import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.read_csv("kumpula.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= { 'date': [0, 1, 2, 3]},usecols=[0,1,2,3,5])
data['avg'] = data.radiation.rolling( 150).mean()
data1 = pd.read_csv("tahtela.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= { 'date': [0, 1, 2, 3]},usecols=[0,1,2,3,5])
data1['avg'] = data1.radiation.rolling( 150).mean()
plt.figure(figsize=(16,6))
sns.lineplot(x = 'date',
y = 'avg',
data = data,
label = 'Kumpula')
sns.lineplot(x = 'date',
y = 'avg',
data = data1,
label = 'Tähtelä')
plt.title("Moving average of direct solar radiation in the year 2021")
plt.ylabel("W/m2")
data = pd.read_csv("kumpula1.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= { 'date': [0, 1, 2, 3]},usecols=[0,1,2,3,5])
data['avg'] = data.radiation.rolling( 150).mean()
data1 = pd.read_csv("tahtela1.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= { 'date': [0, 1, 2, 3]},usecols=[0,1,2,3,5])
data1['avg'] = data1.radiation.rolling( 150).mean()
plt.figure(figsize=(16,6))
sns.lineplot(x = 'date',
y = 'avg',
data = data,
label = 'Kumpula')
sns.lineplot(x = 'date',
y = 'avg',
data = data1,
label = 'Tähtelä')
plt.title("Moving average of direct solar radiation in the year 2020")
plt.ylabel("W/m2")
data = pd.read_csv("kumpula_temp.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= { 'date': [0, 1, 2, 3]},usecols=[0,1,2,3,5])
data['avg'] = data.temperature.rolling( 5).mean()
data1 = pd.read_csv("tahtela_temp.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= { 'date': [0, 1, 2, 3]},usecols=[0,1,2,3,5])
data1['avg'] = data1.temperature.rolling( 5).mean()
plt.figure(figsize=(16,6))
sns.lineplot(x = 'date',
y = 'avg',
data = data,
label = 'Kumpula')
sns.lineplot(x = 'date',
y = 'avg',
data = data1,
label = 'Tähtelä')
plt.title("Moving average of air temperature in the year 2021")
plt.ylabel("°C")
data = pd.read_csv("kumpula_temp1.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= { 'date': [0, 1, 2, 3]},usecols=[0,1,2,3,5])
data['avg'] = data.temperature.rolling( 5).mean()
data1 = pd.read_csv("tahtela_temp1.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= { 'date': [0, 1, 2, 3]},usecols=[0,1,2,3,5])
data1['avg'] = data1.temperature.rolling( 5).mean()
plt.figure(figsize=(16,6))
sns.lineplot(x = 'date',
y = 'avg',
data = data,
label = 'Kumpula')
sns.lineplot(x = 'date',
y = 'avg',
data = data1,
label = 'Tähtelä')
plt.title("Moving average of air temperature in the year 2020")
plt.ylabel("°C")
import numpy as np
import matplotlib.dates as mdates
data = pd.read_csv("ilmala_july16.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= [[0, 1, 2, 3]],usecols=[0,1,2,3,5])
data1 = pd.read_csv("consumption_july16.csv",dayfirst=True,sep=";",
header=0,decimal=b".",index_col=0,
parse_dates= [[0, 1, 2, 3]],usecols=[0,1,2,3,5])
fig, ax = plt.subplots(1,figsize=(16,6))
plt.plot(0.195*data*5100*0.001*0.75,color='blue', marker='',linestyle='-')
plt.title('Production')
plt.xlabel('date')
plt.tight_layout()
xfmt = mdates.DateFormatter('%H:%M')
ax.xaxis.set_major_formatter(xfmt)
plt.ylim(0, 650)
plt.show()
fig, ax = plt.subplots(1,figsize=(16,6))
plt.plot(data1*0.05,color='green', marker='',linestyle='-')
plt.title('Consumption')
plt.xlabel('date')
plt.tight_layout()
xfmt = mdates.DateFormatter('%H:%M')
ax.xaxis.set_major_formatter(xfmt)
plt.ylim(0, 650)
plt.show()