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a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ license = "GPL" packages = [{include='pysus'}] [tool.poetry.dependencies] -python = ">=3.10,<3.12" +python = ">=3.10,<3.13" python-dateutil = "2.8.2" cffi = "1.15.1" dbfread = "2.0.7" @@ -54,7 +54,6 @@ sphinx-rtd-theme = "^1.3.0" nbsphinx = "^0.9.3" [tool.poetry.group.geo.dependencies] -geobr = { version = "^0.2.0", extras=["preprocessing"] } geocoder = { version = "^1.38.1", extras=["preprocessing"] } jsonschema = "^4.19.0" descartes = "^1.1.0" diff --git a/pysus/preprocessing/__init__.py b/pysus/preprocessing/__init__.py index a2f17c1..3762bb8 100644 --- a/pysus/preprocessing/__init__.py +++ b/pysus/preprocessing/__init__.py @@ -1,5 +1,4 @@ -# -*- coding:utf-8 -*- -u""" +""" Created on 19/07/16 by fccoelho license: GPL V3 or Later diff --git a/pysus/preprocessing/geodata.py b/pysus/preprocessing/geodata.py index 1fc3008..a9e5c9a 100644 --- a/pysus/preprocessing/geodata.py +++ b/pysus/preprocessing/geodata.py @@ -1,4 +1,3 @@ -# -*- coding:utf-8 -*- """ This module contains a set of functions to deal with geodatadecode commonly encoded variables @@ -9,72 +8,80 @@ __docformat__ = "restructuredtext en" import pandas as pd -from geobr import read_municipality +# from geobr import read_municipality +import warnings +warnings.warn( + "The 'geodata' module is deprecated and will be removed in a future release. " + "Please use 'new_module' instead.", + DeprecationWarning, + stacklevel=2 +) -def add_data_to_municipality( - counts, - map_year=2019, - codmun_col="CODMUNRES", - title_cols=["SEXO", "IDADE_ANOS"], - value_col="COUNTS", - nan_string="nan", -): - """ - Adiciona dados de mortalidade aos seus respectivos municípios. Gera um GeoDataFrame do GeoPandas. - :param counts: dataframe contendo os dados a serem agregados. - :param map_year: ano do mapa a ser usado (biblioteca geobr). - :param codmun_col: coluna com geocode do município - :param title_cols: colunas que serão utilizadas para formar o título das colunas no GeoDataFrame. - :param value_col: coluna com o valor a ser adicionado ao GeoDataFrame - :return: - """ - - # Extrai código do estado dos municípios. - # 2 primeiros dígitos do código são o estado - states = ( - counts[counts[codmun_col] != nan_string][codmun_col] - .apply(lambda x: str(x)[:2]) - .unique() - ) - geo_df = read_municipality(code_muni=states[0], year=map_year) - - all_fields = [codmun_col] + title_cols + [value_col] - - if len(states) > 1: - for state in states[1:]: - geo_df = geo_df.append(read_municipality(code_muni=state, year=map_year)) - - column_names = column_name_list(counts, title_cols) - geo_df[column_names] = 0 - - for i, mun in geo_df.iterrows(): - data = counts[ - (counts[codmun_col] == mun["code_muni"]) & (counts[value_col] > 0.0) - ] - for _, item in data.iterrows(): - geo_df.loc[i, column_name(item, title_cols)] = item[value_col] - - return geo_df.fillna(0) - - -def column_name_list(df, title_cols): - unique_indices = pd.DataFrame(df.groupby(title_cols).indices.keys()) - titles = unique_indices.apply(column_name, axis=1) - return titles.tolist() - - -def column_name(item, title_cols=None): - if title_cols: - name = item[title_cols[0]] - if len(title_cols) > 1: - for col in title_cols[1:]: - name = f"{name}-{item[col]}" - else: - name = item[0] - if len(item) > 1: - for col in item[1:]: - name = f"{name}-{col}" - - return name +# def add_data_to_municipality( +# counts, +# map_year=2019, +# codmun_col="CODMUNRES", +# title_cols=["SEXO", "IDADE_ANOS"], +# value_col="COUNTS", +# nan_string="nan", +# ): +# +# """ +# Adiciona dados de mortalidade aos seus respectivos municípios. Gera um GeoDataFrame do GeoPandas. +# :param counts: dataframe contendo os dados a serem agregados. +# :param map_year: ano do mapa a ser usado (biblioteca geobr). +# :param codmun_col: coluna com geocode do município +# :param title_cols: colunas que serão utilizadas para formar o título das colunas no GeoDataFrame. +# :param value_col: coluna com o valor a ser adicionado ao GeoDataFrame +# :return: +# """ +# +# # Extrai código do estado dos municípios. +# # 2 primeiros dígitos do código são o estado +# states = ( +# counts[counts[codmun_col] != nan_string][codmun_col] +# .apply(lambda x: str(x)[:2]) +# .unique() +# ) +# geo_df = read_municipality(code_muni=states[0], year=map_year) +# +# all_fields = [codmun_col] + title_cols + [value_col] +# +# if len(states) > 1: +# for state in states[1:]: +# geo_df = geo_df.append(read_municipality(code_muni=state, year=map_year)) +# +# column_names = column_name_list(counts, title_cols) +# geo_df[column_names] = 0 +# +# for i, mun in geo_df.iterrows(): +# data = counts[ +# (counts[codmun_col] == mun["code_muni"]) & (counts[value_col] > 0.0) +# ] +# for _, item in data.iterrows(): +# geo_df.loc[i, column_name(item, title_cols)] = item[value_col] +# +# return geo_df.fillna(0) +# +# +# def column_name_list(df, title_cols): +# unique_indices = pd.DataFrame(df.groupby(title_cols).indices.keys()) +# titles = unique_indices.apply(column_name, axis=1) +# return titles.tolist() +# +# +# def column_name(item, title_cols=None): +# if title_cols: +# name = item[title_cols[0]] +# if len(title_cols) > 1: +# for col in title_cols[1:]: +# name = f"{name}-{item[col]}" +# else: +# name = item[0] +# if len(item) > 1: +# for col in item[1:]: +# name = f"{name}-{col}" +# +# return name