388 lines
28 KiB
Plaintext
388 lines
28 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Analyse 2M++ resims"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/scratch/pyenvs/versions/3.6.5/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
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" from ._conv import register_converters as _register_converters\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pylab as plt\n",
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"from src.read_all_h5 import explore_chain, rebuild_spliced_h5\n",
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"from src.special_regions import *\n",
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"import healpy as hp\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Just some helper Routines"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"#little reading routine\n",
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"def load_resim(fname,ncpu=1):\n",
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" print (\"reading : \", fname)\n",
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" \n",
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" if ncpu>0:\n",
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" a=rebuild_spliced_h5(fname,['u_pos','u_vel'],ncpu,verbose=False)\n",
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" return np.array(a[\"u_pos\"][...]),np.array(a[\"u_vel\"][...])\n",
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" else:\n",
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" print ('Error: need number of processes to read files !')\n",
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" \n",
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" return None\n",
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"#print special regions\n",
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"special_coords.keys()\n",
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"\n",
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"#set observer\n",
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"corner=np.array([-0.5*677.77,-0.5*677.77,-0.5*677.77])\n",
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"\n",
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"#change coordinates\n",
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"def cart2sphere(pos,corner=np.array([0,0,0]),rmax=100):\n",
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" r = np.sqrt((pos[:,0]+corner[0])**2+(pos[:,1]+corner[1])**2+(pos[:,2]+corner[2])**2)\n",
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" foo=np.where(r<rmax)\n",
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" r = r[foo]\n",
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" \n",
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" phi,theta=hp.vec2ang(np.array([pos[foo][:,0]+corner[0],pos[foo][:,1]+corner[1],pos[foo][:,2]+corner[2]]).T, lonlat=True)\n",
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" \n",
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" return r,phi,theta\n",
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"\n",
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"\n",
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"def get_densities(r,ra,dec,rmax,region='full'):\n",
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" rho_mean=(512./677.77)**3\n",
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" \n",
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" if(region=='full'):\n",
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" hist, binEdges = np.histogram(r,bins=600, density=False,range=[0,rmax])\n",
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" dv = np.pi*4./3.*(binEdges[1:]**3-binEdges[:-1]**3)\n",
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" else:\n",
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" foo,da = sky_region(region,ra,dec)\n",
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" hist, binEdges = np.histogram(r[foo],bins=600, density=False,range=[0,rmax])\n",
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" dv = da/3.*(binEdges[1:]**3-binEdges[:-1]**3)\n",
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" \n",
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" \n",
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" bincenters = 0.5*(binEdges[1:]+binEdges[:-1])\n",
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" \n",
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" rho = hist/dv\n",
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" delta = rho/rho_mean -1.\n",
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" \n",
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" aux = np.cumsum(hist)\n",
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" auxv = 4./3.*np.pi*binEdges[1:]**3\n",
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" rho_cum = aux/auxv\n",
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" delta_cum = rho_cum/rho_mean-1.\n",
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" \n",
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" return bincenters,delta, binEdges[1:], delta_cum \n",
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"\n",
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"def find_ncpu(fbase):\n",
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" import glob\n",
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" nn = len(glob.glob(fbase+\"*\"))\n",
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" return nn\n",
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"\n",
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"def plot_radial_density(r,ra,dec):\n",
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" \n",
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" rmax=300\n",
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" foo=sky_region('R3',ra,dec)\n",
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" x_R1,delta_R1,x_cum_R1,delta_cum_R1=get_densities(r[foo],rmax)\n",
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"\n",
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" f, (ax1, ax2, ax3) = plt.subplots(1,3, sharex=True, sharey=True,figsize=(12,4))\n",
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" \n",
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" \n",
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" ax1.set_xlim([0,rmax])\n",
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" ax1.set_ylim([-1,1])\n",
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" ax1.set_xlabel(r'$r$')\n",
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" ax1.set_ylabel(r'$\\delta(r)$')\n",
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" ax1.plot(x_R1,delta_R1,color='red')\n",
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" ax1.plot(x_R1,delta_R1*0.,color='black',linestyle='--')\n",
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" \n",
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" ax2.set_xlim([0,rmax])\n",
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" ax2.set_ylim([-1,1])\n",
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" ax2.set_xlabel(r'$r$')\n",
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" ax2.yaxis.set_ticks_position('none') \n",
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" ax2.plot(x_R1,delta_R1,color='red')\n",
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" ax2.plot(x_R1,delta_R1*0.,color='black',linestyle='--')\n",
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" \n",
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" ax3.set_xlim([0,rmax])\n",
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" ax3.set_ylim([-1,1])\n",
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" ax3.set_xlabel(r'$r$')\n",
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" ax3.yaxis.set_ticks_position('none') \n",
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" ax3.plot(x_R1,delta_R1,color='red')\n",
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" ax3.plot(x_R1,delta_R1*0.,color='black',linestyle='--')\n",
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" \n",
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" \n",
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" plt.show()\n",
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" \n",
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"fins=['output_7430.h5',\n",
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"'output_7440.h5',\n",
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"'output_7450.h5',\n",
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"'output_7460.h5',\n",
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"'output_7470.h5',\n",
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"'output_7480.h5',\n",
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"'output_7490.h5',\n",
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"'output_7500.h5',\n",
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"'output_7510.h5',\n",
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"'output_7520.h5',\n",
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"'output_7530.h5',\n",
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"'output_7540.h5',\n",
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"'output_7550.h5',\n",
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"'output_7570.h5',\n",
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"'output_7590.h5',\n",
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"'output_7610.h5',\n",
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"'output_7630.h5',\n",
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"'output_7650.h5',\n",
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"'output_7670.h5',\n",
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"'output_7690.h5',\n",
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"'output_7710.h5',\n",
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"'output_7730.h5',\n",
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"'output_7750.h5',\n",
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"'output_7770.h5',\n",
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"'output_7790.h5',\n",
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"'output_7810.h5',\n",
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"'output_7830.h5',\n",
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"'output_7850.h5',\n",
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"'output_7870.h5',\n",
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"'output_7890.h5',\n",
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"'output_7910.h5',\n",
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"'output_7930.h5',\n",
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"'output_7950.h5',\n",
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"'output_7970.h5',\n",
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"'output_7990.h5',\n",
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"'output_8010.h5',\n",
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"'output_8030.h5',\n",
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"'output_8050.h5',\n",
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"'output_8070.h5',\n",
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"'output_8090.h5',\n",
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"'output_8110.h5',\n",
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"'output_8130.h5',\n",
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"'output_8150.h5',\n",
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"'output_8170.h5',\n",
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"'output_8190.h5',\n",
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"'output_8210.h5',\n",
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"'output_8230.h5',\n",
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"'output_8250.h5',\n",
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"'output_8270.h5',\n",
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"'output_8290.h5',\n",
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"'output_8310.h5',\n",
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"'output_8330.h5',\n",
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"'output_8350.h5',\n",
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"'output_8370.h5',\n",
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"'output_8390.h5',\n",
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"'output_8410.h5',\n",
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"'output_8430.h5',\n",
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"'output_8450.h5',\n",
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"'output_8470.h5',\n",
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"'output_8490.h5',\n",
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"'output_8510.h5',\n",
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"'output_8530.h5',\n",
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"'output_8550.h5',\n",
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"'output_8570.h5',\n",
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"'output_8590.h5',\n",
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"'output_8610.h5',\n",
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"'output_8630.h5',\n",
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"'output_8650.h5',\n",
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"'output_8670.h5',\n",
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"'output_8690.h5',\n",
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"'output_8710.h5',\n",
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"'output_8730.h5',\n",
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"'output_8750.h5',\n",
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"'output_8770.h5',\n",
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"'output_8790.h5',\n",
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"'output_8810.h5',\n",
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"'output_8830.h5',\n",
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"'output_8850.h5',\n",
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"'output_8870.h5',\n",
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"'output_8890.h5',\n",
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"'output_8910.h5',\n",
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"'output_8930.h5',\n",
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"'output_8950.h5',\n",
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"'output_8970.h5',\n",
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"'output_8990.h5']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"reading : /data/lavaux/PM_HOT_0.30_try2_epsilon_0.01/output_7430.h5\n",
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"hi 7.379306853242552e-07 359.9999969390372 -89.99126552546272 89.99550581118774\n",
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"reading : /data/lavaux/PM_HOT_0.30_try2_epsilon_0.01/output_7440.h5\n",
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"hi 2.9451484856277518e-06 359.999997570849 -89.9889787218639 89.99511084033364\n",
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"reading : /data/lavaux/PM_HOT_0.30_try2_epsilon_0.01/output_7450.h5\n",
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"hi 3.385283781994141e-07 359.99999553714446 -89.98808898802002 89.99533411827332\n",
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"reading : /data/lavaux/PM_HOT_0.30_try2_epsilon_0.01/output_7460.h5\n",
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"hi 1.8074473344467155e-06 359.9999977874951 -89.98371284950048 89.99201676085508\n",
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"reading : /data/lavaux/PM_HOT_0.30_try2_epsilon_0.01/output_7470.h5\n",
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"hi 3.2910328257955735e-06 359.99999773190456 -89.99272054208683 89.99425095008178\n",
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"reading : /data/lavaux/PM_HOT_0.30_try2_epsilon_0.01/output_7480.h5\n",
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"hi 1.1389157106431394e-05 359.99999830154223 -89.98472794623657 89.99811377865286\n",
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"reading : /data/lavaux/PM_HOT_0.30_try2_epsilon_0.01/output_7490.h5\n",
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"hi 2.874475098358866e-06 359.999990777863 -89.99649122931581 89.97667660204161\n",
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"reading : /data/lavaux/PM_HOT_0.30_try2_epsilon_0.01/output_7500.h5\n"
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]
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}
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],
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"source": [
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"#get Cartesian coordinates for coma:\n",
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"pcoma,rc=get_objpos_range('coma')\n",
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"\n",
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"#load particles\n",
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"rmax=300\n",
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"xx_full=[]\n",
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"xx_R1=[]\n",
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"xx_R2=[]\n",
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"xx_R3=[]\n",
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"\n",
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"\n",
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"rcoma=[]\n",
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"racoma=[]\n",
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"deccoma=[]\n",
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"velcoma=[]\n",
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"for fname in fins:\n",
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" ncpu=find_ncpu('/data/lavaux/PM_HOT_0.30_try2_epsilon_0.01/'+fname)\n",
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" pos1,vel1=load_resim('/data/lavaux/PM_HOT_0.30_try2_epsilon_0.01/'+fname,ncpu=ncpu)\n",
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" \n",
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" dcoma = np.sqrt((pos1[:,0]+corner[0]-pcoma[0])**2+(pos1[:,1]+corner[1]-pcoma[1])**2+(pos1[:,2]+corner[2]-pcoma[2])**2)\n",
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" foo=np.where(dcoma<20.)\n",
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" \n",
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" r,ra,dec = cart2sphere(pos1,corner=corner,rmax=2000)\n",
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" \n",
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" print ('hi',np.min(ra),np.max(ra),np.min(dec),np.max(dec))\n",
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" \n",
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" velcoma.append(vel1[foo])\n",
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" rcoma.append(r[foo])\n",
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" racoma.append(ra[foo])\n",
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" deccoma.append(dec[foo])\n",
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" \n",
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" np.savez('/scratch/jasche/coma_particles', r = rcoma, ra = racoma, dec = deccoma, vel=velcoma)\n",
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" \n",
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" xx_full.append(get_densities(r,ra,dec,rmax))\n",
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" \n",
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" xx_R1.append(get_densities(r,ra,dec,rmax,region='R1'))\n",
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" xx_R2.append(get_densities(r,ra,dec,rmax,region='R2'))\n",
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" xx_R3.append(get_densities(r,ra,dec,rmax,region='R3'))\n",
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" np.savez('/scratch/jasche/home_radial_dens', full = xx_full, R1=xx_R1, R2=xx_R2,R3=xx_R3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"r,ra,dec = cart2sphere(pos1,corner=corner,rmax=300)"
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]
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},
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{
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"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x288 with 3 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_radial_density(r,ra,dec)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 66,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"511.99999999999983\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print (np.shape(pos1)[0]**(1./3.))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"10\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import glob\n",
|
||
|
|
"aux = glob.glob(\"/data/lavaux/PM_HOT_0.30_try2_epsilon_0.01/output_7610.h5_*\")\n",
|
||
|
|
"print(len(aux))\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"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": "3.6.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|