In [1]:

    

from __future__ import print_function, division

%matplotlib inline
import matplotlib.pyplot as plt

import seaborn as sns
sns.set(context="poster")

# from IPython.display import display, Math
from ipywidgets import interactive, interact_manual, \
                       RadioButtons, FloatSlider, fixed

import numpy as np

# from astropy.modeling import models, fitting

## Boilerplate path hack to give access to full clustered_SNe package
import sys, os
if __package__ is None:
    if os.pardir not in sys.path[0]:
        file_dir = os.getcwd()
        sys.path.insert(0, os.path.join(file_dir, 
                                        os.pardir, 
                                        os.pardir))

from clustered_SNe.analysis.visualize_helpers import parameter_study_wrapper
from clustered_SNe.analysis.thornton_helpers import ThorntonParameterStudy, \
                                                    energies


    

    




/Users/egentry/anaconda/lib/python3.4/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.
  "You should import from ipykernel or jupyter_client instead.", ShimWarning)

In [2]:

    

data_dir = "../saved_runs/thornton"
thornton_parameter_study = ThorntonParameterStudy(data_dir)


    

In [3]:

    

run_summary = RunSummary()
result_widget_parameter_study = interact_manual(parameter_study_wrapper,
                                data_dir = fixed(data_dir),
                                log_Z = FloatSlider(min=-3.0, max=+0.5, step=0.5, value=0),
                                log_n = FloatSlider(min=-3.0, max=+3.0, step=1.0, value=-1),
                                T     = fixed(1e4),
                                with_cooling = True)
def update_run_summary_parameter_study(widget):
    """Copy the latest result into run_summarya
    
    Don't reuse this function definition; needs to be bound to the correct result_widget"""
    run_summary.replace_with(result_widget_parameter_study.widget.result)
result_widget_parameter_study.widget.children[-1].on_click(update_run_summary_parameter_study)


    

In [4]:

    

thornton_parameter_study.df.head()


    

    
Out[4]:






  

    

      

      
background_density
      
background_temperature
      
metallicity
      
number_density
      
E_R_kin
      
E_R_int
      
E_R_tot
      
t_f
      
R_shock
    
  
  

    

      
0
      
2.224587e-21
      
10000
      
0.000020
      
1329.999845
      
6.505033e+49
      
2.374112e+49
      
8.879145e+49
      
6.855601e+11
      
1.023776e+19
    
    

      
1
      
2.224587e-21
      
10000
      
0.000632
      
1329.999845
      
6.929573e+49
      
1.998836e+49
      
8.928409e+49
      
5.750614e+11
      
9.414944e+18
    
    

      
2
      
2.224587e-22
      
10000
      
0.000020
      
132.999984
      
6.467735e+49
      
3.157304e+49
      
9.625039e+49
      
3.525105e+12
      
2.922131e+19
    
    

      
3
      
2.224587e-24
      
10000
      
0.000632
      
1.330000
      
6.024864e+49
      
8.219634e+49
      
1.424450e+50
      
3.427176e+13
      
1.988724e+20
    
    

      
4
      
2.224587e-23
      
10000
      
0.006325
      
13.299998
      
6.899039e+49
      
2.202135e+49
      
9.101174e+49
      
6.212072e+12
      
5.803480e+19
    
  

In [5]:

    

interactive(thornton_parameter_study.plot_one_number_density,
            energy = RadioButtons(options=energies))


    

    






$$E = 11.07 \cdot 10^{49} \cdot n_0^{ -0.08 } \text{ [ergs]}$$






    

In [6]:

    

interactive(thornton_parameter_study.plot_one_metallicity,
            energy = RadioButtons(options=energies))


    

    






$$E = 10.61 \cdot 10^{49} \cdot (Z / Z_\odot)^{ -0.22 } \text{ [ergs]}$$