import h5py
import pandas as pd
import re

import matplotlib as plt

from Model import Model
from fitter import Fitter
from Data import Data

file = "ltcc_current.h5"


def print_attrs(name, obj):
    #  print(f"\nAttributes for {name}:")
    #  for key, val in obj.attrs.items():
    #  print(f"  {key}: {val}")
    pass


with h5py.File(file, "r") as h5_file:
    h5_file.visititems(print_attrs)


dfs_by_sex_tag_spid = {}

with h5py.File(file, "r") as h5_file:
    for eid in h5_file.keys():
        attributes = h5_file[eid].attrs
        sex = attributes.get("sex")
        tag = attributes.get("tag")
        spid = attributes.get("spid")

        key = f"{sex}_{tag}_{spid}"

        if key not in dfs_by_sex_tag_spid:
            dfs_by_sex_tag_spid[key] = pd.DataFrame()

        row_data = {"experiment_id": eid, "sex": sex, "tag": tag, "spid": spid}
        temp_df = pd.DataFrame([row_data])

        dfs_by_sex_tag_spid[key] = pd.concat(
            [dfs_by_sex_tag_spid[key], temp_df], ignore_index=True
        )

#  for key, df in dfs_by_sex_tag_spid.items():
#  print(f"DataFrame for {key}:")
#  print(df)
#  print()


def fit_and_plot_dataframes(dfs_by_sex_tag_spid):
    for key, df in dfs_by_sex_tag_spid.items():
        print(f"Fitting and plotting data for {key}...")

        combined_data = []

        for eid in df["experiment_id"].tolist():
            data = Data(file, group_key=eid)
            combined_data.append(data)

        collective_data = Data.combine(combined_data)

        fit = Fitter(Model, collective_data)
        fit.optimize()
        res, fig = fit.optimize()

        plt.figure()
        plt.title(f"Fit results for {key}")

        for single_data in combined_data:
            plt.plot(single_data.x, single_data.y,
                     label=f"Experiment {single_data.eid}")

        plt.plot(collective_data.x, collective_data.y, 'k-',
                 label="Combined Fit")
        plt.legend()
        plt.xlabel("X")
        plt.ylabel("Y")

        key_cleaned = re.sub(r"[^\w.-]", "", key)
        plt.savefig(f"combined_plot_{key_cleaned}.png")
        plt.savefig(f"combined_plot_{key_cleaned}.pdf")
        plt.close()

        fit_hist = pd.DataFrame.from_dict(fit.fit_results, orient="index").T
        fit_hist.index.name = "Iterations"

        res_filename = f"combined_fit_results_{key}.csv"
        res_filename = res_filename.replace(" ", "_").replace(":", "-")
        fit_hist.to_csv(res_filename, index=True)

        print(f"Finished fitting for {key}. Results saved.")


fit_and_plot_dataframes(dfs_by_sex_tag_spid)

"""
def fit_data():
    filename = "ltcc_current.h5"
    with h5py.File(filename, "r") as h5:
        eids = list(h5.keys())

    for eid in eids:
        data = Data(filename, group_key=eid)
        fit = Fitter(Model, data)
        fit.optimize()
        res, fig = fit.optimize()

        fit_hist = pd.DataFrame.from_dict(fit.fit_results, orient="index").T
        fit_hist.index.name = "Iterations"

        res_filename = f"fit_results_{eid}.csv"
        res_filename = res_filename.replace(" ", "_").replace(":", "-")
        fit_hist.to_csv(res_filename, index=True)

        eid_cleaned = re.sub(r"[^w.-]", "", eid)  # Eemaldab kõik eritähed
        fig.savefig(f"plot_{eid_cleaned}.png")
        fig.savefig(f"plot_{eid_cleaned}.pdf")


fit_data()

"""