Refactoring phase 1

README.md

@@ -1,5 +1,4 @@
-DABEST-Python
-================
+# DABEST-Python
 
 <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 

dabest/__init__.py

@@ -1,5 +1,6 @@
 from ._api import load, prop_dataset
 from ._stats_tools import effsize as effsize
-from ._classes import TwoGroupsEffectSize, PermutationTest
+from ._effsize_objects import TwoGroupsEffectSize, PermutationTest
+from ._dabest_object import Dabest
 
 __version__ = "2023.03.29"

dabest/_api.py

@@ -4,11 +4,24 @@
 __all__ = ['load', 'prop_dataset']
 
 # %% ../nbs/API/load.ipynb 4
-def load(data, idx=None, x=None, y=None, paired=None, id_col=None,
-        ci=95, resamples=5000, random_seed=12345, proportional=False, 
-        delta2 = False, experiment = None, experiment_label = None,
-        x1_level = None, mini_meta=False):
-    '''
+def load(
+    data,
+    idx=None,
+    x=None,
+    y=None,
+    paired=None,
+    id_col=None,
+    ci=95,
+    resamples=5000,
+    random_seed=12345,
+    proportional=False,
+    delta2=False,
+    experiment=None,
+    experiment_label=None,
+    x1_level=None,
+    mini_meta=False,
+):
+    """
     Loads data in preparation for estimation statistics.
 
     This is designed to work with pandas DataFrames.
@@ -22,15 +35,15 @@ def load(data, idx=None, x=None, y=None, paired=None, id_col=None,
         with each individual tuple producing its own contrast plot
     x : string or list, default None
         Column name(s) of the independent variable. This can be expressed as
-        a list of 2 elements if and only if 'delta2' is True; otherwise it 
+        a list of 2 elements if and only if 'delta2' is True; otherwise it
         can only be a string.
     y : string, default None
         Column names for data to be plotted on the x-axis and y-axis.
     paired : string, default None
-        The type of the experiment under which the data are obtained. If 'paired' 
+        The type of the experiment under which the data are obtained. If 'paired'
         is None then the data will not be treated as paired data in the subsequent
-        calculations. If 'paired' is 'baseline', then in each tuple of x, other 
-        groups will be paired up with the first group (as control). If 'paired' is 
+        calculations. If 'paired' is 'baseline', then in each tuple of x, other
+        groups will be paired up with the first group (as control). If 'paired' is
         'sequential', then in each tuple of x, each group will be paired up with
         its previous group (as control).
     id_col : default None.
@@ -45,7 +58,7 @@ def load(data, idx=None, x=None, y=None, paired=None, id_col=None,
         This integer is used to seed the random number generator during
         bootstrap resampling, ensuring that the confidence intervals
         reported are replicable.
-    proportional : boolean, default False. 
+    proportional : boolean, default False.
         An indicator of whether the data is binary or not. When set to True, it
         specifies that the data consists of binary data, where the values are
         limited to 0 and 1. The code is not suitable for analyzing proportion
@@ -55,76 +68,112 @@ def load(data, idx=None, x=None, y=None, paired=None, id_col=None,
     delta2 : boolean, default False
         Indicator of delta-delta experiment
     experiment : String, default None
-        The name of the column of the dataframe which contains the label of 
+        The name of the column of the dataframe which contains the label of
         experiments
     experiment_lab : list, default None
         A list of String to specify the order of subplots for delta-delta plots.
-        This can be expressed as a list of 2 elements if and only if 'delta2' 
-        is True; otherwise it can only be a string. 
+        This can be expressed as a list of 2 elements if and only if 'delta2'
+        is True; otherwise it can only be a string.
     x1_level : list, default None
         A list of String to specify the order of subplots for delta-delta plots.
-        This can be expressed as a list of 2 elements if and only if 'delta2' 
-        is True; otherwise it can only be a string. 
+        This can be expressed as a list of 2 elements if and only if 'delta2'
+        is True; otherwise it can only be a string.
     mini_meta : boolean, default False
         Indicator of weighted delta calculation.
 
     Returns
     -------
     A `Dabest` object.
-    '''
-    from ._classes import Dabest
-
-    return Dabest(data, idx, x, y, paired, id_col, ci, resamples, random_seed, proportional, delta2, experiment, experiment_label, x1_level, mini_meta)
-
-
+    """
+    from dabest import Dabest
+
+    return Dabest(
+        data,
+        idx,
+        x,
+        y,
+        paired,
+        id_col,
+        ci,
+        resamples,
+        random_seed,
+        proportional,
+        delta2,
+        experiment,
+        experiment_label,
+        x1_level,
+        mini_meta,
+    )
 
 # %% ../nbs/API/load.ipynb 5
 import numpy as np
 from typing import Union, Optional
+import pandas as pd
 
-def prop_dataset(group:Union[list, tuple, np.ndarray, dict], #Accepts lists, tuples, or numpy ndarrays of numeric types.
-                 group_names: Optional[list] = None):
-    '''
+
+def prop_dataset(
+    group: Union[
+        list, tuple, np.ndarray, dict
+    ],  # Accepts lists, tuples, or numpy ndarrays of numeric types.
+    group_names: Optional[list] = None,
+):
+    """
     Convenient function to generate a dataframe of binary data.
-    '''
-    import pandas as pd
+    """
 
     if isinstance(group, dict):
         # If group_names is not provided, use the keys of the dict as group_names
         if group_names is None:
             group_names = list(group.keys())
         elif not set(group_names) == set(group.keys()):
             # Check if the group_names provided is the same as the keys of the dict
-            raise ValueError('group_names must be the same as the keys of the dict.')
+            raise ValueError("group_names must be the same as the keys of the dict.")
         # Check if the values in the dict are numeric
-        if not all([isinstance(group[name], (list, tuple, np.ndarray)) for name in group_names]):
-            raise ValueError('group must be a dict of lists, tuples, or numpy ndarrays of numeric types.')
+        if not all(
+            [isinstance(group[name], (list, tuple, np.ndarray)) for name in group_names]
+        ):
+            raise ValueError(
+                "group must be a dict of lists, tuples, or numpy ndarrays of numeric types."
+            )
         # Check if the values in the dict only have two elements under each parent key
         if not all([len(group[name]) == 2 for name in group_names]):
-            raise ValueError('Each parent key should have only two elements.')
+            raise ValueError("Each parent key should have only two elements.")
         group_val = group
 
     else:
         if group_names is None:
-            raise ValueError('group_names must be provided if group is not a dict.')
+            raise ValueError("group_names must be provided if group is not a dict.")
         # Check if the length of group is two times of the length of group_names
         if not len(group) == 2 * len(group_names):
-            raise ValueError('The length of group must be two times of the length of group_names.')
-        group_val = {group_names[i]: [group[i*2], group[i*2+1]] for i in range(len(group_names))}
+            raise ValueError(
+                "The length of group must be two times of the length of group_names."
+            )
+        group_val = {
+            group_names[i]: [group[i * 2], group[i * 2 + 1]]
+            for i in range(len(group_names))
+        }
 
     # Check if the sum of values in group_val under each key are the same
-    if not all([sum(group_val[name]) == sum(group_val[group_names[0]]) for name in group_val.keys()]):
-        raise ValueError('The sum of values under each key must be the same.')
-
-    id_col = pd.Series(range(1, sum(group_val[group_names[0]])+1))
-
+    if not all(
+        [
+            sum(group_val[name]) == sum(group_val[group_names[0]])
+            for name in group_val.keys()
+        ]
+    ):
+        raise ValueError("The sum of values under each key must be the same.")
+
+    id_col = pd.Series(range(1, sum(group_val[group_names[0]]) + 1))
+
     final_df = pd.DataFrame()
 
     for name in group_val.keys():
-        col = np.repeat(0, group_val[name][0]).tolist() + np.repeat(1, group_val[name][1]).tolist()
-        df = pd.DataFrame({name:col})
+        col = (
+            np.repeat(0, group_val[name][0]).tolist()
+            + np.repeat(1, group_val[name][1]).tolist()
+        )
+        df = pd.DataFrame({name: col})
         final_df = pd.concat([final_df, df], axis=1)
 
-    final_df['ID'] = id_col
+    final_df["ID"] = id_col
 
     return final_df