resampling

来源： https://stackoverflow.com/questions/36222928/pandas-ohlc-aggregation-on-ohlc-data

来源： https://stackoverflow.com/questions/36222928/pandas-ohlc-aggregation-on-ohlc-data

来源： https://stackoverflow.com/questions/20816813/resampling-a-dataframe-to-hourly-15min-and-5min-periods-in-julia

来源： https://stackoverflow.com/questions/20816813/resampling-a-dataframe-to-hourly-15min-and-5min-periods-in-julia

问题 df1 = data.frame(id=c('A1','2','B3','4','5','6','7','8','9','10'),s1c1=c(0,0.2,0,0.5,0.8,0,0,0,0,0),s1c2=c(0,0,0.3,0,0,0.9,0.3,0,0,0),s1c3=c(0.1,0,0,0,0,0,0,0.2,0.8,0.1)) df2 = data.frame(id=c('A1','2','B3','4','5','6','7','8','9','10'),s2c1=c(0,0.22,0,0.35,0.8,0,0,0,0,0),s2c2=c(0,0,0.23,0,0,0.7,0.3,0,0,0),s2c3=c(0.2,0,0,0,0,0,0,0.4,0.9,0.4)) df <- merge(df1,df2, by="id",all=TRUE) df$class <- c(0,0,0,0,0,1,1,0,0,0) > df id s1c1 s1c2 s1c3 s2c1 s2c2 s2c3 class 10 0.0 0.0 0.1 0.00 0.00 0.4 0 2 0

问题 How can i resample audio(PCM data) using Audio Unit at runtime/live ? I have an Audio Unit setup as follows. - (void) setUpAudioUnit { OSStatus status; AudioComponentInstance audioUnit; AudioComponent inputComponent; AudioComponentDescription audioComponentDescription; AudioStreamBasicDescription audioStreamBasicDescription; // Describe audio component audioComponentDescription.componentType = kAudioUnitType_Output; audioComponentDescription.componentSubType = kAudioUnitSubType

问题 How can i resample audio(PCM data) using Audio Unit at runtime/live ? I have an Audio Unit setup as follows. - (void) setUpAudioUnit { OSStatus status; AudioComponentInstance audioUnit; AudioComponent inputComponent; AudioComponentDescription audioComponentDescription; AudioStreamBasicDescription audioStreamBasicDescription; // Describe audio component audioComponentDescription.componentType = kAudioUnitType_Output; audioComponentDescription.componentSubType = kAudioUnitSubType

问题 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)

问题 I am trying to simulate data via bootstrapping to create confidence bands for my real data with a funnel plot. I am building on the strategy of the accepted answer to a previous question. Instead of using a single probability distribution for simulating my data I want to modify it to use different probability distributions depending on the part of the data being simulated. I greatly appreciate anyone who can help answer the question or help me phrase the question more clearly. My problem is

问题 I have a pandas df 'instr_bar' with tick data as follows: time 2016-07-29 16:07:24 5.72 2016-07-29 16:07:24 5.72 2016-07-29 16:07:24 5.72 2016-07-29 16:07:58 5.72 2016-07-29 16:07:58 5.72 2016-07-29 16:09:49 5.70 2016-07-29 16:09:50 5.73 2016-07-29 16:11:14 5.73 2016-07-29 16:11:14 5.73 2016-07-29 16:14:53 5.77 2016-07-29 16:14:53 5.77 2016-07-29 16:17:27 5.75 2016-07-29 16:17:43 5.76 2016-07-29 16:17:43 5.76 I want to turn this into 5 minute OHLC. The index is not unique in many instances. I