Merge pull request #20 from josesho/v0.1.7

v0.1.7

Author: josesho

.travis.yml

@@ -34,7 +34,7 @@ install:
   - conda update conda --yes
   - conda create -n testenv --yes pip python=$PYTHON matplotlib
   - source activate testenv
-  - pip install pytest==3.7
+  - pip install pytest==4.0
   - pip install .
 
 script: pytest

README.md

@@ -22,6 +22,8 @@ DABEST powers [estimationstats.com](https://www.estimationstats.com/), allowing
 
 DABEST has been tested on Python 2.7, 3.5, 3.6, and 3.7.
 
+_Python 2.7 will no longer be supported from dabest v0.2.0 onwards, as [Python 2.7 will no longer be supported after January 1 2020](https://python3statement.org/)._ dabest v.0.2.0 is under development, and will be released before the end of 2019.
+
 In addition, the following packages are also required:
 - [numpy](https://www.numpy.org) (1.15)
 - [scipy](https://www.scipy.org) (1.1)

dabest/__init__.py

@@ -1,4 +1,4 @@
 from ._api import plot
 from ._bootstrap_tools import bootstrap
 
-__version__="0.1.6"
+__version__="0.1.7"

dabest/_api.py

@@ -34,6 +34,7 @@ def plot(
         ci_linewidth=3,
         summary_linewidth=3,
         multiplot_horizontal_spacing=0.75,
+        cumming_vertical_spacing=0.1,
         font_scale=1,
         dpi=100,
         tick_length=10,
@@ -170,6 +171,12 @@ def plot(
             this determines the horizontal spacing between each plot. This
             number is a fraction of the axes width.
 
+        cumming_vertical_spacing: float, default 0.05
+            The amount of height reserved for space between the swarmplot and
+            the contrast plots, expressed as a fraction of the average axis
+            height.
+
+
         font_scale: float, default 1.
             The font size will be scaled by this number. This value is passed
             on to seaborn.set().
@@ -529,6 +536,12 @@ def plot(
     else:
         ws = 0
 
+    # Set appropriate vertical spacing between subplots,
+    # based on whether the contrast is floating.
+    if float_contrast is False:
+        hs = cumming_vertical_spacing
+    else:
+        hs = 0
 
     # infer the figsize.
     if color_col is None:
@@ -553,7 +566,7 @@ def plot(
     if fig_size is None:
         fig_size = fsize
 
-    gridspecs = {'width_ratios': widthratio, 'wspace' : ws, 'hspace': 0}
+    gridspecs = {'width_ratios': widthratio, 'wspace' : ws, 'hspace': hs}
     subplots_args = {'ncols': ncols, 'figsize':fig_size, 'dpi':dpi,
                      'gridspec_kw': gridspecs}
 

dabest/_bootstrap_tools.py

@@ -30,7 +30,7 @@ class bootstrap:
 
         alpha: float, default 0.05
             Denotes the likelihood that the confidence interval produced
-            _does not_ include the true summary statistic. When alpha = 0.05,
+            does not include the true summary statistic. When alpha = 0.05,
             a 95% confidence interval is produced.
 
         reps: int, default 5000

dabest/tests/test_bootstrap.py

@@ -14,7 +14,6 @@
 
 
 
-@pytest.fixture
 def create_dummy_dataset(seed=None, n=30, base_mean=0, expt_groups=6,
                          scale_means=2, scale_std=1.2):
     """
@@ -53,58 +52,39 @@ def create_dummy_dataset(seed=None, n=30, base_mean=0, expt_groups=6,
     return random_seed, max_mean_diff, df
 
 
-@pytest.fixture
 def is_difference(result):
     assert result.is_difference == True
 
-
-@pytest.fixture
 def is_paired(result):
     assert result.is_paired == True
 
-
-@pytest.fixture
 def check_pvalue_1samp(result):
     assert result.pvalue_1samp_ttest != 'NIL'
 
-
-@pytest.fixture
 def check_pvalue_2samp_unpaired(result):
     assert result.pvalue_2samp_ind_ttest != 'NIL'
 
-
-@pytest.fixture
 def check_pvalue_2samp_paired(result):
     assert result.pvalue_2samp_related_ttest != 'NIL'
 
-
-@pytest.fixture
 def check_mann_whitney(result):
     """Nonparametric unpaired"""
     assert result.pvalue_mann_whitney != 'NIL'
     assert result.pvalue_wilcoxon == 'NIL'
 
-
-@pytest.fixture
 def check_wilcoxon(result):
     """Nonparametric Paired"""
     assert result.pvalue_wilcoxon != 'NIL'
     assert result.pvalue_mann_whitney == 'NIL'
 
+# def test_mean_within_ci_bca(mean, result):
+#     assert mean >= result.bca_ci_low
+#     assert mean <= result.bca_ci_high
+#
+# def test_mean_within_ci_pct(mean, result):
+#     assert mean >= result.pct_ci_low
+#     assert mean <= result.pct_ci_high
 
-@pytest.fixture
-def test_mean_within_ci_bca(mean, result):
-    assert mean >= result.bca_ci_low
-    assert mean <= result.bca_ci_high
-
-
-@pytest.fixture
-def test_mean_within_ci_pct(mean, result):
-    assert mean >= result.pct_ci_low
-    assert mean <= result.pct_ci_high
-
-
-@pytest.fixture
 def single_samp_stat_tests(sample, result):
 
     assert result.is_difference == False
@@ -113,8 +93,6 @@ def single_samp_stat_tests(sample, result):
     ttest_result = sp.stats.ttest_1samp(sample, 0).pvalue
     assert result.pvalue_1samp_ttest == pytest.approx(ttest_result)
 
-
-@pytest.fixture
 def unpaired_stat_tests(control, expt, result):
     is_difference(result)
     check_pvalue_2samp_unpaired(result)
@@ -130,8 +108,6 @@ def unpaired_stat_tests(control, expt, result):
                                                 alternative='two-sided').pvalue
     assert result.pvalue_mann_whitney == pytest.approx(mann_whitney_result)
 
-
-@pytest.fixture
 def paired_stat_tests(control, expt, result):
     is_difference(result)
     is_paired(result)
@@ -146,9 +122,7 @@ def paired_stat_tests(control, expt, result):
     wilcoxon_result = sp.stats.wilcoxon(control, expt).pvalue
     assert result.pvalue_wilcoxon == pytest.approx(wilcoxon_result)
 
-
-@pytest.fixture
-def does_ci_captures_mean_diff(control, expt, paired, nreps=100, alpha=0.05):
+def does_ci_capture_mean_diff(control, expt, paired, nreps=100, alpha=0.05):
     if expt is None:
         mean_diff = control.mean()
     else:
@@ -206,7 +180,7 @@ def test_single_sample_bootstrap(mean=100, sd=10, n=25, nreps=100, alpha=0.05):
     results = bst.bootstrap(sample, alpha_level=alpha)
     single_samp_stat_tests(sample, results)
 
-    does_ci_captures_mean_diff(sample, None, False, nreps, alpha)
+    does_ci_capture_mean_diff(sample, None, False, nreps, alpha)
 
 
 
@@ -225,7 +199,7 @@ def test_unpaired_difference(mean=100, sd=10, n=25, nreps=100, alpha=0.05):
     results = bst.bootstrap(sample1, sample2, paired=False, alpha_level=alpha)
     unpaired_stat_tests(sample1, sample2, results)
 
-    does_ci_captures_mean_diff(sample1, sample2, False, nreps, alpha)
+    does_ci_capture_mean_diff(sample1, sample2, False, nreps, alpha)
 
 
 
@@ -248,4 +222,4 @@ def test_paired_difference(mean=100, sd=10, n=25, nreps=100, alpha=0.05):
     results = bst.bootstrap(sample1, sample2, alpha_level=alpha, paired=True)
     paired_stat_tests(sample1, sample2, results)
 
-    does_ci_captures_mean_diff(sample1, sample2, True, nreps, alpha)
+    does_ci_capture_mean_diff(sample1, sample2, True, nreps, alpha)

dabest/tests/test_plotting.py

@@ -14,8 +14,6 @@
 from .. import _api
 
 
-# Fixtures.
-@pytest.fixture
 def create_dummy_dataset(seed=None, n=30, base_mean=0, expt_groups=6,
                          scale_means=2, scale_std=1.2):
     """
@@ -56,7 +54,6 @@ def create_dummy_dataset(seed=None, n=30, base_mean=0, expt_groups=6,
 
 
 
-@pytest.fixture
 def get_swarm_yspans(coll, round_result=False, decimals=12):
     """
     Given a matplotlib Collection, will obtain the y spans

docs/source/dabest-r.rst renamed to docs/source/_archive/dabest-r.rst0

@@ -57,7 +57,7 @@
 # The short X.Y version.
 version = '0.1'
 # The full version, including alpha/beta/rc tags.
-release = '0.1.5'
+release = '0.1.7'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/source/conf.py

@@ -8,30 +8,32 @@ DABEST
 -----------------------------------------------
 Data Analysis with Bootstrap-coupled ESTimation
 -----------------------------------------------
-*version 0.1.6*
+*version 0.1.7*
 
 Analyze your data with effect sizes!
 
 .. image:: _images/showpiece.png
 
+
 News
 ----
+January 2019
+  - Release of v0.1.7. *Added `cumming_vertical_spacing` option.* See the :doc:`release-notes`.
+
 October 2018
  - Release of v0.1.6. *Added more keywords for control of plot elements.*  See the :doc:`release-notes`.
 
-
 July 2018
  - Release of v0.1.5. *bugfix for setup and package management*
  - Release of v0.1.4.
 
-
 June 2018
  - Release of v0.1.3.
- - Added a short tutorial :doc:`dabest-r`!
 
 December 2017
  - We have made a `webapp <https://www.estimationstats.com>`_ that produces Gardner-Altman and Cumming plots!
 
+
 Contents
 --------
 
@@ -41,7 +43,6 @@ Contents
    background
    getting-started
    tutorial
-   dabest-r
    release-notes
    about
    api