chore: update get_cmap to colormaps[ ]

chansigit · Jul 5, 2024 · 9ed280c · 9ed280c
1 parent d28b8ac
commit 9ed280c
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Showing 5 changed files with 10 additions and 10 deletions.
diff --git a/dynamo/plot/markers.py b/dynamo/plot/markers.py
@@ -217,7 +217,7 @@ def bubble(
     )
 
     if color_key is None:
-        cmap_ = matplotlib.cm.get_cmap(color_key_cmap)
+        cmap_ = matplotlib.colormaps[color_key_cmap]
         cmap_.set_bad("lightgray")
         unique_labels = np.unique(clusters)
         num_labels = unique_labels.shape[0]

diff --git a/dynamo/plot/scatters.py b/dynamo/plot/scatters.py
@@ -705,7 +705,7 @@ def _plot_basis_layer(cur_b, cur_l):
                 if stack_colors:
                     main_debug("stack colors: changing cmap")
                     _cmap = stack_colors_cmaps[ax_index % len(stack_colors_cmaps)]
-                    max_color = matplotlib.cm.get_cmap(_cmap)(float("inf"))
+                    max_color = matplotlib.colormaps[_cmap](float("inf"))
                     legend_circle = Line2D(
                         [0],
                         [0],
@@ -2276,7 +2276,7 @@ def scatters_single_input(
         main_debug("stack colors: changing cmap")
         cur_title = stack_colors_title
         cmap = stack_colors_cmaps[(ax_index - 1) % len(stack_colors_cmaps)]
-        max_color = matplotlib.cm.get_cmap(cmap)(float("inf"))
+        max_color = matplotlib.colormaps[cmap](float("inf"))
         # TODO: consider remove the legend because it is not helpful
         legend_circle = Line2D(
             [0],

diff --git a/dynamo/plot/topography.py b/dynamo/plot/topography.py
@@ -321,7 +321,7 @@ def plot_fixed_points_2d(
     if ax is None:
         ax = plt.gca()
 
-    cm = matplotlib.cm.get_cmap(_cmap) if type(_cmap) is str else _cmap
+    cm = matplotlib.colormaps[_cmap] if type(_cmap) is str else _cmap
     for i in range(len(Xss)):
         cur_ftype = ftype[i]
         marker_ = markers.MarkerStyle(marker=marker, fillstyle=filltype[int(cur_ftype + 1)])
@@ -457,7 +457,7 @@ def plot_fixed_points(
             vecfld_dict["confidence"],
         )
 
-    cm = matplotlib.cm.get_cmap(_cmap) if type(_cmap) is str else _cmap
+    cm = matplotlib.colormaps[_cmap] if type(_cmap) is str else _cmap
     colors = [c if confidence is None else np.array(cm(confidence[i])) for i in range(len(confidence))]
     text_colors = ["black" if cur_ftype == -1 else "blue" if cur_ftype == 0 else "red" for cur_ftype in ftype]
 

diff --git a/dynamo/plot/utils.py b/dynamo/plot/utils.py
@@ -125,7 +125,7 @@ def calculate_colors(
             )
         if color_key is None:
             main_debug("color_key is None")
-            cmap = copy.copy(matplotlib.cm.get_cmap(color_key_cmap))
+            cmap = copy.copy(matplotlib.colormaps[color_key_cmap])
             cmap.set_bad("lightgray")
             colors = None
 
@@ -814,7 +814,7 @@ def _matplotlib_points(
             cb.locator = MaxNLocator(nbins=3, integer=True)
             cb.update_ticks()
 
-        cmap = matplotlib.cm.get_cmap(cmap)
+        cmap = matplotlib.colormaps[cmap]
         colors = cmap(values)
     # No color (just pick the midpoint of the cmap)
     else:
@@ -919,7 +919,7 @@ def _datashade_points(
             aggregation = canvas.points(data, "x", "y", agg=ds.count_cat("label"))
             result = tf.shade(aggregation, how="eq_hist")
         elif color_key is None:
-            cmap = matplotlib.cm.get_cmap(color_key_cmap)
+            cmap = matplotlib.colormaps[color_key_cmap]
             cmap.set_bad("lightgray")
             # add plotnonfinite=True to canvas.points
 
@@ -960,7 +960,7 @@ def _datashade_points(
 
     # Color by values
     elif values is not None:
-        cmap_ = matplotlib.cm.get_cmap(cmap)
+        cmap_ = matplotlib.colormaps[cmap]
         cmap_.set_bad("lightgray")
 
         if values.shape[0] != points.shape[0]:

diff --git a/dynamo/vectorfield/vector_calculus.py b/dynamo/vectorfield/vector_calculus.py
@@ -737,7 +737,7 @@ def sensitivity(
 
     S_det = [np.linalg.det(S[:, :, i]) for i in np.arange(S.shape[2])]
     adata.obs["sensitivity_det_" + basis] = np.nan
-    adata.obs.loc[cell_idx, "sensitivity_det_" + basis] = S_det
+    adata.obs["sensitivity_det_" + basis][cell_idx] = S_det
     if store_in_adata:
         skey = "sensitivity" if basis is None else "sensitivity_" + basis
         adata.uns[skey] = ret_dict