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Script to parse the Webtime Tracker CSV file and plot it
#!/usr/bin/env python
import argparse
from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def smoothed(t: np.ndarray, smoothing: int) -> np.ndarray:
if smoothing <= 0:
return t
left, right = smoothing // 2, (smoothing - 1) // 2
window = np.hanning(smoothing)
window /= window.sum()
smooth_t = np.convolve(t, window)[left:-right]
return smooth_t
def main(args: argparse.Namespace) -> None:
data = pd.read_csv(args.csv)
largest = data.loc[data.sum(1).nlargest(args.num_sites).index.values]
domains = list(largest["Domain"])
times = largest.to_numpy()[:, 1:]
dates = [
datetime.strptime(date_string, '%Y-%m-%d')
for date_string in data.columns[1:]
]
fig = plt.figure(figsize=(10, 5))
for domain, time in zip(domains, times):
time = (np.array(time, dtype=np.int32) + 30) // 60
plt.plot(dates, smoothed(time, args.smoothing), label=domain)
plt.legend(title="Sites", bbox_to_anchor=(1.05, 1), loc="upper left")
fig.autofmt_xdate()
plt.xlabel("Date")
plt.ylabel("Minutes")
plt.tight_layout()
plt.savefig("time.svg")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Parses Webtime tracker data")
parser.add_argument("csv", help="Path to the Webtime CSV")
parser.add_argument("-n", "--num-sites", type=int, default=10,
help="Number of sites to look at")
parser.add_argument("-s", "--smoothing", type=int, default=20,
help="Smoothing window length")
args = parser.parse_args()
main(args)
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