csv data analysis for single file and pie chart

scripts/single_csv.py

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+import glob
+import pandas as pd
+import openpyxl
+import os
+from tqdm import tqdm
+import platform
+from PyQt5.QtWidgets import QApplication, QWidget, QFileDialog
+
+def consolidate_and_perform_calculations(curdir, rootdir, path_delim, validation_file):
+
+    print(os.path.split(curdir)[1])
+    
+    # list all csv files only  
+    csv_files = glob.glob(curdir + path_delim + '/*.{}'.format('csv'))
+    if len(csv_files) == 0:
+        print("no csv files in the sub folder")
+    # print(csv_files)
+
+    df_csv_append = pd.DataFrame()
+    
+    first = True
+
+    # merge the CSV files
+    for file in csv_files:
+        if first:
+            df_csv_append = pd.read_csv(file)
+            colname = file.split('.')[0].split(path_delim)[-1] #file.split('.')[0]
+            df_csv_append.rename(columns={'ca': colname}, inplace = True)
+            df_csv_append = df_csv_append.drop(['1'], axis=1)
+            first = False
+        else:
+            df = pd.read_csv(file)
+            colname = file.split('.')[0].split(path_delim)[-1] #file.split('.')[0]
+            df.rename(columns={'ca': colname}, inplace = True)
+            df = df.drop(['1'], axis=1)
+            df_csv_append = df_csv_append.merge(df, on='tv')
+    
+    df_csv_append = df_csv_append[df_csv_append['tv'].between(300, 700)]
+
+    wavelength_col = "123_tv" # to make sorting columns simpler
+
+    df_csv_append.rename(columns={'tv': wavelength_col}, inplace = True)
+
+    df_csv_append = df_csv_append.reindex(sorted(df_csv_append.columns), axis=1)
+
+    outfile = rootdir + path_delim + os.path.split(curdir)[1] + "_analysis.xlsx"
+
+    # df_csv_append.to_excel(outfile, sheet_name="merged_data", index=False)
+    # df_csv_append.to_csv("D8 Merged.csv", index=False)
+
+    # calculations
+    df_427 = df_csv_append.loc[(df_csv_append[wavelength_col] >= 427) & (df_csv_append[wavelength_col] < 428)]
+
+    df_555 = df_csv_append.loc[(df_csv_append[wavelength_col] >= 555) & (df_csv_append[wavelength_col] < 556)]
+
+    df_validation = pd.read_excel(validation_file)
+
+    df_analysis = df_427.iloc[0] + df_555.iloc[0]
+    # print(df_analysis)
+    # print(min(df_csv_append[0:5]))
+
+    midpoint1 = 427
+    midpoint2 = 555
+    bandwidth1 = 25
+    bandwidth2 = 10
+
+    df = df_analysis.rename(columns = {"NM":"Wavelength","CA":"Absorbance"}, inplace = True)
+
+    # 427 nm range
+    df1 = df[ (df['Wavelength'] > (midpoint1-bandwidth1)) & (df['Wavelength'] < (midpoint1+bandwidth1)) ]
+
+    # 555 nm range
+    df2 = df[ (df['Wavelength'] > (midpoint2-bandwidth2)) & (df['Wavelength'] < (midpoint2+bandwidth2)) ]
+    
+    procData.append({"Sample ID": sampleID, 
+                     "max_427": round(df1["Absorbance"].max() , 3),
+                     "wvmax_427": round(df1.at[df1["Absorbance"].idxmax(),"Wavelength"], 3),
+                     "avg_427": round(df1["Absorbance"].mean(), 3),
+                     "max_555": round(df2["Absorbance"].max(), 3),
+                     "wvmax_555": round(df2.at[df2["Absorbance"].idxmax(),"Wavelength"], 3),
+                     "avg_555": round(df2["Absorbance"].mean(), 3),
+                     "ratio_max": round(df2["Absorbance"].max()/df1["Absorbance"].max(), 3),
+                     "ratio_avg": round(df2["Absorbance"].mean()/df1["Absorbance"].mean(), 3)
+                    })
+
+    writer = pd.ExcelWriter(outfile, engine = 'openpyxl')
+    df_analysis.to_excel(writer, sheet_name = 'analysis', index=False)
+    df_csv_append.to_excel(writer, sheet_name = 'merged_data', index=False)
+    writer.close()
+
+
+sampleID = 1
+allDF = pd.DataFrame()
+procData = []
+
+
+if __name__ == "__main__":
+
+    file = os.getcwd() + "/D26-100umol-1.csv"
+    df = pd.read_csv(file)
+    
+    midpoint1 = 427
+    midpoint2 = 555
+    bandwidth1 = 25
+    bandwidth2 = 10
+
+    df.rename(columns = {"tv":"Wavelength","ca":"Absorbance"}, inplace = True)
+
+    # 427 nm range
+    df1 = df[ (df['Wavelength'] > (midpoint1-bandwidth1)) & (df['Wavelength'] < (midpoint1+bandwidth1)) ]
+
+    # 555 nm range
+    df2 = df[ (df['Wavelength'] > (midpoint2-bandwidth2)) & (df['Wavelength'] < (midpoint2+bandwidth2)) ]
+    
+    procData.append({"Sample ID": sampleID, 
+                     "max_427": round(df1["Absorbance"].max() , 3),
+                     "wvmax_427": round(df1.at[df1["Absorbance"].idxmax(),"Wavelength"], 3),
+                     "avg_427": round(df1["Absorbance"].mean(), 3),
+                     "max_555": round(df2["Absorbance"].max(), 3),
+                     "wvmax_555": round(df2.at[df2["Absorbance"].idxmax(),"Wavelength"], 3),
+                     "avg_555": round(df2["Absorbance"].mean(), 3),
+                     "ratio_max": round(df2["Absorbance"].max()/df1["Absorbance"].max(), 3),
+                     "ratio_avg": round(df2["Absorbance"].mean()/df1["Absorbance"].mean(), 3)
+                    })
+    print(procData)