Interpolation

[1]:

import urllib.request
import hepi
import numpy as np
import hepi.resummino as rs
import hepi.spheno as sp
sp.set_path("SPheno")
rs.set_path("resummino")

[2]:

for a,b in [(2000002,1000022)]:
    for pdf,nlopdf in [("CT14lo","CT14lo")]:
        li = [hepi.Input(hepi.Order.LO,13000,a,b,"slha.in",pdf,nlopdf,1., 1.)]
        #li=hepi.slha_scan_rel(li,lambda r : [["EXTPAR",1,r],["EXTPAR",2,r]],np.linspace(500.,1500.,16*2))

        li=hepi.mass_scan(li,a,np.linspace(500.,1500.,5))
        li=hepi.mass_scan(li,b,np.linspace(500.,1500.,5))
        dl = rs.run(li,True,True)

skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 25 jobs

[3]:

hepi.mapplot(dl,"mass_2000002","mass_1000022","LO",xaxis="$M_1$",yaxis="$M_2$",zaxis="$\sigma_{LO}$",show=True)
../_images/examples_test_interpolate_3_0.png 
[4]:

from smpl import data
xx,yy = data.flatmesh(np.linspace(500,1500,5),np.linspace(500,1500,5))
dll = hepi.interpolate_2d(dl,"mass_1000022","mass_2000002","LO",xx,yy,interpolator="linear")
hepi.mapplot(dll,"mass_2000002","mass_1000022","LO",xaxis="$M_1$",yaxis="$M_2$",zaxis="$\sigma_{LO}$",show=True,fill_missing=False)
#print(dll["LO"])

/usr/lib/python3.8/site-packages/scipy/interpolate/_fitpack_impl.py:977: RuntimeWarning: No more knots can be added because the number of B-spline
coefficients already exceeds the number of data points m.
Probable causes: either s or m too small. (fp>s)
        kx,ky=1,1 nx,ny=7,8 m=25 fp=0.000000 s=0.000000
  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))
../_images/examples_test_interpolate_4_1.png 
[5]:

(dl["LO"].to_numpy()-dll["LO"].to_numpy())/dl["LO"].to_numpy()

[5]:

array([0.0+/-0.0016154263871670225,
       1.5539029389273503e-16+/-0.0015909845253920855,
       0.0+/-0.0015822520490067892, 0.0+/-0.0015819508546576252,
       0.0+/-0.001587055618200895,
       2.6772946370592586e-16+/-0.0014827480507491023,
       0.0+/-0.0014453499859720636,
       -2.515267754258356e-16+/-0.0014266141005399458,
       1.192233620234001e-16+/-0.0014207667154714826,
       3.3356726725471718e-16+/-0.0014217864616281873,
       -2.0907546578191998e-15+/-0.0014085512991080533,
       -2.0359474544460697e-15+/-0.0013673252967128261,
       -1.8262344226844117e-15+/-0.0013448653411289781,
       -1.6382129552163417e-15+/-0.0013341627341679062,
       -1.7302908222722817e-15+/-0.0013314060188193053,
       0.0+/-0.001361907903727031,
       1.1779974384479693e-16+/-0.0013226213444908178,
       0.0+/-0.0012988387152938253,
       1.7286326595444938e-16+/-0.0012854820635717283,
       0.0+/-0.001279159351555103, 0.0+/-0.0013320552441462043,
       -1.390146825835857e-16+/-0.001296338469651002,
       0.0+/-0.0012723021368171658,
       1.9054767539780033e-16+/-0.0012573895051737369,
       0.0+/-0.0012490700235172464], dtype=object)

[6]:

from smpl import data
xx,yy = data.flatmesh(np.linspace(500,1500,16),np.linspace(500,1500,16))
dll = hepi.interpolate_2d(dl,"mass_1000022","mass_2000002","LO",xx,yy,interpolator="cubic")
hepi.mapplot(dll,"mass_2000002","mass_1000022","LO",xaxis="$M_1$",yaxis="$M_2$",zaxis="$\sigma_{LO}$",show=True,fill_missing=False)
#print(dll["LO"])
../_images/examples_test_interpolate_6_0.png 
[7]:

for a,b in [(2000002,1000022)]:
    for pdf,nlopdf in [("CT14lo","CT14lo")]:
        li = [hepi.Input(hepi.Order.LO,13000,a,b,"slha.in",pdf,nlopdf,1., 1.)]
        #li=hepi.slha_scan_rel(li,lambda r : [["EXTPAR",1,r],["EXTPAR",2,r]],np.linspace(500.,1500.,16*2))

        li=hepi.mass_scan(li,a,np.linspace(500.,1500.,16))
        li=hepi.mass_scan(li,b,np.linspace(500.,1500.,16))
        dl2 = rs.run(li,True,True)
hepi.mapplot(dl2,"mass_2000002","mass_1000022","LO",xaxis="$M_1$",yaxis="$M_2$",zaxis="$\sigma_{LO}$",show=True)

skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 256 jobs
../_images/examples_test_interpolate_7_1.png 
[10]:

from smpl import stat

dll = hepi.interpolate_2d(dl,"mass_1000022","mass_2000002","LO",xx,yy,interpolator="cubic")
print(stat.mean(np.abs((dl2["LO"].to_numpy()-dll["LO"].to_numpy())/dl2["LO"].to_numpy())))

dll = hepi.interpolate_2d(dl,"mass_1000022","mass_2000002","LO",xx,yy,interpolator="linear")
print(stat.mean(np.abs((dl2["LO"].to_numpy()-dll["LO"].to_numpy())/dl2["LO"].to_numpy())))

# exponential dependency
dll = hepi.interpolate_2d(dl,"mass_1000022","mass_2000002","LO",xx,yy,interpolator="linear",pre=np.log,post=np.exp)
print(stat.mean(np.abs((dl2["LO"].to_numpy()-dll["LO"].to_numpy())/dl2["LO"].to_numpy())))

dll = hepi.interpolate_2d(dl,"mass_1000022","mass_2000002","LO",xx,yy,interpolator="cubic",pre=np.log,post=np.exp)
print(stat.mean(np.abs((dl2["LO"].to_numpy()-dll["LO"].to_numpy())/dl2["LO"].to_numpy())))

0.013+/-0.013
0.22+/-0.25
0.009+/-0.009
0.0003+/-0.0004

[11]:

(dl2["LO"].to_numpy()-dll["LO"].to_numpy())/dl2["LO"].to_numpy()

[11]:

array([0.0+/-0.0016154263871666335,
       0.00016204064752406365+/-0.0016061995454333942,
       7.899466661134942e-05+/-0.0015996085411351794,
       1.0198409639310502e-05+/-0.0015943027134063172,
       3.381517513823047e-06+/-0.0015900286956441652,
       1.642938008983535e-05+/-0.0015867754012363767,
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       0.00018373391712617177+/-0.00125056986360162,
       -1.6102817093322409e-16+/-0.001249070023516848], dtype=object)

[26]:

dl = hepi.load(
    urllib.request.urlopen(
        "https://raw.githubusercontent.com/APN-Pucky/xsec/master/json/pp13_hinosplit_N2N1_NLO%2BNLL.json"
    ),
    dimensions=2,
)
hepi.mapplot(dl,"N1","N2","NLO_PLUS_NLL",xaxis="$m_{\\tilde{\\chi}_1^0}$ [GeV]",yaxis="$m_{\\tilde{\\chi}_2^0}$ [GeV]" , zaxis="$\\sigma_{\\mathrm{NLO+NLL}}$ [pb]")

xx,yy = data.flatmesh(np.linspace(50,320,100),np.linspace(80,320,100))
dll = hepi.interpolate_2d(dl,"N1","N2","NLO_PLUS_NLL",xx,yy,interpolator="linearnd",pre=np.log,post=np.exp)
hepi.mapplot(dll,"N1","N2","NLO_PLUS_NLL",xaxis="$m_{\\tilde{\\chi}_1^0}$ [GeV]",yaxis="$m_{\\tilde{\\chi}_2^0}$ [GeV]" , zaxis="$\\sigma_{\\mathrm{NLO+NLL}}$ [pb]",fill_missing=False)
../_images/examples_test_interpolate_10_0.png
../_images/examples_test_interpolate_10_1.png 
[ ]: