sampling

问题 I asked this question and I got an answer which works for a general case with sequential and non missing data but not for my case specifically. I have a DF that looks as follows. eventTime MeteredEnergy Demand RunningHoursLamps 6/7/2018 0:00 67.728 64 1037.82 6/7/2018 1:00 67.793 64 1038.82 6/7/2018 2:00 67.857 64 1039.82 6/7/2018 3:00 67.922 64 1040.82 6/7/2018 4:00 67.987 64 1041.82 6/7/2018 5:00 64 1042.82 6/7/2018 6:00 1043.43 6/7/2018 23:00 68.288 6/8/2018 0:00 67.728 64 1037.82 6/8/2018

问题 I'm developing an Android app, for a research, and im reading several Sensor data like accelerometer, gyroscope, barometer etc. So I have 4 Nexus 6P devices all with the newest Factory Image and freshly set up with no other app installed than the standard once which are pre-installed. So the Problem that occurs now is that one of the phones is constantly lagging behind, so for example i record for half an hour the accelerometer at 105 Hz (so the max possible rate for the accelerometer is

问题 I have an audio sample of about 14 seconds in 8khz Sample Rate. Im using librosa to extract some features from this audio file. y, sr = librosa.load(file_name) stft = np.abs(librosa.stft(y, n_fft=n_fft)) # file_length = 14.650022675736961 #sec # defaults # n_fft =2048 # hop_length = 512 # win_length/4 = n_fft/4 = 512 (win_length = n_fft default) #windowsTime = n_fft * Ts # (1/sr) stft.shape # (1025, 631) Specshow : librosa.display.specshow(stft, x_axis='time', y_axis='log') [![stft sr = 22050

问题 I have an audio sample of about 14 seconds in 8khz Sample Rate. Im using librosa to extract some features from this audio file. y, sr = librosa.load(file_name) stft = np.abs(librosa.stft(y, n_fft=n_fft)) # file_length = 14.650022675736961 #sec # defaults # n_fft =2048 # hop_length = 512 # win_length/4 = n_fft/4 = 512 (win_length = n_fft default) #windowsTime = n_fft * Ts # (1/sr) stft.shape # (1025, 631) Specshow : librosa.display.specshow(stft, x_axis='time', y_axis='log') [![stft sr = 22050

问题 I have the following data: MTU (CET) Day-ahead Price [EUR/MWh] 0 09.10.2017 00:00 - 09.10.2017 01:00 43.13 1 09.10.2017 01:00 - 09.10.2017 02:00 34.80 2 09.10.2017 02:00 - 09.10.2017 03:00 33.31 3 09.10.2017 03:00 - 09.10.2017 04:00 32.24 ....... 22 09.10.2017 22:00 - 09.10.2017 23:00 49.06 23 09.10.2017 23:00 - 10.10.2017 00:00 38.46 From which I would like to have data for every 5 minutes. By using: price = pd.read_csv(price_data) price_x = price.set_index(pd.DatetimeIndex(price['MTU (CET)'

问题 I have the following data: MTU (CET) Day-ahead Price [EUR/MWh] 0 09.10.2017 00:00 - 09.10.2017 01:00 43.13 1 09.10.2017 01:00 - 09.10.2017 02:00 34.80 2 09.10.2017 02:00 - 09.10.2017 03:00 33.31 3 09.10.2017 03:00 - 09.10.2017 04:00 32.24 ....... 22 09.10.2017 22:00 - 09.10.2017 23:00 49.06 23 09.10.2017 23:00 - 10.10.2017 00:00 38.46 From which I would like to have data for every 5 minutes. By using: price = pd.read_csv(price_data) price_x = price.set_index(pd.DatetimeIndex(price['MTU (CET)'

来源： https://stackoverflow.com/questions/50908047/sampling-from-multivariate-customised-cumulative-distribution-function-in-matlab

来源： https://stackoverflow.com/questions/50908047/sampling-from-multivariate-customised-cumulative-distribution-function-in-matlab

来源： https://stackoverflow.com/questions/64159397/sample-from-a-grouped-dataframe-with-specified-probabilities-in-r

来源： https://stackoverflow.com/questions/64159397/sample-from-a-grouped-dataframe-with-specified-probabilities-in-r