105 lines
3.2 KiB
Plaintext
105 lines
3.2 KiB
Plaintext
---
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title: File Stimulus
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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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### File Stimulus
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```{python}
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import pathlib
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import rlxnix as rlx
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import plotly.graph_objects as go
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import numpy as np
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import scipy.signal as signal
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from plotly.subplots import make_subplots
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from util import trial_plot, plot_line_comparision
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dataset_path = pathlib.Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2-recording.nix")
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relacs_path = pathlib.Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2_relacs/2025-10-08-aa-invivo-2.nix")
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dataset = rlx.Dataset(str(dataset_path))
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relacs = rlx.Dataset(str(relacs_path))
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#INFO: Select the first stimulus of the calibration repro
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repro_d = dataset.repro_runs("FileStimulus_1")[0].stimuli[2]
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repro_r = relacs.repro_runs("FileStimulus_1")[0].stimuli[2]
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sinus, t = repro_d.trace_data("sinus")
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sinus_r, t_r = repro_r.trace_data("V-1")
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stimulus_oe, t = repro_d.trace_data("stimulus")
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stimulus_re, t_r = repro_r.trace_data("GlobalEFieldStimulus")
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local_eod_oe, t = repro_d.trace_data("local-eod")
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local_eod_re, t_r = repro_r.trace_data("LocalEOD-1")
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global_eod_oe, t = repro_d.trace_data("global-eod")
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global_eod_re, t_r = repro_r.trace_data("EOD")
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ttl, t = repro_d.trace_data("ttl-line")
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```
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### Plotting the First trial
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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.
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```{python}
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#| echo: False
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# 2. Add traces to the FIRST subplot (row=1, col=1)
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# Note: Plotly rows and columns are 1-indexed
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fig = trial_plot(repro_d, repro_r)
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fig.show()
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```
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### Correlation between the Signals
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```{python}
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print(f"Duration of the dataset {repro_d.duration}")
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print(f"Duration of the relacs {repro_r.duration}")
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# Resample the open-ephys data
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sinus_resampled = signal.resample(sinus, len(sinus_r))
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```
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```{python}
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#| echo: False
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fig= plot_line_comparision(t_r, t_r, sinus_r, sinus_resampled, ["sinus-relacs", "sinus-resampled-open-ephys"])
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fig.show()
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```
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We need to scale the two signals
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```{python}
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oephys_lanes = [sinus, local_eod_oe, global_eod_oe, stimulus_oe]
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relacs_lanes = [sinus_r, local_eod_re, global_eod_re, stimulus_re]
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names_lanes = ["sinus", "local-eod", "global-eod", "stimulus"]
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lags_lanes = []
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for oephys_lane, relacs_lane, names_lane in zip(oephys_lanes, relacs_lanes, names_lanes, strict=True):
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print(oephys_lane.shape)
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print(relacs_lane.shape)
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oephys_lane_resampled = signal.resample(oephys_lane, len(relacs_lane))
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correlation = signal.correlate(oephys_lane_resampled, relacs_lane, mode="full")
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lags = signal.correlation_lags(oephys_lane_resampled.size, relacs_lane.size, mode="full")
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lag = lags[np.argmax(correlation)]
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lags_lanes.append(lag)
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print(f"{names_lane} has a lag of {lag}")
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```
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```{python}
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#| echo: False
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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"])
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fig.show()
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print(f"The lag of the whitenoise is {lags_lanes[-1] * (1/20_000) * 1000} milli seconds")
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```
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