diff --git a/notebooks/common.py b/notebooks/common.py
new file mode 100644
index 0000000..4c45fdf
--- /dev/null
+++ b/notebooks/common.py
@@ -0,0 +1,18 @@
+import numpy as np
+import csv
+import matplotlib.pyplot as plt
+
+def import_csv_data(filename):
+    stylus_x = []
+    stylus_y = []
+    stylus_pressure = []
+    time = []
+    with open(filename) as csv_fp:
+        reader = csv.reader(csv_fp)
+        next(reader) # skip header
+        for row in reader:
+            for i, v in enumerate([stylus_x, stylus_y, stylus_pressure, time]):
+                v.append(float(row[i]))
+    stylus_pos = np.array([stylus_x, stylus_y])
+    time = np.array(time)
+    return time, stylus_pos
\ No newline at end of file
diff --git a/notebooks/fast_data.csv b/notebooks/fast_data.csv
new file mode 100644
index 0000000..14b7764
--- /dev/null
+++ b/notebooks/fast_data.csv
@@ -0,0 +1,217 @@
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diff --git a/notebooks/frequency_analysis.ipynb b/notebooks/frequency_analysis.ipynb
new file mode 100644
index 0000000..64708a0
--- /dev/null
+++ b/notebooks/frequency_analysis.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import common\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import fft\n",
+    "\n",
+    "time, pos = common.import_csv_data('fast_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average time delta [s] = 0.007605088372093023\n",
+      "Min time delta [s] = 0.0009239999999999999\n",
+      "Estimated sampling frequency [Hz] = 131.49091122589894\n",
+      "Frequency upper bound [Hz] 1082.2510822510824\n"
+     ]
+    }
+   ],
+   "source": [
+    "time_deltas = (time[1:] - time[:-1])*1e-6\n",
+    "print('Average time delta [s] =', np.mean(time_deltas))\n",
+    "print('Min time delta [s] =', np.min(time_deltas))\n",
+    "print('Estimated sampling frequency [Hz] =', 1/np.mean(time_deltas))\n",
+    "print('Frequency upper bound [Hz]', 1/np.min(time_deltas))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f912f709710>]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BosqgBTADKSKkiHSlhtKWyK3VdZFDZ6LKoAV4ngGQJtKBprQlDREbUu+1M1Fl0AJcZqTIhIlUGShtiG5Up6gyaAEuM1IpQjpFanUpbUloozrtox2JKoMW4DKQIuicgdK2SCNFd9btTFQZtADHZaSJkCK1upT2JPTSmSqDjkSVwQJjrSwiQipFanUpbYmGiRRVBguMHVjpFOlqIqVtCYeJFrEgyqKhymCBsWMsRZ53oEaX0o6oZ6CoMlhg7FJSbzWRruFW2hPdtVRRZbDA2IGVNm8gq9WltCOqDBRVBguMHWS6HYXSzoS3sF7EgiiLhiqDBcafM0gRUim1upT2RDeqU2ZVBkR0ExENEdEzIu2TRHSIiLaZv8vFbx8jogEi2kNEbxfpl5q0ASK6TqSfTUSPmvTvEVFuPh9wsXF9z8ALFelAU9oRJ7QdhfbRTmQunsG3AVwak/4lZr7Q/G0CACI6H8A7AVxgrvkqEaWJKA3gKwAuA3A+gHeZcwHgcyavVwEYBXDVyTxQu+HPGaR0zkBpXxz1DDqeWZUBM/8cwMgc87sCwO3MXGXmFwAMAHiL+Rtg5n3MXANwO4AriIgAvBXA9831NwN4x8t8hrbGaXrpbJELpCgxuDpn0PGczJzBB4louwkjLTNpawAcEOccNGlJ6SsAjDFzI5IeCxFdTURbiWjr8PDwSRS9dVjhr9tRKO1M+Etn2kc7kRNVBjcAOAfAhQCOAPjCvJVoBpj5RmbeyMwb+/v7W3HLk8YRcwYpnTNQ2hRdWqpkTuQiZh60x0T0dQD3mP8eArBOnLrWpCEh/TiApUSUMd6BPP+0QL50pspAaVdCbyBrH+1ITsgzIKLV4r9/BMCuNLobwDuJKE9EZwM4F8BjAB4HcK5ZOZSDN8l8N3u7tj0I4E/M9VcCuOtEytSu2I+GpMh8z0DHmdKGhD2DRSyIsmjM6hkQ0W0AfhfASiI6COATAH6XiC4EwABeBPB+AGDmnUR0B4BdABoArmFmx+TzQQD3AkgDuImZd5pbXAvgdiL6DICnAHxz3p6uDQhWE0HnDJS2xdU5g45nVmXAzO+KSU4U2Mz8WQCfjUnfBGBTTPo+eKuNTkusy50yq4naPUw0Vq7hi5ufw8cvfy26sunFLo7SInSjOkXfQF5gWCqDU2DO4FfPH8ctj+zHs0cnF7soSgtxdQK541FlsMA4cs6AKPTh8XZkYroOAGjoYvOOQl86U1QZLDByzoCo/VdqTFQ8ZVB32rucyvwS3o5i8cqhLB6qDBYYG38ls5qo3T97OTHtvf/XaHcXRplXXGakyDvWOYPORJXBAhN+A7n99yaynkFDPYOOouEwsmlPHOhqos5ElcEC468mSsGsJlrkAs2CP2fQ7gVV5hWXGTmrDLTpOxJVBguMG1pN1P6TcxMVEybSCeSOwnEZmbQXJ2r3eS1lYVBlsMC44ktnp8L3DCbtBLKahx2Fw4x0ikCEtp/XUhYGVQYLjJWp3kCjtt8e2J9AbveCKvOK63rKIH0KzGspC4MqgwUmWE3kLS9td6tLJ5A7E8flYJFDm/dRZWFQZbDAWOF/yqwmmrZhIvUMOgmH2f9Ot+qCzkSVwQLjyC2s23xvoobjolRzAOha805Dw0SKKoMFxo6rYG+ixS3PTExWGv6xvoHcWTREmKidDRZl4VBlsMAEq4mAdJsvLZXKQCeQOwvXDxORvnTWoagyWGCCvYnaf87ATh4D+tJZpxFMIOt7Bp2KKoMFxhHvGaRS1NaTc3byGADq6hl0FI7rzWvp1/g6F1UGC0x4zqC9J2ZDnoHOGXQULrP5Gp+GiToVVQYLjCv2Jkq3+Woi+8IZoGGiTsNxGelUSieQOxhVBguMK94zoDYfaGHPQMNEnYTLjDR5Bos2fWcy6zeQlZMj9D2DNl9aOlFpgAgo5jLqGXQYjhvsTdTOBouycMzqGRDRTUQ0RETPiLTlRLSZiPaaf5eZdCKi64logIi2E9GbxDVXmvP3EtGVIv3NRLTDXHM9EdF8P+Ri4n/PIHUKzBlM11HMZ5DLpHQCucNouOxtptjmoUxl4ZhLmOjbAC6NpF0H4H5mPhfA/eb/AHAZgHPN39UAbgA85QHgEwAuAvAWAJ+wCsSc8z5xXfRebcfhsWkcHa/M6VxHvGeQ9Aby7iMTODw2DcALz8zn/kUTlXpTfiOlGm5/7KXYc/u6ssikaV4mkO/bNYhH9x0/6XxmY3Bibm1xKlOpOwuav76BrMyqDJj55wBGIslXALjZHN8M4B0i/Rb22AJgKRGtBvB2AJuZeYSZRwFsBnCp+a2PmbewJ7FuEXktCLc88iLu2X4YAPDgniEMTVTAzLh1y35MVuqoNhzc9thLcF3G0EQFdz55sCmP//X9p/FXP9wBAHjk+eM4OFoGANy17RBGSzUwM/594BiYOfI9g2Clxu4jE3hi/ygA4L/f9hT+6Wd74LqM//CPD+COrQdC96s2HPzbU4eahHqp2sDxqSoAT/D++8AxAMBjL4xgcKKCiUodF332fty7cxAjpRqu+vbjGCnVcM/2w7juzh0YmqjgqZdG8a1/fwGAN4Hc151FJpUK7U204+A4StUGXJdx22MvoVJ3UKo28K9b9oOZMTA0hZt+6eXx3Udfwmfu2QUA+Py9e/C1h58HAHzqR7vwq4FjcF3G5376LI6OVzBaquF/fm+bv222ZbJS9+tU8rOdR7Hn6CQcl/Hfb3sKT700in3DU7j4H+7HE/tHsf3gGP7TFx/GZKWOwYkKHtozBAAYn65jeLIa1x18XjhWgusymBnPHp1o+v2xF0bwr1v2AwA+dud2/O1dnqP88HPD2H+8lJhv3XHxzKFxAMD+4yX8ZMcRUzfP4iN3PA0AePDZIf+cH28/giPj02g4Lv7hJ7sxUqrhyZdG8Wuf/BmOjE9j5+Fx/P2m3WBmbDsw5vflgaFJvHjMK8fTB8ZwzPSL3UcmUGu4GCvX8IHvPIGxcg2Hx6bxxc3PgZmx4+A4bt2yP7KFdfgZpqoNX0GMlWsAvD23XjD3Ozw2jR897ZVjx8FxPPDsIADgxWMlHBn3jJwHnh302/SR54+jUncwNFnBe7/1GMbKNYxP13HvzqMAgONTVew+4rXBrsMTft1Ivvf4SxicqMBxGTf98gVU6g6YGYeMUXVobNqv66cPjOFB0xeGJisYL3v97Yubn8O2A2NgZtyx9QBKVW8BhXzW+3YNNt37keeP+0bO8akqKnUHDcfF1bdsxbYDYwCAJ18aBTOH2v/gaBn37/by23N0Ek++5I3/p14a9evygWcHMTTpPdc/3/ccxso1TFbquP7+vQs+j3eiE8irmPmIOT4KYJU5XgNASrKDJm2m9IMx6bEQ0dVEtJWItg4PD59Qwb+z5SX86OnDcF3G+27eilu37Me+YyX8zb89g5/tHMTPnzuGj925A88cHscPnjyE/3nH06GJVQA4PlXDsZI3KK757pP4+s/34fhUFR+6fRvu2nYITx8cx3/+xqN49IURsZqIkEmTv83DP927B5+42xMoY+Uaxst1lOsOhiareOFYWBA++OwQPvy9bXj26CReOFbCN36xD4AnbN/9zccAAF+67zl89aEBAMBVNz9uylTDdN3BwdEyth8cw/1G6Ng3jaeqDfzgyYP43E+fNf+vo5hPI5sOrMNqw8H/fcOvcNtjL2HXkQl87M4deGjPEO7bPYi//rdnsHdoCj986iA+dc8uVBsOHtwzhJ884w3qUq2BUs0bpN/+1QvYvHsQL42UccNDz+O+3YN4Yv8o7nzqEHYcGsfjL47gz7++BXXHxZcfGMA7b9zS1HYf/+Ez+MYv9mGsXMOPnj6MXz1/HEfHK2AGjoxP45lDExgYmsLhsQpufWQ/3nfLVjAzPvWjXXj/rVsBAN9/4iAefs7rOz96+jAOjJQxOFHB277wEO7bPYgt+0Zw6T//AnsHJ0P3vv2xl/DFzc8BALYfHPcH+P/43jZ8/Rf74LqMT9z1DAaGJjE4UcF/+uLDODBSxqYdR/CHX/4lhia9Mn3oe9sAAE8fGMe2A54w+OSPduKGh59HreHig7c9idsfO4A9g5P4l4f34aE9Q3jxWAk1x8WR8Qo27xrEjT/fh1LNwbf+/QV85p7dAIBrf7ADnzZK+L3fegz/8vDzGC3V8Af/55f40dOHse3AGDbtOIqnD47jZzuP4vr79+LIeAV3bD2Av//xbrgiTCQ9g7rj4rc/9wC+/8QBHBqbxps/cx8ef3EEDz03jLd+4SEcGpvG7Y8fwP9z+1OoNVx87eHn8cm7vXJ8+Hvb8Kkfeccf+M6TuOWR/RicqOBdX9+CTTuOYPuBcTy0Zxi7Dk/gh08exPtvfQIjpRq+/OAA3vstr19/dtMuPw/L+HQd1/5gB+588hC2HxzDp+7ZhZ8/N4z7dw/hd/73gxiaqOC7j+7HB297Cq7L+MqDA76B8v5bn8Cn7tkFx2Vcf/9ebNpxBC8eL+P//f52/PSZo9h5eByv/Zuf4uBoGd9/4iD+6y1bm4yVz9/7LL5g+sKffO0RXH//Xhwv1fCzXYPYsu84njk0jj/+6q/w+IujQftPVHDLI/vxl995EsyMz9+7B3/9Q2/8f/T/expf2vwcGo6L/3rzVnz30Zewd2gS/3zfXjzw7BB+/twxfHHzc9h5uNlImU9OejWRsehb4lcy843MvJGZN/b3959QHoV8GuWag1KtgYbLmJiuh4SjbfjJSgNTVe/YWgyWqWoD5WoDzN714yKPyUoDIyXPKhsp1VCpe9q8K5NCIZdBzXFRd1xMVOr+Us5S1StP2dynXAvfb9y8DDYxXcdd2w7hMz/ejVK14YWrTIhkqtpAqerAcRmTlQbGp+t+uUtVB2WzAV251vDzL9cclKsOKnUXjsso1xwU8hmkU0GYqFR1UHNcjJRqoWeUx6Wql/d0zUG51kBJ5l9roNpw4TJQrjqYEs/on1d18PiLI/jV88dxfKqGwYlKrCU/Wan7z+k/s32uquM/V6nmPX/dYUwbC3Rwwsvvyw/sxa2PvAjHZXzo9qdw22MvYXiyCpe9cNPQpFef0fuXag2/Pss1B6Wq47f/xHQDw1NV3PzIfjzw7BCeG5zEwNAUdh+ZwPBkFczAaMkre63hotZwvT5kyj5VaWCq4rULM0K/lWqO/4yVmoNpEy4qVb3y2DocLdcwWq7BdRmj5TpGy3WMlGtwXMbwVDVof1FnNo/puoO6w8Fb8sI1KFcdjJbrODjqhUYdl3FgpOwr4eHJKqYqXrnLtQYmqw2/jUdKNYyUaqg1XFTqLiYrdYwaz2KkVPPLXqo5/hf2Jit1/zrb98enw8LY5j9VrYvjBo6MT8NxGUOTVUxMe97MdN3rc/a8oYkqhiYr/r3DY76OF4ziPTAy7d9XbtNir5kyaYfGpnFkvBL0jWrD98qOTVVxbKoGZmDEWPi1houa43r9xtx33ByXqg5c9jz0YOyK44hcmG9OVBkMmhAPzL9DJv0QgHXivLUmbab0tTHpC0Yhn2kabGVR2SXRuazQiSoDT8h5QrLhMkq1QMhN1cLCyqYX8hkU8t7iLU8oesLLdtiyGPT2eotfjlrQCUtGmE75ncYL3UiBGAgv+VxO6LkCwe3lVchlkE0HE8hR4TPTsfe8noKxv3mCM6ibsnhG+Vyheq85qDbckFvccFxUG64RjsHACz9voBh8QVMNCwN7XKo14LJVZoFQmhLH0Ta3ZbL15rd/KH8nNHhDbSfqTLaJfX75u680q4GRMF13UBGCfMrUPTObdnBQrgulX3Wa8ivVnNDz2nqarNb9UKYMR1YaNj8H07WZ+4I1kkK/JfQ9q1Cb8/PS6w6j1nBDBoQlfK58luZ+XTL9qRwZk+VqoBxj60aM/6hxVjJjt+54il2eWxLPNRV5rqmq6PtNYyE6FoP8gr6wsPNGJ6oM7gZgVwRdCeAukf4es6roYgDjJpx0L4BLiGiZmTi+BMC95rcJIrrYrCJ6j8hrQSjkMqEOFh2EvqVZCzekJBjMYaEHGEEUyTubJuQzKRRyaQBWKHq/JQnb6P0AM5hq4UFTa3ieRiB4gw4WKLywYvA9g7r0GLwBU8invQlkEyqYrsXcWwy8sujUZaNYa46LSt0TnmXplVTjy4MGOwgAACAASURBVBFSDNX4eo9as345hHAp1wKhFBVWVmj6yiAisGZrAykwrDHgC9taI9Lmsg2kApDne3k0HM9qjgqRshQu5rrpeuAZ2DK4DL+erTC295aGQrkaUw5xPDHdQCbVHCayE9eewdIs1KeqUog5IUVuDSqpvJMs3qBvResy+M3vCyEjJ65fRPuIlwcz+0pSKu+woogzUCLjvxYxemqRPpTQn8qRZ7dzMdN1p0mxhdurNZ7BrO8ZENFtAH4XwEoiOghvVdA/AriDiK4CsB/An5nTNwG4HMAAgDKAvwAAZh4hok8DeNyc9ylmtpPSH4C3YqkbwE/M34LRk083DTapoVNmZWtY8AaNYC1UFulRi2Iq0uiFfAZEJDwDL926zkAwuL38Ip1fDF7rsoYsskrDd4fjOqVnKUurpFlx2Wt7chlvAtlY5TK8JAdeteH69SStbnu+DbOEB0pQp1NVB4UZ6sxeu6Qn698/eK7mkFfUsooK3oYJg1XqricoRWhQCjby2z/iDUa8qbqTCgnbqZh2CT9XWDjbMOXYdD2Ur63zOKU0XXMwXbf1HtxnsuJdm05Tk/cRrbOohzLl51H3lz/LxUQ2zDkdCVHV/P4R9aa944mK10dKkfqVCrvbGEfliDEQ9W6j26lLg0cqmrCXLPKrNvzwS93hUDnKtfCY6c6mg/YSeYfv30A2nfJ/t16+V+aId1GL6QtmLq3WcP39wKLtIsdrXYRsHZeRImAhVuDPqgyY+V0JP70t5lwGcE1CPjcBuCkmfSuA181WjvmimM8kDohSteErg7LoxKWag4efG8buIxP484teAQCoNVw/pigbPRQLNgKgkMv497bn2HtaoWnnMeyx6zK+9vPn8V8uXh9rTcmBNzxl8whbRNJyCVmowrKSVqKnuLwJZDtnMF1rHshRYeAPLCHwbNx0OhSWiHoGQX3Icsv2ePDZITguY8PKHr9uytVgEIY8gxjXWgqMQTG/MhVTplD71xp45tA4vvPofnz2Ha/3zz9uYsDVhuvHfGUfCuUdtQyFkLNlDdo/GioL6slSqQehGqmcj5eq/pxMKaYOpmS9R+ZWbDlcjv8AU0WEnWRfsO0/FTEubN527iWq5KZEe8Va8lHjou6A2RtvuUzKTw+uC8ZabFhMHNsyNXsUgeDtzjaPk6lqAwdGyjgyXsGF65ai7jDqTmAAeIpoZs81anRGx27UY5P9pu6k/ONv/GIfPn/vHmz/5CXoyc3vO8Mdtx1FjwkThd33eItMHt/55EH8y8PPhyw1K/DC4Y/mPKwS6DGW0GSl4VtcUhjIzrz76AT+90/34P7dgxEF0Nzhh8zkaN3xJg/9e4eeS7q0YkCac46XanAZ/gSyDRWURVhChkqmqoFgmIpROva5Gi5jTJYpZtDYkFnQHkHeX3lwAP/nwYHY5w7NGUQGU1wIx062h63VSDhFWHsPPDuE2x47gNFyrUnIee1fC9olJjQgBaUUXOPlui9MbT3Jto2GKP0J+rqD6brMO9z+Ncf1J2iThEu4D4Xj8WlC0zbrQZjITRRWMu9onyzX470mOfkfKquop2OTNX+Za6nawH/+xhZ87eHnwx6P7EMhg0woPJP3kPVWo3MNMYojVE81rx9+8LtPxo5/qThKkeeKMwwmK3W/TIN+n3TCSq4WfzxZaYAB34OZTzpuO4piPm0GjXTPZJgA5jg8kL3Z/0aoMwzFDOSoZThV9axtAH6YSK5UsXnUHcbYtDeQyzXHX2k0MR0WNJPSavI7eSCghkICL0bhJViGtkx2Anmq4Z1TiRuwVW9VTDTvsXLNd2mtxSPzDg88B9KDihMo5ZqDiUrdn6T1yywm/32FV4/EpmPK5Asok2/0uaaqDaRSgWdo+4JcMRVqO1HXsg/FC83gGWV72fwqdelphCdDbTmma27sJO5QTH+SCkWGCcOKJmg7wGxhHdk/q9KwYSIxv1UL2r8snneyUveNHFsOZiE0IyEZaxxFvUFfyYl6mqo2sO2lMSzryeGis5eLeooT6oECHSnV/fkvm1/Ncf3wnOwrpVoDXdXACpfhzZFSDWPlekh52vyifVb25XKMULdGBBD0yem644eAo/OO0jOoNlz0dmUWJ0x0umFdK98ij0yuGVnQNGgmK95kj2zIUIhHWChSuExVHSzp9uLeVhnIwTscc1yuNXzBMGmWnHn5BfcZLdX8Tm6XTcq8mz0AYb1bS1MIU3vvnlwaGbG0NPwswcCr+vHjYOANyXJMxDxXtRESSlOizuI8hqlqAxPTDX/FFmCEph3I4lmkUJKCQZZDvqkcpzTLtQbSpgOUag3Y9+6Ol2q+JR9qu6lAydm5n9AcSi3egh6KaS9ZT+GwWQMEr0yeZ+CVY8R4cl4e4rkmA0szdlVLqC8Ex4C3mWIqBYj3DUMTyNNCedcazZ6VNADCZYq3yHtCbRdTT6JuJip1lGqOt2Q6ZmGE9AZl35LlCI8TuSQ7yCOfCRRUdPzbJdZ+HhNy/MeFnaLhoxjjLbH9gzay47xU89qgt2thxHbHKYOiL5BFZxDWhbUMwzHywJKU21DIUMiI755H3L1qA2uWdgGA7yHEDV6ZX6kaWAkTlfhJqMHJeCEXsjTF5FRIKNWsu9ocrirmM8jELC2Nhr+kZxA3eEOegRCa9t2NqWoDxZjnOi6EnFWK0jMI5RdpOynUrcCQZToq6unoRDDw5MoSEgsI7NLWkBKJaS953BRCkXMupi4Thaa5j5yvKgthXak7vnAeCim2ZiXsWZqi/WvNgne8XPPbEQi+xtfgIK0SWb1knyvkGcYpuZjjUkTp92Sl4LXzH0H7y3q3405651EPKryQYW5lkvMYpaqDrqzsk4FAtuPfvlHtlS8Y/354LjQvFvFW4soUIwvkyjjpGZSNUurrymIh6Dhl0OMLZK9Bqg1XvPwlPYOw62qF8xGpDEKDutmqs5Z8dAI5zmqWx9N1x3/tfyL08phQBjEKwCuHjGlL70dMZFWThVJPPuNNILvNE8hyIFthMFkJwgdxzxI9PjYZhMLilifK5xovB7HVOIus+WU673isXPfLJ/OLO44Kb7/9q8GKqSMxBoBXZzECrxofJz42VfXj30ntH3h1gdKUSnC6FljnSQol1C9iY9rNk5cW+6Uz+dJZNWk1UUOsaqrN3P62fKGQUbXhL7UOh4bi6/ewaYPJ6SBU43mGMnxoQ4P1IFyZMDbCc1rB3E9XNgjJSIEcKIN4wyDkkcm5i5j2T+xDwquXYSfrpU8ZI1E9g3nCj9vHhTFEmGBiOoh/lmqOb2UfFZZBqPNPBHlErRV7z+5sGkQzuIkxykXGq8emAyGX1MnjQhByolgOvGMxg7eQSyOTSvlWsRUA0WVwthzHYjyAaPqxGMEVDs8ECkqW/0jIkm8eeNP14M1VOReSNNikV2ePXfasUVumlD9nFCg52eZDMW0OhFcqSWMgCFfMLJSi59hwZKnqBHMG4j2DRIs3VkFFvdXmPAD5zY0gzb505oWJAiHn98M5KKVYL6HawJSYM/ANlIn4+rUbOY5P10U4KBxnt5Z8kieX1AZynmXKzBlMirBjudbwDcawMoj3fmJXMiV4A3HP6ynNQEHl0qmgHJU61i3vwULQccogGiYCgEERMrJhAinAbLwSCHeGYzGdq+4IS6MSXk1ERCjkMrGdCAgLhsAtrsdazeEOP7MwkJNTMtYcp3wK+Yw3Z+CG5wzkM5arjm81z0XIJVm//vxHOYjJD042C2wgeRAeE9ZUEJONv7eMGQ/GKFDZ/jK/wwmegaw/m181suQ4+t7FTOWLtTRrDdipwpAySLQu45Wm3D4kLg8AZtdShD57OVuYaDYrN/m5gjmD8bKck4kX5EeMMpio1P1VQ0AwTqUSHkwytmYpX6nqIJ+xK5mC373tV7w2tUopKT9mb/mxzS8IV8aXaTDhef1+XW2gkQm8FQ0TzSN2BUOc0PQ8gXrT74PSokwMzzRbiXb9d1G4dYV8OtaVj+bnKwMRJkoabIkxbZG33d30WML9bB0UcpnQFtZy62TfW6k2/DmFJK9kNmXgiHmWJCtXKoAjYwnWuRUGISHXHIefqaxSkLN9CbAqvcEkazBpctI7HinV/GWaQwnKO1FZCQvavvsghXCS0BxMaoOY9pfXAUYZNL2B7N3P7qcEeIK3EfdcibHwmdt/LgrFKuS6wxEjLPCgbfsPJnlNiSFD7xxvlVEtthy2SkJ9IdEDCca/3/5J4zXJqxNevR1rNkKxUGGijnvPwFrp0uKdFgLPdn75+5GY8AIAf5ln9Hybh41b2jAR4Alb+zuA0AReVRwf9RVKsDRSnmvX7sv7RY/l+dZCkW9zyg24bB305NPIpFNouMFLZdFnrDVcP/4p71dNeBa5YkWWyV4rz5VL95I8A5mHP5EZU//RdPns8j6yLmUs3NbP0YR7Jz2X3Phw1nPFOXHP5XLwPWo5bzKXNp+oNOcd108t3hbW8S+dyfvXHQ7W/88yjqL3LMfUQynh9/AYnBbHlaZz5L1lHcj0pD4S1yfn0g+T8rPHSXUg02X55Dny/rbfTlW8dyn6VBnMD/KtPblUd6bjqCVv5xWiyOsy4pxiPnhBRCoGS1x+VhnYe2cS7mlJJZQ/6bKk9GI+g2wq2Gp7OiIw5lJnM6VF05PqEggPwsGJSuJzzWXJtb3PXNqOKBK6Mm1gz0nNoX6TmK3OZnouu2IlLr+5lCPpnCCPmPcMRPtLZRQtX9zxXPrKXMsdne+ZS/vPds5c2pFo5n4okX1rrrIlqWzR9BR5socZ6OtemDBRxymDohDGKwo5cZxPSM+FvgfMDCzpziKb9lpKammZR39vcFwQCsiGqQBgmdl3Z2UxuN9yc29rrVihLPOz18myLuvJ+R06VP5iwnUivdfUSYqAfCbleQZ2All4Bk15z1J/y3uC35fGlDn67NHnqsldS10O30OUX95neSH++AxTf/3iuiXd8n750LH0Imz723LbT4NGyyGfJbme5PPatgvKsbIYLodktBTeylme432UiJrusTyhL8S1XToF7z2DmAlkoFkZJI8ZL122S2J9yHaMOSeXSaGYz4Tao+a4MzzLzM+bVNcrZ8gv2g+L+Qzypv3D+c2l/Zv7bWJ7Rfq7DTlpmGie6MqmfKHZ39vlp5/RKwV5V+yxpSeX9j0MKaRlHmf0BdfJOQO5NUVvl1UG4t7FsACIy29Vnyyfd77cIjtU/qIsX5AeumefySPnvdmYSRHqYgJZvvqeVDf22YmAFWZQLOnJ+ishZDnC1838XKE66I2vJ3muHJD2/HSK/EG2rJDzheaZoTrNxx5LbLkL+Yy/LFKWSZb/jIRjm0dPLu33hVAf6ovvTz25tC+UrCCS5xRysv0T+mRvfJ359RT5Gh8QDhM1XPYNh+b8mtuurzvrl3W2ftN0bOqsmM+EDDg/j6T+1Necd4oCIbuimPfHf6iu++LrJm78y7o+I6Evz/a8+UzKt/BXFmdvL3m8UBPIHacM7IoeINr5Zm8Eaw0WQ8JAdkQhGMR1sjMXfGWQ8b2Evq5g0Mg8pOsaN1AyKfKty55cetbn6k8QBnZg2bJlxJfOpuvOyxIuPdlAyBVyGX9nyrnUb5IAte73yqTyz3Lck0v7z9abz/jKeU5tng6GiFU0ScbAqhhBlHRcyAftXxTHSf0pzorNpAjLClk/D9snk5RqkjKwxzO9Z+DfO/S8zXl3ZVO+5VrIB31hLkIurqyFfNB2MgwTd++kY2koyeOQITKLQgGCUK1Xprn361UxSqeYz/h59HVlfYNrLoZBryqD+cO+eDaXDiqP1yzt9q7PpdFjOtTyQs7vpElCODSBnLcCQAjNfNoXBtLiWb2k2z+WHerMvuZOLjtXkqeRZEH7QtNcn0ml4JjvAU/XnFhrO+m4R5TJU1DNz5XU4a0CzaVTWGqspt6ujB/O6e2SQjPhGWM8IWldFvKB0lyV4G3J47OWBpZ8IOQyoh2DMiV5F3ECryAUlKcYMjNet0K0wUop5HLiuWI9g3jLNc5L9D0DoQzkBCcQrt84pROq61xmDmOtuUzZNPlhRWmF27aIXtefIHht+Qo5UU9zMJpWJZT1LDP+i6Lel/UEnmZSOeL6qmzzHqFc5qK8NUw0j9jOtTKpUya4j6uXNIcJPOHSbNVJSyPkGeSEAAh5Cc0D2SqfaH5hIRdsgudbv0Jozta5pHdhy2bnQ+xnI0Nu7CyutRSO8hmXdGd9zypJkPuDVwi2vq6s3/mLop6SPB5r1REBK3tzTXVT7Mr67RG22OKtwTXLuoNnyQkhJ45jLc2+5v6Uy6R8xSY9Qy9k1Kw0ZX4yfryyYNtLGhTh9rdv0ia1lz0uCCWXStkwkX8aKnXHbzdg9hBUTy5ihZt6WipChnF1EypTkyXv1c26ZT3x18V4Z5kUYant10LYyvyWFXKxIeOk8gXGYJKnEa8o7XEunfKVXE9C2yUpbJmfTiDPI3J7iEJsGKM55l7MZ0IhGV+r5wJL6IwYqwQIewZWOBZyUpAHnXVJTzARaAWRzLsrKwVK4KFIi7eYj1cucaGhnpCFajwDM2gbrrdt8Ypizl/ZENdZu7JB/FOGq7qFZ9CTEGfvjxl4Utn2dmX8GGlPPl5oxnlCPdm0H9+WQrOYT/thor6ubCA0E8JVVgCEBW/QXj3Cw0ucM7BzDaKupXUpFYqMs4fDRHJiNFBy1vL28gi8lbmGZ3qE8s74H7cJzxnIieCQl2iFnFByYUMpKEePKN9soZVCTl4X9KflhVysZxivlNJhQ0mO+XzzcVwoBwj6U3c2Ha53+4xy7meWMKE0cuS9C0KeLBNKMzlMpJ7BvBFy8WcYNN3ZYJK3rysThAlExy7k40NGdlKWCP6GXN49xXVCofSEBLl3jvQM5ISatEqkSx4WUM3eSjhM1Cx47YCxyqjuMCo1B93ZYDAlDV6ZRyCg0v6cgezwcVa4ZzUFq3WkcOzrblZyS3tyweR0XBhGWvLCgwoJg65MrCBfFVIGnjUatuTD7WXrfbmYnI4LNci67smHw0SFXHObJoaJioEgT8oj3hiID+vImHzcS2fLCs0hRVk+OY7CCi8YG7Lt4jxUb+FBIDTlfEpR9gU7HkNKs1kJh4VtNHQZFzKa2cjp6874x3IuJORpCEMubgK5R4SrpHEk+2dPPj60JvNTZTCP+A0itHpcKERagL1d2cD6jQjycOzXxI99azDj74Rqz/HyyMR2KJlfyDMITYYJwZaTecQJzaCDnuF3ynRocPSIMgGBMmg4Lsp1x3hCMVZdrFufFvWbCR3b510h3HNpNfkDQrRLX1cGvflsUx5euZNDYYWm+p1ZMcTN/WRSFJnwk88o2ivG6gyHBoIwXFhAScUQPHvcc8VNIEthFhVQsZPT5lnymVTgbQnvLUUxXzprOFheCMISSRP0YSs8qJtiyEAJFLkNRcqFB72hNmpuuyXd2cADycUrzZCRI+sjps17Iorc9slVMW3e2xUoorChkUZPzBj0Q4MiNBT2StKRviDS41Yqiraz22zPN52pDEJCMxD2dkb/DKnJfask479TEB1s0iqOhoxsZ/PvbQVHLuxq9ghrJeoZpCjo5NJS8oR6vKcRJxiCwRtvldh/bZhouu59c7VbusLFQMgt6Y4Jm0UHSl4eN7vtZ/jzBGFL3p7b1yU9g3R4UEcGTbopThwXJop4UzEemZ1L6hPCp0coAM/7iRPCzSGjcLw6IvRjwkTRyUmroOT689AEcpzCS4hH9ycISukZRL+BXK27fp0CwTs2cml0MdLmcRPjxXw0pBW2yMOhl0gIJaYv9OSblWZ3Nu2P0Z6QoZRGMSSwY7xEWU+hcRIYJfbehYjyC/en8Iou2SfCSj88BkMGlHjefMZbCm/fRViolURAB+5NBAQCOuxme52YwbEWgPQMCrkM6g1vxERj7nLisyub8v8f3Ft2jGaXUZbpjL48smlCVyZ+cEdDRnYZoBSa1m2fqDRECEWGbzIhJQIEE8h2W4XurHD9zeR0PpMKDaqiGLyyw9sQWZOCyqcxVWv4MWiZRzQ0ECzpi7fqVhalhRo8S0j5xNSZDBPZa13zUpHX5pnQCiLpQdk5lKgg972fomdpNgleKZTEBHJcmMgaGo7LoTfn5QSyzaOYz/gWvSyrNVCGJ6uRpbbh/gEEXzqLfvZySXcWRN4Ll12mL2TTqaa6s/cLQkPh9oiu6JqsNPwQVNS7lIsQrJG2RISJpNJc2u2FDMPtHJ407hFljQsZ2WeoO64IBwnB3B32DDJpNyiHUPC+p2lWGTUbjnFjNyhrXBi41nDFeFg4kX1SngERvUhEO4hoGxFtNWnLiWgzEe01/y4z6URE1xPRABFtJ6I3iXyuNOfvJaIrT+6RZidokPBgsp1UCtJQuKJLdqhmt9haBtk0+W9Oypd0vPPjPAphQQvPwFtJkzVhKW/76/Ayyfi4aFRo9uTSSJH3DJkURUJbYcEAeEtLAfgbtXXn5OBM+zHz0D2kAhADLBDI4TwKeU9R5DPppoEslbBnkQVholCd+YraWz0j2y6xnqLCNjIIZXv2ydBASODJ0IAYyF1efrlMCln7TMK6LETqvSehHaPpPUIgZtMUEVZCmAlBU4zUe08uja6s1zej4UXbLsEW1mFl0J1Nh5S6VTBS2UrhXRR9IeQVReZZCrkg35CHlYtvu3DcXva/NGyYMXY8RJRmXHjWtn94Yj9s3Nl7h73zSHg2n0GK4BuCUU8+GCfpkMKWY0b2F+sByXDVQjEfaub3mPmY+P91AO5n5n8kouvM/68FcBmAc83fRQBuAHARES0H8AkAGwEwgCeI6G5mHp2HssXSE9OQtpMzA9l0CrlMKtTJo5aBfT0+7BlkQp1UHlvC7qC8d3MM2ltJ48052JflooNAuqtVs3WAzK+YCzo4EfnCrifmfjbfTMQzsFZTJkXIpVMo5j0h7g/kXPNAt88aN39g03vycjDL68JzNbY8UatJCl7rCYQGnlDSVil7AjteSRTynmeQSafMG6IRz0B4b9YziNZfU9hAhgakN5gQqpNzJ91Z7xm8UJ2noLuy0f4W5GFluFSmvkIR7RDqs0LwplNeqCj0BnLDRVc2he5cxtt22rRNNp0Ktae0+sOLIcKCzdafLVcqRX7ILM6Tk4o3OmfQk4u0f0LopdnQiPGKjXKqOxmkU+QL85Ai6grqMJ1y/Oct5MLj0R9rpg/GGSLhNg8bKEE/8/LKpl0/XLRQm9QBCxMmugLA75rjmwE8BE8ZXAHgFmZmAFuIaCkRrTbnbmbmEQAgos0ALgVw2wKUDUAQG7daO+cL/3TgauestSEEs1g+abeVbXb3gk64YUUhNAnsnSPd2OYBZC3GFHmdta876w/ydct7sH55T2DNiM5czKdRbTS7oN3RgWY6Z1yMN5hANp6B2c7ZCqBC3uvkPbkM8llvD6Ng0IQ7sD2OxomJAiEXtgTTkYESDEJbHpl3ITL4rUVlB03YAkzjvNW9OH91H157Zh+qZlJ8RTEXav++rgxsZXteXTg0KNvOLgko5DNY1dflK9zerkg4JJ8JCcdEb1AKePNcqRSZ0AWjKxsoiDhlW8xnwKLvRq3sYqT9QyEUk0eKwm8g1x0XjsvoyqRDymhJdxaZVArpFPkhRGlgxbWjPLZvqYeUWnSeI0aQe2GioG7jQotBn4zUU0jZhtsnmybkM+mQkSe9FyDsGRTyad9AiS4KiHsHoTvbrHyi3qVUlMV8JuRdZhuub8gt1FYUwMkrAwbwMyJiAP/CzDcCWMXMR8zvRwGsMsdrABwQ1x40aUnpTRDR1QCuBoBXvOIVJ1zo887sw5ql3VhZzOO8M3vx2rP6AAAXrlvqbx17Tn8R5/QX0duVwYYVPXjt6j6c01/Ea1b14oKzlmDbgTEAXud/VX8Ra5Z2ozubxu+8uh9rjQL4+ns2Nu2GeNbSLpy/ug+vX7vUH7xn9nX5nbU7l/YVTypF+PUNy/0Y7g/+8jeQNZO7r15VxAVr+vCaVb148/pleN2aJRgt1fGaVb1Ys6wbr17Vi1euLPhKzp9s7etCf28+5BnY+La/iiYdfO3NOyeN88/q8zcqO+/MXv9FpF9buxTnr+7D8kLO1FUh7ApnAyXxmjM9gZxKEVYv6fKt62svOw+rl3ShO5vGH79pDX773H6sXdaDlcUczjuzD13ZNDas6MGGFQWcf1YfXnVGEcWuDFYWc/7EqrUUiQirl3TjzCXec65Z2o3zzuzD6iXd2PSh3wYAvL3vTDz68ZXo7crizeuX+buSfvSS1/jbab9+7RK8bk0flnRnsaKQw4aVBZy1tBvpFGHdsh4cMt9XKOYzePdvrMfbLzgT6RThfb/9Sn9//D/duA7LzSTweWf24txVvTijrwuFXBqv7C/6BkVPLoPfPGcl/q/Xr8aKQg6/de5Kv7+cuaQLDYd9gdKd84Tx0p4s1q/owTlnFPHqVUWcv7oPhXwGf/m7Zbyyv4iVRtF1ZVP4wzes9rdeXlHMY0XBi7GvXtKF9St6sHpJF3pyaaxb3oOB4Sl/AtnuS9SVTaMnG3hcf/sHF/htd/5ZfXjNmUUs78lhaU8W5/QXcN6Zvdi4fplff2uWeu1x3pl9uOAsr/1/7zVn+N9WWFHIYWUxj+5sGhvXL8OF65bgFct78Mp+r703rCjg45efhwvOWgLA++JfMZ/B+hUF/7l+be0SP4Ty0UtegzetX4aVxTz6ujI454wCzl5ZRD6TwoYVBeQzaaxd1o3+3jw2rl+GYfP9gff+hw3+G9cXnb0CF65bimI+g7OWdOHVq3rxmlW9uOx1Z+LXNyzH4EQFvfkM1izrxuvXeH2lO5vGBWf1+cbbu39jPZb443gZXr92CVYv6cI5/QW8bs0SrCjmsaovj1f2F1FruDij12ub/r68P9n/B7+22v+2yNkrC3hlfwELBjOf8B+ANebfMwA8DeB3AIxFzhk1/94D4LdEooPgwgAACZ9JREFU+v3wQkMfBfDXIv1vAHx0tnu/+c1v5oXEdV12XTfx9/3HSnzVtx/ncrVxUvd56XiJmZmfH5rkv//xLnZdlweGJvm+XUdPKl/JfbuO8u2P7Wdm5qPj0zxaqrLruvzmT2/mb/5iHzMz7x2cYMfxnvf+3Ud5/bX38PX3Pcfrr72HH3/h+JzuM11rsOu6XK42+JN3P8OTlTr/cu8wv+Hv7uWJ6Vro3PHpGh+fqp7Ucw1PVvj5oUlmZr7uB9v57zft8tOnayfXLpJKveH3hbGy9xyTlTp/65f7ZuwjSTQcr2+NlWr8kTu28WSlnnjuWLnGY+UaHx4r8/pr7+G3f+lhZvbq2rZXHFOVOu8dnGxKf/HYFA+OTzflYf/93E928zkf+zEzMw9NVHj9tffwLb96ga/48i95/bX38Ggpuc3sc71c9h8r8fBk5WVfV284s7bzbOO4HZmYrvlyQVJrONyYoc3nCoCtHCNTT8ozYOZD5t8hIvohgLcAGCSi1cx8xISBhszphwCsE5evNWmHEISVbPpDJ1Ou+YCSNhk3vGJFD75x5caTvo/9nukr+4v42OWvBRB4JfPF2167yj+WL1Td/5H/6LvBrzqj10+3YRn74RW78mg2uoT1+ok/vAAA8JuvWoltf3tJ07nz4e6uLOb9lUT/8MevD6XPJ3Jd9xIxifje3zz7hPKzy0WX9GTxT3/6hhnPtfdjY27aOrb/JlHIZ/CqM5r70PoVgWUp87Dvwsi9iaxnkM+mfc9kpvvO9G2KmXjFihP7pm8mncJsS+5nG8ftSK9ZOBIlm17YNwFOOHciKhBRrz0GcAmAZwDcDcCuCLoSwF3m+G4A7zGrii4GMM5eOOleAJcQ0TKz8ugSk6YsMEu6s/47BZJgAtmsJppF8CgLj5wzWEi8l8485WMXJHSZOR77vQvl9ORkPINVAH5oNG8GwHeZ+adE9DiAO4joKgD7AfyZOX8TgMsBDAAoA/gLAGDmESL6NIDHzXmfYjOZrCwO1gKZmLariRZuBYMyN/KZlLe1yRy9tBMlbSxpl4OvnHVlUv5ChFPR0lbmxgmPcmbeB6DJx2Xm4wDeFpPOAK5JyOsmADedaFmU+cW6+xPqGbQNRN7Kna4FVgY20uMyhyaQe7uCFTnK6YmafEoTWTNncHyq5r3otoBrm5W5051No2uB9qWx2LkDx+XAM8im8d/+4zn4wzectaD3VhYXHeVKE3bOYGiyiqXd2ROeGFTml//x+6/Ga87snf3EkyBlwkDMcmlpCutXFEKTz8rphyoDpQm7N9HxUhWvXKkCoF34LxevX/B72PUEDjMqYgJZOf3RpQFKE3ZpKXN4t0zl9Md6Bo7rffIU0DmjTkGVgdKEDAupMugsgjARo2Lexs5nVUx0AtrKShPy5Zblhfl9gUtpb9JyArmmYaJOQpWB0oSdQAYQ+sqVcvoTLC0FBicq6MqmmrZhV05PVBkoTdilpYB6Bp2GXVrqMuPw+DTOWtqtL5p1CKoMlCakZ7BC5ww6irSYQD40Ou1/elU5/VFloDQhlcEyVQYdRYoCz+DQmCqDTkKVgdJERoSJ1DPoLGyYaLrm4NhUTZVBB6HKQGkinSL/4yW6tLSzsBPIB0e9j/ecpcqgY1BloMRiJ5FVGXQWdmnpgdEyADR9tlU5fdE1Y0osmTQhk07rGvMOw84ZHBgxykA9g45BlYESSyZF/gfAlc4hUAbTIPK+wax0BhomUmLJpFM6edyB2JfPD4yWsaq3a8E/tai0D9rSSiyZFOmy0g6ERJhI5ws6C1UGSiwri3ls0P3rOw770tlEpYF1qgw6Cp0zUGK59aq3oHuBP7GotB8bNyzDn1/0CqxZ2o0rLtQvm3USqgyUWFYUdU+iTmRpTw5//0evX+xiKItA24SJiOhSItpDRANEdN1il0dRFKWTaAtlQERpAF8BcBmA8wG8i4jOX9xSKYqidA5toQwAvAXAADPvY+YagNsBXLHIZVIURekY2kUZrAFwQPz/oEkLQURXE9FWIto6PDzcssIpiqKc7rSLMpgTzHwjM29k5o39/f2LXRxFUZTThnZRBocArBP/X2vSFEVRlBbQLsrgcQDnEtHZRJQD8E4Ady9ymRRFUTqGtnjPgJkbRPRBAPcCSAO4iZl3LnKxFEVROgZi5sUuwwlBRMMA9p/g5SsBHJvH4pxOaN0ko3WTjNZNMu1WN+uZuWnS9ZRVBicDEW1l5o2LXY52ROsmGa2bZLRukjlV6qZd5gwURVGURUSVgaIoitKxyuDGxS5AG6N1k4zWTTJaN8mcEnXTkXMGiqIoSphO9QwURVEUgSoDRVEUpbOUgX4zoRkiepGIdhDRNiLaatKWE9FmItpr/l222OVsBUR0ExENEdEzIi22LsjjetOXthPRmxav5AtPQt18kogOmb6zjYguF799zNTNHiJ6++KUujUQ0ToiepCIdhHRTiL6kEk/pfpOxygD/WbCjPweM18o1kJfB+B+Zj4XwP3m/53AtwFcGklLqovLAJxr/q4GcEOLyrhYfBvNdQMAXzJ950Jm3gQAZly9E8AF5pqvmvF3utIA8BFmPh/AxQCuMXVwSvWdjlEG0G8mvByuAHCzOb4ZwDsWsSwtg5l/DmAkkpxUF1cAuIU9tgBYSkSrW1PS1pNQN0lcAeB2Zq4y8wsABuCNv9MSZj7CzE+a40kAu+FtwX9K9Z1OUgZz+mZCB8IAfkZETxDR1SZtFTMfMcdHAaxanKK1BUl1of3J44Mm1HGTCCd2bN0Q0QYAbwTwKE6xvtNJykCJ57eY+U3wXNdriOh35I/srT3W9cfQuojhBgDnALgQwBEAX1jc4iwuRFQE8AMAH2bmCfnbqdB3OkkZ6DcTYmDmQ+bfIQA/hOfOD1q31fw7tHglXHSS6qLj+xMzDzKzw8wugK8jCAV1XN0QURaeIvgOM99pkk+pvtNJykC/mRCBiApE1GuPAVwC4Bl49XKlOe1KAHctTgnbgqS6uBvAe8zKkIsBjIuQQEcQiXP/Eby+A3h1804iyhPR2fAmSh9rdflaBRERgG8C2M3MXxQ/nVp9h5k75g/A5QCeA/A8gL9a7PIs9h+AVwJ42vzttHUCYAW81Q97AdwHYPlil7VF9XEbvHBHHV4c96qkugBA8FanPQ9gB4CNi13+RaibW82zb4cn4FaL8//K1M0eAJctdvkXuG5+C14IaDuAbebv8lOt7+h2FIqiKEpHhYkURVGUBFQZKIqiKKoMFEVRFFUGiqIoClQZKIqiKFBloCiKokCVgaIoigLg/wc4MdZaLM/diQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(time_deltas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f912b796890>]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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HR/WZ24dabnPVrY/qtl/u000PUJsHcOIOjRb0nz/epU/84Jc6Mtb8SfmdTx3R1T99XH++ca3OXL14VtqUuXCXpFe96GT92dmr9dk7hvQXX7hLt+/cV133m4Oj1ZLNjx55ultNBJAhd+zcp2LZdWS8qOt+9mR1+d5Do7r8m/fpHZ+/Uyf153XF5g2z1qb8rO1pln1syxlataRf371vr97zpXt0wRkr9NJVi3TnrmfkkjafuVK3PrJPz44XtbAvs90AYBbc/PDTWr6wVy8aWKjP/2SXDo8VdGSsqG/9fLcKpbI2n7lKf/XqdR39co5GmU21Rf09+vsLNuh956/Xp3/0qP5rx2798KH0F/DBN75Yr3jBUv3gwaf0P7/ar80vW9Xt5gKI1ESxrB/v3K83v2yV3nXuKfrQ9ffpP24f0ryenDauW6Z/fssZWrd8way3K7PhXtGXz+nyTRt0+aYNGiuU1JdPZGYqlspatqBXn71jl1734gEt4OgdwHHY9pNdena8qDedsUIvX7NEt3zw9RovltSbS7OmWzqSaGa2SdKnJeUkfdHdP96J/Ryr2g8N5HOJ/vWtL9fffXWH/vbaQX3irS/X2mXzJUlHxgp68sCopPRbnsYKJSVmGiuWtPfgmE573kKtWTqvq784ADPvyFhB9z15SPN6Ew0s7FehXFZfPtFEsayDowUtntejZfN7tWhejwqlsq7+6eP65M2/0paznq83vGTyE6d9+c5+QGk6zH1mP+xjZjlJv5L0Rkm7Jd0j6Z3u/nC7+2zcuNEHBwdntB3T9c0du/WR7zygUtk1cFKfRgslHZzGuPCL+vNatXieVi7u18pF/Voyv0ej4U2grydRPjEdGSuqVHb15XPq70lqpon6e3Lq60nUn5+c5hLTRKmsiWJZZZfyOVM+MeWTpDrfk0uUS0w9OVMuSfeTz5ncJZdUDh/ecpfMKo+RqCeX3jefM/UkSdOARZXXQe3LwUy8gaGOu6tYdpXK6XSq/HBJpVK6bbly35Kr5Ol9K/dOH2byNVh2aaxQ0shESWOFkia3bK9Qch0aKejAyITGCiX15hONTZR0ZLyo8WJZJ/XnlU9MxZKrUHKVymUVyq6xQkl7hke1e3hUvzk0qulEopnUm0s0Xixr0xkrddU7z1ZvfvavTzGzHe6+sdW6Thy5v1LSkLs/FnZ+naQtktqGeze97RVr9NrTluvaO3+t/UfG1ZtPdMqy+Vq7bL5M0sHRgub15ORy5ZNEKxf365d7D2to37Pae2hMTx0e08N7D+vQaEHze3Mql13jxbKKZdfCvrx6cqaxQlnjxZIKpfg+NZuYlE8SJYmUM0vfFFwqhz/Msnv6xhKempmUS0w5s8n5xJreJNr9ASWWPkZipqTmPu6usksur76RVd7AkrC9KX0zSpL0ttdu75OPU9l1ZftKuyv7rbSv8b6tVJpYmTZuXxt+lX0liWQyha5UqZzupxQCsBz6t7aNledZe7/Ktl5zn3L5dzy/sLzs6UFA7b4qz7fuuamm/+Whfe37Yi4ykxb25tXXk+jwaFEl96aDpN5coucvmadXnrpM605eoHNesETFkuuZoxPh7zd9o1g8r0dHxoo6cHRCw0cndGS8qD966Qq95rTl3X6aLXUi3FdLerLm9m5Jf9C4kZltlbRVkk455ZQONGP6Vi7u1+Wbpn+J0u+vW3Zc+ymWypoolathXzctlFQsu3rziXpziRIzFcvpm0Sx5Ol8OAIqlsLycjkcgbhMIZhs8o+6cpRVLKXbVbYvlMrpEX7lDz+0rxpUMrm8GgLVo65SOrWaME0SqwsR98kQKFfnPYRVfcA3/lNQOXorlzW5f/dqoFkIt8p/FGaVMPWaoAttcJ8MxvAkax+n9g3Cld6oBOZkf9TftzbA05ZWZ6qT2u0rz9HUvK9KeytvTEmStq3yRpjU7MtDv3qL+1W2Tazy3Cb7ZbJf64+Q031MvlnX/j6r92nx+s2HN+p0miiXqO4NuJ3a+1Qeo/IGV3t3q2mDmTSvJ6d5vTnN68lNez9L5vdo2YJe9edzmiiV07+lDg6rO5d17Syiu2+TtE1KyzLdasdsyucS5XOJOvi1iQCC/qT7de9u6kSRaI+ktTW314RlAIBZ0olwv0fSejM71cx6JV0s6cYO7AcA0MaMl2XcvWhm75X0Q6WXQl7j7g/N9H4AAO11pObu7jdJuqkTjw0AmFomBw4DgOc6wh0AMohwB4AMItwBIINmfGyZ42qE2X5J7b+f6ndbLum3M9icLKFv2qNv2qNvWpuL/fICdx9otWJOhPuJMLPBdgPnPNfRN+3RN+3RN63F1i+UZQAggwh3AMigLIT7tm43YA6jb9qjb9qjb1qLql+ir7kDAJpl4cgdANCAcAeADIo63M1sk5ntNLMhM7ui2+3pNjP7tZk9YGb3mtlgWLbMzG4xs0fDdGm32zkbzOwaM9tnZg/WLGvZF5a6KryO7jezc7rX8s5q0y8fM7M94XVzr5ldWLPuw6FfdprZBd1p9ewws7VmdruZPWxmD5nZ+8PyKF830YZ7+CLuz0jaLOl0Se80s9O726o54Q3uflbN9bhXSLrV3ddLujXcfi74sqRNDcva9cVmSevDz1ZJn5ulNnbDl9XcL5J0ZXjdnBVGdVX4e7pY0hnhPp8Nf3dZVZT0IXc/XdK5ki4LfRDl6ybacFfNF3G7+4Skyhdxo94WSdvD/HZJF3WxLbPG3X8i6UDD4nZ9sUXStZ66S9ISM1s1Oy2dXW36pZ0tkq5z93F3f1zSkNK/u0xy973u/vMwf0TSI0q/EzrK103M4d7qi7hXd6ktc4VLutnMdoQvIJekFe6+N8w/JWlFd5o2J7TrC15L0ntDaeGamtLdc7ZfzGydpLMl3a1IXzcxhzuavdbdz1H67+JlZva62pWeXvfKta+iLxp8TtKLJJ0laa+kf+tuc7rLzBZK+pakD7j74dp1Mb1uYg53voi7gbvvCdN9kr6j9F/opyv/Kobpvu61sOva9cVz+rXk7k+7e8ndy5K+oMnSy3OuX8ysR2mwf83dvx0WR/m6iTnc+SLuGma2wMxOqsxLepOkB5X2ySVhs0sk3dCdFs4J7friRknvDlc/nCvpUM2/4ZnXUCf+U6WvGyntl4vNrM/MTlV64vBns92+2WJmJulqSY+4+6dqVsX5unH3aH8kXSjpV5J2SfpIt9vT5b54oaT7ws9Dlf6QdLLSM/yPSvqRpGXdbuss9cfXlZYYCkproZe26wtJpvTKq12SHpC0sdvtn+V++Up43vcrDaxVNdt/JPTLTkmbu93+DvfNa5WWXO6XdG/4uTDW1w3DDwBABsVclgEAtEG4A0AGEe4AkEGEOwBkEOEOABlEuANABhHuAJBB/w9WyYrXZeZCpwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_ft = fft.fft(pos[0])\n",
+    "freqs = np.linspace(0, )\n",
+    "plt.plot(np.abs(x_ft)/len(pos[0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/shape_analysis.ipynb b/notebooks/shape_analysis.ipynb
new file mode 100644
index 0000000..56cd743
--- /dev/null
+++ b/notebooks/shape_analysis.ipynb
@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import common\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "time, pos = common.import_csv_data('w_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f7fb0145390>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(pos[0], -pos[1], s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "215"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "216"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/w_data.csv b/notebooks/w_data.csv
new file mode 100644
index 0000000..cfebcf6
--- /dev/null
+++ b/notebooks/w_data.csv
@@ -0,0 +1,205 @@
+x,y,pressure,time_usec
+18.822888654389487,97.13282904689864,0.10256410256410256,11399014806
+18.492017564761547,95.62239031770045,0.1111111111111111,11399024788
+18.01409265752119,93.53101361573374,0.12087912087912088,11399029781
+17.572931204683936,91.0910741301059,0.13333333333333333,11399039790
+17.2788235694591,88.99969742813919,0.14481074481074482,11399044778
+17.168533206249787,87.60544629349471,0.15873015873015872,11399054783
+17.09500629744358,86.90832072617246,0.1728937728937729,11399059831
+17.09500629744358,86.90832072617246,0.1838827838827839,11399069794
+17.20529666065289,87.83782148260212,0.19462759462759463,11399074789
+17.42587738707152,89.58063540090771,0.20415140415140415,11399084790
+17.75674847669946,92.25295007564297,0.21343101343101342,11399089814
+18.197909929536713,95.50620272314674,0.22271062271062272,11399099785
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