fixed extract_data

Author: sangyu

README.md

@@ -1,6 +1,5 @@
 # DABEST-Python
 
-
 <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 
 [![minimal Python

dabest/multi.py

@@ -285,11 +285,11 @@ def _validate_individual_dabest_obj(self, dabest_obj, position: int):
 
  def _extract_data(self) -> Tuple[List, List, List, List]:
  """
- Extract bootstrap, effect sizes, and CI data.
+ Extract bootstrap, effect sizes, CI low bounds and CI high bounds.
  Handles mixed contrast types for vortexmap.
  """
  if self._bootstrap_data is not None:
- return self._bootstrap_data, self._effect_data, self._ci_data
+ return self._bootstrap_data, self._effect_data, self._ci_lows, self._ci_highs
 
  # Process effect size attribute name
  effect_attr = "hedges_g" if self.effect_size == 'delta_g' else self.effect_size
@@ -334,7 +334,8 @@ def _extract_data(self) -> Tuple[List, List, List, List]:
  # Cache results
  self._bootstrap_data = bootstraps
  self._effect_data = differences
- self._ci_data = (ci_lows, ci_highs)
+ self._ci_lows = ci_lows
+ self._ci_highs = ci_highs
 
  return bootstraps, differences, ci_lows, ci_highs
 
@@ -383,7 +384,7 @@ def confidence_intervals(self) -> Tuple[List, List]:
  _, _, ci_lows, ci_highs = self._extract_data()
  return ci_lows, ci_highs
 
- def forest_plot(self, **kwargs):
+ def forest_plot(self, **forest_plot_kwargs):
  """
  Create forest plot using validated data.
  
@@ -414,23 +415,22 @@ def forest_plot(self, **kwargs):
  'ci_type': self.ci_type,
  'labels': self.structure['col_labels'],
  }
- forest_kwargs.update(kwargs) # kwargs can override defaults
+ forest_kwargs.update(forest_plot_kwargs) # kwargs can override defaults
 
  # Call existing forest_plot with validated dabest objects
  return forest_plot(data=all_dabest_objs, **forest_kwargs)
 
- def vortexmap(self, **kwargs):
+ def vortexmap(self, **heatmap_kwargs):
  """
  Create vortexmap using validated data.
  
- This uses the enhanced vortexmap that can handle both homogeneous
+ This uses the vortexmap that can handle both homogeneous
  and mixed contrast types.
  """
- # Import here to avoid circular imports 
  from .multi import vortexmap
 
- # Call enhanced vortexmap with self as the multi_contrast object
- return vortexmap(multi_contrast=self, **kwargs) 
+ # Call vortexmap with self as the multi_contrast object
+ return vortexmap(multi_contrast=self, **heatmap_kwargs) 
  def get_bootstrap_by_position(self, row: int, col: int):
  """
  Get bootstrap data for a specific position in the grid.
@@ -611,7 +611,7 @@ def _spiralize(fill, m, n):
 
 # %% ../nbs/API/multi.ipynb 12
 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):
+ reverse_neg=True, abs_rank=False, chop_tail=0, ax=None,fig_size=None, heatmap_kwargs=None):
  """
  Create a vortexmap visualization of multiple contrasts.
  
@@ -633,18 +633,44 @@ def vortexmap(multi_contrast, n=21, sort_by=None, cmap = 'vlag', vmax = None, vm
  Percentage of extreme values to exclude
  ax : matplotlib.Axes, optional
  Existing axes to plot on
- 
+ fig_size : tuple, optional
+ Figure size (width, height) in inches
+ heatmap_kwargs : dict, optional
+ Additional keyword arguments passed to sns.heatmap().
+ Common options include:
+ - 'cmap': colormap (overrides direct cmap parameter)
+ - 'vmin', 'vmax': color scale limits (override direct parameters)
+ - 'center': center value for colormap
+ - 'annot': whether to annotate cells with values
+ - 'fmt': format string for annotations
+ - 'linewidths': width of lines between cells
+ - 'linecolor': color of lines between cells
+ - 'cbar': whether to show colorbar
+ - 'cbar_kws': colorbar customization dict
+ - 'square': whether to make cells square
+ - 'xticklabels', 'yticklabels': tick label control
+ - 'mask': boolean array to mask cells
+ plot_kwargs : dict, optional
+ Additional keyword arguments for plot styling and layout.
+ Available options:
+ - 'title': plot title
+ - 'xlabel', 'ylabel': axis labels
+ - 'xlabel_rotation', 'ylabel_rotation': label rotation angles
+ - 'grid': whether to show grid 
  Returns
  -------
  tuple
  (figure, axes, mean_delta_dataframe) if ax is None, 
  else (axes, mean_delta_dataframe)
  """
+
+ if heatmap_kwargs is None:
+ heatmap_kwargs = {}
  structure = multi_contrast.structure
 
  n_rows = structure['n_rows']
  n_cols = structure['n_cols']
- col_labels = structure['col_labels']
+ col_labels = structure['col_labels'] 
  row_labels = structure['row_labels']
  was_1d = (structure['type'] == '1D')
 
@@ -694,10 +720,13 @@ def vortexmap(multi_contrast, n=21, sort_by=None, cmap = 'vlag', vmax = None, vm
  else:
  cbar_orientation = 'vertical'
  cbar_location = 'right'
- 
+ heatmap_kwargs.setdefault('cmap', cmap)
+ heatmap_kwargs.setdefault('vmax', vmax)
+ heatmap_kwargs.setdefault('vmin', vmin)
+ heatmap_kwargs.setdefault('center', 0)
  # 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)
+ sns.heatmap(spirals, cbar_kws={"shrink": 1, "pad": .17, "orientation": cbar_orientation, "location": cbar_location}, 
+ ax=a, **heatmap_kwargs)
 
  # Set labels
  a.set_xticks(np.linspace(n/2, n_cols*n-n/2, n_cols))

nbs/API/multi.ipynb

@@ -351,11 +351,11 @@
  " \n",
  " def _extract_data(self) -> Tuple[List, List, List, List]:\n",
  " \"\"\"\n",
- " Extract bootstrap, effect sizes, and CI data.\n",
+ " Extract bootstrap, effect sizes, CI low bounds and CI high bounds.\n",
  " Handles mixed contrast types for vortexmap.\n",
  " \"\"\"\n",
  " if self._bootstrap_data is not None:\n",
- " return self._bootstrap_data, self._effect_data, self._ci_data\n",
+ " return self._bootstrap_data, self._effect_data, self._ci_lows, self._ci_highs\n",
  " \n",
  " # Process effect size attribute name\n",
  " effect_attr = \"hedges_g\" if self.effect_size == 'delta_g' else self.effect_size\n",
@@ -400,7 +400,8 @@
  " # Cache results\n",
  " self._bootstrap_data = bootstraps\n",
  " self._effect_data = differences\n",
- " self._ci_data = (ci_lows, ci_highs)\n",
+ " self._ci_lows = ci_lows\n",
+ " self._ci_highs = ci_highs\n",
  " \n",
  " return bootstraps, differences, ci_lows, ci_highs\n",
  " \n",
@@ -449,7 +450,7 @@
  " _, _, ci_lows, ci_highs = self._extract_data()\n",
  " return ci_lows, ci_highs\n",
  " \n",
- " def forest_plot(self, **kwargs):\n",
+ " def forest_plot(self, **forest_plot_kwargs):\n",
  " \"\"\"\n",
  " Create forest plot using validated data.\n",
  " \n",
@@ -480,23 +481,22 @@
  " 'ci_type': self.ci_type,\n",
  " 'labels': self.structure['col_labels'],\n",
  " }\n",
- " forest_kwargs.update(kwargs) # kwargs can override defaults\n",
+ " forest_kwargs.update(forest_plot_kwargs) # kwargs can override defaults\n",
  " \n",
  " # Call existing forest_plot with validated dabest objects\n",
  " return forest_plot(data=all_dabest_objs, **forest_kwargs)\n",
  "\n",
- " def vortexmap(self, **kwargs):\n",
+ " def vortexmap(self, **heatmap_kwargs):\n",
  " \"\"\"\n",
  " Create vortexmap using validated data.\n",
  " \n",
- " This uses the enhanced vortexmap that can handle both homogeneous\n",
+ " This uses the vortexmap that can handle both homogeneous\n",
  " and mixed contrast types.\n",
  " \"\"\"\n",
- " # Import here to avoid circular imports \n",
  " from .multi import vortexmap\n",
  " \n",
- " # Call enhanced vortexmap with self as the multi_contrast object\n",
- " return vortexmap(multi_contrast=self, **kwargs) \n",
+ " # Call vortexmap with self as the multi_contrast object\n",
+ " return vortexmap(multi_contrast=self, **heatmap_kwargs) \n",
  " def get_bootstrap_by_position(self, row: int, col: int):\n",
  " \"\"\"\n",
  " Get bootstrap data for a specific position in the grid.\n",
@@ -727,7 +727,7 @@
  "source": [
  "#| export\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",
+ " reverse_neg=True, abs_rank=False, chop_tail=0, ax=None,fig_size=None, heatmap_kwargs=None):\n",
  " \"\"\"\n",
  " Create a vortexmap visualization of multiple contrasts.\n",
  " \n",
@@ -749,18 +749,44 @@
  " Percentage of extreme values to exclude\n",
  " ax : matplotlib.Axes, optional\n",
  " Existing axes to plot on\n",
- " \n",
+ " fig_size : tuple, optional\n",
+ " Figure size (width, height) in inches\n",
+ " heatmap_kwargs : dict, optional\n",
+ " Additional keyword arguments passed to sns.heatmap().\n",
+ " Common options include:\n",
+ " - 'cmap': colormap (overrides direct cmap parameter)\n",
+ " - 'vmin', 'vmax': color scale limits (override direct parameters)\n",
+ " - 'center': center value for colormap\n",
+ " - 'annot': whether to annotate cells with values\n",
+ " - 'fmt': format string for annotations\n",
+ " - 'linewidths': width of lines between cells\n",
+ " - 'linecolor': color of lines between cells\n",
+ " - 'cbar': whether to show colorbar\n",
+ " - 'cbar_kws': colorbar customization dict\n",
+ " - 'square': whether to make cells square\n",
+ " - 'xticklabels', 'yticklabels': tick label control\n",
+ " - 'mask': boolean array to mask cells\n",
+ " plot_kwargs : dict, optional\n",
+ " Additional keyword arguments for plot styling and layout.\n",
+ " Available options:\n",
+ " - 'title': plot title\n",
+ " - 'xlabel', 'ylabel': axis labels\n",
+ " - 'xlabel_rotation', 'ylabel_rotation': label rotation angles\n",
+ " - 'grid': whether to show grid \n",
  " Returns\n",
  " -------\n",
  " tuple\n",
  " (figure, axes, mean_delta_dataframe) if ax is None, \n",
  " else (axes, mean_delta_dataframe)\n",
  " \"\"\"\n",
+ "\n",
+ " if heatmap_kwargs is None:\n",
+ " heatmap_kwargs = {}\n",
  " structure = multi_contrast.structure\n",
  "\n",
  " n_rows = structure['n_rows']\n",
  " n_cols = structure['n_cols']\n",
- " col_labels = structure['col_labels']\n",
+ " col_labels = structure['col_labels'] \n",
  " row_labels = structure['row_labels']\n",
  " was_1d = (structure['type'] == '1D')\n",
  "\n",
@@ -810,10 +836,13 @@
  " else:\n",
  " cbar_orientation = 'vertical'\n",
  " cbar_location = 'right'\n",
- " \n",
+ " heatmap_kwargs.setdefault('cmap', cmap)\n",
+ " heatmap_kwargs.setdefault('vmax', vmax)\n",
+ " heatmap_kwargs.setdefault('vmin', vmin)\n",
+ " heatmap_kwargs.setdefault('center', 0)\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",
+ " sns.heatmap(spirals, cbar_kws={\"shrink\": 1, \"pad\": .17, \"orientation\": cbar_orientation, \"location\": cbar_location}, \n",
+ " ax=a, **heatmap_kwargs)\n",
  " \n",
  " # Set labels\n",
  " a.set_xticks(np.linspace(n/2, n_cols*n-n/2, n_cols))\n",

nbs/_quarto.yml

@@ -16,8 +16,7 @@ website:
  style: floating
  contents:
  - auto: "/0*.ipynb"
- - auto: "tutorials/0*.ipynb" # Autogenerate a section of tutorial notebooks
- - auto: "tutorials/1*.ipynb" # Autogenerate a section of tutorial notebooks
+ - auto: "tutorials/[012]*.ipynb" # Autogenerate a section of tutorial notebooks
  - section: API
  contents: API/*
  favicon: images/Favicon-3-outline.svg