Interpolation

[1]:

import urllib.request
import hepi
import numpy as np
from hepi.run import resummino as rs
from hepi.run import 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)

/home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/hepi/util.py:101: UserWarning: LHAPDF python binding not installed? Make sure you set PYTHONPATH correctly (i.e. correct python version) if you want to compute PDF uncertainties.
  warnings.warn(

Skipped: 25 Not skipped: 0

[3]:

hepi.mapplot(dl,"mass_2000002","mass_1000022","LO",xaxis="$M_1$",yaxis="$M_2$",zaxis="$\sigma_{LO}$",show=True)

../_images/examples_demo_02_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"])

../_images/examples_demo_02_interpolate_4_0.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,
       1.665699711616912e-16+/-0.0015822520490069067,
       0.0+/-0.0015819508546576252, 0.0+/-0.001587055618200895,
       0.0+/-0.0014827480507491498,
       -1.3039619234566496e-16+/-0.001445349985972156,
       1.257633877129178e-16+/-0.0014266141005402125,
       2.384467240468002e-16+/-0.00142076671547182,
       1.1118908908490573e-16+/-0.0014217864616282267,
       0.0+/-0.0014085512991095304,
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       -1.1779974384479693e-16+/-0.0013226213444905682,
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       0.0+/-0.001279159351555103, 0.0+/-0.0013320552441462043,
       0.0+/-0.0012963384696511003,
       2.2802366353531297e-16+/-0.001272302136817206,
       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_demo_02_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)

Skipped: 256 Not skipped: 0

../_images/examples_demo_02_interpolate_7_13.png

[8]:

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

[9]:

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

[9]:

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       -3.5117582447420925e-16+/-0.001332055244145646,
       -0.00035937095846981114+/-0.0013212454297169308,
       -0.0004409792096084742+/-0.0013115559915172536,
       -0.0002432852316549673+/-0.0013025906102734559,
       7.767929102692437e-05+/-0.001294351732591518,
       0.000256981222052556+/-0.0012869006720839112,
       0.00021018666902285357+/-0.0012804582323263482,
       7.028200240307518e-05+/-0.0012748346532102895,
       -5.868459704079617e-05+/-0.0012699385580342223,
       -0.00012617141125908254+/-0.0012655949706406222,
       -0.00011252231730407305+/-0.0012616831987138997,
       -2.777991257122016e-05+/-0.0012581935763035373,
       8.919720156827438e-05+/-0.0012551529032158745,
       0.00018462963780929018+/-0.0012526029315386102,
       0.00018373391712617177+/-0.0012505698636003345,
       -1.6102817093322409e-16+/-0.001249070023516848], dtype=object)

[10]:

import matplotlib.pyplot as plt

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_NOERR",xaxis="$m_{\\tilde{\\chi}_1^0}$ [GeV]",yaxis="$m_{\\tilde{\\chi}_2^0}$ [GeV]" , zaxis="$\\sigma_{\\mathrm{NLO+NLL}}$ [pb]")
plt.show()
xx,yy = data.flatmesh(np.linspace(50,320,100),np.linspace(80,320,100))
dll = hepi.interpolate_2d(dl,"N1","N2","NLO_PLUS_NLL_NOERR",xx,yy,interpolator="linearnd",pre=np.log,post=np.exp)
hepi.mapplot(dll,"N1","N2","NLO_PLUS_NLL_NOERR",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_demo_02_interpolate_10_0.png

../_images/examples_demo_02_interpolate_10_1.png

[ ]: