Commit cd40ae35 authored by Andreas Schmidt's avatar Andreas Schmidt
Browse files

~= update notebook

parent da4fca55
%% Cell type:markdown id: tags:
# 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, multi_correlation
from xlap.analyse.latency import analyse
import matplotlib.pyplot as plt
import pandas as pd
import copy
%matplotlib inline
colors = ["#E69F00", "#009E73", "#56B4E9", "#CC79A7", "#D55E00"]
```
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## Data Retrieval
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``` python
config = parse_config()
data_files = {
"sender": "~/Work/Publications/rtn-2018/eval/20180420_base1/",
"receiver": "~/Work/Publications/rtn-2018/eval/20180420_base1/"
"sender": "rtn2018/20180417_testbed/",
"receiver": "rtn2018/20180417_testbed/"
}
x= 4050
original1 = evaluate(data_files["sender"] + "sender-1000000.csv", data_files["receiver"] + "receiver-1000000.csv", config=config, kind=0).iloc[0:x]
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).iloc[0:x]
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).iloc[0:x]
original3 = evaluate(data_files["sender"] + "sender-3000000.csv", data_files["receiver"] + "receiver-3000000.csv", config=config, kind=0)
original3.name = "3GHz"
data_files = {
"sender": "~/Work/Publications/rtn-2018/eval/20180420_changed/",
"receiver": "~/Work/Publications/rtn-2018/eval/20180420_changed/"
}
original4 = evaluate(data_files["sender"] + "sender-1000000.csv", data_files["receiver"] + "receiver-1000000.csv", config=config, kind=0).iloc[0:x]
original4.name = "1GHz [2]"
original5 = evaluate(data_files["sender"] + "sender-2000000.csv", data_files["receiver"] + "receiver-2000000.csv", config=config, kind=0).iloc[0:x]
original5.name = "2GHz [2]"
original6 = evaluate(data_files["sender"] + "sender-3000000.csv", data_files["receiver"] + "receiver-3000000.csv", config=config, kind=0).iloc[0:x]
original6.name = "3GHz [2]"
dfs = [original1, original4, original2, original5, original3, original6]
dfs = [original1, original2, original3]
```
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## Traces
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``` python
traces(original1, config)
traces(original1, config, global_xaxis=True)
```
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## Jitter Analysis
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``` python
def multi_trace_jitter(dfs, config):
for df in dfs:
print("############################ {} ############################".format(df.name))
jitter.trace_jitter(df, config=config, threshold=500)
jitter.trace_jitter(df, config=config, threshold=200)
multi_trace_jitter(dfs, config)
```
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## CDFs
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``` python
multi_cdf(dfs, config, export=True)
cfg = copy.deepcopy(config)
d = cfg["durations"]
l=("Decoding","ReceiverIPC","HandlePacket", "Feedback", "SenderIPC","SenderEnqueued","Enqueue")
for e in l:
if e in l:
del d[e]
multi_cdf(dfs, cfg, colors=colors)
```
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## Correlation
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``` python
multi_correlation(dfs, config, export=True)
multi_correlation(dfs, config, colors=colors, figsize=(3.0,2.0), cols=4)
```
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## Latency Criticality
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``` python
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)
```
%% Cell type:code id: tags:
``` python
timing_behaviour(original1, original3, config)
```
%% Cell type:code id: tags:
``` python
timing_behaviour(original2, original3, config)
```
......
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