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txrx_walkthrough

-                      v2
+                      v3
 Notice the indexing trick in the code above. The variable {{{rx_ap_idx_7}}} contains booleans indicating which elements passed the test, {{{True}}} here for receptions with rate of 7. This boolean array is then used as the slice argument to the {{{timestamp}}} array to select only the rate = 7 entries. Using a boolean array as a slice argument is a very fast way to filter a numpy array for further processing.
+Another way to visualize the increasing rates is to plot the cumulative number of bytes received by one node. The rate of bytes-per-time (i.e. throughput) should be faster with increasing rate.
+{{{#!python
+rx_ap_lens_acc = np.cumsum(rx_ap['length'])
+step(rx_ap['timestamp'], rx_ap_lens_acc)
+}}}
+ pattern is clear- each rate was used for about 1 minute. And this explains the distribute of rates above- a higher rate means shorter packet duration, so in a fixed interval (i.e. 1 minute) more packets can be transmitted.
+This will produce a plot:
+[[Image(wlan_exp_log_txrx_ex_t_vs_bytes.png)]]
+Clearly the rate of bytes-per-time is increasing with time, consistent with our earlier observation of rates vs. time.