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fast-konno-ohmachi-0.1.0
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مشاهده بیشترتوضیحات
A Python library that performs Konno-Ohmachi spectral smoothing very fast
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fast-konno-ohmachi-0.1.0 |
| نام | fast-konno-ohmachi |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/jsh9/fast-konno-ohmachi |
| آدرس اینترنتی | https://pypi.org/project/fast-konno-ohmachi/ |
| مجوز | MIT |
# _Fast_ Konno-Ohmachi
A Python library that performs Konno-Ohmachi spectral smoothing very fast (2x speedup compared to the "vanilla" Konno-Ohmachi smoothing algorithm).
## Background
Konno-Ohmachi is a smoothing algorithm proposed by Konno & Ohmachi (1998) [[abstract](http://bssa.geoscienceworld.org/content/88/1/228.short), [PDF](http://www.eq.db.shibaura-it.ac.jp/papers/Konno&Ohmachi1998.pdf)], which achieves a "uniform-span" smoothing to frequency spectra in the logarithmic scale.
For lower frequencies, the Konno-Ohmachi smoothing window is narrower (i.e., less smoothing), and for higher frequencies, the window is wider (i.e., more smoothing).
This makes the Konno-Ohmachi filter particularly appealing to seismologists, who often try to avoid over-smoothing lower frequencies (< 10 Hz) of seismic wave signals.
The plot below shows the result of Konno-Ohmachi filter versus a regular [median value filter](https://en.wikipedia.org/wiki/Median_filter). The two filters yield similar results for frequency > 5 Hz, but for lower frequencies, the median filter over-smoothes the original signal, which is undesirable.
#### (The raw signal used in this example is the Fourier amplitude spectrum of a ground acceleration waveform recorded during the [Magnitude-9.0 Tohoku-Oki Earthquake on March 11, 2011](https://en.wikipedia.org/wiki/2011_T%C5%8Dhoku_earthquake_and_tsunami).)
## Computation speed
Conventionally, Konno-Ohmachi filtering is time-consuming because its smoothing windows are different at each frequency and need to be calculated one by one.
This library achieves a 2x speedup by pre-calculating smoothing windows (i.e., trading memory space for speed).
It can speed up calculation even further by performing parallel computing (the `faster_konno_ohmachi()` function).
The only minor compromises in order to achieve the 2x speedup are:
- Only even integer smoothing strengths from 2 to 100 are supported. This is not an issue in reality because people rarely need non-integer smoothing strengths.
- The smoothing results from `fast_konno_ohmachi()` and `faster_konno_ohmachi()` are not entirely identical to the smoothing result from `slow_konno_ohmachi()` (the "vanilla" algorithm). However, the differences are too minor to have any practical implications.
## Installation
```bash
pip install fast-konno-ohmachi
```
## Usage
```python
import numpy as np
import fast_konno_ohmachi as fko
freq = np.arange(0, 2, 0.1) # just an arbitrary example
signal = np.sin(freq)
smoothed = fko.fast_konno_ohmachi(signal, freq, smooth_coeff=40, progress_bar=True)
```
or (calculate in parallel)
```python
smoothed = fko.faster_konno_ohmachi(signal, freq, smooth_coeff=40, n_cores=4)
```
or (if you'd like to see how slow the "vanilla" implementation can be)
```python
smoothed = fko.slow_konno_ohmachi(signal, freq, smooth_coeff=40, progress_bar=True)
```
You can also try to run `demo/Demo_konno_ohmachi_smooth.py` to smooth a real-world signal. (You'd need `scipy` and `matplotlib` to run the demo script.)
## Additional notes on parallel computing
1. When using `faster_konno_ohmachi()`, the user should to protect the main script with `if __name__ == '__main__'` (see the demo script). This is **mandatory** for Windows, and **highly recommended** for Mac/Linux.
2. The `faster_konno_ohmachi()` function uses multiple CPU cores, but it is not necessarily faster than `fast_konno_ohmachi()`, because the data I/O between the CPU cores takes extra time ("computation overhead"). Below is a benchmarking of the running time for input signals with different length:
| Length of signal (x1000) | 1 | 3 | 5 | 7 | 9 | 11 | 13 | 15 | 17 | 19 | 21 | 23 | 25 | 27 | 29 | 31 |
| ------------------------- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
| Time of "fast" (sec) | 0.1 | 0.4 | 0.9 | 1.5 | 2.2 | 3.3 | 4.4 | 5.4 | 6.8 | 8.1 | 9.6 | 11.2 | 13.1 | 14.9 | 16.7 | 18.8 |
| Time of "faster" (sec) | 2.4 | 2.4 | 2.8 | 3.0 | 3.3 | 3.9 | 4.1 | 4.6 | 4.9 | 5.5 | 5.8 | 6.7 | 7.5 | 8.2 | 9.0 | 9.8 |
Or as shown in this figure:
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl fast-konno-ohmachi-0.1.0:
pip install fast-konno-ohmachi-0.1.0.whl
نصب پکیج tar.gz fast-konno-ohmachi-0.1.0:
pip install fast-konno-ohmachi-0.1.0.tar.gz