---
title: File Stimulus
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---

### File Stimulus

```{python}
import pathlib

import rlxnix as rlx
import plotly.graph_objects as go
import numpy as np
import scipy.signal as signal
from plotly.subplots import make_subplots

from util import trial_plot, plot_line_comparision



dataset_path = pathlib.Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2-recording.nix")
relacs_path  = pathlib.Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2_relacs/2025-10-08-aa-invivo-2.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("FileStimulus_1")[0].stimuli[2]
repro_r = relacs.repro_runs("FileStimulus_1")[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
# 2. Add traces to the FIRST subplot (row=1, col=1)
# Note: Plotly rows and columns are 1-indexed
fig = trial_plot(repro_d, repro_r)

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")
```