Favorite:","def test_get(self, init_db, favorite):\n assert Favorite.get(favorite.id) == favorite","def favorite(user, wine):\n\n favorite = Favorite(user=user, wine=wine)\n\n db.session.add(favorite)\n db.session.commit()\n\n # return favorite","def update_favorites():\n\n check_favorite = Favorite.query.filter(Favorite.favorited_item==session[\"athlete_id\"]).first()\n route = f'/athletes/{session[\"athlete_id\"]}'\n\n if check_favorite is None:\n new_update = Favorite(id=current_user.id, favorited_item=session[\"athlete_id\"])\n db.session.add(new_update) \n \n else:\n db.session.delete(check_favorite)\n \n db.session.commit()\n \n return redirect(route)","def test_user_model(self):\n\n u = User(\n email=\"testy@test.com\",\n username=\"testuser\",\n password=\"HASHED_PASSWORD\",\n phone_number=None,\n image_url=None,\n )\n\n db.session.add(u)\n db.session.commit()\n\n self.assertEqual(len(u.favorites), 0)","def insert_in_favourite(self, food_id, substitute_id):\n\n ref = (food_id, substitute_id)\n print(\"\"\"\\n Souhaitez-vous ajouter cette recherche dans vos favoris ?\n 1. Oui\n 0. Non \"\"\")\n\n choice = int(input(\"Entrez votre choix: \\n\"))\n if choice == 1:\n self.cursor.execute(\"\"\"INSERT INTO favourite\n (food_id, substitute_id)\n VALUES (%s, %s)\"\"\", ref)\n else:\n return","def add_favourite(recipe_id):\r\n if \"user\" in session:\r\n user = coll_users.find_one(\r\n {\"username_lower\": session[\"user\"]})[\"_id\"]\r\n coll_users.update_one(\r\n {\"_id\": ObjectId(user)},\r\n {\"$push\": {\"user_favs\": ObjectId(recipe_id)}})\r\n coll_recipes.update(\r\n {\"_id\": ObjectId(recipe_id)}, {\"$inc\": {\"favourites\": 1}})\r\n return redirect(url_for(\r\n \"recipes.recipe_detail\",\r\n recipe_id=recipe_id))\r\n else:\r\n flash(\"You must be logged in to perform that action!\")\r\n return redirect(url_for(\"users.login\"))","def favourite():\n\n # user is adding or deleting a favourite\n if request.method == \"POST\":\n\n # user is adding a station from 'stations.html'\n if request.form.get(\"add\"):\n\n # max limit of 5 favourites per user\n if len(Favourite.query.filter(Favourite.user_id==session[\"user_id\"]).all()) > 4:\n\n return redirect(url_for(\"stations\", error=\"limit\"))\n\n # remember id of station to add\n station_id = request.form.get(\"add\")\n\n # check user hasn't already favourited station\n if(Favourite.query.filter(Favourite.user_id==session[\"user_id\"],Favourite.station_id==station_id).first()):\n\n return redirect(url_for(\"stations\", error=\"taken\"))\n\n # add favourite to db for user\n addFav = Favourite(user_id=session[\"user_id\"],station_id=station_id)\n db.session.add(addFav)\n db.session.commit()\n\n return redirect(url_for(\"stations\", success=True))\n\n # user is deleting a station from 'favourites.html'\n elif request.form.get(\"delete\"):\n\n station_id = request.form.get(\"delete\")\n\n delFav = Favourite.query.filter(Favourite.user_id==session[\"user_id\"],Favourite.station_id==station_id).first()\n db.session.delete(delFav)\n db.session.commit()\n\n return redirect(url_for(\"favourite\", deleted=True))\n\n # user is viewing favourites via GET\n else:\n favourites = Favourite.query.filter(Favourite.user_id==session[\"user_id\"]).all()\n\n return render_template(\"favourites.html\", username=get_username(), favourites=favourites)","def add_favorite(request):\n print(\"La fonction pour ajouté un produit est appelé\")\n query = request.GET.get('_substitute_product','')\n print(query)\n # query_favorite = query.id\n query_name = Product.objects.get(name=query)\n print(query_name)\n print(\"ID DU PRODUIT\")\n username = request.user\n user_id = request.user.id\n # user = User.objects.get(id=username)\n print(username)\n print(\"ID DE L'USER\")\n if query_name is not None:\n try: \n UserFavorite.objects.get(user_name=username, product=query_name)\n print(\"Ce produit est déjà dans vos favoris.\")\n except ObjectDoesNotExist:\n new_favorite = UserFavorite.objects.create(user_name=username,product=query_name)\n new_favorite.save()\n print(\"Le produit a bien été enregistré.\")\n else:\n pass\n return redirect('favorits')\n # return render(request,'index.html')","def add_visit():\n\n # checks to see if user is logged in\n\n if session.get('username'):\n username = session['username']\n user = User.query.filter_by(username=username).first()\n\n # finds the friend searched for on the database\n friend = request.args.get(\"friend\")\n friend_user = User.query.filter_by(username=friend).first()\n\n when = request.args.get(\"when\")\n user_rating = Decimal(request.args.get(\"rating\"))\n\n # finds the restaurant's ID, adds the restaurant to the database if not in yet\n restaurant = request.args.get(\"name\")\n yelp_id = request.args.get(\"id\")\n avg_rating = request.args.get(\"avg_rating\")\n price_lvl = request.args.get(\"price\")\n review_count = request.args.get(\"rc\")\n categs = request.args.get(\"categs\")\n list_categs = categs.split(\",\")\n\n if not Restaurant.query.filter_by(name=restaurant).all():\n new_restaurant = Restaurant(yelp_id=yelp_id,\n name=restaurant,\n rating=avg_rating,\n price=turn_to_nums(price_lvl),\n review_count=review_count)\n db.session.add(new_restaurant)\n db.session.commit()\n\n rest_id = db.session.query(Restaurant.id).filter_by(yelp_id=yelp_id).first()[0]\n if not Category.query.filter_by(rest_id=rest_id).all():\n if len(list_categs) == 3:\n categ1, categ2, categ3 = list_categs\n elif len(list_categs) == 2:\n categ1, categ2 = list_categs\n categ3 = None\n else:\n categ1 = list_categs\n categ2 = None\n categ3 = None\n new_categs = Category(rest_id=rest_id,\n categ1=categ1,\n categ2=categ2,\n categ3=categ3)\n db.session.add(new_categs)\n db.session.commit()\n\n # Adding to the visits and uservisits tables\n new_visit = Visit(rest_id=rest_id, date=when)\n db.session.add(new_visit)\n db.session.commit()\n new_visit_id = db.session.query(Visit.id).filter_by(rest_id=rest_id,\n date=when).order_by(Visit.date.desc()).first()[0]\n new_visit_exp = UserExp(visit_id=new_visit_id,\n user_id=user.id,\n rating=user_rating)\n f_new_visit_exp = UserExp(visit_id=new_visit_id,\n user_id=friend_user.id)\n db.session.add(new_visit_exp)\n db.session.add(f_new_visit_exp)\n db.session.commit()\n return \" <span class='label label-success'>Saved!</span>\"\n\n # if not logged in, cannot save\n else:\n return \" <a href='/login'><span class='label label-default'>Login to save</span></a>\"","def save_to_favorites_list():\n\n #get show id from the event handler/post request\n show_id = str(request.form.get(\"id\"))\n #get button content from the event handler/post request\n button_content = request.form.get(\"button_content\")\n\n button_content_encoded = button_content.encode('utf-8')\n\n #save utf-8 encoded checkmark as a string variable\n check_mark = \"\\xe2\\x9c\\x93\"\n\n #find the current logged in user\n email = session.get(\"current_user\")\n\n if email:\n\n #use email to find the user_id\n user_id = User.find_user_id_with_email(email)\n\n #if the show has not been favorited yet\n if check_mark not in button_content_encoded:\n #add row in favorites table\n favorite = Favorite.add_to_favorites(show_id, user_id)\n\n #pass back the show_id and that the show has been favorited\n payload = {\"show_id\":show_id,\"favorite\":\"True\"}\n return jsonify(payload)\n else:\n #delete row in favorites table\n Favorite.delete_favorite(show_id)\n\n #pass back the show_id and that the show has been unfavorited\n payload = {\"show_id\":show_id,\"favorite\":\"False\"}\n return jsonify(payload)\n else:\n flash(\"You need to be logged in to see that page.\")\n return redirect(\"/login\")","def test_save(self, init_db, category1):\n params = {\n 'title' : fake.alphanumeric(15),\n 'description' : fake.alphanumeric(200),\n 'ranking' : 1,\n 'meta_data' : {\n 'color' : 'red',\n 'quantity' : 2,\n 'date_purchased' : '2019-02-05',\n 'condition' : 'bad'\n },\n 'category_id' : category1.id\n }\n\n favorite = Favorite(**params)\n assert favorite == favorite.save()","def insert_favorite_food(self, user_answer_id_food, name_substitute):\n self.cursor = self.data_base.cursor(MySQLCursorPrepared)\n save_favorite_food = \"\"\"INSERT INTO Favorite\n (id_food, id_substitute_chooses)\n VALUES({0}, \n (SELECT id FROM Food WHERE name_food = {1}))\"\"\" \\\n .format(int(user_answer_id_food),\n \"\\'\" + name_substitute + \"\\'\")\n self.cursor.execute(save_favorite_food)\n self.data_base.commit()","def favorite():\n # handle pre-flight for browsers CORS access\n if request.method == \"OPTIONS\":\n return generate_response()\n # part1: verify the token\n checked_and_verified, response = check_verify_token(request,login_session)\n if checked_and_verified == False: return response\n # handle the edge case where user is authorized to perform create user but not other method\n if not is_loggedin(login_session):\n response = generate_message(MESSAGE_USER_NOT_LOGGED_IN,401)\n return response\n # handles the get request\n if request.method == \"GET\":\n favorites = read_criteria(Favorite,{\"user_id\":login_session[\"user_id\"]},session,\"m\") or []\n favorites_room_json = [room_json(favorite.room, session,app.config[\"OFFLINE_TESTING\"], login_session) for favorite in favorites]\n return generate_response(elem={\"favorites\":favorites_room_json})\n # part2: check json, handle POST request\n checked_json, response, requested_json = check_json_form(request,MESSAGE_BAD_JSON,MESSAGE_GET_FAV_NO_JSON)\n if checked_json != True: return response\n # verify room id type, with strict mode\n requested_json[\"user_id\"] = login_session[\"user_id\"]\n correct_format,valid_update_pairs, response = process_request_json(Favorite,requested_json, True, access_mode=\"read\",nondb_type_map={\"action\":str})\n if correct_format == False: \n return response\n room = get_row_if_exists(Room, session, ** {\"id\": requested_json[\"room_id\"]})\n user = get_row_if_exists(User, session, ** {\"id\": login_session[\"user_id\"]})\n # if the room id in the request doesn't fit any entry in db, return error message\n if room is None:\n response = generate_message(MESSAGE_FAV_ROOM_NOT_EXIST,404)\n return response\n if requested_json[\"action\"] == \"add\":\n # the add favorite already handle duplicates add\n # it treats multiple adds as one add and every duplicate add afterwards is counted as success\n add_favorite(room,user, session)\n response = generate_message(MESSAGE_POST_FAV_ADD_SUCCESS,201)\n return response\n elif requested_json[\"action\"] == \"delete\":\n # the delete favorite already handle duplicates delete\n # it treats multiple delete as one delete and every duplicate delete afterwards is counted as success\n remove_entry(Favorite,requested_json[\"room_id\"], session)\n response = generate_message(MESSAGE_POST_FAV_DEL_SUCCESS,200)\n return response\n else: # method not supported\n response = generate_message(MESSAGE_POST_FAV_METHOD_NOT_SUPPORTED,405)\n return response","def test_display_favorite(self):\n\n result = self.client.get(\"/view_favorites\")\n self.assertIn(b\"s1925148\", result.data)","def add_to_fav(show_id, name):\n db = get_db()\n db.execute(\n 'INSERT INTO shows_users (show_id, user_id)'\n ' VALUES (?, ?)',\n (show_id, session['user_id'])\n )\n\n flash('\\\"%s\\\" has been successfully added to your favourite TV Shows!' % name)\n db.commit()\n return redirect(request.referrer)","def favourite_create(self, data, sesh):\n\n\t\t# Verify fields\n\t\ttry: DictHelper.eval(data, ['id'])\n\t\texcept ValueError as e: return Services.Effect(error=(1001, [(f, \"missing\") for f in e.args]))\n\n\t\t# If someone tries to add themselves\n\t\tif data['id'] == sesh['thrower']['_id']:\n\t\t\treturn Services.Effect(False);\n\n\t\t# Make sure the thrower exists\n\t\tif not Thrower.exists(data['id']):\n\t\t\treturn Services.Effect(error=(1104, data['id']))\n\n\t\t# Add the thrower to the logged in thrower's favourites and return the\n\t\t#\tresult\n\t\treturn Services.Effect(\n\t\t\tFavourites.add(sesh['thrower']['_id'], data['id'])\n\t\t)","def testing_favourites(self, email, password, song):\n add_to_favourites = Favourites(self.driver, email, password, song)\n add_to_favourites.open_login_page()\n add_to_favourites.set_user_inputs()\n add_to_favourites.open_home_page()\n add_to_favourites.selecting_a_random_song()\n assert add_to_favourites.check_the_song_is_displayed() is True, 'Song is not displaying'\n add_to_favourites.add_to_wishlist()\n add_to_favourites.go_to_favourites_page()\n assert add_to_favourites.check_the_song_is_added_to_wishlist() == song, 'Not the song'","def add_testing_site():\n\n test_id = request.form.get('test_id')\n favorite = { \n 'status': None,\n }\n try:\n if 'user_id' in session:\n user_id = session['user_id']\n\n already_favorited = check_testing_saved_location_in_favorites(user_id, test_id)\n\n if already_favorited:\n favorite['status'] = 'already_favorited'\n flash('Already saved to favorites.')\n return jsonify(favorite)\n\n else:\n favorite['status'] = 'added'\n saved_location = create_testing_saved_locations(user_id, test_id) \n location = get_testing_location_by_test_id(test_id)\n flash(f'Testing Location {location.alternate_name} saved to profile!')\n return jsonify(favorite)\n\n else:\n flash('Please login to save a location!')\n\n except Exception as e:\n msg = f\"Error. Tried adding {test_id} to db failed: \\n {e}.\"\n return jsonify(msg) \n\n return jsonify('Success!')","def FoodCheckIn(sc, event):\n channel = sc.api_call('channels.info', channel=event['channel'])\n food = event['text'][9:]\n if food:\n if 'pizza' in food:\n sc.api_call('reactions.add', as_user='true', channel=event['channel'],\n timestamp=event['ts'], name='pizza')\n user = sc.api_call('users.info', user=event['user'])\n db = pymysql.connect(host='localhost', user='pizzabot', db='pizzachat')\n cursor = db.cursor()\n query = 'INSERT INTO foodlist (who, what) VALUES (%s, %s)'\n cursor.execute(query, (user['user']['name'], food.encode('utf-8')))\n db.commit()\n db.close()","def test_user_own_recipes(self):\n\n recipe1 = Recipe(uri=\"testuri\", name=\"testname\", image_url=\"test_image_url\", user_id=self.uid)\n recipe2 = Recipe(uri=\"testuri2\", name=\"testname2\", image_url=\"test_image_url2\")\n\n db.session.add_all([recipe1, recipe2])\n db.session.commit()\n\n self.assertEqual(recipe1.user_id, self.uid)\n self.assertNotEqual(recipe2.user_id, self.uid)\n self.assertEqual(recipe2.user_id, None)","def test_create_ingredient_successful(self):\n payload = {'name':'Cabbage'}\n self.client.post(INGREDIENTS_URL, payload)\n exists = Ingredient.objects.all().filter(user=self.user, name=payload['name']).exists\n self.assertTrue(exists)","def test_add_remove_from_wishlist(self):\n url = reverse('add-to-wishlist')\n data = {\n 'igdb': self.game.igdb,\n 'name': self.game.name,\n 'slug': self.game.slug,\n 'cover_id': self.game.cover_id,\n 'backdrop_id': self.game.backdrop_id\n }\n\n add = self.client.post(url, data, format='json')\n self.assertEqual(True, add.data['value'])\n\n remove = self.client.post(url, data, format='json')\n self.assertEqual(False, remove.data['value'])","def favorited(self: Article, request: Request):\n if not request.user:\n return False\n\n if self in request.user.favorites:\n return True\n\n return False","def setUp(self):\n self.prod_1 = Product.objects.create(\n pk=1,\n ean='3350033118072',\n name='test 1',\n category='cat 1',\n image='product_default.png',\n nutriscore='u'\n )\n\n self.user_1 = User.objects.create_user(\n pk=1,\n username='Fav Models Unit Test 1',\n email='boggusmail@boggusmail.net'\n )\n\n self.fav_1 = Favourite.objects.create(\n pk=1,\n date_added='2019-12-20 09:00:00',\n user=self.user_1,\n product=self.prod_1\n )","def favourite(self, favourite):\n\n self._favourite = favourite","def add_fav_drinks(self, user_id, drinks): \n assert type(user_id) == str\n assert type(drinks) == list\n\n fav_drinks = self.get_fav_drinks(user_id)\n user_check = self.users.get_user_name(user_id)\n drinks_check = [self.drinks.get_drinks_by_flavor_and_type(d.get('flavor'), d.get('type')) for d in drinks]\n\n # make sure that at least one drink exists in the list\n if all(x is None for x in drinks_check):\n print(\"All drinks provided do not exist. We will not add favorite drinks since one of the drinks must already exist.\")\n \n # user does not exist\n elif user_check is None: \n print(\"User Id {} does not exist.\".format(user_id))\n \n # add fav drinks\n else : \n # user has existing fav drinks\n if fav_drinks is not None:\n for d in drinks:\n # add the drink if it does not exist \n drink_id = self.drinks.add_drink(d.get('type'), d.get('flavor'))\n fav_drinks.append(drink_id)\n # user has no existing fav drinks\n else :\n ids = []\n for d in drinks:\n # add the drink if it does not exist \n ids.append(self.drinks.add_drink(d.get('type'), d.get('flavor')))\n\n fd_id = self.__generate_id()\n self.favorite_drinks.append({\"id\": fd_id, \"user_id\": user_id, \"drink_id\": ids})","def test_create_ingredient(self):\n\n ingredient_payload = {'name': 'Test Ingredient'}\n self.client.post(URL_INGREDIENTS, ingredient_payload)\n\n is_ingredient_created = Ingredient.objects.filter(\n user=self.user,\n name=ingredient_payload['name']\n ).exists()\n\n self.assertTrue(is_ingredient_created)","def toggle_favorite(self, user, article, is_favoriting):\n if user not in article.favorited_by.all() and is_favoriting:\n article.favorited_by.add(user)\n if user in article.favorited_by.all() and not is_favoriting:\n article.favorited_by.remove(user)\n article.favoritesCount = article.favorited_by.all().count()\n article.save()","def test_create_ingredient_successful(self):\n payload = {\n 'name': 'turmeric'\n }\n\n self.client.post(INGREDIENT_URL, payload)\n\n exists = Ingredients.objects.filter(\n user=self.user,\n name=payload['name']\n ).exists()\n\n self.assertTrue(exists)","def added_by(self, user):\n return ChefsHasRecipes.objects.filter(recipe=self, chef=user).exists()","def test_create_ingredient_succesfull(self):\n\n payload = {'name': 'Cabbage'}\n self.client.post(INGREDIENTS_URL, payload)\n\n exist = Ingredient.objects.filter(\n user=self.user,\n name=payload['name'],\n ).exists()\n self.assertTrue(exist)","def test_new_flight_succeeds(self, init_db, new_flight):\n assert new_flight == new_flight.save()","def set_favorite(request):\n company_id = request.data.get('id')\n company = Company.objects.get(id=company_id)\n\n request.user.profile.companies.add(company)\n return Response({'favorite': True})","def create(self, validated_data):\n user_data = validated_data.pop('user')\n user = User.object.get(id=user_data.id)\n restaurant, created = Restaurant.objects.update_or_create(user=user, data=validated_data)\n return restaurant","def update_favourites(self, item_info, status):\r\n if status == \"Add\":\r\n return self.model.add_to_favourites(item_info)\r\n elif status == \"Remove\":\r\n return self.model.delete_from_favourites(item_info)","def add_fav_drink(self, user_id, drink_id):\n assert type(user_id) == str\n assert type(drink_id) == str \n\n existing_drink = False if self.drinks.get_drink_by_id(drink_id) is None else True\n existing_user = False if self.users.get_user_name(user_id) is None else True\n if not existing_drink:\n print(\"Drink does not exist.\")\n elif not existing_user:\n print(\"User does not exist.\")\n else :\n fav_drinks = self.get_fav_drinks(user_id)\n if fav_drinks is not None:\n if drink_id not in fav_drinks:\n fav_drinks.append(drink_id)\n else : # user exists but has no fav drinks\n fd_id = self.__generate_id()\n self.favorite_drinks.append({\"id\": fd_id, \"user_id\": user_id, \"drink_id\": [drink_id]})","def test_create_ingredient_successful(self):\n payload = {'name': \"Test ingredient\"}\n\n # in order to create user must be already authenticated\n self.client.post(INGREDIENTS_URL, payload)\n\n exists = Ingredient.objects.filter(\n user=self.user,\n name=payload['name'],\n ).exists()\n\n self.assertTrue(exists)","def test_update(self, init_db, favorite):\n params = {\n \"title\": \"Category\",\n \"description\": fake.alphanumeric(100),\n }\n favorite.update(**params)\n assert favorite.title == params['title']\n assert favorite.description == params['description']","def mark_favorite(request, object_id):\n feed_item = get_object_or_404(FeedItem, id=object_id)\n fav_item, is_new = FavoriteItem.objects.get_or_create(feed_item=feed_item)\n if request.is_ajax():\n return JSONResponse({'status': 'ok', 'text': 'Marked as favorite'}, False)\n return redirect(request.META.get('HTTP_REFERER', 'feed_item_list'))","def test_if_user_can_add_and_retrieve_data(self):\n # take the first three drinks\n drinks = self.test_data[\"drinks\"][:3]\n # create drink objects from the json data\n drinks = [Drink(**i) for i in drinks]\n Drink.objects.bulk_create(drinks)\n\n data = self.test_data[\"data\"][0]\n # use drink ids added to the db for this particular\n # test\n data[\"drink_id\"] = drinks[random.randint(0, 2)]._id\n\n response = self.client.post(\"/data/data_collected/\",\n data, format='json')\n\n # assert it data was added correctly\n self.assertEqual(response.status_code,\n status.HTTP_201_CREATED)\n\n # retrieve the data added\n response = self.client.get(\"/data/data_collected/\")\n\n # assert if the response is 200\n self.assertEqual(response.status_code, 200)\n\n # get the number of added data records\n data_added_count = len(response.json())\n\n # assert if the data added is one\n self.assertEqual(data_added_count, 1)","def update_favorite_things():\n data = request.data\n favorite_things = json.loads(data)\n print(favorite_things)\n connection = mongo_connect()\n if(favorite_things[\"action\"] == \"add\"):\n connection[\"users\"].update_one(\n {\"_id\": ObjectId(session[\"_id\"])},\n {\"$push\": {\n favorite_things[\"type\"]: ObjectId(favorite_things[\"item_id\"])\n }\n }\n )\n else:\n connection[\"users\"].update_one(\n {\"_id\": ObjectId(session[\"_id\"])},\n {\"$pull\":\n {\n favorite_things[\"type\"]:\n ObjectId(favorite_things[\"item_id\"])\n }\n }\n )\n resp = jsonify(success=True)\n return resp","def test_api_can_add_food_to_a_meal(self):\n response = self.client.post(f'/api/v1/meals/{self.breakfast.id}/foods/{self.oatmeal.id}')\n # import code; code.interact(local=dict(globals(), **locals()))\n\n self.assertEqual(response.data['message'], \"Successfully added oatmeal to breakfast\")","def test_delete(self, init_db, favorite):\n favorite.delete()\n assert Favorite.get(favorite.id) == None","def NewFood(r, foods):\n hasError = False\n for i in foods:\n try:\n food_form = FoodForm(i)\n if food_form.is_valid():\n food = food_form.save(commit=False)\n\n # store base64 type image into food.src\n if isBase64Image(i[\"src\"]):\n file = getImgContentFile(i[\"src\"])\n try:\n Image.open(file)\n food.src = file\n except IOError:\n print(\"Not a image.\")\n hasError = True\n food.restaurant = r\n food.save()\n else:\n hasError = True\n except:\n print(\"new error\")\n hasError = True\n return hasError","def test_create_ingredient_successful(self):\n\n payload = {'name': 'Salt'}\n\n res = self.client.post(INGREDIENTS_URL, payload)\n\n exists = Ingredient.objects.filter(\n user=self.user,\n name=payload['name']\n ).exists()\n\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n self.assertTrue(exists)","def saveUserFavorites(request, userid):\n try:\n User.objects.get(id=userid)\n newFavorite = FavoriteSerializer(data=request.data)\n print(request.data)\n if newFavorite.is_valid():\n try:\n Item.objects.get(id=request.data.get(\"item\"))\n newFavorite.save()\n return Response(newFavorite.data, status=status.HTTP_201_CREATED)\n \n except Item.DoesNotExist:\n fail = {\n \"Item\":\"doesnotexist\"\n }\n return JsonResponse(fail)\n \n #for testing ONLY\n print(newFavorite.errors)\n #f##########\n\n fail = {\n \"user\": \"requestdatanotvalid\",\n \"item\":\"requestdatanotvalid\"\n }\n return JsonResponse(fail)\n\n except User.DoesNotExist:\n fail = {\n \"user\":\"doesnotexist\"\n }\n return JsonResponse(fail)","def test_view_url_propose_product_already_in_favorites(self):\r\n self.client.login(username='test', password='test')\r\n response = self.client.get(reverse('search_results'),\r\n {'query': '', 'name': 'nutella'})\r\n self.assertEqual(response.status_code, 200)\r\n self.assertTemplateUsed(response, 'purbeurre/search_results.html')","def fav_place_check(uri):\n return veggiesailor.StorageFav().exists(uri)","def test_given_a_user_when_I_add_a_todo_Then_I_can_access_it_from_user_todo_collection(self):\n from .models import Tag\n from .models import TodoUser\n from .models import TodoItem\n\n user = TodoUser(\n email=u'king.arthur@example.com',\n first_name=u'Arthur',\n last_name=u'Pendragon',\n )\n self.session.add(user)\n\n tags = [u'quest', u'ni', u'knight']\n\n todo = TodoItem(user.email,\n u'Find a shrubbery', \n [u'quest', u'ni', u'knight'] \n ) \n self.session.add(todo)\n \n user_todo = user.todo_list.one()\n self.assertTrue(todo is user_todo)","def test_addUser(self):\n self.new_user.saveUser()\n self.assertEqual(len(User.users_list),1)","def test_favs(app):\n res = app.get('/favs')\n assert res.status_code == 200\n assert b'bloom' in res.data","def test_new_likes(self):\n\n new_user = User.signup(\"newuser\", \"newuser@gmail.com\", \"password123\", None)\n db.session.add(new_user)\n db.session.commit()\n\n new_msg = Message(text=\"A new message from a new user\", user_id=new_user.id)\n db.session.add(new_msg)\n db.session.commit()\n\n new_user.likes.append(new_msg)\n db.session.commit()\n\n likes = Likes.query.all()\n self.assertEqual(len(likes), 1)\n self.assertEqual(likes[0].user_id, new_user.id)\n self.assertEqual(likes[0].message_id, new_msg.id)","def test_returns_projects_favourited_by_user_if_favourited_by_me_is_true(self):\n # Arrange\n # Make all projects to be accessible for user\n self.test_project_2.private = False\n self.test_project_2.save()\n self.test_project_3.status = ProjectStatus.PUBLISHED.value\n self.test_project_3.save()\n self.test_user.favorites = [self.test_project_1]\n self.test_user.save()\n # Act\n response = self.client.get(\n self.url,\n headers={\"Authorization\": self.user_session_token},\n query_string={\"favoritedByMe\": \"true\"},\n )\n self.assertEqual(response.status_code, 200)\n self.assertEqual(len(response.json[\"results\"]), 1)\n self.assertEqual(\n response.json[\"results\"][0][\"projectId\"], self.test_project_1.id\n )","def add_to_fav(request, q_id):\n if request.method == 'POST':\n Quotes.objects.add_to_user_fav(request.session['id'], q_id)\n return redirect('/quotes')","def connect_user(self, user):\n\t\tis_user_added = False\n\t\tif not user in self.users.all():\n\t\t\tself.users.add(user)\n\t\t\tself.save()\n\t\t\tis_user_added = True\n\t\telif user in self.users.all():\n\t\t\tis_user_added = True\n\t\treturn is_user_added","def favorite(self) -> bool:\n return self._favorite","def post(self):\n # fetch parameter\n get_parser = reqparse.RequestParser(bundle_errors=True)\n get_parser.add_argument(\"user_id\", required=True, help=\"User ID required to ad to their favourite projects\")\n get_parser.add_argument(\"project_id\", required=True, help=\"Project ID required to add to the favourite projects\")\n args = get_parser.parse_args(strict=True)\n\n # get user_id and project_id\n user_id = args[\"user_id\"]\n project_id = args[\"project_id\"]\n\n # convert parameter ids into objectids\n try:\n user_id = ObjectId(user_id)\n project_id = ObjectId(project_id)\n except:\n return {\"message\": \"invalid user id or project id\"}, 400\n\n # add project to the user's favourites\n if ('user_id' or 'project_id') not in args.keys():\n return {\"message\": \"both user and project id are required\"}, 400\n else:\n # check if user is valid\n user = self.users.find_one({\"_id\": user_id})\n project = self.projects.find_one({\"_id\": project_id})\n if user is None:\n return {\"message\": \"user not found\"}, 404\n elif project is None:\n return {\"message\": \"project not found\"}, 404\n else:\n # add project to favourites\n user_favourites = self.favourites.find_one({\"user_id\": user_id})\n if user_favourites is None:\n # insert a new doc into favourites collection\n favourites_list = []\n favourites_list.append(deepcopy(project)) \n self.favourites.insert({\n \"user_id\": user_id,\n \"favourite_projects\": favourites_list\n })\n else:\n new_favourite_list = user_favourites[\"favourite_projects\"]\n\n # check if this project is already in the user's favourites\n for proj in new_favourite_list:\n if proj[\"_id\"] == project_id:\n return {\"message\": \"project is already in the favourites list\"}, 400\n\n new_favourite_list.append(deepcopy(project))\n updated_list = {\"favourite_projects\": new_favourite_list}\n\n self.favourites.update({\"user_id\": user_id}, {\"$set\": updated_list}, upsert=False)\n \n return {\"status\": \"project has been added to favourites successfully\"}, 200","def save(self, substitute_choice, product_choice):\n self.db.query(f\"\"\"\n INSERT INTO {self.table} (substitut_id, original_id)\n VALUES (:substitut_id, :original_id)\n ON DUPLICATE KEY UPDATE substitut_id = :substitut_id\n \"\"\", substitut_id=substitute_choice.id, original_id=product_choice.id)\n favorite = (substitute_choice.id, product_choice.id)\n return favorite","def test_add(self):\n # add a todo\n self.add(title=\"Sample task todo\", description=\"for sample\", state=\"todo\")\n task = Task.query.filter_by(title='Sample task todo').first()\n self.assertEqual(task.description, 'for sample')\n self.assertEqual(task.state, 'todo')\n\n # add a doing\n self.add(title=\"Sample task doing\", description=\"for sample\", state=\"doing\")\n task = Task.query.filter_by(title=\"Sample task doing\").first()\n self.assertEqual(task.description, 'for sample')\n self.assertEqual(task.state, 'doing')\n\n # add a done\n self.add(title=\"Sample task done\", description=\"for sample\", state=\"done\")\n task = Task.query.filter_by(title='Sample task done').first()\n self.assertEqual(task.description, 'for sample')\n self.assertEqual(task.state, 'done')","def favorite(request, movie_id):\n\n movie = get_object_or_404(Movie, pk=movie_id)\n try:\n if movie.liked:\n movie.liked = False\n else:\n movie.liked = True\n movie.save()\n except (KeyError, Movie.DoesNotExist):\n return JsonResponse({'success': False})\n else:\n return JsonResponse({'success': True})","def saving_search(request, id_movie):\n movie = Movie.objects.get(id_code=id_movie)\n Favorite.objects.get_or_create(user_link=request.user,\n movie_saved=movie)\n return redirect('home')","def test_save_users(self):\n\n self.new_users.save_users() # saving the new user\n self.assertEqual(len(User.user_list), 1)","def favorite(self, favorite: bool):\n if favorite is None:\n raise ValueError(\"Invalid value for `favorite`, must not be `None`\")\n\n self._favorite = favorite","def post_favorite(request, pk=None):\n post = Post.objects.get(pk=pk).original_or_self()\n if post.favorites.filter(pk=request.user.pk).exists():\n post.favorites.remove(request.user)\n else:\n post.favorites.add(request.user)\n post.save()\n\n referer = request.META['HTTP_REFERER']\n if referer:\n return redirect(referer)\n else:\n return redirect('posts:post', pk=post.pk)","def restaurants_new():\n # If the user isn't logged in, send to the login page\n if helper.handle_login(login_session) is False:\n return redirect('/login')\n\n if request.method == 'POST':\n if len(request.form['name']) > 0:\n new_restaurant = Restaurant(name=request.form['name'],\n address=request.form['address'],\n phone=request.form['phone'],\n web=helper.check_restaurant_URL(request.form['web']),\n description=request.form['description'],\n user_id=login_session['user_id'])\n session.add(new_restaurant)\n session.commit()\n flash(\"New restaurant created - {}\".format(new_restaurant.name))\n tag_line = request.form['tag_line']\n tag_list = tag_line.split(',')\n for tag in tag_list:\n helper.add_tag_if_not_exists(tag, new_restaurant.id)\n return redirect(url_for('restaurants_page'))\n else:\n flash(\"Incorrect Restaurant details - Please include a name!\")\n\n user_info = helper.get_user_if_exists(login_session)\n return render_template('newrestaurant.html', user_info=user_info)","def test_get_and_set_star(self):\n email = 'user2@example.com'\n feature_id = self.feature_1.key.integer_id()\n notifier.FeatureStar.set_star(email, feature_id)\n actual = notifier.FeatureStar.get_star(email, feature_id)\n self.assertEqual(email, actual.email)\n self.assertEqual(feature_id, actual.feature_id)\n self.assertTrue(actual.starred)\n updated_feature = models.Feature.get_by_id(feature_id)\n self.assertEqual(1, updated_feature.star_count)\n\n notifier.FeatureStar.set_star(email, feature_id, starred=False)\n actual = notifier.FeatureStar.get_star(email, feature_id)\n self.assertEqual(email, actual.email)\n self.assertEqual(feature_id, actual.feature_id)\n self.assertFalse(actual.starred)\n updated_feature = models.Feature.get_by_id(feature_id)\n self.assertEqual(0, updated_feature.star_count)","def test_save(self):\n self.assertEqual(CalendallUser.objects.count(), len(self.data))","def test_favorites_posts(self):\n self.resource._request.register_uri(\n 'GET', '/users/dotzero/favorites/posts?page=2', 'fixture_post.json')\n\n response = self.resource.favorites_posts('dotzero', 2)\n\n self.assertTrue('data' in response)\n self.assertTrue('server_time' in response)","def test_create_shelf(self, *_):\n form = forms.ShelfForm()\n form.data[\"user\"] = self.local_user.id\n form.data[\"name\"] = \"new shelf name\"\n form.data[\"description\"] = \"desc\"\n form.data[\"privacy\"] = \"unlisted\"\n request = self.factory.post(\"\", form.data)\n request.user = self.local_user\n\n views.create_shelf(request)\n\n shelf = models.Shelf.objects.get(name=\"new shelf name\")\n self.assertEqual(shelf.privacy, \"unlisted\")\n self.assertEqual(shelf.description, \"desc\")\n self.assertEqual(shelf.user, self.local_user)","def add_favourites_field():\n existing = read_json()\n\n if 'favourites' not in existing[0].keys(): # if the field has not already been added, add it.\n for club in existing:\n club['favourites'] = 0\n write_json(existing)","def accept(self):\n receiver_friend_list = FriendList.objects.filter(user_id=self.receiver_id)\n sender_friend_list = FriendList.objects.filter(user_id=self.sender_id)\n if(receiver_friend_list.exists()):\n receiver_friend_list = receiver_friend_list[0]\n else:\n receiver_friend_list = FriendList.objects.create(user_id=self.receiver_id)\n\n if(sender_friend_list.exists()):\n sender_friend_list = sender_friend_list[0]\n else:\n sender_friend_list = FriendList.objects.create(user_id=self.sender_id)\n\n if receiver_friend_list:\n receiver_friend_list.add_friend(self.sender_id)\n if sender_friend_list:\n sender_friend_list.add_friend(self.receiver_id)\n self.is_active = False\n self.save()","def test_add_or_remove_like_from_db(self):\n session = UnifiedAlchemyMagicMock()\n with mock.patch(\"app.DB.session\", session):\n app.add_or_remove_like_from_db(\"username\", 0)\n is_liked = (\n session.query(app.models.Likes.id)\n .filter_by(username=\"username\", post_id=0)\n .scalar()\n is not None\n )\n session.commit()\n self.assertEqual(is_liked, True)","def test_update_restaurant(self):\n from espresso import db\n from espresso import Restaurant\n\n name = 'Restaurant Mexicano'\n zip_code = \"94110\"\n db.session.add(Restaurant(name=name, creator='test-user@gmail.com', zip_code=zip_code))\n db.session.commit()\n\n headers = {'Content-Type': 'application/json'}\n headers.update(auth_header_cru_restaurants)\n website = 'www.mexicano-nj.com'\n email = 'mexicano-nj@gmail.com'\n info = {'website': website, 'email': email}\n resp = self.test_client.put(self.API_BASE + '/1', headers=headers, data=json.dumps(info))\n\n self.assertEqual(resp.status_code, 200)\n resp_dict = json.loads(resp.data)\n self.assertEqual(resp_dict['id'], 1)\n self.assertTrue(name in resp_dict['message'])\n\n # Check that all restaurant fields are returned.\n self.assertTrue('restaurant' in resp_dict)\n self.assertEqual(resp_dict['restaurant']['id'], 1)\n self.assertEqual(resp_dict['restaurant']['website'], website)\n self.assertEqual(resp_dict['restaurant']['email'], email)\n self.assertEqual(resp_dict['restaurant']['zip_code'], zip_code)\n\n self.assertTrue('name' in resp_dict['restaurant'])\n self.assertTrue('street' in resp_dict['restaurant'])\n self.assertTrue('suite' in resp_dict['restaurant'])\n self.assertTrue('city' in resp_dict['restaurant'])\n self.assertTrue('state' in resp_dict['restaurant'])\n self.assertTrue('phone_num' in resp_dict['restaurant'])\n self.assertTrue('date_established' in resp_dict['restaurant'])\n self.assertTrue('creator' in resp_dict['restaurant'])\n\n # -----------------------------\n # Make a separate request to retrieve the restaurant and assert that updated fields are as intended\n resp = self.test_client.get(self.API_BASE + '/1', headers=auth_header_cru_restaurants)\n\n self.assertEqual(resp.status_code, 200)\n resp_dict = json.loads(resp.data)\n self.assertEqual(resp_dict['restaurant']['id'], 1)\n self.assertEqual(resp_dict['restaurant']['website'], website)\n self.assertEqual(resp_dict['restaurant']['email'], email)\n self.assertEqual(resp_dict['restaurant']['zip_code'], zip_code) # Make sure this has not changed","def test_like_message(self):\n\n self.u.likes.append(self.m_u2)\n db.session.commit()\n\n self.assertEqual(len(self.u.likes), 1)\n self.assertEqual(self.u.likes[0], self.m_u2)","def test_create_pizza(self):\n url = reverse('pizzas-list')\n data = {'name': 'Quattro Formaggio'}\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(Pizza.objects.count(), 1)\n self.assertEqual(Pizza.objects.get().name, 'Quattro Formaggio')","def test_add(self):\n r = main.List.connection()\n main.List.add(r, \"ToDo\", 1, \"Buy apples\", 2, \"20.05.2015\")\n task = r.get(\"ToDo\")\n self.assertTrue(task, \"No such entry in DB. Adding failed.\")","def SetNewFav(self, newFav):\n self.favouriteFood = newFav","def save(self, *args, **kwargs):\n if not self.require_confirm_email:\n User.objects.filter(is_active=False, deactivation_reason=\"pending\").update(\n is_active=True, deactivation_reason=None\n )\n if not self.invite_question_text:\n self.invite_question_text = \"What is your favourite book?\"\n super().save(*args, **kwargs)","def test_check_add_flavor(self):\n for flavor_id, flavor in OPENSTACK_FLAVOR.items():\n self.cmd._add_flavor(flavor, flavor_id)\n ralph_flavor = CloudFlavor.objects.get(flavor_id=flavor_id)\n self.assertEqual(ralph_flavor.name, flavor['name'])\n self.assertEqual(ralph_flavor.cloudprovider, self.cloud_provider)\n self.assertIn(flavor['tag'], ralph_flavor.tags.names())\n self.assertEqual(flavor['cores'], ralph_flavor.cores)\n self.assertEqual(flavor['memory'], ralph_flavor.memory)\n self.assertEqual(flavor['disk'], ralph_flavor.disk)","def add_restaurant_review():\n username = sign_up.get_username()\n if username:\n add_var = dict(user=username, restaurant_name=\"\", restaurant_address=\"\",\n restaurant_item=\"\", item_comments=\"\", item_price=\"\",\n restaurant_ranking=\"\", restaurant_rating=\"\",\n restaurant_rating_reason=\"\", address=\"\", restaurant_chosen=\"\",\n address_chosen=\"\")\n return bottle.template('add_review', add_var=add_var)\n else:\n return bottle.template('login',\n dict(user_error=\"Sorry, you need to be logged in to submit a review, please log below:\", pw_error=\"\"))","def post(self):\n args = UpdateLikeList.post_parser.parse_args()\n user_name = args.get('user_name')\n restaurant_name = args.get('restaurant_name')\n #rating = args.get('rating')\n newlike = {\n 'user_name':args.get('user_name'),\n 'restaurant_name':args.get('restaurant_name')\n #'rating':args.get('rating')\n }\n conn = db.create_connection(db.connection_config_dict)\n cursor = conn.cursor()\n\n # To get user's user_id\n user_id = []\n sql_1 = 'SELECT user_id FROM User WHERE user_name = \"{user_name}\"'.format(user_name=user_name)\n print(sql_1)\n cursor.execute(sql_1)\n for u in cursor:\n user_id.append(u)\n print(user_id) \n\n # To get restaurant's restaurant_id\n restaurant_id = []\n sql_2 = 'SELECT restaurant_id FROM Restaurant WHERE name = \"{restaurant_name}\"'.format(restaurant_name=restaurant_name)\n print(sql_2)\n cursor.execute(sql_2)\n for u in cursor:\n restaurant_id.append(u)\n print(restaurant_id)\n\n # Insert new restaurant into LikeList table\n # neo4j may need insert data here\n # user id is user_id[0][0], restaurant id is restaurant_id[0][0].\n sql_3 = \"INSERT INTO LikeList (user_id, restaurant_id) VALUES ({user_id}, {restaurant_id});\".format(user_id=user_id[0][0], restaurant_id=restaurant_id[0][0])\n print(sql_3)\n cursor.execute(sql_3)\n\n conn.commit()\n return newlike, 201","def test_create_recipe_with_ingredient(self):\n ingredient1 = sample_ingredient(user=self.user, name='Prawns')\n ingrident2 = sample_ingredient(user=self.user, name ='Ginger')\n\n payload = {\n 'title': 'Thai prawn and curry',\n 'ingredient': [ingredient1.id,ingrident2.id],\n 'time_minuts':60,\n 'price': 250\n }\n res = self.client.post(RECIPE_URL,payload)\n\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n\n recipe = Recipe.objects.get(id=res.data['id'])\n ingredients = recipe.ingredient.all()\n self.assertEqual(ingredients.count(),2)\n self.assertIn(ingredient1, ingredients)\n self.assertIn(ingrident2,ingredients)","def count_favorite(self, obj):\n\n return obj.recipe_fav.count()","def test_func(self):\n return (Student.objects.filter(user=self.request.user).exists())","def test_create_like(self):\n pokemon = Pokemon.objects.all().first()\n r = self.client.post(\n reverse('create_like'),\n {'pokemon-like-id': pokemon.id},\n HTTP_X_REQUESTED_WITH='XMLHttpRequest'\n )\n\n self.assertEqual(Like.objects.all().count(), 1)","def test_is_following(self):\n\n self.u1.following.append(self.u2)\n db.session.commit()\n\n self.assertTrue(self.u1.is_following(self.u2))\n self.assertFalse(self.u2.is_following(self.u1))","def test_is_followed_by(self):\n\n self.u1.followers.append(self.u2)\n db.session.commit()\n\n self.assertTrue(self.u1.is_followed_by(self.u2))\n self.assertFalse(self.u2.is_followed_by(self.u1))","def test_save_user(self):\n self.new_user.save_user()\n self.assertEqual(len(User.user_list), 1)","def test_save_user(self):\n self.new_user.save_user()\n self.assertEqual(len(User.user_list), 1)","def test_ingredients_limited_to_user(self):\n\n user2 = get_user_model().objects.create_user(\n 'zohaib@123.com',\n 'zohaib123'\n )\n Ingredients.objects.create(user=user2, name='Vinegar')\n\n ingredient = Ingredients.objects.create(\n user=self.user, name=\"Turmeric\"\n )\n res = self.client.get(INGREDIENT_URL)\n\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n self.assertEqual(len(res.data), 1)\n self.assertEqual(res.data[0]['name'], ingredient.name)","def test_get_restaurant_by_id(self):\n from espresso import db\n from espresso import Restaurant\n\n name = 'Restaurant Greco'\n db.session.add(Restaurant(name=name, creator='test-user@gmail.com'))\n db.session.commit()\n\n # Since this is a freshly created table, the first id should be 1\n resp = self.test_client.get(self.API_BASE + '/1', headers=auth_header_cru_restaurants)\n self.assertEqual(resp.status_code, 200)\n resp_dict = json.loads(resp.data)\n self.assertEqual(resp_dict['restaurant']['name'], name)","def test_get_is_book_on_shelf(self, *_):\n shelf = self.local_user.shelf_set.first()\n self.assertFalse(shelf_tags.get_is_book_on_shelf(self.book, shelf))\n models.ShelfBook.objects.create(\n shelf=shelf, book=self.book, user=self.local_user\n )\n self.assertTrue(shelf_tags.get_is_book_on_shelf(self.book, shelf))","def add_user_to_db(json_details):\n return True","def newMenuItem(restaurant_id):\n\n if 'access_token' not in flask_session:\n return logInRedirect()\n restaurant = session.query(Restaurant).filter_by(id = restaurant_id).first()\n user_id = getUserId(flask_session['email'],flask_session['google_plus_id'])\n if not restaurant.user_id == user_id:\n flash(\"Only restaurant owners can add new items.\")\n return redirect(url_for(\"publicMenu\",restaurant_id = restaurant_id))\n\n if request.method == \"POST\":\n new_name = request.form['new_name']\n print \"\\nnewMenuItem POST triggered, name is: \", new_name\n newMenuItem = MenuItem( name=new_name,\n restaurant_id=restaurant.id )\n session.add(newMenuItem)\n session.commit()\n flash( \"new item '\" + new_name + \"' created!\")\n print \"POST worked!\"\n return redirect(url_for(\"showMenu\", restaurant_id=restaurant.id))\n\n else:\n return render_template('newMenuItem.html', restaurant = restaurant)","def test_get_feature_starrers__some_starrers(self):\n app_user_1 = models.AppUser(email='user16@example.com')\n app_user_1.put()\n app_user_2 = models.AppUser(email='user17@example.com')\n app_user_2.put()\n feature_1_id = self.feature_1.key.integer_id()\n notifier.FeatureStar.set_star(app_user_1.email, feature_1_id)\n notifier.FeatureStar.set_star(app_user_2.email, feature_1_id)\n\n actual = notifier.FeatureStar.get_feature_starrers(feature_1_id)\n self.assertItemsEqual(\n [app_user_1.email, app_user_2.email],\n [au.email for au in actual])","def test_user_retrieve_restaurant(self):\n response = self.client.get(f'/api/places/1/', format='json')\n self.assertNotEqual(response.status_code, status.HTTP_403_FORBIDDEN)","def test_update_restaurant_blank_name(self):\n from espresso import db\n from espresso import Restaurant\n\n name = 'Restaurant Mexicano'\n db.session.add(Restaurant(name=name, creator='test-user@gmail.com'))\n db.session.commit()\n\n headers = {'Content-Type': 'application/json'}\n headers.update(auth_header_cru_restaurants)\n info = {'name': ''}\n resp = self.test_client.put(self.API_BASE + '/1', headers=headers, data=json.dumps(info))\n self.assertEqual(resp.status_code, 400)","def post(self, request, slug):\n try:\n article = Article.objects.get(slug=slug)\n except Article.DoesNotExist:\n return Response({\"Message\": [\n \"That article does not exist\"\n ]}, status.HTTP_204_NO_CONTENT)\n favorite = dict()\n favorite[\"user\"] = request.user.id\n favorite[\"article\"] = article.pk\n serializer = self.serializer_class(data=favorite)\n serializer.is_valid(raise_exception=True)\n serializer.save()\n article_serializer = ArticleSerializer(\n instance=article, context={'request': request})\n data = dict(article=article_serializer.data)\n data[\"article\"][\"favorited\"] = True\n data[\"message\"] = \"favorited\"\n return Response(data, status.HTTP_200_OK)"],"string":"[\n \"def test_Favourite(self):\\n self.assertEquals(self.fav_1.pk, 1)\\n self.assertEquals(self.fav_1.date_added, '2019-12-20 09:00:00')\\n self.assertEquals(self.fav_1.user.pk, 1)\\n self.assertEquals(self.fav_1.product.pk, 1)\",\n \"async def create(self, favorite: Favorite) -> Favorite:\",\n \"def test_get(self, init_db, favorite):\\n assert Favorite.get(favorite.id) == favorite\",\n \"def favorite(user, wine):\\n\\n favorite = Favorite(user=user, wine=wine)\\n\\n db.session.add(favorite)\\n db.session.commit()\\n\\n # return favorite\",\n \"def update_favorites():\\n\\n check_favorite = Favorite.query.filter(Favorite.favorited_item==session[\\\"athlete_id\\\"]).first()\\n route = f'/athletes/{session[\\\"athlete_id\\\"]}'\\n\\n if check_favorite is None:\\n new_update = Favorite(id=current_user.id, favorited_item=session[\\\"athlete_id\\\"])\\n db.session.add(new_update) \\n \\n else:\\n db.session.delete(check_favorite)\\n \\n db.session.commit()\\n \\n return redirect(route)\",\n \"def test_user_model(self):\\n\\n u = User(\\n email=\\\"testy@test.com\\\",\\n username=\\\"testuser\\\",\\n password=\\\"HASHED_PASSWORD\\\",\\n phone_number=None,\\n image_url=None,\\n )\\n\\n db.session.add(u)\\n db.session.commit()\\n\\n self.assertEqual(len(u.favorites), 0)\",\n \"def insert_in_favourite(self, food_id, substitute_id):\\n\\n ref = (food_id, substitute_id)\\n print(\\\"\\\"\\\"\\\\n Souhaitez-vous ajouter cette recherche dans vos favoris ?\\n 1. Oui\\n 0. Non \\\"\\\"\\\")\\n\\n choice = int(input(\\\"Entrez votre choix: \\\\n\\\"))\\n if choice == 1:\\n self.cursor.execute(\\\"\\\"\\\"INSERT INTO favourite\\n (food_id, substitute_id)\\n VALUES (%s, %s)\\\"\\\"\\\", ref)\\n else:\\n return\",\n \"def add_favourite(recipe_id):\\r\\n if \\\"user\\\" in session:\\r\\n user = coll_users.find_one(\\r\\n {\\\"username_lower\\\": session[\\\"user\\\"]})[\\\"_id\\\"]\\r\\n coll_users.update_one(\\r\\n {\\\"_id\\\": ObjectId(user)},\\r\\n {\\\"$push\\\": {\\\"user_favs\\\": ObjectId(recipe_id)}})\\r\\n coll_recipes.update(\\r\\n {\\\"_id\\\": ObjectId(recipe_id)}, {\\\"$inc\\\": {\\\"favourites\\\": 1}})\\r\\n return redirect(url_for(\\r\\n \\\"recipes.recipe_detail\\\",\\r\\n recipe_id=recipe_id))\\r\\n else:\\r\\n flash(\\\"You must be logged in to perform that action!\\\")\\r\\n return redirect(url_for(\\\"users.login\\\"))\",\n \"def favourite():\\n\\n # user is adding or deleting a favourite\\n if request.method == \\\"POST\\\":\\n\\n # user is adding a station from 'stations.html'\\n if request.form.get(\\\"add\\\"):\\n\\n # max limit of 5 favourites per user\\n if len(Favourite.query.filter(Favourite.user_id==session[\\\"user_id\\\"]).all()) > 4:\\n\\n return redirect(url_for(\\\"stations\\\", error=\\\"limit\\\"))\\n\\n # remember id of station to add\\n station_id = request.form.get(\\\"add\\\")\\n\\n # check user hasn't already favourited station\\n if(Favourite.query.filter(Favourite.user_id==session[\\\"user_id\\\"],Favourite.station_id==station_id).first()):\\n\\n return redirect(url_for(\\\"stations\\\", error=\\\"taken\\\"))\\n\\n # add favourite to db for user\\n addFav = Favourite(user_id=session[\\\"user_id\\\"],station_id=station_id)\\n db.session.add(addFav)\\n db.session.commit()\\n\\n return redirect(url_for(\\\"stations\\\", success=True))\\n\\n # user is deleting a station from 'favourites.html'\\n elif request.form.get(\\\"delete\\\"):\\n\\n station_id = request.form.get(\\\"delete\\\")\\n\\n delFav = Favourite.query.filter(Favourite.user_id==session[\\\"user_id\\\"],Favourite.station_id==station_id).first()\\n db.session.delete(delFav)\\n db.session.commit()\\n\\n return redirect(url_for(\\\"favourite\\\", deleted=True))\\n\\n # user is viewing favourites via GET\\n else:\\n favourites = Favourite.query.filter(Favourite.user_id==session[\\\"user_id\\\"]).all()\\n\\n return render_template(\\\"favourites.html\\\", username=get_username(), favourites=favourites)\",\n \"def add_favorite(request):\\n print(\\\"La fonction pour ajouté un produit est appelé\\\")\\n query = request.GET.get('_substitute_product','')\\n print(query)\\n # query_favorite = query.id\\n query_name = Product.objects.get(name=query)\\n print(query_name)\\n print(\\\"ID DU PRODUIT\\\")\\n username = request.user\\n user_id = request.user.id\\n # user = User.objects.get(id=username)\\n print(username)\\n print(\\\"ID DE L'USER\\\")\\n if query_name is not None:\\n try: \\n UserFavorite.objects.get(user_name=username, product=query_name)\\n print(\\\"Ce produit est déjà dans vos favoris.\\\")\\n except ObjectDoesNotExist:\\n new_favorite = UserFavorite.objects.create(user_name=username,product=query_name)\\n new_favorite.save()\\n print(\\\"Le produit a bien été enregistré.\\\")\\n else:\\n pass\\n return redirect('favorits')\\n # return render(request,'index.html')\",\n \"def add_visit():\\n\\n # checks to see if user is logged in\\n\\n if session.get('username'):\\n username = session['username']\\n user = User.query.filter_by(username=username).first()\\n\\n # finds the friend searched for on the database\\n friend = request.args.get(\\\"friend\\\")\\n friend_user = User.query.filter_by(username=friend).first()\\n\\n when = request.args.get(\\\"when\\\")\\n user_rating = Decimal(request.args.get(\\\"rating\\\"))\\n\\n # finds the restaurant's ID, adds the restaurant to the database if not in yet\\n restaurant = request.args.get(\\\"name\\\")\\n yelp_id = request.args.get(\\\"id\\\")\\n avg_rating = request.args.get(\\\"avg_rating\\\")\\n price_lvl = request.args.get(\\\"price\\\")\\n review_count = request.args.get(\\\"rc\\\")\\n categs = request.args.get(\\\"categs\\\")\\n list_categs = categs.split(\\\",\\\")\\n\\n if not Restaurant.query.filter_by(name=restaurant).all():\\n new_restaurant = Restaurant(yelp_id=yelp_id,\\n name=restaurant,\\n rating=avg_rating,\\n price=turn_to_nums(price_lvl),\\n review_count=review_count)\\n db.session.add(new_restaurant)\\n db.session.commit()\\n\\n rest_id = db.session.query(Restaurant.id).filter_by(yelp_id=yelp_id).first()[0]\\n if not Category.query.filter_by(rest_id=rest_id).all():\\n if len(list_categs) == 3:\\n categ1, categ2, categ3 = list_categs\\n elif len(list_categs) == 2:\\n categ1, categ2 = list_categs\\n categ3 = None\\n else:\\n categ1 = list_categs\\n categ2 = None\\n categ3 = None\\n new_categs = Category(rest_id=rest_id,\\n categ1=categ1,\\n categ2=categ2,\\n categ3=categ3)\\n db.session.add(new_categs)\\n db.session.commit()\\n\\n # Adding to the visits and uservisits tables\\n new_visit = Visit(rest_id=rest_id, date=when)\\n db.session.add(new_visit)\\n db.session.commit()\\n new_visit_id = db.session.query(Visit.id).filter_by(rest_id=rest_id,\\n date=when).order_by(Visit.date.desc()).first()[0]\\n new_visit_exp = UserExp(visit_id=new_visit_id,\\n user_id=user.id,\\n rating=user_rating)\\n f_new_visit_exp = UserExp(visit_id=new_visit_id,\\n user_id=friend_user.id)\\n db.session.add(new_visit_exp)\\n db.session.add(f_new_visit_exp)\\n db.session.commit()\\n return \\\" <span class='label label-success'>Saved!</span>\\\"\\n\\n # if not logged in, cannot save\\n else:\\n return \\\" <a href='/login'><span class='label label-default'>Login to save</span></a>\\\"\",\n \"def save_to_favorites_list():\\n\\n #get show id from the event handler/post request\\n show_id = str(request.form.get(\\\"id\\\"))\\n #get button content from the event handler/post request\\n button_content = request.form.get(\\\"button_content\\\")\\n\\n button_content_encoded = button_content.encode('utf-8')\\n\\n #save utf-8 encoded checkmark as a string variable\\n check_mark = \\\"\\\\xe2\\\\x9c\\\\x93\\\"\\n\\n #find the current logged in user\\n email = session.get(\\\"current_user\\\")\\n\\n if email:\\n\\n #use email to find the user_id\\n user_id = User.find_user_id_with_email(email)\\n\\n #if the show has not been favorited yet\\n if check_mark not in button_content_encoded:\\n #add row in favorites table\\n favorite = Favorite.add_to_favorites(show_id, user_id)\\n\\n #pass back the show_id and that the show has been favorited\\n payload = {\\\"show_id\\\":show_id,\\\"favorite\\\":\\\"True\\\"}\\n return jsonify(payload)\\n else:\\n #delete row in favorites table\\n Favorite.delete_favorite(show_id)\\n\\n #pass back the show_id and that the show has been unfavorited\\n payload = {\\\"show_id\\\":show_id,\\\"favorite\\\":\\\"False\\\"}\\n return jsonify(payload)\\n else:\\n flash(\\\"You need to be logged in to see that page.\\\")\\n return redirect(\\\"/login\\\")\",\n \"def test_save(self, init_db, category1):\\n params = {\\n 'title' : fake.alphanumeric(15),\\n 'description' : fake.alphanumeric(200),\\n 'ranking' : 1,\\n 'meta_data' : {\\n 'color' : 'red',\\n 'quantity' : 2,\\n 'date_purchased' : '2019-02-05',\\n 'condition' : 'bad'\\n },\\n 'category_id' : category1.id\\n }\\n\\n favorite = Favorite(**params)\\n assert favorite == favorite.save()\",\n \"def insert_favorite_food(self, user_answer_id_food, name_substitute):\\n self.cursor = self.data_base.cursor(MySQLCursorPrepared)\\n save_favorite_food = \\\"\\\"\\\"INSERT INTO Favorite\\n (id_food, id_substitute_chooses)\\n VALUES({0}, \\n (SELECT id FROM Food WHERE name_food = {1}))\\\"\\\"\\\" \\\\\\n .format(int(user_answer_id_food),\\n \\\"\\\\'\\\" + name_substitute + \\\"\\\\'\\\")\\n self.cursor.execute(save_favorite_food)\\n self.data_base.commit()\",\n \"def favorite():\\n # handle pre-flight for browsers CORS access\\n if request.method == \\\"OPTIONS\\\":\\n return generate_response()\\n # part1: verify the token\\n checked_and_verified, response = check_verify_token(request,login_session)\\n if checked_and_verified == False: return response\\n # handle the edge case where user is authorized to perform create user but not other method\\n if not is_loggedin(login_session):\\n response = generate_message(MESSAGE_USER_NOT_LOGGED_IN,401)\\n return response\\n # handles the get request\\n if request.method == \\\"GET\\\":\\n favorites = read_criteria(Favorite,{\\\"user_id\\\":login_session[\\\"user_id\\\"]},session,\\\"m\\\") or []\\n favorites_room_json = [room_json(favorite.room, session,app.config[\\\"OFFLINE_TESTING\\\"], login_session) for favorite in favorites]\\n return generate_response(elem={\\\"favorites\\\":favorites_room_json})\\n # part2: check json, handle POST request\\n checked_json, response, requested_json = check_json_form(request,MESSAGE_BAD_JSON,MESSAGE_GET_FAV_NO_JSON)\\n if checked_json != True: return response\\n # verify room id type, with strict mode\\n requested_json[\\\"user_id\\\"] = login_session[\\\"user_id\\\"]\\n correct_format,valid_update_pairs, response = process_request_json(Favorite,requested_json, True, access_mode=\\\"read\\\",nondb_type_map={\\\"action\\\":str})\\n if correct_format == False: \\n return response\\n room = get_row_if_exists(Room, session, ** {\\\"id\\\": requested_json[\\\"room_id\\\"]})\\n user = get_row_if_exists(User, session, ** {\\\"id\\\": login_session[\\\"user_id\\\"]})\\n # if the room id in the request doesn't fit any entry in db, return error message\\n if room is None:\\n response = generate_message(MESSAGE_FAV_ROOM_NOT_EXIST,404)\\n return response\\n if requested_json[\\\"action\\\"] == \\\"add\\\":\\n # the add favorite already handle duplicates add\\n # it treats multiple adds as one add and every duplicate add afterwards is counted as success\\n add_favorite(room,user, session)\\n response = generate_message(MESSAGE_POST_FAV_ADD_SUCCESS,201)\\n return response\\n elif requested_json[\\\"action\\\"] == \\\"delete\\\":\\n # the delete favorite already handle duplicates delete\\n # it treats multiple delete as one delete and every duplicate delete afterwards is counted as success\\n remove_entry(Favorite,requested_json[\\\"room_id\\\"], session)\\n response = generate_message(MESSAGE_POST_FAV_DEL_SUCCESS,200)\\n return response\\n else: # method not supported\\n response = generate_message(MESSAGE_POST_FAV_METHOD_NOT_SUPPORTED,405)\\n return response\",\n \"def test_display_favorite(self):\\n\\n result = self.client.get(\\\"/view_favorites\\\")\\n self.assertIn(b\\\"s1925148\\\", result.data)\",\n \"def add_to_fav(show_id, name):\\n db = get_db()\\n db.execute(\\n 'INSERT INTO shows_users (show_id, user_id)'\\n ' VALUES (?, ?)',\\n (show_id, session['user_id'])\\n )\\n\\n flash('\\\\\\\"%s\\\\\\\" has been successfully added to your favourite TV Shows!' % name)\\n db.commit()\\n return redirect(request.referrer)\",\n \"def favourite_create(self, data, sesh):\\n\\n\\t\\t# Verify fields\\n\\t\\ttry: DictHelper.eval(data, ['id'])\\n\\t\\texcept ValueError as e: return Services.Effect(error=(1001, [(f, \\\"missing\\\") for f in e.args]))\\n\\n\\t\\t# If someone tries to add themselves\\n\\t\\tif data['id'] == sesh['thrower']['_id']:\\n\\t\\t\\treturn Services.Effect(False);\\n\\n\\t\\t# Make sure the thrower exists\\n\\t\\tif not Thrower.exists(data['id']):\\n\\t\\t\\treturn Services.Effect(error=(1104, data['id']))\\n\\n\\t\\t# Add the thrower to the logged in thrower's favourites and return the\\n\\t\\t#\\tresult\\n\\t\\treturn Services.Effect(\\n\\t\\t\\tFavourites.add(sesh['thrower']['_id'], data['id'])\\n\\t\\t)\",\n \"def testing_favourites(self, email, password, song):\\n add_to_favourites = Favourites(self.driver, email, password, song)\\n add_to_favourites.open_login_page()\\n add_to_favourites.set_user_inputs()\\n add_to_favourites.open_home_page()\\n add_to_favourites.selecting_a_random_song()\\n assert add_to_favourites.check_the_song_is_displayed() is True, 'Song is not displaying'\\n add_to_favourites.add_to_wishlist()\\n add_to_favourites.go_to_favourites_page()\\n assert add_to_favourites.check_the_song_is_added_to_wishlist() == song, 'Not the song'\",\n \"def add_testing_site():\\n\\n test_id = request.form.get('test_id')\\n favorite = { \\n 'status': None,\\n }\\n try:\\n if 'user_id' in session:\\n user_id = session['user_id']\\n\\n already_favorited = check_testing_saved_location_in_favorites(user_id, test_id)\\n\\n if already_favorited:\\n favorite['status'] = 'already_favorited'\\n flash('Already saved to favorites.')\\n return jsonify(favorite)\\n\\n else:\\n favorite['status'] = 'added'\\n saved_location = create_testing_saved_locations(user_id, test_id) \\n location = get_testing_location_by_test_id(test_id)\\n flash(f'Testing Location {location.alternate_name} saved to profile!')\\n return jsonify(favorite)\\n\\n else:\\n flash('Please login to save a location!')\\n\\n except Exception as e:\\n msg = f\\\"Error. Tried adding {test_id} to db failed: \\\\n {e}.\\\"\\n return jsonify(msg) \\n\\n return jsonify('Success!')\",\n \"def FoodCheckIn(sc, event):\\n channel = sc.api_call('channels.info', channel=event['channel'])\\n food = event['text'][9:]\\n if food:\\n if 'pizza' in food:\\n sc.api_call('reactions.add', as_user='true', channel=event['channel'],\\n timestamp=event['ts'], name='pizza')\\n user = sc.api_call('users.info', user=event['user'])\\n db = pymysql.connect(host='localhost', user='pizzabot', db='pizzachat')\\n cursor = db.cursor()\\n query = 'INSERT INTO foodlist (who, what) VALUES (%s, %s)'\\n cursor.execute(query, (user['user']['name'], food.encode('utf-8')))\\n db.commit()\\n db.close()\",\n \"def test_user_own_recipes(self):\\n\\n recipe1 = Recipe(uri=\\\"testuri\\\", name=\\\"testname\\\", image_url=\\\"test_image_url\\\", user_id=self.uid)\\n recipe2 = Recipe(uri=\\\"testuri2\\\", name=\\\"testname2\\\", image_url=\\\"test_image_url2\\\")\\n\\n db.session.add_all([recipe1, recipe2])\\n db.session.commit()\\n\\n self.assertEqual(recipe1.user_id, self.uid)\\n self.assertNotEqual(recipe2.user_id, self.uid)\\n self.assertEqual(recipe2.user_id, None)\",\n \"def test_create_ingredient_successful(self):\\n payload = {'name':'Cabbage'}\\n self.client.post(INGREDIENTS_URL, payload)\\n exists = Ingredient.objects.all().filter(user=self.user, name=payload['name']).exists\\n self.assertTrue(exists)\",\n \"def test_add_remove_from_wishlist(self):\\n url = reverse('add-to-wishlist')\\n data = {\\n 'igdb': self.game.igdb,\\n 'name': self.game.name,\\n 'slug': self.game.slug,\\n 'cover_id': self.game.cover_id,\\n 'backdrop_id': self.game.backdrop_id\\n }\\n\\n add = self.client.post(url, data, format='json')\\n self.assertEqual(True, add.data['value'])\\n\\n remove = self.client.post(url, data, format='json')\\n self.assertEqual(False, remove.data['value'])\",\n \"def favorited(self: Article, request: Request):\\n if not request.user:\\n return False\\n\\n if self in request.user.favorites:\\n return True\\n\\n return False\",\n \"def setUp(self):\\n self.prod_1 = Product.objects.create(\\n pk=1,\\n ean='3350033118072',\\n name='test 1',\\n category='cat 1',\\n image='product_default.png',\\n nutriscore='u'\\n )\\n\\n self.user_1 = User.objects.create_user(\\n pk=1,\\n username='Fav Models Unit Test 1',\\n email='boggusmail@boggusmail.net'\\n )\\n\\n self.fav_1 = Favourite.objects.create(\\n pk=1,\\n date_added='2019-12-20 09:00:00',\\n user=self.user_1,\\n product=self.prod_1\\n )\",\n \"def favourite(self, favourite):\\n\\n self._favourite = favourite\",\n \"def add_fav_drinks(self, user_id, drinks): \\n assert type(user_id) == str\\n assert type(drinks) == list\\n\\n fav_drinks = self.get_fav_drinks(user_id)\\n user_check = self.users.get_user_name(user_id)\\n drinks_check = [self.drinks.get_drinks_by_flavor_and_type(d.get('flavor'), d.get('type')) for d in drinks]\\n\\n # make sure that at least one drink exists in the list\\n if all(x is None for x in drinks_check):\\n print(\\\"All drinks provided do not exist. We will not add favorite drinks since one of the drinks must already exist.\\\")\\n \\n # user does not exist\\n elif user_check is None: \\n print(\\\"User Id {} does not exist.\\\".format(user_id))\\n \\n # add fav drinks\\n else : \\n # user has existing fav drinks\\n if fav_drinks is not None:\\n for d in drinks:\\n # add the drink if it does not exist \\n drink_id = self.drinks.add_drink(d.get('type'), d.get('flavor'))\\n fav_drinks.append(drink_id)\\n # user has no existing fav drinks\\n else :\\n ids = []\\n for d in drinks:\\n # add the drink if it does not exist \\n ids.append(self.drinks.add_drink(d.get('type'), d.get('flavor')))\\n\\n fd_id = self.__generate_id()\\n self.favorite_drinks.append({\\\"id\\\": fd_id, \\\"user_id\\\": user_id, \\\"drink_id\\\": ids})\",\n \"def test_create_ingredient(self):\\n\\n ingredient_payload = {'name': 'Test Ingredient'}\\n self.client.post(URL_INGREDIENTS, ingredient_payload)\\n\\n is_ingredient_created = Ingredient.objects.filter(\\n user=self.user,\\n name=ingredient_payload['name']\\n ).exists()\\n\\n self.assertTrue(is_ingredient_created)\",\n \"def toggle_favorite(self, user, article, is_favoriting):\\n if user not in article.favorited_by.all() and is_favoriting:\\n article.favorited_by.add(user)\\n if user in article.favorited_by.all() and not is_favoriting:\\n article.favorited_by.remove(user)\\n article.favoritesCount = article.favorited_by.all().count()\\n article.save()\",\n \"def test_create_ingredient_successful(self):\\n payload = {\\n 'name': 'turmeric'\\n }\\n\\n self.client.post(INGREDIENT_URL, payload)\\n\\n exists = Ingredients.objects.filter(\\n user=self.user,\\n name=payload['name']\\n ).exists()\\n\\n self.assertTrue(exists)\",\n \"def added_by(self, user):\\n return ChefsHasRecipes.objects.filter(recipe=self, chef=user).exists()\",\n \"def test_create_ingredient_succesfull(self):\\n\\n payload = {'name': 'Cabbage'}\\n self.client.post(INGREDIENTS_URL, payload)\\n\\n exist = Ingredient.objects.filter(\\n user=self.user,\\n name=payload['name'],\\n ).exists()\\n self.assertTrue(exist)\",\n \"def test_new_flight_succeeds(self, init_db, new_flight):\\n assert new_flight == new_flight.save()\",\n \"def set_favorite(request):\\n company_id = request.data.get('id')\\n company = Company.objects.get(id=company_id)\\n\\n request.user.profile.companies.add(company)\\n return Response({'favorite': True})\",\n \"def create(self, validated_data):\\n user_data = validated_data.pop('user')\\n user = User.object.get(id=user_data.id)\\n restaurant, created = Restaurant.objects.update_or_create(user=user, data=validated_data)\\n return restaurant\",\n \"def update_favourites(self, item_info, status):\\r\\n if status == \\\"Add\\\":\\r\\n return self.model.add_to_favourites(item_info)\\r\\n elif status == \\\"Remove\\\":\\r\\n return self.model.delete_from_favourites(item_info)\",\n \"def add_fav_drink(self, user_id, drink_id):\\n assert type(user_id) == str\\n assert type(drink_id) == str \\n\\n existing_drink = False if self.drinks.get_drink_by_id(drink_id) is None else True\\n existing_user = False if self.users.get_user_name(user_id) is None else True\\n if not existing_drink:\\n print(\\\"Drink does not exist.\\\")\\n elif not existing_user:\\n print(\\\"User does not exist.\\\")\\n else :\\n fav_drinks = self.get_fav_drinks(user_id)\\n if fav_drinks is not None:\\n if drink_id not in fav_drinks:\\n fav_drinks.append(drink_id)\\n else : # user exists but has no fav drinks\\n fd_id = self.__generate_id()\\n self.favorite_drinks.append({\\\"id\\\": fd_id, \\\"user_id\\\": user_id, \\\"drink_id\\\": [drink_id]})\",\n \"def test_create_ingredient_successful(self):\\n payload = {'name': \\\"Test ingredient\\\"}\\n\\n # in order to create user must be already authenticated\\n self.client.post(INGREDIENTS_URL, payload)\\n\\n exists = Ingredient.objects.filter(\\n user=self.user,\\n name=payload['name'],\\n ).exists()\\n\\n self.assertTrue(exists)\",\n \"def test_update(self, init_db, favorite):\\n params = {\\n \\\"title\\\": \\\"Category\\\",\\n \\\"description\\\": fake.alphanumeric(100),\\n }\\n favorite.update(**params)\\n assert favorite.title == params['title']\\n assert favorite.description == params['description']\",\n \"def mark_favorite(request, object_id):\\n feed_item = get_object_or_404(FeedItem, id=object_id)\\n fav_item, is_new = FavoriteItem.objects.get_or_create(feed_item=feed_item)\\n if request.is_ajax():\\n return JSONResponse({'status': 'ok', 'text': 'Marked as favorite'}, False)\\n return redirect(request.META.get('HTTP_REFERER', 'feed_item_list'))\",\n \"def test_if_user_can_add_and_retrieve_data(self):\\n # take the first three drinks\\n drinks = self.test_data[\\\"drinks\\\"][:3]\\n # create drink objects from the json data\\n drinks = [Drink(**i) for i in drinks]\\n Drink.objects.bulk_create(drinks)\\n\\n data = self.test_data[\\\"data\\\"][0]\\n # use drink ids added to the db for this particular\\n # test\\n data[\\\"drink_id\\\"] = drinks[random.randint(0, 2)]._id\\n\\n response = self.client.post(\\\"/data/data_collected/\\\",\\n data, format='json')\\n\\n # assert it data was added correctly\\n self.assertEqual(response.status_code,\\n status.HTTP_201_CREATED)\\n\\n # retrieve the data added\\n response = self.client.get(\\\"/data/data_collected/\\\")\\n\\n # assert if the response is 200\\n self.assertEqual(response.status_code, 200)\\n\\n # get the number of added data records\\n data_added_count = len(response.json())\\n\\n # assert if the data added is one\\n self.assertEqual(data_added_count, 1)\",\n \"def update_favorite_things():\\n data = request.data\\n favorite_things = json.loads(data)\\n print(favorite_things)\\n connection = mongo_connect()\\n if(favorite_things[\\\"action\\\"] == \\\"add\\\"):\\n connection[\\\"users\\\"].update_one(\\n {\\\"_id\\\": ObjectId(session[\\\"_id\\\"])},\\n {\\\"$push\\\": {\\n favorite_things[\\\"type\\\"]: ObjectId(favorite_things[\\\"item_id\\\"])\\n }\\n }\\n )\\n else:\\n connection[\\\"users\\\"].update_one(\\n {\\\"_id\\\": ObjectId(session[\\\"_id\\\"])},\\n {\\\"$pull\\\":\\n {\\n favorite_things[\\\"type\\\"]:\\n ObjectId(favorite_things[\\\"item_id\\\"])\\n }\\n }\\n )\\n resp = jsonify(success=True)\\n return resp\",\n \"def test_api_can_add_food_to_a_meal(self):\\n response = self.client.post(f'/api/v1/meals/{self.breakfast.id}/foods/{self.oatmeal.id}')\\n # import code; code.interact(local=dict(globals(), **locals()))\\n\\n self.assertEqual(response.data['message'], \\\"Successfully added oatmeal to breakfast\\\")\",\n \"def test_delete(self, init_db, favorite):\\n favorite.delete()\\n assert Favorite.get(favorite.id) == None\",\n \"def NewFood(r, foods):\\n hasError = False\\n for i in foods:\\n try:\\n food_form = FoodForm(i)\\n if food_form.is_valid():\\n food = food_form.save(commit=False)\\n\\n # store base64 type image into food.src\\n if isBase64Image(i[\\\"src\\\"]):\\n file = getImgContentFile(i[\\\"src\\\"])\\n try:\\n Image.open(file)\\n food.src = file\\n except IOError:\\n print(\\\"Not a image.\\\")\\n hasError = True\\n food.restaurant = r\\n food.save()\\n else:\\n hasError = True\\n except:\\n print(\\\"new error\\\")\\n hasError = True\\n return hasError\",\n \"def test_create_ingredient_successful(self):\\n\\n payload = {'name': 'Salt'}\\n\\n res = self.client.post(INGREDIENTS_URL, payload)\\n\\n exists = Ingredient.objects.filter(\\n user=self.user,\\n name=payload['name']\\n ).exists()\\n\\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\\n self.assertTrue(exists)\",\n \"def saveUserFavorites(request, userid):\\n try:\\n User.objects.get(id=userid)\\n newFavorite = FavoriteSerializer(data=request.data)\\n print(request.data)\\n if newFavorite.is_valid():\\n try:\\n Item.objects.get(id=request.data.get(\\\"item\\\"))\\n newFavorite.save()\\n return Response(newFavorite.data, status=status.HTTP_201_CREATED)\\n \\n except Item.DoesNotExist:\\n fail = {\\n \\\"Item\\\":\\\"doesnotexist\\\"\\n }\\n return JsonResponse(fail)\\n \\n #for testing ONLY\\n print(newFavorite.errors)\\n #f##########\\n\\n fail = {\\n \\\"user\\\": \\\"requestdatanotvalid\\\",\\n \\\"item\\\":\\\"requestdatanotvalid\\\"\\n }\\n return JsonResponse(fail)\\n\\n except User.DoesNotExist:\\n fail = {\\n \\\"user\\\":\\\"doesnotexist\\\"\\n }\\n return JsonResponse(fail)\",\n \"def test_view_url_propose_product_already_in_favorites(self):\\r\\n self.client.login(username='test', password='test')\\r\\n response = self.client.get(reverse('search_results'),\\r\\n {'query': '', 'name': 'nutella'})\\r\\n self.assertEqual(response.status_code, 200)\\r\\n self.assertTemplateUsed(response, 'purbeurre/search_results.html')\",\n \"def fav_place_check(uri):\\n return veggiesailor.StorageFav().exists(uri)\",\n \"def test_given_a_user_when_I_add_a_todo_Then_I_can_access_it_from_user_todo_collection(self):\\n from .models import Tag\\n from .models import TodoUser\\n from .models import TodoItem\\n\\n user = TodoUser(\\n email=u'king.arthur@example.com',\\n first_name=u'Arthur',\\n last_name=u'Pendragon',\\n )\\n self.session.add(user)\\n\\n tags = [u'quest', u'ni', u'knight']\\n\\n todo = TodoItem(user.email,\\n u'Find a shrubbery', \\n [u'quest', u'ni', u'knight'] \\n ) \\n self.session.add(todo)\\n \\n user_todo = user.todo_list.one()\\n self.assertTrue(todo is user_todo)\",\n \"def test_addUser(self):\\n self.new_user.saveUser()\\n self.assertEqual(len(User.users_list),1)\",\n \"def test_favs(app):\\n res = app.get('/favs')\\n assert res.status_code == 200\\n assert b'bloom' in res.data\",\n \"def test_new_likes(self):\\n\\n new_user = User.signup(\\\"newuser\\\", \\\"newuser@gmail.com\\\", \\\"password123\\\", None)\\n db.session.add(new_user)\\n db.session.commit()\\n\\n new_msg = Message(text=\\\"A new message from a new user\\\", user_id=new_user.id)\\n db.session.add(new_msg)\\n db.session.commit()\\n\\n new_user.likes.append(new_msg)\\n db.session.commit()\\n\\n likes = Likes.query.all()\\n self.assertEqual(len(likes), 1)\\n self.assertEqual(likes[0].user_id, new_user.id)\\n self.assertEqual(likes[0].message_id, new_msg.id)\",\n \"def test_returns_projects_favourited_by_user_if_favourited_by_me_is_true(self):\\n # Arrange\\n # Make all projects to be accessible for user\\n self.test_project_2.private = False\\n self.test_project_2.save()\\n self.test_project_3.status = ProjectStatus.PUBLISHED.value\\n self.test_project_3.save()\\n self.test_user.favorites = [self.test_project_1]\\n self.test_user.save()\\n # Act\\n response = self.client.get(\\n self.url,\\n headers={\\\"Authorization\\\": self.user_session_token},\\n query_string={\\\"favoritedByMe\\\": \\\"true\\\"},\\n )\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(len(response.json[\\\"results\\\"]), 1)\\n self.assertEqual(\\n response.json[\\\"results\\\"][0][\\\"projectId\\\"], self.test_project_1.id\\n )\",\n \"def add_to_fav(request, q_id):\\n if request.method == 'POST':\\n Quotes.objects.add_to_user_fav(request.session['id'], q_id)\\n return redirect('/quotes')\",\n \"def connect_user(self, user):\\n\\t\\tis_user_added = False\\n\\t\\tif not user in self.users.all():\\n\\t\\t\\tself.users.add(user)\\n\\t\\t\\tself.save()\\n\\t\\t\\tis_user_added = True\\n\\t\\telif user in self.users.all():\\n\\t\\t\\tis_user_added = True\\n\\t\\treturn is_user_added\",\n \"def favorite(self) -> bool:\\n return self._favorite\",\n \"def post(self):\\n # fetch parameter\\n get_parser = reqparse.RequestParser(bundle_errors=True)\\n get_parser.add_argument(\\\"user_id\\\", required=True, help=\\\"User ID required to ad to their favourite projects\\\")\\n get_parser.add_argument(\\\"project_id\\\", required=True, help=\\\"Project ID required to add to the favourite projects\\\")\\n args = get_parser.parse_args(strict=True)\\n\\n # get user_id and project_id\\n user_id = args[\\\"user_id\\\"]\\n project_id = args[\\\"project_id\\\"]\\n\\n # convert parameter ids into objectids\\n try:\\n user_id = ObjectId(user_id)\\n project_id = ObjectId(project_id)\\n except:\\n return {\\\"message\\\": \\\"invalid user id or project id\\\"}, 400\\n\\n # add project to the user's favourites\\n if ('user_id' or 'project_id') not in args.keys():\\n return {\\\"message\\\": \\\"both user and project id are required\\\"}, 400\\n else:\\n # check if user is valid\\n user = self.users.find_one({\\\"_id\\\": user_id})\\n project = self.projects.find_one({\\\"_id\\\": project_id})\\n if user is None:\\n return {\\\"message\\\": \\\"user not found\\\"}, 404\\n elif project is None:\\n return {\\\"message\\\": \\\"project not found\\\"}, 404\\n else:\\n # add project to favourites\\n user_favourites = self.favourites.find_one({\\\"user_id\\\": user_id})\\n if user_favourites is None:\\n # insert a new doc into favourites collection\\n favourites_list = []\\n favourites_list.append(deepcopy(project)) \\n self.favourites.insert({\\n \\\"user_id\\\": user_id,\\n \\\"favourite_projects\\\": favourites_list\\n })\\n else:\\n new_favourite_list = user_favourites[\\\"favourite_projects\\\"]\\n\\n # check if this project is already in the user's favourites\\n for proj in new_favourite_list:\\n if proj[\\\"_id\\\"] == project_id:\\n return {\\\"message\\\": \\\"project is already in the favourites list\\\"}, 400\\n\\n new_favourite_list.append(deepcopy(project))\\n updated_list = {\\\"favourite_projects\\\": new_favourite_list}\\n\\n self.favourites.update({\\\"user_id\\\": user_id}, {\\\"$set\\\": updated_list}, upsert=False)\\n \\n return {\\\"status\\\": \\\"project has been added to favourites successfully\\\"}, 200\",\n \"def save(self, substitute_choice, product_choice):\\n self.db.query(f\\\"\\\"\\\"\\n INSERT INTO {self.table} (substitut_id, original_id)\\n VALUES (:substitut_id, :original_id)\\n ON DUPLICATE KEY UPDATE substitut_id = :substitut_id\\n \\\"\\\"\\\", substitut_id=substitute_choice.id, original_id=product_choice.id)\\n favorite = (substitute_choice.id, product_choice.id)\\n return favorite\",\n \"def test_add(self):\\n # add a todo\\n self.add(title=\\\"Sample task todo\\\", description=\\\"for sample\\\", state=\\\"todo\\\")\\n task = Task.query.filter_by(title='Sample task todo').first()\\n self.assertEqual(task.description, 'for sample')\\n self.assertEqual(task.state, 'todo')\\n\\n # add a doing\\n self.add(title=\\\"Sample task doing\\\", description=\\\"for sample\\\", state=\\\"doing\\\")\\n task = Task.query.filter_by(title=\\\"Sample task doing\\\").first()\\n self.assertEqual(task.description, 'for sample')\\n self.assertEqual(task.state, 'doing')\\n\\n # add a done\\n self.add(title=\\\"Sample task done\\\", description=\\\"for sample\\\", state=\\\"done\\\")\\n task = Task.query.filter_by(title='Sample task done').first()\\n self.assertEqual(task.description, 'for sample')\\n self.assertEqual(task.state, 'done')\",\n \"def favorite(request, movie_id):\\n\\n movie = get_object_or_404(Movie, pk=movie_id)\\n try:\\n if movie.liked:\\n movie.liked = False\\n else:\\n movie.liked = True\\n movie.save()\\n except (KeyError, Movie.DoesNotExist):\\n return JsonResponse({'success': False})\\n else:\\n return JsonResponse({'success': True})\",\n \"def saving_search(request, id_movie):\\n movie = Movie.objects.get(id_code=id_movie)\\n Favorite.objects.get_or_create(user_link=request.user,\\n movie_saved=movie)\\n return redirect('home')\",\n \"def test_save_users(self):\\n\\n self.new_users.save_users() # saving the new user\\n self.assertEqual(len(User.user_list), 1)\",\n \"def favorite(self, favorite: bool):\\n if favorite is None:\\n raise ValueError(\\\"Invalid value for `favorite`, must not be `None`\\\")\\n\\n self._favorite = favorite\",\n \"def post_favorite(request, pk=None):\\n post = Post.objects.get(pk=pk).original_or_self()\\n if post.favorites.filter(pk=request.user.pk).exists():\\n post.favorites.remove(request.user)\\n else:\\n post.favorites.add(request.user)\\n post.save()\\n\\n referer = request.META['HTTP_REFERER']\\n if referer:\\n return redirect(referer)\\n else:\\n return redirect('posts:post', pk=post.pk)\",\n \"def restaurants_new():\\n # If the user isn't logged in, send to the login page\\n if helper.handle_login(login_session) is False:\\n return redirect('/login')\\n\\n if request.method == 'POST':\\n if len(request.form['name']) > 0:\\n new_restaurant = Restaurant(name=request.form['name'],\\n address=request.form['address'],\\n phone=request.form['phone'],\\n web=helper.check_restaurant_URL(request.form['web']),\\n description=request.form['description'],\\n user_id=login_session['user_id'])\\n session.add(new_restaurant)\\n session.commit()\\n flash(\\\"New restaurant created - {}\\\".format(new_restaurant.name))\\n tag_line = request.form['tag_line']\\n tag_list = tag_line.split(',')\\n for tag in tag_list:\\n helper.add_tag_if_not_exists(tag, new_restaurant.id)\\n return redirect(url_for('restaurants_page'))\\n else:\\n flash(\\\"Incorrect Restaurant details - Please include a name!\\\")\\n\\n user_info = helper.get_user_if_exists(login_session)\\n return render_template('newrestaurant.html', user_info=user_info)\",\n \"def test_get_and_set_star(self):\\n email = 'user2@example.com'\\n feature_id = self.feature_1.key.integer_id()\\n notifier.FeatureStar.set_star(email, feature_id)\\n actual = notifier.FeatureStar.get_star(email, feature_id)\\n self.assertEqual(email, actual.email)\\n self.assertEqual(feature_id, actual.feature_id)\\n self.assertTrue(actual.starred)\\n updated_feature = models.Feature.get_by_id(feature_id)\\n self.assertEqual(1, updated_feature.star_count)\\n\\n notifier.FeatureStar.set_star(email, feature_id, starred=False)\\n actual = notifier.FeatureStar.get_star(email, feature_id)\\n self.assertEqual(email, actual.email)\\n self.assertEqual(feature_id, actual.feature_id)\\n self.assertFalse(actual.starred)\\n updated_feature = models.Feature.get_by_id(feature_id)\\n self.assertEqual(0, updated_feature.star_count)\",\n \"def test_save(self):\\n self.assertEqual(CalendallUser.objects.count(), len(self.data))\",\n \"def test_favorites_posts(self):\\n self.resource._request.register_uri(\\n 'GET', '/users/dotzero/favorites/posts?page=2', 'fixture_post.json')\\n\\n response = self.resource.favorites_posts('dotzero', 2)\\n\\n self.assertTrue('data' in response)\\n self.assertTrue('server_time' in response)\",\n \"def test_create_shelf(self, *_):\\n form = forms.ShelfForm()\\n form.data[\\\"user\\\"] = self.local_user.id\\n form.data[\\\"name\\\"] = \\\"new shelf name\\\"\\n form.data[\\\"description\\\"] = \\\"desc\\\"\\n form.data[\\\"privacy\\\"] = \\\"unlisted\\\"\\n request = self.factory.post(\\\"\\\", form.data)\\n request.user = self.local_user\\n\\n views.create_shelf(request)\\n\\n shelf = models.Shelf.objects.get(name=\\\"new shelf name\\\")\\n self.assertEqual(shelf.privacy, \\\"unlisted\\\")\\n self.assertEqual(shelf.description, \\\"desc\\\")\\n self.assertEqual(shelf.user, self.local_user)\",\n \"def add_favourites_field():\\n existing = read_json()\\n\\n if 'favourites' not in existing[0].keys(): # if the field has not already been added, add it.\\n for club in existing:\\n club['favourites'] = 0\\n write_json(existing)\",\n \"def accept(self):\\n receiver_friend_list = FriendList.objects.filter(user_id=self.receiver_id)\\n sender_friend_list = FriendList.objects.filter(user_id=self.sender_id)\\n if(receiver_friend_list.exists()):\\n receiver_friend_list = receiver_friend_list[0]\\n else:\\n receiver_friend_list = FriendList.objects.create(user_id=self.receiver_id)\\n\\n if(sender_friend_list.exists()):\\n sender_friend_list = sender_friend_list[0]\\n else:\\n sender_friend_list = FriendList.objects.create(user_id=self.sender_id)\\n\\n if receiver_friend_list:\\n receiver_friend_list.add_friend(self.sender_id)\\n if sender_friend_list:\\n sender_friend_list.add_friend(self.receiver_id)\\n self.is_active = False\\n self.save()\",\n \"def test_add_or_remove_like_from_db(self):\\n session = UnifiedAlchemyMagicMock()\\n with mock.patch(\\\"app.DB.session\\\", session):\\n app.add_or_remove_like_from_db(\\\"username\\\", 0)\\n is_liked = (\\n session.query(app.models.Likes.id)\\n .filter_by(username=\\\"username\\\", post_id=0)\\n .scalar()\\n is not None\\n )\\n session.commit()\\n self.assertEqual(is_liked, True)\",\n \"def test_update_restaurant(self):\\n from espresso import db\\n from espresso import Restaurant\\n\\n name = 'Restaurant Mexicano'\\n zip_code = \\\"94110\\\"\\n db.session.add(Restaurant(name=name, creator='test-user@gmail.com', zip_code=zip_code))\\n db.session.commit()\\n\\n headers = {'Content-Type': 'application/json'}\\n headers.update(auth_header_cru_restaurants)\\n website = 'www.mexicano-nj.com'\\n email = 'mexicano-nj@gmail.com'\\n info = {'website': website, 'email': email}\\n resp = self.test_client.put(self.API_BASE + '/1', headers=headers, data=json.dumps(info))\\n\\n self.assertEqual(resp.status_code, 200)\\n resp_dict = json.loads(resp.data)\\n self.assertEqual(resp_dict['id'], 1)\\n self.assertTrue(name in resp_dict['message'])\\n\\n # Check that all restaurant fields are returned.\\n self.assertTrue('restaurant' in resp_dict)\\n self.assertEqual(resp_dict['restaurant']['id'], 1)\\n self.assertEqual(resp_dict['restaurant']['website'], website)\\n self.assertEqual(resp_dict['restaurant']['email'], email)\\n self.assertEqual(resp_dict['restaurant']['zip_code'], zip_code)\\n\\n self.assertTrue('name' in resp_dict['restaurant'])\\n self.assertTrue('street' in resp_dict['restaurant'])\\n self.assertTrue('suite' in resp_dict['restaurant'])\\n self.assertTrue('city' in resp_dict['restaurant'])\\n self.assertTrue('state' in resp_dict['restaurant'])\\n self.assertTrue('phone_num' in resp_dict['restaurant'])\\n self.assertTrue('date_established' in resp_dict['restaurant'])\\n self.assertTrue('creator' in resp_dict['restaurant'])\\n\\n # -----------------------------\\n # Make a separate request to retrieve the restaurant and assert that updated fields are as intended\\n resp = self.test_client.get(self.API_BASE + '/1', headers=auth_header_cru_restaurants)\\n\\n self.assertEqual(resp.status_code, 200)\\n resp_dict = json.loads(resp.data)\\n self.assertEqual(resp_dict['restaurant']['id'], 1)\\n self.assertEqual(resp_dict['restaurant']['website'], website)\\n self.assertEqual(resp_dict['restaurant']['email'], email)\\n self.assertEqual(resp_dict['restaurant']['zip_code'], zip_code) # Make sure this has not changed\",\n \"def test_like_message(self):\\n\\n self.u.likes.append(self.m_u2)\\n db.session.commit()\\n\\n self.assertEqual(len(self.u.likes), 1)\\n self.assertEqual(self.u.likes[0], self.m_u2)\",\n \"def test_create_pizza(self):\\n url = reverse('pizzas-list')\\n data = {'name': 'Quattro Formaggio'}\\n response = self.client.post(url, data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n self.assertEqual(Pizza.objects.count(), 1)\\n self.assertEqual(Pizza.objects.get().name, 'Quattro Formaggio')\",\n \"def test_add(self):\\n r = main.List.connection()\\n main.List.add(r, \\\"ToDo\\\", 1, \\\"Buy apples\\\", 2, \\\"20.05.2015\\\")\\n task = r.get(\\\"ToDo\\\")\\n self.assertTrue(task, \\\"No such entry in DB. Adding failed.\\\")\",\n \"def SetNewFav(self, newFav):\\n self.favouriteFood = newFav\",\n \"def save(self, *args, **kwargs):\\n if not self.require_confirm_email:\\n User.objects.filter(is_active=False, deactivation_reason=\\\"pending\\\").update(\\n is_active=True, deactivation_reason=None\\n )\\n if not self.invite_question_text:\\n self.invite_question_text = \\\"What is your favourite book?\\\"\\n super().save(*args, **kwargs)\",\n \"def test_check_add_flavor(self):\\n for flavor_id, flavor in OPENSTACK_FLAVOR.items():\\n self.cmd._add_flavor(flavor, flavor_id)\\n ralph_flavor = CloudFlavor.objects.get(flavor_id=flavor_id)\\n self.assertEqual(ralph_flavor.name, flavor['name'])\\n self.assertEqual(ralph_flavor.cloudprovider, self.cloud_provider)\\n self.assertIn(flavor['tag'], ralph_flavor.tags.names())\\n self.assertEqual(flavor['cores'], ralph_flavor.cores)\\n self.assertEqual(flavor['memory'], ralph_flavor.memory)\\n self.assertEqual(flavor['disk'], ralph_flavor.disk)\",\n \"def add_restaurant_review():\\n username = sign_up.get_username()\\n if username:\\n add_var = dict(user=username, restaurant_name=\\\"\\\", restaurant_address=\\\"\\\",\\n restaurant_item=\\\"\\\", item_comments=\\\"\\\", item_price=\\\"\\\",\\n restaurant_ranking=\\\"\\\", restaurant_rating=\\\"\\\",\\n restaurant_rating_reason=\\\"\\\", address=\\\"\\\", restaurant_chosen=\\\"\\\",\\n address_chosen=\\\"\\\")\\n return bottle.template('add_review', add_var=add_var)\\n else:\\n return bottle.template('login',\\n dict(user_error=\\\"Sorry, you need to be logged in to submit a review, please log below:\\\", pw_error=\\\"\\\"))\",\n \"def post(self):\\n args = UpdateLikeList.post_parser.parse_args()\\n user_name = args.get('user_name')\\n restaurant_name = args.get('restaurant_name')\\n #rating = args.get('rating')\\n newlike = {\\n 'user_name':args.get('user_name'),\\n 'restaurant_name':args.get('restaurant_name')\\n #'rating':args.get('rating')\\n }\\n conn = db.create_connection(db.connection_config_dict)\\n cursor = conn.cursor()\\n\\n # To get user's user_id\\n user_id = []\\n sql_1 = 'SELECT user_id FROM User WHERE user_name = \\\"{user_name}\\\"'.format(user_name=user_name)\\n print(sql_1)\\n cursor.execute(sql_1)\\n for u in cursor:\\n user_id.append(u)\\n print(user_id) \\n\\n # To get restaurant's restaurant_id\\n restaurant_id = []\\n sql_2 = 'SELECT restaurant_id FROM Restaurant WHERE name = \\\"{restaurant_name}\\\"'.format(restaurant_name=restaurant_name)\\n print(sql_2)\\n cursor.execute(sql_2)\\n for u in cursor:\\n restaurant_id.append(u)\\n print(restaurant_id)\\n\\n # Insert new restaurant into LikeList table\\n # neo4j may need insert data here\\n # user id is user_id[0][0], restaurant id is restaurant_id[0][0].\\n sql_3 = \\\"INSERT INTO LikeList (user_id, restaurant_id) VALUES ({user_id}, {restaurant_id});\\\".format(user_id=user_id[0][0], restaurant_id=restaurant_id[0][0])\\n print(sql_3)\\n cursor.execute(sql_3)\\n\\n conn.commit()\\n return newlike, 201\",\n \"def test_create_recipe_with_ingredient(self):\\n ingredient1 = sample_ingredient(user=self.user, name='Prawns')\\n ingrident2 = sample_ingredient(user=self.user, name ='Ginger')\\n\\n payload = {\\n 'title': 'Thai prawn and curry',\\n 'ingredient': [ingredient1.id,ingrident2.id],\\n 'time_minuts':60,\\n 'price': 250\\n }\\n res = self.client.post(RECIPE_URL,payload)\\n\\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\\n\\n recipe = Recipe.objects.get(id=res.data['id'])\\n ingredients = recipe.ingredient.all()\\n self.assertEqual(ingredients.count(),2)\\n self.assertIn(ingredient1, ingredients)\\n self.assertIn(ingrident2,ingredients)\",\n \"def count_favorite(self, obj):\\n\\n return obj.recipe_fav.count()\",\n \"def test_func(self):\\n return (Student.objects.filter(user=self.request.user).exists())\",\n \"def test_create_like(self):\\n pokemon = Pokemon.objects.all().first()\\n r = self.client.post(\\n reverse('create_like'),\\n {'pokemon-like-id': pokemon.id},\\n HTTP_X_REQUESTED_WITH='XMLHttpRequest'\\n )\\n\\n self.assertEqual(Like.objects.all().count(), 1)\",\n \"def test_is_following(self):\\n\\n self.u1.following.append(self.u2)\\n db.session.commit()\\n\\n self.assertTrue(self.u1.is_following(self.u2))\\n self.assertFalse(self.u2.is_following(self.u1))\",\n \"def test_is_followed_by(self):\\n\\n self.u1.followers.append(self.u2)\\n db.session.commit()\\n\\n self.assertTrue(self.u1.is_followed_by(self.u2))\\n self.assertFalse(self.u2.is_followed_by(self.u1))\",\n \"def test_save_user(self):\\n self.new_user.save_user()\\n self.assertEqual(len(User.user_list), 1)\",\n \"def test_save_user(self):\\n self.new_user.save_user()\\n self.assertEqual(len(User.user_list), 1)\",\n \"def test_ingredients_limited_to_user(self):\\n\\n user2 = get_user_model().objects.create_user(\\n 'zohaib@123.com',\\n 'zohaib123'\\n )\\n Ingredients.objects.create(user=user2, name='Vinegar')\\n\\n ingredient = Ingredients.objects.create(\\n user=self.user, name=\\\"Turmeric\\\"\\n )\\n res = self.client.get(INGREDIENT_URL)\\n\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\\n self.assertEqual(len(res.data), 1)\\n self.assertEqual(res.data[0]['name'], ingredient.name)\",\n \"def test_get_restaurant_by_id(self):\\n from espresso import db\\n from espresso import Restaurant\\n\\n name = 'Restaurant Greco'\\n db.session.add(Restaurant(name=name, creator='test-user@gmail.com'))\\n db.session.commit()\\n\\n # Since this is a freshly created table, the first id should be 1\\n resp = self.test_client.get(self.API_BASE + '/1', headers=auth_header_cru_restaurants)\\n self.assertEqual(resp.status_code, 200)\\n resp_dict = json.loads(resp.data)\\n self.assertEqual(resp_dict['restaurant']['name'], name)\",\n \"def test_get_is_book_on_shelf(self, *_):\\n shelf = self.local_user.shelf_set.first()\\n self.assertFalse(shelf_tags.get_is_book_on_shelf(self.book, shelf))\\n models.ShelfBook.objects.create(\\n shelf=shelf, book=self.book, user=self.local_user\\n )\\n self.assertTrue(shelf_tags.get_is_book_on_shelf(self.book, shelf))\",\n \"def add_user_to_db(json_details):\\n return True\",\n \"def newMenuItem(restaurant_id):\\n\\n if 'access_token' not in flask_session:\\n return logInRedirect()\\n restaurant = session.query(Restaurant).filter_by(id = restaurant_id).first()\\n user_id = getUserId(flask_session['email'],flask_session['google_plus_id'])\\n if not restaurant.user_id == user_id:\\n flash(\\\"Only restaurant owners can add new items.\\\")\\n return redirect(url_for(\\\"publicMenu\\\",restaurant_id = restaurant_id))\\n\\n if request.method == \\\"POST\\\":\\n new_name = request.form['new_name']\\n print \\\"\\\\nnewMenuItem POST triggered, name is: \\\", new_name\\n newMenuItem = MenuItem( name=new_name,\\n restaurant_id=restaurant.id )\\n session.add(newMenuItem)\\n session.commit()\\n flash( \\\"new item '\\\" + new_name + \\\"' created!\\\")\\n print \\\"POST worked!\\\"\\n return redirect(url_for(\\\"showMenu\\\", restaurant_id=restaurant.id))\\n\\n else:\\n return render_template('newMenuItem.html', restaurant = restaurant)\",\n \"def test_get_feature_starrers__some_starrers(self):\\n app_user_1 = models.AppUser(email='user16@example.com')\\n app_user_1.put()\\n app_user_2 = models.AppUser(email='user17@example.com')\\n app_user_2.put()\\n feature_1_id = self.feature_1.key.integer_id()\\n notifier.FeatureStar.set_star(app_user_1.email, feature_1_id)\\n notifier.FeatureStar.set_star(app_user_2.email, feature_1_id)\\n\\n actual = notifier.FeatureStar.get_feature_starrers(feature_1_id)\\n self.assertItemsEqual(\\n [app_user_1.email, app_user_2.email],\\n [au.email for au in actual])\",\n \"def test_user_retrieve_restaurant(self):\\n response = self.client.get(f'/api/places/1/', format='json')\\n self.assertNotEqual(response.status_code, status.HTTP_403_FORBIDDEN)\",\n \"def test_update_restaurant_blank_name(self):\\n from espresso import db\\n from espresso import Restaurant\\n\\n name = 'Restaurant Mexicano'\\n db.session.add(Restaurant(name=name, creator='test-user@gmail.com'))\\n db.session.commit()\\n\\n headers = {'Content-Type': 'application/json'}\\n headers.update(auth_header_cru_restaurants)\\n info = {'name': ''}\\n resp = self.test_client.put(self.API_BASE + '/1', headers=headers, data=json.dumps(info))\\n self.assertEqual(resp.status_code, 400)\",\n \"def post(self, request, slug):\\n try:\\n article = Article.objects.get(slug=slug)\\n except Article.DoesNotExist:\\n return Response({\\\"Message\\\": [\\n \\\"That article does not exist\\\"\\n ]}, status.HTTP_204_NO_CONTENT)\\n favorite = dict()\\n favorite[\\\"user\\\"] = request.user.id\\n favorite[\\\"article\\\"] = article.pk\\n serializer = self.serializer_class(data=favorite)\\n serializer.is_valid(raise_exception=True)\\n serializer.save()\\n article_serializer = ArticleSerializer(\\n instance=article, context={'request': request})\\n data = dict(article=article_serializer.data)\\n data[\\\"article\\\"][\\\"favorited\\\"] = True\\n data[\\\"message\\\"] = \\\"favorited\\\"\\n return Response(data, status.HTTP_200_OK)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72114295","0.6636154","0.6632463","0.65658706","0.64831656","0.6482696","0.6476188","0.64745235","0.6445702","0.6381491","0.6374218","0.6319528","0.6261317","0.6236881","0.6179164","0.6172634","0.6163744","0.6161769","0.6146276","0.6124457","0.60840327","0.6082842","0.6063424","0.6043306","0.6040604","0.60332847","0.60214037","0.6017845","0.5996445","0.59783953","0.59630066","0.59426045","0.5907134","0.5890616","0.5873086","0.58557093","0.5843716","0.5838309","0.5833724","0.58273315","0.58217096","0.58204156","0.57307446","0.57043576","0.56848633","0.5684691","0.56819737","0.56725836","0.5670301","0.5669386","0.56667906","0.56558275","0.56395274","0.5638145","0.56235874","0.56175506","0.5587593","0.55795","0.55683464","0.5561002","0.5560598","0.55406415","0.553992","0.5533672","0.55330646","0.55322725","0.552966","0.5528444","0.5528023","0.55215436","0.5520078","0.55152476","0.5507108","0.54975736","0.5497368","0.5484245","0.54831713","0.5466831","0.5453766","0.54491746","0.54470766","0.5444997","0.5444987","0.54427916","0.54321253","0.5427761","0.54244006","0.54219854","0.54069823","0.53986573","0.53986573","0.5396267","0.53886825","0.5383246","0.53826976","0.537803","0.5376038","0.537137","0.5369109","0.5359738"],"string":"[\n \"0.72114295\",\n \"0.6636154\",\n \"0.6632463\",\n \"0.65658706\",\n \"0.64831656\",\n \"0.6482696\",\n \"0.6476188\",\n \"0.64745235\",\n \"0.6445702\",\n \"0.6381491\",\n \"0.6374218\",\n \"0.6319528\",\n \"0.6261317\",\n \"0.6236881\",\n \"0.6179164\",\n \"0.6172634\",\n \"0.6163744\",\n \"0.6161769\",\n \"0.6146276\",\n \"0.6124457\",\n \"0.60840327\",\n \"0.6082842\",\n \"0.6063424\",\n \"0.6043306\",\n \"0.6040604\",\n \"0.60332847\",\n \"0.60214037\",\n \"0.6017845\",\n \"0.5996445\",\n \"0.59783953\",\n \"0.59630066\",\n \"0.59426045\",\n \"0.5907134\",\n \"0.5890616\",\n \"0.5873086\",\n \"0.58557093\",\n \"0.5843716\",\n \"0.5838309\",\n \"0.5833724\",\n \"0.58273315\",\n \"0.58217096\",\n \"0.58204156\",\n \"0.57307446\",\n \"0.57043576\",\n \"0.56848633\",\n \"0.5684691\",\n \"0.56819737\",\n \"0.56725836\",\n \"0.5670301\",\n \"0.5669386\",\n \"0.56667906\",\n \"0.56558275\",\n \"0.56395274\",\n \"0.5638145\",\n \"0.56235874\",\n \"0.56175506\",\n \"0.5587593\",\n \"0.55795\",\n \"0.55683464\",\n \"0.5561002\",\n \"0.5560598\",\n \"0.55406415\",\n \"0.553992\",\n \"0.5533672\",\n \"0.55330646\",\n \"0.55322725\",\n \"0.552966\",\n \"0.5528444\",\n \"0.5528023\",\n \"0.55215436\",\n \"0.5520078\",\n \"0.55152476\",\n \"0.5507108\",\n \"0.54975736\",\n \"0.5497368\",\n \"0.5484245\",\n \"0.54831713\",\n \"0.5466831\",\n \"0.5453766\",\n \"0.54491746\",\n \"0.54470766\",\n \"0.5444997\",\n \"0.5444987\",\n \"0.54427916\",\n \"0.54321253\",\n \"0.5427761\",\n \"0.54244006\",\n \"0.54219854\",\n \"0.54069823\",\n \"0.53986573\",\n \"0.53986573\",\n \"0.5396267\",\n \"0.53886825\",\n \"0.5383246\",\n \"0.53826976\",\n \"0.537803\",\n \"0.5376038\",\n \"0.537137\",\n \"0.5369109\",\n \"0.5359738\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78729564"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":993,"cells":{"query":{"kind":"string","value":"Stuff to do before every test."},"document":{"kind":"string","value":"def setUp(self):\n\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 and add sample data\n db.create_all()\n users()\n reviews()\n\n\n with self.client as c:\n with c.session_transaction() as sess:\n sess['user_id'] = 1"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def before_run_tests(cls):\n pass","def do_before(self):\r\n pass","def before_test(self, func, *args, **kwargs):\n pass","def setUp(self):\r\n # nothing to do, all tests use different things\r\n pass","def setUp(self):\n print(\"New test by Nikolay Melnik\")","def beforeTest(self, test):\n self.setupLoghandler()","def before(self) -> None:\n pass","def setUp(self):\r\n pass # nothing used by all\r","def setUp(self):\n pass #because we dont have anything to setup.","def setUp(self) -> None:\n pass","def setUp(self) -> None:\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\n pass","def setUp(self):\r\n pass # nothing required by all\r","def setUp(self):\n\n pass","def setUp(self):\n\n pass","def setUp(self) :\n pass","def setUp(self):\n\n return","def setUp(self):\r\n pass","def setUp(self):\n print('Calling \\'setUp\\'')","def setUp(self):\n self","def setUp(self):\n self","def setUp(self):\n \n pass","def setUp(self):\r\n print('---------------------------------------------\\n')\r\n print('STARTING TEST...')","def setUp(self):\n\n BaseTest.setUp(self)","def startTestHook(self):","def setUp(self):\n MainTests.setUp(self)","def setUp(self):\n setUp()","def setUp(self):\n super(BasicTestCase, self).setUp()","def setUp(self):\n test_env_setup()","def setUp_extra(self):\n pass","def setUp(self):\n print(\"\\nIn setUp()...\")","def setUp(self):\n\n # ISSUE007\n # TODO, pyunit's bright idea is to call setup before each test. It\n # was defining multiple patterns which was annoying but not a problem.\n # The cleanest way to do things is probably to remove patterns after\n # the test, but we don't have that functionality. For now just create\n # one pattern to avoid confusion, but do it by hacking in a global\n # variable\n\n global firstTime\n\n if not firstTime:\n return\n firstTime = False\n\n # get the full source name for even and odd sources\n out_of_order_numbers = quilt_test_core.get_source_name(\n \"out_of_order_numbers\")\n\n # TODO REad the pattern id from the std output then query that one\n # See ISSUE007 and ISSUE008\n # call quilt_define with the pattern code and name query\n # dups_follows\n quilt_test_core.call_quilt_script('quilt_define.py', ['-n',\n 'out_of_order',\n 'source(\"' + out_of_order_numbers + '\",\"grep\")'])","def setUp(self):\n\t\tprint(\"\\n-------------------------------------\\nIn Test_RMT_Util:\", self._testMethodName)","def XXsetUp(self):\n print(\"FooTest:setUp_:begin\")\n ## do something...\n print(\"FooTest:setUp_:end\")","def XXsetUp(self):\n print(\"FooTest:setUp_:begin\")\n ## do something...\n print(\"FooTest:setUp_:end\")","def setUpTestCase(self):\n pass","def setUp(self):\n raise NotImplementedError","def setUp(self) -> None:\n return super().setUp()","def setUp(self):\n\n self._set_up()","def setUp(self):\n \n \n pass","def setUp(self):\n \n \n pass","def setUp(self):\n \n \n pass","def setUp(self):\n \n \n pass","def setUp(self):\n \n \n pass","def setUp(self):\n \n \n pass","def setUp(self):\n \n \n pass","def setUp(self):\n \n \n pass","def setUp(self):\n \n \n pass","def setUp(self):\n self.setup_beets()","def setUp(self):\n print 'unittest.setUp()'\n pass","def setUp(self):\n logging.debug('setting up')","def setUp(self):\n logging.debug('setting up')","def setUp(self):\n if not self.flag:\n self.fail(self.err_msg)","def setUp(self):\n # use self.attribute to keep anything which needs to be accessed later\n print('setUp method\\n')","def before_each_test(self, request):\n self.test_counter = Counter()\n self.check_ref = request.config.getvalue(\"check_ref\")\n self.create_ref = request.config.getvalue(\"create_ref\")","def setUp(self):\n super(TestCase, self).setUp()\n self._context = CallContext()","def on_before_execution(self):\n pass","def setUp(self):\n util.create_mocks()","def setUp(self):\n util.create_mocks()","def setUp(self):\n util.create_mocks()","def setUp(self):\n util.create_mocks()","def setUp(self):\n util.create_mocks()","def setUp(self):\n util.create_mocks()","def setUp(self):\n self.setUpPyfakefs()","def startTestRun(self):","def setUp(self):\n assert COMMANDS.keys() == EXPCT_RESULTS.keys()\n self.tests = []\n self.test_numbers = deque(sorted(COMMANDS.keys()))","def __call__(self, result=None):\n self._pre_setup()\n super(TestCase, self).__call__(result)\n self._post_tearDown()","def _fixture_setup(self):\n pass","def test_begin(self):","def setUp(self):\n self.Reinitialize()","def setUp(self):\n\t\tprint(\"\\n-------------------------------------\\nIn Test_ValidParams:\", self._testMethodName)","def setUp(self):","def setUp(self):","def setUp(self):","def setUp(self):"],"string":"[\n \"def before_run_tests(cls):\\n pass\",\n \"def do_before(self):\\r\\n pass\",\n \"def before_test(self, func, *args, **kwargs):\\n pass\",\n \"def setUp(self):\\r\\n # nothing to do, all tests use different things\\r\\n pass\",\n \"def setUp(self):\\n print(\\\"New test by Nikolay Melnik\\\")\",\n \"def beforeTest(self, test):\\n self.setupLoghandler()\",\n \"def before(self) -> None:\\n pass\",\n \"def setUp(self):\\r\\n pass # nothing used by all\\r\",\n \"def setUp(self):\\n pass #because we dont have anything to setup.\",\n \"def setUp(self) -> None:\\n pass\",\n \"def setUp(self) -> None:\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\n pass\",\n \"def setUp(self):\\r\\n pass # nothing required by all\\r\",\n \"def setUp(self):\\n\\n pass\",\n \"def setUp(self):\\n\\n pass\",\n \"def setUp(self) :\\n pass\",\n \"def setUp(self):\\n\\n return\",\n \"def setUp(self):\\r\\n pass\",\n \"def setUp(self):\\n print('Calling \\\\'setUp\\\\'')\",\n \"def setUp(self):\\n self\",\n \"def setUp(self):\\n self\",\n \"def setUp(self):\\n \\n pass\",\n \"def setUp(self):\\r\\n print('---------------------------------------------\\\\n')\\r\\n print('STARTING TEST...')\",\n \"def setUp(self):\\n\\n BaseTest.setUp(self)\",\n \"def startTestHook(self):\",\n \"def setUp(self):\\n MainTests.setUp(self)\",\n \"def setUp(self):\\n setUp()\",\n \"def setUp(self):\\n super(BasicTestCase, self).setUp()\",\n \"def setUp(self):\\n test_env_setup()\",\n \"def setUp_extra(self):\\n pass\",\n \"def setUp(self):\\n print(\\\"\\\\nIn setUp()...\\\")\",\n \"def setUp(self):\\n\\n # ISSUE007\\n # TODO, pyunit's bright idea is to call setup before each test. It\\n # was defining multiple patterns which was annoying but not a problem.\\n # The cleanest way to do things is probably to remove patterns after\\n # the test, but we don't have that functionality. For now just create\\n # one pattern to avoid confusion, but do it by hacking in a global\\n # variable\\n\\n global firstTime\\n\\n if not firstTime:\\n return\\n firstTime = False\\n\\n # get the full source name for even and odd sources\\n out_of_order_numbers = quilt_test_core.get_source_name(\\n \\\"out_of_order_numbers\\\")\\n\\n # TODO REad the pattern id from the std output then query that one\\n # See ISSUE007 and ISSUE008\\n # call quilt_define with the pattern code and name query\\n # dups_follows\\n quilt_test_core.call_quilt_script('quilt_define.py', ['-n',\\n 'out_of_order',\\n 'source(\\\"' + out_of_order_numbers + '\\\",\\\"grep\\\")'])\",\n \"def setUp(self):\\n\\t\\tprint(\\\"\\\\n-------------------------------------\\\\nIn Test_RMT_Util:\\\", self._testMethodName)\",\n \"def XXsetUp(self):\\n print(\\\"FooTest:setUp_:begin\\\")\\n ## do something...\\n print(\\\"FooTest:setUp_:end\\\")\",\n \"def XXsetUp(self):\\n print(\\\"FooTest:setUp_:begin\\\")\\n ## do something...\\n print(\\\"FooTest:setUp_:end\\\")\",\n \"def setUpTestCase(self):\\n pass\",\n \"def setUp(self):\\n raise NotImplementedError\",\n \"def setUp(self) -> None:\\n return super().setUp()\",\n \"def setUp(self):\\n\\n self._set_up()\",\n \"def setUp(self):\\n \\n \\n pass\",\n \"def setUp(self):\\n \\n \\n pass\",\n \"def setUp(self):\\n \\n \\n pass\",\n \"def setUp(self):\\n \\n \\n pass\",\n \"def setUp(self):\\n \\n \\n pass\",\n \"def setUp(self):\\n \\n \\n pass\",\n \"def setUp(self):\\n \\n \\n pass\",\n \"def setUp(self):\\n \\n \\n pass\",\n \"def setUp(self):\\n \\n \\n pass\",\n \"def setUp(self):\\n self.setup_beets()\",\n \"def setUp(self):\\n print 'unittest.setUp()'\\n pass\",\n \"def setUp(self):\\n logging.debug('setting up')\",\n \"def setUp(self):\\n logging.debug('setting up')\",\n \"def setUp(self):\\n if not self.flag:\\n self.fail(self.err_msg)\",\n \"def setUp(self):\\n # use self.attribute to keep anything which needs to be accessed later\\n print('setUp method\\\\n')\",\n \"def before_each_test(self, request):\\n self.test_counter = Counter()\\n self.check_ref = request.config.getvalue(\\\"check_ref\\\")\\n self.create_ref = request.config.getvalue(\\\"create_ref\\\")\",\n \"def setUp(self):\\n super(TestCase, self).setUp()\\n self._context = CallContext()\",\n \"def on_before_execution(self):\\n pass\",\n \"def setUp(self):\\n util.create_mocks()\",\n \"def setUp(self):\\n util.create_mocks()\",\n \"def setUp(self):\\n util.create_mocks()\",\n \"def setUp(self):\\n util.create_mocks()\",\n \"def setUp(self):\\n util.create_mocks()\",\n \"def setUp(self):\\n util.create_mocks()\",\n \"def setUp(self):\\n self.setUpPyfakefs()\",\n \"def startTestRun(self):\",\n \"def setUp(self):\\n assert COMMANDS.keys() == EXPCT_RESULTS.keys()\\n self.tests = []\\n self.test_numbers = deque(sorted(COMMANDS.keys()))\",\n \"def __call__(self, result=None):\\n self._pre_setup()\\n super(TestCase, self).__call__(result)\\n self._post_tearDown()\",\n \"def _fixture_setup(self):\\n pass\",\n \"def test_begin(self):\",\n \"def setUp(self):\\n self.Reinitialize()\",\n \"def setUp(self):\\n\\t\\tprint(\\\"\\\\n-------------------------------------\\\\nIn Test_ValidParams:\\\", self._testMethodName)\",\n \"def setUp(self):\",\n \"def setUp(self):\",\n \"def setUp(self):\",\n \"def setUp(self):\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.8209909\",\n \"0.8028296\",\n \"0.80253834\",\n \"0.7872079\",\n \"0.76552856\",\n \"0.76427346\",\n \"0.7627397\",\n \"0.7540311\",\n \"0.7515845\",\n \"0.74945575\",\n \"0.74945575\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.74365586\",\n \"0.7428322\",\n \"0.7426078\",\n \"0.7426078\",\n \"0.7420724\",\n \"0.7384354\",\n \"0.7378374\",\n \"0.7370478\",\n \"0.73638576\",\n \"0.73638576\",\n \"0.7355973\",\n \"0.73555076\",\n \"0.7353191\",\n \"0.73489285\",\n \"0.7347254\",\n \"0.73437107\",\n \"0.7263831\",\n \"0.72633225\",\n \"0.72391665\",\n \"0.7169371\",\n \"0.71394217\",\n \"0.713784\",\n \"0.7133868\",\n \"0.7133868\",\n \"0.7119319\",\n \"0.7118144\",\n \"0.71014655\",\n \"0.70950025\",\n \"0.7085565\",\n \"0.7085565\",\n \"0.7085565\",\n \"0.7085565\",\n \"0.7085565\",\n \"0.7085565\",\n \"0.7085565\",\n \"0.7085565\",\n \"0.7085565\",\n \"0.6989451\",\n \"0.6986\",\n \"0.6980955\",\n \"0.6980955\",\n \"0.69528717\",\n \"0.69458896\",\n \"0.6935865\",\n \"0.68862945\",\n \"0.68795764\",\n \"0.68782437\",\n \"0.68782437\",\n \"0.68782437\",\n \"0.68782437\",\n \"0.68782437\",\n \"0.68782437\",\n \"0.68605286\",\n \"0.6854516\",\n \"0.68527067\",\n \"0.6852522\",\n \"0.6850463\",\n \"0.68246025\",\n \"0.6805653\",\n \"0.68054616\",\n \"0.6795788\",\n \"0.6795788\",\n \"0.6795788\",\n \"0.6795788\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":994,"cells":{"query":{"kind":"string","value":"Do at end of every test."},"document":{"kind":"string","value":"def tearDown(self):\n\n db.session.close()\n db.drop_all()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_finished(self):\n\n # We'll start the next test in an idle, so that the current one is\n # properly terminated, and we do not execute in its context\n\n GLib.idle_add(self._do_test)","def test_run_ended(self):","def finished_tests(self):\n self.testing = 0","def tearDown(self):\n\t\tprint(\"end test\")\n\t\tpass","def after_test(self, test_results):\n pass","def endOfTestcase(self):\n pass # nothing to do here. Hence pass statement is called.","def on_test_end(self, logs=None):","def after_all(self) -> None:","def on_test_end(self):\n for callback in self.callbacks:\n callback.on_test_end(self, self.get_model())","def after_test(self, func, *args, **kwargs):\n pass","def XXtearDown(self):\n print(\"FooTest:tearDown_:begin\")\n ## do something...\n print(\"FooTest:tearDown_:end\")","def XXtearDown(self):\n print(\"FooTest:tearDown_:begin\")\n ## do something...\n print(\"FooTest:tearDown_:end\")","def tearDown(self):\n pass\n # teardown called after each test\n # e.g. maybe write test results to some text file","def tearDown(self):\n super(TestCase, self).tearDown()\n self._context.check_done()","def pytest_finished_handling_group(session, worker):","def after(self):\n pass","def after(self):\n pass","def do_after(self):\r\n pass","def teardown(self) -> None:","def teardown(self) -> None:","def teardown(self) -> None:","def eof(self):\n self.report_scenario_completed()\n self.report_feature_completed()\n self.report_failures()\n self.stream.flush()\n self.reset()","def teardown(self):","def teardown(self):","def teardown(self):","def finish():\n pass","def done(self):\n log.debug(\"Test run concluded\")\n if self._startTime is not None:\n self.report['startTime'] = self._startTime\n self.report['runTime'] = time.time() - self._startTime\n self.report['testsRun'] = self.testsRun\n self.report['tests'] = self._tests\n self.writeReport()","def tearDown(self):\n self.logger.info(\"tearDown begin\")\n self.logger.info(\"tearDown end\\n\")","def finish(self):\n pass","def finish(self):\n pass","def _end(self):\n\n self.logger.msg1(\"Done\")","def Finish(self):\n pass","def done(self):","def done(self):","def end(self):\n self._log.debug('%s: doing ..', __class__.__name__)\n self._log.debug('%s: done.', __class__.__name__)","def finish():","def finish():","def finish():","def finish():","def teardown_method(self):\n world.clear_paths()\n print(\"\\nEnd of tests in: %s\\n-------------------\\n\" % __name__)\n self.bigml = {}","def finished_tests(self):\n self.testing = 0\n if not self.closing:\n self.enable_menus(1)\n self.parent.finished_tests()","def teardown(self):\n pass","def teardown(self):\n pass","def teardown(self):\n pass","def end(self):\n ...","def tearDown(self):\n print('Calling \\'tearDown\\'')","def end(self):\n self._log.debug('doing ..')\n super().end()\n\n self._log.debug('done')","def teardown(self):\n pass # pylint: disable=unnecessary-pass","def finish(self) -> None:","def finish(self) -> None:","def after(self) -> None:\n pass","def afterWork(self):\n pass","def teardown(self) -> None:\n pass","def teardown(self) -> None:\n pass","def finished(self):\n\t\telog(\"finished\")","def finish(self):","def finish(self):","def teardown(self, rc):\n pass","def end(self):\n pass","def end(self):\n pass","def end(self):\n pass","def end(c: Composition) -> None:\n c.run(\"testdrive\", \"verify-data.td\")","def _do_test(self):\n\n process_all_events()\n\n if self.list:\n (callback, args, kwargs) = self.list.pop(0)\n callback(*args, **kwargs)\n else:\n safe_exit(force=1)","def teardown(self):\n self.tcex.log.trace('teardown')","def tearDown(self) :\n pass","def tearDown(self) :\n pass","def tearDown(self) :\n pass","def tearDown(self):\n\t\tpass","def tearDown(self):\r\n self.app.application_close(self.util.client, self.app_name)\r\n\r\n self.common.generate_report(self.util.client, False)\r\n # Releases the client so that other clients can approach the agent in the near future.\r\n self.common.release_client(self.util.client)\r\n self.logger.info(\"==============Results=================\")\r\n self.logger.info(\"Number of Strings verified: \" + str(len(Config.results_list)/2))\r\n for i in range(0, len(Config.results_list), 2):\r\n self.logger.info(str(Config.results_list[i]) + \"{:>36}\".format('=====> ')\r\n + str(Config.results_list[i+1]))\r\n self.logger.info(\"Testcase tear-down: COMPLETED\")","def _teardown(self):\n # No-op base implementation","def tearDown(self):\r\n pass","def tearDown(self):\r\n pass","def tearDown(self):\r\n pass","def tearDown(self):\r\n pass","def tearDown(self):\r\n pass","def clean_up(self) -> None:\n print('Doing some clean-up work...')","def tearDown(self):\n self.loop.close()","def tearDown(self):\n self.loop.close()","def tearDown(self):\n self.loop.close()","def tearDown(self):\n self.loop.close()","def tearDown(self) -> None:\n pass","def tearDown(self) -> None:\n pass","def tearDown(self) -> None:\n pass","def tearDown(self):\n pass","def tearDown(self):\n self.teardown_beets()","def tearDown(self):\n pass #because we dont have anything to tearDown.","def execute(self):\n for test in self.tests:\n test.execute()\n self.logger.dump()\n print(\"Finished!\")","def end(self) -> None:","def finish(self):\n if self.failed:\n print \"%s failed. %s of %s assertions passed.\" % (\n self.failed, self.passed, self.failed + self.passed)\n else:\n print \"%s of %s assertions passed.\" % (self.passed, self.passed)\n\n self.failed = self.passed = 0","def test_teardown(self):\n assert self.search_behaviour.teardown() is None\n self.assert_quantity_in_outbox(0)","def tearDown(self):\r\n testing.tearDown()","def runTest(self):\n unittest.main()\n ChoreTest.clean_up()","def finish(self) -> None:\n self.__exit__(None, None, None)","def test_teardown(self):\n assert self.http_handler.teardown() is None\n self.assert_quantity_in_outbox(0)","def tearDown(self) -> None:\n\n logging.info(f\"{'=' * 20}Test completed!{'=' * 20}\")\n logging.info(\"Failed to execute the following parameter combinations: \")\n if self.error_params:\n for each in self.error_params:\n logging.info(each)","def complete_run():\n pass","def tearDown(self):\n pass","def tearDown(self):\n pass","def tearDown(self):\n pass","def tearDown(self):\n pass","def tearDown(self):\n pass"],"string":"[\n \"def test_finished(self):\\n\\n # We'll start the next test in an idle, so that the current one is\\n # properly terminated, and we do not execute in its context\\n\\n GLib.idle_add(self._do_test)\",\n \"def test_run_ended(self):\",\n \"def finished_tests(self):\\n self.testing = 0\",\n \"def tearDown(self):\\n\\t\\tprint(\\\"end test\\\")\\n\\t\\tpass\",\n \"def after_test(self, test_results):\\n pass\",\n \"def endOfTestcase(self):\\n pass # nothing to do here. Hence pass statement is called.\",\n \"def on_test_end(self, logs=None):\",\n \"def after_all(self) -> None:\",\n \"def on_test_end(self):\\n for callback in self.callbacks:\\n callback.on_test_end(self, self.get_model())\",\n \"def after_test(self, func, *args, **kwargs):\\n pass\",\n \"def XXtearDown(self):\\n print(\\\"FooTest:tearDown_:begin\\\")\\n ## do something...\\n print(\\\"FooTest:tearDown_:end\\\")\",\n \"def XXtearDown(self):\\n print(\\\"FooTest:tearDown_:begin\\\")\\n ## do something...\\n print(\\\"FooTest:tearDown_:end\\\")\",\n \"def tearDown(self):\\n pass\\n # teardown called after each test\\n # e.g. maybe write test results to some text file\",\n \"def tearDown(self):\\n super(TestCase, self).tearDown()\\n self._context.check_done()\",\n \"def pytest_finished_handling_group(session, worker):\",\n \"def after(self):\\n pass\",\n \"def after(self):\\n pass\",\n \"def do_after(self):\\r\\n pass\",\n \"def teardown(self) -> None:\",\n \"def teardown(self) -> None:\",\n \"def teardown(self) -> None:\",\n \"def eof(self):\\n self.report_scenario_completed()\\n self.report_feature_completed()\\n self.report_failures()\\n self.stream.flush()\\n self.reset()\",\n \"def teardown(self):\",\n \"def teardown(self):\",\n \"def teardown(self):\",\n \"def finish():\\n pass\",\n \"def done(self):\\n log.debug(\\\"Test run concluded\\\")\\n if self._startTime is not None:\\n self.report['startTime'] = self._startTime\\n self.report['runTime'] = time.time() - self._startTime\\n self.report['testsRun'] = self.testsRun\\n self.report['tests'] = self._tests\\n self.writeReport()\",\n \"def tearDown(self):\\n self.logger.info(\\\"tearDown begin\\\")\\n self.logger.info(\\\"tearDown end\\\\n\\\")\",\n \"def finish(self):\\n pass\",\n \"def finish(self):\\n pass\",\n \"def _end(self):\\n\\n self.logger.msg1(\\\"Done\\\")\",\n \"def Finish(self):\\n pass\",\n \"def done(self):\",\n \"def done(self):\",\n \"def end(self):\\n self._log.debug('%s: doing ..', __class__.__name__)\\n self._log.debug('%s: done.', __class__.__name__)\",\n \"def finish():\",\n \"def finish():\",\n \"def finish():\",\n \"def finish():\",\n \"def teardown_method(self):\\n world.clear_paths()\\n print(\\\"\\\\nEnd of tests in: %s\\\\n-------------------\\\\n\\\" % __name__)\\n self.bigml = {}\",\n \"def finished_tests(self):\\n self.testing = 0\\n if not self.closing:\\n self.enable_menus(1)\\n self.parent.finished_tests()\",\n \"def teardown(self):\\n pass\",\n \"def teardown(self):\\n pass\",\n \"def teardown(self):\\n pass\",\n \"def end(self):\\n ...\",\n \"def tearDown(self):\\n print('Calling \\\\'tearDown\\\\'')\",\n \"def end(self):\\n self._log.debug('doing ..')\\n super().end()\\n\\n self._log.debug('done')\",\n \"def teardown(self):\\n pass # pylint: disable=unnecessary-pass\",\n \"def finish(self) -> None:\",\n \"def finish(self) -> None:\",\n \"def after(self) -> None:\\n pass\",\n \"def afterWork(self):\\n pass\",\n \"def teardown(self) -> None:\\n pass\",\n \"def teardown(self) -> None:\\n pass\",\n \"def finished(self):\\n\\t\\telog(\\\"finished\\\")\",\n \"def finish(self):\",\n \"def finish(self):\",\n \"def teardown(self, rc):\\n pass\",\n \"def end(self):\\n pass\",\n \"def end(self):\\n pass\",\n \"def end(self):\\n pass\",\n \"def end(c: Composition) -> None:\\n c.run(\\\"testdrive\\\", \\\"verify-data.td\\\")\",\n \"def _do_test(self):\\n\\n process_all_events()\\n\\n if self.list:\\n (callback, args, kwargs) = self.list.pop(0)\\n callback(*args, **kwargs)\\n else:\\n safe_exit(force=1)\",\n \"def teardown(self):\\n self.tcex.log.trace('teardown')\",\n \"def tearDown(self) :\\n pass\",\n \"def tearDown(self) :\\n pass\",\n \"def tearDown(self) :\\n pass\",\n \"def tearDown(self):\\n\\t\\tpass\",\n \"def tearDown(self):\\r\\n self.app.application_close(self.util.client, self.app_name)\\r\\n\\r\\n self.common.generate_report(self.util.client, False)\\r\\n # Releases the client so that other clients can approach the agent in the near future.\\r\\n self.common.release_client(self.util.client)\\r\\n self.logger.info(\\\"==============Results=================\\\")\\r\\n self.logger.info(\\\"Number of Strings verified: \\\" + str(len(Config.results_list)/2))\\r\\n for i in range(0, len(Config.results_list), 2):\\r\\n self.logger.info(str(Config.results_list[i]) + \\\"{:>36}\\\".format('=====> ')\\r\\n + str(Config.results_list[i+1]))\\r\\n self.logger.info(\\\"Testcase tear-down: COMPLETED\\\")\",\n \"def _teardown(self):\\n # No-op base implementation\",\n \"def tearDown(self):\\r\\n pass\",\n \"def tearDown(self):\\r\\n pass\",\n \"def tearDown(self):\\r\\n pass\",\n \"def tearDown(self):\\r\\n pass\",\n \"def tearDown(self):\\r\\n pass\",\n \"def clean_up(self) -> None:\\n print('Doing some clean-up work...')\",\n \"def tearDown(self):\\n self.loop.close()\",\n \"def tearDown(self):\\n self.loop.close()\",\n \"def tearDown(self):\\n self.loop.close()\",\n \"def tearDown(self):\\n self.loop.close()\",\n \"def tearDown(self) -> None:\\n pass\",\n \"def tearDown(self) -> None:\\n pass\",\n \"def tearDown(self) -> None:\\n pass\",\n \"def tearDown(self):\\n pass\",\n \"def tearDown(self):\\n self.teardown_beets()\",\n \"def tearDown(self):\\n pass #because we dont have anything to tearDown.\",\n \"def execute(self):\\n for test in self.tests:\\n test.execute()\\n self.logger.dump()\\n print(\\\"Finished!\\\")\",\n \"def end(self) -> None:\",\n \"def finish(self):\\n if self.failed:\\n print \\\"%s failed. %s of %s assertions passed.\\\" % (\\n self.failed, self.passed, self.failed + self.passed)\\n else:\\n print \\\"%s of %s assertions passed.\\\" % (self.passed, self.passed)\\n\\n self.failed = self.passed = 0\",\n \"def test_teardown(self):\\n assert self.search_behaviour.teardown() is None\\n self.assert_quantity_in_outbox(0)\",\n \"def tearDown(self):\\r\\n testing.tearDown()\",\n \"def runTest(self):\\n unittest.main()\\n ChoreTest.clean_up()\",\n \"def finish(self) -> None:\\n self.__exit__(None, None, None)\",\n \"def test_teardown(self):\\n assert self.http_handler.teardown() is None\\n self.assert_quantity_in_outbox(0)\",\n \"def tearDown(self) -> None:\\n\\n logging.info(f\\\"{'=' * 20}Test completed!{'=' * 20}\\\")\\n logging.info(\\\"Failed to execute the following parameter combinations: \\\")\\n if self.error_params:\\n for each in self.error_params:\\n logging.info(each)\",\n \"def complete_run():\\n pass\",\n \"def tearDown(self):\\n pass\",\n \"def tearDown(self):\\n pass\",\n \"def tearDown(self):\\n pass\",\n \"def tearDown(self):\\n pass\",\n \"def tearDown(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78425974","0.77754915","0.77742684","0.7766872","0.76616216","0.75920033","0.7470884","0.73606","0.73159856","0.7297722","0.7293865","0.7293865","0.72588974","0.7235985","0.72320426","0.7224789","0.7224789","0.72181326","0.72043455","0.72043455","0.72043455","0.71976244","0.7169287","0.7169287","0.7169287","0.71564865","0.7147801","0.71392965","0.7076554","0.7076554","0.7069717","0.7041966","0.7038115","0.7038115","0.70337486","0.70182955","0.70182955","0.70182955","0.70182955","0.70081335","0.7004758","0.6997527","0.6997527","0.6997527","0.69892746","0.69694585","0.69613737","0.6956021","0.69547766","0.69547766","0.6952943","0.6912461","0.6906216","0.6906216","0.68874747","0.6876898","0.6876898","0.6872803","0.68528396","0.68528396","0.68528396","0.684885","0.6844017","0.6826692","0.68262887","0.68262887","0.68262887","0.6800694","0.67869633","0.67826825","0.6779946","0.6779946","0.6779946","0.6779946","0.6779946","0.677544","0.677034","0.677034","0.677034","0.677034","0.6764208","0.6764208","0.6764208","0.67621267","0.6758972","0.67563933","0.6751442","0.6749756","0.67355955","0.67334735","0.67305505","0.67238015","0.672133","0.6718853","0.67131215","0.6708315","0.66984904","0.66984904","0.66984904","0.66984904","0.66984904"],"string":"[\n \"0.78425974\",\n \"0.77754915\",\n \"0.77742684\",\n \"0.7766872\",\n \"0.76616216\",\n \"0.75920033\",\n \"0.7470884\",\n \"0.73606\",\n \"0.73159856\",\n \"0.7297722\",\n \"0.7293865\",\n \"0.7293865\",\n \"0.72588974\",\n \"0.7235985\",\n \"0.72320426\",\n \"0.7224789\",\n \"0.7224789\",\n \"0.72181326\",\n \"0.72043455\",\n \"0.72043455\",\n \"0.72043455\",\n \"0.71976244\",\n \"0.7169287\",\n \"0.7169287\",\n \"0.7169287\",\n \"0.71564865\",\n \"0.7147801\",\n \"0.71392965\",\n \"0.7076554\",\n \"0.7076554\",\n \"0.7069717\",\n \"0.7041966\",\n \"0.7038115\",\n \"0.7038115\",\n \"0.70337486\",\n \"0.70182955\",\n \"0.70182955\",\n \"0.70182955\",\n \"0.70182955\",\n \"0.70081335\",\n \"0.7004758\",\n \"0.6997527\",\n \"0.6997527\",\n \"0.6997527\",\n \"0.69892746\",\n \"0.69694585\",\n \"0.69613737\",\n \"0.6956021\",\n \"0.69547766\",\n \"0.69547766\",\n \"0.6952943\",\n \"0.6912461\",\n \"0.6906216\",\n \"0.6906216\",\n \"0.68874747\",\n \"0.6876898\",\n \"0.6876898\",\n \"0.6872803\",\n \"0.68528396\",\n \"0.68528396\",\n \"0.68528396\",\n \"0.684885\",\n \"0.6844017\",\n \"0.6826692\",\n \"0.68262887\",\n \"0.68262887\",\n \"0.68262887\",\n \"0.6800694\",\n \"0.67869633\",\n \"0.67826825\",\n \"0.6779946\",\n \"0.6779946\",\n \"0.6779946\",\n \"0.6779946\",\n \"0.6779946\",\n \"0.677544\",\n \"0.677034\",\n \"0.677034\",\n \"0.677034\",\n \"0.677034\",\n \"0.6764208\",\n \"0.6764208\",\n \"0.6764208\",\n \"0.67621267\",\n \"0.6758972\",\n \"0.67563933\",\n \"0.6751442\",\n \"0.6749756\",\n \"0.67355955\",\n \"0.67334735\",\n \"0.67305505\",\n \"0.67238015\",\n \"0.672133\",\n \"0.6718853\",\n \"0.67131215\",\n \"0.6708315\",\n \"0.66984904\",\n \"0.66984904\",\n \"0.66984904\",\n \"0.66984904\",\n \"0.66984904\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":995,"cells":{"query":{"kind":"string","value":"Find restaurant name by zipcode."},"document":{"kind":"string","value":"def test_process_searchbox_with_mock(self):\n\n result = self.client.get('/process_searchbox', data={'zipcode': '94043', 'cuisine': 'indian'})\n self.assertIn(b\"Dosa Paratha\", result.data)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def county_name(zipcode): \n search = SearchEngine(simple_zipcode=True) # set simple_zipcode=False to use rich info database\n zipcode_query = search.by_zipcode(str(zipcode))\n zipcode_query_dict = zipcode_query.to_dict()\n county = zipcode_query_dict['county']\n if county is None:\n print('Invalid County')\n else :\n if 'County' in county:\n county = county[:-7]\n if county in county_list:\n print('County is County List')\n print(county)\n return county","def get_zipsearch(zipcode=u''):\n from x84.bbs import getterminal, LineEditor, echo\n term = getterminal()\n echo(u''.join((u'\\r\\n\\r\\n',\n term.bold_yellow(u' -'),\n term.reverse_yellow(u':'),\n u' ')))\n return LineEditor(width=min(30, term.width - 5), content=zipcode).read()","def find_one(cls, zipcode ):\n qry = cls.session.query(cls).filter(cls.ZIPCODE.ilike(f'{zipcode}'))\n zc = qry.one()\n return zc","def find_zip_codes(self, zip_code):\n zip_code = str(zip_code).strip()\n cursor = self.households.find({\"addresses.zip_code\":zip_code})\n results = [Household.from_dict(dct) for dct in cursor]\n\n cursor = self.businesses.find({\"address.zip_code\":zip_code})\n results += [Business.from_dict(dct) for dct in cursor]\n\n return results","def lookup_usaf_station_by_zipcode(zipcode):\n\n usaf = zipcode_usaf.get(zipcode, None)\n return usaf","def get_zipcode_names(add):\r\n lng=get_address(add)[1]\r\n lat=get_address(add)[0]\r\n engine = get_sql_engine()\r\n query = text(\r\n \"\"\"\r\n SELECT\r\n code\r\n FROM philly_zipcode\r\n WHERE ST_Intersects(geom, ST_SetSRID(ST_MakePoint(:lng, :lat), 4326))\r\n \"\"\"\r\n )\r\n resp = engine.execute(query,lng=lng, lat=lat).fetchall()\r\n # get a list of names\r\n names = [row[\"code\"] for row in resp][0]\r\n return names","def get_info_on_postalcode(_, postalcode):\n fourpp = int(postalcode[0:4])\n chars = postalcode[4:6]\n streets = get_streets(fourpp, chars)\n if streets:\n street = streets[0]\n town = street.postcode.city.get_official_name()\n address = street.street\n data = {'found': True, 'address': address, 'town': town}\n else:\n data = {'found': False}\n j = json.dumps(data)\n return HttpResponse(j, content_type='application/json')","def get_city(zip_code):\r\n\r\n # API key, retrieved from configure.py\r\n api_key = configure.ZIP_KEY\r\n\r\n # API endpoint\r\n url = f'https://www.zipcodeapi.com/rest/{api_key}/info.json/{zip_code}/degrees'\r\n\r\n # API call\r\n response = requests.get(url)\r\n\r\n # Collect response in json format\r\n data = response.json()\r\n\r\n if 'error_code' in data or 'error_msg' in data:\r\n return {\r\n 'success': False,\r\n 'query': zip_code\r\n }\r\n\r\n else:\r\n return {\r\n 'success': True,\r\n 'query': data['zip_code'],\r\n 'city': data['city'],\r\n 'state': data['state'],\r\n 'lat': data['lat'],\r\n 'lon': data['lng']\r\n }","def searchZipcode(zipcode, jurisdictions):\n try:\n if len(str(zipcode)) != 5:\n return jurisdictions.none()\n\n zipcode = Zipcode.objects.get(code=zipcode)\n j = jurisdictions.filter(geometry__intersects=zipcode.geometry)\n return j\n except Exception as e:\n print(e)\n return jurisdictions.none()","def get(self, zipcode):\n response = hereService.getWeatherByZipcode(zipcode)\n return response","def get_zipcode_hospitals(add):\r\n name=get_zipcode_names(add)\r\n engine = get_sql_engine()\r\n hospital_info = text(\r\n \"\"\"\r\n SELECT\r\n\t \"HOSPITAL_NAME\" AS name, \"STREET_ADDRESS\" as address,\r\n \"PHONE_NUMBER\" as contact, geom,\r\n ST_X(geom) AS lon, ST_Y(geom) AS lat\r\n FROM philly_hospital\r\n WHERE \"ZIP_CODE\" = :name\r\n \"\"\"\r\n )\r\n hospitals = gpd.read_postgis(hospital_info, con=engine, params={\"name\": name})\r\n return hospitals","def match_city(self, city, dpt_code, zip_code = None):\n city = format_str_city_insee(city)\n dpt_code = dpt_code.rjust(2, '0')\n if zip_code:\n zip_code.rjust(5, '0')\n # Based on zip code and city name\n ls_matching = []\n found_indicator = False\n if zip_code:\n if zip_code in self.dict_corr_zip_insee:\n for city_insee, zip_insee, dpt_insee, code_insee in self.dict_corr_zip_insee[zip_code]:\n if city == city_insee:\n ls_matching.append((city_insee, zip_insee, code_insee))\n found_indicator = True\n if found_indicator:\n return (ls_matching, 'zip_city_match')\n # If no exact zip, city match: check if city name in insee city names\n for city_insee, zip_insee, dpt_insee, code_insee in self.dict_corr_zip_insee[zip_code]:\n if city in city_insee:\n ls_matching.append((city_insee, zip_insee, code_insee))\n found_indicator = True\n if found_indicator:\n return (ls_matching, 'zip_city_in_match(es)')\n # Based on dpt code and city name\n for city_insee, zip_insee, dpt_insee, code_insee in self.dict_corr_dpt_insee[dpt_code]:\n if city == city_insee:\n ls_matching.append((city_insee, zip_insee, code_insee))\n found_indicator = True\n if found_indicator:\n return (ls_matching, 'dpt_city_match')\n # If no exact dpt, city match: check if city name in insee city names\n for city_insee, zip_insee, dpt_insee, code_insee in self.dict_corr_dpt_insee[dpt_code]:\n if city in city_insee:\n ls_matching.append((city_insee, zip_insee, code_insee))\n found_indicator = True\n if found_indicator:\n return (ls_matching, 'dpt_city_in_match(es)')\n # No match\n return (None, 'no_match')","def get_city_by_code(post_code):\n post_code = post_code.replace(' ', '').encode('utf-8')\n error = ''\n city = ''\n opener = urllib2.build_opener()\n url = 'http://maps.googleapis.com/maps/api/geocode/json?address={0}&sensor=false'.format(post_code)\n response = opener.open(url).read()\n response_dict = json.loads(response)\n request_status = response_dict['status']\n if request_status == 'OK':\n logger.debug('Google response')\n logger.debug(response_dict)\n results = response_dict['results']\n \"\"\"\n first get all results\n with required zip code\n \"\"\"\n results_with_required_zip_code = []\n for result in results:\n address_components = result['address_components']\n for address_component in address_components:\n types = address_component['types']\n for t in types:\n if t == 'postal_code' and address_component['short_name'].replace(' ', '').lower() == post_code.lower():\n results_with_required_zip_code.append(result)\n if not results_with_required_zip_code:\n error = {\n 'status': '8',\n 'message': POST_CODE_DOES_NOT_EXISTS,\n 'title': POST_CODE_DOES_NOT_EXISTS_TITLE\n }\n # error = 'No location with post code %s' % post_code\n else:\n \"\"\"\n next we need all results in GB\n \"\"\"\n results_with_required_zip_code_in_GB = ''\n for good_result in results_with_required_zip_code:\n address_components = good_result['address_components']\n for address_component in address_components:\n types = address_component['types']\n for t in types:\n if t == 'country' and address_component['short_name'].lower() == 'GB'.lower():\n results_with_required_zip_code_in_GB = good_result\n if not results_with_required_zip_code_in_GB:\n error = {\n 'status': '7',\n 'message': POST_CODE_DOES_NOT_EXISTS_IN_GB,\n 'title': POST_CODE_DOES_NOT_EXISTS_IN_GB_TITLE\n }\n # error = 'No city with post code %s in GB' % post_code\n else:\n \"\"\"\n finally find city name\n \"\"\"\n address_components = results_with_required_zip_code_in_GB['address_components']\n # first try get postal city\n searching_city = get_city_by_key(address_components, 'postal_town')\n if not searching_city:\n # next by administrative_area_level_2\n searching_city = get_city_by_key(address_components, 'administrative_area_level_2')\n if not searching_city:\n print url\n error = {\n 'status': '7',\n 'message': POST_CODE_DOES_NOT_EXISTS_IN_GB,\n 'title': POST_CODE_DOES_NOT_EXISTS_IN_GB_TITLE\n }\n # error = 'No city with post code %s in GB' % post_code\n else:\n city = searching_city\n elif request_status == 'ZERO_RESULTS':\n error = {\n 'status': '8',\n 'message': POST_CODE_DOES_NOT_EXISTS,\n 'title': POST_CODE_DOES_NOT_EXISTS_TITLE\n }\n else:\n error = request_status\n return {\n 'error': error,\n 'data': city\n }","def get_postal_code_by_name(self, name):\n raise NotImplementedError()","def zillow_query(address, zipcode, key=keys.zillow):\n zillow_data = ZillowWrapper(key)\n deep_search_response = zillow_data.get_deep_search_results(\n address, zipcode)\n result = GetDeepSearchResults(deep_search_response)\n return result","def compute_zip_code(zip_code_text):\n zip_code = None\n if zip_code_text and len(zip_code_text) >= 5 and zip_code_text.isdigit():\n zip_code = zip_code_text[:5]\n return zip_code","def main(postalcode):\n places = postalcodes_mexico.places(postalcode)\n click.echo(places)\n return 0","def patrons_in_zipcode(session, zipcode):\n\n query = {\n 'target': {\n 'record': {\n 'type': 'patron'\n },\n 'id': 80010\n },\n 'expr': {\n 'op': 'equals',\n 'operands': [\n str(zipcode),\n ''\n ]\n }\n }\n\n url = '/patrons/query?offset=0&limit={}'.format(str(api_limit))\n headers = {'content-type': 'application/json'}\n data = json.dumps(query)\n r = session.post(api_url_base + url, data=data, headers=headers)\n entries = json.loads(r.text)['entries']\n record_ids = [x['link'].split('/')[-1] for x in entries]\n return record_ids","def correct_zipcode(business_tag, zipcode):\n try:\n address = business_tag.find('div', {'class': 'secondary-attributes'}).find('address').text\n zipcode_found = re.search(re.compile('(^|[^\\d])\\d{5}($|[^\\d])'), address).group(0)\n zipcode_found = re.search(re.compile('\\d{5}'), zipcode_found).group(0)\n return zipcode_found == zipcode\n except:\n return False","def find_places(query):\n parts = str(query).split(' ')\n for i, p in enumerate(parts):\n p = p.replace('-', ' ').strip()\n try:\n postal_code = int(p)\n if len(postal_code) == 4:\n print(postal_code, parts[i+1])\n # Check \n #response = get_osm_location(\"{postal_code} {name}\")\n #lon = response['lon']\n #lat = response['lat']\n #poly = \n except Exception as e:\n continue","def type_zip_code(self, zip_code):\n\n\t\twith allure.step(\"Type payee zip code\"):\n\t\t\telement = Element(driver=self.driver,\n\t\t\t explicit_wait_time=self.explicit_wait_time,\n\t\t\t locator=BillPayPageLocator.ZIP_CODE_INPUT)\n\t\t\telement.write(zip_code)\n\t\t\treturn None","def search_using_magento_code(cls, code):\n countries = cls.search([('code', '=', code)])\n\n if not countries:\n return cls.raise_user_error(\n \"country_not_found\", error_args=(code, )\n )\n\n return countries[0]","def postal_code():\r\n\r\n cursor.execute('SELECT col_1 FROM zipcodes order by RANDOM() limit 1;'\r\n )\r\n return cursor.fetchone()[0]","def extract_zipcode(full_address):\n full_address = full_address.strip()\n last_space_index = full_address.rindex(\" \")\n zipcode = full_address[last_space_index + 1 : ]\n return zipcode","def get_postal_zone(self, postal_code: str) -> PostalZone:\n key = postal_code.lower()\n try:\n return self._postal_zones[key]\n except KeyError:\n postal_code = PostalZone(postal_code=postal_code, city=self)\n self._postal_zones[key] = postal_code\n return postal_code","def get_testing_locations_by_zip():\n\n zip_code = request.args.get('zip_code')\n\n testing_info = get_testing_location_by_zipcode(zip_code)\n data = format_data(d=testing_info, key='test_id')\n\n return jsonify(data)","def search_address(query: str) -> Tuple[int, str]:\n\n url = 'https://api.n1.ru/api/v1/geo/geocoder/with_cities/'\n params = _search_params.copy()\n params['q'] = query\n\n try:\n r = requests.get(url, params=params, headers=_headers)\n response = r.json()\n\n if not 'result' in response or not response['result']:\n raise NotFoundException('Result not found or empty.')\n \n address = None\n house_number = query.split(',')[-1].strip()\n for x in response['result']:\n if x['name_ru'].lower() == house_number:\n address = x\n break\n \n if address is None:\n raise NotFoundException(f'Not found house number {house_number} in result: {response[\"result\"]}')\n \n return address['street']['id'], address['name_ru']\n except requests.RequestException as e:\n raise ParserException(f'Fail make request. query: {query}') from e\n except NotFoundException as e:\n raise ParserException('Invalid result.') from e\n except (KeyError, IndexError) as e:\n raise ParserException(f'Fail get street id or house number. value: {response[\"result\"]}') from e","def test_can_lookup_postcode(self):\n postcode_to_lookup = \"SW1A 1AA\"\n os_places_key = self.app.config.get(\"OS_PLACES_API_KEY\")\n addresses = AddressLookup(key=os_places_key).by_postcode(postcode_to_lookup)\n self.assertGreater(len(addresses), 0)\n result_postcode = addresses[0].get(\"DPA\", {}).get(\"POSTCODE\")\n self.assertEqual(result_postcode, postcode_to_lookup)","def find_in_county(cls, county_code, ma_region, pdp_region, name='*'):\n flter = or_(cls.COUNTY_CODE == county_code,\n cls.MA_REGION_CODE == ma_region,\n cls.PDP_REGION_CODE == pdp_region\n )\n if not name == '*':\n look_for = f\"{name.lower()}%\"\n flter = and_(flter, cls.PLAN_NAME.ilike(look_for))\n\n qry = cls.session.query(Plans.PLAN_NAME).filter(flter).distinct(cls.PLAN_NAME).all()\n results = [r.PLAN_NAME for r in qry]\n return results","def findLocationByCode(cls, code):\r\n return cls.query.filter_by(code = code).first()","def geocode_zip():\n\n # Get user location \n zipcode = request.args.get('zipcode')\n location_result = client.geocode(zipcode)\n\n # Save needed geolocation in the session\n session['lat'] = location_result[\"results\"][0][\"location\"][\"lat\"]\n session['lng']= location_result[\"results\"][0][\"location\"][\"lng\"]\n\n city = location_result[\"results\"][0][\"address_components\"][\"city\"]\n state = location_result[\"results\"][0][\"address_components\"][\"state\"]\n session['user_facing_location'] = city + \", \" + state\n\n return jsonify(location_result)","def lookup(name, phonebook):\n\n phonebook_data = read_phonebook(phonebook)\n\n match = False\n for entry_name in phonebook_data:\n if name.lower() in entry_name.lower():\n match = True\n print entry_name, phonebook_data[entry_name]\n\n if not match:\n print \"No matches found.\"","def geocode(postcode):\n key = current_app.config.get(\"OS_PLACES_API_KEY\")\n formatted_addresses = FormattedAddressLookup(key=key).by_postcode(postcode)\n response = [{\"formatted_address\": address} for address in formatted_addresses if address]\n return Response(json.dumps(response), mimetype=\"application/json\")","def get_closest_station_by_zipcode(zipcode):\n\n station_lookup_method_by_zipcode = lookup_usaf_station_by_zipcode(zipcode)\n try:\n station, warnings, lat, lon = _get_closest_station_by_zcta_ranked(zipcode)\n\n isd_metadata = get_isd_file_metadata(str(station))\n if len(isd_metadata) == 0:\n logging.warning(\"Zipcode %s mapped to station %s, but no ISD metadata was found.\" % (zipcode, station))\n return station_lookup_method_by_zipcode\n\n except UnrecognizedUSAFIDError as e:\n logging.warning(\"Closest station %s is not a recognized station. Using backup-method station %s for zipcode %s instead.\" % (\n str(station),\n station_lookup_method_by_zipcode,\n zipcode))\n return station_lookup_method_by_zipcode\n\n except UnrecognizedZCTAError as e:\n logging.warning(\"Unrecognized ZCTA %s\" % e)\n return None\n\n if str(station) != station_lookup_method_by_zipcode:\n logging.debug(\"Previously would have selected station %s instead of %s for zip code %s\" % (\n station_lookup_method_by_zipcode,\n str(station),\n zipcode))\n\n if warnings:\n logging.warning(\"Station %s is %d meters over maximum %d meters (%d meters) (zip code %s is at lat/lon %f, %f)\" % (\n str(station),\n int(warnings[0].data['distance_meters'] - warnings[0].data['max_distance_meters']),\n int(warnings[0].data['max_distance_meters']),\n int(warnings[0].data['distance_meters']),\n zipcode,\n lat,\n lon,\n ))\n logging.warning(\"Closest station %s is too far. Using backup-method station %s instead.\" % (\n str(station),\n station_lookup_method_by_zipcode))\n return station_lookup_method_by_zipcode\n\n return str(station)","def get_zipcode_stations(add):\r\n name=get_zipcode_names(add)\r\n engine = get_sql_engine()\r\n neighborhood_stations = text(\r\n \"\"\"\r\n SELECT\r\n \"name\" as name,\r\n \"addressStreet\" as address,\r\n \"bikesAvailable\" as available_bikes,\r\n v.geom as geom,\r\n ST_X(v.geom) as lon, ST_Y(v.geom)as lat\r\n FROM indego_rt1130 as v\r\n JOIN philly_zipcode as n\r\n ON ST_Intersects(v.geom, n.geom)\r\n WHERE n.code = :name\r\n \"\"\"\r\n )\r\n stations = gpd.read_postgis(neighborhood_stations, con=engine, params={\"name\": name})\r\n return stations","def show_restaurants(customer_score, seed_number=1, area=None, rank=None, search_key=None,page=1):\n\tcuisine_match = {\n\t\t'american': ['American','Steak','New American','French-American'],\n\t\t'french': ['French','French/Italian','French-American','French| Gluten Free'],\n\t\t'italian': ['Italian','French/Italian'],\n\t\t'asian': ['Asian','Japanese','Middle Eastern','Chinese','Korean','Indian','Thai'],\n\t}\n\trank_match = {'Platinum': 5, 'Gold': 4, 'Silver': 3, 'Bronze': 2, 'Blue': 1}\n\n\tquery = Restaurants.query.filter(Restaurants.dinerscode_rank >= 1, Restaurants.dinerscode_rank <= get_maximum_rank(customer_score), Restaurants.is_deleted == 'N')\n\n\tif area and area != 'ALL':\n\t\tquery = query.filter(Restaurants.area == area)\n \n\tif rank in rank_match:\n\t\tquery = query.filter(Restaurants.dinerscode_rank == rank_match[rank])\n\n\tif search_key:\n\t\tsearch_key = search_key.lower()\n\t\tcuisine_lst = cuisine_match[search_key] if search_key in cuisine_match else (search_key,)\n\t\tquery = query.filter(or_(Restaurants.restaurant_name.ilike('%{}%'.format(search_key)),\n\t\t \t\t Restaurants.cuisine_type.in_(cuisine_lst),\n\t\t \t\t Restaurants.cuisine_type.ilike('%{}%'.format(search_key))))\n\t\t# restaurants = [res for res in restaurants if search_key in res.restaurant_name.lower() or search_key in res.cuisine_type.lower()]\n\n\tquery = query.order_by(func.rand(seed_number)).offset(offset).paginate(page, 12)\n\n\treturn query","def find_with_postcode(self, postcode, predicate=None, skip_cache=False):\n now = _time_ms(datetime.datetime.utcnow())\n if skip_cache or now - self._last_updated > CACHE_LIMIT:\n self._process_stations()\n\n info = self.pc.get(postcode)\n if not info:\n raise InvalidPostcodeException(\"No known postcode %s\" % postcode)\n if 'geo' not in info or not set(['lat', 'lng']) <= set(info['geo']):\n raise InvalidDataException(\"Missing latitude and/or longitude\")\n lat, lng = float(info['geo']['lat']), float(info['geo']['lng'])\n return self.find_with_geo(lat, lng, predicate=predicate)","def suggestions(self, input, borough_code=None):\n parsed = parser.address(input)\n if borough_code:\n parsed['BOROUGH_CODE'] = borough_code\n self.similiar_names = []\n self.results = []\n if parsed['PHN'] and parsed['STREET']:\n if not parsed['BOROUGH_CODE'] and not parsed['ZIP']:\n # iterate borocodes\n for x in range(1, 6):\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], borough_code=x)\n # try address with borough code if present\n elif parsed['BOROUGH_CODE']:\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], borough_code=parsed['BOROUGH_CODE'])\n # try address with zip code if present\n elif parsed['ZIP']:\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], zip=parsed['ZIP'])\n # validate and retrieve any addresses\n if len(self.similiar_names):\n for name in self.similiar_names:\n self._geocode(phn=parsed['PHN'], street=name['street'], borough_code=name['borough_code'])\n if None in self.results:\n self.results = list(filter(lambda v: v is not None, self.results))\n\n return self.results","def lookup_muni(name_muni=None, code_muni=None, verbose=False):\n # Get metadata with data url addresses\n temp_meta = utils.select_metadata(geo=\"lookup_muni\", year=2010)\n\n # Read DataFrame available at provided url\n lookup_table = utils.download_metadata(\n temp_meta.loc[:, \"download_path\"].to_list()[0]\n )\n lookup_table[\"name_muni_format\"] = lookup_table[\"name_muni_format\"].str.lower()\n\n # Search by inputs\n if (\n code_muni == \"all\"\n or name_muni == \"all\"\n or (code_muni is None and name_muni is None)\n ):\n if verbose:\n print(f\"Returning results for all municipalities\")\n return lookup_table.iloc[:, :-1]\n\n elif code_muni is not None:\n if name_muni is not None:\n if verbose:\n print(\"Ignoring argument name_muni\")\n try:\n output = lookup_table[lookup_table[\"code_muni\"] == int(code_muni)].iloc[\n :, :-1\n ]\n if verbose:\n print(\n \"Returning results for municipality \",\n f'{output.loc[:, \"name_muni\"].to_list()[0]}',\n )\n return output\n\n except KeyError:\n raise Exception(\n f\"The `code_muni` argument {code_muni}\",\n \"was not found in the database.\",\n )\n\n elif name_muni is not None:\n # Cleaning from accents and turning into lower cases without spaces\n name_muni = utils.strip_accents(str(name_muni).lower().strip())\n output = lookup_table[lookup_table[\"name_muni_format\"] == name_muni]\n\n if len(output) == 0:\n if verbose:\n print(\"Please insert a valid municipality name\")\n raise Exception(\n f\"The `name_muni` argument {name_muni} \",\n \"was not found in the database.\",\n )\n else:\n if verbose:\n print(\n \"Returning results for municipality\"\n f'{output.loc[:, \"name_muni\"].to_list()[0]}'\n )\n return output.iloc[:, :-1]\n\n elif code_muni == \"all\" and name_muni == \"all\":\n if verbose:\n print(\"Please insert either a municipality \", \"name or a municipality code\")","def find_airport_code_by_city(city):\n airports = get_airports()\n\n if city == 'London':\n return 'LHR'\n\n for airport_code in airports:\n if airports[airport_code].lower() == city.lower():\n return airport_code\n return None","def city_state_zip(**kwargs):\r\n result = \"{city_name}, {state_code}\".format(**kwargs)\r\n if kwargs[\"five_digit_zip_code\"]:\r\n # RLID for some reason has two spaces between state & ZIP.\r\n result += \" {five_digit_zip_code}\".format(**kwargs)\r\n return result","def postal_code(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"postal_code\")","def postal_code(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"postal_code\")","def postal_code(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"postal_code\")","def findARestaurant(mealType,location):\n\t#1. Use getGeocodeLocation to get the latitude and longitude coordinates of the location string.\n\t# lat_lng = f\"{getGeocodeLocation(location)}\"\n\t# lat_lng_formatted = lat_lng[lat_lng.find(\"(\")+1:lat_lng.find(\")\")]\n\tlatitude, longitude = getGeocodeLocation(location)\n\n\t#2. Use foursquare API to find a nearby restaurant with the latitude, longitude, and mealType strings.\n\t#HINT: format for url will be something like https://api.foursquare.com/v2/venues/search?client_id=CLIENT_ID&client_secret=CLIENT_SECRET&v=20130815&ll=40.7,-74&query=sushi\n\turl = (f\"https://api.foursquare.com/v2/venues/search?client_id={foursquare_client_id}&client_secret={foursquare_client_secret}&v={version}&ll={latitude},{longitude}&intent=browse&radius=10000&query={mealType}&limit=10\")\n\th = httplib2.Http()\n\tresult = json.loads(h.request(url, \"GET\")[1])\n\n\t#3. Grab the first restaurant\n\tvenue_id = result[\"response\"][\"venues\"][0][\"id\"]\n\tvenue_name = result[\"response\"][\"venues\"][0][\"name\"]\n\tvenue_location = result[\"response\"][\"venues\"][0][\"location\"]\n\n\t#4. Get a 300x300 picture of the restaurant using the venue_id (you can change this by altering the 300x300 value in the URL or replacing it with 'orginal' to get the original picture\n\timg_url = (f\"https://api.foursquare.com/v2/venues/{venue_id}/photos?client_id={foursquare_client_id}&client_secret={foursquare_client_secret}&v={version}&group=venue&limit=10\")\n\timg_h = httplib2.Http()\n\timg_result = json.loads(img_h.request(img_url, \"GET\")[1])\n\tprint(img_result)\n\n\t#5. Grab the first image\n\tif len(img_result[\"response\"][\"photos\"][\"items\"]) > 0:\n\t\timg_url = f\"{img_url_pre_lim['prefix']}300x300{img_url_pre_lim['suffix']}\"\n\n\t#6. If no image is available, insert default a image url\n\telse:\n\t\timg_url = \"https://cps-static.rovicorp.com/3/JPG_400/MI0003/711/MI0003711195.jpg?partner=allrovi.com\"\n\n\t#7. Return a dictionary containing the restaurant name, address, and image url\t\n\tresult = {\"name\": venue_name, \"address\": venue_location.get(\"address\",\"\"), \"img_url\": img_url}\n\tprint(result)\n\treturn result","def geo_coder(house_number, boro_code, street_name, zip_code): \r\n wa1 = '1B{}{}{}{}{}C{}{}'.format(rightpad(house_number, 16), rightpad('', 38), boro_code, rightpad('', 10), rightpad(street_name, 32), rightpad('', 113), rightpad(zip_code, 5))\r\n wa1 = rightpad(wa1, 1200)\r\n wa2 = rightpad('', 4300)\r\n NYCGeo.NYCgeo(wa1, wa2)\r\n return wa1, wa2","def get_supplier_address_by_name(supplier_name: str) -> str:\n # Open a new connection\n db, cursor = db_connector.cursor()\n\n query = \"select address from supplier where name = '{}';\".format(supplier_name)\n cursor.execute(query)\n data = cursor.fetchall()\n db.disconnect()\n return data[0][0]","def zip_code(self):\n\n\t\telement = Element(driver=self.driver,\n\t\t explicit_wait_time=self.explicit_wait_time,\n\t\t locator=BillPayPageLocator.ZIP_CODE_INPUT)\n\t\treturn element.element_value","def search(self,name=None):\n\t\taddresses = discover_devices()\n\t\t#if len(addresses) == 0:\n\t\t#\treturn None\n\t\tnames = []\n\t\tfor adr in addresses:\n\t\t\tnames.append(lookup_name(adr))\n\t\t\tif name != None and name == names[-1]:\n\t\t\t\treturn adr\n\n\t\treturn zip(addresses,names)","def get_full_address(postal_code):\n from app.models.address import Address\n\n address = Address.find_one(postal_code)\n if address:\n return jsonify(address.to_dict())\n return jsonify(dict())","def test_get_restaurants(self):\n address = {'number': '375',\n 'street': 'Noe St',\n 'city': 'San Francisco',\n 'zip': '94114'}\n\n with self.app.app_context():\n restaurants = ordrin.get_restaurants(address)\n\n # Ordr.in returns a test entry as the first item in the list when\n # when hitting their testing servers.\n entry = restaurants[0]\n self.assertEquals(entry['na'], 'Test Merchant 20130315')\n self.assertEquals(entry['id'], 23917)","def replace_zip_code(zip_code):\r\n if len(zip_code)>5:\r\n return zip_code[0:5]\r\n else:\r\n return zip_code","def search():\n\n # Store the 'q' part of the URL as a string called 'q'. Check 'q' loaded, and produce runtime error if not.\n # e.g. '12589'\n q = request.args.get(\"q\")\n if not q:\n raise RuntimeError(\"missing location\")\n\n # Rewrites user input as lowercase\n q = str.lower(q)\n\n # Select the entire row from database 'places' that at least contains the value of 'q' in one of the 'postal_code', 'place_name', or 'admin_name1' fields.\n # e.g. [{'country_code':'US','postal_code':'12589'}]\n q_info = db.execute(\"SELECT * FROM places WHERE postal_code LIKE :q OR LOWER(place_name) LIKE :q OR LOWER(admin_name1) LIKE :q LIMIT 10\", q='%'+q+'%')\n\n # Run 'q_info' dict through 'jsonify()' function to convert some elements to JSON compatible(?)\n return jsonify(q_info)","def zipcode_validation(add):\r\n lng=get_address(add)[1]\r\n lat=get_address(add)[0]\r\n engine = get_sql_engine()\r\n query = text(\r\n \"\"\"\r\n SELECT\r\n code\r\n FROM philly_zipcode\r\n WHERE ST_Intersects(geom, ST_SetSRID(ST_MakePoint(:lng, :lat), 4326))\r\n \"\"\"\r\n )\r\n resp = engine.execute(query,lng=lng, lat=lat).fetchall()\r\n return resp","def search():\n # q is the name of the http parameter\n request.args.get(\"q\")\n\n #check for missing arguments\n if not(request.args.get(\"q\")):\n raise RuntimeError(\"Missing geo!\")\n\n #\"%\":match any number of characters\n q=request.args.get(\"q\") + \"%\"\n\n #retrieve data from database\n rows=db.execute(\"SELECT * from places WHERE postal_code LIKE :pc OR place_name LIKE :city OR admin_name1 LIKE :state\", pc=q,city=q,state=q)\n\n return jsonify(rows)","def search_food(cls, name):\n obj = cls.objects(name=name).first()\n return obj","def get_city_by_name(request, city_prefix):\n cities = City.objects.filter(city_name__istartswith=city_prefix)[:5]\n serializer = AllCitiesSerializer(cities, many=True)\n return Response(serializer.data)","def zipcode(self, zipcode):\n self._zipcode = zipcode","def search_zipcode(driver, zipcode, links_file, min_page):\n time.sleep(LOADING_TIME)\n driver = fill_forms(driver, zipcode)\n\n # If a minimum page number was inputted, go to that url directly\n if min_page:\n url = driver.current_url\n page_url = url.split('&ns=1')[0] + '&start=' + str((min_page - 1) * 10)\n driver.get(page_url)\n\n source = driver.page_source\n soup = BeautifulSoup(source, \"html.parser\")\n \n # If it hit a captcha, stop everything\n if check_captcha(soup):\n sys.exit('Hit Captcha at zipcode ' + zipcode)\n\n # If it didn't find anything for that zip code, skip it\n if not soup.find(text = \"Sorry, but we didn't understand the location you entered.\"):\n # Go to each business link on search page\n driver = go_through_businesses(driver, soup, links_file, zipcode)\n\n # Go to next page of businesses in search, if there is a next page\n while True:\n try:\n # next_tag = driver.find_element_by_xpath(\"//a[@class='page-option prev-next next']\")\n next_tag = driver.find_element_by_xpath(\"//a[@class='u-decoration-none next pagination-links_anchor']\")\n # If there's no next button, you're done\n except:\n break\n else:\n driver.execute_script(\"return arguments[0].scrollIntoView();\", next_tag)\n time.sleep(LOADING_TIME)\n\n next_tag.click()\n time.sleep(LOADING_TIME)\n source = driver.page_source\n soup = BeautifulSoup(source, \"html.parser\")\n driver = go_through_businesses(driver, soup, links_file, zipcode)\n\n return driver","def query_business_name():\n print()\n business_name = input(\n 'Please enter full business name or type \"back\" or \"quit\": ')\n print()\n if business_name == \"quit\":\n print(\"Goodbye!\")\n sys.exit()\n if business_name == \"back\":\n return \"back\"\n\n business_object = business_col.find_one({\"name\": business_name})\n if business_object is None:\n print(\"No business found with given name.\")\n\n return business_object","def zipcode(self, zipcode):\n if zipcode is None:\n raise ValueError(\"Invalid value for `zipcode`, must not be `None`\") # noqa: E501\n\n self._zipcode = zipcode","def get_zipcode_takeouts(add):\r\n name=get_zipcode_names(add)\r\n engine = get_sql_engine()\r\n zipcode_takeouts = text(\r\n \"\"\"\r\n SELECT\r\n \"NAME\" as name, \"ADDRESS\" as address,\r\n geom, ST_X(geom) as lon, ST_Y(geom)as lat\r\n FROM chinese_takeout\r\n WHERE \"ZIP\" = :name\r\n\r\n \"\"\"\r\n )\r\n ctakeouts = gpd.read_postgis(zipcode_takeouts, con=engine, params={\"name\": name})\r\n return ctakeouts","def retrieve_location(self, street, zip, city, country):\n \n query = u\"%s, %s, %s\" %(street, city, country)\n query = urllib.quote(query.encode(\"utf-8\"))\n\n url = \"https://api.mapbox.com/v4/geocode/mapbox.places/%s.json?access_token=%s\" %(query, self.config.mapbox_access_token)\n response = requests.get(url)\n if response.status_code!=200:\n raise LocationNotFound()\n\n data = response.json()['features']\n\n if len(data)==0:\n query = u\"%s, %s\" %(city, country)\n query = urllib.quote(query.encode(\"utf-8\"))\n\n # trying again but only with city\n url = \"https://api.mapbox.com/v4/geocode/mapbox_places/%s.json?access_token=%s\" %(query, self.config.mapbox_access_token)\n data = requests.get(url).json()\n data = data['features']\n\n if len(data)==0:\n raise LocationNotFound()\n\n # for some reason in geojson it is (long,lat). Oh yeah\n return tuple(reversed(data[0]['center']))","def run_script():\n var=raw_input(\"Enter a Zipcode: \")\n address='http://www.uszip.com/zip/'+var\n conn=urllib.urlopen(address)\n t=[]\n for line in conn.fp:\n\tline=line.strip()\n\tif '<title>' in line:\n\t line.split()\n\t print line[7:-16]\n\tif 'Total population' in line:\n\t line=line.strip('z')\n\t loc=line.index('Total population')\n\t loc2=line.index('<span')\n\t print line[(loc+25):loc2]","def principal_zip_code(self, principal_zip_code):\n\n self._principal_zip_code = principal_zip_code","def find_address():\n while True:\n business_object = query_business_name()\n if business_object == \"back\":\n return\n elif business_object is None:\n continue\n\n print(f'{business_object[\"name\"]}\\'s address is:'\n f'{business_object[\"address\"]}, {business_object[\"city\"]} '\n f'{business_object[\"state\"]}')","def postal_code(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"postal_code\")","def fuzzy_pcode(\n self, countryiso3: str, name: str, logname: Optional[str] = None\n ) -> Optional[str]:\n if (\n self.countries_fuzzy_try is not None\n and countryiso3 not in self.countries_fuzzy_try\n ):\n if logname:\n self.ignored.add((logname, countryiso3))\n return None\n name_to_pcode = self.name_to_pcode.get(countryiso3)\n if not name_to_pcode:\n if logname:\n self.errors.add((logname, countryiso3))\n return None\n adm_name_lookup = clean_name(name)\n adm_name_lookup2 = multiple_replace(\n adm_name_lookup, self.admin_name_replacements\n )\n pcode = name_to_pcode.get(\n adm_name_lookup, name_to_pcode.get(adm_name_lookup2)\n )\n if not pcode and name.lower() in self.admin_fuzzy_dont:\n if logname:\n self.ignored.add((logname, countryiso3, name))\n return None\n if not pcode:\n for map_name in name_to_pcode:\n if adm_name_lookup in map_name:\n pcode = name_to_pcode[map_name]\n if logname:\n self.matches.add(\n (\n logname,\n countryiso3,\n name,\n self.pcode_to_name[pcode],\n \"substring\",\n )\n )\n break\n for map_name in name_to_pcode:\n if adm_name_lookup2 in map_name:\n pcode = name_to_pcode[map_name]\n if logname:\n self.matches.add(\n (\n logname,\n countryiso3,\n name,\n self.pcode_to_name[pcode],\n \"substring\",\n )\n )\n break\n if not pcode:\n map_names = list(name_to_pcode.keys())\n lower_mapnames = [x.lower() for x in map_names]\n\n def al_transform_1(name):\n if name[:3] == \"al \":\n return f\"ad {name[3:]}\"\n else:\n return None\n\n def al_transform_2(name):\n if name[:3] == \"al \":\n return name[3:]\n else:\n return None\n\n matching_index = self.phonetics.match(\n lower_mapnames,\n adm_name_lookup,\n alternative_name=adm_name_lookup2,\n transform_possible_names=[al_transform_1, al_transform_2],\n )\n\n if matching_index is None:\n if logname:\n self.errors.add((logname, countryiso3, name))\n return None\n\n map_name = map_names[matching_index]\n pcode = name_to_pcode[map_name]\n if logname:\n self.matches.add(\n (\n logname,\n countryiso3,\n name,\n self.pcode_to_name[pcode],\n \"fuzzy\",\n )\n )\n return pcode","def _extract_zipcode(self):\n self.col_etl = self.col_etl.apply(lambda x: x[0:2])\n self.col_etl.name = 'deliv_sector'","def zip_code(self, value):\n regex = config.get('validators', 'zip_code')\n zipcode = re.search(regex,\n value)\n if not zipcode:\n raise ZipCodeError(\"ZipCodeError: 'zip_code' must be 5 non-float digits\")\n else:\n self._zip_code = value","def search_for_customer(f_name: str, l_name: str):\n return cr.search_for_customer(f_name=f_name, l_name=l_name)","async def async_query_locations(self,\n zip_code: str = None,\n latitude: str = None,\n longitude: str = None):\n\n location_url = f\"{API_BASE_URL}/locations\"\n headers = {\n \"Accept\": \"application/json\"\n }\n params = {}\n if latitude and longitude:\n params[\"filter.lat.near\"] = latitude\n params[\"filter.lon.near\"] = longitude\n elif zip_code:\n params[\"filter.zipCode.near\"] = zip_code\n else:\n params['filter.lat.near'] = str(self._hass.config.latitude)\n params['filter.lon.near'] = str(self._hass.config.longitude)\n\n try:\n location_resp = await self._oauth_session.async_request('GET',\n location_url,\n params=params,\n headers=headers)\n location_resp.raise_for_status()\n json_response = await location_resp.json()\n locations = [\n {\n 'locationId': item['locationId'],\n 'name': item['name']\n }\n for item\n in json_response['data']]\n return locations\n except Exception as ex:\n _LOGGER.error(\"Unable to retrieve locations: %s\",\n str(ex))\n return None","def get_address_by_name(name, limit):\n request = \"{}/{}?key={}&q={}&type=json&limit={}\".format(config.GEOCODE_URL, config.GEOCODE_SEARCH_PATH, config.GEOCODE_KEY, name, limit)\n response = requests.get(request).json()\n return response","def list_near_postcode(code):\n index_postcode = \"\".join(code.split()).upper()\n postcode = models.Postcode.query.get(index_postcode)\n\n if postcode is None:\n raise NotFound(\n Markup(\"Postcode <strong>{}</strong> does not exist.\").format(code)\n )\n if postcode.text != code:\n # Redirect to correct URL, eg 'W1A+1AA' instead of 'w1a1aa'\n return redirect(url_for(\".list_near_postcode\", code=postcode.text),\n code=302)\n\n stops = postcode.stops_in_range(db.undefer(models.StopPoint.lines))\n groups = _group_lines_stops(stops)\n\n return render_template(\"postcode.html\", postcode=postcode, list_stops=stops,\n groups=groups)","def zip_code(self):\n return self._zip_code","def getZipCode(dbpath) -> (int, float, float):\n conn = sqlite3.connect(str(dbpath))\n c = conn.cursor()\n c.execute(\"select zipcode, lat, long from user where id=1\")\n conn.commit()\n zipcode = c.fetchone()\n conn.close()\n return zipcode[0], zipcode[1], zipcode[2]","def find_zip_code(x):\n i = 0\n j = 4\n for i in range(1,len(x)-6):\n string = x[i-1:i+6]\n cond = (string[1:-1].isnumeric(), not string[0].isnumeric(), not string[-1].isnumeric())\n if all(cond):\n return x[i:i+5]","def _geocode(self, phn, street, borough_code=None, zip=None):\n try:\n r = self._g[self.geofunction](house_number=phn, street=street, borough_code=borough_code, zip=zip)\n self.results.append(r)\n except GeosupportError as ge:\n if 'SIMILAR NAMES' in ge.result[\"Message\"]:\n list_of_street_names = ge.result['List of Street Names']\n r = [{\n 'street': s,\n 'borough_code': borough_code\n } for s in list_of_street_names]\n self.similiar_names.extend(r)","def Collection_search_name(C:list, name:str) -> list:\r\n restaurants = []\r\n for r in C:\r\n for dish in r.menu:\r\n if name in dish.name:\r\n restaurants.append(r)\r\n return restaurants","def get_zipcode_markets(add):\r\n name=get_zipcode_names(add)\r\n engine = get_sql_engine()\r\n zipcode_markets = text(\r\n \"\"\"\r\n SELECT\r\n \"NAME\" as name, \"ADDRESS\" as address,\r\n \"TIME\" as time, geom,\r\n ST_X(geom) as lon, ST_Y(geom)as lat\r\n FROM farmers_markets\r\n WHERE \"ZIP\" = :name\r\n \"\"\"\r\n )\r\n fmarkets = gpd.read_postgis(zipcode_markets, con=engine, params={\"name\": name})\r\n return fmarkets","def schoolNameFromPassCode(pass_code):\n try:\n return Team.objects.get(pass_code=pass_code).organization.name\n except Team.DoesNotExist:\n return None","def lookup(addr, num, street, city, code, geo_dict, failure_set):\n try:\n address_url = \"https://geocoding.geo.census.gov/geocoder/locations/address?\" + \\\n \"street=\" + str(num) + \"+\" + street.replace(\" \", \"+\") + \"&city=\" + city + \"&zip=\" + \\\n str(code) + \"&benchmark=9&format=json\"\n geo_data = json.load(req.urlopen(address_url).decode('utf-8'))['result']\n except Exception:\n try:\n address_url = \"https://geocoding.geo.census.gov/geocoder/locations/address?\" + \\\n \"street=\" + str(num) + \"+\" + street.replace(\" \", \"+\") + \"&city=\" + city + \"&zip=\" + \\\n str(code) + \"&benchmark=9&format=json\"\n geo_data = json.loads(req.urlopen(address_url).read().decode('utf-8'))['result']\n except Exception as e:\n print(e, addr)\n failure_set.add(addr)\n return None\n if len(geo_data['addressMatches']) == 0:\n print(addr, ': Failure')\n failure_set.add(addr)\n return None\n print(addr, ': Success')\n location = geo_data['addressMatches'][0]['coordinates']\n latlong = ','.join([str(location['y']), str(location['x'])])\n geo_dict[addr] = latlong\n return tuple(float(geo) for geo in latlong.split(','))","def find(self, text: unicode) -> ghidra.program.model.address.Address:\n ...","def read_street_city_names(path=work_david, filter_neighborhoods=True,\n fix_alley=True):\n import pandas as pd\n df = pd.read_csv(path/'street_names_israel.csv', encoding=\"cp1255\")\n df.columns = ['city_code', 'city_name', 'street_code', 'street_name']\n df['city_name'] = df['city_name'].str.replace(\n 'תל אביב - יפו', 'תל אביב יפו')\n if filter_neighborhoods:\n df = df[df['street_code'] <= 5999]\n if fix_alley:\n df['street_name'] = df['street_name'].str.replace('סמ ', 'סמטת ')\n return df","def postal_code(self, instance):\r\n return instance.user.profile.postal_code","def parse_restaurant_name(text):\n stripped = text.lower()\n\n for name_list in RESTAURANT_NAMES:\n for name in name_list:\n if name.lower() in stripped:\n return name_list[0]\n\n return \"\"","def get_city(address):\n geolocator = Nominatim(user_agent=\"specify_your_app_name_here\")\n \n while True:\n try:\n location = geolocator.geocode(address)\n break\n except Exception:\n None\n \n city = citipy.nearest_city(location.latitude, location.longitude)\n return [city.city_name.title(), city.country_code.title()]","def findName(zippedFile):\n pp = linesFromZippedFile(zippedFile, 'project.properties')\n if pp:\n return pp[1][:-1].split('=')[1]\n return \"\"","def get_my_zip_code(\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = GetMyZipCode.create(\n namespace=namespace,\n )\n return run_request(request, additional_headers=x_additional_headers, **kwargs)","def get_name(book, phone):\n # поиск в словаре\n if str(type(book)) == \"<class 'dict'>\":\n abonent = book.get(str(phone))\n return abonent\n # поиск в списке\n else:\n for b in book:\n if DEBUG:\n print(b[0], phone, phone==b[0], b[1])\n if b[0]==phone:\n return b[1]\n \n return None","def searchByName(database):\n firstname=str(input(\"What is his first name :\"))\n usr,find=getByName(database,firstname)\n if find:\n print(usr)","def GeoLocZip(zip_code, cntry):\r\n nb_error = 0\r\n #Try connection with OSM server\r\n while(nb_error < 100):\r\n try :\r\n #connection succeed\r\n time.sleep(1)\r\n g = geocoder.osm(str(zip_code)+' '+str(cntry))\r\n break\r\n except:\r\n #connection failed\r\n #try again\r\n nb_error += 1\r\n print(\"error req - nb_error : \"+str(nb_error))\r\n continue\r\n #g.osm['x'] = longitude\r\n #g.osm['y'] = latitude\r\n return g.osm['x'], g.osm['y']","def test_lookup_entry_by_name(self):\n self.phonebook.add(\"Bob\", \"12345\")\n self.assertEqual(\"12345\", self.phonebook.lookup(\"Bob\"))","def geocode_one(self, postcode: str, address: Optional[str] = None) -> pd.Series:\n if postcode is None and address is None:\n raise utils.GenericException(\"You must pass either postcode or address, or both.\")\n if self.gmaps_key is None:\n self.gmaps_key = self._load_key()\n if self.gmaps_key is not None:\n self.gmaps_client = googlemaps.Client(key=self.gmaps_key)\n if self.cache is None:\n self._load_cache()\n sep = \", \" if address and postcode else \"\"\n postcode = postcode if postcode is not None else \"\"\n address = address if address is not None else \"\"\n search_term = f\"{address}{sep}{postcode}\"\n if search_term in self.cache:\n logging.debug(\"Loading GMaps Geocoder API result from cache: '%s'\", search_term)\n geocode_result = self.cache[search_term]\n else:\n logging.debug(\"Querying Google Maps Geocoder API for '%s'\", search_term)\n if self.gmaps_key is None:\n return pd.Series({\"latitude\": np.nan, \"longitude\": np.nan, \"match_status\": 0})\n geocode_result = self.gmaps_client.geocode(search_term, region=\"uk\")\n self.cache[search_term] = geocode_result\n self.cache_modified = True\n if not geocode_result or len(geocode_result) > 1:\n return pd.Series({\"latitude\": np.nan, \"longitude\": np.nan, \"match_status\": 0})\n geometry = geocode_result[0][\"geometry\"]\n ok_loc_types = [\"ROOFTOP\", \"GEOMETRIC_CENTER\"]\n if geometry[\"location_type\"] in ok_loc_types or \\\n geocode_result[0][\"types\"] == [\"postal_code\"]:\n return pd.Series({\"latitude\": geometry[\"location\"][\"lat\"],\n \"longitude\": geometry[\"location\"][\"lng\"],\n \"match_status\": 3})\n return pd.Series({\"latitude\": np.nan, \"longitude\": np.nan, \"match_status\": 0})","def getEntry(self, zip_file):\n \n return self.getAllEntries().get(zip_file)","def fix_location(r):\n \n # all is fine: just change zipcode datatype to str\n if not np.isnan(r['zip']) and not np.isnan(r['lat']):\n return [str(int(r['zip'])), r['lng'], r['lat']]\n \n # try to locate within zipcode polygons\n if not np.isnan(r['lat']):\n query = \"\"\"\n SELECT t.geoid as zip, {} as lng, {} as lat\n FROM us_zcta5 t JOIN usps_zcta5 z ON t.geoid = z.zip\n WHERE ST_Contains(t.shape, ST_GeomFromText('POINT({} {})', 2))\n \"\"\"\n res = pd.read_sql(query.format(r['lng'], r['lat'], r['lng'], r['lat']), con = con)\n if len(res) == 1:\n return res.values[0].tolist()\n\n # use zipcode center as location proxy: geocoding is prefered in this case, but might be quite expensive\n if not np.isnan(r['zip']):\n res = zipcodes[zipcodes['zip'] == str(int(r['zip']))]\n if len(res) == 1:\n return res.values[0].tolist()[:3]\n\n return [None, None, None]","def searchCountry(host):\n process = subprocess.Popen(\"geoiplookup \"+host,stdout=subprocess.PIPE, shell=True)\n (output, err) = process.communicate()\n secondPart = output.split(\"GeoIP Country Edition: \", 1)[1]\n country = secondPart.split(\"\\nGeoIP City Edition\", 1)[0]\n return country","def get_country_code(country_name):\n for code, name in COUNTRIES.items():\n if name == country_name:\n return code\n # if string isn't found returns None\n else:\n continue","def country(name):\n return location_db().find(name=name)[\"country\"]","def get_city(self, territory_id: str = \"\"):","def get_city(self, territory_id: str = \"\"):"],"string":"[\n \"def county_name(zipcode): \\n search = SearchEngine(simple_zipcode=True) # set simple_zipcode=False to use rich info database\\n zipcode_query = search.by_zipcode(str(zipcode))\\n zipcode_query_dict = zipcode_query.to_dict()\\n county = zipcode_query_dict['county']\\n if county is None:\\n print('Invalid County')\\n else :\\n if 'County' in county:\\n county = county[:-7]\\n if county in county_list:\\n print('County is County List')\\n print(county)\\n return county\",\n \"def get_zipsearch(zipcode=u''):\\n from x84.bbs import getterminal, LineEditor, echo\\n term = getterminal()\\n echo(u''.join((u'\\\\r\\\\n\\\\r\\\\n',\\n term.bold_yellow(u' -'),\\n term.reverse_yellow(u':'),\\n u' ')))\\n return LineEditor(width=min(30, term.width - 5), content=zipcode).read()\",\n \"def find_one(cls, zipcode ):\\n qry = cls.session.query(cls).filter(cls.ZIPCODE.ilike(f'{zipcode}'))\\n zc = qry.one()\\n return zc\",\n \"def find_zip_codes(self, zip_code):\\n zip_code = str(zip_code).strip()\\n cursor = self.households.find({\\\"addresses.zip_code\\\":zip_code})\\n results = [Household.from_dict(dct) for dct in cursor]\\n\\n cursor = self.businesses.find({\\\"address.zip_code\\\":zip_code})\\n results += [Business.from_dict(dct) for dct in cursor]\\n\\n return results\",\n \"def lookup_usaf_station_by_zipcode(zipcode):\\n\\n usaf = zipcode_usaf.get(zipcode, None)\\n return usaf\",\n \"def get_zipcode_names(add):\\r\\n lng=get_address(add)[1]\\r\\n lat=get_address(add)[0]\\r\\n engine = get_sql_engine()\\r\\n query = text(\\r\\n \\\"\\\"\\\"\\r\\n SELECT\\r\\n code\\r\\n FROM philly_zipcode\\r\\n WHERE ST_Intersects(geom, ST_SetSRID(ST_MakePoint(:lng, :lat), 4326))\\r\\n \\\"\\\"\\\"\\r\\n )\\r\\n resp = engine.execute(query,lng=lng, lat=lat).fetchall()\\r\\n # get a list of names\\r\\n names = [row[\\\"code\\\"] for row in resp][0]\\r\\n return names\",\n \"def get_info_on_postalcode(_, postalcode):\\n fourpp = int(postalcode[0:4])\\n chars = postalcode[4:6]\\n streets = get_streets(fourpp, chars)\\n if streets:\\n street = streets[0]\\n town = street.postcode.city.get_official_name()\\n address = street.street\\n data = {'found': True, 'address': address, 'town': town}\\n else:\\n data = {'found': False}\\n j = json.dumps(data)\\n return HttpResponse(j, content_type='application/json')\",\n \"def get_city(zip_code):\\r\\n\\r\\n # API key, retrieved from configure.py\\r\\n api_key = configure.ZIP_KEY\\r\\n\\r\\n # API endpoint\\r\\n url = f'https://www.zipcodeapi.com/rest/{api_key}/info.json/{zip_code}/degrees'\\r\\n\\r\\n # API call\\r\\n response = requests.get(url)\\r\\n\\r\\n # Collect response in json format\\r\\n data = response.json()\\r\\n\\r\\n if 'error_code' in data or 'error_msg' in data:\\r\\n return {\\r\\n 'success': False,\\r\\n 'query': zip_code\\r\\n }\\r\\n\\r\\n else:\\r\\n return {\\r\\n 'success': True,\\r\\n 'query': data['zip_code'],\\r\\n 'city': data['city'],\\r\\n 'state': data['state'],\\r\\n 'lat': data['lat'],\\r\\n 'lon': data['lng']\\r\\n }\",\n \"def searchZipcode(zipcode, jurisdictions):\\n try:\\n if len(str(zipcode)) != 5:\\n return jurisdictions.none()\\n\\n zipcode = Zipcode.objects.get(code=zipcode)\\n j = jurisdictions.filter(geometry__intersects=zipcode.geometry)\\n return j\\n except Exception as e:\\n print(e)\\n return jurisdictions.none()\",\n \"def get(self, zipcode):\\n response = hereService.getWeatherByZipcode(zipcode)\\n return response\",\n \"def get_zipcode_hospitals(add):\\r\\n name=get_zipcode_names(add)\\r\\n engine = get_sql_engine()\\r\\n hospital_info = text(\\r\\n \\\"\\\"\\\"\\r\\n SELECT\\r\\n\\t \\\"HOSPITAL_NAME\\\" AS name, \\\"STREET_ADDRESS\\\" as address,\\r\\n \\\"PHONE_NUMBER\\\" as contact, geom,\\r\\n ST_X(geom) AS lon, ST_Y(geom) AS lat\\r\\n FROM philly_hospital\\r\\n WHERE \\\"ZIP_CODE\\\" = :name\\r\\n \\\"\\\"\\\"\\r\\n )\\r\\n hospitals = gpd.read_postgis(hospital_info, con=engine, params={\\\"name\\\": name})\\r\\n return hospitals\",\n \"def match_city(self, city, dpt_code, zip_code = None):\\n city = format_str_city_insee(city)\\n dpt_code = dpt_code.rjust(2, '0')\\n if zip_code:\\n zip_code.rjust(5, '0')\\n # Based on zip code and city name\\n ls_matching = []\\n found_indicator = False\\n if zip_code:\\n if zip_code in self.dict_corr_zip_insee:\\n for city_insee, zip_insee, dpt_insee, code_insee in self.dict_corr_zip_insee[zip_code]:\\n if city == city_insee:\\n ls_matching.append((city_insee, zip_insee, code_insee))\\n found_indicator = True\\n if found_indicator:\\n return (ls_matching, 'zip_city_match')\\n # If no exact zip, city match: check if city name in insee city names\\n for city_insee, zip_insee, dpt_insee, code_insee in self.dict_corr_zip_insee[zip_code]:\\n if city in city_insee:\\n ls_matching.append((city_insee, zip_insee, code_insee))\\n found_indicator = True\\n if found_indicator:\\n return (ls_matching, 'zip_city_in_match(es)')\\n # Based on dpt code and city name\\n for city_insee, zip_insee, dpt_insee, code_insee in self.dict_corr_dpt_insee[dpt_code]:\\n if city == city_insee:\\n ls_matching.append((city_insee, zip_insee, code_insee))\\n found_indicator = True\\n if found_indicator:\\n return (ls_matching, 'dpt_city_match')\\n # If no exact dpt, city match: check if city name in insee city names\\n for city_insee, zip_insee, dpt_insee, code_insee in self.dict_corr_dpt_insee[dpt_code]:\\n if city in city_insee:\\n ls_matching.append((city_insee, zip_insee, code_insee))\\n found_indicator = True\\n if found_indicator:\\n return (ls_matching, 'dpt_city_in_match(es)')\\n # No match\\n return (None, 'no_match')\",\n \"def get_city_by_code(post_code):\\n post_code = post_code.replace(' ', '').encode('utf-8')\\n error = ''\\n city = ''\\n opener = urllib2.build_opener()\\n url = 'http://maps.googleapis.com/maps/api/geocode/json?address={0}&sensor=false'.format(post_code)\\n response = opener.open(url).read()\\n response_dict = json.loads(response)\\n request_status = response_dict['status']\\n if request_status == 'OK':\\n logger.debug('Google response')\\n logger.debug(response_dict)\\n results = response_dict['results']\\n \\\"\\\"\\\"\\n first get all results\\n with required zip code\\n \\\"\\\"\\\"\\n results_with_required_zip_code = []\\n for result in results:\\n address_components = result['address_components']\\n for address_component in address_components:\\n types = address_component['types']\\n for t in types:\\n if t == 'postal_code' and address_component['short_name'].replace(' ', '').lower() == post_code.lower():\\n results_with_required_zip_code.append(result)\\n if not results_with_required_zip_code:\\n error = {\\n 'status': '8',\\n 'message': POST_CODE_DOES_NOT_EXISTS,\\n 'title': POST_CODE_DOES_NOT_EXISTS_TITLE\\n }\\n # error = 'No location with post code %s' % post_code\\n else:\\n \\\"\\\"\\\"\\n next we need all results in GB\\n \\\"\\\"\\\"\\n results_with_required_zip_code_in_GB = ''\\n for good_result in results_with_required_zip_code:\\n address_components = good_result['address_components']\\n for address_component in address_components:\\n types = address_component['types']\\n for t in types:\\n if t == 'country' and address_component['short_name'].lower() == 'GB'.lower():\\n results_with_required_zip_code_in_GB = good_result\\n if not results_with_required_zip_code_in_GB:\\n error = {\\n 'status': '7',\\n 'message': POST_CODE_DOES_NOT_EXISTS_IN_GB,\\n 'title': POST_CODE_DOES_NOT_EXISTS_IN_GB_TITLE\\n }\\n # error = 'No city with post code %s in GB' % post_code\\n else:\\n \\\"\\\"\\\"\\n finally find city name\\n \\\"\\\"\\\"\\n address_components = results_with_required_zip_code_in_GB['address_components']\\n # first try get postal city\\n searching_city = get_city_by_key(address_components, 'postal_town')\\n if not searching_city:\\n # next by administrative_area_level_2\\n searching_city = get_city_by_key(address_components, 'administrative_area_level_2')\\n if not searching_city:\\n print url\\n error = {\\n 'status': '7',\\n 'message': POST_CODE_DOES_NOT_EXISTS_IN_GB,\\n 'title': POST_CODE_DOES_NOT_EXISTS_IN_GB_TITLE\\n }\\n # error = 'No city with post code %s in GB' % post_code\\n else:\\n city = searching_city\\n elif request_status == 'ZERO_RESULTS':\\n error = {\\n 'status': '8',\\n 'message': POST_CODE_DOES_NOT_EXISTS,\\n 'title': POST_CODE_DOES_NOT_EXISTS_TITLE\\n }\\n else:\\n error = request_status\\n return {\\n 'error': error,\\n 'data': city\\n }\",\n \"def get_postal_code_by_name(self, name):\\n raise NotImplementedError()\",\n \"def zillow_query(address, zipcode, key=keys.zillow):\\n zillow_data = ZillowWrapper(key)\\n deep_search_response = zillow_data.get_deep_search_results(\\n address, zipcode)\\n result = GetDeepSearchResults(deep_search_response)\\n return result\",\n \"def compute_zip_code(zip_code_text):\\n zip_code = None\\n if zip_code_text and len(zip_code_text) >= 5 and zip_code_text.isdigit():\\n zip_code = zip_code_text[:5]\\n return zip_code\",\n \"def main(postalcode):\\n places = postalcodes_mexico.places(postalcode)\\n click.echo(places)\\n return 0\",\n \"def patrons_in_zipcode(session, zipcode):\\n\\n query = {\\n 'target': {\\n 'record': {\\n 'type': 'patron'\\n },\\n 'id': 80010\\n },\\n 'expr': {\\n 'op': 'equals',\\n 'operands': [\\n str(zipcode),\\n ''\\n ]\\n }\\n }\\n\\n url = '/patrons/query?offset=0&limit={}'.format(str(api_limit))\\n headers = {'content-type': 'application/json'}\\n data = json.dumps(query)\\n r = session.post(api_url_base + url, data=data, headers=headers)\\n entries = json.loads(r.text)['entries']\\n record_ids = [x['link'].split('/')[-1] for x in entries]\\n return record_ids\",\n \"def correct_zipcode(business_tag, zipcode):\\n try:\\n address = business_tag.find('div', {'class': 'secondary-attributes'}).find('address').text\\n zipcode_found = re.search(re.compile('(^|[^\\\\d])\\\\d{5}($|[^\\\\d])'), address).group(0)\\n zipcode_found = re.search(re.compile('\\\\d{5}'), zipcode_found).group(0)\\n return zipcode_found == zipcode\\n except:\\n return False\",\n \"def find_places(query):\\n parts = str(query).split(' ')\\n for i, p in enumerate(parts):\\n p = p.replace('-', ' ').strip()\\n try:\\n postal_code = int(p)\\n if len(postal_code) == 4:\\n print(postal_code, parts[i+1])\\n # Check \\n #response = get_osm_location(\\\"{postal_code} {name}\\\")\\n #lon = response['lon']\\n #lat = response['lat']\\n #poly = \\n except Exception as e:\\n continue\",\n \"def type_zip_code(self, zip_code):\\n\\n\\t\\twith allure.step(\\\"Type payee zip code\\\"):\\n\\t\\t\\telement = Element(driver=self.driver,\\n\\t\\t\\t explicit_wait_time=self.explicit_wait_time,\\n\\t\\t\\t locator=BillPayPageLocator.ZIP_CODE_INPUT)\\n\\t\\t\\telement.write(zip_code)\\n\\t\\t\\treturn None\",\n \"def search_using_magento_code(cls, code):\\n countries = cls.search([('code', '=', code)])\\n\\n if not countries:\\n return cls.raise_user_error(\\n \\\"country_not_found\\\", error_args=(code, )\\n )\\n\\n return countries[0]\",\n \"def postal_code():\\r\\n\\r\\n cursor.execute('SELECT col_1 FROM zipcodes order by RANDOM() limit 1;'\\r\\n )\\r\\n return cursor.fetchone()[0]\",\n \"def extract_zipcode(full_address):\\n full_address = full_address.strip()\\n last_space_index = full_address.rindex(\\\" \\\")\\n zipcode = full_address[last_space_index + 1 : ]\\n return zipcode\",\n \"def get_postal_zone(self, postal_code: str) -> PostalZone:\\n key = postal_code.lower()\\n try:\\n return self._postal_zones[key]\\n except KeyError:\\n postal_code = PostalZone(postal_code=postal_code, city=self)\\n self._postal_zones[key] = postal_code\\n return postal_code\",\n \"def get_testing_locations_by_zip():\\n\\n zip_code = request.args.get('zip_code')\\n\\n testing_info = get_testing_location_by_zipcode(zip_code)\\n data = format_data(d=testing_info, key='test_id')\\n\\n return jsonify(data)\",\n \"def search_address(query: str) -> Tuple[int, str]:\\n\\n url = 'https://api.n1.ru/api/v1/geo/geocoder/with_cities/'\\n params = _search_params.copy()\\n params['q'] = query\\n\\n try:\\n r = requests.get(url, params=params, headers=_headers)\\n response = r.json()\\n\\n if not 'result' in response or not response['result']:\\n raise NotFoundException('Result not found or empty.')\\n \\n address = None\\n house_number = query.split(',')[-1].strip()\\n for x in response['result']:\\n if x['name_ru'].lower() == house_number:\\n address = x\\n break\\n \\n if address is None:\\n raise NotFoundException(f'Not found house number {house_number} in result: {response[\\\"result\\\"]}')\\n \\n return address['street']['id'], address['name_ru']\\n except requests.RequestException as e:\\n raise ParserException(f'Fail make request. query: {query}') from e\\n except NotFoundException as e:\\n raise ParserException('Invalid result.') from e\\n except (KeyError, IndexError) as e:\\n raise ParserException(f'Fail get street id or house number. value: {response[\\\"result\\\"]}') from e\",\n \"def test_can_lookup_postcode(self):\\n postcode_to_lookup = \\\"SW1A 1AA\\\"\\n os_places_key = self.app.config.get(\\\"OS_PLACES_API_KEY\\\")\\n addresses = AddressLookup(key=os_places_key).by_postcode(postcode_to_lookup)\\n self.assertGreater(len(addresses), 0)\\n result_postcode = addresses[0].get(\\\"DPA\\\", {}).get(\\\"POSTCODE\\\")\\n self.assertEqual(result_postcode, postcode_to_lookup)\",\n \"def find_in_county(cls, county_code, ma_region, pdp_region, name='*'):\\n flter = or_(cls.COUNTY_CODE == county_code,\\n cls.MA_REGION_CODE == ma_region,\\n cls.PDP_REGION_CODE == pdp_region\\n )\\n if not name == '*':\\n look_for = f\\\"{name.lower()}%\\\"\\n flter = and_(flter, cls.PLAN_NAME.ilike(look_for))\\n\\n qry = cls.session.query(Plans.PLAN_NAME).filter(flter).distinct(cls.PLAN_NAME).all()\\n results = [r.PLAN_NAME for r in qry]\\n return results\",\n \"def findLocationByCode(cls, code):\\r\\n return cls.query.filter_by(code = code).first()\",\n \"def geocode_zip():\\n\\n # Get user location \\n zipcode = request.args.get('zipcode')\\n location_result = client.geocode(zipcode)\\n\\n # Save needed geolocation in the session\\n session['lat'] = location_result[\\\"results\\\"][0][\\\"location\\\"][\\\"lat\\\"]\\n session['lng']= location_result[\\\"results\\\"][0][\\\"location\\\"][\\\"lng\\\"]\\n\\n city = location_result[\\\"results\\\"][0][\\\"address_components\\\"][\\\"city\\\"]\\n state = location_result[\\\"results\\\"][0][\\\"address_components\\\"][\\\"state\\\"]\\n session['user_facing_location'] = city + \\\", \\\" + state\\n\\n return jsonify(location_result)\",\n \"def lookup(name, phonebook):\\n\\n phonebook_data = read_phonebook(phonebook)\\n\\n match = False\\n for entry_name in phonebook_data:\\n if name.lower() in entry_name.lower():\\n match = True\\n print entry_name, phonebook_data[entry_name]\\n\\n if not match:\\n print \\\"No matches found.\\\"\",\n \"def geocode(postcode):\\n key = current_app.config.get(\\\"OS_PLACES_API_KEY\\\")\\n formatted_addresses = FormattedAddressLookup(key=key).by_postcode(postcode)\\n response = [{\\\"formatted_address\\\": address} for address in formatted_addresses if address]\\n return Response(json.dumps(response), mimetype=\\\"application/json\\\")\",\n \"def get_closest_station_by_zipcode(zipcode):\\n\\n station_lookup_method_by_zipcode = lookup_usaf_station_by_zipcode(zipcode)\\n try:\\n station, warnings, lat, lon = _get_closest_station_by_zcta_ranked(zipcode)\\n\\n isd_metadata = get_isd_file_metadata(str(station))\\n if len(isd_metadata) == 0:\\n logging.warning(\\\"Zipcode %s mapped to station %s, but no ISD metadata was found.\\\" % (zipcode, station))\\n return station_lookup_method_by_zipcode\\n\\n except UnrecognizedUSAFIDError as e:\\n logging.warning(\\\"Closest station %s is not a recognized station. Using backup-method station %s for zipcode %s instead.\\\" % (\\n str(station),\\n station_lookup_method_by_zipcode,\\n zipcode))\\n return station_lookup_method_by_zipcode\\n\\n except UnrecognizedZCTAError as e:\\n logging.warning(\\\"Unrecognized ZCTA %s\\\" % e)\\n return None\\n\\n if str(station) != station_lookup_method_by_zipcode:\\n logging.debug(\\\"Previously would have selected station %s instead of %s for zip code %s\\\" % (\\n station_lookup_method_by_zipcode,\\n str(station),\\n zipcode))\\n\\n if warnings:\\n logging.warning(\\\"Station %s is %d meters over maximum %d meters (%d meters) (zip code %s is at lat/lon %f, %f)\\\" % (\\n str(station),\\n int(warnings[0].data['distance_meters'] - warnings[0].data['max_distance_meters']),\\n int(warnings[0].data['max_distance_meters']),\\n int(warnings[0].data['distance_meters']),\\n zipcode,\\n lat,\\n lon,\\n ))\\n logging.warning(\\\"Closest station %s is too far. Using backup-method station %s instead.\\\" % (\\n str(station),\\n station_lookup_method_by_zipcode))\\n return station_lookup_method_by_zipcode\\n\\n return str(station)\",\n \"def get_zipcode_stations(add):\\r\\n name=get_zipcode_names(add)\\r\\n engine = get_sql_engine()\\r\\n neighborhood_stations = text(\\r\\n \\\"\\\"\\\"\\r\\n SELECT\\r\\n \\\"name\\\" as name,\\r\\n \\\"addressStreet\\\" as address,\\r\\n \\\"bikesAvailable\\\" as available_bikes,\\r\\n v.geom as geom,\\r\\n ST_X(v.geom) as lon, ST_Y(v.geom)as lat\\r\\n FROM indego_rt1130 as v\\r\\n JOIN philly_zipcode as n\\r\\n ON ST_Intersects(v.geom, n.geom)\\r\\n WHERE n.code = :name\\r\\n \\\"\\\"\\\"\\r\\n )\\r\\n stations = gpd.read_postgis(neighborhood_stations, con=engine, params={\\\"name\\\": name})\\r\\n return stations\",\n \"def show_restaurants(customer_score, seed_number=1, area=None, rank=None, search_key=None,page=1):\\n\\tcuisine_match = {\\n\\t\\t'american': ['American','Steak','New American','French-American'],\\n\\t\\t'french': ['French','French/Italian','French-American','French| Gluten Free'],\\n\\t\\t'italian': ['Italian','French/Italian'],\\n\\t\\t'asian': ['Asian','Japanese','Middle Eastern','Chinese','Korean','Indian','Thai'],\\n\\t}\\n\\trank_match = {'Platinum': 5, 'Gold': 4, 'Silver': 3, 'Bronze': 2, 'Blue': 1}\\n\\n\\tquery = Restaurants.query.filter(Restaurants.dinerscode_rank >= 1, Restaurants.dinerscode_rank <= get_maximum_rank(customer_score), Restaurants.is_deleted == 'N')\\n\\n\\tif area and area != 'ALL':\\n\\t\\tquery = query.filter(Restaurants.area == area)\\n \\n\\tif rank in rank_match:\\n\\t\\tquery = query.filter(Restaurants.dinerscode_rank == rank_match[rank])\\n\\n\\tif search_key:\\n\\t\\tsearch_key = search_key.lower()\\n\\t\\tcuisine_lst = cuisine_match[search_key] if search_key in cuisine_match else (search_key,)\\n\\t\\tquery = query.filter(or_(Restaurants.restaurant_name.ilike('%{}%'.format(search_key)),\\n\\t\\t \\t\\t Restaurants.cuisine_type.in_(cuisine_lst),\\n\\t\\t \\t\\t Restaurants.cuisine_type.ilike('%{}%'.format(search_key))))\\n\\t\\t# restaurants = [res for res in restaurants if search_key in res.restaurant_name.lower() or search_key in res.cuisine_type.lower()]\\n\\n\\tquery = query.order_by(func.rand(seed_number)).offset(offset).paginate(page, 12)\\n\\n\\treturn query\",\n \"def find_with_postcode(self, postcode, predicate=None, skip_cache=False):\\n now = _time_ms(datetime.datetime.utcnow())\\n if skip_cache or now - self._last_updated > CACHE_LIMIT:\\n self._process_stations()\\n\\n info = self.pc.get(postcode)\\n if not info:\\n raise InvalidPostcodeException(\\\"No known postcode %s\\\" % postcode)\\n if 'geo' not in info or not set(['lat', 'lng']) <= set(info['geo']):\\n raise InvalidDataException(\\\"Missing latitude and/or longitude\\\")\\n lat, lng = float(info['geo']['lat']), float(info['geo']['lng'])\\n return self.find_with_geo(lat, lng, predicate=predicate)\",\n \"def suggestions(self, input, borough_code=None):\\n parsed = parser.address(input)\\n if borough_code:\\n parsed['BOROUGH_CODE'] = borough_code\\n self.similiar_names = []\\n self.results = []\\n if parsed['PHN'] and parsed['STREET']:\\n if not parsed['BOROUGH_CODE'] and not parsed['ZIP']:\\n # iterate borocodes\\n for x in range(1, 6):\\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], borough_code=x)\\n # try address with borough code if present\\n elif parsed['BOROUGH_CODE']:\\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], borough_code=parsed['BOROUGH_CODE'])\\n # try address with zip code if present\\n elif parsed['ZIP']:\\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], zip=parsed['ZIP'])\\n # validate and retrieve any addresses\\n if len(self.similiar_names):\\n for name in self.similiar_names:\\n self._geocode(phn=parsed['PHN'], street=name['street'], borough_code=name['borough_code'])\\n if None in self.results:\\n self.results = list(filter(lambda v: v is not None, self.results))\\n\\n return self.results\",\n \"def lookup_muni(name_muni=None, code_muni=None, verbose=False):\\n # Get metadata with data url addresses\\n temp_meta = utils.select_metadata(geo=\\\"lookup_muni\\\", year=2010)\\n\\n # Read DataFrame available at provided url\\n lookup_table = utils.download_metadata(\\n temp_meta.loc[:, \\\"download_path\\\"].to_list()[0]\\n )\\n lookup_table[\\\"name_muni_format\\\"] = lookup_table[\\\"name_muni_format\\\"].str.lower()\\n\\n # Search by inputs\\n if (\\n code_muni == \\\"all\\\"\\n or name_muni == \\\"all\\\"\\n or (code_muni is None and name_muni is None)\\n ):\\n if verbose:\\n print(f\\\"Returning results for all municipalities\\\")\\n return lookup_table.iloc[:, :-1]\\n\\n elif code_muni is not None:\\n if name_muni is not None:\\n if verbose:\\n print(\\\"Ignoring argument name_muni\\\")\\n try:\\n output = lookup_table[lookup_table[\\\"code_muni\\\"] == int(code_muni)].iloc[\\n :, :-1\\n ]\\n if verbose:\\n print(\\n \\\"Returning results for municipality \\\",\\n f'{output.loc[:, \\\"name_muni\\\"].to_list()[0]}',\\n )\\n return output\\n\\n except KeyError:\\n raise Exception(\\n f\\\"The `code_muni` argument {code_muni}\\\",\\n \\\"was not found in the database.\\\",\\n )\\n\\n elif name_muni is not None:\\n # Cleaning from accents and turning into lower cases without spaces\\n name_muni = utils.strip_accents(str(name_muni).lower().strip())\\n output = lookup_table[lookup_table[\\\"name_muni_format\\\"] == name_muni]\\n\\n if len(output) == 0:\\n if verbose:\\n print(\\\"Please insert a valid municipality name\\\")\\n raise Exception(\\n f\\\"The `name_muni` argument {name_muni} \\\",\\n \\\"was not found in the database.\\\",\\n )\\n else:\\n if verbose:\\n print(\\n \\\"Returning results for municipality\\\"\\n f'{output.loc[:, \\\"name_muni\\\"].to_list()[0]}'\\n )\\n return output.iloc[:, :-1]\\n\\n elif code_muni == \\\"all\\\" and name_muni == \\\"all\\\":\\n if verbose:\\n print(\\\"Please insert either a municipality \\\", \\\"name or a municipality code\\\")\",\n \"def find_airport_code_by_city(city):\\n airports = get_airports()\\n\\n if city == 'London':\\n return 'LHR'\\n\\n for airport_code in airports:\\n if airports[airport_code].lower() == city.lower():\\n return airport_code\\n return None\",\n \"def city_state_zip(**kwargs):\\r\\n result = \\\"{city_name}, {state_code}\\\".format(**kwargs)\\r\\n if kwargs[\\\"five_digit_zip_code\\\"]:\\r\\n # RLID for some reason has two spaces between state & ZIP.\\r\\n result += \\\" {five_digit_zip_code}\\\".format(**kwargs)\\r\\n return result\",\n \"def postal_code(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"postal_code\\\")\",\n \"def postal_code(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"postal_code\\\")\",\n \"def postal_code(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"postal_code\\\")\",\n \"def findARestaurant(mealType,location):\\n\\t#1. Use getGeocodeLocation to get the latitude and longitude coordinates of the location string.\\n\\t# lat_lng = f\\\"{getGeocodeLocation(location)}\\\"\\n\\t# lat_lng_formatted = lat_lng[lat_lng.find(\\\"(\\\")+1:lat_lng.find(\\\")\\\")]\\n\\tlatitude, longitude = getGeocodeLocation(location)\\n\\n\\t#2. Use foursquare API to find a nearby restaurant with the latitude, longitude, and mealType strings.\\n\\t#HINT: format for url will be something like https://api.foursquare.com/v2/venues/search?client_id=CLIENT_ID&client_secret=CLIENT_SECRET&v=20130815&ll=40.7,-74&query=sushi\\n\\turl = (f\\\"https://api.foursquare.com/v2/venues/search?client_id={foursquare_client_id}&client_secret={foursquare_client_secret}&v={version}&ll={latitude},{longitude}&intent=browse&radius=10000&query={mealType}&limit=10\\\")\\n\\th = httplib2.Http()\\n\\tresult = json.loads(h.request(url, \\\"GET\\\")[1])\\n\\n\\t#3. Grab the first restaurant\\n\\tvenue_id = result[\\\"response\\\"][\\\"venues\\\"][0][\\\"id\\\"]\\n\\tvenue_name = result[\\\"response\\\"][\\\"venues\\\"][0][\\\"name\\\"]\\n\\tvenue_location = result[\\\"response\\\"][\\\"venues\\\"][0][\\\"location\\\"]\\n\\n\\t#4. Get a 300x300 picture of the restaurant using the venue_id (you can change this by altering the 300x300 value in the URL or replacing it with 'orginal' to get the original picture\\n\\timg_url = (f\\\"https://api.foursquare.com/v2/venues/{venue_id}/photos?client_id={foursquare_client_id}&client_secret={foursquare_client_secret}&v={version}&group=venue&limit=10\\\")\\n\\timg_h = httplib2.Http()\\n\\timg_result = json.loads(img_h.request(img_url, \\\"GET\\\")[1])\\n\\tprint(img_result)\\n\\n\\t#5. Grab the first image\\n\\tif len(img_result[\\\"response\\\"][\\\"photos\\\"][\\\"items\\\"]) > 0:\\n\\t\\timg_url = f\\\"{img_url_pre_lim['prefix']}300x300{img_url_pre_lim['suffix']}\\\"\\n\\n\\t#6. If no image is available, insert default a image url\\n\\telse:\\n\\t\\timg_url = \\\"https://cps-static.rovicorp.com/3/JPG_400/MI0003/711/MI0003711195.jpg?partner=allrovi.com\\\"\\n\\n\\t#7. Return a dictionary containing the restaurant name, address, and image url\\t\\n\\tresult = {\\\"name\\\": venue_name, \\\"address\\\": venue_location.get(\\\"address\\\",\\\"\\\"), \\\"img_url\\\": img_url}\\n\\tprint(result)\\n\\treturn result\",\n \"def geo_coder(house_number, boro_code, street_name, zip_code): \\r\\n wa1 = '1B{}{}{}{}{}C{}{}'.format(rightpad(house_number, 16), rightpad('', 38), boro_code, rightpad('', 10), rightpad(street_name, 32), rightpad('', 113), rightpad(zip_code, 5))\\r\\n wa1 = rightpad(wa1, 1200)\\r\\n wa2 = rightpad('', 4300)\\r\\n NYCGeo.NYCgeo(wa1, wa2)\\r\\n return wa1, wa2\",\n \"def get_supplier_address_by_name(supplier_name: str) -> str:\\n # Open a new connection\\n db, cursor = db_connector.cursor()\\n\\n query = \\\"select address from supplier where name = '{}';\\\".format(supplier_name)\\n cursor.execute(query)\\n data = cursor.fetchall()\\n db.disconnect()\\n return data[0][0]\",\n \"def zip_code(self):\\n\\n\\t\\telement = Element(driver=self.driver,\\n\\t\\t explicit_wait_time=self.explicit_wait_time,\\n\\t\\t locator=BillPayPageLocator.ZIP_CODE_INPUT)\\n\\t\\treturn element.element_value\",\n \"def search(self,name=None):\\n\\t\\taddresses = discover_devices()\\n\\t\\t#if len(addresses) == 0:\\n\\t\\t#\\treturn None\\n\\t\\tnames = []\\n\\t\\tfor adr in addresses:\\n\\t\\t\\tnames.append(lookup_name(adr))\\n\\t\\t\\tif name != None and name == names[-1]:\\n\\t\\t\\t\\treturn adr\\n\\n\\t\\treturn zip(addresses,names)\",\n \"def get_full_address(postal_code):\\n from app.models.address import Address\\n\\n address = Address.find_one(postal_code)\\n if address:\\n return jsonify(address.to_dict())\\n return jsonify(dict())\",\n \"def test_get_restaurants(self):\\n address = {'number': '375',\\n 'street': 'Noe St',\\n 'city': 'San Francisco',\\n 'zip': '94114'}\\n\\n with self.app.app_context():\\n restaurants = ordrin.get_restaurants(address)\\n\\n # Ordr.in returns a test entry as the first item in the list when\\n # when hitting their testing servers.\\n entry = restaurants[0]\\n self.assertEquals(entry['na'], 'Test Merchant 20130315')\\n self.assertEquals(entry['id'], 23917)\",\n \"def replace_zip_code(zip_code):\\r\\n if len(zip_code)>5:\\r\\n return zip_code[0:5]\\r\\n else:\\r\\n return zip_code\",\n \"def search():\\n\\n # Store the 'q' part of the URL as a string called 'q'. Check 'q' loaded, and produce runtime error if not.\\n # e.g. '12589'\\n q = request.args.get(\\\"q\\\")\\n if not q:\\n raise RuntimeError(\\\"missing location\\\")\\n\\n # Rewrites user input as lowercase\\n q = str.lower(q)\\n\\n # Select the entire row from database 'places' that at least contains the value of 'q' in one of the 'postal_code', 'place_name', or 'admin_name1' fields.\\n # e.g. [{'country_code':'US','postal_code':'12589'}]\\n q_info = db.execute(\\\"SELECT * FROM places WHERE postal_code LIKE :q OR LOWER(place_name) LIKE :q OR LOWER(admin_name1) LIKE :q LIMIT 10\\\", q='%'+q+'%')\\n\\n # Run 'q_info' dict through 'jsonify()' function to convert some elements to JSON compatible(?)\\n return jsonify(q_info)\",\n \"def zipcode_validation(add):\\r\\n lng=get_address(add)[1]\\r\\n lat=get_address(add)[0]\\r\\n engine = get_sql_engine()\\r\\n query = text(\\r\\n \\\"\\\"\\\"\\r\\n SELECT\\r\\n code\\r\\n FROM philly_zipcode\\r\\n WHERE ST_Intersects(geom, ST_SetSRID(ST_MakePoint(:lng, :lat), 4326))\\r\\n \\\"\\\"\\\"\\r\\n )\\r\\n resp = engine.execute(query,lng=lng, lat=lat).fetchall()\\r\\n return resp\",\n \"def search():\\n # q is the name of the http parameter\\n request.args.get(\\\"q\\\")\\n\\n #check for missing arguments\\n if not(request.args.get(\\\"q\\\")):\\n raise RuntimeError(\\\"Missing geo!\\\")\\n\\n #\\\"%\\\":match any number of characters\\n q=request.args.get(\\\"q\\\") + \\\"%\\\"\\n\\n #retrieve data from database\\n rows=db.execute(\\\"SELECT * from places WHERE postal_code LIKE :pc OR place_name LIKE :city OR admin_name1 LIKE :state\\\", pc=q,city=q,state=q)\\n\\n return jsonify(rows)\",\n \"def search_food(cls, name):\\n obj = cls.objects(name=name).first()\\n return obj\",\n \"def get_city_by_name(request, city_prefix):\\n cities = City.objects.filter(city_name__istartswith=city_prefix)[:5]\\n serializer = AllCitiesSerializer(cities, many=True)\\n return Response(serializer.data)\",\n \"def zipcode(self, zipcode):\\n self._zipcode = zipcode\",\n \"def search_zipcode(driver, zipcode, links_file, min_page):\\n time.sleep(LOADING_TIME)\\n driver = fill_forms(driver, zipcode)\\n\\n # If a minimum page number was inputted, go to that url directly\\n if min_page:\\n url = driver.current_url\\n page_url = url.split('&ns=1')[0] + '&start=' + str((min_page - 1) * 10)\\n driver.get(page_url)\\n\\n source = driver.page_source\\n soup = BeautifulSoup(source, \\\"html.parser\\\")\\n \\n # If it hit a captcha, stop everything\\n if check_captcha(soup):\\n sys.exit('Hit Captcha at zipcode ' + zipcode)\\n\\n # If it didn't find anything for that zip code, skip it\\n if not soup.find(text = \\\"Sorry, but we didn't understand the location you entered.\\\"):\\n # Go to each business link on search page\\n driver = go_through_businesses(driver, soup, links_file, zipcode)\\n\\n # Go to next page of businesses in search, if there is a next page\\n while True:\\n try:\\n # next_tag = driver.find_element_by_xpath(\\\"//a[@class='page-option prev-next next']\\\")\\n next_tag = driver.find_element_by_xpath(\\\"//a[@class='u-decoration-none next pagination-links_anchor']\\\")\\n # If there's no next button, you're done\\n except:\\n break\\n else:\\n driver.execute_script(\\\"return arguments[0].scrollIntoView();\\\", next_tag)\\n time.sleep(LOADING_TIME)\\n\\n next_tag.click()\\n time.sleep(LOADING_TIME)\\n source = driver.page_source\\n soup = BeautifulSoup(source, \\\"html.parser\\\")\\n driver = go_through_businesses(driver, soup, links_file, zipcode)\\n\\n return driver\",\n \"def query_business_name():\\n print()\\n business_name = input(\\n 'Please enter full business name or type \\\"back\\\" or \\\"quit\\\": ')\\n print()\\n if business_name == \\\"quit\\\":\\n print(\\\"Goodbye!\\\")\\n sys.exit()\\n if business_name == \\\"back\\\":\\n return \\\"back\\\"\\n\\n business_object = business_col.find_one({\\\"name\\\": business_name})\\n if business_object is None:\\n print(\\\"No business found with given name.\\\")\\n\\n return business_object\",\n \"def zipcode(self, zipcode):\\n if zipcode is None:\\n raise ValueError(\\\"Invalid value for `zipcode`, must not be `None`\\\") # noqa: E501\\n\\n self._zipcode = zipcode\",\n \"def get_zipcode_takeouts(add):\\r\\n name=get_zipcode_names(add)\\r\\n engine = get_sql_engine()\\r\\n zipcode_takeouts = text(\\r\\n \\\"\\\"\\\"\\r\\n SELECT\\r\\n \\\"NAME\\\" as name, \\\"ADDRESS\\\" as address,\\r\\n geom, ST_X(geom) as lon, ST_Y(geom)as lat\\r\\n FROM chinese_takeout\\r\\n WHERE \\\"ZIP\\\" = :name\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n )\\r\\n ctakeouts = gpd.read_postgis(zipcode_takeouts, con=engine, params={\\\"name\\\": name})\\r\\n return ctakeouts\",\n \"def retrieve_location(self, street, zip, city, country):\\n \\n query = u\\\"%s, %s, %s\\\" %(street, city, country)\\n query = urllib.quote(query.encode(\\\"utf-8\\\"))\\n\\n url = \\\"https://api.mapbox.com/v4/geocode/mapbox.places/%s.json?access_token=%s\\\" %(query, self.config.mapbox_access_token)\\n response = requests.get(url)\\n if response.status_code!=200:\\n raise LocationNotFound()\\n\\n data = response.json()['features']\\n\\n if len(data)==0:\\n query = u\\\"%s, %s\\\" %(city, country)\\n query = urllib.quote(query.encode(\\\"utf-8\\\"))\\n\\n # trying again but only with city\\n url = \\\"https://api.mapbox.com/v4/geocode/mapbox_places/%s.json?access_token=%s\\\" %(query, self.config.mapbox_access_token)\\n data = requests.get(url).json()\\n data = data['features']\\n\\n if len(data)==0:\\n raise LocationNotFound()\\n\\n # for some reason in geojson it is (long,lat). Oh yeah\\n return tuple(reversed(data[0]['center']))\",\n \"def run_script():\\n var=raw_input(\\\"Enter a Zipcode: \\\")\\n address='http://www.uszip.com/zip/'+var\\n conn=urllib.urlopen(address)\\n t=[]\\n for line in conn.fp:\\n\\tline=line.strip()\\n\\tif '<title>' in line:\\n\\t line.split()\\n\\t print line[7:-16]\\n\\tif 'Total population' in line:\\n\\t line=line.strip('z')\\n\\t loc=line.index('Total population')\\n\\t loc2=line.index('<span')\\n\\t print line[(loc+25):loc2]\",\n \"def principal_zip_code(self, principal_zip_code):\\n\\n self._principal_zip_code = principal_zip_code\",\n \"def find_address():\\n while True:\\n business_object = query_business_name()\\n if business_object == \\\"back\\\":\\n return\\n elif business_object is None:\\n continue\\n\\n print(f'{business_object[\\\"name\\\"]}\\\\'s address is:'\\n f'{business_object[\\\"address\\\"]}, {business_object[\\\"city\\\"]} '\\n f'{business_object[\\\"state\\\"]}')\",\n \"def postal_code(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"postal_code\\\")\",\n \"def fuzzy_pcode(\\n self, countryiso3: str, name: str, logname: Optional[str] = None\\n ) -> Optional[str]:\\n if (\\n self.countries_fuzzy_try is not None\\n and countryiso3 not in self.countries_fuzzy_try\\n ):\\n if logname:\\n self.ignored.add((logname, countryiso3))\\n return None\\n name_to_pcode = self.name_to_pcode.get(countryiso3)\\n if not name_to_pcode:\\n if logname:\\n self.errors.add((logname, countryiso3))\\n return None\\n adm_name_lookup = clean_name(name)\\n adm_name_lookup2 = multiple_replace(\\n adm_name_lookup, self.admin_name_replacements\\n )\\n pcode = name_to_pcode.get(\\n adm_name_lookup, name_to_pcode.get(adm_name_lookup2)\\n )\\n if not pcode and name.lower() in self.admin_fuzzy_dont:\\n if logname:\\n self.ignored.add((logname, countryiso3, name))\\n return None\\n if not pcode:\\n for map_name in name_to_pcode:\\n if adm_name_lookup in map_name:\\n pcode = name_to_pcode[map_name]\\n if logname:\\n self.matches.add(\\n (\\n logname,\\n countryiso3,\\n name,\\n self.pcode_to_name[pcode],\\n \\\"substring\\\",\\n )\\n )\\n break\\n for map_name in name_to_pcode:\\n if adm_name_lookup2 in map_name:\\n pcode = name_to_pcode[map_name]\\n if logname:\\n self.matches.add(\\n (\\n logname,\\n countryiso3,\\n name,\\n self.pcode_to_name[pcode],\\n \\\"substring\\\",\\n )\\n )\\n break\\n if not pcode:\\n map_names = list(name_to_pcode.keys())\\n lower_mapnames = [x.lower() for x in map_names]\\n\\n def al_transform_1(name):\\n if name[:3] == \\\"al \\\":\\n return f\\\"ad {name[3:]}\\\"\\n else:\\n return None\\n\\n def al_transform_2(name):\\n if name[:3] == \\\"al \\\":\\n return name[3:]\\n else:\\n return None\\n\\n matching_index = self.phonetics.match(\\n lower_mapnames,\\n adm_name_lookup,\\n alternative_name=adm_name_lookup2,\\n transform_possible_names=[al_transform_1, al_transform_2],\\n )\\n\\n if matching_index is None:\\n if logname:\\n self.errors.add((logname, countryiso3, name))\\n return None\\n\\n map_name = map_names[matching_index]\\n pcode = name_to_pcode[map_name]\\n if logname:\\n self.matches.add(\\n (\\n logname,\\n countryiso3,\\n name,\\n self.pcode_to_name[pcode],\\n \\\"fuzzy\\\",\\n )\\n )\\n return pcode\",\n \"def _extract_zipcode(self):\\n self.col_etl = self.col_etl.apply(lambda x: x[0:2])\\n self.col_etl.name = 'deliv_sector'\",\n \"def zip_code(self, value):\\n regex = config.get('validators', 'zip_code')\\n zipcode = re.search(regex,\\n value)\\n if not zipcode:\\n raise ZipCodeError(\\\"ZipCodeError: 'zip_code' must be 5 non-float digits\\\")\\n else:\\n self._zip_code = value\",\n \"def search_for_customer(f_name: str, l_name: str):\\n return cr.search_for_customer(f_name=f_name, l_name=l_name)\",\n \"async def async_query_locations(self,\\n zip_code: str = None,\\n latitude: str = None,\\n longitude: str = None):\\n\\n location_url = f\\\"{API_BASE_URL}/locations\\\"\\n headers = {\\n \\\"Accept\\\": \\\"application/json\\\"\\n }\\n params = {}\\n if latitude and longitude:\\n params[\\\"filter.lat.near\\\"] = latitude\\n params[\\\"filter.lon.near\\\"] = longitude\\n elif zip_code:\\n params[\\\"filter.zipCode.near\\\"] = zip_code\\n else:\\n params['filter.lat.near'] = str(self._hass.config.latitude)\\n params['filter.lon.near'] = str(self._hass.config.longitude)\\n\\n try:\\n location_resp = await self._oauth_session.async_request('GET',\\n location_url,\\n params=params,\\n headers=headers)\\n location_resp.raise_for_status()\\n json_response = await location_resp.json()\\n locations = [\\n {\\n 'locationId': item['locationId'],\\n 'name': item['name']\\n }\\n for item\\n in json_response['data']]\\n return locations\\n except Exception as ex:\\n _LOGGER.error(\\\"Unable to retrieve locations: %s\\\",\\n str(ex))\\n return None\",\n \"def get_address_by_name(name, limit):\\n request = \\\"{}/{}?key={}&q={}&type=json&limit={}\\\".format(config.GEOCODE_URL, config.GEOCODE_SEARCH_PATH, config.GEOCODE_KEY, name, limit)\\n response = requests.get(request).json()\\n return response\",\n \"def list_near_postcode(code):\\n index_postcode = \\\"\\\".join(code.split()).upper()\\n postcode = models.Postcode.query.get(index_postcode)\\n\\n if postcode is None:\\n raise NotFound(\\n Markup(\\\"Postcode <strong>{}</strong> does not exist.\\\").format(code)\\n )\\n if postcode.text != code:\\n # Redirect to correct URL, eg 'W1A+1AA' instead of 'w1a1aa'\\n return redirect(url_for(\\\".list_near_postcode\\\", code=postcode.text),\\n code=302)\\n\\n stops = postcode.stops_in_range(db.undefer(models.StopPoint.lines))\\n groups = _group_lines_stops(stops)\\n\\n return render_template(\\\"postcode.html\\\", postcode=postcode, list_stops=stops,\\n groups=groups)\",\n \"def zip_code(self):\\n return self._zip_code\",\n \"def getZipCode(dbpath) -> (int, float, float):\\n conn = sqlite3.connect(str(dbpath))\\n c = conn.cursor()\\n c.execute(\\\"select zipcode, lat, long from user where id=1\\\")\\n conn.commit()\\n zipcode = c.fetchone()\\n conn.close()\\n return zipcode[0], zipcode[1], zipcode[2]\",\n \"def find_zip_code(x):\\n i = 0\\n j = 4\\n for i in range(1,len(x)-6):\\n string = x[i-1:i+6]\\n cond = (string[1:-1].isnumeric(), not string[0].isnumeric(), not string[-1].isnumeric())\\n if all(cond):\\n return x[i:i+5]\",\n \"def _geocode(self, phn, street, borough_code=None, zip=None):\\n try:\\n r = self._g[self.geofunction](house_number=phn, street=street, borough_code=borough_code, zip=zip)\\n self.results.append(r)\\n except GeosupportError as ge:\\n if 'SIMILAR NAMES' in ge.result[\\\"Message\\\"]:\\n list_of_street_names = ge.result['List of Street Names']\\n r = [{\\n 'street': s,\\n 'borough_code': borough_code\\n } for s in list_of_street_names]\\n self.similiar_names.extend(r)\",\n \"def Collection_search_name(C:list, name:str) -> list:\\r\\n restaurants = []\\r\\n for r in C:\\r\\n for dish in r.menu:\\r\\n if name in dish.name:\\r\\n restaurants.append(r)\\r\\n return restaurants\",\n \"def get_zipcode_markets(add):\\r\\n name=get_zipcode_names(add)\\r\\n engine = get_sql_engine()\\r\\n zipcode_markets = text(\\r\\n \\\"\\\"\\\"\\r\\n SELECT\\r\\n \\\"NAME\\\" as name, \\\"ADDRESS\\\" as address,\\r\\n \\\"TIME\\\" as time, geom,\\r\\n ST_X(geom) as lon, ST_Y(geom)as lat\\r\\n FROM farmers_markets\\r\\n WHERE \\\"ZIP\\\" = :name\\r\\n \\\"\\\"\\\"\\r\\n )\\r\\n fmarkets = gpd.read_postgis(zipcode_markets, con=engine, params={\\\"name\\\": name})\\r\\n return fmarkets\",\n \"def schoolNameFromPassCode(pass_code):\\n try:\\n return Team.objects.get(pass_code=pass_code).organization.name\\n except Team.DoesNotExist:\\n return None\",\n \"def lookup(addr, num, street, city, code, geo_dict, failure_set):\\n try:\\n address_url = \\\"https://geocoding.geo.census.gov/geocoder/locations/address?\\\" + \\\\\\n \\\"street=\\\" + str(num) + \\\"+\\\" + street.replace(\\\" \\\", \\\"+\\\") + \\\"&city=\\\" + city + \\\"&zip=\\\" + \\\\\\n str(code) + \\\"&benchmark=9&format=json\\\"\\n geo_data = json.load(req.urlopen(address_url).decode('utf-8'))['result']\\n except Exception:\\n try:\\n address_url = \\\"https://geocoding.geo.census.gov/geocoder/locations/address?\\\" + \\\\\\n \\\"street=\\\" + str(num) + \\\"+\\\" + street.replace(\\\" \\\", \\\"+\\\") + \\\"&city=\\\" + city + \\\"&zip=\\\" + \\\\\\n str(code) + \\\"&benchmark=9&format=json\\\"\\n geo_data = json.loads(req.urlopen(address_url).read().decode('utf-8'))['result']\\n except Exception as e:\\n print(e, addr)\\n failure_set.add(addr)\\n return None\\n if len(geo_data['addressMatches']) == 0:\\n print(addr, ': Failure')\\n failure_set.add(addr)\\n return None\\n print(addr, ': Success')\\n location = geo_data['addressMatches'][0]['coordinates']\\n latlong = ','.join([str(location['y']), str(location['x'])])\\n geo_dict[addr] = latlong\\n return tuple(float(geo) for geo in latlong.split(','))\",\n \"def find(self, text: unicode) -> ghidra.program.model.address.Address:\\n ...\",\n \"def read_street_city_names(path=work_david, filter_neighborhoods=True,\\n fix_alley=True):\\n import pandas as pd\\n df = pd.read_csv(path/'street_names_israel.csv', encoding=\\\"cp1255\\\")\\n df.columns = ['city_code', 'city_name', 'street_code', 'street_name']\\n df['city_name'] = df['city_name'].str.replace(\\n 'תל אביב - יפו', 'תל אביב יפו')\\n if filter_neighborhoods:\\n df = df[df['street_code'] <= 5999]\\n if fix_alley:\\n df['street_name'] = df['street_name'].str.replace('סמ ', 'סמטת ')\\n return df\",\n \"def postal_code(self, instance):\\r\\n return instance.user.profile.postal_code\",\n \"def parse_restaurant_name(text):\\n stripped = text.lower()\\n\\n for name_list in RESTAURANT_NAMES:\\n for name in name_list:\\n if name.lower() in stripped:\\n return name_list[0]\\n\\n return \\\"\\\"\",\n \"def get_city(address):\\n geolocator = Nominatim(user_agent=\\\"specify_your_app_name_here\\\")\\n \\n while True:\\n try:\\n location = geolocator.geocode(address)\\n break\\n except Exception:\\n None\\n \\n city = citipy.nearest_city(location.latitude, location.longitude)\\n return [city.city_name.title(), city.country_code.title()]\",\n \"def findName(zippedFile):\\n pp = linesFromZippedFile(zippedFile, 'project.properties')\\n if pp:\\n return pp[1][:-1].split('=')[1]\\n return \\\"\\\"\",\n \"def get_my_zip_code(\\n namespace: Optional[str] = None,\\n x_additional_headers: Optional[Dict[str, str]] = None,\\n **kwargs\\n):\\n if namespace is None:\\n namespace, error = get_services_namespace()\\n if error:\\n return None, error\\n request = GetMyZipCode.create(\\n namespace=namespace,\\n )\\n return run_request(request, additional_headers=x_additional_headers, **kwargs)\",\n \"def get_name(book, phone):\\n # поиск в словаре\\n if str(type(book)) == \\\"<class 'dict'>\\\":\\n abonent = book.get(str(phone))\\n return abonent\\n # поиск в списке\\n else:\\n for b in book:\\n if DEBUG:\\n print(b[0], phone, phone==b[0], b[1])\\n if b[0]==phone:\\n return b[1]\\n \\n return None\",\n \"def searchByName(database):\\n firstname=str(input(\\\"What is his first name :\\\"))\\n usr,find=getByName(database,firstname)\\n if find:\\n print(usr)\",\n \"def GeoLocZip(zip_code, cntry):\\r\\n nb_error = 0\\r\\n #Try connection with OSM server\\r\\n while(nb_error < 100):\\r\\n try :\\r\\n #connection succeed\\r\\n time.sleep(1)\\r\\n g = geocoder.osm(str(zip_code)+' '+str(cntry))\\r\\n break\\r\\n except:\\r\\n #connection failed\\r\\n #try again\\r\\n nb_error += 1\\r\\n print(\\\"error req - nb_error : \\\"+str(nb_error))\\r\\n continue\\r\\n #g.osm['x'] = longitude\\r\\n #g.osm['y'] = latitude\\r\\n return g.osm['x'], g.osm['y']\",\n \"def test_lookup_entry_by_name(self):\\n self.phonebook.add(\\\"Bob\\\", \\\"12345\\\")\\n self.assertEqual(\\\"12345\\\", self.phonebook.lookup(\\\"Bob\\\"))\",\n \"def geocode_one(self, postcode: str, address: Optional[str] = None) -> pd.Series:\\n if postcode is None and address is None:\\n raise utils.GenericException(\\\"You must pass either postcode or address, or both.\\\")\\n if self.gmaps_key is None:\\n self.gmaps_key = self._load_key()\\n if self.gmaps_key is not None:\\n self.gmaps_client = googlemaps.Client(key=self.gmaps_key)\\n if self.cache is None:\\n self._load_cache()\\n sep = \\\", \\\" if address and postcode else \\\"\\\"\\n postcode = postcode if postcode is not None else \\\"\\\"\\n address = address if address is not None else \\\"\\\"\\n search_term = f\\\"{address}{sep}{postcode}\\\"\\n if search_term in self.cache:\\n logging.debug(\\\"Loading GMaps Geocoder API result from cache: '%s'\\\", search_term)\\n geocode_result = self.cache[search_term]\\n else:\\n logging.debug(\\\"Querying Google Maps Geocoder API for '%s'\\\", search_term)\\n if self.gmaps_key is None:\\n return pd.Series({\\\"latitude\\\": np.nan, \\\"longitude\\\": np.nan, \\\"match_status\\\": 0})\\n geocode_result = self.gmaps_client.geocode(search_term, region=\\\"uk\\\")\\n self.cache[search_term] = geocode_result\\n self.cache_modified = True\\n if not geocode_result or len(geocode_result) > 1:\\n return pd.Series({\\\"latitude\\\": np.nan, \\\"longitude\\\": np.nan, \\\"match_status\\\": 0})\\n geometry = geocode_result[0][\\\"geometry\\\"]\\n ok_loc_types = [\\\"ROOFTOP\\\", \\\"GEOMETRIC_CENTER\\\"]\\n if geometry[\\\"location_type\\\"] in ok_loc_types or \\\\\\n geocode_result[0][\\\"types\\\"] == [\\\"postal_code\\\"]:\\n return pd.Series({\\\"latitude\\\": geometry[\\\"location\\\"][\\\"lat\\\"],\\n \\\"longitude\\\": geometry[\\\"location\\\"][\\\"lng\\\"],\\n \\\"match_status\\\": 3})\\n return pd.Series({\\\"latitude\\\": np.nan, \\\"longitude\\\": np.nan, \\\"match_status\\\": 0})\",\n \"def getEntry(self, zip_file):\\n \\n return self.getAllEntries().get(zip_file)\",\n \"def fix_location(r):\\n \\n # all is fine: just change zipcode datatype to str\\n if not np.isnan(r['zip']) and not np.isnan(r['lat']):\\n return [str(int(r['zip'])), r['lng'], r['lat']]\\n \\n # try to locate within zipcode polygons\\n if not np.isnan(r['lat']):\\n query = \\\"\\\"\\\"\\n SELECT t.geoid as zip, {} as lng, {} as lat\\n FROM us_zcta5 t JOIN usps_zcta5 z ON t.geoid = z.zip\\n WHERE ST_Contains(t.shape, ST_GeomFromText('POINT({} {})', 2))\\n \\\"\\\"\\\"\\n res = pd.read_sql(query.format(r['lng'], r['lat'], r['lng'], r['lat']), con = con)\\n if len(res) == 1:\\n return res.values[0].tolist()\\n\\n # use zipcode center as location proxy: geocoding is prefered in this case, but might be quite expensive\\n if not np.isnan(r['zip']):\\n res = zipcodes[zipcodes['zip'] == str(int(r['zip']))]\\n if len(res) == 1:\\n return res.values[0].tolist()[:3]\\n\\n return [None, None, None]\",\n \"def searchCountry(host):\\n process = subprocess.Popen(\\\"geoiplookup \\\"+host,stdout=subprocess.PIPE, shell=True)\\n (output, err) = process.communicate()\\n secondPart = output.split(\\\"GeoIP Country Edition: \\\", 1)[1]\\n country = secondPart.split(\\\"\\\\nGeoIP City Edition\\\", 1)[0]\\n return country\",\n \"def get_country_code(country_name):\\n for code, name in COUNTRIES.items():\\n if name == country_name:\\n return code\\n # if string isn't found returns None\\n else:\\n continue\",\n \"def country(name):\\n return location_db().find(name=name)[\\\"country\\\"]\",\n \"def get_city(self, territory_id: str = \\\"\\\"):\",\n \"def get_city(self, territory_id: str = \\\"\\\"):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72173095","0.669443","0.66253436","0.66198653","0.64643675","0.6446394","0.6327299","0.62487876","0.6163434","0.6108556","0.6016015","0.5976856","0.59515876","0.5928086","0.57584274","0.57179135","0.5710414","0.5634522","0.56298053","0.5622634","0.55962557","0.55466205","0.55463","0.5541144","0.55284435","0.5525873","0.5525492","0.54803157","0.54749537","0.5468904","0.54457366","0.54452455","0.5412685","0.5376579","0.53727096","0.53326607","0.5331357","0.53236043","0.53039724","0.5287272","0.527973","0.52766496","0.52766496","0.52766496","0.5198934","0.5194489","0.51744586","0.5156221","0.51405466","0.5140064","0.51244974","0.5099282","0.509161","0.5088811","0.5087377","0.50869673","0.5080285","0.50790596","0.50707585","0.50634885","0.5042655","0.5040994","0.50223315","0.5003732","0.49967054","0.4995582","0.4994981","0.49834424","0.49768955","0.49507967","0.49494335","0.49488053","0.49443567","0.4939537","0.4939483","0.4937237","0.4936235","0.49275413","0.4925153","0.49232978","0.49161","0.4914992","0.49138454","0.4909963","0.48948297","0.4891174","0.48813117","0.48795304","0.48759368","0.4875739","0.4873573","0.48651558","0.48649505","0.48640317","0.48613998","0.48588765","0.48588225","0.48549655","0.48469034","0.4844906","0.4844906"],"string":"[\n \"0.72173095\",\n \"0.669443\",\n \"0.66253436\",\n \"0.66198653\",\n \"0.64643675\",\n \"0.6446394\",\n \"0.6327299\",\n \"0.62487876\",\n \"0.6163434\",\n \"0.6108556\",\n \"0.6016015\",\n \"0.5976856\",\n \"0.59515876\",\n \"0.5928086\",\n \"0.57584274\",\n \"0.57179135\",\n \"0.5710414\",\n \"0.5634522\",\n \"0.56298053\",\n \"0.5622634\",\n \"0.55962557\",\n \"0.55466205\",\n \"0.55463\",\n \"0.5541144\",\n \"0.55284435\",\n \"0.5525873\",\n \"0.5525492\",\n \"0.54803157\",\n \"0.54749537\",\n \"0.5468904\",\n \"0.54457366\",\n \"0.54452455\",\n \"0.5412685\",\n \"0.5376579\",\n \"0.53727096\",\n \"0.53326607\",\n \"0.5331357\",\n \"0.53236043\",\n \"0.53039724\",\n \"0.5287272\",\n \"0.527973\",\n \"0.52766496\",\n \"0.52766496\",\n \"0.52766496\",\n \"0.5198934\",\n \"0.5194489\",\n \"0.51744586\",\n \"0.5156221\",\n \"0.51405466\",\n \"0.5140064\",\n \"0.51244974\",\n \"0.5099282\",\n \"0.509161\",\n \"0.5088811\",\n \"0.5087377\",\n \"0.50869673\",\n \"0.5080285\",\n \"0.50790596\",\n \"0.50707585\",\n \"0.50634885\",\n \"0.5042655\",\n \"0.5040994\",\n \"0.50223315\",\n \"0.5003732\",\n \"0.49967054\",\n \"0.4995582\",\n \"0.4994981\",\n \"0.49834424\",\n \"0.49768955\",\n \"0.49507967\",\n \"0.49494335\",\n \"0.49488053\",\n \"0.49443567\",\n \"0.4939537\",\n \"0.4939483\",\n \"0.4937237\",\n \"0.4936235\",\n \"0.49275413\",\n \"0.4925153\",\n \"0.49232978\",\n \"0.49161\",\n \"0.4914992\",\n \"0.49138454\",\n \"0.4909963\",\n \"0.48948297\",\n \"0.4891174\",\n \"0.48813117\",\n \"0.48795304\",\n \"0.48759368\",\n \"0.4875739\",\n \"0.4873573\",\n \"0.48651558\",\n \"0.48649505\",\n \"0.48640317\",\n \"0.48613998\",\n \"0.48588765\",\n \"0.48588225\",\n \"0.48549655\",\n \"0.48469034\",\n \"0.4844906\",\n \"0.4844906\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":996,"cells":{"query":{"kind":"string","value":"Return the nth Fibonacci number"},"document":{"kind":"string","value":"def fibonacci(n):\n if n in (0,1):\n return n\n\n return (fibonacci(n-2) + fibonacci(n-1))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def fibonacci(n):","def nthFibonacci(n):\n\n # Run some basic error checking\n try:\n n = int(n)\n except: # if this fails not a number inputed\n sys.stderr.write('Incorrect data input\\n')\n return None\n if n < 0:\n sys.stderr.write('Only positive integers allowed\\n')\n return None\n \n # since the error checking slows down the recursion we run it as a seperate function\n [Fnm,Fn] = fastrecursivefibonacci(n)\n return Fnm","def fibonacci_iterative(nth_nmb: int) -> int:\n old, new = 0, 1\n if nth_nmb in (0, 1):\n return nth_nmb\n for __ in range(nth_nmb - 1):\n old, new = new, old + new\n return new","def fibi(n):\n a, b = 0, 1\n for i in range(n):\n # fibonacci series is next no. is sum of previous two number.\n temp = a\n a = b\n # now nth fibonacci no. is sum of previous two number.\n b = temp+b\n # returning a because a changing each places\n return a","def fibi(n):\n if n == 0: return 0\n if n == 1: return 1\n f_n2, f_n1 = 1, 1\n for i in range(3, n+1):\n f_n2, f_n1 = f_n1, f_n2+f_n1\n return f_n1","def fibonacci(n):\n if n == 0:\n return 0\n elif n == 1:\n return 1\n else:\n nth = fibonacci(n-1) + fibonacci(n-2)\n return nth","def fib(n:int) -> int:\n if n<= 2:\n return 1\n else:\n return fibonacci.fib(n-1) + fibonacci.fib(n-2)","def fibi(n: int) -> int:\n if n == 0:\n return 0\n if n == 1:\n return 1\n f_n2, f_n1 = 1, 1\n for _ in range(3, n+1):\n f_n2, f_n1 = f_n1, f_n2+f_n1\n return f_n1","def fib(n):\n n = int(n)\n if n <= 1:\n return 1\n\n return fib(n-1) + fib(n-2)","def fast_fibonacci(n):\n return _fast_fibonacci(n)[0]","def fib(n: int) -> int:\n if n == 0: return 0\n if n == 1: return 1\n return fib(n-1) + fib(n-2)","def fib(n):\n print(\"fib({})\".format(n))\n if(n <= 2):\n return 1\n else:\n return fib(n-1) + fib(n-2)","def fib(n):\n if n < 2:\n return n\n else:\n return fib(n-1) + fib(n-2)","def fibonacci(n):\n if n < 2:\n return n\n return fibonacci(n-1) + fibonacci(n-2)","def fibonacci(n):\n if n < 2:\n return n\n return fibonacci(n-1) + fibonacci(n-2)","def fib (n):\r\n if n == 0 or n == 1:\r\n return 1\r\n else:\r\n return fib(n-1) + fib(n-2)","def fibonacci(n):\n if n < 2:\n return n\n return fibonacci(n - 1) + fibonacci(n - 2)","def fib(n):\n if n == 0: return 0\n if n == 1: return 1\n return fib(n-1) + fib(n-2)","def fib(n):\n if n in (0, 1): return n\n return fib(n-1) + fib(n-2)","def fib(i):\n if i < 2: return 1\n return fib(i-1) + fib(i-2)","def fib_iterative(n: int) -> int:\n print(n)\n return 0","def fib(n: int) -> int:\n if n == 0:\n return 0\n if n == 1:\n return 1\n return fib(n-1) + fib(n-2)","def fibonacci(n):\n if n < 0:\n raise ValueError(\"n cannot be negative\")\n elif n < 2:\n return n\n else:\n a, b = 0, 1\n for _ in range(n):\n a, b = b, add(a, b)\n return a","def fib(n):\n if n == 1 or n == 2:\n result = 1\n else:\n result = fib(n-1) + fib(n-2)\n return result","def fib(n): #Describe \"n\" as a variable in fib sequence\n while n == 0:\n return 0 #establish that 0 position is equal to 0\n if n == 1:\n return 1\n else:\n return fib(n-1) + fib(n-2)","def fibonacci(n):\n if n in (0, 1):\n return n\n return fibonacci(n - 2) + fibonacci(n - 1)","def fib(n):\n a, b = 1, 1\n while n:\n a, b = b, a + b\n n -= 1\n return a","def fib(n):\n if n == 0 or n == 1:\n return n\n else:\n return fib(n-2) + fib(n-1)","def fibonacci(n):\n if n <= 1:\n return n \n else:\n return fibonacci(n-1) + fibonacci(n-2)","def fibonacci(n):\n print(n)\n if n == 0 or n == 1:\n return 1\n\n return fibonacci(n - 1) + fibonacci(n - 2)","def fibonacci(n):\n fibval = sum_series(n, 0, 1)\n print(fibval)\n return fibval","def fibonacci (n):\n\tif n == 0:\n\t\treturn 0\n\telif n == 1:\n\t\treturn 1\n\telse:\n\t\treturn fibonacci(n-2) + fibonacci(n-1)","def fib_iterative(n: int) -> int:\n if n < 0:\n raise ValueError\n number1 = 0\n number2 = 1\n counter = 1\n while counter < n:\n counter += 1\n number1, number2 = number2, number1 + number2\n return number2","def fib(n): \n if n == 0:\n return 0\n elif n == 1:\n return 1\n\n else:\n return fib(n-1) + fib(n-2)","def fib(n):\n if n == 0:\n return 0\n elif n == 1:\n return 1\n else:\n return fib(n - 2) + fib(n - 1)","def fib(n):\n if n == 0:\n return 0\n elif n == 1:\n return 1\n else:\n return fib(n - 1) + fib(n - 2)","def fibonacci(n):\n\tif n == 0:\n\t\treturn 0\n\telif n == 1:\n\t\treturn 1\n\telse:\n\t\treturn fibonacci(n-1) + fibonacci(n-2)","def fibonacci_recursive(nth_nmb: int) -> int:\n def fib(_n):\n return _n if _n <= 1 else fib(_n - 1) + fib(_n - 2)\n return fib(nth_nmb)","def fibonacci(n):\n if n in (0, 1):\n return n\n return (fibonacci(n-2) + fibonacci(n-1))","def fibonacci(self, n):\n\n if n == 1:\n return 1\n elif n <= 0:\n return 0\n else:\n return self.fibonacci(n - 1) + self.fibonacci(n - 2)","def fib(n):\n if n == 1:\n return 1\n else:\n return n + fib(n-1)","def fibonacci(n):\n\tfib_seq = []\n\tnth_term = 0\n\t\n\tfor i in range(0,n+1):\n\t\tif i == 0:\n\t\t\tfib_seq.append(0)\n\t\tif i == 1:\n\t\t\tfib_seq.append(1)\n\t\tif i > 1:\n\t\t\tnth_term = fib_seq[-1] + fib_seq[-2]\n\t\t\tfib_seq.append(nth_term)\n\t\n\tprint(fib_seq)\n\tprint(fib_seq[n])\n\treturn(fib_seq[n])","def fib3(n):\n if n < 2:\n return n\n return fib1(n-1) + fib1(n-2)","def fib(n):\n if n==1 or n==2:\n return 1\n else:\n return fib(n-1)+fib(n-2)","def fibonacci(n):\n\n if n <= 1:\n return n\n else:\n return (fibonacci(n-1) + fibonacci(n-2))","def fibonacci(n):\n\n if (n == 0):\n return 0\n elif (n == 1):\n return 1\n else:\n return fibonacci(n - 1) + fibonacci(n - 2)","def fibonacci(n):\n if n == 0:\n return 0\n elif n == 1:\n return 1\n else:\n return fibonacci(n - 1) + fibonacci(n - 2)","def fibonacci_number(n):\r\n l = [0, 1] \r\n for i in range(n - 1):\r\n l = [*l, l[-1] + l[-2]]\r\n return l[n - 1]","def fibonacci(n):\n if n == 1:\n return 0\n elif n == 2:\n return 1\n else:\n return fibonacci(n-2) + fibonacci(n-1)","def fibonacci_term(n):\n return int(((1+sqrt(5))**n-(1-sqrt(5))**n)/(2**n*sqrt(5)))","def fibonacci(n):\n\n if n == 0:\n return 0\n if n == 1:\n return 1\n \n previous = 0\n current = 1\n\n for i in range(n-1):\n previous, current = current, current + previous\n\n return current","def find_fib(n):\n # fibo = 2.078087 * math.log(n) + 1.672276\n return 0 # fibo","def fib(n):\n i = 0\n j = 1\n n = n - 1\n\n while n >= 0:\n i, j = j, i + j\n n = n - 1\n return i","def fibonacci(n):\r\n\r\n if n in past_fib:\r\n return past_fib[n]\r\n \r\n if n == 0 or n == 1:\r\n past_fib[n] = 1\r\n return 1\r\n\r\n total = fibonacci(n-1) + fibonacci(n-2)\r\n past_fib[n] = total\r\n return total","def fibonacci(n):\n if n==0 :\n return 0\n elif n==1:\n return 1\n else:\n return fibonacci(n-1) + fibonacci(n-2)","def fibo(n):\r\n if n==1:\r\n return 0\r\n elif n==2:\r\n return 1\r\n else:\r\n return fibo(n-1)+fibo(n-2)","def fibonacci(n):\n sequence = [0, 1]\n for i in range(n + 1):\n value = add(sequence[-2], sequence[-1])\n sequence.append(value)\n return sequence[n]","def fib(n):\n fib = [0, 1]\n if n > 2:\n for i in range(n):\n fib.append(fib[-1] + fib[-2])\n return fib[n-1]\n else:\n return fib[n-1]","def optimized_fibonacci(f):\n a = 0\n b = 1\n if f < 2:\n return f\n else:\n for i in range(1, f):\n c = a + b\n a = b\n b = c\n return b","def fibonacci(n):\n\n if n == 1:\n return 1\n elif n < 1:\n return 0\n else:\n return fibonacci(n-1) + fibonacci(n-2)","def fib_formula(n):\n if n <= 1:\n return n\n else:\n return (fib_formula(n - 1) + fib_formula(n - 2))","def fibonacci(n):\n assert n >= 0 and int(\n n) == n, 'n has to be greater than or equal to 0 and has to be an integer'\n if n in [0, 1]:\n return n\n else:\n return fibonacci(n - 1) + fibonacci(n - 2)","def fibonacciN(n):\n previous, current = 1, 1\n k = 2\n while k < n:\n previous, current = current, previous + current\n k += 1\n return current","def fibonacci(n):\n if n == 0:\n return 0\n elif n == 1:\n return 1\n else:\n return (fibonacci(n-1) + fibonacci(n-2))","def fibonacci(n):\n\n ## Auxiliary functions for working in our polynomial ring.\n def poly_sqr((a, b)):\n a2 = a*a\n return 2*a*b + a2, a2 + b*b\n def poly_mul((a, b), (c, d)):\n ac = a*c\n return a*d + b*c + ac, ac + b*d\n\n ## Do the job. For negative indices, we take powers of t^{-1}.\n if n < 0: return power((1, -1), -n, (0, 1), poly_sqr, poly_mul)\n else: return power((1, 0), n, (0, 1), poly_sqr, poly_mul)","def fibonacci0(n):\n assert n == int(n) and n > 0\n if n in [1, 2]:\n return 1\n return fibonacci0(n-1) + fibonacci0(n-2)","def fib(n):\n i = 0\n j = 1\n n = n - 1\n\n while n >= 0:\n i, j = j, i + j\n n = n - 1\n \n return i","def fib(n):\n i = 0\n j = 1\n n = n - 1\n\n while n >= 0:\n i, j = j, i + j\n n = n - 1\n \n return i","def fib(n):\n i = 0\n j = 1\n n = n - 1\n\n while n >= 0:\n i, j = j, i + j\n n = n - 1\n \n return i","def fibonacci_n(n):\n sqrt5 = math.sqrt(5)\n phi = (1 + sqrt5) / 2\n psi = (1 - sqrt5) / 2\n return (phi**n - psi**n) // sqrt5","def fibonacci(n):\n\n if n == 1:\n v = 0\n elif n == 2:\n v = 1\n else:\n v = fibonacci(n - 2) + fibonacci(n - 1)\n\n return v","def fibonacci0(n):\n assert n == int(n) and n > 0\n if n in [1, 2]:\n return 1\n return fibonacci0(n-1) + fibonacci0(n-2)","def fibonacci(n: int) -> int:\n m = 1 << (n.bit_length() - 1)\n Fn = 0\n Fnm1 = 1\n while m:\n Fn2 = Fn * Fn\n Fn = 2 * Fnm1 * Fn + Fn2\n Fnm1 = Fnm1 * Fnm1 + Fn2\n if n & m:\n Fnm1, Fn = Fn, Fnm1 + Fn\n m >>= 1\n return Fn","def next_fib(f):\n for f in fib:\n i = fib.index(f)\n return f+fib[i-1]","def fibonacci():\n return sum_series(a=0, b=1)","def fibonacci(n):\n k, m = 1, 1\n\n if n < 2:\n return n\n\n for i in range(2, n):\n k, m = m, k + m\n\n return m","def fib(n):\n if n < 0:\n raise ValueError\n\n if n > 1:\n return fib(n - 1) + fib(n - 2)\n\n return n","def fibonacci(n: int) -> int:\n if n <= 0:\n raise ValueError(\"The number must be greater than zero.\")\n exit(code=1)\n if n == 1:\n return 0\n elif n == 2:\n return 1\n else:\n return fibonacci(n-1) + fibonacci(n - 2)","def fibonacci1(n):\n if n in (0, 1):\n return n\n return fibonacci1(n - 2) + fibonacci1(n - 1)","def fibonacci_iter(n):\n f = []\n for x in range(n + 1):\n if x == 0:\n f.append(x)\n elif x == 1:\n f.append(x)\n else:\n f.append(f[-1] + f[-2])\n return f[-1]","def fib1(n):\n if n < 2:\n return n\n return fib1(n-1) + fib1(n-2)","def fibonacci(n, a = 0, b = 1):\n if n == 0:\n return a\n elif n == 1:\n return b\n else:\n return fibonacci(n - 1, b, a + b)","def fib(n): # this line defines the function 'fib' where n is the input value\n i = 0\n j = 1\n n = n - 1\n\n while n >= 0:\n i, j = j, i + j\n n = n - 1\n \n return i","def fib (n):\n a,b=0,1\n while b<n:\n\tprint b,\n\ta,b = b,a+b","def fibonacci(n):\n if n <= 1:\n return 1\n n0 = 0\n n1 = 1\n result = 1\n level = 2\n while level <= n:\n result = n0 + n1\n n0 = n1\n n1 = result\n level += 1\n return result","def fib(n):\n\ta,b=0,1\n\twhile b<n:\n\t\tprint b,\n\t\ta,b=b,a+b","def fib(n):\n\ta, b = 0, 1\n\twhile a < n:\n\t\tprint a,\n\t\ta, b = b, a+b","def fibnormal (n):\n\n if n < 3:\n return 1\n f1 = f2 = 1\n for i in range (n-1):\n f3 = f1 + f2\n f1, f2 = f2, f3\n\n return f3","def get_fibonacci_last_digit_fast(n):\n fibonacci = [0 for i in range(n + 1)]\n fibonacci[1] = 1\n\n for i in range(2, n + 1):\n fibonacci[i] = (fibonacci[i - 1] + fibonacci[i - 2]) % 10\n\n return fibonacci[n]","def fib(index):\n return round((GR**index)/R5)","def fibonacci(num):\n if num == 0 or num ==1:\n return 1\n return fibonacci(num-1) + fibonacci (num-2)","def fibo_element(n):\n f = ()\n if n < 0:\n print(\"Incorrect number\")\n elif n == 0:\n return 0\n elif n == 1:\n return 1\n else:\n return fibo_element(n-1) + fibo_element(n-2)","def fib(n):\n global counter\n if n==0 or n==1:\n return 1\n else:\n if n-1==2 or n-2==2:\n counter+=1\n return fib(n-1) + fib(n-2)","def fibclassic (n):\n\n return 1 if n < 3 else fibclassic (n-2) + fibclassic (n-1)","def fibi2(n: int) -> int:\n f = [0, 1] + [None for _ in range(2, n+1)]\n for i in range(2, n+1):\n f[i] = f[i-1]+f[i-2]\n return f[n]","def fibonacci(number: int) -> int:\n fibs = [0] * (number + 2)\n fibs[0] = 0\n fibs[1] = 1\n for i in range(2, number + 1):\n fibs[i] = fibs[i - 1] + fibs[i - 2]\n return fibs[number]","def fib_recursive(n: int) -> int:\n print(n)\n return 0","def fib(N):\n sqrt5 = math.sqrt(5)\n phi = (sqrt5 + 1) / 2\n return int(round(math.pow(phi, N) / sqrt5))","def fib(n):\n a, b = 0, 1\n while b < n:\n print b,\n a, b = b, a+b","def fib2(n):\n if n <= 2:\n return 1\n i, res = 1, 1\n for _ in range(n - 2):\n i, res = res, res + i\n return res"],"string":"[\n \"def fibonacci(n):\",\n \"def nthFibonacci(n):\\n\\n # Run some basic error checking\\n try:\\n n = int(n)\\n except: # if this fails not a number inputed\\n sys.stderr.write('Incorrect data input\\\\n')\\n return None\\n if n < 0:\\n sys.stderr.write('Only positive integers allowed\\\\n')\\n return None\\n \\n # since the error checking slows down the recursion we run it as a seperate function\\n [Fnm,Fn] = fastrecursivefibonacci(n)\\n return Fnm\",\n \"def fibonacci_iterative(nth_nmb: int) -> int:\\n old, new = 0, 1\\n if nth_nmb in (0, 1):\\n return nth_nmb\\n for __ in range(nth_nmb - 1):\\n old, new = new, old + new\\n return new\",\n \"def fibi(n):\\n a, b = 0, 1\\n for i in range(n):\\n # fibonacci series is next no. is sum of previous two number.\\n temp = a\\n a = b\\n # now nth fibonacci no. is sum of previous two number.\\n b = temp+b\\n # returning a because a changing each places\\n return a\",\n \"def fibi(n):\\n if n == 0: return 0\\n if n == 1: return 1\\n f_n2, f_n1 = 1, 1\\n for i in range(3, n+1):\\n f_n2, f_n1 = f_n1, f_n2+f_n1\\n return f_n1\",\n \"def fibonacci(n):\\n if n == 0:\\n return 0\\n elif n == 1:\\n return 1\\n else:\\n nth = fibonacci(n-1) + fibonacci(n-2)\\n return nth\",\n \"def fib(n:int) -> int:\\n if n<= 2:\\n return 1\\n else:\\n return fibonacci.fib(n-1) + fibonacci.fib(n-2)\",\n \"def fibi(n: int) -> int:\\n if n == 0:\\n return 0\\n if n == 1:\\n return 1\\n f_n2, f_n1 = 1, 1\\n for _ in range(3, n+1):\\n f_n2, f_n1 = f_n1, f_n2+f_n1\\n return f_n1\",\n \"def fib(n):\\n n = int(n)\\n if n <= 1:\\n return 1\\n\\n return fib(n-1) + fib(n-2)\",\n \"def fast_fibonacci(n):\\n return _fast_fibonacci(n)[0]\",\n \"def fib(n: int) -> int:\\n if n == 0: return 0\\n if n == 1: return 1\\n return fib(n-1) + fib(n-2)\",\n \"def fib(n):\\n print(\\\"fib({})\\\".format(n))\\n if(n <= 2):\\n return 1\\n else:\\n return fib(n-1) + fib(n-2)\",\n \"def fib(n):\\n if n < 2:\\n return n\\n else:\\n return fib(n-1) + fib(n-2)\",\n \"def fibonacci(n):\\n if n < 2:\\n return n\\n return fibonacci(n-1) + fibonacci(n-2)\",\n \"def fibonacci(n):\\n if n < 2:\\n return n\\n return fibonacci(n-1) + fibonacci(n-2)\",\n \"def fib (n):\\r\\n if n == 0 or n == 1:\\r\\n return 1\\r\\n else:\\r\\n return fib(n-1) + fib(n-2)\",\n \"def fibonacci(n):\\n if n < 2:\\n return n\\n return fibonacci(n - 1) + fibonacci(n - 2)\",\n \"def fib(n):\\n if n == 0: return 0\\n if n == 1: return 1\\n return fib(n-1) + fib(n-2)\",\n \"def fib(n):\\n if n in (0, 1): return n\\n return fib(n-1) + fib(n-2)\",\n \"def fib(i):\\n if i < 2: return 1\\n return fib(i-1) + fib(i-2)\",\n \"def fib_iterative(n: int) -> int:\\n print(n)\\n return 0\",\n \"def fib(n: int) -> int:\\n if n == 0:\\n return 0\\n if n == 1:\\n return 1\\n return fib(n-1) + fib(n-2)\",\n \"def fibonacci(n):\\n if n < 0:\\n raise ValueError(\\\"n cannot be negative\\\")\\n elif n < 2:\\n return n\\n else:\\n a, b = 0, 1\\n for _ in range(n):\\n a, b = b, add(a, b)\\n return a\",\n \"def fib(n):\\n if n == 1 or n == 2:\\n result = 1\\n else:\\n result = fib(n-1) + fib(n-2)\\n return result\",\n \"def fib(n): #Describe \\\"n\\\" as a variable in fib sequence\\n while n == 0:\\n return 0 #establish that 0 position is equal to 0\\n if n == 1:\\n return 1\\n else:\\n return fib(n-1) + fib(n-2)\",\n \"def fibonacci(n):\\n if n in (0, 1):\\n return n\\n return fibonacci(n - 2) + fibonacci(n - 1)\",\n \"def fib(n):\\n a, b = 1, 1\\n while n:\\n a, b = b, a + b\\n n -= 1\\n return a\",\n \"def fib(n):\\n if n == 0 or n == 1:\\n return n\\n else:\\n return fib(n-2) + fib(n-1)\",\n \"def fibonacci(n):\\n if n <= 1:\\n return n \\n else:\\n return fibonacci(n-1) + fibonacci(n-2)\",\n \"def fibonacci(n):\\n print(n)\\n if n == 0 or n == 1:\\n return 1\\n\\n return fibonacci(n - 1) + fibonacci(n - 2)\",\n \"def fibonacci(n):\\n fibval = sum_series(n, 0, 1)\\n print(fibval)\\n return fibval\",\n \"def fibonacci (n):\\n\\tif n == 0:\\n\\t\\treturn 0\\n\\telif n == 1:\\n\\t\\treturn 1\\n\\telse:\\n\\t\\treturn fibonacci(n-2) + fibonacci(n-1)\",\n \"def fib_iterative(n: int) -> int:\\n if n < 0:\\n raise ValueError\\n number1 = 0\\n number2 = 1\\n counter = 1\\n while counter < n:\\n counter += 1\\n number1, number2 = number2, number1 + number2\\n return number2\",\n \"def fib(n): \\n if n == 0:\\n return 0\\n elif n == 1:\\n return 1\\n\\n else:\\n return fib(n-1) + fib(n-2)\",\n \"def fib(n):\\n if n == 0:\\n return 0\\n elif n == 1:\\n return 1\\n else:\\n return fib(n - 2) + fib(n - 1)\",\n \"def fib(n):\\n if n == 0:\\n return 0\\n elif n == 1:\\n return 1\\n else:\\n return fib(n - 1) + fib(n - 2)\",\n \"def fibonacci(n):\\n\\tif n == 0:\\n\\t\\treturn 0\\n\\telif n == 1:\\n\\t\\treturn 1\\n\\telse:\\n\\t\\treturn fibonacci(n-1) + fibonacci(n-2)\",\n \"def fibonacci_recursive(nth_nmb: int) -> int:\\n def fib(_n):\\n return _n if _n <= 1 else fib(_n - 1) + fib(_n - 2)\\n return fib(nth_nmb)\",\n \"def fibonacci(n):\\n if n in (0, 1):\\n return n\\n return (fibonacci(n-2) + fibonacci(n-1))\",\n \"def fibonacci(self, n):\\n\\n if n == 1:\\n return 1\\n elif n <= 0:\\n return 0\\n else:\\n return self.fibonacci(n - 1) + self.fibonacci(n - 2)\",\n \"def fib(n):\\n if n == 1:\\n return 1\\n else:\\n return n + fib(n-1)\",\n \"def fibonacci(n):\\n\\tfib_seq = []\\n\\tnth_term = 0\\n\\t\\n\\tfor i in range(0,n+1):\\n\\t\\tif i == 0:\\n\\t\\t\\tfib_seq.append(0)\\n\\t\\tif i == 1:\\n\\t\\t\\tfib_seq.append(1)\\n\\t\\tif i > 1:\\n\\t\\t\\tnth_term = fib_seq[-1] + fib_seq[-2]\\n\\t\\t\\tfib_seq.append(nth_term)\\n\\t\\n\\tprint(fib_seq)\\n\\tprint(fib_seq[n])\\n\\treturn(fib_seq[n])\",\n \"def fib3(n):\\n if n < 2:\\n return n\\n return fib1(n-1) + fib1(n-2)\",\n \"def fib(n):\\n if n==1 or n==2:\\n return 1\\n else:\\n return fib(n-1)+fib(n-2)\",\n \"def fibonacci(n):\\n\\n if n <= 1:\\n return n\\n else:\\n return (fibonacci(n-1) + fibonacci(n-2))\",\n \"def fibonacci(n):\\n\\n if (n == 0):\\n return 0\\n elif (n == 1):\\n return 1\\n else:\\n return fibonacci(n - 1) + fibonacci(n - 2)\",\n \"def fibonacci(n):\\n if n == 0:\\n return 0\\n elif n == 1:\\n return 1\\n else:\\n return fibonacci(n - 1) + fibonacci(n - 2)\",\n \"def fibonacci_number(n):\\r\\n l = [0, 1] \\r\\n for i in range(n - 1):\\r\\n l = [*l, l[-1] + l[-2]]\\r\\n return l[n - 1]\",\n \"def fibonacci(n):\\n if n == 1:\\n return 0\\n elif n == 2:\\n return 1\\n else:\\n return fibonacci(n-2) + fibonacci(n-1)\",\n \"def fibonacci_term(n):\\n return int(((1+sqrt(5))**n-(1-sqrt(5))**n)/(2**n*sqrt(5)))\",\n \"def fibonacci(n):\\n\\n if n == 0:\\n return 0\\n if n == 1:\\n return 1\\n \\n previous = 0\\n current = 1\\n\\n for i in range(n-1):\\n previous, current = current, current + previous\\n\\n return current\",\n \"def find_fib(n):\\n # fibo = 2.078087 * math.log(n) + 1.672276\\n return 0 # fibo\",\n \"def fib(n):\\n i = 0\\n j = 1\\n n = n - 1\\n\\n while n >= 0:\\n i, j = j, i + j\\n n = n - 1\\n return i\",\n \"def fibonacci(n):\\r\\n\\r\\n if n in past_fib:\\r\\n return past_fib[n]\\r\\n \\r\\n if n == 0 or n == 1:\\r\\n past_fib[n] = 1\\r\\n return 1\\r\\n\\r\\n total = fibonacci(n-1) + fibonacci(n-2)\\r\\n past_fib[n] = total\\r\\n return total\",\n \"def fibonacci(n):\\n if n==0 :\\n return 0\\n elif n==1:\\n return 1\\n else:\\n return fibonacci(n-1) + fibonacci(n-2)\",\n \"def fibo(n):\\r\\n if n==1:\\r\\n return 0\\r\\n elif n==2:\\r\\n return 1\\r\\n else:\\r\\n return fibo(n-1)+fibo(n-2)\",\n \"def fibonacci(n):\\n sequence = [0, 1]\\n for i in range(n + 1):\\n value = add(sequence[-2], sequence[-1])\\n sequence.append(value)\\n return sequence[n]\",\n \"def fib(n):\\n fib = [0, 1]\\n if n > 2:\\n for i in range(n):\\n fib.append(fib[-1] + fib[-2])\\n return fib[n-1]\\n else:\\n return fib[n-1]\",\n \"def optimized_fibonacci(f):\\n a = 0\\n b = 1\\n if f < 2:\\n return f\\n else:\\n for i in range(1, f):\\n c = a + b\\n a = b\\n b = c\\n return b\",\n \"def fibonacci(n):\\n\\n if n == 1:\\n return 1\\n elif n < 1:\\n return 0\\n else:\\n return fibonacci(n-1) + fibonacci(n-2)\",\n \"def fib_formula(n):\\n if n <= 1:\\n return n\\n else:\\n return (fib_formula(n - 1) + fib_formula(n - 2))\",\n \"def fibonacci(n):\\n assert n >= 0 and int(\\n n) == n, 'n has to be greater than or equal to 0 and has to be an integer'\\n if n in [0, 1]:\\n return n\\n else:\\n return fibonacci(n - 1) + fibonacci(n - 2)\",\n \"def fibonacciN(n):\\n previous, current = 1, 1\\n k = 2\\n while k < n:\\n previous, current = current, previous + current\\n k += 1\\n return current\",\n \"def fibonacci(n):\\n if n == 0:\\n return 0\\n elif n == 1:\\n return 1\\n else:\\n return (fibonacci(n-1) + fibonacci(n-2))\",\n \"def fibonacci(n):\\n\\n ## Auxiliary functions for working in our polynomial ring.\\n def poly_sqr((a, b)):\\n a2 = a*a\\n return 2*a*b + a2, a2 + b*b\\n def poly_mul((a, b), (c, d)):\\n ac = a*c\\n return a*d + b*c + ac, ac + b*d\\n\\n ## Do the job. For negative indices, we take powers of t^{-1}.\\n if n < 0: return power((1, -1), -n, (0, 1), poly_sqr, poly_mul)\\n else: return power((1, 0), n, (0, 1), poly_sqr, poly_mul)\",\n \"def fibonacci0(n):\\n assert n == int(n) and n > 0\\n if n in [1, 2]:\\n return 1\\n return fibonacci0(n-1) + fibonacci0(n-2)\",\n \"def fib(n):\\n i = 0\\n j = 1\\n n = n - 1\\n\\n while n >= 0:\\n i, j = j, i + j\\n n = n - 1\\n \\n return i\",\n \"def fib(n):\\n i = 0\\n j = 1\\n n = n - 1\\n\\n while n >= 0:\\n i, j = j, i + j\\n n = n - 1\\n \\n return i\",\n \"def fib(n):\\n i = 0\\n j = 1\\n n = n - 1\\n\\n while n >= 0:\\n i, j = j, i + j\\n n = n - 1\\n \\n return i\",\n \"def fibonacci_n(n):\\n sqrt5 = math.sqrt(5)\\n phi = (1 + sqrt5) / 2\\n psi = (1 - sqrt5) / 2\\n return (phi**n - psi**n) // sqrt5\",\n \"def fibonacci(n):\\n\\n if n == 1:\\n v = 0\\n elif n == 2:\\n v = 1\\n else:\\n v = fibonacci(n - 2) + fibonacci(n - 1)\\n\\n return v\",\n \"def fibonacci0(n):\\n assert n == int(n) and n > 0\\n if n in [1, 2]:\\n return 1\\n return fibonacci0(n-1) + fibonacci0(n-2)\",\n \"def fibonacci(n: int) -> int:\\n m = 1 << (n.bit_length() - 1)\\n Fn = 0\\n Fnm1 = 1\\n while m:\\n Fn2 = Fn * Fn\\n Fn = 2 * Fnm1 * Fn + Fn2\\n Fnm1 = Fnm1 * Fnm1 + Fn2\\n if n & m:\\n Fnm1, Fn = Fn, Fnm1 + Fn\\n m >>= 1\\n return Fn\",\n \"def next_fib(f):\\n for f in fib:\\n i = fib.index(f)\\n return f+fib[i-1]\",\n \"def fibonacci():\\n return sum_series(a=0, b=1)\",\n \"def fibonacci(n):\\n k, m = 1, 1\\n\\n if n < 2:\\n return n\\n\\n for i in range(2, n):\\n k, m = m, k + m\\n\\n return m\",\n \"def fib(n):\\n if n < 0:\\n raise ValueError\\n\\n if n > 1:\\n return fib(n - 1) + fib(n - 2)\\n\\n return n\",\n \"def fibonacci(n: int) -> int:\\n if n <= 0:\\n raise ValueError(\\\"The number must be greater than zero.\\\")\\n exit(code=1)\\n if n == 1:\\n return 0\\n elif n == 2:\\n return 1\\n else:\\n return fibonacci(n-1) + fibonacci(n - 2)\",\n \"def fibonacci1(n):\\n if n in (0, 1):\\n return n\\n return fibonacci1(n - 2) + fibonacci1(n - 1)\",\n \"def fibonacci_iter(n):\\n f = []\\n for x in range(n + 1):\\n if x == 0:\\n f.append(x)\\n elif x == 1:\\n f.append(x)\\n else:\\n f.append(f[-1] + f[-2])\\n return f[-1]\",\n \"def fib1(n):\\n if n < 2:\\n return n\\n return fib1(n-1) + fib1(n-2)\",\n \"def fibonacci(n, a = 0, b = 1):\\n if n == 0:\\n return a\\n elif n == 1:\\n return b\\n else:\\n return fibonacci(n - 1, b, a + b)\",\n \"def fib(n): # this line defines the function 'fib' where n is the input value\\n i = 0\\n j = 1\\n n = n - 1\\n\\n while n >= 0:\\n i, j = j, i + j\\n n = n - 1\\n \\n return i\",\n \"def fib (n):\\n a,b=0,1\\n while b<n:\\n\\tprint b,\\n\\ta,b = b,a+b\",\n \"def fibonacci(n):\\n if n <= 1:\\n return 1\\n n0 = 0\\n n1 = 1\\n result = 1\\n level = 2\\n while level <= n:\\n result = n0 + n1\\n n0 = n1\\n n1 = result\\n level += 1\\n return result\",\n \"def fib(n):\\n\\ta,b=0,1\\n\\twhile b<n:\\n\\t\\tprint b,\\n\\t\\ta,b=b,a+b\",\n \"def fib(n):\\n\\ta, b = 0, 1\\n\\twhile a < n:\\n\\t\\tprint a,\\n\\t\\ta, b = b, a+b\",\n \"def fibnormal (n):\\n\\n if n < 3:\\n return 1\\n f1 = f2 = 1\\n for i in range (n-1):\\n f3 = f1 + f2\\n f1, f2 = f2, f3\\n\\n return f3\",\n \"def get_fibonacci_last_digit_fast(n):\\n fibonacci = [0 for i in range(n + 1)]\\n fibonacci[1] = 1\\n\\n for i in range(2, n + 1):\\n fibonacci[i] = (fibonacci[i - 1] + fibonacci[i - 2]) % 10\\n\\n return fibonacci[n]\",\n \"def fib(index):\\n return round((GR**index)/R5)\",\n \"def fibonacci(num):\\n if num == 0 or num ==1:\\n return 1\\n return fibonacci(num-1) + fibonacci (num-2)\",\n \"def fibo_element(n):\\n f = ()\\n if n < 0:\\n print(\\\"Incorrect number\\\")\\n elif n == 0:\\n return 0\\n elif n == 1:\\n return 1\\n else:\\n return fibo_element(n-1) + fibo_element(n-2)\",\n \"def fib(n):\\n global counter\\n if n==0 or n==1:\\n return 1\\n else:\\n if n-1==2 or n-2==2:\\n counter+=1\\n return fib(n-1) + fib(n-2)\",\n \"def fibclassic (n):\\n\\n return 1 if n < 3 else fibclassic (n-2) + fibclassic (n-1)\",\n \"def fibi2(n: int) -> int:\\n f = [0, 1] + [None for _ in range(2, n+1)]\\n for i in range(2, n+1):\\n f[i] = f[i-1]+f[i-2]\\n return f[n]\",\n \"def fibonacci(number: int) -> int:\\n fibs = [0] * (number + 2)\\n fibs[0] = 0\\n fibs[1] = 1\\n for i in range(2, number + 1):\\n fibs[i] = fibs[i - 1] + fibs[i - 2]\\n return fibs[number]\",\n \"def fib_recursive(n: int) -> int:\\n print(n)\\n return 0\",\n \"def fib(N):\\n sqrt5 = math.sqrt(5)\\n phi = (sqrt5 + 1) / 2\\n return int(round(math.pow(phi, N) / sqrt5))\",\n \"def fib(n):\\n a, b = 0, 1\\n while b < n:\\n print b,\\n a, b = b, a+b\",\n \"def fib2(n):\\n if n <= 2:\\n return 1\\n i, res = 1, 1\\n for _ in range(n - 2):\\n i, res = res, res + i\\n return res\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8549428","0.83808696","0.8357446","0.8267962","0.82505155","0.8213346","0.8213339","0.8212905","0.81149447","0.8098057","0.80716306","0.8040074","0.8032861","0.8029916","0.8029916","0.8019259","0.8019077","0.8012156","0.8007875","0.8001539","0.79852134","0.798478","0.7971879","0.796173","0.79607636","0.7956564","0.7947912","0.79437095","0.7939163","0.793087","0.7926684","0.792416","0.79177755","0.7917123","0.791375","0.79056567","0.79043746","0.790302","0.79014206","0.7884692","0.78817505","0.7878559","0.7878249","0.7870866","0.78684634","0.7853879","0.78508687","0.7848421","0.7848355","0.78410333","0.7835929","0.78329885","0.78276616","0.781293","0.78108233","0.7807304","0.7806639","0.78050643","0.7802335","0.77912503","0.7791148","0.7784265","0.77827543","0.77812916","0.7771224","0.77694684","0.7767021","0.7767021","0.7767021","0.7756825","0.77567786","0.7749564","0.7724282","0.77222544","0.77169573","0.7709786","0.7707383","0.7706818","0.76979643","0.76971465","0.7690063","0.7678807","0.7673536","0.7662299","0.7655996","0.7653059","0.76496464","0.7639371","0.7636822","0.7633434","0.76321936","0.76279294","0.76244855","0.7597176","0.7591671","0.75871646","0.75850254","0.7578626","0.7568075","0.75604355"],"string":"[\n \"0.8549428\",\n \"0.83808696\",\n \"0.8357446\",\n \"0.8267962\",\n \"0.82505155\",\n \"0.8213346\",\n \"0.8213339\",\n \"0.8212905\",\n \"0.81149447\",\n \"0.8098057\",\n \"0.80716306\",\n \"0.8040074\",\n \"0.8032861\",\n \"0.8029916\",\n \"0.8029916\",\n \"0.8019259\",\n \"0.8019077\",\n \"0.8012156\",\n \"0.8007875\",\n \"0.8001539\",\n \"0.79852134\",\n \"0.798478\",\n \"0.7971879\",\n \"0.796173\",\n \"0.79607636\",\n \"0.7956564\",\n \"0.7947912\",\n \"0.79437095\",\n \"0.7939163\",\n \"0.793087\",\n \"0.7926684\",\n \"0.792416\",\n \"0.79177755\",\n \"0.7917123\",\n \"0.791375\",\n \"0.79056567\",\n \"0.79043746\",\n \"0.790302\",\n \"0.79014206\",\n \"0.7884692\",\n \"0.78817505\",\n \"0.7878559\",\n \"0.7878249\",\n \"0.7870866\",\n \"0.78684634\",\n \"0.7853879\",\n \"0.78508687\",\n \"0.7848421\",\n \"0.7848355\",\n \"0.78410333\",\n \"0.7835929\",\n \"0.78329885\",\n \"0.78276616\",\n \"0.781293\",\n \"0.78108233\",\n \"0.7807304\",\n \"0.7806639\",\n \"0.78050643\",\n \"0.7802335\",\n \"0.77912503\",\n \"0.7791148\",\n \"0.7784265\",\n \"0.77827543\",\n \"0.77812916\",\n \"0.7771224\",\n \"0.77694684\",\n \"0.7767021\",\n \"0.7767021\",\n \"0.7767021\",\n \"0.7756825\",\n \"0.77567786\",\n \"0.7749564\",\n \"0.7724282\",\n \"0.77222544\",\n \"0.77169573\",\n \"0.7709786\",\n \"0.7707383\",\n \"0.7706818\",\n \"0.76979643\",\n \"0.76971465\",\n \"0.7690063\",\n \"0.7678807\",\n \"0.7673536\",\n \"0.7662299\",\n \"0.7655996\",\n \"0.7653059\",\n \"0.76496464\",\n \"0.7639371\",\n \"0.7636822\",\n \"0.7633434\",\n \"0.76321936\",\n \"0.76279294\",\n \"0.76244855\",\n \"0.7597176\",\n \"0.7591671\",\n \"0.75871646\",\n \"0.75850254\",\n \"0.7578626\",\n \"0.7568075\",\n \"0.75604355\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78968835"},"document_rank":{"kind":"string","value":"39"}}},{"rowIdx":997,"cells":{"query":{"kind":"string","value":"Get the eol mode map"},"document":{"kind":"string","value":"def EOLModeMap():\n # Maintenance Note: ints must be kept in sync with EDSTC_EOL_* in edstc\n return { EOL_MODE_CR : _(\"Old Machintosh (\\\\r)\"),\n EOL_MODE_LF : _(\"Unix (\\\\n)\"),\n EOL_MODE_CRLF : _(\"Windows (\\\\r\\\\n)\")}"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_eol_for_open(self) -> str:\n map = {\n EOLTypes.CRLF: WINDOWS_EOL,\n EOLTypes.LF: UNIX_EOL,\n EOLTypes.NATIVE: linesep,\n }\n\n return map[self]","def get_modes(self):\n return [i for i, j in enumerate(self._modemap._map) if j is not None]","def getModeLookupTable(self):\n mode_table = []\n header = None\n for line in open(PublicTransit.MODE_LOOKUP_FILE_NAME):\n line = line.strip()\n if len(line) == 0: \n continue\n line = map(str.strip,line.split(\",\"))\n if header is None:\n header = line\n #CPT_AGENCYID\tAGENCYNAME\tCPT_MODE\tSCH_ROUTEDESIGNATOR\tMODECODE\tMODEGROUP\n continue\n data = {}\n for i in range(len(line)):\n data[header[i]] = line[i]\n mode_table.append(data)\n return mode_table","def _get_modes(self):\n return self.__modes","def get_modes(self, code_block):\r\n # FUCK YOU INDEX ERRORS, LIST COMPS, AND EVEN YOU LAMBDAS I DON'T NEED PRETTY\r\n # 0 = pos mode\r\n # 1 = imm mode\r\n modes, mode_codes = [0, 0], list(reversed(str(code_block[0])))[2:]\r\n x = 0\r\n for mode in mode_codes:\r\n modes[x] = int(mode)\r\n x += 1\r\n print('Get modes: ')\r\n print(modes)\r\n return modes","def get_modes(self):\n return self.circuit.get_modes()","def modes(self, exp_id: int) -> List[str]:\n return list(self.state[exp_id].keys())","def __mode_modesetid(self, mode):\n\t\tfor key,val in self.ms_all.iteritems():\n\t\t\tix = val.index(mode)\n\t\t\tif ix is not None:\n\t\t\t\treturn key, ix","def all_modes(self):\n\n # Find \"post-proj all modes\"\n # Jump to first value, ignoring text.\n # Move through data, adding it to a list\n # continue onto next line.\n # Repeat until the following line is known to be empty.\n\n # output.dat is the psi4 output file.\n with open('output.dat', 'r') as file:\n lines = file.readlines()\n for count, line in enumerate(lines):\n if \"post-proj all modes\" in line:\n start_of_vals = count\n break\n else:\n raise EOFError('Cannot locate modes in output.dat file.')\n\n # Barring the first (and sometimes last) line, dat file has 6 values per row.\n end_of_vals = start_of_vals + (3 * len(self.molecule.molecule['input'])) // 6\n\n structures = lines[start_of_vals][24:].replace(\"'\", \"\").split()\n structures = structures[6:]\n\n for row in range(1, end_of_vals - start_of_vals):\n # Remove double strings and weird formatting.\n structures += lines[start_of_vals + row].replace(\"'\", \"\").replace(\"]\", \"\").split()\n\n all_modes = [float(val) for val in structures]\n\n return array(all_modes)","def _get_applicable_modes(command):\n mode_dict = {}\n _add_applicable_modes(command, mode_dict)\n return mode_dict.keys()","def parse_session_mode_and_map(log_data):\n try:\n match = search(\n r\"<\\d{2}:\\d{2}> [^d]* Loading level \\w+\\/(\\w+), \\w+ (\\w+)\",\n log_data)\n line_map, line_mode = match.groups()\n return (line_mode, line_map)\n except Exception:\n print(\"Something is wrong with the log file!\")","def modes(self):\n return np.hstack(tuple(self.operator.modes))","def all_modes(self):\n\n # Find \"post-proj all modes\"\n # Jump to first value, ignoring text.\n # Move through data, adding it to a list\n # continue onto next line.\n # Repeat until the following line is known to be empty.\n\n # output.dat is the psi4 output file.\n with open(\"output.dat\", \"r\") as file:\n lines = file.readlines()\n for count, line in enumerate(lines):\n if \"post-proj all modes\" in line:\n start_of_vals = count\n break\n else:\n raise EOFError(\"Cannot locate modes in output.dat file.\")\n\n # Barring the first (and sometimes last) line, dat file has 6 values per row.\n end_of_vals = start_of_vals + (3 * len(self.molecule.atoms)) // 6\n\n structures = lines[start_of_vals][24:].replace(\"'\", \"\").split()\n structures = structures[6:]\n\n for row in range(1, end_of_vals - start_of_vals):\n # Remove double strings and weird formatting.\n structures += (\n lines[start_of_vals + row].replace(\"'\", \"\").replace(\"]\", \"\").split()\n )\n\n all_modes = [float(val) for val in structures]\n\n return np.array(all_modes)","def __convertEOL(self):\n aw = self.activeWindow()\n aw.convertEols(aw.eolMode())","def get_modes(self):\n modes = set()\n for er in self.exercise_recordings:\n if er.mode not in modes:\n modes.add(er.mode)\n return list(modes)","def modes(self) -> List[str]:\n return [m.name for m in self._modes]","def GetPackageModes(self):\n return self._modes","def values(self):\n return self._modes.values()","def get_focus_mode_names(self):\n names = []\n for focus_mode in self.focus_modes:\n names.append(focus_mode['modeName'])\n return names","def _modes(self):\n answer = []\n for i in dir(self):\n if i.startswith('handle_'):\n answer.append(i.replace('handle_', ''))\n return answer","def _get_mode(self):\n self._validate_mode()\n return deepcopy(self.mode)","def common_mode(self):\n return self._common_mode","def common_mode(self):\n return self._common_mode","def getmode(self, mode):\r\n modes = {}\r\n # core modes\r\n for m, (basemode, basetype, bands) in _MODEINFO.items():\r\n modes[m] = ModeDescriptor(m, bands, basemode, basetype)\r\n # extra experimental modes\r\n modes[\"RGBa\"] = ModeDescriptor(\"RGBa\",\r\n (\"R\", \"G\", \"B\", \"a\"), \"RGB\", \"L\")\r\n modes[\"LA\"] = ModeDescriptor(\"LA\", (\"L\", \"A\"), \"L\", \"L\")\r\n modes[\"La\"] = ModeDescriptor(\"La\", (\"L\", \"a\"), \"L\", \"L\")\r\n modes[\"PA\"] = ModeDescriptor(\"PA\", (\"P\", \"A\"), \"RGB\", \"L\")\r\n # mapping modes\r\n modes[\"I;16\"] = ModeDescriptor(\"I;16\", \"I\", \"L\", \"L\")\r\n modes[\"I;16L\"] = ModeDescriptor(\"I;16L\", \"I\", \"L\", \"L\")\r\n modes[\"I;16B\"] = ModeDescriptor(\"I;16B\", \"I\", \"L\", \"L\")\r\n # set global mode cache atomically\r\n _modes = modes\r\n return _modes[mode]","def line_styles (self):\n return self._line_styles","def get_map(self):\n\n self.mp = defaultdict(lambda : ord('x'))\n y, x = 0, 0\n while True:\n cond, output = self.ic()\n\n if cond: break\n # New row of the print out\n if output == 10:\n y += 1\n x = 0\n # Assign the value to the map\n else:\n self.mp[y,x] = output\n x += 1\n \n return self.mp","def get_keymap(self):\n return self.keymap","def keys(self):\n return self._modes.keys()","def _get_mode(self):\n raise NotImplementedError","def get_all_color_modes(self):\n return self._all_color_modes","def preset_modes(self):\n return self._preset_modes","def get_mode(self) -> str:\n\n return self.send(self.cmd.GET_MODE)","def map_rule4(self):\n odml.terminology.terminologies['map'] = parse(\"\"\"\n S1[T1]\n - P2\n S2[T2]\n - P1\n S3[T3]\n - P1\n - P2\n - P3\n \"\"\")","def get_heatmode_stringlist(self):\n return text_heatmode","def get_current_mode(self):\n return self.read(0xa2)","def get_mode(self):\n summary = self.get_version_summary()\n pattern = '\\$.*? .*? (.*?) .*? .*? .*? .*? .*? .*? \\r\\n' \n mode = re.findall(pattern,summary).pop()\n return mode","def get_keymap(lines: str):\n parsed = [line for line in lines.split(\"\\n\") if line]\n\n start = end = 0\n for i, line in enumerate(parsed):\n if \"qmkformat start\" in line:\n start = i + 1\n if \"qmkformat end\" in line:\n end = i\n break\n\n layout = \"\".join(parsed[start:end])\n return layout[layout.find(\"{\") + 1 : layout.find(\"}\")]","def default_trace_codes() -> Mapping[int, str]:\n with open(Path(__file__).resolve().parent.joinpath('trace.codes'), 'r') as fd:\n return from_trace_codes_text(fd.read())","def GetMapMode(*args, **kwargs):\n return _gdi_.DC_GetMapMode(*args, **kwargs)","def raw_mode(self) -> ContextManager[None]:","def modes(self):\n try:\n order = self._current_order\n except AttributeError:\n raise AttributeError('Cannot iterate over modes without iterating over orders!') from None\n mode = -order\n while mode <= order:\n yield mode\n mode += 1","def config_mode(self):\n return \"\"","def ActiveHlt2Lines(self) :\n return []","def get_mode(self):\r\n return self._api.get_mode()","def getDisplayModes(self, obj):\n modes = []\n return modes","def getColorMapFlags():\n\treturn colorMap_flag","def get_pers_modes(self, pcut):\n return self.get_allpers_modes()[pcut]","def get_all_servers_modes():\n return _get_list(\n lambda server: server.mode,\n lambda server: server.mode_name_long\n )","def getmode(self):\n return self.mode","def cursor_mode(self):\n return self._cursor_mode","def preset_modes(self) -> Optional[List[str]]:\n return [PRESET_MODE_WHOOSH]","def find_mode_range(self):\n\n if (len(self.n) < 1): return -1,-1,-1,-1\n nmin = np.nanmin(self.modes['n'])\n nmax = np.nanmax(self.modes['n'])\n lmin = np.nanmin(self.modes['l'])\n lmax = np.nanmax(self.modes['l'])\n return nmin, nmax, lmin, lmax","def mode(self):\n return self._lift(\"mode\")","def get_lines(self) -> list[str]:\n\n chars = self.get_char(\"border\")\n assert isinstance(chars, list)\n left_border, _, right_border, _ = chars\n\n lines = super().get_lines()\n last_line = lines.pop()\n\n for line in range(256 // self.grid_cols):\n buff = left_border\n\n for num in range(self.grid_cols):\n col = str(line * self.grid_cols + num)\n if col == \"0\":\n buff += \" \"\n continue\n\n buff += self._layer_functions[self.layer](f\"{col:>3}\", col) + \" \"\n\n buff = buff[:-1]\n lines.append(buff + \"\" + right_border)\n\n lines.append(last_line)\n\n return lines","def preset_modes(self) -> list:\n return self._preset_modes","def get_mode(self):\r\n return self.mode","def get_mode(state):\n node = state\n for key in (\"layers\", \"mode\"):\n node = node.get(key, {})\n return node.get(\"state\", \"add\")","def getSchemaMap(self):\n\t\trSchemaMap = {}\n\t\tfor schemaName in self.schemaList:\n\t\t\tmodeList = [str(tKey) for tKey in self.schemaList[schemaName].modes.keys()]\n\n\t\t\tif len(modeList) == 1 and None in self.schemaList[schemaName].modes:\n\t\t\t\trSchemaMap[schemaName] = 0\n\t\t\telse:\n\t\t\t\trSchemaMap[schemaName] = modeList\n\n\t\treturn rSchemaMap","def supported_modes(self):\n return [OFF, SYNC, CHARGE]","def modesets(self):\n\t\tcopy_ms_all = {}\n\t\tfor mode_set_id,mode_set in self.ms_all.iteritems():\n\t\t\tcopy_ms_all[mode_set_id] = copy.deepcopy(mode_set.simple())\n\t\treturn copy_ms_all","def get_macros(self):\n return LEVELS.keys()","def _get_mode():\n return context.get_context('mode')","def lines(self):\n if not PY2:\n return self.curbuf\n\n return [l.decode(self.options.get('encoding')) for l in self.curbuf]","def mode(self) -> str:\r\n ...","def geef_kolom_keuzes(self):\n return self._kolom_codelijst.geef_terminals()","def getMode(self):\n return self._mode","def _get_app_flags_mode(self, string):\n\n app = ''\n flags = ''\n mode = 0\n split = string.split()\n\n if len(split) == 0:\n pass\n\n elif len(split) == 1:\n part = split[0]\n if self._is_app(part):\n app = part\n elif self._is_flags(part):\n flags = part\n elif self._is_mode(part):\n mode = part\n\n elif len(split) == 2:\n part0 = split[0]\n part1 = split[1]\n\n if self._is_app(part0):\n app = part0\n if self._is_flags(part1):\n flags = part1\n elif self._is_mode(part1):\n mode = part1\n elif self._is_flags(part0):\n flags = part0\n if self._is_mode(part1):\n mode = part1\n elif self._is_mode(part0):\n mode = part0\n if self._is_flags(part1):\n flags = part1\n\n elif len(split) >= 3:\n part0 = split[0]\n part1 = split[1]\n part2 = split[2]\n\n if self._is_app(part0):\n app = part0\n if self._is_flags(part1):\n flags = part1\n if self._is_mode(part2):\n mode = part2\n elif self._is_mode(part1):\n mode = part1\n if self._is_flags(part2):\n flags = part2\n elif self._is_flags(part0):\n flags = part0\n if self._is_mode(part1):\n mode = part1\n elif self._is_mode(part0):\n mode = part0\n if self._is_flags(part1):\n flags = part1\n #Always run in new window\n flags = 'f'\n return app, flags, int(mode)","def getting_flags_locations(self):\n print(self.flags)\n self.line_finder.find_line(self.html)","def read_all_status_characters(self):\n return self.STATUS_CHARACTERS","def get_modes_of_operation(self):\n return [\"Online\", \"Offline\"]","def getZaptelConf(self):\n output = []\n for portInd, portLine in enumerate(self.portLines):\n if self[portInd]['type'] != 'na':\n values = self[portInd]\n values['type'] = values['type'] == 'fxs' and \"fxo\" or 'fxs' #Hmm crazy zaptel idea that your fxo is your fxs in zapata but the correct way around in zaptel\n output.append(\"%(type)s%(signalling)s=\" % self[portInd] + str(portLine[0]))\n return output","def getVKBEditMode(self):\r\n return eval(self.phone.sx('(send (get-text-editor-manager) get-edit-mode)', convertToString=True).title())","def mapping_description(self):\n\n mapping_description_lines = []\n\n if self.valuemap:\n for value in sorted(self.valuemap.keys()):\n mapping = self.valuemap[value]\n mapping_description_lines.append(\"'{}' <-> '{}'\\n\".format(value, mapping))\n return mapping_description_lines","def GetEOLChar(self):\n m_id = self.GetEOLMode()\n if m_id == wx.stc.STC_EOL_CR:\n return u'\\r'\n elif m_id == wx.stc.STC_EOL_CRLF:\n return u'\\r\\n'\n else:\n return u'\\n'","def getCharMapping(tweets):\n text = map(lambda x: x.getText(), tweets)\n allChars = [c for s in text for c in s]\n x = collections.Counter(allChars)\n chars_used = x.most_common()[:max_chars]\n charset = map(lambda x: x[0], chars_used)\n # Add padding, start, end and unknown characters\n mapping = dict((c, i) for i, c in enumerate(charset + ['<s>', '</s>', '<pad>', '<unknown>', '<unknown_test>']))\n dump(mapping, open(char_mapping_filename, 'wb'))\n return mapping","def exit_config_mode(self):\n return \"\"","def get_lines():\n buf = vim.current.buffer\n return buf","def _get_lsp_config_path_select_mode_configured(self):\n return self.__lsp_config_path_select_mode_configured","def partial_modes(self, level, node=None):\n if node:\n return self.operator.modes[self._index_list(level, node)]\n\n indeces = [self._index_list(level, i) for i in range(2**level)]\n return np.hstack(tuple([self.operator.modes[idx] for idx in indeces]))","def _generate_character_map(self):\n self._ct = [-1] * 256\n index = 0\n for c_range in self._meta.character_ranges:\n for c_pos in range(c_range['min'], c_range['max'] + 1):\n self._ct[c_pos] = index\n index += 1","def get_color_mode(self):\n mode=lib.is_SetColorMode(self.hcam,0x8000)\n return self._color_modes_inv.get(mode,mode)","def formats(self):\n return self._map.keys()","def ActiveHlt2Lines(self) :\n lines = [\n 'Hlt2B2HH',\n 'Hlt2B2PiPi',\n 'Hlt2B2KPi',\n 'Hlt2B2KK',\n 'Hlt2Lb2PK',\n 'Hlt2Lb2PPi'\n ]\n\n return lines","def getModes(this):\n\t\tthis.checkInit()\n\t\t\n\t\t# On sauvegarde la config actuelle\n\t\tinit = this.config(get=True)\n\t\t\n\t\t# Ensembles de modes\n\t\tformats = Camera.formats.copy()\n\t\tmodes = set()\n\t\t\n\t\t# On averti du départ\n\t\tprint '\\nLooping modes for the camera... (%d modes)' % (len(formats))\n\t\t\t\n\t\t# Pour chaques formats\n\t\twhile formats:\n\t\t\t\n\t\t\t# On récupère le format à tester\n\t\t\tformat = formats.pop()\n\t\t\t\n\t\t\t# Configuration actuelle\n\t\t\tmode = this.config(\n\t\t\t\theight = float(format[1]),\n\t\t\t\twidth = float(format[0])\n\t\t\t)\n\t\t\t\n\t\t\t# On enregistre le mode\n\t\t\tcurrentFormat = (mode['width'], mode['height'])\n\t\t\tmodes.add(currentFormat)\n\t\t\tif currentFormat in formats:\n\t\t\t\tformats.remove(currentFormat)\n\t\t\t\n\t\t\t# On affiche l'itération courante\n\t\t\tprintf('%d%5s\\r' % (len(formats), ''))\n\t\t###\n\t\t\n\t\t# On remet comme avant et on retourne la liste de modes\n\t\tthis.config(params=init); print 'Done, found %d.' % (len(modes))\n\t\treturn [(int(mode[0]), int(mode[1])) for mode in modes]","def build_exception_map(cls, tokens):\r\n exception_ranges = defaultdict(list)\r\n for token in tokens:\r\n token_type, _, token_start, token_end = token[0:4]\r\n if token_type in (tokenize.COMMENT, tokenize.STRING):\r\n if token_start[0] == token_end[0]:\r\n exception_ranges[token_start[0]].append((token_start[1], token_end[1]))\r\n else:\r\n exception_ranges[token_start[0]].append((token_start[1], sys.maxint))\r\n for line in range(token_start[0] + 1, token_end[0]):\r\n exception_ranges[line].append((0, sys.maxint))\r\n exception_ranges[token_end[0]].append((0, token_end[1]))\r\n return exception_ranges","def preset_modes(self) -> list[str]:\n # Use the Vallox profile names for the preset names.\n return list(STR_TO_VALLOX_PROFILE_SETTABLE.keys())","def parse_layout(keymap_c: str):\n lines = get_keymap(keymap_c)\n lines = remove_excess_white_space(lines)\n layout_strs = get_layouts_strs(lines)\n return get_layout_keys(layout_strs)","def _get_modes(self, L=0):\n l = np.arange(L + 1).reshape((-1, 1))\n z = np.zeros((L + 1, 2))\n return np.hstack([l, z])","def get_mode(self):\n self.read(\":FUNC?\")","def get_config_lines(self, key):\n return \" \".join([key, self._config[key][\"value\"], \"#\", self._config[key][\"comments\"]] if self._config[key][\"comments\"] is not None\n else [key, self._config[key][\"value\"]])","def init_modes(self):\n \n self.deleteMode = delete_Mode()\n self.commandMode = command_Mode()\n self.visualMode = visual_Mode()\n self.insertMode = insert_Mode()\n self.exMode = ex_Mode()\n self.yankMode = yank_Mode()\n self.gmodeMode = gmode_Mode()\n self.cmodeMode = cmode_Mode()\n self.rmodeMode = rmode_Mode()\n self.tmodeMode = tmode_Mode()\n self.selectionMode = selection_Mode()\n self.indentMode = indent_Mode()","def modes(self, avg=False):\n if not self.fp_init:\n self._init_full_props\n if avg:\n return self._modes_avg, self._num_modes\n return self._modes, self._num_modes","def get_opcode_mode(op):\n op_str = f\"{op:05}\"\n DE = int(op_str[-2:])\n C = int(op_str[2])\n B = int(op_str[1]) \n A = int(op_str[0]) \n\n return A, B, C, DE","def currentMode(self):\n logger.debug(\"Func: currentMode/getter\")\n\n return self._currentsDict[\"currentMode\"]","def separate_mode_type(mode):\n m = stat.S_IMODE(mode)\n t = stat.S_IFMT(mode)\n return m, mode_to_unix(t)","def ActiveHlt2Lines(self) :\n\n lines = [\n 'Hlt2SingleMuon',\n 'Hlt2SingleMuonHighPT',\n 'Hlt2SingleMuonLowPT',\n ]\n \n return lines","def extra_state_attributes(self) -> dict[str, str]:\n if self._cmode is None:\n return {}\n return {ATTR_CMODE: self._cmode}","def _get_lsp_config_path_select_mode_unconditional(self):\n return self.__lsp_config_path_select_mode_unconditional","def get_map(self):\n return self.get_raw_ys()","def branches(self):\n return self.in_lines + self.out_lines"],"string":"[\n \"def get_eol_for_open(self) -> str:\\n map = {\\n EOLTypes.CRLF: WINDOWS_EOL,\\n EOLTypes.LF: UNIX_EOL,\\n EOLTypes.NATIVE: linesep,\\n }\\n\\n return map[self]\",\n \"def get_modes(self):\\n return [i for i, j in enumerate(self._modemap._map) if j is not None]\",\n \"def getModeLookupTable(self):\\n mode_table = []\\n header = None\\n for line in open(PublicTransit.MODE_LOOKUP_FILE_NAME):\\n line = line.strip()\\n if len(line) == 0: \\n continue\\n line = map(str.strip,line.split(\\\",\\\"))\\n if header is None:\\n header = line\\n #CPT_AGENCYID\\tAGENCYNAME\\tCPT_MODE\\tSCH_ROUTEDESIGNATOR\\tMODECODE\\tMODEGROUP\\n continue\\n data = {}\\n for i in range(len(line)):\\n data[header[i]] = line[i]\\n mode_table.append(data)\\n return mode_table\",\n \"def _get_modes(self):\\n return self.__modes\",\n \"def get_modes(self, code_block):\\r\\n # FUCK YOU INDEX ERRORS, LIST COMPS, AND EVEN YOU LAMBDAS I DON'T NEED PRETTY\\r\\n # 0 = pos mode\\r\\n # 1 = imm mode\\r\\n modes, mode_codes = [0, 0], list(reversed(str(code_block[0])))[2:]\\r\\n x = 0\\r\\n for mode in mode_codes:\\r\\n modes[x] = int(mode)\\r\\n x += 1\\r\\n print('Get modes: ')\\r\\n print(modes)\\r\\n return modes\",\n \"def get_modes(self):\\n return self.circuit.get_modes()\",\n \"def modes(self, exp_id: int) -> List[str]:\\n return list(self.state[exp_id].keys())\",\n \"def __mode_modesetid(self, mode):\\n\\t\\tfor key,val in self.ms_all.iteritems():\\n\\t\\t\\tix = val.index(mode)\\n\\t\\t\\tif ix is not None:\\n\\t\\t\\t\\treturn key, ix\",\n \"def all_modes(self):\\n\\n # Find \\\"post-proj all modes\\\"\\n # Jump to first value, ignoring text.\\n # Move through data, adding it to a list\\n # continue onto next line.\\n # Repeat until the following line is known to be empty.\\n\\n # output.dat is the psi4 output file.\\n with open('output.dat', 'r') as file:\\n lines = file.readlines()\\n for count, line in enumerate(lines):\\n if \\\"post-proj all modes\\\" in line:\\n start_of_vals = count\\n break\\n else:\\n raise EOFError('Cannot locate modes in output.dat file.')\\n\\n # Barring the first (and sometimes last) line, dat file has 6 values per row.\\n end_of_vals = start_of_vals + (3 * len(self.molecule.molecule['input'])) // 6\\n\\n structures = lines[start_of_vals][24:].replace(\\\"'\\\", \\\"\\\").split()\\n structures = structures[6:]\\n\\n for row in range(1, end_of_vals - start_of_vals):\\n # Remove double strings and weird formatting.\\n structures += lines[start_of_vals + row].replace(\\\"'\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\").split()\\n\\n all_modes = [float(val) for val in structures]\\n\\n return array(all_modes)\",\n \"def _get_applicable_modes(command):\\n mode_dict = {}\\n _add_applicable_modes(command, mode_dict)\\n return mode_dict.keys()\",\n \"def parse_session_mode_and_map(log_data):\\n try:\\n match = search(\\n r\\\"<\\\\d{2}:\\\\d{2}> [^d]* Loading level \\\\w+\\\\/(\\\\w+), \\\\w+ (\\\\w+)\\\",\\n log_data)\\n line_map, line_mode = match.groups()\\n return (line_mode, line_map)\\n except Exception:\\n print(\\\"Something is wrong with the log file!\\\")\",\n \"def modes(self):\\n return np.hstack(tuple(self.operator.modes))\",\n \"def all_modes(self):\\n\\n # Find \\\"post-proj all modes\\\"\\n # Jump to first value, ignoring text.\\n # Move through data, adding it to a list\\n # continue onto next line.\\n # Repeat until the following line is known to be empty.\\n\\n # output.dat is the psi4 output file.\\n with open(\\\"output.dat\\\", \\\"r\\\") as file:\\n lines = file.readlines()\\n for count, line in enumerate(lines):\\n if \\\"post-proj all modes\\\" in line:\\n start_of_vals = count\\n break\\n else:\\n raise EOFError(\\\"Cannot locate modes in output.dat file.\\\")\\n\\n # Barring the first (and sometimes last) line, dat file has 6 values per row.\\n end_of_vals = start_of_vals + (3 * len(self.molecule.atoms)) // 6\\n\\n structures = lines[start_of_vals][24:].replace(\\\"'\\\", \\\"\\\").split()\\n structures = structures[6:]\\n\\n for row in range(1, end_of_vals - start_of_vals):\\n # Remove double strings and weird formatting.\\n structures += (\\n lines[start_of_vals + row].replace(\\\"'\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\").split()\\n )\\n\\n all_modes = [float(val) for val in structures]\\n\\n return np.array(all_modes)\",\n \"def __convertEOL(self):\\n aw = self.activeWindow()\\n aw.convertEols(aw.eolMode())\",\n \"def get_modes(self):\\n modes = set()\\n for er in self.exercise_recordings:\\n if er.mode not in modes:\\n modes.add(er.mode)\\n return list(modes)\",\n \"def modes(self) -> List[str]:\\n return [m.name for m in self._modes]\",\n \"def GetPackageModes(self):\\n return self._modes\",\n \"def values(self):\\n return self._modes.values()\",\n \"def get_focus_mode_names(self):\\n names = []\\n for focus_mode in self.focus_modes:\\n names.append(focus_mode['modeName'])\\n return names\",\n \"def _modes(self):\\n answer = []\\n for i in dir(self):\\n if i.startswith('handle_'):\\n answer.append(i.replace('handle_', ''))\\n return answer\",\n \"def _get_mode(self):\\n self._validate_mode()\\n return deepcopy(self.mode)\",\n \"def common_mode(self):\\n return self._common_mode\",\n \"def common_mode(self):\\n return self._common_mode\",\n \"def getmode(self, mode):\\r\\n modes = {}\\r\\n # core modes\\r\\n for m, (basemode, basetype, bands) in _MODEINFO.items():\\r\\n modes[m] = ModeDescriptor(m, bands, basemode, basetype)\\r\\n # extra experimental modes\\r\\n modes[\\\"RGBa\\\"] = ModeDescriptor(\\\"RGBa\\\",\\r\\n (\\\"R\\\", \\\"G\\\", \\\"B\\\", \\\"a\\\"), \\\"RGB\\\", \\\"L\\\")\\r\\n modes[\\\"LA\\\"] = ModeDescriptor(\\\"LA\\\", (\\\"L\\\", \\\"A\\\"), \\\"L\\\", \\\"L\\\")\\r\\n modes[\\\"La\\\"] = ModeDescriptor(\\\"La\\\", (\\\"L\\\", \\\"a\\\"), \\\"L\\\", \\\"L\\\")\\r\\n modes[\\\"PA\\\"] = ModeDescriptor(\\\"PA\\\", (\\\"P\\\", \\\"A\\\"), \\\"RGB\\\", \\\"L\\\")\\r\\n # mapping modes\\r\\n modes[\\\"I;16\\\"] = ModeDescriptor(\\\"I;16\\\", \\\"I\\\", \\\"L\\\", \\\"L\\\")\\r\\n modes[\\\"I;16L\\\"] = ModeDescriptor(\\\"I;16L\\\", \\\"I\\\", \\\"L\\\", \\\"L\\\")\\r\\n modes[\\\"I;16B\\\"] = ModeDescriptor(\\\"I;16B\\\", \\\"I\\\", \\\"L\\\", \\\"L\\\")\\r\\n # set global mode cache atomically\\r\\n _modes = modes\\r\\n return _modes[mode]\",\n \"def line_styles (self):\\n return self._line_styles\",\n \"def get_map(self):\\n\\n self.mp = defaultdict(lambda : ord('x'))\\n y, x = 0, 0\\n while True:\\n cond, output = self.ic()\\n\\n if cond: break\\n # New row of the print out\\n if output == 10:\\n y += 1\\n x = 0\\n # Assign the value to the map\\n else:\\n self.mp[y,x] = output\\n x += 1\\n \\n return self.mp\",\n \"def get_keymap(self):\\n return self.keymap\",\n \"def keys(self):\\n return self._modes.keys()\",\n \"def _get_mode(self):\\n raise NotImplementedError\",\n \"def get_all_color_modes(self):\\n return self._all_color_modes\",\n \"def preset_modes(self):\\n return self._preset_modes\",\n \"def get_mode(self) -> str:\\n\\n return self.send(self.cmd.GET_MODE)\",\n \"def map_rule4(self):\\n odml.terminology.terminologies['map'] = parse(\\\"\\\"\\\"\\n S1[T1]\\n - P2\\n S2[T2]\\n - P1\\n S3[T3]\\n - P1\\n - P2\\n - P3\\n \\\"\\\"\\\")\",\n \"def get_heatmode_stringlist(self):\\n return text_heatmode\",\n \"def get_current_mode(self):\\n return self.read(0xa2)\",\n \"def get_mode(self):\\n summary = self.get_version_summary()\\n pattern = '\\\\$.*? .*? (.*?) .*? .*? .*? .*? .*? .*? \\\\r\\\\n' \\n mode = re.findall(pattern,summary).pop()\\n return mode\",\n \"def get_keymap(lines: str):\\n parsed = [line for line in lines.split(\\\"\\\\n\\\") if line]\\n\\n start = end = 0\\n for i, line in enumerate(parsed):\\n if \\\"qmkformat start\\\" in line:\\n start = i + 1\\n if \\\"qmkformat end\\\" in line:\\n end = i\\n break\\n\\n layout = \\\"\\\".join(parsed[start:end])\\n return layout[layout.find(\\\"{\\\") + 1 : layout.find(\\\"}\\\")]\",\n \"def default_trace_codes() -> Mapping[int, str]:\\n with open(Path(__file__).resolve().parent.joinpath('trace.codes'), 'r') as fd:\\n return from_trace_codes_text(fd.read())\",\n \"def GetMapMode(*args, **kwargs):\\n return _gdi_.DC_GetMapMode(*args, **kwargs)\",\n \"def raw_mode(self) -> ContextManager[None]:\",\n \"def modes(self):\\n try:\\n order = self._current_order\\n except AttributeError:\\n raise AttributeError('Cannot iterate over modes without iterating over orders!') from None\\n mode = -order\\n while mode <= order:\\n yield mode\\n mode += 1\",\n \"def config_mode(self):\\n return \\\"\\\"\",\n \"def ActiveHlt2Lines(self) :\\n return []\",\n \"def get_mode(self):\\r\\n return self._api.get_mode()\",\n \"def getDisplayModes(self, obj):\\n modes = []\\n return modes\",\n \"def getColorMapFlags():\\n\\treturn colorMap_flag\",\n \"def get_pers_modes(self, pcut):\\n return self.get_allpers_modes()[pcut]\",\n \"def get_all_servers_modes():\\n return _get_list(\\n lambda server: server.mode,\\n lambda server: server.mode_name_long\\n )\",\n \"def getmode(self):\\n return self.mode\",\n \"def cursor_mode(self):\\n return self._cursor_mode\",\n \"def preset_modes(self) -> Optional[List[str]]:\\n return [PRESET_MODE_WHOOSH]\",\n \"def find_mode_range(self):\\n\\n if (len(self.n) < 1): return -1,-1,-1,-1\\n nmin = np.nanmin(self.modes['n'])\\n nmax = np.nanmax(self.modes['n'])\\n lmin = np.nanmin(self.modes['l'])\\n lmax = np.nanmax(self.modes['l'])\\n return nmin, nmax, lmin, lmax\",\n \"def mode(self):\\n return self._lift(\\\"mode\\\")\",\n \"def get_lines(self) -> list[str]:\\n\\n chars = self.get_char(\\\"border\\\")\\n assert isinstance(chars, list)\\n left_border, _, right_border, _ = chars\\n\\n lines = super().get_lines()\\n last_line = lines.pop()\\n\\n for line in range(256 // self.grid_cols):\\n buff = left_border\\n\\n for num in range(self.grid_cols):\\n col = str(line * self.grid_cols + num)\\n if col == \\\"0\\\":\\n buff += \\\" \\\"\\n continue\\n\\n buff += self._layer_functions[self.layer](f\\\"{col:>3}\\\", col) + \\\" \\\"\\n\\n buff = buff[:-1]\\n lines.append(buff + \\\"\\\" + right_border)\\n\\n lines.append(last_line)\\n\\n return lines\",\n \"def preset_modes(self) -> list:\\n return self._preset_modes\",\n \"def get_mode(self):\\r\\n return self.mode\",\n \"def get_mode(state):\\n node = state\\n for key in (\\\"layers\\\", \\\"mode\\\"):\\n node = node.get(key, {})\\n return node.get(\\\"state\\\", \\\"add\\\")\",\n \"def getSchemaMap(self):\\n\\t\\trSchemaMap = {}\\n\\t\\tfor schemaName in self.schemaList:\\n\\t\\t\\tmodeList = [str(tKey) for tKey in self.schemaList[schemaName].modes.keys()]\\n\\n\\t\\t\\tif len(modeList) == 1 and None in self.schemaList[schemaName].modes:\\n\\t\\t\\t\\trSchemaMap[schemaName] = 0\\n\\t\\t\\telse:\\n\\t\\t\\t\\trSchemaMap[schemaName] = modeList\\n\\n\\t\\treturn rSchemaMap\",\n \"def supported_modes(self):\\n return [OFF, SYNC, CHARGE]\",\n \"def modesets(self):\\n\\t\\tcopy_ms_all = {}\\n\\t\\tfor mode_set_id,mode_set in self.ms_all.iteritems():\\n\\t\\t\\tcopy_ms_all[mode_set_id] = copy.deepcopy(mode_set.simple())\\n\\t\\treturn copy_ms_all\",\n \"def get_macros(self):\\n return LEVELS.keys()\",\n \"def _get_mode():\\n return context.get_context('mode')\",\n \"def lines(self):\\n if not PY2:\\n return self.curbuf\\n\\n return [l.decode(self.options.get('encoding')) for l in self.curbuf]\",\n \"def mode(self) -> str:\\r\\n ...\",\n \"def geef_kolom_keuzes(self):\\n return self._kolom_codelijst.geef_terminals()\",\n \"def getMode(self):\\n return self._mode\",\n \"def _get_app_flags_mode(self, string):\\n\\n app = ''\\n flags = ''\\n mode = 0\\n split = string.split()\\n\\n if len(split) == 0:\\n pass\\n\\n elif len(split) == 1:\\n part = split[0]\\n if self._is_app(part):\\n app = part\\n elif self._is_flags(part):\\n flags = part\\n elif self._is_mode(part):\\n mode = part\\n\\n elif len(split) == 2:\\n part0 = split[0]\\n part1 = split[1]\\n\\n if self._is_app(part0):\\n app = part0\\n if self._is_flags(part1):\\n flags = part1\\n elif self._is_mode(part1):\\n mode = part1\\n elif self._is_flags(part0):\\n flags = part0\\n if self._is_mode(part1):\\n mode = part1\\n elif self._is_mode(part0):\\n mode = part0\\n if self._is_flags(part1):\\n flags = part1\\n\\n elif len(split) >= 3:\\n part0 = split[0]\\n part1 = split[1]\\n part2 = split[2]\\n\\n if self._is_app(part0):\\n app = part0\\n if self._is_flags(part1):\\n flags = part1\\n if self._is_mode(part2):\\n mode = part2\\n elif self._is_mode(part1):\\n mode = part1\\n if self._is_flags(part2):\\n flags = part2\\n elif self._is_flags(part0):\\n flags = part0\\n if self._is_mode(part1):\\n mode = part1\\n elif self._is_mode(part0):\\n mode = part0\\n if self._is_flags(part1):\\n flags = part1\\n #Always run in new window\\n flags = 'f'\\n return app, flags, int(mode)\",\n \"def getting_flags_locations(self):\\n print(self.flags)\\n self.line_finder.find_line(self.html)\",\n \"def read_all_status_characters(self):\\n return self.STATUS_CHARACTERS\",\n \"def get_modes_of_operation(self):\\n return [\\\"Online\\\", \\\"Offline\\\"]\",\n \"def getZaptelConf(self):\\n output = []\\n for portInd, portLine in enumerate(self.portLines):\\n if self[portInd]['type'] != 'na':\\n values = self[portInd]\\n values['type'] = values['type'] == 'fxs' and \\\"fxo\\\" or 'fxs' #Hmm crazy zaptel idea that your fxo is your fxs in zapata but the correct way around in zaptel\\n output.append(\\\"%(type)s%(signalling)s=\\\" % self[portInd] + str(portLine[0]))\\n return output\",\n \"def getVKBEditMode(self):\\r\\n return eval(self.phone.sx('(send (get-text-editor-manager) get-edit-mode)', convertToString=True).title())\",\n \"def mapping_description(self):\\n\\n mapping_description_lines = []\\n\\n if self.valuemap:\\n for value in sorted(self.valuemap.keys()):\\n mapping = self.valuemap[value]\\n mapping_description_lines.append(\\\"'{}' <-> '{}'\\\\n\\\".format(value, mapping))\\n return mapping_description_lines\",\n \"def GetEOLChar(self):\\n m_id = self.GetEOLMode()\\n if m_id == wx.stc.STC_EOL_CR:\\n return u'\\\\r'\\n elif m_id == wx.stc.STC_EOL_CRLF:\\n return u'\\\\r\\\\n'\\n else:\\n return u'\\\\n'\",\n \"def getCharMapping(tweets):\\n text = map(lambda x: x.getText(), tweets)\\n allChars = [c for s in text for c in s]\\n x = collections.Counter(allChars)\\n chars_used = x.most_common()[:max_chars]\\n charset = map(lambda x: x[0], chars_used)\\n # Add padding, start, end and unknown characters\\n mapping = dict((c, i) for i, c in enumerate(charset + ['<s>', '</s>', '<pad>', '<unknown>', '<unknown_test>']))\\n dump(mapping, open(char_mapping_filename, 'wb'))\\n return mapping\",\n \"def exit_config_mode(self):\\n return \\\"\\\"\",\n \"def get_lines():\\n buf = vim.current.buffer\\n return buf\",\n \"def _get_lsp_config_path_select_mode_configured(self):\\n return self.__lsp_config_path_select_mode_configured\",\n \"def partial_modes(self, level, node=None):\\n if node:\\n return self.operator.modes[self._index_list(level, node)]\\n\\n indeces = [self._index_list(level, i) for i in range(2**level)]\\n return np.hstack(tuple([self.operator.modes[idx] for idx in indeces]))\",\n \"def _generate_character_map(self):\\n self._ct = [-1] * 256\\n index = 0\\n for c_range in self._meta.character_ranges:\\n for c_pos in range(c_range['min'], c_range['max'] + 1):\\n self._ct[c_pos] = index\\n index += 1\",\n \"def get_color_mode(self):\\n mode=lib.is_SetColorMode(self.hcam,0x8000)\\n return self._color_modes_inv.get(mode,mode)\",\n \"def formats(self):\\n return self._map.keys()\",\n \"def ActiveHlt2Lines(self) :\\n lines = [\\n 'Hlt2B2HH',\\n 'Hlt2B2PiPi',\\n 'Hlt2B2KPi',\\n 'Hlt2B2KK',\\n 'Hlt2Lb2PK',\\n 'Hlt2Lb2PPi'\\n ]\\n\\n return lines\",\n \"def getModes(this):\\n\\t\\tthis.checkInit()\\n\\t\\t\\n\\t\\t# On sauvegarde la config actuelle\\n\\t\\tinit = this.config(get=True)\\n\\t\\t\\n\\t\\t# Ensembles de modes\\n\\t\\tformats = Camera.formats.copy()\\n\\t\\tmodes = set()\\n\\t\\t\\n\\t\\t# On averti du départ\\n\\t\\tprint '\\\\nLooping modes for the camera... (%d modes)' % (len(formats))\\n\\t\\t\\t\\n\\t\\t# Pour chaques formats\\n\\t\\twhile formats:\\n\\t\\t\\t\\n\\t\\t\\t# On récupère le format à tester\\n\\t\\t\\tformat = formats.pop()\\n\\t\\t\\t\\n\\t\\t\\t# Configuration actuelle\\n\\t\\t\\tmode = this.config(\\n\\t\\t\\t\\theight = float(format[1]),\\n\\t\\t\\t\\twidth = float(format[0])\\n\\t\\t\\t)\\n\\t\\t\\t\\n\\t\\t\\t# On enregistre le mode\\n\\t\\t\\tcurrentFormat = (mode['width'], mode['height'])\\n\\t\\t\\tmodes.add(currentFormat)\\n\\t\\t\\tif currentFormat in formats:\\n\\t\\t\\t\\tformats.remove(currentFormat)\\n\\t\\t\\t\\n\\t\\t\\t# On affiche l'itération courante\\n\\t\\t\\tprintf('%d%5s\\\\r' % (len(formats), ''))\\n\\t\\t###\\n\\t\\t\\n\\t\\t# On remet comme avant et on retourne la liste de modes\\n\\t\\tthis.config(params=init); print 'Done, found %d.' % (len(modes))\\n\\t\\treturn [(int(mode[0]), int(mode[1])) for mode in modes]\",\n \"def build_exception_map(cls, tokens):\\r\\n exception_ranges = defaultdict(list)\\r\\n for token in tokens:\\r\\n token_type, _, token_start, token_end = token[0:4]\\r\\n if token_type in (tokenize.COMMENT, tokenize.STRING):\\r\\n if token_start[0] == token_end[0]:\\r\\n exception_ranges[token_start[0]].append((token_start[1], token_end[1]))\\r\\n else:\\r\\n exception_ranges[token_start[0]].append((token_start[1], sys.maxint))\\r\\n for line in range(token_start[0] + 1, token_end[0]):\\r\\n exception_ranges[line].append((0, sys.maxint))\\r\\n exception_ranges[token_end[0]].append((0, token_end[1]))\\r\\n return exception_ranges\",\n \"def preset_modes(self) -> list[str]:\\n # Use the Vallox profile names for the preset names.\\n return list(STR_TO_VALLOX_PROFILE_SETTABLE.keys())\",\n \"def parse_layout(keymap_c: str):\\n lines = get_keymap(keymap_c)\\n lines = remove_excess_white_space(lines)\\n layout_strs = get_layouts_strs(lines)\\n return get_layout_keys(layout_strs)\",\n \"def _get_modes(self, L=0):\\n l = np.arange(L + 1).reshape((-1, 1))\\n z = np.zeros((L + 1, 2))\\n return np.hstack([l, z])\",\n \"def get_mode(self):\\n self.read(\\\":FUNC?\\\")\",\n \"def get_config_lines(self, key):\\n return \\\" \\\".join([key, self._config[key][\\\"value\\\"], \\\"#\\\", self._config[key][\\\"comments\\\"]] if self._config[key][\\\"comments\\\"] is not None\\n else [key, self._config[key][\\\"value\\\"]])\",\n \"def init_modes(self):\\n \\n self.deleteMode = delete_Mode()\\n self.commandMode = command_Mode()\\n self.visualMode = visual_Mode()\\n self.insertMode = insert_Mode()\\n self.exMode = ex_Mode()\\n self.yankMode = yank_Mode()\\n self.gmodeMode = gmode_Mode()\\n self.cmodeMode = cmode_Mode()\\n self.rmodeMode = rmode_Mode()\\n self.tmodeMode = tmode_Mode()\\n self.selectionMode = selection_Mode()\\n self.indentMode = indent_Mode()\",\n \"def modes(self, avg=False):\\n if not self.fp_init:\\n self._init_full_props\\n if avg:\\n return self._modes_avg, self._num_modes\\n return self._modes, self._num_modes\",\n \"def get_opcode_mode(op):\\n op_str = f\\\"{op:05}\\\"\\n DE = int(op_str[-2:])\\n C = int(op_str[2])\\n B = int(op_str[1]) \\n A = int(op_str[0]) \\n\\n return A, B, C, DE\",\n \"def currentMode(self):\\n logger.debug(\\\"Func: currentMode/getter\\\")\\n\\n return self._currentsDict[\\\"currentMode\\\"]\",\n \"def separate_mode_type(mode):\\n m = stat.S_IMODE(mode)\\n t = stat.S_IFMT(mode)\\n return m, mode_to_unix(t)\",\n \"def ActiveHlt2Lines(self) :\\n\\n lines = [\\n 'Hlt2SingleMuon',\\n 'Hlt2SingleMuonHighPT',\\n 'Hlt2SingleMuonLowPT',\\n ]\\n \\n return lines\",\n \"def extra_state_attributes(self) -> dict[str, str]:\\n if self._cmode is None:\\n return {}\\n return {ATTR_CMODE: self._cmode}\",\n \"def _get_lsp_config_path_select_mode_unconditional(self):\\n return self.__lsp_config_path_select_mode_unconditional\",\n \"def get_map(self):\\n return self.get_raw_ys()\",\n \"def branches(self):\\n return self.in_lines + self.out_lines\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63044417","0.6147848","0.5959693","0.5748074","0.57146144","0.5593236","0.54169565","0.54102844","0.53679585","0.5359293","0.5346539","0.5331634","0.53012353","0.5280056","0.52593654","0.52485204","0.51640564","0.5128611","0.51207393","0.51181734","0.5070815","0.50445783","0.50445783","0.5026303","0.5024813","0.5011286","0.4998007","0.4982108","0.49743688","0.49624553","0.4946604","0.49225214","0.49156147","0.49013764","0.48998696","0.48776537","0.4872287","0.48685196","0.48679757","0.48463494","0.4843349","0.48277453","0.48260796","0.4804955","0.48033822","0.48012325","0.4771022","0.47652483","0.47610146","0.4739435","0.47350392","0.47309792","0.47213328","0.47153068","0.47123528","0.47106335","0.47102326","0.4706636","0.47035414","0.47001228","0.46847597","0.46842253","0.46753025","0.46725753","0.46582302","0.46556345","0.46523872","0.46483147","0.4645053","0.46333164","0.4632547","0.4622022","0.46153834","0.45971","0.45969278","0.4593864","0.45911032","0.45879862","0.45861897","0.45856938","0.4584695","0.45794022","0.45739627","0.45658514","0.45586914","0.4558479","0.45564532","0.4555265","0.45515016","0.4550603","0.4547667","0.45463377","0.45452863","0.4544144","0.4543211","0.45391715","0.4531764","0.45269614","0.4526361","0.45251802"],"string":"[\n \"0.63044417\",\n \"0.6147848\",\n \"0.5959693\",\n \"0.5748074\",\n \"0.57146144\",\n \"0.5593236\",\n \"0.54169565\",\n \"0.54102844\",\n \"0.53679585\",\n \"0.5359293\",\n \"0.5346539\",\n \"0.5331634\",\n \"0.53012353\",\n \"0.5280056\",\n \"0.52593654\",\n \"0.52485204\",\n \"0.51640564\",\n \"0.5128611\",\n \"0.51207393\",\n \"0.51181734\",\n \"0.5070815\",\n \"0.50445783\",\n \"0.50445783\",\n \"0.5026303\",\n \"0.5024813\",\n \"0.5011286\",\n \"0.4998007\",\n \"0.4982108\",\n \"0.49743688\",\n \"0.49624553\",\n \"0.4946604\",\n \"0.49225214\",\n \"0.49156147\",\n \"0.49013764\",\n \"0.48998696\",\n \"0.48776537\",\n \"0.4872287\",\n \"0.48685196\",\n \"0.48679757\",\n \"0.48463494\",\n \"0.4843349\",\n \"0.48277453\",\n \"0.48260796\",\n \"0.4804955\",\n \"0.48033822\",\n \"0.48012325\",\n \"0.4771022\",\n \"0.47652483\",\n \"0.47610146\",\n \"0.4739435\",\n \"0.47350392\",\n \"0.47309792\",\n \"0.47213328\",\n \"0.47153068\",\n \"0.47123528\",\n \"0.47106335\",\n \"0.47102326\",\n \"0.4706636\",\n \"0.47035414\",\n \"0.47001228\",\n \"0.46847597\",\n \"0.46842253\",\n \"0.46753025\",\n \"0.46725753\",\n \"0.46582302\",\n \"0.46556345\",\n \"0.46523872\",\n \"0.46483147\",\n \"0.4645053\",\n \"0.46333164\",\n \"0.4632547\",\n \"0.4622022\",\n \"0.46153834\",\n \"0.45971\",\n \"0.45969278\",\n \"0.4593864\",\n \"0.45911032\",\n \"0.45879862\",\n \"0.45861897\",\n \"0.45856938\",\n \"0.4584695\",\n \"0.45794022\",\n \"0.45739627\",\n \"0.45658514\",\n \"0.45586914\",\n \"0.4558479\",\n \"0.45564532\",\n \"0.4555265\",\n \"0.45515016\",\n \"0.4550603\",\n \"0.4547667\",\n \"0.45463377\",\n \"0.45452863\",\n \"0.4544144\",\n \"0.4543211\",\n \"0.45391715\",\n \"0.4531764\",\n \"0.45269614\",\n \"0.4526361\",\n \"0.45251802\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.808408"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":998,"cells":{"query":{"kind":"string","value":"Base Class for all controls. On its own, this is just a Label in a BoxLayout."},"document":{"kind":"string","value":"def __init__(self,\n\t label:str=None,\n\t variable_name:str=None,\n\t value:typing.Any=None,\n\t parent:QtWidgets.QWidget=None,\n\t on_change:typing.Callable=None):\n\t\tQtWidgets.QWidget.__init__(self, parent=parent)\n\n\t\tif label is None:\n\t\t\tif variable_name is None:\n\t\t\t\tlabel = \"\"\n\t\t\telse:\n\t\t\t\tlabel = app.translator(variable_name)\n\n\t\tself._make_label_widget(label)\n\t\tself.layout = self._formset()\n\t\tself.setLayout(self.layout)\n\t\tself.label = label\n\n\t\tValueMixin.__init__(self, variable_name=variable_name, on_change=on_change, value=value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_label(self):\n\n self.pc_label = Label(self.form_box, text=\"Primary Current [A]:\", anchor='nw', width=32,\n bg=self.design.color.secondary, font=('Arial', 15))\n self.sc_label = Label(self.form_box, text=\"Secondary Current [A]:\", anchor='nw', width=32,\n bg=self.design.color.secondary, font=('Arial', 15))\n self.avg_t_label = Label(self.form_box, text=\"Average Time [s]: \", anchor='nw', width=32,\n bg=self.design.color.secondary, font=('Arial', 15))\n self.nwt_label = Label(self.form_box, text=\"network type (static/dynamic):\", anchor='nw', width=32,\n bg=self.design.color.secondary, font=('Arial', 15))\n self.nw_ip_label = Label(self.form_box, text=\"IpAddress:\", anchor='nw', width=32,\n bg=self.design.color.secondary, font=('Arial', 15))\n self.nw_gw_label = Label(self.form_box, text=\"Gateway:\", anchor='nw', width=32, bg=self.design.color.secondary,\n font=('Arial', 15))\n self.nw_sm_label = Label(self.form_box, text=\"subnet mask:\", anchor='nw', width=32,\n bg=self.design.color.secondary, font=('Arial', 15))\n self.nw_mca_label = Label(self.form_box, text=\"Mac Address:\", anchor='nw', width=32,\n bg=self.design.color.secondary, font=('Arial', 15))","def create_widget(self):\n self.widget = UILabel()","def GridLabel(Parent,Text,Row,Column):\r\n L = Label(Parent,text=Text)\r\n L.grid(row=Row,column=Column)\r\n return L","def BaseLabel(self, *args):\n return _XCAFDoc.XCAFDoc_ShapeTool_BaseLabel(self, *args)","def add_label(self, side=\"left\", **kwargs):\n widget = ttk.Label(self, **kwargs)\n widget.pack(side=side)\n\n return widget","def label(self, color):\r\n return Label(self, color)","def __init__(self, parent, text):\n tk.Frame.__init__(self, parent)\n\n label = tk.Label(self, text=\"De Centrale\", font=(\"Verdana\", 25))\n label.pack(fill=tk.BOTH, side='left', anchor='w')\n\n label = tk.Label(self, text=text, anchor=\"e\", font=(\"Verdana\", 12))\n label.pack(expand=True, fill=tk.BOTH, side='right', anchor='e')","def create_label(self, on, text: str):\n return tk.Label(on, font=self.FONT, bg=self.BG_COLOR, text=text)","def __init__(self, parent, text_link=None):\n Frame.__init__(self, parent)\n if text_link is None:\n text_link = StringVar(value=\"Running\")\n self._text = text_link\n self._label = Label(self, bd=1, relief=SUNKEN, anchor=W, textvariable=self._text)\n self._label.pack(fill=X)","def __init__(self, label=None):\n super().__init__(\"h\", 1, [], label=label)","def customWidgets(self):\n \n # to be called a property value needs to change\n def onValueChanged(widget, prop):\n # set the corresponding property of the videobalance element\n self.balance.set_property(prop, widget.get_value())\n\n # videobalance has several properties, with the following range\n # and defaults\n properties = [(\"contrast\", 0, 2, 1),\n (\"brightness\", -1, 1, 0), \n (\"hue\", -1, 1, 0),\n (\"saturation\", 0, 2, 1)]\n \n # create a place to hold our controls\n controls = gtk.VBox()\n labels = gtk.VBox()\n # for every propety, create a control and set its attributes\n for prop, lower, upper, default in properties:\n widget = gtk.HScale(); label = gtk.Label(prop)\n\n # set appropriate atributes\n widget.set_update_policy(gtk.UPDATE_CONTINUOUS)\n widget.set_value(default)\n widget.set_draw_value(True)\n widget.set_range(lower, upper)\n\n # connect to our signal handler, specifying the property\n # to adjust\n widget.connect(\"value-changed\", onValueChanged, prop)\n \n # pack widget into box\n controls.pack_start(widget, True, True)\n labels.pack_start(label, True, False)\n\n layout = gtk.HBox()\n layout.pack_start(labels, False, False)\n layout.pack_end(controls, True, True)\n return layout","def BaseLabel(self, *args):\n return _XCAFDoc.XCAFDoc_ColorTool_BaseLabel(self, *args)","def __init__(self,name,value,*args,**kargs):\n self._plain = kargs.get('plain',False)\n self.input = QtGui.QLabel()\n maxw,maxh = maxSize()\n self.input.setMaximumSize(int(0.75*maxw),maxh)\n InputItem.__init__(self,name,*args,**kargs)\n self.setValue(value)\n self.layout().insertWidget(1,self.input)\n self.label.setWordWrap(True)\n self.setSize()","def __init__(self, text=\"\", widget=None):\n self._label_text = text\n self._widget = widget\n self._widget.on_change = self._update\n super().__init__(text=f\"{text} {widget.value}\")","def BaseLabel(self, *args):\n return _XCAFDoc.XCAFDoc_LayerTool_BaseLabel(self, *args)","def __init__(self, font, color, text=\"\", top=0, left=0, bottom=None, right=None):\n self.text = text\n self.font = font\n self.color = color\n self.top = top\n self.left = left\n self.bottom = bottom\n self.right = right\n self.renderLabel()","def Label(self) -> str:","def DrawControlLabel(self, dc, wnd, item, rect):\r\n\r\n label_size = GetLabelSize(dc, item.GetLabel(), item.GetOrientation() != AUI_TBTOOL_HORIZONTAL)\r\n text_height = label_size.GetHeight()\r\n text_width = label_size.GetWidth()\r\n\r\n dc.SetFont(self._font)\r\n\r\n if self._agwFlags & AUI_TB_TEXT:\r\n \r\n tx, text_height = dc.GetTextExtent(\"ABCDHgj\") \r\n\r\n text_width, ty = dc.GetTextExtent(item.GetLabel())\r\n\r\n # don't draw the label if it is wider than the item width\r\n if text_width > rect.width:\r\n return\r\n\r\n # set the label's text colour\r\n dc.SetTextForeground(wx.BLACK)\r\n\r\n text_x = rect.x + (rect.width/2) - (text_width/2) + 1\r\n text_y = rect.y + rect.height - text_height - 1\r\n\r\n if self._agwFlags & AUI_TB_TEXT and item.GetLabel() != \"\": \r\n dc.DrawText(item.GetLabel(), text_x, text_y)","def label_grid(self):\n\n self.pc_label.grid(row=0, sticky=\"nw\", pady=2, padx=3)\n self.sc_label.grid(row=1, sticky=\"nw\", pady=2, padx=3)\n self.avg_t_label.grid(row=2, sticky=\"nw\", pady=2, padx=3)\n self.nwt_label.grid(row=4, sticky=\"nw\", pady=2, padx=3)\n self.nw_ip_label.grid(row=5, sticky=\"nw\", pady=2, padx=3)\n self.nw_gw_label.grid(row=6, sticky=\"nw\", pady=2, padx=3)\n self.nw_sm_label.grid(row=7, sticky=\"nw\", pady=2, padx=3)\n self.nw_mca_label.grid(row=8, sticky=\"nw\", pady=2, padx=3)","def BaseLabel(self, *args):\n return _XCAFDoc.XCAFDoc_MaterialTool_BaseLabel(self, *args)","def createLabel(self, text, font):\n label = QLabel(self)\n label.setFont(font)\n label.setText(text)\n label.setFixedHeight(40)\n label.setAlignment(Qt.AlignCenter)\n label.setStyleSheet('background-color: rgba(0,0,0,0);color: white; border: 0px solid black; ')\n return label","def __init__(self, parent) -> None:\n super().__init__(parent)\n\n self.setText(\"\")\n self.setPixmap(QPixmap(self.image))\n\n self.setScaledContents(False)\n self.setObjectName(\"Label_Bild\")\n self.setAlignment(QtCore.Qt.AlignCenter)","def __init__(self, label):\n self.label = label","def __init__(self,\n rect: Rect,\n text: str = \"\",\n style: Style = None,\n parent: Component = None) -> None:\n Component.__init__(self, rect, style=style, parent=parent)\n self.set_text(text)","def __init__(self, parent, *args, **kwargs):\n tk.LabelFrame.__init__(self, parent, *args, **kwargs)\n self.canvas = MainCanvas(self, bg=\"orange\")\n self.canvas.pack(side='top', fill='both', expand=True)","def create_widgets(self):\n #create description label\n Label(self,\n text = \"Patient Info:\"\n ).grid(row = 0, column = 0, sticky = W)","def __init__(self, x, y, align_bottom_left, text, font, color):\n super(TextBox, self).__init__(x, y)\n\n self.align_bottom_left = align_bottom_left\n self.__text = text\n self.font = font\n self.color = color\n self.original_x = x\n self.original_y = y\n self.image = None\n self.image_rect = None\n\n self.set_text(text)","def myEntLabel(name='myText', \r\n\t\t\t numb=None,\r\n\t\t\t call=None, data=['dati'],\r\n\t\t\t nLab='Label', cLab=\"#333\",\r\n\t\t\t tLab='h', aLab=[False, False, 1]):\r\n#myEntry\r\n\t# entr, call\r\n\tentr,call = myEntry(name=name,\r\n\t\t\t \t\t\tnumb=numb, \r\n\t\t\t \t\t\tcall=call, data=data)\r\n#myLabel\r\n\tif cLab == None:\r\n\t\tcLab=Gdk.color_parse('blue')\r\n\t# labe\r\n\tlabe = myLabel(name=nLab, \r\n\t\t\t\t\tleng=len(nLab)+1, prea=' ', post=' ', \r\n\t\t\t\t\tfont='Courier 10', \r\n\t\t\t\t\tcolo=cLab)\r\n#xBox \r\n\txBox = myBox(tLab)\r\n\t#child, expand=True, fill=True, padding=0\r\n\txBox.pack_start(labe, *aLab)\r\n\txBox.pack_start(entr, *aLab)\t\r\n# <- \r\n\treturn xBox, [entr, call, labe]","def __init__(self, text='', **kwargs):\n Control.__init__(self, text=text, **kwargs)","def create_controls(self):\n\n self.button_frame = tk.LabelFrame(self, text=\"Controls\", padx=5, pady=5)\n self.button_frame.grid(row=0, column=1, padx=5, pady=5, sticky=\"n\")\n self.load_data = tk.Button(\n self.button_frame, text=\"Load Data\", command=self.update_stats\n )\n self.load_data.grid(row=0)\n\n self.print_data = tk.Button(\n self.button_frame, text=\"Print Data\", command=self.print_raw_data,\n )\n self.print_data.grid(row=1)\n\n self.quit = tk.Button(\n self.button_frame, text=\"Quit\", fg=\"red\", command=self.master.destroy\n )\n self.quit.grid(row=2)","def create_ui(self):\n main_sizer = wx.BoxSizer(wx.VERTICAL)\n\n self.text_ctrl = wx.TextCtrl(self, style=wx.TE_MULTILINE)\n self.text_ctrl.Bind(wx.EVT_TEXT, self.on_text)\n main_sizer.Add(self.text_ctrl, 1, wx.ALL | wx.EXPAND, 5)\n\n self.SetSizer(main_sizer)","def BaseLabel(self, *args):\n return _XCAFDoc.XCAFDoc_DimTolTool_BaseLabel(self, *args)","def __init__( self ):\n \n Frame.__init__( self ) # initializes Frame instance\n \n # frame fills all available space\n self.pack( expand = YES, fill = BOTH )\n self.master.title( \"Labels\" )\n \n self.Label1 = Label( self, text = \"Label with text\" )\n \n # resize frame to accommodate Label\n self.Label1.pack()\n \n self.Label2 = Label( self,\n text = \"Labels with text and a bitmap\" )\n \n # insert Label against left side of frame\n self.Label2.pack( side = LEFT )\n \n # using default bitmap image as label\n self.Label3 = Label( self, bitmap = \"warning\" )\n self.Label3.pack( side = LEFT )","def __init__(self, dat, frame, box_size, centre,\n label=False, **kwargs):\n\n super().__init__(dat, frame, box_size, centre) # initialise superclass\n\n self.label = label # write labels\n\n self.draw()","def __init__(self, name, label, class_to_use, size, state, left_window_label=None, left_window_value=None):\n self.name = name\n self.label = label\n self.class_to_use = class_to_use\n self.size = size\n self.state = state\n self.left_window_label = left_window_label\n self.left_window_value = left_window_value","def __init__(self, ax, labels, bw=None, bh=None, colors=None, actives=None):\n AxesWidget.__init__(self, ax)\n\n labels = copy.deepcopy(labels)\n\n labels.append(\"select all\")\n labels.append(\"unselect all\")\n print(\"colors\", colors)\n colors = colors+[\"#000000\"]*2\n\n ax.set_xticks([])\n ax.set_yticks([])\n ax.set_navigate(False)\n\n if actives is None:\n actives = [False] * len(labels)\n actives = actives+[False]*2\n\n if len(labels) > 1:\n dy = 1. / (len(labels) + 1)\n ys = np.linspace(1 - dy, dy, len(labels))\n else:\n dy = 0.25\n ys = [0.5]\n\n axcolor = ax.get_facecolor()\n\n self.labels = []\n self.lines = []\n self.rectangles = []\n\n lineparams = {'color': 'k', 'linewidth': 1.2,\n 'transform': ax.transAxes, 'solid_capstyle': 'butt'}\n\n for index, (y, label, active) in enumerate(zip(ys, labels, actives)):\n if colors is None:\n t = ax.text(0.25, y, label, transform=ax.transAxes,\n horizontalalignment='left',\n verticalalignment='center')\n else:\n t = ax.text(0.25, y, label, transform=ax.transAxes,\n horizontalalignment='left',\n verticalalignment='center', color=colors[index])\n\n if bw is None:\n w = dy / 2\n else:\n w = bw\n\n if bh is None:\n h = dy / 2\n else:\n h = bh\n\n x, y = 0.05, y - h / 2\n\n p = Rectangle(xy=(x, y), width=w, height=h, edgecolor='black',\n facecolor=axcolor, transform=ax.transAxes)\n\n l1 = Line2D([x, x + w], [y + h, y], **lineparams)\n l2 = Line2D([x, x + w], [y, y + h], **lineparams)\n\n l1.set_visible(active)\n l2.set_visible(active)\n self.labels.append(t)\n self.rectangles.append(p)\n self.lines.append((l1, l2))\n ax.add_patch(p)\n ax.add_line(l1)\n ax.add_line(l2)\n\n self.connect_event('button_press_event', self._clicked)\n\n self.cnt = 0\n self.observers = {}","def label(self):\r\n raise NotImplementedError","def __display_controls(self):\n self.__fill_data_variables()\n self.__fill_smoothing_method()\n self.__fill_smooth_factor()\n\n left_box = VBox([self.data_vars])\n center_box = VBox([self.smoothing_methods])\n right_box = VBox([self.smooth_factor])\n #_HBox = HBox([left_box, center_box, right_box],\n _HBox = HBox([left_box, center_box, right_box],\n layout={'height': '80px',\n 'width' : '99%'}\n )\n display(_HBox)","def add_variable(self, side=\"left\", **kwargs):\n widget = ttk.Label(self, **kwargs)\n widget.pack(side=side)\n\n return widget","def __init__(self):\n\t\tself.root = Tk()\n\t\tself.root.geometry(\"1600x1600\")\n\t\ttitle_arabic=u'\\uFEB3\\uFEE8\\uFECC\\uFEEE\\uFEA9'\n\t\tself.root.title(title_arabic)\n\t\tself.root.geometry(\"1600x900\")\t\t\n\t\tself.root.resizable(width=False, height=False)\n\t\tmylabel=Label(text=\"Loading . . .\",fg=\"#ff1a75\",bg=\"black\",font=(\"Times New Roman\",18))\n\t\tmylabel.place(x=665,y=300)\n\t\tmylabel3=Text()\n\t\tmylabel3.place(x=550,y=395)\n\t\tmylabel3['width']='50'\n\t\tmylabel3['height']=0.6\n\t\tmylabel3.configure(state='disabled')\n\t\tself.root.configure(background='black')\n\t\tself.load()\n\t\tself.root.mainloop()","def init ( self, parent ):\n # Create a panel to hold all of the buttons:\n self.control = panel = wx.Panel( parent, -1 )\n sizer = wx.BoxSizer( wx.VERTICAL )\n \n # Add the standard font control:\n font = self._font = wx.TextCtrl( panel, -1, self.str_value )\n wx.EVT_KILL_FOCUS( font, self.update_object )\n wx.EVT_TEXT_ENTER( panel, font.GetId(), self.update_object )\n sizer.Add( font, 0, wx.EXPAND | wx.BOTTOM, 3 )\n \n # Add all of the font choice controls:\n sizer2 = wx.BoxSizer( wx.HORIZONTAL )\n facenames = all_facenames()\n control = self._facename = wx.Choice( panel, -1, wx.Point( 0, 0 ), \n wx.Size( choice_width( facenames ), 20 ), \n facenames )\n \n sizer2.Add( control, 2, wx.EXPAND )\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts )\n \n control = self._point_size = wx.Choice( panel, -1, \n wx.Point( 0, 0 ), wx.Size( 30, 20 ), \n PointSizes )\n sizer2.Add( control, 1, wx.EXPAND | wx.RIGHT, 3 )\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts ) \n \n sizer.Add( sizer2, 0, wx.EXPAND )\n \n # Set-up the layout:\n panel.SetAutoLayout( True )\n panel.SetSizer( sizer )\n sizer.Fit( panel )","def create_widget(self):\n pass","def create_labels(self):\n for name in self.names:\n temp_button = Label(text=name)\n self.root.ids.label_box.add_widget(temp_button)","def initWidgets(self):\n self.loctext.setText(\"{0:g}\".format(self.loc))\n self.scaletext.setText(\"{0:g}\".format(self.scale))","def __init__(self, title=None, dimensions=None, style=None, spacing=None, margin=None, layout=None):\n super(customGroupBox, self).__init__()\n\n if layout:\n self.setLayout(layout)\n else:\n self.setLayout(QtGui.QVBoxLayout())\n\n if title:\n self.setTitle(title)\n\n if dimensions:\n self.setFixedSize(dimensions[0], dimensions[1])\n\n if style:\n self.setStyle(QtGui.QStyleFactory.create(style))\n\n if margin:\n self.layout().setContentsMargins(margin, margin, margin, margin)\n\n if spacing:\n self.layout().setSpacing(spacing)","def __init__(self, parent=None):\n QLabel.__init__(self, parent)\n self.start_animation(self.SLOW_DURATION)","def text(self) -> None:\n label_space = tk.Label(self)\n label_space.grid(row=0)\n label_book_number = tk.Label(self, text=f'Номер книги:')\n label_book_number.grid(row=1, column=0, ipady=5)\n label_title = tk.Label(self, text='Название книги:')\n label_title.grid(row=2, column=0, padx=5)\n label_author = tk.Label(self, text='Автор:')\n label_author.grid(row=3, column=0, pady=5)\n label_genre = tk.Label(self, text='Жанр:')\n label_genre.grid(row=4, column=0)","def toControls(self,widget):","def renderLabel(self):\n self.render = self.font.render(self.text, True, self.color)\n self.rect = self.render.get_rect()","def _initLayout(self):\n\t\tpanel = wx.Panel(self)\n\n\t\t# Create a font object\n\t\tfont = wx.SystemSettings.GetFont(wx.SYS_SYSTEM_FONT)\n\t\tfont.SetPointSize(9)\n\n\t\t# Vertical sizer will contain multiple horizontal sizers as rows\n\t\tvbox = wx.BoxSizer(wx.VERTICAL)\n\n\t\t# First Row: The text we need to categorize\n\t\thbox1 = wx.BoxSizer(wx.HORIZONTAL)\n\t\tst1 = wx.StaticText(panel, label='Text')\n\t\tst1.SetFont(font)\n\t\thbox1.Add(st1, flag=wx.RIGHT, border=8)\n\t\ttc = wx.TextCtrl(panel)\n\t\tself._textControl = tc\n\t\thbox1.Add(tc, proportion=1)\n\t\tvbox.Add(hbox1, flag=wx.EXPAND|wx.LEFT|wx.RIGHT|wx.TOP, border=10)\n\t\t\n\t\t# The existing class assignment\n\t\thboxExisting = wx.BoxSizer(wx.HORIZONTAL)\n\t\tlabel = wx.StaticText(panel, label='Current')\n\t\tlabel.SetFont(font)\n\t\thboxExisting.Add(label, flag=wx.RIGHT, border=8)\n\t\t\n\t\tlabel = wx.StaticText(panel, label=\"(unassigned)\")\n\t\tself._existingClass = \"(unassigned)\"\n\t\tself._existingClassLabel = label\n\t\tlabel.SetFont(font)\n\t\thboxExisting.Add(label, flag=wx.RIGHT, border=8)\n\t\tvbox.Add(hboxExisting, flag=wx.EXPAND|wx.LEFT|wx.RIGHT|wx.TOP, border=10)\n\t\t\n\t\t# Button to keep the current class assignment\n\t\tbutton = wx.Button(panel, label=\"KEEP\", name=\"*KEEP\")\n\t\thboxExisting.Add(button, flag=wx.RIGHT)\n\n\t\t# Button to skip this record, i.e., move to next record without writing this one out\n\t\tbutton = wx.Button(panel, label=\"DELETE\", name=\"*KILL\")\n\t\thboxExisting.Add(button, flag=wx.RIGHT)\n\n\t\tvbox.Add((-1, 10))\n\n\t\t# Buttons for classes that can be assigned to the text\n\t\thbox2 = wx.BoxSizer(wx.HORIZONTAL)\n\t\tst2 = wx.StaticText(panel, label='Reassign to...')\n\t\tst2.SetFont(font)\n\t\thbox2.Add(st2)\n\t\tvbox.Add(hbox2, flag=wx.LEFT | wx.TOP, border=10)\n\n\t\tvbox.Add((-1, 10))\n\n\t\t# Grid of buttons, one for each class label\n\t\thbox3 = wx.GridSizer(8,5,50)\n\t\n\t\tfor label in sorted(labels.LABELS):\n\t\t\tbutton = MyButton(panel, label=label, size=(70, 30), name=label)\n\t\t\thbox3.Add(button)\n\n\t\tvbox.Add(hbox3, flag=wx.LEFT|wx.RIGHT|wx.EXPAND, \n\t\t\tborder=10)\n\t\t\n\t\tpanel.SetSizer(vbox)","def __init__(self):\n super(GraphVisualizerCase, self).__init__()\n\n self._v_layout = QVBoxLayout()\n self.setLayout(self._v_layout)\n\n top_label_layout = QHBoxLayout()\n self._v_layout.addLayout(top_label_layout)\n\n self._top_left_label = MathTextLabel()\n top_label_layout.addWidget(self._top_left_label)\n self._top_right_label = MathTextLabel()\n top_label_layout.addWidget(self._top_right_label)\n\n self._center_widget = QFrame()\n self._center_text_label = QLabel()\n self._center_text_label.setAlignment(Qt.AlignCenter)\n layout = QHBoxLayout()\n layout.addWidget(self._center_text_label)\n self._center_widget.setLayout(layout)\n\n self._v_layout.addWidget(self._center_widget)\n\n bot_label_layout = QHBoxLayout()\n self._v_layout.addLayout(bot_label_layout)\n\n self._bot_left_label = MathTextLabel()\n bot_label_layout.addWidget(self._bot_left_label)\n self._bot_right_label = MathTextLabel()\n bot_label_layout.addWidget(self._bot_right_label)","def _create_label(self, x, y, text, width=50, **config):\n\n self.main_canvas.create_text(x, y, text='%6s' % text, width=width, **config)","def _setup_ui(self):\n from functools import partial\n\n self.setStyleSheet(\n \"\"\"\n QLabel[labelField=\"true\"] {\n font-weight: bold;\n }\n \"\"\"\n )\n\n # The main layout\n self.main_layout = QtWidgets.QVBoxLayout(self)\n self.main_layout.setContentsMargins(0, 0, 0, 0)\n\n # the form layout\n self.form_layout = QtWidgets.QFormLayout()\n self.form_layout.setLabelAlignment(\n QtCore.Qt.AlignRight | QtCore.Qt.AlignTrailing | QtCore.Qt.AlignVCenter\n )\n\n # store roles\n label_role = QtWidgets.QFormLayout.LabelRole\n field_role = QtWidgets.QFormLayout.FieldRole\n\n self.main_layout.addLayout(self.form_layout)\n\n i = -1\n\n # Reviewer\n i += 1\n reviewer_name_label = QtWidgets.QLabel(self)\n reviewer_name_label.setText(\"Reviewer\")\n self.form_layout.setWidget(i, label_role, reviewer_name_label)\n\n self.reviewer_name_widget = QtWidgets.QLabel(self)\n self.form_layout.setWidget(i, field_role, self.reviewer_name_widget)\n\n # Task Name field\n i += 1\n task_name_label = QtWidgets.QLabel(self)\n task_name_label.setText(\"Task\")\n self.form_layout.setWidget(i, label_role, task_name_label)\n\n self.task_name_widget = QtWidgets.QLabel(self)\n self.form_layout.setWidget(i, field_role, self.task_name_widget)\n\n # # Version Info field\n # from anima.ui.widgets.version import VersionDetailsWidget\n # self.latest_version_widget = VersionDetailsWidget(parent=self)\n # self.main_layout.insertWidget(0, self.latest_version_widget)\n\n # Review Type Field\n i += 1\n review_type_label = QtWidgets.QLabel(self)\n review_type_label.setText(\"Review Type\")\n self.form_layout.setWidget(i, label_role, review_type_label)\n\n self.review_type_widget = ReviewTypeWidget(self)\n self.review_type_widget.currentIndexChanged.connect(\n partial(self.review_type_changed_callback)\n )\n\n self.form_layout.setWidget(i, field_role, self.review_type_widget)\n\n # Timing Field\n i += 1\n effort_label = QtWidgets.QLabel(self)\n effort_label.setText(\"Timing\")\n self.form_layout.setWidget(i, label_role, effort_label)\n\n effort_layout = QtWidgets.QHBoxLayout()\n self.form_layout.setLayout(i, field_role, effort_layout)\n\n from anima.ui.widgets.timing import ScheduleTimingWidget\n from anima import defaults\n\n self.timing_widget = ScheduleTimingWidget(\n self, timing_resolution=defaults.timing_resolution\n )\n self.timing_widget.setEnabled(False)\n # set the default to 1 hour\n self.timing_widget.set_schedule_info(timing=1, unit=\"h\")\n effort_layout.addWidget(self.timing_widget)\n\n # Description Field\n i += 1\n description_label = QtWidgets.QLabel(self)\n description_label.setText(\"Description\")\n self.form_layout.setWidget(i, label_role, description_label)\n\n self.description_widget = QtWidgets.QTextEdit(self)\n self.form_layout.setWidget(i, field_role, self.description_widget)","def create_widgets(self):","def set_ui(self):\r\n\r\n self.canvas = tk.Canvas(self)\r\n self.canvas.pack()\r\n\r\n self.entry = ttk.Entry(self.canvas, justify=\"center\", font=(\"Calibri\", 12))\r\n\r\n self.grid = Grid(self.canvas)","def __init__(self, text, separator_line_thickness, label_type, dpi=(600, 600)):\n \n def get_text_on_label(text, label_type):\n \"\"\"Format how the text will look on the label.\n \n text - Text to be placed on the label.\n label_type - One of the types specifying the label layout.\n \"\"\"\n text_on_label = \"\".join([c for c in text if c in string.ascii_letters + string.digits])\n if label_type == 0:\n text_on_label = \"\"\n elif label_type == 1 or label_type == 2 or label_type == 4:\n text_on_label = \"\\n\".join([text_on_label[:4],\n text_on_label[4:8],\n text_on_label[8:12],\n text_on_label[12:]])\n elif label_type == 3:\n text_on_label = \"\\n\".join([\"-\".join([text_on_label[:4],\n text_on_label[4:8]]),\n \"-\".join([text_on_label[8:12],\n text_on_label[12:]])])\n else:\n text_on_label = \"\"\n return text_on_label\n \n self.label_image = None\n self.text_on_label = get_text_on_label(text, label_type)\n self.label_type = label_type\n self.separator_line_thickness = separator_line_thickness\n self.dpi = dpi","def __init__(\r\n self, text=\"Ok\", font=None, size=30, state: int = Button.INACTIVE\r\n ) -> None:\r\n super().__init__(state)\r\n self._font = Font(font, size)\r\n self._text = text\r\n self.rect = Rect(0, 0, 0, 0)\r\n self.redraw()","def InitUI(self, frame):\n\t\timport wx\n\t\tframe.SetSize((200,210))\n\t\t\n\t\tpanel = wx.Panel(frame)\n\t\t\n\t\tvbox = wx.BoxSizer(wx.VERTICAL)\n\t\t\n\t\thbox1 = wx.BoxSizer(wx.HORIZONTAL)\n\t\tstartButton = wx.Button(panel, label='Start Baselining', size=(170, 25))\n\t\thbox1.Add(startButton)\n\t\tvbox.Add(hbox1, flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTRE, border=5)\n\t\t\n\t\tvbox.Add((-1, 5))\n\t\t\n\t\thbox2 = wx.BoxSizer(wx.HORIZONTAL)\n\t\tstopButton = wx.Button(panel, label='Stop Baselining', size=(170, 25))\n\t\thbox2.Add(stopButton)\n\t\tvbox.Add(hbox2, flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTRE, border=5)\n\t\t\n\t\tvbox.Add((-1, 5))\n\t\t\n\t\thbox6 = wx.BoxSizer(wx.HORIZONTAL)\n\t\tresetButton = wx.Button(panel, label='Reset', size=(170, 25))\n\t\thbox6.Add(resetButton)\n\t\tvbox.Add(hbox6, flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTRE, border=5)\n\t\t\n\t\tvbox.Add((-1, 10))\n\t\t\n\t\thbox3 = wx.BoxSizer(wx.HORIZONTAL)\n\t\tself.onOffText = wx.StaticText(panel, label='Off')\n\t\thbox3.Add(self.onOffText)\n\t\tvbox.Add(hbox3, flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTRE, border=5)\n\t\t\n\t\tvbox.Add((-1, 20))\n\t\t\n\t\thbox4 = wx.BoxSizer(wx.HORIZONTAL)\n\t\tminText = wx.StaticText(panel, label='Min', style=wx.ALIGN_CENTRE)\n\t\tmaxText = wx.StaticText(panel, label='Max', style=wx.ALIGN_CENTRE)\n\t\thbox4.Add(minText, -1)\n\t\thbox4.Add(maxText, -1)\n\t\tvbox.Add(hbox4, flag=wx.EXPAND | wx.LEFT | wx.RIGHT, border=5)\n\t\t\n\t\tvbox.Add((-1, 5))\n\t\t\n\t\thbox5 = wx.BoxSizer(wx.HORIZONTAL)\n\t\tself.minBox = wx.TextCtrl(panel, style=wx.TE_READONLY)\n\t\tself.maxBox = wx.TextCtrl(panel, style=wx.TE_READONLY)\n\t\tself.minBox.SetBackgroundColour((210,210,210))\n\t\tself.maxBox.SetBackgroundColour((210,210,210))\n\t\thbox5.Add(self.minBox, -1, wx.EXPAND)\n\t\thbox5.Add(self.maxBox, -1, wx.EXPAND)\n\t\tvbox.Add(hbox5, flag=wx.LEFT | wx.RIGHT | wx.EXPAND, border=5)\n\t\t\n\t\tpanel.SetSizer(vbox)\n\t\t\n\t\t# Bind items\n\t\tframe.Bind(wx.EVT_BUTTON, self.OnStartPress, id=startButton.GetId())\n\t\tframe.Bind(wx.EVT_BUTTON, self.OnStopPress, id=stopButton.GetId())\n\t\tframe.Bind(wx.EVT_BUTTON, self.OnResetPress, id=resetButton.GetId())","def createLabels(self):\r\n # Grid layout to organize the widgets\r\n self.grid = QGridLayout()\r\n self.labelWidgets = {\r\n\r\n \"<h2>FECHA</h2>\": (0, 0),\r\n \"<h2>BILL AREA</h2>\": (0, 3, 1, 1),\r\n \"<h2>HORA</h2>\": (1, 0),\r\n \"<h2>NOMBRE</h2>\": (2, 0),\r\n \"<h2>CUMPLEAÑOS</h2>\": (3, 0),\r\n \"<h2>CELULAR</h2>\": (4, 0),\r\n \"<h2>DIRECCIÓN</h2>\": (5, 0),\r\n \"<h2>CIUDAD</h2>\": (6, 0),\r\n \"<h2>MÉTODO DE PAGO</h2>\": (7, 0),\r\n \"<h2>PEDIDO</h2>\": (8, 0, 1, 3),\r\n \"<h2>TOTAL POLLO</h2>\": (9, 0),\r\n \"<h2>TOTAL CARNE</h2>\": (10, 0),\r\n \"<h2>EMPANACHOS</h2>\": (11, 0),\r\n \"<h2>TOTAL PRODUCTOS</h2>\": (12, 0),\r\n \"<h2>VALOR ($)</h2>\": (13, 0)\r\n }\r\n for labelName, position in self.labelWidgets.items():\r\n if len(position) == 4:\r\n self.label = QLabel(labelName)\r\n self.label.setAlignment(Qt.AlignCenter)\r\n self.label.setStyleSheet(\"\"\"\r\n color: #cac03f; font-family: times;\r\n font-weight: bold;\r\n border: 5px inset #cac03f;\r\n font-size: 15px;\r\n \"\"\")\r\n self.grid.addWidget(\r\n self.label, position[0], position[1], position[2], position[3])\r\n\r\n else:\r\n self.label = QLabel(labelName)\r\n self.label.setStyleSheet(\"\"\"\r\n color: #A8DBC5;\r\n font-family: times;\r\n font-weight: bold;\"\"\")\r\n self.grid.addWidget(self.label, position[0], position[1])\r\n # CREATING THE SPECIAL BILL LABEL\r\n self.bill = QTextEdit()\r\n self.bill.setFixedWidth(320)\r\n self.bill.setStyleSheet(\"\"\"\r\n font-family: times;\r\n font-size: large;\r\n background-color : white;\r\n border: 4px solid #A8DBC5;\r\n font-size: 15px;\r\n \"\"\")\r\n self.grid.addWidget(self.bill, 1, 3, 12, 1)\r\n # setting the main layout in order to add the logo at the top\r\n # and the labels, line Edit widgets and buttons at the bottom\r\n self.mainLayout = QVBoxLayout()\r\n # Creating the Image Label for the Business logo\r\n self.labelImg = QLabel()\r\n self.labelImg.setAlignment(Qt.AlignCenter)\r\n self.pixmap = QPixmap('kafflogo.png')\r\n self.labelImg.setPixmap(self.pixmap)\r\n # Setting the vertical layout as the main Layout of the tab 1\r\n self.tab1.setLayout(self.mainLayout)\r\n self.mainLayout.addWidget(self.labelImg)\r\n # Adding the grid layout under the image\r\n self.mainLayout.addLayout(self.grid)","def add_label(self, text, location=(None,0)):\n label=QtWidgets.QLabel(self)\n label.setText(str(text))\n label.setAlignment(QtCore.Qt.AlignLeft)\n location=self._normalize_location(location)\n self.formLayout.addWidget(label,*location)\n return label","def set_controls(self):\n # Image control\n image = pyxbmct.Image(addonfolder+artsfolder+'/tvh.png')\n self.placeControl(image, 0, 0, rowspan=8, columnspan=16)\n\n\t\t# Label information\n image = pyxbmct.Image(addonfolder+artsfolder+'/users.png')\n self.placeControl(image, 8, 1, rowspan=1, columnspan=14)\n\t\t\n\t\t# Username input\n image = pyxbmct.Image(addonfolder+artsfolder+'/username.png')\n self.placeControl(image, 10, 1, rowspan=1, columnspan=3)\n self.username_input = pyxbmct.Edit('')\n self.placeControl(self.username_input, 10, 4, rowspan=1, columnspan=4)\n\n\t\t# Password input\n image = pyxbmct.Image(addonfolder+artsfolder+'/password.png')\n self.placeControl(image, 11, 1, rowspan=1, columnspan=3)\n self.password_input = pyxbmct.Edit('', isPassword=True)\n self.placeControl(self.password_input, 11, 4, rowspan=1, columnspan=4)\n\n\t\t# Next button\n self.next_button = pyxbmct.Button('Next')\n self.placeControl(self.next_button, 13, 14, rowspan=1, columnspan=1)\n # Connect close button\n self.connect(self.next_button, lambda: self.page())\n\t\t\n\t\t# Close button\n self.close_button = pyxbmct.Button('Exit')\n self.placeControl(self.close_button, 13, 15, rowspan=1, columnspan=1)\n self.connect(self.close_button, lambda: self.closepage())","def add_text_label(self, name, value=None, label=None, location=(None,0)):\n widget=widget_label.LVTextLabel(self,value=value)\n widget.setObjectName(_fromUtf8(self.name+\"_\"+name))\n return self.add_simple_widget(name,widget,label=label,add_indicator=False,location=location)","def __init__(self, transform, size, label, loc,\n pad=0.1, borderpad=0.1, sep=2, prop=None, frameon=True):\n self.size_bar = AuxTransformBox(transform)\n self.size_bar.add_artist(Rectangle((0, 0), size, 0, fc='none', color='white', lw=3))\n\n self.txt_label = TextArea(label, dict(color='white', size='x-large', weight='normal'),\n minimumdescent=False)\n\n self._box = VPacker(children=[self.size_bar, self.txt_label],\n align=\"center\",\n pad=0, sep=sep)\n\n AnchoredOffsetbox.__init__(self, loc, pad=pad, borderpad=borderpad,\n child=self._box,\n prop=prop,\n frameon=frameon)","def fromControls(self,widget):","def getWidget(self):","def create_widgets( self ):","def create_gen_labels(master: Widget) -> None:\r\n\r\n gen_label = Label(master, text='Gen:', font=self.FONT_NORMAL, bg=self.MAIN_BG)\r\n gen_label.pack(side=LEFT)\r\n self.gen_number = Label(master, text=0, font=self.FONT_NORMAL, bg=self.MAIN_BG)\r\n self.gen_number.pack(side=LEFT)","def create_widgets(self):\n # only ever shown card in player's hand, so create widgets when dealt\n self.name_label = tk.Label(self, text=self.name)\n self.ability_label = tk.Label(self, text=self.ability)\n self.food_label = tk.Label(self, text=\"Food: \" + str(self.food))\n self.use_button = TraitUseButton(self, text=\"USE\", command=self.use)","def makeInstructionLabel(self, textInstruction):\n Label(self.sideFrame, text=textInstruction,\n font=self.sideFont, anchor='w').pack(fill=X, padx=10)","def _create_signal_label(self, master, row, column, signame, minimum, maximum, unit):\n lbl_name = Label(master, text=signame, bg=COLUMN_COLOR_LIST[column], font=(\"Helvetica\", 12))\n lbl_min = Label(master, text=minimum, bg=COLUMN_COLOR_LIST[column], font=(\"Helvetica\", 9))\n lbl_max = Label(master, text=maximum, bg=COLUMN_COLOR_LIST[column + 1], font=(\"Helvetica\", 9))\n lbl_unit = Label(master, text=unit, bg=COLUMN_COLOR_LIST[column + 2], font=(\"Helvetica\", 9))\n lbl_name.grid(row=row, column=column, columnspan=3, sticky=W+E)\n lbl_min.grid(row=row+1, column=column, sticky=W+E)\n lbl_max.grid(row=row+1, column=column+1, sticky=W+E)\n lbl_unit.grid(row=row+1, column=column+2, sticky=W+E)","def __repr__(self):\n self.makeLabel() # update the label in case it's changed.\n return self.label","def set_controls(self):\n # Image control\n image = pyxbmct.Image(addonfolder+artsfolder+'/osc.png')\n self.placeControl(image, 0, 0, rowspan=7, columnspan=16)\n\n\t\t# Label information\n image = pyxbmct.Image(addonfolder+artsfolder+'/readers.png')\n self.placeControl(image, 7, 1, rowspan=1, columnspan=14)\n\t\t\n\t\t# Hostname input\n image = pyxbmct.Image(addonfolder+artsfolder+'/hostname.png')\n self.placeControl(image, 9, 0, rowspan=1, columnspan=4)\n self.hostname_input = pyxbmct.Edit('')\n self.placeControl(self.hostname_input, 9, 4, rowspan=1, columnspan=5)\n\n\t\t# Port input\n image = pyxbmct.Image(addonfolder+artsfolder+'/port.png')\n self.placeControl(image, 12, 1, rowspan=1, columnspan=3)\n self.port_input = pyxbmct.Edit('')\n self.placeControl(self.port_input, 12, 4, rowspan=1, columnspan=2)\n\n\t\t# Username input\n image = pyxbmct.Image(addonfolder+artsfolder+'/username.png')\n self.placeControl(image, 10, 1, rowspan=1, columnspan=3)\n self.username_input = pyxbmct.Edit('')\n self.placeControl(self.username_input, 10, 4, rowspan=1, columnspan=4)\n\t\t\n\t\t# Password input\n image = pyxbmct.Image(addonfolder+artsfolder+'/password.png')\n self.placeControl(image, 11, 1, rowspan=1, columnspan=3)\n self.password_input = pyxbmct.Edit('', isPassword=True)\n self.placeControl(self.password_input, 11, 4, rowspan=1, columnspan=4)\n\n\t\t# Close button\n self.close_button = pyxbmct.Button('Exit')\n self.placeControl(self.close_button, 13, 15, rowspan=1, columnspan=1)\n self.connect(self.close_button, lambda: self.page(Readers))","def __init__(self, name, header, desc=None, prop=None, style=None, attr=None,\n disabled=False, css_cls=None):\n Widget.__init__(self, name, desc=desc, prop=prop, style=style, attr=attr,\n css_cls=css_cls)\n self._header = header\n self._disabled = disabled","def pack(self, label, *widgets, **kwargs):\n col = 0\n border = kwargs.get(\"border\", 10)\n font = self.GetFont()\n if \"size\" in kwargs:\n font.SetPointSize(kwargs[\"size\"])\n if kwargs.get(\"bold\", False):\n font.SetWeight(wx.BOLD)\n\n if label != \"\":\n text = wx.StaticText(self, label=label)\n text.SetFont(font)\n if \"textcol\" in kwargs:\n text.SetForegroundColour(kwargs[\"textcol\"])\n if not widgets:\n span = (1, 2)\n else:\n span = (1, 1)\n self.sizer.Add(text, pos=(self.row, col), border=border, flag=wx.ALIGN_CENTRE_VERTICAL | wx.LEFT, span=span)\n col += 1\n else:\n text = None\n\n for w in widgets:\n span = (1, 1)\n w.label = text\n if hasattr(w, \"span\"):\n span = (1, w.span)\n w.SetFont(font)\n w.Enable(col == 0 or kwargs.get(\"enable\", True))\n self.sizer.Add(w, pos=(self.row, col), border=border, flag=wx.ALIGN_CENTRE_VERTICAL | wx.EXPAND | wx.LEFT, span=span)\n col += span[1]\n self.row += 1","def setLabel( self, cCtrlName, cLabel ):\n self.setControlModelProperty( cCtrlName, \"Label\", cLabel )","def create_widgets(self):\n self.instruction = Label(self, text=\"Enter the password \")\n self.instruction.grid(row=0, column=0, columnspan=2, sticky=W)\n\n self.password_entry = Entry(self)\n self.password_entry.grid(row=0, column=1, sticky=W)\n\n self.submit_button = Button(self, text=\"Submit\",\n command=self.reveal, width=10)\n self.submit_button.grid(row=2, column=0, sticky=W)\n\n self.exit_button = Button(self, text=\"Exit\",\n command=self.quit, width=10)\n self.exit_button.grid(row=2, column=1, sticky=W)\n\n #self.close_button = Button(self, text = \"Close\", command = self.quit)\n #self.close_button.grid(row = 2, column = 0, sticky = E)\n\n self.text = Text(self, width=35, height=5, wrap=WORD)\n self.text.grid(row=3, column=0, columnspan=2, sticky=W)","def create_title(self):\n label_title = Label(self.frame, text=\"Brick Breaker\", font=(\"Arial\", 40), bg='light blue',\n fg='white')\n label_title.pack()","def __init__(self, label: str):\n pass","def create_widgets(self):\n for name in self.names:\n new_label = Label(text=name, id=name, font_size=50)\n self.root.ids.name_entries.add_widget(new_label)","def init_infobox(self):\n infobox = tk.Label(self, text=\"\", justify=\"left\")\n infobox.grid(row=0, column=1, sticky=\"n\")\n self.infobox = infobox","def create_base(self):\n if self.debug:\n print(\"Creating base\")\n self.console_panel = ConsolePanel(self.root)\n self.side_panel = SidePanel(self.root, self.populate_main_panel)\n self.side_panel.set_separator(\"word_word\")\n self.main_panel = MainPanel(self.root, action=\"word_word\")","def create_labels(self):\n for name in self.names:\n new_label = Label(text=name)\n self.root.ids.names_box.add_widget(new_label)","def create_widget(parent, control_name, control_value, trait,\n label_class=None, user_data=None):\n # Create the list widget: a frame\n frame = QtGui.QFrame(parent=parent)\n frame.setFrameShape(QtGui.QFrame.StyledPanel)\n\n # Create tools to interact with the list widget: expand or collapse -\n # add a list item - remove a list item\n tool_widget = QtGui.QWidget(parent)\n layout = QtGui.QHBoxLayout()\n layout.addStretch(1)\n layout.setContentsMargins(0, 0, 0, 0)\n layout.setSpacing(2)\n tool_widget.setLayout(layout)\n # Create the tool buttons\n resize_button = QtGui.QToolButton()\n layout.addWidget(resize_button)\n # Set the tool icons\n icon = QtGui.QIcon()\n icon.addPixmap(QtGui.QPixmap(\n _fromUtf8(\":/soma_widgets_icons/nav_down\")),\n QtGui.QIcon.Normal, QtGui.QIcon.Off)\n resize_button.setIcon(icon)\n resize_button.setFixedSize(30, 22)\n frame.user_data = user_data\n\n editable_labels = False\n handler = getattr(trait, 'handler', trait)\n if handler.inner_traits():\n editable_labels = True\n frame.inner_trait = trait.handler.inner_traits()[0]\n\n add_button = QtGui.QToolButton()\n delete_button = QtGui.QToolButton()\n layout.addWidget(add_button)\n # Set the tool icons\n icon = QtGui.QIcon()\n icon.addPixmap(\n QtGui.QPixmap(_fromUtf8(\":/soma_widgets_icons/add\")),\n QtGui.QIcon.Normal, QtGui.QIcon.Off)\n add_button.setIcon(icon)\n add_button.setFixedSize(30, 22)\n delete_button.setFixedSize(30, 22)\n # Add list item callback\n add_hook = partial(\n ControllerControlWidget.add_item, weak_proxy(parent),\n control_name, weak_proxy(frame))\n add_button.clicked.connect(add_hook)\n\n # Create the associated controller widget\n controller_widget = ControllerWidget(control_value, parent=frame,\n live=True,\n editable_labels=editable_labels,\n user_data=user_data)\n\n # Store some parameters in the list widget\n frame.trait = trait\n frame.controller = control_value\n frame.controller_widget = controller_widget\n frame.connected = False\n\n # Add the list controller widget to the list widget\n frame.setLayout(controller_widget.layout())\n\n # Set some callback on the controller control tools\n # Resize callback\n resize_hook = partial(\n ControllerControlWidget.expand_or_collapse, weak_proxy(frame),\n weak_proxy(resize_button))\n resize_button.clicked.connect(resize_hook)\n\n if getattr(trait, 'expanded') is False:\n ControllerControlWidget.set_expanded(frame, resize_button, False)\n\n # Create the label associated with the controller widget\n control_label = trait.label\n if control_label is None:\n control_label = control_name\n if label_class is None:\n label_class = QtGui.QLabel\n if control_label is not None:\n label = label_class(control_label, parent)\n else:\n label = None\n\n return (frame, (label, tool_widget))","def __init__(self):\n\n self.boxes = [\n urwid.AttrMap(urwid.Text('QMap'), 'menu header'),\n urwid.Divider('=')\n ]\n\n self.widget = urwid.Padding(None, align='center')\n self.update()\n\n BaseTimedWidgetWrap.__init__(self, self.widget)","def __init__(self,name,*args,**kargs):\n QtGui.QWidget.__init__(self,*args)\n layout = QtGui.QHBoxLayout()\n #layout.setSpacing(0)\n layout.setMargin(0)\n self.setLayout(layout)\n self.key = str(name)\n if 'text' in kargs:\n text = kargs['text']\n else:\n text = self.key\n if text:\n self.label = QtGui.QLabel()\n text = standardIcon(text)\n if isinstance(text,QtGui.QPixmap):\n self.label.setPixmap(text)\n else:\n self.label.setText(text)\n if 'b' in kargs.get('stretch',''):\n layout.addStretch()\n layout.addWidget(self.label)\n if 'a' in kargs.get('stretch',''):\n print 'test'\n layout.addStretch()\n\n if 'data' in kargs:\n self.data = kargs['data']\n\n if 'enabled' in kargs:\n self.setEnabled(kargs['enabled'])\n\n if 'readonly' in kargs:\n try:\n self.input.setReadOnly(kargs['readonly'])\n except:\n print \"Can not set readonly: %s,%s\" % (name,kargs)\n\n if 'width' in kargs:\n try:\n print 'SETTING WIDTH',self.input\n self.input.setMinimumWidth(kargs['width'])\n except:\n pass\n \n if 'tooltip' in kargs:\n self.setToolTip(kargs['tooltip'])\n ## if hasattr(self,'label'):\n ## self.label.setToolTip(kargs['tooltip'])\n ## try:\n ## self.input.setToolTip(kargs['tooltip'])\n ## except:\n ## pass\n\n if 'buttons' in kargs and kargs['buttons']:\n #print kargs\n self.buttons = dialogButtons(self,kargs['buttons'])\n layout.addItem(self.buttons)","def create_widgets(self):\n Label(self, text=\"Choose your favorite movie type\").grid(row=0, column=0, sticky=W)\n\n # instructions\n Label(self, text=\"Select all that apply:\").grid(row=1, column=0, sticky=W)\n\n # comedy check button\n self.comedy = BooleanVar()\n Checkbutton(self, text=\"Comedy\", variable=self.comedy, command=self.update_text).grid(row=2, column=0, sticky=W)\n\n # drama check button\n self.drama = BooleanVar()\n Checkbutton(self, text=\"Drama\", variable=self.drama, command=self.update_text).grid(row=3, column=0, sticky=W)\n\n # romance check button\n self.romance = BooleanVar()\n Checkbutton(self, text=\"Romance\", variable=self.romance, command=self.update_text).grid(row=4, column=0, sticky=W)\n\n self.result = Text(self, width=40, height=5, wrap=WORD) # wrap=WORD when dropping 1 line down the words will not cut in the middle, drop line prior or after word end\n self.result.grid(row=5, column=0, columnspan=3)","def __init__(self, gui, shapes, x, y, callback=None, label=None,\r\n label_pos='left', shortcut=None):\r\n if not (type(shapes) is list or type(shapes) is tuple):\r\n self.shapes = [shapes]\r\n else:\r\n self.shapes = shapes\r\n self.toggle = len(self.shapes) > 1 #this widget can toggle between two\r\n self.clicked = False\r\n self.callback = callback\r\n self.shortcut = shortcut\r\n self.label_pos = label_pos\r\n if label:\r\n self.labelobj = pi3d.String(font=gui.font, string=label, is_3d=False,\r\n camera=gui.camera, justify='L')\r\n self.labelobj.set_shader(gui.shader)\r\n else:\r\n self.labelobj = None\r\n self.relocate(x, y)\r\n if not (self in gui.widgets): #because TextBox re-runs Widget.__init__\r\n gui.widgets.append(self)\r\n self.visible = True","def widget(self, p_int): # real signature unknown; restored from __doc__\n pass","def widget(self, p_int): # real signature unknown; restored from __doc__\n pass","def getControls(self):","def TCFieldWidget(field, request):\n widget = FieldWidget(field, TCWidget(request))\n # widget.label = u'' # don't show the label twice\n return widget","def _build_gui(self):\n box = qt.QHBoxLayout(self)\n box.addWidget(self._but)\n\n lab = self._lab\n Pol = qt.QSizePolicy\n lab.setSizePolicy(Pol.Expanding, Pol.Preferred)\n lab.setFrameStyle(qt.QLabel.Panel)\n box.addWidget(lab)","def _build_gui(self):\n box = qt.QHBoxLayout(self)\n box.addWidget(self._but)\n\n lab = self._lab\n Pol = qt.QSizePolicy\n lab.setSizePolicy(Pol.Expanding, Pol.Preferred)\n lab.setFrameStyle(qt.QLabel.Panel)\n box.addWidget(lab)","def __init__(self,name,value,*args,**kargs):\n \n kargs['text'] = '' # Force no label\n self.input = value\n InputItem.__init__(self,name,*args,**kargs)\n self.layout().insertWidget(1,self.input)","def __init__(self):\n self.defaultTheme = \"DarkAmber\"\n self.version = 1.4\n self.versionName = \"class update\"\n self.title = \"Lms GUI default window\"\n self.layout = [[sg.Text(\"This is the base window class layout.\")]]\n self.elementJustification = 'c'\n self.location=(500, 300)\n self.running = True\n self.window = None\n self.event = \"\"\n self.values = []\n self.nextAction = None","def makeTitle(self):\n l1=Label(self.app, text=\"Asset Allocation Combinations\")\n l1.grid(row=0, column=0)","def __init__(self, x, y, parent, game, app, *args, **kwargs):\n Label.__init__(self, parent, *args, **kwargs)\n self.configure(bg=Color.MID_TONE)\n\n # Helper to create mouse-event listeners for tiles, so they don't\n # trigger when game has ended.\n def create_marker_listener(func):\n def listener(_event):\n if game.get_state() is GameState.PLAYING:\n func(_event)\n\n return listener\n\n # Highlight tile when mouse is over it\n def mouse_over(_event):\n if game.get_tile(x, y) is MarkerType.NONE:\n self.configure(bg=Color.HIGH_TONE)\n\n # Return back to normal when mouse off\n def mouse_leave(_event):\n self.configure(bg=Color.MID_TONE)\n\n self.bind(\"<Enter>\", create_marker_listener(mouse_over))\n self.bind(\"<Leave>\", create_marker_listener(mouse_leave))\n self.bind(\"<Button-1>\", create_marker_listener(\n lambda _e: app.grid_clicked(x, y)))\n self.bind(\"<ButtonRelease-1>\",\n create_marker_listener(mouse_leave))","def buildmainframe(self):\n self.mainframewidgets=[]\n for x in range(3):\n thislabel = Label(self.mainframe, text=str(x))\n thislabel.grid()\n self.mainframewidgets.append(thislabel)","def __init__(self, dotNumber, isRaised=False):\n\n Gtk.Alignment.__init__(self)\n if dotNumber in [1, 2, 3, 7]:\n self.set(1.0, 0.5, 0.0, 0.0)\n self.set_padding(0, 0, 3, 0)\n else:\n self.set(0.0, 0.5, 0.0, 0.0)\n self.set_padding(0, 0, 0, 3)\n\n self.label = Gtk.Label()\n self.add(self.label)\n if isRaised:\n self.raiseDot()\n else:\n self.lowerDot()","def create_widgets(self):\r\n\r\n # we pass self since 'self' is a ttk frame\r\n # the tic tac toe buttons\r\n self.btn_tl = ttk.Button(self)\r\n self.btn_tm = ttk.Button(self)\r\n self.btn_tr = ttk.Button(self)\r\n self.btn_ml = ttk.Button(self)\r\n self.btn_mm = ttk.Button(self)\r\n self.btn_mr = ttk.Button(self)\r\n self.btn_bl = ttk.Button(self)\r\n self.btn_bm = ttk.Button(self)\r\n self.btn_br = ttk.Button(self)\r\n\r\n # the reset button\r\n self.btn_reset = ttk.Button(self)"],"string":"[\n \"def create_label(self):\\n\\n self.pc_label = Label(self.form_box, text=\\\"Primary Current [A]:\\\", anchor='nw', width=32,\\n bg=self.design.color.secondary, font=('Arial', 15))\\n self.sc_label = Label(self.form_box, text=\\\"Secondary Current [A]:\\\", anchor='nw', width=32,\\n bg=self.design.color.secondary, font=('Arial', 15))\\n self.avg_t_label = Label(self.form_box, text=\\\"Average Time [s]: \\\", anchor='nw', width=32,\\n bg=self.design.color.secondary, font=('Arial', 15))\\n self.nwt_label = Label(self.form_box, text=\\\"network type (static/dynamic):\\\", anchor='nw', width=32,\\n bg=self.design.color.secondary, font=('Arial', 15))\\n self.nw_ip_label = Label(self.form_box, text=\\\"IpAddress:\\\", anchor='nw', width=32,\\n bg=self.design.color.secondary, font=('Arial', 15))\\n self.nw_gw_label = Label(self.form_box, text=\\\"Gateway:\\\", anchor='nw', width=32, bg=self.design.color.secondary,\\n font=('Arial', 15))\\n self.nw_sm_label = Label(self.form_box, text=\\\"subnet mask:\\\", anchor='nw', width=32,\\n bg=self.design.color.secondary, font=('Arial', 15))\\n self.nw_mca_label = Label(self.form_box, text=\\\"Mac Address:\\\", anchor='nw', width=32,\\n bg=self.design.color.secondary, font=('Arial', 15))\",\n \"def create_widget(self):\\n self.widget = UILabel()\",\n \"def GridLabel(Parent,Text,Row,Column):\\r\\n L = Label(Parent,text=Text)\\r\\n L.grid(row=Row,column=Column)\\r\\n return L\",\n \"def BaseLabel(self, *args):\\n return _XCAFDoc.XCAFDoc_ShapeTool_BaseLabel(self, *args)\",\n \"def add_label(self, side=\\\"left\\\", **kwargs):\\n widget = ttk.Label(self, **kwargs)\\n widget.pack(side=side)\\n\\n return widget\",\n \"def label(self, color):\\r\\n return Label(self, color)\",\n \"def __init__(self, parent, text):\\n tk.Frame.__init__(self, parent)\\n\\n label = tk.Label(self, text=\\\"De Centrale\\\", font=(\\\"Verdana\\\", 25))\\n label.pack(fill=tk.BOTH, side='left', anchor='w')\\n\\n label = tk.Label(self, text=text, anchor=\\\"e\\\", font=(\\\"Verdana\\\", 12))\\n label.pack(expand=True, fill=tk.BOTH, side='right', anchor='e')\",\n \"def create_label(self, on, text: str):\\n return tk.Label(on, font=self.FONT, bg=self.BG_COLOR, text=text)\",\n \"def __init__(self, parent, text_link=None):\\n Frame.__init__(self, parent)\\n if text_link is None:\\n text_link = StringVar(value=\\\"Running\\\")\\n self._text = text_link\\n self._label = Label(self, bd=1, relief=SUNKEN, anchor=W, textvariable=self._text)\\n self._label.pack(fill=X)\",\n \"def __init__(self, label=None):\\n super().__init__(\\\"h\\\", 1, [], label=label)\",\n \"def customWidgets(self):\\n \\n # to be called a property value needs to change\\n def onValueChanged(widget, prop):\\n # set the corresponding property of the videobalance element\\n self.balance.set_property(prop, widget.get_value())\\n\\n # videobalance has several properties, with the following range\\n # and defaults\\n properties = [(\\\"contrast\\\", 0, 2, 1),\\n (\\\"brightness\\\", -1, 1, 0), \\n (\\\"hue\\\", -1, 1, 0),\\n (\\\"saturation\\\", 0, 2, 1)]\\n \\n # create a place to hold our controls\\n controls = gtk.VBox()\\n labels = gtk.VBox()\\n # for every propety, create a control and set its attributes\\n for prop, lower, upper, default in properties:\\n widget = gtk.HScale(); label = gtk.Label(prop)\\n\\n # set appropriate atributes\\n widget.set_update_policy(gtk.UPDATE_CONTINUOUS)\\n widget.set_value(default)\\n widget.set_draw_value(True)\\n widget.set_range(lower, upper)\\n\\n # connect to our signal handler, specifying the property\\n # to adjust\\n widget.connect(\\\"value-changed\\\", onValueChanged, prop)\\n \\n # pack widget into box\\n controls.pack_start(widget, True, True)\\n labels.pack_start(label, True, False)\\n\\n layout = gtk.HBox()\\n layout.pack_start(labels, False, False)\\n layout.pack_end(controls, True, True)\\n return layout\",\n \"def BaseLabel(self, *args):\\n return _XCAFDoc.XCAFDoc_ColorTool_BaseLabel(self, *args)\",\n \"def __init__(self,name,value,*args,**kargs):\\n self._plain = kargs.get('plain',False)\\n self.input = QtGui.QLabel()\\n maxw,maxh = maxSize()\\n self.input.setMaximumSize(int(0.75*maxw),maxh)\\n InputItem.__init__(self,name,*args,**kargs)\\n self.setValue(value)\\n self.layout().insertWidget(1,self.input)\\n self.label.setWordWrap(True)\\n self.setSize()\",\n \"def __init__(self, text=\\\"\\\", widget=None):\\n self._label_text = text\\n self._widget = widget\\n self._widget.on_change = self._update\\n super().__init__(text=f\\\"{text} {widget.value}\\\")\",\n \"def BaseLabel(self, *args):\\n return _XCAFDoc.XCAFDoc_LayerTool_BaseLabel(self, *args)\",\n \"def __init__(self, font, color, text=\\\"\\\", top=0, left=0, bottom=None, right=None):\\n self.text = text\\n self.font = font\\n self.color = color\\n self.top = top\\n self.left = left\\n self.bottom = bottom\\n self.right = right\\n self.renderLabel()\",\n \"def Label(self) -> str:\",\n \"def DrawControlLabel(self, dc, wnd, item, rect):\\r\\n\\r\\n label_size = GetLabelSize(dc, item.GetLabel(), item.GetOrientation() != AUI_TBTOOL_HORIZONTAL)\\r\\n text_height = label_size.GetHeight()\\r\\n text_width = label_size.GetWidth()\\r\\n\\r\\n dc.SetFont(self._font)\\r\\n\\r\\n if self._agwFlags & AUI_TB_TEXT:\\r\\n \\r\\n tx, text_height = dc.GetTextExtent(\\\"ABCDHgj\\\") \\r\\n\\r\\n text_width, ty = dc.GetTextExtent(item.GetLabel())\\r\\n\\r\\n # don't draw the label if it is wider than the item width\\r\\n if text_width > rect.width:\\r\\n return\\r\\n\\r\\n # set the label's text colour\\r\\n dc.SetTextForeground(wx.BLACK)\\r\\n\\r\\n text_x = rect.x + (rect.width/2) - (text_width/2) + 1\\r\\n text_y = rect.y + rect.height - text_height - 1\\r\\n\\r\\n if self._agwFlags & AUI_TB_TEXT and item.GetLabel() != \\\"\\\": \\r\\n dc.DrawText(item.GetLabel(), text_x, text_y)\",\n \"def label_grid(self):\\n\\n self.pc_label.grid(row=0, sticky=\\\"nw\\\", pady=2, padx=3)\\n self.sc_label.grid(row=1, sticky=\\\"nw\\\", pady=2, padx=3)\\n self.avg_t_label.grid(row=2, sticky=\\\"nw\\\", pady=2, padx=3)\\n self.nwt_label.grid(row=4, sticky=\\\"nw\\\", pady=2, padx=3)\\n self.nw_ip_label.grid(row=5, sticky=\\\"nw\\\", pady=2, padx=3)\\n self.nw_gw_label.grid(row=6, sticky=\\\"nw\\\", pady=2, padx=3)\\n self.nw_sm_label.grid(row=7, sticky=\\\"nw\\\", pady=2, padx=3)\\n self.nw_mca_label.grid(row=8, sticky=\\\"nw\\\", pady=2, padx=3)\",\n \"def BaseLabel(self, *args):\\n return _XCAFDoc.XCAFDoc_MaterialTool_BaseLabel(self, *args)\",\n \"def createLabel(self, text, font):\\n label = QLabel(self)\\n label.setFont(font)\\n label.setText(text)\\n label.setFixedHeight(40)\\n label.setAlignment(Qt.AlignCenter)\\n label.setStyleSheet('background-color: rgba(0,0,0,0);color: white; border: 0px solid black; ')\\n return label\",\n \"def __init__(self, parent) -> None:\\n super().__init__(parent)\\n\\n self.setText(\\\"\\\")\\n self.setPixmap(QPixmap(self.image))\\n\\n self.setScaledContents(False)\\n self.setObjectName(\\\"Label_Bild\\\")\\n self.setAlignment(QtCore.Qt.AlignCenter)\",\n \"def __init__(self, label):\\n self.label = label\",\n \"def __init__(self,\\n rect: Rect,\\n text: str = \\\"\\\",\\n style: Style = None,\\n parent: Component = None) -> None:\\n Component.__init__(self, rect, style=style, parent=parent)\\n self.set_text(text)\",\n \"def __init__(self, parent, *args, **kwargs):\\n tk.LabelFrame.__init__(self, parent, *args, **kwargs)\\n self.canvas = MainCanvas(self, bg=\\\"orange\\\")\\n self.canvas.pack(side='top', fill='both', expand=True)\",\n \"def create_widgets(self):\\n #create description label\\n Label(self,\\n text = \\\"Patient Info:\\\"\\n ).grid(row = 0, column = 0, sticky = W)\",\n \"def __init__(self, x, y, align_bottom_left, text, font, color):\\n super(TextBox, self).__init__(x, y)\\n\\n self.align_bottom_left = align_bottom_left\\n self.__text = text\\n self.font = font\\n self.color = color\\n self.original_x = x\\n self.original_y = y\\n self.image = None\\n self.image_rect = None\\n\\n self.set_text(text)\",\n \"def myEntLabel(name='myText', \\r\\n\\t\\t\\t numb=None,\\r\\n\\t\\t\\t call=None, data=['dati'],\\r\\n\\t\\t\\t nLab='Label', cLab=\\\"#333\\\",\\r\\n\\t\\t\\t tLab='h', aLab=[False, False, 1]):\\r\\n#myEntry\\r\\n\\t# entr, call\\r\\n\\tentr,call = myEntry(name=name,\\r\\n\\t\\t\\t \\t\\t\\tnumb=numb, \\r\\n\\t\\t\\t \\t\\t\\tcall=call, data=data)\\r\\n#myLabel\\r\\n\\tif cLab == None:\\r\\n\\t\\tcLab=Gdk.color_parse('blue')\\r\\n\\t# labe\\r\\n\\tlabe = myLabel(name=nLab, \\r\\n\\t\\t\\t\\t\\tleng=len(nLab)+1, prea=' ', post=' ', \\r\\n\\t\\t\\t\\t\\tfont='Courier 10', \\r\\n\\t\\t\\t\\t\\tcolo=cLab)\\r\\n#xBox \\r\\n\\txBox = myBox(tLab)\\r\\n\\t#child, expand=True, fill=True, padding=0\\r\\n\\txBox.pack_start(labe, *aLab)\\r\\n\\txBox.pack_start(entr, *aLab)\\t\\r\\n# <- \\r\\n\\treturn xBox, [entr, call, labe]\",\n \"def __init__(self, text='', **kwargs):\\n Control.__init__(self, text=text, **kwargs)\",\n \"def create_controls(self):\\n\\n self.button_frame = tk.LabelFrame(self, text=\\\"Controls\\\", padx=5, pady=5)\\n self.button_frame.grid(row=0, column=1, padx=5, pady=5, sticky=\\\"n\\\")\\n self.load_data = tk.Button(\\n self.button_frame, text=\\\"Load Data\\\", command=self.update_stats\\n )\\n self.load_data.grid(row=0)\\n\\n self.print_data = tk.Button(\\n self.button_frame, text=\\\"Print Data\\\", command=self.print_raw_data,\\n )\\n self.print_data.grid(row=1)\\n\\n self.quit = tk.Button(\\n self.button_frame, text=\\\"Quit\\\", fg=\\\"red\\\", command=self.master.destroy\\n )\\n self.quit.grid(row=2)\",\n \"def create_ui(self):\\n main_sizer = wx.BoxSizer(wx.VERTICAL)\\n\\n self.text_ctrl = wx.TextCtrl(self, style=wx.TE_MULTILINE)\\n self.text_ctrl.Bind(wx.EVT_TEXT, self.on_text)\\n main_sizer.Add(self.text_ctrl, 1, wx.ALL | wx.EXPAND, 5)\\n\\n self.SetSizer(main_sizer)\",\n \"def BaseLabel(self, *args):\\n return _XCAFDoc.XCAFDoc_DimTolTool_BaseLabel(self, *args)\",\n \"def __init__( self ):\\n \\n Frame.__init__( self ) # initializes Frame instance\\n \\n # frame fills all available space\\n self.pack( expand = YES, fill = BOTH )\\n self.master.title( \\\"Labels\\\" )\\n \\n self.Label1 = Label( self, text = \\\"Label with text\\\" )\\n \\n # resize frame to accommodate Label\\n self.Label1.pack()\\n \\n self.Label2 = Label( self,\\n text = \\\"Labels with text and a bitmap\\\" )\\n \\n # insert Label against left side of frame\\n self.Label2.pack( side = LEFT )\\n \\n # using default bitmap image as label\\n self.Label3 = Label( self, bitmap = \\\"warning\\\" )\\n self.Label3.pack( side = LEFT )\",\n \"def __init__(self, dat, frame, box_size, centre,\\n label=False, **kwargs):\\n\\n super().__init__(dat, frame, box_size, centre) # initialise superclass\\n\\n self.label = label # write labels\\n\\n self.draw()\",\n \"def __init__(self, name, label, class_to_use, size, state, left_window_label=None, left_window_value=None):\\n self.name = name\\n self.label = label\\n self.class_to_use = class_to_use\\n self.size = size\\n self.state = state\\n self.left_window_label = left_window_label\\n self.left_window_value = left_window_value\",\n \"def __init__(self, ax, labels, bw=None, bh=None, colors=None, actives=None):\\n AxesWidget.__init__(self, ax)\\n\\n labels = copy.deepcopy(labels)\\n\\n labels.append(\\\"select all\\\")\\n labels.append(\\\"unselect all\\\")\\n print(\\\"colors\\\", colors)\\n colors = colors+[\\\"#000000\\\"]*2\\n\\n ax.set_xticks([])\\n ax.set_yticks([])\\n ax.set_navigate(False)\\n\\n if actives is None:\\n actives = [False] * len(labels)\\n actives = actives+[False]*2\\n\\n if len(labels) > 1:\\n dy = 1. / (len(labels) + 1)\\n ys = np.linspace(1 - dy, dy, len(labels))\\n else:\\n dy = 0.25\\n ys = [0.5]\\n\\n axcolor = ax.get_facecolor()\\n\\n self.labels = []\\n self.lines = []\\n self.rectangles = []\\n\\n lineparams = {'color': 'k', 'linewidth': 1.2,\\n 'transform': ax.transAxes, 'solid_capstyle': 'butt'}\\n\\n for index, (y, label, active) in enumerate(zip(ys, labels, actives)):\\n if colors is None:\\n t = ax.text(0.25, y, label, transform=ax.transAxes,\\n horizontalalignment='left',\\n verticalalignment='center')\\n else:\\n t = ax.text(0.25, y, label, transform=ax.transAxes,\\n horizontalalignment='left',\\n verticalalignment='center', color=colors[index])\\n\\n if bw is None:\\n w = dy / 2\\n else:\\n w = bw\\n\\n if bh is None:\\n h = dy / 2\\n else:\\n h = bh\\n\\n x, y = 0.05, y - h / 2\\n\\n p = Rectangle(xy=(x, y), width=w, height=h, edgecolor='black',\\n facecolor=axcolor, transform=ax.transAxes)\\n\\n l1 = Line2D([x, x + w], [y + h, y], **lineparams)\\n l2 = Line2D([x, x + w], [y, y + h], **lineparams)\\n\\n l1.set_visible(active)\\n l2.set_visible(active)\\n self.labels.append(t)\\n self.rectangles.append(p)\\n self.lines.append((l1, l2))\\n ax.add_patch(p)\\n ax.add_line(l1)\\n ax.add_line(l2)\\n\\n self.connect_event('button_press_event', self._clicked)\\n\\n self.cnt = 0\\n self.observers = {}\",\n \"def label(self):\\r\\n raise NotImplementedError\",\n \"def __display_controls(self):\\n self.__fill_data_variables()\\n self.__fill_smoothing_method()\\n self.__fill_smooth_factor()\\n\\n left_box = VBox([self.data_vars])\\n center_box = VBox([self.smoothing_methods])\\n right_box = VBox([self.smooth_factor])\\n #_HBox = HBox([left_box, center_box, right_box],\\n _HBox = HBox([left_box, center_box, right_box],\\n layout={'height': '80px',\\n 'width' : '99%'}\\n )\\n display(_HBox)\",\n \"def add_variable(self, side=\\\"left\\\", **kwargs):\\n widget = ttk.Label(self, **kwargs)\\n widget.pack(side=side)\\n\\n return widget\",\n \"def __init__(self):\\n\\t\\tself.root = Tk()\\n\\t\\tself.root.geometry(\\\"1600x1600\\\")\\n\\t\\ttitle_arabic=u'\\\\uFEB3\\\\uFEE8\\\\uFECC\\\\uFEEE\\\\uFEA9'\\n\\t\\tself.root.title(title_arabic)\\n\\t\\tself.root.geometry(\\\"1600x900\\\")\\t\\t\\n\\t\\tself.root.resizable(width=False, height=False)\\n\\t\\tmylabel=Label(text=\\\"Loading . . .\\\",fg=\\\"#ff1a75\\\",bg=\\\"black\\\",font=(\\\"Times New Roman\\\",18))\\n\\t\\tmylabel.place(x=665,y=300)\\n\\t\\tmylabel3=Text()\\n\\t\\tmylabel3.place(x=550,y=395)\\n\\t\\tmylabel3['width']='50'\\n\\t\\tmylabel3['height']=0.6\\n\\t\\tmylabel3.configure(state='disabled')\\n\\t\\tself.root.configure(background='black')\\n\\t\\tself.load()\\n\\t\\tself.root.mainloop()\",\n \"def init ( self, parent ):\\n # Create a panel to hold all of the buttons:\\n self.control = panel = wx.Panel( parent, -1 )\\n sizer = wx.BoxSizer( wx.VERTICAL )\\n \\n # Add the standard font control:\\n font = self._font = wx.TextCtrl( panel, -1, self.str_value )\\n wx.EVT_KILL_FOCUS( font, self.update_object )\\n wx.EVT_TEXT_ENTER( panel, font.GetId(), self.update_object )\\n sizer.Add( font, 0, wx.EXPAND | wx.BOTTOM, 3 )\\n \\n # Add all of the font choice controls:\\n sizer2 = wx.BoxSizer( wx.HORIZONTAL )\\n facenames = all_facenames()\\n control = self._facename = wx.Choice( panel, -1, wx.Point( 0, 0 ), \\n wx.Size( choice_width( facenames ), 20 ), \\n facenames )\\n \\n sizer2.Add( control, 2, wx.EXPAND )\\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts )\\n \\n control = self._point_size = wx.Choice( panel, -1, \\n wx.Point( 0, 0 ), wx.Size( 30, 20 ), \\n PointSizes )\\n sizer2.Add( control, 1, wx.EXPAND | wx.RIGHT, 3 )\\n wx.EVT_CHOICE( panel, control.GetId(), self.update_object_parts ) \\n \\n sizer.Add( sizer2, 0, wx.EXPAND )\\n \\n # Set-up the layout:\\n panel.SetAutoLayout( True )\\n panel.SetSizer( sizer )\\n sizer.Fit( panel )\",\n \"def create_widget(self):\\n pass\",\n \"def create_labels(self):\\n for name in self.names:\\n temp_button = Label(text=name)\\n self.root.ids.label_box.add_widget(temp_button)\",\n \"def initWidgets(self):\\n self.loctext.setText(\\\"{0:g}\\\".format(self.loc))\\n self.scaletext.setText(\\\"{0:g}\\\".format(self.scale))\",\n \"def __init__(self, title=None, dimensions=None, style=None, spacing=None, margin=None, layout=None):\\n super(customGroupBox, self).__init__()\\n\\n if layout:\\n self.setLayout(layout)\\n else:\\n self.setLayout(QtGui.QVBoxLayout())\\n\\n if title:\\n self.setTitle(title)\\n\\n if dimensions:\\n self.setFixedSize(dimensions[0], dimensions[1])\\n\\n if style:\\n self.setStyle(QtGui.QStyleFactory.create(style))\\n\\n if margin:\\n self.layout().setContentsMargins(margin, margin, margin, margin)\\n\\n if spacing:\\n self.layout().setSpacing(spacing)\",\n \"def __init__(self, parent=None):\\n QLabel.__init__(self, parent)\\n self.start_animation(self.SLOW_DURATION)\",\n \"def text(self) -> None:\\n label_space = tk.Label(self)\\n label_space.grid(row=0)\\n label_book_number = tk.Label(self, text=f'Номер книги:')\\n label_book_number.grid(row=1, column=0, ipady=5)\\n label_title = tk.Label(self, text='Название книги:')\\n label_title.grid(row=2, column=0, padx=5)\\n label_author = tk.Label(self, text='Автор:')\\n label_author.grid(row=3, column=0, pady=5)\\n label_genre = tk.Label(self, text='Жанр:')\\n label_genre.grid(row=4, column=0)\",\n \"def toControls(self,widget):\",\n \"def renderLabel(self):\\n self.render = self.font.render(self.text, True, self.color)\\n self.rect = self.render.get_rect()\",\n \"def _initLayout(self):\\n\\t\\tpanel = wx.Panel(self)\\n\\n\\t\\t# Create a font object\\n\\t\\tfont = wx.SystemSettings.GetFont(wx.SYS_SYSTEM_FONT)\\n\\t\\tfont.SetPointSize(9)\\n\\n\\t\\t# Vertical sizer will contain multiple horizontal sizers as rows\\n\\t\\tvbox = wx.BoxSizer(wx.VERTICAL)\\n\\n\\t\\t# First Row: The text we need to categorize\\n\\t\\thbox1 = wx.BoxSizer(wx.HORIZONTAL)\\n\\t\\tst1 = wx.StaticText(panel, label='Text')\\n\\t\\tst1.SetFont(font)\\n\\t\\thbox1.Add(st1, flag=wx.RIGHT, border=8)\\n\\t\\ttc = wx.TextCtrl(panel)\\n\\t\\tself._textControl = tc\\n\\t\\thbox1.Add(tc, proportion=1)\\n\\t\\tvbox.Add(hbox1, flag=wx.EXPAND|wx.LEFT|wx.RIGHT|wx.TOP, border=10)\\n\\t\\t\\n\\t\\t# The existing class assignment\\n\\t\\thboxExisting = wx.BoxSizer(wx.HORIZONTAL)\\n\\t\\tlabel = wx.StaticText(panel, label='Current')\\n\\t\\tlabel.SetFont(font)\\n\\t\\thboxExisting.Add(label, flag=wx.RIGHT, border=8)\\n\\t\\t\\n\\t\\tlabel = wx.StaticText(panel, label=\\\"(unassigned)\\\")\\n\\t\\tself._existingClass = \\\"(unassigned)\\\"\\n\\t\\tself._existingClassLabel = label\\n\\t\\tlabel.SetFont(font)\\n\\t\\thboxExisting.Add(label, flag=wx.RIGHT, border=8)\\n\\t\\tvbox.Add(hboxExisting, flag=wx.EXPAND|wx.LEFT|wx.RIGHT|wx.TOP, border=10)\\n\\t\\t\\n\\t\\t# Button to keep the current class assignment\\n\\t\\tbutton = wx.Button(panel, label=\\\"KEEP\\\", name=\\\"*KEEP\\\")\\n\\t\\thboxExisting.Add(button, flag=wx.RIGHT)\\n\\n\\t\\t# Button to skip this record, i.e., move to next record without writing this one out\\n\\t\\tbutton = wx.Button(panel, label=\\\"DELETE\\\", name=\\\"*KILL\\\")\\n\\t\\thboxExisting.Add(button, flag=wx.RIGHT)\\n\\n\\t\\tvbox.Add((-1, 10))\\n\\n\\t\\t# Buttons for classes that can be assigned to the text\\n\\t\\thbox2 = wx.BoxSizer(wx.HORIZONTAL)\\n\\t\\tst2 = wx.StaticText(panel, label='Reassign to...')\\n\\t\\tst2.SetFont(font)\\n\\t\\thbox2.Add(st2)\\n\\t\\tvbox.Add(hbox2, flag=wx.LEFT | wx.TOP, border=10)\\n\\n\\t\\tvbox.Add((-1, 10))\\n\\n\\t\\t# Grid of buttons, one for each class label\\n\\t\\thbox3 = wx.GridSizer(8,5,50)\\n\\t\\n\\t\\tfor label in sorted(labels.LABELS):\\n\\t\\t\\tbutton = MyButton(panel, label=label, size=(70, 30), name=label)\\n\\t\\t\\thbox3.Add(button)\\n\\n\\t\\tvbox.Add(hbox3, flag=wx.LEFT|wx.RIGHT|wx.EXPAND, \\n\\t\\t\\tborder=10)\\n\\t\\t\\n\\t\\tpanel.SetSizer(vbox)\",\n \"def __init__(self):\\n super(GraphVisualizerCase, self).__init__()\\n\\n self._v_layout = QVBoxLayout()\\n self.setLayout(self._v_layout)\\n\\n top_label_layout = QHBoxLayout()\\n self._v_layout.addLayout(top_label_layout)\\n\\n self._top_left_label = MathTextLabel()\\n top_label_layout.addWidget(self._top_left_label)\\n self._top_right_label = MathTextLabel()\\n top_label_layout.addWidget(self._top_right_label)\\n\\n self._center_widget = QFrame()\\n self._center_text_label = QLabel()\\n self._center_text_label.setAlignment(Qt.AlignCenter)\\n layout = QHBoxLayout()\\n layout.addWidget(self._center_text_label)\\n self._center_widget.setLayout(layout)\\n\\n self._v_layout.addWidget(self._center_widget)\\n\\n bot_label_layout = QHBoxLayout()\\n self._v_layout.addLayout(bot_label_layout)\\n\\n self._bot_left_label = MathTextLabel()\\n bot_label_layout.addWidget(self._bot_left_label)\\n self._bot_right_label = MathTextLabel()\\n bot_label_layout.addWidget(self._bot_right_label)\",\n \"def _create_label(self, x, y, text, width=50, **config):\\n\\n self.main_canvas.create_text(x, y, text='%6s' % text, width=width, **config)\",\n \"def _setup_ui(self):\\n from functools import partial\\n\\n self.setStyleSheet(\\n \\\"\\\"\\\"\\n QLabel[labelField=\\\"true\\\"] {\\n font-weight: bold;\\n }\\n \\\"\\\"\\\"\\n )\\n\\n # The main layout\\n self.main_layout = QtWidgets.QVBoxLayout(self)\\n self.main_layout.setContentsMargins(0, 0, 0, 0)\\n\\n # the form layout\\n self.form_layout = QtWidgets.QFormLayout()\\n self.form_layout.setLabelAlignment(\\n QtCore.Qt.AlignRight | QtCore.Qt.AlignTrailing | QtCore.Qt.AlignVCenter\\n )\\n\\n # store roles\\n label_role = QtWidgets.QFormLayout.LabelRole\\n field_role = QtWidgets.QFormLayout.FieldRole\\n\\n self.main_layout.addLayout(self.form_layout)\\n\\n i = -1\\n\\n # Reviewer\\n i += 1\\n reviewer_name_label = QtWidgets.QLabel(self)\\n reviewer_name_label.setText(\\\"Reviewer\\\")\\n self.form_layout.setWidget(i, label_role, reviewer_name_label)\\n\\n self.reviewer_name_widget = QtWidgets.QLabel(self)\\n self.form_layout.setWidget(i, field_role, self.reviewer_name_widget)\\n\\n # Task Name field\\n i += 1\\n task_name_label = QtWidgets.QLabel(self)\\n task_name_label.setText(\\\"Task\\\")\\n self.form_layout.setWidget(i, label_role, task_name_label)\\n\\n self.task_name_widget = QtWidgets.QLabel(self)\\n self.form_layout.setWidget(i, field_role, self.task_name_widget)\\n\\n # # Version Info field\\n # from anima.ui.widgets.version import VersionDetailsWidget\\n # self.latest_version_widget = VersionDetailsWidget(parent=self)\\n # self.main_layout.insertWidget(0, self.latest_version_widget)\\n\\n # Review Type Field\\n i += 1\\n review_type_label = QtWidgets.QLabel(self)\\n review_type_label.setText(\\\"Review Type\\\")\\n self.form_layout.setWidget(i, label_role, review_type_label)\\n\\n self.review_type_widget = ReviewTypeWidget(self)\\n self.review_type_widget.currentIndexChanged.connect(\\n partial(self.review_type_changed_callback)\\n )\\n\\n self.form_layout.setWidget(i, field_role, self.review_type_widget)\\n\\n # Timing Field\\n i += 1\\n effort_label = QtWidgets.QLabel(self)\\n effort_label.setText(\\\"Timing\\\")\\n self.form_layout.setWidget(i, label_role, effort_label)\\n\\n effort_layout = QtWidgets.QHBoxLayout()\\n self.form_layout.setLayout(i, field_role, effort_layout)\\n\\n from anima.ui.widgets.timing import ScheduleTimingWidget\\n from anima import defaults\\n\\n self.timing_widget = ScheduleTimingWidget(\\n self, timing_resolution=defaults.timing_resolution\\n )\\n self.timing_widget.setEnabled(False)\\n # set the default to 1 hour\\n self.timing_widget.set_schedule_info(timing=1, unit=\\\"h\\\")\\n effort_layout.addWidget(self.timing_widget)\\n\\n # Description Field\\n i += 1\\n description_label = QtWidgets.QLabel(self)\\n description_label.setText(\\\"Description\\\")\\n self.form_layout.setWidget(i, label_role, description_label)\\n\\n self.description_widget = QtWidgets.QTextEdit(self)\\n self.form_layout.setWidget(i, field_role, self.description_widget)\",\n \"def create_widgets(self):\",\n \"def set_ui(self):\\r\\n\\r\\n self.canvas = tk.Canvas(self)\\r\\n self.canvas.pack()\\r\\n\\r\\n self.entry = ttk.Entry(self.canvas, justify=\\\"center\\\", font=(\\\"Calibri\\\", 12))\\r\\n\\r\\n self.grid = Grid(self.canvas)\",\n \"def __init__(self, text, separator_line_thickness, label_type, dpi=(600, 600)):\\n \\n def get_text_on_label(text, label_type):\\n \\\"\\\"\\\"Format how the text will look on the label.\\n \\n text - Text to be placed on the label.\\n label_type - One of the types specifying the label layout.\\n \\\"\\\"\\\"\\n text_on_label = \\\"\\\".join([c for c in text if c in string.ascii_letters + string.digits])\\n if label_type == 0:\\n text_on_label = \\\"\\\"\\n elif label_type == 1 or label_type == 2 or label_type == 4:\\n text_on_label = \\\"\\\\n\\\".join([text_on_label[:4],\\n text_on_label[4:8],\\n text_on_label[8:12],\\n text_on_label[12:]])\\n elif label_type == 3:\\n text_on_label = \\\"\\\\n\\\".join([\\\"-\\\".join([text_on_label[:4],\\n text_on_label[4:8]]),\\n \\\"-\\\".join([text_on_label[8:12],\\n text_on_label[12:]])])\\n else:\\n text_on_label = \\\"\\\"\\n return text_on_label\\n \\n self.label_image = None\\n self.text_on_label = get_text_on_label(text, label_type)\\n self.label_type = label_type\\n self.separator_line_thickness = separator_line_thickness\\n self.dpi = dpi\",\n \"def __init__(\\r\\n self, text=\\\"Ok\\\", font=None, size=30, state: int = Button.INACTIVE\\r\\n ) -> None:\\r\\n super().__init__(state)\\r\\n self._font = Font(font, size)\\r\\n self._text = text\\r\\n self.rect = Rect(0, 0, 0, 0)\\r\\n self.redraw()\",\n \"def InitUI(self, frame):\\n\\t\\timport wx\\n\\t\\tframe.SetSize((200,210))\\n\\t\\t\\n\\t\\tpanel = wx.Panel(frame)\\n\\t\\t\\n\\t\\tvbox = wx.BoxSizer(wx.VERTICAL)\\n\\t\\t\\n\\t\\thbox1 = wx.BoxSizer(wx.HORIZONTAL)\\n\\t\\tstartButton = wx.Button(panel, label='Start Baselining', size=(170, 25))\\n\\t\\thbox1.Add(startButton)\\n\\t\\tvbox.Add(hbox1, flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTRE, border=5)\\n\\t\\t\\n\\t\\tvbox.Add((-1, 5))\\n\\t\\t\\n\\t\\thbox2 = wx.BoxSizer(wx.HORIZONTAL)\\n\\t\\tstopButton = wx.Button(panel, label='Stop Baselining', size=(170, 25))\\n\\t\\thbox2.Add(stopButton)\\n\\t\\tvbox.Add(hbox2, flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTRE, border=5)\\n\\t\\t\\n\\t\\tvbox.Add((-1, 5))\\n\\t\\t\\n\\t\\thbox6 = wx.BoxSizer(wx.HORIZONTAL)\\n\\t\\tresetButton = wx.Button(panel, label='Reset', size=(170, 25))\\n\\t\\thbox6.Add(resetButton)\\n\\t\\tvbox.Add(hbox6, flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTRE, border=5)\\n\\t\\t\\n\\t\\tvbox.Add((-1, 10))\\n\\t\\t\\n\\t\\thbox3 = wx.BoxSizer(wx.HORIZONTAL)\\n\\t\\tself.onOffText = wx.StaticText(panel, label='Off')\\n\\t\\thbox3.Add(self.onOffText)\\n\\t\\tvbox.Add(hbox3, flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTRE, border=5)\\n\\t\\t\\n\\t\\tvbox.Add((-1, 20))\\n\\t\\t\\n\\t\\thbox4 = wx.BoxSizer(wx.HORIZONTAL)\\n\\t\\tminText = wx.StaticText(panel, label='Min', style=wx.ALIGN_CENTRE)\\n\\t\\tmaxText = wx.StaticText(panel, label='Max', style=wx.ALIGN_CENTRE)\\n\\t\\thbox4.Add(minText, -1)\\n\\t\\thbox4.Add(maxText, -1)\\n\\t\\tvbox.Add(hbox4, flag=wx.EXPAND | wx.LEFT | wx.RIGHT, border=5)\\n\\t\\t\\n\\t\\tvbox.Add((-1, 5))\\n\\t\\t\\n\\t\\thbox5 = wx.BoxSizer(wx.HORIZONTAL)\\n\\t\\tself.minBox = wx.TextCtrl(panel, style=wx.TE_READONLY)\\n\\t\\tself.maxBox = wx.TextCtrl(panel, style=wx.TE_READONLY)\\n\\t\\tself.minBox.SetBackgroundColour((210,210,210))\\n\\t\\tself.maxBox.SetBackgroundColour((210,210,210))\\n\\t\\thbox5.Add(self.minBox, -1, wx.EXPAND)\\n\\t\\thbox5.Add(self.maxBox, -1, wx.EXPAND)\\n\\t\\tvbox.Add(hbox5, flag=wx.LEFT | wx.RIGHT | wx.EXPAND, border=5)\\n\\t\\t\\n\\t\\tpanel.SetSizer(vbox)\\n\\t\\t\\n\\t\\t# Bind items\\n\\t\\tframe.Bind(wx.EVT_BUTTON, self.OnStartPress, id=startButton.GetId())\\n\\t\\tframe.Bind(wx.EVT_BUTTON, self.OnStopPress, id=stopButton.GetId())\\n\\t\\tframe.Bind(wx.EVT_BUTTON, self.OnResetPress, id=resetButton.GetId())\",\n \"def createLabels(self):\\r\\n # Grid layout to organize the widgets\\r\\n self.grid = QGridLayout()\\r\\n self.labelWidgets = {\\r\\n\\r\\n \\\"<h2>FECHA</h2>\\\": (0, 0),\\r\\n \\\"<h2>BILL AREA</h2>\\\": (0, 3, 1, 1),\\r\\n \\\"<h2>HORA</h2>\\\": (1, 0),\\r\\n \\\"<h2>NOMBRE</h2>\\\": (2, 0),\\r\\n \\\"<h2>CUMPLEAÑOS</h2>\\\": (3, 0),\\r\\n \\\"<h2>CELULAR</h2>\\\": (4, 0),\\r\\n \\\"<h2>DIRECCIÓN</h2>\\\": (5, 0),\\r\\n \\\"<h2>CIUDAD</h2>\\\": (6, 0),\\r\\n \\\"<h2>MÉTODO DE PAGO</h2>\\\": (7, 0),\\r\\n \\\"<h2>PEDIDO</h2>\\\": (8, 0, 1, 3),\\r\\n \\\"<h2>TOTAL POLLO</h2>\\\": (9, 0),\\r\\n \\\"<h2>TOTAL CARNE</h2>\\\": (10, 0),\\r\\n \\\"<h2>EMPANACHOS</h2>\\\": (11, 0),\\r\\n \\\"<h2>TOTAL PRODUCTOS</h2>\\\": (12, 0),\\r\\n \\\"<h2>VALOR ($)</h2>\\\": (13, 0)\\r\\n }\\r\\n for labelName, position in self.labelWidgets.items():\\r\\n if len(position) == 4:\\r\\n self.label = QLabel(labelName)\\r\\n self.label.setAlignment(Qt.AlignCenter)\\r\\n self.label.setStyleSheet(\\\"\\\"\\\"\\r\\n color: #cac03f; font-family: times;\\r\\n font-weight: bold;\\r\\n border: 5px inset #cac03f;\\r\\n font-size: 15px;\\r\\n \\\"\\\"\\\")\\r\\n self.grid.addWidget(\\r\\n self.label, position[0], position[1], position[2], position[3])\\r\\n\\r\\n else:\\r\\n self.label = QLabel(labelName)\\r\\n self.label.setStyleSheet(\\\"\\\"\\\"\\r\\n color: #A8DBC5;\\r\\n font-family: times;\\r\\n font-weight: bold;\\\"\\\"\\\")\\r\\n self.grid.addWidget(self.label, position[0], position[1])\\r\\n # CREATING THE SPECIAL BILL LABEL\\r\\n self.bill = QTextEdit()\\r\\n self.bill.setFixedWidth(320)\\r\\n self.bill.setStyleSheet(\\\"\\\"\\\"\\r\\n font-family: times;\\r\\n font-size: large;\\r\\n background-color : white;\\r\\n border: 4px solid #A8DBC5;\\r\\n font-size: 15px;\\r\\n \\\"\\\"\\\")\\r\\n self.grid.addWidget(self.bill, 1, 3, 12, 1)\\r\\n # setting the main layout in order to add the logo at the top\\r\\n # and the labels, line Edit widgets and buttons at the bottom\\r\\n self.mainLayout = QVBoxLayout()\\r\\n # Creating the Image Label for the Business logo\\r\\n self.labelImg = QLabel()\\r\\n self.labelImg.setAlignment(Qt.AlignCenter)\\r\\n self.pixmap = QPixmap('kafflogo.png')\\r\\n self.labelImg.setPixmap(self.pixmap)\\r\\n # Setting the vertical layout as the main Layout of the tab 1\\r\\n self.tab1.setLayout(self.mainLayout)\\r\\n self.mainLayout.addWidget(self.labelImg)\\r\\n # Adding the grid layout under the image\\r\\n self.mainLayout.addLayout(self.grid)\",\n \"def add_label(self, text, location=(None,0)):\\n label=QtWidgets.QLabel(self)\\n label.setText(str(text))\\n label.setAlignment(QtCore.Qt.AlignLeft)\\n location=self._normalize_location(location)\\n self.formLayout.addWidget(label,*location)\\n return label\",\n \"def set_controls(self):\\n # Image control\\n image = pyxbmct.Image(addonfolder+artsfolder+'/tvh.png')\\n self.placeControl(image, 0, 0, rowspan=8, columnspan=16)\\n\\n\\t\\t# Label information\\n image = pyxbmct.Image(addonfolder+artsfolder+'/users.png')\\n self.placeControl(image, 8, 1, rowspan=1, columnspan=14)\\n\\t\\t\\n\\t\\t# Username input\\n image = pyxbmct.Image(addonfolder+artsfolder+'/username.png')\\n self.placeControl(image, 10, 1, rowspan=1, columnspan=3)\\n self.username_input = pyxbmct.Edit('')\\n self.placeControl(self.username_input, 10, 4, rowspan=1, columnspan=4)\\n\\n\\t\\t# Password input\\n image = pyxbmct.Image(addonfolder+artsfolder+'/password.png')\\n self.placeControl(image, 11, 1, rowspan=1, columnspan=3)\\n self.password_input = pyxbmct.Edit('', isPassword=True)\\n self.placeControl(self.password_input, 11, 4, rowspan=1, columnspan=4)\\n\\n\\t\\t# Next button\\n self.next_button = pyxbmct.Button('Next')\\n self.placeControl(self.next_button, 13, 14, rowspan=1, columnspan=1)\\n # Connect close button\\n self.connect(self.next_button, lambda: self.page())\\n\\t\\t\\n\\t\\t# Close button\\n self.close_button = pyxbmct.Button('Exit')\\n self.placeControl(self.close_button, 13, 15, rowspan=1, columnspan=1)\\n self.connect(self.close_button, lambda: self.closepage())\",\n \"def add_text_label(self, name, value=None, label=None, location=(None,0)):\\n widget=widget_label.LVTextLabel(self,value=value)\\n widget.setObjectName(_fromUtf8(self.name+\\\"_\\\"+name))\\n return self.add_simple_widget(name,widget,label=label,add_indicator=False,location=location)\",\n \"def __init__(self, transform, size, label, loc,\\n pad=0.1, borderpad=0.1, sep=2, prop=None, frameon=True):\\n self.size_bar = AuxTransformBox(transform)\\n self.size_bar.add_artist(Rectangle((0, 0), size, 0, fc='none', color='white', lw=3))\\n\\n self.txt_label = TextArea(label, dict(color='white', size='x-large', weight='normal'),\\n minimumdescent=False)\\n\\n self._box = VPacker(children=[self.size_bar, self.txt_label],\\n align=\\\"center\\\",\\n pad=0, sep=sep)\\n\\n AnchoredOffsetbox.__init__(self, loc, pad=pad, borderpad=borderpad,\\n child=self._box,\\n prop=prop,\\n frameon=frameon)\",\n \"def fromControls(self,widget):\",\n \"def getWidget(self):\",\n \"def create_widgets( self ):\",\n \"def create_gen_labels(master: Widget) -> None:\\r\\n\\r\\n gen_label = Label(master, text='Gen:', font=self.FONT_NORMAL, bg=self.MAIN_BG)\\r\\n gen_label.pack(side=LEFT)\\r\\n self.gen_number = Label(master, text=0, font=self.FONT_NORMAL, bg=self.MAIN_BG)\\r\\n self.gen_number.pack(side=LEFT)\",\n \"def create_widgets(self):\\n # only ever shown card in player's hand, so create widgets when dealt\\n self.name_label = tk.Label(self, text=self.name)\\n self.ability_label = tk.Label(self, text=self.ability)\\n self.food_label = tk.Label(self, text=\\\"Food: \\\" + str(self.food))\\n self.use_button = TraitUseButton(self, text=\\\"USE\\\", command=self.use)\",\n \"def makeInstructionLabel(self, textInstruction):\\n Label(self.sideFrame, text=textInstruction,\\n font=self.sideFont, anchor='w').pack(fill=X, padx=10)\",\n \"def _create_signal_label(self, master, row, column, signame, minimum, maximum, unit):\\n lbl_name = Label(master, text=signame, bg=COLUMN_COLOR_LIST[column], font=(\\\"Helvetica\\\", 12))\\n lbl_min = Label(master, text=minimum, bg=COLUMN_COLOR_LIST[column], font=(\\\"Helvetica\\\", 9))\\n lbl_max = Label(master, text=maximum, bg=COLUMN_COLOR_LIST[column + 1], font=(\\\"Helvetica\\\", 9))\\n lbl_unit = Label(master, text=unit, bg=COLUMN_COLOR_LIST[column + 2], font=(\\\"Helvetica\\\", 9))\\n lbl_name.grid(row=row, column=column, columnspan=3, sticky=W+E)\\n lbl_min.grid(row=row+1, column=column, sticky=W+E)\\n lbl_max.grid(row=row+1, column=column+1, sticky=W+E)\\n lbl_unit.grid(row=row+1, column=column+2, sticky=W+E)\",\n \"def __repr__(self):\\n self.makeLabel() # update the label in case it's changed.\\n return self.label\",\n \"def set_controls(self):\\n # Image control\\n image = pyxbmct.Image(addonfolder+artsfolder+'/osc.png')\\n self.placeControl(image, 0, 0, rowspan=7, columnspan=16)\\n\\n\\t\\t# Label information\\n image = pyxbmct.Image(addonfolder+artsfolder+'/readers.png')\\n self.placeControl(image, 7, 1, rowspan=1, columnspan=14)\\n\\t\\t\\n\\t\\t# Hostname input\\n image = pyxbmct.Image(addonfolder+artsfolder+'/hostname.png')\\n self.placeControl(image, 9, 0, rowspan=1, columnspan=4)\\n self.hostname_input = pyxbmct.Edit('')\\n self.placeControl(self.hostname_input, 9, 4, rowspan=1, columnspan=5)\\n\\n\\t\\t# Port input\\n image = pyxbmct.Image(addonfolder+artsfolder+'/port.png')\\n self.placeControl(image, 12, 1, rowspan=1, columnspan=3)\\n self.port_input = pyxbmct.Edit('')\\n self.placeControl(self.port_input, 12, 4, rowspan=1, columnspan=2)\\n\\n\\t\\t# Username input\\n image = pyxbmct.Image(addonfolder+artsfolder+'/username.png')\\n self.placeControl(image, 10, 1, rowspan=1, columnspan=3)\\n self.username_input = pyxbmct.Edit('')\\n self.placeControl(self.username_input, 10, 4, rowspan=1, columnspan=4)\\n\\t\\t\\n\\t\\t# Password input\\n image = pyxbmct.Image(addonfolder+artsfolder+'/password.png')\\n self.placeControl(image, 11, 1, rowspan=1, columnspan=3)\\n self.password_input = pyxbmct.Edit('', isPassword=True)\\n self.placeControl(self.password_input, 11, 4, rowspan=1, columnspan=4)\\n\\n\\t\\t# Close button\\n self.close_button = pyxbmct.Button('Exit')\\n self.placeControl(self.close_button, 13, 15, rowspan=1, columnspan=1)\\n self.connect(self.close_button, lambda: self.page(Readers))\",\n \"def __init__(self, name, header, desc=None, prop=None, style=None, attr=None,\\n disabled=False, css_cls=None):\\n Widget.__init__(self, name, desc=desc, prop=prop, style=style, attr=attr,\\n css_cls=css_cls)\\n self._header = header\\n self._disabled = disabled\",\n \"def pack(self, label, *widgets, **kwargs):\\n col = 0\\n border = kwargs.get(\\\"border\\\", 10)\\n font = self.GetFont()\\n if \\\"size\\\" in kwargs:\\n font.SetPointSize(kwargs[\\\"size\\\"])\\n if kwargs.get(\\\"bold\\\", False):\\n font.SetWeight(wx.BOLD)\\n\\n if label != \\\"\\\":\\n text = wx.StaticText(self, label=label)\\n text.SetFont(font)\\n if \\\"textcol\\\" in kwargs:\\n text.SetForegroundColour(kwargs[\\\"textcol\\\"])\\n if not widgets:\\n span = (1, 2)\\n else:\\n span = (1, 1)\\n self.sizer.Add(text, pos=(self.row, col), border=border, flag=wx.ALIGN_CENTRE_VERTICAL | wx.LEFT, span=span)\\n col += 1\\n else:\\n text = None\\n\\n for w in widgets:\\n span = (1, 1)\\n w.label = text\\n if hasattr(w, \\\"span\\\"):\\n span = (1, w.span)\\n w.SetFont(font)\\n w.Enable(col == 0 or kwargs.get(\\\"enable\\\", True))\\n self.sizer.Add(w, pos=(self.row, col), border=border, flag=wx.ALIGN_CENTRE_VERTICAL | wx.EXPAND | wx.LEFT, span=span)\\n col += span[1]\\n self.row += 1\",\n \"def setLabel( self, cCtrlName, cLabel ):\\n self.setControlModelProperty( cCtrlName, \\\"Label\\\", cLabel )\",\n \"def create_widgets(self):\\n self.instruction = Label(self, text=\\\"Enter the password \\\")\\n self.instruction.grid(row=0, column=0, columnspan=2, sticky=W)\\n\\n self.password_entry = Entry(self)\\n self.password_entry.grid(row=0, column=1, sticky=W)\\n\\n self.submit_button = Button(self, text=\\\"Submit\\\",\\n command=self.reveal, width=10)\\n self.submit_button.grid(row=2, column=0, sticky=W)\\n\\n self.exit_button = Button(self, text=\\\"Exit\\\",\\n command=self.quit, width=10)\\n self.exit_button.grid(row=2, column=1, sticky=W)\\n\\n #self.close_button = Button(self, text = \\\"Close\\\", command = self.quit)\\n #self.close_button.grid(row = 2, column = 0, sticky = E)\\n\\n self.text = Text(self, width=35, height=5, wrap=WORD)\\n self.text.grid(row=3, column=0, columnspan=2, sticky=W)\",\n \"def create_title(self):\\n label_title = Label(self.frame, text=\\\"Brick Breaker\\\", font=(\\\"Arial\\\", 40), bg='light blue',\\n fg='white')\\n label_title.pack()\",\n \"def __init__(self, label: str):\\n pass\",\n \"def create_widgets(self):\\n for name in self.names:\\n new_label = Label(text=name, id=name, font_size=50)\\n self.root.ids.name_entries.add_widget(new_label)\",\n \"def init_infobox(self):\\n infobox = tk.Label(self, text=\\\"\\\", justify=\\\"left\\\")\\n infobox.grid(row=0, column=1, sticky=\\\"n\\\")\\n self.infobox = infobox\",\n \"def create_base(self):\\n if self.debug:\\n print(\\\"Creating base\\\")\\n self.console_panel = ConsolePanel(self.root)\\n self.side_panel = SidePanel(self.root, self.populate_main_panel)\\n self.side_panel.set_separator(\\\"word_word\\\")\\n self.main_panel = MainPanel(self.root, action=\\\"word_word\\\")\",\n \"def create_labels(self):\\n for name in self.names:\\n new_label = Label(text=name)\\n self.root.ids.names_box.add_widget(new_label)\",\n \"def create_widget(parent, control_name, control_value, trait,\\n label_class=None, user_data=None):\\n # Create the list widget: a frame\\n frame = QtGui.QFrame(parent=parent)\\n frame.setFrameShape(QtGui.QFrame.StyledPanel)\\n\\n # Create tools to interact with the list widget: expand or collapse -\\n # add a list item - remove a list item\\n tool_widget = QtGui.QWidget(parent)\\n layout = QtGui.QHBoxLayout()\\n layout.addStretch(1)\\n layout.setContentsMargins(0, 0, 0, 0)\\n layout.setSpacing(2)\\n tool_widget.setLayout(layout)\\n # Create the tool buttons\\n resize_button = QtGui.QToolButton()\\n layout.addWidget(resize_button)\\n # Set the tool icons\\n icon = QtGui.QIcon()\\n icon.addPixmap(QtGui.QPixmap(\\n _fromUtf8(\\\":/soma_widgets_icons/nav_down\\\")),\\n QtGui.QIcon.Normal, QtGui.QIcon.Off)\\n resize_button.setIcon(icon)\\n resize_button.setFixedSize(30, 22)\\n frame.user_data = user_data\\n\\n editable_labels = False\\n handler = getattr(trait, 'handler', trait)\\n if handler.inner_traits():\\n editable_labels = True\\n frame.inner_trait = trait.handler.inner_traits()[0]\\n\\n add_button = QtGui.QToolButton()\\n delete_button = QtGui.QToolButton()\\n layout.addWidget(add_button)\\n # Set the tool icons\\n icon = QtGui.QIcon()\\n icon.addPixmap(\\n QtGui.QPixmap(_fromUtf8(\\\":/soma_widgets_icons/add\\\")),\\n QtGui.QIcon.Normal, QtGui.QIcon.Off)\\n add_button.setIcon(icon)\\n add_button.setFixedSize(30, 22)\\n delete_button.setFixedSize(30, 22)\\n # Add list item callback\\n add_hook = partial(\\n ControllerControlWidget.add_item, weak_proxy(parent),\\n control_name, weak_proxy(frame))\\n add_button.clicked.connect(add_hook)\\n\\n # Create the associated controller widget\\n controller_widget = ControllerWidget(control_value, parent=frame,\\n live=True,\\n editable_labels=editable_labels,\\n user_data=user_data)\\n\\n # Store some parameters in the list widget\\n frame.trait = trait\\n frame.controller = control_value\\n frame.controller_widget = controller_widget\\n frame.connected = False\\n\\n # Add the list controller widget to the list widget\\n frame.setLayout(controller_widget.layout())\\n\\n # Set some callback on the controller control tools\\n # Resize callback\\n resize_hook = partial(\\n ControllerControlWidget.expand_or_collapse, weak_proxy(frame),\\n weak_proxy(resize_button))\\n resize_button.clicked.connect(resize_hook)\\n\\n if getattr(trait, 'expanded') is False:\\n ControllerControlWidget.set_expanded(frame, resize_button, False)\\n\\n # Create the label associated with the controller widget\\n control_label = trait.label\\n if control_label is None:\\n control_label = control_name\\n if label_class is None:\\n label_class = QtGui.QLabel\\n if control_label is not None:\\n label = label_class(control_label, parent)\\n else:\\n label = None\\n\\n return (frame, (label, tool_widget))\",\n \"def __init__(self):\\n\\n self.boxes = [\\n urwid.AttrMap(urwid.Text('QMap'), 'menu header'),\\n urwid.Divider('=')\\n ]\\n\\n self.widget = urwid.Padding(None, align='center')\\n self.update()\\n\\n BaseTimedWidgetWrap.__init__(self, self.widget)\",\n \"def __init__(self,name,*args,**kargs):\\n QtGui.QWidget.__init__(self,*args)\\n layout = QtGui.QHBoxLayout()\\n #layout.setSpacing(0)\\n layout.setMargin(0)\\n self.setLayout(layout)\\n self.key = str(name)\\n if 'text' in kargs:\\n text = kargs['text']\\n else:\\n text = self.key\\n if text:\\n self.label = QtGui.QLabel()\\n text = standardIcon(text)\\n if isinstance(text,QtGui.QPixmap):\\n self.label.setPixmap(text)\\n else:\\n self.label.setText(text)\\n if 'b' in kargs.get('stretch',''):\\n layout.addStretch()\\n layout.addWidget(self.label)\\n if 'a' in kargs.get('stretch',''):\\n print 'test'\\n layout.addStretch()\\n\\n if 'data' in kargs:\\n self.data = kargs['data']\\n\\n if 'enabled' in kargs:\\n self.setEnabled(kargs['enabled'])\\n\\n if 'readonly' in kargs:\\n try:\\n self.input.setReadOnly(kargs['readonly'])\\n except:\\n print \\\"Can not set readonly: %s,%s\\\" % (name,kargs)\\n\\n if 'width' in kargs:\\n try:\\n print 'SETTING WIDTH',self.input\\n self.input.setMinimumWidth(kargs['width'])\\n except:\\n pass\\n \\n if 'tooltip' in kargs:\\n self.setToolTip(kargs['tooltip'])\\n ## if hasattr(self,'label'):\\n ## self.label.setToolTip(kargs['tooltip'])\\n ## try:\\n ## self.input.setToolTip(kargs['tooltip'])\\n ## except:\\n ## pass\\n\\n if 'buttons' in kargs and kargs['buttons']:\\n #print kargs\\n self.buttons = dialogButtons(self,kargs['buttons'])\\n layout.addItem(self.buttons)\",\n \"def create_widgets(self):\\n Label(self, text=\\\"Choose your favorite movie type\\\").grid(row=0, column=0, sticky=W)\\n\\n # instructions\\n Label(self, text=\\\"Select all that apply:\\\").grid(row=1, column=0, sticky=W)\\n\\n # comedy check button\\n self.comedy = BooleanVar()\\n Checkbutton(self, text=\\\"Comedy\\\", variable=self.comedy, command=self.update_text).grid(row=2, column=0, sticky=W)\\n\\n # drama check button\\n self.drama = BooleanVar()\\n Checkbutton(self, text=\\\"Drama\\\", variable=self.drama, command=self.update_text).grid(row=3, column=0, sticky=W)\\n\\n # romance check button\\n self.romance = BooleanVar()\\n Checkbutton(self, text=\\\"Romance\\\", variable=self.romance, command=self.update_text).grid(row=4, column=0, sticky=W)\\n\\n self.result = Text(self, width=40, height=5, wrap=WORD) # wrap=WORD when dropping 1 line down the words will not cut in the middle, drop line prior or after word end\\n self.result.grid(row=5, column=0, columnspan=3)\",\n \"def __init__(self, gui, shapes, x, y, callback=None, label=None,\\r\\n label_pos='left', shortcut=None):\\r\\n if not (type(shapes) is list or type(shapes) is tuple):\\r\\n self.shapes = [shapes]\\r\\n else:\\r\\n self.shapes = shapes\\r\\n self.toggle = len(self.shapes) > 1 #this widget can toggle between two\\r\\n self.clicked = False\\r\\n self.callback = callback\\r\\n self.shortcut = shortcut\\r\\n self.label_pos = label_pos\\r\\n if label:\\r\\n self.labelobj = pi3d.String(font=gui.font, string=label, is_3d=False,\\r\\n camera=gui.camera, justify='L')\\r\\n self.labelobj.set_shader(gui.shader)\\r\\n else:\\r\\n self.labelobj = None\\r\\n self.relocate(x, y)\\r\\n if not (self in gui.widgets): #because TextBox re-runs Widget.__init__\\r\\n gui.widgets.append(self)\\r\\n self.visible = True\",\n \"def widget(self, p_int): # real signature unknown; restored from __doc__\\n pass\",\n \"def widget(self, p_int): # real signature unknown; restored from __doc__\\n pass\",\n \"def getControls(self):\",\n \"def TCFieldWidget(field, request):\\n widget = FieldWidget(field, TCWidget(request))\\n # widget.label = u'' # don't show the label twice\\n return widget\",\n \"def _build_gui(self):\\n box = qt.QHBoxLayout(self)\\n box.addWidget(self._but)\\n\\n lab = self._lab\\n Pol = qt.QSizePolicy\\n lab.setSizePolicy(Pol.Expanding, Pol.Preferred)\\n lab.setFrameStyle(qt.QLabel.Panel)\\n box.addWidget(lab)\",\n \"def _build_gui(self):\\n box = qt.QHBoxLayout(self)\\n box.addWidget(self._but)\\n\\n lab = self._lab\\n Pol = qt.QSizePolicy\\n lab.setSizePolicy(Pol.Expanding, Pol.Preferred)\\n lab.setFrameStyle(qt.QLabel.Panel)\\n box.addWidget(lab)\",\n \"def __init__(self,name,value,*args,**kargs):\\n \\n kargs['text'] = '' # Force no label\\n self.input = value\\n InputItem.__init__(self,name,*args,**kargs)\\n self.layout().insertWidget(1,self.input)\",\n \"def __init__(self):\\n self.defaultTheme = \\\"DarkAmber\\\"\\n self.version = 1.4\\n self.versionName = \\\"class update\\\"\\n self.title = \\\"Lms GUI default window\\\"\\n self.layout = [[sg.Text(\\\"This is the base window class layout.\\\")]]\\n self.elementJustification = 'c'\\n self.location=(500, 300)\\n self.running = True\\n self.window = None\\n self.event = \\\"\\\"\\n self.values = []\\n self.nextAction = None\",\n \"def makeTitle(self):\\n l1=Label(self.app, text=\\\"Asset Allocation Combinations\\\")\\n l1.grid(row=0, column=0)\",\n \"def __init__(self, x, y, parent, game, app, *args, **kwargs):\\n Label.__init__(self, parent, *args, **kwargs)\\n self.configure(bg=Color.MID_TONE)\\n\\n # Helper to create mouse-event listeners for tiles, so they don't\\n # trigger when game has ended.\\n def create_marker_listener(func):\\n def listener(_event):\\n if game.get_state() is GameState.PLAYING:\\n func(_event)\\n\\n return listener\\n\\n # Highlight tile when mouse is over it\\n def mouse_over(_event):\\n if game.get_tile(x, y) is MarkerType.NONE:\\n self.configure(bg=Color.HIGH_TONE)\\n\\n # Return back to normal when mouse off\\n def mouse_leave(_event):\\n self.configure(bg=Color.MID_TONE)\\n\\n self.bind(\\\"<Enter>\\\", create_marker_listener(mouse_over))\\n self.bind(\\\"<Leave>\\\", create_marker_listener(mouse_leave))\\n self.bind(\\\"<Button-1>\\\", create_marker_listener(\\n lambda _e: app.grid_clicked(x, y)))\\n self.bind(\\\"<ButtonRelease-1>\\\",\\n create_marker_listener(mouse_leave))\",\n \"def buildmainframe(self):\\n self.mainframewidgets=[]\\n for x in range(3):\\n thislabel = Label(self.mainframe, text=str(x))\\n thislabel.grid()\\n self.mainframewidgets.append(thislabel)\",\n \"def __init__(self, dotNumber, isRaised=False):\\n\\n Gtk.Alignment.__init__(self)\\n if dotNumber in [1, 2, 3, 7]:\\n self.set(1.0, 0.5, 0.0, 0.0)\\n self.set_padding(0, 0, 3, 0)\\n else:\\n self.set(0.0, 0.5, 0.0, 0.0)\\n self.set_padding(0, 0, 0, 3)\\n\\n self.label = Gtk.Label()\\n self.add(self.label)\\n if isRaised:\\n self.raiseDot()\\n else:\\n self.lowerDot()\",\n \"def create_widgets(self):\\r\\n\\r\\n # we pass self since 'self' is a ttk frame\\r\\n # the tic tac toe buttons\\r\\n self.btn_tl = ttk.Button(self)\\r\\n self.btn_tm = ttk.Button(self)\\r\\n self.btn_tr = ttk.Button(self)\\r\\n self.btn_ml = ttk.Button(self)\\r\\n self.btn_mm = ttk.Button(self)\\r\\n self.btn_mr = ttk.Button(self)\\r\\n self.btn_bl = ttk.Button(self)\\r\\n self.btn_bm = ttk.Button(self)\\r\\n self.btn_br = ttk.Button(self)\\r\\n\\r\\n # the reset button\\r\\n self.btn_reset = ttk.Button(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68835676","0.66542274","0.63613844","0.6344261","0.63232493","0.630243","0.6290771","0.61542106","0.6145763","0.6128528","0.60839635","0.60793793","0.6052032","0.60399705","0.60251766","0.6019413","0.5956044","0.59486943","0.5947741","0.5929713","0.592145","0.59027714","0.5897525","0.58900255","0.5885295","0.58769643","0.5871108","0.5863109","0.58589906","0.5858728","0.5858443","0.5853256","0.58291","0.58266807","0.5817648","0.5778226","0.5695481","0.5694086","0.5693424","0.5693087","0.56831545","0.5679762","0.5672391","0.56670046","0.5660985","0.5657537","0.5648342","0.5645032","0.56372285","0.56364197","0.5634726","0.56341195","0.5606234","0.5590015","0.55875874","0.5585702","0.55826604","0.5578148","0.557277","0.5563365","0.55500185","0.55492276","0.5540989","0.55287045","0.5524254","0.55202866","0.5519667","0.55154884","0.5503924","0.55005586","0.54928833","0.5487588","0.54811376","0.5474216","0.54681647","0.54602295","0.54589826","0.5445203","0.5443228","0.5431134","0.54264784","0.5423422","0.54200536","0.5418309","0.5411461","0.5411225","0.54037434","0.5378702","0.5378702","0.5366552","0.53653085","0.5365219","0.5365219","0.5359049","0.5358661","0.5353649","0.53518176","0.5350866","0.53450125","0.5341609"],"string":"[\n \"0.68835676\",\n \"0.66542274\",\n \"0.63613844\",\n \"0.6344261\",\n \"0.63232493\",\n \"0.630243\",\n \"0.6290771\",\n \"0.61542106\",\n \"0.6145763\",\n \"0.6128528\",\n \"0.60839635\",\n \"0.60793793\",\n \"0.6052032\",\n \"0.60399705\",\n \"0.60251766\",\n \"0.6019413\",\n \"0.5956044\",\n \"0.59486943\",\n \"0.5947741\",\n \"0.5929713\",\n \"0.592145\",\n \"0.59027714\",\n \"0.5897525\",\n \"0.58900255\",\n \"0.5885295\",\n \"0.58769643\",\n \"0.5871108\",\n \"0.5863109\",\n \"0.58589906\",\n \"0.5858728\",\n \"0.5858443\",\n \"0.5853256\",\n \"0.58291\",\n \"0.58266807\",\n \"0.5817648\",\n \"0.5778226\",\n \"0.5695481\",\n \"0.5694086\",\n \"0.5693424\",\n \"0.5693087\",\n \"0.56831545\",\n \"0.5679762\",\n \"0.5672391\",\n \"0.56670046\",\n \"0.5660985\",\n \"0.5657537\",\n \"0.5648342\",\n \"0.5645032\",\n \"0.56372285\",\n \"0.56364197\",\n \"0.5634726\",\n \"0.56341195\",\n \"0.5606234\",\n \"0.5590015\",\n \"0.55875874\",\n \"0.5585702\",\n \"0.55826604\",\n \"0.5578148\",\n \"0.557277\",\n \"0.5563365\",\n \"0.55500185\",\n \"0.55492276\",\n \"0.5540989\",\n \"0.55287045\",\n \"0.5524254\",\n \"0.55202866\",\n \"0.5519667\",\n \"0.55154884\",\n \"0.5503924\",\n \"0.55005586\",\n \"0.54928833\",\n \"0.5487588\",\n \"0.54811376\",\n \"0.5474216\",\n \"0.54681647\",\n \"0.54602295\",\n \"0.54589826\",\n \"0.5445203\",\n \"0.5443228\",\n \"0.5431134\",\n \"0.54264784\",\n \"0.5423422\",\n \"0.54200536\",\n \"0.5418309\",\n \"0.5411461\",\n \"0.5411225\",\n \"0.54037434\",\n \"0.5378702\",\n \"0.5378702\",\n \"0.5366552\",\n \"0.53653085\",\n \"0.5365219\",\n \"0.5365219\",\n \"0.5359049\",\n \"0.5358661\",\n \"0.5353649\",\n \"0.53518176\",\n \"0.5350866\",\n \"0.53450125\",\n \"0.5341609\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60125613"},"document_rank":{"kind":"string","value":"16"}}},{"rowIdx":999,"cells":{"query":{"kind":"string","value":"fit le modele puis retourne ses performances en test"},"document":{"kind":"string","value":"def score(self, archi:ArchitectureNN):\n archi.fit_model(self.train_data, **self.train_params)\n \n return archi.compute_test_score(self.test_data)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def fit_test(self):","def experiment_models(train, test, train_target, test_target):\n # Linear models\n linear_models = [(LinearRegression, {\"n_jobs\": -1}),\n (Lasso, {\"alpha\": 3}),\n (Ridge, {\"alpha\": 3}),\n (LinearSVR, {\"random_state\": 0, \"tol\": 1e-5})]\n\n # Add polynomial features\n poly = preprocessing.PolynomialFeatures(2)\n\n # scaler\n scaler = preprocessing.StandardScaler().fit(train)\n\n print(\"Use linear models with linear features\")\n for model_ in linear_models:\n scaled_train = scaler.transform(train)\n scaled_test = scaler.transform(test)\n model = model_[0](**model_[1])\n model.fit(scaled_train, train_target.to_numpy())\n train_pred = model.predict(scaled_train)\n valid_pred = model.predict(scaled_test)\n print(\"=========================================\")\n print(f\"Model : {model_}\")\n compute_metrics(train_pred, train_target, valid_pred, test_target)\n print(\"=========================================\")\n\n print(\"Use linear models with polynomial features\")\n train = poly.fit_transform(train)\n test = poly.transform(test)\n scaler = preprocessing.StandardScaler().fit(train)\n for model_ in linear_models:\n scaled_train = scaler.transform(train)\n scaled_test = scaler.transform(test)\n model = model_[0](**model_[1])\n model.fit(scaled_train, train_target.to_numpy())\n train_pred = model.predict(scaled_train)\n valid_pred = model.predict(scaled_test)\n print(\"=========================================\")\n print(f\"Model : {model_}\")\n compute_metrics(train_pred, train_target, valid_pred, test_target)\n print(\"=========================================\")","def eval_perf_total(model, X_train, y_train, X_test, y_test):\n\n y_hat_train = model.predict(X_train)\n y_hat_test = model.predict(X_test)\n \n train_mae = metrics.mean_absolute_error(y_train, y_hat_train)\n train_mse = metrics.mean_squared_error(y_train, y_hat_train)\n train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_hat_train))\n train_r = metrics.r2_score(y_train, y_hat_train)\n\n print('Evaluating Performance on Training Data:\\n')\n print(f' Train Mean Absolute Error: {train_mae:,.2f}')\n print(f' Train Mean Squared Error: {train_mse:,.2f}\\n')\n print(f'Train Root Mean Squared Error: {train_rmse:,.2f}')\n print(f'Train R-Square Value: {round(train_r,2)}')\n\n print('\\n'+'---'*25+'\\n')\n\n test_mae = metrics.mean_absolute_error(y_test, y_hat_test)\n test_mse = metrics.mean_squared_error(y_test, y_hat_test)\n test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat_test))\n test_r = metrics.r2_score(y_test, y_hat_test)\n\n print('Evaluating Performance on Testing Data:\\n')\n print(f' Test Mean Absolute Error: {test_mae:,.2f}')\n print(f' Test Mean Squared Error: {test_mse:,.2f}\\n')\n print(f'Test Root Mean Squared Error: {test_rmse:,.2f}')\n print(f'Test R-Square Value: {round(test_r,2)}')","def eval_perf_train(model, X_train=None, y_train=None):\n\n # if X_train != None and y_train != None:\n\n y_hat_train = model.predict(X_train)\n \n train_mae = metrics.mean_absolute_error(y_train, y_hat_train)\n train_mse = metrics.mean_squared_error(y_train, y_hat_train)\n train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_hat_train))\n train_r = metrics.r2_score(y_train, y_hat_train)\n\n print('Evaluating Performance on Training Data:\\n')\n print(f'Train Mean Absolute Error: {train_mae:,.2f}')\n print(f'Train Mean Squared Error: {train_mse:,.2f}\\n')\n print(f'Train Root Mean Squared Error: {train_rmse:,.2f}')\n print(f'Train R-Square Value: {round(train_r,2)}')\n\n # if X_test != None and y_test != None:\n\n # y_hat_test = model.predict(X_test)\n\n # test_mae = metrics.mean_absolute_error(y_test, y_hat_test)\n # test_mse = metrics.mean_squared_error(y_test, y_hat_test)\n # test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat_test))\n # test_r = metrics.r2_score(y_test, y_hat_test)\n\n # print('Evaluating Performance on Testing Data:\\n')\n # print(f'Test Mean Absolute Error: {test_mae:,.2f}')\n # print(f'Test Mean Squared Error: {test_mse:,.2f}\\n')\n # print(f'Test Root Mean Squared Error: {test_rmse:,.2f}')\n # print(f'Test R-Square Value: {round(test_r,2)}')","def run_model(Xp_train=None,y_train=None,Xp_test=None,y_test=None,mt='bl', params={}):\n models = { \n \"bl\": lambda : None,\n \"lr\": LinearRegression,\n \"dtr\": DecisionTreeRegressor,\n \"gbr\": GradientBoostingRegressor\n }\n\n # Select Model, returns None for baseline\n model = models[mt](**params)\n\n # Fit Model and make predictions\n if mt != 'bl': # If model not baseline\n # Dummy encoding , (each category of n levels or attributes is converted into n-1 dichotomous variables)\n X_train = pd.get_dummies(Xp_train, columns=['type', 'duration','location'],drop_first=True)\n X_test = pd.get_dummies(Xp_test, columns=['type', 'duration','location'],drop_first=True)\n model.fit(X_train,y_train)\n # Make Predictions\n y_pred = model.predict(X_test)\n #print X_test.shape\n y_predtr = model.predict(X_train) \n else: # Compute baseline\n y_pred = np.median(y_train).repeat(len(y_test))\n y_predtr = np.median(y_train).repeat(len(y_train))\n \n \n\n # Report metrics\n print(\"Model name: %s\" % (model.__class__.__name__ if model else \"Price Median Baseline\"))\n if mt != 'bl':\n print(\"hyper-parameters: \" + \", \".join( \"{0}: {1}\".format(k,v) for (k,v) in params.items() ) )\n print(\"Mean absolute error training set: %.2f\" % mean_absolute_error(y_train, y_predtr)) \n print(\"Mean absolute error testing set: %.2f \\n\" % mean_absolute_error(y_test, y_pred))","def test_models(directorio=''):\r\n \r\n print('The trained models will be tested now')\r\n start = time.time()\r\n \r\n busqueda = \"ls \" + directorio + \"/*.h5 > model_names.txt\"\r\n\r\n os.system(busqueda)\r\n\r\n X = np.load(directorio + '/Xtest.npy')\r\n diccio = np.load(directorio + '/feature_standarisation.npy').item()\r\n y = pd.read_csv(directorio + '/dbtest.csv')['target'].values\r\n\r\n X = (X - diccio['mean'])/diccio['std']\r\n x = np.reshape(X,(X.shape[0],X.shape[2]))\r\n \r\n with open('model_names.txt','r') as f:\r\n for line in f:\r\n modelo = models.load_model(line[:len(line)-1])\r\n nombre = line.split('/')[1]\r\n outpred = modelo.predict(x)\r\n prediction = outpred >= 0.5\r\n \r\n cost = -(np.dot(y,np.log10(outpred)) + \\\r\n np.dot((1-y),np.log10(1-outpred)))/y.shape[0]\r\n precision,recall,fscore,support = PRFS(y, prediction)\r\n \r\n with open(directorio + '/test_results.txt','a') as tr:\r\n tr.write(nombre + '\\n')\r\n tr.write('cost function: '+str(cost[0])+'\\n')\r\n tr.write('samples: '+str(support)+'\\n')\r\n tr.write('precision: '+str(np.round(precision*100,2))+'\\n')\r\n tr.write('recall: '+str(np.round(recall*100,2))+'\\n')\r\n tr.write('f1-score: '+str(np.round(fscore*100,2))+'\\n')\r\n tr.write('\\n')\r\n tr.close()\r\n \r\n print('The test of all trained models lasted ', round(time.time()-start,2),' s')\r\n os.system('rm model_names.txt')\r\n \r\n return","def _evaluate_during_fit(self, test_loader, epoch):","def OTU_table_ML(OTU_table,metadata,obj_col):\n for ele in OTU_table.index:\n #print(ele)\n X.append(df.loc[ele])\n Y.append(metadata[obj_col][ele])\n precisions = []\n for train_time in range(100): \n X,Y = shuffle(X,Y)\n sample_num = len(X)\n sep_num = int(0.8*sample_num)\n train_set = [X[:sep_num],Y[:sep_num]]\n test_set = [X[sep_num:],Y[sep_num:]]\n clf = svm.SVC(gamma='scale')\n clf.fit(train_set[0], train_set[1]) \n predict_result = clf.predict(test_set[0])\n count = 0\n for i in range(len(predict_result)):\n if predict_result[i] == test_set[1][i]:\n count += 1\n else:\n pass\n precisions.append(1.0*count/len(predict_result))\n print(np.mean(precisions))","def get_perf(self) :\n self.train()\n\n prediction = self.clf.predict(self.df_test.drop(columns = 'up')[:-1])\n self.accuracy = accuracy_score(df_test['up'][length:].values, prediction)\n tn, fp, fn, tp = confusion_matrix(df_test['up'][length:].values, prediction).ravel()\n self.recall = tp/(tp+fn)\n self.specificity = tn / (tn+fp)\n\n\n self.df_true = self.df_true[self.length:]\n\n profit = 1\n mini = 1\n maxi = 1\n self.df_true['close'] = self.df_true['close'].map(lambda x : np.exp(x))\n for s in range(1,len(self.df_true)):\n if prediction[x-1] == 1 :\n result = ((self.df_true['close'].iloc[s] -self.df_true['close'].iloc[s-1]) / self.df_true['close'].iloc[s-1]) + 1\n profit = profit * result\n if result < mini :\n mini = result\n if maxi < result :\n maxi = result\n self.mini = mini\n self.maxi = maxi\n self.profit = profit","def fit(self) -> None:\n start_time = time.time()\n # ---- START -----\n train_df = self.processed_train_df[self.processed_train_df[self.filter_col_name]].dropna()\n train_features = train_df[self.feature_list]\n for label, model in zip(self.label_list, self.models):\n model.fit(train_features, train_df[label])\n # ---- END -----\n end_time = time.time()\n print(\"Finished fitting : elasped time : \" + str(end_time - start_time))","def test_predictor():","def test_training(self):\n\t\tpass","def testModel( self, classTest, classPred):","def test_variational():\n # iris\n #pres = \"Test pour le data set Iris (facile, classique)\"\n #test_from_func_variational(pres, 15, 10, 3, True, Iris)\n\n # breast cancer\n pres = \"Test pour le data set Breast Cancer (facile, classique)\"\n test_from_func_variational(pres, 15, 10, 3, True, Breast_cancer)\n\n # digits\n # pres = \"Test pour le data set Digits (difficile, classique)\"\n # test_from_func(pres, 10, 10, 10, True, Digits, quantum_instance)\n\n # wine\n # pres = \"Test pour le data set Wine (moyen, classique)\"\n # test_from_func(pres, 15, 10, 5, True, Wine, quantum_instance)\n\n # gaussian\n pres = \"Test pour des données gaussiennes (moyen, classique)\"\n for _ in range(1):\n print(\"\\n\")\n print(\"New iteration\")\n test_from_func_variational(pres, 25, 10, 2, True, Gaussian)\n print(\"\\n\")\n\n # small adn strings\n pres = \"Test pour des séquences ADN courtes (difficile, classique)\"\n test_from_func_variational(pres, 10, 15, 14, True, Sequence)\n\n #Quantum data\n pres = \"Test pour des données générées par ordinateur quantique (facile, quantique)\"\n print(pres)\n _, samp_train, samp_test, labels = ad_hoc_data(15, 10, 2, 0.3, True)\n sample_m, sample_p = stock_get(20, 0.3)\n\n labels_me = [-1, 1]\n samp_train_me = {-1: np.array(sample_m[:15]), 1: np.array(sample_p[:15])}\n samp_test_me = {-1: np.array(sample_m[15:]), 1: np.array(sample_p[15:])}\n print(samp_train)\n print(samp_train_me)\n print(samp_test)\n print(samp_test_me)\n\n my_impl_variational(samp_train, samp_test, labels)\n print(\"Pour autres données quantiques\")\n my_impl_variational(samp_train_me, samp_test_me, labels_me)","def test_fit(self):\n X = self.generate_X()\n task = mmRDTR()\n fit_result = task.fit(X)","def test(x_test, m_labels_test, ques_test, alpha, checkpoint_name, model_name, output_file_name):\n bestModel = pick_model(model_name, alpha)\n bestModel.load_state_dict(torch.load(checkpoint_name))\n bestModel.eval()\n best_scores = {}\n\n with torch.no_grad():\n test_pred = bestModel(x_test, m_labels_test)\n prec, recall, f1, df_output = get_qald_metrics(test_pred, m_labels_test, ques_test, mode='test')\n df_output.to_csv(output_file_name)\n print(\"Test -- f1 is {} \".format(f1))\n print(\"Test -- prec, recall, f1\", prec, recall, f1)\n best_scores['precision'] = prec\n best_scores['recall'] = recall\n best_scores['f1'] = f1\n\n # for name, mod in bestModel.named_modules():\n # if type(mod) == nn.ModuleList:\n # for name1, mod1 in mod.named_modules():\n # if 'cdd' not in name1 and 'AND' not in name1:\n # if 'batch' in name1.lower():\n # continue\n # elif 'or_max' in name1.lower():\n # continue\n # elif 'and' in name1.lower():\n # print(name1, mod1.cdd())\n # elif 'or' in name1.lower():\n # print(name1, mod1.AND.cdd())\n # else:\n # if 'cdd' not in name and 'AND' not in name:\n # if 'batch' in name.lower():\n # continue\n # elif 'or_max' in name.lower():\n # continue\n # elif 'and' in name.lower():\n # print(name, mod.cdd())\n # elif 'or' in name.lower():\n # print(name, mod.AND.cdd())\n return test_pred, best_scores","def train(self): \n start_time = time()\n\n # reset previous results\n self.best_result = pd.DataFrame()\n self.result = pd.DataFrame()\n\n # Generate dictionaries of all posible parameter permutations\n keys, values = zip(*self.params.items())\n self.permutations_dict = [dict(zip(keys, v)) for v in itertools.product(*values)] \n\n # Run through all models in parallel threads\n with Pool(self.thread_cnt) as p:\n result = p.map(self.analyze_model, self.permutations_dict)\n\n\n # wrap up results\n if self.classes_names: # acts as trigger for computation of cms\n for i, dic in enumerate(result):\n dic[\"id\"] = i\n self.cms = [(dic[\"id\"] ,dic.pop(\"cm\")) for dic in result]\n\n self.result = pd.DataFrame(result)\n self.best_result = self.result.iloc[self.result[\"score\"].argmax()] # store row with the best score\n self.best_result = self.result.iloc[self.result[\"f1_score\"].argmax()] # store row with the best score\n self.best_result = self.result.iloc[self.result[\"recall\"].argmax()] # store row with the best score\n self.best_result = self.result.iloc[self.result[\"precision\"].argmax()] # store row with the best score\n end_time = time()\n print(\"Finished evaluation\")\n print(\"Best parameteters found with:\", self.best_parameter_set())\n print(\"score=\", self.best_score())\n #print(\"f1_score=\", self.best_f1_score())\n #print(\"recall_score=\", self.best_recall_score())\n #print(\"precision_score=\", self.best_precision_score())\n print(\"Total evaluation time = {:.2f}s\".format(end_time-start_time))\n\n return self.best_parameter_set(), self.best_score()","def test(self, plot=False):\n accuracy_list = []\n fobj_avg = self.load_stats()\n\n for ii in range(settings.PARS['maxIters']):\n model = self.load_model(ii)\n D1 = model['D']\n W1 = model['W']\n\n # classification\n tic = time.time()\n accuracy_list.append(self.classification(D1, W1)[1])\n toc = time.time()\n print(\n 'Final recognition rate for OnlineDL is : {} , objective function value: {}, time: {}'\n .format(accuracy_list[ii], fobj_avg[ii], toc-tic)\n )\n\n accuracy_list = np.asarray(accuracy_list)\n\n print('Best recognition rate for OnlineDL is {} at iteration {}'.format(\n accuracy_list.max(), accuracy_list.argmax()))\n\n if plot:\n # plot the objective function values for all iterations\n plt.clf()\n plt.plot(list(fobj_avg.keys()), list(fobj_avg.values()), 'mo--', linewidth=2)\n plt.xlabel('Iterations')\n plt.ylabel('Average objective function value')\n plt.xticks(list(range(0, 20)), list(range(1, 21)))\n plt.show()\n\n plt.clf()\n plt.plot(accuracy_list, 'rs--', linewidth=2)\n plt.xticks(list(range(0, 20)), list(range(1, 21)))\n plt.xlabel('Iterations')\n plt.ylabel('Accuracy')\n plt.show()","def model(classifier, data):\n print(\"Beggining to test model\")\n train, test = cross_validation.train_test_split(data, test_size=.30)\n f,c = train[:,1:], train[:,0]\n classifier.fit(f,c,False)\n print(\"Score: \" + classifier.score(f,c))\n print(\"Finished testing model\")","def train(X : list, y : list, model_name : str, data, mode,*vals,**args):\n # 훈련 결과 dictionary\n scores = {}\n\n # 훈련 모드 선택\n\n model = MODELS[mode][model_name]\n\n X_train, X_val, y_train, y_val = train_test_split(data[X], data[y], train_size=0.7)\n \n model.fit(X_train, y_train)\n\n scores['train'] = validation(model, X_train, y_train, mode)\n scores['validation'] = validation(model, X_val, y_val, mode)\n\n return scores","def do_stats_model(x, y):\n Xx = sm.add_constant(x)\n sm_logit = sm.Logit(y, Xx)\n result = sm_logit.fit()\n print result.summary()\n result.pred_table()\n # linear model\n print \"linear regression model:\\n\"\n sm_linear = sm.OLS(y, Xx)\n result = sm_linear.fit()\n print result.summary()","def test_modele(self, data):\n\t\tif not isinstance(data, ndarray):\n\t\t\traise TypeError(\"erreur data = {} n'est pas de type numpy.ndarray \".format(type(data)))\t\t\t\n\n\t\t#result = self.session.run(tf.argmax(self.variable_mnsit[\"y_conv\"],1), feed_dict={self.variable_mnsit[\"x\"]: [data]})\n\t\t[tableau_pourcent, result] = self.session.run([self.y_conv, tf.argmax(self.y_conv, 1)], feed_dict={self.x : [data], self.keep_prob: 1.0})\n\t\tprint (\"l'ordinateur voit un ... {}\", result)\n\t\tprint (\"tableaux: {}\".format(tableau_pourcent))","def model_performance_comparison(self, yvar, prev_group, C_FP, C_FN):\n trn_bl_df = self.reweigh()\n # sample_weights = self.reweigh(bl_df=trn_bl_df)\n\n s_weights = trn_bl_df.instance_weights\n print(s_weights)\n\n trainset = trn_bl_df.convert_to_dataframe()[0]\n testset = trainset\n\n X = trainset.loc[:, trainset.columns != yvar]\n y = trainset[yvar]\n #X_test = testset.loc[:, trainset.columns != yvar]\n #y_test = testset[yvar]\n\n X_test = X\n y_test = y\n\n clf_ww = sklearn.linear_model.LogisticRegression(random_state=999).fit(X, y, sample_weight=s_weights)\n clf_wow = sklearn.linear_model.LogisticRegression(random_state=999).fit(X, y)\n\n output_probabilities_to_csv(model=clf_ww, x_test=X_test, path='probs_ww_withprvgroup.csv',priv_group_col=trainset[prev_group], actuals=y_test)\n output_probabilities_to_csv(model=clf_wow, x_test=X_test, path='probs_wow_withprvgroup.csv', priv_group_col=trainset[prev_group], actuals=y_test)\n\n print(\"------------------------------------------\")\n print(\"Accuracy of Vanila Logistic Model\")\n print(\"------------------------------------------\")\n print(\"Without Weights : \", round(clf_wow.score(X_test, y_test), 3))\n print(\"With Weights : \", round(clf_ww.score(X_test, y_test), 3))\n\n X_test_age1 = testset.loc[:, trainset.columns != yvar][testset[prev_group] == 1.0]\n y_test_age1 = testset[yvar][testset[prev_group] == 1.0]\n X_test_age0 = testset.loc[:, trainset.columns != yvar][testset[prev_group] == 0.0]\n y_test_age0 = testset[yvar][testset[prev_group] == 0.0]\n\n wow = round(abs(clf_wow.score(X_test_age0, y_test_age0) - clf_wow.score(X_test_age1, y_test_age1)), 3)\n ww = round(abs(clf_ww.score(X_test_age0, y_test_age0) - clf_ww.score(X_test_age1, y_test_age1)), 3)\n\n #output_probabilities_to_csv(model=clf_ww, x_test=X_test_age0, path='probs_unpriv_ww.csv')\n #output_probabilities_to_csv(model=clf_ww, x_test=X_test_age1, path='probs_priv_ww.csv')\n #output_probabilities_to_csv(model=clf_wow, x_test=X_test_age0, path='probs_unpriv_wow.csv')\n #output_probabilities_to_csv(model=clf_wow, x_test=X_test_age1, path='probs_priv_wow.csv')\n\n print(\"\")\n print(\"\")\n print(\"--------------------------------------------------------------\")\n print(\"Difference in accuracy between privileged and unprivileged\")\n print(\"--------------------------------------------------------------\")\n print(\"without weights : \", wow)\n print(\"with weights : \", ww)\n\n Ypredclf = clf_ww.predict(X_test)\n Ypredclf2 = clf_wow.predict(X_test)\n withw = confusion_matrix(y_test, Ypredclf)\n without = confusion_matrix(y_test, Ypredclf2)\n print(\"\")\n print(\"\")\n print(\"--------------------------------------------------------------\")\n print(\"Confusion Matrix\")\n print(\"--------------------------------------------------------------\")\n print(\"without weights\")\n print(without)\n print(\"\")\n print(\"\")\n print(\"with weights\")\n print(withw)\n\n a, b, c, d = without.ravel() #(tn, fp, fn, tp)\n a1, b1, c1, d1 = withw.ravel() #(tn, fp, fn, tp)\n\n withweights = b1 * C_FP + c1 * C_FN\n withoutweights = b * C_FP + c * C_FN\n\n print(\"\")\n print(\"\")\n print(\"cost with weights: \", withweights)\n print(\"cost without weights: \", withoutweights)\n print(\"Has cost decreased after reweighing?\", withweights < withoutweights)\n\n print('')\n print('SUMMARY TABLE')\n\n cost = fr.CostingFairness(input_dataframe=self.data,\n label_names=['credit'],\n protected_attribute_names=['Age_previliged'],\n trained_model=clf_ww)\n\n metrics_table = self.generate_pre_train_metrics_table(model_without_weights=clf_wow,\n model_with_weights=clf_ww,\n test_set=testset,\n target=yvar,\n privileged=prev_group,\n false_positive_cost=C_FP,\n false_negative_cost=C_FN)\n priv_diff_table = generate_privileged_diff(metrics_table)\n delta_table = generate_delta_table(metrics_table)\n costs_table = cost.return_cost_fairness_accuracy_optimised()\n\n # pdf = PDF()\n # pdf.add_page()\n # pdf.write_table_to_pdf(metrics_table)\n # pdf.write_table_to_pdf(priv_diff_table)\n # pdf.write_table_to_pdf(delta_table)\n # pdf.output('TEST01.pdf', 'F')\n\n print(\"\")\n print(\"What we see is interesting, after re-weighing the bias of the model has decreased significantly by {}%, \"\n \"with a very slight decrease in accuracy as shown earlier\".format(round((wow - ww) * 100)))\n\n return metrics_table, priv_diff_table, delta_table, costs_table","def test_all_false(directorio = str(), database = 'red_cod-db.pkl', \r\n local_function = 'fij_2.0_25_diccio'):\r\n df = pd.read_pickle(database)\r\n collection = pd.read_csv(directorio + '/compounds_collection.csv')\r\n \r\n cifs = [i for i in collection['cif']]\r\n maxsites = np.max(collection['sitios'])\r\n \r\n df = df[df['sitios'] > 0][df['sitios'] <= maxsites].reset_index(drop=True)\r\n df = df.loc[~df['cif'].isin(cifs)].reset_index(drop=True)\r\n \r\n x, _, df = inout_creator(df=df)\r\n \r\n x = compute_quotients(X=x)\r\n x, df = append_local_functions(X = x,df=df)\r\n \r\n busqueda = \"ls \" + directorio + \"/*.h5 > model_names.txt\"\r\n os.system(busqueda)\r\n \r\n diccio = np.load(directorio + '/feature_standarisation.npy').item()\r\n \r\n X = (x - diccio['mean'])/diccio['std']\r\n x = np.reshape(X,(X.shape[0],X.shape[2]))\r\n \r\n with open('model_names.txt','r') as f:\r\n for line in f:\r\n modelo = models.load_model(line[:len(line)-1])\r\n nombre = line.split('/')[1]\r\n \r\n outpred = modelo.predict(x)\r\n prediction = outpred >= 0.5\r\n df['y_pred'] = np.ravel(prediction)\r\n \r\n with open(directorio+'/test_with_all_false.txt','a') as tr:\r\n tr.write(nombre + '\\n')\r\n \r\n for sitios in range(1, max(df['sitios']) + 1):\r\n \r\n acc = df[df['sitios'] == sitios][df['y_pred'] == False].shape[0]\r\n miniset = df[df['sitios'] == sitios].shape[0]\r\n percent = round(100*acc/miniset,2)\r\n \r\n \r\n tr.write('With '+ str(sitios) + ' sites:' + str(percent) +\\\r\n '(' + str(miniset) + ' samples)' + '\\n')\r\n tr.close()\r\n return","def train_eval_model(model, model_name, X_train, y_train, X_test, y_test):\n\n model_predictions_train = model.predict(X_train) # Wyniki regresji dla zbioru treningowego\n model_mse_train = mean_squared_error(y_train, model_predictions_train) # MSE dla zbioru treningowego\n model_rmse_train = np.sqrt(model_mse_train) # RMSE dla zbioru treningowego\n model_predictions_test = model.predict(X_test)\n model_mse_test = mean_squared_error(y_test, model_predictions_test)\n model_rmse_test = np.sqrt(model_mse_test)\n # Kroswalidacja modelu\n model_scores = cross_val_score(model, X_train, y_train, scoring=\"neg_mean_squared_error\", cv=10)\n model_rmse_scores = np.sqrt(-model_scores)\n\n model_result = ResultDataRegressors(model_name, model, model_rmse_train, model_rmse_test, model_rmse_scores)\n return model_result","def eval_perf_test(model, X_test, y_test):\n\n y_hat_test = model.predict(X_test)\n\n test_mae = metrics.mean_absolute_error(y_test, y_hat_test)\n test_mse = metrics.mean_squared_error(y_test, y_hat_test)\n test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat_test))\n test_r = metrics.r2_score(y_test, y_hat_test)\n\n print('Evaluating Performance on Testing Data:\\n')\n print(f'Test Mean Absolute Error: {test_mae:,.2f}')\n print(f'Test Mean Squared Error: {test_mse:,.2f}\\n')\n print(f'Test Root Mean Squared Error: {test_rmse:,.2f}')\n print(f'Test R-Square Value: {round(test_r,2)}')","def train_full_model(X,y_train):\n scaler = MinMaxScaler()\n x_train = scaler.fit_transform(X)\n\n #chose model\n # model = RandomForestClassifier()\n model = GradientBoostingClassifier()\n\n\n #train\n print(\"train model\")\n tic = time.time()\n model.fit(x_train,y_train)\n tac = time.time()\n print(\"elapsed time\", tac-tic)\n\n #save data\n save_data(model,scaler,x_train,y_train)","def _model_train(df,tag,test=False):\r\n ## start timer for runtime\r\n time_start = time.time()\r\n \r\n X,y,dates = engineer_features(df)\r\n\r\n if test:\r\n n_samples = int(np.round(0.3 * X.shape[0]))\r\n subset_indices = np.random.choice(np.arange(X.shape[0]),n_samples,\r\n replace=False).astype(int)\r\n mask = np.in1d(np.arange(y.size),subset_indices)\r\n y=y[mask]\r\n X=X[mask]\r\n dates=dates[mask]\r\n \r\n ## Perform a train-test split\r\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,\r\n shuffle=True, random_state=42)\r\n ## train a random forest model\r\n param_grid_rf = {\r\n 'rf__criterion': ['mse','mae'],\r\n 'rf__n_estimators': [50,100,250,500,1000],\r\n 'rf__max_depth': [5, 10, 20, 50],\r\n 'rf__min_samples_split': [2, 4, 8],\r\n 'rf__min_samples_leaf': [1, 2, 4]\r\n }\r\n pipe_rf = Pipeline(steps=[('rf', RandomForestRegressor())]) #Scaling not required for tree methods, robust to outliers\r\n grid_rf = RandomizedSearchCV(pipe_rf, param_distributions=param_grid_rf, cv=5, iid=False, n_jobs=-1)\r\n grid_rf.fit(X_train, y_train)\r\n y_pred = grid_rf.predict(X_test)\r\n eval_rmse_rf = round(np.sqrt(mean_squared_error(y_test,y_pred)),3)\r\n \r\n param_grid_mlp = {\r\n 'mlp__hidden_layer_sizes': [(30,),(30,10),(50,)],\r\n 'mlp__solver': [\"lbfgs\"],\r\n 'mlp__alpha': [0.01 ,0.001, 0.0001],\r\n 'mlp__max_iter': [500,1000],\r\n 'mlp__activation': ['relu','tanh']\r\n }\r\n pipe_mlp = Pipeline(steps=[('scaler', MinMaxScaler()),\r\n ('mlp', MLPRegressor())])\r\n grid_mlp = RandomizedSearchCV(pipe_mlp, param_distributions=param_grid_mlp, cv=5, iid=False, n_jobs=-1)\r\n grid_mlp.fit(X_train, y_train)\r\n y_pred = grid_mlp.predict(X_test)\r\n eval_rmse_mlp = round(np.sqrt(mean_squared_error(y_test,y_pred)),3)\r\n \r\n alg_dict={'rf':eval_rmse_rf,'mlp':eval_rmse_mlp}\r\n grid_dict={'rf':grid_rf,'mlp':grid_mlp}\r\n \r\n grid=grid_dict[max(alg_dict, key=alg_dict.get)]\r\n eval_rmse=max(eval_rmse_rf,eval_rmse_mlp)\r\n ## retrain using all data\r\n grid.fit(X, y)\r\n model_name = re.sub(\"\\.\",\"_\",str(MODEL_VERSION))\r\n if test:\r\n saved_model = os.path.join(MODEL_DIR,\r\n \"test-{}-{}.joblib\".format(tag,model_name))\r\n print(\"... saving test version of model: {}\".format(saved_model))\r\n else:\r\n saved_model = os.path.join(MODEL_DIR,\r\n \"sl-{}-{}.joblib\".format(tag,model_name))\r\n print(\"... saving model: {}\".format(saved_model))\r\n \r\n joblib.dump(grid,saved_model)\r\n\r\n m, s = divmod(time.time()-time_start, 60)\r\n h, m = divmod(m, 60)\r\n runtime = \"%03d:%02d:%02d\"%(h, m, s)\r\n\r\n ## update log\r\n msg='Model training - {0} model saved for country {1} after achieving accuracy {2}, versioned {3}. Training completed in {4}'.format(max(alg_dict, key=alg_dict.get),tag,eval_rmse,MODEL_VERSION,runtime)\r\n update_train_log(msg)","def main():\n df = prepro_last()\n X, y = train_build(df)\n fit_store(X, y)","def test_fit() -> None:\n mapie = MapieRegressor()\n mapie.fit(X_toy, y_toy)","def _test(self,model, dataset):\n avg_loss = tfe.metrics.Mean('loss')\n accuracy = tfe.metrics.Accuracy('accuracy')\n\n for (images, labels) in tfe.Iterator(dataset):\n logits = model(images, training=False)\n avg_loss(self._loss(logits, labels))\n accuracy(\n tf.argmax(logits, axis=1, output_type=tf.int64),\n tf.cast(labels, tf.int64))\n print('Test set: Average loss: %.4f, Accuracy: %4f%%\\n' %\n (avg_loss.result(), 100 * accuracy.result()))\n with tf.contrib.summary.always_record_summaries():\n tf.contrib.summary.scalar('loss', avg_loss.result())\n tf.contrib.summary.scalar('accuracy', accuracy.result())","def test(which, fitted_model_filename):\n click.echo(\"Mode: test.\")\n defaults = get_defaults()\n\n # bootstrap input\n fitted_model_path = os.path.join(defaults.OUTPUT.FITTED_MODELS_PATH, fitted_model_filename)\n new_options = [\"OUTPUT.FITTED_MODEL_PATH\", fitted_model_path]\n\n mode = \"{}_test\".format(which)\n boot_data = bootstrap(new_options, mode=mode)\n\n model = boot_data['model']\n X_test, y_test = boot_data['data']\n defaults = boot_data['defaults']\n\n eval_metrics = get_evaluation_metrics()\n # model = RandomForestClassifier(random_state=defaults.MISC.SEED, class_weight='balanced')\n\n # X_train, y_train = load_data(defaults, which='train')\n # scaler = StandardScaler()\n # numeric_cols = X_train.select_dtypes(include=np.number).columns.tolist()\n # X_train.loc[:, numeric_cols] = scaler.fit_transform(X_train[numeric_cols])\n\n # model.fit(X_train, y_train)\n\n test_results = test_performance(conf=defaults,\n model=model,\n X_test=X_test, y_test=y_test,\n eval_metrics=eval_metrics)\n results = pd.DataFrame(test_results.values(), index=test_results.keys(), columns=[\"test\"])\n\n results_filename = \"{}_results_{}.csv\".format(mode, fitted_model_filename.split(\".\")[0])\n results_path = os.path.join(defaults.OUTPUT.RESULTS_PATH, results_filename)\n results.to_csv(results_path)","def fit(model, data, test_ids, exp_name, datasets):\n if model.model_type == 'torch':\n size = len(data[0])\n else:\n size = data[0].shape[0]\n \n train_ids = [i for i in range(size) if i not in test_ids]\n scaler = pka_scaler(data[1][train_ids])\n if model.data_type == 'descriptors':\n desc_scaler = StandardScaler()\n desc_scaler.fit(data[0][train_ids])\n data[0] = desc_scaler.transform(data[0])\n \n trained_model = train(model, train_ids, data, scaler, datasets)\n results = test(model, trained_model, test_ids, data, scaler)\n model.experiments.append({'name':exp_name,'model':trained_model, 'results':results, 'scaler':scaler})\n return results","def fit():\n pass","def test_evaluate():\n X_train, X_test, y_train, y_test = src.load()\n clf, score = src.train(X_train, y_train)\n test_score = src.evaluate(clf, X_test, y_test)\n assert isinstance(test_score, float)","def test(self):\n with torch.no_grad():\n self.model.eval()\n p10_forecast, p10_forecast, p90_forecast, target = None, None, None, None\n\n t = time()\n for step, sample in enumerate(self.test_loader):\n\n # Hide future predictions from input vector, set to 0 (or 1) values where timestep > encoder_steps\n steps = self.cnf.all_params['num_encoder_steps']\n pred_len = sample['outputs'].shape[1]\n x = sample['inputs'].float().to(self.cnf.device)\n x[:, steps:, 0] = 1\n\n # Feed input to the model\n if self.cnf.all_params[\"model\"] == \"transformer\" or self.cnf.all_params[\"model\"] == \"grn_transformer\":\n\n # Auto-regressive prediction\n for i in range(pred_len):\n output = self.model.forward(x)\n x[:, steps + i, 0] = output[:, i, 1]\n output = self.model.forward(x)\n\n elif self.cnf.all_params[\"model\"] == \"tf_transformer\":\n output, _, _ = self.model.forward(x)\n else:\n raise NameError\n\n output = output.squeeze()\n y, y_pred = sample['outputs'].squeeze().float().to(self.cnf.device), output\n\n # Compute loss\n loss, _ = self.loss(y_pred, y)\n smape = symmetric_mean_absolute_percentage_error(output[:, :, 1].detach().cpu().numpy(),\n sample['outputs'][:, :, 0].detach().cpu().numpy())\n\n # De-Normalize to compute metrics\n target = unnormalize_tensor(self.data_formatter, y, sample['identifier'][0][0])\n p10_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 0], sample['identifier'][0][0])\n p50_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 1], sample['identifier'][0][0])\n p90_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 2], sample['identifier'][0][0])\n\n # Compute metrics\n self.test_losses['p10'].append(self.loss.numpy_normalised_quantile_loss(p10_forecast, target, 0.1))\n self.test_losses['p50'].append(self.loss.numpy_normalised_quantile_loss(p50_forecast, target, 0.5))\n self.test_losses['p90'].append(self.loss.numpy_normalised_quantile_loss(p90_forecast, target, 0.9))\n\n self.test_loss.append(loss.item())\n self.test_smape.append(smape)\n\n # Plot serie prediction\n p1, p2, p3, target = np.expand_dims(p10_forecast, axis=-1), np.expand_dims(p50_forecast, axis=-1), \\\n np.expand_dims(p90_forecast, axis=-1), np.expand_dims(target, axis=-1)\n p = np.concatenate((p1, p2, p3), axis=-1)\n plot_temporal_serie(p, target)\n\n # Log stuff\n for k in self.test_losses.keys():\n mean_test_loss = np.mean(self.test_losses[k])\n print(f'\\t● AVG {k} Loss on TEST-set: {mean_test_loss:.6f} │ T: {time() - t:.2f} s')\n\n # log log log\n mean_test_loss = np.mean(self.test_loss)\n mean_smape = np.mean(self.test_smape)\n print(f'\\t● AVG Loss on TEST-set: {mean_test_loss:.6f} │ T: {time() - t:.2f} s')\n print(f'\\t● AVG SMAPE on TEST-set: {mean_smape:.6f} │ T: {time() - t:.2f} s')","def test(self, test_iter, step, corpus_type, id):\n\n self.model.eval()\n stats = Statistics()\n if not os.path.exists(self.args.result_path):\n os.makedirs(self.args.result_path)\n if not os.path.exists(self.args.story_path):\n os.makedirs(self.args.story_path)\n can_path = self.args.result_path + corpus_type + '.' + id + '_step%d.candidate' % step\n gold_path = self.args.result_path + corpus_type + '.' + id + '_step%d.gold' % step\n story_path = self.args.story_path + corpus_type + '.' + id + '.story'\n with open(story_path, 'w') as save_story:\n with open(can_path, 'w') as save_pred:\n with open(gold_path, 'w') as save_gold:\n with torch.no_grad():\n for batch in test_iter:\n src = batch.src\n labels = batch.labels\n segs = batch.segs\n clss = batch.clss\n mask = batch.mask\n mask_cls = batch.mask_cls\n weight = batch.weight\n index = batch.index\n\n pred = []\n\n sents_vec, sent_scores, mask, cluster_weight = self.model(src, segs, clss, mask, mask_cls)\n loss = self.loss(sent_scores, labels.float())\n weight_loss = self.weight_loss(cluster_weight, weight)\n loss = (loss * mask.float()).sum()\n total_loss = loss + weight_loss * 10\n batch_stats = Statistics(float(total_loss.cpu().data.numpy()), len(labels))\n stats.update(batch_stats)\n\n sent_scores = sent_scores + mask.float()\n sent_scores = sent_scores.cpu().data.numpy()\n cluster_weight = cluster_weight.cpu().data.numpy()\n selected_ids = np.argsort(-sent_scores, 1)\n cluster_weight = np.argsort(cluster_weight)\n # print(selected_ids)\n # selected_ids = np.sort(selected_ids,1)\n cluster_num = len(cluster_weight)\n for i, idx in enumerate(selected_ids):\n rank = np.where(cluster_weight == i)[0][0]\n\n if rank <= max(cluster_num // 6, 6):\n for j in range(5):\n sen_ind = selected_ids[i][j]\n _pred = batch.src_str[i][sen_ind].strip()\n pred.append((index[i][sen_ind], _pred))\n elif rank <= max(cluster_num // 3, 10):\n for j in range(3):\n sen_ind = selected_ids[i][j]\n _pred = batch.src_str[i][sen_ind].strip()\n pred.append((index[i][sen_ind], _pred))\n elif rank <= max(cluster_num * 2 // 3, 15):\n for j in range(2):\n sen_ind = selected_ids[i][j]\n _pred = batch.src_str[i][sen_ind].strip()\n pred.append((index[i][sen_ind], _pred))\n else:\n sen_ind = selected_ids[i][0]\n _pred = batch.src_str[i][sen_ind].strip()\n pred.append((index[i][sen_ind], _pred))\n\n gold_summary = (batch.tgt_str[0].strip())\n pred.sort(key=lambda x: x[0])\n for i in range(len(pred)):\n save_story.write(pred[i][1].strip() + '\\n')\n if i == 0:\n save_pred.write(pred[i][1].strip())\n else:\n save_pred.write('<q> ' + pred[i][1].strip())\n save_gold.write(gold_summary)\n for sent in gold_summary.split('<q>'):\n save_story.write('@highlight {}\\n'.format(sent))\n if self.args.test_txt:\n return stats\n else:\n rouges = calculate_rouge(can_path, gold_path)\n logger.info('Rouges at step %d \\n%s' % (step, rouge_results_to_str(rouges)))\n self._report_step(0, step, valid_stats=stats)\n return stats, rouges","def eval(self):\n self.train(mode=False)","def learn(model: KW_Model,\n trainloader: DataLoader,\n testloader: DataLoader,\n optimizer: optim.Optimizer,\n nb_epoch: int,\n device: torch.device,\n eval_fn: Callable[[List[bool], List[Qid]], Dict[Qid, float]],\n mean_window: int = 50,\n entropy_lambda: float = 0.025,\n smt_lambda: float = 1.0,\n reinforce_lambda: float = 1.0,\n ) -> Tuple[nn.Module, Dict[str, List[torch.tensor]], Dict[str, List[torch.tensor]]]:\n print(\"Memory usage: %s (kb)\" % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)\n past_rewards = {str(q_id.long().item()): deque(maxlen=mean_window)\n for _, _, q_ids, _ in chain(trainloader, testloader)\n for q_id in q_ids}\n \n logs = [\"reward\",\n \"scaled_entropy\",\n \"scaled_reinforce\",\n \"scaled_smt\",\n \"total_loss\",\n \"accuracy\"]\n train_logs = {log: list() for log in logs}\n test_logs = {log: list() for log in logs}\n del logs\n \n for epoch in range(nb_epoch):\n running_loss, running_reward = [], []\n entropies, reinforces, smts = [], [], []\n nb_correct, nb_total = 0, 0\n print(f\"\\nEpoch {epoch}\")\n \n print(\"Begin epoch: %s (kb)\" % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)\n model.train()\n for x, y, q_id, masks in trainloader:\n x = x.to(device)\n y = y.to(device)\n masks = masks.to(device)\n\n # batch x seq , batch x seq , batch x seq\n sample, logits, entropy, params = model(x, masks)\n batch_reward, rewards = eval_fn(sample.detach().t().tolist(), q_id)\n losses = compute_losses(y, params, rewards, logits, past_rewards,\n entropy, entropy_lambda, reinforce_lambda, smt_lambda, device)\n\n # entropy_lambda = min(1.01*entropy_lambda, 0.025)\n # reinforce_lambda = min(1.01*reinforce_lambda, 1.0)\n # smt_lambda = max(0.99*smt_lambda, 0.05)\n loss, reinforce_loss, entropy, smt_loss = losses\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n temp = (sample.long() == y.detach().long()).float() * masks\n nb_correct += temp.byte().cpu().sum().tolist()\n nb_total += masks.cpu().sum().tolist()\n\n running_loss.append(loss.item())\n running_reward.extend(rewards.values())\n print(f\"\\rTr Loss {mean(running_loss): .3f} Rewa {mean(running_reward): .5f}\", end=\"\")\n \n reinforces.append(reinforce_loss.item())\n entropies.append(entropy.item())\n smts.append(smt_loss.item())\n\n # Logs\n train_logs[\"reward\"].append(mean(running_reward))\n train_logs[\"scaled_entropy\"].append(mean(entropies))\n train_logs[\"scaled_reinforce\"].append(mean(reinforces))\n train_logs[\"scaled_smt\"].append(mean(smts))\n train_logs[\"total_loss\"].append(mean(running_loss))\n train_logs[\"accuracy\"].append(nb_correct / nb_total)\n \n \n train_loss, train_reward = mean(running_loss), mean(running_reward)\n running_loss, running_reward = [], []\n entropies, reinforces, smts = [], [], []\n nb_correct, nb_total = 0, 0\n model.eval()\n for x, y, q_id, masks in testloader:\n x = x.to(device)\n y = y.to(device)\n masks = masks.to(device)\n\n # batch x seq , batch x seq , batch x seq\n sample, logits, entropy, params = model(x, masks)\n batch_reward, rewards = eval_fn(sample.detach().t().tolist(), q_id)\n\n losses = compute_losses(y, params, rewards, logits, past_rewards,\n entropy, entropy_lambda, reinforce_lambda, smt_lambda, device)\n loss, reinforce_loss, entropy, smt_loss = losses\n \n temp = (sample.long() == y.detach().long()).float() * masks\n nb_correct += temp.byte().sum().tolist()\n nb_total += masks.sum().tolist()\n\n running_loss.append(loss.item())\n running_reward.extend(rewards.values())\n print(f\"\\rTr Loss {train_loss: .3f} Rewa {train_reward: .3f}\",\n f\"Te Loss{mean(running_loss): .3f} Rewa {mean(running_reward): .3f}\",\n end=\"\")\n \n reinforces.append(reinforce_loss.item())\n entropies.append(entropy.item())\n smts.append(smt_loss.item())\n \n \n # Logs\n test_logs[\"reward\"].append(mean(running_reward))\n test_logs[\"scaled_entropy\"].append(mean(entropies))\n test_logs[\"scaled_reinforce\"].append(mean(reinforces))\n test_logs[\"scaled_smt\"].append(mean(smts))\n test_logs[\"total_loss\"].append(mean(running_loss))\n test_logs[\"accuracy\"].append(nb_correct / nb_total)\n \n\n return model, train_logs, test_logs","def model_evaluate(model,x_train,n_y_array,x_val, vald_array):\n\n scores = model.evaluate(x_train, n_y_array, verbose=1)\n\n scores2 = model.evaluate(x_val, vald_array, verbose=1)\n\n\n print(\"for traininf set\")\n\n print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n\n print(\"%s: %.2f%%\" % (model.metrics_names[0], scores[0]))\n\n\n\n print(\"for validation set : \") \n\n print(\"%s: %.2f%%\" % (model.metrics_names[1], scores2[1]*100))\n\n print(\"%s: %.2f%%\" % (model.metrics_names[0], scores2[0]))","def final_eval(self):\n # Test performance - Load best model\n self.load_model(self.best_model_path, model_type='best')\n logging.info(\"Loading best model after epoch: %d\" %\n self.train_info['epoch'])\n\n perf_file = path.join(self.model_dir, \"perf.json\")\n if self.slurm_id:\n parent_dir = path.dirname(path.normpath(self.model_dir))\n perf_dir = path.join(parent_dir, \"perf\")\n if not path.exists(perf_dir):\n os.makedirs(perf_dir)\n perf_file = path.join(perf_dir, self.slurm_id + \".json\")\n\n output_dict = {'model_dir': self.model_dir}\n for key, val in vars(self.args).items():\n output_dict[key] = val\n\n for split in ['dev', 'test']:\n logging.info('\\n')\n logging.info('%s' % split.capitalize())\n result_dict = self.eval_model(split, final_eval=True)\n if split != 'test':\n logging.info('Calculated F1: %.3f' % result_dict['fscore'])\n\n output_dict[split] = result_dict\n\n json.dump(output_dict, open(perf_file, 'w'), indent=2)\n\n logging.info(\"Final performance summary at %s\" % perf_file)\n sys.stdout.flush()","def modelfit(alg, data, features, target):\n\n # Split data into train and test.\n X_train, X_test, y_train, y_test = model_selection.train_test_split(data[features], data[target],\n test_size=.2)\n\n # Baseline fit.\n alg.fit(X_train, y_train)\n train_predictions = alg.predict(X_train)\n print(\"RMSE Train (Pre-CV): %.4g\" % np.sqrt(metrics.mean_squared_error(y_train, train_predictions)))\n\n\n # Cross_validation/hyper-parameter selection.\n #dtr_cv(alg, X_train, y_train)\n\n # Report results + visualize.\n test_predictions = alg.predict(X_test)\n print(\"RMSE Test: %.4g\" % np.sqrt(metrics.mean_squared_error(y_test, test_predictions)))\n\n plot_results(y_test, test_predictions)","def test_per_dqn(self):\n model = PERDQNLightning(self.hparams)\n result = self.trainer.fit(model)\n\n self.assertEqual(result, 1)","def test(self):\n self.model.eval()\n\n for step, sample in enumerate(self.test_loader):\n x, _, _ = sample\n x = x.to(self.device)\n\n y_pred = self.model.forward(x)\n loss = nn.MSELoss()(y_pred, x)\n self.test_losses.append(loss.item())\n\n if step % (max(8, len(self.test_loader)) // 8) == 0:\n out_img = torch.cat([x[0], torch.clamp(y_pred[0], 0, 1)], dim=2)\n self.sw.add_image(tag=f'sample_{step}', img_tensor=out_img, global_step=self.epoch)\n\n # log average loss on test set\n mean_test_loss = np.mean(self.test_losses)\n self.test_losses = []\n print(f'\\t● AVG Loss on TEST-set: {mean_test_loss:.6f} │ patience: ', end='')\n self.sw.add_scalar(tag='test_loss', scalar_value=mean_test_loss, global_step=self.epoch)\n\n # save best model and update training patience\n if self.best_test_loss is None or mean_test_loss < self.best_test_loss:\n self.best_test_loss = mean_test_loss\n self.patience = conf.FX_PATIENCE\n torch.save(self.model.state_dict(), self.log_path / 'best.pth')\n else:\n self.patience = self.patience - 1\n print(f'{self.patience}/{conf.FX_PATIENCE}')\n\n if self.patience == 0:\n self.show_completion_msg()","def computeRmse(model, data, n , sc):\n truth = data.map( lambda x: ((x[0], x[1]), x[2]) )\n truth.cache()\n ##print 'test zhou 0.....', truth.count() , '............', truth.take(10)\n\n predictions = model.predictAll(data.map(lambda x: (x[0], x[1])))\n predictions.cache()\n # here let's rescale predicted ratings to 0-10 scale\n maxPrediction = predictions.map(lambda x: x[2]).max()\n minPrediction = predictions.map(lambda x: x[2]).min()\n maxRate = RatingScale\n minRate = RatingScaleMin\n ##print 'test zhou 1......', predictions.count(), '............', predictions.take(10)\n\n #predictionsAndRatings = predictions.map(lambda x: ((x[0], x[1]), (x[2]-minPrediction)/(maxPrediction-minPrediction)*(maxRate-minRate)+minRate )).join(data.map(lambda x: ((x[0], x[1]), x[2]))).values()\n\n\n #predictedRating = predictions.map(lambda x: ((x[0], x[1]), (x[2]-minPrediction)/(maxPrediction-minPrediction)*(maxRate-minRate)+minRate ) )\n predictedRating = predictions.map(lambda x: ((x[0], x[1]), x[2] ) )\n predictedRating.cache()\n ##predictedRating.checkpoint()\n ##print 'test zhou 2.......', predictedRating.count(), '............', predictedRating.take(10)\n\n\n \n\n\n predictionsAndRatings = predictedRating.join(truth).values()\n #predictionsAndRatings = sc.union(predictedRating, truth)\n predictionsAndRatings.cache()\n #print 'test zhou 3........', predictionsAndRatings.count(), '............', predictionsAndRatings.take(10)\n #predictionsAndRatings = predictions.map(lambda x: ((x[0], x[1]), x[2])).join(data.map(lambda x: ((x[0], x[1]), x[2]))).values()\n \n return sqrt(predictionsAndRatings.map(lambda x: (x[0] - x[1]) ** 2).reduce(add) / float(n))\n #return 1.0","def model_evaluation(X_train, y_train, X_test, y_test, k=16):\n print(\">>>>>>> x.shape\", X_train.shape)\n p_matrix, X_reduce = dimension_reduction(X_train, k=k)\n print(\"model training ...\")\n bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2), n_estimators=30, learning_rate=1)\n bdt.fit(X_reduce, y_train)\n print(\"fit succeed\")\n\n X_test = np.dot(X_test, p_matrix)\n y_pred = bdt.predict(X_test)\n print(classification_report(y_test, y_pred, target_names=['benign', 'gafgyt', 'miari'], digits=4))","def compute_test():\n model.eval()\n sets = list(features.keys())\n for dset, loaders in zip(sets, [train_loaders, val_loaders, test_loaders]):\n final_specific_loss = 0\n final_total_loss = 0\n for loader in loaders:\n loader_total_loss = 0\n loader_specific_loss = 0\n for data in loader:\n output = model(data.to(device))\n specific_loss = specific_loss_torch_geom(output, (data.pos, data.y),\n data.batch, batch_sizes[dset]).detach()\n loader_specific_loss += specific_loss\n loader_total_loss += torch.mean(specific_loss)\n # Average the loss over each loader\n loader_specific_loss /= len(loader)\n loader_total_loss /= len(loader)\n # Average the loss over the different loaders\n final_specific_loss += loader_specific_loss / len(loaders)\n final_total_loss += loader_total_loss / len(loaders)\n del output, loader_specific_loss\n\n print(\"Test set results \", dset, \": loss= {:.4f}\".format(final_total_loss))\n print(dset, \": \", final_specific_loss)\n print(\"Results in log scale\", np.log10(final_specific_loss.detach().cpu()),\n np.log10(final_total_loss.detach().cpu().numpy()))\n if args.wandb:\n wandb.run.summary[\"test results\"] = np.log10(final_specific_loss.detach().cpu())\n # free unnecessary data\n\n\n final_specific_numpy = np.log10(final_specific_loss.detach().cpu())\n del final_total_loss, final_specific_loss\n torch.cuda.empty_cache()\n return final_specific_numpy","def train(self,path,mode):\n if mode == \"porto\":\n self.prepare_data(path)\n else:\n self.prepare_sumo_data(path)\n self.poly_regression()","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def test_machine_learning():","def test_model_performance(model_es,test_file):\n test_label = get_test_label(test_file)\n result = model_es.predict(input_fn = lambda :input_fn(test_file,1,False,100,True))\n predict_list = []\n for one_res in result:\n if \"probabilities\" in one_res:\n predict_list.append(one_res['probabilities'][1])\n get_auc(predict_list,test_label)","def eval_regression_performance(dset, model, scaler, measure):\n X = dset[\"X\"]\n y = dset[\"y\"]\n y = y.reshape((len(y), 1))\n yhat = model.predict(X)\n\n # afecta en algo aplicar invers_stransform sobre una sola columna que con tra la matriz?\n prediction = scaler.inverse_transform(yhat)\n\n # invert scaling for actual\n true = scaler.inverse_transform(y)\n\n # calculate MAE\n performance = measure(true, prediction)\n return performance","def test(self):\n for data_tier in self.data_tiers:\n tot = len(self.preprocessed_data[data_tier]['features'])\n p = int(math.floor(tot*0.2))\n test_features = np.array(self.preprocessed_data[data_tier]['features'][p:])\n trend_test_classifications = np.array(self.preprocessed_data[data_tier]['trend_classifications'][p:])\n avg_test_classifications = np.array(self.preprocessed_data[data_tier]['avg_classifications'][p:])\n accuracy_trend = self.clf_trend[data_tier].score(test_features, trend_test_classifications)\n accuracy_avg = self.clf_avg[data_tier].score(test_features, avg_test_classifications)\n self.logger.info('The accuracy of %s trend classifier for data tier %s is %.3f', self.name, data_tier, accuracy_trend)\n self.logger.info('The accuracy of %s avg regressor for data tier %s is %.3f', self.name, data_tier, accuracy_avg)","def get_perf(self) :\n self.train()\n self.df_true = self.df_true[self.length:]\n self.accuracy , self.recall, self.specificity, self.profit, self.min , self.max = get_accuracy_LSTM(self.df_test, self.df_true,self.model, self.length)","def main():\n housing = pd.read_csv(\"Data/train_original.csv\")\n housing[\"TotalSF\"] = (\n housing[\"TotalBsmtSF\"] + housing[\"1stFlrSF\"] + housing[\"2ndFlrSF\"]\n )\n training_features, testing_features, training_target, testing_target = impute_dummify_and_split(\n housing, drop_target=False\n )\n\n p_values = [\n (c, pearsonr(training_features[\"SalePrice\"], training_features[c])[1])\n for c in training_features.columns\n ]\n\n p_value_limits = [0.05]\n\n result = []\n ps_and_cols = {}\n\n for p_value_limit in p_value_limits:\n\n high_ps = list(\n map(lambda t: t[0], sorted(p_values, key=lambda t1: t1[1])[:15])\n )\n\n print(training_features[high_ps].corr())\n\n columns = [p[0] for p in p_values if p[1] < p_value_limit]\n\n training_features_restricted = training_features[columns].drop(\n \"SalePrice\", axis=\"columns\"\n )\n\n testing_features_restricted = testing_features[columns].drop(\n \"SalePrice\", axis=\"columns\"\n )\n\n for model in (\n linear_model.Lasso(alpha=2.1),\n linear_model.Ridge(alpha=2.1),\n ):\n\n model.fit(training_features_restricted, training_target)\n\n train_score = model.score(\n training_features_restricted, training_target\n )\n\n test_score = model.score(\n testing_features_restricted, testing_target\n )\n\n name = str(model).split(\"(\")[0]\n\n result = result + [\n (\n \"_2_restrict_features\",\n name,\n \"p value limit: {:.3f}, alpha: 2.1\".format(p_value_limit),\n train_score,\n test_score,\n )\n ]\n\n print(ps_and_cols)\n return training_features[high_ps].corr()","def assess_perf(self, bases: list, set_type, epoch):\n self.prepare_calc_performance()\n self.base_dfl = dict() # base dict of lists with prediction data frames\n for base in bases:\n self.base_dfl[base] = list()\n ohlcv_list = ad.SplitSets.split_sets(set_type, self.predictor.ohlcv.load_data(base))\n features_list = ad.SplitSets.split_sets(set_type, self.predictor.features.load_data(base))\n targets_list = ad.SplitSets.split_sets(set_type, self.predictor.targets.load_data(base))\n assert len(ohlcv_list) == len(features_list)\n assert len(ohlcv_list) == len(targets_list)\n for ix in range(len(ohlcv_list)):\n odf = ohlcv_list[ix]\n fdf = features_list[ix]\n tdf = targets_list[ix]\n if (fdf is None) or fdf.empty or (tdf is None) or tdf.empty:\n logger.warning(f\"empty data for {base} between {odf.index[0]} and {odf.index[-1]}\")\n continue\n [fdf, tdf] = ccd.common_timerange([fdf, tdf])\n\n if self.predictor.scaler is not None:\n fdf_scaled = self.predictor.scaler.transform(fdf.values)\n pred = self.predictor.kerasmodel.predict_on_batch(fdf_scaled)\n else:\n logger.error(\"missing scaler\")\n pred = self.predictor.kerasmodel.predict_on_batch(fdf.values)\n if pred is None:\n logger.warning(f\"no prediction data for {base} between {odf.index[0]} and {odf.index[-1]}\")\n continue\n pdf = pd.DataFrame(data=pred, index=fdf.index, columns=self.predictor.targets.target_dict().keys())\n pdf.loc[pdf.index[-1], ct.SELL] = 1 # force sell at end of data range\n if pdf.empty:\n logger.warning(f\"empty prediction data for {base} between {odf.index[0]} and {odf.index[-1]}\")\n continue\n pdf = pd.concat([odf.close, tdf.target, pdf], axis=1, join=\"inner\")\n self.base_dfl[base].append(self.calc_performance(pdf))\n logger.info(f\"\\n performance results \\n{self.total}\\n\")\n logger.info(f\"\\n precision results \\n{self.confusion}\\n\")\n return self.find_best()","def test_model(self, batch_size):\n (_, gen_val, gen_test) = self.dataset.data_loaders(\n batch_size=batch_size,\n split=(0.01, 0.01)\n )\n print('Num Test Batches: ', len(gen_test))\n mean_loss_test, mean_accuracy_test = self.loss_and_acc_test(gen_test)\n print('Test Epoch:')\n print(\n '\\tTest Loss: ', mean_loss_test, '\\n'\n '\\tTest Accuracy: ', mean_accuracy_test * 100\n )","def test_10_test_model(self, example):\n res = example.calc_model()\n print(example.trips_ij)\n total_trips_target = example.persons_gi.sum()\n total_trips_actual = example.trips_ij.sum()\n np.testing.assert_almost_equal(total_trips_target, total_trips_actual)","def main_stats_model(y_train: pd.DataFrame, y_test: pd.DataFrame, y_pred: np.ndarray,\n model_name: str = '',\n model_parameters: dict = None,\n model_preprocessing: str = '',\n sequence_origin: str = '',\n primers_origin: str = '',\n taxonomy_level: Union[List[int], int] = '',\n selected_primer: Union[List[str], str] = '',\n test_size: float = 0.2,\n feature_importances: np.ndarray = None,\n k: int = 4,\n save_csv: bool = False,\n xgb_model=None,\n rf_model=None,\n save_model=False,\n save_tree: int = 0):\n model_path = folder_paths['model_results'] + model_name + '{}'.format(slash)\n if not os.path.exists(model_path):\n os.makedirs(model_path)\n\n folder_number = get_new_model_folder_number(model_name=model_name)\n analysis_path = model_path + '{:0>5d}_analysis_{}_{}{}'.format(folder_number, selected_primer, taxonomy_level, slash)\n os.makedirs(analysis_path)\n\n log_path = analysis_path + 'model_results.txt'\n logger = StatLogger(log_path=log_path)\n\n # Basic information on configuration\n test_size = get_model_info(y_test, model_name, model_parameters, model_preprocessing, sequence_origin,\n primers_origin, taxonomy_level, selected_primer, test_size, logger)\n\n # Metrics of model results\n main_class_prop, accuracy = get_metrics_model(y_train, y_test, y_pred, logger, feature_importances, k, save_tree,\n xgb_model,\n analysis_path=analysis_path)\n\n if save_csv:\n add_optimal_model_params(folder_number, selected_primer, taxonomy_level, accuracy, model_parameters,\n model_path=model_path)\n\n if save_model:\n if xgb_model is not None:\n xgb_model.save_model(analysis_path+'0001.model')\n if rf_model is not None:\n filename = analysis_path+'0001.model'\n pickle.dump(rf_model, open(filename, 'wb'))\n\n logger.close_file()\n\n return test_size, main_class_prop, accuracy","def test_model(net, data_loader):\n net.eval()\n running_loss = 0.0\n with torch.no_grad():\n for data in data_loader:\n X = data['X']\n y_d = data['y_descreen']\n outputs = net(X)\n loss = criterion(outputs, y_d)\n running_loss += loss\n return running_loss","def master_modelisation(X,y,test_size,model_function_list) :\n models = model_function_list\n\n output = pd.DataFrame()\n model = []\n RMSE = []\n MAPE = []\n tenth_perc = []\n ninetieth_perc = []\n\n for i in models:\n output_row = pd.DataFrame()\n\n model.append(i)\n\n y_pred, y_test = i(df, variables, test_size)\n\n RMSE.append(compute_rmse(y_test, y_pred))\n MAPE.append(compute_mape(y_test, y_pred))\n tenth_perc.append(tenth_percentile(y_test, y_pred))\n ninetieth_perc.append(ninetieth_percentile(y_test, y_pred))\n\n output['Model'] = model\n output['RMSE'] = RMSE\n output['MAPE'] = MAPE\n output['tenth_perc'] = tenth_perc\n output['ninetieth_perc'] = ninetieth_perc\n\n return output","def test_model():\n pass","def test_model(model: nn.Module, test_set: data.DataLoader, number_of_classes: int) -> Tuple[score.FloatScore, score.DictScore]:\n # model.eval is used for ImageNet models, batchnorm or dropout layers will work in eval mode.\n model.eval()\n\n def test_average() -> score.FloatScore:\n correct = 0\n total = 0\n\n with torch.set_grad_enabled(False):\n for (inputs, yreal) in tqdm(test_set, unit=\"images\", desc=\"Testing model (average)\", leave=True, ascii=True):\n inputs, yreal = inputs.cuda(), yreal.cuda()\n\n ypred = model(inputs)\n _, predicted = torch.max(ypred.data, 1)\n\n total += yreal.size(0)\n correct += (predicted == yreal).sum().item()\n\n accuracy = 100 * correct / total\n log.info(\"Accuracy of the network on the {} test images (average): {}\".format(total, accuracy))\n with open('epoch_logs.txt', 'a+') as file:\n file.write('Test Acc: {}\\n'.format(accuracy))\n return score.FloatScore(accuracy)\n\n def test_per_class() -> score.DictScore:\n class_correct = list(0. for _ in range(number_of_classes))\n class_total = list(0. for _ in range(number_of_classes))\n total = 0\n\n with torch.no_grad():\n for (inputs, yreal) in tqdm(test_set, unit=\"images\", desc=\"Testing model (per class)\", leave=True, ascii=True):\n inputs, yreal = inputs.cuda(), yreal.cuda()\n\n total += yreal.size(0)\n\n ypred = model(inputs)\n _, predicted = torch.max(ypred, 1)\n c = (predicted == yreal).squeeze()\n for i in range(yreal.shape[0]):\n label = yreal[i]\n class_correct[label] += c[i].item()\n class_total[label] += 1\n\n log.info(\"Accuracy of the network on the {} test images (per-class):\".format(total))\n\n per_class_accuracy = {}\n for i in range(number_of_classes):\n accuracy = 100 * class_correct[i] / (class_total[i] + 0.0001)\n per_class_accuracy[i] = accuracy\n print('Accuracy of %5s : %2d %%' % (\n i, accuracy))\n\n return score.DictScore(per_class_accuracy)\n\n return test_average(), test_per_class()","def train():\n pass","def evaluate(self, train_set, test_set, shuffle_batch=True,\n epochs=25, lr_decay=0.95, sqr_norm_lim=9,labels=None,model=None): \n cost = self.negative_log_likelihood(self.y) \n dropout_cost = self.dropout_negative_log_likelihood(self.y)\n # adadelta upgrades: dict of variable:delta\n grad_updates = self.sgd_updates_adadelta(dropout_cost, lr_decay, 1e-6, sqr_norm_lim)\n # shuffle dataset and assign to mini batches.\n # if dataset size is not a multiple of batch size, replicate \n # extra data (at random)\n np.random.seed(3435)\n batch_size = self.batch_size\n if train_set.shape[0] % batch_size > 0:\n extra_data_num = batch_size - train_set.shape[0] % batch_size\n #extra_data = train_set[np.random.choice(train_set.shape[0], extra_data_num)]\n perm_set = np.random.permutation(train_set) \n extra_data = perm_set[:extra_data_num]\n new_data = np.append(train_set, extra_data, axis=0)\n else:\n new_data = train_set\n \n shuffled_data = np.random.permutation(new_data) # Attardi\n n_batches = shuffled_data.shape[0]/batch_size\n # divide train set into 90% train, 10% validation sets\n n_train_batches = int(np.round(n_batches*0.8))\n n_val_batches = n_batches - n_train_batches\n train_set = shuffled_data[:n_train_batches*batch_size,:]\n val_set = shuffled_data[n_train_batches*batch_size:,:] \n # push data to gpu \n # the dataset has the format [word_indices,padding,user,label]\n train_set_x, train_set_y = shared_dataset(train_set[:,:-2], train_set[:,-1]) \n train_set_u = theano.shared(np.asarray(train_set[:,-2],dtype='int32')) \n # val_set_x = val_set[:,:-2]\n # val_set_u = val_set[:,-2]\n # val_set_y = val_set[:,-1]\n val_set_x, val_set_y = shared_dataset(val_set[:,:-2], val_set[:,-1])\n val_set_u = theano.shared(np.asarray(val_set[:,-2],dtype='int32')) \n test_set_x = test_set[:,:-2]\n test_set_u = test_set[:,-2]\n test_set_y = test_set[:,-1] \n batch_start = self.index * batch_size\n batch_end = batch_start + batch_size\n\n # compile Theano functions to get train/val/test errors\n \n \n test_y_pred = self.predict(test_set_x,test_set_u)\n test_error = T.mean(T.neq(test_y_pred, self.y))\n # errors on train set\n if self.Users is not None:\n train_model = theano.function([self.index], cost, updates=grad_updates,\n givens={\n self.x: train_set_x[batch_start:batch_end],\n self.y: train_set_y[batch_start:batch_end],\n self.u: train_set_u[batch_start:batch_end]\n },\n allow_input_downcast = True)\n\n train_error = theano.function([self.index], self.errors(self.y),\n givens={\n self.x: train_set_x[batch_start:batch_end],\n self.y: train_set_y[batch_start:batch_end],\n self.u: train_set_u[batch_start:batch_end]},\n allow_input_downcast=True)\n val_model = theano.function([self.index], self.errors(self.y),\n givens={\n self.x: val_set_x[batch_start:batch_end],\n self.y: val_set_y[batch_start:batch_end], \n self.u: val_set_u[batch_start:batch_end]},\n allow_input_downcast=True)\n test_model = theano.function([self.x, self.u, self.y], test_error, allow_input_downcast=True)\n else:\n train_model = theano.function([self.index], cost, updates=grad_updates,\n givens={\n self.x: train_set_x[batch_start:batch_end],\n self.y: train_set_y[batch_start:batch_end]},\n allow_input_downcast = True)\n\n train_error = theano.function([self.index], self.errors(self.y),\n givens={\n self.x: train_set_x[batch_start:batch_end],\n self.y: train_set_y[batch_start:batch_end]},\n allow_input_downcast=True)\n\n val_model = theano.function([self.index], self.errors(self.y),\n givens={\n self.x: val_set_x[batch_start:batch_end],\n self.y: val_set_y[batch_start:batch_end]},\n allow_input_downcast=True)\n test_model = theano.function([self.x, self.y], test_error, allow_input_downcast=True)\n\n # start training over mini-batches\n print 'training...' \n best_val_perf = 0\n test_perf = 0 \n patience = 5\n drops = 0\n prev_val_perf = 0 \n for epoch in xrange(epochs):\n start_time = time.time()\n # FIXME: should permute whole set rather than minibatch indexes\n if shuffle_batch:\n for minibatch_index in np.random.permutation(range(n_train_batches)):\n cost_epoch = train_model(minibatch_index)\n self.set_zero(self.zero_vec) # CHECKME: Why?\n else:\n for minibatch_index in xrange(n_train_batches):\n cost_epoch = train_model(minibatch_index) \n self.set_zero(self.zero_vec)\n train_losses = [train_error(i) for i in xrange(n_train_batches)]\n train_perf = 1 - np.mean(train_losses)\n val_losses = [val_model(i) for i in xrange(n_val_batches)]\n val_perf = 1 - np.mean(val_losses) \n info = 'epoch: %i\\%i (%.2f secs) train acc: %.2f %% | val acc: %.2f %%' % (\n epoch,epochs, time.time()-start_time, train_perf * 100., val_perf*100.) \n # from ipdb import set_trace; set_trace()\n if val_perf > prev_val_perf: \n drops=0\n if val_perf >= best_val_perf:\n best_val_perf = val_perf\n info+= \" **\"\n if model:\n # print \"save model\"\n self.save(model)\n if self.Users is not None:\n test_loss = test_model(test_set_x, test_set_u, test_set_y)\n else:\n test_loss = test_model(test_set_x, test_set_y)\n test_perf = 1 - test_loss \n else: \n drops+=1\n if drops >= patience:\n print \"Ran out of patience...\"\n break\n prev_val_perf = val_perf\n print info\n # set_trace() \n return test_perf","def fit_model(X_train, X_test, y_train, y_test, model):\n \n if model == 'LinearRegression':\n \n regressor=LinearRegression()\n regressor.fit(X_train,y_train)\n y_pred =regressor.predict(X_test)\n r2 = r2_score(y_test, y_pred)\n \n elif model == 'Lasso':\n \n lasso = Lasso()\n lasso.fit(X_train, y_train)\n lasso_pred = lasso.predict(X_test)\n r2 = r2_score(y_test, lasso_pred)\n\n elif model == 'Ridge':\n \n ridge = Ridge()\n ridge.fit(X_train, y_train)\n ridge_pred = ridge.predict(X_test)\n r2 = r2_score(y_test, ridge_pred)\n \n \n else:\n model = make_pipeline(PolynomialFeatures(2), LinearRegression())\n model.fit(X_train, y_train)\n y_pred = model.predict(X_test)\n r2= r2_score(y_test,y_pred)\n\n\n return r2","def train(models, X_train, y_train, X_test, y_test):\n \n # Train and test each model in a for lop\n accuracies = []\n \n for model in models:\n clf = model.fit(X_train, y_train) # Train\n score = clf.score(X_test, y_test) # Test\n accuracies.append(score)\n\n return accuracies","def train_model(algorithm, X_train, y_train, X_test, y_test, cv_type='rand', transformation_type='tf'):\n \n model = algorithm(X_train, y_train, cv_type=cv_type)\n model_preds = model.predict(X_test)\n model_score = f1_score(y_test, model_preds, average='weighted')\n \n return model, model_score, transformation_type","def one_experiment():\n\n # set the name of the experiment\n now = datetime.datetime.now()\n experiment_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute)\n experiment_name = 'overfit_' + str(experiment_id)\n\n # define if you want to use preprocessed data from file\n use_prep_data = False\n if use_prep_data:\n set_params(preproc_data_id='16_5_10.16.47')\n\n # define the changing parameter and its value\n changing_param_name = 'class_weights'\n changing_param_value = [{0: 15, 1: 85}]\n # {0:15, 1:85}]#, {0:4, 1:100}, {0:3, 1:100}, {0:2, 1:100}, {0:1, 1:100}] #[{0:1, 1:1}, {0:15, 1:85}]#\n\n features_to_use = ['user', 'countries', 'session', 'format', 'token']\n # set constant parameters\n set_params(use_word_emb=1)\n set_params(epochs=40)\n set_params(features_to_use=features_to_use)\n\n # save constant parameters to a new \"experiment_..\" filgithx+P@2ub\n save_constant_parameters(experiment_name, changing_param_name)\n\n # run experiment for every parameter value\n for value in changing_param_value:\n process = psutil.Process(os.getpid())\n print(\"-----MEMORY before starting experiment ------\", int(process.memory_info().rss/(8*10**3)), \"KB\")\n\n # update the parameter value\n set_params(class_weights_1=value)\n\n # update the model_id for this new model\n now = datetime.datetime.now()\n new_model_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute) + \".\" + str(now.second)\n\n set_params(model_id=new_model_id)\n\n # evaluate the new model and save the results in the experiment file\n oneExperiment = Process(target=run_experiment, args=(experiment_name,\n new_model_id, changing_param_name, value,))\n oneExperiment.start()\n oneExperiment.join()","def computeModel(self, name, data):\n\t\tif name == \"Weibull\":\n\t\t\tmodel = Weibull(data)\n\t\t\tnamePL = \"Weibull\"\n\n\t\telif name == \"Exponential\":\n\t\t\tmodel = Exponential(data)\n#\t\t\tnamePL = \"Wykładniczy (Goel-Okumoto)\"\n\t\t\tnamePL = \"Wykładniczy\"\n\n\t\telif name == \"Gamma\":\n\t\t\tmodel = Gamma(data)\n#\t\t\tnamePL = \"Gamma (S-kształtny)\"\n\t\t\tnamePL = \"Gamma\"\n\n\t\telif name == \"Logarithmic\":\n\t\t\tmodel = Logarithmic(data)\n#\t\t\tnamePL = \"Logarytmiczny (Musa-Okumoto)\"\n\t\t\tnamePL = \"Logarytmiczny\"\n\n\t\telif name == \"Power\":\n\t\t\tmodel = Power(data)\n#\t\t\tnamePL = \"Potęgowy (Duane)\"\n\t\t\tnamePL = \"Potęgowy\"\n\n\t\telse:\n\t\t\traise Exception, \"Nieznany model '%s'\" % name\n\n\t\tprint \"\\n---\\n\", name, \"estimating...\",\n\n\t\ttry:\n\t\t\tparam, chi, ret, akaike = model.fit()\n\t\texcept Exception, e:\n\t\t\tprint \"Wyjatek!\", e\n\t\t\treturn [None, \"brak\", \"brak\", INF, name, namePL, INF]\n\n\t\t# oblicza pozostale wartosci wykresu\n\t\tbeg = len(ret) + 1\n\t\tend = beg + int((self.white2 - self.black2)/self.granSec) + 1\n\t\tcont = model.calculate(param, range(beg, end))\n\t\tret.extend(cont)\n\n\t\tprint \" alfa,beta=\", param,\n\t\tprint \" chi=\", chi\n\t\tprint \" akaike=\", akaike\n\t\tprint ret\n\n\t\t# formatowanie\n\t\talfa = round(param[0], 4)\n\t\tif len(param) == 2: \n\t\t\tbeta = round(param[1], 4) \n\t\telse: \n\t\t\tbeta = ' ';\t\t# nie ma beta w expotencjalnym\n\t\tchi = int(chi)\n\t\takaike = round(akaike, 2)\n\n\t\treturn [ret, alfa, beta, chi, name, namePL, akaike]","def test_02_train(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-train-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n \n ## update the log\n country = 'india'\n date_range = ('2017-11-29', '2019-05-24')\n metric = {'rmse':0.5}\n runtime = \"00:00:01\"\n model_version = 0.1\n model_version_note = \"test model\"\n \n update_train_log(country, date_range, metric, runtime,\n model_version, model_version_note ,test=True, prefix=LOG_PREFIX)\n\n df = pd.read_csv(log_file)\n logged_metric = [literal_eval(i) for i in df['metric'].copy()][-1]\n self.assertEqual(metric,logged_metric)","def test_one_epoch_model(self, model: nn.Module) -> Tuple[float, Dict[str, float]]:\n losses = []\n l1_criterion = nn.L1Loss()\n model.eval()\n\n # testloaders contain same length(iteration) of batch dataset\n for sample_batched in progressbar(self.testloader, prefix=\"[Test]\\t\"):\n image = torch.autograd.Variable(sample_batched['image'].cuda())\n depth = torch.autograd.Variable(sample_batched['depth'].cuda(non_blocking=True))\n maxDepth = 1000.0\n depth_n = maxDepth/depth\n #No use self.criterion \n\n output = self.model(image)\n # Compute the loss\n\n l_depth = l1_criterion(output, depth_n)\n\n l_ssim = torch.clamp((1 - ssim(output, depth_n, val_range = 1000.0 / 10.0)) * 0.5, 0, 1)\n\n loss = (1.0 * l_ssim) + (0.1 * l_depth)\n\n \n\n if self.half:\n images = images.half()\n\n # forward + backward + optimize\n \n self._count_correct_prediction(output, depth_n)\n losses.append(loss.item())\n\n avg_loss = sum(losses) / len(losses)\n acc = self._get_epoch_acc(is_test=True)\n return avg_loss, acc\n\n # # testloaders contain same length(iteration) of batch dataset\n # for data in progressbar(self.testloader, prefix=\"[Test]\\t\"):\n # images, labels = data[0].to(self.device), data[1].to(self.device)\n\n # if self.half:\n # images = images.half()\n\n # # forward + backward + optimize\n # loss, outputs = self.criterion(model, images=images, labels=labels)\n # self._count_correct_prediction(outputs, labels)\n # losses.append(loss.item())\n\n # avg_loss = sum(losses) / len(losses)\n # acc = self._get_epoch_acc(is_test=True)\n # return avg_loss, acc","def compareModel(self):\n\n # --------------------------------------------------------------------------------------------- #\n # Store the Model parameters\n lines = open(self.model, 'r').readlines()\n MyModel = {}\n for line in lines:\n if ('<source' in line) & ('name=' in line):\n srcNam = line.split('\"')[3]\n MyModel[ srcNam ] = {}\n elif ('<parameter' in line) & ('free=\"1\"' in line):\n parNam = line.split('\"')[7]\n parVal = float(line.split('\"')[11])\n MyModel[ srcNam ][ parNam ] = parVal\n else:\n pass\n\n # --------------------------------------------------------------------------------------------- #\n # Store the fitted results\n lines = open(self.outgtlike, 'r').readlines()\n CodeString = ''\n for line in lines:\n if not 'Upper limit' in line:\n CodeString += line[:-1]\n MyData = eval(CodeString) # create a dictionnary\n\n # --------------------------------------------------------------------------------------------- #\n # Compare\n for key in MyData.keys():\n if 'TS value' in MyData[key].keys():\n # The source has been fitted\n print(\"--- {} ---\".format(key))\n for k in MyModel[key].keys():\n difference = 100* (MyModel[key][k] - float(MyData[key][k].split()[0])) / MyModel[key][k] \n print(\"{0:s} differs by {1:.2f} per cent\".format(k, difference))\n return","def test(self, load_file):\n self.load(load_file)\n table = self.truthtable()\n print \" ..... Test: Model Computes:\", table","def test_best_val(self, te_acc):\n self.test_val = te_acc","def test_fit():\n args = get_layer('fit', 'manual', 'temporal', False, False, window=2, step_size=3)\n run_layer(*args)","def model_qua_launch(self, dict):\r\n list_result = []\r\n if \"SVR\" in dict:\r\n SVR = dict[\"SVR\"]\r\n if SVR[\"Auto\"]:\r\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\"Auto\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"SVR Result Auto\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\"Auto\"], [SVR[\"C\"],\r\n SVR[\"Kernel\"],\r\n SVR[\"Degree\"]])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"SVR Result\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n if \"LR\" in dict:\r\n LR = dict[\"LR\"]\r\n if LR[\"Auto\"]:\r\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\"Auto\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Linear Regression Result Auto\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\"Auto\"], LR[\"fit_intercept\"], LR[\"normalize\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Linear Regression Result\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n\r\n if \"RT\" in dict:\r\n RT = dict[\"RT\"]\r\n if RT[\"Auto\"]:\r\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\"Auto\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Regression Tree Result Auto\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\"Auto\"], [RT[\"Criterion\"],\r\n RT[\"Min_Samples_Split\"],\r\n RT[\"Min_Samples_Leaf\"]])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Regression Tree Result\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n\r\n if \"KNN\" in dict:\r\n KNN = dict[\"KNN\"]\r\n if KNN[\"Auto\"]:\r\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\"Auto\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"KNN Result Auto\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\"Auto\"],\r\n [KNN[\"leaf_size\"], KNN[\"n_neighbors\"], KNN[\"p\"], KNN[\"metric\"]])\r\n model, matrix, dict_cr, graph, time = result\r\n self.model_quali.close()\r\n result_win = Window()\r\n result_win.setWindowTitle(\"KNN Result\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n if \"LogiR\" in dict:\r\n LogiR = dict[\"LogiR\"]\r\n if LogiR[\"Auto\"]:\r\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\"Auto\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Logistic Regression Result Auto\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\"Auto\"], [LogiR['C'],\r\n LogiR['penalty']])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Logistic Regression Result\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n\r\n if \"DTC\" in dict:\r\n DTC = dict[\"DTC\"]\r\n if DTC[\"Auto\"]:\r\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\"Auto\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Tree Decision Classification Auto\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n\r\n else:\r\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\"Auto\"],DTC[\"max_leaf_nodes\"],DTC[\"max_depth\"],DTC[\"min_samples_split\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Tree Decision Classification\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n\r\n\r\n self.close()\r\n self.trigger_result.emit(list_result)","def model_fit(train_features, train_actuals):\n for name in models.keys():\n est = models[name]\n est_params = params[name]\n gscv = GridSearchCV(estimator=est, param_grid=est_params, cv=5,\n scoring='neg_mean_absolute_error', return_train_score=True)\n gscv.fit(train_actuals, train_features)\n cvres = gscv.cv_results_\n print(cvres)\n print(\"best parameters are: {}\".format(gscv.best_estimator_))\n for mean_score,par in zip(cvres[\"mean_test_score\"],cvres[\"params\"]):\n print(-mean_score, par)","def model_test(epo, natural):\n\tmodel.eval()\n\twith torch.no_grad():\n\t\tn = batch_size\n\n\t\tif natural:\n\t\t\tloader = nat_test_loader\n\t\t\tprefix = \"nat\"\n\t\telse:\n\t\t\tloader = syn_test_loader\n\t\t\tprefix = \"syn\"\n\n\t\tlog_cor_file = open(directory + \"/logs/test_\" + prefix + \"_cor_log.txt\", \"a\") # Correct\n\t\tlog_mae_file = open(directory + \"/logs/test_\" + prefix + \"_mae_log.txt\", \"a\") # MAE\n\t\tlog_dev_file = open(directory + \"/logs/test_\" + prefix + \"_dev_log.txt\", \"a\") # DEV\n\t\tlog_sam_file = open(directory + \"/logs/test_\" + prefix + \"_sam_log.txt\", \"a\") # Sample\n\n\t\tccs = []\n\t\tlabls = []\n\t\tnum_unlabeled = 0\n\t\tfor batch_idx, (data, labels) in enumerate(loader):\n\t\t\tdata = data.cuda()\n\t\t\tlabels = labels.float().cuda()\n\n\t\t\tmodel.mode = 'natural' if natural else 'synth'\n\t\t\trecon_batch, mu, logvar, cc = model(data)\n\n\t\t\tcc[labels == 0] = 0 # Sets the counted cells to 0 for unlabeled data, so that regressor_loss=0\n\t\t\tnum_unlabeled += (labels == 0).sum()\n\t\t\t_, _, _ = loss_function(recon_batch, data, mu, logvar, cc, labels, natural)\n\n\t\t\tccs.append(cc.cpu().detach().numpy())\n\t\t\tlabls.append(labels.cpu().detach().numpy())\n\n\t\t\tif batch_idx == 0 and epo % 1000 == 0:\n\t\t\t\t# Save test sample\n\t\t\t\tcomparison = torch.cat([data[:n], recon_batch.view(batch_size, 1, img_size, img_size)[:n]])\n\t\t\t\tsave_image(comparison.cpu(), directory + \"/\" + prefix + \"_\" + str(epo) + \".png\", nrow=n)\n\n\t\t\t\t# Save switch sample\n\t\t\t\tmodel.mode = 'synth' if natural else 'natural'\n\t\t\t\trecon_batch, _, _, _ = model(data)\n\t\t\t\tcomparison = torch.cat([data[:n], recon_batch.view(batch_size, 1, img_size, img_size)[:n]])\n\t\t\t\tsave_image(comparison.cpu(), directory + \"/switch_\" + prefix + \"_\" + str(epo) + \".png\", nrow=n)\n\n\t\tpreds = np.concatenate(ccs, axis=None) # Elementwise round of cellcounts\n\t\tlbls = np.concatenate(labls, axis=None) # Elementswise round of labels\n\n\t\tlog_sam_file.write(str(np.round(preds, 2)) + \"\\n\" + str(lbls) + \"\\n\")\n\t\tpreds = np.around(preds)\n\t\t#lbls = np.around(lbls)\n\n\t\tcorrect = np.sum(preds == lbls) # Count elementwise equality of predictions and labels\n\t\tlen_set = len(loader.dataset)\n\t\tcorrect -= num_unlabeled # Remove zero_indices from numerator\n\t\tcorrect = float(correct) / float(len_set - num_unlabeled) # Remove zero_indices from denominator\n\n\t\tdist_sum = np.sum(np.abs(np.subtract(preds, lbls))) # Elementwise addition of dist between preds and lbls\n\t\tMAE = dist_sum / float(len_set - num_unlabeled)\n\n\t\tlen_labeled = float(len_set - num_unlabeled)\n\t\tdev = np.ones(len_set) - np.divide(preds, lbls) # Deviation contains NaNs because syn data has lbl=0\n\t\tavg_dev = np.sum(np.abs(np.where(np.isnan(dev), 0, dev))) / len_labeled # Take the avg only of those deviations that weren't NaN\n\n\t\tlog_cor_file.write(str(correct)+\"\\n\")\n\t\tlog_mae_file.write(str(MAE)+\"\\n\")\n\t\tlog_dev_file.write(str(avg_dev)+\"\\n\")\n\n\t\t#logfile.write(str(correct) + \" correct, MAE: \" + str(MAE) + \", DEV: \" + str(avg_dev) + \" in \" + prefix + \" set in epoch \" + str(epoch) + \"\\n\\n\")\n\t\tlog_cor_file.close()\n\t\tlog_mae_file.close()\n\t\tlog_dev_file.close()\n\t\tlog_sam_file.close()\n\n\t\tglobal distance_sum\n\t\tdistance_sum = dist_sum\n\t\treturn correct, MAE","def estimatePerformance(self, model):\n Y_pred = model.computeLOO()\n #performance = self.measure.multiOutputPerformance(self.Y, Y_pred)\n #performance = self.measure.getPerformance(self.Y, Y_pred)\n #performance = measure_utilities.aggregate(performance)\n performance = self.measure(self.Y, Y_pred)\n self.predictions.append(Y_pred)\n return performance","def eval_model(config, period, test_data):\n if config.network == 'MLPwithGAN':\n model = MLPwithGAN(config)\n elif config.network == 'MLP':\n model = MLP(config)\n elif config.network == 'LSTM':\n model = VanillaLSTM(config)\n elif config.network == 'CNN':\n model = CNNfeature(config)\n else:\n raise Exception('Unknown model type:{}'.format(config.network))\n\n if config.ensemble:\n m = model\n model = []\n\n for i in glob(gen_path(config.path, str(period)) + '/m*'):\n m.load_state_dict(\n torch.load(gen_path(i, filename=config.network + '.pkl')))\n m.to(config.device)\n m.eval()\n model.append(m)\n else:\n model.load_state_dict(\n torch.load(gen_path(config.path, str(period), 'model', filename=config.network + '.pkl')))\n model.to(config.device)\n model.eval()\n dataloader_test = test_data[0]\n test_date = test_data[1]\n test_symbol = test_data[2]\n sc_y = joblib.load(gen_path(config.path, str(period), 'scaler', filename='training_sc_y.pkl'))\n predict_y_test, real_y_test, valid_index_test = make_prediction(dataloader_test, sc_y, model, config)\n\n stock_score = pd.DataFrame()\n stock_score[\"symbol\"] = test_symbol[valid_index_test]\n stock_score[\"score\"] = predict_y_test\n stock_score['truth'] = real_y_test\n stock_score[\"date\"] = test_date[valid_index_test]\n stock_score = stock_score.sort_values(by=[\"date\"])\n stock_score.to_csv(gen_path(config.path, 'stock_score', filename=str(period) + '.csv'), index=False)","def test_model(loader, model):\n correct = 0\n total = 0\n model.eval()\n for data, labels in loader:\n data_batch, label_batch = data.to(device), labels.to(device)\n outputs = F.softmax(model(data_batch), dim=1)\n predicted = outputs.max(1, keepdim=True)[1]\n total += label_batch.size(0)\n correct += predicted.eq(label_batch.view_as(predicted)).sum().item()\n return (100 * correct / total)","def model_performance_summary(df, data_all_df, cov_buck, fisc_calender, mod_start, pred_start, summ_start, summ_end):\n var = \"pv_yr\" \n agg_col = \"LINE_ORDERS\"\n select_cols = [ \"LINE_ORDERS_ACT\", \"Prediction_Trf\", \"LEAD_MNTS_ACT\", \"LD_UNCLAIMED_ACT\", \"LD_DRIVE_ACT\", \n \"IDV_LEAD_MNTS_Pred\", \"IDV_LD_UNCLAIMED_Pred\", \"IDV_LD_DRIVE_Pred\",\n \"IDV_LEAD_MNTS_ACT\", \"IDV_LD_UNCLAIMED_ACT\", \"IDV_LD_DRIVE_ACT\",\n \"HELPER_MNTS_ACT\", \"HP_UNCLAIMED_ACT\", \"HP_DRIVE_ACT\",\n \"IDV_HELPER_MNTS_Pred\", \"IDV_HP_UNCLAIMED_Pred\", \"IDV_HP_DRIVE_Pred\",\n \"IDV_HELPER_MNTS_ACT\", \"IDV_HP_UNCLAIMED_ACT\", \"IDV_HP_DRIVE_ACT\",\n \"HELPER_OVR_ACT\", \"HPN_UNCLAIMED_ACT\", \"HPN_DRIVE_ACT\",\n \"IDV_HELPER_OVR_Pred\", \"IDV_HPN_UNCLAIMED_Pred\", \"IDV_HPN_DRIVE_Pred\",\n \"IDV_HELPER_OVR_ACT\", \"IDV_HPN_UNCLAIMED_ACT\", \"IDV_HPN_DRIVE_ACT\"\n ]\n\n for i in select_cols:\n if str(i) not in data_all_df.columns:\n data_all_df[str(i)] = np.nan\n\n cov_grp_mm = dp.cov_check(\"RSS_MM\", df, cov_buck, agg_col, fisc_calender, summ_start, summ_end)\n cov_mm = dp.cov_check(\"MM\", df, cov_buck, agg_col, fisc_calender, summ_start, summ_end)\n \n sample = data_all_df[data_all_df.Variable_Type == var].copy()\n sample = pd.merge(sample, cov_grp_mm[[\"RSS_MM\", \"CAT\"]])\n sample.CAT = sample.CAT.astype(int)\n sample[\"MAPE\"] = abs(sample[\"LINE_ORDERS_ACT\"]-sample[\"Prediction_Trf\"])/sample[\"LINE_ORDERS_ACT\"]\n bins= [-1, 0.1, 0.15, 0.20, 0.30, 0.50, 1.00, np.inf]\n labels = [1, 2, 3, 4, 5, 6, 7]\n sample[\"MAPE_BIN\"] = pd.cut(sample['MAPE'], bins=bins, labels=labels)\n wk_list = fisc_calender[fisc_calender.FISC_WK_OF_MTH_ID >= pred_start].FISC_WK_OF_MTH_ID.unique()\n ly_start = fisc_calender[fisc_calender.FISC_WK_OF_MTH_ID == wk_list[4]].LY_FISC_WK_OF_MTH_ID.values\n ly_end = fisc_calender[fisc_calender.FISC_WK_OF_MTH_ID == wk_list[16]].LY_FISC_WK_OF_MTH_ID.values\n train = sample[((sample.FISC_WK_OF_MTH_ID >= mod_start) & (sample.FISC_WK_OF_MTH_ID < pred_start))]\n test_in_train = sample[((sample.FISC_WK_OF_MTH_ID >= ly_start[0]) & (sample.FISC_WK_OF_MTH_ID <= ly_end[0]))]\n # The 4 th index of the list has the prediction start date after excluding the 4 week hold out period\n # for analysis purpose we are considereing only 13 weeks of test period\n test = sample[((sample.FISC_WK_OF_MTH_ID >= wk_list[4]) & (sample.FISC_WK_OF_MTH_ID <= wk_list[16]))]\n fy20 = sample[(sample.FISC_WK_OF_MTH_ID >= summ_start) & (sample.FISC_WK_OF_MTH_ID <= summ_end)]\n \n ####################################################################################\n \n df_grp_summary = fy20.groupby([\"RSS\"]).sum()[\"LINE_ORDERS_ACT\"].reset_index()\n df_grp_summary[\"LINE_ORDERS_PERC\"] = df_grp_summary[\"LINE_ORDERS_ACT\"]/df_grp_summary[\"LINE_ORDERS_ACT\"].sum()\n ####################################################################################\n \n df_sample_list = []\n df_sample_list = cu.mape_stability(train, \"Train CAT\", \"CAT\", \"MAPE\", \"LINE_ORDERS_ACT\", df_sample_list)\n df_sample_list = cu.mape_stability(test_in_train, \"Test_in_train CAT\", \"CAT\", \"MAPE\", \"LINE_ORDERS_ACT\", df_sample_list)\n df_sample_list = cu.mape_stability(test, \"Test CAT\", \"CAT\", \"MAPE\", \"LINE_ORDERS_ACT\", df_sample_list)\n df_sample_list = cu.mape_stability(fy20, \"FY20 CAT\", \"CAT\", \"MAPE\", \"LINE_ORDERS_ACT\", df_sample_list)\n stability = pd.concat(df_sample_list)\n stability.reset_index(inplace = True)\n ####################################################################################\n \n df_sample_res = cu.mape_stability_2d(fy20, \"FISC_MTH_NBR\", \"CAT\")\n grp_sum = fy20.groupby(\"FISC_MTH_NBR\").sum()[\"LINE_ORDERS_ACT\"].reset_index()\n monthly_stability_check = df_sample_res.merge(grp_sum)\n ####################################################################################\n \n mape_cov = cu.mape_stability_2d(fy20, \"CAT\", \"MAPE_BIN\")\n ####################################################################################\n \n mape_grp = cu.mape_stability_2d(fy20, \"RSS\", \"MAPE_BIN\")\n mape_grp[\"TYPE\"] = \"MAPE Buckets\"\n ####################################################################################\n \n mape_cov_grp = cu.mape_stability_2d(fy20, \"RSS\", \"CAT\")\n mape_cov_grp[\"TYPE\"] = \"Covariance Category\"\n ####################################################################################\n \n fy20 = cu.sh_mape(fy20)\n \n sh_summary = []\n for grp in fy20[\"RSS\"].unique():\n sample_df = fy20[fy20[\"RSS\"] == grp]\n\n sh_summary = cu.mape_stability(sample_df, grp, \"MAPE_LD_BIN\", \"MAPE_LD\", \"TOTAL_LEAD_ACT\", sh_summary)\n sh_summary = cu.mape_stability(sample_df, grp, \"MAPE_HP_BIN\", \"MAPE_HP\", \"TOTAL_HELPER_ACT\", sh_summary)\n sh_summary = cu.mape_stability(sample_df, grp, \"MAPE_HPN_BIN\", \"MAPE_HPN\", \"TOTAL_HELPERN_ACT\", sh_summary)\n sh_summary_grp = pd.concat(sh_summary)\n sh_summary_grp.reset_index(inplace = True)\n\n ####################################################################################\n\n sh_summary = []\n var_list = [\"pv_yr\", \"pv_roll_5wk\"]\n for var in var_list:\n sample_df = data_all_df[data_all_df.Variable_Type == var].groupby([\"FISC_WK_OF_MTH_ID\", \"FISC_YR_NBR\", \"MM\"]).sum()[select_cols].reset_index()\n sample_df = pd.merge(sample_df, cov_mm[[\"MM\", \"CAT\"]])\n sample_df = sample_df[sample_df.CAT>0]\n sample_df = sample_df[(sample.FISC_WK_OF_MTH_ID >= summ_start) & (sample.FISC_WK_OF_MTH_ID <= summ_end)]\n sample_df = cu.sh_mape(sample_df)\n sh_summary = cu.mape_stability(sample_df, var, \"MAPE_LDA_BIN\", \"MAPE_LD_ACT\", \"TOTAL_LEAD_ACT\", sh_summary)\n sh_summary = cu.mape_stability(sample_df, var, \"MAPE_HPA_BIN\", \"MAPE_HP_ACT\", \"TOTAL_HELPER_ACT\", sh_summary)\n sh_summary = cu.mape_stability(sample_df, var, \"MAPE_HPNA_BIN\", \"MAPE_HPN_ACT\", \"TOTAL_HELPERN_ACT\", sh_summary)\n sh_summary = cu.mape_stability(sample_df, var, \"MAPE_LD_BIN\", \"MAPE_LD\", \"TOTAL_LEAD_ACT\", sh_summary)\n sh_summary = cu.mape_stability(sample_df, var, \"MAPE_HP_BIN\", \"MAPE_HP\", \"TOTAL_HELPER_ACT\", sh_summary)\n sh_summary = cu.mape_stability(sample_df, var, \"MAPE_HPN_BIN\", \"MAPE_HPN\", \"TOTAL_HELPERN_ACT\", sh_summary)\n sh_mm_summary_df = pd.concat(sh_summary)\n sh_mm_summary_df.reset_index(inplace = True)\n\n ####################################################################################\n\n sh_summary = []\n for var in data_all_df.Variable_Type.unique():\n sample_df = data_all_df[data_all_df.Variable_Type == var].groupby([\"FISC_WK_OF_MTH_ID\", \"FISC_YR_NBR\", \"MM\"]).sum()[select_cols].reset_index()\n sample_df[select_cols] = sample_df[select_cols].replace({0:np.nan})\n sample_df = pd.merge(sample_df, cov_mm[[\"MM\", \"CAT\"]])\n sample_df = sample_df[sample_df.CAT>0]\n sample_df = sample_df[(sample.FISC_WK_OF_MTH_ID >= summ_start) & (sample.FISC_WK_OF_MTH_ID <= summ_end)]\n sample_df = cu.sh_mape(sample_df)\n sh_summary = cu.mape_stability(sample_df, var, \"MAPE_LDAP_BIN\", \"MAPE_LD_ACT_PRED\", \"TOTAL_LEAD_ACT\", sh_summary)\n sh_summary = cu.mape_stability(sample_df, var, \"MAPE_HPAP_BIN\", \"MAPE_HP_ACT_PRED\", \"TOTAL_HELPER_ACT\", sh_summary)\n sh_summary = cu.mape_stability(sample_df, var, \"MAPE_HPNAP_BIN\", \"MAPE_HPN_ACT_PRED\", \"TOTAL_HELPERN_ACT\", sh_summary)\n sh_prop_summary = pd.concat(sh_summary)\n sh_prop_summary.reset_index(inplace = True)\n \n best_mthd = sh_prop_summary.groupby(['IDENTIFIER']).mean()['WEIGHTED MAPE'].reset_index().sort_values(by = [\"WEIGHTED MAPE\"], ascending = True)\n var_list = best_mthd[\"IDENTIFIER\"][:2].to_list()\n ####################################################################################\n\n sample_df = fy20.copy()\n sample_df = sample_df[sample_df.MM.str.contains(\"HD\")]\n sample_df = sample_df.groupby([\"FISC_WK_OF_MTH_ID\", \"MM\"]).sum()[[\"LINE_ORDERS_ACT\", \"Prediction_Trf\"]].reset_index()\n df_grp_all = sample_df.copy()\n df_grp_all[[\"LINE_ORDERS_ACT\"]] = df_grp_all[[\n \"LINE_ORDERS_ACT\"]].replace({0:np.nan})\n df_grp_all[\"MAPE_TA\"] = abs(df_grp_all[\"LINE_ORDERS_ACT\"]-df_grp_all[\"Prediction_Trf\"])/df_grp_all[\"LINE_ORDERS_ACT\"]\n bins= [-1, 0.1, 0.15, 0.20, 0.30, 0.50, 1.00, np.inf]\n labels = [1, 2, 3, 4, 5, 6, 7]\n df_grp_all[\"MAPE_TA_BIN\"] = pd.cut(df_grp_all['MAPE_TA'], bins=bins, labels=labels)\n\n var = \"pv_yr\" \n sample = data_all_df[data_all_df.Variable_Type == var].copy()\n \n sample = sample.merge(cov_grp_mm[[\"RSS_MM\", \"CAT\"]])\n sample = sample[sample.CAT > 0]\n sample = sample[(sample.FISC_WK_OF_MTH_ID >= summ_start) & (sample.FISC_WK_OF_MTH_ID <= summ_end)]\n \n \n sample = data_all_df[data_all_df.Variable_Type == var].copy()\n sample = pd.merge(sample, cov_mm[[\"MM\", \"CAT\"]])\n sample = sample[sample.CAT > 0]\n sample = sample[(sample.FISC_WK_OF_MTH_ID >= summ_start) & (sample.FISC_WK_OF_MTH_ID <= summ_end)]\n fy20_mm = sample.groupby([\"FISC_WK_OF_MTH_ID\", \"MM\", \"CAT\"]).sum()[[\"LINE_ORDERS_ACT\", \"Prediction_Trf\"]].reset_index()\n fy20_mm[[\"LINE_ORDERS_ACT\" ]] = fy20_mm[[\"LINE_ORDERS_ACT\"]].replace({0:np.nan})\n fy20_mm[\"MAPE\"] = abs(fy20_mm[\"LINE_ORDERS_ACT\"]-fy20_mm[\"Prediction_Trf\"])/fy20_mm[\"LINE_ORDERS_ACT\"]\n bins= [-1, 0.1, 0.15, 0.20, 0.30, 0.50, 1.00, np.inf]\n labels = [1, 2, 3, 4, 5, 6, 7]\n fy20_mm[\"MAPE_BIN\"] = pd.cut(fy20_mm['MAPE'], bins=bins, labels=labels)\n \n df_sample_list = []\n df_sample_list = cu.mape_stability(fy20, \"FY20 RSS_MM\", \"CAT\", \"MAPE\", \"LINE_ORDERS_ACT\", df_sample_list)\n df_sample_list = cu.mape_stability(fy20_mm, \"FY20 MM\", \"CAT\", \"MAPE\", \"LINE_ORDERS_ACT\", df_sample_list)\n df_overall_summay = pd.concat(df_sample_list)\n df_overall_summay.reset_index(inplace = True)\n return df_grp_summary, stability, monthly_stability_check, mape_cov, mape_cov_grp, mape_grp, sh_mm_summary_df, sh_summary_grp, sh_prop_summary, df_overall_summay, var_list","def test(self, dataset):\n test_accuracy = 0\n test_loss = 0\n num_examples_tested = 0\n # Put model into evaluation mode\n self.model.eval()\n for num, batch in enumerate(dataset.loader):\n xs, ys = batch\n batch_size = len(xs)\n num_examples_tested += batch_size\n iloss, iaccuracy = self.model(xs, ys)\n test_loss += iloss.cpu().data.numpy().item() * batch_size\n test_accuracy += iaccuracy.cpu().data.numpy().item() * batch_size\n test_accuracy = test_accuracy / num_examples_tested\n test_loss = test_loss / num_examples_tested\n # Return accuracy and loss for this model on the test set\n return test_accuracy, test_loss","def train_model(regressor=DecisionTreeRegressor(max_depth=3, random_state=42),\n dataset_file_name=DATASET_FILE_NAME,\n model_file_name=MODEL_FILE_NAME):\n df = pd.read_csv(dataset_file_name)\n\n # A minumum amount of feature engineering: The player's and opponent's\n # exact score may not be that important for our decisions. The difference,\n # however, certainly is. Moreover, the card value itself is not that\n # important. Here, the sum is.\n df['score_difference'] = df.self_score - df.opp_score\n df.drop(columns=['opp_score'], inplace=True)\n df['score_if_card_played'] = df.self_score + df.result_card_val\n df.drop(columns=['result_card_val'], inplace=True)\n\n # Strategy will be to let our model predict the score for different actions\n # Hence, we're going to train the model on that now\n X, y = df.drop(columns='score'), df.score\n X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n regressor.fit(X_train, y_train)\n\n feature_names = ['self_score', 'opp_stands', 'result_stand',\n 'score_difference', 'score_if_card_played']\n\n score = regressor.score(X_test, y_test)\n print(f\"Score on the test set: {score}.\")\n if isinstance(regressor, DecisionTreeRegressor):\n export_graphviz(regressor, feature_names=feature_names,\n out_file=GRAPHVIZ_FILE_NAME, filled=True)\n\n # For persistence, we export the generated model\n dump(regressor, model_file_name)\n return score","def test(self, test):\r\n self.ml_data.set_target(test[0])\r\n self.ml_data.set_features(test[1])\r\n if self.ml_data.target_type.all() == np.float64 or self.ml_data.target_type.all() == np.int64:\r\n self.model_qua.open()\r\n else:\r\n self.model_quali.open()","def fit(model, data, test_ids, exp_name, train_ids=None):\n if model.model_type == 'torch':\n size = len(data[0])\n else:\n size = data[0].shape[0]\n \n if train_ids == None:\n train_ids = [i for i in range(size) if i not in test_ids]\n scaler = pka_scaler(data[1][train_ids])\n \n if model.data_type == 'descriptors':\n desc_scaler = StandardScaler()\n scaling_data = data[0][train_ids]\n desc_scaler.fit(scaling_data)\n data[0] = desc_scaler.transform(data[0])\n else:\n scaling_data = None\n \n trained_model = train(model, train_ids, data, scaler)\n results = test(model, trained_model, test_ids, data, scaler)\n model.experiments[exp_name] = {'model':trained_model, 'results':results, 'scaler':scaler, 'desc scaling data':scaling_data}\n return results","def test_model(model, dataObj, index):\n\t(s,m,l), img = dataObj.__getitem__(index)\n\timg = img.float().unsqueeze(0)\n\t\n\tif next(model.parameters()).is_cuda:\n\t\toutput = model(img.cuda()) \n\telse:\n\t\toutput = model(img)\n\n\ts_pred,m_pred,l_pred = output[0].squeeze(0).cpu(), output[1].squeeze(0).cpu(), output[2].squeeze(0).cpu()\n\ts_pred = s_pred.detach().numpy()\n\tm_pred = m_pred.detach().numpy()\n\tl_pred = l_pred.detach().numpy()\n\n\timg = img.float().squeeze(0)\n\timg = img.permute(1,2,0)\n\n\tfor j in range(22):\n\t\tvisualize(img, s[j], m[j], l[j], s_pred[j], m_pred[j], l_pred[j])\n\t\tk = np.array(s[j])","def evaluate_model(model, X_test, Y_test, category_names):\n\n print(\"Testing Performance\")\n print(classification_report(Y_test, model.predict(X_test), target_names=category_names))\n\n #Todo cat names","def test(self):\n self.training = False","def evaluate_model(model, X_test, y_test):\n # run prediction with test data\n y_pred = model.predict(X_test)\n\n # print precision, recall and f1-score\n i = 0\n for col in y_test:\n print('Evaluation for \"{}\": \\n {} \\n\\n'.format(col, classification_report(y_test[col], y_pred[:,i])))\n i += 1","def trainModel( self, featureTrain, classTrain):","def train(self):\n\t\traise NotImplementedError","def main():\n data = load_data()\n analyze_features(data['full_features'])\n model = train(data)\n\n with open('model.pickle', 'wb') as f:\n pickle.dump(model, f)\n evaluate(model, data)","def _train_and_evaluate(estimator, output_dir):\n \n \"\"\"X_train, y_train =utils._feature_label_split(df_train,\"is_churn\",\"msno\")\n df_val = utils.read_from_bigquery(\"amiable-octane-267022.kkbox.output_val_1\",\"amiable-octane-267022\")\n X_val, y_val =utils._feature_label_split(df_val,\"is_churn\",\"msno\")\"\"\"\n \n df_train=utils.over_sample(\"amiable-octane-267022.kkbox.output_train_1\",\"amiable-octane-267022\")\n X_train, y_train =utils._feature_label_split(df_train,\"is_churn\",\"msno\")\n df_val=utils.over_sample(\"amiable-octane-267022.kkbox.output_val_1\",\"amiable-octane-267022\")\n X_val, y_val =utils._feature_label_split(df_val,\"is_churn\",\"msno\")\n\n estimator.fit(X_train, y_train)\n f1_scorer = make_scorer(f1_score)\n accuracy_scorer =make_scorer(accuracy_score)\n\n if metadata.HYPERPARAMTER_TUNING:\n scores=model_selection.cross_val_score(estimator, X_val, y_val, cv=3,scoring=f1_scorer)\n #,scoring=f1_scorer\n\n logging.info('Score: %s', scores)\n\n #tune hyper\n hpt = hypertune.HyperTune()\n hpt.report_hyperparameter_tuning_metric(\n hyperparameter_metric_tag='F1_SCORE',\n metric_value=np.mean(scores),\n global_step=10000)\n \n#joblib.dump(estimator, 'model.joblib')\n\n # Write model and eval metrics to `output_dir`\n model_output_path = os.path.join(output_dir, 'model',metadata.MODEL_FILE_NAME)\n \n utils.dump_object(estimator, model_output_path)","def train_and_eval():\n # train_file_name = 'adult.data'\n # test_file_name = 'adult.test'\n train_file_name = 'poker-hand-testing.data'\n test_file_name = 'poker-hand-training-true.data'\n #test_file_name = maybe_download()\n df_train = pd.read_csv(\n tf.gfile.Open(\"/opt/tensor/race_result_clean.csv\"),\n names=COLUMNS,\n skipinitialspace=True,\n skiprows=1)\n df_test = pd.read_csv(\n tf.gfile.Open(\"/opt/tensor/race_result_clean.csv\"),\n names=COLUMNS,\n skipinitialspace=True,\n skiprows=1)\n\n #df_train[LABEL_COLUMN] = (df_train[\"CLASS_Poker_Hand\"].apply(lambda x: x>5)).astype(int)\n #df_test[LABEL_COLUMN] = (df_test[\"CLASS_Poker_Hand\"].apply(lambda x: x>5)).astype(int)\n\n model_dir = tempfile.mkdtemp() if not FLAGS.model_dir else FLAGS.model_dir\n print(\"model directory = %s\" % model_dir)\n m = build_estimator(model_dir)\n print(m)\n m.fit(input_fn=lambda: input_fn(df_train), steps=FLAGS.train_steps)\n results = m.evaluate(input_fn=lambda: input_fn(df_test), steps=1)\n for key in sorted(results):\n print(\"%s: %s\" % (key, results[key]))","def evaluate_model(model, X_test, Y_test, category_names):\n# Print out Precision , recall F1_score and support for each column using classification_report function\n y_pred_test = model.predict(X_test)\n print(classification_report(Y_test, y_pred_test, target_names=category_names))"],"string":"[\n \"def fit_test(self):\",\n \"def experiment_models(train, test, train_target, test_target):\\n # Linear models\\n linear_models = [(LinearRegression, {\\\"n_jobs\\\": -1}),\\n (Lasso, {\\\"alpha\\\": 3}),\\n (Ridge, {\\\"alpha\\\": 3}),\\n (LinearSVR, {\\\"random_state\\\": 0, \\\"tol\\\": 1e-5})]\\n\\n # Add polynomial features\\n poly = preprocessing.PolynomialFeatures(2)\\n\\n # scaler\\n scaler = preprocessing.StandardScaler().fit(train)\\n\\n print(\\\"Use linear models with linear features\\\")\\n for model_ in linear_models:\\n scaled_train = scaler.transform(train)\\n scaled_test = scaler.transform(test)\\n model = model_[0](**model_[1])\\n model.fit(scaled_train, train_target.to_numpy())\\n train_pred = model.predict(scaled_train)\\n valid_pred = model.predict(scaled_test)\\n print(\\\"=========================================\\\")\\n print(f\\\"Model : {model_}\\\")\\n compute_metrics(train_pred, train_target, valid_pred, test_target)\\n print(\\\"=========================================\\\")\\n\\n print(\\\"Use linear models with polynomial features\\\")\\n train = poly.fit_transform(train)\\n test = poly.transform(test)\\n scaler = preprocessing.StandardScaler().fit(train)\\n for model_ in linear_models:\\n scaled_train = scaler.transform(train)\\n scaled_test = scaler.transform(test)\\n model = model_[0](**model_[1])\\n model.fit(scaled_train, train_target.to_numpy())\\n train_pred = model.predict(scaled_train)\\n valid_pred = model.predict(scaled_test)\\n print(\\\"=========================================\\\")\\n print(f\\\"Model : {model_}\\\")\\n compute_metrics(train_pred, train_target, valid_pred, test_target)\\n print(\\\"=========================================\\\")\",\n \"def eval_perf_total(model, X_train, y_train, X_test, y_test):\\n\\n y_hat_train = model.predict(X_train)\\n y_hat_test = model.predict(X_test)\\n \\n train_mae = metrics.mean_absolute_error(y_train, y_hat_train)\\n train_mse = metrics.mean_squared_error(y_train, y_hat_train)\\n train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_hat_train))\\n train_r = metrics.r2_score(y_train, y_hat_train)\\n\\n print('Evaluating Performance on Training Data:\\\\n')\\n print(f' Train Mean Absolute Error: {train_mae:,.2f}')\\n print(f' Train Mean Squared Error: {train_mse:,.2f}\\\\n')\\n print(f'Train Root Mean Squared Error: {train_rmse:,.2f}')\\n print(f'Train R-Square Value: {round(train_r,2)}')\\n\\n print('\\\\n'+'---'*25+'\\\\n')\\n\\n test_mae = metrics.mean_absolute_error(y_test, y_hat_test)\\n test_mse = metrics.mean_squared_error(y_test, y_hat_test)\\n test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat_test))\\n test_r = metrics.r2_score(y_test, y_hat_test)\\n\\n print('Evaluating Performance on Testing Data:\\\\n')\\n print(f' Test Mean Absolute Error: {test_mae:,.2f}')\\n print(f' Test Mean Squared Error: {test_mse:,.2f}\\\\n')\\n print(f'Test Root Mean Squared Error: {test_rmse:,.2f}')\\n print(f'Test R-Square Value: {round(test_r,2)}')\",\n \"def eval_perf_train(model, X_train=None, y_train=None):\\n\\n # if X_train != None and y_train != None:\\n\\n y_hat_train = model.predict(X_train)\\n \\n train_mae = metrics.mean_absolute_error(y_train, y_hat_train)\\n train_mse = metrics.mean_squared_error(y_train, y_hat_train)\\n train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_hat_train))\\n train_r = metrics.r2_score(y_train, y_hat_train)\\n\\n print('Evaluating Performance on Training Data:\\\\n')\\n print(f'Train Mean Absolute Error: {train_mae:,.2f}')\\n print(f'Train Mean Squared Error: {train_mse:,.2f}\\\\n')\\n print(f'Train Root Mean Squared Error: {train_rmse:,.2f}')\\n print(f'Train R-Square Value: {round(train_r,2)}')\\n\\n # if X_test != None and y_test != None:\\n\\n # y_hat_test = model.predict(X_test)\\n\\n # test_mae = metrics.mean_absolute_error(y_test, y_hat_test)\\n # test_mse = metrics.mean_squared_error(y_test, y_hat_test)\\n # test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat_test))\\n # test_r = metrics.r2_score(y_test, y_hat_test)\\n\\n # print('Evaluating Performance on Testing Data:\\\\n')\\n # print(f'Test Mean Absolute Error: {test_mae:,.2f}')\\n # print(f'Test Mean Squared Error: {test_mse:,.2f}\\\\n')\\n # print(f'Test Root Mean Squared Error: {test_rmse:,.2f}')\\n # print(f'Test R-Square Value: {round(test_r,2)}')\",\n \"def run_model(Xp_train=None,y_train=None,Xp_test=None,y_test=None,mt='bl', params={}):\\n models = { \\n \\\"bl\\\": lambda : None,\\n \\\"lr\\\": LinearRegression,\\n \\\"dtr\\\": DecisionTreeRegressor,\\n \\\"gbr\\\": GradientBoostingRegressor\\n }\\n\\n # Select Model, returns None for baseline\\n model = models[mt](**params)\\n\\n # Fit Model and make predictions\\n if mt != 'bl': # If model not baseline\\n # Dummy encoding , (each category of n levels or attributes is converted into n-1 dichotomous variables)\\n X_train = pd.get_dummies(Xp_train, columns=['type', 'duration','location'],drop_first=True)\\n X_test = pd.get_dummies(Xp_test, columns=['type', 'duration','location'],drop_first=True)\\n model.fit(X_train,y_train)\\n # Make Predictions\\n y_pred = model.predict(X_test)\\n #print X_test.shape\\n y_predtr = model.predict(X_train) \\n else: # Compute baseline\\n y_pred = np.median(y_train).repeat(len(y_test))\\n y_predtr = np.median(y_train).repeat(len(y_train))\\n \\n \\n\\n # Report metrics\\n print(\\\"Model name: %s\\\" % (model.__class__.__name__ if model else \\\"Price Median Baseline\\\"))\\n if mt != 'bl':\\n print(\\\"hyper-parameters: \\\" + \\\", \\\".join( \\\"{0}: {1}\\\".format(k,v) for (k,v) in params.items() ) )\\n print(\\\"Mean absolute error training set: %.2f\\\" % mean_absolute_error(y_train, y_predtr)) \\n print(\\\"Mean absolute error testing set: %.2f \\\\n\\\" % mean_absolute_error(y_test, y_pred))\",\n \"def test_models(directorio=''):\\r\\n \\r\\n print('The trained models will be tested now')\\r\\n start = time.time()\\r\\n \\r\\n busqueda = \\\"ls \\\" + directorio + \\\"/*.h5 > model_names.txt\\\"\\r\\n\\r\\n os.system(busqueda)\\r\\n\\r\\n X = np.load(directorio + '/Xtest.npy')\\r\\n diccio = np.load(directorio + '/feature_standarisation.npy').item()\\r\\n y = pd.read_csv(directorio + '/dbtest.csv')['target'].values\\r\\n\\r\\n X = (X - diccio['mean'])/diccio['std']\\r\\n x = np.reshape(X,(X.shape[0],X.shape[2]))\\r\\n \\r\\n with open('model_names.txt','r') as f:\\r\\n for line in f:\\r\\n modelo = models.load_model(line[:len(line)-1])\\r\\n nombre = line.split('/')[1]\\r\\n outpred = modelo.predict(x)\\r\\n prediction = outpred >= 0.5\\r\\n \\r\\n cost = -(np.dot(y,np.log10(outpred)) + \\\\\\r\\n np.dot((1-y),np.log10(1-outpred)))/y.shape[0]\\r\\n precision,recall,fscore,support = PRFS(y, prediction)\\r\\n \\r\\n with open(directorio + '/test_results.txt','a') as tr:\\r\\n tr.write(nombre + '\\\\n')\\r\\n tr.write('cost function: '+str(cost[0])+'\\\\n')\\r\\n tr.write('samples: '+str(support)+'\\\\n')\\r\\n tr.write('precision: '+str(np.round(precision*100,2))+'\\\\n')\\r\\n tr.write('recall: '+str(np.round(recall*100,2))+'\\\\n')\\r\\n tr.write('f1-score: '+str(np.round(fscore*100,2))+'\\\\n')\\r\\n tr.write('\\\\n')\\r\\n tr.close()\\r\\n \\r\\n print('The test of all trained models lasted ', round(time.time()-start,2),' s')\\r\\n os.system('rm model_names.txt')\\r\\n \\r\\n return\",\n \"def _evaluate_during_fit(self, test_loader, epoch):\",\n \"def OTU_table_ML(OTU_table,metadata,obj_col):\\n for ele in OTU_table.index:\\n #print(ele)\\n X.append(df.loc[ele])\\n Y.append(metadata[obj_col][ele])\\n precisions = []\\n for train_time in range(100): \\n X,Y = shuffle(X,Y)\\n sample_num = len(X)\\n sep_num = int(0.8*sample_num)\\n train_set = [X[:sep_num],Y[:sep_num]]\\n test_set = [X[sep_num:],Y[sep_num:]]\\n clf = svm.SVC(gamma='scale')\\n clf.fit(train_set[0], train_set[1]) \\n predict_result = clf.predict(test_set[0])\\n count = 0\\n for i in range(len(predict_result)):\\n if predict_result[i] == test_set[1][i]:\\n count += 1\\n else:\\n pass\\n precisions.append(1.0*count/len(predict_result))\\n print(np.mean(precisions))\",\n \"def get_perf(self) :\\n self.train()\\n\\n prediction = self.clf.predict(self.df_test.drop(columns = 'up')[:-1])\\n self.accuracy = accuracy_score(df_test['up'][length:].values, prediction)\\n tn, fp, fn, tp = confusion_matrix(df_test['up'][length:].values, prediction).ravel()\\n self.recall = tp/(tp+fn)\\n self.specificity = tn / (tn+fp)\\n\\n\\n self.df_true = self.df_true[self.length:]\\n\\n profit = 1\\n mini = 1\\n maxi = 1\\n self.df_true['close'] = self.df_true['close'].map(lambda x : np.exp(x))\\n for s in range(1,len(self.df_true)):\\n if prediction[x-1] == 1 :\\n result = ((self.df_true['close'].iloc[s] -self.df_true['close'].iloc[s-1]) / self.df_true['close'].iloc[s-1]) + 1\\n profit = profit * result\\n if result < mini :\\n mini = result\\n if maxi < result :\\n maxi = result\\n self.mini = mini\\n self.maxi = maxi\\n self.profit = profit\",\n \"def fit(self) -> None:\\n start_time = time.time()\\n # ---- START -----\\n train_df = self.processed_train_df[self.processed_train_df[self.filter_col_name]].dropna()\\n train_features = train_df[self.feature_list]\\n for label, model in zip(self.label_list, self.models):\\n model.fit(train_features, train_df[label])\\n # ---- END -----\\n end_time = time.time()\\n print(\\\"Finished fitting : elasped time : \\\" + str(end_time - start_time))\",\n \"def test_predictor():\",\n \"def test_training(self):\\n\\t\\tpass\",\n \"def testModel( self, classTest, classPred):\",\n \"def test_variational():\\n # iris\\n #pres = \\\"Test pour le data set Iris (facile, classique)\\\"\\n #test_from_func_variational(pres, 15, 10, 3, True, Iris)\\n\\n # breast cancer\\n pres = \\\"Test pour le data set Breast Cancer (facile, classique)\\\"\\n test_from_func_variational(pres, 15, 10, 3, True, Breast_cancer)\\n\\n # digits\\n # pres = \\\"Test pour le data set Digits (difficile, classique)\\\"\\n # test_from_func(pres, 10, 10, 10, True, Digits, quantum_instance)\\n\\n # wine\\n # pres = \\\"Test pour le data set Wine (moyen, classique)\\\"\\n # test_from_func(pres, 15, 10, 5, True, Wine, quantum_instance)\\n\\n # gaussian\\n pres = \\\"Test pour des données gaussiennes (moyen, classique)\\\"\\n for _ in range(1):\\n print(\\\"\\\\n\\\")\\n print(\\\"New iteration\\\")\\n test_from_func_variational(pres, 25, 10, 2, True, Gaussian)\\n print(\\\"\\\\n\\\")\\n\\n # small adn strings\\n pres = \\\"Test pour des séquences ADN courtes (difficile, classique)\\\"\\n test_from_func_variational(pres, 10, 15, 14, True, Sequence)\\n\\n #Quantum data\\n pres = \\\"Test pour des données générées par ordinateur quantique (facile, quantique)\\\"\\n print(pres)\\n _, samp_train, samp_test, labels = ad_hoc_data(15, 10, 2, 0.3, True)\\n sample_m, sample_p = stock_get(20, 0.3)\\n\\n labels_me = [-1, 1]\\n samp_train_me = {-1: np.array(sample_m[:15]), 1: np.array(sample_p[:15])}\\n samp_test_me = {-1: np.array(sample_m[15:]), 1: np.array(sample_p[15:])}\\n print(samp_train)\\n print(samp_train_me)\\n print(samp_test)\\n print(samp_test_me)\\n\\n my_impl_variational(samp_train, samp_test, labels)\\n print(\\\"Pour autres données quantiques\\\")\\n my_impl_variational(samp_train_me, samp_test_me, labels_me)\",\n \"def test_fit(self):\\n X = self.generate_X()\\n task = mmRDTR()\\n fit_result = task.fit(X)\",\n \"def test(x_test, m_labels_test, ques_test, alpha, checkpoint_name, model_name, output_file_name):\\n bestModel = pick_model(model_name, alpha)\\n bestModel.load_state_dict(torch.load(checkpoint_name))\\n bestModel.eval()\\n best_scores = {}\\n\\n with torch.no_grad():\\n test_pred = bestModel(x_test, m_labels_test)\\n prec, recall, f1, df_output = get_qald_metrics(test_pred, m_labels_test, ques_test, mode='test')\\n df_output.to_csv(output_file_name)\\n print(\\\"Test -- f1 is {} \\\".format(f1))\\n print(\\\"Test -- prec, recall, f1\\\", prec, recall, f1)\\n best_scores['precision'] = prec\\n best_scores['recall'] = recall\\n best_scores['f1'] = f1\\n\\n # for name, mod in bestModel.named_modules():\\n # if type(mod) == nn.ModuleList:\\n # for name1, mod1 in mod.named_modules():\\n # if 'cdd' not in name1 and 'AND' not in name1:\\n # if 'batch' in name1.lower():\\n # continue\\n # elif 'or_max' in name1.lower():\\n # continue\\n # elif 'and' in name1.lower():\\n # print(name1, mod1.cdd())\\n # elif 'or' in name1.lower():\\n # print(name1, mod1.AND.cdd())\\n # else:\\n # if 'cdd' not in name and 'AND' not in name:\\n # if 'batch' in name.lower():\\n # continue\\n # elif 'or_max' in name.lower():\\n # continue\\n # elif 'and' in name.lower():\\n # print(name, mod.cdd())\\n # elif 'or' in name.lower():\\n # print(name, mod.AND.cdd())\\n return test_pred, best_scores\",\n \"def train(self): \\n start_time = time()\\n\\n # reset previous results\\n self.best_result = pd.DataFrame()\\n self.result = pd.DataFrame()\\n\\n # Generate dictionaries of all posible parameter permutations\\n keys, values = zip(*self.params.items())\\n self.permutations_dict = [dict(zip(keys, v)) for v in itertools.product(*values)] \\n\\n # Run through all models in parallel threads\\n with Pool(self.thread_cnt) as p:\\n result = p.map(self.analyze_model, self.permutations_dict)\\n\\n\\n # wrap up results\\n if self.classes_names: # acts as trigger for computation of cms\\n for i, dic in enumerate(result):\\n dic[\\\"id\\\"] = i\\n self.cms = [(dic[\\\"id\\\"] ,dic.pop(\\\"cm\\\")) for dic in result]\\n\\n self.result = pd.DataFrame(result)\\n self.best_result = self.result.iloc[self.result[\\\"score\\\"].argmax()] # store row with the best score\\n self.best_result = self.result.iloc[self.result[\\\"f1_score\\\"].argmax()] # store row with the best score\\n self.best_result = self.result.iloc[self.result[\\\"recall\\\"].argmax()] # store row with the best score\\n self.best_result = self.result.iloc[self.result[\\\"precision\\\"].argmax()] # store row with the best score\\n end_time = time()\\n print(\\\"Finished evaluation\\\")\\n print(\\\"Best parameteters found with:\\\", self.best_parameter_set())\\n print(\\\"score=\\\", self.best_score())\\n #print(\\\"f1_score=\\\", self.best_f1_score())\\n #print(\\\"recall_score=\\\", self.best_recall_score())\\n #print(\\\"precision_score=\\\", self.best_precision_score())\\n print(\\\"Total evaluation time = {:.2f}s\\\".format(end_time-start_time))\\n\\n return self.best_parameter_set(), self.best_score()\",\n \"def test(self, plot=False):\\n accuracy_list = []\\n fobj_avg = self.load_stats()\\n\\n for ii in range(settings.PARS['maxIters']):\\n model = self.load_model(ii)\\n D1 = model['D']\\n W1 = model['W']\\n\\n # classification\\n tic = time.time()\\n accuracy_list.append(self.classification(D1, W1)[1])\\n toc = time.time()\\n print(\\n 'Final recognition rate for OnlineDL is : {} , objective function value: {}, time: {}'\\n .format(accuracy_list[ii], fobj_avg[ii], toc-tic)\\n )\\n\\n accuracy_list = np.asarray(accuracy_list)\\n\\n print('Best recognition rate for OnlineDL is {} at iteration {}'.format(\\n accuracy_list.max(), accuracy_list.argmax()))\\n\\n if plot:\\n # plot the objective function values for all iterations\\n plt.clf()\\n plt.plot(list(fobj_avg.keys()), list(fobj_avg.values()), 'mo--', linewidth=2)\\n plt.xlabel('Iterations')\\n plt.ylabel('Average objective function value')\\n plt.xticks(list(range(0, 20)), list(range(1, 21)))\\n plt.show()\\n\\n plt.clf()\\n plt.plot(accuracy_list, 'rs--', linewidth=2)\\n plt.xticks(list(range(0, 20)), list(range(1, 21)))\\n plt.xlabel('Iterations')\\n plt.ylabel('Accuracy')\\n plt.show()\",\n \"def model(classifier, data):\\n print(\\\"Beggining to test model\\\")\\n train, test = cross_validation.train_test_split(data, test_size=.30)\\n f,c = train[:,1:], train[:,0]\\n classifier.fit(f,c,False)\\n print(\\\"Score: \\\" + classifier.score(f,c))\\n print(\\\"Finished testing model\\\")\",\n \"def train(X : list, y : list, model_name : str, data, mode,*vals,**args):\\n # 훈련 결과 dictionary\\n scores = {}\\n\\n # 훈련 모드 선택\\n\\n model = MODELS[mode][model_name]\\n\\n X_train, X_val, y_train, y_val = train_test_split(data[X], data[y], train_size=0.7)\\n \\n model.fit(X_train, y_train)\\n\\n scores['train'] = validation(model, X_train, y_train, mode)\\n scores['validation'] = validation(model, X_val, y_val, mode)\\n\\n return scores\",\n \"def do_stats_model(x, y):\\n Xx = sm.add_constant(x)\\n sm_logit = sm.Logit(y, Xx)\\n result = sm_logit.fit()\\n print result.summary()\\n result.pred_table()\\n # linear model\\n print \\\"linear regression model:\\\\n\\\"\\n sm_linear = sm.OLS(y, Xx)\\n result = sm_linear.fit()\\n print result.summary()\",\n \"def test_modele(self, data):\\n\\t\\tif not isinstance(data, ndarray):\\n\\t\\t\\traise TypeError(\\\"erreur data = {} n'est pas de type numpy.ndarray \\\".format(type(data)))\\t\\t\\t\\n\\n\\t\\t#result = self.session.run(tf.argmax(self.variable_mnsit[\\\"y_conv\\\"],1), feed_dict={self.variable_mnsit[\\\"x\\\"]: [data]})\\n\\t\\t[tableau_pourcent, result] = self.session.run([self.y_conv, tf.argmax(self.y_conv, 1)], feed_dict={self.x : [data], self.keep_prob: 1.0})\\n\\t\\tprint (\\\"l'ordinateur voit un ... {}\\\", result)\\n\\t\\tprint (\\\"tableaux: {}\\\".format(tableau_pourcent))\",\n \"def model_performance_comparison(self, yvar, prev_group, C_FP, C_FN):\\n trn_bl_df = self.reweigh()\\n # sample_weights = self.reweigh(bl_df=trn_bl_df)\\n\\n s_weights = trn_bl_df.instance_weights\\n print(s_weights)\\n\\n trainset = trn_bl_df.convert_to_dataframe()[0]\\n testset = trainset\\n\\n X = trainset.loc[:, trainset.columns != yvar]\\n y = trainset[yvar]\\n #X_test = testset.loc[:, trainset.columns != yvar]\\n #y_test = testset[yvar]\\n\\n X_test = X\\n y_test = y\\n\\n clf_ww = sklearn.linear_model.LogisticRegression(random_state=999).fit(X, y, sample_weight=s_weights)\\n clf_wow = sklearn.linear_model.LogisticRegression(random_state=999).fit(X, y)\\n\\n output_probabilities_to_csv(model=clf_ww, x_test=X_test, path='probs_ww_withprvgroup.csv',priv_group_col=trainset[prev_group], actuals=y_test)\\n output_probabilities_to_csv(model=clf_wow, x_test=X_test, path='probs_wow_withprvgroup.csv', priv_group_col=trainset[prev_group], actuals=y_test)\\n\\n print(\\\"------------------------------------------\\\")\\n print(\\\"Accuracy of Vanila Logistic Model\\\")\\n print(\\\"------------------------------------------\\\")\\n print(\\\"Without Weights : \\\", round(clf_wow.score(X_test, y_test), 3))\\n print(\\\"With Weights : \\\", round(clf_ww.score(X_test, y_test), 3))\\n\\n X_test_age1 = testset.loc[:, trainset.columns != yvar][testset[prev_group] == 1.0]\\n y_test_age1 = testset[yvar][testset[prev_group] == 1.0]\\n X_test_age0 = testset.loc[:, trainset.columns != yvar][testset[prev_group] == 0.0]\\n y_test_age0 = testset[yvar][testset[prev_group] == 0.0]\\n\\n wow = round(abs(clf_wow.score(X_test_age0, y_test_age0) - clf_wow.score(X_test_age1, y_test_age1)), 3)\\n ww = round(abs(clf_ww.score(X_test_age0, y_test_age0) - clf_ww.score(X_test_age1, y_test_age1)), 3)\\n\\n #output_probabilities_to_csv(model=clf_ww, x_test=X_test_age0, path='probs_unpriv_ww.csv')\\n #output_probabilities_to_csv(model=clf_ww, x_test=X_test_age1, path='probs_priv_ww.csv')\\n #output_probabilities_to_csv(model=clf_wow, x_test=X_test_age0, path='probs_unpriv_wow.csv')\\n #output_probabilities_to_csv(model=clf_wow, x_test=X_test_age1, path='probs_priv_wow.csv')\\n\\n print(\\\"\\\")\\n print(\\\"\\\")\\n print(\\\"--------------------------------------------------------------\\\")\\n print(\\\"Difference in accuracy between privileged and unprivileged\\\")\\n print(\\\"--------------------------------------------------------------\\\")\\n print(\\\"without weights : \\\", wow)\\n print(\\\"with weights : \\\", ww)\\n\\n Ypredclf = clf_ww.predict(X_test)\\n Ypredclf2 = clf_wow.predict(X_test)\\n withw = confusion_matrix(y_test, Ypredclf)\\n without = confusion_matrix(y_test, Ypredclf2)\\n print(\\\"\\\")\\n print(\\\"\\\")\\n print(\\\"--------------------------------------------------------------\\\")\\n print(\\\"Confusion Matrix\\\")\\n print(\\\"--------------------------------------------------------------\\\")\\n print(\\\"without weights\\\")\\n print(without)\\n print(\\\"\\\")\\n print(\\\"\\\")\\n print(\\\"with weights\\\")\\n print(withw)\\n\\n a, b, c, d = without.ravel() #(tn, fp, fn, tp)\\n a1, b1, c1, d1 = withw.ravel() #(tn, fp, fn, tp)\\n\\n withweights = b1 * C_FP + c1 * C_FN\\n withoutweights = b * C_FP + c * C_FN\\n\\n print(\\\"\\\")\\n print(\\\"\\\")\\n print(\\\"cost with weights: \\\", withweights)\\n print(\\\"cost without weights: \\\", withoutweights)\\n print(\\\"Has cost decreased after reweighing?\\\", withweights < withoutweights)\\n\\n print('')\\n print('SUMMARY TABLE')\\n\\n cost = fr.CostingFairness(input_dataframe=self.data,\\n label_names=['credit'],\\n protected_attribute_names=['Age_previliged'],\\n trained_model=clf_ww)\\n\\n metrics_table = self.generate_pre_train_metrics_table(model_without_weights=clf_wow,\\n model_with_weights=clf_ww,\\n test_set=testset,\\n target=yvar,\\n privileged=prev_group,\\n false_positive_cost=C_FP,\\n false_negative_cost=C_FN)\\n priv_diff_table = generate_privileged_diff(metrics_table)\\n delta_table = generate_delta_table(metrics_table)\\n costs_table = cost.return_cost_fairness_accuracy_optimised()\\n\\n # pdf = PDF()\\n # pdf.add_page()\\n # pdf.write_table_to_pdf(metrics_table)\\n # pdf.write_table_to_pdf(priv_diff_table)\\n # pdf.write_table_to_pdf(delta_table)\\n # pdf.output('TEST01.pdf', 'F')\\n\\n print(\\\"\\\")\\n print(\\\"What we see is interesting, after re-weighing the bias of the model has decreased significantly by {}%, \\\"\\n \\\"with a very slight decrease in accuracy as shown earlier\\\".format(round((wow - ww) * 100)))\\n\\n return metrics_table, priv_diff_table, delta_table, costs_table\",\n \"def test_all_false(directorio = str(), database = 'red_cod-db.pkl', \\r\\n local_function = 'fij_2.0_25_diccio'):\\r\\n df = pd.read_pickle(database)\\r\\n collection = pd.read_csv(directorio + '/compounds_collection.csv')\\r\\n \\r\\n cifs = [i for i in collection['cif']]\\r\\n maxsites = np.max(collection['sitios'])\\r\\n \\r\\n df = df[df['sitios'] > 0][df['sitios'] <= maxsites].reset_index(drop=True)\\r\\n df = df.loc[~df['cif'].isin(cifs)].reset_index(drop=True)\\r\\n \\r\\n x, _, df = inout_creator(df=df)\\r\\n \\r\\n x = compute_quotients(X=x)\\r\\n x, df = append_local_functions(X = x,df=df)\\r\\n \\r\\n busqueda = \\\"ls \\\" + directorio + \\\"/*.h5 > model_names.txt\\\"\\r\\n os.system(busqueda)\\r\\n \\r\\n diccio = np.load(directorio + '/feature_standarisation.npy').item()\\r\\n \\r\\n X = (x - diccio['mean'])/diccio['std']\\r\\n x = np.reshape(X,(X.shape[0],X.shape[2]))\\r\\n \\r\\n with open('model_names.txt','r') as f:\\r\\n for line in f:\\r\\n modelo = models.load_model(line[:len(line)-1])\\r\\n nombre = line.split('/')[1]\\r\\n \\r\\n outpred = modelo.predict(x)\\r\\n prediction = outpred >= 0.5\\r\\n df['y_pred'] = np.ravel(prediction)\\r\\n \\r\\n with open(directorio+'/test_with_all_false.txt','a') as tr:\\r\\n tr.write(nombre + '\\\\n')\\r\\n \\r\\n for sitios in range(1, max(df['sitios']) + 1):\\r\\n \\r\\n acc = df[df['sitios'] == sitios][df['y_pred'] == False].shape[0]\\r\\n miniset = df[df['sitios'] == sitios].shape[0]\\r\\n percent = round(100*acc/miniset,2)\\r\\n \\r\\n \\r\\n tr.write('With '+ str(sitios) + ' sites:' + str(percent) +\\\\\\r\\n '(' + str(miniset) + ' samples)' + '\\\\n')\\r\\n tr.close()\\r\\n return\",\n \"def train_eval_model(model, model_name, X_train, y_train, X_test, y_test):\\n\\n model_predictions_train = model.predict(X_train) # Wyniki regresji dla zbioru treningowego\\n model_mse_train = mean_squared_error(y_train, model_predictions_train) # MSE dla zbioru treningowego\\n model_rmse_train = np.sqrt(model_mse_train) # RMSE dla zbioru treningowego\\n model_predictions_test = model.predict(X_test)\\n model_mse_test = mean_squared_error(y_test, model_predictions_test)\\n model_rmse_test = np.sqrt(model_mse_test)\\n # Kroswalidacja modelu\\n model_scores = cross_val_score(model, X_train, y_train, scoring=\\\"neg_mean_squared_error\\\", cv=10)\\n model_rmse_scores = np.sqrt(-model_scores)\\n\\n model_result = ResultDataRegressors(model_name, model, model_rmse_train, model_rmse_test, model_rmse_scores)\\n return model_result\",\n \"def eval_perf_test(model, X_test, y_test):\\n\\n y_hat_test = model.predict(X_test)\\n\\n test_mae = metrics.mean_absolute_error(y_test, y_hat_test)\\n test_mse = metrics.mean_squared_error(y_test, y_hat_test)\\n test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat_test))\\n test_r = metrics.r2_score(y_test, y_hat_test)\\n\\n print('Evaluating Performance on Testing Data:\\\\n')\\n print(f'Test Mean Absolute Error: {test_mae:,.2f}')\\n print(f'Test Mean Squared Error: {test_mse:,.2f}\\\\n')\\n print(f'Test Root Mean Squared Error: {test_rmse:,.2f}')\\n print(f'Test R-Square Value: {round(test_r,2)}')\",\n \"def train_full_model(X,y_train):\\n scaler = MinMaxScaler()\\n x_train = scaler.fit_transform(X)\\n\\n #chose model\\n # model = RandomForestClassifier()\\n model = GradientBoostingClassifier()\\n\\n\\n #train\\n print(\\\"train model\\\")\\n tic = time.time()\\n model.fit(x_train,y_train)\\n tac = time.time()\\n print(\\\"elapsed time\\\", tac-tic)\\n\\n #save data\\n save_data(model,scaler,x_train,y_train)\",\n \"def _model_train(df,tag,test=False):\\r\\n ## start timer for runtime\\r\\n time_start = time.time()\\r\\n \\r\\n X,y,dates = engineer_features(df)\\r\\n\\r\\n if test:\\r\\n n_samples = int(np.round(0.3 * X.shape[0]))\\r\\n subset_indices = np.random.choice(np.arange(X.shape[0]),n_samples,\\r\\n replace=False).astype(int)\\r\\n mask = np.in1d(np.arange(y.size),subset_indices)\\r\\n y=y[mask]\\r\\n X=X[mask]\\r\\n dates=dates[mask]\\r\\n \\r\\n ## Perform a train-test split\\r\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,\\r\\n shuffle=True, random_state=42)\\r\\n ## train a random forest model\\r\\n param_grid_rf = {\\r\\n 'rf__criterion': ['mse','mae'],\\r\\n 'rf__n_estimators': [50,100,250,500,1000],\\r\\n 'rf__max_depth': [5, 10, 20, 50],\\r\\n 'rf__min_samples_split': [2, 4, 8],\\r\\n 'rf__min_samples_leaf': [1, 2, 4]\\r\\n }\\r\\n pipe_rf = Pipeline(steps=[('rf', RandomForestRegressor())]) #Scaling not required for tree methods, robust to outliers\\r\\n grid_rf = RandomizedSearchCV(pipe_rf, param_distributions=param_grid_rf, cv=5, iid=False, n_jobs=-1)\\r\\n grid_rf.fit(X_train, y_train)\\r\\n y_pred = grid_rf.predict(X_test)\\r\\n eval_rmse_rf = round(np.sqrt(mean_squared_error(y_test,y_pred)),3)\\r\\n \\r\\n param_grid_mlp = {\\r\\n 'mlp__hidden_layer_sizes': [(30,),(30,10),(50,)],\\r\\n 'mlp__solver': [\\\"lbfgs\\\"],\\r\\n 'mlp__alpha': [0.01 ,0.001, 0.0001],\\r\\n 'mlp__max_iter': [500,1000],\\r\\n 'mlp__activation': ['relu','tanh']\\r\\n }\\r\\n pipe_mlp = Pipeline(steps=[('scaler', MinMaxScaler()),\\r\\n ('mlp', MLPRegressor())])\\r\\n grid_mlp = RandomizedSearchCV(pipe_mlp, param_distributions=param_grid_mlp, cv=5, iid=False, n_jobs=-1)\\r\\n grid_mlp.fit(X_train, y_train)\\r\\n y_pred = grid_mlp.predict(X_test)\\r\\n eval_rmse_mlp = round(np.sqrt(mean_squared_error(y_test,y_pred)),3)\\r\\n \\r\\n alg_dict={'rf':eval_rmse_rf,'mlp':eval_rmse_mlp}\\r\\n grid_dict={'rf':grid_rf,'mlp':grid_mlp}\\r\\n \\r\\n grid=grid_dict[max(alg_dict, key=alg_dict.get)]\\r\\n eval_rmse=max(eval_rmse_rf,eval_rmse_mlp)\\r\\n ## retrain using all data\\r\\n grid.fit(X, y)\\r\\n model_name = re.sub(\\\"\\\\.\\\",\\\"_\\\",str(MODEL_VERSION))\\r\\n if test:\\r\\n saved_model = os.path.join(MODEL_DIR,\\r\\n \\\"test-{}-{}.joblib\\\".format(tag,model_name))\\r\\n print(\\\"... saving test version of model: {}\\\".format(saved_model))\\r\\n else:\\r\\n saved_model = os.path.join(MODEL_DIR,\\r\\n \\\"sl-{}-{}.joblib\\\".format(tag,model_name))\\r\\n print(\\\"... saving model: {}\\\".format(saved_model))\\r\\n \\r\\n joblib.dump(grid,saved_model)\\r\\n\\r\\n m, s = divmod(time.time()-time_start, 60)\\r\\n h, m = divmod(m, 60)\\r\\n runtime = \\\"%03d:%02d:%02d\\\"%(h, m, s)\\r\\n\\r\\n ## update log\\r\\n msg='Model training - {0} model saved for country {1} after achieving accuracy {2}, versioned {3}. Training completed in {4}'.format(max(alg_dict, key=alg_dict.get),tag,eval_rmse,MODEL_VERSION,runtime)\\r\\n update_train_log(msg)\",\n \"def main():\\n df = prepro_last()\\n X, y = train_build(df)\\n fit_store(X, y)\",\n \"def test_fit() -> None:\\n mapie = MapieRegressor()\\n mapie.fit(X_toy, y_toy)\",\n \"def _test(self,model, dataset):\\n avg_loss = tfe.metrics.Mean('loss')\\n accuracy = tfe.metrics.Accuracy('accuracy')\\n\\n for (images, labels) in tfe.Iterator(dataset):\\n logits = model(images, training=False)\\n avg_loss(self._loss(logits, labels))\\n accuracy(\\n tf.argmax(logits, axis=1, output_type=tf.int64),\\n tf.cast(labels, tf.int64))\\n print('Test set: Average loss: %.4f, Accuracy: %4f%%\\\\n' %\\n (avg_loss.result(), 100 * accuracy.result()))\\n with tf.contrib.summary.always_record_summaries():\\n tf.contrib.summary.scalar('loss', avg_loss.result())\\n tf.contrib.summary.scalar('accuracy', accuracy.result())\",\n \"def test(which, fitted_model_filename):\\n click.echo(\\\"Mode: test.\\\")\\n defaults = get_defaults()\\n\\n # bootstrap input\\n fitted_model_path = os.path.join(defaults.OUTPUT.FITTED_MODELS_PATH, fitted_model_filename)\\n new_options = [\\\"OUTPUT.FITTED_MODEL_PATH\\\", fitted_model_path]\\n\\n mode = \\\"{}_test\\\".format(which)\\n boot_data = bootstrap(new_options, mode=mode)\\n\\n model = boot_data['model']\\n X_test, y_test = boot_data['data']\\n defaults = boot_data['defaults']\\n\\n eval_metrics = get_evaluation_metrics()\\n # model = RandomForestClassifier(random_state=defaults.MISC.SEED, class_weight='balanced')\\n\\n # X_train, y_train = load_data(defaults, which='train')\\n # scaler = StandardScaler()\\n # numeric_cols = X_train.select_dtypes(include=np.number).columns.tolist()\\n # X_train.loc[:, numeric_cols] = scaler.fit_transform(X_train[numeric_cols])\\n\\n # model.fit(X_train, y_train)\\n\\n test_results = test_performance(conf=defaults,\\n model=model,\\n X_test=X_test, y_test=y_test,\\n eval_metrics=eval_metrics)\\n results = pd.DataFrame(test_results.values(), index=test_results.keys(), columns=[\\\"test\\\"])\\n\\n results_filename = \\\"{}_results_{}.csv\\\".format(mode, fitted_model_filename.split(\\\".\\\")[0])\\n results_path = os.path.join(defaults.OUTPUT.RESULTS_PATH, results_filename)\\n results.to_csv(results_path)\",\n \"def fit(model, data, test_ids, exp_name, datasets):\\n if model.model_type == 'torch':\\n size = len(data[0])\\n else:\\n size = data[0].shape[0]\\n \\n train_ids = [i for i in range(size) if i not in test_ids]\\n scaler = pka_scaler(data[1][train_ids])\\n if model.data_type == 'descriptors':\\n desc_scaler = StandardScaler()\\n desc_scaler.fit(data[0][train_ids])\\n data[0] = desc_scaler.transform(data[0])\\n \\n trained_model = train(model, train_ids, data, scaler, datasets)\\n results = test(model, trained_model, test_ids, data, scaler)\\n model.experiments.append({'name':exp_name,'model':trained_model, 'results':results, 'scaler':scaler})\\n return results\",\n \"def fit():\\n pass\",\n \"def test_evaluate():\\n X_train, X_test, y_train, y_test = src.load()\\n clf, score = src.train(X_train, y_train)\\n test_score = src.evaluate(clf, X_test, y_test)\\n assert isinstance(test_score, float)\",\n \"def test(self):\\n with torch.no_grad():\\n self.model.eval()\\n p10_forecast, p10_forecast, p90_forecast, target = None, None, None, None\\n\\n t = time()\\n for step, sample in enumerate(self.test_loader):\\n\\n # Hide future predictions from input vector, set to 0 (or 1) values where timestep > encoder_steps\\n steps = self.cnf.all_params['num_encoder_steps']\\n pred_len = sample['outputs'].shape[1]\\n x = sample['inputs'].float().to(self.cnf.device)\\n x[:, steps:, 0] = 1\\n\\n # Feed input to the model\\n if self.cnf.all_params[\\\"model\\\"] == \\\"transformer\\\" or self.cnf.all_params[\\\"model\\\"] == \\\"grn_transformer\\\":\\n\\n # Auto-regressive prediction\\n for i in range(pred_len):\\n output = self.model.forward(x)\\n x[:, steps + i, 0] = output[:, i, 1]\\n output = self.model.forward(x)\\n\\n elif self.cnf.all_params[\\\"model\\\"] == \\\"tf_transformer\\\":\\n output, _, _ = self.model.forward(x)\\n else:\\n raise NameError\\n\\n output = output.squeeze()\\n y, y_pred = sample['outputs'].squeeze().float().to(self.cnf.device), output\\n\\n # Compute loss\\n loss, _ = self.loss(y_pred, y)\\n smape = symmetric_mean_absolute_percentage_error(output[:, :, 1].detach().cpu().numpy(),\\n sample['outputs'][:, :, 0].detach().cpu().numpy())\\n\\n # De-Normalize to compute metrics\\n target = unnormalize_tensor(self.data_formatter, y, sample['identifier'][0][0])\\n p10_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 0], sample['identifier'][0][0])\\n p50_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 1], sample['identifier'][0][0])\\n p90_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 2], sample['identifier'][0][0])\\n\\n # Compute metrics\\n self.test_losses['p10'].append(self.loss.numpy_normalised_quantile_loss(p10_forecast, target, 0.1))\\n self.test_losses['p50'].append(self.loss.numpy_normalised_quantile_loss(p50_forecast, target, 0.5))\\n self.test_losses['p90'].append(self.loss.numpy_normalised_quantile_loss(p90_forecast, target, 0.9))\\n\\n self.test_loss.append(loss.item())\\n self.test_smape.append(smape)\\n\\n # Plot serie prediction\\n p1, p2, p3, target = np.expand_dims(p10_forecast, axis=-1), np.expand_dims(p50_forecast, axis=-1), \\\\\\n np.expand_dims(p90_forecast, axis=-1), np.expand_dims(target, axis=-1)\\n p = np.concatenate((p1, p2, p3), axis=-1)\\n plot_temporal_serie(p, target)\\n\\n # Log stuff\\n for k in self.test_losses.keys():\\n mean_test_loss = np.mean(self.test_losses[k])\\n print(f'\\\\t● AVG {k} Loss on TEST-set: {mean_test_loss:.6f} │ T: {time() - t:.2f} s')\\n\\n # log log log\\n mean_test_loss = np.mean(self.test_loss)\\n mean_smape = np.mean(self.test_smape)\\n print(f'\\\\t● AVG Loss on TEST-set: {mean_test_loss:.6f} │ T: {time() - t:.2f} s')\\n print(f'\\\\t● AVG SMAPE on TEST-set: {mean_smape:.6f} │ T: {time() - t:.2f} s')\",\n \"def test(self, test_iter, step, corpus_type, id):\\n\\n self.model.eval()\\n stats = Statistics()\\n if not os.path.exists(self.args.result_path):\\n os.makedirs(self.args.result_path)\\n if not os.path.exists(self.args.story_path):\\n os.makedirs(self.args.story_path)\\n can_path = self.args.result_path + corpus_type + '.' + id + '_step%d.candidate' % step\\n gold_path = self.args.result_path + corpus_type + '.' + id + '_step%d.gold' % step\\n story_path = self.args.story_path + corpus_type + '.' + id + '.story'\\n with open(story_path, 'w') as save_story:\\n with open(can_path, 'w') as save_pred:\\n with open(gold_path, 'w') as save_gold:\\n with torch.no_grad():\\n for batch in test_iter:\\n src = batch.src\\n labels = batch.labels\\n segs = batch.segs\\n clss = batch.clss\\n mask = batch.mask\\n mask_cls = batch.mask_cls\\n weight = batch.weight\\n index = batch.index\\n\\n pred = []\\n\\n sents_vec, sent_scores, mask, cluster_weight = self.model(src, segs, clss, mask, mask_cls)\\n loss = self.loss(sent_scores, labels.float())\\n weight_loss = self.weight_loss(cluster_weight, weight)\\n loss = (loss * mask.float()).sum()\\n total_loss = loss + weight_loss * 10\\n batch_stats = Statistics(float(total_loss.cpu().data.numpy()), len(labels))\\n stats.update(batch_stats)\\n\\n sent_scores = sent_scores + mask.float()\\n sent_scores = sent_scores.cpu().data.numpy()\\n cluster_weight = cluster_weight.cpu().data.numpy()\\n selected_ids = np.argsort(-sent_scores, 1)\\n cluster_weight = np.argsort(cluster_weight)\\n # print(selected_ids)\\n # selected_ids = np.sort(selected_ids,1)\\n cluster_num = len(cluster_weight)\\n for i, idx in enumerate(selected_ids):\\n rank = np.where(cluster_weight == i)[0][0]\\n\\n if rank <= max(cluster_num // 6, 6):\\n for j in range(5):\\n sen_ind = selected_ids[i][j]\\n _pred = batch.src_str[i][sen_ind].strip()\\n pred.append((index[i][sen_ind], _pred))\\n elif rank <= max(cluster_num // 3, 10):\\n for j in range(3):\\n sen_ind = selected_ids[i][j]\\n _pred = batch.src_str[i][sen_ind].strip()\\n pred.append((index[i][sen_ind], _pred))\\n elif rank <= max(cluster_num * 2 // 3, 15):\\n for j in range(2):\\n sen_ind = selected_ids[i][j]\\n _pred = batch.src_str[i][sen_ind].strip()\\n pred.append((index[i][sen_ind], _pred))\\n else:\\n sen_ind = selected_ids[i][0]\\n _pred = batch.src_str[i][sen_ind].strip()\\n pred.append((index[i][sen_ind], _pred))\\n\\n gold_summary = (batch.tgt_str[0].strip())\\n pred.sort(key=lambda x: x[0])\\n for i in range(len(pred)):\\n save_story.write(pred[i][1].strip() + '\\\\n')\\n if i == 0:\\n save_pred.write(pred[i][1].strip())\\n else:\\n save_pred.write('<q> ' + pred[i][1].strip())\\n save_gold.write(gold_summary)\\n for sent in gold_summary.split('<q>'):\\n save_story.write('@highlight {}\\\\n'.format(sent))\\n if self.args.test_txt:\\n return stats\\n else:\\n rouges = calculate_rouge(can_path, gold_path)\\n logger.info('Rouges at step %d \\\\n%s' % (step, rouge_results_to_str(rouges)))\\n self._report_step(0, step, valid_stats=stats)\\n return stats, rouges\",\n \"def eval(self):\\n self.train(mode=False)\",\n \"def learn(model: KW_Model,\\n trainloader: DataLoader,\\n testloader: DataLoader,\\n optimizer: optim.Optimizer,\\n nb_epoch: int,\\n device: torch.device,\\n eval_fn: Callable[[List[bool], List[Qid]], Dict[Qid, float]],\\n mean_window: int = 50,\\n entropy_lambda: float = 0.025,\\n smt_lambda: float = 1.0,\\n reinforce_lambda: float = 1.0,\\n ) -> Tuple[nn.Module, Dict[str, List[torch.tensor]], Dict[str, List[torch.tensor]]]:\\n print(\\\"Memory usage: %s (kb)\\\" % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)\\n past_rewards = {str(q_id.long().item()): deque(maxlen=mean_window)\\n for _, _, q_ids, _ in chain(trainloader, testloader)\\n for q_id in q_ids}\\n \\n logs = [\\\"reward\\\",\\n \\\"scaled_entropy\\\",\\n \\\"scaled_reinforce\\\",\\n \\\"scaled_smt\\\",\\n \\\"total_loss\\\",\\n \\\"accuracy\\\"]\\n train_logs = {log: list() for log in logs}\\n test_logs = {log: list() for log in logs}\\n del logs\\n \\n for epoch in range(nb_epoch):\\n running_loss, running_reward = [], []\\n entropies, reinforces, smts = [], [], []\\n nb_correct, nb_total = 0, 0\\n print(f\\\"\\\\nEpoch {epoch}\\\")\\n \\n print(\\\"Begin epoch: %s (kb)\\\" % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)\\n model.train()\\n for x, y, q_id, masks in trainloader:\\n x = x.to(device)\\n y = y.to(device)\\n masks = masks.to(device)\\n\\n # batch x seq , batch x seq , batch x seq\\n sample, logits, entropy, params = model(x, masks)\\n batch_reward, rewards = eval_fn(sample.detach().t().tolist(), q_id)\\n losses = compute_losses(y, params, rewards, logits, past_rewards,\\n entropy, entropy_lambda, reinforce_lambda, smt_lambda, device)\\n\\n # entropy_lambda = min(1.01*entropy_lambda, 0.025)\\n # reinforce_lambda = min(1.01*reinforce_lambda, 1.0)\\n # smt_lambda = max(0.99*smt_lambda, 0.05)\\n loss, reinforce_loss, entropy, smt_loss = losses\\n optimizer.zero_grad()\\n loss.backward()\\n optimizer.step()\\n \\n temp = (sample.long() == y.detach().long()).float() * masks\\n nb_correct += temp.byte().cpu().sum().tolist()\\n nb_total += masks.cpu().sum().tolist()\\n\\n running_loss.append(loss.item())\\n running_reward.extend(rewards.values())\\n print(f\\\"\\\\rTr Loss {mean(running_loss): .3f} Rewa {mean(running_reward): .5f}\\\", end=\\\"\\\")\\n \\n reinforces.append(reinforce_loss.item())\\n entropies.append(entropy.item())\\n smts.append(smt_loss.item())\\n\\n # Logs\\n train_logs[\\\"reward\\\"].append(mean(running_reward))\\n train_logs[\\\"scaled_entropy\\\"].append(mean(entropies))\\n train_logs[\\\"scaled_reinforce\\\"].append(mean(reinforces))\\n train_logs[\\\"scaled_smt\\\"].append(mean(smts))\\n train_logs[\\\"total_loss\\\"].append(mean(running_loss))\\n train_logs[\\\"accuracy\\\"].append(nb_correct / nb_total)\\n \\n \\n train_loss, train_reward = mean(running_loss), mean(running_reward)\\n running_loss, running_reward = [], []\\n entropies, reinforces, smts = [], [], []\\n nb_correct, nb_total = 0, 0\\n model.eval()\\n for x, y, q_id, masks in testloader:\\n x = x.to(device)\\n y = y.to(device)\\n masks = masks.to(device)\\n\\n # batch x seq , batch x seq , batch x seq\\n sample, logits, entropy, params = model(x, masks)\\n batch_reward, rewards = eval_fn(sample.detach().t().tolist(), q_id)\\n\\n losses = compute_losses(y, params, rewards, logits, past_rewards,\\n entropy, entropy_lambda, reinforce_lambda, smt_lambda, device)\\n loss, reinforce_loss, entropy, smt_loss = losses\\n \\n temp = (sample.long() == y.detach().long()).float() * masks\\n nb_correct += temp.byte().sum().tolist()\\n nb_total += masks.sum().tolist()\\n\\n running_loss.append(loss.item())\\n running_reward.extend(rewards.values())\\n print(f\\\"\\\\rTr Loss {train_loss: .3f} Rewa {train_reward: .3f}\\\",\\n f\\\"Te Loss{mean(running_loss): .3f} Rewa {mean(running_reward): .3f}\\\",\\n end=\\\"\\\")\\n \\n reinforces.append(reinforce_loss.item())\\n entropies.append(entropy.item())\\n smts.append(smt_loss.item())\\n \\n \\n # Logs\\n test_logs[\\\"reward\\\"].append(mean(running_reward))\\n test_logs[\\\"scaled_entropy\\\"].append(mean(entropies))\\n test_logs[\\\"scaled_reinforce\\\"].append(mean(reinforces))\\n test_logs[\\\"scaled_smt\\\"].append(mean(smts))\\n test_logs[\\\"total_loss\\\"].append(mean(running_loss))\\n test_logs[\\\"accuracy\\\"].append(nb_correct / nb_total)\\n \\n\\n return model, train_logs, test_logs\",\n \"def model_evaluate(model,x_train,n_y_array,x_val, vald_array):\\n\\n scores = model.evaluate(x_train, n_y_array, verbose=1)\\n\\n scores2 = model.evaluate(x_val, vald_array, verbose=1)\\n\\n\\n print(\\\"for traininf set\\\")\\n\\n print(\\\"%s: %.2f%%\\\" % (model.metrics_names[1], scores[1]*100))\\n\\n print(\\\"%s: %.2f%%\\\" % (model.metrics_names[0], scores[0]))\\n\\n\\n\\n print(\\\"for validation set : \\\") \\n\\n print(\\\"%s: %.2f%%\\\" % (model.metrics_names[1], scores2[1]*100))\\n\\n print(\\\"%s: %.2f%%\\\" % (model.metrics_names[0], scores2[0]))\",\n \"def final_eval(self):\\n # Test performance - Load best model\\n self.load_model(self.best_model_path, model_type='best')\\n logging.info(\\\"Loading best model after epoch: %d\\\" %\\n self.train_info['epoch'])\\n\\n perf_file = path.join(self.model_dir, \\\"perf.json\\\")\\n if self.slurm_id:\\n parent_dir = path.dirname(path.normpath(self.model_dir))\\n perf_dir = path.join(parent_dir, \\\"perf\\\")\\n if not path.exists(perf_dir):\\n os.makedirs(perf_dir)\\n perf_file = path.join(perf_dir, self.slurm_id + \\\".json\\\")\\n\\n output_dict = {'model_dir': self.model_dir}\\n for key, val in vars(self.args).items():\\n output_dict[key] = val\\n\\n for split in ['dev', 'test']:\\n logging.info('\\\\n')\\n logging.info('%s' % split.capitalize())\\n result_dict = self.eval_model(split, final_eval=True)\\n if split != 'test':\\n logging.info('Calculated F1: %.3f' % result_dict['fscore'])\\n\\n output_dict[split] = result_dict\\n\\n json.dump(output_dict, open(perf_file, 'w'), indent=2)\\n\\n logging.info(\\\"Final performance summary at %s\\\" % perf_file)\\n sys.stdout.flush()\",\n \"def modelfit(alg, data, features, target):\\n\\n # Split data into train and test.\\n X_train, X_test, y_train, y_test = model_selection.train_test_split(data[features], data[target],\\n test_size=.2)\\n\\n # Baseline fit.\\n alg.fit(X_train, y_train)\\n train_predictions = alg.predict(X_train)\\n print(\\\"RMSE Train (Pre-CV): %.4g\\\" % np.sqrt(metrics.mean_squared_error(y_train, train_predictions)))\\n\\n\\n # Cross_validation/hyper-parameter selection.\\n #dtr_cv(alg, X_train, y_train)\\n\\n # Report results + visualize.\\n test_predictions = alg.predict(X_test)\\n print(\\\"RMSE Test: %.4g\\\" % np.sqrt(metrics.mean_squared_error(y_test, test_predictions)))\\n\\n plot_results(y_test, test_predictions)\",\n \"def test_per_dqn(self):\\n model = PERDQNLightning(self.hparams)\\n result = self.trainer.fit(model)\\n\\n self.assertEqual(result, 1)\",\n \"def test(self):\\n self.model.eval()\\n\\n for step, sample in enumerate(self.test_loader):\\n x, _, _ = sample\\n x = x.to(self.device)\\n\\n y_pred = self.model.forward(x)\\n loss = nn.MSELoss()(y_pred, x)\\n self.test_losses.append(loss.item())\\n\\n if step % (max(8, len(self.test_loader)) // 8) == 0:\\n out_img = torch.cat([x[0], torch.clamp(y_pred[0], 0, 1)], dim=2)\\n self.sw.add_image(tag=f'sample_{step}', img_tensor=out_img, global_step=self.epoch)\\n\\n # log average loss on test set\\n mean_test_loss = np.mean(self.test_losses)\\n self.test_losses = []\\n print(f'\\\\t● AVG Loss on TEST-set: {mean_test_loss:.6f} │ patience: ', end='')\\n self.sw.add_scalar(tag='test_loss', scalar_value=mean_test_loss, global_step=self.epoch)\\n\\n # save best model and update training patience\\n if self.best_test_loss is None or mean_test_loss < self.best_test_loss:\\n self.best_test_loss = mean_test_loss\\n self.patience = conf.FX_PATIENCE\\n torch.save(self.model.state_dict(), self.log_path / 'best.pth')\\n else:\\n self.patience = self.patience - 1\\n print(f'{self.patience}/{conf.FX_PATIENCE}')\\n\\n if self.patience == 0:\\n self.show_completion_msg()\",\n \"def computeRmse(model, data, n , sc):\\n truth = data.map( lambda x: ((x[0], x[1]), x[2]) )\\n truth.cache()\\n ##print 'test zhou 0.....', truth.count() , '............', truth.take(10)\\n\\n predictions = model.predictAll(data.map(lambda x: (x[0], x[1])))\\n predictions.cache()\\n # here let's rescale predicted ratings to 0-10 scale\\n maxPrediction = predictions.map(lambda x: x[2]).max()\\n minPrediction = predictions.map(lambda x: x[2]).min()\\n maxRate = RatingScale\\n minRate = RatingScaleMin\\n ##print 'test zhou 1......', predictions.count(), '............', predictions.take(10)\\n\\n #predictionsAndRatings = predictions.map(lambda x: ((x[0], x[1]), (x[2]-minPrediction)/(maxPrediction-minPrediction)*(maxRate-minRate)+minRate )).join(data.map(lambda x: ((x[0], x[1]), x[2]))).values()\\n\\n\\n #predictedRating = predictions.map(lambda x: ((x[0], x[1]), (x[2]-minPrediction)/(maxPrediction-minPrediction)*(maxRate-minRate)+minRate ) )\\n predictedRating = predictions.map(lambda x: ((x[0], x[1]), x[2] ) )\\n predictedRating.cache()\\n ##predictedRating.checkpoint()\\n ##print 'test zhou 2.......', predictedRating.count(), '............', predictedRating.take(10)\\n\\n\\n \\n\\n\\n predictionsAndRatings = predictedRating.join(truth).values()\\n #predictionsAndRatings = sc.union(predictedRating, truth)\\n predictionsAndRatings.cache()\\n #print 'test zhou 3........', predictionsAndRatings.count(), '............', predictionsAndRatings.take(10)\\n #predictionsAndRatings = predictions.map(lambda x: ((x[0], x[1]), x[2])).join(data.map(lambda x: ((x[0], x[1]), x[2]))).values()\\n \\n return sqrt(predictionsAndRatings.map(lambda x: (x[0] - x[1]) ** 2).reduce(add) / float(n))\\n #return 1.0\",\n \"def model_evaluation(X_train, y_train, X_test, y_test, k=16):\\n print(\\\">>>>>>> x.shape\\\", X_train.shape)\\n p_matrix, X_reduce = dimension_reduction(X_train, k=k)\\n print(\\\"model training ...\\\")\\n bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2), n_estimators=30, learning_rate=1)\\n bdt.fit(X_reduce, y_train)\\n print(\\\"fit succeed\\\")\\n\\n X_test = np.dot(X_test, p_matrix)\\n y_pred = bdt.predict(X_test)\\n print(classification_report(y_test, y_pred, target_names=['benign', 'gafgyt', 'miari'], digits=4))\",\n \"def compute_test():\\n model.eval()\\n sets = list(features.keys())\\n for dset, loaders in zip(sets, [train_loaders, val_loaders, test_loaders]):\\n final_specific_loss = 0\\n final_total_loss = 0\\n for loader in loaders:\\n loader_total_loss = 0\\n loader_specific_loss = 0\\n for data in loader:\\n output = model(data.to(device))\\n specific_loss = specific_loss_torch_geom(output, (data.pos, data.y),\\n data.batch, batch_sizes[dset]).detach()\\n loader_specific_loss += specific_loss\\n loader_total_loss += torch.mean(specific_loss)\\n # Average the loss over each loader\\n loader_specific_loss /= len(loader)\\n loader_total_loss /= len(loader)\\n # Average the loss over the different loaders\\n final_specific_loss += loader_specific_loss / len(loaders)\\n final_total_loss += loader_total_loss / len(loaders)\\n del output, loader_specific_loss\\n\\n print(\\\"Test set results \\\", dset, \\\": loss= {:.4f}\\\".format(final_total_loss))\\n print(dset, \\\": \\\", final_specific_loss)\\n print(\\\"Results in log scale\\\", np.log10(final_specific_loss.detach().cpu()),\\n np.log10(final_total_loss.detach().cpu().numpy()))\\n if args.wandb:\\n wandb.run.summary[\\\"test results\\\"] = np.log10(final_specific_loss.detach().cpu())\\n # free unnecessary data\\n\\n\\n final_specific_numpy = np.log10(final_specific_loss.detach().cpu())\\n del final_total_loss, final_specific_loss\\n torch.cuda.empty_cache()\\n return final_specific_numpy\",\n \"def train(self,path,mode):\\n if mode == \\\"porto\\\":\\n self.prepare_data(path)\\n else:\\n self.prepare_sumo_data(path)\\n self.poly_regression()\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def test_machine_learning():\",\n \"def test_model_performance(model_es,test_file):\\n test_label = get_test_label(test_file)\\n result = model_es.predict(input_fn = lambda :input_fn(test_file,1,False,100,True))\\n predict_list = []\\n for one_res in result:\\n if \\\"probabilities\\\" in one_res:\\n predict_list.append(one_res['probabilities'][1])\\n get_auc(predict_list,test_label)\",\n \"def eval_regression_performance(dset, model, scaler, measure):\\n X = dset[\\\"X\\\"]\\n y = dset[\\\"y\\\"]\\n y = y.reshape((len(y), 1))\\n yhat = model.predict(X)\\n\\n # afecta en algo aplicar invers_stransform sobre una sola columna que con tra la matriz?\\n prediction = scaler.inverse_transform(yhat)\\n\\n # invert scaling for actual\\n true = scaler.inverse_transform(y)\\n\\n # calculate MAE\\n performance = measure(true, prediction)\\n return performance\",\n \"def test(self):\\n for data_tier in self.data_tiers:\\n tot = len(self.preprocessed_data[data_tier]['features'])\\n p = int(math.floor(tot*0.2))\\n test_features = np.array(self.preprocessed_data[data_tier]['features'][p:])\\n trend_test_classifications = np.array(self.preprocessed_data[data_tier]['trend_classifications'][p:])\\n avg_test_classifications = np.array(self.preprocessed_data[data_tier]['avg_classifications'][p:])\\n accuracy_trend = self.clf_trend[data_tier].score(test_features, trend_test_classifications)\\n accuracy_avg = self.clf_avg[data_tier].score(test_features, avg_test_classifications)\\n self.logger.info('The accuracy of %s trend classifier for data tier %s is %.3f', self.name, data_tier, accuracy_trend)\\n self.logger.info('The accuracy of %s avg regressor for data tier %s is %.3f', self.name, data_tier, accuracy_avg)\",\n \"def get_perf(self) :\\n self.train()\\n self.df_true = self.df_true[self.length:]\\n self.accuracy , self.recall, self.specificity, self.profit, self.min , self.max = get_accuracy_LSTM(self.df_test, self.df_true,self.model, self.length)\",\n \"def main():\\n housing = pd.read_csv(\\\"Data/train_original.csv\\\")\\n housing[\\\"TotalSF\\\"] = (\\n housing[\\\"TotalBsmtSF\\\"] + housing[\\\"1stFlrSF\\\"] + housing[\\\"2ndFlrSF\\\"]\\n )\\n training_features, testing_features, training_target, testing_target = impute_dummify_and_split(\\n housing, drop_target=False\\n )\\n\\n p_values = [\\n (c, pearsonr(training_features[\\\"SalePrice\\\"], training_features[c])[1])\\n for c in training_features.columns\\n ]\\n\\n p_value_limits = [0.05]\\n\\n result = []\\n ps_and_cols = {}\\n\\n for p_value_limit in p_value_limits:\\n\\n high_ps = list(\\n map(lambda t: t[0], sorted(p_values, key=lambda t1: t1[1])[:15])\\n )\\n\\n print(training_features[high_ps].corr())\\n\\n columns = [p[0] for p in p_values if p[1] < p_value_limit]\\n\\n training_features_restricted = training_features[columns].drop(\\n \\\"SalePrice\\\", axis=\\\"columns\\\"\\n )\\n\\n testing_features_restricted = testing_features[columns].drop(\\n \\\"SalePrice\\\", axis=\\\"columns\\\"\\n )\\n\\n for model in (\\n linear_model.Lasso(alpha=2.1),\\n linear_model.Ridge(alpha=2.1),\\n ):\\n\\n model.fit(training_features_restricted, training_target)\\n\\n train_score = model.score(\\n training_features_restricted, training_target\\n )\\n\\n test_score = model.score(\\n testing_features_restricted, testing_target\\n )\\n\\n name = str(model).split(\\\"(\\\")[0]\\n\\n result = result + [\\n (\\n \\\"_2_restrict_features\\\",\\n name,\\n \\\"p value limit: {:.3f}, alpha: 2.1\\\".format(p_value_limit),\\n train_score,\\n test_score,\\n )\\n ]\\n\\n print(ps_and_cols)\\n return training_features[high_ps].corr()\",\n \"def assess_perf(self, bases: list, set_type, epoch):\\n self.prepare_calc_performance()\\n self.base_dfl = dict() # base dict of lists with prediction data frames\\n for base in bases:\\n self.base_dfl[base] = list()\\n ohlcv_list = ad.SplitSets.split_sets(set_type, self.predictor.ohlcv.load_data(base))\\n features_list = ad.SplitSets.split_sets(set_type, self.predictor.features.load_data(base))\\n targets_list = ad.SplitSets.split_sets(set_type, self.predictor.targets.load_data(base))\\n assert len(ohlcv_list) == len(features_list)\\n assert len(ohlcv_list) == len(targets_list)\\n for ix in range(len(ohlcv_list)):\\n odf = ohlcv_list[ix]\\n fdf = features_list[ix]\\n tdf = targets_list[ix]\\n if (fdf is None) or fdf.empty or (tdf is None) or tdf.empty:\\n logger.warning(f\\\"empty data for {base} between {odf.index[0]} and {odf.index[-1]}\\\")\\n continue\\n [fdf, tdf] = ccd.common_timerange([fdf, tdf])\\n\\n if self.predictor.scaler is not None:\\n fdf_scaled = self.predictor.scaler.transform(fdf.values)\\n pred = self.predictor.kerasmodel.predict_on_batch(fdf_scaled)\\n else:\\n logger.error(\\\"missing scaler\\\")\\n pred = self.predictor.kerasmodel.predict_on_batch(fdf.values)\\n if pred is None:\\n logger.warning(f\\\"no prediction data for {base} between {odf.index[0]} and {odf.index[-1]}\\\")\\n continue\\n pdf = pd.DataFrame(data=pred, index=fdf.index, columns=self.predictor.targets.target_dict().keys())\\n pdf.loc[pdf.index[-1], ct.SELL] = 1 # force sell at end of data range\\n if pdf.empty:\\n logger.warning(f\\\"empty prediction data for {base} between {odf.index[0]} and {odf.index[-1]}\\\")\\n continue\\n pdf = pd.concat([odf.close, tdf.target, pdf], axis=1, join=\\\"inner\\\")\\n self.base_dfl[base].append(self.calc_performance(pdf))\\n logger.info(f\\\"\\\\n performance results \\\\n{self.total}\\\\n\\\")\\n logger.info(f\\\"\\\\n precision results \\\\n{self.confusion}\\\\n\\\")\\n return self.find_best()\",\n \"def test_model(self, batch_size):\\n (_, gen_val, gen_test) = self.dataset.data_loaders(\\n batch_size=batch_size,\\n split=(0.01, 0.01)\\n )\\n print('Num Test Batches: ', len(gen_test))\\n mean_loss_test, mean_accuracy_test = self.loss_and_acc_test(gen_test)\\n print('Test Epoch:')\\n print(\\n '\\\\tTest Loss: ', mean_loss_test, '\\\\n'\\n '\\\\tTest Accuracy: ', mean_accuracy_test * 100\\n )\",\n \"def test_10_test_model(self, example):\\n res = example.calc_model()\\n print(example.trips_ij)\\n total_trips_target = example.persons_gi.sum()\\n total_trips_actual = example.trips_ij.sum()\\n np.testing.assert_almost_equal(total_trips_target, total_trips_actual)\",\n \"def main_stats_model(y_train: pd.DataFrame, y_test: pd.DataFrame, y_pred: np.ndarray,\\n model_name: str = '',\\n model_parameters: dict = None,\\n model_preprocessing: str = '',\\n sequence_origin: str = '',\\n primers_origin: str = '',\\n taxonomy_level: Union[List[int], int] = '',\\n selected_primer: Union[List[str], str] = '',\\n test_size: float = 0.2,\\n feature_importances: np.ndarray = None,\\n k: int = 4,\\n save_csv: bool = False,\\n xgb_model=None,\\n rf_model=None,\\n save_model=False,\\n save_tree: int = 0):\\n model_path = folder_paths['model_results'] + model_name + '{}'.format(slash)\\n if not os.path.exists(model_path):\\n os.makedirs(model_path)\\n\\n folder_number = get_new_model_folder_number(model_name=model_name)\\n analysis_path = model_path + '{:0>5d}_analysis_{}_{}{}'.format(folder_number, selected_primer, taxonomy_level, slash)\\n os.makedirs(analysis_path)\\n\\n log_path = analysis_path + 'model_results.txt'\\n logger = StatLogger(log_path=log_path)\\n\\n # Basic information on configuration\\n test_size = get_model_info(y_test, model_name, model_parameters, model_preprocessing, sequence_origin,\\n primers_origin, taxonomy_level, selected_primer, test_size, logger)\\n\\n # Metrics of model results\\n main_class_prop, accuracy = get_metrics_model(y_train, y_test, y_pred, logger, feature_importances, k, save_tree,\\n xgb_model,\\n analysis_path=analysis_path)\\n\\n if save_csv:\\n add_optimal_model_params(folder_number, selected_primer, taxonomy_level, accuracy, model_parameters,\\n model_path=model_path)\\n\\n if save_model:\\n if xgb_model is not None:\\n xgb_model.save_model(analysis_path+'0001.model')\\n if rf_model is not None:\\n filename = analysis_path+'0001.model'\\n pickle.dump(rf_model, open(filename, 'wb'))\\n\\n logger.close_file()\\n\\n return test_size, main_class_prop, accuracy\",\n \"def test_model(net, data_loader):\\n net.eval()\\n running_loss = 0.0\\n with torch.no_grad():\\n for data in data_loader:\\n X = data['X']\\n y_d = data['y_descreen']\\n outputs = net(X)\\n loss = criterion(outputs, y_d)\\n running_loss += loss\\n return running_loss\",\n \"def master_modelisation(X,y,test_size,model_function_list) :\\n models = model_function_list\\n\\n output = pd.DataFrame()\\n model = []\\n RMSE = []\\n MAPE = []\\n tenth_perc = []\\n ninetieth_perc = []\\n\\n for i in models:\\n output_row = pd.DataFrame()\\n\\n model.append(i)\\n\\n y_pred, y_test = i(df, variables, test_size)\\n\\n RMSE.append(compute_rmse(y_test, y_pred))\\n MAPE.append(compute_mape(y_test, y_pred))\\n tenth_perc.append(tenth_percentile(y_test, y_pred))\\n ninetieth_perc.append(ninetieth_percentile(y_test, y_pred))\\n\\n output['Model'] = model\\n output['RMSE'] = RMSE\\n output['MAPE'] = MAPE\\n output['tenth_perc'] = tenth_perc\\n output['ninetieth_perc'] = ninetieth_perc\\n\\n return output\",\n \"def test_model():\\n pass\",\n \"def test_model(model: nn.Module, test_set: data.DataLoader, number_of_classes: int) -> Tuple[score.FloatScore, score.DictScore]:\\n # model.eval is used for ImageNet models, batchnorm or dropout layers will work in eval mode.\\n model.eval()\\n\\n def test_average() -> score.FloatScore:\\n correct = 0\\n total = 0\\n\\n with torch.set_grad_enabled(False):\\n for (inputs, yreal) in tqdm(test_set, unit=\\\"images\\\", desc=\\\"Testing model (average)\\\", leave=True, ascii=True):\\n inputs, yreal = inputs.cuda(), yreal.cuda()\\n\\n ypred = model(inputs)\\n _, predicted = torch.max(ypred.data, 1)\\n\\n total += yreal.size(0)\\n correct += (predicted == yreal).sum().item()\\n\\n accuracy = 100 * correct / total\\n log.info(\\\"Accuracy of the network on the {} test images (average): {}\\\".format(total, accuracy))\\n with open('epoch_logs.txt', 'a+') as file:\\n file.write('Test Acc: {}\\\\n'.format(accuracy))\\n return score.FloatScore(accuracy)\\n\\n def test_per_class() -> score.DictScore:\\n class_correct = list(0. for _ in range(number_of_classes))\\n class_total = list(0. for _ in range(number_of_classes))\\n total = 0\\n\\n with torch.no_grad():\\n for (inputs, yreal) in tqdm(test_set, unit=\\\"images\\\", desc=\\\"Testing model (per class)\\\", leave=True, ascii=True):\\n inputs, yreal = inputs.cuda(), yreal.cuda()\\n\\n total += yreal.size(0)\\n\\n ypred = model(inputs)\\n _, predicted = torch.max(ypred, 1)\\n c = (predicted == yreal).squeeze()\\n for i in range(yreal.shape[0]):\\n label = yreal[i]\\n class_correct[label] += c[i].item()\\n class_total[label] += 1\\n\\n log.info(\\\"Accuracy of the network on the {} test images (per-class):\\\".format(total))\\n\\n per_class_accuracy = {}\\n for i in range(number_of_classes):\\n accuracy = 100 * class_correct[i] / (class_total[i] + 0.0001)\\n per_class_accuracy[i] = accuracy\\n print('Accuracy of %5s : %2d %%' % (\\n i, accuracy))\\n\\n return score.DictScore(per_class_accuracy)\\n\\n return test_average(), test_per_class()\",\n \"def train():\\n pass\",\n \"def evaluate(self, train_set, test_set, shuffle_batch=True,\\n epochs=25, lr_decay=0.95, sqr_norm_lim=9,labels=None,model=None): \\n cost = self.negative_log_likelihood(self.y) \\n dropout_cost = self.dropout_negative_log_likelihood(self.y)\\n # adadelta upgrades: dict of variable:delta\\n grad_updates = self.sgd_updates_adadelta(dropout_cost, lr_decay, 1e-6, sqr_norm_lim)\\n # shuffle dataset and assign to mini batches.\\n # if dataset size is not a multiple of batch size, replicate \\n # extra data (at random)\\n np.random.seed(3435)\\n batch_size = self.batch_size\\n if train_set.shape[0] % batch_size > 0:\\n extra_data_num = batch_size - train_set.shape[0] % batch_size\\n #extra_data = train_set[np.random.choice(train_set.shape[0], extra_data_num)]\\n perm_set = np.random.permutation(train_set) \\n extra_data = perm_set[:extra_data_num]\\n new_data = np.append(train_set, extra_data, axis=0)\\n else:\\n new_data = train_set\\n \\n shuffled_data = np.random.permutation(new_data) # Attardi\\n n_batches = shuffled_data.shape[0]/batch_size\\n # divide train set into 90% train, 10% validation sets\\n n_train_batches = int(np.round(n_batches*0.8))\\n n_val_batches = n_batches - n_train_batches\\n train_set = shuffled_data[:n_train_batches*batch_size,:]\\n val_set = shuffled_data[n_train_batches*batch_size:,:] \\n # push data to gpu \\n # the dataset has the format [word_indices,padding,user,label]\\n train_set_x, train_set_y = shared_dataset(train_set[:,:-2], train_set[:,-1]) \\n train_set_u = theano.shared(np.asarray(train_set[:,-2],dtype='int32')) \\n # val_set_x = val_set[:,:-2]\\n # val_set_u = val_set[:,-2]\\n # val_set_y = val_set[:,-1]\\n val_set_x, val_set_y = shared_dataset(val_set[:,:-2], val_set[:,-1])\\n val_set_u = theano.shared(np.asarray(val_set[:,-2],dtype='int32')) \\n test_set_x = test_set[:,:-2]\\n test_set_u = test_set[:,-2]\\n test_set_y = test_set[:,-1] \\n batch_start = self.index * batch_size\\n batch_end = batch_start + batch_size\\n\\n # compile Theano functions to get train/val/test errors\\n \\n \\n test_y_pred = self.predict(test_set_x,test_set_u)\\n test_error = T.mean(T.neq(test_y_pred, self.y))\\n # errors on train set\\n if self.Users is not None:\\n train_model = theano.function([self.index], cost, updates=grad_updates,\\n givens={\\n self.x: train_set_x[batch_start:batch_end],\\n self.y: train_set_y[batch_start:batch_end],\\n self.u: train_set_u[batch_start:batch_end]\\n },\\n allow_input_downcast = True)\\n\\n train_error = theano.function([self.index], self.errors(self.y),\\n givens={\\n self.x: train_set_x[batch_start:batch_end],\\n self.y: train_set_y[batch_start:batch_end],\\n self.u: train_set_u[batch_start:batch_end]},\\n allow_input_downcast=True)\\n val_model = theano.function([self.index], self.errors(self.y),\\n givens={\\n self.x: val_set_x[batch_start:batch_end],\\n self.y: val_set_y[batch_start:batch_end], \\n self.u: val_set_u[batch_start:batch_end]},\\n allow_input_downcast=True)\\n test_model = theano.function([self.x, self.u, self.y], test_error, allow_input_downcast=True)\\n else:\\n train_model = theano.function([self.index], cost, updates=grad_updates,\\n givens={\\n self.x: train_set_x[batch_start:batch_end],\\n self.y: train_set_y[batch_start:batch_end]},\\n allow_input_downcast = True)\\n\\n train_error = theano.function([self.index], self.errors(self.y),\\n givens={\\n self.x: train_set_x[batch_start:batch_end],\\n self.y: train_set_y[batch_start:batch_end]},\\n allow_input_downcast=True)\\n\\n val_model = theano.function([self.index], self.errors(self.y),\\n givens={\\n self.x: val_set_x[batch_start:batch_end],\\n self.y: val_set_y[batch_start:batch_end]},\\n allow_input_downcast=True)\\n test_model = theano.function([self.x, self.y], test_error, allow_input_downcast=True)\\n\\n # start training over mini-batches\\n print 'training...' \\n best_val_perf = 0\\n test_perf = 0 \\n patience = 5\\n drops = 0\\n prev_val_perf = 0 \\n for epoch in xrange(epochs):\\n start_time = time.time()\\n # FIXME: should permute whole set rather than minibatch indexes\\n if shuffle_batch:\\n for minibatch_index in np.random.permutation(range(n_train_batches)):\\n cost_epoch = train_model(minibatch_index)\\n self.set_zero(self.zero_vec) # CHECKME: Why?\\n else:\\n for minibatch_index in xrange(n_train_batches):\\n cost_epoch = train_model(minibatch_index) \\n self.set_zero(self.zero_vec)\\n train_losses = [train_error(i) for i in xrange(n_train_batches)]\\n train_perf = 1 - np.mean(train_losses)\\n val_losses = [val_model(i) for i in xrange(n_val_batches)]\\n val_perf = 1 - np.mean(val_losses) \\n info = 'epoch: %i\\\\%i (%.2f secs) train acc: %.2f %% | val acc: %.2f %%' % (\\n epoch,epochs, time.time()-start_time, train_perf * 100., val_perf*100.) \\n # from ipdb import set_trace; set_trace()\\n if val_perf > prev_val_perf: \\n drops=0\\n if val_perf >= best_val_perf:\\n best_val_perf = val_perf\\n info+= \\\" **\\\"\\n if model:\\n # print \\\"save model\\\"\\n self.save(model)\\n if self.Users is not None:\\n test_loss = test_model(test_set_x, test_set_u, test_set_y)\\n else:\\n test_loss = test_model(test_set_x, test_set_y)\\n test_perf = 1 - test_loss \\n else: \\n drops+=1\\n if drops >= patience:\\n print \\\"Ran out of patience...\\\"\\n break\\n prev_val_perf = val_perf\\n print info\\n # set_trace() \\n return test_perf\",\n \"def fit_model(X_train, X_test, y_train, y_test, model):\\n \\n if model == 'LinearRegression':\\n \\n regressor=LinearRegression()\\n regressor.fit(X_train,y_train)\\n y_pred =regressor.predict(X_test)\\n r2 = r2_score(y_test, y_pred)\\n \\n elif model == 'Lasso':\\n \\n lasso = Lasso()\\n lasso.fit(X_train, y_train)\\n lasso_pred = lasso.predict(X_test)\\n r2 = r2_score(y_test, lasso_pred)\\n\\n elif model == 'Ridge':\\n \\n ridge = Ridge()\\n ridge.fit(X_train, y_train)\\n ridge_pred = ridge.predict(X_test)\\n r2 = r2_score(y_test, ridge_pred)\\n \\n \\n else:\\n model = make_pipeline(PolynomialFeatures(2), LinearRegression())\\n model.fit(X_train, y_train)\\n y_pred = model.predict(X_test)\\n r2= r2_score(y_test,y_pred)\\n\\n\\n return r2\",\n \"def train(models, X_train, y_train, X_test, y_test):\\n \\n # Train and test each model in a for lop\\n accuracies = []\\n \\n for model in models:\\n clf = model.fit(X_train, y_train) # Train\\n score = clf.score(X_test, y_test) # Test\\n accuracies.append(score)\\n\\n return accuracies\",\n \"def train_model(algorithm, X_train, y_train, X_test, y_test, cv_type='rand', transformation_type='tf'):\\n \\n model = algorithm(X_train, y_train, cv_type=cv_type)\\n model_preds = model.predict(X_test)\\n model_score = f1_score(y_test, model_preds, average='weighted')\\n \\n return model, model_score, transformation_type\",\n \"def one_experiment():\\n\\n # set the name of the experiment\\n now = datetime.datetime.now()\\n experiment_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute)\\n experiment_name = 'overfit_' + str(experiment_id)\\n\\n # define if you want to use preprocessed data from file\\n use_prep_data = False\\n if use_prep_data:\\n set_params(preproc_data_id='16_5_10.16.47')\\n\\n # define the changing parameter and its value\\n changing_param_name = 'class_weights'\\n changing_param_value = [{0: 15, 1: 85}]\\n # {0:15, 1:85}]#, {0:4, 1:100}, {0:3, 1:100}, {0:2, 1:100}, {0:1, 1:100}] #[{0:1, 1:1}, {0:15, 1:85}]#\\n\\n features_to_use = ['user', 'countries', 'session', 'format', 'token']\\n # set constant parameters\\n set_params(use_word_emb=1)\\n set_params(epochs=40)\\n set_params(features_to_use=features_to_use)\\n\\n # save constant parameters to a new \\\"experiment_..\\\" filgithx+P@2ub\\n save_constant_parameters(experiment_name, changing_param_name)\\n\\n # run experiment for every parameter value\\n for value in changing_param_value:\\n process = psutil.Process(os.getpid())\\n print(\\\"-----MEMORY before starting experiment ------\\\", int(process.memory_info().rss/(8*10**3)), \\\"KB\\\")\\n\\n # update the parameter value\\n set_params(class_weights_1=value)\\n\\n # update the model_id for this new model\\n now = datetime.datetime.now()\\n new_model_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute) + \\\".\\\" + str(now.second)\\n\\n set_params(model_id=new_model_id)\\n\\n # evaluate the new model and save the results in the experiment file\\n oneExperiment = Process(target=run_experiment, args=(experiment_name,\\n new_model_id, changing_param_name, value,))\\n oneExperiment.start()\\n oneExperiment.join()\",\n \"def computeModel(self, name, data):\\n\\t\\tif name == \\\"Weibull\\\":\\n\\t\\t\\tmodel = Weibull(data)\\n\\t\\t\\tnamePL = \\\"Weibull\\\"\\n\\n\\t\\telif name == \\\"Exponential\\\":\\n\\t\\t\\tmodel = Exponential(data)\\n#\\t\\t\\tnamePL = \\\"Wykładniczy (Goel-Okumoto)\\\"\\n\\t\\t\\tnamePL = \\\"Wykładniczy\\\"\\n\\n\\t\\telif name == \\\"Gamma\\\":\\n\\t\\t\\tmodel = Gamma(data)\\n#\\t\\t\\tnamePL = \\\"Gamma (S-kształtny)\\\"\\n\\t\\t\\tnamePL = \\\"Gamma\\\"\\n\\n\\t\\telif name == \\\"Logarithmic\\\":\\n\\t\\t\\tmodel = Logarithmic(data)\\n#\\t\\t\\tnamePL = \\\"Logarytmiczny (Musa-Okumoto)\\\"\\n\\t\\t\\tnamePL = \\\"Logarytmiczny\\\"\\n\\n\\t\\telif name == \\\"Power\\\":\\n\\t\\t\\tmodel = Power(data)\\n#\\t\\t\\tnamePL = \\\"Potęgowy (Duane)\\\"\\n\\t\\t\\tnamePL = \\\"Potęgowy\\\"\\n\\n\\t\\telse:\\n\\t\\t\\traise Exception, \\\"Nieznany model '%s'\\\" % name\\n\\n\\t\\tprint \\\"\\\\n---\\\\n\\\", name, \\\"estimating...\\\",\\n\\n\\t\\ttry:\\n\\t\\t\\tparam, chi, ret, akaike = model.fit()\\n\\t\\texcept Exception, e:\\n\\t\\t\\tprint \\\"Wyjatek!\\\", e\\n\\t\\t\\treturn [None, \\\"brak\\\", \\\"brak\\\", INF, name, namePL, INF]\\n\\n\\t\\t# oblicza pozostale wartosci wykresu\\n\\t\\tbeg = len(ret) + 1\\n\\t\\tend = beg + int((self.white2 - self.black2)/self.granSec) + 1\\n\\t\\tcont = model.calculate(param, range(beg, end))\\n\\t\\tret.extend(cont)\\n\\n\\t\\tprint \\\" alfa,beta=\\\", param,\\n\\t\\tprint \\\" chi=\\\", chi\\n\\t\\tprint \\\" akaike=\\\", akaike\\n\\t\\tprint ret\\n\\n\\t\\t# formatowanie\\n\\t\\talfa = round(param[0], 4)\\n\\t\\tif len(param) == 2: \\n\\t\\t\\tbeta = round(param[1], 4) \\n\\t\\telse: \\n\\t\\t\\tbeta = ' ';\\t\\t# nie ma beta w expotencjalnym\\n\\t\\tchi = int(chi)\\n\\t\\takaike = round(akaike, 2)\\n\\n\\t\\treturn [ret, alfa, beta, chi, name, namePL, akaike]\",\n \"def test_02_train(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-train-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n \\n ## update the log\\n country = 'india'\\n date_range = ('2017-11-29', '2019-05-24')\\n metric = {'rmse':0.5}\\n runtime = \\\"00:00:01\\\"\\n model_version = 0.1\\n model_version_note = \\\"test model\\\"\\n \\n update_train_log(country, date_range, metric, runtime,\\n model_version, model_version_note ,test=True, prefix=LOG_PREFIX)\\n\\n df = pd.read_csv(log_file)\\n logged_metric = [literal_eval(i) for i in df['metric'].copy()][-1]\\n self.assertEqual(metric,logged_metric)\",\n \"def test_one_epoch_model(self, model: nn.Module) -> Tuple[float, Dict[str, float]]:\\n losses = []\\n l1_criterion = nn.L1Loss()\\n model.eval()\\n\\n # testloaders contain same length(iteration) of batch dataset\\n for sample_batched in progressbar(self.testloader, prefix=\\\"[Test]\\\\t\\\"):\\n image = torch.autograd.Variable(sample_batched['image'].cuda())\\n depth = torch.autograd.Variable(sample_batched['depth'].cuda(non_blocking=True))\\n maxDepth = 1000.0\\n depth_n = maxDepth/depth\\n #No use self.criterion \\n\\n output = self.model(image)\\n # Compute the loss\\n\\n l_depth = l1_criterion(output, depth_n)\\n\\n l_ssim = torch.clamp((1 - ssim(output, depth_n, val_range = 1000.0 / 10.0)) * 0.5, 0, 1)\\n\\n loss = (1.0 * l_ssim) + (0.1 * l_depth)\\n\\n \\n\\n if self.half:\\n images = images.half()\\n\\n # forward + backward + optimize\\n \\n self._count_correct_prediction(output, depth_n)\\n losses.append(loss.item())\\n\\n avg_loss = sum(losses) / len(losses)\\n acc = self._get_epoch_acc(is_test=True)\\n return avg_loss, acc\\n\\n # # testloaders contain same length(iteration) of batch dataset\\n # for data in progressbar(self.testloader, prefix=\\\"[Test]\\\\t\\\"):\\n # images, labels = data[0].to(self.device), data[1].to(self.device)\\n\\n # if self.half:\\n # images = images.half()\\n\\n # # forward + backward + optimize\\n # loss, outputs = self.criterion(model, images=images, labels=labels)\\n # self._count_correct_prediction(outputs, labels)\\n # losses.append(loss.item())\\n\\n # avg_loss = sum(losses) / len(losses)\\n # acc = self._get_epoch_acc(is_test=True)\\n # return avg_loss, acc\",\n \"def compareModel(self):\\n\\n # --------------------------------------------------------------------------------------------- #\\n # Store the Model parameters\\n lines = open(self.model, 'r').readlines()\\n MyModel = {}\\n for line in lines:\\n if ('<source' in line) & ('name=' in line):\\n srcNam = line.split('\\\"')[3]\\n MyModel[ srcNam ] = {}\\n elif ('<parameter' in line) & ('free=\\\"1\\\"' in line):\\n parNam = line.split('\\\"')[7]\\n parVal = float(line.split('\\\"')[11])\\n MyModel[ srcNam ][ parNam ] = parVal\\n else:\\n pass\\n\\n # --------------------------------------------------------------------------------------------- #\\n # Store the fitted results\\n lines = open(self.outgtlike, 'r').readlines()\\n CodeString = ''\\n for line in lines:\\n if not 'Upper limit' in line:\\n CodeString += line[:-1]\\n MyData = eval(CodeString) # create a dictionnary\\n\\n # --------------------------------------------------------------------------------------------- #\\n # Compare\\n for key in MyData.keys():\\n if 'TS value' in MyData[key].keys():\\n # The source has been fitted\\n print(\\\"--- {} ---\\\".format(key))\\n for k in MyModel[key].keys():\\n difference = 100* (MyModel[key][k] - float(MyData[key][k].split()[0])) / MyModel[key][k] \\n print(\\\"{0:s} differs by {1:.2f} per cent\\\".format(k, difference))\\n return\",\n \"def test(self, load_file):\\n self.load(load_file)\\n table = self.truthtable()\\n print \\\" ..... Test: Model Computes:\\\", table\",\n \"def test_best_val(self, te_acc):\\n self.test_val = te_acc\",\n \"def test_fit():\\n args = get_layer('fit', 'manual', 'temporal', False, False, window=2, step_size=3)\\n run_layer(*args)\",\n \"def model_qua_launch(self, dict):\\r\\n list_result = []\\r\\n if \\\"SVR\\\" in dict:\\r\\n SVR = dict[\\\"SVR\\\"]\\r\\n if SVR[\\\"Auto\\\"]:\\r\\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\\\"Auto\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"SVR Result Auto\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\\\"Auto\\\"], [SVR[\\\"C\\\"],\\r\\n SVR[\\\"Kernel\\\"],\\r\\n SVR[\\\"Degree\\\"]])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"SVR Result\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n if \\\"LR\\\" in dict:\\r\\n LR = dict[\\\"LR\\\"]\\r\\n if LR[\\\"Auto\\\"]:\\r\\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\\\"Auto\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Linear Regression Result Auto\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\\\"Auto\\\"], LR[\\\"fit_intercept\\\"], LR[\\\"normalize\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Linear Regression Result\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n if \\\"RT\\\" in dict:\\r\\n RT = dict[\\\"RT\\\"]\\r\\n if RT[\\\"Auto\\\"]:\\r\\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\\\"Auto\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Regression Tree Result Auto\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\\\"Auto\\\"], [RT[\\\"Criterion\\\"],\\r\\n RT[\\\"Min_Samples_Split\\\"],\\r\\n RT[\\\"Min_Samples_Leaf\\\"]])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Regression Tree Result\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n if \\\"KNN\\\" in dict:\\r\\n KNN = dict[\\\"KNN\\\"]\\r\\n if KNN[\\\"Auto\\\"]:\\r\\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\\\"Auto\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"KNN Result Auto\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\\\"Auto\\\"],\\r\\n [KNN[\\\"leaf_size\\\"], KNN[\\\"n_neighbors\\\"], KNN[\\\"p\\\"], KNN[\\\"metric\\\"]])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n self.model_quali.close()\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"KNN Result\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n if \\\"LogiR\\\" in dict:\\r\\n LogiR = dict[\\\"LogiR\\\"]\\r\\n if LogiR[\\\"Auto\\\"]:\\r\\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\\\"Auto\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Logistic Regression Result Auto\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\\\"Auto\\\"], [LogiR['C'],\\r\\n LogiR['penalty']])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Logistic Regression Result\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n if \\\"DTC\\\" in dict:\\r\\n DTC = dict[\\\"DTC\\\"]\\r\\n if DTC[\\\"Auto\\\"]:\\r\\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\\\"Auto\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Tree Decision Classification Auto\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n else:\\r\\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\\\"Auto\\\"],DTC[\\\"max_leaf_nodes\\\"],DTC[\\\"max_depth\\\"],DTC[\\\"min_samples_split\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Tree Decision Classification\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n\\r\\n self.close()\\r\\n self.trigger_result.emit(list_result)\",\n \"def model_fit(train_features, train_actuals):\\n for name in models.keys():\\n est = models[name]\\n est_params = params[name]\\n gscv = GridSearchCV(estimator=est, param_grid=est_params, cv=5,\\n scoring='neg_mean_absolute_error', return_train_score=True)\\n gscv.fit(train_actuals, train_features)\\n cvres = gscv.cv_results_\\n print(cvres)\\n print(\\\"best parameters are: {}\\\".format(gscv.best_estimator_))\\n for mean_score,par in zip(cvres[\\\"mean_test_score\\\"],cvres[\\\"params\\\"]):\\n print(-mean_score, par)\",\n \"def model_test(epo, natural):\\n\\tmodel.eval()\\n\\twith torch.no_grad():\\n\\t\\tn = batch_size\\n\\n\\t\\tif natural:\\n\\t\\t\\tloader = nat_test_loader\\n\\t\\t\\tprefix = \\\"nat\\\"\\n\\t\\telse:\\n\\t\\t\\tloader = syn_test_loader\\n\\t\\t\\tprefix = \\\"syn\\\"\\n\\n\\t\\tlog_cor_file = open(directory + \\\"/logs/test_\\\" + prefix + \\\"_cor_log.txt\\\", \\\"a\\\") # Correct\\n\\t\\tlog_mae_file = open(directory + \\\"/logs/test_\\\" + prefix + \\\"_mae_log.txt\\\", \\\"a\\\") # MAE\\n\\t\\tlog_dev_file = open(directory + \\\"/logs/test_\\\" + prefix + \\\"_dev_log.txt\\\", \\\"a\\\") # DEV\\n\\t\\tlog_sam_file = open(directory + \\\"/logs/test_\\\" + prefix + \\\"_sam_log.txt\\\", \\\"a\\\") # Sample\\n\\n\\t\\tccs = []\\n\\t\\tlabls = []\\n\\t\\tnum_unlabeled = 0\\n\\t\\tfor batch_idx, (data, labels) in enumerate(loader):\\n\\t\\t\\tdata = data.cuda()\\n\\t\\t\\tlabels = labels.float().cuda()\\n\\n\\t\\t\\tmodel.mode = 'natural' if natural else 'synth'\\n\\t\\t\\trecon_batch, mu, logvar, cc = model(data)\\n\\n\\t\\t\\tcc[labels == 0] = 0 # Sets the counted cells to 0 for unlabeled data, so that regressor_loss=0\\n\\t\\t\\tnum_unlabeled += (labels == 0).sum()\\n\\t\\t\\t_, _, _ = loss_function(recon_batch, data, mu, logvar, cc, labels, natural)\\n\\n\\t\\t\\tccs.append(cc.cpu().detach().numpy())\\n\\t\\t\\tlabls.append(labels.cpu().detach().numpy())\\n\\n\\t\\t\\tif batch_idx == 0 and epo % 1000 == 0:\\n\\t\\t\\t\\t# Save test sample\\n\\t\\t\\t\\tcomparison = torch.cat([data[:n], recon_batch.view(batch_size, 1, img_size, img_size)[:n]])\\n\\t\\t\\t\\tsave_image(comparison.cpu(), directory + \\\"/\\\" + prefix + \\\"_\\\" + str(epo) + \\\".png\\\", nrow=n)\\n\\n\\t\\t\\t\\t# Save switch sample\\n\\t\\t\\t\\tmodel.mode = 'synth' if natural else 'natural'\\n\\t\\t\\t\\trecon_batch, _, _, _ = model(data)\\n\\t\\t\\t\\tcomparison = torch.cat([data[:n], recon_batch.view(batch_size, 1, img_size, img_size)[:n]])\\n\\t\\t\\t\\tsave_image(comparison.cpu(), directory + \\\"/switch_\\\" + prefix + \\\"_\\\" + str(epo) + \\\".png\\\", nrow=n)\\n\\n\\t\\tpreds = np.concatenate(ccs, axis=None) # Elementwise round of cellcounts\\n\\t\\tlbls = np.concatenate(labls, axis=None) # Elementswise round of labels\\n\\n\\t\\tlog_sam_file.write(str(np.round(preds, 2)) + \\\"\\\\n\\\" + str(lbls) + \\\"\\\\n\\\")\\n\\t\\tpreds = np.around(preds)\\n\\t\\t#lbls = np.around(lbls)\\n\\n\\t\\tcorrect = np.sum(preds == lbls) # Count elementwise equality of predictions and labels\\n\\t\\tlen_set = len(loader.dataset)\\n\\t\\tcorrect -= num_unlabeled # Remove zero_indices from numerator\\n\\t\\tcorrect = float(correct) / float(len_set - num_unlabeled) # Remove zero_indices from denominator\\n\\n\\t\\tdist_sum = np.sum(np.abs(np.subtract(preds, lbls))) # Elementwise addition of dist between preds and lbls\\n\\t\\tMAE = dist_sum / float(len_set - num_unlabeled)\\n\\n\\t\\tlen_labeled = float(len_set - num_unlabeled)\\n\\t\\tdev = np.ones(len_set) - np.divide(preds, lbls) # Deviation contains NaNs because syn data has lbl=0\\n\\t\\tavg_dev = np.sum(np.abs(np.where(np.isnan(dev), 0, dev))) / len_labeled # Take the avg only of those deviations that weren't NaN\\n\\n\\t\\tlog_cor_file.write(str(correct)+\\\"\\\\n\\\")\\n\\t\\tlog_mae_file.write(str(MAE)+\\\"\\\\n\\\")\\n\\t\\tlog_dev_file.write(str(avg_dev)+\\\"\\\\n\\\")\\n\\n\\t\\t#logfile.write(str(correct) + \\\" correct, MAE: \\\" + str(MAE) + \\\", DEV: \\\" + str(avg_dev) + \\\" in \\\" + prefix + \\\" set in epoch \\\" + str(epoch) + \\\"\\\\n\\\\n\\\")\\n\\t\\tlog_cor_file.close()\\n\\t\\tlog_mae_file.close()\\n\\t\\tlog_dev_file.close()\\n\\t\\tlog_sam_file.close()\\n\\n\\t\\tglobal distance_sum\\n\\t\\tdistance_sum = dist_sum\\n\\t\\treturn correct, MAE\",\n \"def estimatePerformance(self, model):\\n Y_pred = model.computeLOO()\\n #performance = self.measure.multiOutputPerformance(self.Y, Y_pred)\\n #performance = self.measure.getPerformance(self.Y, Y_pred)\\n #performance = measure_utilities.aggregate(performance)\\n performance = self.measure(self.Y, Y_pred)\\n self.predictions.append(Y_pred)\\n return performance\",\n \"def eval_model(config, period, test_data):\\n if config.network == 'MLPwithGAN':\\n model = MLPwithGAN(config)\\n elif config.network == 'MLP':\\n model = MLP(config)\\n elif config.network == 'LSTM':\\n model = VanillaLSTM(config)\\n elif config.network == 'CNN':\\n model = CNNfeature(config)\\n else:\\n raise Exception('Unknown model type:{}'.format(config.network))\\n\\n if config.ensemble:\\n m = model\\n model = []\\n\\n for i in glob(gen_path(config.path, str(period)) + '/m*'):\\n m.load_state_dict(\\n torch.load(gen_path(i, filename=config.network + '.pkl')))\\n m.to(config.device)\\n m.eval()\\n model.append(m)\\n else:\\n model.load_state_dict(\\n torch.load(gen_path(config.path, str(period), 'model', filename=config.network + '.pkl')))\\n model.to(config.device)\\n model.eval()\\n dataloader_test = test_data[0]\\n test_date = test_data[1]\\n test_symbol = test_data[2]\\n sc_y = joblib.load(gen_path(config.path, str(period), 'scaler', filename='training_sc_y.pkl'))\\n predict_y_test, real_y_test, valid_index_test = make_prediction(dataloader_test, sc_y, model, config)\\n\\n stock_score = pd.DataFrame()\\n stock_score[\\\"symbol\\\"] = test_symbol[valid_index_test]\\n stock_score[\\\"score\\\"] = predict_y_test\\n stock_score['truth'] = real_y_test\\n stock_score[\\\"date\\\"] = test_date[valid_index_test]\\n stock_score = stock_score.sort_values(by=[\\\"date\\\"])\\n stock_score.to_csv(gen_path(config.path, 'stock_score', filename=str(period) + '.csv'), index=False)\",\n \"def test_model(loader, model):\\n correct = 0\\n total = 0\\n model.eval()\\n for data, labels in loader:\\n data_batch, label_batch = data.to(device), labels.to(device)\\n outputs = F.softmax(model(data_batch), dim=1)\\n predicted = outputs.max(1, keepdim=True)[1]\\n total += label_batch.size(0)\\n correct += predicted.eq(label_batch.view_as(predicted)).sum().item()\\n return (100 * correct / total)\",\n \"def model_performance_summary(df, data_all_df, cov_buck, fisc_calender, mod_start, pred_start, summ_start, summ_end):\\n var = \\\"pv_yr\\\" \\n agg_col = \\\"LINE_ORDERS\\\"\\n select_cols = [ \\\"LINE_ORDERS_ACT\\\", \\\"Prediction_Trf\\\", \\\"LEAD_MNTS_ACT\\\", \\\"LD_UNCLAIMED_ACT\\\", \\\"LD_DRIVE_ACT\\\", \\n \\\"IDV_LEAD_MNTS_Pred\\\", \\\"IDV_LD_UNCLAIMED_Pred\\\", \\\"IDV_LD_DRIVE_Pred\\\",\\n \\\"IDV_LEAD_MNTS_ACT\\\", \\\"IDV_LD_UNCLAIMED_ACT\\\", \\\"IDV_LD_DRIVE_ACT\\\",\\n \\\"HELPER_MNTS_ACT\\\", \\\"HP_UNCLAIMED_ACT\\\", \\\"HP_DRIVE_ACT\\\",\\n \\\"IDV_HELPER_MNTS_Pred\\\", \\\"IDV_HP_UNCLAIMED_Pred\\\", \\\"IDV_HP_DRIVE_Pred\\\",\\n \\\"IDV_HELPER_MNTS_ACT\\\", \\\"IDV_HP_UNCLAIMED_ACT\\\", \\\"IDV_HP_DRIVE_ACT\\\",\\n \\\"HELPER_OVR_ACT\\\", \\\"HPN_UNCLAIMED_ACT\\\", \\\"HPN_DRIVE_ACT\\\",\\n \\\"IDV_HELPER_OVR_Pred\\\", \\\"IDV_HPN_UNCLAIMED_Pred\\\", \\\"IDV_HPN_DRIVE_Pred\\\",\\n \\\"IDV_HELPER_OVR_ACT\\\", \\\"IDV_HPN_UNCLAIMED_ACT\\\", \\\"IDV_HPN_DRIVE_ACT\\\"\\n ]\\n\\n for i in select_cols:\\n if str(i) not in data_all_df.columns:\\n data_all_df[str(i)] = np.nan\\n\\n cov_grp_mm = dp.cov_check(\\\"RSS_MM\\\", df, cov_buck, agg_col, fisc_calender, summ_start, summ_end)\\n cov_mm = dp.cov_check(\\\"MM\\\", df, cov_buck, agg_col, fisc_calender, summ_start, summ_end)\\n \\n sample = data_all_df[data_all_df.Variable_Type == var].copy()\\n sample = pd.merge(sample, cov_grp_mm[[\\\"RSS_MM\\\", \\\"CAT\\\"]])\\n sample.CAT = sample.CAT.astype(int)\\n sample[\\\"MAPE\\\"] = abs(sample[\\\"LINE_ORDERS_ACT\\\"]-sample[\\\"Prediction_Trf\\\"])/sample[\\\"LINE_ORDERS_ACT\\\"]\\n bins= [-1, 0.1, 0.15, 0.20, 0.30, 0.50, 1.00, np.inf]\\n labels = [1, 2, 3, 4, 5, 6, 7]\\n sample[\\\"MAPE_BIN\\\"] = pd.cut(sample['MAPE'], bins=bins, labels=labels)\\n wk_list = fisc_calender[fisc_calender.FISC_WK_OF_MTH_ID >= pred_start].FISC_WK_OF_MTH_ID.unique()\\n ly_start = fisc_calender[fisc_calender.FISC_WK_OF_MTH_ID == wk_list[4]].LY_FISC_WK_OF_MTH_ID.values\\n ly_end = fisc_calender[fisc_calender.FISC_WK_OF_MTH_ID == wk_list[16]].LY_FISC_WK_OF_MTH_ID.values\\n train = sample[((sample.FISC_WK_OF_MTH_ID >= mod_start) & (sample.FISC_WK_OF_MTH_ID < pred_start))]\\n test_in_train = sample[((sample.FISC_WK_OF_MTH_ID >= ly_start[0]) & (sample.FISC_WK_OF_MTH_ID <= ly_end[0]))]\\n # The 4 th index of the list has the prediction start date after excluding the 4 week hold out period\\n # for analysis purpose we are considereing only 13 weeks of test period\\n test = sample[((sample.FISC_WK_OF_MTH_ID >= wk_list[4]) & (sample.FISC_WK_OF_MTH_ID <= wk_list[16]))]\\n fy20 = sample[(sample.FISC_WK_OF_MTH_ID >= summ_start) & (sample.FISC_WK_OF_MTH_ID <= summ_end)]\\n \\n ####################################################################################\\n \\n df_grp_summary = fy20.groupby([\\\"RSS\\\"]).sum()[\\\"LINE_ORDERS_ACT\\\"].reset_index()\\n df_grp_summary[\\\"LINE_ORDERS_PERC\\\"] = df_grp_summary[\\\"LINE_ORDERS_ACT\\\"]/df_grp_summary[\\\"LINE_ORDERS_ACT\\\"].sum()\\n ####################################################################################\\n \\n df_sample_list = []\\n df_sample_list = cu.mape_stability(train, \\\"Train CAT\\\", \\\"CAT\\\", \\\"MAPE\\\", \\\"LINE_ORDERS_ACT\\\", df_sample_list)\\n df_sample_list = cu.mape_stability(test_in_train, \\\"Test_in_train CAT\\\", \\\"CAT\\\", \\\"MAPE\\\", \\\"LINE_ORDERS_ACT\\\", df_sample_list)\\n df_sample_list = cu.mape_stability(test, \\\"Test CAT\\\", \\\"CAT\\\", \\\"MAPE\\\", \\\"LINE_ORDERS_ACT\\\", df_sample_list)\\n df_sample_list = cu.mape_stability(fy20, \\\"FY20 CAT\\\", \\\"CAT\\\", \\\"MAPE\\\", \\\"LINE_ORDERS_ACT\\\", df_sample_list)\\n stability = pd.concat(df_sample_list)\\n stability.reset_index(inplace = True)\\n ####################################################################################\\n \\n df_sample_res = cu.mape_stability_2d(fy20, \\\"FISC_MTH_NBR\\\", \\\"CAT\\\")\\n grp_sum = fy20.groupby(\\\"FISC_MTH_NBR\\\").sum()[\\\"LINE_ORDERS_ACT\\\"].reset_index()\\n monthly_stability_check = df_sample_res.merge(grp_sum)\\n ####################################################################################\\n \\n mape_cov = cu.mape_stability_2d(fy20, \\\"CAT\\\", \\\"MAPE_BIN\\\")\\n ####################################################################################\\n \\n mape_grp = cu.mape_stability_2d(fy20, \\\"RSS\\\", \\\"MAPE_BIN\\\")\\n mape_grp[\\\"TYPE\\\"] = \\\"MAPE Buckets\\\"\\n ####################################################################################\\n \\n mape_cov_grp = cu.mape_stability_2d(fy20, \\\"RSS\\\", \\\"CAT\\\")\\n mape_cov_grp[\\\"TYPE\\\"] = \\\"Covariance Category\\\"\\n ####################################################################################\\n \\n fy20 = cu.sh_mape(fy20)\\n \\n sh_summary = []\\n for grp in fy20[\\\"RSS\\\"].unique():\\n sample_df = fy20[fy20[\\\"RSS\\\"] == grp]\\n\\n sh_summary = cu.mape_stability(sample_df, grp, \\\"MAPE_LD_BIN\\\", \\\"MAPE_LD\\\", \\\"TOTAL_LEAD_ACT\\\", sh_summary)\\n sh_summary = cu.mape_stability(sample_df, grp, \\\"MAPE_HP_BIN\\\", \\\"MAPE_HP\\\", \\\"TOTAL_HELPER_ACT\\\", sh_summary)\\n sh_summary = cu.mape_stability(sample_df, grp, \\\"MAPE_HPN_BIN\\\", \\\"MAPE_HPN\\\", \\\"TOTAL_HELPERN_ACT\\\", sh_summary)\\n sh_summary_grp = pd.concat(sh_summary)\\n sh_summary_grp.reset_index(inplace = True)\\n\\n ####################################################################################\\n\\n sh_summary = []\\n var_list = [\\\"pv_yr\\\", \\\"pv_roll_5wk\\\"]\\n for var in var_list:\\n sample_df = data_all_df[data_all_df.Variable_Type == var].groupby([\\\"FISC_WK_OF_MTH_ID\\\", \\\"FISC_YR_NBR\\\", \\\"MM\\\"]).sum()[select_cols].reset_index()\\n sample_df = pd.merge(sample_df, cov_mm[[\\\"MM\\\", \\\"CAT\\\"]])\\n sample_df = sample_df[sample_df.CAT>0]\\n sample_df = sample_df[(sample.FISC_WK_OF_MTH_ID >= summ_start) & (sample.FISC_WK_OF_MTH_ID <= summ_end)]\\n sample_df = cu.sh_mape(sample_df)\\n sh_summary = cu.mape_stability(sample_df, var, \\\"MAPE_LDA_BIN\\\", \\\"MAPE_LD_ACT\\\", \\\"TOTAL_LEAD_ACT\\\", sh_summary)\\n sh_summary = cu.mape_stability(sample_df, var, \\\"MAPE_HPA_BIN\\\", \\\"MAPE_HP_ACT\\\", \\\"TOTAL_HELPER_ACT\\\", sh_summary)\\n sh_summary = cu.mape_stability(sample_df, var, \\\"MAPE_HPNA_BIN\\\", \\\"MAPE_HPN_ACT\\\", \\\"TOTAL_HELPERN_ACT\\\", sh_summary)\\n sh_summary = cu.mape_stability(sample_df, var, \\\"MAPE_LD_BIN\\\", \\\"MAPE_LD\\\", \\\"TOTAL_LEAD_ACT\\\", sh_summary)\\n sh_summary = cu.mape_stability(sample_df, var, \\\"MAPE_HP_BIN\\\", \\\"MAPE_HP\\\", \\\"TOTAL_HELPER_ACT\\\", sh_summary)\\n sh_summary = cu.mape_stability(sample_df, var, \\\"MAPE_HPN_BIN\\\", \\\"MAPE_HPN\\\", \\\"TOTAL_HELPERN_ACT\\\", sh_summary)\\n sh_mm_summary_df = pd.concat(sh_summary)\\n sh_mm_summary_df.reset_index(inplace = True)\\n\\n ####################################################################################\\n\\n sh_summary = []\\n for var in data_all_df.Variable_Type.unique():\\n sample_df = data_all_df[data_all_df.Variable_Type == var].groupby([\\\"FISC_WK_OF_MTH_ID\\\", \\\"FISC_YR_NBR\\\", \\\"MM\\\"]).sum()[select_cols].reset_index()\\n sample_df[select_cols] = sample_df[select_cols].replace({0:np.nan})\\n sample_df = pd.merge(sample_df, cov_mm[[\\\"MM\\\", \\\"CAT\\\"]])\\n sample_df = sample_df[sample_df.CAT>0]\\n sample_df = sample_df[(sample.FISC_WK_OF_MTH_ID >= summ_start) & (sample.FISC_WK_OF_MTH_ID <= summ_end)]\\n sample_df = cu.sh_mape(sample_df)\\n sh_summary = cu.mape_stability(sample_df, var, \\\"MAPE_LDAP_BIN\\\", \\\"MAPE_LD_ACT_PRED\\\", \\\"TOTAL_LEAD_ACT\\\", sh_summary)\\n sh_summary = cu.mape_stability(sample_df, var, \\\"MAPE_HPAP_BIN\\\", \\\"MAPE_HP_ACT_PRED\\\", \\\"TOTAL_HELPER_ACT\\\", sh_summary)\\n sh_summary = cu.mape_stability(sample_df, var, \\\"MAPE_HPNAP_BIN\\\", \\\"MAPE_HPN_ACT_PRED\\\", \\\"TOTAL_HELPERN_ACT\\\", sh_summary)\\n sh_prop_summary = pd.concat(sh_summary)\\n sh_prop_summary.reset_index(inplace = True)\\n \\n best_mthd = sh_prop_summary.groupby(['IDENTIFIER']).mean()['WEIGHTED MAPE'].reset_index().sort_values(by = [\\\"WEIGHTED MAPE\\\"], ascending = True)\\n var_list = best_mthd[\\\"IDENTIFIER\\\"][:2].to_list()\\n ####################################################################################\\n\\n sample_df = fy20.copy()\\n sample_df = sample_df[sample_df.MM.str.contains(\\\"HD\\\")]\\n sample_df = sample_df.groupby([\\\"FISC_WK_OF_MTH_ID\\\", \\\"MM\\\"]).sum()[[\\\"LINE_ORDERS_ACT\\\", \\\"Prediction_Trf\\\"]].reset_index()\\n df_grp_all = sample_df.copy()\\n df_grp_all[[\\\"LINE_ORDERS_ACT\\\"]] = df_grp_all[[\\n \\\"LINE_ORDERS_ACT\\\"]].replace({0:np.nan})\\n df_grp_all[\\\"MAPE_TA\\\"] = abs(df_grp_all[\\\"LINE_ORDERS_ACT\\\"]-df_grp_all[\\\"Prediction_Trf\\\"])/df_grp_all[\\\"LINE_ORDERS_ACT\\\"]\\n bins= [-1, 0.1, 0.15, 0.20, 0.30, 0.50, 1.00, np.inf]\\n labels = [1, 2, 3, 4, 5, 6, 7]\\n df_grp_all[\\\"MAPE_TA_BIN\\\"] = pd.cut(df_grp_all['MAPE_TA'], bins=bins, labels=labels)\\n\\n var = \\\"pv_yr\\\" \\n sample = data_all_df[data_all_df.Variable_Type == var].copy()\\n \\n sample = sample.merge(cov_grp_mm[[\\\"RSS_MM\\\", \\\"CAT\\\"]])\\n sample = sample[sample.CAT > 0]\\n sample = sample[(sample.FISC_WK_OF_MTH_ID >= summ_start) & (sample.FISC_WK_OF_MTH_ID <= summ_end)]\\n \\n \\n sample = data_all_df[data_all_df.Variable_Type == var].copy()\\n sample = pd.merge(sample, cov_mm[[\\\"MM\\\", \\\"CAT\\\"]])\\n sample = sample[sample.CAT > 0]\\n sample = sample[(sample.FISC_WK_OF_MTH_ID >= summ_start) & (sample.FISC_WK_OF_MTH_ID <= summ_end)]\\n fy20_mm = sample.groupby([\\\"FISC_WK_OF_MTH_ID\\\", \\\"MM\\\", \\\"CAT\\\"]).sum()[[\\\"LINE_ORDERS_ACT\\\", \\\"Prediction_Trf\\\"]].reset_index()\\n fy20_mm[[\\\"LINE_ORDERS_ACT\\\" ]] = fy20_mm[[\\\"LINE_ORDERS_ACT\\\"]].replace({0:np.nan})\\n fy20_mm[\\\"MAPE\\\"] = abs(fy20_mm[\\\"LINE_ORDERS_ACT\\\"]-fy20_mm[\\\"Prediction_Trf\\\"])/fy20_mm[\\\"LINE_ORDERS_ACT\\\"]\\n bins= [-1, 0.1, 0.15, 0.20, 0.30, 0.50, 1.00, np.inf]\\n labels = [1, 2, 3, 4, 5, 6, 7]\\n fy20_mm[\\\"MAPE_BIN\\\"] = pd.cut(fy20_mm['MAPE'], bins=bins, labels=labels)\\n \\n df_sample_list = []\\n df_sample_list = cu.mape_stability(fy20, \\\"FY20 RSS_MM\\\", \\\"CAT\\\", \\\"MAPE\\\", \\\"LINE_ORDERS_ACT\\\", df_sample_list)\\n df_sample_list = cu.mape_stability(fy20_mm, \\\"FY20 MM\\\", \\\"CAT\\\", \\\"MAPE\\\", \\\"LINE_ORDERS_ACT\\\", df_sample_list)\\n df_overall_summay = pd.concat(df_sample_list)\\n df_overall_summay.reset_index(inplace = True)\\n return df_grp_summary, stability, monthly_stability_check, mape_cov, mape_cov_grp, mape_grp, sh_mm_summary_df, sh_summary_grp, sh_prop_summary, df_overall_summay, var_list\",\n \"def test(self, dataset):\\n test_accuracy = 0\\n test_loss = 0\\n num_examples_tested = 0\\n # Put model into evaluation mode\\n self.model.eval()\\n for num, batch in enumerate(dataset.loader):\\n xs, ys = batch\\n batch_size = len(xs)\\n num_examples_tested += batch_size\\n iloss, iaccuracy = self.model(xs, ys)\\n test_loss += iloss.cpu().data.numpy().item() * batch_size\\n test_accuracy += iaccuracy.cpu().data.numpy().item() * batch_size\\n test_accuracy = test_accuracy / num_examples_tested\\n test_loss = test_loss / num_examples_tested\\n # Return accuracy and loss for this model on the test set\\n return test_accuracy, test_loss\",\n \"def train_model(regressor=DecisionTreeRegressor(max_depth=3, random_state=42),\\n dataset_file_name=DATASET_FILE_NAME,\\n model_file_name=MODEL_FILE_NAME):\\n df = pd.read_csv(dataset_file_name)\\n\\n # A minumum amount of feature engineering: The player's and opponent's\\n # exact score may not be that important for our decisions. The difference,\\n # however, certainly is. Moreover, the card value itself is not that\\n # important. Here, the sum is.\\n df['score_difference'] = df.self_score - df.opp_score\\n df.drop(columns=['opp_score'], inplace=True)\\n df['score_if_card_played'] = df.self_score + df.result_card_val\\n df.drop(columns=['result_card_val'], inplace=True)\\n\\n # Strategy will be to let our model predict the score for different actions\\n # Hence, we're going to train the model on that now\\n X, y = df.drop(columns='score'), df.score\\n X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\\n regressor.fit(X_train, y_train)\\n\\n feature_names = ['self_score', 'opp_stands', 'result_stand',\\n 'score_difference', 'score_if_card_played']\\n\\n score = regressor.score(X_test, y_test)\\n print(f\\\"Score on the test set: {score}.\\\")\\n if isinstance(regressor, DecisionTreeRegressor):\\n export_graphviz(regressor, feature_names=feature_names,\\n out_file=GRAPHVIZ_FILE_NAME, filled=True)\\n\\n # For persistence, we export the generated model\\n dump(regressor, model_file_name)\\n return score\",\n \"def test(self, test):\\r\\n self.ml_data.set_target(test[0])\\r\\n self.ml_data.set_features(test[1])\\r\\n if self.ml_data.target_type.all() == np.float64 or self.ml_data.target_type.all() == np.int64:\\r\\n self.model_qua.open()\\r\\n else:\\r\\n self.model_quali.open()\",\n \"def fit(model, data, test_ids, exp_name, train_ids=None):\\n if model.model_type == 'torch':\\n size = len(data[0])\\n else:\\n size = data[0].shape[0]\\n \\n if train_ids == None:\\n train_ids = [i for i in range(size) if i not in test_ids]\\n scaler = pka_scaler(data[1][train_ids])\\n \\n if model.data_type == 'descriptors':\\n desc_scaler = StandardScaler()\\n scaling_data = data[0][train_ids]\\n desc_scaler.fit(scaling_data)\\n data[0] = desc_scaler.transform(data[0])\\n else:\\n scaling_data = None\\n \\n trained_model = train(model, train_ids, data, scaler)\\n results = test(model, trained_model, test_ids, data, scaler)\\n model.experiments[exp_name] = {'model':trained_model, 'results':results, 'scaler':scaler, 'desc scaling data':scaling_data}\\n return results\",\n \"def test_model(model, dataObj, index):\\n\\t(s,m,l), img = dataObj.__getitem__(index)\\n\\timg = img.float().unsqueeze(0)\\n\\t\\n\\tif next(model.parameters()).is_cuda:\\n\\t\\toutput = model(img.cuda()) \\n\\telse:\\n\\t\\toutput = model(img)\\n\\n\\ts_pred,m_pred,l_pred = output[0].squeeze(0).cpu(), output[1].squeeze(0).cpu(), output[2].squeeze(0).cpu()\\n\\ts_pred = s_pred.detach().numpy()\\n\\tm_pred = m_pred.detach().numpy()\\n\\tl_pred = l_pred.detach().numpy()\\n\\n\\timg = img.float().squeeze(0)\\n\\timg = img.permute(1,2,0)\\n\\n\\tfor j in range(22):\\n\\t\\tvisualize(img, s[j], m[j], l[j], s_pred[j], m_pred[j], l_pred[j])\\n\\t\\tk = np.array(s[j])\",\n \"def evaluate_model(model, X_test, Y_test, category_names):\\n\\n print(\\\"Testing Performance\\\")\\n print(classification_report(Y_test, model.predict(X_test), target_names=category_names))\\n\\n #Todo cat names\",\n \"def test(self):\\n self.training = False\",\n \"def evaluate_model(model, X_test, y_test):\\n # run prediction with test data\\n y_pred = model.predict(X_test)\\n\\n # print precision, recall and f1-score\\n i = 0\\n for col in y_test:\\n print('Evaluation for \\\"{}\\\": \\\\n {} \\\\n\\\\n'.format(col, classification_report(y_test[col], y_pred[:,i])))\\n i += 1\",\n \"def trainModel( self, featureTrain, classTrain):\",\n \"def train(self):\\n\\t\\traise NotImplementedError\",\n \"def main():\\n data = load_data()\\n analyze_features(data['full_features'])\\n model = train(data)\\n\\n with open('model.pickle', 'wb') as f:\\n pickle.dump(model, f)\\n evaluate(model, data)\",\n \"def _train_and_evaluate(estimator, output_dir):\\n \\n \\\"\\\"\\\"X_train, y_train =utils._feature_label_split(df_train,\\\"is_churn\\\",\\\"msno\\\")\\n df_val = utils.read_from_bigquery(\\\"amiable-octane-267022.kkbox.output_val_1\\\",\\\"amiable-octane-267022\\\")\\n X_val, y_val =utils._feature_label_split(df_val,\\\"is_churn\\\",\\\"msno\\\")\\\"\\\"\\\"\\n \\n df_train=utils.over_sample(\\\"amiable-octane-267022.kkbox.output_train_1\\\",\\\"amiable-octane-267022\\\")\\n X_train, y_train =utils._feature_label_split(df_train,\\\"is_churn\\\",\\\"msno\\\")\\n df_val=utils.over_sample(\\\"amiable-octane-267022.kkbox.output_val_1\\\",\\\"amiable-octane-267022\\\")\\n X_val, y_val =utils._feature_label_split(df_val,\\\"is_churn\\\",\\\"msno\\\")\\n\\n estimator.fit(X_train, y_train)\\n f1_scorer = make_scorer(f1_score)\\n accuracy_scorer =make_scorer(accuracy_score)\\n\\n if metadata.HYPERPARAMTER_TUNING:\\n scores=model_selection.cross_val_score(estimator, X_val, y_val, cv=3,scoring=f1_scorer)\\n #,scoring=f1_scorer\\n\\n logging.info('Score: %s', scores)\\n\\n #tune hyper\\n hpt = hypertune.HyperTune()\\n hpt.report_hyperparameter_tuning_metric(\\n hyperparameter_metric_tag='F1_SCORE',\\n metric_value=np.mean(scores),\\n global_step=10000)\\n \\n#joblib.dump(estimator, 'model.joblib')\\n\\n # Write model and eval metrics to `output_dir`\\n model_output_path = os.path.join(output_dir, 'model',metadata.MODEL_FILE_NAME)\\n \\n utils.dump_object(estimator, model_output_path)\",\n \"def train_and_eval():\\n # train_file_name = 'adult.data'\\n # test_file_name = 'adult.test'\\n train_file_name = 'poker-hand-testing.data'\\n test_file_name = 'poker-hand-training-true.data'\\n #test_file_name = maybe_download()\\n df_train = pd.read_csv(\\n tf.gfile.Open(\\\"/opt/tensor/race_result_clean.csv\\\"),\\n names=COLUMNS,\\n skipinitialspace=True,\\n skiprows=1)\\n df_test = pd.read_csv(\\n tf.gfile.Open(\\\"/opt/tensor/race_result_clean.csv\\\"),\\n names=COLUMNS,\\n skipinitialspace=True,\\n skiprows=1)\\n\\n #df_train[LABEL_COLUMN] = (df_train[\\\"CLASS_Poker_Hand\\\"].apply(lambda x: x>5)).astype(int)\\n #df_test[LABEL_COLUMN] = (df_test[\\\"CLASS_Poker_Hand\\\"].apply(lambda x: x>5)).astype(int)\\n\\n model_dir = tempfile.mkdtemp() if not FLAGS.model_dir else FLAGS.model_dir\\n print(\\\"model directory = %s\\\" % model_dir)\\n m = build_estimator(model_dir)\\n print(m)\\n m.fit(input_fn=lambda: input_fn(df_train), steps=FLAGS.train_steps)\\n results = m.evaluate(input_fn=lambda: input_fn(df_test), steps=1)\\n for key in sorted(results):\\n print(\\\"%s: %s\\\" % (key, results[key]))\",\n \"def evaluate_model(model, X_test, Y_test, category_names):\\n# Print out Precision , recall F1_score and support for each column using classification_report function\\n y_pred_test = model.predict(X_test)\\n print(classification_report(Y_test, y_pred_test, target_names=category_names))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70706695","0.64831716","0.64393735","0.6433011","0.64119494","0.6411802","0.6275987","0.624839","0.62458396","0.62132657","0.62124383","0.6203546","0.62026036","0.6191927","0.6183838","0.615125","0.61467177","0.61371404","0.6132172","0.6127471","0.6120949","0.6096613","0.60960996","0.60893327","0.6084784","0.60640055","0.6060898","0.60434383","0.6042966","0.6038365","0.6030956","0.60306555","0.6020445","0.6016485","0.6006332","0.60006744","0.6000377","0.599327","0.5992853","0.59862757","0.5985439","0.5984907","0.5984182","0.5983504","0.5973042","0.59484214","0.5947783","0.59403145","0.5936721","0.5936721","0.5936721","0.5936721","0.5936721","0.59289986","0.59288764","0.59279","0.5910644","0.59035456","0.59029704","0.59016174","0.58927417","0.58919364","0.58910054","0.58811814","0.58795","0.5879478","0.5877612","0.586734","0.5865855","0.58624196","0.5855473","0.5842631","0.5834897","0.5831262","0.5830949","0.58277047","0.5822493","0.582085","0.58127457","0.5811517","0.5804938","0.57972646","0.5796262","0.5792668","0.5791089","0.5789042","0.5788226","0.5786669","0.57853216","0.578384","0.57795805","0.57728654","0.5771019","0.5769778","0.57696056","0.5769117","0.57681394","0.57611364","0.5758028","0.57576656","0.5757561"],"string":"[\n \"0.70706695\",\n \"0.64831716\",\n \"0.64393735\",\n \"0.6433011\",\n \"0.64119494\",\n \"0.6411802\",\n \"0.6275987\",\n \"0.624839\",\n \"0.62458396\",\n \"0.62132657\",\n \"0.62124383\",\n \"0.6203546\",\n \"0.62026036\",\n \"0.6191927\",\n \"0.6183838\",\n \"0.615125\",\n \"0.61467177\",\n \"0.61371404\",\n \"0.6132172\",\n \"0.6127471\",\n \"0.6120949\",\n \"0.6096613\",\n \"0.60960996\",\n \"0.60893327\",\n \"0.6084784\",\n \"0.60640055\",\n \"0.6060898\",\n \"0.60434383\",\n \"0.6042966\",\n \"0.6038365\",\n \"0.6030956\",\n \"0.60306555\",\n \"0.6020445\",\n \"0.6016485\",\n \"0.6006332\",\n \"0.60006744\",\n \"0.6000377\",\n \"0.599327\",\n \"0.5992853\",\n \"0.59862757\",\n \"0.5985439\",\n \"0.5984907\",\n \"0.5984182\",\n \"0.5983504\",\n \"0.5973042\",\n \"0.59484214\",\n \"0.5947783\",\n \"0.59403145\",\n \"0.5936721\",\n \"0.5936721\",\n \"0.5936721\",\n \"0.5936721\",\n \"0.5936721\",\n \"0.59289986\",\n \"0.59288764\",\n \"0.59279\",\n \"0.5910644\",\n \"0.59035456\",\n \"0.59029704\",\n \"0.59016174\",\n \"0.58927417\",\n \"0.58919364\",\n \"0.58910054\",\n \"0.58811814\",\n \"0.58795\",\n \"0.5879478\",\n \"0.5877612\",\n \"0.586734\",\n \"0.5865855\",\n \"0.58624196\",\n \"0.5855473\",\n \"0.5842631\",\n \"0.5834897\",\n \"0.5831262\",\n \"0.5830949\",\n \"0.58277047\",\n \"0.5822493\",\n \"0.582085\",\n \"0.58127457\",\n \"0.5811517\",\n \"0.5804938\",\n \"0.57972646\",\n \"0.5796262\",\n \"0.5792668\",\n \"0.5791089\",\n \"0.5789042\",\n \"0.5788226\",\n \"0.5786669\",\n \"0.57853216\",\n \"0.578384\",\n \"0.57795805\",\n \"0.57728654\",\n \"0.5771019\",\n \"0.5769778\",\n \"0.57696056\",\n \"0.5769117\",\n \"0.57681394\",\n \"0.57611364\",\n \"0.5758028\",\n \"0.57576656\",\n \"0.5757561\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":9,"numItemsPerPage":100,"numTotalItems":95772,"offset":900,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MzU5MDg5MSwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzM1OTQ0OTEsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.XaTFoWdfC_bfKhzWr6HIeauj1AfWfNTsFTP93wedDW4Iuz2NmAe6AAMtWkaduJxxMmHe68cahQZk3X5ZDdkFBQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102 values |
|---|---|---|---|---|---|---|
List the names of available readers Note that this will import all readers. | def names() -> Tuple[str, ...]:
return plugins.list_all(package_name=__name__) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_readers():\n return all_readers",
"def get_reader_funcs():\n return READERS",
"def list_reads(cls) -> list:\n return [cls.FWREAD, cls.RVREAD];",
"def namelist(self):\n return self._handle.getnames()",
"def namelist(self):\n return self._handle.getnames()",
"def list_con... | [
"0.7477495",
"0.64536035",
"0.63747376",
"0.5518427",
"0.5518427",
"0.5415075",
"0.5403663",
"0.5381984",
"0.5381984",
"0.53160554",
"0.5299087",
"0.527088",
"0.52304727",
"0.52028954",
"0.5191611",
"0.5185727",
"0.51809806",
"0.5179094",
"0.5153803",
"0.51439035",
"0.513742"... | 0.47953343 | 78 |
Check whether the given reader exists | def exists(reader_name: str) -> bool:
return plugins.exists(package_name=__name__, plugin_name=reader_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _haveReadLocks(self): \n readLockFileName = ReadLock.fileName\n for name in os.listdir(self.dir):\n if name.startswith(readLockFileName):\n return True\n return False",
"def exists(identifier, network):\n foo = next(load(identifier, network), None)... | [
"0.6164184",
"0.5904849",
"0.58638656",
"0.58638656",
"0.5770025",
"0.575396",
"0.56898904",
"0.5686118",
"0.56144536",
"0.56005126",
"0.5580309",
"0.5566034",
"0.5542885",
"0.55397743",
"0.5537704",
"0.55220896",
"0.5509569",
"0.54929805",
"0.549026",
"0.54900736",
"0.547761... | 0.66304976 | 0 |
Get one line documentation for readers If no readers are specified, documentation for all available readers are returned. | def short_docs(*readers: str) -> List[Tuple[str, str]]:
if not readers:
readers = names()
return [(r, plugins.doc(__name__, r, long_doc=False)) for r in readers] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_readers():\n return all_readers",
"def get_docs(self):\n return self.retrieve_docstring()",
"def get_docs(self):\n return self.retrieve_docstring()",
"def get_docs(self):\n return self.retrieve_docstring()",
"def read_documentation(self, fid):\r\n\r\n lin = self.read_... | [
"0.6139211",
"0.5529351",
"0.5529351",
"0.5529351",
"0.5385095",
"0.53439957",
"0.52958757",
"0.5215697",
"0.5117727",
"0.49581614",
"0.48913068",
"0.4863882",
"0.48443574",
"0.48011702",
"0.47767526",
"0.47586027",
"0.47446275",
"0.4685375",
"0.46740708",
"0.4643249",
"0.462... | 0.67184293 | 0 |
Read a bytes stream with a given reader If the reader is not specified, an attempt to guess at an appropriate reader is made. A NoReaderFound error is raised if no such appropriate reader is found. | def read_stream(
input_stream: IO[bytes], reader_name: Optional[str] = None, **reader_args: Any
) -> Reader:
if reader_name is None:
reader_name = identify(input_stream)
reader = plugins.call(
package_name=__name__,
plugin_name=reader_name,
input_stream=input_stream,
**reader_args,
)
reader.read()
return reader | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_reader(fn):\n if is_bed(fn):\n return BedReader(fn)\n elif is_vcf(fn):\n return VcfReader(fn)\n else:\n raise ValueError(\"Could not get reader for %s\" % fn)",
"def read_file(\n file_path: Union[str, pathlib.Path],\n reader_name: Optional[str] = None,\n **reader_ar... | [
"0.6057923",
"0.6019735",
"0.5995071",
"0.59576786",
"0.5762868",
"0.5712855",
"0.5576423",
"0.544311",
"0.54237616",
"0.54231197",
"0.5403411",
"0.53777176",
"0.53776944",
"0.52933985",
"0.5291875",
"0.52114725",
"0.5191779",
"0.5167444",
"0.514691",
"0.51206833",
"0.510413"... | 0.6454055 | 0 |
Read a file with a given reader If the reader is not specified, an attempt to guess at an appropriate reader is made. A NoReaderFound error is raised if no such appropriate reader is found. | def read_file(
file_path: Union[str, pathlib.Path],
reader_name: Optional[str] = None,
**reader_args: Any,
) -> Reader:
with open(file_path, mode="rb") as input_stream:
return read_stream(input_stream, reader_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read(filePath, reader='infer'):\n if isinstance(reader, str):\n if reader == 'infer':\n loader = inferReader(filePath)\n else:\n if reader in READERS:\n loader = READERS[reader]\n else:\n raise SerpentToolsException(\n ... | [
"0.73507124",
"0.6649994",
"0.6541662",
"0.6465156",
"0.6096228",
"0.5905837",
"0.5818582",
"0.5808917",
"0.57055247",
"0.5697342",
"0.5619084",
"0.5508928",
"0.5467147",
"0.5462562",
"0.5446384",
"0.5420934",
"0.5397053",
"0.5380353",
"0.53490704",
"0.5324267",
"0.53047097",... | 0.7362303 | 0 |
Identify a reader that can read a given file A NoReaderFound error is raised if no such appropriate reader is found. | def identify(input_stream: IO[bytes]) -> str:
import IPython
where_am_i = "readers.__init__.identify"
IPython.embed()
raise exceptions.NoReaderFound(
f"Found no reader that can read {input_stream.name}"
) from None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_reader(fn):\n if is_bed(fn):\n return BedReader(fn)\n elif is_vcf(fn):\n return VcfReader(fn)\n else:\n raise ValueError(\"Could not get reader for %s\" % fn)",
"def inferReader(filePath):\n for reg, reader in six.iteritems(REGEXES):\n match = re.match(reg, filePat... | [
"0.67069465",
"0.6362347",
"0.60579586",
"0.5630345",
"0.54816085",
"0.5466467",
"0.544671",
"0.5360829",
"0.53323615",
"0.53214943",
"0.5317366",
"0.5301518",
"0.5290404",
"0.52551913",
"0.52348626",
"0.51969",
"0.5128019",
"0.5122039",
"0.5115842",
"0.50972366",
"0.5093929"... | 0.48551324 | 39 |
Shortcut for symbol creation to test "function" and "indexed" API. | def symbol(name, dimensions, value=0., mode='function'):
assert(mode in ['function', 'indexed'])
s = DenseData(name=name, dimensions=dimensions)
s.data[:] = value
return s.indexify() if mode == 'indexed' else s | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def FakeSymbol(*args, _op, **kwargs):\n return symbol.Custom(*args, _op=_op, op_type=\"_fake\", **kwargs)",
"def __call__(fun_name):",
"def input_shortcut(symbol, name=None):\n def input_method(self, *args, **kwargs):\n return self.input(symbol, *args, **kwargs)\n input_method.__nam... | [
"0.6120692",
"0.6099593",
"0.5789169",
"0.5777273",
"0.5620227",
"0.56047827",
"0.55795795",
"0.55412346",
"0.55109686",
"0.54903716",
"0.5484314",
"0.5438553",
"0.5425026",
"0.54227895",
"0.5353313",
"0.5350989",
"0.53228265",
"0.53207916",
"0.5307697",
"0.5287561",
"0.52811... | 0.0 | -1 |
Tests basic pointwise arithmetic on twodimensional data | def test_flat(self, expr, result, mode):
i, j = dimify('i j')
a = symbol(name='a', dimensions=(i, j), value=2., mode=mode)
b = symbol(name='b', dimensions=(i, j), value=3., mode=mode)
fa = a.base.function if mode == 'indexed' else a
fb = b.base.function if mode == 'indexed' else b
eqn = eval(expr)
Operator(eqn)(fa, fb)
assert np.allclose(fa.data, result, rtol=1e-12) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_data():\n x = np.array([8, 67, 79, 10, 52, 53, 98, 34, 15, 58], dtype=float)\n y = np.array([24, 87, 48, 94, 98, 66, 14, 24, 60, 16], dtype=float)\n z = np.array([0.064, 4.489, 6.241, 0.1, 2.704, 2.809, 9.604, 1.156,\n 0.225, 3.364], dtype=float)\n\n return x, y, z",
"def Te... | [
"0.6385182",
"0.6282518",
"0.61600417",
"0.61573863",
"0.6152449",
"0.61160505",
"0.60762215",
"0.6071223",
"0.60207266",
"0.6017882",
"0.6017163",
"0.59854347",
"0.59580594",
"0.59534204",
"0.5947321",
"0.591167",
"0.5898243",
"0.58920926",
"0.58749056",
"0.58674467",
"0.586... | 0.0 | -1 |
Tests basic pointwise arithmetic on multidimensional data | def test_deep(self, expr, result, mode):
i, j, k, l = dimify('i j k l')
a = symbol(name='a', dimensions=(i, j, k, l), value=2., mode=mode)
b = symbol(name='b', dimensions=(j, k), value=3., mode=mode)
fa = a.base.function if mode == 'indexed' else a
fb = b.base.function if mode == 'indexed' else b
eqn = eval(expr)
Operator(eqn)(fa, fb)
assert np.allclose(fa.data, result, rtol=1e-12) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_multidimensional_operation(self):\n # start with something (1, 2, 3)\n data = np.array([[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]])\n\n # split 1st dim (2, 2, 3)\n coefficients = np.ones((1, 2)) / 2\n expected = np.array(\n [[[0.0, 0.5, 1.0], [1.5, 2.0, 2.5]], [[0.0,... | [
"0.6647943",
"0.6466135",
"0.6279551",
"0.62339604",
"0.6172996",
"0.61697084",
"0.6152088",
"0.6150274",
"0.6137789",
"0.6136748",
"0.6109442",
"0.6009509",
"0.5945426",
"0.591577",
"0.5912449",
"0.590353",
"0.5883301",
"0.58808935",
"0.5876383",
"0.587444",
"0.58706623",
... | 0.0 | -1 |
Tests pointwise increments with stencil offsets in one dimension | def test_indexed_increment(self, expr, result):
j, l = dimify('j l')
a = symbol(name='a', dimensions=(j, l), value=2., mode='indexed').base
fa = a.function
fa.data[1:, 1:] = 0
eqn = eval(expr)
Operator(eqn)(fa)
assert np.allclose(fa.data, result, rtol=1e-12) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def coordination(c, stencil=nn_stencil):\n\n coordination = np.zeros_like(c, dtype=int)\n for dx, dy in stencil:\n tmp = np.array(c, dtype=bool, copy=True)\n if dx != 0:\n tmp = np.roll(tmp, dx, 0)\n if dy != 0:\n tmp = np.roll(tmp, dy, 1)\n coordination += t... | [
"0.65050405",
"0.54784226",
"0.5474315",
"0.5348384",
"0.5347258",
"0.5336783",
"0.53343135",
"0.53305495",
"0.5314463",
"0.52894896",
"0.5283575",
"0.5209135",
"0.52068275",
"0.5183291",
"0.5156076",
"0.5149829",
"0.51383203",
"0.51378614",
"0.5116575",
"0.51146805",
"0.5108... | 0.0 | -1 |
Test pointwise arithmetic with stencil offsets across two functions in indexed expression format | def test_indexed_stencil(self, expr, result):
j, l = dimify('j l')
a = symbol(name='a', dimensions=(j, l), value=0., mode='indexed').base
fa = a.function
b = symbol(name='b', dimensions=(j, l), value=2., mode='indexed').base
fb = b.function
eqn = eval(expr)
Operator(eqn)(fa, fb)
assert np.allclose(fa.data[1:-1, 1:-1], result[1:-1, 1:-1], rtol=1e-12) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_offsets():\n B = 100\n H = 20\n E = 210000\n sections = ((B, H, 0, E),)\n sections2 = ((B, H, 12.435, E),)\n EI, top, bot = bm.EI(sections, E)\n EI2, top2, bot2 = bm.EI(sections2, E)\n assert 0.99 < EI / EI2 < 1.01\n assert 0.99 < top / top2 < 1.01\n assert 0.99 < bot / bot2 ... | [
"0.54499704",
"0.54236096",
"0.54200417",
"0.54183537",
"0.53868484",
"0.5386813",
"0.5386671",
"0.53676295",
"0.53601813",
"0.5349417",
"0.53437924",
"0.53436583",
"0.5304866",
"0.53009784",
"0.5292884",
"0.52877325",
"0.5283028",
"0.5282718",
"0.5279577",
"0.52767044",
"0.5... | 0.70558316 | 0 |
Test pointwise arithmetic with stencil offsets across a single functions with buffering dimension in indexed expression format | def test_indexed_buffered(self, expr, result):
i, j, l = dimify('i j l')
a = symbol(name='a', dimensions=(i, j, l), value=2., mode='indexed').base
fa = a.function
eqn = eval(expr)
Operator(eqn)(fa)
assert np.allclose(fa.data[1, 1:-1, 1:-1], result[1:-1, 1:-1], rtol=1e-12) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_indexed_stencil(self, expr, result):\n j, l = dimify('j l')\n a = symbol(name='a', dimensions=(j, l), value=0., mode='indexed').base\n fa = a.function\n b = symbol(name='b', dimensions=(j, l), value=2., mode='indexed').base\n fb = b.function\n\n eqn = eval(expr)\n... | [
"0.70109046",
"0.5731927",
"0.5725207",
"0.5534058",
"0.5485873",
"0.54508567",
"0.5420271",
"0.5402067",
"0.53938526",
"0.5361206",
"0.53606236",
"0.5359261",
"0.5359202",
"0.5320887",
"0.5293664",
"0.5291382",
"0.5265721",
"0.52385396",
"0.523383",
"0.52290106",
"0.52282166... | 0.5903518 | 1 |
Test pointwise arithmetic with stencil offsets and open loop boundaries in indexed expression format | def test_indexed_open_loops(self, expr, result):
i, j, l = dimify('i j l')
pushed = [d.size for d in [j, l]]
j.size = None
l.size = None
a = DenseData(name='a', dimensions=(i, j, l), shape=(3, 5, 6)).indexed
fa = a.function
fa.data[0, :, :] = 2.
eqn = eval(expr)
Operator(eqn)(fa)
assert np.allclose(fa.data[1, 1:-1, 1:-1], result[1:-1, 1:-1], rtol=1e-12)
j.size, l.size = pushed | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_indexed_stencil(self, expr, result):\n j, l = dimify('j l')\n a = symbol(name='a', dimensions=(j, l), value=0., mode='indexed').base\n fa = a.function\n b = symbol(name='b', dimensions=(j, l), value=2., mode='indexed').base\n fb = b.function\n\n eqn = eval(expr)\n... | [
"0.6950202",
"0.58628595",
"0.56040186",
"0.5595086",
"0.5519812",
"0.54418904",
"0.54108006",
"0.533119",
"0.53181374",
"0.53098184",
"0.5292402",
"0.5292149",
"0.5247795",
"0.5246134",
"0.523379",
"0.5228938",
"0.5216184",
"0.51775336",
"0.51391",
"0.513825",
"0.51371264",
... | 0.6362784 | 1 |
Test that the calltime overriding of Operator arguments works | def test_override_cache_aliasing(self):
i, j, k, l = dimify('i j k l')
a = symbol(name='a', dimensions=(i, j, k, l), value=2.,
mode='indexed').base.function
a1 = symbol(name='a', dimensions=(i, j, k, l), value=3.,
mode='indexed').base.function
a2 = symbol(name='a', dimensions=(i, j, k, l), value=4.,
mode='indexed').base.function
eqn = Eq(a, a+3)
op = Operator(eqn)
op()
op(a=a1)
op(a=a2)
shape = [d.size for d in [i, j, k, l]]
assert(np.allclose(a.data, np.zeros(shape) + 5))
assert(np.allclose(a1.data, np.zeros(shape) + 6))
assert(np.allclose(a2.data, np.zeros(shape) + 7)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(a, b):",
"def _OverloadOperator(operator): # pylint: disable=invalid-name\n\n tensor_oper = getattr(ops.Tensor, operator)\n\n def _run_op(a, *args):\n # pylint: disable=protected-access\n value = a._AsTensor()\n return tensor_oper(value, *args)\n\n ... | [
"0.6805851",
"0.6484445",
"0.6379259",
"0.6364742",
"0.6364742",
"0.6306603",
"0.6228619",
"0.61764985",
"0.61253047",
"0.61219794",
"0.6098357",
"0.60761136",
"0.59945655",
"0.5985499",
"0.5953652",
"0.5947286",
"0.59347874",
"0.59075207",
"0.5906247",
"0.590326",
"0.5891721... | 0.0 | -1 |
Test calltime symbols overrides with other symbols | def test_override_symbol(self):
i, j, k, l = dimify('i j k l')
a = symbol(name='a', dimensions=(i, j, k, l), value=2.)
a1 = symbol(name='a1', dimensions=(i, j, k, l), value=3.)
a2 = symbol(name='a2', dimensions=(i, j, k, l), value=4.)
op = Operator(Eq(a, a + 3))
op()
op(a=a1)
op(a=a2)
shape = [d.size for d in [i, j, k, l]]
assert(np.allclose(a.data, np.zeros(shape) + 5))
assert(np.allclose(a1.data, np.zeros(shape) + 6))
assert(np.allclose(a2.data, np.zeros(shape) + 7)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testSymbolHash(self):\n gScope = pykd.diaLoadPdb( str(target.module.pdb()) )\n symSet = set([ gScope[\"g_structTest\"], gScope[\"EnumWindowsProc1\"], gScope[\"g_structTest\"] ])\n self.assertEqual( 2, len(symSet) )\n self.assertTrue( gScope[\"g_structTest\"] in symSet )\n sel... | [
"0.6088717",
"0.5891969",
"0.586841",
"0.57884955",
"0.57511026",
"0.5713668",
"0.5713668",
"0.57010543",
"0.5664404",
"0.5631575",
"0.5563887",
"0.55595315",
"0.55191725",
"0.5508196",
"0.54970926",
"0.54917103",
"0.5480295",
"0.54737866",
"0.5444322",
"0.54287255",
"0.54046... | 0.58977205 | 1 |
Test calltime symbols overrides with numpy arrays | def test_override_array(self):
i, j, k, l = dimify('i j k l')
shape = tuple(d.size for d in (i, j, k, l))
a = symbol(name='a', dimensions=(i, j, k, l), value=2.)
a1 = np.zeros(shape=shape, dtype=np.float32) + 3.
a2 = np.zeros(shape=shape, dtype=np.float32) + 4.
op = Operator(Eq(a, a + 3))
op()
op(a=a1)
op(a=a2)
shape = [d.size for d in [i, j, k, l]]
assert(np.allclose(a.data, np.zeros(shape) + 5))
assert(np.allclose(a1, np.zeros(shape) + 6))
assert(np.allclose(a2, np.zeros(shape) + 7)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_TimeArray_repr():",
"def test_format_signature_numpy():",
"def test_TimeArray_convert_unit():",
"def test_TimeArray_copyflag():\r\n\r\n #These two should both generate a TimeArray, with one picosecond.\r\n #This one holds time_unit='s'\r\n t1 = ts.TimeArray(np.array([1], dtype=np.int64), co... | [
"0.6313198",
"0.6234536",
"0.61531353",
"0.60441715",
"0.5897211",
"0.58689487",
"0.5791092",
"0.5729955",
"0.5682435",
"0.5682435",
"0.5633471",
"0.55964816",
"0.55935395",
"0.5578403",
"0.5577986",
"0.5554115",
"0.5551436",
"0.555043",
"0.554579",
"0.5534603",
"0.55257624",... | 0.6475489 | 0 |
Test that the dimension sizes are being inferred correctly | def test_dimension_size_infer(self, nt=100):
i, j, k = dimify('i j k')
shape = tuple([d.size for d in [i, j, k]])
a = DenseData(name='a', shape=shape).indexed
b = TimeData(name='b', shape=shape, save=True, time_dim=nt).indexed
eqn = Eq(b[time, x, y, z], a[x, y, z])
op = Operator(eqn)
_, op_dim_sizes = op.arguments()
assert(op_dim_sizes[time.name] == nt) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_size_check(self):\n [x1, y1, s1, g1] = self.data.diffusion_data.shape\n [x2, y2, s2, g2] = module_05.run_module(self.data).diffusion_data.shape\n self.assertEqual(x1, x2)\n self.assertEqual(y1, y2)\n self.assertEqual(s1, s2)\n self.assertEqual(g1, g2)",
"def dim... | [
"0.76725674",
"0.75539386",
"0.74125654",
"0.7359512",
"0.73051524",
"0.72323257",
"0.7225344",
"0.7185799",
"0.71210706",
"0.6941943",
"0.68519884",
"0.6851906",
"0.68469083",
"0.68195313",
"0.6812142",
"0.67658305",
"0.6746414",
"0.6741667",
"0.6688384",
"0.66588515",
"0.66... | 0.76781756 | 0 |
Test explicit overrides for the leading time dimension | def test_dimension_size_override(self, nt=100):
i, j, k = dimify('i j k')
a = TimeData(name='a', dimensions=(i, j, k))
one = symbol(name='one', dimensions=(i, j, k), value=1.)
op = Operator(Eq(a.forward, a + one))
# Test dimension override via the buffered dimenions
a.data[0] = 0.
op(a=a, t=6)
assert(np.allclose(a.data[1], 5.))
# Test dimension override via the parent dimenions
a.data[0] = 0.
op(a=a, time=5)
assert(np.allclose(a.data[0], 4.)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_general_subset_invalid_time():\n pass",
"def test_fix_metadata_correct_time(self):\n fixed_cube = self.fix.fix_metadata([self.cube])[0]\n time_coord = fixed_cube.coord('time')\n np.testing.assert_allclose(time_coord.points, [0, 1])\n assert time_coord.bounds is None",
"d... | [
"0.6225104",
"0.61606276",
"0.60171777",
"0.59855527",
"0.5899436",
"0.5890807",
"0.57126755",
"0.56421477",
"0.5499632",
"0.5453681",
"0.54009974",
"0.53884566",
"0.53690064",
"0.534088",
"0.5325772",
"0.5315858",
"0.530437",
"0.5300429",
"0.52890176",
"0.5282546",
"0.527451... | 0.6123395 | 2 |
Emulates a potential implementation of boundary condition loops | def test_directly_indexed_expression(self, fa, ti0, t0, exprs):
eqs = EVAL(exprs, ti0.base, t0)
op = Operator(eqs, dse='noop', dle='noop')
trees = retrieve_iteration_tree(op)
assert len(trees) == 2
assert trees[0][-1].nodes[0].expr.rhs == eqs[0].rhs
assert trees[1][-1].nodes[0].expr.rhs == eqs[1].rhs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def simulate_boundary(self,print_every=1000,do_F_bound=True):\n n_t = self.t_span.size\n self.n_t = n_t\n x = self.x0.copy()\n self._triangulate(x)\n self.assign_vertices()\n x = self.check_boundary(x)\n self.x = x.copy()\n sel... | [
"0.6390884",
"0.633568",
"0.63244265",
"0.6194868",
"0.6181194",
"0.6133254",
"0.5930542",
"0.58581144",
"0.58474624",
"0.58428305",
"0.5813989",
"0.5792263",
"0.57295126",
"0.57068944",
"0.5669466",
"0.5665661",
"0.5663369",
"0.56552625",
"0.56534404",
"0.56359583",
"0.56296... | 0.0 | -1 |
Test the generation of a specific box profile against a known result. | def test_box():
savedImg = galsim.fits.read(os.path.join(imgdir, "box_1.fits"))
myImg = galsim.ImageF(savedImg.bounds, scale=0.2)
myImg.setCenter(0,0)
test_flux = 1.8
pixel = galsim.Pixel(scale=1, flux=1)
pixel.drawImage(myImg, method="sb", use_true_center=False)
np.testing.assert_array_almost_equal(
myImg.array, savedImg.array, 5,
err_msg="Using GSObject Pixel disagrees with expected result")
np.testing.assert_array_equal(
pixel.scale, 1,
err_msg="Pixel scale returned wrong value")
# Check with default_params
pixel = galsim.Pixel(scale=1, flux=1, gsparams=default_params)
pixel.drawImage(myImg, method="sb", use_true_center=False)
np.testing.assert_array_almost_equal(
myImg.array, savedImg.array, 5,
err_msg="Using GSObject Pixel with default_params disagrees with expected result")
pixel = galsim.Pixel(scale=1, flux=1, gsparams=galsim.GSParams())
pixel.drawImage(myImg, method="sb", use_true_center=False)
np.testing.assert_array_almost_equal(
myImg.array, savedImg.array, 5,
err_msg="Using GSObject Pixel with GSParams() disagrees with expected result")
# Use non-unity values.
pixel = galsim.Pixel(flux=1.7, scale=2.3)
gsp = galsim.GSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
pixel2 = galsim.Pixel(flux=1.7, scale=2.3, gsparams=gsp)
assert pixel2 != pixel
assert pixel2 == pixel.withGSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
# Test photon shooting.
do_shoot(pixel,myImg,"Pixel")
# Check picklability
do_pickle(pixel, lambda x: x.drawImage(method='no_pixel'))
do_pickle(pixel)
do_pickle(galsim.Pixel(1))
# Check that non-square Box profiles work correctly
scale = 0.2939 # Use a strange scale here to make sure that the centers of the pixels
# never fall on the box edge, otherwise it gets a bit weird to know what
# the correct SB value is for that pixel.
im = galsim.ImageF(16,16, scale=scale)
gsp = galsim.GSParams(maximum_fft_size = 30000)
for (width,height) in [ (3,2), (1.7, 2.7), (2.2222, 3.1415) ]:
box = galsim.Box(width=width, height=height, flux=test_flux, gsparams=gsp)
check_basic(box, "Box with width,height = %f,%f"%(width,height))
do_shoot(box,im,"Box with width,height = %f,%f"%(width,height))
if __name__ == '__main__':
# These are slow because they require a pretty huge fft.
# So only do them if running as main.
do_kvalue(box,im,"Box with width,height = %f,%f"%(width,height))
cen = galsim.PositionD(0, 0)
np.testing.assert_equal(box.centroid, cen)
np.testing.assert_almost_equal(box.kValue(cen), (1+0j) * test_flux)
np.testing.assert_almost_equal(box.flux, test_flux)
np.testing.assert_almost_equal(box.xValue(cen), box.max_sb)
np.testing.assert_almost_equal(box.xValue(width/2.-0.001, height/2.-0.001), box.max_sb)
np.testing.assert_almost_equal(box.xValue(width/2.-0.001, height/2.+0.001), 0.)
np.testing.assert_almost_equal(box.xValue(width/2.+0.001, height/2.-0.001), 0.)
np.testing.assert_almost_equal(box.xValue(width/2.+0.001, height/2.+0.001), 0.)
np.testing.assert_array_equal(
box.width, width,
err_msg="Box width returned wrong value")
np.testing.assert_array_equal(
box.height, height,
err_msg="Box height returned wrong value")
gsp2 = galsim.GSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
box2 = galsim.Box(width=width, height=height, flux=test_flux, gsparams=gsp2)
assert box2 != box
assert box2 == box.withGSParams(gsp2)
assert box2 != box.withGSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
assert box2.withGSParams(maximum_fft_size=30000) == box.withGSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
# Check picklability
do_pickle(box, lambda x: x.drawImage(method='no_pixel'))
do_pickle(box)
do_pickle(galsim.Box(1,1))
# Check sheared boxes the same way
box = galsim.Box(width=3, height=2, flux=test_flux, gsparams=gsp)
box = box.shear(galsim.Shear(g1=0.2, g2=-0.3))
check_basic(box, "Sheared Box", approx_maxsb=True)
do_shoot(box,im, "Sheared Box")
if __name__ == '__main__':
do_kvalue(box,im, "Sheared Box")
do_pickle(box, lambda x: x.drawImage(method='no_pixel'))
do_pickle(box)
cen = galsim.PositionD(0, 0)
np.testing.assert_equal(box.centroid, cen)
np.testing.assert_almost_equal(box.kValue(cen), (1+0j) * test_flux)
np.testing.assert_almost_equal(box.flux, test_flux)
np.testing.assert_almost_equal(box.xValue(cen), box.max_sb)
# This is also a profile that may be convolved using real space convolution, so test that.
if __name__ == '__main__':
conv = galsim.Convolve(box, galsim.Pixel(scale=scale), real_space=True)
check_basic(conv, "Sheared Box convolved with pixel in real space",
approx_maxsb=True, scale=0.2)
do_kvalue(conv,im, "Sheared Box convolved with pixel in real space")
do_pickle(conv, lambda x: x.xValue(0.123,-0.456))
do_pickle(conv) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_game_boxscore(self):\n pass",
"def testProfileCreation(self):\n small_tree1_equality = self.checkProfileEquality(self.profiles[0], self.small_profile1)\n small_tree2_equality = self.checkProfileEquality(self.profiles[1], self.small_profile2)\n known_tree1_equality = self.... | [
"0.61851823",
"0.58061886",
"0.5779419",
"0.5712594",
"0.5674602",
"0.5597677",
"0.55736697",
"0.5516804",
"0.5508091",
"0.5494304",
"0.5471239",
"0.5412909",
"0.5381883",
"0.5344296",
"0.53197837",
"0.531884",
"0.53139627",
"0.52807105",
"0.52799165",
"0.52634877",
"0.525860... | 0.5113333 | 37 |
Test the generation of a specific tophat profile against a known result. | def test_tophat():
savedImg = galsim.fits.read(os.path.join(imgdir, "tophat_101.fits"))
myImg = galsim.ImageF(savedImg.bounds, scale=0.2)
myImg.setCenter(0,0)
test_flux = 1.8
# There are numerical issues with using radius = 1, since many points are right on the edge
# of the circle. e.g. (+-1,0), (0,+-1), (+-0.6,+-0.8), (+-0.8,+-0.6). And in practice, some
# of these end up getting drawn and not others, which means it's not a good choice for a unit
# test since it wouldn't be any less correct for a different subset of these points to be
# drawn. Using r = 1.01 solves this problem and makes the result symmetric.
tophat = galsim.TopHat(radius=1.01, flux=1)
tophat.drawImage(myImg, method="sb", use_true_center=False)
np.testing.assert_array_almost_equal(
myImg.array, savedImg.array, 5,
err_msg="Using GSObject TopHat disagrees with expected result")
np.testing.assert_array_equal(
tophat.radius, 1.01,
err_msg="TopHat radius returned wrong value")
# Check with default_params
tophat = galsim.TopHat(radius=1.01, flux=1, gsparams=default_params)
tophat.drawImage(myImg, method="sb", use_true_center=False)
np.testing.assert_array_almost_equal(
myImg.array, savedImg.array, 5,
err_msg="Using GSObject TopHat with default_params disagrees with expected result")
tophat = galsim.TopHat(radius=1.01, flux=1, gsparams=galsim.GSParams())
tophat.drawImage(myImg, method="sb", use_true_center=False)
np.testing.assert_array_almost_equal(
myImg.array, savedImg.array, 5,
err_msg="Using GSObject TopHat with GSParams() disagrees with expected result")
# Use non-unity values.
tophat = galsim.TopHat(flux=1.7, radius=2.3)
gsp = galsim.GSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
tophat2 = galsim.TopHat(flux=1.7, radius=2.3, gsparams=gsp)
assert tophat2 != tophat
assert tophat2 == tophat.withGSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
# Test photon shooting.
do_shoot(tophat,myImg,"TopHat")
# Test shoot and kvalue
scale = 0.2939
im = galsim.ImageF(16,16, scale=scale)
# The choices of radius here are fairly specific. If the edge of the circle comes too close
# to the center of one of the pixels, then the test will fail, since the Fourier draw method
# will blur the edge a bit and give some flux to that pixel.
for radius in [ 1.2, 0.93, 2.11 ]:
tophat = galsim.TopHat(radius=radius, flux=test_flux)
check_basic(tophat, "TopHat with radius = %f"%radius)
do_shoot(tophat,im,"TopHat with radius = %f"%radius)
do_kvalue(tophat,im,"TopHat with radius = %f"%radius)
# This is also a profile that may be convolved using real space convolution, so test that.
conv = galsim.Convolve(tophat, galsim.Pixel(scale=scale), real_space=True)
check_basic(conv, "TopHat convolved with pixel in real space",
approx_maxsb=True, scale=0.2)
do_kvalue(conv,im, "TopHat convolved with pixel in real space")
cen = galsim.PositionD(0, 0)
np.testing.assert_equal(tophat.centroid, cen)
np.testing.assert_almost_equal(tophat.kValue(cen), (1+0j) * test_flux)
np.testing.assert_almost_equal(tophat.flux, test_flux)
np.testing.assert_almost_equal(tophat.xValue(cen), tophat.max_sb)
np.testing.assert_almost_equal(tophat.xValue(radius-0.001, 0.), tophat.max_sb)
np.testing.assert_almost_equal(tophat.xValue(0., radius-0.001), tophat.max_sb)
np.testing.assert_almost_equal(tophat.xValue(radius+0.001, 0.), 0.)
np.testing.assert_almost_equal(tophat.xValue(0., radius+0.001), 0.)
# Check picklability
do_pickle(tophat, lambda x: x.drawImage(method='no_pixel'))
do_pickle(tophat)
do_pickle(galsim.TopHat(1))
# Check sheared tophat the same way
tophat = galsim.TopHat(radius=1.2, flux=test_flux)
# Again, the test is very sensitive to the choice of shear here. Most values fail because
# some pixel center gets too close to the resulting ellipse for the fourier draw to match
# the real-space draw at the required accuracy.
tophat = tophat.shear(galsim.Shear(g1=0.15, g2=-0.33))
check_basic(tophat, "Sheared TopHat")
do_shoot(tophat,im, "Sheared TopHat")
do_kvalue(tophat,im, "Sheared TopHat")
cen = galsim.PositionD(0, 0)
np.testing.assert_equal(tophat.centroid, cen)
np.testing.assert_almost_equal(tophat.kValue(cen), (1+0j) * test_flux)
np.testing.assert_almost_equal(tophat.flux, test_flux)
np.testing.assert_almost_equal(tophat.xValue(cen), tophat.max_sb)
# Check picklability
do_pickle(tophat, lambda x: x.drawImage(method='no_pixel'))
do_pickle(tophat)
# Check real-space convolution of the sheared tophat.
conv = galsim.Convolve(tophat, galsim.Pixel(scale=scale), real_space=True)
check_basic(conv, "Sheared TopHat convolved with pixel in real space",
approx_maxsb=True, scale=0.2)
do_kvalue(conv,im, "Sheared TopHat convolved with pixel in real space") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_run_profile(dpa_model_spec, dpa_init):\n test_times = np.array(\n DateTime(['2019:001:00:00:00', '2019:001:12:00:00', '2019:002:00:00:00', '2019:003:00:00:00']).secs)\n pitch = np.array([150, 90, 156, 156])\n roll = np.array([0, -5, 10, 0])\n test_schedule = {'pitch': pitch, 'roll': rol... | [
"0.60760397",
"0.59960103",
"0.578803",
"0.57689196",
"0.56904364",
"0.5670949",
"0.5598614",
"0.5586479",
"0.55442274",
"0.5540673",
"0.5539732",
"0.5532514",
"0.5515403",
"0.55088836",
"0.54860824",
"0.54771906",
"0.54092103",
"0.5376669",
"0.53727055",
"0.53688246",
"0.534... | 0.0 | -1 |
Test Box with photon shooting. Particularly the flux of the final image. | def test_box_shoot():
rng = galsim.BaseDeviate(1234)
obj = galsim.Box(width=1.3, height=2.4, flux=1.e4)
im = galsim.Image(100,100, scale=1)
im.setCenter(0,0)
added_flux, photons = obj.drawPhot(im, poisson_flux=False, rng=rng.duplicate())
print('obj.flux = ',obj.flux)
print('added_flux = ',added_flux)
print('photon fluxes = ',photons.flux.min(),'..',photons.flux.max())
print('image flux = ',im.array.sum())
assert np.isclose(added_flux, obj.flux)
assert np.isclose(im.array.sum(), obj.flux)
photons2 = obj.makePhot(poisson_flux=False, rng=rng)
assert photons2 == photons, "Box makePhot not equivalent to drawPhot"
obj = galsim.Pixel(scale=9.3, flux=1.e4)
added_flux, photons = obj.drawPhot(im, poisson_flux=False, rng=rng.duplicate())
print('obj.flux = ',obj.flux)
print('added_flux = ',added_flux)
print('photon fluxes = ',photons.flux.min(),'..',photons.flux.max())
print('image flux = ',im.array.sum())
assert np.isclose(added_flux, obj.flux)
assert np.isclose(im.array.sum(), obj.flux)
photons2 = obj.makePhot(poisson_flux=False, rng=rng)
assert photons2 == photons, "Pixel makePhot not equivalent to drawPhot"
obj = galsim.TopHat(radius=4.7, flux=1.e4)
added_flux, photons = obj.drawPhot(im, poisson_flux=False, rng=rng.duplicate())
print('obj.flux = ',obj.flux)
print('added_flux = ',added_flux)
print('photon fluxes = ',photons.flux.min(),'..',photons.flux.max())
print('image flux = ',im.array.sum())
assert np.isclose(added_flux, obj.flux)
assert np.isclose(im.array.sum(), obj.flux)
photons2 = obj.makePhot(poisson_flux=False, rng=rng)
assert photons2 == photons, "TopHat makePhot not equivalent to drawPhot" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_box():\n savedImg = galsim.fits.read(os.path.join(imgdir, \"box_1.fits\"))\n myImg = galsim.ImageF(savedImg.bounds, scale=0.2)\n myImg.setCenter(0,0)\n test_flux = 1.8\n\n pixel = galsim.Pixel(scale=1, flux=1)\n pixel.drawImage(myImg, method=\"sb\", use_true_center=False)\n np.testing... | [
"0.6883019",
"0.60247976",
"0.5729917",
"0.5662367",
"0.5657805",
"0.56022036",
"0.5602087",
"0.55716115",
"0.5532589",
"0.5502499",
"0.5473831",
"0.54660267",
"0.54527724",
"0.54423535",
"0.5430135",
"0.5401244",
"0.5400176",
"0.53518057",
"0.53516215",
"0.53498757",
"0.5337... | 0.72881216 | 0 |
Test base.py GSObjects for notequals. | def test_ne():
# Define some universal gsps
gsp = galsim.GSParams(maxk_threshold=1.1e-3, folding_threshold=5.1e-3)
# Pixel. Params include scale, flux, gsparams.
# gsparams.
# The following should all test unequal:
gals = [galsim.Pixel(scale=1.0),
galsim.Pixel(scale=1.1),
galsim.Pixel(scale=1.0, flux=1.1),
galsim.Pixel(scale=1.0, gsparams=gsp)]
all_obj_diff(gals)
# Box. Params include width, height, flux, gsparams.
# gsparams.
# The following should all test unequal:
gals = [galsim.Box(width=1.0, height=1.0),
galsim.Box(width=1.1, height=1.0),
galsim.Box(width=1.0, height=1.1),
galsim.Box(width=1.0, height=1.0, flux=1.1),
galsim.Box(width=1.0, height=1.0, gsparams=gsp)]
all_obj_diff(gals)
# TopHat. Params include radius, flux, gsparams.
# gsparams.
# The following should all test unequal:
gals = [galsim.TopHat(radius=1.0),
galsim.TopHat(radius=1.1),
galsim.TopHat(radius=1.0, flux=1.1),
galsim.TopHat(radius=1.0, gsparams=gsp)]
all_obj_diff(gals) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_not_equal_on_equal(self):\n a = objects.OpaqueObject(self.bytes_a, enums.OpaqueDataType.NONE)\n b = objects.OpaqueObject(self.bytes_a, enums.OpaqueDataType.NONE)\n self.assertFalse(a != b)\n self.assertFalse(b != a)",
"def test_not_equal_on_not_equal_value(self):\n a =... | [
"0.74571276",
"0.73648274",
"0.73306084",
"0.72958326",
"0.7260039",
"0.72194594",
"0.7127766",
"0.70569617",
"0.7055979",
"0.7028425",
"0.70240885",
"0.7012719",
"0.6914279",
"0.68538886",
"0.68433744",
"0.6823976",
"0.6806015",
"0.6802378",
"0.6794009",
"0.675546",
"0.67511... | 0.0 | -1 |
Decide whether to enter hotspot mode or wifi mode and then do so | def set_wifi_mode(args):
pass
"""+
try:
if args['mode'] == 'hotspot':
logger.info('will enter hotspot mode')
#TODO - Need to capture the line that contains interface [some lan id] and uncomment it.
change_file_line(path.join('/etc', 'dhcpcd.conf'),
interface_l1_res, 'interface {}\n'.format()
return True if args['silent'] else 'Ok'
if args['mode'] == 'wi-fi':
logger.info('will enter wi-fi mode')
return True if args['silent'] else 'Ok'
else:
logger.error('Unknown wi-fi mode: {}'.format(args['mode']))
return False if args['silent'] else 'ERROR'
except:
logger.error('Exception in set_wifi_mode: {}, {}'.format(exc_info()[0], exc_info()[1]))
return False if args['silent'] else 'ERROR'
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _cycle_wifi(mode=None):\n call(['ifdown', settings.WIFI_INTERFACE])\n if mode is not None:\n call(['iwconfig', settings.WIFI_INTERFACE, 'mode', mode])\n call(['ifup', settings.WIFI_INTERFACE])",
"def switch_network(self,type = None):\n network_type = self.appconfig(type... | [
"0.6191428",
"0.60795987",
"0.60477906",
"0.5971737",
"0.59445924",
"0.58588445",
"0.57119495",
"0.570681",
"0.57007",
"0.5689363",
"0.5664943",
"0.5628451",
"0.5603096",
"0.5597446",
"0.55574036",
"0.55567616",
"0.5528155",
"0.55259633",
"0.5525236",
"0.5521546",
"0.5498724"... | 0.71384984 | 0 |
Get a pointer for function name with provided argtypes and restype | def ptr(self, space, w_name, w_argtypes, w_restype, flags=FUNCFLAG_CDECL):
resshape = unpack_resshape(space, w_restype)
if resshape is None:
w_resshape = space.w_None
else:
w_resshape = resshape
argtypes_w = space.fixedview(w_argtypes)
w_argtypes = space.newtuple(argtypes_w)
w_key = space.newtuple([w_name, w_argtypes, w_resshape])
try:
return space.getitem(self.w_cache, w_key)
except OperationError as e:
if e.match(space, space.w_KeyError):
pass
else:
raise
# Array arguments not supported directly (in C, an array argument
# will be just a pointer). And the result cannot be an array (at all).
argshapes = unpack_argshapes(space, w_argtypes)
ffi_argtypes = [shape.get_basic_ffi_type() for shape in argshapes]
if resshape is not None:
ffi_restype = resshape.get_basic_ffi_type()
else:
ffi_restype = ffi_type_void
if space.isinstance_w(w_name, space.w_text):
name = space.text_w(w_name)
try:
ptr = self.cdll.getrawpointer(name, ffi_argtypes, ffi_restype,
flags)
except KeyError:
raise oefmt(space.w_AttributeError,
"No symbol %s found in library %s",
name, self.name)
except LibFFIError:
raise got_libffi_error(space)
elif (_MS_WINDOWS and space.isinstance_w(w_name, space.w_int)):
ordinal = space.int_w(w_name)
try:
ptr = self.cdll.getrawpointer_byordinal(ordinal, ffi_argtypes,
ffi_restype, flags)
except KeyError:
raise oefmt(space.w_AttributeError,
"No symbol %d found in library %s",
ordinal, self.name)
except LibFFIError:
raise got_libffi_error(space)
else:
raise oefmt(space.w_TypeError,
"function name must be string or integer")
w_funcptr = W_FuncPtr(space, ptr, argshapes, resshape)
space.setitem(self.w_cache, w_key, w_funcptr)
return w_funcptr | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_func(name, argtypes=None, restype=c_int, lib=libDE):\n logger.debug(\"Getting NewWordFinder API function: 'name': '{}', 'argtypes': '{}',\"\n \" 'restype': '{}'.\".format(name, argtypes, restype))\n func = getattr(lib, name)\n if argtypes is not None:\n func.argtypes = argty... | [
"0.66590554",
"0.61740726",
"0.61581415",
"0.59001285",
"0.5855032",
"0.58468574",
"0.5705164",
"0.5677757",
"0.5639868",
"0.5615557",
"0.5614763",
"0.56135756",
"0.5601841",
"0.55988264",
"0.5483516",
"0.54684794",
"0.5429972",
"0.53992546",
"0.53571665",
"0.53392667",
"0.53... | 0.6079239 | 3 |
Set up a root logger showing all entries in the console. | def logger(request):
log = logging.getLogger()
hdlr = logging.StreamHandler()
fmt = '%(asctime)s %(name)s %(levelname)s %(message)s'
formatter = logging.Formatter(fmt)
hdlr.setFormatter(formatter)
log.addHandler(hdlr)
log.setLevel(logging.DEBUG)
log.propagate = False
return log | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_logger():\n root = logging.getLogger()\n root.setLevel(LOGGING_LEVEL)\n formatter = logging.Formatter('%(asctime)s - %(message)s')\n ch = logging.StreamHandler(sys.stdout)\n ch.setLevel(LOGGING_LEVEL)\n ch.setFormatter(formatter)\n root.addHandler(ch)",
"def setup_logging():\n f... | [
"0.7975957",
"0.7763486",
"0.7686616",
"0.75928164",
"0.7581792",
"0.7508208",
"0.73854136",
"0.73814046",
"0.73402363",
"0.7313796",
"0.7308403",
"0.73038954",
"0.7274763",
"0.7260364",
"0.72556174",
"0.7249487",
"0.7241462",
"0.72125137",
"0.71615887",
"0.71491545",
"0.7109... | 0.0 | -1 |
Set up a mongo connection reset and ready to roll. | def mongodb(request):
from pp.user.model import db as mongo
log = get_log('mongodb')
db_name = "testingdb-{}".format(uuid.uuid4().hex)
mongo.init(dict(db_name=db_name))
db = mongo.db()
db.hard_reset()
log.info('database ready for testing "{}"'.format(db_name))
def db_teardown(x=None):
db.hard_reset()
log.warn('teardown database for testing "{}"'.format(db_name))
request.addfinalizer(db_teardown)
return db | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mongodb_init(cls, host=\"127.0.0.1\", port=27017, username=\"\", password=\"\", dbname=\"admin\"):\n if username and password:\n uri = \"mongodb://{username}:{password}@{host}:{port}/{dbname}\".format(username=quote_plus(username),\n ... | [
"0.70171124",
"0.68669546",
"0.6740658",
"0.6647585",
"0.6619428",
"0.6484099",
"0.6464825",
"0.6440319",
"0.6433522",
"0.6415763",
"0.6397156",
"0.6346596",
"0.6345212",
"0.62915",
"0.6224574",
"0.61763144",
"0.61705923",
"0.6150122",
"0.6147837",
"0.613134",
"0.60872823",
... | 0.6147428 | 19 |
Open a tab delimited file and return links and time | def parser(list_of_text):
# Youtube link regex
yt_link = re.compile(r"http(s)?:\/\/www\.youtu.*")
pron_link = re.compile(r".*pornhub.*")
pic_link = re.compile(r"^http(s)?:\/\/.*jpg.*")
pics = [link.split() for link in list_of_text if re.match(pic_link, link)]
found_yt_links = [line.split()
for line in list_of_text if re.match(yt_link, line)]
found_pron = [line.split()
for line in list_of_text if re.match(pron_link, line)]
joined_links = found_yt_links + found_pron
return joined_links, pics | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readTab(file_name):\n data = []\n meta = []\n l=0\n for line in open(file_name):\n if l<3:\n meta.append(line.strip(\"\\n\").split(\"\\t\"))\n else:\n if len(line.strip(\"\\n\").split(\"\\t\")) == len(meta[0]):\n data.append(line.strip(\"\\n\").spl... | [
"0.6074462",
"0.5697214",
"0.55812126",
"0.55209875",
"0.5477201",
"0.54759246",
"0.544581",
"0.5430003",
"0.54228246",
"0.53985965",
"0.5344914",
"0.53337824",
"0.5330407",
"0.53107756",
"0.5296276",
"0.5268797",
"0.5236354",
"0.52124465",
"0.52045405",
"0.51980215",
"0.5169... | 0.0 | -1 |
Function that returns true if a string contains a number | def hasNumbers(inputString):
return any(char.isdigit() for char in inputString) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __has_numbers(self, input_string):\n return bool(re.search(r'\\d', input_string))",
"def has_number(any_string):\n return any(char.isdigit() for char in any_string)",
"def has_num(text):\n return any(str.isdigit(c) for c in text)",
"def is_number(s):\r\n try:\r\n int(s)\r\n ... | [
"0.86861813",
"0.83668447",
"0.82383734",
"0.7930955",
"0.79304606",
"0.7924505",
"0.78930366",
"0.78647584",
"0.77653086",
"0.7743263",
"0.7714202",
"0.7706238",
"0.77058214",
"0.77046204",
"0.77044505",
"0.7690902",
"0.766317",
"0.7639565",
"0.7527607",
"0.7527464",
"0.7508... | 0.8393111 | 1 |
Small function to change time format. Used for make_time func | def tedoius_time(time_string):
start = ['start', 'begin', 'beginning', 'head', 'first']
end = ['slut', 'end', 'tail', 'finish',
'finito', 'fin', 'done', 'finished']
if time_string.lower() in start:
time_string = "00:00:00"
# We need this exact string for later
elif time_string.lower() in end:
return time_string
elif len(time_string) == 1:
time_string = f"00:00:0{time_string}"
elif len(time_string) == 2:
time_string = f"00:00:{time_string}"
elif len(time_string) == 3:
time_string = f"00:00{time_string}"
elif len(time_string) == 4:
time_string = f"00:0{time_string}"
elif len(time_string) == 5:
time_string = f"00:{time_string}"
elif len(time_string) == 6:
time_string = f"00{time_string}"
elif len(time_string) == 7:
time_string = f"0{time_string}"
elif len(time_string) > 8:
raise('Time string too long!')
return time_string | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_time(self, time):\n hh = time[0:2]\n mm = time[2:4]\n ss = time[4:]\n return \"%s:%s:%s UTC\" % (hh,mm,ss)",
"def time_hack(self):\n now = datetime.datetime.now()\n monthnames = ['jan', 'feb', 'mar', 'apr', 'may', 'jun',\n 'jul', 'aug', 'sep', 'oct', ... | [
"0.7315664",
"0.72966367",
"0.7183788",
"0.7177541",
"0.71764636",
"0.7169046",
"0.7167851",
"0.7160244",
"0.7139336",
"0.7091297",
"0.7036572",
"0.70216924",
"0.6875871",
"0.6864549",
"0.6824766",
"0.6812519",
"0.68091184",
"0.680905",
"0.68039894",
"0.67950267",
"0.6790418"... | 0.0 | -1 |
Function that saves the return_list from make_time to a file called yt_vids.txt Optional, default False | def save_link_time(return_list, path_to_download):
# Opens a new file and writes lines to it and saves it at the spot provided
with open(os.path.join(path_to_download, "yt_vids.txt"), "w") as w:
w.write('\n'.join('{} {} {}'.format(
x[0], x[1][0], x[1][1]) for x in return_list)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_save_list_to_file(self):\n task_list = TaskList()\n task1 = Task()\n output_file_path = self.testing_files[0]\n task1.set_name('Test Task One')\n task1.set_minutes(30)\n task1.set_notes('This is a great test task')\n task_list.add_task(task1)\n\n sel... | [
"0.5606279",
"0.54776007",
"0.5325414",
"0.527905",
"0.5232085",
"0.5220749",
"0.5214294",
"0.5194305",
"0.5157583",
"0.5149266",
"0.51426595",
"0.51125914",
"0.50968117",
"0.50955224",
"0.50598377",
"0.50564367",
"0.5056409",
"0.5055913",
"0.50520384",
"0.5035325",
"0.503340... | 0.7020351 | 0 |
Function that downloads a whole video when no interval is supplied Downloaded to the same place where yt_vids is saved to (from save_link_time func) | def download_whole(no_interval):
print(os.getcwd())
SAVE_PATH = 'tmp'
ydl_opts = {"nocheckcertificate": True, "noplaylist": True,
'outtmpl': f'{SAVE_PATH}/%(title)s.%(ext)s'}
with youtube_dl.YoutubeDL(ydl_opts) as ydl:
for video in range(len(no_interval)):
try:
ydl.download([no_interval[video]])
except youtube_dl.utils.ExtractorError or youtube_dl.utils.DownloadError:
print(f"Couldn't download {no_interval[video]}")
continue | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download(df_shorter,folderName):\n os.mkdir(str(folderName))\n path = os.getcwd()+'\\\\'+str(folderName)+'\\\\'\n #add column with video link generated from IDs\n df_shorter['urls'] = df_shorter['id'].apply(lambda x: generateLinkFromId(x))\n vid_dl = []\n i = 1\n for url in df_shorter['url... | [
"0.73994005",
"0.72689897",
"0.7202489",
"0.7171504",
"0.7060031",
"0.6903402",
"0.6774965",
"0.67710614",
"0.67318517",
"0.6675659",
"0.66156524",
"0.66117424",
"0.6610987",
"0.6610455",
"0.6599049",
"0.6573128",
"0.6570862",
"0.6523697",
"0.651439",
"0.65080476",
"0.6456765... | 0.74180853 | 0 |
Function to download videos in specified intervals Takes a list (interval_list) and a path as inputs | def download_interval(interval_list):
start = ['start', 'begin', 'beginning', 'head', 'first']
end = ['slut', 'end', 'tail', 'finish',
'finito', 'fin', 'done', 'finished']
# Iterate over the list
for link in range(len(interval_list)):
try:
video = pafy.new(interval_list[link][0], ydl_opts={
'nocheckcertificate': True, "noplaylist": True})
# Only downloads the video if the video hasn't been downloaded before
if not os.path.exists(os.path.join("tmp", f"{video.title}.mp4")):
video_s = video.getbestvideo()
# TODO: add a way to get the second best stream (third etc.) when an error occurs using Pafy.videostreams and going through the list
video_a = video.getbestaudio()
# Checks if the end point is a string
if interval_list[link][1][1].lower() in end:
# Where is the stream, where should we start, how long should it run
mp4_vid = ffmpeg.input(
video_s.url, ss=interval_list[link][1][0], t=video.duration)
mp4_aud = ffmpeg.input(
video_a.url, ss=interval_list[link][1][0], t=video.duration)
else:
# Where is the stream, where should we start, how long should it run
mp4_vid = ffmpeg.input(
video_s.url, ss=interval_list[link][1][0], t=interval_list[link][1][1])
mp4_aud = ffmpeg.input(
video_a.url, ss=interval_list[link][1][0], t=interval_list[link][1][1])
# Do the processing
try:
(
ffmpeg
.concat(
# Specify what you want from the streams (v for video and a for audio)
mp4_vid['v'],
mp4_aud['a'],
# One video stream and one audio stream
v=1,
a=1
)
# Output is title of video with mp4 ending
.output(os.path.join("tmp", f'{video.title}.mp4'))
.run()
)
except TypeError as e:
print(f"An error occurred e 0: {e}")
except ffmpeg._run.Error as e:
print(f"An error occurred e 1: {e}")
except Exception as e:
print(f"I couldn't download {interval_list[link]} due to: {e}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_whole(no_interval):\n print(os.getcwd())\n SAVE_PATH = 'tmp'\n ydl_opts = {\"nocheckcertificate\": True, \"noplaylist\": True,\n 'outtmpl': f'{SAVE_PATH}/%(title)s.%(ext)s'}\n\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\n for video in range(len(no_interval)):\n ... | [
"0.6634631",
"0.6432747",
"0.63896453",
"0.6305958",
"0.60365444",
"0.59619087",
"0.5885181",
"0.5815725",
"0.56896555",
"0.5573953",
"0.5560245",
"0.5557694",
"0.55472976",
"0.5520894",
"0.5493748",
"0.5459092",
"0.54333603",
"0.54173565",
"0.54105514",
"0.54061747",
"0.5402... | 0.79695 | 0 |
Function to download pictures from the input sequence | def download_pics(pics_links):
for link in range(len(pics_links)):
r = requests.get(pics_links[link][0])
with open(os.path.join("tmp", f"{link}.jpg"), "wb") as dl:
dl.write(r.content) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def downloadLocal(url_list,path):\n print(\"You are downloading {} images\".format(parser_arguments().limit),end=\" \");print(\"of {} class.\".format(parser_arguments().classes))\n print(\"Please, be patient :)\")\n for i in range(len(url_list)):\n filename= url_list[i].split(\"/\")[-1] # name of t... | [
"0.66714674",
"0.6665392",
"0.662957",
"0.65895194",
"0.6587865",
"0.6502355",
"0.64724034",
"0.64450634",
"0.64316654",
"0.6391709",
"0.6382724",
"0.6302078",
"0.6292136",
"0.6259905",
"0.6259636",
"0.6252637",
"0.6219414",
"0.6212664",
"0.61892736",
"0.61513776",
"0.6140734... | 0.6981115 | 0 |
r""" Calculate drainage curve based on the image produced by the ``porosimetry`` function. Returns | def get_drainage_data(self):
im = self.result
sizes = sp.unique(im)
R = []
Snwp = []
Vp = sp.sum(im > 0)
for r in sizes[1:]:
R.append(r)
Snwp.append(sp.sum(im >= r))
Snwp = [s/Vp for s in Snwp]
data = namedtuple('xy_data', ('radius', 'saturation'))
return data(R, Snwp) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_drainage_efficiency(self):#, PLOT, FIGURE, DISTRIBUTION):\n\n print ' Reading drainage efficiency'\n\n self.drainage_efficiency = {}\n\n drainage = np.zeros(self.ATTM_nrows * self.ATTM_ncols)\n\n for i in range(0, self.ATTM_nrows * self.ATTM_ncols):\n if self.ATTM_Total_Fractional_Ar... | [
"0.5753122",
"0.56378067",
"0.55967945",
"0.55486226",
"0.5391032",
"0.53572434",
"0.5309457",
"0.52709997",
"0.5266623",
"0.5236764",
"0.5235034",
"0.52270055",
"0.520521",
"0.51976097",
"0.5173026",
"0.51659435",
"0.5153408",
"0.51311153",
"0.51155776",
"0.51090014",
"0.510... | 0.54940647 | 4 |
computes the sigmoid of z z z can be a matrix, vector or scalar sigmoid | def sigmoid(z):
g = np.zeros(z.shape)
# ====================== YOUR CODE HERE ======================
# Instructions: Compute the sigmoid of each value of z (z can be a matrix,
# vector or scalar).
return 1/(1+np.exp(-z)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sigmoid(z):\r\n \r\n return vSigmoid(z);",
"def sigmoid(z): \n return 1/(1 + np.e**(-z))",
"def sigmoid(z):\r\n \r\n return 1.0 / (1.0+np.exp(-z))",
"def sigmoid(z):\n\treturn 1.0/(1.0+np.exp(-z))",
"def sigmoid(z):\n return 1 / (1 + np.exp(-1 * z))",
"def sigmoid(z):\n\n s = 1... | [
"0.9156451",
"0.89855045",
"0.89530945",
"0.88890535",
"0.88774705",
"0.8873134",
"0.88591874",
"0.88549364",
"0.8827887",
"0.88264954",
"0.88236195",
"0.8823586",
"0.8804889",
"0.8797912",
"0.87720233",
"0.8767488",
"0.87623954",
"0.87474865",
"0.8720549",
"0.8660843",
"0.86... | 0.8674715 | 19 |
Parse vistrone image and annotation file | def parse_anno(self, img_path, anno_path) -> dict:
anno_obj = dict()
img = cv2.imread(img_path)
if len(img.shape) == 3:
h, w, d = img.shape[:3]
anno_obj['size'] = (w, h, d)
else:
h, w = img.shape[:2]
anno_obj['size'] = (w, h, 1)
anno_array = np.loadtxt(anno_path, dtype=np.str, delimiter=',')
objects = list()
if len(anno_array.shape) == 1:
# Just one annotation object
obj = self.create_anno(anno_array)
if obj:
objects.append(obj)
else:
for anno_line in anno_array:
obj = self.create_anno(anno_line)
if obj:
objects.append(obj)
if len(objects) == 0:
return dict()
anno_obj['objects'] = objects
return anno_obj | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unpack_annotation(path):\n buffer = []\n with open(path, 'r') as file:\n lines = file.read()\n\n lines = lines.splitlines()\n for line in lines:\n if not line.startswith('#') and line:\n buffer.append(line)\n\n # Filename to match annotation with photo\n filename = ''... | [
"0.66137487",
"0.6137905",
"0.6088444",
"0.60218704",
"0.6018169",
"0.6017987",
"0.6013739",
"0.59796065",
"0.5911451",
"0.58495057",
"0.584628",
"0.5830778",
"0.58306414",
"0.5818035",
"0.58085626",
"0.5796074",
"0.57908374",
"0.5716362",
"0.57042414",
"0.5697437",
"0.569646... | 0.6034063 | 3 |
recourse through an attribute chain to get the ultimate value. | def safe_chain_getattr(obj, attr):
return reduce(getattr, attr.split('.'), obj) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next(self):\n nextattr = self.iterobj.next()\n return (nextattr.name, self.attrs[nextattr.name])",
"def _get_effective_attribute(self, attribute_name):\n result = []\n tmp = self[attribute_name]\n if tmp != None:\n result.append( tmp )\n if tmp == None or ... | [
"0.594284",
"0.59209895",
"0.5796574",
"0.54958975",
"0.54589653",
"0.54450005",
"0.5416079",
"0.53792626",
"0.5287629",
"0.52547",
"0.5249592",
"0.5235394",
"0.5232",
"0.522848",
"0.52224046",
"0.520934",
"0.52058285",
"0.51851255",
"0.51851255",
"0.51851255",
"0.51851255",
... | 0.5363558 | 8 |
Get chain attribute for an object. | def chain_getattr(obj, attr, value=None):
try:
return _resolve_value(safe_chain_getattr(obj, attr))
except AttributeError:
return value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def safe_chain_getattr(obj, attr):\n return reduce(getattr, attr.split('.'), obj)",
"def chained_getattr(obj, path):\n target = obj\n for attr in path:\n target = corner_case_getattr(target, attr)\n return target",
"def deepgetattr(obj, attr):\n\t\treturn reduce(getattr, attr.split('.'), obj)",
"def... | [
"0.6868703",
"0.65075165",
"0.6485146",
"0.64567274",
"0.62154573",
"0.61727905",
"0.61658573",
"0.6000796",
"0.5964722",
"0.5916216",
"0.5916216",
"0.5876859",
"0.5874207",
"0.5869589",
"0.5869589",
"0.5850275",
"0.58463216",
"0.58114004",
"0.5795329",
"0.57904404",
"0.57904... | 0.7126414 | 0 |
trim the list to make total length no more than limit.If split specified,a string is return. | def trim_iterable(iterable, limit, *, split=None, prefix='', postfix=''):
if split is None:
sl = 0
join = False
else:
sl = len(split)
join = True
result = []
rl = 0
for element in iterable:
element = prefix + element + postfix
el = len(element)
if len(result) > 0:
el += sl
rl += el
if rl <= limit:
result.append(element)
else:
break
if join:
result = split.join(result)
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def trim(self, input_words_list):\n def to_be_trimmed(x):\n if len(x) < 3:\n return False\n else:\n return True\n self.trimmed_words_list = list(filter(to_be_trimmed, input_words_list))\n # print('the filtered words are:')\n # for word... | [
"0.61260587",
"0.61242557",
"0.60332996",
"0.5976751",
"0.59576887",
"0.59357154",
"0.59291357",
"0.575979",
"0.5731277",
"0.56639487",
"0.5637736",
"0.5636854",
"0.5605196",
"0.5510604",
"0.5455997",
"0.54495543",
"0.5449324",
"0.544276",
"0.54360414",
"0.5429126",
"0.542896... | 0.66803694 | 0 |
It decrypts encrypted messages. | def test_decrypt_encrypted(self):
encrypted = encrypt('message')
decrypted = decrypt(encrypted)
assert decrypted == 'message' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decrypt_message(encrypted_message):",
"def decrypt(self, data):",
"def decrypt_message(self):\r\n\r\n\t\t#Will not let user input useless messages that cannot be decrypted.\r\n\t\twhile True:\r\n\t\t\tself.message = input(\"Please enter a message you would like to decrypt. --> \")\r\n\t\t\tif self.message ... | [
"0.87886894",
"0.7859907",
"0.7717388",
"0.7570975",
"0.7548102",
"0.74472094",
"0.735235",
"0.7315939",
"0.73016477",
"0.7261866",
"0.7248655",
"0.72426134",
"0.71901",
"0.7162575",
"0.7121405",
"0.7052611",
"0.7006807",
"0.6988077",
"0.6984153",
"0.6959311",
"0.6911666",
... | 0.72249025 | 12 |
It decrypts encoded messages as UTF8 strings. | def test_decrypt_encoding(self):
encrypted = encrypt('méssåge')
decrypted = decrypt(encrypted)
assert decrypted == 'méssåge' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decrypt_message(encrypted_message):",
"def decrypt_message(msg):\n with urllib.request.urlopen(format_url(main_url+\"decrypt.php\",msg)) as f:\n decryptedmessage = f.read().decode('utf-8',\"strict\")\n return decryptedmessage",
"def decrypt(self, data):",
"def _decrypt(self, msg):\r\n ... | [
"0.7515797",
"0.72070503",
"0.7080225",
"0.70087695",
"0.68919706",
"0.6883082",
"0.6702499",
"0.6700832",
"0.6643625",
"0.6547494",
"0.65426725",
"0.65246123",
"0.65195185",
"0.6513772",
"0.6512258",
"0.6473068",
"0.645004",
"0.644082",
"0.6412383",
"0.63955086",
"0.6384664"... | 0.70483595 | 3 |
It raises an error when trying to decrypt a nonencrypted value. | def test_decrypt_format(self):
with pytest.raises(EncryptionError):
decrypt('message') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_decrypt_key_invalid(self):\n encrypted = encrypt('message', key=b'0' * 32)\n\n with pytest.raises(EncryptionError):\n decrypt(encrypted, key=b'0' * 31)",
"def test_decrypt_key_incorrect(self):\n right_key = b'0' * 32\n wrong_key = b'1' * 32\n\n encrypted = e... | [
"0.7508658",
"0.73156184",
"0.7232054",
"0.6764023",
"0.66845644",
"0.657834",
"0.6562093",
"0.65003294",
"0.6460941",
"0.6459456",
"0.6367024",
"0.63215554",
"0.6274202",
"0.62535083",
"0.62495065",
"0.62276834",
"0.6201334",
"0.6190526",
"0.618059",
"0.61519164",
"0.6145261... | 0.76258427 | 0 |
It accepts a custom decryption key. | def test_decrypt_key(self):
key = b'0' * 32
encrypted = encrypt('message', key=key)
assert decrypt(encrypted, key=key) == 'message' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decrypt(ciphertext: str, key: str) -> str:\n return encrypt(ciphertext, key)",
"def test_decryption(d, c):\n\n#\td = int(raw_input(\"\\nEnter d from public key\\n\"))\n#\tc = int(raw_input(\"\\nEnter c from public key\\n\"))\n\n x = int(raw_input(\"\\nEnter number to decrypt\\n\"))\n decode(endecryp... | [
"0.6593434",
"0.6539305",
"0.6523048",
"0.65177274",
"0.65156114",
"0.6480838",
"0.645648",
"0.63991714",
"0.63842076",
"0.63480246",
"0.634022",
"0.6332533",
"0.6332092",
"0.631312",
"0.62985706",
"0.6246969",
"0.6217749",
"0.6211802",
"0.6208556",
"0.62013805",
"0.6200206",... | 0.69840544 | 0 |
It requires a 32byte key. | def test_decrypt_key_invalid(self):
encrypted = encrypt('message', key=b'0' * 32)
with pytest.raises(EncryptionError):
decrypt(encrypted, key=b'0' * 31) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, key: bytes):\n\n if len(key) != 32:\n raise ValueError('Key must be 32 bytes long')\n self.key = key",
"def create_key ():",
"def __init__(self, key):\n self.bs = 16\n self.key = hashlib.sha256(key.encode()).digest()",
"def load_key():",
"def test_l... | [
"0.7998701",
"0.7112175",
"0.70670855",
"0.70627075",
"0.7016668",
"0.6956229",
"0.6922364",
"0.69079906",
"0.683496",
"0.6793663",
"0.6758785",
"0.6736807",
"0.6699168",
"0.66464436",
"0.66464436",
"0.6582861",
"0.6575875",
"0.6575875",
"0.6530098",
"0.6464395",
"0.64615387"... | 0.0 | -1 |
It raises an error when an incorrect key is provided. | def test_decrypt_key_incorrect(self):
right_key = b'0' * 32
wrong_key = b'1' * 32
encrypted = encrypt('message', key=right_key)
with pytest.raises(EncryptionError):
decrypt(encrypted, key=wrong_key) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_key(self, key):\n raise NotImplementedError",
"def test_handle_key_error():\n\n @handle_key_error\n def get_item(key):\n data = {\"A\": 1, \"B\": 2}\n return data[key]\n\n value = get_item(\"A\")\n assert value == 1\n\n with pytest.raises(InvalidParameter) as exc:\n... | [
"0.7584052",
"0.74336976",
"0.7209498",
"0.7197816",
"0.71823823",
"0.71537316",
"0.70963115",
"0.70610356",
"0.7014894",
"0.6917326",
"0.6872797",
"0.68682355",
"0.6839676",
"0.6820613",
"0.6819844",
"0.68171906",
"0.68070513",
"0.67946887",
"0.67675406",
"0.6766828",
"0.668... | 0.6123274 | 82 |
It gets its default key from settings. | def test_decrypt_key_default(self, settings):
settings.CHITON_ENCRYPTION_KEY = b'0' * 32
encrypted = encrypt('message')
assert decrypt(encrypted) == 'message'
settings.CHITON_ENCRYPTION_KEY = b'1' * 32
with pytest.raises(EncryptionError):
decrypt(encrypted) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def default(self, key):\r\n return self.inherited_settings[key.field_name]",
"def get(self, key, default=None):\n return self.settings.get(key, default)",
"def initial(self):\n from setman import settings\n return getattr(settings, self.name, self.default)",
"def get_setting_defau... | [
"0.7820905",
"0.76434904",
"0.7315344",
"0.7273974",
"0.7266542",
"0.7164663",
"0.708993",
"0.7069916",
"0.7067126",
"0.70258087",
"0.70121825",
"0.69252306",
"0.686531",
"0.6851818",
"0.6849022",
"0.6843253",
"0.678574",
"0.6693812",
"0.66881984",
"0.664332",
"0.6612321",
... | 0.0 | -1 |
It encrypts messages as base64encoded strings. | def test_encrypt_encoding(self):
encrypted = encrypt('message')
assert encrypted
assert encrypted != 'message'
assert type(encrypted) == str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encrypted(data: str) -> str:\n return b64encode(data.encode('ascii')).decode('ascii')",
"def encrypt(strings):\r\n rd = ''.join(random.sample(upperchr+lowcarsechr+otherchr+numchr,10))\r\n output = base64.encodestring(strings+mselment+rd)\r\n print output",
"def test_encryption_of_string(cipher)... | [
"0.69580686",
"0.69413096",
"0.6922633",
"0.68996924",
"0.67965823",
"0.6779057",
"0.677835",
"0.6773155",
"0.67529523",
"0.67321116",
"0.6706291",
"0.6699885",
"0.66843873",
"0.66808355",
"0.66681325",
"0.6639239",
"0.6632102",
"0.6602281",
"0.6573491",
"0.65729386",
"0.6543... | 0.6409957 | 33 |
It accepts a custom encryption key. | def test_encrypt_key(self):
encrypted = encrypt('message', key=b'0' * 32)
assert encrypted
assert encrypted != 'message' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encryption_key(self) -> typing.Optional[aws_cdk.aws_kms.IKey]:\n ...",
"def set_encryption(key):\n global_scope['enc'] = Encryption(key.encode())",
"def encryption_key(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"encryption_key\")",
"def encryption_key(self) -> Optio... | [
"0.6910627",
"0.6908906",
"0.6589015",
"0.6589015",
"0.65658104",
"0.64406216",
"0.64385355",
"0.6403656",
"0.6396212",
"0.6384059",
"0.63731337",
"0.6366203",
"0.633905",
"0.6292146",
"0.6261021",
"0.62500477",
"0.62206835",
"0.62103766",
"0.61971515",
"0.6182859",
"0.616854... | 0.6712674 | 2 |
It requires a 32byte key. | def test_encrypt_key_invalid(self):
with pytest.raises(EncryptionError):
encrypt('message', key=b'0' * 31) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, key: bytes):\n\n if len(key) != 32:\n raise ValueError('Key must be 32 bytes long')\n self.key = key",
"def create_key ():",
"def __init__(self, key):\n self.bs = 16\n self.key = hashlib.sha256(key.encode()).digest()",
"def load_key():",
"def test_l... | [
"0.79996747",
"0.71131784",
"0.70681524",
"0.7063389",
"0.70164996",
"0.6956813",
"0.69228464",
"0.69088066",
"0.6835613",
"0.67935926",
"0.6759126",
"0.6737713",
"0.6699715",
"0.66468084",
"0.66468084",
"0.65837467",
"0.65774214",
"0.65774214",
"0.65307164",
"0.6464985",
"0.... | 0.60107034 | 69 |
It gets its default key from settings. | def test_encrypt_key_default(self, settings):
settings.CHITON_ENCRYPTION_KEY = None
with pytest.raises(EncryptionError):
encrypt('message') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def default(self, key):\r\n return self.inherited_settings[key.field_name]",
"def get(self, key, default=None):\n return self.settings.get(key, default)",
"def initial(self):\n from setman import settings\n return getattr(settings, self.name, self.default)",
"def get_setting_defau... | [
"0.7820905",
"0.76434904",
"0.7315344",
"0.7273974",
"0.7266542",
"0.7164663",
"0.708993",
"0.7069916",
"0.7067126",
"0.70258087",
"0.70121825",
"0.69252306",
"0.686531",
"0.6851818",
"0.6849022",
"0.6843253",
"0.678574",
"0.6693812",
"0.66881984",
"0.664332",
"0.6612321",
... | 0.0 | -1 |
It does not produce the same message using the same key. | def test_encrypt_nonce(self):
key = b'0' * 32
message = 'message'
assert encrypt(message, key=key) != encrypt(message, key=key) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_rekey(self):\n old_key = b'0' * 32\n new_key = b'1' * 32\n\n old_encrypted = encrypt('message', key=old_key)\n new_encrypted = rekey(old_encrypted, old_key=old_key, new_key=new_key)\n\n assert decrypt(new_encrypted, key=new_key) == 'message'",
"def create_key(message, ... | [
"0.6353171",
"0.6112065",
"0.6050746",
"0.60195196",
"0.60014725",
"0.5931781",
"0.59107494",
"0.58862585",
"0.58743024",
"0.5822409",
"0.5816319",
"0.5803703",
"0.5773151",
"0.57707894",
"0.5769746",
"0.57522786",
"0.5739855",
"0.56940407",
"0.5691956",
"0.5683916",
"0.56509... | 0.55720454 | 26 |
It reencrypts an encrypted message using a new key. | def test_rekey(self):
old_key = b'0' * 32
new_key = b'1' * 32
old_encrypted = encrypt('message', key=old_key)
new_encrypted = rekey(old_encrypted, old_key=old_key, new_key=new_key)
assert decrypt(new_encrypted, key=new_key) == 'message' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_rekey_defaults(self, settings):\n old_key = b'0' * 32\n new_key = b'1' * 32\n\n settings.CHITON_ENCRYPTION_KEY = new_key\n settings.CHITON_PREVIOUS_ENCRYPTION_KEY = old_key\n\n encrypted = encrypt('message', key=old_key)\n rekeyed = rekey(encrypted)\n\n ass... | [
"0.7298857",
"0.7298796",
"0.71268785",
"0.66500294",
"0.6324676",
"0.62568414",
"0.6190534",
"0.6128457",
"0.61111814",
"0.6098401",
"0.60541093",
"0.59321904",
"0.5931756",
"0.5884302",
"0.587639",
"0.58716655",
"0.5832455",
"0.58202076",
"0.5819175",
"0.5817855",
"0.580720... | 0.7975824 | 0 |
It raises an error when trying to rekey a nonencrypted value. | def test_rekey_non_encrypted(self):
with pytest.raises(EncryptionError):
rekey('message', old_key=b'0' * 32, new_key=b'1' * 32) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_rekey_key_format(self):\n old_key = b'0' * 32\n encrypted = encrypt('message', key=old_key)\n\n with pytest.raises(EncryptionError):\n rekey(encrypted, old_key=old_key, new_key=b'1' * 31)",
"def test_encrypt_key_invalid(self):\n with pytest.raises(EncryptionError):... | [
"0.7787494",
"0.7165129",
"0.7140279",
"0.71330994",
"0.69257516",
"0.6686824",
"0.6646899",
"0.6348278",
"0.6192973",
"0.6057841",
"0.60305434",
"0.6001477",
"0.5970502",
"0.58401686",
"0.5836722",
"0.5806258",
"0.5804228",
"0.57949257",
"0.57405263",
"0.57361877",
"0.571489... | 0.82164097 | 0 |
It raises an error when given an invalid new key. | def test_rekey_key_format(self):
old_key = b'0' * 32
encrypted = encrypt('message', key=old_key)
with pytest.raises(EncryptionError):
rekey(encrypted, old_key=old_key, new_key=b'1' * 31) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _newKey(self, key):\n pass",
"def _check_key(self, key):\n raise NotImplementedError",
"def test_rekey_non_encrypted(self):\n with pytest.raises(EncryptionError):\n rekey('message', old_key=b'0' * 32, new_key=b'1' * 32)",
"def test_set_invalid_key(test_file):\n md = OSX... | [
"0.71822304",
"0.70056105",
"0.69519156",
"0.6922246",
"0.6791061",
"0.6753472",
"0.6749198",
"0.67076725",
"0.66495234",
"0.6584771",
"0.6557083",
"0.65199524",
"0.65018636",
"0.64907044",
"0.6454335",
"0.64416265",
"0.6438558",
"0.6431475",
"0.6424363",
"0.6396235",
"0.6389... | 0.7050168 | 1 |
It uses the settings for the default old and new key. | def test_rekey_defaults(self, settings):
old_key = b'0' * 32
new_key = b'1' * 32
settings.CHITON_ENCRYPTION_KEY = new_key
settings.CHITON_PREVIOUS_ENCRYPTION_KEY = old_key
encrypted = encrypt('message', key=old_key)
rekeyed = rekey(encrypted)
assert decrypt(rekeyed) == 'message' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_key(self):\n self.__prev_key = self.__new_key",
"def update_dict(new,old):",
"def _newKey(self, key):\n pass",
"def update_default_from_dict(self, key, value):\n pass",
"def test_overwrite(self):\n set_default_for_missing_keys('hello world')\n set_default_for_m... | [
"0.65626293",
"0.6550737",
"0.6313369",
"0.6242143",
"0.61064005",
"0.60898244",
"0.6082216",
"0.60713875",
"0.5890576",
"0.5830049",
"0.58023095",
"0.5726684",
"0.5720197",
"0.5707687",
"0.56511974",
"0.5633935",
"0.5569308",
"0.54979086",
"0.5470096",
"0.5455716",
"0.545565... | 0.62328917 | 4 |
Fetches domain by its name | def get_by_name(name):
return database.get_all(Domain, name, field="name").all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_domain_for_name(self, name):\n domain = self.connection.lookupByName(name)\n return domain",
"def lookup(self, domain_name, validate=True):\n try:\n domain = self.get_domain(domain_name, validate)\n except:\n domain = None\n return domain",
"def... | [
"0.82388127",
"0.7296342",
"0.72872925",
"0.71128905",
"0.70768034",
"0.7075855",
"0.70597684",
"0.69037956",
"0.68828833",
"0.6841402",
"0.6727464",
"0.65337366",
"0.64684",
"0.6401447",
"0.63366103",
"0.63004",
"0.6292597",
"0.62468135",
"0.62434566",
"0.62265456",
"0.61929... | 0.654201 | 11 |
Return True if domain is marked sensitive | def is_domain_sensitive(name):
query = database.session_query(Domain)
query = query.filter(and_(Domain.sensitive, Domain.name == name))
return database.find_all(query, Domain, {}).all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def indomain(url, domain):\n if url and domain:\n return url.startswith(domain)\n return False",
"def is_secure_site_enabled(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"is_secure_site_enabled\")",
"def is_secure_site_enabled(self) -> Optional[pulumi.Input[bool]]:\n ... | [
"0.5945297",
"0.5943389",
"0.5943389",
"0.58801776",
"0.5860374",
"0.5823208",
"0.58100474",
"0.5775172",
"0.57475305",
"0.57442623",
"0.57067436",
"0.56709236",
"0.56709236",
"0.56709236",
"0.56595856",
"0.56003463",
"0.55976045",
"0.5583748",
"0.5570339",
"0.5522135",
"0.55... | 0.7324499 | 0 |
Create a new domain | def create(name, sensitive):
domain = Domain(name=name, sensitive=sensitive)
return database.create(domain) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_domain(DomainName=None):\n pass",
"def create_domain(self, domain: str) -> Session:\n uri = f\"{self.uri}/domains\"\n data = {\n \"hostname\": domain\n }\n response = self.request(uri=uri, method=\"POST\", data=data)\n\n return response",
"def create_... | [
"0.85276437",
"0.7999616",
"0.7635129",
"0.7572509",
"0.745609",
"0.7389668",
"0.73356265",
"0.7306637",
"0.72217894",
"0.72094274",
"0.71779686",
"0.71715486",
"0.7072683",
"0.70593244",
"0.7049416",
"0.7021447",
"0.70118153",
"0.6965163",
"0.6953879",
"0.68060184",
"0.67575... | 0.743728 | 5 |
Update an existing domain | def update(domain_id, name, sensitive):
domain = get(domain_id)
domain.name = name
domain.sensitive = sensitive
database.update(domain) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def edit_domain(domain_name):\n\n if request.method == \"POST\":\n domain = session.query(Domain).filter(\n Domain.domain_name == domain_name).first()\n\n # Check if domain.provider object exists to make sure\n # duplicate Provider.provider_url is not created\n provider = ... | [
"0.7216133",
"0.6992674",
"0.69599164",
"0.6770144",
"0.6691671",
"0.6631902",
"0.66250306",
"0.66209924",
"0.6600841",
"0.65592813",
"0.6530738",
"0.65130335",
"0.6412239",
"0.63919693",
"0.63866",
"0.6360199",
"0.6326602",
"0.6326602",
"0.63022065",
"0.6297731",
"0.62386405... | 0.77741724 | 0 |
Helper to parse REST Api requests | def render(args):
query = database.session_query(Domain)
filt = args.pop("filter")
certificate_id = args.pop("certificate_id", None)
if filt:
terms = filt.split(";")
query = database.filter(query, Domain, terms)
if certificate_id:
query = query.join(Certificate, Domain.certificates)
query = query.filter(Certificate.id == certificate_id)
return database.sort_and_page(query, Domain, args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse (self, request):\n\n data = {}\n body_start = request.find('\\r\\n\\r\\n')\n if body_start == -1:\n data['body'] = None\n else:\n data['body'] = request[body_start+4:]\n parts = request.split(' ', 2)\n data['method'] = parts[0]\n data... | [
"0.6661732",
"0.6361013",
"0.6351881",
"0.63334227",
"0.6265376",
"0.62463653",
"0.61369115",
"0.61359483",
"0.6097976",
"0.6052464",
"0.6047303",
"0.60433483",
"0.59891516",
"0.58926797",
"0.5875995",
"0.5862154",
"0.5862154",
"0.5862154",
"0.5862154",
"0.581381",
"0.579058"... | 0.0 | -1 |
Create a logfile that the rest of the script can write to. | def log_start():
scriptDir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
scriptName = os.path.splitext(os.path.basename(__file__))[0]
log = logging.getLogger('cam_server')
hdlr = logging.FileHandler(scriptDir+'/logs/'+scriptName+'.log')
formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
hdlr.setFormatter(formatter)
log.addHandler(hdlr)
log.setLevel(logging.INFO)
return log | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_logfile(self):\r\n if not self.console_redirect:\r\n return None\r\n\r\n # PCU_logs.robot need a timestamp for console logs as can be run several times\r\n if self.name == self.log_test.replace('.robot', ''):\r\n return open('{0}\\{1}_console_log_{2}'.format(\... | [
"0.77096045",
"0.7498354",
"0.70676357",
"0.7021383",
"0.69633764",
"0.69220144",
"0.6866777",
"0.68400943",
"0.6803973",
"0.6764277",
"0.6756204",
"0.6751284",
"0.6738857",
"0.67022955",
"0.66923296",
"0.66836953",
"0.6674281",
"0.66629696",
"0.6649449",
"0.664781",
"0.65934... | 0.6588597 | 24 |
Establish a TCP connection to the indiserver via port 7624 | def connect_to_indi():
indiclient=IndiClient()
indiclient.setServer("localhost",7624)
# Ensure the indiserver is running
if (not(indiclient.connectServer())):
print("No indiserver running on "+indiclient.getHost()+":"+str(indiclient.getPort())+" - Try to run")
print(" indiserver indi_sx_ccd")
sys.exit(1)
return indiclient | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SCPI_sock_connect(ipaddress,port=5025):\n\n try:\n session=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n #session.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 0)\n #session.setsockopt(socket.SOL_SOCKET, socket.SO_LINGER, 0)\n session.connect((ipaddress,port))\n exce... | [
"0.62255913",
"0.62050253",
"0.6195879",
"0.6171926",
"0.6135462",
"0.6120585",
"0.606939",
"0.60673463",
"0.602371",
"0.6017615",
"0.60021055",
"0.60011834",
"0.5976623",
"0.5967753",
"0.5963107",
"0.59526026",
"0.59498817",
"0.5947324",
"0.5940068",
"0.5910819",
"0.59102845... | 0.68877107 | 0 |
Connection routine for the CCD (given below in ccd variable). The following CCD properties are accessed. More can be found by going to indilib.org. CONNECTION Switch CCD_EXPOSURE Number CCD1 BLOB CCD_BINNING Number CCD_ABORT_EXPOSURE Number CCD_TEMPERATURE Number CCD_COOLER Switch CCD_FRAME_TYPE Switch | def connect_to_ccd():
ccd="SX CCD SXVR-H694"
device_ccd=indiclient.getDevice(ccd)
while not(device_ccd):
time.sleep(0.5)
device_ccd=indiclient.getDevice(ccd)
print("Searching for device...")
print("Found device")
ccd_connect=device_ccd.getSwitch("CONNECTION")
while not(ccd_connect):
time.sleep(0.5)
ccd_connect=device_ccd.getSwitch("CONNECTION")
if not(device_ccd.isConnected()):
ccd_connect[0].s=PyIndi.ISS_ON # the "CONNECT" switch
ccd_connect[1].s=PyIndi.ISS_OFF # the "DISCONNECT" switch
indiclient.sendNewSwitch(ccd_connect)
ccd_exposure=device_ccd.getNumber("CCD_EXPOSURE")
while not(ccd_exposure):
time.sleep(0.5)
ccd_exposure=device_ccd.getNumber("CCD_EXPOSURE")
# inform the indi server that we want to receive the
# "CCD1" blob from this device
indiclient.setBLOBMode(PyIndi.B_ALSO, ccd, "CCD1")
ccd_ccd1=device_ccd.getBLOB("CCD1")
while not(ccd_ccd1):
time.sleep(0.5)
ccd_ccd1=device_ccd.getBLOB("CCD1")
# get access to setting the CCD's binning value
ccd_bin=device_ccd.getNumber("CCD_BINNING")
while not(ccd_bin):
time.sleep(0.5)
ccd_bin=device_ccd.getNumber("CCD_BINNING")
# get access to aborting the CCD's exposure
ccd_abort=device_ccd.getSwitch("CCD_ABORT_EXPOSURE")
while not(ccd_abort):
time.sleep(0.5)
ccd_abort=device_ccd.getSwitch("CCD_ABORT_EXPOSURE")
# get access to the CCD's temperature value
ccd_temp=device_ccd.getNumber("CCD_TEMPERATURE")
while not(ccd_temp):
time.sleep(0.5)
ccd_temp=device_ccd.getNumber("CCD_TEMPERATURE")
# get access to switching the CCD's cooler on/off
ccd_cooler=device_ccd.getSwitch("CCD_COOLER")
while not(ccd_cooler):
time.sleep(0.5)
ccd_cooler=device_ccd.getSwitch("CCD_COOLER")
# get access to switching the CCD's image frame type
ccd_frame=device_ccd.getSwitch("CCD_FRAME_TYPE")
while not(ccd_frame):
time.sleep(0.5)
ccd_frame=device_ccd.getSwitch("CCD_FRAME_TYPE")
return ccd_exposure, ccd_ccd1, ccd_bin, ccd_abort, ccd_temp, ccd_cooler, ccd_frame | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open_circ():\n\n set_mode(mode_cc) # set operation mode to CC\n time.sleep(.250)\n set_CC_current(cc_current=0) # set CC mode current to 0 amps\n time.sleep(.1)\n \n oc_vals = get_input_values() # read open circuits levels\n oc_data_point = data_point(oc_vals) # create data point for ope... | [
"0.5991389",
"0.5908873",
"0.563912",
"0.5434294",
"0.54057074",
"0.53454",
"0.53262013",
"0.5306355",
"0.52807844",
"0.52574426",
"0.5246854",
"0.5221757",
"0.5210567",
"0.51662695",
"0.51509064",
"0.5141476",
"0.51294994",
"0.5121288",
"0.5110107",
"0.5095082",
"0.50932145"... | 0.7094732 | 0 |
Find the last numbered image in the current directory. | def last_image(fileDir):
lastNum = 0
lastImg = ''
# find the name and number of the last image in the current directory
for f in os.listdir(fileDir):
if os.path.isfile(os.path.join(fileDir, f)):
file_name = os.path.splitext(f)[0]
file_name2 = file_name[4:]
try:
file_num = int(file_name2)
if file_num > lastNum:
lastNum = file_num
lastImg = os.path.join(fileDir, f)
except ValueError:
'The file name "%s" is not an integer. Skipping' % file_name
return lastNum, lastImg | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_last_counter():\n counter = imageNumStart\n if imageNumOn:\n image_ext = \".jpg\"\n search_str = imagePath + \"/*\" + image_ext\n file_prefix_len = len(imagePath + imageNamePrefix)+1\n try:\n # Scan image folder for most recent jpg file\n # and try to e... | [
"0.7381124",
"0.71163946",
"0.6752403",
"0.65669954",
"0.651449",
"0.6455671",
"0.6257701",
"0.62144595",
"0.61207634",
"0.6083774",
"0.60167074",
"0.6005787",
"0.5981818",
"0.59797287",
"0.5971447",
"0.597075",
"0.59043884",
"0.5898508",
"0.58921754",
"0.5875937",
"0.581396"... | 0.7685043 | 0 |
Sends an exposure command to the CCD given the type of frame and exposure time. The received BLOB is of FITS type and is | def exposure(frameType, expTime):
blobEvent.clear()
# set the specified frame type
if frameType.lower() == 'light':
ccd_frame[0].s = PyIndi.ISS_ON
ccd_frame[1].s = PyIndi.ISS_OFF
ccd_frame[2].s = PyIndi.ISS_OFF
ccd_frame[3].s = PyIndi.ISS_OFF
indiclient.sendNewSwitch(ccd_frame)
elif frameType.lower() == 'bias':
ccd_frame[0].s = PyIndi.ISS_OFF
ccd_frame[1].s = PyIndi.ISS_ON
ccd_frame[2].s = PyIndi.ISS_OFF
ccd_frame[3].s = PyIndi.ISS_OFF
indiclient.sendNewSwitch(ccd_frame)
elif frameType.lower() == 'dark':
ccd_frame[0].s = PyIndi.ISS_OFF
ccd_frame[1].s = PyIndi.ISS_OFF
ccd_frame[2].s = PyIndi.ISS_ON
ccd_frame[3].s = PyIndi.ISS_OFF
indiclient.sendNewSwitch(ccd_frame)
elif frameType.lower() == 'flat':
ccd_frame[0].s = PyIndi.ISS_OFF
ccd_frame[1].s = PyIndi.ISS_OFF
ccd_frame[2].s = PyIndi.ISS_OFF
ccd_frame[3].s = PyIndi.ISS_ON
indiclient.sendNewSwitch(ccd_frame)
# set the value for the next exposure
ccd_exposure[0].value=expTime
indiclient.sendNewNumber(ccd_exposure)
# wait for the exposure
blobEvent.wait()
for blob in ccd_ccd1:
# pyindi-client adds a getblobdata() method to IBLOB item
# for accessing the contents of the blob, which is a bytearray in Python
image_data=blob.getblobdata()
# write the byte array out to a FITS file
global imgNum
global imgName
imgNum += 1
fileName = fileDir+'raw-'+str(imgNum).zfill(8)+'.fits'
f = open(fileName, 'wb')
f.write(image_data)
f.close()
imgName = fileName
return fileName | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expose(self, cmd, expTime, expType):\n\n if not expType:\n expType = 'test'\n if cmd:\n cmd.inform('exposureState=\"exposing\"')\n if expType not in ('bias', 'test') and expTime > 0:\n time.sleep(expTime + self._exposureOverheadTime())\n\n if cmd:\n ... | [
"0.66172343",
"0.66024214",
"0.61997694",
"0.6147396",
"0.6001152",
"0.5911744",
"0.5895627",
"0.5890128",
"0.5692038",
"0.5651894",
"0.5623061",
"0.56096864",
"0.54805845",
"0.54146963",
"0.5291824",
"0.5205684",
"0.5177268",
"0.51427567",
"0.51357037",
"0.5105987",
"0.50998... | 0.7639681 | 0 |
Changes CCD parameters/settings based on the given arguments | def setParams(commandList):
for i in commandList:
# set the bin mode (1x1 or 2x2)
if 'bin=' in i:
try:
bin = int(i.replace('bin=',''))
if bin >= 1 and bin <= 2:
ccd_bin[0].value = bin
ccd_bin[1].value = bin
indiclient.sendNewNumber(ccd_bin)
response = 'OK: Bin mode set to '+str(bin)+'x'+str(bin)
else:
response = 'BAD: Invalid Bin Mode'
except ValueError:
response = 'BAD: Invalid Bin Mode'
# turn the cooler on/off
elif 'cooler=' in i:
cooler = i.replace('cooler=','')
if cooler.lower() == 'on':
ccd_cooler[0].s=PyIndi.ISS_ON # the "COOLER_ON" switch
ccd_cooler[1].s=PyIndi.ISS_OFF # the "COOLER_OFF" switch
indiclient.sendNewSwitch(ccd_cooler)
response = 'OK: Cooler turned '+cooler
elif cooler.lower() == 'off':
ccd_cooler[0].s=PyIndi.ISS_OFF # the "COOLER_ON" switch
ccd_cooler[1].s=PyIndi.ISS_ON # the "COOLER_OFF" switch
indiclient.sendNewSwitch(ccd_cooler)
response = 'OK: Cooler turned '+cooler
else:
response = 'BAD: Invalid cooler set'
# set the temperature setpoint (-40C - 0C)
elif 'temp=' in i:
try:
temp = float(i.replace('temp=',''))
if temp >= -40 and temp <= 0:
response = 'OK: Setting temperature setpoint to '+str(temp)
ccd_temp[0].value = temp
indiclient.sendNewNumber(ccd_temp)
else:
response = 'BAD: Invalid temperature setpoint'
except ValueError:
response = 'BAD: Invalid temperature setpoint'
# set the image output directory
elif 'fileDir=' in i:
try:
global imgNum
global imgName
global fileDir
tempFileDir = i.replace('fileDir=','')
if tempFileDir[0] == '~':
tempFileDir = os.path.expanduser('~')+tempFileDir[1:]
if tempFileDir[len(tempFileDir)-1] != '/':
tempFileDir = tempFileDir+'/'
if not os.path.exists(tempFileDir):
os.makedirs(tempFileDir)
imgNum, imgName = last_image(tempFileDir)
fileDir = tempFileDir
response = 'OK: File directory set to '+fileDir
#run_image_display(fileDir)
except FileNotFoundError:
response = 'BAD: Directory does not exist'
# set the temperature setpoint (-40C - 0C)
elif 'frameType=' in i:
try:
frameType = i.replace('frameType=','')
if frameType.lower() == 'light':
ccd_frame[0].s = PyIndi.ISS_ON
ccd_frame[1].s = PyIndi.ISS_OFF
ccd_frame[2].s = PyIndi.ISS_OFF
ccd_frame[3].s = PyIndi.ISS_OFF
indiclient.sendNewSwitch(ccd_frame)
response = 'OK: CCD frame type set to '+frameType
elif frameType.lower() == 'bias':
ccd_frame[0].s = PyIndi.ISS_OFF
ccd_frame[1].s = PyIndi.ISS_ON
ccd_frame[2].s = PyIndi.ISS_OFF
ccd_frame[3].s = PyIndi.ISS_OFF
indiclient.sendNewSwitch(ccd_frame)
response = 'OK: CCD frame type set to '+frameType
elif frameType.lower() == 'dark':
ccd_frame[0].s = PyIndi.ISS_OFF
ccd_frame[1].s = PyIndi.ISS_OFF
ccd_frame[2].s = PyIndi.ISS_ON
ccd_frame[3].s = PyIndi.ISS_OFF
indiclient.sendNewSwitch(ccd_frame)
response = 'OK: CCD frame type set to '+frameType
elif frameType.lower() == 'flat':
ccd_frame[0].s = PyIndi.ISS_OFF
ccd_frame[1].s = PyIndi.ISS_OFF
ccd_frame[2].s = PyIndi.ISS_OFF
ccd_frame[3].s = PyIndi.ISS_ON
indiclient.sendNewSwitch(ccd_frame)
response = 'OK: CCD frame type set to '+frameType
else:
response = 'BAD: Invalid frame type'
except ValueError:
response = 'BAD: Invalid frame type'
else:
response = 'BAD: Invalid Set'+'\n'+response
return response | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setCcdMode(*argv):",
"def set_parameter_values(self, c1, c2):\n self.c1 = c1\n self.c2 = c2",
"def set_parameters(self, args):\n self.args = args\n\n if args.testing:\n self.delay_close()\n\n if args.source == \"simulation\":\n log.info(\"Create simu... | [
"0.7167858",
"0.60452235",
"0.60324895",
"0.5878222",
"0.57493514",
"0.5714063",
"0.56342685",
"0.56257355",
"0.5574821",
"0.5573054",
"0.55587506",
"0.55421853",
"0.55330527",
"0.55330527",
"0.55330527",
"0.55330527",
"0.55330527",
"0.55182",
"0.5511414",
"0.55045736",
"0.54... | 0.528423 | 39 |
Determines what to do with the incoming data, whether it is sending an exposure command or setting a parameter. This is a separate method from handle_client() because it is called as a new thread, so ensure the exposure is nonblocking. | def handle_command(log, writer, data):
response = 'BAD: Invalid Command'
commandList = data.split()
try:
if commandList[0] == 'expose':
if len(commandList) == 3:
if commandList[1] == 'light' or commandList[1] == 'dark' or commandList[1] == 'flat':
expType = commandList[1]
expTime = commandList[2]
try:
float(expTime)
if float(expTime) > 0:
expTime = float(expTime)
fileName = exposure(expType, expTime)
response = 'OK\n'+'FILENAME = '+fileName
else:
response = 'BAD: Invalid Exposure Time'
except ValueError:
response = 'BAD: Invalid Exposure Time'
elif len(commandList) == 2:
if commandList[1] == 'bias':
expType = commandList[1]
try:
fileName = exposure(expType, 0.0)
response = 'OK\n'+'FILENAME: '+fileName
except ValueError:
response = 'BAD: Invalid Exposure Time'
elif commandList[0] == 'set':
if len(commandList) >= 1:
response = setParams(commandList[1:])
except IndexError:
response = 'BAD: Invalid Command'
# tell the client the result of their command & log it
#log.info('RESPONSE = '+response)
#writer.write((response+'\n---------------------------------------------------\n').encode('utf-8'))
writer.write((response+'\nDONE\n').encode('utf-8')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def command(self):\n saw_error = False\n try:\n analog_gain = float(self.value_analog.get())\n except:\n print(\"analog must be floating point value\")\n self.value_analog.set(str(self.tcp_comms.tcp_params.analog_gain_target))\n saw_error = True\n ... | [
"0.61772895",
"0.6057597",
"0.59728074",
"0.5919234",
"0.57503784",
"0.5750175",
"0.5683027",
"0.5636862",
"0.5570763",
"0.5540126",
"0.5496406",
"0.5487305",
"0.54842824",
"0.5481451",
"0.54694",
"0.54545176",
"0.5411232",
"0.54028386",
"0.5396897",
"0.53862023",
"0.5382583"... | 0.57042426 | 6 |
This is the method that receives the client's data and decides what to do with it. It runs in a loop to always be accepting new connections. If the data is 'status', the CCD status is returned. If the data is 'stop', the current exposure is stopped. If the data is anything else, a new thread is created and the data is sent to handle_command(). | async def handle_client(reader, writer):
request = None
# loop to continually handle incoming data
while request != 'quit':
request = (await reader.read(255)).decode('utf8')
print(request.encode('utf8'))
#log.info('COMMAND = '+request)
writer.write(('COMMAND = '+request.upper()+'\n').encode('utf8'))
response = 'BAD'
# check if data is empty, a status query, or potential command
dataDec = request
if dataDec == '':
break
elif 'status' in dataDec.lower():
response = 'OK'
# check if the command thread is running
try:
if exposureState() > 0:
response = response + '\nBUSY'
else:
response = response + '\nIDLE'
except:
response = response + '\nIDLE'
if ccd_frame[0].s == PyIndi.ISS_ON:
frameType = 'LIGHT'
elif ccd_frame[1].s == PyIndi.ISS_ON:
frameType = 'BIAS'
elif ccd_frame[2].s == PyIndi.ISS_ON:
frameType = 'DARK'
elif ccd_frame[3].s == PyIndi.ISS_ON:
frameType = 'FLAT'
response = response+\
'\nBIN MODE = '+str(ccd_bin[0].value)+'x'+str(ccd_bin[1].value)+\
'\nCCD TEMP = '+str(ccd_temp[0].value)+\
'C\nLAST FRAME TYPE = '+str(frameType)+\
'\nFILE DIR = '+str(fileDir)+\
'\nLAST IMAGE = '+str(imgName)
# send current status to open connection & log it
#log.info('RESPONSE: '+response)
writer.write((response+'\nDONE\n').encode('utf-8'))
elif 'stop' in dataDec.lower():
# check if the command thread is running
try:
if comThread.is_alive():
response = 'OK: aborting exposure'
ccd_abort[0].s=PyIndi.ISS_ON
indiclient.sendNewSwitch(ccd_abort)
blobEvent.set() #Ends the currently running thread.
response = response+'\nExposure Aborted'
else:
response = 'OK: idle'
except:
response = 'OK: idle'
# send current status to open connection & log it
#log.info('RESPONSE = '+response)
writer.write((response+'\nDONE\n').encode('utf-8'))
else:
# check if the command thread is running, may fail if not created yet, hence try/except
try:
if comThread.is_alive():
response = 'BAD: busy'
# send current status to open connection & log it
#log.info('RESPONSE = '+response)
writer.write((response+'\nDONE\n').encode('utf-8'))
else:
# create a new thread for the command
comThread = threading.Thread(target=handle_command, args=(log, writer, dataDec,))
comThread.start()
except:
# create a new thread for the command
comThread = threading.Thread(target=handle_command, args=(log, writer, dataDec,))
comThread.start()
await writer.drain()
writer.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handleRecvData(self, data):\n\n\t\t#Look for commands\n\t\tif data == 'Hello':\n\t\t\t#Inform client it is 'connected'\n\t\t\tself.transmit(\"Welcome\")\n\n\t\telif data == 'kill':\t\n\t\t\t#Stop the server running\n\t\t\tself.running = False\n\n\t\telif data == 'control':\n\t\t\t#Print out if in control of ca... | [
"0.7136164",
"0.66953665",
"0.6398843",
"0.6384446",
"0.63746595",
"0.6355251",
"0.6276334",
"0.62578547",
"0.6232429",
"0.6153676",
"0.61311704",
"0.60923487",
"0.60865486",
"0.6026433",
"0.59538",
"0.5909406",
"0.5902352",
"0.58999",
"0.58918047",
"0.58892024",
"0.58742887"... | 0.6803808 | 1 |
Generates a sample configuration. | def create_default(self):
self.database.lifetime = 604800
self.database.path_media = '../data/media.db'
self.database.path_playlist = '../data/playlist.db'
self.indexing.audio.rules = [IndexerRuleConfig()]
self.indexing.audio.rules[0].directory = '/mnt/hdd/Audio'
self.indexing.audio.rules[0].extensions = ['.flac', '.mp3', '.ogg', '.wav']
self.indexing.audio.rules[0].pattern = '{}/{}/{} {}'.format(
get_complete_tag(TAG_ARTIST),
get_complete_tag(TAG_ALBUM),
get_complete_tag(TAG_NUMBER),
get_complete_tag(TAG_TITLE))
self.indexing.image.rules = [IndexerRuleConfig()]
self.indexing.image.rules[0].directory = '/mnt/hdd/Image'
self.indexing.image.rules[0].extensions = ['.gif', '.jpg', '.jpeg', '.png']
self.indexing.image.rules[0].pattern = '{}/{}'.format(
get_complete_tag(TAG_ALBUM),
get_complete_tag(TAG_TITLE))
self.indexing.video.ignore_revisions = False
self.indexing.video.subtitle_rules = [IndexerRuleConfig()]
self.indexing.video.subtitle_rules[0].directory = '/mnt/hdd/Video'
self.indexing.video.subtitle_rules[0].extensions = ['.srt']
self.indexing.video.subtitle_rules[0].pattern = '{}/Subtitle/{}/{}/{}/{}/{}'.format(
get_complete_tag(TAG_TITLE),
get_complete_tag(TAG_QUALITY),
get_complete_tag(TAG_LANGUAGES),
get_complete_tag(TAG_LANGUAGE),
get_complete_tag(TAG_ANY),
get_complete_tag(TAG_EPISODE_TITLE))
self.indexing.video.video_rules = [IndexerRuleConfig()]
self.indexing.video.video_rules[0].directory = '/mnt/hdd/Video'
self.indexing.video.video_rules[0].extensions = ['.avi', '.flv', '.mkv', '.mp4']
self.indexing.video.video_rules[0].pattern = '{}/Content/{}/{}/{}/{}'.format(
get_complete_tag(TAG_TITLE),
get_complete_tag(TAG_QUALITY),
get_complete_tag(TAG_LANGUAGES),
get_complete_tag(TAG_ANY),
get_complete_tag(TAG_EPISODE_TITLE))
self.logging.enabled = True
self.logging.level = 'error'
self.logging.max_size_bytes = 524288
self.logging.path = '../data/log.txt'
self.multimedia.av_player = 'vlc'
self.multimedia.av_player_path = '/usr/bin/vlc-wrapper'
self.multimedia.image_viewer = 'feh'
self.multimedia.image_viewer_path = '/usr/bin/feh'
self.web.port = 8095 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _sample_hyperparameters(self):\n\t\tconfig = {}\n\t\tfor attr, option in self._config_options.items():\n\t\t\tprint('Sampling', attr)\n\t\t\tconfig[attr] = option.sample()\n\t\treturn config",
"def random_configuration(self):\n raise NotImplementedError",
"def generate_configuration(directory):\n ... | [
"0.65736026",
"0.6416811",
"0.6385334",
"0.6240616",
"0.621911",
"0.6212363",
"0.60647005",
"0.60343623",
"0.6023183",
"0.6003032",
"0.5997811",
"0.5997811",
"0.59653294",
"0.59653294",
"0.59533024",
"0.5914431",
"0.5902483",
"0.5882451",
"0.5871193",
"0.5864147",
"0.5847218"... | 0.0 | -1 |
Find all prime numbers between 0 and n | def get_primes(n):
primes = [True] * (n / 2)
for i in range(int((n / 2 - 1) / 2) >> 1):
for j in range((i * (i + 3) << 1) + 3, n / 2, (i << 1) + 3):
primes[j] = False
return [2] + [((i << 1) + 3) for i in range(n / 2) if (primes[i])] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def primes(n):\n return [i for i in xrange(1, n + 1) if mr_prime(i)]",
"def find_n_primes(n):\n primes = [ ]\n\n if n < 2:\n return None;\n\n primes.append(2)\n\n for i in range(3, n + 1, 2):\n is_prime = True\n for p in primes:\n if i % p is 0:\n is_prime = False\n continue\n ... | [
"0.8414769",
"0.8174812",
"0.8154782",
"0.8066897",
"0.79940116",
"0.7972602",
"0.7941718",
"0.79004717",
"0.78921247",
"0.7884613",
"0.786188",
"0.786017",
"0.7860028",
"0.78575575",
"0.78575575",
"0.7856777",
"0.78546125",
"0.78280634",
"0.78155655",
"0.7812109",
"0.7803009... | 0.77787405 | 21 |
Get all data spread across multiple pages | def _get_all_data(self, resource):
response = self._get_raising('{}{}?per_page=100&page=1'.format(
self.GH_API_ENDPOINT, resource
))
yield from response.json()
while 'next' in response.links:
response = self._get_raising(response.links['next']['url'])
yield from response.json() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _paginatedRequest(allPages, *args):\n data = []\n currentPage = 0\n while True:\n newData = Gw2Spidy._request(*(args + (str(currentPage),)))\n if not allPages:\n return newData['results']\n data.extend(newData['results'])\n current... | [
"0.75263435",
"0.739832",
"0.69225633",
"0.6902741",
"0.6879651",
"0.68711656",
"0.6810762",
"0.67072725",
"0.67072725",
"0.6681827",
"0.66562927",
"0.66062623",
"0.65945363",
"0.6549788",
"0.6540712",
"0.6535249",
"0.6503686",
"0.64717466",
"0.6464984",
"0.6408598",
"0.63991... | 0.638738 | 23 |
Get list of names of accessible repositories (including owner) | def list_repositories(self):
data = self._get_all_data('/user/repos')
return [repo['full_name'] for repo in data] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def repositories(self, user_name=None):\n user_name = user_name if user_name else self._auth[0]\n data = self._request('GET', 'users', user_name)\n return data.repositories\n #ret_val = []\n #for repository in data.repositories:\n # ret_val.append(repository.name)\n ... | [
"0.7608418",
"0.7267118",
"0.7101556",
"0.70416945",
"0.6976196",
"0.69618136",
"0.6874849",
"0.6862077",
"0.68582284",
"0.68060064",
"0.6804673",
"0.6765574",
"0.67367995",
"0.67278445",
"0.67108905",
"0.66979",
"0.6602462",
"0.6571694",
"0.65691054",
"0.6552383",
"0.6520411... | 0.78467643 | 0 |
Get dict of labels with colors for given repository slug | def list_labels(self, repository):
data = self._get_all_data('/repos/{}/labels'.format(repository))
return {l['name']: str(l['color']) for l in data} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def colors_for_labels():\n colors = [(i * np.array([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1]) % 255).astype(np.uint8) for i in range(len(CATEGORY))]\n #colors = np.array(range(len(COCO_INSTANCE_CATEGORY_NAMES))) * np.array([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])\n #colors = (colors % 255).numpy().astype(\"... | [
"0.65194696",
"0.5914808",
"0.5822218",
"0.5784747",
"0.57322896",
"0.5691123",
"0.5686196",
"0.5633396",
"0.56304544",
"0.56205434",
"0.55804044",
"0.5575821",
"0.5575821",
"0.5575821",
"0.5575821",
"0.5560394",
"0.55267113",
"0.55267113",
"0.5519801",
"0.5494467",
"0.549199... | 0.7589527 | 0 |
Create new label in given repository | def create_label(self, repository, name, color, **kwargs):
data = {'name': name, 'color': color}
response = self.session.post(
'{}/repos/{}/labels'.format(self.GH_API_ENDPOINT, repository),
json=data
)
if response.status_code != 201:
raise GitHubError(response) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_label(self, org, name):\n pass",
"def test_issue_create_label(self):\n pass",
"async def new_label(event, gh, *args, **kwargs):\n if event.data[\"label\"][\"name\"] == TRIVIAL_LABEL:\n issue_number_found = ISSUE_RE.search(\n event.data[\"pull_request\"][\"title\"])... | [
"0.7274326",
"0.7140176",
"0.67611367",
"0.6694404",
"0.6539713",
"0.6458034",
"0.64482284",
"0.6406368",
"0.63728607",
"0.6343112",
"0.6218493",
"0.6180373",
"0.6086001",
"0.6058881",
"0.60423875",
"0.6024324",
"0.6002186",
"0.5988165",
"0.5969681",
"0.59262604",
"0.5906312"... | 0.77359784 | 0 |
Update existing label in given repository | def update_label(self, repository, name, color, old_name=None, **kwargs):
data = {'name': name, 'color': color}
response = self.session.patch(
'{}/repos/{}/labels/{}'.format(
self.GH_API_ENDPOINT, repository, old_name or name
),
json=data
)
if response.status_code != 200:
raise GitHubError(response) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_labels(repo: Repository, labels: list[Label]):\n\n log.info(f\"Fetching existing labels from {repo.full_name}\")\n existing_labels = {label.name.casefold(): label for label in repo.get_labels()}\n log.info(f\"Found {len(existing_labels)} existing labels\")\n\n for label in labels:\n qual... | [
"0.6681116",
"0.6653021",
"0.64337254",
"0.6265893",
"0.62407804",
"0.6226869",
"0.61520106",
"0.6127353",
"0.61121166",
"0.61037016",
"0.6094828",
"0.603661",
"0.60320926",
"0.59240484",
"0.59008676",
"0.58834106",
"0.58732027",
"0.5860932",
"0.5839947",
"0.58363944",
"0.580... | 0.72466385 | 0 |
Delete existing label in given repository | def delete_label(self, repository, name, **kwargs):
response = self.session.delete(
'{}/repos/{}/labels/{}'.format(
self.GH_API_ENDPOINT, repository, name
)
)
if response.status_code != 204:
raise GitHubError(response) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_issue_delete_label(self):\n pass",
"def delete(ctx: click.Context, repository_path):\n root_commands.cmd_delete(ctx.obj, repository_path)",
"def repository_delete(ctx: click.Context, repository_name):\n subcommand_repository.cmd_delete(ctx.obj, repository_name)",
"def delete_label(id):\... | [
"0.7348744",
"0.716896",
"0.7127642",
"0.6931051",
"0.6877669",
"0.67660433",
"0.67263836",
"0.67033213",
"0.6610836",
"0.6601114",
"0.6599512",
"0.64673704",
"0.64669335",
"0.64562386",
"0.6455143",
"0.63759565",
"0.6242319",
"0.6198236",
"0.61135364",
"0.60569805",
"0.60212... | 0.8369633 | 0 |
Ask for a number between low and high until actually given one. Ask for a number, and if the response is outside the bounds keep asking until you get a number that you think is OK | def stubborn_asker(low, high):
import random
a=random.randint(1,100)
for i in range(1,10):
n=input('enter the number: ')
if n.isdigit():
n=int(n)
if n==a:
return('Correct')
break
elif n>a:
return('The number is bigger.')
elif n<a:
return('The number is smaller.')
else:
return('please enter an integer.')
i+=1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ask_number(question, low, high):\n response = None\n while response not in range(low, high, 1):\n response = input(question)\n return response",
"def ask_number(question, low, high):\n response = None\n while response not in range (low, high):\n response = int(input(question)... | [
"0.8282392",
"0.8165286",
"0.8158761",
"0.8158761",
"0.8125007",
"0.7887655",
"0.74961793",
"0.7476491",
"0.7303107",
"0.70028126",
"0.69437313",
"0.69389933",
"0.69310325",
"0.689875",
"0.68148214",
"0.67976105",
"0.67912346",
"0.6782751",
"0.66932416",
"0.6617285",
"0.65194... | 0.67488945 | 18 |
Extracts feature vectors from a given model and dataset and writes them, along with labels, to a file. This function works for any model whose forward() method returns, on any given input x, the pair (prediction on x, feature vector for x) and more generally, any model whose second return value is a feature vector. | def extract_feature_vectors(model, data_loader, parameters, features_file_path):
feature_vectors, label_vectors = [], []
# Set model to evaluation mode
model.eval()
# Show progress bar while iterating over mini-batches
with tqdm(total=len(data_loader)) as progress_bar:
for i, (X_batch, Y_batch) in enumerate(data_loader):
# Dimensions of the input Tensor
batch_size, channels, height, width = X_batch.size()
# If GPU available, enable CUDA on data
if parameters.cuda:
X_batch = X_batch.cuda()
Y_batch = Y_batch.cuda()
# Wrap the input tensor in a Torch Variable
X_batch_variable = Variable(X_batch, volatile=True)
# Run the model on this batch of inputs, obtaining a Variable of predicted labels and a Variable of features
Y_predicted, features = model(X_batch_variable)
# Convert the features Variable (of size [batch_size, 1024]) to a Tensor, move it to
# CPU, and convert it to a NumPy array
features_numpy = features.data.cpu().numpy()
# Move the labels Tensor (of size [batch_size, 14]) to CPU and convert it to a NumPy array
Y_numpy = Y_batch.cpu().numpy()
# For each example in the batch, record its features and labels
for j in range(batch_size):
feature_vectors.append(features_numpy[j,:])
label_vectors.append(Y_numpy[j,:])
progress_bar.update()
utils.write_feature_and_label_vectors(features_file_path, feature_vectors, label_vectors) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_vectors (feat_vec = None, labels = None, file_extension = None):\n\n feat_file_name = 'output/' + file_extension + '.feature'\n label_file_name = 'output/' + file_extension + '.label'\n\n prettyPrint('Saving feature vector file: {0} ... \\n'\n 'Saving Labels file: {1} ... '.format(... | [
"0.6803554",
"0.6240717",
"0.6221068",
"0.6063516",
"0.6009722",
"0.5950848",
"0.5824239",
"0.58020353",
"0.57663727",
"0.57563233",
"0.5755955",
"0.57133436",
"0.5701105",
"0.5675284",
"0.56728137",
"0.56672686",
"0.56518734",
"0.56423616",
"0.56261265",
"0.56195384",
"0.560... | 0.6868015 | 0 |
Returns the average distance between pairs of vectors in a given list of vectors. | def average_distance_between_vectors(vectors, distance):
vectors = numpy.array(vectors)
vectors = vectors - numpy.mean(vectors, axis=0)
vectors = normalize(vectors)
vectors = list(vectors)
average_distance = utils.RunningAverage()
for vector_1, vector_2 in itertools.combinations(vectors, r=2): # All pairs of vectors
average_distance.update(distance(vector_1, vector_2))
return average_distance() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def average_vectors(vectors_list):\n return np.mean(vectors_list, axis=0)",
"def compute_average(vec_list):\r\n return np.sum(vec_list, axis = 0)/len(vec_list)",
"def average(cls, vectors):\n return cls.sum(vectors) / len(vectors)",
"def vector_mean(vectors: List[Vector]) -> Vector:\n n = len... | [
"0.7456902",
"0.7266837",
"0.7161427",
"0.66369",
"0.66369",
"0.6613315",
"0.6526983",
"0.6526983",
"0.652161",
"0.652161",
"0.652161",
"0.6493121",
"0.6426592",
"0.641932",
"0.61453825",
"0.6135802",
"0.6098236",
"0.60969055",
"0.60628074",
"0.6040219",
"0.6035585",
"0.601... | 0.8062336 | 0 |
Reads feature vectors and labels from a file and prints information about their clustering properties. Here, we think of the space of feature vectors, and consider a vector v_i to be in cluster j if j is one of the labels for example i. | def analyze_feature_vector_clusters(features_file_path, distance=utils.L2_distance):
feature_vectors, label_vectors = utils.read_feature_and_label_vectors(features_file_path)
logging.info('Building clusters...')
# Map from (integer j) --> (list of indices i such that feature_vectors[i] is in cluster j)
# Cluster 0 indicates no disease
indices_for_label = map_labels_to_example_indices(label_vectors)
logging.info('...done.')
logging.info('Computing global and within-cluster average distances')
# Compute average distance between vectors overall
global_average_distance = average_distance_between_vectors(feature_vectors, distance)
logging.info('Global average ' + distance.__name__ + ' between vectors: ' + str(global_average_distance))
# Compute average distance within each cluster
for j, vector_indices in indices_for_label.items():
vectors_in_cluster = [feature_vectors[index] for index in vector_indices]
average_cluster_distance = average_distance_between_vectors(vectors_in_cluster, distance)
logging.info('Average ' + distance.__name__ + ' between vectors in cluster ' + str(j) + ': ' + str(average_cluster_distance)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_vectors (file_extension = None):\n \n feat_file_name = 'output/' + file_extension + '.feature'\n label_file_name = 'output/' + file_extension + '.label'\n \n prettyPrint( \"Loading feature vectors and labels from disk ... \", color.CYAN)\n if not os.path.isfile(feat_file_name) or not os.... | [
"0.5931401",
"0.58759993",
"0.5818201",
"0.5670178",
"0.55925786",
"0.5564548",
"0.5553166",
"0.5509292",
"0.5509292",
"0.5505033",
"0.55044186",
"0.5498727",
"0.5490172",
"0.54632276",
"0.54203254",
"0.54109955",
"0.5390733",
"0.5373782",
"0.5334791",
"0.5330502",
"0.5268046... | 0.65529263 | 0 |
Make custom attributes and methods to native | def native_methods(self):
base_attributes = {
*dir(TapiAdapter),
*dir(TapiClientExecutor),
*dir(JSONAdapterMixin),
"serializer",
}
a = [
attr
for attr in dir(self)
if not attr.startswith("_") and attr not in base_attributes
]
return a | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def attributes(self):\n raise NotImplementedError",
"def __attrs_post_init__(self):",
"def _set_attributes(self):",
"def __setattr__(self, *args, **kwargs): # real signature unknown\n pass",
"def __setattr__(self, *args, **kwargs): # real signature unknown\n pass",
"def __setattr__(self,... | [
"0.6156814",
"0.60756165",
"0.5929101",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
"0.5887519",
... | 0.0 | -1 |
Create of url request | def fill_resource_template_url(self, template, params, resource):
try:
return template.format(**params)
except KeyError:
all_keys = re.findall(r"{(.[^\}]*)", template)
range_not_set_keys = set(all_keys) - set(params.keys())
not_set_keys = "', '".join(range_not_set_keys)
raise TypeError(
"{}() missing {} required url params: '{}'".format(
resource, len(range_not_set_keys), not_set_keys
)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_request_url():\n url = 'http'\n if _config['save']:\n url += 's'\n url += '://{}:{}/move'.format(_config['ip'], _config['port'])\n return url",
"def _CreateRequest(self, url, data=None):\r\n logging.debug(\"Creating request for: '%s' with payload:\\n%s\", url, data)\r\n req =... | [
"0.7095145",
"0.70785993",
"0.7020341",
"0.69661885",
"0.6836974",
"0.67563176",
"0.6740314",
"0.6718406",
"0.6607577",
"0.6582395",
"0.65728194",
"0.65544945",
"0.6512967",
"0.6509939",
"0.6411456",
"0.6373932",
"0.63712084",
"0.63712084",
"0.6340278",
"0.62835985",
"0.62759... | 0.0 | -1 |
Adding parameters to a request | def get_request_kwargs(self, api_params, *args, **kwargs):
serialized = self.serialize_data(kwargs.get("data"))
kwargs["data"] = self.format_data_to_request(serialized)
return kwargs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_params(self, params: t.Mapping[str, str]) -> 'Request':\n return replace(self, params={**self.params, **params})",
"def additional_access_token_request_parameters(self, parameters, request):",
"def get_request_extra_params(self, **kwargs):\n params = self.request_extra_params.copy()\n ... | [
"0.74066496",
"0.72835517",
"0.6995281",
"0.6825499",
"0.68013364",
"0.65432173",
"0.6506432",
"0.64661425",
"0.6460794",
"0.63875496",
"0.6370902",
"0.6263036",
"0.62520313",
"0.6237418",
"0.6223249",
"0.61894953",
"0.6144124",
"0.61332905",
"0.6104013",
"0.60877264",
"0.607... | 0.58247703 | 53 |
Get error from response. | def get_error_message(self, data, response=None):
return str(data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getError(self):\n \n return self.resp[\"error\"]",
"def _get_error_message(response):\n try:\n return response.json()[\"detail\"]\n except (KeyError, _JSONDecodeError):\n return response.text",
"def error(self):\n error = self._wrapped.error\n ... | [
"0.83358467",
"0.78637624",
"0.7812136",
"0.7469619",
"0.74155784",
"0.7169605",
"0.7166862",
"0.7096074",
"0.7082572",
"0.704426",
"0.7022822",
"0.69620997",
"0.6958218",
"0.6933554",
"0.6891388",
"0.68128866",
"0.67896175",
"0.67648655",
"0.6737759",
"0.66620314",
"0.664474... | 0.6469981 | 34 |
Wrapper for throwing custom exceptions. When, for example, the server responds with 200, and errors are passed inside json. | def error_handling(
self,
tapi_exception,
error_message,
repeat_number,
response,
request_kwargs,
api_params,
**kwargs
):
raise tapi_exception | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_json_error(ex):\n if isinstance(ex, HTTPException):\n return ex;\n elif isinstance(ex, ResourceException):\n info = ex.to_dict()\n status_code = ex.http_status\n info[\"type\"] = \"exception\"\n else:\n message = \"There was an in... | [
"0.73746616",
"0.73421353",
"0.73373085",
"0.7094187",
"0.7060387",
"0.70266795",
"0.7011387",
"0.69837064",
"0.69805527",
"0.6943718",
"0.6921114",
"0.6909217",
"0.69065756",
"0.6879482",
"0.67980826",
"0.67973834",
"0.67954606",
"0.678849",
"0.6778472",
"0.67188394",
"0.671... | 0.0 | -1 |
Conditions for repeating a request. If it returns True, the request will be repeated. | def retry_request(
self,
tapi_exception,
error_message,
repeat_number,
response,
request_kwargs,
api_params,
**kwargs
):
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _do_request(self):\n\n if time.time() < self._next_request:\n return False\n else:\n return True",
"def valid_in_request(self):\n return self._repeatable[0] is not None",
"def valid_multiple_in_request(self):\n return self._repeatable[0] is True",
"def va... | [
"0.7024639",
"0.6854883",
"0.6750136",
"0.6601198",
"0.6564489",
"0.64355195",
"0.61150056",
"0.60088533",
"0.5957636",
"0.58485484",
"0.57753676",
"0.574291",
"0.57246184",
"0.5706755",
"0.55322963",
"0.549886",
"0.5494331",
"0.5449525",
"0.5437684",
"0.5399534",
"0.5398021"... | 0.5810275 | 10 |
Stuff to do before every test. | def setUp(self):
# Get the Flask test client
self.client = app.test_client()
#Shows Flask errors that happen during tests
app.config['TESTING'] = True
#To test sessions we need to set Secret key
app.config['SECRET_KEY'] = 'key'
# Connect to test database
connect_to_db(app, "postgresql:///testdb")
# Create tables and add sample data
db.create_all()
users()
reviews() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def before_run_tests(cls):\n pass",
"def do_before(self):\r\n pass",
"def before_test(self, func, *args, **kwargs):\n pass",
"def setUp(self):\r\n # nothing to do, all tests use different things\r\n pass",
"def setUp(self):\n print(\"New test by Nikolay Melnik\")",... | [
"0.8211315",
"0.802689",
"0.80261695",
"0.7873411",
"0.76571673",
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"0.75419456",
"0.75174975",
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"0.74386895",
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"0.74386895",
"0.74386895",
"0.74386895",
"0... | 0.0 | -1 |
Stuff to do before every test. | def setUp(self):
# Get the Flask test client
self.client = app.test_client()
# Show Flask errors that happen during tests
app.config['TESTING'] = True
#To test sessions we need to set Secret key
app.config['SECRET_KEY'] = 'key'
# Connect to test database
connect_to_db(app, "postgresql:///testdb")
# Create tables and add sample data
db.create_all()
users()
reviews()
with self.client as c:
with c.session_transaction() as sess:
sess['user_id'] = 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def before_run_tests(cls):\n pass",
"def do_before(self):\r\n pass",
"def before_test(self, func, *args, **kwargs):\n pass",
"def setUp(self):\r\n # nothing to do, all tests use different things\r\n pass",
"def setUp(self):\n print(\"New test by Nikolay Melnik\")",... | [
"0.8211315",
"0.802689",
"0.80261695",
"0.7873411",
"0.76571673",
"0.76434696",
"0.76261467",
"0.75419456",
"0.75174975",
"0.7496759",
"0.7496759",
"0.74386895",
"0.74386895",
"0.74386895",
"0.74386895",
"0.74386895",
"0.74386895",
"0.74386895",
"0.74386895",
"0.74386895",
"0... | 0.0 | -1 |
Do at end of every test. | def tearDown(self):
db.session.close()
db.drop_all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_finished(self):\n\n # We'll start the next test in an idle, so that the current one is\n # properly terminated, and we do not execute in its context\n\n GLib.idle_add(self._do_test)",
"def test_run_ended(self):",
"def finished_tests(self):\n self.testing = 0",
... | [
"0.7842646",
"0.7775997",
"0.77741504",
"0.77669877",
"0.76620615",
"0.7591832",
"0.74713165",
"0.7361014",
"0.73159444",
"0.7298099",
"0.72939897",
"0.72939897",
"0.7259042",
"0.723601",
"0.72323287",
"0.7225132",
"0.7225132",
"0.72186595",
"0.720453",
"0.720453",
"0.720453"... | 0.0 | -1 |
Test this page can only be reached if user is in session | def test_user_profile_page(self):
result = self.client.get("/profile", follow_redirects=True)
self.assertIn(b"User ID", result.data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_session_not_accessed(self):\n response = self.client.get(\"/auth_processor_no_attr_access/\")\n self.assertContains(response, \"Session not accessed\")",
"def test_not_logged_user_cannot_access(self):\n\n utils.test_not_logged_cannot_access(self, self.url)",
"def test_not_logged_u... | [
"0.7483035",
"0.743996",
"0.743996",
"0.743996",
"0.743996",
"0.73823667",
"0.7071074",
"0.7004314",
"0.7000037",
"0.69217306",
"0.6881494",
"0.68084276",
"0.68084276",
"0.6783943",
"0.67708766",
"0.67426723",
"0.6722217",
"0.6721236",
"0.6721236",
"0.6721236",
"0.6698859",
... | 0.0 | -1 |
Test this page can only be reached if user is in session | def test_search_page(self):
result = self.client.get("/search")
self.assertIn(b"Search", result.data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_session_not_accessed(self):\n response = self.client.get(\"/auth_processor_no_attr_access/\")\n self.assertContains(response, \"Session not accessed\")",
"def test_not_logged_user_cannot_access(self):\n\n utils.test_not_logged_cannot_access(self, self.url)",
"def test_not_logged_u... | [
"0.7482324",
"0.7440397",
"0.7440397",
"0.7440397",
"0.7440397",
"0.7383309",
"0.707093",
"0.7003852",
"0.6998608",
"0.6921593",
"0.68799853",
"0.6807848",
"0.6807848",
"0.6783388",
"0.67709535",
"0.6742237",
"0.67224073",
"0.6720221",
"0.6720221",
"0.6720221",
"0.669822",
... | 0.0 | -1 |
Test user's if favourite restaurant is added to DB | def test_add_to_fav_(self):
result = self.client.post("/add_to_fav", data={"yelp_biz_id":"JA_V9TqDCrkgknqrcUndIQ",
"yelp_rest_name":"Siam", "yelp_rating":"4",
"yelp_category":"Thai", "yelp_price":"$$",
"yelp_image_url":"https://s3-media2.fl.yelpcdn.com/bphoto/1SkZwZrRZkQSzRMn_Trs3w/o.jpg" })
DB_result = Restaurant_details.query.filter_by(biz_id = "JA_V9TqDCrkgknqrcUndIQ").first()
self.assertIsNotNone(DB_result) #testing that the returned result is not NONE
self.assertEqual(DB_result.restaurant_name, 'Siam') #testing restaurant name is what it should be
self.assertIn(b"Your Favourite has been saved", result.data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_Favourite(self):\n self.assertEquals(self.fav_1.pk, 1)\n self.assertEquals(self.fav_1.date_added, '2019-12-20 09:00:00')\n self.assertEquals(self.fav_1.user.pk, 1)\n self.assertEquals(self.fav_1.product.pk, 1)",
"async def create(self, favorite: Favorite) -> Favorite:",
"de... | [
"0.72114295",
"0.6636154",
"0.6632463",
"0.65658706",
"0.64831656",
"0.6482696",
"0.6476188",
"0.64745235",
"0.6445702",
"0.6381491",
"0.6374218",
"0.6319528",
"0.6261317",
"0.6236881",
"0.6179164",
"0.6172634",
"0.6163744",
"0.6161769",
"0.6146276",
"0.6124457",
"0.60840327"... | 0.78729564 | 0 |
Stuff to do before every test. | def setUp(self):
# Get the Flask test client
self.client = app.test_client()
# Show Flask errors that happen during tests
app.config['TESTING'] = True
# Connect to test database
connect_to_db(app, "postgresql:///testdb")
# Create tables and add sample data
db.create_all()
users()
reviews()
with self.client as c:
with c.session_transaction() as sess:
sess['user_id'] = 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def before_run_tests(cls):\n pass",
"def do_before(self):\r\n pass",
"def before_test(self, func, *args, **kwargs):\n pass",
"def setUp(self):\r\n # nothing to do, all tests use different things\r\n pass",
"def setUp(self):\n print(\"New test by Nikolay Melnik\")",... | [
"0.8209909",
"0.8028296",
"0.80253834",
"0.7872079",
"0.76552856",
"0.76427346",
"0.7627397",
"0.7540311",
"0.7515845",
"0.74945575",
"0.74945575",
"0.74365586",
"0.74365586",
"0.74365586",
"0.74365586",
"0.74365586",
"0.74365586",
"0.74365586",
"0.74365586",
"0.74365586",
"0... | 0.0 | -1 |
Do at end of every test. | def tearDown(self):
db.session.close()
db.drop_all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_finished(self):\n\n # We'll start the next test in an idle, so that the current one is\n # properly terminated, and we do not execute in its context\n\n GLib.idle_add(self._do_test)",
"def test_run_ended(self):",
"def finished_tests(self):\n self.testing = 0",
... | [
"0.78425974",
"0.77754915",
"0.77742684",
"0.7766872",
"0.76616216",
"0.75920033",
"0.7470884",
"0.73606",
"0.73159856",
"0.7297722",
"0.7293865",
"0.7293865",
"0.72588974",
"0.7235985",
"0.72320426",
"0.7224789",
"0.7224789",
"0.72181326",
"0.72043455",
"0.72043455",
"0.7204... | 0.0 | -1 |
Find restaurant name by zipcode. | def test_process_searchbox_with_mock(self):
result = self.client.get('/process_searchbox', data={'zipcode': '94043', 'cuisine': 'indian'})
self.assertIn(b"Dosa Paratha", result.data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def county_name(zipcode): \n search = SearchEngine(simple_zipcode=True) # set simple_zipcode=False to use rich info database\n zipcode_query = search.by_zipcode(str(zipcode))\n zipcode_query_dict = zipcode_query.to_dict()\n county = zipcode_query_dict['county']\n if county is None:\n print('I... | [
"0.72173095",
"0.669443",
"0.66253436",
"0.66198653",
"0.64643675",
"0.6446394",
"0.6327299",
"0.62487876",
"0.6163434",
"0.6108556",
"0.6016015",
"0.5976856",
"0.59515876",
"0.5928086",
"0.57584274",
"0.57179135",
"0.5710414",
"0.5634522",
"0.56298053",
"0.5622634",
"0.55962... | 0.0 | -1 |
Return the nth Fibonacci number | def fibonacci(n):
if n in (0,1):
return n
return (fibonacci(n-2) + fibonacci(n-1)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fibonacci(n):",
"def nthFibonacci(n):\n\n # Run some basic error checking\n try:\n n = int(n)\n except: # if this fails not a number inputed\n sys.stderr.write('Incorrect data input\\n')\n return None\n if n < 0:\n sys.stderr.write('Only positive integers allowed\\n')\n return None\n \n ... | [
"0.8549428",
"0.83808696",
"0.8357446",
"0.8267962",
"0.82505155",
"0.8213346",
"0.8213339",
"0.8212905",
"0.81149447",
"0.8098057",
"0.80716306",
"0.8040074",
"0.8032861",
"0.8029916",
"0.8029916",
"0.8019259",
"0.8019077",
"0.8012156",
"0.8007875",
"0.8001539",
"0.79852134"... | 0.78968835 | 39 |
Get the eol mode map | def EOLModeMap():
# Maintenance Note: ints must be kept in sync with EDSTC_EOL_* in edstc
return { EOL_MODE_CR : _("Old Machintosh (\\r)"),
EOL_MODE_LF : _("Unix (\\n)"),
EOL_MODE_CRLF : _("Windows (\\r\\n)")} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_eol_for_open(self) -> str:\n map = {\n EOLTypes.CRLF: WINDOWS_EOL,\n EOLTypes.LF: UNIX_EOL,\n EOLTypes.NATIVE: linesep,\n }\n\n return map[self]",
"def get_modes(self):\n return [i for i, j in enumerate(self._modemap._map) if j is not None]",
... | [
"0.63044417",
"0.6147848",
"0.5959693",
"0.5748074",
"0.57146144",
"0.5593236",
"0.54169565",
"0.54102844",
"0.53679585",
"0.5359293",
"0.5346539",
"0.5331634",
"0.53012353",
"0.5280056",
"0.52593654",
"0.52485204",
"0.51640564",
"0.5128611",
"0.51207393",
"0.51181734",
"0.50... | 0.808408 | 0 |
Base Class for all controls. On its own, this is just a Label in a BoxLayout. | def __init__(self,
label:str=None,
variable_name:str=None,
value:typing.Any=None,
parent:QtWidgets.QWidget=None,
on_change:typing.Callable=None):
QtWidgets.QWidget.__init__(self, parent=parent)
if label is None:
if variable_name is None:
label = ""
else:
label = app.translator(variable_name)
self._make_label_widget(label)
self.layout = self._formset()
self.setLayout(self.layout)
self.label = label
ValueMixin.__init__(self, variable_name=variable_name, on_change=on_change, value=value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_label(self):\n\n self.pc_label = Label(self.form_box, text=\"Primary Current [A]:\", anchor='nw', width=32,\n bg=self.design.color.secondary, font=('Arial', 15))\n self.sc_label = Label(self.form_box, text=\"Secondary Current [A]:\", anchor='nw', width=32,\n ... | [
"0.68835676",
"0.66542274",
"0.63613844",
"0.6344261",
"0.63232493",
"0.630243",
"0.6290771",
"0.61542106",
"0.6145763",
"0.6128528",
"0.60839635",
"0.60793793",
"0.6052032",
"0.60399705",
"0.60251766",
"0.6019413",
"0.5956044",
"0.59486943",
"0.5947741",
"0.5929713",
"0.5921... | 0.60125613 | 16 |
fit le modele puis retourne ses performances en test | def score(self, archi:ArchitectureNN):
archi.fit_model(self.train_data, **self.train_params)
return archi.compute_test_score(self.test_data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit_test(self):",
"def experiment_models(train, test, train_target, test_target):\n # Linear models\n linear_models = [(LinearRegression, {\"n_jobs\": -1}),\n (Lasso, {\"alpha\": 3}),\n (Ridge, {\"alpha\": 3}),\n (LinearSVR, {\"random_state\":... | [
"0.70706695",
"0.64831716",
"0.64393735",
"0.6433011",
"0.64119494",
"0.6411802",
"0.6275987",
"0.624839",
"0.62458396",
"0.62132657",
"0.62124383",
"0.6203546",
"0.62026036",
"0.6191927",
"0.6183838",
"0.615125",
"0.61467177",
"0.61371404",
"0.6132172",
"0.6127471",
"0.61209... | 0.0 | -1 |
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