#Upgrading the library to v2.3 ! pip install --upgrade networkx

import networkx as nx #https://networkx.github.io/ import matplotlib.pyplot as plt #https://matplotlib.org/ import scipy as sp import numpy as np

G = nx.DiGraph() #Graph with 4 ring nodes and one hub node G.add_node("A") G.add_node("B") G.add_node("C") G.add_node("D") G.add_node("E") #Add directional edges to create a hub structure G.add_edges_from([("B", "A"),("C", "A"),("D", "A"),("E", "A")]) nx.draw_networkx(G, node_color='black', font_color='white', edge_color='black', font_weight='bold', node_size=700) plt.axis('off') plt.show()

#Import libraries import pandas as pd #https://pandas.pydata.org/ import matplotlib.pyplot as plt #https://matplotlib.org/ import matplotlib.dates as mdates #https://matplotlib.org/ import numpy as np #http://www.numpy.org/ #from datetime import date #https://docs.python.org/3/library/datetime.html

#Read csv file using pandas #Node B - Temperature NodeB = pd.read_csv("NodeB_LPRAirport_temperature_072018.csv",dayfirst=True,sep=",", header=0,decimal=b".",index_col=0, parse_dates=[[0,1,2,3]], usecols=[0,1,2,3,5]) #Node D - Temperature NodeD = pd.read_csv("NodeD_LPRLepola_temperature_072018.csv",dayfirst=True,sep=",", header=0,decimal=b".",index_col=0, parse_dates=[[0,1,2,3]], usecols=[0,1,2,3,5]) #Node C - Wind NodeC = pd.read_csv("NodeC_LPRAirport_wind_072018.csv",dayfirst=True,sep=",", header=0,decimal=b".",index_col=0, parse_dates=[[0,1,2,3]], usecols=[0,1,2,3,5]) #Node E - Wind NodeE = pd.read_csv("NodeE_LPRLepola_wind_072018.csv",dayfirst=True,sep=",", header=0,decimal=b".",index_col=0, parse_dates=[[0,1,2,3]], usecols=[0,1,2,3,5]) #NodeB.head()

#Original values can also be presented if needed #plt.figure(figsize=(20,9)) #plt.plot(NodeB,color='blue', marker='.', linestyle = 'none') #plt.title("Air Temperature 07/2018 NodeB") #plt.ylabel("Celsius") #plt.grid(True) #plt.show()

#Update values for Node B for row in NodeB.itertuples(): if NodeB.at[row.Index, "Air temperature (degC)"] > 25: NodeB.at[row.Index, "Air temperature (degC)"] = 1 else: NodeB.at[row.Index, "Air temperature (degC)"] = 0 plt.figure(figsize=(20,9)) plt.plot(NodeB,color='blue', marker='.', linestyle = 'none') plt.title("Air Temperature 07/2018 NodeB") plt.ylabel("Celsius") plt.grid(True) plt.show() #Update values for Node D for row in NodeD.itertuples(): if NodeD.at[row.Index, "Air temperature (degC)"] > 25: NodeD.at[row.Index, "Air temperature (degC)"] = 1 else: NodeD.at[row.Index, "Air temperature (degC)"] = 0 plt.figure(figsize=(20,9)) plt.plot(NodeD,color='red', marker='.', linestyle = 'none') plt.title("Air Temperature 07/2018 NodeD") plt.ylabel("Celsius") plt.grid(True) plt.show() #Update values for Node C for row in NodeC.itertuples(): if NodeC.at[row.Index, "Wind speed (m/s)"] > 5: NodeC.at[row.Index, "Wind speed (m/s)"] = 1 else: NodeC.at[row.Index, "Wind speed (m/s)"] = 0 plt.figure(figsize=(20,9)) plt.plot(NodeD,color='green', marker='.', linestyle = 'none') plt.title("Wind Speed 07/2018 NodeC") plt.ylabel("Celsius") plt.grid(True) plt.show() #Update values for Node E for row in NodeC.itertuples(): if NodeE.at[row.Index, "Wind speed (m/s)"] > 5: NodeE.at[row.Index, "Wind speed (m/s)"] = 1 else: NodeE.at[row.Index, "Wind speed (m/s)"] = 0 plt.figure(figsize=(20,9)) plt.plot(NodeD,color='brown', marker='.', linestyle = 'none') plt.title("Wind Speed 07/2018 NodeE") plt.ylabel("Celsius") plt.grid(True) plt.show()

#plotting #if temperature in airport of Lepola is above 25 AND wind in airport or Lepola is above 5 send binary 1. Otherwise send 0. Input values are ready as binary #summary=((NodeB['2018-07'].values > 25) | (NodeD['2018-07'].values > 25)) & ((NodeC['2018-07'].values > 5) | (NodeE['2018-07'].values > 5)) summary=((NodeB['2018-07'].values == 1) | (NodeD['2018-07'].values ==1)) & ((NodeC['2018-07'].values ==1) | (NodeE['2018-07'].values ==1)) plt.figure(figsize=(16,6)) plt.plot(NodeB['2018-07'].index, summary,color='black', marker='.',linestyle='') plt.title("Output for Node A 07/2018") plt.grid(True) plt.show()