Add VU NWB protocol support and extraction example

README.rst

@@ -61,7 +61,8 @@ To install BluePyEfe, run:
 Quick Start and Operating Principle
 ===================================
 
-For a hands-on introduction to BluePyEfe, have a look at the notebook `examples/example_of_extraction.ipynb <examples/example_of_extraction.ipynb>`_
+For a hands-on introduction to BluePyEfe, have a look at the notebook `examples/example_of_extraction.ipynb <examples/example_of_extraction.ipynb>`_.
+For an NWB-focused example, see `examples/nwb_extraction.ipynb <examples/nwb_extraction.ipynb>`_.
 
 The goal of the present package is to extract meaningful electrophysiological features (e-features) from voltage time series.
 The e-features considered in the present package are the one implemented in the `eFEL python library <https://github.com/openbraininstitute/eFEL>`_. See `this pdf <https://bluebrain.github.io/eFEL/efeature-documentation.pdf>`_ for a list of available e-features.

bluepyefe/ecode/__init__.py

@@ -18,7 +18,9 @@
 from . import DeHyperPol
 from . import HyperDePol
 from . import SpikeRec
+from . import capCheck
 from . import negCheops
+from . import pinkNoise
 from . import posCheops
 from . import ramp
 from . import sAHP
@@ -63,4 +65,6 @@
     "poscheops": posCheops.PosCheops,
     "spikerec": SpikeRec.SpikeRec,
     "sinespec": sineSpec.SineSpec,
+    "pinknoise": pinkNoise.PinkNoise,
+    "capcheck": capCheck.CapCheck,
 }

bluepyefe/ecode/capCheck.py

@@ -0,0 +1,152 @@
+"""CapCheck eCode class"""
+
+"""
+Copyright 2026 Open Brain Institute
+
+ This file is part of BluePyEfe <https://github.com/openbraininstitute/BluePyEfe>
+
+ This library is free software; you can redistribute it and/or modify it under
+ the terms of the GNU Lesser General Public License version 3.0 as published
+ by the Free Software Foundation.
+
+ This library is distributed in the hope that it will be useful, but WITHOUT
+ ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+ FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more
+ details.
+
+ You should have received a copy of the GNU Lesser General Public License
+ along with this library; if not, write to the Free Software Foundation, Inc.,
+ 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+"""
+import logging
+import numpy
+
+from ..recording import Recording
+from .tools import base_current
+
+logger = logging.getLogger(__name__)
+
+
+class CapCheck(Recording):
+    """Capacitance-check current stimulus.
+
+    This protocol applies a repeated capacitance-check current command.
+    The stimulus contains alternating positive/negative pulses around the holding current.
+    It is used to determine membrane capacitance from the passive voltage response.
+    """
+
+    def __init__(
+        self,
+        config_data,
+        reader_data,
+        protocol_name="CapCheck",
+        efel_settings=None
+    ):
+
+        super(CapCheck, self).__init__(config_data, reader_data, protocol_name)
+
+        self.ton = None
+        self.toff = None
+        self.tend = None
+        self.amp = None
+        self.hypamp = None
+        self.dt = None
+        self.waveform = None
+
+        self.amp_rel = None
+        self.hypamp_rel = None
+
+        if self.t is not None and self.current is not None:
+            self.interpret(
+                self.t, self.current, self.config_data, self.reader_data
+            )
+
+        if self.voltage is not None:
+            self.set_autothreshold()
+            self.compute_spikecount(efel_settings)
+
+        self.export_attr = ["ton", "toff", "tend", "amp", "hypamp", "dt",
+                            "waveform", "amp_rel", "hypamp_rel"]
+
+    def get_stimulus_parameters(self):
+        """Returns the eCode parameters"""
+        ecode_params = {
+            "delay": self.ton,
+            "amp": self.amp,
+            "thresh_perc": self.amp_rel,
+            "duration": self.toff - self.ton,
+            "totduration": self.tend,
+            "dt": self.dt,
+            "waveform": self.waveform,
+        }
+        return ecode_params
+
+    def _get_timing_index(self, name, config_data, reader_data):
+        if name in config_data and config_data[name] is not None:
+            return int(round(config_data[name] / self.dt))
+        if name in reader_data and reader_data[name] is not None:
+            return int(round(reader_data[name]))
+        return None
+
+    def _detect_stimulus_indexes(self, current):
+        deviation = numpy.abs(numpy.asarray(current) - self.hypamp)
+        edge = min(max(1, int(round(10.0 / self.dt))), len(deviation))
+        noise_level = numpy.std(
+            numpy.concatenate((deviation[:edge], deviation[-edge:]))
+        )
+        threshold = max(4.5 * noise_level, 0.02 * numpy.max(deviation), 1e-5)
+        active = numpy.flatnonzero(deviation > threshold)
+
+        if len(active) == 0:
+            logger.warning(
+                "The automatic cap-check detection failed for the recording "
+                f"{self.protocol_name} in files {self.files}. The whole trace "
+                "will be used as the stimulus waveform."
+            )
+            return 0, len(deviation)
+
+        return active[0], active[-1] + 1
+
+    def interpret(self, t, current, config_data, reader_data):
+        """Analyse a current array and extract from it the parameters
+        needed to reconstruct the array"""
+        self.dt = t[1]
+
+        ton = self._get_timing_index("ton", config_data, reader_data)
+        toff = self._get_timing_index("toff", config_data, reader_data)
+
+        hypamp_value = base_current(current, idx_ton=300 if ton is None else ton)
+        self.set_amplitudes_ecode("hypamp", config_data, reader_data, hypamp_value)
+
+        if ton is None or toff is None:
+            detected_ton, detected_toff = self._detect_stimulus_indexes(current)
+            ton = detected_ton if ton is None else ton
+            toff = detected_toff if toff is None else toff
+
+        ton = max(0, min(ton, len(current) - 1))
+        toff = max(ton + 1, min(toff, len(current)))
+
+        stimulus = numpy.asarray(current[ton:toff]) - self.hypamp
+        amp_value = numpy.max(numpy.abs(stimulus)) if len(stimulus) else 0.0
+        self.set_amplitudes_ecode("amp", config_data, reader_data, amp_value)
+
+        if self.amp == 0.0:
+            self.waveform = numpy.zeros(stimulus.shape)
+        else:
+            self.waveform = stimulus / self.amp
+
+        self.ton = t[ton]
+        self.toff = t[toff] if toff < len(t) else len(t) * self.dt
+        self.tend = len(t) * self.dt
+
+    def generate(self):
+        """Generate the current array from the parameters of the ecode"""
+        t = numpy.arange(0.0, self.tend, self.dt)
+        current = numpy.full(t.shape, numpy.float64(self.hypamp))
+
+        waveform = numpy.asarray(self.waveform)
+        ton = int(self.ton / self.dt)
+        toff = min(ton + len(waveform), len(current))
+        current[ton:toff] += numpy.float64(self.amp) * waveform[:toff - ton]
+
+        return t, current

bluepyefe/ecode/pinkNoise.py

@@ -0,0 +1,156 @@
+"""PinkNoise eCode class"""
+
+"""
+Copyright 2026 Open Brain Institute
+
+ This file is part of BluePyEfe <https://github.com/openbraininstitute/BluePyEfe>
+
+ This library is free software; you can redistribute it and/or modify it under
+ the terms of the GNU Lesser General Public License version 3.0 as published
+ by the Free Software Foundation.
+
+ This library is distributed in the hope that it will be useful, but WITHOUT
+ ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+ FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more
+ details.
+
+ You should have received a copy of the GNU Lesser General Public License
+ along with this library; if not, write to the Free Software Foundation, Inc.,
+ 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+"""
+import logging
+import numpy
+
+from ..recording import Recording
+from .tools import base_current
+from .tools import scipy_signal2d
+
+logger = logging.getLogger(__name__)
+
+
+class PinkNoise(Recording):
+    """Rheobase-scaled pink-noise stimulation protocol.
+
+    This protocol applies a pink-noise current command scaled relative to the
+    rheobase. The stimulus uses three amplitude levels: 0.75x, 1x, and 1.5x
+    rheobase. It is used to measure the response to suprathreshold noisy
+    stimulation and for model fitting and validation.
+    """
+
+    def __init__(
+        self,
+        config_data,
+        reader_data,
+        protocol_name="PinkNoise",
+        efel_settings=None
+    ):
+
+        super(PinkNoise, self).__init__(config_data, reader_data, protocol_name)
+
+        self.ton = None
+        self.toff = None
+        self.tend = None
+        self.amp = None
+        self.hypamp = None
+        self.dt = None
+        self.waveform = None
+
+        self.amp_rel = None
+        self.hypamp_rel = None
+
+        if self.t is not None and self.current is not None:
+            self.interpret(
+                self.t, self.current, self.config_data, self.reader_data
+            )
+
+        if self.voltage is not None:
+            self.set_autothreshold()
+            self.compute_spikecount(efel_settings)
+
+        self.export_attr = ["ton", "toff", "tend", "amp", "hypamp", "dt",
+                            "waveform", "amp_rel", "hypamp_rel"]
+
+    def get_stimulus_parameters(self):
+        """Returns the eCode parameters"""
+        ecode_params = {
+            "delay": self.ton,
+            "amp": self.amp,
+            "thresh_perc": self.amp_rel,
+            "duration": self.toff - self.ton,
+            "totduration": self.tend,
+            "dt": self.dt,
+            "waveform": self.waveform,
+        }
+        return ecode_params
+
+    def _get_timing_index(self, name, config_data, reader_data):
+        if name in config_data and config_data[name] is not None:
+            return int(round(config_data[name] / self.dt))
+        if name in reader_data and reader_data[name] is not None:
+            return int(round(reader_data[name]))
+        return None
+
+    def _detect_stimulus_indexes(self, smooth_current):
+        deviation = numpy.abs(numpy.asarray(smooth_current) - self.hypamp)
+        edge = min(max(1, int(round(10.0 / self.dt))), len(deviation))
+        noise_level = numpy.std(
+            numpy.concatenate((deviation[:edge], deviation[-edge:]))
+        )
+        threshold = max(4.5 * noise_level, 0.02 * numpy.max(deviation), 1e-5)
+        active = numpy.flatnonzero(deviation > threshold)
+
+        if len(active) == 0:
+            logger.warning(
+                "The automatic pink-noise detection failed for the recording "
+                f"{self.protocol_name} in files {self.files}. The whole trace "
+                "will be used as the stimulus waveform."
+            )
+            return 0, len(deviation)
+
+        return active[0], active[-1] + 1
+
+    def interpret(self, t, current, config_data, reader_data):
+        """Analyse a current array and extract from it the parameters
+        needed to reconstruct the array"""
+        self.dt = t[1]
+
+        smooth_current = scipy_signal2d(current, 85)
+
+        ton = self._get_timing_index("ton", config_data, reader_data)
+        toff = self._get_timing_index("toff", config_data, reader_data)
+
+        hypamp_value = base_current(current, idx_ton=300 if ton is None else ton)
+        self.set_amplitudes_ecode("hypamp", config_data, reader_data, hypamp_value)
+
+        if ton is None or toff is None:
+            detected_ton, detected_toff = self._detect_stimulus_indexes(smooth_current)
+            ton = detected_ton if ton is None else ton
+            toff = detected_toff if toff is None else toff
+
+        ton = max(0, min(ton, len(current) - 1))
+        toff = max(ton + 1, min(toff, len(current)))
+
+        stimulus = numpy.asarray(current[ton:toff]) - self.hypamp
+        amp_value = numpy.max(numpy.abs(stimulus)) if len(stimulus) else 0.0
+        self.set_amplitudes_ecode("amp", config_data, reader_data, amp_value)
+
+        if self.amp == 0.0:
+            self.waveform = numpy.zeros(stimulus.shape)
+        else:
+            self.waveform = stimulus / self.amp
+
+        self.ton = t[ton]
+        self.toff = t[toff] if toff < len(t) else len(t) * self.dt
+        self.tend = len(t) * self.dt
+
+    def generate(self):
+        """Generate the current array from the parameters of the ecode"""
+        t = numpy.arange(0.0, self.tend, self.dt)
+        current = numpy.full(t.shape, numpy.float64(self.hypamp))
+
+        waveform = numpy.asarray(self.waveform)
+        ton = int(self.ton / self.dt)
+        toff = min(ton + len(waveform), len(current))
+        current[ton:toff] += numpy.float64(self.amp) * waveform[:toff - ton]
+
+        return t, current