def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() duration = end_time - start_time debug(duration, f"{func.__name__} 执行时间(秒)") return result return wrapper @measure_performance def complex_calculation(data): # 复...
-fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fsigned-char -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -W...
(S12) ** 2) # Coherence calculation #coherence = S12_smoothed / (((S1_smoothed) **2) * ((S2_smoothed)**2)) coherence = S12_smoothed / (np.sqrt(S1_smoothed) * np.sqrt(S2_smoothed)) return coherence, freqs, [coeffs1,coeffs2] def coherence_plot(data1,data2,frame_rate): audio_...
toaster.show_toast("Execution complete", "Your calculation completed", duration=10) 1. 2. 3. 4. 5. 6. 7. 8. 我们在程序完成执行时为我们进行提示。 import winsound # set an alarm of 440HZ for one second (1000ms) duration = 1000 freq = 440 winsound.Beep(freq, duration) 1. 2. 3...
defestimate_pi(n_points: int,show_estimate: bool,)->None:"""Simple Monte Carlo Pi estimation calculation.Parameters---n_pointsnumber of random numbers used to for estimation.show_estimateif True, will show the estimation of Pi, otherwisewill not output...
# PREDICTION AND ACCURACY CALCULATION correct_prediction = tf.equal(y_pred_cls, tf.argmax(y, axis=1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # SAVER merged = tf.summary.merge_all() saver = tf.train.Sa...
["RO_Skid_Running"] = "On" # Convert 'None' or NaN values to np.nan for calculation for ts, entry in data.items(): if entry["Level_PV"] is None or np.isnan(entry.get("Level_PV", np.nan)): entry["Level_PV"] = np.nan # Convert data to DataFrame for better visualization ...
"Your calculation completed", duration=10) 我们在程序完成执行时为我们进行提示。 import winsound # set an alarm of 440HZ for one second (1000ms) duration = 1000 freq = 440 winsound.Beep(freq, duration) Mac和Linux可以使用os模块,使用afplay命令(在macOS上)或aplay命令(在Linux上)播放声音。
def estimate_pi( n_points: int, show_estimate: bool, ) -> None: """ Simple Monte Carlo Pi estimation calculation. Parameters --- n_points number of random numbers used to for estimation. show_estimate if True, will show the estimation of Pi, otherwise will not output anything. ""...
One of Seaborn's notable features is its support for statistical estimation and automatic calculation of confidence intervals within plots, making it a valuable tool for exploring data and understanding the underlying patterns. Whether you're working on exploratory data analysis, data storytelling, or ...