import pandas as pd
import openpyxl
import xlsxwriter
import os
import platform
import numpy as np
pd.options.mode.chained_assignment = None  # default='warn'
pd.options.display.float_format = '{:.4f}'.format

precision_tolerance = 0.009
accuracy_tolerance = 0.02

def perform_calculations(curdir, path_delim, report_file):
    df = pd.read_excel(curdir + path_delim + "hemocube_qc_09_08_2023_data.xlsx", sheet_name="qc_data")
    df_ref = pd.read_excel(curdir + path_delim + "hemocube_qc_09_08_2023_data.xlsx", sheet_name="reference_values")
    
    print(df_ref)
    
    writer = pd.ExcelWriter(report_file, engine = 'xlsxwriter')

    # TODO rename led1Average to Abs 427 and led2Average to Abs555
    df = df.drop(["led1Sample", "deviceRatio", "led1Buffer", "testTime", "led2Sample", "led2Buffer"], axis=1)

    # duplicate columns
    df["device"] = df["deviceId"]
    df["identifier"] = ""
    for idx, row in df.iterrows():
        df['identifier'][idx] = row["deviceId"] + ", " + row["sol"] + ", " + row["wavelength"]

    df_max = df.groupby(['deviceId', "sol"]).max()
    df_max['identifier'] = ""

    df_min = df.groupby(['deviceId', "sol"]).min()
    df_min['identifier'] = ""

    for group, row in df_max.iterrows():
        df_max["identifier"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]

    df_max.to_excel(writer, sheet_name="max", index=False)

    for group, row in df_min.iterrows():
        df_min["identifier"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]

    # TODO: combine 427 mean and 555 mean using df.groupby(['identifier'])[["Abs 427", "Abs555"]].mean()
    df_427nm_mean = df.groupby(['identifier'])["Abs 427"].mean().to_frame()
    df_427nm_mean.rename(columns={'Abs 427': "Abs427_mean"}, inplace = True)

    df_555nm_mean = df.groupby(['identifier'])["Abs555"].mean().to_frame()
    df_555nm_mean.rename(columns={'Abs555': "Abs555_mean"}, inplace = True)

    # df_mean = df.copy()
    df_mean = df_427nm_mean.merge(df_555nm_mean, on="identifier")

    df_describe = df.groupby(['deviceId', "sol"]).describe()
    df_describe.to_excel(writer, sheet_name="describe")

    df_max_min = df_max.merge(df_min, on="identifier")
    df_max_min_mean = df_max_min.merge(df_mean, on="identifier")
    df_max_min_mean_ref = df_max_min_mean.merge(df_ref, on="identifier")

    df_min.to_excel(writer, sheet_name="min", index=False)
    df_max_min_mean_ref.to_excel(writer, sheet_name="df_max_min_mean_ref", index=False)

    df_result = df_max_min.copy()
    df_result["precision_427nm"] = df_max_min["Abs 427_x"] - df_max_min["Abs 427_y"]
    df_result["precision_555nm"] = df_max_min["Abs555_x"] - df_max_min["Abs555_y"]
    df_result["accuracy_427nm"] = df_max_min_mean_ref["ref_device_abs"] - df_max_min_mean_ref["Abs427_mean"]
    df_result["accuracy_555nm"] = df_max_min_mean_ref["ref_device_abs"] - df_max_min_mean_ref["Abs555_mean"]

    df_result = df_result.drop(["Abs 427_x", "Abs555_x", "wavelength_x", "Abs 427_y", "Abs555_y", "wavelength_y", "device_y"], axis=1)
    df_result = df_result.groupby('device_x')[['device_x', "identifier", "precision_427nm", "precision_555nm", "accuracy_427nm", "accuracy_555nm"]].apply(lambda x: x)

    df_result.to_excel(writer, sheet_name="precision", index=False)
    workbook = writer.book
    worksheet = writer.sheets["precision"]
    format1 = workbook.add_format({"num_format": "#,##0.00000"})
    worksheet.set_column(180, 1, 35, format1)

    # Add a header format.
    header_format = workbook.add_format(
        {
            "bold": True,
            "text_wrap": True,
            "valign": "top",
            "fg_color": "#D7E4BC",
            "border": 1,
        }
    )

    # Write the column headers with the defined format.
    for col_num, value in enumerate(df_result.columns.values):
        worksheet.write(0, col_num, value, header_format)

    writer.close()
    
if __name__ == "__main__":
    rootdir = os.getcwd()
    path_delim = ''
    if platform.system() == 'Windows':
        path_delim = '\\'
    else:
        path_delim = '/'
    report_file = rootdir + path_delim + 'report.xlsx'
    print(report_file)
    
    perform_calculations(rootdir, path_delim, report_file)