Commit f6dd9a07 authored by Andreas Schmidt's avatar Andreas Schmidt
Browse files

~= rename X-Lap notebook headings

parent 27d0f7e8
......@@ -48,7 +48,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Traces"
"## Individual Packet Traces"
]
},
{
......@@ -65,7 +65,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Jitter Analysis"
"## Trace Jitter Analysis"
]
},
{
......@@ -82,7 +82,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## CDFs"
"## Latency Distributions"
]
},
{
......@@ -109,7 +109,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Correlation"
"## Correlation with E2E Latency"
]
},
{
......@@ -175,7 +175,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kolmogorov\n"
"## Timing Behaviour"
]
},
{
......
%% Cell type:markdown id: tags:
# X-Lap in Action
%% Cell type:code id: tags:
``` python
%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 = {
"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
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
from xlap.analyse.jitter import multi_trace_jitter
multi_trace_jitter(dfs, config)
```
%% Cell type:markdown id: tags:
## CDFs
## Latency Distributions
%% Cell type:code id: tags:
``` python
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
## 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:
## Latency Criticality
%% Cell type:code id: tags:
``` python
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:
# Kolmogorov
## 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)
```
......
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