### Install from source with Git LFS

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/install.rst

Clones the repository, pulls large files with Git LFS, and installs in editable mode. Recommended for using examples with pre-computed data. Requires several gigabytes of disk space.

```bash
git clone https://github.com/xiaziyna/PyStarshade.git PyStarshade
cd PyStarshade
git lfs pull
pip install -e .
```

--------------------------------

### Install pystarshade with pip

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/install.rst

Installs the package from PyPI for a barebones installation without pre-computed data.

```bash
pip install pystarshade
```

--------------------------------

### Install with GPU support

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/install.rst

Installs the package with GPU support, adding PyTorch as a dependency. Requires an NVIDIA GPU with CUDA; if available, gen_pupil_field() will automatically use the GPU.

```bash
pip install -e ".[gpu]"
```

--------------------------------

### Install PyStarshade from source with Git LFS

Source: https://github.com/xiaziyna/pystarshade/blob/main/README.md

Clones the repository, pulls large files with Git LFS, and installs the package in editable mode. Requires several GB of disk space and is intended for using pre-generated data.

```bash
$ git clone https://github.com/xiaziyna/PyStarshade.git PyStarshade
$ cd PyStarshade
$ git lfs pull
pip install -e .
```

--------------------------------

### Example drm configuration dictionary

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/usage.rst

Example drm dictionary defining starshade parameters: focal length, telescope diameter, starshade radius, distance, petal count, wavelength bands, pixel samplings, and inner working angle. The 'grey_mask_dx' values describe pixel sampling in physical units (e.g., '01m' means Δs = 0.01 m).

```python
'hwo': {
    'focal_length_lens': 10,
    'diameter_telescope_m': 6,
    'radius_lens': 3,
    'ss_radius': 30,
    'dist_ss_t': [9.52e+07],
    'num_pet': 24,
    'wl_bands': [[500, 600]],
    'dx_': [0.04, 0.001],
    'grey_mask_dx': ['04m', '001m'],
    'iwa': [30 / 9.52e+07 / mas_to_rad]  # R_ss / dist_ss_t in MAS
}
```

--------------------------------

### grey_mask_dx parameter example

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/solar_system.ipynb

Shows the mapping of the grey_mask_dx parameter value "01m" to a physical pixel sampling of 0.01 meters.

```text
"01m" → Δs = 0.01 m
```

--------------------------------

### StarshadeProp example: hwo mission workflow

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/test.rst

Shows the typical workflow for the StarshadeProp class: instantiate with the 'hwo' design reference mission, generate the pupil field, generate the PSF basis for a hexagonal pupil, and generate the scene intensity at 500 nm. The source_field argument is a placeholder for an existing array.

```pycon
>>> hwo_starshade = StarshadeProp(drm='hwo')
>>> hwo_starshade.gen_pupil_field()
>>> hwo_starshade.gen_psf_basis(pupil_type='hex')
>>> hwo_starshade.gen_scene(pupil_type='hex', source_field, 500e-9)
```

--------------------------------

### Commit message example for PyStarshade

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/contribute.rst

Use this as a commit message format when contributing code. It follows the conventional commit style and clearly describes the change.

```python
feat: add widget-alignment option
```

--------------------------------

### Run tests with pytest

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/install.rst

Runs all unit tests in the tests/ directory from the repository root. Passing tests confirm the installation works.

```bash
pytest tests/
```

--------------------------------

### Generate and use a PSF basis with StarshadeProp

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/usage.rst

Complete workflow to generate a PSF basis and simulate a scene. Initialize with a DRM, generate the pupil field (computationally intensive), generate the PSF basis for a specific pupil type, then create a source field and call gen_scene to get the focal intensity. Note that gen_pupil_field may take hours and gen_psf_basis may take minutes to an hour.

```python
from pystarshade.propagator import StarshadeProp
import numpy as np

# 1. Initialize with a DRM
drm = 'hwo'
starshade = StarshadeProp(drm=drm)

# 2. Generate the pupil field (this is computationally intensive)
# Optional parameters: wl_override, N_x_override, dx_override, chunk=1
starshade.gen_pupil_field()

# 3. Generate PSF basis for a specific telescope pupil
pupil_type = 'hex'  # Can be 'hex', 'circular', or a custom pupil
starshade.gen_psf_basis(pupil_type=pupil_type)

# 4. Use the generated PSF basis to simulate a scene
# Load or create a toy model of a source field
source_field = np.zeros((251, 251), dtype=np.float32)
source_field[126, 126] = 1 #star
source_field[70, 126] = 1e-10 #toy planet
wavelength = 500e-9  # 500 nm
focal_intensity = starshade.gen_scene(pupil_type, source_field, wavelength)
```

--------------------------------

### Generate HWO PSF basis with StarshadeProp

Source: https://github.com/xiaziyna/pystarshade/blob/main/examples/solar_system.ipynb

Generates a full PSF basis for the HWO starshade and saves it to disk. Requires the StarshadeProp class and the pre-computed diffracted fields from the git-LFS install. The pupil_type 'hwopupil_onaxis' is used for the on-axis HWO pupil.

```python
hwo_starshade = StarshadeProp(drm='hwo')
hwo_starshade.gen_pupil_field()
hwo_starshade.gen_psf_basis(pupil_type='hwopupil_onaxis')
```

--------------------------------

### Load and print WFIRST telescope parameters

Source: https://github.com/xiaziyna/pystarshade/blob/main/examples/solar_system.ipynb

Loads the WFIRST telescope parameters from the DRM file and prints them. The output shows the focal length, telescope diameter, inner working angle, and other mission parameters.

```python
from pystarshade.data.drm import telescope_params
# Load the 'wfirst' parameters
wfirst_params = telescope_params['wfirst']

# Print the WFIRST parameters
print("WFIRST Telescope Parameters:")
for key, value in wfirst_params.items():
    print(f"{key}: {value}")
```

--------------------------------

### Force GPU or CPU for chunked FFT

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/usage.rst

Controls GPU usage for the chunked FFT when a CUDA-capable GPU and PyTorch are available (install with `pip install pystarshade[gpu]`). Setting use_gpu=True forces GPU execution; use_gpu=False forces CPU execution.

```python
hwo_starshade.gen_pupil_field(use_gpu=True)   # force GPU
hwo_starshade.gen_pupil_field(use_gpu=False)  # force CPU
```

--------------------------------

### Import libraries and download ExoVista scene

Source: https://github.com/xiaziyna/pystarshade/blob/main/examples/solar_system.ipynb

Imports the required libraries and pystarshade modules, downloads the ExoVista scene file, and sets the local zodiacal light magnitude to 22.5 mag/arcsec^2.

```python
from astropy.io import fits
import numpy as np
import download_exovista
download_exovista.exovista_scenes_file()
from pystarshade.data.scenes.Scene import *
from pystarshade.diffraction.util import mas_to_rad, au_to_meter, pc_to_meter, data_file_path #, flux_to_mag,
from pystarshade.propagator import StarshadeProp
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from matplotlib import rc
local_zodi_mag_v_arcsec2 = 22.5 #local zodi
```

--------------------------------

### Fresnel

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/test.rst

Class representing a Fresnel diffraction computation setup. Stores spatial sampling, input size, propagation distance, and wavelength.

```APIDOC
## Fresnel

### Description
Represents a Fresnel diffraction computation setup.

### Constructor Parameters
- **d_x** (float) - Required - Spatial sampling interval of the input field [m].
- **N_in** (int) - Required - Number of non-zero input samples.
- **z** (float) - Required - Propagation distance [m].
- **wavelength** (float) - Required - Wavelength of the light [m].

### Attributes
- **wl_z** (float) - Product of the wavelength and propagation distance.
- **k** (float) - Wave number.
- **max_freq** (float) - Maximum frequency of the input field.
```

--------------------------------

### Output: WFIRST telescope parameters

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/solar_system.ipynb

Output from the previous cell showing the WFIRST telescope parameters as printed by the loop.

```text
Output:
WFIRST Telescope Parameters:
focal_length_lens: 30
diameter_telescope_m: 2.36
radius_lens: 1.18
dist_ss_t: [37242256.68350351 26120376.72633376 21191429.69119565]
iwa_mas: 72
num_pet: 16
ss_radius: 13
grey_mask_dx: ['01m', '005m', '002m', '001m', '0005m']
dx_: [0.01, 0.005, 0.002, 0.001, 0.0005]
wl_bands: [[425, 552], [606, 787], [747, 970]]
iwa: [ 72.         102.65711361 126.53428864]
```

--------------------------------

### Add parent directory to sys.path

Source: https://github.com/xiaziyna/pystarshade/blob/main/examples/solar_system.ipynb

Adds the parent directory to the Python path so that the notebook can import the pystarshade package when run from the examples folder.

```python
import os
import gc
import sys
notebook_dir = os.getcwd()
parent_dir = os.path.abspath(os.path.join(notebook_dir, ".."))
if parent_dir not in sys.path:
    sys.path.insert(0, parent_dir)
```

--------------------------------

### Load ExoVista scene and print scene properties

Source: https://github.com/xiaziyna/pystarshade/blob/main/examples/solar_system.ipynb

Loads an ExoVista scene, builds a source field at 500 nm, and prints scene properties (star/planet brightness, semi-major axes, distance, etc.) before displaying the input scene. Requires prior definitions of `file_path`, `dist_xo_ss`, `hdul`, `n_planet`, and unit conversion constants. The printed output shows the scene's magnitude, inclination, number of Zodis, and physical extent.

```python
t = 0 # timestep
scene = Scene(file_path)
wl_range = scene.lambdas * 1e-6 

# Define the wavelength (lam_ref), make sure this matches up with the PSF basis
lam_ref = np.float64(5*1e-7)
wl_str = int(lam_ref*1e9)
w_i = np.where(wl_range >= lam_ref)[0][0]
wl = wl_range[w_i]

bg_scene = scene.getDiskImage()[w_i]
star_xy = scene.getXYstar()
star_spectra = scene.getStarSpec(time = t)[w_i]
bg_scene[int(star_xy[0]), int(star_xy[1])] += star_spectra

planet_xy = scene.getXYplanets(time = t).astype(int)
planet_spectra = scene.getPlanetSpec(time = t)[:, w_i]
ds_mas = scene.getPixScale()

stel_ang_diam = scene.getAngDiam()
ds = dist_xo_ss * ds_mas * mas_to_rad
ds_au = ds / au_to_meter

N_wl = len(wl_range)
N_dust = np.shape(bg_scene)[1]
N_dust_half = N_dust//2

N_s = 1001
source_field = np.zeros((N_s, N_s), dtype=np.complex128) 
half_N = N_s // 2
half_in_N = N_dust // 2
bit_arr = N_dust % 2 
source_field[half_N - half_in_N : half_N + half_in_N + bit_arr,
       half_N - half_in_N : half_N + half_in_N + bit_arr] = bg_scene

n_planet = 0

for i in range(scene.nplanets):
    if np.all(planet_xy[i] - N_dust_half + half_N  <= N_s-1) and np.all(planet_xy[i] - N_dust_half + half_N >= 0):
        source_field[planet_xy[i, 1]- N_dust_half + half_N , planet_xy[i, 0]- N_dust_half + half_N] += planet_spectra[i]
        n_planet +=1

print ('Magnitude of star: ', -2.5*np.log10((500**2)*source_field[int(star_xy[0]), int(star_xy[1])])+8.9)
print ('Magnitude at ~ 1 AU: ', - 2.5*np.log10((500**2)*source_field[int(star_xy[0]) - 50, int(star_xy[1])]) + 8.9)
print ('inclination: ', hdul[2].header['I-0'])
print ('number of Zodis: ', hdul[2].header['NZODIS-0'])
print ('Distance from exo-scene to starshade [pc]: ', dist_xo_ss/pc_to_meter)
print ('Number of planets included in scene: ', n_planet)
print ('Star brightness: ', star_spectra)
print ('Planet brightness: ', planet_spectra)
print ('Plan/Star brightness: ', (planet_spectra/(star_spectra)))
print ('Semi major axis[au]: ', np.sqrt(2)*ds_au*np.hypot(planet_xy[:, 0] - N_dust_half, planet_xy[:, 1] - N_dust_half))
print ('wavelength [m]', wl)
print ('ZODIs: ', hdul[2].header['NZODIS-0'])
print ('Physical extent (AU)', N_s * ds * (1/au_to_meter))
print ( '1 source pixel is (AU): ', ds/au_to_meter)
print ('Distance of system (pc): ', dist_xo_ss/pc_to_meter)

plt.figure()
plt.title('ExoVista input scene', fontsize=14)
plt.text(-940, 900, r'$\lambda = $%s nm' % (wl_str), color='white', fontsize=12)
plt.imshow(np.abs(source_field),  norm=LogNorm(), cmap='jet', extent=(-N_s, N_s, -N_s, N_s))
plt.xlabel('x [mas]', fontsize=14)
plt.ylabel('y [mas]', fontsize=14)
cbar = plt.colorbar()
cbar.set_label('Contrast', fontsize=14)
plt.tight_layout()
plt.show()

del scene
del star_xy
del star_spectra
del planet_xy
del planet_spectra
del bg_scene
_ = __ = ___ = None 
gc.collect()    
```

--------------------------------

### Select starshade drm configuration

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/usage.rst

Selects the 'hwo' design reference mission (drm) configuration for PyStarshade. Other provided options are 'wfirst' and 'habex'.

```python
drm = 'hwo'
```

--------------------------------

### StarshadeProp

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/test.rst

Class for pre-computing files and propagating light past a starshade. Constructor initializes with mission parameters and resolution settings.

```APIDOC
## StarshadeProp

### Description
Class for pre-computing files and propagating light past a starshade.

### Constructor Parameters
- **drm** (str) - Required - Design reference mission (e.g., 'wfirst' or 'hwo').
- **d_s_mas** (float, optional) - Source angular resolution in milliarcseconds, defaults to 2.
- **d_t_mas** (float, optional) - Pixel shift in the pupil plane corresponding to the angular field, defaults to 0.05.
- **d_p_mas** (float, optional) - Pixel size in the focal plane in milliarcseconds, defaults to 2.
- **d_wl** (float, optional) - Wavelength step size in nanometers, defaults to 50.

### Example
```pycon
>>> hwo_starshade = StarshadeProp(drm='hwo')
>>> hwo_starshade.gen_pupil_field()
>>> hwo_starshade.gen_psf_basis(pupil_type='hex')
>>> hwo_starshade.gen_scene(pupil_type='hex', source_field, 500e-9)
```
```

--------------------------------

### Clone and branch PyStarshade repository

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/contribute.rst

Use this to set up a local development branch for contributing to PyStarshade. It clones the repository, changes into the project directory, and creates a new branch named feature/short-name from the current main branch.

```python
git clone git@github.com:xiaziyna/PyStarshade.git
cd PyStarshade
git checkout -b feature/short-name
```

--------------------------------

### Load ExoVista scene from FITS file

Source: https://github.com/xiaziyna/pystarshade/blob/main/examples/solar_system.ipynb

Loads a solar system scene from a FITS file, extracting the distance and inclination from the headers. Requires the 'fname_faceon' variable defined earlier and the 'pc_to_meter' conversion factor. The scene is used for imaging simulations with PyStarshade.

```python
# Handle directories and load solar system scene
data_dir = os.path.abspath(os.path.join(notebook_dir, '..', 'pystarshade', 'data'))
file_path = os.path.join(data_dir, 'scenes', fname_faceon)
hdul = fits.open(file_path)
dist_xo_ss = hdul[4].header['dist'] * pc_to_meter
inclination = hdul[2].header['I-0']
```

--------------------------------

### Initialize StarshadeProp and generate pupil field

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/usage.rst

Initializes the StarshadeProp class for the 'hwo' drm and generates the pupil field, storing it on disk. Optional parameters can change pupil or wavelength sampling; the 'chunk' setting enables a chunked FFT using a memory-mapped starshade mask to avoid RAM bottlenecks.

```python
from pystarshade.propagator import StarshadeProp

drm = 'hwo'
hwo_starshade = StarshadeProp(drm = drm)
hwo_starshade.gen_pupil_field()
```

--------------------------------

### Output: Scene.py already exists

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/solar_system.ipynb

Output from the previous cell indicating that the Scene.py file already exists in the specified path.

```text
Output:
Scene.py already exists in /home/ielo/sandbox/test_/PyStarshade/pystarshade/data/scenes.
```

--------------------------------

### Simulate with GMT aperture

Source: https://github.com/xiaziyna/pystarshade/blob/main/examples/solar_system.ipynb

Runs the simulation with a different aperture (GMT) by setting drm='hwo' and pupil_type='GMT'. It generates the PSF basis, scene, and pupil mask, then plots the aperture. The output shows a ComplexWarning and a figure with 1 Axes.

```python
drm = 'hwo'
pupil_type = 'GMT'

hwo_starshade = StarshadeProp(drm = drm)
hwo_starshade.N_wl = 1 # edit an internal variable so that only a single wavelength PSF basis is calculated
hwo_starshade.gen_psf_basis(pupil_type = pupil_type)
focal_intensity = hwo_starshade.gen_scene(pupil_type, source_field.astype(np.float32), lam_ref, pupil_symmetry = False)

hwo_starshade.gen_pupil(pupil_type = pupil_type)
aperture = hwo_starshade.pupil_mask

plt.figure()
# Plot the first image
plt.imshow(aperture,extent=(-3, 3, -3, 3))
plt.xlabel('x [m]',fontsize=14)
plt.ylabel('y [m]',fontsize=14)
plt.show()
```

```text
Output:
/tmp/ipykernel_426246/4103030377.py:7: ComplexWarning: Casting complex values to real discards the imaginary part
  focal_intensity = hwo_starshade.gen_scene(pupil_type, source_field.astype(np.float32), lam_ref, pupil_symmetry = False)
```

```text
Result:
<Figure size 640x480 with 1 Axes>
```

--------------------------------

### Define ExoVista solar system scene filenames

Source: https://github.com/xiaziyna/pystarshade/blob/main/examples/solar_system.ipynb

Defines filenames for pre-simulated solar system scenes at 60 degrees inclination and face-on, located in the 'data/scenes' directory. These are used with the Scene.py script to load the scenes.

```python
fname_60 = '999-HIP_-TYC_SUN-mv_4.83-L_1.00-d_10.00-Teff_5778.60.fits' #solar system at 60 deg inclination
fname_faceon = '999-HIP_-TYC_SUN-mv_4.83-L_1.00-d_10.00-Teff_5778.00.fits' #solar system face-on
```

--------------------------------

### Load and plot WFIRST and HWO starshade masks

Source: https://github.com/xiaziyna/pystarshade/blob/main/examples/solar_system.ipynb

Loads the WFIRST and HWO starshade masks from the provided data files, converts them to full masks, and plots them side by side with the appropriate extents. The plot shows the starshade masks for the Roman (WFIRST) and HWO missions.

```python
import matplotlib.pyplot as plt
from pystarshade.apodization.apodize import qu_mask_to_full

drm = 'wfirst'
wfirst_starshade = StarshadeProp(drm = drm)
wfirst_starshade.set_mission_params(drm, mask_choice=0, band_i=0)
d_x_w = wfirst_starshade.d_x
ss_mask_fname = wfirst_starshade.ss_mask_fname_partial + '_qu.npz'
starshade_qu = np.load(ss_mask_fname)
wfirst_starshade = qu_mask_to_full(starshade_qu['grey_mask'])

drm = 'hwo'
hwo_starshade = StarshadeProp(drm = drm)
hwo_starshade.set_mission_params(drm, mask_choice=0)
d_x_h = hwo_starshade.d_x
ss_mask_fname = hwo_starshade.ss_mask_fname_partial + '_qu.npz'
starshade_qu = np.load(ss_mask_fname)
hwo_starshade = qu_mask_to_full(starshade_qu['grey_mask'])

N1 = wfirst_starshade.shape[0]
N2 = hwo_starshade.shape[0]

extent1 = (-N1*d_x_w/2, N1*d_x_w/2, -N1*d_x_w/2, N1*d_x_w/2)
extent2 = (-N2*d_x_h/2, N2*d_x_h/2, -N2*d_x_h/2, N2*d_x_h/2)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

im1 = ax1.imshow(wfirst_starshade, extent=extent1, origin='lower', aspect='equal')
ax1.set(title='Roman rendezvous (WFIRST)', xlabel='x [m]', ylabel='y [m]')

im2 = ax2.imshow(hwo_starshade, extent=extent2, origin='lower', aspect='equal')
ax2.set(title='HWO', xlabel='x [m]', ylabel='y [m]')
```

--------------------------------

### Simulate starshade imaging with StarshadeProp

Source: https://github.com/xiaziyna/pystarshade/blob/main/README.md

Simulates imaging for a source field with a default 2 mas sampling using the StarshadeProp class. Initializes with the HWO design reference mission, generates the pupil field and PSF basis for a hexagonal pupil, creates a point source, and produces a focal intensity image with a log-normalized colorbar.

```python
import numpy as np
from pystarshade.propagator import StarshadeProp
import matplotlib.pyplot as plt

# 1. Initialize the Starshade Propagator with HWO (Habitable Worlds Observatory) configuration
starshade = StarshadeProp(drm='hwo')  # drm = design reference mission

# 2. Generate the pupil field (this handles the Fresnel diffraction from starshade to telescope)
starshade.gen_pupil_field()

# 3. Generate PSF basis for a hexagonal pupil (this handles the Fraunhofer diffraction to focal plane)
pupil_type = 'hex'
starshade.gen_psf_basis(pupil_type=pupil_type)

# 4. Create a simple point source (representing a star)
# Using 2 mas (milliarcsecond) sampling as default
nx = ny = 100  # image size
source_field = np.zeros((nx, ny), dtype=np.float32)
source_field[nx//2, ny//2] = 1.0  # place a point source in the center

# 5. Generate the final image
wavelength = 500e-9  # 500 nm
focal_intensity = starshade.gen_scene(pupil_type, source_field, wavelength)

# 6. Visualize the result
plt.figure(figsize=(8, 8))
plt.imshow(focal_intensity, norm=plt.LogNorm())
plt.colorbar()
plt.title('Simulated Starshade Image')
plt.xlabel('Pixels')
plt.ylabel('Pixels')
plt.show()
```

--------------------------------

### FresnelSingle.nchunk_zoom_fresnel_single_fft(x_file, N_out, N_chunk=None)

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/test.rst

Performs single FFT Fresnel diffraction using an N_chunk*N_chunk-way chunked Bluestein FFT to cap peak memory usage. If use_gpu was set at construction, uses GPU-accelerated Bluestein FFT.

```APIDOC
## FresnelSingle.nchunk_zoom_fresnel_single_fft(x_file, N_out, N_chunk=None)

### Description
Performs single FFT Fresnel diffraction using an N_chunk*N_chunk-way chunked Bluestein FFT to cap peak memory usage. If use_gpu was set at construction, uses GPU-accelerated Bluestein FFT.

### Method
nchunk_zoom_fresnel_single_fft

### Endpoint
FresnelSingle.nchunk_zoom_fresnel_single_fft(x_file, N_out, N_chunk=None)

### Parameters
#### Path Parameters
- **x_file** (np.memmap) - Required - Input mask filename as a memory-mapped object.
- **N_out** (int) - Required - Number of output samples.
- **N_chunk** (int, optional) - Optional - Number of chunks along each axis. Default is 8.

### Returns
- **np.ndarray** - Propagated output field.
- **float** - Output grid sampling.
```

--------------------------------

### FresnelSingle.calc_zero_padding()

Source: https://github.com/xiaziyna/pystarshade/blob/main/docs/content/test.rst

Calculates the zero-padding factor (ZP * N_in) to achieve a specified frequency spacing (d_f) for a given spatial sampling (d_x) using the Fresnel single FT method.

```APIDOC
## FresnelSingle.calc_zero_padding()

### Description
Calculates the zero-padding factor (ZP * N_in) to achieve a specified frequency spacing (d_f) for a given spatial sampling (d_x) using the Fresnel single FT method.

### Method
calc_zero_padding

### Endpoint
FresnelSingle.calc_zero_padding()

### Parameters
None

### Returns
- **Zero-padding factor** (float) - ZP * N_in to achieve the desired frequency spacing.
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