Creating a pandas time series datetime with sub-milisecond datetime and downsampling it-CodePudding

So I am quite a novice to pandas timeseries but I think I need it for some applications.

I have a dataset of an voltage recording which was sampled at a rate of 2500Hz for an hour it needs to be downsampled to 1500hz.

How can I A) create a datetime index/object for this data and B) downsample it to 1500Hz?

EDIT (here is an example):

original_hz = 1/2500 # 2500 hz
downsample_to_hz = 1/1500 # 1500 hz

# 1 second time index at the two sampling frequencies
time_2500hz = np.arange(0, 1, original_hz) 
time_1500hz = np.arange(0, 1, downsample_to_hz)

# example sine wave of recording at 2500hz
amplitude   = np.sin(time)

How do I downsample and interpolate amplitude so it lines up with the time index from sampling at 1500hz?

I would like to use pandas timeseries (https://pandas.pydata.org/docs/user_guide/timeseries.html) for this but examples in numpy will also be useful.

CodePudding user response：

Here is one approach using resample and resample.interpolate:

amplitude   = np.sin(time_2500hz)

# set up DataFrame 
df = pd.DataFrame({'signal': amplitude})
# Timedelta index at 2500Hz
df.index = pd.to_timedelta(df.index/2500, unit='s')

# calculate resampling factor
# must be an integer so use microseconds
rate_microsec = 1_000_000//1500

# resample with interpolation
df2 = df.resample(f'{rate_microsec}U').interpolate()

Interpolated output:

                          signal
0 days 00:00:00         0.000000
0 days 00:00:00.000666  0.000664
0 days 00:00:00.001332  0.001328
0 days 00:00:00.001998  0.001992
0 days 00:00:00.002664  0.002656
...                          ...
0 days 00:00:00.996336  0.803052
0 days 00:00:00.997002  0.803052
0 days 00:00:00.997668  0.803052
0 days 00:00:00.998334  0.803052
0 days 00:00:00.999000  0.803052

[1501 rows x 1 columns]

2500Hz data before resampling:

                          signal
0 days 00:00:00         0.000000
0 days 00:00:00.000400  0.000400
0 days 00:00:00.000800  0.000800
0 days 00:00:00.001200  0.001200
0 days 00:00:00.001600  0.001600
...                          ...
0 days 00:00:00.998000  0.840389
0 days 00:00:00.998400  0.840605
0 days 00:00:00.998800  0.840822
0 days 00:00:00.999200  0.841038
0 days 00:00:00.999600  0.841255

[2500 rows x 1 columns]