[stimulus_recreation] lowering repro duration

doc/_quarto.yml

@@ -34,10 +34,16 @@ website:
     contents:
       - text: "Introduction"
         href: "index.qmd"
-      - section: "Tutorials"
+      - text: "Usage"
+        href: "usage.qmd"
+      - text: "Sample Rates"
+        href: "samplerates.qmd"
+      - section: "Delays"
         contents:
-        - "usage.qmd"
+        - "baseline.qmd"
         - "calibration.qmd"
+        - "fi_curve.qmd"
+        - "filestimulus.qmd"
       - section: "API"
         href: "api/index.qmd"
         contents:

doc/baseline.qmd

@@ -0,0 +1,107 @@
+---
+title: Baseline
+format:
+  html:
+    toc: true
+    toc-title: Contents
+    code-block-bg: true
+    code-block-border-left: "#31BAE9"
+    code-line-numbers: true
+    highlight-style: atom-one
+    link-external-icon: true
+    link-external-newwindow: true
+    eqn-number: true
+---
+
+### 1. Loading
+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
+
+from util import trial_plot, plot_line_comparision
+
+
+
+dataset_path = pathlib.Path("../oephys2nix/test/AllStimuli/2025-10-20-aa-invivo-2-recording.nix")
+relacs_path  = pathlib.Path("../oephys2nix/test/AllStimuli/2025-10-20-aa-invivo-2_relacs/2025-10-20-aa-invivo-2_relacs.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("BaselineActivity")[0]
+repro_r = relacs.repro_runs("BaselineActivity")[0]
+
+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 = trial_plot(repro_d, repro_r, 1.0)
+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= plot_line_comparision(t_r, t_r, sinus_r, sinus_resampled, ["sinus-relacs", "sinus-resampled-open-ephys"])
+fig.show()
+```
+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"]
+samples_20kHz = t[-1] * 20_000
+print(samples_20kHz)
+print(f"Total duration {t[-1]}")
+print(repro_d.duration)
+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, int(samples_20kHz))
+  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 = plot_line_comparision(t_r, t_r, np.roll(sinus_r, lags_lanes[0]), sinus_resampled, ["rolled sinus-relacs", "sinus-resampled-open-ephys"])
+fig.show()
+```

doc/calibration.qmd

@@ -26,6 +26,8 @@ import numpy as np
 import scipy.signal as signal
 from plotly.subplots import make_subplots
 
+from util import trial_plot, plot_line_comparision
+
 
 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")
@@ -56,91 +58,7 @@ If you zoom in you can see a little delay between the different recording system
 
 ```{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 = trial_plot(repro_d, repro_r)
 fig.show()
 ```
 ### Correlation between the Signals
@@ -155,22 +73,8 @@ 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])
+fig = plot_line_comparision(t_r, t, sinus_r, sinus, ["sinus-relacs", "sinus-oephys"])
+fig.show()
 ```
 We need to scale the two signals
 
@@ -192,21 +96,6 @@ for oephys_lane, relacs_lane, names_lane in zip(oephys_lanes, relacs_lanes, name
 
 ```{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])
+fig = plot_line_comparision(t_r, t_r, np.roll(sinus_r, lags_lanes[0]),sinus_resampled, ["sinus-relacs", "sinus-resampled-openepyhs"])
+fig.show()
 ```

doc/fi_curve.qmd

@@ -0,0 +1,103 @@
+---
+title: FI Curve
+format:
+  html:
+    toc: true
+    toc-title: Contents
+    code-block-bg: true
+    code-block-border-left: "#31BAE9"
+    code-line-numbers: true
+    highlight-style: atom-one
+    link-external-icon: true
+    link-external-newwindow: true
+    eqn-number: true
+---
+
+### FI Curve
+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
+
+from util import trial_plot, plot_line_comparision
+
+
+
+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("FICurve_1")[0].stimuli[2]
+repro_r = relacs.repro_runs("FICurve_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 = trial_plot(repro_d, repro_r)
+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= plot_line_comparision(t_r, t_r, sinus_r, sinus_resampled, ["sinus-relacs", "sinus-resampled-open-ephys"])
+fig.show()
+```
+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 = plot_line_comparision(t_r, t_r, np.roll(sinus_r, lags_lanes[0]), sinus_resampled, ["rolled sinus-relacs", "sinus-resampled-open-ephys"])
+fig.show()
+```

doc/filestimulus.qmd

@@ -0,0 +1,104 @@
+---
+title: File Stimulus
+format:
+  html:
+    toc: true
+    toc-title: Contents
+    code-block-bg: true
+    code-block-border-left: "#31BAE9"
+    code-line-numbers: true
+    highlight-style: atom-one
+    link-external-icon: true
+    link-external-newwindow: true
+    eqn-number: true
+---
+
+### File Stimulus
+
+```{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
+
+from util import trial_plot, plot_line_comparision
+
+
+
+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")
+```
+### 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 = trial_plot(repro_d, repro_r)
+
+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= plot_line_comparision(t_r, t_r, sinus_r, sinus_resampled, ["sinus-relacs", "sinus-resampled-open-ephys"])
+fig.show()
+```
+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 = plot_line_comparision(t_r, t_r, np.roll(sinus_r, lags_lanes[0]), sinus_resampled, ["rolled sinus-relacs", "sinus-resampled-open-ephys"])
+fig.show()
+```

doc/samplerates.qmd

@@ -0,0 +1,182 @@
+---
+title: Differences between sample rates
+format:
+  html:
+    toc: true
+    toc-title: Contents
+    code-block-bg: true
+    code-block-border-left: "#31BAE9"
+    code-line-numbers: true
+    highlight-style: atom-one
+    link-external-icon: true
+    link-external-newwindow: true
+    eqn-number: true
+---
+
+### 1. General Idea
+The two aquisition systems have a different default sampling rate and currently
+there is a delay and maybe this is due to the different sampling rates.
+
+`open-ephys` has a sample-rate of 30 kHz and `relacs` one of 20 kHz. In this
+test we have two different recordings with one where the open-epyhs has 30 kHz
+and the other with 20 kHz.
+
+### 2. Loading the data 
+
+```{python}
+from pathlib import Path
+
+import rlxnix as rlx
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import scipy.signal as signal
+import numpy as np
+
+# Path to test recording with different samplerate open-epyhs 30kHz and relacs 20kHz
+dataset_path_diff_fs = Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2-recording.nix")
+relacs_path_diff_fs = Path("../oephys2nix/test/Test1/2025-10-08-aa-invivo-2_relacs/2025-10-08-aa-invivo-2.nix")
+
+# Path to test recording with same samplerate open-epyhs 20kHz and relacs 20kHz
+dataset_path_same_fs = Path("../oephys2nix/test/Test2/2025-10-08-ab-invivo-2-recording.nix")
+relacs_path_same_fs = Path("../oephys2nix/test/Test2/2025-10-08-ab-invivo-2_relacs/2025-10-08-ab-invivo-2.nix")
+
+
+dataset_diff_fs = rlx.Dataset(str(dataset_path_diff_fs))
+relacs_diff_fs = rlx.Dataset(str(relacs_path_diff_fs))
+
+dataset_same_fs = rlx.Dataset(str(dataset_path_same_fs))
+relacs_same_fs = rlx.Dataset(str(relacs_path_same_fs))
+
+
+repro_diff_fs_d = dataset_diff_fs.repro_runs("FileStimulus_1")[0].stimuli[2]
+repro_diff_fs_r = relacs_diff_fs.repro_runs("FileStimulus_1")[0].stimuli[2]
+
+repro_same_fs_d = dataset_same_fs.repro_runs("FileStimulus_1")[0].stimuli[2]
+repro_same_fs_r = relacs_same_fs.repro_runs("FileStimulus_1")[0].stimuli[2]
+
+#sinus, t = repro_diff_fs_d.trace_data("sinus")
+#sinus_r, t_r = repro_diff_fs_r.trace_data("V-1")
+
+stimulus_diff_oe, t_diff = repro_diff_fs_d.trace_data("stimulus")
+stimulus_diff_re, t_diff_r = repro_diff_fs_r.trace_data("GlobalEFieldStimulus")
+
+stimulus_same_oe, t_same = repro_same_fs_d.trace_data("stimulus")
+stimulus_same_re, t_same_r = repro_same_fs_r.trace_data("GlobalEFieldStimulus")
+
+```
+
+### 3. Samples in the different recordings for one stimulus
+```{python}
+#| echo: False
+
+print(f"Samples open-epyhs [30 kHz] for one trial: {stimulus_diff_oe.shape}")
+print(f"Samples relacs for one trial: {stimulus_diff_re.shape}")
+
+print(f"Samples open-epyhs [20 kHz] for one trial: {stimulus_same_oe.shape}")
+print(f"Samples relacs for one trial: {stimulus_same_re.shape}")
+```
+
+### 4. Plotting first trial
+Here we plot the different output stimulus, different sample rates
+
+```{python}
+#| echo: False
+x_lim = 0.05
+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=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!

doc/util.py

@@ -0,0 +1,182 @@
+import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+
+def trial_plot(repro_d, repro_r, x_lim: int = 1.0):
+    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")
+
+    mask = t < x_lim
+    mask_r = t_r < x_lim
+
+    t = t[mask]
+    t_r = t_r[mask_r]
+    sinus = sinus[mask]
+    sinus_r = sinus_r[mask_r]
+    stimulus_oe = stimulus_oe[mask]
+    stimulus_re = stimulus_re[mask_r]
+    local_eod_oe = local_eod_oe[mask]
+    local_eod_re = local_eod_re[mask_r]
+    global_eod_oe = global_eod_oe[mask]
+    global_eod_re = global_eod_re[mask_r]
+    ttl = ttl[mask]
+
+    fig = make_subplots(
+        rows=5,
+        cols=1,
+        shared_xaxes=True,
+        subplot_titles=(
+            "TTL-Line",
+            "Stimulus",
+            "Local EOD",
+            "Global EOD",
+            "Sinus",
+        ),
+    )
+
+    fig.add_trace(
+        go.Scattergl(x=t, y=ttl, name="ttl-line", line_color="magenta"),
+        row=1,
+        col=1,
+    )
+
+    fig.add_trace(
+        go.Scattergl(x=t_r, y=stimulus_re, line_color="blue"),
+        row=2,
+        col=1,
+    )
+    fig.add_trace(
+        go.Scattergl(
+            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.Scattergl(x=t_r, y=local_eod_re, line_color="blue", showlegend=False),
+        row=3,
+        col=1,
+    )
+    fig.add_trace(
+        go.Scattergl(x=t, y=local_eod_oe, showlegend=False, line_color="red"),
+        row=3,
+        col=1,
+    )
+
+    # 4. Add traces to the THIRD subplot (row=3, col=1)
+    fig.add_trace(
+        go.Scattergl(x=t_r, y=global_eod_re, showlegend=False, line_color="blue"),
+        row=4,
+        col=1,
+    )
+    fig.add_trace(
+        go.Scattergl(x=t, y=global_eod_oe, showlegend=False, line_color="red"),
+        row=4,
+        col=1,
+    )
+
+    fig.add_trace(
+        go.Scattergl(x=t_r, y=sinus_r, showlegend=False, line_color="blue"),
+        row=5,
+        col=1,
+    )
+    fig.add_trace(
+        go.Scattergl(x=t, y=sinus, showlegend=False, 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(
+            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
+            orientation="h",
+            yanchor="bottom",
+            y=1.05,
+            xanchor="right",
+            x=0.72,
+        ),
+    )
+    # Add a label to the shared x-axis (targeting the last subplot)
+    fig.update_xaxes(title_text="Time (s)", row=4, col=1)
+    fig.update_xaxes(range=[0, 0.5])
+
+    return fig
+
+
+def plot_line_comparision(
+    time_relacs,
+    time_oephys,
+    data_relacs,
+    data_oephys,
+    labels,
+):
+    x_lim = 1.0
+    mask = time_oephys < x_lim
+    mask_r = time_relacs < x_lim
+
+    time_oephys = time_oephys[mask]
+    time_relacs = time_relacs[mask_r]
+
+    data_oephys = data_oephys[mask]
+    data_relacs = data_relacs[mask_r]
+
+    fig = go.Figure()
+    fig.add_trace(
+        go.Scattergl(
+            x=time_relacs,
+            y=data_relacs,
+            name=labels[0],
+            line_color="blue",
+            mode="lines+markers",
+        )
+    )
+    fig.add_trace(
+        go.Scattergl(
+            x=time_oephys,
+            y=data_oephys,
+            name=labels[1],
+            line_color="red",
+            mode="lines+markers",
+        )
+    )
+    fig.update_layout(
+        template="plotly_dark",
+        height=500,  # Set the figure height in pixels
+        legend=dict(
+            bgcolor="rgba(0,0,0,0)",
+            bordercolor="#444",
+            borderwidth=0,
+            font_color="#e5ecf6",
+            orientation="h",
+            yanchor="bottom",
+            y=1.05,
+            xanchor="right",
+            x=0.72,
+        ),
+    )
+    fig.update_xaxes(range=[0, 0.01])
+    return fig

oephys2nix/stimulus_recreation.py

@@ -225,7 +225,7 @@ class StimulusToNix:
         for i, repro in enumerate(self.dataset.repro_runs()):
             log.debug(repro.name)
             log.debug(f"Current Position {current_position.item()}")
-            if repro.duration < 1.0:
+            if repro.duration < 0.05:
                 log.warning(f"Skipping repro {repro.name} because it is two short")
                 continue