def plotObj3D(self, gen, gen_tot):
"""
:param gen:
:param gen_tot:
:return:
"""
color = iter(cm.gray(np.linspace(1, 0.1, gen_tot)))
# color = iter(cm.rainbow(np.linspace(0,1,gen_tot)))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for index, item in enumerate(gen):
obj = item["objective"][0]
x = []
y = []
z = []
# r = index / gen_tot
# g = index / gen_tot
# b = index / gen_tot
for iobj in obj:
x.append(iobj[0])
y.append(iobj[1])
z.append(iobj[2])
# print '['+'\t'.join(map(str,iobj))+']'
ax.scatter(x, y, z, 'o', color=next(color), label=str(index))
ax.scatter(x, y, z, '.', c='red')
ax.set_title('moea.json')
ax.set_xlabel('obj1')
ax.set_ylabel('obj2')
ax.set_zlabel('obj3')
plt.grid(True)
plt.savefig(self.fileName + '.png')
plt.clf()
def plotObj2D(self, gen, gen_tot):
"""
:param gen:
:param gen_tot:
:return:
"""
color = iter(cm.gray(np.linspace(1, 0.1, gen_tot)))
# color = iter(cm.rainbow(np.linspace(0,1,gen_tot)))
for index, item in enumerate(gen):
obj = item["objective"][0]
x = []
y = []
# r = index / gen_tot
# g = index / gen_tot
# b = index / gen_tot
for iobj in obj:
x.append(iobj[0])
y.append(iobj[1])
# print '['+'\t'.join(map(str,iobj))+']'
plt.plot(x, y, 'o', color=next(color), label=str(index))
plt.plot(x, y, '.', mec='red', mfc='red')
plt.title('moea.json')
plt.xlabel('obj1')
plt.ylabel('obj2')
plt.grid(True)
plt.savefig(self.fileName + '.png')
plt.clf()
def savePlot(self):
with open(self.fileName) as data_file:
data = json.load(data_file)
gen = data["generation"]
gen_tot = len(gen)
color = iter(cm.gray(np.linspace(1, 0.1, gen_tot)))
# color = iter(cm.rainbow(np.linspace(0,1,gen_tot)))
for index, item in enumerate(gen):
obj = item["objective"][0]
obj_tot = len(obj)
x = []
y = []
r = index / gen_tot
g = index / gen_tot
b = index / gen_tot
for iobj in obj:
x.append(iobj[0])
y.append(iobj[1])
# print '['+'\t'.join(map(str,iobj))+']'
plt.plot(x, y, 'o', color=next(color), label=str(index))
# minmax = [min(x), max(x), min(y), max(y)]
plt.title('moea.json')
plt.xlabel('obj1')
# if minmax[0]==0.0 and minmax[1]==0.0:
# plt.xlim([minmax[0]-0.05,minmax[1]+0.05])
# elif minmax[0]==0.0 and minmax[1]!=0.0:
# plt.xlim([minmax[0]-0.05,minmax[1]+0.05*abs(minmax[1])])
# elif minmax[0]!=0.0 and minmax[1]==0.0:
# plt.xlim([minmax[0]-0.05*abs(minmax[0]),minmax[1]+0.05])
# else:
# plt.xlim([minmax[0]-0.05*abs(minmax[0]),minmax[1]+0.05*abs(minmax[1])])
# # plt.xlim([minmax[0]-0.05*abs(minmax[0]),minmax[1]+0.05*abs(minmax[1])])
plt.ylabel('obj2')
# if minmax[2]==0.0 and minmax[3]==0.0:
# plt.ylim([minmax[2]-0.05,minmax[3]+0.05])
# elif minmax[2]==0.0 and minmax[3]!=0.0:
# plt.ylim([minmax[2]-0.05,minmax[3]+0.05*abs(minmax[3])])
# elif minmax[2]!=0.0 and minmax[3]==0.0:
# plt.ylim([minmax[2]-0.05*abs(minmax[2]),minmax[3]+0.05])
# else:
# plt.ylim([minmax[2]-0.05*abs(minmax[2]),minmax[3]+0.05*abs(minmax[3])])
# # plt.ylim([minmax[2]-0.05*abs(minmax[2]),minmax[3]+0.05*abs(minmax[3])])
plt.grid(True)
# plt.legend(loc='best')
plt.savefig(self.fileName + '.png')
# plt.show()
plt.clf()
def main():
opts = docopt(__doc__)
if opts['--verbose']:
logging.basicConfig(level=logging.INFO)
else:
logging.basicConfig(level=logging.WARN)
logging.info('Using filename suffix: {0}'.format(opts['--suffix']))
if len(opts['<IMAGE>']) == 0:
logging.info('No images specified. Exiting successfully.')
sys.exit(0)
logging.info('Pre-scanning images...')
min_val, max_val = np.inf, -np.inf
for filename in opts['<IMAGE>']:
logging.info('Scanning {0}'.format(filename))
A = imread(filename)
max_val = max(np.max(A), max_val)
min_val = min(np.min(A), min_val)
logging.info('Input images have values on interval [{0}, {1}].'.format(
min_val, max_val))
if max_val == min_val:
logging.error(
'Images all have the same value pixels. Rescaling is meaningless.')
sys.exit(1)
for filename in opts['<IMAGE>']:
out_filename = filename + opts['--suffix']
logging.info('Re-scaling {0} to {1}'.format(filename, out_filename))
A = np.array(imread(filename), dtype=np.float32)
A -= min_val
A /= max_val - min_val
if len(A.shape) == 2:
A = cm.gray(A)
imsave(out_filename, A, format='png')
logging.info('Finished')
def main():
opts = docopt(__doc__)
if opts['--verbose']:
logging.basicConfig(level=logging.INFO)
else:
logging.basicConfig(level=logging.WARN)
logging.info('Using filename suffix: {0}'.format(opts['--suffix']))
if len(opts['<IMAGE>']) == 0:
logging.info('No images specified. Exiting successfully.')
sys.exit(0)
logging.info('Pre-scanning images...')
min_val, max_val = np.inf, -np.inf
for filename in opts['<IMAGE>']:
logging.info('Scanning {0}'.format(filename))
A = imread(filename)
max_val = max(np.max(A), max_val)
min_val = min(np.min(A), min_val)
logging.info('Input images have values on interval [{0}, {1}].'.format(min_val, max_val))
if max_val == min_val:
logging.error('Images all have the same value pixels. Rescaling is meaningless.')
sys.exit(1)
for filename in opts['<IMAGE>']:
out_filename = filename + opts['--suffix']
logging.info('Re-scaling {0} to {1}'.format(filename, out_filename))
A = np.array(imread(filename), dtype=np.float32)
A -= min_val
A /= max_val - min_val
if len(A.shape) == 2:
A = cm.gray(A)
imsave(out_filename, A, format='png')
logging.info('Finished')
def figureCaRatio(ax1, ax2):
stimlist = [
simDataPath +
'PSim_ConstrainUp_noGABAcontrol_16spinesp3tertdend1_81.8e-05TimeDelay0Mirror_0BranchOffset_0_',
simDataPath +
'PSim_ConstrainUp_GABAAfast_tertdend1_8_0_16spinesp3tertdend1_81.8e-05TimeDelay0Mirror_0BranchOffset_0.1_',
simDataPath +
'PSim_ConstrainUp_GABAAslow_tertdend1_8_0_16spinesp3tertdend1_81.8e-05TimeDelay0Mirror_0BranchOffset_0.1_',
]
labels = ['Control', 'Fast GABA', 'Slow GABA']
colors = [
cm.gray(5 / 10.),
cm.Reds(np.linspace(1, 0, 10))[1],
cm.Reds(np.linspace(1, 0, 10))[4]
]
ax = ax1
normdata = importGABA(stimlist, labels)
handles = []
for i, key in enumerate(normdata):
spy = normdata[key]['spy']
cay = normdata[key]['cay']
mspy = normdata[key]['mspy']
handle, = ax.plot(mspy[:, 0],
mspy[:, 1],
label='',
marker='s',
markersize=4,
c=colors[i])
handles.append(handle)
handle, = ax2.plot(cay[:, 0],
cay[:, 1],
label='',
marker='o',
c=colors[i],
markersize=4,
linestyle=':',
alpha=.9)
handles.append(handle)
ax.legend([handles[i] for i in [0, 2, 4]],
labels,
title='Non-Stim Spine',
frameon=True,
fancybox=True,
loc='upper left')
ax2.legend([handles[i] for i in [1, 3, 5]],
labels,
title='Dendrite',
frameon=True,
fancybox=True,
loc='upper left')
format0(ax)
ax.set_xlabel(u'Distance from Soma (µm)', labelpad=0)
ax.set_ylabel('Peak $\it{[Ca^{2+}]}$ / Stim Spine Peak $\it{[Ca^{2+}]}$',
labelpad=0)
ax.set_xlim([20, 220])
ax.set_ylim([-.01, .36])
format1(ax2)
ax2.set_xlabel(u'Distance from Soma (µm)', labelpad=0)
ax2.set_xlim(ax.get_xlim())
ax2.set_ylim([-.01, .36])
return ax, normdata
def plotJson(folder, fileName, issave=False):
import matplotlib as mpl
if os.environ.get('DISPLAY', '') == '':
# print('--py:Warning:: No display found. Using non-interactive Agg backend')
mpl.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import json
from matplotlib.pyplot import cm
fileName = folder + '/' + fileName
with open(fileName) as data_file:
data = json.load(data_file)
gen = data["generation"]
gen_tot = len(gen)
color = iter(cm.gray(np.linspace(1, 0.1, gen_tot)))
# color = iter(cm.rainbow(np.linspace(0,1,gen_tot)))
for index, item in enumerate(gen):
obj = item["objective"][0]
obj_tot = len(obj)
x = []
y = []
r = index / gen_tot
g = index / gen_tot
b = index / gen_tot
for iobj in obj:
x.append(iobj[0])
y.append(iobj[1])
# print '['+'\t'.join(map(str,iobj))+']'
plt.plot(x, y, 'o', color=next(color), label=str(index))
minmax = [min(x), max(x), min(y), max(y)]
plt.title(folder)
plt.xlabel('obj1')
if minmax[0] == 0.0 and minmax[1] == 0.0:
plt.xlim([minmax[0] - 0.05, minmax[1] + 0.05])
elif minmax[0] == 0.0 and minmax[1] != 0.0:
plt.xlim([minmax[0] - 0.05, minmax[1] + 0.05 * abs(minmax[1])])
elif minmax[0] != 0.0 and minmax[1] == 0.0:
plt.xlim([minmax[0] - 0.05 * abs(minmax[0]), minmax[1] + 0.05])
else:
plt.xlim([
minmax[0] - 0.05 * abs(minmax[0]),
minmax[1] + 0.05 * abs(minmax[1])
])
# plt.xlim([minmax[0]-0.05*abs(minmax[0]),minmax[1]+0.05*abs(minmax[1])])
plt.ylabel('obj2')
if minmax[2] == 0.0 and minmax[3] == 0.0:
plt.ylim([minmax[2] - 0.05, minmax[3] + 0.05])
elif minmax[2] == 0.0 and minmax[3] != 0.0:
plt.ylim([minmax[2] - 0.05, minmax[3] + 0.05 * abs(minmax[3])])
elif minmax[2] != 0.0 and minmax[3] == 0.0:
plt.ylim([minmax[2] - 0.05 * abs(minmax[2]), minmax[3] + 0.05])
else:
plt.ylim([
minmax[2] - 0.05 * abs(minmax[2]),
minmax[3] + 0.05 * abs(minmax[3])
])
# plt.ylim([minmax[2]-0.05*abs(minmax[2]),minmax[3]+0.05*abs(minmax[3])])
plt.grid(True)
# plt.legend(loc='best')
if issave:
plt.savefig(fileName + '.png')
plt.clf()
else:
plt.show()
return obj