Skip to content

Commit

Permalink
change .gitignore
Browse files Browse the repository at this point in the history
  • Loading branch information
@FredDeCeuster
FredDeCeuster committed Sep 27, 2023
1 parent 02b2806 commit 9ea8dfd
Show file tree
Hide file tree
Showing 6 changed files with 2,436 additions and 23 deletions.
1 change: 1 addition & 0 deletions .gitignore
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
*.npz
*.fits
*.FITS
*.ascii
docs/build/

!tests/data/header_only.fits
Expand Down
2 changes: 1 addition & 1 deletion docs/src/background/spectral_line_formation.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
"metadata": {},
"source": [
"### Line optical depth\n",
"Velocity gradients play a key role in line radiative transfer. Since spectral lines are narrowly peaked in frequency space, they are very sensitve to Doppler shifts, and thus motion (gradients), along the line-of-sight.\n",
"Velocity gradients play a key role in line radiative transfer. Since spectral lines are narrowly peaked in frequency space, they are very sensitive to Doppler shifts, and thus motion (gradients), along the line-of-sight.\n",
"Therefore, when numerically solving a line transfer problem, it is key to properly trace the velocity (gradient) along the line-of-sight. \n",
"Since we assume to know the line profile function analytically, we can take care of this sharp frequency dependence by resolving its dependence analytically."
]
Expand Down
316 changes: 316 additions & 0 deletions docs/src/examples/Example_for_Ke.ipynb
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,316 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import numpy as np\n",
"\n",
"from astropy import units, constants\n",
"\n",
"from p3droslo.plot import plot_cube_2D\n",
"from p3droslo.model import TensorModel\n",
"from p3droslo.lines import Line\n",
"from p3droslo.haar import Haar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download the data\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# link to the model, and file name\n",
"input_link = \"https://owncloud.ster.kuleuven.be/index.php/s/6mCZjZ2erTsXq5Y/download\"\n",
"input_file = \"model_Phantom_3D.ascii\"\n",
"# link to the molecular line data, and file name\n",
"lamda_link = \"https://home.strw.leidenuniv.nl/~moldata/datafiles/co.dat\"\n",
"lamda_file = \"co.dat\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2023-09-27 12:41:54-- https://owncloud.ster.kuleuven.be/index.php/s/6mCZjZ2erTsXq5Y/download\n",
"Resolving owncloud.ster.kuleuven.be (owncloud.ster.kuleuven.be)... 134.58.130.75\n",
"Connecting to owncloud.ster.kuleuven.be (owncloud.ster.kuleuven.be)|134.58.130.75|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 388714637 (371M) [application/octet-stream]\n",
"Saving to: ‘model_Phantom_3D.ascii’\n",
"\n",
"model_Phantom_3D.as 100%[===================>] 370.71M 67.4MB/s in 5.5s \n",
"\n",
"2023-09-27 12:42:00 (67.0 MB/s) - ‘model_Phantom_3D.ascii’ saved [388714637/388714637]\n",
"\n",
"--2023-09-27 12:42:09-- https://home.strw.leidenuniv.nl/~moldata/datafiles/co.dat\n",
"Resolving home.strw.leidenuniv.nl (home.strw.leidenuniv.nl)... 132.229.214.179\n",
"Connecting to home.strw.leidenuniv.nl (home.strw.leidenuniv.nl)|132.229.214.179|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 444204 (434K)\n",
"Saving to: ‘co.dat’\n",
"\n",
"co.dat 100%[===================>] 433.79K 1.69MB/s in 0.3s \n",
"\n",
"2023-09-27 12:42:12 (1.69 MB/s) - ‘co.dat’ saved [444204/444204]\n",
"\n"
]
}
],
"source": [
"# Download the model and the line data\n",
"!wget $input_link --output-document $input_file\n",
"!wget $lamda_link --output-document $lamda_file"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Read the Phantom ascii file\n",
"(x,y,z, h, rho, vx,vy,vz, u) = np.loadtxt(input_file, skiprows=14, usecols=(0,1,2,4,5,6,7,8,9), unpack=True)\n",
"\n",
"# Constants that can be read from ascii file\n",
"velocity_cte = 2.9784608e+06\n",
"density_cte = 5.9410314e-07\n",
"energy_cte = 8.8712277e+12\n",
"\n",
"keep = np.logical_and(h>0.0, rho>0.0)\n",
"\n",
"# Exclude unphysical points and points with zero abundance\n",
"x = x [keep]\n",
"y = y [keep]\n",
"z = z [keep]\n",
"vx = vx [keep]\n",
"vy = vy [keep]\n",
"vz = vz [keep]\n",
"u = u [keep]\n",
"rho = rho[keep]\n",
"\n",
"# Extract the number of points\n",
"npoints = len(x)\n",
"\n",
"# Convert rho (total density) to abundances\n",
"nH2 = rho * density_cte * 1.0e+6 * constants.N_A.si.value / 2.02\n",
"nCO = nH2 * 1.0e-4\n",
"\n",
"position = np.array((x, y, z )).T\n",
"velocity = np.array((vx,vy,vz)).T\n",
"\n",
"# Convert units\n",
"position *= constants.au.si.value # convert au to m\n",
"velocity *= velocity_cte * 1.0e-2 # convert cm/s to m/s\n",
"\n",
"# Derive temperature from internal energy (assuming adiabatic heating/cooling)\n",
"gamma = 1.2\n",
"mu = 2.381\n",
"tmp = mu * (gamma-1.0) * u * energy_cte * 1.00784 * (units.erg/units.g * constants.u/constants.k_B).to(units.K).value\n",
"\n",
"# Clamp temperatures below 2.725 K\n",
"tmp[tmp<2.725] = 2.725\n",
"\n",
"# Define turbulence at 150 m/s\n",
"trb = 150.0 * np.ones(npoints)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"# Convert point data to cartesian grid and put it ina TensorModel\n",
"haar = Haar(position, q=8)\n",
"model = TensorModel(shape=tuple([2**(haar.q-1)] * 3), sizes=haar.xyz_L)\n",
"model['density' ] = haar.map_data(nCO, interpolate=True)[-1]\n",
"model['temperature'] = haar.map_data(tmp, interpolate=True)[-1]\n",
"model['v_turbulence'] = haar.map_data(trb, interpolate=True)[-1]\n",
"model['velocity_los'] = haar.map_data(velocity[:,2], interpolate=True)[-1] # velocity along z-axis\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You have selected line:\n",
" co(J=1-0)\n",
"Please check the properties that were inferred:\n",
" Frequency 1.152712018e+11 Hz\n",
" Einstein A coeff 7.203000000e-08 1/s\n",
" Molar mass 28.0 g/mol\n"
]
}
],
"source": [
"line = Line(\n",
" species_name = \"co\",\n",
" transition = 0,\n",
" datafile = lamda_file,\n",
" molar_mass = 28.0\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"vdiff = 1500 # velocity increment size [m/s]\n",
"nfreq = 31 # number of frequencies\n",
"dd = vdiff / constants.c.si.value * nfreq\n",
"fmin = line.frequency - line.frequency*dd\n",
"fmax = line.frequency + line.frequency*dd\n",
"\n",
"frequencies = torch.linspace(fmin, fmax, nfreq)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"img = line.LTE_image_along_last_axis(\n",
" density = model['density' ],\n",
" temperature = model['temperature' ],\n",
" v_turbulence = model['v_turbulence'],\n",
" velocity_los = model['velocity_los'],\n",
" frequencies = frequencies,\n",
" dx = model.dx(3-1)\n",
")\n",
"\n",
"# Avoid negative values (should probably avoid these earlier...)\n",
"img = torch.abs(img)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "be3aff1bc931448daa764e6346cc0d0f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=15, description='z', max=30), Output()), _dom_classes=('widget-interact'…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function p3droslo.plot.plot_cube_2D.<locals>.plot(z)>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_cube_2D(np.log10(img+1.0e-20))"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a34b237d0f0d41a497cb2cc726b365d3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=63, description='i', max=127), IntSlider(value=63, description='j', max=…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function p3droslo.plot.plot_spectrum.<locals>.plot(i, j)>"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_spectrum(img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "magritte",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
Loading

0 comments on commit 9ea8dfd

Please sign in to comment.