def _get_dP(self, i):
key = (i, self.theta % 360)
if key in self._cache:
return self._cache[key]
if self.TS is not None:
self.F = self.TS[i]
th = np.pi / 180 * self.theta
N = [np.sin(th), 0, np.cos(th)]
print('projecting')
Bx = yt.off_axis_projection(self.F,
self.C,
N,
self.W,
self.px,
'magnetic_field_x',
north_vector=[0, 1, 0],
num_threads=4)
By = yt.off_axis_projection(self.F,
self.C,
N,
self.W,
self.px,
'magnetic_field_y',
north_vector=[0, 1, 0],
num_threads=4)
Bz = yt.off_axis_projection(self.F,
self.C,
N,
self.W,
self.px,
'magnetic_field_z',
north_vector=[0, 1, 0],
num_threads=4)
print('done')
# S = [ cos(th), 0, -sin(th)]
# T = [ 0, 1, 0 ]
# U = N
Bs = cm * ((Bx * np.cos(th) - Bz * np.sin(th)) / cm
) # .to_equivalent('gauss', 'CGS')
Bt = cm * (By / cm) # .to_equivalent('gauss', 'CGS')
if self.smoothing > 0:
Bs = gaussian_filter(Bs /
(gauss * cm), self.smoothing) * gauss * cm
Bt = gaussian_filter(Bt /
(gauss * cm), self.smoothing) * gauss * cm
# The momenta here will be converted to PIC units (P / (m_e c))
dPs = -((q * Bt) / (m * c**2)).to('1').v
dPt = +((q * Bs) / (m * c**2)).to('1').v
dPs *= self.strength_scale
dPt *= self.strength_scale
self._cache[key] = (dPs, dPt)
return dPs, dPt
def proton_dP(self, theta, resolution_um, energy_MeV):
th = np.pi / 180 * theta
N = [np.sin(th), 0, np.cos(th)]
resolution = Q(resolution_um, 'um')
px_s = int(self.Ws / resolution) + 1
px_t = int(self.Wt / resolution) + 1
px = [px_s, px_t]
Bx = yt.off_axis_projection(self.F,
self.C,
N,
self.W,
px,
'magnetic_field_x',
north_vector=[0, 1, 0])
By = yt.off_axis_projection(self.F,
self.C,
N,
self.W,
px,
'magnetic_field_y',
north_vector=[0, 1, 0])
Bz = yt.off_axis_projection(self.F,
self.C,
N,
self.W,
px,
'magnetic_field_z',
north_vector=[0, 1, 0])
# S = [ cos(th), 0, -sin(th)]
# T = [ 0, 1, 0 ]
# U = N
Bs = cm * ((Bx * np.cos(th) - Bz * np.sin(th)) / cm).to_equivalent(
'gauss', 'CGS')
Bt = cm * (By / cm).to_equivalent('gauss', 'CGS')
s0 = np.linspace(-self.Ws, self.Ws, px_s) / 2
t0 = np.linspace(-self.Wt, self.Wt, px_t) / 2
[S0, T0] = np.meshgrid(s0, t0, indexing='ij')
# The momenta here will be converted to PIC units (P / (m_e c))
# F ~= N x B = [
dPs = -(q * Bt) / (m * c**2)
dPt = +(q * Bs) / (m * c**2)
E_normalised = Q(energy_MeV, 'MeV') / (m * c**2)
P0 = np.sqrt((E_normalised + M / m)**2 - (M / m)**2)
return S0, T0, dPs, dPt, P0
def project_density(self, data_object=None):
"""
Project density on a plane
Input:
data_object: a yt data object. Optional. Defalt is all_data
"""
if data_object is None:
ad = self.ds
else:
ad = data_object
if ((self.derive_field()) and (self.window_created)):
c = self.window_center
L = self.los
W = self.window_width
N = self.window_resolution
if isinstance(self.window_north_vector, str):
if self.window_north_vector == "pos_north":
north_vector = self.pos_north
elif self.window_north_vector == "pos_east":
north_vector = self.pos_east
else:
north_vector = self.window_north_vector
self.proj_density = yt.off_axis_projection(ad,c, L, W, N, "density",
north_vector=north_vector,
no_ghost=False,
num_threads = 2)
else:
sys.exit("Error: Projection window is not defined")
return True
def project_LOS_b(self, data_object=None):
"""
Integral LOS magnetic field along LOS
Input:
data_object: a yt data object. Optional. Defalt is all_data
"""
if data_object is None:
ad = self.ds
else:
ad = data_object
if ((self.add_LOS_bfield()) and (self.window_created)):
c = self.window_center
L = self.los
W = self.window_width
N = self.window_resolution
if isinstance(self.window_north_vector, str):
if self.window_north_vector == "pos_north":
north_vector = self.pos_north
elif self.window_north_vector == "pos_east":
north_vector = self.pos_east
else:
north_vector = self.window_north_vector
self.integral_LOS_b = yt.off_axis_projection(ad,c, L, W, N,
"magnetic_field_LOS",
#weight="density",
north_vector=north_vector,
no_ghost=False,
num_threads = 2)
else:
sys.exit("Error: Projection window is not defined")
return True
def project_stokes(self, data_object=None):
"""
Project stokes parameter on a plane
Input:
data_object: a yt data object. Optional. Defalt is all_data
"""
if data_object is None:
ad = self.ds
else:
ad = data_object
if ((self.derive_field()) and (self.window_created)):
c = self.window_center
L = self.los
W = self.window_width
N = self.window_resolution
if isinstance(self.window_north_vector, str):
if self.window_north_vector == "pos_north":
north_vector = self.pos_north
elif self.window_north_vector == "pos_east":
north_vector = self.pos_east
else:
north_vector = self.window_north_vector
self.proj_Q = yt.off_axis_projection(ad,c, L, W, N, "stokes_Q",
weight="density",
north_vector=north_vector,
no_ghost=False,
num_threads = 2)
self.proj_U = yt.off_axis_projection(ad,c, L, W, N, "stokes_U",
weight="density",
north_vector=north_vector,
no_ghost=False,
num_threads = 2)
self.DOP=np.sqrt(self.proj_Q**2+self.proj_U**2)
self.offset=.5*np.arctan2(self.proj_U,self.proj_Q)
else:
sys.exit("Error: Projection window is not defined")
return True
def thinnest_edge(ds, r, cen, wid, res):
# r is the current radius around which the edge-on galaxy will be searched for
rs = np.arange(r - 5, r + 6)
rad = []
sig = []
for current_rad in rs:
# Defines data sphere at current radius
sp = ds.sphere(cen, (current_rad, 'kpc'))
# Calculates angular momentum vector and orthogonal vector at the current radius
L = sp.quantities.angular_momentum_vector()
L /= np.linalg.norm(L)
Lx = np.cross(L, [1., 0., 0.])
Lx /= np.linalg.norm(Lx)
image = yt.off_axis_projection(ds,
cen,
Lx,
wid * kpc,
res,
'density',
north_vector=L)
pix = [range(res)[i] * wid / res - wid / 2 for i in range(res)]
# Take average density of each row of pixels
avg_rho = [np.mean(image[:, i]) for i in range(len(pix))]
# Fits a gaussian to the average density versus column number
fit, er = opt.curve_fit(gaus, pix, avg_rho)
# adds the current radius and sigma value to a list for comparison
sig.append(fit[2])
rad.append(current_rad)
return rad[sig.index(min(sig))]
if i > N/2.:
cn = 1.*(i - N/2.)
x_w = max(W_kpc_initial - cn, W_kpc_final)
y_w = max(W_kpc_initial - cn, W_kpc_final)
z_w = max(W_kpc_initial - cn, W_kpc_final)
else:
x_w = W_kpc_initial
y_w = W_kpc_initial
z_w = W_kpc_initial
W = YTArray([x_w, y_w, z_w], 'kpc')
print W, L
N = 512
image1 = yt.off_axis_projection(ds_1, cen_1, L, W, N, ('gas', 'density'), north_vector = north_vector)
image2 = yt.off_axis_projection(ds_2, cen_2, L, W, N, ('gas', 'density'), north_vector = north_vector)
fig, axes = subplots(1,2, figsize = (10.8, 5))
image1 = image1.in_units('Msun * kpc**-2')
image2 = image2.in_units('Msun * kpc**-2')
im1 = axes[0].imshow(np.log10(image1), vmin = 4.5, vmax = 9.5)
im2 = axes[1].imshow(np.log10(image2), vmin = 4.5, vmax = 9.5)
bar_len_kpc = 10.
bar_len_pix = 1.*N/x_w * bar_len_kpc
def generate_ray_image_data(field_list,
weight_list,
model='P0',
output=3195,
ray_data_file='',
data_loc='.',
ion_list='all',
redshift=None):
# load data set with yt, galaxy center, and the bulk velocity of the halo
ds, gcenter, bulk_velocity = ds, gcenter, bv = spg.load_simulation_properties(
model)
# for annoying reasons... need to convert ray positions to "code units"
code_unit_conversion = ds.domain_right_edge.d / ds.domain_right_edge.in_units(
'kpc').d
print(ray_data_file)
ray_id_list, impact, bvx, bvy, bvz, xi, yi, zi, xf, yf, zf, cx, cy, cz =\
np.loadtxt(ray_data_file, skiprows = 1, unpack = True)
bulk_velocity = YTArray([bvx[0], bvy[0], bvz[0]], 'km/s')
# set field parameters so that trident knows to subtract off bulk velocity
ad = ds.all_data()
ad.set_field_parameter('bulk_velocity', bulk_velocity)
ad.set_field_parameter('center', gcenter)
width = ds.arr([300., 20., 10], 'kpc')
for i in [5]:
# generate the coordinates of the random sightline
# write ray id, impact parameter, bulk velocity, and start/end coordinates out to file
h5file = h5.File(
'%s/ray_image_data_%s_%i_%i.h5' %
(data_loc, model, output, ray_id_list[i]), 'w')
ray_start = ds.arr([xi[i], yi[i], zi[i]], 'kpc')
ray_end = ds.arr([xf[i], yf[i], zf[i]], 'kpc')
ray_direction = ray_end - ray_start
ray_center = ray_start + 0.5 * ray_direction
normal_vector = ds.arr(gcenter, 'kpc') - ray_center
normal_vector = np.cross(normal_vector, ray_direction)
for field, weight in zip(field_list, weight_list):
print(field)
sys.stdout.flush()
if weight is not None:
weight = ('gas', weight)
if field not in h5file.keys():
#left_edge =
#box = ds.region(ray_center, )
image = yt.off_axis_projection(ds,
center=ray_center,
normal_vector=normal_vector,
width=width,
resolution=[1200, 80],
item=('gas', field),
weight=weight)
h5file.create_dataset(field, data=image)
h5file.flush()
h5file.close()
print("saved sightline data %i\n" % (i))
def snapshot_to_tfrec(snapshot_file, save_dir, num_of_projections,
number_of_virtual_nodes, number_of_neighbours, plotting):
"""
load snapshot plt file, rotate for different viewing angles, make projections and corresponding graph nets. Save to
tfrec files.
Args:
snapshot: snaphot
save_dir: location of tfrecs
Returns:
"""
print('loading particle and plt file {}...'.format(snapshot_file))
t_0 = default_timer()
# filename = 'turbsph_hdf5_plt_cnt_{:04d}'.format(snapshot) # only plt file, will automatically find part file
#
# file_path = folder_path + filename
# print(f'file: {file_path}')
ds = yt.load(
snapshot_file
) # loads in data into data set class. This is what we will use to plot field values
ad = ds.all_data(
) # Can call on the data set's property .all_data() to generate a dictionary
# containing all data available to be parsed through.
# e.g. print ad['mass'] will print the list of all cell masses.
# if particles exist, print ad['particle_position'] gives a list of each particle [x,y,z]
print('done, time: {}'.format(default_timer() - t_0))
snapshot = int(snapshot_file[-4:])
field = 'density'
width = np.max(ad['x'].to_value()) - np.min(ad['x'].to_value())
resolution = 256
print('making property_values...')
t_0 = default_timer()
property_values = []
# property_transforms = [lambda x: x, lambda x: x, lambda x: x, lambda x: x, lambda x: x, lambda x: x, lambda x: x,
# np.log10, np.log10, np.log10, np.log10]
property_names = [
'x', 'y', 'z', 'velocity_x', 'velocity_y', 'velocity_z',
'gravitational_potential', 'density', 'temperature', 'cell_mass',
'cell_volume'
]
unit_names = [
'pc', 'pc', 'pc', '10*km/s', '10*km/s', '10*km/s', 'J/mg',
'Msun/pc**3', 'K', 'Msun', 'pc**3'
]
_values = []
for name, unit in zip(property_names, unit_names):
if name == 'gravitational_potential':
_values.append(-ad[name].in_units(unit).to_value())
else:
_values.append(ad[name].in_units(unit).to_value())
# property_values = np.array(property_values) # n f
property_values = np.array(_values).T # n f
print('done, time: {}'.format(default_timer() - t_0))
print('making projections and rotating coordinates')
t_0 = default_timer()
positions = []
properties = []
proj_images = []
extra_info = []
projections = 0
while projections < num_of_projections:
print(projections)
V = np.eye(3)
R = _random_special_ortho_matrix(3)
Vprime = np.linalg.inv(R) @ V
north_vector = Vprime[:, 1]
viewing_vec = Vprime[:, 2]
_extra_info = [
snapshot, projections, viewing_vec[0], viewing_vec[1],
viewing_vec[2], north_vector[0], north_vector[1], north_vector[2],
resolution, width
] #, field]
proj_image = yt.off_axis_projection(ds,
center=[0, 0, 0],
normal_vector=viewing_vec,
north_vector=north_vector,
item=field,
width=width,
resolution=resolution)
proj_image = np.log10(np.where(proj_image < 1e-5, 1e-5, proj_image))
xyz = property_values[:, :3] # n 3
velocity_xyz = property_values[:, 3:6] # n 3
rotated_xyz = (R @ xyz.T).T
rotated_velocity_xyz = (R @ velocity_xyz.T).T
_properties = property_values.copy() # n f
_properties[:, :3] = rotated_xyz # n f
_properties[:, 3:6] = rotated_velocity_xyz # n f
_properties[:, 6] = _properties[:, 6] # n f
_positions = xyz # n 3
positions.append(_positions)
properties.append(_properties)
proj_images.append(proj_image)
extra_info.append(_extra_info)
projections += 1
generate_data(positions=positions,
properties=properties,
proj_images=proj_images,
extra_info=extra_info,
save_dir=save_dir,
number_of_virtual_nodes=number_of_virtual_nodes,
plotting=plotting,
number_of_neighbours=number_of_neighbours)
print('done, time: {}'.format(default_timer() - t_0))
hc = HaloCatalog(data_ds=ds, halos_ds=halos_ds)
hc.load()
halos = hc.halo_list
fieldname = 'gas'
fieldvalue = fields[fieldname]
index = 0
halo = halos[index]
# PROJECTION PLOT
com = [halo.quantities['particle_position_x'], halo.quantities['particle_position_y'], halo.quantities['particle_position_z']]
sp = ds.sphere(com, (30, 'kpc'))
amv = sp.quantities.angular_momentum_vector()
amv = amv / np.sqrt((amv**2).sum())
center = sp.quantities.center_of_mass()
res = 1024
width = [0.01, 0.01, 0.01]
image = yt.off_axis_projection(ds, center, amv, width, res, fieldvalue)
yt.write_image(np.log10(image), '%s_%d_%s_offaxis_projection.png' % (ds, index, fieldname))
# PROFILE PLOT
com = [halo.quantities['particle_position_x'], halo.quantities['particle_position_y'], halo.quantities['particle_position_z']]
sp = ds.sphere(com, (30, 'kpc'))
profiles = yt.create_profile(sp, 'radius', fields.values())
for fieldname, field in fields.iteritems():
plt.loglog(profiles.x, profiles[field], label=fieldname)
plt.xlim([profiles.x.min(), profiles.x.max()])
plt.xlabel('Radius $[kpc]$')
plt.ylabel('$\\rho [M_{\odot} kpc^{-3}]$')
plt.legend(loc='best')
plt.savefig('%s_%d_profile.png' % (ds, index))
frame = 1
stepAlpha=np.pi/100
stepBeta=np.pi/100
frames=math.ceil(2*np.pi/stepAlpha)
for i in np.arange(0, frames):
L = yksikkovektori(alpha, beta)
N = yksikkovektori(alpha+stepAlpha, beta+stepBeta)
# Now we call the off_axis_projection function, which handles the rest.
# Note that we set no_ghost equal to False, so that we *do* include ghost
# zones in our data. This takes longer to calculate, but the results look
# much cleaner than when you ignore the ghost zones.
# Also note that we set the field which we want to project as "density", but
# really we could use any arbitrary field like "temperature", "metallicity"
# or whatever.
image = yt.off_axis_projection(ds, tiheysmaksimi, L, W, Npixels, "density", north_vector=N, no_ghost=False, steady_north=True)
# Image is now an NxN array representing the intensities of the various pixels.
# And now, we call our direct image saver. We save the log of the result.
#yt.write_projection(image, "slice/kaanto/offaxis_projection_colorbar%04i.png" %frame, colorbar_label="Column Density (cm$^{-2}$)")
yt.write_projection(image, "kuvat/%04i.png" %frame, cmap_name='hot')
frame+=1
alpha+=stepAlpha
beta+=stepBeta
def generate_ray_image_data(field_list,
weight_list,
model='P0',
output=3195,
ray_data_file='../../data/P0_z0.25_ray_data.dat',
data_loc='.',
ion_list='all',
redshift=None):
# load data set with yt, galaxy center, and the bulk velocity of the halo
if model == 'P0':
ds = yt.load('~/Work/galaxy/P0/P0.003195')
ds.add_field(('gas', 'mass'),
function=_mass2,
units='g',
sampling_type='particle')
trident.add_ion_fields(ds, ions=ion_list)
# for annoying reasons... need to convert ray positions to "code units"
code_unit_conversion = ds.domain_right_edge.d / ds.domain_right_edge.in_units(
'kpc').d
ray_id_list, impact, bvx, bvy, bvz, xi, yi, zi, xf, yf, zf, cx, cy, cz =\
np.loadtxt(ray_data_file, skiprows = 1, unpack = True)
gcenter_kpc = [cx[0], cy[0], cz[0]
] # assuming galaxy center is the same for all sightlines
gcenter = gcenter_kpc * code_unit_conversion
bulk_velocity = YTArray([bvx[0], bvy[0], bvz[0]], 'km/s')
# set field parameters so that trident knows to subtract off bulk velocity
ad = ds.all_data()
ad.set_field_parameter('bulk_velocity', bulk_velocity)
ad.set_field_parameter('center', gcenter)
width = np.array([300., 20., 20.]) # kpc
width *= code_unit_conversion
ray_start_list = np.ndarray(shape=(0, 3))
ray_end_list = np.ndarray(shape=(0, 3))
for i in range(len(xi)):
ray_start_list = np.vstack(
(ray_start_list, [xi[i], yi[i], zi[i]] * code_unit_conversion))
ray_end_list = np.vstack(
(ray_end_list, [xf[i], yf[i], zf[i]] * code_unit_conversion))
for i in [0, 10]:
# generate the coordinates of the random sightline
# write ray id, impact parameter, bulk velocity, and start/end coordinates out to file
h5file = h5.File(
'%s/ray_image_data_%s_%i_%i.h5' %
(data_loc, model, output, ray_id_list[i]), 'a')
ray_center = ray_start_list[i] + 0.5 * (ray_end_list[i] -
ray_start_list[i])
ray_direction = ray_end_list[i] - ray_start_list[i]
print(ray_center, ray_direction, width)
# ray_center = [-0.42299158, -0.30013312, 0.13297239]
# ray_direction = [0.6779612, -0.68934122, -0.25529845]
# image = yt.off_axis_projection(ds, ray_center, ray_direction, width,
# [1200, 80], ('gas', 'temperature'), weight = ('gas', 'density'))
for field, weight in zip(field_list, weight_list):
print(field)
sys.stdout.flush()
if weight is not None:
weight = ('gas', weight)
if field not in h5file.keys():
image = yt.off_axis_projection(ds,
ray_center,
ray_direction,
width, [1200, 80],
('gas', field),
weight=weight)
h5file.create_dataset(field, data=image)
h5file.flush()
h5file.close()
print("saved sightline data %i\n" % (i))
ceny = cen_fits['SAT_%.2i' % sat_n].data['box_avg'][1]
cenz = cen_fits['SAT_%.2i' % sat_n].data['box_avg'][2]
cen = yt.YTArray([cenx, ceny, cenz], 'kpc')
#vel_vec = array([anchor_vxs_box_avg, anchor_vys_box_avg, anchor_vzs_box_avg])
#L = vel_vec/np.linalg.norm(vel_vec)
#L = [1*math.cos(pi*(DD)/100.),0, 1*math.sin(pi*(DD)/100.)] # vector normal to cutting plane
fig, axes = plt.subplots(3, 2, figsize=(10.8, 15))
for i in arange(3):
L = Ls[i]
image1 = yt.off_axis_projection(ds,
cen,
L,
W1,
N, ('gas', 'density'),
north_vector=north_vector)
image2 = yt.off_axis_projection(ds,
cen,
L,
W2,
N, ('gas', 'density'),
north_vector=north_vector)
image1 = image1.in_units('Msun * kpc**-2')
image2 = image2.in_units('Msun * kpc**-2')
im1 = axes[i, 0].imshow(np.log10(image1), vmin=4.5, vmax=9.5)
im2 = axes[i, 1].imshow(np.log10(image2), vmin=4.5, vmax=9.5)
# dataset, we'll just choose an off-axis value at random. This gets normalized
# automatically.
L = [0.5, 0.4, 0.7]
# Our "width" is the width of the image plane as well as the depth.
# The first element is the left to right width, the second is the
# top-bottom width, and the last element is the back-to-front width
# (all in code units)
W = [0.04, 0.04, 0.4]
# The number of pixels along one side of the image.
# The final image will have Npixel^2 pixels.
Npixels = 512
# Now we call the off_axis_projection function, which handles the rest.
# Note that we set no_ghost equal to False, so that we *do* include ghost
# zones in our data. This takes longer to calculate, but the results look
# much cleaner than when you ignore the ghost zones.
# Also note that we set the field which we want to project as "density", but
# really we could use any arbitrary field like "temperature", "metallicity"
# or whatever.
image = yt.off_axis_projection(ds, c, L, W, Npixels, "density", no_ghost=False)
# Image is now an NxN array representing the intensities of the various pixels.
# And now, we call our direct image saver. We save the log of the result.
yt.write_projection(
image,
"offaxis_projection_colorbar.png",
colorbar_label="Column Density (cm$^{-2}$)",
)
frames = math.ceil(2 * np.pi / stepAlpha)
for i in np.arange(0, frames):
L = yksikkovektori(alpha, beta)
N = yksikkovektori(alpha + stepAlpha, beta + stepBeta)
# Now we call the off_axis_projection function, which handles the rest.
# Note that we set no_ghost equal to False, so that we *do* include ghost
# zones in our data. This takes longer to calculate, but the results look
# much cleaner than when you ignore the ghost zones.
# Also note that we set the field which we want to project as "density", but
# really we could use any arbitrary field like "temperature", "metallicity"
# or whatever.
image = yt.off_axis_projection(ds,
tiheysmaksimi,
L,
W,
Npixels,
"density",
north_vector=N,
no_ghost=False,
steady_north=True)
# Image is now an NxN array representing the intensities of the various pixels.
# And now, we call our direct image saver. We save the log of the result.
#yt.write_projection(image, "slice/kaanto/offaxis_projection_colorbar%04i.png" %frame, colorbar_label="Column Density (cm$^{-2}$)")
yt.write_projection(image, "kuvat/%04i.png" % frame, cmap_name='hot')
frame += 1
alpha += stepAlpha
beta += stepBeta
index = 0
halo = halos[index]
# PROJECTION PLOT
com = [
halo.quantities['particle_position_x'],
halo.quantities['particle_position_y'],
halo.quantities['particle_position_z']
]
sp = ds.sphere(com, (30, 'kpc'))
amv = sp.quantities.angular_momentum_vector()
amv = amv / np.sqrt((amv**2).sum())
center = sp.quantities.center_of_mass()
res = 1024
width = [0.01, 0.01, 0.01]
image = yt.off_axis_projection(ds, center, amv, width, res, fieldvalue)
yt.write_image(np.log10(image),
'%s_%d_%s_offaxis_projection.png' % (ds, index, fieldname))
# PROFILE PLOT
com = [
halo.quantities['particle_position_x'],
halo.quantities['particle_position_y'],
halo.quantities['particle_position_z']
]
sp = ds.sphere(com, (30, 'kpc'))
profiles = yt.create_profile(sp, 'radius', fields.values())
for fieldname, field in fields.iteritems():
plt.loglog(profiles.x, profiles[field], label=fieldname)
plt.xlim([profiles.x.min(), profiles.x.max()])
plt.xlabel('Radius $[kpc]$')
def graph_img_plotting(plotting, virtual_properties, senders, receivers,
xray_image, positions, hot_gas_positions, base_data_dir,
center, vprime, dataset, sphere):
max_pos = np.max(positions.T, axis=1)
min_pos = np.min(positions.T, axis=1)
box_size = max_pos - min_pos
if plotting == 1:
print('Plotting xray...')
fig, ax = plt.subplots(1, 3, figsize=(18, 6))
ax[0].scatter(center[0] + hot_gas_positions[:, 0],
center[1] + hot_gas_positions[:, 1],
s=10**(2.5 - np.log10(positions.shape[0])))
ax[0].set_xlim(center[0] - 0.5 * box_size[0],
center[0] + 0.5 * box_size[0])
ax[0].set_ylim(center[1] - 0.5 * box_size[1],
center[1] + 0.5 * box_size[1])
ax[0].set_title('Hot gas particles')
ax[1].imshow(xray_image)
# fig.colorbar(xray_plot, ax=ax[1])
ax[1].set_title('Xray')
ax[2].scatter(center[0] + positions.T[0],
center[1] + positions.T[1],
s=10**(2.5 - np.log10(positions.shape[0])))
ax[2].set_xlim(center[0] - 0.5 * box_size[0],
center[0] + 0.5 * box_size[0])
ax[2].set_ylim(center[1] - 0.5 * box_size[1],
center[1] + 0.5 * box_size[1])
ax[2].set_title('Gas particles')
plt.savefig(os.path.join(base_data_dir, 'images/xray.png'))
plt.show()
if plotting > 1:
graph = nx.OrderedMultiDiGraph()
n_nodes = virtual_properties.shape[
0] # number of nodes is the number of positions
virtual_box_size = (np.min(virtual_properties[:, :3]),
np.max(virtual_properties[:, :3]))
pos = dict() # for plotting node positions.
edgelist = []
# Now put the data in the directed graph: first the nodes with their positions and properties
# pos just takes the x and y coordinates of the position so a 2D plot can be made
for node, feature, position in zip(np.arange(n_nodes),
virtual_properties,
virtual_properties[:, :3]):
graph.add_node(node, features=feature)
pos[node] = (position[:2] - virtual_box_size[0]) / (
virtual_box_size[1] - virtual_box_size[0])
# Next add the edges using the receivers and senders arrays we just created
# Note that an edge is added for both directions
# The features of the edge are dummy arrays at the moment
# The edgelist is for the plotting
# edges = np.stack([senders, receivers], axis=-1) + sibling_node_offset
for u, v in zip(senders, receivers):
graph.add_edge(u, v, features=np.array([1., 0.]))
edgelist.append((u, v))
print(f'Particle graph nodes : {graph.number_of_nodes()}')
print(f'Particle graph edges : {graph.number_of_edges()}')
dens_list = []
for n in list(graph.nodes.data('features')):
dens_list.append(n[1][6])
if plotting == 2:
# Plotting
fig, ax = plt.subplots(2, 2, figsize=(12, 12))
print('Plotting multiplot...')
draw(graph,
ax=ax[0, 0],
pos=pos,
edge_color='red',
node_size=10**(4 - np.log10(len(dens_list))),
width=0.1,
arrowstyle='-',
node_color=dens_list,
cmap='viridis')
# draw(graph, ax=ax[0, 0], pos=pos, node_color='blue', edge_color='red', node_size=10, width=0.1)
ax[0, 1].scatter(center[0] + hot_gas_positions[:, 0],
center[1] + hot_gas_positions[:, 1],
s=10**(2.5 - np.log10(positions.shape[0])))
ax[0, 1].set_xlim(center[0] - 0.5 * box_size[0],
center[0] + 0.5 * box_size[0])
ax[0, 1].set_ylim(center[1] - 0.5 * box_size[1],
center[1] + 0.5 * box_size[1])
ax[0, 1].set_title('Hot gas particles')
ax[1, 0].imshow(xray_image)
# fig.colorbar(xray_plot, ax=ax[1])
# ax[1].set_title('Xray')
ax[1, 1].scatter(center[0] + positions.T[0],
center[1] + positions.T[1],
s=10**(2.5 - np.log10(positions.shape[0])))
ax[1, 1].set_xlim(center[0] - 0.5 * box_size[0],
center[0] + 0.5 * box_size[0])
ax[1, 1].set_ylim(center[1] - 0.5 * box_size[1],
center[1] + 0.5 * box_size[1])
ax[1, 1].set_title('Gas particles')
print('Multiplot done, showing...')
plt.savefig(os.path.join(base_data_dir, 'images/multiplot.png'))
plt.show()
if plotting == 3:
interp_virtual_positions = virtual_properties[:, :3] / 1e4
fig, ax = plt.subplots(figsize=(8, 8))
_x = np.linspace(np.min(interp_virtual_positions[:, 0]),
np.max(interp_virtual_positions[:, 0]), 300)
_y = np.linspace(np.min(interp_virtual_positions[:, 1]),
np.max(interp_virtual_positions[:, 1]), 300)
_z = np.linspace(np.min(interp_virtual_positions[:, 2]),
np.max(interp_virtual_positions[:, 2]), 300)
x, y, z = np.meshgrid(_x, _y, _z, indexing='ij')
interp = interpolate.griddata(
(interp_virtual_positions[:, 0],
interp_virtual_positions[:, 1], interp_virtual_positions[:,
2]),
dens_list,
xi=(x, y, z),
fill_value=0.0)
im = np.mean(interp, axis=2).T[::-1, ]
im = np.log10(
np.where(im / np.max(im) < 1e-3, 1e-3, im / np.max(im)))
ax.imshow(im)
plt.savefig(os.path.join(base_data_dir, 'images/interp.png'))
plt.show()
if plotting == 4:
print('Plotting off-axis projection...')
east_vector = vprime[:, 0]
north_vector = vprime[:, 1]
viewing_vec = vprime[:, 2]
fig, ax = plt.subplots(figsize=(8, 8))
off_axis_image = yt.off_axis_projection(data_source=dataset,
center=sphere.center,
normal_vector=viewing_vec,
width=0.01 *
dataset.domain_width,
item='Density',
resolution=[400, 400],
north_vector=east_vector)
off_axis_image = np.log10(
np.where(off_axis_image < 1e17, 1e17, off_axis_image))
# off_axis_image = off_axis_image.to_ndarray() / np.max(off_axis_image.to_ndarray())
# off_axis_image = np.log10(np.where(off_axis_image < 1e-5, 1e-5, off_axis_image))
# print(f'Maximum : {np.max(off_axis_image)}')
off_axis_image = ax.imshow(off_axis_image)
fig.colorbar(off_axis_image, ax=ax)
plt.savefig(os.path.join(base_data_dir,
'images/off_axis_proj.png'))
plt.show()
# yt.write_image(np.log10(off_axis_image), os.path.join(base_data_dir, 'images/off_axis_proj.png'))
if plotting == 5:
print('Plotting 3D image...')
mlab.points3d(virtual_properties.T[0],
virtual_properties.T[1],
virtual_properties.T[2],
dens_list,
resolution=8,
scale_factor=0.15,
scale_mode='none',
colormap='viridis')
for u, v in zip(senders, receivers):
mlab.plot3d(
[virtual_properties[u][0], virtual_properties[v][0]],
[virtual_properties[u][1], virtual_properties[v][1]],
[virtual_properties[u][2], virtual_properties[v][2]],
tube_radius=None,
tube_sides=3,
opacity=0.1)
mlab.show()
"""
Make simple off-axis plot using data
"""
from GL import *
from cycler import cycler
import queb3
from queb3 import powerlaw_fit as plfit
import davetools as dt
import h5py
from matplotlib.pyplot import cm
import yt
reload(queb3)
simdir = "half_half"
frame = 30
plotdir = "/Users/Kye/512reruns/512rerunplots/offaxisplots"
ds = yt.load("/Users/Kye/512reruns/frbs/half_half/DD%04d/data%04d" %
(frame, frame))
L = [1, 1, 0] # vector normal to cutting plane
north_vector = [-1, 1, 0]
W = [0.02, 0.02, 0.02]
c = [0.5, 0.5, 0.5]
N = 512
image = yt.off_axis_projection(ds, c, L, W, N, "density")
yt.write_image(
np.log10(image),
"%s/%s/%04d_offaxis_density_projection.png" % (plotdir, simdir, frame))
width_kpc = radius * 2
le = center - radius
re = center + radius
box = ds.box(le, re)
# temperature cut mask (same used for mock images 1e5 K)
min_temperature = 1e5
box = box.cut_region('obj["temperature"] > %f' % min_temperature)
for field in fields:
print field
# density weighted projection same normal vector as mock images
proj = yt.off_axis_projection(box,
center=center,
normal_vector=[1, 1, 1],
width=width_kpc,
resolution=nside,
item=field,
weight='number_density')
proj = proj.v.T # transpose to get same orientation as mock image
residual = RadialProfile.residual_field(proj)
group.create_dataset('residual_%s' % field, data=residual)
# Make mask for large density fluctuations associated with small cold clumps
if field == 'number_density':
max_fluc = 5.
mask = residual < max_fluc
mask_group.attrs[
'large_density_fluctuation_volume_fraction'] = mask[
mask == 0].size / float(mask.size)
mask_group.attrs['max_dens_fluc'] = max_fluc
xs_box, ys_box, zs_box, vxs_box, vys_box, vzs_box, xs, ys, zs, vxs, vys, vzs = \
np.load('/Users/rsimons/Dropbox/rcs_foggie/outputs/nref11n_orig_%s_sat%i.npy'%(sname, sat_n))[()]
cen = yt.YTArray([xs_box, ys_box, zs_box], 'kpc')
L = yt.YTArray([vxs_box, vys_box, vzs_box], 'km/s')
L_init = L.copy()
L_init_mag = sqrt(sum(L_init**2))
L_mag = sqrt(sum(L**2))
L_n = L / L_mag
x = 16
fig, ax = plt.subplots(8, x, figsize=((12 / 8.) * x, 12))
print 'gas density off-axis projection'
sat_dens = yt.off_axis_projection(ds,
cen,
-L_n,
W,
N, ('gas', 'density'),
north_vector=north_vector)
print 'gas stellar density off-axis projection'
sat_mstar_dens = yt.off_axis_projection(ds,
cen,
-L_n,
W,
N, ('deposit', 'io_density'),
north_vector=north_vector)
kern = Gaussian2DKernel(0.5 * (N / 15.))
sat_mstar_dens_c = yt.YTArray(convolve_fft(sat_mstar_dens, kern),
sat_mstar_dens.units)
for i in arange(x):
cenS_i = cen + yt.YTArray([-x + 2 * i], 'kpc') * L_n
# Our image plane will be normal to some vector. For things like collapsing
# objects, you could set it the way you would a cutting plane -- but for this
# dataset, we'll just choose an off-axis value at random. This gets normalized
# automatically.
L = [0.5, 0.4, 0.7]
# Our "width" is the width of the image plane as well as the depth.
# The first element is the left to right width, the second is the
# top-bottom width, and the last element is the back-to-front width
# (all in code units)
W = [0.04,0.04,0.4]
# The number of pixels along one side of the image.
# The final image will have Npixel^2 pixels.
Npixels = 512
# Now we call the off_axis_projection function, which handles the rest.
# Note that we set no_ghost equal to False, so that we *do* include ghost
# zones in our data. This takes longer to calculate, but the results look
# much cleaner than when you ignore the ghost zones.
# Also note that we set the field which we want to project as "density", but
# really we could use any arbitrary field like "temperature", "metallicity"
# or whatever.
image = yt.off_axis_projection(ds, c, L, W, Npixels, "density", no_ghost=False)
# Image is now an NxN array representing the intensities of the various pixels.
# And now, we call our direct image saver. We save the log of the result.
yt.write_projection(image, "offaxis_projection_colorbar.png",
colorbar_label="Column Density (cm$^{-2}$)")
ds.add_field(("gas", "Byz"), function=_Byz, units="G", force_override=True)
#fig=plt.figure()
ad=ds.all_data()
#field = ["Byz"]
#p=yt.ProjectionPlot(ds, "x", field, weight_field="density")
#p.zoom(1)
#p.annotate_contour("Byz")
#p.annotate_magnetic_field()
#get(annotate_timestamp)
#p.run_callbacks()
#p.save()
L=np.array([2,1,1])
oap=yt.off_axis_projection(ds, ds.domain_center, L, ds.domain_width, (800, 800), "density")
#yt.write_oap()
oapp=yt.OffAxisProjectionPlot(ds, L, "Byz", weight_field="density")
oapp.annotate_contour(("gas", "density"), ncont=5, factor=4)
#oapp.annotate_magnetic_field()
#oapp.save('contours+vectors_density')
#oapp.save() #to save and include name use oapp.save("%s_oapp.png" % ds)
prj=ds.proj("Byz", "x", weight_field="density")
print prj
#prj.save_object("Byz","Byz_data")
frb=prj.to_frb((4, "AU"), [10000,10000])
#plt.savefig("frb.png")