标签: resampling

import pandas as pd
import numpy as np

dates = pd.date_range('20141229',periods=14, name='Day')
df = pd.DataFrame({'Sum1': [1667, 1229, 1360, 9232, 8866, 4083, 3671, 10085, 10005, 8730, 10056, 10176, 3792, 3518],
                   'Sum2': [91, 75, 75, 254, 239, 108, 99, 259, 395, 355, 332, 386, 96, 111],
                   'Sum3': [365.95, 398.97, 285.12, 992.17, 1116.57, 512.11, 504.47, 1190.96, 1753.6, 1646.25, 1344.05, 1582.67, 560.95, 736.44],
                   'Sum4': [5, 5, 1, 5, 8, 8, 2, 10, 12, 16, 16, 6, 6, 3]},index=dates); print(df)

在df制作这个样子的：

             Sum1  Sum2 …

我有这个数据帧:

startTime     endTime  emails_received
index                                             
2014-01-24 14:00:00  1390568400  1390569600    684
2014-01-24 14:00:00  1390568400  1390569300    700
2014-01-24 14:05:00  1390568700  1390569300    438
2014-01-24 14:05:00  1390568700  1390569900    586
2014-01-24 16:00:00  1390575600  1390576500    752
2014-01-24 16:00:00  1390575600  1390576500    743
2014-01-24 16:00:00  1390575600  1390576500    672
2014-01-24 16:00:00  1390575600  1390576200    712
2014-01-24 16:00:00  1390575600  1390576800    708

我运行resample("10min",how ="median").dropna()然后我得到:

                  startTime     endTime  emails_received
start                                             
2014-01-24 14:00:00  1390568550  1390569450    635
2014-01-24 16:00:00  1390575600  1390576500    712

哪个是对的.有没有什么方法可以通过熊猫轻松获得平均值的标准偏差？

我正在尝试使用ffmpeg / libswresample对我的c ++应用程序中的流音频进行重新采样。更改样本宽度效果很好，结果听起来像人们期望的那样。但是，更改采样率时，结果会有些混乱。我不确定这是由于对libswresample库的使用不正确，还是由于我误解了重采样理论。

这是我的重采样过程，为演示起见简化了：

//Externally supplied data
const uint8_t* in_samples //contains the audio data to be resampled
int in_num_samples = 256

//Set up resampling context
SwrContext *swr = swr_alloc();
av_opt_set_channel_layout(swr, "in_channel_layout", AV_CH_LAYOUT_STEREO, 0);
av_opt_set_channel_layout(swr, "out_channel_layout", AV_CH_LAYOUT_STEREO, 0);
av_opt_set_int(swr, "in_sample_rate", 44100, 0);
av_opt_set_int(swr, "out_sample_rate", 22050, 0);
av_opt_set_sample_fmt(swr, "in_sample_fmt", AV_SAMPLE_FMT_FLT, 0);
av_opt_set_sample_fmt(swr, "out_sample_fmt", AV_SAMPLE_FMT_FLT, 0);
swr_init(swr);

//Perform the resampe
uint8_t* out_samples;
int out_num_samples = av_rescale_rnd(swr_get_delay(swr, in_samplerate) + in_num_samples, out_samplerate, in_samplerate, AV_ROUND_UP);
av_samples_alloc(&out_samples, NULL, out_num_channels, out_num_samples, AV_SAMPLE_FMT_FLT, 0);
out_num_samples = swr_convert(swr, &out_samples, …

我正在尝试基于 5 秒时间步长进行成交量加权价格聚合，我有多个数据点。我可以通过传递聚合类型的字典来获得各个字段的简单平均值和总和聚合。但是，要生成交易量加权聚合，我需要使用定价和交易量字段为每个步骤生成此聚合。

                    TS          P           Q
D           
2018-01-01 00:00:00 1514764800  1673574.0   0.164012
2018-01-01 00:00:00 1514764800  1673954.0   0.006000
2018-01-01 00:00:00 1514764800  1673967.0   0.005808
2018-01-01 00:00:00 1514764800  1673949.0   0.040000
2018-01-01 00:00:00 1514764800  1673573.0   0.159234
2018-01-01 00:00:00 1514764800  1673569.0   0.007000
2018-01-01 00:00:00 1514764800  1673949.0   0.100000
2018-01-01 00:00:00 1514764800  1673569.0   0.008000
2018-01-01 00:00:00 1514764800  1673949.0   0.033000
2018-01-01 00:00:00 1514764800  1673346.0   0.033000
2018-01-01 00:00:01 1514764801  1673967.0   0.212200
2018-01-01 00:00:02 1514764802  1673954.0   0.006765
2018-01-01 00:00:03 1514764803  1673950.0   0.012000
2018-01-01 00:00:03 1514764803  1673955.0   0.005700
2018-01-01 00:00:03 1514764803 …