Repository: MannLabs/alphapept (language: python)

Issue:
Low peptide and protein IDs for datasets with large fragment offset
**Describe the bug**
There was a user reporting low number of peptides / proteins identified for an OrbiTrap dataset.
Investigation revealed that there was quite a large fragment offset. Switching from the default `frag_tol` of `30` to the current maximum of `99`, the identifications increased approx. 4x. The std deviation of the fragment offset for this dataset was 56 ppm, so that a tolerance of 168 (3x std) would potentially be ideal.

**User note**
If you experience low IDs, investigate the log entry for `Calibrating fragments`. Here, the offset will be reported, e.g. `Median fragment offset 5.51 - std 55.77 ppm`.  In case you have large fragment offsets, for now, the workaround is to set the frag_tol to the maximum (99). 

**Potential Updates**
- Increase the maximum value for fragment offset
- Dynamically set the fragment tolerance for search

mzML support
Is there already support for mzML files because Alphapept doesn't seem to recognize the files? 

Best,
Bart 
crash on startup (windows quickstart)
hi y'all!
version 0.3.26 .exe crashes immediately after start. 
when executing via command line this is the output:

Microsoft Windows [Version 10.0.19041.1110]
(c) Microsoft Corporation. All rights reserved.

C:\Users\admin>"C:\Program Files (x86)\AlphaPept\alphapept.exe"


     ___    __      __          ____             __
    /   |  / /___  / /_  ____  / __ \___  ____  / /_
   / /| | / / __ \/ __ \/ __ \/ /_/ / _ \/ __ \/ __/
  / ___ |/ / /_/ / / / / /_/ / ____/ ___/ /_/ / /_
 /_/  |_/_/ .___/_/ /_/\__,_/_/    \___/ .___/\__/
         /_/                          /_/
....................................................
.      https://github.com/MannLabs/alphapept       .
.                    Mann Labs                     .
.                      0.3.26                      .
....................................................


Usage: alphapept.exe [OPTIONS] COMMAND [ARGS]...

Options:
  -h, --help  Show this message and exit.

Commands:
  database     Create a database from a fasta file.
  export       Export protein table from `.ms_data.hdf` files as `.csv`.
  features     Find features in a `.ms_data.hdf` file.
  gui          Start graphical user interface for AlphaPept.
  import       Import and convert raw data from vendor to `.ms_data.hdf` file.
  quantify     Quantify and compare multiple `.ms_data.hdf` files.
  recalibrate  Recalibrate a `.ms_data.hdf` file.
  score        Score PSM from a `.ms_data.hdf` file.
  search       Search and identify feature in a `.ms_data.hdf` file.
  workflow     Run the complete AlphaPept workflow.

C:\Users\admin>

Am I doing something wrong?
best,
ben :)


Discussion / hints:


Hi,
Do you receive an additional error message, or is this the output that you receive?

From what I see, this is actually the desired output from the `cmd`:
If no command is passed, it shows you all existing commands and then quits.

You need to pass an additional command so that It does something (e.g. `alphapept.exe gui`)

I hope that helps, and let me know if this solves the problem.
adding 'gui' to commandline worked! thanks for the quick answer :) 
but running the .exe straight from windows start-menu still causes it to shut down immediately. is there a way to add the 'gui' to the .exe so that i don't need to go through the command prompt every time?
Ah, very good point.
There should actually be a desktop shortcut to directly start the GUI.
I'll keep this PR open so that we can add functionality to better launch from start-menu.

Fix patch:
diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 9270337e..e6be6a6e 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.27
+current_version = 0.3.28
 commit = True
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?
diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 00000000..5e03f82f
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,2 @@
+*.py linguist-detectable=true
+*.ipynb linguist-detectable=false
diff --git a/.github/workflows/cla_assistant.yml b/.github/workflows/cla_assistant.yml
index 8da9714b..50e3a441 100644
--- a/.github/workflows/cla_assistant.yml
+++ b/.github/workflows/cla_assistant.yml
@@ -21,6 +21,4 @@ jobs:
           path-to-cla-document: 'https://github.com/MannLabs/alphapept/blob/master/CLA.md'
           # branch should not be protected
           branch: 'cla'
-          allowlist: straussmaximilian, bot*
-
-    
+          allowlist: straussmaximilian, dependabot
diff --git a/alphapept/__init__.py b/alphapept/__init__.py
index 5c0d6020..818ad96a 100644
--- a/alphapept/__init__.py
+++ b/alphapept/__init__.py
@@ -1,1 +1,1 @@
-__version__ = "0.3.27"
+__version__ = "0.3.28"
diff --git a/alphapept/__main__.py b/alphapept/__main__.py
index 55ad3ad8..dd9e68bf 100644
--- a/alphapept/__main__.py
+++ b/alphapept/__main__.py
@@ -15,7 +15,11 @@ def main():
         print('.'*52)
         print(f"\n{e}\n")
         print('.'*52)
-        print('Please visit https://github.com/MannLabs/alphapept and report this issue. Thanks.\n')
+
+        if 'No module named' in str(e):
+            print('\nPlease make sure to run AlphaPept in the right environment and have all required python packages installed (pip install -r requirements.txt).')
+        else:
+            print('\nPlease visit https://github.com/MannLabs/alphapept and report this issue or search for potential solutions. Thanks.\n')
 
         input("Press Enter to continue...")
 
diff --git a/alphapept/__version__.py b/alphapept/__version__.py
index 51f8b8b9..54bd21ac 100644
--- a/alphapept/__version__.py
+++ b/alphapept/__version__.py
@@ -33,7 +33,7 @@
 AUTHOR_EMAIL = "straussmaximilian@gmail.com"
 COPYRIGHT = "Mann Labs"
 BRANCH = "master"
-VERSION_NO = "0.3.27"
+VERSION_NO = "0.3.28"
 MIN_PYTHON = "3.6"
 MAX_PYTHON = "4"
 AUDIENCE = "Developers"
diff --git a/alphapept/_nbdev.py b/alphapept/_nbdev.py
index ad60ec54..14ffd234 100644
--- a/alphapept/_nbdev.py
+++ b/alphapept/_nbdev.py
@@ -143,6 +143,8 @@
          "remove_column": "05_search.ipynb",
          "get_hits": "05_search.ipynb",
          "score": "11_interface.ipynb",
+         "LOSS_DICT": "05_search.ipynb",
+         "LOSSES": "05_search.ipynb",
          "get_sequences": "05_search.ipynb",
          "get_score_columns": "05_search.ipynb",
          "plot_psms": "05_search.ipynb",
@@ -150,6 +152,7 @@
          "search_db": "05_search.ipynb",
          "search_fasta_block": "05_search.ipynb",
          "filter_top_n": "05_search.ipynb",
+         "ion_extractor": "05_search.ipynb",
          "search_parallel": "05_search.ipynb",
          "filter_score": "06_score.ipynb",
          "filter_precursor": "06_score.ipynb",
@@ -213,6 +216,7 @@
          "averagine_aa": "10_constants.ipynb",
          "averagine_avg": "10_constants.ipynb",
          "protease_dict": "10_constants.ipynb",
+         "loss_dict": "10_constants.ipynb",
          "tqdm_wrapper": "11_interface.ipynb",
          "check_version_and_hardware": "11_interface.ipynb",
          "wrapped_partial": "11_interface.ipynb",
diff --git a/alphapept/constants.py b/alphapept/constants.py
index 72e3d9c3..48f57daf 100644
--- a/alphapept/constants.py
+++ b/alphapept/constants.py
@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: nbs/10_constants.ipynb (unless otherwise specified).
 
 __all__ = ['AAs', 'mass_dict', 'get_mass_dict', 'Isotope', 'spec', 'isotopes', 'averagine_aa', 'averagine_avg',
-           'protease_dict']
+           'protease_dict', 'loss_dict']
 
 # Cell
 AAs = set('ACDEFGHIKLMNPQRSTUVWY')
@@ -392,4 +392,11 @@ def __init__(self, m0:int, dm:int, intensities:np.ndarray):
 protease_dict["trypsin_full"] = "([KR](?=[^P]))|((?<=W)K(?=P))|((?<=M)R(?=P))"
 protease_dict["trypsin_exception"] = "((?<=[CD])K(?=D))|((?<=C)K(?=[HY]))|((?<=C)R(?=K))|((?<=R)R(?=[HR]))"
 protease_dict["non-specific"] = "()"
-protease_dict["trypsin"] = "([KR](?=[^P]))"
\ No newline at end of file
+protease_dict["trypsin"] = "([KR](?=[^P]))"
+
+# Cell
+from numba.typed import Dict
+loss_dict = Dict()
+loss_dict[''] = 0.0
+loss_dict['-H2O'] = 18.01056468346
+loss_dict['-NH3'] = 17.03052
\ No newline at end of file
diff --git a/alphapept/default_settings.yaml b/alphapept/default_settings.yaml
index a8a38289..023fb6a4 100644
--- a/alphapept/default_settings.yaml
+++ b/alphapept/default_settings.yaml
@@ -67,7 +67,7 @@ features:
 search:
   prec_tol: 30
   frag_tol: 30
-  min_frag_hits: 7
+  min_frag_hits: 6
   ppm: true
   calibrate: true
   calibration_std: 3
diff --git a/alphapept/fasta.py b/alphapept/fasta.py
index 508e5a0c..affd7a8d 100644
--- a/alphapept/fasta.py
+++ b/alphapept/fasta.py
@@ -13,7 +13,7 @@
 from alphapept import constants
 import re
 
-def get_missed_cleavages(sequences:list, n_missed_cleavages:int):
+def get_missed_cleavages(sequences:list, n_missed_cleavages:int) -> list:
     """
     Combine cleaved sequences to get sequences with missed cleavages
     Args:
@@ -73,7 +73,7 @@ def cleave_sequence(
 import re
 from alphapept import constants
 
-def count_missed_cleavages(sequence:str="", protease:str="trypsin", **kwargs):
+def count_missed_cleavages(sequence:str="", protease:str="trypsin", **kwargs) -> int:
     """
     Counts the number of missed cleavages for a given sequence and protease
     Args:
@@ -88,7 +88,7 @@ def count_missed_cleavages(sequence:str="", protease:str="trypsin", **kwargs):
     n_missed = len(p.findall(sequence))
     return n_missed
 
-def count_internal_cleavages(sequence:str="", protease:str="trypsin", **kwargs):
+def count_internal_cleavages(sequence:str="", protease:str="trypsin", **kwargs) -> int:
     """
     Counts the number of internal cleavage sites for a given sequence and protease
     Args:
@@ -132,9 +132,13 @@ def parse(peptide:str)->List:
 
     return parsed
 
-def list_to_numba(a_list):
+def list_to_numba(a_list) -> List:
     """
-    Convert python list to numba.typed.List
+    Convert Python list to numba.typed.List
+    Args:
+        a_list (list): Python list.
+    Return:
+        List (numba.typed.List): Numba typed list.
     """
     numba_list = List()
 
@@ -175,9 +179,15 @@ def get_decoy_sequence(peptide:str, pseudo_reverse:bool=False, AL_swap:bool=Fals
 
 
 @njit
-def swap_KR(peptide):
+def swap_KR(peptide:str)->str:
     """
     Swaps a terminal K or R. Note: Only if AA is not modified.
+
+    Args:
+        peptide (str): peptide.
+
+    Returns:
+        str: peptide with swapped KRs.
     """
     if peptide[-1] == 'K':
         peptide[-1] = 'R'
@@ -188,9 +198,14 @@ def swap_KR(peptide):
 
 
 @njit
-def swap_AL(peptide):
+def swap_AL(peptide:str)->str:
     """
     Swaps a A with L. Note: Only if AA is not modified.
+    Args:
+        peptide (str): peptide.
+
+    Returns:
+        str: peptide with swapped ALs.
     """
     i = 0
     while i < len(range(len(peptide) - 1)):
@@ -245,7 +260,15 @@ def add_fixed_mods(seqs:list, mods_fixed:list, **kwargs)->list:
         return seqs
 
 # Cell
-def add_variable_mod(peps:list, mods_variable_dict:dict):
+def add_variable_mod(peps:list, mods_variable_dict:dict)->list:
+    """
+    Function to add variable modification to a list of peptides.
+    Args:
+        peps (list): List of peptides.
+        mods_variable_dict (dict): Dicitionary with modifications. The key is AA, and value is the modified form (e.g. oxM).
+    Returns:
+        list : the list of peptide forms for the given peptide.
+    """
     peptides = []
     for pep_ in peps:
         pep, min_idx = pep_
@@ -263,7 +286,7 @@ def get_isoforms(mods_variable_dict:dict, peptide:str, isoforms_max:int, n_modif
     Function to generate modified forms (with variable modifications) for a given peptide - returns a list of modified forms.
     The original sequence is included in the list
     Args:
-        mods_variable_dict (dict): the key is AA, and value is the modified form (e.g. oxM).
+        mods_variable_dict (dict): Dicitionary with modifications. The key is AA, and value is the modified form (e.g. oxM).
         peptide (str): the peptide sequence to generate modified forms.
         isoforms_max (int): max number of modified forms to generate per peptide.
         n_modifications_max (int, optional): max number of variable modifications per peptide.
@@ -873,6 +896,7 @@ def generate_spectra(to_add:list, mass_dict:dict, callback = None)->list:
     return spectra
 
 # Cell
+from typing import Generator
 
 def block_idx(len_list:int, block_size:int = 1000)->list:
     """
@@ -894,14 +918,14 @@ def block_idx(len_list:int, block_size:int = 1000)->list:
 
     return blocks
 
-def blocks(l:int, n:int)->tuple:
+def blocks(l:int, n:int)->Generator[list, None, None]:
     """
-    Helper function to reate blocks from a given list
+    Helper function to create blocks from a given list
     Args:
         l (list): the list
         n (int): size per block
     Returns:
-        tuple: tuple of split blocks
+        Generator: List with splitted elements
     """
     n = max(1, n)
     return (l[i:i+n] for i in range(0, len(l), n))
@@ -1035,10 +1059,12 @@ def pept_dict_from_search(settings:dict):
 def save_database(spectra:list, pept_dict:dict, fasta_dict:dict, database_path:str, **kwargs):
     """
     Function to save a database to the *.hdf format. Write the database into hdf.
+
     Args:
-        spectra (list): list: theoretical spectra. See generate_spectra()
-        pept_dict (dict): peptide dict. See add_to_pept_dict()
-        fasta_dict (dict): fasta_dict. See generate_fasta_list()
+        spectra (list): list: theoretical spectra. See generate_spectra().
+        pept_dict (dict): peptide dict. See add_to_pept_dict().
+        fasta_dict (dict): fasta_dict. See generate_fasta_list().
+        database_path (str): Path to database.
     """
 
     precmasses, seqs, fragmasses, fragtypes = zip(*spectra)
diff --git a/alphapept/feature_finding.py b/alphapept/feature_finding.py
index 98f167af..235a7a2c 100644
--- a/alphapept/feature_finding.py
+++ b/alphapept/feature_finding.py
@@ -1889,6 +1889,8 @@ def find_features(to_process:tuple, callback:Union[Callable, None] = None, paral
             datatype='thermo'
         elif ext.lower() == '.d':
             datatype='bruker'
+        elif ext.lower() == '.mzml':
+            datatype='mzml'
         else:
             raise NotImplementedError('File extension {} not understood.'.format(ext))
 
@@ -1917,7 +1919,7 @@ def find_features(to_process:tuple, callback:Union[Callable, None] = None, paral
             if not settings['workflow']["find_features"]:
                 features = query_data_to_features(query_data)
             else:
-                if datatype == 'thermo':
+                if datatype in ['thermo','mzml']:
 
                     from .constants import averagine_aa, isotopes
 
diff --git a/alphapept/gui/experiment.py b/alphapept/gui/experiment.py
index b5be7c03..501a5bda 100644
--- a/alphapept/gui/experiment.py
+++ b/alphapept/gui/experiment.py
@@ -44,7 +44,7 @@ def parse_folder(file_folder: str) -> (list, list, list):
     raw_files = [
         _
         for _ in os.listdir(file_folder)
-        if _.lower().endswith(".raw") or _.lower().endswith(".d")
+        if _.lower().endswith(".raw") or _.lower().endswith(".d") or _.lower().endswith(".mzml")
     ]
     fasta_files = [_ for _ in os.listdir(file_folder) if _.lower().endswith(".fasta")]
     db_files = [
@@ -333,6 +333,12 @@ def experiment():
                 )
                 recorder["experiment"]["fasta_paths"] = selection
 
+                #TODO
+
+                if len(recorder["experiment"]["fasta_paths"]) == 0:
+                    st.warning(f"Warning: No FASTA files selected.")
+                    error += 1
+
                 recorder["experiment"]["shortnames"] = shortnames
                 recorder["experiment"]["file_paths"] = [
                     os.path.join(file_folder, _)
diff --git a/alphapept/interface.py b/alphapept/interface.py
index d541fcf9..903fae40 100644
--- a/alphapept/interface.py
+++ b/alphapept/interface.py
@@ -374,11 +374,24 @@ def search_data(
                 logging.info('No calibration found.')
                 offsets = None
 
+            try:
+                frag_tols = [float(
+                    alphapept.io.MS_Data_File(
+                        ms_file_name
+                    ).read(dataset_name="estimated_max_fragment_ppm")[0] * settings['search']['calibration_std']) for ms_file_name in ms_files
+                ]
+
+            except KeyError:
+                logging.info('Fragment tolerance not calibrated found.')
+                frag_tols = None
+
+
             logging.info('Starting second search.')
 
             fasta_dict = alphapept.search.search_parallel(
                 settings,
                 calibration=offsets,
+                fragment_calibration=frag_tols,
                 callback=cb
             )
             pept_dict = None
@@ -947,7 +960,7 @@ def extract_median_unique(settings: dict) -> tuple:
     cols = [_ for _ in ['protein','protein_group','precursor','naked_sequence','sequence'] if _ in protein_fdr.columns]
     n_unique = protein_fdr.groupby('filename')[cols].nunique()
     n_unique.index = [os.path.split(_)[1][:-12] for _ in n_unique.index]
-    cols = [_ for _ in ['fwhm','int_sum','rt_length','rt_tail','o_mass_ppm_raw'] if _ in protein_fdr.columns]
+    cols = [_ for _ in ['fwhm','int_sum','rt_length','rt_tail','prec_offset_raw_ppm '] if _ in protein_fdr.columns]
     median = protein_fdr.groupby('filename')[cols].median()
     median.index = [os.path.split(_)[1][:-12] for _ in median.index]
 
@@ -983,7 +996,7 @@ def get_file_summary(ms_data: alphapept.io.MS_Data_File) -> dict:
                     f_summary['id_rate (peptide_fdr)'] = float(df['raw_idx'].nunique() / n_ms2)
 
             if key in ['feature_table','peptide_fdr']:
-                for field in ['fwhm','int_sum','rt_length','rt_tail','o_mass_ppm_raw']:
+                for field in ['fwhm','int_sum','rt_length','rt_tail','prec_offset_raw_ppm ']:
                     if field in df.columns:
                         f_summary[f'{field} ({key}, median)'] = float(df[field].median())
 
diff --git a/alphapept/io.py b/alphapept/io.py
index 83b3f5e1..8668dd2d 100644
--- a/alphapept/io.py
+++ b/alphapept/io.py
@@ -279,8 +279,11 @@ def extract_mzml_info(input_dict: dict) -> tuple:
     ms_order = input_dict.get('ms level')
     prec_mass = mono_mz = charge = 0
     if ms_order == 2:
-        charge = int(input_dict.get('precursorList').get('precursor')[0].get('selectedIonList').get('selectedIon')[0].get(
+        try:
+            charge = int(input_dict.get('precursorList').get('precursor')[0].get('selectedIonList').get('selectedIon')[0].get(
                 'charge state'))
+        except TypeError:
+            charge = 0
         mono_mz = input_dict.get('precursorList').get('precursor')[0].get('selectedIonList').get('selectedIon')[0].get(
                 'selected ion m/z')
         prec_mass = calculate_mass(mono_mz, charge)
@@ -356,7 +359,7 @@ def load_mzml_data(
         except SystemExit as e:
             raise e
         except Exception as e:
-            logging.info(f"Bad scan={i} in mzML file '{filename}'")
+            logging.info(f"Bad scan={i} in mzML file '{filename}' {e}")
 
         if callback:
             callback((idx+1)/len(spec_indices))
diff --git a/alphapept/matching.py b/alphapept/matching.py
index a837eec7..b84078c4 100644
--- a/alphapept/matching.py
+++ b/alphapept/matching.py
@@ -60,9 +60,17 @@ def calculate_distance(table_1: pd.DataFrame, table_2: pd.DataFrame, offset_dict
 
 def calib_table(table: pd.DataFrame, delta: pd.Series, offset_dict: dict):
     """
-    Apply offset to a table
-    If not _calib table exist, create a new one.
+    Apply offset to a table. Different operations for offsets exist.
+    Offsets will be saved with a '_calib'-suffix. If this does not already exist,
+    it will be created.
 
+    Args:
+        table_1 (pd.DataFrame): Dataframe with data.
+        delta (pd.Series): Series cotaining the offset.
+        offset_dict (dict): Dictionary with column names and how the distance should be calculated.
+
+    Raises:
+        NotImplementedError: If the type of vonversion is not implemented.
     """
     for col in offset_dict:
 
@@ -193,7 +201,14 @@ def calculate_deltas(combos: list, calib:bool = False, callback:Callable=None) -
 
 #There is no unit test for align_files and align_datasets as they are wrappers and should be covered by the quick_test
 def align_files(filenames: list, alignment: pd.DataFrame, offset_dict: dict):
+    """
+    Wrapper function that aligns a list of files.
 
+    Args:
+        filenames (list): A list with raw file names.
+        alignment (pd.DataFrame): A pandas dataframe containing the alignment information.
+        offset_dict (dict): Dictionary with column names and how the distance should be calculated.
+    """
     for idx, filename in enumerate(filenames):
 
         file = os.path.splitext(filename)[0] + '.ms_data.hdf'
@@ -207,7 +222,14 @@ def align_files(filenames: list, alignment: pd.DataFrame, offset_dict: dict):
             ms_file.write(df, dataset_name=column)
 
 
-def align_datasets(settings, callback=None):
+def align_datasets(settings:dict, callback:callable=None):
+    """
+    Wrapper function that aligns all experimental files specified a settings file.
+
+    Args:
+        settings (dict): A list with raw file names.
+        callback (Callable): Callback function to indicate progress.
+    """
     filenames = settings['experiment']['file_paths']
 
     if callback:
@@ -267,7 +289,11 @@ def progress_wrapper(current, step, n_steps):
 from scipy import stats
 def get_probability(df: pd.DataFrame, ref: pd.DataFrame, sigma:pd.DataFrame, index:int)-> float:
     """Probablity estimate of a transfered identification using the Mahalanobis distance.
-    This functions that the dataframes are matched, meaning that the first entry in df matches to the first entry in ref
+
+    The function calculates the probability that a feature is a reference feature.
+    The reference features containing std deviations so that a probability can be estimated.
+
+    It is required that the data frames are matched, meaning that the first entry in df matches to the first entry in ref.
 
     Args:
         df (pd.DataFrame): Dataset containing transferered features
diff --git a/alphapept/performance.py b/alphapept/performance.py
index d9fe6f14..0b7265e9 100644
--- a/alphapept/performance.py
+++ b/alphapept/performance.py
@@ -385,7 +385,7 @@ def AlphaPool(process_count: int) -> multiprocessing.Pool:
     Args:
         process_count (int): The number of processes.
             If larger than available cores, it is trimmed to the available maximum.
-            If 0, it is set to the maximum cores available (at most 50).
+
 
     Returns:
         multiprocessing.Pool: A Pool object to parallelize functions with multiple processes.
@@ -393,6 +393,9 @@ def AlphaPool(process_count: int) -> multiprocessing.Pool:
     """
     max_processes = psutil.cpu_count()
     new_max = min(process_count, 50, max_processes)
+
+    if new_max == 0:
+        new_max = 1
     logging.info(f"AlphaPool was set to {process_count} processes. Setting max to {new_max}.")
 
     return Pool(new_max)
\ No newline at end of file
diff --git a/alphapept/recalibration.py b/alphapept/recalibration.py
index a89eaf4b..b8e12c4a 100644
--- a/alphapept/recalibration.py
+++ b/alphapept/recalibration.py
@@ -12,11 +12,11 @@ def remove_outliers(
     df:  pd.DataFrame,
     outlier_std: float) -> pd.DataFrame:
     """Helper function to remove outliers from a dataframe.
-    Outliers are removed based on the precursor offset mass (o_mass).
+    Outliers are removed based on the precursor offset mass (prec_offset).
     All values within x standard deviations to the median are kept.
 
     Args:
-        df (pd.DataFrame): Input dataframe that contains a o_mass_ppm-column.
+        df (pd.DataFrame): Input dataframe that contains a prec_offset_ppm-column.
         outlier_std (float): Range of standard deviations to filter outliers
 
     Raises:
@@ -26,14 +26,14 @@ def remove_outliers(
         pd.DataFrame: A dataframe w/o outliers.
     """
 
-    if 'o_mass_ppm' not in df.columns:
-        raise ValueError(f"Column o_mass_ppm not in df")
+    if 'prec_offset_ppm' not in df.columns:
+        raise ValueError(f"Column prec_offset_ppm not in df")
     else:
         # Remove outliers for calibration
-        o_mass_std = np.abs(df['o_mass_ppm'].std())
-        o_mass_median = df['o_mass_ppm'].median()
+        o_mass_std = np.abs(df['prec_offset_ppm'].std())
+        o_mass_median = df['prec_offset_ppm'].median()
 
-        df_sub = df.query('o_mass_ppm < @o_mass_median+@outlier_std*@o_mass_std and o_mass_ppm > @o_mass_median-@outlier_std*@o_mass_std').copy()
+        df_sub = df.query('prec_offset_ppm < @o_mass_median+@outlier_std*@o_mass_std and prec_offset_ppm > @o_mass_median-@outlier_std*@o_mass_std').copy()
 
         return df_sub
 
@@ -154,7 +154,7 @@ def get_calibration(
 
     if len(df) > calib_n_neighbors:
 
-        target = 'o_mass_ppm'
+        target = 'prec_offset_ppm'
         cols = ['mz','rt']
 
         if 'mobility' in df.columns:
@@ -171,11 +171,18 @@ def get_calibration(
         corrected_mass = (1-y_hat/1e6) * features['mass_matched']
 
         y_hat_std = y_hat.std()
-        return corrected_mass, y_hat_std
+
+        mad_offset = np.median(np.absolute(y_hat - np.median(y_hat)))
+
+        return corrected_mass, y_hat_std, mad_offset
+
 
     else:
         logging.info('Not enough data points present. Skipping recalibration.')
-        return features['mass_matched'], np.abs(df['o_mass_ppm'].std())
+
+        mad_offset = np.median(np.absolute(df['prec_offset_ppm'].values - np.median(df['prec_offset_ppm'].values)))
+
+        return features['mass_matched'], np.abs(df['prec_offset_ppm'].std()), mad_offset
 
 # Cell
 
@@ -221,7 +228,7 @@ def calibrate_hdf(
                 verbose=False,
                 **settings["search"]
             )
-            corrected_mass, o_mass_ppm_std = get_calibration(
+            corrected_mass, prec_offset_ppm_std, prec_offset_ppm_mad = get_calibration(
                 df,
                 features,
                 **settings["calibration"]
@@ -239,10 +246,10 @@ def calibrate_hdf(
                 group_name="features"
             )
 
-            o_mass_ppm_std = 0
+            prec_offset_ppm_std = 0
 
         ms_file_.write(
-            o_mass_ppm_std,
+            prec_offset_ppm_std,
             dataset_name="corrected_mass",
             group_name="features",
             attr_name="estimated_max_precursor_ppm"
@@ -266,6 +273,8 @@ def calibrate_hdf(
             delta_ppm = ((ions['db_mass'] - ions['ion_mass'])/((ions['db_mass'] + ions['ion_mass'])/2)*1e6).values
             median_offset = -np.median(delta_ppm)
             std_offset = np.std(delta_ppm)
+            mad_offset = np.median(np.absolute(delta_ppm - np.median(delta_ppm)))
+
             mass_list_ms2 = ms_file_.read(dataset_name = 'mass_list_ms2', group_name = "Raw/MS2_scans")
 
             try:
@@ -275,13 +284,15 @@ def calibrate_hdf(
 
             offset += median_offset
 
-            logging.info(f'Median fragment offset {median_offset:.2f} - std {std_offset:.2f} ppm')
+            logging.info(f'Median fragment offset {median_offset:.2f} - std {std_offset:.2f} ppm - mad {mad_offset:.2f} ppm')
 
             ms_file_.write(
                 offset,
                 dataset_name="corrected_fragment_mzs",
             )
 
+            ms_file_.write(np.array([mad_offset]), dataset_name="estimated_max_fragment_ppm")
+
         return True
     except Exception as e:
         logging.error(f'Calibration of file {ms_file} failed. Exception {e}.')
diff --git a/alphapept/search.py b/alphapept/search.py
index f3ddf083..c4e6ff85 100644
--- a/alphapept/search.py
+++ b/alphapept/search.py
@@ -2,8 +2,8 @@
 
 __all__ = ['compare_frags', 'ppm_to_dalton', 'get_idxs', 'compare_spectrum_parallel', 'query_data_to_features',
            'get_psms', 'frag_delta', 'intensity_fraction', 'add_column', 'remove_column', 'get_hits', 'score',
-           'get_sequences', 'get_score_columns', 'plot_psms', 'store_hdf', 'search_db', 'search_fasta_block',
-           'mass_dict', 'filter_top_n', 'search_parallel']
+           'LOSS_DICT', 'LOSSES', 'get_sequences', 'get_score_columns', 'plot_psms', 'store_hdf', 'search_db',
+           'search_fasta_block', 'mass_dict', 'filter_top_n', 'ion_extractor', 'search_parallel']
 
 # Cell
 import logging
@@ -228,7 +228,8 @@ def get_psms(
     ppm: bool,
     min_frag_hits: int,
     callback: Callable = None,
-    m_offset_calibrated:float = None,
+    prec_tol_calibrated:float = None,
+    frag_tol_calibrated:float = None,
     **kwargs
 )->(np.ndarray, int):
     """[summary]
@@ -243,7 +244,8 @@ def get_psms(
         ppm (bool): Flag to use ppm instead of Dalton.
         min_frag_hits (int): Minimum number of frag hits to report a PSMs.
         callback (Callable, optional): Optional callback. Defaults to None.
-        m_offset_calibrated (float, optional): Precursor tolerance if calibration exists. Defaults to None.
+        prec_tol_calibrated (float, optional): Precursor tolerance if calibration exists. Defaults to None.
+        frag_tol_calibrated (float, optional): Fragment tolerance if calibration exists. Defaults to None.
 
     Returns:
         np.ndarray: Numpy recordarray storing the PSMs.
@@ -263,9 +265,12 @@ def get_psms(
     query_frags = query_data['mass_list_ms2']
     query_ints = query_data['int_list_ms2']
 
+    if frag_tol_calibrated:
+        frag_tol = frag_tol_calibrated
+
     if features is not None:
-        if m_offset_calibrated:
-            prec_tol = m_offset_calibrated
+        if prec_tol_calibrated:
+            prec_tol = prec_tol_calibrated
             query_masses = features['corrected_mass'].values
         else:
             query_masses = features['mass_matched'].values
@@ -292,8 +297,8 @@ def get_psms(
         )
         query_indices = indices
     else:
-        if m_offset_calibrated:
-            prec_tol = m_offset_calibrated
+        if prec_tol_calibrated:
+            prec_tol = prec_tol_calibrated
 
         query_masses = query_data['prec_mass_list2']
         query_mz = query_data['mono_mzs2']
@@ -497,6 +502,9 @@ def get_hits(query_frag:np.ndarray, query_int:np.ndarray, db_frag:np.ndarray, db
 
 
 # Cell
+from alphapept import constants
+LOSS_DICT = constants.loss_dict
+LOSSES = np.array(list(LOSS_DICT.values()))
 
 #This function is a wrapper and ist tested by the quick_test
 @njit
@@ -543,8 +551,6 @@ def score(
 
     psms_ = np.zeros(len(psms), dtype=psms_dtype)
 
-    losses = [0, 18.01056468346, 17.03052] #H2O, NH3
-
     ions_ = List()
 
     ion_count = 0
@@ -560,17 +566,17 @@ def score(
         frag_type = frag_types[db_indices[db_idx]:db_indices[db_idx+1]]
 
         if db_ints is None:
-            db_int = np.zeros(len(db_frag))
+            db_int = np.ones(len(db_frag))
         else:
             db_int = db_ints[i]
 
-        ions = get_hits(query_frag, query_int, db_frag, db_int, frag_type, mtol, ppm, losses)
+        ions = get_hits(query_frag, query_int, db_frag, db_int, frag_type, mtol, ppm, LOSSES)
 
-        psms_['o_mass'][i] = query_masses[query_idx] - db_masses[db_idx]
-        psms_['o_mass_ppm'][i] = 2 * psms_['o_mass'][i] / (query_masses[query_idx]  + db_masses[db_idx] ) * 1e6
+        psms_['prec_offset'][i] = query_masses[query_idx] - db_masses[db_idx]
+        psms_['prec_offset_ppm'][i] = 2 * psms_['prec_offset'][i] / (query_masses[query_idx]  + db_masses[db_idx] ) * 1e6
 
-        psms_['o_mass_raw'][i] = query_masses_raw[query_idx] - db_masses[db_idx]
-        psms_['o_mass_ppm_raw'][i] = 2 * psms_['o_mass'][i] / (query_masses_raw[query_idx]  + db_masses[db_idx] ) * 1e6
+        psms_['prec_offset_raw '][i] = query_masses_raw[query_idx] - db_masses[db_idx]
+        psms_['prec_offset_raw_ppm '][i] = 2 * psms_['prec_offset'][i] / (query_masses_raw[query_idx]  + db_masses[db_idx] ) * 1e6
 
         psms_['delta_m'][i] = np.mean(ions[:,4]-ions[:,5])
         psms_['delta_m_ppm'][i] = np.mean(2 * psms_['delta_m'][i] / (ions[:,4]  + ions[:,5] ) * 1e6)
@@ -578,7 +584,7 @@ def score(
         psms_['total_int'][i] = np.sum(query_int)
         psms_['matched_int'][i] = np.sum(ions[:,2])
         psms_['matched_int_ratio'][i] = psms_['matched_int'][i] / psms_['total_int'][i]
-        psms_['int_ratio'][i] = np.mean(ions[:,3]/ions[:,2])
+        psms_['int_ratio'][i] = np.mean(ions[:,2]/ions[:,3]) #3 is db_int, 2 is query_int
 
         psms_['b_hits'][i] = np.sum(ions[ions[:,1]==0][:,0]>0)
         psms_['y_hits'][i] = np.sum(ions[ions[:,1]==0][:,0]<0)
@@ -629,7 +635,8 @@ def get_score_columns(
     frag_tol:float,
     prec_tol:float,
     ppm:bool,
-    m_offset_calibrated:Union[None, float]=None,
+    prec_tol_calibrated:Union[None, float]=None,
+    frag_tol_calibrated:float = None,
     **kwargs
 ) -> (np.ndarray, np.ndarray):
     """Wrapper function to extract score columns.
@@ -643,7 +650,8 @@ def get_score_columns(
         frag_tol (float): Fragment tolerance for search.
         prec_tol (float): Precursor tolerance for search.
         ppm (bool): Flag to use ppm instead of Dalton.
-        m_offset_calibrated (Union[None, float], optional): Calibrated offset mass. Defaults to None.
+        prec_tol_calibrated (Union[None, float], optional): Calibrated offset mass. Defaults to None.
+        frag_tol_calibrated (float, optional): Fragment tolerance if calibration exists. Defaults to None.
 
     Returns:
         np.recarray: Recordarray containing PSMs with additional columns.
@@ -656,6 +664,9 @@ def get_score_columns(
     query_ints = query_data['int_list_ms2']
     query_scans = query_data['scan_list_ms2']
 
+    if frag_tol_calibrated:
+        frag_tol = frag_tol_calibrated
+
     if 'prec_id2' in query_data.keys():
         bruker = True
         query_prec_id = query_data['prec_id2']
@@ -685,7 +696,7 @@ def get_score_columns(
             db_ints = None
 
     if features is not None:
-        if m_offset_calibrated:
+        if prec_tol_calibrated:
             query_masses = features['corrected_mass'].values
         else:
             query_masses = features['mass_matched'].values
@@ -694,7 +705,7 @@ def get_score_columns(
 
         query_mz = features['mz_matched'].values
         query_rt = features['rt_matched'].values
-        query_charges = query_charges[features['query_idx'].values]
+        query_charges = features['charge_matched'].values
         query_scans = query_scans[features['query_idx'].values]
 
         if bruker:
@@ -726,18 +737,12 @@ def get_score_columns(
         query_mz = query_data['mono_mzs2']
         query_rt = query_data['rt_list_ms2']
 
-
-    loss_dict = Dict()
-    loss_dict[''] = 0.0
-    loss_dict['-H2O'] = 18.01056468346
-    loss_dict['-NH3'] = 17.03052
-
-    float_fields = ['o_mass', 'o_mass_ppm', 'o_mass_raw','o_mass_ppm_raw','delta_m','delta_m_ppm','matched_int_ratio','int_ratio']
-    int_fields = ['total_int','matched_int','n_ions','ion_idx'] + [a+_+'_hits' for _ in loss_dict for a in ['b','y']]
+    float_fields = ['prec_offset', 'prec_offset_ppm', 'prec_offset_raw ','prec_offset_raw_ppm ','delta_m','delta_m_ppm','matched_int_ratio','int_ratio']
+    int_fields = ['total_int','matched_int','n_ions','ion_idx'] + [a+_+'_hits' for _ in LOSS_DICT for a in ['b','y']]
 
     psms_dtype = np.dtype([(_,np.float32) for _ in float_fields] + [(_,np.int64) for _ in int_fields])
 
-    psms_, ions_,  = score(
+    psms_, ions,  = score(
         psms,
         query_masses,
         query_masses_raw,
@@ -752,7 +757,7 @@ def get_score_columns(
         ppm,
         psms_dtype)
 
-    ions_ = np.vstack(ions_)
+    ions_ = np.vstack(ions)
 
     for _ in psms_.dtype.names:
         psms = add_column(psms, psms_[_], _)
@@ -928,14 +933,21 @@ def search_db(to_process:tuple, callback:Callable = None, parallel:bool=False, f
                 if calibration == 0:
                     logging.info('Calibration is 0, skipping second database search.')
                     skip = True
-
                 else:
-                    settings['search']['m_offset_calibrated'] = calibration*settings['search']['calibration_std']
-                    calib = settings['search']['m_offset_calibrated']
+                    settings['search']['prec_tol_calibrated'] = calibration*settings['search']['calibration_std']
+                    calib = settings['search']['prec_tol_calibrated']
                     logging.info(f"Found calibrated prec_tol with value {calib:.2f}")
             except KeyError as e:
                 logging.info(f'{e}')
 
+            try:
+                fragment_std = float(ms_file_.read(dataset_name="estimated_max_fragment_ppm")[0])
+                skip = False
+                settings['search']['frag_tol_calibrated'] = fragment_std*settings['search']['calibration_std']
+                calib = settings['search']['frag_tol_calibrated']
+                logging.info(f"Found calibrated frag_tol with value {calib:.2f}")
+            except KeyError as e:
+                logging.info(f'{e}')
 
         if not skip:
             db_data_path = settings['experiment']['database_path']
@@ -1033,7 +1045,6 @@ def search_fasta_block(to_process:tuple) -> (list, int):
 
             n_frags = sum(lens)
 
-
             frags = np.zeros(n_frags, dtype=fragmasses[0].dtype)
             frag_types = np.zeros(n_frags, dtype=fragtypes[0].dtype)
 
@@ -1102,6 +1113,8 @@ def filter_top_n(temp:pd.DataFrame, top_n:int = 10)-> pd.DataFrame:
     """
     pept_dict_ = {}
 
+    temp['temp_idx'] = np.arange(len(temp))
+
     for k, v in temp[['sequence','fasta_index']].values:
         if k in pept_dict_:
             new_set = pept_dict_[k]
@@ -1121,14 +1134,72 @@ def filter_top_n(temp:pd.DataFrame, top_n:int = 10)-> pd.DataFrame:
 
 
 # Cell
+import psutil
+import alphapept.constants as constants
+from .fasta import get_fragmass, parse
+
+def ion_extractor(df: pd.DataFrame, ms_file, frag_tol:float, ppm:bool)->(np.ndarray, np.ndarray):
+    """Extracts the matched hits (ions) from a dataframe.
+
+    Args:
+        df (pd.DataFrame): Pandas dataframe containing the results of the first search.
+        ms_file : MsFile
+        frag_tol (float): Fragment tolerance for search.
+        ppm (bool): Flag to use ppm instead of Dalton.
+
+    Returns:
+        np.ndarray: Numpy recordarray storing the PSMs.
+        np.ndarray: Numpy recordarray storing the ions.
+    """
+
+    query_data = ms_file.read_DDA_query_data()
+    query_indices = query_data["indices_ms2"]
+    query_frags = query_data['mass_list_ms2']
+    query_ints = query_data['int_list_ms2']
+
+    psms = df.to_records()
+
+    ion_count = 0
+
+    ions_ = List()
+
+    for i in range(len(psms)):
+        query_idx = psms[i]["raw_idx"]
+        db_idx = psms[i]["db_idx"]
+        query_idx_start = query_indices[query_idx]
+        query_idx_end = query_indices[query_idx + 1]
+        query_frag = query_frags[query_idx_start:query_idx_end]
+        query_int = query_ints[query_idx_start:query_idx_end]
+
+        seq = psms[i]['sequence']
+
+        db_frag, frag_type = get_fragmass(parse(seq), constants.mass_dict)
+        db_int = np.ones_like(db_frag)
+
+        ions = get_hits(query_frag, query_int, db_frag, db_int, frag_type, frag_tol, ppm, LOSSES)
+
+        n_ions = len(ions)
+
+        psms['n_ions'][i] = n_ions
+        psms['ion_idx'][i] = ion_count
+
+        ion_count += n_ions
+        ions_.append(ions)
+
+    ions_ = np.vstack(ions_)
+
+    return psms, ions_
+
 
 #This function is a wrapper and ist tested by the quick_test
-def search_parallel(settings: dict, calibration:Union[list, None] = None, callback: Union[Callable, None] = None) -> dict:
+def search_parallel(settings: dict, calibration:Union[list, None] = None, fragment_calibration:Union[list, None] = None, callback: Union[Callable, None] = None) -> dict:
     """Function to search multiple ms_data files in parallel.
+    This function will additionally calculate fragments and precursor masses from a given FASTA file.
 
     Args:
         settings (dict): Settings file containg the experimental definitions.
-        calibration (Union[list, None], optional): List of calibrated offsets.. Defaults to None.
+        calibration (Union[list, None], optional): List of calibrated offsets. Defaults to None.
+        fragment_calibration (Union[list, None], optional): List of calibrated fragment offsets. Defaults to None.
         callback (Union[Callable, None], optional): Callback function. Defaults to None.
 
     Returns:
@@ -1148,17 +1219,27 @@ def search_parallel(settings: dict, calibration:Union[list, None] = None, callba
         custom_settings = []
         for _ in calibration:
             settings_ = copy.deepcopy(settings)
-            settings_["search"]["m_offset_calibrated"] = _
+            settings_["search"]["prec_tol_calibrated"] = _
             custom_settings.append(settings_)
     else:
         custom_settings = [settings for _ in ms_file_path]
 
+    if fragment_calibration:
+        for idx, _ in enumerate(fragment_calibration):
+            custom_settings[idx]["search"]["frag_tol_calibrated"] = _
+
 
     logging.info(f"Number of FASTA entries: {len(fasta_list):,} - FASTA settings {settings['fasta']}")
     to_process = [(idx_start, fasta_list[idx_start:idx_end], ms_file_path, custom_settings) for idx_start, idx_end in block_idx(len(fasta_list), fasta_block)]
 
+    memory_available = psutil.virtual_memory().available/1024**3
+
+    n_processes = int(memory_available // 4 )
+
+    logging.info(f'Setting Process limit to {n_processes}')
+
     n_processes = alphapept.performance.set_worker_count(
-        worker_count=settings['general']['n_processes'],
+        worker_count=n_processes,
         set_global=False
     )
 
@@ -1168,42 +1249,56 @@ def search_parallel(settings: dict, calibration:Union[list, None] = None, callba
         ms_file = alphapept.io.MS_Data_File(_+'_', is_new_file=True) #Create temporary files for writing
 
     df_cache = {}
+    ion_cache = {}
 
     with alphapept.performance.AlphaPool(n_processes) as p:
         max_ = len(to_process)
 
-        for i, (_, n_seqs) in enumerate(p.imap_unordered(search_fasta_block, to_process)):
+        for i, (psm_container, n_seqs) in enumerate(p.imap_unordered(search_fasta_block, to_process)):
             n_seqs_ += n_seqs
 
-
             logging.info(f'Block {i+1} of {max_} complete - {((i+1)/max_*100):.2f} % - created peptides {n_seqs:,} ')
-            for j in range(len(_)): #Temporary hdf files for avoiding saving issues
-                output = [_ for _ in _[j]]
+            for j in range(len(psm_container)): #Temporary hdf files for avoiding saving issues
+                output = [_ for _ in psm_container[j]]
                 if len(output) > 0:
-
                     psms = pd.concat(output)
-
                     if ms_file_path[j] in df_cache:
-                        df_cache[ms_file_path[j]] = filter_top_n(pd.concat([df_cache[ms_file_path[j]], psms]))
+                        temp = filter_top_n(pd.concat([df_cache[ms_file_path[j]], psms]))
+                        selector = temp['temp_idx'].values
+                        df_cache[ms_file_path[j]] = temp
                     else:
                         df_cache[ms_file_path[j]] = psms
 
-
             if callback:
                 callback((i+1)/max_)
 
-    for _ in ms_file_path:
+    for idx, _ in enumerate(ms_file_path):
         if _ in df_cache:
             x = df_cache[_]
             ms_file = alphapept.io.MS_Data_File(_)
 
             x['fasta_index'] = x['fasta_index'].apply(lambda x: ','.join(str(_) for _ in x))
 
+
+            if 'frag_tol_calibrated' in custom_settings[idx]['search']:
+                frag_tol = custom_settings[idx]['search']['frag_tol_calibrated']
+            else:
+                frag_tol = custom_settings[idx]['search']['frag_tol']
+
+            ppm = custom_settings[idx]['search']['ppm']
+
             if calibration:
-                store_hdf(x, ms_file, 'second_search', replace = True)
+                save_field = 'first_search'
             else:
-                store_hdf(x, ms_file, 'first_search', replace = True)
+                save_field = 'second_search'
+
+            psms, ions = ion_extractor(x, ms_file, frag_tol, ppm)
+
+            store_hdf(pd.DataFrame(psms), ms_file, save_field, replace=True)
+            ion_columns = ['ion_index','ion_type','ion_int','db_int','ion_mass','db_mass','query_idx','db_idx']
+            store_hdf(pd.DataFrame(ions, columns = ion_columns), ms_file, 'ions', replace=True)
 
+    #Todo? Callback
     logging.info(f'Complete. Created peptides {n_seqs_:,}')
 
     return fasta_dict
\ No newline at end of file
diff --git a/alphapept/settings.py b/alphapept/settings.py
index 2597f520..377925a9 100644
--- a/alphapept/settings.py
+++ b/alphapept/settings.py
@@ -9,13 +9,13 @@ def print_settings(settings: dict):
     """Print a yaml settings file
 
     Args:
-        settings (dict): A yaml dictionary
+        settings (dict): A yaml dictionary.
     """
     print(yaml.dump(settings, default_flow_style=False))
 
 
 def load_settings(path: str):
-    """Load a yaml settings file
+    """Load a yaml settings file.
 
     Args:
         path (str): Path to the settings file.
@@ -37,7 +37,7 @@ def load_settings_as_template(path: str):
         if _ in settings:
             settings.pop(_)
 
-    _ = 'm_offset_calibrated'
+    _ = 'prec_tol_calibrated'
     if 'search' in settings:
         if _ in settings['search']:
             settings['search'].pop(_)
@@ -46,10 +46,10 @@ def load_settings_as_template(path: str):
 
 
 def save_settings(settings: dict, path: str):
-    """Save settings file to path
+    """Save settings file to path.
 
     Args:
-        settings (dict): A yaml dictionary
+        settings (dict): A yaml dictionary.
         path (str): Path to the settings file.
     """
     with open(path, "w") as file:
diff --git a/alphapept/settings_template.yaml b/alphapept/settings_template.yaml
index 484decd6..f62c7e4f 100644
--- a/alphapept/settings_template.yaml
+++ b/alphapept/settings_template.yaml
@@ -397,20 +397,20 @@ search:
   prec_tol:
     type: spinbox
     min: 1
-    max: 99
+    max: 500
     default: 30
-    description: Precursor mass offset.
+    description: Maximum allowed precursor mass offset.
   frag_tol:
     type: spinbox
     min: 1
-    max: 99
+    max: 500
     default: 30
-    description: MSMS mass offset.
+    description: Maximum fragment mass tolerance.
   min_frag_hits:
     type: spinbox
     min: 1
     max: 99
-    default: 7
+    default: 6
     description: Minimum number of fragment hits.
   ppm:
     type: checkbox
diff --git a/changelog.md b/changelog.md
new file mode 100644
index 00000000..2f6a467c
--- /dev/null
+++ b/changelog.md
@@ -0,0 +1,14 @@
+# Changelog
+
+Last change: 17-AUG-2021, MTS
+
+## 0.3.28
+
+* Automatic fragment tolerance estimation for better support of MS2=Iontrap Thermo runs #305
+* Updated error message handling
+* consistent renaming of o_mass to prec_offset
+* Fixed a bug in how `int_ratio` is calculated in first_search
+* Enabled handling of `mzML`-files in GUI / Core
+* Improved Documentation
+* Ion export for search parallel
+* Moved loss_dict to constants.
diff --git a/docs/file_formats.html b/docs/file_formats.html
index d6342b4c..1de03b78 100644
--- a/docs/file_formats.html
+++ b/docs/file_formats.html
@@ -211,19 +211,19 @@ <h3 id="protein_fdr">protein_fdr<a class="anchor-link" href="#protein_fdr"> </a>
 <td>naked sequence (i.e. withouth modifications)</td>
 </tr>
 <tr>
-<td>o_mass</td>
+<td>prec_offset</td>
 <td>mass offset from theoretical precursor mass to actual precursor mass</td>
 </tr>
 <tr>
-<td>o_mass_ppm</td>
-<td>o_mass in ppm</td>
+<td>prec_offset_ppm</td>
+<td>prec_offset in ppm</td>
 </tr>
 <tr>
-<td>o_mass_ppm_raw</td>
-<td>o_mass_raw in ppm</td>
+<td>prec_offset_raw_ppm </td>
+<td>prec_offset_raw  in ppm</td>
 </tr>
 <tr>
-<td>o_mass_raw</td>
+<td>prec_offset_raw </td>
 <td>mass offset from theoretical precursor mass to actual precursor mass (before recalibration)</td>
 </tr>
 <tr>
diff --git a/docs/recalibration.html b/docs/recalibration.html
index b82287d7..5b72f282 100644
--- a/docs/recalibration.html
+++ b/docs/recalibration.html
@@ -67,10 +67,10 @@ <h3 id="Fragment-mass-calibration">Fragment mass calibration<a class="anchor-lin
 <h4 id="remove_outliers" class="doc_header"><code>remove_outliers</code><a href="https://github.com/mannlabs/alphapept/tree/master/alphapept/recalibration.py#L11" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>remove_outliers</code>(<strong><code>df</code></strong>:<code>DataFrame</code>, <strong><code>outlier_std</code></strong>:<code>float</code>)</p>
 </blockquote>
 <p>Helper function to remove outliers from a dataframe.
-Outliers are removed based on the precursor offset mass (o_mass).
+Outliers are removed based on the precursor offset mass (prec_offset).
 All values within x standard deviations to the median are kept.</p>
 <p>Args:
-    df (pd.DataFrame): Input dataframe that contains a o_mass_ppm-column.
+    df (pd.DataFrame): Input dataframe that contains a prec_offset_ppm-column.
     outlier_std (float): Range of standard deviations to filter outliers</p>
 <p>Raises:
     ValueError: An error if the column is not present in the dataframe.</p>
diff --git a/docs/search.html b/docs/search.html
index e1bcd270..f66b94bd 100644
--- a/docs/search.html
+++ b/docs/search.html
@@ -392,7 +392,7 @@ <h4 id="query_data_to_features" class="doc_header"><code>query_data_to_features<
 
 
 <div class="output_markdown rendered_html output_subarea ">
-<h4 id="get_psms" class="doc_header"><code>get_psms</code><a href="https://github.com/mannlabs/alphapept/tree/master/alphapept/search.py#L221" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_psms</code>(<strong><code>query_data</code></strong>:<code>dict</code>, <strong><code>db_data</code></strong>:<code>dict</code>, <strong><code>features</code></strong>:<code>DataFrame</code>, <strong><code>parallel</code></strong>:<code>bool</code>, <strong><code>frag_tol</code></strong>:<code>float</code>, <strong><code>prec_tol</code></strong>:<code>float</code>, <strong><code>ppm</code></strong>:<code>bool</code>, <strong><code>min_frag_hits</code></strong>:<code>int</code>, <strong><code>callback</code></strong>:<code>Callable</code>=<em><code>None</code></em>, <strong><code>m_offset_calibrated</code></strong>:<code>float</code>=<em><code>None</code></em>, <strong>**<code>kwargs</code></strong>)</p>
+<h4 id="get_psms" class="doc_header"><code>get_psms</code><a href="https://github.com/mannlabs/alphapept/tree/master/alphapept/search.py#L221" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_psms</code>(<strong><code>query_data</code></strong>:<code>dict</code>, <strong><code>db_data</code></strong>:<code>dict</code>, <strong><code>features</code></strong>:<code>DataFrame</code>, <strong><code>parallel</code></strong>:<code>bool</code>, <strong><code>frag_tol</code></strong>:<code>float</code>, <strong><code>prec_tol</code></strong>:<code>float</code>, <strong><code>ppm</code></strong>:<code>bool</code>, <strong><code>min_frag_hits</code></strong>:<code>int</code>, <strong><code>callback</code></strong>:<code>Callable</code>=<em><code>None</code></em>, <strong><code>prec_tol_calibrated</code></strong>:<code>float</code>=<em><code>None</code></em>, <strong>**<code>kwargs</code></strong>)</p>
 </blockquote>
 <p>[summary]</p>
 <p>Args:
@@ -405,7 +405,7 @@ <h4 id="get_psms" class="doc_header"><code>get_psms</code><a href="https://githu
     ppm (bool): Flag to use ppm instead of Dalton.
     min_frag_hits (int): Minimum number of frag hits to report a PSMs.
     callback (Callable, optional): Optional callback. Defaults to None.
-    m_offset_calibrated (float, optional): Precursor tolerance if calibration exists. Defaults to None.</p>
+    prec_tol_calibrated (float, optional): Precursor tolerance if calibration exists. Defaults to None.</p>
 <p>Returns:
     np.ndarray: Numpy recordarray storing the PSMs.
     int: 0</p>
@@ -767,7 +767,7 @@ <h4 id="get_sequences" class="doc_header"><code>get_sequences</code><a href="htt
 
 
 <div class="output_markdown rendered_html output_subarea ">
-<h4 id="get_score_columns" class="doc_header"><code>get_score_columns</code><a href="https://github.com/mannlabs/alphapept/tree/master/alphapept/search.py#L623" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_score_columns</code>(<strong><code>psms</code></strong>:<code>recarray</code>, <strong><code>query_data</code></strong>:<code>dict</code>, <strong><code>db_data</code></strong>:<code>Union</code>[<code>dict</code>, <code>str</code>], <strong><code>features</code></strong>:<code>DataFrame</code>, <strong><code>parallel</code></strong>:<code>bool</code>, <strong><code>frag_tol</code></strong>:<code>float</code>, <strong><code>prec_tol</code></strong>:<code>float</code>, <strong><code>ppm</code></strong>:<code>bool</code>, <strong><code>m_offset_calibrated</code></strong>:<code>Union</code>[<code>NoneType</code>, <code>float</code>]=<em><code>None</code></em>, <strong>**<code>kwargs</code></strong>)</p>
+<h4 id="get_score_columns" class="doc_header"><code>get_score_columns</code><a href="https://github.com/mannlabs/alphapept/tree/master/alphapept/search.py#L623" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_score_columns</code>(<strong><code>psms</code></strong>:<code>recarray</code>, <strong><code>query_data</code></strong>:<code>dict</code>, <strong><code>db_data</code></strong>:<code>Union</code>[<code>dict</code>, <code>str</code>], <strong><code>features</code></strong>:<code>DataFrame</code>, <strong><code>parallel</code></strong>:<code>bool</code>, <strong><code>frag_tol</code></strong>:<code>float</code>, <strong><code>prec_tol</code></strong>:<code>float</code>, <strong><code>ppm</code></strong>:<code>bool</code>, <strong><code>prec_tol_calibrated</code></strong>:<code>Union</code>[<code>NoneType</code>, <code>float</code>]=<em><code>None</code></em>, <strong>**<code>kwargs</code></strong>)</p>
 </blockquote>
 <p>Wrapper function to extract score columns.</p>
 <p>Args:
@@ -779,7 +779,7 @@ <h4 id="get_score_columns" class="doc_header"><code>get_score_columns</code><a h
     frag_tol (float): Fragment tolerance for search.
     prec_tol (float): Precursor tolerance for search.
     ppm (bool): Flag to use ppm instead of Dalton.
-    m_offset_calibrated (Union[None, float], optional): Calibrated offset mass. Defaults to None.</p>
+    prec_tol_calibrated (Union[None, float], optional): Calibrated offset mass. Defaults to None.</p>
 <p>Returns:
     np.recarray: Recordarray containing PSMs with additional columns.
     np.ndarray: NumPy array containing ion information.</p>
diff --git a/installer/one_click_windows/alphapept_innoinstaller.iss b/installer/one_click_windows/alphapept_innoinstaller.iss
index 4a67c760..4f65f950 100644
--- a/installer/one_click_windows/alphapept_innoinstaller.iss
+++ b/installer/one_click_windows/alphapept_innoinstaller.iss
@@ -2,7 +2,7 @@
 ; SEE THE DOCUMENTATION FOR DETAILS ON CREATING INNO SETUP SCRIPT FILES!
 
 #define MyAppName "AlphaPept"
-#define MyAppVersion "0.3.27"
+#define MyAppVersion "0.3.28"
 #define MyAppPublisher "MannLabs"
 #define MyAppURL "https://github.com/MannLabs/alphapept"
 #define MyAppExeName "alphapept.exe"
@@ -39,7 +39,7 @@ Name: "desktopicon"; Description: "{cm:CreateDesktopIcon}"; GroupDescription: "{
 Source: "dist\\alphapept\*"; DestDir: "{app}"; Flags: recursesubdirs createallsubdirs
 
 [Icons]
-Name: "{autoprograms}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"
+Name: "{autoprograms}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; Parameters: "gui"
 Name: "{autodesktop}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; Parameters: "gui"; Tasks: desktopicon
 
 [Run]
diff --git a/installer/one_click_windows/create_installer_windows.bat b/installer/one_click_windows/create_installer_windows.bat
index 669f6dc3..00909dc9 100644
--- a/installer/one_click_windows/create_installer_windows.bat
+++ b/installer/one_click_windows/create_installer_windows.bat
@@ -11,9 +11,10 @@ call DEL /F/Q/S dist > NUL
 call RMDIR /Q/S build
 call RMDIR /Q/S dist
 call python setup.py sdist bdist_wheel
-call pip install dist/alphapept-0.3.27-py3-none-any.whl
+call pip install dist/alphapept-0.3.28-py3-none-any.whl
 call pip install pyinstaller==4.2
 call cd installer/one_click_windows
 call pyinstaller ../alphapept.spec -y
 call conda deactivate
+call robocopy dist/alphapept/site-packages/st_aggrid dist/alphapept/st_aggrid /E
 call "C:\Program Files (x86)\Inno Setup 6\ISCC.exe" alphapept_innoinstaller.iss
diff --git a/nbs/00_settings.ipynb b/nbs/00_settings.ipynb
index ac3dea4b..3510d07c 100644
--- a/nbs/00_settings.ipynb
+++ b/nbs/00_settings.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,13 +59,13 @@
     "    \"\"\"Print a yaml settings file\n",
     "\n",
     "    Args:\n",
-    "        settings (dict): A yaml dictionary\n",
+    "        settings (dict): A yaml dictionary.\n",
     "    \"\"\"\n",
     "    print(yaml.dump(settings, default_flow_style=False))\n",
     "\n",
     "\n",
     "def load_settings(path: str):\n",
-    "    \"\"\"Load a yaml settings file\n",
+    "    \"\"\"Load a yaml settings file.\n",
     "\n",
     "    Args:\n",
     "        path (str): Path to the settings file.\n",
@@ -87,7 +87,7 @@
     "        if _ in settings:\n",
     "            settings.pop(_)\n",
     "\n",
-    "    _ = 'm_offset_calibrated'\n",
+    "    _ = 'prec_tol_calibrated'\n",
     "    if 'search' in settings:\n",
     "        if _ in settings['search']:\n",
     "            settings['search'].pop(_)\n",
@@ -96,10 +96,10 @@
     "    \n",
     "\n",
     "def save_settings(settings: dict, path: str):\n",
-    "    \"\"\"Save settings file to path\n",
+    "    \"\"\"Save settings file to path.\n",
     "\n",
     "    Args:\n",
-    "        settings (dict): A yaml dictionary\n",
+    "        settings (dict): A yaml dictionary.\n",
     "        path (str): Path to the settings file.\n",
     "    \"\"\"\n",
     "    with open(path, \"w\") as file:\n",
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -286,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -411,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -426,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -518,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -748,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -788,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -906,7 +906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -914,9 +914,9 @@
     "# Search Settings\n",
     "search = {}\n",
     "\n",
-    "search[\"prec_tol\"] = {'type':'spinbox', 'min':1, 'max':99, 'default':30, 'description':\"Precursor mass offset.\"}\n",
-    "search[\"frag_tol\"] = {'type':'spinbox', 'min':1, 'max':99, 'default':30, 'description':\"MSMS mass offset.\"}\n",
-    "search[\"min_frag_hits\"] = {'type':'spinbox', 'min':1, 'max':99, 'default':7, 'description':\"Minimum number of fragment hits.\"}\n",
+    "search[\"prec_tol\"] = {'type':'spinbox', 'min':1, 'max':500, 'default':30, 'description':\"Maximum allowed precursor mass offset.\"}\n",
+    "search[\"frag_tol\"] = {'type':'spinbox', 'min':1, 'max':500, 'default':30, 'description':\"Maximum fragment mass tolerance.\"}\n",
+    "search[\"min_frag_hits\"] = {'type':'spinbox', 'min':1, 'max':99, 'default':6, 'description':\"Minimum number of fragment hits.\"}\n",
     "search[\"ppm\"] = {'type':'checkbox', 'default':True, 'description':\"Use ppm instead of Dalton.\"}\n",
     "search[\"calibrate\"] = {'type':'checkbox', 'default':True, 'description':\"Recalibrate masses.\"}\n",
     "search[\"calibration_std\"] = {'type':'spinbox', 'min':1, 'max':5, 'default':3, 'description':\"Std range for search after calibration.\"}\n",
@@ -930,7 +930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -949,12 +949,12 @@
       "  type: spinbox\n",
       "frag_tol:\n",
       "  default: 30\n",
-      "  description: MSMS mass offset.\n",
-      "  max: 99\n",
+      "  description: Maximum fragment mass tolerance.\n",
+      "  max: 500\n",
       "  min: 1\n",
       "  type: spinbox\n",
       "min_frag_hits:\n",
-      "  default: 7\n",
+      "  default: 6\n",
       "  description: Minimum number of fragment hits.\n",
       "  max: 99\n",
       "  min: 1\n",
@@ -975,8 +975,8 @@
       "  type: checkbox\n",
       "prec_tol:\n",
       "  default: 30\n",
-      "  description: Precursor mass offset.\n",
-      "  max: 99\n",
+      "  description: Maximum allowed precursor mass offset.\n",
+      "  max: 500\n",
       "  min: 1\n",
       "  type: spinbox\n",
       "protein_fdr:\n",
@@ -1008,7 +1008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1022,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1053,7 +1053,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1072,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1126,7 +1126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1143,7 +1143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1179,7 +1179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1196,7 +1196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1229,7 +1229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1252,7 +1252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1271,13 +1271,13 @@
       "Converted 09_matching.ipynb.\n",
       "Converted 10_constants.ipynb.\n",
       "Converted 11_interface.ipynb.\n",
-      "Converted 12_speed.ipynb.\n",
+      "Converted 12_performance.ipynb.\n",
       "Converted 13_export.ipynb.\n",
-      "Converted 13_performance.ipynb.\n",
       "Converted additional_code.ipynb.\n",
       "Converted contributing.ipynb.\n",
       "Converted file_formats.ipynb.\n",
-      "Converted index.ipynb.\n"
+      "Converted index.ipynb.\n",
+      "Converted Untitled.ipynb.\n"
      ]
     }
    ],
@@ -1293,6 +1293,18 @@
    "display_name": "Python [conda env:alphapept] *",
    "language": "python",
    "name": "conda-env-alphapept-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/02_io.ipynb b/nbs/02_io.ipynb
index f7ee3fee..c6a63012 100644
--- a/nbs/02_io.ipynb
+++ b/nbs/02_io.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -423,8 +423,11 @@
     "    ms_order = input_dict.get('ms level')\n",
     "    prec_mass = mono_mz = charge = 0\n",
     "    if ms_order == 2:\n",
-    "        charge = int(input_dict.get('precursorList').get('precursor')[0].get('selectedIonList').get('selectedIon')[0].get(\n",
+    "        try:\n",
+    "            charge = int(input_dict.get('precursorList').get('precursor')[0].get('selectedIonList').get('selectedIon')[0].get(\n",
     "                'charge state'))\n",
+    "        except TypeError:\n",
+    "            charge = 0\n",
     "        mono_mz = input_dict.get('precursorList').get('precursor')[0].get('selectedIonList').get('selectedIon')[0].get(\n",
     "                'selected ion m/z')\n",
     "        prec_mass = calculate_mass(mono_mz, charge)\n",
@@ -500,7 +503,7 @@
     "        except SystemExit as e:\n",
     "            raise e\n",
     "        except Exception as e:\n",
-    "            logging.info(f\"Bad scan={i} in mzML file '{filename}'\")\n",
+    "            logging.info(f\"Bad scan={i} in mzML file '{filename}' {e}\")\n",
     "\n",
     "        if callback:\n",
     "            callback((idx+1)/len(spec_indices))\n",
@@ -571,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -626,7 +629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -641,7 +644,7 @@
        "  'taxonomyId': None}}"
       ]
      },
-     "execution_count": null,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -660,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -695,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -704,7 +707,7 @@
        "'AFQPFFVELToxMPYSVIR'"
       ]
      },
-     "execution_count": null,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -736,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -800,7 +803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -809,7 +812,7 @@
        "[(2, 6, 9), (9, 13, 16)]"
       ]
      },
-     "execution_count": null,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -821,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -850,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -936,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -959,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1012,7 +1015,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1063,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1108,7 +1111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1163,7 +1166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1265,7 +1268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1424,7 +1427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1722,7 +1725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1935,7 +1938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1955,7 +1958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2144,7 +2147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2181,7 +2184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2266,7 +2269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2301,7 +2304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2359,7 +2362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -2378,9 +2381,10 @@
       "Converted 09_matching.ipynb.\n",
       "Converted 10_constants.ipynb.\n",
       "Converted 11_interface.ipynb.\n",
-      "Converted 12_speed.ipynb.\n",
+      "Converted 12_performance.ipynb.\n",
       "Converted 13_export.ipynb.\n",
-      "Converted 13_performance.ipynb.\n",
+      "Converted additional_code.ipynb.\n",
+      "Converted contributing.ipynb.\n",
       "Converted file_formats.ipynb.\n",
       "Converted index.ipynb.\n"
      ]
@@ -2398,6 +2402,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/03_fasta.ipynb b/nbs/03_fasta.ipynb
index 3f038d2b..86f6ee7a 100644
--- a/nbs/03_fasta.ipynb
+++ b/nbs/03_fasta.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,7 +60,7 @@
     "from alphapept import constants\n",
     "import re\n",
     "\n",
-    "def get_missed_cleavages(sequences:list, n_missed_cleavages:int):\n",
+    "def get_missed_cleavages(sequences:list, n_missed_cleavages:int) -> list:\n",
     "    \"\"\"\n",
     "    Combine cleaved sequences to get sequences with missed cleavages\n",
     "    Args:\n",
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -128,7 +128,7 @@
        "['ABCDEFGHIJK', 'LMNOPQR']"
       ]
      },
-     "execution_count": null,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,7 +177,7 @@
     "import re\n",
     "from alphapept import constants\n",
     "\n",
-    "def count_missed_cleavages(sequence:str=\"\", protease:str=\"trypsin\", **kwargs):\n",
+    "def count_missed_cleavages(sequence:str=\"\", protease:str=\"trypsin\", **kwargs) -> int:\n",
     "    \"\"\"\n",
     "    Counts the number of missed cleavages for a given sequence and protease\n",
     "    Args:\n",
@@ -192,7 +192,7 @@
     "    n_missed = len(p.findall(sequence))\n",
     "    return n_missed\n",
     "\n",
-    "def count_internal_cleavages(sequence:str=\"\", protease:str=\"trypsin\", **kwargs):\n",
+    "def count_internal_cleavages(sequence:str=\"\", protease:str=\"trypsin\", **kwargs) -> int:\n",
     "    \"\"\"\n",
     "    Counts the number of internal cleavage sites for a given sequence and protease\n",
     "    Args:\n",
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -235,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,9 +294,13 @@
     "\n",
     "    return parsed\n",
     "\n",
-    "def list_to_numba(a_list):\n",
+    "def list_to_numba(a_list) -> List:\n",
     "    \"\"\"\n",
-    "    Convert python list to numba.typed.List\n",
+    "    Convert Python list to numba.typed.List\n",
+    "    Args: \n",
+    "        a_list (list): Python list.\n",
+    "    Return:\n",
+    "        List (numba.typed.List): Numba typed list.\n",
     "    \"\"\"\n",
     "    numba_list = List()\n",
     "\n",
@@ -308,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -327,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -354,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -390,9 +394,15 @@
     "\n",
     "\n",
     "@njit\n",
-    "def swap_KR(peptide):\n",
+    "def swap_KR(peptide:str)->str:\n",
     "    \"\"\"\n",
     "    Swaps a terminal K or R. Note: Only if AA is not modified.\n",
+    "    \n",
+    "    Args:\n",
+    "        peptide (str): peptide.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: peptide with swapped KRs.\n",
     "    \"\"\"\n",
     "    if peptide[-1] == 'K':\n",
     "        peptide[-1] = 'R'\n",
@@ -403,9 +413,14 @@
     "\n",
     "\n",
     "@njit\n",
-    "def swap_AL(peptide):\n",
+    "def swap_AL(peptide:str)->str:\n",
     "    \"\"\"\n",
     "    Swaps a A with L. Note: Only if AA is not modified.\n",
+    "    Args:\n",
+    "        peptide (str): peptide.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: peptide with swapped ALs.\n",
     "    \"\"\"\n",
     "    i = 0\n",
     "    while i < len(range(len(peptide) - 1)):\n",
@@ -445,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -464,7 +479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -481,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -498,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -548,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -572,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -581,7 +596,7 @@
        "['AbBcCDEF']"
       ]
      },
-     "execution_count": null,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -595,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -604,7 +619,7 @@
        "['caAbBcCDEF']"
       ]
      },
-     "execution_count": null,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -618,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -647,12 +662,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
     "#export\n",
-    "def add_variable_mod(peps:list, mods_variable_dict:dict):\n",
+    "def add_variable_mod(peps:list, mods_variable_dict:dict)->list:\n",
+    "    \"\"\"\n",
+    "    Function to add variable modification to a list of peptides.\n",
+    "    Args:\n",
+    "        peps (list): List of peptides.\n",
+    "        mods_variable_dict (dict): Dicitionary with modifications. The key is AA, and value is the modified form (e.g. oxM). \n",
+    "    Returns:\n",
+    "        list : the list of peptide forms for the given peptide.\n",
+    "    \"\"\"\n",
     "    peptides = []\n",
     "    for pep_ in peps:\n",
     "        pep, min_idx = pep_\n",
@@ -670,7 +693,7 @@
     "    Function to generate modified forms (with variable modifications) for a given peptide - returns a list of modified forms.\n",
     "    The original sequence is included in the list\n",
     "    Args:\n",
-    "        mods_variable_dict (dict): the key is AA, and value is the modified form (e.g. oxM).\n",
+    "        mods_variable_dict (dict): Dicitionary with modifications. The key is AA, and value is the modified form (e.g. oxM).\n",
     "        peptide (str): the peptide sequence to generate modified forms.\n",
     "        isoforms_max (int): max number of modified forms to generate per peptide.\n",
     "        n_modifications_max (int, optional): max number of variable modifications per peptide.\n",
@@ -713,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -744,7 +767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -763,7 +786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -802,7 +825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -811,7 +834,7 @@
        "['AMA', 'AoxMA', 'AAC', 'AAamC']"
       ]
      },
-     "execution_count": null,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -827,7 +850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -875,7 +898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -927,7 +950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -954,7 +977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1002,7 +1025,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1052,16 +1075,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['AMAMA', 'xAMAMA']"
+       "['xAMAMA', 'AMAMA']"
       ]
      },
-     "execution_count": null,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1073,7 +1096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1102,7 +1125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1179,23 +1202,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['PEPTIDEM',\n",
-       " 'PEPTIDEoxM',\n",
-       " 'MEDITPEP_decoy',\n",
-       " 'oxMEDITPEP_decoy',\n",
-       " 'aPEPTIDEM',\n",
+       "['aPEPTIDEM',\n",
        " 'aPEPTIDEoxM',\n",
        " 'MEDITPEaP_decoy',\n",
-       " 'oxMEDITPEaP_decoy']"
+       " 'oxMEDITPEaP_decoy',\n",
+       " 'PEPTIDEM',\n",
+       " 'PEPTIDEoxM',\n",
+       " 'MEDITPEP_decoy',\n",
+       " 'oxMEDITPEP_decoy']"
       ]
      },
-     "execution_count": null,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1221,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1264,7 +1287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1293,7 +1316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1302,7 +1325,7 @@
        "799.35996420346"
       ]
      },
-     "execution_count": null,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1313,7 +1336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1342,7 +1365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1389,7 +1412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1401,7 +1424,7 @@
        " array([ 1,  2,  3,  4,  5,  6, -1, -2, -3, -4, -5, -6], dtype=int8))"
       ]
      },
-     "execution_count": null,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1412,7 +1435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1432,7 +1455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1464,7 +1487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1484,7 +1507,7 @@
        " 'y6': 703.31447681033}"
       ]
      },
-     "execution_count": null,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1495,7 +1518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1527,7 +1550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1584,7 +1607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1635,7 +1658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1654,7 +1677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1689,7 +1712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1773,7 +1796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1785,7 +1808,7 @@
        " 'sequence': 'MKLFGFRSRRGQTVLGSIDHLYTGSGYRIRYSELQKIHKAAVKGDAAEMERCLARRSGDLDALDKQHRTALHLACASGHVKVVTLLVNRKCQIDIYDKENRTPLIQAVHCQEEACAVILLEHGANPNLKDIYGNTALHYAVYSESTSLAEKLLFHGENIEALDKV'}"
       ]
      },
-     "execution_count": null,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1809,7 +1832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1839,7 +1862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1865,7 +1888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1888,7 +1911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1921,7 +1944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -1930,7 +1953,7 @@
        "{'ABC': [0, 3, 4], 'DEF': [0, 1], 'GHI': [1], 'JKL': [5, 6], 'MNO': [7]}"
       ]
      },
-     "execution_count": null,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1944,7 +1967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1970,7 +1993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2018,7 +2041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2034,7 +2057,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2088,7 +2111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2113,7 +2136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2152,7 +2175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2181,11 +2204,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
     "#export\n",
+    "from typing import Generator\n",
     "\n",
     "def block_idx(len_list:int, block_size:int = 1000)->list:\n",
     "    \"\"\"\n",
@@ -2207,14 +2231,14 @@
     "\n",
     "    return blocks\n",
     "\n",
-    "def blocks(l:int, n:int)->tuple:\n",
+    "def blocks(l:int, n:int)->Generator[list, None, None]:\n",
     "    \"\"\"\n",
-    "    Helper function to reate blocks from a given list\n",
+    "    Helper function to create blocks from a given list\n",
     "    Args:\n",
     "        l (list): the list\n",
     "        n (int): size per block\n",
     "    Returns:\n",
-    "        tuple: tuple of split blocks\n",
+    "        Generator: List with splitted elements\n",
     "    \"\"\"\n",
     "    n = max(1, n)\n",
     "    return (l[i:i+n] for i in range(0, len(l), n))"
@@ -2222,7 +2246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2241,7 +2265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2338,7 +2362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2409,7 +2433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2420,10 +2444,12 @@
     "def save_database(spectra:list, pept_dict:dict, fasta_dict:dict, database_path:str, **kwargs):\n",
     "    \"\"\"\n",
     "    Function to save a database to the *.hdf format. Write the database into hdf.\n",
+    "    \n",
     "    Args:\n",
-    "        spectra (list): list: theoretical spectra. See generate_spectra()\n",
-    "        pept_dict (dict): peptide dict. See add_to_pept_dict()\n",
-    "        fasta_dict (dict): fasta_dict. See generate_fasta_list()\n",
+    "        spectra (list): list: theoretical spectra. See generate_spectra().\n",
+    "        pept_dict (dict): peptide dict. See add_to_pept_dict().\n",
+    "        fasta_dict (dict): fasta_dict. See generate_fasta_list().\n",
+    "        database_path (str): Path to database.\n",
     "    \"\"\"\n",
     "    \n",
     "    precmasses, seqs, fragmasses, fragtypes = zip(*spectra)\n",
@@ -2491,7 +2517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2546,7 +2572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2573,7 +2599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -2592,13 +2618,13 @@
       "Converted 09_matching.ipynb.\n",
       "Converted 10_constants.ipynb.\n",
       "Converted 11_interface.ipynb.\n",
-      "Converted 12_speed.ipynb.\n",
+      "Converted 12_performance.ipynb.\n",
       "Converted 13_export.ipynb.\n",
-      "Converted 13_performance.ipynb.\n",
       "Converted additional_code.ipynb.\n",
       "Converted contributing.ipynb.\n",
       "Converted file_formats.ipynb.\n",
-      "Converted index.ipynb.\n"
+      "Converted index.ipynb.\n",
+      "Converted Untitled.ipynb.\n"
      ]
     }
    ],
@@ -2611,9 +2637,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:alphapept]",
+   "display_name": "Python [conda env:alphapept] *",
    "language": "python",
    "name": "conda-env-alphapept-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/04_feature_finding.ipynb b/nbs/04_feature_finding.ipynb
index ca78744b..1687dd95 100644
--- a/nbs/04_feature_finding.ipynb
+++ b/nbs/04_feature_finding.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -386,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -421,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -637,7 +637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -653,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -754,7 +754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -869,7 +869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1023,7 +1023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1062,7 +1062,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1084,7 +1084,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1129,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1186,7 +1186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1261,7 +1261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1330,7 +1330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1500,7 +1500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1542,7 +1542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1644,7 +1644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1690,7 +1690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1831,7 +1831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1882,7 +1882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1961,7 +1961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2030,7 +2030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2119,7 +2119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2234,7 +2234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2295,7 +2295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2406,7 +2406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2575,7 +2575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2613,6 +2613,8 @@
     "            datatype='thermo'\n",
     "        elif ext.lower() == '.d':\n",
     "            datatype='bruker'\n",
+    "        elif ext.lower() == '.mzml':\n",
+    "            datatype='mzml'\n",
     "        else:\n",
     "            raise NotImplementedError('File extension {} not understood.'.format(ext))\n",
     "\n",
@@ -2641,7 +2643,7 @@
     "            if not settings['workflow'][\"find_features\"]:\n",
     "                features = query_data_to_features(query_data)\n",
     "            else:\n",
-    "                if datatype == 'thermo':\n",
+    "                if datatype in ['thermo','mzml']:\n",
     "\n",
     "                    from alphapept.constants import averagine_aa, isotopes\n",
     "\n",
@@ -2740,7 +2742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2884,7 +2886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2894,7 +2896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -2934,6 +2936,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/05_search.ipynb b/nbs/05_search.ipynb
index 9acff2b6..0e50b6a4 100644
--- a/nbs/05_search.ipynb
+++ b/nbs/05_search.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -109,7 +109,7 @@
        "array([0, 2, 3, 0], dtype=int16)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -260,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -296,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -447,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -499,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -535,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -552,7 +552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -570,7 +570,8 @@
     "    ppm: bool,\n",
     "    min_frag_hits: int,\n",
     "    callback: Callable = None,\n",
-    "    m_offset_calibrated:float = None,\n",
+    "    prec_tol_calibrated:float = None,\n",
+    "    frag_tol_calibrated:float = None,\n",
     "    **kwargs\n",
     ")->(np.ndarray, int):\n",
     "    \"\"\"[summary]\n",
@@ -585,7 +586,8 @@
     "        ppm (bool): Flag to use ppm instead of Dalton.\n",
     "        min_frag_hits (int): Minimum number of frag hits to report a PSMs.\n",
     "        callback (Callable, optional): Optional callback. Defaults to None.\n",
-    "        m_offset_calibrated (float, optional): Precursor tolerance if calibration exists. Defaults to None.\n",
+    "        prec_tol_calibrated (float, optional): Precursor tolerance if calibration exists. Defaults to None.\n",
+    "        frag_tol_calibrated (float, optional): Fragment tolerance if calibration exists. Defaults to None.\n",
     "\n",
     "    Returns:\n",
     "        np.ndarray: Numpy recordarray storing the PSMs.\n",
@@ -604,10 +606,13 @@
     "    query_indices = query_data[\"indices_ms2\"]\n",
     "    query_frags = query_data['mass_list_ms2']\n",
     "    query_ints = query_data['int_list_ms2']\n",
-    "\n",
+    "    \n",
+    "    if frag_tol_calibrated:\n",
+    "        frag_tol = frag_tol_calibrated\n",
+    "    \n",
     "    if features is not None:\n",
-    "        if m_offset_calibrated:\n",
-    "            prec_tol = m_offset_calibrated\n",
+    "        if prec_tol_calibrated:\n",
+    "            prec_tol = prec_tol_calibrated\n",
     "            query_masses = features['corrected_mass'].values\n",
     "        else:\n",
     "            query_masses = features['mass_matched'].values\n",
@@ -634,8 +639,8 @@
     "        )\n",
     "        query_indices = indices\n",
     "    else:\n",
-    "        if m_offset_calibrated:\n",
-    "            prec_tol = m_offset_calibrated\n",
+    "        if prec_tol_calibrated:\n",
+    "            prec_tol = prec_tol_calibrated\n",
     "\n",
     "        query_masses = query_data['prec_mass_list2']\n",
     "        query_mz = query_data['mono_mzs2']\n",
@@ -714,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -742,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -770,7 +775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -799,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -829,7 +834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -873,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -918,7 +923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -989,7 +994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1022,11 +1027,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
     "#export\n",
+    "from alphapept import constants\n",
+    "LOSS_DICT = constants.loss_dict\n",
+    "LOSSES = np.array(list(LOSS_DICT.values()))\n",
     "\n",
     "#This function is a wrapper and ist tested by the quick_test\n",
     "@njit\n",
@@ -1073,8 +1081,6 @@
     "\n",
     "    psms_ = np.zeros(len(psms), dtype=psms_dtype)\n",
     "\n",
-    "    losses = [0, 18.01056468346, 17.03052] #H2O, NH3\n",
-    "\n",
     "    ions_ = List()\n",
     "\n",
     "    ion_count = 0\n",
@@ -1090,17 +1096,17 @@
     "        frag_type = frag_types[db_indices[db_idx]:db_indices[db_idx+1]]\n",
     "\n",
     "        if db_ints is None:\n",
-    "            db_int = np.zeros(len(db_frag))\n",
+    "            db_int = np.ones(len(db_frag))\n",
     "        else:\n",
     "            db_int = db_ints[i]\n",
     "\n",
-    "        ions = get_hits(query_frag, query_int, db_frag, db_int, frag_type, mtol, ppm, losses)\n",
+    "        ions = get_hits(query_frag, query_int, db_frag, db_int, frag_type, mtol, ppm, LOSSES)\n",
     "\n",
-    "        psms_['o_mass'][i] = query_masses[query_idx] - db_masses[db_idx]\n",
-    "        psms_['o_mass_ppm'][i] = 2 * psms_['o_mass'][i] / (query_masses[query_idx]  + db_masses[db_idx] ) * 1e6\n",
+    "        psms_['prec_offset'][i] = query_masses[query_idx] - db_masses[db_idx]\n",
+    "        psms_['prec_offset_ppm'][i] = 2 * psms_['prec_offset'][i] / (query_masses[query_idx]  + db_masses[db_idx] ) * 1e6\n",
     "\n",
-    "        psms_['o_mass_raw'][i] = query_masses_raw[query_idx] - db_masses[db_idx]\n",
-    "        psms_['o_mass_ppm_raw'][i] = 2 * psms_['o_mass'][i] / (query_masses_raw[query_idx]  + db_masses[db_idx] ) * 1e6\n",
+    "        psms_['prec_offset_raw '][i] = query_masses_raw[query_idx] - db_masses[db_idx]\n",
+    "        psms_['prec_offset_raw_ppm '][i] = 2 * psms_['prec_offset'][i] / (query_masses_raw[query_idx]  + db_masses[db_idx] ) * 1e6\n",
     "\n",
     "        psms_['delta_m'][i] = np.mean(ions[:,4]-ions[:,5])\n",
     "        psms_['delta_m_ppm'][i] = np.mean(2 * psms_['delta_m'][i] / (ions[:,4]  + ions[:,5] ) * 1e6)\n",
@@ -1108,7 +1114,7 @@
     "        psms_['total_int'][i] = np.sum(query_int)\n",
     "        psms_['matched_int'][i] = np.sum(ions[:,2])\n",
     "        psms_['matched_int_ratio'][i] = psms_['matched_int'][i] / psms_['total_int'][i]\n",
-    "        psms_['int_ratio'][i] = np.mean(ions[:,3]/ions[:,2])\n",
+    "        psms_['int_ratio'][i] = np.mean(ions[:,2]/ions[:,3]) #3 is db_int, 2 is query_int\n",
     "\n",
     "        psms_['b_hits'][i] = np.sum(ions[ions[:,1]==0][:,0]>0)\n",
     "        psms_['y_hits'][i] = np.sum(ions[ions[:,1]==0][:,0]<0)\n",
@@ -1132,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1156,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1171,7 +1177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1188,7 +1194,8 @@
     "    frag_tol:float,\n",
     "    prec_tol:float,\n",
     "    ppm:bool,\n",
-    "    m_offset_calibrated:Union[None, float]=None,\n",
+    "    prec_tol_calibrated:Union[None, float]=None,\n",
+    "    frag_tol_calibrated:float = None,\n",
     "    **kwargs\n",
     ") -> (np.ndarray, np.ndarray):\n",
     "    \"\"\"Wrapper function to extract score columns.\n",
@@ -1202,7 +1209,8 @@
     "        frag_tol (float): Fragment tolerance for search.\n",
     "        prec_tol (float): Precursor tolerance for search.\n",
     "        ppm (bool): Flag to use ppm instead of Dalton.\n",
-    "        m_offset_calibrated (Union[None, float], optional): Calibrated offset mass. Defaults to None.\n",
+    "        prec_tol_calibrated (Union[None, float], optional): Calibrated offset mass. Defaults to None.\n",
+    "        frag_tol_calibrated (float, optional): Fragment tolerance if calibration exists. Defaults to None.\n",
     "\n",
     "    Returns:\n",
     "        np.recarray: Recordarray containing PSMs with additional columns.\n",
@@ -1214,6 +1222,9 @@
     "    query_frags = query_data['mass_list_ms2']\n",
     "    query_ints = query_data['int_list_ms2']\n",
     "    query_scans = query_data['scan_list_ms2']\n",
+    "    \n",
+    "    if frag_tol_calibrated:\n",
+    "        frag_tol = frag_tol_calibrated\n",
     "\n",
     "    if 'prec_id2' in query_data.keys():\n",
     "        bruker = True\n",
@@ -1244,7 +1255,7 @@
     "            db_ints = None\n",
     "\n",
     "    if features is not None:\n",
-    "        if m_offset_calibrated:\n",
+    "        if prec_tol_calibrated:\n",
     "            query_masses = features['corrected_mass'].values\n",
     "        else:\n",
     "            query_masses = features['mass_matched'].values\n",
@@ -1253,7 +1264,7 @@
     "\n",
     "        query_mz = features['mz_matched'].values\n",
     "        query_rt = features['rt_matched'].values\n",
-    "        query_charges = query_charges[features['query_idx'].values]\n",
+    "        query_charges = features['charge_matched'].values\n",
     "        query_scans = query_scans[features['query_idx'].values]\n",
     "\n",
     "        if bruker:\n",
@@ -1285,18 +1296,12 @@
     "        query_mz = query_data['mono_mzs2']\n",
     "        query_rt = query_data['rt_list_ms2']\n",
     "\n",
-    "\n",
-    "    loss_dict = Dict()\n",
-    "    loss_dict[''] = 0.0\n",
-    "    loss_dict['-H2O'] = 18.01056468346\n",
-    "    loss_dict['-NH3'] = 17.03052\n",
-    "\n",
-    "    float_fields = ['o_mass', 'o_mass_ppm', 'o_mass_raw','o_mass_ppm_raw','delta_m','delta_m_ppm','matched_int_ratio','int_ratio']\n",
-    "    int_fields = ['total_int','matched_int','n_ions','ion_idx'] + [a+_+'_hits' for _ in loss_dict for a in ['b','y']]\n",
+    "    float_fields = ['prec_offset', 'prec_offset_ppm', 'prec_offset_raw ','prec_offset_raw_ppm ','delta_m','delta_m_ppm','matched_int_ratio','int_ratio']\n",
+    "    int_fields = ['total_int','matched_int','n_ions','ion_idx'] + [a+_+'_hits' for _ in LOSS_DICT for a in ['b','y']]\n",
     "\n",
     "    psms_dtype = np.dtype([(_,np.float32) for _ in float_fields] + [(_,np.int64) for _ in int_fields])\n",
     "\n",
-    "    psms_, ions_,  = score(\n",
+    "    psms_, ions,  = score(\n",
     "        psms,\n",
     "        query_masses,\n",
     "        query_masses_raw,\n",
@@ -1310,8 +1315,8 @@
     "        db_indices,\n",
     "        ppm,\n",
     "        psms_dtype)\n",
-    "\n",
-    "    ions_ = np.vstack(ions_)\n",
+    "    \n",
+    "    ions_ = np.vstack(ions)\n",
     "\n",
     "    for _ in psms_.dtype.names:\n",
     "        psms = add_column(psms, psms_[_], _)\n",
@@ -1370,7 +1375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1436,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1469,7 +1474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1541,15 +1546,22 @@
     "                if calibration == 0:\n",
     "                    logging.info('Calibration is 0, skipping second database search.')\n",
     "                    skip = True\n",
-    "\n",
     "                else:\n",
-    "                    settings['search']['m_offset_calibrated'] = calibration*settings['search']['calibration_std']\n",
-    "                    calib = settings['search']['m_offset_calibrated']\n",
+    "                    settings['search']['prec_tol_calibrated'] = calibration*settings['search']['calibration_std']\n",
+    "                    calib = settings['search']['prec_tol_calibrated']\n",
     "                    logging.info(f\"Found calibrated prec_tol with value {calib:.2f}\")\n",
     "            except KeyError as e:\n",
     "                logging.info(f'{e}')\n",
     "\n",
-    "\n",
+    "            try:\n",
+    "                fragment_std = float(ms_file_.read(dataset_name=\"estimated_max_fragment_ppm\")[0])\n",
+    "                skip = False \n",
+    "                settings['search']['frag_tol_calibrated'] = fragment_std*settings['search']['calibration_std']\n",
+    "                calib = settings['search']['frag_tol_calibrated']\n",
+    "                logging.info(f\"Found calibrated frag_tol with value {calib:.2f}\")\n",
+    "            except KeyError as e:\n",
+    "                logging.info(f'{e}')                \n",
+    "     \n",
     "        if not skip:\n",
     "            db_data_path = settings['experiment']['database_path']\n",
     "\n",
@@ -1594,7 +1606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1660,7 +1672,6 @@
     "\n",
     "            n_frags = sum(lens)\n",
     "\n",
-    "\n",
     "            frags = np.zeros(n_frags, dtype=fragmasses[0].dtype)\n",
     "            frag_types = np.zeros(n_frags, dtype=fragtypes[0].dtype)\n",
     "\n",
@@ -1717,7 +1728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1735,6 +1746,8 @@
     "        pd.DataFrame: Filtered DataFrame.\n",
     "    \"\"\"    \n",
     "    pept_dict_ = {}\n",
+    "    \n",
+    "    temp['temp_idx'] = np.arange(len(temp))\n",
     "\n",
     "    for k, v in temp[['sequence','fasta_index']].values:\n",
     "        if k in pept_dict_:\n",
@@ -1756,7 +1769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1779,19 +1792,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
     "#export\n",
+    "import psutil\n",
+    "import alphapept.constants as constants\n",
+    "from alphapept.fasta import get_fragmass, parse\n",
+    "\n",
+    "def ion_extractor(df: pd.DataFrame, ms_file, frag_tol:float, ppm:bool)->(np.ndarray, np.ndarray):\n",
+    "    \"\"\"Extracts the matched hits (ions) from a dataframe.\n",
+    "\n",
+    "    Args:\n",
+    "        df (pd.DataFrame): Pandas dataframe containing the results of the first search.\n",
+    "        ms_file : MsFile\n",
+    "        frag_tol (float): Fragment tolerance for search.\n",
+    "        ppm (bool): Flag to use ppm instead of Dalton.\n",
+    "\n",
+    "    Returns:\n",
+    "        np.ndarray: Numpy recordarray storing the PSMs.\n",
+    "        np.ndarray: Numpy recordarray storing the ions.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    query_data = ms_file.read_DDA_query_data()\n",
+    "    query_indices = query_data[\"indices_ms2\"]\n",
+    "    query_frags = query_data['mass_list_ms2']\n",
+    "    query_ints = query_data['int_list_ms2']\n",
+    "    \n",
+    "    psms = df.to_records()\n",
+    "    \n",
+    "    ion_count = 0\n",
+    "    \n",
+    "    ions_ = List()\n",
+    "\n",
+    "    for i in range(len(psms)):\n",
+    "        query_idx = psms[i][\"raw_idx\"]\n",
+    "        db_idx = psms[i][\"db_idx\"]\n",
+    "        query_idx_start = query_indices[query_idx]\n",
+    "        query_idx_end = query_indices[query_idx + 1]\n",
+    "        query_frag = query_frags[query_idx_start:query_idx_end]\n",
+    "        query_int = query_ints[query_idx_start:query_idx_end]\n",
+    "\n",
+    "        seq = psms[i]['sequence']\n",
+    "\n",
+    "        db_frag, frag_type = get_fragmass(parse(seq), constants.mass_dict)\n",
+    "        db_int = np.ones_like(db_frag)\n",
+    "\n",
+    "        ions = get_hits(query_frag, query_int, db_frag, db_int, frag_type, frag_tol, ppm, LOSSES)\n",
+    "\n",
+    "        n_ions = len(ions)\n",
+    "\n",
+    "        psms['n_ions'][i] = n_ions\n",
+    "        psms['ion_idx'][i] = ion_count\n",
+    "\n",
+    "        ion_count += n_ions\n",
+    "        ions_.append(ions)\n",
+    "        \n",
+    "    ions_ = np.vstack(ions_)\n",
+    "\n",
+    "    return psms, ions_\n",
+    "\n",
     "\n",
     "#This function is a wrapper and ist tested by the quick_test\n",
-    "def search_parallel(settings: dict, calibration:Union[list, None] = None, callback: Union[Callable, None] = None) -> dict:\n",
+    "def search_parallel(settings: dict, calibration:Union[list, None] = None, fragment_calibration:Union[list, None] = None, callback: Union[Callable, None] = None) -> dict:\n",
     "    \"\"\"Function to search multiple ms_data files in parallel.\n",
+    "    This function will additionally calculate fragments and precursor masses from a given FASTA file.\n",
     "\n",
     "    Args:\n",
     "        settings (dict): Settings file containg the experimental definitions.\n",
-    "        calibration (Union[list, None], optional): List of calibrated offsets.. Defaults to None.\n",
+    "        calibration (Union[list, None], optional): List of calibrated offsets. Defaults to None.\n",
+    "        fragment_calibration (Union[list, None], optional): List of calibrated fragment offsets. Defaults to None.\n",
     "        callback (Union[Callable, None], optional): Callback function. Defaults to None.\n",
     "\n",
     "    Returns:\n",
@@ -1811,17 +1882,27 @@
     "        custom_settings = []\n",
     "        for _ in calibration:\n",
     "            settings_ = copy.deepcopy(settings)\n",
-    "            settings_[\"search\"][\"m_offset_calibrated\"] = _\n",
+    "            settings_[\"search\"][\"prec_tol_calibrated\"] = _\n",
     "            custom_settings.append(settings_)\n",
     "    else:\n",
     "        custom_settings = [settings for _ in ms_file_path]\n",
     "\n",
-    "\n",
+    "    if fragment_calibration:\n",
+    "        for idx, _ in enumerate(fragment_calibration):\n",
+    "            custom_settings[idx][\"search\"][\"frag_tol_calibrated\"] = _\n",
+    "        \n",
+    "        \n",
     "    logging.info(f\"Number of FASTA entries: {len(fasta_list):,} - FASTA settings {settings['fasta']}\")\n",
     "    to_process = [(idx_start, fasta_list[idx_start:idx_end], ms_file_path, custom_settings) for idx_start, idx_end in block_idx(len(fasta_list), fasta_block)]\n",
     "\n",
+    "    memory_available = psutil.virtual_memory().available/1024**3\n",
+    "\n",
+    "    n_processes = int(memory_available // 4 )\n",
+    "    \n",
+    "    logging.info(f'Setting Process limit to {n_processes}')\n",
+    "        \n",
     "    n_processes = alphapept.performance.set_worker_count(\n",
-    "        worker_count=settings['general']['n_processes'],\n",
+    "        worker_count=n_processes,\n",
     "        set_global=False\n",
     "    )\n",
     "\n",
@@ -1831,42 +1912,56 @@
     "        ms_file = alphapept.io.MS_Data_File(_+'_', is_new_file=True) #Create temporary files for writing\n",
     "        \n",
     "    df_cache = {} \n",
+    "    ion_cache = {}\n",
     "                \n",
     "    with alphapept.performance.AlphaPool(n_processes) as p:\n",
     "        max_ = len(to_process)\n",
     "\n",
-    "        for i, (_, n_seqs) in enumerate(p.imap_unordered(search_fasta_block, to_process)):\n",
+    "        for i, (psm_container, n_seqs) in enumerate(p.imap_unordered(search_fasta_block, to_process)):\n",
     "            n_seqs_ += n_seqs\n",
     "\n",
-    "\n",
     "            logging.info(f'Block {i+1} of {max_} complete - {((i+1)/max_*100):.2f} % - created peptides {n_seqs:,} ')\n",
-    "            for j in range(len(_)): #Temporary hdf files for avoiding saving issues\n",
-    "                output = [_ for _ in _[j]]\n",
+    "            for j in range(len(psm_container)): #Temporary hdf files for avoiding saving issues\n",
+    "                output = [_ for _ in psm_container[j]]\n",
     "                if len(output) > 0:\n",
-    "\n",
     "                    psms = pd.concat(output)\n",
-    "\n",
     "                    if ms_file_path[j] in df_cache:\n",
-    "                        df_cache[ms_file_path[j]] = filter_top_n(pd.concat([df_cache[ms_file_path[j]], psms]))\n",
+    "                        temp = filter_top_n(pd.concat([df_cache[ms_file_path[j]], psms]))\n",
+    "                        selector = temp['temp_idx'].values\n",
+    "                        df_cache[ms_file_path[j]] = temp\n",
     "                    else:\n",
     "                        df_cache[ms_file_path[j]] = psms\n",
-    "\n",
-    "\n",
+    "                                                     \n",
     "            if callback:\n",
     "                callback((i+1)/max_)\n",
     "\n",
-    "    for _ in ms_file_path:\n",
+    "    for idx, _ in enumerate(ms_file_path):\n",
     "        if _ in df_cache:\n",
     "            x = df_cache[_]\n",
     "            ms_file = alphapept.io.MS_Data_File(_)\n",
     "\n",
     "            x['fasta_index'] = x['fasta_index'].apply(lambda x: ','.join(str(_) for _ in x))\n",
-    "\n",
+    "            \n",
+    "            \n",
+    "            if 'frag_tol_calibrated' in custom_settings[idx]['search']:\n",
+    "                frag_tol = custom_settings[idx]['search']['frag_tol_calibrated']\n",
+    "            else:\n",
+    "                frag_tol = custom_settings[idx]['search']['frag_tol']\n",
+    "                \n",
+    "            ppm = custom_settings[idx]['search']['ppm']\n",
+    "            \n",
     "            if calibration:\n",
-    "                store_hdf(x, ms_file, 'second_search', replace = True)\n",
+    "                save_field = 'first_search'\n",
     "            else:\n",
-    "                store_hdf(x, ms_file, 'first_search', replace = True)\n",
+    "                save_field = 'second_search'\n",
+    "                \n",
+    "            psms, ions = ion_extractor(x, ms_file, frag_tol, ppm)\n",
     "\n",
+    "            store_hdf(pd.DataFrame(psms), ms_file, save_field, replace=True)\n",
+    "            ion_columns = ['ion_index','ion_type','ion_int','db_int','ion_mass','db_mass','query_idx','db_idx']\n",
+    "            store_hdf(pd.DataFrame(ions, columns = ion_columns), ms_file, 'ions', replace=True)\n",
+    "            \n",
+    "    #Todo? Callback\n",
     "    logging.info(f'Complete. Created peptides {n_seqs_:,}')\n",
     "\n",
     "    return fasta_dict"
@@ -1874,7 +1969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1884,7 +1979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1908,7 +2003,10 @@
       "Converted additional_code.ipynb.\n",
       "Converted contributing.ipynb.\n",
       "Converted file_formats.ipynb.\n",
-      "Converted index.ipynb.\n"
+      "Converted index.ipynb.\n",
+      "Converted Untitled.ipynb.\n",
+      "Converted Untitled1.ipynb.\n",
+      "Converted Untitled2.ipynb.\n"
      ]
     }
    ],
@@ -1921,9 +2019,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:alphapept] *",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-alphapept-py"
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.11"
   }
  },
  "nbformat": 4,
diff --git a/nbs/06_score.ipynb b/nbs/06_score.ipynb
index ac1bef06..c92ef749 100644
--- a/nbs/06_score.ipynb
+++ b/nbs/06_score.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -534,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -559,12 +559,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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/+9+pW7cuF154IWPGjNliKoBHHnmEPfbYgyeeSGYIXbGi/K7CyuQEY2ZWhu7du9O2bdty66xcuZJ//OMfnHLKKRvL1qxZU+Gxi4uLOe2001i4cCH//ve/NzlPnz59qF+/PgAvvfTSxmH8e/fuTZMmTQB49tlnmTZtGt26dQNg1apVW4x3Bsn8Lz/72c+4/PLLOemkkzjssMMqjK2yOMGYmZVh55133rhcp04dNmzYsHG9ZDj9DRs20Lhx400mD8vHj3/8Yy699FL69OnDpEmTNna3bX7eskQEAwYM4H//93/LrbfPPvswffp0nnzySX7xi1/Qq1cvrr766q2KdVv5HoyZGVsOa7+5li1bsmTJEpYtW8aaNWt4/PHHAdhll11o27Ytf/rTn4DkF/+//vWvCo+fOx3A6NGjyzxvz549GTduHAATJkzgk08+AaBXr178+c9/3jgVwPLly/nggw+ATacC+Oijj2jQoAFnnXUWl112GdOnT8/vA6kEbsGYWfVUwWPFla1Zs2b07NmTDh06cPzxx28xRH/dunW5+uqr6d69O61atWK//fbbuG3MmDFccMEFXHfddaxdu5bTTz99i2HwNx82f9iwYZxyyik0adKEo48+mvfff7/UuK655hrOOOMM/vjHP3LIIYfwta99jUaNGtG8eXOuu+46jj32WDZs2EDdunW588472XvvvTeZCuCcc87hsssuY4cddqBu3bqMGDGi8j+8Mni4fttuHq7fKoOH6y/dmjVrKCoqok6dOvzzn//kggsu2OruuK1V7YfrlzRK0hJJb+aUDZO0QNLr6euEnG1XSJoj6R1Jx+WU907L5kgamlPeVtLLafnDknbM6lrMzArlww8/pFu3bnTs2JFLLrmEu+++u9Ah5S3LLrL7gDuA+zcrvzkiNhlvWlJ74HRgf2APYKKkfdLNdwLHAMXAq5Iei4iZwG/SY42VNBL4IVB1bT8zsyrQrl07XnvttUKHsU0ya8FExGRgeZ7VTwbGRsSaiHgfmAN0T19zImJuRPwbGAucLEnA0cCf0/1HA30rM34zM9s+hXiK7GJJb6RdaE3SslbA/Jw6xWlZWeXNgE8jYt1m5aWSNFjSVElTly5dWlnXYWZm5ajqBDMC+DrQCVgI3FQVJ42IuyKia0R0bdGiRVWc0szsK69KH1OOiMUly5LuBh5PVxcAe+ZUbZ2WUUb5MqCxpDppKya3vpmZVQNVmmAk7R4RC9PVfkDJE2aPAQ9K+h3JTf52wCuAgHaS2pIkkNOB70dESHoe+B7JfZkBwKNVdyVmlrV8HlPfGlv7SPuwYcM2GTvMtl5mCUbSQ8CRQHNJxcA1wJGSOgEBzAPOA4iItySNA2YC64CLImJ9epyLgaeBImBURLyVnuJyYKyk64DXgHuyuhYzM9t6WT5FdkZE7B4RdSOidUTcExFnR8QBEXFgRPTJac0QEddHxNcjYt+I+FtO+ZMRsU+67fqc8rkR0T0ivhERp0RExaPLmZmV4/rrr2efffbh0EMP5Z133gHKHop/8eLF9OvXj44dO9KxY8eNc7387ne/o0OHDnTo0IFbbrkFgKuvvnrjMsBVV13FrbfeysKFCzn88MPp1KkTHTp04MUXX6zS682ah4oxMwOmTZvG2LFjef3111m3bh1dunThoIMOKnMo/ksuuYQjjjiC8ePHs379elauXMm0adO49957efnll4kIevTowRFHHMGgQYPo378/P/nJT9iwYQNjx47llVde4b777uO4447jqquuYv369Xz55ZeF/hgqlROMmRnw4osv0q9fPxo0aAAkQ+avXr26zKH4n3vuOe6/P/keeVFREbvuuisvvfQS/fr12zgacv/+/XnxxRe55JJLaNasGa+99hqLFy+mc+fONGvWjG7dujFo0CDWrl1L37596dSpU9VedMacYMzMyrCtQ/GX5txzz+W+++5j0aJFDBo0CIDDDz+cyZMn88QTTzBw4EAuvfRSzjnnnO0+V3Xh4frNzEh+2f/lL39h1apVfP755/z1r3+lQYMGZQ7F36tXr40jE69fv54VK1Zw2GGH8Ze//IUvv/ySL774gvHjx2+c4Ktfv3489dRTvPrqqxx3XDLc4gcffEDLli350Y9+xLnnnlulQ+lXBbdgzKxaquqRsrt06cJpp51Gx44d2W233TbOFFnWUPy33norgwcP5p577qGoqIgRI0ZwyCGHMHDgQLp37w4krZbOnTsDsOOOO3LUUUfRuHFjioqKgGRa5htvvJG6devSsGHDjV1utYWH67ft5uH6rTLU9uH6N2zYQJcuXfjTn/5Eu3btCh1Ouar9cP1mZpaYOXMm3/jGN+jVq1e1Ty6VyV1kZmYZa9++PXPnzi10GFXOLRgzqza+al321VFl/hu4BWO1QpuhT1RYZ94NJ1ZYxwqnXr16LFu2jGbNmpFM+WRVLSJYtmwZ9erVq5TjOcFYlXIisLK0bt2a4uJiPGdTYdWrV4/WrVtXyrGcYMysWqhbty5t27YtdBhWiXwPxszMMuEEY2ZmmXCCMTOzTDjBmJlZJpxgzMwsE04wZmaWCScYMzPLhBOMmZllwgnGzMwykVmCkTRK0hJJb+aU3ShplqQ3JI2X1DgtbyNplaTX09fInH0OkjRD0hxJtykdpEhSU0nPSJqdvjfJ6lrMzGzrZdmCuQ/ovVnZM0CHiDgQeBe4ImfbexHRKX2dn1M+AvgR0C59lRxzKPBsRLQDnk3XzcysmsgswUTEZGD5ZmUTImJdujoFKHdENUm7A7tExJRIxpC+H+ibbj4ZGJ0uj84pNzOzaqCQ92AGAX/LWW8r6TVJL0g6LC1rBRTn1ClOywBaRsTCdHkR0LKsE0kaLGmqpKkeqdXMrGoUJMFIugpYB4xJixYCe0VEZ+BS4EFJu+R7vLR1U+YsORFxV0R0jYiuLVq02I7IzcwsX1U+XL+kgcBJQK80MRARa4A16fI0Se8B+wAL2LQbrXVaBrBY0u4RsTDtSltSRZdgZmZ5qNIWjKTewM+BPhHxZU55C0lF6fJ/kdzMn5t2gX0m6eD06bFzgEfT3R4DBqTLA3LKzcysGsisBSPpIeBIoLmkYuAakqfGdgKeSZ82npI+MXY4cK2ktcAG4PyIKHlA4EKSJ9Lqk9yzKblvcwMwTtIPgQ+AU7O6FjMz23qZJZiIOKOU4nvKqPsI8EgZ26YCHUopXwb02p4YzcwsO/4mv5mZZcIJxszMMuEEY2ZmmXCCMTOzTDjBmJlZJpxgzMwsE04wZmaWCScYMzPLhBOMmZllwgnGzMwy4QRjZmaZcIIxM7NMOMGYmVkmnGDMzCwTTjBmZpYJJxgzM8uEE4yZmWXCCcbMzDLhBGNmZplwgjEzs0w4wZiZWSYyTTCSRklaIunNnLKmkp6RNDt9b5KWS9JtkuZIekNSl5x9BqT1Z0sakFN+kKQZ6T63SVKW12NmZvnLugVzH9B7s7KhwLMR0Q54Nl0HOB5ol74GAyMgSUjANUAPoDtwTUlSSuv8KGe/zc9lZmYFkmmCiYjJwPLNik8GRqfLo4G+OeX3R2IK0FjS7sBxwDMRsTwiPgGeAXqn23aJiCkREcD9OccyM7MCK8Q9mJYRsTBdXgS0TJdbAfNz6hWnZeWVF5dSvgVJgyVNlTR16dKl238FZmZWobwSjKRn8ynbWmnLI7b3OHmc566I6BoRXVu0aJH16czMjAoSjKR66T2Q5pKapDfom0pqQxmthTwsTru3SN+XpOULgD1z6rVOy8orb11KuZmZVQMVtWDOA6YB+6XvJa9HgTu28ZyPASVPgg1Ij1VSfk76NNnBwIq0K+1p4Ng0wTUBjgWeTrd9Jung9Omxc3KOZWZmBVanvI0RcStwq6QfR8TtW3twSQ8BR5K0gIpJnga7ARgn6YfAB8CpafUngROAOcCXwA/SGJZL+hXwalrv2ogoeXDgQpIn1eoDf0tfZmZWDZSbYEpExO2SvgW0yd0nIu6vYL8zytjUq5S6AVxUxnFGAaNKKZ8KdCgvBjMzK4y8EoykPwJfB14H1qfFJY8Gm5mZbSGvBAN0BdqnrQwzM7MK5fs9mDeBr2UZiJmZ1S75tmCaAzMlvQKsKSmMiD6ZRGVmZjVevglmWJZBmJlZ7ZPvU2QvZB2ImZnVLvk+RfY5/xnSZUegLvBFROySVWBmZlaz5duCaVSynH5r/mTg4KyCMjOzmm+rR1NOh9P/C8kw+mZmZqXKt4usf87qDiTfi1mdSURmZlYr5PsU2XdyltcB80i6yczMzEqV7z2YH2QdiJmZ1S75TjjWWtJ4SUvS1yOSWle8p5mZfVXle5P/XpL5WvZIX39Ny8zMzEqVb4JpERH3RsS69HUf4LmHzcysTPkmmGWSzpJUlL7OApZlGZiZmdVs+SaYQSQzTy4CFgLfAwZmFJOZmdUC+T6mfC0wICI+AZDUFBhOknjMzMy2kG8L5sCS5AIQEcuBztmEZGZmtUG+CWYHSU1KVtIWTL6tHzMz+wrKN0ncBPxT0p/S9VOA67MJyczMaoO8WjARcT/QH1icvvpHxB+35YSS9pX0es7rM0k/kTRM0oKc8hNy9rlC0hxJ70g6Lqe8d1o2R9LQbYnHzMyykXc3V0TMBGZu7wkj4h2gE4CkImABMB74AXBzRAzPrS+pPXA6sD/JlzwnSton3XwncAxQDLwq6bE0TjMzK7BC30fpBbwXER8k08yU6mRgbESsAd6XNAfonm6bExFzASSNTes6wZiZVQNbPR9MJTsdeChn/WJJb0galfNQQStgfk6d4rSsrPItSBosaaqkqUuXLq286M3MrEwFSzCSdgT6ACUPDowAvk7SfbaQ5MGCShERd0VE14jo2qKFR7gxM6sKhewiOx6YHhGLAUreASTdDTyeri4A9szZr3VaRjnlZmZWYIXsIjuDnO4xSbvnbOsHvJkuPwacLmknSW2BdsArwKtAO0lt09bQ6WldMzOrBgrSgpG0M8nTX+flFP9WUicgSGbMPA8gIt6SNI7k5v064KKIWJ8e52LgaaAIGBURb1XVNZiZWfkKkmAi4gug2WZlZ5dT/3pK+WJnRDwJPFnpAZqZ2XYr9FNkZmZWSznBmJlZJpxgzMwsE4X+Jr9VkQNGH1BhnRkDZlRBJIXnz8KsargFY2ZmmXCCMTOzTDjBmJlZJpxgzMwsE04wZmaWCScYMzPLhBOMmZllwgnGzMwy4QRjZmaZcIIxM7NMOMGYmVkmnGDMzCwTTjBmZpYJJxgzM8uEE4yZmWXC88HYFtoMfaLCOvNuOLEKIjGzmswJxqwcTrZm265gXWSS5kmaIel1SVPTsqaSnpE0O31vkpZL0m2S5kh6Q1KXnOMMSOvPljSgUNdjZmabKvQ9mKMiolNEdE3XhwLPRkQ74Nl0HeB4oF36GgyMgCQhAdcAPYDuwDUlScnMzAqr0AlmcycDo9Pl0UDfnPL7IzEFaCxpd+A44JmIWB4RnwDPAL2rOGYzMytFIRNMABMkTZM0OC1rGREL0+VFQMt0uRUwP2ff4rSsrPJNSBosaaqkqUuXLq3MazAzszIU8ib/oRGxQNJuwDOSZuVujIiQFJVxooi4C7gLoGvXrpVyTDMzK1/BWjARsSB9XwKMJ7mHsjjt+iJ9X5JWXwDsmbN767SsrHIzMyuwgiQYSTtLalSyDBwLvAk8BpQ8CTYAeDRdfgw4J32a7GBgRdqV9jRwrKQm6c39Y9MyMzMrsEJ1kbUExksqieHBiHhK0qvAOEk/BD4ATk3rPwmcAMwBvgR+ABARyyX9Cng1rXdtRCyvusswM7OyFCTBRMRcoGMp5cuAXqWUB3BRGccaBYyq7BjNzGz7VLfHlM3MrJZwgjEzs0w4wZiZWSacYMzMLBNOMGZmlgknGDMzy4QTjJmZZcIJxszMMuEZLc1qm2G75lFnRfZx2FeeWzBmZpYJJxgzM8uEE4yZmWXCCcbMzDLhBGNmZplwgjEzs0w4wZiZWSacYMzMLBNOMGZmlgknGDMzy4QTjJmZZcIJxszMMlHlCUbSnpKelzRT0luShqTlwyQtkPR6+johZ58rJM2R9I6k43LKe6dlcyQNreprMTOzshViNOV1wM8iYrqkRsA0Sc+k226OiOG5lSW1B04H9gf2ACZK2ifdfCdwDFAMvCrpsYiYWSVXYWZm5aryBBMRC4GF6fLnkt4GWpWzy8nA2IhYA7wvaQ7QPd02JyLmAkgam9Z1gjEzqwYKeg9GUhugM/ByWnSxpDckjZLUJC1rBczP2a04LSur3MzMqoGCTTgmqSHwCPCTiPhM0gjgV0Ck7zcBgyrpXIOBwQB77bVXZRzS7KvHE5nZVipIgpFUlyS5jImI/wOIiMU52+8GHk9XFwB75uzeOi2jnPJNRMRdwF0AXbt2jUq4BLPt51/YVssV4ikyAfcAb0fE73LKd8+p1g94M11+DDhd0k6S2gLtgFeAV4F2ktpK2pHkQYDHquIazMysYoVowfQEzgZmSHo9LbsSOENSJ5IusnnAeQAR8ZakcSQ379cBF0XEegBJFwNPA0XAqIh4q+ouw8zMylOIp8heAlTKpifL2ed64PpSyp8sbz8zMyscf5PfzMwy4QRjZmaZcIIxM7NMOMGYmVkmnGDMzCwTTjBmZpYJJxgzM8uEE4yZmWXCCcbMzDLhBGNmZplwgjEzs0w4wZiZWSYKNuGYle6A0QdUWGfGgBlVEIltC//7mf2HWzBmZpYJJxgzM8uEu8hqsDZDn6iwzrwbTqyCSMzMtuQWjJmZZcItGLNyzKv3/Txqrcg8jlpv2K551PHnXNO4BWNmZplwgjEzs0w4wZiZWSacYMzMLBM1PsFI6i3pHUlzJA0tdDxmZpao0QlGUhFwJ3A80B44Q1L7wkZlZmZQ8x9T7g7MiYi5AJLGAicDM7M+8daMOeUvRGavtj9O7DHOrCZSRBQ6hm0m6XtA74g4N10/G+gRERdvVm8wMDhd3Rd4J4NwmgMfZ3DcLNW0mGtavOCYq0JNixdqZsz7RkSjrdmhprdg8hIRdwF3ZXkOSVMjomuW56hsNS3mmhYvOOaqUNPihZob89buU6PvwQALgD1z1lunZWZmVmA1PcG8CrST1FbSjsDpwGMFjsnMzKjhXWQRsU7SxcDTQBEwKiLeKlA4mXbBZaSmxVzT4gXHXBVqWrzwFYm5Rt/kNzOz6qumd5GZmVk15QRjZmaZcILJgKSfSQpJzQsdS0Uk3ShplqQ3JI2X1LjQMZWmpg0JJGlPSc9LminpLUlDCh1TPiQVSXpN0uOFjiUfkhpL+nP6M/y2pEMKHVN5JP00/Xl4U9JDkuoVOqbNSRolaYmkN3PKmkp6RtLs9L1JPsdygqlkkvYEjgU+LHQseXoG6BARBwLvAlcUOJ4t1NAhgdYBP4uI9sDBwEU1IGaAIcDbhQ5iK9wKPBUR+wEdqcaxS2oFXAJ0jYgOJA8mnV7YqEp1H9B7s7KhwLMR0Q54Nl2vkBNM5bsZ+DlQI56eiIgJEbEuXZ1C8l2i6mbjkEAR8W+gZEigaisiFkbE9HT5c5JffK0KG1X5JLUGTgT+UOhY8iFpV+Bw4B6AiPh3RHxa0KAqVgeoL6kO0AD4qMDxbCEiJgPLNys+GRidLo8G+uZzLCeYSiTpZGBBRPyr0LFso0HA3wodRClaAfNz1oup5r+sc0lqA3QGXi5wKBW5heSPow0FjiNfbYGlwL1pt94fJO1c6KDKEhELgOEkvRsLgRURMaGwUeWtZUQsTJcXAS3z2ckJZitJmpj2n27+Ohm4Eri60DFuroKYS+pcRdKtM6ZwkdY+khoCjwA/iYjPCh1PWSSdBCyJiGmFjmUr1AG6ACMiojPwBXl23RRCet/iZJLEuAews6SzChvV1ovkuy159dDU6C9aFkJEfLu0ckkHkPzg/EsSJF1N0yV1j4hFVRjiFsqKuYSkgcBJQK+onl+MqpFDAkmqS5JcxkTE/xU6ngr0BPpIOgGoB+wi6YGIqM6/AIuB4ogoaRn+mWqcYIBvA+9HxFIASf8HfAt4oKBR5WexpN0jYqGk3YEl+ezkFkwliYgZEbFbRLSJiDYkP/xdCp1cKiKpN0m3SJ+I+LLQ8ZShxg0JpOSvjHuAtyPid4WOpyIRcUVEtE5/dk8HnqvmyYX0/9Z8SfumRb2ogqk6tsOHwMGSGqQ/H72oxg8lbOYxYEC6PAB4NJ+d3IKxO4CdgGfSlteUiDi/sCFtqpoNCZSvnsDZwAxJr6dlV0bEk4ULqVb6MTAm/cNjLvCDAsdTpoh4WdKfgekk3dGvUQ2HjJH0EHAk0FxSMXANcAMwTtIPgQ+AU/M6VvXsETEzs5rOXWRmZpYJJxgzM8uEE4yZmWXCCcbMzDLhBGNmZplwgjEzs0w4wZhVc+nAiGY1jhOMWQYk7SzpCUn/Ssd9O01SN0n/SMtekdRIUj1J90qakQ7YeFS6/0BJj0l6Dng2Pd6odL/XcseRM6uu/JeRWTZ6Ax9FxImwcWj514DTIuJVSbsAq0jmX4mIOEDSfsAESfukx+gCHBgRyyX9mmT4lkHppHCvSJoYEV9U9YWZ5cstGLNszACOkfQbSYcBewELI+JVgIj4LJ2H51DSwQ4jYhbJMBwlCeaZiCiZl+NYYGg67MwkkgEp96qiazHbJm7BmGUgIt6V1AU4AbgOeG4bDpPbOhHw3Yh4pzLiM6sKbsGYZUDSHsCXEfEAcCPQA9hdUrd0e6P05v2LwJlp2T4krZLSksjTwI/TUXiR1Dn7qzDbPm7BmGXjAOBGSRuAtcAFJK2Q2yXVJ7n/8m3g98AISTNIRtgdGBFr0jyS61ckM06+IWkH4H2SOXzMqi2PpmxmZplwF5mZmWXCCcbMzDLhBGNmZplwgjEzs0w4wZiZWSacYMzMLBNOMGZmlon/B7iINfv4nkUOAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -589,12 +589,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -619,12 +619,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -636,7 +636,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -660,12 +660,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -677,7 +677,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -689,7 +689,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -701,7 +701,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -727,12 +727,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -781,7 +781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -832,12 +832,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -864,12 +864,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -881,7 +881,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -905,12 +905,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -922,7 +922,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -934,7 +934,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -946,7 +946,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -972,12 +972,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -989,7 +989,7 @@
     },
     {
      "data": {
-      "image/png": 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1MpSkFpGGYcKEICisXRtMj/H881UKDtGJ51i1BAhWeKsryeh4VIMQkfrt+++DZqR//xu6dIFVq4IBbxWIlVeo7Cjoml7hrbapBiEi9ZM7jBkD++4Lr7wS5Bk++QT22KPCU+N1US1v3qRIda0pKR7VIESk/ho/PggQTzwBe++d8Gmxagr5BUVkmlEUY2hATnYWzZs0SsmiPsmkACEi9UdxMTz6KJx2WpCIHjcOttsOMspvLIluTopXUyhyj5l4TsdR0DVBTUwiUj988UWw3OeVV5aeXC+B4BDdnGRxji1JNNflxHNlqAYhInVbQQEMHx5MqNe8ebAu9G9/G/PQRBPPDlj4b4mSvEJdTzxXhgKEiNRtw4bBbbdB377BqOidd455WLwBbfF6JTlBDaG+5RUqI6kBwsxOBv4GZAJPuPuwqP1NgLFAN2AVcK67LzGzLOAJ4KCwjGPdfWgyyyoidcjmzUH31fbtYeBAOOAAOPPMck+pbOI53eZMSoWkBQgzywQeBk4ElgHTzGySu8+POOwSYI27dzSzfsC9wLnAOUATd+9sZs2A+WY2zt2XJKu8IlJHvP9+MLlekyYwaxa0ahUzOFQ38VwfuqlWVzKT1IcAi9x9sbtvBcYD0e/imcCY8PELQA8zK2n6a25mjYBsYCuwPollFZF099NPwYC3o4+GrVvh/vvjTq6nxHPNSGYTUy6wNOL5MuDQeMe4e6GZrQNaEwSLM4HvgGbANe6+OvoFzKw/0B+gXbt2NV1+EUkXCxcGk+stXQpXXQV//jO0aBH3cCWea0a6JqkPAYqANsD2wHtm9l93Xxx5kLuPBEZCsB5ErZdSRJKrZHK99u3hsMOCgW+HH17qkMpMta3Ec+UkM0DkAW0jnu8abot1zLKwOakVQbL6fOB1dy8AfjSzD4DuwGJEpP5zD+ZOuucemDo1GM/w7LNlDqvsVNtKPFdOMnMQ04A9zayDmTUG+gGToo6ZBFwUPu4LTPVgibtvgeMBzKw5cBjwRRLLKiLp4rvv4Oyz4ZxzgtrDqlVxD43XM8k9aD6KpMRz5SUtQLh7ITAAmAJ8Djzn7vPMbIiZnREeNgpobWaLgGuBweH2h4EWZjaPINA86e5zklVWEUkD7vDkk8HcSa+9BvfeCx9/XO7kevGakurDVNvpQGtSi0h6cIdTT4WNG4PJ9fbaq8JT6sq6z+msvDWpNReTiKROURE8+CAsWRI0J40fD2+/nVBwABjUs5OakpIoXXsxiUh9N38+XHopfPQRrFkDt98eDHqLI1ZvpZImo3jbpXoUIESkdhUUBPmFu+6Cli2DdaHPP7/cU+L1VgI0hiGJ1MQkIrVr2DC49VY466ygFnHBBUHzUjni9VYaPmVBMkva4KkGISLJl58fTK7XoUMwuV7XrnD66XEPT3QepXi9mKRmKECISHK9+26Qa2jadNvkemFwiJVXAMo0J0VPkVGiTU52Ld1Ew6QAISLJsX49DB4MjzwS1BxGjCg1uV68vELTrIxKzaMkyaMAISI1b+FC6NEDli2Da64JEtLNm5c6JF5eQQv4pA8FCBGpOZGT6x15ZDDz6mGHxTy0svkDDX6rfQoQIlJ97vDcc3DPPbz64DPc89EPLN/tQtq8vYlBTfLo3TW3TL4h3oR6OdlZbCks1gI+aUABQkSqJy8P/vhHmDSJNfsdyAPPf0Jei18Gu8K8wvRvVvPvGXml8g1ZGUZWplFQtC2zkJ2VyR1n7Ado8Fs6UIAQkapxD+ZMuv76YPDbX/7CGVsOYOn6raUOyy8oYtzHS8us+1xQ7ORkZ9G8SaOYgUABIfUUIESk6iZOhIMOgscfh44dWTb41ZiHRQeHEuvyC/j09pOSWECpDgUIEUlcURE89BCceSZ06MArg//KsPeWkffEAtrkfBs3r5BpFjNIaBxDetNUGyKSmM8+C3omXXMNjB3LxFl5DPrPEpat34IT5BU2bC4kK7P0tBnZWZmcd2hbzbpaBylAiEj5tm7l88uvo6BLV1bN+Zzbz72Fib0uiTmOoaDYad64UZmFev7cu7MW8KmDtGCQiJTr8yuuZ59H/8rEfY9lSI/+rG7WiuyszLgD2gz4ethptVtIqbLyFgxSDkJEytq0KVgbeo89GLjTseT2bcHbexz88+78giLlFRoANTGJSGlvvQWdO0Pv3lBUxKLNGaWCQ4kid+UV6jkFCBEJrFvH12dfCMcfz5LV+Qw4/PdMnPN93BpBSR5BeYX6S01MIgJffkn+UcfQbuUKHjukDyOOOp/NWU15c8Jczu6WW2oUNGyrKWg1t/pNAUKkISsuhowM6NCBd3P35+FTTmPOLnv9vDu/oIi3vljB0D6dNfVFA6QAIdIQuTN96MPkPHQ/Z587lBY770hez2tiHrp8bb5qCg2UAoRIPRc9i+qtXVrS5Z6b6f7ef5m1Sydabt7AsrUttGqblKEAIVKPlVq1zZ1j357AUbePJtOLuev4S3myWy+KM4KeSFq1TaIpQIjUY9GjnXss+oTZu+zJ4JMHsjRn5zLHa9U2iaQAIVKP/bB6A5dNe4nXOx3B0pydGdhrEBsbZwervsWgVdskUlLHQZjZyWa2wMwWmdngGPubmNmz4f6Pzax9xL4DzOwjM5tnZnPNrGkyyypS78yZw8vP3MAtb4+m97y3ANjYpBmYkZOdpUFuUqFKBwgzyzGzWxI4LhN4GDgF2Bc4z8z2jTrsEmCNu3cERgD3huc2Av4FXO7u+wHHAWXnEBaRsrZsgdtug27d6JC/iqv73MxDR/T7eXfJqm0a5CYVidvEZGZtgVuBNsBEYBwwBPhN+LgihwCL3H1xeL3xwJnA/IhjzgTuCB+/APzdzAw4CZjj7rMB3H1Vwnck0tDdey/cdRf85jc0HTGC477dzLQ4YxgUEKQ85eUgxgLvAP8GTgamA58CB7j79wlcOxdYGvF8GXBovGPcvdDM1gGtgb0AN7MpwI7AeHe/L/oFzKw/0B+gXbt2CRRJpJ7auBG+/x722AOuvhoOPRR69gSgd2sFAqma8gLEDu5+R/h4ipmdA1zg7sXJLxaNgKOAg4FNwJvhlLRvRh7k7iOBkRBM910L5RJJC5FjG3qt/Jyhrz9E8+23Y+KTrzL8v4tYvraQNrOmqheSVEu5vZjMbHuCrtEAq4BWYRMQ7r66gmvnAW0jnu8abot1zLIw79AqfJ1lwLvuvjIsx2TgIOBNRBq4krENWT+tY+hbo+k35z8s2SGXcVdcy19fmv9zt9a8tfncNGEuoBqEVE15AaIVMINtAQJgZvivA7tXcO1pwJ5m1oEgEPQDzo86ZhJwEfAR0BeY6u4lTUs3mFkzYCtwLEESW6RBiR4FPahnJ4ZPWcAu33/DuPE303rjWh45tC8PHHkehRuaUuSlF/HJLyhi+JQFChBSJXEDhLu3r86Fw5zCAGAKkAmMdvd5ZjYEmO7uk4BRwD/NbBGwmiCI4O5rzOx+giDjwGR3f7U65RGpa0qNgiaoEdz879lsKnQa5ezM+7sdyJPdz+SznTsGJ8RZHXL52vzaKrLUM+UuORo2+5wC7B1umg9McffCWihbpWjJUakrYtUKenfNLbN909ZC1mwKe3e7c9a8t7jify/Q78L7WN20RZnrxlvhTYPfpDxVWnLUzHKBqcB3wCyCpqbTgfvN7FfuvjwZhRWpz2LVCm6aMJfp36wuteZCXsS3/jbrf+TuKQ/zq8UzmNFmb5pt2Uh+y1Zl1mcob90GkaooLwdxN/CIuz8QudHMBgJDCXIHIlIJ0XMjQZAnGPfx0rLf/t25cNZkBr/zFObO7Sf8gX92PZVddmjB0DAXEV0L6b7bDlq3QWpMeQHiMHf/XfRGd3/QzBYkr0gi9Ve8fECspiGA4xZPZ2abvbn55AEsa7VThSu5ad0GqUnlBYjyMlubarogIvVRdF4hp1nWtrxChJL8QWZxEZdOe5HJnY5iac7O3PrrW8ho2YLl6zaTqxqB1LJyu7maWZ8Y2w3YLknlEamTYiWegTL5hqwMIyvTKCjaVmMoyR98Nvk97pp0P51/+IqsokJGHXsBN/bpooAgKVNegHgX6FXOPhEhfuK5aVZGmXxDQbGTk51F8yaNfg4mNx63G2e88iTFT97LmqYtuaL3Tcw59ASGqrYgKVbeOIjf1WI5ROqseInn6G0l1uUX8OntJ23bcOedcPfdZFx0Ea3vv59HdtghmcUVSVjc6b7N7KmIx+qxJBJHZQeitcnJhg0bYOHCYMM118B//gNPPQUKDpJGylsP4sCIx1cluyAidVWbnOyY2+MtynPfdt/D/vtDnz5QXAzbbQcnnlgbRRWplPIChGZHFUnAoJ6dYgaC6EV59m5cwOuzn+TIKy+Apk3h0UchI6mLOopUS3lJ6l3N7EGCXkslj3/m7gOTWjKROqIkkRxvgFrvrrnwxRdw3HGwciXcfDPcemsQJETSWHkBYlDEY01yJFKOuAPUiouDWkLHjnDKKXDVVdClS62XT6QqyuvFNKY2CyJSr7jDmDFw333w/vtB8vnJJ1NdKpFKKXfBIBEpK95srD9bsgT+8IegZ9JRR8FPP6l3ktRJypCJVELJoLi8tfk42wbFTZyVF9Qa/v73oIfShx8Gj995B3bbLdXFFqkSBQiRSog3KG74lHD+ypJaw2efwZVXqpeS1GkV/vWa2V5m9qaZfRY+P8DM/pT8oomkn+hBcY2KCvnjR8/R6OvFYAbjxsFrr6nWIPVCIl9vHgduAgoA3H0O4dKgIg1N5KC4/b5fxEtjr+WGd8dy/pKPgo3NmweBQqQeSCRANHP3T6K2pd2SoyK1YVDPTuRQyA3vPMVLY69lx41r+L++f2KnYUNSXTSRGpdIL6aVZrYH4chqM+tLsAypSIPTu2sunR67n33+9wLPHnAST/b+I5f37q5ZV6VeMo+zktXPB5jtDowEjgDWAF8DF7r7kqSXrhK6d+/u06drPJ8kyU8/wXffwV57BY+nTYPjj091qUSqzcxmuHv3WPsqrEG4+2LgBDNrDmS4+081XUCRtPb668G4hpYtYc6c4F8FB2kAKgwQZnZb1HMA3F2NrlK/rVoF114LY8fCPvvA44+r26o0KInkIDZGPG4KnA58npziiKSJL76AY4+F1auDifVuuQWaNEl1qURqVSJNTH+NfG5mfwGmJK1EIqlUVASZmcHkeqefDgMHwoEHVnyeSD1UlfpyM2DXmi6ISEq5w+jRsN9+QdNSo0YwapSCgzRoieQg5rJt8aBMYEdA+QepPxYvhv794c034ZhjYONGaN061aUSSblEchCnRzwuBH5w94QGypnZycDfCALLE+4+LGp/E2As0A1YBZwb2X3WzNoB84E73P0vibymSMLc4W9/C/ILmZnwyCNBoAgT0RXO2ipSz5UbIMwsE5ji7ntX9sLhuQ8DJwLLgGlmNsnd50ccdgmwxt07mlk/4F7g3Ij99wOvVfa1RRL21lvwq18FwaFt2583l8zaWjIxX8msrYCChDQY5eYg3L0IWBB+k6+sQ4BF7r7Y3bcC44Ezo445EyhZmOgFoIeF/WjNrDfBoLx5VXhtkdi2boW774ZFi7ZNrvfyy6WCAyQwa6tIA5BIE9P2wDwz+4SILq/ufkYF5+UCSyOeLwMOjXeMuxea2TqgtZltBm4kqH1cH+8FzKw/0B+gXbuqxDBpUKZPh0suCQa7ZWTATTdBs2YxD42etbWi7SL1USIB4takl6KsO4AR7r7BypkZ091HEkwDQvfu3cufM0Qarvx8uP12+OtfYeed4aWX4Izyv9+0yckmL0YwiJzNVaS+S6Sb66nu/k7kD3BqAuflAZH19l3DbTGPMbNGQCuCZPWhwH1mtgS4GrjZzAYk8JoiZd17LwwfHtQe5s2rMDhAMGtrdlZmqW3ZWZkM6tkpWaUUSTuJBIgTY2w7JYHzpgF7mlkHM2tMsIbEpKhjJgEXhY/7AlM9cLS7t3f39sADwD3u/vcEXlMksH59MBoa4Prrg2T0yJGQk5PQ6b275jK0T2dyc7IxIDcnm6F9OitBLQ1K3CYmM7sC+COwu5nNidjVEvigoguHOYUBBKOuM4HR7j7PzIYA0919EjAK+KeZLQJWo4WIpCa8+ipcfjm0ahXkG1q0gOOOq/RlenfNVUCQBi3udN9m1oogQT0UGByx6yd3X10LZasUTfctrFwJV18NTz8N++8fjIQ+5JAKT9N4B2nIqjTdt7uvA9YB5yWrYCI15osv4OijYd06uOOOoIdS48YVnqbxDiLxae5iqduKwrEKHTvCWWfBzJlBj6UEggNovINIeRQgpG5yD9Zn2GefbZPrjRwZNC1VgsY7iMSXyDgIkfTy1Vdw2WVBz6TjjoNNmxKeXC8635DTLIs1mwrKHKfxDiIKEFKXuMOIEfCnP0FWVlBjuPRSJn66nOFPTy2VZAbKJJ6BMvmGrAwjK9MoKNrWWUPjHUQCcXsx1TXqxVT/TZy5jB1+cy5btxbyUN9r+f2vjwJKf+gDZGUYGGU+9JtmZcSsLeRkZ9G8SSP1YpIGqbxeTAoQkt62boWhQ3mjy/EMnL6R4k2b2NKoMZiV+6FfGQZ8Pey0mimvSB1TpW6uIin3ySdw8cUwbx4LT15G/oG9IWvbutD5BUVleiBVhfINIrGpF5Okn02b4Lrr4PDDg3ENr7zC8AN7V/uyOdlZml9JpBJUg5D0c999cP/9vHhoL247/DdsNzebnGaFcfMHWwqLE8pB3HHGfkDZ5LXyDSKxKUBIeli3DpYvh3324eUTz+fZr1vw/i77APBTOb2N4n3ox9pWEggUEEQSowAhqffyy8Hkejk5MHcuw95bRl4YHEoUFHu5vY1ifegrEIhUjwKEpM6KFTBwIIwfD507w+jRkJERdxTzuvwCPr39pFoupEjDpQAhqfH558HkeuvXw5AhcOONP8+fpNXcRNKDejFJ7SosDP7dc0/o25c3n3mdI7OOpMNtb3DksKlMnJWn1dxE0oQChNSO4mJ49FHYe+9g3YZGjZj4h1sZMHsreWvzcUpPta3V3ERST01MknwLF8Kll8K770KPHrB5M1D+VNsfDD5eAUEkxVSDkOQpLg7GNBxwAAUzP+Wes6+nQ7erOfJfXzJxVp6m2hZJc6pBSPKYwUcfsfzwY+l3wG/4tmkOsK0pSVNti6Q31SCkZm3ZEqzotnBhECDGjeOcEwf9HBxK5BcU4Y6S0SJpTAFCas5HH0HXrkG31YkTg21Nm7J83eaYh6/LL1AyWiSNqYlJqm/jRrjlFnjwQdh1Vz58aCyDNuSyfPCrFa7a1rtrrgKCSJpSDUKqb/hw+Nvf4I9/5JVn3uCSH3Ys1XV1w+ZCsjKt1ClqShJJfwoQUjVr1wajoQEGDYL334e//52h7+eV6bpaUOw0b9xITUkidYyamCQhE2fl/Tw76rl5M7j99X9Q0CqHUy/+O3nrtwST5zWL33VV8yiJ1D0KEFKhibPyuGnCXJqvXclDbzzG6Qve5/NfduCmY65g2fotgLquitRHChBSoeFTFpD73dc8//SNNCvI575jfsvIQ/pQmFn6zye/oIgmjTLIzsos1cykfINI3ZTUAGFmJwN/AzKBJ9x9WNT+JsBYoBuwCjjX3ZeY2YnAMKAxsBUY5O5Tk1lWCUQ2JbXJyeaG43dn+dp8bIdcXt7nGMZ0O52vWreNe/66/AJGnNtFq7aJ1ANJCxBmlgk8DJwILAOmmdkkd58fcdglwBp372hm/YB7gXOBlUAvd19uZvsDUwB9wiRZSVNSfkER5sUcP/V5Drp3Ih36/43FGc257aQrKryGuq6K1B/J7MV0CLDI3Re7+1ZgPHBm1DFnAmPCxy8APczM3H2Wuy8Pt88DssPahiRRyeR5u69axrPPDOauNx7l65xdaFRYWGbEc8kSoJHUlCRSvySziSkXWBrxfBlwaLxj3L3QzNYBrQlqECXOBma6+5boFzCz/kB/gHbt2tVcyRuo79Zs5PKPJ3DN+0+zuVFjrj/1al7Yvwdmxog+nSu17rOI1H1pnaQ2s/0Imp1i9o9095HASIDu3bt7rGMkcbts35wDv/uSqXsczG0nXsGKFtsD5TcbKSCI1F/JDBB5QGQ2c9dwW6xjlplZI6AVQbIaM9sVeBH4rbt/lcRy1nvRiefIb/8rV67jppkTyB3Yn0E9OzF43Y2s823NSWo2Emm4khkgpgF7mlkHgkDQDzg/6phJwEXAR0BfYKq7u5nlAK8Cg939gySWsd6LTDxDMF5h0POzweCAb+Yx5rUH6bh6GcMzmrDn8Du589fd1GwkIkASA0SYUxhA0AMpExjt7vPMbAgw3d0nAaOAf5rZImA1QRABGAB0BG4zs9vCbSe5+4/JKm99FWvVtqzNmxj07lgumvEKy7fbkd+ecyfv7t6NXK3kJiIRkpqDcPfJwOSobbdFPN4MnBPjvD8Df05m2eqjWE1Jsaa+6P/JBC6a8Qpjup3O8GN+y6bGwShnreQmIpHSOkktiYvVlBQ59UWr/J/45YbVLNxxNx475Gze7XAQM3P3KXUNTYchIpE0m2s9EaspqWTVtjMWfcR/R13Bwy/di3kxhU2zmdtu31LHKhktItFUg6gnYjUP7bhhDUNefIRTvvyQBW06cs1JA2mzfXONYRCRhChA1FHR+YboWVT3XPENLzx9A02LtsKwYXS67jomNyr9disgiEh5FCDqoFj5hpKpL3xrAYWZjfiq9a680vl4drr5ek4465gUl1hE6iLlIOqgWPmGwqIiLpn1Ku+OupzWm9axyw4taD7yEQUHEaky1SDSXCJdV/dYuZR7X3+Q7nmfw8knM2PwcdCmTWoKLCL1hmoQaaykKSlvbT5O6a6rAObFXPnhs0x+6v/YY9UyhpwzGCZPVnAQkRqhAJHGyuu6mp2ViVsG+/3wFW/seTi9Ln+MA24aAGZxriYiUjlqYkpjsbquNinYwmXvjKHTDQO4fUEh1/QaxC9+sZ26qYpIjVOASGNtcrLJiwgSBy/9jGGvP8Qeq/Ng8aGcMPi6FJZOROo7NTGlsUE9O5GdlUnzLZsY8p9HeP6ZwTQpKuSDR8bBdQoOIpJcqkGkkVg9lob26cyqQTdz4azJPHvk2TS7byi9jtgz1UUVkQbA3OvHQmzdu3f36dOnp7oYVRY9+C0nfz3tNq/j4st70XuvHPjsMzg0esVWEZHqMbMZ7t491j7VINLEzz2W3Dl1wQfc+cajrG62HZfs1pHeXU9QcBCRWqcAkSaWr83nlz+t4q43HqHnwv8xZ+eO3HDKVeSt35LqoolIA6UAkSaO3PID/xg1kMZFBdxz3O8ZdXBvijIyydUaDSKSIgoQqbZ1KzRuTN8LTuClD3owustpfL1DMJ5BazSISCqpm2sSTZyVx5HDptJh8KscOWwqE2fl/bz96HveYEiP/ny/U1smvzmb3t3b0fKxf7B1944YkJuTzdA+nTX4TURSRjWIJIm3BOj0b1Yz+/UPeXDSCLp+t4A39ziYYS/PY+sOv6B311wFBBFJGwoQSRJrHqXNWwtoff99/PuD8Wxo0oyBva5n0j7HghnDpyxQcBCRtKIAUUOiB7nlxZhHyS2DvX78mtc7HcEdJ/yB1c1a/bwv1rxLIiKppABRA2I1JxngQNOCzVz1wXhe6NyDr1q35bozBrE5o+yvvY16K4lImlGAqAGxmpMcOOzbuQx9/UE6rPmOlc1asXzn9pzdrR3/npFX6nj1VhKRdKQAUQOim4dabtnI4Lef5IJPXydvh104v9/dfHPgYQwNp+TuvtsOZeZcUv5BRNKNAkQNiM45XDJtIv1m/4dxR/XlvCljeKZZs1LHq7eSiNQFGgdRAwb17MQuBRvotGIJAI8eejb9fn8/2Q8+AFHBQUSkrlANorrc6b3gPU4ZfSXLG7ekx+8eovWO23PBhYepliAidVpSA4SZnQz8DcgEnnD3YVH7mwBjgW7AKuBcd18S7rsJuAQoAga6+5RklrVK8vLgiivg5ZdpcsghdBg1isX775/qUomI1IikBQgzywQeBk4ElgHTzGySu8+POOwSYI27dzSzfsC9wLlmti/QD9gPaAP818z2cvfSXYVS6bPP4MgjoaAA7r8fBg6EzMxUl0pEpMYkMwdxCLDI3Re7+1ZgPHBm1DFnAmPCxy8APczMwu3j3X2Lu38NLAqvl3pbwum399kHLr4Y5s6Fa65RcBCReieZASIXWBrxfFm4LeYx7l4IrANaJ3guZtbfzKab2fQVK1bUYNFjKCoKagp77gk//hgEhBEjYI89kvu6IiIpUqd7Mbn7SHfv7u7dd9xxx+S90GefwRFHwHXXQZcuUFycvNcSEUkTyQwQeUDbiOe7httiHmNmjYBWBMnqRM5NvuJiuOMOOOggWLwYxo2Dl16CnXeu9aKIiNS2ZAaIacCeZtbBzBoTJJ0nRR0zCbgofNwXmOruHm7vZ2ZNzKwDsCfwSRLLGltGBsybB+ecA59/Dv36gVmtF0NEJBWS1ovJ3QvNbAAwhaCb62h3n2dmQ4Dp7j4JGAX808wWAasJggjhcc8B84FC4Mpa68G0cWNQa7jkEth7b3jmGcjKqpWXFhFJJ0kdB+Huk4HJUdtui3i8GTgnzrl3A3cns3xlTJ0Kl10WNCftumsQIBQcRKSBqtNJ6hqzbl0QGHr0CJqV3n4brroq1aUSEUkpBQgIuquOHg033ABz5sCxx6a6RCIiKddw52L68Uf4/ns44IAgMPTqBd26pbpUIiJpo+HVINzh6adh333hvPOCrqzNmik4iIhEaVgBYunSoKZw4YXBiOjnngtyDiIiUkbDaWIqGQ1dVAQPPAADBmj+JBGRctT/ALF5MzRtGjQpXXYZXHkl7L57qkslIpL26m/7SmEh3HcfdOwIP/wQNCX99a8KDiIiCaqfAWL2bDjsMLjxRjj44FSXRkSkTqpfAaK4GG69Fbp3DxLSzz8PEybATjulumQiInVO/QoQGRnw5Zdw/vkwfz707avJ9UREqqj+JKk3bw7+/de/NH+SiEgNsGB27brPzFYA39TiS/4CWFmLr1fbdH91W32+v/p8b1D797ebu8dcca3eBIjaZmbT3b17qsuRLLq/uq0+3199vjdIr/urXzkIERGpMQoQIiISkwJE1Y1MdQGSTPdXt9Xn+6vP9wZpdH/KQYiISEyqQYiISEwKECIiEpMCRAxmdrKZLTCzRWY2OMb+Jmb2bLj/YzNrH7HvpnD7AjPrWasFT1BV78/MTjSzGWY2N/z3+FovfAWq896F+9uZ2QYzu77WCl0J1fzbPMDMPjKzeeF72LRWC5+AavxtZpnZmPC+Pjezm2q98AlI4P6OMbOZZlZoZn2j9l1kZgvDn4tqpcDurp+IHyAT+ArYHWgMzAb2jTrmj8Cj4eN+wLPh433D45sAHcLrZKb6nmrw/roCbcLH+wN5qb6fmrq3iP0vAM8D16f6fmr4vWsEzAEODJ+3rmd/m+cD48PHzYAlQPtU31MV7q89cAAwFugbsX0HYHH47/bh4+2TXWbVIMo6BFjk7ovdfSswHjgz6pgzgTHh4xeAHmZm4fbx7r7F3b8GFoXXSydVvj93n+Xuy8Pt84BsM2tSK6VOTHXeO8ysN/A1wb2lo+rc30nAHHefDeDuq9y9qJbKnajq3J8Dzc2sEZANbAXW106xE1bh/bn7EnefAxRHndsTeMPdV7v7GuAN4ORkF1gBoqxcYGnE82XhtpjHuHshsI7gG1ki56Zade4v0tnATHffkqRyVkWV783MWgA3AnfWQjmrqjrv3V6Am9mUsAnjhloob2VV5/5eADYC3wHfAn9x99XJLnAlVefzISWfLfVnsj6pNWa2H3AvwbfS+uIOYIS7b7D6OQNwI+Ao4GBgE/Cmmc1w9zdTW6wacwhQBLQhaIJ5z8z+6+6LU1usuk01iLLygLYRz3cNt8U8JqzStgJWJXhuqlXn/jCzXYEXgd+6+1dJL23lVOfeDgXuM7MlwNXAzWY2IMnlrazq3N8y4F13X+num4DJwEFJL3HlVOf+zgded/cCd/8R+ABIi/mMIlTn8yE1ny2pTtyk2w/BN63FBEnmkkTSflHHXEnpRNlz4eP9KJ2kXkz6JQKrc3854fF9Un0fNX1vUcfcQXomqavz3m0PzCRI4DYC/guclup7qsH7uxF4MnzcHJgPHJDqe6rs/UUc+xRlk9Rfh+/j9uHjHZJe5lT/0tLxBzgV+JKgx8Et4bYhwBnh46YEPV0WAZ8Au0ece0t43gLglFTfS03eH/AngnbeTyN+fpnq+6mp9y7iGmkZIGrgb/NCggT8Z8B9qb6XGv7bbBFunxcGh0Gpvpcq3t/BBLW9jQQ1o3kR514c3vci4Pe1UV5NtSEiIjEpByEiIjEpQIiISEwKECIiEpMChIiIxKQAISIiMSlAiITM7I5UzOJqZkVm9mnET3szO87M1pnZrHD2z3fN7PSosuaFx883s/Nqu9xS/2mqDZHUy3f3LpEbwmms33P308PnXYCJZpbv26bHGOHufzGzPYEZZvaCuxfUYrmlnlMNQho0M7vFzL40s/eBThHb9zCz18N1L94zs73D7TuZ2YtmNjv8OSLcfq2ZfRb+XB1uG1LyOHx+t5ldVZVyuvunBAOqykz/4e4LCeZX2r4q1xaJRzUIabDMrBvBdA1dCP4vzARmhLtHApe7+0IzOxT4B3A88CDwjrufZWaZQIvwOr8nmM/JgI/N7B1gNDABeMDMMsLXijX9e7aZfRo+/trdz4pT5JnAoBj3cRCw0IM5iERqjAKENGRHAy96MHkdZjYp/LcFcATwfMTMriXrXhwP/BbAg/UU1pnZUeF1NobnTwCOdvcHzWyVmXUFdgJmufuqGOUo08QUR/Q0s9eY2e8JpvLulcgNi1SGAoRIWRnA2gQ/tCvyBPA7YGeCGkV1dAU+j3hekoM4AxhlZnu4++ZqvobIz5SDkIbsXaC3mWWbWUvCb+Huvh742szOAbDAgeE5bwJXhNszzawV8F54nWZm1hw4K9wGwdToJxNMwjalqgU1swOAW4GHo/e5+yRgOlA76xRLg6EAIQ2Wu88EniWYdvk1YFrE7guAS8xsNsEMoSVLQ14F/MrM5hLkK/YNr/MUweyiHwNPuPus8DW2Am8RTEtd2SU+jy7p5koQGAZ6/AV+hgDXhrkOkRqh2VxFkij8wJ4JnBP2NhKpM/RtQyRJzGxfgrn731RwkLpINQgREYlJNQgREYlJAUJERGJSgBARkZgUIEREJCYFCBERien/AazQ/H5PBIadAAAAAElFTkSuQmCC\n",
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eeuu6LqOIZJp7+N2+Pey3H7z9Ntx1F7Rqlc1SNRiZDBCLgO0Snm8bbYs9JmpOag18D5wGvOzu69z9W+BtoEcGyyoiucQdRo+G7t03JNd7+mnYf/9sl6xByWSAmATsZGYdzKwp0A8YV+GYccCZ0eO+wAR3d0Kz0mEAZtYS2A+Ym8Gyikiu+OqrkFjv5JPDcFUl18uajAWIqE/hEmA88AHwjLvPNrNBZtYnOuxhYCszmw/8HigfCnsv0MrMZhMCzaPuPjNTZRWRHOAOjz4acie99BIMHQoTJyq5XhaZl7fx1XM9evTwyZMnZ7sYIlJT7vDLX8LKlSG53s47Z7tEDYKZTXH32CZ8JesTkewpLYV77w1zGtq3D9lXN9tM+ZNyhD4FEcmOOXPCnIbLLgvrNkCY9KbgkDP0SYhI3Vq3Dm6+Gbp1gw8/DOtC33BDtkslMRQgRKRuDRkC118PJ5wQahGnn67kejlKfRAiknnFxWFt6A4dQnK9bt3gmGOyXSqpgmoQIpJZb7wRkusdd9yG5HoKDvWCAoSIZMaKFXDRRfCLX0BJCQwfruR69YyamEQk/T76CHr2hIUL4fLL4aaboGXLbJdKqkkBQkTSxz10OLdvDwceGIaw7rdftkslNaQmJhGpPfeQTK9rV1i6NCTXe+opBYd6TgFCRGpn0SI4/njo1w+aNQsBQnLe2GmLOHDIBJpu07F7smPUxCQiNeMeciZdeWWY/Hb77fC736kjOgeNnbaIYePn8eWyYtoUFXJop615bsoiiteVVnqeAoSI1NzYsbDXXvDgg9CxY7ZL0+BVDAT9e+0CwIAxs9YHg0XLinny3c9JJU2rAoSIpK60FP785zCnoUOH0M/QqpXyJ+WAsdMWbRIIBoyZRfOCRpvUFFLN4a1PVURS8/77YWTS5ZfDyJFh2+abKzjkiGHj520SCIrXlbJ01boav6ZqECJSubVrYfBguOWWMAv6b38LHdKSNXFNSV8uK67WaxhV1yQU+kWkckOGwMCBYQnQOXPg1FOVXC+LypuSFi0rxtnQlFTUoiD2+KLCAgoLNh44UFjQmNP3a0fbosJK30s1CBHZ1KpVYW3oHXcMI5N69AirvUnWJWtKatakEYUFjTfaV1jQmIF9uqw/L7HGcXy3tgDYgPlTkr2XAoSIbOy11+Dcc6FFC5g+PfQzKDjkjGRNScuL1zH8lK5JA0H57+pQgBCRYPlyuOoqGDEi1Bz+/GfNachBbYoKWRQTJNoUFXJ8t7Y1CgTJqA9CRMLKbp07b5j4NnMmHHJItkslMfr32iW2T6F8zkM6KUCINGRlZeF3hw5w6KHw7rswbFhoXpKcUJ4So8M1L3DgkAkADD5xd9oWFWJA26JCBp+4e1prDuXMPdUpE7mtR48ePnny5GwXQ6R+cA+T3IYMgddfhy22yHaJJEbFyW8QagvpDAhmNsXde8TtUx+ESEOzcCFceCH885+w776wbJkCRA6Im9uQbMTSsPHzMlJjqEgBQqShcA8d0P37hxXe7rwzrA+tjug6V1XyvPK5DcmS6VV3UlxNKUCINCTPPw977x2S6+2wQ7ZL0yDF5UyKS55XvK6UxmaUxnQDtKliglu6KECI5LOSErjrLjjxxBAQypPraSZ01sQ1GyXrCS51j538lokRS3EyOorJzHqb2Twzm29m18Tsb2ZmT0f7J5pZ+4R9e5jZO2Y228xmmVnzTJZVJO/MnAn77x+alJ58MmzbbDMFhyyrTvNQ+QiluhixFKfaNQgzKwIudvdbqjiuMXAvcASwEJhkZuPcfU7CYecAS929o5n1A4YCp5hZE+AJ4H/dfYaZbQXUPCWhSEOyZk1IrDd4MGy5JTzzDPTtm+1SSSTZRLeKyfPKawrpnvxWHUlrEGa2nZmNMLN/mtm5ZtbSzO4APgT+J4XX3geY7+4L3H0tMAo4rsIxxwGPR49HAz3NzIAjgZnuPgPA3b9398qXPhKRYOhQuOmmkFRvzpyQZE+1hpyRbKJbefK8bNQUkqmsBjESeB14DugNTAamA3u4+9cpvHZb4IuE5wuBfZMd4+4lZrYc2ArYGXAzGw9sDYxy99sqvoGZnQ+cD9CuXbsUiiSSp1auhK+/3pBcb999oVevbJeqwYsbulp+00+2PZdUFiC2dPeB0ePxZnYycLq7l2W+WDQBDgL2BlYBr0aTOV5NPMjdRwAjIEyUq4NyieSeV1+F886Dli03JNdTcKhTqS71OWDMLICsNhtVR6Wd1Ga2hZltaWZbAt8DrROeV2URsF3C822jbbHHRP0OraP3WQi84e7fufsq4EVgr1QuSKTBWLYsZF09/HBo0gTuvVdzGrIg2foMf3x+dtJJbvVFZTWI1sAUQt9JuanRbweqGkQ9CdjJzDoQAkE/4LQKx4wDzgTeAfoCE9y9vGnpKjNrAawFfgEMr/pyRBqIefNC7qRvv4Wrr4Ybb4TCuhkbLxtLNts525Pc0iFpgHD39rV54ahP4RJgPNAYeMTdZ5vZIGCyu48DHgb+ambzgSWEIIK7LzWzOwlBxoEX3f2F2pRHJC+UlYU1oHfYIdQcLrsMunfPdqkatOre8Otqkls6VDrMNWr2OQroFG2aA4x395JUXtzdXyQ0DyVuuyHh8Wrg5CTnPkEY6ioi7vDEEyG53ptvhuGrI0dmu1RC8mGrRYUFrCkpy9okt3RIGiDMrC0wAfgKmEZoajoGuNPMDnX3L+umiCIN3OefwwUXwEsvhYlvK1aEACFZUVUeJUhtqc/6IGm6bzN7DJju7ndV2H4p0N3dz8x46apB6b4l77jDffeFPgb3MPHtoovUEV1HUhmZBCEYnNS9La/NXVwvA0Fl6b4rCxBz3b1Tkn3z3D2n6kkKEJJ33KFPnzAzesQIaN8+2yXKW6nWCpoXNGLpqk2TOrQtKuTtaw6ryyKnTU3Xg6is52VV7YokIrHK03D37bshuV7LlpoJnSapzldIll01H0YmVUelw1zN7MSY7QZsnqHyiDRcM2bA2WfD1Kmwdi384Q8h86qkRVya7QFjZtG8oFHK2VWTqU8jk6qjsgDxBnBsJftEJB1Wr4abbw45lLbaCkaPhpNOynap8k515yvEyYeRSdVR2TyIs+qwHCIN19ChIfvqmWeG5iWNUMqI6jYDxWVXzYeRSdVR2TDXx8qDhJmd6e6PJztWRKrpxx/hq69gp53g8svhgAPgiCOyXaq8Vt35CpWNTMrXgFBRZU1MeyY8vowNablFpDb+9S84//yweM+MGSG5noJD2jWk+QqZUlmAUHZUkXRasgSuuAIeewx22QXuvz+kzZC0i+uQfm7KItUKqqmyALGtmd1NaIorf7yeu1+a0ZKJ5JO5c+GQQ+C77+Daa+H666G5VtHNlGQd0q/NXVxv5ytkQ2UBon/CY81AE6mJ8uR6HTvCUUeF5Hpdu2a7VHkvWYd0vs5XyJTKRjGpz0Gkptzh8cfhttvgrbfCyKRHH812qRqMZB3S+TpfIVPUACqSbp9+Cr17w69/HeY1/PBDtkuU18ZOW8SBQybQ4ZoXOHDIBMZOW5R03ed8na+QKQoQIuniDvfcA7vtBv/9b3j8+uuw/fbZLlneSraaG8DgE3enbVEhRsiVNPjE3dURXU2VrgchItX0r3/BQQfBAw8oMNSBZJ3Rw8bP4+1rDlNAqKUqA4SZ7QzcB/zU3Xczsz2APu5+c8ZLJ5Lr1q2D22+HX/0KdtwxJNdr0ULJ9TIgLtGeOqMzK5UaxIOEEU0PALj7TDP7G6AAIQ3b1KlwzjkwfXpoXrr22pB5VWqtqklu5U1JRS0KYtNvqzM6PVLpg2jh7u9V2JbSkqMieam4GAYMgH32ga+/hjFjQnCQtIjrV3jy3c9jm5LcUWd0BqVSg/jOzHYkmlltZn0Jy5CKNEy33RbWhj7nHBg2DLbYItslqrfimo3i+hWSpXVYXryO4ad0VZqMDEm6otz6A8x2AEYABwBLgU+AM9z904yXrhq0opxk1A8/hOR6O+8cHk+aBIdpRm5tVEyHAeHbf3XSb9fnldxyRU1XlAPA3RcAh5tZS6CRu2tQtzQsL78Mv/lNSK43c2b4reBQa8lGIDU2ozTmi2tc+m01JWVWKqOYbqjwHAB3H5ShMonkhu+/h9//HkaOhF13hQcfVHK9WqjYnBQ30xmg1H2TmkRV6bclM1Lpg1iZ8Lg5cAzwQWaKI5Ij5s6FX/wiZGC9/nq47jpo1izbpaq34rKrVqwRlGub0BehYJBdqTQx3ZH43MxuB8ZnrEQi2VRaCo0bh+R6xxwDl14Ke+5Z9XmyXnU6npM1Gx3fra0CQg6oSX25BbBtugsiklXu8Mgj0KVLaFpq0gQefljBoZqSpb5I1pzkoHQYOSyVPohZbAjyjYGtAfU/SP5YsCCs8Pbqq/Dzn8PKlSHJnqwXVys4vlvbTbavWltSrY5njULKban0QRyT8LgE+MbdU5ooZ2a9gT8RAstD7j6kwv5mwEigO/A9cEri8FkzawfMAQa6++2pvKdIytzhT38K/QuNG8N994VAoY7ojcT1HwwYM4vJny3ZZHZzMsk6njUKKbdV+j/BzBoD4939s+hnUTWCQ2PgXuAooDNwqpl1rnDYOcBSd+8IDAeGVth/J/BSKu8nUiOvvQaHHgqzZ8MFFyg4xEg2HPWpiV+kPGehvPlIzUn1S6U1CHcvNbN5ZtbO3T+v5mvvA8yP5lFgZqOA4wg1gnLHAQOjx6OBe8zM3N3N7HjCpLzEUVQitbN2bZj9fMopoSP6qaegsFDJ9SLVSYgX12QURx3P9VcqTUxbALPN7D0Sbtbu3qeK89oCXyQ8Xwjsm+wYdy8xs+XAVma2GrgaOAK4MtkbmNn5wPkA7dq1S+FSpEGbPDmkx5g5M9QUBgwImVcFSN6UlCwhXrJ+haLCAlo2a6IhqnkglQBxfcZLsamBwHB3/9Eq+Wbn7iMIaUDo0aNHal9npOEpLoYbb4Q77oBttoF//AP6VPX9puFJ1pTUrEmjpBPXEvsgyrcP7NNFASFPpBIgfunuVyduMLOhwOtVnLcI2C7h+bbRtrhjFppZE6A1obN6X6Cvmd0GFAFlZrba3e9JobwiGxs6NDQrnXdeSLRXVJTtEuWEVGc2V5YQr8f2W2pCWx5LJVnfVHffq8K2me6+RxXnNQE+BHoSAsEk4DR3n51wzMXA7u5+gZn1A050919VeJ2BwI9VjWJSsj7ZyIoV8OWX0KkT/PhjaF465JBsl6raUh1eWtl2IHZbxUR5lc1s1lDU/FVZsr6kAcLMLgQuAnYAPk7YtRnwtrufkcIb/xK4izDM9RF3v8XMBgGT3X2cmTUH/gp0A5YA/co7tRNeYyAKEFIdL7wQRiS1br2hv6EeSpbtNFnTTtz2gkYGButKfaNjmxc0iu1XiJvZrNFG+a2mAaI1oYN6MHBNwq4f3H1J2ktZSwoQwnffwe9+B08+CbvtFmZC77NPtkuVsrhJZ9XpHE62vbraFhWqyagBqVG6b3dfDiwHTs1UwUTSZu5cOPhgWL4cBg4MI5SaNs12qVIWN4IomWRBIF3BQc1JUi6VTmqR3JWYXO+EE0Jyvd12y3ap1ku1TyBuBFEy6ahBFBUWsKakTDObpVJVdlLXF2piamDc4aGHwuikd97JydxJcX0IyfoEUg0O6eqDGHzi7sCmgUrNSQ1PrVaUE8k5H38chqy+9loYmbRqVU4GiLhawbqyTb+QVZbMLtmks2TDS+O2l5clLhAoIEhlVIOQ+sMdhg+HP/wBCgrg9tvh3HNzIk1GXFPS5U9Pjx02mkzcZDSNIJJMq6wGUT/H/0nD9cYbcPjhMGdOqEXkSHCIWwOhqEVByq+hZHaSi9TEJLlt7VoYPBhOOw122ikk12vePCcCQ7nqpKhI1iegZHaSixQgJHe99x6cfXZIxd28OVx9dci8mmW1TVEB6hyW+kEBQnLPqlVw/fVw113Qpg38859w9NEZe7vqpq2oOF8hWYqKNkWFSWsFCghSHyhASO657Ta4886QLmPoUNh884y9VXVWSxswZhbNCxpt0pzkxKeo0JwCqe8UICQ3LF8ekuvtuitceSX07BlmRmdYZaulVRx2WryuNOl8BUcpKiT/KEBI9j3/fKgtFBXBrFnQqlWdBAeg1qullVOKCslHGuYq2bN4MZx6ali8Z6ut4PHH6zzzapui+E7vxklGSRUVFlBY0HijbWpOknylACHZ8cEHoTnpuedg0KCwXkOP2Lk6GdW/1y6xN/xT990udvvAPl00X0EaDDUxSd0qKYEmTcKchr594be/hS5d6uzt40YmDT5x92qvlqaAIA2BUm1I3SgrgxEjQnqMd9+Fn/wko2+XyhBVUDoLESXrk+z66KOQM+mNN8LopNWr0/ryFYPBoZ22TnmIavG6UoaNn6cAIRJDfRCSOWVlYU7DHnvAjBlhhbdXXoFtt03bW8TlQXry3c9jA0Hc6myQfCSTSEOnGoRkjllYq6F3b7j33jAruhbimo3i5jFUt9E02UgmkYZOAULSa80auPVWOOOMDcn1mjWrdXK9ZDOeU11oB7SKmkh1KUBI+rzzDpxzThjC2qoV9O8fkuzVQMXawqq1JbHNRskW2olLfTGwTxgtpUR5IqlRgJDaW7kSrrsO7r479C+8+CIcdVSNXy6utpBMqXvsQjsndW/La3MXa4iqSC0oQEjtDRsGf/oTXHxxWLths81SPjXVfoVk2iaco1qBSHppHoTUzLJl8NVXYTb0ypUwfToceGC1XqJiTQE2XXazMprDIFJ7WnJU0mvsWOjcGU4+OQxlbdmy2sEBkmdSrSwPklJciNQdNTFJ6r75JqTGePZZ2HPPMK+hFsn1KsukGtevMLBPFwUEkTqkACGpmTMnpOD+8Ue45ZYwQqmgIOXT4/oaki3XqX4FkdyQ0T4IM+sN/AloDDzk7kMq7G8GjAS6A98Dp7j7p2Z2BDAEaAqsBfq7+4TK3kt9EBmybl0IBKWlcOmloQbRqROQ+lKdFVNfwIaRRnHb1XQkUncq64PIWIAws8bAh8ARwEJgEnCqu89JOOYiYA93v8DM+gEnuPspZtYN+MbdvzSz3YDx7l7pHUMBIs3KyuC+++COO2DiRMYuXFvjm36yNZtVUxDJvmwl69sHmO/uC6JCjAKOA+YkHHMcMDB6PBq4x8zM3aclHDMbKDSzZu6+JoPllXLz5oXkem+9BUccwctTP2PAW99tNC/hyXc/3+Smn2ypzmRfQb5cVszx3doqIIjkqEyOYmoLfJHwfGG0LfYYdy8BlgNbVTjmJGBqXHAws/PNbLKZTV68eHHaCt5glZXB0KGhA/r99+HRR2H8eG6atiLlfEfVWapTOZBEcltOD3M1sy7AUOA3cfvdfYS793D3HltvvXXdFi4fNWoE770HRx8d0mWcdRaYVSvbabIhqhW3KgeSSO7LZIBYBGyX8HzbaFvsMWbWBGhN6KzGzLYF/g78n7t/nMFyNmyrV8P118OHH4bnTz4ZlgHdZpv1hyT7ph9300+2VOfp+7XTHAaReiaTfRCTgJ3MrAMhEPQDTqtwzDjgTOAdoC8wwd3dzIqAF4Br3P3tDJaxYXv77dDXMHcubL550uR6/XvtEjvjOVm+o8qW6hSR+iNjAcLdS8zsEmA8YZjrI+4+28wGAZPdfRzwMPBXM5sPLCEEEYBLgI7ADWZ2Q7TtSHf/NlPlbVB+/BGuvRbuuQfatYOXX4ZevZIeXn5zT/Wmr45nkfygXEwN0cCBMGhQmNNwyy0hNXck2dwGEclPWpNaYMmSkFyvS5fQlNSrF+y//0aHJFuUB5QiW6QhUoBoCJ57LqTi3mormDULWrZkbPN2DBsyoco028XrShk2fp4ChEgDlNPDXKWWvv4a+vYNP23ahBFKjRqtryksWlaMs6GmkGxhnuoMcxWR/KEaRL6aPRsOOgiKi2HIELjiCmgSPu7K0mzHTXTThDaRhkkBIt+UJ9fr1AlOPx1++1vGrmrFsNvfWN+clKymkCzNtia0iTRMamLKF2Vl8Oc/w847w+LF0Lgx3HMPY1e12qQ5KX6u84YJbJrQJiKgGkR++OCDMOHtv/+F3r1DLSIS15zkbJphtbymoDkMIlJOAaI+KyuDwYPDnIZWrZgy6C4ubbIbX949jTZFc+nfa5ekHcxOqCFovoOIJKMAUU+VT2j7w8gXabrLAbz3+xsZ+fFqileuBjaMTCpqUcDSVes2Ob9tUSFvX3NYXRdbROoRBYj6priYeRdfxQPN92TR5j/jsmP7s7ZJATZ3Zez6DM2aNFLHs4jUiDqp65M334SuXdnl0Xs4cO47AKxtEtaFTpYwZXnxOnU8i0iNqAZRH/zwA1xzDfzlL9C+PWeccjNvte+a0qltigrV8SwiNaIaRI4bO20RD59wCWV/uY+nDzyJ55/8F5903T/2WC3KIyLppBpEDknMpNqpYC1H/cS577sWsEcfnm+zJ9Pb7ELhSx9zUve2PDdlUcrrM4iI1IQCRI5Yn0l1bQm/nPc2f3zlfpa02JzhZ9+DN23O9DahJlC8rpTX5i5m8Im7Ky23iGSUAkSOGDZ+Hpst+Za7XrmPXh+9y8xtOnLVUZfhtmkr4JfLitWvICIZpwCRI1p+NJfRT15F09J13HrIr3l47+MpbdQ49lglzxORuqAAkW1r10LTphTvuBPP7XYYI/c6hk+23FAzSJYSQ0Qk0zSKKVtKS+Guu2CnneDbb7niqM7cdtRFGwWHwoLGnL5fO81hEJGsUA0iG2bPhnPOgYkT4eijobR0/U1fHc8ikisUIOpSWRnccgtlg25iRdNCbjz2Sibv35v+X5dx/M9Qx7OI5BQFiAxKnNdQXiPY+42JTN/5AK7veT5LWrSG5asZMGYWgIKDiOQUBYgMKZ/X4KtWctXboxi9e08GjFlLq/0vZPHajY8tXlfKsPHzFCBEJKcoQGTIsPHz2OPj6Qx5+W46LP2K71q05uGttiN+dQaSrtsgIpItChCZsHw5Fz09jNOnv8xnRdtwar9beGf7PSs9RXMbRCTXKEBkwvDh9JvxLx7c+3juOPgMVhc0X7+rqLCANSVlWp9BRHKeAkSavDhhFk+Nm8hbzX9Ghxb7cPLQJ7h7xRasrhAIBvbpAmg4q4jkPgWI2nJn0tD72O+mAXQsLKLXOfewYFUj7l63RaXZVRUQRCTXZTRAmFlv4E9AY+Ahdx9SYX8zYCTQHfgeOMXdP432DQDOAUqBS919fCbLWiOLFsGFF7L3888z/Wc7c9VRl65PrleedVXrPotIfZWxAGFmjYF7gSOAhcAkMxvn7nMSDjsHWOruHc2sHzAUOMXMOgP9gC5AG+DfZrazu5eSK95/Hw48ENat46bDzuXR7sdSViG5nkYmiUh9lslcTPsA8919gbuvBUYBx1U45jjg8ejxaKCnmVm0fZS7r3H3T4D50etl35o14feuu8LZZ8OsWbx8xKmbBAfQyCQRqd8yGSDaAl8kPF8YbYs9xt1LgOXAVimei5mdb2aTzWzy4sWL01j0GKWlcOed65Pr0bgxDB8OO+5I/167UFiwcYDQyCQRqe/qdTZXdx/h7j3cvcfWW2+duTd6/3044AC44gro2jXkVEpwfLe2DD5xd2VdFZG8kslO6kXAdgnPt422xR2z0MyaAK0JndWpnJt5ZWUwaBDceiu0bg1PPQWnnAJmmxyqRHsikm8yWYOYBOxkZh3MrCmh03lchWPGAWdGj/sCE9zdo+39zKyZmXUAdgLey2BZ4zVqFFJzn3wyfPAB9OsXGxxERPJRxmoQ7l5iZpcA4wnDXB9x99lmNgiY7O7jgIeBv5rZfGAJIYgQHfcMMAcoAS6usxFMK1fCwIFhvYZOneBvf4OCgjp5axGRXJLReRDu/iLwYoVtNyQ8Xg2cnOTcW4BbMlm+TUyYAOedBwsWwLbbhgCh4CAiDVS97qROm+XLQ2Do2TM0K/3nP3DZZdkulYhIVilAQBiu+sgjcNVVMHMm/OIX2S6RiEjWNdxcTN9+C19/DXvsEQLDscdC9+7ZLpWISM5oeDUId3jySejcGU49NQxlbdFCwUFEpIKGFSC++CLUFM44I8yIfuaZ0OcgIiKbaDhNTOWzoUtL4a674JJLQroMERGJlf8BYvVqaN48NCmddx5cfDHssEO2SyUikvPyt32lpARuuw06doRvvglNSXfcoeAgIpKi/AwQM2bAfvvB1VfD3ntnuzQiIvVSfgWIsjK4/nro0SN0SD/7LIwZAz/9abZLJiJS7+RXgGjUCD78EE47DebMgb59lVxPRKSG8qeTevXq8PuJJ5Q/SUQkDSxk167/zGwx8FkdvuVPgO/q8P3qmq6vfsvn68vna4O6v77t3T12xbW8CRB1zcwmu3uPbJcjU3R99Vs+X18+Xxvk1vXlVx+EiIikjQKEiIjEUoCouRHZLkCG6frqt3y+vny+Nsih61MfhIiIxFINQkREYilAiIhILAWIGGbW28zmmdl8M7smZn8zM3s62j/RzNon7BsQbZ9nZr3qtOApqun1mdkRZjbFzGZFvw+r88JXoTafXbS/nZn9aGZX1lmhq6GW/zb3MLN3zGx29Bk2r9PCp6AW/zYLzOzx6Lo+MLMBdV74FKRwfT83s6lmVmJmfSvsO9PMPop+zqyTAru7fhJ+gMbAx8AOQFNgBtC5wjEXAfdHj/sBT0ePO0fHNwM6RK/TONvXlMbr6wa0iR7vBizK9vWk69oS9o8GngWuzPb1pPmzawLMBPaMnm+VZ/82TwNGRY9bAJ8C7bN9TTW4vvbAHsBIoG/C9i2BBdHvLaLHW2S6zKpBbGofYL67L3D3tcAo4LgKxxwHPB49Hg30NDOLto9y9zXu/gkwP3q9XFLj63P3ae7+ZbR9NlBoZs3qpNSpqc1nh5kdD3xCuLZcVJvrOxKY6e4zANz9e3cvraNyp6o21+dASzNrAhQCa4EVdVPslFV5fe7+qbvPBMoqnNsLeMXdl7j7UuAVoHemC6wAsam2wBcJzxdG22KPcfcSYDnhG1kq52Zbba4v0UnAVHdfk6Fy1kSNr83MWgFXA3+sg3LWVG0+u50BN7PxURPGVXVQ3uqqzfWNBlYCXwGfA7e7+5JMF7iaanN/yMq9JX+S9UmdMbMuwFDCt9J8MRAY7u4/Wn5mAG4CHATsDawCXjWzKe7+anaLlTb7AKVAG0ITzJtm9m93X5DdYtVvqkFsahGwXcLzbaNtscdEVdrWwPcpnptttbk+zGxb4O/A/7n7xxkvbfXU5tr2BW4zs0+B3wHXmtklGS5vddXm+hYCb7j7d+6+CngR2CvjJa6e2lzfacDL7r7O3b8F3gZyIp9RgtrcH7Jzb8l2x02u/RC+aS0gdDKXdyR1qXDMxWzcUfZM9LgLG3dSLyD3OgJrc31F0fEnZvs60n1tFY4ZSG52Utfms9sCmErowG0C/Bs4OtvXlMbruxp4NHrcEpgD7JHta6ru9SUc+xibdlJ/En2OW0SPt8x4mbP9R8vFH+CXwIeEEQfXRdsGAX2ix80JI13mA+8BOySce1103jzgqGxfSzqvD/gDoZ13esLP/2T7etL12SW8Rk4GiDT82zyD0AH/PnBbtq8lzf82W0XbZ0fBoX+2r6WG17c3oba3klAzmp1w7tnRdc8Hfl0X5VWqDRERiaU+CBERiaUAISIisRQgREQklgKEiIjEUoAQEZFYChAiETMbmI0srmZWambTE37am9khZrbczKZF2T/fMLNjKpR1UXT8HDM7ta7LLflPqTZEsq/Y3bsmbojSWL/p7sdEz7sCY82s2Dekxxju7reb2U7AFDMb7e7r6rDckudUg5AGzcyuM7MPzewtYJeE7Tua2cvRuhdvmlmnaPtPzezvZjYj+jkg2v57M3s/+vldtG1Q+ePo+S1mdllNyunu0wkTqjZJ/+HuHxHyK21Rk9cWSUY1CGmwzKw7IV1DV8L/hanAlGj3COACd//IzPYF/gIcBtwNvO7uJ5hZY6BV9Dq/JuRzMmCimb0OPAKMAe4ys0bRe8Wlfy80s+nR40/c/YQkRZ4K9I+5jr2AjzzkIBJJGwUIacgOBv7uIXkdZjYu+t0KOAB4NiGza/m6F4cB/wfgYT2F5WZ2UPQ6K6PzxwAHu/vdZva9mXUDfgpMc/fvY8qxSRNTEhXTzF5uZr8mpPI+NpULFqkOBQiRTTUClqV4067KQ8BZwDaEGkVtdAM+SHhe3gfRB3jYzHZ099W1fA+R9dQHIQ3ZG8DxZlZoZpsRfQt39xXAJ2Z2MoAFe0bnvApcGG1vbGatgTej12lhZi2BE6JtEFKj9yYkYRtf04Ka2R7A9cC9Ffe5+zhgMlA36xRLg6EAIQ2Wu08FniakXX4JmJSw+3TgHDObQcgQWr405GXAoWY2i9Bf0Tl6nccI2UUnAg+5+7ToPdYCrxHSUld3ic+Dy4e5EgLDpZ58gZ9BwO+jvg6RtFA2V5EMim7YU4GTo9FGIvWGvm2IZIiZdSbk7n9VwUHqI9UgREQklmoQIiISSwFCRERiKUCIiEgsBQgREYmlACEiIrH+H1FbMv9V85r4AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1001,7 +1001,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1013,7 +1013,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1058,7 +1058,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1150,7 +1150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1192,7 +1192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1227,7 +1227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1277,7 +1277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1525,7 +1525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1769,7 +1769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1828,7 +1828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1880,7 +1880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1919,7 +1919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1937,7 +1937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1964,7 +1964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1989,7 +1989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2132,7 +2132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2142,7 +2142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -2161,13 +2161,13 @@
       "Converted 09_matching.ipynb.\n",
       "Converted 10_constants.ipynb.\n",
       "Converted 11_interface.ipynb.\n",
-      "Converted 12_speed.ipynb.\n",
+      "Converted 12_performance.ipynb.\n",
       "Converted 13_export.ipynb.\n",
-      "Converted 13_performance.ipynb.\n",
       "Converted additional_code.ipynb.\n",
       "Converted contributing.ipynb.\n",
       "Converted file_formats.ipynb.\n",
-      "Converted index.ipynb.\n"
+      "Converted index.ipynb.\n",
+      "Converted Untitled.ipynb.\n"
      ]
     }
    ],
@@ -2183,6 +2183,18 @@
    "display_name": "Python [conda env:alphapept] *",
    "language": "python",
    "name": "conda-env-alphapept-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/07_recalibration.ipynb b/nbs/07_recalibration.ipynb
index 1b7ae785..2033f0c1 100644
--- a/nbs/07_recalibration.ipynb
+++ b/nbs/07_recalibration.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,11 +77,11 @@
     "    df:  pd.DataFrame,\n",
     "    outlier_std: float) -> pd.DataFrame:\n",
     "    \"\"\"Helper function to remove outliers from a dataframe.\n",
-    "    Outliers are removed based on the precursor offset mass (o_mass).\n",
+    "    Outliers are removed based on the precursor offset mass (prec_offset).\n",
     "    All values within x standard deviations to the median are kept.\n",
     "\n",
     "    Args:\n",
-    "        df (pd.DataFrame): Input dataframe that contains a o_mass_ppm-column.\n",
+    "        df (pd.DataFrame): Input dataframe that contains a prec_offset_ppm-column.\n",
     "        outlier_std (float): Range of standard deviations to filter outliers\n",
     "\n",
     "    Raises:\n",
@@ -91,21 +91,21 @@
     "        pd.DataFrame: A dataframe w/o outliers.\n",
     "    \"\"\"    \n",
     "\n",
-    "    if 'o_mass_ppm' not in df.columns:\n",
-    "        raise ValueError(f\"Column o_mass_ppm not in df\")\n",
+    "    if 'prec_offset_ppm' not in df.columns:\n",
+    "        raise ValueError(f\"Column prec_offset_ppm not in df\")\n",
     "    else:\n",
     "        # Remove outliers for calibration\n",
-    "        o_mass_std = np.abs(df['o_mass_ppm'].std())\n",
-    "        o_mass_median = df['o_mass_ppm'].median()\n",
+    "        o_mass_std = np.abs(df['prec_offset_ppm'].std())\n",
+    "        o_mass_median = df['prec_offset_ppm'].median()\n",
     "\n",
-    "        df_sub = df.query('o_mass_ppm < @o_mass_median+@outlier_std*@o_mass_std and o_mass_ppm > @o_mass_median-@outlier_std*@o_mass_std').copy()\n",
+    "        df_sub = df.query('prec_offset_ppm < @o_mass_median+@outlier_std*@o_mass_std and prec_offset_ppm > @o_mass_median-@outlier_std*@o_mass_std').copy()\n",
     "\n",
     "        return df_sub"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +113,7 @@
     "\n",
     "def test_remove_outliers():\n",
     "\n",
-    "    df = pd.DataFrame({'o_mass_ppm':[1,1,1,1,1,3,5]})\n",
+    "    df = pd.DataFrame({'prec_offset_ppm':[1,1,1,1,1,3,5]})\n",
     "\n",
     "    assert remove_outliers(df, 2).values.max() == 3\n",
     "    assert remove_outliers(df, 1).values.max() == 1\n",
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -250,11 +250,11 @@
     "def test_kneighbors_calibration():\n",
     "    \n",
     "    scaling_dict = {'A':('relative', 10), 'B':('absolute', 20)}\n",
-    "    df = pd.DataFrame({'o_mass_ppm':[1,1,1,2], 'A':np.array([1,2,3,4],dtype=float)})\n",
+    "    df = pd.DataFrame({'prec_offset_ppm':[1,1,1,2], 'A':np.array([1,2,3,4],dtype=float)})\n",
     "    features = pd.DataFrame({'mass':[1,1,1,1], 'A_matched':np.array([1,2,3,4],dtype=float)})\n",
     "\n",
     "    cols = ['A']\n",
-    "    target = 'o_mass_ppm'\n",
+    "    target = 'prec_offset_ppm'\n",
     "    calib_n_neighbors = 3\n",
     "\n",
     "    assert np.allclose(kneighbors_calibration(df, features, cols, target, scaling_dict, calib_n_neighbors), np.array([1,1,1,2]))\n",
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,7 +301,7 @@
     "\n",
     "    if len(df) > calib_n_neighbors:\n",
     "\n",
-    "        target = 'o_mass_ppm'\n",
+    "        target = 'prec_offset_ppm'\n",
     "        cols = ['mz','rt']\n",
     "\n",
     "        if 'mobility' in df.columns:\n",
@@ -318,23 +318,30 @@
     "        corrected_mass = (1-y_hat/1e6) * features['mass_matched']\n",
     "\n",
     "        y_hat_std = y_hat.std()\n",
-    "        return corrected_mass, y_hat_std\n",
+    "        \n",
+    "        mad_offset = np.median(np.absolute(y_hat - np.median(y_hat)))\n",
+    "        \n",
+    "        return corrected_mass, y_hat_std, mad_offset\n",
     "\n",
+    "    \n",
     "    else:\n",
     "        logging.info('Not enough data points present. Skipping recalibration.')\n",
-    "        return features['mass_matched'], np.abs(df['o_mass_ppm'].std())"
+    "        \n",
+    "        mad_offset = np.median(np.absolute(df['prec_offset_ppm'].values - np.median(df['prec_offset_ppm'].values)))\n",
+    "        \n",
+    "        return features['mass_matched'], np.abs(df['prec_offset_ppm'].std()), mad_offset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "#hide\n",
     "\n",
     "def test_get_calibration():\n",
-    "    df = pd.DataFrame({'o_mass_ppm':[0,0,0,0], \n",
+    "    df = pd.DataFrame({'prec_offset_ppm':[0,0,0,0], \n",
     "                       'mz':np.array([10,10,10,10], dtype=float),\n",
     "                       'rt':np.array([1,2,3,4], dtype=float)})\n",
     "\n",
@@ -343,7 +350,7 @@
     "                       'rt_matched':np.array([1,2,3,4], dtype=float)})\n",
     "\n",
     "\n",
-    "    corrected_mass, y_hat_std = get_calibration(df, features, calib_n_neighbors=3)\n",
+    "    corrected_mass, y_hat_std, mad_offset = get_calibration(df, features, calib_n_neighbors=3)\n",
     "\n",
     "    assert np.allclose(corrected_mass.values, np.array([100,100,100,100]))\n",
     "    assert y_hat_std == 0\n",
@@ -357,9 +364,9 @@
     "    features = ms_data.read(dataset_name=\"features\")\n",
     "    df = ms_data.read(dataset_name=\"first_search\")\n",
     "    \n",
-    "    corrected_mass, y_hat_std = get_calibration(df, features, calib_n_neighbors = 10)\n",
+    "    corrected_mass, y_hat_std, mad_offset = get_calibration(df, features, calib_n_neighbors = 10)\n",
     "    \n",
-    "    assert y_hat_std < df['o_mass_ppm'].std()\n",
+    "    assert y_hat_std < df['prec_offset_ppm'].std()\n",
     "\n",
     "\n",
     "test_get_calibration()"
@@ -367,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -415,7 +422,7 @@
     "                verbose=False,\n",
     "                **settings[\"search\"]\n",
     "            )\n",
-    "            corrected_mass, o_mass_ppm_std = get_calibration(\n",
+    "            corrected_mass, prec_offset_ppm_std, prec_offset_ppm_mad = get_calibration(\n",
     "                df,\n",
     "                features,\n",
     "                **settings[\"calibration\"]\n",
@@ -433,10 +440,10 @@
     "                group_name=\"features\"\n",
     "            )\n",
     "\n",
-    "            o_mass_ppm_std = 0\n",
+    "            prec_offset_ppm_std = 0\n",
     "\n",
     "        ms_file_.write(\n",
-    "            o_mass_ppm_std,\n",
+    "            prec_offset_ppm_std,\n",
     "            dataset_name=\"corrected_mass\",\n",
     "            group_name=\"features\",\n",
     "            attr_name=\"estimated_max_precursor_ppm\"\n",
@@ -460,6 +467,8 @@
     "            delta_ppm = ((ions['db_mass'] - ions['ion_mass'])/((ions['db_mass'] + ions['ion_mass'])/2)*1e6).values\n",
     "            median_offset = -np.median(delta_ppm)\n",
     "            std_offset = np.std(delta_ppm)\n",
+    "            mad_offset = np.median(np.absolute(delta_ppm - np.median(delta_ppm)))\n",
+    "            \n",
     "            mass_list_ms2 = ms_file_.read(dataset_name = 'mass_list_ms2', group_name = \"Raw/MS2_scans\")\n",
     "\n",
     "            try:\n",
@@ -469,12 +478,14 @@
     "\n",
     "            offset += median_offset\n",
     "\n",
-    "            logging.info(f'Median fragment offset {median_offset:.2f} - std {std_offset:.2f} ppm')\n",
+    "            logging.info(f'Median fragment offset {median_offset:.2f} - std {std_offset:.2f} ppm - mad {mad_offset:.2f} ppm')\n",
     "\n",
     "            ms_file_.write(\n",
     "                offset,\n",
     "                dataset_name=\"corrected_fragment_mzs\",\n",
     "            )\n",
+    "            \n",
+    "            ms_file_.write(np.array([mad_offset]), dataset_name=\"estimated_max_fragment_ppm\")\n",
     "\n",
     "        return True\n",
     "    except Exception as e:\n",
@@ -493,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -565,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -677,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -724,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -744,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -763,9 +774,9 @@
       "Converted 09_matching.ipynb.\n",
       "Converted 10_constants.ipynb.\n",
       "Converted 11_interface.ipynb.\n",
-      "Converted 12_speed.ipynb.\n",
+      "Converted 12_performance.ipynb.\n",
       "Converted 13_export.ipynb.\n",
-      "Converted 13_performance.ipynb.\n",
+      "Converted additional_code.ipynb.\n",
       "Converted contributing.ipynb.\n",
       "Converted file_formats.ipynb.\n",
       "Converted index.ipynb.\n"
@@ -784,6 +795,18 @@
    "display_name": "Python [conda env:alphapept] *",
    "language": "python",
    "name": "conda-env-alphapept-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/09_matching.ipynb b/nbs/09_matching.ipynb
index 142813e3..06c38c8e 100644
--- a/nbs/09_matching.ipynb
+++ b/nbs/09_matching.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,15 +34,22 @@
    "source": [
     "## Dataset Alignment\n",
     "\n",
-    "For matching MS2-identifications to MS1-features, we first need to align the datasets on top of each other to be able to correctly transfer identifications. Datasets are aligned by comparing shared precursors and calculating the median offset.\n",
-    "When comparing all files to each other we get an overdetermined linear equation system. By solving this, we find offset parameters that minimize hte shift of all files to each other.\n",
+    "For matching MS2-identifications to MS1-features, we first need to align the datasets on top of each other to be able to transfer identifications correctly. Datasets are aligned by comparing shared precursors and calculating the median offset.\n",
+    "When comparing all files to each other, we get an overdetermined linear equation system. By solving this, we find offset parameters that minimize the shift of all files to each other.\n",
+    "Offset is either applied relative (mz, mobility) or absolute (rt).\n",
     "\n",
-    "Offset is either applied relative (mz, mobility) or absolute (rt)."
+    "### Relative offset\n",
+    "\n",
+    "For some parameters, we would like to have a relative correction of values. Consider the case of different `mz`-values, e.g. 300 and 600. If we assume that the offset is larger for larger m/z values, we would not want an absolute correction of e.g. +0.5 Da (300.5 and 600.5) but rather a relative correction of e.g. +0.1% (300.3 and 600.6).\n",
+    "\n",
+    "### Absolute correction\n",
+    "\n",
+    "In contrast to the relative correction, sometimes absolute correction is more applicable. Consider the case of retention time. Here one would rather not expect a relative offset but rather an absolute offset. As an example, consider a lag time of 0.5 Minutes. This would be constant for all retention times and not differ e.g., for later retention times."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,9 +138,17 @@
     "\n",
     "def calib_table(table: pd.DataFrame, delta: pd.Series, offset_dict: dict):\n",
     "    \"\"\"\n",
-    "    Apply offset to a table\n",
-    "    If not _calib table exist, create a new one.\n",
+    "    Apply offset to a table. Different operations for offsets exist.\n",
+    "    Offsets will be saved with a '_calib'-suffix. If this does not already exist,\n",
+    "    it will be created.\n",
+    "\n",
+    "    Args:\n",
+    "        table_1 (pd.DataFrame): Dataframe with data.\n",
+    "        delta (pd.Series): Series cotaining the offset.\n",
+    "        offset_dict (dict): Dictionary with column names and how the distance should be calculated.\n",
     "\n",
+    "    Raises:\n",
+    "        NotImplementedError: If the type of vonversion is not implemented.\n",
     "    \"\"\"\n",
     "    for col in offset_dict:\n",
     "\n",
@@ -152,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -307,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +351,14 @@
     "\n",
     "#There is no unit test for align_files and align_datasets as they are wrappers and should be covered by the quick_test\n",
     "def align_files(filenames: list, alignment: pd.DataFrame, offset_dict: dict):\n",
-    "\n",
+    "    \"\"\"\n",
+    "    Wrapper function that aligns a list of files.\n",
+    "    \n",
+    "    Args:\n",
+    "        filenames (list): A list with raw file names.\n",
+    "        alignment (pd.DataFrame): A pandas dataframe containing the alignment information.\n",
+    "        offset_dict (dict): Dictionary with column names and how the distance should be calculated.\n",
+    "    \"\"\"\n",
     "    for idx, filename in enumerate(filenames):\n",
     "\n",
     "        file = os.path.splitext(filename)[0] + '.ms_data.hdf'\n",
@@ -350,7 +372,14 @@
     "            ms_file.write(df, dataset_name=column)\n",
     "\n",
     "\n",
-    "def align_datasets(settings, callback=None):\n",
+    "def align_datasets(settings:dict, callback:callable=None):\n",
+    "    \"\"\"\n",
+    "    Wrapper function that aligns all experimental files specified a settings file.\n",
+    "    \n",
+    "    Args:\n",
+    "        settings (dict): A list with raw file names.\n",
+    "        callback (Callable): Callback function to indicate progress.\n",
+    "    \"\"\"\n",
     "    filenames = settings['experiment']['file_paths']\n",
     "\n",
     "    if callback:\n",
@@ -420,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,7 +457,11 @@
     "from scipy import stats\n",
     "def get_probability(df: pd.DataFrame, ref: pd.DataFrame, sigma:pd.DataFrame, index:int)-> float:\n",
     "    \"\"\"Probablity estimate of a transfered identification using the Mahalanobis distance.\n",
-    "    This functions that the dataframes are matched, meaning that the first entry in df matches to the first entry in ref\n",
+    "    \n",
+    "    The function calculates the probability that a feature is a reference feature.\n",
+    "    The reference features containing std deviations so that a probability can be estimated.\n",
+    "    \n",
+    "    It is required that the data frames are matched, meaning that the first entry in df matches to the first entry in ref.\n",
     "    \n",
     "    Args:\n",
     "        df (pd.DataFrame): Dataset containing transferered features\n",
@@ -458,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -485,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -503,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -623,7 +656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -642,12 +675,13 @@
       "Converted 09_matching.ipynb.\n",
       "Converted 10_constants.ipynb.\n",
       "Converted 11_interface.ipynb.\n",
-      "Converted 12_speed.ipynb.\n",
+      "Converted 12_performance.ipynb.\n",
       "Converted 13_export.ipynb.\n",
-      "Converted 13_performance.ipynb.\n",
+      "Converted additional_code.ipynb.\n",
       "Converted contributing.ipynb.\n",
       "Converted file_formats.ipynb.\n",
-      "Converted index.ipynb.\n"
+      "Converted index.ipynb.\n",
+      "Converted Untitled.ipynb.\n"
      ]
     }
    ],
@@ -663,6 +697,18 @@
    "display_name": "Python [conda env:alphapept] *",
    "language": "python",
    "name": "conda-env-alphapept-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/10_constants.ipynb b/nbs/10_constants.ipynb
index df59dc38..6ee270b9 100644
--- a/nbs/10_constants.ipynb
+++ b/nbs/10_constants.ipynb
@@ -944,6 +944,48 @@
     "test_get_protease_dict()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Losses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#export\n",
+    "from numba.typed import Dict\n",
+    "loss_dict = Dict()\n",
+    "loss_dict[''] = 0.0\n",
+    "loss_dict['-H2O'] = 18.01056468346\n",
+    "loss_dict['-NH3'] = 17.03052"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      0.0\n",
+      "-H2O  18.01056468346\n",
+      "-NH3  17.03052\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Entries in the loss_dict:\n",
+    "for _ in loss_dict:\n",
+    "    print(f\"{_:<5} {loss_dict[_]}\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -986,6 +1028,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/11_interface.ipynb b/nbs/11_interface.ipynb
index a69fc0e2..f342fd1a 100644
--- a/nbs/11_interface.ipynb
+++ b/nbs/11_interface.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -296,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -483,11 +483,24 @@
     "                logging.info('No calibration found.')\n",
     "                offsets = None\n",
     "\n",
+    "            try:\n",
+    "                frag_tols = [float(\n",
+    "                    alphapept.io.MS_Data_File(\n",
+    "                        ms_file_name\n",
+    "                    ).read(dataset_name=\"estimated_max_fragment_ppm\")[0] * settings['search']['calibration_std']) for ms_file_name in ms_files\n",
+    "                ]\n",
+    "        \n",
+    "            except KeyError:\n",
+    "                logging.info('Fragment tolerance not calibrated found.')\n",
+    "                frag_tols = None\n",
+    "                \n",
+    "                \n",
     "            logging.info('Starting second search.')\n",
     "\n",
     "            fasta_dict = alphapept.search.search_parallel(\n",
     "                settings,\n",
     "                calibration=offsets,\n",
+    "                fragment_calibration=frag_tols,\n",
     "                callback=cb\n",
     "            )\n",
     "            pept_dict = None\n",
@@ -498,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -542,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -598,7 +611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -670,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -741,7 +754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -870,7 +883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -913,7 +926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1086,7 +1099,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1112,7 +1125,7 @@
     "    cols = [_ for _ in ['protein','protein_group','precursor','naked_sequence','sequence'] if _ in protein_fdr.columns]\n",
     "    n_unique = protein_fdr.groupby('filename')[cols].nunique()\n",
     "    n_unique.index = [os.path.split(_)[1][:-12] for _ in n_unique.index]\n",
-    "    cols = [_ for _ in ['fwhm','int_sum','rt_length','rt_tail','o_mass_ppm_raw'] if _ in protein_fdr.columns]\n",
+    "    cols = [_ for _ in ['fwhm','int_sum','rt_length','rt_tail','prec_offset_raw_ppm '] if _ in protein_fdr.columns]\n",
     "    median = protein_fdr.groupby('filename')[cols].median()\n",
     "    median.index = [os.path.split(_)[1][:-12] for _ in median.index]\n",
     "\n",
@@ -1148,7 +1161,7 @@
     "                    f_summary['id_rate (peptide_fdr)'] = float(df['raw_idx'].nunique() / n_ms2)\n",
     "\n",
     "            if key in ['feature_table','peptide_fdr']:\n",
-    "                for field in ['fwhm','int_sum','rt_length','rt_tail','o_mass_ppm_raw']:\n",
+    "                for field in ['fwhm','int_sum','rt_length','rt_tail','prec_offset_raw_ppm ']:\n",
     "                    if field in df.columns:\n",
     "                        f_summary[f'{field} ({key}, median)'] = float(df[field].median())\n",
     "\n",
@@ -1319,7 +1332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1577,7 +1590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1596,11 +1609,15 @@
       "Converted 09_matching.ipynb.\n",
       "Converted 10_constants.ipynb.\n",
       "Converted 11_interface.ipynb.\n",
-      "Converted 12_speed.ipynb.\n",
+      "Converted 12_performance.ipynb.\n",
       "Converted 13_export.ipynb.\n",
-      "Converted 13_performance.ipynb.\n",
+      "Converted additional_code.ipynb.\n",
+      "Converted contributing.ipynb.\n",
       "Converted file_formats.ipynb.\n",
-      "Converted index.ipynb.\n"
+      "Converted index.ipynb.\n",
+      "Converted Untitled.ipynb.\n",
+      "Converted Untitled1.ipynb.\n",
+      "Converted Untitled2.ipynb.\n"
      ]
     }
    ],
@@ -1616,6 +1633,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/12_performance.ipynb b/nbs/12_performance.ipynb
index 587ac5cd..1e781c67 100644
--- a/nbs/12_performance.ipynb
+++ b/nbs/12_performance.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "6b389571",
    "metadata": {},
    "outputs": [],
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "0b133da9",
    "metadata": {},
    "outputs": [],
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "0474545b",
    "metadata": {},
    "outputs": [],
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "bab19525",
    "metadata": {},
    "outputs": [],
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "2374af62",
    "metadata": {},
    "outputs": [],
@@ -217,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "c88f3372",
    "metadata": {},
    "outputs": [],
@@ -348,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "336d8237",
    "metadata": {},
    "outputs": [],
@@ -439,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "8cf25b1c",
    "metadata": {},
    "outputs": [],
@@ -615,7 +615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "94b12ba5",
    "metadata": {},
    "outputs": [
@@ -623,9 +623,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Wall time: 4.01 s\n",
-      "Wall time: 6.51 s\n",
-      "Wall time: 752 ms\n"
+      "Wall time: 3.27 s\n",
+      "Wall time: 6.19 s\n",
+      "Wall time: 665 ms\n"
      ]
     }
    ],
@@ -687,7 +687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "eb95b609",
    "metadata": {},
    "outputs": [],
@@ -701,7 +701,7 @@
     "    Args:\n",
     "        process_count (int): The number of processes.\n",
     "            If larger than available cores, it is trimmed to the available maximum.\n",
-    "            If 0, it is set to the maximum cores available (at most 50).\n",
+    "\n",
     "\n",
     "    Returns:\n",
     "        multiprocessing.Pool: A Pool object to parallelize functions with multiple processes.\n",
@@ -709,6 +709,9 @@
     "    \"\"\"\n",
     "    max_processes = psutil.cpu_count()\n",
     "    new_max = min(process_count, 50, max_processes)\n",
+    "    \n",
+    "    if new_max == 0:\n",
+    "        new_max = 1\n",
     "    logging.info(f\"AlphaPool was set to {process_count} processes. Setting max to {new_max}.\")\n",
     "\n",
     "    return Pool(new_max)"
@@ -716,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "96bb511d",
    "metadata": {},
    "outputs": [
@@ -741,7 +744,8 @@
       "Converted additional_code.ipynb.\n",
       "Converted contributing.ipynb.\n",
       "Converted file_formats.ipynb.\n",
-      "Converted index.ipynb.\n"
+      "Converted index.ipynb.\n",
+      "Converted Untitled.ipynb.\n"
      ]
     }
    ],
@@ -754,9 +758,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/file_formats.ipynb b/nbs/file_formats.ipynb
index 4f3c75a5..95db1ed0 100644
--- a/nbs/file_formats.ipynb
+++ b/nbs/file_formats.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "0e33659e",
    "metadata": {},
    "outputs": [],
@@ -44,6 +44,11 @@
     "Whenever an experiment fails, the `*.yaml`-file will be moved to the `Failed`-folder. It can be moved from there to the `Queue`-folder for reprocessing.\n",
     "\n",
     "\n",
+    "### FASTA files\n",
+    "\n",
+    "When FASTA files are downloaded via the AlphaPept GUI, the files are stored in the FASTA folder in the alphapept home directory (e.g. `C:\\Users\\username\\.alphapept\\fasta`).\n",
+    "\n",
+    "\n",
     "### History and Results\n",
     "\n",
     "AlphaPept screens all `*.yaml`-files in the finished folder and plots a run history based on the summary information. This is especially useful for QC or comparison purposes. Additionally, the `*.yaml`-files can be used to investigate the results of a run.\n",
@@ -102,7 +107,7 @@
     "fwhm | fwhm of the feature\n",
     "hits | total number of b- and y-ion hits. A hit occurs when a theoretical fragment can be found within the `frag_tol` of an experimentally recorded fragment\n",
     "int_apex | intensity at feature apex\n",
-    "int_ratio | mean intensity ratio: matched database fragment intensity divided by experimental fragment intensity (if no db intensity is available it is set to 1)\n",
+    "int_ratio | mean intensity ratio: experimental fragment intensity divided by theoretical intensity (if no db intensity is available db intensity is set to 1) for each matched ion\n",
     "int_sum | summed intensity of the MS1-feature\n",
     "ion_idx | index to ion dataframe for this PSM\n",
     "ion_int | intensity of each matched ion\n",
@@ -117,10 +122,10 @@
     "n_ions | number of matched ions\n",
     "n_missed | number of missed cleavages\n",
     "naked_sequence | naked sequence (i.e. withouth modifications)\n",
-    "o_mass | mass offset from theoretical precursor mass to actual precursor mass\n",
-    "o_mass_ppm | o_mass in ppm\n",
-    "o_mass_ppm_raw | o_mass_raw in ppm\n",
-    "o_mass_raw | mass offset from theoretical precursor mass to actual precursor mass (before recalibration)\n",
+    "prec_offset | mass offset from theoretical precursor mass to actual precursor mass\n",
+    "prec_offset_ppm | prec_offset in ppm\n",
+    "prec_offset_raw_ppm  | prec_offset_raw  in ppm\n",
+    "prec_offset_raw  | mass offset from theoretical precursor mass to actual precursor mass (before recalibration)\n",
     "precursor | peptide + charge\n",
     "q_value | q_value for FDR estiamtion\n",
     "query_idx | index to query (for the spectrum index use raw_idx)\n",
@@ -200,6 +205,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/sandbox/Systems_check.ipynb b/sandbox/Systems_check.ipynb
index 67500fe6..9ff3f0a1 100644
--- a/sandbox/Systems_check.ipynb
+++ b/sandbox/Systems_check.ipynb
@@ -977,7 +977,7 @@
       "2021-06-05 01:28:18> The train accuracy was 0.9375449759497027\n",
       "2021-06-05 01:28:18> Testing on 52807 targets and 52808 decoys\n",
       "2021-06-05 01:28:18> The test accuracy was 0.9120768830185106\n",
-      "2021-06-05 01:28:18> Top features {'y_hits': 0.25201998880892984, 'x_tandem': 0.19904685994420376, 'hits': 0.17195714349201746, 'matched_ion_fraction': 0.07454271584891049, 'n_ions': 0.042067537454321444, 'matched_int_ratio': 0.04114609590298047, 'matched_int': 0.028741739095552207, 'n_missed': 0.028141098265042186, 'b_hits': 0.019242241955068257, 'dist': 0.013153068407932624, 'mz': 0.011432925921745727, 'o_mass_ppm': 0.009885530940336867, 'o_mass_ppm_raw': 0.009804386820815563, 'y-NH3_hits': 0.008997724180330721, 'b-H2O_hits': 0.008801278312162349, 'o_mass': 0.007618288580360598, 'mass': 0.007181766500458176, 'int_apex': 0.006727921265581443, 'y-H2O_hits': 0.006710237484733788, 'n_AA': 0.006530381057705235, 'int_sum': 0.006312872261313745, 'total_int': 0.005226752095698083, 'o_mass_raw': 0.0037907451751688255, 'rt_end': 0.0037428383009209137, 'rt_apex': 0.0036024929947588878, 'rt': 0.003435713995701453, 'rt_start': 0.0034089820233858554, 'fwhm': 0.003070837728250657, 'abs_delta_m_ppm': 0.002896119293633937, 'scan_no': 0.0028954934568110407, 'delta_m_ppm': 0.0028600128562269456, 'delta_m': 0.0026314906965032524, 'b-NH3_hits': 0.0010606477974798233, 'charge': 0.0009199372938580769, 'n_internal': 0.00039613379109945337, 'int_ratio': 0.0}\n",
+      "2021-06-05 01:28:18> Top features {'y_hits': 0.25201998880892984, 'x_tandem': 0.19904685994420376, 'hits': 0.17195714349201746, 'matched_ion_fraction': 0.07454271584891049, 'n_ions': 0.042067537454321444, 'matched_int_ratio': 0.04114609590298047, 'matched_int': 0.028741739095552207, 'n_missed': 0.028141098265042186, 'b_hits': 0.019242241955068257, 'dist': 0.013153068407932624, 'mz': 0.011432925921745727, 'prec_offset_ppm': 0.009885530940336867, 'o_mass_ppm_raw': 0.009804386820815563, 'y-NH3_hits': 0.008997724180330721, 'b-H2O_hits': 0.008801278312162349, 'prec_offset': 0.007618288580360598, 'mass': 0.007181766500458176, 'int_apex': 0.006727921265581443, 'y-H2O_hits': 0.006710237484733788, 'n_AA': 0.006530381057705235, 'int_sum': 0.006312872261313745, 'total_int': 0.005226752095698083, 'o_mass_raw': 0.0037907451751688255, 'rt_end': 0.0037428383009209137, 'rt_apex': 0.0036024929947588878, 'rt': 0.003435713995701453, 'rt_start': 0.0034089820233858554, 'fwhm': 0.003070837728250657, 'abs_delta_m_ppm': 0.002896119293633937, 'scan_no': 0.0028954934568110407, 'delta_m_ppm': 0.0028600128562269456, 'delta_m': 0.0026314906965032524, 'b-NH3_hits': 0.0010606477974798233, 'charge': 0.0009199372938580769, 'n_internal': 0.00039613379109945337, 'int_ratio': 0.0}\n",
       "2021-06-05 01:28:18> Filter df with x_tandem score\n",
       "2021-06-05 01:28:22> Global FDR on precursor\n",
       "2021-06-05 01:28:22> 51,715 target (517 decoy) of 86846 PSMs. fdr 0.009997 for a cutoff of 0.76 (set fdr was 0.01)\n",
@@ -1252,7 +1252,7 @@
       "2021-06-05 01:29:23> The train accuracy was 0.9375449759497027\n",
       "2021-06-05 01:29:23> Testing on 52807 targets and 52808 decoys\n",
       "2021-06-05 01:29:24> The test accuracy was 0.9120768830185106\n",
-      "2021-06-05 01:29:24> Top features {'y_hits': 0.25201998880892984, 'x_tandem': 0.19904685994420376, 'hits': 0.17195714349201746, 'matched_ion_fraction': 0.07454271584891049, 'n_ions': 0.042067537454321444, 'matched_int_ratio': 0.04114609590298047, 'matched_int': 0.028741739095552207, 'n_missed': 0.028141098265042186, 'b_hits': 0.019242241955068257, 'dist': 0.013153068407932624, 'mz': 0.011432925921745727, 'o_mass_ppm': 0.009885530940336867, 'o_mass_ppm_raw': 0.009804386820815563, 'y-NH3_hits': 0.008997724180330721, 'b-H2O_hits': 0.008801278312162349, 'o_mass': 0.007618288580360598, 'mass': 0.007181766500458176, 'int_apex': 0.006727921265581443, 'y-H2O_hits': 0.006710237484733788, 'n_AA': 0.006530381057705235, 'int_sum': 0.006312872261313745, 'total_int': 0.005226752095698083, 'o_mass_raw': 0.0037907451751688255, 'rt_end': 0.0037428383009209137, 'rt_apex': 0.0036024929947588878, 'rt': 0.003435713995701453, 'rt_start': 0.0034089820233858554, 'fwhm': 0.003070837728250657, 'abs_delta_m_ppm': 0.002896119293633937, 'scan_no': 0.0028954934568110407, 'delta_m_ppm': 0.0028600128562269456, 'delta_m': 0.0026314906965032524, 'b-NH3_hits': 0.0010606477974798233, 'charge': 0.0009199372938580769, 'n_internal': 0.00039613379109945337, 'int_ratio': 0.0}\n"
+      "2021-06-05 01:29:24> Top features {'y_hits': 0.25201998880892984, 'x_tandem': 0.19904685994420376, 'hits': 0.17195714349201746, 'matched_ion_fraction': 0.07454271584891049, 'n_ions': 0.042067537454321444, 'matched_int_ratio': 0.04114609590298047, 'matched_int': 0.028741739095552207, 'n_missed': 0.028141098265042186, 'b_hits': 0.019242241955068257, 'dist': 0.013153068407932624, 'mz': 0.011432925921745727, 'prec_offset_ppm': 0.009885530940336867, 'o_mass_ppm_raw': 0.009804386820815563, 'y-NH3_hits': 0.008997724180330721, 'b-H2O_hits': 0.008801278312162349, 'prec_offset': 0.007618288580360598, 'mass': 0.007181766500458176, 'int_apex': 0.006727921265581443, 'y-H2O_hits': 0.006710237484733788, 'n_AA': 0.006530381057705235, 'int_sum': 0.006312872261313745, 'total_int': 0.005226752095698083, 'o_mass_raw': 0.0037907451751688255, 'rt_end': 0.0037428383009209137, 'rt_apex': 0.0036024929947588878, 'rt': 0.003435713995701453, 'rt_start': 0.0034089820233858554, 'fwhm': 0.003070837728250657, 'abs_delta_m_ppm': 0.002896119293633937, 'scan_no': 0.0028954934568110407, 'delta_m_ppm': 0.0028600128562269456, 'delta_m': 0.0026314906965032524, 'b-NH3_hits': 0.0010606477974798233, 'charge': 0.0009199372938580769, 'n_internal': 0.00039613379109945337, 'int_ratio': 0.0}\n"
      ]
     },
     {
@@ -4259,7 +4259,7 @@
       "2021-06-05 03:56:01> The train accuracy was 0.9359463584823375\n",
       "2021-06-05 03:56:01> Testing on 73376 targets and 73376 decoys\n",
       "2021-06-05 03:56:03> The test accuracy was 0.921445704317488\n",
-      "2021-06-05 03:56:03> Top features {'hits': 0.22283315315843608, 'y_hits': 0.20750783808742532, 'x_tandem': 0.14313745807021594, 'matched_ion_fraction': 0.059521825508770596, 'matched_int_ratio': 0.054373517502535565, 'total_int': 0.051515485522970195, 'n_ions': 0.03904978615331172, 'dist': 0.029410259616077798, 'int_sum': 0.02773321560740404, 'n_missed': 0.025384439729050117, 'b_hits': 0.01667244857561743, 'o_mass_ppm': 0.015941556543346926, 'o_mass': 0.014872634724752821, 'o_mass_ppm_raw': 0.014742783327369238, 'o_mass_raw': 0.010112254360787519, 'matched_int': 0.00959673155249209, 'mass': 0.0075545802330231485, 'n_AA': 0.0059522414122282425, 'mobility': 0.005481861801640103, 'y-NH3_hits': 0.004541550085043558, 'mz': 0.004419392001182855, 'charge': 0.003686823528481866, 'feature_id': 0.003653968208952646, 'b-NH3_hits': 0.0024614543137067985, 'rt_end': 0.0024282373156542963, 'parent': 0.0023839970489349226, 'delta_m_ppm': 0.0022595860954326154, 'delta_m': 0.0022121311294211726, 'rt_apex': 0.0021489313392836517, 'rt': 0.002148736482717601, 'rt_start': 0.002128076479372576, 'abs_delta_m_ppm': 0.0020384440304450804, 'b-H2O_hits': 0.0011632891877200759, 'y-H2O_hits': 0.0008411404310696654, 'n_internal': 9.017083512560373e-05, 'int_ratio': 0.0}\n",
+      "2021-06-05 03:56:03> Top features {'hits': 0.22283315315843608, 'y_hits': 0.20750783808742532, 'x_tandem': 0.14313745807021594, 'matched_ion_fraction': 0.059521825508770596, 'matched_int_ratio': 0.054373517502535565, 'total_int': 0.051515485522970195, 'n_ions': 0.03904978615331172, 'dist': 0.029410259616077798, 'int_sum': 0.02773321560740404, 'n_missed': 0.025384439729050117, 'b_hits': 0.01667244857561743, 'prec_offset_ppm': 0.015941556543346926, 'prec_offset': 0.014872634724752821, 'o_mass_ppm_raw': 0.014742783327369238, 'o_mass_raw': 0.010112254360787519, 'matched_int': 0.00959673155249209, 'mass': 0.0075545802330231485, 'n_AA': 0.0059522414122282425, 'mobility': 0.005481861801640103, 'y-NH3_hits': 0.004541550085043558, 'mz': 0.004419392001182855, 'charge': 0.003686823528481866, 'feature_id': 0.003653968208952646, 'b-NH3_hits': 0.0024614543137067985, 'rt_end': 0.0024282373156542963, 'parent': 0.0023839970489349226, 'delta_m_ppm': 0.0022595860954326154, 'delta_m': 0.0022121311294211726, 'rt_apex': 0.0021489313392836517, 'rt': 0.002148736482717601, 'rt_start': 0.002128076479372576, 'abs_delta_m_ppm': 0.0020384440304450804, 'b-H2O_hits': 0.0011632891877200759, 'y-H2O_hits': 0.0008411404310696654, 'n_internal': 9.017083512560373e-05, 'int_ratio': 0.0}\n",
       "2021-06-05 03:56:03> Filter df with x_tandem score\n",
       "2021-06-05 03:56:07> Global FDR on precursor\n",
       "2021-06-05 03:56:07> 75,987 target (759 decoy) of 100425 PSMs. fdr 0.009989 for a cutoff of 0.44 (set fdr was 0.01)\n",
@@ -22965,7 +22965,7 @@
       "2021-06-05 10:33:36> The train accuracy was 0.7998606553692633\n",
       "2021-06-05 10:33:36> Testing on 88988 targets and 88988 decoys\n",
       "2021-06-05 10:33:38> The test accuracy was 0.7501685620533105\n",
-      "2021-06-05 10:33:38> Top features {'x_tandem': 0.19005923137398673, 'y_hits': 0.18209530608382116, 'hits': 0.1476075346052496, 'dist': 0.10744160797304303, 'matched_ion_fraction': 0.048936497167915455, 'n_ions': 0.040729906063940675, 'matched_int_ratio': 0.03327799116101763, 'mass': 0.03101247101324796, 'n_AA': 0.02264474574627197, 'mz': 0.01956790465688868, 'o_mass': 0.011325708334918352, 'b_hits': 0.011290548820343808, 'int_apex': 0.01069085259682615, 'o_mass_ppm_raw': 0.01063959481128551, 'int_sum': 0.010325620874015362, 'matched_int': 0.009798818651385287, 'o_mass_ppm': 0.009635094588587741, 'y-NH3_hits': 0.009612523301067329, 'b-H2O_hits': 0.009185891418499346, 'n_missed': 0.008789944079014289, 'y-H2O_hits': 0.008540603431497788, 'total_int': 0.007553621417831869, 'o_mass_raw': 0.00754256518872208, 'abs_delta_m_ppm': 0.006562298086997074, 'fwhm': 0.005807758997132404, 'delta_m_ppm': 0.005431665076329272, 'delta_m': 0.005282899001724042, 'rt_start': 0.005120497826432484, 'scan_no': 0.004673053190834252, 'rt_apex': 0.004419762853906235, 'rt_end': 0.004374431112811891, 'rt': 0.004168926704827139, 'charge': 0.0039247260022841615, 'b-NH3_hits': 0.0019192376198658993, 'n_internal': 1.0160167477321592e-05, 'int_ratio': 0.0}\n",
+      "2021-06-05 10:33:38> Top features {'x_tandem': 0.19005923137398673, 'y_hits': 0.18209530608382116, 'hits': 0.1476075346052496, 'dist': 0.10744160797304303, 'matched_ion_fraction': 0.048936497167915455, 'n_ions': 0.040729906063940675, 'matched_int_ratio': 0.03327799116101763, 'mass': 0.03101247101324796, 'n_AA': 0.02264474574627197, 'mz': 0.01956790465688868, 'prec_offset': 0.011325708334918352, 'b_hits': 0.011290548820343808, 'int_apex': 0.01069085259682615, 'o_mass_ppm_raw': 0.01063959481128551, 'int_sum': 0.010325620874015362, 'matched_int': 0.009798818651385287, 'prec_offset_ppm': 0.009635094588587741, 'y-NH3_hits': 0.009612523301067329, 'b-H2O_hits': 0.009185891418499346, 'n_missed': 0.008789944079014289, 'y-H2O_hits': 0.008540603431497788, 'total_int': 0.007553621417831869, 'o_mass_raw': 0.00754256518872208, 'abs_delta_m_ppm': 0.006562298086997074, 'fwhm': 0.005807758997132404, 'delta_m_ppm': 0.005431665076329272, 'delta_m': 0.005282899001724042, 'rt_start': 0.005120497826432484, 'scan_no': 0.004673053190834252, 'rt_apex': 0.004419762853906235, 'rt_end': 0.004374431112811891, 'rt': 0.004168926704827139, 'charge': 0.0039247260022841615, 'b-NH3_hits': 0.0019192376198658993, 'n_internal': 1.0160167477321592e-05, 'int_ratio': 0.0}\n",
       "2021-06-05 10:33:38> Filter df with x_tandem score\n",
       "2021-06-05 10:33:46> Global FDR on precursor\n",
       "2021-06-05 10:33:46> 43,081 target (430 decoy) of 95694 PSMs. fdr 0.009981 for a cutoff of 0.81 (set fdr was 0.01)\n",
@@ -23963,7 +23963,7 @@
       "2021-06-05 11:13:30> The train accuracy was 0.8010878181978331\n",
       "2021-06-05 11:13:30> Testing on 90825 targets and 90825 decoys\n",
       "2021-06-05 11:13:31> The test accuracy was 0.7576768510872557\n",
-      "2021-06-05 11:13:31> Top features {'y_hits': 0.19341830463516335, 'x_tandem': 0.15707904149369276, 'hits': 0.14290113657465703, 'matched_ion_fraction': 0.09253537268494087, 'dist': 0.06442128708501854, 'matched_int_ratio': 0.05026688728569285, 'n_ions': 0.03816474336386547, 'mass': 0.026138052621694177, 'matched_int': 0.024957613383679526, 'n_missed': 0.019521465497425474, 'n_AA': 0.01818398877112089, 'b_hits': 0.016851669230205875, 'mz': 0.014215154231496744, 'o_mass': 0.010037387685466699, 'int_apex': 0.009303978137399728, 'o_mass_ppm': 0.008897032367230797, 'y-H2O_hits': 0.008806869287511643, 'int_sum': 0.008591900746738133, 'abs_delta_m_ppm': 0.007936962865461689, 'y-NH3_hits': 0.007917743818584863, 'o_mass_ppm_raw': 0.007313495051916128, 'total_int': 0.007130392012850771, 'o_mass_raw': 0.007127430948488335, 'scan_no': 0.007078716150699843, 'rt_apex': 0.006971778661951278, 'fwhm': 0.006278111723651434, 'rt_start': 0.006116613401483698, 'rt_end': 0.005792029541756065, 'b-H2O_hits': 0.005651568067091318, 'delta_m': 0.0055445648197111, 'delta_m_ppm': 0.005415405024571821, 'rt': 0.004984774357593448, 'charge': 0.003212910864316486, 'b-NH3_hits': 0.0012110225384802879, 'n_internal': 2.4595068390943825e-05, 'int_ratio': 0.0}\n",
+      "2021-06-05 11:13:31> Top features {'y_hits': 0.19341830463516335, 'x_tandem': 0.15707904149369276, 'hits': 0.14290113657465703, 'matched_ion_fraction': 0.09253537268494087, 'dist': 0.06442128708501854, 'matched_int_ratio': 0.05026688728569285, 'n_ions': 0.03816474336386547, 'mass': 0.026138052621694177, 'matched_int': 0.024957613383679526, 'n_missed': 0.019521465497425474, 'n_AA': 0.01818398877112089, 'b_hits': 0.016851669230205875, 'mz': 0.014215154231496744, 'prec_offset': 0.010037387685466699, 'int_apex': 0.009303978137399728, 'prec_offset_ppm': 0.008897032367230797, 'y-H2O_hits': 0.008806869287511643, 'int_sum': 0.008591900746738133, 'abs_delta_m_ppm': 0.007936962865461689, 'y-NH3_hits': 0.007917743818584863, 'o_mass_ppm_raw': 0.007313495051916128, 'total_int': 0.007130392012850771, 'o_mass_raw': 0.007127430948488335, 'scan_no': 0.007078716150699843, 'rt_apex': 0.006971778661951278, 'fwhm': 0.006278111723651434, 'rt_start': 0.006116613401483698, 'rt_end': 0.005792029541756065, 'b-H2O_hits': 0.005651568067091318, 'delta_m': 0.0055445648197111, 'delta_m_ppm': 0.005415405024571821, 'rt': 0.004984774357593448, 'charge': 0.003212910864316486, 'b-NH3_hits': 0.0012110225384802879, 'n_internal': 2.4595068390943825e-05, 'int_ratio': 0.0}\n",
       "2021-06-05 11:13:31> Filter df with x_tandem score\n",
       "2021-06-05 11:13:36> Global FDR on precursor\n",
       "2021-06-05 11:13:37> 53,032 target (530 decoy) of 88404 PSMs. fdr 0.009994 for a cutoff of 0.70 (set fdr was 0.01)\n",
diff --git a/settings.ini b/settings.ini
index c327174b..d27fea3b 100644
--- a/settings.ini
+++ b/settings.ini
@@ -7,7 +7,7 @@ author = Maximilian T. Strauss
 author_email = straussmaximilian@gmail.com
 copyright = Mann Labs
 branch = master
-version = 0.3.27
+version = 0.3.28
 min_python = 3.6
 max_python = 4
 audience = Developers


Test patch:
diff --git a/test_ci.py b/test_ci.py
index d8b676c2..db60df7e 100644
--- a/test_ci.py
+++ b/test_ci.py
@@ -323,6 +323,7 @@ def main(runtype = None, password = None, new_files = True):
         MONGODB_URL = 'ci.yue0n.mongodb.net/'
         config_test_paths(BASE_DIR, TEST_DIR, ARCHIVE_DIR, MONGODB_USER, MONGODB_URL)
 
+    AVAILABLE = ['bruker_irt', 'bruker_hela', 'thermo_irt', 'thermo_hela', 'thermo_hela_large_fasta', 'thermo_hela_modifications', 'PXD006109', 'PXD010012']
 
     if runtype == 'bruker_irt':
         files = ['bruker_IRT.d']
@@ -378,7 +379,7 @@ def main(runtype = None, password = None, new_files = True):
         test_run.run(password=password)
 
     else:
-        raise NotImplementedError(runtype)
+        raise NotImplementedError(f"Runtime {runtype} not found. Available are {AVAILABLE}")
 
 
 if __name__ == "__main__":
diff --git a/testfiles/test.ms_data.hdf b/testfiles/test.ms_data.hdf
index 0ca9fc51..125dd225 100644
Binary files a/testfiles/test.ms_data.hdf and b/testfiles/test.ms_data.hdf differ