---
title: SAM
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---

### SAM

```{python}
import pathlib

import numpy as np
import plotly.graph_objects as go
import rlxnix as rlx
import scipy.signal as signal
from plotly.subplots import make_subplots
from util import plot_line_comparision, trial_plot

dataset_path = pathlib.Path("../oephys2nix/test/AllStimuli/2025-10-20-aa-invivo-2-recording.nix")
relacs_path = pathlib.Path(
    "../oephys2nix/test/AllStimuli/2025-10-20-aa-invivo-2_relacs/2025-10-20-aa-invivo-2_relacs.nix"
)

dataset = rlx.Dataset(str(dataset_path))
relacs = rlx.Dataset(str(relacs_path))

# INFO: Select the first stimulus of the calibration repro
repro_d = dataset.repro_runs("SAM")[0].stimuli[2]
repro_r = relacs.repro_runs("SAM")[0].stimuli[2]

sinus, t = repro_d.trace_data("sinus")
sinus_r, t_r = repro_r.trace_data("V-1")

stimulus_oe, t = repro_d.trace_data("stimulus")
stimulus_re, t_r = repro_r.trace_data("GlobalEFieldStimulus")

local_eod_oe, t = repro_d.trace_data("local-eod")
local_eod_re, t_r = repro_r.trace_data("LocalEOD-1")

global_eod_oe, t = repro_d.trace_data("global-eod")
global_eod_re, t_r = repro_r.trace_data("EOD")

ttl, t = repro_d.trace_data("ttl-line")
```
### Plotting the First trial
If you zoom in you can see a little delay between the different recording systems. It seems that open-ephys is before the relacs recording.

```{python}
# | echo: False
fig = trial_plot(repro_d, repro_r, 0.2)
fig.update_xaxes(range=[0, 0.2])
fig.show()
```
### Correlation between the Signals

```{python}
print(f"Duration of the dataset {repro_d.duration}")
print(f"Duration of the relacs {repro_r.duration}")
# Resample the open-ephys data
sinus_resampled = signal.resample(sinus, len(sinus_r))
```

```{python}
# | echo: False
fig = plot_line_comparision(
    t_r, t_r, sinus_r, sinus_resampled, ["sinus-relacs", "sinus-resampled-open-ephys"]
)
fig.show()
```
We need to scale the two signals

```{python}
oephys_lanes = [sinus, local_eod_oe, global_eod_oe, stimulus_oe]
relacs_lanes = [sinus_r, local_eod_re, global_eod_re, stimulus_re]
names_lanes = ["sinus", "local-eod", "global-eod", "stimulus"]
lags_lanes = []
for oephys_lane, relacs_lane, names_lane in zip(
    oephys_lanes, relacs_lanes, names_lanes, strict=True
):
    print(oephys_lane.shape)
    print(relacs_lane.shape)
    oephys_lane_resampled = signal.resample(oephys_lane, len(relacs_lane))
    correlation = signal.correlate(oephys_lane_resampled, relacs_lane, mode="full")
    lags = signal.correlation_lags(oephys_lane_resampled.size, relacs_lane.size, mode="full")
    lag = lags[np.argmax(correlation)]
    lags_lanes.append(lag)
    print(f"{names_lane} has a lag of {lag}")
```

```{python}
# | echo: False
fig = plot_line_comparision(
    t_r,
    t,
    np.roll(stimulus_re, lags_lanes[-1]),
    stimulus_oe - np.mean(stimulus_oe),
    ["rolled sinus-relacs", "sinus-resampled-open-ephys"],
)
fig.show()

print(f"The lag of the whitenoise is {lags_lanes[-1] * (1 / 20_000) * 1000} milli seconds")
```