import numpy as np
from scipy.io import savemat

# Example MNE/CSP output: (n_trials, n_components, n_times)
X_csp = csp.fit_transform(epochs.get_data(), labels)

# Julia expects: (n_features, n_samples, n_trials)
features_julia = np.transpose(X_csp, (1, 2, 0))

savemat("features_for_julia.mat", {
    "features": features_julia.astype("float64"),
    "labels": labels.astype("int64"),
    "sampling_rate": float(epochs.info["sfreq"]),
})

using MAT
using NimbusSDK

data = matread("features_for_julia.mat")
features = Float64.(data["features"])
labels = Int.(vec(data["labels"]))

metadata = BCIMetadata(
    sampling_rate = Float64(data["sampling_rate"]),
    paradigm = :motor_imagery,
    feature_type = :csp,
    n_features = size(features, 1),
    n_classes = length(unique(labels)),
    chunk_size = nothing
)

bci_data = BCIData(features, metadata, labels)

% features: n_features x n_samples x n_trials
% labels: 1 x n_trials, preferably 1-indexed
save("features_for_nimbus.mat", "features", "labels", "sampling_rate")

using MAT

data = matread("features_for_nimbus.mat")
features = Float64.(data["features"])
labels = Int.(vec(data["labels"]))

if minimum(labels) == 0
    labels .+= 1
end

import numpy as np
import pandas as pd
from scipy.io import savemat

df = pd.read_csv("openvibe_features.csv")

# Example columns: timestamp, f1, f2, ..., label, trial_id
feature_cols = [c for c in df.columns if c.startswith("f")]
trial_ids = df["trial_id"].unique()

trials = []
labels = []
for trial_id in trial_ids:
    trial = df[df["trial_id"] == trial_id]
    trials.append(trial[feature_cols].to_numpy().T)
    labels.append(int(trial["label"].iloc[-1]))

features = np.stack(trials, axis=2)  # (n_features, n_samples, n_trials)

savemat("openvibe_for_nimbus.mat", {
    "features": features.astype("float64"),
    "labels": np.asarray(labels, dtype="int64"),
})

import numpy as np

def validate_export(features, labels):
    assert features.ndim == 3, "Expected (features, samples, trials)"
    assert features.shape[2] == len(labels), "Labels must match trials"
    assert np.isfinite(features).all(), "Features contain NaN or Inf"
    assert len(np.unique(labels)) >= 2, "Need at least two classes"

using NimbusSDK

report = diagnose_preprocessing(bci_data)
if !isempty(report.errors)
    error("Preprocessing export failed validation: $(report.errors)")
end

norm_params = estimate_normalization_params(train_features; method=:zscore)
train_norm = apply_normalization(train_features, norm_params)
test_norm = apply_normalization(test_features, norm_params)