plot time series LFPs and CSDs with signal variances as function of depth
    for the cases with all synapses intact, or knocking out excitatory
    input or inhibitory input to the postsynaptic target region
    
    args:
    ::
        fig : 
        axes :  
        savefolders : list of simulation output folders
        T : list of ints, first and last time sample
        transient : int, duration of transient period
        
    returns:
    ::
        
        matplotlib.figure.Figure object

def fig_exc_inh_contrib(fig, axes, params, savefolders, T=[800, 1000], transient=200,
                        panel_labels = 'FGHIJ', show_xlabels=True):
    '''
    plot time series LFPs and CSDs with signal variances as function of depth
    for the cases with all synapses intact, or knocking out excitatory
    input or inhibitory input to the postsynaptic target region
    
    args:
    ::
        fig : 
        axes :  
        savefolders : list of simulation output folders
        T : list of ints, first and last time sample
        transient : int, duration of transient period
        
    returns:
    ::
        
        matplotlib.figure.Figure object
    
    '''
    # params = multicompartment_params()
    # ana_params = analysis_params.params()

    
    #file name types
    file_names = ['CSDsum.h5', 'LFPsum.h5']
    
    #panel titles
    panel_titles = [
        'LFP&CSD\nexc. syn.',
        'LFP&CSD\ninh. syn.',
        'LFP&CSD\ncompound',
        'CSD variance',
        'LFP variance',]
    
    #labels
    labels = [
        'exc. syn.',
        'inh. syn.',
        'SUM']
    
    #some colors for traces
    if analysis_params.bw:
        colors = ['k', 'gray', 'k']
        # lws = [0.75, 0.75, 1.5]
        lws = [1.25, 1.25, 1.25]
    else:
        colors = [analysis_params.colorE, analysis_params.colorI, 'k']
        # colors = 'rbk'
        # lws = [0.75, 0.75, 1.5]
        lws = [1.25, 1.25, 1.25]

    
    #scalebar labels
    units = ['$\mu$A mm$^{-3}$', 'mV']
    
    #depth of each contact site
    depth = params.electrodeParams['z']


    # #set up figure
    # #figure aspect
    # ana_params.set_PLOS_2column_fig_style(ratio=0.5)
    # fig, axes = plt.subplots(1,5)
    # fig.subplots_adjust(left=0.06, right=0.96, wspace=0.4, hspace=0.2)
    
    #clean up
    for ax in axes.flatten():
        phlp.remove_axis_junk(ax)


    for i, file_name in enumerate(file_names):
        #get the global data scaling bar range for use in latter plots
        #TODO: find nicer solution without creating figure
        dum_fig, dum_ax = plt.subplots(1)
        vlim_LFP = 0
        vlim_CSD = 0
        for savefolder in savefolders:
            vlimround0 = plot_signal_sum(dum_ax, params,
                                         os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
                                         rasterized=False)
            if vlimround0 > vlim_LFP:
                vlim_LFP = vlimround0
            im = plot_signal_sum_colorplot(dum_ax, params, os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
                                           cmap=plt.get_cmap('gray', 21) if analysis_params.bw else plt.get_cmap('bwr_r', 21),
                                           rasterized=False)
            if abs(im.get_array()).max() > vlim_CSD:
                vlim_CSD = abs(im.get_array()).max()
                                                  
        plt.close(dum_fig)


        for j, savefolder in enumerate(savefolders):
            ax = axes[j]
            if i == 1:
                plot_signal_sum(ax, params, os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
                            unit=units[i], T=T,
                            color='k',
                            # color='k' if analysis_params.bw else colors[j],
                            vlimround=vlim_LFP, rasterized=False)
            elif i == 0:
                im = plot_signal_sum_colorplot(ax, params, os.path.join(os.path.split(params.savefolder)[0], savefolder, file_name),
                              unit=r'($\mu$Amm$^{-3}$)', T=T, ylabels=True,
                              colorbar=False,
                              fancy=False, cmap=plt.get_cmap('gray', 21) if analysis_params.bw else plt.get_cmap('bwr_r', 21),
                              absmax=vlim_CSD,
                              rasterized=False)
            
            
            ax.axis((T[0], T[1], -1550, 50))
            ax.set_title(panel_titles[j], va='baseline')
            
            if i == 0:
                phlp.annotate_subplot(ax, ncols=1, nrows=1, letter=panel_labels[j])
            if j != 0:
                ax.set_yticklabels([])
            
            if i == 0:#and j == 2:
                cb = phlp.colorbar(fig, ax, im,
                                   width=0.05, height=0.5,
                                   hoffset=-0.05, voffset=0.5)
                cb.set_label('($\mu$Amm$^{-3}$)', labelpad=0.)
            ax.xaxis.set_major_locator(plt.MaxNLocator(3))
            if show_xlabels:
                ax.set_xlabel(r'$t$ (ms)', labelpad=0.)
            else:
                ax.set_xlabel('')

            
    #power in CSD
    ax = axes[3]
    datas = []    
    for j, savefolder in enumerate(savefolders):
        f = h5py.File(os.path.join(os.path.split(params.savefolder)[0], savefolder, 'CSDsum.h5'))
        var = f['data'].value[:, transient:].var(axis=1)
        ax.semilogx(var, depth,
                    color=colors[j], label=labels[j], lw=lws[j], clip_on=False)
        datas.append(f['data'].value[:, transient:])
        f.close()
    #control variances
    vardiff = datas[0].var(axis=1) + datas[1].var(axis=1) + np.array([2*np.cov(x,y)[0,1] for (x,y) in zip(datas[0], datas[1])]) - datas[2].var(axis=1)
    #ax.semilogx(abs(vardiff), depth, color='gray', lw=1, label='control')
    ax.axis(ax.axis('tight'))
    ax.set_ylim(-1550, 50)
    ax.set_yticks(-np.arange(16)*100)
    if show_xlabels:
        ax.set_xlabel(r'$\sigma^2$ ($(\mu$Amm$^{-3})^2$)', labelpad=0.)
    ax.set_title(panel_titles[3], va='baseline')
    phlp.annotate_subplot(ax, ncols=1, nrows=1, letter=panel_labels[3])
    ax.set_yticklabels([])


    #power in LFP
    ax = axes[4]

    datas = []
    for j, savefolder in enumerate(savefolders):
        f = h5py.File(os.path.join(os.path.split(params.savefolder)[0], savefolder, 'LFPsum.h5'))
        var = f['data'].value[:, transient:].var(axis=1)
        ax.semilogx(var, depth,
                    color=colors[j], label=labels[j], lw=lws[j], clip_on=False)
        datas.append(f['data'].value[:, transient:])
        f.close()
    #control variances
    vardiff = datas[0].var(axis=1) + datas[1].var(axis=1) + np.array([2*np.cov(x,y)[0,1] for (x,y) in zip(datas[0], datas[1])]) - datas[2].var(axis=1)
    ax.axis(ax.axis('tight'))
    ax.set_ylim(-1550, 50)
    ax.set_yticks(-np.arange(16)*100)
    if show_xlabels:
        ax.set_xlabel(r'$\sigma^2$ (mV$^2$)', labelpad=0.)
    ax.set_title(panel_titles[4], va='baseline')
    phlp.annotate_subplot(ax, ncols=1, nrows=1, letter=panel_labels[4])

    ax.legend(bbox_to_anchor=(1.3, 1.0), frameon=False)
    ax.set_yticklabels([])