swapped create_stimulation_timeseries for neuroconv version

frozen_dependencies.txt

@@ -2,6 +2,7 @@ aiobotocore==2.11.1
 aiohttp==3.9.2
 aioitertools==0.11.0
 aiosignal==1.3.1
+annotated-types==0.7.0
 appdirs==1.4.4
 appnope==0.1.4
 arrow==1.3.0
@@ -23,11 +24,12 @@ comm==0.2.1
 contourpy==1.2.0
 cycler==0.12.1
 dandi==0.59.1
-dandischema==0.8.4
+dandischema==0.8.2
 debugpy==1.8.1
 decorator==5.1.1
 distlib==0.3.8
 dnspython==2.5.0
+docstring_parser==0.16
 email-validator==2.1.0.post1
 entrypoints==0.4
 et-xmlfile==1.1.0
@@ -44,7 +46,7 @@ fsspec==2023.12.2
 gast==0.4.0
 h5py==3.10.0
 hdmf==3.13.0
-hdmf_zarr==0.5.0
+hdmf_zarr==0.7.0
 humanize==4.9.0
 identify==2.5.33
 idna==3.6
@@ -93,7 +95,7 @@ ndx-photometry @ git+https://github.com/catalystneuro/ndx-photometry.git@c1284c9
 ndx-spectrum==0.2.2
 nest-asyncio==1.6.0
 networkx==3.2.1
-neuroconv==0.4.7
+neuroconv @ git+https://github.com/catalystneuro/neuroconv.git@f45e77e152369916459a764c918d4c5cbbf1fad1
 nodeenv==1.8.0
 numcodecs==0.11.0
 numpy==1.26.3
@@ -116,9 +118,10 @@ ptyprocess==0.7.0
 pure-eval==0.2.2
 py2vega==0.6.1
 pycryptodomex==3.20.0
-pydantic==1.10.14
+pydantic==2.7.1
+pydantic_core==2.18.2
 Pygments==2.17.2
-pynwb==2.6.0
+pynwb==2.7.0
 pyout==0.7.3
 pyparsing==3.1.1
 pytest==8.1.1

requirements.txt

@@ -1,12 +1,12 @@
 hdmf
-neuroconv
+neuroconv @ git+https://github.com/catalystneuro/neuroconv.git@main
 nwbwidgets
 nwbinspector
 dandi
 pre-commit
 pytest
 ndx-events
-ndx-fiber-photometry @ git+https://github.com/catalystneuro/ndx-fiber-photometry.git@add_fiber_photometry_lab_meta_data
+ndx-fiber-photometry @ git+https://github.com/catalystneuro/ndx-fiber-photometry.git@main
 ipykernel
 tdt
 openpyxl

src/lerner_lab_to_nwb/seiler_2024/seiler_2024optogeneticinterface.py

@@ -4,6 +4,7 @@
 from pynwb.ogen import OptogeneticSeries
 from neuroconv.basedatainterface import BaseDataInterface
 from neuroconv.utils import DeepDict
+from neuroconv.tools.optogenetics import create_optogenetic_stimulation_timeseries
 from typing import Literal
 from hdmf.backends.hdf5.h5_utils import H5DataIO
 from datetime import datetime, time
@@ -157,7 +158,7 @@ def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict):
             location=f"Injection location: {opto_metadata['injection_location']} \n Stimulation location: {opto_metadata['stimulation_location']}",
             excitation_lambda=opto_metadata["excitation_lambda"],
         )
-        timestamps, data = self.create_stimulation_timeseries(
+        timestamps, data = create_optogenetic_stimulation_timeseries(
             stimulation_onset_times=stim_times,
             duration=opto_metadata["duration"],
             frequency=opto_metadata["frequency"],
@@ -172,51 +173,3 @@ def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict):
             description=opto_metadata["ogen_series_description"],
         )
         nwbfile.add_stimulus(ogen_series)
-
-    def create_stimulation_timeseries(  # TODO: Move to neuroconv
-        self, stimulation_onset_times: np.ndarray, duration: float, frequency: float, pulse_width: float, power: float
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """Create a continuous stimulation time series from stimulation onset times and parameters.
-
-        In the resulting data array, the offset time of each pulse is represented by a 0 power value.
-
-        Parameters
-        ----------
-        stimulation_onset_times : np.ndarray
-            Array of stimulation onset times.
-        duration : float
-            Duration of stimulation in seconds.
-        frequency : float
-            Frequency of stimulation in Hz.
-        pulse_width : float
-            Pulse width of stimulation in seconds.
-        power : float
-            Power of stimulation in W.
-
-        Returns
-        -------
-        np.ndarray
-            Stimulation timestamps.
-        np.ndarray
-            Instantaneous stimulation power.
-
-        Notes
-        -----
-        For continuous stimulation of a desired duration, simply set
-        ```
-        pulse_width = duration
-        frequency = 1 / duration
-        ```
-        """
-        num_pulses = int(duration * frequency)
-        inter_pulse_interval = 1 / frequency
-        timestamps, data = [0], [0]
-        for onset_time in stimulation_onset_times:
-            for i in range(num_pulses):
-                pulse_onset_time = onset_time + i * inter_pulse_interval
-                timestamps.append(pulse_onset_time)
-                data.append(power)
-                pulse_offset_time = pulse_onset_time + pulse_width
-                timestamps.append(pulse_offset_time)
-                data.append(0)
-        return np.array(timestamps, dtype=np.float64), np.array(data, dtype=np.float64)