---
title: Calibration
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---

### Calibration of the Amplitude
Lets look at the calibration and the first trial of the recording.

```{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


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("CalibEfield_1")[0].stimuli[2]
repro_r = relacs.repro_runs("CalibEfield_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 = make_subplots( rows=5, cols=1, shared_xaxes=True, subplot_titles=("TTL-Line",
  "Stimulus Comparison", "Local EOD Comparison", "Global EOD Comparison",
  "Sinus Comparison"))

fig.add_trace(
    go.Scatter(x=t, y=ttl, name="ttl-line", line_color="magenta"),
    row=1,
    col=1,
)

fig.add_trace(
    go.Scatter(x=t_r, y=stimulus_re, name="stimulus (relacs)", line_color="blue"),
    row=2,
    col=1,
)
fig.add_trace(
    go.Scatter(
        x=t,
        y=stimulus_oe - np.mean(stimulus_oe), # The same data transformation
        name="stimulus (open-ephys)",
        line_color="red",
    ),
    row=2,
    col=1,
)


# 3. Add traces to the SECOND subplot (row=2, col=1)
fig.add_trace(
    go.Scatter(x=t_r, y=local_eod_re, name="local EOD (relacs)", line_color="blue"),
    row=3,
    col=1,
)
fig.add_trace(
    go.Scatter(x=t, y=local_eod_oe, name="local EOD (open-ephys)", line_color="red"),
    row=3,
    col=1,
)


# 4. Add traces to the THIRD subplot (row=3, col=1)
fig.add_trace(
    go.Scatter(x=t_r, y=global_eod_re, name="global EOD (relacs)", line_color="blue"),
    row=4,
    col=1,
)
fig.add_trace(
    go.Scatter(
        x=t, y=global_eod_oe, name="global EOD (open-ephys)", line_color="red"
    ),
    row=4,
    col=1,
)

# 5. Add traces to the FOURTH subplot (row=4, col=1)
fig.add_trace(
    go.Scatter(x=t_r, y=sinus_r, name="sinus (relacs)", line_color="blue"),
    row=5,
    col=1,
)
fig.add_trace(
    go.Scatter(x=t, y=sinus, name="sinus (open-ephys)", line_color="red"),
    row=5,
    col=1,
)

# 6. Update the layout for a cleaner look
fig.update_layout(
    template="plotly_dark",
    height=800, # Set the figure height in pixels
    # Control the legend
    #legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
    legend=dict(
    bgcolor="rgba(0,0,0,0)",   # transparent dark (or use "#1f2630" to match bg)
    bordercolor="#444",
    borderwidth=0,
    font=dict(color="#e5ecf6") # matches plotly_dark foreground
  )
)
# Add a label to the shared x-axis (targeting the last subplot)
fig.update_xaxes(title_text="Time (s)", row=4, col=1)

# 7. Show the figure
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 = go.Figure()
fig.add_trace( go.Scatter(x=t_r, y=sinus_r, name="sinus (relacs)", line_color="blue", mode="lines+markers"))
fig.add_trace( go.Scatter(x=t_r, y=sinus_resampled, name="sinus-resampled (open-ephys)", line_color="red", mode="lines+markers"))
fig.update_layout(
    template="plotly_dark",
    height=500, # Set the figure height in pixels
    # Control the legend
    #legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
    legend=dict(
    bgcolor="rgba(0,0,0,0)",   # transparent dark (or use "#1f2630" to match bg)
    bordercolor="#444",
    borderwidth=0,
    font=dict(color="#e5ecf6") # matches plotly_dark foreground
  )
)
fig.update_xaxes(range=[0, 0.01])
```
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 = go.Figure()
fig.add_trace( go.Scatter(x=t_r, y=sinus_r, name="sinus (relacs)", line_color="blue", mode="lines+markers"))
fig.add_trace( go.Scatter(x=t_r, y=np.roll(sinus_resampled, -lags_lanes[0]), name="sinus-resampled (open-ephys)", line_color="red", mode="lines+markers"))
fig.update_layout(
    title="Sinus",
    template="plotly_dark",
    height=500, # Set the figure height in pixels
    # Control the legend
    #legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
    legend=dict(
    bgcolor="rgba(0,0,0,0)",   # transparent dark (or use "#1f2630" to match bg)
    bordercolor="#444",
    borderwidth=0,
    font=dict(color="#e5ecf6") # matches plotly_dark foreground
  )
)
fig.update_xaxes(range=[0, 0.01])
```