diff --git a/README.md b/README.md
index 1d80066..5a84352 100644
--- a/README.md
+++ b/README.md
@@ -76,8 +76,8 @@ julia> ax.set_ylabel("y-axis [tan(y)]")
 This code makes use of caching and computes the envelope for each point to be drawn.
 Hence it is memory friendly and is quite fast.
 Benchmarking with 100,000 points.
-```
-julia> using BenchmarkTools
+```julia
+using BenchmarkTools
 for i = 1:n
     if iseven(i)
         xy[1,i] = missing
@@ -85,16 +85,52 @@ for i = 1:n
         xy[2,i] = missing
     end
 end
-julia> @benchmark irsample!(xy,knots,prob)
+@benchmark irsample!(xy,knots,prob)
+```
+```
+BenchmarkTools.Trial: 
+  memory estimate:  29.86 MiB
+  allocs estimate:  600029
+  --------------
+  minimum time:     43.686 ms (4.48% GC)
+  median time:      46.023 ms (4.69% GC)
+  mean time:        48.114 ms (7.55% GC)
+  maximum time:     112.981 ms (51.23% GC)
+  --------------
+  samples:          104
+  evals/sample:     1
+```
+```julia
+using BenchmarkTools
+@benchmark irsample(knots,prob,n)
+```
+```
+BenchmarkTools.Trial: 
+  memory estimate:  37.66 MiB
+  allocs estimate:  735487
+  --------------
+  minimum time:     27.273 ms (6.16% GC)
+  median time:      29.676 ms (11.63% GC)
+  mean time:        31.125 ms (11.67% GC)
+  maximum time:     100.308 ms (62.43% GC)
+  --------------
+  samples:          161
+  evals/sample:     1
+```
+```julia
+using BenchmarkTools
+@benchmark irsample(X,sin.(X),n)
+```
+```
 BenchmarkTools.Trial: 
-  memory estimate:  107.72 KiB
-  allocs estimate:  21
+  memory estimate:  7.19 MiB
+  allocs estimate:  143
   --------------
-  minimum time:     664.329 μs (0.00% GC)
-  median time:      738.891 μs (0.00% GC)
-  mean time:        792.704 μs (1.73% GC)
-  maximum time:     56.139 ms (98.27% GC)
+  minimum time:     6.809 ms (0.00% GC)
+  median time:      7.327 ms (0.00% GC)
+  mean time:        7.811 ms (5.67% GC)
+  maximum time:     76.786 ms (87.52% GC)
   --------------
-  samples:          6271
+  samples:          640
   evals/sample:     1
 ```
diff --git a/docs/README.ipynb b/docs/README.ipynb
index 04ae16b..eb5f86d 100644
--- a/docs/README.ipynb
+++ b/docs/README.ipynb
@@ -18,7 +18,7 @@
    ],
    "source": [
     "import Pkg\n",
-    "Pkg.activate(\"../../.\")\n"
+    "Pkg.activate(\"../.\")\n"
    ]
   },
   {
@@ -59,6 +59,14 @@
    "execution_count": 3,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "┌ Info: Recompiling stale cache file /home/mark/.julia/compiled/v1.1/PyPlot/oatAj.ji for PyPlot [d330b81b-6aea-500a-939a-2ce795aea3ee]\n",
+      "└ @ Base loading.jl:1184\n"
+     ]
+    },
     {
      "data": {
       "image/png": 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",
@@ -87,17 +95,9 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Info: Recompiling stale cache file /home/mark/.julia/compiled/v1.1/InterpolatedRejectionSampling/Xklfj.ji for InterpolatedRejectionSampling [9608d442-472c-11e9-3e14-5b72dbacd115]\n",
-      "└ @ Base loading.jl:1184\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
       ]
@@ -131,7 +131,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
       ]
@@ -173,15 +173,15 @@
      "data": {
       "text/plain": [
        "BenchmarkTools.Trial: \n",
-       "  memory estimate:  107.72 KiB\n",
-       "  allocs estimate:  21\n",
+       "  memory estimate:  29.86 MiB\n",
+       "  allocs estimate:  600029\n",
        "  --------------\n",
-       "  minimum time:     578.331 μs (0.00% GC)\n",
-       "  median time:      630.302 μs (0.00% GC)\n",
-       "  mean time:        744.237 μs (2.45% GC)\n",
-       "  maximum time:     80.901 ms (98.97% GC)\n",
+       "  minimum time:     43.686 ms (4.48% GC)\n",
+       "  median time:      46.023 ms (4.69% GC)\n",
+       "  mean time:        48.114 ms (7.55% GC)\n",
+       "  maximum time:     112.981 ms (51.23% GC)\n",
        "  --------------\n",
-       "  samples:          6656\n",
+       "  samples:          104\n",
        "  evals/sample:     1"
       ]
      },
@@ -202,6 +202,68 @@
     "@benchmark irsample!(xy,knots,prob)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BenchmarkTools.Trial: \n",
+       "  memory estimate:  37.66 MiB\n",
+       "  allocs estimate:  735487\n",
+       "  --------------\n",
+       "  minimum time:     27.273 ms (6.16% GC)\n",
+       "  median time:      29.676 ms (11.63% GC)\n",
+       "  mean time:        31.125 ms (11.67% GC)\n",
+       "  maximum time:     100.308 ms (62.43% GC)\n",
+       "  --------------\n",
+       "  samples:          161\n",
+       "  evals/sample:     1"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "using BenchmarkTools\n",
+    "@benchmark irsample(knots,prob,n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BenchmarkTools.Trial: \n",
+       "  memory estimate:  7.19 MiB\n",
+       "  allocs estimate:  143\n",
+       "  --------------\n",
+       "  minimum time:     6.809 ms (0.00% GC)\n",
+       "  median time:      7.327 ms (0.00% GC)\n",
+       "  mean time:        7.811 ms (5.67% GC)\n",
+       "  maximum time:     76.786 ms (87.52% GC)\n",
+       "  --------------\n",
+       "  samples:          640\n",
+       "  evals/sample:     1"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "using BenchmarkTools\n",
+    "@benchmark irsample(X,sin.(X),n)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/output_3_0.png b/docs/output_3_0.png
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diff --git a/docs/output_5_0.png b/docs/output_5_0.png
index a748f22..837b2f6 100644
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