From 6b22506f77290da1e42ca025bc96ee34be564a43 Mon Sep 17 00:00:00 2001
From: APN-Pucky <APN-Pucky@noreply.github.com>
Date: Thu, 9 Jun 2022 04:18:16 +0200
Subject: [PATCH 1/8] Switch to more recent smpl

---
 hepi/madgraph/run.py  |  14 ++-
 hepi/nllfast/run.py   |   2 +-
 hepi/output.py        |  20 ++--
 hepi/plot.py          | 227 +++++++++---------------------------------
 hepi/resummino/run.py |  23 +++--
 hepi/run.py           |  25 +++--
 hepi/spheno/run.py    |  12 ++-
 hepi/util.py          |   2 +-
 setup.py              |   2 +-
 9 files changed, 122 insertions(+), 205 deletions(-)

diff --git a/hepi/madgraph/run.py b/hepi/madgraph/run.py
index 92951bfa4..448c90bbd 100644
--- a/hepi/madgraph/run.py
+++ b/hepi/madgraph/run.py
@@ -8,6 +8,7 @@
 from .. import Input, Result, get_output_dir
 from .result import is_valid, parse_single
 import time
+import os
 
 
 class MadGraphRunParams(RunParam):
@@ -25,6 +26,15 @@ class MadGraphRunner(Runner):
     def orders(self) -> List[Order]:
         return [Order.LO, Order.NLO]
 
+    def _check_path(self) -> bool:
+        if os.path.exists(os.path.expanduser(self.get_path() +
+                                             "/bin/mg5_aMC")):
+            self.set_path(self.get_path() + "/bin/mg5_aMC")
+            return True
+        if self.get_path().endswith("mg5_aMC"):
+            return True
+        return False
+
     def _check_input(self, param: Input, **kwargs) -> bool:
         """Checks input parameter for compatibility with Prospino"""
         return True
@@ -57,9 +67,9 @@ def _run(self,
                 ' {dir}  && cp {slha} {dir}/Cards/param_card.dat && cp {run} {dir}/Cards/run_card.dat && echo "nb_core = 1" >> {dir}/Cards/amcatnlo_configuration.txt ' + lo+ '&& nice -n 5 {dir}/bin/calculate_xsect -f >> {out}  && rm -rf {dir}'
             print(rps[0].dic["out"])
         if not rps[0].skip:
-            mgcom = 'bin/mg5_aMC'
+            mgcom = ''
             if rps[0].madstr:
-                mgcom = 'bin/mg5_aMC --mode="MadSTR"'
+                mgcom = ' --mode="MadSTR"'
             com = self.get_path() + mgcom + \
                 ' --file {in} >> {out} && cp {slha} {bdir}/Cards/param_card.dat && cp {run} {bdir}/Cards/run_card.dat && sed -i \'s/.*= req_acc_FO/ 1 = req_acc_FO/g\' {bdir}/Cards/run_card.dat && echo "automatic_html_opening = False" >> {bdir}/Cards/amcatnlo_configuration.txt && nice -n 5 {bdir}/bin/calculate_xsect -f'
             pp = subprocess.Popen(com.format(**rps[0].dic), shell=True)
diff --git a/hepi/nllfast/run.py b/hepi/nllfast/run.py
index f378755a8..1359888a5 100644
--- a/hepi/nllfast/run.py
+++ b/hepi/nllfast/run.py
@@ -50,7 +50,7 @@ def _get_nf_input(self, p: Input) -> dict:
             "nf_gluino_mass"] = self._get_ps_proc(p)
         return d
 
-    def _check_input(self, p: Input,**kwargs) -> bool:
+    def _check_input(self, p: Input, **kwargs) -> bool:
         """Checks input parameter for compatibility with Prospino"""
         if p.mu_f != 1. or p.mu_r != 1.:
             warnings.warn(
diff --git a/hepi/output.py b/hepi/output.py
index 75f4859b1..f0389b70c 100644
--- a/hepi/output.py
+++ b/hepi/output.py
@@ -1,9 +1,10 @@
 import json
 from smpl import plot
 import numpy as np
+import pkg_resources as pkg
 
 from .input import Order, order_to_string
-from .util import LD2DF, get_name
+from .util import DL2DF, get_name
 from smpl import io
 
 splot = plot
@@ -144,7 +145,7 @@ def write_csv(dict_list: list, filename: str):
     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,320.0,300.0,0.04+/-0.05
     <BLANKLINE>
  """
-    df = LD2DF(dict_list)
+    df = DL2DF(dict_list)
     df.to_csv(filename, index=False)
 
 
@@ -162,7 +163,7 @@ def write_json(dict_list: list,
 
 
  Args:
-    output (writeable) : Should support a function `.write()`.
+    output (writeable or file name str) : Should support a function `.write()`.
 
  Examples:
 
@@ -176,6 +177,7 @@ def write_json(dict_list: list,
     ...     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"]]}
  """
+
     jd = {}
     jd["initial state"] = "pp"  # TODO add more such cases + filters, also in resummino
     if o == Order.LO:
@@ -188,9 +190,15 @@ def write_json(dict_list: list,
         jd["order"] = "NNLOapprox+NNLL"
     else:
         raise ValueError("Order not supported by write_json.")
-    jd["source"] = "HEPi"
+    package = "hepi"
+    try:
+        version = pkg.require(package)[0].version
+    except pkg.DistributionNotFound:
+        version = "dirty"
+    jd["source"] = package + "-" + version
+
     jd["contact"] = "?"
-    jd["tool"] = dict_list["code"][0]
+    jd["tool"] = dict_list["runner"][0].replace("Runner", "")
     jd["process_latex"] = "$" + get_name(dict_list["particle1"][0]) + get_name(
         dict_list["particle2"][0]) + "$"
     jd["comment"] = dict_list["id"][0]
@@ -214,4 +222,4 @@ def write_json(dict_list: list,
             }
     jd["data"] = dat
     jd["parameters"] = [[parameter]]
-    output.write(json.dumps(jd))
+    io.write(output, json.dumps(jd))
diff --git a/hepi/plot.py b/hepi/plot.py
index 0d1248bc4..e974492c9 100644
--- a/hepi/plot.py
+++ b/hepi/plot.py
@@ -149,8 +149,8 @@ def plot(dict_list,
     if label == "":
         label = None
 
-    vx = dict_list[x][mask]
-    vy = dict_list[y][mask]
+    vx = dict_list[x].to_numpy()[mask]
+    vy = dict_list[y].to_numpy()[mask]
 
     if K:
         yaxis = "$K$"
@@ -245,61 +245,39 @@ def vplot(x,
         else:
             bl, = plt.gca().plot([], [])
         color = bl.get_color()
-    if plot_data:
+    iii = splot.data(vx,
+                            vy * yscale,
+                            label=label,
+                            xaxis=xaxis,
+                            yaxis=yaxis,
+                            logy=logy,
+                            data_color=color,
+                            also_data=plot_data,
+                            interpolate=interpolate,
+                            fmt=data_fmt,
+                            sigmas=0 if not fill else 1,
+                            **kwargs)
+    if iii is not None:
+        ii = iii[0]
+        ix = iii[1]
+        iy = iii[2]
+    if print_area:
+        print('computed AUC using sklearn.metrics.auc: {}'.format(auc(ix, iy)))
+    if ((np.any(np.less(vy, 0)) or (interpolate and np.any(np.less(iy, 0))))
+            and logy):
         splot.data(vx,
-                   vy * yscale,
-                   label=label,
+                   -vy * yscale,
+                   label="-" + label,
                    xaxis=xaxis,
                    yaxis=yaxis,
                    logy=logy,
                    data_color=color,
+                   also_data=plot_data,
+                   interpolate=interpolate,
+                   interpolate_fmt=(0, (3, 1, 3, 1, 1, 1)),
                    fmt=data_fmt,
+                   sigmas=0 if not fill else 1,
                    **kwargs)
-    if interpolate:
-        kargs = {}
-        if not plot_data:
-            kargs = {'xaxis': xaxis, 'yaxis': yaxis, 'label': label}
-        if print_area:
-            print('computed AUC using sklearn.metrics.auc: {}'.format(
-                auc(xnew, power_smooth * yscale)))
-        splot.data(xnew,
-                   power_smooth * yscale,
-                   logy=logy,
-                   fmt=fmt,
-                   init=False,
-                   data_color=color,
-                   **kargs,
-                   **kwargs)
-    if fill:
-        plt.fill_between(xnew,
-                         power_up_smooth * yscale,
-                         power_down_smooth * yscale,
-                         alpha=0.3,
-                         color=color)
-    if ((np.any(np.less(vy, 0)) or
-         (interpolate and np.any(np.less(power_smooth, 0)))) and logy):
-        if plot_data:
-            splot.data(vx,
-                       -vy * yscale,
-                       label="-" + label,
-                       xaxis=xaxis,
-                       yaxis=yaxis,
-                       logy=logy,
-                       data_color=color,
-                       fmt=data_fmt,
-                       **kwargs)
-        if interpolate:
-            if not plot_data:
-                kargs = {'xaxis': xaxis, 'yaxis': yaxis, 'label': "-" + label}
-            splot.data(xnew,
-                       -power_smooth * yscale,
-                       logy=logy,
-                       fmt=None,
-                       linestyle=(0, (3, 1, 3, 1, 1, 1)),
-                       init=False,
-                       data_color=color,
-                       **kargs,
-                       **kwargs)
 
 
 def mass_mapplot(dict_list,
@@ -346,84 +324,17 @@ def mapplot(dict_list, x, y, z, xaxis=None, yaxis=None, zaxis=None, **kwargs):
     vx = dict_list[x]
     vy = dict_list[y]
     vz = dict_list[z]
-    map_vplot(vx, vy, vz, xaxis=xaxis, yaxis=yaxis, zaxis=zaxis, **kwargs)
-
-
-def map_vplot(tvx,
-              tvy,
-              tvz,
-              xaxis=None,
-              yaxis=None,
-              zaxis=None,
-              logz=True,
-              sort=True,
-              fill_missing=True,
-              zscale=1.):
-    vx = np.copy(tvx)
-    vy = np.copy(tvy)
-    vz = np.copy(tvz)
-    if fill_missing:
-        for x in vx:
-            for y in vy:
-                ex = False
-                for i in range(len(vx)):
-                    if vx[i] == x and vy[i] == y:
-                        ex = True
-                if not ex:
-                    vx = np.append(vx, x)
-                    vy = np.append(vy, y)
-                    vz = np.append(vz, 0)
-    if sort:
-        p1 = vx.argsort(kind='stable')
-        vx = np.copy(vx[p1])
-        vy = np.copy(vy[p1])
-        vz = np.copy(vz[p1])
-        p2 = vy.argsort(kind='stable')
-        vx = vx[p2]
-        vy = vy[p2]
-        vz = vz[p2]
-    s = 1
-    while vy[s] == vy[s - 1]:
-        s = s + 1
-    if s == 1:
-        #print("flipped x y ")
-        while vx[s] == vx[s - 1]:
-            s = s + 1
-        if s == 1:
-            print("error too small map")
-            return
-        #x, y = y, x
-        xaxis, yaxis = yaxis, xaxis
-        vx, vy = vy, vx
-
-    grid = splot.unv(vz).reshape((int(np.rint(np.size(vx) / s)), s)) * zscale
-
-    fig, ax = plt.subplots(nrows=1, ncols=1, constrained_layout=True)
-    im = None
-    xl = vx.min() + (vx.min() / 2) - vx[vx != vx.min()].min() / 2
-    xm = vx.max() + (vx.max() / 2) - vx[vx != vx.max()].max() / 2
-    yl = vy.min() + (vy.min() / 2) - vy[vy != vy.min()].min() / 2
-    ym = vy.max() + (vy.max() / 2) - vy[vy != vy.max()].max() / 2
-    im = NonUniformImage(
-        ax,
-        origin="lower",
-        cmap='viridis',
-        interpolation='nearest',
-        extent=(xl, xm, yl, ym),
-        norm=colors.LogNorm() if logz else None,
-    )
-
-    im.set_data(np.unique(vx), np.unique(vy), grid)
-    ax.images.append(im)
-    ax.set_xlim(xl, xm)
-    ax.set_ylim(yl, ym)
-
-    cb = plt.colorbar(im)
-    cb.set_label(zaxis)
-    plt.xlabel(xaxis)
-    plt.ylabel(yaxis)
-    plt.show()
-
+    splot.plot2d(vx,
+                 vy,
+                 vz,
+                 style="image",
+                 xaxis=xaxis,
+                 yaxis=yaxis,
+                 zaxis=zaxis,
+                 **kwargs)
+
+map_vplot =  lambda *a,**da : splot.plot2d(*a,style="image",**da)
+scatter_vplot =  lambda *a,**da : splot.plot2d(*a,style="scatter",**da)
 
 def scatterplot(dict_list,
                 x,
@@ -459,58 +370,14 @@ def scatterplot(dict_list,
     vx = dict_list[x]
     vy = dict_list[y]
     vz = dict_list[z]
-    scatter_vplot(vx, vy, vz, xaxis=xaxis, yaxis=yaxis, zaxis=zaxis, **kwargs)
-
-
-def scatter_vplot(vx,
-                  vy,
-                  vz,
-                  xaxis=None,
-                  yaxis=None,
-                  zaxis=None,
-                  logz=True,
-                  sort=True,
-                  fill_missing=True,
-                  zscale=1.):
-    if sort:
-        p1 = vx.argsort(kind='stable')
-        vx = np.copy(vx[p1])
-        vy = np.copy(vy[p1])
-        vz = np.copy(vz[p1])
-        p2 = vy.argsort(kind='stable')
-        vx = vx[p2]
-        vy = vy[p2]
-        vz = vz[p2]
-
-    fig, ax = plt.subplots(nrows=1, ncols=1, constrained_layout=True)
-    im = None
-    xl = vx.min() + (vx.min() / 2) - vx[vx != vx.min()].min() / 2
-    xm = vx.max() + (vx.max() / 2) - vx[vx != vx.max()].max() / 2
-    yl = vy.min() + (vy.min() / 2) - vy[vy != vy.min()].min() / 2
-    ym = vy.max() + (vy.max() / 2) - vy[vy != vy.max()].max() / 2
-
-    s = plt.scatter(np.concatenate((vx, vx, vx)),
-                    np.concatenate((vy, vy, vy)),
-                    c=np.concatenate(
-                        (splot.unv(vz) + splot.usd(vz),
-                         splot.unv(vz) - splot.usd(vz), splot.unv(vz))),
-                    s=np.concatenate(
-                        ([(3 * plt.rcParams['lines.markersize'])**2
-                          for i in range(len(vx))], [
-                              (2 * plt.rcParams['lines.markersize'])**2
-                              for i in range(len(vx))
-                          ], [(plt.rcParams['lines.markersize'])**2
-                              for i in range(len(vx))])),
-                    norm=colors.LogNorm() if logz else None)
-
-    ax.set_xlim(xl, xm)
-    ax.set_ylim(yl, ym)
-
-    cb = plt.colorbar(s)
-    cb.set_label(zaxis)
-    plt.xlabel(xaxis)
-    plt.ylabel(yaxis)
-    plt.show()
+    splot.plot2d(vx,
+                 vy,
+                 vz,
+                 style="scatter",
+                 xaxis=xaxis,
+                 yaxis=yaxis,
+                 zaxis=zaxis,
+                 **kwargs)
 
 
 fig = None
diff --git a/hepi/resummino/run.py b/hepi/resummino/run.py
index bd1a1b65b..4d4458b38 100644
--- a/hepi/resummino/run.py
+++ b/hepi/resummino/run.py
@@ -19,20 +19,29 @@ class ResumminoRunner(Runner):
     def orders(self) -> List[Order]:
         return [Order.LO, Order.NLO, Order.NLO_PLUS_NLL, Order.aNNLO_PLUS_NNLL]
 
+    def get_version(self) -> str:
+        p = os.path.expanduser(self.get_path())
+        ret = self._sub_run([p, "--version"])
+        return ret.split("\n")[-2]
+
     def _check_path(self) -> bool:
-        if os.path.exists(get_path() + "build/bin/resummino"):
-            self.set_path(get_path() + "build/bin/resummino")
+        if os.path.exists(
+                os.path.expanduser(self.get_path() + "/build/bin/resummino")):
+            self.set_path(self.get_path() + "/build/bin/resummino")
             return True
-        if os.path.exists(get_path() + "bin/resummino"):
-            self.set_path(get_path() + "bin/resummino")
+        if os.path.exists(
+                os.path.expanduser(self.get_path() + "/bin/resummino")):
+            self.set_path(self.get_path() + "/bin/resummino")
             return True
-        return True
+        if self.get_path().endswith("resummino"):
+            return True
+        return False
 
     def _check_input(self, p: Input, **kwargs) -> bool:
 
         if p.order == Order.aNNLO_PLUS_NNLL and (
-                p.has_gluino() and p.has_weakino()) or (p.has_squark()
-                                                        and p.has_weakino()):
+            (p.has_gluino() and p.has_weakino()) or
+            (p.has_squark() and p.has_weakino())):
             warnings.warn(
                 "Resummino does not support stong-weak mixed aNNLO+NNLL.")
             return False
diff --git a/hepi/run.py b/hepi/run.py
index ba73740b6..70230cd3a 100644
--- a/hepi/run.py
+++ b/hepi/run.py
@@ -1,13 +1,15 @@
 from logging import warning
 import os
 import subprocess
+from subprocess import Popen, PIPE
 from typing import List
 import warnings
 from hepi.input import Input, Order, get_input_dir, get_output_dir, get_pre
 from hepi.results import Result
-from hepi.util import LD2DL, DictData, namehash
+from hepi.util import DL2DF, LD2DL, DictData, namehash
 from smpl.parallel import par
 import time
+import pandas as pd
 
 
 class RunParam(DictData):
@@ -55,6 +57,18 @@ def get_name(self) -> str:
         """Returns the name of the runner."""
         return type(self).__name__
 
+    def get_version(self) -> str:
+        return "?"
+
+    def _sub_run(self, coms: List[str]) -> str:
+        process = Popen(coms, stdout=PIPE)
+        (output, err) = process.communicate()
+        exit_code = process.wait()
+        if exit_code != 0:
+            return err.decode()
+        else:
+            return output.decode()
+
     def _check_path(self) -> bool:
         """Checks if the passed path is valid."""
         return True
@@ -62,7 +76,7 @@ def _check_path(self) -> bool:
     def _prepare(self, p: Input, **kwargs) -> RunParam:
         skip_ = kwargs["skip"]
         d = p.__dict__
-        d["runner"] = type(self).__name__
+        d["runner"] = str(type(self).__name__) + "-" + self.get_version()
         name = namehash("_".join("".join(str(_[0]) + "_" + str(_[1]))
                                  for _ in d.items()).replace("/", "-"))
         #print(name)
@@ -119,8 +133,7 @@ def run(self,
             run (bool): Actually start/queue runner.
 
 		Returns:
-		    :obj:`dict` : combined dictionary of results and parameters. Each member therein is a list.
-		        The dictionary is empty if `parse` is set to False.
+		    :obj:`pd.DataFrame` : combined dataframe of results and parameters. The dataframe is empty if `parse` is set to False.
 
 		"""
         if not self._check_path():
@@ -140,8 +153,8 @@ def run(self,
             results = self.parse(outs)
             rdl = LD2DL(results)
             pdl = LD2DL(params)
-            return {**rdl, **pdl}
-        return {}
+            return DL2DF({**rdl, **pdl})
+        return DL2DF({})
 
     def _run(self,
              rps: List[RunParam],
diff --git a/hepi/spheno/run.py b/hepi/spheno/run.py
index 823221f41..b4abf45fc 100644
--- a/hepi/spheno/run.py
+++ b/hepi/spheno/run.py
@@ -8,6 +8,14 @@
 
 class SPhenoRunner(Runner):
 
+    def _check_path(self) -> bool:
+        if os.path.exists(os.path.expanduser(self.get_path() + "/bin/SPheno")):
+            self.set_path(self.get_path() + "/bin/SPheno")
+            return True
+        if self.get_path().endswith("SPheno"):
+            return True
+        return False
+
     def run(self, slhas: List[Input], **kwargs) -> List[Input]:
         """
         Run the passed list of parameters for SPheno.
@@ -17,13 +25,15 @@ def run(self, slhas: List[Input], **kwargs) -> List[Input]:
         Returns:
             :obj:`list` of :class:`Input`
         """
+        if not self._check_path():
+            warnings.warn("The path is not valid for " + self.get_name())
         if os.path.exists("Messages.out"):
             os.remove("Messages.out")
         for s in slhas:
             # Remove Creation time for hash-/caching
             comm = "cp " + self.get_output_dir(
             ) + s.slha + " spheno_tmp.in && " + get_path(
-            ) + "bin/SPheno spheno_tmp.in && mv " + "SPheno.spc " + self.get_output_dir(
+            ) + " spheno_tmp.in && mv " + "SPheno.spc " + self.get_output_dir(
             ) + s.slha + " && sed -i '/Created/d' " + self.get_output_dir(
             ) + s.slha
             proc = subprocess.Popen(comm,
diff --git a/hepi/util.py b/hepi/util.py
index d1db5f870..2ceb54965 100644
--- a/hepi/util.py
+++ b/hepi/util.py
@@ -51,7 +51,7 @@ def LD2DL(l: List, actual_dict=False) -> dict:
     }
 
 
-def LD2DF(ld: dict) -> pd.DataFrame:
+def DL2DF(ld: dict) -> pd.DataFrame:
     """
     Convert a `dict` of `list`s to a `pandas.DataFrame`.
     """
diff --git a/setup.py b/setup.py
index 93607c5b1..bd55784ff 100644
--- a/setup.py
+++ b/setup.py
@@ -26,7 +26,7 @@
         "scipy",
         "sympy",
         "scikit-learn",
-        "smpl>=0.0.118",
+        "smpl>=0.0.128",
         "pyslha",
         "enlighten",
         "particle",

From fb8d66d86cd66510e05b8d15102661188240c3ec Mon Sep 17 00:00:00 2001
From: APN-Pucky <APN-Pucky@noreply.github.com>
Date: Thu, 9 Jun 2022 04:19:26 +0200
Subject: [PATCH 2/8] Update and rerun docs

---
 .../test_madgraph-checkpoint.ipynb            | 334 ++++--
 .../test_pyslha-checkpoint.ipynb              |  60 +-
 .../test_spheno-checkpoint.ipynb              | 241 ++---
 .../test_write-checkpoint.ipynb               | 491 +++++----
 docs/source/examples/MG5_debug                |  44 +-
 docs/source/examples/Messages.out             |   0
 docs/source/examples/demo_resummino.ipynb     | 987 ++++--------------
 docs/source/examples/input/hino.in            | 157 +++
 docs/source/examples/input/slha.in            | 521 +++++++++
 docs/source/examples/input/wino.in            | 158 +++
 docs/source/examples/out.csv                  |  20 +-
 docs/source/examples/out.json                 |   2 +-
 docs/source/examples/py.py                    |  62 +-
 docs/source/examples/spheno_tmp.in            |  49 +
 docs/source/examples/test_distribute.ipynb    |  49 +-
 docs/source/examples/test_madgraph.ipynb      | 653 +++++++++++-
 docs/source/examples/test_pyslha.ipynb        |  57 +-
 docs/source/examples/test_spheno.ipynb        | 308 +++++-
 docs/source/examples/test_write.ipynb         | 256 ++---
 19 files changed, 2802 insertions(+), 1647 deletions(-)
 create mode 100644 docs/source/examples/Messages.out
 create mode 100644 docs/source/examples/input/hino.in
 create mode 100644 docs/source/examples/input/slha.in
 create mode 100644 docs/source/examples/input/wino.in
 create mode 100644 docs/source/examples/spheno_tmp.in

diff --git a/docs/source/examples/.ipynb_checkpoints/test_madgraph-checkpoint.ipynb b/docs/source/examples/.ipynb_checkpoints/test_madgraph-checkpoint.ipynb
index f132abd72..2c1e92c46 100644
--- a/docs/source/examples/.ipynb_checkpoints/test_madgraph-checkpoint.ipynb
+++ b/docs/source/examples/.ipynb_checkpoints/test_madgraph-checkpoint.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "b583970e",
    "metadata": {},
    "outputs": [
@@ -18,8 +18,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1.4.42+dirty\n",
-      "/opt/MG5_aMC_v2_7_0/\n"
+      "0.1.8.9+dirty\n",
+      "/opt/MG5_aMC_v2_7_3/\n"
      ]
     }
    ],
@@ -31,18 +31,10 @@
     "import hepi.madgraph as mg\n",
     "import hepi.util as util\n",
     "import matplotlib.pyplot as plt\n",
-    "mg.set_path(\"/opt/MG5_aMC_v2_7_0/\")\n",
+    "mg.set_path(\"/opt/MG5_aMC_v2_7_3/\")\n",
     "print (mg.get_path())"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7d048757",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "2dd526dd",
@@ -53,17 +45,144 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "ed7216a4",
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Running: 15 jobs\n",
-      "./output/4bec207f684440baa1701f222feffd61ef9ed63a626da77e10441653c9a42084.out\n",
-      "No module named madgraph\n",
+      "./output/09f0fa80d6ffa4d16b6c3b7f888df66d3c6debf643b46bfa1441c5a941d5bc4a.out\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:510: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  _mg5_version = LooseVersion(line[9:].strip())\n",
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:456: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  if tool_name in ['lhapdf6', 'lhapdf'] and MG5_version and MG5_version < LooseVersion(\"2.6.1\"):\n",
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:255: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  ( lambda MG5version: MG5version < LooseVersion(\"2.6.1\"),\n",
+      "Detected 'ninja' missing dependency: 'oneloop'. Will install it now.\n",
+      "Fetching data with command:\n",
+      "  wget --no-check-certificate http://helac-phegas.web.cern.ch/helac-phegas/tar-files/OneLOop-3.6.tgz\n",
+      "--2022-06-08 11:43:47--  http://helac-phegas.web.cern.ch/helac-phegas/tar-files/OneLOop-3.6.tgz\n",
+      "Resolving helac-phegas.web.cern.ch... 188.184.100.128\n",
+      "Connecting to helac-phegas.web.cern.ch|188.184.100.128|:80... connected.\n",
+      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
+      "Location: https://helac-phegas.web.cern.ch/tar-files/OneLOop-3.6.tgz [following]\n",
+      "--2022-06-08 11:43:48--  https://helac-phegas.web.cern.ch/tar-files/OneLOop-3.6.tgz\n",
+      "Connecting to helac-phegas.web.cern.ch|188.184.100.128|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 111734 (109K) [application/x-compressed]\n",
+      "Saving to: ‘OneLOop-3.6.tgz’\n",
+      "\n",
+      "     0K .......... .......... .......... .......... .......... 45% 1.40M 0s\n",
+      "    50K .......... .......... .......... .......... .......... 91% 2.99M 0s\n",
+      "   100K .........                                             100%  156M=0.05s\n",
+      "\n",
+      "2022-06-08 11:43:48 (2.08 MB/s) - ‘OneLOop-3.6.tgz’ saved [111734/111734]\n",
+      "\n",
+      "Installing tool 'oneloop'...\n",
+      "    > Follow the installation progress by running the command below in a separate terminal)\n",
+      "    > tail -f /opt/MG5_aMC_v2_7_3/HEPTools/oneloop/oneloop_install.log\n",
+      "    > Successful installation of dependency 'oneloop' in '/opt/MG5_aMC_v2_7_3/HEPTools'.\n",
+      "    > See installation log at '/opt/MG5_aMC_v2_7_3/HEPTools/oneloop/oneloop_install.log'.\n",
+      "Fetching data with command:\n",
+      "  wget --no-check-certificate https://ninja.hepforge.org/downloads//ninja-1.1.0.tar.gz\n",
+      "--2022-06-08 11:44:27--  https://ninja.hepforge.org/downloads//ninja-1.1.0.tar.gz\n",
+      "Resolving ninja.hepforge.org... 129.234.186.186\n",
+      "Connecting to ninja.hepforge.org|129.234.186.186|:443... connected.\n",
+      "HTTP request sent, awaiting response... 302 Found\n",
+      "Location: /downloads?f=/ninja-1.1.0.tar.gz [following]\n",
+      "--2022-06-08 11:44:27--  https://ninja.hepforge.org/downloads?f=/ninja-1.1.0.tar.gz\n",
+      "Reusing existing connection to ninja.hepforge.org:443.\n",
+      "HTTP request sent, awaiting response... 302 Found\n",
+      "Location: /downloads?f=ninja-1.1.0.tar.gz [following]\n",
+      "--2022-06-08 11:44:27--  https://ninja.hepforge.org/downloads?f=ninja-1.1.0.tar.gz\n",
+      "Reusing existing connection to ninja.hepforge.org:443.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: unspecified [application/x-gzip]\n",
+      "Saving to: ‘ninja-1.1.0.tar.gz’\n",
+      "\n",
+      "     0K .......... .......... .......... .......... ..........  771K\n",
+      "    50K .......... .......... .......... .......... .......... 1.53M\n",
+      "   100K .......... .......... .......... .......... ..........  224M\n",
+      "   150K .......... .......... .......... .......... .......... 1.63M\n",
+      "   200K .......... .......... .......... .......... .......... 17.9M\n",
+      "   250K .......... .......... .......... .......... ..........  181M\n",
+      "   300K .......... .......... .......... .......... ..........  206M\n",
+      "   350K .......... .......... .......... .......... .......... 1.63M\n",
+      "   400K .......... .......... .......... .......... .......... 36.0M\n",
+      "   450K .......... .......... .......... .......... ..........  180M\n",
+      "   500K .......... .......... .......... .......... ..........  163M\n",
+      "   550K .......... .......                                      219M=0.2s\n",
+      "\n",
+      "2022-06-08 11:44:27 (3.42 MB/s) - ‘ninja-1.1.0.tar.gz’ saved [581566]\n",
+      "\n",
+      "Installing tool 'ninja'...\n",
+      "    > Follow the installation progress by running the command below in a separate terminal)\n",
+      "    > tail -f /opt/MG5_aMC_v2_7_3/HEPTools/ninja/ninja_install.log\n",
+      "Successful installation of 'ninja' in '/opt/MG5_aMC_v2_7_3/HEPTools'.\n",
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:510: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  _mg5_version = LooseVersion(line[9:].strip())\n",
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:456: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  if tool_name in ['lhapdf6', 'lhapdf'] and MG5_version and MG5_version < LooseVersion(\"2.6.1\"):\n",
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:255: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  ( lambda MG5version: MG5version < LooseVersion(\"2.6.1\"),\n",
+      "Fetching data with command:\n",
+      "  wget --no-check-certificate http://collier.hepforge.org//collier-latest.tar.gz\n",
+      "--2022-06-08 11:49:29--  http://collier.hepforge.org//collier-latest.tar.gz\n",
+      "Resolving collier.hepforge.org... 129.234.186.186\n",
+      "Connecting to collier.hepforge.org|129.234.186.186|:80... connected.\n",
+      "HTTP request sent, awaiting response... 302 Found\n",
+      "Location: https://collier.hepforge.org/collier-latest.tar.gz [following]\n",
+      "--2022-06-08 11:49:29--  https://collier.hepforge.org/collier-latest.tar.gz\n",
+      "Connecting to collier.hepforge.org|129.234.186.186|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 933676 (912K) [application/x-gzip]\n",
+      "Saving to: ‘collier-latest.tar.gz’\n",
+      "\n",
+      "     0K .......... .......... .......... .......... ..........  5%  808K 1s\n",
+      "    50K .......... .......... .......... .......... .......... 10% 1.54M 1s\n",
+      "   100K .......... .......... .......... .......... .......... 16% 55.5M 0s\n",
+      "   150K .......... .......... .......... .......... .......... 21% 1.51M 0s\n",
+      "   200K .......... .......... .......... .......... .......... 27%  146M 0s\n",
+      "   250K .......... .......... .......... .......... .......... 32%  136M 0s\n",
+      "   300K .......... .......... .......... .......... .......... 38%  137M 0s\n",
+      "   350K .......... .......... .......... .......... .......... 43% 1.61M 0s\n",
+      "   400K .......... .......... .......... .......... .......... 49% 24.1M 0s\n",
+      "   450K .......... .......... .......... .......... .......... 54%  207M 0s\n",
+      "   500K .......... .......... .......... .......... .......... 60%  229M 0s\n",
+      "   550K .......... .......... .......... .......... .......... 65%  175M 0s\n",
+      "   600K .......... .......... .......... .......... .......... 71% 8.41M 0s\n",
+      "   650K .......... .......... .......... .......... .......... 76% 15.2M 0s\n",
+      "   700K .......... .......... .......... .......... .......... 82%  212M 0s\n",
+      "   750K .......... .......... .......... .......... .......... 87% 2.76M 0s\n",
+      "   800K .......... .......... .......... .......... .......... 93% 98.5M 0s\n",
+      "   850K .......... .......... .......... .......... .......... 98% 19.4M 0s\n",
+      "   900K .......... .                                          100%  313M=0.2s\n",
+      "\n",
+      "2022-06-08 11:49:29 (4.66 MB/s) - ‘collier-latest.tar.gz’ saved [933676/933676]\n",
+      "\n",
+      "Installing tool 'collier'...\n",
+      "    > Follow the installation progress by running the command below in a separate terminal)\n",
+      "    > tail -f /opt/MG5_aMC_v2_7_3/HEPTools/collier/collier_install.log\n",
+      "Successful installation of 'collier' in '/opt/MG5_aMC_v2_7_3/HEPTools'.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No module named 'madgraph'\n",
+      " No hepmc reader since No module named 'madgraph' \u001b[1;30m[lhe_parser.py at line 50]\u001b[0m\n",
       "INFO: ************************************************************\n",
       "*                                                          *\n",
       "*           W E L C O M E  to  M A D G R A P H 5           *\n",
@@ -75,7 +194,7 @@
       "*                   *        * *        *                  *\n",
       "*                 *                       *                *\n",
       "*                                                          *\n",
-      "*         VERSION 5.2.7.3               20xx-xx-xx         *\n",
+      "*         VERSION 5.2.7.3.py3           20xx-xx-xx         *\n",
       "*                                                          *\n",
       "*    The MadGraph5_aMC@NLO Development Team - Find us at   *\n",
       "*                 http://amcatnlo.cern.ch                  *\n",
@@ -83,16 +202,15 @@
       "*               Type 'help' for in-line help.              *\n",
       "*                                                          *\n",
       "************************************************************ \n",
-      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/4bec207f684440baa1701f222feffd61ef9ed63a626da77e10441653c9a42084.bdir/Cards/amcatnlo_configuration.txt  \n",
-      "INFO: load configuration from /opt/MG5_aMC_v2_7_0/input/mg5_configuration.txt  \n",
-      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/4bec207f684440baa1701f222feffd61ef9ed63a626da77e10441653c9a42084.bdir/Cards/amcatnlo_configuration.txt  \n",
-      "Using default eps viewer \"gv\". Set another one in ./input/mg5_configuration.txt\n",
+      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/09f0fa80d6ffa4d16b6c3b7f888df66d3c6debf643b46bfa1441c5a941d5bc4a.bdir/Cards/amcatnlo_configuration.txt  \n",
+      "INFO: load configuration from /opt/MG5_aMC_v2_7_3/input/mg5_configuration.txt  \n",
+      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/09f0fa80d6ffa4d16b6c3b7f888df66d3c6debf643b46bfa1441c5a941d5bc4a.bdir/Cards/amcatnlo_configuration.txt  \n",
+      "Using default eps viewer \"evince\". Set another one in ./input/mg5_configuration.txt\n",
       "Using default web browser \"firefox\". Set another one in ./input/mg5_configuration.txt\n",
       "calculate_xsect -f\n",
       "INFO: will run in mode: NLO \n",
       "INFO: Starting run \n",
-      "INFO: Compiling the code \n",
-      "write ./param_card.dat\n"
+      "INFO: Compiling the code \n"
      ]
     },
     {
@@ -114,15 +232,15 @@
       " Check the MG5_aMC option 'output_dependencies'.\n",
       " This will prevent the use of HERWIG6/Pythia6 shower. \u001b[0m\n",
       "INFO: Compiling directories... \n",
-      "INFO: Compiling on 16 cores \n",
+      "INFO: Compiling on 8 cores \n",
       "INFO:  Compiling P0_uux_elmelp... \n",
       "INFO:  Compiling P0_ddx_elmelp... \n",
       "INFO:  Compiling P0_uxu_elmelp... \n",
       "INFO:  Compiling P0_dxd_elmelp... \n",
-      "INFO:     P0_dxd_elmelp done. \n",
-      "INFO:     P0_uux_elmelp done. \n",
       "INFO:     P0_ddx_elmelp done. \n",
       "INFO:     P0_uxu_elmelp done. \n",
+      "INFO:     P0_uux_elmelp done. \n",
+      "INFO:     P0_dxd_elmelp done. \n",
       "INFO: Checking test output: \n",
       "INFO: P0_uux_elmelp \n",
       "INFO:  Result for test_ME: \n",
@@ -145,26 +263,35 @@
       "INFO:  Result for check_poles: \n",
       "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
       "INFO: Starting run \n",
-      "INFO: Using 16 cores \n",
+      "INFO: Using 8 cores \n",
       "INFO: Cleaning previous results \n",
       "INFO: Doing fixed order NLO \n",
       "INFO: Setting up grids \n",
-      "INFO:  Idle: 0,  Running: 0,  Completed: 4 [ current time: 02h24 ] \n",
+      "INFO:  Idle: 0,  Running: 4,  Completed: 0 [ current time: 11h58 ] \n",
+      "INFO:  Idle: 0,  Running: 3,  Completed: 1 [  0.33s  ] \n",
+      "INFO:  Idle: 0,  Running: 2,  Completed: 2 [  0.36s  ] \n",
+      "INFO:  Idle: 0,  Running: 1,  Completed: 3 [  0.48s  ] \n",
+      "INFO:  Idle: 0,  Running: 0,  Completed: 4 [  0.69s  ] \n",
       "INFO: \n",
       "      Results after grid setup:\n",
-      "      Total cross section:      2.708e-01 +- 1.6e-03 pb\n",
+      "      Total cross section:      2.489e-01 +- 1.5e-03 pb\n",
       " \n",
       "INFO: Refining results, step 1 \n",
-      "INFO:  Idle: 0,  Running: 0,  Completed: 4 [ current time: 02h24 ] \n",
+      "INFO:  Idle: 0,  Running: 5,  Completed: 0 [ current time: 11h58 ] \n",
+      "INFO:  Idle: 0,  Running: 4,  Completed: 1 [  0.25s  ] \n",
+      "INFO:  Idle: 0,  Running: 3,  Completed: 2 [  0.33s  ] \n",
+      "INFO:  Idle: 0,  Running: 2,  Completed: 3 [  1.1s  ] \n",
+      "INFO:  Idle: 0,  Running: 1,  Completed: 4 [  1.2s  ] \n",
+      "INFO:  Idle: 0,  Running: 0,  Completed: 5 [  1.2s  ] \n",
       "INFO: \n",
       "   --------------------------------------------------------------\n",
       "      Final results and run summary:\n",
       "      Process p p > 1000011 -1000011 [QCD]\n",
       "      Run at p-p collider (6500.0 + 6500.0 GeV)\n",
-      "      Total cross section: 2.692e-01 +- 9.7e-04 pb\n",
+      "      Total cross section: 2.468e-01 +- 9.1e-04 pb\n",
       "   --------------------------------------------------------------\n",
       " \n",
-      "INFO: The results of this run and the HwU and GnuPlot files with the plots have been saved in /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/4bec207f684440baa1701f222feffd61ef9ed63a626da77e10441653c9a42084.bdir/Events/run_01 \n",
+      "INFO: The results of this run and the HwU and GnuPlot files with the plots have been saved in /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/09f0fa80d6ffa4d16b6c3b7f888df66d3c6debf643b46bfa1441c5a941d5bc4a.bdir/Events/run_01 \n",
       "INFO: Run complete \n",
       "INFO:  \n",
       "quit\n",
@@ -175,31 +302,31 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "stty: stty: 'standard input''standard input': Inappropriate ioctl for device\n",
+      ": Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for devicestty: \n",
+      "'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: stty: 'standard input''standard input': Inappropriate ioctl for device\n",
+      ": Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for devicestty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "\n",
+      "stty: stty: stty: stty: 'standard input''standard input': Inappropriate ioctl for device\n",
+      ": Inappropriate ioctl for device\n",
+      "'standard input': Inappropriate ioctl for device\n",
+      "stty: stty: stty: 'standard input': Inappropriate ioctl for device\n",
+      "'standard input': Inappropriate ioctl for device\n",
+      "'standard input': Inappropriate ioctl for device\n",
+      "'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
@@ -239,7 +366,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -259,7 +386,7 @@
     "     ]\n",
     "for pa,pb in pss:\n",
     "    for param in params:\n",
-    "        i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.)\n",
+    "        i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.,model=\"/opt/MG5_aMC_v2_7_3/models/MSSMatNLO_UFO\")\n",
     "        li = [i]\n",
     "        li = hepi.mass_scan([i],pa, np.linspace(100,1000,7+8))\n",
     "        mg_dl = mg.run(li,skip=False,madstr=False)\n",
@@ -279,17 +406,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "id": "4628188f",
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Running: 15 jobs\n",
-      "./output/bc70cb3af5e83ab35b937bca29b2536487b5662454482b4f2315ada2001d6774.out\n",
-      "No module named madgraph\n",
+      "./output/90bcfe8bc719ea89d80f8ce26ccfc425f6ff8937aec2e0a3f8031ea161b0d4cc.out\n",
+      "No module named 'madgraph'\n",
+      " No hepmc reader since No module named 'madgraph' \u001b[1;30m[lhe_parser.py at line 50]\u001b[0m\n",
       "INFO: ************************************************************\n",
       "*                                                          *\n",
       "*           W E L C O M E  to  M A D G R A P H 5           *\n",
@@ -301,7 +431,7 @@
       "*                   *        * *        *                  *\n",
       "*                 *                       *                *\n",
       "*                                                          *\n",
-      "*         VERSION 5.2.7.3               20xx-xx-xx         *\n",
+      "*         VERSION 5.2.7.3.py3           20xx-xx-xx         *\n",
       "*                                                          *\n",
       "*    The MadGraph5_aMC@NLO Development Team - Find us at   *\n",
       "*                 http://amcatnlo.cern.ch                  *\n",
@@ -309,16 +439,15 @@
       "*               Type 'help' for in-line help.              *\n",
       "*                                                          *\n",
       "************************************************************ \n",
-      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/bc70cb3af5e83ab35b937bca29b2536487b5662454482b4f2315ada2001d6774.bdir/Cards/amcatnlo_configuration.txt  \n",
-      "INFO: load configuration from /opt/MG5_aMC_v2_7_0/input/mg5_configuration.txt  \n",
-      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/bc70cb3af5e83ab35b937bca29b2536487b5662454482b4f2315ada2001d6774.bdir/Cards/amcatnlo_configuration.txt  \n",
-      "Using default eps viewer \"gv\". Set another one in ./input/mg5_configuration.txt\n",
+      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/90bcfe8bc719ea89d80f8ce26ccfc425f6ff8937aec2e0a3f8031ea161b0d4cc.bdir/Cards/amcatnlo_configuration.txt  \n",
+      "INFO: load configuration from /opt/MG5_aMC_v2_7_3/input/mg5_configuration.txt  \n",
+      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/90bcfe8bc719ea89d80f8ce26ccfc425f6ff8937aec2e0a3f8031ea161b0d4cc.bdir/Cards/amcatnlo_configuration.txt  \n",
+      "Using default eps viewer \"evince\". Set another one in ./input/mg5_configuration.txt\n",
       "Using default web browser \"firefox\". Set another one in ./input/mg5_configuration.txt\n",
       "calculate_xsect -f\n",
       "INFO: will run in mode: NLO \n",
       "INFO: Starting run \n",
-      "INFO: Compiling the code \n",
-      "write ./param_card.dat\n"
+      "INFO: Compiling the code \n"
      ]
     },
     {
@@ -333,6 +462,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "write ./param_card.dat\n",
       "INFO: MadSTR: Forcing width MDL_WSUL to zero inside param_card.inc \n",
       "INFO: MadSTR: Forcing width MP__MDL_WSUL to zero inside param_card.inc \n",
       "INFO: MadSTR: Forcing width MDL_WSCL to zero inside param_card.inc \n",
@@ -364,27 +494,27 @@
       " Check the MG5_aMC option 'output_dependencies'.\n",
       " This will prevent the use of HERWIG6/Pythia6 shower. \u001b[0m\n",
       "INFO: Compiling directories... \n",
-      "INFO: Compiling on 16 cores \n",
+      "INFO: Compiling on 8 cores \n",
       "INFO:  Compiling P0_uux_n1n1... \n",
       "INFO:  Compiling P0_ccx_n1n1... \n",
       "INFO:  Compiling P0_ddx_n1n1... \n",
+      "INFO:  Compiling P0_ssx_n1n1... \n",
       "INFO:  Compiling P0_uxu_n1n1... \n",
       "INFO:  Compiling P0_cxc_n1n1... \n",
-      "INFO:  Compiling P0_ssx_n1n1... \n",
       "INFO:  Compiling P0_dxd_n1n1... \n",
       "INFO:  Compiling P0_sxs_n1n1... \n",
+      "INFO:     P0_uux_n1n1 done. \n",
       "INFO:  Compiling P0_bbx_n1n1... \n",
+      "INFO:     P0_ddx_n1n1 done. \n",
       "INFO:  Compiling P0_bxb_n1n1... \n",
-      "INFO:     P0_bxb_n1n1 done. \n",
+      "INFO:     P0_sxs_n1n1 done. \n",
       "INFO:     P0_uxu_n1n1 done. \n",
-      "INFO:     P0_ccx_n1n1 done. \n",
-      "INFO:     P0_dxd_n1n1 done. \n",
-      "INFO:     P0_bbx_n1n1 done. \n",
       "INFO:     P0_cxc_n1n1 done. \n",
-      "INFO:     P0_sxs_n1n1 done. \n",
-      "INFO:     P0_ddx_n1n1 done. \n",
-      "INFO:     P0_uux_n1n1 done. \n",
+      "INFO:     P0_dxd_n1n1 done. \n",
+      "INFO:     P0_ccx_n1n1 done. \n",
       "INFO:     P0_ssx_n1n1 done. \n",
+      "INFO:     P0_bxb_n1n1 done. \n",
+      "INFO:     P0_bbx_n1n1 done. \n",
       "INFO: Checking test output: \n",
       "INFO: P0_uux_n1n1 \n",
       "INFO:  Result for test_ME: \n",
@@ -437,46 +567,46 @@
       "INFO:  Result for check_poles: \n",
       "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
       "INFO: Starting run \n",
-      "INFO: Using 16 cores \n",
+      "INFO: Using 8 cores \n",
       "INFO: Cleaning previous results \n",
       "INFO: Doing fixed order NLO \n",
       "INFO: Setting up grids \n",
-      "INFO:  Idle: 0,  Running: 10,  Completed: 0 [ current time: 02h31 ] \n",
-      "INFO:  Idle: 0,  Running: 9,  Completed: 1 [  5.1s  ] \n",
-      "INFO:  Idle: 0,  Running: 8,  Completed: 2 [  5.5s  ] \n",
-      "INFO:  Idle: 0,  Running: 7,  Completed: 3 [  6s  ] \n",
-      "INFO:  Idle: 0,  Running: 6,  Completed: 4 [  6.2s  ] \n",
-      "INFO:  Idle: 0,  Running: 5,  Completed: 5 [  7.6s  ] \n",
-      "INFO:  Idle: 0,  Running: 4,  Completed: 6 [  7.6s  ] \n",
-      "INFO:  Idle: 0,  Running: 3,  Completed: 7 [  7.8s  ] \n",
-      "INFO:  Idle: 0,  Running: 2,  Completed: 8 [  9.5s  ] \n",
-      "INFO:  Idle: 0,  Running: 1,  Completed: 9 [  10.8s  ] \n",
-      "INFO:  Idle: 0,  Running: 0,  Completed: 10 [  11.2s  ] \n",
+      "INFO:  Idle: 2,  Running: 8,  Completed: 0 [ current time: 13h11 ] \n",
+      "INFO:  Idle: 1,  Running: 8,  Completed: 1 [  13.4s  ] \n",
+      "INFO:  Idle: 0,  Running: 8,  Completed: 2 [  14.2s  ] \n",
+      "INFO:  Idle: 0,  Running: 7,  Completed: 3 [  15.1s  ] \n",
+      "INFO:  Idle: 0,  Running: 6,  Completed: 4 [  15.2s  ] \n",
+      "INFO:  Idle: 0,  Running: 5,  Completed: 5 [  15.6s  ] \n",
+      "INFO:  Idle: 0,  Running: 4,  Completed: 6 [  17.9s  ] \n",
+      "INFO:  Idle: 0,  Running: 3,  Completed: 7 [  21.1s  ] \n",
+      "INFO:  Idle: 0,  Running: 2,  Completed: 8 [  21.7s  ] \n",
+      "INFO:  Idle: 0,  Running: 1,  Completed: 9 [  23.3s  ] \n",
+      "INFO:  Idle: 0,  Running: 0,  Completed: 10 [  23.4s  ] \n",
       "INFO: \n",
       "      Results after grid setup:\n",
-      "      Total cross section:      7.307e-06 +- 2.4e-07 pb\n",
+      "      Total cross section:      7.316e-06 +- 2.4e-07 pb\n",
       " \n",
       "INFO: Refining results, step 1 \n",
-      "INFO:  Idle: 0,  Running: 10,  Completed: 0 [ current time: 02h31 ] \n",
-      "INFO:  Idle: 0,  Running: 9,  Completed: 1 [  4.7s  ] \n",
-      "INFO:  Idle: 0,  Running: 8,  Completed: 2 [  4.9s  ] \n",
-      "INFO:  Idle: 0,  Running: 7,  Completed: 3 [  5s  ] \n",
-      "INFO:  Idle: 0,  Running: 6,  Completed: 4 [  5.9s  ] \n",
-      "INFO:  Idle: 0,  Running: 5,  Completed: 5 [  6.4s  ] \n",
-      "INFO:  Idle: 0,  Running: 4,  Completed: 6 [  6.5s  ] \n",
-      "INFO:  Idle: 0,  Running: 3,  Completed: 7 [  7.6s  ] \n",
-      "INFO:  Idle: 0,  Running: 2,  Completed: 8 [  8s  ] \n",
-      "INFO:  Idle: 0,  Running: 1,  Completed: 9 [  9.1s  ] \n",
-      "INFO:  Idle: 0,  Running: 0,  Completed: 10 [  9.9s  ] \n",
+      "INFO:  Idle: 2,  Running: 8,  Completed: 0 [ current time: 13h11 ] \n",
+      "INFO:  Idle: 1,  Running: 8,  Completed: 1 [  16.8s  ] \n",
+      "INFO:  Idle: 0,  Running: 8,  Completed: 2 [  18.1s  ] \n",
+      "INFO:  Idle: 0,  Running: 7,  Completed: 3 [  18.5s  ] \n",
+      "INFO:  Idle: 0,  Running: 6,  Completed: 4 [  18.8s  ] \n",
+      "INFO:  Idle: 0,  Running: 5,  Completed: 5 [  21.4s  ] \n",
+      "INFO:  Idle: 0,  Running: 4,  Completed: 6 [  24.9s  ] \n",
+      "INFO:  Idle: 0,  Running: 3,  Completed: 7 [  26.9s  ] \n",
+      "INFO:  Idle: 0,  Running: 2,  Completed: 8 [  28.7s  ] \n",
+      "INFO:  Idle: 0,  Running: 1,  Completed: 9 [  30.8s  ] \n",
+      "INFO:  Idle: 0,  Running: 0,  Completed: 10 [  31s  ] \n",
       "INFO: \n",
       "   --------------------------------------------------------------\n",
       "      Final results and run summary:\n",
       "      Process p p > 1000022 1000022 [QCD]\n",
       "      Run at p-p collider (6500.0 + 6500.0 GeV)\n",
-      "      Total cross section: 7.402e-06 +- 1.2e-07 pb\n",
+      "      Total cross section: 7.403e-06 +- 1.2e-07 pb\n",
       "   --------------------------------------------------------------\n",
       " \n",
-      "INFO: The results of this run and the HwU and GnuPlot files with the plots have been saved in /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/bc70cb3af5e83ab35b937bca29b2536487b5662454482b4f2315ada2001d6774.bdir/Events/run_01 \n",
+      "INFO: The results of this run and the HwU and GnuPlot files with the plots have been saved in /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/90bcfe8bc719ea89d80f8ce26ccfc425f6ff8937aec2e0a3f8031ea161b0d4cc.bdir/Events/run_01 \n",
       "INFO: Run complete \n",
       "INFO:  \n",
       "quit\n",
@@ -487,8 +617,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "stty: 'standard input'stty: 'standard input': Inappropriate ioctl for device\n",
-      ": Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
@@ -510,8 +638,10 @@
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
-      "stty: stty: 'standard input': Inappropriate ioctl for device\n",
-      "'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
       "stty: 'standard input': Inappropriate ioctl for device\n",
@@ -551,7 +681,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -571,7 +701,7 @@
     "     ]\n",
     "for pa,pb in pss:\n",
     "    for param in params:\n",
-    "        i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.,model_path=\"/opt/MG5_aMC_v2_7_0/models/EWKino_NLO_UFO_py3\")\n",
+    "        i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.,model=\"/opt/MG5_aMC_v2_7_3/models/EWKino_NLO_UFO\")\n",
     "        li = [i]\n",
     "        li = hepi.mass_scan([i],pa, np.linspace(100,1000,7+8))\n",
     "        mg_dl = mg.run(li,skip=False,madstr=True)\n",
@@ -599,7 +729,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/source/examples/.ipynb_checkpoints/test_pyslha-checkpoint.ipynb b/docs/source/examples/.ipynb_checkpoints/test_pyslha-checkpoint.ipynb
index 989c16a73..5f5567815 100644
--- a/docs/source/examples/.ipynb_checkpoints/test_pyslha-checkpoint.ipynb
+++ b/docs/source/examples/.ipynb_checkpoints/test_pyslha-checkpoint.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "b583970e",
    "metadata": {},
    "outputs": [
@@ -18,8 +18,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1.4.18+dirty\n",
-      "~/git/resummino/\n"
+      "0.1.8.9+dirty\n",
+      "~/git/resummino_release\n"
      ]
     }
    ],
@@ -31,13 +31,13 @@
     "import hepi.resummino as rs\n",
     "import hepi.util as util\n",
     "import matplotlib.pyplot as plt\n",
-    "rs.set_path(\"~/git/resummino\")\n",
+    "rs.set_path(\"~/git/resummino_release\")\n",
     "print (rs.get_path())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "7d048757",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "ed7216a4",
    "metadata": {},
    "outputs": [
@@ -62,6 +62,17 @@
      "text": [
       "Running: 1 jobs\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0    (9.721+/-0.008)e-06\n",
+       "Name: NLO, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -70,17 +81,36 @@
     "        i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.,id=\"5\")\n",
     "        #i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"CT14lo\",\"CT14lo\",1., 1.,model_path=model_path,id=\"5\")\n",
     "        li = [i]\n",
-    "        li = hepi.mass_scan([i],pa, np.linspace(2.4876e2,2.4876e2,1))\n",
+    "        li = hepi.mass_scan([i],pa, np.linspace(2.48758955E+02,2.48758955E+02,1))\n",
     "        rs_dl = rs.run(li,noskip=False)\n",
-    "        print(rs_dl)"
+    "rs_dl[\"NLO\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "a74a3e82",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running: 1 jobs\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0    (9.721+/-0.008)e-06\n",
+       "Name: NLO, dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "for pa,pb in pss:\n",
     "    for param in params:\n",
@@ -89,7 +119,7 @@
     "        li = [i]\n",
     "        #li = hepi.mass_scan([i],pa, np.linspace(2.4876e2,2.4876e2,1))\n",
     "        rs_dl = rs.run(li,noskip=False)\n",
-    "        print(rs_dl)"
+    "rs_dl[\"NLO\"]"
    ]
   },
   {
@@ -99,6 +129,14 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02b72a93",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/source/examples/.ipynb_checkpoints/test_spheno-checkpoint.ipynb b/docs/source/examples/.ipynb_checkpoints/test_spheno-checkpoint.ipynb
index 09124a85b..929f354e3 100644
--- a/docs/source/examples/.ipynb_checkpoints/test_spheno-checkpoint.ipynb
+++ b/docs/source/examples/.ipynb_checkpoints/test_spheno-checkpoint.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SPheno example"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -9,9 +16,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0.2.60+dirty\n",
-      "~/git/resummino_ug_to_UX_vNLO/\n",
-      "~/git/SPheno-3.3.8/\n"
+      "0.1.8.9+dirty\n",
+      "~/git/resummino_release\n",
+      "SPheno\n"
      ]
     }
    ],
@@ -25,30 +32,52 @@
     "import hepi.spheno as sp\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "rs.set_path(\"~/git/resummino_ug_to_UX_vNLO\")\n",
-    "sp.set_path(\"~/git/SPheno-3.3.8\")\n",
+    "rs.set_path(\"~/git/resummino_release\")\n",
+    "sp.set_path(\"SPheno\")\n",
     "print (rs.get_path())\n",
     "print (sp.get_path())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 2,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.8/site-packages/hepi/input.py:283: RuntimeWarning: Could not set new central scale to average of masses.\n",
+      "  warnings.warn(\"Could not set new central scale to average of masses.\",\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running: 16 jobs\n",
-      "Running: 16 jobs\n"
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 16 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.8/site-packages/hepi/input.py:283: RuntimeWarning: Could not set new central scale to average of masses.\n",
+      "  warnings.warn(\"Could not set new central scale to average of masses.\",\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 16 jobs\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x864 with 3 Axes>"
       ]
@@ -60,7 +89,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x864 with 3 Axes>"
       ]
@@ -77,36 +106,47 @@
     "    fig.subplots_adjust(hspace=0)\n",
     "    for pdf,nlopdf in [(\"CT14lo\",\"CT14lo\")]:\n",
     "        li = [hepi.Input(hepi.Order.LO,13000,sq,1000022,\"LesHouches.in\",pdf,nlopdf,1., 1.,id=\"test\")]\n",
-    "        li=hepi.slha_scan_rel(li,lambda r : [[\"EXTPAR\",1,r],[\"EXTPAR\",2,490]],np.linspace(450.,600.,16))\n",
+    "        li=hepi.slha_scan_rel(li,lambda r : [[\"EXTPAR\",1,510],[\"EXTPAR\",2,r]],np.linspace(470.,530.,16))\n",
     "        sp.run(li)\n",
-    "        dl = rs.run(li,False,False)\n",
+    "        dl = rs.run(li,True,True)\n",
     "        for p in [1000022,1000023,1000025,1000035]:\n",
-    "            hepi.slha_plot(li,[\"EXTPAR\",1],[\"MASS\",p],axes=axs[0],logy=True,xaxis=\"$M_2$ [GeV]\",yaxis=\"$M$\",label=\"$\"+hepi.util.get_name(p)+\"$\",tight=False)\n",
+    "            hepi.slha_plot(li,[\"EXTPAR\",2],[\"MASS\",p],axes=axs[0],logy=True,xaxis=\"$M_2$ [GeV]\",yaxis=\"$M$\",label=\"$\"+hepi.util.get_name(p)+\"$\",tight=False)\n",
     "        for nm1 in [1]:\n",
     "            for nm2 in [1,2,3,4]:\n",
-    "                hepi.slha_plot(li,[\"EXTPAR\",1],[\"NMIX\",(nm1,nm2)],fmt=\"-\",interpolate=False,xaxis=\"$M_2$ [GeV]\",yaxis=\"Mixing\",logy=False,axes=axs[1],label=\"$\"+\"N_{\"+ str(nm1)+ str(nm2)+\"}$\",tight=False)\n",
-    "        hepi.vplot(hepi.slha_data(li,[\"EXTPAR\",1]),dl[\"lo\"],plot_data=True,axes=axs[2],xaxis=\"$M_2$ [GeV]\",yaxis= \"$\\sigma$ [pb]\",tight=False,label=\"$\\sigma_{lo}$\")"
+    "                hepi.slha_plot(li,[\"EXTPAR\",2],[\"NMIX\",(nm1,nm2)],fmt=\"-\",interpolate=False,xaxis=\"$M_2$ [GeV]\",yaxis=\"Mixing\",logy=False,axes=axs[1],label=\"$\"+\"N_{\"+ str(nm1)+ str(nm2)+\"}$\",tight=False)\n",
+    "        hepi.vplot(hepi.slha_data(li,[\"EXTPAR\",2]),dl[\"LO\"],plot_data=True,axes=axs[2],xaxis=\"$M_2$ [GeV]\",yaxis= \"$\\sigma$ [pb]\",tight=False,label=\"$\\sigma_{LO}$\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.8/site-packages/hepi/input.py:283: RuntimeWarning: Could not set new central scale to average of masses.\n",
+      "  warnings.warn(\"Could not set new central scale to average of masses.\",\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running: 256 jobs\n",
-      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskip1\n",
-      "flipped x y \n"
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 256 jobs\n",
+      "1\n",
+      "2\n",
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 256 jobs\n",
+      "1\n",
+      "2\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -116,17 +156,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n",
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -136,16 +168,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -155,18 +180,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running: 256 jobs\n",
-      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskip1\n",
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -176,17 +192,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n",
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -196,16 +204,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -225,19 +226,19 @@
     "        li=hepi.slha_scan(li,\"EXTPAR\",1,np.linspace(500.,1500.,16))\n",
     "        li=hepi.slha_scan(li,\"EXTPAR\",2,np.linspace(500.,1500.,16))\n",
     "        sp.run(li)\n",
-    "        dl = rs.run(li,False,False)\n",
+    "        dl = rs.run(li,True,True)\n",
     "        for nm1 in [1]:\n",
     "            for nm2 in [1,2]:\n",
     "                print(nm2)\n",
     "                hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),\n",
-    "                               hepi.slha_data(li,[\"NMIX\",(nm1,nm2)]),logz=False,xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"Mixing\")\n",
+    "                               hepi.slha_data(li,[\"NMIX\",(nm1,nm2)]),logz=False,xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"Mixing\",show=True)\n",
     "        \n",
-    "        hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),dl[\"lo\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{lo}$\")"
+    "        hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),dl[\"LO\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{LO}$\",show=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 4,
    "metadata": {
     "scrolled": false
    },
@@ -246,14 +247,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running: 121 jobs\n",
-      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskip1\n",
-      "flipped x y \n"
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 121 jobs\n",
+      "1\n",
+      "2\n",
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 121 jobs\n",
+      "1\n",
+      "2\n"
      ]
     },
     {
      "data": {
-      "image/png": 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/WZc0t++m1dzNdd84Yv0uSXN3d1zPT5rZ3kzVjF/V67EREaaH4bW6OZ+9ihH5qE60reX3AO8Zedx4SPqw7SskfXyUXS4Z6/iMFhYRxQ1xh/n76veOs/LuzqRLuE3OadbkHGQAz23wXDMantTslCeaPd8vp9IcZNDsvGEDOAdZ04b0yV5sf7V+v348x0/+P9mIGDjD2sKVtHKsz22fNdbnSbgRUVTDvRRK+3dUD1p8AfgunfsBjyoJNyKKG9YWLtXYMacDZ1N1P/t74Au2N3Rz8B7P+BAR0YtWL4VuXoPG9jO2b7Z9LvBKqhl6b5c02gy+u0gLNyKKG+KSApL2pXoQ7GyqmWo+zm6GZWxJwo2I4oY14Uq6Hvi3VAPg/Lnte3s5Pgk3IorqYSSwQfQO4EngGOCS+sk1qG6e2faBYx2chBsRxQ1rwrW9R/e9knAjoqimH+0dJkm4EVHcsNZw91QSbkQUN6wlhT2VhBsRRQ35k2Z7JAk3IopLCzciopC0cCMiCkgvhYiIglJSiIgoZKqWFDJaWEQU5R5e3ZA0T9L9kjZJWtzhc0n6eP35PZJ+s5EvMg5JuBFR3I4uX7sjaRpwNXAmcCxwtqRjR+x2JjC7fi0CPtXAVxiXSVVS2Itm5yFrcg4ygBn7NXeu059s7lwAT2YOssE63yTXYElhLrDJ9oMAkpYBC4CNbfssAP7GtoE7JR0saabtx5oLoztp4UZEUa0HH5po4QJHUk1507K53tbrPkXkv+WIKK6HFu50SWvb1pfaXtq23mlOsZHl3272KSIJNyKK6vHR3m2254zx+WbgqLb1WcCWcexTREoKEVFcg70U1gCzJR0taR9gITByKvOVwDvr3gqvBP6pH/VbSAs3Ivqgqd/nbW+XdDHwdWAacJ3tDZIurD+/BlgFzKea8PGfgXc3dPmeJeFGRFFNP9prexVVUm3fdk3bsoGLGrzkuCXhRkRxU/VJsyTciCguYylERBQwlQcgL9pLQdI0Sd+XdFO9foKk70haL+mrkg5s23dJ/ezz/ZLOKBlnREysJsdSGCalu4W9D7ivbf1aYLHt44EVwIcA6mehFwLHAfOAT9bPTEfEJNDgk2ZDpVjClTQLeB1Vkm15EXBHvbwaeHO9vABYZvsp2w9RdeeYWyrWiJg4rV4K3bwmm5It3KuAD7Prf1z3AmfVy29h59MgA/Psc0Q0Ly3cCSTp9cBW2+tGfHQecJGkdcABwNOtQzqcpmNJR9IiSWslrf1FYxFHxESaqjXcUr0UTgLOkjQf2A84UNLnbJ8DvBZA0jFUJQfo4dnneiCLpQC/Lk3GP6OISWWyJtNuFGnh2l5ie5btF1DdDPuG7XMkzQCQtBfwx0Dr6ZCVwEJJ+0o6mmrg4LtKxBoRE2+qlhT63Q/3bEmtR+5uBP4aoH4WejnVIMLbgYtsT8YaesSUk1l7C7J9O3B7vfwx4GOj7HcZcFmxwCKimKlaUuh3CzcipqDJWC7oxqRKuHsB+zd4vuc3OUEaMP+XzZ3riR/8bXMnAzj6Nc2eb98Dmj1f5iCbVNLCjYgoYCqPpZCEGxHFpYUbEVFAeilERBSUkkJERCEpKUREFDCVb5plmvSIKK7Eo72SDpW0WtID9fshHfY5StI/SLpP0gZJ79vDy44pCTciiis0Wthi4Dbbs4Hb6vWRtgN/ZPslwCupRi88ds8v3VkSbkQUVXAA8gXA9fXy9cAbnxWL/Zjt79XLT1DNSDNhY2+nhhsRxfXQep0uaW3b+tJ6SNZuHGb7MagSa2t0wtFIegHwMuC73YfXmyTciCiuh/rsNttzRvtQ0q3A4R0++kgv8Uh6HvAl4P22J2wugyTciCiqyQHIbY86CIikxyXNrFu3M4Gto+z3HKpk+3nbNzYUWkep4UZEcYUGIF8JnFsvnwt8ZeQOkgR8BrjP9l/t+SXHloQbEUUVvGl2OXC6pAeA0+t1JB0haVW9z0nAO4BTJd1dv+bv+aU7S0khIoor8aSZ7Z8Ap3XYvgWYXy9/i86T1k6IJNyIKG6qPmmWhBsRRU3lWXuTcCOiuLRwJ4G9gec3+I1e/3Rz5wL4eZPT4rzwjObOBZkSJ4pKCzciogBTDWAwFSXhRkRxaeFGRBSShBsRUcBUHoA8CTciikvCjYgoJCWFiIgCMk16RERBKSlERBSQR3sjIgpKwo2IKCQlhYiIAnLTLCKioJQUIiIKmaolhaJzmkmaJun7km6q10+UdGc9j9BaSXPb9l0iaZOk+yU1PBZhRPSLe3hNNqUnkXwfcF/b+hXAn9s+EfiTeh1JxwILgeOAecAnJU0rG2pETJQSs/ZKOlTSakkP1O+HjLHvLo3BiVIs4UqaBbwOuLZts4ED6+WDgC318gJgme2nbD8EbALmEhGTQqEW7mLgNtuzgdvq9dGMbAxOiJIt3KuAD7Prf1zvB66U9Cjwl8CSevuRwKNt+22utz2LpEV1OWLtzxsOOCKaV3Ca9AXA9fXy9cAbO+00SmNwQhRJuJJeD2y1vW7ER+8FPmD7KOADwGdah3Q4Tcf/8GwvtT3H9pyDmwo4IiZUDyWF6a0GVf1a1MNlDrP9GED9PmOU/a7i2Y3BCVGql8JJwFmS5gP7AQdK+hzwBqqmPMAX2fk/zGbgqLbjZ7Gz3DCqTcAbG5y742dNzkEGzc5DljnIYoj1UC7YZnvOaB9KuhU4vMNHH+nm5O2NQUkndx/W+BT5V2F7CXW5oP5SH7R9jqT7gFcDtwOnAg/Uh6wEbpD0V8ARwGzgrhKxRsTEanIActuvGe0zSY9Lmmn7MUkzga0dduvYGLR9TkMh7qLfzZALgI9J2hv4JbAIwPYGScuBjVTzzV1ke6o+nBIx6RTq8rUSOBe4vH7/yrPiGKUxOFEBFU+4tm+natFi+1vAy0fZ7zLgsmKBRUQxhR58uBxYLul84BHgLQCSjgCutT2/TBg79buFGxFTTKmxFGz/BDitw/YtwLOSbXtjcKIk4UZEcZPxKbJuJOFGRFGZtTcioqAk3IiIQlJSiIgoIAOQR0QUlJJCREQhKSlERBQwWQcX70YSbkQUl5JCREQhaeFGRBSQXgoREQWlpBARUUhKChERBWQshYiIgqZqC1f25Pnqkp4A7u93HD2YDmzrdxBdGqZYYbjiHaZYAV5ke9yT6km6meo7d2Ob7XnjvdagmWwJd+1YE84NmmGKd5hiheGKd5hiheGLd5AUmSY9IiKScCMiiplsCXdpvwPo0TDFO0yxwnDFO0yxwvDFOzAmVQ03ImKQTbYWbkTEwErCjYgoZOgSrqRpkr4v6aZ6/c8k/UjS3fVrftu+SyRtknS/pDP6HWu97Q/reDZIumJQYu0Ur6S/a/u5Pizp7kGJt0OsJ0q6s451raS5gxLrKPGeIOk7ktZL+qqkAwcl3vrPen3rZ1lvO1TSakkP1O+HDEq8Q8X2UL2A/wjcANxUr/8Z8MEO+x0L/ADYFzga+F/AtD7HegpwK7BvvT5jUGLtFO+Iz/4r8CeDEm+Hn+0twJn18nzg9kGJdZR41wCvrpfPA/5iUOIFHgamj9h2BbC4Xl4MfHRQ4h2m11C1cCXNAl4HXNvF7guAZbafsv0QsAmYu5tjGjNKrO8FLrf9FIDtrYMQ6xjxtj4T8FbgC/WmQfzZGmi1Eg8CtgxCrDBqvC8C7qiXVwNvrpf7Hu8oFgDX18vXA29s2z6I8Q6koUq4wFXAh3n22BcXS7pH0nVtv+ocCTzats/melspV/HsWI8BXiXpu5K+Kem36u39jhVG/9kCvAp43PYD9Xq/472KZ8f6fuBKSY8Cfwksqbf3O1boHO+9wFn18luAo+rlQYjXwC2S1klaVG87zPZjAPX7jHr7IMQ7NIYm4Up6PbDV9roRH30KeCFwIvAY1a++AOpwmiJ94MaIdW/gEOCVwIeA5XXrsW+xwpjxtpzNztYtDObP9r3AB2wfBXwA+EzrkA6nGYSf7XnARZLWAQcAT7cO6XCa0n03T7L9m8CZVDH+zhj7DkK8Q2OYRgs7CTirvim2H3CgpM/ZPqe1g6T/AbRuUG1mZ6sBYBY7f83sS6x1TDe6Kn7dJWkH1SAe/Yx11HhtnyNpb+BNwMvb9h/En+0bgPfV+3yRnb++D+zPFngtgKRjqEoOgxAvtrfU71slraAqETwuaabtxyTNBFrlsL7HO1T6XUQezws4mZ03H2a2bf8AVT0J4Dh2LeY/SH9ulrTHeiHwn+vlY6h+FdOgxDoy3np9HvDNEfsMRLwjfrb3ASfXy6cB6wYp1g7xtm6Y7gX8DXDeIMQL7A8c0Lb87frvwJXsetPsikGId9hew9TCHc0Vkk6k+jXmYeD3AWxvkLQc2AhsBy6y3e+plK4DrpN0L9WvkOe6+ls7iLG2LGTXcsKg/mwvAD5Wt8h/CSyCgY0V4GxJF9XLNwJ/DQMR72HAiqrSxd7ADbZvlrSGqgR2PvAIVd15EOIdKnm0NyKikKG5aRYRMeyScCMiCknCjYgoJAk3IqKQJNyIiEKScCMiCknCjWIk/b4kS3p127aL622v6WdsESUk4UZJLwXuAV4CIOm5wPnAj4H1fYwroogk3CjpeKqn1l5cr19CNe7BDtuP9y2qiEKScKOklwDLgRdLOgh4G9Wz+vf2NaqIQvJobxQh6Shgte0XS/oe8DWq2QH2pmrx3kE1YtYM4Grbt/Qt2IgJkhZulPJSdtZpn6Aagep6qjLDettftn0B8C6qlm/EpDMZRguL4XA8OxPulcBPbD8j6Xjgs237/TFwdeHYIopIwo1Sjge+BGD7prbtxwIb65kvLge+Zvt7fYgvYsKlhhsDQdIlwLlUs9nebfuaPocU0bgk3IiIQnLTLCKikCTciIhCknAjIgpJwo2IKCQJNyKikCTciIhCknAjIgpJwo2IKCQJNyKikP8PQ6A0rwkjT8kAAAAASUVORK5CYII=\n",
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jbSewRURMV31cfqtdz/EympX5DwXeKunQIafdASyx/TLgWuBPJ1J8SZ+RtEfH8X+VdFM313Y1FcD2j2x/BDgWOBD4uqRXjau0ERFRhNz91oWjgA22H7S9nWYx4+WdJ9j+Z9vb2sNbaNaFnIh/AW6VtEzSO2gek13SzYXdDCj5JeDgdjsEeAnwQ2Dv8ZY2IiIK6K1bcoGkdR3HK22v7DjeD3io43gjMFoj52zgH3sqwRC2Py7pXuCfgc3AK2w/2s213TxzWwvcSROlL7X93fEVMyIiiuotuG22vaQf2Uo6DVhCMzBkIvf5TeB/AqcDLwPWSDrL9jfHurab4PY7wGHAScAfSNpCM1LybuAe218cb8EjImJy9NDd2K2Hgf07jhe2aT+er/Q64H3AL9t+ZoJ5vhH4RdubgL+TdB2wii4WDxkzuNn+eOexpIXA4TRR9I0079iJiIjppr9TAW4DFks6gCaonUrzbrXnSHoF8HFgaRuQJsT2G4Ycf13SUd1c2/MKJbY30vS1TqgvNSIiJlkfW262d0h6F3ADzVSAT9q+V9KHgXW2VwN/BvwU8HlJAP9h+5Re85J0vu0/lXTpCKe8e6x75K0AERGV6vcKJbbXAGuGpH2gY/91fcpqfft5+3hvkOAWEVGrGbr8lu1/aD9XjfceVQU3Y3Z6oFh+A4X/5TzjZ4vmt377vKL5/eFhryma3wN/cXjR/AB23drzW6YmZO7TZZdf0o6i2aFyP+5Tkt+E9H9ASTGSVo/2fTddnVUFt4iI6DBDgxvwapo5dX8H3Ar0/FtagltERK1mbnB7KXAC8FaaEZlfBv7O9r3d3qBsH0lERBTT5+W3irG90/ZXbJ8BHA1sANa2ozW7kpZbRERMO5J2o1k85K3AIuBS4Lpur09wi4io1TRrkXVL0pU0K2OtAf7Q9j293iPBLSKiRtOwu7EHpwH/CZwLvLudEA7NwBLbnj/WDRLcIiJqNUODm+0JjwcpOqBE0lxJd0i6vj3+P5LubLfvS/pimy5Jl0raIOkuSUeWLGdERBX6+LLSmaZ0y+1cmmVV5gPYfm7WrqQvAF9qD08EFrfbq4DLGf29QRER0UHM6G7JCSvWcmvfJnAScMUw380HjuP5NwwsB6504xZgT0n7liprREQVZnHLrWS35CXA+cBwC9i8AbjJ9g/a4+He+LrfZBYuIqIqPcxxq7GFVyS4SToZ2GR7pBWe30qzzMp47r1C0jpJ6zZvmUkLv0VETLK03CbdMcApkr4LXA0cJ+kqAEkLgKNollcZ1NUbXwFsr7S9xPaSBXtnwZWIiOckuE0u2xfaXmh7Ec3bW2+2fVr79ZuA620/3XHJauD0dtTk0cBW24+UKGtERC1mc7fkdJjndipw8ZC0NcAymvXEtgFnlS5URMSMZoYf4TBLFA9uttcCazuOXzvMOQbOKVaoiIgK1dgi69Z0aLlFRMRkSHCLiIjapOUWERH1SXCLiIiqVDrEv1sJbhERFVK7zVaZ9RwRUas+T+KWtFTS/e0bWy4Y5vvdJH2u/f5WSYv6Uo9xqK7lNlCwHf6Mny2WF8A3t88rmt8fH/6asU/qo/v/9LCi+e2ytfzvdnOfKfu79Jyy/0RR4XlVpfMr2s3Xh7z6OaBE0lzgMuAEmvV+b5O02vZ9HaedDTxp+0BJpwIfAd7Sv1J0Ly23iIha9bfldhSwwfaDtrfTLKW4fMg5y4FV7f61wPHqeI12SQluERG16i24LRhchL7dVgy5Wzdva3nuHNs7gK3A3n2sUdeq65aMiAiee+VNDzbbXjJJpSkuLbeIiFr1t1uym7e1PHeOpF2APYAt4y3+RCS4RURUqs9vBbgNWCzpAEnzaBa9Xz3knNXAGe3+m2jeADMls+3SLRkRUas+hhXbOyS9C7gBmAt80va9kj4MrLO9GvgE8BlJG4AnaALglEhwi4ioVL/XlrS9huaVZJ1pH+jYfxr4tf7mOj4JbhERNcryWxERUaUEt4iIqInIK28iIqJGCW4REVEbTc0o/GkhwS0iokYZUBIRETXKM7eIiKhPgltERNQmLbeIiKhPgltERFSl91feVCXBLSKiVgluERFREwEamL3RLcEtIqJS6ZasxADmGT9bLL9vbp9XLC+Ai454bdH87r/4kKL57bq17Ltz5zyjovkBzNlRNj8N1J1f6W633Z4ol6F2TvAGmcQdERE1Kv7LxjSS4BYRUau03CIiojZ55hYREXUxkLcCREREbdJyi4iI+iS4RURETcTsbrkVnVgkaa6kOyRd3x5L0kWSHpC0XtK7O9IvlbRB0l2SjixZzoiIGc/ubatM2VmzcC6wvuP4TGB/4GDbhwBXt+knAovbbQVwecEyRkRUQe5+m1A+0l6SbpT07fbzRcOcc4Skf5N0b9toecvEch1dseAmaSFwEnBFR/LvAB+2PQBge1Obvhy40o1bgD0l7VuqrBERVXAP28RcANxkezFwU3s81DbgdNs/DywFLpG054RzHkHJltslwPlA55z5nwXeImmdpH+UtLhN3w94qOO8jW1aRER0qVTLjaZBsqrdXwW8YegJth+w/e12//vAJuDFE855BEWCm6STgU22bx/y1W7A07aXAH8DfHIc917RBsd1W7bM4rVmIiI6GRhw9xssGPy/tN1W9JDbPrYfafcfBfYZ7WRJRwHzgH8fT9W6UWq05DHAKZKWAbsD8yVdRdMi+/v2nOuAT7X7D9M8ixu0sE37CbZXAisBXvHyefU9FY2IGK/e/kfc3DY0hiXpn4CXDvPV+34sS9vSyG3B9hHTZ4AzBh9JTYYiwc32hcCFAJJeC7zH9mmSLgaOBb4D/DLwQHvJauBdkq4GXgVs7fitICIiutDPqQC2XzdiPtJjkva1/UgbvDaNcN584MvA+9rxFJOm9GjJoS4G3ijpbuBPgLe36WuAB4ENNN2V75ya4kVEzGDlpgKsBs5o988AvjT0BEnzaHrorrR97UQzHEvxSdy21wJr2/2naEZQDj3HwDklyxURUZuCk7gvBq6RdDbwPeDNAJKWAL9t++1t2i8Be0s6s73uTNt3TkaBskJJRESNCr6s1PYW4Phh0tfR9sjZvgq4qkyJEtwiIqrULL81e8fYJbhFRNRqFs+OSnCLiKhUWm4REVGXgs/cpqMEt4iIKtW52n+3qgpuP7L45vZ5xfK76Mhji+UF8K0/Prhofrv+oOw0yDnPqGx+O4pmB4AKPwMpnV/plsJuTxTOsOw/0Qmbze9zqyq4RUREh7TcIiKiKp6Clvs0kuAWEVGrgbTcIiKiMpkKEBER9Ulwi4iIqpisUBIREXURTrdkRERUKMEtIiKqk+AWERFVyTO3iIioUZ65RUREfRLcIiKiLnkrQERE1MYkuEVERIUyoCQiImqTASUREVGfWRzcyr5qOSIiyjDNK2+63SZA0l6SbpT07fbzRaOcO1/SRkn/e0KZjiHBLSKiSu1oyW63ibkAuMn2YuCm9ngkfwR8baIZjiXBLSKiVuWC23JgVbu/CnjDcCdJeiWwD/DViWY4lgS3iIha9RbcFkha17Gt6CGnfWw/0u4/ShPAfoykOcCfA++ZcL26UNWAkkfWz+eiX3hdsfy+9YcHFcsLYNcfqGh+c58pm592FM0OTcGzdpUeml04v92eKvyHWvafaPn8JmLwmVv3NtteMtKXkv4JeOkwX73vx7K1LQ370/VOYI3tjdLk/0FWFdwiImKQwf377cb2iC0HSY9J2tf2I5L2BTYNc9qrgddIeifwU8A8Sf/P9mjP58YtwS0iolblpgKsBs4ALm4/v/STRfFvDO5LOhNYMlmBDfLMLSKiTgWnAtAEtRMkfRt4XXuMpCWSrpjozccjLbeIiFoVarnZ3gIcP0z6OuDtw6R/Gvj0ZJYpwS0iolazeIWSBLeIiCrllTcREVEbAwOz97UARQeUSJor6Q5J17fHn5b0HUl3ttsRbbokXSppg6S7JB1ZspwREVUot0LJtFO65XYusB6Y35F2nu1rh5x3IrC43V4FXN5+RkREVww703KbdJIWAicB3QwLXQ5c6cYtwJ7txMCIiOiGwR7oeqtNyW7JS4Dz+ckFgS5qux4/Kmm3Nm0/4KGOcza2aRER0a1y89ymnSLBTdLJwCbbtw/56kLgYOAXgL2A947j3isGF/rcPvD0xAsbEVGLWfzMrVTL7RjgFEnfBa4GjpN0le1H2q7HZ4BPAUe15z8M7N9x/cI27SfYXml7ie0l8+bsPnk1iIiYSexmtGS3W2WKBDfbF9peaHsRcCpws+3TBp+jqVki+g3APe0lq4HT21GTRwNbO16nEBER3ZjFLbepnuf2t5JeTPMiiTuB327T1wDLgA3ANuCsKSldRMQM5gpbZN0qHtxsrwXWtvvHjXCOgXPKlSoiojZ1tsi6NdUtt4iImAy9v6y0KgluERG1qnD+WrcS3CIiKmTAablFRERV7LTcIiKiPmm5RUREfWZxy02uaKiopMeB7011ObqwANg81YWYRKnfzJb6TQ//1faLx3uxpK/Q1LVbm20vHW9+001VwW2mkLTO9pKpLsdkSf1mttQvalD0ZaURERElJLhFRER1EtymxsqpLsAkS/1mttQvZrw8c4uIiOqk5RYREdVJcJskkuZKukPS9e3x8ZK+IelOSf8i6cA2fTdJn5O0QdKtkhZNacG7NEz9jmvrd4+kVZJ2adMl6dK2fndJOnJqSz42Sd+VdHf7d7WuTdtL0o2Svt1+vqhNr6V+vybpXkkDkpYMOf/Ctn73S/qVqSl1b0ao459J+lb793SdpD07zp9xdYzRJbhNnnOB9R3HlwO/YfsI4LPA+9v0s4EnbR8IfBT4SMlCTsBz9ZM0B1gFnGr7MJq5hme0550ILG63FTR/DjPBsbaP6BgyfgFwk+3FwE3tMdRTv3uAXwW+1nmSpENpXjD888BS4GOS5hYt6fgNreONwGG2XwY8AFwIM76OMYIEt0kgaSFwEnBFR7KB+e3+HsD32/3lNIEB4Frg+PbN5NPWMPXbG9hu+4H2+Ebgje3+cuBKN24B9hx8A/sM0/n3tIrmzfGD6TO+frbX275/mK+WA1fbfsb2d2heIHxU2dL1h+2v2t7RHt4CLGz3q6ljPC/BbXJcApwPdK5983ZgjaSNwG8CF7fp+wEPAbQ/eFtpgsV0dgk/Xr/NwC4d3VlvAvZv95+rX2tjmzadGfiqpNslrWjT9rH9SLv/KLBPu19L/UYyE+sHY9fxbcA/tvsztY4xigS3PpN0MrDJ9u1Dvvo9YJnthcCngL8oXrg+GK5+7ZvTTwU+KunrwA+BnVNUxH74RdtH0nQ5niPplzq/bOs7k4cZj1q/SoxYR0nvA3YAfztVhYvJl4WT++8Y4BRJy4DdgfmSvgwcbPvW9pzPAV9p9x+maeVsbAdh7AFsKVzmXgxXv6tsnwa8BkDS64GD2vMH6zdoYZs2bdl+uP3cJOk6mi6qxyTta/uRtttxU3t6LfX72ginz7j6wch1lHQmcDJwvJ+fBzUj6xijS8utz2xfaHuh7UU0rZmbafr095A0+B/+CTw/2GQ1zw++eBNwc8cP3bQzXP1snybpJdCM/gTeC/x1e8lq4PR2VOHRwNaO7r1pR9ILJL1wcB94Pc1gi86/pzOAL7X7tdRvJKuBU9tRvQfQDJz5+uSXdPxGqqOkpTTd6afY3tZxyYyrY4wtLbcCbO+Q9A7gC5IGgCdp+vwBPgF8RtIG4AmagDETndd2Wc4BLrd9c5u+BlhG85B+G3DWFJWvW/sA17VjenYBPmv7K5JuA66RdDbNaNA3t+fXUr//AfwV8GLgy5LutP0rtu+VdA1wH01X3jm2p3uX80h13ADsBtzYfneL7d+eoXWMMWSFkoiIqE66JSMiojoJbhERUZ0Et4iIqE6CW0REVCfBLSIiqpPgFhER1Ulwi4iI6iS4RYxC0m9JsqTXdqSd06adMHUli4jRJLhFjO5w4JvAwQCS/gvNGx4eB+6awnJFxCgS3CJG9zLgatrgBrwb+DwwYPuxKStVRIwqwS1idIcA1wAHS9oTeAvwr4y+2HBETLEEt4gRSNof2GL7QeAlwHk0iwsfBNwt6WckfULStVNZzoj4SQluESM7HLi73f8hsBRY1abfZftB22dPVeEiYmR55U3EyF7G88Htz2hacTslHU4T5CJimkpwixjZ4cAXAGxf35F+KHDvlJQoIrqS97lFjJOkvYGLaN6sfoXtP5niIkVEK8EtIiKqkwElERFRnQS3iIioToJbRERUJ8EtIiKqk+AWERHVSXCLiIjqJLhFRER1EtwiIqI6CW4REVGd/w+sqFDezGPshAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -263,17 +267,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n",
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -283,16 +279,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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1xy4qJzDeIOn0nHGaWWulnAi9E+UuNVwBPFX3/mvAVRFxLLAc+DCApFkUz1YfA8wBvlxObGxmXaCKS//klC3xSpoCnEGRbGuOBFaV2/cD55Tbc4GlEbE1Ip4FNlJMbGxmHa42qqGRV7fK2eO9DvgIO/8iWwecXW6fy445boabxHhyi+Mzs0x6/cm1LIm3XLVzc0SsGfKt+cBCSWuAccDva6cM08ywJR9JCyStlrT6N8kiNrNWaqLG+1JELIiIlW0JtEVyjWo4GThb0jspFpM7SNItEXEBcBqApJkUpQhoYhLjchmQAYDDpW6ux5t1hW6/cdaILD3eiFgUEVMiYhrFTbPvRsQFkiYCSNoH+DhwQ3nKCmCepLGSpgMzgEdzxGpmrdfrN9faPY73fEkLy+3bga8DRMR6ScsollveBiyMiG6utZv1DD8y3IbEGxEPAg+W218AvjDCcYuBxdkCM7NsXGowM8us10c1tLvUkNT+FMXgVPpfm7Ax4HUXJxwR997l6doCeGXKT470a5p5jbTRq+Bn5/l4zcwySr3mWidy4jWz7Hq9xuvEa2ZZeVSDE6+ZtYFLDWZmmfV6qcHDycwsKy/v7h6vmbVBr69A4cRrZtn1eqnBidfMsvKoBideM2sD93jNzDLzcDIzs4w8EboTr5m1gXu8ZmYZ+eaaE6+ZtUGvlxr85JqZZecn18zMMmry5pqfXDMzS8E317rIAfvBGw9P197MSyelawzg/JXp2kq9VM+Y/dO2V8HlZqw6er3G638dZpZVANvaHUSbOfGaWXbu8ZqZZebEa2aWkVcZduI1szZw4jUzy8ylBjOzjDxXgxOvmbWBSw1mZhl5Pl4nXjNrg15PvJ6dzMyy8+xkZmYZNXlzzbOTmZml0OulBideM8uu10c1ZK3xSuqT9GNJd5TvT5D0iKTHJa2WNLvu2EWSNkraIOn0nHGaWetEE69ulbvHewXwFHBQ+f5a4JMRcbekd5bvT5E0C5gHHAMcDjwgaWZE9Pq4a7Ou4B5vJpKmAGcAX6vbHexIwuOBTeX2XGBpRGyNiGeBjcBszKwruMebz3XAR4Bxdfs+BNwr6XMUvwT+tNw/GXik7rjBct8uJC0AFgAc3pc0XjNrAT8ynKnHK+lMYHNErBnyrUuBKyNiKnAlcGPtlGGaGfYXYEQMRER/RPQf7MRr1hGaGMfblXL1eE8Gzi7ruPsDB0m6BTiLou4L8C12lCEGgal1509hRxliRPu/qo8jLxmfLGjed3e6tgBedXS6tlKvaeY10iyjbi4jNCJLjzciFkXElIiYRnHT7LsRcQFFMn1zedhbgJ+W2yuAeZLGSpoOzAAezRGrmbVWbSJ093jb54PAFySNAX5HWauNiPWSlgFPUqyLt9AjGsy6R6/3eLMn3oh4EHiw3P4+cOIIxy0GFmcLzMyy6ebebCPa3eM1sx7jUQ1OvGbWBi41mJll5FWGnXjNrA2ceM3MMnOpwcwsI99cc+I1szbo9VKD11wzs+yqNjuZpFMkfU/SDZJOafX1nHjNLKvUE6FLuknSZknrhuyfUy6ksFHSVQ2E9WuKuWQGG/7DjJJLDWaWXeJSwxLgS8A3ajsk9QHXA2+nSKSPSVoB9AHXDDl/PvC9iHhI0iTgH4D3pQ1xZ068ZpZdE2WEQyWtrns/EBEDO7UVsUrStCHnzQY2RsQzAJKWAnMj4hrgzN1c75fA2MbDGx0nXjPLqslRDVsion8Ul5kMPF/3fhA4aaSDJb0HOB2YQNF7biknXjPLLsOohoYXUwCIiNuB21sXzs6ceM0suwwjFka1mEIuHtVgZlk1ORH6eEkDks5q8jKPATMkTZe0H8UCDCtSxJ+Ce7xmll0TPd6XImLB7g6QdBtwCsWNuEHg6oi4UdJlwL0UIxluioj1ow44MUV0z1PTkn4FbGh3HE04FNjS7iAa1EmxQmfF20mxAhwZEeP2fNjwJN1D8WduxJaImDPaa1VVtyXe1aO8A9oWnRRvJ8UKnRVvJ8UKnRdvFbnGa2aWmROvmVlm3ZZ4B/Z8SKV0UrydFCt0VrydFCt0XryV01U1XjOzTtBtPV4zs8pz4jUzy6zjEq+kPkk/lnRH+f4Tkv5H0uPl6511xy4q5+LcIOn0dsda7vvLMp71kq6tSqzDxSvpm3Wf63OSHq9KvMPEeoKkR8pYV0uaXZVYR4j3eEkPS1oraaWkg6oSb/l3vbb2WZb7DpF0v6Sfll8Prkq8HSkiOuoF/BVwK3BH+f4TwN8Mc9ws4CcUU7xNB/4L6GtzrKcCDwBjy/cTqxLrcPEO+d7ngb+tSrzDfLb3Ae8ot98JPFiVWEeI9zHgzeX2fOBTVYkXeA44dMi+a4Gryu2rgM9UJd5OfHVUj1fSFOAM4GsNHD4XWBoRWyPiWWAjxRydWYwQ66XApyNiK0BEbK5CrLuJt/Y9AecBt5W7qvjZBlDrNY5nx4QoVf1sjwRWldv3A+eU222PdwRzgZvL7ZuBd9Xtr2K8ldZRiRe4DvgIu84qd5mkJ8olQGr/BRpuPs7JrQ/xD65j11hnAm+S9ENJD0l6Y7m/3bHCyJ8twJuAFyPip+X7dsd7HbvG+iHgs5KeBz4HLCr3tztWGD7edcDZ5fa57JhJqwrxBnCfpDWSavMkTIqIFwDKrxPL/VWIt+N0TOKVdCawOSLWDPnWV4DXAScAL1D8lxianI8zpd3EOgY4GPhj4MPAsrI32bZYYbfx1pzPjt4uVPOzvRS4MiKmAlcCN9ZOGaaZKny284GFktYA44Df104ZppncYz5Pjog3AO+giPHPdnNsFeLtOJ00O9nJwNnlzbP9gYMk3RIRF9QOkPRPQO1GVjvn4xw21jKm26Mojj0qaTvFZCHtnjt0xM9W0hjgPcCJdcdX8bM9C7iiPOZb7PhvfWU/W+A0AEkzKUoRVYiXiNhUft0saTlF6eBFSYdFxAuSDgNqZbK2x9uR2l1kHs2LYgq42k2Kw+r2X0lRbwI4hp2L/s/Qnpsq9bFeAvxduT2T4r9oqkqsQ+Mt388BHhpyTCXiHfLZPgWcUm6/FVhTpViHibd2Y3UfikUa51chXuBAYFzd9g/Kn4HPsvPNtWurEG+nvjqpxzuSayWdQPHfm+eAiwEiYr2kZcCTwDZgYUQ0sdRTS9wE3KRiGerfAxdG8dNbxVhr5rFzmaGqn+0HgS+UPfTfAQugsrECnC9pYbl9O/B1qES8k4DlRQWMMcCtEXGPpMcoSmMXAT+jqEtXId6O5EeGzcwy65iba2Zm3cKJ18wsMydeM7PMnHjNzDJz4jUzy8yJ18wsMydey0bSxZJC0pvr9l1W7ntbO2Mzy8mJ13I6DngCOBpA0gHARcDPgbVtjMssKydey+lYiqfgjirfX04xr8L2iHixbVGZZebEazkdDSwDjpI0HngvxVwA69oalVlmfmTYspA0Fbg/Io6S9CPgborVCsZQ9IBXUczQNRG4PiLua1uwZi3mHq/lchw76ri/opjx6maK8sPaiPh2RHwQeD9FT9isa3XD7GTWGY5lR+L9LPCLiHhZ0rHAkrrjPg5cnzk2s6yceC2XY4F/BYiIO+r2zwKeLFfi+DRwd0T8qA3xmWXjGq9VgqTLgQspVt99PCJuaHNIZi3jxGtmlplvrpmZZebEa2aWmROvmVlmTrxmZpk58ZqZZebEa2aWmROvmVlmTrxmZpk58ZqZZfb/wNLbkFQe01MAAAAASUVORK5CYII=\n",
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Jbuu3byb5RF+eJBcnWZ/k9iRHD9lOSZpYjTysdHcN3XM7D7gb2Begql4x84UkHwP+qn/5GmBVv70cuKT/U5K0A8FhyRmD9dz6pwmcClw6x9f2pXucwSf6ojOAq6pzM7B/koOHaqskTSx7bsCww5IXAW+FOdeQ+jngs1X17f71IcA3Zn39/r5MkrQjIy6a3HIvb5BwS3IasHHm+TxzeD3bVoAe9bPPSbIuybqHN0/Nu42S1AR7bsBwPbfjgNOT3AdcDZyQ5AMASQ4EjuEHV39+ADh01uuVfdnTVNWaqlpdVatfcMCyxWi7JE0Oww0YKNyq6oKqWllVhwFnAjdW1Vn9l18HXFtV35v1lrXAG/pZk8cCW6rqwSHaKkmTzGHJzjgsv3UmTx+SvA64B1gP/Bnw74dulCRNnKKb1TDKNqAkz+0vI5222HUNfhN3Vd0E3DTr9fFzHFPAuYM1SpIasRi9sSSXAzNzJ46YVX4y8MfAMuDSqnrnTj7qbcBHFr6FT+cKJZLUksUZarwCeA9w1UxBkmXAe4GT6Ga035JkLV3QXbjd+98EvAz4B2CQJ/cabpLUkHn03A5Msm7W6zVVtWb2AVX1uSSHbfe+Y4D1VXUPQJKrgTOq6kK6Xt4Ptis5Hngu8OPAd5NcV1WLNjBquElSS0YPt01VtXoeNc11P/IOV5Kqqt8FSPKrfZ2LesXPcJOkVkzA9P6qumKIegw3SWpE+m0gu3w/8lIYh1sBJEkLZfSbuPdLsibJa0es6RZgVZIXJdmL7rautQtxCguhqZ5bUUwt7jDuD3iSYZf72jD1xKD1/fGm4wetb+1dRw5a3173DjJpC4B9Hhp2rGjF5mFvYFr+yNZB69try/cHrW+Pb393sLoytXs/u3lMKNlSVec842cmHwKOp5t8cj/wjqq6LMlvAtfTzZC8vKruGr3Fi6OpcJOkZ71F+H9UVb1+B+XX0S26MXYMN0lqyZhPKBmK4SZJrWh8vchROKFEkloy3ISSsWbPTZIashgTSiaR4SZJLXFYEjDcJKkpXnPrGG6S1IoJWH5rKE4okaSWOKEEsOcmSc0ITiiZYbhJUksclgQMN0lqSsp0A8NNktrhhJKnGG6S1BBvBeg4W1KSWuJsScCemyQ1xdmSHcNNklrisCRguElSO3zkzVMMN0lqieEGGG6S1IwAmTbdwHCTpKY4LNlpLtymB+yTb5h6YrC6AP7nxlcOWt+1d7100PqW37v3oPWteGi4vysrNk8PVhfA3o88OWh9z9ky7O/CHt/+7qD15bF/Gq6yqan5v3d+N3Hvl2QN8Mmq+uT8Kx8vzYWbJD2bZfT/R3krgCRpzDksCRhuktQUr7l1DDdJakUBPhUAMNwkqSn23DqGmyS1xHADDDdJakaw5zZj0EfeJFmW5MtJru1fJ8kfJPlakruT/Pas8ouTrE9ye5Kjh2ynJE2kqtE3H3mzIM4D7gb27V//KnAocHhVTSd5YV/+GmBVv70cuKT/U5L0DHzkTWewnluSlcCpwKWzit8C/Neqmgaoqo19+RnAVdW5Gdg/ycFDtVWSJtboDytt0pDDkhcBbwVm3z//L4BfTrIuyaeTrOrLDwG+Meu4+/sySdIzSI22tWqQcEtyGrCxqm7d7kvLge9V1Wrgz4DL5/HZ5/ThuO7hzbuxJpskTboCpmu0rVFD9dyOA05Pch9wNXBCkg/Q9cg+3h9zDXBkv/8A3bW4GSv7sqepqjVVtbqqVr/ggGWL0XZJmhwOSwIDhVtVXVBVK6vqMOBM4MaqOgv4BDCz1P2/Ar7W768F3tDPmjyW7oLng0O0VZImmcOSnaW+z+2dwF8k+R3gO8Cv9eXXAacA64HHgTcuTfMkacK4/BawBOFWVTcBN/X736KbQbn9MQWcO2S7JKkFLffGRrHUPTdJ0kJp/DraKAw3SWpEt/yW6QYDL78lSVpk0yNuLr8lSRp38+i5Nbn8luEmSa3wmttTDDdJakZ5K0CvqXDbWtM8OPXdwer7HxtOHKwugE/decSg9e197/JB69tnw7C/lCs2Te/8oAWy/NEnB6sL4DlbvjdofXt8e7jfO4A89k+D1jf92HeGq2xq9/5eeitAp6lwk6RnPXtugOEmSe0oyHADEmPNcJOkljS80v8oDDdJaog3cXcMN0lqieEGGG6S1I5iZtWRZz3DTZIaEcphyZ7hJkktMdwAw02S2mK4AYabJLXDa25PMdwkqSFec+v4PDdJaknVaJvPc5Mkjbd5PRXA57lJksZY4YSSnuEmSS1xQglguElSU5xQ0jHcJKklhhtguElSOwofedMz3CSpGfOaLdkkw02SWmK4AYabJLXFcAMaC7cHn9yXd27414PV9+k7jhisLoAV9+41aH37PDTsL8mKzcPOYV7+6NbB6trzW08MVhfAHo89Pmh9PPZPg1Y3/dh3hq3v8eG+n1W78XvgNbenNBVukvTsVrA74dgQw02SWuKwJGC4SVI7HJZ8iuEmSS2x5wYYbpLUFsMNMNwkqSHexD3DcJOkVhQw7WxJGPhJ3EmWJflykmv711ckuTfJbf12VF+eJBcnWZ/k9iRHD9lOSZpYoz+Ju0lD99zOA+4G9p1Vdn5VfXS7414DrOq3lwOX9H9KknaoYMqeGwzYc0uyEjgVuHQXDj8DuKo6NwP7Jzl4URsoSZOuuhVORtlaNeSw5EXAW3n6c2L/oB96fHeS5X3ZIcA3Zh1zf18mSXom0zXa1qhBwi3JacDGqrp1uy9dABwO/BTwfOBt8/jsc5KsS7Lue48Ou36fJI0dr7kBw/XcjgNOT3IfcDVwQpIPVNWD/dDjE8D7gWP64x8ADp31/pV92dNU1ZqqWl1Vq/d+3vK5DpGkZ4eqbrbkKNtAkhyf5G+TvC/J8Ytd3yDhVlUXVNXKqjoMOBO4sarOmrmOliTAzwF39m9ZC7yhnzV5LLClqh4coq2SNNEWoeeW5PIkG5PcuV35yUm+2s9sf/vOWgZ8B9ib7lLTolrq+9z+IskLgAC3Ab/Rl18HnAKsBx4H3rgkrZOkCVOL0xu7AngPcNVMQZJlwHuBk+jC6pYka4FlwIXbvf9NwN9W1f9JchDwR8CvLEZDZwweblV1E3BTv3/CDo4p4NzhWiVJLZjXdbQDk6yb9XpNVa35gU+t+lySw7Z73zHA+qq6ByDJ1cAZVXUhcNoz1PcosOjXkJa65yZJWijzeyrApqpaPY/a5prVvsP7kZP8PPBqYH+6XuCiMtwkqSVjeu9aVX0c+PhQ9RluktSIAmq4e9d2eVb7Uhh0bUlJ0iKq6npuo2ywX5I1SV47Ym23AKuSvCjJXnQz4dcu9CnNlz03SWrIPHpuW6rqnGc6IMmHgOPpJp/cD7yjqi5L8pvA9XQzJC+vqrvm0eRFYbhJUksW4ZpbVb1+B+XX0d26NXZSDS2/kuRh4OtL3Y5dcCCwaakbsUhaPjfw/CbZpJzbj1bVC+bzxiSfoTvPUWyqqpPnU984ayrcJkWSdfOcejv2Wj438PwmWcvnpqdzQokkqTmGmySpOYbb0liz80MmVsvnBp7fJGv53LQdr7lJkppjz02S1BzDbZEkWZbky0mu7V+fmORLSW5L8ndJXtyXL0/y4f55SF+YY+XtsTPHuZ3Qn9udSa5MsmdfniQX9+d2e5Kjl7blO5fkviR39D+ndX3Z85PckOQf+z+f15e3cn6/mOSuJNNJVm93/AX9+X01yauXptW7bgfn94dJvtL/jK5Jsv+s4yfq/LTrDLfFcx5w96zXlwC/UlVHAR8Efq8vfzPwaFW9GHg38K4hGzlPT51bkj2AK4Ezq+oIuvsMz+6Pew2wqt/OofseTIJXVtVRs6aNvx34bFWtAj7bv4Z2zu9O4OeBz80+KMmP0y2p9C+Bk4E/7Z/hNe62P78bgCOq6kjga8AFMNHnp11guC2CJCuBU4FLZxUXsG+/vx/wzX7/DLpwAPgocGL/ZPKxNMe5HQB8v6q+1r++AfiFfv8M4Krq3AzsP/P09Qkz+2d0Jd1T42fKJ/78quruqvrqHF86A7i6qp6oqnvpHh58zLCt231V9ddV9WT/8ma6BX6hkfPT3Ay3xXER8FZg9jo4vwZc16/L9m+Bd/blTz0Tqf8F3EIXGOPqIn7w3DYBe84aznod21YKn+t5T4cM0MbdUcBfJ7k1ycx6ewdV1YP9/kPAQf1+K+e3Iy2e35uAT/f7k3h+2kWG2wJLchqwsapu3e5LvwOcUlUrgffTPWZ9osx1bv1T088E3p3ki8BjwNQSNXEh/ExVHU035Hhukp+d/cX+fCd5ivEznl8Ddnh+SX4XeBL4i6VqnIbjwskL7zjg9CSnAHsD+yb5FHB4VX2hP+bDwGf6/ZlnIt3fT8TYD9g8cJt31Vzn9oGqOgt4BUCSVwEv6Y8f6+c9zaWqHuj/3JjkGrphqg1JDq6qB/thx4394a2c3+d2cHgz55fkV4HTgBNr2/1PE3d+2nX23BZYVV1QVSur6jC6Hs2NdGP7+yWZ+Uf/JLZNNlnLtgkYrwNunPXLN1bmOreqOivJC6Gb+Qm8DXhf/5a1wBv6WYXH0j1a48G5PnscJHlukh+a2QdeRTfZYvbP6Gzgr/r9Vs5vR9YCZ/Yzel9EN3Hmi4vf0vnZ0fklOZluKP30qnp81lsm6vw0GntuA6iqJ5P8OvCxJNPAo3Rj/wCXAX+eZD3wCF1oTJrz+yHLPYBLqurGvvw64BS6C/WPA29covbtqoOAa/r5PHsCH6yqzyS5BfhIkjfTzQb9pf74Vs7v3wB/ArwA+FSS26rq1VV1V5KPAP9AN5x3blWN85Dzjs5vPbAcuKH/2s1V9RsTeH4agSuUSJKa47CkJKk5hpskqTmGmySpOYabJKk5hpskqTmGmySpOYabJKk5hpu0A0n+XZJKcvyssnP7spOWrmWSdsZwk3bspcDfA4cDJNmH7ukODwO3L2G7JO2E4Sbt2JHA1fThBvw28JfAdFVtWLJWSdopw03asR8DPgIcnmR/4JeBz/PMiw1LGgOGmzSHJIcCm6vqHuCFwPl0iwu/BLgjyT9PclmSjy5lOyXNzXCT5vZS4I5+/zHgZODKvvz2qrqnqt68VI2T9Mx85I00tyPZFm5/SNeLm0ryUrqQkzTGDDdpbi8FPgZQVdfOKv9x4K4laZGkXebz3KR5SHIA8Ad0T1W/tKouXOImSZrFcJMkNccJJZKk5hhukqTmGG6SpOYYbpKk5hhukqTmGG6SpOYYbpKk5hhukqTmGG6SpOb8f85vbgbHlo29AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -302,16 +291,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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0ma0mc9v/U9JXqJL6pVTLbM61B//rqOtX1JMRK6jKJr+U9Np6VP5aqnoRbPsMcUQMIdNjbeDMtkX1g2q0/iPg7f120tfSRNu/t30xVW17H+BuSW8dJFpJO48dMitpZ+C4OvAbgNPqZqcB19f3bwDmS9qhniXeF7h7kH1GxHByn7cZ4hyqgeoSqjnJ1VTVj/9OtVKmL/1MgB5JVU55Q/3nbKrlha8aMODdgRWSxvb7ddu3SLoHWC7pTOBnwMkAtlfVyyAfovoiPysrWSJGwwxK1FvVVPWjn5r5d6mOmroaWGL78cFCrdh+lOpEN93bnwLeMc5rLgIumsz+ImJ4Zf5zc/Xk58X1Uu9PUVU/zrb9/X776CeZfww4mGpB/V9IeoqqnvMgsNL2dQNHHhEzlkky79RU9aOfCdAvde14DvAmqgT/AeC6QXYYEZEyy2YaqX4MfASo7bVUK00mtUA+ImY2U52fI17SSPUjZ02MiOIyMt+kqepHkvkEhrmu1/g1Oxvu76mGO3z6p832NwMus/mSYbxowTD/35pqk61+JJlHRHEZmTcvyTwiispqlnYkmUdEcRmZNy/JPCKKymqWdiSZR0RxKbM0L8k8IopLMm9eknlEFDXDzohYTJJ5RBSXZN68JPOIKCoToO1IMo+I4lIzb16SeUQUlzJL85LMI6KoHAHajiTziCguI/PmJZlHRHEZmTcvyTwiispqlnYkmUdEcSmzNC/JPCKKS5mleUnmEVFUDudvR5J5RBSXkXnzRi6ZD/M3fpOTPo1fs7Ph/n7bcH9quL+mDeN1NodZknnzRi6ZR8Rwy0FD7Ugyj4jiksybl2QeEUVlZN6OJPOIKG6Y57amq8zbRERx7vM2nUl6r6QrJF0v6bi295dkHhFFjR3O38+tH5JmSfqBpBvHef6TklZJWinpakk7TiZuSVdKWi9pZY/n5kpaLWmNpPMAbF9neyFwOnDKZPY5iCTziChuY5+3Pp0LPNzrCUl7AucAh9k+CJgFzO9qM1vSLl3b9unR3VeBuT32MQu4DDgeOABYIOmAjibn18+3Ksk8IooboMyym6Slkt7Tqx9Jc4B3A1+eYHfbA38oaXtgJ2Bd1/NHAdePjdglLQSWbBGzfQfw6x79Hw6ssf2o7eeBZcA8VS4GbrZ9/wTxNSIToBFR1ICrWZ62vWiC5y8FPgXs0utJ2z+X9HngZ8Dvgdts39bV5hpJewPLJF0DnAG8s/8Q2RN4ouPxWuCtwMeBY6m+kPax/cUB+hxY0ZF5d21L0iGS7pT0oKRvStq1o+3iuv60WtK7SsYZEe1qYgJU0onAetv3TdDmFcA8YG9gD2BnSaduEY99CfAscDlwku1nBng7vQ5Qtu0ltt9i+6NtJ3IoX2bprm19GTjP9sHACuCvAOp603zgQKoa1T/UdamIGAEN1cyPAE6S9DhVaeMYSVd1tTkWeMz2r2y/AFwL/KfujiQdCRxElYcuGPDtrAX26ng8hy1LOa0rlszHqW3tD9xR378d+EB9fx6wzPZzth8D1lDVpSJimmtqNYvtxbbn2H4d1eDvn213j7p/BvyJpJ0kCXgHXZOlkt4MXEGVdz4CvFLShQO8pXuAfSXtLelldSw3DPD6RpQcmV9KVdvq/MJdCZxU3z+ZTd9uvWpQe7YcX0QU0vBqli1IuknSHra/D/wf4H7gQaqct7Sr+U7AybYfsb0ROA34aY8+rwbuBPaXtFbSmQC2NwBnA7dSfVEst71qG8KflCIToJ21LUlHdzx1BrBE0t9QfZONnQywZw1qnL4XARNNkETEkGn6gCDb3wG+0/H4hI77FzBB6cT297oev0A1Uu9ut2CCPm4Cbhok5qaVWs0yVts6AdgR2FXSVfVPouMAJO1HVYaBAWpQtpdSf9NuJ033g8YiRt4oHN05jIqUWcarbUmaDSBpO6qF9WMzvjcA8yXtUC8Z2he4u0SsEdG+tsssM9FUrzNfIOms+v61wD8C2F4laTnwELABOMt2LugdMQLGJkCjWcWTeWdty/YXgC+M0+4i4KJigUVEMSmzNG+qR+YRMQOlhNK8JPMJNP0P7oUG+3qywb5g+K/ZmZMIjZaMzJuXZB4RReVKQ+1IMo+I4jIyb16SeUQUldUs7Ugyj4jiUmZpXpJ5RBSXMkvzkswjoqhMgLYjyTwiiksyb16SeUQUlzJL85LMI6KorGZpR5J5RBSXkXnzkswjorjUzJuXZB4RReXiFO1IMo+I4jIyb16SeUQUlQnQdiSZR0RxKbM0L8k8IopLmaV5SeYRUVQmQNuRZB4RxWVk3ryRSuZNn8Cnycu8ATzVYF+/abAvGP7Lsg17fDGYjMybN1LJPCKGn4ENUx3ECEoyj4jiMjJvXpJ5RBSXZN68JPOIKCoXp2hHknlEFJdk3rwk84goLmWW5iWZR0RROTdLO5LMI6K4lFmal2QeEUXlcP52JJlHRHFJ5s3LUdIRUdzGPm/TmaT3SrpC0vWSjmt7f0nmEVHU2ARoP7d+SJol6QeSbuzavr+kBzpuv5H0icnGLelKSeslrezaPlfSaklrJJ330vu0r7O9EDgdOGWy++1XknlEFOc+b306F3h4i33Yq20favtQ4C3A74AV3e0kzZa0S9e2fXrs56vA3K52s4DLgOOBA4AFkg7oet35dZtWJZlHRHFNlVkkzQHeDXx5K03fATxi+6c9njsKuF7SjnWfC4El3Y1s3wH8umvz4cAa24/afh5YBsyr+5Gki4Gbbd/fx9vZJkWTeffPIUmHSrqr/gl0r6TDO9ourn+2rJb0rpJxRkR7+h2V1yPz3SQtlfSecbq7FPgUW8/984Gre8ZjXwPcAiyT9KfAGcCH+nw7ewJPdDxeW28D+DhwLPBBSR/ts79JK72aZezn0K7140uA/2r7Zkkn1I+Prn+mzAcOBPYAvi1pP9s51iBiBAwwufm07UW9npB0IrDe9n2Sjh6vA0kvA04CFo/XxvYlkpYBlwOvt/1Mn/GpV3d1n0voMcJvS7GR+Tg/h8ymxL4bsK6+Pw9YZvs5248Ba6h+zkTECGioZn4EcJKkx6nKG8dIuqpHu+OB+23/cryOJB0JHERVU7+g/3fCWmCvjsdz2JTHiipZZrmULX8OfQL4nKQngM+z6Ztzop8um5G0qC7R3Nt0wBHRvKZWs9hebHuO7ddR/ZL/Z9un9mi6gHFKLACS3gxcQTWI/AjwSkkX9vl27gH2lbR3/QtgPnBDn69tVJFk3vlzqOupjwGftL0X8EngK2Mv6dFNzy9q20ttH2b7sMYCjohWtb3OXNJNkvaQtBPwTuDaCZrvBJxs+xHbG4HTgC0mSiVdDdwJ7C9praQzbW8AzgZupSohL7e9ahtCnzTZ7R+LJenvgP9MdbWoHalKK9cC7wFebtuSRFUf21XSYgDbf1e//lbgM7bv3Mp+Gn0zr2myM5qdoGj6W7jXt2f0byYtC/sZ3Lctg6cdJc/ps+0j27ivmaTIv8EJfg6to1oWBHAM8JP6/g3AfEk7SNob2Be4u0SsEdGusYtTjPoRoKVN9blZFgJfkLQ98CywCMD2KknLgYeoRvNnZSVLxOjIuVmaV6TMUkrKLJOXMsu2SZmlfztI/qNC+5pJpnpkHhEzTC5O0Y4k84gobnTqAcMjyTwiihqbAI1mJZlHRHFJ5s1LMo+I4lJmaV6SeUQUlQnQdiSZR0RxKbM0L8k8IopLmaV5SeYRUdSAl4SLPiWZR0RxKbM0L8k8IorLyLx5SeYRUVRWs7QjyTwiikuZpXlJ5hFRXMoszUsyj4iicm6WdiSZR0RxGZk3b9SS+TPA6qY6+1VTHY3v1cCT7e+mEdMpVphe8U6nWAH238bX37qxes/9mE6fy5QatSsN3TudrkoyneKdTrHC9Ip3OsUK0y/emWImXe0qImJkJZlHRIyAUUvmS6c6gAFNp3inU6wwveKdTrHC9It3RhipmnlExEw1aiPziIgZKck8ImIETLtkLmmWpB9IurF+/BlJP5f0QH07oaPtYklrJK2W9K6pjrXe9vE6nlWSLhmWWHvFK+kbHZ/r45IeGJZ4e8R6qKS76ljvlXT4sMQ6TryHSLpT0oOSvilp12GJt/67fnDss6y3vVLS7ZJ+Uv/5imGJN2q2p9UN+C/A14Eb68efAf6yR7sDgB8COwB7A48As6Y41rcD3wZ2qB/PHpZYe8Xb9dzfA38zLPH2+GxvA46v758AfGdYYh0n3nuAo+r7ZwD/bVjiBR4HXt217RLgvPr+ecDFwxJvbtVtWo3MJc0B3g18uY/m84Bltp+z/RiwBjh8K69pzDixfgz4rO3nAGyvH4ZYJ4h37DkBHwKurjcN42drYGx0uxuwbhhihXHj3R+4o75/O/CB+v6UxzuOecDX6vtfA97bsX0Y451xplUyBy4FPsWW5+k5W9KPJF3Z8fNvT+CJjjZr622lXMqWse4HHCnp+5K+K+k/1NunOlYY/7MFOBL4pe2f1I+nOt5L2TLWTwCfk/QE8Hlgcb19qmOF3vGuBE6q758M7FXfH4Z4Ddwm6T5Ji+ptu9v+BUD95+x6+zDEG0yjZC7pRGC97fu6nroceD1wKPALqnIAgHp0U2Qd5gSxbg+8AvgT4K+A5fWod8pihQnjHbOATaNyGM7P9mPAJ23vBXwS+MrYS3p0Mwyf7RnAWZLuA3YBnh97SY9uSq8fPsL2HwPHU8X4tgnaDkO8wfQ60dYRwEn1BOeOwK6SrrJ96lgDSVcAY5ONa9k02gGYw6af3lMSax3Tta6KjXdL2kh1wqGpjHXceG2fKml74P3AWzraD+Nn+x7g3LrNNWwqaQztZwscByBpP6oyzDDEi+119Z/rJa2gKpv8UtJrbf9C0muBsRLhlMcbtaku2k/mBhzNpomk13Zs/yRV/Q7gQDafmHmUqZn46oz1o8Df1vf3o/p5qmGJtTve+vFc4LtdbYYi3q7P9mHg6Pr+O4D7hinWHvGOTX5vB/wv4IxhiBfYGdil4/6/1v8GPsfmE6CXDEO8uW26TaeR+XgukXQo1U+7x4E/B7C9StJy4CFgA3CW7am+9OCVwJWSVlL9rD7N1f+IYYx1zHw2L7EM62e7EPhC/UviWWARDG2sAAsknVXfvxb4RxiKeHcHVlTVP7YHvm77Fkn3UJUFzwR+RlXnH4Z4o5bD+SMiRsC0mQCNiIjxJZlHRIyAJPOIiBGQZB4RMQKSzCMiRkCSeUTECEgyj2Ik/bkkSzqqY9vZ9bZjpzK2iOkuyTxKehPwI+CNAJJ2As4EfgU8OIVxRUx7SeZR0sFUR5O+oX58DtV5VDba/uWURRUxApLMo6Q3AsuBN0jaDTiF6twfK6c0qogRkMP5owhJewG3236DpPuBm6muSrM91Uj9DqozB84GLrN925QFGzENZWQepbyJTXXx31Kdie9rVKWXB21fZ3shcDrViD0iBjAKZ02M6eFgNiXzzwFP2X5R0sHAVzvanQ9cVji2iGkvyTxKORj4JwDbN3ZsPwB4qL7i0meBm23fPwXxRUxrqZnHUJB0DnAa1VXrH7D9xSkOKWJaSTKPiBgBmQCNiBgBSeYRESMgyTwiYgQkmUdEjIAk84iIEZBkHhExApLMIyJGQJJ5RMQISDKPiBgB/x/mRqKnglj6JQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -321,18 +303,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running: 121 jobs\n",
-      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskip1\n",
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -342,17 +315,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n",
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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V2T4VuK+8F3RkZyfmtmRERFv1ekuyfUH428AQ8Pmy/JdSNhd4485OTM8tIqK1Bvsdbba3A3/UZfemnZ2bcIuIaLP29cYmrEyq/w7gIDryyvbR452bcIuIaLMBDjfg74G/Aa4AehoumHCLiGizwQ63HbYvn8yJPQ8okXScpL+VdHjZXj6ZiiMiYopGZyjpZWmXf5T0h5L2l7Tv6DKREyczWvLNwHnAaZKOBg6fxGdEREQfyL0t436edLykb0vaJOmCmv27S/pM2X+TpIM69l1Yyr8t6TV9uLxlVHnzdeCWsqyfyImTuS35U9s/Ad4h6RLg1ybxGRER0Q99vC1Znh/7EHAcsBm4WdJq23d0HHYW8KDt50taCrwHOFXSL1O9VPpXgP8MfFXSC2xPemoN2wdP9txxw03SMcCttn9ciq7tqPgCSd2GaUZExC7W54e4jwA22b4bQNJVwBKgM9yWAH9e1q8GPihJpfwq248C35O0qXzev/TaCElH214r6bfq9tv+h/E+YyI9t+uBbZJGgNuAjZLmAxuB221/oJdGR0REH/X+PdoCSZ239lbaXlnWDwDu7di3Gfj1Mec/cYztHZIeAp5Vym8cc+4BvTaueAWwFviNmn0G+hJuf0TVDf0s1X3P/wr8KnAGcCjw3Im1NSIi+mpys45st724/43pH9t/Vn6eOdnPGHdAie0PAUdR/RKuAB4HzrX9KtsJtoiI6dTf6be2UL1iZtRQKas9RtI8YG/g/gme2xNJH5e0d8f2gZJumMi5Exotafvntt8DvAp4PvANSWO7qhER0bA+j5a8GVgk6WBJu1ENEFk95pjVVKMYAU4B1tp2KV9aRlMeDCwCvjHFy/tn4CZJJ0h6C9XXZCsmcuJEBpS8HDikLIcCzwF+SnWPNSIiplMfB5SU79DeClxHNTnxR23fLuliYL3t1cBHgI+XASMPUAUg5bjPUg0+2QGcM5WRkuUzPyzpduBrwHbgxbZ/NJFzJ/Kd2zpgA9UrBy6zfc/kmhkREX3X5xlKbK8B1owpe1fH+i+AN3Q5993Au/vVFkm/C/wP4HTgRcAaSWfa/tfxzp1IuP0B8ELgdcCfSLqfaqTkRuA225+fbMMjImLyJvpgdou9Hnip7W3ApyVdA6xiApOHjBtutj/cuS1pCDiMKkVfT/WOnYiImA7tm1JrwmyfPGb7G5KOmMi5Pc9QYnsz1fMLX+r13IiI6LMB7LlJOt/2X0q6rMshbxvvM/JWgIiIFhvQ25J3lp+3TPYDEm4REW02gOFm+x/Lz1WT/YyBC7c5w83df57zWGNVMe/h5uoC2Pv7Oxqr64Mrut152DXOX/Tyxuryjscbq6uqcAD/pYvuBnRAiaSxz9Y9he2TxvuMgQu3iIhZZQDDDfjvVPNXfhq4Cei515Jwi4hos8EMt+dSvXbnTcBvA18EPm379ol+wGReVhoRETNEv19WOhPYHrb9ZdvLgCOBTcC6MnvKhKTnFhERM46k3akmD3kTcBBwGXDNRM9PuEVEtFlLemO9kHQl1cxYa4D/afu2Xj8j4RYR0VYtutXYo9OAh4FzgbdVL/oGqoEltr3XeB+QcIuIaLMBDDfbUx4P0uiAEklzJX1L0rVl+/9J2lCWH0r6fCmXpMskbZJ0q6SXNNnOiIjW6O/LSgdG0z23c6mmVdkLwPbLRndI+hzwhbL5WqoX3S0Cfh24vPyMiIhCDOxtySlrrOdW3ibwOuCKmn17AUfz5BsGlgBXunIjsI+k/Ztqa0REa6TnVqvJ25IrgPOBkZp9JwM32P73sn0A1dPpozaXsoiIGNXjM26zqZfXSLhJOhHYZrvbDM9voppmZTKfvVzSeknrhx9ueALGiIjplp5braZ6bkcBJ0m6B7gKOFrSJwAkLQCOoJpeZdQWYGHH9lApexrbK20vtr147p577oq2R0TMXAm3Wo2Em+0LbQ/ZPghYCqy1fVrZfQpwre1fdJyyGji9jJo8EnjI9tYm2hoR0Sa5LVlvJjznthS4ZEzZGuAEqvnEHgHObLpREREznqkfxRDNh5vtdcC6ju1X1hxj4JzGGhUR0VKzqTfWi7wVICKizRr8zk3SvpKul3RX+Tm/5pjDJf2LpNvLJBynduz7O0nf65i84/Cptai7hFtERIs1/J3bBVSPbS0CbijbYz0CnG77V4DjgRWS9unYf57tw8uyYcot6iLhFhHRZs2OllwCrCrrq6ieUX5qc+zv2L6rrP8Q2AY8e8o19yjhFhHRVr0G29TDbb+Okes/Avbb2cGSjgB2A77bUfzucrvy0vLOtl1iJoyWjIiISVBZerRA0vqO7ZW2Vz7xmdJXgefWnHdR54ZtS91vdJYpEz8OLLM9OqbzQqpQ3A1YCbwTuLj3Sxhfwi0ios16741tt72468fZx3bbJ+k+Sfvb3lrCa1uX4/aimpjjojI/8Ohnj/b6HpX0MeAdPbd+ggYq3DQCcx5rrr55Dc72tff3dzRXGXD5ivc3VtefLHpFY3UBeMfjDVaWcdqxazX8KMBqYBnVs8nLePJNLk+2R9oNuIZq8vurx+wbDUZRfV/X8xu2JyrfuUVEtFmz37ldAhwn6S7g2LKNpMWSRt/48kbg5cAZNUP+PylpI7ARWAD8xZRb1MVA9dwiImadBntutu8HjqkpXw+cXdY/AXyiy/lH79IGdki4RUS01SybL7IXCbeIiDZLuNVKuEVEtFh6bvUSbhERbZZwq5Vwi4hosfTc6iXcIiLaapa9XbsXCbeIiDZLuNVKuEVEtJTIbcluEm4REW2WcKuVcIuIaDFl/tJaCbeIiLbKgJKuEm4RES2W79zqJdwiItos4VYr4RYR0WLpudVLuEVEtFnCrVbCLSKirfLKm64SbhERbZZwq5Vwi4hoKQEaSbrVSbhFRLRYbkvWG6xwG4F5DzdX3d737Gisrg+/f0VjdQG8fdErG6vLOx5vrK6qwvxrEDt33Q83NFbXEa95ZPIn5yHurgYr3CIiZhmNTHcLZqaEW0REm6XnVmvOdDcgIiImT+5tmVJd0r6Srpd0V/k5v8txw5I2lGV1R/nBkm6StEnSZyTtNrUWdZdwi4hoK1N9h9zLMjUXADfYXgTcULbr/Nz24WU5qaP8PcCltp8PPAicNdUGdZNwi4hosSZ7bsASYFVZXwWcPOF2SgKOBq6ezPm9SrhFRLSZe1ymZj/bW8v6j4D9uhy3h6T1km6UdHIpexbwE9ujw8w3AwdMuUVdZEBJRERLiUn1xhZIWt+xvdL2yic+U/oq8Nya8y7q3LBtqWvtB9reIul5wFpJG4GHem7pFDQabpLmAuuBLbZPLN3UvwDeAAwDl9u+rJS/HzgBeAQ4w/Y3m2xrRMSMN7nv0bbbXtz9I31st32S7pO0v+2tkvYHtnX5jC3l592S1gEvBj4H7CNpXum9DQFbem38RDV9W/Jc4M6O7TOAhcAhtg8FrirlrwUWlWU5cHmDbYyIaI2Gv3NbDSwr68uALzytPdJ8SbuX9QXAUcAdtg18DThlZ+f3S2PhJmkIeB1wRUfxHwAX2x4BsD36v4AlwJWu3EiV9vs31daIiNZo9ju3S4DjJN0FHFu2kbRY0ui/7YcC6yX9K1WYXWL7jrLvncAfS9pE9R3cR6bcoi6avC25AjgfeGZH2S8Bp0r6TeDHwNts30X1JeO9HceNfvG4lYiIeEKTc0vavh84pqZ8PXB2Wf86cFiX8+8GjtiVbRzVSM9N0onANtu3jNm1O/CLcv/3b4GPTuKzl5dROeuHf97gxJIREdPNwIh7W2aJpnpuRwEnSToB2APYS9InqHpk/1COuQb4WFnfQvVd3KiuXzyWUT4rAf7Dfgtnz+9cRARk+q0uGum52b7Q9pDtg4ClwFrbpwGfB15VDnsF8J2yvho4XZUjgYc6nq2IiIii4QElrTHdz7ldAnxS0tuBn1Hu2QJrqB4D2ET1KMCZ09O8iIgZLq9wqtV4uNleB6wr6z+hGkE59hgD5zTZroiINppNvbFeTHfPLSIiJisvK+0q4RYR0VLV9FtJtzoJt4iINsubuGsl3CIiWiw9t3oJt4iItsp3bl0l3CIiWqsvb9ceSAMVbnMfM3vfs2P8A/vkw+9f0Vhdb1/0ysbqAvCOxxusLH85Y3zX/XDDdDdhRsqjAPUGKtwiImad/OewVsItIqKtDMpoyVoJt4iINptFM/33IuEWEdFieRSgXsItIqLNEm61Em4REW1lMkNJFwm3iIiWEs5tyS4SbhERbZZwq5Vwi4hos4RbrYRbRERb5Tu3ruZMdwMiImLyZPe0TKkuaV9J10u6q/ycX3PMqyRt6Fh+Ienksu/vJH2vY9/hU2rQTiTcIiLazO5tmZoLgBtsLwJuKNtjmuOv2T7c9uHA0cAjwFc6DjlvdL/tDVNtUDcJt4iI1uox2KYebkuAVWV9FXDyOMefAnzJ9iNTrbhXCbeIiLYyTYfbfra3lvUfAfuNc/xS4NNjyt4t6VZJl0rafaoN6iYDSiIi2qz3ASULJK3v2F5pe+XohqSvAs+tOe+izg3blrq/cEfS/sBhwHUdxRdSheJuwErgncDFPV/BBCTcIiJabBKDRLbbXtxtp+1ju9Yl3Sdpf9tbS3ht20k9bwSusf3EyyE7en2PSvoY8I4e2z5huS0ZEdFmzd6WXA0sK+vLgC/s5Ng3MeaWZAlEJInq+7rbptqgbhJuERFtZapX3vSyTM0lwHGS7gKOLdtIWizpitGDJB0ELAT+75jzPylpI7ARWAD8xVQb1E1uS0ZEtFZfemMTr82+Hzimpnw9cHbH9j3AATXHHb0r29cp4RYR0WaZfqtWwi0ios0SbrUGKtwOWriNKy67tLH63vZLr2isLg8/Pv5Bfa0wf2Fi56774YbpbkKMfucWTzNQ4RYRMbsYnJmT6yTcIiLaLHdZaiXcIiLaKrclu0q4RUS0WXputRJuERFtlnCrlXCLiGitZh/ibpOEW0REWxkYyWjJOo3OLSlprqRvSbq2bNe+clyVyyRtKu/9eUmT7YyIaI1mJ05ujaZ7bucCdwJ7dZSdZ/vqMce9FlhUll8HLi8/IyLiCYbh9NzqNNZzkzQEvA64YrxjqV5lfqUrNwL7jL4qISIiCoM90tMyWzR5W3IFcD5Pf29s3SvHDwDu7ThmMzUzTEdEzHrNvvKmNRoJN0knAtts3zJm14XAIcCvAftSvXK8189eLmm9pPUPPjB7/lcSEQHkO7cumuq5HQWcJOke4CrgaEmfsL213Hp8FPgYcEQ5fgvVi+5GDZWyp7G90vZi24vn75t3r0bELGJXoyV7WWaJRtLA9oW2h2wfBCwF1to+bSevHF8NnF5GTR4JPGR7axNtjYholfTcak33c26flPRsQMAG4PdL+RrgBGAT8Ahw5rS0LiJihvMs6o31ovFws70OWFfWa185btvAOc21KiKijWZXb6wX091zi4iIycpbAbpKuEVEtNksenatFwm3iIiWMuD03Gol3CIi2spOz62LhFtERIul51Yv4RYR0WbpudWSB2gYqaQfA9+f7naMYwGwfbobsYvk2top1za9DrT97MmcKOnLVNfYi+22j59MfW0yUOHWBpLW21483e3YFXJt7ZRri0GUyRgjImLgJNwiImLgJNyat3K6G7AL5draKdcWAyffuUVExMBJzy0iIgZOwm0XkDRX0rckXVu2j5H0TUkbJP2zpOeX8t0lfUbSJkk3STpoWhs+ATXXdnS5ttskrZI0r5RL0mXl2m6V9JLpbfnOSbpH0sbye7S+lO0r6XpJd5Wf80v5IFzbGyTdLmlE0uIxx19Yru3bkl4zPa2emC7X9l5J/1Z+b66RtE/H8a25tpiahNuucS5wZ8f25cDv2D4c+BTwp6X8LOBB288HLgXe02QjJ+mJa5M0B1gFLLX9QqpnDJeV414LLCrLcqpfg5nuVbYP7xg6fgFwg+1FwA1lGwbj2m4Dfgv4p86DJP0y1QuFfwU4HvhrSXMbbWnvxl7b9cALbb8I+A5wIbT22mKSEm59JmkIeB1wRUexgb3K+t7AD8v6EqpwALgaOKa8lXxGqrm2ZwGP2f5O2b4eeH1ZXwJc6cqNwD6jb15vkc7fn1VUb4sfLW/1tdm+0/a3a3YtAa6y/ajt71G9MPiIZls3Nba/YntH2bwRGCrrrb+2mLiEW/+tAM4HOufEORtYI2kz8LvAJaX8AOBegPKX8SGqwJipVvDUa9sOzOu4rXUKsLCsP3FtxeZSNlMZ+IqkWyQtL2X72d5a1n8E7FfWB+Hauhm0a3sz8KWy3rZriylIuPWRpBOBbbZvGbPr7cAJtoeAjwHva7xxU1R3beWN6UuBSyV9A/gpMDxNTZyql9p+CdUtx3MkvbxzZ7nWtg4t3um1tVzXa5N0EbAD+OR0NS6mTyZO7q+jgJMknQDsAewl6YvAIbZvKsd8BvhyWd9C1dPZXAZi7A3c33CbJ6ru2j5h+zTgZQCSXg28oBw/em2jhkrZjGR7S/m5TdI1VLer7pO0v+2t5bbjtnL4IFzbP3U5fCCuTdIZwInAMX7yeadWXVtMTXpufWT7QttDtg+i6tGspbrPv7ek0X/0j+PJwSareXIAxinA2o6/iDNK3bXZPk3Sc6Aa+Qm8E/ibcspq4PQysvBI4KGOW3wziqQ9JT1zdB14NdWAi87fn2XAF8r6IFxbN6uBpWUk78FUg2a+setb2rtu1ybpeKrb5yfZfqTjlNZcW0xdem67mO0dkt4CfE7SCPAg1fcAAB8BPi5pE/AAVWi0zXnlluUc4HLba0v5GuAEqi/tHwHOnKb2TcR+wDVlLM884FO2vyzpZuCzks6iGgn6xnL8IFzbbwIfAJ4NfFHSBtuvsX27pM8Cd1Dd0jvH9ky91dzt2jYBuwPXl3032v79ll1bTFFmKImIiIGT25IRETFwEm4RETFwEm4RETFwEm4RETFwEm4RETFwEm4RETFwEm4RETFwEm4RY0j6PUmW9MqOsnNK2XHT17KImKiEW8TTHQb8K3AIgKT/SPVmhx8Dt05juyJighJuEU/3IuAqSrgBbwP+Hhixfd+0tSoiJizhFvF0hwKfBQ6RtA9wKvB1dj7hcETMIAm3iA6SFgL3274beA5wHtUEwy8ANkp6nqSPSLp6OtsZETuXcIt4qsOAjWX9p8DxwKpSfqvtu22fNV2Ni4iJyStvIp7qRTwZbu+l6sUNSzqMKuQiogUSbhFPdRjwOQDb13aU/zJw+7S0KCJ6lve5RfRA0rOAd1O9Uf0K2/97mpsUETUSbhERMXAyoCQiIgZOwi0iIgZOwi0iIgZOwi0iIgZOwi0iIgZOwi0iIgZOwi0iIgZOwi0iIgZOwi0iIgbO/wc+ivTCDzHD7AAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -362,16 +327,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -381,16 +339,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -400,16 +351,9 @@
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "flipped x y \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -431,7 +375,7 @@
     "        li=hepi.slha_scan(li,\"EXTPAR\",1,np.linspace(475.,525.,10+1))\n",
     "        li=hepi.slha_scan(li,\"EXTPAR\",2,np.linspace(450.,500.,10+1))\n",
     "        sp.run(li)\n",
-    "        dl = rs.run(li,False,False)\n",
+    "        dl = rs.run(li,True,True)\n",
     "        for nm1 in [1]:\n",
     "            for nm2 in [1,2]:\n",
     "                print(nm2)\n",
@@ -439,27 +383,20 @@
     "                               hepi.slha_data(li,[\"NMIX\",(nm1,nm2)]),logz=False,xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"Mixing\")\n",
     "        \n",
     "        hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),\n",
-    "                       dl[\"lo\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{lo}$\")\n",
+    "                       dl[\"LO\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{LO}$\")\n",
     "        hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),\n",
     "                       hepi.slha_data(li,[\"MASS\",1000022]),xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$MX$\")\n",
     "        dll[sq] = dl\n",
     "\n",
     "hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),\n",
-    "                       dll[sqs[0]][\"lo\"]+dll[sqs[1]][\"lo\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{lo+lo}$\")\n",
+    "                       dll[sqs[0]][\"LO\"]+dll[sqs[1]][\"LO\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{LO+LO}$\")\n",
     "    "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -473,7 +410,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/examples/.ipynb_checkpoints/test_write-checkpoint.ipynb b/docs/source/examples/.ipynb_checkpoints/test_write-checkpoint.ipynb
index bec5c936a..7fa427eca 100644
--- a/docs/source/examples/.ipynb_checkpoints/test_write-checkpoint.ipynb
+++ b/docs/source/examples/.ipynb_checkpoints/test_write-checkpoint.ipynb
@@ -18,8 +18,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1.4.38+dirty\n",
-      "~/git/resummino_test/\n"
+      "0.1.8.9+dirty\n",
+      "~/git/resummino_release\n"
      ]
     }
    ],
@@ -31,7 +31,7 @@
     "import hepi.resummino as rs\n",
     "import hepi.util as util\n",
     "import matplotlib.pyplot as plt\n",
-    "rs.set_path(\"~/git/resummino_test\")\n",
+    "rs.set_path(\"~/git/resummino_release\")\n",
     "print (rs.get_path())"
    ]
   },
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "ed7216a4",
    "metadata": {},
    "outputs": [
@@ -68,90 +68,63 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running: 8 jobs\n",
-      "RESTART None None None ./output/1ae08df7442cd61d93fad544813d2e60e103232bd2bb6e421f19672f8a944a32.out\n",
-      "RESTART None None None ./output/ecabbd21c33198dead14d14469f2fd5e7eb46a473426243de093d39b474e4f75.out\n",
-      "{'LO': array([0.19827892+/-0.00028789131, 0.01027317+/-1.3097878e-05,\n",
-      "       0.0017420349+/-2.0549253e-06, 0.00045177946+/-5.0512563e-07,\n",
-      "       0.00014600929+/-1.5697746e-07, 5.3950657e-05+/-5.6279928e-08,\n",
-      "       2.1797802e-05+/-2.2197746e-08, 9.3808373e-06+/-9.3677641e-09],\n",
-      "      dtype=object), 'NLO': array([0.26266219+/-0.00035483454, 0.012685717+/-1.4738104e-05,\n",
-      "       0.0020719289+/-2.2534652e-06, 0.00052389967+/-5.389595e-07,\n",
-      "       0.00016619981+/-1.6377336e-07, 6.0600809e-05+/-5.7603335e-08,\n",
-      "       2.427351e-05+/-2.2387002e-08, 1.0401314e-05+/-9.3348503e-09],\n",
-      "      dtype=object), 'NLO_PLUS_NLL': array([0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0,\n",
-      "       0.0+/-0], dtype=object), 'K_LO': array([1.0+/-0, 1.0+/-0, 1.0+/-2.336179591111431e-19, 1.0+/-0,\n",
-      "       1.0+/-1.4277016816777176e-19, 1.0+/-0, 1.0+/-0, 1.0+/-0],\n",
-      "      dtype=object), 'K_NLO': array([1.3247106147239456+/-0.0026271840746797293,\n",
-      "       1.2348395870018698+/-0.002129971888538812,\n",
-      "       1.1893727846669433+/-0.0019083394068543829,\n",
-      "       1.1596358763189456+/-0.0017618913585157765,\n",
-      "       1.1382824339464974+/-0.0016600577186290635,\n",
-      "       1.1232635962153343+/-0.0015852483215957802,\n",
-      "       1.1135760385381976+/-0.001529955842518204,\n",
-      "       1.1087831147012859+/-0.0014886891411263526], dtype=object), 'K_NLO_PLUS_NLL': array([0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0,\n",
-      "       0.0+/-0], dtype=object), 'NLO_PLUS_NLL_OVER_NLO': array([0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0,\n",
-      "       0.0+/-0], dtype=object), 'VNLO': array([0.01041012+/-8.2978566e-05, 0.00047278892+/-3.391944e-06,\n",
-      "       7.4065143e-05+/-4.9588442e-07, 1.8149926e-05+/-1.1663387e-07,\n",
-      "       5.6104445e-06+/-3.4817007e-08, 2.0008648e-06+/-1.2049935e-08,\n",
-      "       7.8639375e-07+/-4.6203322e-09, 3.3146904e-07+/-1.9062864e-09],\n",
-      "      dtype=object), 'P_PLUS_K': array([0.029414681+/-0.00016576186, 0.0011170681+/-4.2910112e-06,\n",
-      "       0.00017377771+/-6.5157433e-07, 4.4930904e-05+/-1.4775241e-07,\n",
-      "       1.4930485e-05+/-4.4496754e-08, 5.767099e-06+/-1.5919381e-08,\n",
-      "       2.4531312e-06+/-6.3706045e-09, 1.1157919e-06+/-2.7409542e-09],\n",
-      "      dtype=object), 'RNLOG': array([-0.002174362+/-3.1345446e-05, -7.7689646e-05+/-1.9432754e-06,\n",
-      "       -9.9472127e-06+/-2.0311267e-07, -2.0116495e-06+/-3.5696284e-08,\n",
-      "       -5.2072331e-07+/-8.7746246e-09, -1.6210965e-07+/-1.9971823e-09,\n",
-      "       -5.5458201e-08+/-9.4045928e-10, -2.0154761e-08+/-3.9732887e-10],\n",
-      "      dtype=object), 'RNLOQ': array([0.0097501419+/-2.4417777e-05, 0.00026621965+/-6.1568973e-07,\n",
-      "       2.9919662e-05+/-6.5509652e-08, 5.6986393e-06+/-1.187522e-08,\n",
-      "       1.4389141e-06+/-2.8871619e-09, 4.3330739e-07+/-8.3701255e-10,\n",
-      "       1.4707696e-07+/-2.7656597e-10, 5.4399032e-08+/-9.9166043e-11],\n",
-      "      dtype=object), 'VNLO_PLUS_P_PLUS_K': array([0.039824801+/-0.00018537107823518736,\n",
-      "       0.00158985702+/-5.469740507342322e-06,\n",
-      "       0.000247842853+/-8.188103965593532e-07,\n",
-      "       6.308083e-05+/-1.8823983184221398e-07,\n",
-      "       2.05409295e-05+/-5.6499425598625026e-08,\n",
-      "       7.767963799999999e-06+/-1.9965661144259312e-08,\n",
-      "       3.23952495e-06+/-7.869693217259304e-09,\n",
-      "       1.44726094e-06+/-3.3386760497722146e-09], dtype=object), 'RNLO': array([0.0075757799+/-3.973367361018416e-05,\n",
-      "       0.000188530004+/-2.038478139169668e-06,\n",
-      "       1.99724493e-05+/-2.1341572392794774e-07,\n",
-      "       3.6869898e-06+/-3.761974935399033e-08,\n",
-      "       9.1819079e-07+/-9.237409848422703e-09,\n",
-      "       2.7119774e-07+/-2.1654854301728264e-09,\n",
-      "       9.1618759e-08+/-9.802817927005271e-10,\n",
-      "       3.4244271e-08+/-4.095169532751419e-10], dtype=object), 'RNLO_PLUS_VNLO_PLUS_P_PLUS_K': array([0.0474005809+/-0.00018958164854393633,\n",
-      "       0.001778387024+/-5.837247154398559e-06,\n",
-      "       0.00026781530230000005+/-8.461659037880073e-07,\n",
-      "       6.67678198e-05+/-1.919621833420376e-07,\n",
-      "       2.145912029e-05+/-5.724958369876851e-08,\n",
-      "       8.03916154e-06+/-2.0082752602063216e-08,\n",
-      "       3.3311437090000002e-06+/-7.930512198267981e-09,\n",
-      "       1.481505211e-06+/-3.3636976529323134e-09], dtype=object), 'order': array([1, 1, 1, 1, 1, 1, 1, 1]), 'energy': array([13000, 13000, 13000, 13000, 13000, 13000, 13000, 13000]), 'energyhalf': array([6500., 6500., 6500., 6500., 6500., 6500., 6500., 6500.]), 'particle1': array([1000011, 1000011, 1000011, 1000011, 1000011, 1000011, 1000011,\n",
-      "       1000011]), 'particle2': array([-1000011, -1000011, -1000011, -1000011, -1000011, -1000011,\n",
-      "       -1000011, -1000011]), 'slha': array(['mastercode_with_gm2.in_mass_1000011_100.0',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_228.57142857142858',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_357.14285714285717',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_485.7142857142858',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_614.2857142857143',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_742.8571428571429',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_871.4285714285716',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_1000.0'], dtype='<U54'), 'pdf_lo': array(['cteq6l1', 'cteq6l1', 'cteq6l1', 'cteq6l1', 'cteq6l1', 'cteq6l1',\n",
-      "       'cteq6l1', 'cteq6l1'], dtype='<U7'), 'pdfset_lo': array([0, 0, 0, 0, 0, 0, 0, 0]), 'pdf_nlo': array(['cteq66', 'cteq66', 'cteq66', 'cteq66', 'cteq66', 'cteq66',\n",
-      "       'cteq66', 'cteq66'], dtype='<U6'), 'pdfset_nlo': array([0, 0, 0, 0, 0, 0, 0, 0]), 'pdf_lo_id': array([10042, 10042, 10042, 10042, 10042, 10042, 10042, 10042]), 'pdf_nlo_id': array([10550, 10550, 10550, 10550, 10550, 10550, 10550, 10550]), 'mu_f': array([1., 1., 1., 1., 1., 1., 1., 1.]), 'mu_r': array([1., 1., 1., 1., 1., 1., 1., 1.]), 'precision': array([0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]), 'max_iters': array([50, 50, 50, 50, 50, 50, 50, 50]), 'invariant_mass': array(['auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto'],\n",
-      "      dtype='<U4'), 'pt': array(['auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto'],\n",
-      "      dtype='<U4'), 'result': array(['total', 'total', 'total', 'total', 'total', 'total', 'total',\n",
-      "       'total'], dtype='<U5'), 'id': array(['5', '5', '5', '5', '5', '5', '5', '5'], dtype='<U1'), 'model_path': array(['/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO'], dtype='<U40'), 'mu': array([ 100.        ,  228.57142857,  357.14285714,  485.71428571,\n",
-      "        614.28571429,  742.85714286,  871.42857143, 1000.        ]), 'mass_1000011': array([ 100.        ,  228.57142857,  357.14285714,  485.71428571,\n",
-      "        614.28571429,  742.85714286,  871.42857143, 1000.        ]), 'code': array(['RS', 'RS', 'RS', 'RS', 'RS', 'RS', 'RS', 'RS'], dtype='<U2')}\n"
+      "skipskipskipskipskipskipskipskipskipRunning: 9 jobs\n",
+      "                        LO                      NLO NLO_PLUS_NLL  \\\n",
+      "0        0.20208+/-0.00029          0.2679+/-0.0004      0.0+/-0   \n",
+      "1      0.013713+/-0.000018      0.017043+/-0.000020      0.0+/-0   \n",
+      "2    0.0026091+/-0.0000031    0.0031281+/-0.0000035      0.0+/-0   \n",
+      "3    0.0007347+/-0.0000008    0.0008593+/-0.0000009      0.0+/-0   \n",
+      "4  0.00025475+/-0.00000028  0.00029242+/-0.00000029      0.0+/-0   \n",
+      "5  0.00010047+/-0.00000011  0.00011369+/-0.00000011      0.0+/-0   \n",
+      "6      (4.322+/-0.004)e-05      (4.841+/-0.005)e-05      0.0+/-0   \n",
+      "7    (1.9790+/-0.0020)e-05    (2.2010+/-0.0020)e-05      0.0+/-0   \n",
+      "8      (9.489+/-0.009)e-06    (1.0515+/-0.0009)e-05      0.0+/-0   \n",
+      "\n",
+      "  aNNLO_PLUS_NNLL                                             K_LO  \\\n",
+      "0            None  1.00000000000000000000+/-0.00000000000000000026   \n",
+      "1            None                                          1.0+/-0   \n",
+      "2            None                                          1.0+/-0   \n",
+      "3            None                                          1.0+/-0   \n",
+      "4            None  1.00000000000000000000+/-0.00000000000000000013   \n",
+      "5            None                                          1.0+/-0   \n",
+      "6            None                                          1.0+/-0   \n",
+      "7            None                                          1.0+/-0   \n",
+      "8            None                                          1.0+/-0   \n",
+      "\n",
+      "             K_NLO K_NLO_PLUS_NLL NLO_PLUS_NLL_OVER_NLO K_aNNLO_PLUS_NNLL  \\\n",
+      "0  1.3255+/-0.0026        0.0+/-0               0.0+/-0              None   \n",
+      "1  1.2429+/-0.0022        0.0+/-0               0.0+/-0              None   \n",
+      "2  1.1989+/-0.0020        0.0+/-0               0.0+/-0              None   \n",
+      "3  1.1697+/-0.0018        0.0+/-0               0.0+/-0              None   \n",
+      "4  1.1479+/-0.0017        0.0+/-0               0.0+/-0              None   \n",
+      "5  1.1316+/-0.0016        0.0+/-0               0.0+/-0              None   \n",
+      "6  1.1199+/-0.0016        0.0+/-0               0.0+/-0              None   \n",
+      "7  1.1122+/-0.0015        0.0+/-0               0.0+/-0              None   \n",
+      "8  1.1081+/-0.0015        0.0+/-0               0.0+/-0              None   \n",
+      "\n",
+      "  aNNLO_PLUS_NNLL_OVER_NLO  ... precision max_iters invariant_mass    pt  \\\n",
+      "0                     None  ...      0.01        50           auto  auto   \n",
+      "1                     None  ...      0.01        50           auto  auto   \n",
+      "2                     None  ...      0.01        50           auto  auto   \n",
+      "3                     None  ...      0.01        50           auto  auto   \n",
+      "4                     None  ...      0.01        50           auto  auto   \n",
+      "5                     None  ...      0.01        50           auto  auto   \n",
+      "6                     None  ...      0.01        50           auto  auto   \n",
+      "7                     None  ...      0.01        50           auto  auto   \n",
+      "8                     None  ...      0.01        50           auto  auto   \n",
+      "\n",
+      "  result id model      mu  mass_1000011                           runner  \n",
+      "0  total  5         100.0         100.0  ResumminoRunner-resummino 3.1.1  \n",
+      "1  total  5         212.5         212.5  ResumminoRunner-resummino 3.1.1  \n",
+      "2  total  5         325.0         325.0  ResumminoRunner-resummino 3.1.1  \n",
+      "3  total  5         437.5         437.5  ResumminoRunner-resummino 3.1.1  \n",
+      "4  total  5         550.0         550.0  ResumminoRunner-resummino 3.1.1  \n",
+      "5  total  5         662.5         662.5  ResumminoRunner-resummino 3.1.1  \n",
+      "6  total  5         775.0         775.0  ResumminoRunner-resummino 3.1.1  \n",
+      "7  total  5         887.5         887.5  ResumminoRunner-resummino 3.1.1  \n",
+      "8  total  5        1000.0        1000.0  ResumminoRunner-resummino 3.1.1  \n",
+      "\n",
+      "[9 rows x 41 columns]\n"
      ]
     }
    ],
@@ -161,7 +134,7 @@
     "        i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.,id=\"5\")\n",
     "        #i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"CT14lo\",\"CT14lo\",1., 1.,model_path=model_path,id=\"5\")\n",
     "        li = [i]\n",
-    "        li = hepi.mass_scan([i],pa, np.linspace(100,1000,8))\n",
+    "        li = hepi.mass_scan([i],pa, np.linspace(100,1000,9))\n",
     "        rs_dl = rs.run(li,noskip=False)\n",
     "        print(rs_dl)"
    ]
@@ -176,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "id": "7dedc960",
    "metadata": {},
    "outputs": [
@@ -204,13 +177,13 @@
        "      <th>LO</th>\n",
        "      <th>NLO</th>\n",
        "      <th>NLO_PLUS_NLL</th>\n",
+       "      <th>aNNLO_PLUS_NNLL</th>\n",
        "      <th>K_LO</th>\n",
        "      <th>K_NLO</th>\n",
        "      <th>K_NLO_PLUS_NLL</th>\n",
        "      <th>NLO_PLUS_NLL_OVER_NLO</th>\n",
-       "      <th>VNLO</th>\n",
-       "      <th>P_PLUS_K</th>\n",
-       "      <th>RNLOG</th>\n",
+       "      <th>K_aNNLO_PLUS_NNLL</th>\n",
+       "      <th>aNNLO_PLUS_NNLL_OVER_NLO</th>\n",
        "      <th>...</th>\n",
        "      <th>precision</th>\n",
        "      <th>max_iters</th>\n",
@@ -218,25 +191,25 @@
        "      <th>pt</th>\n",
        "      <th>result</th>\n",
        "      <th>id</th>\n",
-       "      <th>model_path</th>\n",
+       "      <th>model</th>\n",
        "      <th>mu</th>\n",
        "      <th>mass_1000011</th>\n",
-       "      <th>code</th>\n",
+       "      <th>runner</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.19828+/-0.00029</td>\n",
-       "      <td>0.26266+/-0.00035</td>\n",
+       "      <td>0.20208+/-0.00029</td>\n",
+       "      <td>0.2679+/-0.0004</td>\n",
        "      <td>0.0+/-0</td>\n",
-       "      <td>1.0+/-0</td>\n",
-       "      <td>1.3247+/-0.0026</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1.00000000000000000000+/-0.00000000000000000026</td>\n",
+       "      <td>1.3255+/-0.0026</td>\n",
        "      <td>0.0+/-0</td>\n",
        "      <td>0.0+/-0</td>\n",
-       "      <td>0.01041+/-0.00008</td>\n",
-       "      <td>0.02941+/-0.00017</td>\n",
-       "      <td>-0.002174+/-0.000031</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>...</td>\n",
        "      <td>0.01</td>\n",
        "      <td>50</td>\n",
@@ -244,23 +217,23 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>RS</td>\n",
+       "      <td></td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.010273+/-0.000013</td>\n",
-       "      <td>0.012686+/-0.000015</td>\n",
+       "      <td>0.013713+/-0.000018</td>\n",
+       "      <td>0.017043+/-0.000020</td>\n",
        "      <td>0.0+/-0</td>\n",
+       "      <td>None</td>\n",
        "      <td>1.0+/-0</td>\n",
-       "      <td>1.2348+/-0.0021</td>\n",
+       "      <td>1.2429+/-0.0022</td>\n",
        "      <td>0.0+/-0</td>\n",
        "      <td>0.0+/-0</td>\n",
-       "      <td>0.0004728+/-0.0000034</td>\n",
-       "      <td>0.001117+/-0.000004</td>\n",
-       "      <td>(-7.77+/-0.19)e-05</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>...</td>\n",
        "      <td>0.01</td>\n",
        "      <td>50</td>\n",
@@ -268,23 +241,23 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
-       "      <td>228.571429</td>\n",
-       "      <td>228.571429</td>\n",
-       "      <td>RS</td>\n",
+       "      <td></td>\n",
+       "      <td>212.5</td>\n",
+       "      <td>212.5</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.0017420+/-0.0000021</td>\n",
-       "      <td>0.0020719+/-0.0000023</td>\n",
+       "      <td>0.0026091+/-0.0000031</td>\n",
+       "      <td>0.0031281+/-0.0000035</td>\n",
        "      <td>0.0+/-0</td>\n",
-       "      <td>1.00000000000000000000+/-0.00000000000000000023</td>\n",
-       "      <td>1.1894+/-0.0019</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1.0+/-0</td>\n",
+       "      <td>1.1989+/-0.0020</td>\n",
        "      <td>0.0+/-0</td>\n",
        "      <td>0.0+/-0</td>\n",
-       "      <td>(7.41+/-0.05)e-05</td>\n",
-       "      <td>0.0001738+/-0.0000007</td>\n",
-       "      <td>(-9.95+/-0.20)e-06</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>...</td>\n",
        "      <td>0.01</td>\n",
        "      <td>50</td>\n",
@@ -292,23 +265,23 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
-       "      <td>357.142857</td>\n",
-       "      <td>357.142857</td>\n",
-       "      <td>RS</td>\n",
+       "      <td></td>\n",
+       "      <td>325.0</td>\n",
+       "      <td>325.0</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.0004518+/-0.0000005</td>\n",
-       "      <td>0.0005239+/-0.0000005</td>\n",
+       "      <td>0.0007347+/-0.0000008</td>\n",
+       "      <td>0.0008593+/-0.0000009</td>\n",
        "      <td>0.0+/-0</td>\n",
+       "      <td>None</td>\n",
        "      <td>1.0+/-0</td>\n",
-       "      <td>1.1596+/-0.0018</td>\n",
+       "      <td>1.1697+/-0.0018</td>\n",
        "      <td>0.0+/-0</td>\n",
        "      <td>0.0+/-0</td>\n",
-       "      <td>(1.815+/-0.012)e-05</td>\n",
-       "      <td>(4.493+/-0.015)e-05</td>\n",
-       "      <td>(-2.01+/-0.04)e-06</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>...</td>\n",
        "      <td>0.01</td>\n",
        "      <td>50</td>\n",
@@ -316,23 +289,23 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
-       "      <td>485.714286</td>\n",
-       "      <td>485.714286</td>\n",
-       "      <td>RS</td>\n",
+       "      <td></td>\n",
+       "      <td>437.5</td>\n",
+       "      <td>437.5</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.00014601+/-0.00000016</td>\n",
-       "      <td>0.00016620+/-0.00000016</td>\n",
+       "      <td>0.00025475+/-0.00000028</td>\n",
+       "      <td>0.00029242+/-0.00000029</td>\n",
        "      <td>0.0+/-0</td>\n",
-       "      <td>1.00000000000000000000+/-0.00000000000000000014</td>\n",
-       "      <td>1.1383+/-0.0017</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1.00000000000000000000+/-0.00000000000000000013</td>\n",
+       "      <td>1.1479+/-0.0017</td>\n",
        "      <td>0.0+/-0</td>\n",
        "      <td>0.0+/-0</td>\n",
-       "      <td>(5.610+/-0.035)e-06</td>\n",
-       "      <td>(1.493+/-0.004)e-05</td>\n",
-       "      <td>(-5.21+/-0.09)e-07</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>...</td>\n",
        "      <td>0.01</td>\n",
        "      <td>50</td>\n",
@@ -340,23 +313,23 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
-       "      <td>614.285714</td>\n",
-       "      <td>614.285714</td>\n",
-       "      <td>RS</td>\n",
+       "      <td></td>\n",
+       "      <td>550.0</td>\n",
+       "      <td>550.0</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>(5.395+/-0.006)e-05</td>\n",
-       "      <td>(6.060+/-0.006)e-05</td>\n",
+       "      <td>0.00010047+/-0.00000011</td>\n",
+       "      <td>0.00011369+/-0.00000011</td>\n",
        "      <td>0.0+/-0</td>\n",
+       "      <td>None</td>\n",
        "      <td>1.0+/-0</td>\n",
-       "      <td>1.1233+/-0.0016</td>\n",
+       "      <td>1.1316+/-0.0016</td>\n",
        "      <td>0.0+/-0</td>\n",
        "      <td>0.0+/-0</td>\n",
-       "      <td>(2.001+/-0.012)e-06</td>\n",
-       "      <td>(5.767+/-0.016)e-06</td>\n",
-       "      <td>(-1.621+/-0.020)e-07</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>...</td>\n",
        "      <td>0.01</td>\n",
        "      <td>50</td>\n",
@@ -364,23 +337,23 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
-       "      <td>742.857143</td>\n",
-       "      <td>742.857143</td>\n",
-       "      <td>RS</td>\n",
+       "      <td></td>\n",
+       "      <td>662.5</td>\n",
+       "      <td>662.5</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>(2.1798+/-0.0022)e-05</td>\n",
-       "      <td>(2.4274+/-0.0022)e-05</td>\n",
+       "      <td>(4.322+/-0.004)e-05</td>\n",
+       "      <td>(4.841+/-0.005)e-05</td>\n",
        "      <td>0.0+/-0</td>\n",
+       "      <td>None</td>\n",
        "      <td>1.0+/-0</td>\n",
-       "      <td>1.1136+/-0.0015</td>\n",
+       "      <td>1.1199+/-0.0016</td>\n",
        "      <td>0.0+/-0</td>\n",
        "      <td>0.0+/-0</td>\n",
-       "      <td>(7.86+/-0.05)e-07</td>\n",
-       "      <td>(2.453+/-0.006)e-06</td>\n",
-       "      <td>(-5.55+/-0.09)e-08</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>...</td>\n",
        "      <td>0.01</td>\n",
        "      <td>50</td>\n",
@@ -388,23 +361,47 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
-       "      <td>871.428571</td>\n",
-       "      <td>871.428571</td>\n",
-       "      <td>RS</td>\n",
+       "      <td></td>\n",
+       "      <td>775.0</td>\n",
+       "      <td>775.0</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>(9.381+/-0.009)e-06</td>\n",
-       "      <td>(1.0401+/-0.0009)e-05</td>\n",
+       "      <td>(1.9790+/-0.0020)e-05</td>\n",
+       "      <td>(2.2010+/-0.0020)e-05</td>\n",
+       "      <td>0.0+/-0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1.0+/-0</td>\n",
+       "      <td>1.1122+/-0.0015</td>\n",
+       "      <td>0.0+/-0</td>\n",
+       "      <td>0.0+/-0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>50</td>\n",
+       "      <td>auto</td>\n",
+       "      <td>auto</td>\n",
+       "      <td>total</td>\n",
+       "      <td>5</td>\n",
+       "      <td></td>\n",
+       "      <td>887.5</td>\n",
+       "      <td>887.5</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>(9.489+/-0.009)e-06</td>\n",
+       "      <td>(1.0515+/-0.0009)e-05</td>\n",
        "      <td>0.0+/-0</td>\n",
+       "      <td>None</td>\n",
        "      <td>1.0+/-0</td>\n",
-       "      <td>1.1088+/-0.0015</td>\n",
+       "      <td>1.1081+/-0.0015</td>\n",
        "      <td>0.0+/-0</td>\n",
        "      <td>0.0+/-0</td>\n",
-       "      <td>(3.315+/-0.019)e-07</td>\n",
-       "      <td>(1.1158+/-0.0027)e-06</td>\n",
-       "      <td>(-2.02+/-0.04)e-08</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>...</td>\n",
        "      <td>0.01</td>\n",
        "      <td>50</td>\n",
@@ -412,87 +409,82 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
-       "      <td>1000.000000</td>\n",
-       "      <td>1000.000000</td>\n",
-       "      <td>RS</td>\n",
+       "      <td></td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>8 rows × 38 columns</p>\n",
+       "<p>9 rows × 41 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                        LO                      NLO NLO_PLUS_NLL  \\\n",
-       "0        0.19828+/-0.00029        0.26266+/-0.00035      0.0+/-0   \n",
-       "1      0.010273+/-0.000013      0.012686+/-0.000015      0.0+/-0   \n",
-       "2    0.0017420+/-0.0000021    0.0020719+/-0.0000023      0.0+/-0   \n",
-       "3    0.0004518+/-0.0000005    0.0005239+/-0.0000005      0.0+/-0   \n",
-       "4  0.00014601+/-0.00000016  0.00016620+/-0.00000016      0.0+/-0   \n",
-       "5      (5.395+/-0.006)e-05      (6.060+/-0.006)e-05      0.0+/-0   \n",
-       "6    (2.1798+/-0.0022)e-05    (2.4274+/-0.0022)e-05      0.0+/-0   \n",
-       "7      (9.381+/-0.009)e-06    (1.0401+/-0.0009)e-05      0.0+/-0   \n",
-       "\n",
-       "                                              K_LO            K_NLO  \\\n",
-       "0                                          1.0+/-0  1.3247+/-0.0026   \n",
-       "1                                          1.0+/-0  1.2348+/-0.0021   \n",
-       "2  1.00000000000000000000+/-0.00000000000000000023  1.1894+/-0.0019   \n",
-       "3                                          1.0+/-0  1.1596+/-0.0018   \n",
-       "4  1.00000000000000000000+/-0.00000000000000000014  1.1383+/-0.0017   \n",
-       "5                                          1.0+/-0  1.1233+/-0.0016   \n",
-       "6                                          1.0+/-0  1.1136+/-0.0015   \n",
-       "7                                          1.0+/-0  1.1088+/-0.0015   \n",
+       "0        0.20208+/-0.00029          0.2679+/-0.0004      0.0+/-0   \n",
+       "1      0.013713+/-0.000018      0.017043+/-0.000020      0.0+/-0   \n",
+       "2    0.0026091+/-0.0000031    0.0031281+/-0.0000035      0.0+/-0   \n",
+       "3    0.0007347+/-0.0000008    0.0008593+/-0.0000009      0.0+/-0   \n",
+       "4  0.00025475+/-0.00000028  0.00029242+/-0.00000029      0.0+/-0   \n",
+       "5  0.00010047+/-0.00000011  0.00011369+/-0.00000011      0.0+/-0   \n",
+       "6      (4.322+/-0.004)e-05      (4.841+/-0.005)e-05      0.0+/-0   \n",
+       "7    (1.9790+/-0.0020)e-05    (2.2010+/-0.0020)e-05      0.0+/-0   \n",
+       "8      (9.489+/-0.009)e-06    (1.0515+/-0.0009)e-05      0.0+/-0   \n",
        "\n",
-       "  K_NLO_PLUS_NLL NLO_PLUS_NLL_OVER_NLO                   VNLO  \\\n",
-       "0        0.0+/-0               0.0+/-0      0.01041+/-0.00008   \n",
-       "1        0.0+/-0               0.0+/-0  0.0004728+/-0.0000034   \n",
-       "2        0.0+/-0               0.0+/-0      (7.41+/-0.05)e-05   \n",
-       "3        0.0+/-0               0.0+/-0    (1.815+/-0.012)e-05   \n",
-       "4        0.0+/-0               0.0+/-0    (5.610+/-0.035)e-06   \n",
-       "5        0.0+/-0               0.0+/-0    (2.001+/-0.012)e-06   \n",
-       "6        0.0+/-0               0.0+/-0      (7.86+/-0.05)e-07   \n",
-       "7        0.0+/-0               0.0+/-0    (3.315+/-0.019)e-07   \n",
+       "  aNNLO_PLUS_NNLL                                             K_LO  \\\n",
+       "0            None  1.00000000000000000000+/-0.00000000000000000026   \n",
+       "1            None                                          1.0+/-0   \n",
+       "2            None                                          1.0+/-0   \n",
+       "3            None                                          1.0+/-0   \n",
+       "4            None  1.00000000000000000000+/-0.00000000000000000013   \n",
+       "5            None                                          1.0+/-0   \n",
+       "6            None                                          1.0+/-0   \n",
+       "7            None                                          1.0+/-0   \n",
+       "8            None                                          1.0+/-0   \n",
        "\n",
-       "                P_PLUS_K                 RNLOG  ... precision max_iters  \\\n",
-       "0      0.02941+/-0.00017  -0.002174+/-0.000031  ...      0.01        50   \n",
-       "1    0.001117+/-0.000004    (-7.77+/-0.19)e-05  ...      0.01        50   \n",
-       "2  0.0001738+/-0.0000007    (-9.95+/-0.20)e-06  ...      0.01        50   \n",
-       "3    (4.493+/-0.015)e-05    (-2.01+/-0.04)e-06  ...      0.01        50   \n",
-       "4    (1.493+/-0.004)e-05    (-5.21+/-0.09)e-07  ...      0.01        50   \n",
-       "5    (5.767+/-0.016)e-06  (-1.621+/-0.020)e-07  ...      0.01        50   \n",
-       "6    (2.453+/-0.006)e-06    (-5.55+/-0.09)e-08  ...      0.01        50   \n",
-       "7  (1.1158+/-0.0027)e-06    (-2.02+/-0.04)e-08  ...      0.01        50   \n",
+       "             K_NLO K_NLO_PLUS_NLL NLO_PLUS_NLL_OVER_NLO K_aNNLO_PLUS_NNLL  \\\n",
+       "0  1.3255+/-0.0026        0.0+/-0               0.0+/-0              None   \n",
+       "1  1.2429+/-0.0022        0.0+/-0               0.0+/-0              None   \n",
+       "2  1.1989+/-0.0020        0.0+/-0               0.0+/-0              None   \n",
+       "3  1.1697+/-0.0018        0.0+/-0               0.0+/-0              None   \n",
+       "4  1.1479+/-0.0017        0.0+/-0               0.0+/-0              None   \n",
+       "5  1.1316+/-0.0016        0.0+/-0               0.0+/-0              None   \n",
+       "6  1.1199+/-0.0016        0.0+/-0               0.0+/-0              None   \n",
+       "7  1.1122+/-0.0015        0.0+/-0               0.0+/-0              None   \n",
+       "8  1.1081+/-0.0015        0.0+/-0               0.0+/-0              None   \n",
        "\n",
-       "  invariant_mass    pt  result  id                                model_path  \\\n",
-       "0           auto  auto   total   5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   \n",
-       "1           auto  auto   total   5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   \n",
-       "2           auto  auto   total   5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   \n",
-       "3           auto  auto   total   5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   \n",
-       "4           auto  auto   total   5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   \n",
-       "5           auto  auto   total   5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   \n",
-       "6           auto  auto   total   5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   \n",
-       "7           auto  auto   total   5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   \n",
+       "  aNNLO_PLUS_NNLL_OVER_NLO  ... precision max_iters invariant_mass    pt  \\\n",
+       "0                     None  ...      0.01        50           auto  auto   \n",
+       "1                     None  ...      0.01        50           auto  auto   \n",
+       "2                     None  ...      0.01        50           auto  auto   \n",
+       "3                     None  ...      0.01        50           auto  auto   \n",
+       "4                     None  ...      0.01        50           auto  auto   \n",
+       "5                     None  ...      0.01        50           auto  auto   \n",
+       "6                     None  ...      0.01        50           auto  auto   \n",
+       "7                     None  ...      0.01        50           auto  auto   \n",
+       "8                     None  ...      0.01        50           auto  auto   \n",
        "\n",
-       "            mu  mass_1000011 code  \n",
-       "0   100.000000    100.000000   RS  \n",
-       "1   228.571429    228.571429   RS  \n",
-       "2   357.142857    357.142857   RS  \n",
-       "3   485.714286    485.714286   RS  \n",
-       "4   614.285714    614.285714   RS  \n",
-       "5   742.857143    742.857143   RS  \n",
-       "6   871.428571    871.428571   RS  \n",
-       "7  1000.000000   1000.000000   RS  \n",
+       "  result id model      mu  mass_1000011                           runner  \n",
+       "0  total  5         100.0         100.0  ResumminoRunner-resummino 3.1.1  \n",
+       "1  total  5         212.5         212.5  ResumminoRunner-resummino 3.1.1  \n",
+       "2  total  5         325.0         325.0  ResumminoRunner-resummino 3.1.1  \n",
+       "3  total  5         437.5         437.5  ResumminoRunner-resummino 3.1.1  \n",
+       "4  total  5         550.0         550.0  ResumminoRunner-resummino 3.1.1  \n",
+       "5  total  5         662.5         662.5  ResumminoRunner-resummino 3.1.1  \n",
+       "6  total  5         775.0         775.0  ResumminoRunner-resummino 3.1.1  \n",
+       "7  total  5         887.5         887.5  ResumminoRunner-resummino 3.1.1  \n",
+       "8  total  5        1000.0        1000.0  ResumminoRunner-resummino 3.1.1  \n",
        "\n",
-       "[8 rows x 38 columns]"
+       "[9 rows x 41 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "hepi.LD2DF(rs_dl)"
+    "rs_dl"
    ]
   },
   {
@@ -505,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "a74a3e82",
    "metadata": {},
    "outputs": [
@@ -513,15 +505,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LO,NLO,NLO_PLUS_NLL,K_LO,K_NLO,K_NLO_PLUS_NLL,NLO_PLUS_NLL_OVER_NLO,VNLO,P_PLUS_K,RNLOG,RNLOQ,VNLO_PLUS_P_PLUS_K,RNLO,RNLO_PLUS_VNLO_PLUS_P_PLUS_K,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_path,mu,mass_1000011,code\n",
-      "0.19828+/-0.00029,0.26266+/-0.00035,0.0+/-0,1.0+/-0,1.3247+/-0.0026,0.0+/-0,0.0+/-0,0.01041+/-0.00008,0.02941+/-0.00017,-0.002174+/-0.000031,0.009750+/-0.000024,0.03982+/-0.00019,0.00758+/-0.00004,0.04740+/-0.00019,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_100.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,100.0,100.0,RS\n",
-      "0.010273+/-0.000013,0.012686+/-0.000015,0.0+/-0,1.0+/-0,1.2348+/-0.0021,0.0+/-0,0.0+/-0,0.0004728+/-0.0000034,0.001117+/-0.000004,(-7.77+/-0.19)e-05,0.0002662+/-0.0000006,0.001590+/-0.000005,0.0001885+/-0.0000020,0.001778+/-0.000006,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_228.57142857142858,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,228.57142857142858,228.57142857142858,RS\n",
-      "0.0017420+/-0.0000021,0.0020719+/-0.0000023,0.0+/-0,1.00000000000000000000+/-0.00000000000000000023,1.1894+/-0.0019,0.0+/-0,0.0+/-0,(7.41+/-0.05)e-05,0.0001738+/-0.0000007,(-9.95+/-0.20)e-06,(2.992+/-0.007)e-05,0.0002478+/-0.0000008,(1.997+/-0.021)e-05,0.0002678+/-0.0000008,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_357.14285714285717,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,357.14285714285717,357.14285714285717,RS\n",
-      "0.0004518+/-0.0000005,0.0005239+/-0.0000005,0.0+/-0,1.0+/-0,1.1596+/-0.0018,0.0+/-0,0.0+/-0,(1.815+/-0.012)e-05,(4.493+/-0.015)e-05,(-2.01+/-0.04)e-06,(5.699+/-0.012)e-06,(6.308+/-0.019)e-05,(3.69+/-0.04)e-06,(6.677+/-0.019)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_485.7142857142858,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,485.7142857142858,485.7142857142858,RS\n",
-      "0.00014601+/-0.00000016,0.00016620+/-0.00000016,0.0+/-0,1.00000000000000000000+/-0.00000000000000000014,1.1383+/-0.0017,0.0+/-0,0.0+/-0,(5.610+/-0.035)e-06,(1.493+/-0.004)e-05,(-5.21+/-0.09)e-07,(1.4389+/-0.0029)e-06,(2.054+/-0.006)e-05,(9.18+/-0.09)e-07,(2.146+/-0.006)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_614.2857142857143,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,614.2857142857143,614.2857142857143,RS\n",
-      "(5.395+/-0.006)e-05,(6.060+/-0.006)e-05,0.0+/-0,1.0+/-0,1.1233+/-0.0016,0.0+/-0,0.0+/-0,(2.001+/-0.012)e-06,(5.767+/-0.016)e-06,(-1.621+/-0.020)e-07,(4.333+/-0.008)e-07,(7.768+/-0.020)e-06,(2.712+/-0.022)e-07,(8.039+/-0.020)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_742.8571428571429,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,742.8571428571429,742.8571428571429,RS\n",
-      "(2.1798+/-0.0022)e-05,(2.4274+/-0.0022)e-05,0.0+/-0,1.0+/-0,1.1136+/-0.0015,0.0+/-0,0.0+/-0,(7.86+/-0.05)e-07,(2.453+/-0.006)e-06,(-5.55+/-0.09)e-08,(1.4708+/-0.0028)e-07,(3.240+/-0.008)e-06,(9.16+/-0.10)e-08,(3.331+/-0.008)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_871.4285714285716,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,871.4285714285716,871.4285714285716,RS\n",
-      "(9.381+/-0.009)e-06,(1.0401+/-0.0009)e-05,0.0+/-0,1.0+/-0,1.1088+/-0.0015,0.0+/-0,0.0+/-0,(3.315+/-0.019)e-07,(1.1158+/-0.0027)e-06,(-2.02+/-0.04)e-08,(5.440+/-0.010)e-08,(1.4473+/-0.0033)e-06,(3.42+/-0.04)e-08,(1.4815+/-0.0034)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_1000.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,1000.0,1000.0,RS\n",
+      "LO,NLO,NLO_PLUS_NLL,aNNLO_PLUS_NNLL,K_LO,K_NLO,K_NLO_PLUS_NLL,NLO_PLUS_NLL_OVER_NLO,K_aNNLO_PLUS_NNLL,aNNLO_PLUS_NNLL_OVER_NLO,VNLO,P_PLUS_K,RNLOG,RNLOQ,VNLO_PLUS_P_PLUS_K,RNLO,RNLO_PLUS_VNLO_PLUS_P_PLUS_K,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,mass_1000011,runner\n",
+      "0.20208+/-0.00029,0.2679+/-0.0004,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000026,1.3255+/-0.0026,0.0+/-0,0.0+/-0,,,0.01062+/-0.00008,0.02998+/-0.00017,-0.00218+/-0.00005,0.009949+/-0.000025,0.04059+/-0.00019,0.00777+/-0.00005,0.04837+/-0.00020,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_100.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,100.0,100.0,ResumminoRunner-resummino 3.1.1\n",
+      "0.013713+/-0.000018,0.017043+/-0.000020,0.0+/-0,,1.0+/-0,1.2429+/-0.0022,0.0+/-0,0.0+/-0,,,0.000639+/-0.000005,0.001524+/-0.000006,-0.0001066+/-0.0000020,0.0003784+/-0.0000009,0.002163+/-0.000008,0.0002719+/-0.0000022,0.002435+/-0.000008,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_212.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,212.5,212.5,ResumminoRunner-resummino 3.1.1\n",
+      "0.0026091+/-0.0000031,0.0031281+/-0.0000035,0.0+/-0,,1.0+/-0,1.1989+/-0.0020,0.0+/-0,0.0+/-0,,,0.0001128+/-0.0000008,0.0002628+/-0.0000010,(-1.554+/-0.029)e-05,(4.906+/-0.011)e-05,0.0003757+/-0.0000013,(3.352+/-0.031)e-05,0.0004092+/-0.0000013,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_325.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,325.0,325.0,ResumminoRunner-resummino 3.1.1\n",
+      "0.0007347+/-0.0000008,0.0008593+/-0.0000009,0.0+/-0,,1.0+/-0,1.1697+/-0.0018,0.0+/-0,0.0+/-0,,,(3.010+/-0.020)e-05,(7.281+/-0.025)e-05,(-3.50+/-0.06)e-06,(1.0308+/-0.0022)e-05,0.00010290+/-0.00000032,(6.81+/-0.07)e-06,0.00010971+/-0.00000032,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_437.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,437.5,437.5,ResumminoRunner-resummino 3.1.1\n",
+      "0.00025475+/-0.00000028,0.00029242+/-0.00000029,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000013,1.1479+/-0.0017,0.0+/-0,0.0+/-0,,,(9.99+/-0.06)e-06,(2.562+/-0.008)e-05,(-1.005+/-0.017)e-06,(2.820+/-0.006)e-06,(3.561+/-0.010)e-05,(1.815+/-0.018)e-06,(3.743+/-0.010)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_550.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,550.0,550.0,ResumminoRunner-resummino 3.1.1\n",
+      "0.00010047+/-0.00000011,0.00011369+/-0.00000011,0.0+/-0,,1.0+/-0,1.1316+/-0.0016,0.0+/-0,0.0+/-0,,,(3.805+/-0.023)e-06,(1.0433+/-0.0030)e-05,(-3.438+/-0.033)e-07,(9.123+/-0.018)e-07,(1.424+/-0.004)e-05,(5.68+/-0.04)e-07,(1.481+/-0.004)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_662.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,662.5,662.5,ResumminoRunner-resummino 3.1.1\n",
+      "(4.322+/-0.004)e-05,(4.841+/-0.005)e-05,0.0+/-0,,1.0+/-0,1.1199+/-0.0016,0.0+/-0,0.0+/-0,,,(1.590+/-0.010)e-06,(4.678+/-0.013)e-06,(-1.245+/-0.016)e-07,(3.312+/-0.006)e-07,(6.269+/-0.016)e-06,(2.067+/-0.017)e-07,(6.475+/-0.016)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_775.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,775.0,775.0,ResumminoRunner-resummino 3.1.1\n",
+      "(1.9790+/-0.0020)e-05,(2.2010+/-0.0020)e-05,0.0+/-0,,1.0+/-0,1.1122+/-0.0015,0.0+/-0,0.0+/-0,,,(7.11+/-0.04)e-07,(2.242+/-0.006)e-06,(-4.90+/-0.09)e-08,(1.3073+/-0.0025)e-07,(2.954+/-0.007)e-06,(8.17+/-0.09)e-08,(3.035+/-0.007)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_887.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,887.5,887.5,ResumminoRunner-resummino 3.1.1\n",
+      "(9.489+/-0.009)e-06,(1.0515+/-0.0009)e-05,0.0+/-0,,1.0+/-0,1.1081+/-0.0015,0.0+/-0,0.0+/-0,,,(3.351+/-0.019)e-07,(1.1287+/-0.0028)e-06,(-2.06+/-0.04)e-08,(5.497+/-0.010)e-08,(1.4638+/-0.0034)e-06,(3.43+/-0.04)e-08,(1.4981+/-0.0034)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_1000.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,1000.0,1000.0,ResumminoRunner-resummino 3.1.1\n",
       "\n"
      ]
     }
@@ -541,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "949b48eb",
    "metadata": {},
    "outputs": [
@@ -549,7 +542,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{\"initial state\": \"pp\", \"order\": \"NLO\", \"source\": \"HEPi\", \"contact\": \"?\", \"tool\": \"RS\", \"process_latex\": \"$\\\\tilde{e}_{L}^{-}\\\\tilde{e}_{L}^{+}$\", \"comment\": \"?\", \"reference\": \"?\", \"Ecom [GeV]\": \"13000\", \"process_id\": \"pp_13000_1000011_-1000011\", \"PDF set\": \"cteq66\", \"data\": {\"100.0\": {\"xsec_pb\": 0.26266219, \"unc_pb\": 0.00035483454}, \"228.57142857142858\": {\"xsec_pb\": 0.012685717, \"unc_pb\": 1.4738104e-05}, \"357.14285714285717\": {\"xsec_pb\": 0.0020719289, \"unc_pb\": 2.2534652e-06}, \"485.7142857142858\": {\"xsec_pb\": 0.00052389967, \"unc_pb\": 5.389595e-07}, \"614.2857142857143\": {\"xsec_pb\": 0.00016619981, \"unc_pb\": 1.6377336e-07}, \"742.8571428571429\": {\"xsec_pb\": 6.0600809e-05, \"unc_pb\": 5.7603335e-08}, \"871.4285714285716\": {\"xsec_pb\": 2.427351e-05, \"unc_pb\": 2.2387002e-08}, \"1000.0\": {\"xsec_pb\": 1.0401314e-05, \"unc_pb\": 9.3348503e-09}}, \"parameters\": \"?\"}\n"
+      "{\"initial state\": \"pp\", \"order\": \"NLO\", \"source\": \"hepi-0.1.8.9+dirty\", \"contact\": \"?\", \"tool\": \"Resummino-resummino 3.1.1\", \"process_latex\": \"$\\\\tilde{e}_{L}^{-}\\\\tilde{e}_{L}^{+}$\", \"comment\": \"5\", \"reference\": \"?\", \"Ecom [GeV]\": \"13000\", \"process_id\": \"pp_13000_1000011_-1000011\", \"PDF set\": \"cteq66\", \"data\": {\"100.0\": {\"xsec_pb\": 0.26786916, \"unc_pb\": 0.00036328325}, \"212.5\": {\"xsec_pb\": 0.017042798, \"unc_pb\": 1.9927369e-05}, \"325.0\": {\"xsec_pb\": 0.0031280684, \"unc_pb\": 3.4502997e-06}, \"437.5\": {\"xsec_pb\": 0.00085929957, \"unc_pb\": 8.9960357e-07}, \"550.0\": {\"xsec_pb\": 0.00029242159, \"unc_pb\": 2.9376357e-07}, \"662.5\": {\"xsec_pb\": 0.00011369143, \"unc_pb\": 1.1027916e-07}, \"775.0\": {\"xsec_pb\": 4.8409699e-05, \"unc_pb\": 4.5616627e-08}, \"887.5\": {\"xsec_pb\": 2.2010282e-05, \"unc_pb\": 2.0217058e-08}, \"1000.0\": {\"xsec_pb\": 1.0515472e-05, \"unc_pb\": 9.4331156e-09}}, \"parameters\": [[\"mass_1000011\"]]}\n"
      ]
     }
    ],
@@ -577,7 +570,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/source/examples/MG5_debug b/docs/source/examples/MG5_debug
index 0c5c74ac3..2058895c0 100644
--- a/docs/source/examples/MG5_debug
+++ b/docs/source/examples/MG5_debug
@@ -8,7 +8,7 @@
 #*                *                       *                 *
 #*                                                          *
 #*                                                          *
-#*         VERSION 2.7.3                 2020-06-21         *
+#*         VERSION 2.7.3.py3             2020-06-28         *
 #*                                                          *
 #*    The MadGraph5_aMC@NLO Development Team - Find us at   *
 #*    https://server06.fynu.ucl.ac.be/projects/madgraph     *
@@ -20,41 +20,39 @@
 #*     run as ./bin/mg5_aMC  filename                       *
 #*                                                          *
 #************************************************************
-set default_unset_couplings 99
 set group_subprocesses Auto
 set ignore_six_quark_processes False
-set loop_optimized_output True
 set low_mem_multicore_nlo_generation False
-set loop_color_flows False
-set gauge unitary
 set complex_mass_scheme False
+set gauge unitary
+set loop_optimized_output True
+set loop_color_flows False
 set max_npoint_for_channel 0
+set default_unset_couplings 99
 set automatic_html_opening False
-import model /opt/MG5_aMC_v2_7_3_py3/models/EWKino_NLO_UFO_py3
+import model /opt/MG5_aMC_v2_7_0/models/EWKino_NLO_UFO_py3
 Traceback (most recent call last):
-  File "/opt/MG5_aMC_v2_7_0/madgraph/interface/extended_cmd.py", line 1515, in onecmd
+  File "/opt/MG5_aMC_v2_7_3/madgraph/interface/extended_cmd.py", line 1541, in onecmd
     return self.onecmd_orig(line, **opt)
-  File "/opt/MG5_aMC_v2_7_0/madgraph/interface/extended_cmd.py", line 1464, in onecmd_orig
+  File "/opt/MG5_aMC_v2_7_3/madgraph/interface/extended_cmd.py", line 1490, in onecmd_orig
     return func(arg, **opt)
-  File "/opt/MG5_aMC_v2_7_0/madgraph/interface/master_interface.py", line 280, in do_import
+  File "/opt/MG5_aMC_v2_7_3/madgraph/interface/master_interface.py", line 281, in do_import
     self.cmd.do_import(self, *args, **opts)
-  File "/opt/MG5_aMC_v2_7_0/madgraph/interface/madgraph_interface.py", line 5466, in do_import
+  File "/opt/MG5_aMC_v2_7_3/madgraph/interface/madgraph_interface.py", line 5536, in do_import
     self.import_command_file(args[1])
-  File "/opt/MG5_aMC_v2_7_0/madgraph/interface/extended_cmd.py", line 1661, in import_command_file
+  File "/opt/MG5_aMC_v2_7_3/madgraph/interface/extended_cmd.py", line 1687, in import_command_file
     self.exec_cmd(line, precmd=True)
-  File "/opt/MG5_aMC_v2_7_0/madgraph/interface/extended_cmd.py", line 1544, in exec_cmd
+  File "/opt/MG5_aMC_v2_7_3/madgraph/interface/extended_cmd.py", line 1570, in exec_cmd
     stop = Cmd.onecmd_orig(current_interface, line, **opt)
-  File "/opt/MG5_aMC_v2_7_0/madgraph/interface/extended_cmd.py", line 1464, in onecmd_orig
+  File "/opt/MG5_aMC_v2_7_3/madgraph/interface/extended_cmd.py", line 1490, in onecmd_orig
     return func(arg, **opt)
-  File "/opt/MG5_aMC_v2_7_0/madgraph/interface/master_interface.py", line 280, in do_import
+  File "/opt/MG5_aMC_v2_7_3/madgraph/interface/master_interface.py", line 281, in do_import
     self.cmd.do_import(self, *args, **opts)
-  File "/opt/MG5_aMC_v2_7_0/madgraph/interface/madgraph_interface.py", line 5412, in do_import
-    complex_mass_scheme=self.options['complex_mass_scheme'])
-  File "/opt/MG5_aMC_v2_7_0/models/import_ufo.py", line 239, in import_model
-    model = import_full_model(model_path, decay, prefix)
-  File "/opt/MG5_aMC_v2_7_0/models/import_ufo.py", line 383, in import_full_model
-    ufo_model = ufomodels.load_model(model_path, decay)
-  File "/opt/MG5_aMC_v2_7_0/models/__init__.py", line 48, in load_model
-    (path_split[-1], model_path, sys_path)
-Exception: name EWKino_NLO_UFO_py3 already consider as a python library cann't be reassigned(/opt/MG5_aMC_v2_7_3_py3/models/EWKino_NLO_UFO_py3!=/opt/MG5_aMC_v2_7_0/models/EWKino_NLO_UFO_py3)
+  File "/opt/MG5_aMC_v2_7_3/madgraph/interface/madgraph_interface.py", line 5481, in do_import
+    self._curr_model = import_ufo.import_model(args[1], prefix=prefix,
+  File "/opt/MG5_aMC_v2_7_3/models/import_ufo.py", line 206, in import_model
+    model_path = find_ufo_path(model_name)
+  File "/opt/MG5_aMC_v2_7_3/models/import_ufo.py", line 95, in find_ufo_path
+    raise UFOImportError("Path %s is not a valid pathname" % model_name)
+models.import_ufo.UFOImportError: Path /opt/MG5_aMC_v2_7_0/models/EWKino_NLO_UFO_py3 is not a valid pathname
 Fail to write options with error No model currently active, please import a model!
\ No newline at end of file
diff --git a/docs/source/examples/Messages.out b/docs/source/examples/Messages.out
new file mode 100644
index 000000000..e69de29bb
diff --git a/docs/source/examples/demo_resummino.ipynb b/docs/source/examples/demo_resummino.ipynb
index 74b0123ac..18b9e14a2 100644
--- a/docs/source/examples/demo_resummino.ipynb
+++ b/docs/source/examples/demo_resummino.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "b583970e",
    "metadata": {},
    "outputs": [
@@ -18,7 +18,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1.6.20+dirty\n",
+      "0.1.8.9+dirty\n",
       "~/git/resummino_release/\n"
      ]
     }
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "ed7216a4",
    "metadata": {
     "scrolled": false
@@ -55,9 +55,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running: 15 jobs\n",
-      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipCPU times: user 885 ms, sys: 139 ms, total: 1.02 s\n",
-      "Wall time: 1.09 s\n"
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 15 jobs\n",
+      "CPU times: user 1.56 s, sys: 250 ms, total: 1.81 s\n",
+      "Wall time: 2.01 s\n"
      ]
     },
     {
@@ -85,6 +85,7 @@
     "for pa,pb in pss:\n",
     "    for param in params:\n",
     "        i = hepi.Input(hepi.Order.aNNLO_PLUS_NNLL,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.)\n",
+    "        #i = hepi.Input(hepi.Order.LO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.)\n",
     "        li = [i]\n",
     "        li = hepi.mass_scan([i],pa, np.linspace(100,1000,7+8))\n",
     "        rs_dl = rs.run(li,skip=True)\n",
@@ -105,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "19e4273a",
    "metadata": {},
    "outputs": [
@@ -113,7 +114,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running: 15 jobs\n"
+      "skipskipskipskipskipskipskipRunning: 15 jobs\n"
      ]
     },
     {
@@ -203,63 +204,63 @@
       "A5 =  -23.88253 # +-2494614091611.60693\n",
       "A6 =  18.51094 # +-1767242881915.25708\n",
       "A7 =  -5.99116 # +-175440025782.63754\n",
-      "Performing PDF fit with 5 flavors with M^2/S = 0.134784, Q^2 = 5.69462e+06\n",
-      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.134784 \n",
-      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
-      "Fitting gluon PDF...Performing PDF fit with 5 flavors with M^2/S = 0.10092, Q^2 = 4.26386e+06\n",
-      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.10092 \n",
+      "Performing PDF fit with 5 flavors with M^2/S = 0.12762, Q^2 = 5.39194e+06\n",
+      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.12762 \n",
       " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
       "Fitting gluon PDF... done.\n",
       "Fit result:\n",
-      "A0 =  0.67018 # +-5246206.06733\n",
-      "A1 =  -1.65804 # +-444637.72326\n",
-      "A2 =  1.05805 # +-66615414.46520\n",
-      "A3 =  -3.87963 # +-62902081.95089\n",
-      "A4 =  5.87249 # +-888596872.00247\n",
-      "A5 =  -4.21889 # +-1970943503.28569\n",
-      "A6 =  1.32080 # +-980644900.59228\n",
-      "A7 =  -0.09418 # +-77527121.70000\n",
+      "A0 =  0.34011 # +-64122379.67891\n",
+      "A1 =  -1.79231 # +-18373939.51759\n",
+      "A2 =  1.38168 # +-61320047.68627\n",
+      "A3 =  -2.31208 # +-4628073170.19697\n",
+      "A4 =  -0.31595 # +-71049992834.37181\n",
+      "A5 =  5.31583 # +-166846442446.56094\n",
+      "A6 =  -5.49598 # +-84772388993.22758\n",
+      "A7 =  1.81016 # +-6604420481.96470\n",
       "Fitting valence down quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  4.21985 # +-819133160.18674\n",
-      "A1 =  -0.34477 # +-3027588.70468\n",
-      "A2 =  3.79994 # +-3252959721.53213\n",
-      "A3 =  -4.09498 # +-64270951.67348\n",
-      "A4 =  10.70730 # +-1209008729.06855\n",
-      "A5 =  -17.12007 # +-23752481923.49548\n",
-      "A6 =  13.66539 # +-54684825985.87606\n",
-      "A7 =  -4.14458 # +-12030343217.36676\n",
+      "A0 =  4.39692 # +-2537478442.14297\n",
+      "A1 =  -0.33466 # +-13034327.50355\n",
+      "A2 =  3.89651 # +-30879383722.64828\n",
+      "A3 =  -4.11103 # +-1328276253.87668\n",
+      "A4 =  10.64029 # +-26416457123.99702\n",
+      "A5 =  -16.73092 # +-320672123181.33270\n",
+      "A6 =  13.15118 # +-625118814391.66101\n",
+      "A7 =  -3.93298 # +-130265689872.05547\n",
       "Fitting valence up quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  4.34401 # +-3369368518.02619\n",
-      "A1 =  -0.44127 # +-6083335.30932\n",
-      "A2 =  3.70206 # +-16947285.50715\n",
-      "A3 =  -2.22542 # +-1096652031.18501\n",
-      "A4 =  4.52944 # +-8290938708.38723\n",
-      "A5 =  -5.86820 # +-21582041424.80259\n",
-      "A6 =  2.86913 # +-19862271556.02154\n",
-      "A7 =  -0.18548 # +-3008212477.92347\n",
+      "A0 =  7.05171 # +-1653794414.46120\n",
+      "A1 =  -0.35154 # +-1695316.78204\n",
+      "A2 =  2.73124 # +-94281328.38860\n",
+      "A3 =  -3.42720 # +-46217019.60109\n",
+      "A4 =  7.81735 # +-235055644.89374\n",
+      "A5 =  -12.01261 # +-28544851.87925\n",
+      "A6 =  9.42707 # +-422429434.63980\n",
+      "A7 =  -2.80410 # +-108387790.48083\n",
       "Fitting sea down quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  0.62204 # +-7175700445.49436\n",
-      "A1 =  -0.89122 # +-475347094.08255\n",
-      "A2 =  1.80056 # +-16595634.39016\n",
-      "A3 =  -1.99742 # +-296632958887.43616\n",
-      "A4 =  -6.55641 # +-8033851898394.40918\n",
-      "A5 =  23.78872 # +-30495199740071.10156\n",
-      "A6 =  -25.28186 # +-22726356704426.71875\n",
-      "A7 =  9.06578 # +-2362833624590.60059\n",
+      "A0 =  1.03373 # +-133560135140.21777\n",
+      "A1 =  -0.71672 # +-3379562512.45025\n",
+      "A2 =  2.48389 # +-79866771.66947\n",
+      "A3 =  -2.29957 # +-1788242641027.99854\n",
+      "A4 =  -5.16574 # +-49297357399816.57031\n",
+      "A5 =  21.48642 # +-190882058446675.37500\n",
+      "A6 =  -23.68304 # +-145357005464547.71875\n",
+      "A7 =  8.68596 # +-15464210539818.04688\n",
       "Fitting strange quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  0.13699 # +-60007483.71025\n",
-      "A1 =  -1.42275 # +-120270309.76528\n",
-      "A2 =  2.29776 # +-3778815306.12046\n",
-      "A3 =  -4.10524 # +-17637535714.32787\n",
-      "A4 =  5.91070 # +-366497428241.26288\n",
-      "A5 =  -2.59009 # +-1147008929815.82910\n",
-      "A6 =  -1.20404 # +-743096823865.15234\n",
-      "A7 =  0.99181 # +-69579871951.66164\n",
-      "Fitting bottom quark PDF... done.\n",
+      "A0 =  0.26900 # +-1180297.34588\n",
+      "A1 =  -1.28752 # +-1785851.40048\n",
+      "A2 =  0.72543 # +-804814387.30909\n",
+      "A3 =  -5.61018 # +-42714952.69193\n",
+      "A4 =  12.81910 # +-824702956.14051\n",
+      "A5 =  -14.86113 # +-2371540286.70679\n",
+      "A6 =  8.70986 # +-1415857293.00850\n",
+      "A7 =  -2.05773 # +-123662038.10189\n",
+      "Fitting bottom quark PDF...Performing PDF fit with 5 flavors with M^2/S = 0.134784, Q^2 = 5.69462e+06\n",
+      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.134784 \n",
+      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
+      "Fitting gluon PDF... done.\n",
       "Fit result:\n",
       "A0 =  1.34325 # +-501503835.40527\n",
       "A1 =  -1.50889 # +-15229493.63171\n",
@@ -281,16 +282,6 @@
       "A7 =  -3.96035 # +-155054357182.49445\n",
       "Fitting valence up quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  -0.00142 # +-363140.90650\n",
-      "A1 =  -2.42989 # +-130958941252.55901\n",
-      "A2 =  5.62565 # +-38031144342.28274\n",
-      "A3 =  -16.51797 # +-72220528870354.73438\n",
-      "A4 =  56.91472 # +-1618608017644562.75000\n",
-      "A5 =  -109.60812 # +-5945954295388392.00000\n",
-      "A6 =  115.80512 # +-4852190416986976.00000\n",
-      "A7 =  -58.04092 # +-574366661283276.75000\n",
-      "Fitting sea up quark PDF... done.\n",
-      "Fit result:\n",
       "A0 =  7.15981 # +-2371375635.96233\n",
       "A1 =  -0.34813 # +-2405626.09368\n",
       "A2 =  2.75178 # +-112920069.37558\n",
@@ -319,92 +310,7 @@
       "A5 =  -14.86122 # +-2813943832.63866\n",
       "A6 =  8.71227 # +-1683763786.50343\n",
       "A7 =  -2.05888 # +-147542691.48520\n",
-      "Fitting bottom quark PDF...Performing PDF fit with 5 flavors with M^2/S = 0.120651, Q^2 = 5.09752e+06\n",
-      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.120651 \n",
-      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
-      "Fitting gluon PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  1.33392 # +-218051021.55538\n",
-      "A1 =  -1.51080 # +-6329763.36777\n",
-      "A2 =  3.69658 # +-157925984.96105\n",
-      "A3 =  -4.91977 # +-194682597.84311\n",
-      "A4 =  11.80537 # +-3664718877.16462\n",
-      "A5 =  -16.18111 # +-11057696144.19761\n",
-      "A6 =  12.07516 # +-7445076875.64641\n",
-      "A7 =  -3.79707 # +-759491271.66643\n",
-      "Fitting valence down quark PDF...Performing PDF fit with 5 flavors with M^2/S = 0.12762, Q^2 = 5.39194e+06\n",
-      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.12762 \n",
-      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fitting gluon PDF...Performing PDF fit with 5 flavors with M^2/S = 0.104086, Q^2 = 4.39764e+06\n",
-      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.104086 \n",
-      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
-      "Fitting gluon PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.34011 # +-64122379.67891\n",
-      "A1 =  -1.79231 # +-18373939.51759\n",
-      "A2 =  1.38168 # +-61320047.68627\n",
-      "A3 =  -2.31208 # +-4628073170.19697\n",
-      "A4 =  -0.31595 # +-71049992834.37181\n",
-      "A5 =  5.31583 # +-166846442446.56094\n",
-      "A6 =  -5.49598 # +-84772388993.22758\n",
-      "A7 =  1.81016 # +-6604420481.96470\n",
-      "Fitting valence down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.63467 # +-6450586.48014\n",
-      "A1 =  -1.66924 # +-612398.96956\n",
-      "A2 =  1.11527 # +-64978707.19435\n",
-      "A3 =  -3.77043 # +-85574765.83645\n",
-      "A4 =  5.44347 # +-1233528632.27163\n",
-      "A5 =  -3.55944 # +-2791523960.70656\n",
-      "A6 =  0.84893 # +-1411465291.41003\n",
-      "A7 =  0.03818 # +-112670153.59776\n",
-      "Fitting valence down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.39692 # +-2537478442.14297\n",
-      "A1 =  -0.33466 # +-13034327.50355\n",
-      "A2 =  3.89651 # +-30879383722.64828\n",
-      "A3 =  -4.11103 # +-1328276253.87668\n",
-      "A4 =  10.64029 # +-26416457123.99702\n",
-      "A5 =  -16.73092 # +-320672123181.33270\n",
-      "A6 =  13.15118 # +-625118814391.66101\n",
-      "A7 =  -3.93298 # +-130265689872.05547\n",
-      "Fitting valence up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  -0.00987 # +-3961.63111\n",
-      "A1 =  -4.78121 # +-26118922.89910\n",
-      "A2 =  8.36636 # +-30350827.46402\n",
-      "A3 =  -14.04376 # +-4449144.67961\n",
-      "A4 =  79.13975 # +-571348352.67568\n",
-      "A5 =  -224.13363 # +-10397378228.49435\n",
-      "A6 =  320.24569 # +-38002888385.73756\n",
-      "A7 =  -186.76869 # +-21360563859.47148\n",
-      "Fitting charm quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.37471 # +-1666326669.57200\n",
-      "A1 =  -0.33592 # +-8344685.39755\n",
-      "A2 =  3.88726 # +-22310518618.66117\n",
-      "A3 =  -4.10935 # +-964901016.37741\n",
-      "A4 =  10.65386 # +-18437765862.46499\n",
-      "A5 =  -16.78548 # +-225757180581.90750\n",
-      "A6 =  13.21308 # +-444981184016.84863\n",
-      "A7 =  -3.95578 # +-93316000757.84415\n",
-      "Fitting valence up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.27402 # +-827497710.49621\n",
-      "A1 =  -0.34164 # +-3331730.09935\n",
-      "A2 =  3.83346 # +-5746079846.46096\n",
-      "A3 =  -4.10033 # +-166270529.94446\n",
-      "A4 =  10.69428 # +-3131107623.92452\n",
-      "A5 =  -17.00947 # +-48188221280.21503\n",
-      "A6 =  13.50427 # +-103557244314.46658\n",
-      "A7 =  -4.07434 # +-22374112735.29279\n",
-      "Fitting valence up quark PDF... done.\n",
+      "Fitting bottom quark PDF... done.\n",
       "Fit result:\n",
       "A0 =  -0.00169 # +-1170595.01525\n",
       "A1 =  -2.45948 # +-1114929131858.84448\n",
@@ -416,36 +322,6 @@
       "A7 =  -53.61609 # +-1781776395444919.75000\n",
       "Fitting sea up quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  7.05171 # +-1653794414.46120\n",
-      "A1 =  -0.35154 # +-1695316.78204\n",
-      "A2 =  2.73124 # +-94281328.38860\n",
-      "A3 =  -3.42720 # +-46217019.60109\n",
-      "A4 =  7.81735 # +-235055644.89374\n",
-      "A5 =  -12.01261 # +-28544851.87925\n",
-      "A6 =  9.42707 # +-422429434.63980\n",
-      "A7 =  -2.80410 # +-108387790.48083\n",
-      "Fitting sea down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  1.03373 # +-133560135140.21777\n",
-      "A1 =  -0.71672 # +-3379562512.45025\n",
-      "A2 =  2.48389 # +-79866771.66947\n",
-      "A3 =  -2.29957 # +-1788242641027.99854\n",
-      "A4 =  -5.16574 # +-49297357399816.57031\n",
-      "A5 =  21.48642 # +-190882058446675.37500\n",
-      "A6 =  -23.68304 # +-145357005464547.71875\n",
-      "A7 =  8.68596 # +-15464210539818.04688\n",
-      "Fitting strange quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.26900 # +-1180297.34588\n",
-      "A1 =  -1.28752 # +-1785851.40048\n",
-      "A2 =  0.72543 # +-804814387.30909\n",
-      "A3 =  -5.61018 # +-42714952.69193\n",
-      "A4 =  12.81910 # +-824702956.14051\n",
-      "A5 =  -14.86113 # +-2371540286.70679\n",
-      "A6 =  8.70986 # +-1415857293.00850\n",
-      "A7 =  -2.05773 # +-123662038.10189\n",
-      "Fitting bottom quark PDF... done.\n",
-      "Fit result:\n",
       "A0 =  4.90809 # +-295281633563143.81250\n",
       "A1 =  -0.29254 # +-336658127696.20929\n",
       "A2 =  7.39982 # +-4721977333.09973\n",
@@ -456,46 +332,6 @@
       "A7 =  19.04747 # +-7602993407707350.00000\n",
       "Fitting charm quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  4.52069 # +-3792785483.98147\n",
-      "A1 =  -0.43383 # +-6450536.93272\n",
-      "A2 =  3.69553 # +-18938605.35807\n",
-      "A3 =  -2.31927 # +-1080523731.95969\n",
-      "A4 =  4.77548 # +-8166847116.12351\n",
-      "A5 =  -6.24784 # +-21063383767.93995\n",
-      "A6 =  3.23184 # +-19219039225.40825\n",
-      "A7 =  -0.32812 # +-2896046114.97765\n",
-      "Fitting sea down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.66007 # +-10190253196.36709\n",
-      "A1 =  -0.87153 # +-603659784.81009\n",
-      "A2 =  1.88049 # +-20049026.97495\n",
-      "A3 =  -2.03818 # +-368675519755.92175\n",
-      "A4 =  -6.36992 # +-10008769642467.89453\n",
-      "A5 =  23.48257 # +-38093538148279.23438\n",
-      "A6 =  -25.07188 # +-28471943614352.00000\n",
-      "A7 =  9.01690 # +-2969438871128.36719\n",
-      "Fitting strange quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  5.20458 # +-7269351863.17183\n",
-      "A1 =  -0.40697 # +-10157575.30365\n",
-      "A2 =  3.67161 # +-30725049.44138\n",
-      "A3 =  -2.63003 # +-1253230602.90327\n",
-      "A4 =  5.58156 # +-9233837844.15755\n",
-      "A5 =  -7.46887 # +-22499296653.10953\n",
-      "A6 =  4.38772 # +-19792616445.26697\n",
-      "A7 =  -0.78183 # +-2935797844.66017\n",
-      "Fitting sea down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.13115 # +-76419807.12303\n",
-      "A1 =  -1.43157 # +-166653821.19256\n",
-      "A2 =  2.32522 # +-4055695105.91602\n",
-      "A3 =  -4.01074 # +-24959772559.76839\n",
-      "A4 =  5.47595 # +-519763612754.44226\n",
-      "A5 =  -1.81545 # +-1628949341749.38940\n",
-      "A6 =  -1.83138 # +-1056197165869.32629\n",
-      "A7 =  1.18492 # +-98942341042.20357\n",
-      "Fitting bottom quark PDF... done.\n",
-      "Fit result:\n",
       "A0 =  0.16470 # +-1198514613.88380\n",
       "A1 =  -1.34364 # +-2905219103.43554\n",
       "A2 =  4.49076 # +-28142186922.18334\n",
@@ -504,123 +340,50 @@
       "A5 =  -23.43747 # +-2365949015693.53906\n",
       "A6 =  18.18919 # +-1735882239126.84497\n",
       "A7 =  -5.91162 # +-179460369612.55646\n",
-      " done.\n",
-      "Fit result:\n",
-      "A0 =  -0.00123 # +-1486200.42360\n",
-      "A1 =  -2.25791 # +-154286511211.92456\n",
-      "A2 =  5.69082 # +-48010264548.85862\n",
-      "A3 =  -25.90176 # +-1727434163963762.50000\n",
-      "A4 =  97.40217 # +-33006708258563876.00000\n",
-      "A5 =  -188.14638 # +-133119379635588208.00000\n",
-      "A6 =  191.46205 # +-134546416883660272.00000\n",
-      "A7 =  -89.58398 # +-25874701252301888.00000\n",
-      "Fitting sea up quark PDF...Performing PDF fit with 5 flavors with M^2/S = 0.107301, Q^2 = 4.53349e+06\n",
-      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.107301 \n",
-      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
-      "Fitting gluon PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  1.25634 # +-89618127.14347\n",
-      "A1 =  -1.52469 # +-2736493.50503\n",
-      "A2 =  3.62659 # +-111579703.53622\n",
-      "A3 =  -4.83641 # +-108528996.51061\n",
-      "A4 =  11.45259 # +-2202384150.38222\n",
-      "A5 =  -15.58613 # +-7084165559.09992\n",
-      "A6 =  11.60522 # +-5079450991.12183\n",
-      "A7 =  -3.65369 # +-552967119.00986\n",
-      "Fitting valence down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.31526 # +-851563466.43089\n",
-      "A1 =  -0.33928 # +-3710660.71753\n",
-      "A2 =  3.85573 # +-9011716656.83899\n",
-      "A3 =  -4.10445 # +-333873305.47128\n",
-      "A4 =  10.68344 # +-6107400337.83596\n",
-      "A5 =  -16.92835 # +-82285469028.34207\n",
-      "A6 =  13.39080 # +-169703400047.50909\n",
-      "A7 =  -4.02615 # +-36243081674.89472\n",
-      "Fitting valence up quark PDF...Performing PDF fit with 5 flavors with M^2/S = 0.117241, Q^2 = 4.95342e+06\n",
-      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.117241 \n",
-      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
-      "Fitting gluon PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.69315 # +-4280435971.09928\n",
-      "A1 =  -0.42677 # +-6886822.50146\n",
-      "A2 =  3.68917 # +-21131797.48199\n",
-      "A3 =  -2.40499 # +-1074781362.70386\n",
-      "A4 =  4.99959 # +-8112260799.98491\n",
-      "A5 =  -6.59115 # +-20722240168.31796\n",
-      "A6 =  3.55829 # +-18752010926.71669\n",
-      "A7 =  -0.45629 # +-2812417456.19271\n",
-      "Fitting sea down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.70111 # +-14523933336.82618\n",
-      "A1 =  -0.85133 # +-767835257.43650\n",
-      "A2 =  1.96176 # +-24264814.83467\n",
-      "A3 =  -2.07806 # +-459065318638.71191\n",
-      "A4 =  -6.18708 # +-12491668922148.25000\n",
-      "A5 =  23.18151 # +-47668101204363.97656\n",
-      "A6 =  -24.86451 # +-35729875964367.00000\n",
-      "A7 =  8.96829 # +-3737751677562.64893\n",
-      "Fitting strange quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.12745 # +-95696884.35455\n",
-      "A1 =  -1.43740 # +-221209941.10119\n",
-      "A2 =  2.34177 # +-4348019167.86702\n",
-      "A3 =  -3.94886 # +-33375832077.21353\n",
-      "A4 =  5.19133 # +-696562856211.40076\n",
-      "A5 =  -1.30846 # +-2186416045034.67407\n",
-      "A6 =  -2.24189 # +-1419235425577.24194\n",
-      "A7 =  1.31127 # +-133071920480.73090\n"
+      "Performing PDF fit with 5 flavors with M^2/S = 0.120651, Q^2 = 5.09752e+06\n",
+      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.120651 \n",
+      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Fitting bottom quark PDF... done.\n",
+      "Fitting gluon PDF... done.\n",
       "Fit result:\n",
-      "A0 =  1.23191 # +-166330461.72498\n",
-      "A1 =  -1.52936 # +-5477517.52692\n",
-      "A2 =  3.63503 # +-144575552.52886\n",
-      "A3 =  -4.82227 # +-194631670.51847\n",
-      "A4 =  11.40824 # +-3679424834.63370\n",
-      "A5 =  -15.53324 # +-11155193530.51843\n",
-      "A6 =  11.57969 # +-7565078267.16062\n",
-      "A7 =  -3.65096 # +-781010949.85675\n",
+      "A0 =  1.33392 # +-218051021.55538\n",
+      "A1 =  -1.51080 # +-6329763.36777\n",
+      "A2 =  3.69658 # +-157925984.96105\n",
+      "A3 =  -4.91977 # +-194682597.84311\n",
+      "A4 =  11.80537 # +-3664718877.16462\n",
+      "A5 =  -16.18111 # +-11057696144.19761\n",
+      "A6 =  12.07516 # +-7445076875.64641\n",
+      "A7 =  -3.79707 # +-759491271.66643\n",
       "Fitting valence down quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  4.36933 # +-1333808861.65538\n",
-      "A1 =  -0.33622 # +-6566347.57456\n",
-      "A2 =  3.88322 # +-18962558481.25160\n",
-      "A3 =  -4.10930 # +-831520114.84785\n",
-      "A4 =  10.66049 # +-15493214576.03262\n",
-      "A5 =  -16.80776 # +-189494154576.65582\n",
-      "A6 =  13.23755 # +-375132517777.97498\n",
-      "A7 =  -3.96476 # +-78905089419.56444\n",
+      "A0 =  4.37471 # +-1666326669.57200\n",
+      "A1 =  -0.33592 # +-8344685.39755\n",
+      "A2 =  3.88726 # +-22310518618.66117\n",
+      "A3 =  -4.10935 # +-964901016.37741\n",
+      "A4 =  10.65386 # +-18437765862.46499\n",
+      "A5 =  -16.78548 # +-225757180581.90750\n",
+      "A6 =  13.21308 # +-444981184016.84863\n",
+      "A7 =  -3.95578 # +-93316000757.84415\n",
       "Fitting valence up quark PDF...Performing PDF fit with 5 flavors with M^2/S = 0.124111, Q^2 = 5.2437e+06\n",
       " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.124111 \n",
       " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
       "Fitting gluon PDF... done.\n",
       "Fit result:\n",
-      "A0 =  5.06410 # +-6374227623.43593\n",
-      "A1 =  -0.41229 # +-9249040.88775\n",
-      "A2 =  3.67636 # +-27882693.45824\n",
-      "A3 =  -2.57268 # +-1212604745.90933\n",
-      "A4 =  5.43443 # +-8987164162.03094\n",
-      "A5 =  -7.25010 # +-22160784686.81456\n",
-      "A6 =  4.18247 # +-19639519082.43780\n",
-      "A7 =  -0.70138 # +-2921874766.67406\n",
+      "A0 =  5.20458 # +-7269351863.17183\n",
+      "A1 =  -0.40697 # +-10157575.30365\n",
+      "A2 =  3.67161 # +-30725049.44138\n",
+      "A3 =  -2.63003 # +-1253230602.90327\n",
+      "A4 =  5.58156 # +-9233837844.15755\n",
+      "A5 =  -7.46887 # +-22499296653.10953\n",
+      "A6 =  4.38772 # +-19792616445.26697\n",
+      "A7 =  -0.78183 # +-2935797844.66017\n",
       "Fitting sea down quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  4.53364 # +-5196162.95599\n",
-      "A1 =  -0.45182 # +-30988.49972\n",
-      "A2 =  0.72264 # +-1789690.47048\n",
-      "A3 =  -6.26227 # +-45001.36009\n",
-      "A4 =  15.64728 # +-1173256.91956\n",
-      "A5 =  -19.48430 # +-4282581.97861\n",
-      "A6 =  12.08460 # +-3066305.08981\n",
-      "A7 =  -2.98550 # +-306060.74014\n",
-      "Fitting strange quark PDF... done.\n",
-      "Fit result:\n",
       "A0 =  1.34247 # +-270032761.25336\n",
       "A1 =  -1.50929 # +-7861622.56335\n",
       "A2 =  3.70668 # +-172549827.71356\n",
@@ -641,16 +404,6 @@
       "A7 =  -3.94082 # +-112410193174.99202\n",
       "Fitting valence up quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  0.11518 # +-196567575.31702\n",
-      "A1 =  -1.45795 # +-552557441.37504\n",
-      "A2 =  2.38519 # +-5225671444.31941\n",
-      "A3 =  -3.72539 # +-88385223482.63004\n",
-      "A4 =  4.16246 # +-1855688586007.97363\n",
-      "A5 =  0.52524 # +-5848451764318.04590\n",
-      "A6 =  -3.72693 # +-3807799037611.66553\n",
-      "A7 =  1.76840 # +-358002352176.18146\n",
-      "Fitting bottom quark PDF... done.\n",
-      "Fit result:\n",
       "A0 =  6.97509 # +-1376193427.33736\n",
       "A1 =  -0.35400 # +-1426580.97537\n",
       "A2 =  2.71697 # +-85349360.04706\n",
@@ -681,26 +434,6 @@
       "A7 =  1.90576 # +-599159144343.11694\n",
       "Fitting bottom quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  -0.00126 # +-5186987.26132\n",
-      "A1 =  -2.26660 # +-387802663319.59412\n",
-      "A2 =  5.69205 # +-71941605067.37842\n",
-      "A3 =  -24.96363 # +-4257167320972044.00000\n",
-      "A4 =  93.43831 # +-77881467767753440.00000\n",
-      "A5 =  -180.25876 # +-305157146158540416.00000\n",
-      "A6 =  183.46604 # +-302686722947403136.00000\n",
-      "A7 =  -85.93418 # +-58419711182113184.00000\n",
-      "Fitting sea up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  10.09053 # +-1479590330.58863\n",
-      "A1 =  -0.29387 # +-1547232.71537\n",
-      "A2 =  4.32347 # +-48701728.20643\n",
-      "A3 =  -7.37206 # +-459027.44052\n",
-      "A4 =  22.26108 # +-14403117.29495\n",
-      "A5 =  -33.83670 # +-72356158.70913\n",
-      "A6 =  25.64871 # +-77973822.50931\n",
-      "A7 =  -7.72034 # +-12211498.56573\n",
-      "Fitting charm quark PDF... done.\n",
-      "Fit result:\n",
       "A0 =  -0.00154 # +-800238.78326\n",
       "A1 =  -2.45471 # +-618152995207.04785\n",
       "A2 =  5.63976 # +-74847983382.45068\n",
@@ -719,7 +452,17 @@
       "A5 =  27.98271 # +-20617563471943712.00000\n",
       "A6 =  -41.59293 # +-24337013986374952.00000\n",
       "A7 =  19.62124 # +-4018802001284139.50000\n",
-      "Fitting charm quark PDF...Performing PDF fit with 5 flavors with M^2/S = 0.131178, Q^2 = 5.54225e+06\n",
+      "Fitting charm quark PDF... done.\n",
+      "Fit result:\n",
+      "A0 =  0.05108 # +-1861982125570.62207\n",
+      "A1 =  -1.54096 # +-18347394930090.04688\n",
+      "A2 =  6.36234 # +-29578539062.34410\n",
+      "A3 =  -2.26259 # +-10917636170781266.00000\n",
+      "A4 =  -3.56916 # +-303063294863491776.00000\n",
+      "A5 =  22.52158 # +-1615444451920048128.00000\n",
+      "A6 =  -33.97962 # +-2025506150495536640.00000\n",
+      "A7 =  18.76357 # +-440006982080365248.00000\n",
+      "Performing PDF fit with 5 flavors with M^2/S = 0.131178, Q^2 = 5.54225e+06\n",
       " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.131178 \n",
       " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
       "Fitting gluon PDF... done.\n",
@@ -742,17 +485,10 @@
       "A5 =  -16.74928 # +-349054441175.94879\n",
       "A6 =  13.18065 # +-683663988064.44348\n",
       "A7 =  -3.94505 # +-142183070828.14114\n",
-      "Fitting valence up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.05108 # +-1861982125570.62207\n",
-      "A1 =  -1.54096 # +-18347394930090.04688\n",
-      "A2 =  6.36234 # +-29578539062.34410\n",
-      "A3 =  -2.26259 # +-10917636170781266.00000\n",
-      "A4 =  -3.56916 # +-303063294863491776.00000\n",
-      "A5 =  22.52158 # +-1615444451920048128.00000\n",
-      "A6 =  -33.97962 # +-2025506150495536640.00000\n",
-      "A7 =  18.76357 # +-440006982080365248.00000\n",
-      " done.\n",
+      "Fitting valence up quark PDF...Performing PDF fit with 5 flavors with M^2/S = 0.117241, Q^2 = 4.95342e+06\n",
+      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.117241 \n",
+      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
+      "Fitting gluon PDF... done.\n",
       "Fit result:\n",
       "A0 =  7.10455 # +-1979277564.03232\n",
       "A1 =  -0.34988 # +-2018881.87153\n",
@@ -784,6 +520,56 @@
       "A7 =  -2.05837 # +-136580392.27377\n",
       "Fitting bottom quark PDF... done.\n",
       "Fit result:\n",
+      "A0 =  1.23191 # +-166330461.72498\n",
+      "A1 =  -1.52936 # +-5477517.52692\n",
+      "A2 =  3.63503 # +-144575552.52886\n",
+      "A3 =  -4.82227 # +-194631670.51847\n",
+      "A4 =  11.40824 # +-3679424834.63370\n",
+      "A5 =  -15.53324 # +-11155193530.51843\n",
+      "A6 =  11.57969 # +-7565078267.16062\n",
+      "A7 =  -3.65096 # +-781010949.85675\n",
+      "Fitting valence down quark PDF... done.\n",
+      "Fit result:\n",
+      "A0 =  4.36933 # +-1333808861.65538\n",
+      "A1 =  -0.33622 # +-6566347.57456\n",
+      "A2 =  3.88322 # +-18962558481.25160\n",
+      "A3 =  -4.10930 # +-831520114.84785\n",
+      "A4 =  10.66049 # +-15493214576.03262\n",
+      "A5 =  -16.80776 # +-189494154576.65582\n",
+      "A6 =  13.23755 # +-375132517777.97498\n",
+      "A7 =  -3.96476 # +-78905089419.56444\n",
+      "Fitting valence up quark PDF... done.\n",
+      "Fit result:\n",
+      "A0 =  5.06410 # +-6374227623.43593\n",
+      "A1 =  -0.41229 # +-9249040.88775\n",
+      "A2 =  3.67636 # +-27882693.45824\n",
+      "A3 =  -2.57268 # +-1212604745.90933\n",
+      "A4 =  5.43443 # +-8987164162.03094\n",
+      "A5 =  -7.25010 # +-22160784686.81456\n",
+      "A6 =  4.18247 # +-19639519082.43780\n",
+      "A7 =  -0.70138 # +-2921874766.67406\n",
+      "Fitting sea down quark PDF... done.\n",
+      "Fit result:\n",
+      "A0 =  4.53364 # +-5196162.95599\n",
+      "A1 =  -0.45182 # +-30988.49972\n",
+      "A2 =  0.72264 # +-1789690.47048\n",
+      "A3 =  -6.26227 # +-45001.36009\n",
+      "A4 =  15.64728 # +-1173256.91956\n",
+      "A5 =  -19.48430 # +-4282581.97861\n",
+      "A6 =  12.08460 # +-3066305.08981\n",
+      "A7 =  -2.98550 # +-306060.74014\n",
+      "Fitting strange quark PDF... done.\n",
+      "Fit result:\n",
+      "A0 =  0.11518 # +-196567575.31702\n",
+      "A1 =  -1.45795 # +-552557441.37504\n",
+      "A2 =  2.38519 # +-5225671444.31941\n",
+      "A3 =  -3.72539 # +-88385223482.63004\n",
+      "A4 =  4.16246 # +-1855688586007.97363\n",
+      "A5 =  0.52524 # +-5848451764318.04590\n",
+      "A6 =  -3.72693 # +-3807799037611.66553\n",
+      "A7 =  1.76840 # +-358002352176.18146\n",
+      "Fitting bottom quark PDF... done.\n",
+      "Fit result:\n",
       "A0 =  -0.00080 # +-65402511.24669\n",
       "A1 =  -2.21416 # +-1152930272122.52563\n",
       "A2 =  5.69872 # +-119620894841.06369\n",
@@ -791,7 +577,13 @@
       "A4 =  151.66317 # +-3079621149297991680.00000\n",
       "A5 =  -292.80470 # +-11856776855389329408.00000\n",
       "A6 =  295.32608 # +-11918242334075416576.00000\n",
-      "A7 =  -136.94587 # +-2456313824826118656.00000\n",
+      "A7 =  -136.94587 # +-2456313824826118656.00000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "Fitting sea up quark PDF... done.\n",
       "Fit result:\n",
       "A0 =  4.14173 # +-168406905238730.34375\n",
@@ -804,6 +596,26 @@
       "A7 =  19.23525 # +-6157860609330402.00000\n",
       "Fitting charm quark PDF... done.\n",
       "Fit result:\n",
+      "A0 =  -0.00126 # +-5186987.26132\n",
+      "A1 =  -2.26660 # +-387802663319.59412\n",
+      "A2 =  5.69205 # +-71941605067.37842\n",
+      "A3 =  -24.96363 # +-4257167320972044.00000\n",
+      "A4 =  93.43831 # +-77881467767753440.00000\n",
+      "A5 =  -180.25876 # +-305157146158540416.00000\n",
+      "A6 =  183.46604 # +-302686722947403136.00000\n",
+      "A7 =  -85.93418 # +-58419711182113184.00000\n",
+      "Fitting sea up quark PDF... done.\n",
+      "Fit result:\n",
+      "A0 =  10.09053 # +-1479590330.58863\n",
+      "A1 =  -0.29387 # +-1547232.71537\n",
+      "A2 =  4.32347 # +-48701728.20643\n",
+      "A3 =  -7.37206 # +-459027.44052\n",
+      "A4 =  22.26108 # +-14403117.29495\n",
+      "A5 =  -33.83670 # +-72356158.70913\n",
+      "A6 =  25.64871 # +-77973822.50931\n",
+      "A7 =  -7.72034 # +-12211498.56573\n",
+      "Fitting charm quark PDF... done.\n",
+      "Fit result:\n",
       "A0 =  0.05561 # +-3913936040358.61377\n",
       "A1 =  -1.51910 # +-33061771252704.05859\n",
       "A2 =  6.38928 # +-50330611068.92439\n",
@@ -811,323 +623,39 @@
       "A4 =  -3.17495 # +-525636347550134784.00000\n",
       "A5 =  21.81291 # +-2810219258933194240.00000\n",
       "A6 =  -33.25616 # +-3516416756981644288.00000\n",
-      "A7 =  18.36012 # +-757206424030137984.00000\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Performing PDF fit with 5 flavors with M^2/S = 0.113879, Q^2 = 4.81137e+06\n",
-      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.113879 \n",
-      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
-      "Fitting gluon PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  1.31771 # +-142735895.04798\n",
-      "A1 =  -1.51387 # +-4118866.26670\n",
-      "A2 =  3.68104 # +-133137508.04601\n",
-      "A3 =  -4.90272 # +-140546599.11924\n",
-      "A4 =  11.73782 # +-2754115320.03280\n",
-      "A5 =  -16.07522 # +-8601524684.42912\n",
-      "A6 =  11.99887 # +-5989454298.85466\n",
-      "A7 =  -3.77637 # +-631742279.24307\n",
-      "Fitting valence down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.35946 # +-1096466308.10730\n",
-      "A1 =  -0.33677 # +-5253165.98666\n",
-      "A2 =  3.87759 # +-15695382231.18183\n",
-      "A3 =  -4.10867 # +-683374197.51095\n",
-      "A4 =  10.66739 # +-12500457117.55791\n",
-      "A5 =  -16.83587 # +-154187976167.46933\n",
-      "A6 =  13.27060 # +-307367400818.60449\n",
-      "A7 =  -3.97739 # +-64880350365.36554\n",
-      "Fitting valence up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.96923 # +-5546428942.08440\n",
-      "A1 =  -0.41590 # +-8236685.28581\n",
-      "A2 =  3.67936 # +-25684792.68373\n",
-      "A3 =  -2.53177 # +-1136881549.06938\n",
-      "A4 =  5.32922 # +-8498310182.47657\n",
-      "A5 =  -7.09179 # +-21229947139.71768\n",
-      "A6 =  4.03251 # +-18933853896.56854\n",
-      "A7 =  -0.64236 # +-2821351580.45388\n",
-      "Fitting sea down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.79357 # +-29845976098.60060\n",
-      "A1 =  -0.80924 # +-1248358692.47689\n",
-      "A2 =  2.12859 # +-35747673.59439\n",
-      "A3 =  -2.15521 # +-715812693505.62769\n",
-      "A4 =  -5.83237 # +-19565931931649.31641\n",
-      "A5 =  22.59514 # +-75042244944028.26562\n",
-      "A6 =  -24.45821 # +-56558643040919.64844\n",
-      "A7 =  8.87211 # +-5951504646469.99219\n",
-      "Fitting strange quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.12327 # +-145225735.05022\n",
-      "A1 =  -1.44434 # +-361948623.09631\n",
-      "A2 =  2.35771 # +-4968242258.13304\n",
-      "A3 =  -3.87981 # +-54058262824.21140\n",
-      "A4 =  4.87354 # +-1133047550056.62793\n",
-      "A5 =  -0.74203 # +-3567507720563.16455\n",
-      "A6 =  -2.70083 # +-2321442996437.01367\n",
-      "A7 =  1.45264 # +-218168131492.35831\n",
-      "Fitting bottom quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  -0.00145 # +-741823.32891\n",
-      "A1 =  -2.31998 # +-298092121439.50854\n",
-      "A2 =  5.67567 # +-61529488885.02886\n",
-      "A3 =  -20.45616 # +-895457656948073.12500\n",
-      "A4 =  74.53151 # +-17045267300752714.00000\n",
-      "A5 =  -143.50649 # +-65872751846993800.00000\n",
-      "A6 =  147.42314 # +-62803968217960664.00000\n",
-      "A7 =  -70.20576 # +-11145767781436702.00000\n",
-      "Fitting sea up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  1.94709 # +-12008324476061.22070\n",
-      "A1 =  -0.61266 # +-82655181468.81192\n",
-      "A2 =  6.45250 # +-1798709684.32098\n",
-      "A3 =  -3.14822 # +-48041129290256.35938\n",
-      "A4 =  -3.94360 # +-1850017613606691.25000\n",
-      "A5 =  29.59370 # +-10840859985299122.00000\n",
-      "A6 =  -43.21481 # +-12777462194162632.00000\n",
-      "A7 =  20.22750 # +-2104668959865520.25000\n",
-      "Fitting charm quark PDF...Performing PDF fit with 5 flavors with M^2/S = 0.0947339, Q^2 = 4.00251e+06\n",
-      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.0947339 \n",
-      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
-      "Fitting gluon PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.31940 # +-26693445081.88694\n",
-      "A1 =  -1.75588 # +-5871993143.30024\n",
-      "A2 =  5.22294 # +-16958003.50873\n",
-      "A3 =  -0.31728 # +-5616297226584.73828\n",
-      "A4 =  -7.88510 # +-100732787389304.32812\n",
-      "A5 =  24.14803 # +-413683831123746.81250\n",
-      "A6 =  -29.57261 # +-435131053399942.37500\n",
-      "A7 =  14.42513 # +-82647481973156.68750\n",
-      "Fitting valence down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.11880 # +-667229180.67379\n",
-      "A1 =  -0.35061 # +-2258352.24090\n",
-      "A2 =  3.72867 # +-1250815705.51063\n",
-      "A3 =  -4.08381 # +-21266580.37034\n",
-      "A4 =  10.72602 # +-205851903.09146\n",
-      "A5 =  -17.32988 # +-6708640094.46388\n",
-      "A6 =  13.98488 # +-18158360459.34797\n",
-      "A7 =  -4.28690 # +-4166165847.81338\n",
-      "Fitting valence up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.06996 # +-2620230528.07811\n",
-      "A1 =  -0.45320 # +-5201380.06054\n",
-      "A2 =  3.71166 # +-13953273.06830\n",
-      "A3 =  -2.06542 # +-1067251111.50982\n",
-      "A4 =  4.10955 # +-8104061082.69146\n",
-      "A5 =  -5.21329 # +-21519395073.78424\n",
-      "A6 =  2.23767 # +-20109642613.05399\n",
-      "A7 =  0.06391 # +-3069537131.56564\n",
-      "Fitting sea down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.55397 # +-3596300050.60732\n",
-      "A1 =  -0.92913 # +-296206479.09519\n",
-      "A2 =  1.64457 # +-11430011.65253\n",
-      "A3 =  -1.91327 # +-193118317373.68863\n",
-      "A4 =  -6.93998 # +-5204681578677.30371\n",
-      "A5 =  24.41535 # +-19646948716849.42969\n",
-      "A6 =  -25.70857 # +-14553513728526.75195\n",
-      "A7 =  9.16392 # +-1503366907714.29028\n",
-      "Fitting strange quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.14087 # +-40534506.10663\n",
-      "A1 =  -1.41692 # +-75763890.04524\n",
-      "A2 =  2.27589 # +-3262195358.62605\n",
-      "A3 =  -4.16170 # +-11448382989.01150\n",
-      "A4 =  6.16978 # +-236582537789.78653\n",
-      "A5 =  -3.05117 # +-737351706675.14417\n",
-      "A6 =  -0.83074 # +-476154387707.20581\n",
-      "A7 =  0.87688 # +-44462250682.34723\n",
-      "Fitting bottom quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  -0.00122 # +-689578.94348\n",
-      "A1 =  -2.23973 # +-73808001879.12042\n",
-      "A2 =  5.70186 # +-35102236868.33260\n",
-      "A3 =  -27.25368 # +-964474524316828.50000\n",
-      "A4 =  103.95492 # +-19222509659334980.00000\n",
-      "A5 =  -202.05039 # +-80181050223545296.00000\n",
-      "A6 =  205.85318 # +-83207464261962928.00000\n",
-      "A7 =  -95.79457 # +-16147253236101206.00000\n",
-      "Fitting sea up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  -0.01193 # +-5535.94216\n",
-      "A1 =  -4.44685 # +-24845807.41368\n",
-      "A2 =  8.62088 # +-29336278.38613\n",
-      "A3 =  -14.35581 # +-5368652.08746\n",
-      "A4 =  82.66669 # +-722351713.05304\n",
-      "A5 =  -239.12090 # +-13721144216.73136\n",
-      "A6 =  348.67063 # +-51583559338.60519\n",
-      "A7 =  -207.89172 # +-30004618665.62924\n",
-      "Fitting charm quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.12664 # +-83567119.55045\n",
-      "A1 =  -1.40957 # +-265153833.98280\n",
-      "A2 =  4.30604 # +-10579765371.70145\n",
-      "A3 =  -5.73334 # +-7640630178.31815\n",
-      "A4 =  15.03882 # +-205170737365.67078\n",
-      "A5 =  -21.57010 # +-868604324749.67505\n",
-      "A6 =  16.68006 # +-782926424852.43896\n",
-      "A7 =  -5.46490 # +-101097070416.39142\n",
-      "Performing PDF fit with 5 flavors with M^2/S = 0.110566, Q^2 = 4.6714e+06\n",
-      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.110566 \n",
-      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
-      "Fitting gluon PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  1.37383 # +-120113325.09490\n",
-      "A1 =  -1.50426 # +-3176930.19059\n",
-      "A2 =  3.70961 # +-122906778.98498\n",
-      "A3 =  -4.95076 # +-107407493.98712\n",
-      "A4 =  11.93628 # +-2170135301.46669\n",
-      "A5 =  -16.40439 # +-6946357985.97298\n",
-      "A6 =  12.25576 # +-4943506957.25312\n",
-      "A7 =  -3.85390 # +-530824673.80122\n",
-      "Fitting valence down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.34122 # +-943780695.51818\n",
-      "A1 =  -0.33780 # +-4342563.52803\n",
-      "A2 =  3.86876 # +-12313590306.15841\n",
-      "A3 =  -4.10699 # +-509514062.78831\n",
-      "A4 =  10.67491 # +-9261270362.75935\n",
-      "A5 =  -16.87546 # +-117627796979.77629\n",
-      "A6 =  13.32060 # +-237501426117.90277\n",
-      "A7 =  -3.99730 # +-50375766031.67002\n",
-      "Fitting valence up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.85942 # +-4843300256.93994\n",
-      "A1 =  -0.42015 # +-7405846.68170\n",
-      "A2 =  3.68306 # +-23528871.41027\n",
-      "A3 =  -2.48264 # +-1079771808.73275\n",
-      "A4 =  5.20198 # +-8128978893.40699\n",
-      "A5 =  -6.89904 # +-20556487708.62658\n",
-      "A6 =  3.84979 # +-18454665371.75239\n",
-      "A7 =  -0.57056 # +-2756009017.65946\n",
-      "Fitting sea down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.74552 # +-20783758599.50990\n",
-      "A1 =  -0.83056 # +-978385863.54275\n",
-      "A2 =  2.04449 # +-29424740.43574\n",
-      "A3 =  -2.11710 # +-572765320582.43738\n",
-      "A4 =  -6.00772 # +-15621086572406.24219\n",
-      "A5 =  22.88538 # +-59762748841461.55469\n",
-      "A6 =  -24.65968 # +-44920419622640.41406\n",
-      "A7 =  8.91994 # +-4713173681170.12695\n",
-      "Fitting strange quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.13008 # +-111223651.57474\n",
-      "A1 =  -1.43345 # +-251033868.79143\n",
-      "A2 =  2.33203 # +-4686034388.56271\n",
-      "A3 =  -3.99787 # +-35951885586.39740\n",
-      "A4 =  5.41727 # +-752153730775.11267\n",
-      "A5 =  -1.71135 # +-2365586352515.93555\n",
-      "A6 =  -1.91566 # +-1538315582448.95166\n",
-      "A7 =  1.21092 # +-144505438840.82385\n",
-      "Fitting bottom quark PDF...Performing PDF fit with 5 flavors with M^2/S = 0.0978024, Q^2 = 4.13215e+06\n",
-      " and weights: valence: x^-1.6, sea: x^-1.6, gluon: x^-1.6 and xmin = 0.0978024 \n",
-      " Fit function: f = A0 * x^A1 * (1 - x)^A2 * ( 1 + A3 * x^(1/2) + A4 * x + A5 * x^(3/2) + A6 * x^2 + A7 * x^(5/2) )\n",
-      "Fitting gluon PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.68232 # +-4472600.25359\n",
-      "A1 =  -1.65433 # +-361801.87275\n",
-      "A2 =  1.03829 # +-64501633.95934\n",
-      "A3 =  -3.91242 # +-52789297.95676\n",
-      "A4 =  6.00096 # +-737196032.71520\n",
-      "A5 =  -4.41524 # +-1618134214.73510\n",
-      "A6 =  1.46017 # +-799260227.84604\n",
-      "A7 =  -0.13290 # +-62986937.29694\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fitting valence down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.16414 # +-762618224.82504\n",
-      "A1 =  -0.34800 # +-2659840.32840\n",
-      "A2 =  3.76175 # +-1890717655.27712\n",
-      "A3 =  -4.08913 # +-29557197.39205\n",
-      "A4 =  10.71863 # +-440949353.16139\n",
-      "A5 =  -17.23574 # +-11715931770.86440\n",
-      "A6 =  13.83970 # +-29365725933.49950\n",
-      "A7 =  -4.22192 # +-6602431744.73790\n",
-      "Fitting valence up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  4.15980 # +-3004488164.10243\n",
-      "A1 =  -0.44927 # +-5793501.21573\n",
-      "A2 =  3.70886 # +-15126049.08201\n",
-      "A3 =  -2.12042 # +-1128394965.36207\n",
-      "A4 =  4.25372 # +-8520490584.15877\n",
-      "A5 =  -5.43993 # +-22375396731.41305\n",
-      "A6 =  2.45801 # +-20780529598.92421\n",
-      "A7 =  -0.02348 # +-3165012477.61833\n",
-      "Fitting sea down quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.58672 # +-5069240683.86341\n",
-      "A1 =  -0.91043 # +-374840623.59333\n",
-      "A2 =  1.72184 # +-13759253.15872\n",
-      "A3 =  -1.95573 # +-239070598006.34586\n",
-      "A4 =  -6.74667 # +-6459133978812.33105\n",
-      "A5 =  24.10007 # +-24450672565330.23047\n",
-      "A6 =  -25.49440 # +-18167285514274.35156\n",
-      "A7 =  9.11486 # +-1882803890897.04468\n",
-      "Fitting strange quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.13644 # +-50575851.97292\n",
-      "A1 =  -1.42341 # +-100958353.11608\n",
-      "A2 =  2.29998 # +-3522580994.41742\n",
-      "A3 =  -4.09311 # +-15314768933.56689\n",
-      "A4 =  5.85466 # +-317253600569.07336\n",
-      "A5 =  -2.49009 # +-990462866310.65295\n",
-      "A6 =  -1.28497 # +-640314811358.74451\n",
-      "A7 =  1.01667 # +-59832667866.89417\n",
-      "Fitting bottom quark PDF... done.\n",
+      "A7 =  18.36012 # +-757206424030137984.00000\n",
+      " done.\n",
       "Fit result:\n",
-      "A0 =  -0.00141 # +-920109.58375\n",
-      "A1 =  -2.29852 # +-234527209217.07443\n",
-      "A2 =  5.68280 # +-56277895197.77177\n",
-      "A3 =  -21.76978 # +-1001943070692477.87500\n",
-      "A4 =  80.21519 # +-19250908883362848.00000\n",
-      "A5 =  -154.67340 # +-75941401226505136.00000\n",
-      "A6 =  158.37051 # +-74205352893261200.00000\n",
-      "A7 =  -74.88324 # +-13559630668699734.00000\n",
+      "A0 =  -0.00001 # +-170210159.09184\n",
+      "A1 =  -2.15125 # +-1189663846161.68579\n",
+      "A2 =  5.70219 # +-121091733882.22702\n",
+      "A3 =  -5783.68329 # +-211416047410287672207343616.00000\n",
+      "A4 =  23437.12658 # +-3474768323870621916100296704.00000\n",
+      "A5 =  -45702.57511 # +-13216536871259048099455172608.00000\n",
+      "A6 =  45892.73367 # +-13327052195724752254226399232.00000\n",
+      "A7 =  -21134.21707 # +-2825901508208181882237485056.00000\n",
       "Fitting sea up quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  1.70580 # +-7339888828752.14062\n",
-      "A1 =  -0.65617 # +-65251689382.28885\n",
-      "A2 =  6.30997 # +-1523929666.99744\n",
-      "A3 =  -3.09253 # +-38981886036855.46094\n",
-      "A4 =  -4.21053 # +-1494886329312760.00000\n",
-      "A5 =  30.14838 # +-8746169876902551.00000\n",
-      "A6 =  -43.76907 # +-10302921820026376.00000\n",
-      "A7 =  20.43530 # +-1695850796012437.75000\n",
+      "A0 =  3.51186 # +-96729917926353.84375\n",
+      "A1 =  -0.41151 # +-211816151147.63715\n",
+      "A2 =  7.06648 # +-3450814101.79245\n",
+      "A3 =  -3.35567 # +-110678628220132.48438\n",
+      "A4 =  -2.93850 # +-4327693734485864.50000\n",
+      "A5 =  27.46934 # +-25490381747643096.00000\n",
+      "A6 =  -41.07321 # +-30103391616164592.00000\n",
+      "A7 =  19.42803 # +-4975873365340340.00000\n",
       "Fitting charm quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  0.04310 # +-530175179465.33502\n",
-      "A1 =  -1.58045 # +-7032043845991.97168\n",
-      "A2 =  6.31023 # +-16339555396.81250\n",
-      "A3 =  -1.95587 # +-4824124687845548.00000\n",
-      "A4 =  -4.81202 # +-130573325781099152.00000\n",
-      "A5 =  24.91146 # +-689903232493750144.00000\n",
-      "A6 =  -36.40659 # +-865558170154023296.00000\n",
-      "A7 =  19.97354 # +-190669187640564704.00000\n",
+      "A0 =  0.05084 # +-2920368216839.42432\n",
+      "A1 =  -1.54221 # +-29065980121785.03906\n",
+      "A2 =  6.36244 # +-51155278787.88869\n",
+      "A3 =  -2.25572 # +-17299696205445334.00000\n",
+      "A4 =  -3.59592 # +-479771698437130496.00000\n",
+      "A5 =  22.57024 # +-2556297960974659072.00000\n",
+      "A6 =  -34.02583 # +-3204795388557147136.00000\n",
+      "A7 =  18.78557 # +-696397415572618880.00000\n",
       " done.\n",
       "Fit result:\n",
-      "A0 =  -0.00127 # +-696071.32161\n",
-      "A1 =  -2.25297 # +-92894977557.46347\n",
-      "A2 =  5.69707 # +-38526369179.01037\n",
-      "A3 =  -25.72931 # +-908081331693166.00000\n",
-      "A4 =  97.31979 # +-18029608654980308.00000\n",
-      "A5 =  -188.72578 # +-74529036243812144.00000\n",
-      "A6 =  192.42520 # +-76526384873018320.00000\n",
-      "A7 =  -89.85958 # +-14671321394120572.00000\n",
-      "Fitting sea up quark PDF... done.\n",
-      "Fit result:\n",
       "A0 =  0.00004 # +-370991646798.19250\n",
       "A1 =  -0.55943 # +-1352190496.21885\n",
       "A2 =  10.34611 # +-455831995.02060\n",
@@ -1138,16 +666,6 @@
       "A7 =  8325773.02905 # +-14571560432996098350716858191052800.00000\n",
       "Fitting strange quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  -0.01089 # +-4725.49761\n",
-      "A1 =  -4.60973 # +-25542100.76206\n",
-      "A2 =  8.49688 # +-29882326.52815\n",
-      "A3 =  -14.19903 # +-4924863.94188\n",
-      "A4 =  80.88463 # +-646874124.66758\n",
-      "A5 =  -231.50884 # +-12014671676.76902\n",
-      "A6 =  334.18314 # +-44524194060.29395\n",
-      "A7 =  -197.08254 # +-25462212025.30371\n",
-      "Fitting charm quark PDF... done.\n",
-      "Fit result:\n",
       "A0 =  0.11781 # +-228387517.53027\n",
       "A1 =  -1.45358 # +-624593711.05610\n",
       "A2 =  2.37833 # +-5671046166.80551\n",
@@ -1158,16 +676,6 @@
       "A7 =  1.65456 # +-384993631511.36316\n",
       "Fitting bottom quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  0.03733 # +-160066764582.11169\n",
-      "A1 =  -1.61073 # +-2708977977229.71240\n",
-      "A2 =  6.26578 # +-9482052544.68059\n",
-      "A3 =  -1.62278 # +-2156646286405933.75000\n",
-      "A4 =  -6.22401 # +-57083080318967320.00000\n",
-      "A5 =  27.68613 # +-298676848248770304.00000\n",
-      "A6 =  -39.20728 # +-373901151712065088.00000\n",
-      "A7 =  21.30734 # +-83125714555893520.00000\n",
-      " done.\n",
-      "Fit result:\n",
       "A0 =  -0.00152 # +-1729679.24323\n",
       "A1 =  -2.52104 # +-482446984638.37592\n",
       "A2 =  5.62461 # +-62440027140.95965\n",
@@ -1188,56 +696,6 @@
       "A7 =  -7.74522 # +-13435497.64919\n",
       "Fitting charm quark PDF... done.\n",
       "Fit result:\n",
-      "A0 =  -0.00001 # +-39878812.41112\n",
-      "A1 =  -2.11288 # +-243835977647.21582\n",
-      "A2 =  5.74248 # +-61211092987.51338\n",
-      "A3 =  -2460.71799 # +-1193952294490684990160896.00000\n",
-      "A4 =  10371.44971 # +-21260404040305624632786944.00000\n",
-      "A5 =  -20688.46444 # +-84667725143717263968305152.00000\n",
-      "A6 =  20975.37204 # +-87052569945277299236536320.00000\n",
-      "A7 =  -9520.60357 # +-17930081031846390466084864.00000\n",
-      "Fitting sea up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  -0.00001 # +-170210159.09184\n",
-      "A1 =  -2.15125 # +-1189663846161.68579\n",
-      "A2 =  5.70219 # +-121091733882.22702\n",
-      "A3 =  -5783.68329 # +-211416047410287672207343616.00000\n",
-      "A4 =  23437.12658 # +-3474768323870621916100296704.00000\n",
-      "A5 =  -45702.57511 # +-13216536871259048099455172608.00000\n",
-      "A6 =  45892.73367 # +-13327052195724752254226399232.00000\n",
-      "A7 =  -21134.21707 # +-2825901508208181882237485056.00000\n",
-      "Fitting sea up quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  3.51186 # +-96729917926353.84375\n",
-      "A1 =  -0.41151 # +-211816151147.63715\n",
-      "A2 =  7.06648 # +-3450814101.79245\n",
-      "A3 =  -3.35567 # +-110678628220132.48438\n",
-      "A4 =  -2.93850 # +-4327693734485864.50000\n",
-      "A5 =  27.46934 # +-25490381747643096.00000\n",
-      "A6 =  -41.07321 # +-30103391616164592.00000\n",
-      "A7 =  19.42803 # +-4975873365340340.00000\n",
-      "Fitting charm quark PDF... done.\n",
-      "Fit result:\n",
-      "A0 =  0.05084 # +-2920368216839.42432\n",
-      "A1 =  -1.54221 # +-29065980121785.03906\n",
-      "A2 =  6.36244 # +-51155278787.88869\n",
-      "A3 =  -2.25572 # +-17299696205445334.00000\n",
-      "A4 =  -3.59592 # +-479771698437130496.00000\n",
-      "A5 =  22.57024 # +-2556297960974659072.00000\n",
-      "A6 =  -34.02583 # +-3204795388557147136.00000\n",
-      "A7 =  18.78557 # +-696397415572618880.00000\n",
-      " done.\n",
-      "Fit result:\n",
-      "A0 =  -0.00000 # +-55134464.83824\n",
-      "A1 =  -1.89610 # +-20149081484.88355\n",
-      "A2 =  6.18929 # +-5346519922.84952\n",
-      "A3 =  -427438.09574 # +-119899842168821648822516215447552.00000\n",
-      "A4 =  2192617.34889 # +-3155031859617409337491133894230016.00000\n",
-      "A5 =  -5078929.54091 # +-16928733767003532800511762348638208.00000\n",
-      "A6 =  5786704.02318 # +-21975735942238255135561911107584000.00000\n",
-      "A7 =  -2797189.45580 # +-5134815610622729759880962904162304.00000\n",
-      " done.\n",
-      "Fit result:\n",
       "A0 =  -0.00000 # +-231149449.00169\n",
       "A1 =  -1.86076 # +-51110867936.79800\n",
       "A2 =  6.25128 # +-8378375424.16280\n",
@@ -1268,13 +726,7 @@
       "A4 =  10.63084 # +-30543096594.22620\n",
       "A5 =  -16.79662 # +-420595401147.02911\n",
       "A6 =  13.25074 # +-829311996772.82886\n",
-      "A7 =  -3.97298 # +-171467589544.47324\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "A7 =  -3.97298 # +-171467589544.47324\n",
       "Fitting valence up quark PDF... done.\n",
       "Fit result:\n",
       "A0 =  7.23078 # +-2852226235.61323\n",
@@ -1337,31 +789,14 @@
       "A7 =  17.70068 # +-1060062584830966400.00000\n",
       " done.\n",
       "Fit result:\n",
-      "A0 =  -0.00000 # +-174043641.56273\n",
-      "A1 =  -1.86714 # +-42221557219.50706\n",
-      "A2 =  6.24069 # +-7630611294.39664\n",
-      "A3 =  -375334.25984 # +-180308619452005498360861867114496.00000\n",
-      "A4 =  1954695.17095 # +-4890404447863341009311848992866304.00000\n",
-      "A5 =  -4554249.46625 # +-26547439498803153487674878508138496.00000\n",
-      "A6 =  5191011.71053 # +-34490062929007659762159844109320192.00000\n",
-      "A7 =  -2490952.87193 # +-7941844766748132977863926185197568.00000\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/hepi/run.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, params, skip, parse, parallel, sleep, run, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0msleep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mparse\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             self._run(rps,\n\u001b[0m\u001b[1;32m    123\u001b[0m                       \u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m                       \u001b[0mparallel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparallel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/hepi/run.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, rps, wait, parallel, sleep, **kwargs)\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;31m# Collect statuses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprocesses\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/hepi/run.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;31m# Collect statuses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprocesses\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/lib/python3.8/subprocess.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1081\u001b[0m             \u001b[0mendtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1082\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1083\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1084\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1085\u001b[0m             \u001b[0;31m# https://bugs.python.org/issue25942\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/lib/python3.8/subprocess.py\u001b[0m in \u001b[0;36m_wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1804\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1805\u001b[0m                             \u001b[0;32mbreak\u001b[0m  \u001b[0;31m# Another thread waited.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1806\u001b[0;31m                         \u001b[0;34m(\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msts\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1807\u001b[0m                         \u001b[0;31m# Check the pid and loop as waitpid has been known to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1808\u001b[0m                         \u001b[0;31m# return 0 even without WNOHANG in odd situations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/lib/python3.8/subprocess.py\u001b[0m in \u001b[0;36m_try_wait\u001b[0;34m(self, wait_flags)\u001b[0m\n\u001b[1;32m   1762\u001b[0m             \u001b[0;34m\"\"\"All callers to this function MUST hold self._waitpid_lock.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1763\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1764\u001b[0;31m                 \u001b[0;34m(\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msts\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait_flags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1765\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mChildProcessError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1766\u001b[0m                 \u001b[0;31m# This happens if SIGCLD is set to be ignored or waiting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+      "A0 =  -0.00000 # +-324709210.72320\n",
+      "A1 =  -1.83984 # +-58496055723.32387\n",
+      "A2 =  6.28112 # +-9011260104.28988\n",
+      "A3 =  -326430.69099 # +-157687924958996691070291105808384.00000\n",
+      "A4 =  1719505.71264 # +-4375537209899051611962101605597184.00000\n",
+      "A5 =  -4020149.43999 # +-23917249197168426688517589597945856.00000\n",
+      "A6 =  4578057.46138 # +-31016335746503832376023436715098112.00000\n",
+      "A7 =  -2181769.91656 # +-7044440100927084608256984844599296.00000\n"
      ]
     }
    ],
diff --git a/docs/source/examples/input/hino.in b/docs/source/examples/input/hino.in
new file mode 100644
index 000000000..b2a8f8fad
--- /dev/null
+++ b/docs/source/examples/input/hino.in
@@ -0,0 +1,157 @@
+BLOCK MODSEL  # Model selection
+     1     0   # Low energy spectrum
+#
+BLOCK SMINPUTS  # Standard Model inputs
+         1     1.32507000E+02   # alpha_em^-1(M_Z)^MSbar
+         2     1.16630000E-05   # G_F [GeV^-2]
+         3     1.17200000E-01   # alpha_S(M_Z)^MSbar
+         4     9.11880000E+01   # M_Z pole mass
+         5     4.25000000E+00   # mb(mb)^MSbar
+         6     1.75000000E+02   # mt pole mass
+         7     1.77700000E+00   # mtau pole mass
+#
+BLOCK MASS  # Mass Spectrum
+# PDG code           mass       particle
+        24     8.04190000E+01   # W+
+        25     1.25000000E+02   # h
+        35     1.00000000E+05   # H
+        36     1.00000000E+05   # A
+        37     1.00000000E+05   # H+
+         5     4.87884274E+00   # b-quark pole mass
+   1000001     1.00000000E+05   # ~d_L
+   2000001     1.00000000E+05   # ~d_R
+   1000002     1.00000000E+05   # ~u_L
+   2000002     1.00000000E+05   # ~u_R
+   1000003     1.00000000E+05   # ~s_L
+   2000003     1.00000000E+05   # ~s_R
+   1000004     1.00000000E+05   # ~c_L
+   2000004     1.00000000E+05   # ~c_R
+   1000005     1.00000000E+05   # ~b_1
+   2000005     1.00000000E+05   # ~b_2
+   1000006     1.00000000E+05   # ~t_1
+   2000006     1.00000000E+05   # ~t_2
+   1000011     5.00000000E+02             # ~e_L
+   2000011     1.00000000E+03             # ~e_R
+   1000012     1.00000000E+03            # ~nu_eL
+   1000013     1.00000000E+03             # ~mu_L
+   2000013     1.00000000E+03             # ~mu_R
+   1000014     1.00000000E+03            # ~nu_muL
+   1000015     1.00000000E+03            # ~tau_1
+   2000015     1.00000000E+03            # ~tau_2
+   1000016     1.00000000E+03            # ~nu_tauL
+   1000021     1.00000000E+05   # ~g
+   1000022     1.00000000E+03              # ~chi_10
+   1000023     -1.00000000E+03             # ~chi_20
+   1000025     1.00000000E+05   # ~chi_30
+   1000035     1.00000000E+05   # ~chi_40
+   1000024     1.00000000E+03             # ~chi_1+
+   1000037     1.00000000E+05   # ~chi_2+
+#
+BLOCK NMIX  # Neutralino Mixing Matrix
+  1  1     0.00000000E+00   # N_11
+  1  2     0.00000000E+00  # N_12
+  1  3     0.70710678E+00  # N_13
+  1  4     0.70710678E+00  # N_14
+  2  1     0.00000000E+00  # N_21
+  2  2     0.00000000E+00  # N_22
+  2  3     0.70710678E+00  # N_23
+  2  4    -0.70710678E+00  # N_24
+  3  1     0.00000000E+00  # N_31
+  3  2     0.00000000E+00  # N_32
+  3  3     0.00000000E+00  # N_33
+  3  4     0.00000000E+00  # N_34
+  4  1     0.00000000E+00  # N_41
+  4  2     0.00000000E+00  # N_42
+  4  3     0.00000000E+00  # N_43
+  4  4     0.00000000E+00  # N_44
+#
+BLOCK UMIX  # Chargino Mixing Matrix U
+  1  1     0.00000000E+00   # U_11
+  1  2     1.00000000E+00   # U_12
+  2  1     0.00000000E+00   # U_21
+  2  2     0.00000000E+00   # U_22
+#
+BLOCK VMIX  # Chargino Mixing Matrix V
+  1  1     0.00000000E+00   # V_11
+  1  2     1.00000000E+00   # V_12
+  2  1     0.00000000E+00   # V_21
+  2  2     0.00000000E+00   # V_22
+#
+BLOCK STOPMIX  # Stop Mixing Matrix
+  1  1     0.00000000E+00   # cos(theta_t)
+  1  2     0.00000000E+00   # sin(theta_t)
+  2  1     0.00000000E+00   # -sin(theta_t)
+  2  2     0.00000000E+00   # cos(theta_t)
+#
+BLOCK SBOTMIX  # Sbottom Mixing Matrix
+  1  1     0.00000000E+00   # cos(theta_b)
+  1  2     0.00000000E+00   # sin(theta_b)
+  2  1     0.00000000E+00   # -sin(theta_b)
+  2  2     0.00000000E+00   # cos(theta_b)
+#
+BLOCK STAUMIX  # Stau Mixing Matrix
+  1  1     1.00000000E+00   # cos(theta_tau)
+  1  2     0.00000000E+00   # sin(theta_tau)
+  2  1    -0.00000000E+00   # -sin(theta_tau)
+  2  2     1.00000000E+00   # cos(theta_tau)
+#
+BLOCK ALPHA  # Higgs mixing
+           0.00000000E+00   # Mixing angle in the neutral Higgs boson sector
+#
+BLOCK HMIX Q=  1.00000000E+05  # DRbar Higgs Parameters
+         1     1.00000000E+05   # mu(Q)
+         2     1.00000000E+05   # tanbeta(Q)
+         3     1.00000000E+05   # vev(Q)
+         4     1.00000000E+05   # MA^2(Q)
+#
+BLOCK AU Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_u(Q) DRbar
+  2  2    0.00000000E+00   # A_c(Q) DRbar
+  3  3    0.00000000E+00   # A_t(Q) DRbar
+#
+BLOCK AD Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_d(Q) DRbar
+  2  2    0.00000000E+00   # A_s(Q) DRbar
+  3  3    0.00000000E+00   # A_b(Q) DRbar
+#
+BLOCK AE Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_e(Q) DRbar
+  2  2    0.00000000E+00   # A_mu(Q) DRbar
+  3  3    0.00000000E+00   # A_tau(Q) DRbar
+#
+BLOCK Yu Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_u(Q) DRbar
+  2  2     0.00000000E+00   # y_c(Q) DRbar
+  3  3     8.74064983E-01   # y_t(Q) DRbar
+#
+BLOCK Yd Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_d(Q) DRbar
+  2  2     0.00000000E+00   # y_s(Q) DRbar
+  3  3     1.35994628E-01   # y_b(Q) DRbar
+#
+BLOCK Ye Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_e(Q) DRbar
+  2  2     0.00000000E+00   # y_mu(Q) DRbar
+  3  3     1.00415339E-01   # y_tau(Q) DRbar
+#
+BLOCK MSOFT Q=  1.00000000E+03  # The soft SUSY breaking masses at the scale Q
+         1     1.00000000E+05   # M_1
+         2     1.00000000E+05   # M_2
+         3     1.00000000E+05   # M_3
+        21     1.00000000E+05   # M^2_Hd
+        22     1.00000000E+05   # M^2_Hu
+        31     1.00000000E+05   # M_eL
+        32     1.00000000E+05   # M_muL
+        33     1.00000000E+05   # M_tauL
+        34     1.00000000E+05   # M_eR
+        35     1.00000000E+05   # M_muR
+        36     1.00000000E+05   # M_tauR
+        41     1.00000000E+05   # M_q1L
+        42     1.00000000E+05   # M_q2L
+        43     1.00000000E+05   # M_q3L
+        44     1.00000000E+05   # M_uR
+        45     1.00000000E+05   # M_cR
+        46     1.00000000E+05   # M_tR
+        47     1.00000000E+05   # M_dR
+        48     1.00000000E+05   # M_sR
+        49     1.00000000E+05   # M_bR
diff --git a/docs/source/examples/input/slha.in b/docs/source/examples/input/slha.in
new file mode 100644
index 000000000..014811525
--- /dev/null
+++ b/docs/source/examples/input/slha.in
@@ -0,0 +1,521 @@
+#
+#                               =====================
+#                               | THE SDECAY OUTPUT |
+#                               =====================
+#
+#
+#              -----------------------------------------------------
+#              |  SUSY Les Houches Accord - MSSM Spectrum + Decays |
+#              |                                                   |
+#              |                     SDECAY 1.3                    |
+#              |                                                   |
+#              |  Authors: M.Muhlleitner, A.Djouadi and Y.Mambrini |
+#              |  Ref.:    Comput.Phys.Commun.168(2005)46          |
+#              |           [hep-ph/0311167]                        |
+#              |                                                   |
+#              |  In case of problems please send an email to      |
+#              |           muehlleitner@lapp.in2p3.fr              |
+#              |           djouadi@mail.cern.ch                    |
+#              |           yann.mambrini@th.u-psud.fr              |
+#              |                                                   |
+#              |  If not stated otherwise all DRbar couplings and  |
+#              |  soft SUSY breaking masses are given at the scale |
+#              |  Q=  0.60000000E+03
+#              |                                                   |
+#              -----------------------------------------------------
+#
+#
+BLOCK DCINFO  # Decay Program information
+     1   SDECAY      # decay calculator
+     2   1.3         # version number
+#
+BLOCK SPINFO  # Spectrum calculator information
+     1   SuSpect     # RGE +Spectrum calculator            
+     2   2.41         # version number                     
+#
+BLOCK MODSEL  # Model selection
+     1     1   # #SUGRA                                            
+#
+BLOCK SMINPUTS  # Standard Model inputs
+         1     1.27934000E+02   # alpha_em^-1(M_Z)^MSbar
+         2     1.16639000E-05   # G_F [GeV^-2]
+         3     1.17200000E-01   # alpha_S(M_Z)^MSbar
+         4     9.11870000E+01   # M_Z pole mass
+         5     4.25000000E+00   # mb(mb)^MSbar
+         6     1.75000000E+02   # mt pole mass
+         7     1.77700000E+00   # mtau pole mass
+#
+BLOCK MINPAR  # Input parameters - minimal models
+         1     4.00000000E+02   # m0                  
+         2     6.00000000E+02   # m_1                 
+         3     4.00000000E+01   # tanbeta(mZ)         
+         4     1.00000000E+00   # sign(mu)            
+         5    -5.00000000E+02   # A0                  
+#
+BLOCK EXTPAR  # Input parameters - non-minimal models
+         0     6.00000000E+02   # High                
+#
+BLOCK MASS  # Mass Spectrum
+# PDG code           mass       particle
+        24     8.04946925E+01   # W+
+        25     1.18954791E+02   # h
+        35     7.16884995E+02   # H
+        36     7.16927517E+02   # A
+        37     7.21784915E+02   # H+
+         5     4.87884274E+00   # b-quark pole mass calculated from mb(mb)_Msbar
+   1000001     1.29829240E+03   # ~d_L
+   2000001     1.24915852E+03   # ~d_R
+   1000002     1.29591166E+03   # ~u_L
+   2000002     1.25355093E+03   # ~u_R
+   1000003     1.29829240E+03   # ~s_L
+   2000003     1.24915852E+03   # ~s_R
+   1000004     1.29591166E+03   # ~c_L
+   2000004     1.25355093E+03   # ~c_R
+   1000005     1.09927705E+03   # ~b_1
+   2000005     1.16730975E+03   # ~b_2
+   1000006     9.39158273E+02   # ~t_1
+   2000006     1.16219715E+03   # ~t_2
+   1000011     5.64014880E+02   # ~e_L
+   2000011     4.58179126E+02   # ~e_R
+   1000012     5.58565085E+02   # ~nu_eL
+   1000013     5.64014880E+02   # ~mu_L
+   2000013     4.58179126E+02   # ~mu_R
+   1000014     5.58565085E+02   # ~nu_muL
+   1000015     2.96197906E+02   # ~tau_1
+   2000015     5.36875146E+02   # ~tau_2
+   1000016     5.11819790E+02   # ~nu_tauL
+   1000021     1.29829240E+03   # ~g
+   1000022     2.51682932E+02   # ~chi_10
+   1000023     4.78532131E+02   # ~chi_20
+   1000025    -8.14299486E+02   # ~chi_30
+   1000035     8.22623630E+02   # ~chi_40
+   1000024     4.78503814E+02   # ~chi_1+
+   1000037     8.23036312E+02   # ~chi_2+
+#
+BLOCK NMIX  # Neutralino Mixing Matrix
+  1  1     9.97871763E-01   # N_11
+  1  2    -7.36651593E-03   # N_12
+  1  3     6.14677197E-02   # N_13
+  1  4    -2.04792392E-02   # N_14
+  2  1     1.81108316E-02   # N_21
+  2  2     9.85071061E-01   # N_22
+  2  3    -1.46386634E-01   # N_23
+  2  4     8.87578527E-02   # N_24
+  3  1     2.85848518E-02   # N_31
+  3  2    -4.15004616E-02   # N_32
+  3  3    -7.04783851E-01   # N_33
+  3  4    -7.07630088E-01   # N_34
+  4  1     5.57391188E-02   # N_41
+  4  2    -1.66908512E-01   # N_42
+  4  3    -6.91427795E-01   # N_43
+  4  4     7.00687022E-01   # N_44
+#
+BLOCK UMIX  # Chargino Mixing Matrix U
+  1  1    -9.78179259E-01   # U_11
+  1  2     2.07762696E-01   # U_12
+  2  1     2.07762696E-01   # U_21
+  2  2     9.78179259E-01   # U_22
+#
+BLOCK VMIX  # Chargino Mixing Matrix V
+  1  1    -9.92028693E-01   # V_11
+  1  2     1.26012195E-01   # V_12
+  2  1     1.26012195E-01   # V_21
+  2  2     9.92028693E-01   # V_22
+#
+BLOCK STOPMIX  # Stop Mixing Matrix
+  1  1     4.22253359E-01   # cos(theta_t)
+  1  2     9.06477855E-01   # sin(theta_t)
+  2  1    -9.06477855E-01   # -sin(theta_t)
+  2  2     4.22253359E-01   # cos(theta_t)
+#
+BLOCK SBOTMIX  # Sbottom Mixing Matrix
+  1  1     7.96092330E-01   # cos(theta_b)
+  1  2     6.05175183E-01   # sin(theta_b)
+  2  1    -6.05175183E-01   # -sin(theta_b)
+  2  2     7.96092330E-01   # cos(theta_b)
+#
+BLOCK STAUMIX  # Stau Mixing Matrix
+  1  1     3.17046387E-01   # cos(theta_tau)
+  1  2     9.48410032E-01   # sin(theta_tau)
+  2  1    -9.48410032E-01   # -sin(theta_tau)
+  2  2     3.17046387E-01   # cos(theta_tau)
+#
+BLOCK ALPHA  # Higgs mixing
+          -2.60210643E-02   # Mixing angle in the neutral Higgs boson sector
+#
+BLOCK HMIX Q=  6.00000000E+02  # DRbar Higgs Parameters
+         1     8.11396873E+02   # mu(Q)               
+         2     3.91444173E+01   # tanbeta(Q)          
+         3     2.44116686E+02   # vev(Q)              
+         4     6.53955449E+05   # MA^2(Q)             
+#
+BLOCK AU Q=  6.00000000E+02  # The trilinear couplings
+  1  1    -1.67576609E+03   # A_u(Q) DRbar
+  2  2    -1.67576609E+03   # A_c(Q) DRbar
+  3  3    -1.15797042E+03   # A_t(Q) DRbar
+#
+BLOCK AD Q=  6.00000000E+02  # The trilinear couplings
+  1  1    -1.95453411E+03   # A_d(Q) DRbar
+  2  2    -1.95453411E+03   # A_s(Q) DRbar
+  3  3    -1.64427268E+03   # A_b(Q) DRbar
+#
+BLOCK AE Q=  6.00000000E+02  # The trilinear couplings
+  1  1    -6.65357167E+02   # A_e(Q) DRbar
+  2  2    -6.65357167E+02   # A_mu(Q) DRbar
+  3  3    -5.57296093E+02   # A_tau(Q) DRbar
+#
+BLOCK Yu Q=  6.00000000E+02  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_u(Q) DRbar
+  2  2     0.00000000E+00   # y_c(Q) DRbar
+  3  3     8.59812423E-01   # y_t(Q) DRbar
+#
+BLOCK Yd Q=  6.00000000E+02  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_d(Q) DRbar
+  2  2     0.00000000E+00   # y_s(Q) DRbar
+  3  3     4.93140732E-01   # y_b(Q) DRbar
+#
+BLOCK Ye Q=  6.00000000E+02  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_e(Q) DRbar
+  2  2     0.00000000E+00   # y_mu(Q) DRbar
+  3  3     4.23164240E-01   # y_tau(Q) DRbar
+#
+BLOCK MSOFT Q=  6.00000000E+02  # The soft SUSY breaking masses at the scale Q
+         1     2.54716660E+02   # M_1                 
+         2     4.70754807E+02   # M_2                 
+         3     1.32343001E+03   # M_3                 
+        21    -1.34011334E+05   # M^2_Hd              
+        22    -6.46439200E+05   # M^2_Hu              
+        31     5.62166488E+02   # M_eL                
+        32     5.62166488E+02   # M_muL               
+        33     5.15747712E+02   # M_tauL              
+        34     4.56041151E+02   # M_eR                
+        35     4.56041151E+02   # M_muR               
+        36     3.25467747E+02   # M_tauR              
+        41     1.25980012E+03   # M_q1L               
+        42     1.25980012E+03   # M_q2L               
+        43     1.08326624E+03   # M_q3L               
+        44     1.21611036E+03   # M_uR                
+        45     1.21611036E+03   # M_cR                
+        46     9.42107984E+02   # M_tR                
+        47     1.21083783E+03   # M_dR                
+        48     1.21083783E+03   # M_sR                
+        49     1.10296193E+03   # M_bR                
+#
+#
+#
+#                             =================
+#                             |The decay table|
+#                             =================
+#
+# - The QCD corrections to the decays gluino -> squark  + quark
+#                                     squark -> gaugino + quark_prime
+#                                     squark -> squark_prime + Higgs
+#                                     squark -> gluino  + quark
+#   are included.
+#
+# - The multi-body decays for the inos, stops and sbottoms are included.
+#
+# - The loop induced decays for the gluino, neutralinos and stops
+#   are included.
+#
+# - The SUSY decays of the top quark are included.
+#
+#
+#         PDG            Width
+DECAY         6     1.49643014E+00   # top decays
+#          BR         NDA      ID1       ID2
+     1.00000000E+00    2           5        24   # BR(t ->  b    W+)
+#
+#         PDG            Width
+DECAY   1000021     1.43411999E+01   # gluino decays
+#          BR         NDA      ID1       ID2
+     8.32052813E-03    2     1000001        -1   # BR(~g -> ~d_L  db)
+     8.32052813E-03    2    -1000001         1   # BR(~g -> ~d_L* d )
+     2.38346440E-02    2     2000001        -1   # BR(~g -> ~d_R  db)
+     2.38346440E-02    2    -2000001         1   # BR(~g -> ~d_R* d )
+     8.89887411E-03    2     1000002        -2   # BR(~g -> ~u_L  ub)
+     8.89887411E-03    2    -1000002         2   # BR(~g -> ~u_L* u )
+     2.21524258E-02    2     2000002        -2   # BR(~g -> ~u_R  ub)
+     2.21524258E-02    2    -2000002         2   # BR(~g -> ~u_R* u )
+     8.32052813E-03    2     1000003        -3   # BR(~g -> ~s_L  sb)
+     8.32052813E-03    2    -1000003         3   # BR(~g -> ~s_L* s )
+     2.38346440E-02    2     2000003        -3   # BR(~g -> ~s_R  sb)
+     2.38346440E-02    2    -2000003         3   # BR(~g -> ~s_R* s )
+     8.89887411E-03    2     1000004        -4   # BR(~g -> ~c_L  cb)
+     8.89887411E-03    2    -1000004         4   # BR(~g -> ~c_L* c )
+     2.21524258E-02    2     2000004        -4   # BR(~g -> ~c_R  cb)
+     2.21524258E-02    2    -2000004         4   # BR(~g -> ~c_R* c )
+     1.04614168E-01    2     1000005        -5   # BR(~g -> ~b_1  bb)
+     1.04614168E-01    2    -1000005         5   # BR(~g -> ~b_1* b )
+     6.38359593E-02    2     2000005        -5   # BR(~g -> ~b_2  bb)
+     6.38359593E-02    2    -2000005         5   # BR(~g -> ~b_2* b )
+     1.47158544E-01    2     1000006        -6   # BR(~g -> ~t_1  tb)
+     1.47158544E-01    2    -1000006         6   # BR(~g -> ~t_1* t )
+     5.79783847E-02    2     2000006        -6   # BR(~g -> ~t_2  tb)
+     5.79783847E-02    2    -2000006         6   # BR(~g -> ~t_2* t )
+#
+#         PDG            Width
+DECAY   1000006     3.99039291E+00   # stop1 decays
+#          BR         NDA      ID1       ID2
+     3.83558880E-01    2     1000022         6   # BR(~t_1 -> ~chi_10 t )
+     1.39492213E-01    2     1000023         6   # BR(~t_1 -> ~chi_20 t )
+     3.34173416E-01    2     1000024         5   # BR(~t_1 -> ~chi_1+ b )
+     1.42775491E-01    2     1000037         5   # BR(~t_1 -> ~chi_2+ b )
+#
+#         PDG            Width
+DECAY   2000006     1.60873447E+01   # stop2 decays
+#          BR         NDA      ID1       ID2
+     2.60232438E-02    2     1000022         6   # BR(~t_2 -> ~chi_10 t )
+     1.29178002E-01    2     1000023         6   # BR(~t_2 -> ~chi_20 t )
+     7.23341671E-02    2     1000025         6   # BR(~t_2 -> ~chi_30 t )
+     1.56750834E-01    2     1000035         6   # BR(~t_2 -> ~chi_40 t )
+     2.88908237E-01    2     1000024         5   # BR(~t_2 -> ~chi_1+ b )
+     1.26772948E-01    2     1000037         5   # BR(~t_2 -> ~chi_2+ b )
+     6.86324784E-02    2     1000006        25   # BR(~t_2 -> ~t_1    h )
+     1.31400091E-01    2     1000006        23   # BR(~t_2 -> ~t_1    Z )
+#
+#         PDG            Width
+DECAY   1000005     9.71519704E+00   # sbottom1 decays
+#          BR         NDA      ID1       ID2
+     4.74055354E-02    2     1000022         5   # BR(~b_1 -> ~chi_10 b )
+     2.35329396E-01    2     1000023         5   # BR(~b_1 -> ~chi_20 b )
+     4.93178131E-02    2     1000025         5   # BR(~b_1 -> ~chi_30 b )
+     3.68468060E-02    2     1000035         5   # BR(~b_1 -> ~chi_40 b )
+     4.17502710E-01    2    -1000024         6   # BR(~b_1 -> ~chi_1- t )
+     1.10117849E-01    2    -1000037         6   # BR(~b_1 -> ~chi_2- t )
+     1.03479890E-01    2     1000006       -24   # BR(~b_1 -> ~t_1    W-)
+#
+#         PDG            Width
+DECAY   2000005     8.98452780E+00   # sbottom2 decays
+#          BR         NDA      ID1       ID2
+     3.43050750E-02    2     1000022         5   # BR(~b_2 -> ~chi_10 b )
+     8.27428859E-02    2     1000023         5   # BR(~b_2 -> ~chi_20 b )
+     8.20519758E-02    2     1000025         5   # BR(~b_2 -> ~chi_30 b )
+     9.24420008E-02    2     1000035         5   # BR(~b_2 -> ~chi_40 b )
+     1.45917493E-01    2    -1000024         6   # BR(~b_2 -> ~chi_1- t )
+     3.53079512E-01    2    -1000037         6   # BR(~b_2 -> ~chi_2- t )
+     2.09461057E-01    2     1000006       -24   # BR(~b_2 -> ~t_1    W-)
+#
+#         PDG            Width
+DECAY   1000002     1.19203129E+01   # sup_L decays
+#          BR         NDA      ID1       ID2
+     1.31724993E-02    2     1000022         2   # BR(~u_L -> ~chi_10 u)
+     3.24167272E-01    2     1000023         2   # BR(~u_L -> ~chi_20 u)
+     2.24195415E-04    2     1000025         2   # BR(~u_L -> ~chi_30 u)
+     4.10173135E-03    2     1000035         2   # BR(~u_L -> ~chi_40 u)
+     6.53016036E-01    2     1000024         1   # BR(~u_L -> ~chi_1+ d)
+     5.31826563E-03    2     1000037         1   # BR(~u_L -> ~chi_2+ d)
+#
+#         PDG            Width
+DECAY   2000002     2.63531939E+00   # sup_R decays
+#          BR         NDA      ID1       ID2
+     9.98221646E-01    2     1000022         2   # BR(~u_R -> ~chi_10 u)
+     2.66089154E-04    2     1000023         2   # BR(~u_R -> ~chi_20 u)
+     3.21892338E-04    2     1000025         2   # BR(~u_R -> ~chi_30 u)
+     1.19037281E-03    2     1000035         2   # BR(~u_R -> ~chi_40 u)
+#
+#         PDG            Width
+DECAY   1000001     1.18348714E+01   # sdown_L decays
+#          BR         NDA      ID1       ID2
+     1.55485647E-02    2     1000022         1   # BR(~d_L -> ~chi_10 d)
+     3.22872074E-01    2     1000023         1   # BR(~d_L -> ~chi_20 d)
+     3.82899724E-04    2     1000025         1   # BR(~d_L -> ~chi_30 d)
+     5.34832850E-03    2     1000035         1   # BR(~d_L -> ~chi_40 d)
+     6.41196487E-01    2    -1000024         2   # BR(~d_L -> ~chi_1- u)
+     1.46516462E-02    2    -1000037         2   # BR(~d_L -> ~chi_2- u)
+#
+#         PDG            Width
+DECAY   2000001     6.56423559E-01   # sdown_R decays
+#          BR         NDA      ID1       ID2
+     9.98236054E-01    2     1000022         1   # BR(~d_R -> ~chi_10 d)
+     2.65638188E-04    2     1000023         1   # BR(~d_R -> ~chi_20 d)
+     3.19006207E-04    2     1000025         1   # BR(~d_R -> ~chi_30 d)
+     1.17930135E-03    2     1000035         1   # BR(~d_R -> ~chi_40 d)
+#
+#         PDG            Width
+DECAY   1000004     1.19203129E+01   # scharm_L decays
+#          BR         NDA      ID1       ID2
+     1.31724993E-02    2     1000022         4   # BR(~c_L -> ~chi_10 c)
+     3.24167272E-01    2     1000023         4   # BR(~c_L -> ~chi_20 c)
+     2.24195415E-04    2     1000025         4   # BR(~c_L -> ~chi_30 c)
+     4.10173135E-03    2     1000035         4   # BR(~c_L -> ~chi_40 c)
+     6.53016036E-01    2     1000024         3   # BR(~c_L -> ~chi_1+ s)
+     5.31826563E-03    2     1000037         3   # BR(~c_L -> ~chi_2+ s)
+#
+#         PDG            Width
+DECAY   2000004     2.63531939E+00   # scharm_R decays
+#          BR         NDA      ID1       ID2
+     9.98221646E-01    2     1000022         4   # BR(~c_R -> ~chi_10 c)
+     2.66089154E-04    2     1000023         4   # BR(~c_R -> ~chi_20 c)
+     3.21892338E-04    2     1000025         4   # BR(~c_R -> ~chi_30 c)
+     1.19037281E-03    2     1000035         4   # BR(~c_R -> ~chi_40 c)
+#
+#         PDG            Width
+DECAY   1000003     1.18348714E+01   # sstrange_L decays
+#          BR         NDA      ID1       ID2
+     1.55485647E-02    2     1000022         3   # BR(~s_L -> ~chi_10 s)
+     3.22872074E-01    2     1000023         3   # BR(~s_L -> ~chi_20 s)
+     3.82899724E-04    2     1000025         3   # BR(~s_L -> ~chi_30 s)
+     5.34832850E-03    2     1000035         3   # BR(~s_L -> ~chi_40 s)
+     6.41196487E-01    2    -1000024         4   # BR(~s_L -> ~chi_1- c)
+     1.46516462E-02    2    -1000037         4   # BR(~s_L -> ~chi_2- c)
+#
+#         PDG            Width
+DECAY   2000003     6.56423559E-01   # sstrange_R decays
+#          BR         NDA      ID1       ID2
+     9.98236054E-01    2     1000022         3   # BR(~s_R -> ~chi_10 s)
+     2.65638188E-04    2     1000023         3   # BR(~s_R -> ~chi_20 s)
+     3.19006207E-04    2     1000025         3   # BR(~s_R -> ~chi_30 s)
+     1.17930135E-03    2     1000035         3   # BR(~s_R -> ~chi_40 s)
+#
+#         PDG            Width
+DECAY   1000011     9.84550968E-01   # selectron_L decays
+#          BR         NDA      ID1       ID2
+     4.65695474E-01    2     1000022        11   # BR(~e_L -> ~chi_10 e-)
+     1.82174335E-01    2     1000023        11   # BR(~e_L -> ~chi_20 e-)
+     3.52130191E-01    2    -1000024        12   # BR(~e_L -> ~chi_1- nu_e)
+#
+#         PDG            Width
+DECAY   2000011     1.16269430E+00   # selectron_R decays
+#          BR         NDA      ID1       ID2
+     1.00000000E+00    2     1000022        11   # BR(~e_R -> ~chi_10 e-)
+#
+#         PDG            Width
+DECAY   1000013     9.84550968E-01   # smuon_L decays
+#          BR         NDA      ID1       ID2
+     4.65695474E-01    2     1000022        13   # BR(~mu_L -> ~chi_10 mu-)
+     1.82174335E-01    2     1000023        13   # BR(~mu_L -> ~chi_20 mu-)
+     3.52130191E-01    2    -1000024        14   # BR(~mu_L -> ~chi_1- nu_mu)
+#
+#         PDG            Width
+DECAY   2000013     1.16269430E+00   # smuon_R decays
+#          BR         NDA      ID1       ID2
+     1.00000000E+00    2     1000022        13   # BR(~mu_R -> ~chi_10 mu-)
+#
+#         PDG            Width
+DECAY   1000015     1.13035976E-01   # stau_1 decays
+#          BR         NDA      ID1       ID2
+     1.00000000E+00    2     1000022        15   # BR(~tau_1 -> ~chi_10  tau-)
+#
+#         PDG            Width
+DECAY   2000015     2.79227495E+00   # stau_2 decays
+#          BR         NDA      ID1       ID2
+     1.86174676E-01    2     1000022        15   # BR(~tau_2 -> ~chi_10  tau-)
+     2.73148052E-02    2     1000023        15   # BR(~tau_2 -> ~chi_20  tau-)
+     5.19980804E-02    2    -1000024        16   # BR(~tau_2 -> ~chi_1-  nu_tau)
+     3.08462299E-01    2     1000015        25   # BR(~tau_2 -> ~tau_1   h)
+     4.26050139E-01    2     1000015        23   # BR(~tau_2 -> ~tau_1   Z)
+#
+#         PDG            Width
+DECAY   1000012     9.45884236E-01   # snu_eL decays
+#          BR         NDA      ID1       ID2
+     5.00875798E-01    2     1000022        12   # BR(~nu_eL -> ~chi_10 nu_e)
+     1.62447384E-01    2     1000023        12   # BR(~nu_eL -> ~chi_20 nu_e)
+     3.36676818E-01    2     1000024        11   # BR(~nu_eL -> ~chi_1+ e-)
+#
+#         PDG            Width
+DECAY   1000014     9.45884236E-01   # snu_muL decays
+#          BR         NDA      ID1       ID2
+     5.00875798E-01    2     1000022        14   # BR(~nu_muL -> ~chi_10 nu_mu)
+     1.62447384E-01    2     1000023        14   # BR(~nu_muL -> ~chi_20 nu_mu)
+     3.36676818E-01    2     1000024        13   # BR(~nu_muL -> ~chi_1+ mu-)
+#
+#         PDG            Width
+DECAY   1000016     2.45863075E+00   # snu_tauL decays
+#          BR         NDA      ID1       ID2
+     1.59805087E-01    2     1000022        16   # BR(~nu_tauL -> ~chi_10 nu_tau)
+     1.28143478E-02    2     1000023        16   # BR(~nu_tauL -> ~chi_20 nu_tau)
+     2.66728065E-02    2     1000024        15   # BR(~nu_tauL -> ~chi_1+ tau-)
+     8.00707759E-01    2    -1000015       -24   # BR(~nu_tauL -> ~tau_1+ W-)
+#
+#         PDG            Width
+DECAY   1000024     1.52035047E-01   # chargino1+ decays
+#          BR         NDA      ID1       ID2
+     9.48919414E-01    2    -1000015        16   # BR(~chi_1+ -> ~tau_1+  nu_tau)
+     5.10805864E-02    2     1000022        24   # BR(~chi_1+ -> ~chi_10  W+)
+#
+#         PDG            Width
+DECAY   1000037     6.11594492E+00   # chargino2+ decays
+#          BR         NDA      ID1       ID2
+     2.54308513E-03    2     1000012       -11   # BR(~chi_2+ -> ~nu_eL   e+  )
+     2.54308513E-03    2     1000014       -13   # BR(~chi_2+ -> ~nu_muL  mu+ )
+     8.93130621E-02    2     1000016       -15   # BR(~chi_2+ -> ~nu_tau1 tau+)
+     6.68351430E-03    2    -1000011        12   # BR(~chi_2+ -> ~e_L+    nu_e)
+     6.68351430E-03    2    -1000013        14   # BR(~chi_2+ -> ~mu_L+   nu_mu)
+     1.24494313E-01    2    -1000015        16   # BR(~chi_2+ -> ~tau_1+  nu_tau)
+     2.93136022E-02    2    -2000015        16   # BR(~chi_2+ -> ~tau_2+  nu_tau)
+     2.23143366E-01    2     1000024        23   # BR(~chi_2+ -> ~chi_1+  Z )
+     7.87117731E-02    2     1000022        24   # BR(~chi_2+ -> ~chi_10  W+)
+     2.26192534E-01    2     1000023        24   # BR(~chi_2+ -> ~chi_20  W+)
+     2.10378150E-01    2     1000024        25   # BR(~chi_2+ -> ~chi_1+  h )
+#
+#         PDG            Width
+DECAY   1000022     0.00000000E+00   # neutralino1 decays
+#
+#         PDG            Width
+DECAY   1000023     1.56478737E-01   # neutralino2 decays
+#          BR         NDA      ID1       ID2
+     5.24827566E-03    2     1000022        23   # BR(~chi_20 -> ~chi_10   Z )
+     4.76765073E-02    2     1000022        25   # BR(~chi_20 -> ~chi_10   h )
+     1.81709087E-05    2     2000011       -11   # BR(~chi_20 -> ~e_R-     e+)
+     1.81709087E-05    2    -2000011        11   # BR(~chi_20 -> ~e_R+     e-)
+     1.81709087E-05    2     2000013       -13   # BR(~chi_20 -> ~mu_R-    mu+)
+     1.81709087E-05    2    -2000013        13   # BR(~chi_20 -> ~mu_R+    mu-)
+     4.73501267E-01    2     1000015       -15   # BR(~chi_20 -> ~tau_1-   tau+)
+     4.73501267E-01    2    -1000015        15   # BR(~chi_20 -> ~tau_1+   tau-)
+#
+#         PDG            Width
+DECAY   1000025     5.97586100E+00   # neutralino3 decays
+#          BR         NDA      ID1       ID2
+     7.04378267E-02    2     1000022        23   # BR(~chi_30 -> ~chi_10   Z )
+     2.03536653E-01    2     1000023        23   # BR(~chi_30 -> ~chi_20   Z )
+     2.20499995E-01    2     1000024       -24   # BR(~chi_30 -> ~chi_1+   W-)
+     2.20499995E-01    2    -1000024        24   # BR(~chi_30 -> ~chi_1-   W+)
+     1.74184638E-02    2     1000022        25   # BR(~chi_30 -> ~chi_10   h )
+     1.15779524E-02    2     1000023        25   # BR(~chi_30 -> ~chi_20   h )
+     4.84443941E-05    2     1000011       -11   # BR(~chi_30 -> ~e_L-     e+)
+     4.84443941E-05    2    -1000011        11   # BR(~chi_30 -> ~e_L+     e-)
+     1.35904382E-04    2     2000011       -11   # BR(~chi_30 -> ~e_R-     e+)
+     1.35904382E-04    2    -2000011        11   # BR(~chi_30 -> ~e_R+     e-)
+     4.84443941E-05    2     1000013       -13   # BR(~chi_30 -> ~mu_L-    mu+)
+     4.84443941E-05    2    -1000013        13   # BR(~chi_30 -> ~mu_L+    mu-)
+     1.35904382E-04    2     2000013       -13   # BR(~chi_30 -> ~mu_R-    mu+)
+     1.35904382E-04    2    -2000013        13   # BR(~chi_30 -> ~mu_R+    mu-)
+     8.59641412E-02    2     1000015       -15   # BR(~chi_30 -> ~tau_1-   tau+)
+     8.59641412E-02    2    -1000015        15   # BR(~chi_30 -> ~tau_1+   tau-)
+     4.08232702E-02    2     2000015       -15   # BR(~chi_30 -> ~tau_2-   tau+)
+     4.08232702E-02    2    -2000015        15   # BR(~chi_30 -> ~tau_2+   tau-)
+     2.59715279E-04    2     1000012       -12   # BR(~chi_30 -> ~nu_eL    nu_eb)
+     2.59715279E-04    2    -1000012        12   # BR(~chi_30 -> ~nu_eL*   nu_e )
+     2.59715279E-04    2     1000014       -14   # BR(~chi_30 -> ~nu_muL   nu_mub)
+     2.59715279E-04    2    -1000014        14   # BR(~chi_30 -> ~nu_muL*  nu_mu )
+     3.39016965E-04    2     1000016       -16   # BR(~chi_30 -> ~nu_tau1  nu_taub)
+     3.39016965E-04    2    -1000016        16   # BR(~chi_30 -> ~nu_tau1* nu_tau )
+#
+#         PDG            Width
+DECAY   1000035     6.16941974E+00   # neutralino4 decays
+#          BR         NDA      ID1       ID2
+     1.77348803E-02    2     1000022        23   # BR(~chi_40 -> ~chi_10   Z )
+     1.40973099E-02    2     1000023        23   # BR(~chi_40 -> ~chi_20   Z )
+     2.22369777E-01    2     1000024       -24   # BR(~chi_40 -> ~chi_1+   W-)
+     2.22369777E-01    2    -1000024        24   # BR(~chi_40 -> ~chi_1-   W+)
+     6.67573900E-02    2     1000022        25   # BR(~chi_40 -> ~chi_10   h )
+     1.94526544E-01    2     1000023        25   # BR(~chi_40 -> ~chi_20   h )
+     1.40386337E-03    2     1000011       -11   # BR(~chi_40 -> ~e_L-     e+)
+     1.40386337E-03    2    -1000011        11   # BR(~chi_40 -> ~e_L+     e-)
+     5.15133272E-04    2     2000011       -11   # BR(~chi_40 -> ~e_R-     e+)
+     5.15133272E-04    2    -2000011        11   # BR(~chi_40 -> ~e_R+     e-)
+     1.40386337E-03    2     1000013       -13   # BR(~chi_40 -> ~mu_L-    mu+)
+     1.40386337E-03    2    -1000013        13   # BR(~chi_40 -> ~mu_L+    mu-)
+     5.15133272E-04    2     2000013       -13   # BR(~chi_40 -> ~mu_R-    mu+)
+     5.15133272E-04    2    -2000013        13   # BR(~chi_40 -> ~mu_R+    mu-)
+     7.13946347E-02    2     1000015       -15   # BR(~chi_40 -> ~tau_1-   tau+)
+     7.13946347E-02    2    -1000015        15   # BR(~chi_40 -> ~tau_1+   tau-)
+     4.55705781E-02    2     2000015       -15   # BR(~chi_40 -> ~tau_2-   tau+)
+     4.55705781E-02    2    -2000015        15   # BR(~chi_40 -> ~tau_2+   tau-)
+     3.11822739E-03    2     1000012       -12   # BR(~chi_40 -> ~nu_eL    nu_eb)
+     3.11822739E-03    2    -1000012        12   # BR(~chi_40 -> ~nu_eL*   nu_e )
+     3.11822739E-03    2     1000014       -14   # BR(~chi_40 -> ~nu_muL   nu_mub)
+     3.11822739E-03    2    -1000014        14   # BR(~chi_40 -> ~nu_muL*  nu_mu )
+     4.03249993E-03    2     1000016       -16   # BR(~chi_40 -> ~nu_tau1  nu_taub)
+     4.03249993E-03    2    -1000016        16   # BR(~chi_40 -> ~nu_tau1* nu_tau )
diff --git a/docs/source/examples/input/wino.in b/docs/source/examples/input/wino.in
new file mode 100644
index 000000000..0ef34e3c5
--- /dev/null
+++ b/docs/source/examples/input/wino.in
@@ -0,0 +1,158 @@
+BLOCK MODSEL  # Model selection
+     1     0   # Low energy spectrum
+#
+BLOCK SMINPUTS  # Standard Model inputs
+         1     1.27934000E+02   # alpha_em^-1(M_Z)^MSbar
+         2     1.16639000E-05   # G_F [GeV^-2]
+         3     1.17200000E-01   # alpha_S(M_Z)^MSbar
+         4     9.11870000E+01   # M_Z pole mass
+         5     4.25000000E+00   # mb(mb)^MSbar
+         6     1.75000000E+02   # mt pole mass
+         7     1.77700000E+00   # mtau pole mass
+#
+BLOCK MASS  # Mass Spectrum
+# PDG code           mass       particle
+        24     8.04961219E+01   # W+
+        25     1.25000000E+02   # h
+        35     1.00000000E+05   # H
+        36     1.00000000E+05   # A
+        37     1.00000000E+05   # H+
+         5     4.87884274E+00   # b-quark pole mass
+   1000001     1.00000000E+05   # ~d_L
+   2000001     1.00000000E+05   # ~d_R
+   1000002     1.00000000E+05   # ~u_L
+   2000002     1.00000000E+05   # ~u_R
+   1000003     1.00000000E+05   # ~s_L
+   2000003     1.00000000E+05   # ~s_R
+   1000004     1.00000000E+05   # ~c_L
+   2000004     1.00000000E+05   # ~c_R
+   1000005     1.00000000E+05   # ~b_1
+   2000005     1.00000000E+05   # ~b_2
+   1000006     1.00000000E+05   # ~t_1
+   2000006     1.00000000E+05   # ~t_2
+   1000011     500             # ~e_L
+   2000011     500             # ~e_R
+   1000012     500            # ~nu_eL
+   1000013     500             # ~mu_L
+   2000013     500             # ~mu_R
+   1000014     500            # ~nu_muL
+   1000015     500            # ~tau_1
+   2000015     500            # ~tau_2
+   1000016     500            # ~nu_tauL
+   1000021     1.00000000E+05   # ~g
+   1000022     500              # ~chi_10
+   1000023     500             # ~chi_20
+   1000025     1.00000000E+05   # ~chi_30
+   1000035     1.00000000E+05   # ~chi_40
+   1000024     500             # ~chi_1+
+   1000037     1.00000000E+05   # ~chi_2+
+#
+BLOCK NMIX  # Neutralino Mixing Matrix
+  1  1     1.00000000E+00   # N_11
+  1  2     0.00000000E+00  # N_12
+  1  3     0.00000000E+00  # N_13
+  1  4     0.00000000E+00  # N_14
+  2  1     0.00000000E+00  # N_21
+  2  2     1.00000000E+00  # N_22
+  2  3     0.00000000E+00  # N_23
+  2  4     0.00000000E+00  # N_24
+  3  1     0.00000000E+00  # N_31
+  3  2     0.00000000E+00  # N_32
+  3  3     0.00000000E+00  # N_33
+  3  4     0.00000000E+00  # N_34
+  4  1     0.00000000E+00  # N_41
+  4  2     0.00000000E+00  # N_42
+  4  3     0.00000000E+00  # N_43
+  4  4     0.00000000E+00  # N_44
+#
+BLOCK UMIX  # Chargino Mixing Matrix U
+  1  1     1.00000000E+00   # U_11
+  1  2     0.00000000E+00   # U_12
+  2  1     0.00000000E+00   # U_21
+  2  2     0.00000000E+00   # U_22
+#
+BLOCK VMIX  # Chargino Mixing Matrix V
+  1  1     1.00000000E+00   # V_11
+  1  2     0.00000000E+00   # V_12
+  2  1     0.00000000E+00   # V_21
+  2  2     0.00000000E+00   # V_22
+#
+BLOCK STOPMIX  # Stop Mixing Matrix
+  1  1     0.00000000E+00   # cos(theta_t)
+  1  2     0.00000000E+00   # sin(theta_t)
+  2  1     0.00000000E+00   # -sin(theta_t)
+  2  2     0.00000000E+00   # cos(theta_t)
+#
+BLOCK SBOTMIX  # Sbottom Mixing Matrix
+  1  1     0.00000000E+00   # cos(theta_b)
+  1  2     0.00000000E+00   # sin(theta_b)
+  2  1     0.00000000E+00   # -sin(theta_b)
+  2  2     0.00000000E+00   # cos(theta_b)
+#
+BLOCK STAUMIX  # Stau Mixing Matrix
+  1  1    1.00000000E+00 # cos(theta_tau)
+  1  2    0.00000000E+00 # sin(theta_tau)
+  2  1    0.00000000E+00 # -sin(theta_tau)
+  2  2    1.00000000E+00 # cos(theta_tau)
+#
+BLOCK ALPHA  # Higgs mixing
+           0.00000000E+00   # Mixing angle in the neutral Higgs boson sector
+#
+BLOCK HMIX Q=  1.00000000E+05  # DRbar Higgs Parameters
+         1     1.00000000E+05   # mu(Q)               
+         2     1.00000000E+05   # tanbeta(Q)          
+         3     1.00000000E+05   # vev(Q)              
+         4     1.00000000E+05   # MA^2(Q)             
+
+#
+BLOCK AU Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_u(Q) DRbar
+  2  2    0.00000000E+00   # A_c(Q) DRbar
+  3  3    0.00000000E+00   # A_t(Q) DRbar
+#
+BLOCK AD Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_d(Q) DRbar
+  2  2    0.00000000E+00   # A_s(Q) DRbar
+  3  3    0.00000000E+00   # A_b(Q) DRbar
+#
+BLOCK AE Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_e(Q) DRbar
+  2  2    0.00000000E+00   # A_mu(Q) DRbar
+  3  3    0.00000000E+00   # A_tau(Q) DRbar
+#
+BLOCK Yu Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_u(Q) DRbar
+  2  2     0.00000000E+00   # y_c(Q) DRbar
+  3  3     8.74064983E-01   # y_t(Q) DRbar
+#
+BLOCK Yd Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_d(Q) DRbar
+  2  2     0.00000000E+00   # y_s(Q) DRbar
+  3  3     1.35994628E-01   # y_b(Q) DRbar
+#
+BLOCK Ye Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_e(Q) DRbar
+  2  2     0.00000000E+00   # y_mu(Q) DRbar
+  3  3     1.00415339E-01   # y_tau(Q) DRbar
+#
+BLOCK MSOFT Q=  1.00000000E+03  # The soft SUSY breaking masses at the scale Q
+         1     1.00000000E+05   # M_1
+         2     1.00000000E+05   # M_2
+         3     1.00000000E+05   # M_3
+        21     1.00000000E+05   # M^2_Hd
+        22     1.00000000E+05   # M^2_Hu
+        31     1.00000000E+05   # M_eL
+        32     1.00000000E+05   # M_muL
+        33     1.00000000E+05   # M_tauL
+        34     1.00000000E+05   # M_eR
+        35     1.00000000E+05   # M_muR
+        36     1.00000000E+05   # M_tauR
+        41     1.00000000E+05   # M_q1L
+        42     1.00000000E+05   # M_q2L
+        43     1.00000000E+05   # M_q3L
+        44     1.00000000E+05   # M_uR
+        45     1.00000000E+05   # M_cR
+        46     1.00000000E+05   # M_tR
+        47     1.00000000E+05   # M_dR
+        48     1.00000000E+05   # M_sR
+        49     1.00000000E+05   # M_bR
diff --git a/docs/source/examples/out.csv b/docs/source/examples/out.csv
index 7e09d198d..d31b0cd1c 100644
--- a/docs/source/examples/out.csv
+++ b/docs/source/examples/out.csv
@@ -1,10 +1,10 @@
-LO,NLO,NLO_PLUS_NLL,aNNLO_PLUS_NNLL,K_LO,K_NLO,K_NLO_PLUS_NLL,NLO_PLUS_NLL_OVER_NLO,K_aNNLO_PLUS_NNLL,aNNLO_PLUS_NNLL_OVER_NLO,VNLO,P_PLUS_K,RNLOG,RNLOQ,VNLO_PLUS_P_PLUS_K,RNLO,RNLO_PLUS_VNLO_PLUS_P_PLUS_K,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_path,mu,mass_1000011,runner
-0.20208+/-0.00029,0.2679+/-0.0004,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000026,1.3255+/-0.0026,0.0+/-0,0.0+/-0,,,0.01062+/-0.00008,0.02998+/-0.00017,-0.00218+/-0.00005,0.009949+/-0.000025,0.04059+/-0.00019,0.00777+/-0.00005,0.04837+/-0.00020,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_100.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,100.0,100.0,ResumminoRunner
-0.013713+/-0.000018,0.017043+/-0.000020,0.0+/-0,,1.0+/-0,1.2429+/-0.0022,0.0+/-0,0.0+/-0,,,0.000639+/-0.000005,0.001524+/-0.000006,-0.0001066+/-0.0000020,0.0003784+/-0.0000009,0.002163+/-0.000008,0.0002719+/-0.0000022,0.002435+/-0.000008,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_212.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,212.5,212.5,ResumminoRunner
-0.0026091+/-0.0000031,0.0031281+/-0.0000035,0.0+/-0,,1.0+/-0,1.1989+/-0.0020,0.0+/-0,0.0+/-0,,,0.0001128+/-0.0000008,0.0002628+/-0.0000010,(-1.554+/-0.029)e-05,(4.906+/-0.011)e-05,0.0003757+/-0.0000013,(3.352+/-0.031)e-05,0.0004092+/-0.0000013,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_325.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,325.0,325.0,ResumminoRunner
-0.0007347+/-0.0000008,0.0008593+/-0.0000009,0.0+/-0,,1.0+/-0,1.1697+/-0.0018,0.0+/-0,0.0+/-0,,,(3.010+/-0.020)e-05,(7.281+/-0.025)e-05,(-3.50+/-0.06)e-06,(1.0308+/-0.0022)e-05,0.00010290+/-0.00000032,(6.81+/-0.07)e-06,0.00010971+/-0.00000032,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_437.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,437.5,437.5,ResumminoRunner
-0.00025475+/-0.00000028,0.00029242+/-0.00000029,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000013,1.1479+/-0.0017,0.0+/-0,0.0+/-0,,,(9.99+/-0.06)e-06,(2.562+/-0.008)e-05,(-1.005+/-0.017)e-06,(2.820+/-0.006)e-06,(3.561+/-0.010)e-05,(1.815+/-0.018)e-06,(3.743+/-0.010)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_550.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,550.0,550.0,ResumminoRunner
-0.00010047+/-0.00000011,0.00011369+/-0.00000011,0.0+/-0,,1.0+/-0,1.1316+/-0.0016,0.0+/-0,0.0+/-0,,,(3.805+/-0.023)e-06,(1.0433+/-0.0030)e-05,(-3.438+/-0.033)e-07,(9.123+/-0.018)e-07,(1.424+/-0.004)e-05,(5.68+/-0.04)e-07,(1.481+/-0.004)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_662.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,662.5,662.5,ResumminoRunner
-(4.322+/-0.004)e-05,(4.841+/-0.005)e-05,0.0+/-0,,1.0+/-0,1.1199+/-0.0016,0.0+/-0,0.0+/-0,,,(1.590+/-0.010)e-06,(4.678+/-0.013)e-06,(-1.245+/-0.016)e-07,(3.312+/-0.006)e-07,(6.269+/-0.016)e-06,(2.067+/-0.017)e-07,(6.475+/-0.016)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_775.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,775.0,775.0,ResumminoRunner
-(1.9790+/-0.0020)e-05,(2.2010+/-0.0020)e-05,0.0+/-0,,1.0+/-0,1.1122+/-0.0015,0.0+/-0,0.0+/-0,,,(7.11+/-0.04)e-07,(2.242+/-0.006)e-06,(-4.90+/-0.09)e-08,(1.3073+/-0.0025)e-07,(2.954+/-0.007)e-06,(8.17+/-0.09)e-08,(3.035+/-0.007)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_887.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,887.5,887.5,ResumminoRunner
-(9.489+/-0.009)e-06,(1.0515+/-0.0009)e-05,0.0+/-0,,1.0+/-0,1.1081+/-0.0015,0.0+/-0,0.0+/-0,,,(3.351+/-0.019)e-07,(1.1287+/-0.0028)e-06,(-2.06+/-0.04)e-08,(5.497+/-0.010)e-08,(1.4638+/-0.0034)e-06,(3.43+/-0.04)e-08,(1.4981+/-0.0034)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_1000.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,1000.0,1000.0,ResumminoRunner
+LO,NLO,NLO_PLUS_NLL,aNNLO_PLUS_NNLL,K_LO,K_NLO,K_NLO_PLUS_NLL,NLO_PLUS_NLL_OVER_NLO,K_aNNLO_PLUS_NNLL,aNNLO_PLUS_NNLL_OVER_NLO,VNLO,P_PLUS_K,RNLOG,RNLOQ,VNLO_PLUS_P_PLUS_K,RNLO,RNLO_PLUS_VNLO_PLUS_P_PLUS_K,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,mass_1000011,runner
+0.20208+/-0.00029,0.2679+/-0.0004,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000026,1.3255+/-0.0026,0.0+/-0,0.0+/-0,,,0.01062+/-0.00008,0.02998+/-0.00017,-0.00218+/-0.00005,0.009949+/-0.000025,0.04059+/-0.00019,0.00777+/-0.00005,0.04837+/-0.00020,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_100.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,100.0,100.0,ResumminoRunner-resummino 3.1.1
+0.013713+/-0.000018,0.017043+/-0.000020,0.0+/-0,,1.0+/-0,1.2429+/-0.0022,0.0+/-0,0.0+/-0,,,0.000639+/-0.000005,0.001524+/-0.000006,-0.0001066+/-0.0000020,0.0003784+/-0.0000009,0.002163+/-0.000008,0.0002719+/-0.0000022,0.002435+/-0.000008,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_212.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,212.5,212.5,ResumminoRunner-resummino 3.1.1
+0.0026091+/-0.0000031,0.0031281+/-0.0000035,0.0+/-0,,1.0+/-0,1.1989+/-0.0020,0.0+/-0,0.0+/-0,,,0.0001128+/-0.0000008,0.0002628+/-0.0000010,(-1.554+/-0.029)e-05,(4.906+/-0.011)e-05,0.0003757+/-0.0000013,(3.352+/-0.031)e-05,0.0004092+/-0.0000013,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_325.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,325.0,325.0,ResumminoRunner-resummino 3.1.1
+0.0007347+/-0.0000008,0.0008593+/-0.0000009,0.0+/-0,,1.0+/-0,1.1697+/-0.0018,0.0+/-0,0.0+/-0,,,(3.010+/-0.020)e-05,(7.281+/-0.025)e-05,(-3.50+/-0.06)e-06,(1.0308+/-0.0022)e-05,0.00010290+/-0.00000032,(6.81+/-0.07)e-06,0.00010971+/-0.00000032,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_437.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,437.5,437.5,ResumminoRunner-resummino 3.1.1
+0.00025475+/-0.00000028,0.00029242+/-0.00000029,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000013,1.1479+/-0.0017,0.0+/-0,0.0+/-0,,,(9.99+/-0.06)e-06,(2.562+/-0.008)e-05,(-1.005+/-0.017)e-06,(2.820+/-0.006)e-06,(3.561+/-0.010)e-05,(1.815+/-0.018)e-06,(3.743+/-0.010)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_550.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,550.0,550.0,ResumminoRunner-resummino 3.1.1
+0.00010047+/-0.00000011,0.00011369+/-0.00000011,0.0+/-0,,1.0+/-0,1.1316+/-0.0016,0.0+/-0,0.0+/-0,,,(3.805+/-0.023)e-06,(1.0433+/-0.0030)e-05,(-3.438+/-0.033)e-07,(9.123+/-0.018)e-07,(1.424+/-0.004)e-05,(5.68+/-0.04)e-07,(1.481+/-0.004)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_662.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,662.5,662.5,ResumminoRunner-resummino 3.1.1
+(4.322+/-0.004)e-05,(4.841+/-0.005)e-05,0.0+/-0,,1.0+/-0,1.1199+/-0.0016,0.0+/-0,0.0+/-0,,,(1.590+/-0.010)e-06,(4.678+/-0.013)e-06,(-1.245+/-0.016)e-07,(3.312+/-0.006)e-07,(6.269+/-0.016)e-06,(2.067+/-0.017)e-07,(6.475+/-0.016)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_775.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,775.0,775.0,ResumminoRunner-resummino 3.1.1
+(1.9790+/-0.0020)e-05,(2.2010+/-0.0020)e-05,0.0+/-0,,1.0+/-0,1.1122+/-0.0015,0.0+/-0,0.0+/-0,,,(7.11+/-0.04)e-07,(2.242+/-0.006)e-06,(-4.90+/-0.09)e-08,(1.3073+/-0.0025)e-07,(2.954+/-0.007)e-06,(8.17+/-0.09)e-08,(3.035+/-0.007)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_887.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,887.5,887.5,ResumminoRunner-resummino 3.1.1
+(9.489+/-0.009)e-06,(1.0515+/-0.0009)e-05,0.0+/-0,,1.0+/-0,1.1081+/-0.0015,0.0+/-0,0.0+/-0,,,(3.351+/-0.019)e-07,(1.1287+/-0.0028)e-06,(-2.06+/-0.04)e-08,(5.497+/-0.010)e-08,(1.4638+/-0.0034)e-06,(3.43+/-0.04)e-08,(1.4981+/-0.0034)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_1000.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,1000.0,1000.0,ResumminoRunner-resummino 3.1.1
diff --git a/docs/source/examples/out.json b/docs/source/examples/out.json
index c7d339c3a..8265d11a9 100644
--- a/docs/source/examples/out.json
+++ b/docs/source/examples/out.json
@@ -1 +1 @@
-{"initial state": "pp", "order": "NLO", "source": "HEPi", "contact": "?", "tool": "RS", "process_latex": "$\\tilde{e}_{L}^{-}\\tilde{e}_{L}^{+}$", "comment": "?", "reference": "?", "Ecom [GeV]": "13000", "process_id": "pp_13000_1000011_-1000011", "PDF set": "cteq66", "data": {"100.0": {"xsec_pb": 0.26266219, "unc_pb": 0.00035483454}, "212.5": {"xsec_pb": 0.016796404, "unc_pb": 1.9662724e-05}, "325.0": {"xsec_pb": 0.0030864231, "unc_pb": 3.409076e-06}, "437.5": {"xsec_pb": 0.00084837761, "unc_pb": 8.8924337e-07}, "550.0": {"xsec_pb": 0.00028882589, "unc_pb": 2.9046289e-07}, "662.5": {"xsec_pb": 0.00011233356, "unc_pb": 1.0906148e-07}, "775.0": {"xsec_pb": 4.7848073e-05, "unc_pb": 4.5120962e-08}, "887.5": {"xsec_pb": 2.1762029e-05, "unc_pb": 2.0001873e-08}, "1000.0": {"xsec_pb": 1.0401314e-05, "unc_pb": 9.3348503e-09}}, "parameters": "?"}
\ No newline at end of file
+{"initial state": "pp", "order": "NLO", "source": "hepi-0.1.8.9+dirty", "contact": "?", "tool": "Resummino-resummino 3.1.1", "process_latex": "$\\tilde{e}_{L}^{-}\\tilde{e}_{L}^{+}$", "comment": "5", "reference": "?", "Ecom [GeV]": "13000", "process_id": "pp_13000_1000011_-1000011", "PDF set": "cteq66", "data": {"100.0": {"xsec_pb": 0.26786916, "unc_pb": 0.00036328325}, "212.5": {"xsec_pb": 0.017042798, "unc_pb": 1.9927369e-05}, "325.0": {"xsec_pb": 0.0031280684, "unc_pb": 3.4502997e-06}, "437.5": {"xsec_pb": 0.00085929957, "unc_pb": 8.9960357e-07}, "550.0": {"xsec_pb": 0.00029242159, "unc_pb": 2.9376357e-07}, "662.5": {"xsec_pb": 0.00011369143, "unc_pb": 1.1027916e-07}, "775.0": {"xsec_pb": 4.8409699e-05, "unc_pb": 4.5616627e-08}, "887.5": {"xsec_pb": 2.2010282e-05, "unc_pb": 2.0217058e-08}, "1000.0": {"xsec_pb": 1.0515472e-05, "unc_pb": 9.4331156e-09}}, "parameters": [["mass_1000011"]]}
\ No newline at end of file
diff --git a/docs/source/examples/py.py b/docs/source/examples/py.py
index fcdd32170..f5ee4a562 100644
--- a/docs/source/examples/py.py
+++ b/docs/source/examples/py.py
@@ -5,9 +5,9 @@
 
 _lr_method = 'LALR'
 
-_lr_signature = '28d5f5e5f60bc36e780e1fb8ba154a6c'
+_lr_signature = b'(\xd5\xf5\xe5\xf6\x0b\xc3nx\x0e\x1f\xb8\xba\x15Jl'
     
-_lr_action_items = {'NUMBER':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,]),'COMPLEX':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,]),'ASEC':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,]),'CONJ':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,]),'POWER':([1,5,12,17,19,21,22,23,24,25,26,27,28,29,31,32,33,40,41,42,43,44,45,46,47,48,49,50,52,54,55,56,57,59,60,62,63,],[-11,-4,-5,-7,34,-15,-19,-7,34,34,34,-16,-13,-14,-12,-17,-18,34,-8,-9,-3,34,-6,34,34,34,34,34,-20,34,34,-10,-21,34,-22,34,-23,]),')':([1,5,12,17,21,22,25,26,27,28,29,31,32,33,41,42,43,44,45,46,47,48,49,50,52,54,55,56,57,59,60,62,63,],[-11,-4,-5,-7,-15,-19,43,-2,-16,-13,-14,-12,-17,-18,-8,-9,-3,52,-6,-25,-27,-26,-28,-24,-20,56,57,-10,-21,60,-22,63,-23,]),'(':([0,2,3,4,6,7,8,9,10,13,14,15,16,18,20,30,34,35,36,37,38,39,51,53,58,61,],[7,20,7,7,7,7,7,7,7,7,30,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,]),'+':([1,5,12,17,19,21,22,23,24,25,26,27,28,29,31,32,33,40,41,42,43,44,45,46,47,48,49,50,52,54,55,56,57,59,60,62,63,],[-11,-4,-5,-7,35,-15,-19,-7,35,35,-2,-16,-13,-14,-12,-17,-18,35,-8,-9,-3,35,-6,-25,-27,-26,-28,35,-20,35,35,-10,-21,35,-22,35,-23,]),'*':([1,5,12,17,19,21,22,23,24,25,26,27,28,29,31,32,33,40,41,42,43,44,45,46,47,48,49,50,52,54,55,56,57,59,60,62,63,],[-11,-4,-5,-7,36,-15,-19,-7,36,36,-2,-16,-13,-14,-12,-17,-18,36,-8,-9,-3,36,-6,36,-27,36,-28,36,-20,36,36,-10,-21,36,-22,36,-23,]),'-':([0,1,5,6,7,8,12,17,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,],[8,-11,-4,8,8,8,-5,-7,37,8,-15,-19,-7,37,37,-2,-16,-13,-14,8,-12,-17,-18,8,8,8,8,8,8,37,-8,-9,-3,37,-6,-25,-27,-26,-28,37,8,-20,8,37,37,-10,-21,8,37,-22,8,37,-23,]),',':([1,5,12,17,21,22,26,27,28,29,31,32,33,40,41,42,43,44,45,46,47,48,49,50,52,55,56,57,59,60,63,],[-11,-4,-5,-7,-15,-19,-2,-16,-13,-14,-12,-17,-18,51,-8,-9,-3,53,-6,-25,-27,-26,-28,-24,-20,58,-10,-21,61,-22,-23,]),'/':([1,5,12,17,19,21,22,23,24,25,26,27,28,29,31,32,33,40,41,42,43,44,45,46,47,48,49,50,52,54,55,56,57,59,60,62,63,],[-11,-4,-5,-7,38,-15,-19,-7,38,38,-2,-16,-13,-14,-12,-17,-18,38,-8,-9,-3,38,-6,38,-27,38,-28,38,-20,38,38,-10,-21,38,-22,38,-23,]),'RE':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,]),'SEC':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,]),'PI':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,]),'=':([1,5,12,17,19,21,22,23,24,25,26,27,28,29,31,32,33,40,41,42,43,44,45,46,47,48,49,50,52,54,55,56,57,59,60,62,63,],[-11,-4,-5,-7,39,-15,-19,-7,39,39,-2,-16,-13,-14,-12,-17,-18,39,-8,-9,-3,39,-6,-25,-27,-26,-28,-24,-20,39,39,-10,-21,39,-22,39,-23,]),'ACSC':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,]),'$end':([1,5,11,12,17,19,21,22,26,27,28,29,31,32,33,41,42,43,45,46,47,48,49,50,52,56,57,60,63,],[-11,-4,0,-5,-7,-1,-15,-19,-2,-16,-13,-14,-12,-17,-18,-8,-9,-3,-6,-25,-27,-26,-28,-24,-20,-10,-21,-22,-23,]),'FUNCTION':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,]),'CSC':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,]),'IM':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,]),'VARIABLE':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[17,23,17,17,17,17,17,17,17,17,17,17,17,17,17,17,]),"'":([0,1,5,6,7,8,12,17,20,21,22,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,41,42,43,45,46,47,48,49,50,51,52,53,56,57,58,60,61,63,],[6,-11,-4,6,6,6,-5,-7,6,-15,-19,41,42,-2,-16,-13,-14,6,-12,-17,-18,6,6,6,6,6,6,-8,-9,-3,-6,-25,-27,-26,-28,-24,6,-20,6,-10,-21,6,-22,6,-23,]),'SQRT':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,]),}
+_lr_action_items = {'-':([0,2,3,4,5,6,7,9,10,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,],[3,23,3,-4,-5,-7,3,3,-11,3,3,3,3,3,3,-2,-7,23,3,23,-12,-13,-14,-15,-16,-17,-18,-19,3,-6,23,-25,-26,-27,-28,-8,-9,23,-3,23,3,-20,3,23,23,-10,3,-21,23,3,-22,23,-23,]),'PI':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,]),'VARIABLE':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[6,6,27,6,6,6,6,6,6,6,6,6,6,6,6,6,]),"'":([0,3,4,5,6,7,9,10,20,21,22,23,24,25,26,27,28,29,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,49,51,52,53,56,57,58,60,61,63,],[7,7,-4,-5,-7,7,7,-11,7,7,7,7,7,7,-2,46,47,7,-12,-13,-14,-15,-16,-17,-18,-19,7,-6,-24,-25,-26,-27,-28,-8,-9,-3,7,-20,7,-10,7,-21,7,-22,-23,]),'COMPLEX':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,]),'NUMBER':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,]),'CSC':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,]),'SEC':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,]),'ACSC':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,]),'ASEC':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,]),'RE':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,]),'IM':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,]),'SQRT':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,]),'CONJ':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,]),'FUNCTION':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,]),'(':([0,3,7,8,9,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,29,39,51,53,57,60,],[9,9,9,29,9,9,9,9,9,9,9,9,9,39,9,9,9,9,9,9,9,9,9,9,9,9,]),'$end':([1,2,4,5,6,10,26,31,32,33,34,35,36,37,38,40,41,42,43,44,45,46,47,49,52,56,58,61,63,],[0,-1,-4,-5,-7,-11,-2,-12,-13,-14,-15,-16,-17,-18,-19,-6,-24,-25,-26,-27,-28,-8,-9,-3,-20,-10,-21,-22,-23,]),'POWER':([2,4,5,6,10,26,27,28,30,31,32,33,34,35,36,37,38,40,41,42,43,44,45,46,47,48,49,50,52,54,55,56,58,59,61,62,63,],[20,-4,-5,-7,-11,20,-7,20,20,-12,-13,-14,-15,-16,-17,-18,-19,-6,20,20,20,20,20,-8,-9,20,-3,20,-20,20,20,-10,-21,20,-22,20,-23,]),'=':([2,4,5,6,10,26,27,28,30,31,32,33,34,35,36,37,38,40,41,42,43,44,45,46,47,48,49,50,52,54,55,56,58,59,61,62,63,],[21,-4,-5,-7,-11,-2,-7,21,21,-12,-13,-14,-15,-16,-17,-18,-19,-6,-24,-25,-26,-27,-28,-8,-9,21,-3,21,-20,21,21,-10,-21,21,-22,21,-23,]),'+':([2,4,5,6,10,26,27,28,30,31,32,33,34,35,36,37,38,40,41,42,43,44,45,46,47,48,49,50,52,54,55,56,58,59,61,62,63,],[22,-4,-5,-7,-11,-2,-7,22,22,-12,-13,-14,-15,-16,-17,-18,-19,-6,22,-25,-26,-27,-28,-8,-9,22,-3,22,-20,22,22,-10,-21,22,-22,22,-23,]),'*':([2,4,5,6,10,26,27,28,30,31,32,33,34,35,36,37,38,40,41,42,43,44,45,46,47,48,49,50,52,54,55,56,58,59,61,62,63,],[24,-4,-5,-7,-11,-2,-7,24,24,-12,-13,-14,-15,-16,-17,-18,-19,-6,24,24,24,-27,-28,-8,-9,24,-3,24,-20,24,24,-10,-21,24,-22,24,-23,]),'/':([2,4,5,6,10,26,27,28,30,31,32,33,34,35,36,37,38,40,41,42,43,44,45,46,47,48,49,50,52,54,55,56,58,59,61,62,63,],[25,-4,-5,-7,-11,-2,-7,25,25,-12,-13,-14,-15,-16,-17,-18,-19,-6,25,25,25,-27,-28,-8,-9,25,-3,25,-20,25,25,-10,-21,25,-22,25,-23,]),')':([4,5,6,10,26,30,31,32,33,34,35,36,37,38,40,41,42,43,44,45,46,47,49,50,52,54,55,56,58,59,61,62,63,],[-4,-5,-7,-11,-2,49,-12,-13,-14,-15,-16,-17,-18,-19,-6,-24,-25,-26,-27,-28,-8,-9,-3,52,-20,56,58,-10,-21,61,-22,63,-23,]),',':([4,5,6,10,26,31,32,33,34,35,36,37,38,40,41,42,43,44,45,46,47,48,49,50,52,55,56,58,59,61,63,],[-4,-5,-7,-11,-2,-12,-13,-14,-15,-16,-17,-18,-19,-6,-24,-25,-26,-27,-28,-8,-9,51,-3,53,-20,57,-10,-21,60,-22,-23,]),}
 
 _lr_action = { }
 for _k, _v in _lr_action_items.items():
@@ -16,7 +16,7 @@
       _lr_action[_x][_k] = _y
 del _lr_action_items
 
-_lr_goto_items = {'group':([0,3,4,6,7,8,9,10,13,15,16,18,20,30,34,35,36,37,38,39,51,53,58,61,],[5,21,22,5,5,5,27,28,29,31,32,33,5,5,5,5,5,5,5,5,5,5,5,5,]),'expression':([0,6,7,8,20,30,34,35,36,37,38,39,51,53,58,61,],[19,24,25,26,40,44,45,46,47,48,49,50,54,55,59,62,]),'statement':([0,],[11,]),}
+_lr_goto_items = {'statement':([0,],[1,]),'expression':([0,3,7,9,20,21,22,23,24,25,29,39,51,53,57,60,],[2,26,28,30,40,41,42,43,44,45,48,50,54,55,59,62,]),'group':([0,3,7,9,11,12,13,14,15,16,17,18,20,21,22,23,24,25,29,39,51,53,57,60,],[4,4,4,4,31,32,33,34,35,36,37,38,4,4,4,4,4,4,4,4,4,4,4,4,]),}
 
 _lr_goto = { }
 for _k, _v in _lr_goto_items.items():
@@ -26,32 +26,32 @@
 del _lr_goto_items
 _lr_productions = [
   ("S' -> statement","S'",1,None,None,None),
-  ('statement -> expression','statement',1,'p_statement_expr','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',155),
-  ('expression -> - expression','expression',2,'p_expression_uminus','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',167),
-  ('group -> ( expression )','group',3,'p_group_parentheses','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',171),
-  ('expression -> group','expression',1,'p_expression_group','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',175),
-  ('expression -> PI','expression',1,'p_expression_pi','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',218),
-  ('expression -> expression POWER expression','expression',3,'p_expression_power','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',223),
-  ('expression -> VARIABLE','expression',1,'p_expression_variable','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',237),
-  ("expression -> ' VARIABLE '",'expression',3,'p_expression_variable2','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',241),
-  ("expression -> ' expression '",'expression',3,'p_expression_expression','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',245),
-  ('expression -> COMPLEX ( expression , expression )','expression',6,'p_expression_complex','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',249),
-  ('expression -> NUMBER','expression',1,'p_expression_number','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',253),
-  ('expression -> CSC group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',259),
-  ('expression -> SEC group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',260),
-  ('expression -> ACSC group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',261),
-  ('expression -> ASEC group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',262),
-  ('expression -> RE group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',263),
-  ('expression -> IM group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',264),
-  ('expression -> SQRT group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',265),
-  ('expression -> CONJ group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',266),
-  ('expression -> FUNCTION ( expression )','expression',4,'p_expression_function1','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',272),
-  ('expression -> FUNCTION ( expression , expression )','expression',6,'p_expression_function2','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',286),
-  ('expression -> FUNCTION ( expression , expression , expression )','expression',8,'p_expression_function3','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',300),
-  ('expression -> FUNCTION ( expression , expression , expression , expression )','expression',10,'p_expression_function4','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',315),
-  ('expression -> expression = expression','expression',3,'p_expression_binop','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',329),
-  ('expression -> expression + expression','expression',3,'p_expression_binop','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',330),
-  ('expression -> expression - expression','expression',3,'p_expression_binop','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',331),
-  ('expression -> expression * expression','expression',3,'p_expression_binop','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',332),
-  ('expression -> expression / expression','expression',3,'p_expression_binop','/opt/MG5_aMC_v2_7_0/aloha/aloha_parsers.py',333),
+  ('statement -> expression','statement',1,'p_statement_expr','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',158),
+  ('expression -> - expression','expression',2,'p_expression_uminus','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',170),
+  ('group -> ( expression )','group',3,'p_group_parentheses','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',174),
+  ('expression -> group','expression',1,'p_expression_group','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',178),
+  ('expression -> PI','expression',1,'p_expression_pi','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',221),
+  ('expression -> expression POWER expression','expression',3,'p_expression_power','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',226),
+  ('expression -> VARIABLE','expression',1,'p_expression_variable','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',241),
+  ("expression -> ' VARIABLE '",'expression',3,'p_expression_variable2','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',245),
+  ("expression -> ' expression '",'expression',3,'p_expression_expression','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',249),
+  ('expression -> COMPLEX ( expression , expression )','expression',6,'p_expression_complex','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',253),
+  ('expression -> NUMBER','expression',1,'p_expression_number','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',257),
+  ('expression -> CSC group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',263),
+  ('expression -> SEC group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',264),
+  ('expression -> ACSC group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',265),
+  ('expression -> ASEC group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',266),
+  ('expression -> RE group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',267),
+  ('expression -> IM group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',268),
+  ('expression -> SQRT group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',269),
+  ('expression -> CONJ group','expression',2,'p_expression_func','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',270),
+  ('expression -> FUNCTION ( expression )','expression',4,'p_expression_function1','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',276),
+  ('expression -> FUNCTION ( expression , expression )','expression',6,'p_expression_function2','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',290),
+  ('expression -> FUNCTION ( expression , expression , expression )','expression',8,'p_expression_function3','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',304),
+  ('expression -> FUNCTION ( expression , expression , expression , expression )','expression',10,'p_expression_function4','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',319),
+  ('expression -> expression = expression','expression',3,'p_expression_binop','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',333),
+  ('expression -> expression + expression','expression',3,'p_expression_binop','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',334),
+  ('expression -> expression - expression','expression',3,'p_expression_binop','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',335),
+  ('expression -> expression * expression','expression',3,'p_expression_binop','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',336),
+  ('expression -> expression / expression','expression',3,'p_expression_binop','/opt/MG5_aMC_v2_7_3/aloha/aloha_parsers.py',337),
 ]
diff --git a/docs/source/examples/spheno_tmp.in b/docs/source/examples/spheno_tmp.in
new file mode 100644
index 000000000..207f38688
--- /dev/null
+++ b/docs/source/examples/spheno_tmp.in
@@ -0,0 +1,49 @@
+BLOCK SPINFO
+   1   FeynHiggs
+   2   built on Jul 10,,2016
+#
+BLOCK MODSEL
+   1   0
+   2   1
+   3   0
+   4   0
+   5   0
+   6   0
+#
+BLOCK SMINPUTS
+   1    127.94
+   2    1.16637e-05
+   3    0.1185
+   4    91.187828
+   5    4.18
+   6    173.37379
+   7    1.777
+#
+BLOCK MINPAR
+   3    32.629439
+#
+BLOCK EXTPAR
+   0   -1
+   1    525
+   2   500.0
+   3   3000
+   11   -3411.3194
+   12   -3411.3194
+   13   -3411.3194
+   23   -943.64072
+   26    3045.7282
+   31    1851.1828
+   32    1851.1828
+   33    1334.3428
+   34    1851.1828
+   35    1851.1828
+   36    1334.3428
+   41    883.82311
+   42    883.82311
+   43    1960.8922
+   44    883.82311
+   45    883.82311
+   46    1960.8922
+   47    883.82311
+   48    883.82311
+   49    1960.8922
\ No newline at end of file
diff --git a/docs/source/examples/test_distribute.ipynb b/docs/source/examples/test_distribute.ipynb
index d8b168ef4..96ae51430 100644
--- a/docs/source/examples/test_distribute.ipynb
+++ b/docs/source/examples/test_distribute.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "id": "6c1a697f",
    "metadata": {},
    "outputs": [
@@ -24,8 +24,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1.4.28+dirty\n",
-      "~/git/resummino/\n"
+      "0.1.8.9+dirty\n",
+      "~/git/resummino\n"
      ]
     }
    ],
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 2,
    "id": "f8611906",
    "metadata": {},
    "outputs": [
@@ -60,13 +60,9 @@
      "output_type": "stream",
      "text": [
       "Running: 1 jobs\n",
-      "RESTART None None None ./output/b961f87398318a753d6877b7bc41860014d39418ff8f9103d3ac7299a823e7e5.out\n",
       "Running: 1 jobs\n",
-      "RESTART None None None ./output/3c61d60948d6bb02fc05a4e857e668171c247958624c3c0dbe872e848f2b7000.out\n",
       "Running: 1 jobs\n",
-      "RESTART None None None ./output/31ef23f79e10ff50d0dcd4a71f2df9b409c22bb210869d139952edfe0d3215e9.out\n",
-      "Running: 1 jobs\n",
-      "RESTART None None None ./output/086a5e6f74677e9101881b8a9f6b0b8c452310ceb102bf7b941a0369c01ac7bf.out\n"
+      "Running: 1 jobs\n"
      ]
     }
    ],
@@ -98,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 3,
    "id": "e423f880",
    "metadata": {
     "scrolled": false
@@ -108,16 +104,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running: 9 jobs\n",
-      "RESTART None None None ./output/4bb70513d182bfd9166009c7629547997ddb69645082620dd7fef27fd22274d0.out\n",
-      "RESTART None None None ./output/061cc97365576ff137f4f3c5d1f779f5062296401e7173c779cb5767e9596850.out\n",
-      "RESTART None None None ./output/063bfb7d7c05793201cb3200ff8c60b1e4d814cf7ce6fc9bd08bde07100c3ebb.out\n",
-      "RESTART None None None ./output/2a260536e188f13e6d734bce7f0d3a592bf193e40d079627b5a6bf21732e18b5.out\n",
-      "RESTART None None None ./output/6d436042ce35991fdd665cc1b5052439a78e11ebee862a1c38cb5514ae164347.out\n",
-      "RESTART None None None ./output/e926d3664adb9d3a9f68c59c9e04ce3040eceb9c31363c47ab78a5cde2031470.out\n",
-      "RESTART None None None ./output/a35b6d30eb7678eae5f9e09058d8cc7dafbb8879f2dd5a81d3fa10e7ee9b36ba.out\n",
-      "RESTART None None None ./output/972c523cdf3c4ec63a6635d8ec256666219b288bb500fd281a064da620e4b40a.out\n",
-      "RESTART None None None ./output/b625f50b5def5c62b99c7c23391b7e64efc1ee5c2375798fe8eb473232c1b6aa.out\n"
+      "Running: 9 jobs\n"
      ]
     }
    ],
@@ -148,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "id": "de19c4fa",
    "metadata": {},
    "outputs": [
@@ -157,21 +144,7 @@
      "output_type": "stream",
      "text": [
       "Running: 7 jobs\n",
-      "RESTART None None None ./output/fe497ab2506f42a4ebe99958c56c61764f813a5bd24387f9950ee0e6f77ed390.out\n",
-      "RESTART None None None ./output/fdba21a290334224592298e06bc8400cc3e7194584c725b84b166555c5c91081.out\n",
-      "RESTART None None None ./output/974fbefdf05dd9c0b71fa6085561260e4210fc0b5f792a0ada9637ab16c5a891.out\n",
-      "RESTART None None None ./output/f3a23c0778650618c68e63613ebfb09d72615faf22e6dce50baa8a13fb136002.out\n",
-      "RESTART None None None ./output/ab2c8aaefc8d24a6d81fb23c7d7fd5f7e8153e0d8ebaeb6b742a65a8da2403e5.out\n",
-      "RESTART None None None ./output/94dc758487ebb02ac1b37dc6207783de826ce878585a096aec7c5dd0a03ff46e.out\n",
-      "RESTART None None None ./output/9357abf1f0344236676fa5f45560764a03b380bea63dc64dd375199fc0108037.out\n",
-      "Running: 7 jobs\n",
-      "RESTART None None None ./output/a83b4a9ab8796248db0b78c9798b092a22e2f5904390afd8d7bf693859fa447e.out\n",
-      "RESTART None None None ./output/2ae783fb6a31fcca5d01408bc9fd609354daa7b67a8d7e423ff54d56a1210924.out\n",
-      "RESTART None None None ./output/8dc1e45faa60c87d5f1937d6152e712c20dbfd3c2bce735216a24fb1d9050b0f.out\n",
-      "RESTART None None None ./output/a54496c7d0d5e77aee97c2e8e032278a9d1101b4299686824aa80ef3479048a7.out\n",
-      "RESTART None None None ./output/81240db73da22087f57d379a1998920d58ef3056a71a7d0d82735899e60f9fd7.out\n",
-      "RESTART None None None ./output/9c62620744db8acb5ce5d92cadb56c8a52963e4c3a40808de8854b350c59aed9.out\n",
-      "RESTART None None None ./output/6929de6ef65f92978e5901fffcdd780b20a7888ccd8198b9a63a797fa232d0d0.out\n"
+      "Running: 7 jobs\n"
      ]
     }
    ],
@@ -202,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "484741b8",
    "metadata": {},
    "outputs": [
@@ -216,7 +189,7 @@
    ],
    "source": [
     "params = [\n",
-    "    \"scenarioB.in\",\n",
+    "    \"slha.in\",\n",
     "]\n",
     "pss = [\n",
     "    (1000011,-1000011),\n",
diff --git a/docs/source/examples/test_madgraph.ipynb b/docs/source/examples/test_madgraph.ipynb
index 9109f5142..2c1e92c46 100644
--- a/docs/source/examples/test_madgraph.ipynb
+++ b/docs/source/examples/test_madgraph.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "b583970e",
    "metadata": {},
    "outputs": [
@@ -18,8 +18,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1.6.20+dirty\n",
-      "/opt/MG5_aMC_v2_7_0/\n"
+      "0.1.8.9+dirty\n",
+      "/opt/MG5_aMC_v2_7_3/\n"
      ]
     }
    ],
@@ -31,18 +31,10 @@
     "import hepi.madgraph as mg\n",
     "import hepi.util as util\n",
     "import matplotlib.pyplot as plt\n",
-    "mg.set_path(\"/opt/MG5_aMC_v2_7_0/\")\n",
+    "mg.set_path(\"/opt/MG5_aMC_v2_7_3/\")\n",
     "print (mg.get_path())"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7d048757",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "2dd526dd",
@@ -53,55 +45,336 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "id": "ed7216a4",
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Running: 15 jobs\n",
-      "./output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.out\n"
+      "./output/09f0fa80d6ffa4d16b6c3b7f888df66d3c6debf643b46bfa1441c5a941d5bc4a.out\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/bin/sh: line 1: /opt/MG5_aMC_v2_7_0/bin/mg5_aMC: No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n",
-      "cp: cannot stat './output/dd6217160a629c3e78f6d632d334ec2a5503b99be880523928b954ab6f432446.bdir': No such file or directory\n"
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:510: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  _mg5_version = LooseVersion(line[9:].strip())\n",
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:456: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  if tool_name in ['lhapdf6', 'lhapdf'] and MG5_version and MG5_version < LooseVersion(\"2.6.1\"):\n",
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:255: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  ( lambda MG5version: MG5version < LooseVersion(\"2.6.1\"),\n",
+      "Detected 'ninja' missing dependency: 'oneloop'. Will install it now.\n",
+      "Fetching data with command:\n",
+      "  wget --no-check-certificate http://helac-phegas.web.cern.ch/helac-phegas/tar-files/OneLOop-3.6.tgz\n",
+      "--2022-06-08 11:43:47--  http://helac-phegas.web.cern.ch/helac-phegas/tar-files/OneLOop-3.6.tgz\n",
+      "Resolving helac-phegas.web.cern.ch... 188.184.100.128\n",
+      "Connecting to helac-phegas.web.cern.ch|188.184.100.128|:80... connected.\n",
+      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
+      "Location: https://helac-phegas.web.cern.ch/tar-files/OneLOop-3.6.tgz [following]\n",
+      "--2022-06-08 11:43:48--  https://helac-phegas.web.cern.ch/tar-files/OneLOop-3.6.tgz\n",
+      "Connecting to helac-phegas.web.cern.ch|188.184.100.128|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 111734 (109K) [application/x-compressed]\n",
+      "Saving to: ‘OneLOop-3.6.tgz’\n",
+      "\n",
+      "     0K .......... .......... .......... .......... .......... 45% 1.40M 0s\n",
+      "    50K .......... .......... .......... .......... .......... 91% 2.99M 0s\n",
+      "   100K .........                                             100%  156M=0.05s\n",
+      "\n",
+      "2022-06-08 11:43:48 (2.08 MB/s) - ‘OneLOop-3.6.tgz’ saved [111734/111734]\n",
+      "\n",
+      "Installing tool 'oneloop'...\n",
+      "    > Follow the installation progress by running the command below in a separate terminal)\n",
+      "    > tail -f /opt/MG5_aMC_v2_7_3/HEPTools/oneloop/oneloop_install.log\n",
+      "    > Successful installation of dependency 'oneloop' in '/opt/MG5_aMC_v2_7_3/HEPTools'.\n",
+      "    > See installation log at '/opt/MG5_aMC_v2_7_3/HEPTools/oneloop/oneloop_install.log'.\n",
+      "Fetching data with command:\n",
+      "  wget --no-check-certificate https://ninja.hepforge.org/downloads//ninja-1.1.0.tar.gz\n",
+      "--2022-06-08 11:44:27--  https://ninja.hepforge.org/downloads//ninja-1.1.0.tar.gz\n",
+      "Resolving ninja.hepforge.org... 129.234.186.186\n",
+      "Connecting to ninja.hepforge.org|129.234.186.186|:443... connected.\n",
+      "HTTP request sent, awaiting response... 302 Found\n",
+      "Location: /downloads?f=/ninja-1.1.0.tar.gz [following]\n",
+      "--2022-06-08 11:44:27--  https://ninja.hepforge.org/downloads?f=/ninja-1.1.0.tar.gz\n",
+      "Reusing existing connection to ninja.hepforge.org:443.\n",
+      "HTTP request sent, awaiting response... 302 Found\n",
+      "Location: /downloads?f=ninja-1.1.0.tar.gz [following]\n",
+      "--2022-06-08 11:44:27--  https://ninja.hepforge.org/downloads?f=ninja-1.1.0.tar.gz\n",
+      "Reusing existing connection to ninja.hepforge.org:443.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: unspecified [application/x-gzip]\n",
+      "Saving to: ‘ninja-1.1.0.tar.gz’\n",
+      "\n",
+      "     0K .......... .......... .......... .......... ..........  771K\n",
+      "    50K .......... .......... .......... .......... .......... 1.53M\n",
+      "   100K .......... .......... .......... .......... ..........  224M\n",
+      "   150K .......... .......... .......... .......... .......... 1.63M\n",
+      "   200K .......... .......... .......... .......... .......... 17.9M\n",
+      "   250K .......... .......... .......... .......... ..........  181M\n",
+      "   300K .......... .......... .......... .......... ..........  206M\n",
+      "   350K .......... .......... .......... .......... .......... 1.63M\n",
+      "   400K .......... .......... .......... .......... .......... 36.0M\n",
+      "   450K .......... .......... .......... .......... ..........  180M\n",
+      "   500K .......... .......... .......... .......... ..........  163M\n",
+      "   550K .......... .......                                      219M=0.2s\n",
+      "\n",
+      "2022-06-08 11:44:27 (3.42 MB/s) - ‘ninja-1.1.0.tar.gz’ saved [581566]\n",
+      "\n",
+      "Installing tool 'ninja'...\n",
+      "    > Follow the installation progress by running the command below in a separate terminal)\n",
+      "    > tail -f /opt/MG5_aMC_v2_7_3/HEPTools/ninja/ninja_install.log\n",
+      "Successful installation of 'ninja' in '/opt/MG5_aMC_v2_7_3/HEPTools'.\n",
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:510: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  _mg5_version = LooseVersion(line[9:].strip())\n",
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:456: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  if tool_name in ['lhapdf6', 'lhapdf'] and MG5_version and MG5_version < LooseVersion(\"2.6.1\"):\n",
+      "/opt/MG5_aMC_v2_7_3/HEPTools/HEPToolsInstallers/HEPToolInstaller.py:255: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  ( lambda MG5version: MG5version < LooseVersion(\"2.6.1\"),\n",
+      "Fetching data with command:\n",
+      "  wget --no-check-certificate http://collier.hepforge.org//collier-latest.tar.gz\n",
+      "--2022-06-08 11:49:29--  http://collier.hepforge.org//collier-latest.tar.gz\n",
+      "Resolving collier.hepforge.org... 129.234.186.186\n",
+      "Connecting to collier.hepforge.org|129.234.186.186|:80... connected.\n",
+      "HTTP request sent, awaiting response... 302 Found\n",
+      "Location: https://collier.hepforge.org/collier-latest.tar.gz [following]\n",
+      "--2022-06-08 11:49:29--  https://collier.hepforge.org/collier-latest.tar.gz\n",
+      "Connecting to collier.hepforge.org|129.234.186.186|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 933676 (912K) [application/x-gzip]\n",
+      "Saving to: ‘collier-latest.tar.gz’\n",
+      "\n",
+      "     0K .......... .......... .......... .......... ..........  5%  808K 1s\n",
+      "    50K .......... .......... .......... .......... .......... 10% 1.54M 1s\n",
+      "   100K .......... .......... .......... .......... .......... 16% 55.5M 0s\n",
+      "   150K .......... .......... .......... .......... .......... 21% 1.51M 0s\n",
+      "   200K .......... .......... .......... .......... .......... 27%  146M 0s\n",
+      "   250K .......... .......... .......... .......... .......... 32%  136M 0s\n",
+      "   300K .......... .......... .......... .......... .......... 38%  137M 0s\n",
+      "   350K .......... .......... .......... .......... .......... 43% 1.61M 0s\n",
+      "   400K .......... .......... .......... .......... .......... 49% 24.1M 0s\n",
+      "   450K .......... .......... .......... .......... .......... 54%  207M 0s\n",
+      "   500K .......... .......... .......... .......... .......... 60%  229M 0s\n",
+      "   550K .......... .......... .......... .......... .......... 65%  175M 0s\n",
+      "   600K .......... .......... .......... .......... .......... 71% 8.41M 0s\n",
+      "   650K .......... .......... .......... .......... .......... 76% 15.2M 0s\n",
+      "   700K .......... .......... .......... .......... .......... 82%  212M 0s\n",
+      "   750K .......... .......... .......... .......... .......... 87% 2.76M 0s\n",
+      "   800K .......... .......... .......... .......... .......... 93% 98.5M 0s\n",
+      "   850K .......... .......... .......... .......... .......... 98% 19.4M 0s\n",
+      "   900K .......... .                                          100%  313M=0.2s\n",
+      "\n",
+      "2022-06-08 11:49:29 (4.66 MB/s) - ‘collier-latest.tar.gz’ saved [933676/933676]\n",
+      "\n",
+      "Installing tool 'collier'...\n",
+      "    > Follow the installation progress by running the command below in a separate terminal)\n",
+      "    > tail -f /opt/MG5_aMC_v2_7_3/HEPTools/collier/collier_install.log\n",
+      "Successful installation of 'collier' in '/opt/MG5_aMC_v2_7_3/HEPTools'.\n"
      ]
     },
     {
-     "ename": "ValueError",
-     "evalue": "Array must not contain infs or nans.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_3679/832802357.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mmg_dl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mli\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mskip\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmadstr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mhepi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmass_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmg_dl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"LO\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpa\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlogy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0mhepi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmass_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmg_dl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"NLO\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpa\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlogy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mhepi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mli\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscenario\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"mastercode\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/hepi/plot.py\u001b[0m in \u001b[0;36mmass_plot\u001b[0;34m(dict_list, y, part, logy, yaxis, yscale, label, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m               **kwargs):\n\u001b[1;32m     63\u001b[0m     \u001b[0mdict_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"mass_\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_mass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m     _plot(dict_list,\n\u001b[0m\u001b[1;32m     65\u001b[0m           \u001b[0;34m\"mass_\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m           \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/hepi/plot.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(dict_list, x, y, label, xaxis, yaxis, ratio, K, K_plus_1, logy, yscale, mask, **kwargs)\u001b[0m\n\u001b[1;32m    146\u001b[0m         \u001b[0mvy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvy\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m     \u001b[0m_vplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxaxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myaxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/hepi/plot.py\u001b[0m in \u001b[0;36m_vplot\u001b[0;34m(x, y, label, xaxis, yaxis, logy, yscale, interpolate, plot_data, data_color, mask, fill, data_fmt, fmt, print_area, **kwargs)\u001b[0m\n\u001b[1;32m    204\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minterpolate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0;31m#print(vx,vy)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m         \u001b[0mspl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_interp_spline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: BSpline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m         \u001b[0mpower_smooth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxnew\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/lib/python3.8/site-packages/scipy/interpolate/_bsplines.py\u001b[0m in \u001b[0;36mmake_interp_spline\u001b[0;34m(x, y, k, t, bc_type, axis, check_finite)\u001b[0m\n\u001b[1;32m   1238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1239\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_as_float_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_finite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1240\u001b[0;31m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_as_float_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_finite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1242\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoveaxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m    \u001b[0;31m# now internally interp axis is zero\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/lib/python3.8/site-packages/scipy/interpolate/_bsplines.py\u001b[0m in \u001b[0;36m_as_float_array\u001b[0;34m(x, check_finite)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtyp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcheck_finite\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Array must not contain infs or nans.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: Array must not contain infs or nans."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No module named 'madgraph'\n",
+      " No hepmc reader since No module named 'madgraph' \u001b[1;30m[lhe_parser.py at line 50]\u001b[0m\n",
+      "INFO: ************************************************************\n",
+      "*                                                          *\n",
+      "*           W E L C O M E  to  M A D G R A P H 5           *\n",
+      "*                       a M C @ N L O                      *\n",
+      "*                                                          *\n",
+      "*                 *                       *                *\n",
+      "*                   *        * *        *                  *\n",
+      "*                     * * * * 5 * * * *                    *\n",
+      "*                   *        * *        *                  *\n",
+      "*                 *                       *                *\n",
+      "*                                                          *\n",
+      "*         VERSION 5.2.7.3.py3           20xx-xx-xx         *\n",
+      "*                                                          *\n",
+      "*    The MadGraph5_aMC@NLO Development Team - Find us at   *\n",
+      "*                 http://amcatnlo.cern.ch                  *\n",
+      "*                                                          *\n",
+      "*               Type 'help' for in-line help.              *\n",
+      "*                                                          *\n",
+      "************************************************************ \n",
+      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/09f0fa80d6ffa4d16b6c3b7f888df66d3c6debf643b46bfa1441c5a941d5bc4a.bdir/Cards/amcatnlo_configuration.txt  \n",
+      "INFO: load configuration from /opt/MG5_aMC_v2_7_3/input/mg5_configuration.txt  \n",
+      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/09f0fa80d6ffa4d16b6c3b7f888df66d3c6debf643b46bfa1441c5a941d5bc4a.bdir/Cards/amcatnlo_configuration.txt  \n",
+      "Using default eps viewer \"evince\". Set another one in ./input/mg5_configuration.txt\n",
+      "Using default web browser \"firefox\". Set another one in ./input/mg5_configuration.txt\n",
+      "calculate_xsect -f\n",
+      "INFO: will run in mode: NLO \n",
+      "INFO: Starting run \n",
+      "INFO: Compiling the code \n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Using LHAPDF v6.3.0 interface for PDFs \n",
+      "INFO: Compiling source... \n",
+      "INFO:           ...done, continuing with P* directories \n",
+      "\u001b[1;34mWARNING: Could not compile StdHEP because its source directory could not be found in the SOURCE folder.\n",
+      " Check the MG5_aMC option 'output_dependencies'.\n",
+      " This will prevent the use of HERWIG6/Pythia6 shower. \u001b[0m\n",
+      "INFO: Compiling directories... \n",
+      "INFO: Compiling on 8 cores \n",
+      "INFO:  Compiling P0_uux_elmelp... \n",
+      "INFO:  Compiling P0_ddx_elmelp... \n",
+      "INFO:  Compiling P0_uxu_elmelp... \n",
+      "INFO:  Compiling P0_dxd_elmelp... \n",
+      "INFO:     P0_ddx_elmelp done. \n",
+      "INFO:     P0_uxu_elmelp done. \n",
+      "INFO:     P0_uux_elmelp done. \n",
+      "INFO:     P0_dxd_elmelp done. \n",
+      "INFO: Checking test output: \n",
+      "INFO: P0_uux_elmelp \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_ddx_elmelp \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_uxu_elmelp \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_dxd_elmelp \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: Starting run \n",
+      "INFO: Using 8 cores \n",
+      "INFO: Cleaning previous results \n",
+      "INFO: Doing fixed order NLO \n",
+      "INFO: Setting up grids \n",
+      "INFO:  Idle: 0,  Running: 4,  Completed: 0 [ current time: 11h58 ] \n",
+      "INFO:  Idle: 0,  Running: 3,  Completed: 1 [  0.33s  ] \n",
+      "INFO:  Idle: 0,  Running: 2,  Completed: 2 [  0.36s  ] \n",
+      "INFO:  Idle: 0,  Running: 1,  Completed: 3 [  0.48s  ] \n",
+      "INFO:  Idle: 0,  Running: 0,  Completed: 4 [  0.69s  ] \n",
+      "INFO: \n",
+      "      Results after grid setup:\n",
+      "      Total cross section:      2.489e-01 +- 1.5e-03 pb\n",
+      " \n",
+      "INFO: Refining results, step 1 \n",
+      "INFO:  Idle: 0,  Running: 5,  Completed: 0 [ current time: 11h58 ] \n",
+      "INFO:  Idle: 0,  Running: 4,  Completed: 1 [  0.25s  ] \n",
+      "INFO:  Idle: 0,  Running: 3,  Completed: 2 [  0.33s  ] \n",
+      "INFO:  Idle: 0,  Running: 2,  Completed: 3 [  1.1s  ] \n",
+      "INFO:  Idle: 0,  Running: 1,  Completed: 4 [  1.2s  ] \n",
+      "INFO:  Idle: 0,  Running: 0,  Completed: 5 [  1.2s  ] \n",
+      "INFO: \n",
+      "   --------------------------------------------------------------\n",
+      "      Final results and run summary:\n",
+      "      Process p p > 1000011 -1000011 [QCD]\n",
+      "      Run at p-p collider (6500.0 + 6500.0 GeV)\n",
+      "      Total cross section: 2.468e-01 +- 9.1e-04 pb\n",
+      "   --------------------------------------------------------------\n",
+      " \n",
+      "INFO: The results of this run and the HwU and GnuPlot files with the plots have been saved in /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/09f0fa80d6ffa4d16b6c3b7f888df66d3c6debf643b46bfa1441c5a941d5bc4a.bdir/Events/run_01 \n",
+      "INFO: Run complete \n",
+      "INFO:  \n",
+      "quit\n",
+      "INFO:  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "stty: stty: 'standard input''standard input': Inappropriate ioctl for device\n",
+      ": Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for devicestty: \n",
+      "'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: stty: 'standard input''standard input': Inappropriate ioctl for device\n",
+      ": Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for devicestty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "\n",
+      "stty: stty: stty: stty: 'standard input''standard input': Inappropriate ioctl for device\n",
+      ": Inappropriate ioctl for device\n",
+      "'standard input': Inappropriate ioctl for device\n",
+      "stty: stty: stty: 'standard input': Inappropriate ioctl for device\n",
+      "'standard input': Inappropriate ioctl for device\n",
+      "'standard input': Inappropriate ioctl for device\n",
+      "'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -113,7 +386,7 @@
     "     ]\n",
     "for pa,pb in pss:\n",
     "    for param in params:\n",
-    "        i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.,model=\"/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO\")\n",
+    "        i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.,model=\"/opt/MG5_aMC_v2_7_3/models/MSSMatNLO_UFO\")\n",
     "        li = [i]\n",
     "        li = hepi.mass_scan([i],pa, np.linspace(100,1000,7+8))\n",
     "        mg_dl = mg.run(li,skip=False,madstr=False)\n",
@@ -133,10 +406,292 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "4628188f",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running: 15 jobs\n",
+      "./output/90bcfe8bc719ea89d80f8ce26ccfc425f6ff8937aec2e0a3f8031ea161b0d4cc.out\n",
+      "No module named 'madgraph'\n",
+      " No hepmc reader since No module named 'madgraph' \u001b[1;30m[lhe_parser.py at line 50]\u001b[0m\n",
+      "INFO: ************************************************************\n",
+      "*                                                          *\n",
+      "*           W E L C O M E  to  M A D G R A P H 5           *\n",
+      "*                       a M C @ N L O                      *\n",
+      "*                                                          *\n",
+      "*                 *                       *                *\n",
+      "*                   *        * *        *                  *\n",
+      "*                     * * * * 5 * * * *                    *\n",
+      "*                   *        * *        *                  *\n",
+      "*                 *                       *                *\n",
+      "*                                                          *\n",
+      "*         VERSION 5.2.7.3.py3           20xx-xx-xx         *\n",
+      "*                                                          *\n",
+      "*    The MadGraph5_aMC@NLO Development Team - Find us at   *\n",
+      "*                 http://amcatnlo.cern.ch                  *\n",
+      "*                                                          *\n",
+      "*               Type 'help' for in-line help.              *\n",
+      "*                                                          *\n",
+      "************************************************************ \n",
+      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/90bcfe8bc719ea89d80f8ce26ccfc425f6ff8937aec2e0a3f8031ea161b0d4cc.bdir/Cards/amcatnlo_configuration.txt  \n",
+      "INFO: load configuration from /opt/MG5_aMC_v2_7_3/input/mg5_configuration.txt  \n",
+      "INFO: load configuration from /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/90bcfe8bc719ea89d80f8ce26ccfc425f6ff8937aec2e0a3f8031ea161b0d4cc.bdir/Cards/amcatnlo_configuration.txt  \n",
+      "Using default eps viewer \"evince\". Set another one in ./input/mg5_configuration.txt\n",
+      "Using default web browser \"firefox\". Set another one in ./input/mg5_configuration.txt\n",
+      "calculate_xsect -f\n",
+      "INFO: will run in mode: NLO \n",
+      "INFO: Starting run \n",
+      "INFO: Compiling the code \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "write ./param_card.dat\n",
+      "INFO: MadSTR: Forcing width MDL_WSUL to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MP__MDL_WSUL to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MDL_WSCL to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MP__MDL_WSCL to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MDL_WSUR to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MP__MDL_WSUR to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MDL_WSCR to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MP__MDL_WSCR to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MDL_WSDL to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MP__MDL_WSDL to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MDL_WSSL to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MP__MDL_WSSL to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MDL_WSBL to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MP__MDL_WSBL to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MDL_WSDR to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MP__MDL_WSDR to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MDL_WSSR to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MP__MDL_WSSR to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MDL_WSBR to zero inside param_card.inc \n",
+      "INFO: MadSTR: Forcing width MP__MDL_WSBR to zero inside param_card.inc \n",
+      "\u001b[1;34mWARNING: The replacements above ensure poles cancelation, and affect all widths\n",
+      "   EXCEPT those which enter the resonance-treatment counterterms, which\n",
+      "   are taken from the param_card.\n",
+      "   Do NOT set these widths to zero in the param_card. \u001b[0m\n",
+      "INFO: Using LHAPDF v6.3.0 interface for PDFs \n",
+      "INFO: Compiling source... \n",
+      "INFO:           ...done, continuing with P* directories \n",
+      "\u001b[1;34mWARNING: Could not compile StdHEP because its source directory could not be found in the SOURCE folder.\n",
+      " Check the MG5_aMC option 'output_dependencies'.\n",
+      " This will prevent the use of HERWIG6/Pythia6 shower. \u001b[0m\n",
+      "INFO: Compiling directories... \n",
+      "INFO: Compiling on 8 cores \n",
+      "INFO:  Compiling P0_uux_n1n1... \n",
+      "INFO:  Compiling P0_ccx_n1n1... \n",
+      "INFO:  Compiling P0_ddx_n1n1... \n",
+      "INFO:  Compiling P0_ssx_n1n1... \n",
+      "INFO:  Compiling P0_uxu_n1n1... \n",
+      "INFO:  Compiling P0_cxc_n1n1... \n",
+      "INFO:  Compiling P0_dxd_n1n1... \n",
+      "INFO:  Compiling P0_sxs_n1n1... \n",
+      "INFO:     P0_uux_n1n1 done. \n",
+      "INFO:  Compiling P0_bbx_n1n1... \n",
+      "INFO:     P0_ddx_n1n1 done. \n",
+      "INFO:  Compiling P0_bxb_n1n1... \n",
+      "INFO:     P0_sxs_n1n1 done. \n",
+      "INFO:     P0_uxu_n1n1 done. \n",
+      "INFO:     P0_cxc_n1n1 done. \n",
+      "INFO:     P0_dxd_n1n1 done. \n",
+      "INFO:     P0_ccx_n1n1 done. \n",
+      "INFO:     P0_ssx_n1n1 done. \n",
+      "INFO:     P0_bxb_n1n1 done. \n",
+      "INFO:     P0_bbx_n1n1 done. \n",
+      "INFO: Checking test output: \n",
+      "INFO: P0_uux_n1n1 \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_ccx_n1n1 \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_ddx_n1n1 \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_ssx_n1n1 \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_uxu_n1n1 \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_cxc_n1n1 \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_dxd_n1n1 \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_sxs_n1n1 \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_bbx_n1n1 \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: P0_bxb_n1n1 \n",
+      "INFO:  Result for test_ME: \n",
+      "INFO:    Passed. \n",
+      "INFO:  Result for check_poles: \n",
+      "INFO:    Poles successfully cancel for 20 points over 20 (tolerance=1.0e-05) \n",
+      "INFO: Starting run \n",
+      "INFO: Using 8 cores \n",
+      "INFO: Cleaning previous results \n",
+      "INFO: Doing fixed order NLO \n",
+      "INFO: Setting up grids \n",
+      "INFO:  Idle: 2,  Running: 8,  Completed: 0 [ current time: 13h11 ] \n",
+      "INFO:  Idle: 1,  Running: 8,  Completed: 1 [  13.4s  ] \n",
+      "INFO:  Idle: 0,  Running: 8,  Completed: 2 [  14.2s  ] \n",
+      "INFO:  Idle: 0,  Running: 7,  Completed: 3 [  15.1s  ] \n",
+      "INFO:  Idle: 0,  Running: 6,  Completed: 4 [  15.2s  ] \n",
+      "INFO:  Idle: 0,  Running: 5,  Completed: 5 [  15.6s  ] \n",
+      "INFO:  Idle: 0,  Running: 4,  Completed: 6 [  17.9s  ] \n",
+      "INFO:  Idle: 0,  Running: 3,  Completed: 7 [  21.1s  ] \n",
+      "INFO:  Idle: 0,  Running: 2,  Completed: 8 [  21.7s  ] \n",
+      "INFO:  Idle: 0,  Running: 1,  Completed: 9 [  23.3s  ] \n",
+      "INFO:  Idle: 0,  Running: 0,  Completed: 10 [  23.4s  ] \n",
+      "INFO: \n",
+      "      Results after grid setup:\n",
+      "      Total cross section:      7.316e-06 +- 2.4e-07 pb\n",
+      " \n",
+      "INFO: Refining results, step 1 \n",
+      "INFO:  Idle: 2,  Running: 8,  Completed: 0 [ current time: 13h11 ] \n",
+      "INFO:  Idle: 1,  Running: 8,  Completed: 1 [  16.8s  ] \n",
+      "INFO:  Idle: 0,  Running: 8,  Completed: 2 [  18.1s  ] \n",
+      "INFO:  Idle: 0,  Running: 7,  Completed: 3 [  18.5s  ] \n",
+      "INFO:  Idle: 0,  Running: 6,  Completed: 4 [  18.8s  ] \n",
+      "INFO:  Idle: 0,  Running: 5,  Completed: 5 [  21.4s  ] \n",
+      "INFO:  Idle: 0,  Running: 4,  Completed: 6 [  24.9s  ] \n",
+      "INFO:  Idle: 0,  Running: 3,  Completed: 7 [  26.9s  ] \n",
+      "INFO:  Idle: 0,  Running: 2,  Completed: 8 [  28.7s  ] \n",
+      "INFO:  Idle: 0,  Running: 1,  Completed: 9 [  30.8s  ] \n",
+      "INFO:  Idle: 0,  Running: 0,  Completed: 10 [  31s  ] \n",
+      "INFO: \n",
+      "   --------------------------------------------------------------\n",
+      "      Final results and run summary:\n",
+      "      Process p p > 1000022 1000022 [QCD]\n",
+      "      Run at p-p collider (6500.0 + 6500.0 GeV)\n",
+      "      Total cross section: 7.403e-06 +- 1.2e-07 pb\n",
+      "   --------------------------------------------------------------\n",
+      " \n",
+      "INFO: The results of this run and the HwU and GnuPlot files with the plots have been saved in /home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/docs/source/examples/output/90bcfe8bc719ea89d80f8ce26ccfc425f6ff8937aec2e0a3f8031ea161b0d4cc.bdir/Events/run_01 \n",
+      "INFO: Run complete \n",
+      "INFO:  \n",
+      "quit\n",
+      "INFO:  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n",
+      "stty: 'standard input': Inappropriate ioctl for device\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "params = [\n",
     "    \"mastercode_with_gm2.in\",\n",
@@ -146,7 +701,7 @@
     "     ]\n",
     "for pa,pb in pss:\n",
     "    for param in params:\n",
-    "        i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.,model=\"/opt/MG5_aMC_v2_7_0/models/EWKino_NLO_UFO_py3\")\n",
+    "        i = hepi.Input(hepi.Order.NLO,13000,pa,pb,param,\"cteq6l1\",\"cteq66\",1., 1.,model=\"/opt/MG5_aMC_v2_7_3/models/EWKino_NLO_UFO\")\n",
     "        li = [i]\n",
     "        li = hepi.mass_scan([i],pa, np.linspace(100,1000,7+8))\n",
     "        mg_dl = mg.run(li,skip=False,madstr=True)\n",
diff --git a/docs/source/examples/test_pyslha.ipynb b/docs/source/examples/test_pyslha.ipynb
index 30d7d3b89..5f5567815 100644
--- a/docs/source/examples/test_pyslha.ipynb
+++ b/docs/source/examples/test_pyslha.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "b583970e",
    "metadata": {},
    "outputs": [
@@ -18,8 +18,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1.4.28+dirty\n",
-      "~/git/resummino_test/\n"
+      "0.1.8.9+dirty\n",
+      "~/git/resummino_release\n"
      ]
     }
    ],
@@ -31,13 +31,13 @@
     "import hepi.resummino as rs\n",
     "import hepi.util as util\n",
     "import matplotlib.pyplot as plt\n",
-    "rs.set_path(\"~/git/resummino_test\")\n",
+    "rs.set_path(\"~/git/resummino_release\")\n",
     "print (rs.get_path())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "7d048757",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "ed7216a4",
    "metadata": {},
    "outputs": [
@@ -60,9 +60,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running: 1 jobs\n",
-      "{'LO': array([8.7592441e-06+/-7.8475953e-09], dtype=object), 'NLO': array([1.0663038e-05+/-9.2752399e-09], dtype=object), 'NLO_PLUS_NLL': array([0.0+/-0], dtype=object), 'K_LO': array([1.0+/-0], dtype=object), 'K_NLO': array([1.2173468256239144+/-0.0015201312515062064], dtype=object), 'K_NLO_PLUS_NLL': array([0.0+/-0], dtype=object), 'NLO_PLUS_NLL_OVER_NLO': array([0.0+/-0], dtype=object), 'VNLO': array([1.2408607e-06+/-4.5813373e-09], dtype=object), 'P_PLUS_K': array([3.8807666e-07+/-2.0758299e-09], dtype=object), 'RNLOG': array([-4.1688858e-08+/-4.0857279e-10], dtype=object), 'RNLOQ': array([1.6592239e-07+/-4.9756549e-10], dtype=object), 'VNLO_PLUS_P_PLUS_K': array([1.62893736e-06+/-5.029684008971667e-09], dtype=object), 'RNLO': array([1.24233532e-07+/-6.438191839075038e-10], dtype=object), 'RNLO_PLUS_VNLO_PLUS_P_PLUS_K': array([1.7531708919999999e-06+/-5.070722273174959e-09], dtype=object), 'order': array([1]), 'energy': array([13000]), 'energyhalf': array([6500.]), 'particle1': array([1000022]), 'particle2': array([1000022]), 'slha': array(['mastercode_with_gm2.in_mass_1000022_248.758955'], dtype='<U46'), 'pdf_lo': array(['cteq6l1'], dtype='<U7'), 'pdfset_lo': array([0]), 'pdf_nlo': array(['cteq66'], dtype='<U6'), 'pdfset_nlo': array([0]), 'pdf_lo_id': array([10042]), 'pdf_nlo_id': array([10550]), 'mu_f': array([1.]), 'mu_r': array([1.]), 'precision': array([0.01]), 'max_iters': array([50]), 'invariant_mass': array(['auto'], dtype='<U4'), 'pt': array(['auto'], dtype='<U4'), 'result': array(['total'], dtype='<U5'), 'id': array(['5'], dtype='<U1'), 'model_path': array(['/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO'], dtype='<U40'), 'mu': array([248.758955]), 'mass_1000022': array([248.758955]), 'code': array(['RS'], dtype='<U2')}\n"
+      "Running: 1 jobs\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0    (9.721+/-0.008)e-06\n",
+       "Name: NLO, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -73,12 +83,12 @@
     "        li = [i]\n",
     "        li = hepi.mass_scan([i],pa, np.linspace(2.48758955E+02,2.48758955E+02,1))\n",
     "        rs_dl = rs.run(li,noskip=False)\n",
-    "        print(rs_dl)"
+    "rs_dl[\"NLO\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "a74a3e82",
    "metadata": {},
    "outputs": [
@@ -86,10 +96,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running: 1 jobs\n",
-      "RESTART None None None ./output/01306087b8a5d7c92a02239ba9a08dd7cd8e055cbeecb8db7a1b22b3ec70a1a4.out\n",
-      "{'LO': array([8.7592443e-06+/-7.8475955e-09], dtype=object), 'NLO': array([1.0662875e-05+/-9.274417e-09], dtype=object), 'NLO_PLUS_NLL': array([0.0+/-0], dtype=object), 'K_LO': array([1.0+/-1.1419754446251318e-19], dtype=object), 'K_NLO': array([1.2173281889169367+/-0.0015200538159673982], dtype=object), 'K_NLO_PLUS_NLL': array([0.0+/-0], dtype=object), 'NLO_PLUS_NLL_OVER_NLO': array([0.0+/-0], dtype=object), 'VNLO': array([1.2408607e-06+/-4.5813374e-09], dtype=object), 'P_PLUS_K': array([3.8807668e-07+/-2.0758299e-09], dtype=object), 'RNLOG': array([-4.1688859e-08+/-4.085728e-10], dtype=object), 'RNLOQ': array([1.6575915e-07+/-4.8197672e-10], dtype=object), 'VNLO_PLUS_P_PLUS_K': array([1.62893738e-06+/-5.029684100057653e-09], dtype=object), 'RNLO': array([1.2407029100000002e-07+/-6.318491050257161e-10], dtype=object), 'RNLO_PLUS_VNLO_PLUS_P_PLUS_K': array([1.753007671e-06+/-5.0692164520657985e-09], dtype=object), 'order': array([1]), 'energy': array([13000]), 'energyhalf': array([6500.]), 'particle1': array([1000022]), 'particle2': array([1000022]), 'slha': array(['mastercode_with_gm2.in'], dtype='<U22'), 'pdf_lo': array(['cteq6l1'], dtype='<U7'), 'pdfset_lo': array([0]), 'pdf_nlo': array(['cteq66'], dtype='<U6'), 'pdfset_nlo': array([0]), 'pdf_lo_id': array([10042]), 'pdf_nlo_id': array([10550]), 'mu_f': array([1.]), 'mu_r': array([1.]), 'precision': array([0.01]), 'max_iters': array([50]), 'invariant_mass': array(['auto'], dtype='<U4'), 'pt': array(['auto'], dtype='<U4'), 'result': array(['total'], dtype='<U5'), 'id': array(['5'], dtype='<U1'), 'model_path': array(['/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO'], dtype='<U40'), 'mu': array([248.758955]), 'code': array(['RS'], dtype='<U2')}\n"
+      "Running: 1 jobs\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0    (9.721+/-0.008)e-06\n",
+       "Name: NLO, dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -100,7 +119,7 @@
     "        li = [i]\n",
     "        #li = hepi.mass_scan([i],pa, np.linspace(2.4876e2,2.4876e2,1))\n",
     "        rs_dl = rs.run(li,noskip=False)\n",
-    "        print(rs_dl)"
+    "rs_dl[\"NLO\"]"
    ]
   },
   {
@@ -110,11 +129,19 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02b72a93",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/source/examples/test_spheno.ipynb b/docs/source/examples/test_spheno.ipynb
index f0dfed663..929f354e3 100644
--- a/docs/source/examples/test_spheno.ipynb
+++ b/docs/source/examples/test_spheno.ipynb
@@ -16,9 +16,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1.4.15+dirty\n",
-      "~/git/resummino_ug_to_UX_vNLO/\n",
-      "~/git/SPheno-3.3.8/\n"
+      "0.1.8.9+dirty\n",
+      "~/git/resummino_release\n",
+      "SPheno\n"
      ]
     }
    ],
@@ -32,8 +32,8 @@
     "import hepi.spheno as sp\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "rs.set_path(\"~/git/resummino_ug_to_UX_vNLO\")\n",
-    "sp.set_path(\"~/git/SPheno-3.3.8\")\n",
+    "rs.set_path(\"~/git/resummino_release\")\n",
+    "sp.set_path(\"SPheno\")\n",
     "print (rs.get_path())\n",
     "print (sp.get_path())"
    ]
@@ -46,23 +46,50 @@
    },
    "outputs": [
     {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: './input/LesHouches.in'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_12727/649120515.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhspace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mpdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnlopdf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"CT14lo\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"CT14lo\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0mli\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mhepi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhepi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOrder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLO\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m13000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1000022\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"LesHouches.in\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnlopdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"test\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mli\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhepi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslha_scan_rel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mli\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"EXTPAR\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m510\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"EXTPAR\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m470.\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m530.\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mli\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/hepi/input.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, order, energy, particle1, particle2, slha, pdf_lo, pdf_nlo, mu_f, mu_r, pdfset_lo, pdfset_nlo, precision, max_iters, invariant_mass, result, pt, id, model_path, update)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m             \u001b[0mupdate_slha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate_slha\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mInput\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/hepi/input.py\u001b[0m in \u001b[0;36mupdate_slha\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \"\"\"\n\u001b[0;32m--> 163\u001b[0;31m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpyslha\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_input_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mignorenomass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         i.mu = (abs(b.blocks[\"MASS\"][abs(i.particle1)]) +\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pyslha.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(spcfile, **kwargs)\u001b[0m\n\u001b[1;32m   1546\u001b[0m     \u001b[0mOther\u001b[0m \u001b[0mkeyword\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0mare\u001b[0m \u001b[0mpassed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mreadSLHA\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mreadISAWIG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1547\u001b[0m     \"\"\"\n\u001b[0;32m-> 1548\u001b[0;31m     \u001b[0mtxt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspcfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1549\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspcfile\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mspcfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".isa\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1550\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mreadISAWIG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtxt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pyslha.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(f)\u001b[0m\n\u001b[1;32m    235\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mff\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    238\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './input/LesHouches.in'"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.8/site-packages/hepi/input.py:283: RuntimeWarning: Could not set new central scale to average of masses.\n",
+      "  warnings.warn(\"Could not set new central scale to average of masses.\",\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 16 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.8/site-packages/hepi/input.py:283: RuntimeWarning: Could not set new central scale to average of masses.\n",
+      "  warnings.warn(\"Could not set new central scale to average of masses.\",\n"
      ]
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 16 jobs\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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IiIiIiAcUzEVEREREPKBgLiIiIiLiAQVzEREREREPKJiLiIiIiHhAwVxERERExAMK5iIiIiIiHlAwFxERERHxgIK5iIiIiIgHFMxFRERERDygYC4iIiIi4gEFcxERERERDyiYi4iIiIh4QMFcRERERMQDCuYiIiIiIh5IincBIiIiIiKVysvLyc/Pp6SkpEnP07lzZ1avXt1k/aekpJCenk5ycnKDj1EwFxERERFv5Ofn06lTJ/r374+ZNdl5ioqK6NSpU5P07ZyjoKCA/Px8BgwY0ODjNJVFRERERLxRUlJCWlpak4bypmZmpKWlHfOov4K5iIiIiHilJYfySsfzHBTMRUREREQ8oGAuIiIiIuIBBXMREREREQ+06ruymNllwDeAE4BHnHNvxbciEREREWlJJk6cSE5ODoWFhdx7773ccccdBAIBxo8fzzXXXBPTczXLiLmZ/ZuZfWpmK81stpmlHGc/j5rZLjNbWcu2i8xsrZltMLN7AJxzLzrnbgamAlc17lmIiIiISFuyZ88ezj//fAKBACUlJcydO5dJkyYxc+ZM5s6dG/PzNfmIuZn1Ae4AhjnnDpnZM8DVwONR+/QADjnniqLaMpxzG2p09zjwZ+DJGudIBB4ELgDygYVmNtc5tyqyy08i20VEREREGqRbt27ceeedVev33XcfI0aMACAxMTHm52uuOeZJQHszSwJSgc9rbD8beNHM2gGY2c3An2p24px7H9hTS/85wAbn3CbnXBkwB7jUwu4HXnfOLYnd0xERERGR1urAgQP06dOHH/3oRwAsXLiQrKws0tPTyc/PByAUCsX8vE0ezJ1z24HfAZ8BXwD7as71ds49C7wJPG1m1wA3At86htP0AbZFredH2r4PnA9MMrOptR1oZhPNbMa+ffuO4XQiIiIi4ovFWwt58L0NLN5aGJP+OnTowPLly5k9ezYHDx7kxhtv5PHHH+fyyy/n+eef57bbbmPixIkxOVe05pjK0hW4FBgA7AWeNbPvOOf+Gr2fc+43ZjYHeAg4xTlX3NhzO+f+CPzxKPu8DLycnZ19c2PPJyIiIiLNa/HWQq55eD5lFSECSQnMumkcY/p1bXS/aWlppKamMmXKFK699lqysrIAeOyxxxrdd12aYyrL+cBm59xu51w58ALwlZo7mdlZwHDg78C9x3iO7UDfqPX0SJuIiIiItGLzNxVQVhEi5KC8IsT8TQUx6zszM5MdO3Zw1113xazP+jRHMP8MGGdmqRb+bNLzgNXRO5jZKGAG4ZH17wJpZvbLYzjHQmCQmQ0wswDhN5fG/q2yIiIiIuKVcQPTCCQlkGiQnJTAuIFpMel39+7dvPfee1xxxRUkJIQj86ZNm5gyZQqTJk2KyTlqao455p8AzwFLgBWRc86osVsqcKVzbqNzLgRcB2yt2ZeZzQY+BgabWb6ZTYmcowK4nfA89dXAM865T5voKYmIiIiIJ8b068qsm8bxgwsHx2waC8CUKVM499xzWbZsWVXbwIEDeeSRR2LSf22a5QOGnHP3Us/0FOfchzXWy4GZtew3uZ4+XgNea0SZIiIiItICjenXNWaBHGD69Om0b9+e+++/nwkTJsSs36NprtslioiIiIh4b/369TzwwANMmzaNvn370rt3by688MJmObeCuYiIiIhIxKBBg1izZg1du4ZH4OfNm8dbb4Xv9F1QUMDUqVNZunQp9913X8zP3SxTWUREREREWrq0tDSmTZvWZP1rxFxERERExAMK5iIiIiIiHlAwFxERERHxgIK5iIiIiIgHFMxFRERERDygYC4iIiIi4gEFcxERERERDyiYi4iIiIh4QMFcRERERMQD+uRPEREREZE6TJw4kZycHAoLC7n33nu54447CAQCjB8/nmuuuSam59KIuYiIiIhILfbs2cP5559PIBCgpKSEuXPnMmnSJGbOnMncuXNjfj6NmIuIiIiI1KJbt27ceeedVev33XcfI0aMACAxMTHm59OIuYiIiIi0bNsWwAf/G36MgQMHDtCnTx9+9KMfAbBw4UKysrJIT08nPz8fgFAoFJNzRdOIuYiIiIi0XNsWwBOXQLAMEgNw/Vzom9OoLjt06MDy5cvJzs7mJz/5CTfeeCNPPfUUgwYN4vbbb+fVV19l4sSJMXoChymYi4iIiEjLteWDcCh3wfDjlg8aHcwB0tLSSE1NZcqUKVx77bVkZWUB8NhjjzW677poKouIiIiItFz9zwqPlFti+LH/WTHrOjMzkx07dnDXXXfFrM/6aMRcRERERFquvjnh6StbPgiH8hiMlgPs3r2b9957j5/85CckJITHsl988UVeffVV9u/fz5QpU7jwwgtjcq5KCuYiIiIi0rL1zYlZIK80ZcoUzj33XJYtW1bVdtlll3HZZZdRWFjIXXfdFfNgrqksIiIiIiJRpk+fTvv27bn//vv5+OOPj9j+y1/+ku9973sxP6+CuYiIiIhIxPr163nggQeYNm0affv2pXfv3lUj48457r77biZMmMDo0aNjfm5NZRERERERiRg0aBBr1qypWp83b17V8p/+9Cfefvtt9u3bx4YNG5g6dWpMz61gLiIiIiLSAHfccQd33HFHk/WvqSwiIiIiIh5QMBcRERER8YCCuYiIiIiIBxTMRUREREQ80Krf/GlmlwHfAE4AHnHOvRXfikREREREatfkI+ZmNtjM8qK+9pvZ/zvOvh41s11mtrKWbReZ2Voz22Bm9wA45150zt0MTAWuatQTERERERFpQk0ezJ1za51zWc65LGAMcBD4e/Q+ZtbDzDrVaMuopbvHgYtqNppZIvAgMAEYBkw2s2FRu/wksl1ERERExEvNPcf8PGCjc25rjfazgRfNrB2Amd0M/Knmwc6594E9tfSbA2xwzm1yzpUBc4BLLex+4HXn3JLaCjKziWY2Y9++fcf/rEREREREGqm555hfDcyu2eice9bMBgBPm9mzwI3ABcfQbx9gW9R6PnA68H3gfKCzmWU456bVcu6XgZezs7NvPobziYiIiEgbMHHiRHJycigsLOTee+/ljjvuIBAIMH78eK655pqYnqvZRszNLABcAjxb23bn3G+AEuAh4BLnXHFjz+mc+6NzboxzbmptoVxEREREpC579uzh/PPPJxAIUFJSwty5c5k0aRIzZ85k7ty5MT9fc46YTwCWOOd21rbRzM4ChhOef34vcPsx9L0d6Bu1nh5pExERERE5Lt26dePOO++sWr/vvvsYMWIEAImJiTE/31FHzM3sPDM7MQbnmkwt01gi5xgFzAAuBb4LpJnZL4+h74XAIDMbEBmZvxqI/a8xIiIiItLqHThwgD59+vCjH/0IgIULF5KVlUV6ejr5+fkAhEKhmJ+3IVNZ5gErzOxzM3vLzP7XzK43s9GVb9Y8GjPrQHjO+At17JIKXOmc2+icCwHXATXfIIqZzQY+BgabWb6ZTQFwzlUQHmF/E1gNPOOc+7QhtYmIiIhIy5a3K4+HVzxM3q68mPTXoUMHli9fzuzZszl48CA33ngjjz/+OJdffjnPP/88t912GxMnTozJuaI1ZCrL94EpwDPAR8Bgwrc9vAEYCvQ6WgfOuQNAWj3bP6yxXg7MrGW/yfX08Rrw2tFqEREREZHWI29XHje/dTNlwTICiQFmXjiTrB5Zje43LS2N1NRUpkyZwrXXXktWVrjPxx57rNF91+WoI+bOuQeBrwIO+D+gHLjTOXeOc+6ooVxEREREpKks2rmIsmAZIUKUh8pZtHNRzPrOzMxkx44d3HXXXTHrsz4NuiuLc+6Qc+5+4BwgA1hgZqc3aWUiIiIiIkeR3TObQGKAREskOSGZ7J7ZMel39+7dvPfee1xxxRUkJIQj8+rVq5k6dSqTJk3ioYceisl5oh11KouZfQ0YEvkaCvQAiqhnaoqIiIiISHPI6pHFzAtnsmjnIrJ7ZsdkGgvAlClTOPfcc1m2bFlV29ChQ5k2bRqhUIjrrruO2267LSbnqtSQOea5QB7hT9P8o3NuS0wrEBERERFphKweWTEL5ADTp0+nffv23H///UyYMKHatrlz5/LQQw9x7bXXxux8lRoyleU24EPgG8AnZrbKzJ42s5+Y2WUxr0hEREREJE7Wr1/PAw88wLRp0+jbty+9e/fmwgsvrNp+ySWX8PrrrzNr1qyYn/uoI+bOuenR62aWDowAMoErgBdjXpWIiIiISBwMGjSINWvWVK3Pmzevajk3N5cXXniB0tJSLr744pif+5g/+dM5lw/kA6/HvBoREREREU+NHz+e8ePHN1n/Dbori4iIiIiINC0FcxERERERDyiYi4iIiIh4QMFcRERERMQDCuYiIiIiIh5QMBcRERER8YCCuYiIiIiIBxTMRUREREQ8oGAuIiIiIuKBY/7kTxERERGRtmjixInk5ORQWFjIvffeyx133EEgEGD8+PFcc801je5fI+YiIiIiIkexZ88ezj//fAKBACUlJcydO5dJkyYxc+ZM5s6dG5NzaMRcREREROQounXrxp133lm1ft999zFixAgAEhMTY3IOjZiLiIiIiNThwIED9OnThx/96EcALFy4kKysLNLT08nPzwcgFArF5FwK5iIiIiLSouXtyuPhFQ+Ttysv5n136NCB5cuXM3v2bA4ePMiNN97I448/zuWXX87zzz/PbbfdxsSJE2NyLk1lEREREZEWK29XHje/dTNlwTICiQFmXjiTrB5ZMT1HWloaqampTJkyhWuvvZasrHD/jz32WEzPoxFzEREREWmxFu1cRFmwjBAhykPlLNq5qNF9Pvjgg2RlZZGVlcXnn38OQGZmJjt27OCuu+5qdP91UTAXERERkRYru2c2gcQAiZZIckIy2T2zG93n9773PfLy8sjLy+Okk05i9+7dvPfee1xxxRUkJITj8+rVq5k6dSqTJk3ioYceavQ5QVNZRERERKQFy+qRxcwLZ7Jo5yKye2bHfBoLwJQpUzj33HNZtmxZVdvQoUOZNm0aoVCI6667jttuu63R51EwFxEREZEWLatHVpMEcoDp06fTvn177r//fiZMmFBt29y5c3nooYe49tprY3IuTWUREREREanF+vXreeCBB5g2bRp9+/ald+/eXHjhhVXbL7nkEl5//XVmzZoVk/NpxFxEREREpBaDBg1izZo1Vevz5s2rWs7NzeWFF16gtLSUiy++OCbnUzAXERERETlG48ePZ/z48THtU1NZREREREQ8oGAuIiIiIuIBBXMREREREQ8omIuIiIiIeEDBXERERETEA636rixmdhnwDeAE4BHn3FvxrUhEREREpHbNMmJuZl3M7DkzW2Nmq83sjOPs51Ez22VmK2vZdpGZrTWzDWZ2D4Bz7kXn3M3AVOCqxj0LEREREZGm01xTWf4AvOGcGwKMBFZHbzSzHmbWqUZbRi39PA5cVLPRzBKBB4EJwDBgspkNi9rlJ5HtIiIiIiJeavJgbmadga8BjwA458qcc3tr7HY28KKZtYscczPwp5p9OefeB/bUcpocYINzbpNzrgyYA1xqYfcDrzvnlsTqOYmIiIiIxFpzzDEfAOwGHjOzkcBi4E7n3IHKHZxzz5rZAOBpM3sWuBG44BjO0QfYFrWeD5wOfB84H+hsZhnOuWk1DzSzicDEjIzaBuhFREREpC2bOHEiOTk5FBYWcu+993LHHXcQCAQYP34811xzTUzP1RxTWZKA0cBDzrlRwAHgnpo7Oed+A5QADwGXOOeKG3ti59wfnXNjnHNTawvlkX1eds7d0rlz58aeTkRERERakT179nD++ecTCAQoKSlh7ty5TJo0iZkzZzJ37tyYn685RszzgXzn3CeR9eeoJZib2VnAcODvwL3A7cdwju1A36j19EibiIiIiMhx6datG3feeWfV+n333ceIESMASExMjPn5mnzE3Dm3A9hmZoMjTecBq6L3MbNRwAzgUuC7QJqZ/fIYTrMQGGRmA8wsAFwNxP7XGBERERFp9Q4cOECfPn340Y9+BMDChQvJysoiPT2d/Px8AEKhUMzP21x3Zfk+MMvMlgNZwP/U2J4KXOmc2+icCwHXAVtrdmJms4GPgcFmlm9mUwCccxWER9jfJHzHl2ecc5821ZMREREREX8cXLqUL6fP4ODSpTHpr0OHDixfvpzZs2dz8OBBbrzxRh5//HEuv/xynn/+eW677TYmTpwYk3NFa5YPGHLO5QHZ9Wz/sMZ6OTCzlv0m19PHa8Brx1+liIiIiLQ0B5cu5bPv3ogrK8MCAU5+7FFSR41qdL9paWmkpqYyZcoUrr32WrKysgB47LHHGt13XZprxFxEREREJOYOLliIKyuDUAhXXs7BBQtj1ndmZiY7duzgrrvuilmf9VEwFxEREZEWKzVnLBYIQGIilpxMas7YmPS7e/du3nvvPa644goSEg5H5gMHDpCdnc0rr7wSk/NEa5apLCIiIiIiTSF11ChOfuxRDi5YSGrO2JhMYwGYMmUK5557LsuWLavWfv/993PllVfG5Bw1KZiLiIiISIuWOmpUzAI5wPTp02nfvj33338/EyZMqGqfN28ew4YNo6SkJGbniqZgLiIiIiISsX79eh544AE+/vhjunbtSu/evbnwwgt56623yM3N5cCBA6xatYr27dtz8cUXV5vm0lgK5iIiIiIiEYMGDWLNmjVV6/Pmzata/tWvfgXA448/Tvfu3WMaykHBXERERETkmNxwww1N0q/uyiIiIiIi4gEFcxERERERDyiYi4iIiIh4QMFcRERERLzinIt3CY12PM9BwVxEREREvJGSkkJBQUGLDufOOQoKCkhJSTmm43RXFhERERHxRnp6Ovn5+ezevbtJz1NSUnLMwflYpKSkkJ6efkzHKJiLiIiIiDeSk5MZMGBAk58nNzeXUTH8tNBY0FQWEREREREPKJiLiIiIiHhAwVxERERExAMK5iIiIiIiHlAwFxERERHxgIK5iIiIiIgHdLtEEREREWn9guVQWhT++uxjBm54DU5Jhb458a6sioK5iIiIiPgpOkyXFkFZcS3LxVC6v8Z6EZQVVV8Pllbrui/AE2/B9XO9CecK5iIiIiISO1s/go3vQK+R0G1gTMN0nZLaQ7tO0K5j+DHQCU5Ij1rvCO1OCK9v/QhWv4IRgmAZbPlAwVxEREREPFHfyHTVekPC9L5wXw1xLGG6aj3SFuh4+NhAJ0g8hkjbZwysn0eoopSExAD0P+v4XrMmoGAuIiIi0pJsWxAe5T35DDhxSAzCdAxHpneths/mAw4wGH4FZE1ufJiOpb45cP1ctrz7JAPPvc6b0XJQMBcRERFpPo0dmT7wJRR9cWznPK6R6U41lhsYprctgCcuCU8RSQzA6bd6FXyr9M3hs34HGehZbQrmIiIiIpUqR6P7n3U4UFaU1TFP+mhvSAyH6dP37oYFFbEZmXYOinZQNSI96EIYdknjwnQsRUajj3gNpUEUzEVERKT1OZ4wvS8fti0EQoBBSmcoP9ToML0/1JX2J2fUPzIdPYe6vjBdc0T6a3f5F3775vhXUwuhYC4iIiLNp7YR6UqNHJlu9Jzp8kOEQ3lE2inQ76uNDtOrc3PpOX78cb1cR9CIdKumYC4iIiLH71jC9J5NsO5NcEEgAboNCC835a3xjiVM1xyNvujXfgZfjUi3WgrmIiIircW2BZy89TnYdpRPMzyWMF3tDYmNHJlOSIiEcoAQJCRC79HHEKYjj001Z1qj0RJnCuYiIiItwdHC9K5VsPBhBoQq4NG/wYCzwqO+pcXN86EtDQnTNUekL33Qv/Cr0WiJIwVzERGRhqhvbnRdjmtkuqhRYdogPCr9xXLo0rfxYTqWNCItUi8FcxERkWi1hen8RfD2zyBUAQlJMHIypJwQszBNcmrUpxoe58j0l+th9mRCwTISktrBt5/2M/hqRFqkTgrmIiLih+MZka7UmJHpqnnTDQzToXJY+mRswnQsR6a79IUbXvby0wxFpGEUzEVEJD6iw/RnH8Pc70OwIvyGwK/cDh1OjF2YrlRbmO7cgDC9dxu8/sPwpzYmBuC6F6HfGU368hwXTz/NUEQaRsFcREQarsbI9An7VsP68tiOTIdC8M8HDq9XhemoTzJsSJiO9ch0z2GaGy0iTUrBXESkNXMOKkph8/uw5X3oMQy69o+aD118ZKA+hjA9GmBpLec91jBdtANy7w/P4U5MhkmPQv8zm/bWeMdKc6NFpIl58r+diIhUcQ7KDlQPyUe8wTC6vb62onDYbYiqW+M1fGR62drNjBz71diMTA/4mkakRaRNUzAXEYmFrR/Dxnegd1b4Y7zr/GCW4gaE7GLANeCkVv1TDCuXO/aoPlr9RR5szI30mQBZk2HMd6sfc5xhunB3buxCtEakRaSNUzAXkbancnpH2YFwCC47cPTl+kJ2yb7wB6Y0REJy9RHpdh2hfVfocnL1tpoj0DWPadcpPF3E7Ojn3LYAts4//KEuY26AvmMb9RKKiEjsKZiLiL9CIag4BFs/YsDGv0Hal5CWAeWHjiFU17Fe9bHgDVDrbfH6HA7Mu9fAZ/MJj0gbDL88fJ/rmse06whJ7Zrq1aqbPtRFRKRFUDAXkYbdPzpYEX7jX0XkK1gavkNHRUl4JLaitPpysAzKD0J5SeTxUPixouTwcvmhGl+RtorIekVJ1en7AWx7of7nYQnhABzoEPXVETr2rL4e6ACB1KjlDnUvJ6eGb993tNcv+mPGT5/qX/jVNBEREe+16mBuZpcB3wBOAB5xzr0V34pq2LaAk7c+B9tS/f2Bebwf+OFc1KOr3sZRttW6Xx3b8hfDZx/ByWfASaNqqeH4+m9Xsjt832IXivpyNdZrftWxfdcq2LEceg6H7oOOv5/Kr1Aw8lUeflNfsCL8GKqItAUj7ZHt0fvWtv+hQtjxKRACDDr3ARKODOEu1KBLX6/kVEhuf/gxKSUyGt0hfM/q5PaQnFJ9v/yFsOEdqkajM688PBpdM0gntWvY1I5Y65vDmq//lcJV79J12LkM8fD7efHWQuZvKmDcwDTG9Osa73Jq5XuNvtcH4Rpf2VhGpwGFXtfo8+voe33gf42+1wf+fq+YqwolTXgSsy1AERAEKpxz2cfZz6PAN4FdzrnhNbZdBPwBSAQeds79OmpbV+B3zrkpdfWdnZ3tFi1adDxlHZ9tC+CxCbhQBYZBh+7hkbamCK+ujv2O2n8oHO4qJSQdvQ+JH0sMX6OEpPCb+CqXE5LDI76JyVFtUV9FX8DerYf76XEa9M4M/3tMahf+Sqx8DITDdFLgcFvV9lraktsfDtjHG5q3LSD0+ERcsAxLDJBww8u1/pLonCMYcgSdIxSCYGQ9VNXmqAiF25yDkHM4Io+R75FQ5J++I9yHI7ytsi36uHB7eHnNF0X84pVPKQ86khKNH108lEE9OoX7idrPucP91dZPZXu4msPPq/rzDNdSuVztscYxletbvjzAjA82EQw6EhONm88cyMlpqTWOdbX2RVRfh893uG3D+g2ckpFx7DVX7578woM8vXAbwZAjMcG4Mrsvfbq2r3rNajvG1fP/Tn0/2ur936qOAz/fV8Lfl+YTDEFiAvzLqHR6d05p0Pmaqtaax+3YX8LLy7ZX1Tgx8yR6RtXYkE7rP1/jn8PO/SW8vvKLqhonDO9Nj0611Mjxv2ZHU9/z2FlUyrxVO6rqu2BYL3p0anfUcx6t1s8//5yTTjqpluPqq7P29i+LS3l3zc6qGs8d0pPuHeubIhebn88Nfc2/LC7lvTW7CDpINDhnSI9q9cUidtb3ejfEl8Vl/GNtuMaU5ARm3TSuWcO5mS2uKws3ZzDPds59Wcf2HsAh51xRVFuGc25Djf2+BhQDT0YHczNLBNYBFwD5wEJgsnNuVWT7/wKznHNL6qqx2YP5B/8L7/yCqlHA3iOhV+VTioSXqhBj1ZerttWyX33bat2PI/erXM9fGL7TRGWN/b4CfU+P0bmpZ1sD+9/8D1g/73B9gy6EU86t59wc2Ucd/a9du5bBQ4aGp0ZU+7Ja2urZZ+VzsOSvhEejEyD7esj6zrH3U/mFRQJ1JIRXhm1LhIQEjksk+BIsxyUmU/GdFynplU1peZDSihClFUFKykPh5ai28HqIsmCIimCIipCLLDvKgyHKI48VwRBlQUdFMBRuDznKK8L7l1e2RbZX7lcZsIMhR0l5kJMPrGRcwmrmh4ayLjAMIxy8K6KCdzP8VyZtRG2/P9b892W17GdH+cWzvq31HWr1HRm1KRj5xbNSUoKRmFD7scd7vvqPq2db5MCyivD/GZUCiQm0S6rn/67jPF/0OWvfVnt7SXn4/7tK7ZMTSUk+XF+9fdZTS3l5GYFA4JiPrO10B0srOFB2+D0yHQKJpLarfwJErP6O2JCxlQOlQYpLD9+itWO7JDq0qz4dsN5/0zGspS7FpRUUlYRrTDT4wYWD+d45GY2uqaHqC+a+TGU5G5hqZhc750rN7GbgcmBC9E7OuffNrH8tx+cAG5xzmwDMbA5wqZmtBn4NvF5fKI+L/mdBUgqhilISktrBxb/1bzpLzXmz5//Mrxr75sDmDw7X97W7YlbfF0W5DB49vvEdJbeH5c8ernHktyH9uP5gRDDkOFhWwcGyIAdLghwsq+BQ2aHwelmQQ+XhbYci6+Hl8H/g4bYKSspDlFQEKS0/HK6LSysYeOiecPAtHcqS6XuAxs36MoPkxAQCiQkkJRrJiQkkJxjJSQkkJUTWo7alJCeQnJJEUkICyYnhMJGYYCSasW5nEUuKT2VJ8FQMyOrZkay+XUg0IzExvE9igpFgh48LL3NEW1KCkRBZTrDw9sr/3CuXjfC2cHvlcvjHSEJC5AdK5bGR57phVzH/89pqgpER8598cxhDep1Qdz+1nbdGn5FXsur1PLx2uJ/q26rvG30tVmzfx/+bs5TyCkdykvGnyaMZkd75iGNqnLbW7ZXBpHLfDz/8kDPP/OrhH7Q1fudtaM1LPivku48tqKrxiRtPZ/TJXau2R/dTvV/qdLSgfCwWby3kmofnU14RIjmp+UfYGqKyxrLyEIE4jAI2hO+vY836/nrT6TGpLzc3l/Hjxze+QI6s8ckpsakxVmrW98SNOV7VB9W/V5KTEhg3MC3eJVVprhHzzUAh4aHN6c65GbXs80PgK8CzwO3ABc654lr26w+8UmPEfBJwkXPupsj6tcDphEfRryc8gp7nnJtWS38TgYkZGRk3r1+/vrFP9dhsW8Cmd59k4LnX+RV4ox3vHPPm0kT1NfY/0VDIcaCsggOlQdYtfoeS9f+gLP0rJPcfx4HSCg6UVlBcGow8htcPVIbuqCB9qCzIwfJwW1nFsc3xDiQm0D6QSGog8fBjciIpyYm0S0qgXeRxw65ilufvA8Jh56xB3Tl7cA9SkhNolxTZNynhiONSkhMJJIWDdHJCQrXQXdco3fFoCWED/J9T2VT1xTpwtMXXMJYWby1k9tsLmXz+WK9r9Pl1bIr6Yvl9Am3zNYy1eH6v+DCVpY9zbntkyso84PvOufdr2W8OcDFwinNudx199aeBwdw5d3tDa2z2qSwRsf5mlePjnKO0IhQJzUFyP/yYoZmjKC45HJyLI9sOlEWF6Uh75bbK9oNlDbsVX4JBh3ZJkT/1JVWF59RAIqmBpOrBOjmparlDu0TaR9YPB++kqH0TSUps2NQW30ewoGWEjbZK/4f5R9fEP7omforXdYn7VBbn3PbI4y4z+zvhqSfVgrmZnQUMB/4O3Et41LyhtgN9o9bTI23Sys3fVMAH63czrHdn+qWlUlRSGZTLq5YP1AjNxaW1t1eEavyS+tHHtZ4zJTmhKkh3CIRD9Ykd29E/LYlOKeG2yrC9cMse5q3aWfl5i3znjH7c+NUBVdtTkhNi+uf24zGmX1dm3TTO69GNMf26UnRKwMvaREREYqXJg7mZdQASnHNFkeULgZ/X2GcUMIPwHVc2A7PM7JfOuZ808DQLgUFmNoBwIL8a+HasnoPEXnkwVDUavb+kvGq5qKSCotIKiksqKCopD4fokgr2l4TDduU+4bZyyoNH/4tPzVHpjpGvHp3a1dreoV0SW9av4fQxI6tvD4TfwNLQkWiA0f268v763VWj0Zdm9aF/9w6NeemaxJh+XRV6RURE4qw5Rsx7An+PjAomAX9zzr1RY59U4Ern3EYAM7sOuKFmR2Y2GxgPdDezfOBe59wjzrkKM7sdeJPw7RIfdc592kTPp02pOU+sIhjiQGmQoqgR6cqQXFwVqKNCdmV71Xo4YEe/670uiQlGx3bhUejKxx6dUhjYPYmOKUls2FnMwi17Ku/JwqVZJ3Fldl86pSTTMSUcoju1Sz6uUencog2cNejE43vRorSE0WgRERHxQ5MH88idUkYeZZ8Pa6yXAzNr2W9yPX28Brx2nGW2esHImxErR5uLS8vDo9BR4bqopDxqtLqCz/cdYsX2fVW3CUtJSqCkAW9ATDAiQTo5PAqdkkS3DgFO7pZKp5So9kjYDgfv5PBjShKdIsceLVDXnBt97Rn9vQy+Go0WERGRhvDldoltUkM+dSoUchwsD4ZHniPTPKLDdVG1EeryGiPThwP3gQa8GdEMOgbC4bhjuyQOlFVUhXIDhvfpzJmDutMpJZlOkcAdPZpdGbhTA4nNMm9ao9EiIiLSmiiYx8nirYVcPeNjyoOOv2/4iNMHppGcmFBtXnVRSQXFUeG4Ph0CiVWBulNKePS5d+eUqtHojilJnBDZ3jEqRHeKCtcdAkkkRN3mruaI9H9ePNS78KvRaBEREWktFMzjZP6mgqo3LgYdrNlRRN+u7ekYmUddGbJPSKkcwU6uGqHuVCNcd2yXFNP7RlfSiLSIiIhI81Ewj5NxA9Nol2SUVzgCyQnMvC7by+CrEWkRERGR5tHw+75JTI3p15W/3XwGlw9K9vIDXURERESkeWnEPI70oSkiIiIiUkkj5iIiIiIiHlAwFxERERHxgIK5iIiIiIgHFMxFRERERDygYC4iIiIi4gEFcxERERERD+h2iRFbtmwhOzu72c974MABOnTo0OznlfrpuvhH18RPui7+0TXxj66Jn+J4XUbXtUHBPKJ///4sWrSo2c+bm5vL+PHjm/28Uj9dF//omvhJ18U/uib+0TXxU7yui5ktqWubprKIiIiIiHhAwVxERERExAMK5iIiIiIiHtAc83qUl5eTn59PSUlJk52jc+fOrF69usn6ryklJYX09HSSk5Ob7ZwiIiIicnTeBnMzexT4JrDLOTe8lu0G/AG4GDgI3OCcWxLZdj3wk8iuv3TOPXE8NeTn59OpUyf69+9P+HSxV1RURKdOnZqk75qccxQUFJCfn8+AAQOa5ZwiIiIi0jA+T2V5HLionu0TgEGRr1uAhwDMrBtwL3A6kAPca2Zdj6eAkpIS0tLSmiyUNzczIy0trUn/AiAiIiIix8fbYO6cex/YU88ulwJPurD5QBcz6w18HZjnnNvjnCsE5lF/wK9XawnllVrb8xERkcMWby3klY1lLN5aGO9S6rR4ayEPvrfB2xp9rw/8r9H3+sDf7xVvp7I0QB9gW9R6fqStrnYREfHY4q2FzN9UwLiBaYzpd1x/6GxSLaG+ax6eT2l5iFe2zGfWTeO8q7OyxrKKEIGkBO9q9L0+8L9G3+sDv79XWnIwbzQzu4XwNBh69uxJbm5ute2dO3emqKioSWsIBoNNfo6aSkpKjniuUl1xcbFeI8/omjTOhsIga/YEGdItkYyuiTHrN1bXZUNhkN8sLKE8BMkJ8MOxKTGts7F8rw/glY1llJaHcEBZeYjZby+k6JRAvMuqxvcam6q+WP7/1VZfw1jyucaWHMy3A32j1tMjbduB8TXac2vrwDk3A5gBkJ2d7Wp++tPq1aub/I2ZDXnz5/Tp05k6dSqrVq1i6NChAAwdOpTXXnvtuN7EmZKSwqhRo46r3rZCn9LmH12T47d4ayG/e6dyBCsY09GhWF2XT9/bQIVbiwOCDkq79GP8+IxG9xsrvtcH0GlAIa9smU9ZeYhAcgKTzx/rzShgpcoayytCJCf5V2NT1RfL/7/a6msYSz5/r7TkYD4XuN3M5hB+o+c+59wXZvYm8D9Rb/i8EPjPeBUZCytWrCArK4tXX32VoUOHUlJSws6dO+nfv3+8SxORFmD+pgLKKkKEHJRXhJi/qcCbH0KVxg1MI5CUUPXDfNzAtHiXVI3v9QGM6deVWTeNY/bbC70KGtEqa/R1SpDv9YH/NfpeH/j9veJtMDez2YRHvrubWT7hO60kAzjnpgGvEb5V4gbCt0v8bmTbHjP7BbAw0tXPnXP1vYnUe8uXL+fuu+9m+vTp3HXXXaxatYohQ4bojZwiHvF5/nFLCpW+voa+11dpTL+uFJ0S8LY+CNeo+hrH9xp9rw/8/V7xNpg75yYfZbsDvlfHtkeBR5uirqNpih/Oq1at4tJLL+UXv/gF+/btY8WKFWRmZsakbxFpPN/f7NSSQqWvtYH/9YlIy+dtMG+JmuKH87Zt20hLS6N9+/ZccMEFvPnmmyxfvpwRI0awadMmfvWrX7Fv3z6ee+65I9ZFpHm0hKkiCpUiIv7z9j7mLVFtP5wba8WKFYwYMQKAiy++mFdffbVqxHzgwIE88sgjVfvWXBeR5lE5VSTR8HaqiIiI+E8j5jHUFPM4K0fHAc4++2xuvfVWDh06VNUmIvHXUqaKiIiI3xTMY6gpfjivWLGCK664AoB27dqRmZnJ0qVL6dKlS6P7FpHY0VQRERFpLE1libEx/bryvXMyYvYDetasWVx++eVV6y+99BKfffYZAAUFBUydOpWlS5dy3333HbEuIiIiIi2HRsxbsLS0NKZNm1atrea6iIiIiLQMGjEXEREREfGAgrmIeG/x1kJe2VjG4q2F8S5FRESkySiYi4jXKj8f4Pn15Vzz8HyFcxERabUUzEXEa5WfD+CI3ecDiIiI+EjBXES8Vvn5AAnow3tERKR1UzAXEa9Vfj7A5YOSmXXTON0rXEREWi3dLlFEvDemX1eKTgkolIuISKumEXMREREREQ8omIuIiIiIeEDBvAWYPn06Zsbq1aur2oYOHcrmzZvjWJWIiIiIxJK3wdzMLjKztWa2wczuqWX7A2aWF/laZ2Z7o7YFo7bNbdbCm8CKFSvIysri1VdfBaCkpISdO3fSv3//+BYmIiIiIjHjZTA3s0TgQWACMAyYbGbDovdxzv2bcy7LOZcF/Al4IWrzocptzrlLmqtuALYtgA/+N/wYI8uXL+fuu++uCuarVq1iyJAhmFnMziEiIiIi8eVlMAdygA3OuU3OuTJgDnBpPftPBmY3S2X12bYAnrgE3v1V+DFG4XzVqlVceuml7Nq1i3379rFixQoyMzNj0reIiIiI+MHX2yX2AbZFrecDp9e2o5n1AwYA70Y1p5jZIqAC+LVz7sU6jr0FuAWgZ8+e5ObmVtveuXNnioqKGlx0YO07BIJlmAvigmWUrX2Hsi5D6z0mGAzWe478/Hy6du1KRUUFZ599Ni+++CKLFi1i0KBBLF++nN/97nfs37+fp556ildeeYU333yToqIirr32Ws4777xa+ywpKTniuUp1xcXFeo08o2viJ10X/+ia+EfXxE8+Xhdfg/mxuBp4zjkXjGrr55zbbmYDgXfNbIVzbmPNA51zM4AZANnZ2W78+PHVtq9evZpOnTo1vJLB58H8P0CwDEsM0G7webQ7yvFFRUX1nmPz5s2MHDmSTp06cdlllzFr1iy++OILrrzySjIzM3nyySeZNGkSnTp1YvLkyUyePJnCwkLuuusuLrvsslr7TElJYdSoUQ1/Xm1Qbm4uNf89SHzpmvhJ18U/uib+0TXxk4/Xxddgvh3oG7WeHmmrzdXA96IbnHPbI4+bzCwXGAUcEcxjrm8OXD8XtnwA/c8KrzfS8uXLGTFiBABnn302t956K4cOHapqq80vf/lLvve979W5XURERET84+sc84XAIDMbYGYBwuH7iLurmNkQoCvwcVRbVzNrF1nuDnwVWNUsVUM4jJ/17zEJ5RC+I0tlCG/Xrh2ZmZkEAgG6dOlyxL7OOe6++24mTJjA6NGjY3J+EREREWkeXo6YO+cqzOx24E0gEXjUOfepmf0cWOScqwzpVwNznHMu6vChwHQzCxH+xePXzrnmC+YxNmvWrGrrL730UtVyQUEBP/7xj1m6dCn33XcfHTp04O2332bfvn1s2LCBqVOnNne5IiIiInKcvAzmAM6514DXarT9V431n9Vy3EdA3fM8WpG0tDSmTZtWre2OO+6IUzUiIiIi0hi+TmUREREREWlTFMxFRERERDygYC4iIiIi4gEFcxERERERDyiYi4iIiIh4QMFcRERERMQDCuYiIiIiIh5QMBcRFm8t5MH3NrB4a2G8SxEREWmzvP2AIRFpHou3FnLNw/MpqwgRSEpg1k3jGNOva7zLEhERaXM0Yt4CTJ8+HTNj9erVVW1Dhw5l8+bNcaxKWov5mwooqwgRclBeEWL+poJ4lyQiItImKZi3ACtWrCArK4tXX30VgJKSEnbu3En//v3jW5i0CuMGphFISiDRIDkpgXED0+JdkoiISJukYB5jebvyeHjFw+TtyotZn8uXL+fuu++uCuarVq1iyJAhmFnMziFt15h+XZl10zh+cOFgTWMRERGJI80xj6G8XXnc/NbNlAXLCCQGmHnhTLJ6ZDW631WrVnHppZfyi1/8gn379rFixQoyMzMbX7BIxJh+XRXIRURE4kwj5jG0aOciyoJlhAhRHipn0c5Fje5z27ZtpKWl0b59ey644ALefPNNli9fzogRI9i0aRNTpkxh0qRJAKxevZqpU6cyadIkHnrooUafW0RERESaj4J5DGX3zCaQGCDREklOSCa7Z3aj+1yxYgUjRowA4OKLL+bVV1+tGjEfOHAgjzzySNW+Q4cOZdq0aTzzzDN8+OGHjT63iIiIiDQfTWWJoaweWcy8cCaLdi4iu2d2TKaxVI6OA5x99tnceuutHDp0qKqtprlz5/LQQw9x7bXXNvrcIiIiItJ8vB0xN7OLzGytmW0ws3tq2X6Dme02s7zI101R2643s/WRr+ubs+6sHlncNOKmmIRyqD5i3q5dOzIzMwkEAnTp0qXW/S+55BJef/11Zs2aFZPzi4iIiEjz8HLE3MwSgQeBC4B8YKGZzXXOraqx69POudtrHNsNuBfIBhywOHJsi/xIw5oB+6WXXqpaLigo4Mc//jFLly7lvvvu44wzzuCFF16gtLSUiy++uLlLFREREZFG8DKYAznABufcJgAzmwNcCtQM5rX5OjDPObcncuw84CJgdhPVGjdpaWlMmzatWtv48ePjU4yIiIiINIqvwbwPsC1qPR84vZb9rjCzrwHrgH9zzm2r49g+tZ3EzG4BbgHo2bMnubm51bZ37tyZoqKi43wKDRMMBpv8HDWVlJQc8VyluuLiYr1GntE18ZOui390Tfyja+InH6+Lr8G8IV4GZjvnSs3sVuAJ4Nxj6cA5NwOYAZCdne1qjjavXr2aTp06xabaOhQVFTX5OWpKSUlh1KhRzXrOliY3N1d/ffCMromfdF38o2viH10TP/l4XXx98+d2oG/UenqkrYpzrsA5VxpZfRgY09BjRURERER842swXwgMMrMBZhYArgbmRu9gZr2jVi8BVkeW3wQuNLOuZtYVuDDSJiIiIiLiLS+nsjjnKszsdsKBOhF41Dn3qZn9HFjknJsL3GFmlwAVwB7ghsixe8zsF4TDPcDPK98IKiIiIiLiKy+DOYBz7jXgtRpt/xW1/J/Af9Zx7KPAo01aoIiIiIhIDPk6lUVEREREpE1RMBcRERER8YCCuYiIiIiIBxTMW4Dp06djZqxevbqqbejQoWzevDmOVYmIiIhILCmYtwArVqwgKyuLV199FQh/cufOnTvp379/fAsTERERkZhRMI+xg0uX8uX0GRxcujRmfS5fvpy77767KpivWrWKIUOGYGYxO4eIiIiIxJeCeQwdXLqUz757I7v/8Ac+++6NMQvnq1at4tJLL2XXrl3s27ePFStWkJmZGZO+RURERMQPCuYxdHDBQlxZGYRCuPJyDi5YePSDjmLbtm2kpaXRvn17LrjgAt58802WL1/OiBEj2LRpE1OmTGHSpElV+x84cIDs7GxeeeWVRp9bYmPx1kIefG8Di7cWxrsUERER8ZiCeQyl5ozFAgFITMSSk0nNGdvoPlesWMGIESMAuPjii3n11VerRswHDhzII488Um3/+++/nyuvvLLR55XYWLy1kGsens//vrWWax6er3AuIiIidfL2kz9botRRozj5sUc5uGAhqTljSR01qtF9Vo6OA5x99tnceuutHDp0qKot2rx58xg2bBglJSWNPq/ExvxNBZRVhAg5KK8IMX9TAWP6dY13WSIiIuIhBfMYSx01KiaBvNKKFSu44oorAGjXrh2ZmZksXbqULl26HLFvbm4uBw4cYNWqVbRv356LL76YhAT9USSexg1MI5CUQHlFiOSkBMYNTIt3SSIiIuIpBXPPzZo1q9r6Sy+9VLVcUFDAj3/8Y5YuXcp9993Hr371KwAef/xxunfvrlDugTH9ujLrpnHM31TAuIFpGi0XERGROimYt2BpaWlMmzbtiPYbbrih+YuROo3p11WBXERERI5KQ6oiIiIiIh5QMBcRERER8YCCuYiIiIiIB7wN5mZ2kZmtNbMNZnZPLdt/YGarzGy5mb1jZv2itgXNLC/yNbcxdTjnGnO4d1rb8xERERFpLbwM5maWCDwITACGAZPNbFiN3ZYC2c65TOA54DdR2w4557IiX5ccbx0pKSkUFBS0mjDrnKOgoICUlJR4lyIiIiIiNfh6V5YcYINzbhOAmc0BLgVWVe7gnHsvav/5wHdiXUR6ejr5+fns3r071l1XKSkpadagnJKSQnp6erOdT0REREQaxnwcDTazScBFzrmbIuvXAqc7526vY/8/Azucc7+MrFcAeUAF8Gvn3It1HHcLcAtAz549x8yZMyfGz+ToiouL6dixY7OfV+qn6+IfXRM/6br4R9fEP7omforXdTnnnHMWO+eya9vm64h5g5nZd4Bs4Oyo5n7Oue1mNhB418xWOOc21jzWOTcDmAGQnZ3txo8f3xwlV5Obm0s8zttaLN5a2CQf3qPr4h9dEz/puvhH18Q/uiZ+8vG6+BrMtwN9o9bTI23VmNn5wI+Bs51zpZXtzrntkcdNZpYLjAKOCObSsi3eWsg1D8+nrCJEICmBWTeN0wf5iIiISIvl5Zs/gYXAIDMbYGYB4Gqg2t1VzGwUMB24xDm3K6q9q5m1iyx3B75K1Nx0aT3mbyqgrCJEyEF5RYj5mwriXZKIiIjIcfNyxNw5V2FmtwNvAonAo865T83s58Ai59xc4LdAR+BZMwP4LHIHlqHAdDMLEf7F49fOOQXzVmjcwDQCSQmUV4RITkpg3MC0eJckIiIicty8DOYAzrnXgNdqtP1X1PL5dRz3ETCiaasTH4zp15VZN41rkjnmIiIiIs3N22Au0hBj+nVVIBcREZFWwdc55uKJxVsLefC9DSzeWhjvUkRERERaNY2YS5101xMRERGR5qMRc6mT7noiIiIi0nwUzKVOlXc9STR01xMRERGRJtboqSxm1q2W5iLnXHlj+27tFm8t5JWNZXQaUOjlFBHd9URERESk+cRijvkSwp/SWQgY0AXYYWY7gZudc4tjcI5Wp3L+dml5iFe2zPd2/rbueiIiIiLSPGIxlWUecLFzrrtzLg2YALwC/Cvwlxj03ypVzt92aP62iIiIiMQmmI9zzr1ZueKcews4wzk3H2gXg/5bpcr52wlo/raIiIiIxCaYf2Fmd5tZv8jXD4GdZpYIhGLQf6tUOX/78kHJ3k5jEREREZHmE4s55t8G7gVejKx/GGlLBK6MQf+t1ph+XSk6JaBQLiIiIiKND+bOuS+B79exeUNj+xcRERERaQticbvEU4G7gP7R/Tnnzm1s3yIiIiIibUUsprI8C0wDHgaCMehPRERERKTNiUUwr3DOPRSDfkRERERE2qxY3JXlZTP7VzPrbWbdKr9i0K+IiIiISJsRi2B+PfAfwEfA4sjXosZ2amYXmdlaM9tgZvfUsr2dmT0d2f6JmfWP2vafkfa1Zvb1xtYiIiIiItLUYnFXlgGxKCRa5B7oDwIXAPnAQjOb65xbFbXbFKDQOZdhZlcD9wNXmdkw4GrgNOAk4G0zO9U5p/nvIiIiIuKt4w7mZnauc+5dM7u8tu3OuReOvyxygA3OuU2Rc80BLgWig/mlwM8iy88BfzYzi7TPcc6VApvNbEOkv48bUY+IiIiISJNqzIj52cC7wMRatjmgMcG8D7Ataj0fOL2ufZxzFWa2D0iLtM+vcWyf2k5iZrcAtwD07NmT3NzcRpR8fIqLi+NyXqmfrot/dE38pOviH10T/+ia+MnH63Lcwdw5d2/k8buxK6d5OedmADMAsrOz3fjx45u9htzcXOJxXqmfrot/dE38pOviH10T/+ia+MnH69LoN3+a2VNm1jlqvZ+ZvdPIbrcDfaPW0yNtte5jZklAZ6CggceKiIhnDi5dypfTZ3Bw6dJ4l1Ir3+uDcI2pb7zhfY0+v46+1wf+1+h7feDv90os7mP+T+ATM/sB4Skj/wH8eyP7XAgMMrMBhEP11cC3a+wzl/AdYT4GJgHvOuecmc0F/mZmvyf85s9BwIJG1iMiUq+DS5dycMFCUnPGkjpqVLzLOUJLqO+z796IKyvDAgFOfuxRr+r0vT44XGPH0lI+e+NNr2v09XX0vT7wv0bf6wO/v1caPWLunJsO3AS8BPwc+Jpz7uVG9lkB3A68CawGnnHOfWpmPzezSyK7PQKkRd7c+QPgnsixnwLPEH6j6BvA93y9I4uvv61F8/23Xt/rk9jw/Xul8j/53X/4A59990bv6vS9PoCDCxbiysogFMKVl3NwwcJ4l1SN7/XB4RrNOe9r9PV19L0+8L9G3+uDGt8rZWVe1djoEXMzuxb4KXAdkAm8Zmbfdc4ta0y/zrnXgNdqtP1X1HIJ8K06jv0V8KvGnL+p+fzbWiXff+v1vb5Kvo9Ugt81tojvlVp+EPlUo+/1AaTmjMUCAVx5OZacTGrO2HiXVI3v9cHhGkNlZSR4XqOvr6Pv9UG4RpeUgCsPYYnmXY2+1weQ2r8jzoI4A7Mgqf07xrukKrGYynIFcKZzbhcw28z+DjwBZMWg71artpEN335Q+v7D3Pf6oGX88uB7jS3he8X3H+a+1weQOmoUpb//Tz7/5zxOOvMC/66x5/XB4Ro/nfs0p11yldc1+vo6po4aRelPJ/P5B/M46Sz/6gNYl7ya+68MMWibsb5viLuTV5OFP3X6Xh/AuoRl3D85gUHbYH1fuDthGVlHzJiOj1h8wNBlNdYXmFlOY/tt7TSy0Xi+1wct5JcHz2tsEd8ro0Zx8mOPevtXB9/DEEDerjxu/vy3lPUrI/B5HjN3nUpWj6x4l1XF9/rgcI2lo0pp9/lv/a1x+68p61dOYPti72rMW/k3bt7/JGVZENj/JDNXppM13I/AVmnRpjdZlW6s7JtAonMs2vSmVzX6Xh/AopQUVqUnsLKvhWtMSfFmNPm455ib2Q8jj3+s+QX8LmYVtlKVP8iLL5no3QhlpcoaT7zjDi9r9L0+OBwqSUz09pcH32tsCd8rAOv6GC9+JYF1fSzepRwhb1ceN33+W+7pt4CbPv8tebvy4l3SERbtXERZsIwQIcpD5SzauSjeJVXje31wuEaH87fGtX+PvI5QHixj0dq/x7ukahZtepMyg5AZ5RZe9032wK8TcJDoHMkuvO4T3+sDyB78LwQSAyQAyYkBsgf/S7xLqtKYEfPVkcfFsSikLUodNYqD+/Z5GzQgXKPqO36+j6RCy6hxXR/jxTMSCPQxb0Y1ouXtyuPmt26mLFhGIDHAzAtnejUKWFuo9Kk+gOye2QQSA5SHyklOSCa7Z3a8S6rG9/ogUmNCImWhEMmW4GeNJSUEnKMcSHaO7JKSeJdUTfbArxMoWEw5/obKrOHfZibhXxqyB37du9Fo3+sDyOqRxcyvP8ozHz/DlWdc6dX/h435gKGXI49PxK6ctiVvVx5v7XuLLru6ePWPIlrerjwW7VxEds9sL2v0vT7w/5cH8LvGytBbGixl3lvzvAu94H/wbQmhMqtHFjMvnOnt97Pv9QFklZYx84tdLExOYGx5iKzSsniXdISsIVcwc+ksFgUSyC4LkXX+FfEuqZqWECohXKevtYH/9UH4e3pv573efS8fdzCP3C+8Ts65S+rb3ta1hLDh+yig7/VVagm/PPhcY21/nvetRt+Db0sIlRCu09faIBx8s/bugy7+BV4AtnxA1qGDZB0MgiXClg+gr2dv+eqbQ9bkF8ja8gH0P8u/+mgZoVJar8ZMZTkD2AbMBj4B/JtY6bGWEDZ8HwX0vT5oGb88+F5jZegtC5Z5GXqhZQRf30Ov97YtgCcugWAZJAbg+rn+hcr+Z0FigFBFKQmJgfC6j/rm+PfaiXiiMR8w1Av4ETAc+ANwAfClc+4fzrl/xKK41qwybCSQ4G3YqKwx0RK9rNH3+qBlvWHM1xorQ+83unzDu18aomX1yOKmETd5W5800pYPwqHcBcOPWz6Id0VH6psD189ly4Br/PzFQUSOqjFzzIOEP1nzDTNrB0wGcs3sv51zf45Vga1VZdjw8Y0HlXwfBfS9PvB/igO0jBp9nQsoMbZtQTjw+jjFITIaXTVi7vFo9Gf9DjLQt9dPRBqkUfcxjwTybxAO5f2BPwJ+3fvIYy0hbPj+5++WUJ/vvzy0hBqlDfB9qkhkNNrbXxxEpFVozJs/nyQ8jeU14L+dcytjVpVIK+L7Lw/QMmqUVq62qSK+hV/NjRaRJtaYEfPvAAeAO4E7zKre+2mAc86d0MjaRESkrWgpU0VERJpQY+aYN+aNoyIiIodpqoiISOPmmIuISAvh8xsrK2mqiIi0cQrmIiKtne9vrBQREaBx9zEXEZGWoCXcg1tERPwL5mbWzczmmdn6yGPXWvbJMrOPzexTM1tuZldFbXvczDabWV7kK6tZn4CIiG8q31hpiXpjpYiIx3ycynIP8I5z7tdmdk9k/e4a+xwErnPOrTezk4DFZvamc25vZPt/OOeea76SRUQ8pjdWioi0CD4G80uB8ZHlJ4BcagRz59y6qOXPzWwXcCKwt1kqFBFpafTGShER75lzLt41VGNme51zXSLLBhRWrtexfw7hAH+acy5kZo8DZwClwDvAPc650jqOvQW4BaBnz55j5syZE8Nn0jDFxcV07Nix2c8r9dN18Y+uiZ90Xfyja+IfXRM/xeu6nHPOOYudc9m1bYtLMDezt4FetWz6MfBEdBA3s0Ln3BHzzCPbehMeUb/eOTc/qm0HEABmABudcz8/Wk3Z2dlu0aJFx/hMGi83N5fx48c3+3mlfrou/tE18ZOui390Tfyja+KneF0XM6szmMdlKotz7vy6tpnZTjPr7Zz7IhKyd9Wx3wnAq8CPK0N5pO8vIoulZvYYcFcMSxcRERERaRLe3ZUFmAtcH1m+Hnip5g5mFgD+DjxZ802ekTBfOQ3mMmBlUxYrIgKE7xX+wf+GH0VERI6Dj2/+/DXwjJlNAbYCVwKYWTYw1Tl3U6Tta0Camd0QOe4G51weMMvMTgQMyAOmNmv1ItL26AN8REQkBrwL5s65AuC8WtoXATdFlv8K/LWO489t0gJFRGqq7QN8FMxFROQY+TiVRUSkZdEH+IiISAx4N2IuItLi6AN8REQkBhTMRURiQR/gIyIijaSpLCIiIiIiHlAwFxERERHxgIK5iIiIiIgHFMxFRERERDygYC4iIiIi4gEFcxERERERDyiYi4iIiIh4QMFcRERERMQDCuYiIiIiIh5QMBcRERER8YCCuYiIiIiIBxTMRUREREQ8oGAuIiIiIuIB74K5mXUzs3lmtj7y2LWO/YJmlhf5mhvVPsDMPjGzDWb2tJkFmq96EREREZHj410wB+4B3nHODQLeiazX5pBzLivydUlU+/3AA865DKAQmNK05YpIk9u2gJO3PgfbFsS7EhERkSbjYzC/FHgisvwEcFlDDzQzA84Fnjue40XEQ9sWwBOXMGDzLHjiEoVzERFptcw5F+8aqjGzvc65LpFlAwor12vsVwHkARXAr51zL5pZd2B+ZLQcM+sLvO6cG17HuW4BbgHo2bPnmDlz5sT+CR1FcXExHTt2bPbzSv10Xfxx8tbnGLB5FkaIEAlsGXANn/WbFO+yJELfK/7RNfGPromf4nVdzjnnnMXOuezatiU1dzEAZvY20KuWTT+OXnHOOTOr6zeHfs657WY2EHjXzFYA+46lDufcDGAGQHZ2ths/fvyxHB4Tubm5xOO8Uj9dF49sS4UnniNUUUpCUjsGnnsdA/vmxLsqidD3in90Tfyja+InH69LXIK5c+78uraZ2U4z6+2c+8LMegO76uhje+Rxk5nlAqOA54EuZpbknKsA0oHtMX8CItJ8+ubA9XPZ8u6TDDz3uvC6iIhIK+TjHPO5wPWR5euBl2ruYGZdzaxdZLk78FVglQvPy3kPmFTf8SLSwvTNCU9fUSgXEZFWzMdg/mvgAjNbD5wfWcfMss3s4cg+Q4FFZraMcBD/tXNuVWTb3cAPzGwDkAY80qzVi4iIiIgch7hMZamPc64AOK+W9kXATZHlj4ARdRy/CdCwmoiIiIi0KD6OmIuIiIiItDkK5iIiIiIiHlAwFxERERHxgIK5iIiIiIgHFMxFRERERDygYC4iIiIi4gEFcxERERERDyiYi4iIiIh4QMFcRERERMQDCuYiIiIiIh5QMBcRERER8YCCuYiIiIiIBxTMRUREREQ8oGAuIiIiIuIBBXMREREREQ8omIuIiIiIeMC7YG5m3cxsnpmtjzx2rWWfc8wsL+qrxMwui2x73Mw2R23Lau7nICIiIiJyrLwL5sA9wDvOuUHAO5H1apxz7znnspxzWcC5wEHgrahd/qNyu3MurxlqFhERERFpFB+D+aXAE5HlJ4DLjrL/JOB159zBpixKRERERKQpmXMu3jVUY2Z7nXNdIssGFFau17H/u8DvnXOvRNYfB84ASomMuDvnSus49hbgFoCePXuOmTNnTuyeSAMVFxfTsWPHZj+v1E/XxT+6Jn7SdfGProl/dE38FK/rcs455yx2zmXXti0uwdzM3gZ61bLpx8AT0UHczAqdc0fMM49s6w0sB05yzpVHte0AAsAMYKNz7udHqyk7O9stWrToWJ9Ko+Xm5jJ+/PhmP6/UT9fFP7omftJ18Y+uiX90TfwUr+tiZnUG86TmLgbAOXd+XdvMbKeZ9XbOfREJ2bvq6epK4O+VoTzS9xeRxVIzewy4KyZFi7Rm2xbAlg+g/1nQNyfe1YiIiLRJPs4xnwtcH1m+Hnipnn0nA7OjGyJhvnIazGXAytiXKNKKbFsAT1wC7/4q/LhtQbwrEhERaZN8DOa/Bi4ws/XA+ZF1zCzbzB6u3MnM+gN9gX/UOH6Wma0AVgDdgV82R9EiLdaWDyBYBi4YftzyQbwrEhERaZPiMpWlPs65AuC8WtoXATdFrW8B+tSy37lNWZ9Iq9P/LEgMhEN5YiC8LiIiIs3Ou2AuIs2sbw5cP1dzzEVEROJMwVxEwmFcgVxERCSufJxjLiIiIiLS5iiYi4iIiIh4QMFcRERERMQDCuYiIiIiIh5QMBcRERER8YCCuYiIiIiIBxTMRUREREQ8oGAuIiIiIuIBBXORprZtAXzwv+FHERERkTrokz9FmtK2BfDEJRAsg8QAXD9Xn7ApIiIitdKIuUhT2vJBOJS7YPhxywfxrkhEREQ8pWAu0pT6nxUeKbfE8GP/s+JdkYiIiHhKU1mkZdu2IDwK3f8sP6eI9M0JT1/xuUYRERHxgoK51M/n4NtS5m/3zfGzLhEREfGKd1NZzOxbZvapmYXMLLue/S4ys7VmtsHM7olqH2Bmn0TanzazQPNUfhy2LeDkrc/5e7eOyuD77q/Cj77VqfnbIiIi0op4F8yBlcDlwPt17WBmicCDwARgGDDZzIZFNt8PPOCcywAKgSlNW+5xioTeAZtn+Rl6wf/gq/nbIiIi0op4F8ydc6udc2uPslsOsME5t8k5VwbMAS41MwPOBZ6L7PcEcFmTFdsYkdBrhPwMveB/8K2cv33uj/2dxiIiIiLSQC11jnkfYFvUej5wOpAG7HXOVUS196mrEzO7BbgFoGfPnuTm5jZJsbU5YV8HRloi5hzOElm2pwP7m/H8DXXCiJ/RZe9K9nYZzv6NB2FjbrxLqsUYiHFtxcXFzfrvQY5O18RPui7+0TXxj66Jn3y8LnEJ5mb2NtCrlk0/ds691Fx1OOdmADMAsrOz3fjx45vr1MB4GD2aTe8+ycBzr2O0t6O94+NdQFzk5ubSvP8e5Gh0Tfyk6+IfXRP/6Jr4ycfrEpdg7pw7v5FdbAf6Rq2nR9oKgC5mlhQZNa9s91PfHD7rd5CB3oZyEREREWku3s0xb6CFwKDIHVgCwNXAXOecA94DJkX2ux5othF4EREREZHj5V0wN7N/MbN84AzgVTN7M9J+kpm9BhAZDb8deBNYDTzjnPs00sXdwA/MbAPhOeePNPdzEBERERE5Vt69+dM593fg77W0fw5cHLX+GvBaLfttInzXFhERERGRFsO7EXMRERERkbZIwVxERERExAMK5iIiIiIiHvBujnm8bNmyhezs7GY/74EDB+jQoUOzn1fqp+viH10TP+m6+EfXxD+6Jn6K43UZXdeGNh/MzWwiMDEjI4NFixY1+/l9vLm96Lr4SNfET7ou/tE18Y+uiZ/idV3MbEld29r8VBbn3MvOuVs6d+4c71JEREREpA1r88FcRERERMQHCuYiIiIiIh5o83PM61NeXk5+fj4lJSVNdo7OnTuzevXqJus/WkpKCunp6SQnJzfL+URERESk4RTM65Gfn0+nTp3o378/ZtYk5ygqKqJTp05N0nc05xwFBQXk5+czYMCAJj+fiIiIiBwbTWWpR0lJCWlpaU0WypuTmZGWltako/8iIiIiLcHirYW8srGMxVsL411KNRoxP4qjhfIH5q3jD++sP2o/d543iH+74NRYlXVcWsMvGCIi8bJ4ayHzNxUwbmAaY/p1jXc5taoMG50GFHpZo3OOJVsL+XhTAacPTGP0yV1xzuGqtkcecVXLdbW7SH+Hlzm8oYH7OlzU/mHLt+1j8Wd7GH1yV4b36RxVW901Va5X2zdqv8+LQ2zYVXTEsdHnrdke3XfNbau/2M+ybfvITO/M0N4n1LlfTQ3tv2YXLmrjkduq9wiwdkcRK7fv57STTmBwr05HHHdEHXX0X1+9NQup6zhXY0fnYP2uIu5/fS3lwRCvbJnPrJvGefP9omDeSP92wanVAvdV0z8G4Olbz4hXSSIiXgqFHBUhRzDkKA+FCAYPr1eEQizbtpfFn+1lxEmdObVXx0i7oyIY3l65Hgw6Qi78FQxRtVxtPeQIOkfIhc8b3hYOVsGq5aMf7xwEQ46d+0t4Z80ugiFHohlnDkqja2qAoAvvXxnaQlWPAFFtULUMh/ervt1VhbvD26sfG91HzWMPlQX5Yl8JDnh+/Uec2ClAcmJi9fMRfk2q913ZdvhccHi98ly1BVDqaK81MLd1/3w/3hVILUrKQ1zx0EdV6/EeSG3zwTz6A4ZERFoL5xxlwRAlZSEOlQdZtHUPCzcXMrhXJ/qlpXKoLMih8vBXaXmQsqCjrCJEWUWI8mCIsmB4ufKxPOqxNGq/gr2HaLf0H5RHjq8ZoCsiobsi5LwOaGaQaEaCGQkJkGBGaXmIYC1FB53jH+u+rFpPSU4gvWsqCQaGYRb+C6UBCQm1tFVbNrDKtgTMwm3R+1e1RZajj/1kcwGFB8uPqNEBu4rKqtZP7BjgnCE9jtJ3ZFukXqBqHzh8TOWGyrXIU4haPtwO8MKSfLbvPfo0yvSu7bl6bN9qf909XEcdNUW1V9ZYuc+RtVXvo3LlkQ82saXg4FHrG9C9A7eNP6V6fzXrrOX5A6xevZrThg2r1l+1ujnyOR/eBr+ft471u4qPWuOgHh256+uDqdFF9df0iG1Hr6nmgTX7eHHpdl7M+/yo9f3LqD5cMTr9iHMd0Wc9dVg9ddT2b6dyv6cXbuPZxflHrTHe2nwwd869DLycnZ19cyz6KyopZ39JBYu3xu7PiMuWLeP73/8+X375JWvWrME5x09/+lN+/vOfx6R/EfFDKOTYX1LO/kMVFJWWU1xSQXFp+Kuocrkker28qu1AWZBDZUFKImG7pDwYGbU9dgkGgaQEAokJVY/JUevJkcfUQBLBdkbv7h1JTkogOdFITkggKdFISjASI8uJCUZyjfWkBOOpj7eydc/RA9GQXp24d+JpJEeOTUyIBGirXIaEBDsiWCcmWFXgDi9H7V/j+KNN9Vu8tZBrHp5PeUWI5KQEr/70XamyxrLyEIFkf2r89wsHVy37+DpeO65f1XJT1dd573rGjzzpuI+fMKJ3k9fYGOMH9+D/rh7lbX0A2f278dtvjaxaX7y1kNlvL2Ty+WO9qK9Smw/msbR4ayFrdhQRcnDNw7GZs1RSUsJVV13Fk08+SU5ODj/96U8pKSnhv//7v2NUtYjEQs35x8459h+qYHdxCbuLyviyuJS9B8vYe7CcwoPl4eVD5RRG2vYeLGPfofKjhukEg47tkuiUkkzHdkl0TEmiS2qAk7ok0j6QSPvk8NdrK7/g8waMUmald2bG9dm0S0wMh/CkBBITGv5+lPBHWo9p8P7RbjprYNWyrz/MK43p15VZN43zeo55ZY0+ho1Kvr+OvtcH/tfoe32VxvTrStEpAe/qUzCPofmbCqp+qJZXhJi/qaDRF/ztt99m9OjR5OTkAJCZmckbb7yBmTFx4kRefvnlqn2feOIJPvzwQ5KTk+nSpQu/+tWvGnVuETlSZeDevvcQX+w7xOf7Sli+rZAXln5OMOQwg7QOAfYfqqAsGKq1jw6BRLqkBuiSmkzX1AAndWlP18hy5/bJdG6fTKeUZDqlJFWF706Rx/bJiQ16I/dPvnn4z+a+h15oGT/Mx/Tr6mVd0XwNG9F8fx19rw/8r9H3+nymYB5D4wamkWDhN/0kJyUwbmBao/tcuXIlI0aMqFpfsmQJo0ePZsuWLfTv37+q/cMPP2TVqlXMmDEDgKlTp7J27VoGDx5cs0sROYrSiiCfFRxk05cH2LT7AJ/tOcjnew9VfR0oC9Z5rHPwZfHhub2nnNiBX1w6nLSO7ejaIRy62yUlNsfTqNISQi/oh7mIiIJ5DI3p15UhvTqxv6SCP1w9KiY/YNLS0nj33XcBWLduHS+88AIfffQR//jHPxg9enTVfo888sgRc84DgUCjzy/SmpUHQ2zYVcyqz/ez6ov9rN9VzOYvi8kvPFTtjYrdOgTo06U9A0/swJmDutOnS3tOinz17pzC1oIDXPfoAu9HpH2rSUREqlMwj7Hwn5+TY/YDcPLkycydO5fhw4fTvXt3Zs+eTVpaGosXL+aqq66q2q+8vLzqz9ubN29m165d+oRPkSjOOTbuLmbRlkKWfFbIp5/vZ/3O4qrpJu2SEhjUsyOj+nbl8lHpDDyxAwO6d6B/9w6ckJJcb989T0hpESPSIiLiNwVzz3Xs2LHaPPJKS5YsoaCggKSkJL71rW/xwx/+kH//93+nb9++7Nu3jwcffDAO1Yr4wznHqi/288H6L1m4eQ+LPytkb+S2cl1TkxnepzPfPbM/w3qfwGknnUD/tA4kJR7/hyFrRFpERBpLwbyR6vrkz/73vFptPdY3rH/99dePaJszZ07M+hdpifaXlPPeml38Y91uPlj/JbuLSgEYeGIHLhzWk+x+3RjTvysDu3fQJ+GKiIh3FMwbqeYnf4pI89p3qJx5q3by2oov+GD9bsqDjq6pyZw56ETOPvVEvjaoOz1OSIl3mSIiIkfV5oO5PvlTpOWpCIZ4f/1unl64jXfX7KI86OjTpT3Xn9GfCSN6k9W3yzHdi1tERMQHbT6Yx/qTP0Uk9hZvLeSVjWVU9NjJsvy9PLsonx37S0jrEOD6M/rzjcxwGNf0FBERacnafDA/Gudcq/lh79xxfj63SBwt3lrIt2fOp7QixHPrFwEwfvCJ/OySYZw7pCeBpON/w6aIiIhPFMzrkZKSQkFBAWlpaS0+nDvnKCgoICVFc23Ff1dN/5hPNu+pc3vu2t3krt3N6QO68fStZzRjZSIiIk1Hwbwe6enp5Ofns3v37iY7R0lJSbOF5ZSUFNLT05vlXCKN8YvLhvP7t9bxxqc76NgukZLyEKGQI5Ds54f3iIiIxIKCeT2Sk5Ob/EN6cnNzGTVqVJOeQ6Sl+LK4lN++sZZnFm+jQyCJ/3f+IKacOYB1O4uZ/fZCJp8/VqFcRERaLQVzEYm78mCIpz7eygNvr+NQWZApXx3A987JoGuHABD+8J6iUwIK5SIi0qopmItIXC3eWsh/vrCcdTuLOWtQd+6deBoZPTrGuywREZFmp2AuInFxqCzI795ay6Mfbuakzu2Zce0YLhjWs8W/0VpEROR4KZiLSLObv6mAu59fztaCg1w7rh93TxhCx3b670hERNo2/SQUkWZTHgzxwLx1PPSPjZzcLZU5t4xj3MC0eJclIiLiBQVzEWkWnxUc5I45S8nbtpersvty7yXDSA3ovyAREZFK+qkoIk3u1eVfcPfzyzGDB789mm9k9o53SSIiIt5RMBeRJlMRDPHbN9cy/f1NjD65C3+cPIr0rqnxLktERMRLbT6Ym9lEYGJGRka8SxFpVfYcKOP7s5fw4YYCvjPuZP7rm6cRSEqId1kiIiLeavM/JZ1zLzvnbuncuXO8SxFpNVZu38fEP/2ThVsK+c2kTH552QiFchERkaNo8yPmIhJb76zeye1/W0qX1GSevfUMRvbtEu+SREREWgQFcxGJmafmb+Xel1Zy2kmdeeSGbHp0Sol3SSIiIi2GgrmINFoo5Pj1G2uY8f4mzh/agz9OHqVbIYqIiBwj/eQUkUYprQjyg2eW8eryL7h2XD9+dslpJCZYvMsSERFpcRTMReS4HSoLcutfF/P+ut3854Qh3PK1gZgplIuIiBwPBXMROS77S8qZ8vhCFm8t5DdXZHLl2L7xLklERKRFUzAXkWO250AZ1z+6gNVf7OePk0fxzcyT4l2SiIhIi6dgLiLHZFdRCdfM/ITP9hxk5nXZnDOkR7xLEhERaRUUzEWkwXYXlfLtmZ/w+d5DPP7dHM44JS3eJYmIiLQaCuYi0iBfFpdyzcPz2V54iMe/O5bTByqUi4iIxJI+I1tEjmrPgTK+83B4+sqjNyiUi4iINAUFcxGp196DZVzz8Cds/vIAj1w/VtNXREREmoimsohInYpLK7j+0QVs3F3Mw9dl89WM7vEuSUREpNXSiLmI1Kq0IsjUpxaz8vP9PPjt0Xzt1BPjXZKIiEirpmAuIkcIhhz/9nQe/9zwJb+5IpMLhvWMd0kiIiKtnoK5iFTjnOMnL67gtRU7+Mk3hnLFmPR4lyQiItImKJiLSDW/fXMtsxds43vnnMJNZw2MdzkiIiJthoK5iFSZ+f4m/pK7kck5J3PXhYPjXY6IiEib0uaDuZlNNLMZ+/bti3cpInH1Ut52fvXaai4e0YtfXjYcM4t3SSIiIm1Kmw/mzrmXnXO3dO7cOd6liMTNUx9v4QdPL2No7048cFUWiQkK5SIiIs2tzQdzkbZubt52fvrSpwSdY9PuA6zcvj/eJYmIiLRJ+oAhkTboqukf88nmPUe0l1aEuOKhj6rWTx/QjadvPaM5SxMREWmzFMxF2qCnbz2Dg2UVTJ4xn9Vf7AczgsEQyUkJzLppHGP6dY13iSIiIm2OgrlIGxQMOe6Yncfy7fuY/p0xpHVsx/xNBYwbmKZQLiIiEicK5iJtjHOOn7/8KW+v3snPJg7jwtN6ASiQi4iIxJne/CnSxjzyz8088fFWbjpzADd8dUC8yxEREZEIBXORNuTdNTv51Wuruei0Xvzo4qHxLkdERESiKJiLtBHrdhZxx+w8hvU+gd9fNZIE3atcRETEKwrmIm3AngNlTHliIe0DiTx8fTapAb29RERExDf66SzSypVVhJj618Xs3F/K07eMo3fn9vEuSURERGqhEXORVsw5x09fXMmCzXv47aRMRp2sO6+IiIj4SsFcpBV79MMtPL1oG7efk8GlWX3iXY6IiIjUQ8FcpJV6b+0ufvXqKr5+Wk9+cMGp8S5HREREjkLBXKQV2rCriDv+tpQhvU7ggauydAcWERGRFkDBXKSV2V9Szi1PLqZdcoLuwCIiItKC6Ce2SCsSCjn+bU4en+05yN9uHsdJXXQHFhERkZZCI+Yircgf3lnPO2t28V8Th5EzoFu8yxEREZFjoGAu0krMW7WTP7yznklj0rl2XL94lyMiIiLHSMFcpBXYsKuYf3s6j8z0zvzysuGY6c2eIiIiLY2CuUgLV1RSzq1PLaJdUgLTvjOGlOTEeJckIiIix0Fv/hRpwUIhx78/s4wtBQf565TT9WZPERGRFkwj5iIt2IPvbeCtVTv58cVDOeOUtHiXIyIiIo2gYC7SQr2/bje/f3sd/zKqD9/9av94lyMiIiKNpGAu0gLt2FfC/3s6j1N7dOJ//mWE3uwpIiLSCiiYi7QwFcEQd8xeSkl5kAevGU37gN7sKSIi0hrozZ8iLczv561jwZY9/N9VWWT06BjvckRERCRGNGIu0oK8t3YXf8ndyOScvlw2qk+8yxEREZEYapXB3MyGmtk0M3vOzG6Ldz0isfDFvkP84Ok8hvTqxL0TT4t3OSIiIhJj3gVzM3vUzHaZ2coa7ReZ2Voz22Bm99TXh3NutXNuKnAl8NWmrFekOZQHQ3z/b0spqwjxl2tG60OEREREWiHvgjnwOHBRdIOZJQIPAhOAYcBkMxtmZiPM7JUaXz0ix1wCvAq81rzli8Te795ay6Kthdx3RSYDT9S8chERkdbIuzd/OufeN7P+NZpzgA3OuU0AZjYHuNQ5dx/wzTr6mQvMNbNXgb81YckiTeqd1TuZ/o9NXHP6yVwy8qR4lyMiIiJNxJxz8a7hCJFg/opzbnhkfRJwkXPupsj6tcDpzrnb6zh+PHA50A5Y7px7sI79bgFuAejZs+eYOXPmxPaJNEBxcTEdO2oE1De+XJeCQyH+66NDpKUk8JNxKQQS2+79yn25JlKdrot/dE38o2vip3hdl3POOWexcy67tm3ejZjHgnMuF8htwH4zgBkA2dnZbvz48U1aV21yc3OJx3mlfj5cl/JgiKumf4wllPPkrWfSv3uHuNYTbz5cEzmSrot/dE38o2viJx+vS0sJ5tuBvlHr6ZE2kVbrN2+sYclne3nw26PbfCgXERFpC3x882dtFgKDzGyAmQWAq4G5ca5JpMnMW7WTmR9s5roz+vGNzN7xLkdERESagXfB3MxmAx8Dg80s38ymOOcqgNuBN4HVwDPOuU/jWadIU8kvPMhdzy5jeJ8T+PE3hsa7HBEREWkm3k1lcc5NrqP9NZrg1odmNhGYmJGREeuuRY5ZWUWI7/1tKaGQ48Fvj6Zdku5XLiIi0lZ4N2Le3JxzLzvnbuncuXO8SxHh16+vYdm2vfxmUib90jSvXEREpC2JyYi5mXVrwG4h59zeWJxPpDV689MdPPrhZm74Sn8mjNC8chERkbYmVlNZPo981XeT5UTg5BidT6RV2bYnPK98ZHpn/vPiIfEuR0REROIgVsF8tXNuVH07mNnSGJ1LpFUprQjyvb8tAeDPmlcuIiLSZsVqjvkZMdqn2ZnZRDObsW/fvniXIm3Ufa+tYXn+Pn47aSR9u6XGuxwRERGJk5gEc+dcSSz2iQe9+VPi6fUVX/D4R1u48asDuGh4r3iXIyIiInEU09slmlkK8K/AmYAD/gk85GsoF4mnrQUH+OFzyxnZtwv3TNC8chERkbYu1vcxfxIoAv4UWf828BTwrRifR6RFKykPzys3gz9PHkUgqc3fuVRERKTNi3UwH+6cGxa1/p6ZrYrxOURavP95bTUrt+9n5nXZmlcuIiIiQOw/YGiJmY2rXDGz04FFMT6HSIv2yvLPefLjrdx81gAuGNYz3uWIiIiIJ2L1AUMrCM8pTwY+MrPPIptOBtbE4hwircHmLw9wz/MrGHVyF354keaVi4iIyGGxmsryzRj10+zMbCIwMSMjI96lSCtXUh7ke7OWkJRo/Pnbo0lO1LxyEREROSxWt0vc6pzbCuwErgAeAH4PXB5p85ZulyjN5RevrGLVF/v5/ZUj6dOlfbzLEREREc/oriwizeClvO3M+uQzbv3aQM4donnlIiIiciTdlUWkic3N285dzy5jaK9O3PX1wfEuR0RERDylu7KINKGPNnzJnXPyKA86Nn15gOX5++JdkoiIiHgq1iPmYzjyrixrK+/a4pzLjPH5RLxz1fSP+WTzniPaSytCXPHQR1Xrpw/oxtO3ntGcpYmIiIjHYh3ML4pxfyItTmXY/tsnn/Gjv68gKcFwzpGclMCsm8Yxpl/XOFcoIiIiPoppMI/cmaVF0e0SpSms3L6Pn738KV879UTuODeDTzbvYdzANIVyERERqVNM5pib2ZJY7BMPul2ixNq+Q+X866wlpHUI8H9XZZHdvxvfOydDoVxERETqFasR86Fmtrye7QYo+Uqr55zjP55dxud7D/H0rWfQrUMg3iWJiIhICxGrYN6QzxYPxuhcIt76S+5G3lq1k59+c5hGyEVEROSYxCSYt8S55SKx9t6aXfzurbVclnUSN361f7zLERERkRYm1vcxF2mTtnx5gDvmLGVorxO47/JMzCzeJYmIiEgLE9NgbmY/i2V/Ii1BcWkFtzy1iKQEY/q1Y2gfSIx3SSIiItICxfo+5v9lZu2BbsASYI5zrjDG5xDxRuWbPTfsKuapKafTt1tqvEsSERGRFirWU1kcUAK8CfQl/CmgI2N8DhFv/CV3I6+v3MGPLh7KVzO6x7scERERacFiPWK+xjl3b2T5OTN7HJgGnBvj88SMPmBIjlflmz0vzTqJKWcOiHc5IiIi0sLFesT8SzMbU7ninFsHnBjjc8SUPmBIjseaHfv5/uylDOt9Ar/Wmz1FREQkBmI9Yn4HMMfMFgMrgExgc4zPIRJXu4pKmPL4Ijq0S+SR68fqzZ4iIiISEzEdMXfOLQOygNmRpveAybE8h0g8lZQHufnJxew5UMYj14+lV+eUeJckIiIirUSsR8xxzpUCr0a+RFqNUMjx788sY3n+XqZ9ZwzD+2j6k4iIiMSOPmBIpIF+P28dr674gv+cMISvn9Yr3uWIiIhIK6NgLtIAzyzcxp/f28DVY/ty81kD412OiIiItEIK5iJHMW/VTu55YTlnDerOLy4brjuwiIiISJNQMBepx4LNe7j9b0sYkd6Fad8ZQ3KivmVERESkaShliNRhzY79THliIX26tuexG8bSoV3M3ystIiIiUkXBXKQWuw+GuO6RBaQGEnnyxhy6dQjEuyQRERFp5dp8MDeziWY2Y9++ffEuRTyxY18Jv1lYwqHyIE/cmEN619R4lyQiIiJtQJsP5s65l51zt3TurHtSS/hTPb89cz5FZY4nb8xhSK8T4l2SiIiItBFtPpiLVPqyuJRrZn7Cjv0l/CA7hVEnd413SSIiItKGKJiLAIUHyvjOw5+wrfAgj1w/llO7Jsa7JBEREWljFMylzSsoLuWahz9h05cHePi6sZxxSlq8SxIREZE2SPd/kzZtx74Srnl4Ptv3HmLmddmcOah7vEsSERGRNkrBXNqszwoOcs0j8yk8UM6TN55OzoBu8S5JRERE2jAFc2mT1u8s4pqHP6EsGOJvN59OZnqXeJckIiIibZyCubQ5Sz4rZMrjC0lKTODpW85gcK9O8S5JRERERG/+lLbljZU7mDxjPp1Sknn2VoVyERER8YdGzKXNePzDzfz3K6sYmd6FR67PJq1ju3iXJCIiIlJFwVxavVDI8T+vrebhf27mwmE9+cPVo2gf0H3KRURExC8K5tKq7TtUzr89nce7a3Zxw1f689NvDiMxweJdloiIiMgR2nwwN7OJwMSMjIx4lyIxtnZHEbc+tYj8wkP84tLTuPaM/vEuSURERKRObf7Nn865l51zt3Tu3DnepUgMvbL8c/7lLx9yoCzI07eOUygXERER77X5EXNpXSqCIX775lqmv7+JMf268tA1o+lxQkq8yxIRERE5KgVzaTX2HCjj+7OX8OGGAq4d14+ffnMYgaQ2/0chERERaSEUzKVVWJG/j6l/Xczu4lJ+OymTb2X3jXdJIiIiIsdEwVxatMVbC3n4g03MW72THh3b8dzUM8hM7xLvskRERESOmYK5tFifbCrg2w9/QjDkSDD41b8MVygXERGRFkvBXFqUq6Z/zCeb9xzRHnLw3ccXVa2fPqAbT996RnOWJiIiItIoCubSojx96xks3rqH2/66hL0Hy3E4QiFHclICs24ax5h+XeNdooiIiMhxUTCXFsM5x1/nb+Xnr6zipC7teWrK6RSXVjB/UwHjBqYplIuIiEiLpmAuLUJJeZCfvLiS5xbnc87gE/m/q0fRuX0ygAK5iIiItAoK5uK97XsPMfWpxazYvo87zxvEnecNIiHB4l2WiIiISEwpmIvXPtrwJbfPXkp5RYiHr8vm/GE9412SiIiISJNQMBcvOeeY+cEmfv36Gk45sSPTrx3DwBM7xrssERERkSajYC7eOVhWwQ+fW84ry7/g4hG9+M2kkXRsp3+qIiIi0rop7YhXtnx5gFufWsz6XUXcfdEQpp49EDPNJxcREZHWT8FcvPHeml3cOWcpCQnGEzfmcNagE+NdkoiIiEizUTCXuAuFHH9+bwMPvL2OYb1PYNp3xtC3W2q8yxIRERFpVgrmElf7S8r5wdPLeHv1Ti4f1Yf/uXwEKcmJ8S5LREREpNkpmEvcrN9ZxK1PLeazPQf52cRhXP+V/ppPLiIiIm1Wmw/mZjYRmJiRkRHvUtqU11d8wV3PLqN9IIm/3TyOnAHd4l2SiIiISFwlxLuAeHPOveycu6Vz587xLqVNqAiG+J/XVnPbrCWc2qsTr3z/TIVyERERETRiLs1od1Ep35+9hPmb9nDtuH785JtDaZek+eQiIiIioGAuzWTx1j3866wl7DtUzu+vHMnlo9PjXZKIiIiIVxTMpUk553jy46384pVV9OnansduyGHYSSfEuywRERER7yiYS5M5WFbBj15YwYt5n3P+0B7875VZdG6fHO+yRERERLykYC5NYvOXB5j61GLW7SrirgtP5V/HZ5CQoFshioiIiNRFwVxi7q1Pd/DvzywjMdF44rs5fO3UE+NdkoiIiIj3FMwlZoIhx/++tZa/5G4kM70zf7lmNOldU+NdloiIiEiLoGAuMVFQXMqdc/L454YvmZzTl3snnkZKsm6FKCIiItJQCubSaEs/K+R7s5bw5YEyfnNFJleO7RvvkkRERERaHAVzOW7OOR77cAv3vb6aHp1SeOG2rzC8jz5BVUREROR4KJjLcdlfUs4Pn13OG5/u4PyhPfjdt0bSJTUQ77JEREREWiwFczlmK7fv419nLWH73kP8+OKh3HTWAMx0K0QRERGRxlAwlwZzzvHXTz7jFy+vIq1jgGduHceYft3iXZaIiIhIq6BgLg1SXFrBf76wgpeXfc74wSfy+yuz6NZBU1dEREREYkXBXOq1eGshLy/7nLc+3cGO/SX8x9cHc9vZp+hTPEVERERiTMFc6rR4yx6unjmf8qAD4BeXnsa1Z/SPb1EiIiIirZSCuVRz1fSP+WTznlq3/fSlT/npS58CcPqAbjx96xnNWZqIiIhIq6ZgLtU8fesZLNqyhzvn5LFj3yHMDOccyUkJzLppHGP6dY13iSIiIiKtkoK5VAmGHA++t4H/e3sd6V1Tef5fv0ow5Ji/qYBxA9MUykVERESakIK5APD53kP8v6fzWLB5D/8yqg8/v/Q0OqUkAyiQi4iIiDQDBXPhjZVfcPfzK6gIhvj9lSO5fHR6vEsSERERaXMUzNuw/SXl/PzlVTy3OJ+R6Z35w9Wj6N+9Q7zLEhEREWmTFMzbqI82fsl/PLucHftL+P65GXz/3EEEkhLiXZaIiIhIm6Vg3saUlAf5zRtrefTDzQzs3oHnpp7BqJM1h1xEREQk3hTM25Bl2/byg2fy2Lj7ADd8pT93XzSE9oHEeJclIiIiIiiYtwmlFUEefHcDD+ZupEendvx1yumcOah7vMsSERERkSgK5q3c4q2F3P38cjbsKubyUX2495LT6Nw+Od5liYiIiEgNCuat1IHSCn775lqe+HgLJ3Vuz2PfHcs5g3vEuywRERERqUOrDeZm1gH4B/Az59wr8a6nOf1j3W5+9MIKPt/3/9u7+2Cr6nqP4++PICJ45UENH8CDCWUIysDJ7BlxvOBTaFEeg7LSqHtx9N4/zMvYVDNND46VmtcnRi3yKsjVREQsGB+yRhA8RCqGhAgBlkeUSzA+IJzv/WP9jmxwbyA8e+3l3p/XDHPW+q2n3+bD2ufL2r+19ut8+eQmLht7HAcdULdRm5mZmdWFwlVrkm4DzgLaImJoSftY4FqgC3BLRPx4D7u6HJhZtY4WUNvmN/jR3OXc+8f1HHtY9sSVkU19a90tMzMzM9sLhSvMgV8C/w38qqNBUhfgeuA0YB2wWNJssiL9R7ts/zXgROBZoHsO/a25bdvbuX3hGn42bwVvbmvnktGDmDx6EAd09RNXzMzMzN4rFBG17sM7SBoIzOm4Yi7po2RDUsak+SkAEbFrUd6x/Q+AnsAQ4HXg3IhoL7PeJGASQL9+/UbOmDGj81/MHmzZsoWDDjpon7dfsXE7tz+7lbWb2xl6SBcmDunG4T39RUHv1rvNxTqfMykm51I8zqR4nEkx1SqXU045pTUimsstK+IV83KOAtaWzK8DPlJp5Yi4AkDSV4AN5YrytN5UYCpAc3NzjBo1qpO6u/ceffRR9uW4bf94gyt/8xz3LFnHkb26c9PEIYw5/nAkdX4nG9C+5mLV40yKybkUjzMpHmdSTEXM5b1SmO+TiPhlrfvQ2V7buo2pj61i6mOreGt7O/8+6lguHj2IHt3qOkozMzOzuvdeqebWAwNK5vuntoaxvT24u3UtP523grbNb3LmsCP41tgP0nRIz1p3zczMzMw6wXulMF8MDJZ0DFlB3gJ8sbZdykdE8OiKl7nyweUs//tmRhzdmxsnjvDTVszMzMzqTOEKc0nTgVHAoZLWAd+NiFslXQz8luxJLLdFxLJOOt7ZwNmDBg3qjN11mojgDys3cPX8FSz56/9xdN8e3DBhBKcP9ThyMzMzs3pUuMI8Is6v0D4XmFuF490P3N/c3Pz1zt73vnr8+awgX7x6I0f26s4Pzx3G+JH96dbVT1sxMzMzq1eFK8wbVXt78Mhzbdz8u1UsWv0qhx/cne+PO54vfHiAn0duZmZm1gBcmNdQ65qNzF65lcVvLmfespf4S9sWjup9IN87ewgtJx1N9/1dkJuZmZk1ChfmNdK6ZiMtUxfw1vaAlc/TdEgPrm0ZzhnDjmD/Lh6yYmZmZtZoXJjn6LybF/DEC6+WXbbmlde4dMZSLp2xlI8c05e7vvHRnHtnZmZmZrXU8IV5nk9lKS22W9dsZMItC9n6Vjvd9t+POy46mZFNfareBzMzMzMrpoYfMxER90fEpF69euV63JFNfbjjopP57OD9XZSbmZmZma+Y19LIpj5sPrabi3IzMzMz8xVzMzMzM7MicGFuZmZmZlYALszNzMzMzAqg4QtzSWdLmrpp06Zad8XMzMzMGljDF+a1eiqLmZmZmVmphi/MzczMzMyKQBFR6z4UgqSXgTU1OPShwIYaHNd2z7kUjzMpJudSPM6keJxJMdUql6aIOKzcAhfmNSbpyYhornU/bGfOpXicSTE5l+JxJsXjTIqpiLl4KIuZmZmZWQG4MDczMzMzKwAX5rU3tdYdsLKcS/E4k2JyLsXjTIrHmRRT4XLxGHMzMzMzswLwFXMzMzMzswJwYW5mZmZmVgAuzHMgqYukP0qak+Z/L2lp+vOipFmpXZJ+LmmlpKckjahpx+tcmVxOlbQk5fIHSYNS+wGS7kq5PCFpYE07XsfKZDI6ZfKMpGmSuqZ2nys5kbRa0tPpvHgytfWVNF/SX9LPPqndueSgQiafl7RMUruk5l3Wn5IyeU7SmNr0uv5VyOUqScvT+XCvpN4l6zuXKquQyfdTHkslzZN0ZGovxPuXC/N8XAr8uWMmIj4ZEcMjYjiwAPh1WnQ6MDj9mQTcmHM/G81OuZD9fU9IudwJfDu1XwhsjIhBwNXAlXl2ssG8nYmk/YBpQEtEDCX7ArAL0no+V/J1SnrP6ij4/gt4KCIGAw+leXAuedo1k2eAzwKPla4kaQjQAhwPjAVukNQl1542ll1zmQ8MjYgTgBXAFHAuOds1k6si4oT0u34O8J3UXoj3LxfmVSapP3AmcEuZZQcDo4FZqWkc8KvILAR6Szoir742kgq5BHBwmu4FvJimx5EViAB3A6dKUh79bCRlMjkE2BoRK9L8fOBzadrnSm2VnhPTgHNK2p1LDUTEnyPiuTKLxgEzIuLNiHgBWAmclG/vGldEzIuIbWl2IdA/TTuXGomIf5TM9iT73Q8Fef9yYV591wDfAtrLLDuH7KpTxz+So4C1JcvXpTbrfNfwzlwuAuZKWgd8Cfhxan87l/QGu4msaLTOdQ07Z7IB6Frysfx4YECa9rmSnwDmSWqVNCm19YuIv6XpvwP90rRzyUe5TCpxJvnZUy5fAx5M084lH2UzkfQDSWuBCey4Yl6ITFyYV5Gks4C2iGitsMr5wPQcu2TsNpf/BM6IiP7AL4Cf5d65BlUuk8ie5doCXC1pEbAZ2F6jLjayT0TECLKPeSdL+lTpwpSTn7ubr91mYjVTMRdJVwDbgDtq1bkGVTaTiLgiIgaQ5XFxLTu4Kxfm1fVx4DOSVgMzgNGS/gdA0qFkH1s9ULL+enZcEYTsI6/1+XS1oZTL5QHgxIh4Iq1zF/CxNP12Lunmw17AK7n2uP6VPVciYkG6J+MksrGzHcNafK7kJCLWp59twL1k71svdXzEm362pdWdSw4qZFKJM8lJpVwkfQU4i+wepo7/xDqXHOzFuXIHO4ZIFiITF+ZVFBFTIqJ/RAwku/L3cERMTIvHA3Mi4o2STWYDX053Bp8MbCr5uNg6SblcyMaW9ZL0gbTaaey4MXQ2O246HE+Wo68QdqJK54qk90H2ZBzgcuCmtInPlRxI6inpXzqmgX8lu8mw9Jy4ALgvTTuXKttNJpXMBlqUPV3qGLIb2xZVv6eNpVIuksaSDdH7TES8VrKJc6my3WQyuGS1ccDyNF2I96+ueR/Q3tbCjjHMHeYCZ5DdBPIa8NW8O9WoImKbpK8D90hqBzaSjQcEuBW4XdJK4FWy7Cwfl6VhLvsBN0bEw6nd50o++gH3pnuduwJ3RsRvJC0GZkq6kOxpOV9I6zuX6quUybnAdcBhwAOSlkbEmIhYJmkm8CzZUIrJEeEhYZ2vUi4rgQOA+WnZwoj4pnPJRaVM7pH0QbL7mdYA30zrF+L9S77wZ2ZmZmZWex7KYmZmZmZWAC7MzczMzMwKwIW5mZmZmVkBuDA3MzMzMysAF+ZmZmZmZgXgwtzMzMzMrABcmJuZNQBJ35AUkkaVtE1ObaftYduBkl6XtLSkrZ+kOyWtktQqaUF6lvbu9vOIpDG7tP2HpBslHShpqaSt6ZuRzcwajgtzM7PGMAz4E3AcgKQewEXAy8BTe7H98xExPG0rYBbwWES8PyJGkn3xVv897GM67/yCrhZgekS8nvb/4t68GDOzeuTC3MysMZwAzCAV5sAlwP8C7RHx0j+5r9HA1oi4qaMhItZExHUd85ImSlqUroLfLKkLcDdwpqRuaZ2BwJHA7/f1RZmZ1RMX5mZmjeFDwEzgOEm9gfOAx4Fn9mFfxwNLKi2U9KG0/4+nq+DbgQkR8SqwCDg9rdoCzAx/BbWZGQBda90BMzOrLkkDgFciYpWk9wGXAdcBHwCelnQOcCZwMHBrRMz7J/d/PfAJsqvoHwZOBUYCi7NRLxwItKXVO4az3Jd+XvjuXp2ZWf1wYW5mVv+GAU+n6c3AWOAk4BpgSUTMAmZJ6gP8BNhTYb4M+FzHTERMTjdsPpmaBEyLiClltr0PuFrSCKBHRLTu0ysyM6tDHspiZlb/TmBHYX4VcHFEbGfngh3g28D1e7G/h4Hukv6tpK1HyfRDwPh0dR5JfSU1AUTEFuAR4Dayq+dmZpa4MDczq3/DSGPJI2JORCxI7UOAZcpcCTwYERXHjndIY8LPAT4t6QVJi4BpwOVp+bNkRf48SU8B84EjSnYxHTgRF+ZmZjuR77kxM2tski4BLgAWA0tLn7aSlg8E5kTE0Bz6shpojogN1T6WmVnRuDA3M7PdSjePPk52A+nwKh3jQGABcBgwLD3BxcysobgwNzMzMzMrAI8xNzMzMzMrABfmZmZmZmYF4MLczMzMzKwAXJibmZmZmRWAC3MzMzMzswJwYW5mZmZmVgAuzM3MzMzMCsCFuZmZmZlZAfw/BVkyDJsOk3YAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x864 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x864 with 3 Axes>"
       ]
@@ -81,22 +108,115 @@
     "        li = [hepi.Input(hepi.Order.LO,13000,sq,1000022,\"LesHouches.in\",pdf,nlopdf,1., 1.,id=\"test\")]\n",
     "        li=hepi.slha_scan_rel(li,lambda r : [[\"EXTPAR\",1,510],[\"EXTPAR\",2,r]],np.linspace(470.,530.,16))\n",
     "        sp.run(li)\n",
-    "        dl = rs.run(li,False,False)\n",
+    "        dl = rs.run(li,True,True)\n",
     "        for p in [1000022,1000023,1000025,1000035]:\n",
     "            hepi.slha_plot(li,[\"EXTPAR\",2],[\"MASS\",p],axes=axs[0],logy=True,xaxis=\"$M_2$ [GeV]\",yaxis=\"$M$\",label=\"$\"+hepi.util.get_name(p)+\"$\",tight=False)\n",
     "        for nm1 in [1]:\n",
     "            for nm2 in [1,2,3,4]:\n",
     "                hepi.slha_plot(li,[\"EXTPAR\",2],[\"NMIX\",(nm1,nm2)],fmt=\"-\",interpolate=False,xaxis=\"$M_2$ [GeV]\",yaxis=\"Mixing\",logy=False,axes=axs[1],label=\"$\"+\"N_{\"+ str(nm1)+ str(nm2)+\"}$\",tight=False)\n",
-    "        hepi.vplot(hepi.slha_data(li,[\"EXTPAR\",2]),dl[\"lo\"],plot_data=True,axes=axs[2],xaxis=\"$M_2$ [GeV]\",yaxis= \"$\\sigma$ [pb]\",tight=False,label=\"$\\sigma_{lo}$\")"
+    "        hepi.vplot(hepi.slha_data(li,[\"EXTPAR\",2]),dl[\"LO\"],plot_data=True,axes=axs[2],xaxis=\"$M_2$ [GeV]\",yaxis= \"$\\sigma$ [pb]\",tight=False,label=\"$\\sigma_{LO}$\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.8/site-packages/hepi/input.py:283: RuntimeWarning: Could not set new central scale to average of masses.\n",
+      "  warnings.warn(\"Could not set new central scale to average of masses.\",\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 256 jobs\n",
+      "1\n",
+      "2\n",
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 256 jobs\n",
+      "1\n",
+      "2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "for sq in [2000002,1000002]:\n",
     "    for pdf,nlopdf in [(\"CT14lo\",\"CT14lo\")]:\n",
@@ -106,23 +226,144 @@
     "        li=hepi.slha_scan(li,\"EXTPAR\",1,np.linspace(500.,1500.,16))\n",
     "        li=hepi.slha_scan(li,\"EXTPAR\",2,np.linspace(500.,1500.,16))\n",
     "        sp.run(li)\n",
-    "        dl = rs.run(li,False,False)\n",
+    "        dl = rs.run(li,True,True)\n",
     "        for nm1 in [1]:\n",
     "            for nm2 in [1,2]:\n",
     "                print(nm2)\n",
     "                hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),\n",
-    "                               hepi.slha_data(li,[\"NMIX\",(nm1,nm2)]),logz=False,xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"Mixing\")\n",
+    "                               hepi.slha_data(li,[\"NMIX\",(nm1,nm2)]),logz=False,xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"Mixing\",show=True)\n",
     "        \n",
-    "        hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),dl[\"lo\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{lo}$\")"
+    "        hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),dl[\"LO\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{LO}$\",show=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 121 jobs\n",
+      "1\n",
+      "2\n",
+      "skipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipskipRunning: 121 jobs\n",
+      "1\n",
+      "2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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jbSewRURMV31cfqtdz/EympX5DwXeKunQIafdASyx/TLgWuBPJ1J8SZ+RtEfH8X+VdFM313Y1FcD2j2x/BDgWOBD4uqRXjau0ERFRhNz91oWjgA22H7S9nWYx4+WdJ9j+Z9vb2sNbaNaFnIh/AW6VtEzSO2gek13SzYXdDCj5JeDgdjsEeAnwQ2Dv8ZY2IiIK6K1bcoGkdR3HK22v7DjeD3io43gjMFoj52zgH3sqwRC2Py7pXuCfgc3AK2w/2s213TxzWwvcSROlL7X93fEVMyIiiuotuG22vaQf2Uo6DVhCMzBkIvf5TeB/AqcDLwPWSDrL9jfHurab4PY7wGHAScAfSNpCM1LybuAe218cb8EjImJy9NDd2K2Hgf07jhe2aT+er/Q64H3AL9t+ZoJ5vhH4RdubgL+TdB2wii4WDxkzuNn+eOexpIXA4TRR9I0079iJiIjppr9TAW4DFks6gCaonUrzbrXnSHoF8HFgaRuQJsT2G4Ycf13SUd1c2/MKJbY30vS1TqgvNSIiJlkfW262d0h6F3ADzVSAT9q+V9KHgXW2VwN/BvwU8HlJAP9h+5Re85J0vu0/lXTpCKe8e6x75K0AERGV6vcKJbbXAGuGpH2gY/91fcpqfft5+3hvkOAWEVGrGbr8lu1/aD9XjfceVQU3Y3Z6oFh+A4X/5TzjZ4vmt377vKL5/eFhryma3wN/cXjR/AB23drzW6YmZO7TZZdf0o6i2aFyP+5Tkt+E9H9ASTGSVo/2fTddnVUFt4iI6DBDgxvwapo5dX8H3Ar0/FtagltERK1mbnB7KXAC8FaaEZlfBv7O9r3d3qBsH0lERBTT5+W3irG90/ZXbJ8BHA1sANa2ozW7kpZbRERMO5J2o1k85K3AIuBS4Lpur09wi4io1TRrkXVL0pU0K2OtAf7Q9j293iPBLSKiRtOwu7EHpwH/CZwLvLudEA7NwBLbnj/WDRLcIiJqNUODm+0JjwcpOqBE0lxJd0i6vj3+P5LubLfvS/pimy5Jl0raIOkuSUeWLGdERBX6+LLSmaZ0y+1cmmVV5gPYfm7WrqQvAF9qD08EFrfbq4DLGf29QRER0UHM6G7JCSvWcmvfJnAScMUw380HjuP5NwwsB6504xZgT0n7liprREQVZnHLrWS35CXA+cBwC9i8AbjJ9g/a4+He+LrfZBYuIqIqPcxxq7GFVyS4SToZ2GR7pBWe30qzzMp47r1C0jpJ6zZvmUkLv0VETLK03CbdMcApkr4LXA0cJ+kqAEkLgKNollcZ1NUbXwFsr7S9xPaSBXtnwZWIiOckuE0u2xfaXmh7Ec3bW2+2fVr79ZuA620/3XHJauD0dtTk0cBW24+UKGtERC1mc7fkdJjndipw8ZC0NcAymvXEtgFnlS5URMSMZoYf4TBLFA9uttcCazuOXzvMOQbOKVaoiIgK1dgi69Z0aLlFRMRkSHCLiIjapOUWERH1SXCLiIiqVDrEv1sJbhERFVK7zVaZ9RwRUas+T+KWtFTS/e0bWy4Y5vvdJH2u/f5WSYv6Uo9xqK7lNlCwHf6Mny2WF8A3t88rmt8fH/6asU/qo/v/9LCi+e2ytfzvdnOfKfu79Jyy/0RR4XlVpfMr2s3Xh7z6OaBE0lzgMuAEmvV+b5O02vZ9HaedDTxp+0BJpwIfAd7Sv1J0Ly23iIha9bfldhSwwfaDtrfTLKW4fMg5y4FV7f61wPHqeI12SQluERG16i24LRhchL7dVgy5Wzdva3nuHNs7gK3A3n2sUdeq65aMiAiee+VNDzbbXjJJpSkuLbeIiFr1t1uym7e1PHeOpF2APYAt4y3+RCS4RURUqs9vBbgNWCzpAEnzaBa9Xz3knNXAGe3+m2jeADMls+3SLRkRUas+hhXbOyS9C7gBmAt80va9kj4MrLO9GvgE8BlJG4AnaALglEhwi4ioVL/XlrS9huaVZJ1pH+jYfxr4tf7mOj4JbhERNcryWxERUaUEt4iIqInIK28iIqJGCW4REVEbTc0o/GkhwS0iokYZUBIRETXKM7eIiKhPgltERNQmLbeIiKhPgltERFSl91feVCXBLSKiVgluERFREwEamL3RLcEtIqJS6ZasxADmGT9bLL9vbp9XLC+Ai454bdH87r/4kKL57bq17Ltz5zyjovkBzNlRNj8N1J1f6W633Z4ol6F2TvAGmcQdERE1Kv7LxjSS4BYRUau03CIiojZ55hYREXUxkLcCREREbdJyi4iI+iS4RURETcTsbrkVnVgkaa6kOyRd3x5L0kWSHpC0XtK7O9IvlbRB0l2SjixZzoiIGc/ubatM2VmzcC6wvuP4TGB/4GDbhwBXt+knAovbbQVwecEyRkRUQe5+m1A+0l6SbpT07fbzRcOcc4Skf5N0b9toecvEch1dseAmaSFwEnBFR/LvAB+2PQBge1Obvhy40o1bgD0l7VuqrBERVXAP28RcANxkezFwU3s81DbgdNs/DywFLpG054RzHkHJltslwPlA55z5nwXeImmdpH+UtLhN3w94qOO8jW1aRER0qVTLjaZBsqrdXwW8YegJth+w/e12//vAJuDFE855BEWCm6STgU22bx/y1W7A07aXAH8DfHIc917RBsd1W7bM4rVmIiI6GRhw9xssGPy/tN1W9JDbPrYfafcfBfYZ7WRJRwHzgH8fT9W6UWq05DHAKZKWAbsD8yVdRdMi+/v2nOuAT7X7D9M8ixu0sE37CbZXAisBXvHyefU9FY2IGK/e/kfc3DY0hiXpn4CXDvPV+34sS9vSyG3B9hHTZ4AzBh9JTYYiwc32hcCFAJJeC7zH9mmSLgaOBb4D/DLwQHvJauBdkq4GXgVs7fitICIiutDPqQC2XzdiPtJjkva1/UgbvDaNcN584MvA+9rxFJOm9GjJoS4G3ijpbuBPgLe36WuAB4ENNN2V75ya4kVEzGDlpgKsBs5o988AvjT0BEnzaHrorrR97UQzHEvxSdy21wJr2/2naEZQDj3HwDklyxURUZuCk7gvBq6RdDbwPeDNAJKWAL9t++1t2i8Be0s6s73uTNt3TkaBskJJRESNCr6s1PYW4Phh0tfR9sjZvgq4qkyJEtwiIqrULL81e8fYJbhFRNRqFs+OSnCLiKhUWm4REVGXgs/cpqMEt4iIKtW52n+3qgpuP7L45vZ5xfK76Mhji+UF8K0/Prhofrv+oOw0yDnPqGx+O4pmB4AKPwMpnV/plsJuTxTOsOw/0Qmbze9zqyq4RUREh7TcIiKiKp6Clvs0kuAWEVGrgbTcIiKiMpkKEBER9Ulwi4iIqpisUBIREXURTrdkRERUKMEtIiKqk+AWERFVyTO3iIioUZ65RUREfRLcIiKiLnkrQERE1MYkuEVERIUyoCQiImqTASUREVGfWRzcyr5qOSIiyjDNK2+63SZA0l6SbpT07fbzRaOcO1/SRkn/e0KZjiHBLSKiSu1oyW63ibkAuMn2YuCm9ngkfwR8baIZjiXBLSKiVuWC23JgVbu/CnjDcCdJeiWwD/DViWY4lgS3iIha9RbcFkha17Gt6CGnfWw/0u4/ShPAfoykOcCfA++ZcL26UNWAkkfWz+eiX3hdsfy+9YcHFcsLYNcfqGh+c58pm592FM0OTcGzdpUeml04v92eKvyHWvafaPn8JmLwmVv3NtteMtKXkv4JeOkwX73vx7K1LQ370/VOYI3tjdLk/0FWFdwiImKQwf377cb2iC0HSY9J2tf2I5L2BTYNc9qrgddIeifwU8A8Sf/P9mjP58YtwS0iolblpgKsBs4ALm4/v/STRfFvDO5LOhNYMlmBDfLMLSKiTgWnAtAEtRMkfRt4XXuMpCWSrpjozccjLbeIiFoVarnZ3gIcP0z6OuDtw6R/Gvj0ZJYpwS0iolazeIWSBLeIiCrllTcREVEbAwOz97UARQeUSJor6Q5J17fHn5b0HUl3ttsRbbokXSppg6S7JB1ZspwREVUot0LJtFO65XYusB6Y35F2nu1rh5x3IrC43V4FXN5+RkREVww703KbdJIWAicB3QwLXQ5c6cYtwJ7txMCIiOiGwR7oeqtNyW7JS4Dz+ckFgS5qux4/Kmm3Nm0/4KGOcza2aRER0a1y89ymnSLBTdLJwCbbtw/56kLgYOAXgL2A947j3isGF/rcPvD0xAsbEVGLWfzMrVTL7RjgFEnfBa4GjpN0le1H2q7HZ4BPAUe15z8M7N9x/cI27SfYXml7ie0l8+bsPnk1iIiYSexmtGS3W2WKBDfbF9peaHsRcCpws+3TBp+jqVki+g3APe0lq4HT21GTRwNbO16nEBER3ZjFLbepnuf2t5JeTPMiiTuB327T1wDLgA3ANuCsKSldRMQM5gpbZN0qHtxsrwXWtvvHjXCOgXPKlSoiojZ1tsi6NdUtt4iImAy9v6y0KgluERG1qnD+WrcS3CIiKmTAablFRERV7LTcIiKiPmm5RUREfWZxy02uaKiopMeB7011ObqwANg81YWYRKnfzJb6TQ//1faLx3uxpK/Q1LVbm20vHW9+001VwW2mkLTO9pKpLsdkSf1mttQvalD0ZaURERElJLhFRER1EtymxsqpLsAkS/1mttQvZrw8c4uIiOqk5RYREdVJcJskkuZKukPS9e3x8ZK+IelOSf8i6cA2fTdJn5O0QdKtkhZNacG7NEz9jmvrd4+kVZJ2adMl6dK2fndJOnJqSz42Sd+VdHf7d7WuTdtL0o2Svt1+vqhNr6V+vybpXkkDkpYMOf/Ctn73S/qVqSl1b0ao459J+lb793SdpD07zp9xdYzRJbhNnnOB9R3HlwO/YfsI4LPA+9v0s4EnbR8IfBT4SMlCTsBz9ZM0B1gFnGr7MJq5hme0550ILG63FTR/DjPBsbaP6BgyfgFwk+3FwE3tMdRTv3uAXwW+1nmSpENpXjD888BS4GOS5hYt6fgNreONwGG2XwY8AFwIM76OMYIEt0kgaSFwEnBFR7KB+e3+HsD32/3lNIEB4Frg+PbN5NPWMPXbG9hu+4H2+Ebgje3+cuBKN24B9hx8A/sM0/n3tIrmzfGD6TO+frbX275/mK+WA1fbfsb2d2heIHxU2dL1h+2v2t7RHt4CLGz3q6ljPC/BbXJcApwPdK5983ZgjaSNwG8CF7fp+wEPAbQ/eFtpgsV0dgk/Xr/NwC4d3VlvAvZv95+rX2tjmzadGfiqpNslrWjT9rH9SLv/KLBPu19L/UYyE+sHY9fxbcA/tvsztY4xigS3PpN0MrDJ9u1Dvvo9YJnthcCngL8oXrg+GK5+7ZvTTwU+KunrwA+BnVNUxH74RdtH0nQ5niPplzq/bOs7k4cZj1q/SoxYR0nvA3YAfztVhYvJl4WT++8Y4BRJy4DdgfmSvgwcbPvW9pzPAV9p9x+maeVsbAdh7AFsKVzmXgxXv6tsnwa8BkDS64GD2vMH6zdoYZs2bdl+uP3cJOk6mi6qxyTta/uRtttxU3t6LfX72ginz7j6wch1lHQmcDJwvJ+fBzUj6xijS8utz2xfaHuh7UU0rZmbafr095A0+B/+CTw/2GQ1zw++eBNwc8cP3bQzXP1snybpJdCM/gTeC/x1e8lq4PR2VOHRwNaO7r1pR9ILJL1wcB94Pc1gi86/pzOAL7X7tdRvJKuBU9tRvQfQDJz5+uSXdPxGqqOkpTTd6afY3tZxyYyrY4wtLbcCbO+Q9A7gC5IGgCdp+vwBPgF8RtIG4AmagDETndd2Wc4BLrd9c5u+BlhG85B+G3DWFJWvW/sA17VjenYBPmv7K5JuA66RdDbNaNA3t+fXUr//AfwV8GLgy5LutP0rtu+VdA1wH01X3jm2p3uX80h13ADsBtzYfneL7d+eoXWMMWSFkoiIqE66JSMiojoJbhERUZ0Et4iIqE6CW0REVCfBLSIiqpPgFhER1Ulwi4iI6iS4RYxC0m9JsqTXdqSd06adMHUli4jRJLhFjO5w4JvAwQCS/gvNGx4eB+6awnJFxCgS3CJG9zLgatrgBrwb+DwwYPuxKStVRIwqwS1idIcA1wAHS9oTeAvwr4y+2HBETLEEt4gRSNof2GL7QeAlwHk0iwsfBNwt6WckfULStVNZzoj4SQluESM7HLi73f8hsBRY1abfZftB22dPVeEiYmR55U3EyF7G88Htz2hacTslHU4T5CJimkpwixjZ4cAXAGxf35F+KHDvlJQoIrqS97lFjJOkvYGLaN6sfoXtP5niIkVEK8EtIiKqkwElERFRnQS3iIioToJbRERUJ8EtIiKqk+AWERHVSXCLiIjqJLhFRER1EtwiIqI6CW4REVGd/w+sqFDezGPshAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "dll = {}\n",
     "sqs=[2000002,1000002]\n",
@@ -134,7 +375,7 @@
     "        li=hepi.slha_scan(li,\"EXTPAR\",1,np.linspace(475.,525.,10+1))\n",
     "        li=hepi.slha_scan(li,\"EXTPAR\",2,np.linspace(450.,500.,10+1))\n",
     "        sp.run(li)\n",
-    "        dl = rs.run(li,False,False)\n",
+    "        dl = rs.run(li,True,True)\n",
     "        for nm1 in [1]:\n",
     "            for nm2 in [1,2]:\n",
     "                print(nm2)\n",
@@ -142,22 +383,15 @@
     "                               hepi.slha_data(li,[\"NMIX\",(nm1,nm2)]),logz=False,xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"Mixing\")\n",
     "        \n",
     "        hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),\n",
-    "                       dl[\"lo\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{lo}$\")\n",
+    "                       dl[\"LO\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{LO}$\")\n",
     "        hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),\n",
     "                       hepi.slha_data(li,[\"MASS\",1000022]),xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$MX$\")\n",
     "        dll[sq] = dl\n",
     "\n",
     "hepi.map_vplot(hepi.slha_data(li,[\"EXTPAR\",1]),hepi.slha_data(li,[\"EXTPAR\",2]),\n",
-    "                       dll[sqs[0]][\"lo\"]+dll[sqs[1]][\"lo\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{lo+lo}$\")\n",
+    "                       dll[sqs[0]][\"LO\"]+dll[sqs[1]][\"LO\"],xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{LO+LO}$\")\n",
     "    "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/source/examples/test_write.ipynb b/docs/source/examples/test_write.ipynb
index 6166b3a20..7fa427eca 100644
--- a/docs/source/examples/test_write.ipynb
+++ b/docs/source/examples/test_write.ipynb
@@ -18,8 +18,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1.6.19+dirty\n",
-      "~/git/resummino_release/\n"
+      "0.1.8.9+dirty\n",
+      "~/git/resummino_release\n"
      ]
     }
    ],
@@ -68,97 +68,63 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running: 9 jobs\n",
-      "{'LO': array([0.20208405+/-0.00029302563, 0.013712558+/-1.7676046e-05,\n",
-      "       0.0026090769+/-3.1243321e-06, 0.00073465133+/-8.3509755e-07,\n",
-      "       0.00025475179+/-2.7854162e-07, 0.00010047166+/-1.0658448e-07,\n",
-      "       4.3224927e-05+/-4.4747701e-08, 1.9789732e-05+/-2.0079547e-08,\n",
-      "       9.4894125e-06+/-9.4681338e-09], dtype=object), 'NLO': array([0.26786916+/-0.00036328325, 0.017042798+/-1.9927369e-05,\n",
-      "       0.0031280684+/-3.4502997e-06, 0.00085929957+/-8.9960357e-07,\n",
-      "       0.00029242159+/-2.9376357e-07, 0.00011369143+/-1.1027916e-07,\n",
-      "       4.8409699e-05+/-4.5616627e-08, 2.2010282e-05+/-2.0217058e-08,\n",
-      "       1.0515472e-05+/-9.4331156e-09], dtype=object), 'NLO_PLUS_NLL': array([0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0,\n",
-      "       0.0+/-0, 0.0+/-0], dtype=object), 'aNNLO_PLUS_NNLL': array([None, None, None, None, None, None, None, None, None], dtype=object), 'K_LO': array([1.0+/-2.602590409850336e-19, 1.0+/-0, 1.0+/-0, 1.0+/-0,\n",
-      "       1.0+/-1.2666606380662415e-19, 1.0+/-0, 1.0+/-0, 1.0+/-0, 1.0+/-0],\n",
-      "      dtype=object), 'K_NLO': array([1.3255334104794516+/-0.002631717384869068,\n",
-      "       1.2428605953754217+/-0.0021629996223859554,\n",
-      "       1.1989176708436613+/-0.0019519208991142045,\n",
-      "       1.1696699303600253+/-0.0018075671400151847,\n",
-      "       1.1478686371546203+/-0.0017043776737595882,\n",
-      "       1.1315771034339435+/-0.001626583762921709,\n",
-      "       1.1199486583285612+/-0.0015677821392965133,\n",
-      "       1.112207178955228+/-0.0015222201496370444,\n",
-      "       1.108126767594938+/-0.0014868134066253849], dtype=object), 'K_NLO_PLUS_NLL': array([0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0,\n",
-      "       0.0+/-0, 0.0+/-0], dtype=object), 'NLO_PLUS_NLL_OVER_NLO': array([0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0, 0.0+/-0,\n",
-      "       0.0+/-0, 0.0+/-0], dtype=object), 'K_aNNLO_PLUS_NNLL': array([None, None, None, None, None, None, None, None, None], dtype=object), 'aNNLO_PLUS_NNLL_OVER_NLO': array([None, None, None, None, None, None, None, None, None], dtype=object), 'VNLO': array([0.010615536+/-8.4512336e-05, 0.00063926004+/-4.632389e-06,\n",
-      "       0.00011283865+/-7.642409e-07, 3.0095916e-05+/-1.9609956e-07,\n",
-      "       9.9948463e-06+/-6.2984029e-08, 3.8048342e-06+/-2.3308244e-08,\n",
-      "       1.5903164e-06+/-9.5087621e-09, 7.1148582e-07+/-4.1666038e-09,\n",
-      "       3.3509692e-07+/-1.926412e-09], dtype=object), 'P_PLUS_K': array([0.029977367+/-0.00016932363, 0.0015237026+/-6.0107947e-06,\n",
-      "       0.00026283529+/-1.0235514e-06, 7.2806828e-05+/-2.5010226e-07,\n",
-      "       2.5616614e-05+/-7.9953718e-08, 1.0433286e-05+/-3.0123817e-08,\n",
-      "       4.6784194e-06+/-1.2700738e-08, 2.2422485e-06+/-5.7793878e-09,\n",
-      "       1.1286971e-06+/-2.7714589e-09], dtype=object), 'RNLOG': array([-0.0021761091+/-4.7592963e-05, -0.00010655742+/-1.9718797e-06,\n",
-      "       -1.5536608e-05+/-2.907792e-07, -3.5006405e-06+/-6.3650574e-08,\n",
-      "       -1.0050803e-06+/-1.7184012e-08, -3.4380552e-07+/-3.2594707e-09,\n",
-      "       -1.2446762e-07+/-1.5589143e-09, -4.8978122e-08+/-8.7732056e-10,\n",
-      "       -2.0648145e-08+/-3.8538584e-10], dtype=object), 'RNLOQ': array([0.0099487103+/-2.4886658e-05, 0.00037843043+/-8.8163305e-07,\n",
-      "       4.905887e-05+/-1.0890402e-07, 1.0307804e-05+/-2.1819124e-08,\n",
-      "       2.8204412e-06+/-5.7653472e-09, 9.122583e-07+/-1.8026799e-09,\n",
-      "       3.3116334e-07+/-6.3405971e-10, 1.307273e-07+/-2.4517185e-10,\n",
-      "       5.4966422e-08+/-1.0015655e-10], dtype=object), 'VNLO_PLUS_P_PLUS_K': array([0.040592903+/-0.00018924277162563912,\n",
-      "       0.00216296264+/-7.588720628200059e-06,\n",
-      "       0.00037567393999999996+/-1.2773885946237228e-06,\n",
-      "       0.00010290274400000001+/-3.178146911162245e-07,\n",
-      "       3.56114603e-05+/-1.0178204621197378e-07,\n",
-      "       1.42381202e-05+/-3.808829989659587e-08,\n",
-      "       6.2687358e-06+/-1.5865853346701538e-08,\n",
-      "       2.95373432e-06+/-7.124739333404646e-09,\n",
-      "       1.46379402e-06+/-3.3752107531431587e-09], dtype=object), 'RNLO': array([0.0077726012+/-5.370694436968401e-05,\n",
-      "       0.00027187300999999996+/-2.1599968486376067e-06,\n",
-      "       3.352226199999999e-05+/-3.105038304510919e-07,\n",
-      "       6.807163500000001e-06+/-6.728647518377562e-08,\n",
-      "       1.8153609e-06+/-1.8125382665000258e-08,\n",
-      "       5.6845278e-07+/-3.724755571312365e-09,\n",
-      "       2.0669572e-07+/-1.6829276605338016e-09,\n",
-      "       8.174917800000001e-08+/-9.109339169397176e-10,\n",
-      "       3.4318277e-08+/-3.9818787045866686e-10], dtype=object), 'RNLO_PLUS_VNLO_PLUS_P_PLUS_K': array([0.0483655042+/-0.00019671619782336716,\n",
-      "       0.00243483565+/-7.890137334609168e-06,\n",
-      "       0.00040919620199999997+/-1.3145852008902164e-06,\n",
-      "       0.00010970990750000001+/-3.248594274943519e-07,\n",
-      "       3.74268212e-05+/-1.0338333728337975e-07,\n",
-      "       1.480657298e-05+/-3.8269993377044725e-08,\n",
-      "       6.47543152e-06+/-1.5954859696331715e-08,\n",
-      "       3.035483498e-06+/-7.18273702497832e-09,\n",
-      "       1.498112297e-06+/-3.3986175436953212e-09], dtype=object), 'order': array([1, 1, 1, 1, 1, 1, 1, 1, 1]), 'energy': array([13000, 13000, 13000, 13000, 13000, 13000, 13000, 13000, 13000]), 'energyhalf': array([6500., 6500., 6500., 6500., 6500., 6500., 6500., 6500., 6500.]), 'particle1': array([1000011, 1000011, 1000011, 1000011, 1000011, 1000011, 1000011,\n",
-      "       1000011, 1000011]), 'particle2': array([-1000011, -1000011, -1000011, -1000011, -1000011, -1000011,\n",
-      "       -1000011, -1000011, -1000011]), 'slha': array(['mastercode_with_gm2.in_mass_1000011_100.0',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_212.5',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_325.0',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_437.5',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_550.0',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_662.5',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_775.0',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_887.5',\n",
-      "       'mastercode_with_gm2.in_mass_1000011_1000.0'], dtype='<U42'), 'pdf_lo': array(['cteq6l1', 'cteq6l1', 'cteq6l1', 'cteq6l1', 'cteq6l1', 'cteq6l1',\n",
-      "       'cteq6l1', 'cteq6l1', 'cteq6l1'], dtype='<U7'), 'pdfset_lo': array([0, 0, 0, 0, 0, 0, 0, 0, 0]), 'pdf_nlo': array(['cteq66', 'cteq66', 'cteq66', 'cteq66', 'cteq66', 'cteq66',\n",
-      "       'cteq66', 'cteq66', 'cteq66'], dtype='<U6'), 'pdfset_nlo': array([0, 0, 0, 0, 0, 0, 0, 0, 0]), 'pdf_lo_id': array([10042, 10042, 10042, 10042, 10042, 10042, 10042, 10042, 10042]), 'pdf_nlo_id': array([10550, 10550, 10550, 10550, 10550, 10550, 10550, 10550, 10550]), 'mu_f': array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), 'mu_r': array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), 'precision': array([0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]), 'max_iters': array([50, 50, 50, 50, 50, 50, 50, 50, 50]), 'invariant_mass': array(['auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
-      "       'auto'], dtype='<U4'), 'pt': array(['auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
-      "       'auto'], dtype='<U4'), 'result': array(['total', 'total', 'total', 'total', 'total', 'total', 'total',\n",
-      "       'total', 'total'], dtype='<U5'), 'id': array(['5', '5', '5', '5', '5', '5', '5', '5', '5'], dtype='<U1'), 'model_path': array(['/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO',\n",
-      "       '/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO'], dtype='<U40'), 'mu': array([ 100. ,  212.5,  325. ,  437.5,  550. ,  662.5,  775. ,  887.5,\n",
-      "       1000. ]), 'mass_1000011': array([ 100. ,  212.5,  325. ,  437.5,  550. ,  662.5,  775. ,  887.5,\n",
-      "       1000. ]), 'runner': array(['ResumminoRunner', 'ResumminoRunner', 'ResumminoRunner',\n",
-      "       'ResumminoRunner', 'ResumminoRunner', 'ResumminoRunner',\n",
-      "       'ResumminoRunner', 'ResumminoRunner', 'ResumminoRunner'],\n",
-      "      dtype='<U15')}\n"
+      "skipskipskipskipskipskipskipskipskipRunning: 9 jobs\n",
+      "                        LO                      NLO NLO_PLUS_NLL  \\\n",
+      "0        0.20208+/-0.00029          0.2679+/-0.0004      0.0+/-0   \n",
+      "1      0.013713+/-0.000018      0.017043+/-0.000020      0.0+/-0   \n",
+      "2    0.0026091+/-0.0000031    0.0031281+/-0.0000035      0.0+/-0   \n",
+      "3    0.0007347+/-0.0000008    0.0008593+/-0.0000009      0.0+/-0   \n",
+      "4  0.00025475+/-0.00000028  0.00029242+/-0.00000029      0.0+/-0   \n",
+      "5  0.00010047+/-0.00000011  0.00011369+/-0.00000011      0.0+/-0   \n",
+      "6      (4.322+/-0.004)e-05      (4.841+/-0.005)e-05      0.0+/-0   \n",
+      "7    (1.9790+/-0.0020)e-05    (2.2010+/-0.0020)e-05      0.0+/-0   \n",
+      "8      (9.489+/-0.009)e-06    (1.0515+/-0.0009)e-05      0.0+/-0   \n",
+      "\n",
+      "  aNNLO_PLUS_NNLL                                             K_LO  \\\n",
+      "0            None  1.00000000000000000000+/-0.00000000000000000026   \n",
+      "1            None                                          1.0+/-0   \n",
+      "2            None                                          1.0+/-0   \n",
+      "3            None                                          1.0+/-0   \n",
+      "4            None  1.00000000000000000000+/-0.00000000000000000013   \n",
+      "5            None                                          1.0+/-0   \n",
+      "6            None                                          1.0+/-0   \n",
+      "7            None                                          1.0+/-0   \n",
+      "8            None                                          1.0+/-0   \n",
+      "\n",
+      "             K_NLO K_NLO_PLUS_NLL NLO_PLUS_NLL_OVER_NLO K_aNNLO_PLUS_NNLL  \\\n",
+      "0  1.3255+/-0.0026        0.0+/-0               0.0+/-0              None   \n",
+      "1  1.2429+/-0.0022        0.0+/-0               0.0+/-0              None   \n",
+      "2  1.1989+/-0.0020        0.0+/-0               0.0+/-0              None   \n",
+      "3  1.1697+/-0.0018        0.0+/-0               0.0+/-0              None   \n",
+      "4  1.1479+/-0.0017        0.0+/-0               0.0+/-0              None   \n",
+      "5  1.1316+/-0.0016        0.0+/-0               0.0+/-0              None   \n",
+      "6  1.1199+/-0.0016        0.0+/-0               0.0+/-0              None   \n",
+      "7  1.1122+/-0.0015        0.0+/-0               0.0+/-0              None   \n",
+      "8  1.1081+/-0.0015        0.0+/-0               0.0+/-0              None   \n",
+      "\n",
+      "  aNNLO_PLUS_NNLL_OVER_NLO  ... precision max_iters invariant_mass    pt  \\\n",
+      "0                     None  ...      0.01        50           auto  auto   \n",
+      "1                     None  ...      0.01        50           auto  auto   \n",
+      "2                     None  ...      0.01        50           auto  auto   \n",
+      "3                     None  ...      0.01        50           auto  auto   \n",
+      "4                     None  ...      0.01        50           auto  auto   \n",
+      "5                     None  ...      0.01        50           auto  auto   \n",
+      "6                     None  ...      0.01        50           auto  auto   \n",
+      "7                     None  ...      0.01        50           auto  auto   \n",
+      "8                     None  ...      0.01        50           auto  auto   \n",
+      "\n",
+      "  result id model      mu  mass_1000011                           runner  \n",
+      "0  total  5         100.0         100.0  ResumminoRunner-resummino 3.1.1  \n",
+      "1  total  5         212.5         212.5  ResumminoRunner-resummino 3.1.1  \n",
+      "2  total  5         325.0         325.0  ResumminoRunner-resummino 3.1.1  \n",
+      "3  total  5         437.5         437.5  ResumminoRunner-resummino 3.1.1  \n",
+      "4  total  5         550.0         550.0  ResumminoRunner-resummino 3.1.1  \n",
+      "5  total  5         662.5         662.5  ResumminoRunner-resummino 3.1.1  \n",
+      "6  total  5         775.0         775.0  ResumminoRunner-resummino 3.1.1  \n",
+      "7  total  5         887.5         887.5  ResumminoRunner-resummino 3.1.1  \n",
+      "8  total  5        1000.0        1000.0  ResumminoRunner-resummino 3.1.1  \n",
+      "\n",
+      "[9 rows x 41 columns]\n"
      ]
     }
    ],
@@ -225,7 +191,7 @@
        "      <th>pt</th>\n",
        "      <th>result</th>\n",
        "      <th>id</th>\n",
-       "      <th>model_path</th>\n",
+       "      <th>model</th>\n",
        "      <th>mu</th>\n",
        "      <th>mass_1000011</th>\n",
        "      <th>runner</th>\n",
@@ -251,10 +217,10 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
+       "      <td></td>\n",
        "      <td>100.0</td>\n",
        "      <td>100.0</td>\n",
-       "      <td>ResumminoRunner</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -275,10 +241,10 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
+       "      <td></td>\n",
        "      <td>212.5</td>\n",
        "      <td>212.5</td>\n",
-       "      <td>ResumminoRunner</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -299,10 +265,10 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
+       "      <td></td>\n",
        "      <td>325.0</td>\n",
        "      <td>325.0</td>\n",
-       "      <td>ResumminoRunner</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -323,10 +289,10 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
+       "      <td></td>\n",
        "      <td>437.5</td>\n",
        "      <td>437.5</td>\n",
-       "      <td>ResumminoRunner</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -347,10 +313,10 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
+       "      <td></td>\n",
        "      <td>550.0</td>\n",
        "      <td>550.0</td>\n",
-       "      <td>ResumminoRunner</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -371,10 +337,10 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
+       "      <td></td>\n",
        "      <td>662.5</td>\n",
        "      <td>662.5</td>\n",
-       "      <td>ResumminoRunner</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -395,10 +361,10 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
+       "      <td></td>\n",
        "      <td>775.0</td>\n",
        "      <td>775.0</td>\n",
-       "      <td>ResumminoRunner</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -419,10 +385,10 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
+       "      <td></td>\n",
        "      <td>887.5</td>\n",
        "      <td>887.5</td>\n",
-       "      <td>ResumminoRunner</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -443,10 +409,10 @@
        "      <td>auto</td>\n",
        "      <td>total</td>\n",
        "      <td>5</td>\n",
-       "      <td>/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO</td>\n",
+       "      <td></td>\n",
        "      <td>1000.0</td>\n",
        "      <td>1000.0</td>\n",
-       "      <td>ResumminoRunner</td>\n",
+       "      <td>ResumminoRunner-resummino 3.1.1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -498,27 +464,16 @@
        "7                     None  ...      0.01        50           auto  auto   \n",
        "8                     None  ...      0.01        50           auto  auto   \n",
        "\n",
-       "  result id                                model_path      mu  mass_1000011  \\\n",
-       "0  total  5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   100.0         100.0   \n",
-       "1  total  5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   212.5         212.5   \n",
-       "2  total  5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   325.0         325.0   \n",
-       "3  total  5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   437.5         437.5   \n",
-       "4  total  5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   550.0         550.0   \n",
-       "5  total  5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   662.5         662.5   \n",
-       "6  total  5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   775.0         775.0   \n",
-       "7  total  5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO   887.5         887.5   \n",
-       "8  total  5  /opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO  1000.0        1000.0   \n",
-       "\n",
-       "            runner  \n",
-       "0  ResumminoRunner  \n",
-       "1  ResumminoRunner  \n",
-       "2  ResumminoRunner  \n",
-       "3  ResumminoRunner  \n",
-       "4  ResumminoRunner  \n",
-       "5  ResumminoRunner  \n",
-       "6  ResumminoRunner  \n",
-       "7  ResumminoRunner  \n",
-       "8  ResumminoRunner  \n",
+       "  result id model      mu  mass_1000011                           runner  \n",
+       "0  total  5         100.0         100.0  ResumminoRunner-resummino 3.1.1  \n",
+       "1  total  5         212.5         212.5  ResumminoRunner-resummino 3.1.1  \n",
+       "2  total  5         325.0         325.0  ResumminoRunner-resummino 3.1.1  \n",
+       "3  total  5         437.5         437.5  ResumminoRunner-resummino 3.1.1  \n",
+       "4  total  5         550.0         550.0  ResumminoRunner-resummino 3.1.1  \n",
+       "5  total  5         662.5         662.5  ResumminoRunner-resummino 3.1.1  \n",
+       "6  total  5         775.0         775.0  ResumminoRunner-resummino 3.1.1  \n",
+       "7  total  5         887.5         887.5  ResumminoRunner-resummino 3.1.1  \n",
+       "8  total  5        1000.0        1000.0  ResumminoRunner-resummino 3.1.1  \n",
        "\n",
        "[9 rows x 41 columns]"
       ]
@@ -529,7 +484,7 @@
     }
    ],
    "source": [
-    "hepi.LD2DF(rs_dl)"
+    "rs_dl"
    ]
   },
   {
@@ -550,16 +505,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LO,NLO,NLO_PLUS_NLL,aNNLO_PLUS_NNLL,K_LO,K_NLO,K_NLO_PLUS_NLL,NLO_PLUS_NLL_OVER_NLO,K_aNNLO_PLUS_NNLL,aNNLO_PLUS_NNLL_OVER_NLO,VNLO,P_PLUS_K,RNLOG,RNLOQ,VNLO_PLUS_P_PLUS_K,RNLO,RNLO_PLUS_VNLO_PLUS_P_PLUS_K,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_path,mu,mass_1000011,runner\n",
-      "0.20208+/-0.00029,0.2679+/-0.0004,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000026,1.3255+/-0.0026,0.0+/-0,0.0+/-0,,,0.01062+/-0.00008,0.02998+/-0.00017,-0.00218+/-0.00005,0.009949+/-0.000025,0.04059+/-0.00019,0.00777+/-0.00005,0.04837+/-0.00020,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_100.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,100.0,100.0,ResumminoRunner\n",
-      "0.013713+/-0.000018,0.017043+/-0.000020,0.0+/-0,,1.0+/-0,1.2429+/-0.0022,0.0+/-0,0.0+/-0,,,0.000639+/-0.000005,0.001524+/-0.000006,-0.0001066+/-0.0000020,0.0003784+/-0.0000009,0.002163+/-0.000008,0.0002719+/-0.0000022,0.002435+/-0.000008,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_212.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,212.5,212.5,ResumminoRunner\n",
-      "0.0026091+/-0.0000031,0.0031281+/-0.0000035,0.0+/-0,,1.0+/-0,1.1989+/-0.0020,0.0+/-0,0.0+/-0,,,0.0001128+/-0.0000008,0.0002628+/-0.0000010,(-1.554+/-0.029)e-05,(4.906+/-0.011)e-05,0.0003757+/-0.0000013,(3.352+/-0.031)e-05,0.0004092+/-0.0000013,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_325.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,325.0,325.0,ResumminoRunner\n",
-      "0.0007347+/-0.0000008,0.0008593+/-0.0000009,0.0+/-0,,1.0+/-0,1.1697+/-0.0018,0.0+/-0,0.0+/-0,,,(3.010+/-0.020)e-05,(7.281+/-0.025)e-05,(-3.50+/-0.06)e-06,(1.0308+/-0.0022)e-05,0.00010290+/-0.00000032,(6.81+/-0.07)e-06,0.00010971+/-0.00000032,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_437.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,437.5,437.5,ResumminoRunner\n",
-      "0.00025475+/-0.00000028,0.00029242+/-0.00000029,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000013,1.1479+/-0.0017,0.0+/-0,0.0+/-0,,,(9.99+/-0.06)e-06,(2.562+/-0.008)e-05,(-1.005+/-0.017)e-06,(2.820+/-0.006)e-06,(3.561+/-0.010)e-05,(1.815+/-0.018)e-06,(3.743+/-0.010)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_550.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,550.0,550.0,ResumminoRunner\n",
-      "0.00010047+/-0.00000011,0.00011369+/-0.00000011,0.0+/-0,,1.0+/-0,1.1316+/-0.0016,0.0+/-0,0.0+/-0,,,(3.805+/-0.023)e-06,(1.0433+/-0.0030)e-05,(-3.438+/-0.033)e-07,(9.123+/-0.018)e-07,(1.424+/-0.004)e-05,(5.68+/-0.04)e-07,(1.481+/-0.004)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_662.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,662.5,662.5,ResumminoRunner\n",
-      "(4.322+/-0.004)e-05,(4.841+/-0.005)e-05,0.0+/-0,,1.0+/-0,1.1199+/-0.0016,0.0+/-0,0.0+/-0,,,(1.590+/-0.010)e-06,(4.678+/-0.013)e-06,(-1.245+/-0.016)e-07,(3.312+/-0.006)e-07,(6.269+/-0.016)e-06,(2.067+/-0.017)e-07,(6.475+/-0.016)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_775.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,775.0,775.0,ResumminoRunner\n",
-      "(1.9790+/-0.0020)e-05,(2.2010+/-0.0020)e-05,0.0+/-0,,1.0+/-0,1.1122+/-0.0015,0.0+/-0,0.0+/-0,,,(7.11+/-0.04)e-07,(2.242+/-0.006)e-06,(-4.90+/-0.09)e-08,(1.3073+/-0.0025)e-07,(2.954+/-0.007)e-06,(8.17+/-0.09)e-08,(3.035+/-0.007)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_887.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,887.5,887.5,ResumminoRunner\n",
-      "(9.489+/-0.009)e-06,(1.0515+/-0.0009)e-05,0.0+/-0,,1.0+/-0,1.1081+/-0.0015,0.0+/-0,0.0+/-0,,,(3.351+/-0.019)e-07,(1.1287+/-0.0028)e-06,(-2.06+/-0.04)e-08,(5.497+/-0.010)e-08,(1.4638+/-0.0034)e-06,(3.43+/-0.04)e-08,(1.4981+/-0.0034)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_1000.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,/opt/MG5_aMC_v2_7_0/models/MSSMatNLO_UFO,1000.0,1000.0,ResumminoRunner\n",
+      "LO,NLO,NLO_PLUS_NLL,aNNLO_PLUS_NNLL,K_LO,K_NLO,K_NLO_PLUS_NLL,NLO_PLUS_NLL_OVER_NLO,K_aNNLO_PLUS_NNLL,aNNLO_PLUS_NNLL_OVER_NLO,VNLO,P_PLUS_K,RNLOG,RNLOQ,VNLO_PLUS_P_PLUS_K,RNLO,RNLO_PLUS_VNLO_PLUS_P_PLUS_K,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,mass_1000011,runner\n",
+      "0.20208+/-0.00029,0.2679+/-0.0004,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000026,1.3255+/-0.0026,0.0+/-0,0.0+/-0,,,0.01062+/-0.00008,0.02998+/-0.00017,-0.00218+/-0.00005,0.009949+/-0.000025,0.04059+/-0.00019,0.00777+/-0.00005,0.04837+/-0.00020,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_100.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,100.0,100.0,ResumminoRunner-resummino 3.1.1\n",
+      "0.013713+/-0.000018,0.017043+/-0.000020,0.0+/-0,,1.0+/-0,1.2429+/-0.0022,0.0+/-0,0.0+/-0,,,0.000639+/-0.000005,0.001524+/-0.000006,-0.0001066+/-0.0000020,0.0003784+/-0.0000009,0.002163+/-0.000008,0.0002719+/-0.0000022,0.002435+/-0.000008,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_212.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,212.5,212.5,ResumminoRunner-resummino 3.1.1\n",
+      "0.0026091+/-0.0000031,0.0031281+/-0.0000035,0.0+/-0,,1.0+/-0,1.1989+/-0.0020,0.0+/-0,0.0+/-0,,,0.0001128+/-0.0000008,0.0002628+/-0.0000010,(-1.554+/-0.029)e-05,(4.906+/-0.011)e-05,0.0003757+/-0.0000013,(3.352+/-0.031)e-05,0.0004092+/-0.0000013,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_325.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,325.0,325.0,ResumminoRunner-resummino 3.1.1\n",
+      "0.0007347+/-0.0000008,0.0008593+/-0.0000009,0.0+/-0,,1.0+/-0,1.1697+/-0.0018,0.0+/-0,0.0+/-0,,,(3.010+/-0.020)e-05,(7.281+/-0.025)e-05,(-3.50+/-0.06)e-06,(1.0308+/-0.0022)e-05,0.00010290+/-0.00000032,(6.81+/-0.07)e-06,0.00010971+/-0.00000032,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_437.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,437.5,437.5,ResumminoRunner-resummino 3.1.1\n",
+      "0.00025475+/-0.00000028,0.00029242+/-0.00000029,0.0+/-0,,1.00000000000000000000+/-0.00000000000000000013,1.1479+/-0.0017,0.0+/-0,0.0+/-0,,,(9.99+/-0.06)e-06,(2.562+/-0.008)e-05,(-1.005+/-0.017)e-06,(2.820+/-0.006)e-06,(3.561+/-0.010)e-05,(1.815+/-0.018)e-06,(3.743+/-0.010)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_550.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,550.0,550.0,ResumminoRunner-resummino 3.1.1\n",
+      "0.00010047+/-0.00000011,0.00011369+/-0.00000011,0.0+/-0,,1.0+/-0,1.1316+/-0.0016,0.0+/-0,0.0+/-0,,,(3.805+/-0.023)e-06,(1.0433+/-0.0030)e-05,(-3.438+/-0.033)e-07,(9.123+/-0.018)e-07,(1.424+/-0.004)e-05,(5.68+/-0.04)e-07,(1.481+/-0.004)e-05,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_662.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,662.5,662.5,ResumminoRunner-resummino 3.1.1\n",
+      "(4.322+/-0.004)e-05,(4.841+/-0.005)e-05,0.0+/-0,,1.0+/-0,1.1199+/-0.0016,0.0+/-0,0.0+/-0,,,(1.590+/-0.010)e-06,(4.678+/-0.013)e-06,(-1.245+/-0.016)e-07,(3.312+/-0.006)e-07,(6.269+/-0.016)e-06,(2.067+/-0.017)e-07,(6.475+/-0.016)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_775.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,775.0,775.0,ResumminoRunner-resummino 3.1.1\n",
+      "(1.9790+/-0.0020)e-05,(2.2010+/-0.0020)e-05,0.0+/-0,,1.0+/-0,1.1122+/-0.0015,0.0+/-0,0.0+/-0,,,(7.11+/-0.04)e-07,(2.242+/-0.006)e-06,(-4.90+/-0.09)e-08,(1.3073+/-0.0025)e-07,(2.954+/-0.007)e-06,(8.17+/-0.09)e-08,(3.035+/-0.007)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_887.5,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,887.5,887.5,ResumminoRunner-resummino 3.1.1\n",
+      "(9.489+/-0.009)e-06,(1.0515+/-0.0009)e-05,0.0+/-0,,1.0+/-0,1.1081+/-0.0015,0.0+/-0,0.0+/-0,,,(3.351+/-0.019)e-07,(1.1287+/-0.0028)e-06,(-2.06+/-0.04)e-08,(5.497+/-0.010)e-08,(1.4638+/-0.0034)e-06,(3.43+/-0.04)e-08,(1.4981+/-0.0034)e-06,1,13000,6500.0,1000011,-1000011,mastercode_with_gm2.in_mass_1000011_1000.0,cteq6l1,0,cteq66,0,10042,10550,1.0,1.0,0.01,50,auto,auto,total,5,,1000.0,1000.0,ResumminoRunner-resummino 3.1.1\n",
       "\n"
      ]
     }
@@ -584,15 +539,10 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'code'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_29869/1301378516.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhepi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrs_dl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhepi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOrder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNLO\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"mass_1000011\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"out.json\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0merror_sym\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'out.json'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/hepi/output.py\u001b[0m in \u001b[0;36mwrite_json\u001b[0;34m(dict_list, o, parameter, filename, error_sym, error_asym)\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[0mjd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"source\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"HEPi\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0mjd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"contact\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"?\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m     \u001b[0mjd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"tool\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"code\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m     jd[\"process_latex\"] = \"$\" + get_name(dict_list[\"particle1\"][0]) + get_name(\n\u001b[1;32m     93\u001b[0m         dict_list[\"particle2\"][0]) + \"$\"\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'code'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"initial state\": \"pp\", \"order\": \"NLO\", \"source\": \"hepi-0.1.8.9+dirty\", \"contact\": \"?\", \"tool\": \"Resummino-resummino 3.1.1\", \"process_latex\": \"$\\\\tilde{e}_{L}^{-}\\\\tilde{e}_{L}^{+}$\", \"comment\": \"5\", \"reference\": \"?\", \"Ecom [GeV]\": \"13000\", \"process_id\": \"pp_13000_1000011_-1000011\", \"PDF set\": \"cteq66\", \"data\": {\"100.0\": {\"xsec_pb\": 0.26786916, \"unc_pb\": 0.00036328325}, \"212.5\": {\"xsec_pb\": 0.017042798, \"unc_pb\": 1.9927369e-05}, \"325.0\": {\"xsec_pb\": 0.0031280684, \"unc_pb\": 3.4502997e-06}, \"437.5\": {\"xsec_pb\": 0.00085929957, \"unc_pb\": 8.9960357e-07}, \"550.0\": {\"xsec_pb\": 0.00029242159, \"unc_pb\": 2.9376357e-07}, \"662.5\": {\"xsec_pb\": 0.00011369143, \"unc_pb\": 1.1027916e-07}, \"775.0\": {\"xsec_pb\": 4.8409699e-05, \"unc_pb\": 4.5616627e-08}, \"887.5\": {\"xsec_pb\": 2.2010282e-05, \"unc_pb\": 2.0217058e-08}, \"1000.0\": {\"xsec_pb\": 1.0515472e-05, \"unc_pb\": 9.4331156e-09}}, \"parameters\": [[\"mass_1000011\"]]}\n"
      ]
     }
    ],

From c070fddaa964cc10dfeecead2c0732355b03d821 Mon Sep 17 00:00:00 2001
From: APN-Pucky <APN-Pucky@noreply.github.com>
Date: Thu, 9 Jun 2022 04:30:00 +0200
Subject: [PATCH 3/8] Bump smpl version

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index bd55784ff..d269bbe22 100644
--- a/setup.py
+++ b/setup.py
@@ -26,7 +26,7 @@
         "scipy",
         "sympy",
         "scikit-learn",
-        "smpl>=0.0.128",
+        "smpl>=0.0.131",
         "pyslha",
         "enlighten",
         "particle",

From f48d3156cad369fa37408ec3341fba55161b613e Mon Sep 17 00:00:00 2001
From: APN-Pucky <APN-Pucky@users.noreply.github.com>
Date: Thu, 9 Jun 2022 06:09:16 +0200
Subject: [PATCH 4/8] Update load.py

---
 hepi/load.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/hepi/load.py b/hepi/load.py
index 7fbdbc04b..4826014a2 100644
--- a/hepi/load.py
+++ b/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)
\ No newline at end of file
+    return DL2DF(LD2DL(dat, actual_dict=True))

From d0ffb690879063f72b8912bf7fb4bce69a531ac7 Mon Sep 17 00:00:00 2001
From: APN-Pucky <APN-Pucky@users.noreply.github.com>
Date: Thu, 9 Jun 2022 06:53:04 +0200
Subject: [PATCH 5/8] Update output.py

---
 hepi/output.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/hepi/output.py b/hepi/output.py
index f0389b70c..11d77091f 100644
--- a/hepi/output.py
+++ b/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

From 1feb0e712b981d8428bb8176f599bfb87501108f Mon Sep 17 00:00:00 2001
From: APN-Pucky <APN-Pucky@users.noreply.github.com>
Date: Thu, 9 Jun 2022 06:57:34 +0200
Subject: [PATCH 6/8] Update output.py

---
 hepi/output.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/hepi/output.py b/hepi/output.py
index 11d77091f..7fea5efb3 100644
--- a/hepi/output.py
+++ b/hepi/output.py
@@ -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 = {}

From ef5aca42eb1fd810de114d68b516befa703cd761 Mon Sep 17 00:00:00 2001
From: APN-Pucky <APN-Pucky@users.noreply.github.com>
Date: Thu, 9 Jun 2022 07:05:59 +0200
Subject: [PATCH 7/8] Update plot.py

---
 hepi/plot.py | 19 ++-----------------
 1 file changed, 2 insertions(+), 17 deletions(-)

diff --git a/hepi/plot.py b/hepi/plot.py
index e974492c9..385ee16c4 100644
--- a/hepi/plot.py
+++ b/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:

From 850ac3f7b0e96b5692e9f3ccabfa6758aa73c4f5 Mon Sep 17 00:00:00 2001
From: APN-Pucky <APN-Pucky@users.noreply.github.com>
Date: Thu, 9 Jun 2022 07:08:24 +0200
Subject: [PATCH 8/8] Update run.py

---
 hepi/run.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/hepi/run.py b/hepi/run.py
index 70230cd3a..760cffe6d 100644
--- a/hepi/run.py
+++ b/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):