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plot_transmission_timeline.py
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| time_range = pd.date_range(transmission_df.index.min(), transmission_df.index.max(), freq="{}s".format(args.interval)) | time_range = pd.date_range(transmission_df.index.min(), transmission_df.index.max(), freq="{}s".format(args.interval)) | ||||
| # create bins by pd.cut, aggregate sum | # create bins by pd.cut, aggregate sum | ||||
| transmission_df["goodput"] = transmission_df.groupby(pd.cut(transmission_df.index, bins=time_range, labels=time_range[:-1]))["payload_size"].sum().reset_index() | |||||
| transmission_df["goodput"] = transmission_df.groupby(pd.cut(transmission_df.index, bins=time_range, labels=time_range[:-1]))["payload_size"].sum().reset_index()["payload_size"] | |||||
| #transmission_df["goodput"] = transmission_df["payload_size"].rolling("{}s".format(args.interval)).sum() | #transmission_df["goodput"] = transmission_df["payload_size"].rolling("{}s".format(args.interval)).sum() | ||||
| transmission_df["goodput"] = transmission_df["goodput"].apply( | transmission_df["goodput"] = transmission_df["goodput"].apply( | ||||
| lambda x: ((x * 8) / args.interval) / 10**6 | lambda x: ((x * 8) / args.interval) / 10**6 |
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