diff --git a/python/LIController.py b/python/LIController.py
index 1e74c8a0d..a2c6e7e4e 100644
--- a/python/LIController.py
+++ b/python/LIController.py
@@ -1,6 +1,7 @@
 import h5py
 import numpy as np
 import awkward as ak
+import time
 
 from . import utilities as _utilities
 from . import detector as _detector
@@ -27,6 +28,8 @@ def __init__(self, events_to_inject, experiment, seed=0):
         :param int seed: Optional random number generator seed
         """
 
+        self.global_start = time.time()
+
         self.resources_dir = _util.resource_package_dir()
 
         # Initialize a random number generator
@@ -382,10 +385,16 @@ def GenerateEvents(self, N=None, fill_tables_at_exit=True):
         if N is None:
             N = self.events_to_inject
         count = 0
+        self.gen_times,self.global_times = [],[]
+        prev_time = time.time()
         while (self.injector.InjectedEvents() < self.events_to_inject) and (count < N):
             print("Injecting Event %d/%d  " % (count, N), end="\r")
             tree = self.injector.GenerateEvent()
             self.events.append(tree)
+            t = time.time()
+            self.gen_times.append(t-prev_time)
+            self.global_times.append(t-self.global_start)
+            prev_time = t
             count += 1
         if hasattr(self, "DN_processes"):
             self.DN_processes.SaveCrossSectionTables(fill_tables_at_exit=fill_tables_at_exit)
@@ -396,6 +405,8 @@ def SaveEvents(self, filename, fill_tables_at_exit=True, hdf5=True, parquet=True
         # A dictionary containing each dataset we'd like to save
         datasets = {
             "event_weight":[], # weight of entire event
+            "event_gen_time":[], # generation time of each event
+            "event_global_time":[], # global time of each event
             "num_interactions":[], # number of interactions per event
             "vertex":[], # vertex of each interaction in an event
             "in_fiducial":[], # whether or not each vertex is in the fiducial volume
@@ -410,6 +421,8 @@ def SaveEvents(self, filename, fill_tables_at_exit=True, hdf5=True, parquet=True
         for ie, event in enumerate(self.events):
             print("Saving Event %d/%d  " % (ie, len(self.events)), end="\r")
             datasets["event_weight"].append(self.weighter.EventWeight(event))
+            datasets["event_gen_time"].append(self.gen_times[ie])
+            datasets["event_global_time"].append(self.global_times[ie])
             # add empty lists for each per interaction dataset
             for k in ["vertex",
                       "in_fiducial",
diff --git a/python/LIDarkNews.py b/python/LIDarkNews.py
index 4f9c96106..67b5a33c3 100644
--- a/python/LIDarkNews.py
+++ b/python/LIDarkNews.py
@@ -204,7 +204,7 @@ def __init__(
         table_dir=None,  # table to store
         tolerance=1e-6,  # supposed to represent machine epsilon
         interp_tolerance=5e-2,  # relative interpolation tolerance
-        always_interpolate=False, # bool whether to always interpolate the total/differential cross section
+        always_interpolate=True, # bool whether to always interpolate the total/differential cross section
     ):
         DarkNewsCrossSection.__init__(self)  # C++ constructor
 
@@ -380,7 +380,7 @@ def _query_interpolation_table(self, inputs, mode):
             # check if energy is within table range
             
             if interpolator is None or inputs[0] > interp_table[-1,0]:
-                print("Requested interpolation at %2.2f GeV. Either this above the table boundary or the interpolator doesn't yet exist. Filling %s table"%(inputs[0],mode))
+                print("Requested interpolation at %2.2f GeV. Either this is above the table boundary or the interpolator doesn't yet exist. Filling %s table"%(inputs[0],mode))
                 n = self.FillInterpolationTables(total=(mode=="total"),
                                                  diff=(mode=="differential"),
                                                  Emax = (1+self.interp_tolerance)*inputs[0])
@@ -390,9 +390,11 @@ def _query_interpolation_table(self, inputs, mode):
             elif inputs[0] < interp_table[0,0]:
                 print("Requested interpolation at %2.2f GeV below table boundary. Requring calculation"%inputs[0])
                 return 0
-            val = interpolator(inputs)
+            val = max(0,interpolator(inputs))
             if val<0:
-                print("WARNING: negative interpolated value for %s cross section at,"%mode,inputs) 
+                print("WARNING: negative interpolated value for %s-%s %s cross section at,"%(self.ups_case.nuclear_target.name,
+                                                                                             self.ups_case.scattering_regime,
+                                                                                             mode),inputs) 
             return val
         
         UseSinglePoint, Interpolate, closest_idx = self._interpolation_flags(
@@ -407,7 +409,7 @@ def _query_interpolation_table(self, inputs, mode):
         elif Interpolate:
             return interpolator(inputs)
         else:
-            return 0
+            return -1
 
     def FillTableAtEnergy(self, E, total=True, diff=True, factor=0.8):
         num_added_points = 0
@@ -581,7 +583,7 @@ def DifferentialCrossSection(self, arg1, target=None, energy=None, Q2=None):
         if self.always_interpolate:
             # Check if we can interpolate
             val = self._query_interpolation_table([energy, z], mode="differential")
-            if val > 0:
+            if val >= 0:
                 # we have recovered the differential cross section from the interpolation table
                 return val
 
@@ -632,7 +634,7 @@ def TotalCrossSection(self, arg1, energy=None, target=None):
 
         # Check if we can interpolate
         val = self._query_interpolation_table([energy], mode="total")
-        if val > 0:
+        if val >= 0:
             # we have recovered the cross section from the interpolation table
             return val
 
diff --git a/resources/Examples/Example2/DipolePortal_CCM.py b/resources/Examples/Example2/DipolePortal_CCM.py
index 5bf8daab6..10922a301 100644
--- a/resources/Examples/Example2/DipolePortal_CCM.py
+++ b/resources/Examples/Example2/DipolePortal_CCM.py
@@ -23,7 +23,7 @@
 }
 
 # Number of events to inject
-events_to_inject = 10000
+events_to_inject = 100000
 
 # Expeirment to run
 experiment = "CCM"
@@ -40,7 +40,8 @@
     xs_path,
     "Dipole_M%2.2e_mu%2.2e" % (model_kwargs["m4"], model_kwargs["mu_tr_mu4"]),
 )
-controller.InputDarkNewsModel(primary_type, table_dir, **model_kwargs, **xs_kwargs)
+controller.InputDarkNewsModel(primary_type, table_dir,
+                              **model_kwargs, **xs_kwargs)
 
 # Primary distributions
 primary_injection_distributions = {}
diff --git a/resources/Examples/Example2/DipolePortal_MINERvA.py b/resources/Examples/Example2/DipolePortal_MINERvA.py
index de5592248..e626a7500 100644
--- a/resources/Examples/Example2/DipolePortal_MINERvA.py
+++ b/resources/Examples/Example2/DipolePortal_MINERvA.py
@@ -18,7 +18,7 @@
 
 # cross section class arguments
 xs_kwargs = {
-    "always_interpolate": True
+    "always_interpolate": True,
 }
 
 # Number of events to inject
diff --git a/resources/Examples/Example2/DipolePortal_MiniBooNE.py b/resources/Examples/Example2/DipolePortal_MiniBooNE.py
index 17bb918b2..a9b8a67fd 100644
--- a/resources/Examples/Example2/DipolePortal_MiniBooNE.py
+++ b/resources/Examples/Example2/DipolePortal_MiniBooNE.py
@@ -39,8 +39,7 @@
     xs_path,
     "Dipole_M%2.2e_mu%2.2e" % (model_kwargs["m4"], model_kwargs["mu_tr_mu4"]),
 )
-controller.InputDarkNewsModel(primary_type, table_dir,
-                              **model_kwargs, **xs_kwargs)
+controller.InputDarkNewsModel(primary_type, table_dir, **model_kwargs, **xs_kwargs)
 
 # Primary distributions
 primary_injection_distributions = {}
diff --git a/resources/Examples/Example2/PaperPlots.ipynb b/resources/Examples/Example2/PaperPlots.ipynb
index 307adad3b..369f405c7 100644
--- a/resources/Examples/Example2/PaperPlots.ipynb
+++ b/resources/Examples/Example2/PaperPlots.ipynb
@@ -275,6 +275,68 @@
     "        "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b3c5ba51-84f4-4cfd-b10f-c3b7eebadddb",
+   "metadata": {},
+   "source": [
+    "# Generation Time Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "7777d8cc-71dc-44b1-811e-e1be421be5c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plt.subplots(1,2,figsize=(12,6))\n",
+    "color = [\"goldenrod\",\"lightseagreen\",\"mediumvioletred\"]\n",
+    "alpha = 0.7\n",
+    "for c,k in zip(color,filename.keys()):\n",
+    "    \n",
+    "    #if k==\"MINERvA\": continue\n",
+    "    \n",
+    "    # iterative\n",
+    "    data = awk.from_parquet(\"output/iterative_tol5/\"+filename[k])\n",
+    "    #ax[0].plot(data[\"event_gen_time\"],color=c,alpha=alpha)\n",
+    "    ax[1].plot([0]+list(data[\"event_global_time\"]),color=c,alpha=alpha)\n",
+    "    data = awk.from_parquet(\"output/iterative_tol10/\"+filename[k])\n",
+    "    #ax[0].plot(data[\"event_gen_time\"],ls=\"--\",color=c,alpha=alpha)\n",
+    "    ax[1].plot([0]+list(data[\"event_global_time\"]),ls=\"--\",color=c,alpha=alpha)\n",
+    "    \n",
+    "    # precomputed\n",
+    "    data = awk.from_parquet(\"output/precomputed_tol5/\"+filename[k])\n",
+    "    ax[0].plot([0]+list(data[\"event_global_time\"]),label=k,color=c,alpha=alpha)\n",
+    "    data = awk.from_parquet(\"output/precomputed_tol10/\"+filename[k])\n",
+    "    ax[0].plot([0]+list(data[\"event_global_time\"]),ls=\"--\",color=c,alpha=alpha)\n",
+    "    \n",
+    "ax[0].plot([],[],color=\"black\",label=\"5% Interpolation Tolerance\")\n",
+    "ax[0].plot([],[],color=\"black\",ls=\"--\",label=\"10% Interpolation Tolerance\")\n",
+    "ax[0].set_xlabel(\"Generated Event Number\")\n",
+    "ax[1].set_xlabel(\"Generated Event Number\")\n",
+    "ax[0].set_ylabel(\"Elapsed Time [s]\",labelpad=-4)\n",
+    "ax[1].set_ylabel(\"Elapsed Time [s]\",labelpad=-4)\n",
+    "ax[0].set_ylim(0,100)\n",
+    "ax[1].set_ylim(0,1000)\n",
+    "ax[0].text(4000,1.5,\"Pre-computed Cross Section Tables\")\n",
+    "ax[1].text(2700,15,\"Iteratively-generated Cross Section Tables\")\n",
+    "ax[0].legend()\n",
+    "plt.savefig(\"figures/GenerationTiming.pdf\",dpi=100)\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6315336d-3031-4b4c-a3f2-e1a5b4348591",
@@ -285,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 144,
    "id": "c9d9ce02-1e38-4ef5-a70d-f7351fd0b199",
    "metadata": {},
    "outputs": [
@@ -293,21 +355,46 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "---------------------------------------------------------\n",
-      "   ______           _        _   _                     \n",
-      "   |  _  \\         | |      | \\ | |                    \n",
-      "   | | | |__ _ _ __| | __   |  \\| | _____      _____   \n",
-      "   | | | / _  | ___| |/ /   | .   |/ _ \\ \\ /\\ / / __|  \n",
-      "   | |/ / (_| | |  |   <    | |\\  |  __/\\ V  V /\\__ \\  \n",
-      "   |___/ \\__,_|_|  |_|\\_\\   \\_| \\_/\\___| \\_/\\_/ |___/  \n",
-      "\n",
-      "---------------------------------------------------------\n",
-      "Model:\n",
-      "\t1 dirac heavy neutrino(s).\n",
-      "\n",
+      "Initializing the three-portal model.\n",
+      "Warning: nuclear density for He4 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for Mn55 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for N14 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for Na23 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for Be9 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for W183 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Initializing the three-portal model.\n",
       "Warning: nuclear density for He4 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
-      "Warning: nuclear density for N14 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n"
+      "Warning: nuclear density for Mn55 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for N14 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for Na23 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for Be9 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for W183 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Initializing the three-portal model.\n",
+      "Warning: nuclear density for He4 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for N14 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Initializing the three-portal model.\n",
+      "Warning: nuclear density for He4 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for N14 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Initializing the three-portal model.\n",
+      "Warning: nuclear density for He4 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for N14 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for Cl35 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for Mn55 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Initializing the three-portal model.\n",
+      "Warning: nuclear density for He4 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for N14 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for Cl35 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n",
+      "Warning: nuclear density for Mn55 not tabulated in Nuclear Data Table. Using symmetrized Fermi form factor instead.\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -329,80 +416,89 @@
     "}\n",
     "\n",
     "# Number of events to inject\n",
-    "events_to_inject = 5000\n",
+    "events_to_inject = 1\n",
     "\n",
-    "# Expeirment to run\n",
-    "experiment = \"MiniBooNE\"\n",
+    "# number of points for cross section tables\n",
+    "N = 1000\n",
     "\n",
-    "# Define the controller\n",
-    "controller = LIController(events_to_inject, experiment)\n",
+    "experiments = [\"CCM\",\"MiniBooNE\",\"MINERvA\"]\n",
+    "Emaxs = [0.03,10,20]\n",
     "\n",
-    "# Particle to inject\n",
-    "primary_type = LI.dataclasses.Particle.ParticleType.NuMu\n",
+    "# Expeirment to run\n",
+    "for experiment,Emax in zip(experiments,Emaxs):\n",
+    "#for experiment in [\"MiniBooNE\"]:  \n",
     "\n",
-    "xs_path = LI.utilities.get_cross_section_model_path(f\"DarkNewsTables-v{LI.utilities.darknews_version()}\", must_exist=False)\n",
-    "# Define DarkNews Model\n",
-    "table_dir = os.path.join(\n",
-    "    xs_path,\n",
-    "    \"Dipole_M%2.2e_mu%2.2e\" % (model_kwargs[\"m4\"], model_kwargs[\"mu_tr_mu4\"]),\n",
-    ")\n",
-    "controller.InputDarkNewsModel(primary_type, table_dir, **model_kwargs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ce7b2a65-71f5-4440-85b5-f6b8b2453956",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N = 1000\n",
-    "for xs in controller.DN_processes.cross_sections:\n",
-    "    \n",
-    "    int_type = xs.ups_case.nuclear_target.name+\"_\"+xs.ups_case.scattering_regime\n",
-    "    \n",
-    "    directory = \"figures/DarkNewsCrossSections/%s/\"%int_type\n",
-    "    os.makedirs(directory,exist_ok=True)\n",
-    "    \n",
-    "    Erange=np.logspace(np.log10(xs.total_cross_section_table[0,0]),\n",
-    "                       np.log10(xs.total_cross_section_table[-1,0]),N)\n",
-    "    plt.plot(xs.total_cross_section_table[:,0],xs.total_cross_section_table[:,1],\n",
-    "             color=\"red\",label=\"Analytic\")\n",
-    "    plt.plot(Erange,xs.total_cross_section_interpolator(Erange),\n",
-    "             ls=\"--\",color=\"dodgerblue\",label=\"Interpolated\")\n",
-    "    plt.loglog()\n",
-    "    plt.xlabel(r\"$E~[{\\rm GeV}]$\")\n",
-    "    plt.ylabel(r\"$\\sigma~[{\\rm cm}^2]$\")\n",
-    "    plt.legend(title=int_type)\n",
-    "    \n",
-    "    plt.savefig(\"%s/total.pdf\"%directory,dpi=100)\n",
-    "    plt.clf()\n",
-    "    \n",
-    "    Erange=np.logspace(np.log10(xs.differential_cross_section_table[0,0]),\n",
-    "                       np.log10(xs.differential_cross_section_table[-1,0]),5)\n",
-    "    zrange=np.logspace(-6,-0.0001,N)\n",
-    "    \n",
-    "    for E in Erange:\n",
-    "        \n",
-    "        record = LI.dataclasses.InteractionRecord\n",
-    "        record.primary_momentum = [E,0,0,0]\n",
-    "        Q2min, Q2max = xs.Q2Min(record),xs.Q2Max(record)\n",
-    "        Q2 = (Q2min + (Q2max-Q2min)*zrange)\n",
+    "    if experiment==\"CCM\":\n",
+    "        model_kwargs[\"m4\"] = 0.0235\n",
+    "        model_kwargs[\"mu_tr_mu4\"] = 3e-7\n",
+    "    else:\n",
+    "        model_kwargs[\"m4\"] = 0.47\n",
+    "        model_kwargs[\"mu_tr_mu4\"] = 1.25e-6\n",
     "        \n",
-    "        plt.plot(zrange,xs.ups_case.diff_xsec_Q2(E,np.array(Q2)),color=\"red\",label=\"Analytic\")\n",
-    "        plt.plot(zrange,xs.differential_cross_section_interpolator(E,zrange),ls=\"--\",color=\"dodgerblue\",label=\"Interpolated\")\n",
-    "        plt.xlabel(r\"$Q^2~[{\\rm GeV}^2]$\")\n",
-    "        plt.ylabel(r\"$d\\sigma/dQ^2~[{\\rm cm}^2{\\rm GeV}^{-2}]$\")\n",
-    "        plt.legend(title=\"%s\\n\"%int_type+r\"$E_\\nu$ = %2.2f GeV\"%(E))\n",
-    "        plt.loglog()\n",
-    "        plt.savefig(\"%s/differential_Enu%2.2f.pdf\"%(directory,E),dpi=100)\n",
-    "        plt.clf()"
+    "\n",
+    "    # Define the controller\n",
+    "    controller = LIController(events_to_inject, experiment)\n",
+    "\n",
+    "    # Particle to inject\n",
+    "    primary_type = LI.dataclasses.Particle.ParticleType.NuMu\n",
+    "\n",
+    "    for tol in [10,5]:\n",
+    "        xs_path = \"output/cross_sections_tol%s/\"%str(tol)\n",
+    "        # Define DarkNews Model\n",
+    "        table_dir = os.path.join(\n",
+    "            xs_path,\n",
+    "            \"Dipole_M%2.2e_mu%2.2e\" % (model_kwargs[\"m4\"], model_kwargs[\"mu_tr_mu4\"]),\n",
+    "        )\n",
+    "        controller.InputDarkNewsModel(primary_type, table_dir, **model_kwargs)\n",
+    "\n",
+    "\n",
+    "        for xs in controller.DN_processes.cross_sections:\n",
+    "\n",
+    "            int_type = xs.ups_case.nuclear_target.name+\"_\"+xs.ups_case.scattering_regime\n",
+    "\n",
+    "            directory = \"figures/DarkNewsCrossSections/%s/tol%s/%s/\"%(experiment,tol,int_type)\n",
+    "            os.makedirs(directory,exist_ok=True)\n",
+    "\n",
+    "            Erange=np.logspace(np.log10(xs.total_cross_section_table[0,0]),\n",
+    "                               np.log10(xs.total_cross_section_table[-1,0]),N)\n",
+    "            plt.scatter(xs.total_cross_section_table[:,0],xs.total_cross_section_table[:,1],\n",
+    "                        color=\"red\",label=\"Analytic\")\n",
+    "            plt.plot(Erange,xs.total_cross_section_interpolator(Erange),\n",
+    "                     ls=\"--\",color=\"dodgerblue\",label=\"Interpolated\")\n",
+    "            plt.loglog()\n",
+    "            plt.xlabel(r\"$E~[{\\rm GeV}]$\")\n",
+    "            plt.ylabel(r\"$\\sigma~[{\\rm cm}^2]$\")\n",
+    "            plt.legend(title=int_type)\n",
+    "\n",
+    "            plt.savefig(\"%s/total.pdf\"%directory,dpi=100)\n",
+    "            plt.clf()\n",
+    "\n",
+    "            Erange=np.logspace(np.log10(xs.differential_cross_section_table[0,0]),\n",
+    "                               np.log10(Emax),5)#np.log10(xs.differential_cross_section_table[-1,0]),5)\n",
+    "            zrange=np.logspace(-6,-0.0001,N)\n",
+    "\n",
+    "            for E in Erange:\n",
+    "                \n",
+    "                record = LI.dataclasses.InteractionRecord\n",
+    "                record.primary_momentum = [E,0,0,0]\n",
+    "                Q2min, Q2max = xs.Q2Min(record),xs.Q2Max(record)\n",
+    "                Q2 = (Q2min + (Q2max-Q2min)*zrange)\n",
+    "\n",
+    "                plt.plot(Q2,xs.ups_case.diff_xsec_Q2(E,np.array(Q2)),color=\"red\",label=\"Analytic\")\n",
+    "                plt.plot(Q2,xs.differential_cross_section_interpolator(E,zrange),ls=\"--\",color=\"dodgerblue\",label=\"Interpolated\")\n",
+    "                #plt.xlim(zrange[0],zrange[-1])\n",
+    "                plt.xlabel(r\"$Q^2~[{\\rm GeV}^2]$\")\n",
+    "                plt.ylabel(r\"$d\\sigma/dQ^2~[{\\rm cm}^2{\\rm GeV}^{-2}]$\")\n",
+    "                plt.legend(title=\"%s\\n\"%int_type+r\"$E_\\nu$ = %2.3f GeV\"%(E))\n",
+    "                plt.loglog()\n",
+    "                plt.savefig(\"%s/differential_Enu%2.3f.pdf\"%(directory,E),dpi=100)\n",
+    "                plt.clf()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c8746489-6621-4a9f-b98f-ce9b997f6647",
+   "id": "c2cfd0da-1393-401a-be3f-b4e49a42c52d",
    "metadata": {},
    "outputs": [],
    "source": []