Created
June 3, 2023 23:42
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CPU/Memory Monitoring (Python)
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| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import psutil | |
| import time | |
| import datetime | |
| import scipy | |
| ##### PARAMETERS ################### | |
| SAMPLE_RATE = 5 # hz | |
| SMOOTHING = 0.01 | |
| SAVE_AS_CSV = True | |
| SAVE_AS_PNG = True | |
| SAVE_AS_SVG = True | |
| SHOW_FIGURE = True | |
| #################################### | |
| byte_to_gb = 1/1024/1024/1024 | |
| total_memory = psutil.virtual_memory()[0] * byte_to_gb | |
| start_time = time.time() | |
| filename = "cpu_mem_monitor_" + datetime.datetime.now().strftime("%Y_%m_%d-%H_%M_%S") | |
| print("Press Ctrl+C to stop monitoring...") | |
| data_x = [] | |
| data_mem = [] | |
| data_cpu = [] | |
| try: | |
| while True: | |
| data_x.append( time.time() - start_time ) | |
| data_mem.append( psutil.virtual_memory()[3] * byte_to_gb ) | |
| data_cpu.append( psutil.cpu_percent() ) | |
| time.sleep(1/SAMPLE_RATE) | |
| except KeyboardInterrupt: | |
| print('interrupted!') | |
| min_length = len(data_x) | |
| if (len(data_mem) < min_length): | |
| min_length = len(data_mem) | |
| if (len(data_cpu) < min_length): | |
| min_length = len(data_cpu) | |
| data_x = data_x[:min_length] | |
| data_mem = data_mem[:min_length] | |
| data_cpu = data_cpu[:min_length] | |
| # smooth data | |
| if SMOOTHING>0: | |
| sigma = len(data_x)*SMOOTHING | |
| data_mem_smooth = scipy.ndimage.gaussian_filter1d(data_mem, sigma=sigma, mode="nearest") | |
| data_cpu_smooth = scipy.ndimage.gaussian_filter1d(data_cpu, sigma=sigma, mode="nearest") | |
| else: | |
| data_mem_smooth = data_mem | |
| data_cpu_smooth = data_cpu | |
| fig, ax1 = plt.subplots(figsize=(12,8)) | |
| color = 'tab:red' | |
| ax1.set_xlabel('time (s)') | |
| ax1.set_ylabel('Memory Usage (GiB)', color=color) | |
| ax1.plot(data_x, data_mem, ".", color=color, alpha=0.25) | |
| ax1.plot(data_x, data_mem_smooth, "-", color=color) | |
| ax1.tick_params(axis='y', labelcolor=color) | |
| ax2 = ax1.twinx() | |
| color = 'tab:blue' | |
| ax2.set_ylabel('CPU Usage (Percent)', color=color) | |
| ax2.plot(data_x, data_cpu, ".", color=color, alpha=0.25) | |
| ax2.plot(data_x, data_cpu_smooth, "-", color=color) | |
| ax2.tick_params(axis='y', labelcolor=color) | |
| fig.tight_layout() | |
| if SAVE_AS_CSV: | |
| combined = np.array([data_x, data_mem, data_cpu]).T | |
| np.savetxt(filename + ".csv", combined, fmt="%.5f", delimiter=",", header="Time (seconds), Memory Usage (GiB), CPU Usage (Percent)") | |
| if SAVE_AS_PNG: | |
| plt.savefig(filename + ".png") | |
| if SAVE_AS_SVG: | |
| plt.savefig(filename + ".svg") | |
| if SHOW_FIGURE: | |
| plt.show() |
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Example outputs: