Commit 31fa1851 authored by Andreas Schmidt's avatar Andreas Schmidt
Browse files

Add evaluation and data for RTN2017 workshop.

parent e70b31d8
%% Cell type:markdown id: tags:
# Definitions
%% Cell type:code id: tags:
``` python
import numpy as np
import math
import pandas as pd
import collections
from operator import itemgetter
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (16,9)
plt.rcParams.update({'figure.autolayout': True})
from sklearn import datasets, linear_model
def _evaluate_file(fileName,kind,sender=False):
# Remove first line, as this is the dummy line for intermittently storing data.
df = pd.read_csv(fileName)[1:]
df = df[df["Kind"] == kind].drop(["Kind"],axis=1).set_index("SeqNo")
if sender:
df.drop(["LinkReceive_T",
"LinkReceive_C",
"PrrtDeliver_T",
"PrrtDeliver_C",
"SendFeedbackStart_T",
"SendFeedbackStart_C",
"SendFeedbackEnd_T",
"SendFeedbackEnd_C",
"DecodeStart_T",
"DecodeStart_C",
"DecodeEnd_T",
"DecodeEnd_C",
"HandlePacketStart_T",
"HandlePacketStart_C",
"HandlePacketEnd_T",
"HandlePacketEnd_C",
"CopyOutputStart_T",
"CopyOutputStart_C",
"CopyOutputEnd_T",
"CopyOutputEnd_C",
"PrrtReturnPackage_T",
"PrrtReturnPackage_C",
"PrrtReceivePackage_T",
"PrrtReceivePackage_C"
],axis=1,inplace=True)
df = df[df["LinkTransmitEnd_T"] != 0] # remove empty rows
else:
df.drop(["PrrtSendStart_T",
"PrrtSendStart_C",
"PrrtSendEnd_T",
"PrrtSendEnd_C",
"PrrtSubmitPackage_T",
"PrrtSubmitPackage_C",
"PrrtEncodeStart_T",
"PrrtEncodeStart_C",
"PrrtEncodeEnd_T",
"PrrtEncodeEnd_C",
"PrrtTransmitStart_T",
"PrrtTransmitStart_C",
"PrrtTransmitEnd_T",
"PrrtTransmitEnd_C",
"LinkTransmitStart_T",
"LinkTransmitStart_C",
"LinkTransmitEnd_T",
"LinkTransmitEnd_C",
],axis=1,inplace=True)
df = df[df["LinkReceive_T"] != 0] # remove empty rows
return df
def _restore_timestamp(df, column, cycle_time, base_c, base_t):
df[column + "_T"] = ((df[column + "_C"] - base_c) * cycle_time + base_t).astype(int)
def _diff_t_c(df, name, start, stop):
time = df[stop + "_T"] - df[start + "_T"]
cycles = (df[stop + "_C"] - df[start + "_C"])
return (time.astype(float), cycles.astype(float))
def _generate_processing_durations(df, name, start, stop):
time, cycles = _diff_t_c(df, name, start, stop)
df[name + "TotalTime"] = time
df[name + "TotalCycles"] = cycles
def _generate_cycle_time(df, name, start, stop):
time, cycles = _diff_t_c(df, name, start, stop)
df[name + "CycleTime"] = time / cycles
def _generate_duration(df, name, start, stop, cycleTimeColumn):
diff = df[stop + "_C"] - df[start + "_C"]
df[name + "Cycles"] = diff
df[name + "Time"] = diff * df[cycleTimeColumn]
def evaluate(sender_file, receiver_file, kind=0):
df1 = _evaluate_file(sender_file,kind,True)
df2 = _evaluate_file(receiver_file,kind)
df = df1.join(df2)
# Processing Times and Cycle Durations
_generate_processing_durations(df, "Sender", "PrrtSendStart", "LinkTransmitEnd")
_generate_cycle_time(df, "Sender", "PrrtSendStart", "LinkTransmitEnd")
_generate_processing_durations(df, "Receiver", "LinkReceive", "PrrtDeliver")
_generate_cycle_time(df, "Receiver", "PrrtReceivePackage", "PrrtDeliver")
df["ChannelTime"] = df["LinkReceive_T"] - df["LinkTransmitEnd_T"]
df["EndToEndTime"] = df["SenderTotalTime"] + df["ReceiverTotalTime"]
# Correlate Receiver Times with Sender Times
df["LinkReceive_T"] -= df["ChannelTime"]
df["PrrtReceivePackage_T"] -= df["ChannelTime"]
df["PrrtDeliver_T"] -= df["ChannelTime"]
# Durations
_generate_duration(df, "Send", "PrrtSendStart", "PrrtSendEnd", "SenderCycleTime")
_generate_duration(df, "PrrtTransmit", "PrrtTransmitStart", "PrrtTransmitEnd", "SenderCycleTime")
_generate_duration(df, "LinkTransmit", "LinkTransmitStart", "LinkTransmitEnd", "SenderCycleTime")
_generate_duration(df, "Submit", "PrrtSendStart", "PrrtSubmitPackage", "SenderCycleTime")
_generate_duration(df, "Enqueue", "PrrtSubmitPackage", "PrrtSendEnd", "SenderCycleTime")
_generate_duration(df, "SenderIPC", "PrrtSubmitPackage", "PrrtTransmitStart", "SenderCycleTime")
_generate_duration(df, "SenderEnqueued", "PrrtSendEnd", "LinkTransmitStart", "SenderCycleTime")
_generate_duration(df, "Encoding", "PrrtEncodeStart", "PrrtEncodeEnd", "SenderCycleTime")
_generate_duration(df, "ReceiverIPC", "PrrtReturnPackage", "PrrtReceivePackage", "ReceiverCycleTime")
_generate_duration(df, "HandlePacket", "HandlePacketStart", "HandlePacketEnd", "ReceiverCycleTime")
_generate_duration(df, "Feedback", "SendFeedbackStart", "SendFeedbackEnd", "ReceiverCycleTime")
_generate_duration(df, "Decoding", "DecodeStart", "DecodeEnd", "ReceiverCycleTime")
# Recreate missing timestamps from cycles
senderStamps = ["LinkTransmitStart",
"PrrtSubmitPackage",
"PrrtTransmitStart",
"PrrtTransmitEnd",
"PrrtEncodeStart",
"PrrtEncodeEnd"]
for stamp in senderStamps:
_restore_timestamp(df, stamp, df["SenderCycleTime"], df["PrrtSendStart_C"], df["PrrtSendStart_T"])
receiverStamps = ["DecodeStart",
"DecodeEnd",
"SendFeedbackStart",
"SendFeedbackEnd",
"HandlePacketStart",
"HandlePacketEnd",
"PrrtReturnPackage"]
for stamp in receiverStamps:
_restore_timestamp(df, stamp, df["ReceiverCycleTime"], df["LinkReceive_C"], df["LinkReceive_T"])
return df
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
model = linear_model.LinearRegression()
model.fit(x,y)
print("R-Score:", model.score(x,y))
plt.scatter(x,y)
plt.grid()
plt.plot(x,model.predict(x),color="red",linewidth=3)
def trace(df,title):
fig, ax = plt.subplots(figsize=(8, 4.5))
plt.grid()
base = df["PrrtSendStart_T"]
sender_color = "#AAAAAA"
receiver_color = "#888888"
series = np.transpose(np.array([
["PrrtSendStart_T", "PrrtDeliver_T", "black", "EndToEnd"],
["PrrtSendStart_T", "LinkTransmitEnd_T", sender_color, "SenderTotal"],
["PrrtSendStart_T", "PrrtSendEnd_T", sender_color, "Send"],
["PrrtSendStart_T", "PrrtSubmitPackage_T", sender_color, "Submit"],
["PrrtSubmitPackage_T", "PrrtTransmitStart_T", sender_color, "SenderIPC"],
["PrrtSubmitPackage_T", "PrrtSendEnd_T", sender_color, "Enqueue"],
["PrrtSendEnd_T", "LinkTransmitStart_T", sender_color, "SenderEnqueued"],
["PrrtTransmitStart_T", "PrrtTransmitEnd_T", sender_color, "PrrtTransmit"],
["LinkTransmitStart_T", "LinkTransmitEnd_T", sender_color, "LinkTransmit"],
["LinkReceive_T", "PrrtDeliver_T", receiver_color, "ReceiverTotal"],
#["DecodeStart_T", "DecodeEnd_T", receiver_color, "Decoding"],
["HandlePacketStart_T", "HandlePacketEnd_T", receiver_color, "HandlePacket"],
["PrrtReturnPackage_T", "PrrtReceivePackage_T", receiver_color, "ReceiverIPC"],
["SendFeedbackStart_T", "SendFeedbackEnd_T", receiver_color, "Feedback"],
]))
n = series.shape[1]
starts = df[series[0]] - base
ends = df[series[1]] - base
plt.hlines(range(n), starts, ends, series[2],linewidths=[5])
plt.xlabel("Time [us]")
fig.canvas.draw()
ax.set_yticklabels(series[3])
ax.yaxis.set_ticks(np.arange(0, n, 1))
plt.savefig(title)
plt.show()
def box(df_data,title):
ax = df_data.plot.box(vert=False,grid=True)
fig=ax.get_figure()
ax.set_yticklabels(list(map(lambda x: x.get_text().replace("Time", ""), ax.get_yticklabels())))
plt.xlabel("Time [us]")
fig.set_size_inches(8, 4.5, forward=True)
fig.savefig(title)
def describe_table(df):
stats = df.describe()
stats.drop(["count"],inplace=True)
stats.columns = list(map(lambda x: x.replace("Time", ""), stats.columns))
table = stats.to_latex(float_format=lambda x: "%.3f" % x)
print(table)
return stats
def get_outlier_treshold(stats):
q75 = stats["75%"]
iqr = q75 - stats["25%"]
return q75 + 1.5 * iqr
def _jitter_causes(df,title="JitterCause.pdf"):
stats = df["EndToEndTime"].describe()
tresh = get_outlier_treshold(stats)
outliers = df[df["EndToEndTime"] > tresh]
reasons = ["SendTime",
"PrrtTransmitTime",
"LinkTransmitTime",
"SubmitTime",
"SenderIPCTime",
"SenderEnqueuedTime",
#"EncodingTime",
"EnqueueTime",
"DecodingTime",
"HandlePacketTime",
"ReceiverIPCTime",
"FeedbackTime"]
df_reasons = pd.DataFrame(index=outliers.index)
for r in reasons:
r_tresh = get_outlier_treshold(df[r].describe())
df_reasons[r] = 0
df_reasons[r] = outliers[outliers[r] > r_tresh].notnull()
df_sum = df_reasons.sum().sort_values(ascending=False)
ax = df_sum.plot.bar(x="Reason",y="Frequency",rot=45,grid=True,legend=False,color="black")
fig=ax.get_figure()
plt.ylabel("Frequency")
ax.set_xticklabels(list(map(lambda x: x.get_text().replace("Time", ""), ax.get_xticklabels())))
fig.set_size_inches(8, 3, forward=True)
fig.savefig(title)
print("Outliers:",len(outliers),";","Threshold[us]:",tresh)
def jitter_analysis(df_data):
df_box = df_data[["EndToEndTime",
"SenderTotalTime",
"SendTime",
"SubmitTime",
"EnqueueTime",
"SenderIPCTime",
"SenderEnqueuedTime",
"PrrtTransmitTime",
"LinkTransmitTime",
"ReceiverTotalTime",
"ReceiverIPCTime",
"DecodingTime",
"HandlePacketTime",
"FeedbackTime",
]]
thresh = get_outlier_treshold(df_box["EndToEndTime"].describe())
df_no = df_box[df_box["EndToEndTime"] <= thresh]
box(df_no, "TraceJitter.pdf")
print("No of non-outliers:",len(df_no))
plt.show()
_jitter_causes(df_box)
def correlation(df_data,title="Correlation.pdf"):
columns = list(["SenderTotalTime",
"SendTime",
#"SubmitTime",
"SenderIPCTime",
#"EnqueueTime",
#"PrrtTransmitTime",
"LinkTransmitTime",
"ReceiverTotalTime",
"ReceiverIPCTime",
"HandlePacketTime",
"FeedbackTime",
#"DecodingTime",
])
cols=4
rows=math.ceil(len(columns) / cols)
fig, axes = plt.subplots(nrows=rows, ncols=cols)
fig.set_size_inches(4*cols, 3.5*rows, forward=True)
i = 0
for column in columns:
ax = df_data.plot.scatter(ax=axes[i//cols,i % cols],y="EndToEndTime",x=column,grid=True,marker="+",color="black")
fig2 = ax.get_figure()
ax.set_ylabel("EndToEnd [us]")
ax.margins(0.1,0.1)
ax.set_xlabel("{} [us]".format(column.replace("Time", "")))
i += 1
fig.savefig(title)
```
%% Cell type:markdown id: tags:
# Analysis
%% Cell type:code id: tags:
``` python
df_data = evaluate("results/on/2017_03_28_09_33_00_Sender.csv",
"results/on/2017_03_28_09_33_00_Receiver.csv",kind=0)
df_data = df_data[df_data["EndToEndTime"] < 175]
```
%% Cell type:markdown id: tags:
## Correlation
%% Cell type:code id: tags:
``` python
correlation(df_data)
```
%%%% Output: display_data
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)
%% Cell type:markdown id: tags:
## Trace
%% Cell type:code id: tags:
``` python
element = df_data[df_data["DecodingTime"] == df_data["DecodingTime"].median()].iloc[0]
trace(element, "PacketTrace.pdf")
```
%%%% Output: display_data
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)
%% Cell type:markdown id: tags:
## Jitter
%% Cell type:code id: tags:
``` python
jitter_analysis(df_data)
```
%%%% Output: display_data
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)
%%%% Output: display_data
![](data:image/png;base64,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)
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