[doc] adding different repos for delays
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107
doc/baseline.qmd
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107
doc/baseline.qmd
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---
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title: Baseline
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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. Loading
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Lets look at the calibration and the first trial of the recording.
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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/AllStimuli/2025-10-20-aa-invivo-2-recording.nix")
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relacs_path = pathlib.Path("../oephys2nix/test/AllStimuli/2025-10-20-aa-invivo-2_relacs/2025-10-20-aa-invivo-2_relacs.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("BaselineActivity")[0]
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repro_r = relacs.repro_runs("BaselineActivity")[0]
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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, 1.0)
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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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samples_20kHz = t[-1] * 20_000
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print(samples_20kHz)
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print(f"Total duration {t[-1]}")
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print(repro_d.duration)
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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, int(samples_20kHz))
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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_r, np.roll(sinus_r, lags_lanes[0]), sinus_resampled, ["rolled sinus-relacs", "sinus-resampled-open-ephys"])
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fig.show()
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```
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103
doc/fi_curve.qmd
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doc/fi_curve.qmd
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---
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title: FI Curve
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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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### FI Curve
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Lets look at the calibration and the first trial of the recording.
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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("FICurve_1")[0].stimuli[2]
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repro_r = relacs.repro_runs("FICurve_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_r, np.roll(sinus_r, lags_lanes[0]), sinus_resampled, ["rolled sinus-relacs", "sinus-resampled-open-ephys"])
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fig.show()
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```
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104
doc/filestimulus.qmd
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104
doc/filestimulus.qmd
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---
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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_r, np.roll(sinus_r, lags_lanes[0]), sinus_resampled, ["rolled sinus-relacs", "sinus-resampled-open-ephys"])
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fig.show()
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```
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182
doc/samplerates.qmd
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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],
|
||||
showlegend=False, line_color="blue",
|
||||
mode="markers+lines"), row=1, col=1)
|
||||
fig.add_trace( go.Scattergl(x=t_diff[t_diff<x_lim],
|
||||
y=stimulus_diff_oe[t_diff<x_lim],
|
||||
showlegend=False,
|
||||
line_color="red", mode="markers+lines"), row=1, col=1)
|
||||
|
||||
fig.add_trace( go.Scattergl(x=t_same_r[t_same_r<x_lim],
|
||||
y=stimulus_same_re[t_same_r<x_lim],
|
||||
name="GlobalStimulus (relacs)", line_color="blue",
|
||||
mode="markers+lines") , row=2, col=1)
|
||||
fig.add_trace( go.Scattergl(x=t_same[t_same<x_lim],
|
||||
y=stimulus_same_oe[t_same<x_lim],
|
||||
name="GlobalStimulus (open-ephys)",
|
||||
line_color="red", mode="markers+lines"),row=2, col=1)
|
||||
fig.update_layout(
|
||||
template="plotly_dark",
|
||||
height=400,
|
||||
legend=dict(
|
||||
bgcolor="rgba(0,0,0,0)",
|
||||
bordercolor="#444",
|
||||
borderwidth=0,
|
||||
font=dict(color="#e5ecf6"),
|
||||
orientation="h",
|
||||
yanchor="bottom",
|
||||
y=1.06,
|
||||
xanchor="right",
|
||||
x=0.72,
|
||||
)
|
||||
)
|
||||
|
||||
fig.update_xaxes(range=[0, 0.01])
|
||||
```
|
||||
|
||||
### 5. Lags in recodings
|
||||
|
||||
```{python}
|
||||
# resample to 20 kHz
|
||||
stimulus_diff_oe_resampled = signal.resample(stimulus_diff_oe, len(stimulus_same_re))
|
||||
correlation_diff = signal.correlate(stimulus_diff_oe_resampled, stimulus_diff_re, mode="full")
|
||||
lags_diff = signal.correlation_lags(stimulus_diff_oe_resampled.size, stimulus_diff_re.size, mode="full")
|
||||
lag_diff = lags_diff[np.argmax(correlation_diff)]
|
||||
|
||||
correlation_same = signal.correlate(stimulus_same_oe, stimulus_same_re, mode="full")
|
||||
lags_same = signal.correlation_lags(stimulus_same_oe.size, stimulus_same_re.size, mode="full")
|
||||
lag_same = lags_same[np.argmax(correlation_same)]
|
||||
|
||||
print(f"The lag in with different sampling rates is {lag_diff}, and with the same sample rate is {lag_same}")
|
||||
```
|
||||
|
||||
```{python}
|
||||
#| echo: False
|
||||
fig = make_subplots( rows=2, cols=1, shared_xaxes=True, subplot_titles=("Different fs [30 khz and 20 kHz]", "Same fs [20kHz]"))
|
||||
|
||||
fig.add_trace( go.Scattergl(x=t_diff_r[t_diff_r<x_lim],
|
||||
y=np.roll(stimulus_diff_re[t_diff_r<x_lim], lag_diff),
|
||||
line_color="blue",
|
||||
showlegend=False,
|
||||
mode="markers+lines"), row=1, col=1)
|
||||
fig.add_trace( go.Scattergl(x=t_diff[t_diff<x_lim],
|
||||
y=stimulus_diff_oe[t_diff<x_lim], showlegend=False,
|
||||
line_color="red", mode="markers+lines"), row=1,
|
||||
col=1)
|
||||
|
||||
fig.add_trace( go.Scattergl(x=t_same_r[t_same_r<x_lim],
|
||||
y=np.roll(stimulus_same_re[t_same_r<x_lim], lag_same),
|
||||
name="GlobalStimulus (relacs)", line_color="blue",
|
||||
mode="markers+lines") , row=2, col=1)
|
||||
fig.add_trace( go.Scattergl(x=t_same[t_same<x_lim],
|
||||
y=stimulus_same_oe[t_same<x_lim],
|
||||
name="GlobalStimulus (open-ephys)",
|
||||
line_color="red", mode="markers+lines"),row=2, col=1)
|
||||
fig.update_layout(
|
||||
template="plotly_dark",
|
||||
height=400,
|
||||
legend=dict(
|
||||
bgcolor="rgba(0,0,0,0)",
|
||||
bordercolor="#444",
|
||||
borderwidth=0,
|
||||
font=dict(color="#e5ecf6"),
|
||||
orientation="h",
|
||||
yanchor="bottom",
|
||||
y=1.06,
|
||||
xanchor="right",
|
||||
x=0.72,
|
||||
)
|
||||
)
|
||||
|
||||
fig.update_xaxes(range=[0, 0.01])
|
||||
```
|
||||
### 6. Conculsion
|
||||
|
||||
Lags of simuliar magnitude exists in both recordings therefor the sample rate is not the problem!
|
||||
Loading…
Reference in New Issue
Block a user