{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example `Dysmalpy` 1D fitting, using fitting wrapper\n", " \n", " \n", "In this example, we use `dysmalpy` to measure the kinematics of galaxy **GS4_43501** at $z=1.613$ in 1D, using a fitting wrapper which simplifies the implementation of the fitting algorithm to the user. In this specific case, the fittign method is $\\texttt{MPFIT}$, as specified at the bottom of the **fitting_1D_mpfit.params** file. The notebook shows how to find the best fit models for the one-dimensional velocity and velocity dispersion profiles ($v(r)$ and $\\sigma(r)$). These fits allow us to measure quantities such as the total mass (disk+bulge), the effective radius $r_\\mathrm{eff}$, dark matter fraction $f_\\mathrm{DM}$ and velocity dispersion $\\sigma_0$.\n", "\n", "**The fitting includes the following components:**\n", "\n", " - Disk + Bulge\n", " - NFW halo\n", " - Constant velocity dispersion\n", " \n", " **The structure of the notebook is the following:**\n", " \n", " 1) Setup steps (and load the params file)\n", " 2) Run `Dysmalpy` fitting: 1D wrapper, with fit method= MPFIT\n", " 3) Examine results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1) Setup steps ##\n", "\n", "**First import modules**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:numexpr.utils:Note: NumExpr detected 10 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" ] } ], "source": [ "from __future__ import (absolute_import, division, print_function,\n", " unicode_literals)\n", "\n", "from dysmalpy import fitting\n", "from dysmalpy.fitting_wrappers import dysmalpy_fit_single, utils_io\n", "\n", "import os\n", "\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "**Setup notebook**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Setup plotting\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "mpl.rcParams['figure.dpi']= 300\n", "mpl.rc(\"savefig\", dpi=300)\n", "\n", "from IPython.core.display import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "**Set data, output paths**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Note this will override the `datadir` and `outdir` specified in the param file. \n", "\n", " * *(This is useful for the example here. When running from command line, it's recommended to properly set the directories in the param file.)*" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Data directory (datadir = /YOUR/DATA/PATH/)\n", "filepath = os.path.abspath(fitting.__file__)\n", "datadir = os.sep.join(os.path.dirname(filepath).split(os.sep)[:-1]+[\"tests\", \"test_data\", \"\"])\n", "\n", "# Load the parameters file from the examples directory\n", "param_path = os.sep.join(os.path.dirname(filepath).split(os.sep)[:-1]+[\"examples\", \"examples_param_files\", \"\"])\n", "param_filename = param_path+'fitting_1D_mpfit.params'\n", "\n", "# Where to save output files (output = /YOUR/OUTPUTS/PATH)\n", "outdir = '/Users/sedona/data/dysmalpy_test_examples/JUPYTER_OUTPUT_1D_FITTING_WRAPPER/'\n", "outdir = '/Users/jespejo/Dropbox/Postdoc/Data/dysmalpy_test_examples/JUPYTER_OUTPUT_1D_FITTING_WRAPPER/'\n", "outdir_mpfit = outdir + 'MPFIT/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Settings in parameter file:**\n", "\n", "Note there are many commented out options / parameters. These given an more complete overview of the settings & parameters that can be specified with the fitting wrapper parameter files." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Example parameters file for fitting a single object with 1D data\n", "# Note: DO NOT CHANGE THE NAMES IN THE 1ST COLUMN AND KEEP THE COMMAS!!\n", "# See README for a description of each parameter and its available options.\n", "\n", "# ******************************* OBJECT INFO **********************************\n", "galID, GS4_43501 # Name of your object\n", "z, 1.613 # Redshift\n", "\n", "\n", "# ****************************** DATA INFO *************************************\n", "\n", "datadir, None # Optional: Full path to data directory.\n", "\n", "fdata, GS4_43501.obs_prof.txt # Full path to your data. Alternatively, just the filename if 'datadir' is set.\n", "data_inst_corr, True # Is the dispersion corrected for\n", " # instrumental broadening?\n", "slit_width, 0.55 # arcsecs\n", "slit_pa, 142. # Degrees from N towards blue\n", "symmetrize_data, False # Symmetrize data before fitting?\n", "\n", "\n", "######## Data extraction apertures: ########\n", "## Rectangular apertures:\n", "profile1d_type, rect_ap_cube # 1D aperture extraction shape\n", "pix_perp, 5 # Aperture width (e.g., slit width), in number of pixels\n", " # So arcsec width = pix_perp * pixscale (under 'Instrument Setup')\n", "pix_parallel, 3 # Aperture length (e.g., how many pixels along the slit direction), in npix\n", "\n", "\n", "# ## Circular apertures:\n", "# profile1d_type, circ_ap_cube # 1D aperture extraction shape\n", "# aperture_radius, 0.275 # Circular aperture radius, in ARCSEC. Have used half slit width in past\n", " # -- Eg, aperture diam = slit width\n", "############################################\n", "\n", "\n", "# ***************************** OUTPUT *****************************************\n", "outdir, GS4_43501_1D_out/ # Full path for output directory\n", "\n", "\n", "# ***************************** OBSERVATION SETUP ******************************\n", "\n", "# Instrument Setup\n", "# ------------------\n", "pixscale, 0.125 # Pixel scale in arcsec/pixel\n", "fov_npix, 37 # Number of pixels on a side of model cube\n", "spec_type, velocity # DON'T CHANGE!\n", "spec_start, -1000. # Starting value for spectral axis // generally don't change\n", "spec_step, 10. # Step size for spectral axis in km/s // generally don't change\n", "nspec, 201 # Number of spectral steps // generally don't change\n", "\n", "# LSF Setup\n", "# ---------\n", "use_lsf, True # True/False if using an LSF\n", "sig_inst_res, 51.0 # Instrumental dispersion in km/s\n", "\n", "\n", "# PSF Setup\n", "# ---------\n", "psf_type, Gaussian # Gaussian, Moffat, or DoubleGaussian\n", "psf_fwhm, 0.55 # PSF FWHM in arcsecs\n", "psf_beta, -99. # Beta parameter for a Moffat PSF\n", "\n", "# ## ELLIPTICAL PSF:\n", "# psf_type, Gaussian # Gaussian, Moffat, or DoubleGaussian\n", "# psf_fwhm_major, 0.55 # PSF major axis FWHM in arcsecs\n", "# psf_fwhm_minor, 0.25 # PSF minor axis FWHM in arcsecs\n", "# psf_PA, 0. # PA of PSF major axis, in deg E of N. (0=N, 90=E)\n", "# psf_beta, -99. # Beta parameter for a Moffat PSF\n", "\n", "# # DoubleGaussian: settings instead of psf_fwhm\n", "# psf_type, DoubleGaussian\n", "# psf_fwhm1, 0.16 # FWHM of PSF component 1, in arcsecs. SINFONI AO: 0.16\n", "# psf_fwhm2, 0.48 # FWHM of PSF component 1, in arcsecs. SINFONI AO: 0.48\n", "# psf_scale1, 0.368 # Flux scaling (*not* peak height) of component 1. SINFONI AO: 0.368\n", "# psf_scale2, 0.632 # Flux scaling (*not* peak height) of component 2. SINFONI AO: 0.632\n", "\n", "\n", "# **************************** SETUP MODEL *************************************\n", "\n", "# Model Settings\n", "# -------------\n", "# List of components to use: SEPARATE WITH SPACES\n", "## MUST always keep: geometry zheight_gaus\n", "## RECOMMENDED: always keep: disk+bulge const_disp_prof\n", "components_list, disk+bulge const_disp_prof geometry zheight_gaus halo\n", "\n", "# possible options:\n", "# disk+bulge, sersic, blackhole\n", "# const_disp_prof, geometry, zheight_gaus, halo,\n", "# radial_flow, uniform_planar_radial_flow, uniform_bar_flow, uniform_wedge_flow,\n", "# unresolved_outflow, biconical_outflow,\n", "# CAUTION: azimuthal_planar_radial_flow, variable_bar_flow, spiral_flow\n", "\n", "# List of components that emit light. SEPARATE WITH SPACES\n", "## Current options: disk+bulge / bulge / disk [corresponding to the mass disk+bulge component],\n", "## also: light_sersic, light_gaussian_ring\n", "light_components_list, disk\n", "# NOTE: if a separate light profile (eg light_sersic) is used,\n", "# this MUST be changed to e.g., 'light_components_list, light_sersic'\n", "\n", "adiabatic_contract, False # Apply adiabatic contraction?\n", "pressure_support, True # Apply assymmetric drift correction?\n", "noord_flat, True # Apply Noordermeer flattenning?\n", "oversample, 1 # Spatial oversample factor\n", "oversize, 1 # Oversize factor\n", "\n", "\n", "moment_calc, False # If False, observed profiles fit with GAUSSIANS\n", "\n", "zcalc_truncate, True # Truncate in zgal direction when calculating or not\n", "n_wholepix_z_min, 3 # Minimum number of whole pixels in zgal dir, if zcalc_truncate=True\n", "\n", "\n", "# ********************************************************************************\n", "# DISK + BULGE\n", "# ------------\n", "\n", "# Initial Values\n", "total_mass, 11.0 # Total mass of disk and bulge log(Msun)\n", "bt, 0.3 # Bulge-to-Total Ratio\n", "r_eff_disk, 5.0 # Effective radius of disk in kpc\n", "n_disk, 1.0 # Sersic index for disk\n", "invq_disk, 5.0 # disk scale length to zheight ratio for disk\n", "\n", "n_bulge, 4.0 # Sersic index for bulge\n", "invq_bulge, 1.0 # disk scale length to zheight ratio for bulge\n", "r_eff_bulge, 1.0 # Effective radius of bulge in kpc\n", "\n", "# Fixed? True if its a fixed parameter, False otherwise\n", "total_mass_fixed, False\n", "r_eff_disk_fixed, False\n", "\n", "bt_fixed, True\n", "n_disk_fixed, True\n", "r_eff_bulge_fixed, True\n", "n_bulge_fixed, True\n", "\n", "# Parameter bounds. Lower and upper bounds\n", "total_mass_bounds, 10.0 13.0\n", "bt_bounds, 0.0 1.0\n", "r_eff_disk_bounds, 0.1 30.0\n", "n_disk_bounds, 1.0 8.0\n", "r_eff_bulge_bounds, 1.0 5.0\n", "n_bulge_bounds, 1.0 8.0\n", "\n", "\n", "\n", "# # ********************************************************************************\n", "# # BLACK HOLE\n", "# # ------------\n", "#\n", "# # Initial Values\n", "# BH_mass, 11. # log(Msun)\n", "#\n", "# # Fixed? True if its a fixed parameter, False otherwise\n", "# BH_mass_fixed, False\n", "#\n", "# # Parameter bounds. Lower and upper bounds\n", "# BH_mass_bounds, 6. 18.\n", "\n", "\n", "\n", "\n", "# # ********************************************************************************\n", "# # Separate light profile: (Truncated) Sersic profile\n", "# # ------------\n", "# # Initial values\n", "# L_tot_sersic, 1. # arbitrary units\n", "# lr_eff, 4. # kpc\n", "# lsersic_n, 1. # Sersic index of light profile\n", "# lsersic_rinner, 0. # [kpc] Inner truncation radius of sersic profile. 0 = no truncation\n", "# lsersic_router, inf # [kpc] Outer truncation radius of sersic profile. inf = no truncation\n", "#\n", "# # Fixed? True if its a fixed parameter, False otherwise\n", "# L_tot_sersic_fixed, True\n", "# lr_eff_fixed, False\n", "# lsersic_n_fixed, True\n", "# lsersic_rinner_fixed, True\n", "# lsersic_router_fixed, True\n", "#\n", "# # Parameter bounds. Lower and upper bounds\n", "# L_tot_sersic_bounds, 0. 2. # arbitrary units\n", "# lr_eff_bounds, 0.5 15. # kpc\n", "# lsersic_n_bounds, 0.5 8.\n", "# lsersic_rinner_bounds, 0. 5.\t # kpc\n", "# lsersic_router_bounds, 4. 20. # kpc\n", "\n", "\n", "# # ********************************************************************************\n", "# # Separate light profile: Gaussian ring\n", "# # ------------\n", "# # Initial values\n", "# L_tot_gaus_ring, 1. # arbitrary units\n", "# R_peak_gaus_ring, 6. # kpc\n", "# FWHM_gaus_ring, 1. # kpc\n", "#\n", "# # Fixed? True if its a fixed parameter, False otherwise\n", "# L_tot_gaus_ring_fixed, True\n", "# R_peak_gaus_ring_fixed, True\n", "# FWHM_gaus_ring_fixed, True\n", "#\n", "# # Parameter bounds. Lower and upper bounds\n", "# L_tot_gaus_ring_bounds, 0. 2. # arbitrary units\n", "# R_peak_gaus_ring_bounds, 0. 15. # kpc\n", "# FWHM_gaus_ring_bounds, 0.1 10. # kpc\n", "\n", "\n", "\n", "\n", "# ********************************************************************************\n", "# ********************************************************************************\n", "# ********************************************************************************\n", "# DARK MATTER HALO\n", "# ----------------\n", "\n", "# Halo type: options: NFW / twopowerhalo / burkert / einasto / dekelzhao\n", "halo_profile_type, NFW\n", "\n", "# ** NOTE **: Uncomment the section below corresponding to the selected halo type.\n", "\n", "# ********************************************************************************\n", "# NFW halo\n", "\n", "# Initial Values\n", "mvirial, 11.5 # Halo virial mass in log(Msun)\n", "halo_conc, 5.0 # Halo concentration parameter\n", "fdm, 0.5 # Dark matter fraction at r_eff_disk\n", "\n", "# Fixed? True if its a fixed parameter, False otherwise. Also set False if it will be tied (below)\n", "mvirial_fixed, False\n", "halo_conc_fixed, True\n", "fdm_fixed, False\n", "\n", "# Parameter bounds. Lower and upper bounds\n", "mvirial_bounds, 10.0 13.0\n", "halo_conc_bounds, 1.0 20.0\n", "fdm_bounds, 0.0 1.0\n", "\n", "# Tie the parameters?\n", "fdm_tied, True # for NFW, fdm_tied=True determines fDM from Mvirial (+baryons)\n", "mvirial_tied, False # for NFW, mvirial_tied=True determines Mvirial from fDM (+baryons)\n", "# ********************************************************************************\n", "\n", "# # ********************************************************************************\n", "# # Two-power halo\n", "#\n", "# # Initial Values\n", "# mvirial, 11.5 # Halo virial mass in log(Msun)\n", "# halo_conc, 5.0 # Halo concentration parameter\n", "# fdm, 0.5 # Dark matter fraction at r_eff_disk\n", "# alpha, 1. # TPH: inner slope. NFW has alpha=1\n", "# beta, 3. # TPH: outer slope. NFW has beta=3\n", "#\n", "# # Fixed? True if its a fixed parameter, False otherwise. Also set False if it will be tied (below)\n", "# mvirial_fixed, False\n", "# halo_conc_fixed, True\n", "# fdm_fixed, False\n", "# alpha_fixed, False\n", "# beta_fixed, True\n", "#\n", "# # Parameter bounds. Lower and upper bounds\n", "# mvirial_bounds, 10.0 13.0\n", "# halo_conc_bounds, 1.0 20.0\n", "# fdm_bounds, 0.0 1.0\n", "# alpha_bounds, 0.0 3.0\n", "# beta_bounds, 1.0 4.0\n", "#\n", "# # Tie the parameters?\n", "# fdm_tied, True # for non-NFW, fdm_tied=True determines fDM from other halo params (+baryons)\n", "# mvirial_tied, True # for non-NFW, mvirial_tied=True determines Mvirial from SMHM+fgas + baryon total_mass\n", "# alpha_tied, False # for TPH, alpha_tied=True determines alpha from free fDM + other parameters.\n", "#\n", "# ### OTHER SETTINGS:\n", "# mhalo_relation, Moster18 ## SMHM relation to use for tying Mvir to Mbar. options: Moster18 / Behroozi13\n", "#\n", "# fgas, 0.5 # Gas fraction for SMHM inference of Mvir if 'mvirial_tied=True'\n", "# lmstar, -99. # Currently code uses fgas to infer lmstar\n", "# # from fitting baryon total_mass for SMHM relation\n", "# # ********************************************************************************\n", "\n", "# # ********************************************************************************\n", "# # Burkert halo\n", "#\n", "# # Initial Values\n", "# mvirial, 11.5 # Halo virial mass in log(Msun)\n", "# halo_conc, 5.0 # Halo concentration parameter\n", "# fdm, 0.5 # Dark matter fraction at r_eff_disk\n", "# rB, 10. # Burkert: Halo core radius, in kpc\n", "#\n", "# # Fixed? True if its a fixed parameter, False otherwise. Also set False if it will be tied (below)\n", "# mvirial_fixed, False\n", "# halo_conc_fixed, True\n", "# fdm_fixed, False\n", "# rB_fixed, False\n", "#\n", "# # Parameter bounds. Lower and upper bounds\n", "# mvirial_bounds, 10.0 13.0\n", "# halo_conc_bounds, 1.0 20.0\n", "# fdm_bounds, 0.0 1.0\n", "# rB_bounds, 1.0 20.0\n", "#\n", "# # Tie the parameters?\n", "# fdm_tied, True # for non-NFW, fdm_tied=True determines fDM from other halo params (+baryons)\n", "# mvirial_tied, True # for non-NFW, mvirial_tied=True determines Mvirial from SMHM+fgas + baryon total_mass\n", "# rB_tied, False # for Burkert, rB_tied=True determines rB from free fDM + other parameters.\n", "#\n", "# ### OTHER SETTINGS:\n", "# mhalo_relation, Moster18 ## SMHM relation to use for tying Mvir to Mbar. options: Moster18 / Behroozi13\n", "#\n", "# fgas, 0.5 # Gas fraction for SMHM inference of Mvir if 'mvirial_tied=True'\n", "# lmstar, -99. # Currently code uses fgas to infer lmstar\n", "# # from fitting baryon total_mass for SMHM relation\n", "# # ********************************************************************************\n", "\n", "# # ********************************************************************************\n", "# # Einasto halo\n", "# # Initial Values\n", "# mvirial, 11.5 # Halo virial mass in log(Msun)\n", "# halo_conc, 5.0 # Halo concentration parameter\n", "# fdm, 0.5 # Dark matter fraction at r_eff_disk\n", "# alphaEinasto, 1. # Einasto: Halo profile index\n", "#\n", "# # Fixed? True if its a fixed parameter, False otherwise. Also set False if it will be tied (below)\n", "# mvirial_fixed, False\n", "# halo_conc_fixed, True\n", "# fdm_fixed, False\n", "# alphaEinasto_fixed, False\n", "#\n", "# # Parameter bounds. Lower and upper bounds\n", "# mvirial_bounds, 10.0 13.0\n", "# halo_conc_bounds, 1.0 20.0\n", "# fdm_bounds, 0.0 1.0\n", "# alphaEinasto_bounds, 0.0 2.0\n", "#\n", "# # Tie the parameters?\n", "# fdm_tied, True # for non-NFW, fdm_tied=True determines fDM from other halo params (+baryons)\n", "# mvirial_tied, True # for non-NFW, mvirial_tied=True determines Mvirial from SMHM+fgas + baryon total_mass\n", "# alphaEinasto_tied, False # for Einasto, alphaEinasto_tied=True determines alphaEinasto from free fDM + other params.\n", "#\n", "# ### OTHER SETTINGS:\n", "# mhalo_relation, Moster18 ## SMHM relation to use for tying Mvir to Mbar. options: Moster18 / Behroozi13\n", "#\n", "# fgas, 0.5 # Gas fraction for SMHM inference of Mvir if 'mvirial_tied=True'\n", "# lmstar, -99. # Currently code uses fgas to infer lmstar\n", "# # from fitting baryon total_mass for SMHM relation\n", "# # ********************************************************************************\n", "\n", "\n", "# # ********************************************************************************\n", "# # Dekel-Zhao halo\n", "# # Initial Values\n", "# mvirial, 12.0 # Halo virial mass in log(Msun)\n", "# s1, 1.5 # Inner logarithmic slope (at resolution r1=0.01*Rvir)\n", "# c2, 25.0 # Concentration parameter (defined relative to c, a)\n", "# fdm, 0.5 # Dark matter fraction at r_eff_disk\n", "#\n", "# # Fixed? True if its a fixed parameter, False otherwise. Also set False if it will be tied (below)\n", "# mvirial_fixed, False\n", "# s1_fixed, False\n", "# c2_fixed, False\n", "# fdm_fixed, False\n", "#\n", "# # Parameter bounds. Lower and upper bounds\n", "# mvirial_bounds, 10.0 13.0 # log(Msun)\n", "# s1_bounds, 0.0 2.0\n", "# c2_bounds, 0.0 40.0\n", "# fdm_bounds, 0.0 1.0\n", "#\n", "# # Tie the parameters?\n", "# mvirial_tied, True # mvirial_tied=True determines Mvirial from fDM, s1, c2.\n", "# s1_tied, True # Tie the s1 to M*/Mvir using best-fit Freundlich+20 (Eqs 45, 47, 48, Table 1)\n", "# c2_tied, True # Tie the c2 to M*/Mvir using best-fit Freundlich+20 (Eqs 47, 49, Table 1)\n", "# fdm_tied, False # for non-NFW, fdm_tied=True determines fDM from other halo params (+baryons)\n", "#\n", "# ### OTHER SETTINGS:\n", "# lmstar, 10.5 # Used to infer s1, c2 if s1_tied or c2_tied = True\n", "#\n", "# # ********************************************************************************\n", "\n", "\n", "# ********************************************************************************\n", "# INTRINSIC DISPERSION PROFILE\n", "# ------------------\n", "\n", "# Initial Values\n", "sigma0, 39.0 # Constant intrinsic dispersion value\n", "\n", "# Fixed? True if its a fixed parameter, False otherwise\n", "sigma0_fixed, False\n", "\n", "# Parameter bounds. Lower and upper bounds\n", "sigma0_bounds, 5.0 300.0\n", "\n", "\n", "\n", "# ********************************************************************************\n", "# ********************************************************************************\n", "# ********************************************************************************\n", "# HIGHER ORDER COMPONENTS: INFLOW, OUTFLOW\n", "# ----------------------------------------\n", "\n", "# # ********************************************************************************\n", "# # UNIFORM SPHERICAL RADIAL FLOW -- in rhat direction in spherical coordinates\n", "# # radial_flow\n", "# # -------------------\n", "#\n", "# vr, -90. # Radial flow [km/s]. Positive: Outflow. Negative: Inflow.\n", "\n", "\n", "# # ********************************************************************************\n", "# # UNIFORM PLANAR RADIAL FLOW -- in Rhat direction in cylindrical coordinates\n", "# # (eg, radial in galaxy midplane)\n", "# # uniform_planar_radial_flow\n", "# # -------------------\n", "#\n", "# vr, -90. # Radial flow [km/s]. Positive: Outflow. Negative: Inflow.\n", "\n", "\n", "# # ********************************************************************************\n", "# # UNIFORM BAR FLOW -- in xhat direction along bar in cartesian coordinates,\n", "# # with bar at an angle relative to galaxy major axis (blue)\n", "# # uniform_bar_flow\n", "# # -------------------\n", "#\n", "# vbar, -90. # Bar flow [km/s]. Positive: Outflow. Negative: Inflow.\n", "# phi, 90. # Azimuthal angle of bar [degrees], counter-clockwise from blue major axis.\n", "# # Default is 90 (eg, along galaxy minor axis)\n", "# bar_width, 2 # Width of the bar perpendicular to bar direction.\n", "# # Bar velocity only is nonzero between -bar_width/2 < ygal < bar_width/2.\n", "\n", "\n", "# # ********************************************************************************\n", "# # UNIFORM WEDGE FLOW -- in planar radial flow in cylindrical coordinates, restricted to pos, neg wedges\n", "# # uniform_wedge_flow\n", "# # -------------------\n", "#\n", "# vr, -90. # Radial flow [km/s]. Positive: Outflow. Negative: Inflow.\n", "# theta, 60. # Opening angle of wedge [deg]. (the full angular span)\n", "# phi, 90. # Angle offset relative to the galaxy angle, so the wedge center is at phi.\n", "# # Default: 90 deg, so centered along minor axis\n", "\n", "\n", "# # ********************************************************************************\n", "# # UNRESOLVED OUTFLOW -- at galaxy center (ie, AGN unresolved outflow)\n", "# # unresolved_outflow\n", "# # -------------------\n", "#\n", "# vcenter, 0. # Central velocity of the Gaussian in km/s\n", "# fwhm, 1000. # FWHM of the Gaussian in km/s\n", "# amplitude, 1.e12 # Amplitude of the Gaussian, for flux in ~M/L=1 luminosity units\n", "# # with the dimming applied ... roughly ....\n", "\n", "\n", "# # ********************************************************************************\n", "# # BICONICAL OUTFLOW\n", "# # biconical_outflow\n", "# # -------------------\n", "#\n", "# n, 0.5 # Power law index\n", "# vmax, 500. # Maximum velocity of the outflow in km/s\n", "# rturn, 5. # Turn-over radius in kpc of the velocty profile\n", "# thetain, 30. # Half inner opening angle in degrees. Measured from the bicone axis\n", "# dtheta, 20. # Difference between inner and outer opening angle in degrees\n", "# rend, 10. # Maximum radius of the outflow in kpc\n", "# norm_flux, 8. # Log flux amplitude of the outflow at r = 0.\n", "# # Need to check dimming/flux conventions\n", "# tau_flux, 1. # Exponential decay rate of the flux\n", "# biconical_profile_type, both # Type of velocity profile:\n", "# # 'both', 'increase', 'decrease', 'constant'\n", "# biconical_outflow_dispersion, 80. # Dispersion (stddev of gaussian) of biconical outflow, km/s\n", "\n", "\n", "# # ********************************************************************************\n", "# # VARIABLE BAR FLOW -- in xhat direction along bar in cartesian coordinates,\n", "# # with bar at an angle relative to galaxy major axis (blue)\n", "# # CAUTION!!!\n", "# # variable_bar_flow\n", "# # -------------------\n", "#\n", "# vbar_func_bar_flow, -90.*np.exp(-R/5.) # Bar flow FUNCTION [km/s]. Positive: Outflow. Negative: Inflow.\n", "# phi, 90. # Azimuthal angle of bar [degrees], counter-clockwise from blue major axis.\n", "# # Default is 90 (eg, along galaxy minor axis)\n", "# bar_width, 2 # Width of the bar perpendicular to bar direction.\n", "# # Bar velocity only is nonzero between -bar_width/2 < ygal < bar_width/2.\n", "\n", "\n", "# # ********************************************************************************\n", "# # AZIMUTHAL PLANAR RADIAL FLOW -- in Rhat direction in cylindrical coordinates\n", "# # (eg, radial in galaxy midplane), with an added azimuthal term\n", "# # CAUTION!!!\n", "# # azimuthal_planar_radial_flow\n", "# # -------------------\n", "#\n", "# vr_func_azimuthal_planar_flow, -90.*np.exp(-R/5.) # Radial flow [km/s].\n", "# # Positive: Outflow. Negative: Inflow.\n", "# m, 2 # Number of modes in the azimuthal pattern. m=0 gives a purely radial profile.\n", "# phi0, 0. # Angle offset relative to the galaxy angle [deg],\n", "# # so the azimuthal variation goes as cos(m(phi_gal - phi0))\n", "\n", "\n", "\n", "# # ********************************************************************************\n", "# # SPIRAL DENSIY WAVE FLOW -- as in Davies et al. 2009, ApJ, 702, 114\n", "# # Here assuming CONSTANT velocity -- try to match real Vrot...\n", "# # CAUTION!!! NO SPACES IN FUNCTION DEFINITONS!\n", "# # spiral_flow\n", "# # -------------------\n", "#\n", "# Vrot_func_spiral_flow, 150.+0.*R # Unperturbed rotation velocity of the galaxy\n", "# dVrot_dR_func_spiral_flow, 0.*R # Derivative of Vrot(R) -- ideally evaluated analytically, otherwise very slow.\n", "# rho0_func_spiral_flow, 1.e11*np.exp(-R/5.) # Unperturbed midplane density profile of the galaxy\n", "# f_func_spiral_flow, (np.sqrt(m**2-2.)*Vrot(R)/cs)*np.log(R) # Function describing the spiral shape, m*phi = f(R)\n", "# # with k = df/dR\n", "# k_func_spiral_flow, (np.sqrt(m**2-2.)*Vrot(R)/cs)/R # Function for radial wavenumber\n", "#\n", "# m, 2 # Number of photometric/density spiral arms.\n", "# cs, 10. # Sound speed of medium, in km/s.\n", "# epsilon, 1. # Density contrast of perturbation (unitless).\n", "# Om_p, 0. # Angular speed of the driving force, Omega_p\n", "# phi0, 0. # Angle offset of the arm winding, in degrees. Default: 0.\n", "\n", "\n", "# ********************************************************************************\n", "# ********************************************************************************\n", "# ********************************************************************************\n", "\n", "\n", "# ********************************************************************************\n", "# ZHEIGHT PROFILE\n", "# ---------------\n", "\n", "# Initial Values\n", "sigmaz, 0.9 # Gaussian width of the galaxy in z, in kpc\n", "\n", "# Fixed? True if its a fixed parameter, False otherwise\n", "sigmaz_fixed, False\n", "\n", "# Parameter bounds. Lower and upper bounds\n", "sigmaz_bounds, 0.1 1.0\n", "\n", "\n", "# Tie the zheight to the effective radius of the disk?\n", "# If set to True, make sure sigmaz_fixed is False\n", "zheight_tied, True\n", "\n", "\n", "# GEOMETRY\n", "# --------\n", "\n", "# Initial Values\n", "inc, 62. # Inclination of galaxy, 0=face-on, 90=edge-on\n", "\n", "# Fixed? True if its a fixed parameter, False otherwise\n", "inc_fixed, True\n", "\n", "# Parameter bounds. Lower and upper bounds\n", "inc_bounds, 0.0 90.0\n", "\n", "\n", "# **************************** Fitting Settings ********************************\n", "\n", "fit_method, mpfit # mcmc, nested, or mpfit\n", "\n", "do_plotting, True # Produce all output plots?\n", "\n", "fitdispersion, True # Simultaneously fit the velocity and dispersion?\n", "fitflux, False # Also fit for the flux?\n", "\n", "\n", "# MPFIT Settings\n", "#----------------\n", "maxiter, 200 # Maximum number of iterations before mpfit quits\n", "\n" ] } ], "source": [ "with open(param_filename, 'r') as f:\n", " print(f.read())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Add some settings for this notebook example:**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "plot_type = 'png'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "--------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2) Run `Dysmalpy` fitting: 1D wrapper, with fit method= MPFIT ##" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:DysmalPy:*************************************\n", "INFO:DysmalPy: Fitting: GS4_43501 using MPFIT\n", "INFO:DysmalPy: obs: OBS\n", "INFO:DysmalPy: velocity file: /Users/jespejo/anaconda3/envs/test_dysmalpy/lib/python3.11/site-packages/dysmalpy/tests/test_data/GS4_43501.obs_prof.txt\n", "INFO:DysmalPy: nSubpixels: 1\n", "INFO:DysmalPy: mvirial_tied: False\n", "INFO:DysmalPy:\n", "MPFIT Fitting:\n", "Start: 2024-01-03 10:10:44.647732\n", "\n", "INFO:DysmalPy:Iter 1 CHI-SQUARE = 82.00732713 DOF = 32\n", " disk+bulge:total_mass = 11 \n", " disk+bulge:r_eff_disk = 5 \n", " halo:mvirial = 11.5 \n", " dispprof_LINE:sigma0 = 39 \n", "\n", "INFO:DysmalPy:Iter 2 CHI-SQUARE = 71.78611963 DOF = 32\n", " disk+bulge:total_mass = 10.98404781 \n", " disk+bulge:r_eff_disk = 5.431735616 \n", " halo:mvirial = 11.69606647 \n", " dispprof_LINE:sigma0 = 40.2897216 \n", "\n", "INFO:DysmalPy:Iter 3 CHI-SQUARE = 68.0789783 DOF = 32\n", " disk+bulge:total_mass = 10.93780574 \n", " disk+bulge:r_eff_disk = 5.645223846 \n", " halo:mvirial = 12.13248856 \n", " dispprof_LINE:sigma0 = 43.27809695 \n", "\n", "INFO:DysmalPy:Iter 4 CHI-SQUARE = 64.55272712 DOF = 32\n", " disk+bulge:total_mass = 10.87549154 \n", " disk+bulge:r_eff_disk = 5.025363644 \n", " halo:mvirial = 12.21393822 \n", " dispprof_LINE:sigma0 = 43.71883553 \n", "\n", "INFO:DysmalPy:Iter 5 CHI-SQUARE = 60.90507325 DOF = 32\n", " disk+bulge:total_mass = 10.76106807 \n", " disk+bulge:r_eff_disk = 3.544651118 \n", " halo:mvirial = 12.39674668 \n", " dispprof_LINE:sigma0 = 42.85289749 \n", "\n", "INFO:DysmalPy:Iter 6 CHI-SQUARE = 58.86424672 DOF = 32\n", " disk+bulge:total_mass = 10.66780383 \n", " disk+bulge:r_eff_disk = 2.712423548 \n", " halo:mvirial = 12.54275972 \n", " dispprof_LINE:sigma0 = 39.47363979 \n", "\n", "INFO:DysmalPy:Iter 7 CHI-SQUARE = 58.59151209 DOF = 32\n", " disk+bulge:total_mass = 10.67734926 \n", " disk+bulge:r_eff_disk = 2.762632506 \n", " halo:mvirial = 12.49244494 \n", " dispprof_LINE:sigma0 = 38.10677514 \n", "\n", "INFO:DysmalPy:Iter 8 CHI-SQUARE = 58.59027979 DOF = 32\n", " disk+bulge:total_mass = 10.67811434 \n", " disk+bulge:r_eff_disk = 2.772714672 \n", " halo:mvirial = 12.49263336 \n", " dispprof_LINE:sigma0 = 38.18435198 \n", "\n", "INFO:DysmalPy:Iter 9 CHI-SQUARE = 58.59022498 DOF = 32\n", " disk+bulge:total_mass = 10.67826899 \n", " disk+bulge:r_eff_disk = 2.775022027 \n", " halo:mvirial = 12.4926511 \n", " dispprof_LINE:sigma0 = 38.2000539 \n", "\n", "INFO:DysmalPy:Iter 10 CHI-SQUARE = 58.59022442 DOF = 32\n", " disk+bulge:total_mass = 10.67828168 \n", " disk+bulge:r_eff_disk = 2.775243281 \n", " halo:mvirial = 12.49266183 \n", " dispprof_LINE:sigma0 = 38.20157538 \n", "\n", "INFO:DysmalPy:Iter 11 CHI-SQUARE = 58.59022439 DOF = 32\n", " disk+bulge:total_mass = 10.67828485 \n", " disk+bulge:r_eff_disk = 2.775292025 \n", " halo:mvirial = 12.49266282 \n", " dispprof_LINE:sigma0 = 38.201916 \n", "\n", "INFO:DysmalPy:\n", "End: 2024-01-03 10:10:47.219127\n", "\n", "******************\n", "Time= 2.57 (sec), 0:2.57 (m:s)\n", "MPFIT Status = 1\n", "MPFIT Error/Warning Message = None\n", "******************\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------\n", " Dysmalpy MPFIT fitting complete for: GS4_43501\n", " output folder: /Users/jespejo/Dropbox/Postdoc/Data/dysmalpy_test_examples/JUPYTER_OUTPUT_1D_FITTING_WRAPPER/MPFIT/\n", "------------------------------------------------------------------\n", " \n" ] } ], "source": [ "dysmalpy_fit_single.dysmalpy_fit_single(param_filename=param_filename, \n", " datadir=datadir, outdir=outdir_mpfit, \n", " plot_type=plot_type, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3) Examine results ##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Result plots ###\n", "\n", "**Read in parameter file**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "params = utils_io.read_fitting_params(fname=param_filename)\n", "\n", "# Override data + output paths:\n", "params['datadir'] = datadir\n", "params['outdir'] = outdir_mpfit\n", "\n", "# Add the plot type:\n", "params['plot_type'] = plot_type\n", "\n", "\n", "f_galmodel = params['outdir'] + '{}_model.pickle'.format(params['galID'])\n", "f_results = params['outdir'] + '{}_{}_results.pickle'.format(params['galID'], \n", " params['fit_method'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "**Best-fit plot**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 9, "metadata": { "image/png": { "width": 600 } }, "output_type": "execute_result" } ], "source": [ "# Look at best-fit saved plot:\n", "filepath = outdir_mpfit+\"{}_{}_bestfit_{}.{}\".format(params['galID'], \n", " params['fit_method'], \n", " params['obs_1_name'], \n", " params['plot_type'])\n", "Image(filename=filepath, width=600)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Directly generating result plots ####\n", "\n", "**Reload the galaxy, results files:**" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "gal, results = fitting.reload_all_fitting(filename_galmodel=f_galmodel, \n", " filename_results=f_results, \n", " fit_method=params['fit_method'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "**Plot the best-fit results:**" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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7dkwLFixw2PbYY4/VOO68885T//797W2LxaJ3333X0+EBAAAA/mUySSkpNT4OSDogudSXRIDgQDIAAABAqCsu9t66YFLF3DUtPwAAAOBFn376qcrOWN7o8ssvV5s2bVwaO2rUKId2Xl6eR2MDAAAAAH8hZQMAEJ4OHDh7G2WNAAAAgKD0ySefOLSzs7NdHnvllVfKZDLJ8nulow0bNqiwsFBpaWmeDBEAAAAAfI7KAACA8NSunZSa6vhITq5YVx0AAABAUNm8ebND+/LLL3d5bFxcnC644AKHbVu2bPFIXAAAAADgTyQDAABgw9rnABD8kpKkhATvzZ+QULEPAEBA+eGHHxzarVu3dmt8q1atHNpbt26tc0wAAAAA4G/UQgYA4Ey2tc9TUvwdiffYLpR5aw15LpQB8CeTSZo+vSK5y9PHuYSEirlrs6SMxVLx+8UZs1nJtudFRdIZa19XieVtAECSdOjQIR06dMhhW/Pmzd2ao3L/bdu21TkuT/j1118VFxfn0TmbNWsmkxu/P3bt2qXY2FiPxtCoUSNFR0fX2G/fvn06fvy4R/dtk5ycrPj4+Br7HThwQKWlpV6JITExUYmJiTX2O3z4sA4fPlxjv7Iz/nZw9esWHx+v5OTkGvuVlpbqQFXL7XlAdHS0GjVqVGO/48ePa9++fV6JwWQyqVmzZjX2s1gs2rNnj1dikKQWLVq41G/nzp1ei8HVY8SePXvsy6t4GseICp4+Rrii8nEkNTXVb8eIspo+D1XCMeK/OEZUqOkYUZvfmzbeOEa4G483jhHuxsDfERVcPUb4jRU1MgzDGhERYY2IiLA+99xz/g4HIW7z5s1WSfbH5s2b/R0Sgkxpaan9+6e0tNTf4dTO/v1Wq+S/x/79/v4fqLMavw/efttqTUjw/P9dQkLF3LBarQH08+iLn6kQ+LmpLGC+fnUUKu/DbSdPVnxfOnmUFhRYkyVrsmQtLSiosb/15MnaxRJkx9xQ+p4JpPfC3/nBi3MCge+XX35x+PmKjY11e45nn33WYY4RI0Z4PlAXVD5WeONRUFBQYxxnHj+98Vi+fLlL/x9XXXWV12KYPXu2SzGMGDHCazFMmDDBpRgmTJjgtRhc/V6fPXu212K46qqrXIph+fLlXoshPT3dpRgKCgq8FoPk+ql6b8bgyjHCarVa09PTvRYDx4iKB8eI/z5c+VueY8R/eTMGjhEVD44RFY9AOEYEw98R/j4fwG0kCAsFBQX67rvvtHfvXpWWlqpx48ZKT09Xly5dVK9ePX+HBwC+l5MjDRtW412qZrNZLTIyJEk7CwpqviuJu1QBBAqTqeYqL7Gxsuelp6RIHr7zUlJFRQBvVCmQ/ru8zbBhHHsBhLXKd1vFxMS4PUflMUePHq1TTJK0f/9+FRUVuTVm+/btdd4vAAAAANhwxgghbcGCBZo6daq++uqrKl8/99xzNXToUE2aNMmlUiYAEFIC5UJZLZjNZrf6e7q0KryHry1CTnGx95ZlkcJjeRsAqEHlZABXysZWVjkZwBMln1955RXl5ubWeR4AAAAAqC2SARCSSktLddddd+m9995z2u/QoUOaMWOG8vLy9Oabb6p3794+ihCAU95e0x5Bz5U1uc5ktVq9FAk8ja8tAACoK8MwfDIGAAAAAAJdhL8DADzt1KlTGjp06FmJACkpKerVq5eGDBmiiy66yOGDfmFhoW688UatWrXK1+ECqIrJJE2fXpEQAAAAAABOVE4mPHbsmNtzVB7jboIiAAAAAAQiKgMg5Dz++ONatGiRvV2vXj1NnTpVf/rTnxQVFWXfvnXrVt155532JQTKy8vVv39/ff/992rcuLHP4wZQSXVr2h84ILVr55+YEDAql201m81KS0uTVJHgRen44MXXFgAAuCtQkwFGjx6tIUOGuDVm+/bt6t+/f533DQAAAAASyQAIMTt27NC0adMctr3//vu68cYbz+rbrl07LV26VNdcc409IeDgwYPKzc3VP/7xD5/EC6AGrqxpj7Dk7IJwXFwcF4yDGF9bAADgroRKFcXKyspkNpvd+rth//79Du3ExMQ6x5WamqrU1NQ6zfHpp5/qvPPOq3MsZ2rWrJlb/bds2aLY2FiPxtCoUSOX+r333ns6fvy4R/dtk5yc7FK/559/XhMnTvRKDK5+nz344IMaOXJkjf3Kysr0xz/+UZLrXzdXE18GDx6s7Oxsl/q6Kzo62qV+nTt3dnvugoICl/qZTK6dJm/WrJnLc3qTN2Nw9RixatUqWSwWr8TAMaKCp48Rrqh8HHH195g3jhFnxuKKzp07e+1ng2PEf4XSMaI2vzdtvHGMcDcebxwj3I0h2P6O8Pcxwl8COzrATbm5uTp58qS9PXLkyCoTAWxiYmI0Z84cXXDBBTpx4oQk6fXXX9ejjz6qli1bej1eAAAAAABQNw0bNlRSUpKKz6gqtnv3brVt29blOXbt2uXQzszM9Fh8ddG0aVO1aNHCrzGkp6f7LSHT1ZP93pScnOzyCX9vSUxMdOmEv9lstj/39NctPj7e78tnREdHu11JzNPfuyaTye8/k5ICIgZ3E4u8gWNEBVePEa6o7XHEG8eIM2NxRXR0tN9/NjhG/FcwHCO8+XvTxp1jhLficecY4a0YAuXviED42fCHCH8HAHjKsWPHtGDBAodtjz32WI3jzjvvPIcSfBaLRe+++66nwwMAAAAAAF5S+cL/9u3b3Rq/Y8cOp/MBCAy2amFnPlx5DQAAIFyRDICQ8emnn6qsrMzevvzyy9WmTRuXxo4aNcqhnZeX59HYAMDbzGbzWQ9XXgMAAABCQfv27R3atuUAXWE2m7Vp0yan8wEAAABAMGKZABdZrVYZhuHvMODEJ5984tB2Z/2RK6+8UiaTyb6GzYYNG1RYWGgvMwYAgc5ZmaWqjmVWq9Wb4QAAAIQUzgkEvj59+ujVV1+1t1esWOHy2JUrVzqsaZuVlcX5AAAAgBBT+QapyjdTVUaFGYQKkgFccN9999mfd+rUyY+RwJnNmzc7tC+//HKXx8bFxemCCy7Qhg0b7Nu2bNnCh38AABDYLBbpjPWRq2Q2y746XVGRdEYlpSolJUkmPiYAgA3nBIJD7969FRMTo2PHjkmqqAzw448/ulQxcM6cOQ7tAQMGeCNEAAAA+BE3UyFccZbPBS+99JK/Q4ALfvjhB4d269at3RrfqlUrh2SArVu36uqrr/ZIbHXx66+/ejwDrVmzZjK5cJJ/z549DndHeFKjRo0UHR1dY799+/bp+PHjXokhOTnZ6R8ANgcOHFBpaalXYkhMTFRiYmKN/Q4fPqzDhw+7NOeZy2Xs2rVLsbGxTvvHx8crOTnZaR9JKi0t1YEDB1yKwV3R0dFq1KhRjf2OHz+u/bt3q7lXonCNxWLRnj17vDZ/ixYtXOq3c+dOh/aWLVvc2k/l8WcKxmOEu9/3rvDEMaKucXnqGBFx8KDXf25KS0t1wAtLUJTVdOH6DMePH9e+ffu8su9ff/1V5513Xo1jfHqMmDtXGjNGKilxOiZOUpGtkZFR4z5On3OODubmylzNhZBgPEZUpy4/o64eIw4ePKiGLs/qHe78HSG59/8SbH9HFBUV1divNkwmk5o1a+aVueF/nBMIDrGxsRo8eLDefvtt+7bnnntOs2fPdjru559/Vn5+vr1tMpl08803ey1OAAAAAPApKxACDh48aJXk8CgtLXVrjocffthh/AMPPOClaJ3bvHnzWe/F04+CggKXYklPT/daDMuXL3cphquuusprMcyePdulGEaMGOG1GCZMmOBSDBMmTPBaDCNGjHAphtmzZ3sthquuusqlGJYvX25NlqxWbz/27682hoKCAq/9P0iu/1r2ZgxnHiNKS0vt2ysfV8P+GHHypNW6f791zNCh1mTJI4/ISjF46hjhi5+bd6dN8+r3ZVXfg5UtX77c5bki3fzaXNikScWxwdnj5EnfHSNOnrRaExK89vUsruL7sapjhDNhf4z43ZihQ/36e8tqDe+/I878PbZo0SKvxZCenl7j/0Hlv/M3b97s0v8dANf98ssv1nr16jn8rC1cuLDa/seOHbN26dLFof/dd9/tw4jPFijHCmefAxC4wvHrFo7vGfCmQPqZCqRYEBpKS0vdevgjPn9/zwdCDKHI33/jUxkAIaHynU6xsbFu302fmprq0C6p4S47V+zfv9/tu4+2b99e5/0CALzsjDuyX5LkqfsFD0saI+kdD82Hqt0iabqkRHcG7d0rVfpb4SwJCYqbMKHWcbmluLjGigB1kSgpSZJ37uMGAMA7WrZsqbFjx+r555+3bxs8eLCmTp2qP/3pT4qKirJv/+GHH3TnnXdqzZo19m0NGzbUBF/9LgcAAA5Yzx3exvcMwhXJAAgJlcszx8TEuD1H5TFHjx6tU0yS9Morryg3N7fO8wAAAojF4lJp9tpIVMVF6vcknfLgvMWqSDRI9OCcDhISdCJIPlBFqhaJAK4qKVHDCRMUKc9+/QBPilRFooennHP8eMVx0YWlIwDAF5599llt2bJFixcvliSdPHlS999/vyZPnqyLLrpI55xzjnbs2KH169c7rAMbFRWl/Px8NW7c2F+hAwAQ1ljPHQC8I8LfAQCeUDkZwJU1ZCurnAzgrTXiAQBBzkd3ZHvSKVVUHDjs4XklSQkJ0vTpskZGemN2j0uSF5MiJEUcPerxrx/gKbeootpDkQcfL82bJyUnV1RMAYAAEBkZqfnz52vo0KEO2/fv369PPvlE77//vr799luHCwipqalauHChrrzySl+HCwAAAABexe0bCEmGYfhkDAAAweIdVVQccPdC9bChQ/XSS04WQkhKqrgjeM6c2gcHwOu8XRVDY8ZIw4ZRIQBAQIiPj9d7772nwYMHa8qUKfr666+r7Hfuuedq6NChys3NVUpKio+jBIDAUlUZdmcotw1P4+Y8APAOztQgJFQuIXTs2DG356g8xllZIleNHj1aQ4YMcWvM9u3b1b9//zrvGwCAyk7J/TXgj0ZHS5wcB4Ket6tiqKSkonIKxwsAAWTw4MEaPHiwCgoKtH79eu3du1dms1mNGjVSenq6unbtqqioKH+HCQABwd1zoZRoh6eRYAIA3kEyAEJCoCYDpKamKjU1tU5zfPrppzrvvPPqHMuZmjVr5lK/VatWyWKxeHTfNo0aNXKp33vvvafjx497JYbk5GSX+j3//POaOHGiV2JITEx0qd+DDz6okSNHutS3rKxMf/zjHyVJW7ZsUWxsrNP+rn6vDx48WNnZ2S71dZerS3t07txZ32zbptMXXaSIo0e9EosSEirudK5Gs2bNVFBQ4J19u8GbMQTjMcLd73tXuHqM8IZv163T6YYNvXKMqEnl/0tXf4956xhxZjw16dy5c40/GxEHD0oXX+yJ0Kpl+/oFu+reRzAeI2wyMjLc2o+z7ydXjxETJ06U5s1za7+e9uCDD+r266/3+ve+M4Hwd8Sll17qtd+fJioiAAEtIyPD7d8BAAAAABAKOGOBkJCQkODQLisrk9lsdiubcP/+/Q5tVy/AeFvTpk3VokULv+zb1ZP93uTqyX5vSk5O9utFQani+9HV78kzy7qlp6d7LKs2Pj7eI0kydREdHa0WrVtLr7xSUY7Y0+u2/772ubMSxyaTyW8/k2cKhBgC6Rjhre97f2nevLlbd9e6c4yoSW3/L711jHCnVGV0dHTNPxs++N5w9+sXqOr6PgLpGFFbnjjWNgyAxJDExEQlNm/u1xgC5e+IQPh6AAAABKLKJdrNZrPS0tIkSYWFhUH/ORsAgHBFMgBCQsOGDZWUlKTi4mL7tt27d6tt27Yuz7Fr1y6HdmZmpsfiA+BhOTkV6xIXF8tsNqvF73f57CwocPhw6uy1KtnWPgcAhCTWoAQAAACq5uycSVxcHMkAIapyAv6Z7aqS8/k+AIDgwxUPhIy2bdtqzZo19vb27dvdSgbYsWPHWfMBCGAmU8Udq7Gx/10DPSXF8a5fZ68BAMKOX05cJSVVVJ7xdDUbmxqWtwEAAACA6jir3mWrDHEmq9XqzXAAAF4Q4e8AAE9p3769Q/urr75yeazZbNamTZuczgcAAAC4zWSqWIKm0rJWHuHC8jYAAAAAAAAIX5w1Qsjo06ePXn31VXt7xYoVLo9duXKlLBaLvZ2VlVVl5iMAAADgtjOWt3GG5W0AAACA4FVVWX1nAqHkPkupAUDo48wRQkbv3r0VExOjY8eOSaqoDPDjjz+qTZs2NY6dM2eOQ3vAgAHeCBEAAADhyra8jTMsbwMAAAAELWcl96sSCCX3AyEhAQDgXSwTgJARGxurwYMHO2x77rnnahz3888/Kz8/3942mUy6+eabPR4fAAAAAAAAAAAAAPgKlQE8aNeuXTpw4IDKyspqzOrr1q2bj6IKLxMnTtR7772nkydPSqq443/AgAG64YYbqux//PhxjRo1SidOnLBvu+OOO9SqVSufxAsAgaZySbsz21WVuyODHAAAoALnBAAACG+VS+6bzWb7UrSFhYWcQwEA+AXJAHW0c+dOPfPMM8rLy1NxDWuA2hiG4bA+PTynZcuWGjt2rJ5//nn7tsGDB2vq1Kn605/+pKioKPv2H374QXfeeafWrFlj39awYUNNmDDBpzEDQCBxVtLO9gH2TIFQ0g4AAMBfOCcAAABsnF3sj4uLIxkAgANuyoKvkAxQBytWrNCAAQN05MgRLoYEkGeffVZbtmzR4sWLJUknT57U/fffr8mTJ+uiiy7SOeecox07dmj9+vUOX7eoqCjl5+ercePG/godAAAAABAkOCcAAAAAoLa4KQu+QjJALR06dEiDBw9WSUmJYmJidNdddykpKUm5ubkyDEOzZs3SwYMH9e9//1sffvihTp48qSuvvFKjRo3yd+ghLzIyUvPnz9edd96pefPm2bfv379fn3zySZVjUlNT9eabb+rKK6/0VZgAEJAql7QDAADA2TgnAAAAAAAIBiQD1NKMGTN06NAhmUwmffnll+rUqZO2bNmi3NxcSXL4gL9r1y7ddNNNWrlypbp3704Zeh+Ij4/Xe++9p8GDB2vKlCn6+uuvq+x37rnnaujQocrNzVVKSoqPowSAwEO5KQAAgJpxTgAAAABAXXBTFnyFZIBa+vTTT2UYhoYNG6ZOnTo57Zuenq5PP/1U7du31+TJk9WnTx9ddtllPoo0vA0ePFiDBw9WQUGB1q9fr71798psNqtRo0ZKT09X165dFRUV5e8wAQAAAABBhHMCAAAAAOqCm7LgKyQD1NKPP/4oSbr++uurfP306dOKiIiwtxMTEzV27Fg99thjmjlzJh/8fSwjI0MZGRn+DgMAAAAAEAI4JwAAAAAACAYRNXdBVQ4fPiypIsPfpn79+vbnZWVlZ43p0qWLJOnLL7/0bnAAAAAIbUlJUkKC9+ZPSKjYBwCgSpwTAAAAAAAEA5IBasn2IT8yMtK+rUGDBvbne/fuPWtMvXr1JEn79u3zcnQAAAAIaSaTNH26dxICEhIq5jZRRAwAqsM5AQAAAABAMOAMXy394Q9/0E8//aSioiL7ttTUVMXHx8tsNmvdunU677zzHMbYyggCAIAgZrsju6TEO/NzR7Z3hdLXLydHGjZMKi522s1sNqvF78sl7SwoqHlNuqQkEgEAoAacEwAAAAAABAMqA9RShw4dJEnff/+9w/bOnTvLarVqxowZslqt9u3Hjh3TlClTJOmsEwIAACCIcEd2cAu1r5/JJKWk1Pg4IOmA5FJfvv8AoGacEwAAAAAQ7Mxm81kPV15DcOFMXy1dddVVmj9/vj7//HM98sgj9u233nqrlixZojVr1qhbt24aOnSozGaz5s6dqy1btsgwDA0aNMiPkQMAgDrjjuzgxtcP4SiUqmIAAYBzAgAAAACCXXx8fLWvpaWlnbXtzIRnBA/OVtbSjTfeqPvuu0/Lly/X7t271bx5c0lSTk6OXn/9dX355Zdas2aN1qxZ4zDu/PPP10MPPeSPkAEgJFTOQKycreisL4KUxVLjRVuZzUq2PS8qksrKnPf3xEVb2x3ZzsTGVtyNLVX0reliMnyHrx/Cja0qxpgxnk8IoKoJwhDnBAAAAAAAwYCzNbXUpEkTHTx4UKdPn1aDBg3s2w3D0EcffaSHHnpI77zzjsrLyyVJERER6t+/v2bMmKHY2Fh/hQ0AQc/dbEUEublzXbpwFSfJvmLv73dyO2W7cJWTU9cIASB4UBUD8BjOCQAAAAAIdqWlpf4OAT7AGZs6SKqmDOY555yjWbNm6cUXX9TPP/8si8WizMzMavsDAIAqWCzeuYNVqphzzJiKi2JcwAIQTqiKAXgM5wQAAIHA3aqINSZ6AgDCBr8TwgNnv70oPj5eF110kb/DAICQ4k62otlsplpAMCsu9t7a1lLF3MXFNV8UAwAAqAXOCQAAfMFZBcWqsN4zAADhhWQAAEBQIVsR8KzKd5Gc2a7qDhN+BgEAAAAAAAAgOJAMUEtXX321DMPQG2+8ofT0dJfG7N27Vzk5OTIMQ0uXLvVyhAAAADVzdhdJVZU1uIsEAADOCQAAAkflCopnVkksLCwkoRsAgDBHMkAtrVixQoZhuLUm07Fjx+zjAAAAAABAcOKcAAAgUDi72B8XF0cyAAAAYY5kAAAAgDBW+S4SAAAAAAAAAEBoIBnAh06cOCFJioqK8nMkAAAAFbhLBAAA3+CcAAAAAADA1yL8HUA42bx5sySpYcOGfo4EAAAAAAD4EucEAAAAAAC+RmUAF3355ZdVbv/mm2904MABp2PLy8v1888/629/+5sMw1BWVpY3QgQAAAAAAF7AOQEAAAAAQDAiGcBF2dnZMgzDYZvVatXtt9/u8hxWq1WGYbg1BgAAAEBoMpvN1bYrvyaxrAfgT5wTAAAAAAAEI5IB3GC1Wl3aVp34+Hg98cQTGjBggCfDAhBOLBapuNh5H7NZybbnRUVSWZnz/klJkolfBwAA+Fp8fHy1r6WlpZ21zZ3PHgA8j3MCAAAAAIBgw9UfF82ePduhPWrUKBmGocmTJ6tp06bVjjMMQ9HR0WratKkuuugixcTEeDtUAKFq7lxpzBippMRptzhJRbZGRkbN8yYkSNOnSzk5dY0QAAAACEmcEwAAP+CGCAAAgDrjLx8XjRgxwqE9atQoSVL//v3Vrl07f4QEIJxYLC4lAtRKSUnF3MOG8YEYAAAfKi0t9XcIAFzEOQEA8DFuiAAAAPAIrvrU0vLlyyVJGa78kQkAdVVc7J1EAJuSkop9pKR4bx8AAMBBXFycv0MAUEucEwAAL+KGCAAAAI/hL55auuqqq/wdAgAAQHCj7CcAIEhxTgAAvIgbIgAAADyGM6UetGvXLh04cEBlZWWyWq1O+3br1s1HUQEAAAQgyn4CAAAAAAAAgFeRDFBHO3fu1DPPPKO8vDwV13Rn2+8Mw5DFYvFyZAAAAAGKsp8AgBDEDQIAAAAAgEDDGdI6WLFihQYMGKAjR47U+EEfAAAAv6PsJwAgRHCDAAAAAAAgkJEMUEuHDh3S4MGDVVJSopiYGN11111KSkpSbm6uDMPQrFmzdPDgQf373//Whx9+qJMnT+rKK6/UqFGj/B06AAAAAACoI24QAAAAAAAEOpIBamnGjBk6dOiQTCaTvvzyS3Xq1ElbtmxRbm6uJDlc9N+1a5duuukmrVy5Ut27d9eECRP8FTYAAAAAAKgjbhAAAAAAAASDCH8HEKw+/fRTGYahYcOGqVOnTk77pqen69NPP1Xjxo01efJkrV271kdRAgAAAAAAT6t8g8CLL76owYMH218fNWqUxo8fr/nz5+unn37SxRdfrJUrV2rnzp0aMWKEHyMHAAAAAIQTkgFq6ccff5QkXX/99VW+fvr0aYd2YmKixo4dq9OnT2vmzJlejw8AgKCXlCQlJHhv/oSEin0AIcZsNp/1cOU1AIDruEEAAAAAABAMSAaopcOHD0uq+FBvU79+ffvzsrKys8Z06dJFkvTll196NzgACFNcAAsxJpM0fbp3EgISEirmNrFiEkJPfHy8wyMtLc3+Wlpa2lmvAwDcxw0CAAAAAIBgwBnwWqpfv77KysoUGRlp39agQQP787179+q8885zGFOvXj1J0r59+3wTJACEGWcXtc68GGZjtVq9GQ48ISdHGjZMKi522s1sNqtFRoYkaWdBgeLi4pzPm5REIgDOUjlJqHJCUWU1fp8BAEKWKzcIVP7blBsEAAABw2Kp8XO2zGYl254XFUlV3PzmgM/ZAAAEJH4719If/vAH/fTTTyoqKrJvS01NVXx8vMxms9atW3dWMoDtzgEAAOAGk0lKSXHeJzZWB2zPU1IkLtKiFkIloai0tNTfIQBAyOMGAQBA0Jo7VxozRiopcdotTpL9zPfvyfdO2Srw5eTUNUIAAOBBJAPUUocOHfTTTz/p+++/V58+fezbO3furCVLlmjGjBkaPny4DMOQJB07dkxTpkyRpLNOCAAAPIMLYABAxQK4L2irYnBHG/yIGwQAAEHJYnEpEaBWSkoq5h42jL+nAAAIIPxWrqWrrrpK8+fP1+eff65HHnnEvv3WW2/VkiVLtGbNGnXr1k1Dhw6V2WzW3LlztWXLFhmGoUGDBvkxcgAIXQFzcQJAUCKhCOEqKKticEcb/IwbBAAAQam42DuJADYlJRX7qKm6HwAA8JkIfwcQrG688UZJ0vLly7V792779pycHHXr1k1Wq1Vr1qzR2LFj9T//8z/aunWrJOn888/XQw895JeYAQAAUL24uDi3HgD8xBd3tFksnp8bIeWq/9/enYdHXd2L439PiCgEDMgmIsoirohLL1ZQq6BctdWfWrW17li1LpRia21d6nJbbq1cxSoupVTQopVqxe1Wq7LUKup1RcEFkaByUQgQkAQECfP7gy9zHcieTCaZvF7PM485Z87yzkiS+Zx5f845/PBIJpPx7LPPptWfddZZERGpGwTGjRsXv/vd7+Kggw6Kd955xw0CAEBEbNqBqzYPAKgrOwPU0U477RTLly+PjRs3pp0LmEgk4oknnojLLrss7r///li3bl1EROTl5cWJJ54Yd911V7Rt2zZbYQMAAKRpdrtiuKONJuCEE06ISy+9NHWDwC677BIRm24Q+NOf/hTPP/98zJo1K2bNmpXWzw0CAEBE1btzVaRJ7M4FQLMkGaAeOnbsWGF9+/btY8KECXHrrbfGvHnzYsOGDdGvX79K2wMAAGSLnS6g9twgAAAAQHMgGSCD2rVrFwceeGC2wwAAAAAamBsEACA7arttflNMft1yd66ysrLo1q1bREQsWbKkScYMQPMkGQAAAACggblBAAAyIxe22K/qw/6CggLJAAA0mLxsBwAAAAAAAAAANCw7AwA0Bx07RhQWRqxalZnxCws3zQEAAAAATZgt9gGg5iQDVKNPnz4NPmYikYiPPvqowccFclh+fsS4cREjRjR8QkBh4aax8/1JAACAr7MmAJAFboigGrbYB4Ca88lPNRYuXFjjtolEYqvzhyqrA6i1M8+MOO20iJKSKpuVlZVFr969IyJiYVFR9RdAHTtKBAAAgApYEwDIggzeEJEsLIx1N98c21kHAQBaCO96qvGtb32rygv1xYsXx/z58yOZTEYymYxevXrFjjvuGMlkMpYsWZJaOEgkEtGvX7/o3r17I0UO5KT8/IguXapu07ZtLNv8dZcuEbKhAQCgTqwJAGRJHW6IqImSVaui/PzzI/nDH9Y3QgCAZkEyQDVmzpxZ6XMzZsyIU089Ndq0aRO//OUv44ILLkidTbTZ0qVLY/z48fG73/0uli9fHnfddVcMGTIkw1EDADRhtv0EoJmwJgCQRbW9IQIAgK3kZTuA5urTTz+Nk08+Ob788suYMWNGXHPNNVtd9EdEdO3aNa655pqYMWNGrF27Nk455ZT49NNPsxAxAEATsXnbz8LChh+7sHDT2Lb9BCCDrAkAND1LliyJ0tLSKC0tjSVLllRYv/kBANBSSAaoo1tvvTVWrlwZP/nJT2LgwIHVtv+3f/u3+MlPfhIlJSVxyy23NEKEAEBjKysr2+pRk+dapDPPjFi2LGLp0iofZUVF0SUiukREWVFRte1j2bJNYwNABlkTAGh6CgoK0h6V1Rc4ThEAaEEkA9TRf//3f0cikYhjjjmmxn02t33qqacyFRYAkEXt2rVLe3z9DsFu3bpt9XyLt3nbz2oeyyI2bf1Zg7Z2BACgMVgTAAAAoDmQDFBHixYtioiItm3b1rjP5rab+wIAAADNjzUBAAAAmgO3TtVR69atY+3atfH222/HN77xjRr1eeedd1J9AYDc4+xJAGgZrAkAAADQHEgGqKP+/fvHCy+8EDfffHOcfvrpse2221bZft26dXHzzTdHIpGIffbZp5GiBAAak7MnAaBlsCZAs7ZhQ0RJSdVtysqi8+avi4sj1qypun3Hjo5rAlqWhv5dWlYWrSKivIHCA4DNHBNQR2eddVZERLz33ntxzDHHxCeffFJp208++SSOPfbYmDt3bkREnH322Y0SIwAAANDwrAnQbE2eHNG5c0TXrlU+Cnr3juKIKI6Igt69q20fnTtvGhugJcjA79KC3r1jWUSckeVvDYDcI2W3js4///y477774sUXX4znn38++vXrF0OHDo2DDz44unbtGolEIpYsWRIvv/xyTJ8+PTZs2BAREYMHD47zzz8/y9EDAAAAdWVNgGZpw4aIESMiVq1q+LFXrdo09mmn2SEAyG0Z/F3aISLGbZ4DABqId+d1lEgk4u9//3ucdNJJMX369Pjqq6/imWeeiWeeeWartslkMiIijjjiiJg6dWokEonGDhcAaGLKyspq1d4RBADQdFgToFkqKclMIsBmq1ZtmqNLl8zNAdRPx44RhYWZ+11QWLhpjlyW4d+lHSKirKRk02sJAA2gxRwTcN5550VxcXGDjtm+fft47rnnYuLEiXHAAQdEMpms8LH//vvHPffcE9OnT49Cf8QBgIho165drR4AQNNiTQCAZic/P2LcuMx80FxYuGlsu4MAQJPSYv4yT5o0KR555JG49tprY+TIkZHfgG9KzjnnnDjnnHOiuLg43n777Vi+fHkkk8no1KlT7LffftFFRjQAAEDDcEcbGfDnP/859t9//9h7772jVatWteprTQCARrNhw6Y706tSVhadN39dXByxZk3680cfHfHeexErV24qd+hQ4Qf4ZWVl0at374iIWFhUVP1udR07SgQAgCaoRf11Xr16dfz85z+PCRMmxNixY+Poo49u0PG7dOkSRx55ZIOOCQDkptLS0rRyWVlZdOvWLSIilixZ4lgAgMpsvqMtE2e1uqOtxTrnnHMikUhE69at44gjjoinnnqq1mNYEwAgoyZPrtH7n4KISO2P+/8+zK/S5vc/Z56ZXt+2bSzb/HWXLhGuUQGgWWpxKxzJZDLef//9+Pa3vx3HHXdc3HLLLdG3b99shwUAtDBVfdhfUFAgGQCgKmeeGXHaadXeGeeONmpr3bp1MXfu3GyHAQDpNmzITCJkxKYxR4zY9N7K+yAAyDkt5q97//79Y86cOZFIJCJiU1LAk08+Gc8880z89Kc/jauvvjratm2b5SgBAACokfz8TXepVcUdbQBALigpydwRSRGbxi4pqf69FQDQ7LSYZIC33norxo8fH9ddd10UFxenkgLWrVsXN954Y9x7771x0003xemnn57lSAEAAIBs+/DDD+OVV16JDz/8MNavXx+tW7eOXr16xZ577hkHHXRQtGrVKtshAgAAQJVaTDJAXl5eXHTRRXH66afHDTfcEHfccUesX78+EolEJJPJWLx4cZx11llx1113xe233x77779/tkMGAAAAGtmqVati8ODB8corr1Tapl27dnHEEUfEWWedFd/97ncjLy+vESMEAACAmmlxV6vbb7993HzzzTFnzpw4/vjjI5lMRiKRSCUFvPjiizFw4MD40Y9+FMuWLat+QAAAACBnlJaWxiuvvBLJZDL1iIi08urVq+PJJ5+M73//+9GrV6/4wx/+kOWoAQAAYGstLhlgs9122y0ee+yxePbZZ2OfffZJSwooLy+PCRMmxO677x633357bNy4MdvhAgAAAI1g84f/m48X/HrdZpufSyaTsWjRorjkkkvisMMOiwULFjReoAAAAFCNFpsMsNmRRx4Zb731Vtx5553RuXPnVFJAMpmMlStXxqhRo2K//faL6dOnZztUAAAAIMM2rwkkk8n45je/GSNHjozf/va38Z//+Z9xySWXxNChQ2O77bZLSxrYvNPgIYccErNnz87ydwAAAACb5Gc7gKYgLy8vLrroojj99NPjhhtuiHHjxsVXX32VuqCfO3duDBs2LE466aS4+eabY9ddd812yADQopWVlVVa3vK5iIiCgoKMxwQA5I4DDjgg7rvvvthnn30qfH79+vXx3HPPxR/+8Id48sknUzsFLFmyJI444oh49dVXY7fddmvMkAEAAGArLX5ngK/bfvvt4+abb445c+bEcccdl3Z0QDKZjKlTp8Zee+0V1113Xaxduzbb4QJAi9WuXbu0R7du3VLPdevWbavnAQBqauedd47p06dXmggQEdG6dev49re/HY899li8/PLLsf/++6fWEFatWhWnnnpqrFu3rhGjBgAAgK1JBqhAv3794vHHH49nnnkm9t5779QFfUTEl19+Gb/5zW9izz33jIceeijLkQIAAAANJZFIxKWXXhqFhYU17jNw4MCYNWtWfP/7308dHfD222/HzTffnKkwAQAAoEYkA1ThqKOOitmzZ8cdd9wRnTp1SiUFJJPJ+PTTT+O0006LIUOGxDvvvJPtUAGgRSktLa3VAwCgOps/yP/Wt75V677bbrtt3H///XHMMcekxho7dmysWbOmQWMEAACA2pAMUI28vLy4+OKL48MPP4xRo0ZFfn5+REQqKeCf//xnHHjggTFixIgoKSnJcrQA0DIUFBTU6gEAUJU2bdqkvu7cuXOdxsjLy4uJEydG+/btIyJ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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results.plot_results(gal)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Results reports ###\n", "\n", "We now look at the results reports, which include the best-fit values and uncertainties (as well as other fitting settings and output)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "###############################\n", " Fitting for GS4_43501\n", "\n", "Date: 2024-01-03 10:10:48.930775\n", "\n", " obs: OBS\n", " Datafiles:\n", " vel : /Users/jespejo/anaconda3/envs/test_dysmalpy/lib/python3.11/site-packages/dysmalpy/tests/test_data/GS4_43501.obs_prof.txt\n", " apertures: RectApertures\n", " fit_velocity: True\n", " fit_dispersion: True\n", " fit_flux: False\n", " moment: False\n", " partial_weight: True\n", " zcalc_truncate: True\n", " n_wholepix_z_min: 3\n", " oversample: 1\n", " oversize: 1\n", "\n", "\n", "Fitting method: MPFIT\n", " fit status: 1\n", "\n", "pressure_support: True\n", "pressure_support_type: 1\n", "\n", "###############################\n", " Fitting results\n", "-----------\n", " disk+bulge\n", " total_mass 10.6783 +/- 0.0400\n", " r_eff_disk 2.7753 +/- 0.3250\n", "\n", " n_disk 1.0000 [FIXED]\n", " r_eff_bulge 1.0000 [FIXED]\n", " n_bulge 4.0000 [FIXED]\n", " bt 0.3000 [FIXED]\n", " mass_to_light 1.0000 [FIXED]\n", "\n", " noord_flat True\n", "-----------\n", " halo\n", " mvirial 12.4927 +/- 0.1405\n", "\n", " fdm 0.2684 [TIED]\n", " conc 5.0000 [FIXED]\n", "-----------\n", " dispprof_LINE\n", " sigma0 38.2020 +/- 4.4594\n", "-----------\n", " zheightgaus\n", " sigmaz 0.4714 [TIED]\n", "-----------\n", " geom_1\n", " inc 62.0000 [FIXED]\n", " pa 142.0000 [FIXED]\n", " xshift 0.0000 [FIXED]\n", " yshift 0.0000 [FIXED]\n", " vel_shift 0.0000 [FIXED]\n", "\n", "-----------\n", "Adiabatic contraction: False\n", "\n", "-----------\n", "Red. chisq: 1.8309\n", "\n", "\n", "\n" ] } ], "source": [ "# Print report\n", "print(results.results_report(gal=gal))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**To directly save the results report** to a file, we can use the following:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Save report to file:\n", "f_report = params['outdir'] + '{}_fit_report.txt'.format(params['galID'])\n", "results.results_report(gal=gal, filename=f_report)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Also note the fitting wrappers automatically save two versions of the report files:**" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "fbase = '{}_{}_bestfit_results'.format(params['galID'], params['fit_method'])\n", "f_report_pretty = params['outdir'] + fbase + '_report.info'\n", "f_report_machine = params['outdir'] + fbase + '.dat'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"pretty\" version, automatically saved as `*_best_fit_results_report.info`, is formatted to be human-readable, and includes more information on the fit settings at the beginning (for reference)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "###############################\n", " Fitting for GS4_43501\n", "\n", "Date: 2024-01-03 10:10:47.831461\n", "\n", " obs: OBS\n", " Datafiles:\n", " vel : /Users/jespejo/anaconda3/envs/test_dysmalpy/lib/python3.11/site-packages/dysmalpy/tests/test_data/GS4_43501.obs_prof.txt\n", " apertures: RectApertures\n", " fit_velocity: True\n", " fit_dispersion: True\n", " fit_flux: False\n", " moment: False\n", " partial_weight: True\n", " zcalc_truncate: True\n", " n_wholepix_z_min: 3\n", " oversample: 1\n", " oversize: 1\n", "\n", "\n", "Fitting method: MPFIT\n", " fit status: 1\n", "\n", "pressure_support: True\n", "pressure_support_type: 1\n", "\n", "###############################\n", " Fitting results\n", "-----------\n", " disk+bulge\n", " total_mass 10.6783 +/- 0.0400\n", " r_eff_disk 2.7753 +/- 0.3250\n", "\n", " n_disk 1.0000 [FIXED]\n", " r_eff_bulge 1.0000 [FIXED]\n", " n_bulge 4.0000 [FIXED]\n", " bt 0.3000 [FIXED]\n", " mass_to_light 1.0000 [FIXED]\n", "\n", " noord_flat True\n", "-----------\n", " halo\n", " mvirial 12.4927 +/- 0.1405\n", "\n", " fdm 0.2684 [TIED]\n", " conc 5.0000 [FIXED]\n", "-----------\n", " dispprof_LINE\n", " sigma0 38.2020 +/- 4.4594\n", "-----------\n", " zheightgaus\n", " sigmaz 0.4714 [TIED]\n", "-----------\n", " geom_1\n", " inc 62.0000 [FIXED]\n", " pa 142.0000 [FIXED]\n", " xshift 0.0000 [FIXED]\n", " yshift 0.0000 [FIXED]\n", " vel_shift 0.0000 [FIXED]\n", "\n", "-----------\n", "Adiabatic contraction: False\n", "\n", "-----------\n", "Red. chisq: 1.8309\n", "\n", "\n" ] } ], "source": [ "with open(f_report_pretty, 'r') as f:\n", " lines = [line.rstrip() for line in f]\n", " for line in lines: print(line)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"machine\" version, automatically saved as `*_best_fit_results.dat`, is formatted as a machine-readable space-separated ascii file. It includes key parameter fit information, as well as the best-fit reduced chisq." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# component param_name fixed best_value l68_err u68_err\n", "disk+bulge total_mass False 10.6783 0.0400 0.0400\n", "disk+bulge r_eff_disk False 2.7753 0.3250 0.3250\n", "disk+bulge n_disk True 1.0000 -99.0000 -99.0000\n", "disk+bulge r_eff_bulge True 1.0000 -99.0000 -99.0000\n", "disk+bulge n_bulge True 4.0000 -99.0000 -99.0000\n", "disk+bulge bt True 0.3000 -99.0000 -99.0000\n", "disk+bulge mass_to_light True 1.0000 -99.0000 -99.0000\n", "halo mvirial False 12.4927 0.1405 0.1405\n", "halo fdm TIED 0.2684 -99.0000 -99.0000\n", "halo conc True 5.0000 -99.0000 -99.0000\n", "dispprof_LINE sigma0 False 38.2020 4.4594 4.4594\n", "zheightgaus sigmaz TIED 0.4714 -99.0000 -99.0000\n", "geom_1 inc True 62.0000 -99.0000 -99.0000\n", "geom_1 pa True 142.0000 -99.0000 -99.0000\n", "geom_1 xshift True 0.0000 -99.0000 -99.0000\n", "geom_1 yshift True 0.0000 -99.0000 -99.0000\n", "geom_1 vel_shift True 0.0000 -99.0000 -99.0000\n", "mvirial ----- ----- 12.4927 -99.0000 -99.0000\n", "fit_status ----- ----- 1 -99.0000 -99.0000\n", "adiab_contr ----- ----- False -99.0000 -99.0000\n", "redchisq ----- ----- 1.8309 -99.0000 -99.0000\n", "noord_flat ----- ----- True -99.0000 -99.0000\n", "pressure_support ----- ----- True -99.0000 -99.0000\n", "pressure_support_type ----- ----- 1 -99.0000 -99.0000\n", "obs:OBS:apertures ----- ----- RectApertures -99.0000 -99.0000\n", "obs:OBS:moment ----- ----- False -99.0000 -99.0000\n", "obs:OBS:partial_weight ----- ----- True -99.0000 -99.0000\n" ] } ], "source": [ "with open(f_report_machine, 'r') as f:\n", " lines = [line.rstrip() for line in f]\n", " for line in lines: print(line)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.6" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }