Sorry, we couldn’t find that page.
+Try using the search box or go to the homepage.
+
+from typing import List
+import re
+import pandas as pd
+import osmnx as ox
+from shapely.geometry import Point, Polygon, MultiPolygon
+from loguru import logger
+import pymorphy2
+from sloyka.src.utils.constants import NUM_CITY_OBJ
+from sloyka.src.geocoder.objects_address_extractor_by_rules import AddressExtractorExtra
+from sloyka.src.utils.data_getter.geo_data_getter import GeoDataGetter
+from rapidfuzz import fuzz
+import numpy as np
+
+
+
+[docs]
+class OtherGeoObjects:
+
+[docs]
+ @staticmethod
+ def get_and_process_osm_data(osm_id: int, tags: dict) -> pd.DataFrame:
+ """
+ Retrieves and processes OSM data for different urban objects.
+ """
+ df = GeoDataGetter.get_osm_data(osm_id, tags)
+ df["geometry"] = df["geometry"].apply(OtherGeoObjects.calculate_centroid)
+ df.rename(columns={df.columns[-1]: "geo_obj_tag"}, inplace=True)
+ return df
+
+
+
+[docs]
+ @staticmethod
+ def run_osm_dfs(osm_id: int) -> pd.DataFrame:
+ """
+ Collects dataframes with OSM spatial data, finds centroids and combines them into one.
+ """
+ tags_list = [
+ {"leisure": ["park", "garden", "recreation_ground"]},
+ {"amenity": ["hospital", "clinic", "school", "kindergarten"]},
+ {"landuse": ["cemetery"]},
+ {"natural": ["beach", "water"]},
+ {"railway": ["station", "subway"]},
+ {"tourism": ["attraction", "museum"]},
+ {"historic": ["monument", "memorial"]},
+ {"place": ["square"]},
+ ]
+
+ osm_dfs = list()
+ for tags in tags_list:
+ logger.debug(f'getting {osm_id, tags}')
+ try:
+ tmp_df = OtherGeoObjects.get_and_process_osm_data(osm_id, tags)
+ osm_dfs.append(tmp_df)
+ except RuntimeError:
+ logger.warning(f'Runtime error during fetching {osm_id, tags}')
+ continue
+ if osm_dfs:
+ osm_combined_df = pd.concat(osm_dfs, axis=0)
+ logger.debug(f'got {osm_id, tags}')
+ logger.debug(f'{osm_combined_df.shape}')
+ return osm_combined_df
+ else:
+ logger.warning(f'No data were gathered about city objects in {osm_id}')
+ return pd.DataFrame()
+
+
+
+[docs]
+ @staticmethod
+ def calculate_centroid(geometry) -> Point:
+ """
+ Calculates the centroid for polygons.
+ """
+ if isinstance(geometry, (Polygon, MultiPolygon)):
+ return geometry.centroid
+ elif isinstance(geometry, Point):
+ return geometry
+ else:
+ return None
+
+
+
+[docs]
+ @staticmethod
+ def extract_geo_obj(text) -> List[str]:
+ """
+ The function extracts location entities from the text, using the Natasha library.
+ """
+ if text is None:
+ return None
+ morph = pymorphy2.MorphAnalyzer()
+ extractor = AddressExtractorExtra(morph)
+
+ other_geo_obj = []
+
+ matches = extractor(text)
+ if not matches:
+ return other_geo_obj
+ try:
+ for match in matches:
+ if not match:
+ continue
+ part = match.fact
+ if part.value and part.type:
+ combined_phrase = f"{part.value} {part.type}"
+ other_geo_obj.append(combined_phrase)
+ elif part.value:
+ other_geo_obj.append(part.value)
+ elif part.type:
+ other_geo_obj.append(part.type)
+ if not other_geo_obj:
+ return other_geo_obj
+ except Exception as e:
+ # logger.warning(f"Error extracting geo objects: {e}")
+ return other_geo_obj
+ return other_geo_obj
+
+
+
+[docs]
+ @staticmethod
+ def restoration_of_normal_form(other_geo_obj, osm_combined_df, threshold=0.7) -> List[str]:
+ """
+ This function compares the extracted location entity with an OSM array and returns a normalized form if the percentage of similarity is at least 70%.
+ """
+ osm_name_obj = osm_combined_df["name"].tolist()
+ similarity_matrix = np.zeros((len(other_geo_obj), len(osm_name_obj)))
+
+ def extract_numbers(s):
+ return re.findall(r"\d+", s)
+
+ for i, word1 in enumerate(other_geo_obj):
+ numbers_from_extraction = extract_numbers(word1)
+ for j, word2 in enumerate(osm_name_obj):
+ numbers_from_OSM_name = extract_numbers(word2)
+ if numbers_from_extraction == numbers_from_OSM_name:
+ similarity = fuzz.ratio(word1, word2) / 100.0
+ else:
+ similarity = 0
+ similarity_matrix[i, j] = similarity
+
+ restoration_list = other_geo_obj.copy()
+ for i in range(len(other_geo_obj)):
+ max_index = np.argmax(similarity_matrix[i])
+ if similarity_matrix[i, max_index] > threshold:
+ restoration_list[i] = osm_name_obj[max_index]
+ else:
+ restoration_list[i] = ""
+
+ return restoration_list
+
+
+ @staticmethod
+ def find_num_city_obj(text, NUM_CITY_OBJ) -> List[str]:
+ """
+ This function searches for urban objects in the text, the names of which are represented as a number. For example, "school No. 6".
+ """
+ text = str(text)
+ text = text.lower()
+ num_obj_list = []
+ for key, forms in NUM_CITY_OBJ.items():
+ for form in forms:
+ pattern = rf"\b{re.escape(form)}\b\s+№?\s*(\d+)"
+ matches = re.findall(pattern, text)
+ for match in matches:
+ num_obj_list.append(f"{key} № {match}")
+ num_obj_list = list(set(num_obj_list))
+ num_obj_list_clear = {}
+ for obj in num_obj_list:
+ key = obj.split(" № ")[1]
+ if key in num_obj_list_clear:
+ if len(obj.split(" № ")[0]) > len(num_obj_list_clear[key].split(" № ")[0]):
+ num_obj_list_clear[key] = obj
+ else:
+ num_obj_list_clear[key] = obj
+
+ return list(num_obj_list_clear.values())
+
+ @staticmethod
+ def combine_city_obj(df_obj) -> pd.DataFrame:
+ """
+ Combines the found named urban objects and urban objects whose names are in the form of numbers.
+ """
+ df_obj["other_geo_obj"] = df_obj["other_geo_obj"] + df_obj["other_geo_obj_num"]
+ df_obj.drop(columns=["other_geo_obj_num"], inplace=True)
+ return df_obj
+
+ @staticmethod
+ def expand_toponym(df_obj) -> pd.DataFrame:
+ """
+ Splits the list of found entities into different rows for further analysis.
+ """
+ expanded_df = df_obj.copy()
+ expanded_df["other_geo_obj"] = expanded_df["other_geo_obj"].apply(
+ lambda x: x if isinstance(x, list) and x else None
+ )
+ expanded_df = expanded_df.explode("other_geo_obj").reset_index(drop=True)
+ return expanded_df
+
+ @staticmethod
+ def find_geometry(toponym, osm_combined_df) -> Point:
+ """
+ Finds the coordinate in the OSM array by the name of the city object.
+ """
+ if toponym is None:
+ return None
+ match = osm_combined_df[osm_combined_df["name"] == toponym]
+ if not match.empty:
+ return match.iloc[0, 1]
+ else:
+ return None
+
+ @staticmethod
+ def find_geo_obj_tag(toponym, osm_combined_df) -> str:
+ """
+ Finds the geo_obj_tag in the OSM array by the name of the city object.
+ """
+ if toponym is None:
+ return None
+ match = osm_combined_df[osm_combined_df["name"] == toponym]
+ if not match.empty:
+ return match.iloc[0, -1]
+ else:
+ return None
+
+ @staticmethod
+ def get_unique_part_types(df):
+ return df["other_geo_obj"].unique()
+
+
+[docs]
+ @staticmethod
+ def run(osm_id: int, df: pd.DataFrame, text_column: str) -> pd.DataFrame:
+ """
+ Launches the module for extracting urban objects from texts that do not relate to streets.
+ """
+ df_obj = df.copy()
+ df_obj["Numbers"] = pd.NA
+ # osm_combined_df = OtherGeoObjects.run_osm_dfs(osm_id)
+
+ df_obj["other_geo_obj"] = df_obj[text_column].apply(OtherGeoObjects.extract_geo_obj)
+ df_obj["other_geo_obj_num"] = df_obj[text_column].apply(
+ lambda x: OtherGeoObjects.find_num_city_obj(x, NUM_CITY_OBJ)
+ )
+
+ df_obj = OtherGeoObjects.combine_city_obj(df_obj)
+
+ osm_combined_df = OtherGeoObjects.run_osm_dfs(osm_id)
+
+ if not osm_combined_df.empty:
+ df_obj["other_geo_obj"] = df_obj["other_geo_obj"].apply(
+ lambda x: OtherGeoObjects.restoration_of_normal_form(x, osm_combined_df)
+ )
+ df_obj = OtherGeoObjects.expand_toponym(df_obj)
+
+ df_obj["geometry"] = df_obj["other_geo_obj"].apply(lambda x: OtherGeoObjects.find_geometry(x, osm_combined_df))
+ df_obj["geo_obj_tag"] = df_obj["other_geo_obj"].apply(
+ lambda x: OtherGeoObjects.find_geo_obj_tag(x, osm_combined_df)
+ )
+ df_obj = df_obj[df_obj["geometry"].notna()]
+
+ return df_obj
+
+
+
+"""
+TODO: add spellchecker since there might be misspelled words.
+
+This module is aimed to provide necessary tools to find mentioned
+location in the text.
+
+@class:Location:
+A class aimed to efficiently geocode addresses using Nominatim. Geocoded addresses are stored in the 'book' dictionary argument.
+Thus, if the address repeats, it would be taken from the book.
+
+@class:Streets:
+A class encapsulating functionality for retrieving street data
+for a specified city from OSM and processing it to extract useful information for geocoding purposes.
+
+@class:Geocoder:
+A class providing functionality for simple geocoding and address extraction.
+"""
+import numpy as np
+import re
+import warnings
+import os
+import flair
+import geopandas as gpd
+import pandas as pd
+import pymorphy2
+import torch
+import string
+import math
+from rapidfuzz import fuzz
+from nltk.stem.snowball import SnowballStemmer
+from sloyka.src.utils.data_getter.historical_geo_data_getter import HistGeoDataGetter
+from sloyka.src.utils.constants import (
+ AREA_STOPWORDS,
+ GROUP_STOPWORDS,
+ REGEX_PATTERN,
+ REPLACEMENT_STRING,
+)
+
+from flair.models import SequenceTagger
+from shapely.geometry import Point
+from tqdm import tqdm
+# from natasha import (
+# Segmenter,
+# MorphVocab,
+# NewsEmbedding,
+# NewsMorphTagger,
+# NewsSyntaxParser,
+# NewsNERTagger,
+# Doc,
+# )
+
+from loguru import logger
+
+from pandarallel import pandarallel
+from sloyka.src.geocoder.city_objects_getter import OtherGeoObjects
+from sloyka.src.utils.data_getter.street_getter import Streets
+from sloyka.src.utils.data_getter.location_getter import Location
+from sloyka.src.utils.data_getter.geo_data_getter import GeoDataGetter
+from sloyka.src.geocoder.street_extractor import StreetExtractor
+from sloyka.src.geocoder.word_form_matcher import WordFormFinder
+
+import warnings
+
+warnings.simplefilter("ignore")
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+pandarallel.initialize(progress_bar=True, nb_workers=-1)
+
+# segmenter = Segmenter()
+# morph_vocab = MorphVocab()
+morph = pymorphy2.MorphAnalyzer()
+
+# emb = NewsEmbedding()
+# morph_tagger = NewsMorphTagger(emb)
+# syntax_parser = NewsSyntaxParser(emb)
+# ner_tagger = NewsNERTagger(emb)
+warnings.simplefilter(action="ignore", category=FutureWarning)
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+
+
+
+stemmer = SnowballStemmer("russian")
+
+tqdm.pandas()
+
+
+
+[docs]
+class Geocoder:
+ """
+ This class provides a functionality of simple geocoder
+ """
+
+ dir_path = os.path.dirname(os.path.realpath(__file__))
+
+ global_crs: int = 4326
+
+ def __init__(
+ self,
+ model_path: str = "Geor111y/flair-ner-addresses-extractor",
+ device: str = "cpu",
+ osm_id: int = None,
+ city_tags: dict ={"place": ["state"]}
+ ):
+ self.device = device
+ flair.device = torch.device(device)
+ self.classifier = SequenceTagger.load(model_path)
+ self.osm_id = osm_id
+ self.osm_city_name = (
+ GeoDataGetter()
+ .get_features_from_id(osm_id=self.osm_id,tags=city_tags, selected_columns=["name", "geometry"])
+ .iloc[0]["name"]
+ )
+ self.street_names = Streets.run(self.osm_id)
+
+
+ @staticmethod
+ def get_stem(street_names_df: pd.DataFrame) -> pd.DataFrame:
+ """
+ Function finds the stem of the word to find this stem in the street
+ names dictionary (df).
+ """
+ logger.info("get_stem started")
+
+ morph = pymorphy2.MorphAnalyzer()
+ cases = ["nomn", "gent", "datv", "accs", "ablt", "loct"]
+
+ for case in cases:
+ street_names_df[case] = street_names_df["street_name"].apply(
+ lambda x: morph.parse(x)[0].inflect({case}).word if morph.parse(x)[0].inflect({case}) else None
+ )
+ return street_names_df
+
+
+ @staticmethod
+ def get_level(row: pd.Series) -> str:
+ """
+ Addresses in the messages are recognized on different scales:
+ 1. Where we know the street name and house number -- house level;
+ 2. Where we know only street name -- street level (with the centroid
+ geometry of the street);
+ 3. Where we don't know any info but the city -- global level.
+ """
+
+ if (not pd.isna(row["Street"])) and (row["Numbers"] == ""):
+ return "street"
+ elif (not pd.isna(row["Street"])) and (row["Numbers"] != ""):
+ return "house"
+ else:
+ return "global"
+
+
+ def create_gdf(self, df: pd.DataFrame) -> gpd.GeoDataFrame:
+ """
+ Function simply creates gdf from the recognised geocoded geometries.
+ """
+ logger.info("create_gdf started")
+
+ df["Location"] = df["addr_to_geocode"].progress_apply(Location().query)
+ df = df.dropna(subset=["Location"])
+ df["geometry"] = df['Location'].apply(lambda x: Point(x.longitude, x.latitude))
+ df["Location"] = df['Location'].apply(lambda x: x.address)
+ df["Numbers"].astype(str)
+ gdf = gpd.GeoDataFrame(df, geometry="geometry", crs=Geocoder.global_crs)
+
+ return gdf
+
+ def set_global_repr_point(self, gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
+ """
+ This function set the centroid (actually, representative point) of the
+ geocoded addresses to those texts that weren't geocoded (or didn't
+ contain any addresses according to the trained NER model).
+ """
+
+ try:
+ gdf.loc[gdf["level"] == "global", "geometry"] = gdf.loc[
+ gdf["level"] != "global", "geometry"
+ ].unary_union.representative_point()
+ except AttributeError:
+ pass
+
+ return gdf
+
+ def merge_to_initial_df(self, gdf: gpd.GeoDataFrame, initial_df: pd.DataFrame) -> gpd.GeoDataFrame:
+ """
+ This function merges geocoded df to the initial df in order to keep
+ all original attributes.
+ """
+
+ # initial_df.drop(columns=['key_0'], inplace=True)
+ gdf = initial_df.join(
+ gdf[
+ [
+ "Street",
+ "initial_street",
+ "only_full_street_name",
+ "Numbers",
+ "Score",
+ "location_options",
+ "Location",
+ "geometry",
+ ]
+ ],
+ how="outer",
+ )
+ gdf = gpd.GeoDataFrame(gdf, geometry="geometry", crs=Geocoder.global_crs)
+
+ return
+
+ def assign_street(variable):
+ if isinstance(variable, float) and math.isnan(variable):
+ return "street"
+ return variable
+
+ def get_df_areas(self, osm_id, tags):
+ """
+ Retrieves the GeoDataFrame of areas corresponding to the given OSM ID and tags.
+
+ Args:
+ osm_id (int): The OpenStreetMap ID.
+ tags (dict): The tags to filter by.
+ date (str): The date of the data to retrieve.
+
+ Returns:
+ gpd.GeoDataFrame: The GeoDataFrame containing the areas.
+
+ This function first checks if the GeoDataFrame corresponding to the given OSM ID is already in the cache.
+ If it is, it returns the cached GeoDataFrame. Otherwise, it retrieves the GeoDataFrame from the HistGeoDataGetter,
+ filters out the 'way' elements, and adds it to the cache. Finally, it returns the GeoDataFrame from the cache.
+ """
+ area_cache = {}
+ if osm_id not in area_cache:
+ geo_data_getter = HistGeoDataGetter()
+ df_areas = geo_data_getter.get_features_from_id(osm_id=osm_id, tags=tags)
+ df_areas = df_areas[df_areas["element_type"] != "way"]
+ area_cache[osm_id] = df_areas
+ return area_cache[osm_id]
+
+ def preprocess_group_name(self, group_name):
+ """
+ Preprocesses a group name by converting it to lowercase, removing special characters, and removing specified stopwords.
+
+ Args:
+ group_name (str): The group name to preprocess.
+
+ Returns:
+ str: The preprocessed group name.
+ """
+ group_name = group_name.lower()
+ group_name = re.sub(REGEX_PATTERN, REPLACEMENT_STRING, group_name)
+ words_to_remove = GROUP_STOPWORDS
+ for word in words_to_remove:
+ group_name = re.sub(word, "", group_name, flags=re.IGNORECASE)
+ return group_name
+
+ def preprocess_area_names(self, df_areas):
+ """
+ Preprocesses the area names in the given DataFrame by removing specified stopwords, converting the names to lowercase,
+ and stemming them.
+
+ Parameters:
+ df_areas (DataFrame): The DataFrame containing the area names.
+
+ Returns:
+ DataFrame: The DataFrame with preprocessed area names, where the 'area_name' column contains the original names
+ with stopwords removed, the 'area_name_processed' column contains the lowercase names with special characters
+ removed, and the 'area_stems' column contains the stemmed names.
+ """
+ words_to_remove = AREA_STOPWORDS
+ for word in words_to_remove:
+ df_areas["area_name"] = df_areas["name"].str.replace(word, "", regex=True)
+
+ df_areas["area_name_processed"] = df_areas["area_name"].str.lower()
+ df_areas["area_name_processed"] = df_areas["area_name_processed"].str.replace(
+ REGEX_PATTERN, REPLACEMENT_STRING, regex=True
+ )
+ df_areas["area_stems"] = df_areas["area_name_processed"].apply(
+ lambda x: [stemmer.stem(word) for word in x.split()]
+ )
+ return df_areas
+
+ def match_group_to_area(self, group_name, df_areas):
+ """
+ Matches a given group name to an area in a DataFrame of areas.
+
+ Args:
+ group_name (str): The name of the group to match.
+ df_areas (DataFrame): The DataFrame containing the areas to match against.
+
+ Returns:
+ tuple: A tuple containing the best match for the group name and the admin level of the match.
+ If no match is found, returns (None, None).
+ """
+ group_name_stems = [stemmer.stem(word) for word in group_name.split()]
+ max_partial_ratio = 20
+ max_token_sort_ratio = 20
+ best_match = None
+ admin_level = None
+
+ for _, row in df_areas.iterrows():
+ area_stems = row["area_stems"]
+
+ partial_ratio = fuzz.partial_ratio(group_name, row["area_name_processed"])
+ token_sort_ratio = fuzz.token_sort_ratio(group_name_stems, area_stems)
+
+ if partial_ratio > max_partial_ratio and token_sort_ratio > max_token_sort_ratio:
+ max_partial_ratio = partial_ratio
+ max_token_sort_ratio = token_sort_ratio
+ best_match = row["area_name"]
+ admin_level = row["key"]
+
+ return best_match, admin_level
+
+
+[docs]
+ def run(
+ self, df: pd.DataFrame, tags:dict|None=None, text_column: str = "text", group_column: str | None = "group_name", search_for_objects=False
+ ):
+ """
+ Runs the data processing pipeline on the input DataFrame.
+
+ Args:
+ tags (dict): The tags to filter by.
+ date (str): The date of the data to retrieve.
+ df (pd.DataFrame): The input DataFrame.
+ text_column (str, optional): The name of the text column in the DataFrame. Defaults to "text".
+
+ Returns:
+ gpd.GeoDataFrame: The processed DataFrame after running the data processing pipeline.
+
+ This function retrieves the GeoDataFrame of areas corresponding to the given OSM ID and tags.
+ It then preprocesses the area names and matches each group name to an area. The best match
+ and admin level are assigned to the DataFrame. The function also retrieves other geographic
+ objects and street names, preprocesses the street names, finds the word form, creates a GeoDataFrame,
+ merges it with the other geographic objects, assigns the street tag, and returns the final GeoDataFrame.
+ """
+
+ # initial_df = df.copy()
+ if tags:
+ df_areas = self.get_df_areas(self.osm_id, tags)
+ df_areas = self.preprocess_area_names(df_areas)
+
+ if group_column and group_column in df.columns:
+ for i, group_name in enumerate(df[group_column]):
+ processed_group_name = self.preprocess_group_name(group_name)
+ best_match, admin_level = self.match_group_to_area(processed_group_name, df_areas)
+ df.at[i, "territory"] = best_match
+ df.at[i, "key"] = admin_level
+ del df_areas
+ # df = AreaMatcher.run(self, df, osm_id, tags, date)
+
+ df[text_column] = df[text_column].astype(str).str.replace('\n', ' ')
+ df_reconstruction = df.copy()
+ df[text_column] = df[text_column].apply(str)
+
+
+ df = StreetExtractor.process_pipeline(df, text_column, self.classifier)
+ street_names = self.get_stem(self.street_names)
+
+ df = WordFormFinder(self.osm_city_name).find_word_form(df, street_names)
+
+ del street_names
+ gdf = self.create_gdf(df)
+ del df
+
+ if search_for_objects:
+ df_obj = OtherGeoObjects.run(self.osm_id, df, text_column)
+ gdf = pd.concat([gdf, df_obj], ignore_index=True)
+ del df_obj
+ gdf["geo_obj_tag"] = gdf["geo_obj_tag"].apply(Geocoder.assign_street)
+
+ gdf = pd.concat(
+ [gdf, df_reconstruction[~df_reconstruction[text_column].isin(gdf[text_column])]], ignore_index=True
+ )
+
+ # gdf2 = self.merge_to_initial_df(gdf, initial_df)
+
+ # # Add a new 'level' column using the get_level function
+ # gdf2["level"] = gdf2.progress_apply(self.get_level, axis=1)
+ # gdf2 = self.set_global_repr_point(gdf2)
+ gdf.set_crs(4326, inplace=True)
+ return gdf
+
+
+
+
+ # def extract_ner_street(self, text: str) -> pd.Series:
+ # """
+ # Function calls the pre-trained custom NER model to extract mentioned
+ # addresses from the texts (usually comment) in social networks in
+ # russian language.
+ # The model scores 0.8 in F1 and other metrics.
+ # """
+
+ # try:
+ # text = re.sub(r"\[.*?\]", "", text)
+ # except Exception:
+ # return pd.Series([None, None])
+
+ # sentence = Sentence(text)
+ # self.classifier.predict(sentence)
+ # try:
+ # res = sentence.get_labels("ner")[0].labeled_identifier.split("]: ")[1].split("/")[0].replace('"', "")
+ # score = round(sentence.get_labels("ner")[0].score, 3)
+ # if score > 0.7:
+ # return pd.Series([res, score])
+ # else:
+ # return pd.Series([None, None])
+
+ # except IndexError:
+ # return pd.Series([None, None])
+
+ # @staticmethod
+ # def extract_building_num(text, street_name, number) -> string:
+ # """
+ # The function finds the already extracted street name in the text
+ # and searches for numbers related to building numbers in a certain range of indexes
+ # around the street name.
+ # """
+ # if pd.notna(number) and number != "":
+ # return number
+ # if isinstance(text, float) and math.isnan(text):
+ # return ""
+
+ # clear_text = str(text).translate(str.maketrans("", "", string.punctuation))
+ # clear_text = clear_text.lower().split(" ")
+ # positions = [index for index, item in enumerate(clear_text) if item == street_name]
+
+ # if not positions:
+ # return ""
+
+ # position = positions[0]
+ # search_start = max(0, position)
+ # search_end = min(len(clear_text), position + END_INDEX_POSITION)
+
+ # num_result = []
+
+ # for f_index in range(max(0, search_start), min(len(clear_text), search_end)):
+ # element = clear_text[f_index]
+ # if any(character.isdigit() for character in str(element)) and len(str(element)) <= 3:
+ # num_result.append(element)
+ # break
+
+ # if num_result:
+ # return num_result[0]
+ # else:
+ # return ""
+
+ # @staticmethod
+ # def extract_toponym(text, street_name) -> string:
+ # """
+ # The function finds the already extracted street name in the text
+ # and searches for words related to toponyms in a certain range of indexes
+ # around the street name.
+ # """
+ # if isinstance(text, float) and math.isnan(text):
+ # return None
+
+ # clear_text = str(text).translate(str.maketrans("", "", string.punctuation))
+ # clear_text = clear_text.lower().split(" ")
+ # positions = [index for index, item in enumerate(clear_text) if item == street_name]
+
+ # if not positions:
+ # return None
+
+ # position = positions[0]
+ # search_start = max(0, position - int(START_INDEX_POSITION))
+ # search_end = min(len(clear_text), position + int(END_INDEX_POSITION))
+
+ # ad_result = []
+ # for i in range(search_start, min(search_end + 1, len(clear_text))):
+ # word = clear_text[i]
+ # normal_form = morph.parse(word)[0].normal_form
+ # if normal_form in TARGET_TOPONYMS:
+ # ad_result.append(REPLACEMENT_DICT.get(normal_form, normal_form))
+
+ # if ad_result:
+ # return ad_result[0]
+ # else:
+ # return None
+
+ # def get_street(self, df: pd.DataFrame, text_column: str) -> gpd.GeoDataFrame:
+ # """
+ # Function calls NER model and post-process result in order to extract
+ # the address mentioned in the text.
+ # """
+ # logger.info("get_street started")
+
+ # df[text_column].dropna(inplace=True)
+ # df[text_column] = df[text_column].astype(str)
+
+ # logger.info("extract_ner_street started")
+
+ # df[["Street", "Score"]] = df[text_column].progress_apply(lambda t: self.extract_ner_street(t))
+ # df["Street"] = df[[text_column, "Street"]].progress_apply(
+ # lambda row: Geocoder.get_ner_address_natasha(row, text_column),
+ # axis=1,
+ # )
+
+ # df = df[df.Street.notna()]
+ # df = df[df["Street"].str.contains("[а-яА-Я]")]
+
+ # logger.info("pattern1.sub started")
+
+ # pattern1 = re.compile(r"(\D)(\d)(\D)")
+ # df["Street"] = df["Street"].progress_apply(lambda x: pattern1.sub(r"\1 \2\3", x))
+
+ # logger.info("pattern2.findall started")
+
+ # pattern2 = re.compile(r"\d+")
+ # df["Numbers"] = df["Street"].progress_apply(lambda x: " ".join(pattern2.findall(x)))
+
+ # logger.info("pattern2.sub started")
+
+ # df["Street"] = df["Street"].progress_apply(lambda x: pattern2.sub("", x).strip())
+
+ # df["initial_street"] = df["Street"].copy()
+
+ # df["Street"] = df["Street"].str.lower()
+
+ # logger.info("extract_building_num started")
+
+ # df["Numbers"] = df.progress_apply(
+ # lambda row: Geocoder.extract_building_num(row[text_column], row["Street"], row["Numbers"]),
+ # axis=1,
+ # )
+
+ # logger.info("extract_toponym started")
+
+ # df["Toponims"] = df.progress_apply(
+ # lambda row: Geocoder.extract_toponym(row[text_column], row["Street"]),
+ # axis=1,
+ # )
+ # return df
+
+ # df = pd.DataFrame(data={'text': 'На биржевой 14 что-то произошло'}, index=[0])
+ # print(Geocoder().run(df=df, text_column='text'))
+
+
+ # def find_word_form(self, df: pd.DataFrame, strts_df: pd.DataFrame) -> pd.DataFrame:
+ # """
+ # In Russian language any word has different forms.
+ # Since addresses are extracted from the texts in social networks,
+ # they might be in any possible form. This function is aimed to match that
+ # free form to the one that is used in the OSM database.
+
+ # Since the stem is found there would be several streets with that stem
+ # in their name.
+ # However, the searching street name has its specific ending (form) and
+ # not each matched street name could have it.
+ # """
+
+ # df["full_street_name"] = None
+
+ # for idx, row in df.iterrows():
+ # search_val = row["Street"]
+ # search_top = row["Toponims"]
+ # val_num = row["Numbers"]
+
+ # for col in strts_df.columns[2:]:
+ # search_rows = strts_df.loc[strts_df[col] == search_val]
+ # matching_rows = search_rows[search_rows["toponim_name"] == search_top]
+
+ # if not matching_rows.empty:
+ # only_streets_full = matching_rows["street"].values
+ # streets_full = [
+ # street + f" {val_num}" + f" {self.osm_city_name}" + " Россия" for street in only_streets_full
+ # ]
+
+ # df.loc[idx, "full_street_name"] = ",".join(streets_full)
+ # df.loc[idx, "only_full_street_name"] = ",".join(only_streets_full)
+
+ # else:
+ # if search_val in strts_df[col].values:
+ # only_streets_full = strts_df.loc[strts_df[col] == search_val, "street"].values
+ # streets_full = [
+ # street + f" {val_num}" + f" {self.osm_city_name}" + " Россия"
+ # for street in only_streets_full
+ # ]
+
+ # df.loc[idx, "full_street_name"] = ",".join(streets_full)
+ # df.loc[idx, "only_full_street_name"] = ",".join(only_streets_full)
+
+ # df.dropna(subset=["full_street_name", "only_full_street_name"], inplace=True)
+ # df["location_options"] = df["full_street_name"].str.split(",")
+ # df["only_full_street_name"] = df["only_full_street_name"].str.split(",")
+
+ # tmp_df_1 = df["location_options"].explode()
+ # tmp_df_1.name = "addr_to_geocode"
+ # tmp_df_2 = df["only_full_street_name"].explode()
+ # tmp_df_2.name = "only_full_street_name"
+ # new_df = tmp_df_1.to_frame().join(tmp_df_2.to_frame())
+
+ # df.drop(columns=["only_full_street_name"], inplace=True)
+ # df = df.merge(new_df, left_on=df.index, right_on=new_df.index)
+ # df.drop(columns=["key_0"], inplace=True)
+
+ # # new_df = df["only_full_street_name"].explode()
+ # # new_df.name = "only_full_street_name"
+ # # df.drop(columns=['key_0', 'only_full_street_name'], inplace=True)
+ # # df = df.merge(new_df, left_on=df.index, right_on=new_df.index)
+
+ # # print(df.head())
+ # df["only_full_street_name"] = df["only_full_street_name"].astype(str)
+ # df["location_options"] = df["location_options"].astype(str)
+
+ # return df
+
+import pandas as pd
+import re
+from tqdm import tqdm
+from loguru import logger
+from typing import Tuple, List, Optional
+
+import string
+import math
+from typing import Optional
+from loguru import logger
+from flair.data import Sentence
+
+# Initialize morphological analyzer (use the correct library for your context)
+import pymorphy2
+morph = pymorphy2.MorphAnalyzer()
+
+from sloyka.src.geocoder.text_address_extractor_by_rules import NatashaExtractor
+from sloyka.src.utils.constants import (
+ START_INDEX_POSITION,
+ REPLACEMENT_DICT,
+ TARGET_TOPONYMS,
+ END_INDEX_POSITION,
+ SCORE_THRESHOLD
+)
+
+
+
+[docs]
+class StreetExtractor:
+
+ extractor = NatashaExtractor()
+
+
+[docs]
+ @staticmethod
+ def process_pipeline(df: pd.DataFrame, text_column: str, classifier) -> pd.DataFrame:
+
+ local_df = df.copy()
+ """
+ Execute the address extraction pipeline on the DataFrame.
+
+ Args:
+ df (pd.DataFrame): DataFrame containing the text data.
+ text_column (str): Column name in the DataFrame with text data for address extraction.
+
+ Returns:
+ pd.DataFrame: DataFrame with extracted street addresses and additional processing columns.
+ """
+ texts = StreetExtractor._preprocess_text_column(local_df, text_column)
+ extracted_streets = StreetExtractor._extract_streets(texts, classifier)
+ refined_streets = StreetExtractor._refine_street_data(extracted_streets)
+ building_numbers = StreetExtractor._extract_building_numbers(texts, refined_streets)
+ toponyms = StreetExtractor._extract_toponyms(texts, refined_streets)
+
+ # Combine results into a DataFrame
+ processed_df = pd.DataFrame({
+ text_column: texts,
+ 'Street': refined_streets,
+ 'Numbers': building_numbers,
+ 'Toponyms': toponyms
+ })
+
+ StreetExtractor._check_df_len_didnt_change(local_df, processed_df)
+
+ return processed_df
+
+
+ @staticmethod
+ def _check_df_len_didnt_change(df1, df2):
+ try:
+ assert len(df1) == len(df2)
+ except Exception as e:
+ logger.critical('dfs lengths differ')
+ raise e
+
+
+ @staticmethod
+ def _preprocess_text_column(df: pd.DataFrame, text_column: str) -> List[str]:
+ """
+ Preprocess the text column by ensuring non-null values and converting to string type.
+
+ Args:
+ df (pd.DataFrame): DataFrame containing the text data.
+ text_column (str): Column name in the DataFrame with text data.
+
+ Returns:
+ List[str]: List of preprocessed text entries.
+ """
+ try:
+ text_series = df[text_column].dropna().astype(str)
+ return text_series.tolist()
+ except Exception as e:
+ logger.warning(f"Error in _preprocess_text_column: {e}")
+ return []
+
+ @staticmethod
+ def _extract_streets(texts: List[str], classifier) -> List[Tuple[Optional[str], Optional[float]]]:
+ """
+ Extract street names from the text column using NER model.
+
+ Args:
+ texts (List[str]): List of text entries.
+
+ Returns:
+ List[Tuple[Optional[str], Optional[float]]]: List of tuples with extracted street names and confidence scores.
+ """
+ tqdm.pandas()
+ extracted_streets = []
+ for text in tqdm(texts):
+ try:
+ extracted_streets.append(StreetExtractor.extract_ner_street(text, classifier))
+ except Exception as e:
+ logger.warning(f"Error extracting NER street from text '{text}': {e}")
+ extracted_streets.append((None, None))
+ return extracted_streets
+
+ @staticmethod
+ def _refine_street_data(street_data: List[Tuple[Optional[str], Optional[float]]]) -> List[Optional[str]]:
+ """
+ Refine street data by normalizing and cleaning up street names.
+
+ Args:
+ street_data (List[Tuple[Optional[str], Optional[float]]]): List of tuples with extracted street names and confidence scores.
+
+ Returns:
+ List[Optional[str]]: List of refined street names.
+ """
+ refined_streets = []
+ for street, _ in street_data:
+ if street:
+ try:
+ refined_streets.append(StreetExtractor._refine_street_name(street))
+ except Exception as e:
+ logger.warning(f"Error refining street '{street}': {e}")
+ refined_streets.append(None)
+ else:
+ refined_streets.append(None)
+ return refined_streets
+
+ @staticmethod
+ def _refine_street_name(street: str) -> str:
+ """
+ Refine street name by normalizing and cleaning up the street string.
+
+ Args:
+ street (str): Raw street name.
+
+ Returns:
+ str: Refined street name.
+ """
+ try:
+ street = re.sub(r"(\D)(\d)(\D)", r"\1 \2\3", street)
+ street = re.sub(r"\d+", "", street).strip().lower()
+ return street
+ except Exception as e:
+ logger.warning(f"Error in _refine_street_name with street '{street}': {e}")
+ return ""
+
+ @staticmethod
+ def _extract_building_numbers(texts: List[str], streets: List[Optional[str]]) -> List[Optional[str]]:
+ """
+ Extract building numbers from the text data.
+
+ Args:
+ texts (List[str]): List of text entries.
+ streets (List[Optional[str]]): List of refined street names.
+
+ Returns:
+ List[Optional[str]]: List of extracted building numbers.
+ """
+ building_numbers = []
+ for text, street in zip(texts, streets):
+ if street:
+ try:
+ building_numbers.append(StreetExtractor._extract_building_number(text, street))
+ except Exception as e:
+ logger.warning(f"Error extracting building number from text '{text}' with street '{street}': {e}")
+ building_numbers.append(None)
+ else:
+ building_numbers.append(None)
+ return building_numbers
+
+ @staticmethod
+ def _extract_building_number(text: str, street: str) -> str:
+ """
+ Extract building number from the text.
+
+ Args:
+ text (str): Input text for address extraction.
+ street (str): Extracted and refined street name.
+
+ Returns:
+ str: Extracted building number.
+ """
+ try:
+ numbers = " ".join(re.findall(r"\d+", text))
+ return StreetExtractor.extract_building_num(text, street, numbers)
+ except Exception as e:
+ logger.warning(f"Error in _extract_building_number with text '{text}' and street '{street}': {e}")
+ return ""
+
+ @staticmethod
+ def _extract_toponyms(texts: List[str], streets: List[Optional[str]]) -> List[Optional[str]]:
+ """
+ Extract toponyms from the text data.
+
+ Args:
+ texts (List[str]): List of text entries.
+ streets (List[Optional[str]]): List of refined street names.
+
+ Returns:
+ List[Optional[str]]: List of extracted toponyms.
+ """
+ toponyms = []
+ for text, street in zip(texts, streets):
+ if street:
+ try:
+ toponyms.append(StreetExtractor.extract_toponym(text, street))
+ except Exception as e:
+ logger.warning(f"Error extracting toponym from text '{text}' with street '{street}': {e}")
+ toponyms.append(None)
+ else:
+ toponyms.append(None)
+ return toponyms
+
+
+
+[docs]
+ @staticmethod
+ def extract_toponym(text: str, street_name: str) -> Optional[str]:
+ """
+ Extract toponyms near the specified street name in the text.
+
+ This function identifies the position of a street name in the text and searches for related toponyms
+ within a specified range around the street name.
+
+ Args:
+ text (str): The text containing the address.
+ street_name (str): The name of the street to search around.
+
+ Returns:
+ Optional[str]: The first toponym found if present, otherwise None.
+ """
+ try:
+ # Handle the case where text is NaN
+ if isinstance(text, float) and math.isnan(text):
+ return None
+
+ # Clean and split the text into words
+ cleaned_text = StreetExtractor._clean_text(text)
+ words = cleaned_text.split()
+
+ # Find positions of the street name
+ positions = StreetExtractor._find_street_name_positions(words, street_name)
+ if not positions:
+ return None
+
+ # Search for toponyms in the range around the street name
+ toponym = StreetExtractor._search_toponyms(words, positions[0])
+ return toponym
+
+ except Exception as e:
+ logger.warning(f"Error in extract_toponym with text '{text}' and street_name '{street_name}': {e}")
+ return None
+
+
+ @staticmethod
+ def _clean_text(text: str) -> str:
+ """
+ Clean the input text by removing punctuation and converting to lowercase.
+
+ Args:
+ text (str): The input text.
+
+ Returns:
+ str: The cleaned text.
+ """
+ return text.translate(str.maketrans("", "", string.punctuation)).lower()
+
+ @staticmethod
+ def _find_street_name_positions(words: List[str], street_name: str) -> List[int]:
+ """
+ Find positions of the street name in the list of words.
+
+ Args:
+ words (List[str]): List of words from the cleaned text.
+ street_name (str): The name of the street to find.
+
+ Returns:
+ List[int]: List of positions where the street name occurs.
+ """
+ return [index for index, word in enumerate(words) if word == street_name]
+
+ @staticmethod
+ def _search_toponyms(words: List[str], position: int) -> Optional[str]:
+ """
+ Search for toponyms within a specified range around the given position.
+
+ Args:
+ words (List[str]): List of words from the cleaned text.
+ position (int): The position around which to search for toponyms.
+
+ Returns:
+ Optional[str]: The first toponym found if present, otherwise None.
+ """
+ search_start = max(0, position - START_INDEX_POSITION)
+ search_end = min(len(words), position + END_INDEX_POSITION)
+
+ for i in range(search_start, search_end + 1):
+ word = words[i]
+ try:
+ normal_form = morph.parse(word)[0].normal_form
+ except Exception as e:
+ logger.warning(f"Error parsing word '{word}': {e}")
+ continue
+
+ if normal_form in TARGET_TOPONYMS:
+ return REPLACEMENT_DICT.get(normal_form, normal_form)
+
+ return None
+
+
+[docs]
+ @staticmethod
+ def extract_building_num(text: str, street_name: str, number: Optional[str]) -> str:
+ """
+ Extract building numbers near the specified street name in the text.
+
+ This function identifies the position of a street name in the text and searches for related building numbers
+ within a specified range of indexes around the street name.
+
+ Args:
+ text (str): The text containing the address.
+ street_name (str): The name of the street to search around.
+ number (Optional[str]): Previously extracted building number.
+
+ Returns:
+ str: The first building number found if present, otherwise an empty string.
+ """
+ try:
+ if pd.notna(number) and number != "":
+ return number
+
+ if isinstance(text, float) and math.isnan(text):
+ return ""
+
+ cleaned_text = StreetExtractor._clean_text(text)
+ words = cleaned_text.split()
+
+ positions = StreetExtractor._find_street_name_positions(words, street_name)
+ if not positions:
+ return ""
+
+ building_number = StreetExtractor._search_building_number(words, positions[0])
+ return building_number
+
+ except Exception as e:
+ logger.warning(f"Error in extract_building_num with text '{text}', street_name '{street_name}', number '{number}': {e}")
+ return ""
+
+
+
+ @staticmethod
+ def _find_street_name_positions(words: List[str], street_name: str) -> List[int]:
+ """
+ Find positions of the street name in the list of words.
+
+ Args:
+ words (List[str]): List of words from the cleaned text.
+ street_name (str): The name of the street to find.
+
+ Returns:
+ List[int]: List of positions where the street name occurs.
+ """
+ return [index for index, word in enumerate(words) if word == street_name]
+
+ @staticmethod
+ def _search_building_number(words: List[str], position: int) -> str:
+ """
+ Search for building numbers within a specified range around the given position.
+
+ Args:
+ words (List[str]): List of words from the cleaned text.
+ position (int): The position around which to search for building numbers.
+
+ Returns:
+ str: The first building number found if present, otherwise an empty string.
+ """
+ search_start = max(0, position)
+ search_end = min(len(words), position + END_INDEX_POSITION)
+
+ for index in range(search_start, search_end):
+ word = words[index]
+ if StreetExtractor._is_building_number(word):
+ return word
+
+ return ""
+
+ @staticmethod
+ def _is_building_number(word: str) -> bool:
+ """
+ Check if a word is a valid building number.
+
+ Args:
+ word (str): The word to check.
+
+ Returns:
+ bool: True if the word is a valid building number, otherwise False.
+ """
+ return any(character.isdigit() for character in word) and len(word) <= 3
+
+#---------
+
+[docs]
+ @staticmethod
+ def extract_ner_street(text: str, classifier) -> pd.Series:
+ """
+ Extract street addresses from text using a pre-trained custom NER model.
+
+ This function processes text by removing unnecessary content, applies a custom NER model
+ to extract mentioned addresses, and returns the address with a confidence score.
+
+ Args:
+ text (str): The input text to process and extract addresses from.
+
+ Returns:
+ pd.Series: A Series containing the extracted address and confidence score,
+ or [None, None] if extraction fails or the score is below the threshold.
+ """
+ try:
+ cleaned_text = StreetExtractor._clean_text(text)
+ sentence = Sentence(cleaned_text)
+
+ # Predict entities using the classifier
+ classifier.predict(sentence)
+
+ address, score = StreetExtractor._extract_address_and_score(sentence)
+
+ if not address or score < SCORE_THRESHOLD:
+ address = StreetExtractor.extractor.get_ner_address_natasha(text)
+ if address:
+ score = 1
+
+ # Return the result if the score is above the threshold
+ return pd.Series([address, score] if score is not None and score > SCORE_THRESHOLD else [None, None])
+
+ except Exception as e:
+ logger.warning(f"Error in extract_ner_street with text '{text}': {e}")
+ return pd.Series([None, None])
+
+
+ @staticmethod
+ def _clean_text(text: str) -> str:
+ """
+ Clean the input text by removing unwanted patterns.
+
+ Args:
+ text (str): The input text.
+
+ Returns:
+ str: The cleaned text.
+ """
+ try:
+ return re.sub(r"\[.*?\]", "", text)
+ except Exception as e:
+ logger.warning(f"Error in _clean_text with text '{text}': {e}")
+ return text
+
+ @staticmethod
+ def _extract_address_and_score(sentence: Sentence) -> Tuple[Optional[str], Optional[float]]:
+ """
+ Extract address and score from the NER model's predictions.
+
+ Args:
+ sentence (Sentence): The Sentence object containing NER predictions.
+
+ Returns:
+ Tuple[Optional[str], Optional[float]]: Extracted address and its confidence score.
+ """
+ try:
+ labels = sentence.get_labels("ner")
+ if labels:
+ label = labels[0]
+ address = StreetExtractor._parse_address(label.labeled_identifier)
+ score = round(label.score, 3)
+ return address, score
+ return None, None
+ except IndexError as e:
+ logger.warning(f"Error in _extract_address_and_score: {e}")
+ return None, None
+
+ @staticmethod
+ def _parse_address(label_value: str) -> str:
+ """
+ Parse the address from the label value string.
+
+ Args:
+ label_value (str): The labeled identifier from the NER model.
+
+ Returns:
+ str: Parsed address.
+ """
+ try:
+ return label_value.split("]: ")[1].split("/")[0].replace('"', "")
+ except IndexError as e:
+ logger.warning(f"Error in _parse_address with label value '{label_value}': {e}")
+ return ""
+
+
+
+
+"""
+This module contains the EmotionClassifiers class, which is designed to categorise input texts into emotion categories.
+It uses a Huggingface transformer model trained on Bert_Large by default.
+The EmotionClassifiers class has the following method:
+@method:recognize_emotion: Adding an emotion category.
+@method:recognize_average_emotion_from_multiple_models: Adding an average emotion category or the most likely emotion
+category using multiple models.
+"""
+
+from aniemore.recognizers.text import TextRecognizer
+from aniemore.models import HuggingFaceModel
+import torch
+import pandas as pd
+from tqdm import tqdm
+import gc
+
+
+
+[docs]
+class EmotionRecognizer:
+ """
+ This class is designed to categorise input texts into emotion categories.
+
+ Attributes:
+
+ - model: This attribute holds the model used for emotion recognition. It defaults to HuggingFaceModel.Text.Bert_Large,
+ but can be set to any other compatible model during the instantiation of the class.
+
+ - device: the device to use for inference. It automatically selects 'cuda' (GPU) if a compatible GPU
+ is available and CUDA is enabled, otherwise, it falls back to 'cpu'.
+
+ - text: The text to be analyzed.
+
+ - df: The DataFrame containing the text to be analyzed.
+
+ - text_column: The name of the column containing the text to be analyzed.
+ """
+
+ def __init__(self, model_name=HuggingFaceModel.Text.Bert_Large, device=None):
+ self.device = device if device is not None else ("cuda" if torch.cuda.is_available() else "cpu")
+ self.model_name = model_name
+
+ # Define the default model names to avoid repeated initialization
+ self.default_model_names = [
+ HuggingFaceModel.Text.Bert_Tiny,
+ HuggingFaceModel.Text.Bert_Base,
+ HuggingFaceModel.Text.Bert_Large,
+ HuggingFaceModel.Text.Bert_Tiny2,
+ ]
+
+
+[docs]
+ def recognize_emotion(self, text):
+ """
+ Return the emotion for a given text.
+ """
+ recognizer = TextRecognizer(model=self.model_name, device=self.device)
+ emotion = recognizer.recognize(text, return_single_label=True)
+ return emotion
+
+
+
+[docs]
+ def recognize_average_emotion_from_multiple_models(self, df, text_column, models=None, average=True):
+ """
+ Calculate the prevailing emotion using multiple models for a DataFrame column.
+ """
+ if models is None:
+ models = self.default_model_names
+ else:
+ # Validate that the provided models are in the default models list
+ for model in models:
+ if model not in self.default_model_names:
+ raise ValueError(
+ f"Model {model} is not a valid model. Valid models are: {self.default_model_names}"
+ )
+
+ # Initialize scores DataFrame
+ scores = pd.DataFrame(
+ 0, index=df.index, columns=["happiness", "sadness", "anger", "fear", "disgust", "enthusiasm", "neutral"]
+ )
+
+ # Process each model one by one with progress bar
+ for model_name in tqdm(models, desc="Processing models"):
+ try:
+ print(f"Clearing cache and collecting garbage before loading model: {model_name}")
+ torch.cuda.empty_cache()
+ gc.collect()
+
+ print(f"Loading model: {model_name}")
+ recognizer = TextRecognizer(model=model_name, device=self.device)
+ model_results = [recognizer.recognize(text, return_single_label=False) for text in df[text_column]]
+
+ for idx, result in enumerate(model_results):
+ for emotion, score in result.items():
+ if average:
+ scores.at[df.index[idx], emotion] += score
+ else:
+ scores.at[df.index[idx], emotion] = max(scores.at[df.index[idx], emotion], score)
+
+ # Удаление модели из памяти
+ del recognizer
+ torch.cuda.empty_cache() # Очистка кеша CUDA (если используется GPU)
+ gc.collect() # Сборка мусора
+ print(f"Model {model_name} processed and unloaded.")
+ except Exception as e:
+ print(f"Error processing model {model_name}: {e}")
+ torch.cuda.empty_cache()
+ gc.collect()
+
+ if average:
+ # Average the scores by the number of models
+ scores = scores.div(len(models))
+
+ # Determine the prevailing emotion with the highest score
+ prevailing_emotions = scores.idxmax(axis=1)
+
+ return prevailing_emotions
+
+
+
+"""
+This module contains the EventDetection class, which is aimed to generate events and their connections based
+on the application of semantic clustering method (BERTopic) on the texts in the context of an urban spatial model.
+
+The EventDetection class has the following methods:
+
+@method:_get_roads:
+Get the road network of a city as road links and roads.
+
+@method:_get_buildings:
+Get the buildings of a city as a GeoDataFrame.
+
+@method:_collect_population:
+Collect population data for each object (building, street, link).
+
+@method:_preprocess:
+Preprocess the data.
+"""
+import re
+from itertools import chain, combinations
+
+import geopandas as gpd
+import osmnx as ox
+import pandas as pd
+import numpy as np
+from bertopic import BERTopic
+from hdbscan import HDBSCAN
+from shapely.geometry import LineString
+from transformers.pipelines import pipeline
+from umap import UMAP
+
+
+
+[docs]
+class EventDetection:
+ """
+ This class is aimed to generate events and their connections.
+ It is based on the application of semantic clustering method (BERTopic)
+ on the texts in the context of urban spatial model
+ """
+
+ def __init__(self):
+ np.random.seed(42)
+ self.population_filepath = None
+ self.levels = ["building", "link", "road", "global"]
+ self.levels_scale = dict(zip(self.levels, list(range(2, 10, 2))))
+ self.functions_weights = {
+ "Безопасность": 0.12,
+ "Благоустройство": 0.21,
+ "Дороги": 0.18,
+ "ЖКХ": 0.2,
+ "Здравоохранение": 0.2,
+ "Другое": 0.16,
+ "Образование": 0.16,
+ "Социальная защита": 0.13,
+ "Строительство": 0.19,
+ "Обращение с отходами": 0.19,
+ "Транспорт": 0.17,
+ "Экология": 0.22,
+ "Энергетика": 0.19,
+ }
+ self.messages = None
+ self.links = None
+ self.buildings = None
+ self.population = None
+ self.topic_model = None
+ self.events = None
+ self.connections = None
+
+ def _get_roads(self, city_name, city_crs) -> gpd.GeoDataFrame:
+ """
+ Get the road network of a city as road links and roads.
+ Args:
+ city_name (string): The name of the city.
+ city_crs (int): The spatial reference code (CRS) of the city.
+ Returns:
+ links (GeoDataFrame): GeoDataFrame with the city's road links and roads.
+ """
+ links = ox.graph_from_place(city_name, network_type="drive")
+ links = ox.utils_graph.graph_to_gdfs(links, nodes=False).to_crs(city_crs)
+ links = links.reset_index(drop=True)
+ links["link_id"] = links.index
+ links["geometry"] = links["geometry"].buffer(7)
+ links = links.to_crs(4326)
+ links = links[["link_id", "name", "geometry"]]
+ links.loc[links["name"].map(type) == list, "name"] = links[links["name"].map(type) == list]["name"].map(
+ lambda x: ", ".join(x)
+ )
+ road_id_name = dict(enumerate(links.name.dropna().unique().tolist()))
+ road_name_id = {v: k for k, v in road_id_name.items()}
+ links["road_id"] = links["name"].replace(road_name_id)
+ return links
+
+ def _get_buildings(self) -> gpd.GeoDataFrame:
+ """
+ Get the buildings of a city as a GeoDataFrame
+ Args:
+ links(GeoDataFrame): GeoDataFrame with the city's road links and roads.
+ filepath (string): The path to the GeoJSON file with building data. The default is set to 'population.geojson'.
+ Returns:
+ buildings (GeoDataFrame): GeoDataFrame with the city's buildings.
+ """
+ buildings = gpd.read_file(self.population_filepath)
+ buildings = buildings[["address", "building_id", "population_balanced", "geometry"]]
+ buildings = buildings.to_crs(4326)
+ buildings["building_id"] = buildings.index
+ buildings = (
+ gpd.sjoin_nearest(
+ buildings,
+ self.links[["link_id", "road_id", "geometry"]],
+ how="left",
+ max_distance=500,
+ )
+ .drop(columns=["index_right"])
+ .drop_duplicates(subset="building_id")
+ )
+ self.buildings = buildings
+ return buildings
+
+ def _collect_population(self) -> dict:
+ """
+ Collect population data for each object (building, street, link).
+ """
+ buildings = self.buildings.copy()
+ pops_global = {0: buildings.population_balanced.sum()}
+ pops_buildings = buildings["population_balanced"].to_dict()
+ pops_links = (
+ buildings[["population_balanced", "link_id"]].groupby("link_id").sum()["population_balanced"].to_dict()
+ )
+ pops_roads = (
+ buildings[["population_balanced", "road_id"]].groupby("road_id").sum()["population_balanced"].to_dict()
+ )
+ pops = {
+ "global": pops_global,
+ "road": pops_roads,
+ "link": pops_links,
+ "building": pops_buildings,
+ }
+ self.population = pops
+ return pops
+
+ def _preprocess(self) -> gpd.GeoDataFrame:
+ """
+ Preprocess the data
+ """
+ messages = self.messages[
+ [
+ "Текст комментария",
+ "geometry",
+ "Дата и время",
+ "message_id",
+ "cats",
+ ]
+ ]
+ messages = messages.sjoin(self.buildings, how="left")[
+ [
+ "Текст комментария",
+ "address",
+ "geometry",
+ "building_id",
+ "message_id",
+ "Дата и время",
+ "cats",
+ ]
+ ]
+ messages.rename(
+ columns={"Текст комментария": "text", "Дата и время": "date_time"},
+ inplace=True,
+ )
+ messages = messages.sjoin(self.links, how="left")[
+ [
+ "text",
+ "geometry",
+ "building_id",
+ "index_right",
+ "name",
+ "message_id",
+ "date_time",
+ "cats",
+ "road_id",
+ ]
+ ]
+ messages.rename(
+ columns={"index_right": "link_id", "name": "road_name"},
+ inplace=True,
+ )
+ messages = messages.join(
+ self.buildings[["link_id", "road_id"]],
+ on="building_id",
+ rsuffix="_from_building",
+ )
+ messages.loc[messages.link_id.isna(), "link_id"] = messages.loc[messages.link_id.isna()][
+ "link_id_from_building"
+ ]
+ messages.loc[messages.road_id.isna(), "road_id"] = messages.loc[messages.road_id.isna()][
+ "road_id_from_building"
+ ]
+ messages = messages[
+ [
+ "message_id",
+ "text",
+ "geometry",
+ "building_id",
+ "link_id",
+ "road_id",
+ "date_time",
+ "cats",
+ ]
+ ].dropna(subset="text")
+ messages["cats"] = messages.cats.astype(str).str.split("; ").map(lambda x: x[0])
+ messages["importance"] = messages["cats"].map(self.functions_weights)
+ messages["importance"].fillna(0.16, inplace=True)
+ messages["global_id"] = 0
+ return messages
+
+ def _create_model(self, min_event_size):
+ """
+ Create a topic model with a UMAP, HDBSCAN, and a BERTopic model.
+ """
+ umap_model = UMAP(
+ n_neighbors=15,
+ n_components=5,
+ min_dist=0.0,
+ metric="cosine",
+ random_state=42,
+ )
+ hdbscan_model = HDBSCAN(
+ min_cluster_size=min_event_size,
+ min_samples=1,
+ metric="euclidean",
+ cluster_selection_method="eom",
+ prediction_data=True,
+ )
+ embedding_model = pipeline("feature-extraction", model="cointegrated/rubert-tiny2")
+ topic_model = BERTopic(
+ embedding_model=embedding_model,
+ hdbscan_model=hdbscan_model,
+ umap_model=umap_model,
+ calculate_probabilities=True,
+ verbose=True,
+ n_gram_range=(1, 3),
+ )
+ return topic_model
+
+ def _event_from_object(
+ self,
+ messages,
+ topic_model,
+ target_column: str,
+ population: dict,
+ object_id: float,
+ event_level: str,
+ ):
+ """
+ Create a list of events for a given object
+ (building, street, link, total).
+ """
+ local_messages = messages[messages[target_column] == object_id]
+ message_ids = local_messages.message_id.tolist()
+ docs = local_messages.text.tolist()
+ if len(docs) >= 5:
+ try:
+ topics, probs = topic_model.fit_transform(docs)
+ except TypeError:
+ print("Can't reduce dimensionality or some other problem")
+ return
+ try:
+ topics = topic_model.reduce_outliers(docs, topics)
+ topic_model.update_topics(docs, topics=topics)
+ except ValueError:
+ print("Can't distribute all messages in topics")
+ event_model = topic_model.get_topic_info()
+ event_model["level"] = event_level
+ event_model["object_id"] = str(object_id)
+ event_model["id"] = event_model.apply(
+ lambda x: f"{str(x.Topic)}_{str(x.level)}_{str(x.object_id)}",
+ axis=1,
+ )
+ try:
+ event_model["potential_population"] = population[event_level][object_id]
+ except Exception: # need to select type of error
+ event_model["potential_population"] = population["global"][0]
+
+ clustered_messages = pd.DataFrame(data={"id": message_ids, "text": docs, "topic_id": topics})
+ event_model["message_ids"] = [
+ clustered_messages[clustered_messages["topic_id"] == topic]["id"].tolist()
+ for topic in event_model.Topic
+ ]
+ event_model["duration"] = event_model.message_ids.map(
+ lambda x: (
+ pd.to_datetime(messages[messages["message_id"].isin(x)].date_time).max()
+ - pd.to_datetime(messages[messages["message_id"].isin(x)].date_time).min()
+ ).days
+ )
+ event_model["category"] = event_model.message_ids.map(
+ lambda x: ", ".join(messages[messages["message_id"].isin(x)].cats.mode().tolist())
+ )
+ event_model["importance"] = event_model.message_ids.map(
+ lambda x: messages[messages["message_id"].isin(x)].importance.mean()
+ )
+ return event_model
+ else:
+ return
+
+ def _get_events(self, min_event_size) -> gpd.GeoDataFrame:
+ """
+ Create a list of events for all levels.
+ """
+ messages = self.messages.copy()
+ messages_list = messages.text.tolist()
+ index_list = messages.message_id.tolist()
+ pops = self._collect_population()
+ topic_model = self._create_model(min_event_size)
+ events = [
+ [
+ self._event_from_object(messages, topic_model, f"{level}_id", pops, oid, level)
+ for oid in messages[f"{level}_id"].unique().tolist()
+ ]
+ for level in reversed(self.levels)
+ ]
+ events = [item for sublist in events for item in sublist if item is not None]
+ events = pd.concat(list(chain(events)))
+ events["geometry"] = events.message_ids.map(
+ lambda x: messages[messages.message_id.isin(x)].geometry.unary_union.representative_point()
+ )
+ events = gpd.GeoDataFrame(events, geometry="geometry").set_crs(4326)
+ events.rename(
+ columns={
+ "Name": "name",
+ "Representative_Docs": "docs",
+ "Count": "intensity",
+ "potential_population": "population",
+ },
+ inplace=True,
+ )
+ events["docs"] = events["docs"].map(
+ lambda x: ", ".join([str(index_list[messages_list.index(text)]) for text in x])
+ )
+ events.message_ids = events.message_ids.map(lambda x: ", ".join([str(id) for id in x]))
+ events["intensity"] = (events["intensity"] - events["intensity"].min()) / (
+ events["intensity"].max() - events["intensity"].min()
+ )
+ events["duration"] = (events["duration"] - events["duration"].min()) / (
+ events["duration"].max() - events["duration"].min()
+ )
+ events.loc[events.intensity == 0, "intensity"] = 0.1 # fix later
+ events.loc[events.duration.isna(), "duration"] = 1 # fix later
+ events["risk"] = events.intensity * events.duration * events.importance * events.population
+ events["message_ids"] = events.message_ids.map(lambda x: ", ".join(list(set(x.split(", ")))))
+ events["docs"] = events.docs.map(lambda x: ", ".join(list(set(x.split(", ")))))
+ return events
+
+ def _get_event_connections(self) -> gpd.GeoDataFrame:
+ """
+ Create a list of connections between events.
+ """
+ events = self.events.copy()
+ events.index = events.id
+ events.geometry = events.centroid
+ weights = [len((set(c[0]) & set(c[1]))) for c in combinations(self.events.message_ids, 2)]
+ nodes = [c for c in combinations(events.id, 2)]
+ connections = pd.DataFrame(nodes, weights).reset_index()
+ connections.columns = ["weight", "a", "b"]
+ connections = connections[connections["weight"] > 0]
+ connections = connections.join(events.geometry, on="a", rsuffix="_")
+ connections = connections.join(events.geometry, on="b", rsuffix="_")
+ events.reset_index(drop=True, inplace=True)
+ connections["geometry"] = connections.apply(lambda x: LineString([x["geometry"], x["geometry_"]]), axis=1)
+ connections.drop(columns=["geometry_"], inplace=True)
+ connections = gpd.GeoDataFrame(connections, geometry="geometry").set_crs(32636)
+ return connections
+
+ def _rebalance(self, connections, events, levels, event_population: int, event_id: str):
+ """
+ Rebalance the population of an event.
+ """
+ connections_of_event = connections[connections.a == event_id].b
+ if len(connections_of_event) > 0:
+ accounted_pops = events[events.id.isin(connections_of_event) & events.level.isin(levels)].population.sum()
+ if event_population >= accounted_pops:
+ rebalanced_pops = event_population - accounted_pops
+ else:
+ connections_of_event = connections[connections.b == event_id].a
+ accounted_pops = events[
+ events.id.isin(connections_of_event) & events.level.isin(levels)
+ ].population.sum()
+ rebalanced_pops = event_population - accounted_pops
+ return rebalanced_pops
+ else:
+ return event_population
+
+ def _rebalance_events(self) -> gpd.GeoDataFrame:
+ """
+ Rebalance the population of events.
+ """
+ levels = self.levels.copy()
+ events = self.events.copy()
+ connections = self.connections.copy()
+ events_rebalanced = []
+ for level in levels[1:]:
+ levels_to_account = levels[: levels.index(level)]
+ events_for_level = events[events.level == level]
+ events_for_level["rebalanced_population"] = events_for_level.apply(
+ lambda x: self._rebalance(
+ connections,
+ events,
+ levels_to_account,
+ x.population,
+ x.id,
+ ),
+ axis=1,
+ )
+ events_rebalanced.append(events_for_level)
+ events_rebalanced = pd.concat(events_rebalanced)
+ events_rebalanced.loc[
+ events_rebalanced.rebalanced_population.isna(),
+ "rebalanced_population",
+ ] = 0
+ events_rebalanced["population"] = events_rebalanced.rebalanced_population
+ events_rebalanced.drop(columns=["rebalanced_population"], inplace=True)
+ events_rebalanced.population = events_rebalanced.population.astype(int)
+ events_rebalanced["population"] = (events_rebalanced["population"] - events_rebalanced["population"].min()) / (
+ events_rebalanced["population"].max() - events_rebalanced["population"].min()
+ )
+ events_rebalanced.loc[events_rebalanced.population == 0, "population"] = 0.01 # fix later
+ events_rebalanced.loc[
+ events_rebalanced.population.isna() & events_rebalanced.level.isin(["building", "link"]),
+ "population",
+ ] = 0.01 # fix later
+ events_rebalanced.loc[
+ events_rebalanced.population.isna() & events_rebalanced.level.isin(["road", "global"]),
+ "population",
+ ] = 1 # fix later
+ events_rebalanced["risk"] = (
+ events_rebalanced.intensity * (events_rebalanced.duration + 1) * events_rebalanced.importance
+ )
+ events_rebalanced = events_rebalanced[["name", "docs", "level", "id", "risk", "message_ids", "geometry"]]
+ return events_rebalanced
+
+ def _filter_outliers(self):
+ """
+ Filter outliers.
+ """
+ pattern = r"^-1.*"
+ events = self.events
+ connections = self.connections
+ print(
+ len(events[events.name.map(lambda x: True if re.match(pattern, x) else False)]),
+ "outlier clusters of",
+ len(events),
+ "total clusters. Filtering...",
+ )
+ events = events[events.name.map(lambda x: False if re.match(pattern, x) else True)]
+ connections = connections[connections.a.map(lambda x: False if re.match(pattern, x) else True)]
+ connections = connections[connections.b.map(lambda x: False if re.match(pattern, x) else True)]
+ return events, connections
+
+ def _prepare_messages(self):
+ """
+ Prepare messages for export.
+ """
+ messages = self.messages.copy()
+ messages = messages.reset_index(drop=True)
+ messages.rename(columns={"cats": "block"}, inplace=True)
+ messages = messages[["message_id", "text", "geometry", "date_time", "block"]]
+ messages = messages.to_crs(4326)
+ return messages
+
+
+[docs]
+ def run(
+ self,
+ target_texts: gpd.GeoDataFrame,
+ filepath_to_population: str,
+ city_name: str,
+ city_crs: int,
+ min_event_size: int,
+ ):
+ """
+ Returns a GeoDataFrame of events, a GeoDataFrame of
+ connections between events, and a GeoDataFrame of messages.
+ """
+ self.population_filepath = filepath_to_population
+ self.messages = target_texts.copy()
+ print("messages loaded")
+ self.links = self._get_roads(city_name, city_crs)
+ print("road links loaded")
+ self.buildings = self._get_buildings()
+ print("buildings loaded")
+ self.messages = self._preprocess()
+ print("messages preprocessed")
+ self.events = self._get_events(min_event_size)
+ print("events detected")
+ self.connections = self._get_event_connections()
+ print("connections generated")
+ self.events = self._rebalance_events()
+ print("population and risk rebalanced")
+ self.events, self.connections = self._filter_outliers()
+ print("outliers filtered")
+ self.messages = self._prepare_messages()
+ print("done!")
+
+ return self.messages, self.events, self.connections
+
+
+
+"""
+This module contains the TextClassifiers class, which is aimed to classify input texts into themes or structured types of events.
+It uses a Huggingface transformer model trained on rubert-tiny.
+In many cases, the count of messages per theme was too low to efficiently train, so synthetic themes based
+on the categories as the upper level were used (for example, 'unknown_ЖКХ').
+
+Attributes:
+- repository_id (str): The repository ID.
+- number_of_categories (int): The number of categories.
+- device_type (str): The type of device.
+
+The TextClassifiers class has the following methods:
+
+@method:initialize_classifier: Initializes the text classification pipeline with the specified model, tokenizer, and device type.
+
+@method:run_text_classifier_topics:
+ Takes a text as input and returns the predicted themes and probabilities.
+
+@method:run_text_classifier:
+ Takes a text as input and returns the predicted categories and probabilities.
+"""
+import pandas as pd
+from transformers import pipeline
+from sloyka.src.utils.exceptions import InvalidInputError, ClassifierInitializationError, ClassificationError
+
+
+
+[docs]
+class TextClassifiers:
+ def __init__(self, repository_id, number_of_categories=1, device_type=None):
+ self.repository_id = repository_id
+ self.number_of_categories = number_of_categories
+ self.device_type = device_type or -1 # -1 will automatically choose the device based on availability
+ self.classifier = None
+
+
+[docs]
+ def initialize_classifier(self):
+ if not self.classifier:
+ try:
+ self.classifier = pipeline(
+ "text-classification",
+ model=self.repository_id,
+ tokenizer="cointegrated/rubert-tiny2",
+ device=self.device_type,
+ )
+ except Exception as e:
+ raise ClassifierInitializationError(f"Failed to initialize the classifier: {e}")
+
+
+
+[docs]
+ def classify_text(self, text, is_topic=False):
+ if not isinstance(text, str):
+ raise InvalidInputError("Input must be a string.")
+
+ self.initialize_classifier()
+
+ try:
+ predictions = self.classifier(text, top_k=self.number_of_categories)
+ preds_df = pd.DataFrame(predictions)
+ categories = "; ".join(preds_df["label"].tolist())
+ probabilities = "; ".join(preds_df["score"].round(3).astype(str).tolist())
+ except Exception as e:
+ raise ClassificationError(f"Error during text classification: {e}")
+
+ return categories, probabilities
+
+
+
+[docs]
+ def run_text_classifier_topics(self, text):
+ return self.classify_text(text, is_topic=True)
+
+
+
+
+
+
+"""
+@class:Semgraph:
+The main class of the semantic graph module. It is aimed to build a semantic graph based on the provided data
+and parameters.
+More convenient to use after extracting data from geocoder.
+
+The Semgraph class has the following methods:
+
+@method:clean_from_dublicates:
+A function to clean a DataFrame from duplicates based on specified columns.
+
+@method:clean_from_digits:
+Removes digits from the text in the specified column of the input DataFrame.
+
+@method:clean_from_toponyms:
+Clean the text in the specified text column by removing any words that match the toponyms in the name and
+toponym columns.
+
+@method:aggregate_data:
+Creates a new DataFrame by aggregating the data based on the provided text and toponyms columns.
+"""
+
+import nltk
+import pandas as pd
+import geopandas as gpd
+import networkx as nx
+from transformers import BertTokenizer, BertModel # type: ignore
+
+from .g_attrs_adder import add_attributes
+from .keyword_extracter import extract_keywords
+from .semantic_closeness_annotator import get_semantic_closeness
+from .g_text_data_getter import get_tag, get_coordinates, get_text_ids
+from ..utils.data_preprocessing.preprocessor import (
+ clean_from_dublicates,
+ clean_from_digits,
+ clean_from_toponyms,
+ clean_from_links,
+)
+
+nltk.download("stopwords")
+
+
+from sloyka.src.utils.constants import TAG_ROUTER
+
+
+
+[docs]
+class Semgraph:
+ """
+ This is the main class of semantic graph module.
+ It is aimed to build a semantic graph based on the provided data and parameters.
+ More convinient to use after extracting data from geocoder.
+
+ Param:
+ bert_name: the name of the BERT model to use (default is 'DeepPavlov/rubert-base-cased')
+ language: the language of the BERT model (default is 'russian')
+ device: the device to use for inference (default is 'cpu')
+ """
+
+ def __init__(
+ self, bert_name: str = "DeepPavlov/rubert-base-cased", language: str = "russian", device: str = "cpu"
+ ) -> None:
+ self.language = language
+ self.device = device
+ self.tokenizer = BertTokenizer.from_pretrained(bert_name)
+ self.model_name = bert_name
+ self.model = BertModel.from_pretrained(bert_name).to(device)
+
+
+[docs]
+ @staticmethod
+ def convert_df_to_edge_df(
+ data: pd.DataFrame | gpd.GeoDataFrame, toponym_column: str, word_info_column: str = "words_score"
+ ) -> pd.DataFrame | gpd.GeoDataFrame:
+ edge_list = []
+
+ for i in data[toponym_column]:
+ current_df = data.loc[data[toponym_column] == i]
+ for j in range(len(current_df)):
+ toponym = current_df[toponym_column].iloc[j]
+ word_nodes = current_df[word_info_column].iloc[j]
+
+ for k in word_nodes:
+ if k[2] in TAG_ROUTER.keys():
+ edge_list.append([toponym, k[0], k[1], TAG_ROUTER[k[2]]])
+
+ edge_df = pd.DataFrame(edge_list, columns=["FROM", "TO", "distance", "type"])
+
+ return edge_df
+
+
+
+[docs]
+ def build_graph(
+ self,
+ data: pd.DataFrame | gpd.GeoDataFrame,
+ id_column: str,
+ text_column: str,
+ text_type_column: str,
+ toponym_column: str,
+ toponym_name_column: str,
+ toponym_type_column: str,
+ post_id_column: str,
+ parents_stack_column: str,
+ directed: bool = True,
+ location_column: str | None = None,
+ geometry_column: str | None = None,
+ key_score_filter: float = 0.6,
+ semantic_score_filter: float = 0.75,
+ top_n: int = 1,
+ ) -> nx.classes.graph.Graph:
+ """
+ Build a graph based on the provided data.
+
+ Args:
+ data (pd.DataFrame or gpd.GeoDataFrame): The input data to build the graph from.
+ id_column (str): The column containing unique identifiers.
+ text_column (str): The column containing text information.
+ text_type_column (str): The column indicating the type of text.
+ toponym_column (str): The column containing toponym information.
+ toponym_name_column (str): The column containing toponym names.
+ toponym_type_column (str): The column containing toponym types.
+ post_id_column (str): The column containing post identifiers.
+ parents_stack_column (str): The column containing parent-child relationships.
+ directed (bool): Flag indicating if the graph is directed. Defaults to True.
+ location_column (str or None): The column containing location information. Defaults to None.
+ geometry_column (str or None): The column containing geometry information. Defaults to None.
+ key_score_filter (float): The threshold for key score filtering. Defaults to 0.6.
+ semantic_score_filter (float): The threshold for semantic score filtering. Defaults to 0.75.
+ top_n (int): The number of top keywords to extract. Defaults to 1.
+
+ Returns:
+ nx.classes.graph.Graph: The constructed graph.
+ """
+
+ data = clean_from_dublicates(data, id_column)
+
+ data = clean_from_digits(data, text_column)
+
+ data = clean_from_toponyms(data, text_column, toponym_name_column, toponym_type_column)
+
+ data = clean_from_links(data, text_column)
+
+ extracted = extract_keywords(
+ data,
+ text_column,
+ text_type_column,
+ toponym_column,
+ id_column,
+ post_id_column,
+ parents_stack_column,
+ key_score_filter,
+ top_n,
+ )
+
+ df = extracted[0]
+ toponyms_attributes = extracted[1]
+ words_attributes = extracted[2]
+
+ preprocessed_df = self.convert_df_to_edge_df(data=df, toponym_column=toponym_column)
+
+ words_df = get_semantic_closeness(preprocessed_df, "TO", semantic_score_filter)
+
+ graph_df = pd.concat([preprocessed_df, words_df], ignore_index=True)
+ if directed:
+ G = nx.from_pandas_edgelist(
+ graph_df, source="FROM", target="TO", edge_attr=["distance", "type"], create_using=nx.DiGraph()
+ )
+
+ else:
+ G = nx.from_pandas_edgelist(graph_df, source="FROM", target="TO", edge_attr=["distance", "type"])
+
+ nodes = list(G.nodes())
+ attributes = get_tag(nodes, list(set(data[toponym_column])))
+
+ nx.set_node_attributes(G, attributes, "tag")
+ G = add_attributes(G=G, new_attributes=toponyms_attributes, attribute_tag="counts", toponym_attributes=True)
+
+ G = add_attributes(G=G, new_attributes=words_attributes, attribute_tag="counts", toponym_attributes=False)
+
+ if isinstance(data, gpd.GeoDataFrame):
+ G = get_coordinates(
+ G=G,
+ geocoded_data=data,
+ toponym_column=toponym_column,
+ location_column=location_column,
+ geometry_column=geometry_column,
+ )
+
+ G = get_text_ids(G=G, filtered_data=df, toponym_column=toponym_column, text_id_column=id_column)
+
+ return G
+
+
+
+[docs]
+ def update_graph(
+ self,
+ G: nx.classes.graph.Graph,
+ data: pd.DataFrame | gpd.GeoDataFrame,
+ id_column: str,
+ text_column: str,
+ text_type_column: str,
+ toponym_column: str,
+ toponym_name_column: str,
+ toponym_type_column: str,
+ post_id_column: str,
+ parents_stack_column: str,
+ directed: bool = True,
+ counts_attribute: str | None = None,
+ location_column: str | None = None,
+ geometry_column: str | None = None,
+ key_score_filter: float = 0.6,
+ semantic_score_filter: float = 0.75,
+ top_n: int = 1,
+ ) -> nx.classes.graph.Graph:
+ """
+ Update the input graph based on the provided data, returning the updated graph.
+
+ Args:
+ G (nx.classes.graph.Graph): The input graph to be updated.
+ data (pd.DataFrame or gpd.GeoDataFrame): The input data to update the graph.
+ id_column (str): The column containing unique identifiers.
+ text_column (str): The column containing text information.
+ text_type_column (str): The column indicating the type of text.
+ toponym_column (str): The column containing toponym information.
+ toponym_name_column (str): The column containing toponym names.
+ toponym_type_column (str): The column containing toponym types.
+ post_id_column (str): The column containing post identifiers.
+ parents_stack_column (str): The column containing parent-child relationships.
+ directed (bool): Flag indicating if the graph is directed. Defaults to True.
+ counts_attribute (str or None): The attribute to be used for counting. Defaults to None.
+ location_column (str or None): The column containing location information. Defaults to None.
+ geometry_column (str or None): The column containing geometry information. Defaults to None.
+ key_score_filter (float): The threshold for key score filtering. Defaults to 0.6.
+ semantic_score_filter (float): The threshold for semantic score filtering. Defaults to 0.75.
+ top_n (int): The number of top keywords to extract. Defaults to 1.
+
+ Returns:
+ nx.classes.graph.Graph: The updated graph.
+ """
+
+ new_G = self.build_graph(
+ data,
+ id_column,
+ text_column,
+ text_type_column,
+ toponym_column,
+ toponym_name_column,
+ toponym_type_column,
+ post_id_column,
+ parents_stack_column,
+ directed,
+ location_column,
+ geometry_column,
+ key_score_filter,
+ semantic_score_filter,
+ top_n,
+ )
+
+ joined_G = nx.compose(G, new_G)
+
+ if counts_attribute is not None:
+ nodes = list(set(G.nodes) & set(new_G.nodes))
+ for i in nodes:
+ joined_G.nodes[i]["total_counts"] = G.nodes[i][counts_attribute] + new_G.nodes[i]["counts"]
+
+ return joined_G
+
+
+
+
+# debugging
+# if __name__ == '__main__':
+# file = open("C:\\Users\\thebe\\Downloads\\test.geojson", encoding='utf-8')
+# test_gdf = gpd.read_file(file)
+#
+# sm = Semgraph()
+#
+# G = sm.build_graph(test_gdf[:3000],
+# id_column='id',
+# text_column='text',
+# text_type_column='type',
+# toponym_column='only_full_street_name_numbers',
+# toponym_name_column='initial_street',
+# toponym_type_column='Toponims',
+# post_id_column='post_id',
+# parents_stack_column='parents_stack',
+# location_column='Location',
+# geometry_column='geometry')
+#
+# # print(len(G.nodes))
+# #
+# # G = sm.update_graph(G,
+# # test_gdf[3000:],
+# # id_column='id',
+# # text_column='text',
+# # text_type_column='type',
+# # toponym_column='only_full_street_name',
+# # toponym_name_column='initial_street',
+# # toponym_type_column='Toponims',
+# # post_id_column='post_id',
+# # parents_stack_column='parents_stack',
+# # counts_attribute='counts',
+# # location_column='Location',
+# # geometry_column='geometry')
+# #
+# # print(len(G.nodes))
+# #
+# # nx.write_graphml(G, 'name.graphml', encoding='utf-8')
+
+"""
+This module contains classes for retrieving and working with various types of data.
+
+@class:GeoDataGetter:
+This class is used to retrieve geospatial data from OpenStreetMap (OSM) based on given OSM ID and tags.
+
+@class:VKParser:
+A class for parsing and working with VK comments and posts. Combines posts and comments into one dataframe.
+
+@class:Streets:
+A class for working with street data.
+
+"""
+import osmnx as ox
+import geopandas as gpd
+import pandas as pd
+from sloyka.src.utils.constants import (
+ GLOBAL_CRS,
+ GLOBAL_METRIC_CRS,
+)
+from shapely.ops import transform
+from tqdm import tqdm
+import requests
+import sys
+import datetime
+import time
+import osm2geojson
+import random
+from typing import List, Optional
+from osmapi import OsmApi
+import networkx as nx
+from loguru import logger
+
+
+
+[docs]
+class VKParser:
+ API_VERISON = "5.131"
+ COUNT_ITEMS = 100
+ # SLEEP_TIME = 0.5
+ TIMEOUT_LIMIT = 15
+
+
+[docs]
+ @staticmethod
+ def get_group_name(domain, accsess_token):
+ params = {"group_id": domain, "access_token": accsess_token, "v": VKParser.API_VERISON}
+ response = requests.get("https://api.vk.com/method/groups.getById", params=params) # передвинуть повыше
+ data = response.json()
+ if "response" in data and data["response"]:
+ group_name = data["response"][0]["name"]
+ return pd.DataFrame({"group_name": [group_name]})
+ else:
+ print("Error while fetching group name:", data)
+ return pd.DataFrame({"group_name": [None]})
+
+
+
+[docs]
+ @staticmethod
+ def get_owner_id_by_domain(domain, access_token):
+ """
+ Get the owner ID of a VK group by its domain.
+
+ Args:
+ domain (str): The domain of the VK group.
+ access_token (str): The access token for the VK API.
+
+ Returns:
+ int: The owner ID of the VK group, or None if the request was not successful.
+ """
+ url = "https://api.vk.com/method/wall.get"
+ params = {
+ "domain": domain,
+ "access_token": access_token,
+ "v": VKParser.API_VERISON,
+ }
+ response = requests.get(url, params=params)
+ if response.ok:
+ owner_id = response.json()["response"]["items"][0]["owner_id"]
+ else:
+ owner_id = None
+ return owner_id
+
+
+
+[docs]
+ @staticmethod
+ def get_group_post_ids(domain, access_token, post_num_limit, step) -> list:
+ """
+ A static method to retrieve a list of post IDs for a given group, based on the owner ID,
+ access token, post number limit, and step size. Returns a list of post IDs.
+ """
+ offset = 0
+ post_ids = []
+
+ while offset < post_num_limit:
+ print(offset, " | ", post_num_limit, end="\r")
+ res = requests.get(
+ "https://api.vk.com/method/wall.get",
+ params={
+ "access_token": access_token,
+ "v": VKParser.API_VERISON,
+ "domain": domain,
+ "count": step,
+ "offset": offset,
+ },
+ timeout=10,
+ ).json()["response"]
+ # print(res.json().keys())
+ time.sleep(random.random())
+
+ post_ids_new = [k["id"] for k in res["items"]]
+ post_ids += post_ids_new
+ offset += step
+
+ return post_ids
+
+
+
+[docs]
+ @staticmethod
+ def get_subcomments(owner_id, post_id, access_token, params):
+ """
+ Retrieves subcomments from the VK API.
+
+ Args:
+ owner_id (int): The ID of the owner of the comments.
+ post_id (int): The ID of the post.
+ access_token (str): The access token for authentication.
+ params (dict): Additional parameters for the API request.
+
+ Returns:
+ list: A list of subcomments retrieved from the API.
+ """
+ subcomments = []
+
+ response = requests.get("https://api.vk.com/method/wall.getComments", params=params)
+ # print(response.json().keys())
+ time.sleep(random.random())
+ data = response.json()
+
+ if "response" in data:
+ for item in data["response"]["items"]:
+ item["date"] = datetime.datetime.utcfromtimestamp(item["date"]).strftime("%Y-%m-%d %H:%M:%S")
+ if "likes" in item:
+ item["likes.count"] = item["likes"]["count"]
+ subcomments.append(item)
+
+ return subcomments
+
+
+
+[docs]
+ def get_comments(owner_id, post_id, access_token):
+ """
+ Get comments for a post on VK using the specified owner ID, post ID, and access token.
+
+ Parameters:
+ owner_id (int): The ID of the post owner.
+ post_id (int): The ID of the post.
+ access_token (str): The access token for authentication.
+
+ Returns:
+ list: A list of dictionaries containing comment information.
+ """
+ params = {
+ "owner_id": owner_id,
+ "post_id": post_id,
+ "access_token": access_token,
+ "v": VKParser.API_VERISON,
+ "extended": 1,
+ "count": 100,
+ "need_likes": 1,
+ }
+
+ comments = []
+
+ response = requests.get("https://api.vk.com/method/wall.getComments", params=params)
+ # print(response.json().keys())
+ time.sleep(random.random())
+ data = response.json()
+
+ if "response" in data:
+ for item in data["response"]["items"]:
+ if item["text"] == "":
+ continue
+ item["date"] = datetime.datetime.utcfromtimestamp(item["date"]).strftime("%Y-%m-%d %H:%M:%S")
+ if "likes" in item:
+ item["likes.count"] = item["likes"]["count"]
+ comments.append(item)
+ if item["thread"]["count"] > 0:
+ params["comment_id"] = item["id"]
+ subcomments = VKParser.get_subcomments(owner_id, post_id, access_token, params)
+ comments.extend(subcomments)
+ return comments
+
+
+
+[docs]
+ @staticmethod
+ def comments_to_dataframe(comments):
+ """
+ Convert comments to a DataFrame.
+
+ Args:
+ comments: List of comments to be converted.
+
+ Returns:
+ DataFrame: A DataFrame containing specific columns from the input comments.
+ """
+ df = pd.DataFrame(comments)
+ df = df[["id", "from_id", "date", "text", "post_id", "parents_stack", "likes.count"]]
+ return df
+
+
+
+[docs]
+ @staticmethod
+ def run_posts(domain, access_token, cutoff_date, number_of_messages=float("inf"), step=50):
+ """
+ A function to retrieve posts from a social media API based on specified parameters.
+
+ Parameters:
+ owner_id (int): The ID of the owner whose posts are being retrieved.
+ access_token (str): The authentication token for accessing the API.
+ step (int): The number of posts to retrieve in each API call.
+ cutoff_date (str): The date to stop retrieving posts (format: '%Y-%m-%d').
+ number_of_messages (float): The maximum number of messages to retrieve (default is infinity).
+
+ Returns:
+ pandas.DataFrame: A DataFrame containing the retrieved posts.
+ """
+
+ domain = domain
+ offset = 0
+ all_posts = []
+ if step > number_of_messages:
+ step = number_of_messages
+ while offset < number_of_messages:
+ print(offset, " | ", number_of_messages, end="\r")
+
+ response = requests.get(
+ "https://api.vk.com/method/wall.get",
+ params={
+ "access_token": access_token,
+ "v": VKParser.API_VERISON,
+ "domain": domain,
+ "count": step,
+ "offset": offset,
+ }, timeout=600
+ )
+ if response.ok:
+ # print(response.json().keys())
+ data = response.json()["response"]["items"]
+ offset += step
+ current_posts = pd.json_normalize(data)
+ current_posts = current_posts[["date", "id", "text", "views.count", "likes.count", "reposts.count"]]
+ current_posts["date"] = [
+ datetime.datetime.fromtimestamp(current_posts["date"][i]) for i in range(len(current_posts["date"]))
+ ]
+ current_posts["type"] = "post"
+ all_posts.append(current_posts)
+ print(current_posts.date.min())
+ if any(current_posts["date"] < datetime.datetime.strptime(cutoff_date, "%Y-%m-%d")):
+ print("posts downloaded")
+ break
+ else:
+ continue
+ time.sleep(random.random())
+ df_posts = pd.concat(all_posts).reset_index(drop=True)
+ df_posts = df_posts[df_posts.text.map(lambda x: len(x)) > 0]
+ df_posts["text"] = df_posts["text"].str.replace(r"\n", "", regex=True)
+ df_posts["link"] = df_posts["text"].str.extract(r"(https://\S+)")
+ return df_posts
+
+
+
+[docs]
+ @staticmethod
+ def run_comments(domain, post_ids, access_token):
+ owner_id = VKParser.get_owner_id_by_domain(domain, access_token)
+ all_comments = []
+ for post_id in tqdm(post_ids):
+ comments = VKParser.get_comments(owner_id, post_id, access_token)
+ all_comments.extend(comments)
+ df = VKParser.comments_to_dataframe(all_comments)
+ df["type"] = "comment"
+ df = df.reset_index(drop=True)
+ print("comments downloaded")
+ return df
+
+
+
+[docs]
+ @staticmethod
+ def run_parser(domain, access_token, cutoff_date, number_of_messages=float("inf"), step=100):
+ """
+ Runs the parser with the given parameters and returns a combined DataFrame of posts and comments.
+
+ :param owner_id: The owner ID for the parser.
+ :param access_token: The user token for authentication.
+ :param step: The step size for fetching data.
+ :param cutoff_date: The cutoff date for fetching data.
+ :param number_of_messages: The maximum number of messages to fetch. Defaults to positive infinity.
+ :return: A combined DataFrame of posts and comments.
+ """
+ owner_id = VKParser.get_owner_id_by_domain(domain, access_token)
+ df_posts = VKParser.run_posts(domain=owner_id, access_token=access_token, step=step, cutoff_date=cutoff_date, number_of_messages=number_of_messages)
+ post_ids = df_posts["id"].tolist()
+
+ df_comments = VKParser.run_comments(domain=owner_id, post_ids=post_ids, access_token=access_token)
+ df_comments.loc[df_comments["parents_stack"].apply(lambda x: len(x) > 0), "type"] = "reply"
+ for i in range(len(df_comments)):
+ tmp = df_comments["parents_stack"].iloc[i]
+ if tmp is not None:
+ if len(tmp) > 0:
+ df_comments["parents_stack"].iloc[i] = tmp[0]
+ else:
+ df_comments["parents_stack"].iloc[i] = None
+
+ df_combined = df_comments.join(df_posts, on="post_id", rsuffix="_post")
+ df_combined = pd.concat([df_posts, df_comments], ignore_index=True)
+ df_group_name = VKParser.get_group_name(domain, access_token)
+ df_combined["group_name"] = df_group_name["group_name"][0]
+
+ return df_combined
+
+
+
+import pandas as pd
+import numpy as np
+from flair.models import SequenceTagger
+from flair.data import Sentence
+from rapidfuzz import fuzz
+from typing import List
+from sloyka.src.utils.constants import CITY_SERVICES_NAMES
+
+tagger = SequenceTagger.load("Glebosol/city_services")
+
+
+
+[docs]
+class City_services:
+
+[docs]
+ def extraction_services(text):
+ sentence = Sentence(text)
+ tagger.predict(sentence)
+ entities = sentence.get_spans("ner")
+ entity_names = [entity.text for entity in entities]
+ return entity_names
+
+
+
+[docs]
+ def remove_last_letter(words):
+ reduced_words = [word[:-1] for word in words]
+ return reduced_words
+
+
+ # def replace_with_most_similar(entity_names: List[str], CITY_SERVICES_NAMES: List[str]) -> List[str]:
+ # true_city_services_names = [difflib.get_close_matches(word_entity_names, CITY_SERVICES_NAMES, n=1, cutoff=0.0)[0] for word_entity_names in entity_names]
+ # return true_city_services_names
+
+
+[docs]
+ def replace_with_most_similar(list_of_entities):
+ similarity_matrix = np.zeros((len(list_of_entities), len(CITY_SERVICES_NAMES)))
+ for i, word1 in enumerate(list_of_entities):
+ for j, word2 in enumerate(CITY_SERVICES_NAMES):
+ similarity = fuzz.ratio(word1, word2) / 100.0
+ similarity_matrix[i, j] = similarity
+ new_list_of_entities = list_of_entities.copy()
+ for i in range(len(list_of_entities)):
+ max_index = np.argmax(similarity_matrix[i])
+ new_list_of_entities[i] = CITY_SERVICES_NAMES[max_index]
+ return new_list_of_entities
+
+
+
+[docs]
+ def run(self, df, text_column):
+ df["City_services_extraced"] = df[text_column].apply(lambda text: City_services.extraction_services(text))
+ df["City_services_cuted"] = df["City_services_extraced"].apply(
+ lambda row: City_services.remove_last_letter(row)
+ )
+ df["City_services"] = df["City_services_cuted"].apply(lambda row: City_services.replace_with_most_similar(row))
+ df.drop("City_services_cuted", axis=1, inplace=True)
+ return df
+
+
+
+import networkx as nx
+import folium
+import random
+from folium.plugins import MarkerCluster
+
+
+
+[docs]
+def draw_graph_folium(graph_path, output_file=None):
+ """
+ Visualizes a graph from the given graph_path using Folium and MarkerCluster.
+
+ Args:
+ graph_path (str): The path to the graphml file.
+ output_file (str, optional): The file to save the visualization to. Defaults to None.
+
+ Returns:
+ folium.Map: The folium map object representing the visualized graph.
+ """
+ G = nx.read_graphml(graph_path)
+
+ color_mapping = {
+ -1: "gray",
+ 0: "blue",
+ 1: "green",
+ 2: "purple",
+ 3: "cyan",
+ 4: "brown",
+ 5: "orange",
+ 6: "pink",
+ 7: "darkred",
+ 8: "yellow",
+ 9: "beige",
+ 10: "darkgreen",
+ 11: "lightgreen",
+ 12: "darkblue",
+ 13: "lightblue",
+ 14: "darkpurple",
+ 15: "cadetblue",
+ 16: "red",
+ 17: "lightgreen",
+ 18: "lightblue",
+ }
+
+ target_clusters = range(1, 19)
+
+ m = folium.Map(
+ location=[59.9343, 30.3351],
+ zoom_start=10,
+ tiles="cartodbdark_matter",
+ control_scale=True,
+ )
+
+ for c in target_clusters:
+ mc = MarkerCluster(name=f"{c} | cluster")
+
+ for node, data in G.nodes(data=True):
+ if "Lat" in data and "Lon" in data:
+ main_node_location = [data["Lat"], data["Lon"]]
+ for n in G.neighbors(node):
+ if "Cluster" in G.nodes[n] and G.nodes[n]["Cluster"] == c:
+ neighbor_data = G.nodes[n]
+ neighbor_location = [
+ main_node_location[1] + random.uniform(-0.0008, 0.0008),
+ main_node_location[0] + random.uniform(-0.0008, 0.0008),
+ ]
+ folium.CircleMarker(
+ location=neighbor_location,
+ radius=10,
+ color=color_mapping[G.nodes[n]["Cluster"]],
+ fill=True,
+ fill_color=color_mapping[G.nodes[n]["Cluster"]],
+ popup=neighbor_data,
+ name=f'cluster_{G.nodes[n]["Cluster"]}',
+ ).add_to(mc)
+ mc.add_to(m)
+
+ folium.LayerControl().add_to(m)
+ if not output_file is None:
+ m.save(output_file)
+
+ return m
+
+Attention
+
+ You are reading the latest
+ (unstable) version of this documentation, which may document features not available
+ or compatible with the latest Crest packages released on the Unity Asset Store.
+
+ View the stable version of this page. +
+Attention
++ Looks like you are on a version without a published tag. + We will redirect you to the latest documentation automatically. + If it does not redirect automatically in a few seconds, please click the follow: + ${newUrl.href} +
+' + + '' + + _("Hide Search Matches") + + "
" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/docs/build/html/_static/styles/furo-extensions.css b/docs/build/html/_static/styles/furo-extensions.css new file mode 100644 index 0000000..bc447f2 --- /dev/null +++ b/docs/build/html/_static/styles/furo-extensions.css @@ -0,0 +1,2 @@ +#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout a{color:var(--color-foreground-secondary)!important}#furo-readthedocs-versions{background:transparent;display:block;position:static;width:100%}#furo-readthedocs-versions .rst-versions{background:#1a1c1e}#furo-readthedocs-versions .rst-current-version{background:var(--color-sidebar-item-background);cursor:unset}#furo-readthedocs-versions .rst-current-version:hover{background:var(--color-sidebar-item-background)}#furo-readthedocs-versions .rst-current-version .fa-book{color:var(--color-foreground-primary)}#furo-readthedocs-versions>.rst-other-versions{padding:0}#furo-readthedocs-versions>.rst-other-versions small{opacity:1}#furo-readthedocs-versions .injected .rst-versions{position:unset}#furo-readthedocs-versions:focus-within,#furo-readthedocs-versions:hover{box-shadow:0 0 0 1px var(--color-sidebar-background-border)}#furo-readthedocs-versions:focus-within .rst-current-version,#furo-readthedocs-versions:hover .rst-current-version{background:#1a1c1e;font-size:inherit;height:auto;line-height:inherit;padding:12px;text-align:right}#furo-readthedocs-versions:focus-within .rst-current-version .fa-book,#furo-readthedocs-versions:hover .rst-current-version .fa-book{color:#fff;float:left}#furo-readthedocs-versions:focus-within .fa-caret-down,#furo-readthedocs-versions:hover .fa-caret-down{display:none}#furo-readthedocs-versions:focus-within .injected,#furo-readthedocs-versions:focus-within .rst-current-version,#furo-readthedocs-versions:focus-within .rst-other-versions,#furo-readthedocs-versions:hover .injected,#furo-readthedocs-versions:hover .rst-current-version,#furo-readthedocs-versions:hover .rst-other-versions{display:block}#furo-readthedocs-versions:focus-within>.rst-current-version,#furo-readthedocs-versions:hover>.rst-current-version{display:none}.highlight:hover button.copybtn{color:var(--color-code-foreground)}.highlight button.copybtn{align-items:center;background-color:var(--color-code-background);border:none;color:var(--color-background-item);cursor:pointer;height:1.25em;opacity:1;right:.5rem;top:.625rem;transition:color .3s,opacity .3s;width:1.25em}.highlight button.copybtn:hover{background-color:var(--color-code-background);color:var(--color-brand-content)}.highlight button.copybtn:after{background-color:transparent;color:var(--color-code-foreground);display:none}.highlight button.copybtn.success{color:#22863a;transition:color 0ms}.highlight button.copybtn.success:after{display:block}.highlight button.copybtn svg{padding:0}body{--sd-color-primary:var(--color-brand-primary);--sd-color-primary-highlight:var(--color-brand-content);--sd-color-primary-text:var(--color-background-primary);--sd-color-shadow:rgba(0,0,0,.05);--sd-color-card-border:var(--color-card-border);--sd-color-card-border-hover:var(--color-brand-content);--sd-color-card-background:var(--color-card-background);--sd-color-card-text:var(--color-foreground-primary);--sd-color-card-header:var(--color-card-marginals-background);--sd-color-card-footer:var(--color-card-marginals-background);--sd-color-tabs-label-active:var(--color-brand-content);--sd-color-tabs-label-hover:var(--color-foreground-muted);--sd-color-tabs-label-inactive:var(--color-foreground-muted);--sd-color-tabs-underline-active:var(--color-brand-content);--sd-color-tabs-underline-hover:var(--color-foreground-border);--sd-color-tabs-underline-inactive:var(--color-background-border);--sd-color-tabs-overline:var(--color-background-border);--sd-color-tabs-underline:var(--color-background-border)}.sd-tab-content{box-shadow:0 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rgba(0,0,0,.1)!important}.sd-card-hover:hover{transform:none}.sd-cards-carousel{gap:.25rem;padding:.25rem}body{--tabs--label-text:var(--color-foreground-muted);--tabs--label-text--hover:var(--color-foreground-muted);--tabs--label-text--active:var(--color-brand-content);--tabs--label-text--active--hover:var(--color-brand-content);--tabs--label-background:transparent;--tabs--label-background--hover:transparent;--tabs--label-background--active:transparent;--tabs--label-background--active--hover:transparent;--tabs--padding-x:0.25em;--tabs--margin-x:1em;--tabs--border:var(--color-background-border);--tabs--label-border:transparent;--tabs--label-border--hover:var(--color-foreground-muted);--tabs--label-border--active:var(--color-brand-content);--tabs--label-border--active--hover:var(--color-brand-content)}[role=main] .container{max-width:none;padding-left:0;padding-right:0}.shadow.docutils{border:none;box-shadow:0 .2rem .5rem rgba(0,0,0,.05),0 0 .0625rem rgba(0,0,0,.1)!important}.sphinx-bs .card{background-color:var(--color-background-secondary);color:var(--color-foreground)} +/*# sourceMappingURL=furo-extensions.css.map*/ \ No newline at end of file diff --git a/docs/build/html/_static/styles/furo-extensions.css.map b/docs/build/html/_static/styles/furo-extensions.css.map new file mode 100644 index 0000000..9ba5637 --- /dev/null +++ b/docs/build/html/_static/styles/furo-extensions.css.map @@ -0,0 +1 @@ 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at end of file diff --git a/docs/build/html/_static/styles/furo.css b/docs/build/html/_static/styles/furo.css new file mode 100644 index 0000000..e3d4e57 --- /dev/null +++ b/docs/build/html/_static/styles/furo.css @@ -0,0 +1,2 @@ +/*! normalize.css v8.0.1 | MIT License | github.com/necolas/normalize.css */html{line-height:1.15;-webkit-text-size-adjust:100%}body{margin:0}main{display:block}h1{font-size:2em;margin:.67em 0}hr{box-sizing:content-box;height:0;overflow:visible}pre{font-family:monospace,monospace;font-size:1em}a{background-color:transparent}abbr[title]{border-bottom:none;text-decoration:underline;text-decoration:underline 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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto-light\n display: block\n\n @media (prefers-color-scheme: dark)\n .theme-toggle svg.theme-icon-when-auto-dark\n display: block\n .theme-toggle svg.theme-icon-when-auto-light\n display: none\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n --font-stack--headings: var(--font-stack);\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #6b6f76; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #0a4bff;\n --color-brand-content: #2757dd;\n --color-brand-visited: #872ee0;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n\n --color-api-added: #21632c;\n --color-api-added-border: #38a84d;\n --color-api-changed: #046172;\n --color-api-changed-border: #06a1bc;\n --color-api-deprecated: #605706;\n --color-api-deprecated-border: #f0d90f;\n --color-api-removed: #b30000;\n --color-api-removed-border: #ff5c5c;\n\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline--hover: var(--color-foreground-border);\n\n --color-link--visited: var(--color-brand-visited);\n --color-link-underline--visited: var(--color-background-border);\n --color-link--visited--hover: var(--color-brand-visited);\n --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #cfd0d0; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #3d94ff;\n --color-brand-content: #5ca5ff;\n --color-brand-visited: #b27aeb;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n --color-api-added: #3db854;\n --color-api-added-border: #267334;\n --color-api-changed: #09b0ce;\n --color-api-changed-border: #056d80;\n --color-api-deprecated: #b1a10b;\n --color-api-deprecated-border: #6e6407;\n --color-api-removed: #ff7575;\n --color-api-removed-border: #b03b3b;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-family: var(--font-stack--headings)\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:visited\n color: var(--color-link--visited)\n text-decoration-color: var(--color-link-underline--visited)\n &:hover\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &:visited\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\n$icon-size: 1.25rem\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n.skip-to-content\n position: fixed\n padding: 1rem\n border-radius: 1rem\n left: 0.25rem\n top: 0.25rem\n z-index: 40\n background: var(--color-background-primary)\n color: var(--color-foreground-primary)\n\n transform: translateY(-200%)\n transition: transform 300ms ease-in-out\n\n &:focus-within\n transform: translateY(0%)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: $icon-size\n width: $icon-size\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: $icon-size\n width: $icon-size\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page, .view-this-page\n svg\n color: inherit\n height: $icon-size\n width: $icon-size\n\n.sidebar-toggle\n position: absolute\n display: none\n//