Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)

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Accession:153574
A detailed network model of the dual-layer dendro-dendritic inhibitory microcircuits in the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra, Upinder S. Bhalla (2015). All cell morphologies and network connections are in NeuroML v1.8.0. PG and granule cell channels and synapses are also in NeuroML v1.8.0. Mitral cell channels and synapses are in native python.
Reference:
1 . Gilra A, Bhalla US (2015) Bulbar microcircuit model predicts connectivity and roles of interneurons in odor coding. PLoS One 10:e0098045 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell;
Channel(s): I A; I h; I K,Ca; I Sodium; I Calcium; I Potassium;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: Python; MOOSE/PyMOOSE;
Model Concept(s): Sensory processing; Sensory coding; Markov-type model; Olfaction;
Implementer(s): Bhalla, Upinder S [bhalla at ncbs.res.in]; Gilra, Aditya [aditya_gilra -at- yahoo -period- com];
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell; AMPA; NMDA; Gaba; I A; I h; I K,Ca; I Sodium; I Calcium; I Potassium; Gaba; Glutamate;
#!/usr/bin/env python
# -*- coding: utf-8 -*-

########## You need to run: python2.6 average_tuft_inhibition.py <tuftADIresults_foldername>

import os
import sys
import math
import pickle

sys.path.extend(["..","../networks","../generators","../simulations"])

from moose.utils import * # imports moose
from data_utils import *

from stimuliConstants import * # has SETTLETIME
from simset_activinhibition import * # has oninject_ext
from plot_mitral_connectivity_NetworkML import * # get connectivity from netfile

from pylab import * # part of matplotlib that depends on numpy but not scipy

## override sim time settings of stimuliConstants.py (has stimuliConstantsMinimal.py)
## Here in tuftADI, unlike activdep inh,
## ASYM_TEST puts same ORN frate into B as A, rather than const.
ASYM_TEST = False#True
DIRECTED = True
FRAC_DIRECTED = 0.01#0.03
IN_VIVO = True
onInject = oninject_ext # in Hz, from simset_activinhibition_minimal
REVERSED_ADI = False
NONLINEAR_ORNS = False
## if ODORINH, sims used 1x granule bgnd (ensure extraexc_factor=1 in stimuliConstants.py)
## and 10Hz ORN to mitB, else 1x and 5Hz.
## I'm using this to just deliver 0.5x and 1x lat i/p, to show linearity of lat inh.
ODORINH = True#False

if IN_VIVO: invivo_str = '_invivo'
else: invivo_str = ''
if DIRECTED: dirt_str = '_directed'+str(FRAC_DIRECTED)
else: dirt_str = ''
if REVERSED_ADI: rev_str = '_reversed'
else: rev_str = ''
if ASYM_TEST: asym_str = '_asym'
else: asym_str = ''
if ODORINH: odorinh_str = '_odorinh'
else: odorinh_str = '_airinh'

## inh =  (no_singles,no_joints,no_PGs)
inh = (False,False,False)
print "Using NOSINGLES =",inh[0],"NOJOINTS =",inh[1],"NOPGS =",inh[2]
netseeds = [100.0,200.0,300.0,400.0,500.0,600.0,700.0,800.0,900.0,1000.0]

if IN_VIVO:
    mit_distance_list = [50.0,200.0,400.0,600.0,800.0,1000.0,1200.0,1400.0,1600.0,1800.0,1850.0,1900.0] # microns
else:
    mit_distance_list = [50.0] # microns

## whether to calculate and print the number of joint granules connected
SHOW_CONN = False

if len(sys.argv)<2:
    print 'Specify folder name where tuftADI results reside.'
    sys.exit(1)
else:
    filebase = sys.argv[1]
    ## eg: choose '_aug17_2' from '../results/tuftADI_aug17_2/'
    dirextns = filebase.split('/')
    if dirextns[-1]=='': dirextn = dirextns[-2]
    else: dirextn = dirextns[-1]
    dirextn = dirextn.split('tuftADI')[-1]

def get_filename(netseed,inh,mitdistance,lat_mitnum=3,
        _directed=DIRECTED,_frac_directed=FRAC_DIRECTED,
        _asym_str=asym_str,_rev_str=rev_str,_odorinh_str=odorinh_str,
        directed_str=dirt_str,filebase='../results/tuftADI'):
    ### read filename from the output file of automated run
    ## construct the filename
    if inh[0]: singles_str = '_NOSINGLES'
    else: singles_str = '_SINGLES'
    if inh[1]: joints_str = '_NOJOINTS'
    else: joints_str = '_JOINTS'
    if inh[2]: pgs_str = '_NOPGS'
    else: pgs_str = '_PGS'
    ## stable enough that time tags are not needed
    outfilename = filebase+'/tuftADI_'+str(lat_mitnum)+\
        '_seed'+str(netseed)+'_mitdist'+str(mitdistance)+\
        singles_str+joints_str+pgs_str+invivo_str+directed_str+\
        _rev_str+_asym_str+_odorinh_str+'.pickle'
    return outfilename, (singles_str, joints_str, pgs_str)

def get_rates_inh(filename):
    f = open(filename,'r')
    Ainjectarray, both_firingratearrays = pickle.load(f)
    f.close()
    mit_alone = array(both_firingratearrays[0])
    mit_inhibited = array(both_firingratearrays[1])
    diff_frates = mit_alone - mit_inhibited
    max_redux = max(diff_frates)
    avg_redux = mean(diff_frates)
    return Ainjectarray, both_firingratearrays, max_redux, avg_redux

def plot_avg_inh_lat(mit_distance,_rev_str,manyplots=False,directed_str=dirt_str):
    ## load the data
    full_firingratearray = []
    half_firingratearray = []
    redux_frate_array = []
    max_redux_frate_array = []
    avg_redux_frate_array = []
    for netseed in netseeds:
        filename,_ = get_filename(netseed,inh,mit_distance,
            _asym_str=asym_str,_rev_str=_rev_str,_odorinh_str='_odorinh',
            directed_str=directed_str,filebase=filebase)
        ## if the result file for these seeds & tweaks doesn't exist,
        ## then carry on to the next.
        if not os.path.exists(filename):
            print "File not found",filename
            continue
            
        Ainjectarray, both_firingratearrays, max_redux, avg_redux = \
            get_rates_inh(filename)
        full_firingratearray.append(both_firingratearrays)
        max_redux_frate_array.append(max_redux)
        avg_redux_frate_array.append(avg_redux)

        ## load for 0.5x (_airinh) firing rate in B too.
        if not ASYM_TEST:
            filename,_ = get_filename(netseed,inh,mit_distance,
                _asym_str=asym_str,_rev_str=_rev_str,_odorinh_str='_airinh',
                directed_str=directed_str,filebase=filebase)
            ## if the result file for these seeds & tweaks doesn't exist,
            ## then carry on to the next.
            if not os.path.exists(filename): continue
            print filename
            Ainjectarray, both_firingratearrays, max_redux, avg_redux = \
                get_rates_inh(filename)
            half_firingratearray.append(both_firingratearrays)

    ## calculate means and std errs
    full_firingratearray = array(full_firingratearray)
    mean_firingratearray = mean(full_firingratearray, axis=0)
    std_firingratearray = std(full_firingratearray, axis=0)
    half_firingratearray = array(half_firingratearray)
    mean_half_firingratearray = mean(half_firingratearray, axis=0)
    std_half_firingratearray = std(half_firingratearray, axis=0)

    if manyplots:
        ## plot the lone vs inhibited firing rate vs input (Hz) curves
        mainfig = figure(figsize=(columnwidth/2.0,linfig_height),dpi=300,facecolor='w')
        mainaxes = mainfig.add_subplot(111,frameon=False)
        for mitralBinject in [0,1]:
            if ASYM_TEST:
                if mitralBinject == 0: mitB_str = '0.0 Hz'
                else: mitB_str = 'as for A'
            else: mitB_str = str(mitralBinject*onInject)+' Hz'
            mainaxes.errorbar(x=Ainjectarray,\
            y=mean_firingratearray[mitralBinject],\
            yerr=std_firingratearray[mitralBinject],\
            color=['k','b'][mitralBinject], marker=['s','o'][mitralBinject],\
            markersize=marker_size, label="mitB i/p = "+mitB_str, linewidth=linewidth)
        if not ASYM_TEST and 'reverse' not in _rev_str:
            mitB_str = str(0.5*onInject)+' Hz'
            mainaxes.errorbar(x=Ainjectarray,\
            y=mean_half_firingratearray[1],\
            yerr=std_half_firingratearray[1],\
            color='r', marker='d', markersize=marker_size,\
            label="mitB i/p = "+mitB_str, linewidth=linewidth)    
        mainaxes.get_yaxis().set_ticks_position('left')
        mainaxes.get_xaxis().set_ticks_position('bottom')
        xmin, xmax = mainaxes.get_xaxis().get_view_interval()
        ymin, ymax = mainaxes.get_yaxis().get_view_interval()
        mainaxes.set_xlim([0,xmax])
        mainaxes.set_ylim([0,ymax])
        mainaxes.set_xticks([0,xmax])
        mainaxes.set_yticks([0,ymax])
        mainaxes.add_artist(Line2D((0, 0), (0, ymax), color='black', linewidth=axes_linewidth))
        mainaxes.add_artist(Line2D((0, xmax), (0, 0), color='black', linewidth=axes_linewidth))
        ##biglegend("lower right")
        #biglegend("upper left")
        axes_labels(mainaxes,"mitral A ORN rate (Hz)","mitral A firing rate (Hz)")
        mainfig.tight_layout()
        figfilename = '../figures/tuftADI/tuftADI'+'_mitdist'+str(mit_distance) \
            +invivo_str+dirt_str+_rev_str+asym_str+dirextn
        mainfig.savefig(figfilename+'.png', dpi=mainfig.dpi)
        mainfig.savefig(figfilename+'.svg', dpi=mainfig.dpi)

    return max_redux_frate_array, avg_redux_frate_array

def plot_avg_inh_lat_all(mit_distance,_rev_str,manyplots=False):
    mainfig = figure(figsize=(columnwidth*2.0,linfig_height),dpi=300,facecolor='w')
    ax1 = mainfig.add_subplot(1,5,1)
    ax2 = mainfig.add_subplot(1,5,2)
    ax3 = mainfig.add_subplot(1,5,3)
    ax4 = mainfig.add_subplot(1,5,4)
    ax5 = mainfig.add_subplot(1,5,5)
    ## inh =  (no_singles,no_joints,no_PGs)
    inh_options = [
        ('../results/tuftADI/',(False,False,False),ax1),
        ('../results/tuftADI/',(False,False,True),ax2),
        ('../results/tuftADI/',(True,True,False),ax3),
        ('../results/tuftADI_aug17_2_PGmod/',(False,False,False),ax4),
        ('../results/tuftADI_aug17_4_PGmod/',(False,False,False),ax5)]
    maxy = 0.0
    for axnum,(filebase,inh,mainaxes) in enumerate(inh_options):
        ## load the data
        full_firingratearray = []
        half_firingratearray = []
        redux_frate_array = []
        max_redux_frate_array = []
        avg_redux_frate_array = []
        for netseed in netseeds:
            filename,_ = get_filename(netseed,inh,mit_distance,
                _asym_str=asym_str,_rev_str=_rev_str,_odorinh_str='_odorinh',
                filebase=filebase)
            ## if the result file for these seeds & tweaks doesn't exist,
            ## then carry on to the next.
            if not os.path.exists(filename): continue
            print filename
                
            Ainjectarray, both_firingratearrays, max_redux, avg_redux = \
                get_rates_inh(filename)
            full_firingratearray.append(both_firingratearrays)
            max_redux_frate_array.append(max_redux)
            avg_redux_frate_array.append(avg_redux)

            ## load for 0.5x (_airinh) firing rate in B too.
            if not ASYM_TEST:
                filename,_ = get_filename(netseed,inh,mit_distance,
                    _asym_str=asym_str,_rev_str=_rev_str,_odorinh_str='_airinh',
                    filebase=filebase)
                ## if the result file for these seeds & tweaks doesn't exist,
                ## then carry on to the next.
                if not os.path.exists(filename): continue
                print filename
                Ainjectarray, both_firingratearrays, max_redux, avg_redux = \
                    get_rates_inh(filename)
                half_firingratearray.append(both_firingratearrays)

        ## calculate means and std errs
        full_firingratearray = array(full_firingratearray)
        mean_firingratearray = mean(full_firingratearray, axis=0)
        std_firingratearray = std(full_firingratearray, axis=0)
        half_firingratearray = array(half_firingratearray)
        mean_half_firingratearray = mean(half_firingratearray, axis=0)
        std_half_firingratearray = std(half_firingratearray, axis=0)

        if manyplots:
            ## plot the lone vs inhibited firing rate vs input (Hz) curves
            for mitralBinject in [0,1]:
                if ASYM_TEST:
                    if mitralBinject == 0: mitB_str = '0.0 Hz'
                    else: mitB_str = 'as for A'
                else: mitB_str = str(mitralBinject*onInject)+' Hz'
                mainaxes.errorbar(x=Ainjectarray,\
                y=mean_firingratearray[mitralBinject],\
                yerr=std_firingratearray[mitralBinject],\
                color=['k','b'][mitralBinject], marker=['s','o'][mitralBinject],\
                markersize=marker_size, label="mitB i/p = "+mitB_str, linewidth=linewidth)
            if not ASYM_TEST and 'reverse' not in _rev_str:
                mitB_str = str(0.5*onInject)+' Hz'
                mainaxes.errorbar(x=Ainjectarray,\
                y=mean_half_firingratearray[1],\
                yerr=std_half_firingratearray[1],\
                color='r', marker='d', markersize=marker_size,\
                label="mitB i/p = "+mitB_str, linewidth=linewidth)    

            xmin,xmax,ymin,ymax = \
                beautify_plot(mainaxes,x0min=True,y0min=True,xticksposn='bottom',yticksposn='left')
            maxy = max(maxy,ymax)
            if axnum==0:
                axes_labels(mainaxes,"","mitral A firing rate (Hz)")
            elif axnum==2:
                axes_labels(mainaxes,"mitral A ORN rate (Hz)","")
    for ax in [ax1,ax2,ax3,ax4,ax5]:
        ax.set_ylim([0,maxy])
        ax.set_yticks([0,maxy])
    #mainfig.tight_layout()
    mainfig.subplots_adjust(top=0.95,left=0.07,right=0.95,bottom=0.15,wspace=0.25,hspace=0.4)
    figfilename = '../figures/tuftADI/tuftADI_alllat_'+'_mitdist'+str(mit_distance) \
        +invivo_str+dirt_str+_rev_str+asym_str+dirextn
    mainfig.savefig(figfilename+'.png', dpi=mainfig.dpi)
    mainfig.savefig(figfilename+'.svg', dpi=mainfig.dpi)

    return

def plot_avg_inh_self(mit_distance,_rev_str,manyplots=False):
    ## plot the lone vs self-inhibited firing rate vs input (Hz) curves
    mainfig = figure(figsize=(columnwidth/2.0,linfig_height),dpi=300,facecolor='w')
    mainaxes = mainfig.add_subplot(111)
    ## inh =  (no_singles,no_joints,no_PGs)
    inh_list = [(True,True,True),(False,False,True),(True,True,False),(False,False,False)]
    labels_list = ['no PGs+granules','only granules','only PGs','both']
    for inh_i,inh in enumerate(inh_list):
        full_firingratearray = []
        dashes = [(1.5,0.5),(0.5,0.5),(1.5,0.5,0.5,0.5),(1.0,0.0)]
        for netseed in netseeds:
            filename,_ = get_filename(netseed,inh,mit_distance,
                _asym_str=asym_str,_rev_str=_rev_str,_odorinh_str='_odorinh',
                filebase=filebase)
            ## if the result file for these seeds & tweaks doesn't exist,
            ## then carry on to the next.
            if not os.path.exists(filename): continue
                
            ## load the data
            Ainjectarray, both_firingratearrays, max_redux, avg_redux = \
                get_rates_inh(filename)
            ## not interested in lateral inhibition here, so leave that out
            full_firingratearray.append(both_firingratearrays[0])

        ## calculate means and std errs
        full_firingratearray = array(full_firingratearray)
        mean_firingratearray = mean(full_firingratearray, axis=0)
        std_firingratearray = std(full_firingratearray, axis=0)

        mainaxes.errorbar(x=Ainjectarray,\
            y=mean_firingratearray,\
            yerr=std_firingratearray,\
            color=['k','b','c','r'][inh_i], marker=['s','^','o','d'][inh_i],\
            markersize=marker_size, linewidth=linewidth,\
            label=labels_list[inh_i]) # removed dashes=dashes[inh_i]

    beautify_plot(mainaxes,x0min=True,y0min=True,\
        xticksposn='bottom',yticksposn='left',drawxaxis=True,drawyaxis=True)
    ### Legend too big
    #biglegend('lower right',mainaxes,fontsize=label_fontsize-2,\
    #    labelspacing=0.1,borderpad=0.1,markerscale=0.1,columnspacing=0.1,frameon=False)
    ## Legend without the error bars
    ## get handles
    handles, labels = mainaxes.get_legend_handles_labels()
    ## remove the errorbars
    handles = [h[0] for h in handles]
    ## use them in the legend
    leg = mainaxes.legend(handles, labels, loc='upper left',numpoints=1,\
        labelspacing=0.0,borderpad=0.01,markerscale=1,columnspacing=0.0,\
        handletextpad=0.3,prop={'size':label_fontsize-2},frameon=False)
    ### modify legend text sizes -- use prop={'size':...} above,
    ### else below method causes alignment issues of handles with labels
    #for t in leg.get_texts():
    #    t.set_fontsize(label_fontsize-2)
    axes_labels(mainaxes,"mitral A ORN rate (Hz)","mitral A firing rate (Hz)",ypad=-6)
    mainfig.tight_layout()
    figfilename = '../figures/tuftADI/tuftADI'+'_mitdist'+str(mit_distance) \
        +invivo_str+dirt_str+_rev_str+asym_str
    mainfig.savefig(figfilename+'_selfinh.png', dpi=mainfig.dpi)
    mainfig.savefig(figfilename+'_selfinh.svg', dpi=mainfig.dpi)

def plot_avg_diff_inh(mit_distance,_rev_str):
    ## load the data
    full_firingratearray = []
    noinh_firingratearray = []
    noself_firingratearray = []
    redux_frate_array = []
    max_redux_frate_array = []
    avg_redux_frate_array = []
    for netseed in netseeds:
        filename,_ = get_filename(netseed,inh,mit_distance,
            _asym_str=asym_str,_rev_str=_rev_str,_odorinh_str='_odorinh',
            filebase=filebase)
        ## if the result file for these seeds & tweaks doesn't exist,
        ## then carry on to the next.
        if not os.path.exists(filename): continue
        print filename
            
        Ainjectarray, both_firingratearrays, max_redux, avg_redux = \
            get_rates_inh(filename)
        full_firingratearray.append(both_firingratearrays)
        max_redux_frate_array.append(max_redux)
        avg_redux_frate_array.append(avg_redux)

        filename,_ = get_filename(netseed,(True,False,True),mit_distance,
            _asym_str=asym_str,_rev_str=_rev_str,_odorinh_str='_odorinh',
            filebase=filebase)
        ## if the result file for these seeds & tweaks doesn't exist,
        ## then carry on to the next.
        if not os.path.exists(filename): continue
        print filename
            
        Ainjectarray, both_firingratearrays, max_redux, avg_redux = \
            get_rates_inh(filename)
        noself_firingratearray.append(both_firingratearrays)

        filename,_ = get_filename(netseed,(True,True,True),mit_distance,
            _asym_str=asym_str,_rev_str=_rev_str,_odorinh_str='_odorinh',
            filebase=filebase)
        ## if the result file for these seeds & tweaks doesn't exist,
        ## then carry on to the next.
        if not os.path.exists(filename): continue
        print filename
            
        Ainjectarray, both_firingratearrays, max_redux, avg_redux = \
            get_rates_inh(filename)
        noinh_firingratearray.append(both_firingratearrays)

    ## calculate means and std errs
    full_firingratearray = array(full_firingratearray)
    mean_firingratearray = mean(full_firingratearray, axis=0)
    std_firingratearray = std(full_firingratearray, axis=0)
    noinh_firingratearray = array(noinh_firingratearray)
    mean_noinh_firingratearray = mean(noinh_firingratearray, axis=0)
    std_noinh_firingratearray = std(noinh_firingratearray, axis=0)
    noself_firingratearray = array(noself_firingratearray)
    mean_noself_firingratearray = mean(noself_firingratearray, axis=0)
    std_noself_firingratearray = std(noself_firingratearray, axis=0)

    ############## plotting
    ## plot the lone vs inh firing rate vs I curves
    mainfig = figure(figsize=(columnwidth/2.0,linfig_height),dpi=300,facecolor='w')
    mainaxes = mainfig.add_subplot(111,frameon=False)
    ## plot mit cell alone
    mainaxes.errorbar(x=Ainjectarray,\
        y=mean_noinh_firingratearray[0], yerr=std_noinh_firingratearray[0],\
        color='k', marker='s',\
        markersize=marker_size, linewidth=linewidth)
    ## plot mit cell without self inhibition
    mainaxes.errorbar(x=Ainjectarray,\
        y=mean_noself_firingratearray[0], yerr=std_noself_firingratearray[0],\
        color='r', marker='o',\
        markersize=marker_size, linewidth=linewidth)
    ## plot mit cell without self inhibition + lateral odorinh
    mainaxes.errorbar(x=Ainjectarray,\
        y=mean_noself_firingratearray[1], yerr=std_noself_firingratearray[1],\
        color='r', marker='d',\
        markersize=marker_size, linewidth=linewidth)
    for mitralBinject in [0,1]:
        if ASYM_TEST:
            if mitralBinject == 0: mitB_str = '0.0 Hz'
            else: mitB_str = 'as for A'
        else: mitB_str = str(mitralBinject*onInject)+' Hz'
        mainaxes.errorbar(x=Ainjectarray,\
            y=mean_firingratearray[mitralBinject],\
            yerr=std_firingratearray[mitralBinject],\
            color=['b','b'][mitralBinject], marker=['x','+'][mitralBinject],\
            markersize=marker_size, label="mitB i/p = "+mitB_str, linewidth=linewidth)
    mainaxes.get_yaxis().set_ticks_position('left')
    mainaxes.get_xaxis().set_ticks_position('bottom')
    xmin, xmax = mainaxes.get_xaxis().get_view_interval()
    ymin, ymax = mainaxes.get_yaxis().get_view_interval()
    mainaxes.set_xlim([0,xmax])
    mainaxes.set_ylim([0,ymax])
    mainaxes.set_xticks([0,xmax])
    mainaxes.set_yticks([0,ymax])
    mainaxes.add_artist(Line2D((0, 0), (0, ymax), color='black', linewidth=axes_linewidth))
    mainaxes.add_artist(Line2D((0, xmax), (0, 0), color='black', linewidth=axes_linewidth))
    ##biglegend("lower right")
    #biglegend("upper left")
    axes_labels(mainaxes,"mitral A ORN frate (Hz)","mitral A firing rate (Hz)")
    mainfig.tight_layout()
    figfilename = '../figures/tuftADI/tuftADI'+'_mitdist'+str(mit_distance) \
        +invivo_str+dirt_str+_rev_str+asym_str
    #mainfig.savefig(figfilename+'.png', dpi=mainfig.dpi)
    #mainfig.savefig(figfilename+'.svg', dpi=mainfig.dpi)

    return max_redux_frate_array, avg_redux_frate_array

def plot_avg_inh_Bconst_BasA(mit_distance,_rev_str=rev_str,asym_list=['_asym','']):
    ## plot the firing rate vs I curves for lone, BasA, Bconst
    mainfig = figure(figsize=(columnwidth/2.0,linfig_height),dpi=300,facecolor='w')
    mainaxes = mainfig.add_subplot(111,frameon=False)

    curvei = 0
    max_redux_frate_array = []
    avg_redux_frate_array = []
    ## asym (True/False) specifies whether mitB gets sameasA / const tuft input.
    for _asym_str in asym_list:
        ## load the data
        full_firingratearray = []
        for netseed in netseeds:
            filename,_ = get_filename(netseed,inh,mit_distance,\
                _asym_str=_asym_str,_rev_str=_rev_str,filebase=filebase)
            ## if the result file for these seeds & tweaks doesn't exist,
            ## then carry on to the next.
            if not os.path.exists(filename): continue
            print 'Using',filename

            Ainjectarray, both_firingratearrays, max_redux, avg_redux = \
                get_rates_inh(filename)
            full_firingratearray.append(both_firingratearrays)
            ## use only those frate reductions that are with same input as A in mitB.
            if not _asym_str:
                max_redux_frate_array.append(max_redux)
                avg_redux_frate_array.append(avg_redux)

        ## calculate means and std errs
        full_firingratearray = array(full_firingratearray)
        mean_firingratearray = mean(full_firingratearray, axis=0)
        std_firingratearray = std(full_firingratearray, axis=0)

        for mitralBinject in [0,1]:
            if _asym_str: ## if '', then const i/p to mitB
                mitB_str = str(mitralBinject*onInject)+' Hz'
            else:
                if mitralBinject == 0: mitB_str = '0.0 Hz'
                else: mitB_str = 'as for A'
            ## condition to not plot the lone mitA f-vs-I twice
            #if mitralBinject!=0 or _asym_str:
            if True:
                mainaxes.errorbar(x=Ainjectarray,\
                y=mean_firingratearray[mitralBinject],\
                yerr=std_firingratearray[mitralBinject],\
                color=['k','b','r'][curvei], marker=['s','o','v'][curvei],\
                markersize=marker_size, label="mitB i/p = "+mitB_str, linewidth=linewidth)
                curvei += 1

    mainaxes.get_yaxis().set_ticks_position('left')
    mainaxes.get_xaxis().set_ticks_position('bottom')
    xmin, xmax = mainaxes.get_xaxis().get_view_interval()
    ymin, ymax = mainaxes.get_yaxis().get_view_interval()
    mainaxes.set_xlim([0,xmax])
    mainaxes.set_ylim([0,ymax])
    mainaxes.set_xticks([0,xmax])
    mainaxes.set_yticks([0,ymax])
    mainaxes.add_artist(Line2D((0, 0), (0, ymax), color='black', linewidth=axes_linewidth))
    mainaxes.add_artist(Line2D((0, xmax), (0, 0), color='black', linewidth=axes_linewidth))
    ##biglegend("lower right")
    #biglegend("upper left")
    axes_labels(mainaxes,"mitral A ORN frate (Hz)","mitral A firing rate (Hz)")
    mainfig.tight_layout()
    mainfig.savefig('../figures/tuftADI/tuftADI_both' \
        +invivo_str+dirt_str+rev_str+odorinh_str+'.png', \
        dpi=mainfig.dpi)
    mainfig.savefig('../figures/tuftADI/tuftADI_both' \
        +invivo_str+dirt_str+rev_str+odorinh_str+'.svg', \
        dpi=mainfig.dpi)
    return max_redux_frate_array, avg_redux_frate_array

def plot_avg_inh_distancedep(mit_distance_list):
    redux = []
    redux_std = []
    for i,reverse in enumerate([False,True]):
        print "Reversed =", reverse
        if reverse:
            rev_label = "Reversed"
            _rev_str = '_reversed'
        else:
            rev_label = "Forward"
            _rev_str = ''
        redux_directed = []
        redux_directed_std = []
        for mit_distance in mit_distance_list:
            print 'Distance =',mit_distance,'microns:'
            ## the former below, plots for only one of asym=True/False
            ## (i.e. sameasA/const i/p to mitB) set at the top.
            ## whereas the latter below, plots for both in the same graph.
            max_redux_frate_array, avg_redux_frate_array = \
                plot_avg_inh_lat(mit_distance,_rev_str,manyplots=False)
            #max_redux_frate_array, avg_redux_frate_array = \
            #    plot_avg_inh_Bconst_BasA(mit_distance,_rev_str)
            redux_directed.append(mean(avg_redux_frate_array))
            redux_directed_std.append(std(avg_redux_frate_array))
        redux.append((rev_label,redux_directed))
        redux_std.append(redux_directed_std)

    ## plot the reduction-in-frate vs separation-of-mitrals for forward & reverse tuft inh.
    fig = figure(figsize=(columnwidth/2.0,linfig_height),dpi=300,facecolor='w')
    ax = fig.add_subplot(111)
    mit_distance_list = array(mit_distance_list)/1000.0
    errorbar(mit_distance_list,-array(redux[0][1]),yerr=redux_std[0],\
        color=(1,0,0),linewidth=linewidth,marker='o',label=redux[0][0])
    errorbar(mit_distance_list,-array(redux[1][1]),yerr=redux_std[1],\
        color=(0,0,1),linewidth=linewidth,marker='x',label=redux[1][0])
    #biglegend('upper right')#'lower left')
    axes_labels(ax,'separation (mm)','mean $\Delta$ rate (Hz)',ypad=-3) # (0 to 1.5nA) in 400ms
    ax.xaxis.set_label_position('top')
    ax.get_yaxis().set_ticks_position('left')
    ax.get_xaxis().set_ticks_position('top')
    ## set xticks text sizes
    for label in ax.get_xticklabels():
        label.set_fontsize(label_fontsize)
    ## hide the top and left axes
    for loc, spine in ax.spines.items(): # items() returns [(key,value),...]
        spine.set_linewidth(axes_linewidth)
        if loc in ['right','bottom']:
            spine.set_color('none') # don't draw spine
    xmin, xmax = ax.get_xaxis().get_view_interval()
    ymin, ymax = ax.get_yaxis().get_view_interval()
    ax.set_xlim(0,xmax)
    ax.set_ylim(ymin,0)
    ax.set_yticks([ymin,0])
    ax.set_xticks([0,1,2])
    fig_clip_off(fig)
    fig.tight_layout()
    fig.savefig('../figures/tuftADI/tuftADI_redux'+invivo_str+asym_str+'.svg',\
        bbox_inches='tight',dpi=fig.dpi)
    fig.savefig('../figures/tuftADI/tuftADI_redux'+invivo_str+asym_str+'.png',\
        bbox_inches='tight',dpi=fig.dpi)

def plot_avg_inh_distancedep_v2(mit_distance_list):
    redux = []
    redux_std = []
    mit_distance_list = array(mit_distance_list)
    for i,reverse in enumerate([False,True]):
        ## plot the reduction-in-frate vs separation-of-mitrals for forward & reverse tuft inh.
        fig = figure(figsize=(columnwidth/2.0,linfig_height),dpi=300,facecolor='w')
        ax = fig.add_subplot(111)
        print "Reversed =", reverse
        if reverse:
            rev_label = "Reversed"
            _rev_str = '_reversed'
        else:
            rev_label = "Forward"
            _rev_str = ''
        for dirti,directed_str in enumerate(['','_directed0.0','_directed0.01']):
            redux_directed = []
            redux_directed_std = []
            print "Directed str set as",directed_str,"."
            for mit_distance in mit_distance_list:
                print 'Distance =',mit_distance,'microns:'
                ## the former below, plots for only one of asym=True/False
                ## (i.e. sameasA/const i/p to mitB) set at the top.
                ## whereas the latter below, plots for both in the same graph.
                max_redux_frate_array, avg_redux_frate_array = \
                    plot_avg_inh_lat(mit_distance,_rev_str,manyplots=False,\
                    directed_str=directed_str)
                #max_redux_frate_array, avg_redux_frate_array = \
                #    plot_avg_inh_Bconst_BasA(mit_distance,_rev_str)
                redux_directed.append(mean(avg_redux_frate_array))
                redux_directed_std.append(std(avg_redux_frate_array))
            redux.append((rev_label,redux_directed))
            redux_std.append(redux_directed_std)

            errorbar(mit_distance_list/1000.,-array(redux_directed),yerr=redux_directed_std,\
                color=['g','b','r'][dirti],linewidth=linewidth,\
                marker=['o','x','s'][dirti],label=redux[0][0])
        #biglegend('upper right')#'lower left')
        axes_labels(ax,'separation (mm)','mean $\Delta$ rate (Hz)',ypad=-3) # (0 to 1.5nA) in 400ms
        ax.xaxis.set_label_position('top')
        ax.get_yaxis().set_ticks_position('left')
        ax.get_xaxis().set_ticks_position('top')
        ## set xticks text sizes
        for label in ax.get_xticklabels():
            label.set_fontsize(label_fontsize)
        ## hide the top and left axes
        for loc, spine in ax.spines.items(): # items() returns [(key,value),...]
            spine.set_linewidth(axes_linewidth)
            if loc in ['right','bottom']:
                spine.set_color('none') # don't draw spine
        xmin, xmax = ax.get_xaxis().get_view_interval()
        ymin, ymax = ax.get_yaxis().get_view_interval()
        ax.set_xlim(0,xmax)
        ymin = -35 ## hard-coded for my simulations
        ax.set_ylim(ymin,1)
        ax.set_yticks([ymin,0,2])
        ax.set_xticks([0,1,2])
        fig_clip_off(fig)
        fig.tight_layout()
        fig.savefig('../figures/tuftADI/tuftADI_redux'+invivo_str+asym_str+rev_label+'.svg',\
            bbox_inches='tight',dpi=fig.dpi)
        fig.savefig('../figures/tuftADI/tuftADI_redux'+invivo_str+asym_str+rev_label+'.png',\
            bbox_inches='tight',dpi=fig.dpi)

if __name__ == "__main__":
    ### for single / few test runs...
    #plot_avg_inh_Bconst_BasA(600.0,_rev_str='',asym_list=[''])
    #plot_avg_diff_inh(400.0,_rev_str='')
    ## PAPER figure 4: example tuft inh -- lateral
    #plot_avg_inh_lat(400.0,_rev_str='',manyplots=True)
    ## PAPER figure 5 (diff pg inputs) : tuft inh -- lateral
    #plot_avg_inh_lat_all(400.0,_rev_str='',manyplots=True)
    ## For a single plot of lat inh -- air and odor
    ## set the directory on commandline and the inh_options in the function:
    #plot_avg_inh_lat(400.0,_rev_str='',manyplots=True)
    ## PAPER figure 2: tuft inh -- self
    plot_avg_inh_self(400.0,_rev_str='',manyplots=True)
    ## OBSOLETE PAPER figure 4: tuft inh -- distance dependence
    #plot_avg_inh_distancedep(mit_distance_list)
    ## PAPER figure 4: tuft inh -- distance dependence
    #plot_avg_inh_distancedep_v2(mit_distance_list)

    show()

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