Clean up readme and add original dataset and some useful info

gpcnet_tests/README.md

-1) Use 
-       bash get_code.sh
-   to get code and apply modifications.
+## Replicate the GPCNeT plot in the paper
 
-2) cd gpcnet
+* `python3 analyse_data.py 256probes_256congestors_bw_ad0_ad3.pickle`
 
-3) Modify Makefile for your compilation environment and run 
-       make all
+* If you encounter issues, e.g. with Latex, you can do `cp stub_Common_lib.py Common_lib.py`. This will only affect figure aesthetics and can be undone using `git checkout Common_lib.py`.
 
-4) Modify submit.cobalt to suit your submission system and change "benchmark" variable to point to "network_load_test" in your GPCNeT directory.
+## Generate the same kind of data used in the GPCNeT plot in the paper
 
-5) qsub submit.cobalt
+1. `bash get_code.sh` to get the code used in the GPCNeT experiments.
+
+2. `cd gpcnet`
+
+3. Modify the `Makefile` for your compilation environment.
+ 
+4. `make all`
+
+5. Modify `submit.cobalt` to suit your submission system and change "benchmark" variable to point to "network_load_test" executable in your GPCNeT directory.
+
+6. `qsub test_job_theta.cobalt` or similar to launch the job on your system.
+
+6. `python3 gather_data.py ./Probe_ADAPTIVE_*__nnodes_512/summary.out` to process the raw data
+
+7. `python3 analyse_data.py dataset.pickle` to generate the kind of plot used in the paper
 
-6) python3 gather_data.py ./Probe_ADAPTIVE_*__nnodes_512/summary.out
 
-7) To generate plot: 
-       python3 analyse_data.py dataset.pickle

gpcnet_tests/analyse_data.py

@@ -250,7 +250,6 @@ def plot_zdata_data_probes_and_congs_individual(df, num_nodes):
 
 def get_resampled_kde(binv, binc):
     num_samples = 100000
-    print("Drawing", str(num_samples), "samples for plot. For final version this should be 100'000.")
     np.random.seed(20201337)
     resamples = np.random.choice(binv, size=num_samples, p=binc/binc.sum())
     return resamples
@@ -262,7 +261,7 @@ def set_more_printout_pandas():
     pd.set_option('display.width', 1000)
 
 
-def pickout_data(df):
+def pick_paper_data(df):
     df2 = df.loc[(df.benchmark_name == "RR Two-sided BW") & (df.cong == "yes")]
     return df2.loc[(df2.p_ar == "ADAPTIVE_0") | (df2.p_ar == "ADAPTIVE_3")]
 
@@ -270,7 +269,7 @@ def pickout_data(df):
 # Main entry point
 if __name__ == "__main__":
     df = load_dataframe(sys.argv[1])
-    df = pickout_data(df)
+    df = pick_paper_data(df)
     
     if df.isnull().values.any():
         print("There are NULL values in the dataframe. Aborting.")

gpcnet_tests/loaded_modules.txt

+Loaded modules when compiling the code for the GPCNeT experiments presented in the paper:
+
+  1) modules/3.2.11.3
+  2) intel/19.0.5.281
+  3) craype-network-aries
+  4) craype/2.6.1
+  5) cray-libsci/19.06.1
+  6) udreg/2.3.2-6.0.7.1_5.15__g5196236.ari
+  7) ugni/6.0.14.0-6.0.7.1_3.15__gea11d3d.ari
+  8) pmi/5.0.14
+  9) dmapp/7.1.1-6.0.7.1_6.6__g45d1b37.ari
+ 10) gni-headers/5.0.12.0-6.0.7.1_3.13__g3b1768f.ari
+ 11) xpmem/2.2.15-6.0.7.1_5.13__g7549d06.ari
+ 12) job/2.2.3-6.0.7.1_5.48__g6c4e934.ari
+ 13) dvs/2.7_2.2.120-6.0.7.1_12.5__g74cb2cc4
+ 14) alps/6.6.43-6.0.7.1_5.51__ga796da32.ari
+ 15) rca/2.2.18-6.0.7.1_5.52__g2aa4f39.ari
+ 16) atp/2.1.3
+ 17) PrgEnv-intel/6.0.5
+ 18) craype-mic-knl
+ 19) cray-mpich/7.7.10
+ 20) nompirun/nompirun
+ 21) darshan/3.1.5
+ 22) trackdeps
+ 23) xalt
+ 24) papi/5.7.0.2

gpcnet_tests/stub_Common_lib.py

+def init_plotting():
+    pass
+
+def hide_spines():
+    pass