lint

fpfs/image.py

@@ -97,10 +97,7 @@ def __init__(
         self.klim_pix = min(self.klim_pix, self.ngrid // 2 - 1)
 
         self.klim = float(self.klim_pix * self._dk)
-        logging.info(
-            "Maximum |k| is %.3f"
-            % (self.klim)
-        )
+        logging.info("Maximum |k| is %.3f" % (self.klim))
 
         self._indx = jnp.arange(
             self.ngrid // 2 - self.klim_pix,
@@ -110,7 +107,6 @@ def __init__(
         self._ind2d = jnp.ix_(self._indx, self._indx)
         return
 
-
     @partial(jax.jit, static_argnames=["self"])
     def deconvolve(self, data, prder=1.0, frder=1.0):
         """Deconvolves input data with the PSF or PSF power
@@ -306,7 +302,7 @@ def detect_sources(
             self.klim,
         )
         if bound is None:
-            bound = self.ngrid//2 + 5
+            bound = self.ngrid // 2 + 5
         dd = imgutil.find_peaks(img_conv, img_conv_det, thres, thres2, bound).T
         return dd
 

fpfs/imgutil.py

@@ -160,7 +160,7 @@ def shapelets2d(ngrid, nord, sigma, klim):
         ngrid, ngrid, sigma, klim, return_grid=True
     )
     rfunc = np.sqrt(xfunc**2.0 + yfunc**2.0)  # radius
-    r2_over_sigma2 = (rfunc / sigma) ** 2.
+    r2_over_sigma2 = (rfunc / sigma) ** 2.0
     ny, nx = gaufunc.shape
 
     rmask = rfunc != 0.0
@@ -191,7 +191,7 @@ def shapelets2d(ngrid, nord, sigma, klim):
                 pow(-1.0, d1)
                 * pow(cc, 0.5)
                 * lfunc[c1, abs(mm), :, :]
-                * pow(r2_over_sigma2, abs(mm)/2)
+                * pow(r2_over_sigma2, abs(mm) / 2)
                 * gaufunc
                 * eulfunc**mm
                 * (1j) ** nn
@@ -252,9 +252,7 @@ def shapelets2d_real(ngrid, nord, sigma, klim):
             % nord
         )
     # generate the complex shaplet functions
-    chi = shapelets2d(
-        ngrid, nord, sigma, klim
-    )[indm]
+    chi = shapelets2d(ngrid, nord, sigma, klim)[indm]
     # transform to real shapelet functions
     chi_2 = np.zeros((len(name_s), ngrid, ngrid), dtype=np.float64)
     for i, ind in enumerate(ind_s):
@@ -484,8 +482,11 @@ def cond_fun(dist):
             / psf_array[ngrid // 2, ngrid // 2 + dist]
         )
         return jax.lax.cond(
-            v1 < v2, v1, lambda x: x > thres,
-            v2, lambda x: x > thres,
+            v1 < v2,
+            v1,
+            lambda x: x > thres,
+            v2,
+            lambda x: x > thres,
         )
 
     def body_fun(dist):
@@ -498,25 +499,27 @@ def body_fun(dist):
     )
     return klim
 
+
 def truncate_square(arr, rcut):
     if len(arr.shape) != 2 or arr.shape[0] != arr.shape[1]:
         raise ValueError("Input array must be a 2D square array")
 
     ngrid = arr.shape[0]
-    arr[:ngrid//2-rcut, :] = 0
-    arr[ngrid//2+rcut:, :] = 0
-    arr[:, :ngrid//2-rcut] = 0
-    arr[:, ngrid//2+rcut:] = 0
+    arr[: ngrid // 2 - rcut, :] = 0
+    arr[ngrid // 2 + rcut :, :] = 0
+    arr[:, : ngrid // 2 - rcut] = 0
+    arr[:, ngrid // 2 + rcut :] = 0
     return
 
+
 def truncate_circle(arr, rcut):
     if len(arr.shape) != 2 or arr.shape[0] != arr.shape[1]:
         raise ValueError("Input array must be a 2D square array")
     ngrid = arr.shape[0]
     y, x = np.ogrid[0:ngrid, 0:ngrid]
     center_x, center_y = ngrid // 2, ngrid // 2
     # Compute the squared distance to the center
-    distance_squared = (x - center_x)**2 + (y - center_y)**2
+    distance_squared = (x - center_x) ** 2 + (y - center_y) ** 2
     # Mask values outside the circle
     arr[distance_squared > rcut**2] = 0.0
     return

fpfs/simutil.py

@@ -187,7 +187,6 @@ def make_cosmo_sim(
     shear_value=0.02,
     nrot=nrot_default,
 ):
-
     """Makes cosmo-like blended galaxy image simulations.
 
     Args:
@@ -337,6 +336,7 @@ def generate_cosmos_gal(record, truncr=5.0, gsparams=None):
     Returns:
         gal:    Galsim galaxy
     """
+
     # record columns:
     # For 'sersicfit', the result is an array of 8 numbers for each:
     #     SERSICFIT[0]: intensity of light profile at the half-light radius.
@@ -696,10 +696,7 @@ def make_isolate_sim(
         g2 = shear_const
     else:
         raise ValueError("cannot decide g1 or g2")
-    logging.info(
-        "Processing for %s, and shear is %s."
-        % (gname, shear_const)
-    )
+    logging.info("Processing for %s, and shear is %s." % (gname, shear_const))
 
     if do_shift:
         shifts = np.random.uniform(low=-0.5, high=0.5, size=(ngal, 2)) * scale
@@ -747,8 +744,9 @@ def make_isolate_sim(
         np.pad(
             array=oo,
             pad_width=(buff, buff),
-            constant_values=0.,
-        ) for oo in outcome
+            constant_values=0.0,
+        )
+        for oo in outcome
     ]
     return outcome