def count_rois_differences():
"""
counts average differences between no. pixels in ROIs
"""
h =[[len(l) for l in get_animal_rois(get_name('H',i))] for i in INDICES]
d =[[len(l) for l in get_animal_rois(get_name('D',i))] for i in INDICES]
h = np.sum(np.asarray(h),axis=0)
d = np.sum(np.asarray(d),axis=0)
diff = np.abs(h-d)
print (100*np.mean([np.median(diff/h),np.median(diff/d)]))
print ("average difference: {:0.2f}({:0.2f})".format(100*np.mean([np.mean(diff/h),np.mean(diff/d)]),100*np.mean([np.std(diff/h),np.std(diff/d)])))
def get_roi_group_a(atype='H',roi_group=[8,9],a_indices=INDICES):
"""
returns ROI groups for every animal of a given species as a concatenated vector
parameters:
atype = animal type [H,D]
roi_group - list of rois to include
a_indices - animal indices
return:
a dictinary indexed by animals with individual roi vectors
a concatenated roi vector
"""
animals = {}
for a in a_indices:
animals[a]=[]
for a in a_indices:
name=get_name(atype,a)
rois = get_animal_rois(name)
for r in roi_group:
animals[a].append(rois[r-1])
for a in a_indices:
animals[a]=np.concatenate(animals[a])
data = np.concatenate([animals[a] for a in animals])
return animals,data
def get_roi_differences(rid):
"""
returns (and prints) differences between animals in ROI
parameters:
rid: ROI id
returns:
dictionary of differences i.e.
{diff: roi difference,H:<horse roi stats>,D:<donkey roi stats>}
"""
temps = {}
rets = {}
for atype in ATYPES:
temps[atype]=[]
for i in INDICES:
name = get_name(atype,i)
rois = get_animal_rois(name)
temps[atype].append(rois[rid-1])
temps[atype] = np.concatenate(temps[atype])
rmin,rmean,rmedian,rmax = np.min(temps[atype]),np.mean(temps[atype]),np.median(temps[atype]),np.max(temps[atype])
rets[atype] = [rmin,rmean,rmedian,rmax]
dd = np.abs(rets['H'][1]-rets['D'][1])
print ("{}: {:0.2f}, {}".format(rid,dd,rets))
rets['diff'] = dd
return rets
def plot_roi_histo(rid,small=False,show=GLOBAL_SHOW):
"""
plots hhistogram of temperatures for a given ROI
parameters:
rid - ROI id or 0 for all
small - True/False: wheather to generate small (half-size) histograms
show: True/False: show or save image
"""
assert rid>=0 and rid<16,"{}".format(rid)
temps = {}
for atype in ATYPES:
temps[atype]=[]
for i in INDICES:
name = get_name(atype,i)
rois = get_animal_rois(name)
if rid>0:
temps[atype].append(rois[rid-1])
else:
temps[atype].append(np.concatenate(rois))
temps[atype] = np.concatenate(temps[atype])
print ("{} ROI {}: mean: {:0.2f}, std: {:0.2f}, skew:{:0.2f}, kurtosis:{:0.2f}".format(atype,rid
,np.mean(temps[atype])
,np.std(temps[atype])
,skew(temps[atype])
,kurtosis(temps[atype])))
if not small:
plt.rcParams.update({'font.size': 12})
plt.figure(figsize=(4,3),dpi=300)
else:
plt.rcParams.update({'font.size': 10})
plt.figure(figsize=(2,1.5),dpi=300)
nbins = int(np.max([np.sqrt(len(temps[atype])) for atype in ['H','D']]))
plt.hist(temps['H'],color='#DC3220',alpha=0.7,density=True,bins=nbins,label='Horses')
plt.hist(temps['D'],color='#005AB5',alpha=0.7,density=True,bins=nbins,label='Donkeys')
if not small:
plt.xlabel("Temperature")
else:
txt="[ROI {}] Temp.".format(rid) if rid>0 else 'Combined Temp.'
plt.xlabel(txt)
plt.ylabel("Density")
if not small:
plt.legend()
plt.tight_layout(0.1,0.1,0.1)
ss = '_small' if small else ''
if show:
plt.show()
else:
plt.savefig('fig/histo_{}{}.pdf'.format(rid,ss),bbox_inches='tight',pad_inches=0)
plt.close()
def print_global_temperatures():
"""
prints global temperature stats for animals
"""
temps = {}
for atype in ATYPES:
temps[atype] = []
for i in INDICES:
arr, anno = get_animal(get_name(atype, i))
temps[atype].append(arr[anno != 0])
temps[atype] = np.concatenate(temps[atype])
print(
atype,
'min: {:0.2f},mean:{:0.2f}({:0.2f}), median:{:0.2f}, max: {:0.2f}'
.format(np.min(temps[atype]), np.mean(temps[atype]),
np.std(temps[atype]), np.median(temps[atype]),
np.max(temps[atype])))
def get_roi_group_a(atype='H', roi_group=[8, 9]):
"""
returns a GOR of all animals of a given species as a vector
parameters:
atype = animal type [H,D]
roi_group - list of rois to include (GOR)
return:
vector of temperature values of all animals in GOR
"""
data = []
for i in INDICES:
name = get_name(atype, i)
rois = get_animal_rois(name)
for r in roi_group:
data.append(rois[r - 1])
return np.concatenate(data)
def plot_groups(stat=np.mean,normalise=False,show=GLOBAL_SHOW):
"""
Compare ROI features with t-SNE (expecting observable structure in data)
warning: t-SNE is a nondeterministic algorithm
paramters:
stat: feature extraction statistics
normalise: normalise features by removing the global average
show: True/False: show or save image
"""
data = []
y = []
for atype in ATYPES:
for a in INDICES:
rois = get_animal_rois(get_name(atype=atype,index=a))
if rois != None:
y.append(0 if atype =='H' else 1)
if normalise:
gv = np.mean(np.concatenate(rois))
rois = [v-gv for v in rois]
temp = [stat(v) for v in rois]
data.append(temp)
plt.rcParams.update({'font.size': 12})
X = np.array(data)
y=np.array(y)
tsne = TSNE(perplexity=5)
X = tsne.fit_transform(X)
where = y==0
plt.scatter(X[where,0],X[where,1],color = '#DC3220', label='Horses')
where = y==1
plt.scatter(X[where,0],X[where,1],color = '#005AB5', label='Donkeys')
plt.legend()
nstr = '_norm' if normalise else ''
if show:
plt.show()
else:
plt.savefig('fig/tsne_{}{}.pdf'.format(stat.__name__,nstr),bbox_inches='tight',pad_inches=0)
plt.close()
def plot_box(atype='H',show=GLOBAL_SHOW):
"""
plots boxplot of temperatures in rois for a given animal type
parameters:
atype - animal type ['H','D']
"""
arr = []
for i in INDICES:
name = get_name(atype,i)
rois = get_animal_rois(name)
arr.append(rois)
rois = []
for rid in range(15):
rois.append(np.concatenate([v[rid] for v in arr]))
plt.rcParams.update({'font.size': 10})
plt.figure(figsize=(4,3),dpi=300)
medians = [np.median(v) for v in rois]
arg = np.argsort(medians)[::-1]
rois=np.array(rois)
rois=rois[arg]
indices = np.arange(15)+1
plt.boxplot(rois,widths = 0.6,flierprops={'marker':'o','markersize':1,'alpha':0.7,'markeredgecolor':'#DC3220','linestyle':'none'})
plt.ylim(10,30)
plt.xticks(np.arange(15)+1,indices[arg])
plt.xlabel("ROI")
plt.ylabel("Temperature")
plt.tight_layout(0.2,0.2,0.2)
if show:
plt.show()
else:
plt.savefig('fig/box_{}.pdf'.format(atype),bbox_inches='tight',pad_inches=0)#
plt.close()
else:
name = '{}.pdf'.format(
custom_name) if custom_name is not None else 'fig/{}{}.pdf'.format(
name, indstr)
plt.savefig(name, bbox_inches='tight', pad_inches=0)
plt.close()
if __name__ == '__main__':
plot_animal_heatmap(name='D.3',
gmin=8.80,
gmax=30.65,
cutb=[20, 201, 52, 300],
is_bg=False,
custom_name='fig/heat')
plot_animal_heatmap(name='D.3',
gmin=8.80,
gmax=30.65,
cutb=[20, 201, 52, 300],
is_bg=True,
custom_name='fig/heat_all')
print_global_temperatures()
plot_animal_heatmap('D.17', gmin=8.80, gmax=30.65)
plot_animal_heatmap('D.18', gmin=8.80, gmax=30.65)
plot_animal_heatmap('D.12', gmin=None, gmax=None)
plot_animal_heatmap('H.11', gmin=None, gmax=None)
if True:
for atype in ATYPES:
for i in INDICES:
plot_animal_heatmap(get_name(atype, i))