import matplotlib.pyplot as plt
import csv, sys, numpy as np
from math import sqrt

filename = sys.argv[1] 

with open(filename, 'r') as file:
    reader = csv.reader (file, strict = True)
    fieldnames = None
    #fieldnames = reader.fieldnames
    converters = (str, int, int, int, float, lambda x: float(x)*1e-3)

    for row in reader:
        if fieldnames is None:
            fieldnames = [ n.strip() for n in row]
            values = []
            continue

        values.append ( [ c(v) for v, c in zip(row, converters) ] )

request1 = values[:int(len(values)/2)]
request2 = values[int(len(values)/2):]

Ntransforms = 1
while values[0][0:3] == values[Ntransforms][0:3]:
    Ntransforms += 1

splitMethods = ['avg', 'med', 'cen']
type = ["o", "or", "r", ]
BVs = sorted (list (set ([ v[0] for v in request1[::Ntransforms] ])))
xvals = [ BVs.index(v[0]) + len(BVs)*v[2] + 3*len(BVs)*v[1] for v in request1[::Ntransforms] ]
cases = [ v[0] + " " + type[v[1]] + " " + splitMethods[v[2]] for v in request1[::Ntransforms] ]

idx_reorder = sorted (list(range(len(xvals))), key=lambda i: xvals[i])
def reorder (l): return [ l[i] for i in idx_reorder ]

xvals_s = reorder (xvals)
cases_s = reorder (cases)

onlyLB = True
# Time
plt.figure(0)
for i in range(Ntransforms):
    if not onlyLB:
        plt.plot(xvals_s, reorder([ v[5] for v in request1[i::Ntransforms] ]) , '-.o', label=str(i))
    plt.plot(xvals_s, reorder([ v[5] for v in request2[i::Ntransforms] ]) , ':+',  label=str(i)+"+lb")

plt.legend()
plt.xticks(ticks=xvals_s, labels=cases_s, rotation=90)
plt.ylabel('Time (us)')
plt.yscale('log')

# Time
plt.figure(2)
for k in range (0, len(request1), Ntransforms):
    if not onlyLB:
        plt.plot([ xvals[int(k/Ntransforms)], ], sum([ v[5] for v in request1[k:k+Ntransforms] ])/Ntransforms)
    plt.plot([ xvals[int(k/Ntransforms)], ], sum([ v[5] for v in request2[k:k+Ntransforms] ])/Ntransforms)

plt.plot(xvals_s, reorder ([ sum([ v[5] for v in request2[k:k+Ntransforms] ])/Ntransforms for k in range (0, len(request1), Ntransforms) ]))

plt.xticks(ticks=xvals_s, labels=cases_s, rotation=90)
plt.ylabel('Time (us)')
plt.yscale('log')

# Distance lower bound
plt.figure(1)
for i in range(Ntransforms):
    if request2[i][3] > 0: continue
    plt.plot(xvals_s, reorder([ v[4] for v in request2[i::Ntransforms] ]) , ':+',  label=str(i)+"+lb")

plt.legend()
plt.ylabel('Distance lower bound')
plt.xticks(ticks=xvals_s, labels=cases_s, rotation=90)

plt.show()