Adapt to updated nbdev3

Author: Jacobluke-

.github/workflows/deploy.yaml

@@ -11,4 +11,4 @@ on:
 jobs:
   deploy:
     runs-on: ubuntu-latest
-    steps: [uses: fastai/workflows/quarto-ghp@master]
+    steps: [uses: fastai/workflows/quarto-ghp3@master]

.github/workflows/test-nbdev.yaml

@@ -4,4 +4,4 @@ on:  [workflow_dispatch, pull_request, push]
 jobs:
   test-nbdev:
     runs-on: ubuntu-latest
-    steps: [uses: fastai/workflows/nbdev-ci@master]
+    steps: [uses: fastai/workflows/nbdev3-ci@master]

dabest/_api.py

@@ -2,10 +2,10 @@
 
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/load.ipynb.
 
-# %% auto 0
+# %% auto #0
 __all__ = ['load', 'prop_dataset']
 
-# %% ../nbs/API/load.ipynb 4
+# %% ../nbs/API/load.ipynb #3c3d8162
 def load(
     data,
     idx=None,
@@ -112,7 +112,7 @@ def load(
         ps_adjust,
     )
 
-# %% ../nbs/API/load.ipynb 5
+# %% ../nbs/API/load.ipynb #0046e04c
 import numpy as np
 from typing import Union, Optional
 import pandas as pd

dabest/_bootstrap_tools.py

@@ -1,9 +1,9 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/bootstrap.ipynb.
 
-# %% auto 0
+# %% auto #0
 __all__ = ['bootstrap', 'jackknife_indexes', 'bca']
 
-# %% ../nbs/API/bootstrap.ipynb 3
+# %% ../nbs/API/bootstrap.ipynb #4231300f
 import numpy as np
 import pandas as pd
 import seaborn as sns
@@ -12,7 +12,7 @@
 from scipy.stats import mannwhitneyu, wilcoxon, norm
 import warnings
 
-# %% ../nbs/API/bootstrap.ipynb 4
+# %% ../nbs/API/bootstrap.ipynb #e86b4b8d
 class bootstrap:
     """
     Computes the summary statistic and a bootstrapped confidence interval.
@@ -230,7 +230,7 @@ def __repr__(self):
         b = "[{} CI: {}, {}]".format(self.ci, self.bca_ci_low, self.bca_ci_high)
         return "\n".join([a, b])
 
-# %% ../nbs/API/bootstrap.ipynb 5
+# %% ../nbs/API/bootstrap.ipynb #00c814b9
 def jackknife_indexes(data):
     # Taken without modification from scikits.bootstrap package.
     """

dabest/_dabest_object.py

@@ -2,10 +2,10 @@
 
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/dabest_object.ipynb.
 
-# %% auto 0
+# %% auto #0
 __all__ = ['Dabest']
 
-# %% ../nbs/API/dabest_object.ipynb 5
+# %% ../nbs/API/dabest_object.ipynb #d3c6f47a
 # Import standard data science libraries
 import warnings
 from numpy import array, repeat, random, issubdtype, number
@@ -14,7 +14,7 @@
 from scipy.stats import norm
 from scipy.stats import randint
 
-# %% ../nbs/API/dabest_object.ipynb 7
+# %% ../nbs/API/dabest_object.ipynb #350b12c1
 class Dabest(object):
 
     """

dabest/_delta_objects.py

@@ -2,10 +2,10 @@
 
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/delta_objects.ipynb.
 
-# %% auto 0
+# %% auto #0
 __all__ = ['DeltaDelta', 'MiniMetaDelta']
 
-# %% ../nbs/API/delta_objects.ipynb 5
+# %% ../nbs/API/delta_objects.ipynb #af15ef59
 from scipy.stats import norm
 import pandas as pd
 import numpy as np
@@ -15,7 +15,7 @@
 import warnings
 import datetime as dt
 
-# %% ../nbs/API/delta_objects.ipynb 6
+# %% ../nbs/API/delta_objects.ipynb #2df37fab
 class DeltaDelta(object):
     r"""
     A class to compute and store the delta-delta statistics for experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2. The data is divided into two pairs of two groups, and a primary delta is first calculated as the mean difference between each of the pairs:
@@ -382,7 +382,7 @@ def results(self):
             self.__compute_results()
         return self.__results
 
-# %% ../nbs/API/delta_objects.ipynb 10
+# %% ../nbs/API/delta_objects.ipynb #c3198a65
 class MiniMetaDelta(object):
     """
     A class to compute and store the weighted delta.

dabest/_effsize_objects.py

@@ -2,10 +2,10 @@
 
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/effsize_objects.ipynb.
 
-# %% auto 0
+# %% auto #0
 __all__ = ['TwoGroupsEffectSize', 'EffectSizeDataFrame', 'PermutationTest']
 
-# %% ../nbs/API/effsize_objects.ipynb 5
+# %% ../nbs/API/effsize_objects.ipynb #2220814e
 import pandas as pd
 import lqrt
 from scipy.stats import norm
@@ -23,7 +23,7 @@
 from string import Template
 import scipy.stats as spstats
 
-# %% ../nbs/API/effsize_objects.ipynb 6
+# %% ../nbs/API/effsize_objects.ipynb #083e0298
 class TwoGroupsEffectSize(object):
 
     """
@@ -818,7 +818,7 @@ def bec_pct_high(self):
         return self.__bec_pct_high
 
 
-# %% ../nbs/API/effsize_objects.ipynb 10
+# %% ../nbs/API/effsize_objects.ipynb #1f25c41c
 class EffectSizeDataFrame(object):
     """A class that generates and stores the results of bootstrapped effect
     sizes for several comparisons."""
@@ -1658,7 +1658,7 @@ def delta2(self):
     def is_delta_delta(self):
         return self.__delta2
 
-# %% ../nbs/API/effsize_objects.ipynb 29
+# %% ../nbs/API/effsize_objects.ipynb #b9ad9e32
 class PermutationTest:
     """
     A class to compute and report permutation tests.

dabest/_stats_tools/confint_1group.py

@@ -2,17 +2,17 @@
 
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/confint_1group.ipynb.
 
-# %% auto 0
+# %% auto #0
 __all__ = ['create_bootstrap_indexes', 'compute_1group_jackknife', 'compute_1group_acceleration', 'compute_1group_bootstraps',
            'compute_1group_bias_correction', 'summary_ci_1group']
 
-# %% ../../nbs/API/confint_1group.ipynb 4
+# %% ../../nbs/API/confint_1group.ipynb #9181f236
 import numpy as np
 from numpy.random import PCG64, RandomState
 from scipy.stats import norm
 from numpy import sort as npsort
 
-# %% ../../nbs/API/confint_1group.ipynb 5
+# %% ../../nbs/API/confint_1group.ipynb #bac09924
 def create_bootstrap_indexes(array, resamples=5000, random_seed=12345):
     """Given an array-like, returns a generator of bootstrap indexes
     to be used for resampling.

dabest/_stats_tools/confint_2group_diff.py

@@ -2,13 +2,13 @@
 
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/confint_2group_diff.ipynb.
 
-# %% auto 0
+# %% auto #0
 __all__ = ['create_jackknife_indexes', 'create_repeated_indexes', 'compute_meandiff_jackknife', 'bootstrap_indices',
            'compute_bootstrapped_diff', 'delta2_bootstrap_loop', 'compute_delta2_bootstrapped_diff',
            'compute_meandiff_bias_correction', 'compute_interval_limits', 'calculate_group_var',
            'calculate_bootstraps_var', 'calculate_weighted_delta']
 
-# %% ../../nbs/API/confint_2group_diff.ipynb 4
+# %% ../../nbs/API/confint_2group_diff.ipynb #fa733643
 import numpy as np
 from numpy import arange, delete, errstate
 from numpy import mean as npmean
@@ -18,7 +18,7 @@
 from scipy.stats import norm
 from numpy import isnan
 
-# %% ../../nbs/API/confint_2group_diff.ipynb 5
+# %% ../../nbs/API/confint_2group_diff.ipynb #8cf9b1fc
 @njit(cache=True, parallel=True)
 def create_jackknife_indexes(data):
     """

dabest/_stats_tools/effsize.py

@@ -2,7 +2,7 @@
 
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/effsize.ipynb.
 
-# %% ../../nbs/API/effsize.ipynb 4
+# %% ../../nbs/API/effsize.ipynb #9d92d449
 from __future__ import annotations
 import numpy as np
 from numba import njit
@@ -11,10 +11,10 @@
 from scipy.stats import mannwhitneyu
 
 
-# %% auto 0
+# %% auto #0
 __all__ = ['two_group_difference', 'func_difference', 'cohens_d', 'cohens_h', 'hedges_g', 'cliffs_delta', 'weighted_delta']
 
-# %% ../../nbs/API/effsize.ipynb 5
+# %% ../../nbs/API/effsize.ipynb #0547f8a7
 def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types.
                          test:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types.
                          is_paired=None, #If not None, returns the paired Cohen's d
@@ -99,7 +99,7 @@ def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples,
         return cliffs_delta(control, test)
 
 
-# %% ../../nbs/API/effsize.ipynb 6
+# %% ../../nbs/API/effsize.ipynb #c93f36d4
 def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically discarded.
                     test:list|tuple|np.ndarray, # NaNs are automatically discarded.
                     func, # Summary function to apply.
@@ -135,7 +135,7 @@ def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically disc
     return func(test) - func(control)
 
 
-# %% ../../nbs/API/effsize.ipynb 7
+# %% ../../nbs/API/effsize.ipynb #c6dd20e4
 @njit(cache=True)
 def cohens_d(control:list|tuple|np.ndarray,
              test:list|tuple|np.ndarray,
@@ -210,7 +210,7 @@ def cohens_d(control:list|tuple|np.ndarray,
 
     return M / divisor
 
-# %% ../../nbs/API/effsize.ipynb 8
+# %% ../../nbs/API/effsize.ipynb #93688770
 # @njit(cache=True) # It uses np.seterr which is not supported by Numba
 def cohens_h(control:list|tuple|np.ndarray, 
              test:list|tuple|np.ndarray
@@ -247,7 +247,7 @@ def cohens_h(control:list|tuple|np.ndarray,
     return phi_test - phi_control
 
 
-# %% ../../nbs/API/effsize.ipynb 9
+# %% ../../nbs/API/effsize.ipynb #bcd77c32
 def hedges_g(control:list|tuple|np.ndarray, 
              test:list|tuple|np.ndarray, 
              is_paired:str=None)->float:
@@ -271,7 +271,7 @@ def hedges_g(control:list|tuple|np.ndarray,
     correction_factor = _compute_hedges_correction_factor(len_c, len_t)
     return correction_factor * d
 
-# %% ../../nbs/API/effsize.ipynb 10
+# %% ../../nbs/API/effsize.ipynb #8fafb111
 @njit(cache=True)
 def _mann_whitney_u(x, y):
     """Numba-optimized Mann-Whitney U calculation"""
@@ -307,7 +307,7 @@ def cliffs_delta(control:list|tuple|np.ndarray,
     return _cliffs_delta_core(c, t)
 
 
-# %% ../../nbs/API/effsize.ipynb 11
+# %% ../../nbs/API/effsize.ipynb #7a772510
 @njit(cache=True)
 def _compute_standardizers(control, test):
     """
@@ -356,7 +356,7 @@ def _compute_standardizers(control, test):
 
     return pooled, average 
 
-# %% ../../nbs/API/effsize.ipynb 12
+# %% ../../nbs/API/effsize.ipynb #4529e82c
 def _compute_hedges_correction_factor(n1, 
                                       n2
                                      )->float:
@@ -390,7 +390,7 @@ def _compute_hedges_correction_factor(n1,
 
     return out
 
-# %% ../../nbs/API/effsize.ipynb 13
+# %% ../../nbs/API/effsize.ipynb #249e5375
 @njit(cache=True)
 def weighted_delta(difference, bootstrap_dist_var):
     '''