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Add Gaby's code to echem block

Temp. add PSTrace block

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Added a way of ploting .csv from PStrace with echem block

pydatalab/pydatalab/apps/PSTrace/PalmSensReader.py

@@ -6,68 +6,15 @@
 """
 
 
-
 import pandas as pd
 
 filename = (
-    "PalmSense_test_datalab.csv"  # file with experimetnal data as exported "as csv" from PSTrace
+    # file with experimetnal data as exported "as csv" from PSTrace
+    "PalmSense_test_datalab.csv"
 )
 
-<<<<<<< HEAD
-# The following lines adjust the granularity of reporting. 
-#pd.options.display.max_rows = 10
-#pd.options.display.max_columns = 10
-#pd.options.display.float_format = "{:.1f}".format
-<<<<<<< HEAD
-
-<<<<<<< HEAD
-
-<<<<<<< HEAD
-<<<<<<< HEAD
-filename = "PalmSense_test_datalab_shorter.csv"
-file_encoding = 'utf-16 LE'
-=======
-
-
-filename = "PalmSense_test_datalab_shorter.csv"
-file_encoding = 'utf-16 LE'
-
-def getdata():
-    """Loads all the experimental data to a dataframe per excel sheet"""
-            # set file_encoding to the file encoding (utf8, latin1, etc.)
-    #df = pd.read_csv(file, header = 2, encoding = file_encoding, index_col = False, names=range(25))
-
-    with open(filename, 'r', encoding = file_encoding) as temp_f:
-        # get No of columns in each line
-        col_count = [ len(l.split(",")) for l in temp_f.readlines() ]
->>>>>>> cc22c82 (Palmsense reader code now can load the data and separate in different df according to keyword Impedance)
-
-
-def getdata(filename, file_encoding='utf-16 LE', verbose = False):
-=======
-filename = "PalmSense_test_datalab_shorter.csv" # file with experimetnal data as exported "as csv" from PSTrace
-=======
-filename = "PalmSense_test_datalab.csv" # file with experimetnal data as exported "as csv" from PSTrace
-<<<<<<< HEAD
->>>>>>> 4fa42bc (code reads all parts of csv output file from PSTrace)
-=======
-
-<<<<<<< HEAD
-filename = "PalmSense_test_datalab_EIS.csv" # file with experimetnal data as exported "as csv" from PSTrace
->>>>>>> 3378ab0 (solved issues with cases where there are no DC current experiments)
-=======
-filename = "PalmSense_test_datalab.csv" # file with experimetnal data as exported "as csv" from PSTrace
->>>>>>> 611b9a2 (solved bug)
-keyword = "Measurement" #keyword to split input file on
-=======
->>>>>>> 3ca1a83 (cleaned up a bit)
-
-
-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.
@@ -89,131 +36,38 @@ def getdata(filename, file_encoding="utf-16 LE", verbose=False):
     # Generate column names (names will be 0, 1, 2, ..., maximum columns - 1)
     column_names = [i for i in range(0, max(col_count))]
 
-<<<<<<< HEAD
     # Read CSV file into a DataFrame
-    df = pd.read_csv(filename, header=None, names=column_names, encoding=file_encoding)
+    df = pd.read_csv(filename, header=None,
+                     names=column_names, encoding=file_encoding)
 
     # 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 = df.apply(lambda row: row.astype(
+        str).str.contains("Measurement"), 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:
         for key, split_df in split_dfs.items():
-            print(f"DataFrame for splitting keyword = Measurement occurrence {key}:")
+            print(
+                f"DataFrame for splitting keyword = Measurement occurrence {key}:")
             print(split_df)
             print("\n")
 
     return split_dfs
 
-<<<<<<< HEAD
-<<<<<<< HEAD
-
-<<<<<<< HEAD
-<<<<<<< HEAD
-<<<<<<< HEAD
-
-
-
-
-=======
-    ### Read csv
-    df = pd.read_csv(filename, header=None, names=column_names,encoding = file_encoding)
-
-#    print(dfhead)
-  #  print (df.head(n=10))
 
-    df.to_csv("test.csv")
-
-
-    # Keyword to split on
-    keyword = 'Impedance'
-
-    # Find the locations of the keyword in any column
-    mask = df.apply(lambda row: row.astype(str).str.contains(keyword), axis=1)
-    mask['Any'] = mask.any(axis=1)
-    groups = mask["Any"].cumsum()
-
-    # Splitting the DataFrame based on the keyword occurrences
-    split_dfs = {group: df[group == groups] for group in groups.unique()}
-
-    # Display the split DataFrames
-    for key, split_df in split_dfs.items():
-        print(f"DataFrame for '{keyword}' occurrence {key}:")
-        print(split_df)
-        print("\n")
-
-
-
-
-
-
-    return df
->>>>>>> cc22c82 (Palmsense reader code now can load the data and separate in different df according to keyword Impedance)
-=======
-def formatdata(split_dfs):
-=======
-# def formatdata(split_dfs):
->>>>>>> c84820a (Cleaned and documented code for impedance spectroscopy data formating)
-
-#     dfs_with_freq= []
-#     impedance_dfs = {}
-#     for key, df in split_dfs.items():
-
-#         if df.apply(lambda row: row.astype(str).str.contains('freq / Hz')).any().any():
-#             dfs_with_freq.append(key)
-#             df = split_dfs[key].reset_index(drop=True)
-#             name_row = (df[df.apply(lambda row: row.astype(str).str.contains('Measurement'))
-#                                 .any(axis=1)].index[0])
-#             new_name = df.iloc[name_row][1]
-#             date_row = (df[df.apply(lambda row: row.astype(str).str.contains('Date and time'))
-#                                 .any(axis=1)].index[0])
-#             date_time =  df.iloc[date_row][1]
-#             print(date_time)
-
-
-
-
-#             # Find the index of the row containing the string 'freq / Hz'
-#             index_with_freq = (df[df.apply(lambda row: row.astype(str).str.contains('freq / Hz'))
-#                                 .any(axis=1)].index[0])
-
-#             # Set the row with 'freq / Hz' as the header
-#             df.columns = df.iloc[index_with_freq]
-
-#             # Remove the row that contains 'freq / Hz' and rows before it (index < index_with_freq)
-#             df = df.drop(index_with_freq).drop(index=range(0, index_with_freq))
-
-#             # Display the DataFrame after setting the header and removing unnecessary rows
-#             impedance_dfs[new_name]= {"Date and Time" : date_time, "Data": df}
-
-#     if dfs_with_freq:
-#         n = len (dfs_with_freq)
-#         print(impedance_dfs)
-#         print(f"There are {n} Impedance measurements")
-
-#     else:
-#         print("No part of this file contains Impedance measurements")
-
-
-=======
->>>>>>> 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:
     - 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):
@@ -270,21 +124,8 @@ def format_impedance_data(split_dfs):
         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
-
-
->>>>>>> c84820a (Cleaned and documented code for impedance spectroscopy data formating)
-=======
 
 
 def format_DC_data(split_dfs):
@@ -305,7 +146,8 @@ def format_DC_data(split_dfs):
     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:"))
+            df.apply(lambda row: row.astype(str).str.contains(
+                "Date and time measurement:"))
             .any()
             .any()
         ):
@@ -322,7 +164,7 @@ def format_DC_data(split_dfs):
             # (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]
+                f"DC measurement {int(i/2)}": df.iloc[:, i: i + 2]
                 for i in range(0, df.shape[1], 2)
             }
     if DC_data:
@@ -347,7 +189,8 @@ def format_DC_data(split_dfs):
             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}
+            DC_dfs[key] = {"Name": new_name,
+                           "Date and Time": date_time, "Data": df}
             new_key = f"DC measurement ({key})"
 
         n = len(dfs_DC_meas)
@@ -356,20 +199,6 @@ def format_DC_data(split_dfs):
     else:
         print("There are no direct current measurements")
 
-<<<<<<< HEAD
-<<<<<<< HEAD
-
-
-
-
->>>>>>> 4fa42bc (code reads all parts of csv output file from PSTrace)
-=======
-
->>>>>>> 3ca1a83 (cleaned up a bit)
-def main():
-    """ Main program """
-=======
->>>>>>> 2542e1a ([pre-commit.ci] auto fixes from pre-commit.com hooks)
 
 def main():
     """Main program"""