diff --git a/1 - An invitation to sketched learning.ipynb b/1 - An invitation to sketched learning.ipynb
index 6dd02c19ea2752b991266585b4eea2d71d46ea3f..72a021a4f410594382cc6e8bcddb1ffb1306d9f9 100644
--- a/1 - An invitation to sketched learning.ipynb	
+++ b/1 - An invitation to sketched learning.ipynb	
@@ -58,8 +58,7 @@
     "K = 10       # Number of Gaussians\n",
     "n = int(1e7) # Number of samples we want to generate\n",
     "# We use the generatedataset_GMM method from pycle (we ask that the entries are <= 1, and imbalanced clusters)\n",
-    "X = pycle.utils.generatedataset_GMM(d,K,n,normalize='l_inf-unit-ball',balanced=False, separation_scale=6,\n",
-    "                                       output_required=\"GMM\") \n",
+    "X = pycle.utils.generatedataset_GMM(d,K,n,normalize='l_inf-unit-ball',balanced=False, separation_scale=6) \n",
     "\n",
     "# Bounds on the dataset, necessary for compressive k-means\n",
     "bounds = np.array([-np.ones(d),np.ones(d)]) # We assumed the data is normalized between -1 and 1\n",
@@ -80,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -120,19 +119,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, to solve k-means (with $10$ centroids) from this sketch, we call the CLOMPR algorithm. To get a rough idea of the computational advantage of sketching, we record the elapsed time. We also compare to the standard k-means implementation from the `scikit-learn` library."
+    "Now, to solve k-means (with $10$ centroids) from this sketch, we call the CLOMPR algorithm [1]. To get a rough idea of the computational advantage of sketching, we record the elapsed time. We also compare to the standard k-means implementation from the `scikit-learn` library.\n",
+    "\n",
+    "[1] Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez, \"Sketching for Large-Scale Learning of Mixture Models\", Information and Inference: a Journal of the IMA, vol. 7, issue 3, pp. 447-508, 2018 [ArXiv](https://arxiv.org/abs/1606.02838)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time to learn centroids from sketch : 46.79973816871643 seconds\n"
+      "Time to learn centroids from the sketch : 19.64159607887268 seconds\n"
      ]
     }
    ],
@@ -140,19 +141,19 @@
     "start = time()\n",
     "(weights,centroids) = pycle.compressive_learning.CLOMPR(\"k-means\",z,Phi,K,bounds,nRepetitions=1)\n",
     "stop = time()\n",
-    "print(\"Time to learn centroids from sketch : {} seconds\".format(stop-start))"
+    "print(\"Time to learn centroids from the sketch : {} seconds\".format(stop-start))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time to learn centroids from full dataset : 284.3603889942169 seconds\n"
+      "Time to learn centroids from the full dataset : 365.33590602874756 seconds\n"
      ]
     }
    ],
@@ -163,7 +164,7 @@
     "start = time()\n",
     "kmeans_estimator.fit(X)\n",
     "stop = time()\n",
-    "print(\"Time to learn centroids from full dataset : {} seconds\".format(stop-start))"
+    "print(\"Time to learn centroids from the full dataset : {} seconds\".format(stop-start))"
    ]
   },
   {
@@ -175,12 +176,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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Xl11zzTXcdNNNvOUtb+HGG2/k6quv5rd+67cAnwfxKU95Cq94xSu46qqr+MEf/EHSNOXDH/4wd911FzfffDPXXnstaZqyf/9+Xvva13Ldddfxl3/5l3zwgx/ku7/7u3na057Ga1/72uXj7d+/n7m5OQDe9KY38eQnP5lnPOMZfOtb31pu84d/+Ic89alP5eqrr+alL33pGV/natSCeAGzkV7eej28kQCdyfB2vX2udU5nc57nLZ/+g7XFcESZ+l7kGfLVr36V66+//mHL77jjDu655x6+8IUvcODAAe6++24+85nPAHDPPffwqle9iq997WvMzMzwV3/1V/z4j/84N9xwA3/2Z3/GgQMHaDabAOzatYsvfvGLPPOZz+S1r30tf//3f8+BAwe48847+ehHP3rKMe+++24+9KEPceDAAW6//XbuvPNkgqxbb72VL33pS3z5y1/mPe95zxlf52rUglhzWtYSqUj5r8+4MK4mkvDY2PouGDFMF+HIFzfW9vDdm+ZoueOOO7jjjjv4nu/5Hq677jq++c1vcs899wBw5ZVXcu211wJw/fXX8+CDD665n5e85CUA3HnnnTz72c9mz549hGHIzTffvCywIz772c/yohe9iFarxdTUFC94wQuW11199dXcfPPNvP/97ycMNyekuhbEmjNmvKc33jNbTSRHrLT1XVDD280mXQK1wVrDQeTbnwFXXXUVd99998OWO+f49V//dQ4cOMCBAwe49957+Zmf+RkAkiRZbqeUQmu95v43K8nD3/7t3/KqV72KL37xi9x4443rHnOj1IJYc0asZ1McvT7d8HXkTd6I/XKzOK/EtzkDptxYW1v69mfAc5/7XPI857bbblte9uUvf5mpqSne+9730u/3ATh8+DAnTqxfHmllSrBxnv70p/PpT3+aubk5jDF88IMf5FnPetYpbZ75zGfy0Y9+lDRN6fV6/PVf/7W/LGs5ePAgz3nOc3jzm99Mp9NZPq+zoZ66V3PGjPf01hO9ccYTmFrryLQhCOQUB8p4W4C8NCSRWnOfAFrbVfex1rHH9/FYZZjedJo74NLrvHf5dFx2vW9/BogIH/nIR/jlX/5l3vzmN9NoNNi/fz/veMc7mJmZ4fu+7/sAmJiY4P3vfz9KqTX3dcstt/DKV76SZrPJP/7jP56y7pJLLuHWW2/lOc95Ds45fuRHfoQXvvDU+nTXXXcdL3nJS7jmmmvYu3cvN954I+A91C9/+cvpdDo45/jFX/zFTfFK1+m/ajbMsrCMDYtX9gTXEpmVPcqRkI0Pv8eFMFLB8v5LY9HG0m5Ep+xba8uw0LTicEOiuPI8Vzv/9a7hsWJD6b/u/TsfWrOeYyVqwkv+DJ74/Zt7gtuIOv1Xzaaymh1w/PVKkclLQ1GYh+1jNANlNNweCWBemuVtxh0yQSAkkSIMg1WDtQHCMFhVDFcbHq8UuI14z7c0T/x+uOk/eNFbjagJN/3KBS2Gj4R6yFyzJiuHmiORWK92hzaW1FimquHwSNyUFQwOJb53aJwjiRRJpLDWYZxbDsge7Xd0TFFCKzgpfOM9uJViONr3Wuc3vu2a3vPtkhHnmb/qh86feYv3JgeRtxledr1fV4vhGVMLYs2ajIvD+BBzvGe1UmCacUhQmuVh8UicwjAgqHp/w1Kj5GQQdhAIwdh+kkidYqPcqCCvFNaV69bbduV1r7y2LcsTv9//pYvem9ycOWObYc1JakGsWZdxsVvNKVEai7InnSMjgVtpo7PWLdsNW1G4vH60bqU9cTS8HhfHkZNlvaDuaBUr0Fpivh7nuh60cw6RMzhuc0cthCt4JP6RWhBrNsRqvaXRUHgkbKNl4B0j1rrlIfEgK+kXJQ2lCFVAaS2RBCgRMm2IdYBxbtm2t9KeOHo9Es/x8xo/H+BhIv1IhsHncujcaDSYn59n165dZyaKNcs455ifnz/j0qe1INasy8qhJjw87hDjbXdOWA6rAdDOLrdpxiEKwYkfEjfHto+rpA7aWNpRdFLw1MkA79H/vDRobWk0QrS2XnidI1TBcm9yXKTXsxeejnM1XN63bx+HDh1idnb2nBz/fKHRaLBv374z2qYWxJo1WTnUBB7mZBn16Ea2O/AeYVfFGrarYXJpLAZHgJzaiyz9Ppxx9PKCSPy+R8Njax2Z0cTKD5VzbVgqS3bRWBbRlR7jkUivdU0bHTKfK0GMoogrr7zynBz7QqcOu6lZk5XT8uBkUPZ4CM0oNGbUTokggdAKw+W2SoRQnWyntV22ByoRemXB8U7G8cGQYVqSV6mhlAiFsYjD9y5Dtew0iVRAqAICkYcNnccDukeMn/N6bJvQm5pNp+4h1qzLuMd11FsceY9HPUKtLcNSE5ngFCdKMw5JC02/KJmII8TBYpZTGENuLe0wZGg0YkEFAY/b2aYVRRjc8vA8DAOmiYljRZprxOFtkMbSDEOaSUhRGDLtBTSOTwrhqJc5Op9xD/TpZtlsJGznkbItvNcXKLUg1qzKyod23IO7craKEmEiiZaFURyU1tJMQqIgYDr2gjbISgZVyI0AKZpeVoJAQwUkcUhaalwAjUCRFpq8NCjlRUxbi7GORqCWe4BaW0QJMYEfQmu73ANdycrkEqvZRMevd7V7crae53Ptva5Zn1oQa05ho8HXoyGskpMB16MAamsdgfWiNJ9miIOdQYPJVowSITeGblGiC81EHDLQmrQwhEGAC8CVjiAWlBNKZ8iLas6yCjDOMiy9PRLAWMdEEpE0QobDEquEIjM4gUY1x3bllMOR4+V017mSzfA8b6vA7wuQ2oZ4AbKWbWwkHMApTgqtLUVhSHPt/2tNNy/op8Up9sOR4Ay1pqiCs2PxITb9vKQoDBIIcahoBAGI90RnhaFvSrSzFNqQGYOtep6FtiiBodEMyhKjHZoqJMe55eDu4bBkoDXOOOJIEVSCPfJKr7ye0VRANRbWstG6L2dLLYZbl7qHeIGx2nS8lcHX4xSFoZsVy97cJFQoJ1hxdPKSZhqSaUMUBjSjkNJYSmMojCXIhUYUkoSKMPCeaG0sxlokEIy1FAYmkgjrHEXpcGK94GYlzVjR6Rfsnm6gEJpRSKIUaanJSk0UKppVQHfhvE2y0QjJMk1uLaHxYplpQys4aTs0zi0P61d6ytcbRtec/2yKIIrI84B3Agr4L865W9do92Lgw8CNzrk6lc1jxGqit5otbUReegfFKEBaiaCUohmHlJWYNQNFqfwwtigKiqonhkCuLcYYwkZCPysonEWsjz8clCX9zBAp7zFuhiHtOCK3Pp6w1EKiFINCU2hD7iyRCJFSlNbSKwq/LnfsnUloi0+UqvCB2IOsJBChqRQq8h7oOAiWZ8JY607xlAfRyZkywRlM76s5PzlrQRQRBbwb+AHgEHCniHzMOff1Fe0mgV8C/ulsj3mhcibeyfEZHisf8JXiOD5UHoXCjNYXxg99c2OYDAMCKygE4xxxrNDGgoNhrikjS64Nxzsp4nyva1BYnLP0y5KWikAc3SIjyxyBcuxoNkiiAmMdzTggyy1OWWIJiQPFTCIspgXWwd52AyUBu5oJRWJR+OH6SSH2mXOiMAAHoXHkzlJaS1D42EaDtzmCj5fE+v/jc6lrMbxw2Qwb4tOBe51z9zvnCuBDwAtXafc7wJuBbBOOecFxJrFx421XBi2vhqlscSPPrR/2ervhcBTqIgFFYejnJUt5jsURS0BhLZnWZNaQBAEBwo5mwmRbgQiIJdeaonQspinaWabjBo/f0+biqTbNxIuac95DvTDI6fRKEEcUBuydbNCOIgLxw/VYBQyqfIkIpIXmeH9IURrEwVQzJpKAflmSax9204p8ppzRPOrRfOuR/XHl/RkPNaq5sNgMQbwMODj2/lC1bBkRuQ643Dn3t5twvAuSjQjbWm3HM8WsLAA1CqQeiQSc7CVqY1GhoFRAQylECaEIw8xQGENmjR+OBgHWODJjMc6SRAFF6dDaEQDdVDPVDEhzn+kmUgHawcIgBSNYsVgLVkMhJWEUIE4QgYVBQeE0uSk50U/pZgVz/ZS88I4SESiMoVMUHOkN6eUFuTXE8vD6LUmkTolTHL8Ho3szog7OvjB51J0qIhIA/wm4ZQNtfw74OYArrrji0T2xbciZJicYMZ6VZtyLPGpnnIMqbs84R1kYjHMc6Q7Y3WosT5nTpaGfFQxNSVN70Wu0lfcax4qs1LTCiEgJR7sDXJBweGHI8U7KTGsHjSbEgUKU5ehCxnw/J4xhYalA40hCYa5bwLQQhULshGOdjEyXKBOy1CrY2Uh8vKMuIHcY7c9ZAY0wQFtLVloiJcTOkRaagS5pm4hmHC4L4nhqshErTQsXVFnTGmBzBPEwcPnY+33VshGTwNOAf6gyd1wMfExEXrDSseKcuw24DXwJgU04txpOdaSMek1poTHO0YpCVCDLGa2dcyzlBQ0VUFQhLkVZhdqkBZkxtKOQRhgyLHIyazDGMtsbMkgNe2caqAC+MzfAWMeJbkqUOHp5SZo7CpVRljFDci7b2SRNS451UpIkoFQBhdWEoXBiKfVDd20oteOSHTFhqBjmmtlBSqoNM03DVCuioRQGiELfswwFmmFYOWdKhqlBx14AW5UZYRRDOT6DZTSLZVks1eqpxGqRPH/ZDEG8E3iSiFyJF8KXAi8brXTOdYDdo/ci8g/Ar9Re5seWlQ6VUAVEVaxeEAiiIbeGSAKakaIRhkw2NcPckBUZjThAAqEdKBDviIlCIXAwP8woSsdSnpLNGeIIVOhQIQyyksQqipaBwNCKGjywuES/Z1CXCofmhohyNEPFic4QB5RW45ygnWFqKmKQapI4pNA+jnEiimnGAYvDHOMcF0+2UDgEH8qzlJWUztdhSbVGQkesQj/c1xprHRNxhA389Y9CcMAL3kgsVwpfPcvk/OesBdE5p0Xk1cAn8GE373XOfU1Efhu4yzn3sbM9Rs3ZszL0ZnyOL3hBiIIqTEUUCmFPu8mwck4ETsBBaR0SWDpZTifTfCfrsTAouHhnwh7XonCaQwt9wlDRH5ZYnBepfEhnyTA9XdIflmRlQa8X0UtTJpsNlooSI45mFBES0rMpS4MCQWiqEGMN2kDowGAIVURZlgzCkvl+RrMZ+vIEeCePw5EbSyyKXc0GrSSiNJbQQYn3PJfaMhFHxLHPqlMUxs+UMY6wsbqt8dGc41xz7tkUG6Jz7nbg9hXLXr9G22dvxjFrNs7pQm+KymYYUmWmNoYSSxgIzkJpHGHgw2S6mSEKYbabM9n2s1AmEkVTRfQYkuaGhV7KRBzQCwOybEi3NBydhzAQJlu7mGpGtJOIjs4RYxmkQ6IwZqbdoBWFSGCZ66R0+yl5kXHp7mnynmVnK6FXZqQZxKpEBTAVJ0RJQF5qji4NEBsQKmhNKdoqxIjFChSlYag1zSjEVskjgrGpfVpbCmsJrVA4S6hPzfIzYqVtdq3QpprtST1T5TxmPCW/snJKj3A8AYITCBFmhxnaWCYaIaVx9KyjFSpmmrEPsDaOdqIw1jIoCiyKvLQEgeVYb8BgaIibkIQBaWFY6PdxgcK5kjQvuWTvFAuDIWmqsaJRhGhr6KU5E5HFSclcLhwOLHnp6OVdhkWTKIoJRNGIYbFjaU4Ycgu5DshtSUsS0tJiNeyeCiEQppKYRCmO9Upi0WgVYJ3DGEu3KEhEMdWI6WYFSagIRGiEXiCdGavPUmXeXlk2FU51wtRieH5QC+J5yii7NLA8LY0qw3QQCOIgM4YoCJbzCjYjhYsC8sKwlBaUzrJnskFUBBzupsQq4NKpJvODgp2thMPdPq0ooigds90BE82IyIa0GxFp7lhYHNKIvcOjV2a0loQobGIp0QayvEcURXR7A4pmwWJfUdoMjGKiETIsHFMTJS4w9IcZi/2Ivh1w+KHMB2rvaWGW2gwzTaEtvTwnaQgREd2wpB05AnG0Y59STFdzqo2z2MojHQRCFPgZOSh8OYNqmqHCJ67QmaawloZV/j7Cw2q71GJ4flAL4nnKyiSpASfnLY8y1DSUr3ucFprSWhKl0M4yxFA6izGOorBEcUArVORW0y81S2lGHAm9nibaKQx0AcohkePE4pBD8x2UFCxlJZOmQbOlaCMcH1TW8b4AACAASURBVGS0VUEcx/SGfU50IChzMgeXSUmjFUIf5lKDtgajoSeawbAkVDGTU01mkgaLtg/GInqKhXxAO55iMUsZDEsaKmLvTIApQ7SypNqwkGY0whBtLbNpSiMICQNHryiZTmI/37makRMHAXGsyEuz3CMsxdEI/b0Kx+7vekkyVhta12x9akE8jxm3bymR5aQGYRRQ5vqUh7oV+q/CUGsm4xDnYrpZSRgK88OMEk2WWg6WXfpdgyQWF2oeONzjRLdPSwS7ZzelLcAVDIaWqVihbc5DxwztJpQ96DVg9mDBYh8ood0GiWGYgmqkDCzVzBVoTYVkfU230FwyaekOM7TEhI2Yna0WjRYMBiFz6RCsYme7ya6JBpPNyGfiUTGFNgwyw8xMQmH8jJtmFDIVR0ggqCogfVQJsLCWQMspcZoY/388hdha9Z9XS55R2xe3D7UgbnNW632slcxBZ5rMGEJ7skZKWmrS0jsbAhHSvKRbDSsjpbDWcrQzZJD5GERrITcFbugf9kYjZjIXbBCyOOiSpwVKCSUliY052jEsnoCZHTC7BA2BQQm2gOkpmJiGbODX9UuQ0n8pl4DHtYU0hM4RKDJLNz3BdDskjkOyKGC4GBEFloRJ4pZw8WSbiWZEXmi+cXSRhgrYt3uCTpEznC8otGUyjkE5Um2YTmIiFSzbCwEC5wO988IsF65aLqOAv6dp9WOyWuD2SntibV/cXtSCuA1Ya8i1Wu9jrfReo/g6Z08Wi3euqmLnLNpan4LL+p7PRBKR5prFgeZYr4/RQhhbdk+06A4VNtC045hjSx3KIsAGGhsK/VzT6Zc0EogmAtwslAqKErI+2AYoC6mFfh/SPixpiIHJDHoFlBq6QDYscQ56Pb88T+GhYxoJNE+4KGPX3ikmW020aPZEkyxkGb28QAUBjUiIgpDd7SbDouREN6c0hqndEZNhjAiUzlJmPjGEQogjRSlA5UAZiWVpLBiWTRDj0x1XYz3PdM3WphbELc56Q66VvY/R8HdlqvyRp3RkOxwlbs2tT8+PDuhkOQ0V0ogUhbEohNI4yqBgOm7SlSFLSwWDQU4japA0AorC2/dUM6DhHFOtaYryGAe/BQIsTGX0Suh0fYBqIKBimE5gbhYG2hfHi4AU6AxhZ6UxPWB+1q9PgYkcZAIigYaCnoXGcMCgn1MWlk46ZNgzpKZgR6vJzok2YQz9oqQoHTvbMYWzDArD0UGfSCIsPodirv2c7YvbLUIELY64CswemRvG53qPnFTr2QZru+H2pBbELc5aQ67RAzcuhiOv8ik9GSvLnuWRSGa5Hzr38pJmqAgCGKYWo0oakS/mdKI7YH6QE4tCIsPepM2wn5PmFq379NMYrYdYDb1ej24KF+/QDPuOfulFrDsPEwqGgOn6ZUEXjuNf94BLgHlgFzAHiPXtS+AEMMBnIGkBZQHtprc5zh6H7qJhx15DuLjIkW7AjAooRDixUFAYw16Z4rgMuXxmkt2TDZYGBZ0sZ5AKeyehtEIUBUw2YqLA12Sxzi07VkYZtUtrT0kPNsqduJ4Y1nbD7UktiNuAjUwhW+lVHrUDPwslGfOIFtaCc0w1fKZqFQQ0Qz93+USespRm9FJDK1ZkhWGx7x0lBAFT0xGDboY2Q+6fXaIVQZ6DMXD0eMGDh8ECOV7MugY661zb4bH/DbxYjr6UCi+MTeAIcKgHO3uwu+1tkHECUsChvmahA/v2QSMA7YRkqMgmGvQXCrS2LBYpRQ5TSYyK/P3qDgocjrAV0FQhqdaU2hIkEVmmccLJYTMbL3xf2w23L7UgbkPWeuBWDpNHCQpGOQDBC0KhDQ/1MqaSiDgKEGdpxCGRMgy0w5bQiAOcFQa6pN9NmRt2GeaamUbEbD/nonYMDooMDh+D/hCOVuI3AyR4YVxPDFcySpSpq/+L+N6hBQoqO2MIg4HPMXf5HOzZAYUGncNCB3Y0YbIFGYa0X6BDy33HhsTHQy6/aIYn7J4k15bFNKdflkRBwAmbkjcMsfIOFHHe266qH5lmEm54eDwK4B6339ZsH2pB3MKcrnbwWix7P7Vd9qCO8iBmxhDgBa8VK4alpmMM/TRnkFlO9AZkRvPkvTMs5rkP3G4qkoEiaQeIC2goR8cYdiaK1BjiBNoa9qbeGWKBPn5YfDa08fubxotlDJyoHC4a+A6w+wREbWgkEGooHFgCEkK0K0mCBt1sCZImOycStLMc6w6x1rF7qkEzDhhkFh1ZJqIIpYLlKoKBnAxLWplVe8R4b91ax7DQtAhPhunUPcVtRS2IW5SN2KFWCuZ42qqiMMu2Q1ultwpDHzgy1BpdWjqUDIqC492U3rDEKl/XxDro6YLD83063QFpmmFCITSOE8MBJrUcXSiYmoTeInRSH0rj8H/dTboHo/0sVP971f+o+u+Awx2Iqm5oksIlAqZtSU1Odz5lqpXhnBDEwvFOxlJasNBLSeKYqWZMKxZm2v4x6OSl7zWHiiRWlMbP7LHO0YzDVT+HlanVWpzsjddiuP2oBXGLcjo71GrhNaO8fkVhGJYa7SxTKsZaR78omSAiUYrCGKZaMcZYSqWIg4CZyRhXwgODRZphTL9fkpuSrEgpBZoOShcgxpJp6Pd8/OBC7ntuhx6DezI+LyTGD6dzvJ1xAggjyA2kfccgKIhDGOQBE62EhooonEFpR6otl80kREo40UlpN0OSwJcaaFbmBa0tSaSWQ5RWm5Uy7tgavR4PxanFcPtRC+IW5kwN96Maw4W1NJSidLJcS8TmJYO8JNUaY31qf+18XWRjhZl2hEkMdlawseHQwoBeP+VQZ0BTYEFDHENRwPFZ3yNs4h0nj4UYjlOMvR7ZHfcAQQrSBizMduGSnRCEECvFIC0JSGnumCASmGhGZMaSFprpZkSAkFvDIC+xhaOTlUwnEbsmm7Tw5VVDe2qIU16aU+rQ1D3C7U8tiNuYtUJuWnE1DW+oSQvth3si9MoCHEzGEZk1uELTUIqLZhI6WUGaaYbpkGEppP2STA3JehBEcHQBJqcgy+D4wA9fp9m84fHZMgByB0dn4bJFMNrHK1pdMHFZg8AVzHYylvoD0twhkfDUi3bSaoZI4IXMAanRXhyLkqzqKVrryLUvbDVek2U8c1CdJ/H8oBbE84TxaWSjpASjhK9aW8T5Mp2IEBlfTS81Pmi5n+fMd0oaTcfFu2d8sfhJTXchpDMzRzEA63zvsNR+Wp3GO04SfNzguSbDO3MAZrQfRtvj3vEyyLtcdFFCwwq7L5pmZochMhFHugMm44RAhMk4ohH7oXNmNWnpaFVimBnjE85qs6YHeaMziWq2NrUgbnNOSfOFT2M1sn2FKsAYy4k0J1EBSegzYfcLzcIwZabRoNl0WAtLzQxrQ6JEoIgoAoNqBshxODEHsz2g64Vn5D0+k5CaRxs79voEMIUX7WYIzQR2JBHNZosoES5q72BgcmIXIQJpVlIaw6RNKErDznaDJPamh15eMJnElAi5NjStf2RGzqs6HvH8ohbEbc54QPZoCFcUBleV58xLw9IwY+9kE4Uw0YgphinD0pCVQ0IVoK0mkYhDnR4EMBj2mV3KyYZ9cgdxCHEAs9bHGG5lGtWfAEkI020vlj2Tk/csuW1jSsd03CKYsjTCiPvnO+xutDHG4ZwPRwpdiBLoF5rpZkISqFOCskfTIWH9dF+1GG4vakHcomzU9jSevGGU3quw1k8/U8onc3DCYloAvtZxokIun5kgK0uOdVOOdPq4Kvbu24eOMCwL9BBSA/OLMOj64fFWsReuR8ZJR0ugYSqAwyfgyNGSfZeV7FcBYbtFEWhmlwxLSU5gApotQKBIS4alIYoCdrea9MrSmxyUT6prnE8YK0oIRjOB6nRf5w21IJ4DTid2G32oViZvEA1RrAhtldorLVAqYEc7YjKKGeqShWFBK1EYa1hKNe2mIuoqRBxhW9CFod8FbXxShrm+F5hRMMl2EMURDl8OoN3wITlxAt28JBr02RVOMEhzyHwP++C8YyaJuWznJK0kZDKKaSWRrwLoLDq3iAgNQOOICUDV6b7ON2pBfIzZiNht5KFazn5dJW/Q2vqSAManrUpLTb8oicMAhWJoSoyDbpU4dX5QsDgc0kxCekWOGAgaELUjotQwNFAY703urXkWW5tJwFqYm/OhOlkOu6dzSjHML3ZxkTAZN7lk30WU2hE3FJNJDEYIk8BXGxSpnFEQITQa4bJJIi+953nl0HllsPxaacJqth61ID7GjETsdD2IjfYgAXJjSJRaLglQGu9iaEUhoQrIiozj3ZxIoJkoJmJvN0szw1Ke0y9yVOnoDzRlJ6PT8+ErET7w2WzWxT/GHAVsHw7i7YrTHUh2Qm9Rs5T7vIxXXByQ6pLdzRY4YT5N2T3RIteadhIThQFR4EVvNAVSlK9HDSen9I1MFuM/ZFrb5al8tShuD2pBfIwZPTjrpY86HSuni001YsD3WNCWaFQdzkFWapIoZCLWiAKxwtH+kCMLQ4wrCCQiNMLOHS3yE4swESFLJREwOQmTvbOfk3yu6Fd/4If8HQOHjkERwMVTMLVDmJ5UiFYU1hKFwnwvZ6YZcbxn2e0cSgWU2rJnorncG9SZJtM+o/Yo8/holtD4ZxqGQS2G24z6k3qMWSv1/NkweuAGRclilp+cdobjeCfDOUcUKpwGKw5noNSaQWpoJwoRS1oYWhMtdsYRUxMwCyz1fAjL+UAGPAR0+97ZMjkNkTgWhwGdok87CTEWjnUGHFnImE0H4KARqOV6M3lpyDJviugWBYU2y6UH1vpMazHcXtQ9xHPA2YrhanbIMAzY2W7QHeY+0an1Hud+WbLbxUQSYEJDZ1DSTAJmJhtkcyW9POXQXIdGY0BsDQdnM+550McYLp79pW4pRrORpydg0IOpS5rs2dXkssmdZFrTjkMu2dEibECoQzpZQeEsygmI7wU2o5CpRswU8SmOlNq7fH5QC+I2Y9yZsnLGRFEY5gYF09YigdAIFLvbDTp5yTDTHOv3OTqX8pR9M1w81aQ7SOksZuQmZwrFsUHGUjUtb7vaDU9HgZ/ffOQE9LOUJEo4rOfJS5hqJVy6a5IJFdNshexoJ4jzcYxBILSj6JSZKnlpTnGk1GK4/akFcRuxMvkrVbqvUdhNGAbsmohJAsX8MGOAJhBHUTpsYFBBgKbkWHeANo5GHKMnNK1hwvxwyP0PwETr1Kwy5xOCHzofOuKn9rXb0Bv0SLOcOApJQrXsLBHnWBzm9LKSHe2YZhg+LLONEoFg9aw3NduTWhA3kUf6MGx0u1OcKaNttF0OuxlqTSCCBMKuVoPSVevImD9e0isL8rLk8HyXECFuxpTWIqHj6GGwOZjYz08+H3F4U4ADLmtB0oRh3zAzIcQJxLHCOEdfFywOLSWWxEVMxjGFNZBD04bLDhQnvgbMyEkGnOL930jYVM3Worb4bhLjQ6iVy0+3XWnsmtutXL7alLBcGy9sFnCgqyGzQphKYva2WhBYJHAkccjURIPp6Qa7JxuYAhyG+UV4wMI3Ot75cL4yKkUwtceXP+gVEAQl3cxV9Zg13UHBscUh/YFmekqxYyKmNJa5XkZaaorSUBqfMEOU4MzJrDejFGyjz3T88xv/TFf7zGvOPXUP8QxYa86q1nbVX/vlDNajQvFVkO54sO7KuEStfQzhqPylE5bXj6f7GnemNJQijhUpmlwbrPa9nIVhSaAsrTDCBdAbGALxGXAWFjOWwiGdYY/evKHUPpD5+KN5A7cABt9LXDoBSqA1BXv37KY0JdONmLlBThQG7JxImGzGtMOGr2ntHLnVuKq+ta28y+IgNYYyszRjnzcxCdRycarR5w+nTvGr7Y1bk00RRBF5HvBOfKG0/+Kcu3XF+tcAP4tPQDIL/LRz7jubceyz5UyKB41/ucGHYSgRhqWmFYXLoRdF4V0SaalJlAIFw2FJZg2NQNEvS2aayXKarlG2Gmsdc4OUJFBoa5loxEQqoLS+UPr4Q5ZEPtnAcFgy0Jow9NlsRp7lwlqcaOb7mn5UsruV4AwspikEsJQP6C4MmJ219IbekXKh9FdCoF/VfzGH4cscJUlgMGOYnmwwGbfZMdmgHUe0IkU3LemXJXPdzJcTiCImIj90nkgin4zX2IcdZ2Vs4sopfjVbj7MWRBFRwLuBH8AnT75TRD7mnPv6WLMvATc454Yi8vPAHwAvOdtjny3rhUqMhGokcqWxRMr37ka9N20sUqXYysUwLEuaKuRgt09ThcwOU/a0m0jg6A1LVCC04pCltPT1ThQ0gpBOkdMOI7S1fPP4EnumGxgNlwJJqAiDgFAFdAY5Bp92avSgaRztyuC/NMzRzhIFARKGZNrQGfRoJTFJHGCxDDJNqQtcYeguWpyCwPovwsMf6fMTh6/TsgMQgYU+XBTB1FREu53QHeTsKBImVILGIAHsbMYo8Z9fiKBUQCzVd8NaSmdpcmpN7JXfq1oEtz6b0UN8OnCvc+5+ABH5EPBCYFkQnXOfGmv/eeDlm3DcM2a1pJ4rp9FZ65aHrYX2NsFIBWSFxgQBC1lGPysJlYATnHN0ihxtDEt9Q5w47jm8SBAZityRNBU6hbQsILAETpiemQDrt33c7ikemu+yo90A55hdyomjHT6rypJhcVhgrOFxO6fItGGmFRMGfq7yzmaDuJpWFgRCFARY40AgDALiUGjEMZNNRSctaUYhjSTA6ICcjPk+pEPYPQ2tnk/8eiEwiq+cAbSC6aYfPi8uptgiIMfS7ZdMNHLmT1gmWhF7JhpcNNGkUWUjTwKFoapm6BwTcXRKPOKoB1+zvdgMQbwMP110xCHge9dp/zPAxzfhuGfEakWZwNt1RvY5rS1prjkxHKJcQBwGlM4QSsBDS32yQvPQQpcsLUh1TjNOWBr2OXi4Sy+FmQmfEOGe+/0MD8EnKg3w84INfpj2+MkFyhxSC/v2QjcDPYQ9u2DP3ojF4QL9vmH3zgmUQHcp4/5dTSbiFlddvovABaggoDA++QBWuHi6RTMOkQKGpSY1mt5Q04gC5gY5x5dSLt/dYjppML/YQUnC7kZKNgFFNXyce6w/lHPMEpAPYDLyqc6arYyLJ2fYGYZkTnPvsQ7NIGaqHXGik3LxTIvQBOTGYCNHpg2uyqgdycm5ziPGSwzUbA8eU6eKiLwcuAF41hrrfw74OYArrrhiU489PoRZjucbq7ubZRonkJYlS70SFUE+LDlyImWpGHBodo4HDqVYDb0eLAy9wfQhTi16tJLVskrPj6WP+daRsRWHgcMlCSU7gYuilLAFJzrQiHo8bifMdTrs2DHNZdNNBnmDIAhIC40KQUQw2hFHAYkLWEwLJhoB1kY4hhxbGpAby6C0LC2kLFnoH4fZ4sLpHY5j8QbtyxVMN6AVxeSpJmyHDPpDgiBi1+4GgmNxWBCHAbYNpfZmiRAhipQPdVLVlMxozPs/VuemFsXtwWYI4mHg8rH3+6plpyAi/wr4DeBZzrl8tR05524DbgO44YYbNtXGvzKMZSSG2li6g5xeXlIYzZHOkLlhShzCkYUh//zNg5zowJfmfTDvY0GOz9RytOSkopawcBxmO4vMXLTIJZMhu3ZNsnPHBJe2pxkUJY0gRALo55r5wZAjnT47ywYzEyFxAnMnhoSJIw4MpfUxdHEIS8X6on6+sgPfc3fOZ9cmVHT7KUEAQ2PY224ShMLB2ZTpyZBGFBGrgHYUkpcWFfqQm9FUyXExHM9kXovh9mEzBPFO4EkiciVeCF8KvGy8gYh8D/BHwPOcc495voCRI2Rk78FBYQ2DTJPqEuvg2LBHPnQc6Q647/BBjh013HNsa8XkzQFzGfAdeGqoaSaLPP6JA4qLS3ZPNBiEvmJcGAjz/RzEMswLDi90SHPNQrrIcM6Ql5ajs6BLWCguHGfKSob4a08AGwlFlhFEEXkRIlrYM9VCOQhix57JBhNJhDhoRxGZyZiOE+JY0U0LJpIIqHuF252zFkTnnBaRVwOfwI8i3+uc+5qI/DZwl3PuY8Bb8LXE/1J84OpDzrkXnO2xN8J4AXelhM4wx+IYFoZOkVMUGu0M33pwgaVhn8WFLl/4JjzwWJzcWfB1DWjIv1kQu3lQsLMxQRRETE8qhkVJg5CMglwbesMuhXa0YkUUO6YmHIuLPj3WY9Xz3Wocx9t5jy5C4RyXX1ywYzrCCkxOJUwkikMLGQ0ldAaGtBhgq1rW2kBYpf5KQrXs2BoV+KrFcHuyKTZE59ztwO0rlr1+7PW/2ozjPBKWs0prQz8rONJJmWgqmqGiM8g5cN8xFoYdvjObceQ78O1NPPYuOnxX8BAO4Wt2Px0mNnHvnq/m0P1yya7vHOOaKxR7Lt1JaaeZmkjopjnDJUthhiwOM2xhIPYpsB467h0p26kkwKNBACw5CHpQ5PC4Swe4aMCe6Wmm4gaBwI7JNlHomEhissJQlA4CH2caO7UsgnDyB5g6U/a25LyaqTJecGnck7zsSHGWMPRT275xtMtSntIpu3zlWxlf6W1eT+kyZnlD9N+4KfgKhbdSEaP5pLmeN+p/yxzTm3Qkz0PAQx148CuGK++b5SlPWuSpT9hHmhuGJgVCbGY4PoCvHvZDxQvRZrgaBh+ULhqiCIY57GwrmnFAbg37dkwQBgGLfQ2BMB01aDUULXUyzGZUvkEcxJFCiY9NXFlOoGbrc94I4spMMMoKaaEJVUAS+alUONAY5no5X3roGP1ejxNzOfduohjukxP8dfwbTJISiqUxtufnqTt5uvom/3v+e8w+CgU9F4FgCJelmvsPHaM0oLMcCYXFDA4fvjC9yacjA1qACiDNwYYBSRjT7af0J2Jm2jH/P3tvHiVZetZnPt9y14iMiFxr667qRSstsRwasdjsIDDDZrMIg22JxYJzxnjODLbFYgbGLJYYz1geHwbBYLDYBjF4sAVmk80AZgYNaiQkjaRutdRd3V1rbpEZ292+Zf64GVGR2VldVV1ZWZnV8ZyTJyMiIyO+WO573+9dfm8zTIiUpsIyqgTtMJokU7yAUEi8vCYUa6t7VUDt3uaeMYi7OgOm+oEjrShLS2EseWXp9S0XegOkM1zZKHjiuYPdNr49+GnmGKHF85PkgbAs+B4/Gfw8f7/6Rwf4rNfoAZ/8BFxu5ggN1QAKPBtlXWIyY380EMcwyiH1oFPFYKvkmcvbSKM4MZ8ShQ5TOnJnyWJDuDOrWQlBtlOvOvYKZ0mV48k9YxDh2tZ4nESJdB3b6WYFFodWktacZHTV0qtGdEdw6cYPe9M8KC7ziDi/rzEcEwjH58sPs8zWHfESK+CjDlq9ukXtuE7MO2wUIArQcT1DpreZYVTBUuMEcw2FxzLMBN28YKkZMswNOr4m2jEoKhbTePJ4M+mv48k9ZRDh+QOYpBS0nSezhiuDjFFlKMuMYqtie/1gM6yfJR/H3YSiWoXmM+ST/KH7rBvet0Ofk6JLRsgz/gR1XvTGvNSTJbdCQJ1tvzqARQnCVmz3ugwrmI9HPL3m2RqMOL3UpqkDNnqwHVa8QncIfZ1hHg+b2juadC+zUQNHm3vOIMLus7MxDhVIEjQicKyt5vTygssjf+BbSIVD3IRmjAfkDe73iDjP9+l38dfkRygJUFi2afAO8zX8sn39TRneGTdHtfPTAFwGG+t1W2VlYKmzTdMbhoOSaiEljiIGeYkoBWtxRhJpFohpReGu1r29CZXZqIHjwT1pEGF3Mbbw0K9K8syjtaQhFba4NqLyoPiYO4e/CQ8uwPC4v/+6f/98+SF+NvifiSmRAiIMACklb9Hv4vPlh3lz9X0zo3jArAFhBf3LkAZ1X/pjT+RocsIAtnp9rp5YoJEmKCUIY8m8SQikJAk0CEh2xB+mt8V7vcKZMTy63JMGcTqOaLyjX5R0RwUIj9SQS0ca1lulg9wy/5V/mFXf4UFxfZlV5+Fxf47z/tS+f28x5B3BvyQV+xfGpKLg8+RH+A71u/y8/eoDWfeMGstOz6kDWex8N3bS8gtArw+9YpM0gqKC3tmM15w7haQu1lZecKLZwFJ//2KjiGO9r6rSjKPJPWkQ98YRnfcMypJ+ZtnoF/hMcOZ0xH0XigMtxAbBP6q+h18O//l1DVpGxA9W33ndR/gG9Sc33HanouS79e/wb+xX4Wde4oFi9/weswls5vDh83UUNwGyYgOtBafa8+SrFQkxLztd8uB8G70jHCxlPQBsPHdlZhSPNvekQYRrGWetJZFRRCpgqSlJQkWsJVe3tzmxvMba2sHOH/5L/0reWH4/bw9/mg4DAgweMGhWfYd/UP1DPubPXff//6b6v69rTKdJKXmluMDj/mBVgQ6LFgO+Sf0pr5ePobD8hX8Vv2xez2UW7/bSboinLm7/84uwub7O2VPrPHB2kYdP64nOpRV1A0Bh7KQ0Z2YMjz73rEGcVrwOQ0UrCigCicex3GzQzyvOnAlIk4o/evZgt87v86/irxX/C58jP8ZrxVN4BO93L+f9/uXcKEuckt/Uc1jkTd/3qPGF8oP8r8HbEfiJ8X+tf5rvUL/P28wb+EX7VXd5hTdHDnywgO3zEDc3sNayMajjvZ1GyEIcg68zz9NVDzArvTmq3HMGcdcclJ05F5FUzCUho/6Q1e0SIeBTTi9wohPzgSef5TMGGVc265pEc2ArEbzXfQrv5VNu6b+e8yu8jMs3vF9IxWV/9L2pvbxSPMvP7BMjjUT9zv8T/Rtc8Yv8nnshjeGjxXlg8ROweWKLh1zJ6lLKqIqhBYvNZFKGc70ky4yjwz0VgNo73lErOWmlAujEMacXYxpxSCg11gla6TwPnhZ87mvhc5dvtsrvzvFO+xUMfHzD+33EP3Astpd7+V79W0Qv4I8nouQt+tc5biOv/jKHv3gG3v/xEe97/Bme2uxSOY+19XCyaaQUu76XM44O95SHuLfGa9w+NT2p7v5Oi6bOGZoKJRT30t8qSwAAIABJREFUdVKeO9Emq0oeODlg4emr/JdP1EH0u8GfuE/lOb/Mw1wiFPv3w2Y+5G3Vtxzyym4fgeMr5GOoF+jkAVgRXR4Sl3nKnz6klR0MfaC7Ac81hswlQ7LTBUOj8YJJ4fZs6t7R5p4yiLD7izb95Zs2lPPNmHliOlFJFLZZTkcMyopPRhFaa+bmrnJpzTDowWO9wxVQ9Ui+rfwh/vfwxznDOk1xLU6Y+wCP4Puq7+Ev/KsPcVUHQ0R1U4XrBkWb4SGs6OB5CsiehbCxyv1LC4SnAjSCTBqkECSRnhnDI8w9ZxD3Y2+RrPV1sTayloDvpDGx1sgVyQMLczxy/zJPbWwzygrue+YSDQWPPwcf69YxxjudytikxVeV/5wvkR/gO9TvcVaskhPy2/Zz+VX7pawxf4dXcGfICSkJ0Ow7QWJCiOGqP56vEerxD/0tGPmCXlEilUQKQbqjqj1LqBxd7nmDuF+XQEA98F0h8ALwkEYBSaARUlBUhkiElNYgpEJ7zQNnK77YGM5fvcL58yAsbPTqkp2NO7Bui+I97lHe4x69A49+txD8hv0CvlX90XXDAQAf9ee4xNIhrusaMQVfKD9ERwy46jv8mXst5kUcJh+5DA998gJl7nndgyukrcZk2zxLqBxd7nmDeL3eUev9ru3LdHdLEmleqRSVd5xsJXTzAiUUhalox03uX9ymlaasbw0ofMlzz/WxDtY34OnhwdY1Hjye14nH+XT5CTSWS36JP3CPknHjRM5B8A7ztXy9+n/Qfsh+9iDzIT9eHf7YboHjv1H/jr+vfxeHoC7QkjgEP2XewK/aL7+lx1sF/uqZCqtWeeDEHOc6baz0aJglVI4w97xBhOcHsPczklIK5NSs5jBQCCtYaqYspAkCGJV158EoazLfCljvzbPcDNl8WYmS8NzWNk9dWucDj/d53xG0il8p/19+WP8KHTEgxCBw5IT8JD/Pb9gv4ifNt+0ofN85rrDIN5Q/yr8Nfop5+qTkSAEDH2NQfG/1vbzfv+KOrmE/flz/wk5R/PO38z+kf5UmOT9rv+aWHnN1E9xDFYO+YVCWzMnooJY74w7xkjCI+7HfGXp8mzDgxfMnp6VxQCeN2M4KlJYsxZZWEnG24xhWFWEQEIeazBiKj2R8aHRoL+eGvEn9Pm/Rv06yp/6vuRPPe4P6Yz5VPsXfLv8pBeEdXcsn/Rk+v3w7nys/yl+XH0bh+KB7mPe4z3xR29Pb5VXiWf6W+rPnvTdjUlHy3+rf5P+wX8gmrZt+3HXg0lrFc/NbnJpvcv9CXW7jBbtmhM+8xaPDS9YgXo+xJLwSYqJpN4lDKkkca8JwZ7JaY2dWixRoa5mLNK85sYxxjq3tT7L6cbhyl18PwGvFU/yTfYzhNIkoeTXP8oP61/gR86ZDWJXgz90j/Ll75BCe64V5k/p9ghuU5HsE36z+mHfYmx8WmQFKQBRrGrFEeEFWGZJAX5vzM4snHinuqcLsg2BcNDst8Lm3kHbcsD/+rYRACEErCVlsJLx8aYGHTi9x9ogkSr9Hv/sFi6HHJKLkm9QfkxzTlsAXyyPyPFq8cHFVIkpeI87f8mN/8jyURUVWObzwFKYeZzH2DGfxxKPFzCDuwwttp/cjDBWdNGKlmaKEwHvJq86s8Nmf2uFT1J1c6Y1JyPky+f4bFkOPcUj+hvyLO7yqo0Xhby5EMOLWY4AD4OLaJk9f6eFsXdkgxfNrZWccDWZb5ttkfKbXWmKMQ0jBUjOinWoq4xl9VkXjiSGPde9OM9qS2MagJiKzNyKh4D6xfodXdbR4t/tcXi2fobFPQmXMwMf8nnvdLT/2CAilwIcWJcWka2VmCI8mMw/xNhjHgIxxk8tSCDpRRCQ1ZxaaPHL/aT714QZf+TA8fBfWaLy+qe6QMR5B9RI7T/6f9vNfUH3ceeiT8ifu0275sSsgrwo2Nwsu9AcYd5h9TzNulZfWN/+AGSshW++R1KMnx4axQcBp0p1Zv5bFhQFBsErjKfjQIU6JX6VDSUB6k6Ppc0L+yh+86b5PrPIl8gOkFFz0S/yhe/SOZ7NvlgEp317+Y94Zvo0As6tovPCaETF/p/yBFzWyIQY8Cq8MAYpYqcnslZmXePSYGcTbRGu5Swl5/DvYadcqjOXcUpteGTEaGrzdJN2ChRT+v2fg2Tu8Povineb1fLf+bWJx421zn4Q/d7cmWfZCzNPjXwU/zevk43hAYykIeSv/G//afP1O1vbuG4bH/Kv4ivJtfJf6j3yD+jNSCrZp8Gv2S/i35itZp/2iHrcLrG1bzp1WKF2raCspiYK7HFyesS8zg3gA7J2uZr0nUJIk1Ny3MMdCGlNYy4NLLc5v9Lm0OSBtKBYXLnPxco4NYbsLl/r1FmtA/bsDPJTW098aKaRNuHChHvOxegvr+yX7et6o/5DQm327Q8aMfMhPVN/GQRmoOUb8h/CHOSk2d3ldwU4W+x/qf09bDHmb+dYDeb7b5YJf4UfNt/Oj5tsP7DEXgNNLAa88Oc9CHOHFTD37KDMziAfMuAsGmLQCBoHC4llOG7SCiOVmyma/5OFzjvuXLV57AqkY5COSKGC7nzPq9wnjBkJ68JL5+YQsqzi1tM2VrYqnz9fKKjfDBm2+pfxhfj38MRJfTMRYx1gvKAh4u/kGfsd93oG9F9+j382K6F63bzkVBd+u/oB32S++7tCt485KCKeWlzgxl1I4x3wUoQI52zIfUWYG8Q4w2TbvKrjVeOtJAk0aBZxqVrzCtRkUJcY6Vkcjhr0GaUvSOBnRCBVbWUFeGTyeQGpGZUWzEbG2fpEgghuIxuzicX+WLyv+BW/Uv8/fU+8hwOCQBBj+xH0a7zBfc6AtcxrD31XvueE2XeJ4k/oDfvRQisEPn1OLUJUVm/0S42E5SXZlmWeG8WhxIAZRCPGVwL8CFPDz3vu37vl7BPwS8JnU4jBv8N6fP4jnPsqMv+jjIm8ra9mxJNK004iytFSNmFFREYWaZEkihKIRKJSSrA9zKmtpBiEWy+owB+s5d6aBZsjlC9C7hfWs0+Z/Mm/g7eYbOSU2CTCs+TYD0gN/7SdFF30TSpKhsLxOPn7gz39UOLUiWGnPszwfsZSkCLU7vDLrVDla3LZBFEIo4KeBLwcuAO8TQrzbe//Rqbt9J9D13r9MCPEtwNuAN9zucx8ntJZgHBaPMQ4pBUIJEq2JtMIDnSSisBa9E3RPtKZyjkDKSSZ7kFecWJ7nysaLF1C1KC745YN7cdfl5sp9/BFIqtwptBbMz4V4t/NZT/XHX0+Jacbd4yDqEF8HfMJ7/5T3vgR+Hfi6Pff5OuCdO5d/E/hSIcRL7lugtUR4GFWGorKT4LrWkkYU1IISqs4+SimIY00jDghDhRICpSUNHXBqvslcGnCUo26X/cJN1TOWXvNed/zUv2+GCBhknu0sozKerDKTeT9jZsbwaHEQBvEM8NzU9Qs7t+17H++9AbbhGE5IOgDCUNGMgokWY7UzGbARBxMllL0Ula0LwK1jLgl5oNPigTMrvPoVHFntbIvil+yXk/sXNooOwTvtVxzSqg6XeaARaZaSJq1EY6lH4+41ijOODkeqU0UI8WYhxGNCiMfW1tbu9nLuGGNhiOnm/rGnGAVql3CtlHXBdxQoYqnpJAFaK155cpGXP7jCQ01uQZDqcHmH+Vou+SWK6xjFkY/4WfPVPOtPHPLK7jwh8NmfAg+cOcn9iw2W05RGGDxPUm7G0eIgDOJF4P6p6/ft3LbvfYQQGmizj/K+9/7nvPePeu8fXV4+jBjX3Wc/8drr3aYDSShr4dpWI+Rku82DD0lefkQt4pCEry//Gf/FvZbcBwx9ROE1fR/T9wk/Zd7Av7TfeLeXeUf49AV42ZklWmGCd4KRMc9TUZpx9DiILPP7gJcLIR6kNnzfAuyttH038Ebgz4FvBP7Iez/bN9wCWktSpSmwxJGmHDm0VNzXmYczGwgJj23d7VU+nx5Nvqv6x5xgky9WfzVp3fsj9xn3bM+0BM6ehoeWl1huNmgnIZG+NhJ35iEeXW77G+m9N0KIfwD8AXXZzS947z8ihPhnwGPe+3cD/wb4ZSHEJ6hHHh+/ocJ3Eefq3tdRZTA42lHIQhyhVZ3BjOKQ3F+ms1V3sRxFrrLAr9svudvLOBQ+qwkn5xeQSqJ34sKKnXixfb4S+4yjw4Gcor33vwv87p7b/vupyznwTQfxXC81xrVqwtcHUuTrGKMxDmNAWEWahkgDp+rKHgZ3e9EvYR4Azp2Dh04tshAnLKQxTR2gAjmpKpgZw6PLvblnuYeYKOrgSUK9q9i71dAYEXB2vkX5svs42elyZTTkvR+9tV7ng+IV4jnerH6HL1PvR2N5yp/i58xX87vus1+UUsxx49EmvOwhxWsfOssjpxaZiyMirVA7quqz+OHRZ2YQjwHTijrTHmMjDBiVlpVGkxOvSLhycoE//tDHONeuaGzD04e4xr8l/5SfCH6BADOR4/9U8TRvC36Ob3F/xLdXb7lnY4Yh8Oo5eM2rYh44eYqVuQZSKppBQBCoWTLlGDH7lI4J02U4wjPpXOkkIc1IIQnw0hDFLV7zMsVrXgkPHtLO7NXiGX4i+AUSUT5vNklDFHymfJIf1L96OIu5ReZu8/9XgC+4H173SMrnvOJhPvOBZc7Nt4m0RCk5U8c+Ztybp+x7gL3ZSDflIY5VdOJQk3hNFQT0yoJzagHzsCerFrnaHRDrS8x9Ep7N6ya67Tu01u9Wv/2CU+sSUfIG9X/xP5o3MCK+Q6u4dTpACvRfxP+eBprAuYfg0Ved4nMeOoM1gvkkphNFu0YFzPqVjw8zg3gE2dv0P319jJQCLevrVe44MddgkJX05+a4sDHkzHKLdpKwkFziwSKjuwEbl+ETvlY7PMi5el+qPnDDqXUWxWfJJ16UDP+dQFG3Ss1pkKYeGaphYq5XgQRwXMvcnwHuX4ATyxAEdZJrsdHhlSsrNMKQjbIgkoowUFTWYb1H88IzVGZlOEeLmUE8gowTKdN1a9PGMFDXxB7GxjJynjQKuK/ToBEoMmdYFQXBQ/cRXFhnQQ9ZWixZuAiPb9YHvqSugbpdbjTTGGoPNbrJMQaHgQVKagW1hPr9OLUMKgIv4FxRi/KqCFQFWxZOdSSBDFlZbKKIWGzHnFtqcXquiRKCdgJJoCcCweOOpOsxU7s5eswM4hFkvC3G1F7G2BgWVS20GgUKybXyjUBJssqQlQbjHUjJnIxoLgXkJmF7VKIGisQ4mqqHlYZ8BFJCawCXuSVpxefxjD/BK8WFF7xPgOUTfm+L+90jAJYbcOI+WL0CoYSVJWjEmkCGCO0pSui0Ik51FrDSMRfFbI1GSB9wejHmoYUOC0lMHGhya2nH0cQ7RN744JpWu5l5ikeDmUE8gkwfKNPzWsZDrKYPHCkFSagZFhUIWEoSSmPZzHI2M0NZWk7Op7TSiMpUbCcx8/MZn7zUpbsFLQ/BsPaYVnlxNYw/a76aHwt+8bpjPJ2HJ/0ZnvKnX8Sj3xnmgN4Q2v36IDh1ClbmW8RBQhQKgkDTy3JSnZBGAWcW5lhMQ672G1hvONFo1u97ZUijgFTWh1JuLLFWN/QOx+wNicyM4t1lZhCPKNc7MKz3u4zk+L5SXIspKiVJwwDrPH0Eyzplw+esFwbrPKcWF6gKRyi2WehAXkIxhPY69Dw86+uZLjfLu93n8W3+P/EIzxCL3f/pPGREvKV68y2/B3eCaOfnkQ5EHThzMmBjs+JEp0On2eTMSsrVbk4kNQ+daDAXh0RKEymJdZAoRasZ044ipBdUoh5B60Xt2YdSEoa3NkBqpot4dJiV3Rxhxp7DuHVvrI4z/fcxjTigFYcIVd+nGQQ7XRGeSCnSKCAONFGsmU8iluZbzDWaLLdSAmBrG7YFJKouJbkVDJpvLf8p/95+HrkP6PmEvk/IfMjH/Dm+qfwRPubPHcybchu0qDPDCugbaEiock8nCVlaatJJY4RVaDSNWHGm1eT+dhOlJEXl6BWGOJKkMiBRmjBQpFojVD04CpgYxluV+DooY7j3eW90/Xq3vVSZeYhHmOlkynSWubJuMg96/HucyTTG4UT9v9Z5tJBUONphROtEwPk1SV5anIfOfISoBP04J51zJAaWFkBfge3RrW2fC0K+33w3P2n+Dp8tP0aI4eP+Pp70992Bd+bWCagLqM92oL8Fcy3QEUQqQIYCLTS1XRCcWogpSri8PSIKNIlUqEASSkFrp/vECzCujvGGXhGGCu2unazuxhb4etUJ4wQd1HHo6V7q2XZ9NzODeMTZO7DKuWvGb7x1NrnBCc/WqMBRJ2FKbxnmFq8sGEmJ5b5GyiCreHxtg/6oACuImwGNRNJoOjZXYW0T1ke18Yi59fKcHg3e4x496LfhttHUr0l5EBoaChY7TZqNmLLwxEHAq0/MI6hb7HpZTmnAWk8rDViI47rQempL7Fwt+DptXMYdKXfDwOzdeo+rFcaiEoGSu7zB6QqGm13rCyV/7oXE0MwgHhPG3t/YGxzfVpaWflnRcJpgJ4bo8AgnWGqGBEoBno0sJ3eWbpaTDQ1zzYCisBQjS155Ql3X1pXVNSN4dIpkbp8QaEpYmgcrYL4FEs2wKHGlw5SONFRUzpNqRTzXRAlPbhz9omI+risUK+cIueZhKSEmYY1RZWgyDlXcHcOwt5h/b3JnfLksbV08vk+We2w0904G3M+bvJm/HSdmMcRjwnSHyvSXLAwVc2E9j0VKQSAlUggiqWglEbFWlM6hvGJ9kLHWy1AKFtMGc0mCDGrvaC4GqaE/vDam/sYz844PDhg6+PB56G7VZUZBCKpS9LKcfjniI5e6PHW5x9BUFMZivQA8wtaeYSAlhbGTGGFWGEbGYKybvPfAxEAe+Gu4iXjg9N+GWTUZaDZdqWCMo5eXCH/tpFpUlrK0k8tZUc9/GV8fP8709286xj2t/r73b8eJmYd4THihrY3Wkqo0lMZOpva1opDMGLLK0M8qmolmY1hnfZMkYFgYirLCW4nC00hS7lsZURYwL8A6iIYw5NZGnR5FUupkSktBbqEZwfJcyvxcG7Ho4LIkTUKM9ZRUVMYzFwuaoaZfeoLQkRmD9rv9hyTSRE5NDM7k5w54Rsa4ugA/uLZd33tda8loVBHHtTzcVlFQGku7ESGlICtq1e7CWLLKkGqNc56trCDQEms9SGjqAET9nIOy2jlB1qZiusphvy36mOOaOZ8ZxGPEfl+uPDd1gN/WB0zlPMl4DrSv50DPpxFaCk50EpRaorKWtX5GaSwqsKSNBlf72+QF+ACWmnB5vd4+3LgH5WjTpI6FNiWcPg2NGAhBi5DuaMSZcI5TJ9vMJyFYSelgfZQRCEkrrKcgNgO94zF6WmEI7CRNplRspreHB2kExt7odLXBeFDVeKtaVJZ+XhJrxYXekBONhHYaoRAMqgqZCwIh2SoKIqWYi0JipdgqC3DQLysWVESgJcOiopK1kRW+/pFCXHutO57iYFjSbIT7bo+Pm1c4zcwgHmPK0rJdlDS0Jg41jSiYfBm9gKI0IAUKz/qoojAVoZZsZyUVlk4Sc3qpwRMXVom8ZBRYmgGUFqIQouxge54PG0kdO5wHTi5BGMFCR5MPPbkwJCO4Yobcf6bJcrPJQiNgc5RhKkWvqEgiTaglzgv6RUUjqifnjY3hmL295gfVeWKMmyRtAiWRQb29LY0l1GqSLfbOM6gMhTMY6ymspawsrShkWFZs5wVZVZ8EPZ6XLbYpnMVU0EmDunZSqVrV2zsGZVlLzAlJ5QyxruPTY6GKUV5xYXvEfd4Th9emR4691WmDvTejfdQ9xplBPObEO0Pux/GbrDBUzuGso7CeSILWmmboyAvBdlZgsWin8coxKkpGQ0vlLe1EkBeewSb0y1rw4DjGEUPqmsOSeprZ4jw05kCVUFWS3JWc1inN+ZCq9FAGeG/Zzj3DkeDsUshCkqClpHSOVGsiVXefBFIS7hz400mEAHktzjsuhbrFLePYYBjjJo8PTIyvMY61UQa+7rQJlKx3BdYRCNjoFYSBZGQqNjYLtBBkpkIgsc4xLEuyzCHkFs4J8sJQrVu01ZxcimmFIf3CUEeRPY1Qs5VVeF+SNSz3yyajqiJRmsVmiPWetWFGMwyQiIkIrhICpcSuhI6ZakM9ykZxZhCPKcY4RpUhFJLSXzNbUaDwhccoSRJKQqHYzgo8AofDSMdykjAKDM9s9OlnBZv9LlkOgxzKrDYkLof1u/fybosOtSHX1PWHSsNoAIWFzqJhab7NXCcCp2nHimasKIwn0IKFufry2rBgLtZoWW8Vw1BRlpbcWrSTu5IIY698nPSaFvS9Gcb/X1mHMDCqDIGUeOcJd9o1R5VBI2jqgMpZ1ocFsamIpaJbFKz3MnpFxZlOg1FuebbfI/KK9UFOmiiyqqKhI0au5On1AruTKCmNRQeOK8OE0hqWGw1OzicUpcPLkG6e0QpjNkcZc6Hmci8nDgVpoNGBJA3qk0VmDFXp8JmncBYtJJ00mrxvXjBJ4ux97UfJQM4M4jFgvy/NOKuplUTvnHWLqt5OVdahhUAJuZMx9QhpccLR71fY0rOVF/SLgm53k80tcAJaKXQtbGzWPsIDwLPcnvDD3WA8PqENCFEb+KCAKIVGlLLQbiCUxBfQbGvaSUQcKJphyMaoYFiWnO00WGokQG3osp22R7WnwHmyXd4jBnsrxnD8/0oIEJAGmrKyrGc5YVE/rsPTrQxFaQi0ZmQLBj1B4SxPrXVRKFpxwNpwyHMbAy5e7RJFkryoEBJ6/QopDZFUmEDS7+e00wCLxFsI3AYu1JjSMHIF29s51huED0mbkuW4SVZUICXdocd5wXI7ZClJyEtTt4tqTc+VDHNDEiuycidT7RyhlKCunTyOaqnOzCAecfbrPphk+JSceBNQn4HxdQcFO0XEURQRqIr+qEI6SdJQtKOAvLLgHM3mHItLGcMczBCG/dq7EtRb5uNmDMeMO1M6S9COwadw7kRA2miglKIaeWRkWIjmkVJSWk8aKtKwQeUszbhOnoy9Pec8SVB7i9NJgxdb2Dz9OYqdzNWwqCbjIZDQiUIq61gb5oRa0h3kbOUVSVhfrqzjmStrPLXWZbkdkqiIC9t9Ni7Bah+WOjAYwkIbhIXtEorCkmq4NIBOWNEI68SZ89BODL3lK0RX4dIaNFqw0tLofsx60AWvmGs3iIKAs/Nz9DLB1W6GUIJz803SVhMEKClItCarDFIKQimJY73LEx53z+wt1dnbSXPYzAziEWe6fW+vcdRa0pQBZWnZymrTJYBOHE3iN855XO7JtGVOarojjVISqSEzFaHUnJqf5+lLXVZz6I/q+FSX4zu9LwTOCRh4qHIwIegQQtmkqEpOtJsQe6TVtJuaSGi6eUmvMDRDhXGw2stIooq5MKARBbsO0OlWSbj5JMr4gB/rWQZIjHHkxiIri3WeyllGhaFXGJbTiChUlJVhUDou94as9oecaKU8fXGD7bLPxmbB+hpcuFgS6ZLRCJ4panX0T6zvxICz/ddzsWR39f0Q1E6cRAHnroJ9wGDEgNRD5qG50WOx08KJConGVLUB1Ko2qlpKMmNoWo3DMyoq4lCjzbWTybiYfdw9M9syz7hlprdV09sN4WFkDA6Pt55BZZFCMB/Gk8Lafl7RHRWUrs5EbmX11joflpQyp9OaY6EVMOhXrHm4yJ0bN3AYJECrCcsBpCkkCcw3E1oLAdrFGDyx1yx2IsrCs20LQiXwzrExdEjpkRJ6Q4/1ri5014rcWtJ9vBq4ce/y3pa5YMc4VNahEZQ7cwkFgkaoSUKNANYHGVcHGaO84uLGBpuDnDwf8vjVTdZW646brIILw/pEMD0q4sUkxOzU7yeBJ8/X189QhyCaCzAse1y80iOcg5OdmFba4oNPjxiurBA1BIkMcR6UAOM97TACIDJ1D7gSYlIgvreu8W7PrJ4ZxGPAtJc4/QUaZzaTQKOEoDCWyjs8dX1iryzplxWBEEjlyYaOYVXQG4HF4rSjuznkcnfAxUt1MbajPrCOMwPgUh9OpaAbMB9L2s0AZzQEnlYcYEtIooDSeZJQEHhNFCoSKWjoax5hJOsxonhIQ71ret714od72RsnzI0lBoZlvUXuJBFZZiic5Wo/w1lYbAY8tdHj6laOwVBWhk9eWmerD09kcHGzfp0f3Lo1qbYXy8WdH7kJbhMWgJNA9fKchswZFmB8xom5U5xZkQSyFssdFRWNSIGHzSwnCTTeQ+p2qwSNudve4swgHhP2ijxAnWn2gslZtbCWdhTVWxLvGBUV/bykGQZoqZhrWFYHsL49oDAV2ki2Rp48g6yAlXm4OICNu/lCDwC184OGpoZIK3qVBT9CN+fYHBakYUAoFChPqkMCLQhFPTI0VAqHR0uJEKL2vI2hLetTxa447k3ED8ciC1IKwlBNSmsGRUWg6m2zFR6PJ9GKq6OM890ua90RTlRkxvLsxQ3e90S9++3e6TfwBRh7nZs7P9tPwkoD1oawvjnk0195ASdOUlaONAjYHhYY52nHAV5InHMkQUBhLInUSCX2jZHfLWYG8Zgx3Zw/UWcOJINhSVYaAi2pHIzKikFV1TWJ3jPMDf3MkBlD1JSUWwIjKipTF18XBayv3Z0B9wdNArR13aI339EgBT4zuPmQ0hbkPYWak+SuohMkKCFIlKayntxa8NTzlKWcbPHaOiQM1fPiuDej/DL+n71xyDTUJEqTWUNvVNIvS4x1XOxts9nNGJmcKxtbbPTh6efg0mG8ebfIRWB1WHcEXenBx5/NuTo4z4MnFknTiFiEqFDQzwPaScDlzNNp1gXvKy6l3Ywm5ThHoVZxZhCPOPudMccKzWOp+jw3XBlmCGBB13p9o7ICL0l0HfQuvOHiVo/eIMN6hykLtocZGpiTMJTQs/su4dihqQVgFwNwzhBplLh+AAAgAElEQVQFDebSgFgnJGHIiZMxZ9pzKC/pJAG9vKJXVjRCTSAUga6FX7XeSXpYSxrWh8o4K/xCB+z1NAbH9XilqWsAC2fJS8NWXrGd5/SGJc9udXn64iaVKzCl50NPwAi4coffs9uhovZah0ByFbaHoN0GcSPmzEITu2ERSvHwSovT7QbzSUw3KxmaiihXjIwhNde20DMPcca+XO/Amo4fVtahtWQ+DhFAHGq8rbd7FkteOvLSMRiWBCqg0TCUzvH0+jof/hgYV2/DLt61V3mwrADzCqIAtIB+Dql0BO2ATiMiiQOaOmIxiVkbFOTWsl1UzMchgZCkUVDXd2pJLy+JlCKU13qWx1lh4LqjAvZWBjhX95SXziF9XcM4MhVbowIhBN0sw1jYGGZ0+xmjPGeUwYefPdqGcC8ldSJGDaD6OMwv5mgcraanEWnWuhGpCkjCgPkkJLeWwlmGRZ0UjJyaFKLvlTE7LCM5M4hHmP1iVHsH1nvrQQsqt1O+IQTGe5SqC7et8oRKghRkZcZqf4S2huEIuu7aPOJ7hQ3q3mXh684U24XFBcvqlR6juZLlhTZrmzk6AJyiYSSdJGQhiXA73psMBBhHpOqY4ngswLjUKXZqchs8X4NwfL2o6p5ii0chEIBQdRtdL6u42B0yLAs2tgsqn5PnFR9/dpvVNdjMj5cxnMYCH/VwZh3SuCRQI5yPiaIRT16t+61P7HTDdKKIE3PJ5IQzHYa42Qz+QXIvHQv3JHsPtunxAd7uiJLKgKVmQl4YrPcMigLhYTCyDKqCNFKU1uCEQFSGnsnZ2KyLro+7tNdeHHVHisjhmSuwnMKoV1I5EIWgXRZko5K1rWSyfZ2L6iFSzkEaaSgNQgmaYTCJG+7VoNzvYJ3+fLSWE4Vq5TwDU5GVFi1hOy+52O9zYWMbJ6E72ubZ5wYUwAefO5hZ2UeBi8DZHqzFGTrISDSUOmE108wlGrysqyIcuNLvShDebAb/oLktgyiEWADeRd3ldR74Zu99d899Ph34Gep+ewv8hPf+XbfzvC9Vpj1G6TxOeFzp64xdqPECNkYZW4MKLz1RCBu5YWMjo5vl9LZ69KsRz1yxXNw+3rWG18MDF/pwSkMzgLAJkQCvIBSCqoLKeQpnWGk2cN5ivObKdkYz1TS8BgWBuFZI/ELbt70HqxL1ycqVtv5/73G+9hCtdayPCvr9ikvdIVJJtPIUVcHqVr34/iG+V4fBR3pwOoN2E8q8y8pKRlbkCC9YnksZFSGV81gcidbMuZCGDO6anuLteojfD/xn7/1bhRDfv3P9LXvuMwL+nvf+SSHEaeAvhRB/4L3fus3nfkky3R0hpaAVhxOtvEFe4q2g3dRc2cpYG464tNGnbpF1rPb6rK57qmHtHR5f1boXZhNYcTDXAGfg0mZdnN2IYX4+oVUmLCUpSaAwTrHSjCjMjhSWVoxKQxLXh8a0xzftAY6VaPYKtgZK4kztuafoWqfS1wX0C2mEUoJIQa+MyU3FU8+tstqtSGNY3zicmsLDpAf0Knh5F4SCUZ6jwhznHa2XnSWzhmFuiQJJK9gtlnE3kiu3axC/DviincvvBP6YPQbRe//xqcuXhBCrwDIwM4gHgNa1AnK/KLnSz5HKoZ3kam9IZSCJNZVxZHnJKPM8e7U2hPf67IiBg5UmLLQACcLB1iBjbaNHksTkVHQ3iro7IhQsJintsFaWdtYjVH0weuuxil3F8NNxrunf038LZJ2hrpzDWsd2WSF93S758avbGOPolxmPXxyw2QWfHd+Y4c3wJNBfh5MpPHQOcJLtQUkzLom1JlR1QkUJwbCo0FKSRPrYeYgnvPeXdy5fAU680J2FEK+jboT45G0+7z3BrZ4Jp+djjC8b4yYzVKJAkBu4uj0kKx3zc4qqr7i43mWQZ8zFda/zc1xr0bpXuQCklyGSoAJotQXSeKrcYqoRebOB1JIohLKALZET67qjQgd1R8lYYq0pg+cZwL2Xx5/lJNnlPIWr5bUipXDOcmF7RFYYnlvdJpmTrK5tsXEVtm0dvrjXtst7qaAeTVBC7ks28iFJX7PUikkCzaisKGVdX9vaEdcwxu3qDrrT3NAgCiH+E3WXzl5+aPqK994LIa67CxNCnAJ+GXij937fNkshxJuBNwOcPXv2Rks71owb/YGb6t8cDwYKRB1kzq3FGoff8X6kEkhRS4B5J0A4Noee7WFOEHi8EYyGtWd4rxvDMeeBeB2CCJz3tOcEMpZoIZEBuMqxteVpnKhIXUrlLYFUiJ1vZ2UdpbPIPR0q0/NNxlu8orIY62jEAd56nIBQSracAwF56TDGEWpBf9hne+T4+FMF6xau3rV36HDZADaG0P0knBsMCJ2kGUQ004CoKskKiQoES0m8cxLxDIqKJsGhGcUbGkTv/Zdd729CiKtCiFPe+8s7Bm/fRgchRAv4j8APee/f+wLP9XPAzwE8+uij92qIC2DSyD6+fDP3H8t8We/RCJwSmGpHfLOCojJsDDMqZ+kkEauDERfXN5BesNmrWF+Fyzd4nnuJEhgUUBUwHMDpZU8SD2lGDdY2Mh4606GVwMOLHfLSIJEY56i8RxcVSaAJpdo1y2SsUm2mJPOlrAuKC+8xxiGUIJaKsrKs9nMW04ChqXAervT69IclSlty/9IxhtNsAukmXJ7vcf99i/QGJf2sIpCSThJBwiQGOx5dAIdTj3i7W+Z3A28E3rrz+z/svYMQIgR+C/gl7/1v3ubz3VPcyocrpaARBxjjap05BBqBUYJ+XjLIKj6+us3VzW1UqDnRSSmrCoTEupxQwGj40vEOxzwFLAINQKSQ55aT8wHDyhBJTZTWRrCfO+YiQSuK2c4LKu9IgGYUPK/mcFwUP81Yim3Sj6tqT1IhMBZwgkCDQDHXDNjsWdYPuSk5ouSr5Xv5bv3bPCCuIICLfpGft/8Vv2X/OkOSQ1lHCRhbNwX0BgUm9jy02ME4j5OeQErUTv2nQuzywqcHe405SEN5uwbxrcBvCCG+E3gG+GYAIcSjwPd4779r57YvABaFEG/a+b83ee//6jaf+yXBOIbinJ9s1YZFVW+RvcA6h7WeMJCcWogpnWE0KukOMrobfZ67nFN4CEy9XT6ugq+3QwjMNeHsSoCvNHEc0dYBWnkurWd1zCqKGJR1F8+ossRGoaWkGQbXxgLsk0yB3TqHY9XyUCuc9yy3IsrKEWjB05tDhlVOEGg8dSz3sLhPrPEb4f9AixFNcW102ANilR8Qv8Z/p3+Tv13+EE/4Ox+qagPS1bHES2vrxHGTlXZCrCIiNLmzLEQBQD2yYSde7pyn2pGXmJ52eJCF27dlEL33G8CX7nP7Y8B37Vz+FeBXbud5XqoY4xiVhtgp+kWJkpJQyHrSmQe1M3wo0or1LGM4cjSDgLgtcUawGW8TBbD6HLWM/F1+PXeLq0DUg9WrFY05w3BQUMSGwaiJ0Zaq9DTaCuHrcZ2LcT3FMJByImQ6nczSWu4b95VS4MvaOAoPmbW1ZL9ydLOc9fWMS1tXGfZhMKpFKK6j3XqgtBjwm+GPsMQ2ep8wf0MUJL7gXeGP8ZXFW7nC4h1dzyr1yXlYgkHhy4xh30GzwssAZ+qTf5oGk+4gqOdgj5NW15sNfbvc69UXx4r9tmFpWH8J+kU9ZChNA2KpyEx90AHkxqKp79uKI4SXdIcDhrnBsyMO4F5a8cNpHPVB+MR5WN/yDIZ97Ah6VUYoBaOqYnNYYrFs5SXO+0k/8zTjE9Q4wz/NWPB0ZGpFIYsnEAIhRd2hIhTt+YBhD65uwlNrh2MMAf6ueg8dhvsawzFSQIOc/1o/L+p1R7gKVBnIKseKkK0qozKWYeYZmJKNLGc0qnZ9BmPjuLcY/iDjijODeESYHkY+fZvW9YHZjgLSpJ67HIYKIagnnZm6Qb4eZgHdLGdQlvSLkkGWUeW1kGjOvVuIfSME9Rd9RF2o3TcOlTry0tAdVcRaEWtNVTk2RwVX+hmbWU5WGqJAkUTXNlLXE4kdKxC14pDlNCErDENjKIzB+VoNuxmHiKSuCjisEhuJ4zv07xOLG5d8B8LyDepPSQ5hGncKRAmcWjlBs6EItSLWAfcvJMzHMUJA7mpVoPFY1jF3MrEyM4hHhP0G7lTWTeKGoVaTweVuZ/rbJza2eXZzSGUcaagIhaQ7zOllGcp7Al/39baA03f35d1VPLU3VgK5ga1uQbdXcGV1QFEUrHQSFtOQNAo422lw33yDRljHsMaxq6ww9LJy38cfB/rHv8NQEYYKjWArq7i8PWStl9MMIh5caVKKwwtfnGST5BYixwbFy8Wd1z5SwKXL8MzFK5gKRrlBaYFSkkFpSAJNc+czGBQVxrjn7aDuBDNxhyPEfhJf4zjJxEBahxN1rKsoHDYoGG4LenFVT8rLM0aZoTvY4vI2dDdqQ3DcVbBvF0k9PGu4DY0GrHQaCC2QVjOsKkIZ4HCcbKS0k2jXCWk8b3k8MH7MpJVvSsNvfL0VhZTScnU4ojCunmdjBd1sxNbg8OKHoah2prXcHB5BeAgNhJ66la9CULoSbxIUkqrylM6Cv1Z7OD4qDkP1ZuYhHlGmSwkmwWMlifRO76z3tOKQlbmUxVZMOwrYLDLW+yOGxQCnBL4CV9VjJl9q5TZ7qahjiYOyznAWlQErUVKy0csZlSXWejJrKEtbCzT4aych633dlzwV1hjXH45vk1LUsmOVJavqmTZZbjHOIZVgozekzDynlms1lMNgzXcIMDd9/5CKy/7OJlWgDhkoB5H2CAtOQmEqHHU8PDNm4hXanfd7vIMa14XeCWYG8S6x9wPdGzucPvDGZR1FZSmMZZCXrI1yhPLMBQEIuNQb8szVHr3BkLysqApHYYGw3p681A0i1IrOaQzzKWz1BvR7OVHqGY0cF7cHfGJti61RSa8sscaRhJok0jTiuvRGSLFr6iHs9urH3qTzHikE3nmkliylKQ8tzdFKEnzhaaX1NMC5Q3nNCX/iPo2btR9P+Pu5yPKdXRR1XWjchK0uVFIwPxcyGDmeXOszqkqsg2JHiDfSatdOKSvMJHR00MwM4l1gP4O31/PYm0mLglqs1DiHUpJmoFluJDjvWe3lbI4KAiVpRSFWKmJgNABf3d2hREeFBFgO4f5FWBvBE+eHDPIB3c2Kgc9BeLb6JaWvyI1huygZFtWk/tMLSAO9a67K2CuMdlSera+VsSOtJsXF7SjA4ZE+oHAV20B3Da6OOITURc3PmK+luIlZiiMf8a/N3zyEFUFEnVgJ2zAfRdzXaXNiPqKVKlpxRDsKaMT1tnmvyMN+x8dBMTOId4G9H+j09b2ex9g7dK7WPSyMJZQSIQWJ1lQ48rxCCWimIQ/cf5JFHTKMwBoYVS/NYuy9ZMCFEnoZ9DdhOwfjDFujPt54llsJ882QZhARSkU7CpEIstIwKCvywuxSdd47+2Mi2GsMq70RmTFouaPG3YzIfYlx0IxhfgFONeC+Q3rtH/Av56fMGxj56xvFkY/4Jfvl/Gf3mYeyphUB7RU4vdhifq5FbupxF71th3OO0rnnHQvjcqcoUHest3mWVLlL7FfUO111v/c+43hKVhkUgq285GyrSWkVIoRi29LLq3r+svDkG3Uw+ihOartb9IGqrPubF0MoS8d6d5tIBvRGBaWBfl7SSkKkEAhVn6hUaSnFNY9w74B12IlvBYJyYCm9J/EeCWxm9eyUza2SMi9p64h02ZImhueePLzX/ov2b3DRL/ED+tc4IbbweAQCh6BPyr+ovpF/577oUNayRK1PqTS00wRPHRM80U4ItGQxTehX1a4t8bgGNEXvK9x7UMwM4l1ivw90ejhRUdlJN4QSgtxaAilphPUQpEAoRlWFF7CcpiRaQheudnsMBjnWQdqAk9t1T+WMmo8N6taxlRhKY3AKZABKaE7Mw2IjQXoweEJffz5CCVJx7UAcZ533St075wkCxf/f3pvHWJbd932f31nu9tZau7pn6WGLmyibWjyW7T8I2RYJyQwgCUhsC7FgCogkJ4RhxIGCEOAfCWQDkSw4QQAHcBgKFmXANmWBkYhIBrg4CgHHtEhDEimPKFIcLtMzvdf+lrucc/LHfbf6VU11Vw2ra5u+H6BQb7l17+/devd7z/md37JMjJ6N+Je9xwCBgIr7BA/3d3fYfLC71z/6rPy7n/J/nk8VL/K98nXeqV5B4fm6v8bvhXfycC339FkHVovanSPaksaWwtUjxEgZyuD3NfZq4nHnxfC0VpxbQTwHHvcPnZ82N6LZdHczRuFCAAElntu7U1KjiIww2oDcVYiAieHBLRhvP2ws3lLTLBfoCpRW3FgekCYxReFJuhEPdidsGc31ocaJ4EpXZ67MMoaUeth0vnk+31PF+4DWdWpfUTiqEKjEI06RWkvcMaQTy2BoeOZexf3JWS94CX8Y3sofuree6VHn+bM9uLoILzw75Gq/i9ZCFCxaC1oJO5M6TbWYtWFoejXPuyxOK/ym9SGeA0c5hZv33ayc1HxMXLOwsptXbIwn3NvNIQhZF6Z5xfb2iMhYEgGl4N4Zf7aLzmuAFrB9uLq0wHAxxU2FJBJWsgQv4IPHqLofipb6f9SsbBaFo3R+30rnwR4rUN/MdsuSjXFB8LDQi8mMweeaTj9hsNBhZQ2+p8MZ1Zi5GLwAXF+FtZWUfrcLDhIVcW05oRtbBCEoiKyi9J5JUb1uZf80aUeI58RRdzdjFDRhHFVgVNTBskYrVGCWK6vpZwaHpyrgwWjEN+7vYhRMc5j4s1vJvCxooAwwegDFWsHOuqbXifAe7o0muDKw2E3IvSN4KMRhtMKouop2EOoafXZ/1ZsmHKRBqTo42zmPSF2VSIBOR5NXEeOtCdsFdPqwNoJvnMO5OA92gFfvwGChzr5a6KUspJZBnLA5Lnl2EGPUw9avjkAKr/Ovt1Pmp5BGFJUWOpFFQn2hjYqSvHC1EFYeFRS9zLK20GO8O6GQnDt3PIOYC7DEHHhR/oSf0p/hOXWXndDhE+49/Bv/g5Tn8PUbU4chZSPquEIt2Fhzd2dKPFKoWCFjxUIWk1nDqKroq2ivBFjjz2qYH7U0I3uY9WR2Di+QakWlhWGI6Scx37o7pQiOxQySFDbvwRuInb6UDIDnLFx7Fq4OLNeWFijLQFVVTCtDZgydoSUxht28JLaaTlKn7jXnu3FVAKc2ZW4F8Zx5VJvL5kLLS0caG6xWjMuKUAVGZUU/jkE5Si84cWyNcoLXdFNLnjscBVvnLIZDdvhY9Eu8VV4loUDPqq28qP6E/4lf5QPFh/hyuHGmNlnqUWIkEIqKZMkSnOLZlZRBkqKVIrF1PGcR6gKvVil2i5LY+3qErvYXLYU6TnS+pQCAUYpEawpfe3Ijq1jrdtiZFCSpZjDIqHLH5vP3+ebLb+6eKu9YgOECLCWGTq+LaE2eO0oCo7yk9J6FJGEnL0DVsZwHbzwHy36dBq0P8Rx5VIB2U8Shqjy5c3s1+CJR4MFooZtoEhOhQuD+zpTNnYLK56xPpqxPClR4RD+HM8JS8a+if8g75Vt0JN8TQ4CuTFmUXf5l9A+5Lmfbay6lHq1UATYLxzSfMJmUrI8LdvOKinoRpQweK/WqvlJ1vxqr9tdBbGLiDraCUEr2Mlx6SR3CkxlDCNBNDdcXe1zJOsQqYXNScGXB8u4FeLepMzjebFwHXngWbqz1WVkdsLzQ51pvwMpSxlo3ZZBGszjEio1xQaJeH2d4msHY87QjxHNkPsxm/vnD1cr6IszLus1oCDCtKkbTCq88lQtMS8erG7vsTKZoFVh/UDDsgo6gHJ3fZ/sR9QWelbvE8ug11IScv69/g/+2+rtnZtciMExh7Qos9yJwFtOBSGkKV7K5nqMCyJrwTK9LECi9x8ziEg9yMH50XhT3AotnzetTY3DWU3U8sTEsdhMKCiI9oJ9u8fLNbdQd2Ji8eUKl3mXg6nVYW15k0MkgKK4PByx1I+5uF4wLz2o/YSlNMFphjWBmlccPFuE9bTGEVhDPhYPT5IMXU+k8PtRTBKsUuXOMi4peZKm859bWmH5iiWJFGius0jhX8c3X7vPKbZj24JWN83VL/az5bbry+Dm7kcCP6i/QqSZn0s9DgKVFGGiYFLCxXWBVRbezyqgqUUoYdixGFFc62d4oZXOS75VgQ79+v4eFgcxPpzNj9qbfk8rhq6bQqSUOMa4s0Tam01U8qz0LY9i4dbkrnBvg7cC736lx3uFyxy4lnSSmk2p6ScIgq4sZD9IIUULpPcM4rnuNl+eTfd8K4hlzWDZKczE1U2M7i2NrLqpYP8yNlSDYSKEN3N3MeTAZUYSKgMdXECdwd/38m55fl+P1k6vQrMk6Xw/PnLJFtf/w7notNFkfEsm5/kKfYSeiF8WEABZNGtWBwqNxxUIS048jypkfsHQe7eXQKd3B581UGgtmVuS0rvNn2BiXRBYSozFxxPJCSuUqqtzxrbvbDKlvaIbLJYwWSIAbCVx/Hlav9Ih8RJqlXB2kLPWy+mYza0TvfCBWGjMLM9NGTtVHeBStIJ4x89Pk+ZFhk5oUVQrRgqJ+La8c3dgSW40EGBUVwQe8F5R1bNyacmdrnc2dEeu78NrWxah9WB02lDoEhT+z1eaCepV5IYXFDBZWDPlowuZuQbJg0EGRO0+mPKOyQishd3XDKI1QeE+EwmngGA3U56uzNAK5lCVMqookdvSs5W3XFpiUBdPKM4x7lNmUhSLnB96eM5nCqIQv3roc4VN96rS8YReurkBRwu6u5+pyzI3VPoudlOU0YSFNKIOHAErX+d+p1AuHs+SgY/UqPw1aQTwHDgsdUEpIjK4boIuqBbKsKL2jKBW7eclCEtNJNKntsJ5PoBKIKkyk2B7XxRwWNGy684/i+Jx7Nz+m/z+MPD5XZkzCK+H0y001FMBkAm4RiqKiEMXmaJcrg4xuFrPWS+jEESLQMXav5FewggkQRfrQXOZH0fgRm3AcYxShgr6NiKxmpRuzPbWkFha7ETfvj8FrwqLj1vomslnwfX14bbsuUHFRA+2/L4XlJbAxdFO4vnSF7iDmuaU+kViu9BN8qCtil97vjQL1zL96Hv7Cw2gF8Rw52C4g+EDpPVLUjnwXPLe2Jix2HXe3J4zyktc2xjhVsTtyfPX2a2ztFuyOpzy4D3kFN90FCD0EPurez1/Tv4fh8LL7AJNg+Wj1fsIZBTtYYIH6gk0Vs4WNjIV+j1gbtAoM04TCOSalJ4R6imt1HZRt7ENRO64Ylu7hDaGppp0Zg4qE9dGUKgjLHUtqLQtpQuWE2CjWJzll6emnJeXVgnfmcO/OhJfvw1emF6M/ziqwZOuMqH4Prj+TkSUdep2ItWGHd63WNzploKNtnROuDXrmFoK6SpANtTg256dJ1WtHiE8pTX6s02CcMK4qcufQwLT0bIwmfOv+iGeXUsYhp5zAtzbvMx6X5NWU127D7dHFqmzzUniBj1T/GT9rfptMXi+Kk2B5KVznV9z7z8ymptFUNoVxAX3lWBwOuDbMSKyidMJ2keMqiGYi6ENgOy9ITX2pzOfTHnm8uVqJTZB9U+S3n0YM0xhPXVD23nhK31puLPS4F1lWypLlbsr6aEJeBgo/RumYKNvkRlWvROcb8B/PYS7dBdaA5SuwMoSVfozDcHVpkbeuDskLjzUWo2G3dPRDhDaKRGlsVLt+jKlnQRLYmyY3YTXHudmcFq0gXhCMUfjCYSONcQpVgIjw/EJKRSCNxuzkOdoZ7hWb3HztAX/8lUDahZe26wv9ovG/ur/Oqyzz8+bXychnoxpB4/h195f5n6v/8kyzVXJgCCwMQSJIvWGUl7xyf4eVrIdEOQuJxQGdJCFQh8wYqUc0pfOPdPg/rhxVM70+2FIzSQzLKqV0nmEcZumYiu1pySCOsFpzpZ/O8nkX+BK3SeOYJC2ZjCCvJvBHE9IU7t2vF9K2nvA5G1IXcy2pF9iXE1ga1sKxuqJZHfZ49wvXan+4iehEESt9Q8ATEJRXLGYxkdV7NSPRQlE4xmW1L7bzsCrkZ00riOfI/EW0V3HZw6goeW1rQuEqFrsxVoRnF7u8fHubm6MN7txa59ZrgZu+bsB+EcWw4dfdX+Ffux/iRfkqa7LOLimf99/NhORc7KmAQa9ugbl2dZFelpJow7XlhKIUUNAxhsQYqsrTie3e/8g9IhSk8Sk+qnrR/OvGqH2iGkUaClBW2Bzn7JQF48rRTyzDNCGvSgJCXjjetrbMzXiHnk4Y9aZs7EQsrU7oZ7CyCkt34PaD2We0sFvWPtO71O6Cx31PlqkFb51aBFc1dIfQMXXkwsiByuEdN/rEqeL+1oRhaummXca5Y2WQcaWb4BFirUiNxShhMVMkszauZVnfUMzM/TB/Hk6zpNcboRXEc2IvpGbOmex93cjIaMVbFrsUznFnd8SfbkyYMuWb65u8fOsuX/sq3PFPfjRwWgQUXwjvPHfH1xp12XqvQFsIXmGCIjWGQZpAGtCiyYzGiuJBMaVTWLKszqk9+L9qRjXz9REP4zCRbGgWz7qxZZjFFEVdLr8TR0TaURiN0QXOWhDPlW4H7x33RpZYLG9/dor3QhIZ3rLm+MbtdXa3Ic1q395oAm/NIXcQCrAWBinc3YHUQhTBKIcbN2KmGzkv3wYVwVuuCf04ZnNSkqWK5RyqSPHM1UWsGNJ4xFp/wELXEhtLPOurrEWoQiAQKAMkovb8qM35m785zNeTPItMlKNoBfGcmO8zO51WRJFGi7BTVRTOkRnD/d2cr97a5ubmBnc3dpiORzxYhy1f38lb3hhrGXRj6HeFLIoI3jMqSxa6CeOi5EonI68c1Uy5LZoyPKyU/ajRzElGNUrV2UjN34sWljtpXbRDK7paExvNuCrZHlcEcfSTGJSw2k1IIs3X7zzAaMNyt0Ov36WYVlSVI5SenVCxkFju3NvGiyOOU7qpYheH/M8AACAASURBVGG9YLgQ4aYeZTQLvQ7F0JF11kljy3c9s8bKMOWb97aItSWJBfGGt6312BpXvPu5JfpR3ct6VJRYqdtaDJOY0tcNurwPez1oms966Oc/51HhPK0gngEH/UvNqEAjjCcl98c5vcRglGI7L9galUyrEqOENNUMibm7vsXLt+Arm2/uIgCnhQAhgI8hjRWriwu889oSZSXEGja2S/pphdGKrrFkqcVotW+VGB7+L+crr5zkYm7yng9SEYhU3fQ+5BVdiTDdeiGi8oFpHljsWt5xZUhkatGMjWJ9ZHHdwGha0I8jRrkDCSgVE0WKSZ5T5prveqHLQHe4vbODEujGCaajWOl3WEo7PLeUMS4c5SAwSCMWOglGQWosipzVLCWL6xFhWXnsrJCrMYpIvbEQmosihtAK4qmz18x8LrvBGEXmzd50q5cYIl23BHAV9FPNnbtjRHmMFra2S3byCXdbMfyOWKZeYc5SGGZAqcA5unHEFMfGpCCOPDp0EamrNitV/7+284LU15fJ3vTYy7HjEI+iGT0108b5aWXzfWkyXqqpZ6ss6caGFxY7xNYgAfQshKWqAkZrClehg+KZhYzYGHaKgiuDjLLylJVjfTxltdslTuqpdr9jWUxjtBF00HQSgxIhtnWW1LVBZxYGFuhay2KW7Bv5oUAhRHp/u9DjhCVdJDGEVhBPnb2QmrkLqOmgVzqPVsIgjRlPS1wVQHlSa0Ac97cLxtMxf/TybV75Fnz7vD/MJcXPfgoPlHBvXDKsPEXpMVYRl5q+tXSTiKryjKqKaFpf3KX3e21IoQ4NOW4c4pF2PcZ/Nt9PxIWA1YpBFpOauq9IE7ZSOs8gjsmdAwO9xNaP/QQXYBBHXOt1GBUlDyY5m9Oc1V6HpU5Mag3fkF16iaYTWe5uF3QSjwrC3e0pq72E3iAiSyxb45xpVZEaU+d0w17bVYPCaEUamdcVu4DDR4AXZRHlICcSRBFZBD5OXRn8m8DfCCEc2gZYRPrAS8BvhhDOrrzJBeDgBeR9wGiFpq6gMs0rbo8meO/ZmhTc2ZgyLTyVFLy6vkHlYLM6izWJwF+Qr/CT+t+yJut8M6zxz937eCm8cOpHPk16wHIfnl2GKAYdW9YW+hR4+ipm2BEiYygrt5fHHKS+kLvW7vW0medxF/FxRz7H8Z/Np3o2oTpNNaTmvbgT73vN+0Avjqi8J7N2LyXOKMVCElF4Txbb2TRYUfmANXVV8CyylJWrU+sURLruDbOdF0xLT2orjFPESu8Fqw9V/LqR4VGVrS+a77DhpCPEDwGfDSH8ooh8aPb8f3jEtv8A+NwJj3dpORhe06SDSYCpqwNUi8oTW00InrjQ7G5NGY8L8l047UpeMQX/zP4jvld9nZQcJfDnw5/w4/rf8TvuL/DfV3/nzDJKniQW6Cfw3DVIOgqZwHK3z9XFLsEJcapIlYYQWJ+UJFVFZh5WJzf6oRgdZ1TzRkc+xxWE+RFXXjqqmW/T6FqcmkwP5+o0uIGJ99kDEFnNoBNTFI5itlg07CW1X7LyRJnGVR6xhhuLPbpptHe8YRrjk0BizL4sk/n4ysM+++POw0UTQzh5gdgfBz42e/wx4CcO20hE/hxwBfjUCY936WkKXTarylD3R1nK4rrTWFnxyvqIu9sjdrcmqDjwYOv0V5V/2fxTfkB9jY7UYghgxJNJwfv17/H39CdO2YInT5e6o12Zz0JQdjxj71kcdhkmEblz7EwLIqVY6qasdmK6UQSzGoje11kk86uk81Pbw5oeHdzmKA7u46h97j2WWdHaudcPFqtt/JCxrRddmmrfSWLoxnZfoHjhPcEFvECkFP1OXHd4nO2rl9QLK2ls9mIpD37OR712mTipIF4JIdyaPb5NLXr7EBEF/GPg54/amYj8nIh8UUS+eO/eRU1jPxnz05rS1V3FplVFYgwheF7dGnN/a8KoHHFrfcTtV2Fcnq5Nq2zwI/o/ksjhB8ok52fM7xBxyoY8YbrUuctRB9JY04kVi92MQWrxQTEuSorK4whMq4rYGIZpzGJWFyutKk/p/V53PXh9/vmjBOw4PKpi+lH7NKYundWI0/w2TXzrfFgX1OlxzciuybiZ318WGbLMkkWGJDF74taUozsYUP6oz3nZBPAgR06ZReQz1DGtB/nw/JMQQhCRw9xcHwR+J4RwU+TxJyuE8BHgIwAvvvjiRchff6I0TvDmLm61YlpU7OYViS5JIk3PRoy7BeM84DUEf/otxN+jvkyFIn7MNgHhe+XrdYD1JSGmbnn5zBAWhwOUE6LIsj2puGW2cUUgXlD0Isu4cGxPc67EHYxRTKcVooWusntiMM+T8IEd3Mcb2efjFioq9zB2slm1Pqz+5jzzERDzxzjYy+TNzpGCGEJ476PeE5E7InI1hHBLRK5yeBuPvwS8R0Q+SH3TjkRkN4Twoe/Y6kvI/N1/fuUw1hojwvq0QAGDzFL6lGmVsxBbqk6JP+UCh5GUyJFLNoKVM1nZeSIMqW8ksYKqgtFOzvJin2E3pSwgs5a4LyxEad1i1Di28opsWtCJLIX3JEofuqDS8CQE4nFZLN/JvuanzQf3d1zf5vyi0ONE+iKGzZyUky6qfBL4APCLs9+/dXCDEMLfah6LyE8DLz5tYgj7K58oJeChKByihWeHXXbzgu1piVWaOzu77IwdpS/ZPgP/4Zf9W47cJqLkq/65U7bkybEKDBZhoVMvpkRGkUQRgzihjCqWspTlToLVmqlzDKKYONN0s2if/606RiHYi8TjFjCOK4aH9Yc5uA2cXm/k8+Sk/+lfBN4nIl8D3jt7joi8KCIfPalxl5HD/D/zlM4zyStG05KNac60qPA+cHc05c7uhNe2d9namlDlUyrAm7oS8WnyR+EGN8MKLhz+xS6C5nP+3dxncMqWPBl6wNIVWFoANIiGJEsoqwqMp5jCg3HO+rioK5CHQJA6wNn7UKfNqYdVzKvq8UVuLwvHDgV6zKLQ/Kr1Rcg9ftKcaIQYQngA/PAhr38R+JlDXv9V4FdPcsyLzFEhF80osdnWFnW7AFd5+kkd/zWJLEuDLqWqyCcl096YnRGnXkP+vy7/Pp+I/keyMCWa65Q3DZb7DPhQ+bOna8ATYg1Y6tQ/RurFlIV0QBoZdiclqTFcvZrUpfxLhyOQqLpRemw1O5Oi7pBn60WLLDKXaoT4JDhOXOSbTQgb2kyVJ8hxA20b4XQhIFVgpygJBHwQlrsJkRZe3RgjsQILz1+B8Cp85RT7ArwcrvEj+S/xd8z/zV/X/y8dJmzQ49eq9/HP3F9j+xJ0DDbUJawWu5Bk0O90WFkYMOxGDG3Kq4xITF2QIIsssTGI1DUPC+8hr5g6R2L0/sKuLft4s4ohtIL4xHncVGPeJyOBuhm6UiRW471ikAZccGyNNFEEia0v2CyFLAF2T9f2OyzyC9Xf5heqv029enJ5vvhq9vP8IrxlRbMbHFcWB8SRZWuzorMSuHFlwGIWkReeka7IrEYphQ8BAbRR9KgLO3gf2M1LuthWFJ8iWkE8Aw5Opb2vfVbdyO75qrbzgklVQRDKUOKdYnkwJIkiQnBM8ges7h6+jH86XB4xjKjDFzJgsQdxN2K8VVFWOUZqQSuDo6oirFL0OobdqmI69ix36lL+ezF8zKXFqVYInzZaQTwDHjWVbtKtfAgkWtOPI0LwTF2MRjPILLd24OZrmwwyzbOZ4+5FLo99DlwB+gLDIUQW4g6Ijlhd1OgQ000NVxczeiYhNgqjNYkxJNYQQqD0gUlZ1a1G5WFtwsbf+zTF4LW0gnhmHJbi1EzFRmVZh3koYTv3uBKUwDh3GBS9ToKOPPd2t/meV+F2dTF6L58Xirp6jQBrEbzwHHS6mmeWFok6Ea4ITAuHaIVGYXyEKIitJRBAUTdINxoLdGK7N2qf7++hVN0fez47pRXGNzetIJ4DTWA2s3AOR4BQ+wyNqpvy7BYF49sVRDFLzyRU1QKjccmVQY75mufB5Jw/xDnigbdQC2PSq3uImChGGYMvhJVeihfFchZTEuh3NF0bY7UQ/KxxlFYo6uKsSslewY2mrH0+658yHxXwZoy7a9lPK4jngFKCVLUQWq3oRjMnflESGY2S2q84zGJ28wIJiigJXL+6yvrOBmK3eW4Ctzj/hvRniaFe6nlB4C1XAQ2dFFQMmSji2OI8JFHESj9mWgYm0wrvoNSece6wVtOTiFjrvRJfQF3Xzz5Mb5svktD8bsXwzU8riOdAMz3bq3YT6qoqsdakcd3tzQus9FJWOikb4yk7RUEvigj9Ie946xg3rri5Dt+6Wwvj08KzwLUrgIK0C88sDSjyiqSTMUgSFroRFkMkhkkoudrLuDroEHzggQ/EWtX9P3Td/Q1gNC1rX6F6WKX6zVi4oOVoWkE8B+ZHG96HfdOyphrywEWEGIIPiAYZC0Yp0l1L8pbn+Pq3b2O3JiwAXQV3L1EXvjdKxGzxxNT9lK2pu8etpAliNcudHs+vdhkmKcEFppUnihTXsy69KIIAu87RsZpeHO3djPZS87QilodieJJ0vdbPeLlpBfGc2Fdw9EDBzaryFMEjHiKjWUoSqiowSC2xtXRzw+CtKQsLt/hSdwO2oDOCr45gwqWpv/BYrlE3R98BVoDnVmBtoe413Inh+bUF0jSlYxMirVhIMzJtmODQIWCVoR9HJMZQBk+KJtGaLLHkpdsLd2rO/7wYjouKjDeeodL6GS8/rSCeIwf9Uk2JJhcCGkFpwc+KdDo8HRXxTN/w6hZ0YkMSP8PmTkHR9/S2J3Q2YDqB+6O6QfllnUobYDGGtT6EFKZTWB3CcDFj2QlxPyJVHQZJwrNLGc4JqTVERhFZTTcYEq24N8oxOmc5S8mMYeIcKq/QBxZOlN1fGzDxep8YPsmWAC0Xm1YQLxjzJZyqyjMpq7qidpLUdfqKijI4XOkZTxx/5plnSTLh3kbOxu4OD8Yjtu9OeDAFbsEGp54G/UT5LurpsLJQmroXSkhBpQpfOHxsiKoYEk+3o6kKYVJUZLHGVYbVQUxV1eXynQQSbUij+muuROr2nrOm8k340zyNf/dgId8n3RKg5WLSCuI5crA72cFsFhdC7TfUCpUIo2lJRWAxi5nkjsiqugubUmhGDNOEBdfhtt1hNZ+S9caMd8BVsPsA/sjXub7nVfe6Qy3OHWBM7RtMgO9ZhtfuQ8/CyhW4/lzG7u6Y3AnLWYesG6NEYW2EVZqlXkwIimudHkqEKFZkNgICS0nK1jQnBMi0pZ/UfUG2pgWDJMLAXo3Dxnc7f95PUrS15fLTCuI5ctiUef5CPBj60UksVin6ccQDPWVaajJjMFa4LsJ2mTOcxmTErE9G5KVnZHKCCuQLkM7y/qYVbOzWgvQqtd/xNBDquoSKevqeUa8S9xdgMgEbYHEZri4JaSfQiSDOLKsLA66uLDGdTplMApNJwWJ3yI0rXbpJzGRS15HspRGd2KCCUHnPtPIUzjFxjoGO6MSWJKm/4gOiOoVvrkT/Yed9/vVHPW9589IK4jlz3AuxGU2KFuKgWemkaGofY145iEGLYimFd14Zcmd3wlfSlG7H8q3XNrm7uY3VI7LIgnbcueeJU7i+DdMC8gJGUwhVXZDWU8c45tTCuQUMZr9j6pGd5WHWSGf2eDz73QW0hWECcQrfm4ELkCZ1Wa7tHCQHnYDTlu9ai3nbjVVefmWd0W5FUCWxjrh2NWJgMxa7FpwlMoFCwaBj8B52phWREbQo+qmlm0bILAWvmfrCw9L4831F5keFLS3QCuKlYb7idl66va5rW9OC4AOd2BIbTQh10YgHZsJC0iGLhbdeW+HKYpeNcUESa4ITVoYbbO9WLD+f4AVeu7eNUiXiFZvjkqqC8QhyB0ZDFsPGDlQFrK7CYkeYErh9exYGE0E/he0R7IxBR1CN6tzirAO9nsY4wWmPJWLQARMLsc3oJRHGGPpRgijDoJ8gQVHi6JISiaZrE8ZUJBJBVhKJIYk0/chShUDwgczWxTLSyDAuK6SqBbB8RI+RVghbDtIK4iVi/gJuWpkuwF5/lrx0FN6xPp2S58LKYr0ykfUrbiz1WR9PccrhCuHtVwe8cn+XThRRmYLV3oAyOCZ5yVpesj0eEdDEVkjjBGOFcV6gVCA1GcqAKz3PLIyJrTB1CqlKVhbBaMd47IjSut9xrGOgjqeMVUJnYPHOk08gTjT9NGJrVDEuKxZ6GdcWuhSFI4stkVUY0URG0YtTKhewEhHCLEZTCRahlIf9aoxRZJi9YOtYPWzd0Ipgy+NoBfGS4mb9gpPE7OtxEUkdfNyP6gDke6MxZRERR4puFbE1LdA2gBe++9oiN7d2uHfb01+EG70lOqmiKD23tsaMy4JBmjAuKqZ5RWQiOjamDBXDLGFcFCz1M1wuuKhkMoEsUlwbdLi1PWZ3WjAqSowDpz2hUKyspIymHkrD1UXDWr9LN4nYmkyxRrPUyehHEbd3Jix1IjJrESVIELQWtFIM4ojI1P7VIHXanarqUXMTY2i1wqj9C1SK/S1E21Fiy0FaQbxkKFUXJGgez/+OraZ0dZZF10RM8opBEpNbR9dGZAPLUiemcI57o5xYGb5nbZkr3RFWIkQCVinSWJikjl4SMYhjfNfzYDQlm0ZoAa0i+pmBSlheiAgV5M7je4F+FIEEloNnrdNlHHK0Eu5sjNmVCqMMaYDnnsnoJhaNJnclo9wR5YFhP+JKL61FXsHWtGSxG+GCpyiFzCq0Vq/rM2y02tfjw4UAfm7hRLcrxy1H0wriJeRRF/LBZuJWK6QQelG94tpkZxijGCQxjkBROVY6XTwemY2gMmMICiJR9OKIvHLEViMBKufZLR2dSOOBKFgqU7GzW7I6SDBa2MkrglcsDSzFhuPbd3eYlCXPLGe8Y3mR6dCz2o3Jq4AxMNqoWMpi+qml8sJWXpIllo7VjMqKQRSjtLAzKbFG74lbXjrS2NRhS7oWxrL09Q3Ds1dRqKlk87gFrJYWaAXxUnNYBsXBiz62mnTWKKmqPNPKEflAllomeYWJFM55RAlF6epcaqXoOosLgal3WK3o2YjUGpQIu0XBpPRc69fbVE6zaQvGRUWhfF2OXwmJiXhh1XBlMWFzt2AxS1nppezmBVsThzKegUlRZkpsNb0kJjOG0nsmVYUDYqVJIoMEmFpHouteyVXlKb3HzgQvthqLoqQuqWaMeljYdbaNOhBu09JykFYQLylH+cGa99XcyMgYRVQpCu8xXu2tWpfeE2YhPamtvxJZbKm8hwBpZIhmIzOonxel4/5oglaaNBaWOymjsqQKnqudDp3EECnN2FWMJo4r/Q79xNIxlmnlGGagUYgSltJkr2R/ZDQ6qLpZfOX2+s64EEiURmY+QWMUma+F3s1S8JoR8kEacYQ3Zy/hlidHK4iXlKP8YIcFdgMkiUEVbq99gTWKrrL7qkTnZT1FjtHks1FjI7CjvJwVV4WpCyzHGms0w44jKw2T0pEmmkEck6UWtzWiCgEJYJXGh0CqDd3YAtQ1IK2idIFJVRGAQEAFYTPP8R5Up670c7AajQsBw+v7A5fOo31d9PVgrGErhi2PoxXES8xRF/bB95vV6KYf/cGFhvntlH0okNNpRRE8idZ0YouWWmwWMksaWXaLAi2Ka/2Uyns2pgW3RxNWQ8AFuNJN6CYRsdL1foxGtKBFGJoYiPemwBJgKy8QgdQautbSzaI9Qa7LezmSmZ17K8hzYTXay/4p8xs4Zy1PN60gPiXMlxqbF8KqWXTwD8voN9u72cguD7VQNb47pQRXOrpRRPCBqgokVqONItEGkTqDJokMU+8QrwihnrqWhUdbhYSHFcOB2dQ3kCamHoEqIa8cRte+zyb/2KmAme2riTM82PekbQzV8p3SCuJTQjPFbh43o8X5XiLKh/0BzB5MojHVw3i+3aLEitrL+Cidp5tYUmv2fHi9LKIoHFGk6VaWIngiqae8UdB7x2vIS1fHOc58mqLrnifzC0FNNet5oTs4VZ7/nK0YtnwntIL4lDHvX9MirxOVJuuleTw/FQX2ehU3lXhiVS+2uFndRu8D02nFqKq7vWirSJwguv77ZrrtZn5KpaSeBntHHPS+BRClhCjSh5b2f9xIsBXDlu+UVhCfIvaNnppQlLmG9IdV32mEp/HLdRK7b/vmdyNik7yiDJ6OMSRJ3R/GJPVUG2r/ZSOqzahTtOz5FRu/YOn8nmAerFzdjGKlasWv5cnSCuJTxnwIzuNGV/NT6sbf9zi/XCNuaWyIZ2Ex8+1WG/Gdzy5p9me1Irbx3r7mS3QdZK/qT8W+qXRLy5PgRN8kEVkUkU+LyNdmvxcesd3zIvIpEfljEXlJRF44yXFbngyPErdGdOBhEYnD/HWP+pt9KXKzv2uEcK8Iq96fZzz/Xmz1Xr/kZt/zNjc2ZdEb73vS0vI4Tvpt+hDw2RDC24DPzp4fxq8BvxxC+G7gB4G7JzxuyylyUMDmfx/1N40wzotY8/yw4qzz4ji/r3kxPLi/+VFuS8uT5KTfqB8HPjZ7/DHgJw5uICLvAkwI4dMAIYTdEML4hMdtOQMeN3VtmH9/z984lx2zV3nGqEeOMh83Uj1MMFtaTouTCuKVEELT3O02dfvcg7wd2BSRT4jI74vIL4uIPuFxW06Z4wjR/OhtXgwPjtyOO8p81L5bMWw5K45cVBGRzwBrh7z14fknIYQgIocNJwzwHuD7gW8DHwd+GviVQ471c8DPATz//PNHmdZyyhxrmjy3Kt2sRM8Xaj3JsbVvC7q2nC1HCmII4b2Pek9E7ojI1RDCLRG5yuG+wZvAH4QQXp79zW8Cf5FDBDGE8BHgIwAvvvjim6Hf+pueg3GBVJ68dJTe08V+x6LYZpy0nAcnnTJ/EvjA7PEHgN86ZJsvAEMRWZk9/6vASyc8bssFY95XmMZ18YaTjhCP4zs8ysfZ0vJGOKkg/iLwPhH5GvDe2XNE5EUR+ShACMEBPw98VkS+TN2d8v884XFbLiDzvsInsQJ8HDE8zsJPS8txOVFgdgjhAfDDh7z+ReBn5p5/Gnj3SY7Vcr5cxMWNNm+55UnTBnK1HMlFHom1YtjyJGkF8SnnOCLXxgK2PC20gvgU80ZGfq0YtjwNtMUdnmJaH1xLy37aEeJTTiuGLS0PaQWxpaWlZUYriC0tLS0zWkFsaWlpmdEKYktLS8uMVhBbLjUXMVi85fLSCmLLpeUiZ9C0XE7aOMSWS0sbR9nypGlHiC2XmlYMW54krSC2tLS0zGgFsaWlpWVGK4gtLS0tM1pBbGlpaZnRCmJLS0vLjFYQW1paWma0gtjS0tIyoxXElpaWlhmtILa0tLTMaAWxpaWlZYaEcDET40XkHvCtuZeWgfvnZM5xueg2tvadjNa+k3FR7LseQlg57I0LK4gHEZEvhhBePG87HsdFt7G172S09p2Mi24ftFPmlpaWlj1aQWxpaWmZcZkE8SPnbcAxuOg2tvadjNa+k3HR7bs8PsSWlpaW0+YyjRBbWlpaTpULK4gisiginxaRr81+Lzxiu+dF5FMi8sci8pKIvHDRbJxt2xeRmyLyTy6SfSLyfSLy70XkP4nIl0Tkb56yTT8qIn8iIn8qIh865P1YRD4+e/8/nOX/85j2/Xez79mXROSzInL9LO07jo1z2/3nIhJE5ExXdo9jn4j8jdl5/E8i8i/O0r7HEkK4kD/APwI+NHv8IeCXHrHd7wLvmz3uAtlFs3H2/v8G/Avgn1wk+4C3A2+bPb4G3AKGp2SPBr4O3AAi4A+Bdx3Y5oPAP509/kng42d4vo5j319pvmPAf3OW9h3Xxtl2PeBzwOeBFy+SfcDbgN8HFmbPV8/yHD7u58KOEIEfBz42e/wx4CcObiAi7wJMCOHTACGE3RDC+OxMPNpGABH5c8AV4FNnZFfDkfaFEL4aQvja7PFrwF3g0KDVJ8APAn8aQng5hFAA/2pm46Ns/g3gh0XkrBqnHGlfCOH/mfuOfR549oxsO7aNM/4B8EvA9CyN43j2/Szwv4cQNgBCCHfP2MZHcpEF8UoI4dbs8W1qQTnI24FNEfmEiPy+iPyyiOizM/FoG0VEAf8Y+PkztKvhOOdwDxH5Qeq7+tdPyZ5ngFfmnt+cvXboNiGECtgClk7JnoMcx755/ivg35yqRa/nSBtF5AeA50IIv32Whs04zjl8O/B2Efl3IvJ5EfnRM7PuCM61DamIfAZYO+StD88/CSEEETlsOdwA7wG+H/g28HHgp4FfuUA2fhD4nRDCzdMY6DwB+5r9XAX+OfCBEIJ/sla++RCRnwJeBH7ovG2ZZ3YD/l+or4OLiqGeNv9l6hH250Tkz4YQNs/VKs5ZEEMI733UeyJyR0SuhhBuzS7Ww4bVN4E/CCG8PPub3wT+Ik9QEJ+AjX8JeI+IfJDaxxmJyG4I4ZHO8DO2DxHpA78NfDiE8PknYdcjeBV4bu75s7PXDtvmpogYYAA8OEWbDjt2w2H2ISLvpb7h/FAIIT8j2xqOsrEH/Bngd2c34DXgkyLyYyGEL14A+6C+bv9DCKEEviEiX6UWyC+cgX2P5SJPmT8JfGD2+APAbx2yzReAoYg0Pq+/Crx0BrY1HGljCOFvhRCeDyG8QD1t/rUnJYZPwj4RiYD/a2bXb5yyPV8A3iYib5kd9ydnNs4zb/N/AfzbMPO8nwFH2ici3w/8H8CPnZPv67E2hhC2QgjLIYQXZt+5z89sPQsxPNK+Gb9JPTpERJapp9Avn5F9j+e8V3Ue9UPtN/os8DXgM8Di7PUXgY/Obfc+4EvAl4FfBaKLZuPc9j/N2a4yH2kf8FNACfzB3M/3naJN7we+Su2n/PDstV+gvmgBEuBfA38K/B5w44y/d0fZ9xngzty5+uRZ2nccGw9s+7uc4SrzMc+hUE/rX5pdtz951ufwUT9tpkpLS0vLjIs8ZW5paWk5U1pBhRLUQAAAADhJREFUbGlpaZnRCmJLS0vLjFYQW1paWma0gtjS0tIyoxXElpaWlhmtILa0tLTMaAWxpaWlZcb/Dyl/KJghKnzBAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -194,9 +195,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "SSE from sketch: 619060.4839675338\n",
+      "SSE from sketch: 615246.3229068591\n",
       "SSE from k-means: 591302.7260215983\n",
-      "Relative SSE (SSE of usual k-means / SSE of the centroids from the sketch): 1.046943395868805\n"
+      "Relative SSE (SSE of usual k-means / SSE of the centroids recovered from the sketch): 1.0404929587359728\n"
      ]
     }
    ],
@@ -220,7 +221,7 @@
     "SSE_kmeans = pycle.utils.SSE(X,centroids_kmeans)\n",
     "print(\"SSE from k-means: {}\".format(SSE_kmeans))\n",
     "\n",
-    "print(\"Relative SSE (SSE of usual k-means / SSE of the centroids from the sketch): {}\".format(SSE_sketching/SSE_kmeans))\n"
+    "print(\"Relative SSE (SSE of usual k-means / SSE of the centroids recovered from the sketch): {}\".format(SSE_sketching/SSE_kmeans))\n"
    ]
   },
   {
diff --git a/2 - Building intuition for sketched learning.ipynb b/2 - Building intuition for sketched learning.ipynb
index 4d13eefd3061632b645299a5b2248e346dbd384c..5b5f5506de17d80a022a7cf0d57002185252282b 100644
--- a/2 - Building intuition for sketched learning.ipynb	
+++ b/2 - Building intuition for sketched learning.ipynb	
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,13 +39,15 @@
     "import pycle\n",
     "from pycle import sketching, compressive_learning, utils\n",
     "\n",
+    "from plot_utils import do_the_plot_for_notebook_2\n",
+    "\n",
     "# Fix the random seed for reproducibility\n",
     "np.random.seed(0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,813 +80,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support. ' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>');\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option);\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>');\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"999.9\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8e52ba61e931496d93b5cc2427c3d984",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(Dropdown(description='task', options={'1means2D': '1means2D', '2means1D': '2means1D', '1…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tasks = {\n",
     "    '1means2D': '1means2D',\n",
@@ -961,6 +159,7 @@
     "    if task == '1means2D' or task == '2means1D':\n",
     "        thetas_00 = np.linspace(low_pos,upp_pos,nTest)\n",
     "        thetas_11 = np.linspace(low_pos,upp_pos,nTest)\n",
+    "        pos_fine = None\n",
     "    elif task == '1GMM1D':\n",
     "        thetas_00 = np.linspace(low_pos,upp_pos,nTest)\n",
     "        thetas_11 = np.linspace(low_var,upp_var,nTest)\n",
@@ -977,64 +176,8 @@
     "            cost_R[i_0,i_1] = f_cost_R(th_0,th_1,X,task)\n",
     "            cost_L[i_0,i_1] = f_cost_L(th_0,th_1,z,Phi,task)\n",
     "\n",
-    "    # Find minimum of both costs\n",
-    "    index_sol_L = np.unravel_index(np.argmin(cost_L),cost_L.shape)\n",
-    "    index_sol_R = np.unravel_index(np.argmin(cost_R),cost_R.shape)\n",
-    "    index_sol_R = tuple(np.flip(index_sol_R))\n",
-    "    index_sol_L = tuple(np.flip(index_sol_L))\n",
-    "    \n",
-    "    # First plot (data)\n",
-    "    ax1.cla()\n",
-    "    if task == '1means2D':\n",
-    "        ax1.scatter(X[:,0],X[:,1],s=1, alpha=0.1)\n",
-    "        ax1.scatter(thetas_0[index_sol_R],thetas_1[index_sol_R], s=ballsize+8, c=cR, label=\"true params.\")\n",
-    "        ax1.scatter(thetas_0[index_sol_L],thetas_1[index_sol_L], s=ballsize-6, c=cL, label=\"sketched params.\")\n",
-    "        ax1.set_ylim((-1,1))\n",
-    "        ax1.set_xlabel(r'$x_1$')\n",
-    "        ax1.set_ylabel(r'$x_2$')\n",
-    "    elif task == '2means1D':\n",
-    "        ax1.hist(X,bins=40, weights = np.ones(n)/n, alpha=0.55)\n",
-    "        ylim = ax1.get_ylim()\n",
-    "        ax1.plot(thetas_0[index_sol_R],ylim[1]/20, marker=\".\",markersize=markersize, c=cR, label=\"true params.\")\n",
-    "        ax1.plot(thetas_1[index_sol_R],ylim[1]/20, marker=\".\",markersize=markersize, c=cR)\n",
-    "        ax1.plot(thetas_0[index_sol_L],ylim[1]/20, marker=\".\",markersize=markersize-5, c=cL, label=\"sketched params.\")\n",
-    "        ax1.plot(thetas_1[index_sol_L],ylim[1]/20, marker=\".\",markersize=markersize-5, c=cL)   \n",
-    "        ax1.set_xlabel(r'$x_1$')\n",
-    "    elif task == '1GMM1D':\n",
-    "        ax1.hist(X,bins=40, density=True, alpha=0.55)\n",
-    "        ax1.plot(pos_fine,scipy.stats.norm.pdf(pos_fine, loc = thetas_0[index_sol_R],\n",
-    "                                               scale=np.sqrt(10**thetas_1[index_sol_R])),c=cR,lw=lw+1.5, label=\"true params.\")\n",
-    "        ax1.plot(pos_fine,scipy.stats.norm.pdf(pos_fine, loc = thetas_0[index_sol_L],\n",
-    "                                               scale=np.sqrt(10**thetas_1[index_sol_L])),c=cL,lw=lw, label=\"sketched params.\")\n",
-    "\n",
-    "        ax1.set_xlabel(r'$x_1$')\n",
-    "    ax1.set_xlim((-1, +1))\n",
-    "    ax1.set_title(\"Dataset ({}-dimensional)\".format(d))\n",
-    "    ax1.legend()\n",
-    "    \n",
-    "\n",
-    "    # Second plot (true cost)\n",
-    "    ax2.cla()\n",
-    "    cost_R_to_disp = np.log(cost_R - np.min(cost_R) + 1).T\n",
-    "    ax2.contourf(thetas_0,thetas_1,cost_R_to_disp,30)\n",
-    "    ax2.scatter(thetas_0[index_sol_R],thetas_1[index_sol_R], s=ballsize, c=cR)\n",
-    "    ax2.set_title(\"True risk $R$ (log scale)\")\n",
-    "    ax2.set_xlabel(r'$\\theta_1$')\n",
-    "    ax2.set_ylabel(r'$\\theta_2$')\n",
-    "    if task == '1GMM1D':\n",
-    "        old_ticks = ax2.get_yticks()\n",
-    "        ax2.set_yticklabels(['$10^{'+str(t)+'}$' for t in old_ticks])\n",
-    "\n",
-    "    # Second plot (sketched cost)\n",
-    "    ax3.cla()\n",
-    "    ax3.contourf(thetas_0,thetas_1,cost_L.T,30)\n",
-    "    ax3.scatter(thetas_0[index_sol_L],thetas_1[index_sol_L], s=ballsize, c=cL)\n",
-    "    ax3.set_title(\"Sketched cost $L$\")\n",
-    "    ax3.set_xlabel(r'$\\theta_1$')\n",
-    "    ax3.set_ylabel(r'$\\theta_2$')\n",
-    "    if task == '1GMM1D':\n",
-    "        old_ticks = ax3.get_yticks()\n",
-    "        ax3.set_yticklabels(['$10^{'+str(t)+'}$' for t in old_ticks])\n",
+    "    # Call the dedicated plotting subroutine from plot_utils.py\n",
+    "    do_the_plot_for_notebook_2(task,d,ax1,ax2,ax3,thetas_0,thetas_1,X,cost_L,cost_R,ballsize,cR,cL,markersize,lw,pos_fine)\n",
     "\n"
    ]
   },
diff --git a/plot_utils.py b/plot_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccfa2b2016fedf2630778348f71eb577512216e1
--- /dev/null
+++ b/plot_utils.py
@@ -0,0 +1,64 @@
+import numpy as np
+import scipy.stats
+
+
+def do_the_plot_for_notebook_2(task,d,ax1,ax2,ax3,thetas_0,thetas_1,X,cost_L,cost_R,ballsize,cR,cL,markersize,lw,pos_fine):
+    """Plotting utility for the second notebook, creates three subplots ax1, ax2 and ax3 according to the given task."""
+    # Find minimum of both costs
+    index_sol_L = np.unravel_index(np.argmin(cost_L),cost_L.shape)
+    index_sol_R = np.unravel_index(np.argmin(cost_R),cost_R.shape)
+    index_sol_R = tuple(np.flip(index_sol_R))
+    index_sol_L = tuple(np.flip(index_sol_L))
+    
+    # First plot (data)
+    ax1.cla()
+    if task == '1means2D':
+        ax1.scatter(X[:,0],X[:,1],s=1, alpha=0.1)
+        ax1.scatter(thetas_0[index_sol_R],thetas_1[index_sol_R], s=ballsize+8, c=cR, label="true params.")
+        ax1.scatter(thetas_0[index_sol_L],thetas_1[index_sol_L], s=ballsize-6, c=cL, label="sketched params.")
+        ax1.set_ylim((-1,1))
+        ax1.set_xlabel(r'$x_1$')
+        ax1.set_ylabel(r'$x_2$')
+    elif task == '2means1D':
+        ax1.hist(X,bins=40, weights = np.ones(n)/n, alpha=0.55)
+        ylim = ax1.get_ylim()
+        ax1.plot(thetas_0[index_sol_R],ylim[1]/20, marker=".",markersize=markersize, c=cR, label="true params.")
+        ax1.plot(thetas_1[index_sol_R],ylim[1]/20, marker=".",markersize=markersize, c=cR)
+        ax1.plot(thetas_0[index_sol_L],ylim[1]/20, marker=".",markersize=markersize-5, c=cL, label="sketched params.")
+        ax1.plot(thetas_1[index_sol_L],ylim[1]/20, marker=".",markersize=markersize-5, c=cL)   
+        ax1.set_xlabel(r'$x_1$')
+    elif task == '1GMM1D':
+        ax1.hist(X,bins=40, density=True, alpha=0.55)
+        ax1.plot(pos_fine,scipy.stats.norm.pdf(pos_fine, loc = thetas_0[index_sol_R],
+                                               scale=np.sqrt(10**thetas_1[index_sol_R])),c=cR,lw=lw+1.5, label="true params.")
+        ax1.plot(pos_fine,scipy.stats.norm.pdf(pos_fine, loc = thetas_0[index_sol_L],
+                                               scale=np.sqrt(10**thetas_1[index_sol_L])),c=cL,lw=lw, label="sketched params.")
+
+        ax1.set_xlabel(r'$x_1$')
+    ax1.set_xlim((-1, +1))
+    ax1.set_title("Dataset ({}-dimensional)".format(d))
+    ax1.legend()
+
+
+    # Second plot (true cost)
+    ax2.cla()
+    cost_R_to_disp = np.log(cost_R - np.min(cost_R) + 1).T
+    ax2.contourf(thetas_0,thetas_1,cost_R_to_disp,30)
+    ax2.scatter(thetas_0[index_sol_R],thetas_1[index_sol_R], s=ballsize, c=cR)
+    ax2.set_title("True risk $R$ (log scale)")
+    ax2.set_xlabel(r'$\theta_1$')
+    ax2.set_ylabel(r'$\theta_2$')
+    if task == '1GMM1D':
+        old_ticks = ax2.get_yticks()
+        ax2.set_yticklabels(['$10^{'+str(t)+'}$' for t in old_ticks])
+
+    # Second plot (sketched cost)
+    ax3.cla()
+    ax3.contourf(thetas_0,thetas_1,cost_L.T,30)
+    ax3.scatter(thetas_0[index_sol_L],thetas_1[index_sol_L], s=ballsize, c=cL)
+    ax3.set_title("Sketched cost $L$")
+    ax3.set_xlabel(r'$\theta_1$')
+    ax3.set_ylabel(r'$\theta_2$')
+    if task == '1GMM1D':
+        old_ticks = ax3.get_yticks()
+        ax3.set_yticklabels(['$10^{'+str(t)+'}$' for t in old_ticks])
\ No newline at end of file
diff --git a/pycle/utils.py b/pycle/utils.py
index a82979a522f9abb41738c8cc5a8312dbd07d06d5..ed42481573c288b245cce81cf8759495f46bc037 100644
--- a/pycle/utils.py
+++ b/pycle/utils.py
@@ -440,9 +440,7 @@ def loglikelihood_GMM(P,X,robust = True):
     Returns:
         - loglikelihood: real, the loglikelihood value defined above
     """
-    
-    # TODO : avoid recomputations of inv
-    
+        
     # Unpack
     (w,mu,Sig) = P
     (K,d) = mu.shape
@@ -452,7 +450,8 @@ def loglikelihood_GMM(P,X,robust = True):
     
     for k in range(K):
         p += w[k]*scipy.stats.multivariate_normal.pdf(X, mean=mu[k], cov=Sig[k], allow_singular=True)
-    logp = np.log(p)
+    with np.errstate(divide='ignore'): # ignore divide by zero warning
+        logp = np.log(p)
     
     if robust:
         b = np.zeros(K)