fixed implementation of linesource method

LFPy/__init__.py

@@ -29,7 +29,7 @@
   * inputgenerators - functions for synaptic input time generation
 '''
 
-__version__ = "1.1.2"
+__version__ = "1.1.3"
 
 from .pointprocess import Synapse, PointProcess, StimIntElectrode
 from .recextelectrode import RecExtElectrode, RecExtElectrodeSetup

LFPy/lfpcalc.py

@@ -13,7 +13,7 @@
 
 import numpy as np
 
-def calc_lfp_choose(cell, x=0, y=0, z=0, sigma=0.3,
+def calc_lfp_choose(cell, x=0., y=0., z=0., sigma=0.3,
                     r_limit=None,
                     timestep=None, t_indices=None, method='linesource'):
     '''
@@ -46,7 +46,7 @@ def calc_lfp_choose(cell, x=0, y=0, z=0, sigma=0.3,
                                     r_limit=r_limit,
                                     timestep=timestep, t_indices=t_indices)
 
-def calc_lfp_linesource(cell, x=0, y=0, z=0, sigma=0.3,
+def calc_lfp_linesource(cell, x=0., y=0., z=0., sigma=0.3,
                         r_limit=None,
                         timestep=None, t_indices=None):
     '''Calculate electric field potential using the line-source method, all
@@ -102,9 +102,9 @@ def calc_lfp_linesource(cell, x=0, y=0, z=0, sigma=0.3,
 
     #case i, h < 0, l < 0
     [i] = np.where(hnegi & lnegi)
-    #case ii, h < 0, l > 0
+    #case ii, h < 0, l >= 0
     [ii] = np.where(hnegi & lposi)
-    #case iii, h > 0, l > 0
+    #case iii, h >= 0, l >= 0
     [iii] = np.where(hposi & lposi)
 
     Ememi = _Ememi_calc(i, currmem, sigma, deltaS, l, r2, h)
@@ -116,7 +116,7 @@ def calc_lfp_linesource(cell, x=0, y=0, z=0, sigma=0.3,
 
     return Emem.transpose()
 
-def calc_lfp_som_as_point(cell, x=0, y=0, z=0, sigma=0.3,
+def calc_lfp_som_as_point(cell, x=0., y=0., z=0., sigma=0.3,
                           r_limit=None,
                           timestep=None, t_indices=None):
     '''Calculate electric field potential using the line-source method,
@@ -180,7 +180,7 @@ def calc_lfp_som_as_point(cell, x=0, y=0, z=0, sigma=0.3,
                 % (r_soma, s_limit)))
         r_soma = s_limit
 
-    # Check that no segment is close the electrode than r_limit
+    # Check that no segment is closer to the electrode than r_limit
     if np.sum(np.nonzero( r2 < r_limit*r_limit )) > 0:
         for idx in np.nonzero( r2[1:] < r_limit[1:] * r_limit[1:] )[0]+1:
             if (h[idx] < r_limit[idx]) and \
@@ -202,9 +202,9 @@ def calc_lfp_som_as_point(cell, x=0, y=0, z=0, sigma=0.3,
     #Line sources
     #case i,  h < 0,  l < 0
     i = np.where(hnegi & lnegi)
-    #case ii,  h < 0,  l > 0
+    #case ii,  h < 0,  l >= 0
     ii = np.where(hnegi & lposi)
-    #case iii,  h > 0,  l > 0
+    #case iii,  h >= 0,  l >= 0
     iii = np.where(hposi & lposi)
 
     Ememi = _Ememi_calc(i, currmem, sigma, deltaS, l, r2, h)
@@ -281,12 +281,8 @@ def _h_calc(xstart, xend, ystart, yend, zstart, zend, deltaS, x, y, z):
     '''Subroutine used by calc_lfp_*()'''
     aa = np.array([x - xend, y - yend, z-zend])
     bb = np.array([xend - xstart, yend - ystart, zend - zstart])
-    try:
-        cc = np.dot(aa.T, bb).diagonal().copy()
-    except:
-        raise ValueError
+    cc = np.dot(aa.T, bb).diagonal()
     hh = cc / deltaS
-    hh[0] = 0
     return hh
 
 def _r2_calc(xend, yend, zend, x, y, z, h):

LFPy/lfpcalc.pyx

@@ -127,9 +127,9 @@ cpdef np.ndarray[DTYPE_t, ndim=1, negative_indices=False] calc_lfp_linesource(
 
     #case i, h < 0, l < 0
     [i] = np.where(hnegi & lnegi)
-    #case ii, h < 0, l > 0
+    #case ii, h < 0, l >= 0
     [ii] = np.where(hnegi & lposi)
-    #case iii, h > 0, l > 0
+    #case iii, h >= 0, l >= 0
     [iii] = np.where(hposi & lposi)
 
 
@@ -238,9 +238,9 @@ cpdef np.ndarray[DTYPE_t, ndim=1] calc_lfp_som_as_point(cell,
     #Line sources
     #case i,  h < 0,  l < 0
     [i] = np.where(hnegi & lnegi)
-    #case ii,  h < 0,  l > 0
+    #case ii,  h < 0,  l >= 0
     [ii] = np.where(hnegi & lposi)
-    #case iii,  h > 0,  l > 0
+    #case iii,  h >= 0,  l >= 0
     [iii] = np.where(hposi & lposi)
 
     Ememi = _Ememi_calc(i, currmem, sigma, deltaS, l, r2, h)
@@ -407,9 +407,8 @@ cdef np.ndarray[DTYPE_t, ndim=1, negative_indices=False] _h_calc(
     aa = np.array([x - xend, y - yend, z-zend])
     aaT = aa.T
     bb = np.array([xend - xstart, yend - ystart, zend - zstart])
-    cc = np.dot(aaT, bb).diagonal().copy()
+    cc = np.dot(aaT, bb).diagonal()
     hh = cc / deltaS
-    hh[0] = 0
     return hh
 
 

LFPy/pointprocess.py

@@ -121,19 +121,20 @@ def set_spike_times_w_netstim(self, noise=1., start=0., number=1E3,
         Generate a train of pre-synaptic stimulus times by setting up the
         neuron NetStim object associated with this synapse
 
-        Arguments
-        ---------
-        noise : float in [0, 1]
-            Fractional randomness, from deterministic to intervals that drawn
-            from negexp distribution (Poisson spiketimes).
-        start : float
-            ms, (most likely) start time of first spike
-        number : int
-            (average) number of spikes
-        interval : float
-            ms, (mean) time between spikes
-        seed : float
-            random seed value
+        kwargs:
+        ::
+
+            noise : float in [0, 1]
+                Fractional randomness, from deterministic to intervals that drawn
+                from negexp distribution (Poisson spiketimes).
+            start : float
+                ms, (most likely) start time of first spike
+            number : int
+                (average) number of spikes
+            interval : float
+                ms, (mean) time between spikes
+            seed : float
+                random seed value
         '''
         self.cell.netstimlist[-1].noise = noise
         self.cell.netstimlist[-1].start = start

LFPy/recextelectrode.py

@@ -69,18 +69,23 @@ def __init__(self, cell=None, sigma=0.3,
         '''Initialize class RecExtElectrodeSetup'''
         self.cell = cell
         self.sigma = sigma
-        if type(x) == float or type(x) == int:
+        if type(x) is float or type(x) is int:
             self.x = np.array([x])
         else:
             self.x = np.array(x).flatten()
-        if type(y) == float or type(y) == int:
+        if type(y) is float or type(y) is int:
             self.y = np.array([y])
         else:
             self.y = np.array(y).flatten()
-        if type(z) == float or type(z) == int:
+        if type(z) is float or type(z) is int:
             self.z = np.array([z])
         else:
             self.z = np.array(z).flatten()
+        try:
+            assert((self.x.size==self.y.size) and (self.x.size==self.z.size))
+        except AssertionError as ae:
+            raise ae, "The number of elements in [x, y, z] must be identical"
+
         self.color = color
         self.marker = marker
         if N is not None:

documentation/sources/conf.py

@@ -50,7 +50,7 @@
 # The short X.Y version.
 version = '1.1'
 # The full version, including alpha/beta/rc tags.
-release = '1.1.2'
+release = '1.1.3'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

setup.py

@@ -49,7 +49,7 @@
 
 setup(
     name = "LFPy",
-    version = "1.1.2",
+    version = "1.1.3",
     maintainer = "Espen Hagen",
     maintainer_email = 'e.hagen@fz-juelich.de',
     packages = ['LFPy'],