#import libraries
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

#import data
df=pd.read_csv('C:/Users/nithi/Google Drive/Python/Student Data/SCADA.csv',skip_blank_lines=True)

list1=[1,8,25] #list of turbines to plot #plot these three turbines to visualise the difference

for x in list1:
    #filter only data for turbine x
    dfx=df[(df['turbine']==x)].copy()
    
    #filter out curtailment - curtailed when turbine is pitching above (and/or below) a certain threshold
    def f0(c): #0 deg normal pitch
        if c['pitch']==0 or (c['pitch']!=0 and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return 'normal' 
        else:
            return 'curtailed'
    dfx['curtailment0']=dfx.apply(f0,axis=1)

    def f1(c): #3 deg
        if 0<=c['pitch']<=3 or ((c['pitch']>3 or c['pitch']<0) and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                                    c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return 'normal' 
        else:
            return 'curtailed'
    dfx['curtailment1']=dfx.apply(f1,axis=1)
    
    def f2(c): #5 deg
        if 0<=c['pitch']<=5 or ((c['pitch']>5 or c['pitch']<0) and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                                    c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return 'normal' 
        else:
            return 'curtailed'
    dfx['curtailment2']=dfx.apply(f2,axis=1)
    
    def f3(c): #7 deg
        if 0<=c['pitch']<=7 or ((c['pitch']>7 or c['pitch']<0) and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                                    c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return 'normal' 
        else:
            return 'curtailed'
    dfx['curtailment3']=dfx.apply(f3,axis=1)
    
    def f4(c): #10 deg
        if 0<=c['pitch']<=10 or ((c['pitch']>10 or c['pitch']<0) and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                                      c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return 'normal' 
        else:
            return 'curtailed'
    dfx['curtailment4']=dfx.apply(f4,axis=1)
    
    #filter data for plots
    df1=dfx[dfx.curtailment0=='normal'] #normal w/o curtailment 0 deg
    df2=dfx[dfx.curtailment1=='normal'] #normal w/o curtailment 3 deg
    df3=dfx[dfx.curtailment2=='normal'] #normal w/o curtailment 5 deg
    df4=dfx[dfx.curtailment3=='normal'] #normal w/o curtailment 7 deg
    df5=dfx[dfx.curtailment4=='normal'] #normal w/o curtailment 10 deg
    
    #get x and y coordinates
    x1=dfx['ws_av'] #normal w/ curtailment
    y1=dfx['ap_av'] 
    x2=df1['ws_av'] #normal w/o curtailment 0 deg pitch
    y2=df1['ap_av'] 
    x3=df2['ws_av'] #normal w/o curtailment 3 deg
    y3=df2['ap_av']
    x4=df3['ws_av'] #normal w/o curtailment 5 deg
    y4=df3['ap_av']
    x5=df4['ws_av'] #normal w/o curtailment 7 deg
    y5=df4['ap_av']
    x6=df5['ws_av'] #normal w/o curtailment 10 deg
    y6=df5['ap_av']

    #plot the figure
    fig=plt.figure(figsize=(18,9),dpi=1500)
    
    ax1=fig.add_subplot(231)
    ax1.scatter(x1,y1,marker=".",c='C0')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/ curtailment')
    
    ax2=fig.add_subplot(232)
    ax2.scatter(x2,y2,marker=".",c='C1')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/o curtailment (normal pitch =0)')

    ax3=fig.add_subplot(233)
    ax3.scatter(x3,y3,marker=".",c='C2')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/o curtailment (0<= normal pitch <=3)')
    
    ax4=fig.add_subplot(234)
    ax4.scatter(x4,y4,marker=".",c='C3')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/o curtailment (0<= normal pitch <=5)')
    
    ax5=fig.add_subplot(235)
    ax5.scatter(x5,y5,marker=".",c='C4')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/o curtailment (0<= normal pitch <=7)')
    
    ax6=fig.add_subplot(236)
    ax6.scatter(x6,y6,marker=".",c='C5')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/o curtailment (0<= normal pitch <=10)')

    fig.suptitle('Power curves for turbine %s'%x+' at different normal pitch angles')
    plt.tight_layout()
    plt.subplots_adjust(top=0.92)
    plt.show()

#import libraries
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

#import data
df=pd.read_csv('C:/Users/nithi/Google Drive/Python/Student Data/SCADA.csv',skip_blank_lines=True)

list1=[1,8,25] #list of turbines to plot #plot these three turbines to visualise the difference

#narrow down - plot for pitch angles between 3 and 6 deg
for x in list1:
    dfx=df[(df['turbine']==x)].copy()
    
    def f0(c): #2.5 deg
        if 0<=c['pitch']<=2.5 or ((c['pitch']>2.5 or c['pitch']<0) and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                                        c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return 'normal' 
        else:
            return 'curtailed'
    dfx['curtailment0']=dfx.apply(f0,axis=1)

    def f1(c): #3.5 deg
        if 0<=c['pitch']<=3.5 or ((c['pitch']>3.5 or c['pitch']<0) and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                                        c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return 'normal' 
        else:
            return 'curtailed'
    dfx['curtailment1']=dfx.apply(f1,axis=1)
    
    def f2(c): #4 deg
        if 0<=c['pitch']<=4 or ((c['pitch']>4 or c['pitch']<0) and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                                    c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return 'normal' 
        else:
            return 'curtailed'
    dfx['curtailment2']=dfx.apply(f2,axis=1)
    
    def f3(c): #4.5 deg
        if 0<=c['pitch']<=4.5 or ((c['pitch']>4.5 or c['pitch']<0) and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                                        c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return 'normal' 
        else:
            return 'curtailed'
    dfx['curtailment3']=dfx.apply(f3,axis=1)
    
    def f4(c): #5.5 deg
        if 0<=c['pitch']<=5.5 or ((c['pitch']>5.5 or c['pitch']<0) and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                                        c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return 'normal' 
        else:
            return 'curtailed'
    dfx['curtailment4']=dfx.apply(f4,axis=1)
    
    def f5(c): #6 deg
        if 0<=c['pitch']<=6 or ((c['pitch']>6 or c['pitch']<0) and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                                    c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return 'normal' 
        else:
            return 'curtailed'
    dfx['curtailment5']=dfx.apply(f5,axis=1)
    
    df1=dfx[dfx.curtailment0=='normal'] #normal w/o curtailment 2.5 deg
    df2=dfx[dfx.curtailment1=='normal'] #normal w/o curtailment 3.5 deg
    df3=dfx[dfx.curtailment2=='normal'] #normal w/o curtailment 4 deg
    df4=dfx[dfx.curtailment3=='normal'] #normal w/o curtailment 4.5 deg
    df5=dfx[dfx.curtailment4=='normal'] #normal w/o curtailment 5.5 deg
    df6=dfx[dfx.curtailment5=='normal'] #normal w/o curtailment 6 deg
    
    x1=df1['ws_av'] #normal w/o curtailment 2.5 deg pitch
    y1=df1['ap_av'] 
    x2=df2['ws_av'] #normal w/o curtailment 3.5 deg
    y2=df2['ap_av']
    x3=df3['ws_av'] #normal w/o curtailment 4 deg
    y3=df3['ap_av']
    x4=df4['ws_av'] #normal w/o curtailment 4.5 deg
    y4=df4['ap_av']
    x5=df5['ws_av'] #normal w/o curtailment 5.5 deg
    y5=df5['ap_av']
    x6=df6['ws_av'] #normal w/o curtailment 6 deg
    y6=df6['ap_av']

    fig=plt.figure(figsize=(18,9),dpi=1500)
    
    ax1=fig.add_subplot(231)
    ax1.scatter(x1,y1,marker=".",c='C0')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/o curtailment (0<= normal pitch <=2.5)')
    
    ax2=fig.add_subplot(232)
    ax2.scatter(x2,y2,marker=".",c='C1')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/o curtailment (0<= normal pitch <=3.5)')
    
    ax3=fig.add_subplot(233)
    ax3.scatter(x3,y3,marker=".",c='C2')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/o curtailment (0<= normal pitch <=4)')
    
    ax4=fig.add_subplot(234)
    ax4.scatter(x4,y4,marker=".",c='C3')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/o curtailment (0<= normal pitch <=4.5)')
    
    ax5=fig.add_subplot(235)
    ax5.scatter(x5,y5,marker=".",c='C4')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/o curtailment (0<= normal pitch <=5.5)')
    
    ax6=fig.add_subplot(236)
    ax6.scatter(x6,y6,marker=".",c='C5')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.title('w/o curtailment (0<= normal pitch <=6)')

    fig.suptitle('Power curves for turbine %s'%x+' at different normal pitch angles')
    plt.tight_layout()
    plt.subplots_adjust(top=.92)
    plt.show()

#import libraries
import pandas as pd
import itertools
import matplotlib.pyplot as plt
%matplotlib inline

#import data
df=pd.read_csv('C:/Users/nithi/Google Drive/Python/Student Data/SCADA.csv',skip_blank_lines=True)

#plot for all turbines with threshold of 3.5deg
list2=list(df['turbine'].unique()) #list of turbines to plot
list2=sorted(list2,key=int) #sort turbines in ascending order

#create new empty dataframe with the same index
df2=pd.DataFrame(index=df.index)

#apply function for filtering curtailment 
for x in list2:
    dfx=df[(df['turbine']==x)].copy()

    def f(c): #3.5 deg
        if 0<=c['pitch']<=3.5 or ((c['pitch']>3.5 or c['pitch']<0) and (c['ap_av']<=(.1*dfx['ap_av'].max()) or 
                                                                        c['ap_av']>=(.9*dfx['ap_av'].max()))):
            return c['ap_av'] 
    df2['%s'%x]=dfx.apply(f,axis=1)
    
#copy wind speed from original dataframe    
df2['Wind speed (m/s)']=df['ws_av']

#setting wind speed as index
df2=df2.set_index('Wind speed (m/s)')

#create new list for individual subplot titles
list3=['Power curve for turbine %s without curtailment' %x for x in list2]

#plotting all columns (all turbines) in the same figure
ax=df2.plot(subplots=True,style='.',layout=(5,5),figsize=(50,30),sharex=False,title=list3,legend=False,xlim=(0,38))
list4=list(range(0,5)) #based on layout=(x,y), create lists with range(0,x) and (0,y)
list5=list(itertools.product(list4,list4)) #get coordinates of each subplot
for (x,y) in list5:
    ax[x][y].set_ylabel('Average active power (kW)')

#round pitch angle to nearest degree
df['pitch_round']=df['pitch'].round()
dfg=pd.DataFrame(df.groupby('pitch_round').size())
dfg=dfg.sort_values([0],ascending=False)
#get percentage of frequency of pitch angle
dfg['pct']=dfg[0]/dfg[0].sum()*100
dfg

#extract power readings before cut-in (3m/s)
def f(c): 
    if c['ws_av']<=3:
        return c['ap_av'] 
df['power']=df.apply(f,axis=1)

#create pivot table (new dataframe)
p=pd.pivot_table(df,index=['ws_av'],columns=['turbine'],values=['power'])

#removing pivot table values name from heading
p.columns=p.columns.droplevel(0)

list6=['Power curve for turbine %s before cut-in' %x for x in list2]

#rename index name
p.index.name='Wind speed (m/s)'

ax=p.plot(subplots=True,figsize=(50,30),layout=(5,5),style='.',sharex=False,title=list6,legend=False,xlim=(0,3),
          ylim=(-40,200))
for (x,y) in list5:
    ax[x][y].set_ylabel('Average active power (kW)')

#import libraries
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

#import data
df=pd.read_csv('C:/Users/nithi/Google Drive/Python/Student Data/SCADA.csv',skip_blank_lines=True)

list1=[24] #list of turbines to plot #plot these three turbines to visualise the difference

for x in list1:
    #filter only data for turbine x
    dfx=df[(df['turbine']==x)].copy()
    
    #get x and y coordinates
    x1=dfx['ws_av'] #normal w/ curtailment
    y1=dfx['ap_av'] 

    #plot the figure
    fig=plt.figure(figsize=(7,4.5),dpi=500)
    
    ax1=fig.add_subplot(111)
    ax1.scatter(x1,y1,marker=".",c='C0')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.show()

#import libraries
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

#import data
df=pd.read_csv('C:/Users/nithi/Google Drive/Python/Student Data/SCADA.csv',skip_blank_lines=True)

list1=[24] #list of turbines to plot #plot these three turbines to visualise the difference

for x in list1:
    #filter only data for turbine x
    dfx=df[(df['turbine']==x)].copy()
    def f(c):
        if c['ws_av']<3 and c['ap_av']>0:
            return 0
        else:
            return c['ap_av']
    dfx['ap']=dfx.apply(f,axis=1)
    
    #get x and y coordinates
    x1=dfx['ws_av'] #normal w/ curtailment
    y1=dfx['ap'] 

    #plot the figure
    fig=plt.figure(figsize=(7,4.5),dpi=500)
    
    ax1=fig.add_subplot(111)
    ax1.scatter(x1,y1,marker=".",c='C0')
    plt.xlabel('Wind speed (m/s)')
    plt.ylabel('Average active power (kW)')
    plt.show()