Add basic scripts

.gitignore

+build
+__pycache__
+*.egg-info
\ No newline at end of file

README.md

 # Solar limb detection
 
-
+Python routines for finding the solar limb on Ca II and H-$\alpha$ observations.
 
 ## Getting started
 
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+You can install this package directly from the git repository using pip:
 
+```bash
+pip install git+https://gitlab.lis-lab.fr/presage/solar-limb-detection.git
 ```
-cd existing_repo
-git remote add origin https://gitlab.lis-lab.fr/presage/solar-limb-detection.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.lis-lab.fr/presage/solar-limb-detection/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
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-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
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-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
 
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
+Then you can use it:
 
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
+```python
+from solar_limb_detection import fit_limb
 
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
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-
-## Installation
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-
-## Usage
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-
-## Support
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-
-## Roadmap
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-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+limb = fit_limb(smap)
+```
\ No newline at end of file

requirements.txt

+sunpy
+torch
+opencv-python
+numpy
+scikit-image
+matplotlib
\ No newline at end of file

setup.py

+from distutils.core import setup
+from setuptools import find_packages
+
+with open("requirements.txt") as f:
+    required_packages = f.read().splitlines()
+
+setup(
+    name="solar_limb_detection",
+    version="1.0",
+    description="Python routines for finding the solar limb on Ca II and H-$\alpha$ observations.",
+    author="Jay Paul Morgan",
+    author_email="jay.morgan@univ-tln.fr",
+    url="https://gitlab.lis-lab.fr/presage/solar-limb-detection",
+    packages=find_packages(exclude=["tests"]),
+    install_requires=required_packages)
\ No newline at end of file

solar_limb_detection/__init__.py

+from solar_limb_detection.detection import fit_limb
+from solar_limb_detection.darkening import remove_limb_darkening
\ No newline at end of file

solar_limb_detection/darkening.py

+import numpy as np
+import sunpy
+import sunpy.map
+from typing import Union
+
+
+def compute_dist_grid(smap) -> np.ndarray:
+    """Create a 2D matrix where the values in this matrix
+    are the distances of point i,j from the solar centre"""
+    radius = (smap.rsun_obs / smap.scale.axis1).value
+    centre_x, centre_y = smap.meta["crpix1"], smap.meta["crpix2"]
+    xdim, ydim = smap.dimensions.x.to_value(), smap.dimensions.y.to_value()
+    x_grid, y_grid = np.meshgrid(np.arange(xdim), np.arange(ydim), sparse=True)
+    x_2 = (x_grid - centre_x) ** 2
+    y_2 = (y_grid - centre_y) ** 2
+    dist_grid = np.sqrt(x_2 + y_2) / radius
+    dist_grid[np.where(dist_grid > 1)] = 0
+    return dist_grid
+
+
+def remove_limb_darkening(smap: sunpy.map.GenericMap) -> sunpy.map.GenericMap:
+    data = smap.data.copy()
+    dist = compute_dist_grid(smap)
+    data = limb_normalisation(data, dist, smap.meta["wavelnth"])
+    return sunpy.map.Map(data, smap.meta)
+
+
+def _dark_limb(wavelength: int, dtype: str):
+    # assert 4000 < wavelength < 15000
+
+    # coefficients for 5th order poly fit to limb darkening
+    # constant u.  Fit was done on data from Astrophysical
+    # Quantities by Allen.
+
+    if dtype == "u":
+        a = -8.9829751
+        b = 0.0069093916
+        c = -1.8144591e-6
+        d = 2.2540875e-10
+        e = -1.3389747e-14
+        f = 3.0453572e-19
+    else:
+        a = 9.2891180
+        b = -0.0062212632
+        c = 1.5788029e-6
+        d = -1.9359644e-10
+        e = 1.1444469e-14
+        f = -2.599494e-19
+
+    a = np.array([a, b, c, d, e, f])
+    wavelength_series = np.array([wavelength**n for n in range(len(a))])
+    ul = sum(a * wavelength_series)
+    return ul
+
+
+def limb_normalisation(arr: np.ndarray, dist_grid: np.ndarray, wavelength: int):
+    """Remove limb darkening using a 5th polynomial with fitted constants
+    from Allen
+    """
+    ul = _dark_limb(wavelength, dtype="u")
+    vl = _dark_limb(wavelength, dtype="v")
+    limbfilt = (
+        1
+        - ul
+        - vl
+        + ul * np.cos(np.arcsin(dist_grid))
+        + vl * np.cos(np.arcsin(dist_grid)) ** 2
+    )
+    return arr / limbfilt

solar_limb_detection/detection.py

+# internal imports
+from dataclasses import dataclass
+from typing import Union
+
+# external imports
+import cv2
+import torch
+import numpy as np
+import sunpy
+import sunpy.map
+from skimage import filters
+
+
+@dataclass
+class SolarLimb:
+    cx: float
+    cy: float
+    radius: float
+
+
+class CircleFitter(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.ab = torch.nn.Parameter(torch.zeros(2))
+        self.R = torch.nn.Parameter(torch.ones(1))
+
+    def distance(self, points):
+        return torch.abs(torch.sqrt(torch.pow(points - self.ab, 2).sum(1)) - self.R)
+
+    def is_outlier(self, point, T):
+        return self.distance(point) >= self.R / T
+
+    def remove_outliers(self, points, T):
+        return points[~self.is_outlier(points, T)]
+
+    def loss_fn(self, points):
+        return (self.distance(points)**2).sum()
+
+    def get_circle_params(self):
+        return torch.cat([self.ab, self.R], dim=0).cpu().detach().numpy().tolist()
+
+
+
+def find_anchors(smap: sunpy.map.GenericMap) -> torch.Tensor:
+    x,y = smap.shape
+    xs = np.linspace(x//4, x-x//4, 69, dtype=int)
+    ys = np.linspace(y//4, y-y//4, 69, dtype=int)
+
+    k = np.array([-1, 0, +1]).reshape(1, 3)
+    dy = cv2.filter2D(filters.gaussian(smap, 7), -1, k.reshape(-1, 1))
+    dx = cv2.filter2D(filters.gaussian(smap, 7), -1, k)
+
+    points = []
+    for xi in xs:
+        # bottom profile
+        point = np.argmin(dy[y-(y//4):, xi])
+        points.append((xi, y-y//4+point))
+    for yi in ys:
+        # right profile
+        point = np.argmin(dx[yi, x-x//4:])
+        points.append((x-x//4+point, yi))
+    for xi in xs:
+        # top profile
+        point = np.argmax(dy[:y//4, xi])
+        points.append((xi, point))
+    for yi in ys:
+        # left profile
+        point = np.argmax(dx[yi,:y//3])
+        points.append((point, yi))
+
+    return torch.Tensor(points)
+
+
+def fit_limb(smap: Union[np.ndarray, sunpy.map.GenericMap]) -> SolarLimb:
+    """
+    Fit the solar limb to a Ca-II or H-$\alpha$ observation.
+    """
+    if isinstance(smap, sunpy.map.GenericMap):
+        return fit_limb(smap.data)
+    
+    smap = smap.copy()  # don't modify the original version
+
+    def fit(smap):
+        points = find_anchors(smap)
+        L = CircleFitter()
+        O = torch.optim.SGD(L.parameters(), lr=1e-3)
+        for idx, T in enumerate([30, 100, 200]):
+            for i in range(200):
+                loss = L.loss_fn(points)
+                loss.backward()
+                O.step()
+                L.zero_grad()
+            points = L.remove_outliers(points, T)
+            if idx > 0 and torch.sqrt(L.distance(points).mean()) < 2:
+                break
+        return L.get_circle_params()
+
+    params = fit(smap)
+
+    # after first fit, cut off values above median to remove
+    # presence of active regions that might throw off the limb
+    # fitting. After, do the fitting again.
+    xs = np.arange(0, smap.shape[1], 1, dtype=int)
+    ys = np.arange(0, smap.shape[0], 1, dtype=int)
+    mask = (xs[None,:]-params[0])**2 + (ys[:,None]-params[1])**2 <= params[2]**2
+    med_value = np.median(smap[mask])
+
+    smap[smap>med_value] = med_value
+    params = fit(smap)
+    
+    return SolarLimb(*params)

tests/test_solar_limb_darkening.py

+from solar_limb_detection import remove_limb_darkening
+import sunpy
+from pathlib import Path
+import urllib.request
+
+
+def download(url, output):
+    def progress(block_num, block_size, total_size):
+        if block_num % 10 == 0:
+            down_size = f"{(block_num*block_size)/1000000000:.4f}/{total_size/1000000000:.4f} GB"
+            perc_size = f"{round(100*((block_num*block_size)/total_size), 4):.4f}"
+            print(
+                f"Downloading {Path(url).name}: {down_size} ({perc_size}%)",
+                end="\r",
+                flush=True,
+            )
+    urllib.request.urlretrieve(url, output, progress)
+    return output
+
+
+# download the H-alpha test file
+fn = download("https://bass2000.obspm.fr/bass2000/data//pub/sidc/Halpha/2306/UCH20230605064459.fts", "/tmp/halpha.fits")
+
+# open the sunpy map file, reading in the data and header separately (so as to avoid errors)
+smap = sunpy.map.Map("/tmp/halpha.fits")
+
+# and remove the limb-darkening effect:
+smap = remove_limb_darkening(smap)
+
+# and optionally plot the data:
+smap.peek()
\ No newline at end of file

tests/test_solar_limb_detection.py

+from solar_limb_detection import fit_limb
+import sunpy
+from pathlib import Path
+import urllib.request
+
+
+def download(url, output):
+    def progress(block_num, block_size, total_size):
+        if block_num % 10 == 0:
+            down_size = f"{(block_num*block_size)/1000000000:.4f}/{total_size/1000000000:.4f} GB"
+            perc_size = f"{round(100*((block_num*block_size)/total_size), 4):.4f}"
+            print(
+                f"Downloading {Path(url).name}: {down_size} ({perc_size}%)",
+                end="\r",
+                flush=True,
+            )
+    urllib.request.urlretrieve(url, output, progress)
+    return output
+
+
+# download the H-alpha test file
+fn = download("https://bass2000.obspm.fr/bass2000/data//pub/sidc/Halpha/2306/UCH20230605064459.fts", "/tmp/halpha.fits")
+
+# open the sunpy map file, reading in the data and header separately (so as to avoid errors)
+data, header = sunpy.io.read_file(fn)[0]
+
+# using the data, we can fit the solar limb to this image:
+limb = fit_limb(data)
+#SolarLimb(cx=1023.9826049804688, cy=1023.9387817382812, radius=907.2899780273438)
+
+assert limb.radius == 907.2899780273438
+assert limb.cx == 1023.9826049804688
+assert limb.cy == 1023.9387817382812
+
+# this will give us a class of SolarLimb with the class variables:
+# - cx: the solar centre x
+# - cy: the solar centre y
+# - radius: the solar radius
+
+# then, if we like, we can plot the data, overlaying the detected solar limb.
+# import matplotlib.pyplot as plt
+# from matplotlib.patches import Circle
+
+# fig, ax = plt.subplots()
+# ax.imshow(data, cmap="gray")
+# ax.add_patch(Circle((limb.cx, limb.cy), limb.radius, fill=None, edgecolor="red"))
+# ax.invert_yaxis() # correct flip in matplotlib
\ No newline at end of file