In [1]:

    

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

%matplotlib inline


    

In [2]:

    

dark = ["#8c510a", "#01665e", "#762a83"]
med = ["#d8b365", "#5ab4ac", "#af8dc3"]


    

In [3]:

    

def color_variant(hex_color, brightness_offset=1):
    """ takes a color like #87c95f and produces a lighter or darker variant """
    if len(hex_color) != 7:
        raise Exception("Passed %s into color_variant(), needs to be in #87c95f format." % hex_color)
    rgb_hex = [hex_color[x:x+2] for x in [1, 3, 5]]
    new_rgb_int = [int(hex_value, 16) + brightness_offset for hex_value in rgb_hex]
    new_rgb_int = [min([255, max([0, i])]) for i in new_rgb_int] # make sure new values are between 0 and 255
    # hex() produces "0x88", we want just "88"
    return "#" + "".join([hex(i)[2:] for i in new_rgb_int])


    

In [4]:

    

color_variant("#d8b365", 120)


    

    
Out[4]:





'#ffffdd'

In [5]:

    

brwn = "#8c510a"
purp = "#762a83"
teal = "#01665e"
brwns = [color_variant(brwn, (i+2)*10) for i in range(0, 9)]
purps = [color_variant(purp, (i+1)*50) for i in range(0, 3)]
teals = [color_variant(teal, (i+2)*10) for i in range(0, 13)]


    

In [ ]:

    




    

In [47]:

    

pklfile = '../../prep-pkls/nucmoles_opusupdate_aug2019/not-scaled_fiss.pkl'
trainXY = pd.read_pickle(pklfile)
trainXY.reset_index(inplace=True, drop=True)


    

In [48]:

    

def splitXY(dfXY):
    lbls = ['ReactorType', 'CoolingTime', 'Enrichment', 'Burnup', 'OrigenReactor']
    dfX = dfXY.drop(lbls, axis=1)
    if 'total' in dfX.columns:
        dfX.drop('total', axis=1, inplace=True)
    r_dfY = dfXY.loc[:, lbls[0]]
    c_dfY = dfXY.loc[:, lbls[1]]
    e_dfY = dfXY.loc[:, lbls[2]]
    b_dfY = dfXY.loc[:, lbls[3]]
    o_dfY = dfXY.loc[:, lbls[4]]
    return dfX, r_dfY, c_dfY, e_dfY, b_dfY, o_dfY

X, rY, cY, eY, bY, oY = splitXY(trainXY)
yo = trainXY.OrigenReactor
yr = trainXY.ReactorType


    

In [49]:

    

trainXY.sample(10)


    

    
Out[49]:







  

    

      

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

    

      
4536
      
3774.59
      
0.500723
      
1.37
      
w17x17
      
pwr
      
1.1380
      
0.8909
      
0.9809
      
0.0
      
1.0460
      
...
      
0.4388
      
0.7382
      
1.340
      
1.967
      
0.3714
      
0.4169
      
0.9813
      
1.0400
      
1.0670
      
1.0640
    
    

      
19310
      
3244.15
      
0.392519
      
3.12
      
atrium10x10-9
      
bwr
      
0.9705
      
0.7917
      
0.8347
      
0.0
      
0.9050
      
...
      
0.3840
      
0.6403
      
1.146
      
1.612
      
0.3486
      
0.3959
      
0.8321
      
0.8836
      
0.9062
      
0.9087
    
    

      
22402
      
38325.64
      
5.114817
      
3.28
      
svea100-0
      
bwr
      
10.8300
      
10.1700
      
9.0040
      
0.0
      
9.6110
      
...
      
3.5540
      
9.9630
      
13.080
      
19.800
      
5.2870
      
6.7540
      
7.6480
      
8.4390
      
9.0260
      
9.4790
    
    

      
15282
      
22012.47
      
33.154909
      
3.97
      
ge10x10-8
      
bwr
      
6.3030
      
5.9580
      
5.3020
      
0.0
      
5.9970
      
...
      
2.4450
      
5.2650
      
7.578
      
10.960
      
3.3130
      
4.1690
      
4.7290
      
5.1710
      
5.4410
      
5.6510
    
    

      
19177
      
21794.04
      
23.238887
      
1.91
      
atrium10x10-9
      
bwr
      
6.3300
      
5.9580
      
5.3540
      
0.0
      
6.0340
      
...
      
2.4610
      
5.0990
      
7.568
      
11.190
      
3.4620
      
4.3150
      
4.9020
      
5.3200
      
5.5690
      
5.7110
    
    

      
4466
      
39382.50
      
29.382502
      
0.50
      
w17x17
      
pwr
      
11.2100
      
10.6500
      
9.3680
      
0.0
      
9.9720
      
...
      
3.6520
      
10.2300
      
13.520
      
21.670
      
5.6270
      
7.1340
      
8.0420
      
8.8380
      
9.4310
      
9.8200
    
    

      
2822
      
32754.54
      
4735.380227
      
3.07
      
w14x14
      
pwr
      
9.3060
      
8.7910
      
7.7810
      
0.0
      
8.5180
      
...
      
3.2750
      
8.3090
      
11.230
      
17.280
      
4.8240
      
6.1900
      
6.6950
      
7.3670
      
7.8430
      
8.2020
    
    

      
4075
      
31863.64
      
1.101951
      
4.10
      
w15x15
      
pwr
      
9.0150
      
8.3290
      
7.5300
      
0.0
      
8.1970
      
...
      
3.1520
      
8.0700
      
10.890
      
16.520
      
4.3370
      
5.5010
      
6.4410
      
7.1010
      
7.5680
      
7.9430
    
    

      
16144
      
3066.73
      
4214.608653
      
2.03
      
abb8x8-1
      
bwr
      
0.9261
      
0.8886
      
0.7982
      
0.0
      
0.9207
      
...
      
0.4013
      
0.6201
      
1.089
      
1.544
      
0.6245
      
0.7730
      
0.8031
      
0.8516
      
0.8710
      
0.8672
    
    

      
22494
      
4885.18
      
21.421788
      
4.01
      
svea100-0
      
bwr
      
1.4520
      
1.3430
      
1.2460
      
0.0
      
1.4440
      
...
      
0.6259
      
1.0150
      
1.719
      
2.426
      
0.7048
      
0.8259
      
1.2260
      
1.3090
      
1.3430
      
1.3540
    
  

10 rows × 49 columns

In [50]:

    

y = trainXY.ReactorType
lda = LinearDiscriminantAnalysis(n_components=2)
fit = lda.fit(X, y).transform(X)
fit_df = pd.DataFrame(fit, columns=['x1', 'x2'])
df = pd.concat([fit_df, y], axis=1)


    

In [51]:

    

plt.clf()
sns.set_style('white')
sns.set_context('talk')
#sns.set(rc={'figure.figsize':(9,8)})
g = sns.lmplot(x='x1', y='x2', hue="ReactorType", palette=med, data=df, fit_reg=False, scatter_kws={'alpha':0.2})
#g = sns.lmplot(x='x1', y='x2', hue="ReactorType", palette=med, data=df, legend=False, fit_reg=False, scatter_kws={'alpha':0.5})
#plt.legend(loc='upper right', markerscale=2)
#g._legend.set_title("Reactor Type")
for lh in g._legend.legendHandles: 
    lh.set_alpha(1)
#    lh.set_sizes([50]) 
#plt.ylim(-8, 8)
#plt.xlim(-8, 8)
plt.show()


    

    






<Figure size 432x288 with 0 Axes>






    

In [52]:

    

saveme = g.ax.get_figure()
saveme.savefig('lda-trainset_fiss.png', transparent=True)


    

In [8]:

    

lda = LinearDiscriminantAnalysis(n_components=3)
fit = lda.fit(X, yo).transform(X)


    

In [9]:

    

target_names = yo.unique()
target_names


    

    
Out[9]:





array(['ce14x14', 'ce16x16', 'w14x14', 'w15x15', 'w17x17', 's14x14',
       's18x18', 'bw15x15', 'vver1000', 'ge7x7-0', 'ge8x8-1', 'ge9x9-2',
       'ge10x10-8', 'abb8x8-1', 'atrium9x9-9', 'atrium10x10-9',
       'svea64-1', 'svea100-0', 'vver440', 'vver440_3.82', 'vver440_4.25',
       'vver440_4.38', 'candu19', 'candu28', 'candu37'], dtype=object)

In [10]:

    

plt.clf()


    

    






<Figure size 432x288 with 0 Axes>

In [11]:

    

colors = teals[0:9] + brwns + teals[9:13] + purps


    

In [12]:

    

#target_names = ['pwr', 'bwr', 'phwr']
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for color, target_name in zip(colors, target_names):
    ax.scatter(fit[yo == target_name, 0], fit[yo == target_name, 1], fit[yo == target_name, 2], 
                s=10, alpha=.8, color=color, label=target_name)
plt.legend(loc='right', bbox_to_anchor=(1.5, 0.5), shadow=False)
plt.title('LDA of SNF Dataset')
plt.show()


    

In [13]:

    

fit_df = pd.DataFrame(fit, columns=['x1', 'x2'])
df = pd.concat([fit_df, y], axis=1)


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~/.local/lib/python3.6/site-packages/pandas/core/internals/managers.py in create_block_manager_from_blocks(blocks, axes)
   1676                 blocks = [
-> 1677                     make_block(values=blocks[0], placement=slice(0, len(axes[0])))
   1678                 ]

~/.local/lib/python3.6/site-packages/pandas/core/internals/blocks.py in make_block(values, placement, klass, ndim, dtype, fastpath)
   3264 
-> 3265     return klass(values, ndim=ndim, placement=placement)
   3266 

~/.local/lib/python3.6/site-packages/pandas/core/internals/blocks.py in __init__(self, values, placement, ndim)
    127                 "Wrong number of items passed {val}, placement implies "
--> 128                 "{mgr}".format(val=len(self.values), mgr=len(self.mgr_locs))
    129             )

ValueError: Wrong number of items passed 3, placement implies 2

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-13-63a9163dccc4> in <module>
----> 1 fit_df = pd.DataFrame(fit, columns=['x1', 'x2'])
      2 df = pd.concat([fit_df, y], axis=1)

~/.local/lib/python3.6/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
    437                 mgr = init_dict({data.name: data}, index, columns, dtype=dtype)
    438             else:
--> 439                 mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
    440 
    441         # For data is list-like, or Iterable (will consume into list)

~/.local/lib/python3.6/site-packages/pandas/core/internals/construction.py in init_ndarray(values, index, columns, dtype, copy)
    211         block_values = [values]
    212 
--> 213     return create_block_manager_from_blocks(block_values, [columns, index])
    214 
    215 

~/.local/lib/python3.6/site-packages/pandas/core/internals/managers.py in create_block_manager_from_blocks(blocks, axes)
   1685         blocks = [getattr(b, "values", b) for b in blocks]
   1686         tot_items = sum(b.shape[0] for b in blocks)
-> 1687         construction_error(tot_items, blocks[0].shape[1:], axes, e)
   1688 
   1689 

~/.local/lib/python3.6/site-packages/pandas/core/internals/managers.py in construction_error(tot_items, block_shape, axes, e)
   1716         raise ValueError("Empty data passed with indices specified.")
   1717     raise ValueError(
-> 1718         "Shape of passed values is {0}, indices imply {1}".format(passed, implied)
   1719     )
   1720 

ValueError: Shape of passed values is (24795, 3), indices imply (24795, 2)

In [14]:

    

df.head()


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-14-c42a15b2c7cf> in <module>
----> 1 df.head()

NameError: name 'df' is not defined

In [15]:

    

ax = sns.scatterplot(x='x1', y='x2', hue="ReactorType", palette=med, data=df)


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-15-a87311ac2d6e> in <module>
----> 1 ax = sns.scatterplot(x='x1', y='x2', hue="ReactorType", palette=med, data=df)

NameError: name 'df' is not defined

In [ ]: