---
title: Delays anaylsis
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---

### 1. Delays
We noticed a delay in the recodings if you were to plot a comparision between
the base relacs recording and the new generated open-ephys.

```{python}
# | echo: False
import pathlib

import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import rlxnix as rlx
import scipy.signal as signal
from plotly.subplots import make_subplots
from rich.progress import track
from rich.table import Table
from util import calc_lag, plot_line_comparision, trial_plot

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")

fig = plot_line_comparision(
    t_r,
    t,
    stimulus_re,
    stimulus_oe - np.mean(stimulus_oe),
    ["stimulus-relacs", "stimulus-open-ephys"],
)
fig.show()
```

### 2. Look at differnt RePros
Currently implemented repros are:

- [x] [Baseline](baseline.qmd)
- [x] [Calibration](calibration.qmd)
- [x] [FI Curve](fi_curve.qmd)
- [x] [File Stimulus](filestimulus.qmd)
- [ ] Sams
- [ ] Chrips
- [ ] Beats

### 3. General Delay in detail
```{python}
rich_tabel = Table("Repro Run", "Signal", "Lag (samples)")
names_lanes = ["sinus", "local-eod", "global-eod", "stimulus"]
dataframe = []
for repro_idx, (repro_d, repro_r) in enumerate(
    zip(dataset.repro_runs(), relacs.repro_runs(), strict=True)
):
    if not repro_d.stimuli:
        lags = calc_lag(repro_d, repro_r)
        for lag, names_lane in zip(lags, names_lanes, strict=True):
            rich_tabel.add_row(f"{repro_d.name}", f"{names_lane}", f"{lag}")
            dataframe.append(
                {"ReproName": repro_d.name, "Line": names_lane, "Lag": lag, "Trial": 0}
            )

    else:
        lags_lanes = {f"{key}": [] for key in names_lanes}
        for trial, (stim_oe, stim_re) in enumerate(
            zip(repro_d.stimuli, repro_r.stimuli, strict=True)
        ):
            lags = calc_lag(stim_oe, stim_re)
            for lag, names_lane in zip(lags, names_lanes):
                lags_lanes[names_lane].append(lag)
                dataframe.append(
                    {"ReproName": repro_d.name, "Line": names_lane, "Lag": lag, "Trial": trial}
                )
        for lane in lags_lanes:
            mean_lag = np.mean(lags_lanes[lane])
            std_lag = np.std(lags_lanes[lane])
            rich_tabel.add_row(f"{repro_d.name}", f"{lane}", f"{mean_lag:.2f}\u00b1{std_lag:.2f}")

rich_tabel
```

```{python}
repros = dataset.repro_runs("Baseline")
print(repros)
exclude = []
for rep in repros:
    exclude.append(rep.name)

df = pd.DataFrame(dataframe)
df = df[~df["ReproName"].isin(exclude)]
fig = px.box(
    df,
    x="Line",
    y="Lag",
    color="Line",
    title="Lag Distribution Across Different Signals",
    labels={"Line": "Signal", "Lag": "Lag (samples)"},
)
fig.update_layout(template="plotly_dark")
fig.show()

fig = px.box(
    df,
    x="Line",
    y="Lag",
    color="ReproName",
    title="Lag Distribution by Signal and Repro Run",
    labels={"Line": "Signal", "Lag": "Lag (samples)"},
)
fig.update_layout(template="plotly_dark")
fig.show()
```