Refactor config parsing out. Use ruamel.yaml.

pandas==0.20.1

matplotlib==2.0.1

sklearn==0.0

pyyaml==3.12

ruamel.yaml==3.12

# TODO: Refactor.

def hist(df):

    return df.hist(cumulative=True, normed=1, bins=200)

def scatter(df, column):

    plt.scatter(df.index, df[column], grid=True)

def regress(df, column):

    x = df.index.values.reshape(-1, 1)

    y = df[column].values

    print("Outliers:", len(outliers), ";", "Threshold[us]:", threshold)

def trace_jitter(df_filtered, export=False, file_name=None):

    thresh = get_outlier_threshold(df_filtered["EndToEndTime"].describe())

    df_no_outliers = df_filtered[df_filtered["EndToEndTime"] <= thresh]

def trace_jitter(data_frame, export=False, file_name=None):

    """

    Displays (and saves) a stacked boxplot of durations.

    """

    thresh = get_outlier_threshold(data_frame["EndToEndTime"].describe())

    df_no_outliers = data_frame[data_frame["EndToEndTime"] <= thresh]

    box(df_no_outliers, export, file_name)

    print("{} / {} are no outliers.".format(len(df_no_outliers), len(df_filtered)))

    print("{} / {} are no outliers.".format(len(df_no_outliers), len(data_frame)))

    fig = plt.gcf()

    fig.canvas.set_window_title('Jitter Analysis')

    plt.show()

import matplotlib.pyplot as plt

def hist(df):

    return df.hist(cumulative=True, normed=1, bins=200)

def scatter(df, column):

    plt.scatter(df.index, df[column], grid=True)

import argparse

import yaml

from xlap.parse import evaluate

from xlap.parse import evaluate, parse_config

import xlap.analyse.jitter as jitter

tasks = {

}

def parse_config():

    with open("xlap.yml") as f:

        contents = f.read()

    return yaml.load(contents)

def main():

    config = parse_config()

    data_files = config["data_files"]