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adrn/MWtest-Gala.ipynb

Created September 19, 2025 14:33
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MWtest-Gala.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "3946db8c-0df5-40c6-b332-865d59580182",
"metadata": {},
"source": [
"# Use Gala to make orbits in Jason's MW model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f2363216",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'3.12.9 (main, Jun 11 2025, 12:55:16) [GCC 13.3.0]'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sys\n",
"sys.version"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "76484acb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.10.1.dev67\n"
]
}
],
"source": [
"import astropy.units as u\n",
"import gala.coordinates as gc\n",
"import gala.dynamics as gd\n",
"import gala.integrate as gi\n",
"import gala.potential as gp\n",
"from gala.units import SimulationUnitSystem\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"\n",
"import gala\n",
"print(gala.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "323d90ae-9c6c-489a-943a-979eca2fdbd8",
"metadata": {},
"outputs": [],
"source": [
"halo_config_txt = \"\"\"\n",
"---\n",
"id: sphereSL\n",
"parameters :\n",
" numr: 1000\n",
" rmin: 0.0007\n",
" rmax: 600\n",
" Lmax: 10\n",
" nmax: 48\n",
" rmapping: 7.0\n",
" modelname: \"Martin_Ioana_halo.txt\"\n",
" cachename: .slgrid_sph_cache_halo_May10\n",
"...\n",
"\"\"\"\n",
"with open(\"halo_config.yml\", \"w\") as f:\n",
" f.write(halo_config_txt)\n",
"halo_coeffs = 'MW_halo_July26.h5'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cffb108a-147e-4be5-9f0a-99a42617b449",
"metadata": {},
"outputs": [],
"source": [
"disc_config = \"\"\"\n",
"id: cylinder\n",
"parameters:\n",
" acyl: 3.\n",
" hcyl: 1.6\n",
" mmax: 6\n",
" rcylmin: 0.01\n",
" rcylmax: 30.0\n",
" nmax: 72\n",
" ncylnx: 256\n",
" ncylny: 256\n",
" lmaxfid : 72\n",
" nmaxfid : 72\n",
" ncylodd : 36\n",
" rnum: 200\n",
" pnum: 1\n",
" tnum: 80\n",
" logr: true\n",
" cachename: eof.cache.file_IoanaMW_June4\n",
"\"\"\"\n",
"with open(\"disc_config.yml\", \"w\") as f:\n",
" f.write(disc_config)\n",
"disc_coeffs = 'MW_disc_Jul26.h5'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f8cb1d2a-e94d-4ca4-a692-ab0c49eaac18",
"metadata": {},
"outputs": [],
"source": [
"bulge_config=\"\"\"\n",
"---\n",
"id: sphereSL\n",
"parameters :\n",
" numr: 4000\n",
" rmin: 0.0007\n",
" rmax: 7\n",
" Lmax: 10\n",
" nmax: 32\n",
" rmapping: 0.2\n",
" modelname: MW_GridSph_bulge_May10.model\n",
" cachename: slgrid_sph_cache_bulge_Aug22\n",
"...\n",
"\"\"\"\n",
"with open(\"bulge_config.yml\", \"w\") as f:\n",
" f.write(bulge_config)\n",
"bulge_coeffs = 'MW_bulge_Aug.h5'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6b62e54c",
"metadata": {},
"outputs": [],
"source": [
"import astropy.units as u\n",
"import gala.potential as gp\n",
"from gala.units import SimulationUnitSystem"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "05da0a98",
"metadata": {},
"outputs": [],
"source": [
"usys = SimulationUnitSystem(u.kpc, u.Msun)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5ff074bf",
"metadata": {},
"outputs": [],
"source": [
"pot = gp.CCompositePotential()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "05dd8a07",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"---- SLGridSph::ReadH5Cache: successfully read basis cache <.slgrid_sph_cache_halo_May10>\n",
"---- Spherical::orthoTest: worst=0.046905\n"
]
}
],
"source": [
"pot[\"halo\"] = gp.EXPPotential(\n",
" config_file=\"halo_config.yml\",\n",
" coef_file=halo_coeffs,\n",
" snapshot_time_unit=u.Gyr,\n",
" units=usys\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "46d8c705",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"---- EmpCylSL::ReadH5Cache: read <eof.cache.file_IoanaMW_June4>\n",
"---- EmpCylSL::read_cache: table forwarded to all processes\n",
"---- Cylindrical::orthoTest: worst=0.00580151\n"
]
}
],
"source": [
"pot[\"disk\"] = gp.EXPPotential(\n",
" config_file=\"disc_config.yml\",\n",
" coef_file=disc_coeffs,\n",
" snapshot_time_unit=u.Gyr,\n",
" units=usys\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c613a2f0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"---- SLGridSph::ReadH5Cache: successfully read basis cache <slgrid_sph_cache_bulge_Aug22>\n",
"---- Spherical::orthoTest: worst=0.00382804\n"
]
}
],
"source": [
"pot[\"bulge\"] = gp.EXPPotential(\n",
" config_file=\"bulge_config.yml\",\n",
" coef_file=bulge_coeffs,\n",
" snapshot_time_unit=u.Gyr,\n",
" units=usys\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0fe8bbeb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"halo [140.95724693] km / s\n",
"disk [123.26104381] km / s\n",
"bulge [109.5799336] km / s\n",
"total [216.95619887] km / s\n"
]
}
],
"source": [
"for name, comp in pot.items():\n",
" print(name, comp.circular_velocity([8.0, 0, 0] * u.kpc, t=7 * u.Gyr).to(u.km/u.s))\n",
"\n",
"print(\"total\", pot.circular_velocity([8.0, 0, 0] * u.kpc, t=7 * u.Gyr).to(u.km/u.s))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f8ba30cc",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: AstropyDeprecationWarning: The get_name method is deprecated and may be removed in a future version.\n",
" Use name instead. [gala.dynamics.core]\n"
]
}
],
"source": [
"w0 = gd.PhaseSpacePosition([15., 0, 0] * u.kpc, [0, 250, 15] * u.km/u.s)\n",
"orbit = pot.integrate_orbit(\n",
" w0, dt=1*u.Myr, t1=5*u.Gyr, t2=7*u.Gyr, Integrator=gi.LeapfrogIntegrator\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "aa1df385",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: AstropyDeprecationWarning: The get_name method is deprecated and may be removed in a future version.\n",
" Use name instead. [gala.dynamics.orbit]\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n",
"Ignoring fixed y limits to fulfill fixed data aspect with adjustable data limits.\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 864x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = orbit.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0405995d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "scratch-312",
"language": "python",
"name": "scratch-312"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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