Python snippet example on how to compute blink rate per minute.

blinkrate_PI.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

events_df = pd.read_csv("path/events.csv")
blinks_df = pd.read_csv("path/blinks.csv")

# filter blinks only occurring between event name A and B
event_name_A = "recording.begin"
event_name_B = "recording.end"
time_event_A = events_df[events_df["name"] == event_name_A]["timestamp [ns]"].values[0]
time_event_B = events_df[events_df["name"] == event_name_B]["timestamp [ns]"].values[0]
blinks_df_filter = blinks_df[
    (blinks_df["start timestamp [ns]"] > time_event_A)
    & (blinks_df["end timestamp [ns]"] < time_event_B)
]


# Blink rate per minute
blinks_df_filter["minute"] = np.nan
blinks_per_minute = pd.DataFrame(
    columns=["minute", "number of blinks", "avg_blink_duration"]
)
minutes_the_section_last = np.ceil((time_event_B - time_event_A) / 1e9 / 60)
for minute in range(0, int(minutes_the_section_last), 1):
    time_min_start = time_event_A + minute * 60 * 1e9
    idx = blinks_df_filter.loc[
        (blinks_df_filter["start timestamp [ns]"] > time_min_start)
        & (blinks_df_filter["start timestamp [ns]"] < time_min_start + 60 * 1e9)
    ].index
    blinks_df_filter.loc[idx, "minute"] = minute
    avg_blink_duration = blinks_df_filter.loc[idx, "duration [ms]"].mean()
    blinks_per_minute = pd.concat(
        [
            blinks_per_minute,
            pd.DataFrame(
                [[minute, len(idx.to_numpy()), avg_blink_duration]],
                columns=["minute", "number of blinks", "avg_blink_duration"],
            ),
        ],
        ignore_index=True,
    )

blinks_per_minute.to_csv("blinks_per_minute.csv")
blinks_per_minute.plot(x="minute", y="number of blinks")
plt.show()
print(blinks_df_filter)