def s_cone_weights(d, celllist, celldat, params):
'''
'''
lm_midgets = an.compute_s_dist_cone_weight(d, celldat, celllist, params)
# plotting routines
ax, fig1 = pf.get_axes(1, 1, nticks=[4, 5], return_fig=True)
ax[0].plot(lm_midgets[:, 0], lm_midgets[:, 1], 'ko')
ax[0].set_xlabel('distance from S-cone (arcmin)')
ax[0].set_ylabel('S / (L+M+S)')
# histogram of same data
ax2, fig2 = pf.get_axes(1, 1, nticks=[4, 5], return_fig=True)
ax2[0].spines['bottom'].set_smart_bounds(False)
count, bins = np.histogram(lm_midgets[:, 1], bins=15)
count = count / count.sum() * 100
bins, count = pf.histOutline(count, bins)
ax2[0].plot(bins, count, 'k-')
ax2[0].set_xlim([0, 1])
ax2[0].set_ylabel('% of cells')
ax2[0].set_xlabel('S / (L+M+S)')
# Save plots
savedir = util.get_save_dirname(params, check_randomized=True)
fig1.savefig(savedir + 's_weight_scatter.eps', edgecolor='none')
fig2.savefig(savedir + 's_weight_hist.eps', edgecolor='none')
def HueScaling(cm, lPeak=559):
'''
'''
hues = cm.getHueScalingData(
ConeRatio={'fracLvM': 0.70, 's': 0.05, },
maxSens={'l': lPeak, 'm': 530.0, 's': 417.0, })
ax = pf.get_axes()[0]
ax.plot(hues['lambdas'], hues['red'], 'r')
ax.plot(hues['lambdas'], hues['green'], 'g')
ax.plot(hues['lambdas'], hues['blue'], 'b')
ax.plot(hues['lambdas'], hues['yellow'], 'y')
ax.set_xlabel('wavelength (nm)')
ax.set_ylabel('percentage')
ax.set_xlim([390, 750])
plt.show()
def s_cone_dist_analysis(rg, results, model_name):
'''
'''
# checkout proximity to s cone and rg metric
ax, fig = pf.get_axes(1, 1, nticks=[3, 3], return_fig=True)
ax = ax[0]
inds = results[:, 4] < 0.4 # eliminate S-cone center cells
ax.plot(results[inds, 4], np.abs(rg[inds]), 'ko')
ax.set_xlabel('S-cone weight')
ax.set_ylabel('absolute value rg response')
# See if there is a relationship between color names and distance to S-cone
X = sm.add_constant(results[inds, 4], prepend=True)
OLSmodel = sm.OLS(rg[inds], X)
res = OLSmodel.fit()
print '\n\n\n'
print res.rsquared
savedir = util.get_save_dirname(params, check_randomized=True)
fig.savefig(savedir + 's_cone_distance.svg', edgecolor='none')
def fit_linear_model(rg, results, params, opponent=True):
# Switch depending upon opponent option
if not opponent:
predictors = results[:, [2, 3, 4, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27]]
else:
# organize in opponent fashion
N = 2 # center cone and nearest x
predictors = np.zeros((len(results[:, 0]), N + 1))
_inds = [2, 10, 13, 16, 19, 22, 25, 28, 31]
for i in range(N + 1):
predictors[:, i] = ((results[:, _inds[i]] +
results[:, _inds[i] + 2])
- results[:, _inds[i] + 1])
# cone weights in L, M, S order
inds = [2, 10, 13]
X = sm.add_constant(predictors, prepend=True)
OLSmodel = sm.OLS(rg, X) #GLM(rg, X)
res = OLSmodel.fit()
#print res.rsquared
print '\n\n\n'
print (res.summary())
linear_reg = True
ridge_reg = False
## Linear regression on training data
clfs = []
if linear_reg:
clfs.append(LinearRegression())
if ridge_reg:
clfs.append(Ridge())
ax, fig = pf.get_axes(1, 1, nticks=[3, 3], return_fig=True)
ax = ax[0]
Ntrials = 100
mae = np.zeros((Ntrials, 1))
for i in range(Ntrials):
x_train, x_test, y_train, y_test = model_selection.train_test_split(
predictors, rg, test_size=0.15)
for clf in clfs:
clf.fit(x_train, y_train)
mae[i] = mean_absolute_error(y_test, clf.predict(x_test))
ax.plot(y_test, clf.predict(x_test), 'ko', alpha=0.5)
print '\n\n'
print mae.mean(), mae.std()
ax.set_xlabel('observed')
ax.set_ylabel('predicted')
ax.set_aspect('equal')
if y_train.min() < 0:
ax.plot([-1, 1], [-1, 1], 'k-')
ax.set_ylim([-1, 1])
ax.set_xlim([-1, 1])
else:
ax.plot([0, 1], [0, 1], 'k-')
savedir = util.get_save_dirname(params, check_randomized=True)
fig.savefig(params + 'cone_inputs_model_error.svg',
edgecolor='none')
def classify_analysis(d, params, purity_thresh=0.0, nseeds=10):
'''
'''
# --- params --- #
rg_metric = False
background = 'white'
cmdscaling = True
# kernel options=linear, rbf, poly
kernel = 'linear'
# MDScaling options
dims = [0, 1]
test_size = 0.15
# if kernel is set to linear only first entry of 'C' is used, gamma ignored
param_grid = {'C': [1e9],
'gamma': [0.0001, 0.001], }
# -------------- #
human_subjects = ['wt', 'bps']
if params['model_name'].lower() not in human_subjects:
raise('Model must be a human subject with psychopysics data')
# no need to run a bunch of times since lms is easy to classify
if not params['color_cats_switch']:
nseeds = 1
# get some info about the cones
nn_dat = util.get_nn_dat(params['model_name'])
celllist = util.get_cell_list(d)
ncells = len(celllist)
# put responses into a matrix for easy processing
data_matrix = an.get_data_matrix(d, params['cell_type'])
# compute distance matrix
corrmat = an.compute_corr_matrix(data_matrix)
if cmdscaling:
# compute the classical multi-dimensional scaling
config_mat, eigen = dat.cmdscale(corrmat)
else:
pca = PCA(n_components=len(dims)).fit(data_matrix)
config_mat = pca.transform(data_matrix)
# get the location and cone type of each cone
xy_lms = an.get_cone_xy_lms(d, nn_dat, celllist)
# threshold rg that separates red, white, green
if params['color_cats_switch']:
# get response
cone_contrast=params['cone_contrast']
r = an.response(d, params)
output = an.associate_cone_color_resp(r, nn_dat, celllist, params['model_name'],
bkgd=background,
randomized=params['randomized'])
stim_cone_ids = output[:, -1]
stim_cone_inds = np.zeros((1, len(stim_cone_ids)), dtype='int')
for cone in range(len(stim_cone_ids)):
stim_cone_inds[0, cone] = np.where(nn_dat[:, 0] ==
stim_cone_ids[cone])[0]
if rg_metric:
# break rg into three categories: red, green, white
rg, by, high_purity = an.get_rgby_from_naming(output[:, 5:10],
purity_thresh)
rgby_thresh = 0.5
red = rg < -rgby_thresh
green = rg > rgby_thresh
blue = by < -rgby_thresh
yellow = by > rgby_thresh
color_categories = (yellow * 4 + blue * 3 + red * 2 + green).T[0]
else: # use dom response category
max_cat = np.argmax(output[:, 5:10], axis=1)
red = max_cat == 1
green = max_cat == 2
blue = max_cat == 3
yellow = max_cat == 4
color_categories = (yellow * 4 + blue * 3 + red * 2 + green).T
class_cats = color_categories.copy()
config_mat = config_mat[stim_cone_inds, :][0]
data_matrix = data_matrix[stim_cone_inds, :][0]
ncells = len(class_cats)
else:
class_cats = xy_lms[:, 2]
# SVM Classify
print 'running SVM'
print '\tCMDScaling=' + str(cmdscaling)
print '\tkernel=' + str(kernel)
print '\tRGmetric=' + str(rg_metric)
print '\tbackground=' + background
target_names, class_cats = get_target_names_categories(params['color_cats_switch'],
class_cats)
clf, report = an.svm_classify(data_matrix, class_cats, param_grid, target_names,
cmdscaling, dims=dims, display_verbose=True,
rand_seed=2264235, Nseeds=nseeds, test_size=test_size,
kernel=kernel)
print report
# --------------------------------------------------- #
print 'plotting results from SVM'
# undo category shift of plotting
if background == 'blue' and params['color_cats_switch']:
class_cats[class_cats > 0] += 1
# need to order corrmat based on color category
sort_inds = np.argsort(class_cats)
sort_data_matrix = data_matrix[sort_inds, :]
sort_corrmat = an.compute_corr_matrix(sort_data_matrix)
# plot correlation matrix
ax, fig1 = pf.get_axes(1, 1, nticks=[3, 3], return_fig=True)
ax[0].imshow(sort_corrmat)
# change axes to indicate location of category boundaries
# plot MDS configuration matrix in 2 and 3dim
ax, fig2 = pf.get_axes(1, 1, nticks=[3, 3], return_fig=True)
# add decision function
xx, yy = np.meshgrid(np.linspace(-1.5, 1.5, 200), np.linspace(-1.5, 1.5, 200))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
ax[0].contourf(xx, yy, -Z, cmap=plt.cm.Paired, alpha=0.8)
for cone in range(ncells):
if (class_cats[cone] == 0 and params['color_cats_switch'] is False
or class_cats[cone] == 3):
color = [0, 0, 1]
elif class_cats[cone] == 0 and params['color_cats_switch'] is True:
color = [0.7, 0.7, 0.7]
elif class_cats[cone] == 1:
color = [0, 1, 0]
elif class_cats[cone] == 2:
color = [1, 0, 0]
elif class_cats[cone] == 4:
color = [0.8, 0.8, 0] # yellow
else:
raise TypeError('category type must be int [0, 4]')
ax[0].plot(config_mat[cone, 0], config_mat[cone, 1], 'o', markersize=8,
alpha=0.8, color=color, markeredgecolor='k')
# save output
savedir = util.get_save_dirname(params, check_randomized=True)
# save txt file
fhandle = open(savedir + 'classification_report.txt', 'w')
fhandle.write(report)
fhandle.close()
# save figs
#fig1.savefig(savename + '_corr_matrix.eps', edgecolor='none')
fig2.savefig(savedir + 'low_dim_rep.eps', edgecolor='none')
#fig3.savefig(savename + '_3dplot.eps', edgecolor='none')
if cmdscaling:
ax, fig4 = pf.get_axes(1, 1, nticks=[3, 3], return_fig=True)
ax[0].plot(eigen, 'ko')
fig4.savefig(savedir + 'eigenvals.eps', edgecolor='none')
plt.show(block=params['block_plots'])
def plotModel(cm, plotModel=True, plotCurveFamily=False,
plotUniqueHues=False, savefigs=False,
fracLvM=0.25, SHOW=True, OD=None, age=None,
maxSens=None):
"""Plot cone spectral sensitivies and first stage predictions.
"""
if maxSens is None:
maxSens = {'l': 559.0, 'm': 530.0, 's': 417.0, }
if plotCurveFamily:
model = cm.colorModel(age=age)
model.genModel(ConeRatio={'fracLvM': fracLvM, 's': 0.05, },
maxSens=maxSens, OD=OD)
FirstStage = model.returnFirstStage()
SecondStage = model.returnSecondStage()
fig = plt.figure(figsize=(8.5, 8))
fig.set_tight_layout(True)
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
pf.AxisFormat()
pf.TufteAxis(ax1, ['left', ], Nticks=[5, 5])
pf.TufteAxis(ax2, ['left', 'bottom'], Nticks=[5, 5])
ax1.plot(FirstStage['lambdas'],
np.zeros((len(FirstStage['lambdas']))), 'k', linewidth=1.0)
ax2.plot(FirstStage['lambdas'],
np.zeros((len(FirstStage['lambdas']))), 'k', linewidth=1.0)
sortedlist = []
for key in SecondStage['percent']:
sortedlist.append(SecondStage['percent'][key])
#print SecondStage['percent'][key]
sortedlist = sorted(sortedlist, key=itemgetter('probSurround'),
reverse=True)
thresh = sortedlist[15]['probSurround']
for i in SecondStage['lmsV_L']:
if i % 2 == 0 or SecondStage['percent'][i][
'probSurround'] >= thresh:
if SecondStage['percent'][i]['probSurround'] >= thresh:
print SecondStage['percent'][i]
ax1.plot(FirstStage['lambdas'],
SecondStage['lmsV_M'][i][1],
c=(1,0,0), linewidth=1, alpha=0.25)
ax2.plot(FirstStage['lambdas'],
SecondStage['lmsV_L'][i][1],
c=(0,0,1), linewidth=1, alpha=0.25)
else:
ax1.plot(FirstStage['lambdas'],
SecondStage['lmsV_M'][i][1],
c=(0,0,0), linewidth=1, alpha=0.10)
ax2.plot(FirstStage['lambdas'],
SecondStage['lmsV_L'][i][1],
c=(0,0,0), linewidth=1, alpha=0.10)
ax1.set_ylim([-0.4, 0.4])
ax1.set_xlim([FirstStage['wavelen']['startWave'],
FirstStage['wavelen']['endWave']])
ax2.set_xlim([FirstStage['wavelen']['startWave'],
FirstStage['wavelen']['endWave']])
ax1.set_ylabel('sensitivity')
ax2.set_ylabel('sensitivity')
ax2.set_xlabel('wavelength (nm)')
if savefigs:
plt.savefig('familyLMS_' + str(int(fracLvM * 100)) + 'L.eps')
plt.show()
if plotModel:
model = cm.colorModel(age=age)
ax = pf.get_axes(1, 1, nticks=[5, 4])[0]
ax.spines['left'].set_smart_bounds(False)
style = ['-', '--', '-.']
for i, LvM in enumerate(np.array([0.0, 0.2, 0.4]) + fracLvM):
model.genModel(ConeRatio={'fracLvM': LvM, 's': 0.05, },
maxSens=maxSens, OD=OD)
FirstStage = model.returnFirstStage()
ThirdStage = model.returnThirdStage()
# get colors
blue = ThirdStage['lCenter'].clip(0, 1000)
yellow = ThirdStage['lCenter'].clip(-1000, 0)
red = ThirdStage['mCenter'].clip(0, 1000)
green = ThirdStage['mCenter'].clip(-1000, 0)
# plot
ax.plot(FirstStage['lambdas'], blue,
'b' + style[i], label=str(int(LvM * 100)) + "%L")
ax.plot(FirstStage['lambdas'], yellow,
'y' + style[i])
ax.plot(FirstStage['lambdas'], green, 'g' + style[i])
ax.plot(FirstStage['lambdas'], red, 'r' + style[i])
# add black line at zero
ax.plot(FirstStage['lambdas'],
np.zeros((len(FirstStage['lambdas']))), 'k', linewidth=2.0)
ax.set_ylim([-0.28, 0.28])
ax.set_xlim([FirstStage['wavelen']['startWave'],
FirstStage['wavelen']['endWave']])
ax.legend(loc='upper right', fontsize=18)
ax.set_ylabel('sensitivity')
ax.set_xlabel('wavelength (nm)')
if savefigs:
plt.savefig('percent_L.eps')
plt.show()
if plotUniqueHues:
model = cm.colorModel(age=age)
ax = pf.get_axes(1, 1, nticks=[4, 5])[0]
ax.spines['bottom'].set_smart_bounds(False)
style = ['-', '--', '-.']
i = 0
for lPeak in [559.0, 557.25, 555.5]:
model.genModel(maxSens={'l': lPeak, 'm': 530.0, 's': 417.0, },
OD=OD)
model.findUniqueHues()
UniqueHues = model.returnUniqueHues()
ax.plot(UniqueHues['LMratio'], UniqueHues['green'],
'g' + style[i], label=str(int(lPeak)))
ax.plot(UniqueHues['LMratio'], UniqueHues['blue'],
'b' + style[i], label=str(int(lPeak)))
ax.plot(UniqueHues['LMratio'], UniqueHues['yellow'],
'y' + style[i], label=str(int(lPeak)))
i += 1
ax.set_xlim([20, 100])
ax.set_ylim([460, 600])
ax.set_ylabel('wavelength (nm)')
ax.set_xlabel('% L')
if savefigs:
plt.savefig('unique_hues.eps')
if SHOW:
plt.show()
else:
return ax