import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter


matplotlib.style.use('ggplot')

# Roofline Plot

#log x
i = 32
xlbl = []
while i > 1:
    xlbl.append("1/"+repr(i))
    i //= 2
xlbl.append(repr(i)) #1
i += 1
while i<=64:
    xlbl.append(repr(i))
    i *= 2

print(xlbl)    
# memory
values = []
bandwidth = 10.6
peak = 86.4
basepeak = 54.4
ymem = []
ypeak = []
ybasepeak = []
yeight = []
ysix = []

for i in np.arange(0,64,0.1):
    if bandwidth*i < peak:
        values.append(bandwidth*i)
    else:
        values.append(peak)

i=1/32
while i<=64:
    if i < 16 and i > 4:
        yeight.append(21.78)
        ysix.append(peak)
    else:
        yeight.append(None)
        ysix.append(None)
    
    if bandwidth*i < peak and bandwidth*i*2 < peak:
        ymem.append(bandwidth*i)
        ypeak.append(None)
    elif bandwidth*i < peak and bandwidth*i*2 > peak:
        ypeak.append(peak)
        ymem.append(bandwidth*i)
    else:
        ypeak.append(peak)
        ymem.append(None)
    i*=2



#plot data
#data = pd.Series(data=values, name='Peak Memory Bandwidth', index=np.arange(0,64,0.1))

data = {'Peak Memory Bandwidth': pd.Series(ymem, index=xlbl), 'Peak Floating-Point Performance (Turbo)': pd.Series(ypeak, index=xlbl), 'Best 8 OI Kernel': pd.Series(yeight, index=xlbl), 'Best 1/16 OI Kernel': pd.Series(yeight, index=xlbl)}
df = pd.DataFrame(data)

ax = df.plot()
ax.set_xlabel("Operational Itensity (FLOP/Byte)")
ax.set_ylabel("Attainable GFLOP/s")
ax.set_yscale('log', basey=2)
ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))

print(repr(data))




plt.show()