f.init()
print "generated discrete dynamics"
# Allocate memory
u = copy.deepcopy(u0)
v = copy.deepcopy(v0)
h = copy.deepcopy(h0)
# Prepare plotting
if plot_progress:
fig = plt.figure(1)
ax = fig.add_subplot(111, projection="3d")
# plt.clf()
# plt.grid(True)
plt.ion()
plt.hold(False)
plt.draw()
plt.show()
# Measurement
h_meas = []
# Simulate once to generate "measurements"
for k in range(num_measurements):
# Visualize the pool
if plot_progress:
# plt.ioff()
# print h[::numboxes_per_plot]
ax.cla()
surf = ax.plot_surface(
from norml import maml_rl
from norml import tools
from norml.tools import utility
flags.DEFINE_integer('framerate', 25, 'Video framerate.')
flags.DEFINE_bool('render', True, 'Create video?')
flags.DEFINE_string('model_dir', None, 'Checkpoint path for saved model')
flags.DEFINE_string('output_dir', '/tmp', 'Where to store states, rewards...')
flags.DEFINE_integer('test_task_index', 0,
'Which task modifier to use for testing')
flags.DEFINE_bool('eval_meta', True, 'Whether to evaluate the meta policy')
flags.DEFINE_bool('eval_finetune', True,
'Whether to evaluate the finetune policy')
flags.DEFINE_integer('num_finetune_steps', 1,
'Number of finetune steps to perform')
plt.ion()
def _load_config():
if '.ckpt-' in FLAGS.model_dir:
config_path = os.path.dirname(FLAGS.model_dir)
else:
config_path = FLAGS.model_dir
tf.logging.info('Loading config: %s' % config_path)
config = utility.load_config(config_path)
return config
def _save_result(renders, states, returns, actions, output_dir):
"""Saves the results of policy rollouts to file.
def plot_spectrum(result, correct = True, interactive = False):
plt.close('all')
plt.ioff()
if interactive:
plt.ion()
hdu = fits.open(result['ORIGINALFILE'])
galaxy = gaussian_filter(hdu[1].data, 1)
thumbnail = hdu['THUMBNAIL'].data
twoD = hdu['2D'].data
header = hdu[0].header
header1 = hdu[1].header
hdu.close()
lamRange = header1['CRVAL1'] + np.array([0., header1['CD1_1'] * (header1['NAXIS1'] - 1)])
if correct:
zp = 1. + (result['VREL'] / 299792.458)
else:
zp = 1.
wavelength = np.linspace(lamRange[0],lamRange[1], header1['NAXIS1']) / zp
ymin, ymax = np.min(galaxy), np.max(galaxy)
ylim = [ymin, ymax] + np.array([-0.02, 0.1])*(ymax-ymin)
ylim[0] = 0.
xmin, xmax = np.min(wavelength), np.max(wavelength)
### Define multipanel size and properties
fig = plt.figure(figsize=(8,6))
gs = gridspec.GridSpec(200,130,bottom=0.10,left=0.10,right=0.95)
### Plot the object in the sky
ax_obj = fig.add_subplot(gs[0:70,105:130])
ax_obj.imshow(thumbnail, cmap = 'gray', interpolation = 'nearest')
ax_obj.set_xticks([])
ax_obj.set_yticks([])
### Plot the 2D spectrum
ax_2d = fig.add_subplot(gs[0:11,0:100])
ix_start = header['START_{}'.format(int(result['DETECT']))]
ix_end = header['END_{}'.format(int(result['DETECT']))]
ax_2d.imshow(twoD, cmap='spectral',
aspect = "auto", origin = 'lower', extent=[xmin, xmax, 0, 1],
vmin = -0.2, vmax=0.2)
ax_2d.set_xticks([])
ax_2d.set_yticks([])
### Add spectra subpanels
ax_spectrum = fig.add_subplot(gs[11:85,0:100])
ax_blue = fig.add_subplot(gs[110:200,0:50])
ax_red = fig.add_subplot(gs[110:200,51:100])
### Plot some atomic lines
line_wave = [4861., 5175., 5892., 6562.8, 8498., 8542., 8662.]
# ['Hbeta', 'Mgb', 'NaD', 'Halpha', 'CaT', 'CaT', 'CaT']
for i in range(len(line_wave)):
x = [line_wave[i], line_wave[i]]
y = [ylim[0], ylim[1]]
ax_spectrum.plot(x, y, c= 'gray', linewidth=1.0)
ax_blue.plot(x, y, c= 'gray', linewidth=1.0)
ax_red.plot(x, y, c= 'gray', linewidth=1.0)
### Plot the spectrum
ax_spectrum.plot(wavelength, galaxy, 'k', linewidth=1.3)
ax_spectrum.set_ylim(ylim)
ax_spectrum.set_xlim([xmin,xmax])
ax_spectrum.set_ylabel(r'Arbitrary Flux')
ax_spectrum.set_xlabel(r'Restframe Wavelength [ $\AA$ ]')
### Plot blue part of the spectrum
x1, x2 = 300, 750
ax_blue.plot(wavelength[x1:x2], galaxy[x1:x2], 'k', linewidth=1.3)
ax_blue.set_xlim(wavelength[x1],wavelength[x2])
ax_blue.set_ylim(galaxy[x1:x2].min(), galaxy[x1:x2].max())
ax_blue.set_yticks([])
### Plot red part of the spectrum
x1, x2 = 1400, 1500
ax_red.plot(wavelength[x1:x2], galaxy[x1:x2], 'k', linewidth=1.3)
ax_red.set_xlim(wavelength[x1],wavelength[x2])
ax_red.set_ylim(galaxy[x1:x2].min(), galaxy[x1:x2].max())
ax_red.set_yticks([])
### Plot text
#if interactive:
textplot = fig.add_subplot(gs[80:200,105:130])
kwarg = {'va' : 'center', 'ha' : 'left', 'size' : 'medium'}
textplot.text(0.1, 1.0,r'ID = {} \, {}'.format(result.ID, int(result.DETECT)),**kwarg)
textplot.text(0.1, 0.9,r'$v =$ {}'.format(int(result.VREL)), **kwarg)
textplot.text(0.1, 0.8,r'$\delta \, v = $ {}'.format(int(result.VERR)), **kwarg)
textplot.text(0.1, 0.7,r'SN1 = {0:.2f}'.format(result.SN1), **kwarg)
textplot.text(0.1, 0.6,r'SN2 = {0:.2f}'.format(result.SN2), **kwarg)
textplot.text(0.1, 0.5,r'TDR = {0:.2f}'.format(result.TDR), **kwarg)
textplot.text(0.1, 0.4,r'SG = {}'.format(result.SG), **kwarg)
textplot.axis('off')
return fig