def fig_exc_inh_contrib(fig, axes, params, savefolders, T=[<NUM_LIT>, <NUM_LIT:1000>], transient=<NUM_LIT:200>,<EOL>panel_labels = '<STR_LIT>', show_xlabels=True):
<EOL>file_names = ['<STR_LIT>', '<STR_LIT>']<EOL>panel_titles = [<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',]<EOL>labels = [<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>']<EOL>if analysis_params.bw:<EOL><INDENT>colors = ['<STR_LIT:k>', '<STR_LIT>', '<STR_LIT:k>']<EOL>lws = [<NUM_LIT>, <NUM_LIT>, <NUM_LIT>]<EOL><DEDENT>else:<EOL><INDENT>colors = [analysis_params.colorE, analysis_params.colorI, '<STR_LIT:k>']<EOL>lws = [<NUM_LIT>, <NUM_LIT>, <NUM_LIT>]<EOL><DEDENT>units = ['<STR_LIT>', '<STR_LIT>']<EOL>depth = params.electrodeParams['<STR_LIT:z>']<EOL>for ax in axes.flatten():<EOL><INDENT>phlp.remove_axis_junk(ax)<EOL><DEDENT>for i, file_name in enumerate(file_names):<EOL><INDENT>dum_fig, dum_ax = plt.subplots(<NUM_LIT:1>)<EOL>vlim_LFP = <NUM_LIT:0><EOL>vlim_CSD = <NUM_LIT:0><EOL>for savefolder in savefolders:<EOL><INDENT>vlimround0 = plot_signal_sum(dum_ax, params,<EOL>os.path.join(os.path.split(params.savefolder)[<NUM_LIT:0>], savefolder, file_name),<EOL>rasterized=False)<EOL>if vlimround0 > vlim_LFP:<EOL><INDENT>vlim_LFP = vlimround0<EOL><DEDENT>im = plot_signal_sum_colorplot(dum_ax, params, os.path.join(os.path.split(params.savefolder)[<NUM_LIT:0>], savefolder, file_name),<EOL>cmap=plt.get_cmap('<STR_LIT>', <NUM_LIT>) if analysis_params.bw else plt.get_cmap('<STR_LIT>', <NUM_LIT>),<EOL>rasterized=False)<EOL>if abs(im.get_array()).max() > vlim_CSD:<EOL><INDENT>vlim_CSD = abs(im.get_array()).max()<EOL><DEDENT><DEDENT>plt.close(dum_fig)<EOL>for j, savefolder in enumerate(savefolders):<EOL><INDENT>ax = axes[j]<EOL>if i == <NUM_LIT:1>:<EOL><INDENT>plot_signal_sum(ax, params, os.path.join(os.path.split(params.savefolder)[<NUM_LIT:0>], savefolder, file_name),<EOL>unit=units[i], T=T,<EOL>color='<STR_LIT:k>',<EOL>vlimround=vlim_LFP, rasterized=False)<EOL><DEDENT>elif i == <NUM_LIT:0>:<EOL><INDENT>im = plot_signal_sum_colorplot(ax, params, os.path.join(os.path.split(params.savefolder)[<NUM_LIT:0>], savefolder, file_name),<EOL>unit=r'<STR_LIT>', T=T, ylabels=True,<EOL>colorbar=False,<EOL>fancy=False, cmap=plt.get_cmap('<STR_LIT>', <NUM_LIT>) if analysis_params.bw else plt.get_cmap('<STR_LIT>', <NUM_LIT>),<EOL>absmax=vlim_CSD,<EOL>rasterized=False)<EOL><DEDENT>ax.axis((T[<NUM_LIT:0>], T[<NUM_LIT:1>], -<NUM_LIT>, <NUM_LIT:50>))<EOL>ax.set_title(panel_titles[j], va='<STR_LIT>')<EOL>if i == <NUM_LIT:0>:<EOL><INDENT>phlp.annotate_subplot(ax, ncols=<NUM_LIT:1>, nrows=<NUM_LIT:1>, letter=panel_labels[j])<EOL><DEDENT>if j != <NUM_LIT:0>:<EOL><INDENT>ax.set_yticklabels([])<EOL><DEDENT>if i == <NUM_LIT:0>:<EOL><INDENT>cb = phlp.colorbar(fig, ax, im,<EOL>width=<NUM_LIT>, height=<NUM_LIT:0.5>,<EOL>hoffset=-<NUM_LIT>, voffset=<NUM_LIT:0.5>)<EOL>cb.set_label('<STR_LIT>', labelpad=<NUM_LIT:0.>)<EOL><DEDENT>ax.xaxis.set_major_locator(plt.MaxNLocator(<NUM_LIT:3>))<EOL>if show_xlabels:<EOL><INDENT>ax.set_xlabel(r'<STR_LIT>', labelpad=<NUM_LIT:0.>)<EOL><DEDENT>else:<EOL><INDENT>ax.set_xlabel('<STR_LIT>')<EOL><DEDENT><DEDENT><DEDENT>ax = axes[<NUM_LIT:3>]<EOL>datas = []    <EOL>for j, savefolder in enumerate(savefolders):<EOL><INDENT>f = h5py.File(os.path.join(os.path.split(params.savefolder)[<NUM_LIT:0>], savefolder, '<STR_LIT>'))<EOL>var = f['<STR_LIT:data>'].value[:, transient:].var(axis=<NUM_LIT:1>)<EOL>ax.semilogx(var, depth,<EOL>color=colors[j], label=labels[j], lw=lws[j], clip_on=False)<EOL>datas.append(f['<STR_LIT:data>'].value[:, transient:])<EOL>f.close()<EOL><DEDENT>vardiff = datas[<NUM_LIT:0>].var(axis=<NUM_LIT:1>) + datas[<NUM_LIT:1>].var(axis=<NUM_LIT:1>) + np.array([<NUM_LIT:2>*np.cov(x,y)[<NUM_LIT:0>,<NUM_LIT:1>] for (x,y) in zip(datas[<NUM_LIT:0>], datas[<NUM_LIT:1>])]) - datas[<NUM_LIT:2>].var(axis=<NUM_LIT:1>)<EOL>ax.axis(ax.axis('<STR_LIT>'))<EOL>ax.set_ylim(-<NUM_LIT>, <NUM_LIT:50>)<EOL>ax.set_yticks(-np.arange(<NUM_LIT:16>)*<NUM_LIT:100>)<EOL>if show_xlabels:<EOL><INDENT>ax.set_xlabel(r'<STR_LIT>', labelpad=<NUM_LIT:0.>)<EOL><DEDENT>ax.set_title(panel_titles[<NUM_LIT:3>], va='<STR_LIT>')<EOL>phlp.annotate_subplot(ax, ncols=<NUM_LIT:1>, nrows=<NUM_LIT:1>, letter=panel_labels[<NUM_LIT:3>])<EOL>ax.set_yticklabels([])<EOL>ax = axes[<NUM_LIT:4>]<EOL>datas = []<EOL>for j, savefolder in enumerate(savefolders):<EOL><INDENT>f = h5py.File(os.path.join(os.path.split(params.savefolder)[<NUM_LIT:0>], savefolder, '<STR_LIT>'))<EOL>var = f['<STR_LIT:data>'].value[:, transient:].var(axis=<NUM_LIT:1>)<EOL>ax.semilogx(var, depth,<EOL>color=colors[j], label=labels[j], lw=lws[j], clip_on=False)<EOL>datas.append(f['<STR_LIT:data>'].value[:, transient:])<EOL>f.close()<EOL><DEDENT>vardiff = datas[<NUM_LIT:0>].var(axis=<NUM_LIT:1>) + datas[<NUM_LIT:1>].var(axis=<NUM_LIT:1>) + np.array([<NUM_LIT:2>*np.cov(x,y)[<NUM_LIT:0>,<NUM_LIT:1>] for (x,y) in zip(datas[<NUM_LIT:0>], datas[<NUM_LIT:1>])]) - datas[<NUM_LIT:2>].var(axis=<NUM_LIT:1>)<EOL>ax.axis(ax.axis('<STR_LIT>'))<EOL>ax.set_ylim(-<NUM_LIT>, <NUM_LIT:50>)<EOL>ax.set_yticks(-np.arange(<NUM_LIT:16>)*<NUM_LIT:100>)<EOL>if show_xlabels:<EOL><INDENT>ax.set_xlabel(r'<STR_LIT>', labelpad=<NUM_LIT:0.>)<EOL><DEDENT>ax.set_title(panel_titles[<NUM_LIT:4>], va='<STR_LIT>')<EOL>phlp.annotate_subplot(ax, ncols=<NUM_LIT:1>, nrows=<NUM_LIT:1>, letter=panel_labels[<NUM_LIT:4>])<EOL>ax.legend(bbox_to_anchor=(<NUM_LIT>, <NUM_LIT:1.0>), frameon=False)<EOL>ax.set_yticklabels([])<EOL>
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