# troposim
Simulate tropospheric noise for InSAR data
## Installation
```bash
pip install troposim
```
To make an editable installation
```bash
git clone https://github.com/scottstanie/troposim && cd troposim
pip install -e .
```
## Usage
To simulate one turbulence image, you can specify the shape:
```python
from troposim import turbulence
noise = turbulence.simulate(shape=(500, 500))
```
or add a 3rd dimension to simulate a stack of images
```python
noise = turbulence.simulate(shape=(10, 500, 500))
```
The `beta` argument is the slope of the log10(power) vs log10(frequency) graph.
The default is to use a single linear slope of $\beta = 8 / 3$:
$$
P(f) \propto \frac{1}{f^\beta}
$$
For smaller-scale turbulence, you can use a different `beta`:
```python
flatter_noise = turbulence.simulate(beta=2.2)
```
Since real InSAR data typically have a power spectrum that is not a single slope, you can **estimate the spectrum from an image** and use that to simulate new data:
```python
from troposim.turbulence import Psd
psd = Psd.from_image(noise)
new_noise = psd.simulate()
```
Here the `psd` object has attributes
- `p0`: the power at the reference frequency `freq0`
- `beta`: a numpy Polynomial which was fit to the log-log PSD
along with `psd1d`, which are the radially averaged spectrum values at the `psd.freq` frequencies. You can see these with the `.plot()` method.
```python
# assuming maptlotlib is installed
psd.plot()
# Or, to plot a side-by-side of image and 1D PSD
from troposim import plotting
plotting.plot_psd(noise, freq=freq, psd1d=psd1d)
# Or just the PSD plot, no image
plotting.plot_psd1d(psd.freq, psd.psd1d)
```
To simulate a stack of new values from the PSD of one image, you simply pass in a new `shape` argument to `.simulate`:
```python
psd.simulate(shape=(10, 400, 400))
```
Note that the default fit will use a cubic polynomial.
To request only a linear fit,
```python
psd = Psd.from_image(noise, deg=1)
```
You can also save the PSD parameters for later use:
```python
psd.save(outfile="my_psd.npz")
# Later, reload from this file
psd = Psd.load(outfile)
```
## Citation
If you find this library useful, [please consider citing our paper](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024JB029614):
> Staniewicz, S., & Chen, J. (2025). Automatic detection of InSAR deformation and tropospheric noise features using computer vision: A case study over West Texas. Journal of Geophysical Research: Solid Earth, 130(7), e2024JB029614.
Raw data
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