home.md

@@ -51,6 +51,78 @@ All of the tests that have been done for each hardware, along with analysis and
 * [[Laptop Tests]]     
 * [[JETSON TK1]]   
 
+# Using Jupyter
+
+A jupyter notebook is a useful, interactive way to present a report from test results. It combines markdown formatted text with live python code, meaning that plotted data can be analysed in more detail and much easier.
+
+### Installation
+
+Various packages will need to be installed to use jupyter:
+
+```
+$ sudo apt-get install jupyter-notebook
+$ sudo apt-get install python3 pandas
+$ sudo apt-get install python3-matplotlib
+$ sudo apt-get install python-pip
+
+pip install nbconvert
+pip install jupyter_client
+```
+### Producing a notebook with plots
+
+Notebooks are made of cells, each containing either markdown formatted text or python code.
+
+The first stage is to include the necessary libraries. Matplotlib is a good library for plotting data using python.
+
+```python
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+```
+
+Next define a function which takes a data file as an argument, parses the data, and returns a frame containing the data:
+
+```python
+def load_datafile(basename):
+    times = []
+    values = []
+    frequency1 = []
+    frequency2 = []
+    frequency3 = []
+    frequency4 = []
+    with open(basename, "r") as fh:
+        for line in fh:
+            line = line.strip()
+            try:
+                time, value, f1, f2, f3, f4 = line.split(" ")
+                times.append(int(time))
+                values.append(int(value))
+                frequency1.append(int(f1))
+                frequency2.append(int(f2))
+                frequency3.append(int(f3))
+                frequency4.append(int(f4))
+            except ValueError:
+                time = ""
+        tmpVal = times[0]
+        times[:] = [x - tmpVal for x in times]
+    return pd.DataFrame({"Time": times, "Latency": values, "Frequency1": frequency1, "Frequency2": frequency2, "Frequency3": frequency3, "Frequency4": frequency4})
+```
+
+Then to plot, simply use the code:
+
+```python
+load_datafile(<filename>).plot(title=<title>, x="Time", y="Latency", linewidth=0.5)
+```
+
+To add multiple files as subplots, define an array of axes for the plots to use (each subplot is one array element), then in `plot()` add an argument `ax=axes[n]` defining which subplot to use. Note that 2D arrays can be used to plot subplots in grids (i.e. 4 plots in a 2x2 grid).
+
+```python
+fig, axes = plt.subplots(nrows=<number of plots>, ncols=1)
+load_datafile(<filename1>).plot(title=<title>, x="Time", y="Latency", linewidth=0.5, ax=axes[0])
+load_datafile(<filename2>).plot(title=<title>, x="Time", y="Latency", linewidth=0.5, ax=axes[1])
+plt.tight_layout()
+```
+
 # Notes to consider
 
 [[Kernel tuning notes]]