r"""
.. _custom_info_class:

Custom Calibration Info Subclass
================================

Whenever you want to implement your own version of a calibration or simply want to add some meta information to your
calibration info objects, you need to create a new subclass of :class:`~imucal.CalibrationInfo`.

When creating a entirely new calibration, you should subclass from :class:`~imucal.CalibrationInfo` directly.
If you just want to extend an existing object, subclass from the respective class.
"""

# %%
# In the following, we will see how to extend the :class:`~imucal.FerrarisCalibrationInfo`.
#
# Here are the important things to keep in mind:
#
# 1. Each subclass needs to overwrite `CAL_TYPE`. Otherwise it is not recognised as a separate class when loading
#    objects from file.
# 2. The new class needs to inherit from `dataclass`, if you add fields that should be serialized when using the export
#    methods.
# 3. All new attributes need Type annotation and a default value (if in doubt use None)
# 4. Note, that all new attributes need to be json serializable by default, if you want ot use json export.
from dataclasses import dataclass
from typing import Optional

from example_data import EXAMPLE_PATH
from imucal import FerrarisCalibrationInfo, ferraris_regions_from_df


@dataclass
class ExtendedFerrarisCalibrationInfo(FerrarisCalibrationInfo):
    CAL_TYPE = "ExtendedFerraris"
    new_meta_info: Optional[str] = None


# %%
# With that we have a new subclass of the ferraris calibration info.
# As it is marked as dataclass, the `__init__` is created automatically and saving and loading from file is also taken
# care of.
# To use the new class, we need to provide it when initializing our `FerrarisCalibration`.
from imucal import FerrarisCalibration

cal = FerrarisCalibration(calibration_info_class=ExtendedFerrarisCalibrationInfo)

# %%
# Now, we can calculate the calibration as normal (here we just use some dummy data).
# To provide a value for our `new_meta_info` field, we can pass it directly to the `calculate` method.
import pandas as pd

cal_data = ferraris_regions_from_df(pd.read_csv(EXAMPLE_PATH / "annotated_session.csv", header=0, index_col=[0, 1]))

cal_info = cal.compute(
    cal_data, sampling_rate_hz=204.8, from_acc_unit="a.u.", from_gyr_unit="a.u.", new_meta_info="my value"
)

cal_info.new_meta_info

# %%
# And of course we can simply export and reimport the new calibration info to json or hdf5 (we will use a tempfile
# here to not clutter the example folder).
import tempfile
from pathlib import Path

from imucal.management import load_calibration_info

with tempfile.TemporaryDirectory() as d:
    file_name = Path(d) / "my_cal_info.json"
    cal_info.to_json_file(file_name)

    # An load it again
    loaded_cal_info = load_calibration_info(file_name)

loaded_cal_info.new_meta_info