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%matplotlib inline
import pandas as pd
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
import matplotlib
matplotlib.rcParams['figure.figsize'] = (9.0, 5.0)
import matplotlib.pyplot as plt
import seaborn as sns
import re
import statsmodels
import numpy as np
from  matplotlib.ticker import FuncFormatter


    

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sns.set(color_codes=True)
sns.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 2.5})


    

In [ ]:

    

def plot(prefix):
    m = re.match("es-(?P<es>\d+).qd-(?P<qd>\d+).w-(?P<wd>\d+).(?P<iface>\w+)", prefix)
    entry_size = m.group("es")
    qdepth = m.group("qd")
    width = m.group("wd")
    interface = m.group("iface")
    
    iops = pd.read_csv("{}.iops.csv".format(prefix))
    iops["sec"] = (iops.us - min(iops.us)) / 1000000
    ax = sns.regplot(iops.sec, iops.iops, x_bins=10, truncate=True)
    ax.set_title("{}, {}, {}, {}".format(
        entry_size, qdepth, width, interface))
    
    lat = pd.read_csv("{}.latency.csv".format(prefix))
    fig, ax = plt.subplots(figsize=(8, 4))
    ax.plot(latency.Value, latency.Percentile)
    ax.set_title("{}, {}, {}, {}".format(
        entry_size, qdepth, width, interface))
    
plot("es-10.qd-1.w-30.omap")