Data¶
Load snapshot¶
Load data from a single snapshot into Snap and see what arrays are
available with available_arrays().
>>> import plonk
>>> snap = plonk.load_snap('disc_00030.h5')
>>> snap.available_arrays()
['angular_momentum',
'angular_velocity',
'azimuthal_angle',
'density',
'dust_to_gas_ratio',
'id',
'kinetic_energy',
'mass',
'momentum',
'polar_angle',
'position',
'pressure',
'radius_cylindrical',
'radius_spherical',
'smoothing_length',
'sound_speed',
'specific_angular_momentum',
'specific_kinetic_energy',
'stopping_time',
'sub_type',
'temperature',
'timestep',
'type',
'velocity',
'velocity_divergence',
'velocity_radial_cylindrical',
'velocity_radial_spherical']
Load a single snapshot and access particle arrays and properties.
>>> import plonk
>>> snap = plonk.load_snap('disc_00030.h5')
>>> snap['position']
array([[-3.69505001e+12, 7.42032967e+12, -7.45096980e+11],
[-1.63052677e+13, 1.16308971e+13, 1.92879212e+12],
[-7.66283930e+12, 1.62532232e+13, 2.34302988e+11],
...,
[ 1.39571712e+13, -1.16179990e+13, 8.09090354e+11],
[ 9.53716176e+12, 9.98500386e+12, 4.93933367e+11],
[ 1.21421196e+12, 2.08618956e+13, 1.12998892e+12]]) <Unit('meter')>
>>> snap['position'].to('au')
array([[ -24.6998837 , 49.60184016, -4.98066567],
[-108.99398271, 77.74774493, 12.89317897],
[ -51.22291689, 108.64608658, 1.56621873],
...,
[ 93.29792694, -77.66152625, 5.40843496],
[ 63.75198868, 66.74562821, 3.30174062],
[ 8.11650561, 139.453159 , 7.55350939]]) <Unit('astronomical_unit')>
>>> snap.properties['time']
61485663602.558136 <Unit('second')>
Load a single snapshot and access sink arrays via sinks attribute.
>>> import plonk
>>> snap = plonk.load_snap('disc_00030.h5')
>>> sinks = snap.sinks
>>> sinks.available_arrays()
['accretion_radius',
'last_injection_time',
'mass',
'mass_accreted',
'position',
'softening_radius',
'spin',
'velocity']
>>> sinks['spin']
array([[ 3.56866999e+36, -1.17910663e+37, 2.44598074e+40],
[ 4.14083556e+36, 1.19118555e+36, 2.62569386e+39]]) <Unit('kilogram * meter ** 2 / second')>
Load time series data¶
Load a Phantom time series (.ev) file with load_time_series() and see
what columns are available.
>>> import plonk
>>> ts = plonk.load_time_series('disc01.ev')
>>> ts.columns
Index(['time', 'energy_kinetic', 'energy_thermal', 'energy_magnetic',
'energy_potential', 'energy_total', 'momentum', 'angular_momentum',
'density_max', 'density_average', 'timestep', 'entropy',
'mach_number_rms', 'velocity_rms', 'center_of_mass_x',
'center_of_mass_y', 'center_of_mass_z', 'gas_density_max',
'gas_density_average', 'dust_density_max', 'dust_density_average'],
dtype='object')
>>> ts
time energy_kinetic ... dust_density_max dust_density_average
0 0.000000 0.000013 ... 1.720023e-10 8.015937e-12
1 1.593943 0.000013 ... 1.714059e-10 8.015771e-12
2 6.375774 0.000013 ... 1.696885e-10 8.018406e-12
3 25.503096 0.000013 ... 1.636469e-10 8.061417e-12
4 51.006191 0.000013 ... 1.580470e-10 8.210622e-12
.. ... ... ... ... ...
548 12394.504462 0.000013 ... 1.481833e-09 2.482929e-11
549 12420.007557 0.000013 ... 1.020596e-09 2.483358e-11
550 12445.510653 0.000013 ... 8.494835e-10 2.488946e-11
551 12471.013748 0.000013 ... 6.517475e-10 2.497029e-11
552 12496.516844 0.000013 ... 5.205011e-10 2.506445e-11
[553 rows x 21 columns]
Load simulation¶
Load a simulation with load_simulation() and access snapshots and time
series data with snaps and time_series
attributes.
>>> import plonk
>>> sim = plonk.load_simulation(prefix='disc')
>>> sim.snaps
[<plonk.Snap "disc_00000.h5">,
<plonk.Snap "disc_00001.h5">,
<plonk.Snap "disc_00002.h5">,
<plonk.Snap "disc_00003.h5">,
<plonk.Snap "disc_00004.h5">,
<plonk.Snap "disc_00005.h5">,
<plonk.Snap "disc_00006.h5">,
<plonk.Snap "disc_00007.h5">,
<plonk.Snap "disc_00008.h5">,
<plonk.Snap "disc_00009.h5">,
<plonk.Snap "disc_00010.h5">,
<plonk.Snap "disc_00011.h5">,
<plonk.Snap "disc_00012.h5">,
<plonk.Snap "disc_00013.h5">,
<plonk.Snap "disc_00014.h5">,
<plonk.Snap "disc_00015.h5">,
<plonk.Snap "disc_00016.h5">,
<plonk.Snap "disc_00017.h5">,
<plonk.Snap "disc_00018.h5">,
<plonk.Snap "disc_00019.h5">,
<plonk.Snap "disc_00020.h5">,
<plonk.Snap "disc_00021.h5">,
<plonk.Snap "disc_00022.h5">,
<plonk.Snap "disc_00023.h5">,
<plonk.Snap "disc_00024.h5">,
<plonk.Snap "disc_00025.h5">,
<plonk.Snap "disc_00026.h5">,
<plonk.Snap "disc_00027.h5">,
<plonk.Snap "disc_00028.h5">,
<plonk.Snap "disc_00029.h5">,
<plonk.Snap "disc_00030.h5">]
>>> sim.time_series['global']
time [s] ... dust_density_average [kg / m ** 3]
0 0.000000e+00 ... 4.762293e-15
1 8.005946e+06 ... 4.762195e-15
2 3.202378e+07 ... 4.763760e-15
3 1.280951e+08 ... 4.789313e-15
4 2.561903e+08 ... 4.877956e-15
.. ... ... ...
548 6.225423e+10 ... 1.475116e-14
549 6.238233e+10 ... 1.475371e-14
550 6.251042e+10 ... 1.478691e-14
551 6.263852e+10 ... 1.483493e-14
552 6.276661e+10 ... 1.489087e-14
[553 rows x 21 columns]
>>> sim.time_series['sinks']
[ time [s] position_x [m] ... sink_sink_force_y [N] sink_sink_force_z [N]
0 4.002973e+06 -1.068586e+10 ... 2.455750e+18 2.020208e+14
1 8.005946e+06 -1.068584e+10 ... 4.911471e+18 2.020271e+14
2 1.601189e+07 -1.068574e+10 ... 9.822886e+18 2.020559e+14
3 3.202378e+07 -1.068536e+10 ... 1.964551e+19 2.021794e+14
4 6.404757e+07 -1.068380e+10 ... 3.928915e+19 1.407952e+14
... ... ... ... ... ...
1038 6.257447e+10 -9.976304e+09 ... 7.513519e+20 7.902581e+15
1039 6.263852e+10 -9.895056e+09 ... 7.884134e+20 7.664701e+15
1040 6.270257e+10 -9.809975e+09 ... 8.251714e+20 7.628553e+15
1041 6.276661e+10 -9.721096e+09 ... 8.616085e+20 7.705555e+15
1042 6.283066e+10 -9.628452e+09 ... 8.977201e+20 7.391809e+15
[1043 rows x 18 columns],
time [s] position_x [m] ... sink_sink_force_y [N] sink_sink_force_z [N]
0 4.002973e+06 1.120931e+13 ... -2.573360e+21 -2.116959e+17
1 8.005946e+06 1.120928e+13 ... -5.146689e+21 -2.117025e+17
2 1.601189e+07 1.120918e+13 ... -1.029332e+22 -2.117327e+17
3 3.202378e+07 1.120877e+13 ... -2.058637e+22 -2.118621e+17
4 6.404757e+07 1.120715e+13 ... -4.117069e+22 -1.475378e+17
... ... ... ... ... ...
1038 6.257447e+10 1.041137e+13 ... -7.748986e+23 -8.150240e+18
1039 6.263852e+10 1.032805e+13 ... -8.131071e+23 -7.904764e+18
1040 6.270257e+10 1.024073e+13 ... -8.510027e+23 -7.867359e+18
1041 6.276661e+10 1.014946e+13 ... -8.885717e+23 -7.946693e+18
1042 6.283066e+10 1.005427e+13 ... -9.257998e+23 -7.623017e+18
[1043 rows x 18 columns]]