first cleaning README and renaming the campaign files

README.md

@@ -10,9 +10,9 @@ It is a fork from demand-response-user
 - `scripts/run_expe.sh` shell script which launch experiments when workload is prepared.
 - `scripts/compute_stat.sh` shell script which compute stats when the experiments finished.
 - `default.nix` nix file given all the necessary dependencies.
-- `campaign3.py`: Python script preparing and launching in parrallel the experiments. 
-Each experiment corresponds to one instance of `instance3.py`.
-- `analyse_campaign3.ipynb`: Jupyter notebook analysing the results.
+- `campaign.py`: Python script preparing and launching in parrallel the experiments. 
+Each experiment corresponds to one instance of `instance.py`.
+- `analyse_campaign.ipynb`: Jupyter notebook analysing the results.
 
 
 ## Steps to reproduce the experiments
@@ -33,8 +33,8 @@ The main software used (and configured in the file `default.nix`) are:
 - python3, pandas, jupyter, matplotlib etc. for the data analysis
 
 
-All the dependencies of the project are given in the default.nix file to 
-install nix you can launch the command
+All the dependencies of the project are given in the `default.nix` file.
+If you do not have Nix installed on your machine, you can install it with
 ```bash
 scripts/install_nix.sh
 ```
@@ -42,36 +42,40 @@ It might be required to type other commands for the nix command to be
 available in the current shell. In this case it will be indicated by the 
 prompt of the nix installation.
 
-
-To go into the experiments environment you can now type :
+Open a shell with all the dependencies managed:
 ```bash
 nix-shell -A exp_env --pure
 ```
-This will compile and install all the dependencies needed for the experiments 
-it can take some times (in our case, it took 6 minutes)
+
+This will compile and install all the dependencies needed for the experiments.
+It can take some time (in our case, it took 6 minutes).
+
 ### 2. Prepare input workload
-Inside the nix-shell use the following command inside project directory :
+Inside the nix-shell, run the following script to download (from the [Parallel Workloads Archive](https://www.cs.huji.ac.il/labs/parallel/workload/)) and filter the input workload used in the experiments:
+
 ```bash
 scripts/prepare_workload.sh
 ```
 
 ### 3. Launch the campaign
-By default the run_expe scripts only run one seed by experiment 
-to do the 30 experiments you have to modify `--nb_replicat ` argument
-it should look like this : 
+By default the `run_expe` scripts only run one seed by experiment 
+to do the 30 experiments you have to modify `--nb_replicat` argument
+it should look like this:
+
 ```bash 
-python3 campaign3.py --nb-replicat 30 --expe-range -1 --window-mode 8 --nb-days 164 \
+python3 campaign.py --nb-replicat 30 --expe-range -1 --window-mode 8 --nb-days 164 \
 --json-behavior behavior_file/big_effort.json behavior_file/low_effort.json behavior_file/max_effort.json behavior_file/medium_effort.json \
 --compress-mode --production-file data_energy/energy_trace_sizing_solar.csv data_energy/energy_trace_sizing_solar.csv
 ```
-As every experiments can take up to 20 GB of RAM, 
+
+As every experiment can take up to 20 GB of RAM, 
 you might be limited by the memory of your system.
 When you are running the experiments you can limit 
 the number of parallel run using `--threads n` command-line argument 
 with `n` the maximum of experiments to run in parallel.
 By default, it uses every physical cores available.
 
-Once you have done all the previous step, 
+Once you have done all the previous steps, 
 launch the bash script in the nix-shell :
 ```bash
 scripts/run_expe.sh
@@ -83,6 +87,7 @@ Rigid finished, Now monolithic behavior
 It means that the program has finished computing the simulation 
 and is now computing stat on the obtained value. 
 You can stop the program now if you only want raw simulation results
+
 ### 4. Generate the metrics
 The tagged `experiments-version` version forget some metrics in computation.
 After the experiments, to compute the metrics we have to switch to tag 
@@ -92,13 +97,13 @@ using the command :
 scripts/compute_stat.sh
 ```
 The stat will be computed and place in the shell directory  with the names 
-`campaign3_metrics.csv` and `campaign3_metrics_relative.csv`.
+`campaign_metrics.csv` and `campaign_metrics_relative.csv`.
 It will likely differ from the one provided in result_big_expe. 
 In our reproduction we noticed a relative difference of 0.5% in energy related metrics and
 2% in user behaviors related metrics
 
 ### 5. Generate the graph 
-To generate the graph you can launch the notebook `analyse_campaign3.ipynb`.
+To generate the graph you can launch the notebook `analyse_campaign.ipynb`.
 You will have to change the variable `RAW_DATA_DIR` and `OUT_DIR` to match with your setup.
 
 
@@ -121,7 +126,7 @@ is the energy trace of the energy produced by DataZero2 sizing algorithm
 ## Advanced options
 Inside the nix shell exp_env, launch the commands :
 ```bash
-python3 campaign3.py --help
+python3 campaign.py --help
 ```
 You will get a details of every possible arguments.
 You will have to at least give the following arguments :

campaign3.py → campaign.py

@@ -11,7 +11,7 @@ import concurrent.futures
 
 from scripts.util import WL_DIR, ROOT_DIR, ExpeDict, create_dir_rec_if_needed, generate_dict_from_json
 from scripts.generate_file import save_dict_to_json
-from instance3 import start_instance, prepare_input_data, generate_windows_dict, \
+from instance import start_instance, prepare_input_data, generate_windows_dict, \
     compress_expe_result, user_type_to_behavior
 from scripts.stat_tools import plot_exec_time, plot_queue_load
 from compute_metrics_campaign3 import compute_metrics_all_expe_parr