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@@ -7,11 +7,9 @@ grtmeasures = ['meanrate', 'meanrate', 'meanrate', 'blankmeanrate',
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mi = pd.MultiIndex.from_product([mvigrtmsustrs, STIMTYPES, [False, 'prestim', 'cond']],
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names=['msu', 'stimtype', 'blank'])
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fig4 = pd.DataFrame(index=mi, columns=['meanrate', 'meanburstratio'])
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-## NOTE: make sure that rows are not overwritten due to temporarily enabling iteration
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-## over st8 in any of the following loops!
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-# strip, scatter and dumbbell plot movie vs grating FMI for applicable measures:
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+# strip plot movie vs grating FMI for applicable measures:
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np.random.seed(0) # to get identical horizontal jitter in strip plots on every run
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figsize = DEFAULTFIGURESIZE
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linmin, linmax, linstep = -1, 1, 1
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@@ -28,36 +26,14 @@ for mvimeasure, grtmeasure in zip(mvimeasures, grtmeasures):
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else:
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Axislabel = axislabel
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for st8 in ['none']:#ALLST8S:
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- #mvifmis, grtfmis = [], []
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mvimaxfmis, grtmaxfmis, grtmaxfmi2s = [], [], []
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exmplis, exmplmsustrs, normlis = [], [], []
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# collect all movie FMIs:
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- '''
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- for mseustr in mvimseustrs:
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- mvifmi = mviFMI[mvimeasure][mseustr, st8]
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- if pd.isna(mvifmi):
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- continue
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- mvifmis.append(mvifmi)
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- # collect all grating FMIs:
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- for mseustr in grtmseustrs:
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- grtfmi = grtFMI[grtmeasure][mseustr, st8]
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- if pd.isna(grtfmi):
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- continue
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- grtfmis.append(grtfmi)
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- '''
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# collect paired movie and grating max FMIs:
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keptmsui = 0
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for msustr in mvigrtmsustrs:
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mvimaxfmi = maxFMI[mvimeasure][msustr, st8, 'mvi']
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grtmaxfmi = maxFMI[grtmeasure][msustr, st8, 'grt']
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- '''
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- ## hack to replicate Steffen's code in R:
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- if mvimeasure == 'meanrate':
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- othermvimaxfmi = maxFMI['meanburstratio'][msustr, st8, 'mvi']
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- othergrtmaxfmi = maxFMI['meanburstratio'][msustr, st8, 'grt']
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- if pd.isna(othermvimaxfmi) or pd.isna(othergrtmaxfmi):
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- continue
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- '''
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if pd.isna(mvimaxfmi) or pd.isna(grtmaxfmi): # missing one or both maxFMI values
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continue
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if isblank: # add a second grtmaxfmi measure for blank cond
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@@ -78,51 +54,17 @@ for mvimeasure, grtmeasure in zip(mvimeasures, grtmeasures):
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else:
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fig4.loc[msustr, 'mvi', False][measure] = mvimaxfmi # save
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fig4.loc[msustr, 'grt', False][measure] = grtmaxfmi # save
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- #if grtmeasure == 'meanburstratio' and grtmaxfmi > 0.8:
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- # print('OUTLIER: %s grtmaxfmi=%g' % (msustr, grtmaxfmi))
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if msustr in msu2exmpli:
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exmplis.append(keptmsui)
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exmplmsustrs.append(msustr)
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else:
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normlis.append(keptmsui)
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keptmsui += 1 # manually increment
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- #mvifmis = np.asarray(mvifmis)
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- #grtfmis = np.asarray(grtfmis)
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mvimaxfmis = np.asarray(mvimaxfmis)
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grtmaxfmis = np.asarray(grtmaxfmis)
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grtmaxfmi2s = np.asarray(grtmaxfmi2s)
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assert len(mvimaxfmis) == len(grtmaxfmis)
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npairs = len(mvimaxfmis)
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- '''
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- ## strip plot movie and grating FMIs, for all mseus:
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- f, a = plt.subplots(figsize=figsize)
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- wintitle('FMI %s nat movie grating stripplot %s' % (mvimeasure, st8))
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- # plot y=0 line:
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- a.axhline(y=0, ls='--', marker='', color='lightgray', zorder=-np.inf)
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- data = pd.DataFrame(data={'stimtype':['Movie']*len(mvifmis)+['Grating']*len(grtfmis),
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- 'FMI':np.concatenate([mvifmis, grtfmis])})
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- sns.stripplot(x='stimtype', y='FMI', data=data, jitter=True, clip_on=False,
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- marker='.', color='None', edgecolor=st82clr[st8], size=np.sqrt(50))
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- # exclude extreme +/- 1 FMI values from mean and ttest:
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- #mvifmisnon1 = mvifmis[abs(mvifmis) != 1]
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- #grtfmisnon1 = grtfmis[abs(grtfmis) != 1]
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- # plot mean with short horizontal lines:
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- #meanmvifmi, meangrtfmi = mvifmisnon1.mean(), grtfmisnon1.mean()
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- #a.plot([-0.25, 0.25], [meanmvifmi, meanmvifmi], '-', lw=2, c='red', zorder=np.inf)
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- #a.plot([0.75, 1.25], [meangrtfmi, meangrtfmi], '-', lw=2, c='red', zorder=np.inf)
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- a.set_xlabel('')
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- a.set_ylabel('%s FMI' % Axislabel)
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- a.set_ylim(-1, 1)
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- t, p = ttest_ind(mvifmisnon1, grtfmisnon1, equal_var=False) # non-paired t-test
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- a.add_artist(AnchoredText('p$=$%.2g' % p, loc='upper center', frameon=False))
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- # connect the dots:
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- x = np.array([[0]*npairs, [1]*npairs])
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- y = np.array([mvimaxfmis, grtmaxfmis])
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- a.plot(x, y, '-', c='k', alpha=0.2, lw=1)
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- # due to jitter, dots don't perfectly connect. Can get actual data using:
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- #mvixy, grtxy = a.collections[0].get_offsets(), a.collections[1].get_offsets()
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- # but some of those points aren't paired, which makes it complicated
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- '''
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## strip plot paired movie and grating max FMIs:
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if mvimeasure == 'meanrate': # 2 column wide strip plot
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f, a = plt.subplots(figsize=(figsize[0]*1.35, figsize[1]*1.5))
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@@ -176,10 +118,6 @@ for mvimeasure, grtmeasure in zip(mvimeasures, grtmeasures):
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a.plot([1.75, 2.25], [meangrtmaxfmi2, meangrtmaxfmi2], '-', lw=2, c='red',
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zorder=np.inf)
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a.set_ylabel('%s FMI' % Axislabel)
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- #if measure.startswith('meanrate'):
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- # a.set_ylim(-0.6, 1)
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- # a.set_yticks([-0.5, 0, 0.5, 1])
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- #else:
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a.set_ylim(-1, 1)
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a.set_yticks([-1, -0.5, 0, 0.5, 1])
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a.spines['bottom'].set_position(('outward', 5))
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@@ -203,242 +141,3 @@ for mvimeasure, grtmeasure in zip(mvimeasures, grtmeasures):
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#a.plot(x[:, signchangeis], y[:, signchangeis], '-', c='k', alpha=1.0, lw=1)
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# due to jitter, dots don't perfectly connect. Can get actual data using:
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#mvixy, grtxy = a.collections[0].get_offsets(), a.collections[1].get_offsets()
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- ## scatter plot movie vs. grating max FMIs:
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- '''
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- f, a = plt.subplots(figsize=figsize)
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- wintitle('maxFMI %s movie grating scatter' % mvimeasure)
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- # plot x=0 and y=0 lines:
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- a.axhline(y=0, ls='--', marker='', color='lightgray', zorder=-np.inf)
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- a.axvline(x=0, ls='--', marker='', color='lightgray', zorder=-np.inf)
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- # plot y=x line:
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- xyline = [linmin, linmax], [linmin, linmax]
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- a.plot(xyline[0], xyline[1], '--', color='gray', zorder=-1)
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- # plot normal (non-example) points:
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- a.scatter(grtmaxfmis[normlis], mvimaxfmis[normlis], clip_on=False,
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- marker='.', c='None', edgecolor=st82clr[st8], s=DEFSZ)
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- # plot example points, one at a time:
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- for exmpli, msustr in zip(exmplis, exmplmsustrs):
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- marker = exmpli2mrk[msu2exmpli[msustr]]
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- c = exmpli2clr[msu2exmpli[msustr]]
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- sz = exmpli2sz[msu2exmpli[msustr]]
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- lw = exmpli2lw[msu2exmpli[msustr]]
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- a.scatter(grtmaxfmis[exmpli], mvimaxfmis[exmpli], marker=marker, c=c, s=sz, lw=lw)
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- # exclude extreme +/- 1 FMI values from mean and ttest:
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- non1is = (abs(grtmaxfmis) != 1) & (abs(mvimaxfmis) != 1)
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- grtmaxfmisnon1 = grtmaxfmis[non1is]
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- mvimaxfmisnon1 = mvimaxfmis[non1is]
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- # plot mean:
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- a.scatter(np.mean(grtmaxfmisnon1), np.mean(mvimaxfmisnon1),
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- c='red', edgecolor='red', s=50, marker='^')
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- a.set_xlabel('Grating %s FMI' % axislabel)
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- a.set_ylabel('Movie %s FMI' % axislabel)
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- a.set_xlim(linmin, linmax)
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- a.set_ylim(linmin, linmax)
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- a.set_xticks(ticks)
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- a.set_yticks(a.get_xticks()) # make log scale y ticks the same as x ticks
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- a.minorticks_off()
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- a.set_aspect('equal')
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- a.spines['left'].set_position(('outward', 4))
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- a.spines['bottom'].set_position(('outward', 4))
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- t, p = ttest_rel(grtmaxfmisnon1, mvimaxfmisnon1) # paired t-test
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- a.add_artist(AnchoredText('p$=$%.2g' % p, loc='lower right', frameon=False))
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- '''
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- ## dumbbell plot:
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- '''
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- f, a = plt.subplots(figsize=DUMBBELLFIGSIZE)
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- wintitle('maxFMI %s movie grating dumbbell' % mvimeasure)
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- # plot x=0 line:
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- a.axvline(x=0, ls='--', marker='', color='lightgray', zorder=-np.inf)
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- # plot horizontal lines:
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- mvimaxfmisortis = mvimaxfmis.argsort() # y vals for hlines
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- npairs = len(mvimaxfmis)
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- ys = np.arange(npairs)
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- xmvis, xgrts = mvimaxfmis[mvimaxfmisortis], grtmaxfmis[mvimaxfmisortis]
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- a.hlines(ys, xmvis, xgrts, zorder=-100)
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- sz = 100
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- # plot grt points:
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- a.scatter(xgrts, ys, clip_on=False, marker='.', c='white',
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- edgecolor=st82clr[st8], s=sz)
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- # plot mvi points:
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- a.scatter(xmvis, ys, clip_on=False, marker='.', c=st82clr[st8], s=sz)
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- a.set_xlabel('%s FMI' % Axislabel)
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- a.set_ylabel('Neurons')
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- a.set_xlim(linmin, linmax)
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- a.set_ylim(-1, npairs)
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- a.set_xticks(ticks)
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- a.set_yticks([])
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- a.spines['left'].set_position(('outward', 4))
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- a.spines['left'].set_visible(False)
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- '''
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-
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-'''
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-# plot CDFs of spatial suppression index for blankscreen movies/gratings wrt full screen
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-# movies/gratings, for meanrate and meanburstratio, for control and opto conditions:
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-figsize = DEFAULTFIGURESIZE
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-stimtype2resp = {'mvi':mviresp, 'grt':grtresp}
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-for stimtype in ['mvi', 'grt']:
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- resp = stimtype2resp[stimtype]
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- if stimtype == 'mvi':
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- resp = resp.xs('nat', level='kind') # dereference movie 'kind' index level
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- for measure in ['meanrate', 'meanburstratio']:
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- f, a = plt.subplots(figsize=figsize)
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- wintitle('SSI %s %s blank vs fullscreen CDF' % (stimtype, measure))
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- opto2SSI = {}
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- for opto in OPTOS:
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- rows = resp.xs(['none', opto], level=['st8', 'opto']) # only mseu index left
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- fullscreen = rows[measure]
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- blank = rows['blank'+measure]
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- SSI = (blank - fullscreen) / (blank + fullscreen)
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- SSI = np.float64(SSI[SSI.notna()].values) # pull float array out of object Series
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- opto2SSI[opto] = SSI
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- SSIbins = list(np.unique(SSI)) + [10]
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- fb = opto2fb[opto].capitalize()
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- a.hist(SSI, bins=SSIbins, density=True, histtype='step',
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- cumulative=True, clip_on=True, lw=1.5,
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- label=fb, color=opto2clr[opto], alpha=1)
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- a.set_xlabel('Spatial suppression index')
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- a.set_ylabel('Cumulative probability')
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- #a.legend(frameon=False)
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- a.set_xlim(-1, 1)
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- a.set_ylim(0, 1.01)
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- a.set_yticks([0, 0.5, 1])
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- #a.spines['left'].set_position(('outward', 4))
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- _, KS_p = ks_2samp(opto2SSI[False], opto2SSI[True])
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- txt = '$\mathregular{p_{KS}=%.2g}$' % KS_p # prob that distribs are the same
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- a.add_artist(AnchoredText(txt, loc='lower right', frameon=False))
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-
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-
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-# stripplot spatial suppression index for blankscreen movies/gratings wrt full screen
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-# movies/gratings, for meanrate and meanburstratio, for both opto conditions, for all mseu:
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-figsize = DEFAULTFIGURESIZE
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-stimtype2resp = {'mvi':mviresp, 'grt':grtresp}
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-for measure in ['meanrate', 'meanburstratio']:
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- opto2SSI = {}
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- for stimtype in ['mvi', 'grt']:
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- resp = stimtype2resp[stimtype]
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- if stimtype == 'mvi':
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- resp = resp.xs('nat', level='kind') # dereference movie 'kind' index level
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- for opto in OPTOS:
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- rows = resp.xs(['none', opto], level=['st8', 'opto']) # only mseu index left
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- fullscreen = rows[measure]
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- blank = rows['blank'+measure]
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- SSI = (blank - fullscreen) / (blank + fullscreen)
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- #SSI = (blank - fullscreen) / blank
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- SSI = np.float64(SSI[SSI.notna()].values) # pull float array out of object Series
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- opto2SSI[opto] = SSI
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- # stripplot:
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- SSIons, SSIoffs = opto2SSI[True], opto2SSI[False]
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- f, a = plt.subplots(figsize=figsize)
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- wintitle('SSI %s %s blank vs fullscreen stripplot' % (stimtype, measure))
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- data = pd.DataFrame.from_dict({'Feedback':SSIoffs, 'Suppression':SSIons},
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- orient='index').transpose()
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- sns.stripplot(ax=a, data=data, clip_on=False, marker='.',
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- color='None', edgecolor='black', size=np.sqrt(50))
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- # exclude extreme +/- 1 SSI values from mean:
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- SSIonsnon1 = SSIons[abs(SSIons) != 1]
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- SSIoffsnon1 = SSIoffs[abs(SSIoffs) != 1]
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- # plot mean with short horizontal lines:
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- meanSSIon, meanSSIoff = SSIonsnon1.mean(), SSIoffsnon1.mean()
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- a.plot([-0.25, 0.25], [meanSSIoff, meanSSIoff], '-', lw=2, c='red', zorder=np.inf)
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- a.plot([0.75, 1.25], [meanSSIon, meanSSIon], '-', lw=2, c='red', zorder=np.inf)
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- a.set_ylabel('Spatial suppression index')
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- a.set_ylim(-1, 1)
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- a.spines['bottom'].set_position(('outward', 5))
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- a.spines['bottom'].set_visible(False)
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- a.tick_params(bottom=False)
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-
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-
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-# stripplot FMI spatial suppression index for blankscreen movies/gratings wrt full screen
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-# movies/gratings, for meanrate and meanburstratio, for all mseu:
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-figsize = DEFAULTFIGURESIZE
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-stimtype2FMI = {'mvi':mviFMI, 'grt':grtFMI}
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-for measure in ['meanrate', 'meanburstratio']:
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- stimtype2SSI = {}
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- for stimtype in ['mvi', 'grt']:
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- FMI = stimtype2FMI[stimtype]
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- rows = FMI.xs('none', level='st8') # only mseu index left
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- fullscreen = rows[measure]
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- blank = rows['blank'+measure]
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- SSI = (blank - fullscreen) / (abs(blank) + abs(fullscreen))
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- #SSI = (blank - fullscreen) / blank
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- SSI = np.float64(SSI[SSI.notna()].values) # pull float array out of object Series
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- stimtype2SSI[stimtype] = SSI
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- # plot CDF:
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- f, a = plt.subplots(figsize=figsize)
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- wintitle('SSI %s %s FMI blank vs fullscreen' % (stimtype, measure))
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- SSIbins = list(np.unique(SSI)) + [10]
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- a.hist(SSI, bins=SSIbins, density=True, histtype='step',
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- cumulative=True, clip_on=True, lw=1.5, color='black', alpha=1)
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- a.set_xlabel('FMI spatial suppression index')
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- a.set_ylabel('Cumulative probability')
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- #a.legend(frameon=False)
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- a.set_xlim(-1, 1)
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- a.set_ylim(0, 1.01)
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- a.set_yticks([0, 0.5, 1])
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- #a.spines['left'].set_position(('outward', 4))
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- # stripplot:
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- mviSSI, grtSSI = stimtype2SSI['mvi'], stimtype2SSI['grt']
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- f, a = plt.subplots(figsize=figsize)
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- wintitle('SSI %s FMI blank vs fullscreen stripplot' % measure)
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- data = pd.DataFrame.from_dict({'Movie':mviSSI, 'Grating':grtSSI},
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- orient='index').transpose()
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- sns.stripplot(ax=a, data=data, clip_on=False, marker='.',
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- color='None', edgecolor='black', size=np.sqrt(50))
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- # exclude extreme +/- 1 SSI values from mean:
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- mviSSInon1 = mviSSI[abs(mviSSI) != 1]
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- grtSSInon1 = grtSSI[abs(grtSSI) != 1]
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- # plot mean with short horizontal lines:
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- meanmvifmi, meangrtfmi = mviSSInon1.mean(), grtSSInon1.mean()
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- a.plot([-0.25, 0.25], [meanmvifmi, meanmvifmi], '-', lw=2, c='red', zorder=np.inf)
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- a.plot([0.75, 1.25], [meangrtfmi, meangrtfmi], '-', lw=2, c='red', zorder=np.inf)
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- a.set_ylabel('FMI spatial suppression index')
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- a.set_ylim(-1, 1)
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- a.spines['bottom'].set_position(('outward', 5))
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- a.spines['bottom'].set_visible(False)
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- a.tick_params(bottom=False)
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-
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-
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-# dumbbell plot FMI spatial suppression index for blankscreen movies/gratings wrt full screen
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-# movies/gratings, for meanrate and meanburstratio, for all mseu:
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-figsize = DEFAULTFIGURESIZE
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-for measure in ['meanrate', 'meanburstratio']:
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- stimtype2SSI = {}
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- for stimtype in ['mvi', 'grt']:
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- rows = maxFMI.xs(['none', stimtype], level=['st8', 'stimtype']) # only msu index left
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- fullscreen = rows[measure]
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- blank = rows['blank'+measure]
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- SSI = (blank - fullscreen) / (abs(blank) + abs(fullscreen))
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- #SSI = (blank - fullscreen) / blank
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- SSI = np.float64(SSI.values) # pull float array out of object Series
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- stimtype2SSI[stimtype] = SSI
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-
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- mviSSI, grtSSI = stimtype2SSI['mvi'], stimtype2SSI['grt']
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- keepis = pd.notna(mviSSI) & pd.notna(grtSSI) # remove any unit that has NaN in either stim
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- mviSSI, grtSSI = mviSSI[keepis], grtSSI[keepis]
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- f, a = plt.subplots(figsize=figsize)
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- wintitle('SSI %s maxFMI blank vs fullscreen stripplot dumbbell' % measure)
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- # plot x=0 line:
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- a.axvline(x=0, ls='--', marker='', color='lightgray', zorder=-np.inf)
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- # plot horizontal lines:
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- mviSSIsortis = mviSSI.argsort() # y vals for hlines
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- npairs = len(mviSSI)
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- ys = np.arange(npairs)
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- xmvis, xgrts = mviSSI[mviSSIsortis], grtSSI[mviSSIsortis]
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- a.hlines(ys, xmvis, xgrts, zorder=-100)
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- sz = 100
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- # plot grt points:
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- a.scatter(xgrts, ys, marker='.', c='white', edgecolor=st82clr[st8], s=sz)
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- # plot mvi points:
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- a.scatter(xmvis, ys, marker='.', c=st82clr[st8], s=sz)
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- axislabel = measure2axislabel[measure]
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- if axislabel[0].islower():
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- Axislabel = axislabel.capitalize()
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- else:
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- Axislabel = axislabel
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- a.set_xlabel('%s FMI SSI' % Axislabel)
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- a.set_ylabel('Neurons')
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- #a.set_xlim(linmin-eps, linmax+eps)
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- a.set_ylim(-1, npairs)
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- #a.set_xticks(ticks)
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- a.set_yticks([])
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-'''
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