Add scripts used for RPi energy analysis (#34)

* Add scripts used for RPi energy analysis

* remove hardcoded user path

raspberry_pi_experiments/README.md

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+# Model Power Consumption Benchmark Guide
+
+This documents how we ran the power consumption experiments for the models on the Raspberry Pi. We use a [Watts Up Pro](https://www.vernier.com/products/sensors/wu-pro/) meter to measure the power. The results are summarized in our [paper](https://arxiv.org/abs/1711.00333) on arXiv.
+
+## Experiment Setup
+1. Download and install the usb serial from `http://www.ftdichip.com/Drivers/VCP.htm`.
+2. Plug the Raspberry Pi into the Watts Up Pro and adjust the mode to be current Watt mode.
+3. Connect Wattsup Pro with your laptop using usb cable.
+4. After the Raspberry Pi (assume the wifi is connectable) (TODO(tuzhucheng): add intruction to setup wifi) is fully started, connect your laptop to the wifi provided by the Raspberry Pi.
+5. Kick start the `wattsup_server.py` on your laptop, and record the printed out `wattsup_server ip`.
+6. Double check the model name and the keywords in the `power_consumption_benchmark.py` on Raspberry Pi. (also make sure that the idle watt read is greater or equal to the one shown in the script)
+7. Kick start `power_consumption_benchmark.py` on Raspberry Pi with the `wattsup_server ip` and the port your `wattsup_server` is running on and get read from stdout.
+
+## Analysis
+You can save the output of the `power_consumption_benchmark.py` script into a file. `experiment_output_e2e.txt` is our end-to-end experiment result including both preprocessing and inference while `experiment_output_preprocessing.txt` is our experiment result with preprocessing only.
+
+We do some exploratory analysis in a Jupyter Notebook `analysis.ipynb`. Note the dependencies in the notebook (`numpy`, `pandas`, `matplotlib`, `seaborn`) are not in any `requirements*.txt` files so you need to install them to run the commands in the notebook. This notebook also outputs two CSV files for more detailed analysis in R, like p-values and using `ggplot2` for plotting.
+
+The R Markdown file `analysis_plots.Rmd` is used to generate the plots for our paper. It takes as input files outputted by the Jupyter notebook.