# -*- coding: utf-8 -*-
from xml.etree import ElementTree as ET
import string
import sys
import operator # has itemgetter() for sorted()
sys.path.extend(["..","../neuroml","../cells","../channels","../networks","../simulations"])
from neuroml_utils import *
from sim_utils import * # has build_tweaks()
from networkConstants import *
## these are needed to load mitral cell and get segids and synapse locations
from load_channels import *
from load_synapses import *
from load_cells import *
from simset_odor import * # has synchan_activation_correction
load_channels()
## synchan_activation_correction depends on SIMDT,
## hence passed all the way down to
## granule_mitral_GABA synapse from the top level
load_synapses(synchan_activation_correction)
## cellSegmentDict[<cellname>] = { segid1 : [ segname,(proximalx,proximaly,proximalz),
## (distalx,distaly,distalz),diameter,length,[potential_syn1, ... ] ] , ... }
cellSegmentDict = load_cells()
from pylab import *
## USAGE:
## python2.6 plot_mitral_connectivity_NetworkML.py <networkmlfile.xml>
## OR
## python2.6 plot_mitral_connectivity_NetworkML.py SYN_DECAY
## The plots are for weights and numbers of all exc/inh synapses on the central/lateral mitrals.
## The printing on the terminal gives weights located proximally vs distally from central mits.
TWOGLOMS = False#True # whether to check for mits 0,2 (True) or 0,1 (False)
INCLUDEMULTIS = True # whether to include multi-s in joint connectivity
class NetworkML():
def __init__(self,numgloms,Exc_notInh=False,CentralWts_notLateral=True):
self.numgloms=numgloms
if TWOGLOMS: self.mitB=2
else: self.mitB=1
## Whether exc or inh part of the reciprocal synapses should be considered
## False by default, as interested in inhibition onto central 'premits'
self.Exc_notInh = Exc_notInh
if Exc_notInh: self.weight_str = 'exc'
else: self.weight_str = 'inh'
## Take numbers and weights of all connections
## from/to central mits (premits) VS from/to lateral mits (postmits)
self.CentralWts_notLateral = CentralWts_notLateral
if CentralWts_notLateral: self.centlat_str = 'central'
else: self.centlat_str = 'lateral'
print "READ CAREFULLY: Using",self.centlat_str,"mitrals'",self.weight_str,"weights and numbers."
def readNetworkMLFromFile(self,filename,params={}):
print "reading file ... ", filename
tree = ET.parse(filename)
root_element = tree.getroot()
#print "Tweaking model ... "
tweak_model(root_element, params)
#print "Parsing model for mitrals connectivity via joint granules ... "
return self.readNetworkML(root_element,root_element.attrib['lengthUnits'])
def readNetworkML(self,network,lengthUnits="micrometer"):
if lengthUnits in ['micrometer','micron']:
self.length_factor = 1e-6
else:
self.length_factor = 1.0
self.network = network
#print "loading mitral positions ... "
self.loadMitrals() # create cells
#print "figuring joints connections ... "
self.figureJoints() # create list of connections
def loadMitrals(self):
for population in self.network.findall(".//{"+nml_ns+"}population"):
cellname = population.attrib["cell_type"]
populationname = population.attrib["name"]
if populationname != 'mitrals': continue
self.mitralsDict = {}
for instance in population.findall(".//{"+nml_ns+"}instance"):
instanceid = int(instance.attrib['id'])
location = instance.find('./{'+nml_ns+'}location')
x = float(location.attrib['x'])*self.length_factor
y = float(location.attrib['y'])*self.length_factor
z = float(location.attrib['z'])*self.length_factor
self.mitralsDict[instanceid]=(x,y,z)
#print self.mitralsDict
def figureJoints(self):
## make a list of singles and joints connected to each mitral
self.singlesDict={}
self.jointsDict={}
projections = self.network.find(".//{"+nml_ns+"}projections")
for projection in self.network.findall(".//{"+nml_ns+"}projection"):
projectionname = projection.attrib["name"]
## connections listed in joints and multis are used to get
## the mitral cells connected to each other
## Whether exc or inh part of the reciprocal synapses should be considered
if self.Exc_notInh:
proj_joints = ( projectionname == 'mitral_granule_main_exc_joints' )
proj_multis = (INCLUDEMULTIS and projectionname == 'mitral_granule_main_exc_multis')
proj_singles = ( projectionname == 'mitral_granule_main_exc_singles' )
mitpositionstr = 'pre'
granpositionstr = 'post'
else:
proj_joints = ( projectionname == 'granule_mitral_inh_joints' )
proj_multis = (INCLUDEMULTIS and projectionname == 'granule_mitral_inh_multis')
proj_singles = ( projectionname == 'granule_mitral_inh_singles' )
mitpositionstr = 'post'
granpositionstr = 'pre'
if proj_joints or proj_multis:
for connection in projection.findall(".//{"+nml_ns+"}connection"):
mit_cell_id = int(connection.attrib[ mitpositionstr+'_cell_id' ])
gran_cell_id = int(connection.attrib[ granpositionstr+'_cell_id' ])
## joint-s and multi-s granule id-s both start with 0.
## make multi-id-s go negative, starting from -1, to maintain uniqueness.
if proj_multis: gran_cell_id = -gran_cell_id-1
mit_seg_id = int(connection.attrib[ mitpositionstr+'_segment_id' ])
gran_seg_id = int(connection.attrib[ granpositionstr+'_segment_id' ])
props = connection.find(".//{"+nml_ns+"}properties")
weight = float(props.attrib['weight'])
## pre cells are granules, post are mitrals
## joints will have two <connection> tags in the projection
## multis will have multiple <connection> tags in the projection
if mit_cell_id in self.jointsDict:
self.jointsDict[mit_cell_id].append((gran_cell_id,mit_seg_id,gran_seg_id,weight))
else:
self.jointsDict[mit_cell_id] = [(gran_cell_id,mit_seg_id,gran_seg_id,weight)]
elif proj_singles:
for connection in projection.findall(".//{"+nml_ns+"}connection"):
mit_cell_id = int(connection.attrib[ mitpositionstr+'_cell_id' ])
gran_cell_id = int(connection.attrib[ granpositionstr+'_cell_id' ])
mit_seg_id = int(connection.attrib[ mitpositionstr+'_segment_id' ])
gran_seg_id = int(connection.attrib[ granpositionstr+'_segment_id' ])
props = connection.find(".//{"+nml_ns+"}properties")
weight = float(props.attrib['weight'])
if mit_cell_id in self.singlesDict:
self.singlesDict[mit_cell_id].append((gran_cell_id,mit_seg_id,gran_seg_id,weight))
else:
self.singlesDict[mit_cell_id] = [(gran_cell_id,mit_seg_id,gran_seg_id,weight)]
def calc_connections(self):
## collate those joints that are connected to mitrals 0 or 1
## Also calculate distance between mitrals
## also figure out how many singles and joints
## are connected to primary vs secondary dendrites
self.connectivityDict = {0:{},self.mitB:{}}
self.connectivityDictNums = {0:{},self.mitB:{}}
mit_prim_joints = {0:0,self.mitB:0}
mit_somadend_joints = {0:0,self.mitB:0}
mit_sec_joints = {0:0,self.mitB:0}
mit_prim_singles = {0:0,self.mitB:0}
mit_sec_singles = {0:0,self.mitB:0}
if TWOGLOMS: postmitrange = [0,self.mitB]
else: postmitrange = range(self.numgloms*MIT_SISTERS)
for postmitid in postmitrange:
self.connectivityDict[0][postmitid] = [0,0] # distance, inh weights
self.connectivityDict[self.mitB][postmitid] = [0,0] # distance, inh weights
self.connectivityDictNums[0][postmitid] = [0,0] # distance, number of grans
self.connectivityDictNums[self.mitB][postmitid] = [0,0] # distance, number of grans
### print list of segments at which each mitral is connected to joints/multis.
#gran2mainmitids,presegids,postsegids,_ = zip(*self.jointsDict[postmitid])
#print "Mitral",postmitid,"is connected to joints on its segments :",presegids,\
# "total =",len(presegids)
## print list of segments at which mit2/3 has joints/multis. 3 is directed, 2 in not.
## Be very careful in finding segid of directedness if frac_directed is small like 0.3%.
grans2premit,presegids,postsegids,_ = zip(*self.jointsDict[3])
presegids = list(presegids)
presegids.sort()
print "Connections to joints/multis of mitral",3,"are at segments",presegids,\
"total =",len(presegids)
print "Be very careful in finding segid of directedness if frac_directed < 1%."
for premitid in [0,self.mitB]:
#### collate info about joints to mitrals 0 and self.mitB
if premitid in self.jointsDict:
## separate out the granule_id-s to this premit, pre_seg_id-s and post_seg_id-s into separate lists
grans2premit,mitsegids,gransegids,premit_weights = zip(*self.jointsDict[premitid])
#print "Connections to joints of mitral",premitid,"are at segments",presegids,\
# "total =",len(presegids)
#### count total number of joints connected to the two main mits
#### segregate into those connected to primary dendrite vs soma+neardend vs sec dend
#### if TWOGLOMS, count only those that connect to the other main mit.
if premitid==0: postmitid=self.mitB
else: postmitid=0
for i,mitsegid in enumerate(mitsegids):
## if TWOGLOMS, do not count joints to other mitrals, only to the two main ones.
if TWOGLOMS:
gran_id = grans2premit[i]
grans2postmit = zip(*self.jointsDict[postmitid])[0]
if gran_id not in grans2postmit:
continue
## far prim dend
far_prim_dend_segids = [16,17,18,19,20]
## soma & nearest-prim and near-sec dends
## 0 is soma, 15 is the nearest prim dend, rest are near sec dends
close_dend_segids = list( set(proximal_mitral_segids) - set(far_prim_dend_segids) )
if mitsegid in far_prim_dend_segids: mit_prim_joints[premitid]+=premit_weights[i]
elif mitsegid in close_dend_segids: mit_somadend_joints[premitid]+=premit_weights[i]
## rest of the sec dend
else: mit_sec_joints[premitid]+=premit_weights[i]
#### find number of joints from each of the main mits
#### to each of the remaining mitrals noting distance
premitposition = self.mitralsDict[premitid]
for postmitid in postmitrange:
if postmitid == premitid: continue # find granules only between non-self!
(postx,posty,postz) = self.mitralsDict[postmitid]
distance = ((premitposition[0]-postx)**2 + (premitposition[1]-posty)**2 +\
(premitposition[2]-postz)**2)**0.5
self.connectivityDict[premitid][postmitid][0] = distance * 1e6 # microns!
self.connectivityDictNums[premitid][postmitid][0] = distance * 1e6 # microns!
if postmitid not in self.jointsDict: continue
grans2postmit,mitsegids,gransegids,postmit_weights = zip(*self.jointsDict[postmitid])
## Take numbers and weights of connections from/to central mits (premits)
## VS from/to lateral mits (postmits)
if self.CentralWts_notLateral:
grans2mitA = grans2premit
grans2mitB = grans2postmit
mit_weights = premit_weights
else:
grans2mitB = grans2premit
grans2mitA = grans2postmit
mit_weights = postmit_weights
for gran_id in set(grans2mitB): # can be many of the same gran_id-s, take unique set
if gran_id in grans2mitA:
granidxs = where(array(grans2mitA)==gran_id)[0] # multiple syns from granid to premit
for granidx in granidxs:
## Be careful to take the premit weight! weight decays exponentially along dendrite!
self.connectivityDict[premitid][postmitid][1] += mit_weights[granidx]
self.connectivityDictNums[premitid][postmitid][1] += 1
#### collate info about singles to mitrals 0 and self.mitB
## For singles, CentralWts_notLateral has no meaning, always central mit (premit) weights
if premitid in self.singlesDict:
## separate out the gran_cell_id-s, mit_seg_id-s, gran_seg_id-s, weights into separate lists
grans2premit,mitsegids,gransegids,premit_weights = zip(*self.singlesDict[premitid])
## count inh wt of the singles on to the primary dendrite vs sec dend
for i,mitsegid in enumerate(mitsegids):
if mitsegid in [15,16,17,18,19,20]: mit_prim_singles[premitid]+=premit_weights[i]
else: mit_sec_singles[premitid]+=premit_weights[i]
self.mit_posn_grannos = \
(mit_prim_joints,mit_somadend_joints,mit_sec_joints,mit_prim_singles,mit_sec_singles)
return (self.mitralsDict,self.connectivityDict,self.connectivityDictNums,self.mit_posn_grannos)
def print_info(self):
(mit_prim_joints,mit_somadend_joints,mit_sec_joints,mit_prim_singles,mit_sec_singles) = \
self.mit_posn_grannos
print self.weight_str,"central-mit\'s weight of joints at far primary dendrites of mitA and mitB is ",mit_prim_joints
print self.weight_str,"central-mit\'s weight of joints at soma and near prim & sec dendrites of mitA and mitB is ",mit_somadend_joints
print self.weight_str,"central-mit\'s weight of joints at secondary dendrites of mitA and mitB is ",mit_sec_joints
print self.weight_str,"central-mit\'s weight of singles at primary dendrites of mitA and mitB is ",mit_prim_singles
print self.weight_str,"central-mit\'s weight of singles at secondary dendrites of mitA and mitB is ",mit_sec_singles
def calc_synaptic_decay(self,indistance,mitid=0):
""" indistance is max distance of segment in m,
indends is a list of lat/peripheral dendrites e.g. ['d1','p1',...]"""
## if lateral mitral, get weights for only the directed dendrite
if mitid != 0:
## list of segments at which mit2/3 has joints/multis.
count_dict = {}
for _,mit_seg_id,_,_ in self.jointsDict[mitid]:
if mit_seg_id not in count_dict: count_dict[mit_seg_id] = 0
else: count_dict[mit_seg_id] += 1
## get the segment that has the most joints/multis, and is a lat dend
while True:
mit_seg_id_max = max(count_dict.iterkeys(), key=(lambda key: count_dict[key]))
segname = cellSegmentDict['mitral'][str(mit_seg_id_max)][0]
## latdend of lateral mit that directs to mit0 soma
## it should one of d1-d4 or p1-p4
## typically a few p [proximal?] lat dend compts go on to d [distal?] compts
dendnum = segname[14:15]
if dendnum in ['1','2','3','4']:
indends = ['p'+dendnum,'d'+dendnum] # p# and d# belong to dend num #.
print 'Found compartment',segname,'with most syns.'
break
else:
del count_dict[mit_seg_id_max] ## remove for next iteration
print 'Not using compartment',segname,'checking next most...'
## get the weights on each compartment
prim_dist_weight_list = []
sec_dist_weight_list = []
for segment in cellSegmentDict['mitral'].values():
## segment = [ segname,(proximalx,proximaly,proximalz),\
## (distalx,distaly,distalz),diameter,length,[potential_syn1, ... ] ]
## segment[0] is segment name, which has segid at the end after an underscore
segname = segment[0]
segid = int(string.split(segname,'_')[-1])
## use this seg only if granule_mitral is one of the potential synapses here
if "granule_mitral" in segment[5]:
## average segment position from the proximal and distal points
segx1 = segment[1][0]
segx2 = segment[2][0]
segx = ( segx1 + segx2 ) / 2.0
segy1 = segment[1][1]
segy2 = segment[2][1]
segy = ( segy1 + segy2 ) / 2.0
segz1 = segment[1][2]
segz2 = segment[2][2]
segz = ( segz1 + segz2 ) / 2.0
## segid is for the prototype cell whose soma x0,y0,z0 is at origin.
## The start of segment is taken in dend_decay()
## in generate_neuroml.py for exp decay,
## but here, we want the mid-segment for distance of synapses.
distance = sqrt(segx**2+segy**2+segz**2)
## only consider distances up to indistance
if distance>indistance: continue
wt_list = []
## if lateral mitral, keep only lat directed dend or primary dendrite
if mitid != 0:
dendname = segname[13:15] # take 2 chars from string secname
if dendname not in indends and segid not in [0,15,16,17,18,19,20]:
## It is important to have empty wt_list for dendrites that are excluded,
## since avg_synaptic_decay() assumes the same order of segments
## across different network seeds.
sec_dist_weight_list.append((distance,segname,wt_list))
continue
## singly connected granules
for (gran_cell_id,mit_seg_id,gran_seg_id,weight) in self.singlesDict[mitid]:
if mit_seg_id==segid:
if not self.Exc_notInh:
## weight of each inh synapse on a singly-connected granule is actually
## GRANS_CLUB_SINGLES/float(SYNS_PER_CLUBBED_SINGLE) times unit wt,
## Each syn represents (GRANS_CLUB_SINGLES/SYNS_PER_CLUBBED_SINGLE) # of syns
## wt_list is a list of individual synapse weights, undo clubbing below ...
wt_list.extend( \
[weight/(GRANS_CLUB_SINGLES/float(SYNS_PER_CLUBBED_SINGLE))] \
* (GRANS_CLUB_SINGLES/SYNS_PER_CLUBBED_SINGLE) )
else:
## Exc weight from a given mitral to granules connected to it.
wt_list.append(weight)
## jointly and multiply connected granules
for (gran_cell_id,mit_seg_id,gran_seg_id,weight) in self.jointsDict[mitid]:
if mit_seg_id==segid: wt_list.append(weight)
if segid in [0,15,16,17,18,19,20]: # soma & prim dend segments
prim_dist_weight_list.append((distance,segname,wt_list))
else: # sec dend segments
sec_dist_weight_list.append((distance,segname,wt_list))
return prim_dist_weight_list,sec_dist_weight_list
latdistlim = 800 ## plot only lateral 800 microns
def avg_synaptic_decay(seednums,frac_dir_str,\
Exc_notInh=False,CentralWts_notLateral=True,mitid=0):
numgloms = 3
allprimwtlists = None
allsecwtlists = None
for seednum in seednums:
### The _4syndecay named files below, have minimal synvariation=0.01 uniform,
### and allow_multi_granule_conn = False
#filename = \
# '../netfiles/syn_conn_array_10000_singlesclubbed100_jointsclubbed1_numgloms'+\
# str(numgloms)+'_seed'+str(seednum)+'_directed'+frac_dir_str+'_proximal_4syndecay.xml'
## But Upi suggested I use my usual netfiles instead of _4syndecay above,
## and plot an average curve over an example.
filename = \
'../netfiles/syn_conn_array_10000_singlesclubbed100_jointsclubbed1_numgloms'+\
str(numgloms)+'_seed'+str(seednum)+'_directed'+frac_dir_str+'_proximal.xml'
netml = NetworkML(numgloms,Exc_notInh,CentralWts_notLateral)
tweaks = {}
netml.readNetworkMLFromFile(filename,params=tweaks)
prim_dist_weight_list,sec_dist_weight_list = \
netml.calc_synaptic_decay(latdistlim*1e-6,mitid) # microns to m
## zip(* ) is the inverse of zip()
## * is the unpacking operator
sortedprimdists,sortednames,sortedwtlists = zip(*sorted(prim_dist_weight_list))
if allprimwtlists is None: allprimwtlists = sortedwtlists
else:
for i,wtlist in enumerate(sortedwtlists): allprimwtlists[i].extend(wtlist)
## sec_dist_weight_list = [(distance,segname,wt_list),...]
## sort by sec dend num and then distance, same sec dend stays together
## first 15 chars of segname contain dendrite num d1,d2,d3,d4. eg:Seg0_sec_dendd3_4_225
sortedsecdists,sortednames,sortedwtlists = \
zip(*sorted( sec_dist_weight_list,key=(lambda x: x[1][:15]+'%01.6f'%x[0]) ))
if allsecwtlists is None: allsecwtlists = sortedwtlists
else:
for i,wtlist in enumerate(sortedwtlists): allsecwtlists[i].extend(wtlist)
sortedprimwts = [ mean(wtlist) for wtlist in allprimwtlists ]
sortedsecwts = [ mean(wtlist) for wtlist in allsecwtlists ]
return sortedprimdists,sortedprimwts,sortedsecdists,sortedsecwts,sortednames
def bin_by_distance(dist_list,wt_list):
## add one bin at the end which only acts to set limits, but contains nothing
distance_bins = array( \
[15,50,80,120,200,270,350,420,485,575]+range(675,latdistlim+101,100) )*1e-6
dist_numbins = len(distance_bins)
binned_wts = [0.0]*len(distance_bins)
binned_nums = [0.0]*len(distance_bins)
for distnum,dist in enumerate(dist_list):
wt = wt_list[distnum]
if isnan(wt): continue
## small list, not bothering to do binary search, just sequential search
bin_lower = 0
i = 0
while True:
bin_upper = (distance_bins[i]+distance_bins[i+1])/2.0
if dist>=bin_lower and dist<bin_upper:
binned_wts[i] += wt
binned_nums[i] += 1
break
else:
bin_lower = bin_upper
## if dist is outside values in distance_bins, go to next dist
if i>=dist_numbins: break
i += 1
## do not return the last set-limit bin
return distance_bins[0:-1],\
array(binned_wts[0:-1])/array(binned_nums[0:-1]) # element-wise division
def plot_synaptic_decay_paperfigure():
"""The single seed scatter plot calculations
and the directed only mitid=3 plot calculations
may give some floating point warnings,
as some of the compartments have no synapses, or wt_list is set to [],
and so their weight values become nan-s on taking mean. """
seednums = arange(750.0,760.0,1.0)
eg_seednum_idx = 4
latdisttick = 600 ## tick at lateral 600 microns, rest show with clip off
## inh weights on central mit as a function of distance on soma-prim and sec dend
primdists,primwts,secdists,secwts,secnames = avg_synaptic_decay(seednums,'0.01')
sec_distance_bins,binned_secwts = bin_by_distance(secdists,secwts)
_,primwts_undir,_,secwts_undir,_ = avg_synaptic_decay(seednums,'0.0')
_,binned_secwts_undir = bin_by_distance(secdists,secwts_undir)
primdists_um = array(primdists)*1e6
secdists_um = array(secdists)*1e6
sec_distance_bins_um = array(sec_distance_bins)*1e6
#### soma-primary dendrite inh gran-|mit plot
fig = figure(figsize=(columnwidth*7/8.0,linfig_height/3.0),\
dpi=fig_dpi,facecolor='none') # none = transparent
ax = fig.add_subplot(111)
ax.plot(primdists_um,array(primwts_undir)*granule_mitral_GABA_Gbar*1e9,\
color='b',marker=',',ms=marker_size,linewidth=linewidth)
ax.plot(primdists_um,array(primwts)*granule_mitral_GABA_Gbar*1e9,\
color='r',marker=',',ms=marker_size,linewidth=linewidth)
## inh weights, directed, one network seed only
_,primwts_undir,_,_,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.0')
_,primwts,_,_,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.01')
ax.scatter(primdists_um,array(primwts_undir)*granule_mitral_GABA_Gbar*1e9,\
color='b',marker='s',s=marker_size)
ax.scatter(primdists_um,array(primwts)*granule_mitral_GABA_Gbar*1e9,\
color='r',marker='x',s=marker_size)
beautify_plot(ax,xticksposn='bottom',yticksposn='left')
#axes_labels(ax,'$\delta$ ($\mu m$)','$\sigma$ (nS)',fontsize=label_fontsize)
add_scalebar(ax,matchx=False,matchy=False,hidex=False,hidey=False,\
sizex=100,labelx='100 $\mu m$',sizey=1,labely=' 1 nS',\
bbox_to_anchor=[0.8,0.3],bbox_transform=ax.transAxes)
fig_clip_off(fig)
fig.tight_layout()
fig.savefig('../figures/connectivity/soma-primdend-inh-vs-distance.svg',\
dpi=fig.dpi,transparent=True)
#### sec dend inh gran-|mit plot
fig = figure(figsize=(columnwidth/2.0,linfig_height/3.0),\
dpi=fig_dpi,facecolor='none') # none = transparent
ax = fig.add_subplot(111)
ax.plot(sec_distance_bins_um,array(binned_secwts_undir)*granule_mitral_GABA_Gbar*1e9,\
color='b',marker=',',ms=marker_size,linewidth=linewidth)
ax.plot(sec_distance_bins_um,array(binned_secwts)*granule_mitral_GABA_Gbar*1e9,\
color='r',marker=',',ms=marker_size,linewidth=linewidth)
_,_,_,ymax = beautify_plot(ax,xticksposn='bottom',yticksposn='left')
## inh weights, directed, one network seed only, all dendrites
_,_,_,secwts_undir,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.0')
_,_,_,secwts,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.01')
ax.scatter(secdists_um,array(secwts_undir)*granule_mitral_GABA_Gbar*1e9,\
color='b',marker='s',s=marker_size)
ax.scatter(secdists_um,array(secwts)*granule_mitral_GABA_Gbar*1e9,\
color='r',marker='x',s=marker_size)
#axes_labels(ax,'$\delta$ ($\mu m$)','$\sigma$ (nS)',fontsize=label_fontsize)
add_scalebar(ax,matchx=False,matchy=False,hidex=False,hidey=False,\
sizex=250,labelx='250 $\mu m$',sizey=3,labely=' 3 nS',\
bbox_to_anchor=[0.9,0.3],bbox_transform=ax.transAxes)
ax.set_xlim(0,latdisttick)
ax.set_ylim(0,8)
ax.set_yticks([0,8])
fig.tight_layout()
fig_clip_off(fig)
fig.savefig('../figures/connectivity/secdend-inh-vs-distance.svg',\
dpi=fig.dpi,transparent=True)
#### sec dend exc mit->gran plot for central and lateral mitral
fig = figure(figsize=(columnwidth/2.0,linfig_height*1.15),\
dpi=fig_dpi,facecolor='none') # none = transparent
ax = fig.add_subplot(3,1,1)
## exc weights of central mit as a function of distance on sec dend
## Not extra-directed, avg over all networks, all dendrites
_,primwts,_,secwts,_ = avg_synaptic_decay(seednums,'0.0',Exc_notInh=True)
_,binned_wts = bin_by_distance(secdists,secwts)
ax.plot(sec_distance_bins_um,array(binned_wts)*mitral_granule_AMPA_Gbar*1e9,\
color='b',linestyle='solid',marker=',',linewidth=linewidth,ms=marker_size)
## extra-directed, avg over all networks, all dendrites
_,primwts,_,secwts,_ = avg_synaptic_decay(seednums,'0.01',Exc_notInh=True)
distance_bins,binned_wts = bin_by_distance(secdists,secwts)
ax.plot(sec_distance_bins_um,array(binned_wts)*mitral_granule_AMPA_Gbar*1e9,\
color='r',linestyle='solid',marker=',',linewidth=linewidth,ms=marker_size)
## extra-directed, one network, all dendrites (lateral and peripheral)
_,primwts,_,secwts_undir,_ = \
avg_synaptic_decay([seednums[eg_seednum_idx]],'0.0',Exc_notInh=True)
_,primwts,_,secwts,_ = \
avg_synaptic_decay([seednums[eg_seednum_idx]],'0.01',Exc_notInh=True)
ax.scatter(secdists_um,array(secwts_undir)*mitral_granule_AMPA_Gbar*1e9,color='b',\
marker='s',s=marker_size)
ax.scatter(secdists_um,array(secwts)*mitral_granule_AMPA_Gbar*1e9,color='r',\
marker='x',s=marker_size)
_,_,_,ymax = beautify_plot(ax,xticksposn='bottom',yticksposn='left')
#axes_labels(ax,'$\delta$ ($\mu m$)','$\sigma$ (nS)',fontsize=label_fontsize)
add_scalebar(ax,matchx=False,matchy=False,hidex=False,hidey=False,\
sizex=250,labelx='250 $\mu m$',sizey=0.2,labely='0.2 nS',\
bbox_to_anchor=[1.1,-1.0],bbox_transform=ax.transAxes)
ax.set_xlim(0,latdisttick)
ax.set_ylim(0,0.6)
ax.set_xticks([0])
ax.set_yticks([0,0.6])
ax = fig.add_subplot(3,1,3)
## exc weights of lateral mit as a function of distance on its sec dend
## mitid=2 does the trick, CentralWts_notLateral is for later processing not needed here.
## get secdists again as now only directed lat dend is averaged over.
_,primwts,secdists,secwts,_ = avg_synaptic_decay(seednums,'0.0',Exc_notInh=True,\
CentralWts_notLateral=False,mitid=2)
secdists_um = array(secdists)*1e6
sec_distance_bins,binned_wts = bin_by_distance(secdists,secwts)
sec_distance_bins_um = array(sec_distance_bins)*1e6
ax.plot(-sec_distance_bins_um,array(binned_wts)*mitral_granule_AMPA_Gbar*1e9,\
color='b',marker=',',linewidth=linewidth)
_,primwts,_,secwts,_ = avg_synaptic_decay(seednums,'0.01',Exc_notInh=True,\
CentralWts_notLateral=False,mitid=2)
_,binned_wts = bin_by_distance(secdists,secwts)
ax.plot(-sec_distance_bins_um,array(binned_wts)*mitral_granule_AMPA_Gbar*1e9,\
color='g',marker=',',linewidth=linewidth)
_,_,_,ymax = beautify_plot(ax,xticksposn='bottom',yticksposn='right',drawyaxis=False)
## extra-directed, one network, only one directed dendrite
_,primwts,secdists,secwts_undir,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.0',\
Exc_notInh=True,CentralWts_notLateral=False,mitid=2)
secdists_um = array(secdists)*1e6
ax.scatter(-secdists_um,array(secwts_undir)*mitral_granule_AMPA_Gbar*1e9,color='b',\
marker='s',s=marker_size)
# secdists for single seeds can be different, since compartments with no syns are ignored
_,primwts,secdists,secwts,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.01',\
Exc_notInh=True,CentralWts_notLateral=False,mitid=2)
secdists_um = array(secdists)*1e6
ax.scatter(-secdists_um,array(secwts)*mitral_granule_AMPA_Gbar*1e9,color='g',\
marker='x',s=marker_size)
#axes_labels(ax,'$\delta$ ($\mu m$)','$\sigma$ (nS)',fontsize=label_fontsize)
ax.spines['right'].set_color('k') # draw right axis
## xlim and ylim must be same as for above axes, since scale bar is shared
ax.set_xlim(-latdisttick,0)
ax.set_ylim(0,0.6)
ax.set_xticks([0])
ax.set_yticks([0,0.6])
fig.tight_layout()
fig_clip_off(fig)
fig.savefig('../figures/connectivity/secdend-exc-vs-distance.svg',\
dpi=fig.dpi,transparent=True)
show()
def plot_synaptic_decay_paperfigure_v2():
"""The single seed scatter plot calculations
and the directed only mitid=3 plot calculations
may give some floating point warnings,
as some of the compartments have no synapses, or wt_list is set to [],
and so their weight values become nan-s on taking mean. """
seednums = arange(750.0,760.0,1.0)
eg_seednum_idx = 4
latdisttick = 800 ## tick at lateral 600 microns, rest show with clip off
## inh weights on central mit as a function of distance on soma-prim and sec dend
primdists,primwts,secdists,secwts,secnames = avg_synaptic_decay(seednums,'0.01')
sec_distance_bins,binned_secwts = bin_by_distance(secdists,secwts)
_,primwts_undir,_,secwts_undir,_ = avg_synaptic_decay(seednums,'0.0')
_,binned_secwts_undir = bin_by_distance(secdists,secwts_undir)
primdists_um = array(primdists)*1e6
secdists_um = array(secdists)*1e6
sec_distance_bins_um = array(sec_distance_bins)*1e6
#### soma-primary dendrite inh gran-|mit plot
fig = figure(figsize=(columnwidth/2.0,linfig_height/3.0*1.2),\
dpi=fig_dpi,facecolor='w') # none = transparent
ax = fig.add_subplot(111)
ax.plot(primdists_um,array(primwts_undir)*granule_mitral_GABA_Gbar*1e9,\
color='b',marker=',',ms=marker_size,linewidth=linewidth)
ax.plot(primdists_um,array(primwts)*granule_mitral_GABA_Gbar*1e9,\
color='r',marker=',',ms=marker_size,linewidth=linewidth)
## inh weights, directed, one network seed only
_,primwts_undir,_,_,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.0')
_,primwts,_,_,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.01')
ax.scatter(primdists_um,array(primwts_undir)*granule_mitral_GABA_Gbar*1e9,\
color='b',marker='s',s=marker_size)
ax.scatter(primdists_um,array(primwts)*granule_mitral_GABA_Gbar*1e9,\
color='r',marker='x',s=marker_size)
beautify_plot(ax,xticksposn='bottom',yticksposn='left')
ax.set_xlim(0,550)
ax.set_xticks([0,550])
axes_labels(ax,'distance ($\mu m$)',u'G─┤M (nS)',fontsize=label_fontsize,xpad=-4,ypad=2)
#add_scalebar(ax,matchx=False,matchy=False,hidex=False,hidey=False,\
# sizex=100,labelx='100 $\mu m$',sizey=1,labely=' 1 nS',\
# bbox_to_anchor=[0.8,0.3],bbox_transform=ax.transAxes)
fig_clip_off(fig)
fig.tight_layout()
fig.savefig('../figures/connectivity/soma-primdend-inh-vs-distance_v2.svg',\
dpi=fig.dpi,transparent=True)
fig = figure(figsize=(columnwidth/2.0,linfig_height),\
dpi=fig_dpi,facecolor='w') # none = transparent
#### sec dend inh gran-|mit plot
ax = fig.add_subplot(3,1,1)
ax.plot(sec_distance_bins_um,array(binned_secwts_undir)*granule_mitral_GABA_Gbar*1e9,\
color='b',marker=',',ms=marker_size,linewidth=linewidth)
ax.plot(sec_distance_bins_um,array(binned_secwts)*granule_mitral_GABA_Gbar*1e9,\
color='r',marker=',',ms=marker_size,linewidth=linewidth)
_,_,_,ymax = beautify_plot(ax,xticksposn='bottom',yticksposn='left')
## inh weights, directed, one network seed only, all dendrites
_,_,_,secwts_undir,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.0')
_,_,_,secwts,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.01')
ax.scatter(secdists_um,array(secwts_undir)*granule_mitral_GABA_Gbar*1e9,\
color='b',marker='s',s=marker_size)
ax.scatter(secdists_um,array(secwts)*granule_mitral_GABA_Gbar*1e9,\
color='r',marker='x',s=marker_size)
axes_labels(ax,'',u'G─┤M (nS)',fontsize=label_fontsize,ypad=9)
#add_scalebar(ax,matchx=False,matchy=False,hidex=False,hidey=False,\
# sizex=250,labelx='250 $\mu m$',sizey=3,labely=' 3 nS',\
# bbox_to_anchor=[0.9,0.3],bbox_transform=ax.transAxes)
ax.set_xlim(0,latdisttick)
ax.set_xticklabels(['',''])
ax.set_ylim(0,8)
ax.set_yticks([0,8])
#### sec dend exc mit->gran plot for central and lateral mitral
ax = fig.add_subplot(3,1,2)
## exc weights of central mit as a function of distance on sec dend
## Not extra-directed, avg over all networks, all dendrites
_,primwts,_,secwts,_ = avg_synaptic_decay(seednums,'0.0',Exc_notInh=True)
_,binned_wts = bin_by_distance(secdists,secwts)
ax.plot(sec_distance_bins_um,array(binned_wts)*mitral_granule_AMPA_Gbar*1e9,\
color='b',linestyle='solid',marker=',',linewidth=linewidth,ms=marker_size)
## extra-directed, avg over all networks, all dendrites
_,primwts,_,secwts,_ = avg_synaptic_decay(seednums,'0.01',Exc_notInh=True)
distance_bins,binned_wts = bin_by_distance(secdists,secwts)
ax.plot(sec_distance_bins_um,array(binned_wts)*mitral_granule_AMPA_Gbar*1e9,\
color='r',linestyle='solid',marker=',',linewidth=linewidth,ms=marker_size)
## extra-directed, one network, all dendrites (lateral and peripheral)
_,primwts,_,secwts_undir,_ = \
avg_synaptic_decay([seednums[eg_seednum_idx]],'0.0',Exc_notInh=True)
_,primwts,_,secwts,_ = \
avg_synaptic_decay([seednums[eg_seednum_idx]],'0.01',Exc_notInh=True)
ax.scatter(secdists_um,array(secwts_undir)*mitral_granule_AMPA_Gbar*1e9,color='b',\
marker='s',s=marker_size)
ax.scatter(secdists_um,array(secwts)*mitral_granule_AMPA_Gbar*1e9,color='r',\
marker='x',s=marker_size)
_,_,_,ymax = beautify_plot(ax,xticksposn='bottom',yticksposn='left')
axes_labels(ax,'',u'M→G (nS)',fontsize=label_fontsize,ypad=2)
#add_scalebar(ax,matchx=False,matchy=False,hidex=False,hidey=False,\
# sizex=250,labelx='250 $\mu m$',sizey=0.2,labely='0.2 nS',\
# bbox_to_anchor=[1.1,-1.0],bbox_transform=ax.transAxes)
ax.set_xlim(0,latdisttick)
ax.set_xticks([0,latdisttick])
ax.set_xticklabels(['',''])
ax.set_ylim(0,0.6)
ax.set_yticks([0,0.6])
ax.set_yticklabels(['0','0.6'])
ax = fig.add_subplot(3,1,3)
## exc weights of lateral mit as a function of distance on its sec dend
## mitid=2 does the trick, CentralWts_notLateral is for later processing not needed here.
## get secdists again as now only directed lat dend is averaged over.
_,primwts,secdists,secwts,_ = avg_synaptic_decay(seednums,'0.0',Exc_notInh=True,\
CentralWts_notLateral=False,mitid=2)
secdists_um = array(secdists)*1e6
sec_distance_bins,binned_wts = bin_by_distance(secdists,secwts)
sec_distance_bins_um = array(sec_distance_bins)*1e6
ax.plot(sec_distance_bins_um,array(binned_wts)*mitral_granule_AMPA_Gbar*1e9,\
color='b',marker=',',linewidth=linewidth)
_,primwts,_,secwts,_ = avg_synaptic_decay(seednums,'0.01',Exc_notInh=True,\
CentralWts_notLateral=False,mitid=2)
_,binned_wts = bin_by_distance(secdists,secwts)
ax.plot(sec_distance_bins_um,array(binned_wts)*mitral_granule_AMPA_Gbar*1e9,\
color='g',marker=',',linewidth=linewidth)
_,_,_,ymax = beautify_plot(ax,xticksposn='bottom',yticksposn='left')
## extra-directed, one network, only one directed dendrite
_,primwts,secdists,secwts_undir,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.0',\
Exc_notInh=True,CentralWts_notLateral=False,mitid=2)
secdists_um = array(secdists)*1e6
ax.scatter(secdists_um,array(secwts_undir)*mitral_granule_AMPA_Gbar*1e9,color='b',\
marker='s',s=marker_size)
# secdists for single seeds can be different, since compartments with no syns are ignored
_,primwts,secdists,secwts,_ = avg_synaptic_decay([seednums[eg_seednum_idx]],'0.01',\
Exc_notInh=True,CentralWts_notLateral=False,mitid=2)
secdists_um = array(secdists)*1e6
ax.scatter(secdists_um,array(secwts)*mitral_granule_AMPA_Gbar*1e9,color='g',\
marker='x',s=marker_size)
axes_labels(ax,'distance ($\mu m$)',u'M→G (nS)',fontsize=label_fontsize,xpad=-4,ypad=2)
## xlim and ylim must be same as for above axes, since scale bar is shared
ax.set_xlim(0,latdisttick)
ax.set_ylim(0,0.6)
ax.set_xticks([0,latdistlim])
ax.set_yticks([0,0.6])
ax.set_yticklabels(['0','0.6'])
fig.tight_layout()
fig_clip_off(fig)
fig.savefig('../figures/connectivity/secdend-vs-distance_v2.svg',\
dpi=fig.dpi,transparent=True)
show()
if __name__ == "__main__":
err_string = "Please give me a NeuroML filename or SYN_DECAY as argument."
if len(sys.argv) < 2:
print err_string
sys.exit(1)
filename = sys.argv[1]
if 'SYN_DECAY' in sys.argv:
#plot_synaptic_decay_paperfigure()
plot_synaptic_decay_paperfigure_v2()
else:
if TWOGLOMS: numgloms=2
else:
postnumgloms_str = filename.split('numgloms')[1] # take filename after 'numgloms'
numgloms = int(postnumgloms_str.split('_')[0]) # take the integer between 'numgloms' and '_'
netml = NetworkML(numgloms)
#if TWOGLOMS:
# includeProjections = ['granule_baseline']
# tweaks = build_tweaks( mitralsclub=True, nospineinh=False, nosingles=False,
# nojoints=False, nomultis=False, nopgs=False, onlytwomits=True,
# includeProjections=includeProjections, twomitrals=(0,netml.mitB) )
#else: tweaks = {}
tweaks = {}
netml.readNetworkMLFromFile(filename,params=tweaks)
(mitralsDict,connectivityDict,connectivityDictNums,mit_posn_grannos) = netml.calc_connections()
netml.print_info()
## dict.iteritems() returns a list of (key,value) pairs.
## sorted sorts above list after calling itemgetter(1)
## on each item which returns the second element i.e. value
## finally, a list of (key, value) pairs is returned sorted by value
mitAconnectivityList = sorted(connectivityDict[0].iteritems(), key=operator.itemgetter(1))
mitBconnectivityList = sorted(connectivityDict[netml.mitB].iteritems(), key=operator.itemgetter(1))
mitAconnectivityNumsList = sorted(connectivityDictNums[0].iteritems(), key=operator.itemgetter(1))
mitBconnectivityNumsList = sorted(connectivityDictNums[netml.mitB].iteritems(), key=operator.itemgetter(1))
fig = figure()
ax = fig.add_subplot(111)
## zip(* ) is the inverse of zip()
## * is the unpacking operator
## key is postmitid; value is (distance,weights,#joints)
sortedkeys,sortedvalues = zip(*mitAconnectivityList)
for key in sortedkeys:
ax.annotate(str(key), xy=connectivityDict[0][key])
x,y = zip(*sortedvalues)
ax.plot(x,y,'r-o',label='mitral 0')
## zip(* ) is the opposite of zip()
## key is postmitid; value is (distance,#joints)
sortedkeys,sortedvalues = zip(*mitBconnectivityList)
for key in sortedkeys:
ax.annotate(str(key), xy=connectivityDict[netml.mitB][key])
x,y = zip(*sortedvalues)
ax.plot(x,y,'g-x',label='mitral '+str(netml.mitB))
legend(loc='upper left')
xlabel('distance (microns)')
ylabel(netml.centlat_str+'-mit\'s '+netml.weight_str+' weight at joint+multi granules')
title(netml.centlat_str+'-mit\'s '+netml.weight_str+' weight at all joints/multis between mits 0/1 <--> mit x')
fig = figure()
ax = fig.add_subplot(111)
sortedkeys,sortedvalues = zip(*mitAconnectivityNumsList)
for key in sortedkeys:
ax.annotate(str(key), xy=connectivityDictNums[0][key])
x,y = zip(*sortedvalues)
ax.plot(x,y,'r-o',label='mitral 0')
sortedkeys,sortedvalues = zip(*mitBconnectivityNumsList)
for key in sortedkeys:
ax.annotate(str(key), xy=connectivityDictNums[netml.mitB][key])
x,y = zip(*sortedvalues)
ax.plot(x,y,'g-x',label='mitral '+str(netml.mitB))
legend(loc='upper left')
xlabel('distance (microns)')
ylabel('# of '+netml.centlat_str+'-mit\'s '+netml.weight_str+' conns on joint+multi granules')
title('# of '+netml.centlat_str+'-mit\'s '+netml.weight_str+' conns on joints/multis bet. mits 0/1 & mit x')
print "READ CAREFULLY: Using",netml.centlat_str,"mitrals'",netml.weight_str,"weights and numbers."
show()
