import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sys import exit
import wquantiles as wq
import numpy as np

from functools import partial

import sys

def convert64(x):
    return np.int64(int(x, base=16))

def convert8(x):
    return np.int8(int(x, base=16))

df = pd.read_csv(sys.argv[1],
        dtype={
            "main_core": np.int8,
            "helper_core": np.int8,
            # "address": int,
            # "hash": np.int8,
            "time": np.int16,
            "clflush_remote_hit": np.int32,
            "clflush_shared_hit": np.int32,
            "clflush_miss_f": np.int32,
            "clflush_local_hit_f": np.int32,
            "clflush_miss_n": np.int32,
            "clflush_local_hit_n": np.int32,
            "reload_miss": np.int32,
            "reload_remote_hit": np.int32,
            "reload_shared_hit": np.int32,
            "reload_local_hit": np.int32},
        converters={'address': convert64, 'hash': convert8},
        )

sample_columns = [
"clflush_remote_hit",
"clflush_shared_hit",
"clflush_miss_f",
"clflush_local_hit_f",
"clflush_miss_n",
"clflush_local_hit_n",
"reload_miss",
"reload_remote_hit",
"reload_shared_hit",
"reload_local_hit",
]

sample_flush_columns = [
    "clflush_remote_hit",
    "clflush_shared_hit",
    "clflush_miss_f",
    "clflush_local_hit_f",
    "clflush_miss_n",
    "clflush_local_hit_n",
]
print(df.columns)
#df["Hash"] = df["Addr"].apply(lambda x: (x >> 15)&0x3)

print(df.head())

print(df["hash"].unique())

min_time = df["time"].min()
max_time = df["time"].max()

q10s = [wq.quantile(df["time"], df[col], 0.1) for col in sample_flush_columns]
q90s = [wq.quantile(df["time"], df[col], 0.9) for col in sample_flush_columns]

graph_upper = int(((max(q90s) + 19) // 10) * 10)
graph_lower = int(((min(q10s) - 10) // 10) * 10)
# graph_lower = (min_time // 10) * 10
# graph_upper = ((max_time + 9) // 10) * 10

print("graphing between {}, {}".format(graph_lower, graph_upper))

df_main_core_0 = df[df["main_core"] == 0]
#df_helper_core_0 = df[df["helper_core"] == 0]

g = sns.FacetGrid(df_main_core_0, col="helper_core", row="hash", legend_out=True)
g2 = sns.FacetGrid(df, col="main_core", row="hash", legend_out=True)


colours = ["b", "r", "g", "y"]

def custom_hist(x, *y, **kwargs):
    for (i, yi) in enumerate(y):
        kwargs["color"] = colours[i]
        sns.distplot(x, range(graph_lower, graph_upper), hist_kws={"weights": yi, "histtype":"step"}, kde=False, **kwargs)

# Color convention here :
# Blue = miss
# Red = Remote Hit
# Green = Local Hit
# Yellow = Shared Hit

g.map(custom_hist, "time", "clflush_miss_n", "clflush_remote_hit", "clflush_local_hit_n", "clflush_shared_hit")

g2.map(custom_hist, "time", "clflush_miss_n", "clflush_remote_hit", "clflush_local_hit_n", "clflush_shared_hit")

# g.map(sns.distplot, "time", hist_kws={"weights": df["clflush_hit"]}, kde=False)

plt.show()
#plt.figure()


exit(0)

def stat(x, key):
    return wq.median(x["Time"], x[key])


miss = df.groupby(["Core", "Hash"]).apply(stat, "ClflushMiss")
stats = miss.reset_index()
stats.columns = ["Core", "Hash", "Miss"]
hit = df.groupby(["Core", "Hash"]).apply(stat, "ClflushHit")
stats["Hit"] = hit.values


print(stats.to_string())

g = sns.FacetGrid(stats, row="Core")

g.map(sns.distplot, 'Miss', bins=range(100, 480), color="r")
g.map(sns.distplot, 'Hit', bins=range(100, 480))
plt.show()

#stats["clflush_miss_med"] = stats[[0]].apply(lambda x: x["miss_med"])
#stats["clflush_hit_med"] = stats[[0]].apply(lambda x: x["hit_med"])
#del df[[0]]
#print(hit.to_string(), miss.to_string())

# test = pd.DataFrame({"value" : [0, 5], "weight": [5, 1]})
# plt.figure()
# sns.distplot(test["value"], hist_kws={"weights": test["weight"]}, kde=False)

exit(0)