Commit dd12d742 authored by NUNEZ Arturo's avatar NUNEZ Arturo

many missed pushs

parent 56f5c2c5
Pipeline #770 failed with stages
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{
"cells": [],
"metadata": {},
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"nbformat_minor": 1
}
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 1
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 1
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{
"cells": [],
"metadata": {},
"nbformat": 4,
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 1
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pylab as plt\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pulse20150408_Q6.txt\n",
"Pulse20150418_Q6.txt\n",
"Pulse20150420_Q6.txt\n",
"Pulse20150422_Q6.txt\n",
"Pulse20150618_Q6.txt\n",
"Pulse20150906_Q6.txt\n",
"Pulse20151002_Q6.txt\n",
"Pulse20151010_Q6.txt\n",
"Pulse20151028_Q6.txt\n",
"Pulse20151121_Q6.txt\n",
"Pulse20151130_Q6.txt\n",
"Pulse20151203_Q6.txt\n",
"Pulse20151205_Q6.txt\n",
"Pulse20151214_Q6.txt\n",
"Pulse20160104_Q6.txt\n",
"Pulse20160112_Q6.txt\n",
"Pulse20160118_Q6.txt\n",
"Pulse20160120_Q6.txt\n",
"Pulse20160122_Q6.txt\n",
"Pulse20160209_Q6.txt\n",
"Pulse20160215_Q6.txt\n",
"Pulse20160225_Q6.txt\n",
"Pulse20160416_Q6.txt\n",
"Pulse20160503_Q6.txt\n",
"Pulse20160510_Q6.txt\n",
"Pulse20160517_Q6.txt\n",
"Pulse20160524_Q6.txt\n",
"Pulse20160531_Q6.txt\n",
"Pulse20160609_Q6.txt\n",
"Pulse20160617_Q6.txt\n",
"Pulse20160626_Q6.txt\n",
"Pulse20160704_Q6.txt\n",
"Pulse20160707_Q6.txt\n",
"Pulse20160716_Q6.txt\n",
"Pulse20160725_Q6.txt\n",
"Pulse20180108_0.txt\n",
"Pulse20180108_10.txt\n",
"Pulse20180108_1.txt\n",
"Pulse20180108_2.txt\n",
"Pulse20180108_3.txt\n",
"Pulse20180108_4.txt\n",
"Pulse20180108_5.txt\n",
"Pulse20180108_6.txt\n",
"Pulse20180108_7.txt\n",
"Pulse20180108_8.txt\n",
"Pulse20180108_9.txt\n",
"Pulse20180108_Q6.txt\n",
"Pulse20180307_Q6.txt\n",
"Pulse20180313_Q6.txt\n",
"Pulse20180318_Q6.txt\n",
"Pulse20180320_Q6.txt\n",
"Pulse20180413_Q6.txt\n",
"Pulse20180414_Q6.txt\n",
"Pulse20180420_Q6.txt\n",
"Pulse20180427_Q6.txt\n",
"Pulse20180502_Q6.txt\n",
"Pulse20180518_Q6.txt\n",
"Pulse20180522_Q6.txt\n",
"Pulse20180523_Q6.txt\n",
"Pulse20180601_Q6.txt\n",
"Pulse20180608_Q6.txt\n",
"Pulse20180613_Q6.txt\n",
"Pulse20180703_Q6.txt\n",
"Pulse20180831_Q6.txt\n",
"Pulse20180910_Q6.txt\n"
]
}
],
"source": [
"dones = open(\"../../datafiles/donefilesQ6.txt\")\n",
"for l in dones:\n",
" path = l.split(' ')[-1]\n",
" print path.split('/')[2][:-1]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
},
"latex_envs": {
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"import resource\n",
"soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)\n",
"resource.setrlimit(resource.RLIMIT_NOFILE, (hard, hard))\n",
"\n",
"#import __future__\n",
"import numpy as np\n",
"import sys\n",
"import glob\n",
"from root_numpy import root2array, tree2array\n",
"from root_numpy import testdata\n",
"import ROOT\n",
"\n",
"import matplotlib.pylab as plt\n",
"import matplotlib as mpl\n",
"import matplotlib.colors as colors\n",
"from scipy.interpolate import interp1d\n",
"import h5py\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"from matplotlib import rc\n",
"rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
"## for Palatino and other serif fonts use:\n",
"#rc('font',**{'family':'serif','serif':['Palatino']})\n",
"rc('text', usetex=True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"paths = glob.glob(\"/data/ANTARES/Sun_Backgound/AA/old\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"data = ROOT.TFile(paths[0],'OPEN')\n",
"in_hist = data.Get('H_STheta')\n",
"\n",
"Nbins = in_hist.GetNbinsX()\n",
"print Nbins\n",
"xval = np.array([in_hist.GetBinCenter(k) for k in range(Nbins)])\n",
"xerr = np.array([in_hist.GetBinWidth(k) for k in range(Nbins)])\n",
"yval = np.array([in_hist.GetBinContent(k) for k in range(Nbins)])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax =plt.subplots()\n",
"ax.step(xval, yval,where='mid')\n",
"ax.scatter(xval, yval,s=10)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-2.545 -2.455 -2.365 -2.275 -2.185 -2.095 -2.005 -1.915 -1.825 -1.735\n",
" -1.645 -1.555 -1.465 -1.375 -1.285 -1.195 -1.105 -1.015 -0.925 -0.835\n",
" -0.745 -0.655 -0.565 -0.475 -0.385 -0.295 -0.205 -0.115 -0.025 0.065\n",
" 0.155 0.245 0.335 0.425 0.515 0.605 0.695 0.785 0.875 0.965\n",
" 1.055 1.145 1.235 1.325 1.415 1.505 1.595 1.685 1.775 1.865]\n"
]
}
],
"source": [
"print xval"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
},
"latex_envs": {
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0
}
},
"nbformat": 4,
"nbformat_minor": 1
}
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 1
}
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 1
}
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 1
}
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
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......@@ -15,46 +15,60 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from scipy.stats import rv_continuous\n",
"from scipy.interpolate import interp1d\n",
"from scipy.interpolate import UnivariateSpline\n",
"from matplotlib.patches import Circle\n",
"from scipy.special import gamma\n",
"import numpy as np\n",
"import emcee\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"from numpy import exp, sqrt\n",
"from scipy.integrate import quad, dblquad, simps\n",
"from scipy.integrate import quad, dblquad, nquad, simps\n",
"from scipy.stats import rv_continuous\n",
"from scipy.special import gamma\n",
"from scipy.interpolate import interp1d\n",
"from scipy.integrate import quad\n",
"import scipy.optimize as optimize\n",
"import matplotlib.patches as patches\n",
"from itertools import product\n",
"from scipy.integrate import quad\n",
"import scipy.optimize as optimize\n",
"from scipy.interpolate import interp1d\n",
"from scipy.misc import derivative\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"from sklearn.neighbors import KDTree\n",
"import sys\n",
"import lmfit\n",
"from py_unsio import *\n",
"import pymc\n",
"import glob\n",
"from unsio import *\n",
"import os\n",
"from pymodelfit import FunctionModel1DAuto\n",
"import wkbl\n",
"from wkbl.astro.halo_info import *\n",
"from mpl_toolkits.mplot3d import axes3d\n",
"from mpl_toolkits.mplot3d import proj3d\n",
"from matplotlib import cm\n",
"import wkbl.astro.nbody_essentials as nbe\n",
"import cfalcon\n",
"CF =cfalcon.CFalcon()\n",
"import iminuit\n",
"from iminuit import Minuit, describe, Struct\n",
"import probfit\n",
"import warnings\n",
"import cfalcon\n",
"CF =cfalcon.CFalcon()\n",
"from matplotlib.colors import LogNorm\n",
"from mpl_toolkits.axes_grid.inset_locator import inset_axes\n",
"warnings.filterwarnings('ignore')"
"from matplotlib.ticker import FormatStrFormatter\n",
"from matplotlib import rc\n",
"import datetime\n",
"from scipy.misc import derivative\n",
"rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
"## for Palatino and other serif fonts use:\n",
"#rc('font',**{'family':'serif','serif':['Palatino']})\n",
"rc('text', usetex=True)\n",
"labelsize = 30\n",
"tickssize = 19\n",
"textsize = 15"
]
},
{
......@@ -66,7 +80,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {
"collapsed": false
},
......@@ -80,6 +94,7 @@
}
],
"source": [
"dmo = HALOBdmo()\n",
"simname = \"HALO B\"\n",
"pathsim = \"/data/POL/HALOB/DMO/output_00041\"\n",
"#path = \"/media/arturo/ARTUROTECA/OUTPUTS/HaloB/output_00417\"\n",
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 1
}
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 1
}
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......@@ -16,7 +16,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {
"collapsed": false,
"hide_input": false
......@@ -29,7 +29,6 @@
"from matplotlib.patches import Circle\n",
"from scipy.special import gamma\n",
"import numpy as np\n",
"import emcee\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"from numpy import exp, sqrt\n",
"from scipy.integrate import quad, dblquad, nquad\n",
......@@ -43,9 +42,7 @@
"import sys\n",
"import lmfit\n",
"from py_unsio import *\n",
"import pymc\n",
"import os\n",
"from pymodelfit import FunctionModel1DAuto\n",
"import wkbl\n",
"from wkbl.astro.halo_info import *\n",
"from mpl_toolkits.mplot3d import axes3d\n",
......@@ -55,7 +52,6 @@
"CF =cfalcon.CFalcon()\n",
"import iminuit\n",
"from iminuit import Minuit, describe, Struct\n",
"import probfit\n",
"from matplotlib.colors import LogNorm\n",
"from matplotlib.ticker import FormatStrFormatter\n",
"import warnings\n",
......@@ -64,7 +60,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {
"collapsed": false,
"hide_input": false,
......@@ -77,12 +73,7 @@
"text": [
"loading Dark matter..\n",
"loading Stars..\n",
"loading Gas..\n",
"| r_200 = 177.54\n",
"| Diagonal matrix computed \n",
"| | 20, 0, 0|\n",
"| D =| 0, 14, 0|\n",
"| | 0, 0, 2|\n"
"loading Gas..\n"
]
}
],
......@@ -93,7 +84,7 @@
"myhydro = wkbl.Galaxy_Hound(hydro.path)\n",
"\n",
"myhydro.center_shift(hydro.c_dm_com)\n",
"myhydro.r_virial(600,n=25)\n"
"myhydro.r_virial(600,n=2.5,rotate=False)\n"
]
},
{
......@@ -103,15 +94,7 @@
"collapsed": false,
"hide_input": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"34308117 34308117\n"
]
}
],
"outputs": [],
"source": [
"pos_dm = np.array(myhydro.dm.pos3d.reshape(len(myhydro.dm.pos3d)*3),dtype=np.float32)\n",
"pos_gs = np.array(myhydro.gs.pos3d.reshape(len(myhydro.gs.pos3d)*3),dtype=np.float32)\n",
......@@ -137,7 +120,7 @@
},
{
"cell_type": "code",
"execution_count": null,