.gitattributes

+*.ipynb filter=nbstripout
+
+*.ipynb diff=ipynb

.gitignore

@@ -2,3 +2,4 @@ build/
 dist/
 xlap.egg-info/
 .ipynb_checkpoints/
+.venv/

Pipfile

+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+
+[packages]
+jupyter = "*"
+matplotlib = "*"
+pandas = "*"
+numpy = "*"
+scipy = "*"
+ruamel-yaml = "*"
+scikit-learn = "*"
+graphviz = "*"
+jupyterlab = "*"
+nbstripout = "*"
+nbconvert = "*"
+
+[requires]
+python_version = "3.6"

notebook.ipynb

 %% Cell type:markdown id: tags:
 
 # X-Lap in Action
 
-%% Cell type:markdown id: tags:
-
-## Imports
-
 %% Cell type:code id: tags:
 
 ``` 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.regress import linear as linear_regression
-from xlap.analyse.trace import traces
-from xlap.analyse.correlation import correlation
-from xlap.analyse.latency import analyse
-import pandas as pd
 %matplotlib inline
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Data Retrieval
 
 %% Cell type:code id: tags:
 
 ``` python
+from xlap.parse import evaluate, parse_config
 config = parse_config()
-data_files = config["data_files"]
-original = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0)
+data_files = {
+    "sender": "rtn2018/20180417_testbed/",
+    "receiver": "rtn2018/20180417_testbed/"
+}
+df_1GHz = evaluate(data_files["sender"] + "sender-1000000.csv", data_files["receiver"] + "receiver-1000000.csv", config=config, kind=0)
+df_1GHz.name = "1GHz"
+df_2GHz = evaluate(data_files["sender"] + "sender-2000000.csv", data_files["receiver"] + "receiver-2000000.csv", config=config, kind=0)
+df_2GHz.name = "2GHz"
+df_3GHz = evaluate(data_files["sender"] + "sender-3000000.csv", data_files["receiver"] + "receiver-3000000.csv", config=config, kind=0)
+df_3GHz.name = "3GHz"
+dfs = [df_1GHz, df_2GHz, df_3GHz]
 ```
 
 %% Cell type:markdown id: tags:
 
-## Traces
+## Individual Packet Traces
 
 %% Cell type:code id: tags:
 
 ``` python
-traces(original, config)
+from xlap.analyse.trace import traces
+traces(df_1GHz, config, global_xaxis=True)
 ```
 
 %% Cell type:markdown id: tags:
 
-## Jitter Analysis
+## Trace Jitter Analysis
 
 %% Cell type:code id: tags:
 
 ``` python
-df = jitter.prep(original, config=config)
-jitter.trace_jitter(df, threshold=500)
+from xlap.analyse.jitter import multi_trace_jitter
+multi_trace_jitter(dfs, config)
 ```
 
 %% Cell type:markdown id: tags:
 
-## Correlation
+## Latency Distributions
 
 %% Cell type:code id: tags:
 
 ``` python
-correlation(df[df["EndToEnd_D"] < 500], config)
+from xlap.analyse.cdf import multi_cdf
+from xlap.analyse.util import colors
+import copy
+cfg = copy.deepcopy(config)
+list(map(cfg["durations"].pop, ("Decoding",
+                                "ReceiverIPC",
+                                "HandlePacket",
+                                "Feedback",
+                                "SenderIPC",
+                                "SenderEnqueued",
+                                "Enqueue")))
+multi_cdf(dfs, cfg, colors=colors)
 ```
 
 %% Cell type:markdown id: tags:
 
+## Correlation with E2E Latency
+
+%% Cell type:code id: tags:
+
+``` python
+from xlap.analyse.correlation import multi_correlation
+multi_correlation(dfs, config, colors=colors, figsize=(3.0,2.0), cols=4)
+```
+
+%% Cell type:markdown id: tags:
+
+# $\Delta$elta
 ## Latency Criticality
 
 %% Cell type:code id: tags:
 
 ``` python
-d = analyse(original, config)
+from xlap.analyse.latency import analyse
+d = analyse(df_1GHz, config)
 ```
 
 %% Cell type:markdown id: tags:
 
 ### Correlations
 
 %% Cell type:code id: tags:
 
 ``` python
 d.corr.sort_values(ascending=False)
 ```
 
 %% Cell type:markdown id: tags:
 
 ### Control Flow Graph
 
 %% Cell type:code id: tags:
 
 ``` python
 d.cfg
 ```
+
+%% Cell type:markdown id: tags:
+
+## Timing Behaviour
+
+%% Cell type:code id: tags:
+
+``` python
+from xlap.analyse.timing import timing_behaviour
+timing_behaviour(df_1GHz, df_2GHz, config)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+timing_behaviour(df_1GHz, df_3GHz, config)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+timing_behaviour(df_2GHz, df_3GHz, config)
+```

rtn2018/20180419_energy/data.txt

+
+1 40.112488 0.28655806569935959455261300
+2 37.635804 0.13545612357162314105068459
+3 44.867554 0.15736979554175162807913194

rtn2018/20180419_energy/energy-1000000.txt

+Setting cpu: 0
+Setting cpu: 1
+Setting cpu: 2
+Setting cpu: 3
+Setting cpu: 4
+Setting cpu: 5
+Setting cpu: 6
+Setting cpu: 7
+analyzing CPU 0:
+996816
+analyzing CPU 1:
+998867
+analyzing CPU 2:
+999414
+analyzing CPU 3:
+997363
+analyzing CPU 4:
+1000507
+analyzing CPU 5:
+999687
+analyzing CPU 6:
+1000234
+analyzing CPU 7:
+997089
+Setting cpu: 0
+Setting cpu: 1
+Setting cpu: 2
+Setting cpu: 3
+Setting cpu: 4
+Setting cpu: 5
+Setting cpu: 6
+Setting cpu: 7
+analyzing CPU 0:
+925859
+analyzing CPU 1:
+1000097
+analyzing CPU 2:
+999960
+analyzing CPU 3:
+993535
+analyzing CPU 4:
+1000371
+analyzing CPU 5:
+998046
+analyzing CPU 6:
+993261
+analyzing CPU 7:
+992578
+RAPL::Package energy {0}: 16.163391J
+RAPL::PowerPlane0 (core) {0}: 1.458923J
+RAPL::PowerPlane1 (on-core GPU if avail) {0}: 0.000000J
+RAPL::DRAM energy {0}: 4.892883J
+Time: 5.087s
+Found Haswell CPU
+RAPL::Package energy {0}: 23.949097J
+RAPL::PowerPlane0 (core) {0}: 1.922913J
+RAPL::PowerPlane1 (on-core GPU if avail) {0}: 0.000000J
+RAPL::DRAM energy {0}: 5.827271J
+Time: 5.729s
+Found Haswell CPU