In [3]:

    

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable


    

In [10]:

    

trainXY = pd.read_pickle("../pkl_trainsets/23may2018/not-scaled_trainset_nucs_fissact_8dec.pkl")
trainXY.head()


    

    
Out[10]:







  

    

      

      
Burnup
      
CoolingTime
      
Enrichment
      
OrigenReactor
      
ReactorType
      
ba138
      
ce140
      
ce142
      
ce144
      
cs133
      
...
      
xe131
      
xe132
      
xe134
      
xe136
      
y89
      
zr91
      
zr92
      
zr93
      
zr94
      
zr96
    
  
  

    

      
0.000e+00
      
0
      
0.000000
      
1.5
      
ce14x14
      
pwr
      
0.0000
      
0.0000
      
0.0000
      
0.0
      
0.0000
      
...
      
0.0
      
0.0000
      
0.000
      
0.00
      
0.0000
      
0.0000
      
0.0000
      
0.000
      
0.0000
      
0.0000
    
    

      
1.000e+02.1
      
3000
      
0.000694
      
1.5
      
ce14x14
      
pwr
      
0.9070
      
0.6846
      
0.7816
      
0.0
      
0.8243
      
...
      
0.0
      
0.5795
      
1.066
      
1.54
      
0.2827
      
0.3157
      
0.7848
      
0.830
      
0.8538
      
0.8497
    
    

      
1.070e+02
      
3000
      
7.000000
      
1.5
      
ce14x14
      
pwr
      
0.9074
      
0.7367
      
0.7824
      
0.0
      
0.8716
      
...
      
0.0
      
0.6014
      
1.067
      
1.54
      
0.3129
      
0.3509
      
0.7876
      
0.835
      
0.8539
      
0.8497
    
    

      
1.300e+02
      
3000
      
30.000000
      
1.5
      
ce14x14
      
pwr
      
0.9074
      
0.8212
      
0.7824
      
0.0
      
0.9029
      
...
      
0.0
      
0.6075
      
1.067
      
1.54
      
0.3940
      
0.4481
      
0.7876
      
0.835
      
0.8539
      
0.8497
    
    

      
4.653e+02
      
3000
      
365.250000
      
1.5
      
ce14x14
      
pwr
      
0.9074
      
0.8552
      
0.7824
      
0.0
      
0.9045
      
...
      
0.0
      
0.6076
      
1.067
      
1.54
      
0.6106
      
0.7524
      
0.7876
      
0.835
      
0.8539
      
0.8497
    
  

5 rows × 52 columns

In [11]:

    

x = len(trainXY.columns)
dfX = trainXY.iloc[:, 5:x]
dfY_b = trainXY.iloc[:, 0] # burnup
dfY_c = trainXY.iloc[:, 1] # cooling time
dfY_e = trainXY.iloc[:, 2] # enrichment
dfY_o = trainXY.iloc[:, 3] # origen rxtr
dfY_r = trainXY.iloc[:, 4] # rxtr type


    

In [12]:

    

x = dfY_b
y = dfY_e
df = pd.DataFrame({'Burnup [MW/MTHM]': x, 'Enrichmnent [Weight% U235]': y}, 
                  columns=['Burnup [MW/MTHM]', 'Enrichmnent [Weight% U235]'])
#bin stuff
xbin = 1500
ybin = 0.15
xmax = np.max(np.abs(x))
ymax = np.max(np.abs(y))
xlim = (int(xmax/xbin) + 1)*xbin
ylim = (int(ymax/ybin) + 1)*ybin
xbins = np.arange(0, xlim + xbin, xbin)
ybins = np.arange(0, ylim + ybin, ybin)
xcount, xdivs = np.histogram(x, bins=xbins)
ycount, ydivs = np.histogram(y, bins=ybins)


    

In [13]:

    

fig, axScatter = plt.subplots(figsize=(5.5, 5.5))
axScatter.scatter(x, y, s=ycount*0.25, alpha=0.5)
plt.show()


    

In [14]:

    

fig.clf()
fig, axScatter = plt.subplots(figsize=(7.5,7.5))
axScatter.scatter(x, y)
divider = make_axes_locatable(axScatter)
axHistx = divider.append_axes("top", 1.2, pad=0.1, sharex=axScatter)
axHisty = divider.append_axes("right", 1.2, pad=0.1, sharey=axScatter)
axHistx.hist(x, bins=xbins)
axHisty.hist(y, bins=ybins, orientation='horizontal')
#plt.draw()
plt.show()


    

In [14]:

    

#axHistx.set_yticks([0, 50, 100])
#axHisty.set_xticks([0, 50, 100])


    

In [26]:

    

fig.clf()
fig, ax = plt.subplots(figsize=(5.5, 5.5))
scatcolor = ax.scatter(x, y, cmap='inferno')
ax.axis([0, xmax, 0, ymax])
cb = fig.colorbar(scatcolor, ax=ax)
cb.set_label('Count of Instances')


    

    




---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-26-e909243a21a8> in <module>()
      3 scatcolor = ax.scatter(x, y, cmap='inferno')
      4 ax.axis([0, xmax, 0, ymax])
----> 5 cb = fig.colorbar(scatcolor, ax=ax)
      6 cb.set_label('Count of Instances')

~\AppData\Local\Continuum\anaconda3\lib\site-packages\matplotlib\figure.py in colorbar(self, mappable, cax, ax, use_gridspec, **kw)
   1842                 cax, kw = cbar.make_axes(ax, **kw)
   1843         cax._hold = True
-> 1844         cb = cbar.colorbar_factory(cax, mappable, **kw)
   1845 
   1846         self.sca(current_ax)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\matplotlib\colorbar.py in colorbar_factory(cax, mappable, **kwargs)
   1363         cb = ColorbarPatch(cax, mappable, **kwargs)
   1364     else:
-> 1365         cb = Colorbar(cax, mappable, **kwargs)
   1366 
   1367     cid = mappable.callbacksSM.connect('changed', cb.on_mappable_changed)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\matplotlib\colorbar.py in __init__(self, ax, mappable, **kw)
    916         # Ensure the given mappable's norm has appropriate vmin and vmax set
    917         # even if mappable.draw has not yet been called.
--> 918         mappable.autoscale_None()
    919 
    920         self.mappable = mappable

~\AppData\Local\Continuum\anaconda3\lib\site-packages\matplotlib\cm.py in autoscale_None(self)
    346         """
    347         if self._A is None:
--> 348             raise TypeError('You must first set_array for mappable')
    349         self.norm.autoscale_None(self._A)
    350         self.changed()

TypeError: You must first set_array for mappable

In [ ]: