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