diff --git a/.travis.yml b/.travis.yml
index 9f1af04..42b4fde 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,17 +1,22 @@
+# Documentation: http://docs.travis-ci.com/user/languages/julia/
 language: julia
+sudo: required
 os:
   - linux
   # - osx
 julia:
-  - 0.6
-  - nightly
-matrix:
-  allow_failures:
-    - julia: nightly
+  - 0.7
+  - 1.0
+
+addons:
+  apt:
+    packages:
+    - libgtk-3-dev
+
 notifications:
   email: false
 script:
   - if [[ -a .git/shallow ]]; then git fetch --unshallow; fi
-  - julia --check-bounds=yes -e 'Pkg.clone("https://github.com/tawheeler/Vec.jl.git"); Pkg.clone("https://github.com/tawheeler/Records.jl.git"); Pkg.clone("https://github.com/tawheeler/AutomotiveDrivingModels.jl.git"); Pkg.clone(pwd()); Pkg.test("AutoViz"; coverage=true)'
+  - julia --project --check-bounds=yes -e 'using Pkg; Pkg.add(PackageSpec(url="https://github.com/sisl/Vec.jl")); Pkg.add(PackageSpec(url="https://github.com/sisl/Records.jl")); Pkg.add(PackageSpec(url="https://github.com/sisl/AutomotiveDrivingModels.jl")); Pkg.build("AutoViz"); Pkg.test("AutoViz"; coverage=true)'
 after_success:
-  - julia -e 'cd(Pkg.dir("AutoViz")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'
+  - julia --project -e 'using Pkg; cd(Pkg.dir("AutoViz")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'
diff --git a/Project.toml b/Project.toml
new file mode 100644
index 0000000..109bb3d
--- /dev/null
+++ b/Project.toml
@@ -0,0 +1,24 @@
+name = "AutoViz"
+uuid = "82aa6e0c-a491-5edf-8d4b-c16b98e4ea17"
+repo = "https://github.com/sisl/AutoViz.jl.git"
+version = "0.6.2"
+
+[deps]
+Cairo = "159f3aea-2a34-519c-b102-8c37f9878175"
+Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
+Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
+Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+Reel = "71555da5-176e-5e73-a222-aebc6c6e4f2f"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+
+[extras]
+Interact = "c601a237-2ae4-5e1e-952c-7a85b0c7eef1"
+NBInclude = "0db19996-df87-5ea3-a455-e3a50d440464"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Reactive = "a223df75-4e93-5b7c-acf9-bdd599c0f4de"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Interact", "NBInclude", "Random", "Reactive", "Test"]
diff --git a/REQUIRE b/REQUIRE
deleted file mode 100644
index 6033789..0000000
--- a/REQUIRE
+++ /dev/null
@@ -1,10 +0,0 @@
-julia 0.6
-
-Cairo
-Colors
-Cairo
-Reel
-Interact
-NBInclude
-Parameters
-StaticArrays
diff --git a/deps/build.jl b/deps/build.jl
new file mode 100644
index 0000000..3467dcc
--- /dev/null
+++ b/deps/build.jl
@@ -0,0 +1,11 @@
+using Pkg
+packages = keys(Pkg.installed())
+if !in("Vec", packages)
+    Pkg.add(PackageSpec(url="https://github.com/sisl/Vec.jl.git"))
+end
+if !in("Records", packages)
+    Pkg.add(PackageSpec(url="https://github.com/sisl/Records.jl.git"))
+end
+if !in("AutomotiveDrivingModels", packages)
+    Pkg.add(PackageSpec(url="https://github.com/sisl/AutomotiveDrivingModels.jl.git"))
+end
\ No newline at end of file
diff --git a/doc/AutoViz.ipynb b/doc/AutoViz.ipynb
index 17567fd..14d171e 100644
--- a/doc/AutoViz.ipynb
+++ b/doc/AutoViz.ipynb
@@ -9,16 +9,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mPrecompiling module AutoViz.\n",
-      "\u001b[39mWARNING: Method definition (::Type{AutoViz.CarFollowCamera{I} where I})(I, Float64) in module AutoViz at C:\\Users\\ekhla\\AppData\\Local\\JuliaPro-0.6.1.1\\pkgs-0.6.1.1\\v0.6\\AutoViz\\src\\cameras.jl:43 overwritten at C:\\Users\\ekhla\\AppData\\Local\\JuliaPro-0.6.1.1\\pkgs-0.6.1.1\\v0.6\\AutoViz\\src\\cameras.jl:46.\n",
-      "WARNING: Method definition (::Type{AutoViz.CarFollowCamera{I} where I})(I, Float64) in module AutoViz at C:\\Users\\ekhla\\AppData\\Local\\JuliaPro-0.6.1.1\\pkgs-0.6.1.1\\v0.6\\AutoViz\\src\\cameras.jl:43 overwritten at C:\\Users\\ekhla\\AppData\\Local\\JuliaPro-0.6.1.1\\pkgs-0.6.1.1\\v0.6\\AutoViz\\src\\cameras.jl:46.\n"
+      "┌ Info: Precompiling AutoViz [82aa6e0c-a491-5edf-8d4b-c16b98e4ea17]\n",
+      "└ @ Base loading.jl:1187\n",
+      "┌ Warning: Package AutoViz does not have AutomotiveDrivingModels in its dependencies:\n",
+      "│ - If you have AutoViz checked out for development and have\n",
+      "│   added AutomotiveDrivingModels as a dependency but haven't updated your primary\n",
+      "│   environment's manifest file, try `Pkg.resolve()`.\n",
+      "│ - Otherwise you may need to report an issue with AutoViz\n",
+      "└ Loading AutomotiveDrivingModels into AutoViz from project dependency, future warnings for AutoViz are suppressed.\n"
      ]
     }
    ],
@@ -38,17 +43,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "Cairo.CairoSurface{UInt32}(Ptr{Void} @0x0000000004d33cb0, 1000.0, 600.0, #undef)"
+       "Cairo.CairoSurfaceBase{UInt32}(Ptr{Nothing} @0x000000001f9491a0, 1000.0, 600.0)"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -79,17 +84,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "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",
       "text/plain": [
-       "Cairo.CairoSurface{UInt32}(Ptr{Void} @0x0000000004f02ac0, 1000.0, 120.0, #undef)"
+       "Cairo.CairoSurfaceBase{UInt32}(Ptr{Nothing} @0x000000001f9489d0, 1000.0, 120.0)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -116,17 +121,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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",
       "text/plain": [
-       "Cairo.CairoSurface{UInt32}(Ptr{Void} @0x00000000052ccdf0, 1000.0, 600.0, #undef)"
+       "Cairo.CairoSurfaceBase{UInt32}(Ptr{Nothing} @0x000000001f949c90, 1000.0, 600.0)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -146,17 +151,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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N6a76pom9iDDVnnzK4plvvfveT374vRCHRcYjgnscSranPPrEUzqD8ePtB4LBePt7q9Nq01KSU+3WZIvJbDTYrBaj0aDVagKBoMvtHhwa9rjcXp/X4/F6fV630+Xxejweb35+ftMRbpsDgPgkazQ6nc5sNpuMJnOSxWgwmkxGo8FospjTUlMsZrNOpw0GQ16vb9Thcnt9Yy7P0IhjcHhM6KWnMaHVak6YOy3g81539RVjI8OxbgcTjOAeb8orax565NGDLV31R9pj3csE0Gg0Gam29JTklGRLeqrNbDR6fb7Oru7GgwePHDrU0dnR3trS2dHmcjq4hwIA8HlkjcaSZM3LLywoKs7Pyy8rL6+srMzPyzUaDG6Pt39oZHjMNTji6B8ajY8H1ZXFudMrim684fqD9fti3QsmEsE9rpx6xjk//tEP12/b29kzGOteImc06HOz0rLSbHlZ6RpZbm5tb2xs2Ldv767t21qbjwQCE/lbRQBAItPp9EUlZbPmzpsxo7aqqrqkqCAUDnf2DvQOjnb1Dk7sPpYoy8lMXbag7sf33rfm9Zdi3QsmDME9bsi33/2di84//+0N28ec7lg3o5pBryvOy8rNTM3KSHW73QcaGjd+vGHdB+91dbTFujUAQALJzS9cdvKK409YMq26ymw29/YPdfUNtXT2TvYjj/8dq8X0paXzXn399V/+5F5pQg+DRqwQ3OOB3mD81YN/qKys+ODT3eO/BcmenDS/tvKDT3ZG4a+4zWopyc8qKciWJWnrth3vvvPWxg3rHKMj4lcGAOAYrDb78UuWrTjtSwvmzQ2HpaaO7paOvlGHK9Z9qaDVapYvquvu6rr5xmt93ilyEBw+H8F9ysvKyX38L0+PjLm37Dk4zqitkeWZNaWVJXkfbd7bMyDwjZbsjNSSvIzCvKzhkdG1a9e98rcXDzUeELccAADjVFE17dwLLlq+bFmK3dba2dvS1d/TPyR60eQks9PlCY37xPdZ1SW5malXf+XL3Z0dE9IYYoXgPrXVzKh7+OFHt+071NzRO85S2ekpJy6sa2hq39PQPIG3QnyW1WKqLs0vK8ptbmlfvfrNN197aXhwQMRCAAAIkpKWvvKc8888c2VJccGR1q6Gpg6HS9ST7MrivNnTyz/etq+zd7yvrpXkZ82bUXHDDdfV79szIb0hJgjuU9iseQv++Ic/frR1X3ffBPzQn52R6nC6RVyopNVoSguzq0sLZCn8zrvvPfHIn3t7uiZ8FQAAoiklLf3iy64855yzbcnJjU0dDU0d/sDE74O3mI0nLZo55nBt3H4gOL4Tb7LTU5Ytqrv99tu2fPLxRLWHKCO4T1VLlq/4xf0/f2/jzoHhyXu/UkaqbUZlYVZayiebtjzx2CN7d22PdUcAAEyw2llzr772+uMWLegdHN53sE3EvYfTK4rKCnPe+HDzOOuk2ZNPPWHOPd+9Z+17b09IY4gygvuUdMbZ5//g+//x1vptk/Y20KLczFk1pU6X69FHHn7z1Zc4wxEAEN90Ov3Kc8+/9rrrrUmWXfVNrV39se7o30tOMp+5bP5Pfvqz119+Mda9QDWC+9Rz4WVX3nnH7avXbXO4Jt2xjxpZLi3MmVVd0tbR+d+/++0n6z+MdUcAAERV3ZwFt3z9thnTa+oPt9U3tU/CK8wtZuPKZfP/8OeHnn784Vj3AnUI7lPMDTffftWVV7yxdovH64t1L/+HXqerqyyqLM3fuGnLgw/8orXpcKw7AgAgZopKy2+/8+7jFy1obGrfe7BNxPb38TAZ9Gcsm//c8y/88be/inUvUIHgPpV88zvfP2vlmavXbR3/Ye0TSKPR1FYW1pQXvfb6m3/83a9HhoUfjwUAwJRgT0n92q3fOOfslfWHW/cebAuN7+3SiaXXac9cNv+NN1f/6mf3xroXKKWNdQNQ6va771m5cuXqdVsDk+aXbrIslxfmrDhhdkPDgZuuu/r9d9Z4PVzuAADAP3g9ng0fffjC88/X1tWdc9qJUlgaGBqdJFeYhkLhQ61dZ6xYlpqZtWnjhli3A0UI7lPDV6776uWXXbZm3bZAMMJn7RqNZmJPZy/IyThtyZyRoYGv3XTDSy88S2QHAODf8nm9a99/5/XXX1+4YOGpJy7y+v1DI45YNyVJkhQOh4+0dZ97+sl6U9L2LZti3Q6OjeA+Bay6+Mu33nLzm2u3+PwR7pDLzUw7c9n8A4fbJiS7Z6TaTlsyx+91feO2W5989M9joyPjrwkAQHxzOhxvr3lj3bqPTjt1xeI50/uHRl0eb6ybkkLh8JG27ovPO3PM5dm/d3es28ExsMd9sjv1jHN+9KP/fOODzRH/9a4uLZhRWfTWR9vG/w1Cr9MtqKtMtVm+973vbt64fpzVAABITAuPX3rffT8ZHnNu2XNoot5bTU+xRXy1i8loOHv5wnvv+6+33nh5QpqBIDxxn9QWn7Ds/vt/tmbd1sguNJUlaen8Gan25LfWb4v4af1RJflZK06Y/eGHH9x807XtLc3jrAYAQMLqaGt9+qm/pKZmXHr+So/XNzQ6ATtnFs2qLszNaIvo/PhAMNja2XvDVZc1HmpqbT4y/mYgCE/cJ6+62fMeeuihdzbsiPjvs9GgryjO23ewZZydWC2mRbOq/T7PN++4renwwXFWAwAAf5dfWHz/L3+dnZ310ZZ9Y87xXs8yb0ZFRpr9nQ3bI9sZm5Kc9KWl877+9Vu2bf5knJ1AEIL7JFVeNe0v//M/H27a0z8Yyx3ksiTVVhVXleb/8hcPvPrSczHsBACAeHXu+ZfedfedjU0dextbxvkuWm1lcUlB9up1WyM7ejItJXnFcbOuvvrqxgN7x9cIhCC4T0ap6RmvvPrGxp31Pf3DMWzDYjKectysA/UN37rzVpdzUrz/DgBAXLIkWX/+wO+m1VS//8mucb6TVpyf1dM/HPFFjdkZKcfPrjnv3LOGBiLZdQOhCO6Tjkarff5vr/UNORtbOmPWRFgqzMtYPLvm17958K/P/k/M2gAAIJGcterie75995a9B5vbe2PYRnlhTmF26gXnrQxOsgtfQXCfdB743Z9y84s27WqM1R+OVqtZWFdlMelu+eqNvKECAEA05eQV/Pb3fzKaLBu27YvZRelhadGsqq6OtjtvvSk2DeBzcKrM5HLtV29dvnz5ui37YtVAmj35zBPnr1239ravXj88NBirNgAASEyOsdG/vfhcTn7BqjNX9A6OxOasd1nq7Bk4bv5MS7J925ZPY9AAPgfBfRI54aRTvn3XXWs+2h6M9HrUcQlLVSV5C+oqv/nNO1985i8Te80qAABQKBwOf/rxR3v27b/h6itCwdDA8FhMfgnf0tl76aqVh5uaW5oOx2B5/DtslZksCopLX3jhxbc37BgZc0Z/dVmSFtRVWkz6a6+6fLC/L/oNAACA/8eekvrok09rtPr12/bH5IGazWo5/cR5X77skubDh6K/Ov6VJtYNQJIkyWyxPPrYkxt31Mcktet12tNPnNfd1XHBOWeQ2gEAmCRGhocuXnXW3r17Vi6bb9Dro9/AqMO1fuu+hx953JJkjf7q+FdslZkM5IefeHrE6T3U0qV2plajKcnPHh7HjWspyUkrT1rw7HPP/+RH32N7DAAAk0o4HP7wvbd1RsuXLzy7s2cw4kMeI+Zwuk1Gw1lnn/3qS3+N8tL4VwT32Lv+5tvnz5//yc4GtRM1snzGsvkjY66B4dHIls7LSlu2qO6ee+55+YVnIqsAAABE27Ft8/4DjTdde8Wowz3qdEV59d6BkVnTyi3J9u1bNkV5afw/bJWJsapptdd85cp1m1UfIyNL0vLjZnX2DjY0tUe4dHHe3Olll11y8foP34usAgAAiI5PNqy97NJLZk8rqSrOG0+dotzMCF5w/HDTnqu/ckXNjLrxLI3x4+XUWNLrDa+teWd3Q0tX35DauYtn14RCoc27GyNZOCzVVhXlZ6VedsmFw4MDkVQAAABRl2yzP/vCS31DY7samiOrsKCuSqvVfLqzXu3E7PSU+bXlZ35phd8XixMqIUkST9xj6777HxgcdkSQ2mVZDoVCWyJL7ZI0d0ZZuj3pgvPOIrUDADCFjI2OXLjqbKtJd9zsmsgqbN3TaE+2VJXmq53YMzDcOzh6789+Gdm6mBAE95hZfurpixct2LYvkrNRw+Hw5t2NEbxJKkvScbNrNFLgkgvOcTljcIINAAAYD4/bddnFq1zO0aXzZ0SwcSIsSe9t3DmzujQrPUXt3K17Dy1aMP+0M89RvywmBltlYiM1PeO1N1a/+/HOUUf0XjGRJWnJ/OmDA/03XXtlKCZ3PAEAgImg0Wge+O8/l5SUrduyN4JD4awW85zpZeu3qn7Fzmoxf2npnPPOOWugr1ftXIwfwT0GZFl++oWXh12+xqbOaC56ynGzDuzf/607vx4OhaK2LgAAEEHWaH7+wH9Pmz79/U92RfNA5/LCnHSb6fKLV3GKdPRxHGQM3HDz7bNmzd6yJ6qXkJ0wb1rTkcN33XazxF8zAADiQDj87lurZ89dMKeupqUzevcnDo06plUUJ3E6ZCzwxD3aMrNzXnn1jdc/2OTx+aO26OJZVaMjw1+99ooQz9oBAIgjskbzp0f/YrOnbYr0yIoIGPS681YsXnXeuT1dHVFbFBLBPfoef+oFpy98qFX1JakRCkszq4tNBs2Vl14QDASitCgAAIgWjVb7xFPPh7WGHfuORC3ZVRbnWgyaay6/KErrQZIkTpWJshWnn1VYWBC91C5J1WX5afaka664lNQOAEBcCgWD1111eZJBN72iIGqLHmzpKszPO+3Mc6O2IiSeuEeT0WR+690P1m3eOzwWpXMYy4tyyguzLzzvbJfTEZ0VAQBATFiSrH995fXDbT2HW7ujs6LNajnluJmnnbLc63FHZ0XwxD16vvfD+9q7+6OW2nOzUmsriy+/5EJSOwAAcc/ldFx+yYW1lcW5WanRWXHU4Wrr6vvuf94XneUgEdyjpmpa7YqTT9p5oEnVLL1Op5Ej+a2I1WJaMnfGDddfN8TdqAAAJIahwYHrrrnqhLnTrRZzdFbcvr9pxfJlM+pmR2c5cBxkNMiy/NiTT++qbx5R87hdluVzTlnc1Tfk8fpULafTaleetOC+//rJ5k/Wq+wUAABMYUODAy2tnTdcfdnB5s5QSPgB0OFweGhk7JKLLnj+2ac5bzoKCO7RcPUNX5s+fcau+mZV7xQsqKsaHnU0tavbqSZL0orjZ7/+5ptPP/Gwui4BAMDU13T4YJI97fRTTjzcEo3DMBxOT1lhjtFs2bl9axSWS3BslRHOZLZcf+01G3c2qErtqXZrQW7G9v2H1S43t7a8qbn5wV/8VO1EAAAQH373q58fPnxkXm1FBHMXzapOtVtVTJCljTsbrr/2GpPZEsFyUIXgLtzt3/x2U3uP2u0uyxbWrdu0R+1lwiUFWalWyx233CBJ/LoKAICEFb79lhvsVnNJQZbamQ1NHSctmqnqBTuP19fc0XPbN7+tdi2oRXAXy56adu45Z+1uaFE7cfOuxsGRMVVTbFbLvBkV11x1hd+n7ocEAAAQZ/w+37VXXTFvRoXNqu5B+PCoo7tvqKa8UNWsXfUt551zVkpauqpZUIs97mL96Cc/d3nDXf1DaieOOdUdiSrL8ulL595333/t2blN7VoAACD+uJyO5ta2a6+8tKGpQ9XErt7BZYtmNrV1B4JBhVNCoZBGlufMmfv+O2vUdwqleOIuUHZewbIlx+873BaFtebXVnyyacu7a16PwloAAGBK+PDdtzZ+umm+ys3uwVDokx0HcjLTVM3ad7B12ZLj8wqLVc2CKtycKtAfH/1LUGM8JP6d7tystDk1JStPX8EmGQAA8Fl6veH1Ne/sOdjW0SP8apeqkjw54Pna9V8RvVDC4om7KBXV0+tmTIvCtcMGvf7E+TNuu/UWUjsAAPh//H7fLTd/dcm86SaDXvRaB5s7a2dMm1Y7U/RCCYs97qL87o8PNXx3HxgAACAASURBVLZ0q7pxKTInHzfz2eeef2f1a6IXAgAAU9HQQH9YY1j5pZMPtQrfBeBye8740pdeevE50QslJp64CzFn/qL8vNzWzj7RC5UX5QwNDj78h9+KXggAAExdj/359/39/eVFOaIXaunsy8/LmTN/keiFEhN73IV44ukXBp1+0cHdoNedt2LxqvPO7elS97Y4AABINJnZOa++9sar72/y+vxCFyrOy0qx6K654mKhqyQmnrhPvIKikorysjbxj9uPm13zl6eeIbUDAIBj6uvpfuIvTy2eVSX6ksaWzt7K8tLiskjubcUXI7hPvG/c9e09DU2iby7NTrcb9NqH/vCg4HUAAECceOSPvzXotLlZ6s55jMDegy233v5N0askIIL7BEu2pxy3aOFBwUdAyrK8ZH7tPd+5O6T4ZgQAAJDgQqHQt+7+5gnzpmtksZulG5s6jl+80J4q/CeERMOpMhPsG3ffozUmdfUNKp+Sn52u9p7UOdNK9+/b9/STj6rsDgAAJLS+nu6q6XXlpcXd/cPiVglLktGgKyoq+nj9WnGrJCCeuE8knV5/1soz96u5KjUnI7W2qkTVKslJ5rLC3B//4DvqmgMAAJCkH3//O+VFuclJZqGr7D3Yes5ZK/UGg9BVEg1P3CfSldfclFdY1NTeq3zKycfN2rhtv0fN+93LFtT+/g9/2LNzm/oGAQBAovP7/cNjrnPOPO1Im8BrIoOhUKrdajCad+3YKm6VRMMT9wkjy/IVV16+u6FF+ZRUu9UfCA6ruaQpPcUmy+FXXnxGfYMAAACSJEmvvPiMFA5lptlVzVowsyrNnqx8/K76piuvuFwWvJ8+oRDcJ8zS5acGAkFVu9XnzqjYse+wqlWOn1Pz8/t/Fg6LPrQGAADErXA4/LOf/XTRzCpVs1o7eufMKFc+3uHyBIKhpctPVdkdPhfBfcJ85aprDqjZ3S5J0uGWru7+IeXj87LSBoeG1r73tsrWAAAA/o/1H747ODRUkJOhfErPwLDdakkym5RPOXC4/cqrrlbdHD4HwX1iWJKs02uq2rsHVM1q7uhRNX7RrOr77v2hqikAAAD/1r0/+s+FM6tUbWTZeeDIzJoS5ePbuvtn1FQnJdtUtoZ/j+A+MS694uqWjp6QyB0s5YW5h5uad23bIm4JAACQOPbs3HboSFNpYbbyKU3tPbmZKk5nD4fDLZ09F192pfru8G8Q3CfGqlWrGls6xdWXZXnujPJ7f/gf4pYAAACJ5t4f/se8GZXK3x8Nh8MvvbNR1RINRzrOX7VKfWv4NwjuE6C4rCLZmjQ04hC3RFlBzp79B5oOHRS3BAAASDRNhw7u2X+grCBH3BLDY87kZGtpRaW4JRIHwX0CXHfjVxuaOoQuMbOm5MEHfiF0CQAAkIAe+MX9ddXFQpdobOq45tqbhC6RIAju46XRaE46canQKwyyM1J7enoP7N0tbgkAAJCYGg/s7e3rz81MFbfEodauZcuWaDTEzvHiv+B4LTvlSw6n26vm6lO15kwr/f3vfyeuPgAASGS/++1vZ08rk4QdseHzBxxOz0krThe1QMIguI/XpV++vP6IwH0y9uSkcDi07v13xS0BAAAS2foP3w2HQ3ZbkrglGpo6Lrnsy+LqJwiC+7hotNraaTXt3f3ilphVU/LE409I4n4KBgAAiS78+OOPzxS5072tu3/GtGqtVituiURAcB+XBYuXDI85xB3fbjYaMlJtf33+KUH1AQAAJEn663NPZabZzUaDoPrhcHh0zLnguKWC6icIgvu4rDr/gpaOXnH1K0vyVq95OxgIiFsCAAAgGAisXvN2ZUmeuCWaO3vPXXW+uPqJgOA+LgsXzG/p7FM4WJbl8qJcVfWrSgueeOxh9X0BAACo8/ijD1UWiwzu7X0L588TVz8RENwjV1JeKUmy8vNkinIzs9JTlNdPT7F19/R2d7RF1B0AAIAKPZ3tPX39Gak2QfV9fr9Goy2tqBJUPxEQ3CN3wUWXtnT0KB9fVphzpLVL+fhp5QUvPP+8+r4AAAAi8eKLL1SXinzo3tFz3gUXi6sf9wjukVu27MRmxftkJEnKTLf3DgwrHCzLcl5W+qsvEdwBAECUvPzis3lZGRpZVj5l2cI65aObO3pPWrYsgsbwdwT3CNnsKempqSNjToXjM9Ps/YOjyk+fKS3I3rZjp9fjiaw9AAAAtXxe7/Zde0oKspVP0Wo1GWl2hYNHxpwZaan2lLSIugPBPVIrzz2/rUvdeTJ7D7YoH1xTVvCXJx9X2RQAAMC4PPnYIzWlBcrHN7X1lBXmKB/f1tV75jnnqe8LkkRwj9jSpSd29A4pH983OKJ8n4xBrzPotFs++Tii1gAAACK0ddNGg15r0OsUjm/t7C3IyVBev7N3aMnSEyNqDQT3SNVUV3X3qQjuqhTnZW3auo3bUgEAQNSFN2/bXpSXqXB0MBTy+vwWs1Hh+K6+wZqqykh7S3QE90jY7Kl6vd4v7F6kkvys1155SVBxAACAL/DK3/5amq9im/u6zXu8XqWnY/v9QaPRYGObe0QI7pFYuvyUnv5BQcU1smy3WT/9+CNB9QEAAL7Apo0fpdiSZMVny4w53cFQSGl1WeruG1py4kmR9ZbgCO6RWLr0xJ5+pRvW1crNSmtoPBgU9jgfAADgCwSDwYaDh/OyRD0U7xkYXrrsJEHF4xvBPRJ1tbVdAje4Z65+801BxQEAAI5p9eo3SxRvc1erq3dwVl2doOLxjeCumt5gtNttDpeoE9bzczLeXv2aoOIAAADHtOb1V/JzRAV3h8uTYk82GJW+z4qjCO6qzVt43PDomKDiKclJff0DjrFRQfUBAACOyekY6+3vT0lOElR/aGRs7oLjBBWPYwR31U5afnKXmhPcVcnLTt+8eYug4gAAAApt2bo1PztdUPGuvqFlJy0XVDyOEdxVq6ur6x0cEVQ8NyNl/bp1gooDAAAotH7tuuwMu6DiPQMjM2fOFFQ8jhHcVcvLzRkacQgqnpZq37Jpg6DiAAAACm36ZH1aiqjgPjgylpebI6h4HCO4q6PTGwwGfSAYFFHcYjKOjo553G4RxQEAAJTzetxjDofyK1FVCQZDBoNebzCIKB7HCO7qlFZUOdWcJ7Nk3gzlg/Oz0/fs26e+KQAAgIm3Z+++3ExRp7m7XJ6SskpBxeMVwV2dWbPnDAwpPfJFlqSczFTlxbPSbR+vXx9RXwAAABNs48cbstNF7ZYZGBqdOWuOoOLxiuCuzowZtcMOl8LBSRazw6l430tYyslMW7/u/Qg7AwAAmFBr3383JyNVCgspPjTmnFFbK6R0/CK4q1NRUTk4rPQQ9/TU5EHFr7HKGjkUCg/290XaGgAAwEQaHuwPS5KskRWOnzO9vChX6bVNQ6POykq2yqhDcFcnPy9neFRpFk9PsQ0MK91Xk5Kc1NvXH2lfAAAAE6+3r9+u+BqmMac7PdWmcPDQyFh+Xm6kfSUogrsKOp3eaDD4A0qPlOnoGejoGVA4OD3VdvjIkUhbAwAAmHhHjjSlpyQrHDwwPJqmeLA/EDQaDTq9PtLWEhHBXYWikjK3x6t8fE//kMfrUzjYbjXv38+RMgAAYBLZv39fqk3pE/fhUWeK4sfzkiS53d6i4rKI+kpQBHcVyiurRsacgoqnpyTv2blDUHEAAIAI7Nm1M9VmVTg4HA7LstIN8ZIkjTqcZRUVEfWVoAjuKuTlFzjVPHFXxZ5sbTjAE3cAADCJHNi/x6bmIfpbH21TPtjp9ublF6pvKnER3FXIzc11e5RufVFFI8uBYNDlVPraKwAAQBS4nQ4pHNYofo7ucKm4AN7t9efm5UXUV4IiuKuQk5Prcgt54p5stfT1K32NFQAAIGp6+vqTrRYRlZ1uT05OjojK8YrgrkJmZobT7RFR2Wa1dPf0iKgMAAAwHj29vclJZhGVnS5PZobSc98hEdxVSU2xO1xCgnuS2djd3S2iMgAAwHj0dHcnmU0iKjtdnrTUFBGV4xXBXYVkq9Xr84uobDLqu7u6RFQGAAAYj66uLovJIKKyx+tLShKyCSdeEdyVkmVZ1mjC4bCI4haTsbOjXURlAACA8ejsaDcbhVyTFJYkrVar6gTJBEdwV8qSlBwOhQQVt1pMbW1tgooDAABErL29LckiZI+7JEnhcNicpPSceBDclcrIyvb6A4KKJ1nMHW0tgooDAABErK2lOckiZI+7JElenz8zi4NllCK4K2VLtvn9Qja4S5Kk1WhHR4YEFQcAAIjY6PCQTqsTVNzvD1itPHFXiuCulMliDgSDCgdrZLmmrEB58bAUDgj7qQAAACBigYA/LAl5x0+SpGAwaLbwfqpSBHelkpKSAgGlwd2g11WW5KuoLuadVwAAgAkgLKj4A8EkS5Kg4vGH4K6U2WxR/sRdp9MqHyzLckjYa68AAADjFAqHBZ39EgwFTWZRb77GH4K7UmazORhUGq+1Wq3yx/M6rcYfEPXaKwAAwDgFAgGtRmlonFVTajYqPfc9EAiazWyVUYrgrpTZbFEe3HVajZrBWoI7AACYtPyBgE6nVTg4PcVmMRsVDg6GQhb2uCtGcFfKZDIFFG9o8foC3f1KT4nRarV+MReyAgAAjJ/P59dplYbGQDCo1SpN+cFg2GQWddZk/CG4K2VSs1XG4XLvP9SqcLBOq/FxpAwAAJis/H6/miwe0ikfHAqaTexxV4rgrpTJZAyFhLxSrdVqfD6fiMoAAAATwqBTcZS7Qa90sCxr0jMyI+ooEYk6Tj/+6PWGkJizkDQaTVDxETQAAABRZjKZDHptSFlc0eu0RoNO6WC9LoPgrhjBHQAAAF9kcHDQFwhqlG2Acbq9Xl9A6WCXp7OzfXzdJRC2ygAAAGDChMKhMDdLisETdwAAAEyY3fVNAcXneUAVgjsAAAAmjF/xHZRQi60yAAAAwBRAcAcAAACmAII7AAAAMAUQ3AEAAIApgOAOAAAATAEEdyG0Wo3JaIh1FwAAAIgfBHchCnIyaquKY90FAAAA4gfBXQhZkmVJjnUXAAAAiB8EdwAAAGAKILgDAAAAUwDBXYhQKBQKhWLdBQAAAOKHLtYNxKe27v727v5YdwEAABBtVaX5w6PO3oHhWDcShwjuQoTD4XCsewAAAIg+uzXJ5wvEuov4xFYZAAAAYAoguAMAAABTAMEdAAAAmALY4w4AAIAJ09rV53S5Y91FfCK4AwAAYML09A/FuoW4xVYZAAAAYAoguAMAAABTAMEdAAAAmAII7gAAAMAUQHAHAAAApgCCOwAAADAFENyFyM5InVZeGOsuAAAAED8I7kKYjQarxRzrLgAAABA/CO4AAADAFEBwBwAAAKYAgjsAAAAwBehi3UB86hsccbjcse4CAAAg2jJSbS6P1+X2xrqROMQTdyGcbk//0GisuwAAAIi20oKcrLSUWHcRnwjuAAAAwBRAcAcAAACmAII7AAAAMAUQ3AEAADBhnG6Px+uLdRfxiVNlAAAAMGH2H2qNdQtxiyfuAAAAwBRAcAcAAACmAII7AAAAMAUQ3AEAAIApgOAOAAAATAEEdwAAAGAKILgLYbWYM9Psse4CAAAA8YPgLkRGqq0kPzvWXQAAACB+ENwBAACAKYDgrlRZeYXNalE42GI22pOTFA42GvS5OTyeBwAAk5Qsi0qMsiyHQiFBxeMPwV2p/fv2Ol1uhYNHHS6H4sEut7d/YDDSvgAAAMTS63WBYFBEZY0se71eEZXjEsFdKY/Ho9Uo/c8VDIa0WqWDA8Gg0WCItC8AAACxjAZDIKA0uGsU5yVJkrRa2evxRNRUIiK4K+V2u1VlcZ1Wq3ywXq+PtC8AAACx9Hp9MKh0Q8uSedPTU2wKB2u1Gpdb6SYFENyV8njcyp+4qwruwWDIYCC4AwCASUqv1wcUB3e9Xqd827pWo/EQ3BUjuCvlcbt1OqX/uUYdrn2HWhQODgSCOp0u0r4AAADEUrXHXafVqhrs8RDclSK4K+VyubRqHqJ39w0pHRwKKX88DwAAEGVarVb5Q3SdVqP88bxWq3W5nJH2lXAI7kq53W6d4j3uasmyLKgyAADAOKkKKlqtJqjiibvGzVYZxQjuSrldTp1W1IYWWSNLEtkdAABMQuoeMH7wyS6fP6BwsFajcbtckTSVkAjuSjldTuV73NUKhyWj2SyoOAAAQMRMZnM4rGK8w6XieEedTut0slVGKYK7UkODgwZhhzb6/P7MLC5PBQAAk05GVrbP5xNU3GjQDw8rfS0QBHelhgb6xZ394nS6CwuLBRUHAACIWFFxiaqH6KrodbqhgT5BxeMPwV0pn1fgtV5Otze/sFBcfQAAgMjkFxQ6hQX3sCT5vF5BxeMPwV2FQCCg/A4mVdweX15evojKAAAA45GXl+/2Ctkqo9VoAn6/iMrxiuCuwsjomMVsFFHZ6fHm5uWJqAwAADAeubm5Lo+Q4G4xG0dGx0RUjlcEdxUGBgeTLCYRlZ1uT1ZmlojKAAAA45GZmeV0C9kqY7WY+wcHRVSOVwR3FXp7epLMQoL7yJgrN4fgDgAAJp3cnOyRUSEnNiZZTL09vSIqxyuCuwpd3V0Ws0FEZbfHa7PZuD8VAABMKrIs22xWQXvcLSZDV1eniMrxiuCuQldnp8WoYo/79Ioi5YODwWA6R7kDAIDJJD0zOxAICipuNuoJ7qoQ3FXoaGtNUvNy6oxKFcF9cHi0buZc9U0BAACIMnP23MFhUe+PJplNne1tgorHJYK7Co0N9bbkJOXjff6AXvGdTYOjztq6mRH1BQAAIETtzFlDow5Bxe02a2NjvaDicYngrsJAX49Op1U+fnB4LC0lWeHgkVFnzbSaiPoCAAAQoqa6ZnjMpXBwmj1Z1T5hrVY70NsTUV8JiuCuQjgcHh11mE1Kd8sMjTjSFQf3/uHRkiIVX+sAAACilRQXDQyNKhyck5mq/KQNi8k4OjoaDocjbS0REdzVaWlrU57F27v73R6lt/i63F67PVkj5mZWAAAAtTQajc1mdSkOM2kpyQPDSlN+WkpySysb3NUhJqrT2NCQalO6zX14zNnUruIXQF6vv6J6ekR9AQAATLDy6ukeNQdBptmtg8NKN8Sn2a0NDQ0R9ZW4CO7q7Nm9K0XN+6mqdPYOLjvpFEHFAQAAVFl+8orOXhU3mxoNBp/fr3BwSrJlz+6dEfWVuAju6uzeuS091SaoeFff0KLFiwUVBwAAUGXR4sXdfUPKx2/apeKImLQU2+5dO9Q3ldAI7ur09XTrFJ/wqFZP/1BFeamg4gAAAKpUlJb0DAwrH9/a2ad8sE6n6+dIGZUI7uqEw+Exh8NkNIgoHgyFNLKcmZ0rojgAAIBymdk5YUkKBkMiipuMhrGxsXBISPE4RnBXraWlNTPNLqh4d9/gictXCCoOAACg0LKTT+1Rs09Glcw0O0fKRIDgrtrmzZuz0kRtc+8eGFlywhJBxQEAABQ64YSl3QMjgopnp9s2b9okqHgcI7ir9uH77+RlpQsq3tkzMHNmraDiAAAACs2sm9HVOyCoeG5W+toP3hNUPI4R3FVrPnzQaDRoFF8MporH59dotCXlFSKKAwAAKFFcVqnRaDw+pWc7qqKRZYNe33TkoIji8Y3grlo4HG5t6xB3KGRLZ+95F1wiqDgAAMAxXXDhJc0doo58SU+1tbZ38GZqBAjukdi2fWtOZoqg4k3tPcuXLRNUHAAA4JiWnbSsuUPF2Y6q5GSkbN+6VVDx+EZwj8S6Dz7IzUgVVHxkzJmRkWZPSRNUHwAA4Ask2+zpaSkjY05B9fMy09Z++IGg4vGN4B6Jnds3p9iSlY83m4zlRSpOZ2/r7DvznPPU9wUAADBeZ6+6qK1L1ON2SZLsNuuuHTxxjwTBPRIBv79/cDA5yaxwvN8fmD2tTHn91q6+008/I6LWAAAAxuVLp5/e2tkvqHhykrlvYDDg9wmqH98I7hHatWu38kMhA8Gg1+e3WpQG/e6+ocryMoPRFGl3AAAAkTAYTZVlpT39oq5eystK3717t6DicY/gHqE1q98ozFFxmntze09JQZbCwaFwuKOnf9VFl0XUGgAAQITOv/iy9u6+UDiscPy08sKS/Gzl9QtzM956a3VErYHgHqlNG9enpiTLik9zP9zWXVqQo7x+/ZH2iy66KKLWAAAAInTRRRc3NncqH19Zktc7OKxwsEaWU2zWTzesi6g1ENwjFQwEGg4ezs1UevaL2+Pd29isvP7A8FhOdmZOfmEkzQEAAKiXnVeQlZHePzSqcLxep9VqtS63V+H43Ky0xoOHgoFApA0mOoJ75NasfrMkP1P5+KZ2dRcZNDZ1XH3tjSqbAgAAiNC119/U2NyhfHxRXlZ7l4rXWEvyMlevflN9X/gHgnvkVr/+SkGOiuCu1sHmztNOPUX5bhwAAIBxkE87dcXBli7lE0oKsg+3qhifl5Px1huvqm8M/0Bwj5xzbLSnrz/VZhVU3+31+fzBRcefKKg+AADAUQuOX+LzBzxeFQc1bti6b3BkTOHgVLu1t6/fMaZ0Hw7+FcF9XD744INiNbtl1Gpoar/62uvF1QcAAPi76667sf5Iu6opXp9f+eCSvKz33+fC1HEhuI/L3158riQ/W1J6YpJqLR29dTOmZWapOGUJAABArfTMrJm101s6esUtUZyf9fJfnxdXPxEQ3Mels60lEAiYTAZB9UPhcENT+y133CWoPgAAgCRJt3/jWwcOtSo/vl0ts9Hg9wc62loE1U8QBPfxWr9hY1mBwCfiBw63nXrycpPZIm4JAACQyExmyyknL6tvUrdPRpXSguwNH28UVz9BENzH6/FHH6ouKxBX3x8Itnb2XnvjzeKWAAAAiezaG29p6+zzB4LilqguK/ifJx4RVz9BENzHq7Xp0MjoWKpd1NkykiTtbmy5+KILNVqtuCUAAEBi0mi1F190/p6DreKWSLFZR0ZGjxxqFLdEgiC4T4CXX3q5qjhPXH23xzsy5jz3gkvELQEAABLTWedeOOpwO90ecUvUlOa99PJL4uonDoL7BHjumSeL87M1Im9K2t3QfP3110sSlzEBAIAJJN/01Zt21TcJXECWi/KyX3j2KXFLJA6C+wRwOx37DjQU5GSompWdnqJ88MDQmEarO+f8i1W2BgAA8LlWnneBRqMbGFJ6iVIEinIz9h6odzkELpE4CO4T48knHqsuy1c15cSFdUaDXuloWfp0Z8Mdd9zGTncAADAhNBrNHXfcsWlXg9Df6FeXFjz15JMCF0gkBPeJ8fG6961mk4ogLkl7GprqqkqUjx8Zc4463JdfdZ3q5gAAAP7FFdfc6HC6h8ec4pYw6PVmk2H9uvfELZFQCO4TIxwOf7D2o4qiXOVTGps6yotyNRoVfwSbdh+8/rrrDEaj+gYBAAD+SW8wXHvN1Vv3HlY+RaPRLJ1fq2qVqpLcD9etC4dCKrvDv0dwnzCPPfKnaeWFyseHwuFDLZ3VpSo22Lg93s6ega/ddqf67gAAAP7pljvu6uodVHWYTE1ZgdPtVrVKTVnh4488pLI1fC6C+4Rpaz7S1NJalJupfMqexmaP169qlR0Hmi6+8AKrza6yOwAAgH+wWJMvOv98tYfJzKgs2tuo4rj3wtyMw80tbc1HVHaHz8WbjhOprb3j8ssuamzuVDg+GAoNjzpULREMhQx67cxZsz549231DQIAAEj/ed/9AUnX3jOgfEppQY5Go2lq71Y+5cQFM372k//qaBN4tVOi4Yn7RNr66cdutzsj1SZ0lV31zSctXTKjbrbQVQAAQFyqnl530tIlu+ubVc2qqy7ZskfF1afpKckul3vzxvXqmsMX4on7BBtzuFeecVpTe4/QVYbHnJdefMFzzz4lhcNCFwIAAPFEluXH/vLUzvrmUYdL1cSDzR3+QFD5+BPmTHv44T83HNinskF8EZ64T7A3X/ub1WKyWkxCV+nsGZQkzXVf/brQVQAAQJy5+oabJVnb0a1ik8zfhdQ8K7RaTBaz8Y2X/6p2FXwxgvsEC4dCzz73fG1lkdhlZGnjzvrrrr4qMztH7EIAACBepGdk3XD9tRt31Au9cUmSpLqq4meefS7EKZATTfCfW0IyGI0ffvTxq+996vMHhC5UU5avCXpvvOYKoasAAID48NATTwckvfJTNCKj1+lWnXrc8mVLvB51Z0fimHjiPvF8Xu+at9+dVlYgeqGGIx0VFeUnrThd9EIAAGCqO2nF6RXlZQcFp3ZJkqZXFKx++x1Suwg8cRciLT3zjTVvvfzORlWvcUQgxWY9eVHdmWec5hgdEboQAACYuqw2++o173ywaY/ac6jV0uu0q047/qwzTh8c6BO6UGLiibsQgwN9q996p66qRPRCw6OO+iPtv/nvP4leCAAATF2//t0f64+0iU7tkiTNrC5ZveZtUrsgBHdRHrj/vrKiHJPRIHqh/Yfb8vPyrrzmRtELAQCAqeiyr1xXVFi4/3C76IWMBn1ZYe4Dv/ip6IUSFsFdFJfT+cxzL8ydVqZ2ok6rlWV1W5jWbd771ZtuLKuoUrsWAACIb0UlZV+/+WtrN+2JwlrzZpQ//exzLsdYFNZKTOxxF0in07+39qP3Nu52uFS8n1FdWmBPtmzereJyMkmS8rPSp1cUnHPGimBQ7K56AAAwVWi02lfeeLuxtbu9q1/0WlaL6dQT5qxYvtTv84leK2HxxF2gQMD/hz/9eeHMSlWzGps7CnMzU21WVbM6egccLs9//JhfTgEAgH/4/o9/5vb6o5DaJUlaWFf15z8/RGoXiuAu1ovP/MVs1KenJCufEg6H127avXRBrdq1Nu1qPGX5Saedea7aiQAAIP6cdua5pyxftmmXut/hS5J0wtzpaqek2qxGo+7Zpx5XOxGqaGPdQLwLh7t7+y86/5xDLV3KJ7k9vsw0u9lsHBgaVbGUJLV29t1w1WXrN2wY7OdtbgAAEldZRdVvlJetuAAAIABJREFUHnzw7fU7vH6/qonVZQUWs7GtS02QCEvLF9c98KtfHWqsV9clVOKJu3DvrnnD7XbnZqapmrVpV71f/cWrTrdnw7b9Dz38qC0lVe1cAAAQH6w2+6OPPbFxe72qt+wkSTKbjLNryjbvalA1Kzcrze12v/3ma6pmIQIE9ygIf++eb58wb5pGzVkx/kDwSFt3BIt19Q0eONz22JNPa7X8OgUAgISj0Wgeffypxuauzt4BVRNlSVpx/OyPt+9XdX2kLMvHz532g+9/V5LCKjuFagT3aNi3e8f6jZ/OrCmJznKNzZ1hWXvv/Q9EZzkAADB53PfzX8s6/YEjbWonpqUkd/YMtHere5N1Vk3Jho8/3b19q9rlEAEeykbJxx+tu/WWW7p6B70+dVvNItPePbD8hPk6o2XHts1RWA4AAEwGl199/dlnnfXhp7sjePrt9vi6+gZVTUlOMs+vrbjx2q/4/RwmEw0E9ygJBPx9AyOXXnhuY3NHdFZs6ey79PyVTS0dzUcORmdFAAAQQ8tPPfNb37przbptgWhd6nLq8bN/85sH9+7eEZ3lQHCPnoMN+087Y6XRZBoedUZhuWAw1NrZ97XrrjhQ39jW2hyFFQEAQKwsWHzCL3/1y7c+2ubyeKOzYllRTsjv+em9P4jOcpDY4x5ld33jtoUzqw16fXSWc7g8b63f/qtfPTBzzvzorAgAAKJvRt3sB3/723c2bB9zqjtGJmJ6nW5BbdW3774zOsvh73jiHlWOsdEkW+rxi+a1RuUOM0mSvD5/d9/gbV+7ft36DYMDHO4OAEC8KSmvePyJJ9dt3jMwNBa1RU+YU/PGm2++99YbUVsRkiSpOKAQE0Kj1a5558Pt+w/3DIxEbdGcjJQT5k6/9JKLOtpaorYoAAAQLTe/8PkX//rJzvqu3qGoLZqdbp87vfyM05aHorWZHn/HVploCwWD3/7WXUsX1Op10ft1R3f/8JY9jf/z9DPpmVlRWxQAAAiVnpn11DPPbd19MJqpXa/TLl1Q++1v3UVqjz6Cewzs3Lb5xb+9fMLcaZFNN5uMEcxq7erfc7D1xb+9kptfGNm6AABg8sjNL3zxb6/sPdTaqvLk9XFaMnf6S6+8tpPzpmOB4B4bv/3lT/0+b3lhbgRzl8ybXlaYE8HEprae7f/b3n3HSVWdjx8/d/rMlpntvbENFpZeRQSRIGIDe4kFBWuiJt/EV8o3MYlfJYoaDMbEEmvUiAVQ7ICdIm0p29jOLtvLzO6U3am/P5LfggjsMDu7M7N83i//4CLn3gf2zL3PnHvOc0qq31z7dk6+j98ZAABAMMjKzn3jzbVFZTU19S3Det3UBIej7y+PPjScF0U/EvfA8Hg8d92+fMq4nHCd9nTbfr59/6SC7BhDhA/XbWhu/3ZP6UsvvTJ+MnVmAAAISQWFE1791+vf7a883DisZSfCdZpp4/N+cudtHrd7OK+LflSVCRirxdzU1nHDNZcdqjlyWtubuT2ehub2RedMqTrc5MMOCxZbb0uH8ae3Ly8rp747AAAhZtrM2X97+ukvdx5safdlXrtWrVryo1mlVfWn21CSpEVzpjz++BO7v9vqw3XhFyTugVRRXjpxyoyM9JTmduNpNbQ7nJ0m88xJoyvrGn24rq3XfqS5/c7lNzY0tlZVlPtwBgAAMPzmLVi06tFVm7bu6zT6UvlRqZBfeO70bXvLeizW0207ecyomtrqJx9b6cN14S+UgwwwtUb74cef7TxYebq5uxBCJpO5B/GuKlynXTRn8vMvvvjSs0/7fBIAADA8br7truXLln389R6z1ZddlmSSdNH86aWV9RWnP+oXH62fNTH/gvMX9NpOO+OHHzHiHmAup3NP0b67b7u1orbxdLNwj+e0ptgcz+5wVtQ1XXrBgslTZ2z69KNBng0AAAwRuVz+4CNPLL5g8Udf7bb19vl2krkzCg83tpXXHDndhkqFYtGcKXfdfVdjw2HfLg1/IXEPvNbmJqFQX3rBgsrDTcN8abfbXVXXlJ+bdf2Pf/zpJx/zNRoAgGCjj4p+9Y234hOSNm/b58Patn5HWjpbOk779b4kSYvmTH71tdc+2bje50vDX6gqExRe+Mea/QcOTC/MHf5Le4T4bl/FkVbju+vfH1M4YfgDAAAAJ5OdN3rdho1tRutXu4rdg3s37nA6T7uNR0wfn7v/4MF//mPNYC4Nf2HEPVhs/vTjq665VqPWdJp8WW4yKJLoNJk7TeZ77lzR0tZRUV463AEAAIAfWLj40idXP7ltb1ldY2tAAshOT4yO0K64+Xrm0wYJFqcGkaiY2PUbNm7dW9rSYQpIAOE6zYKzJn67dfsffnu/w2EPSAwAAECpVP3hoUdnnzVz09Yis7U3IDHERevnTB275JILO9uHtVo8ToGpMkGkq6P99ttXzJsx3oddmfzCbO3dsHlHWuaoDz7ZNGbc+IDEAADAGS47d/R7H32amzf6vc07ApW16zTq+TPH333nHWTtQYWpMsGlva21sbn9xmsvr6htDNRrqaa2rh6L7ad3rFDpInZ/tz0gMQAAcEaSblh220MPP7T7YGVJVX2gpqfIJGnx3KmPPfb4V19sClAIODES96BTeag0Ki5p/pyZ1Q0tgYrBbO2trGtceO6cS5Zc9sXnm61WS6AiAQDgDKGPin76uRdnz5796dd7uroD+eQ9d+b4LZ9//s9nngpgDDghpsoEo9WP/l9jU9O0cTk+nyFMq1l49mSZzPefr93h/GxrUae5b/17Hyy6eKnP5wEAAANadPHSDz78xO6Wf/z1nl67I4CRTBuX09zc/OhDDwQwBpwMi1ODlFKp+teb79gc7qKSGt9+SvmjUguy0zd+vsPh9L3mqxBCH66bM23skcam+39+b0vTae/aAAAATiEhKeXRx1enJCd/vavYZA7ohioeUZifoQ9TX3vFEmpUBCdG3IOUw2G/4doro8K1BTlpvp2hvLphX1n1xfNnaNWqwURiMls3fr6zy+JYt+G9+//3j3I506sAAPADmUx22933rVv/ntUpNn6xc5BZu0atunTBTI1K6fMZ8rKSE2Iib7j2CrL2oMWIe1ALC494Z/3Girqmqvpm386QHB89sSD7wy92Dj4YpUIxdVx2ZLjuN7+6f++uHYM/IQAAZ6yxhRMfWfW4yyO+3VNqd5z+1kjfF2OI/NHZk77eefBIS4dvZ8hKjS/ITrt86SXdxq5BBoOhQ+Ie7GLi4t9+d0NRSXVdU1DUY0qIMZw9deyOnbse/uPvOzuCIiQAAEJIdEzcbx7404xpU7/ZVdzSYRz8CbNSE6cW5nz81e4ei823M6QmxEwdl3Pl5UvbWn0cKMTwIHEPAcmp6W+ufXvb3tLGtqD4EiyTpIKctILcjI0ffLz6sYetFmrOAAAwMF1Y2L3/85uLL1xUXFFXWlXv9kfdZ7lMNmfauG93F/u8pC0xNmr2lDHXXXN1fV3N4OPBkCJxDw1Z2TmvvvbGl98dbOsMzKaqP6RUyAvzMrLTk9986+2/r3nCYWc+HAAAJyaXy6+7afltK25tauncebDS4Rzs3Bh/iTFEnDdrwk033VhRVhLoWDAwEveQUThp6jP/eGbL9n3tXd2BjuUojVo1aUxWYlzU039/Zu3rL3vc7kBHBABAEJFksqVXXHfPT+82ma3b9x2y9fYFOqKjYqMi58+ccPsdtx/YuyvQscArJO6hZGzhxGeefe7bvaVNrZ2BjuV7wnXaGeNzNWrVK6++8sarLzodgSxACwBAMFAoldfesOzGG27s7bPv2F9htvo4AX2IxMfo50wde9eddx4o2h3oWOAtEvcQk5Wd++LLr+4prjzc1B7oWI4XGa4bm5OWHB/z2ebPn3rysc52lq4CAM5EEZH6ZSvuvGzpElOPZW9pjbHbHOiIjpeaGDt9fO4ty26uLC8NdCw4DSTuoSchKeW1N94srWrwuUbkkFIpFaOzUgpyM3fvLVr1yEO1VZWBjggAgGGSlJJ2z89+MW/unJrDTfsP1fX2BeMCsMyUhAn5GTf8+LqGw7WBjgWnh8Q9JEXFxL72xlsNLZ0llfWD/xmmJMQ0tnT4YWX7MWQyWX5W8ticjOraw6++8tKWTz90uQa1gSsAAEFLLpfPX7j4hhtvHpWZXlxZV17T6A7ORV8ekZeVlJ2WeN01V3a0tQY6Gpw2EvdQFRYe8a833jJZ+4pKB1u8acrYnJTE2M1biyy2Xr/E1k8SIjkhZvSoVENk2NffbHvh+Wcqy1m0DgAYOXLyC25Zftuc2WcZu81l1Q2NrZ3+HQjzr8L8jFh9+PVXX27uCaJCF/AeiXsIU2u0r7y+1uWRfXegYpCnSoyNOmf6uOKKwyUVdUNxx5HLZVkpCWNz091u97vr1r/y4nM9Jj9sOQEAQECER+qvuPr6K6+8whAZWVJ5+FDtEZ/LqJ+CJERBboYQoriibvBnmz4+Vy7cN153VV9vcC2ThfdI3EObUql66pkXUlNTt+zY73IN6q2cXCabNj6vz+7YW1Llr/B+SB+uy8tKyUxNbGpu2bJl87q31zYdqR+6ywEA4EdJKWlLr7hq/vzzkhITahuaD9UcMZmtQ3QtQ0TYvBnj27pM24vKBvuIl8vmzxjf0NDwk9tvcTiCcdo9vETiPgJI9/zPr6664rJPvtnj817Hw08fEZaZHJuekiA8nu927np77Zt7d20P0hmBAIAzmCRJuWPGLb3sijlzztZHRNQ3tdU1trV0DO1741mTxiTEGr7ccaBr0BVpwnWahWdP/vCjTx75v997/LFXKwKIxH2EWLj40gd+/7uvdx1sbg+pKSgeodWoMlMTMlPidRr1oarq7du2ffH55qryEpJ4AECgyGSy7PyCeeeeN3PWrLzsUdbevtojrbUNLbZe+zCkTpIQqUlxDU1tg8+y46L186YXPvzwyg/ee8cPkSHQSNxHjuy8Mc8+93xFbVNZTUOgY/GFXCZLiDUkxUUnxUdr1Mqm5tZdu3Z99cXnu77b5rAH0T5zAIARSalST50+65x5506ZMiU5KaG3197Y2tnc3tXSbnSF5lhSbkZSQXbaHXfcXl5yINCxwD9I3EeUSEPUCy//S5Krvt1d4g7l12GSENGGyKQ4Q1JclD4ywuFwNDY1V1RWlpYU792zu6byEKk8AGAwlCp1Vk7epMlTxhSMzc3JSU5KVCqVpu6epraupjZjp7E7hB+iQkhCTC3M1anlt9z0466OoNuxET4jcR9pZHL5ylVPTpky6dNviuwOR6DD8Q+5TBZtiIjSh0dFhEUbIsJ0WofD0dllbG/vaGltaWpsbGluqqutPVJ/uKOjjZweAPAfSpU6JiYuJS09IzMzITEpKTk5IT4hNjYmOsqgVCotVlunsaerx9JlMncae0J0WP2HVErlwrMn7t6999e/vNfNJiojC4n7yHTT8jtvX3HrVzuLm9u7Ah3LkJDJZBFh2jCtOkyn0WnUWrUyXKcND9MqFQqX261QyO12h1KpsNsdQgibzRbKrx8AAAOIMkQaTT1arUYIoVIpHQ6nSqV0Ol1ymczhdJotNrPVZutzWHv7LNZei62vx2IbqSupEmOjzpk29pnn/vny838PdCzwPxL3EStvzLg1T/2t3WjefbDSj9Nm1CrlxfNn7C+vqaxtDInZOEqFQi6TFAq5XC4PdCwAgCHh8Qi73e5yexxOZ6BjCRhJkgpz09OS4u695+6SA/sCHQ6GBIn7SKZUqv608rGzz5q5ads+U4/FX6dVKZWTCkZlpiSUVdeXVNafyXdJAEBQ8IRGRqNUKApy0t1u94FDtf49c7hOu+CsicUlpT+/546+Xj/vg47gEQrdHINz3vkX/vEPf9hfXnuo9ogfT6uQy8fkpCkV8j3FQ7hhExBAcpdY9IVtepE9ssfHV+oOpVSZqXj3Am17NC98gKEU9Im7VqOeMDorMyW+rLqhuKLOv9usZqcnTirIfnjlnz/cQM3HES64uzn8JComds3Tzxqior787uBQ7MkMjEhLP7Kes2Ogtc7xYcJsF9aTLgR3y6TucOmRuyN71dxvgSET3Im7PiLs3JnjD5TXVtc3+3cLJIVcftak0R63844Vy9pamv14ZgQnBoHOCL0267p31hqi465acoGx29xjtgXzDQ4IBnKXuOVNi2zAJ+zCHLH5RiGTib1NwnGCgXnJIzR9npZ4eWMC91tgKAXxc63P7iirbugyDXYP1O/xiOSE6IVzpmzYsOH+n91tMfv15AhWPEjOIDt3bN2+Y8f111yVnhzf1Nblco3MBfWAX8QYXXO3f3+4XasUEWqhVX7vv7Fx4scTxIJRYvlk4faIfS0/SN89QkjN8YrKLMUwhg+ceYI4cfc7tUp5zrSxiTH6n/7krg/fezfQ4WD4nEndHEIIISRJWrbiruW3LttXVnOo5kgI1IUBAiGuw/WbNd3f+60nzhc/mzVAszaLeHybWLPjuMkzn56j+Wi+1t8xAvj/gnuqjH9lpsRNnzD6jX+vfWr1Ks8ILWqJk5EFOgAMN4/H88Kzf7v4osUeu/WSBTOj9OFDfUV9RNiF86aNyU7TqFVDfS0gwOLCxP2zxXWFgY4DwAhkiAi76NzpMRGaJZdcvOaJR8jaz0C8uj1DdbS13rXipjnnLnzggd+3dph2F1cN3Y5xph7Ll98dGJWWtPDsSXK5/FDNkeKKuiG6FjCEtjWIsN3H/2amQSzM/u+vO23iL9vEX3eIbrbvBUa4KH34qLTE9OT48uqGksrDQ305mSSNzU0flZb08MqHP/vwvaG+HILWGfNiCSeh1mh+9bsHF5537vaisvrm9qG+nFIhN0SGt3WahvpCwCBFmN1/esyLjrp0jHj3atFpE2t2iNXbhfHE5ZPf/5F2y2yNn0ME0G8Yp8qMSkucWphr7LbU1DfXHmkZhlptaYlxMyfmf7b585UP/o4a7Wc4EncIIUR+QeGDD62MiY7evq+8rcNEvwCEEP/7ZHeU0S07de22c7PEWWlizQCj7KuXR9Sl8oYTGDLDmLgrFXKXyz0ce4d7RFyMfubE/I6Ozt/99tflJQeG/IoIeiRoOGr23Pm//vVvFUrltqJyP+606huZJA3HbRE4uew654rXzGq7xy0Tvt0tJY+QPJ4vZ6nXn6/zd3QAjjHiFqdGhGmnjM0J16kfeXTVZx9uCHQ4CBYjq5vDD6QLl1z+s/vuM9v6du6vsNgC9kouNyN5wphRQoiubnOXqaehuaO1wxioYHDGMnS7C8sc+m4fV4DYVVJVhqIqg7F2YIj5mrhLQkSE66L04TGGCIfTdaC81s+BnT6NWjVxdGZCbNTf//HsW6+/5N8NmxDqSNxxAjK5/Pobb71txfLG1o49JdV99pPuCjnkkUhSZLguJirS6XTVNbYGKgwAQFDzKXHPSk2YMi63x2LtNJo7TT0t7Uaz1TYEwXlLqZCPy0vPTk9+8623n37ycZfTGcBgEJxI3HFSKrX6J/f98vLLllQfbiqprLf12QMd0cBiDJHzZ01wu90ej6fP7ui1OzZvLfKm4fTxeUrl0WHR0sr6TlPPgK0yUxNSEmL6D9u7usurGwZsFabVTCwY1X/o8Xi2F5W7vajqM3HMqDDd0QWOlbWNLV68hUiOj8lKS+g/7O6xHjhUO2ArpUI+fXz+sXeI3Qcqer34Cjc2N8MQGdZ/eLixrb6pbcBWcdH6vKyU/kNbb9+e4qoBW0mSNHNivkx2tKztvtIab567uRnJ8bGG/sOm1s7q+oG3CtdHhI3Ly+g/dLnc24vKBmwlhJg2Pk91TO8qq6rvMHrRu1ISUhKP9q6Oru4yL3qXTqOeNDa7/9Dj8ewoKvemZtSE0VnhYUcrzVfVNTW3dw3YKjk+Oistsf+wx2zbX14zYCulQjF9fN73etfByl4v7jAFOenHVrD1snfFRkXmj0rtP+zts+8+WDlgK0mImZNGH9u79pfV9FgG7l05GckJx/Su5rauqsNNA7Y6rne53Z5te0sHbCWEmFqYq1Yp+w+97F0ZyfGpSbH9h53GntKq+gFbadWqyeNyjh57xI595U7XwEszj+td1Yebm9o6B2x1XO8yW3r3lVWf7A+Pz8+Ki9ZLklCplJIQZdUN3vyzBw+tWlWQkzYqPemdd9c/tXqVvY/KVDgxEncMQBcWfvPyO6++6gqzxbaruNLPOzYPGYVcrpDLhBDe5JpCiGh9hFJxdCPhTlOPN4UCdBp1xDFPI4utz5usUZKk+Gh9/6HL7W7v6j7Fn+9niAg79gnd1W2xOwb+22nUKn340QnWvXaHNwsYJCFio/Uy6b+3CI8QbZ0mb97YRobrtMcU7DeZrd4kZEqFIvqYhKzP4TR2e9XTYqMi5cekVm1d3d58BQrXacO06v5Ds7XXm1lhCrk8xhDRf+h0uTuMXv3gftC7zA4vBtK0GnXkoHuX2+Np7zR586JdHxGmOaZ3GXss3rxq81fvau80ebOmJSJMq9Mc/cF1m63eDCgc17vsDmeXd70rxhD5n9vIf7R3dXvzFShcpwnTHv2C7WXvkstlsYbI/kOny+VN/i2EiNKHqxRHvxZ6ee86rndZe/u8+U4iSVJctL4/b/C+dx137/K2d6mU+oijowB9dofx5L1LH65TKOROl9vlcjmdLrvDGSqrpPThuvGjsxJi9Bve/+Dpvz5hMXv1c8cZi8QdXpHL5UuuvG7ZsmVqlWpvaXVj68CDJQAADJMQXJwaH2MYn5ehVSv/vfbtF5/7m9OLgRgg1Lo5Am3arLPvve/nmRnpBw/VHqo5EipDGsNP8nhm7LXP2m2P63BLPv0r9aqliizFB+dpTZHscAz4SOb2zN3eN3W/PbrLxxXGFp2sLEfx4XyNVcsnMYiFTuIuSVJmSsK4vPRuU/eaNX/d8ulHQvAkhbdCpJsjyIwpnHDfz35ROLbgUG1DRW1TAIvPBK2523qXfGLzSNKpsvYItbA5hPOk+YRHkkwR0p/vjuxT81EFfLHkE9vcbQPdoAwaYeo9Re7kkaQjibK/rIh0k7oHrVBI3MO0mtzMpLzM1APFJav/8ljpgX2BjgihRz7wHwF+oL21ZeOGdevWrzcYos6bO2tcboYkSaYei9vNsMF/3fa6Ren0SKd+mJydLjbdJGwOsa9FnOifThJC0+fpjJI1JFNPEDhtKrvn1n9bBk7nrh4nXr1MWOziQOsJ03dJiEizpy5V0R7DQzOIBWviLpfJMlLiZ07Iz05L2Lb121/98udvvv5qe2tLoONCSOIeBN/ZrJZdO7a+/urLu3bvycvJ+dE5MxNiDHaH05tFTiNbmNV9/pf/GeQ75ZMkK0rcO1NcnC9unHCy9N0jSR3R8rIc5QlPAOAUEtvcs3d5UZ1jYqK4bYpYOkZcXnCK9L0+WV6XxlfoIBZ8iXuMIXJCfsaMifltzU2r//LYygf/uPXrL6yW0KjxgOAUfN0cIUuhVF689Mprrrk2JSmx8nBjdX1Lt9ka6KACI9zieXDV9ws1/mGeeGDeAM1qjWLl1+KFvcdOnnHLxDfT1OsuYN9N4LSlNTp//uz3a3T881Jxy6QBmh1sFau+Ff/af9wX6fd/pN0yW3OyRgiwYJoqExmuy05PzE5PamxqeeON199f9xYLT+EvDB7Ab5wOx7q1r69b+3pcfML1N9+6YP55Bn3k4cbWuqb21g4je78NLNMgnlosYnRi5deBDgU4g42LF3+/SMhl4sW9gQ4FIUOSpPgYQ2ZybFpyvNHUvXnzll/85Pk25sPA30jc4X9trS2rH3149aMPh0fqz198yQUXLJ4z9SxTt6XmSEvdkTZvKliPQNVdYtMPtg6J1orJSf/9tcMlXt4nHvpK1A68pxIAH5W0neCTmBQuxsb/99dWh/jHLvHot6KF+QwYmEIuT4qPzkyJS4yNbmhs3rTps3fWvtbZPvC+YIBvgubFEkY0hVI177yFF1+yZOKEQrvdcbi5rbnN2NHVPVKrSar7PH9e6UX+PT9LbL5JOFzi3wfFn74UlSeoju+RxJbZmo0LtD/8XwBOLaHN9au/ebFJ1k0TxUtLhNUhnt8j/vyNaDrxDjjvLNZ9M119wv+FwBvGqTIySYqJikyMM6QnxqlUyqJ9B95/b/0Xmz91OkJgf3GEOkbcMRycDvumjzdu+nijJEkFhRMXXXjR5EmT504b12e3N7Z2tnSYWtqNI2kkvk8ttcbKYzvdslOX2bG7xPN7Tj3KLnlEXSqfU8AXbTHyXrWktosB9lKw2MXjW8WqraceZa9LpZzDmUupUCTEGhJj9UlxMWq18nBD457du59Y+X7JgSImgmI4MeKOgJEkKWNUzvzzFk6fMSM/L1cul7e0dbZ0dLcbu009llC/FebUOG5/zSJ3edy+fsgkIWRuUZyn/Oe1YR6Jjyrgi8kH7Ne/a5GE5JZ8vKX855O4dar6rYtYIx7E/D3iLkmSPiIsNioyIVafEBPlcrkOVVTt2L59y+ZP6qorQ/0JhdBFNoBgYYiOnTt/wcyZZ+XkZCfEx8pksh6ztaOr22i2dRp7jN1mt9sTWh02rsM9ZX9ffIePCYNNI1VlKPYUqjwh9bcGgk1Kk3NSsSPG151TzWFS+SjFwdEq/0YFPxtM4u4RMplkiAyPNkQYwrUxUZER4Tq3293S2l5ZWbV9+9Yvt2wydrb7M1rAV2QECFJqrW50wbjC8RMLCsZmZ49KjI9TKBTW3j6zxWrt7bPa7NbePqutz2Lrtdr6RupceQCAV7xI3GWSpNOqw7QanVat06h1WpVOow4P0+k0aqfT2dzaVlVVXVJSfGB/UVnJwT7bGVrOGEGOxB0hQ63RpKRmpGdmJSenJCUnJyYmxscnRBn0en2ETCaXSZLL7fZ4PHa7w+V2OxxOl9vjcDrcbo/D6XK73YLZJgAwQkXpI0zdZqVCLpNJSoVSLpOUSoVcJlOplJIkyWUyt8fjdrtMpp4uo6m1taX4Osv2AAACk0lEQVS5ubmpsbGx8cjh2pojDXV9vb2B/hsAXiGVwQih1uo0Gq1KrdLro5QqpT7SoFAq9HqDXCGPiNBLdHUAGLlG5eTWVFWYuo0up8tkMjodTlO30WF3mExd9j57b6+NEXQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgFPw/S/D4NWW2DtkAAAAASUVORK5CYII=",
       "text/plain": [
-       "Cairo.CairoSurface{UInt32}(Ptr{Void} @0x0000000004ee0610, 1000.0, 600.0, #undef)"
+       "Cairo.CairoSurfaceBase{UInt32}(Ptr{Nothing} @0x000000001f9486b0, 1000.0, 600.0)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -175,17 +180,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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",
       "text/plain": [
-       "Cairo.CairoSurface{UInt32}(Ptr{Void} @0x000000000414ef40, 1000.0, 150.0, #undef)"
+       "Cairo.CairoSurfaceBase{UInt32}(Ptr{Nothing} @0x000000001f949e20, 1000.0, 150.0)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -205,17 +210,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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",
       "text/plain": [
-       "Cairo.CairoSurface{UInt32}(Ptr{Void} @0x00000000052c79a0, 1000.0, 120.0, #undef)"
+       "Cairo.CairoSurfaceBase{UInt32}(Ptr{Nothing} @0x000000001f9494c0, 1000.0, 120.0)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -235,17 +240,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "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",
       "text/plain": [
-       "Cairo.CairoSurface{UInt32}(Ptr{Void} @0x0000000005ae2a40, 1000.0, 120.0, #undef)"
+       "Cairo.CairoSurfaceBase{UInt32}(Ptr{Nothing} @0x000000001f948070, 1000.0, 120.0)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -269,17 +274,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "Cairo.CairoSurface{UInt32}(Ptr{Void} @0x0000000004f08610, 1000.0, 250.0, #undef)"
+       "Cairo.CairoSurfaceBase{UInt32}(Ptr{Nothing} @0x00000000140eeb00, 1000.0, 250.0)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,17 +296,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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",
       "text/plain": [
-       "Cairo.CairoSurface{UInt32}(Ptr{Void} @0x000000000402e440, 1000.0, 250.0, #undef)"
+       "Cairo.CairoSurfaceBase{UInt32}(Ptr{Nothing} @0x00000000019db990, 1000.0, 250.0)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -319,23 +324,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Base.Random is deprecated, run `using Random` instead\n",
+      "  likely near C:\\Users\\Maxime\\.julia\\packages\\IJulia\\k5o7j\\src\\kernel.jl:38\n"
+     ]
+    },
     {
      "data": {
       "image/png": 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",
       "text/plain": [
-       "Cairo.CairoSurface{UInt32}(Ptr{Void} @0x0000000004fe2600, 1000.0, 600.0, #undef)"
+       "Cairo.CairoSurfaceBase{UInt32}(Ptr{Nothing} @0x00000000020d4330, 1000.0, 600.0)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "srand(0)\n",
+    "Random.seed!(0)\n",
     "timestep = 0.1\n",
     "roadway = gen_stadium_roadway(3, radius=45.0)\n",
     "\n",
@@ -351,6 +364,7 @@
     "carcolors = Dict{Int,Colorant}()\n",
     "base_speed, x, j, id_count = 20.0, 10, 0, 0\n",
     "for roadind in start_roadinds\n",
+    "    global id_count\n",
     "    vehstate = VehicleState(Frenet(roadind, roadway), roadway, base_speed+randn())\n",
     "    for delta_s in start_delta_s    \n",
     "        vehstate2 = move_along(vehstate, roadway, delta_s)\n",
@@ -363,7 +377,7 @@
     "    end\n",
     "end\n",
     "\n",
-    "actions = get_actions!(Array{Any}(length(scene)), scene, roadway, models)\n",
+    "actions = get_actions!(Array{Any}(undef, length(scene)), scene, roadway, models)\n",
     "\n",
     "function drawsim(t, dt=NaN)\n",
     "    get_actions!(actions, scene, roadway, models)\n",
@@ -385,8 +399,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "using Reel\n",
@@ -404,115 +420,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "┌ Info: Precompiling Reactive [a223df75-4e93-5b7c-acf9-bdd599c0f4de]\n",
+      "└ @ Base loading.jl:1187\n",
+      "┌ Info: Precompiling Interact [c601a237-2ae4-5e1e-952c-7a85b0c7eef1]\n",
+      "└ @ Base loading.jl:1187\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
-       "<div id=\"interact-js-shim\">\n",
-       "    <script charset=\"utf-8\">\n",
-       "(function (IPython, $, _, MathJax) {\n",
-       "    $.event.special.destroyed = {\n",
-       "\tremove: function(o) {\n",
-       "\t    if (o.handler) {\n",
-       "\t\to.handler.apply(this, arguments)\n",
-       "\t    }\n",
-       "\t}\n",
-       "    }\n",
-       "\n",
-       "    var OutputArea = IPython.version >= \"4.0.0\" ? require(\"notebook/js/outputarea\").OutputArea : IPython.OutputArea;\n",
+       "    <script class='js-collapse-script'>\n",
+       "        var curMatch =\n",
+       "            window.location.href\n",
+       "            .match(/(.*?)\\/notebooks\\/.*\\.ipynb/);\n",
        "\n",
-       "    var redrawValue = function (container, type, val) {\n",
-       "\tvar selector = $(\"<div/>\");\n",
-       "\tvar oa = new OutputArea(_.extend(selector, {\n",
-       "\t    selector: selector,\n",
-       "\t    prompt_area: true,\n",
-       "\t    events: IPython.events,\n",
-       "\t    keyboard_manager: IPython.keyboard_manager\n",
-       "\t})); // Hack to work with IPython 2.1.0\n",
+       "        curMatch = curMatch ||\n",
+       "            window.location.href\n",
+       "            .match(/(.*?)\\/apps\\/.*\\.ipynb/);\n",
        "\n",
-       "\tswitch (type) {\n",
-       "\tcase \"image/png\":\n",
-       "            var _src = 'data:' + type + ';base64,' + val;\n",
-       "\t    $(container).find(\"img\").attr('src', _src);\n",
-       "\t    break;\n",
-       "\tcase \"text/latex\":\n",
-       "\t\tif (MathJax){\n",
-       "\t\t\tvar math = MathJax.Hub.getAllJax(container)[0];\n",
-       "\t\t\tMathJax.Hub.Queue([\"Text\", math, val.replace(/^\\${1,2}|\\${1,2}$/g, '')]);\n",
-       "\t\t\tbreak;\n",
-       "\t\t}\n",
-       "\tdefault:\n",
-       "\t    var toinsert = OutputArea.append_map[type].apply(\n",
-       "\t\toa, [val, {}, selector]\n",
-       "\t    );\n",
-       "\t    $(container).empty().append(toinsert.contents());\n",
-       "\t    selector.remove();\n",
-       "\t}\n",
-       "    }\n",
-       "\n",
-       "\n",
-       "    $(document).ready(function() {\n",
-       "\tfunction initComm(evt, data) {\n",
-       "\t    var comm_manager = data.kernel.comm_manager;\n",
-       "        //_.extend(comm_manager.targets, require(\"widgets/js/widget\"))\n",
-       "\t    comm_manager.register_target(\"Signal\", function (comm) {\n",
-       "            comm.on_msg(function (msg) {\n",
-       "                var val = msg.content.data.value;\n",
-       "                $(\".signal-\" + comm.comm_id).each(function() {\n",
-       "                var type = $(this).data(\"type\");\n",
-       "                if (typeof(val[type]) !== \"undefined\" && val[type] !== null) {\n",
-       "                    redrawValue(this, type, val[type], type);\n",
-       "                }\n",
-       "                });\n",
-       "                delete val;\n",
-       "                delete msg.content.data.value;\n",
-       "            });\n",
-       "\t    });\n",
-       "\n",
-       "\t    // coordingate with Comm and redraw Signals\n",
-       "\t    // XXX: Test using Reactive here to improve performance\n",
-       "\t    $([IPython.events]).on(\n",
-       "\t\t'output_appended.OutputArea', function (event, type, value, md, toinsert) {\n",
-       "\t\t    if (md && md.reactive) {\n",
-       "                // console.log(md.comm_id);\n",
-       "                toinsert.addClass(\"signal-\" + md.comm_id);\n",
-       "                toinsert.data(\"type\", type);\n",
-       "                // Signal back indicating the mimetype required\n",
-       "                var comm_manager = IPython.notebook.kernel.comm_manager;\n",
-       "                var comm = comm_manager.comms[md.comm_id];\n",
-       "                comm.then(function (c) {\n",
-       "                    c.send({action: \"subscribe_mime\",\n",
-       "                       mime: type});\n",
-       "                    toinsert.bind(\"destroyed\", function() {\n",
-       "                        c.send({action: \"unsubscribe_mime\",\n",
-       "                               mime: type});\n",
-       "                    });\n",
-       "                })\n",
-       "\t\t    }\n",
-       "\t    });\n",
-       "\t}\n",
-       "\n",
-       "\ttry {\n",
-       "\t    // try to initialize right away. otherwise, wait on the status_started event.\n",
-       "\t    initComm(undefined, IPython.notebook);\n",
-       "\t} catch (e) {\n",
-       "\t    $([IPython.events]).on('kernel_created.Kernel kernel_created.Session', initComm);\n",
-       "\t}\n",
-       "    });\n",
-       "})(IPython, jQuery, _, MathJax);\n",
-       "</script>\n",
-       "    <script>\n",
-       "        window.interactLoadedFlag = true\n",
-       "       $(\"#interact-js-shim\").bind(\"destroyed\", function () {\n",
-       "           if (window.interactLoadedFlag) {\n",
-       "               console.warn(\"JavaScript required by Interact will be removed if you remove this cell or run using Interact more than once.\")\n",
-       "           }\n",
-       "       })\n",
-       "       $([IPython.events]).on(\"kernel_starting.Kernel kernel_restarting.Kernel\", function () { window.interactLoadedFlag = false })\n",
-       "   </script>\n",
-       "</div>"
+       "        if ( curMatch ) {\n",
+       "            $('head').append('<base href=\"' + curMatch[1] + '/\">');\n",
+       "        }\n",
+       "    </script>\n"
+      ],
+      "text/plain": [
+       "HTML{String}(\"    <script class='js-collapse-script'>\\n        var curMatch =\\n            window.location.href\\n            .match(/(.*?)\\\\/notebooks\\\\/.*\\\\.ipynb/);\\n\\n        curMatch = curMatch ||\\n            window.location.href\\n            .match(/(.*?)\\\\/apps\\\\/.*\\\\.ipynb/);\\n\\n        if ( curMatch ) {\\n            \\$('head').append('<base href=\\\"' + curMatch[1] + '/\\\">');\\n        }\\n    </script>\\n\")"
       ]
      },
      "metadata": {},
@@ -520,16 +459,50 @@
     },
     {
      "data": {
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      },
-     "execution_count": 13,
-     "metadata": {
-      "comm_id": "a173b2ed-f068-4a0b-a9cd-a593c2d924bf",
-      "reactive": true
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<script class='js-collapse-script' src='/assetserver/1288945514d6baffa4b9b033f5efc240a64e3726-assets/providers/ijulia_setup.js'></script>"
+      ],
+      "text/plain": [
+       "HTML{String}(\"<script class='js-collapse-script' src='/assetserver/1288945514d6baffa4b9b033f5efc240a64e3726-assets/providers/ijulia_setup.js'></script>\")"
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "  <script class='js-collapse-script'>\n",
+       "    $('.js-collapse-script').parent('.output_subarea').css('padding', '0');\n",
+       "  </script>\n"
+      ],
+      "text/plain": [
+       "HTML{String}(\"  <script class='js-collapse-script'>\\n    \\$('.js-collapse-script').parent('.output_subarea').css('padding', '0');\\n  </script>\\n\")"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "5: \"map(map(24 fps))\" = Cairo.CairoSurfaceBase{UInt32}(Ptr{Nothing} @0x00000000019db670, 1000.0, 600.0) Cairo.CairoSurfaceBase{UInt32} "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
      "output_type": "execute_result"
     }
    ],
@@ -555,15 +528,15 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Julia 0.6.1",
+   "display_name": "Julia 0.7.0",
    "language": "julia",
-   "name": "julia-0.6"
+   "name": "julia-0.7"
   },
   "language_info": {
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "0.6.1"
+   "version": "0.7.0"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb
index ac041a6..ac6f7d6 100644
--- a/notebooks/tutorial.ipynb
+++ b/notebooks/tutorial.ipynb
@@ -9,11 +9,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mPrecompiling module AutomotiveDrivingModels.\n",
+      "\u001b[39m"
+     ]
+    }
+   ],
    "source": [
     "using AutomotiveDrivingModels\n",
     "using AutoViz"
@@ -36,9 +43,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -54,7 +59,7 @@
    ],
    "source": [
     "roadway = gen_stadium_roadway(2)\n",
-    "scene = Vector{Any}(0)\n",
+    "scene = Vector{Any}(undef, 0)\n",
     "push!(scene, roadway)\n",
     "render(scene)"
    ]
@@ -98,9 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -121,9 +124,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -179,7 +180,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Julia 0.6.2",
+   "display_name": "Julia 0.6.0",
    "language": "julia",
    "name": "julia-0.6"
   },
@@ -187,7 +188,7 @@
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "0.6.2"
+   "version": "0.6.0"
   }
  },
  "nbformat": 4,
diff --git a/src/2d/roadways.jl b/src/2d/roadways.jl
index de91419..75ffdd4 100644
--- a/src/2d/roadways.jl
+++ b/src/2d/roadways.jl
@@ -22,7 +22,7 @@ function render!(rendermodel::RenderModel, lane::Lane, roadway::Roadway;
     )
 
     n = length(lane.curve)
-    pts = Array{Float64}(2, n + has_next(lane))
+    pts = Array{Float64}(undef, 2, n + has_next(lane))
     for (i,pt) in enumerate(lane.curve)
         pts[1,i] = pt.pos.x
         pts[2,i] = pt.pos.y
@@ -51,7 +51,7 @@ function render!(rendermodel::RenderModel, roadway::Roadway;
             laneR = seg.lanes[1]
             laneL = seg.lanes[end]
 
-            pts = Array{Float64}(2, length(laneL.curve) + has_next(laneL) +
+            pts = Array{Float64}(undef, 2, length(laneL.curve) + has_next(laneL) +
                                         length(laneR.curve) + has_next(laneR) +
                                         2*length(seg.lanes))
             pts_index = 0
@@ -117,7 +117,7 @@ function render!(rendermodel::RenderModel, roadway::Roadway;
             halfwidth = lane.width/2
 
             # always render the left lane marking
-            pts_left = Array{Float64}(2, N)
+            pts_left = Array{Float64}(undef, 2, N)
             for (i,pt) in enumerate(lane.curve)
                 p_left = pt.pos + polar(halfwidth, pt.pos.θ + π/2)
 
@@ -135,7 +135,7 @@ function render!(rendermodel::RenderModel, roadway::Roadway;
 
             # only render the right lane marking if this is the first lane
             if lane.tag.lane == 1
-                pts_right = Array{Float64}(2, N)
+                pts_right = Array{Float64}(undef, 2, N)
 
                 for (i,pt) in enumerate(lane.curve)
                     p_right = pt.pos - polar(halfwidth, pt.pos.θ + π/2)
@@ -160,6 +160,6 @@ function render!(rendermodel::RenderModel, roadway::Roadway;
 end
 
 # for case when there is no roadway
-render!(rendermodel::RenderModel, roadway::Void) = rendermodel
+render!(rendermodel::RenderModel, roadway::Nothing) = rendermodel
 
 Base.show(io::IO, ::MIME"image/png", roadway::Roadway) = show(io, MIME"image/png"(), render(roadway))
diff --git a/src/AutoViz.jl b/src/AutoViz.jl
index f4d1497..285e7d5 100644
--- a/src/AutoViz.jl
+++ b/src/AutoViz.jl
@@ -6,6 +6,7 @@ using Reexport
 using Parameters
 using StaticArrays
 using AutomotiveDrivingModels
+using Printf
 
 @reexport using Colors
 @reexport using Cairo
diff --git a/src/cameras.jl b/src/cameras.jl
index 56215f2..045cb97 100644
--- a/src/cameras.jl
+++ b/src/cameras.jl
@@ -6,7 +6,7 @@ export
     SceneFollowCamera
 
 abstract type Camera end
-camera_set!{S,D,I,R}(::RenderModel, cam::Camera, ::EntityFrame{S,D,I}, ::R, canvas_width::Int, canvas_height::Int) = error("camera_set! not implemented for Camera $cam")
+camera_set!(::RenderModel, cam::Camera, ::EntityFrame{S,D,I}, ::R, canvas_width::Int, canvas_height::Int) where {S,D,I,R} = error("camera_set! not implemented for Camera $cam")
 
 mutable struct StaticCamera <: Camera
     pos::VecE2
@@ -21,7 +21,7 @@ function camera_set!(rendermodel::RenderModel, cam::StaticCamera, canvas_width::
 
     rendermodel
 end
-camera_set!{S,D,I,R}(rendermodel::RenderModel, cam::StaticCamera, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int) = camera_set!(rendermodel, cam, canvas_width, canvas_height)
+camera_set!(rendermodel::RenderModel, cam::StaticCamera, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int) where {S,D,I,R} = camera_set!(rendermodel, cam, canvas_width, canvas_height)
 
 # method for new interface
 camera_set!(rm::RenderModel, cam::StaticCamera, scene, canvas_width::Int, canvas_height::Int) = camera_set!(rm, cam, canvas_width, canvas_height)
@@ -34,7 +34,7 @@ function camera_set!(rendermodel::RenderModel, cam::FitToContentCamera, canvas_w
     camera_fit_to_content!(rendermodel, canvas_width, canvas_height, percent_border=cam.percent_border)
     rendermodel
 end
-camera_set!{S,D,I,R}(rendermodel::RenderModel, cam::FitToContentCamera, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int) = camera_set!(rendermodel, cam, canvas_width, canvas_height)
+camera_set!(rendermodel::RenderModel, cam::FitToContentCamera, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int) where {S,D,I,R} = camera_set!(rendermodel, cam, canvas_width, canvas_height)
 
 # method for new interface
 camera_set!(rendermodel::RenderModel, cam::FitToContentCamera, scene, canvas_width::Int, canvas_height::Int) = camera_set!(rendermodel, cam, canvas_width, canvas_height)
@@ -45,7 +45,7 @@ mutable struct CarFollowCamera{I} <: Camera
 end
 CarFollowCamera(targetid::I) where {I} = CarFollowCamera{I}(targetid, 3.0)
 
-function camera_set!{S<:State1D,D,I,R}(rendermodel::RenderModel, cam::CarFollowCamera{I}, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int)
+function camera_set!(rendermodel::RenderModel, cam::CarFollowCamera{I}, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int) where {S<:State1D,D,I,R}
 
     veh_index = findfirst(scene, cam.targetid)
     if veh_index != 0
@@ -58,7 +58,7 @@ function camera_set!{S<:State1D,D,I,R}(rendermodel::RenderModel, cam::CarFollowC
 
     rendermodel
 end
-function camera_set!{S<:VehicleState,D,I,R}(rendermodel::RenderModel, cam::CarFollowCamera{I}, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int)
+function camera_set!(rendermodel::RenderModel, cam::CarFollowCamera{I}, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int) where {S<:VehicleState,D,I,R}
 
     veh_index = findfirst(scene, cam.targetid)
     if veh_index != 0
@@ -75,7 +75,7 @@ end
 # method for new interface
 function camera_set!(rendermodel::RenderModel, cam::CarFollowCamera, scene, canvas_width::Int, canvas_height::Int)
 
-    inds = find(x -> x isa ArrowCar && id(x) == cam.targetid, scene)
+    inds = findall(x -> x isa ArrowCar && id(x) == cam.targetid, scene)
     if isempty(inds)
         ids = [c.id for c in scene if c isa ArrowCar]
         add_instruction!( rendermodel, render_text, ("CarFollowCamera did not find an ArrowCar with id $(cam.targetid) (found ids: $ids)", 10, 15, 15, colorant"white"), incameraframe=false)
@@ -93,7 +93,7 @@ mutable struct SceneFollowCamera <: Camera
     zoom::Float64 # [pix/meter]
     SceneFollowCamera(zoom::Float64=3.0) = new(zoom)
 end
-function camera_set!{S<:State1D,D,I,R}(rendermodel::RenderModel, cam::SceneFollowCamera, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int)
+function camera_set!(rendermodel::RenderModel, cam::SceneFollowCamera, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int) where {S<:State1D,D,I,R}
 
 
     if length(scene) > 0
@@ -114,7 +114,7 @@ function camera_set!{S<:State1D,D,I,R}(rendermodel::RenderModel, cam::SceneFollo
 
     rendermodel
 end
-function camera_set!{S<:VehicleState,D,I,R}(rendermodel::RenderModel, cam::SceneFollowCamera, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int)
+function camera_set!(rendermodel::RenderModel, cam::SceneFollowCamera, scene::EntityFrame{S,D,I}, roadway::R, canvas_width::Int, canvas_height::Int) where {S<:VehicleState,D,I,R}
 
 
     if length(scene) > 0
diff --git a/src/interface.jl b/src/interface.jl
index a74b173..5d8bde4 100644
--- a/src/interface.jl
+++ b/src/interface.jl
@@ -1,9 +1,9 @@
-function render!{S,D,I}(
+function render!(
     rendermodel::RenderModel,
     scene::EntityFrame{S,D,I};
     car_color::Colorant=COLOR_CAR_OTHER, # default color
     car_colors::Dict{I,Colorant}=Dict{I,Colorant}(), #  id -> color
-    )
+    ) where {S,D,I}
 
     for veh in scene
         render!(rendermodel, veh, get(car_colors, veh.id, car_color))
@@ -12,12 +12,12 @@ function render!{S,D,I}(
     rendermodel
 end
 
-function render{R<:Roadway}(roadway::R;
+function render(roadway::R;
     canvas_width::Int=DEFAULT_CANVAS_WIDTH,
     canvas_height::Int=DEFAULT_CANVAS_HEIGHT,
     rendermodel = RenderModel(),
     cam::Camera = FitToContentCamera(),
-    )
+    ) where {R<:Roadway}
 
     s = CairoRGBSurface(canvas_width, canvas_height)
     ctx = creategc(s)
@@ -30,11 +30,11 @@ function render{R<:Roadway}(roadway::R;
     return s
 end
 
-function render{S,D,I,R}(ctx::CairoContext, scene::EntityFrame{S,D,I}, roadway::R;
+function render(ctx::CairoContext, scene::EntityFrame{S,D,I}, roadway::R;
     rendermodel::RenderModel=RenderModel(),
     cam::Camera=SceneFollowCamera(),
     car_colors::Dict{I,Colorant}=Dict{I,Colorant}(),
-    )
+    ) where {S,D,I,R}
 
     canvas_width = floor(Int, Cairo.width(ctx))
     canvas_height = floor(Int, Cairo.height(ctx))
@@ -49,20 +49,20 @@ function render{S,D,I,R}(ctx::CairoContext, scene::EntityFrame{S,D,I}, roadway::
     render(rendermodel, ctx, canvas_width, canvas_height)
     ctx
 end
-function render{S,D,I,R}(scene::EntityFrame{S,D,I}, roadway::R;
+function render(scene::EntityFrame{S,D,I}, roadway::R;
     canvas_width::Int=DEFAULT_CANVAS_WIDTH,
     canvas_height::Int=DEFAULT_CANVAS_HEIGHT,
     rendermodel::RenderModel=RenderModel(),
     cam::Camera=SceneFollowCamera(),
     car_colors::Dict{I,Colorant}=Dict{I,Colorant}(), # id
-    )
+    ) where {S,D,I,R}
 
     s, ctx = get_surface_and_context(canvas_width, canvas_height)
     render(ctx, scene, roadway, rendermodel=rendermodel, cam=cam, car_colors=car_colors)
     s
 end
 
-function get_pastel_car_colors{S,D,I}(scene::EntityFrame{S,D,I}; saturation::Float64=0.85, value::Float64=0.85)
+function get_pastel_car_colors(scene::EntityFrame{S,D,I}; saturation::Float64=0.85, value::Float64=0.85) where {S,D,I}
     retval = Dict{I,Colorant}()
     n = length(scene)
     for (i,veh) in enumerate(scene)
diff --git a/src/overlays.jl b/src/overlays.jl
index 7e8138f..7d7c346 100644
--- a/src/overlays.jl
+++ b/src/overlays.jl
@@ -14,13 +14,13 @@ export
 
 abstract type SceneOverlay end
 
-function render{S,D,I,O<:SceneOverlay,R}(scene::EntityFrame{S,D,I}, roadway::R, overlays::AbstractVector{O};
+function render(scene::EntityFrame{S,D,I}, roadway::R, overlays::AbstractVector{O};
     canvas_width::Int=DEFAULT_CANVAS_WIDTH,
     canvas_height::Int=DEFAULT_CANVAS_HEIGHT,
     rendermodel::RenderModel=RenderModel(),
     cam::Camera=SceneFollowCamera(),
     car_colors::Dict{I,Colorant}=Dict{I,Colorant}(),
-    )
+    ) where {S,D,I,O<:SceneOverlay,R}
 
     s = CairoRGBSurface(canvas_width, canvas_height)
     ctx = creategc(s)
@@ -76,7 +76,7 @@ end
     line_spacing::Float64 = 1.5 # multiple of font_size
     incameraframe=false
 end
-function render!{S,D,I,R}(rendermodel::RenderModel, overlay::TextOverlay, scene::EntityFrame{S,D,I}, roadway::R)
+function render!(rendermodel::RenderModel, overlay::TextOverlay, scene::EntityFrame{S,D,I}, roadway::R) where {S,D,I,R}
     x = overlay.pos.x
     y = overlay.pos.y
     y_jump = overlay.line_spacing*overlay.font_size
@@ -103,7 +103,7 @@ end
 mutable struct Overwash <: SceneOverlay
     color::Colorant
 end
-function render!{S,D,I,R}(rendermodel::RenderModel, overlay::Overwash, scene::EntityFrame{S,D,I}, roadway::R)
+function render!(rendermodel::RenderModel, overlay::Overwash, scene::EntityFrame{S,D,I}, roadway::R) where {S,D,I,R}
     add_instruction!(rendermodel, render_paint, (overlay.color,))
     rendermodel
 end
diff --git a/src/render_scenes.jl b/src/render_scenes.jl
deleted file mode 100644
index e69de29..0000000
diff --git a/src/renderable.jl b/src/renderable.jl
index 0c383da..8ee60c7 100644
--- a/src/renderable.jl
+++ b/src/renderable.jl
@@ -10,7 +10,7 @@ function isrenderable end
 
 isrenderable(object) = isrenderable(typeof(object))
 isrenderable(::Type{R}) where R <: Renderable = true
-isrenderable(t::Type) = method_exists(render!, Tuple{RenderModel, t})
+isrenderable(t::Type) = hasmethod(render!, Tuple{RenderModel, t})
 isrenderable(t::Type{Roadway}) = true
 
 """
diff --git a/src/rendermodels.jl b/src/rendermodels.jl
index 0c29acd..6b97993 100644
--- a/src/rendermodels.jl
+++ b/src/rendermodels.jl
@@ -45,7 +45,7 @@ mutable struct RenderModel
     camera_rotation  :: Float64        # [rad]
     background_color :: RGB
 
-    RenderModel() = new(Array{Tuple}(0), VecE2(0.0,0.0), 1.0, 0.0, RGB(0, 0, 0))
+    RenderModel() = new(Array{Tuple}(undef, 0), VecE2(0.0,0.0), 1.0, 0.0, RGB(0, 0, 0))
 end
 
 Cairo.move_to(ctx::CairoContext, P::VecE2) = move_to(ctx, P.x, P.y)
@@ -302,11 +302,11 @@ function render_vehicle(
 end
 
 # aggregate
-function render_point_trail{T<:Real}(
+function render_point_trail(
     ctx           :: CairoContext,
     pts           :: Matrix{T}, # 2×n
     color         :: Colorant,
-    circle_radius :: Real = 0.25 )
+    circle_radius :: Real = 0.25 ) where {T<:Real}
 
     save(ctx)
 
@@ -319,13 +319,13 @@ function render_point_trail{T<:Real}(
 
     restore(ctx)
 end
-function render_line{T<:Real}(
+function render_line(
     ctx        :: CairoContext,
     pts        :: Matrix{T}, # 2×n
     color      :: Colorant,
     line_width :: Real = 1.0,
     line_cap   :: Integer=Cairo.CAIRO_LINE_CAP_ROUND, # CAIRO_LINE_CAP_BUTT, CAIRO_LINE_CAP_ROUND, CAIRO_LINE_CAP_SQUARE
-    )
+    ) where {T<:Real}
 
     line_width = user_to_device_distance!(ctx, [line_width,0])[1]
 
@@ -341,13 +341,13 @@ function render_line{T<:Real}(
     stroke(ctx)
     restore(ctx)
 end
-function render_line{T<:Real}(
+function render_line(
     ctx        :: CairoContext,
     pts        :: Vector{VecE2{T}}, # 2×n
     color      :: Colorant,
     line_width :: Real = 1.0,
     line_cap   :: Integer=Cairo.CAIRO_LINE_CAP_ROUND, # CAIRO_LINE_CAP_BUTT, CAIRO_LINE_CAP_ROUND, CAIRO_LINE_CAP_SQUARE
-    )
+    ) where {T<:Real}
 
     line_width = user_to_device_distance!(ctx, [line_width,0])[1]
 
@@ -363,13 +363,13 @@ function render_line{T<:Real}(
     stroke(ctx)
     restore(ctx)
 end
-function render_closed_line{T<:Real}(
+function render_closed_line(
     ctx        :: CairoContext,
     pts        :: Matrix{T}, # 2×n
     color      :: Colorant,
     line_width :: Real = 1.0,
     fill_color :: Colorant = RGBA(0.0,0.0,0.0,0.0),
-    )
+    ) where {T<:Real}
 
     line_width = user_to_device_distance!(ctx, [line_width,0])[1]
 
@@ -394,13 +394,13 @@ function render_closed_line{T<:Real}(
 
     restore(ctx)
 end
-function render_closed_line{T<:Real}(
+function render_closed_line(
     ctx        :: CairoContext,
     pts        :: Vector{VecE2{T}},
     color      :: Colorant,
     line_width :: Real = 1.0,
     fill_color :: Colorant = RGBA(0.0,0.0,0.0,0.0),
-    )
+    ) where {T<:Real}
 
     line_width = user_to_device_distance!(ctx, [line_width,0])[1]
 
@@ -425,11 +425,11 @@ function render_closed_line{T<:Real}(
 
     restore(ctx)
 end
-function render_fill_region{T<:Real}(
+function render_fill_region(
     ctx        :: CairoContext,
     pts        :: Matrix{T}, # 2×n
     color      :: Colorant,
-    )
+    ) where {T<:Real}
 
     save(ctx)
     set_source_rgba(ctx,color)
@@ -494,7 +494,7 @@ function render_line_segment(
     stroke(ctx)
     restore(ctx)
 end
-function render_dashed_line{T<:Real}(
+function render_dashed_line(
     ctx          :: CairoContext,
     pts          :: Matrix{T}, # 2×n
     color        :: Colorant,
@@ -503,7 +503,7 @@ function render_dashed_line{T<:Real}(
     dash_spacing_in :: Real = 1.0,
     dash_offset_in  :: Real = 0.0,
     line_cap :: Integer = Cairo.CAIRO_LINE_CAP_ROUND,
-    )
+    ) where {T<:Real}
 
     line_width   = user_to_device_distance!(ctx, [line_width_in,  0])[1]
     dash_length  = user_to_device_distance!(ctx, [dash_length_in, 0])[1]
@@ -561,7 +561,7 @@ function render_dashed_arc(
 
     restore(ctx)
 end
-function render_arrow{T<:Real}(
+function render_arrow(
     ctx               :: CairoContext,
     pts               :: Matrix{T}, # 2×n
     color             :: Colorant,
@@ -570,7 +570,7 @@ function render_arrow{T<:Real}(
     ARROW_WIDTH_RATIO :: AbstractFloat = 0.8,
     ARROW_ALPHA       :: AbstractFloat = 0.1pi,
     ARROW_BETA        :: AbstractFloat = 0.8pi
-    )
+    ) where {T<:Real}
 
     @assert(size(pts,2) > 1)
 
@@ -617,12 +617,12 @@ function render_arrow{T<:Real}(
     restore(ctx)
 end
 
-function render_colormesh{T<:Real, S<:Real, U<:Real}(
+function render_colormesh(
     ctx::CairoContext,
     C::Matrix{T}, # n×m matrix of 0->1 values
     X::Vector{S}, # n+1 vector of x bin boundaries
     Y::Vector{U} # m+1 vector of y bin boundaries
-    )
+    ) where {T<:Real, S<:Real, U<:Real}
 
     n,m = size(C)
     @assert(length(X) == n+1)
@@ -653,14 +653,15 @@ function render_colormesh{T<:Real, S<:Real, U<:Real}(
 
     restore(ctx)
 end
-function render_colormesh{T<:Real, S<:Real, U<:Real}(
+function render_colormesh(
     ctx::CairoContext,
     C::Matrix{T}, # n×m matrix of 0->1 values
     X::Vector{S}, # n+1 vector of x bin boundaries
     Y::Vector{U}, # m+1 vector of y bin boundaries
     color₀::Colorant, # color for c = 0
     color₁::Colorant, # color for c = 1
-    )
+    ) where {T<:Real, S<:Real, U<:Real}
+
 
     n,m = size(C)
     @assert(length(X) == n+1)
diff --git a/test/runtests.jl b/test/runtests.jl
index 191a022..b4b2565 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -1,11 +1,13 @@
-using Base.Test
-using AutomotiveDrivingModels
+using Test
+using Pkg
 using AutoViz
 using NBInclude
 using Colors
+using Random
+using AutomotiveDrivingModels
 
-nbinclude(Pkg.dir("AutoViz", "doc", "AutoViz.ipynb"))
-nbinclude(Pkg.dir("AutoViz", "notebooks", "tutorial.ipynb"))
+@nbinclude(joinpath(dirname(pathof(AutoViz)),"..", "notebooks", "tutorial.ipynb"))
+@nbinclude(joinpath(dirname(pathof(AutoViz)),"..", "doc", "AutoViz.ipynb"))
 
 rw = gen_straight_roadway(3, 100.0)
 car = ArrowCar(0.0, 0.0, 0.0, id=1)