import h5py
import pandas as pd
import re

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}")


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

# Dict to hold DFs 'sex', 'tag' & 'spid'
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_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()