"""Single-subject infant dataset for testing maxwell_filter with movecomp.

https://openneuro.org/datasets/ds004229
"""

import mne
import numpy as np

bids_root = "~/mne_data/ds004229"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds004229"

task = "amnoise"
crop_runs = (300.0, 600.0)  # 5 minutes from the middle of the recording for speed

find_flat_channels_meg = True
find_noisy_channels_meg = True
use_maxwell_filter = True
mf_destination = mne.transforms.translation(  # rotate backward and move up
    z=0.055,
) @ mne.transforms.rotation(x=np.deg2rad(-15))
mf_mc = True
mf_st_duration = 10
mf_int_order = 6  # lower for smaller heads
mf_mc_t_step_min = 0.5  # just for speed!
mf_mc_t_window = 0.2  # cleaner cHPI filtering on this dataset
mf_filter_chpi = True  # even though we lowpass, set to True for test coverage
mf_mc_rotation_velocity_limit = 30.0  # deg/s for annotations
mf_mc_translation_velocity_limit = 20e-3  # m/s
mf_esss = 8
mf_esss_reject = {"grad": 10000e-13, "mag": 40000e-15}
ch_types = ["meg"]

l_freq = None
h_freq = 40.0

# SSP and peak-to-peak rejection
spatial_filter = "ssp"
n_proj_eog = dict(n_mag=0, n_grad=0)
n_proj_ecg = dict(n_mag=2, n_grad=2)
ssp_ecg_channel = "MEG0113"  # ECG channel is not hooked up in this dataset
reject = ssp_reject_ecg = {"grad": 2000e-13, "mag": 5000e-15}

# Epochs
epochs_tmin = -0.2
epochs_tmax = 1
epochs_decim = 6  # 1200->200 Hz
baseline = (None, 0)
report_add_epochs_image_kwargs = {
    "grad": {"vmin": 0, "vmax": 1e13 * reject["grad"]},  # fT/cm
    "mag": {"vmin": 0, "vmax": 1e15 * reject["mag"]},  # fT
}

# Conditions / events to consider when epoching
conditions = ["auditory"]

# Decoding
decode = False

# Noise estimation
noise_cov = "emptyroom"

  Platform             Linux-5.15.0-1057-aws-x86_64-with-glibc2.35
Python               3.12.4 (main, Jun  8 2024, 23:40:19) [GCC 11.4.0]
Executable           /home/circleci/.pyenv/versions/3.12.4/bin/python3.12
CPU                  Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz (36 cores)
Memory               68.6 GiB

Core
├☑ mne               1.9.0.dev59+ged933b8d9 (devel, latest release is 1.8.0)
├☑ numpy             2.0.2 (OpenBLAS 0.3.27 with 2 threads)
├☑ scipy             1.14.1
└☑ matplotlib        3.9.2 (backend=agg)

Numerical (optional)
├☑ sklearn           1.5.2
├☑ numba             0.60.0
├☑ nibabel           5.2.1
├☑ pandas            2.2.3
├☑ h5io              0.2.4
├☑ h5py              3.12.1
└☐ unavailable       nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.44.1 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.2 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt         0.11.1
├☑ vtk               9.3.1
├☑ qtpy              2.4.1 (PyQt6=6.7.1)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.16.0.dev44+g65da1ae78
├☑ mne-bids-pipeline 1.10.0.dev27+g02cdc20
├☑ edfio             0.4.4
├☑ pybv              0.7.5
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, eeglabio, mffpy