notebook.community

Edit and run

European Extremely Large Telescope site selection

a comparison between real selection and multicriteria-decision-analysis suggestions

Juan B Cabral – Bruno O Sanchez – Manuel Starck Cuffini

Instituto de Astronomía Teórica y Experimental

jbcabral@oac.unc.edu.ar- bruno@oac.unc.edu.ar- mstarck@oac.unc.edu.ar

Data Mangling





In [1]:



    


import pandas as pd

import numpy as np

import skcriteria as sc
from skcriteria.madm import topsis, wsum, moora, wprod, electre



    











In [2]:



    


df = pd.read_csv("sites.csv")[:-3]
df



    


















    
Out[2]:










  
    

      
      Criteria/Alternatives
      Criteria
      Armazones
      Aklim
      ORM
      Ventarrones
      Macon
    

  
  
    

      0
      Final Status
      -
      Selected
      NaN
      Reference North
      Reference South
      NaN
    

    

      1
      Hemisphere
      -
      S
      N
      N
      S
      S
    

    

      2
      Location
      -
      Chile
      Morroco
      Canarias (Spain)
      Chile
      Argentina
    

    

      3
      Altitude (AMSL)
      max
      3064
      2350
      2346
      2837
      4653
    

    

      4
      Total seeing ε (′′)
      min
      0.64
      1.00
      0.80
      0.91
      0.87
    

    

      5
      Isoplanatic angle θ 0 (′′)
      max
      2.04
      1.29
      1.93
      1.96
      1.37
    

    

      6
      Coherence time τ 0 (ms)
      max
      4.6
      3.53
      5.58
      4.90
      3.37
    

    

      7
      Optical étendue G 0 (m 2 ms arcsec 2 )
      min
      NaN
      0.05
      0.38
      0.26
      0.10
    

    

      8
      Free-atmosphere seeing (′′) ε FA
      max
      NaN
      0.52
      0.31
      0.55
      0.66
    

    

      9
      Boundary-layer seeing (′′) ε BL
      NaN
      NaN
      0.77
      0.65
      0.60
      0.51
    

    

      10
      Cloud: clear fraction (%)
      max
      89
      76
      84
      85
      75
    

    

      11
      Night temperature at 2 m (°C)
      min
      7.5
      12.5
      7.3
      10.9
      -0.2
    

    

      12
      Night relative humidity at 2 m (%)
      min
      21
      32
      21
      14
      20
    

    

      13
      Night wind speed at 10 m (m=s)
      min
      7.2*
      6.2
      8.2
      5.9
      11.3
    

    

      14
      Night pressure at 2 m
      min
      NaN
      767.0
      772.4
      727.0
      581.8
    

  




















In [3]:



    


anames = df.columns[2:].values

def to_apply(r):
    new = []
    for e in r:
        if isinstance(e , str):
            e = float(e.replace("*", ""))
        new.append(e)
    return new

mtx = df[anames][3:].dropna()[anames].apply(to_apply).T.values
criteria = [sc.MIN if c == "min" else sc.MAX for c in df.Criteria[3:][~df.Armazones.isnull()].values]

cnames = df["Criteria/Alternatives"][3:].apply(lambda r: r.strip())[~df[3:].Armazones.isnull()].values
cnames = map(lambda s: s.decode("utf8"), cnames)

data = sc.Data(mtx, criteria, anames=anames, cnames=cnames)

Final Data





In [4]:



    


data



    


















    
Out[4]:










 ALT./CRIT.  Altitude (AMSL) (max)  Total seeing ε (′′) (min)  Isoplanatic angle θ 0 (′′) (max)  Coherence time τ 0 (ms) (max)  Cloud: clear fraction (%) (max)  Night temperature at 2 m (°C) (min)  Night relative humidity at 2 m (%) (min)  Night wind speed at 10 m (m=s) (min) 




 Armazones           3064                     0.64                           2.04                             4.6                             89                                 7.5                                     21                                     7.2                  


   Aklim             2350                       1                            1.29                            3.53                             76                                12.5                                     32                                     6.2                  


    ORM              2346                      0.8                           1.93                            5.58                             84                                 7.3                                     21                                     8.2                  


Ventarrones          2837                     0.91                           1.96                             4.9                             85                                10.9                                     14                                     5.9                  


   Macon             4653                     0.87                           1.37                            3.37                             75                                -0.2                                     20                                     11.3

TOPSIS

https://en.wikipedia.org/wiki/TOPSIS





In [5]:



    


dm = topsis.TOPSIS()
topsis_dec = dm.decide(data)
topsis_dec



    


















    
Out[5]:







TOPSIS (mnorm=vector, wnorm=sum) - Solution:



 ALT./CRIT.  Altitude (AMSL) (max)  Total seeing ε (′′) (min)  Isoplanatic angle θ 0 (′′) (max)  Coherence time τ 0 (ms) (max)  Cloud: clear fraction (%) (max)  Night temperature at 2 m (°C) (min)  Night relative humidity at 2 m (%) (min)  Night wind speed at 10 m (m=s) (min)  Rank 




 Armazones           3064                     0.64                           2.04                             4.6                             89                                 7.5                                     21                                     7.2                    2   


   Aklim             2350                       1                            1.29                            3.53                             76                                12.5                                     32                                     6.2                    5   


    ORM              2346                      0.8                           1.93                            5.58                             84                                 7.3                                     21                                     8.2                    3   


Ventarrones          2837                     0.91                           1.96                             4.9                             85                                10.9                                     14                                     5.9                    4   


   Macon             4653                     0.87                           1.37                            3.37                             75                                -0.2                                     20                                     11.3                   1

Weighted Sum Model

https://en.wikipedia.org/wiki/Weighted_sum_model





In [6]:



    


dm = wsum.MDWeightedSum()
wsum_dec = dm.decide(data)
wsum_dec



    


















    
Out[6]:







MDWeightedSum (mnorm=sum, wnorm=sum) - Solution:



 ALT./CRIT.  Altitude (AMSL) (max)  Total seeing ε (′′) (min)  Isoplanatic angle θ 0 (′′) (max)  Coherence time τ 0 (ms) (max)  Cloud: clear fraction (%) (max)  Night temperature at 2 m (°C) (min)  Night relative humidity at 2 m (%) (min)  Night wind speed at 10 m (m=s) (min)  Rank 




 Armazones           3064                     0.64                           2.04                             4.6                             89                                 7.5                                     21                                     7.2                    1   


   Aklim             2350                       1                            1.29                            3.53                             76                                12.5                                     32                                     6.2                    5   


    ORM              2346                      0.8                           1.93                            5.58                             84                                 7.3                                     21                                     8.2                    4   


Ventarrones          2837                     0.91                           1.96                             4.9                             85                                10.9                                     14                                     5.9                    3   


   Macon             4653                     0.87                           1.37                            3.37                             75                                -0.2                                     20                                     11.3                   2

Weighted Product

https://en.wikipedia.org/wiki/Weighted_product_model





In [7]:



    


dm = wprod.WeightedProduct()
wprod_dec = dm.decide(data)
wprod_dec



    


















    
Out[7]:







WeightedProduct (mnorm=sum, wnorm=sum) - Solution:



 ALT./CRIT.  Altitude (AMSL) (max)  Total seeing ε (′′) (min)  Isoplanatic angle θ 0 (′′) (max)  Coherence time τ 0 (ms) (max)  Cloud: clear fraction (%) (max)  Night temperature at 2 m (°C) (min)  Night relative humidity at 2 m (%) (min)  Night wind speed at 10 m (m=s) (min)  Rank 




 Armazones           3064                     0.64                           2.04                             4.6                             89                                 7.5                                     21                                     7.2                    2   


   Aklim             2350                       1                            1.29                            3.53                             76                                12.5                                     32                                     6.2                    5   


    ORM              2346                      0.8                           1.93                            5.58                             84                                 7.3                                     21                                     8.2                    4   


Ventarrones          2837                     0.91                           1.96                             4.9                             85                                10.9                                     14                                     5.9                    3   


   Macon             4653                     0.87                           1.37                            3.37                             75                                -0.2                                     20                                     11.3                   1

ELECTRE 1

https://en.wikipedia.org/wiki/ELECTRE





In [8]:



    


dm = electre.ELECTRE1()
electre_dec = dm.decide(data)
electre_dec



    


















    
Out[8]:







ELECTRE1 (mnorm=sum, p=0.65, q=0.35, wnorm=sum) - Solution:



 ALT./CRIT.  Altitude (AMSL) (max)  Total seeing ε (′′) (min)  Isoplanatic angle θ 0 (′′) (max)  Coherence time τ 0 (ms) (max)  Cloud: clear fraction (%) (max)  Night temperature at 2 m (°C) (min)  Night relative humidity at 2 m (%) (min)  Night wind speed at 10 m (m=s) (min)  Kernel 




 Armazones           3064                     0.64                           2.04                             4.6                             89                                 7.5                                     21                                     7.2                          


   Aklim             2350                       1                            1.29                            3.53                             76                                12.5                                     32                                     6.2                          


    ORM              2346                      0.8                           1.93                            5.58                             84                                 7.3                                     21                                     8.2                          


Ventarrones          2837                     0.91                           1.96                             4.9                             85                                10.9                                     14                                     5.9                          


   Macon             4653                     0.87                           1.37                            3.37                             75                                -0.2                                     20                                     11.3                    @

Results





In [9]:



    


methods = ["TOPSIS", "WSUM", "WPROD"]
kernel = np.array([1 if idx  in electre_dec.kernel_ else 0 for idx, _ in enumerate(anames)])
ranks = np.vstack((topsis_dec.rank_, wsum_dec.rank_, wprod_dec.rank_, kernel)).T
rdf = pd.DataFrame(ranks, index=anames, columns=methods + ["ELECTRE 1 Kernel"])
rdf



    


















    
Out[9]:










  
    

      
      TOPSIS
      WSUM
      WPROD
      ELECTRE 1 Kernel
    

  
  
    

      Armazones
      2
      1
      2
      0
    

    

      Aklim
      5
      5
      5
      0
    

    

      ORM
      3
      4
      4
      0
    

    

      Ventarrones
      4
      3
      3
      0
    

    

      Macon
      1
      2
      1
      1
    

  




















In [10]:



    


rdf[methods].T.describe()



    


















    
Out[10]:










  
    

      
      Armazones
      Aklim
      ORM
      Ventarrones
      Macon
    

  
  
    

      count
      3.000000
      3.0
      3.000000
      3.000000
      3.000000
    

    

      mean
      1.666667
      5.0
      3.666667
      3.333333
      1.333333
    

    

      std
      0.577350
      0.0
      0.577350
      0.577350
      0.577350
    

    

      min
      1.000000
      5.0
      3.000000
      3.000000
      1.000000
    

    

      25%
      1.500000
      5.0
      3.500000
      3.000000
      1.000000
    

    

      50%
      2.000000
      5.0
      4.000000
      3.000000
      1.000000
    

    

      75%
      2.000000
      5.0
      4.000000
      3.500000
      1.500000
    

    

      max
      2.000000
      5.0
      4.000000
      4.000000
      2.000000

Content source: toros-astro/epio2017_EELT_MCDM

Similar notebooks:

notebook.community | gallery | about