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#!/usr/bin/env python3
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import pickle
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from matplotlib import pyplot as plt
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import requests
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import json
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from datetime import datetime
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from dataclasses import dataclass
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from collections import defaultdict
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from pathlib import Path
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import traceback
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import os
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api_url = r'https://review.video.fosdem.org/api/v1/event/1/overview'
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poll_rate = 10*60 # seconds
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pickle_file = Path(r'./talk_data.pickle')
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@dataclass
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class TimeSeries:
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"The object to pickle and unpickle"
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states: set[str]
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jobstates: set[str]
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data: dict[datetime, tuple[dict[str, int], dict[str, int]]] # time → (states(name → count), jobstates(ditto))
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def get_data() -> tuple[datetime, dict[str, int], dict[str, int]]:
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resp = requests.get(api_url)
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if resp.status_code != 200: raise RuntimeError(f'bad status code: {resp.status_code}')
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time = datetime.now().astimezone()
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data = json.loads(resp.content.decode())
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states = defaultdict(lambda: 0)
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jobstates = defaultdict(lambda: 0)
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# should be an array/list
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for talk in data:
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states[talk['state']] += 1
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jobstates[talk['progress']] += 1
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return time, dict(states), dict(jobstates)
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def main():
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if pickle_file.exists():
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with open(pickle_file, 'rb') as f:
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ts = pickle.load(f)
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else:
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print("New time series.")
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ts = TimeSeries(states=set(), jobstates=set(), data=dict())
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plt.ion()
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plt.clf()
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plt.show(block=False)
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while True:
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try:
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time, states, jobstates = get_data()
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print(f'data got at {time}')
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assert time not in ts.data
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ts.data[time] = (states, jobstates)
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ts.states |= states.keys()
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ts.jobstates |= jobstates.keys()
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except RuntimeError as e:
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print('Could not get/save data.')
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traceback.print_exc()
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# Save the pickle
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# Keep the previous pickle in case we fail (e.g. ENOSPC)
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os.rename(pickle_file, pickle_file.with_suffix('.bak'))
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with open(pickle_file, 'wb') as f:
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pickle.dump(ts, f)
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print('pickle saved')
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# Show the plot
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x = sorted(ts.data.keys()) # times
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# first is lowest
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states_low = (
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'lost',
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'ignored',
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'uninteresting',
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'broken',
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'preview',
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'cutting',
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'transcoding',
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'publishing',
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)
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states_high = (
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'done',
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'waiting_for_files',
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)
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states_other = tuple(sorted(ts.states - (set(states_low) | set(states_high))))
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states = states_low + states_other + states_high
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ys = [tuple(ts.data[time][0].get(state, 0) for state in states) for time in x] # numbers per state
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# We do not show jobstates atm. Too lazy.
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# ys are transposed – we need vectors by times, not by states.
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ys = list(zip(*ys))
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last = ts.data[max(ts.data.keys())][0]
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labels = [f'{state}: {last.get(state, 0)}' for state in states]
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plt.clf()
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plt.stackplot(x, ys, labels=labels)
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plt.legend(loc='upper left')
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print('showing plot')
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plt.pause(poll_rate)
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main()
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