diff --git a/Makefile b/Makefile
index 56d1a05..9e48311 100644
--- a/Makefile
+++ b/Makefile
@@ -52,6 +52,7 @@ unit_tests:
 	${PYTHON} testsuite/calculate_net_charge_unit_test.py
 	${PYTHON} testsuite/setup_salt_ions_unit_tests.py
 	${PYTHON} testsuite/globular_protein_unit_tests.py
+	${PYTHON} testsuite/analysis_tests.py
 
 functional_tests:
 	${PYTHON} testsuite/cph_ideal_tests.py
diff --git a/lib/analysis.py b/lib/analysis.py
index d1b380e..65dbd0b 100644
--- a/lib/analysis.py
+++ b/lib/analysis.py
@@ -25,11 +25,11 @@ def add_data_to_df(df, data_dict, index):
     Adds the data in data_dict in the df pandas dataframe
     
     Args:
-        df(`obj`): pandas dataframe
+        df(`Pandas.Dataframe`): pandas dataframe
         data_dict(`dict`): dictionary with the data to be added
         index(`lst`): index of the df where the data should be added
     Returns:
-        updated_df(`obj`): pandas dataframe updated with data_dict
+        updated_df(`Pandas.Dataframe`): pandas dataframe updated with data_dict
 
     """
     if df.empty:
@@ -42,15 +42,20 @@ def add_data_to_df(df, data_dict, index):
                          index=index)])
     return updated_df
 
-def analyze_time_series(path_to_datafolder):
+def analyze_time_series(path_to_datafolder, filename_extension= ".csv", minus_separator = False,):
     """
     Analyzes all time series stored in `path_to_datafolder` using the block binning method.
 
     Args:
         path_to_datafolder(`str`): path to the folder with the files with the time series
+        filename_extension(`str`): extension of the file. Defaults to ".csv"
+        minus_separator(`bool`): switch to enable the minus as a separator. Defaults to False.
 
     Returns:
-        (`obj`): pandas dataframe with
+        data(`Pandas.Dataframe`): Dataframe with the time averages of all the time series in the datafolder.
+
+    Note:
+        - For more information about `minus_separator`, please check the documentation of `get_params_from_file_name`
 
     """
     data=pd.DataFrame()
@@ -58,11 +63,13 @@ def analyze_time_series(path_to_datafolder):
         # Gather all data
         for subitem in subdirectory:
             if subitem.is_file():
-                if 'time_series' in subitem.name:
+                if filename_extension in subitem.name:
                     # Get parameters from the file name
-                    data_dict=get_params_from_dir_name(subitem.name.replace('_time_series.csv', ''))
+                    data_dict=get_params_from_file_name(file_name=subitem.name,
+                                                        filename_extension=filename_extension,
+                                                        minus_separator=minus_separator)
                     # Get the observables for binning analysis
-                    time_series_data=read_csv_file(path=f"{path_to_datafolder}/{subitem.name}")
+                    time_series_data=pd.read_csv(f"{path_to_datafolder}/{subitem.name}")
                     analyzed_data=block_analyze(full_data=time_series_data)
                     value_list=[]
                     index_list=[]
@@ -75,194 +82,27 @@ def analyze_time_series(path_to_datafolder):
                                         index=[len(data)])   
     return data
 
-def do_binning_analysis(list_time_series_df, frac_data_to_discard=0.3):
-    """
-    Does a binning analysis of all Pandas DataFrame objects in `list_time_series_df`.
-    
-    Args:
-        list_time_series_df(`lst`): List of Pandas DataFrame objects
-        frac_data_to_discard(`float`, optional): Fraction of data to be discarted in each Pandas DataFrame. Defaults to 0.3.
-        
-    Returns:
-        mean_series(`obj`): PandasSeries object with the mean, error of the mean, number of effective samples and correlation time.
-
-    Note:
-        - All Pandas DataFrame objects in `list_time_series_df` are merged into a single Pandas DataFrame object for the binning analysis.
-        - All Pandas DataFrame objects in `list_time_series_df` are assumed to have the same observables.
-        - The fraction of data to be discarted as equilibration is discarted in each of the Pandas DataFrame objects.
-    """
-    # Estimate the time interval in which the data was stored
-    gathered_binning_df=merge_time_series_dfs(list_time_series_df=list_time_series_df,
-                         frac_data_to_discard=frac_data_to_discard)
-    # Clean up the dataframe 
-    if "Unnamed: 0" in gathered_binning_df.columns:
-        gathered_binning_df=gathered_binning_df.drop(['Unnamed: 0'], axis=1)
-    # Do the binning analysis
-    mean_series=block_analyze(gathered_binning_df)
-    return mean_series
-
-def merge_time_series_dfs(list_time_series_df,frac_data_to_discard=0,rescale_time=False):
-    """
-    Merges all Pandas DataFrame objects in `list_time_series_df` and rescales the time.
-    
-    Args:
-        list_time_series_df(`lst`): List of Pandas DataFrame objects
-        frac_data_to_discard(`float`, optional): Fraction of data to be discarted in each Pandas DataFrame. Defaults to 0.
-        
-    Returns:
-        gathered_binning_df(`obj`): DataFrame object with all data.
-
-    Note:
-        - All Pandas DataFrame objects in `list_time_series_df` are assumed to have the same observables.
-        - The fraction of data to be discarted as equilibration is discarted in each of the Pandas DataFrame objects.
-    """
-    # Estimate the time interval in which the data was stored
-    dt = get_dt(list_time_series_df[0])
-    for index in range(len(list_time_series_df)):
-        binning_df=list_time_series_df[index]
-        # Drop the data corresponding to equilibration
-        drop_rows = int(binning_df.shape[0]*frac_data_to_discard)
-        binning_df  = binning_df.drop(range(0,drop_rows))
-        if rescale_time:         
-            # Make a continous time evolution
-            binning_df["time"]+=index*binning_df.shape[0]*dt
-        list_time_series_df[index]=binning_df
-
-    # Join all the dataframes for binning analysis
-    gathered_binning_df=pd.concat(list_time_series_df)
-    
-    if 'Unnamed: 0' in gathered_binning_df.columns:
-    # Clean up the dataframe 
-        gathered_binning_df=gathered_binning_df.drop(['Unnamed: 0'], axis=1)
-    
-    # Clean up the dataframe 
-    gathered_binning_df.reset_index(drop=True, inplace=True)
-    return gathered_binning_df
-
-
-def get_params_from_dir_name(name):
-    """
-    Gets the parameters from name assuming a structure 
-    name=obsname1-value1_obsname2-value2...
-    
-    Args:
-        name(`str`): name of the directory
-    
-    Returns:
-        params(`dict`): dictionary with the labels and values of the parameters
-    """
-    entries = name.split('_')
-    params = {}
-    for entry in entries:
-        sp_entry = entry.split('-', 1)
-        params[sp_entry[0]] = sp_entry[-1]     #float(sp_entry[-1])     # creates a dictionary of parameters and their values.
-    return params
-
-
-def split_dataframe_in_equal_blocks(df, start_row, end_row, block_size):
-    """
-    Splits a Pandas dataframe in equally spaced blocks.
-
-    Args:
-        - df (`obj`): PandasDataframe
-        - start_row (`int`): index of the first row
-        - end_row (`int`): index of the last row
-        - block_size (`int`): number of rows per block
-
-    Returns:
-        - (`list`): array of PandasDataframe of equal size
-    """
-    return [df[row:row+block_size] for row in range(start_row,end_row,block_size)]
-
-def split_dataframe(df,n_blocks):
-    """
-    Splits a Pandas Dataframe in n_blocks of approximately the same size.
-
-    Args:
-        - df (`obj`): PandasDataframe
-        - n_blocks (`int`): Number of blocks
-
-    Returns:
-        - (`list`): array of PandasDataframe 
-
-    Notes:
-        - For a `df` of length `l` that should be split into n_blocks, it returns l % n_blocks sub-arrays of size l//n_blocks + 1 and the rest of size l//n_blocks.
-        - The behaviour of this function is the same as numpy.array_split for backwards compatibility, see [docs](https://numpy.org/doc/stable/reference/generated/numpy.array_split.html)
-
-    """
-
-    # Blocks of size 1 (s1) =  df.shape[0]//n_blocks+1
-
-    n_blocks_s1=df.shape[0] % n_blocks
-    block_size_s1=df.shape[0]//n_blocks+1
-    blocks=split_dataframe_in_equal_blocks(df=df,
-                                           start_row=0,
-                                           end_row=n_blocks_s1*block_size_s1,
-                                           block_size=block_size_s1)
-
-
-    # Blocks of size 2 (s2) =  df.shape[0]//n_blocks
-    block_size_s2=df.shape[0]//n_blocks
-    blocks+=split_dataframe_in_equal_blocks(df=df,
-                                           start_row=n_blocks_s1*block_size_s1,
-                                           end_row=df.shape[0],
-                                           block_size=block_size_s2)
-    return blocks
-
-def get_time_series_from_average_df(pd_series, label):
-    """
-    Gets the time series from a PandasSeries object
-    
-    Args:
-        pd_series (`obj`): PandasSeries object
-        label (`str`): column name where the time series are stored
-    
-    Returns:
-        (`obj`): PandasDataFrame with the time series and their statistical error
-    """
-    import numpy as np
-    dist_dict={}
-    expected_labels=["mean", "errmean", "nsamples"]
-    for data_str in pd_series[label]:
-        clean_data_str=data_str.strip("{").strip("}")
-        for data_set in clean_data_str.split("]"):
-            data_list=data_set.split(":")
-            if len(data_list) == 1:
-                continue            
-            # Parse data and label
-            data=list(data_list[1][2:].split(","))
-            data=np.array([float(x) for x in data])
-            clean_label=data_list[0].strip("'").strip(",").strip()
-            label_sts=clean_label.split("_")[-1]
-            label_qty=clean_label[:-len(label_sts)-1].strip("'")            
-            if label_sts not in expected_labels:
-                raise ValueError(f"Error while parsing the df, found label for stats {label_sts}")
-
-            if label_qty in dist_dict.keys():
-                dist_dict[label_qty][label_sts]=data
-            else:
-                dist_dict[label_qty]={}
-                dist_dict[label_qty][label_sts]=data 
-    return pd.DataFrame.from_dict(dist_dict)
-
-
-def block_analyze(full_data, n_blocks=16, time_col = "time", equil=0.1,  columns_to_analyze = "all", verbose = False):
+def block_analyze(full_data, n_blocks=16, time_col = "time", equil=0.1,  columns_to_analyze = "all", verbose = False, dt=None):
     """
     Analyzes the data in `full_data` using a binning procedure.
 
     Args:
-        - full_data(`obj`): pandas dataframe with the observables time series
-        - n_blocks(`int`): number of blocks used in the binning procedure.
-        - time_col(`str`): column name where the time is stored in `full_data`. Defaults to `time`.
-        - equil(`float`,opt): fraction of the data discarded as part of the equilibration. Defaults to 0.1.
-        - columns_to_analyze(`list`): array of column names to be analyzed. Defaults to "all".
-        - verbose(`bool`): switch to activate/deactivate printing the block size. Defaults to False.
+        full_data(`Pandas.Dataframe`): dataframe with the observables time series
+        n_blocks(`int`): number of blocks used in the binning procedure.
+        time_col(`str`): column name where the time is stored in `full_data`. Defaults to `time`.
+        equil(`float`,opt): fraction of the data discarded as part of the equilibration. Defaults to 0.1.
+        columns_to_analyze(`list`): array of column names to be analyzed. Defaults to "all".
+        verbose(`bool`): switch to activate/deactivate printing the block size. Defaults to False.
+        dt(`float`): time step in which the data was stored. If no value is provided, is calculated from the time series. Defaults to None.
 
     Returns:
-        `result`: pandas dataframe with the mean (mean), statistical error (err_mean), number of effective samples (n_eff) and correlation time (tau_int) of each observable.
+        `result`(`Pandas.Series`): Series the mean (mean), statistical error (err_mean), number of effective samples (n_eff) and correlation time (tau_int) of each observable.
     """
+    if dt is None:
+        dt, n_warnings = get_dt(full_data, time_col, verbose = verbose) # check that the data was stored with the same time interval dt
+        if n_warnings > 0 and verbose:
+            print(f"Warning: looks like a repeated time value was encountered {n_warnings} times")
 
-    dt = get_dt(full_data) # check that the data was stored with the same time interval dt
     drop_rows = int(full_data.shape[0]*equil) # calculate how many rows should be dropped as equilibration
     # drop the rows that will be discarded as equlibration
     data = full_data.drop(range(0,drop_rows))
@@ -301,141 +141,160 @@ def block_analyze(full_data, n_blocks=16, time_col = "time", equil=0.1,  columns
 
     # concatenate the observables and atribute a key for each (unique index)
     result = pd.concat( [ mean, err_mean, n_eff, tau_int], keys= [ "mean", "err_mean", "n_eff", "tau_int" ], join="inner")
-    result = pd.concat( [ pd.Series([n_blocks,block_size], index=[('n_blocks',),('block_size',)]), result])
+    
     return result
 
-def get_dt(data):
+def built_output_name(input_dict):
     """
-    Sorts data to calculate the time step of the simulation.
+    Builts the output name for a given set of input parameters.
 
     Args:
-        - data (`obj`): pandas dataframe with the observables time series
+        input_dict(`dict`): dictionary with all terminal inputs.
 
     Returns:
-        dt (`float`): simulation time step.
+        output_name(`str`): name used for the output files
+
+    Note:
+        - The standard formatting rule is parametername1_parametervalue1_parametername2_parametervalue2
     """
-    if 'time' in data:
-        time = data['time']
-    elif 'MC sweep' in data:
-        time = data['MC sweep']
+    output_name=""
+    for label in input_dict:
+        if type(input_dict[label]) in [str,bool]:
+            formatted_variable=f"{input_dict[label]:}"
+        else:
+            formatted_variable=f"{input_dict[label]:.3g}"
+        output_name+=f"{label}_{formatted_variable}_"
+    return output_name[:-1]
+
+def get_dt(data, time_col = "time", relative_tolerance = 0.01, verbose = False):
+    """ 
+    Calculate the time difference between the lines of the time series
+
+    Args:
+        data(`obj`): PandasDataFrame containing the time series
+        time_col(`str`): string denoting the name of the column which contains the time data
+        relative_toleranace(`float`): threshold up to which an inconsistency between consecutive dt values is considered acceptable (see Notes below for more details)
+        verbose(`bool`): Switch to activate/deactivate printing of warning messages.
+
+    Returns:
+        (`float`, `int`) 1. value of time difference between consecutive lines, 2. number of repeated time values
+
+    Note:
+        - `relative_tolerance` is used in two ways to check for irregularities in time intervals of between consecutive lines:
+            - Sometimes, small inconsistencies in time intervals may occur due to rounding errors or because we run a short tuning of cells or Ewald parameters. These inconsistencies are considered negligible, as long as they do not exceed dt * relative_tolerance
+            - Sometimes, consecutive values may have identical times, e.g. if only reactions are performed but there no MD evolution in beteen. In this case, dt is ill-defined. As of May 2024, we only produce warnings but do not raise an error in order to retain backward compatibility. We deprecate such use and plan to remove this compatibility in the future. If necessary, the user can always override get_dt() by providing a custom value on the input.
+    """
+    if time_col in data.columns.to_list():
+        time = data[ time_col ]
     else:
-        raise ValueError("neither 'time' nor 'MC sweep' column found in data, got " + str(data))
+        raise ValueError(f"Column \'{time_col}\' not found in columns: "+str( data.columns.to_list() ) )
     imax = data.shape[0]
     dt_init = time[1] - time[0]
     warn_lines = []
     for i in range(1,imax):
         dt = time[i] - time[i-1]
-        if np.abs((dt_init - dt)/dt) > 0.01:
-            warn_lines.append("Row {} dt = {} = {} - {} not equal to dt_init = {}")
-    if len(warn_lines) > 20:
-        print("\n")
-        for line in warn_lines:
-            print(line)
-    return dt
-
-def read_csv_file(path):
-    """
-    Reads the csv file in path.
+        if np.abs( (dt_init - dt)/dt_init ) > relative_tolerance : # dt is different from before
+            if np.abs( dt/dt_init ) < relative_tolerance : # looks like a repeated value
+                warn_lines.append(f"Row {i} dt = {dt} = {time[i]} - {time[i-1]} not equal to dt_init = {dt_init}")
+                dt = max(dt,dt_init)
+            else:
+                raise ValueError(f"\nget_dt: Row {i} dt = {dt} = {time[i]} - {time[i-1]} not equal to dt_init = {dt_init}\n")
+    if verbose:
+        print(warn_lines)
+    return dt, len(warn_lines)
 
+def get_params_from_file_name(file_name, minus_separator = False, filename_extension=".csv"):
+    """
+    Gets the parameters from `file_name`. By default, it assumes the standard formating rule  `file_name=obsname1_value1_obsname2_value2...`
+    
     Args:
-        - path (`str`): path to the csv file
+        file_name(`str`): name of the file
+        minus_separator(`bool`): switch to enable the minus as a separator, see Notes. Defaults to False.
+        filename_extension(`str`): extension of the file. Defaults to ".csv"
     
     Returns:
-        - `obj`: pandas dataframe with the information stored in the csv file
+        params(`dict`): dictionary with the labels and values of the parameters.
 
+    Note:
+        - If `minus_separator = True`, then the data is parsed assuming the following formating rule  `file_name=obsname1-value1_obsname2-value2...`. 
+        - This is functionality is kept for backwards compatibility.
     """
-    if os.path.exists(path):
-        return pd.read_csv(filepath_or_buffer=path)
+    file_name = os.path.basename(file_name)
+    params = {}
+    if filename_extension in file_name:
+        system_name = file_name.replace(f"{filename_extension}", '')
     else:
-        return None
+        system_name = file_name
+    if minus_separator:
+        entries = system_name.split('_')
+        for entry in entries:
+            splitted_entry = entry.split('-')
+            if splitted_entry[0] == 'N':
+                params[splitted_entry[0]] = int(splitted_entry[-1])
+            else:
+                params[splitted_entry[0]] = splitted_entry[-1]           
+    else:
+        entries = system_name.split('_')
+        if len(entries) % 2:
+            raise ValueError("Wrong file name format. Need even number of entries separated by underscores, got: " + str(entries) + str( len(entries)) )
+        for i in range( len(entries)//2 ):
+            params[ entries[2*i] ] = entries[2*i+1]
+    return params
 
-def get_distribution_from_df(df, key):
+def split_dataframe(df,n_blocks):
     """
-    Gets a list stored in a pandas dataframe `df`.
+    Splits a Pandas Dataframe in n_blocks of approximately the same size.
 
     Args:
-        - df (`obj`): pandas dataframe
-        - key (`str`): column name where the list is stored
+        df(`Pandas.Dataframe`): Dataframe to be split
+        n_blocks(`int`): Number of blocks
 
     Returns:
-        distribution_list (`lst`): list stored under `key`
+        (`lst` of `Pandas.Dataframe`): array with the splitted dataframes.
+
+    Note:
+        - For a `df` of length `l` that should be split into n_blocks, it returns l % n_blocks sub-arrays of size l//n_blocks + 1 and the rest of size l//n_blocks.
+        - The behaviour of this function is the same as numpy.array_split for backwards compatibility, see [docs](https://numpy.org/doc/stable/reference/generated/numpy.array_split.html)
 
     """
-    distribution_list=[]
-    for row in df[key]:
-        if pd.isnull(row):
-            continue
-        clean_row=row.strip("[").strip("]")
-        row_list=[]
-        for element in clean_row.split():
-            row_list.append(float(element))
-        distribution_list.append(row_list)
-    return distribution_list
-
-def create_histogram_df_from_distribution_list(distribution_list, start, end, nbins):
-    """
-    Does a histogram from the data stored in `distribution_list` and stores it into a pandas dataframe.
 
-    Args:
-        start (`float`): start point of the histogram
-        end (`float`): end point of the histogram
-        nbins (`int`): number of bins in the histogram
+    # Blocks of size 1 (s1) =  df.shape[0]//n_blocks+1
 
-    Returns:
-        `obj`: Pandas dataframe with the histogram counts
-    """
+    n_blocks_s1=df.shape[0] % n_blocks
+    block_size_s1=df.shape[0]//n_blocks+1
+    blocks=split_dataframe_in_equal_blocks(df=df,
+                                           start_row=0,
+                                           end_row=n_blocks_s1*block_size_s1,
+                                           block_size=block_size_s1)
+
+
+    # Blocks of size 2 (s2) =  df.shape[0]//n_blocks
+    block_size_s2=df.shape[0]//n_blocks
+    blocks+=split_dataframe_in_equal_blocks(df=df,
+                                           start_row=n_blocks_s1*block_size_s1,
+                                           end_row=df.shape[0],
+                                           block_size=block_size_s2)
+    return blocks
 
-    counts, x_bins = np.histogram(distribution_list[0], bins=nbins, range=(start,end))
-    dict_hist={}
-    for index in range(len(counts)):
-        dict_hist[x_bins[index]]=[]
-    dict_hist["time"]=[]
-    cnt=1
-    for data_set in distribution_list:
-        counts, _  = np.histogram(data_set, bins=nbins, range=(start,end))
-        norm=sum(counts)
-        for index in range(len(counts)):
-            dict_hist[x_bins[index]].append(counts[index]/norm)
-        dict_hist["time"].append(cnt)
-        cnt+=5000
-    return  pd.DataFrame.from_dict(dict_hist)
-
-def find_index_with_value_in_df(df,column_name, value, tol=0.01):
+def split_dataframe_in_equal_blocks(df, start_row, end_row, block_size):
     """
-    Finds the index in the pandas DataFrame `df` with a column `column_name` and a row `value`.
+    Splits a Pandas dataframe in equally spaced blocks.
 
     Args:
-        df (`obj`): Pandas DataFrame.
-        column_name (`str`): Label of the column.
-        value (`any`): Value to be found in `df[column_name]`.
-        tol (`float`): Tolerance in value.
+        df(`Pandas.Dataframe`): Dataframe to be splitted.
+        start_row(`int`): index of the first row.
+        end_row(`int`): index of the last row.
+        block_size(`int`): number of rows per block.
 
     Returns:
-        index (int): Index found.
+        (`lst`): array of dataframes of equal size.
     """
-    index = np.where(abs(df[column_name]-value)/value < tol)
-    return index[0]
+    return [df[row:row+block_size] for row in range(start_row,end_row,block_size)]
+
+
 
-def built_output_name(input_dict):
-    """
-    Builts the output name for a given set of input parameters.
 
-    Args:
-        input_dict (`dict`): dictionary with all terminal inputs.
 
-    Returns:
-        output_name (`str`): name used for the output files
 
-    Note:
-        The standard formatting rule is parametername1-parametervalue1_parametername2-parametervalue2
-    """
-    output_name=""
-    for label in input_dict:
-        if type(input_dict[label]) in [str,bool]:
-            formatted_variable=f"{input_dict[label]:}"
-        else:
-            formatted_variable=f"{input_dict[label]:.3g}"
-        output_name+=f"{label}-{formatted_variable}_"
-    return output_name[:-1]
 
 
diff --git a/pyMBE.py b/pyMBE.py
index 9c32e88..faab288 100644
--- a/pyMBE.py
+++ b/pyMBE.py
@@ -68,10 +68,10 @@ def __init__(self, SEED, temperature=None, unit_length=None, unit_charge=None, K
         and sets up  the `pmb.df` for bookkeeping.
 
         Args:
-            temperature(`obj`,optional): Value of the temperature in the pyMBE UnitRegistry. Defaults to None.
-            unit_length(`obj`, optional): Value of the unit of length in the pyMBE UnitRegistry. Defaults to None.
-            unit_charge (`obj`,optional): Reduced unit of charge defined using the `pmb.units` UnitRegistry. Defaults to None. 
-            Kw (`obj`,optional): Ionic product of water in mol^2/l^2. Defaults to None. 
+            temperature(`pint.Quantity`,optional): Value of the temperature in the pyMBE UnitRegistry. Defaults to None.
+            unit_length(`pint.Quantity`, optional): Value of the unit of length in the pyMBE UnitRegistry. Defaults to None.
+            unit_charge (`pint.Quantity`,optional): Reduced unit of charge defined using the `pmb.units` UnitRegistry. Defaults to None. 
+            Kw (`pint.Quantity`,optional): Ionic product of water in mol^2/l^2. Defaults to None. 
         
         Note:
             - If no `temperature` is given, a value of 298.15 K is assumed by default.
@@ -92,12 +92,12 @@ def add_bond_in_df(self, particle_id1, particle_id2, use_default_bond=False):
         Adds a bond entry on the `pymbe.df` storing the particle_ids of the two bonded particles.
 
         Args:
-            particle_id1 (`int`): particle_id of the type of the first particle type of the bonded particles
-            particle_id2 (`int`): particle_id of the type of the second particle type of the bonded particles
-            use_default_bond (`bool`, optional): Controls if a bond of type `default` is used to bond particle whose bond types are not defined in `pmb.df`. Defaults to False.
+            particle_id1(`int`): particle_id of the type of the first particle type of the bonded particles
+            particle_id2(`int`): particle_id of the type of the second particle type of the bonded particles
+            use_default_bond(`bool`, optional): Controls if a bond of type `default` is used to bond particle whose bond types are not defined in `pmb.df`. Defaults to False.
 
         Returns:
-            index (`int`): Row index where the bond information has been added in pmb.df.
+            index(`int`): Row index where the bond information has been added in pmb.df.
         """
         particle_name1 = self.df.loc[(self.df['particle_id']==particle_id1) & (self.df['pmb_type']=="particle")].name.values[0]
         particle_name2 = self.df.loc[(self.df['particle_id']==particle_id2) & (self.df['pmb_type']=="particle")].name.values[0]
@@ -127,7 +127,7 @@ def add_bonds_to_espresso(self, espresso_system) :
         Adds all bonds defined in `pmb.df` to `espresso_system`.
 
         Args:
-            espresso_system (str): system object of espressomd library
+            espresso_system(`espressomd.system.System`): system object of espressomd library
         """
 
         if 'bond' in self.df.values:
@@ -193,8 +193,8 @@ def assign_molecule_id(self, name, molecule_index, pmb_type, used_molecules_id):
         Args:
             name(`str`): Label of the molecule type to be created. `name` must be defined in `pmb.df`
             pmb_type(`str`): pmb_object_type to assign the `molecule_id` 
-            molecule_index (`int`): index of the current `pmb_object_type` to assign the `molecule_id`
-            used_molecules_id (`lst`): list with the `molecule_id` values already used.
+            molecule_index(`int`): index of the current `pmb_object_type` to assign the `molecule_id`
+            used_molecules_id(`lst`): list with the `molecule_id` values already used.
         
         Returns:
             molecule_id(`int`): Id of the molecule
@@ -229,7 +229,7 @@ def calculate_center_of_mass_of_molecule(self, molecule_id, espresso_system):
 
         Args:
             molecule_id(`int`): Id of the molecule whose center of mass is to be calculated.
-            espresso_system(`obj`): Instance of a system object from the espressomd library.
+            espresso_system(`espressomd.system.System`): Instance of a system object from the espressomd library.
         
         Returns:
             center_of_mass(`lst`): Coordinates of the center of mass.
@@ -250,12 +250,12 @@ def calculate_HH(self, molecule_name, pH_list=None, pka_set=None):
         for molecules with the name `molecule_name`.
 
         Args:
-            molecule_name (`str`): name of the molecule to calculate the ideal charge for
+            molecule_name(`str`): name of the molecule to calculate the ideal charge for
             pH_list(`lst`): pH-values to calculate. 
             pka_set(`dict`): {"name" : {"pka_value": pka, "acidity": acidity}}
 
         Returns:
-            Z_HH (`lst`): Henderson-Hasselbalch prediction of the charge of `sequence` in `pH_list`
+            Z_HH(`lst`): Henderson-Hasselbalch prediction of the charge of `sequence` in `pH_list`
 
         Note:
             - This function supports objects with pmb types: "molecule", "peptide" and "protein".
@@ -315,10 +315,10 @@ def calculate_HH_Donnan(self, c_macro, c_salt, pH_list=None, pka_set=None):
         Calculates the charge on the different molecules according to the Henderson-Hasselbalch equation coupled to the Donnan partitioning.
 
         Args:
-            c_macro ('dict'): {"name": concentration} - A dict containing the concentrations of all charged macromolecular species in the system. 
-            c_salt ('float'): Salt concentration in the reservoir.
-            pH_list ('lst'): List of pH-values in the reservoir. 
-            pka_set ('dict'): {"name": {"pka_value": pka, "acidity": acidity}}.
+            c_macro('dict'): {"name": concentration} - A dict containing the concentrations of all charged macromolecular species in the system. 
+            c_salt('float'): Salt concentration in the reservoir.
+            pH_list('lst'): List of pH-values in the reservoir. 
+            pka_set('dict'): {"name": {"pka_value": pka, "acidity": acidity}}.
 
         Returns:
             {"charges_dict": {"name": charges}, "pH_system_list": pH_system_list, "partition_coefficients": partition_coefficients_list}
@@ -347,11 +347,11 @@ def calc_charges(c_macro, pH):
             Calculates the charges of the different kinds of molecules according to the Henderson-Hasselbalch equation.
 
             Args:
-                c_macro ('dic'): {"name": concentration} - A dict containing the concentrations of all charged macromolecular species in the system. 
-                pH ('float'): pH-value that is used in the HH equation.
+                c_macro('dic'): {"name": concentration} - A dict containing the concentrations of all charged macromolecular species in the system. 
+                pH('float'): pH-value that is used in the HH equation.
 
             Returns:
-                charge ('dict'): {"molecule_name": charge}
+                charge('dict'): {"molecule_name": charge}
             """
             charge = {}
             for name in c_macro:
@@ -363,8 +363,8 @@ def calc_partition_coefficient(charge, c_macro):
             Calculates the partition coefficients of positive ions according to the ideal Donnan theory.
 
             Args:
-                charge ('dict'): {"molecule_name": charge}
-                c_macro ('dic'): {"name": concentration} - A dict containing the concentrations of all charged macromolecular species in the system. 
+                charge('dict'): {"molecule_name": charge}
+                c_macro('dict'): {"name": concentration} - A dict containing the concentrations of all charged macromolecular species in the system. 
             """
             nonlocal ionic_strength_res
             charge_density = 0.0
@@ -401,12 +401,12 @@ def calculate_initial_bond_length(self, bond_object, bond_type, epsilon, sigma,
         based on the minimum of the sum of bonded and short-range (LJ) interactions.
         
         Args:
-            bond_object (`cls`): instance of a bond object from espressomd library
-            bond_type (`str`): label identifying the used bonded potential
-            epsilon (`float`): LJ epsilon of the interaction between the particles
-            sigma (`float`): LJ sigma of the interaction between the particles
-            cutoff (`float`): cutoff-radius of the LJ interaction 
-            offset (`float`): offset of the LJ interaction
+            bond_object(`espressomd.interactions.BondedInteractions`): instance of a bond object from espressomd library
+            bond_type(`str`): label identifying the used bonded potential
+            epsilon(`pint.Quantity`): LJ epsilon of the interaction between the particles
+            sigma(`pint.Quantity`): LJ sigma of the interaction between the particles
+            cutoff(`pint.Quantity`): cutoff-radius of the LJ interaction 
+            offset(`pint.Quantity`): offset of the LJ interaction
         """    
         def truncated_lj_potential(x, epsilon, sigma, cutoff,offset):
             if x>cutoff:
@@ -436,11 +436,11 @@ def calculate_net_charge (self, espresso_system, molecule_name):
         Returns the net charge per molecule and a maps with the net charge per residue and molecule.
 
         Args:
-            espresso_system: system information 
-            molecule_name (str): name of the molecule to calculate the net charge
+            espresso_system(`espressomd.system.System`): system information 
+            molecule_name(`str`): name of the molecule to calculate the net charge
 
         Returns:
-            {"mean": mean_net_charge, "molecules": {mol_id: net_charge_of_mol_id, }, "residues": {res_id: net_charge_of_res_id, }}
+            (`dict`): {"mean": mean_net_charge, "molecules": {mol_id: net_charge_of_mol_id, }, "residues": {res_id: net_charge_of_res_id, }}
 
         Note:
             - The net charge of the molecule is averaged over all molecules of type `name` 
@@ -475,7 +475,7 @@ def center_molecule_in_simulation_box(self, molecule_id, espresso_system):
         
         Args:
             molecule_id(`int`): Id of the molecule to be centered.
-            espresso_system(`obj`): Instance of a system object from the espressomd library.
+            espresso_system(`espressomd.system.System`): Instance of a system object from the espressomd library.
         """
         if len(self.df.loc[self.df['molecule_id']==molecule_id].pmb_type) == 0:
             raise ValueError("The provided molecule_id is not present in the pyMBE dataframe.")      
@@ -530,11 +530,11 @@ def check_dimensionality(self, variable, expected_dimensionality):
         Checks if the dimensionality of `variable` matches `expected_dimensionality`.
 
         Args:
-            `variable`(`pint.Quantity`): Quantity to be checked.
-            `expected_dimensionality(`str`): Expected dimension of the variable.
+            variable(`pint.Quantity`): Quantity to be checked.
+            expected_dimensionality(`str`): Expected dimension of the variable.
 
         Returns:
-            `bool`: `True` if the variable if of the expected dimensionality, `False` otherwise.
+            (`bool`): `True` if the variable if of the expected dimensionality, `False` otherwise.
 
         Note:
             - `expected_dimensionality` takes dimensionality following the Pint standards [docs](https://pint.readthedocs.io/en/0.10.1/wrapping.html?highlight=dimensionality#checking-dimensionality).
@@ -601,7 +601,7 @@ def check_pka_set(self, pka_set):
         Checks that `pka_set` has the formatting expected by the pyMBE library.
        
         Args:
-            pka_set (dict): {"name" : {"pka_value": pka, "acidity": acidity}}
+            pka_set(`dict`): {"name" : {"pka_value": pka, "acidity": acidity}}
         """
         required_keys=['pka_value','acidity']
         for required_key in required_keys:
@@ -659,7 +659,7 @@ def convert_str_to_bond_object (self, bond_str):
         Convert a row read as a `str` to the corresponding bond object. There are two supported bonds: HarmonicBond and FeneBond
 
         Args:
-            bond_str (`str`): string with the information of a bond object
+            bond_str(`str`): string with the information of a bond object
 
         Returns:
             bond_object(`obj`): EsPRESSo bond object 
@@ -720,14 +720,14 @@ def create_added_salt (self, espresso_system, cation_name, anion_name, c_salt, v
         Creates a `c_salt` concentration of `cation_name` and `anion_name` ions into the `espresso_system`.
 
         Args:
-            espresso_system (`obj`): instance of an espresso system object.
+            espresso_system(`espressomd.system.System`): instance of an espresso system object.
             cation_name(`str`): `name` of a particle with a positive charge.
             anion_name(`str`): `name` of a particle with a negative charge.
             c_salt(`float`): Salt concentration.
             verbose(`bool`): switch to activate/deactivate verbose. Defaults to True.
             
         Returns:
-            c_salt_calculated (float): Calculated salt concentration added to `espresso_system`.
+            c_salt_calculated(`float`): Calculated salt concentration added to `espresso_system`.
         """
         cation_name_charge = self.df.loc[self.df['name']==cation_name].state_one.charge.values[0]
         anion_name_charge = self.df.loc[self.df['name']==anion_name].state_one.charge.values[0]     
@@ -761,8 +761,8 @@ def create_bond_in_espresso(self, bond_type, bond_parameters):
         Creates either a harmonic or a FENE bond in ESPREesSo
 
         Args:
-            bond_type (`str`)        : label to identify the potential to model the bond.
-            bond_parameters (`dict`) : parameters of the potential of the bond.
+            bond_type(`str`): label to identify the potential to model the bond.
+            bond_parameters(`dict`): parameters of the potential of the bond.
 
         Note:
             Currently, only HARMONIC and FENE bonds are supported.
@@ -823,13 +823,13 @@ def create_counterions(self, object_name, cation_name, anion_name, espresso_syst
         
         Args:
             object_name(`str`): `name` of a pymbe object.
-            espresso_system(`obj`): Instance of a system object from the espressomd library.
+            espresso_system(`espressomd.system.System`): Instance of a system object from the espressomd library.
             cation_name(`str`): `name` of a particle with a positive charge.
             anion_name(`str`): `name` of a particle with a negative charge.
             verbose(`bool`): switch to activate/deactivate verbose. Defaults to True.
 
         Returns: 
-            counterion_number (dict): {"name": number}
+            counterion_number(`dict`): {"name": number}
         """
         cation_charge = self.df.loc[self.df['name']==cation_name].state_one.charge.iloc[0]
         anion_charge = self.df.loc[self.df['name']==anion_name].state_one.charge.iloc[0]
@@ -871,13 +871,13 @@ def create_molecule(self, name, number_of_molecules, espresso_system, list_of_fi
 
         Args:
             name(`str`): Label of the molecule type to be created. `name` must be defined in `pmb.df`
-            espresso_system(`obj`): Instance of a system object from espressomd library.
+            espresso_system(`espressomd.system.System`): Instance of a system object from espressomd library.
             number_of_molecules(`int`): Number of molecules of type `name` to be created.
             list_of_first_residue_positions(`list`, optional): List of coordinates where the central bead of the first_residue_position will be created, random by default
             use_default_bond(`bool`, optional): Controls if a bond of type `default` is used to bond particle with undefined bonds in `pymbe.df`
 
         Returns:
-            molecules_info (`dict`):  {molecule_id: {residue_id:{"central_bead_id":central_bead_id, "side_chain_ids": [particle_id1, ...]}}} 
+            molecules_info(`dict`):  {molecule_id: {residue_id:{"central_bead_id":central_bead_id, "side_chain_ids": [particle_id1, ...]}}} 
         """
         if list_of_first_residue_positions is not None:
             for item in list_of_first_residue_positions:
@@ -987,10 +987,10 @@ def create_particle(self, name, espresso_system, number_of_particles, position=N
         Creates `number_of_particles` particles of type `name` into `espresso_system` and bookkeeps them into `pymbe.df`.
         
         Args:
-            name (`str`): Label of the particle type to be created. `name` must be a `particle` defined in `pmb_df`. 
-            espresso_system (`cls`): Instance of a system object from the espressomd library.
-            number_of_particles (`int`): Number of particles to be created.
-            position (list of [`float`,`float`,`float`], optional): Initial positions of the particles. If not given, particles are created in random positions. Defaults to None.
+            name(`str`): Label of the particle type to be created. `name` must be a `particle` defined in `pmb_df`. 
+            espresso_system(`espressomd.system.System`): Instance of a system object from the espressomd library.
+            number_of_particles(`int`): Number of particles to be created.
+            position(list of [`float`,`float`,`float`], optional): Initial positions of the particles. If not given, particles are created in random positions. Defaults to None.
             fix(`bool`, optional): Controls if the particle motion is frozen in the integrator, it is used to create rigid objects. Defaults to False.
         Returns:
             created_pid_list(`list` of `float`): List with the ids of the particles created into `espresso_system`.
@@ -1038,7 +1038,7 @@ def create_pmb_object(self, name, number_of_objects, espresso_system, position=N
         Args:
             name(`str`): Unique label of the `pmb object` to be created. 
             number_of_objects(`int`): Number of `pmb object`s to be created.
-            espresso_system(`obj`): Instance of an espresso system object from espressomd library.
+            espresso_system(`espressomd.system.System`): Instance of an espresso system object from espressomd library.
             position(`list`): Coordinates where the particles should be created.
             use_default_bond(`bool`,optional): Controls if a `default` bond is used to bond particles with undefined bonds in `pmb.df`. Defaults to `False`.
 
@@ -1073,7 +1073,7 @@ def create_protein(self, name, number_of_proteins, espresso_system, topology_dic
 
         Args:
             name(`str`): Label of the protein to be created. 
-            espresso_system(`obj`): Instance of a system object from the espressomd library.
+            espresso_system(`espressomd.system.System`): Instance of a system object from the espressomd library.
             number_of_proteins(`int`): Number of proteins to be created.
             positions(`dict`): {'ResidueNumber': {'initial_pos': [], 'chain_id': ''}}
         """
@@ -1135,13 +1135,13 @@ def create_residue(self, name, espresso_system, central_bead_position=None,use_d
 
         Args:
             name(`str`): Label of the residue type to be created. `name` must be defined in `pmb.df`
-            espresso_system(`obj`): Instance of a system object from espressomd library.
+            espresso_system(`espressomd.system.System`): Instance of a system object from espressomd library.
             central_bead_position(`list` of `float`): Position of the central bead.
-            use_default_bond (`bool`): Switch to control if a bond of type `default` is used to bond a particle whose bonds types are not defined in `pmb.df`
-            backbone_vector (`list` of `float`): Backbone vector of the molecule. All side chains are created perpendicularly to `backbone_vector`.
+            use_default_bond(`bool`): Switch to control if a bond of type `default` is used to bond a particle whose bonds types are not defined in `pmb.df`
+            backbone_vector(`list` of `float`): Backbone vector of the molecule. All side chains are created perpendicularly to `backbone_vector`.
 
         Returns:
-            residues_info (`dict`): {residue_id:{"central_bead_id":central_bead_id, "side_chain_ids":[particle_id1, ...]}}
+            residues_info(`dict`): {residue_id:{"central_bead_id":central_bead_id, "side_chain_ids":[particle_id1, ...]}}
         """
         self.check_if_name_is_defined_in_df(name=name,
                                             pmb_type_to_be_defined='residue')
@@ -1318,10 +1318,10 @@ def define_AA_residues(self, sequence, model):
 
         Args:
             sequence(`lst`):  Sequence of the peptide or protein.
-            model (`string`): Model name. Currently only models with 1 bead '1beadAA' or with 2 beads '2beadAA' per amino acid are supported.
+            model(`string`): Model name. Currently only models with 1 bead '1beadAA' or with 2 beads '2beadAA' per amino acid are supported.
 
         Returns:
-            residue_list (`list` of `str`): List of the `name`s of the `residue`s  in the sequence of the `molecule`.             
+            residue_list(`list` of `str`): List of the `name`s of the `residue`s  in the sequence of the `molecule`.             
         """
         residue_list = []
         for residue_name in sequence:
@@ -1348,9 +1348,9 @@ def define_bond(self, bond_type, bond_parameters, particle_pairs):
         Defines a pmb object of type `bond` in `pymbe.df`.
 
         Args:
-            bond_type (`str`)        : label to identify the potential to model the bond.
-            bond_parameters (`dict`) : parameters of the potential of the bond.
-            particle_pairs (`lst`)   : list of the `names` of the `particles` to be bonded.
+            bond_type(`str`): label to identify the potential to model the bond.
+            bond_parameters(`dict`): parameters of the potential of the bond.
+            particle_pairs(`lst`): list of the `names` of the `particles` to be bonded.
 
         Note:
             Currently, only HARMONIC and FENE bonds are supported.
@@ -1405,12 +1405,12 @@ def define_default_bond(self, bond_type, bond_parameters, epsilon=None, sigma=No
         The LJ parameters can be optionally provided to calculate the initial bond length
 
         Args:
-            bond_type (`str`)          : label to identify the potential to model the bond.
-            bond_parameters (`dict`)   : parameters of the potential of the bond.
-            sigma (`float`, optional)  : LJ sigma of the interaction between the particles.
-            epsilon (`float`, optional): LJ epsilon for the interaction between the particles.
-            cutoff (`float`, optional) : cutoff-radius of the LJ interaction.
-            offset (`float`, optional) : offset of the LJ interaction.
+            bond_type(`str`): label to identify the potential to model the bond.
+            bond_parameters(`dict`): parameters of the potential of the bond.
+            sigma(`float`, optional): LJ sigma of the interaction between the particles.
+            epsilon(`float`, optional): LJ epsilon for the interaction between the particles.
+            cutoff(`float`, optional): cutoff-radius of the LJ interaction.
+            offset(`float`, optional): offset of the LJ interaction.
 
         Note:
             - Currently, only harmonic and FENE bonds are supported. 
@@ -1456,8 +1456,8 @@ def define_molecule(self, name, residue_list):
         Defines a pyMBE object of type `molecule` in `pymbe.df`.
 
         Args:
-            name (`str`): Unique label that identifies the `molecule`.
-            residue_list (`list` of `str`): List of the `name`s of the `residue`s  in the sequence of the `molecule`.  
+            name(`str`): Unique label that identifies the `molecule`.
+            residue_list(`list` of `str`): List of the `name`s of the `residue`s  in the sequence of the `molecule`.  
         """
         if self.check_if_name_is_defined_in_df(name=name,pmb_type_to_be_defined='molecule'):
             return
@@ -1491,15 +1491,15 @@ def define_particle(self, name, q=0, acidity='inert', pka=None, sigma=None, epsi
         Defines the properties of a particle object.
 
         Args:
-            name (`str`): Unique label that identifies this particle type.  
-            q (`int`, optional): Permanent charge of this particle type. Defaults to 0.
-            acidity (`str`, optional): Identifies whether if the particle is `acidic` or `basic`, used to setup constant pH simulations. Defaults to `inert`.
-            pka (`float`, optional): If `particle` is an acid or a base, it defines its  pka-value. Defaults to None.
-            sigma (`pint.Quantity`, optional): Sigma parameter used to set up Lennard-Jones interactions for this particle type. Defaults to None.
-            cutoff (`pint.Quantity`, optional): Cutoff parameter used to set up Lennard-Jones interactions for this particle type. Defaults to None.
-            offset (`pint.Quantity`, optional): Offset parameter used to set up Lennard-Jones interactions for this particle type. Defaults to None.
-            epsilon (`pint.Quantity`, optional): Epsilon parameter used to setup Lennard-Jones interactions for this particle tipe. Defaults to None.
-            verbose (`bool`, optional): Switch to activate/deactivate verbose. Defaults to True.
+            name(`str`): Unique label that identifies this particle type.  
+            q(`int`, optional): Permanent charge of this particle type. Defaults to 0.
+            acidity(`str`, optional): Identifies whether if the particle is `acidic` or `basic`, used to setup constant pH simulations. Defaults to `inert`.
+            pka(`float`, optional): If `particle` is an acid or a base, it defines its  pka-value. Defaults to None.
+            sigma(`pint.Quantity`, optional): Sigma parameter used to set up Lennard-Jones interactions for this particle type. Defaults to None.
+            cutoff(`pint.Quantity`, optional): Cutoff parameter used to set up Lennard-Jones interactions for this particle type. Defaults to None.
+            offset(`pint.Quantity`, optional): Offset parameter used to set up Lennard-Jones interactions for this particle type. Defaults to None.
+            epsilon(`pint.Quantity`, optional): Epsilon parameter used to setup Lennard-Jones interactions for this particle tipe. Defaults to None.
+            verbose(`bool`, optional): Switch to activate/deactivate verbose. Defaults to True.
             overwrite(`bool`, optional): Switch to enable overwriting of already existing values in pmb.df. Defaults to False.
 
         Note:
@@ -1553,7 +1553,7 @@ def define_particles(self, parameters, overwrite=False, verbose=True):
             verbose (`bool`, optional): Switch to activate/deactivate verbose. Defaults to True.
 
         Note:
-            parameters = {"particle_name1: {"sigma": sigma_value, "epsilon": epsilon_value, ...}, particle_name2: {...},}
+            - parameters = {"particle_name1: {"sigma": sigma_value, "epsilon": epsilon_value, ...}, particle_name2: {...},}
         '''
         if not parameters:
             return 0
@@ -1649,9 +1649,9 @@ def define_residue(self, name, central_bead, side_chains):
         Defines a pyMBE object of type `residue` in `pymbe.df`.
 
         Args:
-            name (`str`): Unique label that identifies the `residue`.
-            central_bead (`str`): `name` of the `particle` to be placed as central_bead of the `residue`.
-            side_chains (`list` of `str`): List of `name`s of the pmb_objects to be placed as side_chains of the `residue`. Currently, only pmb_objects of type `particle`s or `residue`s are supported.
+            name(`str`): Unique label that identifies the `residue`.
+            central_bead(`str`): `name` of the `particle` to be placed as central_bead of the `residue`.
+            side_chains(`list` of `str`): List of `name`s of the pmb_objects to be placed as side_chains of the `residue`. Currently, only pmb_objects of type `particle`s or `residue`s are supported.
         """
         if self.check_if_name_is_defined_in_df(name=name,pmb_type_to_be_defined='residue'):
             return
@@ -1667,8 +1667,8 @@ def destroy_pmb_object_in_system(self, name, espresso_system):
         Destroys all particles associated with `name` in `espresso_system` amd removes the destroyed pmb_objects from `pmb.df` 
 
         Args:
-            name (str): Label of the pmb object to be destroyed. The pmb object must be defined in `pymbe.df`.
-            espresso_system (cls): Instance of a system class from espressomd library.
+            name(`str`): Label of the pmb object to be destroyed. The pmb object must be defined in `pymbe.df`.
+            espresso_system(`espressomd.system.System`): Instance of a system class from espressomd library.
 
         Note:
             - If `name`  is a object_type=`particle`, only the matching particles that are not part of bigger objects (e.g. `residue`, `molecule`) will be destroyed. To destroy particles in such objects, destroy the bigger object instead.
@@ -1717,15 +1717,15 @@ def determine_reservoir_concentrations(self, pH_res, c_salt_res, activity_coeffi
         This is a modified version of the code by Landsgesell et al. (doi.org/10.18419/darus-2237).
 
         Args:
-            pH_res ('float'): pH-value in the reservoir.
-            c_salt_res ('float'): Concentration of monovalent salt (e.g. NaCl) in the reservoir.
-            activity_coefficient_monovalent_pair ('callable', optional): A function that calculates the activity coefficient of an ion pair as a function of the ionic strength.
+            pH_res('float'): pH-value in the reservoir.
+            c_salt_res('pint.Quantity'): Concentration of monovalent salt (e.g. NaCl) in the reservoir.
+            activity_coefficient_monovalent_pair('callable', optional): A function that calculates the activity coefficient of an ion pair as a function of the ionic strength.
 
         Returns:
-            cH_res ('float'): Concentration of H+ ions.
-            cOH_res ('float'): Concentration of OH- ions.
-            cNa_res ('float'): Concentration of Na+ ions.
-            cCl_res ('float'): Concentration of Cl- ions.
+            cH_res('pint.Quantity'): Concentration of H+ ions.
+            cOH_res('pint.Quantity'): Concentration of OH- ions.
+            cNa_res('pint.Quantity'): Concentration of Na+ ions.
+            cCl_res('pint.Quantity'): Concentration of Cl- ions.
         """
 
         self_consistent_run = 0
@@ -1786,7 +1786,7 @@ def enable_motion_of_rigid_object(self, name, espresso_system):
 
         Args:
             name(`str`): Label of the object.
-            espresso_system(`obj`): Instance of a system object from the espressomd library.
+            espresso_system(`espressomd.system.System`): Instance of a system object from the espressomd library.
 
         Note:
             - It requires that espressomd has the following features activated: ["VIRTUAL_SITES_RELATIVE", "MASS"].
@@ -1815,7 +1815,7 @@ def filter_df(self, pmb_type):
             pmb_type(`str`): pmb_object_type to filter in `pmb.df`.
 
         Returns:
-            pmb_type_df(`obj`): filtered `pmb.df`.
+            pmb_type_df(`Pandas.Dataframe`): filtered `pmb.df`.
         """
         pmb_type_df = self.df.loc[self.df['pmb_type']== pmb_type]
         pmb_type_df = pmb_type_df.dropna( axis=1, thresh=1)
@@ -1969,13 +1969,13 @@ def get_bond_length(self, particle_name1, particle_name2, hard_check=False, use_
         If no bond is found, it prints a message and it does not return anything. If `hard_check` is activated, the code stops if no bond is found.
 
         Args:
-            particle_name1 (str): label of the type of the first particle type of the bonded particles.
-            particle_name2 (str): label of the type of the second particle type of the bonded particles.
-            hard_check (bool, optional): If it is activated, the code stops if no bond is found. Defaults to False. 
-            use_default_bond (bool, optional): If it is activated, the "default" bond is returned if no bond is found between `particle_name1` and `particle_name2`. Defaults to False. 
+            particle_name1(str): label of the type of the first particle type of the bonded particles.
+            particle_name2(str): label of the type of the second particle type of the bonded particles.
+            hard_check(bool, optional): If it is activated, the code stops if no bond is found. Defaults to False. 
+            use_default_bond(bool, optional): If it is activated, the "default" bond is returned if no bond is found between `particle_name1` and `particle_name2`. Defaults to False. 
 
         Returns:
-            l0 (`float`): bond length
+            l0(`pint.Quantity`): bond length
         
         Note:
             - If `use_default_bond`=True and no bond is defined between `particle_name1` and `particle_name2`, it returns the default bond defined in `pmb.df`.
@@ -2415,7 +2415,7 @@ def read_pmb_df (self,filename):
         Reads a pyMBE's Dataframe stored in `filename` and stores the information into pyMBE.
 
         Args:
-            filename(str): path to file.
+            filename(`str`): path to file.
 
         Note:
             This function only accepts files with CSV format. 
@@ -2517,13 +2517,13 @@ def search_bond(self, particle_name1, particle_name2, hard_check=False, use_defa
         If no bond is found, it prints a message and it does not return anything. If `hard_check` is activated, the code stops if no bond is found.
 
         Args:
-            particle_name1 (str): label of the type of the first particle type of the bonded particles.
-            particle_name2 (str): label of the type of the second particle type of the bonded particles.
-            hard_check (bool, optional): If it is activated, the code stops if no bond is found. Defaults to False. 
-            use_default_bond (bool, optional): If it is activated, the "default" bond is returned if no bond is found between `particle_name1` and `particle_name2`. Defaults to False. 
+            particle_name1(`str`): label of the type of the first particle type of the bonded particles.
+            particle_name2(`str`): label of the type of the second particle type of the bonded particles.
+            hard_check(`bool`, optional): If it is activated, the code stops if no bond is found. Defaults to False. 
+            use_default_bond(`bool`, optional): If it is activated, the "default" bond is returned if no bond is found between `particle_name1` and `particle_name2`. Defaults to False. 
 
         Returns:
-            bond (cls): bond object from the espressomd library.
+            bond(`espressomd.interactions.BondedInteractions`): bond object from the espressomd library.
         
         Note:
             - If `use_default_bond`=True and no bond is defined between `particle_name1` and `particle_name2`, it returns the default bond defined in `pmb.df`.
@@ -2583,11 +2583,11 @@ def set_particle_acidity(self, name, acidity='inert', default_charge=0, pka=None
         deprotonated states. In each state is set: `label`,`charge` and `es_type`. If it is inert, it will define only `state_one`.
 
         Args:
-            name (`str`): Unique label that identifies the `particle`. 
-            acidity (`str`): Identifies whether the particle is `acidic` or `basic`, used to setup constant pH simulations. Defaults to `inert`.
+            name(`str`): Unique label that identifies the `particle`. 
+            acidity(`str`): Identifies whether the particle is `acidic` or `basic`, used to setup constant pH simulations. Defaults to `inert`.
             default_charge (`int`): Charge of the particle. Defaults to 0.
-            pka (`float`, optional):  If `particle` is an acid or a base, it defines its pka-value. Defaults to None.
-            verbose (`bool`, optional): Switch to activate/deactivate verbose. Defaults to True.
+            pka(`float`, optional):  If `particle` is an acid or a base, it defines its pka-value. Defaults to None.
+            verbose(`bool`, optional): Switch to activate/deactivate verbose. Defaults to True.
             overwrite(`bool`, optional): Switch to enable overwriting of already existing values in pmb.df. Defaults to False. 
         """
         acidity_valid_keys = ['inert','acidic', 'basic']
@@ -2673,11 +2673,11 @@ def set_reduced_units(self, unit_length=None, unit_charge=None, temperature=None
         Sets the set of reduced units used by pyMBE.units and it prints it.
 
         Args:
-            unit_length (`obj`,optional): Reduced unit of length defined using the `pmb.units` UnitRegistry. Defaults to None. 
-            unit_charge (`obj`,optional): Reduced unit of charge defined using the `pmb.units` UnitRegistry. Defaults to None. 
-            temperature (`obj`,optional): Temperature of the system, defined using the `pmb.units` UnitRegistry. Defaults to None. 
-            Kw (`obj`,optional): Ionic product of water in mol^2/l^2. Defaults to None. 
-            verbose (`bool`, optional): Switch to activate/deactivate verbose. Defaults to True.
+            unit_length(`pint.Quantity`,optional): Reduced unit of length defined using the `pmb.units` UnitRegistry. Defaults to None. 
+            unit_charge(`pint.Quantity`,optional): Reduced unit of charge defined using the `pmb.units` UnitRegistry. Defaults to None. 
+            temperature(`pint.Quantity`,optional): Temperature of the system, defined using the `pmb.units` UnitRegistry. Defaults to None. 
+            Kw(`pint.Quantity`,optional): Ionic product of water in mol^2/l^2. Defaults to None. 
+            verbose(`bool`, optional): Switch to activate/deactivate verbose. Defaults to True.
 
         Note:
             - If no `temperature` is given, a value of 298.15 K is assumed by default.
@@ -2713,12 +2713,12 @@ def setup_cpH (self, counter_ion, constant_pH, exclusion_range=None, pka_set=Non
         Args:
             counter_ion(`str`): `name` of the counter_ion `particle`.
             constant_pH(`float`): pH-value.
-            exclusion_range(`float`, optional): Below this value, no particles will be inserted.
+            exclusion_range(`pint.Quantity`, optional): Below this value, no particles will be inserted.
             use_exclusion_radius_per_type(`bool`,optional): Controls if one exclusion_radius for each espresso_type is used. Defaults to `False`.
             pka_set(`dict`,optional): Desired pka_set, pka_set(`dict`): {"name" : {"pka_value": pka, "acidity": acidity}}. Defaults to None.
 
         Returns:
-            RE (`obj`): Instance of a reaction_ensemble.ConstantpHEnsemble object from the espressomd library.
+            RE(`reaction_methods.ConstantpHEnsemble`): Instance of a reaction_methods.ConstantpHEnsemble object from the espressomd library.
             sucessfull_reactions_labels(`lst`): Labels of the reactions set up by pyMBE.
         """
         from espressomd import reaction_methods
@@ -2763,15 +2763,15 @@ def setup_gcmc(self, c_salt_res, salt_cation_name, salt_anion_name, activity_coe
         For reactive systems coupled to a reservoir, the grand-reaction method has to be used instead.
 
         Args:
-            c_salt_res ('float'): Concentration of monovalent salt (e.g. NaCl) in the reservoir.
+            c_salt_res ('pint.Quantity'): Concentration of monovalent salt (e.g. NaCl) in the reservoir.
             salt_cation_name ('str'): Name of the salt cation (e.g. Na+) particle.
             salt_anion_name ('str'): Name of the salt anion (e.g. Cl-) particle.
             activity_coefficient ('callable', optional): A function that calculates the activity coefficient of an ion pair as a function of the ionic strength.
-            exclusion_range(`float`, optional): For distances shorter than this value, no particles will be inserted.
+            exclusion_range(`pint.Quantity`, optional): For distances shorter than this value, no particles will be inserted.
             use_exclusion_radius_per_type(`bool`,optional): Controls if one exclusion_radius for each espresso_type is used. Defaults to `False`.
 
         Returns:
-            RE (`obj`): Instance of a reaction_ensemble.ReactionEnsemble object from the espressomd library.
+            RE (`reaction_methods.ReactionEnsemble`): Instance of a reaction_methods.ReactionEnsemble object from the espressomd library.
         """
         from espressomd import reaction_methods
         if exclusion_range is None:
@@ -2831,20 +2831,20 @@ def setup_grxmc_reactions(self, pH_res, c_salt_res, proton_name, hydroxide_name,
 
         Args:
             pH_res ('float): pH-value in the reservoir.
-            c_salt_res ('float'): Concentration of monovalent salt (e.g. NaCl) in the reservoir.
+            c_salt_res ('pint.Quantity'): Concentration of monovalent salt (e.g. NaCl) in the reservoir.
             proton_name ('str'): Name of the proton (H+) particle.
             hydroxide_name ('str'): Name of the hydroxide (OH-) particle.
             salt_cation_name ('str'): Name of the salt cation (e.g. Na+) particle.
             salt_anion_name ('str'): Name of the salt anion (e.g. Cl-) particle.
             activity_coefficient ('callable', optional): A function that calculates the activity coefficient of an ion pair as a function of the ionic strength.
-            exclusion_range(`float`, optional): For distances shorter than this value, no particles will be inserted.
+            exclusion_range(`pint.Quantity`, optional): For distances shorter than this value, no particles will be inserted.
             pka_set(`dict`,optional): Desired pka_set, pka_set(`dict`): {"name" : {"pka_value": pka, "acidity": acidity}}. Defaults to None.
             use_exclusion_radius_per_type(`bool`,optional): Controls if one exclusion_radius for each espresso_type is used. Defaults to `False`.
 
         Returns:
             RE (`obj`): Instance of a reaction_ensemble.ReactionEnsemble object from the espressomd library.
             sucessful_reactions_labels(`lst`): Labels of the reactions set up by pyMBE.
-            ionic_strength_res ('float'): Ionic strength of the reservoir (useful for calculating partition coefficients).
+            ionic_strength_res ('pint.Quantity'): Ionic strength of the reservoir (useful for calculating partition coefficients).
         """
         from espressomd import reaction_methods
         if exclusion_range is None:
@@ -3041,16 +3041,16 @@ def setup_grxmc_unified (self, pH_res, c_salt_res, cation_name, anion_name, acti
 
         Args:
             pH_res ('float'): pH-value in the reservoir.
-            c_salt_res ('float'): Concentration of monovalent salt (e.g. NaCl) in the reservoir.
+            c_salt_res ('pint.Quantity'): Concentration of monovalent salt (e.g. NaCl) in the reservoir.
             cation_name ('str'): Name of the cationic particle.
             anion_name ('str'): Name of the anionic particle.
             activity_coefficient ('callable', optional): A function that calculates the activity coefficient of an ion pair as a function of the ionic strength.
-            exclusion_range(`float`, optional): Below this value, no particles will be inserted.
+            exclusion_range(`pint.Quantity`, optional): Below this value, no particles will be inserted.
             pka_set(`dict`,optional): Desired pka_set, pka_set(`dict`): {"name" : {"pka_value": pka, "acidity": acidity}}. Defaults to None.
             use_exclusion_radius_per_type(`bool`,optional): Controls if one exclusion_radius per each espresso_type. Defaults to `False`.
 
         Returns:
-            RE (`obj`): Instance of a reaction_ensemble.ReactionEnsemble object from the espressomd library.
+            RE (`reaction_ensemble.ReactionEnsemble`): Instance of a reaction_ensemble.ReactionEnsemble object from the espressomd library.
             sucessful_reactions_labels(`lst`): Labels of the reactions set up by pyMBE.
             ionic_strength_res ('float'): Ionic strength of the reservoir (useful for calculating partition coefficients).
         """
@@ -3217,7 +3217,7 @@ def setup_lj_interactions(self, espresso_system, shift_potential=True, combining
         Sets up the Lennard-Jones (LJ) potential between all pairs of particle types with values for `sigma`, `offset`, and `epsilon` stored in `pymbe.df`.
 
         Args:
-            espresso_system(`obj`): Instance of a system object from the espressomd library.
+            espresso_system(`espressomd.system.System`): Instance of a system object from the espressomd library.
             shift_potential(`bool`, optional): If True, a shift will be automatically computed such that the potential is continuous at the cutoff radius. Otherwise, no shift will be applied. Defaults to True.
             combining_rule(`string`, optional): combining rule used to calculate `sigma` and `epsilon` for the potential between a pair of particles. Defaults to 'Lorentz-Berthelot'.
             warning(`bool`, optional): switch to activate/deactivate warning messages. Defaults to True.
@@ -3294,7 +3294,7 @@ def write_pmb_df (self, filename):
         Writes the pyMBE dataframe into a csv file
         
         Args:
-            filename (`str`): Path to the csv file 
+            filename(`str`): Path to the csv file 
         '''
 
         self.df.to_csv(filename)
diff --git a/samples/Beyer2024/globular_protein.py b/samples/Beyer2024/globular_protein.py
index 11b6c15..7bc7394 100644
--- a/samples/Beyer2024/globular_protein.py
+++ b/samples/Beyer2024/globular_protein.py
@@ -79,7 +79,7 @@
 pH_value = args.pH 
 
 inputs={"pH": args.pH,
-        "protein_pdb": args.pdb}
+        "pdb": args.pdb}
 
 #System Parameters 
 LANGEVIN_SEED = 77 
diff --git a/testsuite/analysis_tests.py b/testsuite/analysis_tests.py
new file mode 100644
index 0000000..2cd536f
--- /dev/null
+++ b/testsuite/analysis_tests.py
@@ -0,0 +1,155 @@
+#
+# Copyright (C) 2024 pyMBE-dev team  
+#
+# This file is part of pyMBE.
+#
+# pyMBE is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# pyMBE is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+import unittest as ut
+import pandas as pd
+import lib.analysis as ana
+
+
+class Serialization(ut.TestCase):
+
+    def test_analyze_time_series(self):
+        print("*** Unit test: test that analysis.analyze_time_series analyzes all data in a folder correctly ***")
+        analyzed_data = ana.analyze_time_series(path_to_datafolder="testsuite/tests_data",
+                                       filename_extension="_time_series.csv",
+                                       minus_separator=True)
+        analyzed_data[["Dens","eps"]] = analyzed_data[["Dens","eps"]].apply(pd.to_numeric)
+        reference_data = pd.read_csv("testsuite/tests_data/average_data.csv", header=[0,1])
+        analyzed_data.columns = analyzed_data.sort_index(axis=1,level=[0,1],ascending=[True,True]).columns
+        reference_data.columns = reference_data.sort_index(axis=1,level=[0,1],ascending=[True,True]).columns
+        pd.testing.assert_frame_equal(analyzed_data.dropna(),reference_data.dropna(), check_column_type=False, check_dtype=False)
+        print("*** Unit passed ***")
+        
+        return
+    
+    
+    def test_get_dt(self):
+        print("*** Unit test: test that analysis.get_dt returns the right time step ***")
+        data = pd.DataFrame.from_dict( {'time': [0, 1, 2,], 'obs': ['1.0', '2.0', '4.0']} )
+        dt, n_warnings = ana.get_dt(data)
+        self.assertAlmostEqual(dt, 1.0, delta = 1e-7)
+        self.assertEqual(n_warnings, 0)
+        print("*** Unit passed ***")
+
+        print("*** Unit test: test that analysis.get_dt prints a warning if there are values with repeated time steps ***")
+        data = pd.DataFrame.from_dict( {'time': [0, 1, 1,], 'obs': ['1.0', '2.0', '4.0']} )
+        dt, n_warnings = ana.get_dt(data,verbose=True)
+        self.assertAlmostEqual(dt, 1.0, delta = 1e-7)
+        self.assertEqual(n_warnings, 1)
+        print("*** Unit passed ***")
+
+        print("*** Unit test: test that analysis.get_dt raises a ValueError if the column with the time is not found ***")
+        data = pd.DataFrame.from_dict( {'ns': [0, 1, 2,], 'obs': ['1.0', '2.0', '4.0']} )
+        inputs = {"data": data}
+        self.assertRaises(ValueError, ana.get_dt, **inputs)
+        
+        print("*** Unit passed ***")
+
+        print("*** Unit test: test that analysis.get_dt raises a ValueError if the time is stored at uneven intervals ***")
+        data = pd.DataFrame.from_dict( {'time': [0, 1, 4,], 'obs': ['1.0', '2.0', '4.0']} )
+        inputs = {"data": data}
+        self.assertRaises(ValueError, ana.get_dt, **inputs)
+        
+        print("*** Unit passed ***")
+
+    def test_add_data_to_df(self):
+        print("*** Unit test: test that analysis.add_data_to_df creates a Pandas Dataframe from a dictionary correctly ***")
+        data = {'A': [2], 
+                'B': ['1.0']}
+        reference_df = pd.DataFrame(data, 
+                                    index=[0])
+        
+        analysis_df = ana.add_data_to_df(df=pd.DataFrame(),
+                                         data_dict=data,
+                                         index=[0])
+        pd.testing.assert_frame_equal(reference_df,analysis_df)
+        print("*** Unit passed ***")
+        print("*** Unit test: test that analysis.add_data_to_df updates a Pandas Dataframe with new data from dictionary correctly ***")
+        data ["C"] = False
+        reference_df =  pd.concat([reference_df, pd.DataFrame(data,index=[len(analysis_df)])])
+        analysis_df = ana.add_data_to_df(df=analysis_df,
+                                         data_dict=data,
+                                         index=[len(analysis_df)])
+        print("*** Unit passed ***")
+
+
+    def test_get_params_from_file_name(self):
+        print("*** Unit test: test that get_params_from_file_name parses a filename without minus separator ***")
+        filename = 'density_0.001_N_1000_T_2.00.csv'
+        correct_params = {'density': '0.001', 'N': '1000', 'T': '2.00'}
+        params = ana.get_params_from_file_name(filename, 
+                                                minus_separator = False)
+        self.assertEqual(correct_params,params)
+        print("*** Unit passed ***")
+
+        print("*** Unit test: test that get_params_from_file_name parses a filename with minus separator ***")
+        filename = 'N-064_Solvent-good_Init-coil_time_series.csv'
+        correct_params = {'N': 64, 'Solvent': 'good', 'Init': 'coil'}
+        params = ana.get_params_from_file_name(filename, 
+                                                minus_separator = True,
+                                                filename_extension="_time_series.csv")
+        self.assertEqual(correct_params,params)
+        print("*** Unit passed ***")
+
+        print("*** Unit test: test that get_params_from_file_name parses a filename with a different extension ***")
+        filename = 'density_0.001_N_1000_T_2.00_time_series.txt'
+        correct_params = {'density': '0.001', 'N': '1000', 'T': '2.00'}
+        params = ana.get_params_from_file_name(filename, 
+                                                minus_separator = False,
+                                                filename_extension="_time_series.txt")
+        self.assertEqual(correct_params,params)
+        print("*** Unit passed ***")
+
+        print("*** Unit test: test that get_params_from_file_name raises a ValueError if a filename with a wrong formating is provided ***")
+        inputs = {"file_name": 'density_0.001_N_1000_T_f_2.00_time_series.txt',
+                  "filename_extension": "_time_series.txt"}
+        self.assertRaises(ValueError, ana.get_params_from_file_name, **inputs)
+        print("*** Unit passed ***")
+        
+    def test_block_analyze(self):
+        print("*** Unit test: test that block_analyze yields the expected outputs and reports the number of blocks and the block size. It should print that it encountered 1 repeated time value. ***")
+        data = pd.read_csv("testsuite/tests_data/N-064_Solvent-good_Init-coil_time_series.csv")
+        analyzed_data = ana.block_analyze(full_data=data, verbose=True)
+        analyzed_data = ana.add_data_to_df(df=pd.DataFrame(),
+                            data_dict=analyzed_data.to_dict(),
+                            index=[0])
+        reference_data = pd.read_csv("testsuite/tests_data/N-064_Solvent-good_Init-coil_time_series_analyzed.csv", header=[0,1])
+        pd.testing.assert_frame_equal(analyzed_data.dropna(),reference_data.dropna(), check_column_type=False)
+        print("*** Unit passed ***")
+        
+        print("*** Unit test: test that block_analyze analyzes correcly a subset of the data ***")
+        analyzed_data = ana.block_analyze(full_data=data, columns_to_analyze="Rg")
+        analyzed_data = ana.add_data_to_df(df=pd.DataFrame(),
+                            data_dict=analyzed_data.to_dict(),
+                            index=[0])
+        reference_data = pd.read_csv("testsuite/tests_data/N-064_Solvent-good_Init-coil_time_series_analyzed.csv", header=[0,1])
+        reference_data = reference_data[[("mean","Rg"),("err_mean","Rg"),("n_eff","Rg"),("tau_int","Rg")]]
+        pd.testing.assert_frame_equal(analyzed_data.dropna(),reference_data.dropna(), check_column_type=False)
+        print("*** Unit passed ***")
+
+        print("*** Unit test: test that block_analyze raises a ValueError if there is no time column ***")
+        data = pd.DataFrame.from_dict( {'ns': [0, 1, 2,], 'obs': ['1.0', '2.0', '4.0']} )
+        inputs = {"full_data": data, "verbose": False, "dt": 1}
+        self.assertRaises(ValueError, ana.block_analyze, **inputs)
+        
+        print("*** Unit passed ***")
+        
+
+if __name__ == "__main__":
+    print("*** lib.analysis unit tests ***")
+    ut.main()
diff --git a/testsuite/gcmc_tests.py b/testsuite/gcmc_tests.py
index 1cee373..c091816 100644
--- a/testsuite/gcmc_tests.py
+++ b/testsuite/gcmc_tests.py
@@ -38,8 +38,10 @@ def gcmc_test(script_path, mode):
             print(subprocess.list2cmdline(run_command))
             subprocess.check_output(run_command)
         # Analyze all time series
-        data=analysis.analyze_time_series(path_to_datafolder=time_series_path)
+        data=analysis.analyze_time_series(path_to_datafolder=time_series_path,
+                                          filename_extension="_time_series.csv")
 
+    print(data["csalt","value"])
     # Check concentration
     test_concentration=np.sort(data["csalt","value"].to_numpy(dtype=float))
     ref_concentration=np.sort(data["mean","c_salt"].to_numpy())
diff --git a/testsuite/globular_protein_tests.py b/testsuite/globular_protein_tests.py
index c2394d4..a912970 100644
--- a/testsuite/globular_protein_tests.py
+++ b/testsuite/globular_protein_tests.py
@@ -49,7 +49,8 @@ def run_protein_test(script_path, test_pH_values, protein_pdb, rtol, atol,mode="
             print(subprocess.list2cmdline(run_command))
             subprocess.check_output(run_command)
         # Analyze all time series
-        data=analysis.analyze_time_series(path_to_datafolder=time_series_path)
+        data=analysis.analyze_time_series(path_to_datafolder=time_series_path,
+                                          filename_extension="_time_series.csv")
 
         data_path=pmb.get_resource(path="testsuite/globular_protein_tests_data")
         
diff --git a/testsuite/serialization_test.py b/testsuite/serialization_test.py
index 5f5960f..7b29b84 100644
--- a/testsuite/serialization_test.py
+++ b/testsuite/serialization_test.py
@@ -46,8 +46,8 @@ def test_json_encoder(self):
     def test_parameters_to_path(self):
         params = {"kT": 2., "phi": -np.pi, "n": 3, "fene": True, "name": "pep"}
         name = lib.analysis.built_output_name(params)
-        self.assertEqual(name, "kT-2_phi--3.14_n-3_fene-True_name-pep")
-        params_out = lib.analysis.get_params_from_dir_name(name)
+        self.assertEqual(name, "kT_2_phi_-3.14_n_3_fene_True_name_pep")
+        params_out = lib.analysis.get_params_from_file_name(name)
         params_ref = {"kT": "2", "phi": "-3.14", "n": "3",
                       "fene": "True", "name": "pep"}
         self.assertEqual(params_out, params_ref)
diff --git a/testsuite/tests_data/N-064_Solvent-good_Init-coil_time_series.csv b/testsuite/tests_data/N-064_Solvent-good_Init-coil_time_series.csv
new file mode 100644
index 0000000..7523528
--- /dev/null
+++ b/testsuite/tests_data/N-064_Solvent-good_Init-coil_time_series.csv
@@ -0,0 +1,2009 @@
+step,time,Ekin,Epot,Etot,Tkin,Rg,pressure
+     0,     0.000,   99.107173,   52.059782,  151.166955,    1.032366,   10.202262,    0.000000
+  1000,    10.000,   87.302638,   48.741338,  136.043977,    0.909402,    8.397635,    0.000000
+  2000,    20.000,  118.308579,   47.383348,  165.691927,    1.232381,    6.522455,    0.000000
+  3000,    30.000,   85.627667,   53.670441,  139.298108,    0.891955,    6.399166,    0.000000
+  4000,    40.000,  102.803540,   35.967366,  138.770906,    1.070870,    6.482141,    0.000000
+  5000,    50.000,   97.501682,   47.504458,  145.006140,    1.015643,    6.652361,    0.000000
+  6000,    60.000,   89.050920,   44.067975,  133.118895,    0.927614,    5.924682,    0.000000
+  7000,    70.000,   98.059788,   45.910390,  143.970178,    1.021456,    5.662736,    0.000000
+  8000,    80.000,  104.706179,   44.916084,  149.622263,    1.090689,    5.727205,    0.000000
+  9000,    90.000,   93.847664,   41.335563,  135.183227,    0.977580,    5.360753,    0.000000
+ 10000,   100.000,  101.838216,   40.040637,  141.878853,    1.060815,    4.884872,    0.000000
+ 11000,   110.000,   82.680643,   41.011847,  123.692490,    0.861257,    6.263432,    0.000000
+ 12000,   120.000,   92.441704,   42.068458,  134.510162,    0.962934,    6.584052,    0.000000
+ 13000,   130.000,  118.998200,   45.584162,  164.582362,    1.239565,    5.885683,    0.000000
+ 14000,   140.000,   93.000855,   43.227375,  136.228231,    0.968759,    5.660620,    0.000000
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+     0, 20000.010,   86.331185,   42.228914,  128.560099,    0.899283,    6.476240,    0.000000
+  1000, 20010.010,   83.480279,   44.612975,  128.093254,    0.869586,    6.990107,    0.000000
+  2000, 20020.010,  100.247387,   38.955768,  139.203154,    1.044244,    6.597584,    0.000000
+  3000, 20030.010,   91.366094,   41.272112,  132.638207,    0.951730,    6.988043,    0.000000
+  4000, 20040.010,  100.864501,   43.712855,  144.577356,    1.050672,    6.203967,    0.000000
+  5000, 20050.010,   99.534740,   44.918354,  144.453095,    1.036820,    6.488762,    0.000000
+  6000, 20060.010,   92.991086,   50.295877,  143.286963,    0.968657,    6.794900,    0.000000
diff --git a/testsuite/tests_data/N-064_Solvent-good_Init-coil_time_series_analyzed.csv b/testsuite/tests_data/N-064_Solvent-good_Init-coil_time_series_analyzed.csv
new file mode 100644
index 0000000..5093800
--- /dev/null
+++ b/testsuite/tests_data/N-064_Solvent-good_Init-coil_time_series_analyzed.csv
@@ -0,0 +1,3 @@
+mean,mean,mean,mean,mean,mean,mean,err_mean,err_mean,err_mean,err_mean,err_mean,err_mean,err_mean,n_eff,n_eff,n_eff,n_eff,n_eff,n_eff,n_eff,tau_int,tau_int,tau_int,tau_int,tau_int,tau_int,tau_int
+step,Ekin,Epot,Etot,Tkin,Rg,pressure,step,Ekin,Epot,Etot,Tkin,Rg,pressure,step,Ekin,Epot,Etot,Tkin,Rg,pressure,step,Ekin,Epot,Etot,Tkin,Rg,pressure
+1095752.7654867256,95.439669210177,45.354566968473456,140.79423618473453,0.9941632284292035,6.05229448340708,0.0,135740.92503695545,0.1705547398547065,0.1437387612925945,0.22910529225213178,0.0017766090357742472,0.14004234848198663,0.0,14.873125998867755,3321.254709391739,1636.3604915773453,2499.312737817561,3321.2654236179455,59.777386104317905,,607.8076660339049,2.7218629075442404,5.5244550614186645,3.616994329366668,2.721854126958778,151.22775666745008,
diff --git a/testsuite/tests_data/N-128_Dens-0.2_eps-0.71_Init-coil_time_series.csv b/testsuite/tests_data/N-128_Dens-0.2_eps-0.71_Init-coil_time_series.csv
new file mode 100644
index 0000000..2ed6ac1
--- /dev/null
+++ b/testsuite/tests_data/N-128_Dens-0.2_eps-0.71_Init-coil_time_series.csv
@@ -0,0 +1,1000 @@
+step,time,Ekin,Epot,Etot,Tkin,Rg,pressure
+     0,     0.000,  190.671557,151832.174191,152022.845748,    0.993081,   10.251153,    0.200000
+   100,     1.000,  883.563700,  121.168104, 1004.731804,    4.601894,   11.591822,    0.813798
+   200,     2.000,  295.147924,   29.545129,  324.693052,    1.537229,   11.933002,    0.125664
+   300,     3.000,  216.279063,   21.900550,  238.179614,    1.126453,   12.135840,    0.100567
+   400,     4.000,  180.574389,   18.143357,  198.717746,    0.940492,   12.240097,    0.062777
+   500,     5.000,  190.367253,   10.376109,  200.743362,    0.991496,   12.306653,    0.001585
+   600,     6.000,  194.801982,   -3.109730,  191.692251,    1.014594,   12.393261,    0.018662
+   700,     7.000,  199.839795,    4.567590,  204.407385,    1.040832,   12.607743,    0.043487
+   800,     8.000,  186.813956,    5.529598,  192.343554,    0.972989,   12.773825,   -0.022935
+   900,     9.000,  167.120211,    5.522899,  172.643109,    0.870418,   12.929426,    0.018103
+  1000,    10.000,  202.578834,    3.754257,  206.333091,    1.055098,   13.079326,    0.038049
+  1100,    11.000,  231.088035,  -10.530867,  220.557167,    1.203584,   13.128004,    0.089306
+  1200,    12.000,  189.045554,   15.047231,  204.092785,    0.984612,   13.214463,    0.070880
+  1300,    13.000,  202.759824,   -2.918371,  199.841453,    1.056041,   13.202436,    0.132432
+  1400,    14.000,  185.080010,    1.198603,  186.278613,    0.963958,   13.334348,    0.067717
+  1500,    15.000,  201.218079,    7.568177,  208.786256,    1.048011,   13.397167,    0.090046
+  1600,    16.000,  186.406562,    1.670135,  188.076697,    0.970868,   13.399516,   -0.031661
+  1700,    17.000,  219.754444,   -7.436980,  212.317465,    1.144554,   13.517044,    0.106484
+  1800,    18.000,  190.626499,   10.541968,  201.168468,    0.992846,   13.533500,    0.091934
+  1900,    19.000,  212.596409,    0.807779,  213.404187,    1.107273,   13.630527,    0.071331
+  2000,    20.000,  188.552109,    1.788657,  190.340766,    0.982042,   13.692390,    0.003743
+  2100,    21.000,  198.760681,   17.144932,  215.905613,    1.035212,   13.786974,    0.027608
+  2200,    22.000,  218.641563,   -1.456787,  217.184775,    1.138758,   13.831901,   -0.015935
+  2300,    23.000,  204.814481,    3.573563,  208.388044,    1.066742,   13.888252,    0.001214
+  2400,    24.000,  191.884133,    8.861358,  200.745491,    0.999397,   13.924923,    0.067022
+  2500,    25.000,  176.272527,    5.796790,  182.069317,    0.918086,   14.050576,    0.080324
+  2600,    26.000,  209.586296,   -0.808106,  208.778190,    1.091595,   14.180270,    0.014906
+  2700,    27.000,  204.217679,  -10.153807,  194.063872,    1.063634,   14.219185,   -0.018683
+  2800,    28.000,  231.802370,    3.678672,  235.481042,    1.207304,   14.337528,    0.050596
+  2900,    29.000,  186.429565,   -9.933949,  176.495617,    0.970987,   14.396042,    0.055305
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+  3100,    31.000,  197.293008,   10.218340,  207.511348,    1.027568,   14.442979,    0.000091
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+  3900,    39.000,  190.511056,   -8.459609,  182.051447,    0.992245,   14.894881,    0.160013
+  4000,    40.000,  181.599578,  -11.180530,  170.419048,    0.945831,   14.957485,    0.129744
+  4100,    41.000,  183.105845,   -5.086827,  178.019018,    0.953676,   15.007933,    0.072231
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+  5000,    50.000,  179.827050,   -0.998340,  178.828710,    0.936599,   15.474993,    0.031224
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+  5900,    59.000,  188.659744,    4.118786,  192.778530,    0.982603,   15.930487,    0.173449
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+  6100,    61.000,  185.256095,  -14.229039,  171.027056,    0.964875,   16.058595,    0.085499
+  6200,    62.000,  203.627300,    5.961608,  209.588907,    1.060559,   16.149422,    0.000218
+  6300,    63.000,  178.172857,   -2.387704,  175.785153,    0.927984,   16.136079,    0.105647
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+  6600,    66.000,  194.088222,   -2.953793,  191.134429,    1.010876,   16.314682,    0.065466
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+ 89500,   895.000,  172.286041,   13.306073,  185.592114,    0.897323,   43.776191,    0.028740
+ 89600,   896.000,  179.684859,   -0.161132,  179.523727,    0.935859,   43.673172,    0.016772
+ 89700,   897.000,  175.501851,   -2.829116,  172.672734,    0.914072,   43.667780,    0.016605
+ 89800,   898.000,  196.045257,   -5.045303,  190.999954,    1.021069,   43.718669,    0.005860
+ 89900,   899.000,  215.108916,   12.853196,  227.962112,    1.120359,   43.780714,    0.108874
+ 90000,   900.000,  199.143217,    3.994674,  203.137891,    1.037204,   43.738992,    0.055435
+ 90100,   901.000,  181.288001,  -10.940102,  170.347898,    0.944208,   43.732389,    0.070414
+ 90200,   902.000,  181.141130,    5.004921,  186.146052,    0.943443,   43.633899,    0.130940
+ 90300,   903.000,  166.807453,   21.719649,  188.527102,    0.868789,   43.656193,    0.019782
+ 90400,   904.000,  179.876576,   25.888115,  205.764691,    0.936857,   43.748056,    0.014831
+ 90500,   905.000,  204.158956,   14.897756,  219.056712,    1.063328,   43.765830,    0.105564
+ 90600,   906.000,  178.105310,   11.842419,  189.947729,    0.927632,   43.774438,    0.108818
+ 90700,   907.000,  194.297168,   -7.282648,  187.014519,    1.011964,   43.759189,    0.060903
+ 90800,   908.000,  191.406300,    8.562820,  199.969120,    0.996908,   43.743209,    0.056394
+ 90900,   909.000,  215.778857,   15.572231,  231.351088,    1.123848,   43.751212,    0.102212
+ 91000,   910.000,  205.819251,  -15.523882,  190.295368,    1.071975,   43.741360,   -0.059720
+ 91100,   911.000,  205.422272,  -21.505511,  183.916761,    1.069908,   43.689894,   -0.024241
+ 91200,   912.000,  202.559610,  -20.942422,  181.617188,    1.054998,   43.670217,    0.047746
+ 91300,   913.000,  192.089484,   -5.307678,  186.781806,    1.000466,   43.733048,    0.119658
+ 91400,   914.000,  181.603578,  -16.298243,  165.305335,    0.945852,   43.803695,   -0.026163
+ 91500,   915.000,  171.751185,    5.373998,  177.125184,    0.894537,   43.888036,    0.112596
+ 91600,   916.000,  200.470196,   10.086268,  210.556463,    1.044116,   44.023074,    0.103965
+ 91700,   917.000,  185.915867,    7.687344,  193.603211,    0.968312,   44.038195,   -0.003850
+ 91800,   918.000,  203.349629,   -0.395143,  202.954485,    1.059113,   44.088456,   -0.030945
+ 91900,   919.000,  206.855436,    3.441201,  210.296637,    1.077372,   44.051865,   -0.017125
+ 92000,   920.000,  172.551298,   -4.798502,  167.752796,    0.898705,   43.910482,   -0.038323
+ 92100,   921.000,  170.249969,    0.105999,  170.355968,    0.886719,   43.923520,   -0.019753
+ 92200,   922.000,  207.039845,    0.142819,  207.182664,    1.078333,   43.961381,    0.038014
+ 92300,   923.000,  202.661528,   10.864206,  213.525734,    1.055529,   43.982299,    0.038421
+ 92400,   924.000,  214.259878,    7.473076,  221.732954,    1.115937,   43.996727,   -0.011205
+ 92500,   925.000,  198.183387,    6.105253,  204.288639,    1.032205,   44.102786,    0.099596
+ 92600,   926.000,  195.500378,   -6.827419,  188.672959,    1.018231,   44.194753,    0.042886
+ 92700,   927.000,  183.474526,   11.272099,  194.746625,    0.955596,   44.196902,    0.050209
+ 92800,   928.000,  190.231444,   11.018780,  201.250224,    0.990789,   44.217882,    0.098675
+ 92900,   929.000,  195.610021,   10.875854,  206.485875,    1.018802,   44.197498,    0.025396
+ 93000,   930.000,  195.236536,   16.807840,  212.044375,    1.016857,   44.220979,    0.005193
+ 93100,   931.000,  174.263245,   -4.953794,  169.309451,    0.907621,   44.334397,    0.032815
+ 93200,   932.000,  170.756443,    0.050945,  170.807387,    0.889356,   44.360487,    0.035362
+ 93300,   933.000,  215.811046,   -8.999186,  206.811859,    1.124016,   44.347482,    0.109618
+ 93400,   934.000,  206.296900,   -1.995912,  204.300988,    1.074463,   44.420156,    0.089325
+ 93500,   935.000,  195.693411,    2.398910,  198.092321,    1.019237,   44.428537,   -0.035457
+ 93600,   936.000,  169.232916,    4.160517,  173.393433,    0.881421,   44.499107,    0.111691
+ 93700,   937.000,  168.592869,   10.312536,  178.905405,    0.878088,   44.562086,   -0.011045
+ 93800,   938.000,  196.659947,   27.013148,  223.673095,    1.024271,   44.533531,    0.147660
+ 93900,   939.000,  190.977002,    4.970769,  195.947771,    0.994672,   44.567860,    0.094725
+ 94000,   940.000,  197.276917,    3.735119,  201.012036,    1.027484,   44.639184,    0.038727
+ 94100,   941.000,  166.306233,   -9.269074,  157.037158,    0.866178,   44.612351,    0.038818
+ 94200,   942.000,  188.686919,   -0.716742,  187.970177,    0.982744,   44.684855,    0.121287
+ 94300,   943.000,  213.397186,   -9.247037,  204.150149,    1.111444,   44.767287,    0.075847
+ 94400,   944.000,  193.673154,    1.179144,  194.852298,    1.008714,   44.768486,    0.079939
+ 94500,   945.000,  187.104483,   -2.195817,  184.908667,    0.974503,   44.877867,    0.081604
+ 94600,   946.000,  171.989215,   -8.216920,  163.772295,    0.895777,   44.904335,    0.022319
+ 94700,   947.000,  164.585250,   -1.638016,  162.947234,    0.857215,   44.954026,   -0.018348
+ 94800,   948.000,  181.186774,    1.769023,  182.955797,    0.943681,   45.064749,    0.013808
+ 94900,   949.000,  220.158720,   -0.903404,  219.255316,    1.146660,   45.176134,    0.108913
+ 95000,   950.000,  184.343344,   -4.750330,  179.593014,    0.960122,   45.116015,    0.071327
+ 95100,   951.000,  206.020941,  -11.394639,  194.626302,    1.073026,   45.158181,   -0.023237
+ 95200,   952.000,  191.892607,   -1.531248,  190.361359,    0.999441,   45.153418,    0.017608
+ 95300,   953.000,  199.205954,   -7.529677,  191.676277,    1.037531,   45.132417,    0.226269
+ 95400,   954.000,  215.075876,  -10.556119,  204.519757,    1.120187,   45.093547,    0.111094
+ 95500,   955.000,  193.284355,    4.986765,  198.271120,    1.006689,   45.143013,    0.027889
+ 95600,   956.000,  202.302129,  -11.210640,  191.091489,    1.053657,   45.169193,    0.032291
+ 95700,   957.000,  186.146733,   -1.615010,  184.531724,    0.969514,   45.219872,    0.176822
+ 95800,   958.000,  179.895904,   -2.130902,  177.765002,    0.936958,   45.344052,    0.061432
+ 95900,   959.000,  186.517587,  -16.198874,  170.318713,    0.971446,   45.423182,    0.064586
+ 96000,   960.000,  186.742596,    7.733896,  194.476492,    0.972618,   45.325026,    0.064986
+ 96100,   961.000,  171.567467,    3.365035,  174.932503,    0.893581,   45.402558,    0.117264
+ 96200,   962.000,  194.916781,  -22.961644,  171.955136,    1.015192,   45.493950,    0.013791
+ 96300,   963.000,  195.221980,  -11.428713,  183.793268,    1.016781,   45.595195,    0.008715
+ 96400,   964.000,  182.740015,  -11.990897,  170.749119,    0.951771,   45.514552,   -0.022449
+ 96500,   965.000,  161.610772,  -16.353940,  145.256833,    0.841723,   45.568914,    0.140932
+ 96600,   966.000,  195.868171,  -19.921975,  175.946196,    1.020147,   45.650640,    0.061345
+ 96700,   967.000,  182.717542,  -27.490337,  155.227205,    0.951654,   45.655157,    0.048670
+ 96800,   968.000,  216.322786,  -30.880203,  185.442583,    1.126681,   45.721065,    0.070584
+ 96900,   969.000,  189.393389,  -16.801125,  172.592264,    0.986424,   45.782146,    0.028225
+ 97000,   970.000,  198.071794,  -22.565901,  175.505893,    1.031624,   45.794600,    0.042854
+ 97100,   971.000,  199.474316,  -16.943241,  182.531075,    1.038929,   45.794068,   -0.020792
+ 97200,   972.000,  198.718683,   -1.243898,  197.474786,    1.034993,   45.929985,   -0.045088
+ 97300,   973.000,  183.211388,   -1.573112,  181.638276,    0.954226,   45.945736,    0.099736
+ 97400,   974.000,  183.571347,  -14.205320,  169.366027,    0.956101,   46.052415,    0.001623
+ 97500,   975.000,  187.947187,   -8.025754,  179.921433,    0.978892,   46.074700,    0.014614
+ 97600,   976.000,  208.041294,    7.444824,  215.486118,    1.083548,   46.069473,    0.079225
+ 97700,   977.000,  198.980061,   15.923998,  214.904059,    1.036354,   46.204053,    0.066923
+ 97800,   978.000,  178.906392,    0.585780,  179.492172,    0.931804,   46.319397,    0.022753
+ 97900,   979.000,  176.828224,   -1.510608,  175.317615,    0.920980,   46.341299,    0.071944
+ 98000,   980.000,  176.337296,   -5.692649,  170.644646,    0.918423,   46.315245,    0.022845
+ 98100,   981.000,  184.937164,   -1.722569,  183.214595,    0.963214,   46.326849,    0.050311
+ 98200,   982.000,  206.784918,   10.682891,  217.467809,    1.077005,   46.273167,    0.137444
+ 98300,   983.000,  194.093150,    3.231352,  197.324502,    1.010902,   46.278163,    0.029382
+ 98400,   984.000,  216.401372,    4.365080,  220.766452,    1.127090,   46.256717,    0.006820
+ 98500,   985.000,  195.228556,   29.044616,  224.273172,    1.016815,   46.264165,    0.124023
+ 98600,   986.000,  195.876751,   20.349909,  216.226660,    1.020191,   46.301813,    0.049097
+ 98700,   987.000,  186.196828,    0.451122,  186.647950,    0.969775,   46.470735,   -0.004961
+ 98800,   988.000,  197.971589,   -3.697644,  194.273945,    1.031102,   46.567128,    0.095061
+ 98900,   989.000,  199.765932,    2.351737,  202.117669,    1.040448,   46.566554,    0.124584
+ 99000,   990.000,  181.162864,   -4.267804,  176.895060,    0.943557,   46.592108,   -0.024846
+ 99100,   991.000,  205.367424,   -7.910903,  197.456521,    1.069622,   46.770374,    0.127461
+ 99200,   992.000,  163.800454,   10.312817,  174.113271,    0.853127,   46.852653,    0.064736
+ 99300,   993.000,  169.635803,  -17.131556,  152.504248,    0.883520,   46.903993,   -0.010285
+ 99400,   994.000,  187.728335,  -13.230235,  174.498100,    0.977752,   46.907491,   -0.058958
+ 99500,   995.000,  180.658580,  -11.477721,  169.180859,    0.940930,   46.949899,    0.069630
+ 99600,   996.000,  194.836051,    0.613263,  195.449314,    1.014771,   47.139397,    0.052426
+ 99700,   997.000,  188.461426,  -13.564811,  174.896614,    0.981570,   47.180711,    0.045941
+ 99800,   998.000,  206.267493,    3.437841,  209.705334,    1.074310,   47.170557,    0.038951
diff --git a/testsuite/tests_data/average_data.csv b/testsuite/tests_data/average_data.csv
new file mode 100644
index 0000000..0e65c13
--- /dev/null
+++ b/testsuite/tests_data/average_data.csv
@@ -0,0 +1,4 @@
+N,Dens,eps,Init,mean,mean,mean,mean,mean,mean,mean,err_mean,err_mean,err_mean,err_mean,err_mean,err_mean,err_mean,n_eff,n_eff,n_eff,n_eff,n_eff,n_eff,n_eff,tau_int,tau_int,tau_int,tau_int,tau_int,tau_int,tau_int,Solvent
+value,value,value,value,step,Ekin,Epot,Etot,Tkin,Rg,pressure,step,Ekin,Epot,Etot,Tkin,Rg,pressure,step,Ekin,Epot,Etot,Tkin,Rg,pressure,step,Ekin,Epot,Etot,Tkin,Rg,pressure,value
+128,0.2,0.71,coil,54850.0,191.2804732333333,-0.31302169666666674,190.96745150444443,0.9962524566666666,33.73888648777778,0.04605722777777777,6903.116848368006,0.4814484444592045,0.8350703362143954,1.200849464813764,0.0025075409147814172,2.072764872126986,0.002730929328419299,14.180632591333836,802.5729387760522,166.90619557423483,217.24017285607138,802.5751596471125,14.216706264639246,449.14027525678495,31.733422123566406,0.5606967021418184,2.696125200456406,2.071440075211777,0.5606951505923287,31.65290128552986,1.0019141564241227,
+64,,,coil,1095752.7654867256,95.439669210177,45.354566968473456,140.79423618473453,0.9941632284292035,6.05229448340708,0.0,135740.92503695545,0.1705547398547065,0.1437387612925945,0.22910529225213178,0.0017766090357742472,0.14004234848198663,0.0,14.873125998867755,3321.254709391739,1636.3604915773453,2499.312737817561,3321.2654236179455,59.777386104317905,,607.8076660339049,2.7218629075442404,5.5244550614186645,3.616994329366668,2.721854126958778,151.22775666745008,,good
diff --git a/testsuite/weak_polyelectrolyte_dialysis_test.py b/testsuite/weak_polyelectrolyte_dialysis_test.py
index 563b3ae..d4cdb2d 100644
--- a/testsuite/weak_polyelectrolyte_dialysis_test.py
+++ b/testsuite/weak_polyelectrolyte_dialysis_test.py
@@ -44,7 +44,8 @@
         print(subprocess.list2cmdline(run_command))
         subprocess.check_output(run_command)
     # Analyze all time series
-    data=analysis.analyze_time_series(path_to_datafolder=time_series_path)
+    data=analysis.analyze_time_series(path_to_datafolder=time_series_path,
+                                      filename_extension="_time_series.csv")
     data_path=pmb.get_resource(path="testsuite/weak_polyelectrolyte_dialysis_test_data")
 
 # Get reference test data