Merge branch 'lightkurve:main' into main

CHANGES.rst

@@ -1,4 +1,8 @@
-1.1.0dev (2024-09-18)
+1.1.1 (2024-09-24)
+=====================
+- Updated pyproject.toml
+
+1.1.0 (2024-09-24)
 =====================
 
 - Added the option to use the initial TESS commissioning PRF files [#9]

docs/README.md

@@ -20,6 +20,8 @@ You can install lkprf using pip:
 pip install lkprf --upgrade
 ```
 
+> :question: **Note**: If installing in a new environment you may run into issues with the fitsio build that causes an installation error. If so, you can pip install numpy< 2.0.0 first, then pip install lkprf. 
+
 ## Usage
 
 > :question: **Note**: `lkprf` uses tuples to specify positions and uses the Python convention of `(row, column)`.

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "lkprf"
-version = "1.1.0dev"
+version = "1.1.1"
 description = ""
 authors = ["TESS Science Support Center <[email protected]>, Christina Hedges <[email protected]>"]
 license = "MIT"
@@ -10,6 +10,7 @@ packages = [{include = "lkprf", from = "src"}]
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
 scipy = ">=1.8.0"
+numpy = ">=1.20.0,<2.0.0"
 fitsio = "^1.2.4"
 requests = "^2.32.3"
 

src/lkprf/data.py

@@ -7,7 +7,8 @@
 from scipy.ndimage import label, uniform_filter
 
 from . import logger, PACKAGEDIR
-CACHEDIR = PACKAGEDIR + '/data/'
+
+CACHEDIR = PACKAGEDIR + "/data/"
 
 __all__ = [
     "download_kepler_prf_file",
@@ -138,7 +139,7 @@ def open_file(url: str):
         )
         prefix = _tess_prefixes[camera][ccd]
         filename = f"tess-prf-cam{camera}-ccd{ccd}-sec4.fits"
-    
+
     file_path = f"{cache_dir}{filename}"
     # ensure the file_path exists
     if not os.path.exists(file_path):
@@ -170,12 +171,14 @@ def get_kepler_prf_file(module: int, output: int, cache_dir: str = CACHEDIR):
             f"No local files found, building Kepler PRF for Module {module}, output {output}."
         )
         download_kepler_prf_file(module=module, output=output, cache_dir=cache_dir)
-    
+
     hdulist = fitsio.FITS(file_path)
     return hdulist
 
 
-def get_tess_prf_file(camera: int, ccd: int, sector: int = 4, cache_dir: str = CACHEDIR):
+def get_tess_prf_file(
+    camera: int, ccd: int, sector: int = 4, cache_dir: str = CACHEDIR
+):
     """Get a PRF file for a given camera/ccd/sector"""
     if sector <= 3:
         filename = f"tess-prf-cam{camera}-ccd{ccd}-sec1.fits"
@@ -187,7 +190,7 @@ def get_tess_prf_file(camera: int, ccd: int, sector: int = 4, cache_dir: str = C
             f"No local files found, building TESS PRF for Camera {camera}, CCD {ccd}."
         )
         build_tess_prf_file(camera=camera, ccd=ccd, sector=sector, cache_dir=cache_dir)
-    
+
     hdulist = fitsio.FITS(file_path)
     return hdulist
 

src/lkprf/keplerprf.py

@@ -23,20 +23,21 @@ class KeplerPRF(PRF):
     https://archive.stsci.edu/missions/kepler/commissioning_prfs/
     """
 
-    def __init__(self, channel: int, cache_dir: str = PACKAGEDIR + '/data/'):
+    def __init__(self, channel: int, cache_dir: str = PACKAGEDIR + "/data/"):
         super().__init__()
         self.channel = channel
         self.mission = "Kepler"
         self.cache_dir = cache_dir
         self._prepare_prf()
-
 
     def __repr__(self):
         return f"KeplerPRF Object [Channel {self.channel}]"
 
     def _get_prf_data(self):
         module, output = channel_to_module_output(self.channel)
-        return get_kepler_prf_file(module=module, output=output, cache_dir = self.cache_dir)
+        return get_kepler_prf_file(
+            module=module, output=output, cache_dir=self.cache_dir
+        )
 
     def check_coordinates(self, targets: List[Tuple], shape: Tuple):
         row, column = self._unpack_targets(targets)
@@ -63,7 +64,7 @@ def check_coordinates(self, targets: List[Tuple], shape: Tuple):
                 LKPRFWarning,
             )
         return
-    
+
     def _prepare_supersamp_prf(self, targets: List[Tuple], shape: Tuple):
         row, column = self._unpack_targets(targets)
         # Set the row and column for the model
@@ -86,23 +87,23 @@ def _prepare_supersamp_prf(self, targets: List[Tuple], shape: Tuple):
         # Kepler has 5 measurements, so nearest 3 triangulates the measurement
         prf_weights = [
             np.sqrt(
-                (ref_column - self.crval1p[i]) ** 2 + (ref_row - self.crval2p[i]) ** 2) 
-                for i in range(self.PRFdata.shape[0])
-                ]
+                (ref_column - self.crval1p[i]) ** 2 + (ref_row - self.crval2p[i]) ** 2
+            )
+            for i in range(self.PRFdata.shape[0])
+        ]
         idx = np.argpartition(prf_weights, 3)[:3]
 
         for i in idx:
             if prf_weights[i] < min_prf_weight:
                 prf_weights[i] = min_prf_weight
             supersamp_prf += self.PRFdata[i] / prf_weights[i]
 
-
         supersamp_prf /= np.nansum(supersamp_prf) * self.cdelt1p[0] * self.cdelt2p[0]
 
         # Set up the interpolation function
         self.interpolate = RectBivariateSpline(self.PRFrow, self.PRFcol, supersamp_prf)
         return
-    
+
     def _update_coordinates(self, targets: List[Tuple], shape: Tuple):
         row, column = self._unpack_targets(targets)
         # Set the row and column for the model
@@ -123,17 +124,17 @@ def _update_coordinates(self, targets: List[Tuple], shape: Tuple):
         # Kepler has 5 measurements, so nearest 3 triangulates the measurement
         prf_weights = [
             np.sqrt(
-                (ref_column - self.crval1p[i]) ** 2 + (ref_row - self.crval2p[i]) ** 2) 
-                for i in range(self.PRFdata.shape[0])
-                ]
+                (ref_column - self.crval1p[i]) ** 2 + (ref_row - self.crval2p[i]) ** 2
+            )
+            for i in range(self.PRFdata.shape[0])
+        ]
         idx = np.argpartition(prf_weights, 3)[:3]
 
         for i in idx:
             if prf_weights[i] < min_prf_weight:
                 prf_weights[i] = min_prf_weight
             supersamp_prf += self.PRFdata[i] / prf_weights[i]
 
-
         supersamp_prf /= np.nansum(supersamp_prf) * self.cdelt1p[0] * self.cdelt2p[0]
         self.interpolate = RectBivariateSpline(self.PRFrow, self.PRFcol, supersamp_prf)
         return

src/lkprf/prfmodel.py

@@ -67,7 +67,7 @@ def _evaluate(
             The (row, column) origin of the image in pixels.
             Combined with shape this sets the extent of the image.
         shape : Tuple
-            The (N_row, N_col) shape of the image. 
+            The (N_row, N_col) shape of the image.
             Combined with the origin this sets the extent of the image.
 
         Returns
@@ -76,8 +76,8 @@ def _evaluate(
             Three dimensional array representing the PRF values parametrized by flux and centroids.
             Has shape (ntargets, shape[0], shape[1])
         """
-        
-        #self.update_coordinates(targets=targets, shape=shape)
+
+        # self.update_coordinates(targets=targets, shape=shape)
         target_row, target_column = self._unpack_targets(targets)
 
         # Integer extent from the PRF model
@@ -197,7 +197,7 @@ def _prepare_prf(self):
         """
 
         hdulist = self._get_prf_data()
-        self.date = hdulist[0].read_header()['DATE']
+        self.date = hdulist[0].read_header()["DATE"]
         PRFdata, crval1p, crval2p, cdelt1p, cdelt2p = [], [], [], [], []
         for hdu in hdulist[1:]:
             PRFdata.append(hdu.read())
@@ -223,7 +223,6 @@ def _prepare_prf(self):
         PRFcol = (PRFcol - np.size(PRFcol) / 2) * cdelt1p[0]
         PRFrow = (PRFrow - np.size(PRFrow) / 2) * cdelt2p[0]
 
-
         (
             self.PRFrow,
             self.PRFcol,
@@ -234,8 +233,6 @@ def _prepare_prf(self):
             self.cdelt2p,
         ) = (PRFrow, PRFcol, PRFdata, crval1p, crval2p, cdelt1p, cdelt2p)
 
-
-
     @abstractmethod
     def check_coordinates(self, targets, shape):
         """Method to check if selected pxels contain collatoral pixels
@@ -251,4 +248,4 @@ def _prepare_supersamp_prf(self, targets, shape):
 
         This method sets up the RectBivariateSpline function to interpolate the supersampled PRF
         """
-        pass
+        pass

src/lkprf/tessprf.py

@@ -14,9 +14,15 @@
 
 class TESSPRF(PRF):
     """A TESSPRF class. The TESS PRF measurements are supersampled by a factor of 9.
-    Two PRF models were produced, one for sectors 1-3 and a second set for sectors 4+ """
-
-    def __init__(self, camera: int, ccd: int, sector: int = 4, cache_dir: str = PACKAGEDIR + '/data/'):
+    Two PRF models were produced, one for sectors 1-3 and a second set for sectors 4+"""
+
+    def __init__(
+        self,
+        camera: int,
+        ccd: int,
+        sector: int = 4,
+        cache_dir: str = PACKAGEDIR + "/data/",
+    ):
         super().__init__()
         self.camera = camera
         self.ccd = ccd
@@ -29,7 +35,12 @@ def __repr__(self):
         return f"TESSPRF Object [Camera {self.camera}, CCD {self.ccd}, Sector {self.sector}]"
 
     def _get_prf_data(self):
-        return get_tess_prf_file(camera=self.camera, ccd=self.ccd, sector=self.sector, cache_dir=self.cache_dir)
+        return get_tess_prf_file(
+            camera=self.camera,
+            ccd=self.ccd,
+            sector=self.sector,
+            cache_dir=self.cache_dir,
+        )
 
     def check_coordinates(self, targets: List[Tuple], shape: Tuple):
         row, column = self._unpack_targets(targets)
@@ -54,11 +65,10 @@ def check_coordinates(self, targets: List[Tuple], shape: Tuple):
             )
 
         return
-
 
     def _prepare_supersamp_prf(self, targets: List[Tuple], shape: Tuple):
         row, column = self._unpack_targets(targets)
-        # Set the row and column for the model. 
+        # Set the row and column for the model.
 
         # Create one supersampled PRF for each image, used for all targets in the image
         row, column = np.atleast_1d(row), np.atleast_1d(column)
@@ -77,27 +87,26 @@ def _prepare_supersamp_prf(self, targets: List[Tuple], shape: Tuple):
         # Find the 4 measured PRF models nearest the target location
         prf_weights = [
             np.sqrt(
-                (ref_column - self.crval1p[i]) ** 2 + (ref_row - self.crval2p[i]) ** 2) 
-                for i in range(self.PRFdata.shape[0])
-                ]
+                (ref_column - self.crval1p[i]) ** 2 + (ref_row - self.crval2p[i]) ** 2
+            )
+            for i in range(self.PRFdata.shape[0])
+        ]
         idx = np.argpartition(prf_weights, 4)[:4]
 
         for i in idx:
             if prf_weights[i] < min_prf_weight:
                 prf_weights[i] = min_prf_weight
             supersamp_prf += self.PRFdata[i] / prf_weights[i]
 
-
         supersamp_prf /= np.nansum(supersamp_prf) * self.cdelt1p[0] * self.cdelt2p[0]
-        
+
         # Set up the interpolation function
         self.interpolate = RectBivariateSpline(self.PRFrow, self.PRFcol, supersamp_prf)
         return
-
 
     def _update_coordinates(self, targets: List[Tuple], shape: Tuple):
         row, column = self._unpack_targets(targets)
-        # Set the row and column for the model. 
+        # Set the row and column for the model.
         # Disallows models in the collateral pixels
         row, column = np.atleast_1d(row), np.atleast_1d(column)
         row = np.min([np.max([row, row**0 * 0], axis=0), row**0 * 2048], axis=0).mean()
@@ -115,17 +124,17 @@ def _update_coordinates(self, targets: List[Tuple], shape: Tuple):
         # Find the 4 measurements nearest the desired locations
         prf_weights = [
             np.sqrt(
-                (ref_column - self.crval1p[i]) ** 2 + (ref_row - self.crval2p[i]) ** 2) 
-                for i in range(self.PRFdata.shape[0])
-                ]
+                (ref_column - self.crval1p[i]) ** 2 + (ref_row - self.crval2p[i]) ** 2
+            )
+            for i in range(self.PRFdata.shape[0])
+        ]
         idx = np.argpartition(prf_weights, 4)[:4]
 
         for i in idx:
             if prf_weights[i] < min_prf_weight:
                 prf_weights[i] = min_prf_weight
             supersamp_prf += self.PRFdata[i] / prf_weights[i]
 
-
         supersamp_prf /= np.nansum(supersamp_prf) * self.cdelt1p[0] * self.cdelt2p[0]
         self.interpolate = RectBivariateSpline(self.PRFrow, self.PRFcol, supersamp_prf)
         return

src/lkprf/version.py

@@ -1,3 +1,3 @@
 # It is important to store the version number in a separate file
 # so that we can read it without importing the package
-__version__ = "1.1.0dev"
+__version__ = "1.1.1"