def test_covering_grid():
return
# We decompose in different ways
cs = np.mgrid[0.47:0.53:2j, 0.47:0.53:2j, 0.47:0.53:2j]
cs = np.array([a.ravel() for a in cs]).T
length = (1.0 / 128) * 16 # 16 half-widths of a cell
for nprocs in [1, 2, 4, 8]:
ds = fake_random_ds(64, nprocs=nprocs, fields=_fields)
streams = Streamlines(ds, cs, length=length)
streams.integrate_through_volume()
for path in (streams.path(i) for i in range(8)):
yield assert_rel_equal, path['dts'].sum(), 1.0, 14
yield assert_equal, np.all(path['t'] <= (1.0 + 1e-10)), True
path["density"]
def test_covering_grid():
return
# We decompose in different ways
cs = np.mgrid[0.47:0.53:2j,0.47:0.53:2j,0.47:0.53:2j]
cs = np.array([a.ravel() for a in cs]).T
length = (1.0/128) * 16 # 16 half-widths of a cell
for nprocs in [1, 2, 4, 8]:
ds = fake_random_ds(64, nprocs = nprocs, fields = _fields)
streams = Streamlines(ds, cs, length=length)
streams.integrate_through_volume()
for path in (streams.path(i) for i in range(8)):
yield assert_rel_equal, path['dts'].sum(), 1.0, 14
yield assert_equal, np.all(path['t'] <= (1.0 + 1e-10)), True
path["density"]
def streamLines(xc1, xc2, xc3, Bc1, Bc2, Bc3, N):
'''
Computes streamlines. Automatically calls the streamSeeds
function.
'''
#bottom, top = streamSeeds(xc1, xc2, xc3, N)
bottom, top = streamSeeds(xc1, xc2, xc3, Bc3, N)
#top = []
#print(bottom)
#print(top)
bottom = np.array(bottom)
top = np.array(top)
#print(bottom)
#return
#bottom = [[.5,.5,.5],[.5,8.4999,.5]]
ds = makeYtDS(xc1, xc2, xc3, Bc1, Bc2, Bc3)
streamlines = Streamlines(ds,
bottom,
'B_x',
'B_y',
'B_z',
direction=1.0,
length=3. * (xc3[-1] - xc3[0]))
streamlines.integrate_through_volume()
bottomstreamlines = streamlines.streamlines
if len(top) > 0:
streamlines = Streamlines(ds,
top,
'B_x',
'B_y',
'B_z',
direction=-1.0,
length=3. * (xc3[-1] - xc3[0]))
streamlines.integrate_through_volume()
topstreamlines = streamlines.streamlines
else:
topstreamlines = []
streamlines = []
for stream in bottomstreamlines:
stream = stream[np.all(stream != 0.0, axis=1)]
streamlines.append(stream)
for stream in topstreamlines:
stream = stream[np.all(stream != 0.0, axis=1)]
streamlines.append(stream)
return np.array(streamlines)
def _integrate(self):
#inbound
streamlines = Streamlines(self.ds,
self.init_coords.T,
'magnetic_field_x',
'magnetic_field_y',
'magnetic_field_z',
length=self.ds.quan(10, 'code_length'),
get_magnitude=True,
direction=1)
streamlines.integrate_through_volume()
self.streamlines_in = streamlines
#outbound
streamlines = Streamlines(self.ds,
self.init_coords.T,
'magnetic_field_x',
'magnetic_field_y',
'magnetic_field_z',
length=self.ds.quan(10, 'code_length'),
get_magnitude=True,
direction=-1)
streamlines.integrate_through_volume()
self.streamlines_out = streamlines
fname = '/Users/jillnaiman1/data/IsolatedGalaxy/galaxy0030/galaxy0030' # home # now, get stream line ds = yt.load(fname) c = np.array([0.5]*3) N = 10 # number of stream lines scale = 1.0 pos_dx = np.random.random((N,3))*scale-scale/2. pos = c+pos_dx streamlines = Streamlines(ds,pos,'velocity_x', 'velocity_y', 'velocity_z', length=1.0) streamlines.integrate_through_volume() # select out 1 stream... for now stream = streamlines.streamlines[1] stream = stream[np.all(stream != 0.0, axis=1)] # doen something fancy # units stream = stream*ds.length_unit # into cm # now, scale down to what we want stream = stream/yt.units.cm stream = stream/3.1e18 # pc # for this case stream = stream/1e5
# Load the dataset
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
# Define c: the center of the box, N: the number of streamlines,
# scale: the spatial scale of the streamlines relative to the boxsize,
# and then pos: the random positions of the streamlines.
c = ds.arr([0.5]*3, 'code_length')
N = 30
scale = ds.quan(15, 'kpc').in_units('code_length') # 15 kpc in code units
pos_dx = np.random.random((N,3))*scale-scale/2.
pos = c+pos_dx
# Create the streamlines from these positions with the velocity fields as the
# fields to be traced
streamlines = Streamlines(ds, pos, 'velocity_x', 'velocity_y', 'velocity_z', length=1.0)
streamlines.integrate_through_volume()
# Create a 3D matplotlib figure for visualizing the streamlines
fig=pl.figure()
ax = Axes3D(fig)
# Trace the streamlines through the volume of the 3D figure
for stream in streamlines.streamlines:
stream = stream[np.all(stream != 0.0, axis=1)]
# Make the colors of each stream vary continuously from blue to red
# from low-x to high-x of the stream start position (each color is R, G, B)
# can omit and just set streamline colors to a fixed color
x_start_pos = ds.arr(stream[0,0], 'code_length')
x_start_pos -= ds.arr(0.5, 'code_length')
x_start_pos /= scale
def get_streamlines(ds):
from yt.visualization.api import Streamlines
streamlines = Streamlines(ds, ds.domain_center)
streamlines.integrate_through_volume()
stream = streamlines.path(0)
matplotlib.pylab.semilogy(stream['t'], stream['Density'], '-x')
def get_streamlines(ds):
from yt.visualization.api import Streamlines
streamlines = Streamlines(ds, ds.domain_center)
streamlines.integrate_through_volume()
stream = streamlines.path(0)
matplotlib.pylab.semilogy(stream['t'], stream['Density'], '-x')
