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add option to store psd for fft, stft and welch method #276

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Dec 14, 2023
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add option to store psd for fft, stft and welch method #276

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81c7d73
add option to store psd for fft, stft and welch method
timonmerk Dec 14, 2023
df4486c
fix segment length tests
timonmerk Dec 14, 2023
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102 changes: 85 additions & 17 deletions py_neuromodulation/nm_oscillatory.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,15 @@ def test_settings_osc(
assert isinstance(
s[osc_feature_name]["windowlength_ms"], int
), f"windowlength_ms needs to be type int, got {s[osc_feature_name]['windowlength_ms']}"

assert (
s[osc_feature_name]["windowlength_ms"]
<= s["segment_length_features_ms"]
), (
f"oscillatory feature windowlength_ms = ({s[osc_feature_name]['windowlength_ms']})"
f"needs to be smaller than"
f"s['segment_length_features_ms'] = {s['segment_length_features_ms']}",
)
else:
for seg_length in s[osc_feature_name][
"segment_lengths_ms"
Expand Down Expand Up @@ -89,19 +98,33 @@ def update_KF(self, feature_calc: float, KF_name: str) -> float:
feature_calc = self.KF_dict[KF_name].x[0]
return feature_calc

def estimate_osc_features(self, features_compute: dict, data: np.ndarray, feature_name: np.ndarray, est_name: str):
def estimate_osc_features(
self,
features_compute: dict,
data: np.ndarray,
feature_name: np.ndarray,
est_name: str,
):
for feature_est_name in list(self.s[est_name]["features"].keys()):
if self.s[est_name]["features"][feature_est_name] is True:
# switch case for feature_est_name
match feature_est_name:
case "mean":
features_compute[f"{feature_name}_{feature_est_name}"] = np.nanmean(data)
features_compute[
f"{feature_name}_{feature_est_name}"
] = np.nanmean(data)
case "median":
features_compute[f"{feature_name}_{feature_est_name}"] = np.nanmedian(data)
features_compute[
f"{feature_name}_{feature_est_name}"
] = np.nanmedian(data)
case "std":
features_compute[f"{feature_name}_{feature_est_name}"] = np.nanstd(data)
features_compute[
f"{feature_name}_{feature_est_name}"
] = np.nanstd(data)
case "max":
features_compute[f"{feature_name}_{feature_est_name}"] = np.nanmax(data)
features_compute[
f"{feature_name}_{feature_est_name}"
] = np.nanmax(data)

return features_compute

Expand All @@ -122,13 +145,15 @@ def __init__(

window_ms = self.s["fft_settings"]["windowlength_ms"]
self.window_samples = int(-np.floor(window_ms / 1000 * sfreq))
freqs = fft.rfftfreq(-self.window_samples, 1 / np.floor(self.sfreq))
self.freqs = fft.rfftfreq(
-self.window_samples, 1 / np.floor(self.sfreq)
)

self.feature_params = []
for ch_idx, ch_name in enumerate(self.ch_names):
for fband, f_range in self.f_ranges_dict.items():
idx_range = np.where(
(freqs >= f_range[0]) & (freqs < f_range[1])
(self.freqs >= f_range[0]) & (self.freqs < f_range[1])
)[0]
feature_name = "_".join([ch_name, "fft", fband])
self.feature_params.append((ch_idx, feature_name, idx_range))
Expand All @@ -145,10 +170,20 @@ def calc_feature(self, data: np.ndarray, features_compute: dict) -> dict:
Z = np.log10(Z)

for ch_idx, feature_name, idx_range in self.feature_params:

Z_ch = Z[ch_idx, idx_range]

features_compute = self.estimate_osc_features(features_compute, Z_ch, feature_name, "fft_settings")
features_compute = self.estimate_osc_features(
features_compute, Z_ch, feature_name, "fft_settings"
)

for ch_idx, ch_name in enumerate(self.ch_names):
if self.s["fft_settings"]["return_spectrum"]:
features_compute.update(
{
f"{ch_name}_fft_psd_{str(f)}": Z[ch_idx][idx]
for idx, f in enumerate(self.freqs.astype(int))
}
)

return features_compute

Expand Down Expand Up @@ -177,23 +212,39 @@ def test_settings(s: dict, ch_names: Iterable[str], sfreq: int | float):
)

def calc_feature(self, data: np.ndarray, features_compute: dict) -> dict:
f, Z = signal.welch(
freqs, Z = signal.welch(
data,
fs=self.sfreq,
window="hann",
nperseg=self.sfreq,
noverlap=None,
)

if self.log_transform:
Z = np.log10(Z)

for ch_idx, feature_name, f_range in self.feature_params:
Z_ch = Z[ch_idx]

if self.log_transform:
Z_ch = np.log10(Z_ch)
idx_range = np.where((freqs >= f_range[0]) & (freqs <= f_range[1]))[
0
]

idx_range = np.where((f >= f_range[0]) & (f <= f_range[1]))[0]
features_compute = self.estimate_osc_features(
features_compute,
Z_ch[idx_range],
feature_name,
"welch_settings",
)

features_compute = self.estimate_osc_features(features_compute, Z_ch[idx_range], feature_name, "welch_settings")
for ch_idx, ch_name in enumerate(self.ch_names):
if self.s["welch_settings"]["return_spectrum"]:
features_compute.update(
{
f"{ch_name}_welch_psd_{str(f)}": Z[ch_idx][idx]
for idx, f in enumerate(freqs.astype(int))
}
)

return features_compute

Expand Down Expand Up @@ -223,7 +274,7 @@ def test_settings(s: dict, ch_names: Iterable[str], sfreq: int | float):
)

def calc_feature(self, data: np.ndarray, features_compute: dict) -> dict:
f, _, Zxx = signal.stft(
freqs, _, Zxx = signal.stft(
data,
fs=self.sfreq,
window="hamming",
Expand All @@ -235,9 +286,26 @@ def calc_feature(self, data: np.ndarray, features_compute: dict) -> dict:
Z = np.log10(Z)
for ch_idx, feature_name, f_range in self.feature_params:
Z_ch = Z[ch_idx]
idx_range = np.where((f >= f_range[0]) & (f <= f_range[1]))[0]
idx_range = np.where((freqs >= f_range[0]) & (freqs <= f_range[1]))[
0
]

features_compute = self.estimate_osc_features(
features_compute,
Z_ch[idx_range, :],
feature_name,
"stft_settings",
)

features_compute = self.estimate_osc_features(features_compute, Z_ch[idx_range, :], feature_name, "stft_settings")
for ch_idx, ch_name in enumerate(self.ch_names):
if self.s["stft_settings"]["return_spectrum"]:
Z_ch_mean = Z[ch_idx].mean(axis=1)
features_compute.update(
{
f"{ch_name}_stft_psd_{str(f)}": Z_ch_mean[idx]
for idx, f in enumerate(freqs.astype(int))
}
)

return features_compute

Expand Down
15 changes: 9 additions & 6 deletions py_neuromodulation/nm_settings.json
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Original file line number Diff line number Diff line change
Expand Up @@ -93,8 +93,9 @@
"mean": true,
"median": false,
"std": false,
"max" : false
}
"max": false
},
"return_spectrum": true
},
"welch_settings": {
"windowlength_ms": 1000,
Expand All @@ -103,8 +104,9 @@
"mean": true,
"median": false,
"std": false,
"max" : false
}
"max": false
},
"return_spectrum": true
},
"stft_settings": {
"windowlength_ms": 500,
Expand All @@ -113,8 +115,9 @@
"mean": true,
"median": false,
"std": false,
"max" : false
}
"max": false
},
"return_spectrum": true
},
"bandpass_filter_settings": {
"segment_lengths_ms": {
Expand Down
19 changes: 19 additions & 0 deletions tests/test_feature_sampling_rates.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,13 +92,30 @@ def test_different_sampling_rate_0DOT1Hz():
assert np.diff(df["time"].iloc[:2]) / 1000 == (1 / sampling_rate_features)


def test_wrong_initalization_of_segment_length_features_ms_and_osc_window_length():
segment_length_features_ms = 800

arr_test = np.random.random([2, 1200])
settings, nm_channels = get_example_settings(arr_test)

settings["segment_length_features_ms"] = 800
settings["fft_settings"]["windowlength_ms"] = 1000

with pytest.raises(Exception):
stream = nm_stream_offline.Stream(
sfreq=1000, nm_channels=nm_channels, settings=settings, verbose=True
)


def test_different_segment_lengths():
segment_length_features_ms = 800

arr_test = np.random.random([2, 1200])
settings, nm_channels = get_example_settings(arr_test)

settings["segment_length_features_ms"] = segment_length_features_ms
settings["fft_settings"]["windowlength_ms"] = segment_length_features_ms

stream = nm_stream_offline.Stream(
sfreq=1000, nm_channels=nm_channels, settings=settings, verbose=True
)
Expand All @@ -111,6 +128,8 @@ def test_different_segment_lengths():
settings, nm_channels = get_example_settings(arr_test)

settings["segment_length_features_ms"] = segment_length_features_ms
settings["fft_settings"]["windowlength_ms"] = segment_length_features_ms

stream = nm_stream_offline.Stream(
sfreq=1000, nm_channels=nm_channels, settings=settings, verbose=True
)
Expand Down
3 changes: 2 additions & 1 deletion tests/test_osc_features.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,7 +69,8 @@ def test_fft_zero_data():
features_out = fft_obj.calc_feature(data, {})

for f in features_out.keys():
assert features_out[f] == 0
if "psd_0" not in f:
assert np.isclose(features_out[f], 0, atol=1e-6)


def test_fft_random_data():
Expand Down
1 change: 1 addition & 0 deletions tests/test_sampling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@ def get_features(time_end_ms: int, segment_length_features_ms: int):
data = np.random.random([2, time_end_ms])
settings = get_fast_compute_settings()
settings["segment_length_features_ms"] = segment_length_features_ms
settings["fft_settings"]["windowlength_ms"] = segment_length_features_ms

settings["frequency_ranges_hz"] = {
# "high beta" : [20, 35],
Expand Down
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