import re
import numpy as np

from prettytable import PrettyTable


def main():
    table_1 = """+----------+-----------------------+-----------------------+----------------------+\n
                 | Variable |         Value         |      Lower Error      |     Upper Error      |\n
                 +----------+-----------------------+-----------------------+----------------------+\n
                 |   AFB    |  -0.1327941714665493  | -0.011751983569979923 | 0.011695518975582573 |\n
                 |    FL    |   0.778784190310989   | -0.013786738313616998 | 0.01334631516152801  |\n
                 |    S3    |  0.012016752919524067 | -0.014186281678810328 | 0.01390470699489175  |\n
                 |    S4    | 0.0030912112690471196 |  -0.02494551503535478 | 0.02500682819273369  |\n
                 |    S5    |  0.06257482631549156  | -0.022576669869741432 | 0.02267902660423071  |\n
                 |    S7    |  0.026461647341068015 | -0.022870493984060422 | 0.022709046371542273 |\n
                 |    S8    |  0.022528200778115424 | -0.024500119399894143 | 0.02444883875849664  |\n
                 |    S9    | -0.010047596608480971 | -0.015100958783891871 | 0.01497061747614531  |\n
                 +----------+-----------------------+-----------------------+----------------------+"""

    table_2 = """+----------+-----------------------+-----------------------+----------------------+\n
                 | Variable |         Value         |      Lower Error      |     Upper Error      |\n
                 +----------+-----------------------+-----------------------+----------------------+\n
                 |   AFB    | -0.044313915998597596 | -0.011266005942710903 | 0.011376116194169396 |\n
                 |    FL    |   0.8080898316779715  | -0.012903371237386076 | 0.012627755576193413 |\n
                 |    S3    | -0.001350693384098943 |  -0.01425102196417036 | 0.014014236056631763 |\n
                 |    S4    |  -0.16348388538343442 | -0.022982703026786865 | 0.02353050491741336  |\n
                 |    S5    |  -0.12998638915138938 |  -0.02207623402690808 | 0.02230804022923196  |\n
                 |    S7    | -0.019175072605277906 | -0.022219297227352726 | 0.02233298418372788  |\n
                 |    S8    |  0.03896504393685167  |  -0.02306667997644898 | 0.023056355590581525 |\n
                 |    S9    | -0.026410777123662296 |  -0.01497373735832672 | 0.015237714795859537 |\n
                 +----------+-----------------------+-----------------------+----------------------+"""

    table_3 = """+----------+-----------------------+-----------------------+----------------------+\n
                 | Variable |         Value         |      Lower Error      |     Upper Error      |\n
                 +----------+-----------------------+-----------------------+----------------------+\n
                 |   AFB    |  0.06877321968399613  |  -0.00873785505692959 | 0.008866035437567673 |\n
                 |    FL    |   0.7579354189983714  |  -0.01048129883366948 | 0.01017491937670441  |\n
                 |    S3    |  -0.02540601876438032 | -0.012075664333196083 | 0.012192632205060838 |\n
                 |    S4    |  -0.18300822691744986 | -0.015545142299112552 | 0.016136099542591972 |\n
                 |    S5    |  -0.28137307676130335 |  -0.0160784362375397  | 0.01630021297009425  |\n
                 |    S7    |  0.033064765761582675 |  -0.01837491321971226 | 0.018457429889962827 |\n
                 |    S8    | -0.022661825503764606 | -0.018831356033962507 | 0.018816177761568176 |\n
                 |    S9    |  -0.01151502616377776 | -0.012778180225448249 | 0.013091600028830737 |\n
                 +----------+-----------------------+-----------------------+----------------------+"""

    bin_labels     = [r"$0.25 < q^2 < 4.00$", r"$4.00 < q^2 < 8.00$", r"$11.00 < q^2 < 12.50$",
                      r"$15.00 < q^2 < 18.00$", r"$1.10 < q^2 < 6.00$",
                      r"$1.1 < q^2 < 2.5$", r"$2.5 < q^2 < 4.0$", r"$4.0 < q^2 < 6.0$", r"$6.0 < q^2 < 8.0$"]

    tables       = [table_1, table_2, table_3]
    variables    = ["AFB", "FL", "S3", "S4", "S5", "S7", "S8", "S9"]
    select_regex = [".*AFB.*\d+", ".*FL.*\d+", ".*S3.*\d+", ".*S4.*\d+", ".*S5.*\d+", ".*S7.*\d+", ".*S8.*\d+", ".*S9.*\d+"]
    value_regex  = "[- ]\d+.\d+"
    samples      = {'1': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None},
                    '2': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None},
                    '3': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None},
                   }

    for index in range(len(tables)):
        for variable, regex in zip(variables, select_regex):
            splitted = tables[index].splitlines()
            for line in splitted:
                subresult = re.match(regex, line)
                if subresult != None:
                    substring = subresult.group(0)
                    result    = re.findall(value_regex, substring)
                    value     = np.float64(result[0])
                    lower_err = np.float64(result[1])
                    upper_err = np.float64(result[2])

                    samples[str(index + 1)][variable] = (value, lower_err, upper_err)

    fit_variables_split = [[] for _ in range(8)]
    fit_errors_split    = [[] for _ in range(8)]
    for value in samples.values():
        count = 0
        for fitvalue in value.values():
            fit_variables_split[count].append(fitvalue[0])
            fit_errors_split[count].append(np.sqrt(fitvalue[1]**2 + fitvalue[2]**2))
            count += 1

    info_table = PrettyTable(["Variable", "Value", "Lower Error", "Upper Error"])
    info_table.title = f"q2_split_{bin_labels[4]}_average"
    fit_labels = ["AFB", "FL", "S3", "S4", "S5", "S7", "S8", "S9"]
    count = 0
    for name, fit_split_var, fit_err_split in zip(fit_labels, fit_variables_split, fit_errors_split):
        fit_avg     = np.mean(fit_split_var)
        error_split = 1/3 * np.sqrt(fit_err_split[0]**2 + fit_err_split[1]**2 + fit_err_split[2]**2)
        info_table.add_row([name, fit_avg, -error_split, error_split])
        count += 1

    print(info_table)

    
if __name__ == "__main__":
    main()