[docstring]

doc/_quarto.yml

@@ -39,15 +39,20 @@ website:
       - text: "Sample Rates"
         href: "samplerates.qmd"
       - section: "Delays"
+        href: 'delays.qmd'
         contents:
         - "baseline.qmd"
         - "calibration.qmd"
         - "fi_curve.qmd"
         - "filestimulus.qmd"
+        - "sam.qmd"
       - section: "API"
         href: "api/index.qmd"
         contents:
-        - "api/index.qmd"
+        - "api/main.qmd"
+        - "api/metadata.qmd"
+        - "api/stimulus_recreation.qmd"
+        - "api/tonix.qmd"
 
 
 quartodoc:
@@ -62,6 +67,12 @@ quartodoc:
       desc: Terminal client
       contents:
         - main
+    - title: helper
+      desc: Helper Function
+      contents:
+        - metadata
+        - stimulus_recreation
+        - tonix
 
 execute:
   freeze: auto

doc/calibration.qmd

@@ -20,22 +20,22 @@ Lets look at the calibration and the first trial of the recording.
 
 import pathlib
 
-import rlxnix as rlx
-import plotly.graph_objects as go
 import numpy as np
+import plotly.graph_objects as go
+import rlxnix as rlx
 import scipy.signal as signal
 from plotly.subplots import make_subplots
-
-from util import trial_plot, plot_line_comparision
-
+from util import 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")
+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
+# INFO: Select the first stimulus of the calibration repro
 repro_d = dataset.repro_runs("CalibEfield_1")[0].stimuli[2]
 repro_r = relacs.repro_runs("CalibEfield_1")[0].stimuli[2]
 
@@ -57,7 +57,7 @@ ttl, t = repro_d.trace_data("ttl-line")
 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
+# | echo: False
 fig = trial_plot(repro_d, repro_r)
 fig.show()
 ```
@@ -68,11 +68,10 @@ 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
+# | echo: False
 fig = plot_line_comparision(t_r, t, sinus_r, sinus, ["sinus-relacs", "sinus-oephys"])
 fig.show()
 ```
@@ -83,19 +82,27 @@ 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}")
+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, ["sinus-relacs", "sinus-resampled-openepyhs"])
+# | echo: False
+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/delays.qmd

@@ -0,0 +1,147 @@
+---
+title: Delays anaylsis
+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. 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()
+```

doc/filestimulus.qmd

@@ -69,9 +69,7 @@ fig.show()
 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}
@@ -99,6 +97,8 @@ for oephys_lane, relacs_lane, names_lane in zip(oephys_lanes, relacs_lanes, name
 
 ```{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 = 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"])
 fig.show()
+
+print(f"The lag of the whitenoise is {lags_lanes[-1] * (1/20_000) * 1000} milli seconds")
 ```

doc/index.qmd

@@ -38,7 +38,7 @@ Please follow the installation instructions for quarto, and install the extra
 dependencies.
 
 ```bash
-pip install "[.doc]"
+pip install "[.docs]"
 cd doc
 quartodoc build
 quarto preview

doc/sam.qmd

@@ -0,0 +1,111 @@
+---
+title: SAM
+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
+---
+
+### SAM
+
+```{python}
+import pathlib
+
+import numpy as np
+import plotly.graph_objects as go
+import rlxnix as rlx
+import scipy.signal as signal
+from plotly.subplots import make_subplots
+from util import plot_line_comparision, trial_plot
+
+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("SAM")[0].stimuli[2]
+repro_r = relacs.repro_runs("SAM")[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
+fig = trial_plot(repro_d, repro_r, 0.2)
+fig.update_xaxes(range=[0, 0.2])
+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,
+    np.roll(stimulus_re, lags_lanes[-1]),
+    stimulus_oe - np.mean(stimulus_oe),
+    ["rolled sinus-relacs", "sinus-resampled-open-ephys"],
+)
+fig.show()
+
+print(f"The lag of the whitenoise is {lags_lanes[-1] * (1 / 20_000) * 1000} milli seconds")
+```

doc/util.py

@@ -1,5 +1,6 @@
 import numpy as np
 import plotly.graph_objects as go
+import scipy.signal as signal
 from plotly.subplots import make_subplots
 
 
@@ -122,7 +123,7 @@ def trial_plot(repro_d, repro_r, x_lim: int = 1.0):
     )
     # 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])
+    fig.update_xaxes(range=[0, x_lim])
 
     return fig
 
@@ -178,5 +179,31 @@ def plot_line_comparision(
             x=0.72,
         ),
     )
-    fig.update_xaxes(range=[0, 0.01])
+    fig.update_xaxes(title_text="Time (s)", range=[0, 0.01])
     return fig
+
+
+def calc_lag(repro_d, repro_r):
+    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")
+
+    oephys_lanes = [sinus, local_eod_oe, global_eod_oe, stimulus_oe]
+    relacs_lanes = [sinus_r, local_eod_re, global_eod_re, stimulus_re]
+    lags_lanes = []
+    for oephys_lane, relacs_lane in zip(oephys_lanes, relacs_lanes, strict=True):
+        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)
+
+    return lags_lanes

oephys2nix/main.py

@@ -18,7 +18,7 @@ console = Console()
 
 
 @app.command()
-def main(
+def convert(
     data_path: Path = typer.Argument(
         ...,
         help="The source directory containing the Open Ephys data.",
@@ -92,32 +92,68 @@ def main(
         stim = StimulusToNix(open_ephys, str(relacs), str(nix_path))
         stim.create_repros_automatically()
         stim.print_table()
-        # stim.checks()
-
-        # if debug:
-        #     stim.plot_stimulus()
 
 
 @app.command()
 def timeline(
     data_path: Path = typer.Argument(
         ...,
-        help="The source directory containing the Open Ephys data.",
+        help="The source directory containing a dataset.",
         exists=True,
         file_okay=False,
         dir_okay=True,
         readable=True,
         resolve_path=True,
     ),
-):
+) -> None:
+    """Plot the timeline for a given dataset."""
+    dataset = list(Path(data_path).rglob("*.nix"))
+
+    if not dataset:
+        log.error("Did not find any dataset")
+        raise typer.Exit()
+    if len(dataset) > 1:
+        log.info(f"Found multiple datasets {len(dataset)} taking the first one")
+    dataset = rlx.Dataset(str(dataset[0]))
+    dataset.plot_timeline()
+    dataset.close()
+
+
+@app.command()
+def plot(
+    data_path: Path = typer.Argument(
+        ...,
+        help="The source directory containing the open-ephys and relacs data.",
+        exists=True,
+        file_okay=False,
+        dir_okay=True,
+        readable=True,
+        resolve_path=True,
+    ),
+) -> None:
+    """Plot the stimulus, TTL and the relacs stimulus.
+
+    You can run this if you converted already the nix-file.
+    """
     relacs_data_paths = list(Path(data_path).rglob("*relacs/*.nix"))
     if not relacs_data_paths:
         log.error("Did not find any relacs data")
         raise typer.Exit()
 
-    dataset = rlx.Dataset(str(relacs_data_paths[0]))
-    dataset.plot_timeline()
-    dataset.close()
+    open_ephys_data_paths = list(Path(data_path).rglob("*open-ephys"))
+    if not open_ephys_data_paths:
+        log.error("Did not find any open-ephys data")
+        raise typer.Exit()
+
+    nix_file_name = relacs_data_paths[0].parent.name.split("_")[0] + "-recording.nix"
+    nix_path = relacs_data_paths[0].parent.parent / nix_file_name
+    if not nix_path.is_file:
+        log.error("Did not find any converted nix file")
+        raise typer.Exit()
+
+    stim = StimulusToNix(open_ephys_data_paths[0], str(relacs_data_paths[0]), str(nix_path))
+    stim.plot_stimulus()
+    stim.close()
 
 
 if __name__ == "__main__":

oephys2nix/metadata.py

@@ -2,6 +2,15 @@ import nixio
 
 
 def create_metadata_from_dict(d: dict, section: nixio.Section) -> None:
+    """Creating nix section from dictionary.
+
+    Parameters
+    ----------
+    d : dict
+        Dictionary that needs to be saved to section
+    section : nixio.Section
+        Target section in Nix file
+    """
     for key, value in d.items():
         if isinstance(value, dict):
             new_sec = section.create_section(key, f"{type(key)}")
@@ -18,6 +27,18 @@ def create_metadata_from_dict(d: dict, section: nixio.Section) -> None:
 
 
 def create_dict_from_section(section: nixio.Section) -> dict:
+    """Creating dictionary from Nix section
+
+    Parameters
+    ----------
+    section : nixio.Section
+        Source nix section that will be exported to the dictionary
+
+    Returns
+    -------
+    dict: dict
+        Nix section in a dictionary
+    """
     d = {}
     for key, value in section.items():
         if isinstance(value, nixio.Section):

oephys2nix/stimulus_recreation.py

@@ -1,7 +1,6 @@
 import logging
 import pathlib
 import sys
-import tomllib
 
 import matplotlib.pyplot as plt
 import nixio
@@ -22,6 +21,47 @@ console = Console()
 
 
 class StimulusToNix:
+    """Processing the stimulus recreation from the relax dataset and the open-ephys data.
+
+    Parameters
+    ----------
+    open_ephys_path: pathlib.Path
+        Path to open-ephys recording
+    relacs_nix_path : str
+        Path to relacs nix file
+    nix_file : str
+        Path to new nix file
+
+    Attributes
+    ----------
+    relacs_nix_path : str
+        Path to relacs nix file
+    nix_file_path : str
+        Path to new nix file
+    dataset : rlx.Dataset
+        Dataset from the relacs file
+    relacs_nix_file : nixio.File
+        Relacs nix file
+    relacs_block : nixio.Block
+        Relacs nix block
+    relacs_sections :nixio.Section
+        Relacs nix Section
+    neo_data : neo.OpenEphysBinaryIO
+        Open-ephys data
+    fs : float
+        Sample rate of the open-ephys recording
+    nix_file : nixio.File
+        New nix file
+    block : nixio.Block
+        New nix block
+    threshold : float
+        Threshold for TTL line
+    new_start_jiggle : float
+        For finding the new start, ensuring finding next TTL pulse
+
+
+    """
+
     def __init__(self, open_ephys_path: pathlib.Path, relacs_nix_path: str, nix_file: str):
         self.relacs_nix_path = relacs_nix_path
         self.nix_file_path = nix_file
@@ -43,7 +83,8 @@ class StimulusToNix:
         self.threshold = 2
         self.new_start_jiggle = 0.1
 
-    def _append_relacs_tag_mtags(self):
+    def _append_relacs_tag_mtags(self) -> None:
+        """Append relacs tags and multi tags to new nix file."""
         for t in self.relacs_block.tags:
             log.debug(f"Appending relacs tags {t.name}")
             tag = self.block.create_tag(f"relacs_{t.name}", t.type, position=t.position)
@@ -80,7 +121,31 @@ class StimulusToNix:
                 pass
             mtag.metadata = self.nix_file.sections[sec.name]
 
-    def _find_peak_ttl(self, time_ttl, peaks_ttl, lower, upper):
+    def _find_next_ttl(
+        self, time_ttl: np.ndarray, peaks_ttl: np.ndarray, lower: float, upper: float
+    ) -> np.ndarray:
+        """Find the next TTL pulse within a specific duration constrained by lower and upper.
+
+        Parameters
+        ----------
+        time_ttl : np.ndarray
+            Time array of the TTL line
+
+        peaks_ttl : np.ndarray
+            Detected peaks indeces on the TTL line
+
+        lower : float
+            lower bound for searching the TTL pulse
+
+        upper : float
+            upper bound for searching the TTL pulse
+
+        Returns
+        -------
+        np.ndarray
+            time point of the new TTL pulse
+
+        """
         peak = time_ttl[peaks_ttl[(time_ttl[peaks_ttl] > lower) & (time_ttl[peaks_ttl] < upper)]]
 
         if not peak.size > 0:
@@ -92,7 +157,28 @@ class StimulusToNix:
             peak = np.mean(peak)
         return peak
 
-    def _find_peak_ttl_index(self, time_ttl, peaks_ttl, current_position):
+    def _find_peak_ttl_index(
+        self, time_ttl: np.ndarray, peaks_ttl: np.ndarray, current_position: np.ndarray
+    ) -> np.ndarray:
+        """Find the next TTL pulse from the indeces of the detected TTL pulses.
+
+        Parameters
+        ----------
+        time_ttl : np.ndarray
+            Time array of the TTL line
+
+        peaks_ttl : np.ndarray
+            Detected peaks indeces on the TTL line
+
+        current_position : np.ndarray
+            Current time of the TTL pulse
+
+        Returns
+        -------
+        np.ndarray
+            Next time of TTL pulse
+
+        """
         new_repro_start_index = peaks_ttl[
             (time_ttl[peaks_ttl] > current_position - self.new_start_jiggle)
             & (time_ttl[peaks_ttl] < current_position + self.new_start_jiggle)
@@ -112,13 +198,34 @@ class StimulusToNix:
 
     @property
     def _reference_groups(self) -> list[nixio.Group]:
+        """Holds the reference groups.
+
+        Returns
+        -------
+        list[nixio.Group]
+
+        """
         return [
             self.block.groups["neuronal-data"],
             self.block.groups["efish"],
             self.block.groups["relacs"],
         ]
 
-    def _append_mtag(self, repro, positions, extents):
+    def _append_mtag(self, repro: rlx.Dataset, positions: np.ndarray, extents: np.ndarray) -> None:
+        """Apped multi tags of the current repro to the nix file.
+
+        Parameters
+        ----------
+        repro : rlx.Dataset
+            Current Repro
+
+        positions : np.ndarray
+            postions of the multi tags
+
+        extents : np.ndarray
+            extents of the multi tags
+
+        """
         try:
             nix_mtag = self.block.create_multi_tag(
                 f"{repro.name}",
@@ -159,7 +266,20 @@ class StimulusToNix:
             nix_group = self.nix_file.blocks[0].groups[repro.name]
         nix_group.multi_tags.append(nix_mtag)
 
-    def _append_tag(self, repro, position, extent):
+    def _append_tag(self, repro: rlx.Dataset, position: np.ndarray, extent: np.ndarray) -> None:
+        """Append tag of the current repro.
+
+        Parameters
+        ----------
+        repro : rlx.Dataset
+            Current Repro
+
+        position : np.ndarray
+            positions of the multi tags
+
+        extent : np.ndarray
+            extents of the multi tags
+        """
         try:
             nix_tag = self.block.create_tag(
                 f"{repro.name}",
@@ -201,7 +321,8 @@ class StimulusToNix:
             nix_group = self.nix_file.blocks[0].groups[repro.name]
         nix_group.tags.append(nix_tag)
 
-    def create_repros_automatically(self):
+    def create_repros_automatically(self) -> None:
+        """Create the repros form relacs with the TTL pulses."""
         ttl_oeph = self.block.data_arrays["ttl-line"][:]
         time_ttl = np.arange(len(ttl_oeph)) / self.fs
         time_index = np.arange(len(ttl_oeph))
@@ -214,7 +335,7 @@ class StimulusToNix:
         if close_peaks.size > 0:
             peaks_ttl = np.delete(peaks_ttl, close_peaks)
 
-        first_peak = self._find_peak_ttl(
+        first_peak = self._find_next_ttl(
             time_ttl,
             peaks_ttl,
             time_ttl[peaks_ttl[0]] - self.new_start_jiggle,
@@ -229,6 +350,8 @@ class StimulusToNix:
                 log.warning(f"Skipping repro {repro.name} because it is two short")
                 continue
 
+                embed()
+                exit()
             if repro.stimuli:
                 log.debug("Processing MultiTag")
                 repetition = len(repro.stimuli)
@@ -244,7 +367,9 @@ class StimulusToNix:
                         time_ttl, peaks_ttl, current_position
                     )
 
-                # current_position = position_mtags[-1]
+                if "FICurve" in repro.name:
+                    position_mtags += 0.2
+
                 self._append_mtag(repro, position_mtags, extents_mtag)
                 extent = position_mtags[-1] + extents_mtag[-1] - position_mtags[0]
                 self._append_tag(repro, position_mtags[0], extent)
@@ -282,9 +407,11 @@ class StimulusToNix:
                     last_repro_position.reshape(-1, 1),
                     (current_position - last_repro_position).reshape(-1, 1),
                 )
-        # self.close()
 
-    def create_repros_from_config_file(self):
+    def create_repros_from_config_file(self) -> None:
+        """Creates repros form a config file.
+        NOT MAINTAINED.
+        """
         ttl_oeph = self.block.data_arrays["ttl-line"][:]
         peaks_ttl = signal.find_peaks(
             ttl_oeph.flatten(),
@@ -429,7 +556,8 @@ class StimulusToNix:
             ]
             nix_group.multi_tags.append(nix_mtag)
 
-    def print_table(self):
+    def print_table(self) -> None:
+        """Print the converted times in a rich table."""
         nix_data_set = rlx.Dataset(self.nix_file_path)
         table = Table("Repro Name", "start", "stop", "duration")
         for repro_r, repro_n in zip(self.dataset.repro_runs(), nix_data_set.repro_runs()):
@@ -443,7 +571,8 @@ class StimulusToNix:
         console.print(table)
         nix_data_set.close()
 
-    def checks(self):
+    def checks(self) -> None:
+        """Just for debugging currently."""
         important_repros = ["FileStimulus", "SAM", "FICurve"]
         nix_data_set = rlx.Dataset(self.nix_file_path)
 
@@ -508,7 +637,8 @@ class StimulusToNix:
             v_eod, t_eod = repro_n.trace_data("global-eod")
             v_eodr, t_eodr = repro_n.trace_data("EOD")
 
-    def plot_stimulus(self):
+    def plot_stimulus(self) -> None:
+        """Plot the relacs stimulus, open-epyhs and TTL line."""
         ttl_oeph = self.block.data_arrays["ttl-line"][:]
 
         time_index = np.arange(len(ttl_oeph))

oephys2nix/tonix.py

@@ -14,6 +14,36 @@ log = logging.getLogger(__name__)
 
 
 class RawToNix:
+    """Appending all raw data from relacs and open-ephsy to a new nix file.
+
+    Parameters
+    ----------
+    open_ephys_path: pathlib.Path
+        Path to open-ephys recording
+    relacs_nix_path : str
+        Path to relacs nix file
+    nix_file : str
+        Path to new nix file
+
+    Attributes
+    ----------
+    relacs_nix_file : nixio.File
+        Relacs nix file
+    dataset : rlx.Dataset
+        Dataset of the relacs file
+    relacs_block : nixio.Block
+        Relacs block
+    relacs_sections : nixio.Section
+        Relacs section
+    neo_data : neo.OpenEphysBinaryIO
+        Open Ephys data
+    nix_file : nixio.File
+        New nix file
+    block : nixio.Block
+        New nix file block
+
+    """
+
     def __init__(self, open_ephys_path: pathlib.Path, relacs_nix_path: str, nix_file: str):
         self.relacs_nix_file = nixio.File.open(relacs_nix_path, nixio.FileMode.ReadOnly)
         self.dataset = rlx.Dataset(relacs_nix_path)
@@ -25,7 +55,8 @@ class RawToNix:
         self.nix_file.create_block("open-ephys.data", "open-ephys.sampled")
         self.block = self.nix_file.blocks[0]
 
-    def append_section(self):
+    def append_section(self) -> None:
+        """Append sections from relacs."""
         sec = self.nix_file.create_section(
             self.relacs_sections[0].name, self.relacs_sections[0].type
         )
@@ -33,7 +64,8 @@ class RawToNix:
         create_metadata_from_dict(d, sec)
         self.block.metadata = sec
 
-    def append_fish_lines(self):
+    def append_fish_lines(self) -> None:
+        """Append fish lines from open-ephys."""
         efishs = ["ttl-line", "global-eod", "stimulus", "local-eod", "sinus"]
 
         efish_types = [
@@ -63,7 +95,8 @@ class RawToNix:
             )
             efish_group.data_arrays.append(data_array)
 
-    def append_relacs_lines(self):
+    def append_relacs_lines(self) -> None:
+        """Append relacs lines."""
         relacs = [
             "V-1",
             "EOD",
@@ -111,7 +144,8 @@ class RawToNix:
 
             efish_group.data_arrays.append(data_array)
 
-    def append_raw_data(self):
+    def append_raw_data(self) -> None:
+        """Append Open-Ephys Raw data."""
         gr = self.block.create_group("neuronal-data", "open-epyhs.sampled")
         raw_neo_data = self.neo_data[0].segments[0].analogsignals[0].load()
 
@@ -128,6 +162,7 @@ class RawToNix:
         )
         gr.data_arrays.append(nix_data_array)
 
-    def close(self):
+    def close(self) -> None:
+        """Close all nix files."""
         self.nix_file.close()
         self.relacs_nix_file.close()