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refine_weight_percentage [2015/10/29 06:24]
rowlesmr3
refine_weight_percentage [2015/10/29 06:25]
rowlesmr3
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 Macro to aid in the setting or refining of weight percentages. The scale factors are calculated from the given weight fractions. Weight fractions can be either fixed or refined. Macro to aid in the setting or refining of weight percentages. The scale factors are calculated from the given weight fractions. Weight fractions can be either fixed or refined.
  
-The macro can handle up to 10 phases. The equations were calculated in Maple from the standard Hill/Howard quantification algorithm.+The macro can handle up to phases. The equations were calculated in Maple from the standard Hill/Howard quantification algorithm.
  
 Contributors:​ Matthew Rowles, with [[refining_setting_weight_percents_directly|help from Alan]] Contributors:​ Matthew Rowles, with [[refining_setting_weight_percents_directly|help from Alan]]
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 xdd xdd
    prm s1 0.0035    prm s1 0.0035
-   prm w1 = 100 - w2 - w3 - w4;+   prm w1 = 100 - w2 - w3 - w4; : 0 ''​this will just report the value
    prm !w2 20    prm !w2 20
    prm w3 30    prm w3 30
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    prm x##n = w##n / (m##n (100 - w##n));    prm x##n = w##n / (m##n (100 - w##n));
        
-   '​define scale prms {{{ 
        
-   #m_if p == 2; 'there are 2 phases in total+   #m_if p == 2; ''there are 2 phases in total
    ​ #​m_if n == 1;    ​ #​m_if n == 1;
-   ​ '​do nothing, as s1 should already be defined elsewhere+    ''do nothing, as s1 should already be defined elsewhere
    ​ #​m_elseif n == 2;    ​ #​m_elseif n == 2;
     prm s##n = x2*s1*m1;     prm s##n = x2*s1*m1;
    ​ #​m_endif    ​ #​m_endif
         
-   #​m_elseif p == 3; 'there are three phases in total+   #​m_elseif p == 3; ''there are three phases in total
    ​ #​m_if n == 1;    ​ #​m_if n == 1;
     prm m_denom = (m2*m3*x2*x3-1);​     prm m_denom = (m2*m3*x2*x3-1);​
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     prm s##n = -m1*s1*x8*(m2*m3*m4*m5*m6*m7*x2*x3*x4*x5*x6*x7+m2*m3*m4*m5*m6*x2*x3*x4*x5*x6+m2*m3*m4*m5*m7*x2*x3*x4*x5*x7+m2*m3*m4*m6*m7*x2*x3*x4*x6*x7+m2*m3*m5*m6*m7*x2*x3*x5*x6*x7+m2*m4*m5*m6*m7*x2*x4*x5*x6*x7+m3*m4*m5*m6*m7*x3*x4*x5*x6*x7+m2*m3*m4*m5*x2*x3*x4*x5+m2*m3*m4*m6*x2*x3*x4*x6+m2*m3*m4*m7*x2*x3*x4*x7+m2*m3*m5*m6*x2*x3*x5*x6+m2*m3*m5*m7*x2*x3*x5*x7+m2*m3*m6*m7*x2*x3*x6*x7+m2*m4*m5*m6*x2*x4*x5*x6+m2*m4*m5*m7*x2*x4*x5*x7+m2*m4*m6*m7*x2*x4*x6*x7+m2*m5*m6*m7*x2*x5*x6*x7+m3*m4*m5*m6*x3*x4*x5*x6+m3*m4*m5*m7*x3*x4*x5*x7+m3*m4*m6*m7*x3*x4*x6*x7+m3*m5*m6*m7*x3*x5*x6*x7+m4*m5*m6*m7*x4*x5*x6*x7+m2*m3*m4*x2*x3*x4+m2*m3*m5*x2*x3*x5+m2*m3*m6*x2*x3*x6+m2*m3*m7*x2*x3*x7+m2*m4*m5*x2*x4*x5+m2*m4*m6*x2*x4*x6+m2*m4*m7*x2*x4*x7+m2*m5*m6*x2*x5*x6+m2*m5*m7*x2*x5*x7+m2*m6*m7*x2*x6*x7+m3*m4*m5*x3*x4*x5+m3*m4*m6*x3*x4*x6+m3*m4*m7*x3*x4*x7+m3*m5*m6*x3*x5*x6+m3*m5*m7*x3*x5*x7+m3*m6*m7*x3*x6*x7+m4*m5*m6*x4*x5*x6+m4*m5*m7*x4*x5*x7+m4*m6*m7*x4*x6*x7+m5*m6*m7*x5*x6*x7+m2*m3*x2*x3+m2*m4*x2*x4+m2*m5*x2*x5+m2*m6*x2*x6+m2*m7*x2*x7+m3*m4*x3*x4+m3*m5*x3*x5+m3*m6*x3*x6+m3*m7*x3*x7+m4*m5*x4*x5+m4*m6*x4*x6+m4*m7*x4*x7+m5*m6*x5*x6+m5*m7*x5*x7+m6*m7*x6*x7+m2*x2+m3*x3+m4*x4+m5*x5+m6*x6+m7*x7+1)/​m_denom;​     prm s##n = -m1*s1*x8*(m2*m3*m4*m5*m6*m7*x2*x3*x4*x5*x6*x7+m2*m3*m4*m5*m6*x2*x3*x4*x5*x6+m2*m3*m4*m5*m7*x2*x3*x4*x5*x7+m2*m3*m4*m6*m7*x2*x3*x4*x6*x7+m2*m3*m5*m6*m7*x2*x3*x5*x6*x7+m2*m4*m5*m6*m7*x2*x4*x5*x6*x7+m3*m4*m5*m6*m7*x3*x4*x5*x6*x7+m2*m3*m4*m5*x2*x3*x4*x5+m2*m3*m4*m6*x2*x3*x4*x6+m2*m3*m4*m7*x2*x3*x4*x7+m2*m3*m5*m6*x2*x3*x5*x6+m2*m3*m5*m7*x2*x3*x5*x7+m2*m3*m6*m7*x2*x3*x6*x7+m2*m4*m5*m6*x2*x4*x5*x6+m2*m4*m5*m7*x2*x4*x5*x7+m2*m4*m6*m7*x2*x4*x6*x7+m2*m5*m6*m7*x2*x5*x6*x7+m3*m4*m5*m6*x3*x4*x5*x6+m3*m4*m5*m7*x3*x4*x5*x7+m3*m4*m6*m7*x3*x4*x6*x7+m3*m5*m6*m7*x3*x5*x6*x7+m4*m5*m6*m7*x4*x5*x6*x7+m2*m3*m4*x2*x3*x4+m2*m3*m5*x2*x3*x5+m2*m3*m6*x2*x3*x6+m2*m3*m7*x2*x3*x7+m2*m4*m5*x2*x4*x5+m2*m4*m6*x2*x4*x6+m2*m4*m7*x2*x4*x7+m2*m5*m6*x2*x5*x6+m2*m5*m7*x2*x5*x7+m2*m6*m7*x2*x6*x7+m3*m4*m5*x3*x4*x5+m3*m4*m6*x3*x4*x6+m3*m4*m7*x3*x4*x7+m3*m5*m6*x3*x5*x6+m3*m5*m7*x3*x5*x7+m3*m6*m7*x3*x6*x7+m4*m5*m6*x4*x5*x6+m4*m5*m7*x4*x5*x7+m4*m6*m7*x4*x6*x7+m5*m6*m7*x5*x6*x7+m2*m3*x2*x3+m2*m4*x2*x4+m2*m5*x2*x5+m2*m6*x2*x6+m2*m7*x2*x7+m3*m4*x3*x4+m3*m5*x3*x5+m3*m6*x3*x6+m3*m7*x3*x7+m4*m5*x4*x5+m4*m6*x4*x6+m4*m7*x4*x7+m5*m6*x5*x6+m5*m7*x5*x7+m6*m7*x6*x7+m2*x2+m3*x3+m4*x4+m5*x5+m6*x6+m7*x7+1)/​m_denom;​
    ​ #​m_endif    ​ #​m_endif
 +
 +   #​m_elseif p == 9;
 +   ​ #​m_if n == 1;
 +    prm m_denom = 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 +   ​ #​m_elseif n == 2;
 +    prm s##n = -x2*s1*m1*(m3*m4*m5*m6*m7*m8*m9*x3*x4*x5*x6*x7*x8*x9+m3*m4*m5*m6*m7*m8*x3*x4*x5*x6*x7*x8+m3*m4*m5*m6*m7*m9*x3*x4*x5*x6*x7*x9+m3*m4*m5*m6*m8*m9*x3*x4*x5*x6*x8*x9+m3*m4*m5*m7*m8*m9*x3*x4*x5*x7*x8*x9+m3*m4*m6*m7*m8*m9*x3*x4*x6*x7*x8*x9+m3*m5*m6*m7*m8*m9*x3*x5*x6*x7*x8*x9+m4*m5*m6*m7*m8*m9*x4*x5*x6*x7*x8*x9+m3*m4*m5*m6*m7*x3*x4*x5*x6*x7+m3*m4*m5*m6*m8*x3*x4*x5*x6*x8+m3*m4*m5*m6*m9*x3*x4*x5*x6*x9+m3*m4*m5*m7*m8*x3*x4*x5*x7*x8+m3*m4*m5*m7*m9*x3*x4*x5*x7*x9+m3*m4*m5*m8*m9*x3*x4*x5*x8*x9+m3*m4*m6*m7*m8*x3*x4*x6*x7*x8+m3*m4*m6*m7*m9*x3*x4*x6*x7*x9+m3*m4*m6*m8*m9*x3*x4*x6*x8*x9+m3*m4*m7*m8*m9*x3*x4*x7*x8*x9+m3*m5*m6*m7*m8*x3*x5*x6*x7*x8+m3*m5*m6*m7*m9*x3*x5*x6*x7*x9+m3*m5*m6*m8*m9*x3*x5*x6*x8*x9+m3*m5*m7*m8*m9*x3*x5*x7*x8*x9+m3*m6*m7*m8*m9*x3*x6*x7*x8*x9+m4*m5*m6*m7*m8*x4*x5*x6*x7*x8+m4*m5*m6*m7*m9*x4*x5*x6*x7*x9+m4*m5*m6*m8*m9*x4*x5*x6*x8*x9+m4*m5*m7*m8*m9*x4*x5*x7*x8*x9+m4*m6*m7*m8*m9*x4*x6*x7*x8*x9+m5*m6*m7*m8*m9*x5*x6*x7*x8*x9+m3*m4*m5*m6*x3*x4*x5*x6+m3*m4*m5*m7*x3*x4*x5*x7+m3*m4*m5*m8*x3*x4*x5*x8+m3*m4*m5*m9*x3*x4*x5*x9+m3*m4*m6*m7*x3*x4*x6*x7+m3*m4*m6*m8*x3*x4*x6*x8+m3*m4*m6*m9*x3*x4*x6*x9+m3*m4*m7*m8*x3*x4*x7*x8+m3*m4*m7*m9*x3*x4*x7*x9+m3*m4*m8*m9*x3*x4*x8*x9+m3*m5*m6*m7*x3*x5*x6*x7+m3*m5*m6*m8*x3*x5*x6*x8+m3*m5*m6*m9*x3*x5*x6*x9+m3*m5*m7*m8*x3*x5*x7*x8+m3*m5*m7*m9*x3*x5*x7*x9+m3*m5*m8*m9*x3*x5*x8*x9+m3*m6*m7*m8*x3*x6*x7*x8+m3*m6*m7*m9*x3*x6*x7*x9+m3*m6*m8*m9*x3*x6*x8*x9+m3*m7*m8*m9*x3*x7*x8*x9+m4*m5*m6*m7*x4*x5*x6*x7+m4*m5*m6*m8*x4*x5*x6*x8+m4*m5*m6*m9*x4*x5*x6*x9+m4*m5*m7*m8*x4*x5*x7*x8+m4*m5*m7*m9*x4*x5*x7*x9+m4*m5*m8*m9*x4*x5*x8*x9+m4*m6*m7*m8*x4*x6*x7*x8+m4*m6*m7*m9*x4*x6*x7*x9+m4*m6*m8*m9*x4*x6*x8*x9+m4*m7*m8*m9*x4*x7*x8*x9+m5*m6*m7*m8*x5*x6*x7*x8+m5*m6*m7*m9*x5*x6*x7*x9+m5*m6*m8*m9*x5*x6*x8*x9+m5*m7*m8*m9*x5*x7*x8*x9+m6*m7*m8*m9*x6*x7*x8*x9+m3*m4*m5*x3*x4*x5+m3*m4*m6*x3*x4*x6+m3*m4*m7*x3*x4*x7+m3*m4*m8*x3*x4*x8+m3*m4*m9*x3*x4*x9+m3*m5*m6*x3*x5*x6+m3*m5*m7*x3*x5*x7+m3*m5*m8*x3*x5*x8+m3*m5*m9*x3*x5*x9+m3*m6*m7*x3*x6*x7+m3*m6*m8*x3*x6*x8+m3*m6*m9*x3*x6*x9+m3*m7*m8*x3*x7*x8+m3*m7*m9*x3*x7*x9+m3*m8*m9*x3*x8*x9+m4*m5*m6*x4*x5*x6+m4*m5*m7*x4*x5*x7+m4*m5*m8*x4*x5*x8+m4*m5*m9*x4*x5*x9+m4*m6*m7*x4*x6*x7+m4*m6*m8*x4*x6*x8+m4*m6*m9*x4*x6*x9+m4*m7*m8*x4*x7*x8+m4*m7*m9*x4*x7*x9+m4*m8*m9*x4*x8*x9+m5*m6*m7*x5*x6*x7+m5*m6*m8*x5*x6*x8+m5*m6*m9*x5*x6*x9+m5*m7*m8*x5*x7*x8+m5*m7*m9*x5*x7*x9+m5*m8*m9*x5*x8*x9+m6*m7*m8*x6*x7*x8+m6*m7*m9*x6*x7*x9+m6*m8*m9*x6*x8*x9+m7*m8*m9*x7*x8*x9+m3*m4*x3*x4+m3*m5*x3*x5+m3*m6*x3*x6+m3*m7*x3*x7+m3*m8*x3*x8+m3*m9*x3*x9+m4*m5*x4*x5+m4*m6*x4*x6+m4*m7*x4*x7+m4*m8*x4*x8+m4*m9*x4*x9+m5*m6*x5*x6+m5*m7*x5*x7+m5*m8*x5*x8+m5*m9*x5*x9+m6*m7*x6*x7+m6*m8*x6*x8+m6*m9*x6*x9+m7*m8*x7*x8+m7*m9*x7*x9+m8*m9*x8*x9+m3*x3+m4*x4+m5*x5+m6*x6+m7*x7+m8*x8+m9*x9+1)/​m_denom;​
 +   ​ #​m_elseif n == 3;
 +    prm s##n = -(m2*m4*m5*m6*m7*m8*m9*x2*x4*x5*x6*x7*x8*x9+m2*m4*m5*m6*m7*m8*x2*x4*x5*x6*x7*x8+m2*m4*m5*m6*m7*m9*x2*x4*x5*x6*x7*x9+m2*m4*m5*m6*m8*m9*x2*x4*x5*x6*x8*x9+m2*m4*m5*m7*m8*m9*x2*x4*x5*x7*x8*x9+m2*m4*m6*m7*m8*m9*x2*x4*x6*x7*x8*x9+m2*m5*m6*m7*m8*m9*x2*x5*x6*x7*x8*x9+m4*m5*m6*m7*m8*m9*x4*x5*x6*x7*x8*x9+m2*m4*m5*m6*m7*x2*x4*x5*x6*x7+m2*m4*m5*m6*m8*x2*x4*x5*x6*x8+m2*m4*m5*m6*m9*x2*x4*x5*x6*x9+m2*m4*m5*m7*m8*x2*x4*x5*x7*x8+m2*m4*m5*m7*m9*x2*x4*x5*x7*x9+m2*m4*m5*m8*m9*x2*x4*x5*x8*x9+m2*m4*m6*m7*m8*x2*x4*x6*x7*x8+m2*m4*m6*m7*m9*x2*x4*x6*x7*x9+m2*m4*m6*m8*m9*x2*x4*x6*x8*x9+m2*m4*m7*m8*m9*x2*x4*x7*x8*x9+m2*m5*m6*m7*m8*x2*x5*x6*x7*x8+m2*m5*m6*m7*m9*x2*x5*x6*x7*x9+m2*m5*m6*m8*m9*x2*x5*x6*x8*x9+m2*m5*m7*m8*m9*x2*x5*x7*x8*x9+m2*m6*m7*m8*m9*x2*x6*x7*x8*x9+m4*m5*m6*m7*m8*x4*x5*x6*x7*x8+m4*m5*m6*m7*m9*x4*x5*x6*x7*x9+m4*m5*m6*m8*m9*x4*x5*x6*x8*x9+m4*m5*m7*m8*m9*x4*x5*x7*x8*x9+m4*m6*m7*m8*m9*x4*x6*x7*x8*x9+m5*m6*m7*m8*m9*x5*x6*x7*x8*x9+m2*m4*m5*m6*x2*x4*x5*x6+m2*m4*m5*m7*x2*x4*x5*x7+m2*m4*m5*m8*x2*x4*x5*x8+m2*m4*m5*m9*x2*x4*x5*x9+m2*m4*m6*m7*x2*x4*x6*x7+m2*m4*m6*m8*x2*x4*x6*x8+m2*m4*m6*m9*x2*x4*x6*x9+m2*m4*m7*m8*x2*x4*x7*x8+m2*m4*m7*m9*x2*x4*x7*x9+m2*m4*m8*m9*x2*x4*x8*x9+m2*m5*m6*m7*x2*x5*x6*x7+m2*m5*m6*m8*x2*x5*x6*x8+m2*m5*m6*m9*x2*x5*x6*x9+m2*m5*m7*m8*x2*x5*x7*x8+m2*m5*m7*m9*x2*x5*x7*x9+m2*m5*m8*m9*x2*x5*x8*x9+m2*m6*m7*m8*x2*x6*x7*x8+m2*m6*m7*m9*x2*x6*x7*x9+m2*m6*m8*m9*x2*x6*x8*x9+m2*m7*m8*m9*x2*x7*x8*x9+m4*m5*m6*m7*x4*x5*x6*x7+m4*m5*m6*m8*x4*x5*x6*x8+m4*m5*m6*m9*x4*x5*x6*x9+m4*m5*m7*m8*x4*x5*x7*x8+m4*m5*m7*m9*x4*x5*x7*x9+m4*m5*m8*m9*x4*x5*x8*x9+m4*m6*m7*m8*x4*x6*x7*x8+m4*m6*m7*m9*x4*x6*x7*x9+m4*m6*m8*m9*x4*x6*x8*x9+m4*m7*m8*m9*x4*x7*x8*x9+m5*m6*m7*m8*x5*x6*x7*x8+m5*m6*m7*m9*x5*x6*x7*x9+m5*m6*m8*m9*x5*x6*x8*x9+m5*m7*m8*m9*x5*x7*x8*x9+m6*m7*m8*m9*x6*x7*x8*x9+m2*m4*m5*x2*x4*x5+m2*m4*m6*x2*x4*x6+m2*m4*m7*x2*x4*x7+m2*m4*m8*x2*x4*x8+m2*m4*m9*x2*x4*x9+m2*m5*m6*x2*x5*x6+m2*m5*m7*x2*x5*x7+m2*m5*m8*x2*x5*x8+m2*m5*m9*x2*x5*x9+m2*m6*m7*x2*x6*x7+m2*m6*m8*x2*x6*x8+m2*m6*m9*x2*x6*x9+m2*m7*m8*x2*x7*x8+m2*m7*m9*x2*x7*x9+m2*m8*m9*x2*x8*x9+m4*m5*m6*x4*x5*x6+m4*m5*m7*x4*x5*x7+m4*m5*m8*x4*x5*x8+m4*m5*m9*x4*x5*x9+m4*m6*m7*x4*x6*x7+m4*m6*m8*x4*x6*x8+m4*m6*m9*x4*x6*x9+m4*m7*m8*x4*x7*x8+m4*m7*m9*x4*x7*x9+m4*m8*m9*x4*x8*x9+m5*m6*m7*x5*x6*x7+m5*m6*m8*x5*x6*x8+m5*m6*m9*x5*x6*x9+m5*m7*m8*x5*x7*x8+m5*m7*m9*x5*x7*x9+m5*m8*m9*x5*x8*x9+m6*m7*m8*x6*x7*x8+m6*m7*m9*x6*x7*x9+m6*m8*m9*x6*x8*x9+m7*m8*m9*x7*x8*x9+m2*m4*x2*x4+m2*m5*x2*x5+m2*m6*x2*x6+m2*m7*x2*x7+m2*m8*x2*x8+m2*m9*x2*x9+m4*m5*x4*x5+m4*m6*x4*x6+m4*m7*x4*x7+m4*m8*x4*x8+m4*m9*x4*x9+m5*m6*x5*x6+m5*m7*x5*x7+m5*m8*x5*x8+m5*m9*x5*x9+m6*m7*x6*x7+m6*m8*x6*x8+m6*m9*x6*x9+m7*m8*x7*x8+m7*m9*x7*x9+m8*m9*x8*x9+m2*x2+m4*x4+m5*x5+m6*x6+m7*x7+m8*x8+m9*x9+1)*x3*s1*m1/​m_denom;​
 +   ​ #​m_elseif n == 4;
 +    prm s##n = -(m2*m3*m5*m6*m7*m8*m9*x2*x3*x5*x6*x7*x8*x9+m2*m3*m5*m6*m7*m8*x2*x3*x5*x6*x7*x8+m2*m3*m5*m6*m7*m9*x2*x3*x5*x6*x7*x9+m2*m3*m5*m6*m8*m9*x2*x3*x5*x6*x8*x9+m2*m3*m5*m7*m8*m9*x2*x3*x5*x7*x8*x9+m2*m3*m6*m7*m8*m9*x2*x3*x6*x7*x8*x9+m2*m5*m6*m7*m8*m9*x2*x5*x6*x7*x8*x9+m3*m5*m6*m7*m8*m9*x3*x5*x6*x7*x8*x9+m2*m3*m5*m6*m7*x2*x3*x5*x6*x7+m2*m3*m5*m6*m8*x2*x3*x5*x6*x8+m2*m3*m5*m6*m9*x2*x3*x5*x6*x9+m2*m3*m5*m7*m8*x2*x3*x5*x7*x8+m2*m3*m5*m7*m9*x2*x3*x5*x7*x9+m2*m3*m5*m8*m9*x2*x3*x5*x8*x9+m2*m3*m6*m7*m8*x2*x3*x6*x7*x8+m2*m3*m6*m7*m9*x2*x3*x6*x7*x9+m2*m3*m6*m8*m9*x2*x3*x6*x8*x9+m2*m3*m7*m8*m9*x2*x3*x7*x8*x9+m2*m5*m6*m7*m8*x2*x5*x6*x7*x8+m2*m5*m6*m7*m9*x2*x5*x6*x7*x9+m2*m5*m6*m8*m9*x2*x5*x6*x8*x9+m2*m5*m7*m8*m9*x2*x5*x7*x8*x9+m2*m6*m7*m8*m9*x2*x6*x7*x8*x9+m3*m5*m6*m7*m8*x3*x5*x6*x7*x8+m3*m5*m6*m7*m9*x3*x5*x6*x7*x9+m3*m5*m6*m8*m9*x3*x5*x6*x8*x9+m3*m5*m7*m8*m9*x3*x5*x7*x8*x9+m3*m6*m7*m8*m9*x3*x6*x7*x8*x9+m5*m6*m7*m8*m9*x5*x6*x7*x8*x9+m2*m3*m5*m6*x2*x3*x5*x6+m2*m3*m5*m7*x2*x3*x5*x7+m2*m3*m5*m8*x2*x3*x5*x8+m2*m3*m5*m9*x2*x3*x5*x9+m2*m3*m6*m7*x2*x3*x6*x7+m2*m3*m6*m8*x2*x3*x6*x8+m2*m3*m6*m9*x2*x3*x6*x9+m2*m3*m7*m8*x2*x3*x7*x8+m2*m3*m7*m9*x2*x3*x7*x9+m2*m3*m8*m9*x2*x3*x8*x9+m2*m5*m6*m7*x2*x5*x6*x7+m2*m5*m6*m8*x2*x5*x6*x8+m2*m5*m6*m9*x2*x5*x6*x9+m2*m5*m7*m8*x2*x5*x7*x8+m2*m5*m7*m9*x2*x5*x7*x9+m2*m5*m8*m9*x2*x5*x8*x9+m2*m6*m7*m8*x2*x6*x7*x8+m2*m6*m7*m9*x2*x6*x7*x9+m2*m6*m8*m9*x2*x6*x8*x9+m2*m7*m8*m9*x2*x7*x8*x9+m3*m5*m6*m7*x3*x5*x6*x7+m3*m5*m6*m8*x3*x5*x6*x8+m3*m5*m6*m9*x3*x5*x6*x9+m3*m5*m7*m8*x3*x5*x7*x8+m3*m5*m7*m9*x3*x5*x7*x9+m3*m5*m8*m9*x3*x5*x8*x9+m3*m6*m7*m8*x3*x6*x7*x8+m3*m6*m7*m9*x3*x6*x7*x9+m3*m6*m8*m9*x3*x6*x8*x9+m3*m7*m8*m9*x3*x7*x8*x9+m5*m6*m7*m8*x5*x6*x7*x8+m5*m6*m7*m9*x5*x6*x7*x9+m5*m6*m8*m9*x5*x6*x8*x9+m5*m7*m8*m9*x5*x7*x8*x9+m6*m7*m8*m9*x6*x7*x8*x9+m2*m3*m5*x2*x3*x5+m2*m3*m6*x2*x3*x6+m2*m3*m7*x2*x3*x7+m2*m3*m8*x2*x3*x8+m2*m3*m9*x2*x3*x9+m2*m5*m6*x2*x5*x6+m2*m5*m7*x2*x5*x7+m2*m5*m8*x2*x5*x8+m2*m5*m9*x2*x5*x9+m2*m6*m7*x2*x6*x7+m2*m6*m8*x2*x6*x8+m2*m6*m9*x2*x6*x9+m2*m7*m8*x2*x7*x8+m2*m7*m9*x2*x7*x9+m2*m8*m9*x2*x8*x9+m3*m5*m6*x3*x5*x6+m3*m5*m7*x3*x5*x7+m3*m5*m8*x3*x5*x8+m3*m5*m9*x3*x5*x9+m3*m6*m7*x3*x6*x7+m3*m6*m8*x3*x6*x8+m3*m6*m9*x3*x6*x9+m3*m7*m8*x3*x7*x8+m3*m7*m9*x3*x7*x9+m3*m8*m9*x3*x8*x9+m5*m6*m7*x5*x6*x7+m5*m6*m8*x5*x6*x8+m5*m6*m9*x5*x6*x9+m5*m7*m8*x5*x7*x8+m5*m7*m9*x5*x7*x9+m5*m8*m9*x5*x8*x9+m6*m7*m8*x6*x7*x8+m6*m7*m9*x6*x7*x9+m6*m8*m9*x6*x8*x9+m7*m8*m9*x7*x8*x9+m2*m3*x2*x3+m2*m5*x2*x5+m2*m6*x2*x6+m2*m7*x2*x7+m2*m8*x2*x8+m2*m9*x2*x9+m3*m5*x3*x5+m3*m6*x3*x6+m3*m7*x3*x7+m3*m8*x3*x8+m3*m9*x3*x9+m5*m6*x5*x6+m5*m7*x5*x7+m5*m8*x5*x8+m5*m9*x5*x9+m6*m7*x6*x7+m6*m8*x6*x8+m6*m9*x6*x9+m7*m8*x7*x8+m7*m9*x7*x9+m8*m9*x8*x9+m2*x2+m3*x3+m5*x5+m6*x6+m7*x7+m8*x8+m9*x9+1)*m1*s1*x4/​m_denom;​
 +   ​ #​m_elseif n == 5;
 +    prm s##n = -(m7*x7+1)*(m9*x9+1)*s1*m1*(m2*m3*m4*x2*x3*x4+m2*m3*x2*x3+m2*m4*x2*x4+m3*m4*x3*x4+m2*x2+m3*x3+m4*x4+1)*(m8*x8+1)*x5*(m6*x6+1)/​m_denom;​
 +   ​ #​m_elseif n == 6;
 +    prm s##n = -(m8*x8+1)*(m2*m3*m4*m5*x2*x3*x4*x5+m2*m3*m4*x2*x3*x4+m2*m3*m5*x2*x3*x5+m2*m4*m5*x2*x4*x5+m3*m4*m5*x3*x4*x5+m2*m3*x2*x3+m2*m4*x2*x4+m2*m5*x2*x5+m3*m4*x3*x4+m3*m5*x3*x5+m4*m5*x4*x5+m2*x2+m3*x3+m4*x4+m5*x5+1)*m1*s1*(m9*x9+1)*x6*(m7*x7+1)/​m_denom;​
 +   ​ #​m_elseif n == 7;
 +    prm s##n = -(m9*x9+1)*s1*m1*(m2*m3*m4*m5*m6*x2*x3*x4*x5*x6+m2*m3*m4*m5*x2*x3*x4*x5+m2*m3*m4*m6*x2*x3*x4*x6+m2*m3*m5*m6*x2*x3*x5*x6+m2*m4*m5*m6*x2*x4*x5*x6+m3*m4*m5*m6*x3*x4*x5*x6+m2*m3*m4*x2*x3*x4+m2*m3*m5*x2*x3*x5+m2*m3*m6*x2*x3*x6+m2*m4*m5*x2*x4*x5+m2*m4*m6*x2*x4*x6+m2*m5*m6*x2*x5*x6+m3*m4*m5*x3*x4*x5+m3*m4*m6*x3*x4*x6+m3*m5*m6*x3*x5*x6+m4*m5*m6*x4*x5*x6+m2*m3*x2*x3+m2*m4*x2*x4+m2*m5*x2*x5+m2*m6*x2*x6+m3*m4*x3*x4+m3*m5*x3*x5+m3*m6*x3*x6+m4*m5*x4*x5+m4*m6*x4*x6+m5*m6*x5*x6+m2*x2+m3*x3+m4*x4+m5*x5+m6*x6+1)*x7*(m8*x8+1)/​m_denom;​
 +   ​ #​m_elseif n == 8;
 +    prm s##n = -(m2*m3*m4*m5*m6*m7*x2*x3*x4*x5*x6*x7+m2*m3*m4*m5*m6*x2*x3*x4*x5*x6+m2*m3*m4*m5*m7*x2*x3*x4*x5*x7+m2*m3*m4*m6*m7*x2*x3*x4*x6*x7+m2*m3*m5*m6*m7*x2*x3*x5*x6*x7+m2*m4*m5*m6*m7*x2*x4*x5*x6*x7+m3*m4*m5*m6*m7*x3*x4*x5*x6*x7+m2*m3*m4*m5*x2*x3*x4*x5+m2*m3*m4*m6*x2*x3*x4*x6+m2*m3*m4*m7*x2*x3*x4*x7+m2*m3*m5*m6*x2*x3*x5*x6+m2*m3*m5*m7*x2*x3*x5*x7+m2*m3*m6*m7*x2*x3*x6*x7+m2*m4*m5*m6*x2*x4*x5*x6+m2*m4*m5*m7*x2*x4*x5*x7+m2*m4*m6*m7*x2*x4*x6*x7+m2*m5*m6*m7*x2*x5*x6*x7+m3*m4*m5*m6*x3*x4*x5*x6+m3*m4*m5*m7*x3*x4*x5*x7+m3*m4*m6*m7*x3*x4*x6*x7+m3*m5*m6*m7*x3*x5*x6*x7+m4*m5*m6*m7*x4*x5*x6*x7+m2*m3*m4*x2*x3*x4+m2*m3*m5*x2*x3*x5+m2*m3*m6*x2*x3*x6+m2*m3*m7*x2*x3*x7+m2*m4*m5*x2*x4*x5+m2*m4*m6*x2*x4*x6+m2*m4*m7*x2*x4*x7+m2*m5*m6*x2*x5*x6+m2*m5*m7*x2*x5*x7+m2*m6*m7*x2*x6*x7+m3*m4*m5*x3*x4*x5+m3*m4*m6*x3*x4*x6+m3*m4*m7*x3*x4*x7+m3*m5*m6*x3*x5*x6+m3*m5*m7*x3*x5*x7+m3*m6*m7*x3*x6*x7+m4*m5*m6*x4*x5*x6+m4*m5*m7*x4*x5*x7+m4*m6*m7*x4*x6*x7+m5*m6*m7*x5*x6*x7+m2*m3*x2*x3+m2*m4*x2*x4+m2*m5*x2*x5+m2*m6*x2*x6+m2*m7*x2*x7+m3*m4*x3*x4+m3*m5*x3*x5+m3*m6*x3*x6+m3*m7*x3*x7+m4*m5*x4*x5+m4*m6*x4*x6+m4*m7*x4*x7+m5*m6*x5*x6+m5*m7*x5*x7+m6*m7*x6*x7+m2*x2+m3*x3+m4*x4+m5*x5+m6*x6+m7*x7+1)*m1*s1*x8*(m9*x9+1)/​m_denom;​
 +   ​ #​m_elseif n == 9;
 +    prm s##n = -x9*s1*m1*(m2*m3*m4*m5*m6*m7*m8*x2*x3*x4*x5*x6*x7*x8+m2*m3*m4*m5*m6*m7*x2*x3*x4*x5*x6*x7+m2*m3*m4*m5*m6*m8*x2*x3*x4*x5*x6*x8+m2*m3*m4*m5*m7*m8*x2*x3*x4*x5*x7*x8+m2*m3*m4*m6*m7*m8*x2*x3*x4*x6*x7*x8+m2*m3*m5*m6*m7*m8*x2*x3*x5*x6*x7*x8+m2*m4*m5*m6*m7*m8*x2*x4*x5*x6*x7*x8+m3*m4*m5*m6*m7*m8*x3*x4*x5*x6*x7*x8+m2*m3*m4*m5*m6*x2*x3*x4*x5*x6+m2*m3*m4*m5*m7*x2*x3*x4*x5*x7+m2*m3*m4*m5*m8*x2*x3*x4*x5*x8+m2*m3*m4*m6*m7*x2*x3*x4*x6*x7+m2*m3*m4*m6*m8*x2*x3*x4*x6*x8+m2*m3*m4*m7*m8*x2*x3*x4*x7*x8+m2*m3*m5*m6*m7*x2*x3*x5*x6*x7+m2*m3*m5*m6*m8*x2*x3*x5*x6*x8+m2*m3*m5*m7*m8*x2*x3*x5*x7*x8+m2*m3*m6*m7*m8*x2*x3*x6*x7*x8+m2*m4*m5*m6*m7*x2*x4*x5*x6*x7+m2*m4*m5*m6*m8*x2*x4*x5*x6*x8+m2*m4*m5*m7*m8*x2*x4*x5*x7*x8+m2*m4*m6*m7*m8*x2*x4*x6*x7*x8+m2*m5*m6*m7*m8*x2*x5*x6*x7*x8+m3*m4*m5*m6*m7*x3*x4*x5*x6*x7+m3*m4*m5*m6*m8*x3*x4*x5*x6*x8+m3*m4*m5*m7*m8*x3*x4*x5*x7*x8+m3*m4*m6*m7*m8*x3*x4*x6*x7*x8+m3*m5*m6*m7*m8*x3*x5*x6*x7*x8+m4*m5*m6*m7*m8*x4*x5*x6*x7*x8+m2*m3*m4*m5*x2*x3*x4*x5+m2*m3*m4*m6*x2*x3*x4*x6+m2*m3*m4*m7*x2*x3*x4*x7+m2*m3*m4*m8*x2*x3*x4*x8+m2*m3*m5*m6*x2*x3*x5*x6+m2*m3*m5*m7*x2*x3*x5*x7+m2*m3*m5*m8*x2*x3*x5*x8+m2*m3*m6*m7*x2*x3*x6*x7+m2*m3*m6*m8*x2*x3*x6*x8+m2*m3*m7*m8*x2*x3*x7*x8+m2*m4*m5*m6*x2*x4*x5*x6+m2*m4*m5*m7*x2*x4*x5*x7+m2*m4*m5*m8*x2*x4*x5*x8+m2*m4*m6*m7*x2*x4*x6*x7+m2*m4*m6*m8*x2*x4*x6*x8+m2*m4*m7*m8*x2*x4*x7*x8+m2*m5*m6*m7*x2*x5*x6*x7+m2*m5*m6*m8*x2*x5*x6*x8+m2*m5*m7*m8*x2*x5*x7*x8+m2*m6*m7*m8*x2*x6*x7*x8+m3*m4*m5*m6*x3*x4*x5*x6+m3*m4*m5*m7*x3*x4*x5*x7+m3*m4*m5*m8*x3*x4*x5*x8+m3*m4*m6*m7*x3*x4*x6*x7+m3*m4*m6*m8*x3*x4*x6*x8+m3*m4*m7*m8*x3*x4*x7*x8+m3*m5*m6*m7*x3*x5*x6*x7+m3*m5*m6*m8*x3*x5*x6*x8+m3*m5*m7*m8*x3*x5*x7*x8+m3*m6*m7*m8*x3*x6*x7*x8+m4*m5*m6*m7*x4*x5*x6*x7+m4*m5*m6*m8*x4*x5*x6*x8+m4*m5*m7*m8*x4*x5*x7*x8+m4*m6*m7*m8*x4*x6*x7*x8+m5*m6*m7*m8*x5*x6*x7*x8+m2*m3*m4*x2*x3*x4+m2*m3*m5*x2*x3*x5+m2*m3*m6*x2*x3*x6+m2*m3*m7*x2*x3*x7+m2*m3*m8*x2*x3*x8+m2*m4*m5*x2*x4*x5+m2*m4*m6*x2*x4*x6+m2*m4*m7*x2*x4*x7+m2*m4*m8*x2*x4*x8+m2*m5*m6*x2*x5*x6+m2*m5*m7*x2*x5*x7+m2*m5*m8*x2*x5*x8+m2*m6*m7*x2*x6*x7+m2*m6*m8*x2*x6*x8+m2*m7*m8*x2*x7*x8+m3*m4*m5*x3*x4*x5+m3*m4*m6*x3*x4*x6+m3*m4*m7*x3*x4*x7+m3*m4*m8*x3*x4*x8+m3*m5*m6*x3*x5*x6+m3*m5*m7*x3*x5*x7+m3*m5*m8*x3*x5*x8+m3*m6*m7*x3*x6*x7+m3*m6*m8*x3*x6*x8+m3*m7*m8*x3*x7*x8+m4*m5*m6*x4*x5*x6+m4*m5*m7*x4*x5*x7+m4*m5*m8*x4*x5*x8+m4*m6*m7*x4*x6*x7+m4*m6*m8*x4*x6*x8+m4*m7*m8*x4*x7*x8+m5*m6*m7*x5*x6*x7+m5*m6*m8*x5*x6*x8+m5*m7*m8*x5*x7*x8+m6*m7*m8*x6*x7*x8+m2*m3*x2*x3+m2*m4*x2*x4+m2*m5*x2*x5+m2*m6*x2*x6+m2*m7*x2*x7+m2*m8*x2*x8+m3*m4*x3*x4+m3*m5*x3*x5+m3*m6*x3*x6+m3*m7*x3*x7+m3*m8*x3*x8+m4*m5*x4*x5+m4*m6*x4*x6+m4*m7*x4*x7+m4*m8*x4*x8+m5*m6*x5*x6+m5*m7*x5*x7+m5*m8*x5*x8+m6*m7*x6*x7+m6*m8*x6*x8+m7*m8*x7*x8+m2*x2+m3*x3+m4*x4+m5*x5+m6*x6+m7*x7+m8*x8+1)/​m_denom;​
 +   ​ #​m_endif
 +   
        
    #​m_endif    #​m_endif
-   '​}}} 
        
    scale = s##n;    scale = s##n;
 } }
 </​code>​ </​code>​

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