black-formatted files

src/alchemlyb/convergence/__init__.py

@@ -1,10 +1,6 @@
-<<<<<<< HEAD
 from .convergence import (
     forward_backward_convergence,
     fwdrev_cumavg_Rc,
     A_c,
     block_average,
 )
-=======
-from .convergence import forward_backward_convergence, fwdrev_cumavg_Rc, A_c, block_average
->>>>>>> 9eb22a1 (Moving average (#381))

src/alchemlyb/convergence/convergence.py

@@ -39,11 +39,7 @@ def forward_backward_convergence(
            Lower case input is also accepted until release 2.0.0.
     num : int
         The number of blocks used to divide *each* DataFrame and progressively add
-<<<<<<< HEAD
         to assess convergence. Note that if the DataFrames are different lengths,
-=======
-        to assess convergence. Note that if the DataFrames are different lengths, 
->>>>>>> 9eb22a1 (Moving average (#381))
         the number of samples contributed with each block will be different.
     error_tol : float
         The maximum error tolerated for analytic error. If the analytic error is
@@ -52,11 +48,7 @@ def forward_backward_convergence(
         .. versionadded:: 2.3.0
         .. versionchanged:: 2.4.0
            Clarified docstring, removed incorrect estimation of std for cumulative
-<<<<<<< HEAD
            result in bar and added check that only a single lambda state is
-=======
-           result in bar and added check that only a single lambda state is 
->>>>>>> 9eb22a1 (Moving average (#381))
            represented in the indices of each df in df_list.
 
     kwargs : dict
@@ -108,16 +100,11 @@ def forward_backward_convergence(
         # select estimator class by name
         my_estimator = estimators_dispatch[estimator](**kwargs)
         logger.info(f"Use {estimator} estimator for convergence analysis.")
-<<<<<<< HEAD
 
-=======
-
->>>>>>> 9eb22a1 (Moving average (#381))
     # Check that each df in the list has only one value of lambda
     for i, df in enumerate(df_list):
         lambda_values = list(set([x[1:] for x in df.index.to_numpy()]))
         if len(lambda_values) > 1:
-<<<<<<< HEAD
             ind = [
                 j
                 for j in range(len(lambda_values[0]))
@@ -129,13 +116,6 @@ def forward_backward_convergence(
                 )
             )
 
-=======
-            ind  = [j for j in range(len(lambda_values[0])) if len(list(set([x[j] for x in lambda_values]))) > 1][0]
-            raise ValueError(
-                "Provided DataFrame, df_list[{}] has more than one lambda value in df.index[{}]".format(i, ind)
-            )
-
->>>>>>> 9eb22a1 (Moving average (#381))
     logger.info("Begin forward analysis")
     forward_list = []
     forward_error_list = []
@@ -486,7 +466,6 @@ def block_average(df_list, estimator="MBAR", num=10, **kwargs):
     for i, df in enumerate(df_list):
         lambda_values = list(set([x[1:] for x in df.index.to_numpy()]))
         if len(lambda_values) > 1:
-<<<<<<< HEAD
             ind = [
                 j
                 for j in range(len(lambda_values[0]))
@@ -496,23 +475,14 @@ def block_average(df_list, estimator="MBAR", num=10, **kwargs):
                 "Provided DataFrame, df_list[{}] has more than one lambda value in df.index[{}]".format(
                     i, ind
                 )
-=======
-            ind  = [j for j in range(len(lambda_values[0])) if len(list(set([x[j] for x in lambda_values]))) > 1][0]
-            raise ValueError(
-                "Provided DataFrame, df_list[{}] has more than one lambda value in df.index[{}]".format(i, ind)
->>>>>>> 9eb22a1 (Moving average (#381))
             )
 
     if estimator in ["BAR"] and len(df_list) > 2:
         raise ValueError(
             "Restrict to two DataFrames, one with a fep-lambda value and one its forward adjacent state for a "
             "meaningful result."
         )
-<<<<<<< HEAD
 
-=======
-
->>>>>>> 9eb22a1 (Moving average (#381))
     logger.info("Begin Moving Average Analysis")
     average_list = []
     average_error_list = []

src/alchemlyb/estimators/mbar_.py

@@ -94,19 +94,12 @@ class MBAR(BaseEstimator, _EstimatorMixOut):
     .. versionchanged:: 2.1.0
         `n_bootstraps` option added.
     .. versionchanged:: 2.4.0
-<<<<<<< HEAD
         Handle initial estimate, initial_f_k, from bar in the instance
         that not all lambda states represented as column headers are
         represented in the indices of u_nk.
     .. versionchanged:: 2.5.0
         Added computation of enthalpy and entropy
 
-=======
-       Handle initial estimate, initial_f_k, from bar in the instance
-       that not all lambda states represented as column headers are 
-       represented in the indices of u_nk.
-    
->>>>>>> 9eb22a1 (Moving average (#381))
     """
 
     def __init__(

src/alchemlyb/visualisation/convergence.py

@@ -59,11 +59,7 @@ def plot_convergence(dataframe, units=None, final_error=None, ax=None):
      .. versionchanged:: 0.6.0
          data now takes in dataframe
      .. versionadded:: 0.4.0
-<<<<<<< HEAD
 
-=======
-     
->>>>>>> 9eb22a1 (Moving average (#381))
     """
     if units is not None:
         dataframe = get_unit_converter(units)(dataframe)
@@ -196,11 +192,7 @@ def plot_block_average(dataframe, units=None, final_error=None, ax=None):
      `Alchemical Analysis <https://github.com/MobleyLab/alchemical-analysis>`_.
 
      .. versionadded:: 2.4.0
-<<<<<<< HEAD
 
-=======
-     
->>>>>>> 9eb22a1 (Moving average (#381))
     """
     if units is not None:
         dataframe = get_unit_converter(units)(dataframe)

src/alchemlyb/visualisation/dF_state.py

@@ -266,4 +266,3 @@ def plot_dF_state(
 
     leg.get_frame().set_alpha(0.5)
     return fig
-