[doc] adding different repos for delays
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wendtalexander
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doc/samplerates.qmd
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182
doc/samplerates.qmd
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---
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title: Differences between sample rates
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format:
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html:
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toc: true
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toc-title: Contents
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code-block-bg: true
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code-block-border-left: "#31BAE9"
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code-line-numbers: true
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highlight-style: atom-one
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link-external-icon: true
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link-external-newwindow: true
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eqn-number: true
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---
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### 1. General Idea
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The two aquisition systems have a different default sampling rate and currently
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there is a delay and maybe this is due to the different sampling rates.
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`open-ephys` has a sample-rate of 30 kHz and `relacs` one of 20 kHz. In this
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test we have two different recordings with one where the open-epyhs has 30 kHz
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and the other with 20 kHz.
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### 2. Loading the data
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```{python}
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from pathlib import Path
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import rlxnix as rlx
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import scipy.signal as signal
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import numpy as np
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# Path to test recording with different samplerate open-epyhs 30kHz and relacs 20kHz
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dataset_path_diff_fs = Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2-recording.nix")
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relacs_path_diff_fs = Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2_relacs/2025-10-08-aa-invivo-2.nix")
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# Path to test recording with same samplerate open-epyhs 20kHz and relacs 20kHz
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dataset_path_same_fs = Path("../oephys2nix/test/Test2/2025-10-08-ab-invivo-2-recording.nix")
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relacs_path_same_fs = Path("../oephys2nix/test/Test2/2025-10-08-ab-invivo-2_relacs/2025-10-08-ab-invivo-2.nix")
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dataset_diff_fs = rlx.Dataset(str(dataset_path_diff_fs))
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relacs_diff_fs = rlx.Dataset(str(relacs_path_diff_fs))
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dataset_same_fs = rlx.Dataset(str(dataset_path_same_fs))
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relacs_same_fs = rlx.Dataset(str(relacs_path_same_fs))
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repro_diff_fs_d = dataset_diff_fs.repro_runs("FileStimulus_1")[0].stimuli[2]
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repro_diff_fs_r = relacs_diff_fs.repro_runs("FileStimulus_1")[0].stimuli[2]
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repro_same_fs_d = dataset_same_fs.repro_runs("FileStimulus_1")[0].stimuli[2]
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repro_same_fs_r = relacs_same_fs.repro_runs("FileStimulus_1")[0].stimuli[2]
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#sinus, t = repro_diff_fs_d.trace_data("sinus")
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#sinus_r, t_r = repro_diff_fs_r.trace_data("V-1")
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stimulus_diff_oe, t_diff = repro_diff_fs_d.trace_data("stimulus")
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stimulus_diff_re, t_diff_r = repro_diff_fs_r.trace_data("GlobalEFieldStimulus")
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stimulus_same_oe, t_same = repro_same_fs_d.trace_data("stimulus")
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stimulus_same_re, t_same_r = repro_same_fs_r.trace_data("GlobalEFieldStimulus")
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```
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### 3. Samples in the different recordings for one stimulus
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```{python}
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#| echo: False
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print(f"Samples open-epyhs [30 kHz] for one trial: {stimulus_diff_oe.shape}")
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print(f"Samples relacs for one trial: {stimulus_diff_re.shape}")
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print(f"Samples open-epyhs [20 kHz] for one trial: {stimulus_same_oe.shape}")
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print(f"Samples relacs for one trial: {stimulus_same_re.shape}")
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```
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### 4. Plotting first trial
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Here we plot the different output stimulus, different sample rates
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```{python}
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#| echo: False
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x_lim = 0.05
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fig = make_subplots( rows=2, cols=1, shared_xaxes=True, subplot_titles=("Different fs [30 khz and 20 kHz]", "Same fs [20kHz]"))
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fig.add_trace( go.Scattergl(x=t_diff_r[t_diff_r<x_lim],
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y=stimulus_diff_re[t_diff_r<x_lim],
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showlegend=False, line_color="blue",
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mode="markers+lines"), row=1, col=1)
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fig.add_trace( go.Scattergl(x=t_diff[t_diff<x_lim],
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y=stimulus_diff_oe[t_diff<x_lim],
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showlegend=False,
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line_color="red", mode="markers+lines"), row=1, col=1)
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fig.add_trace( go.Scattergl(x=t_same_r[t_same_r<x_lim],
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y=stimulus_same_re[t_same_r<x_lim],
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name="GlobalStimulus (relacs)", line_color="blue",
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mode="markers+lines") , row=2, col=1)
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fig.add_trace( go.Scattergl(x=t_same[t_same<x_lim],
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y=stimulus_same_oe[t_same<x_lim],
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name="GlobalStimulus (open-ephys)",
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line_color="red", mode="markers+lines"),row=2, col=1)
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fig.update_layout(
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template="plotly_dark",
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height=400,
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legend=dict(
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bgcolor="rgba(0,0,0,0)",
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bordercolor="#444",
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borderwidth=0,
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font=dict(color="#e5ecf6"),
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orientation="h",
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yanchor="bottom",
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y=1.06,
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xanchor="right",
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x=0.72,
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)
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)
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fig.update_xaxes(range=[0, 0.01])
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```
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### 5. Lags in recodings
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```{python}
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# resample to 20 kHz
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stimulus_diff_oe_resampled = signal.resample(stimulus_diff_oe, len(stimulus_same_re))
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correlation_diff = signal.correlate(stimulus_diff_oe_resampled, stimulus_diff_re, mode="full")
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lags_diff = signal.correlation_lags(stimulus_diff_oe_resampled.size, stimulus_diff_re.size, mode="full")
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lag_diff = lags_diff[np.argmax(correlation_diff)]
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correlation_same = signal.correlate(stimulus_same_oe, stimulus_same_re, mode="full")
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lags_same = signal.correlation_lags(stimulus_same_oe.size, stimulus_same_re.size, mode="full")
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lag_same = lags_same[np.argmax(correlation_same)]
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print(f"The lag in with different sampling rates is {lag_diff}, and with the same sample rate is {lag_same}")
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```
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```{python}
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#| echo: False
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fig = make_subplots( rows=2, cols=1, shared_xaxes=True, subplot_titles=("Different fs [30 khz and 20 kHz]", "Same fs [20kHz]"))
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fig.add_trace( go.Scattergl(x=t_diff_r[t_diff_r<x_lim],
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y=np.roll(stimulus_diff_re[t_diff_r<x_lim], lag_diff),
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line_color="blue",
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showlegend=False,
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mode="markers+lines"), row=1, col=1)
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fig.add_trace( go.Scattergl(x=t_diff[t_diff<x_lim],
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y=stimulus_diff_oe[t_diff<x_lim], showlegend=False,
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line_color="red", mode="markers+lines"), row=1,
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col=1)
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fig.add_trace( go.Scattergl(x=t_same_r[t_same_r<x_lim],
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y=np.roll(stimulus_same_re[t_same_r<x_lim], lag_same),
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name="GlobalStimulus (relacs)", line_color="blue",
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mode="markers+lines") , row=2, col=1)
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fig.add_trace( go.Scattergl(x=t_same[t_same<x_lim],
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y=stimulus_same_oe[t_same<x_lim],
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name="GlobalStimulus (open-ephys)",
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line_color="red", mode="markers+lines"),row=2, col=1)
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fig.update_layout(
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template="plotly_dark",
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height=400,
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legend=dict(
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bgcolor="rgba(0,0,0,0)",
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bordercolor="#444",
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borderwidth=0,
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font=dict(color="#e5ecf6"),
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orientation="h",
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yanchor="bottom",
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y=1.06,
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xanchor="right",
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x=0.72,
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)
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)
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fig.update_xaxes(range=[0, 0.01])
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```
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### 6. Conculsion
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Lags of simuliar magnitude exists in both recordings therefor the sample rate is not the problem!
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