Improve np.reshape call consistency.

colour-science · Nov 3, 2023 · 42bcdca · 42bcdca
1 parent bcbe33a
commit 42bcdca
Show file tree

Hide file tree

Showing 4 changed files with 9 additions and 9 deletions.
diff --git a/colour/io/luts/cinespace_csp.py b/colour/io/luts/cinespace_csp.py
@@ -204,7 +204,7 @@ def _parse_table_section(lines):
         )
     ):
         LUT = LUT3x1D(
-            domain=pre_LUT.reshape(3, 4).transpose()[0:2],
+            domain=pre_LUT.reshape([3, 4]).transpose()[0:2],
             name=title,
             comments=comments,
             table=table,

diff --git a/colour/plotting/diagrams.py b/colour/plotting/diagrams.py
@@ -340,9 +340,9 @@ def lines_spectral_locus(
         normal_l.append([normal, normal])
         colour_l.append([ij_l, ij_l])
 
-    ij_w = as_float_array(ij_n).reshape(-1, 2)
-    normal_w = as_float_array(normal_l).reshape(-1, 2)
-    colours_w = as_float_array(colour_l).reshape(-1, 2)
+    ij_w = as_float_array(ij_n).reshape([-1, 2])
+    normal_w = as_float_array(normal_l).reshape([-1, 2])
+    colours_w = as_float_array(colour_l).reshape([-1, 2])
 
     colour_w = normalise_maximum(
         XYZ_to_plotting_colourspace(

diff --git a/colour/plotting/temperature.py b/colour/plotting/temperature.py
@@ -368,9 +368,9 @@ def CCT_D_uv_to_plotting_colourspace(CCT_D_uv):
         )
         colour_itl.append(CCT_D_uv_to_plotting_colourspace(CCT_D_uv))
 
-    ij_l = as_float_array(ij_itl).reshape(-1, 2)
-    normal_l = as_float_array(normal_itl).reshape(-1, 2)
-    colour_l = as_float_array(colour_itl).reshape(-1, 3)
+    ij_l = as_float_array(ij_itl).reshape([-1, 2])
+    normal_l = as_float_array(normal_itl).reshape([-1, 2])
+    colour_l = as_float_array(colour_itl).reshape([-1, 3])
 
     lines_l = zeros(
         ij_l.shape[0],

diff --git a/colour/quality/tm3018.py b/colour/quality/tm3018.py
@@ -147,7 +147,7 @@ def colour_fidelity_index_ANSIIESTM3018(
         np.floor(specification.colorimetry_data[1].JMh[:, 2] / 22.5)
     )
 
-    bin_mask = bins == np.arange(16).reshape(-1, 1)
+    bin_mask = bins == np.arange(16).reshape([-1, 1])
 
     # "bin_mask" is used later with Numpy broadcasting and "np.nanmean"
     # to skip a list comprehension and keep all the mean calculation vectorised
@@ -186,7 +186,7 @@ def colour_fidelity_index_ANSIIESTM3018(
 
     # Local colour fidelity indexes, i.e. 16 CFIs for each bin.
     bin_delta_E_s = np.nanmean(
-        specification.delta_E_s.reshape(1, -1) * bin_mask, axis=1
+        specification.delta_E_s.reshape([1, -1]) * bin_mask, axis=1
     )
     R_fs = as_float_array(delta_E_to_R_f(bin_delta_E_s))