Suwi_Project

import pandas as pd #Import the Pandas library
from matplotlib import pyplot as plt #Import the matplotlib library
import numpy as np #Import the numby library

santoamaro = pd.read_csv('santoamaro.csv') #Open the csv of the Santo Amaro measuring station with the parameter MP10(Inhalable Paritciculas)

AnualAve_santoamaro = santoamaro['Media Horaria'].mean() #Calculates the average of parameter MP10 for the entire time interval
print(AnualAve_santoamaro) #Print average result

santoamaro["Data"] = pd.to_datetime(santoamaro["Data"], dayfirst=True) # Convert date column to datetime

MonthAve_santoamaro = santoamaro.groupby(santoamaro['Data'].dt.month)['Media Horaria'].mean() #Calculate the monthly average

y = MonthAve_santoamaro #call 'x' from the monthly average
x = np.array([1,2,3,4,5,6,7,8,9,10,11,12]) #call 'y' the months

plt.bar(x,y) #creates the graph with the variables x and y
plt.ylabel('Media de MP10/µg/m3') #legend the variable y
plt.xlabel('Meses') #legend the variable x
plt.title('Concentração Média do Poluente MP10 na estação de Santo Amaro, 2022') #creates a title

plt.show() #show the graph

pqdompedroii = pd.read_csv('pqdompedroii.csv') #Open the csv of the Parque dom Pedro II easuring station with the parameter MP10(Inhalable Paritciculas)

AnualAve_pqdompedro = pqdompedroii['Media Horaria'].mean #Calculates the average of parameter MP10 for the entire time interval
print(AnualAve_pqdompedro) #Print average result

pqdompedroii["Data"] = pd.to_datetime(pqdompedroii["Data"], dayfirst=True) # Convert date column to datetime

MonthAve_pqdompedro = pqdompedroii.groupby(pqdompedroii['Data'].dt.month)['Media Horaria'].mean() #Calculate the monthly average


y = MonthAve_pqdompedro #call 'x' from the monthly average
x = np.array([1,2,3,4,5,6,7,8,9,10,11,12])  #call 'y' the months

plt.bar(x,y,)  #creates the graph with the variables x and y
plt.ylabel('Media de MP10/µg/m3')#legend the variable y
plt.xlabel('Meses') #legend the variable x
plt.title('Concentração Média do Poluente MP10 na estação de Parque Dom Pedro II, 2022') #creates a title

plt.show() #show the graph

nsenhoradoo = pd.read_csv('nsenhoradoo.csv') #Abre o csv da estação de medição de Nossa Senhora do Ó com o parâmetro MP10(Paritciculas Inalaveis) #Open the csv of the Santo Amaro measuring station with the parameter MP10(Inhalable Paritciculas)

AnualAve_nsenhoradoo = nsenhoradoo['Media Horaria'].mean()#Calculates the average of parameter MP10 for the entire time interval
print(AnualAve_nsenhoradoo) #Print average result

nsenhoradoo["Data"] = pd.to_datetime(nsenhoradoo["Data"], dayfirst=True) # Convert date column to datetime

MonthAve_nsenhorado = nsenhoradoo.groupby(santoamaro['Data'].dt.month)['Media Horaria'].mean() #Calculate the monthly average


y = MonthAve_nsenhorado #call 'x' from the monthly average
x = np.array([1,2,3,4,5,6,7,8,9,10,11,12]) #call 'y' the months

plt.bar(x,y,) #creates the graph with the variables x and y
plt.ylabel('Media de MP10/µg/m3') #legend the variable y
plt.xlabel('Meses') #legend the variable x
plt.title('Concentração Média do Poluente MP10 na estação Nossa Senhora do O, 2022') #creates a title

plt.show()#show the graph

margtiete = pd.read_csv('marg_tiete_ptedosremedios.csv') #Open the csv of the MArginal Tiete easuring station with the parameter MP10(Inhalable Paritciculas)

AnualAve_margtiete = margtiete['Media Horaria'].mean()  #Calculates the average of parameter MP10 for the entire time interval
print(AnualAve_santoamaro) #Print average result


margtiete["Data"] = pd.to_datetime(margtiete["Data"], dayfirst=True) # Convert date column to datetime


MonthAve_margtiete = margtiete.groupby(margtiete['Data'].dt.month)['Media Horaria'].mean() #Calculate the monthly average

y = MonthAve_margtiete #call 'x' from the monthly average
x = np.array([1,2,3,4,5,6,7,8,9,10,11,12])  #call 'y' the months

plt.bar(x,y,)  #creates the graph with the variables x and y
plt.ylabel('Media de MP10/µg/m3') #legend the variable y
plt.xlabel('Meses') #legend the variable x
plt.title('Concentração Média do Poluente MP10 na estação de Marginal Tiete, 2022') #creates a title

plt.show()#show the graph

itaimpaulista = pd.read_csv('itaimpaulista.csv') #Open the csv of the Itaim PAulista easuring station with the parameter MP10(Inhalable Paritciculas)

AnualAve_itaimpaulista = itaimpaulista['Media Horaria'].mean()#Calculates the average of parameter MP10 for the entire time interval
print(AnualAve_itaimpaulista)  #Print average result

itaimpaulista["Data"] = pd.to_datetime(margtiete["Data"], dayfirst=True)  # Convert date column to datetime

MonthAve_itaimpaulista = itaimpaulista.groupby(santoamaro['Data'].dt.month)['Media Horaria'].mean()#Calculate the monthly average



y = MonthAve_itaimpaulista #call 'y' from the monthly average
x = np.array([1,2,3,4,5,6,7,8,9,10,11,12]) #call 'x' the months


plt.bar(x,y,)   #creates the graph with the variables x and y
plt.ylabel('Media de MP10/µg/m3') #legend the variable y
plt.xlabel('Meses') #legend the variable x
plt.title('Concentração Média do Poluente MP10 na estação de Itaim Paulista, 2022') #creates a title

plt.show() #show the graph

interlagos = pd.read_csv('interlagos.csv') #Open the csv of the Interlagos measuring station with the parameter MP10(Inhalable Paritciculas)

AnualAve_interlagos = interlagos['Media Horaria'].mean() #Calculates the average of parameter MP10 for the entire time interval
print(AnualAve_interlagos) #Print average result


interlagos["Data"] = pd.to_datetime(margtiete["Data"], dayfirst=True) # Convert date column to datetime


MonthAve_interlagos = interlagos.groupby(interlagos['Data'].dt.month)['Media Horaria'].mean() #Calculate the monthly average


y = MonthAve_interlagos  #call 'y' from the monthly average
x = np.array([1,2,3,4,5,6,7,8,9,10,11,12]) #call 'x' the months

plt.bar(x,y,)  #creates the graph with the variables x and y
plt.ylabel('Media de MP10/µg/m3') #legend the variable y
plt.xlabel('Meses') #legend the variable x
plt.title('Concentração Média do Poluente MP10 na estação de Interlagos, 2022')  #creates a title

plt.show() #show the graph

congonhas = pd.read_csv('congonhas.csv') #Open the csv of the Congonhaso measuring station with the parameter MP10(Inhalable Paritciculas)
AnualAve_congonhas = congonhas['Media Horaria'].mean()#Calculates the average of parameter MP10 for the entire time interval
print(AnualAve_congonhas) #Print average result

congonhas["Data"] = pd.to_datetime(margtiete["Data"], dayfirst=True)# Convert date column to datetime


MonthAve_congonhas = congonhas.groupby(congonhas['Data'].dt.month)['Media Horaria'].mean() #Calculate the monthly average




y = MonthAve_congonhas #call 'y' from the monthly average
x = np.array([1,2,3,4,5,6,7,8,9,10,11,12])  #call 'x' the months

plt.bar(x,y,)  #creates the graph with the variables x and y
plt.ylabel('Media de MP10/µg/m3') #legend the variable y
plt.xlabel('Meses') #legend the variable x
plt.title('Concentração Média do Poluente MP10 na estação de Congonhas, 2022') #creates a title


plt.show()#show the graph

capaoredondo = pd.read_csv('capaoredondo.csv') #Open the csv of the Capao Redondo measuring station with the parameter MP10(Inhalable Paritciculas)

AnualAve_capaoredondo = capaoredondo['Media Horaria'].mean() #Calculates the average of parameter MP10 for the entire time interval
print(AnualAve_capaoredondo) #Print average result


capaoredondo["Data"] = pd.to_datetime(margtiete["Data"], dayfirst=True) # Convert date column to datetime

MonthAve_capaoredondo = capaoredondo.groupby(capaoredondo['Data'].dt.month)['Media Horaria'].mean() #Calculate the monthly average


y = MonthAve_capaoredondo #call 'y' from the monthly average
x = np.array([1,2,3,4,5,6,7,8,9,10,11,12]) #call 'x' the months

plt.bar(x,y)  #creates the graph with the variables x and y
plt.ylabel('Media de MP10/µg/m3') #legend the variable y
plt.xlabel('Meses') #legend the variable x
plt.title('Concentração Média do Poluente MP10 na estação de Capão Redondo, 2022') #creates a title


plt.show()#show the graph

perus = pd.read_csv('perus.csv') #Open the csv of the Perus measuring station with the parameter MP10(Inhalable Paritciculas)

AnualAve_perus = perus['Media Horaria'].mean() #Calculates the average of parameter MP10 for the entire time interval
print(AnualAve_perus) #Print average result

perus["Data"] = pd.to_datetime(perus["Data"], dayfirst=True) # Convert date column to datetime 

MonthAve_perus = perus.groupby(perus['Data'].dt.month)['Media Horaria'].mean() #Calculate the monthly average


y = MonthAve_perus #call 'y' from the monthly average
x = np.array([1,2,3,4,5,6,7,8,9,10,11,12])  #call 'x' the months

plt.bar(x,y,) #creates the graph with the variables x and y
plt.ylabel('Media de MP10/µg/m3') #legend the variable y
plt.xlabel('Meses') #legend the variable x
plt.title('Concentração Média do Poluente MP10 na estação de Perus, 2022') #creates a title

plt.show() #show the graph

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import BoundaryNorm
from scipy.interpolate import griddata  
import numpy.ma as ma
import folium
from folium.plugins import HeatMap
import branca.colormap as cm

Filename= "cordenadasestacoes.csv" #create a variable to the file
colnames = ['Estacao', 'Media Anual', 'Lat' ,'Lon'] #add title to columns
dado=pd.read_csv(Filename, sep=';', names=colnames) #create a dataframe 

nd = pd.DataFrame(dado, columns=['Lat', 'Lon', 'Media Anual']).groupby(['Lat','Lon'])['Media Anual'].sum().reset_index() #grophy the variables we want to show
p = nd.loc[0:70]
nnd = pd.DataFrame(dado,columns=['Lat', 'Lon'])
nnw = nnd.loc[1:70].values.astype(float)

Lat = p.Lat.tolist() #creates a list with the latitudes
lon = p.Lon.tolist() #creates a list with the longotudelongitude 

m = folium.Map(
    location=[-23, -46],
    tiles='Stamen Terrain',
    zoom_start=5.49
) #create a map with folium library, with locations, the mapa base, and the zoom to startes 
#insert points in the map

HeatMap(list(zip(Lat, lon))).add_to(m)

# Add things in the map

folium.TileLayer('Stamen Terrain').add_to(m) #add themala base
folium.TileLayer('openstreetmap').add_to(m) #add mapa base
folium.map.LayerControl('bottomright', collapsed=False, autoZIndex=True).add_to(m) #add control to the map
# Criated legend 
colormap = cm.LinearColormap(colors=['lightblue','green','yellow','red'],vmin=0,vmax=110) #add the color off tbe pointes with a buffer
colormap.caption= ('Contracao Anual de MP10') #give a legend to the point
colormap.add_to(m) #add the colormap defined to the map

m #print the map