Merge 775b877 into ec7600e

docs/index.md

@@ -112,7 +112,7 @@ Now, sci-analysis should be ready to use. Try the following code:
 ```python
 np.random.seed(987654321)
 data = st.norm.rvs(size=1000)
-analyze(data)
+analyze(xdata=data)
 ```
 
 

sci_analysis.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: sci-analysis
-Version: 2.1.1rc2
+Version: 2.1.1
 Summary: An easy to use and powerful python-based data exploration and analysis tool
 Home-page: https://github.com/cmmorrow/sci-analysis
 Author: chris morrow

sci_analysis.egg-info/SOURCES.txt

@@ -25,6 +25,7 @@ sci_analysis/graphs/vector.py
 sci_analysis/preferences/__init__.py
 sci_analysis/preferences/preferences.py
 sci_analysis/test/__init__.py
+sci_analysis/test/base.py
 sci_analysis/test/test_analyze.py
 sci_analysis/test/test_anova.py
 sci_analysis/test/test_cat_statistics.py

sci_analysis/graphs/vector.py

@@ -318,9 +318,12 @@ def calc_fit(self):
         """
         x = self._data.data
         y = self._data.other
-        p = polyfit(x, y, 1, full=True)
-        fit = polyval(p[0], x)
-        return (x.min(), x.max()), (fit.min(), fit.max())
+        p = polyfit(x, y, 1)
+        fit = polyval(p, x)
+        if p[0] > 0:
+            return (x.min(), x.max()), (fit.min(), fit.max())
+        else:
+            return (x.min(), x.max()), (fit.max(), fit.min())
 
     def draw(self):
         """
@@ -462,9 +465,12 @@ def calc_fit(x, y):
         fit_coordinates : list
             A list of the min and max fit points.
         """
-        p = polyfit(x, y, 1, full=True)
-        fit = polyval(p[0], x)
-        return (x.min(), x.max()), (fit.min(), fit.max())
+        p = polyfit(x, y, 1)
+        fit = polyval(p, x)
+        if p[0] > 0:
+            return (x.min(), x.max()), (fit.min(), fit.max())
+        else:
+            return (x.min(), x.max()), (fit.max(), fit.min())
 
     def draw(self):
         """

sci_analysis/test/test_graph_groupscatter.py

@@ -359,6 +359,19 @@ def test_26_scatter_three_groups_long_group_names(self):
         self.assertTrue(GraphGroupScatter(input_array['a'], input_array['b'], groups=input_array['c'],
                                           save_to='{}test_group_scatter_26'.format(self.save_path)))
 
+    def test_27_scatter_two_groups_negative_corr(self):
+        np.random.seed(987654321)
+        input_1_x = st.norm.rvs(size=100)
+        input_1_y = [x + st.norm.rvs(0, 0.5, size=1)[0] for x in input_1_x]
+        input_2_x = st.norm.rvs(size=100)
+        input_2_y = [2 - (x / 2) + st.norm.rvs(0, 0.2, size=1)[0] for x in input_2_x]
+        grp = [1] * 100 + [2] * 100
+        cs_x = np.concatenate((input_1_x, input_2_x))
+        cs_y = np.concatenate((input_1_y, input_2_y))
+        input_array = pd.DataFrame({'a': cs_x, 'b': cs_y, 'c': grp})
+        self.assertTrue(GraphGroupScatter(input_array['a'], input_array['b'], groups=input_array['c'],
+                                          save_to='{}test_group_scatter_27'.format(self.save_path)))
+
 
 if __name__ == '__main__':
     unittest.main()

sci_analysis/test/test_graph_scatter.py

@@ -314,6 +314,13 @@ def test_131_scatter_empty_vector(self):
         vector = Vector()
         self.assertRaises(NoDataError, lambda: GraphScatter(vector))
 
+    def test_132_negative_corr(self):
+        """Generate a scatter plot with correlating data"""
+        np.random.seed(987654321)
+        input_x_array = st.weibull_min.rvs(2, size=2000)
+        input_y_array = np.array([3 - x + st.norm.rvs(0, 0.5, size=1) for x in input_x_array])
+        self.assertTrue(GraphScatter(input_x_array, input_y_array, save_to='{}test_scatter_132'.format(self.save_path)))
+
 
 if __name__ == '__main__':
     unittest.main()

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='sci_analysis',
-    version='2.1.1',
+    version='2.1.2',
     description='An easy to use and powerful python-based data exploration and analysis tool',
     long_description=long_description,
     long_description_content_type='text/markdown',