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...
Python 代码如下所示: 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, o...
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。 AI检测代码解析 import winsound # set an alarm of 440HZ for one second (1000ms) duration = 1000 freq = 440 winsound.Beep(freq, duratio...
start_time = time() i_global, _, batch_loss, batch_acc = sess.run( [global_step, optimizer, loss, accuracy], feed_dict={x: batch_xs, y: batch_ys, learning_rate: lr(epoch)}) duration = time() - start_time if s % 10 =...
"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。
'CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION', 'CAP_PROP_XI_AUTO_WB', 'CAP_PROP_XI_AVAILABLE_BANDWIDTH', 'CAP_PROP_XI_BINNING_HORIZONTAL', 'CAP_PROP_XI_BINNING_PATTERN', 'CAP_PROP_XI_BINNING_SELECTOR', 'CAP_PROP_XI_BINNING_VERTICAL', 'CAP_PROP_XI_BPC', 'CAP_PROP_XI_BUFFERS_QUEUE_SIZE...
["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 ...
import talib import numba as nb from GolemQ.analysis.timeseries import ( Timeline_duration, ...
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. ""...
rate, beta1=0.9, beta2=0.999, epsilon=1e-08).minimize(loss, global_step=global_step) # 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 ...