Metadata-Version: 2.1
Name: LFPykit
Version: 0.2
Summary: Electrostatic models for multicompartment neuron models
Home-page: https://github.com/LFPy/LFPykit
Author: LFPy-team
Author-email: lfpy@users.noreply.github.com
License: UNKNOWN
Description: LFPykit
        =======
        
        This Python module contain freestanding implementations of electrostatic
        forward models presently incorporated in LFPy
        (https://github.com/LFPy/LFPy, https://LFPy.readthedocs.io).
        
        The aim of the `LFPykit` module is to provide electrostatic models
        in a manner that facilitates forward-model predictions of extracellular
        potentials and related measures from multicompartment neuron models, but
        without explicit dependencies on neural simulation software such as
        NEURON (https://neuron.yale.edu, https://github.com/neuronsimulator/nrn),
        Arbor (https://arbor.readthedocs.io, https://github.com/arbor-sim/arbor),
        or even LFPy.
        The `LFPykit` module can then be more easily incorporated with
        these simulators, or in various projects that utilize them such as
        LFPy (https://LFPy.rtfd.io, https://github.com/LFPy/LFPy).
        BMTK (https://alleninstitute.github.io/bmtk/, https://github.com/AllenInstitute/bmtk),
        etc.
        
        Its main functionality is providing class methods that return two-dimensional
        linear transformation matrices **M**
        between transmembrane currents
        **I** of multicompartment neuron models and some
        measurement **Y** given by **Y**=**MI**.
        
        The presently incorporated volume conductor models have been incorporated in
        LFPy (https://LFPy.rtfd.io, https://github.com/LFPy/LFPy),
        as described in various papers and books:
        
        1. Linden H, Hagen E, Leski S, Norheim ES, Pettersen KH, Einevoll GT
           (2014) LFPy: a tool for biophysical simulation of extracellular
           potentials generated by detailed model neurons. Front.
           Neuroinform. 7:41. doi: 10.3389/fninf.2013.00041
        
        2. Hagen E, Næss S, Ness TV and Einevoll GT (2018) Multimodal Modeling of Neural
           Network Activity: Computing LFP, ECoG, EEG, and MEG
           Signals With LFPy 2.0. Front. Neuroinform. 12:92.
           doi: 10.3389/fninf.2018.00092
        
        3. Ness, T. V., Chintaluri, C., Potworowski, J., Leski, S., Glabska,
           H., Wójcik, D. K., et al. (2015). Modelling and analysis of electrical
           potentials recorded in microelectrode arrays (MEAs).
           Neuroinformatics 13:403–426. doi: 10.1007/s12021-015-9265-6
        
        4. Nunez and Srinivasan, Oxford University Press, 2006
        
        5. Næss S, Chintaluri C, Ness TV, Dale AM, Einevoll GT and Wójcik DK
           (2017). Corrected Four-sphere Head Model for EEG Signals. Front. Hum.
           Neurosci. 11:490. doi: 10.3389/fnhum.2017.00490
        
        
        Build Status
        ------------
        
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        Features
        --------
        
        `LFPykit` presently incorporates different electrostatic forward models for extracellular potentials
        and magnetic signals that has been derived using volume conductor theory.
        In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons.
        Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed.
        
        The module presently incorporates different classes.
        To represent the geometry of a multicompartment neuron model we have:
        
        * `CellGeometry`:
          Base class representing a multicompartment neuron geometry in terms
          of segment x-, y-, z-coordinates and diameter.
        
        Different classes built to map transmembrane currents of `CellGeometry` like instances
        to different measurement modalities:
        
        * `LinearModel`:
          Base class representing a generic forward model
          for subclassing
        * `CurrentDipoleMoment`:
          Class for predicting current dipole moments
        * `PointSourcePotential`:
          Class for predicting extracellular potentials
          assuming point sources and point contacts
        * `LineSourcePotential`:
          Class for predicting extracellular potentials assuming
          line sourcers and point contacts
        * `RecExtElectrode`:
          Class for simulations of extracellular potentials
        * `RecMEAElectrode`:
          Class for simulations of in vitro (slice) extracellular
          potentials
        * `OneSphereVolumeConductor`:
          For computing extracellular potentials within
          sand outside a homogeneous sphere
        * `LaminarCurrentSourceDensity`:
          For computing the 'ground truth' current source density across
          cylindrical volumes aligned with the z-axis
        
        
        Different classes built to map current dipole moments (i.e., computed using `CurrentDipoleMoment`)
        to extracellular measurements:
        
        * `eegmegcalc.FourSphereVolumeConductor`:
          For computing extracellular potentials in
          4-sphere head model (brain, CSF, skull, scalp)
          from current dipole moment
        * `eegmegcalc.InfiniteVolumeConductor`:
          To compute extracellular potentials in infinite volume conductor
          from current dipole moment
        * `eegmegcalc.MEG`:
          Class for computing magnetic field from current dipole moments
        * `eegmegcalc.NYHeadModel`:
          Class for computing extracellular potentials in detailed head volume
          conductor model (https://www.parralab.org/nyhead)
        
        Each class (except `CellGeometry`) should have a public method `get_transformation_matrix()`
        that returns the linear map between the transmembrane currents or current dipole moment
        and corresponding measurements (see Usage below)
        
        
        Usage
        -----
        
        A basic usage example using a mock 3-segment stick like neuron,
        treating each segment as a point source in a linear, isotropic and homogeneous volume conductor,
        computing the extracellular potential in ten different locations
        alongside the cell geometry:
        
            >>> # imports
            >>> import numpy as np
            >>> from lfpykit import CellGeometry, PointSourcePotential
            >>> n_seg = 3
            >>> # instantiate class `CellGeometry`:
            >>> cell = CellGeometry(x=np.array([[0.] * 2] * n_seg),  # (µm)
                                    y=np.array([[0.] * 2] * n_seg),  # (µm)
                                    z=np.array([[10. * x, 10. * (x + 1)]
                                                for x in range(n_seg)]),  # (µm)
                                    d=np.array([1.] * n_seg))  # (µm)
            >>> # instantiate class `PointSourcePotential`:
            >>> psp = PointSourcePotential(cell,
                                           x=np.ones(10) * 10,
                                           y=np.zeros(10),
                                           z=np.arange(10) * 10,
                                           sigma=0.3)
            >>> # get linear response matrix mapping currents to measurements
            >>> M = psp.get_transformation_matrix()
            >>> # transmembrane currents (nA):
            >>> imem = np.array([[-1., 1.],
                                 [0., 0.],
                                 [1., -1.]])
            >>> # compute extracellular potentials (mV)
            >>> V_ex = M @ imem
            >>> V_ex
            array([[-0.01387397,  0.01387397],
                   [-0.00901154,  0.00901154],
                   [ 0.00901154, -0.00901154],
                   [ 0.01387397, -0.01387397],
                   [ 0.00742668, -0.00742668],
                   [ 0.00409718, -0.00409718],
                   [ 0.00254212, -0.00254212],
                   [ 0.00172082, -0.00172082],
                   [ 0.00123933, -0.00123933],
                   [ 0.00093413, -0.00093413]])
        
        
        A basic usage example using a mock 3-segment stick like neuron,
        treating each segment as a point source,
        computing the current dipole moment and computing the potential in ten different
        remote locations away from the cell geometry:
        
            >>> # imports
            >>> import numpy as np
            >>> from lfpykit import CellGeometry, CurrentDipoleMoment, \
            >>>     eegmegcalc
            >>> n_seg = 3
            >>> # instantiate class `CellGeometry`:
            >>> cell = CellGeometry(x=np.array([[0.] * 2] * n_seg),  # (µm)
                                    y=np.array([[0.] * 2] * n_seg),  # (µm)
                                    z=np.array([[10. * x, 10. * (x + 1)]
                                                for x in range(n_seg)]),  # (µm)
                                    d=np.array([1.] * n_seg))  # (µm)
            >>> # instantiate class `CurrentDipoleMoment`:
            >>> cdp = CurrentDipoleMoment(cell)
            >>> M_I_to_P = cdp.get_transformation_matrix()
            >>> # instantiate class `eegmegcalc.InfiniteVolumeConductor` and map dipole moment to
            >>> # extracellular potential at measurement sites
            >>> ivc = eegmegcalc.InfiniteVolumeConductor(sigma=0.3)
            >>> # compute linear response matrix between dipole moment and
            >>> # extracellular potential
            >>> M_P_to_V = ivc.get_transformation_matrix(np.c_[np.ones(10) * 1000,
                                                         np.zeros(10),
                                                         np.arange(10) * 100])
            >>> # transmembrane currents (nA):
            >>> imem = np.array([[-1., 1.],
                                [0., 0.],
                                [1., -1.]])
            >>> # compute extracellular potentials (mV)
            >>> V_ex = M_P_to_V @ M_I_to_P @ imem
            >>> V_ex
            array([[ 0.00000000e+00,  0.00000000e+00],
                  [ 5.22657054e-07, -5.22657054e-07],
                  [ 1.00041193e-06, -1.00041193e-06],
                  [ 1.39855769e-06, -1.39855769e-06],
                  [ 1.69852477e-06, -1.69852477e-06],
                  [ 1.89803345e-06, -1.89803345e-06],
                  [ 2.00697409e-06, -2.00697409e-06],
                  [ 2.04182029e-06, -2.04182029e-06],
                  [ 2.02079888e-06, -2.02079888e-06],
                  [ 1.96075587e-06, -1.96075587e-06]])
        
        
        Physical units
        --------------
        
        Notes on physical units used in `LFPykit`:
        
        - There are no explicit checks for physical units
        
        - Transmembrane currents are assumed to be in units of (nA)
        
        - Spatial information is assumed to be in units of (µm)
        
        - Voltages are assumed to be in units of (mV)
        
        - Extracellular conductivities are assumed to be in units of (S/m)
        
        - current dipole moments are assumed to be in units of (nA µm)
        
        - Magnetic fields are assumed to be in units of (nA/µm)
        
        
        Dimensionality
        --------------
        
        - Transmembrane currents are represented by arrays with shape `(n_seg, n_timesteps)`
          where `n_seg` is the number of segments of the neuron model.
        
        - Current dipole moments are represented by arrays with shape `(3, n_timesteps)`
        
        - Response matrices **M** have shape `(n_points, input.shape[0])` where `n_points` is
          for instance the number of extracellular recording sites and `input.shape[0]`
          the first dimension of the input; that is, the number of segments in case of
          transmembrane currents or 3 in case of current dipole moments.
        
        - predicted signals (except magnetic fields using `eegmegcalc.MEG`) have shape
          `(n_points, n_timesteps)`
        
        
        Documentation
        -------------
        
        The online Documentation of `LFPykit` can be found here:
        https://lfpy-forward-models.readthedocs.io/en/latest
        
        
        Dependencies
        ------------
        
        `LFPykit` is implemented in Python and is written (and continuously tested) for `Python >= 3.7`.
        The main `LFPykit` module depends on `numpy`, `scipy` and `MEAutility` (https://github.com/alejoe91/MEAutility, https://meautility.readthedocs.io/en/latest/).
        
        Running all unit tests and example files may in addition require `py.test`, `matplotlib`,
        `neuron` (https://www.neuron.yale.edu),
        (`arbor` coming) and
        `LFPy` (https://github.com/LFPy/LFPy, https://LFPy.readthedocs.io).
        
        
        Installation
        ------------
        
        First, make sure that the above dependencies are installed in the current Python environment.
        
        Install the current development version on https://GitHub.com using `git` (https://git-scm.com):
        
            $ git clone https://github.com/LFPy/LFPykit.git
            $ cd LFPykit
            $ python setup.py install  # --user optional
        
        or using `pip`:
        
            $ pip install .  # --user optional
        
        For active development, link the repository location
        
            $ python setup.py develop  # --user optional
        
        Installing from the Python Package Index (pypi.org):
        
            $ pip install lfpykit  # --user optional
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Utilities
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 4 - Beta
Provides: lfpykit
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: docs
