natfeedback_code_eLife/fig1S33S1_ntsr.py

"""Figure 1S33S1 Ntsr versions of fig1S33S1.py, use run -i fig1S33S1_ntsr.py. Currently used
for rebuttal figure R2"""

assert EXPTYPE == 'ntsrmvis'

from matplotlib.patches import Rectangle



chirptype = pd.read_csv('csv/ntsr_unit_celltypes_yannik.csv')
chirptype.set_index(['msu'], inplace=True)


## strip plot max FMI of movie and grating meanrate and meanburstratio,
## each msu classified as chirp ON/OFF/transient/mixed by Yannik:
np.random.seed(0) # to get identical horizontal jitter in strip plots on every run
figsize = DEFAULTFIGURESIZE
chirp2clr = {'On':green, 'Off':'red', 'Transient':'black', 'Mixed':'gray'}
for stimtype in STIMTYPES:
    stimtypelabel = stimtype2axislabel[stimtype]
    for measure in ['meanrate', 'meanburstratio']:
        axislabel = measure2axislabel[measure]
        if axislabel.islower():
            axislabel = axislabel.capitalize()
        fmis, onoffs = [], []
        for msustr in mvigrtmsustrs:
            fmi = maxFMI.loc[msustr, 'none', stimtype][measure]
            if pd.isna(fmi):
                continue
            try:
                onoff = chirptype.loc[msustr]['clu_label']
            except KeyError:
                continue # no chirp cell type for this msu
            fmis.append(fmi)
            onoffs.append(onoff)
        fmis = np.asarray(fmis)
        onoffs = np.asarray(onoffs)
        onfmis = fmis[onoffs == 'ON-sust.']
        offfmis = fmis[onoffs == 'OFF-sust.']
        onofffmis = fmis[onoffs == 'ON-OFF-trans.']
        mixedfmis = fmis[onoffs == 'mixed']
        f, a = plt.subplots(figsize=figsize)
        wintitle('maxFMI chirp %s %s stripplot' % (stimtypelabel, measure))
        dfdict = {'On':onfmis, 'Off':offfmis, 'Transient':onofffmis, 'Mixed':mixedfmis}
        data = pd.DataFrame.from_dict(dfdict, orient='index').transpose()
        sns.stripplot(ax=a, data=data, palette=chirp2clr, clip_on=False,
                      marker='.', size=np.sqrt(70))
        a.set_ylabel('FMI')
        a.set_xlim(0, 3)
        a.set_ylim(-1, 1)
        a.set_yticks([-1, 0, 1])
        a.spines['left'].set_position(('outward', 10))
        a.spines['bottom'].set_position(('outward', 5))
        #a.spines['bottom'].set_visible(False)
        #a.tick_params(bottom=False)
        plt.xticks(rotation=45)
        print(stimtype, measure)
        print(data)


## scatter plot grating meanrates, coloured by chirp ON sustained/OFF sustained/transient/mixed
## as classified by Yannik:
figsize = DEFAULTFIGURESIZE
logmin, logmax = -1.2, 2
logticks = np.array([-1, 0, 1, 2])
#log0min = logmin + 0.05
for st8 in ['none']:#ALLST8S:
    f, a = plt.subplots(figsize=figsize)
    wintitle('opto meanrate grating %s chirp' % st8)
    rons, roffs, onoffs = [], [], []
    for mseustr in grtmseustrs:
        meanrate = grtresp.loc[mseustr, st8]['meanrate']
        msustr = mseustr2msustr(mseustr)
        try:
            onoff = chirptype.loc[msustr]['clu_label']
        except KeyError:
            continue # no chirp experiment for cell typing
        if meanrate.isna().any(): # missing one or both meanrates
            #print('%s: missing one or both opto conditions, skipping' % mseustr)
            continue
        rons.append(meanrate[True])
        roffs.append(meanrate[False])
        onoffs.append(onoff)
        #fig3.loc[mseustr, 'meanrate'] = meanrate[False], meanrate[True] # save
    rons = np.asarray(rons)
    roffs = np.asarray(roffs)
    onoffs = np.asarray(onoffs)
    clrs = []
    chirptype2clr = {'ON-sust.':green, 'OFF-sust.':'red', 'ON-OFF-trans.':'black',
                     'mixed':'gray'}
    for onoff in onoffs:
        clr = chirptype2clr[onoff]
        clrs.append(clr)
    # plot y=x line:
    xyline = [10**logmin, 10**logmax], [10**logmin, 10**logmax]
    a.plot(xyline[0], xyline[1], '--', color='gray', zorder=-1)
    # plot all points:
    a.scatter(rons, roffs, marker='.', c='None', edgecolor=clrs, s=DEFSZ)
    a.set_ylabel('Feedback FR (spk/s)')
    a.set_xlabel('Suppression FR (spk/s)')
    a.set_xscale('log')
    a.set_yscale('log')
    a.set_xlim(10**logmin, 10**logmax)
    a.set_ylim(10**logmin, 10**logmax)
    a.set_xticks(10.0**logticks)
    a.set_yticks(a.get_xticks()) # make log scale y ticks the same as x ticks
    axes_disable_scientific(a)
    a.minorticks_off()
    a.set_aspect('equal')


## scatter plot movie meanrates, coloured by chirp ON sustained/OFF sustained/transient/mixed
## as classified by Yannik:
figsize = DEFAULTFIGURESIZE
logmin, logmax = -1.2, 2
logticks = np.array([-1, 0, 1, 2])
#log0min = logmin + 0.05
for st8 in ['none']:#ALLST8S:
    f, a = plt.subplots(figsize=figsize)
    wintitle('opto meanrate movie nat %s chirp' % st8)
    rons, roffs, onoffs = [], [], []
    for mseustr in mvimseustrs:
        meanrate = mviresp.loc[mseustr, 'nat', st8]['meanrate']
        msustr = mseustr2msustr(mseustr)
        try:
            onoff = chirptype.loc[msustr]['clu_label']
        except KeyError:
            continue # no chirp experiment for cell typing
        if meanrate.isna().any(): # missing one or both meanrates
            #print('%s: missing one or both opto conditions, skipping' % mseustr)
            continue
        print(mseustr)
        rons.append(meanrate[True])
        roffs.append(meanrate[False])
        onoffs.append(onoff)
    rons = np.asarray(rons)
    roffs = np.asarray(roffs)
    clrs = []
    chirptype2clr = {'ON-sust.':green, 'OFF-sust.':'red', 'ON-OFF-trans.':'black',
                     'mixed':'gray'}
    for onoff in onoffs:
        clr = chirptype2clr[onoff]
        clrs.append(clr)
    # plot y=x line:
    xyline = [10**logmin, 10**logmax], [10**logmin, 10**logmax]
    a.plot(xyline[0], xyline[1], '--', color='gray', zorder=-1)
    # plot all points:
    a.scatter(rons, roffs, marker='.', c='None', edgecolor=clrs, s=DEFSZ)
    a.set_ylabel('Feedback FR (spk/s)')
    a.set_xlabel('Suppression FR (spk/s)')
    a.set_xscale('log')
    a.set_yscale('log')
    a.set_xlim(10**logmin, 10**logmax)
    a.set_ylim(10**logmin, 10**logmax)
    a.set_xticks(10.0**logticks)
    a.set_yticks(a.get_xticks()) # make log scale y ticks the same as x ticks
    axes_disable_scientific(a)
    a.minorticks_off()
    a.set_aspect('equal')


## scatter plot grating burst ratio, coloured by chirp ON sustained/OFF sustained/transient/mixed
## as classified by Yannik:
figsize = DEFAULTFIGURESIZE
logmin, logmax = -3.55, 0
logticks = np.array([-3, -2, -1, 0])
for st8 in ['none']:#ALLST8S:
    f, a = plt.subplots(figsize=figsize)
    wintitle('opto burst ratio grating %s chirp' % st8)
    brons, broffs, onoffs = [], [], []
    for mseustr in grtmseustrs:
        br = grtresp.loc[mseustr, st8]['meanburstratio']
        msustr = mseustr2msustr(mseustr)
        try:
            onoff = chirptype.loc[msustr]['clu_label']
        except KeyError:
            continue # no chirp experiment for cell typing
        if br.isna().any(): # missing for at least one opto condition
            continue
        brons.append(br[True])
        broffs.append(br[False])
        onoffs.append(onoff)
        #fig3.loc[mseustr, 'meanburstratio'] = br[False], br[True] # save
    brons, broffs, onoffs = np.asarray(brons), np.asarray(broffs), np.asarray(onoffs)
    clrs = []
    chirptype2clr = {'ON-sust.':green, 'OFF-sust.':'red', 'ON-OFF-trans.':'black',
                     'mixed':'gray'}
    for onoff in onoffs:
        clr = chirptype2clr[onoff]
        clrs.append(clr)
    # plot y=x line:
    xyline = [10**logmin, 10**logmax], [10**logmin, 10**logmax]
    a.plot(xyline[0], xyline[1], '--', color='gray', zorder=-1)
    # plot all points:
    a.scatter(brons, broffs, marker='.', c='None', edgecolor=clrs, s=DEFSZ)
    a.set_xlabel('Suppression BR') # keep it short to maximize space for axes
    a.set_ylabel('Feedback BR')
    a.set_xscale('log')
    a.set_yscale('log')
    a.set_xlim(10**logmin, 10**logmax)
    a.set_ylim(10**logmin, 10**logmax)
    a.set_xticks(10.0**logticks)
    a.set_yticks(a.get_xticks()) # make log scale y ticks the same as x ticks
    a.minorticks_off()
    axes_disable_scientific(a)
    a.set_aspect('equal')


## scatter plot movie burst ratio, coloured by chirp ON sustained/OFF sustained/transient/mixed
## as classified by Yannik:
figsize = DEFAULTFIGURESIZE
logmin, logmax = -3.55, 0
logticks = np.array([-3, -2, -1, 0])
for st8 in ['none']:#ALLST8S:
    f, a = plt.subplots(figsize=figsize)
    wintitle('opto burst ratio movie nat %s chirp' % st8)
    brons, broffs, onoffs = [], [], []
    for mseustr in mvimseustrs:
        br = mviresp.loc[mseustr, 'nat', st8]['meanburstratio']
        msustr = mseustr2msustr(mseustr)
        msustr = mseustr2msustr(mseustr)
        try:
            onoff = chirptype.loc[msustr]['clu_label']
        except KeyError:
            continue # no chirp experiment for cell typing
        if br.isna().any(): # missing for at least one opto condition
            continue
        brons.append(br[True])
        broffs.append(br[False])
        onoffs.append(onoff)
        #fig3.loc[mseustr, 'meanburstratio'] = br[False], br[True] # save
    brons, broffs, onoffs = np.asarray(brons), np.asarray(broffs), np.asarray(onoffs)
    clrs = []
    chirptype2clr = {'ON-sust.':green, 'OFF-sust.':'red', 'ON-OFF-trans.':'black',
                     'mixed':'gray'}
    for onoff in onoffs:
        clr = chirptype2clr[onoff]
        clrs.append(clr)
    # plot y=x line:
    xyline = [10**logmin, 10**logmax], [10**logmin, 10**logmax]
    a.plot(xyline[0], xyline[1], '--', color='gray', zorder=-1)
    # plot all points:
    a.scatter(brons, broffs, marker='.', c='None', edgecolor=clrs, s=DEFSZ)
    a.set_xlabel('Suppression BR') # keep it short to maximize space for axes
    a.set_ylabel('Feedback BR')
    a.set_xscale('log')
    a.set_yscale('log')
    a.set_xlim(10**logmin, 10**logmax)
    a.set_ylim(10**logmin, 10**logmax)
    a.set_xticks(10.0**logticks)
    a.set_yticks(a.get_xticks()) # make log scale y ticks the same as x ticks
    a.minorticks_off()
    axes_disable_scientific(a)
    a.set_aspect('equal')


#TRANSTHRESH = 0.2
#ONOFFTHRESH = 0.2


## generate celltype vs chirptype on/off/transient/other matrix, requires that celltype code
## from fig1S33S1.py has been run with EXPTYPE == 'ntsrmvis' set.
## The two classification methods correspond very poorly:
# join on msu index, those rows that don't overlap have chirp type fields left as NaN
celltypechirptype = celltype.join(chirptype)
mat = np.zeros((4, 4), dtype=int)
chrptypestr2i = {'ON-sust.':0, 'OFF-sust.':1, 'ON-OFF-trans.':2, 'mixed':3}
mvitypestr2i  = {'On':0, 'Off':1, 'Trans':2, '':3}
for msustr, row in celltypechirptype.iterrows():
    chrptypestr = row['clu_label']
    if pd.isna(chrptypestr):
        continue # mvi unit doesn't have a chirp experiment?
    onoff = row['onoff']
    trans = row['trans']
    mvitypestr = ''
    if trans >= TRANSTHRESH: # first try and classify by trans
        mvitypestr = 'Trans'
    elif onoff >= ONOFFTHRESH: # try and classify by on/off
        mvitypestr = 'On'
    elif onoff < -ONOFFTHRESH:
        mvitypestr = 'Off'
    chrptypei = chrptypestr2i[chrptypestr]
    mvitypei = mvitypestr2i[mvitypestr]
    mat[mvitypei, chrptypei] += 1
figsize = 4, 3
f, a = plt.subplots(figsize=figsize)
wintitle('celltype vs chirptype matrix ONOFFTHRESH=%.2f TRANSTHRESH=%.2f'
         % (ONOFFTHRESH, TRANSTHRESH))
#a.imshow(mat, origin='upper', aspect='equal', cmap='gray')
img = a.matshow(mat)
#a.set_xticks(np.arange(4))
#a.set_yticks(np.arange(4))
#a.set_xticklabels(list(chrptypestr2i.keys()))
#a.set_yticklabels(list(mvitypestr2i.keys()))
f.colorbar(img, ticks=[mat.min(), mat.max()], label='Unit count')
a.set_xlabel('Chirp type')
a.set_ylabel('Movie onset type')
f.tight_layout(pad=1)