def plot(self):
self.myboom.elements[0].display['color'] = 'blue'
ind = (self.photons['probability'] > 0)
posdat = self.keeppos.format_positions()[ind, :, :]
fig = mlab.figure()
obj = plot_object(self.instrument, viewer=fig)
obj = plot_object(self.myboom, viewer=fig)
rays = plot_rays(posdat,
scalar=np.asarray(self.photons[self.plot_col_color],
dtype=float)[ind])
mlab.savefig(os.path.join(self.outpath, self.filename + '.x3d'))
def make_x3dplot(instrument):
keeppos = simulator.KeepCol('pos')
instrument.postprocess_steps = [keeppos]
target = SkyCoord(30., 30., unit='deg')
star = source.PointSource(coords=target, energy=.5, flux=1.)
pointing = source.FixedPointing(coords=target)
photons = star.generate_photons(5000)
photons = pointing(photons)
photons = instrument(photons)
ind = (photons['probability'] >= 0) & (photons['facet'] >= 0)
posdat = keeppos.format_positions()[ind, :, :]
pp = photons[ind]
fig = mlab.figure()
plot_object(instrument, viewer=fig)
plot_rays(posdat, scalar=pp['order'])
return fig
def plot(self):
ind = (self.photons['probability'] > 0)
posdat = self.keeppos.format_positions()[ind, :, :]
fig = mlab.figure()
obj = plot_object(self.instrument, viewer=fig)
rays = plot_rays(posdat, scalar=self.photons[self.plot_col_color][ind])
mlab.savefig(os.path.join(self.outpath, self.filename + '.x3d'))
my_source = PointSource(coords=SkyCoord(30., 30., unit='deg'), energy=0.25,
polarization=120.,
geomarea=Ageom)
my_pointing = FixedPointing(coords=SkyCoord(30., 30., unit='deg'),
reference_transform=redsox.xyz2zxy)
photons = my_source.generate_photons(1)
photons = my_pointing(photons)
photons = redsox.redsox(photons)
fig = mlab.figure(**kwargsfig)
redsox.mirror.display = copy.deepcopy(redsox.mirror.display)
redsox.mirror.display['color'] = (1., 0.6, 0.)
out = marxsavi.plot_object(redsox.redsox, viewer=fig)
pos_full = redsox.keeppos.format_positions()
ind = np.isfinite(photons['order']) & (photons['grating_id'] < 5000)
pos = np.empty((pos_full.shape[0], pos_full.shape[1] + 1, pos_full.shape[2]))
pos[:, 0:3, :] = pos_full[:, 0:3, :]
pos[:, 3:, :] = pos_full[:, 2:, :]
col = np.zeros_like(pos[:, :, 0])
col[:, 3:] = photons['order'][:, None]
# some tricks to color photons red that are 1st order and then pass through a second overlapping
# grating with no direction change (order 0)
col[ind, 3:] = (photons['CCD_ID'][ind, None] > 0)
col = 0.5 * col # 0.5 is red in this color map
kwargssurface = {'opacity': .6, 'line_width': 1, 'colormap': 'summer'}
out = marxs.visualization.mayavi.plot_rays(pos[ind, :, :],
from astropy.table import Table
from scipy.interpolate import interp1d
from arcus.defaults import DefaultSource, DefaultPointing
import astropy
import astropy.units as u
from mayavi import mlab
from marxs.visualization.mayavi import plot_object, plot_rays
%matplotlib
from arcus.arcus import ArcusForPlot, Arcus
arc = ArcusForPlot()
fig = mlab.figure(size=(800, 640))
arc.elements[0].display['shape'] = 'None'
outinstrum = plot_object(arc, viewer=fig)
EQPegAspec = Table.read('../inputdata/EQPegA_flux.tbl', format='ascii',
names=['energy', 'flux'])
# restrict table to ARCUS energy range
EQPegAspec = EQPegAspec[(EQPegAspec['energy'] > 0.25) &
(EQPegAspec['energy'] < 1.5)]
coord = astropy.coordinates.SkyCoord.from_name("EQ Peg A")
mysource = DefaultSource(coords=coord, energy=EQPegAspec,
geomarea=arc.elements[0].area,
flux=(EQPegAspec['flux'][1:] * np.diff(EQPegAspec['energy'])).sum())
mypointing = DefaultPointing(coords=coord)
mypointing = DefaultPointing() mysource = DefaultSource(energy=e) photons = mysource.generate_photons(n_photons) photons = mypointing(photons) instrum = Arcus() photons = instrum(photons) ind = (photons['probability'] > 0) posdat = visualization.utils.format_saved_positions(arcus.arcus.keeppos4)[ind, :, :] fig = mlab.figure() obj = plot_object(arcus.arcus.arcus4, viewer=fig) rays = plot_rays(posdat, scalar=photons['energy'][ind]) d = np.dstack(keeppos.data) d = np.swapaxes(d, 1, 2) d = h2e(d) marxs.visualization.mayavi.plot_rays(d, scalar=photons['order'], viewer=fig) arcus.arcus.plot(format="mayavi", viewer=fig) theta, phi = np.mgrid[-0.2 + np.pi:0.2 + np.pi:60j, -1:1:60j] arcus.rowland.plot(theta=theta, phi=phi, viewer=fig, format='mayavi') photonsm = mysource.generate_photons(n_photons)
photons = my_source.generate_photons(.2)
photons = my_pointing(photons)
photons = instrum3(photons)
ind = (photons['facet'] >= 0) & (photons['CCD_ID'] >= 0)
positions = [instrum3.KeepPos.format_positions()[ind, :, :]]
colorid = [photons['colorindex'][ind]]
# saving to x3d resets color scale form in to max, ignoring vmin and vmax
# Thus, add a non-visible line here with color 5 to prevent that
positions.append(np.zeros((2, 5, 3)))
colorid.append([0, 5])
fig = mlab.figure(**kwargsfig)
out = marxsavi.plot_object(instrum, viewer=fig)
out = marxs.visualization.mayavi.plot_rays(np.concatenate(positions, axis=0),
scalar=np.hstack(colorid),
kwargssurface={'opacity': .5,
'line_width': 1,
'colormap': 'gist_rainbow',
'vmin': 0,
'vmax': 5})
# overview
mlab.view(100, 60, 2800, [20, -200, 1800], roll=225)
fig.scene.save('../JATIS/mayavi_overview.png')
fig.scene.save('../JATIS/mayavi_overview.pdf')
fig.scene.save('../JATIS/web/REDSoX.x3d')
# gratings
mlab.view(60, 60, 900, [-30, -60, 1600], roll=270)
# object to save intermediate photons positions after every step of the simulation
pos = simulator.KeepCol('pos')
instrum = simulator.Sequence(elements=[aper, mirr, gas, det, projectfp],
postprocess_steps=[pos])
star = source.PointSource(coords=SkyCoord(30., 30., unit='deg'),
energy=1.,
flux=1.)
pointing = source.FixedPointing(coords=SkyCoord(30., 30., unit='deg'))
photons = star.generate_photons(100)
photons = pointing(photons)
photons = instrum(photons)
ind = (photons['probability'] > 0) & (photons['facet'] >= 0)
posdat = pos.format_positions()[ind, :, :]
fig = mlab.figure(bgcolor=(1, 1, 1))
obj = plot_object(instrum, viewer=fig)
rays = plot_rays(posdat,
scalar=photons['colorid'][ind],
kwargssurface={
'opacity': .5,
'line_width': 1,
'colormap': 'blue-red'
})
mlab.view(45, 54, 15000, [7200., -400, -900])
rowland.display['coo2'] = np.linspace(-.2, .2, 60)
obj = plot_object(rowland, viewer=fig)
mlab.savefig('../subaper3d.png')
mlab.savefig('../web/subaper.x3d')
star = source.PointSource(coords=target, energy=.5, flux=1.)
pointing = source.FixedPointing(coords=target)
photons = star.generate_photons(5000)
photons = pointing(photons)
photons = instrument(photons)
ind = (photons['probability'] >= 0) & (photons['facet'] >= 0)
posdat = keeppos.format_positions()[ind, :, :]
pp = photons[ind]
fig = mlab.figure()
plot_object(instrument, viewer=fig)
plot_rays(posdat, scalar=pp['order'])
return fig
fig = make_x3dplot(demo_onaxis_full)
demo_rowland.display['coo2'] = np.linspace(-.2, .2, 60)
obj = plot_object(demo_rowland, viewer=fig)
mlab.savefig(os.path.join(x3dpath, 'toy_chandralike.x3d'))
fig = make_x3dplot(demo_onaxis_sub)
obj = plot_object(demo_rowland, viewer=fig)
mlab.savefig(os.path.join(x3dpath, 'toy_subaper.x3d'))
# ## CAT gratings
#
# - Are illuminated at an angle, thus they work better with a tilted Rowland torus.
# - Diffract almost exculsively to one side.
# - Diffract efficiently into higher orders.
# - Need CCDs to image those high orders, but also want to see zeroth order for wavelength calibration.
alpha = 0.4
from marxs.source import PointSource, FixedPointing
from marxs.visualization.mayavi import plot_object, plot_rays
from redsox.redsox import PerfectRedsox, xyz2zxy
from redsox.gosox import PerfectGosox
from redsox.mirror import Ageom
%matplotlib
#instrum = PerfectRedsox()
instrum = PerfectGosox()
fig = mlab.figure()
mlab.clf()
out = plot_object(instrum, viewer=fig)
my_source = PointSource(coords=SkyCoord(30., 30., unit='deg'), energy=0.25,
polarization=120.,
geomarea=Ageom)
my_pointing = FixedPointing(coords=SkyCoord(30., 30., unit='deg'),
reference_transform=xyz2zxy)
photons = my_source.generate_photons(10)
photons = my_pointing(photons)
photons = instrum(photons)
pos = instrum.KeepPos.format_positions()
ind = np.isfinite(photons['order']) & (photons['facet'] < 2000) & (photons['CCD_ID'] >= 0)
out = marxs.visualization.mayavi.plot_rays(pos[ind, :, :], scalar=photons['order'][ind], viewer=fig)
import os
from mayavi import mlab
import marxs.visualization.mayavi as marxsavi
from settings import figureout, kwargsfig
import sys
sys.path.append('../redsox')
import redsox
%matplotlib
fig = mlab.figure(**kwargsfig)
for g in redsox.grat2.elements:
g.display['color'] = (1.0, 1., 1.)
out = marxsavi.plot_object(redsox.grat1, viewer=fig)
for g in redsox.grat2.elements:
g.display['color'] = (1.0, 0.5, 0.5)
out = marxsavi.plot_object(redsox.grat2, viewer=fig)
for g in redsox.grat3.elements:
g.display['color'] = (0.5, 0.5, 1.0)
out = marxsavi.plot_object(redsox.grat3, viewer=fig)
mlab.plot3d([0, 0], [0, 0], [2e3, 1.1e3], color=(1., 1., 1.),
opacity=0.3, tube_radius=20, tube_sides=20)
mlab.view(azimuth=40, elevation=70, distance=700, focalpoint=[0, 0, 1620])
fig.scene.save(os.path.join(figureout, 'gratings_mayavi.pdf'))
opacity=0.7,
color=(0., 0., 0.))
lines = np.array([[[rgx, rgy, rgz], [0, rgy, rgz]],
[[0, 0, 0], [0, rgy, rgz]],
[[rgx, rgy, rgz], [0, 0, 0]]])
out = marxs.visualization.mayavi.plot_rays(lines,
scalar=np.zeros(3),
kwargssurface={'opacity': 1.,
'line_width': 3,
'colormap': 'blue-red'})
return rgx, rgy, rgz, r
figcoordsys = mlab.figure(bgcolor=(1,1,1), size=(1000, 1000))
out = marxsavi.plot_object(Sequence(elements=instrum.elements[3:]),
viewer=figcoordsys)
out = marxsavi.plot_object(instrums[0].elements[2], viewer=figcoordsys)
rgx, rgy, rgz, r = plot_correct_rg()
#plot_halfplanes(r * 1, rgy, rgz)
plot_axes(x=[-0.1 * r, 1.05 * r], z=[0, rgz * 1.05])
# Mark center of gratings
mlab.points3d(rgx, rgy, rgz, scale_factor=3)
# yz - plane
mlab.triangular_mesh(np.array([0., 0, 0, 0]),
rgy*2 * np.array([-1, 1, 1., -1]),
r * np.array([0, 0., 1, 1]),
[[0, 1, 2], [0, 2, 3]],
opacity=0.3,
color=(.4, .4, .4))
# mlab.view(azimuth=33, elevation=50, distance=300, focalpoint=[20, 14, 62], roll=300)
# display is usually set for all objects of a class.
# To change only one mirror, make a copy of this dictionary first.
m2.display = copy(m2.display)
m2.display['color'] = 'blue'
det = FlatDetector(position=[50, 0, 2e2], zoom=[1., 20., 20.])
# Make an object that keeps the photon position after every simulation step
# for later plotting of photon paths.
pos = KeepCol('pos')
experiment = Sequence(elements=[m1, m2, det], postprocess_steps=[pos])
from mayavi import mlab
from marxs.visualization.mayavi import plot_object, plot_rays
fig = mlab.figure()
for i, angle in enumerate(np.arange(0, .5, .15) * np.pi):
rotmat = np.eye(4)
rotmat[:3, :3] = euler.euler2mat(angle, 0, 0, 'szxy')
light.position = np.dot(rotmat, light_pos)
light.dir = np.dot(rotmat, light_dir)
m1.pos4d = np.dot(rotmat, m1pos4d)
rays = light(100)
pos.data = []
pos(rays)
rays = experiment(rays)
# Now do the plotting
obj = plot_object(experiment, viewer=fig)
rout = plot_rays(pos.format_positions())
mlab.view(-60, 140., 525, [70., 60., 60.], roll=250)
mlab.savefig('../3dpol.pdf')
mlab.savefig('../web/3dpol.x3d')