Remove jitter.prep. Add correlation export. Small fixes.

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# X-Lap in Action

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## Imports

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``` python

import logging

logger = logging.getLogger()

logger.setLevel(logging.DEBUG)

from ipywidgets import interact, interactive, fixed, interact_manual

import ipywidgets as widgets

from xlap.parse import evaluate, evaluate_side, parse_config

import xlap.analyse.jitter as jitter

from xlap.analyse.cdf import multi_cdf

from xlap.analyse.regress import linear as linear_regression

from xlap.analyse.trace import traces

from xlap.analyse.correlation import correlation

from xlap.analyse.correlation import correlation, multi_correlation

from xlap.analyse.latency import analyse

import matplotlib.pyplot as plt

import pandas as pd

%matplotlib inline

```

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## Data Retrieval

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``` python

config = parse_config()

data_files = config["data_files"]

original = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0)

data_files = {

    "sender": "~/Work/Publications/rtn-2018/eval/20180417_testbed/",

    "receiver": "~/Work/Publications/rtn-2018/eval/20180417_testbed/"

}

original1 = evaluate(data_files["sender"] + "sender-1000000.csv", data_files["receiver"] + "receiver-1000000.csv", config=config, kind=0)

original1.name = "1GHz"

original2 = evaluate(data_files["sender"] + "sender-2000000.csv", data_files["receiver"] + "receiver-2000000.csv", config=config, kind=0)

original2.name = "2GHz"

original3 = evaluate(data_files["sender"] + "sender-3000000.csv", data_files["receiver"] + "receiver-3000000.csv", config=config, kind=0)

original3.name = "3GHz"

dfs = [original1, original2, original3]

```

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## Traces

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``` python

traces(original, config)

traces(original1, config)

```

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## Jitter Analysis

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``` python

df = jitter.prep(original, config=config)

jitter.trace_jitter(df, threshold=500)

def multi_trace_jitter(dfs, config):

    for df in dfs:

        print("############################ {} ############################".format(df.name))

        jitter.trace_jitter(df, config=config, threshold=300)

multi_trace_jitter(dfs, config)

```

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## CDFs

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``` python

multi_cdf(dfs, config, export=True)

```

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## Correlation

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``` python

correlation(df[df["EndToEnd_D"] < 500], config)

multi_correlation(dfs, config, export=True)

```

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## Latency Criticality

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``` python

d = analyse(original, config)

d = analyse(original1, config)

```

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### Correlations

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``` python

d.corr.sort_values(ascending=False)

```

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### Control Flow Graph

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``` python

d.cfg

```

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# Kolmogorov

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``` python

from scipy import stats

from xlap.analyse.util import extract_durations

import numpy as np

def timing_behaviour(df1, df2, config, confidence=0.9):

    durations = [x + "_D" for x in extract_durations(config)]

    norm = lambda x: x / np.max(x)

    for duration in durations:

        rvs1 = norm(df1[duration])

        rvs2 = norm(df2[duration])

        stat, pvalue = stats.ks_2samp(rvs1, rvs2)

        result = "CANNOT REJECT"

        if pvalue < 1 - confidence:

            result = "REJECT"

        print(duration.ljust(20), "{:.6f}".format(pvalue), result, sep="\t\t")

timing_behaviour(original1, original2, config)

```

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``` python

timing_behaviour(original1, original3, config)

```

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``` python

timing_behaviour(original2, original3, config)

```

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``` python

```

        Source: sender

        Thread: app_send

        Type: time

    PrrtSendPacketStart:

        Source: sender

        Thread: trans_send

        Type: none

    PrrtSendPacketEnd:

        Source: sender

        Thread: trans_send

        Type: none

    PrrtTransmitStart:

        Source: sender