Merge branch 'repeated_measures_without_paired' into v0.4dev

Author: LI-Yixuan

dabest/plotter.py

@@ -332,23 +332,9 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
             pivot_values = [yvar, color_col]
         pivoted_plot_data = pd.pivot(data=plot_data, index=dabest_obj.id_col,
                                      columns=xvar, values=pivot_values)
-        if is_paired == "baseline":
-            temp_idx = []
-            for i in idx:
-                control = i[0]
-                temp_idx.extend(((control, test) for test in i[1:]))
-            temp_idx = tuple(temp_idx)
-
-            temp_all_plot_groups = []
-            for i in temp_idx:
-                temp_all_plot_groups.extend(list(i))
-        else:
-            temp_idx = idx
-            temp_all_plot_groups = all_plot_groups
-        
         x_start = 0
-        for ii, current_tuple in enumerate(temp_idx):
-            if len(temp_idx) > 1:
+        for ii, current_tuple in enumerate(idx):
+            if len(idx) > 1:
                 # Select only the data for the current tuple.
                 if color_col is None:
                     current_pair = pivoted_plot_data.reindex(columns=current_tuple)
@@ -377,8 +363,8 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
                 rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs)
             x_start  = x_start + grp_count
         # Set the tick labels, because the slopegraph plotting doesn't.
-        rawdata_axes.set_xticks(np.arange(0, len(temp_all_plot_groups)))
-        rawdata_axes.set_xticklabels(temp_all_plot_groups)
+        rawdata_axes.set_xticks(np.arange(0, len(all_plot_groups)))
+        rawdata_axes.set_xticklabels(all_plot_groups)
 
 
     else:
@@ -445,19 +431,12 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
 
     # Plot effect sizes and bootstraps.
     # Take note of where the `control` groups are.
-    if is_paired == "baseline" and show_pairs == True:
-        ticks_to_skip = np.arange(0, len(temp_all_plot_groups), 2).tolist()
-        ticks_to_plot = np.arange(1, len(temp_all_plot_groups), 2).tolist() 
-        ticks_to_skip_contrast = np.cumsum([(len(t)-1)*2 for t in idx])[:-1].tolist()
-        ticks_to_skip_contrast.insert(0, 0)
-
-    else:
-        ticks_to_skip   = np.cumsum([len(t) for t in idx])[:-1].tolist()
-        ticks_to_skip.insert(0, 0)
+    ticks_to_skip   = np.cumsum([len(t) for t in idx])[:-1].tolist()
+    ticks_to_skip.insert(0, 0)
 
         # Then obtain the ticks where we have to plot the effect sizes.
-        ticks_to_plot = [t for t in range(0, len(all_plot_groups))
-                        if t not in ticks_to_skip]
+    ticks_to_plot = [t for t in range(0, len(all_plot_groups))
+                    if t not in ticks_to_skip]
 
 
     # Plot the bootstraps, then the effect sizes and CIs.
@@ -704,55 +683,22 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
         if contrast_ylim_low < 0 < contrast_ylim_high:
             contrast_axes.axhline(y=0, **reflines_kwargs)
 
-        if is_paired == "baseline" and show_pairs == True:
-            rightend_ticks_raw = np.array([len(i)-1 for i in temp_idx]) + np.array(ticks_to_skip)
-            for ax in [rawdata_axes]:
-                sns.despine(ax=ax, bottom=True)
-        
-                ylim = ax.get_ylim()
-                xlim = ax.get_xlim()
-                redraw_axes_kwargs['y'] = ylim[0]
-        
-                for k, start_tick in enumerate(ticks_to_skip):
-                    end_tick = rightend_ticks_raw[k]
-                    ax.hlines(xmin=start_tick, xmax=end_tick,
-                              **redraw_axes_kwargs)
-        
-                ax.set_ylim(ylim)
-                del redraw_axes_kwargs['y']
-
-            rightend_ticks_contrast = np.array([(len(i)-1)*2-1 for i in idx]) + np.array(ticks_to_skip_contrast)
-            for ax in [contrast_axes]:
-                sns.despine(ax=ax, bottom=True)
-        
-                ylim = ax.get_ylim()
-                xlim = ax.get_xlim()
-                redraw_axes_kwargs['y'] = ylim[0]
-        
-                for k, start_tick in enumerate(ticks_to_skip_contrast):
-                    end_tick = rightend_ticks_contrast[k]
-                    ax.hlines(xmin=start_tick, xmax=end_tick,
-                              **redraw_axes_kwargs)
-        
-                ax.set_ylim(ylim)
-                del redraw_axes_kwargs['y']
-        else:
-            # Compute the end of each x-axes line.
-            rightend_ticks = np.array([len(i)-1 for i in idx]) + np.array(ticks_to_skip)
-            for ax in [rawdata_axes, contrast_axes]:
-                sns.despine(ax=ax, bottom=True)
-        
-                ylim = ax.get_ylim()
-                xlim = ax.get_xlim()
-                redraw_axes_kwargs['y'] = ylim[0]
+        # Compute the end of each x-axes line.
+        rightend_ticks = np.array([len(i)-1 for i in idx]) + np.array(ticks_to_skip)
+        for ax in [rawdata_axes, contrast_axes]:
+            sns.despine(ax=ax, bottom=True)
+    
+            ylim = ax.get_ylim()
+            xlim = ax.get_xlim()
+            redraw_axes_kwargs['y'] = ylim[0]
 
-                for k, start_tick in enumerate(ticks_to_skip):
-                    end_tick = rightend_ticks[k]
-                    ax.hlines(xmin=start_tick, xmax=end_tick,
-                              **redraw_axes_kwargs)
+            for k, start_tick in enumerate(ticks_to_skip):
+                end_tick = rightend_ticks[k]
+                ax.hlines(xmin=start_tick, xmax=end_tick,
+                          **redraw_axes_kwargs)
 
-                ax.set_ylim(ylim)
-                del redraw_axes_kwargs['y']
+            ax.set_ylim(ylim)
+            del redraw_axes_kwargs['y']
 
 
 

proportional.py

@@ -0,0 +1,15 @@
+from scipy.stats import norm, bernoulli
+import pandas as pd
+import dabest
+import pylab
+
+control = bernoulli.rvs(0.3, loc=0, size=1000, random_state=12345)
+test    = bernoulli.rvs(0.4, loc=0, size=1000, random_state=12345)
+test2    = bernoulli.rvs(0.5, loc=0, size=1000, random_state=12345)
+
+my_df   = pd.DataFrame({"control": control,
+                            "test": test,
+                        "t2":test2})
+my_dabest_object = dabest.load(my_df, idx=("control", "test", "t2"), proportional=True)
+my_dabest_object.mean_diff.plot()
+pylab.show()