downsampling

问题 This is somewhat of a broad topic, but I will try to pare it to some specific questions. I have noticed a difference between resample and groupby that I am curious to learn about. Here is some hourly time series data: In[]: import pandas as pd dr = pd.date_range('01-01-2020 8:00', periods=10, freq='H') df = pd.DataFrame({'A':range(10), 'B':range(10,20), 'C':range(20,30)}, index=dr) df Out[]: A B C 2020-01-01 08:00:00 0 10 20 2020-01-01 09:00:00 1 11 21 2020-01-01 10:00:00 2 12 22 2020-01-01

问题 This is somewhat of a broad topic, but I will try to pare it to some specific questions. I have noticed a difference between resample and groupby that I am curious to learn about. Here is some hourly time series data: In[]: import pandas as pd dr = pd.date_range('01-01-2020 8:00', periods=10, freq='H') df = pd.DataFrame({'A':range(10), 'B':range(10,20), 'C':range(20,30)}, index=dr) df Out[]: A B C 2020-01-01 08:00:00 0 10 20 2020-01-01 09:00:00 1 11 21 2020-01-01 10:00:00 2 12 22 2020-01-01

问题 This is somewhat of a broad topic, but I will try to pare it to some specific questions. I have noticed a difference between resample and groupby that I am curious to learn about. Here is some hourly time series data: In[]: import pandas as pd dr = pd.date_range('01-01-2020 8:00', periods=10, freq='H') df = pd.DataFrame({'A':range(10), 'B':range(10,20), 'C':range(20,30)}, index=dr) df Out[]: A B C 2020-01-01 08:00:00 0 10 20 2020-01-01 09:00:00 1 11 21 2020-01-01 10:00:00 2 12 22 2020-01-01

问题 I have found the following method to downsample a signal in python. I would like to use this method with a sample_rate of 100.21 but I think currently it only works for integer powers of two. Is there a possibility to downsample my signal with frequency 100.21 Hz to 8 Hz? def interpolateDataTo8Hz(data,sample_rate,startTime): # Downsample idx_range = range(0,len(data)) data = data.iloc[idx_range[0::int(sample_rate)/8]] # Set the index to be 8Hz data.index = pd.DatetimeIndex(start=startTime

问题 I'm trying to use ffmpeg/libswresample to resample streaming audio in my c++ application. Changing the sample width works well and the result sounds as one would expect; however, when changing the sample rate the result is somewhat crackly. I am unsure if it is due to incorrect usage of the libswresample library, or if I'm misunderstanding the resampling theory. Here is my resampling process, simplified for demonstration's sake: //Externally supplied data const uint8_t* in_samples //contains

问题 I try to rescale 2D images (greyscale). The image size is 256x256 and the desired output is 224x224. The pixel values range from 0 to 1300. I tried 2 approaches to rescale them with Lanczos Interpolation: First using PIL Image: import numpy as np from PIL import Image import cv2 array = np.random.randint(0, 1300, size=(10, 256, 256)) array[0] = Image.fromarray(array[0]).resize(size=(224, 224), resample=Image.LANCZOS) resulting in the error message: ValueError: image has wrong mode And then

来源： https://stackoverflow.com/questions/41029912/efficient-way-of-downsample-collection-via-decimating-or-extracting-every-nth-el

来源： https://stackoverflow.com/questions/41029912/efficient-way-of-downsample-collection-via-decimating-or-extracting-every-nth-el

问题 I have a large csv file, example of the data below. I will use an example of eight teams to illustrate. home_team away_team home_score away_score year belgium france 2 2 1990 brazil uruguay 3 1 1990 italy belgium 1 2 1990 sweden mexico 3 1 1990 france chile 3 1 1991 brazil england 2 1 1991 italy belgium 1 2 1991 chile switzerland 2 2 1991 My data runs for many years. I would like to have total number of scores of each team every year, see example below, team total_scores year belgium 4 1990

问题 I am attempting to perform undersampling of the majority class using python scikit learn. Currently my codes look for the N of the minority class and then try to undersample the exact same N from the majority class. And both the test and training data have this 1:1 distribution as a result. But what I really want is to do this 1:1 distribution on the training data ONLY but test it on the original distribution in the testing data. I am not quite sure how to do the latter as there is some dict