Preliminary 2 Results

The code appears to be running so it is now possible to obtain some prelimiary results for the base set of paramters to investigate, $E_{\text{APPLIED}}$=-0.16, -0.18, -0.20, -0.22, -0.24, -0.26, -0.28, -0.30; $c_{\theta}^{\infty}$=0.006, 0.012



In [22]:

    
from extremefill2D.fextreme.tools import get_treant_df, get_by_tags, get_by_uuid, fcompose
import pandas
from extremefill2D.fextreme.plot import vega_plot_treant
from extremefill2D.fextreme import read_json
from toolz.curried import map, pipe
from pprint import pprint
from extremefill2D.fextreme.plot import vega_plot_treants



In [23]:

    
df = get_treant_df(['prelim2'], path='../../data')



In [24]:

    
df_sort = df.sort(columns=['bulkSuppressor', 'appliedPotential'])
df_sort









    



/home/wd15/anaconda/envs/extreme/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
  if __name__ == '__main__':






    Out[24]:






  
    
      
      appliedPotential
      bulkSuppressor
      tags
      uuid
    
  
  
    
      22
      -0.80
      0.003
      [prelim2]
      334dee67
    
    
      6
      -0.70
      0.003
      [prelim2]
      642c8b55
    
    
      25
      -0.70
      0.003
      [prelim2]
      c3437f85
    
    
      10
      -0.60
      0.003
      [prelim2]
      bc3cacdb
    
    
      2
      -0.50
      0.003
      [prelim2]
      8b7eeba9
    
    
      3
      -0.40
      0.003
      [prelim2]
      0bc89986
    
    
      0
      -0.30
      0.003
      [prelim2]
      63d2cfbc
    
    
      16
      -0.28
      0.003
      [prelim2]
      2e05eb8e
    
    
      24
      -0.26
      0.003
      [prelim2]
      1b300121
    
    
      12
      -0.24
      0.003
      [prelim2]
      80663650
    
    
      8
      -0.22
      0.003
      [prelim2]
      280a1451
    
    
      7
      -0.20
      0.003
      [prelim2]
      e677558a
    
    
      5
      -0.18
      0.003
      [prelim2]
      1b23b0b8
    
    
      1
      -0.16
      0.003
      [prelim2]
      0d9a570c
    
    
      21
      -0.80
      0.006
      [prelim2]
      24365f73
    
    
      23
      -0.70
      0.006
      [prelim2]
      b22e3837
    
    
      15
      -0.60
      0.006
      [prelim2]
      950f7398
    
    
      17
      -0.50
      0.006
      [prelim2]
      7153746e
    
    
      4
      -0.40
      0.006
      [prelim2]
      bdb08a0e
    
    
      11
      -0.30
      0.006
      [prelim2]
      d3a85632
    
    
      20
      -0.80
      0.012
      [prelim2]
      795b55ce
    
    
      13
      -0.70
      0.012
      [prelim2]
      97e9f836
    
    
      19
      -0.60
      0.012
      [prelim2]
      d8ae1960
    
    
      9
      -0.50
      0.012
      [prelim2]
      4ce42ce6
    
    
      14
      -0.40
      0.012
      [prelim2]
      6959ec1f
    
    
      18
      -0.30
      0.012
      [prelim2]
      b901726f



In [25]:

    
df_to_treants = fcompose(
    lambda x: x.uuid,
    map(get_by_uuid(path='../../data')),
    list
)

$k^+$ = 0.006



In [26]:

    
df1 = df[df.bulkSuppressor == 0.006].sort(columns=['appliedPotential'])
df1









    



/home/wd15/anaconda/envs/extreme/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
  if __name__ == '__main__':






    Out[26]:






  
    
      
      appliedPotential
      bulkSuppressor
      tags
      uuid
    
  
  
    
      21
      -0.8
      0.006
      [prelim2]
      24365f73
    
    
      23
      -0.7
      0.006
      [prelim2]
      b22e3837
    
    
      15
      -0.6
      0.006
      [prelim2]
      950f7398
    
    
      17
      -0.5
      0.006
      [prelim2]
      7153746e
    
    
      4
      -0.4
      0.006
      [prelim2]
      bdb08a0e
    
    
      11
      -0.3
      0.006
      [prelim2]
      d3a85632



In [27]:

    
out = vega_plot_treants(df_to_treants(df1))
out.display()

$k^+$ = 0.012



In [28]:

    
df2 = df[df.bulkSuppressor == 0.012].sort(columns=['appliedPotential'])
df2









    



/home/wd15/anaconda/envs/extreme/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
  if __name__ == '__main__':






    Out[28]:






  
    
      
      appliedPotential
      bulkSuppressor
      tags
      uuid
    
  
  
    
      20
      -0.8
      0.012
      [prelim2]
      795b55ce
    
    
      13
      -0.7
      0.012
      [prelim2]
      97e9f836
    
    
      19
      -0.6
      0.012
      [prelim2]
      d8ae1960
    
    
      9
      -0.5
      0.012
      [prelim2]
      4ce42ce6
    
    
      14
      -0.4
      0.012
      [prelim2]
      6959ec1f
    
    
      18
      -0.3
      0.012
      [prelim2]
      b901726f



In [29]:

    
out = vega_plot_treants(df_to_treants(df2))
out.display()

$k^+$=0003



In [30]:

    
df3 = df[df.bulkSuppressor == 0.003].sort(columns=['appliedPotential'])
df3









    



/home/wd15/anaconda/envs/extreme/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
  if __name__ == '__main__':






    Out[30]:






  
    
      
      appliedPotential
      bulkSuppressor
      tags
      uuid
    
  
  
    
      22
      -0.80
      0.003
      [prelim2]
      334dee67
    
    
      6
      -0.70
      0.003
      [prelim2]
      642c8b55
    
    
      25
      -0.70
      0.003
      [prelim2]
      c3437f85
    
    
      10
      -0.60
      0.003
      [prelim2]
      bc3cacdb
    
    
      2
      -0.50
      0.003
      [prelim2]
      8b7eeba9
    
    
      3
      -0.40
      0.003
      [prelim2]
      0bc89986
    
    
      0
      -0.30
      0.003
      [prelim2]
      63d2cfbc
    
    
      16
      -0.28
      0.003
      [prelim2]
      2e05eb8e
    
    
      24
      -0.26
      0.003
      [prelim2]
      1b300121
    
    
      12
      -0.24
      0.003
      [prelim2]
      80663650
    
    
      8
      -0.22
      0.003
      [prelim2]
      280a1451
    
    
      7
      -0.20
      0.003
      [prelim2]
      e677558a
    
    
      5
      -0.18
      0.003
      [prelim2]
      1b23b0b8
    
    
      1
      -0.16
      0.003
      [prelim2]
      0d9a570c



In [31]:

    
out = vega_plot_treants(df_to_treants(df3))
out.display()



In [ ]:

	appliedPotential	bulkSuppressor	tags	uuid
22	-0.80	0.003	[prelim2]	334dee67
6	-0.70	0.003	[prelim2]	642c8b55
25	-0.70	0.003	[prelim2]	c3437f85
10	-0.60	0.003	[prelim2]	bc3cacdb
2	-0.50	0.003	[prelim2]	8b7eeba9
3	-0.40	0.003	[prelim2]	0bc89986
0	-0.30	0.003	[prelim2]	63d2cfbc
16	-0.28	0.003	[prelim2]	2e05eb8e
24	-0.26	0.003	[prelim2]	1b300121
12	-0.24	0.003	[prelim2]	80663650
8	-0.22	0.003	[prelim2]	280a1451
7	-0.20	0.003	[prelim2]	e677558a
5	-0.18	0.003	[prelim2]	1b23b0b8
1	-0.16	0.003	[prelim2]	0d9a570c
21	-0.80	0.006	[prelim2]	24365f73
23	-0.70	0.006	[prelim2]	b22e3837
15	-0.60	0.006	[prelim2]	950f7398
17	-0.50	0.006	[prelim2]	7153746e
4	-0.40	0.006	[prelim2]	bdb08a0e
11	-0.30	0.006	[prelim2]	d3a85632
20	-0.80	0.012	[prelim2]	795b55ce
13	-0.70	0.012	[prelim2]	97e9f836
19	-0.60	0.012	[prelim2]	d8ae1960
9	-0.50	0.012	[prelim2]	4ce42ce6
14	-0.40	0.012	[prelim2]	6959ec1f
18	-0.30	0.012	[prelim2]	b901726f

	appliedPotential	bulkSuppressor	tags	uuid
21	-0.8	0.006	[prelim2]	24365f73
23	-0.7	0.006	[prelim2]	b22e3837
15	-0.6	0.006	[prelim2]	950f7398
17	-0.5	0.006	[prelim2]	7153746e
4	-0.4	0.006	[prelim2]	bdb08a0e
11	-0.3	0.006	[prelim2]	d3a85632