Merge branch 'master' of github.com:APN-Pucky/HEPi into pandas_as_def…

…ault

README.md

@@ -8,7 +8,7 @@ Currently, slurm is supported for clusters.
 | [Stable][doc stable]        | [Unstable][doc test]           |
 | ------------- |:-------------:|
 | [![workflow][a s image]][a s link]      | [![test][a t image]][a t link]     |
-| [![Coverage Status][c s i]][c s l] | [![Coverage Status][c t i]][c t l] |
+| - | [![Coverage Status][c t i]][c t l] |
 | -      |[![Codacy Badge][codacy cover image]][codacy cover link] | 
 | -     |[![Codacy Badge][codacy quality image]][codacy quality link] | 
 | [![Documentation][rtd s i]][rtd s l] | [![Documentation][rtd t i]][rtd t l]  | 

hepi/load.py

@@ -3,7 +3,7 @@
 from re import I
 from uncertainties import ufloat
 from hepi.input import Input, order_to_string, xsec_to_order
-from hepi.util import LD2DL
+from hepi.util import LD2DL, DL2DF
 
 
 def load(f, dimensions=1):
@@ -26,7 +26,7 @@ def load(f, dimensions=1):
         pdf_nlo=dict["PDF set"],
         update=False,
     )
-    inpu.code = dict["tool"]
+    inpu.runner = dict["tool"]
     dat = []
     params = dict["parameters"]
     if dimensions == 2:
@@ -49,4 +49,4 @@ def load(f, dimensions=1):
             dicd[order_to_string(inpu.order)] = ufloat(
                 dict["data"][k]["xsec_pb"], dict["data"][k]["unc_pb"])
             dat.append(dicd)
-    return LD2DL(dat, actual_dict=True)
+    return DL2DF(LD2DL(dat, actual_dict=True))

hepi/output.py

@@ -69,7 +69,7 @@ def write_csv(dict_list: list, filename: str):
     >>> hepi.write_csv(dl, open("test.csv", 'w'))
     >>> with open('test.csv', 'r') as f:
     ...     print(f.read())
-    order,energy,energyhalf,particle1,particle2,slha,pdf_lo,pdfset_lo,pdf_nlo,pdfset_nlo,pdf_lo_id,pdf_nlo_id,mu_f,mu_r,precision,max_iters,invariant_mass,pt,result,id,model,mu,code,N2,N1,NLO_PLUS_NLL
+    order,energy,energyhalf,particle1,particle2,slha,pdf_lo,pdfset_lo,pdf_nlo,pdfset_nlo,pdf_lo_id,pdf_nlo_id,mu_f,mu_r,precision,max_iters,invariant_mass,pt,result,id,model,mu,runner,N2,N1,NLO_PLUS_NLL
     2,13000.0,6500.0,-1,-1,$\\tilde\\chi_2^0\\tilde\\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,81.5,80.0,7.746+/-0.023
     2,13000.0,6500.0,-1,-1,$\\tilde\\chi_2^0\\tilde\\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,82.0,80.0,7.646+/-0.024
     2,13000.0,6500.0,-1,-1,$\\tilde\\chi_2^0\\tilde\\chi_1^0$ (higgsino),CTEQ6.6 and MSTW2008nlo90cl,0,CTEQ6.6 and MSTW2008nlo90cl,0,0,0,1.0,1.0,0.01,50,auto,auto,total,,,0.0,Resummino,83.0,80.0,7.451+/-0.024
@@ -175,7 +175,7 @@ def write_json(dict_list: list,
     >>> hepi.write_json(dl, Order.NLO_PLUS_NLL,"N1",open("test.json", 'w'))
     >>> with open('test.json', 'r') as f:
     ...     print(f.read())
-    {"initial state": "pp", "order": "NLO+NLL", "source": "HEPi", "contact": "?", "tool": "Resummino", "process_latex": "$\\\\overline{d}\\\\overline{d}$", "comment": "", "reference": "?", "Ecom [GeV]": "13000.0", "process_id": "pp_13000.0_-1_-1", "PDF set": "CTEQ6.6 and MSTW2008nlo90cl", "data": {"80.0": {"xsec_pb": 2.142151}, "60.0": {"xsec_pb": 4.504708}, "100.0": {"xsec_pb": 1.165897}, "125.0": {"xsec_pb": 0.614697}, "150.0": {"xsec_pb": 0.354984}, "175.0": {"xsec_pb": 0.327625}, "200.0": {"xsec_pb": 0.141817}, "225.0": {"xsec_pb": 0.138083}, "250.0": {"xsec_pb": 0.066363}, "300.0": {"xsec_pb": 0.044674}}, "parameters": [["N1"]]}
+    {"initial state": "pp", "order": "NLO+NLL", "source": "hepi-...", "contact": "?", "tool": "Resummino", "process_latex": "$\\\\overline{d}\\\\overline{d}$", "comment": "", "reference": "?", "Ecom [GeV]": "13000.0", "process_id": "pp_13000.0_-1_-1", "PDF set": "CTEQ6.6 and MSTW2008nlo90cl", "data": {"80.0": {"xsec_pb": 2.142151}, "60.0": {"xsec_pb": 4.504708}, "100.0": {"xsec_pb": 1.165897}, "125.0": {"xsec_pb": 0.614697}, "150.0": {"xsec_pb": 0.354984}, "175.0": {"xsec_pb": 0.327625}, "200.0": {"xsec_pb": 0.141817}, "225.0": {"xsec_pb": 0.138083}, "250.0": {"xsec_pb": 0.066363}, "300.0": {"xsec_pb": 0.044674}}, "parameters": [["N1"]]}
  """
 
     jd = {}

hepi/plot.py

@@ -223,22 +223,7 @@ def vplot(x,
         vx = x
         vy = y
 
-    xnew = np.linspace(
-        vx[0],
-        vx[-1],
-        300,
-    )
-    if interpolate:
-        #print(vx,vy)
-        spl = make_interp_spline(vx, splot.unv(vy), k=3)  # type: BSpline
-        power_smooth = spl(xnew)
-    if fill:
-        spl_up = make_interp_spline(vx, splot.unv(vy) + splot.usd(vy),
-                                    k=3)  # type: BSpline
-        power_up_smooth = spl_up(xnew)
-        spl_down = make_interp_spline(vx, splot.unv(vy) - splot.usd(vy),
-                                      k=3)  # type: BSpline
-        power_down_smooth = spl_down(xnew)
+
     if data_color is None:
         if 'axes' in kwargs and kwargs['axes'] is not None:
             bl, = kwargs['axes'].plot([], [])
@@ -258,7 +243,7 @@ def vplot(x,
                             sigmas=0 if not fill else 1,
                             **kwargs)
     if iii is not None:
-        ii = iii[0]
+        #ii = iii[0]
         ix = iii[1]
         iy = iii[2]
     if print_area:

hepi/run.py

@@ -9,7 +9,6 @@
 from hepi.util import DL2DF, LD2DL, DictData, namehash
 from smpl.parallel import par
 import time
-import pandas as pd
 
 
 class RunParam(DictData):