adopted peptide.py to work with the current version of block_analyze,…

README.md

@@ -63,11 +63,12 @@ sudo apt install python3-venv
 To set up pyMBE, the users need to install its virtual environment, install its Python dependencies and configure the path to the ESPResSo build folder as follows:
 
 ```sh
-python3 -m venv pymbe
-source pymbe/bin/activate
-python3 maintainer/configure_venv.py --espresso_path=/home/user/espresso/build # adapt path
+python3 -m venv pymbe  # creates a local folder named pymbe, which contains the virtual environment
+source pymbe/bin/activate  # activates the pymbe venv
+python3 maintainer/configure_venv.py --espresso_path=/home/user/espresso/build # please, adapt the espresso path accordingly
 python3 -m pip install -r requirements.txt
-deactivate
+python3 simulation_script.py # run the espresso simulation script
+deactivate  # deactivate the virtual environment
 ```
 
 We highlight that the path `/home/user/espresso/build` is just an example of a possible

lib/analysis.py

@@ -42,14 +42,15 @@ def add_data_to_df(df, data_dict, index):
                          index=index)])
     return updated_df
 
-def analyze_time_series(path_to_datafolder, filename_extension= ".csv", minus_separator = False,):
+def analyze_time_series(path_to_datafolder, filename_extension= ".csv", minus_separator = False, ignore_files=None):
     """
     Analyzes all time series stored in `path_to_datafolder` using the block binning method.
 
     Args:
         path_to_datafolder(`str`): path to the folder with the files with the time series
         filename_extension(`str`): extension of the file. Defaults to ".csv"
         minus_separator(`bool`): switch to enable the minus as a separator. Defaults to False.
+        ignore_files(`lst`): list of filenames to be ignored for the bining analysis.
 
     Returns:
         data(`Pandas.Dataframe`): Dataframe with the time averages of all the time series in the datafolder.
@@ -59,10 +60,21 @@ def analyze_time_series(path_to_datafolder, filename_extension= ".csv", minus_se
 
     """
     data=pd.DataFrame()
+    if ignore_files is None:
+        ignore_files=[]
     with os.scandir(path_to_datafolder) as subdirectory:
         # Gather all data
         for subitem in subdirectory:
             if subitem.is_file():
+                ignore_file=False
+                for file in ignore_files:
+                    if set(file.split()) == set(subitem.name.split()):
+                        ignore_file=True
+                if ignore_file:
+                    continue
+                print(subitem.name)
+                from time import sleep
+                sleep(5)
                 if filename_extension in subitem.name:
                     # Get parameters from the file name
                     data_dict=get_params_from_file_name(file_name=subitem.name,

samples/analyze_time_series.py

@@ -0,0 +1,33 @@
+#
+# Copyright (C) 2024 pyMBE-dev team
+#
+# This file is part of pyMBE.
+#
+# pyMBE is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# pyMBE is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+import argparse
+from lib import analysis
+
+parser = argparse.ArgumentParser(description='Sample script analyze time series from the other sample scripts using the binning method.')
+parser.add_argument('--data_folder',
+                    type=str,
+                    required=True,
+                    help='path to the data folder with the time series')
+args = parser.parse_args()
+
+# Read and analyze time series
+analyzed_data=analysis.analyze_time_series(path_to_datafolder=args.data_folder,
+                                            ignore_files=["analyzed_data.csv"])
+analyzed_data.to_csv(f"{args.data_folder}/analyzed_data.csv", 
+                        index=False)