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December 23, 2022 05:19
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Sub-process to plot a live multi-series line graph.
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| from collections import deque | |
| from string import printable | |
| from liveplot import LivePlot | |
| # Create the LivePlot object. The lambda takes the queued data and converts into | |
| # a Tuple consisting of the x-axis value (probably time) and a list of metrics. | |
| # Each metric is a tuple consisting of a name for the series and a value. | |
| plot = LivePlot(lambda counter, queue_depth: (counter, [("depth", queue_depth)])) | |
| # Start the sub-process. | |
| plot.start() | |
| # This is just to demo the live plot | |
| chars = printable.strip() | |
| max_len = 4 | |
| queue = deque([c for c in chars]) | |
| counter = 0 | |
| while queue: | |
| p = queue.popleft() | |
| counter += 1 | |
| # Send data sample to the metric function every 10k iterations | |
| if counter % 10000 == 0: | |
| plot.enqueue(counter//10000, len(queue)) | |
| print(p) | |
| for c in chars: | |
| _p = p + c | |
| if len(_p) <= max_len: | |
| queue.append(_p) |
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| from collections import deque | |
| from matplotlib import pyplot as plt | |
| from matplotlib.animation import FuncAnimation | |
| from multiprocessing import Process, Queue | |
| from typing import Callable, Tuple, Union, List | |
| MetricFuncRet = Tuple[Union[int, float], List[Tuple[str, Union[int, float]]]] | |
| MetricFunc = Callable[[...], MetricFuncRet] | |
| class LivePlot(Process): | |
| def __init__( | |
| self, | |
| metric_fn: MetricFunc, | |
| maxplot=500, | |
| maxqueue=1000000, | |
| interval=500 | |
| ) -> None: | |
| super().__init__() | |
| self._queue = Queue(maxsize=maxqueue) | |
| self._interval = interval | |
| self._maxplot = maxplot | |
| self._mf = metric_fn | |
| def run(self) -> None: | |
| plt.style.use("fivethirtyeight") | |
| xvals = deque(maxlen=self._maxplot) | |
| series = {} | |
| def animate(i): | |
| while not self._queue.empty(): | |
| args, kwargs = self._queue.get_nowait() | |
| xval, metrics = self._mf(*args, **kwargs) | |
| xvals.append(xval) | |
| for k, v in metrics: | |
| series.setdefault(k, deque(maxlen=self._maxplot)).append(v) | |
| plt.cla() | |
| for k, v in series.items(): | |
| plt.plot(xvals, v, label=k) | |
| plt.legend(loc="upper left") | |
| plt.tight_layout() | |
| ani = FuncAnimation(plt.gcf(), animate, interval=self._interval) | |
| plt.show() | |
| def enqueue(self, *args, **kwargs) -> None: | |
| self._queue.put((args, kwargs)) |
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