deleted pssession file because it was very big before pull request

[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

pydatalab/pydatalab/apps/PSTrace/PalmSensReader.py

@@ -6,12 +6,12 @@
 """
 
 
+
 import pandas as pd
-import os.path, glob
-import csv
-import re
-from io import StringIO
-import numpy as np
+
+filename = (
+    "PalmSense_test_datalab.csv"  # file with experimetnal data as exported "as csv" from PSTrace
+)
 
 <<<<<<< HEAD
 # The following lines adjust the granularity of reporting. 
@@ -20,6 +20,7 @@
 #pd.options.display.float_format = "{:.1f}".format
 <<<<<<< HEAD
 
+<<<<<<< HEAD
 
 <<<<<<< HEAD
 <<<<<<< HEAD
@@ -64,6 +65,9 @@ def getdata(filename, file_encoding='utf-16 LE', verbose = False):
 
 def getdata(filename, file_encoding='utf-16 LE', verbose = False ):
 >>>>>>> 2d1266c (Formated EIS part of the output files)
+=======
+def getdata(filename, file_encoding="utf-16 LE", verbose=False):
+>>>>>>> 2542e1a ([pre-commit.ci] auto fixes from pre-commit.com hooks)
     """
     Loads experimental data from a CSV file, splits the DataFrame based on a specified keyword,
     and returns a dictionary containing the resulting DataFrames.
@@ -77,9 +81,8 @@ def getdata(filename, file_encoding='utf-16 LE', verbose = False ):
       it is normally any type of measurement in the first df, and EIS in the following ones
     """
 
-
     # Open the file to get the number of columns in each line
-    with open(filename, 'r', encoding=file_encoding) as temp_f:
+    with open(filename, "r", encoding=file_encoding) as temp_f:
         # Get the number of columns in each line
         col_count = [len(l.split(",")) for l in temp_f.readlines()]
 
@@ -92,15 +95,14 @@ def getdata(filename, file_encoding='utf-16 LE', verbose = False ):
 
     # Find the locations of the keyword "Measurement" in any column. The file onlyhas that when an EIS mesurmment starts
     mask = df.apply(lambda row: row.astype(str).str.contains("Measurement"), axis=1)
-    mask['Any'] = mask.any(axis=1)
+    mask["Any"] = mask.any(axis=1)
     groups = mask["Any"].cumsum()
 
     # Split the DataFrame based on the keyword occurrences and drop columns with all NaN values
-    split_dfs = {group: df[group == groups].dropna(axis=1, how='all') 
-                                         for group in groups.unique()}
-
+    split_dfs = {group: df[group == groups].dropna(axis=1, how="all") for group in groups.unique()}
+
     # Display the split DataFrames if verbose=True, default value is False
-    if verbose: 
+    if verbose:
         for key, split_df in split_dfs.items():
             print(f"DataFrame for splitting keyword = Measurement occurrence {key}:")
             print(split_df)
@@ -109,6 +111,7 @@ def getdata(filename, file_encoding='utf-16 LE', verbose = False ):
     return split_dfs
 
 <<<<<<< HEAD
+<<<<<<< HEAD
 
 <<<<<<< HEAD
 <<<<<<< HEAD
@@ -200,19 +203,23 @@ def formatdata(split_dfs):
 =======
 >>>>>>> 4fa42bc (code reads all parts of csv output file from PSTrace)
 =======
+=======
+
+>>>>>>> 2542e1a ([pre-commit.ci] auto fixes from pre-commit.com hooks)
 def find_row(df, keyword):
     """This function finds the index of a row containiing a keyword
-    Args: 
+    Args:
     - df : DataFrame where we are searching
     - keyword (str): the keyword it searches"""
-    
+
     return df[df.apply(lambda row: row.astype(str).str.contains(keyword)).any(axis=1)].index[0]
 >>>>>>> 3ca1a83 (cleaned up a bit)
 
+
 def format_impedance_data(split_dfs):
     """
     Formats the data extracted from split_dfs, filtering the data related to Impedance measurements.
-    
+
     Args:
     - split_dfs (dict): A dictionary containing values as dataframes for different measurements,
     that are the result of the function getdata(filename)
@@ -221,50 +228,57 @@ def format_impedance_data(split_dfs):
     - impedance_dfs (dict): A dictionary storing dataframes related to Impedance measurements,
       indexed by their respective keys, each containing the measurement name, date, and the actual data.
     """
-    
+
     # Initialize a list to store keys of dataframes with 'freq / Hz'
     dfs_with_freq = []
-    
+
     # Create a dictionary to hold dataframes with impedance measurements
     impedance_dfs = {}
-    
+
     # Check if 'freq / Hz' exists in any dataframe, and append the key to a list
     for key, df in split_dfs.items():
-
-        if df.apply(lambda row: row.astype(str).str.contains('freq / Hz')).any().any():
+        if df.apply(lambda row: row.astype(str).str.contains("freq / Hz")).any().any():
             # Reset the index of the dataframe
-            df = split_dfs[key].reset_index(drop=True)            
+            df = split_dfs[key].reset_index(drop=True)
             dfs_with_freq.append(key)
-            
+
             # Find the row index that contains 'Measurement' to find the name to use
             name_row = find_row(df, "Measurement")
             new_name = df.iloc[name_row][1]
-            
+
             # Find the row index that contains 'Date and time' for the date and time
-            date_row = find_row(df, 'Date and time')
+            date_row = find_row(df, "Date and time")
             date_time = df.iloc[date_row][1]
-            
-            # Find the index of the row containing the string 'freq / Hz' to use as df header            
-            freq_row = find_row(df, 'freq / Hz' )
+
+            # Find the index of the row containing the string 'freq / Hz' to use as df header
+            freq_row = find_row(df, "freq / Hz")
             df.columns = df.iloc[freq_row]
-     
+
             # Remove the row that contains 'freq / Hz' and rows before it
             df = df.drop(freq_row).drop(index=range(0, freq_row))
-            
+
             # Store the extracted information and data in the impedance_dfs dictionary
-            impedance_dfs[f"EIS measurement {key}"] = {"Name" : new_name, "Date and Time": date_time, "Data": df}
-
+            impedance_dfs[f"EIS measurement {key}"] = {
+                "Name": new_name,
+                "Date and Time": date_time,
+                "Data": df,
+            }
+
     # Check if there are dataframes with 'freq / Hz'
     if dfs_with_freq:
         n = len(dfs_with_freq)
         print(f"There are {n} Impedance measurements")
     else:
         print("The are no Impedance measurements")
+<<<<<<< HEAD
 
 <<<<<<< HEAD
 >>>>>>> 2d1266c (Formated EIS part of the output files)
 
 =======
+=======
+
+>>>>>>> 2542e1a ([pre-commit.ci] auto fixes from pre-commit.com hooks)
     return impedance_dfs
 <<<<<<< HEAD
 
@@ -276,56 +290,62 @@ def format_impedance_data(split_dfs):
 def format_DC_data(split_dfs):
     """
     Extracts and formats DC (direct current) measurement data from a collection of DataFrames.
-    
+
     Args:
-    split_dfs (dict): A dictionary containing DataFrames to process, 
+    split_dfs (dict): A dictionary containing DataFrames to process,
     comes from the funtion getdata(filename)
-    
+
     Returns:
     dict: Dictionary of formatted DC measurement data.
     """
-    
+
     dfs_DC_meas = []
     DC_data = False
     # Process each DataFrame in the input dictionary
     for key, df in split_dfs.items():
         # Check for the presence of 'Date and time measurement:', only present in dataframes of DC measurements
-        if df.apply(lambda row: row.astype(str).str.contains('Date and time measurement:')).any().any():
+        if (
+            df.apply(lambda row: row.astype(str).str.contains("Date and time measurement:"))
+            .any()
+            .any()
+        ):
             DC_data = True
             # Reset the index of the dataframe
             df = split_dfs[key].reset_index(drop=True)
-
 
             # Find and remove the row index containing 'File date:' because it belongs to EIS measurements
             # it is an artifact of how the different dataframes were split by getdata(filename)
-            row_filedate = find_row(df, 'File date:')
+            row_filedate = find_row(df, "File date:")
             df = df.drop(df.index[row_filedate])
 
-            # Create a dictionary of DataFrames with two columns each 
+            # Create a dictionary of DataFrames with two columns each
             # (each DC measurements only consists on 2 columns that can change in the magnitude measured
             # possible magnitudes: time(s), Voltage (V), Currrent (microA)
-            DC_dfs = {f"DC measurement {int(i/2)}": df.iloc[:, i:i+2] for i in range(0, df.shape[1], 2)}
+            DC_dfs = {
+                f"DC measurement {int(i/2)}": df.iloc[:, i : i + 2]
+                for i in range(0, df.shape[1], 2)
+            }
     if DC_data:
         # Select the first DataFrame 'DC measurement 0' as example to find rows
         df = DC_dfs["DC measurement 0"]
 
-        # Find the row index for date/time of measurement, name of measurement and units 
-        date_row = find_row(df, 'Date and time measurement:')
+        # Find the row index for date/time of measurement, name of measurement and units
+        date_row = find_row(df, "Date and time measurement:")
         name_row = date_row - 1
         units_row = date_row + 1
-    
+
         # Process each DataFrame in the generated dictionary
         for key, df in DC_dfs.items():
             dfs_DC_meas.append(key)
             # Extract date and time information
             date_time = df.iloc[date_row, 1]
             new_name = df.iloc[name_row, 0].split(":")[0]
-    
+
             # Set column headers as the units row
             df.columns = df.iloc[units_row]
             df = df.drop(units_row).drop(index=range(0, units_row))
-            df.dropna(how='all', inplace=True)
-            
+            df.dropna(how="all", inplace=True)
+
             # Store the extracted information and data in the 'DC_dfs' dictionary
             DC_dfs[key] = {"Name": new_name, "Date and Time": date_time, "Data": df}
             new_key = f"DC measurement ({key})"
@@ -337,6 +357,7 @@ def format_DC_data(split_dfs):
         print("There are no direct current measurements")
 
 <<<<<<< HEAD
+<<<<<<< HEAD
 
 
 
@@ -347,21 +368,22 @@ def format_DC_data(split_dfs):
 >>>>>>> 3ca1a83 (cleaned up a bit)
 def main():
     """ Main program """
+=======
+>>>>>>> 2542e1a ([pre-commit.ci] auto fixes from pre-commit.com hooks)
+
+def main():
+    """Main program"""
+
+    split_dfs = getdata(filename, verbose=False)
 
-    split_dfs = getdata(filename, verbose = False)
-
     eis_data = format_impedance_data(split_dfs)
 
     DC_data = format_DC_data(split_dfs)
-
-    print(eis_data)    
-    print (DC_data)
 
-
-if __name__ == "__main__":
-
-
-
+    print(eis_data)
+    print(DC_data)
 
-    #Call main program
-    main()
+
+if __name__ == "__main__":
+    # Call main program
+    main()