Final_Paper_Plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%javascript\n",
    "var rto = 120;\n",
    "console.log('NB: Increase require timeout to ' + rto + ' seconds');\n",
    "window.requirejs.config({waitSeconds: rto}); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Load Results from Previous Sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/DESI_SciencePaperConfirmation/kmax_0.1h/ps4.db')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 1 - Match Neutrino/Relic Forecasts\n",
    "\n",
    "# GOOD\n",
    "\n",
    "For some cosmology, we wish to show that the fisher forecast produces identical constraints for neutrinos and for an equivalent relic.  \n",
    "\n",
    "### OPERATIONAL NOTE: \n",
    "We want to add the same CMB information to both the Neutrino and Equivalent Relic forecasts. If I start with the same CMB Fisher matrix, where one row/column is dlogP/dM_ncdm, I must appropriately scale it before use as the CMB prior for relics by multiplying that row/column by the factor 3^(3/4). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync1/\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps1_resultsdir = os.path.join(projectdir, 'results', 'ps1')\n",
    "ps1_resultsdir_neutrino = os.path.join(ps1_resultsdir, 'neutrino')\n",
    "ps1_resultsdir_relic = os.path.join(ps1_resultsdir, 'relic')\n",
    "ps1_convergencedir_neutrino = os.path.join(ps1_resultsdir, 'convergence', 'neutrino')\n",
    "ps1_convergencedir_relic = os.path.join(ps1_resultsdir, 'convergence', 'relic')\n",
    "cf.makedirectory(ps1_resultsdir_neutrino)\n",
    "cf.makedirectory(ps1_resultsdir_relic)\n",
    "cf.makedirectory(ps1_convergencedir_neutrino) \n",
    "cf.makedirectory(ps1_convergencedir_relic) \n",
    "\n",
    "# Fiducial Cosmologies\n",
    "ps1_neutrinofid = {\n",
    "        \"A_s\" : 2.22e-9, \n",
    "        \"n_s\" : 0.965,\n",
    "        \"omega_b\" : 0.02222,\n",
    "        \"omega_cdm\" : 0.1120,\n",
    "        \"tau_reio\" : 0.06,\n",
    "        \"h\" : 0.70,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 3., \n",
    "        \"T_ncdm\" : 1.95/2.726, \n",
    "        \"m_ncdm\" : 0.03, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046 - (3 * 1.0132), \n",
    "        \"relic_vary\" : None #Not used for neutrino forecasts\n",
    "        } \n",
    "# For matching the neutrino/relic forecasts, we must modify temp/mass according to equation 44 \n",
    "# of the Cora/Julian paper. That is: \n",
    "# m_chi = m_nu * 3^(1/4) ***where m_nu is the mass of a single neutrino in the degenerate hierarchy\n",
    "# T_chi = 1.95 * 3^(1/4)\n",
    "ps1_relicfid = {\n",
    "        \"A_s\" : 2.22e-9, \n",
    "        \"n_s\" : 0.965,\n",
    "        \"omega_b\" : 0.02222,\n",
    "        \"omega_cdm\" : 0.1120,\n",
    "        \"tau_reio\" : 0.06,\n",
    "        \"h\" : 0.70,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"T_ncdm\" : (1.95/2.726) * np.power(3., 1./4.), # Units [T_cmb]  Are Julian's matrices at 1.64 K? \n",
    "        \"m_ncdm\" : 0.03 * np.power(3., 1./4.), # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046 - (3 * 1.0132), \n",
    "        \"relic_vary\" : \"m_ncdm\" # Fix T_ncdm or M_ncdm \n",
    "        }\n",
    "\n",
    "# DESI survey parameters\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85])\n",
    "dNdz = np.array([309., 2269., 1923., 2094., 1441., 1353., 1337., 523., 466., 329., 126., 0., 0.])\n",
    "skycover = 14000. # Sky coverage of survey in degrees^2\n",
    "\n",
    "#Demonstrate Convergence\n",
    "#neutrinoconvergencetest = cf.convergence(\n",
    "#    classpath, # Path to CLASS installation\n",
    "#    datastore, # Path to directory holding CLASS output data\n",
    "#    'neutrino', # 'relic' or 'neutrino' forecasting scheme \n",
    "#    ps1_neutrinofid, # The fiducial cosmology \n",
    "#    z_table, # Redshift steps in observation\n",
    "#    dNdz, # Redshift noise in observation\n",
    "#    fisher_order=[\n",
    "#        'omega_b',                                    \n",
    "#        'omega_cdm',                                  \n",
    "#        'n_s',                                        \n",
    "#        'A_s',                                        \n",
    "#        'tau_reio',                                   \n",
    "#        'h',                                          \n",
    "#        'M_ncdm',                                                                  \n",
    "#        'sigma_fog',                                   \n",
    "#        'b0',                                         \n",
    "#        'alpha_k2'\n",
    "#    ],\n",
    "#    fcoverage_deg=14000, # Sky coverage in observation\n",
    "#    RSD=True, # Use RSD correction to Pm\n",
    "#    FOG=True, # Use FOG correction to Pm\n",
    "#    AP=True, # Use AP correction to PM\n",
    "#    COV=True, #Use AP Change of Variables correction to PM\n",
    "#    mu_step=mu_integral_step,\n",
    "#    varyfactors=[0.006, 0.007, 0.008, 0.009, 0.010], # Relative factors used to compute convergence\n",
    "#    showplots=True,\n",
    "#    saveplots=True,\n",
    "#    savepath=ps1_convergencedir_neutrino, \n",
    "#    plotparams= ['A_s',                                                          \n",
    "#                'n_s',                                                          \n",
    "#                'omega_b',                                                      \n",
    "#                'omega_cdm',                                                    \n",
    "#                'h',                                                            \n",
    "#                'tau_reio',                                                     \n",
    "#                'omega_ncdm',\n",
    "#                'M_ncdm',\n",
    "#                'sigmafog',                                                     \n",
    "#                'b0',                                                           \n",
    "#                'alphak2']\n",
    "#    )\n",
    "#neutrinoconvergencetest.gen_all_plots() # Display convergence plots\n",
    "#relicconvergencetest = cf.convergence(\n",
    "#    classpath, # Path to CLASS installation\n",
    "#    datastore, # Path to directory holding CLASS output data\n",
    "#    'relic', # 'relic' or 'neutrino' forecasting scheme \n",
    "#    ps1_relicfid, # The fiducial cosmology \n",
    "#    z_table, # Redshift steps in observation\n",
    "#    dNdz, # Redshift noise in observation\n",
    "#    fisher_order=[\n",
    "#        'omega_b',                                    \n",
    "#        'omega_cdm',                                  \n",
    "#        'n_s',                                        \n",
    "#        'A_s',                                        \n",
    "#        'tau_reio',                                   \n",
    "#        'h',                                          \n",
    "#        'M_ncdm',                                                                  \n",
    "#        'sigma_fog',                                   \n",
    "#        'b0',                                         \n",
    "#        'alpha_k2'\n",
    "#    ],\n",
    "#    fcoverage_deg=14000, # Sky coverage in observation\n",
    "#    RSD=True, # Use RSD correction to Pm\n",
    "#    FOG=True, # Use FOG correction to Pm\n",
    "#    AP=True, # Use AP correction to PM\n",
    "#    COV=True, #Use AP Change of Variables correction to PM\n",
    "#    mu_step=mu_integral_step,\n",
    "#    varyfactors=[0.006, 0.007, 0.008, 0.009, 0.010], # Relative factors used to compute convergence\n",
    "#    showplots=True,\n",
    "#    saveplots=True,\n",
    "#    savepath=ps1_convergencedir_relic, \n",
    "#    plotparams= ['A_s',                                                          \n",
    "#                'n_s',                                                          \n",
    "#                'omega_b',                                                      \n",
    "#                'omega_cdm',                                                    \n",
    "#                'h',                                                            \n",
    "#                'tau_reio',                                                     \n",
    "#                'omega_ncdm',\n",
    "#                'M_ncdm',\n",
    "#                'sigmafog',                                                     \n",
    "#                'b0',                                                           \n",
    "#                'alphak2']\n",
    "#    )\n",
    "#relicconvergencetest.gen_all_plots() # Display convergence plots\n",
    "\n",
    "# Run forecasts\n",
    "neutrinoforecast = cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'neutrino', \n",
    "    ps1_neutrinofid, \n",
    "    z_table, \n",
    "    \"DESI\", \n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True)\n",
    "neutrinoforecast.gen_pm()\n",
    "neutrinoforecast.gen_fisher(\n",
    "    fisher_order=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                                                           \n",
    "        'omega_ncdm',                                 \n",
    "        'sigma_fog',                                   \n",
    "        'b0',                                         \n",
    "        'alpha_k2'],\n",
    "    mu_step=mu_integral_step, \n",
    "    skipgen=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "relicforecast = cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    ps1_relicfid, \n",
    "    z_table,\n",
    "    \"DESI\",\n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True)\n",
    "relicforecast.gen_pm()\n",
    "relicforecast.gen_fisher(\n",
    "    fisher_order=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                                                             \n",
    "        'omega_ncdm',\n",
    "        'sigma_fog',                                   \n",
    "        'b0',                                         \n",
    "        'alpha_k2'],\n",
    "    mu_step=mu_integral_step, \n",
    "    skipgen=False)\n",
    "\n",
    "dill.dump_session(os.path.join(ps1_resultsdir, 'ps1.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps1/ps1.db')\n",
    "\n",
    "# Add CMB data\n",
    "neutrinoforecast.load_cmb_fisher(\n",
    "    fisher_order=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                                                             \n",
    "        'omega_ncdm'],\n",
    "    fisherpath=os.path.join(cf.priors_directory(), \"CMBS4_Fisher_Neutrinos.dat\"),\n",
    "    )\n",
    "\n",
    "relicforecast.load_cmb_fisher( # We are specifically adding the CMBS4 fisher information for neutrinos\n",
    "    fisher_order=[             # for the purpose of making a comparison, though we should add the\n",
    "        'omega_b',             # information for relics in the real forecast                          \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                                                             \n",
    "        'omega_ncdm'],\n",
    "    fisherpath=os.path.join(cf.priors_directory(), \"CMBS4_Fisher_Relics.dat\"),\n",
    "    )\n",
    "\n",
    "# Save results\n",
    "neutrinoforecast.export_matrices(ps1_resultsdir_neutrino)\n",
    "relicforecast.export_matrices(ps1_resultsdir_relic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# MAKE PLOTS\n",
    "# Show plots inline, and load main getdist plot module and samples class\n",
    "from __future__ import print_function\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import sys, os\n",
    "sys.path.insert(0,os.path.realpath(os.path.join(os.getcwd(),'..')))\n",
    "from getdist import plots, MCSamples, loadMCSamples\n",
    "from getdist.gaussian_mixtures import GaussianND\n",
    "import getdist\n",
    "# use this *after* importing getdist if you want to use interactive plots\n",
    "# %matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import IPython\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "print('GetDist Version: %s, Matplotlib version: %s'%(getdist.__version__, plt.matplotlib.__version__))\n",
    "# matplotlib 2 may not work very well without usetex on, can uncomment\n",
    "# plt.rcParams['text.usetex']=True\n",
    "\n",
    "paramnames = [r'$\\omega_b$', r'$\\omega_{cdm}$', r'$n_s$', r'$A_s$', r'$\\tau_{reio}$', 'h', r'$\\omega_{ncdm}$', r'$\\sigma_{fog}$', r'$\\beta_0$', r'$\\alpha_{k2}$']\n",
    "fiducials = [0.02222, 0.1120, 0.965, 2.22e-9, 0.06, 0.7, 0.09/93.14, 250000, 1.0, 1.0]\n",
    "#paramnames = [r'$\\omega_b$', r'$\\omega_{cdm}$', r'$n_s$', r'$A_s$', r'$\\tau_{reio}$', 'h', r'$\\omega_\\chi$']\n",
    "#fiducials = [0.02222, 0.1120, 0.965, 2.22e-9, 0.06, 0.7, 0.09/93.14]\n",
    "\n",
    "reliccovmat = np.loadtxt(\"/Users/nicholasdeporzio/Desktop/cfworkspace/results/PAPER_RESULTS/ps1/relic/inv_fullfisher.mat\", skiprows=1)\n",
    "neutrinocovmat = np.loadtxt(\"/Users/nicholasdeporzio/Desktop/cfworkspace/results/PAPER_RESULTS/ps1/neutrino/inv_fullfisher.mat\", skiprows=1)\n",
    "\n",
    "relicgauss = GaussianND(fiducials, reliccovmat, names=paramnames)\n",
    "neutrinogauss = GaussianND(fiducials, neutrinocovmat, names=paramnames)\n",
    "\n",
    "g = plots.get_subplot_plotter()\n",
    "g.settings.axes_fontsize=20\n",
    "g.settings.axes_labelsize=28\n",
    "g.settings.axis_tick_x_rotation=45\n",
    "g.settings.legend_fontsize=28\n",
    "g.triangle_plot(\n",
    "    [relicgauss, neutrinogauss], \n",
    "    #filled=True,\n",
    "    legend_labels=['Equivalent Relic', 'Neutrino'],\n",
    "    legend_loc='upper right',\n",
    "    line_args = [\n",
    "        {'ls':'dotted', 'lw':2, 'color':'#A51C30'},\n",
    "        {'ls':'--', 'lw':2, 'color':'#93a1ad'}],\n",
    "    contour_args=[\n",
    "        {'ls':'dotted', 'lw':2, 'color':'#A51C30', 'filled' : False},\n",
    "        {'ls':'--', 'lw':2, 'color':'#93a1ad', 'filled' : False}])\n",
    "#plt.suptitle('Note: Same CMB fisher used for both cases.', fontsize=16)\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/relicneutrino_match.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 2 - Compare Neutrino Forecast with MCMC for DESI\n",
    "\n",
    "We wish to generate a LCDM forecast for the DESI 2D posteriors overplotted on MCMC contours... We should forecast for:\n",
    "1. relic steps on  \n",
    "2. relic steps off \n",
    "3. shift in parameter values due to relics steps on/off \n",
    "4. overplot (1)-(3) on MCMC contours "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "print(cf.RSD_DELTA_L_NUMERATOR_FACTOR)\n",
    "print(cf.RSD_DELTA_LAMBDACDM)\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "#projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Volumes/SSD01/data.nosync2/\")\n",
    "classpath = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps2_resultsdir = cf.correct_path('/Volumes/SSD01/data.nosync2/results/ps2')\n",
    "#ps2_convergencedir = os.path.join(ps2_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps2_resultsdir)\n",
    "#cf.makedirectory(ps2_convergencedir) \n",
    "\n",
    "\n",
    "# No Relicfast CMBS4_desi_DH 2020-01-30 Chains Bestfit PLUS Forecasted Shift for relicfast\n",
    "ps2_fid = { \n",
    "        \"A_s\" : 2.234591e-09-4.806938031745625e-12, \n",
    "        \"n_s\" : 9.659302e-01+5.311035111640709e-05,\n",
    "        \"omega_b\" : 2.227047e-02+1.1602668787041021e-06,\n",
    "        \"omega_cdm\" : 1.127401e-01-4.108796815856762e-05,\n",
    "        \"tau_reio\" : 6.019503e-02-0.001064158458315645,\n",
    "        \"h\" : 7.007968e-01+0.0010625065009387986,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 3., \n",
    "        \"T_ncdm\" : 1.95/2.726, \n",
    "        \"m_ncdm\" : 3.623808e-02-(0.009823889445796/3), # Units [eV]\n",
    "        \"b0\" : 1.003223e-01-0.004980685985104448, \n",
    "        \"alphak2\" : 9.948011e+00+0.10341895410597579,\n",
    "        \"sigma_fog_0\" : 2.497568e+05+762.6869526527007, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046 - (3 * 1.0132), \n",
    "        \"relic_vary\" : None, #Not used for neutrino forecasts\n",
    "        }\n",
    "\n",
    "ps18_fid = {                                                                    \n",
    "        \"A_s\" : 2.2321e-9,                                                      \n",
    "        \"n_s\" : 0.967,                                                          \n",
    "        \"omega_b\" : 0.02226,                                                    \n",
    "        \"omega_cdm\" : 0.1127,                                                   \n",
    "        \"tau_reio\" : 0.0598,                                                    \n",
    "        \"h\" : 0.701,                                                            \n",
    "        \"T_cmb\" : 2.726, # Units [K]                                            \n",
    "        \"N_ncdm\" : 3.0,                                                          \n",
    "        \"deg_ncdm\" : 1.0,                                                         \n",
    "        \"T_ncdm\" : (1.95/2.726), # Units [T_cmb].                             \n",
    "        \"m_ncdm\" : 0.02, # Units [eV]                                         \n",
    "        \"b0\" : 1.0,                                                             \n",
    "        \"alphak2\" : 1.0,                                                        \n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]                                 \n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm                          \n",
    "        }\n",
    "\n",
    "# DESI survey parameters\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85])\n",
    "dNdz = np.array([309., 2269., 1923., 2094., 1441., 1353., 1337., 523., 466., 329., 126., 0., 0.])\n",
    "skycover = 14000. # Sky coverage of survey in degrees^2\n",
    "\n",
    "#Demonstrate Convergence\n",
    "#ps2_convergencetest = cf.convergence(\n",
    "#    classpath, # Path to CLASS installation\n",
    "#    datastore, # Path to directory holding CLASS output data\n",
    "#    'neutrino', # 'relic' or 'neutrino' forecasting scheme \n",
    "#    ps2_fid, # The fiducial cosmology \n",
    "#    z_table, # Redshift steps in observation\n",
    "#    dNdz, # Redshift noise in observation\n",
    "#    fisher_order=[\n",
    "#        'omega_b',                                    \n",
    "#        'omega_cdm',                                  \n",
    "#        'n_s',                                        \n",
    "#        'A_s',                                        \n",
    "#        'tau_reio',                                   \n",
    "#        'h',                                          \n",
    "#        'M_ncdm',                                                                  \n",
    "#        'sigma_fog',                                   \n",
    "#        'b0',                                         \n",
    "#        'alpha_k2'\n",
    "#    ],\n",
    "#    fcoverage_deg=14000, # Sky coverage in observation\n",
    "#    RSD=True, # Use RSD correction to Pm\n",
    "#    FOG=True, # Use FOG correction to Pm\n",
    "#    AP=True, # Use AP correction to PM\n",
    "#    COV=True, #Use AP Change of Variables correction to PM\n",
    "#    mu_step=mu_integral_step,\n",
    "#    varyfactors=[0.006, 0.007, 0.008, 0.009, 0.010], # Relative factors used to compute convergence\n",
    "#    showplots=True,\n",
    "#    saveplots=True,\n",
    "#    savepath=ps2_convergencedir, \n",
    "#    plotparams= ['A_s',                                                          \n",
    "#                'n_s',                                                          \n",
    "#                'omega_b',                                                      \n",
    "#                'omega_cdm',                                                    \n",
    "#                'h',                                                            \n",
    "#                'tau_reio',                                                     \n",
    "#                'omega_ncdm',\n",
    "#                'M_ncdm',\n",
    "#                'sigmafog',                                                     \n",
    "#                'b0',                                                           \n",
    "#                'alphak2'] \n",
    "#    )\n",
    "#ps2_convergencetest.gen_all_plots() # Display convergence plots\n",
    "\n",
    "# Run Fisher Forecast for cosmology\n",
    "ps2_forecast = cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'neutrino', \n",
    "    ps2_fid, \n",
    "    z_table, \n",
    "    \"DESI\",\n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover,\n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True)\n",
    "ps2_forecast.gen_pm()\n",
    "ps2_forecast.gen_fisher(\n",
    "    fisher_order=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                          \n",
    "        'M_ncdm',                                                                  \n",
    "        'sigma_fog',                                   \n",
    "        'b0',                                         \n",
    "        'alpha_k2'\n",
    "    ],\n",
    "    mu_step=mu_integral_step, \n",
    "    skipgen=False)\n",
    "dill.dump_session(os.path.join(ps2_resultsdir, 'ps2.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#import dill\n",
    "#dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps2/ps2.db')\n",
    "\n",
    "# Add CMB information\n",
    "ps2_forecast.load_cmb_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'M_ncdm'],\n",
    "        fisherpath=os.path.join(cf.priors_directory(), \"CMBS4_Fisher_Neutrinos.dat\"), \n",
    "        colnames=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'M_ncdm'])\n",
    "\n",
    "# Save results\n",
    "ps2_forecast.export_matrices(ps2_resultsdir)\n",
    "\n",
    "\n",
    "# Print errors\n",
    "for i in range(10):\n",
    "    print(ps2_forecast.fisher_order[i], \"uncertainty: \", np.sqrt(ps2_forecast.numpy_full_covariance[i, i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 3 - Compare Neutrino Forecast with MCMC for EUCLID\n",
    "\n",
    "We wish to generate a LCDM forecast for the EUCLID 2D posteriors overplotted on MCMC contours... We should forecast for:\n",
    "1. relic steps on  \n",
    "2. relic steps off \n",
    "3. shift in parameter values due to relics steps on/off \n",
    "4. overplot (1)-(3) on MCMC contours "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync3/\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps3_resultsdir = os.path.join(projectdir, 'results', 'ps3')\n",
    "ps3_convergencedir = os.path.join(ps3_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps3_resultsdir)\n",
    "cf.makedirectory(ps3_convergencedir) \n",
    "\n",
    "\n",
    "\n",
    "# EUCLID survey parameters\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85, 1.95])\n",
    "dNdz = np.array([2434.280, 4364.812, 4728.559, 4825.798, 4728.797, 4507.625, 4269.851, 3720.657, 3104.309, \n",
    "    2308.975, 1514.831, 1474.707, 893.716, 497.613])\n",
    "skycover = 0.3636 # Sky coverage of survey in fraction\n",
    "\n",
    "#Demonstrate Convergence\n",
    "#ps3_convergencetest = cf.convergence(\n",
    "#    classpath, # Path to CLASS installation\n",
    "#    datastore, # Path to directory holding CLASS output data\n",
    "#    'neutrino', # 'relic' or 'neutrino' forecasting scheme \n",
    "#    ps3_fid, # The fiducial cosmology \n",
    "#    z_table, # Redshift steps in observation\n",
    "#    dNdz, # Redshift noise in observation\n",
    "#    fisher_order=[\n",
    "#        'omega_b',                                    \n",
    "#        'omega_cdm',                                  \n",
    "#        'n_s',                                        \n",
    "#        'A_s',                                        \n",
    "#        'tau_reio',                                   \n",
    "#        'h',                                          \n",
    "#        'M_ncdm',                                                                  \n",
    "#        'sigma_fog',                                   \n",
    "#        'b0',                                         \n",
    "#        'alpha_k2'\n",
    "#    ],\n",
    "#    fsky=skycover, # Sky coverage in observation\n",
    "#    RSD=True, # Use RSD correction to Pm\n",
    "#    FOG=True, # Use FOG correction to Pm\n",
    "#    AP=True, # Use AP correction to PM\n",
    "#    COV=True, #Use AP Change of Variables correction to PM\n",
    "#    mu_step=mu_integral_step,\n",
    "#    varyfactors=[0.006, 0.007, 0.008, 0.009, 0.010], # Relative factors used to compute convergence\n",
    "#    showplots=True,\n",
    "#    saveplots=True,\n",
    "#    savepath=ps3_convergencedir, \n",
    "#    plotparams= ['A_s',                                                          \n",
    "#                'n_s',                                                          \n",
    "#                'omega_b',                                                      \n",
    "#                'omega_cdm',                                                    \n",
    "#                'h',                                                            \n",
    "#                'tau_reio',                                                     \n",
    "#                'omega_ncdm',\n",
    "#                'M_ncdm',\n",
    "#                'sigmafog',                                                     \n",
    "#                'b0',                                                           \n",
    "#                'alphak2'] \n",
    "#    )\n",
    "#ps3_convergencetest.gen_all_plots() # Display convergence plots\n",
    "\n",
    "# Run Fisher Forecast\n",
    "ps3_forecast = cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'neutrino', \n",
    "    ps3_fid, \n",
    "    z_table, \n",
    "    \"EUCLID\", \n",
    "    dNdz, \n",
    "    fsky=skycover,\n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True)\n",
    "ps3_forecast.gen_pm()\n",
    "ps3_forecast.gen_fisher(\n",
    "    fisher_order=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                          \n",
    "        'M_ncdm',                                                                    \n",
    "        'sigma_fog',                                   \n",
    "        'beta0',\n",
    "        'beta1',                                       \n",
    "        'alpha_k2'\n",
    "    ],\n",
    "    mu_step=mu_integral_step, \n",
    "    skipgen=False)\n",
    "\n",
    "dill.dump_session(os.path.join(ps3_resultsdir, 'ps3.db'))\n",
    "\n",
    "# Add CMB information\n",
    "ps3_forecast.load_cmb_fisher(\n",
    "    fisher_order=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                                                             \n",
    "        'M_ncdm'],\n",
    "    fisherpath=os.path.join(cf.priors_directory(), \"CMBS4_Fisher_Neutrinos.dat\"),\n",
    "    colnames=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'H_0',                                                                             \n",
    "        'M_ncdm'])\n",
    "\n",
    "# Save results\n",
    "ps3_forecast.export_matrices(ps3_resultsdir)\n",
    "\n",
    "# Print errors\n",
    "for i in range(10):\n",
    "    print(ps3_forecast.fisher_order[i], \"uncertainty: \", np.sqrt(ps3_forecast.numpy_full_covariance[i, i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session(\"/Users/nicholasdeporzio/Desktop/cfworkspace/results/NeutrinoMatch_CMBS4_euclid_beta_3_DH/ps3_CMBS4_norelicfast_beta3bestfit/ps3.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ps3_fid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 4 - Relic Forecast for DESI at 1.5072 [K] \n",
    "\n",
    "We wish to plot the uncertainty in relic temperature as a function of relic mass for masses on the range of 10^-1 to 10^2 [eV] at fiducial relic temperature 1.5072 [K] with the DESI survey. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync4/\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "\n",
    "# Generate output paths  \n",
    "ps4_resultsdir = os.path.join(projectdir, 'results', 'ps4')\n",
    "ps4_convergencedir = os.path.join(ps4_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps4_resultsdir)\n",
    "cf.makedirectory(ps4_convergencedir) \n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps4_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"T_ncdm\" : (1.5072/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"T_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# DESI Parameters\n",
    "z_table = z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85])\n",
    "dNdz = np.array([309., 2269., 1923., 2094., 1441., 1353., 1337., 523., 466., 329., 126., 0., 0.])\n",
    "skycover = 14000. # Sky coverage of survey in degrees^2\n",
    "\n",
    "# Demonstrate Convergence\n",
    "#ps4_convergencetest = cf.convergence(\n",
    "#    classpath, # Path to CLASS installation\n",
    "#    datastore, # Path to directory holding CLASS output data\n",
    "#    'relic', # 'relic' or 'neutrino' forecasting scheme \n",
    "#    ps4_fid, # The fiducial cosmology \n",
    "#    z_table, # Redshift steps in observation\n",
    "#    dNdz, # Redshift noise in observation\n",
    "#    fisher_order=[\n",
    "#        'omega_b',                                    \n",
    "#        'omega_cdm',                                  \n",
    "#        'n_s',                                        \n",
    "#        'A_s',                                        \n",
    "#        'tau_reio',                                   \n",
    "#        'h',                                          \n",
    "#        'T_ncdm', \n",
    "#        'sigma_fog',                                   \n",
    "#        'b0',                                         \n",
    "#        'alpha_k2'\n",
    "#    ],\n",
    "#    fcoverage_deg=skycover, # Sky coverage in observation\n",
    "#    RSD=True, # Use RSD correction to Pm\n",
    "#    FOG=True, # Use FOG correction to Pm\n",
    "#    AP=True, # Use AP correction to PM\n",
    "#    COV=True, #Use AP Change of Variables correction to PM\n",
    "#    mu_step=mu_integral_step,\n",
    "#    varyfactors=[0.006, 0.007, 0.008, 0.009, 0.010], # Relative factors used to compute convergence\n",
    "#    showplots=True,\n",
    "#    saveplots=True,\n",
    "#    savepath=ps4_convergencedir, \n",
    "#    plotparams= ['A_s',                                                          \n",
    "#                'n_s',                                                          \n",
    "#                'omega_b',                                                      \n",
    "#                'omega_cdm',                                                    \n",
    "#                'h',                                                            \n",
    "#                'tau_reio',                                                     \n",
    "#                'omega_ncdm',\n",
    "#                'M_ncdm',\n",
    "#                'sigmafog',                                                     \n",
    "#                'b0',                                                           \n",
    "#                'alphak2'] \n",
    "#    )\n",
    "#ps4_convergencetest.gen_all_plots()\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "omegacdm_set = ps4_fid['omega_cdm'] - ((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(ps4_fid['T_ncdm']*2.726 / 1.95, 3.))                                     \n",
    "ps4_fiducialset = [dict(ps4_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[midx]}) \n",
    "               for midx, mval in enumerate(masses)]\n",
    "ps4_forecastset = [cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"DESI\",\n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(ps4_fiducialset)]\n",
    "for fidx, fcst in enumerate(ps4_forecastset): \n",
    "    fcst.gen_pm()\n",
    "    fcst.gen_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'T_ncdm',                                 \n",
    "            'sigma_fog',                                   \n",
    "            'b0',                                         \n",
    "            'alpha_k2'],\n",
    "        mu_step=mu_integral_step, \n",
    "        skipgen=False)\n",
    "    print(\"Relic Forecast \", fidx, \" complete...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dill.dump_session(os.path.join(ps4_resultsdir, 'ps4.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps4/Low_Masses/Fixed_Kmax/ps4.db')\n",
    "\n",
    "# Save results \n",
    "#inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherCMBS4_bin\"\n",
    "inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherPlanck_bin\"\n",
    "head = 'omega_b \\t omega_cdm \\t n_s \\t A_s \\t tau_reio \\t h \\t T_ncdm \\t sigma_fog \\t b0 \\t alpha_k2'\n",
    "for fidx, fval in enumerate(ps4_forecastset):\n",
    "    fval.load_cmb_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'T_ncdm'],\n",
    "        fisherpath=inpath+str(fidx+1)+\"_T1.5K.txt\"\n",
    "    )\n",
    "    print(\"CMB Fisher information loaded to forecast \" + str(fidx) + \"...\")\n",
    "    outdir=os.path.join(ps4_resultsdir, 'M'+str(fidx+1))\n",
    "    cf.makedirectory(outdir)\n",
    "    fval.export_matrices(outdir)\n",
    "    np.savetxt(outdir + '_Full_Fisher.dat', fval.numpy_full_fisher, delimiter='\\t', header=head)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 5 - Relic Forecast for DESI at 0.9052 [K]\n",
    "\n",
    "We wish to plot the uncertainty in relic temperature as a function of relic mass for masses on the range of 10^-1 to 10^2 [eV] at fiducial relic temperature 0.9052 [K] with the  DESI  survey. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "masses = np.geomspace(0.01, 10., 21)\n",
    "print(masses[0])\n",
    "print(masses[14])\n",
    "print(masses[20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "#projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Volumes/SSD01/data.nosync5/\")\n",
    "classpath = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps5_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps5/\")\n",
    "#ps5_convergencedir = os.path.join(ps5_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps5_resultsdir)\n",
    "#cf.makedirectory(ps5_convergencedir) \n",
    "\n",
    "# Set fiducial cosmology \n",
    "# Linda Fiducial Cosmology \n",
    "ps5_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"T_ncdm\" : (0.9052/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# DESI Parameters\n",
    "z_table = z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85])\n",
    "dNdz = np.array([309., 2269., 1923., 2094., 1441., 1353., 1337., 523., 466., 329., 126., 0., 0.])\n",
    "skycover = 14000. # Sky coverage of survey in degrees^2\n",
    "\n",
    "# Demonstrate Convergence\n",
    "#ps5_convergencetest = cf.convergence(\n",
    "#   classpath, # Path to CLASS installation\n",
    "#    datastore, # Path to directory holding CLASS output data\n",
    "#    'relic', # 'relic' or 'neutrino' forecasting scheme \n",
    "#    ps5_fid, # The fiducial cosmology \n",
    "#    z_table, # Redshift steps in observation\n",
    "#    dNdz, # Redshift noise in observation\n",
    "#    fisher_order=[\n",
    "#        'omega_b',                                    \n",
    "#        'omega_cdm',                                  \n",
    "#        'n_s',                                        \n",
    "#        'A_s',                                        \n",
    "#        'tau_reio',                                   \n",
    "#        'h',                                          \n",
    "#        'T_ncdm', \n",
    "#        'sigma_fog',                                   \n",
    "#        'b0',                                         \n",
    "#        'alpha_k2'\n",
    "#    ],\n",
    "#    fcoverage_deg=skycover, # Sky coverage in observation\n",
    "#    RSD=True, # Use RSD correction to Pm\n",
    "#    FOG=True, # Use FOG correction to Pm\n",
    "#    AP=True, # Use AP correction to PM\n",
    "#    COV=True, #Use AP Change of Variables correction to PM\n",
    "#    mu_step=mu_integral_step,\n",
    "#    varyfactors=[0.006, 0.007, 0.008, 0.009, 0.010], # Relative factors used to compute convergence\n",
    "#    showplots=True,\n",
    "#    saveplots=True,\n",
    "#    savepath=ps5_convergencedir, \n",
    "#    plotparams= ['A_s',                                                          \n",
    "#                'n_s',                                                          \n",
    "#                'omega_b',                                                      \n",
    "#                'omega_cdm',                                                    \n",
    "#                'h',                                                            \n",
    "#                'tau_reio',                                                     \n",
    "#                'omega_ncdm',\n",
    "#                'M_ncdm',\n",
    "#                'sigmafog',                                                     \n",
    "#                'b0',                                                           \n",
    "#                'alphak2'] \n",
    "#    )\n",
    "#ps5_convergencetest.gen_all_plots()\n",
    "\n",
    "# Run Fisher Forecast\n",
    "#masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "masses = np.array([0.01, 1.0, 100.0])\n",
    "\n",
    "omegacdm_set = ps5_fid['omega_cdm'] - ((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(ps5_fid['T_ncdm']*2.726 / 1.95, 3.))                                     \n",
    "ps5_fiducialset = [dict(ps5_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[midx]}) \n",
    "               for midx, mval in enumerate(masses)]\n",
    "ps5_forecastset = [cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"DESI\",\n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(ps5_fiducialset)]\n",
    "for fidx, fcst in enumerate(ps5_forecastset): \n",
    "    fcst.gen_pm()\n",
    "    fcst.gen_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'N_ncdm',                                 \n",
    "            'sigma_fog',                                   \n",
    "            'b0',                                         \n",
    "            'alpha_k2'],\n",
    "        mu_step=mu_integral_step, \n",
    "        skipgen=False)\n",
    "    print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "    dill.dump_session(os.path.join(ps5_resultsdir, 'M'+str(fidx+1)+'_ps5.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps5/Low_Masses/Fixed_Kmax/ps5.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Save results \n",
    "#inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherCMBS4_bin\"\n",
    "inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/Relic_Fine_Priors/Fisher_T0.91K/FisherPlanck_bin\"\n",
    "postpath = '_T0.91K.txt'\n",
    "\n",
    "head = 'omega_b \\t omega_cdm \\t n_s \\t A_s \\t tau_reio \\t h \\t T_ncdm \\t sigma_fog \\t b0 \\t alpha_k2'\n",
    "for fidx, fval in enumerate(ps5_forecastset):\n",
    "    fval.load_cmb_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'N_ncdm'],\n",
    "        fisherpath=inpath+str(fidx+1)+\"_T0.9K.txt\"\n",
    "    )\n",
    "    print(\"CMB Fisher information loaded to forecast \" + str(fidx) + \"...\")\n",
    "    outdir=os.path.join(ps5_resultsdir, 'M'+str(fidx+1))\n",
    "    cf.makedirectory(outdir)\n",
    "    fval.export_matrices(outdir)\n",
    "    np.savetxt(outdir + 'Full_Fisher.dat', fval.numpy_full_fisher, delimiter='\\t', header=head)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 6 - Relic Forecast for EUCLID at 1.5072 [K]\n",
    "\n",
    "We wish to plot the uncertainty in relic temperature as a function of relic mass for masses on the range of 10^-1 to 10^2 [eV] at fiducial relic temperature 1.5072 [K] with the EUCLID survey. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync6/\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "\n",
    "# Generate output paths  \n",
    "ps6_resultsdir = os.path.join(projectdir, 'results', 'ps6')\n",
    "ps6_convergencedir = os.path.join(ps6_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps6_resultsdir)\n",
    "cf.makedirectory(ps6_convergencedir) \n",
    "\n",
    "# Set fiducial cosmology \n",
    "# Linda Fiducial Cosmology \n",
    "ps6_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"T_ncdm\" : (1.5072/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"beta0\" : 1.7, \n",
    "        \"beta1\" : 1.0,\n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"T_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# EUCLID values\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85, 1.95])\n",
    "dNdz = np.array([2434.280, 4364.812, 4728.559, 4825.798, 4728.797, 4507.625, 4269.851, 3720.657, 3104.309, \n",
    "    2308.975, 1514.831, 1474.707, 893.716, 497.613])\n",
    "skycover = 0.3636 # Sky coverage of survey in fraction\n",
    "\n",
    "# Demonstrate Convergence\n",
    "#ps6_convergencetest = cf.convergence(\n",
    "#    classpath, # Path to CLASS installation\n",
    "#    datastore, # Path to directory holding CLASS output data\n",
    "#    'relic', # 'relic' or 'neutrino' forecasting scheme \n",
    "#    ps6_fid, # The fiducial cosmology \n",
    "#    z_table, # Redshift steps in observation\n",
    "#    \"EUCLID\",\n",
    "#    dNdz, # Redshift noise in observation\n",
    "#    fisher_order=[\n",
    "#        'omega_b',                                    \n",
    "#        'omega_cdm',                                  \n",
    "#        'n_s',                                        \n",
    "#        'A_s',                                        \n",
    "#        'tau_reio',                                   \n",
    "#        'h',                                          \n",
    "#        'T_ncdm', \n",
    "#        'sigma_fog',                                   \n",
    "#        'b0',                                         \n",
    "#        'alpha_k2'\n",
    "#    ],\n",
    "#    fsky=skycover, # Sky coverage in observation\n",
    "#    RSD=True, # Use RSD correction to Pm\n",
    "#    FOG=True, # Use FOG correction to Pm\n",
    "#    AP=True, # Use AP correction to PM\n",
    "#    COV=True, #Use AP Change of Variables correction to PM\n",
    "#    mu_step=mu_integral_step,\n",
    "#    varyfactors=[0.006, 0.007, 0.008, 0.009, 0.010], # Relative factors used to compute convergence\n",
    "#    showplots=True,\n",
    "#    saveplots=True,\n",
    "#    savepath=ps6_convergencedir, \n",
    "#    plotparams= ['A_s',                                                          \n",
    "#                'n_s',                                                          \n",
    "#                'omega_b',                                                      \n",
    "#                'omega_cdm',                                                    \n",
    "#                'h',                                                            \n",
    "#                'tau_reio',                                                     \n",
    "#                'omega_ncdm',\n",
    "#                'M_ncdm',\n",
    "#                'sigmafog',                                                     \n",
    "#                'beta0',\n",
    "#                'beta1',\n",
    "#                'alphak2'] \n",
    "#    )\n",
    "#ps6_convergencetest.gen_all_plots()\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "omegacdm_set = ps6_fid['omega_cdm'] - ((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(ps6_fid['T_ncdm']*2.726 / 1.95, 3.))                                     \n",
    "ps6_fiducialset = [dict(ps6_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[midx]}) \n",
    "               for midx, mval in enumerate(masses)]\n",
    "ps6_forecastset = [cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"EUCLID\",\n",
    "    dNdz, \n",
    "    fsky=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(ps6_fiducialset)]\n",
    "for fidx, fcst in enumerate(ps6_forecastset): \n",
    "    fcst.gen_pm()\n",
    "    fcst.gen_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'T_ncdm',                                 \n",
    "            'sigma_fog',                                   \n",
    "            'beta0',\n",
    "            'beta1',\n",
    "            'alpha_k2'],\n",
    "        mu_step=mu_integral_step, \n",
    "        skipgen=False)\n",
    "    print(\"Relic Forecast \", fidx, \" complete...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dill.dump_session(os.path.join(ps6_resultsdir, 'ps6.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps6/Low_Masses/Fixed_Kmax/ps6.db')\n",
    "\n",
    "# Save results \n",
    "#inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherCMBS4_bin\"\n",
    "inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherPlanck_bin\"\n",
    "head = 'omega_b \\t omega_cdm \\t n_s \\t A_s \\t tau_reio \\t h \\t T_ncdm \\t sigma_fog \\t beta0 \\t beta1 \\t alpha_k2'\n",
    "for fidx, fval in enumerate(ps6_forecastset):\n",
    "    fval.load_cmb_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'T_ncdm'],\n",
    "        fisherpath=inpath+str(fidx+1)+\"_T1.5K.txt\"\n",
    "    )\n",
    "    print(\"CMB Fisher information loaded to forecast \" + str(fidx) + \"...\")\n",
    "    outdir=os.path.join(ps6_resultsdir, 'M'+str(fidx+1))\n",
    "    cf.makedirectory(outdir)\n",
    "    fval.export_matrices(outdir)\n",
    "    np.savetxt(outdir + 'Full_Fisher.dat', fval.numpy_full_fisher, delimiter='\\t', header=head)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 7 - Relic Forecast for EUCLID at 0.9052 [K]\n",
    "\n",
    "We wish to plot the uncertainty in relic temperature as a function of relic mass for masses on the range of 10^-1 to 10^2 [eV] at fiducial relic temperature 0.9052 [K] with the EUCLID survey. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync7/\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "#mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "mu_integral_step = 1.0\n",
    "\n",
    "# Generate output paths  \n",
    "ps7_resultsdir = os.path.join(projectdir, 'results', 'ps7')\n",
    "ps7_convergencedir = os.path.join(ps7_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps7_resultsdir)\n",
    "cf.makedirectory(ps7_convergencedir) \n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps7_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"T_ncdm\" : (0.9052/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"beta0\" : 1.7, \n",
    "        \"beta1\" : 1.0,\n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"T_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# EUCLID values\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85, 1.95])\n",
    "dNdz = np.array([2434.280, 4364.812, 4728.559, 4825.798, 4728.797, 4507.625, 4269.851, 3720.657, 3104.309, \n",
    "    2308.975, 1514.831, 1474.707, 893.716, 497.613])\n",
    "skycover = 0.3636 # Sky coverage of survey in fraction\n",
    "\n",
    "#Demonstrate Convergence\n",
    "#ps7_convergencetest = cf.convergence(\n",
    "#    classpath, # Path to CLASS installation\n",
    "#    datastore, # Path to directory holding CLASS output data\n",
    "#    'relic', # 'relic' or 'neutrino' forecasting scheme \n",
    "#    ps7_fid, # The fiducial cosmology \n",
    "#    z_table, # Redshift steps in observation\n",
    "#    dNdz, # Redshift noise in observation\n",
    "#    fisher_order=[\n",
    "#        'omega_b',                                    \n",
    "#        'omega_cdm',                                  \n",
    "#        'n_s',                                        \n",
    "#        'A_s',                                        \n",
    "#        'tau_reio',                                   \n",
    "#        'h',                                          \n",
    "#        'T_ncdm', \n",
    "#        'sigma_fog',                                   \n",
    "#        'b0',                                         \n",
    "#        'alpha_k2'\n",
    "#    ],\n",
    "#    fsky=skycover, # Sky coverage in observation\n",
    "#    RSD=True, # Use RSD correction to Pm\n",
    "#    FOG=True, # Use FOG correction to Pm\n",
    "#    AP=True, # Use AP correction to PM\n",
    "#    COV=True, #Use AP Change of Variables correction to PM\n",
    "#    mu_step=mu_integral_step,\n",
    "#    varyfactors=[0.006, 0.007, 0.008, 0.009, 0.010], # Relative factors used to compute convergence\n",
    "#    showplots=True,\n",
    "#    saveplots=True,\n",
    "#    savepath=ps7_convergencedir, \n",
    "#    plotparams= ['A_s',                                                          \n",
    "#                'n_s',                                                          \n",
    "#                'omega_b',                                                      \n",
    "#                'omega_cdm',                                                    \n",
    "#                'h',                                                            \n",
    "#                'tau_reio',                                                     \n",
    "#                'omega_ncdm',\n",
    "#                'M_ncdm',\n",
    "#                'sigmafog',                                                     \n",
    "#                'b0',                                                           \n",
    "#                'alphak2'] \n",
    "#    )\n",
    "#ps7_convergencetest.gen_all_plots()\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "omegacdm_set = ps7_fid['omega_cdm'] - ((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(ps7_fid['T_ncdm']*2.726 / 1.95, 3.))                                     \n",
    "ps7_fiducialset = [dict(ps7_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[midx]}) \n",
    "               for midx, mval in enumerate(masses)]\n",
    "ps7_forecastset = [cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"EUCLID\",\n",
    "    dNdz, \n",
    "    fsky=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(ps7_fiducialset)]\n",
    "for fidx, fcst in enumerate(ps7_forecastset): \n",
    "    fcst.gen_pm()\n",
    "    fcst.gen_fisher(\n",
    "        fisher_order=[                                                          \n",
    "            'omega_b',                                                          \n",
    "            'omega_cdm',                                                        \n",
    "            'n_s',                                                              \n",
    "            'A_s',                                                              \n",
    "            'tau_reio',                                                         \n",
    "            'h',                                                                \n",
    "            'T_ncdm',                                                           \n",
    "            'sigma_fog',                                                        \n",
    "            'beta0',\n",
    "            'beta1',\n",
    "            'alpha_k2'],\n",
    "        mu_step=mu_integral_step, \n",
    "        skipgen=False\n",
    "    )\n",
    "    print(\"Relic Forecast \", fidx, \" complete...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dill.dump_session(os.path.join(ps7_resultsdir, 'ps7.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps7/Low_Masses/Fixed_Kmax/ps7.db')\n",
    "\n",
    "# Save results \n",
    "#inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherCMBS4_bin\"\n",
    "inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherPlanck_bin\"\n",
    "head = 'omega_b \\t omega_cdm \\t n_s \\t A_s \\t tau_reio \\t h \\t omega_ncdm \\t sigma_fog \\t beta0 \\t beta1 \\t alpha_k2'\n",
    "for fidx, fval in enumerate(ps7_forecastset):\n",
    "    fval.load_cmb_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'T_ncdm'],\n",
    "        fisherpath=inpath+str(fidx+1)+\"_T0.9K.txt\"\n",
    "    )\n",
    "    print(\"CMB Fisher information loaded to forecast \" + str(fidx) + \"...\")\n",
    "    outdir=os.path.join(ps7_resultsdir, 'M'+str(fidx+1))\n",
    "    cf.makedirectory(outdir)\n",
    "    fval.export_matrices(outdir)\n",
    "    np.savetxt(outdir + 'Full_Fisher.dat', fval.numpy_full_fisher, delimiter='\\t', header=head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 8 - Relic Forecast for BOSS at 1.5072[K]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync8/\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps8_resultsdir = os.path.join(projectdir, 'results', 'ps8')\n",
    "ps8_convergencedir = os.path.join(ps8_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps8_resultsdir)\n",
    "cf.makedirectory(ps8_convergencedir) \n",
    "\n",
    "# Set fiducial cosmology \n",
    "# Linda Fiducial Cosmology \n",
    "ps8_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"T_ncdm\" : (1.5072/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"beta0\" : 1.7, \n",
    "        \"beta1\" : 1.0,\n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"T_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# BOSS values\n",
    "z_table = np.array([0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75])\n",
    "dNdz = np.array([8., 50., 125., 222., 332., 447., 208., 30.])\n",
    "skycover = 10000. # Sky coverage of BOSS in degrees^2\n",
    "\n",
    "#Demonstrate Convergence\n",
    "#ps8_convergencetest = cf.convergence(\n",
    "#    classpath, # Path to CLASS installation\n",
    "#    datastore, # Path to directory holding CLASS output data\n",
    "#    'relic', # 'relic' or 'neutrino' forecasting scheme \n",
    "#    ps8_fid, # The fiducial cosmology \n",
    "#    z_table, # Redshift steps in observation\n",
    "#    dNdz, # Redshift noise in observation\n",
    "#    fisher_order=[\n",
    "#        'omega_b',                                    \n",
    "#        'omega_cdm',                                  \n",
    "#        'n_s',                                        \n",
    "#        'A_s',                                        \n",
    "#        'tau_reio',                                   \n",
    "#        'h',                                          \n",
    "#        'T_ncdm', \n",
    "#        'sigma_fog',                                   \n",
    "#        'b0',                                         \n",
    "#        'alpha_k2'\n",
    "#    ],\n",
    "#    fsky=skycover, # Sky coverage in observation\n",
    "#    RSD=True, # Use RSD correction to Pm\n",
    "#    FOG=True, # Use FOG correction to Pm\n",
    "#    AP=True, # Use AP correction to PM\n",
    "#    COV=True, #Use AP Change of Variables correction to PM\n",
    "#    mu_step=mu_integral_step,\n",
    "#    varyfactors=[0.006, 0.007, 0.008, 0.009, 0.010], # Relative factors used to compute convergence\n",
    "#    showplots=True,\n",
    "#    saveplots=True,\n",
    "#    savepath=ps8_convergencedir, \n",
    "#    plotparams= ['A_s',                                                          \n",
    "#                'n_s',                                                          \n",
    "#                'omega_b',                                                      \n",
    "#                'omega_cdm',                                                    \n",
    "#                'h',                                                            \n",
    "#                'tau_reio',                                                     \n",
    "#                'omega_ncdm',\n",
    "#                'M_ncdm',\n",
    "#                'sigmafog',                                                     \n",
    "#                'b0',                                                           \n",
    "#                'alphak2'] \n",
    "#    )\n",
    "#ps8_convergencetest.gen_all_plots()\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "omegacdm_set = ps8_fid['omega_cdm'] - ((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(ps8_fid['T_ncdm']*2.726 / 1.95, 3.))                                     \n",
    "ps8_fiducialset = [dict(ps8_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[midx]}) \n",
    "               for midx, mval in enumerate(masses)]\n",
    "ps8_forecastset = [cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"EUCLID\",\n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(ps8_fiducialset)]\n",
    "for fidx, fcst in enumerate(ps8_forecastset): \n",
    "    fcst.gen_pm()\n",
    "    fcst.gen_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'T_ncdm',                                 \n",
    "            'sigma_fog',                                   \n",
    "            'beta0',\n",
    "            'beta1',\n",
    "            'alpha_k2'],\n",
    "        mu_step=mu_integral_step, \n",
    "        skipgen=False)\n",
    "    print(\"Relic Forecast \", fidx, \" complete...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dill.dump_session(os.path.join(ps8_resultsdir, 'ps8.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps8/Low_Masses/Fixed_Kmax/ps8.db')\n",
    "\n",
    "# Save results \n",
    "#inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherCMBS4_bin\"\n",
    "inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherPlanck_bin\"\n",
    "head = 'omega_b \\t omega_cdm \\t n_s \\t A_s \\t tau_reio \\t h \\t T_ncdm \\t sigma_fog \\t beta0 \\t beta1 \\t alpha_k2'\n",
    "for fidx, fval in enumerate(ps8_forecastset):\n",
    "    fval.load_cmb_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'T_ncdm'],\n",
    "        fisherpath=inpath+str(fidx+1)+\"_T1.5K.txt\"\n",
    "    )\n",
    "    print(\"CMB Fisher information loaded to forecast \" + str(fidx) + \"...\")\n",
    "    outdir=os.path.join(ps8_resultsdir, 'M'+str(fidx+1))\n",
    "    cf.makedirectory(outdir)\n",
    "    fval.export_matrices(outdir)\n",
    "    np.savetxt(outdir + 'Full_Fisher.dat', fval.numpy_full_fisher, delimiter='\\t', header=head)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 9 - Relic Forecast for BOSS/eBOSS at 0.9052[K]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os                                                                       \n",
    "import dill                                                                     \n",
    "import shutil                                                                   \n",
    "import numpy as np                                                              \n",
    "import pandas as pd                                                             \n",
    "import seaborn as sns                                                           \n",
    "import cosmicfish as cf                                                         \n",
    "import matplotlib.pyplot as plt                                                 \n",
    "                                                                                \n",
    "                                                                                \n",
    "# Instruct pyplot to use seaborn                                                \n",
    "sns.set()                                                                       \n",
    "                                                                                \n",
    "# Set project, data, CLASS directories                                          \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")    \n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync9/\")\n",
    "classpath = os.path.join(projectdir, \"class\")                                   \n",
    "                                                                                \n",
    "# Specify resolution of numerical integrals                                     \n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1\n",
    "                                                                                \n",
    "# Generate output paths                                                         \n",
    "ps9_resultsdir = os.path.join(projectdir, 'results', 'ps9')       \n",
    "ps9_convergencedir = os.path.join(ps9_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps9_resultsdir)                                        \n",
    "cf.makedirectory(ps9_convergencedir)                                    \n",
    "                                                                                \n",
    "#Set Fiducial cosmology                                                         \n",
    "# Linda Fiducial Cosmology \n",
    "ps9_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"T_ncdm\" : (0.9052/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"beta0\" : 1.7, \n",
    "        \"beta1\" : 1.0,\n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"T_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# BOSS LRGs                                                               \n",
    "z_table = z_table = np.array([0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75])\n",
    "dNdz = np.array([8., 50., 125., 222., 332., 447., 208., 30.])    \n",
    "skycover = 10000. # Sky coverage of BOSS in degrees^2\n",
    "                                                                                \n",
    "# Demonstrate Convergence                                                       \n",
    "#ps9_convergencetest = cf.convergence(                                  \n",
    "#    classpath, # Path to CLASS installation                                    \n",
    "#    datastore, # Path to directory holding CLASS output data                   \n",
    "#    'relic', # 'relic' or 'neutrino' forecasting scheme                        \n",
    "#    ps9_fid, # The fiducial cosmology                                  \n",
    "#    z_table, # Redshift steps in observation                                   \n",
    "#    dNdz, # Redshift noise in observation                                      \n",
    "#    fisher_order=[                                                             \n",
    "#        'omega_b',                                                             \n",
    "#        'omega_cdm',                                                           \n",
    "#        'n_s',                                                                 \n",
    "#        'A_s',                                                                 \n",
    "#        'tau_reio',                                                            \n",
    "#        'h',                                                                   \n",
    "#        'T_ncdm',                                                              \n",
    "#        'sigma_fog',                                                           \n",
    "#        'b0',                                                                  \n",
    "#        'alpha_k2'                                                             \n",
    "#    ],                                                                         \n",
    "#    fcoverage_deg=skycover, # Sky coverage in observation                      \n",
    "#    RSD=True, # Use RSD correction to Pm                                       \n",
    "#    FOG=True, # Use FOG correction to Pm                                       \n",
    "#    AP=True, # Use AP correction to PM                                         \n",
    "#    COV=True, #Use AP Change of Variables correction to PM                     \n",
    "#    mu_step=mu_integral_step,                                                  \n",
    "#    varyfactors=[0.006, 0.007, 0.008, 0.009, 0.010], # Relative factors used to compute convergence\n",
    "#    showplots=True,                                                            \n",
    "#    saveplots=True,                                                            \n",
    "#    savepath=ps9_convergencedir,                                       \n",
    "#    plotparams= ['A_s',                                                          \n",
    "#                'n_s',                                                          \n",
    "#                'omega_b',                                                      \n",
    "#                'omega_cdm',                                                    \n",
    "#                'h',                                                            \n",
    "#                'tau_reio',                                                     \n",
    "#                'omega_ncdm',                                                  \n",
    "#                'M_ncdm',                                                      \n",
    "#                'sigmafog',                                                     \n",
    "#                'b0',                                                           \n",
    "#                'alphak2']                                                     \n",
    "#    )                                                                          \n",
    "#ps9_convergencetest.gen_all_plots()\n",
    "\n",
    "# Run Fisher Forecast                                                           \n",
    "masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "omegacdm_set = ps9_fid['omega_cdm'] - ((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(ps9_fid['T_ncdm']*2.726 / 1.95, 3.))\n",
    "ps9_fiducialset = [dict(ps9_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[midx]})\n",
    "               for midx, mval in enumerate(masses)]                             \n",
    "ps9_forecastset = [cf.forecast(                                         \n",
    "    classpath,                                                                  \n",
    "    datastore,                                                                  \n",
    "    'relic',                                                                    \n",
    "    fidval,                                                                     \n",
    "    z_table, \n",
    "    \"EUCLID\",\n",
    "    dNdz,                                                                       \n",
    "    fcoverage_deg=skycover,                                                     \n",
    "    dstep=derivative_step,                                                      \n",
    "    RSD=True,                                                                   \n",
    "    FOG=True,                                                                   \n",
    "    AP=True,                                                                    \n",
    "    COV=True) for fididx, fidval in enumerate(ps9_fiducialset)]         \n",
    "for fidx, fcst in enumerate(ps9_forecastset):                           \n",
    "    fcst.gen_pm()                                                               \n",
    "    fcst.gen_fisher(                                                            \n",
    "        fisher_order=[                                                          \n",
    "            'omega_b',                                                          \n",
    "            'omega_cdm',                                                        \n",
    "            'n_s',                                                              \n",
    "            'A_s',                                                              \n",
    "            'tau_reio',                                                         \n",
    "            'h',                                                                \n",
    "            'T_ncdm',                                                           \n",
    "            'sigma_fog',                                                        \n",
    "            'beta0',\n",
    "            'beta1',\n",
    "            'alpha_k2'],                                                        \n",
    "        mu_step=mu_integral_step,                                               \n",
    "        skipgen=False)                                                          \n",
    "    print(\"Relic Forecast \", fidx, \" complete...\")                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dill.dump_session(os.path.join(ps9_resultsdir, 'ps9.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps9/Low_Masses/Fixed_Kmax/ps9.db')\n",
    "\n",
    "# Save results                                                                  \n",
    "#inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherCMBS4_bin\"\n",
    "inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherPlanck_bin\"\n",
    "head = 'omega_b \\t omega_cdm \\t n_s \\t A_s \\t tau_reio \\t h \\t T_ncdm \\t sigma_fog \\t beta0 \\t beta1 \\t alpha_k2'\n",
    "for fidx, fval in enumerate(ps9_forecastset):                     \n",
    "    fval.load_cmb_fisher(                                                       \n",
    "        fisher_order=[                                                          \n",
    "            'omega_b',                                                          \n",
    "            'omega_cdm',                                                        \n",
    "            'n_s',                                                              \n",
    "            'A_s',                                                              \n",
    "            'tau_reio',                                                         \n",
    "            'h',                                                                \n",
    "            'T_ncdm'],                                                          \n",
    "        fisherpath=inpath+str(fidx+1)+\"_T0.9K.txt\"\n",
    "    ) \n",
    "    print(\"CMB Fisher information loaded to forecast \" + str(fidx) + \"...\")     \n",
    "    outdir=os.path.join(ps9_resultsdir, 'M'+str(fidx+1))                  \n",
    "    cf.makedirectory(outdir)                                                    \n",
    "    fval.export_matrices(outdir)                                                \n",
    "    np.savetxt(outdir + 'Full_Fisher.dat', fval.numpy_full_fisher, delimiter='\\t', header=head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 10 - Combine Constraints from Various Surveys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync10/\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Combine plots\n",
    "basepath = '/Users/nicholasdeporzio/Desktop/cfworkspace/results/'\n",
    "\n",
    "# Load data\n",
    "low_masses = np.geomspace(0.001, 0.1, 10)\n",
    "high_masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "lownumpts = len(low_masses)\n",
    "highnumpts = len(high_masses)\n",
    "\n",
    "mattype = '/inv_fullfisher.mat'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "high_plotdatadir = '/Users/nicholasdeporzio/Desktop/plotdata/'\n",
    "cf.makedirectory(high_plotdatadir)\n",
    "\n",
    "np.savetxt(high_plotdatadir+'masses.csv', high_masses)\n",
    "\n",
    "high_desi_cmbs4_1507mK_covs = [pd.read_csv(basepath+'ps4/High_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_desi_cmbs4_905mK_covs = [pd.read_csv(basepath+'ps5/High_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_euclid_cmbs4_1507mK_covs = [pd.read_csv(basepath+'ps6/High_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_euclid_cmbs4_905mK_covs = [pd.read_csv(basepath+'ps7/High_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_boss_cmbs4_1507mK_covs = [pd.read_csv(basepath+'ps8/High_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_boss_cmbs4_905mK_covs = [pd.read_csv(basepath+'ps9/High_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "\n",
    "high_desi_planck_1507mK_covs = [pd.read_csv(basepath+'ps4/High_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_desi_planck_905mK_covs = [pd.read_csv(basepath+'ps5/High_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_euclid_planck_1507mK_covs = [pd.read_csv(basepath+'ps6/High_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_euclid_planck_905mK_covs = [pd.read_csv(basepath+'ps7/High_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_boss_planck_1507mK_covs = [pd.read_csv(basepath+'ps8/High_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_boss_planck_905mK_covs = [pd.read_csv(basepath+'ps9/High_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "\n",
    "high_desi_cmbs4_1507mK_Terrs = [np.sqrt(high_desi_cmbs4_1507mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_desi_cmbs4_905mK_Terrs = [np.sqrt(high_desi_cmbs4_905mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_euclid_cmbs4_1507mK_Terrs = [np.sqrt(high_euclid_cmbs4_1507mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_euclid_cmbs4_905mK_Terrs = [np.sqrt(high_euclid_cmbs4_905mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_boss_cmbs4_1507mK_Terrs = [np.sqrt(high_boss_cmbs4_1507mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_boss_cmbs4_905mK_Terrs = [np.sqrt(high_boss_cmbs4_905mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "\n",
    "high_desi_planck_1507mK_Terrs = [np.sqrt(high_desi_planck_1507mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_desi_planck_905mK_Terrs = [np.sqrt(high_desi_planck_905mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_euclid_planck_1507mK_Terrs = [np.sqrt(high_euclid_planck_1507mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_euclid_planck_905mK_Terrs = [np.sqrt(high_euclid_planck_905mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_boss_planck_1507mK_Terrs = [np.sqrt(high_boss_planck_1507mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_boss_planck_905mK_Terrs = [np.sqrt(high_boss_planck_905mK_covs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "\n",
    "high_desi_cmbs4_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(high_desi_cmbs4_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "high_desi_cmbs4_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(high_desi_cmbs4_905mK_Terrs) * np.power(1.95, -4.))\n",
    "high_euclid_cmbs4_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(high_euclid_cmbs4_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "high_euclid_cmbs4_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(high_euclid_cmbs4_905mK_Terrs) * np.power(1.95, -4.))\n",
    "high_boss_cmbs4_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(high_boss_cmbs4_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "high_boss_cmbs4_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(high_boss_cmbs4_905mK_Terrs) * np.power(1.95, -4.))\n",
    "\n",
    "high_desi_planck_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(high_desi_planck_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "high_desi_planck_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(high_desi_planck_905mK_Terrs) * np.power(1.95, -4.))\n",
    "high_euclid_planck_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(high_euclid_planck_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "high_euclid_planck_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(high_euclid_planck_905mK_Terrs) * np.power(1.95, -4.))\n",
    "high_boss_planck_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(high_boss_planck_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "high_boss_planck_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(high_boss_planck_905mK_Terrs) * np.power(1.95, -4.))\n",
    "\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_desi_1507mK_Terrs.csv', high_desi_cmbs4_1507mK_Terrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_desi_905mK_Terrs.csv', high_desi_cmbs4_905mK_Terrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_euclid_1507mK_Terrs.csv', high_euclid_cmbs4_1507mK_Terrs) \n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_euclid_905mK_Terrs.csv',  high_euclid_cmbs4_905mK_Terrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_boss_1507mK_Terrs.csv', high_boss_cmbs4_1507mK_Terrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_boss_905mK_Terrs.csv', high_boss_cmbs4_905mK_Terrs)\n",
    "\n",
    "np.savetxt(high_plotdatadir+'high_planck_desi_1507mK_Terrs.csv', high_desi_planck_1507mK_Terrs) \n",
    "np.savetxt(high_plotdatadir+'high_planck_desi_905mK_Terrs.csv', high_desi_planck_905mK_Terrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_euclid_1507mK_Terrs.csv', high_euclid_planck_1507mK_Terrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_euclid_905mK_Terrs.csv', high_euclid_planck_905mK_Terrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_boss_1507mK_Terrs.csv', high_boss_planck_1507mK_Terrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_boss_905mK_Terrs.csv', high_boss_planck_905mK_Terrs)\n",
    "\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_desi_1507mK_Nerrs.csv', high_desi_cmbs4_1507mK_Nerrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_desi_905mK_Nerrs.csv', high_desi_cmbs4_905mK_Nerrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_euclid_1507mK_Nerrs.csv', high_euclid_cmbs4_1507mK_Nerrs) \n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_euclid_905mK_Nerrs.csv',  high_euclid_cmbs4_905mK_Nerrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_boss_1507mK_Nerrs.csv', high_boss_cmbs4_1507mK_Nerrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_boss_905mK_Nerrs.csv', high_boss_cmbs4_905mK_Nerrs)\n",
    "\n",
    "np.savetxt(high_plotdatadir+'high_planck_desi_1507mK_Nerrs.csv', high_desi_planck_1507mK_Nerrs) \n",
    "np.savetxt(high_plotdatadir+'high_planck_desi_905mK_Nerrs.csv', high_desi_planck_905mK_Nerrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_euclid_1507mK_Nerrs.csv', high_euclid_planck_1507mK_Nerrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_euclid_905mK_Nerrs.csv', high_euclid_planck_905mK_Nerrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_boss_1507mK_Nerrs.csv', high_boss_planck_1507mK_Nerrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_boss_905mK_Nerrs.csv', high_boss_planck_905mK_Nerrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "high_desi_cmbs4_1507mK_omegacovs = [pd.read_csv(basepath+'ps4/High_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_desi_cmbs4_905mK_omegacovs = [pd.read_csv(basepath+'ps5/High_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_euclid_cmbs4_1507mK_omegacovs = [pd.read_csv(basepath+'ps6/High_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_euclid_cmbs4_905mK_omegacovs = [pd.read_csv(basepath+'ps7/High_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_boss_cmbs4_1507mK_omegacovs = [pd.read_csv(basepath+'ps8/High_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_boss_cmbs4_905mK_omegacovs = [pd.read_csv(basepath+'ps9/High_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "\n",
    "high_desi_planck_1507mK_omegacovs = [pd.read_csv(basepath+'ps4/High_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_desi_planck_905mK_omegacovs = [pd.read_csv(basepath+'ps5/High_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_euclid_planck_1507mK_omegacovs = [pd.read_csv(basepath+'ps6/High_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_euclid_planck_905mK_omegacovs = [pd.read_csv(basepath+'ps7/High_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_boss_planck_1507mK_omegacovs = [pd.read_csv(basepath+'ps8/High_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "high_boss_planck_905mK_omegacovs = [pd.read_csv(basepath+'ps9/High_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(highnumpts)]\n",
    "\n",
    "high_desi_cmbs4_1507mK_omegaerrs = [np.sqrt(high_desi_cmbs4_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_desi_cmbs4_905mK_omegaerrs = [np.sqrt(high_desi_cmbs4_905mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_euclid_cmbs4_1507mK_omegaerrs = [np.sqrt(high_euclid_cmbs4_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_euclid_cmbs4_905mK_omegaerrs = [np.sqrt(high_euclid_cmbs4_905mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_boss_cmbs4_1507mK_omegaerrs = [np.sqrt(high_boss_cmbs4_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_boss_cmbs4_905mK_omegaerrs = [np.sqrt(high_boss_cmbs4_905mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "\n",
    "high_desi_planck_1507mK_omegaerrs = [np.sqrt(high_desi_planck_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_desi_planck_905mK_omegaerrs = [np.sqrt(high_desi_planck_905mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_euclid_planck_1507mK_omegaerrs = [np.sqrt(high_euclid_planck_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_euclid_planck_905mK_omegaerrs = [np.sqrt(high_euclid_planck_905mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_boss_planck_1507mK_omegaerrs = [np.sqrt(high_boss_planck_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "high_boss_planck_905mK_omegaerrs = [np.sqrt(high_boss_planck_905mK_omegacovs[idx].iloc[6,6]) for idx in range(highnumpts)]\n",
    "\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_desi_1507mK_omegaerrs.csv', high_desi_cmbs4_1507mK_omegaerrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_desi_905mK_omegaerrs.csv', high_desi_cmbs4_905mK_omegaerrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_euclid_1507mK_omegaerrs.csv', high_euclid_cmbs4_1507mK_omegaerrs) \n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_euclid_905mK_omegaerrs.csv',  high_euclid_cmbs4_905mK_omegaerrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_boss_1507mK_omegaerrs.csv', high_boss_cmbs4_1507mK_omegaerrs)\n",
    "np.savetxt(high_plotdatadir+'high_cmbs4_boss_905mK_omegaerrs.csv', high_boss_cmbs4_905mK_omegaerrs)\n",
    "\n",
    "np.savetxt(high_plotdatadir+'high_planck_desi_1507mK_omegaerrs.csv', high_desi_planck_1507mK_omegaerrs) \n",
    "np.savetxt(high_plotdatadir+'high_planck_desi_905mK_omegaerrs.csv', high_desi_planck_905mK_omegaerrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_euclid_1507mK_omegaerrs.csv', high_euclid_planck_1507mK_omegaerrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_euclid_905mK_omegaerrs.csv', high_euclid_planck_905mK_omegaerrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_boss_1507mK_omegaerrs.csv', high_boss_planck_1507mK_omegaerrs)\n",
    "np.savetxt(high_plotdatadir+'high_planck_boss_905mK_omegaerrs.csv', high_boss_planck_905mK_omegaerrs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "low_plotdatadir = '/Users/nicholasdeporzio/Desktop/plotdata/'\n",
    "cf.makedirectory(low_plotdatadir)\n",
    "\n",
    "np.savetxt(low_plotdatadir+'masses.csv', low_masses)\n",
    "\n",
    "low_desi_cmbs4_1507mK_covs = [pd.read_csv(basepath+'ps4/Low_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_desi_cmbs4_905mK_covs = [pd.read_csv(basepath+'ps5/Low_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_euclid_cmbs4_1507mK_covs = [pd.read_csv(basepath+'ps6/Low_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_euclid_cmbs4_905mK_covs = [pd.read_csv(basepath+'ps7/Low_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_boss_cmbs4_1507mK_covs = [pd.read_csv(basepath+'ps8/Low_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_boss_cmbs4_905mK_covs = [pd.read_csv(basepath+'ps9/Low_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "\n",
    "low_desi_planck_1507mK_covs = [pd.read_csv(basepath+'ps4/Low_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_desi_planck_905mK_covs = [pd.read_csv(basepath+'ps5/Low_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_euclid_planck_1507mK_covs = [pd.read_csv(basepath+'ps6/Low_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_euclid_planck_905mK_covs = [pd.read_csv(basepath+'ps7/Low_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_boss_planck_1507mK_covs = [pd.read_csv(basepath+'ps8/Low_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_boss_planck_905mK_covs = [pd.read_csv(basepath+'ps9/Low_Masses/Fixed_Kmax/Planck/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "\n",
    "low_desi_cmbs4_1507mK_Terrs = [np.sqrt(low_desi_cmbs4_1507mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_desi_cmbs4_905mK_Terrs = [np.sqrt(low_desi_cmbs4_905mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_euclid_cmbs4_1507mK_Terrs = [np.sqrt(low_euclid_cmbs4_1507mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_euclid_cmbs4_905mK_Terrs = [np.sqrt(low_euclid_cmbs4_905mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_boss_cmbs4_1507mK_Terrs = [np.sqrt(low_boss_cmbs4_1507mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_boss_cmbs4_905mK_Terrs = [np.sqrt(low_boss_cmbs4_905mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "\n",
    "low_desi_planck_1507mK_Terrs = [np.sqrt(low_desi_planck_1507mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_desi_planck_905mK_Terrs = [np.sqrt(low_desi_planck_905mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_euclid_planck_1507mK_Terrs = [np.sqrt(low_euclid_planck_1507mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_euclid_planck_905mK_Terrs = [np.sqrt(low_euclid_planck_905mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_boss_planck_1507mK_Terrs = [np.sqrt(low_boss_planck_1507mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_boss_planck_905mK_Terrs = [np.sqrt(low_boss_planck_905mK_covs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "\n",
    "low_desi_cmbs4_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(low_desi_cmbs4_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "low_desi_cmbs4_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(low_desi_cmbs4_905mK_Terrs) * np.power(1.95, -4.))\n",
    "low_euclid_cmbs4_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(low_euclid_cmbs4_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "low_euclid_cmbs4_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(low_euclid_cmbs4_905mK_Terrs) * np.power(1.95, -4.))\n",
    "low_boss_cmbs4_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(low_boss_cmbs4_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "low_boss_cmbs4_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(low_boss_cmbs4_905mK_Terrs) * np.power(1.95, -4.))\n",
    "\n",
    "low_desi_planck_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(low_desi_planck_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "low_desi_planck_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(low_desi_planck_905mK_Terrs) * np.power(1.95, -4.))\n",
    "low_euclid_planck_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(low_euclid_planck_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "low_euclid_planck_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(low_euclid_planck_905mK_Terrs) * np.power(1.95, -4.))\n",
    "low_boss_planck_1507mK_Nerrs = (4. * np.power(1.5, 3.) * np.array(low_boss_planck_1507mK_Terrs) * np.power(1.95, -4.))\n",
    "low_boss_planck_905mK_Nerrs = (4. * np.power(0.9, 3.) * np.array(low_boss_planck_905mK_Terrs) * np.power(1.95, -4.))\n",
    "\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_desi_1507mK_Terrs.csv', low_desi_cmbs4_1507mK_Terrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_desi_905mK_Terrs.csv', low_desi_cmbs4_905mK_Terrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_euclid_1507mK_Terrs.csv', low_euclid_cmbs4_1507mK_Terrs) \n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_euclid_905mK_Terrs.csv',  low_euclid_cmbs4_905mK_Terrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_boss_1507mK_Terrs.csv', low_boss_cmbs4_1507mK_Terrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_boss_905mK_Terrs.csv', low_boss_cmbs4_905mK_Terrs)\n",
    "\n",
    "np.savetxt(low_plotdatadir+'low_planck_desi_1507mK_Terrs.csv', low_desi_planck_1507mK_Terrs) \n",
    "np.savetxt(low_plotdatadir+'low_planck_desi_905mK_Terrs.csv', low_desi_planck_905mK_Terrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_euclid_1507mK_Terrs.csv', low_euclid_planck_1507mK_Terrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_euclid_905mK_Terrs.csv', low_euclid_planck_905mK_Terrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_boss_1507mK_Terrs.csv', low_boss_planck_1507mK_Terrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_boss_905mK_Terrs.csv', low_boss_planck_905mK_Terrs)\n",
    "\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_desi_1507mK_Nerrs.csv', low_desi_cmbs4_1507mK_Nerrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_desi_905mK_Nerrs.csv', low_desi_cmbs4_905mK_Nerrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_euclid_1507mK_Nerrs.csv', low_euclid_cmbs4_1507mK_Nerrs) \n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_euclid_905mK_Nerrs.csv',  low_euclid_cmbs4_905mK_Nerrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_boss_1507mK_Nerrs.csv', low_boss_cmbs4_1507mK_Nerrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_boss_905mK_Nerrs.csv', low_boss_cmbs4_905mK_Nerrs)\n",
    "\n",
    "np.savetxt(low_plotdatadir+'low_planck_desi_1507mK_Nerrs.csv', low_desi_planck_1507mK_Nerrs) \n",
    "np.savetxt(low_plotdatadir+'low_planck_desi_905mK_Nerrs.csv', low_desi_planck_905mK_Nerrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_euclid_1507mK_Nerrs.csv', low_euclid_planck_1507mK_Nerrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_euclid_905mK_Nerrs.csv', low_euclid_planck_905mK_Nerrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_boss_1507mK_Nerrs.csv', low_boss_planck_1507mK_Nerrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_boss_905mK_Nerrs.csv', low_boss_planck_905mK_Nerrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "low_desi_cmbs4_1507mK_omegacovs = [pd.read_csv(basepath+'ps4/Low_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_desi_cmbs4_905mK_omegacovs = [pd.read_csv(basepath+'ps5/Low_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_euclid_cmbs4_1507mK_omegacovs = [pd.read_csv(basepath+'ps6/Low_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_euclid_cmbs4_905mK_omegacovs = [pd.read_csv(basepath+'ps7/Low_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_boss_cmbs4_1507mK_omegacovs = [pd.read_csv(basepath+'ps8/Low_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_boss_cmbs4_905mK_omegacovs = [pd.read_csv(basepath+'ps9/Low_Masses/Fixed_Kmax/CMBS4/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "\n",
    "low_desi_planck_1507mK_omegacovs = [pd.read_csv(basepath+'ps4/Low_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_desi_planck_905mK_omegacovs = [pd.read_csv(basepath+'ps5/Low_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_euclid_planck_1507mK_omegacovs = [pd.read_csv(basepath+'ps6/Low_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_euclid_planck_905mK_omegacovs = [pd.read_csv(basepath+'ps7/Low_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_boss_planck_1507mK_omegacovs = [pd.read_csv(basepath+'ps8/Low_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "low_boss_planck_905mK_omegacovs = [pd.read_csv(basepath+'ps9/Low_Masses/Fixed_Kmax/Planck/omega_version/M'+str(idx+1)+mattype, sep='\\t', header=0) for idx in range(lownumpts)]\n",
    "\n",
    "low_desi_cmbs4_1507mK_omegaerrs = [np.sqrt(low_desi_cmbs4_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_desi_cmbs4_905mK_omegaerrs = [np.sqrt(low_desi_cmbs4_905mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_euclid_cmbs4_1507mK_omegaerrs = [np.sqrt(low_euclid_cmbs4_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_euclid_cmbs4_905mK_omegaerrs = [np.sqrt(low_euclid_cmbs4_905mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_boss_cmbs4_1507mK_omegaerrs = [np.sqrt(low_boss_cmbs4_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_boss_cmbs4_905mK_omegaerrs = [np.sqrt(low_boss_cmbs4_905mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "\n",
    "low_desi_planck_1507mK_omegaerrs = [np.sqrt(low_desi_planck_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_desi_planck_905mK_omegaerrs = [np.sqrt(low_desi_planck_905mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_euclid_planck_1507mK_omegaerrs = [np.sqrt(low_euclid_planck_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_euclid_planck_905mK_omegaerrs = [np.sqrt(low_euclid_planck_905mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_boss_planck_1507mK_omegaerrs = [np.sqrt(low_boss_planck_1507mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "low_boss_planck_905mK_omegaerrs = [np.sqrt(low_boss_planck_905mK_omegacovs[idx].iloc[6,6]) for idx in range(lownumpts)]\n",
    "\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_desi_1507mK_omegaerrs.csv', low_desi_cmbs4_1507mK_omegaerrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_desi_905mK_omegaerrs.csv', low_desi_cmbs4_905mK_omegaerrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_euclid_1507mK_omegaerrs.csv', low_euclid_cmbs4_1507mK_omegaerrs) \n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_euclid_905mK_omegaerrs.csv',  low_euclid_cmbs4_905mK_omegaerrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_boss_1507mK_omegaerrs.csv', low_boss_cmbs4_1507mK_omegaerrs)\n",
    "np.savetxt(low_plotdatadir+'low_cmbs4_boss_905mK_omegaerrs.csv', low_boss_cmbs4_905mK_omegaerrs)\n",
    "\n",
    "np.savetxt(low_plotdatadir+'low_planck_desi_1507mK_omegaerrs.csv', low_desi_planck_1507mK_omegaerrs) \n",
    "np.savetxt(low_plotdatadir+'low_planck_desi_905mK_omegaerrs.csv', low_desi_planck_905mK_omegaerrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_euclid_1507mK_omegaerrs.csv', low_euclid_planck_1507mK_omegaerrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_euclid_905mK_omegaerrs.csv', low_euclid_planck_905mK_omegaerrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_boss_1507mK_omegaerrs.csv', low_boss_planck_1507mK_omegaerrs)\n",
    "np.savetxt(low_plotdatadir+'low_planck_boss_905mK_omegaerrs.csv', low_boss_planck_905mK_omegaerrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#butt\n",
    "from scipy.integrate import quad\n",
    "def integrand(p, m, T):\n",
    "    return (2./np.power(2 * np.pi, 3) * (1/ (np.exp(p/T) + 1)) \n",
    "            * np.sqrt(np.power(p, 2) + np.power(m, 2))  * (4 * np.pi * np.power(p, 2)))\n",
    "\n",
    "omegashot = np.array([quad(integrand, 0, 10, args=(mval, 1.5e-4 ))[0] for mval in high_masses])\n",
    "omegascold = np.array([quad(integrand, 0, 10, args=(mval, 0.9e-4 ))[0] for mval in high_masses])\n",
    "\n",
    "omegascmb_hot = np.array([quad(integrand, 0, 10, args=(mval, 1.5*1100*1e-4 ))[0] for mval in high_masses])\n",
    "omegascmb_cold = np.array([quad(integrand, 0, 10, args=(mval, 0.9*1100*1e-4 ))[0] for mval in high_masses])\n",
    "\n",
    "relomegaerrhot = omegascmb_hot/omegashot\n",
    "relomegaerrcold = omegascmb_cold/omegascold\n",
    "\n",
    "dstepuphot = np.array([quad(integrand, 0, 10, args=(mval, 1.01*1.5*1e-4 ))[0] for mval in high_masses])\n",
    "dstepdownhot = np.array([quad(integrand, 0, 10, args=(mval, 0.99*1.5*1e-4 ))[0] for mval in high_masses])\n",
    "dstepupcold = np.array([quad(integrand, 0, 10, args=(mval, 1.01*0.9*1e-4 ))[0] for mval in high_masses])\n",
    "dstepdowncold = np.array([quad(integrand, 0, 10, args=(mval, 0.99*0.9*1e-4 ))[0] for mval in high_masses])\n",
    "\n",
    "dstepupcmbhot = np.array([quad(integrand, 0, 10, args=(mval, 1.001*1.5*1100*1e-4 ))[0] for mval in high_masses])\n",
    "dstepdowncmbhot = np.array([quad(integrand, 0, 10, args=(mval, 0.999*1.5*1100*1e-4 ))[0] for mval in high_masses])\n",
    "dstepupcmbcold = np.array([quad(integrand, 0, 10, args=(mval, 1.001*0.9*1100*1e-4 ))[0] for mval in high_masses])\n",
    "dstepdowncmbcold = np.array([quad(integrand, 0, 10, args=(mval, 0.999*0.9*1100*1e-4 ))[0] for mval in high_masses])\n",
    "\n",
    "domegadThot = (dstepuphot-dstepdownhot)/(2*0.01*1.5*1e-4)\n",
    "domegadTcold = (dstepupcold-dstepdowncold)/(2*0.01*0.9*1e-4)\n",
    "\n",
    "domegacmbdThot = (dstepupcmbhot-dstepdowncmbhot)/(2*0.001*1.5*1e-4)\n",
    "domegacmbdTcold = (dstepupcmbcold-dstepdowncmbcold)/(2*0.001*0.9*1e-4)\n",
    "\n",
    "dlogomegadlogT_hot = (1.5*1e-4) * domegadThot / omegashot \n",
    "dlogomegadlogT_cold = (0.9*1e-4) * domegadTcold / omegascold\n",
    "\n",
    "dlogomegacmbdlogT_hot = (1.5*1e-4) * domegacmbdThot / omegascmb_hot \n",
    "dlogomegacmbdlogT_cold = (0.9*1e-4) * domegacmbdTcold / omegascmb_cold\n",
    "\n",
    "deltaTdomegadT_hot = dlogomegadlogT_hot * omegashot * (1/(1.5*1e-4)) * high_euclid_cmbs4_1507mK_Terrs\n",
    "deltaTdomegadT_cold = dlogomegadlogT_cold * omegascold * (1/(0.9*1e-4)) * high_euclid_cmbs4_905mK_Terrs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plotdatadir = '/Users/nicholasdeporzio/Desktop/plotdata/'\n",
    "cf.makedirectory(plotdatadir)\n",
    "######################################\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "plt.semilogx(high_masses, dlogomegacmbdlogT_hot, label=\"$T_{\\chi, 0} = 1.5 K$\")\n",
    "plt.semilogx(high_masses, dlogomegacmbdlogT_cold, label=\"$T_{\\chi, 0} = 0.9 K$\")\n",
    "#plt.title(r\"$T_{\\chi, 0}$ = 1.5 K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$dlog\\omega_\\chi / dlogT_\\chi$\", fontsize=24)\n",
    "plt.legend(fontsize=18)\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/dlogplot.png\")\n",
    "plt.show()\n",
    "\n",
    "######################################\n",
    "plt.figure(figsize=(15,7.5))\n",
    "plt.semilogx(high_masses, np.array(high_boss_cmbs4_1507mK_Terrs) , label=\"CMBS4 + BOSS\", linestyle='dashed', color='red')\n",
    "plt.semilogx(high_masses, np.array(high_desi_cmbs4_1507mK_Terrs), label=\"CMBS4 + DESI\", linestyle='dashed', color='green')\n",
    "plt.semilogx(high_masses, np.array(high_euclid_cmbs4_1507mK_Terrs) , label=\"CMBS4 + EUCLID\", linestyle='dashed', color='blue')\n",
    "plt.semilogx(high_masses, np.array(high_boss_planck_1507mK_Terrs) , label=\"Planck + BOSS\", linestyle='solid', color='red')\n",
    "plt.semilogx(high_masses, np.array(high_desi_planck_1507mK_Terrs), label=\"Planck + DESI\", linestyle='solid', color='green')\n",
    "plt.semilogx(high_masses, np.array(high_euclid_planck_1507mK_Terrs) , label=\"Planck + EUCLID\", linestyle='solid', color='blue')\n",
    "\n",
    "#plt.semilogx(low_masses, np.array(low_boss_cmbs4_1507mK_Terrs) , linestyle='dashed', color='red')\n",
    "#plt.semilogx(low_masses, np.array(low_desi_cmbs4_1507mK_Terrs), linestyle='dashed', color='green')\n",
    "#plt.semilogx(low_masses, np.array(low_euclid_cmbs4_1507mK_Terrs) , linestyle='dashed', color='blue')\n",
    "#plt.semilogx(low_masses, np.array(low_boss_planck_1507mK_Terrs) , linestyle='solid', color='red')\n",
    "#plt.semilogx(low_masses, np.array(low_desi_planck_1507mK_Terrs), linestyle='solid', color='green')\n",
    "#plt.semilogx(low_masses, np.array(low_euclid_planck_1507mK_Terrs) , linestyle='solid', color='blue')\n",
    "\n",
    "#sigmaline1 = (lownumpts + highnumpts) * [1.5072/8]\n",
    "#sigmaline1 = (highnumpts) * [1.5072/8]\n",
    "\n",
    "#sigmaline1 = omegascmb_hot / 3.\n",
    "#plt.semilogx(np.append(low_masses, high_masses), sigmaline1, label=r\"$2\\sigma$\", linestyle='solid', color='black')\n",
    "#plt.semilogx(high_masses, sigmaline1, label=r\"$2\\sigma$\", linestyle='solid', color='black')\n",
    "#np.savetxt(plotdatadir+'1507mK_T_5sigma.csv', [np.append(low_masses, high_masses), sigmaline1])\n",
    "\n",
    "plt.title(r\"$T_{\\chi, 0}$ = 1.5 K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{T_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='center right')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(plotdatadir + \"relicforecast_T1507mk_Terr.png\")\n",
    "plt.show()\n",
    "\n",
    "##########################################\n",
    "\n",
    "##\n",
    "cutmasses=np.delete(high_masses, 3)\n",
    "cut905mKTerrboss = np.delete(high_boss_planck_905mK_Terrs, 3)\n",
    "cut905mKTerrdesi = np.delete(high_desi_planck_905mK_Terrs, 3)\n",
    "cut905mKTerreuclid = np.delete(high_euclid_planck_905mK_Terrs, 3)\n",
    "##\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.semilogx(high_masses, np.array(high_boss_cmbs4_905mK_Terrs) , label=\"CMBS4 + BOSS\", linestyle='dashed', color='red')\n",
    "plt.semilogx(high_masses, np.array(high_desi_cmbs4_905mK_Terrs), label=\"CMBS4 + DESI\", linestyle='dashed', color='green')\n",
    "plt.semilogx(high_masses, np.array(high_euclid_cmbs4_905mK_Terrs) , label=\"CMBS4 + EUCLID\", linestyle='dashed', color='blue')\n",
    "plt.semilogx(cutmasses, np.array(cut905mKTerrboss) , label=\"Planck + BOSS\", linestyle='solid', color='red')\n",
    "plt.semilogx(cutmasses, np.array(cut905mKTerrdesi), label=\"Planck + DESI\", linestyle='solid', color='green')\n",
    "plt.semilogx(cutmasses, np.array(cut905mKTerreuclid) , label=\"Planck + EUCLID\", linestyle='solid', color='blue')\n",
    "\n",
    "#plt.semilogx(low_masses, np.array(low_boss_cmbs4_905mK_Terrs) , linestyle='dashed', color='red')\n",
    "#plt.semilogx(low_masses, np.array(low_desi_cmbs4_905mK_Terrs), linestyle='dashed', color='green')\n",
    "#plt.semilogx(low_masses, np.array(low_euclid_cmbs4_905mK_Terrs) , linestyle='dashed', color='blue')\n",
    "#plt.semilogx(low_masses, np.array(low_boss_planck_905mK_Terrs) , linestyle='solid', color='red')\n",
    "#plt.semilogx(low_masses, np.array(low_desi_planck_905mK_Terrs), linestyle='solid', color='green')\n",
    "#plt.semilogx(low_masses, np.array(low_euclid_planck_905mK_Terrs) , linestyle='solid', color='blue')\n",
    "\n",
    "#sigmaline2 = (lownumpts + highnumpts) * [0.9052/8]\n",
    "#sigmaline2 = (highnumpts) * [0.9052/8]\n",
    "\n",
    "#sigmaline2 = omegascmb_cold / 3.\n",
    "\n",
    "#plt.semilogx(np.append(low_masses, high_masses), sigmaline2, label=r\"$5\\sigma$\", linestyle='solid', color='black')\n",
    "#plt.semilogx(high_masses, sigmaline2, label=r\"$2\\sigma ~N_{eff}$\", linestyle='solid', color='black')\n",
    "#np.savetxt(plotdatadir+'905mK_T_5sigma.csv', [np.append(low_masses, high_masses), sigmaline2])\n",
    "\n",
    "plt.title(r\"$T_{\\chi, 0}$ = 0.9 K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{T_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='upper right')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(plotdatadir+\"relicforecast_T905mk_Terr.png\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#####################\n",
    "plt.figure(figsize=(15,7.5))\n",
    "plt.semilogx(high_masses, np.array(high_boss_cmbs4_1507mK_Nerrs) , label=\"CMBS4 + BOSS\", linestyle='dashed', color='red')\n",
    "plt.semilogx(high_masses, np.array(high_desi_cmbs4_1507mK_Nerrs), label=\"CMBS4 + DESI\", linestyle='dashed', color='green')\n",
    "plt.semilogx(high_masses, np.array(high_euclid_cmbs4_1507mK_Nerrs) , label=\"CMBS4 + EUCLID\", linestyle='dashed', color='blue')\n",
    "plt.semilogx(high_masses, np.array(high_boss_planck_1507mK_Nerrs) , label=\"Planck + BOSS\", linestyle='solid', color='red')\n",
    "plt.semilogx(high_masses, np.array(high_desi_planck_1507mK_Nerrs), label=\"Planck + DESI\", linestyle='solid', color='green')\n",
    "plt.semilogx(high_masses, np.array(high_euclid_planck_1507mK_Nerrs) , label=\"Planck + EUCLID\", linestyle='solid', color='blue')\n",
    "\n",
    "plt.semilogx(low_masses, np.array(low_boss_cmbs4_1507mK_Nerrs) , linestyle='dashed', color='red')\n",
    "plt.semilogx(low_masses, np.array(low_desi_cmbs4_1507mK_Nerrs), linestyle='dashed', color='green')\n",
    "plt.semilogx(low_masses, np.array(low_euclid_cmbs4_1507mK_Nerrs) , linestyle='dashed', color='blue')\n",
    "plt.semilogx(low_masses, np.array(low_boss_planck_1507mK_Nerrs) , linestyle='solid', color='red')\n",
    "plt.semilogx(low_masses, np.array(low_desi_planck_1507mK_Nerrs), linestyle='solid', color='green')\n",
    "plt.semilogx(low_masses, np.array(low_euclid_planck_1507mK_Nerrs) , linestyle='solid', color='blue')\n",
    "\n",
    "sigmaline3 = (lownumpts + highnumpts) * [4. * np.power(1.5072, 3) * np.power(1.95, -4) * (1.5072/5)]\n",
    "plt.semilogx(np.append(low_masses, high_masses), sigmaline3, label=r\"$5\\sigma$\", linestyle='solid', color='black')\n",
    "np.savetxt(plotdatadir+'1507mK_Neff_5sigma.csv', [np.append(low_masses, high_masses), sigmaline3])\n",
    "\n",
    "plt.title(r\"$T_{\\chi, 0}$ = 1.5 K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{Neff}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='center right')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(plotdatadir+\"relicforecast_T1507mk_Nerr.png\")\n",
    "plt.show()\n",
    "\n",
    "#####################\n",
    "plt.figure(figsize=(15,7.5))\n",
    "plt.semilogx(high_masses, np.array(high_boss_cmbs4_905mK_Nerrs) , label=\"CMBS4 + BOSS\", linestyle='dashed', color='red')\n",
    "plt.semilogx(high_masses, np.array(high_desi_cmbs4_905mK_Nerrs), label=\"CMBS4 + DESI\", linestyle='dashed', color='green')\n",
    "plt.semilogx(high_masses, np.array(high_euclid_cmbs4_905mK_Nerrs) , label=\"CMBS4 + EUCLID\", linestyle='dashed', color='blue')\n",
    "plt.semilogx(high_masses, np.array(high_boss_planck_905mK_Nerrs) , label=\"Planck + BOSS\", linestyle='solid', color='red')\n",
    "plt.semilogx(high_masses, np.array(high_desi_planck_905mK_Nerrs), label=\"Planck + DESI\", linestyle='solid', color='green')\n",
    "plt.semilogx(high_masses, np.array(high_euclid_planck_905mK_Nerrs) , label=\"Planck + EUCLID\", linestyle='solid', color='blue')\n",
    "\n",
    "plt.semilogx(low_masses, np.array(low_boss_cmbs4_905mK_Nerrs) , linestyle='dashed', color='red')\n",
    "plt.semilogx(low_masses, np.array(low_desi_cmbs4_905mK_Nerrs), linestyle='dashed', color='green')\n",
    "plt.semilogx(low_masses, np.array(low_euclid_cmbs4_905mK_Nerrs) , linestyle='dashed', color='blue')\n",
    "plt.semilogx(low_masses, np.array(low_boss_planck_905mK_Nerrs) , linestyle='solid', color='red')\n",
    "plt.semilogx(low_masses, np.array(low_desi_planck_905mK_Nerrs), linestyle='solid', color='green')\n",
    "plt.semilogx(low_masses, np.array(low_euclid_planck_905mK_Nerrs) , linestyle='solid', color='blue')\n",
    "\n",
    "sigmaline4 = (lownumpts + highnumpts) * [4. * np.power(0.9052, 3) * np.power(1.95, -4) * (0.9052/5)]\n",
    "plt.semilogx(np.append(low_masses, high_masses), sigmaline4, label=r\"$5\\sigma$\", linestyle='solid', color='black')\n",
    "np.savetxt(plotdatadir+'905mK_Neff_5sigma.csv', [np.append(low_masses, high_masses), sigmaline4])\n",
    "\n",
    "plt.title(r\"$T_{\\chi, 0}$ = 0.9 K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{Neff}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='upper right')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(plotdatadir+\"relicforecast_T905mk_Nerr.png\")\n",
    "plt.show()\n",
    "\n",
    "#####################\n",
    "plt.figure(figsize=(15,7.5))\n",
    "#plt.semilogx(high_masses, np.array(high_boss_cmbs4_1507mK_omegaerrs) , label=\"CMBS4 + BOSS\", linestyle='dashed', color='red')\n",
    "#plt.semilogx(high_masses, np.array(high_desi_cmbs4_1507mK_omegaerrs), label=\"CMBS4 + DESI\", linestyle='dashed', color='green')\n",
    "#plt.semilogx(high_masses, np.array(high_euclid_cmbs4_1507mK_omegaerrs) , label=\"CMBS4 + EUCLID\", linestyle='dashed', color='blue')\n",
    "#plt.semilogx(high_masses, np.array(high_boss_planck_1507mK_omegaerrs) , label=\"Planck + BOSS\", linestyle='solid', color='red')\n",
    "#plt.semilogx(high_masses, np.array(high_desi_planck_1507mK_omegaerrs), label=\"Planck + DESI\", linestyle='solid', color='green')\n",
    "#plt.semilogx(high_masses, np.array(high_euclid_planck_1507mK_omegaerrs) , label=\"Planck + EUCLID\", linestyle='solid', color='blue')\n",
    "\n",
    "#plt.semilogx(low_masses, np.array(low_boss_cmbs4_1507mK_omegaerrs) , linestyle='dashed', color='red')\n",
    "#plt.semilogx(low_masses, np.array(low_desi_cmbs4_1507mK_omegaerrs), linestyle='dashed', color='green')\n",
    "#plt.semilogx(low_masses, np.array(low_euclid_cmbs4_1507mK_omegaerrs) , linestyle='dashed', color='blue')\n",
    "#plt.semilogx(low_masses, np.array(low_boss_planck_1507mK_omegaerrs) , linestyle='solid', color='red')\n",
    "#plt.semilogx(low_masses, np.array(low_desi_planck_1507mK_omegaerrs), linestyle='solid', color='green')\n",
    "#plt.semilogx(low_masses, np.array(low_euclid_planck_1507mK_omegaerrs) , linestyle='solid', color='blue')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.loglog(high_masses, domegacmbdThot * np.array(high_boss_cmbs4_1507mK_Terrs) * 1e-4 , label=\"CMBS4 + BOSS\", linestyle='dashed', color='red')\n",
    "plt.loglog(high_masses, domegacmbdThot * np.array(high_desi_cmbs4_1507mK_Terrs) * 1e-4, label=\"CMBS4 + DESI\", linestyle='dashed', color='green')\n",
    "plt.loglog(high_masses, domegacmbdThot * np.array(high_euclid_cmbs4_1507mK_Terrs) * 1e-4 , label=\"CMBS4 + EUCLID\", linestyle='dashed', color='blue')\n",
    "plt.loglog(high_masses, domegacmbdThot * np.array(high_boss_planck_1507mK_Terrs) * 1e-4 , label=\"Planck + BOSS\", linestyle='solid', color='red')\n",
    "plt.loglog(high_masses, domegacmbdThot * np.array(high_desi_planck_1507mK_Terrs) * 1e-4, label=\"Planck + DESI\", linestyle='solid', color='green')\n",
    "plt.loglog(high_masses, domegacmbdThot * np.array(high_euclid_planck_1507mK_Terrs) * 1e-4 , label=\"Planck + EUCLID\", linestyle='solid', color='blue')\n",
    "\n",
    "#sigmaline5 = high_masses * (1./93.) * 3. * np.power(1.5072, 2.) * np.power(1.95, -3) * (1.5072/5)\n",
    "sigmaline5 = omegascmb_hot / 5.\n",
    "plt.loglog(high_masses, sigmaline5, label=r\"$5\\sigma$\", linestyle='solid', color='black')\n",
    "np.savetxt(plotdatadir+'1507mK_omega_5sigma.csv', [high_masses, sigmaline5])\n",
    "\n",
    "plt.title(r\"$T_{\\chi, 0}$ = 1.5 K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{\\omega_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='upper left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "#plt.ylim((-0.001,0.005))\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/plot1.png\")\n",
    "plt.show()\n",
    "\n",
    "#####################\n",
    "plt.figure(figsize=(15,7.5))\n",
    "#plt.loglog(high_masses, np.array(high_boss_cmbs4_905mK_omegaerrs) , label=\"CMBS4 + BOSS\", linestyle='dashed', color='red')\n",
    "#plt.loglog(high_masses, np.array(high_desi_cmbs4_905mK_omegaerrs), label=\"CMBS4 + DESI\", linestyle='dashed', color='green')\n",
    "#plt.loglog(high_masses, np.array(high_euclid_cmbs4_905mK_omegaerrs) , label=\"CMBS4 + EUCLID\", linestyle='dashed', color='blue')\n",
    "#plt.loglog(high_masses, np.array(high_boss_planck_905mK_omegaerrs) , label=\"Planck + BOSS\", linestyle='solid', color='red')\n",
    "#plt.loglog(high_masses, np.array(high_desi_planck_905mK_omegaerrs), label=\"Planck + DESI\", linestyle='solid', color='green')\n",
    "#plt.loglog(high_masses, np.array(high_euclid_planck_905mK_omegaerrs) , label=\"Planck + EUCLID\", linestyle='solid', color='blue')\n",
    "\n",
    "#plt.loglog(low_masses, np.array(low_boss_cmbs4_905mK_omegaerrs) , linestyle='dashed', color='red')\n",
    "#plt.loglog(low_masses, np.array(low_desi_cmbs4_905mK_omegaerrs), linestyle='dashed', color='green')\n",
    "#plt.loglog(low_masses, np.array(low_euclid_cmbs4_905mK_omegaerrs) , linestyle='dashed', color='blue')\n",
    "#plt.loglog(low_masses, np.array(low_boss_planck_905mK_omegaerrs) , linestyle='solid', color='red')\n",
    "#plt.loglog(low_masses, np.array(low_desi_planck_905mK_omegaerrs), linestyle='solid', color='green')\n",
    "#plt.loglog(low_masses, np.array(low_euclid_planck_905mK_omegaerrs) , linestyle='solid', color='blue')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.loglog(high_masses, domegacmbdTcold * np.array(high_boss_cmbs4_905mK_Terrs) * 1e-4 , label=\"CMBS4 + BOSS\", linestyle='dashed', color='red')\n",
    "plt.loglog(high_masses, domegacmbdTcold * np.array(high_desi_cmbs4_905mK_Terrs) * 1e-4, label=\"CMBS4 + DESI\", linestyle='dashed', color='green')\n",
    "plt.loglog(high_masses, domegacmbdTcold * np.array(high_euclid_cmbs4_905mK_Terrs) * 1e-4 , label=\"CMBS4 + EUCLID\", linestyle='dashed', color='blue')\n",
    "plt.loglog(high_masses, domegacmbdTcold * np.array(high_boss_planck_905mK_Terrs) * 1e-4 , label=\"Planck + BOSS\", linestyle='solid', color='red')\n",
    "plt.loglog(high_masses, domegacmbdTcold * np.array(high_desi_planck_905mK_Terrs) * 1e-4, label=\"Planck + DESI\", linestyle='solid', color='green')\n",
    "plt.loglog(high_masses, domegacmbdTcold * np.array(high_euclid_planck_905mK_Terrs) * 1e-4 , label=\"Planck + EUCLID\", linestyle='solid', color='blue')\n",
    "\n",
    "print(\"Conversion Factor: \")\n",
    "print(domegacmbdTcold)\n",
    "print(\"T errors: \")\n",
    "print(np.array(high_euclid_planck_905mK_Terrs))\n",
    "print(\"Conversion factor * T errors: \")\n",
    "print(domegacmbdTcold * np.array(high_euclid_planck_905mK_Terrs))\n",
    "\n",
    "#sigmaline6 = high_masses * (1./93.) * 3. * np.power(0.9052, 2.) * np.power(1.95, -3) * (1.5072/5)\n",
    "sigmaline6 = omegascmb_cold / 5.\n",
    "\n",
    "print(\"Omega_rec (1sigma line): \")\n",
    "print(sigmaline6)\n",
    "print(\"Reference value of sigma omegas from Fisher: \")\n",
    "print(np.array(low_euclid_planck_905mK_omegaerrs))\n",
    "plt.loglog(high_masses, sigmaline6, label=r\"$5\\sigma$\", linestyle='solid', color='black')\n",
    "np.savetxt(plotdatadir+'905mK_omega_5sigma.csv', [high_masses, sigmaline6])\n",
    "\n",
    "plt.title(r\"$T_{\\chi, 0}$ = 0.9 K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{\\omega_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='upper left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "#plt.ylim(top=0.007)\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/plot2.png\")\n",
    "plt.show()\n",
    "\n",
    "#####################\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.loglog(high_masses, (1/omegascmb_hot) * domegacmbdThot * np.array(high_boss_cmbs4_1507mK_Terrs) * 1e-4 , label=\"CMBS4 + BOSS\", linestyle='dashed', color='red')\n",
    "plt.loglog(high_masses, (1/omegascmb_hot) * domegacmbdThot * np.array(high_desi_cmbs4_1507mK_Terrs) * 1e-4, label=\"CMBS4 + DESI\", linestyle='dashed', color='green')\n",
    "plt.loglog(high_masses, (1/omegascmb_hot) * domegacmbdThot * np.array(high_euclid_cmbs4_1507mK_Terrs) * 1e-4 , label=\"CMBS4 + EUCLID\", linestyle='dashed', color='blue')\n",
    "plt.loglog(high_masses, (1/omegascmb_hot) * domegacmbdThot * np.array(high_boss_planck_1507mK_Terrs) * 1e-4 , label=\"Planck + BOSS\", linestyle='solid', color='red')\n",
    "plt.loglog(high_masses, (1/omegascmb_hot) * domegacmbdThot * np.array(high_desi_planck_1507mK_Terrs) * 1e-4, label=\"Planck + DESI\", linestyle='solid', color='green')\n",
    "plt.loglog(high_masses, (1/omegascmb_hot) * domegacmbdThot * np.array(high_euclid_planck_1507mK_Terrs) * 1e-4 , label=\"Planck + EUCLID\", linestyle='solid', color='blue')\n",
    "\n",
    "sigmaline7 = len(high_masses) * [1./5.]\n",
    "\n",
    "plt.loglog(high_masses, sigmaline7, label=r\"$5\\sigma$\", linestyle='solid', color='black')\n",
    "\n",
    "plt.title(r\"$T_{\\chi, 0}$ = 1.5 K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{\\omega_\\chi}/\\omega_\\chi$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='upper left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/plot3.png\")\n",
    "plt.show()\n",
    "\n",
    "#####################\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.loglog(high_masses, (1/omegascmb_cold) * domegacmbdTcold * np.array(high_boss_cmbs4_905mK_Terrs) * 1e-4 , label=\"CMBS4 + BOSS\", linestyle='dashed', color='red')\n",
    "plt.loglog(high_masses, (1/omegascmb_cold) * domegacmbdTcold * np.array(high_desi_cmbs4_905mK_Terrs) * 1e-4, label=\"CMBS4 + DESI\", linestyle='dashed', color='green')\n",
    "plt.loglog(high_masses, (1/omegascmb_cold) * domegacmbdTcold * np.array(high_euclid_cmbs4_905mK_Terrs) * 1e-4 , label=\"CMBS4 + EUCLID\", linestyle='dashed', color='blue')\n",
    "plt.loglog(high_masses, (1/omegascmb_cold) * domegacmbdTcold * np.array(high_boss_planck_905mK_Terrs) * 1e-4 , label=\"Planck + BOSS\", linestyle='solid', color='red')\n",
    "plt.loglog(high_masses, (1/omegascmb_cold) * domegacmbdTcold * np.array(high_desi_planck_905mK_Terrs) * 1e-4, label=\"Planck + DESI\", linestyle='solid', color='green')\n",
    "plt.loglog(high_masses, (1/omegascmb_cold) * domegacmbdTcold * np.array(high_euclid_planck_905mK_Terrs) * 1e-4 , label=\"Planck + EUCLID\", linestyle='solid', color='blue')\n",
    "\n",
    "sigmaline8 = len(high_masses) * [1./5.]\n",
    "\n",
    "plt.loglog(high_masses, sigmaline8, label=r\"$5\\sigma$\", linestyle='solid', color='black')\n",
    "\n",
    "plt.title(r\"$T_{\\chi, 0}$ = 0.9 K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{\\omega_\\chi}/\\omega_\\chi$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='upper left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "#plt.ylim(top=0.007)\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/plot4.png\")\n",
    "plt.show()\n",
    "\n",
    "#####################\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.loglog(high_masses, (1/omegascmb_hot) * domegacmbdThot * np.array(high_desi_cmbs4_1507mK_Terrs) * 1e-4 , label=\"CMBS4 + DESI, 1.5K\", linestyle='dashed', color='red')\n",
    "plt.loglog(high_masses, (1/omegascmb_cold) * domegacmbdTcold * np.array(high_desi_cmbs4_905mK_Terrs) * 1e-4, label=\"CMBS4 + DESI, 0.9K\", linestyle='dashed', color='green')\n",
    "\n",
    "\n",
    "sigmaline9 = len(high_masses) * [1./3.]\n",
    "\n",
    "plt.loglog(high_masses, sigmaline9, label=r\"$3\\sigma$\", linestyle='solid', color='black')\n",
    "\n",
    "#plt.title(r\"$T_{\\chi, 0}$ = 0.9 K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{\\omega_\\chi}/\\omega_\\chi$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='upper left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/plot5.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#butt\n",
    "from scipy.integrate import quad\n",
    "def integrand(p, m, T):\n",
    "    return (2./np.power(2 * np.pi, 3) * (1/ (np.exp(p/T) + 1)) \n",
    "            * np.sqrt(np.power(p, 2) + np.power(m, 2))  * (4 * np.pi * np.power(p, 2)))\n",
    "\n",
    "omegashot = np.array([quad(integrand, 0, 10, args=(mval, 1.5e-4 ))[0] for mval in high_masses])\n",
    "omegascold = np.array([quad(integrand, 0, 10, args=(mval, 0.9e-4 ))[0] for mval in high_masses])\n",
    "\n",
    "omegascmb_hot = np.array([quad(integrand, 0, 10, args=(mval, 1.5*1100*1e-4 ))[0] for mval in high_masses])\n",
    "omegascmb_cold = np.array([quad(integrand, 0, 10, args=(mval, 0.9*1100*1e-4 ))[0] for mval in high_masses])\n",
    "\n",
    "relomegaerrhot = omegascmb_hot/omegashot\n",
    "relomegaerrcold = omegascmb_cold/omegascold\n",
    "\n",
    "dstepuphot = np.array([quad(integrand, 0, 10, args=(mval, 1.01*1.5*1e-4 ))[0] for mval in high_masses])\n",
    "dstepdownhot = np.array([quad(integrand, 0, 10, args=(mval, 0.99*1.5*1e-4 ))[0] for mval in high_masses])\n",
    "dstepupcold = np.array([quad(integrand, 0, 10, args=(mval, 1.01*0.9*1e-4 ))[0] for mval in high_masses])\n",
    "dstepdowncold = np.array([quad(integrand, 0, 10, args=(mval, 0.99*0.9*1e-4 ))[0] for mval in high_masses])\n",
    "\n",
    "dstepupcmbhot = np.array([quad(integrand, 0, 10, args=(mval, 1.001*1.5*1100*1e-4 ))[0] for mval in high_masses])\n",
    "dstepdowncmbhot = np.array([quad(integrand, 0, 10, args=(mval, 0.999*1.5*1100*1e-4 ))[0] for mval in high_masses])\n",
    "dstepupcmbcold = np.array([quad(integrand, 0, 10, args=(mval, 1.001*0.9*1100*1e-4 ))[0] for mval in high_masses])\n",
    "dstepdowncmbcold = np.array([quad(integrand, 0, 10, args=(mval, 0.999*0.9*1100*1e-4 ))[0] for mval in high_masses])\n",
    "\n",
    "domegadThot = (dstepuphot-dstepdownhot)/(2*0.01*1.5*1e-4)\n",
    "domegadTcold = (dstepupcold-dstepdowncold)/(2*0.01*0.9*1e-4)\n",
    "\n",
    "domegacmbdThot = (dstepupcmbhot-dstepdowncmbhot)/(2*0.001*1.5*1e-4)\n",
    "domegacmbdTcold = (dstepupcmbcold-dstepdowncmbcold)/(2*0.001*0.9*1e-4)\n",
    "\n",
    "dlogomegadlogT_hot = (1.5*1e-4) * domegadThot / omegashot \n",
    "dlogomegadlogT_cold = (0.9*1e-4) * domegadTcold / omegascold\n",
    "\n",
    "dlogomegacmbdlogT_hot = (1.5*1e-4) * domegacmbdThot / omegascmb_hot \n",
    "dlogomegacmbdlogT_cold = (0.9*1e-4) * domegacmbdTcold / omegascmb_cold\n",
    "\n",
    "deltaTdomegacmbdT_hot = domegacmbdThot * high_desi_cmbs4_1507mK_Terrs\n",
    "deltaTdomegacmbdT_cold = domegacmbdTcold * high_desi_cmbs4_905mK_Terrs\n",
    "\n",
    "# wriggle\n",
    "fishmattype='/inv_fullfisher.mat'\n",
    "omegacdm_high_desi_cmbs4_1507mK = [np.sqrt(pd.read_csv(basepath+'ps4/High_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+fishmattype, sep='\\t', header=0).iloc[1,1]) for idx in range(27)]\n",
    "omegacdm_high_desi_cmbs4_905mK = [np.sqrt(pd.read_csv(basepath+'ps5/High_Masses/Fixed_Kmax/CMBS4/M'+str(idx+1)+fishmattype, sep='\\t', header=0).iloc[1,1]) for idx in range(27)]\n",
    "omegancdm_today_desi_cmbs4_1507mK = domegadThot * high_desi_cmbs4_1507mK_Terrs\n",
    "omegancdm_today_desi_cmbs4_905mK = domegadTcold * high_desi_cmbs4_905mK_Terrs\n",
    "#deltaTdomegacmbdT_hot\n",
    "#deltaTdomegacmbdT_cold\n",
    "\n",
    "\n",
    "masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "omegancdm_set1507mK = ((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(1.5072*2.726 / 1.95, 3.))\n",
    "omegacdm_set1507mK = 0.1127 - omegancdm_set1507mK\n",
    "omegancdm_set905mK = ((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(0.9052*2.726 / 1.95, 3.))\n",
    "omegacdm_set905mK = 0.1127 - omegancdm_set905mK\n",
    "#omegascmb_hot = np.array([quad(integrand, 0, 10, args=(mval, 1.5*1100*1e-4 ))[0] for mval in high_masses])\n",
    "#omegascmb_cold = np.array([quad(integrand, 0, 10, args=(mval, 0.9*1100*1e-4 ))[0] for mval in high_masses])\n",
    "\n",
    "#Data\n",
    "data1_omegacdm1507 = omegacdm_high_desi_cmbs4_1507mK/omegacdm_set1507mK\n",
    "data2_omegancdm1507 = omegancdm_today_desi_cmbs4_1507mK/omegancdm_set1507mK\n",
    "data3_omegancdm1507_cmb  = deltaTdomegacmbdT_hot/omegascmb_hot\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.loglog(high_masses, data1_omegacdm1507 , label=\"CMBS4 + DESI, 1.5K, $\\omega_{CDM}$\", linestyle='solid', color='red')\n",
    "plt.loglog(high_masses, data2_omegancdm1507, label=\"CMBS4 + DESI, 1.5K, $\\omega_{NCDM, z=0}$\", linestyle='solid', color='green')\n",
    "plt.loglog(high_masses, data3_omegancdm1507_cmb, label=\"CMBS4 + DESI, 1.5K, $\\omega_{NCDM, z=1100}$\", linestyle='solid', color='blue')\n",
    "\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_\\omega / \\omega$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='upper left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/plot6.png\")\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync10/\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Combine plots\n",
    "basepath = '/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps5/High_Masses/Fixed_Kmax/CMBS4/'\n",
    "\n",
    "mattype='/inv_lssfisher.mat'\n",
    "desi_1507mK_fishers = [(pd.read_csv(basepath+'M'+str(idx+1)+mattype, sep='\\t', header=0).iloc[6,6]) for idx, val in enumerate(np.arange(1, 28))]\n",
    "\n",
    "masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "omegas = (masses/93) * np.power(0.9/1.95, 3)\n",
    "dtdomegas = (93./masses) * np.power(1.95 * 1100, 3) * np.power(0.9 * 1100, -2) * (1./3) \n",
    "dtdomegas2 = np.power(dtdomegas, 2.)\n",
    "relerrs = desi_1507mK_fishers * dtdomegas2 * np.power(omegas, 2)\n",
    "errs = np.sqrt(np.array(desi_1507mK_fishers))\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "plt.loglog(masses, errs, linestyle='solid', color='red')\n",
    "#plt.title(\"DESI + CMBS4, 0.9[K]\")\n",
    "plt.xlabel(\"Relic Mass [eV]\")\n",
    "#plt.ylabel(r\"$F_{\\omega_\\chi, \\omega_\\chi} \\times \\omega_\\chi^2$\")\n",
    "plt.ylabel(r\"$\\sigma  T$\")\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/Plot1.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15,7.5))\n",
    "#plt.semilogx(masses, boss_1500mK_Nerrs, label=\"BOSS, 1.5072K\", linestyle='dotted', color='orange')\n",
    "#plt.semilogx(masses, boss_1100mK_Nerrs, label=\"BOSS, 0.9052K\", linestyle='dotted', color='green')\n",
    "plt.semilogx(masses, desi_1500mK_Nerrs, label=\"DESI, 1.5072K\", linestyle='dashed', color='orange')\n",
    "plt.semilogx(masses, desi_1100mK_Nerrs, label=\"DESI, 0.9052K\", linestyle='dashed', color='green')\n",
    "#plt.semilogx(masses, euclid_1500mK_Nerrs, label=\"EUCLID, 1.5072K\", color='orange')\n",
    "#plt.semilogx(masses, euclid_1100mK_Nerrs, label=\"EUCLID, 0.9052K\", color='green')\n",
    "plt.title(r\"$N_{eff}$ Shift, LSS + CMBS4\")\n",
    "plt.xlabel(\"Relic Mass [eV]\")\n",
    "plt.ylabel(r\"$\\Delta N_{eff}$\")\n",
    "plt.legend()\n",
    "plt.tick_params(axis='x', which='minor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/relicforecast_N.png\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "print(boss_1500mK_Nerrs[0])\n",
    "print(boss_1100mK_Nerrs[0])\n",
    "print(desi_1500mK_Nerrs[0])\n",
    "print(desi_1100mK_Nerrs[0])\n",
    "print(euclid_1500mK_Nerrs[0])\n",
    "print(euclid_1100mK_Nerrs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "errors_Tncdm_cmb = [np.sqrt(fval.numpy_cmb_covariance[6,6]) for fidx, fval in enumerate(forecastset)]    \n",
    "errors_Tncdm_lss = [np.sqrt(fval.numpy_lss_covariance[6,6]) for fidx, fval in enumerate(forecastset)]\n",
    "errors_Tncdm_full = [np.sqrt(fval.numpy_full_covariance[6,6]) for fidx, fval in enumerate(forecastset)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 11 - Demonstrate Shift Formalism in b0\n",
    "# GOOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync/\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps11_resultsdir = os.path.join(projectdir, 'results', 'ps11')\n",
    "ps11_convergencedir = os.path.join(ps11_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps11_resultsdir)\n",
    "cf.makedirectory(ps11_convergencedir) \n",
    "\n",
    "# Set fiducial cosmology\n",
    "ps11_fid = { # Fiducial for DESI/EUCLID MCMCs\n",
    "        \"A_s\" : 2.22e-9, \n",
    "        \"n_s\" : 0.965,\n",
    "        \"omega_b\" : 0.02222,\n",
    "        \"omega_cdm\" : 0.1120,\n",
    "        \"tau_reio\" : 0.06,\n",
    "        \"h\" : 0.70,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 3., \n",
    "        \"T_ncdm\" : 1.95/2.726, \n",
    "        \"m_ncdm\" : 0.03, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046 - (3 * 1.0132), \n",
    "        \"relic_vary\" : None, #Not used for neutrino forecasts\n",
    "        } \n",
    "\n",
    "# DESI survey parameters\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85])\n",
    "dNdz = np.array([309., 2269., 1923., 2094., 1441., 1353., 1337., 523., 466., 329., 126., 0., 0.])\n",
    "skycover = 14000. # Sky coverage of survey in degrees^2\n",
    "\n",
    "# Run Forecast\n",
    "ps11_forecast = cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'neutrino', \n",
    "    ps11_fid, \n",
    "    z_table, \n",
    "    \"DESI\"\n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True)\n",
    "ps11_forecast.gen_pm()\n",
    "ps11_forecast.gen_fisher(\n",
    "    fisher_order=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                          \n",
    "        'h',                                          \n",
    "        'M_ncdm',                                                                    \n",
    "        'sigma_fog',                                   \n",
    "        'b0',                                         \n",
    "        'alpha_k2',\n",
    "        'b_Amp'\n",
    "    ],\n",
    "    mu_step=mu_integral_step, \n",
    "    skipgen=False)\n",
    "\n",
    "# Restructure results\n",
    "DD = np.array(ps11_forecast.fisher[10, 10])\n",
    "Dj = np.array(ps11_forecast.fisher[0:10, 10])\n",
    "Extended_Fisher = np.array(ps11_forecast.fisher)\n",
    "ps11_forecast.fisher = np.array(ps11_forecast.fisher[0:10, 0:10])\n",
    "ps11_forecast.fisher_order.remove('b_Amp')\n",
    "\n",
    "# Add CMB Data\n",
    "ps11_forecast.load_cmb_fisher(\n",
    "    fisher_order=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                                                             \n",
    "        'M_ncdm'],\n",
    "    fisherpath=os.path.join(cf.priors_directory(), \"CMBS4_Fisher_Neutrinos.dat\"))\n",
    "\n",
    "# Save results\n",
    "ps11_forecast.export_matrices(ps11_resultsdir)\n",
    "pd.DataFrame(ps11_forecast.numpy_full_fisher, columns=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                          \n",
    "        'h',                                          \n",
    "        'M_ncdm',                                                                    \n",
    "        'sigma_fog',                                   \n",
    "        'b0',                                         \n",
    "        'alpha_k2'\n",
    "    ]).to_csv(os.path.join(ps11_resultsdir, \"yescmbfisher.csv\"), sep='\\t')\n",
    "pd.DataFrame(ps11_forecast.numpy_lss_fisher, columns=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                          \n",
    "        'h',                                          \n",
    "        'M_ncdm',                                                                    \n",
    "        'sigma_fog',                                   \n",
    "        'b0',                                         \n",
    "        'alpha_k2'\n",
    "    ]).to_csv(os.path.join(ps11_resultsdir, \"nocmbfisher.csv\"), sep='\\t')\n",
    "\n",
    "# Print the uncertainty in the P_g shift amplitude\n",
    "D_spread = np.sqrt(DD)\n",
    "np.savetxt(path+\"shift_root_DD.csv\", np.array([D_spread]), delimiter='\\t')\n",
    "print(r\"Uncertainty in P_g shift amplitude: \", D_spread)\n",
    "\n",
    "#Djvec1=(ps11_forecast.numpy_full_fisher[8,:]  *  0.001)\n",
    "\n",
    "# Print parameter errors\n",
    "for i in range(10): \n",
    "    print(np.sqrt(ps11_forecast.numpy_full_covariance[i, i]), '\\n')\n",
    "\n",
    "# Print shift amplitude\n",
    "#Amp_Projection[4] = 0.\n",
    "pd.DataFrame(np.array(Dj)).to_csv(os.path.join(ps11_resultsdir, \"shift_basis_projection.csv\"), sep='\\t')\n",
    "print(\"Amplitude vector, <D, g_i>:\")\n",
    "print(pd.DataFrame(np.array(Dj)))\n",
    "\n",
    "# Print parameter shift  \n",
    "Delta_A = np.matmul(np.linalg.inv(np.array(ps11_forecast.numpy_full_fisher)), Dj)\n",
    "print('Parameter shift vector, delta a_i: ')\n",
    "pd.DataFrame(np.array(Delta_A)).to_csv(os.path.join(ps11_resultsdir, \"shift_parameter_amplitude.csv\"), sep='\\t')\n",
    "print(pd.DataFrame(np.array(Delta_A)))\n",
    "print(\"Parameter shift/error vector, (delta a_i / sigma_i): \")\n",
    "params = [\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                          \n",
    "        'M_ncdm',                                                                    \n",
    "        'sigma_fog',                                   \n",
    "        'b0',                                         \n",
    "        'alpha_k2'\n",
    "    ]\n",
    "for i in range(10): \n",
    "    print(\"delta a_i / sigma_i for\", params[i], \": \", (Delta_A[i]\n",
    "                                                       /np.sqrt(np.linalg.inv(np.array(ps11_forecast.numpy_full_fisher))[i,i])))\n",
    "dill.dump_session(os.path.join(ps11_resultsdir, 'ps11.db'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 12 - Forecast Shift in Neutrino Cosmology w/ and w/o RelicFast\n",
    "# GOOD\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync4\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps12_resultsdir = os.path.join(projectdir, 'results', 'ps12')\n",
    "ps12_convergencedir = os.path.join(ps12_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps12_resultsdir)\n",
    "cf.makedirectory(ps12_convergencedir) \n",
    "\n",
    "# Set fiducial cosmology\n",
    "#ps12_fid = { # Fiducial for DESI/EUCLID MCMCs\n",
    "#       \"A_s\" : 2.22e-9, \n",
    "#        \"n_s\" : 0.965,\n",
    "#        \"omega_b\" : 0.02222,\n",
    "#        \"omega_cdm\" : 0.1120,\n",
    "#        \"tau_reio\" : 0.06,\n",
    "#        \"h\" : 0.70,\n",
    "#        \"T_cmb\" : 2.726, # Units [K]\n",
    "#        \"N_ncdm\" : 3., \n",
    "#        \"T_ncdm\" : 1.95/2.726, \n",
    "#        \"m_ncdm\" : 0.03, # Units [eV]\n",
    "#        \"b0\" : 1.0, \n",
    "#        \"alphak2\" : 1.0,\n",
    "#        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "#        \"N_eff\" : 3.046 - (3 * 1.0132), \n",
    "#        \"relic_vary\" : None, #Not used for neutrino forecasts\n",
    "#        } \n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps12_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"T_ncdm\" : (0.9052/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"T_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# DESI survey parameters\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85])\n",
    "dNdz = np.array([309., 2269., 1923., 2094., 1441., 1353., 1337., 523., 466., 329., 126., 0., 0.])\n",
    "skycover = 14000. # Sky coverage of survey in degrees^2\n",
    "\n",
    "# EUCLID values\n",
    "#z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85, 1.95])\n",
    "#dNdz = np.array([2434.280, 4364.812, 4728.559, 4825.798, 4728.797, 4507.625, 4269.851, 3720.657, 3104.309, \n",
    "#    2308.975, 1514.831, 1474.707, 893.716, 497.613])\n",
    "#skycover = 0.3636 # Sky coverage of survey in fraction\n",
    "# Run Fisher Forecast\n",
    "\n",
    "masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "omegacdm_set = ps12_fid['omega_cdm'] - ((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(ps12_fid['T_ncdm']*2.726 / 1.95, 3.))                                     \n",
    "ps12_fiducialset = [dict(ps12_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[midx]}) \n",
    "               for midx, mval in enumerate(masses)]\n",
    "ps12_forecastset = [cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"DESI\",\n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(ps12_fiducialset)]\n",
    "for fidx, fcst in enumerate(ps12_forecastset): \n",
    "    fcst.gen_pm()\n",
    "    fcst.gen_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'T_ncdm',                                 \n",
    "            'sigma_fog',                                   \n",
    "            'b0',                                         \n",
    "            'alpha_k2', \n",
    "            'D_Amp'],\n",
    "        mu_step=mu_integral_step, \n",
    "        skipgen=False)\n",
    "    print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "    # Save notebook state\n",
    "    dill.dump_session(os.path.join(ps12_resultsdir, 'ps12.db'))\n",
    "    \n",
    "import datetime\n",
    "print(str(datetime.datetime.now()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps12/ps12.db')\n",
    "\n",
    "# Save results \n",
    "inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherCMBS4_bin\"\n",
    "#inpath = \"/Users/nicholasdeporzio/Documents/Academic/Research/Projects/cosmicfish/cosmicfish/priors/New_Relic_CMB_Fisher_Matrices/FisherPlanck_bin\"\n",
    "head = 'omega_b \\t omega_cdm \\t n_s \\t A_s \\t tau_reio \\t h \\t T_ncdm \\t sigma_fog \\t b0 \\t alpha_k2 \\t D_Amp'\n",
    "for fidx, fval in enumerate(ps12_forecastset):\n",
    "    fval.load_cmb_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'T_ncdm'],\n",
    "#        fisherpath=inpath+str(fidx+1)+\"_T0.9K.txt\",\n",
    "        fisherpath=inpath+str(1)+\"_T0.9K.txt\")\n",
    "    print(\"CMB Fisher information loaded to forecast \" + str(fidx) + \"...\")\n",
    "    outdir=os.path.join(ps12_resultsdir, 'M'+str(fidx+1))\n",
    "    cf.makedirectory(outdir)\n",
    "    fval.export_matrices(outdir)\n",
    "    np.savetxt(outdir + '_Full_Fisher.dat', fval.numpy_full_fisher, delimiter='\\t', header=head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Restructure results\n",
    "DD = np.array(ps12_forecast.fisher[10, 10])\n",
    "Dj = np.array(ps12_forecast.fisher[0:10, 10])\n",
    "Extended_Fisher = np.array(ps12_forecast.fisher)\n",
    "ps12_forecast.fisher = np.array(ps12_forecast.fisher[0:10, 0:10])\n",
    "ps12_forecast.fisher_order.remove('D_Amp')\n",
    "\n",
    "# Save notebook state\n",
    "dill.dump_session(os.path.join(ps12_resultsdir, 'ps12.db'))\n",
    "\n",
    "# Add CMB Data\n",
    "ps12_forecast.load_cmb_fisher(\n",
    "    fisher_order=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                                                             \n",
    "        'M_ncdm'],\n",
    "    fisherpath=os.path.join(cf.priors_directory(), \"CMBS4_Fisher_Neutrinos.dat\"))\n",
    "\n",
    "# Save results\n",
    "ps12_forecast.export_matrices(ps12_resultsdir)\n",
    "pd.DataFrame(ps12_forecast.numpy_full_fisher, columns=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                          \n",
    "        'M_ncdm',                                                                    \n",
    "        'sigma_fog',                                   \n",
    "        'b0',                                    \n",
    "        'alpha_k2'\n",
    "    ]).to_csv(os.path.join(ps12_resultsdir, \"yescmbfisher.csv\"), sep='\\t')\n",
    "pd.DataFrame(ps12_forecast.numpy_lss_fisher, columns=[\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                          \n",
    "        'M_ncdm',                                                                    \n",
    "        'sigma_fog',                                   \n",
    "        'b0',                                     \n",
    "        'alpha_k2'\n",
    "    ]).to_csv(os.path.join(ps12_resultsdir, \"nocmbfisher.csv\"), sep='\\t')\n",
    "\n",
    "# Print the uncertainty in the P_g shift amplitude\n",
    "D_spread = np.sqrt(DD)\n",
    "np.savetxt(os.path.join(ps12_resultsdir, \"shift_root_DD.csv\"), np.array([D_spread]), delimiter='\\t')\n",
    "print(r\"Uncertainty in P_g shift amplitude: \", D_spread)\n",
    "\n",
    "#Djvec1=(ps12_forecast.numpy_full_fisher[8,:]  *  0.001)\n",
    "\n",
    "# Print parameter errors\n",
    "for i in range(10): \n",
    "    print(np.sqrt(ps12_forecast.numpy_full_covariance[i, i]), '\\n')\n",
    "\n",
    "# Print shift amplitude\n",
    "#Amp_Projection[4] = 0.\n",
    "pd.DataFrame(np.array(Dj)).to_csv(os.path.join(ps12_resultsdir, \"shift_basis_projection.csv\"), sep='\\t')\n",
    "print(\"Amplitude vector, <D, g_i>:\")\n",
    "print(pd.DataFrame(np.array(Dj)))\n",
    "\n",
    "# Print parameter shift  \n",
    "Delta_A = np.matmul(np.linalg.inv(np.array(ps12_forecast.numpy_full_fisher)), Dj)\n",
    "print('Parameter shift vector, delta a_i: ')\n",
    "pd.DataFrame(np.array(Delta_A)).to_csv(os.path.join(ps12_resultsdir, \"shift_parameter_amplitude.csv\"), sep='\\t')\n",
    "print(pd.DataFrame(np.array(Delta_A)))\n",
    "print(\"Parameter shift/error vector, (delta a_i / sigma_i): \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "params = [\n",
    "        'omega_b',                                    \n",
    "        'omega_cdm',                                  \n",
    "        'n_s',                                        \n",
    "        'A_s',                                        \n",
    "        'tau_reio',                                   \n",
    "        'h',                                          \n",
    "        'M_ncdm',                                                                    \n",
    "        'sigma_fog',                                   \n",
    "        'b0',\n",
    "        'alpha_k2'\n",
    "    ]\n",
    "for i in range(10): \n",
    "    print(\"delta a_i / sigma_i for\", params[i], \": \", (Delta_A[i]\n",
    "                                                       /np.sqrt(np.linalg.inv(np.array(ps12_forecast.numpy_full_fisher))[i,i])))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 13 - Forecast g_ncdm for DESI at 1.5072 [K]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Volumes/SSD01/data.nosync13/\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps13_resultsdir = os.path.join(projectdir, 'results', 'ps13_test')\n",
    "#ps13_convergencedir = os.path.join(ps13_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps13_resultsdir)\n",
    "#cf.makedirectory(ps13_convergencedir) \n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps13_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"deg_ncdm\" : 1,\n",
    "        \"T_ncdm\" : (1.5072/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# DESI Parameters\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85])\n",
    "dNdz = np.array([309., 2269., 1923., 2094., 1441., 1353., 1337., 523., 466., 329., 126., 0., 0.])\n",
    "skycover = 14000. # Sky coverage of survey in degrees^2\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "omegacdm_set = ps13_fid['omega_cdm'] - ps13_fid[\"N_ncdm\"]*((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(ps13_fid['T_ncdm']*2.726 / 1.95, 3.))                                     \n",
    "ps13_fiducialset = [dict(ps13_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[midx]}) \n",
    "               for midx, mval in enumerate(masses)]\n",
    "ps13_forecastset = [cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"DESI\",\n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(ps13_fiducialset)]\n",
    "for fidx, fcst in enumerate(ps13_forecastset): \n",
    "    fcst.gen_pm()\n",
    "    fcst.gen_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'N_ncdm',                                 \n",
    "            'sigma_fog',                                   \n",
    "            'b0',                                         \n",
    "            'alpha_k2'],\n",
    "        mu_step=mu_integral_step, \n",
    "        skipgen=False)\n",
    "    print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "    dill.dump_session(os.path.join(ps13_resultsdir, 'ps13.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps13/ps13.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prepath = \"/Users/nicholasdeporzio/Desktop/Fisher_CMB/Fisher_T1.5K/FisherCMBS4_bin\"\n",
    "postpath = \"_T1.5K.txt\"\n",
    "\n",
    "for fidx, fval in enumerate(ps13_forecastset):\n",
    "    fval.reorder_fisher(['A_s', 'h', 'n_s', 'omega_b', 'omega_cdm', 'tau_reio', 'N_ncdm', 'sigma_fog', 'b0', 'alpha_k2'])\n",
    "    fval.load_cmb_fisher(\n",
    "        fisher_order=[\n",
    "            'A_s',                                    \n",
    "            'h',                                  \n",
    "            'n_s',                                        \n",
    "            'omega_b',                                        \n",
    "            'omega_cdm',                                   \n",
    "            'tau_reio',                                                                             \n",
    "            'N_ncdm'],\n",
    "        fisherpath = prepath + str(fidx+1) + postpath\n",
    "    )\n",
    "    outdir=os.path.join(ps13_resultsdir, 'M'+str(fidx+1))\n",
    "    cf.makedirectory(outdir)\n",
    "    fval.export_matrices(outdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "masses_ev = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "planckpath = \"/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps13/Planck/M\"\n",
    "cmbs4path = \"/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps13/CMBS4/M\"\n",
    "postpath = \"/inv_lssfisher.mat\"\n",
    "\n",
    "data1 = [np.sqrt(np.loadtxt(planckpath+str(idx+1)+postpath, delimiter='\\t', skiprows=1)[6,6]) for idx in range(27)]\n",
    "data2 = [np.sqrt(np.loadtxt(cmbs4path+str(idx+1)+postpath, delimiter='\\t', skiprows=1)[6,6]) for idx in range(27)]\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.semilogx(masses_ev, data1, label=r\"$\\sigma_{g_\\chi}$, DESI+Planck 1.5K\", linestyle='solid', color='red')\n",
    "plt.semilogx(masses_ev, data2, label=r\"$\\sigma_{g_\\chi}$, DESI+CMBS4, 1.5K\", linestyle='solid', color='green')\n",
    "\n",
    "plt.title(\"T=1.5K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/testing/plotA5.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 14 - Forecast g_ncdm for DESI at 0.9052 [K]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync14/\")\n",
    "classpath = os.path.join(projectdir, \"class2\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps14_resultsdir = os.path.join(projectdir, 'results', 'ps14')\n",
    "cf.makedirectory(ps14_resultsdir)\n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps14_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"deg_ncdm\" : 1,\n",
    "        \"T_ncdm\" : (0.9052/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# DESI Parameters\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85])\n",
    "dNdz = np.array([309., 2269., 1923., 2094., 1441., 1353., 1337., 523., 466., 329., 126., 0., 0.])\n",
    "skycover = 14000. # Sky coverage of survey in degrees^2\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "omegacdm_set = ps14_fid['omega_cdm'] - ps14_fid[\"N_ncdm\"]*((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(ps14_fid['T_ncdm']*2.726 / 1.95, 3.))                                     \n",
    "ps14_fiducialset = [dict(ps14_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[midx]}) \n",
    "               for midx, mval in enumerate(masses)]\n",
    "ps14_forecastset = [cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"DESI\",\n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(ps14_fiducialset)]\n",
    "for fidx, fcst in enumerate(ps14_forecastset): \n",
    "    fcst.gen_pm()\n",
    "    fcst.gen_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'N_ncdm',                                 \n",
    "            'sigma_fog',                                   \n",
    "            'b0',                                         \n",
    "            'alpha_k2'],\n",
    "        mu_step=mu_integral_step, \n",
    "        skipgen=False)\n",
    "    print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "    dill.dump_session(os.path.join(ps14_resultsdir, 'ps14.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps14/ps14.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prepath = \"/Users/nicholasdeporzio/Desktop/Fisher_CMB/Fisher_T0.9K/FisherPlanck_bin\"\n",
    "postpath = \"_T0.9K.txt\"\n",
    "\n",
    "for fidx, fval in enumerate(ps14_forecastset):\n",
    "    fval.reorder_fisher(['A_s', 'h', 'n_s', 'omega_b', 'omega_cdm', 'tau_reio', 'N_ncdm', 'sigma_fog', 'b0', 'alpha_k2'])\n",
    "    fval.load_cmb_fisher(\n",
    "        fisher_order=[\n",
    "            'A_s',                                    \n",
    "            'h',                                  \n",
    "            'n_s',                                        \n",
    "            'omega_b',                                        \n",
    "            'omega_cdm',                                   \n",
    "            'tau_reio',                                                                             \n",
    "            'N_ncdm'],\n",
    "        fisherpath = prepath + str(fidx+1) + postpath\n",
    "    )\n",
    "    outdir=os.path.join(ps14_resultsdir, 'M'+str(fidx+1))\n",
    "    cf.makedirectory(outdir)\n",
    "    fval.export_matrices(outdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "masses_ev = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "planckpath = \"/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps14/Planck/M\"\n",
    "cmbs4path = \"/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps14/CMBS4/M\"\n",
    "postpath = \"/inv_fullfisher.mat\"\n",
    "\n",
    "data1 = [np.sqrt(np.loadtxt(planckpath+str(idx+1)+postpath, delimiter='\\t', skiprows=1)[6,6]) for idx in range(27)]\n",
    "data2 = [np.sqrt(np.loadtxt(cmbs4path+str(idx+1)+postpath, delimiter='\\t', skiprows=1)[6,6]) for idx in range(27)]\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.semilogx(masses_ev, data1, label=r\"$\\sigma_{g_\\chi}$, DESI+Planck 0.9K\", linestyle='solid', color='red')\n",
    "plt.semilogx(masses_ev, data2, label=r\"$\\sigma_{g_\\chi}$, DESI+CMBS4, 0.9K\", linestyle='solid', color='green')\n",
    "\n",
    "plt.title(\"T=0.9K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/testing/plotA6.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 15 - Forecast g_ncdm for EUCLID at 1.5072 [K]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/data.nosync15/\")\n",
    "classpath = os.path.join(projectdir, \"class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps15_resultsdir = os.path.join(projectdir, 'results', 'ps15')\n",
    "#ps15_convergencedir = os.path.join(ps15_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps15_resultsdir)\n",
    "#cf.makedirectory(ps15_convergencedir) \n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps15_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"deg_ncdm\" : 1,\n",
    "        \"T_ncdm\" : (1.5072/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"beta0\" : 1.7, \n",
    "        \"beta1\" : 1.0,\n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# EUCLID Parameters\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85, 1.95])\n",
    "dNdz = np.array([2434.280, 4364.812, 4728.559, 4825.798, 4728.797, 4507.625, 4269.851, 3720.657, 3104.309, \n",
    "    2308.975, 1514.831, 1474.707, 893.716, 497.613])\n",
    "skycover = 0.3636 # Sky coverage of survey in fraction\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "omegacdm_set = ps15_fid['omega_cdm'] - ps15_fid[\"N_ncdm\"]*((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(ps15_fid['T_ncdm']*2.726 / 1.95, 3.))                                     \n",
    "ps15_fiducialset = [dict(ps15_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[midx]}) \n",
    "               for midx, mval in enumerate(masses)]\n",
    "ps15_forecastset = [cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"EUCLID\",\n",
    "    dNdz, \n",
    "    fsky=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(ps15_fiducialset)]\n",
    "for fidx, fcst in enumerate(ps15_forecastset): \n",
    "    fcst.gen_pm()\n",
    "    fcst.gen_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            'N_ncdm',                                 \n",
    "            'sigma_fog',                                   \n",
    "            'beta0',\n",
    "            'beta1',\n",
    "            'alpha_k2'],\n",
    "        mu_step=mu_integral_step, \n",
    "        skipgen=False)\n",
    "    print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "    dill.dump_session(os.path.join(ps15_resultsdir, 'ps15.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps15/ps15.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prepath = \"/Users/nicholasdeporzio/Desktop/Fisher_CMB/Fisher_T1.5K/FisherCMBS4_bin\"\n",
    "postpath = \"_T1.5K.txt\"\n",
    "head = 'A_s \\t h \\t n_s \\t omega_b \\t omega_cdm \\t tau_reio \\t N_ncdm \\t sigma_fog \\t beta0 \\t beta1 \\t alpha_k2'\n",
    "\n",
    "for fidx, fval in enumerate(ps15_forecastset):\n",
    "    fval.reorder_fisher(['A_s', 'h', 'n_s', 'omega_b', 'omega_cdm', 'tau_reio', 'N_ncdm', 'sigma_fog', 'beta0', 'beta1', 'alpha_k2'])\n",
    "    fval.load_cmb_fisher(\n",
    "        fisher_order=[\n",
    "            'A_s',                                    \n",
    "            'h',                                  \n",
    "            'n_s',                                        \n",
    "            'omega_b',                                        \n",
    "            'omega_cdm',                                   \n",
    "            'tau_reio',                                                                             \n",
    "            'N_ncdm'],\n",
    "        fisherpath = prepath + str(fidx+1) + postpath\n",
    "    )\n",
    "    outdir=os.path.join(ps15_resultsdir, 'M'+str(fidx+1))\n",
    "    cf.makedirectory(outdir)\n",
    "    fval.export_matrices(outdir)\n",
    "    np.savetxt(outdir + '_Full_Fisher.dat', fval.numpy_full_fisher, delimiter='\\t', header=head)\n",
    "    fval.pandas_lss_fisher.to_csv(outdir + \"_LSS_Fisher.dat\", sep='\\t', index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "masses_ev = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "planckpath = \"/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps15/Planck/M\"\n",
    "cmbs4path = \"/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps15/CMBS4/M\"\n",
    "postpath = \"/inv_fullfisher.mat\"\n",
    "\n",
    "data1 = [np.sqrt(np.loadtxt(planckpath+str(idx+1)+postpath, delimiter='\\t', skiprows=1)[6,6]) for idx in range(27)]\n",
    "data2 = [np.sqrt(np.loadtxt(cmbs4path+str(idx+1)+postpath, delimiter='\\t', skiprows=1)[6,6]) for idx in range(27)]\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.semilogx(masses_ev, data1, label=r\"$\\sigma_{g_\\chi}$, EUCLID+Planck 1.5K\", linestyle='solid', color='red')\n",
    "plt.semilogx(masses_ev, data2, label=r\"$\\sigma_{g_\\chi}$, EUCLID+CMBS4, 1.5K\", linestyle='solid', color='green')\n",
    "\n",
    "plt.title(\"T=1.5K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/testing/plotA7.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 16 - Forecast g_ncdm for EUCLID at 0.9052 [K]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-11T06:28:08.977435Z",
     "start_time": "2020-04-11T06:18:12.773252Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "#projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "projectdir = cf.correct_path(\"/Volumes/SSD01/\")\n",
    "datastore = cf.correct_path(\"/Volumes/SSD01/data.nosync16p/\")\n",
    "classpath = os.path.join(\"/Users/nicholasdeporzio/Desktop/cfworkspace/class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps16_resultsdir = os.path.join(projectdir, 'results', 'ps16p')\n",
    "cf.makedirectory(ps16_resultsdir)\n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps16_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 0., \n",
    "        \"deg_ncdm\" : 0.,\n",
    "        \"T_ncdm\" : (0.9052/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"beta0\" : 1.7, \n",
    "        \"beta1\" : 1.0,\n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# EUCLID Parameters\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85, 1.95])\n",
    "dNdz = np.array([2434.280, 4364.812, 4728.559, 4825.798, 4728.797, 4507.625, 4269.851, 3720.657, 3104.309, \n",
    "    2308.975, 1514.831, 1474.707, 893.716, 497.613])\n",
    "skycover = 0.3636 # Sky coverage of survey in fraction\n",
    "\n",
    "# Run Fisher Forecast\n",
    "#masses = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "masses = np.array([0.2])\n",
    "\n",
    "omegacdm_set = ps16_fid['omega_cdm'] - ps16_fid[\"N_ncdm\"]*((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(ps16_fid['T_ncdm']*2.726 / 1.95, 3.))                                     \n",
    "ps16_fiducialset = [dict(ps16_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[midx]}) \n",
    "               for midx, mval in enumerate(masses)]\n",
    "ps16_forecastset = [cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"EUCLID\",\n",
    "    dNdz, \n",
    "    fsky=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(ps16_fiducialset)]\n",
    "for fidx, fcst in enumerate(ps16_forecastset): \n",
    "    fcst.gen_pm()\n",
    "    fcst.gen_fisher(\n",
    "        fisher_order=[\n",
    "            'omega_b',                                    \n",
    "            'omega_cdm',                                  \n",
    "            'n_s',                                        \n",
    "            'A_s',                                        \n",
    "            'tau_reio',                                   \n",
    "            'h',                                                                             \n",
    "            #'N_ncdm',                                 \n",
    "            'sigma_fog',                                   \n",
    "            'beta0',\n",
    "            'beta1',\n",
    "            'alpha_k2'],\n",
    "        mu_step=mu_integral_step, \n",
    "        skipgen=False)\n",
    "    print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "    dill.dump_session(os.path.join(ps16_resultsdir, 'ps16p.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T15:50:48.638932Z",
     "start_time": "2020-03-30T15:50:44.315243Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps16_test/ps16o.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-11T07:42:56.956477Z",
     "start_time": "2020-04-11T07:42:56.947217Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savetxt(\"/Users/nicholasdeporzio/Desktop/k_0.txt\", ps16_forecastset[0].k_table[0], delimiter='\\t')\n",
    "np.savetxt(\"/Users/nicholasdeporzio/Desktop/db_0.txt\", ps16_forecastset[0].spectra_mid[0].b_interp_table, delimiter='\\t')\n",
    "np.savetxt(\"/Users/nicholasdeporzio/Desktop/dcdm_0.txt\", ps16_forecastset[0].spectra_mid[0].cdm_interp_table, delimiter='\\t')\n",
    "np.savetxt(\"/Users/nicholasdeporzio/Desktop/Pg_0.txt\", ps16_forecastset[0].Pg[0, :, 20], delimiter='\\t')\n",
    "np.savetxt(\"/Users/nicholasdeporzio/Desktop/Pm_0.txt\", ps16_forecastset[0].Pm[0], delimiter='\\t')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T16:48:42.477767Z",
     "start_time": "2020-03-30T16:48:42.472445Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "print(masses[5])\n",
    "ps16_forecastset[5].spectra_mid[0].datadirectory\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prepath = \"/Users/nicholasdeporzio/Desktop/Fisher_CMB/Fisher_T0.9K/FisherCMBS4_bin\"\n",
    "postpath = \"_T0.9K.txt\"\n",
    "head = 'A_s \\t h \\t n_s \\t omega_b \\t omega_cdm \\t tau_reio \\t N_ncdm \\t sigma_fog \\t beta0 \\t beta1 \\t alpha_k2'\n",
    "\n",
    "for fidx, fval in enumerate(ps16_forecastset):\n",
    "    fval.reorder_fisher(['A_s', 'h', 'n_s', 'omega_b', 'omega_cdm', 'tau_reio', 'N_ncdm', 'sigma_fog', 'beta0', 'beta1', 'alpha_k2'])\n",
    "    fval.load_cmb_fisher(\n",
    "        fisher_order=[\n",
    "            'A_s',                                    \n",
    "            'h',                                  \n",
    "            'n_s',                                        \n",
    "            'omega_b',                                        \n",
    "            'omega_cdm',                                   \n",
    "            'tau_reio',                                                                             \n",
    "            'N_ncdm'],\n",
    "        fisherpath = prepath + str(fidx+1) + postpath\n",
    "    )\n",
    "    outdir=os.path.join(ps16_resultsdir, 'M'+str(fidx+1))\n",
    "    cf.makedirectory(outdir)\n",
    "    fval.export_matrices(outdir)\n",
    "    np.savetxt(outdir + '_Full_Fisher.dat', fval.numpy_full_fisher, delimiter='\\t', header=head)\n",
    "    fval.pandas_lss_fisher.to_csv(outdir + \"_LSS_Fisher.dat\", sep='\\t', index=False)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "masses_ev = np.append(np.array([0.001, 0.01]), np.geomspace(0.1, 22.0, 25))\n",
    "\n",
    "planckpath = \"/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps16/Planck/M\"\n",
    "cmbs4path = \"/Users/nicholasdeporzio/Desktop/cfworkspace/results/ps16/CMBS4/M\"\n",
    "postpath = \"/inv_fullfisher.mat\"\n",
    "\n",
    "data1 = [np.sqrt(np.loadtxt(planckpath+str(idx+1)+postpath, delimiter='\\t', skiprows=1)[6,6]) for idx in range(27)]\n",
    "data2 = [np.sqrt(np.loadtxt(cmbs4path+str(idx+1)+postpath, delimiter='\\t', skiprows=1)[6,6]) for idx in range(27)]\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.semilogx(masses_ev, data1, label=r\"$\\sigma_{g_\\chi}$, EUCLID+Planck 0.9K\", linestyle='solid', color='red')\n",
    "plt.semilogx(masses_ev, data2, label=r\"$\\sigma_{g_\\chi}$, EUCLID+CMBS4, 0.9K\", linestyle='solid', color='green')\n",
    "\n",
    "plt.title(\"T=0.9K\", fontsize=24)\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.savefig(\"/Users/nicholasdeporzio/Desktop/testing/plotA8.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 17 - Grid Plot for DESI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-10T06:40:38.513511Z",
     "start_time": "2020-04-10T06:32:06.730544Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "#projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Volumes/SSD01/data.nosync17/\")\n",
    "classpath = os.path.join(\"/Users/nicholasdeporzio/Desktop/cfworkspace/class\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "ps17_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps17/\")\n",
    "\n",
    "\n",
    "#ps17_convergencedir = os.path.join(ps17_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps17_resultsdir)\n",
    "#cf.makedirectory(ps17_convergencedir) \n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps17_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"deg_ncdm\" : 1,\n",
    "        \"T_ncdm\" : (1.5072/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# DESI Parameters\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85])\n",
    "dNdz = np.array([309., 2269., 1923., 2094., 1441., 1353., 1337., 523., 466., 329., 126., 0., 0.])\n",
    "skycover = 14000. # Sky coverage of survey in degrees^2\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.geomspace(0.01, 10., 10)\n",
    "temps = np.linspace(0.79, 1.5, 12)\n",
    "\n",
    "omegacdm_set = np.array([ps17_fid['omega_cdm'] - ps17_fid[\"N_ncdm\"]*((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(tval / 1.95, 3.)) for tidx, tval in enumerate(temps)])                                  \n",
    "ps17_fiducialset = [[dict(ps17_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[tidx, midx], 'T_ncdm' : temps[tidx]/2.726}) \n",
    "               for midx, mval in enumerate(masses)] for tidx, tval in enumerate(temps)] \n",
    "ps17_forecastset = [[cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"DESI\",\n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(fidrowvals)] for fidrowidx, fidrowvals in enumerate(ps17_fiducialset)]\n",
    "for frowidx, frowval in enumerate(ps17_forecastset): \n",
    "    for fidx, fcst in enumerate(frowval): \n",
    "        fcst.gen_pm()\n",
    "        fcst.gen_fisher(\n",
    "            fisher_order=[\n",
    "                'omega_b',                                    \n",
    "                'omega_cdm',                                  \n",
    "                'n_s',                                        \n",
    "                'A_s',                                        \n",
    "                'tau_reio',                                   \n",
    "                'h',                                                                             \n",
    "                'N_ncdm',                                 \n",
    "                'sigma_fog',                                   \n",
    "                'b0',                                         \n",
    "                'alpha_k2'],\n",
    "            mu_step=mu_integral_step, \n",
    "            skipgen=False)\n",
    "        print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "        dill.dump_session(os.path.join(ps17_resultsdir, 'Apr10_'+str(frowidx)+'_'+str(fidx)+'_ps17.db'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-12T20:20:55.929359Z",
     "start_time": "2020-05-12T20:20:55.925261Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-12T20:22:12.834991Z",
     "start_time": "2020-05-12T20:22:12.831743Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T06:23:17.099237Z",
     "start_time": "2020-03-27T06:23:17.095911Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T10:55:47.903721Z",
     "start_time": "2020-03-27T06:23:50.093201Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-10T09:43:59.718364Z",
     "start_time": "2020-04-10T09:43:58.742404Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T15:02:44.030385Z",
     "start_time": "2020-03-27T15:02:44.026097Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T05:28:24.638617Z",
     "start_time": "2020-04-14T05:23:42.105946Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/ClusterResults_2020_04_11/ps17/52270113/11_9_ps17.db')\n",
    "\n",
    "ps17_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps17/\")\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "prepath = cf.priors_directory()\n",
    "tempnames = ['0.79', '0.85', '0.92', '0.98', '1.05', '1.11', '1.18', '1.24', '1.31', '1.37', '1.44', '1.50']\n",
    "#head = 'omega_b \\t omega_cdm \\t n_s \\t A_s \\t tau_reio \\t h \\t N_ncdm \\t sigma_fog \\t beta0 \\t alpha_k2'\n",
    "head = 'A_s \\t h \\t n_s \\t omega_b \\t omega_cdm \\t tau_reio \\t N_ncdm \\t sigma_fog \\t beta0 \\t alpha_k2'\n",
    "\n",
    "for tidx, tval in enumerate(ps17_forecastset):\n",
    "    for midx, mval in enumerate(tval): \n",
    "        mval.reorder_fisher(['A_s', 'h', 'n_s', 'omega_b', 'omega_cdm', 'tau_reio', 'N_ncdm', 'sigma_fog', 'b0', 'alpha_k2'])\n",
    "        mval.load_cmb_fisher(\n",
    "            fisher_order=[\n",
    "                'A_s',\n",
    "                'h',  \n",
    "                'n_s',\n",
    "                'omega_b',                                    \n",
    "                'omega_cdm',                                                     \n",
    "                'tau_reio',                                                                                    \n",
    "                'N_ncdm'],\n",
    "            fisherpath = cf.priors_directory()+'/Relic_Grid_Priors/Fisher_T'+tempnames[tidx]+'K/Fisher'+cmbsurvey+'_bin'+str(midx+1)+'_T'+tempnames[tidx]+'K.txt'\n",
    "        )\n",
    "        outdir=os.path.join(ps17_resultsdir, cmbsurvey, 'T'+tempnames[tidx], 'M'+str(midx+1))\n",
    "        cf.makedirectory(outdir)\n",
    "        mval.export_matrices(outdir)\n",
    "        np.savetxt(outdir + '_Full_Fisher.dat', mval.numpy_full_fisher, delimiter='\\t', header=head)\n",
    "        mval.pandas_lss_fisher.to_csv(outdir + \"_LSS_Fisher.dat\", sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T05:32:52.888964Z",
     "start_time": "2020-04-14T05:32:48.607013Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "#dtype=\"cmb\"\n",
    "#dtype=\"lss\"\n",
    "dtype=\"full\"\n",
    "\n",
    "paramidx = 6\n",
    "\n",
    "if dtype=='lss': \n",
    "    title = 'DESI Only'\n",
    "    postpend = '_lss.png'\n",
    "elif dtype=='cmb': \n",
    "    title =  cmbsurvey + ' Only'\n",
    "    postpend = '_cmb.png'\n",
    "elif dtype=='full': \n",
    "    title = 'DESI + ' + cmbsurvey\n",
    "    postpend = '_full.png'\n",
    "\n",
    "tempnames = ['0.79', '0.85', '0.92', '0.98', '1.05', '1.11', '1.18', '1.24', '1.31', '1.37', '1.44', '1.50']\n",
    "masses = np.geomspace(0.01, 10., 10)\n",
    "temps = np.linspace(0.79, 1.5, 12)\n",
    "\n",
    "datapath = '/Volumes/SSD01/results/ps17'\n",
    "\n",
    "data = np.array([[np.sqrt(np.loadtxt(\n",
    "    datapath+'/'+cmbsurvey+'/T'+tempnames[tidx]+'/M'+str(midx+1)+'/inv_'+dtype+'fisher.mat', delimiter='\\t', skiprows=1)[paramidx, paramidx])\n",
    "                               for midx, mval in enumerate(masses)] \n",
    "                               for tidx, tval in enumerate(temps)]) \n",
    "\n",
    "flatdata = data.flatten()\n",
    "flatnsigmas = 1./flatdata\n",
    "logflatdata = np.log10(flatdata)\n",
    "logflatnsigmas = np.log10(flatnsigmas)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "for tidx, tval in enumerate(temps): \n",
    "    plt.semilogx(masses, data[tidx, :], 'o', label=r\"T = \" + str(f\"{tval:.2f}\" + ' K'), linestyle='solid')\n",
    "\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{g_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA17a'+postpend)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(25,25))    \n",
    "# Create long format\n",
    "people = np.repeat(np.flip(temps), len(masses))\n",
    "feature = list(masses) * len(temps)\n",
    "df=pd.DataFrame({'Mass [eV]': feature, 'Temperature [K]': people, 'value': flatdata })\n",
    "# plot it\n",
    "df_wide=df.pivot_table( index='Temperature [K]', columns='Mass [eV]', values='value' )\n",
    "ax=sns.heatmap(df_wide, \n",
    "               yticklabels=(np.flip(temps)),\n",
    "               #xticklabels=(np.flip(masses)),\n",
    "               cbar_kws={'label': r'$\\sigma_{g_\\chi}$'}, \n",
    "               #vmin=0.2, \n",
    "               #vmax=1.5,\n",
    "               annot=True\n",
    "              )\n",
    "\n",
    "# format text labels\n",
    "fmt = '{:0.2f}'\n",
    "xticklabels = []\n",
    "for item in ax.get_xticklabels():\n",
    "    item.set_text(fmt.format(float(item.get_text())))\n",
    "    xticklabels += [item]\n",
    "yticklabels = []\n",
    "for item in ax.get_yticklabels():\n",
    "    item.set_text(fmt.format(float(item.get_text())))\n",
    "    yticklabels += [item]\n",
    "\n",
    "ax.set_xticklabels(xticklabels, fontsize=18)\n",
    "ax.set_yticklabels(yticklabels, fontsize=18)\n",
    "\n",
    "ax.set_xlabel(ax.get_xlabel(), fontsize=18)\n",
    "ax.set_ylabel(ax.get_ylabel(), fontsize=18)\n",
    "\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.ax.tick_params(labelsize=18)\n",
    "ax.figure.axes[-1].set_ylabel(ax.figure.axes[-1].get_ylabel(), size=22)\n",
    "\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA17b'+postpend)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-12T20:10:46.829298Z",
     "start_time": "2020-05-12T20:10:46.668108Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.geomspace(0.01, 10., 10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-12T20:10:52.921690Z",
     "start_time": "2020-05-12T20:10:52.918000Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.linspace(0.79, 1.5, 12)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 18 - Minimum Relic Temp Fine Plots for DESI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-10T09:47:49.913869Z",
     "start_time": "2020-04-10T09:47:48.963513Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Volumes/SSD01/data.nosync18/\")\n",
    "classpath = os.path.join(projectdir, \"class2\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "\n",
    "# Generate output paths  \n",
    "#ps18_resultsdir = os.path.join(projectdir, 'results', 'ps18')\n",
    "ps18_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps18/\")\n",
    "\n",
    "\n",
    "#ps18_convergencedir = os.path.join(ps18_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps18_resultsdir)\n",
    "#cf.makedirectory(ps18_convergencedir) \n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps18_fid = {\n",
    "        \"A_s\" : 2.2321e-9, \n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1., \n",
    "        \"deg_ncdm\" : 1,\n",
    "        \"T_ncdm\" : (1.5072/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0, \n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# DESI Parameters\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85])\n",
    "dNdz = np.array([309., 2269., 1923., 2094., 1441., 1353., 1337., 523., 466., 329., 126., 0., 0.])\n",
    "skycover = 14000. # Sky coverage of survey in degrees^2\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.geomspace(0.01, 10., 21)\n",
    "temps = np.array([0.79, 0.91, 0.94, 1.08])\n",
    "\n",
    "omegacdm_set = np.array([ps18_fid['omega_cdm'] - ps18_fid[\"N_ncdm\"]*((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(tval / 1.95, 3.)) for tidx, tval in enumerate(temps)])                                  \n",
    "ps18_fiducialset = [[dict(ps18_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[tidx, midx], 'T_ncdm' : temps[tidx]/2.726}) \n",
    "               for midx, mval in enumerate(masses)] for tidx, tval in enumerate(temps)] \n",
    "ps18_forecastset = [[cf.forecast(\n",
    "    classpath, \n",
    "    datastore, \n",
    "    'relic', \n",
    "    fidval, \n",
    "    z_table, \n",
    "    \"DESI\",\n",
    "    dNdz, \n",
    "    fcoverage_deg=skycover, \n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(fidrowvals)] for fidrowidx, fidrowvals in enumerate(ps18_fiducialset)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-10T23:41:35.608165Z",
     "start_time": "2020-04-10T09:47:50.244199Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "#dill.load_session('/Volumes/SSD01/results/ps18/3_3_ps18.db')\n",
    "for frowidx, frowval in enumerate(ps18_forecastset):\n",
    "    for fidx, fcst in enumerate(frowval):\n",
    "        if type(fcst.fisher)==type(None):\n",
    "            fcst.gen_pm()\n",
    "            fcst.gen_fisher(\n",
    "                fisher_order=[\n",
    "                    'omega_b',\n",
    "                    'omega_cdm',\n",
    "                    'n_s',\n",
    "                    'A_s',\n",
    "                    'tau_reio',\n",
    "                    'h',\n",
    "                    'N_ncdm',\n",
    "                    'sigma_fog',\n",
    "                    'b0',\n",
    "                    'alpha_k2'],\n",
    "                mu_step=mu_integral_step,\n",
    "                skipgen=False)\n",
    "            print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "            dill.dump_session(os.path.join(ps18_resultsdir, str(frowidx)+'_'+str(fidx)+'_ps18.db'))\n",
    "        else:\n",
    "            print('Fisher matrix already generated!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-06T08:07:32.495948Z",
     "start_time": "2020-04-06T08:07:22.680414Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-13T22:21:01.882630Z",
     "start_time": "2020-04-13T22:20:49.863001Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/ClusterResults_2020_04_11/ps18/52270117/3_20_ps18.db')\n",
    "\n",
    "ps18_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps18/\")\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "prepath = cf.priors_directory()\n",
    "tempnames = ['0.79', '0.91', '0.94', '1.08']\n",
    "head = 'A_s \\t h \\t n_s \\t omega_b \\t omega_cdm \\t tau_reio \\t N_ncdm \\t sigma_fog \\t beta0 \\t alpha_k2'\n",
    "\n",
    "\n",
    "for tidx, tval in enumerate(ps18_forecastset):\n",
    "    for midx, mval in enumerate(tval): \n",
    "        mval.reorder_fisher(['A_s', 'h', 'n_s', 'omega_b', 'omega_cdm', 'tau_reio', 'N_ncdm', 'sigma_fog', 'b0', 'alpha_k2'])\n",
    "        mval.load_cmb_fisher(\n",
    "              fisher_order=[\n",
    "                'A_s',\n",
    "                'h',  \n",
    "                'n_s',\n",
    "                'omega_b',                                    \n",
    "                'omega_cdm',                                                     \n",
    "                'tau_reio',                                                                                    \n",
    "                'N_ncdm'],\n",
    "            fisherpath = cf.priors_directory()+'/Relic_Fine_Priors/Fisher_T'+tempnames[tidx]+'K/Fisher'+cmbsurvey+'_bin'+str(midx+1)+'_T'+tempnames[tidx]+'K.txt'\n",
    "        )\n",
    "        outdir=os.path.join(ps18_resultsdir, cmbsurvey, 'T'+tempnames[tidx], 'M'+str(midx+1))\n",
    "        cf.makedirectory(outdir)\n",
    "        mval.export_matrices(outdir)\n",
    "        np.savetxt(outdir + '_Full_Fisher.dat', mval.numpy_full_fisher, delimiter='\\t', header=head)\n",
    "        mval.pandas_lss_fisher.to_csv(outdir + \"_LSS_Fisher.dat\", sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-10T23:49:58.451440Z",
     "start_time": "2020-04-10T23:49:55.113067Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "#dtype=\"cmb\"\n",
    "#dtype=\"lss\"\n",
    "dtype=\"full\"\n",
    "\n",
    "paramidx = 6\n",
    "\n",
    "if dtype=='lss': \n",
    "    title = 'DESI Only'\n",
    "    postpend = '_lss.png'\n",
    "elif dtype=='cmb': \n",
    "    title =  cmbsurvey + ' Only'\n",
    "    postpend = '_cmb.png'\n",
    "elif dtype=='full': \n",
    "    title = 'DESI + ' + cmbsurvey\n",
    "    postpend = '_full.png'\n",
    "\n",
    "tempnames = ['0.79', '0.91', '0.94', '1.08']\n",
    "masses = np.geomspace(0.01, 10., 21)\n",
    "temps = np.array([0.79, 0.91, 0.94, 1.08])\n",
    "\n",
    "datapath = '/Volumes/SSD01/results/ps18'\n",
    "\n",
    "data = np.array([[np.sqrt(np.loadtxt(\n",
    "    datapath+'/'+cmbsurvey+'/T'+tempnames[tidx]+'/M'+str(midx+1)+'/inv_'+dtype+'fisher.mat', delimiter='\\t', skiprows=1)[paramidx, paramidx])\n",
    "                               for midx, mval in enumerate(masses)] \n",
    "                               for tidx, tval in enumerate(temps)]) \n",
    "\n",
    "flatdata = data.flatten()\n",
    "flatnsigmas = 1./flatdata\n",
    "logflatdata = np.log10(flatdata)\n",
    "logflatnsigmas = np.log10(flatnsigmas)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "for tidx, tval in enumerate(temps): \n",
    "    plt.semilogx(masses, data[tidx, :], 'o', label=r\"T = \" + str(f\"{tval:.2f}\" + \" K\"), linestyle='solid')\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{g_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA18a'+postpend)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "for tidx, tval in enumerate(temps): \n",
    "    plt.semilogx(masses, 1./data[tidx, :], 'o', label=r\"T = \" + str(f\"{tval:.2f}\" + ' K'), linestyle='solid')\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$1/\\sigma_{g_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA18b'+postpend)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 19 - Grid Plot for EUCLID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-10T06:54:52.631428Z",
     "start_time": "2020-04-10T06:45:22.328645Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import cosmicfish as cf\n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "#projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Volumes/SSD01/data.nosync19/\")\n",
    "classpath = os.path.join(\"/Users/nicholasdeporzio/Desktop/cfworkspace/class3\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "#ps19_resultsdir = os.path.join(projectdir, 'results', 'ps19')\n",
    "ps19_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps19/\")\n",
    "#ps19_convergencedir = os.path.join(ps19_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps19_resultsdir)\n",
    "#cf.makedirectory(ps19_convergencedir) \n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps19_fid = {\n",
    "        \"A_s\" : 2.2321e-9,\n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1.,\n",
    "        \"deg_ncdm\" : 1,\n",
    "        \"T_ncdm\" : (1.5072/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0,\n",
    "        \"beta0\" : 1.7,\n",
    "        \"beta1\" : 1.0,\n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# EUCLID values\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85, 1.95])\n",
    "dNdz = np.array([2434.280, 4364.812, 4728.559, 4825.798, 4728.797, 4507.625, 4269.851, 3720.657, 3104.309,\n",
    "    2308.975, 1514.831, 1474.707, 893.716, 497.613])\n",
    "skycover = 0.3636 # Sky coverage of survey in fraction\n",
    "\n",
    "# Run Fisher Forecast\n",
    "#masses = np.geomspace(0.01, 10., 10)\n",
    "#temps = np.linspace(0.79, 1.5, 12)\n",
    "masses = np.array([0.01])\n",
    "temps = np.array([0.79])\n",
    "\n",
    "omegacdm_set = np.array([ps19_fid['omega_cdm'] - ps19_fid[\"N_ncdm\"]*((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(tval / 1.95, 3.)) for tidx, tval in enumerate(temps)])                 \n",
    "ps19_fiducialset = [[dict(ps19_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[tidx, midx], 'T_ncdm' : temps[tidx]/2.726})\n",
    "               for midx, mval in enumerate(masses)] for tidx, tval in enumerate(temps)]\n",
    "ps19_forecastset = [[cf.forecast(\n",
    "    classpath,\n",
    "    datastore,\n",
    "    'relic',\n",
    "    fidval,\n",
    "    z_table,\n",
    "    \"EUCLID\",\n",
    "    dNdz,\n",
    "    fsky=skycover,\n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(fidrowvals)] for fidrowidx, fidrowvals in enumerate(ps19_fiducialset)]\n",
    "\n",
    "#dill.load_session('/n/home02/ndeporzio/projects/cosmicfish/cfworkspace/results/51392649/2_9_ps19.db')\n",
    "for frowidx, frowval in enumerate(ps19_forecastset):\n",
    "    for fidx, fcst in enumerate(frowval):\n",
    "        if type(fcst.fisher)==type(None):\n",
    "            fcst.gen_pm()\n",
    "            fcst.gen_fisher(\n",
    "                fisher_order=[\n",
    "                    'omega_b',\n",
    "                    'omega_cdm',\n",
    "                    'n_s',\n",
    "                    'A_s',\n",
    "                    'tau_reio',\n",
    "                    'h',                                                                    \n",
    "                    'N_ncdm',\n",
    "                    'sigma_fog',\n",
    "                    'beta0',\n",
    "                    'beta1',\n",
    "                    'alpha_k2'],\n",
    "                mu_step=mu_integral_step,\n",
    "                skipgen=False)\n",
    "            print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "            dill.dump_session(os.path.join(ps19_resultsdir, str(frowidx)+'_'+str(fidx)+'_ps19.db'))\n",
    "        else:\n",
    "            print('Fisher matrix already generated!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-10T07:01:23.274944Z",
     "start_time": "2020-04-10T07:01:21.901517Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/ClusterResults_2020_04_11/ps19/52270118/11_9_ps19.db')\n",
    "\n",
    "ps19_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps19/\")\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "prepath = cf.priors_directory()\n",
    "tempnames = ['0.79', '0.85', '0.92', '0.98', '1.05', '1.11', '1.18', '1.24', '1.31', '1.37', '1.44', '1.50']\n",
    "head = 'A_s \\t h \\t n_s \\t omega_b \\t omega_cdm \\t tau_reio \\t N_ncdm \\t sigma_fog \\t beta0 \\t beta1 \\t alpha_k2'\n",
    "\n",
    "for tidx, tval in enumerate(ps19_forecastset):\n",
    "    for midx, mval in enumerate(tval): \n",
    "        mval.reorder_fisher(['A_s', 'h', 'n_s', 'omega_b', 'omega_cdm', 'tau_reio', 'N_ncdm', 'sigma_fog', 'beta0', 'beta1', 'alpha_k2'])\n",
    "        mval.load_cmb_fisher(\n",
    "            fisher_order=[\n",
    "                'A_s',\n",
    "                'h',  \n",
    "                'n_s',\n",
    "                'omega_b',                                    \n",
    "                'omega_cdm',                                                     \n",
    "                'tau_reio',                                                                                    \n",
    "                'N_ncdm'],\n",
    "            fisherpath = cf.priors_directory()+'/Relic_Grid_Priors/Fisher_T'+tempnames[tidx]+'K/Fisher'+cmbsurvey+'_bin'+str(midx+1)+'_T'+tempnames[tidx]+'K.txt'\n",
    "        )\n",
    "        outdir=os.path.join(ps19_resultsdir, cmbsurvey, 'T'+tempnames[tidx], 'M'+str(midx+1))\n",
    "        cf.makedirectory(outdir)\n",
    "        mval.export_matrices(outdir)\n",
    "        np.savetxt(outdir + '_Full_Fisher.dat', mval.numpy_full_fisher, delimiter='\\t', header=head)\n",
    "        mval.pandas_lss_fisher.to_csv(outdir + \"_LSS_Fisher.dat\", sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-10T06:24:22.352615Z",
     "start_time": "2020-04-10T06:24:22.349056Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "#dtype=\"cmb\"\n",
    "#dtype=\"lss\"\n",
    "dtype=\"full\"\n",
    "\n",
    "paramidx = 6\n",
    "\n",
    "if dtype=='lss': \n",
    "    title = 'EUCLID Only'\n",
    "    postpend = '_lss.png'\n",
    "elif dtype=='cmb': \n",
    "    title =  cmbsurvey + ' Only'\n",
    "    postpend = '_cmb.png'\n",
    "elif dtype=='full': \n",
    "    title = 'EUCLID + ' + cmbsurvey\n",
    "    postpend = '_full.png'\n",
    "\n",
    "tempnames = ['0.79', '0.85', '0.92', '0.98', '1.05', '1.11', '1.18', '1.24', '1.31', '1.37', '1.44', '1.50']\n",
    "masses = np.geomspace(0.01, 10., 10)\n",
    "temps = np.linspace(0.79, 1.5, 12)\n",
    "\n",
    "datapath = '/Volumes/SSD01/results/ps19'\n",
    "\n",
    "data = np.array([[np.sqrt(np.loadtxt(\n",
    "    datapath+'/'+cmbsurvey+'/T'+tempnames[tidx]+'/M'+str(midx+1)+'/inv_'+dtype+'fisher.mat', delimiter='\\t', skiprows=1)[paramidx, paramidx])\n",
    "                               for midx, mval in enumerate(masses)] \n",
    "                               for tidx, tval in enumerate(temps)]) \n",
    "\n",
    "flatdata = data.flatten()\n",
    "flatnsigmas = 1./flatdata\n",
    "logflatdata = np.log10(flatdata)\n",
    "logflatnsigmas = np.log10(flatnsigmas)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "for tidx, tval in enumerate(temps): \n",
    "    plt.semilogx(masses, data[tidx, :], 'o', label=r\"T = \" + str(f\"{tval:.2f}\" + ' K'), linestyle='solid')\n",
    "\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{g_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA19a'+postpend)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(25,25))    \n",
    "# Create long format\n",
    "people = np.repeat(np.flip(temps), len(masses))\n",
    "feature = list(masses) * len(temps)\n",
    "df=pd.DataFrame({'Mass [eV]': feature, 'Temperature [K]': people, 'value': flatdata })\n",
    "# plot it\n",
    "df_wide=df.pivot_table( index='Temperature [K]', columns='Mass [eV]', values='value' )\n",
    "ax=sns.heatmap(df_wide, \n",
    "               yticklabels=(np.flip(temps)),\n",
    "               #xticklabels=(np.flip(masses)),\n",
    "               cbar_kws={'label': r'$\\sigma_{g_\\chi}$'}, \n",
    "               #vmin=0.2, \n",
    "               #vmax=1.5,\n",
    "               annot=True\n",
    "              )\n",
    "\n",
    "# format text labels\n",
    "fmt = '{:0.2f}'\n",
    "xticklabels = []\n",
    "for item in ax.get_xticklabels():\n",
    "    item.set_text(fmt.format(float(item.get_text())))\n",
    "    xticklabels += [item]\n",
    "yticklabels = []\n",
    "for item in ax.get_yticklabels():\n",
    "    item.set_text(fmt.format(float(item.get_text())))\n",
    "    yticklabels += [item]\n",
    "\n",
    "ax.set_xticklabels(xticklabels, fontsize=18)\n",
    "ax.set_yticklabels(yticklabels, fontsize=18)\n",
    "\n",
    "ax.set_xlabel(ax.get_xlabel(), fontsize=18)\n",
    "ax.set_ylabel(ax.get_ylabel(), fontsize=18)\n",
    "\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.ax.tick_params(labelsize=18)\n",
    "ax.figure.axes[-1].set_ylabel(ax.figure.axes[-1].get_ylabel(), size=22)\n",
    "\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA19b'+postpend)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T04:18:09.192614Z",
     "start_time": "2020-04-14T04:17:51.804003Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T04:23:22.882991Z",
     "start_time": "2020-04-14T04:23:18.465401Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 20 - Minimum Relic Temp Fine Plots for EUCLID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T06:24:59.114872Z",
     "start_time": "2020-04-08T05:46:06.383696Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import cosmicfish as cf\n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Volumes/SSD01/data.nosync20/\")\n",
    "classpath = os.path.join(projectdir, \"class4\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "#ps20_resultsdir = os.path.join(projectdir, 'results', 'ps20')\n",
    "ps20_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps20/\")\n",
    "#ps20_convergencedir = os.path.join(ps20_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps20_resultsdir)\n",
    "#cf.makedirectory(ps20_convergencedir) \n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps20_fid = {\n",
    "        \"A_s\" : 2.2321e-9,\n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1.,\n",
    "        \"deg_ncdm\" : 1,\n",
    "        \"T_ncdm\" : (1.5072/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0,\n",
    "        \"beta0\" : 1.7,\n",
    "        \"beta1\" : 1.0,\n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# EUCLID values\n",
    "z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85, 1.95])\n",
    "dNdz = np.array([2434.280, 4364.812, 4728.559, 4825.798, 4728.797, 4507.625, 4269.851, 3720.657, 3104.309,\n",
    "    2308.975, 1514.831, 1474.707, 893.716, 497.613])\n",
    "skycover = 0.3636 # Sky coverage of survey in fraction\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.geomspace(0.01, 10., 21)\n",
    "temps = np.array([0.79, 0.91, 0.94, 1.08])\n",
    "\n",
    "omegacdm_set = np.array([ps20_fid['omega_cdm'] - ps20_fid[\"N_ncdm\"]*((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(tval / 1.95, 3.)) for tidx, tval in enumerate(temps)])                 \n",
    "ps20_fiducialset = [[dict(ps20_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[tidx, midx], 'T_ncdm' : temps[tidx]/2.726})\n",
    "               for midx, mval in enumerate(masses)] for tidx, tval in enumerate(temps)]\n",
    "ps20_forecastset = [[cf.forecast(\n",
    "    classpath,\n",
    "    datastore,\n",
    "    'relic',\n",
    "    fidval,\n",
    "    z_table,\n",
    "    \"EUCLID\",\n",
    "    dNdz,\n",
    "    fsky=skycover,\n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(fidrowvals)] for fidrowidx, fidrowvals in enumerate(ps20_fiducialset)]\n",
    "\n",
    "\n",
    "#dill.load_session('/n/home02/ndeporzio/projects/cosmicfish/cfworkspace/results/51392653/1_12_ps20.db')\n",
    "for frowidx, frowval in enumerate(ps20_forecastset):\n",
    "    for fidx, fcst in enumerate(frowval):\n",
    "        if type(fcst.fisher)==type(None):\n",
    "            fcst.gen_pm()\n",
    "            fcst.gen_fisher(\n",
    "                fisher_order=[\n",
    "                    'omega_b',\n",
    "                    'omega_cdm',\n",
    "                    'n_s',\n",
    "                    'A_s',\n",
    "                    'tau_reio',\n",
    "                    'h',                                                                    \n",
    "                    'N_ncdm',\n",
    "                    'sigma_fog',\n",
    "                    'beta0',\n",
    "                    'beta1',\n",
    "                    'alpha_k2'],\n",
    "                mu_step=mu_integral_step,\n",
    "                skipgen=False)\n",
    "            print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "            dill.dump_session(os.path.join(ps20_resultsdir, str(frowidx)+'_'+str(fidx)+'_ps20.db'))\n",
    "        else:\n",
    "            print('Fisher matrix already generated!')\n",
    "            \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T06:38:26.481955Z",
     "start_time": "2020-04-08T06:38:23.967985Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/ClusterResults_2020_04_11/ps20/52270119/3_20_ps20.db')\n",
    "\n",
    "ps20_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps20/\")\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "prepath = cf.priors_directory()\n",
    "tempnames = ['0.79', '0.91', '0.94', '1.08']\n",
    "head = 'A_s \\t h \\t n_s \\t omega_b \\t omega_cdm \\t tau_reio \\t N_ncdm \\t sigma_fog \\t beta0 \\t beta1 \\t alpha_k2'\n",
    "\n",
    "\n",
    "for tidx, tval in enumerate(ps20_forecastset):\n",
    "    for midx, mval in enumerate(tval): \n",
    "        mval.reorder_fisher(['A_s', 'h', 'n_s', 'omega_b', 'omega_cdm', 'tau_reio', 'N_ncdm', 'sigma_fog', 'beta0', 'beta1', 'alpha_k2'])\n",
    "        mval.load_cmb_fisher(\n",
    "              fisher_order=[\n",
    "                'A_s',\n",
    "                'h',  \n",
    "                'n_s',\n",
    "                'omega_b',                                    \n",
    "                'omega_cdm',                                                     \n",
    "                'tau_reio',                                                                                    \n",
    "                'N_ncdm'],\n",
    "            fisherpath = cf.priors_directory()+'/Relic_Fine_Priors/Fisher_T'+tempnames[tidx]+'K/Fisher'+cmbsurvey+'_bin'+str(midx+1)+'_T'+tempnames[tidx]+'K.txt'\n",
    "        )\n",
    "        outdir=os.path.join(ps20_resultsdir, cmbsurvey, 'T'+tempnames[tidx], 'M'+str(midx+1))\n",
    "        cf.makedirectory(outdir)\n",
    "        mval.export_matrices(outdir)\n",
    "        np.savetxt(outdir + '_Full_Fisher.dat', mval.numpy_full_fisher, delimiter='\\t', header=head)\n",
    "        mval.pandas_lss_fisher.to_csv(outdir + \"_LSS_Fisher.dat\", sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "#dtype=\"cmb\"\n",
    "#dtype=\"lss\"\n",
    "dtype=\"full\"\n",
    "\n",
    "paramidx = 6\n",
    "\n",
    "if dtype=='lss': \n",
    "    title = 'EUCLID Only'\n",
    "    postpend = '_lss.png'\n",
    "elif dtype=='cmb': \n",
    "    title =  cmbsurvey + ' Only'\n",
    "    postpend = '_cmb.png'\n",
    "elif dtype=='full': \n",
    "    title = 'EUCLID + ' + cmbsurvey\n",
    "    postpend = '_full.png'\n",
    "\n",
    "tempnames = ['0.79', '0.91', '0.94', '1.08']\n",
    "masses = np.geomspace(0.01, 10., 21)\n",
    "temps = np.array([0.79, 0.91, 0.94, 1.08])\n",
    "\n",
    "datapath = '/Volumes/SSD01/results/ps20'\n",
    "\n",
    "data = np.array([[np.sqrt(np.loadtxt(\n",
    "    datapath+'/'+cmbsurvey+'/T'+tempnames[tidx]+'/M'+str(midx+1)+'/inv_'+dtype+'fisher.mat', delimiter='\\t', skiprows=1)[paramidx, paramidx])\n",
    "                               for midx, mval in enumerate(masses)] \n",
    "                               for tidx, tval in enumerate(temps)]) \n",
    "\n",
    "flatdata = data.flatten()\n",
    "flatnsigmas = 1./flatdata\n",
    "logflatdata = np.log10(flatdata)\n",
    "logflatnsigmas = np.log10(flatnsigmas)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "for tidx, tval in enumerate(temps): \n",
    "    plt.semilogx(masses, data[tidx, :], 'o', label=r\"T = \" + str(f\"{tval:.2f}\" + \" K\"), linestyle='solid')\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{g_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA20a'+postpend)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "for tidx, tval in enumerate(temps): \n",
    "    plt.semilogx(masses, 1./data[tidx, :], 'o', label=r\"T = \" + str(f\"{tval:.2f}\" + ' K'), linestyle='solid')\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$1/\\sigma_{g_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA20b'+postpend)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-13T22:33:54.607395Z",
     "start_time": "2020-04-13T22:33:43.273702Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 21 - Grid Plot for BOSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T03:45:29.454186Z",
     "start_time": "2020-04-08T03:35:27.765994Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import cosmicfish as cf\n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Volumes/SSD01/data.nosync21/\")\n",
    "classpath = os.path.join(projectdir, \"class5\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "#ps21_resultsdir = os.path.join(projectdir, 'results', 'ps21')\n",
    "ps21_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps21/\")\n",
    "#ps21_convergencedir = os.path.join(ps21_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps21_resultsdir)\n",
    "#cf.makedirectory(ps21_convergencedir) \n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps21_fid = {\n",
    "        \"A_s\" : 2.2321e-9,\n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1.,\n",
    "        \"deg_ncdm\" : 1,\n",
    "        \"T_ncdm\" : (1.5072/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0,\n",
    "        \"beta0\" : 1.7,\n",
    "        \"beta1\" : 1.0,\n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# BOSS values\n",
    "z_table = np.array([0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75])\n",
    "dNdz = np.array([8., 50., 125., 222., 332., 447., 208., 30.])\n",
    "skycover = 10000. # Sky coverage of BOSS in degrees^2\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.geomspace(0.01, 10., 10)\n",
    "temps = np.linspace(0.79, 1.5, 12)\n",
    "\n",
    "omegacdm_set = np.array([ps21_fid['omega_cdm'] - ps21_fid[\"N_ncdm\"]*((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(tval / 1.95, 3.)) for tidx, tval in enumerate(temps)])                 \n",
    "ps21_fiducialset = [[dict(ps21_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[tidx, midx], 'T_ncdm' : temps[tidx]/2.726})\n",
    "               for midx, mval in enumerate(masses)] for tidx, tval in enumerate(temps)]\n",
    "ps21_forecastset = [[cf.forecast(\n",
    "    classpath,\n",
    "    datastore,\n",
    "    'relic',\n",
    "    fidval,\n",
    "    z_table,\n",
    "    \"EUCLID\",\n",
    "    dNdz,\n",
    "    fcoverage_deg=skycover,\n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(fidrowvals)] for fidrowidx, fidrowvals in enumerate(ps21_fiducialset)]\n",
    "\n",
    "\n",
    "                                                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T03:59:13.618241Z",
     "start_time": "2020-04-08T03:59:11.306510Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "#dill.load_session('/Volumes/SSD01/results/ps21/1_0_ps21.db')\n",
    "for frowidx, frowval in enumerate(ps21_forecastset):\n",
    "    for fidx, fcst in enumerate(frowval):\n",
    "        if type(fcst.fisher)==type(None):\n",
    "            fcst.gen_pm()\n",
    "            fcst.gen_fisher(\n",
    "                fisher_order=[\n",
    "                    'omega_b',\n",
    "                    'omega_cdm',\n",
    "                    'n_s',\n",
    "                    'A_s',\n",
    "                    'tau_reio',\n",
    "                    'h',                                                                    \n",
    "                    'N_ncdm',\n",
    "                    'sigma_fog',\n",
    "                    'beta0',\n",
    "                    'beta1',\n",
    "                    'alpha_k2'],\n",
    "                mu_step=mu_integral_step,\n",
    "                skipgen=False)\n",
    "            print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "            dill.dump_session(os.path.join(ps21_resultsdir, str(frowidx)+'_'+str(fidx)+'_ps21.db'))\n",
    "        else:\n",
    "            print('Fisher matrix already generated!')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/ClusterResults_2020_04_11/ps21/52270120/11_9_ps21.db')\n",
    "\n",
    "ps21_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps21/\")\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "prepath = cf.priors_directory()\n",
    "tempnames = ['0.79', '0.85', '0.92', '0.98', '1.05', '1.11', '1.18', '1.24', '1.31', '1.37', '1.44', '1.50']\n",
    "head = 'A_s \\t h \\t n_s \\t omega_b \\t omega_cdm \\t tau_reio \\t N_ncdm \\t sigma_fog \\t beta0 \\t beta1 \\t alpha_k2'\n",
    "\n",
    "for tidx, tval in enumerate(ps21_forecastset):\n",
    "    for midx, mval in enumerate(tval): \n",
    "        mval.reorder_fisher(['A_s', 'h', 'n_s', 'omega_b', 'omega_cdm', 'tau_reio', 'N_ncdm', 'sigma_fog', 'beta0', 'beta1', 'alpha_k2'])\n",
    "        mval.load_cmb_fisher(\n",
    "            fisher_order=[\n",
    "                'A_s',\n",
    "                'h',  \n",
    "                'n_s',\n",
    "                'omega_b',                                    \n",
    "                'omega_cdm',                                                     \n",
    "                'tau_reio',                                                                                    \n",
    "                'N_ncdm'],\n",
    "            fisherpath = cf.priors_directory()+'/Relic_Grid_Priors/Fisher_T'+tempnames[tidx]+'K/Fisher'+cmbsurvey+'_bin'+str(midx+1)+'_T'+tempnames[tidx]+'K.txt'\n",
    "        )\n",
    "        outdir=os.path.join(ps21_resultsdir, cmbsurvey, 'T'+tempnames[tidx], 'M'+str(midx+1))\n",
    "        cf.makedirectory(outdir)\n",
    "        mval.export_matrices(outdir)\n",
    "        np.savetxt(outdir + '_Full_Fisher.dat', mval.numpy_full_fisher, delimiter='\\t', header=head)\n",
    "        mval.pandas_lss_fisher.to_csv(outdir + \"_LSS_Fisher.dat\", sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "#dtype=\"cmb\"\n",
    "#dtype=\"lss\"\n",
    "dtype=\"full\"\n",
    "\n",
    "paramidx = 6\n",
    "\n",
    "if dtype=='lss': \n",
    "    title = 'BOSS Only'\n",
    "    postpend = '_lss.png'\n",
    "elif dtype=='cmb': \n",
    "    title =  cmbsurvey + ' Only'\n",
    "    postpend = '_cmb.png'\n",
    "elif dtype=='full': \n",
    "    title = 'BOSS + ' + cmbsurvey\n",
    "    postpend = '_full.png'\n",
    "\n",
    "tempnames = ['0.79', '0.85', '0.92', '0.98', '1.05', '1.11', '1.18', '1.24', '1.31', '1.37', '1.44', '1.50']\n",
    "masses = np.geomspace(0.01, 10., 10)\n",
    "temps = np.linspace(0.79, 1.5, 12)\n",
    "\n",
    "datapath = '/Volumes/SSD01/results/ps21'\n",
    "\n",
    "data = np.array([[np.sqrt(np.loadtxt(\n",
    "    datapath+'/'+cmbsurvey+'/T'+tempnames[tidx]+'/M'+str(midx+1)+'/inv_'+dtype+'fisher.mat', delimiter='\\t', skiprows=1)[paramidx, paramidx])\n",
    "                               for midx, mval in enumerate(masses)] \n",
    "                               for tidx, tval in enumerate(temps)]) \n",
    "\n",
    "flatdata = data.flatten()\n",
    "flatnsigmas = 1./flatdata\n",
    "logflatdata = np.log10(flatdata)\n",
    "logflatnsigmas = np.log10(flatnsigmas)\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "for tidx, tval in enumerate(temps): \n",
    "    plt.semilogx(masses, data[tidx, :], 'o', label=r\"T = \" + str(f\"{tval:.2f}\" + ' K'), linestyle='solid')\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{g_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA21a'+postpend)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(25,25))    \n",
    "# Create long format\n",
    "people = np.repeat(np.flip(temps), len(masses))\n",
    "feature = list(masses) * len(temps)\n",
    "df=pd.DataFrame({'Mass [eV]': feature, 'Temperature [K]': people, 'value': flatdata })\n",
    "# plot it\n",
    "df_wide=df.pivot_table( index='Temperature [K]', columns='Mass [eV]', values='value' )\n",
    "ax=sns.heatmap(df_wide, \n",
    "               yticklabels=(np.flip(temps)),\n",
    "               #xticklabels=(np.flip(masses)),\n",
    "               cbar_kws={'label': r'$\\sigma_{g_\\chi}$'}, \n",
    "               #vmin=0.2, \n",
    "               #vmax=1.5,\n",
    "               annot=True\n",
    "              )\n",
    "\n",
    "# format text labels\n",
    "fmt = '{:0.2f}'\n",
    "xticklabels = []\n",
    "for item in ax.get_xticklabels():\n",
    "    item.set_text(fmt.format(float(item.get_text())))\n",
    "    xticklabels += [item]\n",
    "yticklabels = []\n",
    "for item in ax.get_yticklabels():\n",
    "    item.set_text(fmt.format(float(item.get_text())))\n",
    "    yticklabels += [item]\n",
    "\n",
    "ax.set_xticklabels(xticklabels, fontsize=18)\n",
    "ax.set_yticklabels(yticklabels, fontsize=18)\n",
    "\n",
    "ax.set_xlabel(ax.get_xlabel(), fontsize=18)\n",
    "ax.set_ylabel(ax.get_ylabel(), fontsize=18)\n",
    "\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.ax.tick_params(labelsize=18)\n",
    "ax.figure.axes[-1].set_ylabel(ax.figure.axes[-1].get_ylabel(), size=22)\n",
    "\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA21b'+postpend)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-13T21:15:48.677734Z",
     "start_time": "2020-04-13T21:15:37.352195Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T04:27:58.846166Z",
     "start_time": "2020-04-14T04:27:54.434185Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Set 22 - Minimum Relic Temp Fine Plots for BOSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T04:10:44.362934Z",
     "start_time": "2020-04-08T03:59:50.868136Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import cosmicfish as cf\n",
    "import matplotlib.pyplot as plt\n",
    "import dill\n",
    "\n",
    "\n",
    "# Instruct pyplot to use seaborn \n",
    "sns.set()\n",
    "\n",
    "# Set project, data, CLASS directories \n",
    "projectdir = cf.correct_path(\"/Users/nicholasdeporzio/Desktop/cfworkspace/\")\n",
    "datastore = cf.correct_path(\"/Volumes/SSD01/data.nosync22/\")\n",
    "classpath = os.path.join(projectdir, \"class6\")\n",
    "\n",
    "# Specify resolution of numerical integrals\n",
    "derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative\n",
    "mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1 \n",
    "\n",
    "# Generate output paths  \n",
    "#ps22_resultsdir = os.path.join(projectdir, 'results', 'ps22')\n",
    "ps22_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps22/\")\n",
    "#ps22_convergencedir = os.path.join(ps22_resultsdir, 'convergence')\n",
    "cf.makedirectory(ps22_resultsdir)\n",
    "#cf.makedirectory(ps22_convergencedir) \n",
    "\n",
    "# Linda Fiducial Cosmology \n",
    "ps22_fid = {\n",
    "        \"A_s\" : 2.2321e-9,\n",
    "        \"n_s\" : 0.967,\n",
    "        \"omega_b\" : 0.02226,\n",
    "        \"omega_cdm\" : 0.1127,\n",
    "        \"tau_reio\" : 0.0598,\n",
    "        \"h\" : 0.701,\n",
    "        \"T_cmb\" : 2.726, # Units [K]\n",
    "        \"N_ncdm\" : 1.,\n",
    "        \"deg_ncdm\" : 1,\n",
    "        \"T_ncdm\" : (1.5072/2.726), # Units [T_cmb]. \n",
    "        \"m_ncdm\" : 0.0328, # Units [eV]\n",
    "        \"b0\" : 1.0,\n",
    "        \"beta0\" : 1.7,\n",
    "        \"beta1\" : 1.0,\n",
    "        \"alphak2\" : 1.0,\n",
    "        \"sigma_fog_0\" : 250000, #Units [m s^-2]\n",
    "        \"N_eff\" : 3.046, #We allow relativistic neutrinos in addition to our DM relic\n",
    "        \"relic_vary\" : \"N_ncdm\" # Fix T_ncdm or m_ncdm \n",
    "        }\n",
    "\n",
    "# BOSS values\n",
    "z_table = np.array([0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75])\n",
    "dNdz = np.array([8., 50., 125., 222., 332., 447., 208., 30.])\n",
    "skycover = 10000. # Sky coverage of BOSS in degrees^2\n",
    "\n",
    "# Run Fisher Forecast\n",
    "masses = np.geomspace(0.01, 10., 21)\n",
    "temps = np.array([0.79, 1.5])\n",
    "\n",
    "omegacdm_set = np.array([ps22_fid['omega_cdm'] - ps22_fid[\"N_ncdm\"]*((masses/cf.NEUTRINO_SCALE_FACTOR)*np.power(tval / 1.95, 3.)) for tidx, tval in enumerate(temps)])                 \n",
    "ps22_fiducialset = [[dict(ps22_fid, **{'m_ncdm' : masses[midx], 'omega_cdm' : omegacdm_set[tidx, midx], 'T_ncdm' : temps[tidx]/2.726})\n",
    "               for midx, mval in enumerate(masses)] for tidx, tval in enumerate(temps)]\n",
    "ps22_forecastset = [[cf.forecast(\n",
    "    classpath,\n",
    "    datastore,\n",
    "    'relic',\n",
    "    fidval,\n",
    "    z_table,\n",
    "    \"EUCLID\",\n",
    "    dNdz,\n",
    "    fcoverage_deg=skycover,\n",
    "    dstep=derivative_step,\n",
    "    RSD=True,\n",
    "    FOG=True,\n",
    "    AP=True,\n",
    "    COV=True) for fididx, fidval in enumerate(fidrowvals)] for fidrowidx, fidrowvals in enumerate(ps22_fiducialset)]\n",
    "\n",
    "#(0.02226+0.1127+((0.01/93)*(1.5072/1.95)^3))/(0.701^2)=0.274744\n",
    "print(cf.gen_V(h=0.701, omega_b=0.02226, omega_cdm=0.1127, \n",
    "               z=0.3, N_ncdm=1., T_ncdm=1.5, m_ncdm=0.01, fsky=1, z_spacing=0.6)*1.0e-9)         \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#dill.load_session('/n/home02/ndeporzio/projects/cosmicfish/cfworkspace/results/51392655/3_5_ps22.db')\n",
    "for frowidx, frowval in enumerate(ps22_forecastset):\n",
    "    for fidx, fcst in enumerate(frowval):\n",
    "        if type(fcst.fisher)==type(None):\n",
    "            fcst.gen_pm()\n",
    "            fcst.gen_fisher(\n",
    "                fisher_order=[\n",
    "                    'omega_b',\n",
    "                    'omega_cdm',\n",
    "                    'n_s',\n",
    "                    'A_s',\n",
    "                    'tau_reio',\n",
    "                    'h',                                                                    \n",
    "                    'N_ncdm',\n",
    "                    'sigma_fog',\n",
    "                    'beta0',\n",
    "                    'beta1',\n",
    "                    'alpha_k2'],\n",
    "                mu_step=mu_integral_step,\n",
    "                skipgen=False)\n",
    "            print(\"Relic Forecast \", fidx, \" complete...\")\n",
    "            dill.dump_session(os.path.join(ps22_resultsdir, str(frowidx)+'_'+str(fidx)+'_ps22.db'))\n",
    "        else:\n",
    "            print('Fisher matrix already generated!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T06:47:17.221203Z",
     "start_time": "2020-04-08T06:47:14.783894Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "dill.load_session('/Users/nicholasdeporzio/Desktop/ClusterResults_2020_04_11/ps22/52270121/3_20_ps22.db')\n",
    "\n",
    "ps22_resultsdir = cf.correct_path(\"/Volumes/SSD01/results/ps22/\")\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "prepath = cf.priors_directory()\n",
    "tempnames = ['0.79', '0.91', '0.94', '1.08']\n",
    "head = 'A_s \\t h \\t n_s \\t omega_b \\t omega_cdm \\t tau_reio \\t N_ncdm \\t sigma_fog \\t beta0 \\t beta1 \\t alpha_k2'\n",
    "\n",
    "\n",
    "for tidx, tval in enumerate(ps22_forecastset):\n",
    "    for midx, mval in enumerate(tval): \n",
    "        mval.reorder_fisher(['A_s', 'h', 'n_s', 'omega_b', 'omega_cdm', 'tau_reio', 'N_ncdm', 'sigma_fog', 'beta0', 'beta1', 'alpha_k2'])\n",
    "        mval.load_cmb_fisher(\n",
    "              fisher_order=[\n",
    "                'A_s',\n",
    "                'h',  \n",
    "                'n_s',\n",
    "                'omega_b',                                    \n",
    "                'omega_cdm',                                                     \n",
    "                'tau_reio',                                                                                    \n",
    "                'N_ncdm'],\n",
    "            fisherpath = cf.priors_directory()+'/Relic_Fine_Priors/Fisher_T'+tempnames[tidx]+'K/Fisher'+cmbsurvey+'_bin'+str(midx+1)+'_T'+tempnames[tidx]+'K.txt'\n",
    "        )\n",
    "        outdir=os.path.join(ps22_resultsdir, cmbsurvey, 'T'+tempnames[tidx], 'M'+str(midx+1))\n",
    "        cf.makedirectory(outdir)\n",
    "        mval.export_matrices(outdir)\n",
    "        np.savetxt(outdir + '_Full_Fisher.dat', mval.numpy_full_fisher, delimiter='\\t', header=head)\n",
    "        mval.pandas_lss_fisher.to_csv(outdir + \"_LSS_Fisher.dat\", sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-13T21:22:31.824164Z",
     "start_time": "2020-04-13T21:22:24.481794Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import cosmicfish as cf \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#cmbsurvey=\"CMBS4\"\n",
    "cmbsurvey=\"Planck\"\n",
    "\n",
    "#dtype=\"cmb\"\n",
    "#dtype=\"lss\"\n",
    "dtype=\"full\"\n",
    "\n",
    "paramidx = 6\n",
    "\n",
    "if dtype=='lss': \n",
    "    title = 'BOSS Only'\n",
    "    postpend = '_lss.png'\n",
    "elif dtype=='cmb': \n",
    "    title =  cmbsurvey + ' Only'\n",
    "    postpend = '_cmb.png'\n",
    "elif dtype=='full': \n",
    "    title = 'BOSS + ' + cmbsurvey\n",
    "    postpend = '_full.png'\n",
    "\n",
    "tempnames = ['0.79', '0.91', '0.94', '1.08']\n",
    "masses = np.geomspace(0.01, 10., 21)\n",
    "temps = np.array([0.79, 0.91, 0.94, 1.08])\n",
    "\n",
    "datapath = '/Volumes/SSD01/results/ps22'\n",
    "\n",
    "data = np.array([[np.sqrt(np.loadtxt(\n",
    "    datapath+'/'+cmbsurvey+'/T'+tempnames[tidx]+'/M'+str(midx+1)+'/inv_'+dtype+'fisher.mat', delimiter='\\t', skiprows=1)[paramidx, paramidx])\n",
    "                               for midx, mval in enumerate(masses)] \n",
    "                               for tidx, tval in enumerate(temps)]) \n",
    "\n",
    "flatdata = data.flatten()\n",
    "flatnsigmas = 1./flatdata\n",
    "logflatdata = np.log10(flatdata)\n",
    "logflatnsigmas = np.log10(flatnsigmas)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "for tidx, tval in enumerate(temps): \n",
    "    plt.semilogx(masses, data[tidx, :], 'o', label=r\"T = \" + str(f\"{tval:.2f}\" + \" K\"), linestyle='solid')\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$\\sigma_{g_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA22a'+postpend)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(15,7.5))\n",
    "for tidx, tval in enumerate(temps): \n",
    "    plt.semilogx(masses, 1./data[tidx, :], 'o', label=r\"T = \" + str(f\"{tval:.2f}\" + ' K'), linestyle='solid')\n",
    "plt.xlabel(r\"$M_\\chi$ [eV]\", fontsize=24)\n",
    "plt.ylabel(r\"$1/\\sigma_{g_\\chi}$\", fontsize=24)\n",
    "plt.legend(fontsize=18, loc='lower left')\n",
    "plt.tick_params(axis='x', which='minttor')\n",
    "plt.grid(True, which='minor')\n",
    "plt.title(title, fontsize=22)\n",
    "plt.savefig(datapath+'/'+cmbsurvey+'/plotA22b'+postpend)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-13T21:18:12.921956Z",
     "start_time": "2020-04-13T21:18:12.917307Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Save Cl Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-04T16:44:59.461525Z",
     "start_time": "2020-04-04T16:44:17.200117Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import dill\n",
    "\n",
    "prepath='/n/home02/ndeporzio/projects/cosmicfish/cfworkspace/results/FinePlot_'\n",
    "\n",
    "for idx in range(84): \n",
    "    temp_index=(idx // 21)\n",
    "    mass_index=(idx % 21)\n",
    "    \n",
    "    if (temp_index==0) or (temp_index==2): \n",
    "        dill.load_session(prepath+str(idx)+'/fp_'+str(mass_index)+'_'+str(temp_index)+'.db')\n",
    "\n",
    "        varnames = ['A_s', 'n_s', 'omega_b', 'omega_cdm', 'tau_reio', 'h', 'N_ncdm', 'M_ncdm']\n",
    "        for frowidx, frowval in enumerate(fp_forecastset): \n",
    "            for fidx, fval  in enumerate(frowval): \n",
    "                mass = fp_fiducialset[frowidx][fidx]['m_ncdm']\n",
    "                temp = fp_fiducialset[frowidx][fidx]['T_ncdm']*2.726\n",
    "        \n",
    "                omega_b_high = fval.omega_b_high[0].datadirectory\n",
    "                omega_b_low = fval.omega_b_low[0].datadirectory\n",
    "                omega_cdm_high = fval.omega_cdm_high[0].datadirectory\n",
    "                omega_cdm_low = fval.omega_cdm_low[0].datadirectory\n",
    "                h_high = fval.h_high[0].datadirectory\n",
    "                h_low = fval.h_low[0].datadirectory\n",
    "                tau_reio_high = fval.tau_reio_high[0].datadirectory\n",
    "                tau_reio_low = fval.tau_reio_low[0].datadirectory\n",
    "                N_ncdm_high = fval.N_ncdm_high[0].datadirectory\n",
    "                N_ncdm_low = fval.N_ncdm_low[0].datadirectory\n",
    "                M_ncdm_high = fval.M_ncdm_high[0].datadirectory\n",
    "                M_ncdm_low = fval.M_ncdm_low[0].datadirectory\n",
    "                A_s_high = fval.A_s_high[0].datadirectory\n",
    "                A_s_low = fval.A_s_low[0].datadirectory\n",
    "                n_s_high = fval.n_s_high[0].datadirectory\n",
    "                n_s_low = fval.n_s_low[0].datadirectory\n",
    "                for pidx, pval  in enumerate(varnames): \n",
    "                    path = ('/n/holyscratch01/dvorkin_lab/ndeporzio/Fine_Cls/T_ncdm_'+f'{temp:.2f}'+'/m_ncdm_' + f'{mass:.3f}' + '/' + pval + '/')\n",
    "                    print('From: ' + eval((pval+'_high')))\n",
    "                    print('To: ' +path+'high')\n",
    "                    shutil.copytree(eval((pval+'_high')), (path+'high'))\n",
    "                    shutil.copytree(eval((pval+'_low')), (path+'low'))\n",
    "    \n",
    "    %reset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-12T02:43:38.012705Z",
     "start_time": "2020-05-12T02:43:22.350660Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for i in range(84): \n",
    "    print(i)\n",
    "    %reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
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   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
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   "outputs": [],
   "source": []
  }
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