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Compare $A_{sym}$ vs. $E_{min}$

I'm going to create a figure that compares the magnitude of anisotropy vs. energy threshold for the four data sets.

  • Measured
  • ipol
  • cgmf
  • freya

Each of the datap/Asym.csv spreadsheets were calculated using the script scripts/plot_Asym_energy_space.py.

Enable interactive plots





In [1]:



    


%matplotlib notebook



    











In [2]:



    


import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter, AutoMinorLocator)
import pandas as pd

Move up a directory for easier access





In [3]:



    


os.getcwd()



    


















    
Out[3]:








'C:\\Users\\pfsch\\Box Sync\\Projects\\fnpc\\analysis\\compare'

















In [4]:



    


os.chdir('..')



    











In [5]:



    


os.getcwd()



    


















    
Out[5]:








'C:\\Users\\pfsch\\Box Sync\\Projects\\fnpc\\analysis'

















In [6]:



    


sys.path.append('../scripts')



    











In [7]:



    


import bicorr_plot as bicorr_plot
import bicorr_math as bicorr_math



    











In [8]:



    


angle_bin_edges = np.arange(10.01,181,10)
angle_bin_centers = bicorr_math.calc_centers(angle_bin_edges)



    











In [9]:



    


angle_bin_edges.shape



    


















    
Out[9]:








(18,)

Find the other data





In [10]:



    


os.listdir('cgmf/datap')



    


















    
Out[10]:








['Asym_df.csv',
 'bhm_e.npz',
 'by_angle_e_df.csv',
 'det_df_e_filled.csv',
 'Esum_df.csv',
 'num_fissions.mat',
 'singles_e_df_filled.csv',
 'singles_hist.npz',
 'singles_hist_e_n.npz',
 'slices_analysis.npz',
 'sparse_bhm.npz']

















In [11]:



    


Asym_df_meas = pd.read_csv(r'Cf072115_to_Cf072215b/datap/Asym_df.csv', index_col=0)
Asym_df_cgmf = pd.read_csv(r'cgmf/datap/Asym_df.csv', index_col=0)
Asym_df_freya = pd.read_csv(r'freya/datap/Asym_df.csv', index_col=0)
Asym_df_ipol = pd.read_csv(r'ipol/datap/Asym_df.csv', index_col=0)
Asym_df_ipol_noct = pd.read_csv(r'ipol_noct/datap/Asym_df.csv', index_col=0)

Which do I want to plot on the same distribution?





In [45]:



    


Asym_dfs=[Asym_df_meas,Asym_df_cgmf,Asym_df_freya,Asym_df_ipol,Asym_df_ipol_noct]
legends =['Experiment', 'CGMF',      'FREYA',      'PoliMi',    'PoliMi-No CT']
fmts   = ['x',          's',         'D',          'o',         '^']
colors = ['#5d269b',    '#dd673b',   '#80bc31',    '#3cbfe0',   '#4242f4']

to_plot = [0,1,2,3]

General plot settings





In [46]:



    


line_thickness = 1
ebar_width     = 3

Plot as-is

These will be for Figure 7 in the paper.





In [47]:



    


Asym_df_cgmf.head()



    


















    
Out[47]:











  
    

      
      emin
      emax
      emin_real
      emax_real
      Asym
      Asym_err
      Asym_min
      Asym_min_err
    

  
  
    

      0
      1.0
      4
      4.0
      1.000
      1.889400
      0.090331
      1.912181
      0.102630
    

    

      1
      1.2
      4
      4.0
      1.175
      1.931323
      0.095439
      1.949794
      0.105606
    

    

      2
      1.4
      4
      4.0
      1.375
      1.994774
      0.108271
      2.015480
      0.120194
    

    

      3
      1.6
      4
      4.0
      1.575
      2.070092
      0.136786
      2.093130
      0.142185
    

    

      4
      1.8
      4
      4.0
      1.775
      2.183410
      0.172535
      2.218285
      0.178499
    

  




















In [58]:



    


fig = plt.figure(figsize=(4,4))
ax = plt.gca()

for i in to_plot:
    Asym_df = Asym_dfs[i]
    ax.errorbar(Asym_df['emin'],
                 Asym_df['Asym'],
                 yerr=Asym_df['Asym_err'],
                 fmt=fmts[i],
                 markeredgewidth=1,
                 markerfacecolor='none',
                 elinewidth=line_thickness,
                 capthick = line_thickness,
                 capsize = ebar_width,
                 c=colors[i])
ax.set_xlim([0.8,4])
ax.set_ylim([.5,4.5])
ax.set_xlabel('$E_{min}$ (MeV)')
ax.set_ylabel('$A_{sym}$')

# Set up ticks
ax.tick_params(axis='both',
               which='major',
               direction='inout',
               length=6,
               color='k',
               bottom=True, right=True, top=True, left=True)
ax.tick_params(axis='both',
               which='minor',
               direction='in',
               length=3,
               bottom=True, right=True, top=True, left=True)
# Major
ax.xaxis.set_major_locator(MultipleLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(1))
# Minor
ax.xaxis.set_minor_locator(MultipleLocator(.2))
ax.yaxis.set_minor_locator(MultipleLocator(0.2))

ax.text(1,4,'(a)', size=15, backgroundcolor='white')

leg = plt.legend([legends[i] for i in to_plot],bbox_to_anchor=(.46,.53))
leg.get_frame().set_edgecolor('w')

plt.tight_layout()
bicorr_plot.save_fig_to_folder('Asym_vs_Emin_compare',r'compare\fig')



    


















    




















    























In [16]:

Compare simulation to experiment





In [29]:



    


def plot_calcs(Asym_df):
    x = Asym_df['emin']
    y = Asym_df['Asym']
    yerr = Asym_df['Asym_err']
    return x, y, yerr



    











In [44]:



    


Asym_df_exp = Asym_dfs[0]
to_plot = [1,2,3]

fig = plt.figure(figsize=(4,4))
ax = plt.gca()

x, y_exp, yerr_exp = plot_calcs(Asym_df_exp)

for i in to_plot:
    Asym_df = Asym_dfs[i]
    x_sim, y_sim, yerr_sim = plot_calcs(Asym_df)
    y,yerr = bicorr_math.prop_err_division(y_sim,yerr_sim,y_exp,yerr_exp)    
    
    ax.errorbar(x, y, yerr=yerr,
                 fmt=fmts[i],
                 markeredgewidth=1,
                 markerfacecolor='none',
                 elinewidth=line_thickness,
                 capthick = line_thickness,
                 capsize = ebar_width,
                 c=colors[i])
leg = plt.legend([legends[i] for i in to_plot],bbox_to_anchor=(.4,.6))    
leg.get_frame().set_edgecolor('w')

plt.axhline(1.0,color='gray', linewidth=1,linestyle='--')
    
ax.set_xlim([0.8,4])
ax.set_ylim([.5,2])
ax.set_xlabel('$E_{min}$ (MeV)')
ax.set_ylabel(r'$\left[A_{sym}\right]_{SIM} / \left[A_{sym}\right]_{EXP}$')

# Set up ticks
ax.tick_params(axis='both',
               which='major',
               direction='inout',
               length=6,
               color='k',
               bottom=True, right=True, top=True, left=True)
ax.tick_params(axis='both',
               which='minor',
               direction='in',
               length=3,
               bottom=True, right=True, top=True, left=True)
# Major
ax.xaxis.set_major_locator(MultipleLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(.5))
# Minor
ax.xaxis.set_minor_locator(MultipleLocator(.2))
ax.yaxis.set_minor_locator(MultipleLocator(0.1))

ax.text(1,1.83,'(b)', size=15, backgroundcolor='white')

plt.tight_layout()
bicorr_plot.save_fig_to_folder('Asym_vs_Emin_diff',r'compare\fig')



    


















    




















    























In [18]:



    


os.getcwd()



    


















    
Out[18]:








'C:\\Users\\pfschus\\Box Sync\\Projects\\fnpc\\analysis'

Try $A_{sym} = W(180)/W_{min}$

Should we be comparing to the data at 90 degrees, or the minimum W values?





In [17]:



    


Asym_df



    


















    
Out[17]:











  
    

      
      emin
      emax
      emin_real
      emax_real
      Asym
      Asym_err
      Asym_min
      Asym_min_err
    

  
  
    

      0
      1.0
      4
      4.0
      1.000
      1.298544
      0.048786
      1.373203
      0.047016
    

    

      1
      1.2
      4
      4.0
      1.175
      1.311801
      0.050688
      1.387924
      0.047769
    

    

      2
      1.4
      4
      4.0
      1.375
      1.340514
      0.055990
      1.422138
      0.054524
    

    

      3
      1.6
      4
      4.0
      1.575
      1.377363
      0.059335
      1.460167
      0.061494
    

    

      4
      1.8
      4
      4.0
      1.775
      1.403279
      0.066703
      1.492027
      0.071245
    

    

      5
      2.0
      4
      4.0
      1.975
      1.435378
      0.080158
      1.532264
      0.081155
    

    

      6
      2.2
      4
      4.0
      2.175
      1.492052
      0.083743
      1.586427
      0.084978
    

    

      7
      2.4
      4
      4.0
      2.375
      1.528561
      0.105867
      1.622493
      0.108362
    

    

      8
      2.6
      4
      4.0
      2.575
      1.543597
      0.115115
      1.647768
      0.137667
    

    

      9
      2.8
      4
      4.0
      2.775
      1.547370
      0.137527
      1.660936
      0.168662
    

    

      10
      3.0
      4
      4.0
      2.975
      1.586722
      0.155809
      1.698385
      0.178737
    

    

      11
      3.2
      4
      4.0
      3.175
      1.646731
      0.230310
      1.816361
      0.277126
    

    

      12
      3.4
      4
      4.0
      3.375
      1.720735
      0.359721
      1.808602
      0.374548
    

    

      13
      3.6
      4
      4.0
      3.575
      1.875276
      0.575191
      1.972223
      0.622835
    

    

      14
      3.8
      4
      4.0
      3.775
      1.914871
      1.234394
      2.027582
      1.535520
    

  




















In [18]:



    


fig = plt.figure(figsize=(4,4))
ax = plt.gca()

for i in to_plot:
    Asym_df = Asym_dfs[i]
    ax.errorbar(Asym_df['emin'],
                 Asym_df['Asym_min'],
                 yerr=Asym_df['Asym_min_err'],
                 fmt=fmts[i],
                 markeredgewidth=1,
                 markerfacecolor='none',
                 elinewidth=line_thickness,
                 capthick = line_thickness,
                 capsize = ebar_width,
                 c=colors[i])
ax.set_xlim([0,4])
ax.set_ylim([.5,4.5])
ax.set_xlabel('$E_{min}$')
ax.set_ylabel('$A_{sym}$')

# Set up ticks
ax.tick_params(axis='both',
               which='major',
               direction='inout',
               length=6,
               color='k',
               bottom=True, right=True, top=True, left=True)
ax.tick_params(axis='both',
               which='minor',
               direction='in',
               length=3,
               bottom=True, right=True, top=True, left=True)
# Major
ax.xaxis.set_major_locator(MultipleLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(1))
# Minor
ax.xaxis.set_minor_locator(MultipleLocator(.2))
ax.yaxis.set_minor_locator(MultipleLocator(0.2))

plt.legend([legends[i] for i in to_plot])
plt.tight_layout()



    


















    




















    























In [18]:



    


bicorr_plot.save_fig_to_folder('Asym_min_vs_Emin_compare',r'compare\fig')

Try plotting everything together





In [19]:



    


fig = plt.figure(figsize=(6,6))
ax = plt.gca()

for i in to_plot:
    Asym_df = Asym_dfs[i]
    ax.errorbar(Asym_df['emin'],
                 Asym_df['Asym'],
                 yerr=Asym_df['Asym_err'],
                 fmt=fmts[i],
                 markeredgewidth=1,
                 markerfacecolor='k',
                 elinewidth=line_thickness,
                 capthick = line_thickness,
                 capsize = ebar_width,
                 c=colors[i])
    ax.errorbar(Asym_df['emin'],
                 Asym_df['Asym_min'],
                 yerr=Asym_df['Asym_min_err'],
                 fmt=fmts[i],
                 markeredgewidth=1,
                 markerfacecolor='none',
                 elinewidth=line_thickness,
                 capthick = line_thickness,
                 capsize = ebar_width,
                 c=colors[i])
ax.set_xlim([0,4])
ax.set_ylim([1,4])
ax.set_xlabel('$E_{min}$')
ax.set_ylabel('$A_{sym}$')

# Set up ticks
ax.tick_params(axis='both',
               which='major',
               direction='inout',
               length=6,
               color='k',
               bottom=True, right=True, top=True, left=True)
ax.tick_params(axis='both',
               which='minor',
               direction='in',
               length=3,
               bottom=True, right=True, top=True, left=True)
# Major
ax.xaxis.set_major_locator(MultipleLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(1))
# Minor
ax.xaxis.set_minor_locator(MultipleLocator(.2))
ax.yaxis.set_minor_locator(MultipleLocator(0.2))

plt.legend([legends[i] for i in np.repeat(to_plot,2).tolist()])
plt.tight_layout()



    


















    




















    























In [27]:



    


np.repeat(to_plot,2).tolist()



    


















    
Out[27]:








[0, 0, 1, 1, 2, 2, 3, 3, 4, 4]

















In [ ]:

Content source: pfschus/fission_bicorrelation

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