Merge pull request #80 from ACCLAB/v0.2.7

v0.2.7

Author: josesho

.travis.yml

@@ -5,6 +5,7 @@ env:
   - PYTHON=3.5 BACKEND=agg
   - PYTHON=3.6 BACKEND=agg
   - PYTHON=3.7 BACKEND=agg
+  # - PYTHON=3.8 BACKEND=agg
 
 
 before_install:
@@ -21,6 +22,7 @@ install:
   - conda update conda --yes
   - conda config --add channels conda-forge
   - conda create -n testenv --yes pip python=$PYTHON matplotlib
+  # - if [ "$PYTHON" = "3.8" ]; then conda create -n testenv --yes --channel conda-forge/label/pre-3.8 pip python=$PYTHON; else conda create -n testenv --yes pip python=$PYTHON matplotlib; fi
   - source activate testenv
   - pip install .[dev]
 

README.md

@@ -2,11 +2,10 @@
 [![Travis CI build status](https://travis-ci.org/ACCLAB/DABEST-python.svg?branch=master)](https://travis-ci.org/ACCLAB/DABEST-python)
 [![minimal Python version](https://img.shields.io/badge/Python%3E%3D-3.5-6666ff.svg)](https://www.anaconda.com/distribution/)
 [![PyPI version](https://badge.fury.io/py/dabest.svg)](https://badge.fury.io/py/dabest)
-[![PyPI Downloads](https://img.shields.io/pypi/dm/dabest?color=bright-green)](https://pypistats.org/packages/dabest)
+[![Downloads](https://pepy.tech/badge/dabest/month)](https://pepy.tech/project/dabest/month)
 [![Free-to-view citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://rdcu.be/bHhJ4)
 [![License](https://img.shields.io/badge/License-BSD%203--Clause--Clear-orange.svg)](https://spdx.org/licenses/BSD-3-Clause-Clear.html)
 
-
 ## About
 
 DABEST is a package for **D**ata **A**nalysis using **B**ootstrap-Coupled **EST**imation.

dabest/__init__.py

@@ -23,4 +23,4 @@
 from ._stats_tools import effsize as effsize
 from ._classes import TwoGroupsEffectSize 
 
-__version__ = "0.2.6"
+__version__ = "0.2.7"

dabest/_classes.py

@@ -143,6 +143,11 @@ def __init__(self, data, idx, x, y, paired, id_col, ci, resamples,
                                 value_vars=all_plot_groups,
                                 value_name=self.__yvar,
                                 var_name=self.__xvar)
+                                
+        # Added in v0.2.7.
+        # remove any NA rows.
+        plot_data.dropna(axis=0, how='any', subset=[self.__yvar], inplace=True)
+
 
         # Lines 131 to 140 added in v0.2.3.
         # Fixes a bug that jammed up when the xvar column was already 

dabest/tests/baseline_images/test_24_test_wide_df_nan.png

@@ -6,6 +6,9 @@
 import pytest
 import numpy as np
 import pandas as pd
+from scipy.stats import norm
+
+
 import matplotlib as mpl
 mpl.use('Agg')
 import matplotlib.pyplot as plt
@@ -42,13 +45,13 @@
                                      ("Control 3", "Test 4","Test 5", "Test 6")
                                    ))
 
-                             
-                             
+
+
 @pytest.mark.mpl_image_compare
 def test_01_gardner_altman_unpaired_meandiff():
     return two_groups_unpaired.mean_diff.plot();
-    
-    
+
+
 
 @pytest.mark.mpl_image_compare
 def test_02_gardner_altman_unpaired_mediandiff():
@@ -59,15 +62,15 @@ def test_02_gardner_altman_unpaired_mediandiff():
 @pytest.mark.mpl_image_compare
 def test_03_gardner_altman_unpaired_hedges_g():
     return two_groups_unpaired.hedges_g.plot();
-    
-    
-    
+
+
+
 @pytest.mark.mpl_image_compare
 def test_04_gardner_altman_paired_meandiff():
     return two_groups_paired.mean_diff.plot();
-    
-    
-    
+
+
+
 @pytest.mark.mpl_image_compare
 def test_04_gardner_altman_paired_hedges_g():
     return two_groups_paired.hedges_g.plot();
@@ -78,41 +81,40 @@ def test_04_gardner_altman_paired_hedges_g():
 def test_05_cummings_two_group_unpaired_meandiff():
     return two_groups_unpaired.mean_diff.plot(fig_size=(4, 6),
                                               float_contrast=False);
-    
-    
-    
+
+
+
 @pytest.mark.mpl_image_compare
 def test_06_cummings_two_group_paired_meandiff():
     return two_groups_paired.mean_diff.plot(fig_size=(6, 6),
                                             float_contrast=False);
-    
-    
-    
+
+
+
 @pytest.mark.mpl_image_compare
 def test_07_cummings_multi_group_unpaired():
     return multi_2group.mean_diff.plot();
-        
-    
-    
-    
+
+
+
 @pytest.mark.mpl_image_compare
 def test_08_cummings_multi_group_paired():
     return multi_2group_paired.mean_diff.plot(fig_size=(6, 6));
-    
-    
-    
+
+
+
 @pytest.mark.mpl_image_compare
 def test_09_cummings_shared_control():
     return shared_control.mean_diff.plot();
-    
-    
+
+
 
 @pytest.mark.mpl_image_compare
 def test_10_cummings_multi_groups():
     return multi_groups.mean_diff.plot();
-    
-    
-    
+
+
+
 @pytest.mark.mpl_image_compare(tolerance=20)
 def test_11_inset_plots():
 
@@ -164,27 +166,29 @@ def test_12_gardner_altman_ylabel():
 @pytest.mark.mpl_image_compare
 def test_13_multi_2group_color():
     return multi_2group.mean_diff.plot(color_col="Gender");
-    
-    
-    
+
+
+
 @pytest.mark.mpl_image_compare
 def test_14_gardner_altman_paired_color():
     return two_groups_paired.mean_diff.plot(fig_size=(6, 6),
                                             color_col="Gender");
-    
-    
+
+
+
 @pytest.mark.mpl_image_compare
 def test_15_change_palette_a():
     return multi_2group.mean_diff.plot(fig_size=(7, 6),
                                        color_col="Gender", 
                                        custom_palette="Dark2");
-    
-    
+
+
+
 @pytest.mark.mpl_image_compare
 def test_16_change_palette_b():
     return multi_2group.mean_diff.plot(custom_palette="Paired");
-    
-    
+
+
 
 my_color_palette = {"Control 1" : "blue",    
                 "Test 1"    : "purple",
@@ -203,28 +207,28 @@ def test_18_desat():
     return multi_2group.mean_diff.plot(custom_palette=my_color_palette, 
                             swarm_desat=0.75, 
                             halfviolin_desat=0.25);
-                            
-                            
-                                                    
+
+
+
 @pytest.mark.mpl_image_compare                            
 def test_19_dot_sizes():
     return multi_2group.mean_diff.plot(raw_marker_size=3, 
                                        es_marker_size=12);
-                                       
-                                       
-                                       
-                                       
+
+
+
 @pytest.mark.mpl_image_compare
 def test_20_change_ylims():
     return multi_2group.mean_diff.plot(swarm_ylim=(0, 5), 
                                        contrast_ylim=(-2, 2));
-                                       
-                                       
+
+
+
 @pytest.mark.mpl_image_compare
 def test_21_invert_ylim():
     return multi_2group.mean_diff.plot(contrast_ylim=(2, -2), 
                                        contrast_label="More negative is better!");
-                                       
+
 
 
 @pytest.mark.mpl_image_compare
@@ -242,9 +246,9 @@ def test_22_ticker_gardner_altman():
     contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25))
 
     return f
-    
-    
-    
+
+
+
 @pytest.mark.mpl_image_compare
 def test_23_ticker_cumming():
     f = multi_2group.mean_diff.plot(swarm_ylim=(0,6),
@@ -260,11 +264,52 @@ def test_23_ticker_cumming():
     contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25))
 
     return f
-    
-    
-    
+
+
+
+np.random.seed(9999)
+Ns = [20, 10, 21, 20]
+c1 = pd.DataFrame({'Control':norm.rvs(loc=3, scale=0.4, size=Ns[0])})
+t1 = pd.DataFrame({'Test 1': norm.rvs(loc=3.5, scale=0.5, size=Ns[1])})
+t2 = pd.DataFrame({'Test 2': norm.rvs(loc=2.5, scale=0.6, size=Ns[2])})
+t3 = pd.DataFrame({'Test 3': norm.rvs(loc=3, scale=0.75, size=Ns[3])})
+wide_df = pd.concat([c1, t1, t2, t3],axis=1)
+
+
+long_df = pd.melt(wide_df, 
+              value_vars=["Control", "Test 1", "Test 2", "Test 3"],
+                value_name="value",
+                var_name="group")
+long_df['dummy'] = np.repeat(np.nan, len(long_df))
+
+
+
 @pytest.mark.mpl_image_compare
-def test_25_style_sheets():
+def test_24_test_wide_df_nan():
+
+    wide_df_dabest = load(wide_df, 
+                          idx=("Control", "Test 1", "Test 2", "Test 3")
+                          )
+
+    return wide_df_dabest.mean_diff.plot(); 
+
+
+
+@pytest.mark.mpl_image_compare
+def test_25_test_long_df_nan():
+
+    long_df_dabest = load(long_df, x="group", y="value",
+                          idx=("Control", "Test 1", "Test 2", "Test 3")
+                          )
+
+    return long_df_dabest.mean_diff.plot(); 
+
+
+
+@pytest.mark.mpl_image_compare
+def test_99_style_sheets():
+    # Perform this test last so we don't have to reset the plot style.
     plt.style.use("dark_background")
 
-    return multi_2group.mean_diff.plot();
+    return multi_2group.mean_diff.plot();
+    

dabest/tests/baseline_images/test_25_test_long_df_nan.png

@@ -77,7 +77,7 @@
 # The short X.Y version.
 version = '0.2'
 # The full version, including alpha/beta/rc tags.
-release = '0.2.6'
+release = '0.2.7'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

…baseline_images/test_25_style_sheets.png …baseline_images/test_99_style_sheets.pngdabest/tests/baseline_images/test_25_style_sheets.png renamed to dabest/tests/baseline_images/test_99_style_sheets.png dabest/tests/baseline_images/test_25_style_sheets.png renamed to dabest/tests/baseline_images/test_99_style_sheets.png

@@ -19,7 +19,8 @@ Analyze your data with estimation statistics!
 News
 ----
 October 2019:
-  - v0.2.6 released. This release has one new feature (plotting of estimation plot inside any :py:mod:`matplotlib` :py:class:`Axes`; see the section on :ref:`inset_plot` in the :doc:`tutorial`). There are also two bug patches for the handling of bootstrap plotting, and of dependency installation. For more information, see the :doc:`release-notes`.
+  - v0.2.7 released. A minor bugfix in the handling of wide datasets with unequal Ns in each group. For more information, see the :doc:`release-notes`.
+  - v0.2.6 released. This release has one new feature (plotting of estimation plot inside any :py:mod:`matplotlib` :py:class:`Axes`; see the section on :ref:`inset_plot` in the :doc:`tutorial`). There are also two bug patches for the handling of bootstrap plotting, and of dependency installation. 
 
 September 2019:
   - v0.2.5 released. This release addresses two feature requests, and also patches two bugs: one affecting the paired difference CIs, and one involving NaNs in unused/irrelevant columns.