diff --git a/notebooks/perseus.ipynb b/notebooks/perseus.ipynb
deleted file mode 100644
index f8499b1..0000000
--- a/notebooks/perseus.ipynb
+++ /dev/null
@@ -1,165 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "5a38ed25",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-04-12T14:25:46.519408Z",
- "start_time": "2023-04-12T14:25:03.003304Z"
- },
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "import sys\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import scienceplots\n",
- "import astroquery\n",
- "from tqdm import trange, tqdm\n",
- "\n",
- "sys.path.append(\"../\")\n",
- "import csiborgtools\n",
- "\n",
- "%matplotlib widget \n",
- "%load_ext autoreload\n",
- "%autoreload 2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "id": "79919270",
- "metadata": {},
- "outputs": [],
- "source": [
- "# # Norma\n",
- "cluster = {\"RA\": (16 + 15 / 60 + 32.8 / 60**2) * 15,\n",
- " \"DEC\": -60 + 54 / 60 + 30 / 60**2,\n",
- " \"DIST\": 67.8}\n",
- "\n",
- "Xclust = np.array([cluster[\"DIST\"], cluster[\"RA\"], cluster[\"DEC\"]]).reshape(1, -1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "id": "96740b90",
- "metadata": {},
- "outputs": [],
- "source": [
- "paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n",
- "nsims = paths.get_ics(False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "90033fb1",
- "metadata": {},
- "outputs": [],
- "source": [
- "Xclust = np.array([cluster[\"DIST\"], cluster[\"RA\"], cluster[\"DEC\"]]).reshape(1, -1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "194baa83",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 101/101 [00:44<00:00, 2.25it/s]\n"
- ]
- }
- ],
- "source": [
- "matches = np.full(len(nsims), np.nan)\n",
- "\n",
- "for ii in trange(101):\n",
- " cat = csiborgtools.read.CSiBORGHaloCatalogue(nsims[ii], paths, minmass=('M', 1e13))\n",
- " dist, ind = cat.angular_neighbours(Xclust, ang_radius=5, rad_tolerance=10)\n",
- " dist = dist[0]\n",
- " ind = ind[0]\n",
- "\n",
- " if ind.size > 0:\n",
- " matches[ii] = np.max(cat['M'][ind])\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "id": "392f6eee",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6ff7d5b9dc2f4b3fa6f563ee91f59655",
- "version_major": 2,
- "version_minor": 0
- },
- "image/png": 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- "source": [
- "x = np.log10(matches[~np.isnan(matches)])\n",
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- "\n",
- "plt.figure()\n",
- "plt.hist(x, bins=10)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9cc660b0",
- "metadata": {},
- "outputs": [],
- "source": []
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- "metadata": {
- "kernelspec": {
- "display_name": "venv_galomatch",
- "language": "python",
- "name": "python3"
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diff --git a/notebooks/powerspectrum_test.ipynb b/notebooks/powerspectrum_test.ipynb
deleted file mode 100644
index d0e16db..0000000
--- a/notebooks/powerspectrum_test.ipynb
+++ /dev/null
@@ -1,721 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "import csiborgtools\n",
- "from h5py import File\n",
- "from gc import collect"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "with File(\"/mnt/extraspace/rstiskalek/csiborg2/snapshot_099.hdf5\", 'r') as f:\n",
- " # print(f[\"Header\"].attrs[\"BoxSize\"])\n",
- " mhigh = f[\"Header\"].attrs[\"MassTable\"][1]\n",
- " pos1 = f['PartType1']['Coordinates'][:]\n",
- " pos5 = f['PartType5']['Coordinates'][:]\n",
- " mass5 = f['PartType5']['Masses'][:]\n",
- "\n",
- "mass1 = np.ones(len(pos1)) * mhigh * 1e10\n",
- "mass5 *= 1e10\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "80"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pos = np.concatenate((pos1, pos5), axis=0)\n",
- "mass = np.concatenate((mass1, mass5), axis=0).astype(np.float32)\n",
- "\n",
- "del pos1, pos5, mass1, mass5\n",
- "collect()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Using PCS mass assignment scheme with weights\n",
- "Time taken = 58.325 seconds\n",
- "\n"
- ]
- }
- ],
- "source": [
- "import MAS_library as MASL\n",
- "\n",
- "\n",
- "# density field parameters\n",
- "grid = 512 #the 3D field will have grid x grid x grid voxels\n",
- "BoxSize = 677.7 #Mpc/h ; size of box\n",
- "MAS = 'PCS' #mass-assigment scheme\n",
- "verbose = True #print information on progress\n",
- "\n",
- "# define 3D density field\n",
- "delta = np.zeros((grid,grid,grid), dtype=np.float32)\n",
- "# construct 3D density field\n",
- "MASL.MA(pos, delta, BoxSize, MAS, verbose=verbose, W=mass)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "# np.save(\"delta_8600_PCS.npy\", delta)\n",
- "delta_new = np.load(\"delta_8600_PCS.npy\")\n",
- "\n",
- "delta_old = np.load(\"/mnt/extraspace/rstiskalek/CSiBORG/environment/density_PCS_08596_grid512.npy\").T"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "delta_new /= np.mean(delta_new)\n",
- "delta_new -= 1\n",
- "delta_old /= np.mean(delta_old)\n",
- "delta_old -= 1\n",
- "\n",
- "# delta_new = np.log10(delta_new)\n",
- "# delta_old = np.log10(delta_old)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [],
- "source": [
- "imin = 256 - 50\n",
- "imax = 256 + 50\n",
- "\n",
- "delta_new_central = np.zeros_like(delta_new)\n",
- "delta_new_central[imin:imax, imin:imax, imin:imax] = delta_new[imin:imax, imin:imax, imin:imax]\n",
- "\n",
- "delta_old_central = np.zeros_like(delta_old)\n",
- "delta_old_central[imin:imax, imin:imax, imin:imax] = delta_old[imin:imax, imin:imax, imin:imax]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [],
- "source": [
- "# plt.figure()\n",
- "# plt.imshow(delta_new_central[:, 256, :], origin='lower')\n",
- "\n",
- "# plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Computing power spectrum of the field...\n",
- "Time to complete loop = 8.08\n",
- "Time taken = 14.80 seconds\n",
- "\n",
- "Computing power spectrum of the field...\n",
- "Time to complete loop = 8.02\n",
- "Time taken = 14.54 seconds\n"
- ]
- }
- ],
- "source": [
- "knew, pknew = csiborgtools.field.power_spectrum(delta_new_central, 677.7, \"PCS\", threads=2)\n",
- "kold, pkold = csiborgtools.field.power_spectrum(delta_old_central, 677.7, \"PCS\", threads=2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [],
- "source": [
- "import camb\n",
- "from camb import model \n",
- "\n",
- "pars = camb.CAMBparams()\n",
- "h = 0.705\n",
- "pars.set_cosmology(H0=h*100, ombh2=0.04825 * h**2, omch2=(0.307 - 0.04825) * h**2)\n",
- "pars.InitPower.set_params(ns=0.9611)\n",
- "#Note non-linear corrections couples to smaller scales than you want\n",
- "pars.set_matter_power(redshifts=[0.], kmax=40)\n",
- "\n",
- "#Non-Linear spectra (Halofit)\n",
- "pars.NonLinear = model.NonLinear_both\n",
- "results = camb.get_results(pars)\n",
- "results.calc_power_spectra(pars)\n",
- "kh_nonlin, z_nonlin, pk_nonlin = results.get_matter_power_spectrum(minkh=1e-3, maxkh=50, npoints = 200)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
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