added vortexmap 2d functionality

Constructing MultiContrast class with a 2d array in the form of a nested list now works to produce a 2d vortexmap.

Author: sangyu

dabest/multi.py

@@ -95,7 +95,7 @@ def _parse_contrast_structure(contrasts, labels=None):
  # Handle 2D labels
  if labels and isinstance(labels[0], (list, tuple)):
  row_labels = [labels[i][0] for i in range(n_rows)]
- col_labels = labels[0]
+ col_labels = labels[0][1:]
  else:
  row_labels = [f"Row {i+1}" for i in range(n_rows)]
  col_labels = [f"Col {j+1}" for j in range(n_cols)]
@@ -188,8 +188,8 @@ def _spiralize(fill, m, n):
  return array
 
 # %% ../nbs/API/multi.ipynb 12
-def vortexmap(multi_contrast, n=21, sort_by=None, vmax=3, vmin=-3, 
- reverse_neg=True, abs_rank=False, chop_tail=0, ax=None, **kwargs):
+def vortexmap(multi_contrast, n=21, sort_by=None, cmap = 'vlag', vmax = None, vmin = None, 
+ reverse_neg=True, abs_rank=False, chop_tail=0, ax=None,fig_size=None, **kwargs):
  """
  Create a vortexmap visualization of multiple contrasts.
  
@@ -201,7 +201,7 @@ def vortexmap(multi_contrast, n=21, sort_by=None, vmax=3, vmin=-3,
  Size of each spiral (n x n grid per contrast)
  sort_by : list, optional
  Order to sort contrasts by
- vmax, vmin : float, default 3, -3
+ vmax, vmin : float, default None, None
  Color scale limits
  reverse_neg : bool, default True
  Whether to reverse negative values
@@ -227,6 +227,7 @@ def vortexmap(multi_contrast, n=21, sort_by=None, vmax=3, vmin=-3,
  contrasts_2d = structure['contrasts_2d']
 
  spirals = pd.DataFrame(np.zeros((n_rows * n, n_cols * n)))
+ 
  mean_delta = pd.DataFrame(np.zeros((n_rows, n_cols)), 
  columns=col_labels, 
  index=row_labels)
@@ -251,19 +252,40 @@ def vortexmap(multi_contrast, n=21, sort_by=None, vmax=3, vmin=-3,
  f, a = plt.subplots(1, 1)
  else:
  a = ax
+ if vmax is None:
+ vmax = np.max(spirals.values)
+ if vmin is None:
+ vmin = np.min(spirals.values)
+ if structure['was_1d']:
+ cbar_orientation = 'horizontal'
+ cbar_location = 'top'
+ else:
+ cbar_orientation = 'vertical'
+ cbar_location = 'right'
 
- sns.heatmap(spirals, cmap='vlag', cbar_kws={"shrink": 0.2, 'pad': .17}, 
- ax=a, vmax=vmax, vmin=vmin)
+ # Create heatmap
+ sns.heatmap(spirals, cmap=cmap, cbar_kws={"shrink": 1, "pad": .17, "orientation": cbar_orientation, "location": cbar_location}, 
+ ax=a, center = 0, vmax=vmax, vmin=vmin, **kwargs)
 
  # Set labels
  a.set_xticks(np.linspace(n/2, n_cols*n-n/2, n_cols))
  a.set_xticklabels(col_labels, rotation=45, ha='right')
- a.set_yticks(np.linspace(n/2, n_rows*n-n/2, n_rows))
- a.set_yticklabels(row_labels, ha='right', rotation=0)
- 
+
+ if structure['was_1d']:
+ a.set_xlabel('Contrasts')
+ a.set_ylabel(' ')
+ a.set_yticks([])
+ a.set_yticklabels([])
+ else:
+ a.set_yticks(np.linspace(n/2, n_rows*n-n/2, n_rows))
+ a.set_yticklabels(row_labels, ha='right', rotation=0)
+
  if ax is None:
  f.gca().set_aspect('equal')
- f.set_size_inches(n_cols/3, n_rows/3)
+ if fig_size is None:
+ f.set_size_inches(n_cols/3, n_rows/3)
+ else:
+ f.set_size_inches(fig_size)
  return f, a, mean_delta
  else:
  return a, mean_delta

nbs/API/multi.ipynb

@@ -178,7 +178,7 @@
  " # Handle 2D labels\n",
  " if labels and isinstance(labels[0], (list, tuple)):\n",
  " row_labels = [labels[i][0] for i in range(n_rows)]\n",
- " col_labels = labels[0]\n",
+ " col_labels = labels[0][1:]\n",
  " else:\n",
  " row_labels = [f\"Row {i+1}\" for i in range(n_rows)]\n",
  " col_labels = [f\"Col {j+1}\" for j in range(n_cols)]\n",
@@ -305,8 +305,8 @@
  "outputs": [],
  "source": [
  "#| export\n",
- "def vortexmap(multi_contrast, n=21, sort_by=None, vmax=3, vmin=-3, \n",
- " reverse_neg=True, abs_rank=False, chop_tail=0, ax=None, **kwargs):\n",
+ "def vortexmap(multi_contrast, n=21, sort_by=None, cmap = 'vlag', vmax = None, vmin = None, \n",
+ " reverse_neg=True, abs_rank=False, chop_tail=0, ax=None,fig_size=None, **kwargs):\n",
  " \"\"\"\n",
  " Create a vortexmap visualization of multiple contrasts.\n",
  " \n",
@@ -318,7 +318,7 @@
  " Size of each spiral (n x n grid per contrast)\n",
  " sort_by : list, optional\n",
  " Order to sort contrasts by\n",
- " vmax, vmin : float, default 3, -3\n",
+ " vmax, vmin : float, default None, None\n",
  " Color scale limits\n",
  " reverse_neg : bool, default True\n",
  " Whether to reverse negative values\n",
@@ -344,6 +344,7 @@
  " contrasts_2d = structure['contrasts_2d']\n",
  "\n",
  " spirals = pd.DataFrame(np.zeros((n_rows * n, n_cols * n)))\n",
+ " \n",
  " mean_delta = pd.DataFrame(np.zeros((n_rows, n_cols)), \n",
  " columns=col_labels, \n",
  " index=row_labels)\n",
@@ -368,19 +369,40 @@
  " f, a = plt.subplots(1, 1)\n",
  " else:\n",
  " a = ax\n",
+ " if vmax is None:\n",
+ " vmax = np.max(spirals.values)\n",
+ " if vmin is None:\n",
+ " vmin = np.min(spirals.values)\n",
+ " if structure['was_1d']:\n",
+ " cbar_orientation = 'horizontal'\n",
+ " cbar_location = 'top'\n",
+ " else:\n",
+ " cbar_orientation = 'vertical'\n",
+ " cbar_location = 'right'\n",
  " \n",
- " sns.heatmap(spirals, cmap='vlag', cbar_kws={\"shrink\": 0.2, 'pad': .17}, \n",
- " ax=a, vmax=vmax, vmin=vmin)\n",
+ " # Create heatmap\n",
+ " sns.heatmap(spirals, cmap=cmap, cbar_kws={\"shrink\": 1, \"pad\": .17, \"orientation\": cbar_orientation, \"location\": cbar_location}, \n",
+ " ax=a, center = 0, vmax=vmax, vmin=vmin, **kwargs)\n",
  " \n",
  " # Set labels\n",
  " a.set_xticks(np.linspace(n/2, n_cols*n-n/2, n_cols))\n",
  " a.set_xticklabels(col_labels, rotation=45, ha='right')\n",
- " a.set_yticks(np.linspace(n/2, n_rows*n-n/2, n_rows))\n",
- " a.set_yticklabels(row_labels, ha='right', rotation=0)\n",
- " \n",
+ "\n",
+ " if structure['was_1d']:\n",
+ " a.set_xlabel('Contrasts')\n",
+ " a.set_ylabel(' ')\n",
+ " a.set_yticks([])\n",
+ " a.set_yticklabels([])\n",
+ " else:\n",
+ " a.set_yticks(np.linspace(n/2, n_rows*n-n/2, n_rows))\n",
+ " a.set_yticklabels(row_labels, ha='right', rotation=0)\n",
+ "\n",
  " if ax is None:\n",
  " f.gca().set_aspect('equal')\n",
- " f.set_size_inches(n_cols/3, n_rows/3)\n",
+ " if fig_size is None:\n",
+ " f.set_size_inches(n_cols/3, n_rows/3)\n",
+ " else:\n",
+ " f.set_size_inches(fig_size)\n",
  " return f, a, mean_delta\n",
  " else:\n",
  " return a, mean_delta\n",