def findBestPictureWithMethod():
from analysis import getPrCurve
from tools import saveData
d = getPrCurve(allMethods, 1000)
#d[3]['img']['0.jpg']['MEAN'].keys()
d = d[3]['img']
aucs = {
name: {method: d[name][method]['auc']
for method in d[name]}
for name in d
}
# auc = d['13.jpg']['RC']['auc']
saveData(aucs, 'aucs')
def sortGood(myMethod='ME1', compare='DISC2'):
l = [(name, aucs[name][myMethod] - aucs[name][compare])
for name in aucs]
l.sort(key=lambda x: x[1])
return l
# [showpr(i) for i,j in l]
def sortAucByMethod(method='ME1'):
l = [(name, aucs[name][method]) for name in aucs]
l.sort(key=lambda x: x[1])
return l
return sortGood()
def plotPrCurve(precisions, recalls, labels=None, avgPreScore=-1, save=None):
'''
recalls, precision: list of PR curves
labels: each line's name
save: if is string of path, save plot img and data to path
'''
n = len(recalls) if isinstance(recalls, list) else 1
if labels == None:
labels = range(n) if n != 1 else 'line'
if n == 1 and not isinstance(recalls, list):
precisions = [precisions]
recalls = [recalls]
labels = [labels]
plt.clf()
color = ['r', 'b', 'k', 'g', 'c', 'y', 'm']
lineStyle = '-', '--', ':', '-.'
colors = []
for i in lineStyle:
colors += map(lambda x: x + i, color)
for recall, precision, label, i in zip(recalls, precisions, labels,
range(len(recalls))):
plt.plot(recall, precision, colors[i % len(colors)], lw=1, label=label)
plt.xlabel('Recall')
plt.ylabel('Precision')
# plt.axis('equal')
plt.axis([0., 1., 0., 1.])
plt.title('Avg of {0:d} line\'s AUC={1:0.2f}'.format(n, avgPreScore))
plt.legend(bbox_to_anchor=(0., 0, .8, 1.1),
loc=3,
ncol=3,
mode="expand",
borderaxespad=0.)
plt.grid()
if save != None:
if not isinstance(save, str) and not isinstance(save, unicode):
save = 'lastResoult'
name = save
data = recalls, precisions, labels, avgPreScore
plt.savefig('./figs/' + name + '.png', dpi=300)
dirr = './figs/' + name + '/'
if not os.path.isdir(dirr):
os.mkdir(dirr)
plt.savefig(dirr + name + '.svg')
saveData(data, dirr + name + '.pickle')
plt.show()
def findBestPictureWithMethod():
d = getPrCurve(allMethods, 10)
#d[3]['img']['0.jpg']['MEAN'].keys()
d = d[3]['img']
aucs = {
name: {method: d[name][method]['auc']
for method in d[name]}
for name in d
}
saveData(aucs, 'aucs')
def sortAucByMethod(method='ME2'):
l = [(name, aucs[name][method]) for name in aucs]
l.sort(key=lambda x: x[1])
return l
def saveImgData(imgName, methods):
'''
save data to LABEL_DATA_DIR
return default method`s AUC
'''
if isinstance(imgName, int):
imgName = IMG_NAME_LIST[imgName]
imgGt = io.imread(IMG_DIR + imgName[:-3] + 'png') / 255.
coarseDic = getCoarseDic(imgName, methods, COARSE_DIR)
data = {}
data['name'] = imgName
data['method'] = {}
for method, refindImg in coarseDic.items():
data['method'][method] = getAucAndPr(imgGt, refindImg, method)
dataName = LABEL_DATA_DIR + imgName[:-3] + 'data'
AUC_METHOD = "DRFI"
data['aucMethod'] = AUC_METHOD
data['auc'] = data['method'][AUC_METHOD][0]
saveData(data, dataName)
return data['auc']
print("Saving data...")
# Save spikes
# tools.saveData(Simulation_params["experiment_id"],filename,".spikes",all_spikes)
# Save connection matrix
# tools.saveData(Simulation_params["experiment_id"],filename,".connections",connection_matrix)
# Save membrane potentials
# if Simulation_params["computeMP"]:
# tools.saveData(Simulation_params["experiment_id"],filename,".MP_exc",potentials[0])
# tools.saveData(Simulation_params["experiment_id"],filename,".MP_inh",potentials[1])
# Save AMPA and GABA currents
if Simulation_params["decimate"]:
tools.saveData(Simulation_params["experiment_id"],
filename, ".AMPA",
tools.decimate(AMPA_current, 10))
tools.saveData(Simulation_params["experiment_id"],
filename, ".GABA",
tools.decimate(GABA_current, 10))
else:
tools.saveData(Simulation_params["experiment_id"],
filename, ".AMPA", AMPA_current)
tools.saveData(Simulation_params["experiment_id"],
filename, ".GABA", GABA_current)
# # Normalize LFPs
# # Discard first 500 ms for comp. mean and s. deviation
# start_time_pos = int(500.0/Simulation_params["simstep"])
if st_con['source'] in pop_ex:
connection_matrix[ind].append(
pop_ex.index(st_con['source']))
if st_con['source'] in pop_in:
connection_matrix[ind].append(pop_in.index(st_con['source'])+\
len(pop_ex))
# Add connections from external inputs
for j in range(len(pop_parrot_th)):
connection_matrix[j].append(j + len(pop_ex) + len(pop_in))
connection_matrix[j].append(j + len(pop_ex) + len(pop_in) +
len(pop_parrot_th))
print("Saving data...")
# Save spikes
tools.saveData(Simulation_params["experiment_id"], filename, ".spikes",
all_spikes)
# Save connection matrix
tools.saveData(Simulation_params["experiment_id"], filename,
".connections", connection_matrix)
# Save membrane potentials
if Simulation_params["computeMP"]:
tools.saveData(Simulation_params["experiment_id"], filename,
".MP_exc", potentials[0])
tools.saveData(Simulation_params["experiment_id"], filename,
".MP_inh", potentials[1])
# Save AMPA and GABA currents
if Simulation_params["decimate"]:
tools.saveData(Simulation_params["experiment_id"], filename,
len(cluster_2_rate_1[0]) / 100000))
print("Rate 2 sp/s: %s, %s, %s" %
(len(cluster_0_rate_2[0]) / 100000, len(cluster_1_rate_2[0]) / 100000,
len(cluster_2_rate_2[0]) / 100000))
print("mean(g) = %s,%s,%s" %
(np.mean(g_cluster_0), np.mean(g_cluster_1), np.mean(g_cluster_2)))
#######################################################################
# Save data
# os.mkdir('../results/merged_files')
# A) 1.5 sp/s
filename = 'trial_' + str(0) + '_rate_' + str(1.5) + '_gex_' + str(
1.78289473684) + '_gin_' + str(1.0)
# AMPA and GABA
tools.saveData("merged_files", filename, ".AMPA", cluster_0_rate_1[0])
tools.saveData("merged_files", filename, ".GABA", cluster_0_rate_1[1])
# Save time array
tools.saveData("merged_files", filename, ".times", t_sim)
# Save time step
tools.saveData("merged_files", filename, ".dt", simstep)
filename = 'trial_' + str(0) + '_rate_' + str(1.5) + '_gex_' + str(
1.20394736842) + '_gin_' + str(1.0)
# AMPA and GABA
tools.saveData("merged_files", filename, ".AMPA", cluster_1_rate_1[0])
tools.saveData("merged_files", filename, ".GABA", cluster_1_rate_1[1])
# Save time array
tools.saveData("merged_files", filename, ".times", t_sim)
# Save time step
tools.saveData("merged_files", filename, ".dt", simstep)
kurtosis = computeKurtosisCustom(vel)
a2 = computeExcessKurtosis_a2(kurtosis, 2)
if verbose_temperature == True:
print('Temperature: '+'{:.3f}'.format(meanTemperature))
#print('{:.3f}'.format(a2))
# Saving temperature and a2 data
file_t.write('{0:10.6f} {1:10.4f}\n'.format(abs_time, meanTemperature))
file_a2.write('{0:10.6f} {1:10.4f}\n'.format(abs_time, a2))
if verbose_absTime == True:
print('Contador de tiempo absoluto: ', str(abs_time))
# We save positions and velocities data after current collision
saveData(c, data_folder, n_particles, pos, vel)
p = "{:.2f}".format(100*(c/n_collisions)) + " %" # Percent completed
if verbose_percent == True:
print(p)
if verbose_saveData == True:
print('Saving file, collision nº: '+str(c+1)+' / '+str(n_collisions))
if verbose_debug == True:
print(' ')
print('COLLISION Nº: '+str(c))
print('Event list head:')
print(events.eventTimesList.iloc[0:1])
if dt==0:
def saveKs(self, npz_filename):
saveData(npz_filename, tuneX=self.tuneX, tuneY=self.tuneY, k0=self.k0, k1=self.k1, b1=self.b1)
def saveCubes(self, npz_cubefile):
saveData(npz_cubefile, **self.cubes)