55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
import time
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from datetime import datetime, timedelta
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import numpy as np
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import matplotlib.pyplot as pl
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def get_current_time():
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"""Returns the current time in seconds since the epoch."""
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return time.time()
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def generate_timestrings(start_time_str, end_time_str, dt):
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"""Generates a list of timestamps from start_time to end_time, which are strings such
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as 19:00."""
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timestamps = []
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current_time = datetime.strptime(start_time_str, "%H:%M")
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end_time = datetime.strptime(end_time_str, "%H:%M")
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while current_time <= end_time:
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timestamps.append(current_time)
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current_time += timedelta(seconds=dt)
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timestrings = [dt.strftime("%H:%M") for dt in timestamps]
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return timestrings
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def index_peak_times(timestamps, peak_times, peak_durations):
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"""Converts peak times from HH:MM format to seconds since epoch."""
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# start_time is the start time of the batch process in seconds since the epoch
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# peak_times is a list of strings in HH:MM format
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dt = timestamps[1] - timestamps[0] # time step in seconds
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peak_indices = np.zeros(len(timestamps), dtype=int)
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start_datetime = datetime.fromtimestamp(timestamps[0])
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processed_times = []
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peak_occurence_no = 1
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for time_str, duration in zip(peak_times, peak_durations):
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# convert HH:MM to a datetime object
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time_obj = datetime.strptime(time_str, "%H:%M")
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full_datetime = start_datetime.replace(
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hour=time_obj.hour, minute=time_obj.minute, second=0, microsecond=0
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)
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peak_start = int(full_datetime.timestamp())
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peak_end = peak_start + duration * 60 # duration in minutes to seconds
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peak_timestamps = np.arange(peak_start, peak_end, dt, dtype=int)
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common, indices_ts, _ = np.intersect1d(
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timestamps, peak_timestamps, return_indices=True
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)
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peak_indices[indices_ts] = peak_occurence_no
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peak_occurence_no += 1
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pl.plot(peak_indices, label="Peak Indices")
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pl.show()
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return processed_times
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