Run through all the tests after merging into dev

Author: Jacobluke-

dabest/_classes.py

@@ -2608,11 +2608,11 @@ def plot(self, color_col=None,
 
             raw_marker_size=6, es_marker_size=9,
 
-            swarm_label=None, contrast_label=None,
-            swarm_ylim=None, contrast_ylim=None,
+            swarm_label=None, barchart_label=None, contrast_label=None, delta2_label=None,
+            swarm_ylim=None, barchart_ylim=None, contrast_ylim=None, delta2_ylim=None,
 
             custom_palette=None, swarm_desat=0.5, halfviolin_desat=1,
-            halfviolin_alpha=0.8,
+            halfviolin_alpha=0.8, 
 
             #bar plot
             bar_label=None, bar_desat=0.5, bar_width = 0.5,bar_ylim = None,
@@ -2637,6 +2637,7 @@ def plot(self, color_col=None,
             reflines_kwargs=None,
             group_summary_kwargs=None,
             legend_kwargs=None):
+
         """
         Creates an estimation plot for the effect size of interest.
         

dabest/tests/test_03_plotting.py

@@ -25,7 +25,7 @@
 two_groups_unpaired = load(df, idx=("Control 1", "Test 1"))
 
 two_groups_paired   = load(df, idx=("Control 1", "Test 1"),
-                           paired=True, id_col="ID")
+                           paired="baseline", id_col="ID")
 
 multi_2group = load(df, idx=(("Control 1", "Test 1",),
                              ("Control 2", "Test 2"))
@@ -34,7 +34,7 @@
 multi_2group_paired = load(df,
                             idx=(("Control 1", "Test 1"),
                                  ("Control 2", "Test 2")),
-                            paired=True, id_col="ID")
+                            paired="baseline", id_col="ID")
 
 shared_control = load(df, idx=("Control 1", "Test 1",
                                 "Test 2", "Test 3",
@@ -137,7 +137,7 @@ def test_11_inset_plots():
     iris_dabest3 = load(data=iris_melt[iris_melt.species=="setosa"],
                         x="metric", y="value",
                         idx=("sepal_length", "sepal_width"),
-                        paired=True, id_col="index")
+                        paired="baseline", id_col="index")
 
 
 
@@ -356,4 +356,4 @@ 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/test_10_proportion_plot.py

@@ -13,7 +13,7 @@
 two_groups_unpaired = load(df, idx=("Control 1", "Test 1"))
 
 two_groups_paired   = load(df, idx=("Control 1", "Test 1"),
-                           paired=True, id_col="ID")
+                           paired="baseline", id_col="ID")
 
 multi_2group = load(df, idx=(("Control 1", "Test 1",),
                              ("Control 2", "Test 2"))
@@ -22,7 +22,7 @@
 multi_2group_paired = load(df,
                             idx=(("Control 1", "Test 1"),
                                  ("Control 2", "Test 2")),
-                            paired=True, id_col="ID")
+                            paired="baseline", id_col="ID")
 
 shared_control = load(df, idx=("Control 1", "Test 1",
                                 "Test 2", "Test 3",

dabest/tests/test_99_confint.py

@@ -24,7 +24,7 @@ def test_paired_mean_diff_ci():
 
 
     ex_bp = load(data=exercise_bp, idx=("before", "after"), 
-                 paired=True, id_col="subject_id")
+                 paired="baseline", id_col="subject_id")
     paired_mean_diff = ex_bp.mean_diff.results
 
     assert pytest.approx(3.875) == paired_mean_diff.bca_low[0]

dabest/tests/utils.py

@@ -39,6 +39,101 @@ def create_demo_dataset(seed=9999, N=20):
     return df
 
 
+
+def create_demo_dataset_rm(seed=9999, N=20):
+    
+    import numpy as np
+    import pandas as pd
+    from scipy.stats import norm # Used in generation of populations.
+
+    np.random.seed(9999) # Fix the seed so the results are replicable.
+    # pop_size = 10000 # Size of each population.
+
+    # Create samples
+    timepoint0 = norm.rvs(loc=3, scale=0.4, size=N)
+    timepoint1 = norm.rvs(loc=3.5, scale=0.75, size=N)
+    timepoint2 = norm.rvs(loc=3.25, scale=0.4, size=N)
+    timepoint3 = norm.rvs(loc=3.5, scale=0.5, size=N)
+    timepoint4 = norm.rvs(loc=2.5, scale=0.6, size=N)
+    timepoint5 = norm.rvs(loc=3, scale=0.75, size=N)
+    timepoint6 = norm.rvs(loc=3.5, scale=0.75, size=N)
+    timepoint7 = norm.rvs(loc=3.25, scale=0.4, size=N)
+    timepoint8 = norm.rvs(loc=3.25, scale=0.4, size=N)
+
+
+    # Add a `gender` column for coloring the data.
+    grp1 = np.repeat('Group 1', N/2).tolist()
+    grp2 = np.repeat('Group 2', N/2).tolist()
+    grp = grp1 + grp2
+
+    # Add an `id` column for paired data plotting. 
+    id_col = pd.Series(range(1, N+1))
+
+    # Combine samples and gender into a DataFrame.
+    df = pd.DataFrame({'Time Point 0' : timepoint0,     
+                       'Time Point 1' : timepoint1,
+                       'Time Point 2' : timepoint2,     
+                       'Time Point 3' : timepoint3,
+                       'Time Point 4' : timepoint4,     
+                       'Time Point 5' : timepoint5,
+                       'Time Point 6' : timepoint6,     
+                       'Time Point 7' : timepoint7, 
+                       'Time Point 8' : timepoint8,
+                       'Group'        : grp, 
+                       'ID'           : id_col
+                      })
+                      
+    return df
+
+
+def create_demo_dataset_delta(seed=9999, N=20):
+    
+    import numpy as np
+    import pandas as pd
+    from scipy.stats import norm # Used in generation of populations.
+
+    np.random.seed(seed) # Fix the seed so the results are replicable.
+    # pop_size = 10000 # Size of each population.
+
+    from scipy.stats import norm # Used in generation of populations.
+
+    # Create samples
+    y = norm.rvs(loc=3, scale=0.4, size=N*2)
+
+    # Add experiment column
+    e1 = np.repeat('Control', N).tolist()
+    e2 = np.repeat('Test', N).tolist()
+    experiment = e1 + e2 
+
+    # Add a `Light` column as the first variable
+    light = []
+    for i in range(N):
+        light.append('L1')
+        light.append('L2')
+
+    # Add a `genotype` column as the second variable
+    g1 = np.repeat('G1', N/2).tolist()
+    g2 = np.repeat('G2', N/2).tolist()
+    g3 = np.repeat('G3', N).tolist()
+    genotype = g1 + g2 + g3
+
+    # Add an `id` column for paired data plotting.
+    id_col = []
+    for i in range(N):
+        id_col.append(i)
+        id_col.append(i)
+
+    # Combine samples and gender into a DataFrame.
+    df = pd.DataFrame({'ID'        : id_col,
+                      'Light'      : light,
+                       'Genotype'  : genotype, 
+                       'Experiment': experiment,
+                       'Y'         : y
+                    })
+                      
+    return df
+
+
 def create_demo_prop_dataset(seed=9999, N=40):
     import numpy as np
     import pandas as pd
@@ -135,4 +230,4 @@ def get_swarm_yspans(coll, round_result=False, decimals=12):
 #     df["idcol"] = pd.Series(range(1, n+1))
 #     df.columns = [str(c) for c in df.columns]
 # 
-#     return random_seed, max_mean_diff, df
+#     return random_seed, max_mean_diff, df