| @@ -163,29 +163,6 @@ if __name__ == "__main__": | |||
| if args.save: | |||
| plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", ""))) | |||
| # plot correlations | |||
| corr_pairs = [ | |||
| ["goodput_rolling", "RSRQ"], | |||
| ["goodput_rolling", "RSRP"], | |||
| ["goodput_rolling", "RSSI"], | |||
| ["goodput_rolling", "SINR"], | |||
| ["goodput_rolling", "downlink_cqi"], | |||
| ] | |||
| for pair in corr_pairs: | |||
| # spearman and pearson | |||
| sp = transmission_df[pair[0]].corr(transmission_df[pair[1]], method="spearman") | |||
| pe = transmission_df[pair[0]].corr(transmission_df[pair[1]], method="pearson") | |||
| title = "{}/{} spearman: {} pearson: {}".format(pair[0], pair[1], round(sp, 4), round(pe, 4)) | |||
| transmission_df.plot.scatter(x=pair[0], y=pair[1], c="DarkBlue", title=title) | |||
| if args.save: | |||
| plt.savefig("{}{}_corr_{}_and_{}.pdf".format(args.save, csv.replace(".csv", ""), pair[0], pair[1])) | |||
| plt.clf() | |||
| counter += 1 | |||
| plt.clf() | |||