def add_data_to_municipality(
counts,
map_year=2019,
codmun_col='CODMUNRES',
title_cols=['SEXO','IDADE_ANOS'],
value_col='CONTAGEM'):
"""
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'][codmun_col].apply(lambda x: str(x)[:2]).unique()
geo_df = read_municipality(code_muni=states[0],year=map_year)
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 total_veh_domain(lon1, lat1, lon2, lat2, year, UF, veh_sep18):
'''
Parameters
----------
lon1 : float number
longitude 1 of the south-west.
lat1 : float number
latitude 1 of the south-west
lon2 : float number
longitude 2 of the north-east.
lat2 : float number
latitude 2 of the north-east.
year : integer
year analized.
UF : string list
Unidades Federais de Brasil
veh : Pandas DataFrame
Number of vehicle types by UF and municipality.
Returns
-------
Number of vehicles inside the modeling domain area
'''
mun = geobr.read_municipality(code_muni='all', year=2018)
polygon = Polygon([(lon1, lat1), (lon1, lat2), (lon2, lat2), (lon2, lat1),
(lon1, lat1)])
munDomain = gpd.clip(mun, polygon) ## This is very important
munDomain = munDomain.sort_values(by='abbrev_state')
munnames = list(munDomain.name_muni.values)
munNAMES = [x.upper() for x in munnames]
munNAMES = [strip_accents(x) for x in munNAMES]
munDomain['MUNICIPIO'] = munNAMES
UF = ['MG', 'ES', 'RJ', 'SP', 'PR', 'SC', 'RS', 'MS', 'GO']
vehUF = veh_sep18[veh_sep18.UF.isin(UF)]
vehs = [
'AUTOMOVEL', 'CAMINHAO', 'CAMINHAO TRATOR', 'CAMINHONETE', 'CAMIONETA',
'CICLOMOTOR', 'MICRO-ONIBUS', 'MOTOCICLETA', 'MOTONETA', 'ONIBUS',
'TRATOR ESTEI', 'TRATOR RODAS', 'UTILITARIO'
]
vehUF = vehUF.filter(['UF', 'MUNICIPIO'] + vehs)
vehUF['TOTAL'] = vehUF.sum(axis=1)
vehMunDom1 = vehUF[vehUF.MUNICIPIO.isin(munNAMES)]
return vehMunDom1, vehUF
def get_geobr():
for k, v in initials.items():
ufs = v.upper()
df = read_municipality(code_muni=ufs, year=2019)
cols = ['code_muni', 'code_state', 'code_region']
df[cols] = df[cols].applymap(np.int64)
result = df.to_json()
parsed = json.loads(result)
fname = f'{v}-municipalities.json'
to_path = os.path.join(path_, fname)
with open(to_path, 'w') as f:
json.dump(parsed, f)
logger.warning(f'Saving the JSON to {k} state in {to_path}')
print('\n')
logger.warning('All files were downloaded successfully!')
@author: mgavidia
"""
import pandas as pd
import geopandas as gpd
import geobr as gb
import xarray as xr
import matplotlib.pyplot as plt
from shapely.geometry import Polygon
# States in domain
states_codes = ["SP", "RJ", "PR", "MG", "ES", "SC", "MS"]
# Saving shapefile in a dict
states_in_domain = {
code: gb.read_municipality(code_muni=code, year=2014)
for code in states_codes
}
# Merging municipalities in domain
mun_gdf = pd.concat([state for code, state in states_in_domain.items()],
ignore_index=True)
# Reading veicule number from
# https://www.gov.br/infraestrutura/pt-br/assuntos/transito/conteudo-denatran/frota-de-veiculos-2014
veic = pd.read_excel("./frota_por_municipio_e_tipo_out_2014.xlsx",
sheet_name="OUT_2014",
header=3)
# Clip shapefiles to WRF domain
wrf = xr.open_dataset("./geo_em.d01.nc")
def test_read_municipality():
assert isinstance(read_municipality(), gpd.geodataframe.GeoDataFrame)
assert isinstance(read_municipality(code_muni="AC", year=1991),
gpd.geodataframe.GeoDataFrame)
assert isinstance(read_municipality(code_muni="AC", year=2010),
gpd.geodataframe.GeoDataFrame)
assert isinstance(read_municipality(code_muni=11, year=1991),
gpd.geodataframe.GeoDataFrame)
assert isinstance(read_municipality(code_muni=11, year=2010),
gpd.geodataframe.GeoDataFrame)
assert isinstance(read_municipality(code_muni="all", year=1991),
gpd.geodataframe.GeoDataFrame)
assert isinstance(read_municipality(code_muni="all", year=2010),
gpd.geodataframe.GeoDataFrame)
with pytest.raises(Exception):
read_municipality(year=9999999)
read_municipality(code_muni="RJ_ABC")
crs=ccrs.PlateCarree())
# Put a background image on for nice sea rendering.
ax1.stock_img()
# Create a feature for States/Admin 1 regions at 1:50m from Natural Earth
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
ax1.add_feature(cfeature.LAND)
ax1.add_feature(cfeature.COASTLINE)
ax1.add_feature(states_provinces, edgecolor='black')
mun = geobr.read_municipality(code_muni='all', year=2018)
mun.plot(facecolor="none", alpha=1, edgecolor='gray', ax=ax1)
polygon = Polygon([(lon1, lat1), (lon1, lat2), (lon2, lat2), (lon2, lat1),
(lon1, lat1)])
poly_gdf = gpd.GeoDataFrame([1], geometry=[polygon], crs=mun.crs)
poly_gdf.boundary.plot(ax=ax1, color="red")
munDomain = gpd.clip(mun, polygon) ## This is very important
munDomain = munDomain.sort_values(by='abbrev_state')
ax2 = plt.subplot(1, 2, 2, projection=ccrs.PlateCarree())
munDomain.plot(ax=ax2, color="purple", alpha=0.5)
munDomain.boundary.plot(ax=ax2)
poly_gdf.boundary.plot(ax=ax2, color="red")
ax2.set_title("Clipped", fontsize=20)
plt.savefig('04_output/emissions/fig/clip.png',
def plot_map(df, data_type, title, cmap, cbar_title, UF=None):
#df must be in format pd.DataFrame({'id': [...] ,'z':[...]})
year = 2018
var = df.columns[-1]
if data_type == 'state':
df1 = geobr.read_state(code_state='all', year=year)
df1 = df1.sort_values('abbrev_state').reset_index(drop=True)
gdf = gpd.GeoDataFrame(df1[['abbrev_state', 'geometry']])
jdf = json.loads(df1[['abbrev_state', 'geometry']].to_json())
try:
assert list(df1['abbrev_state'].values) == list(df['state'])
except:
print(
"df is not in the same order of IBGE data, try to put it on state name alphabetical order "
)
plot_data = dict(type='choropleth',
geojson=jdf,
locations=gdf.index.astype(str),
locationmode='geojson-id',
colorscale=cmap,
text=gdf['abbrev_state'].astype(str),
z=df[var].astype(float),
colorbar={'title': cbar_title})
layout = dict(title_text=title, geo={'scope': 'south america'})
fig = go.Figure(data=plot_data, layout=layout)
fig.update_geos(fitbounds="locations", visible=False)
elif data_type == 'city':
df1 = geobr.read_municipality(code_muni=UF, year=year)
df1 = df1.sort_values('name_muni').reset_index(drop=True)
df1['code_muni'] = df1['code_muni'].astype(int)
idx = [
pd.Index(df1['code_muni']).get_loc(i) for i in df['city_id'].values
]
idx = np.array(idx)
idxx = list(set(range(len(df1))).difference(idx))
z = np.ones(len(df1))
z[idx] = df[var]
z[idxx] = np.nan
gdf = gpd.GeoDataFrame(df1[['name_muni', 'geometry']])
jdf = json.loads(df1[['name_muni', 'geometry']].to_json())
try:
assert list(df1.loc[idx]['code_muni'].values) == list(
df['city_id'])
except:
print(
"df isnt in the same order of IBGE data, try to put it on city name alphabetical order "
)
plot_data = dict(type='choropleth',
geojson=jdf,
locations=gdf.index.astype(str),
locationmode='geojson-id',
colorscale=cmap,
text=gdf['name_muni'].astype(str),
z=z.astype(float),
colorbar={'title': cbar_title})
layout = dict(title_text=title, geo={'scope': 'south america'})
fig = go.Figure(data=plot_data, layout=layout)
fig.update_geos(fitbounds="locations", visible=False)
plotly.offline.iplot(fig)
import pandas as pd
from geobr import read_municipality
import sys
from colorama import Fore,Style
try:
sys.argv[1]
except:
raise ValueError(Fore.RED+"Please inform the year"+Style.RESET_ALL)
ano=int(sys.argv[1])
df=pd.read_csv('/home/lucas/projects/cartpy/cartpy/data/counties_1872_1991.csv')
mun = read_municipality(code_muni="all", year=ano)
mun.columns=['codigo','nome','estado_code','estado','geometry']
mun['ano']=ano
new=df.append(mun)
new['nome']=[k.title().replace('ç','c').replace('ã','a').replace('á','a').replace('à','a').replace('â','a')\
.replace('ẽ','e').replace('é','e').replace('è','e').replace('ê','e').replace('ĩ','i').\
replace('í','i').replace('ì','i').replace('î','i').replace('õ','o').replace('ó','o').\
replace('ò','o').replace('ô','o').replace('ũ','u').replace('ú','u').\
replace('ù','u').replace('û','u')\
if isinstance(k,str) else k for k in new['nome']]
new.to_csv('/home/lucas/projects/cartpy/cartpy/data/counties_1872_1991.csv',index=False)
print("The year {} was added successfully".format(ano))