Style updates

pierfra-rocci · Jun 23, 2022 · 03ebdf2 · 03ebdf2
1 parent aaea709
commit 03ebdf2
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Showing 4 changed files with 20 additions and 20 deletions.
diff --git a/properimage/operations.py b/properimage/operations.py
@@ -197,21 +197,21 @@ def subtract(
     D_hat_r = fourier_shift(psf_new_hat * ref.interped_hat, (-dx_new, -dy_new))
     D_hat_n = fourier_shift(psf_ref_hat * new.interped_hat, (-dx_ref, -dy_ref))
 
-    norm_b = ref.var ** 2 * psf_new_hat * psf_new_hat_conj
-    norm_a = new.var ** 2 * psf_ref_hat * psf_ref_hat_conj
+    norm_b = ref.var**2 * psf_new_hat * psf_new_hat_conj
+    norm_a = new.var**2 * psf_ref_hat * psf_ref_hat_conj
 
     new_back = sep.Background(new.interped).back()
     ref_back = sep.Background(ref.interped).back()
     gamma = new_back - ref_back
     b = n_zp / r_zp
-    norm = np.sqrt(norm_a + norm_b * b ** 2)
+    norm = np.sqrt(norm_a + norm_b * b**2)
     if beta:
         if shift:  # beta==True & shift==True
 
             def cost(vec):
                 b, dx, dy = vec
-                gammap = gamma / np.sqrt(new.var ** 2 + b ** 2 * ref.var ** 2)
-                norm = np.sqrt(norm_a + norm_b * b ** 2)
+                gammap = gamma / np.sqrt(new.var**2 + b**2 * ref.var**2)
+                norm = np.sqrt(norm_a + norm_b * b**2)
                 dhn = D_hat_n / norm
                 dhr = D_hat_r / norm
                 b_n = (
@@ -257,8 +257,8 @@ def cost(vec):
             bi = b
 
             def F(b):
-                gammap = gamma / np.sqrt(new.var ** 2 + b ** 2 * ref.var ** 2)
-                norm = np.sqrt(norm_a + norm_b * b ** 2)
+                gammap = gamma / np.sqrt(new.var**2 + b**2 * ref.var**2)
+                norm = np.sqrt(norm_a + norm_b * b**2)
                 b_n = (
                     _ifftwn(D_hat_n / norm, norm="ortho")
                     - gammap
@@ -291,8 +291,8 @@ def F(b):
             bi = b
 
             def F(b):
-                gammap = gamma / np.sqrt(new.var ** 2 + b ** 2 * ref.var ** 2)
-                norm = np.sqrt(norm_a + norm_b * b ** 2)
+                gammap = gamma / np.sqrt(new.var**2 + b**2 * ref.var**2)
+                norm = np.sqrt(norm_a + norm_b * b**2)
                 b_n = (
                     _ifftwn(D_hat_n / norm, norm="ortho")
                     - gammap
@@ -322,8 +322,8 @@ def F(b):
     else:
         if shift:  # beta==False & shift==True
             bi = n_zp / r_zp
-            gammap = gamma / np.sqrt(new.var ** 2 + b ** 2 * ref.var ** 2)
-            norm = np.sqrt(norm_a + norm_b * b ** 2)
+            gammap = gamma / np.sqrt(new.var**2 + b**2 * ref.var**2)
+            norm = np.sqrt(norm_a + norm_b * b**2)
             dhn = D_hat_n / norm
             dhr = D_hat_r / norm
 
@@ -369,7 +369,7 @@ def cost(vec):
             dx = 0.0
             dy = 0.0
 
-    norm = norm_a + norm_b * b ** 2
+    norm = norm_a + norm_b * b**2
 
     if dx == 0.0 and dy == 0.0:
         D_hat = (D_hat_n - b * D_hat_r) / np.sqrt(norm)
@@ -382,7 +382,7 @@ def cost(vec):
     if np.any(np.isnan(D.real)):
         pass
 
-    d_zp = b / np.sqrt(ref.var ** 2 * b ** 2 + new.var ** 2)
+    d_zp = b / np.sqrt(ref.var**2 * b**2 + new.var**2)
     P_hat = (psf_ref_hat * psf_new_hat * b) / (np.sqrt(norm) * d_zp)
 
     P = _ifftwn(P_hat, norm="ortho").real
@@ -408,13 +408,13 @@ def cost(vec):
 
     V_en = _ifftwn(
         _fftwn(new.data.filled(0) + 1.0, norm="ortho")
-        * _fftwn(kn ** 2, s=new.data.shape),
+        * _fftwn(kn**2, s=new.data.shape),
         norm="ortho",
     )
 
     V_er = _ifftwn(
         _fftwn(ref.data.filled(0) + 1.0, norm="ortho")
-        * _fftwn(kr ** 2, s=ref.data.shape),
+        * _fftwn(kr**2, s=ref.data.shape),
         norm="ortho",
     )
 

diff --git a/properimage/plot.py b/properimage/plot.py
@@ -309,7 +309,7 @@ def autopsf_coef(
             img = ax.imshow(a, **imshow_kw)
             fig.colorbar(img, ax=ax, **cmap_kw)
 
-            title = title_tpl.format(j=idx + 1, sum=np.sqrt(np.sum(a ** 2)))
+            title = title_tpl.format(j=idx + 1, sum=np.sqrt(np.sum(a**2)))
             ax.set_title(title)
 
         return axs
diff --git a/properimage/single_image.py b/properimage/single_image.py
@@ -605,7 +605,7 @@ def check_margin(stamp, rms=self._bkg.globalrms):
                     xcm = np.array([xcm[0], ycm[0]])
 
                     delta = xcm - np.asarray(new_shape) / 2.0
-                    if np.sqrt(np.sum(delta ** 2)) > new_shape[0] / 5.0:
+                    if np.sqrt(np.sum(delta**2)) > new_shape[0] / 5.0:
                         to_del.append(jj)
                         jj += 1
                         continue
@@ -634,7 +634,7 @@ def check_margin(stamp, rms=self._bkg.globalrms):
                         ymax, xmax, thmax = outl_cat[0]
                         xcm = np.array([xmax, ymax])
                         delta = xcm - np.asarray(new_shape) / 2.0
-                        if np.sqrt(np.sum(delta ** 2)) > new_shape[0] / 5.0:
+                        if np.sqrt(np.sum(delta**2)) > new_shape[0] / 5.0:
                             to_del.append(jj)
                             jj += 1
                             continue
@@ -998,7 +998,7 @@ def s_hat_comp(self):
                     conv = fourier_shift(conv, (+dx, +dy))
                     np.add(conv, s_hat, out=s_hat)
 
-            self._s_hat_comp = (self.zp / (var ** 2)) * s_hat
+            self._s_hat_comp = (self.zp / (var**2)) * s_hat
             self._s_hat_inf_loss = self.inf_loss
 
         return self._s_hat_comp

diff --git a/tests/test_plot.py b/tests/test_plot.py
@@ -273,7 +273,7 @@ def test_plot_autopsf_coef(random_4psf_simage, fig_test, fig_ref):
         img = ax.imshow(a, vmax=med + 2 * std, vmin=med - 2 * std)
         fig_ref.colorbar(img, ax=ax, **cmap_kw)
 
-        title = title_tpl.format(j=idx + 1, sum=np.sqrt(np.sum(a ** 2)))
+        title = title_tpl.format(j=idx + 1, sum=np.sqrt(np.sum(a**2)))
         ax.set_title(title)
 
     # fig test