diff --git a/notebooks/sep_analysis_tools/dynamic_spectrum.ipynb b/notebooks/sep_analysis_tools/dynamic_spectrum.ipynb
index aeafbff..11f96fd 100644
--- a/notebooks/sep_analysis_tools/dynamic_spectrum.ipynb
+++ b/notebooks/sep_analysis_tools/dynamic_spectrum.ipynb
@@ -7,8 +7,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from onset_functions import *\n",
-    "import onset_widgets as w"
+    "from seppy.tools import Event\n",
+    "import seppy.tools.widgets as w\n",
+    "import datetime, os"
    ]
   },
   {
@@ -331,7 +332,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.9.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e3b113a080b82eb2d3a809b09472aaf632ea244e00139a5388ab3f89cf23b31b"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/sep_analysis_tools/onset_determination.ipynb b/notebooks/sep_analysis_tools/onset_determination.ipynb
index 9585677..7bfb3c3 100644
--- a/notebooks/sep_analysis_tools/onset_determination.ipynb
+++ b/notebooks/sep_analysis_tools/onset_determination.ipynb
@@ -15,8 +15,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from onset_functions import *\n",
-    "import onset_widgets as w"
+    "from seppy.tools import Event\n",
+    "import seppy.tools.widgets as w\n",
+    "import datetime, os"
    ]
   },
   {
@@ -330,7 +331,7 @@
     },
     {
      "data": {
-      "image/png": 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VMrYwNzHGHyH3cDs+Ge2qu6Bt9TJ4lf5v/GRma37icPNrfW6P3ngJrT4orXL+MxSZAO7JLfFMXjwJyRiZBSJlFihvlK58+ipJbow/Mtz0XkaGkGBVhgteCBN0Nn0OZ6MsvBAmsJNkY2Kap97LeSlM8UVqxfxuQrGbme5e4LpCABHxrxCZ63vOgiN38fvRe5AA+LN/PXxUtTTuPU1GSoYMVmbGqFTKRusN46gEKVIzZdCaAdfD1UdJOPvgORpVcFKZ/jQ5HXEv0yEBlPElhMCfx+//E2Nl0La6S4HXm5frsa/gPWk/Iqa3Ndgyr8S8xOrQKDhamWFs68oGTQAdvvkEI9ZdhAAwoV0VRD2XIlsmMNzfy6DXCPFJ6Th06wkm77heqOVkZstRZ/ohNKnkjLVDG6qUvUrPws24V3otZ0jQBRy7/QzlHC1x6Et/WOTzO1tenhbwJv+BG0/UkhFC5CQqNp6PhqO1OUY294KJkRF+P3oXMrmATMf18e4rcdh9JQ4AMKn9BxrPqxHxmt9i0KaCszUW9auLvdce4/ej9zTOY2dpijJ2lhjh74VrsS/RtlrRxRwREdG7hAmJd1S1KQfwobcr3BxUbzQJAVx8mIgDN+J11r8em4SYxFQ0rOAEx3xk7xxyPQErBNB6/on8NTyXH/feAgDsvBwHY4kEVV1L4FValsrNbQD4ausVHPyyqcYnSgrrj5D7mH3gtvL3iqVsEJlQ8JvpcrnA2QfPkZYlQ3rWvzdOBATOPXiOpLQsPE3OgDQz5ybc68kIhYXHNH+pzcvUXTdw8MYT/BX+CABgYWqEeb1qo467PazNTTB55w0AOfvcxFiCMZsuq9Sv/+NhrcvuveSs8gauwt5rj/Hr4bsq0z5ff1FnGw/ciMeKk5G4GvsSXiVt4OFkhVc6bgpODL6OYxHP8GPX6sqnh16XJZPj2O2nSMuUqd1c/mx9OJpUcsaEdh+gqmsJlbLYl2n4fsd1pGXJMK51ZdTzcMTT5HRM2H5NZb7lpyKV/09Ky8KXmy9DJheIf5UOY4kE/Rq5Y8quGxrb9iBBin3Xdcfj6+4/S8Ht+GTULmcPq9duqP125A6+blNF72V9vOys2rTNF6KVcbawX110++OM2jzno3JerX+Vnq1yQ/DgjSdYcOQebj7OudCdGPABPihTAoduPlFbxtTd/96QdsrnUwIyuUD4wxc6ExLXYpOUcXYhKhFONmZYeTrnb6WIAW3KOlphaqdqGL3xktZ5Xr8wHbjyPH7pVkO57849UL8xfuLOM5XfZUKg7a8nlb/nvin1874InW1UyH2MauDpiIErz6uULz5+HxIJlH0LKyw4chcLjtzF3i+aqH32X3chKhF7rj5G6IPnsLcyVXmT4M9+dfNM6iWnZ2Pn5VjlMSWvv/e12CRlMkKx/s+gX2J6wRHVY86zVxlo9NMRyIXqeuUC+C74Gm7HJ6NOOXu1ZESmTK5yDjMxkiC7iJP9uY+Xzb21d7kwZdd1rDsbrTItP+fqLzdfhoWpEQ7fyunyYX7vWvlsqaqDN+Kx6nQUgJwnTH/vWweHbj7BL/sj4Gxjjs+aecHHwwHj/7qmPC4O8fNE2+ouuBLzEo+T0tCoghPsrcwQn5SO4Wv/PU9kZMvRedFpNPZywi/damLW/pzP+87LcTj3XUu92qf4PpFbx4Wn0PKDUpjQ7oM860/YfhXbRjSGUa7EXvClR3jwQv3J3F/S3XE4yyHPZabDCKezSsBGIoO2T1WmkGBjZimcyLJDSaMsjLaIhZtRpsZ525k+x74sJ4TJbDFE6o1GJq/QyTQB9UxScDTbXmXeyzIbpEL7TcWwbFtszsz5/IVn2yBB5Hy2HCT/xn11Yymuy3Juwt6RWeKx3Ay1jVNwT6Y9wZkMExzPskNVYylKGqknYuPlppBBgnsyS4Rk2asvQIPvU8ujmnEqRlvk7wnrk9l2sJTIES83w/ZMZ9ySGeaG8s/7ItSO3XIBZUJh84VofLImTKX8o6ql8XEDd7WE2vbwWGwPz9mu/J4jXzdrfwS2f+6r/P3pq3T4zjyqPC80qeSM7wI+wPE7z5QxdvbB8yJNSCg8epGW90x5OHLrCZaceIDzuRLSdT0cEFCjDFIzs3H63nON52RdsmRynL6XAIlEgsZeTrgU80J5Dpix599jyqYLMdg50he1ytkXejsiE6Ro9PORfNXJ6yb5ybsJ2HvtMWqVs4ebvSWyZXK0mntcZyLg8ct0nI9MROXStjj3zz6NSUzD1UdJSEjJgFdJG4RHv9D4dsKOS7FYf+4h3OwtMbNHTbU3YA7dfIJlJx7A2dYMM7vXzNe2ZmTLcOTWE1iYGmt8+G7j+WiVayRHa1NYmZlg/blotXl1uRCViFYflM5XHW0+KFMC2TKhNSGhML6d/t/diYiI/guYkHiHnYtMBCJVp12KeYHuf4bqrBeZIEWH308pf5/QrgrWhD5Elkye51MsKwfVV/k9TY8uUfSh6WaClZkxUjNliEyQYtyWK7j4z837jxu6w6ukDdpUK63xibO/rzyGXM8nze4/S1H5/d7TFI3zKW4EJUo13yi48yQFbX89oXLRkPup8ON3nuHAjZwbtham+idWTIz/3b6XqZlYeSoSTjbqF62O1mZIlGbicVK6yo3Y9Cw5Pl8fDgBYM6SBSp3XkxF5eT0ZAUAtGQHkvKmhy/KTD3AhKudveSPuFW7o8QTX4VtP4O9dEmXtLTXeaF568gHSj2rvq/Pk3QRUKvUI37tWVZm+/3q8so9Wl7PRqOfhiKO3nmpahIpd/zxhpXA+SvdF8OLjqq9vn7z7TMucOd0+tJ5/AjK5gKudBfaOaaJSvujYfXzRspLeXSBo+rvlvmgqY2eBCs7WeJAgRa8loWjg6YigwfXV6ii8/tq9ptjVtx0AsCXsEUZoeTL+ZtwrtJl/Asn/vIklkUhQv7wDapWzhxDApguqF6AZWfnrrzW/XS49KMCbP5nZciSk5BxXO9QsAxtzE7iUsED8q3Rsu6g5aTJsdZjWp/wGrTqvNu31m+iKY6dCwIKTqFXOHssH+iD0wXPMOXAbj16kIne1nZf//Uy/3q3RAz2StNdik7AiV+JO299bF5lcYO+1x1h7Nn9d2CXnegMl93oTUjKUb3Fc1JL8Vey7QY3Lo2OtMjh97znmHboDAPjmr6v4pIknLkS9QK2yhXsjQJPlJyNx9VGSxjJNnzVt5x9NTt1TTcz+si8CH1X99+bLspORaFPNBT7lHZGeJcPuK3HYeF7zDZ3cT5UCwN9XHyNLJlee0x48k8LcxAhZ2XLsufZYOZ9MCNx5kqzcn828SyJocAM8epGqcT1n7j/Hzceq+6P/8nMIeu3cpcvqIQ0Q+E/C7t7TFNx7mgIzYyP4VXRGwwpO+GVfBHZdjkVVVzuVz3949Evce5aCyqVtldNe73ZjbGpFrLSO0CsZAQB/pLsi459ugczx77HplTDBpNTyuJJtjZRcX8MfyC1RI0uK6sZSbMtU7T5xpHkskl7rGulsdgmczS4BR0kWAkxVz0GJwhRns9WPb+eyS6BfchV4Gv97k1GRjACAFyKnTkWjNAwxf4yxqRURJbfEcGllvbYZAKamlVe2+cVrbR4urYwsIUGajmTJ6+7IrXBHboVe5nmfm3PbnlkS2zML3g3lhO3XsO7sQ5jk823Iwxq+Qxy6+QSHbj7R+bagpmNmqw9K4/At9WS/JhmvJTtWh0apJKnPPniOdr+d1Fgnv337X4l5ibCHL1C7nB3qeTjmqy4AdPj9FHwrOqldU+iy4lSkSjICyLl5DQA/743I87wxc38E6nmoxu6q05H4aW9OcqlzbVeV8+DrNl2INkhCQt9uAx+9SMPKU5Go5lpC54MTCp+vD0cZOwuETmiJtCxZntdzHRfmXAcaG0lUvm8MXHlO+aCHuZYu8v4IuYc7T1JwAS/Qt4E7Gr72Zs6ykw+U34s71dL/zS0g54EDxfo1xcvrcaKpe6TXta9ZBo9epOFKzEuN5U0rl0SPemWxNjRKeX1CRERERY8JifdMuoYbclnZcrxKz1L20f30lerTLvo+pWtlZoyqriXwNNlwfXnqsrh/PeWTwLlvACueFm5auSTuP01Re6Ni/2tvh3y19Qpm7Y/Ahk8aoWIp/butcLYxV95I/C7gA9x/loI/Q+7jXORz1Jp2UKWrD003u3Ina3L/XTT9jbT5vJmX8oIgISUTP/x9U22AHJcSFlg7tAE+yuNtlUANNzIVbv3QFs+lGfCbeUzvthWUrtepe9Yri7ikNNhbmuFpcrrKhYFcLlRuiuWmzz7VlKTK/fSVoqsXxb9mJkaY3KFqoV+pf11cUrrKk3evS07PVu6j+FeaY01Xvq3H4tA8E3KK7Z7TsxbK2Fmq9Mt+PjIRV2I03yQtCvGv0lW6/slNU6In9nIadmi4abDveny+3kTxKmn4Ltp+6loD3wVf01o+q0fOk4I7RvrqfELyrpbEKPDvDaSKpWwgkwu1N7q+buONw7ee4FL0S5XpV2JeYuSGcLUbOoZS2OU+fC6F13d7VabVcLPDg2cpyjfKiopEAkztVA0AULucA9aERiEhJRO7r8Rh37XHyJYLGEmgvIGtSOAVVu5z1etvpeji6WytdX8725hpTAo/eZWhFmf7rsfDp7wjtobFKN+e05ciGaHw6EUaPl2r+nZcePQLlXNj/GtP2pZ3ssKSAT44EvFE+cT2Z/8k0JV1XjsGLhlQD1kyOUZt0HyDrq67vdq034/ew+Lj93Fx8kdYd/YhUjKyld3U5Zb73PTzvlsaH1AYJtXezdbrMnKNUZD7/0EZ2p9IlwM4kf1v8qu1aSJam75AbeMUrfUShSnWZ+o/wGmcMEdctu4+6z2MCv89b5GGLqTyO97EFxaP8Ge6K7JgBBk0JwYsIctXgiM/cj8w0amWq9rDCLpULVMC/t4lVcbRUXwvbFLJWWNXgbkN968A79K2eSYkFDEvBLD+3EPEvUxD5dK2ajdqX3+DDgCyZQL+s4/h4XPNSUJNZHKB/svPITkjG0YS4Ma0tgXqGu30veeIeJx39zj3nqbgUvRLnNGRNHmi5bvS617/rv7k1b837XUlIwDd312Lyg+vdXv4adMKWHrigfJ3d0crRCf++7fT9J3x97514Gxjjr4a3pYF1Lcr9/fp15NcQE5y+86Tf4+NmrpJzP3dWt8HxDStX9dDb9rOdZqYGxuhWx03rQkJY0lOfEsAJiSIiIjeICYk3iEi15e6nztWxoTdd7TOW87REl1ru2HB0Xu4/jAd1b/bhcDGHhjf7gOkpUohz8z/xaYcxpBKpZBK0/Wq/79WlTQ+Qa8vM2TCvYQRohI0XyiFXFftM3pmjxr4dpvmm4Hxz9Nx6tYjlLEupzI9Kz1N67b0qOmG9jXLQALAs6QN5sU8hTwzHdeiii4h41XSWjn+QjXXEmjiWQIyucCnH7ri6qMkjRdkskyBVGnB/qYKUqkU9qZGqF7KHFcfJcHT2QqRWvZ7fqWlSiGVWkIIgdgXaXiZlKy1rfXcrDA1IKc/6lEbwlXmy0hLRaaOv1deMtNTIZWq3kTMSEtVLi8rPQ1SqRTp/0zzq1gKyNLvs66Jk40psrIFXqVnw6+SMz6qWhpTNNz0k5nIVNqVmju+JDl/m9fbIJVKIcv15Fju8qh43e1NTZVClpkGeWYGytkaQSqV4stm7th1OQ67rz7+Z54UZGfk7Gtdn4VBjT3wIjUL2TIBRxtTtK7qgu3hsQjWY/DCLrXdsONyznw/BOt++q911dKo7manfNK6oHw8HDCnVy08l2bAu3QJnH2QkK+/ryxTrhJrcomJ8nczEyP4lrfR+LdSTJNKpRBZJrA1AURWemG6DMfUdrUwecd1tfV5OZhgf4bmODl7O++baRMDquB63CuVmzMZaam4G5uAoDORWBuav24RNMlKT1PGmcLdWPX2mglL/Nypstabz7ljrDAk/8SZwl+f1EOTf5KzitsduW/LrOjfCG3mn0B61r+fB3lmThucbc2QninP17ghP3atji1hMUh8mY701JzjVLaWv+HnzbxQvbQFTOuXxrZz6k+GDvBxR7PKJfHL/gicvqd6vnj91lJGWs664p8nFer8AQD34/6tX8HZCg80HDOyM0wglUqV30FkmRKUtTXCQB8XhN+Lx0EN3b7Jjf6NMXMTI/iVz3mDIfRrX0Q/T8X5yOeYfSDnuODtYovsDNVjtm9FJ5y5/xwZmUBC4ivIMtIg15LgSk2VQirNOa7uDY9ULme9zS30S8np9klb6jvDOAPZmemQywo3rkSmJAOZQgZ5ZjqamLzEGOMYQA5kyIHMzAxlm+oZv0Iv82f4NtXw428ZQ2CM+T1cz7RS2Ze1jJNxRWaro6Z2w83jsCRD9yDjqZmZMJLk7GF5ZjrkMEI9swQYZTpADmOkZWbmTM+V1PjJ6gF+TSsLqch/d0cfmz1BW7MXSBZGmJVWDg/lusfa+cDZDJJqTnqd3wCgQTmXf8616rHlXsIIX7fwwMz9t9XKqrmWwI/dqqNyKVvsuhIHeWY6sjL++X6SqnrcNDcxwrSAD/DZuku4/jAdEx6qv50xukVFrV3JvMoEXunRZX7u81hySgqSknNuSMsBPH/5Cg7/vEUsz0yHXMNN7FSp4nuH6g3k1FTpPzGZs+yc74sWKse/H3de1tqu9LRU3ItNQMLLV2r7OSMtVed3/Iy0VGRmZut97Mv85zti7vq66mZn/Dv/63+3gmj5QSl0quqIxYdzkhQ25iZY1b8Gms85rpxHcS6Tpmcp1+db3gZmxkaoVtIM12L1G1NCm7RUKfZeilTZFsV3/Nxy//3SU1O1ns+AnL+hrr8TAHzRoiISUzNVujHsVK0MfL2c8OnaixqTbbllpachI81MZR1ZGf9+D1HG12t/09x/Q8V5KzvDVOU6WpaZ8z06Vce1tdxYpnb9kZdsmVxleYoYlP+zralSKaSm+XsrWBd5aipS5XLluowK8wWViOg/KFWaCbnI+Z4jlUoh8GbHKXxb5D7fCT3OJRKhz1z0Vnj69ClKlzZMf5dERERERERERERERIby5MkTlCql+y1uw48STEUmv09XEBERERERERERERG9Ldhl0zvE2dlZ+f/4+HjY2Og/HgIR6UcqlSrfRHry5AmsrQ0/1gDRfx3jjKjoMc6Iih7jjKjoMc6Iih7jjApLCIHU1Jyue3Pfv9aGCYl3iJHRvy+02NjY8ABBVMSsra0ZZ0RFjHFGVPQYZ0RFj3FGVPQYZ0RFj3FGBZWfB+fZZRMRERERERERERERERU5JiSIiIiIiIiIiIiIiKjIMSFBRERERERERERERERFjgkJIiIiIiIiIiIiIiIqchIhhCjuRhARERERERERERER0fuNb0gQEREREREREREREVGRY0KCiIiIiIiIiIiIiIiKHBMSRERERERERERERERU5JiQICIiIiIiIiIiIiKiIseEBBERERERERERERERFTkmJApg69ataNasGRwcHGBtbY1atWph1qxZyMrK0lnv4sWL6NmzJ0qXLg0LCwt4enpi9OjRePr06XvVHiJDyO/n+syZM/j888/x4Ycfws3NDRYWFrC2tkbVqlUxevRoREVFFagdCxcuRO/evVG1alU4OzvD1NQUjo6OaNq0KRYtWpRnnGVmZmLBggXw8/ODo6MjLCwsULZsWbRr1w6bN28uUJuIDCW/cVa+fHlIJJI8f3744Yd8tSM8PBxz5sxB3759UblyZRgZGUEikWDdunVa62RlZeHIkSP4+uuvUb9+fdjb28PU1BQuLi7o1KkT9uzZk682EBna7du38fvvv2PQoEGoUaMGTExMIJFIMGPGDL3qHz58GAEBAXB2doalpSWqVKmCiRMnIiUlxWBt7NWrlzJudcVbdHQ0Ro0aBW9vb1haWiq/NwYGBuLKlSsGaw9RfhU2znL7448/lPEwbNiwfNdPSkrC1q1bMXToUFStWhVWVlawsLBAhQoVMGTIEFy7di1fy9M3PomKWmHi7Pnz55gwYQJq1KgBa2trmJmZoWzZsujZsydOnDhR4DZlZmZi5syZqFWrFqytreHg4IBmzZph27ZtedYt6P0ToqJSmOuamJgYLFmyBJ9++inq1asHc3PzAp/HFIQQmD59Orp27Qpvb284OjrC1NQUpUqVQuvWrbFu3ToIIXQuIzk5GT/++CN8fHxgZ2cHS0tLeHh4oGvXrjh8+HCB20bvEUH5MmbMGAFAmJiYiNatW4tu3boJe3t7AUD4+fmJ1NRUjfW2bt0qTExMBABRv3590atXL1GhQgUBQJQuXVrcvXv3vWgPkSEU5HM9ceJEAUC4u7uL5s2biz59+oh27dqJ0qVLCwDC2tpaHDt2LN9tcXNzEyYmJqJWrVoiICBA9OnTRzRp0kQZPw0bNhTJycka68bExIiqVasKAMLZ2Vl06NBB9O7dWzRu3FhYWVmJ7t2757s9RIZSkDgbN26cCAwM1PjTsWNHAUAAECdOnMhXWzp37qysm/tn7dq1WuscOnRIOZ+Li4to37696NWrl6hevbpy+qeffirkcnm+9w2RIShi7PWf6dOn51l33rx5AoCQSCSiadOmomfPnsLFxUUAEN7e3uLZs2eFbt+mTZuU69AVb2fPnhW2trYCgHBzcxOdOnUSXbt2FZ6enspjyJYtWwrdHqKCKEyc5Xb//n1hbW2tjIehQ4fmuy2K76IAROXKlUW3bt1E586dRdmyZQUAYWpqKoKCgvRalr7xSfQmFDTO7t27J1xdXQUA4eTkJAICAkSPHj3EBx98oFzG3Llz890eqVQqGjduLAAIe3t70a1bN9G6dWvl9dm4cePy3Jb83j8hKkqFua6ZP3++xvgsyHlMISsrSwAQ5ubmwsfHR3Ts2FH06tVLNGzYUHle6tixo8jOztZY/9q1a8LNzU0AEGXLlhVdunQRPXr0EA0aNBCmpqY6Y5T+O5iQyIfg4GABQNjY2IiLFy8qpz979kzUqFFD68kvNjZWWFlZCQBiyZIlyunZ2dmif//+yqRAfm+avG3tITKEgn6ub968KSIjI9WmZ2RkKL94li1bVutJU5uTJ09qTDg8fPhQeHt7CwBi/PjxauWpqamiSpUqAoCYOnWqyMzMVCmXSqXi0qVL+WoLkaEUNM50mTlzpvImTH79/PPP4rvvvhPbtm0T9+/fF/7+/nnegDly5Ijo3r27xuTHpk2bhLGxsQAgVq9ene/2EBnCsmXLxFdffSXWr18vbt26JQYMGKDXDZzw8HAhkUiEsbGx2Lt3r3K6VCoVLVu2FAAKndCOj48XTk5Ook6dOsLX11dnvNWsWVN5IZz7XCaTycSkSZOUN4TS0tIK1SaigihonOUmk8lEkyZNhI2NjQgMDCzwjZyffvpJjB07Vty5c0dlemZmpvjyyy8FAGFmZpbng1/5iU+iN6GgcdapUycBQLRv316kpKSolC1ZskSZGIiJiclXexTXdjVq1FBJ0IeFhQkbGxsBQOzevVutXlF8/yUyhMJc1+zYsUOMHj1arFq1Sly5ckWZHC9MQkIul4tjx45p/G539epVUapUKQFALF68WK08Pj5elCpVSpiYmIg///xT7b7iixcvxI0bNwrcNnp/MCGRD/Xr1xcAxIwZM9TKTp48qcwgvnz5UqXs66+/FgBEq1at1OolJycLOzs7AUDs37//nW4PkSEU9HOtS2ZmprCwsBAAxNWrVw3W1jVr1ggAom7dumplkydPVt7AIXrbFEWcKRJ0v/zyS6Hbp09CIi9Dhw4VAETLli0L3R4iQ1Dc6MzrBk7Pnj0FADFs2DC1sqioKGFkZCQAiFu3bhW4LZ06dRKmpqbi8uXLOuMtISFB+aTd06dP1cqzs7OFpaWlACDCw8ML3B4iQ9E3znJTvJG0aNEiMWXKlELfyNFEJpOJypUr630TV5/4JCou+saZIjlw/vx5jeWVKlUSAMT27dv1XndiYqIwMzMTAMSpU6fUyqdPny4AiEaNGqmVFcX3X6I3IT/XNUV1Hsvthx9+EABEt27d1MoUCcuffvqpyNZP7weOIaGn2NhYXLhwAQDw8ccfq5X7+fmhXLlyyMjIwN69e1XKgoODtdazsbFBp06dAADbt29/Z9tDZAiF+VzrIpFIYGSUc7gzNzc3TGMBmJiYaFxmVlYW/vzzTwDA119/bbD1ERlCUcTZ6dOncfv2bZiYmCAwMNCg7S2oOnXqAMjpV5XoXZGZmansJ1hTfHp4eMDX1xfAv9/n8mvNmjXYtWsXJkyYgFq1aumcNz/nTGdn5wK1h6g43b59GxMnToS/vz8+++yzIluPkZERatasCUD3eSk/8Un0trOwsNBrvvycP/bu3YvMzEy4u7srz4e5Kc6dZ8+eRVxcnHJ6UV1nEr0Jb9t1jbb7IE+fPsWmTZtgaWmJUaNGFUfT6B3ChISeLl26BABwdHSEp6enxnl8fHxU5gVyBnK5d++eSrk+9RQUA4gGBQW9Fe0hKkoF/VzrIpPJMG3aNKSmpqJq1aqoWLGi2jyKAQNDQkL0buvTp08xc+ZMAFAm8RTCw8ORkJAAV1dXVKxYEdeuXcO0adMwfPhwjB8/Hnv27IFcLtd7XUSGVBRxtnLlSgBAQEAAXFxcNM5TkDgrjLt37wIAypQp80bWR2QId+7cQWpqKoCCfU/LK85iY2MxZswYVK9eHRMnTsyzPTY2NmjSpAkAYNKkSSoDfsrlckydOhVpaWlo164dypUrl+fyiN4mMpkMgYGBkEgkWLFiBSQSSZ51oqKilHEWFRWVr/XldV7Kb3wSve3atWsHAMprsdyWLVuGu3fvokaNGvjwww9VynTFmeLcp+0cWaFCBTg6OgIALl++rFbPkN9/id6UoriuKej57P79+/jjjz8AqN8HOXbsGLKyslCnTh3Y2trizJkzmDhxIoYPH47Jkyfj+PHjBms/vftMirsB74rIyEgAgLu7u9Z5FBdiinkBqAS2trqa6r1r7SEyhIJ+rnOLjo7G999/DwBITEzEpUuX8OjRI1SsWBFbtmxRvimRXxs2bMDBgweRnZ2Nx48f4/Tp08jIyEBgYCDGjh2rMu/Vq1cBAGXLlsX48eMxa9YsCCGU5TNnzkSdOnWwY8cOndtKVBQMEWe5SaVSbNmyBQAwdOhQA7Sw8OLj45WJ/O7duxdvY4jyQRFz9vb2sLW11ThPYb6nDRs2DMnJyVi1ahXMzMz0qrNs2TIEBARg6dKl2LNnD3x8fGBsbIxLly4hNjYWAwYMwMKFC/PdFqLiNnv2bJw7dw7z58+Hl5dXka5r//79uHLlCiQSCbp166ZxnoLEJ9HbbPbs2bh58yb27NkDd3d3NGrUCFZWVrhx4wYiIiLQvn17LFu2TPm0tT70+R5btmxZJCYmqpwnDf39l+hNKe7rmgULFiA8PByZmZmIiYlBaGgohBAYP348+vTpozKv4j6Ii4sLBgwYgHXr1qmUz5gxA61atcKWLVvg4ODwxraB3k5MSOgpOTkZAGBtba11HhsbGwDAq1ev1OrpqqupnoKXlxcsLCxgZ2f3VrSHqCgV9HOdW2JiIlavXq0yrW7duli5ciWqVaumsY63tzcAwMrKSut6z58/r7JciUSCL7/8ElOmTFG7aHz+/DmAnKdrzp8/j5EjR+KLL76Ai4uL8vdLly6hffv2CA8Ph6mpqdb1EhmaIeIsty1btiAlJQUuLi4ICAjQOp8+cWYI2dnZ6N+/P5KSklCjRg0MHz68SNdHZEiFjU9dcbZs2TLs378f48eP1/pkqSbe3t4IDQ3FgAEDcPDgQcTGxirLqlatimbNmqFEiRJ6L4/obXD9+nVMmTIFjRs3xhdffKF3PVNTU2Wc6fv9LS4uTpmw/+STT5RdN+VW0PgkepuVLl0aISEh+Oyzz7Bu3Tpll4RAzs3/Fi1aoGTJkmr1dMVZYe+DGOr7L9GbUJTXNfqez44ePYqdO3eq1Js2bRr+97//qc2ruA+ye/du5Zu0gYGBsLOzQ0hICEaOHInDhw+jT58+OHDggMG2hd5N7LLpLXfkyBFERESga9euxd0UondC7dq1IYSAXC7Ho0ePsGXLFqSmpqJevXpYsGCBxjoRERGIiIhAgwYNtC73119/hRAC6enpiIiIwMSJE7F48WLUqlULN2/eVJlX8TZEVlYW+vbti4ULF6Jy5cooUaIEWrVqhUOHDsHCwgLXr1/Hpk2bDLfxRMVgxYoVAICBAwfqfMJNnzgzhBEjRuDIkSNwcnLCtm3b+JQp/adoi7OHDx9i3Lhx+OCDDzB16tR8LfP06dOoUaMGrl+/jg0bNiA+Ph6JiYnYvXs3srKyMHTo0Lfm7SgifWRnZyMwMBBGRkZYuXJlvt6edXNzU8aZm5tbnvO/evUKHTp0QFxcHBo0aIDffvtNbZ7CxCfR2ywiIgJ16tTB7t278ccffyAmJgZJSUkICQlB6dKlMW7cOAQEBEAmk6nUy2+cEb2vivK6Rt8427FjB4QQkEqluHr1Kj799FNMmjQJH374oco4LYDqfZBvvvkGU6ZMQfny5eHg4ICuXbsiODgYEokEBw8exMmTJw22LfRuYkJCT4rX5qVSqdZ5UlJSAEDlKbHcr9trq6up3rvWHiJDKOjnWhOJRAI3Nzf07NkToaGhKF26NL788ktcuXKlUG00NzeHt7c3pk+fjtWrV+Phw4dqg/jmjjNNTzG4u7ujffv2AIDDhw8Xqj1E+WXIOLtz5w5Onz4NABgyZIiBWlhwY8aMwYoVK+Dg4IBDhw6hcuXKxd0konwxZHwqCCEwZMgQSKVSrFy5Ml8DVb98+RJdu3bFs2fPsH37dvTt2xelS5eGg4MDOnTogP3798PKygorV67EsWPH9F4uUXH68ccfER4ejmnTpimfDi0KKSkpaNeuHS5duoQ6depg//79aoP8FiY+id5m2dnZ6N69O+7du4dly5bhs88+Q9myZVGiRAn4+/vj4MGDcHFxwaFDh7BmzRq9l1vY+yCGPL8SFaW37brGysoKNWrUwMKFC/HLL7/gypUram8Y5nUfpGHDhqhbty4A3gchJiT0Vr58eQC6R7VXlCnmBQAPDw/l/6Ojo/Wu9661h8gQCvq5zou9vT26du0KuVyOXbt2FaaJKrp37w5bW1uEhYWptLlChQoa/5+bYvrjx48N1h4ifRgyzhSDWfv5+RXpTR19jBs3DgsWLIC9vT0OHjyIOnXqFGt7iApCEXMvX75U6WYzt/yeB5OSknD06FFYWVlh/PjxaNasmcqPYtDPH3/8Ec2aNVN5BX/Pnj149uwZKlSogIYNG6otO/d0XljSuyI4OBhATpcSr8eDop/uPXv2KKcVhFQqRfv27XHmzBnUrFkThw4d0thfdmHik+htdu7cOdy8eRPm5uYax01xcHBQDnqdn/OH4tyn7V4GADx69Ehl3tz/N/R1JlFReNuvawYPHgwg5zya+w0nxT0OExMT5Zgsr+N9EFLgGBJ6UhwAnj9/jsjISHh6eqrNExYWBgDKjB+Qk12vWLEi7t27h7CwMNSoUUOveu9ae4gMoaCfa30o+gt9+vRpIVv5LyMjI1haWiI5ORlPnz5VnnTr1q0LiUQCIQQSEhI0nowTEhIA/NtXKdGbYqg4k8lkyifairu7lm+++Qbz5s2DnZ0dDh48yP636Z3l7e0NKysrpKamIiwsDM2bN1ebp6DnwZSUFBw/flxrueK1/dwUN3x0PS2qGOcsMTExX+0hKm6nTp3SWhYfH4/4+PgCLTc1NRXt27fHiRMnULNmTWV3G7oUJD6J3maK84eVlRWMjY01zlOQ84fi3Kc4F77uwYMHyuXlvolblNeZRIb0LlzXKO6tZGZm4uXLl8pzXL169QDkvCH16tUr2Nvbq9XlfRBS4BsSeipbtizq168PANiwYYNa+alTpxATEwNzc3O1QT0V4z9oqpeSkoLdu3cDgMYnB96V9hAZQmE+13k5evQoABj0Vcfr16/j6dOnMDY2VnkTwsXFBX5+fgA0P/GTlZWlvOgs6v70iV5nqDjbu3cvHj9+DFtbW/Ts2bPI2puX8ePHY/bs2bCzs8OhQ4eU20b0LjIzM1N26acpPh8+fIgzZ84AgN7ji9nb20MIofXH398fALB27VoIIRASEqKsq+hTOCIiAklJSWrLzsrKQnh4OABovLlD9Da6fPmy1niYMmUKgJxEu2JafqSlpaFDhw44fvy4Mhnh7Oysdf7CxCfR20xx/njx4gXu3r2rcZ5z584ByN/5IyAgAGZmZoiOjlZ2G5qb4tzZqFEjuLq6KqcX5XUmkaG8K9c1R44cAQA4OTnB0dFROb1BgwZwd3cHABw6dEitXmJiIi5evKicl/7jBOktODhYABA2Njbi4sWLyukJCQmiRo0aAoAYN26cWr3Y2FhhZWUlAIilS5cqp2dnZ4sBAwYIAKJ+/fpCLper1W3RooXw9vYW27dvfyvaQ1TUCvq5/umnn8TTp0/VpicmJopRo0YJAMLOzk7Ex8erzePt7S28vb3FuXPnVKafPHlS7Nq1S2RlZanVuXjxoqhevboAIPr06aNWfvjwYQFAODg4iNDQUOX0rKwsMXr0aAFA2NraamwPUVEraJzl1qVLFwFAfPLJJ3qtU1ucaeLv7y8AiLVr1+qcb+LEiQKAsLe3F+fPn9erHUTFJTAwUAAQ06dP1znfxYsXhUQiEcbGxmLfvn3K6VKpVLRs2VIAEN27d9dYNz9xpqAr3p4+fSqsra0FANGzZ0+RnJysLMvIyBAjR44UAISpqam4f/++3uskKir6xpk2U6ZMEQDE0KFDNZY/evRIGWePHj1SKUtLSxOtWrUSAETNmjXFs2fPCtSG3PQ9HxK9SfrEWWZmpnBzcxMARNOmTVWu02Qymfj5558FAAFAnDhxQqWurjgTQogxY8Yo4ywhIUE5/eLFi8LGxkYAELt371arZ4jvv0RFxVDXNXmdxxR0xdnu3bvFsWPHNN4TPHLkiChbtqwAIMaPH69WvmLFCgFAlC9fXkRERCinS6VS0aNHDwFAuLu7i7S0tAJuIb0vJELk87GP/7gxY8ZgwYIFMDU1RcuWLWFtbY0jR47g5cuX8PX1xaFDh2BpaalWb+vWrejbty9kMhkaNmyI8uXL48KFC3jw4AFKly6NU6dOoWLFimr1ypcvj4cPH2LVqlUYNGhQsbeH6E0oyOdaIpHA2NgYNWrUgJeXF0xMTBAbG4tLly5BKpXCzs4OW7duxUcffaS2PolEAgA4duyYSl/BQUFBGDx4MOzt7VGnTh2UKVMGKSkpiIyMxLVr1wAAvr6++PvvvzW+jjhjxgxMnjwZJiYmaNCgAVxcXBAeHo6oqChYWlpi69atyidhid60gp4/gJyuz8qWLYusrCycPXtWY9/yr9MWZ0BOX93Tp09X/n7z5k0kJyfDy8tL5cnSs2fPKv+/a9cudO7cGQDg4+ODatWqaVyvs7Mz5syZk2f7iAwtPDwcn3/+ufL3+/fvIyEhAWXLllU+OQrk9GdfpkwZlbrz58/H2LFjIZFI4O/vj1KlSuHkyZN4/PgxvL29cerUKY1PXeuKM22aNWuG48ePY+3atejfv79a+bp16zB48GBkZ2ejZMmSqF+/PkxNTREWFobY2FgYGRlh0aJFGDFihF7rIzKkwsSZJlOnTsW0adMwdOhQLF++XK08KipK+TR3ZGSkSl/zY8eOxfz58wEAHTp00NpNk5+fH4YNG6bX9uUVn0RvQkHj7OjRo+jYsSNSU1NRokQJNGzYELa2trhy5Qru378PAPjuu+/w448/qqxPV5wBOd2itWrVCqGhoXBwcECLFi0glUpx5MgRZGVlYezYsZg7d67GbSnM91+iolKY65rHjx+rvDX76NEjxMbGomTJkiq9OPzxxx8q3ZHpijPFubBkyZKoU6cOSpYsiZcvX+Lu3bu4c+cOgJw3dTdu3Ahzc3O1dn7yySdYvnw5LCws0KhRI9jZ2eHcuXOIj4+Ho6Mj9u/f/9a+/UFvUHFnRN5FmzdvFk2bNhUlSpQQlpaWonr16uKXX34RGRkZOuuFhYWJbt26iZIlSwozMzPh4eEhRo4cqfMJaQ8PDwFArFq16q1oD9Gbkt/P9cKFC0Xv3r1F5cqVhb29vTAxMREODg6iUaNGYsqUKTo/1/jn6Zxjx46pTI+MjBSTJ08WzZs3F+XKlRMWFhbCzMxMlC1bVnTs2FGsX79eyGQyndtx4MAB0a5dO+Ho6ChMTU1FuXLlxKBBg8StW7fyvU+IDK2g5485c+YIAKJatWp6r0tbnAkhxKpVq5Tlun4KUsfDw0PvNhIZ0rFjx/T6jEZGRmqsf+jQIdG2bVvh6OgozM3NRaVKlcSECRPEq1evtK5TV5xpo88T2JcvXxaDBg0SFSpUEObm5srvjf369cvX2xhEhlbYOHtdXk+WRkZGal2m4qnxvH4CAwP13j6+IUFvg8LE2f3798XIkSNFlSpVhKWlpTA1NRWurq6ia9eu4uDBgxrXpyvOFDIyMsTPP/8sqlevLiwtLYWdnZ1o2rSp2LJlS57bU9Dvv0RFpTDXNbnjRdePpnsd2uLs6tWr4ptvvhGNGzcWbm5uwtzcXFhYWAhPT0/Rq1cvjW8gvW7jxo3C399f2NnZCTMzM+Hl5SVGjRolYmJiCrGn6H3CNySIiIiIiIiIiIiIiKjIcVBrIiIiIiIiIiIiIiIqckxIEBERERERERERERFRkWNCgoiIiIiIiIiIiIiIihwTEkREREREREREREREVOSYkCAiIiIiIiIiIiIioiLHhAQRERERERERERERERW5dzYhkZWVhSNHjuDrr79G/fr1YW9vD1NTU7i4uKBTp07Ys2ePzvqHDx9GQEAAnJ2dYWlpiSpVqmDixIlISUnROP/t27fx66+/IiAgAG5ubjAzM0OJEiVQv359/Pzzz1rryWQybNu2DRMmTEDr1q3h5OQEiUQCExOTQu+DEydO4KeffkL37t1Rvnx5SCQSSCQSnDp1Ks+6crkcS5YsQcOGDWFrawtbW1s0bNgQS5cuhRCiQO25dOkSfv75Z7Rs2RKlS5eGqakpHBwc0KRJEyxatAhZWVla62ZnZ+OPP/6An58fHBwcYGpqCmdnZ7Rs2RKrV6+GXC4vUJuocBhnjDMqeowzxhkVPcYZ44yKHuOMcUZFj3HGOKOixzhjnNEbIN5Rhw4dEgAEAOHi4iLat28vevXqJapXr66c/umnnwq5XK5Wd968eQKAkEgkomnTpqJnz57CxcVFABDe3t7i2bNnanXc3NwEAGFhYSH8/PxEnz59RPPmzYWFhYUAILy8vMTDhw/V6r148ULZntw/xsbGhd4HtWrV0rjskydP6qyXnZ0tunXrJgAIKysr0bFjR9GxY0dhaWkpAIiePXsKmUyWr7ZkZWUp129jYyOaN28u+vTpI/z8/ISxsbEAIBo0aCBevHihVjc9PV00bdpUABBmZmaiRYsWonfv3qJx48ZCIpEIAKJLly4a/5ZUtBhnjDMqeowzxhkVPcYZ44yKHuOMcUZFj3HGOKOixzhjnFHRe2cTEkeOHBHdu3cXJ06cUCvbtGmT8kO5evVqlbLw8HAhkUiEsbGx2Lt3r3K6VCoVLVu2FABE9+7d1ZbZokULsWLFCpGcnKwyPTIyUlSrVk0AEM2bN1erl5KSIvr16yfmzJkjjh49Ki5fvmywA8RXX30lpk6dKnbt2iUePXokPDw89DpAzJ8/XwAQbm5u4sGDB8rpDx48EK6urgKA+P333/PVlqysLFGvXj2xZcsWkZ6erlJ29epVUaZMGQFADB48WK2u4oDt4eGhdpC9cOGCsLW1FQDExo0b89UmKjzGGeOMih7jjHFGRY9xxjijosc4Y5xR0WOcMc6o6DHOGGdU9N7ZhERehg4dKgCIli1bqkzv2bOnACCGDRumVicqKkoYGRkJAOLWrVt6r+vkyZPKbF1MTIzOeSMjIw12gHidPgcImUymzM6uW7dOrXzt2rUCgHB1dc131lIXxXItLS1FZmamSlmHDh0EADFr1iyNdT/55BMBQHzxxRcGaw8ZBuNMM8YZGRLjTDPGGRkS40wzxhkZEuNMM8YZGRLjTDPGGRkS40wzxhnlxzs7hkRe6tSpAwCIiYlRTsvMzFT29fbxxx+r1fHw8ICvry8AIDg4ON/ren19b6PQ0FDEx8fD3Nwc3bt3Vyvv3r07zMzMEBcXh3PnzhlsvYp9lJaWhoSEBJUyCwsLvZbh7OxssPaQYTDONGOckSExzjRjnJEhMc40Y5yRITHONGOckSExzjRjnJEhMc40Y5xRfry3CYm7d+8CAMqUKaOcdufOHaSmpgIAfHx8NNZTTL906VK+1/X6+t5Giu2qVq2axsC0tLREtWrVVOY1BMU+MjMzg6Ojo0pZu3btAACLFi1CdHS0StnFixexadMmWFpaYsCAAQZrDxkG40wzxhkZEuNMM8YZGRLjTDPGGRkS40wzxhkZEuNMM8YZGRLjTDPGGeXHe5mQiI+PR1BQEACoZOUiIyMBAPb29rC1tdVYt1y5cirz6uOXX34BANStWxfly5cvQIvfHMV2ubu7a52nIPtAFyEEZs2aBQDo0KEDzM3NVcoHDRqEgQMH4uHDh6hUqRJatmyJvn37ws/PD/Xr14enpyeOHDny1u/b/xrGmXaMMzIUxpl2jDMyFMaZdowzMhTGmXaMMzIUxpl2jDMyFMaZdowzyg+T4m6AoWVnZ6N///5ISkpCjRo1MHz4cGVZcnIyAMDa2lprfRsbGwDAq1ev9FpfUFAQNm/eDGNjY/z222+FaPmbURT7IC/Tpk1DaGgobGxslAfT3IyMjBAUFISaNWtiwoQJOHr0qLLMysoKrVq1gpeXl0HaQobBONONcUaGwDjTjXFGhsA4041xRobAONONcUaGwDjTjXFGhsA4041xRvnx3r0hMWLECBw5cgROTk7Ytm0bzMzMimxdR44cUR6AZs2aBT8/vyJb17tqzZo1+OGHH2BkZISVK1eiUqVKavO8evUKHTp0wNdff41Ro0bhzp07kEqluHbtGrp06YJ58+ahQYMGb31/ef8ljLO3C+Ps/cQ4e7swzt5PjLO3C+Ps/cQ4e7swzt5PjLO3C+Ps/cQ4e7swzt5t79UbEmPGjMGKFSvg4OCAQ4cOoXLlyirlitempFKp1mWkpKQAAEqUKKFzXadOnULnzp2RmZmJKVOmYOzYsYVs/b8iIiI0Zvb8/PwwbNiwQi27oPvgl19+QUREhNq8c+bM0Tr4y9atWzFkyBAAwLJly9CzZ0+N840bNw579+7F559/jnnz5imnV69eHevXr8fz589x4MABTJo0CatXr85jC6moMc7yxjijwmKc5Y1xRoXFOMsb44wKi3GWN8YZFRbjLG+MMyosxlneGGeUL+I9MXbsWAFA2NvbiwsXLmic5+rVqwKAACBevXqlcZ4vv/xSABA9evTQuq7Tp08LW1tbAUBMnDgxX+2MjIwUAISxsbHWeY4dO6ZsZ+6fwMBAncv28PAQAMTJkye1zrNgwQIBQNStW1frPHXq1BEAxMKFC5XT/P39NbYpMjJS4zL++usvYWJiIiQSiVi6dKnWdWVnZwtzc3Od7V69erUAIFxdXbUuh94MxhnjjIoe44xxRkWPccY4o6LHOGOcUdFjnDHOqOgxzhhnZHjvRULi66+/FgCEnZ2dOH/+vNb5MjIyhJWVlQAgjh49qnGeJk2aCADip59+0lgeGhqqPDh89913+W6rPgeIgtLnAHHq1CkBQJibm4u0tDS18tTUVGFmZiYAiDNnzhSoHcHBwcLU1FRIJBKxePFinfPGxcUpDzZXrlzROM+OHTsEAGFhYVGg9pBhMM5yMM6oKDHOcjDOqCgxznIwzqgoMc5yMM6oKDHOcjDOqCgxznIwzsjQ3vmExLfffqvXwUGhZ8+eAoAYNmyYWllUVJQwNjYWAMStW7fUys+dOydKlChR4IODEMV/gJDJZMLFxUUAEOvWrVMrX7t2rTI7KJPJ8t2GXbt26X1wECLnoK3IWGrLbI4fP14AEB988EG+20OGwTj7F+OMigrj7F+MMyoqjLN/Mc6oqDDO/sU4o6LCOPsX44yKCuPsX4wzMrR3OiExceJEAeS8NqXPwUEIIS5evCgkEokwNjYW+/btU06XSqWiZcuWAoDo3r27Wr0LFy4IOzu7Qh0chCj+A4QQQsyfP18AEG5ubuLBgwfK6Q8ePBBubm4CgPj999/zvf49e/YIMzMzIZFIxJIlS/Supzholy5dWi1refjwYWFhYSEAiOnTp+e7TVR4jDNVjDMqCowzVYwzKgqMM1WMMyoKjDNVjDMqCowzVYwzKgqMM1WMMzI0iRBC4B20a9cudO7cGQDg4+ODatWqaZzP2dkZc+bMUZk2f/58jB07FhKJBP7+/ihVqhROnjyJx48fw9vbG6dOnVIbOMXR0REvXryAvb29cr2ajB8/HlWqVFGZ9vnnnyM8PBwAkJGRgcuXLwMAGjZsqJynffv2mDx5sn4b/4/ly5dj+fLlyt8vXbqEzMxMVK1aVTmYTJkyZRAcHKxSTyaToWfPnggODoaVlRVatWoFADh8+DBSU1PRo0cPbN68GUZGRnq35enTp3B3d0dGRgbKli2Lli1bap339YFpYmNj0bRpUzx48ADGxsZo1KgR3Nzc8ODBA4SFhQEAWrRogT179sDCwkLvNlHhMc4YZ1T0GGeMMyp6jDPGGRU9xhnjjIoe44xxRkWPccY4ozeguDMiBbVq1SoBqA968vqPh4eHxvqHDh0Sbdu2FY6OjsLc3FxUqlRJTJgwQevgM/qsC4A4duyYWl1tA7Tk/slrABlNpkyZUuDtl8lkYvHixcLHx0dYW1sLa2trUb9+fbF48WIhl8vz3RZFJlafH00D07x69UrMmDFD1K9fX5QoUUIYGxsLR0dH4e/vL5YsWSKys7Pz3SYqPMYZ44yKHuOMcUZFj3HGOKOixzhjnFHRY5wxzqjoMc4YZ1T03tk3JIiIiIiIiIiIiIiI6N2h/zsyREREREREREREREREBcSEBBERERERERERERERFTkmJIiIiIiIiIiIiIiIqMgxIUFEREREREREREREREWOCQkiIiIiIiIiIiIiIipyTEgQEREREREREREREVGRY0KCiIiIiIiIiIiIiIiKHBMSRERERERERERERERU5JiQICIiIiJ6x0kkknz/NGvWDADQrFkzSCQShISEFOs2GMJvv/0GiUSCv/76S2X61KlTVbZZm6CgIEgkEpQvX16trHz58pBIJAgKCtK5DMX+nDp1ar7arqgnkUjQuXNnnfNu3bpV5W/56NEjlfJBgwYpy2rXrq1zWRcuXFBZ1qlTp/LVbiIiIiKi/DAp7gYQEREREVHhBAYGqk2Lj4/HgQMHtJZXqVKlyNv1Jj179gxTp05F/fr10b179+JuTqHs3bsXT548QenSpTWWr1ixQu9lXblyBRcvXkS9evUKvSwiIiIiosJiQoKIiIiI6B2n6an9kJAQZUJC11P9a9asQWpqKtzd3YuodW/GtGnT8PLly3y/mfC28fHxQVhYGNasWYOvv/5arTwmJgaHDh1C/fr1ceHCBb2WtXLlSo0JibS0NGzatAllypSBsbGx2psWRERERESGxi6biIiIiIj+w9zd3VGlShVYWVkVd1MK7OXLlwgKCoKbmxvatm1b3M0plP79+8PMzAyrVq3SWB4UFAS5XI4hQ4bkuaz27dujdOnS2LhxI9LT09XKt23bhqSkJAwcOBDGxsaFbjsRERERUV6YkCAiIiIi+g/TNoaEYhyCoKAg3L59G71790apUqVgbW2N+vXrY+fOncp5z507h06dOqFkyZKwtLTEhx9+iCNHjmhdZ1paGubOnYtGjRrB3t4eFhYW8Pb2xjfffIPnz5/nextWrVoFqVSKAQMGwMjo3b7EcXJyQqdOnXDr1i2EhoaqlAkhEBQUBEtLS/Tt2zfPZZmYmGDAgAF48eIFgoOD1cpXrlwJAHolN4iIiIiIDOHd/rZORERERERFKjw8HPXq1cOVK1fQsmVL1KpVC2FhYejatSu2bduGHTt2oEmTJnj06BFatmwJb29vnD17Fm3bttU4QHJcXBwaNmyIr776Cnfv3kX9+vUREBCAjIwMzJ49Gz4+Pnj48GG+2rhjxw4AQKtWrQyxyUVG3wGvFQkCRcJA4dixY3jw4AG6desGOzs7vdapbVn379/H8ePH4evri8qVK+u5BUREREREhcOEBBERERERafX7779jwoQJuHXrFjZu3IgzZ85gwYIFEELgyy+/xJAhQ7BixQqEh4dj06ZNuHz5Mv73v/8hOzsb06ZNU1mWEAK9evXCtWvXMHToUERFReHQoUPYvn077t27h3HjxiEqKgqDBw/Wu31paWk4e/YsjIyM0KBBA0NvfrFo06YNypYti82bNyM1NVU5XZFUGDp0qN7L+uCDD/Dhhx/i6NGjiI6OVk5ftWoVhBD5WhYRERERUWExIUFERERERFo1aNAA3333HSQSiXLaZ599BkdHRzx69AitWrXCgAEDVOpMmjQJAHDixAlkZWUppx84cACnT59G7dq1sXjxYtja2irLTExMMGvWLFSvXh3Hjh3D9evX9WrfjRs3kJmZibJly6osT5Pjx49DIpFo/dEnETJ48GCdyzh+/LjWuu7u7vD29oazs7POdRgZGSEwMBDJycnYunUrACApKQnbt29HhQoV0KxZszzbmduQIUMgl8uV41LI5XKsXr0aNjY26NWrV76WRURERERUGCbF3QAiIiIiInp7tWvXTiUZAeQkDzw9PZGYmIiAgAC1Ok5OTnB0dERiYiKeP38OFxcXAMCePXsAAN27d4eJifqliJGREZo2bYrr16/jzJkzqF69ep7te/LkiXKdeSldurTOQa/v3buH06dP61yGr68vKlasqLV8//79yja9bs2aNXm2UWHw4MH46aefsHLlSgQGBmLDhg1IS0tTju2RH71798b//vc/BAUF4fvvv8eBAwfw6NEjDBkyBNbW1vlaFhERERFRYTAhQUREREREWrm7u2ucbmNjo7Pc1tYWiYmJSE9PV0578OABAGDy5MmYPHmyzvU+e/ZMr/YlJSUBAEqUKJHnvFWqVEFQUJDW8qCgoDwTEsOGDcOgQYO0ljdr1kxrQiI/vLy80LRpU5w4cQL379/HypUrYWRkpHPd2tja2qJHjx5YvXo1jh49ysGsiYiIiKjYMCFBRERERERaGRnp7uU1r/Lc5HI5AMDPzw9eXl46561WrZpey7S3twcAvHr1Su92vCuGDBmC48eP48svv0RYWBhat26NcuXKFXhZq1evxuzZs3Hs2DF4e3vD19fXwC0mIiIiItKNCQkiIiIiInojFDfTO3fujK+++sogyyxVqhQA4Pnz5wZZ3tukR48eGD16NHbv3g2gcG80NG3aFBUrVsSBAwcAIF8DhxMRERERGQoHtSYiIiIiojeiXbt2AICtW7dCCGGQZVarVg1mZmZ49OgRkpOTDbLMt4WVlRUGDRoEJycneHp6okuXLoVa3ogRI+Dk5IRSpUph4MCBhmkkEREREVE+MCFBRERERERvROfOnVG/fn2cP38egwcP1jhOxIsXL7B48WJkZ2frtUxLS0s0atQIcrkc586dM3STDWrgwIGoUqUKFi5cqHed3377DQkJCXjw4AHMzc0Ltf5x48YhISEBT548QZkyZQq1LCIiIiKigmBCgoiIiIiI3ggjIyPs2LEDtWvXxurVq+Hp6QlfX1/07dsX3bt3R506dVCyZEl89tlneickACjfHDh06FARtdwwoqOjcfv2bSQkJBR3U4iIiIiIigUTEkRERERE9Ma4urri7NmzWLx4MRo0aIDbt29j27ZtOHXqFICcboUOHDgACwsLvZc5ePBgWFtbY926dZDJZEXVdCIiIiIiKiSJMFTnrURERERERMVk1KhRWLRoEXbt2oWOHTsWd3OIiIiIiEgDJiSIiIiIiOid9+zZM1SuXBkVK1bEhQsXirs5RERERESkAbtsIiIiIiKid17JkiUxdepUhIWFYdu2bcXdHCIiIiIi0oBvSBARERERERERERERUZHjGxJERERERERERERERFTkmJAgIiIiIiIiIiIiIqIix4QEEREREREREREREREVOSYkiIiIiIiIiIiIiIioyDEhQURERET0nunfvz8kEgkCAgJ0zvfixQu4ublBIpHAyMgIEokEM2fO1FnnzJkzMDY2hoWFBa5cuQILCwsYGxvjzJkzOuvNnDkTEokE3t7eWLBgASQSCdzc3PDixQud9QICAiCRSNC/f3+d8/3XSCQSSCSSfNcrX748JBIJoqKilNNkMhmqVKkCDw8PpKWlGbCVRERERESqmJAgIiIiInrPLFy4EG5ubti3bx+WLFmidb6RI0ciLi4O7du3x+nTp2FsbIzvv/8e165d0zh/amoqAgMDIZfLMWPGDNSqVQvTp0+HXC5HYGAgpFKpxnrXrl3D999/D2NjY6xevRpffPEFAgICEBcXh88//1xr+5YuXYp9+/bBzc0NCxcuzN9OIL0ZGxtjxowZiI6OxqxZs4q7OURERET0HmNCgoiIiIjoPWNvb48VK1YAAL766is8ePBAbZ5t27Zh48aNcHJywvLly/Hhhx/im2++QWZmJgYMGIDMzEy1Ol9//TXu3bsHPz8/jB07FgAwbtw4+Pn54d69e/jqq6/U6mRlZWHgwIHIzMzEt99+i0aNGgEAli9fDkdHR2zatAlbtmxRqxcZGYlx48YBAFauXAl7e/sC7w/KW48ePVCjRg3MnDkT8fHxxd0cIiIiInpPMSFBRERERPQeatOmDUaMGIGUlBTlWw0KT548wWeffQYA+OOPP+Di4gIAmDp1KmrVqoUrV65g6tSpKss7fPgw/vzzT9jY2GD16tUwMsq5lDAyMkJQUBCsra2xePFiHDhwQKXeDz/8gMuXL6NWrVqYMmWKcnqZMmXwxx9/AAA+++wzPH78WFkml8sxaNAgpKSk4LPPPkPr1q0Nt2NIqyFDhiAtLQ1Lly4t7qYQERER0XuKCQkiIiIiovfUnDlz4OXlhVOnTmHOnDnK6Z9++ikSEhLQt29f9OrVSzndzMwMa9asgZmZGWbNmoXQ0FAAQFJSEoYMGQIhBObOnYsKFSqorMfLywtz584FAAwdOlQ5LsSFCxfw888/w8zMDGvXroWZmZlKvd69e6NPnz5ITEzEsGHDlNN//fVXnDhxAhUrVsTs2bP12tapU6dCIpFg6tSpiIuLw7Bhw+Dq6gpLS0tUr15d+cYIAERERODjjz+Gi4sLLCwsUKtWLWzevFnjcnOPubBv3z40a9YMdnZ2cHBwQIcOHVS6t9qwYQM+/PBD2Nrawt7eHt26dcP9+/f1ar8moaGhaNeuHezt7WFjYwMfHx+sXLkyz3o3b95Ez5494ezsrNz+OXPmQCaT6azXr18/mJiYYMmSJcjOzi5wu4mIiIiItGFCgoiIiIjoPWVtba18m0ExNkRQUBB27doFV1dXLFq0SK1OzZo1MW3aNMhkMgwcOBCpqakYM2YMYmJi0K5dO3z66aca1zV8+HC0bdsWsbGxGDlyJNLT0zFw4EDIZDL88MMPqFGjhsZ6ixYtQpkyZbB3714sXboUt27dwsSJE2FkZITVq1fD2to6X9scHR2NevXq4dChQ2jSpAk+/PBDREREYNiwYZg7dy7Onj2LBg0aIDw8HM2bN0fdunVx9epV9OnTR2tSAgCWLFmC9u3bIzs7G23btkWpUqWwZ88eNG3aFPfv38c333yDwMBAWFlZoW3btihRogSCg4PRtGnTPAfu1mTr1q1o0qQJ9u/fj3LlyqFTp06wtLTEsGHDlF1ZaXLq1Ck0aNAA27Ztg52dHbp06YIyZcrgu+++Q+/evXWus2TJkqhduzbi4uJw4cKFfLeZiIiIiChPgoiIiIiI3mvffPONACCqVasm7OzsBACxd+9erfNnZ2eLDz/8UAAQjRs3FgCEg4ODiI2N1bme2NhY4eDgoFKvcePGQiaT6ay3Z88eAUDY2NiImjVrCgDi22+/zdc2TpkyRQAQAMSIESNEVlaWsmzXrl0CgLC1tRUeHh5ixowZQi6XK8t//fVXAUBUrFhRbbkeHh4CgDA3NxeHDx9WTs/OzhY9e/YUAET16tWFk5OTuHz5srJcKpUq98GMGTPytS2PHz8Wtra2AoCYN2+eStnhw4eFhYWFcltzS0tLE+XKlRMAxP/+9z+RnZ2tLLty5YpwdnZW1ouMjNS47i+++EIAENOnT89Xm4mIiIiI9ME3JIiIiIiI3nOKNxRu3LiBpKQkfPrpp2jXrp3W+Y2NjbFmzRpYWVnhzJkzAHLeZHB1ddW5HldXVyxcuBAAcObMGZU3NHQJCAjAJ598gpSUFFy9ehU1atTADz/8kM+tzOHu7o758+fDxMREOa1jx46oWbMmkpOTUbp0aXz33XeQSCTK8pEjR8LR0RH37t1DdHS0xuV+8cUXaNmypfJ3Y2NjTJgwAQBw/fp1/PDDD6hVq5ay3MrKSvkmw5EjR/K1DStWrEBycjIaNWqEL7/8UqWsZcuWGD58uMZ6f/31F2JiYlCuXDnMmjULxsbGyrKaNWti4sSJea67WrVqAIDw8PB8tZmIiIiISB9MSBARERERvefMzc3x008/KX9XjPegS8WKFZUDX9erVw99+/bVa10ff/wx6tWrBwAYMWIEKlasqFe9efPmKf//008/qY03oa/mzZvDwsJCbXqlSpUAAO3atVNJRgCAiYkJypcvDwCIi4vTuNyAgACty8yrXNsytQkJCQGQM6aDJoGBgTrr9erVC6ampnrXy83JyQlAzsDnRERERESGZpL3LERERERE9K6zsbHR+H996ug7f2Hq6Wrfjh07sGPHDrU6w4YNg5+fn8o0d3d3ncvXVm5rawsASE9P11iuqV7udmoq17TMhIQEfPXVV2rzVqlSBePHjwcAPHr0CADg6empsS3apudVz8HBAXZ2dkhKStJYDgAlSpQAgAKNe0FERERElBcmJIiIiIiI6K12+fJlrF69Wm16s2bN1BISeXUPlVd5Qevpu9yUlBSN2+Lv769MSBQnRbLCwcGhmFtCRERERO8jJiSIiIiIiOitNnXqVEydOrW4m2EQ5cuXhxBC5zxubm6IiIhAVFSUxnJt093c3HSWv3z5UufbEQDw/PlzAEDp0qV1zkdEREREVBAcQ4KIiIiIiOgt4u/vDwBYv369xvI1a9borLdlyxZkZWXpXS+369evA4ByHBAiIiIiIkNiQoKIiIiIiOgtMnToUNjY2CA0NBQLFixQKQsJCcHixYs11uvRowfc3NwQHR2NCRMmQC6XK8uuX7+OGTNm5LnuM2fOAABatGhRiC0gIiIiItKMCQkiIiIiIqK3iKurK5YtWwZjY2OMGTMGNWvWxMcffwx/f3+0aNECI0aM0FjP0tIS69evh5WVFebOnYvKlSujb9++aN26NerWrYsmTZrAw8ND63qfPXuGq1evwtXVFfXr1y+qzSMiIiKi/zAmJIiIiIiIiN4yffr0QUhICNq0aYOHDx9i586dSE5OxuLFizFv3jyt9fz9/XHu3Dl069YNL168QHBwMB49eoQffvgBmzdv1rnOdevWITs7G8OHD4eJCYcbJCIiIiLDk4i8RlQjIiIiIiKi95oQArVq1cK9e/fw4MEDuLi4FHeTiIiIiOg9xDckiIiIiIiI/uO2bduGa9eu4dtvv2UygoiIiIiKDN+QICIiIiIi+g+TyWSoVq0a0tLSEBERAUtLy+JuEhERERG9p5iQICIiIiIiIiIiIiKiIscum4iIiIiIiIiIiIiIqMgxIUFEREREREREREREREWOCQkiIiIiIiIiIiIiIipyJsXdACIiejcIIZCVlQW5XF7cTSEiIiIiIiINTExMYGLC231E9PbiEYqIiHSSyWRISEhAcnIysrKyirs5REREREREpIO1tTWcnZ1hZWVV3E0hIlIjEUKI4m4EERG9nWQyGWJiYpCRkQE7OzvY2NjA2NgYEomkuJtGREREREREuQghkJGRgcTERGRlZcHT0xNmZmbF3SwiIhVMSBARkVZPnjzBy5cv4e7uDktLy+JuDhEREREREeVBJpMhMjISFhYWKFu2bHE3h4hIBQe1JiIijYQQSE5Ohp2dHZMRRERERERE7whjY2PY2dkhNTUVfA6ZiN42TEgQEZFGWVlZyMrKgo2NTXE3hYiIiIiIiPLB0tISMpmM4wAS0VuHCQkiItJILpcDyHm6hoiIiIiIiN4dius4xXUdEdHbggkJIiLSiQNYExERERERvVt4HUdEbysmJIiIiIiIiIiIiIiIqMiZFHcDiIiIiIiIiIj0kZ2UBFlaWnE3I1+MLS1hYmdX3M0gIiJ6KzAhQURERERERERvveykJDxavBhCJivupuSLxNgYZUeMYFKCiIgI7LKJiIjIIIQQ2Lx5M7p164Zy5crBwsICDg4OqF27Nr755htER0cXdxPfWkFBQZBIJBg0aFBxN4XeE+XLl4dEIoFEIsGYMWN0zjt79mzlvCYmfFYnt5s3b6JLly4oVaoUjI2NIZFIMHXq1DzrNWvWDBKJBCEhIUXexoJ429v3rlLEEVFRkqWlvXPJCAAQMplB3+rIfZ7L/WNjY4NatWphwoQJeP78ucHWV9D2RUVFFVsb3jdRUVGQSCQoX758cTeFiKjQeNVFRERUSHFxcejatSvOnz8PiUSCevXqwdfXF6mpqQgNDcXs2bOxYMECzJ07FyNHjizu5uab4gaTECLfdaOiouDp6QkPDw9elFKxWL9+PWbPng0zMzON5StXrnzDLXo3SKVStG/fHlFRUfDx8UGbNm1gbGyM2rVrF3fTiIjoH76+vqhYsSIAQC6XIy4uDmfOnMEvv/yCNWvW4OTJk6hQoUIxt5KIiEgVExJERESF8OLFCzRp0gQPHjxAnTp1sHbtWlSrVk1Znp2djd9++w3ffvstRo0aBZlMhi+++KIYW/z26dq1Kxo1agQ7dmNABubj44OwsDDs3LkTPXv2VCs/c+YMIiIiUL9+fVy4cKEYWvj2unDhAqKiotC4cWOcPn26uJtDREQaDBs2TO0N0/j4ePj7++POnTv45ptvsG3btuJpHBERkRbssomIiKgQRo0ahQcPHsDT0xNHjx5VSUYAgImJCcaNG4fffvsNAPDVV1/h1q1bxdHUt5adnR2qVKmCMmXKFHdT6D0zZMgQANrfglixYoXKfPQvRTdzlSpVKuaWEBFRfri4uODrr78GABw5cqSYW0NERKSOCQkiIqICevDgATZt2gQAmDNnDuzt7bXO+/nnn6NWrVrIysrCrFmzVMqmTp2q7Jv92bNnGDlyJMqVKwczMzOUK1cOo0ePxsuXLzUud+vWrWjVqhWcnJxgamoKJycnVK1aFZ988gmuXr2qsc62bdvQtm1blCxZEmZmZnBzc0P//v1x8+ZNje1SeL2f4ry6YBo0aBA8PT0BAA8fPlSrr6BtDImQkBBIJBI0a9YMGRkZmDZtGipXrgwLCwu4u7vj22+/RXp6OgAgKSkJX331FSpUqAALCwuUL18eU6dORXZ2ttb2HTlyBN26dUOZMmVgZmaGUqVKoWvXrggNDdW5XfTuqFGjBnx8fHDw4EHExsaqlKWkpGDLli0oW7YsWrdurXM52dnZWL58OZo1awZHR0eYm5vD09MTn332GWJiYjTW2b59O4YNG4bq1avDwcEBFhYW8PT0xJAhQ3D79m2NdQYNGgSJRIKgoCBERkZiwIABcHFxgbm5Oby8vDBp0iRkZGQUaF8cOHAAHTp0QKlSpWBmZgZXV1f07t0bYWFhKvMp4i4wMBAAsHr1ao1xq6/jx4+jdevWcHR0hJWVFRo0aIC1a9dqnPfZs2dYsGABAgIC4OnpCUtLS5QoUQI+Pj6YOXOmMt41SU1Nxa+//go/Pz84ODjA3NwcHh4e6NixIzZs2KB3e1etWgUzMzM4ODjg2LFjKmUPHz7EoEGD4OLiAgsLC1SqVAlTpkxBenq61nEpck8/efIkOnbsiJIlS8LIyAhBQUHK+R49eoTRo0ejUqVKsLCwgJ2dHXx9fbFkyRLINPSXn/u8oUnu46e26VlZWZg5cyaqVasGS0tLODk5oVu3bjqT5qGhoWjXrh3s7e1hY2MDHx8fdntG9BZycXEBALXvQQ8fPsTMmTPRokULuLu7w9zcHPb29vDz88OSJUsgl8u1LvPFixf44Ycf4OPjAzs7O1haWqJChQro1asX9u3bp3fbZsyYAYlEgnLlyuHatWsqZdevX0f37t3h7OwMKysr1KhRA7/++ivkcrnWcSlyT9+5cydatGgBR0dHtWNyREQEBg8eDA8PD5ibm8PR0REtW7bEli1bNLYz9zlZE23fX3NPl0qlmDBhAipWrAhzc3O4uLggMDBQ7TtJbn///Tf8/f1ha2sLOzs7NGnSBDt37tQ6PxHRu4hdNhERUYEIISAMODjfmyaxtCz04Ju7d++GXC6Hvb09OnXqpHt9EgkGDBiAK1euYPfu3RBCqK0/JiYGdevWRVZWFnx9fZGeno7Tp09j4cKFOHfuHE6fPg1TU1Pl/D/88AOmTJkCExMTNG7cGG5ubkhKSkJ0dDRWrFiBatWqoWbNmsr5s7Oz0a9fP2zZsgXm5uaoV68e3NzccOfOHaxfvx7bt2/H9u3b0bZtWwBA7dq1ERgYiNWrVwOA8galgo2Njc5t9vPzQ0pKCv766y9YW1ujR48eee9UDTIzM9GmTRtcunQJzZo1g7e3N06ePIlZs2bh5s2bWL16NRo3bozExEQ0bdoUlSpVwokTJzBt2jQ8efIEf/75p9oyv/rqK8ydOxdGRkbw8fFBkyZNEB0djZ07d2L37t1YtmwZBg8eXKD20ttlyJAhCAsLQ1BQECZOnKicvmXLFqSkpGDMmDEwMtL+jE5ycjI6deqEkJAQ2NjYoF69eihZsiSuXbuGxYsXY+vWrTh06BDq1KmjUq9Xr14wNzdH1apV0aJFC2RnZ+P69etYtWoVtmzZgoMHD6Jx48Ya13n58mWMGTMGDg4O8Pf3R2JiIk6fPo0ff/wRN27cQHBwcL72weTJk5U3gBo3bgx3d3fcunULW7ZswV9//YWlS5cq3xJR3Cy5d+8eTp8+DS8vL/j5+eVrfQrBwcFYuHAhqlSpgjZt2iAuLg6nTp3CwIEDcfnyZcydO1dl/gMHDmDMmDFwc3NDxYoV0ahRIzx79gznzp3D+PHjsXPnThw7dgzm5uYq9WJiYtC2bVvcvHkTVlZW8PX1hZOTE2JjY3Hy5Elcu3YNH3/8cZ7t/f777zF9+nSUL18ee/bsQdWqVZVlN2/ehL+/PxISEuDq6orOnTtDKpVi7ty5OHr0qM6beEBO8njx4sWoUqUKWrVqhcTEROV2XLhwAW3btkViYiLc3d3RpUsXJCUlISQkBGfOnEFwcDB27dqldRyUgsjKykJAQADOnDmDpk2b4oMPPsD58+cRHByMY8eO4dKlS2oDp27duhV9+/aFTCZD9erVUaNGDcTExGDYsGG4ceOGwdpGRIV3/vx5AFB7c3ft2rWYPHkyPD09UblyZfj6+uLx48cIDQ3F6dOncfDgQWzbtk3tO+qVK1fQvn17xMbGws7ODn5+frC1tUV0dDT+/vtvPH36FO3atdPZpqysLAwfPhyrVq1C7dq1sWfPHri6uirLjx8/jnbt2iEtLQ1eXl746KOP8Pz5c3z77bc4e/Zsnts8d+5cLFy4ED4+Pmjbti3i4uJgbGwMANizZw969OiB9PR0eHt7o1u3bnj69CmOHz+Oo0eP4sCBA8q3Jg0lKSkJjRs3RnR0NJo0aYLq1asjNDQUa9aswfHjx3HlyhW17krnz5+PsWPHAgAaNGgALy8v3L17F126dFFOJyJ6HzAhQUREBSLS0vC4kndxN6PAyty9DYmVVaGWcfHiRQBAnTp1YGKS9ym1fv36AIDnz58rB3vObeXKlRg0aBAWL16svFEVExODDz/8EBcuXMC2bdvQt29fAEBGRgZ++eUX2NjYICwsDN7eqn+Lhw8fIu21hNGUKVOwZcsWNGzYEBs3blRZ/7Zt29CnTx98/PHHePDgAezt7dGlSxd06dJFmZDQ9oSYNsOGDUOrVq3w119/wdnZOd/1FUJDQ9GgQQM8ePAATk5Oyu2rU6cO/v77bzRr1gyVK1fGpk2bYPXP3zQsLAwffvghli5digkTJsDd3V25vGXLlmHu3LmoWLEi/vrrL5WkzYkTJ9ChQweMGDECfn5+72x3NclLliJl6bI85zOrUR1OQatUpj0fNBiZ167nWdfm009gO/xT5e/ylBQ88W+uV/ucVq2AWa79XpQ+/vhjjBs3Ti0hsXLlSkgkkjy7axoxYgRCQkLQoUMHrFixAqVKlVKW/frrr/jyyy/Ru3dv3Lp1S3njA8gZTLtDhw6wtrZWThNC4M8//8TIkSPx6aef4tq1axoTo7/99hsmTpyIadOmKZd5/fp1NGrUCDt27EBoaCg+/PBDvbZ///79mDFjBiwsLLBr1y589NFHyrIVK1Zg2LBhGDFiBBo2bIhq1aqhSpUqCAoKQlBQEE6fPg0/P78Cx+6CBQvw008/YcKECcppihtO8+bNQ+vWrdGmTRtlWb169RAaGopGjRqpLOfFixfo06cPDh48iAULFii7IgFyBnHt1q0bbt68idatW2PdunUoWbKksjw9PR1Hjx7V2c7MzEwMGTIE69evh4+PD/7++2+ULl1aZZ4BAwYgISEBffr0QVBQkPIYHRsbi5YtW2p960Xhjz/+wKJFi/D555+rTM/IyEDPnj2RmJiIESNGYMGCBcrE84MHD9CyZUscOHAA06ZNw48//qhzHflx5swZ1KlTB/fv31c+SZ2eno4uXbrgwIED+Pnnn7FkyRLl/PHx8Rg6dChkMhnmzZuHL7/8Ull25MgRdOjQwWBtI6KCkcvlePz4MYKDgzFr1iwYGxtj0qRJKvO0adMGXbp0QfXq1VWmx8XFISAgANu3b8e2bdtUxl2SSqXo2LEjYmNjMXDgQCxatEjloZSkpKQ8x2FKSkpCjx49cPjwYbRr1w5btmxRWUZaWhr69euHtLQ0jBs3DrNmzVI+LHDz5k20aNECT5480bmOP//8Ezt37lR7SOjJkyfo168f0tPTMWPGDHz33XfKc29YWBhat26NlStXolGjRvjkk090riM/duzYgTZt2uDkyZMoUaIEgJzzWYsWLXD58mX88ccfKufHq1ev4uuvv4aRkRE2b96s8iDP+vXrMWDAAIO1jYiouLHLJiIiogJ69uwZAKjduNIm93yKurmVLVsWixYtUnn6V9FlEwAcPnxYOf3Vq1dIS0tDhQoV1JIRAODh4YEqVaoof09MTMT8+fNhYWGBv/76Sy0Z0qNHDwwfPhwvXrzAunXr9NqeN0UikWDFihXKZASQs32KC7PIyEgsX75cmYwAcgYzbteuHeRyucrr+nK5XNnFyaZNm1SSEQDQtGlTTJ48GZmZmSo34941IiUF8vj4PH9kzxPV6sqeJ+pVV6SkvLZSoVc9eXw8RGbWG9oTOWOUdOvWDffu3cPx48cBALdv38bp06fh7++PChUqaK1769YtbNy4Ea6urtiwYYNKMgIA/ve//yEgIAB3795V666id+/eKskIIOez/Pnnn+PDDz/EjRs3tHaNU69ePUyfPl0lwVG9enXlZz73sSAvc+bMAZDTbVzuZAQADB06FB06dEBWVpZynBtDqlOnjsrNFgDw9/dX3pR//Q2JDz74QC0ZAQAODg74/fffAeQ8pZ/b7t27ERYWhjJlyuCvv/5SSUYAgIWFBQICArS28cWLF2jdujXWr1+PTp064fjx42rH9JMnTyI8PBw2NjZqx2g3Nze17dCkRYsWaskIxfY8fPgQrq6u+PXXX1XegqtQoYLy7/f777/r7LIqvyQSCVatWqVMRgA5+2ratGkA1D9jK1asQHJyMho1aqSSjACAli1bYvjw4QZrGxHpb/Dgwcou9YyNjVG2bFmMHj0aNWvWxPHjx9WShfXr11dLRgCAq6urskvR14+zy5cvR0xMDGrXro2VK1eqvSFrZ2eHVq1aaW1jdHQ0/Pz8cPjwYQwfPhy7d+9WW8a2bdsQGxsLDw8P/PzzzypvLlatWhWTJ0/Oc18EBgZqfGN52bJlSEpKQr169TBx4kSVBwF8fHyUDyvMnj07z3Xkh7W1NVatWqVMRgA557Px48cDUD/O/v7775DJZOjZs6faW8X9+vXL821sIqJ3Cd+QICKiApFYWqLMXd1PhL7NJJaWb3ydQgid5S1btlS5qa7wwQcfAIBKf7MlS5ZE+fLlcfXqVYwbNw5Dhw5V6V7kdceOHUNaWhpatmwJNzc3jfM0a9YMf/zxB86cOYNRo0bps0lvhLu7u8aLZ8XbC/Xq1VO7UZy7PC4uTjnt0qVLiIuLg5eXF+rVq6dxfYo+18+cOVPYphcbiY0NjHLdaNTG2MlR4zSZHnUlr3fZJZHotU4AkJiZ5j2TASmefl+5ciX8/f2Vfd7n9XbE3r17IYRAu3btYGtrq3GeZs2aYe/evThz5ozajZ979+5h//79uHfvHpKTk5VjASie8rx9+7bGuO3QoYPGNyc0HQt0yc7OxunTpwFArY9rhaFDh+Lvv/9WGy/BEAYOHKhxemBgIObOnYtTp05BJpOpJF5kMpmyq6LHjx8jLS0tp4vAf46fr7+JsH//fgA5b8Lk1Y3c6yIjI/HZZ58hIiICo0aNwm+//aax+y5FIqtt27ZwdFSPmfbt28Pe3l7rWD8AtHZZp0iY9unTR60rKgDo1q0bHBwc8OLFC1y8eBG+vr56bFne3N3dUatWLbXp2j5jinb269dP4/ICAwOLJKlFRLr5+vqiYsWKyt8TEhJw9epVXLhwAV9++SXWr1+v9rZnRkYGDh48iAsXLuDp06fIyMiAEALJyckAtB9nhw4dqnK81kd4eDhGjRqF+Ph4/PLLL/j22281zqc4zvbs2VMlMavQr1+/PL+b5nWcfb3rUYWhQ4fiq6++wt27dxEXF6fSjVRh+Pj4oEyZMmrT8zrO9u/fX+PyAgMDOZYEEb03mJAgIqICkUgkhe7y6F3n7OwMAHm+Qq7w9OlT5f9ff4oXgEq3Qrkpnqx6/enYNWvWoEePHpg3bx7mzZsHR0dHNGzYEB999BEGDBigbB+Q0/UHkNO1Rl5jZ2h6e6M4adsvipuP2soVN5Bz7zfFfrh///47tx/yw3b4pyrdKeXH61046cvIxgZlLurusqG4NG/eHJ6enti2bRt+/fVXrFmzBiVKlMhzXBPF52XFihV59i2d+/Mik8kwatQoLFmyRGci8tWrVxqn5/dYoM3z58+V877+VpSCl5cXAP2THPmhbZ2K6WlpaXj+/LkyoXj37l107dpV53gEr++zhw8fAoDKG2H6+vTTT5GdnY1hw4Yp38DQ5NGjRwCgNqZCbh4eHjoTEtrqKva7tn0lkUjg6emJFy9eGPRvlNdn7PXB0xX7IK+/KRG9WcOGDVNLOGdnZ+P777/Hzz//DH9/f9y+fVv5nejs2bPo3bs3oqOjtS7TkMfZ3r17Izs7GzNmzNCajADyPs7a29vDzs4OSUlJWpdR0OOsvb09HB0dkZiYiEePHhksIZHfczmPs0T0X8KEBBERUQHVq1cP69atQ3h4OLKzs/McR0IxwKCTk5PGiyZdA+tq0qRJE0RFRWHPnj04fvw4zpw5gwMHDmDfvn2YMmUKgoOD0bJlSwBQDrhasWLFPJ+wLcgFZ1HKa7/kZ78p9oOLi4tK3/Wa5E7o0LtNIpFg0KBBmDJlCgIDAxEfH49PP/0Ulnm8KaX4vNSuXVvj0+S5NWzYUPn/3377DYsXL4aLiwvmzZuHxo0bo3Tp0rCwsACQ8zT/xo0btSYr8nsseJfl3gc9evTAjRs30KFDB3zzzTeoWrUqSpQoAVNTU2RmZmp8g6Aw+vfvjzVr1mD9+vXo1q1bngOy6kpi5pXgzOuzZmh5DbL9X/qMEf3XmJiYYMaMGVi2bBkeP36MNWvWYOTIkUhNTUWXLl3w5MkTDB48GJ999hkqVqyIEiVKwNjYGHfu3IG3t3eeb/TmR2BgIFasWIH58+ejbdu2Wt9OVeBxlojov4EJCSIiogLq2LEjxo0bh6SkJOzcuRPdu3fXOq8QAmvXrgWgvTuWgrC0tESPHj2UT3o/e/YMkyZNwtKlSzFkyBDlU23lypUDAHh7exd4gNr3gWI/ODk5/af3w3/RoEGDMG3aNOzevRtA3t01Af9+Xnx9fbFw4UK917VlyxYAwJIlSzT2+Xz37l29l1UYTk5OMDc3R0ZGBh48eKA2Zgrw71sg2rpyK4zIyEiN06OiogDkjFmgGBsmIiICV69eRalSpRAcHKyW4NW2zxRPoEZEROS7fYGBgWjXrh369++PLl26YMOGDRqP44p9o2i3JopjbX4plq34O2ii2I+5/0ZmZmYAoOxixVDt0cbNzQ0RERFa94GufUNEb56RkRHKly+PhIQE5XhFJ06cwJMnT1C3bl1l14W56TrO3rp1CxERETrHitBk0qRJqFq1KsaNG4cWLVpgz5498PPzU5svr+NsUlKSzrfQdFEcv7QdZ5OSkpCYmKjSDqB4jrP3799HVFQUqlWrplbO4ywRvU+YsiUiIiogLy8v9OrVCwDw9ddf67xQ+uOPP3D16lWYmJjg66+/LrI2lSxZUjkoYXR0NF68eAEgZ3wKMzMzhISEqHQdpQ9FX77Z2dn5bo/iYq4gdYtC/fr14ezsjJs3b+rsFobeP+7u7ujcuTOcnJzQqFEjlTcatFE8Mb9r1658DSisuLHh4eGhVnbjxg1cvnxZ72UVhomJifLGj7YEnOKmVPPmzQ2+/nXr1mmcvmbNGgCAn5+fMvGg2Geurq4a3zbTtqy2bdsCADZu3AipVJrvNvbq1QvBwcEwMjJC7969lW3LrWnTpgBy+lFXHFNz27dvn8bp+lCMWbN582aNn7Hg4GC8ePECtra2Kk8WK26aaRsYfc+ePQVqjzb+/v4AgPXr12ss17TfiKj4yOVy5Q1sRReXiuOstq6E8jrOrly5UjkWUn6MHTsWS5cuRUpKCtq0aYNDhw6pzaM4zm7dulXjd8YNGzbke70KiuPs6tWrNZYrzoOVKlVSSUjoOs4KIbBv374Ct0kTHmeJ6L+ECQkiIqJCWLRoEcqXL4/IyEi0aNFC7SZ3dnY25s2bhzFjxgAAZs6cqfGpp/x6+PAhli9frrEPesUT4A4ODsp+akuXLo3Ro0dDKpWiY8eOuHbtmlq9jIwM7Nq1S+1J47JlywJAgW7glyxZEmZmZoiPj1deCBcnU1NTTJkyBUIIdO3aFadOnVKbRyaT4ejRozh79mwxtJCK0vbt25GQkIDQ0FC95q9Tpw66d++OmJgYdOvWTePTiVKpFOvXr1cZS0YxYOWiRYtUunR4/PgxBg4c+EYTdOPGjQMA/Pnnnzhy5IhKWVBQEHbt2gVTU1PlMcqQLl68qEyQKpw6dQqLFi0CAHz55ZfK6ZUrV4axsTGuXbumHNhTYffu3Zg/f77GdXTq1Al16tRBXFwcevbsiefPn6uUp6en53nTqH379ti7dy8sLS0xaNAg/PHHHyrlTZs2Ra1atZCcnIzRo0cjMzNTWRYXF6fcxwXRs2dPuLu7Iy4uDmPHjlX5bERGRiqXPXr0aGWXXwDQokULGBkZ4cCBA8rBYIGcm2QLFizAX3/9VeA2aTJ06FDY2NggNDQUCxYsUCkLCQnB4sWLDbo+Iiq47OxsTJo0CQkJCQCgfFNPcW46cuQIbt68qVJn6dKl2Lx5s8blDRs2DGXLlsWlS5fwySefqCV/X716hcOHD+ts0yeffIJ169YhMzMTHTt2xI4dO1TKe/bsiTJlyiAqKgoTJ05UOXdGRETghx9+yHvDday7RIkSCA8Px08//aTSJdWlS5cwY8YMAFB7YEjxNsjatWtV9ldWVha+/fZbXLhg2HGzRo8eDWNjY2zZsgXBwcEqZZs2bVLbZ0RE7zJ22URERFQIjo6OOHXqFLp06YKwsDDUqFEDPj4+8PLyQmpqKkJDQ/Hs2TOYmZlh7ty5Brvp9+LFC3zyySf4/PPPUbt2beVAd3fv3sWlS5cgkUgwe/ZsGBsbK+v88ssvePz4MTZs2KDsE79ChQowMTHBo0ePcPnyZUilUuzbt09lHInu3btjzpw5aNWqFVq0aKEcGHHmzJnK7la0MTU1RadOnbBt2zbUrl0bfn5+sPpnMPTly5cbZF/k16hRoxAdHY3Zs2ejSZMmqFatGipWrAhLS0vEx8fj8uXLePnyJf788080atSoWNpIb49Vq1bh5cuX2LdvH7y9vVGrVi14enpCCIGoqChcuXIFmZmZuHXrFkqXLg0A+O6777B//34sW7YMx44dQ926dfHq1SscP34cFSpUQNeuXdVuNhSVdu3aYdKkSZgxYwY++ugj+Pr6wt3dHREREQgPD4exsTEWL15skETp67744gtMmDABa9asQc2aNREXF4eTJ09CLpdjzJgxCAgIUM7r7OyMUaNG4bfffkPLli3RpEkTuLq64vbt2wgPD1duw+uMjIwQHByMNm3aYN++fXB3d4efnx+cnJwQGxuLK1euwN7ePs+uLpo3b47Dhw+jXbt2GDlyJJKTk5UDsEokEqxbtw7+/v5Yv349QkJC4Ovri9TUVBw7dgy1a9fGhx9+iNDQUOVbYfoyNzfHtm3b0LZtW/z555/Yu3cvGjVqhOTkZBw9ehTp6elo06YNpkyZolKvXLlyGD16tMr+cnR0xJUrVxAdHY3x48fjl19+yVdbdHF1dcWyZcvQv39/jBkzBsuXL0f16tURGxuLkydP4n//+5/WpBERFZ3ly5erJHGfP3+OK1euICYmBgAwceJENG7cGEBOkr1z587YuXMn6tSpg2bNmsHR0RGXL1/G7du38d133+HHH39UW4eNjQ127dqFgIAArFq1CsHBwfD19YWNjQ1iYmJw6dIlNGjQIM/unPr27Qtra2v06tULPXv2RFBQEPr16wcAsLKywrp169C+fXvMmjUL27dvh4+PDxITExESEoLOnTvj3LlziI6OzvdxtnTp0li/fj169uyJiRMnYu3atahTpw6ePn2K48ePIzs7G4MHD8Ynn3yiUs/X11e5v3x8fODn5wdLS0uEh4fj1atXGDNmDH777bd8tUWX2rVr4+eff8Y333yDbt26oWHDhvDy8sLdu3dx4cIFfPnllzzOEtF7g29IEBERFZKbmxvOnTuHjRs3olOnToiNjcX27dsREhICFxcXjBs3Drdv3zboE8heXl749ddf0aFDB7x8+RJ79+7Fnj17IJVKMXDgQFy4cAFDhw5VqWNiYoL169dj79696NKlC54+fYpdu3bhwIEDSExMRMeOHbFhwwbla/MK06dPxzfffAN7e3vs2LEDK1aswIoVK7T2qfu6JUuWYPjw4ZBIJNi2bZuyfnGaNWsWTp8+jX79+iElJQX79+/Hnj17EBcXh2bNmmH58uXo3bt3sbaR3g62trY4ePAgNmzYgFatWiE6OhrBwcE4evQo0tLS0K9fPwQHB8PLy0tZp2HDhggLC0OnTp0glUqxa9cu3L9/H6NHj0ZoaKjyzaU3Zfr06di3bx/atWuHW7duYcuWLco3Cs6cOaPXeBoF0bVrVxw6dAguLi7Yu3cvzp8/j7p16yIoKAi//vqr2vzz58/HihUrUKdOHVy8eBF79+6FlZUVNm3ahOnTp2tdj4eHB8LCwpRvoIWGhmL79u14+PAh/P39MXPmTL3a27BhQ4SEhKBUqVIYP348Jk2apCyrXr06Ll68iAEDBiArKws7duzArVu3MGbMGBw6dEj5hoyzs3P+dhJyupK7fPkyRo4cCWNjYwQHB+PkyZOoU6cO/vzzT/z9998ab8DNnz8fc+fOReXKlXHmzBmEhISgatWqOHv2LNq0aZPvduSlT58+CAkJQZs2bfDw4UPs3LkTycnJWLx4MebNm2fw9RFpYmxpCUmuhy3eFRJjYxgXwaDLp0+fxurVq5U/Bw8eVHZBd+zYMbVE7tatWzF79mx4e3vj1KlTOHjwINzd3XHgwAEMGzZM63rq1KmDa9euYdKkSShXrhxCQkKwa9cuxMfHo1OnTpgwYYJe7e3UqRP27NkDc3NzDBw4EEuXLlWWtWjRAufOnUPXrl2RmJiIHTt24NGjR/jxxx+xbt06xMfHw8jICI6OjvneTx06dEB4eDgCAwORkpKCbdu24eLFi2jSpAk2bdqkcUwNIKc7vUmTJqFMmTIICQnB2bNn0aRJE4SHh6N27dr5bkdevv76a+zcuRN+fn64fv268i3Gbdu24YsvvjD4+oiIiotE5H5fjYiI6B/p6emIjIyEp6enSjcRRERE9K/IyEhUrFgRtra2SExMhJERn/kiKkrZSUmQpaUVdzPyxdjSEiZ2dsXdjHfWiRMn4O/vjxo1auDq1avF3Zx3Bq/niOhtxS6biIiIiIiIdJBKpYiKilLr2urhw4fo168f5HI5AgMDmYwgegNM7Ox4c/899OzZM6SkpCi7IVW4fv26sjulwYMHF0fTiIjIwJiQICIiIiIi0uHZs2eoXr06vLy8ULlyZZQoUQLR0dEIDw9HRkYGatWqpbNbKSIi0u3GjRto3rw5qlatigoVKsDS0hKRkZEIDw+HXC7HRx99hNGjRxd3M4mIyACYkCAiIiIiItLB2dkZX331FY4ePYoLFy7g5cuXsLKyQs2aNdG9e3eMHj0aVlZWxd1MIqJ3VuXKlTFy5EgcP34cp0+fRnJyMmxtbdG4cWN8/PHH+OSTT2BiwltYRETvA44hQUREGrHPUSIiIiIioncTr+eI6G3FTk6JiIiIiIiIiIiIiKjIMSFBRERERERERERERERFjgkJIiIiIiIiIiIiIiIqckxIEBERERERERERERFRkWNCgoiIiIiIiIiIiIiIihwTEkREREREREREREREVOSYkCAiIiIiIiIiIiIioiLHhAQRERERERERERERERU5JiSIiIiIiIiIiIiIiKjIMSFBRERERERERERERERFjgkJIiKiQihfvjwkEonKj7m5Odzd3dG7d2+cPHmy2NoWFRUFiUSC8uXLv7F1hoSEQCKRoFmzZm9snUSvyx2XY8aM0Tnv7NmzlfOamJi8oRa+f27fvo1ff/0VAQEBcHNzg5mZGUqUKIH69evj559/RkpKSqGWHxQU9EaPZYU1depUSCQSTJ069Y2s7+bNm+jSpQtKlSoFY2NjvdfdrFkzSCQShISEFHkbC+Jtb9+7SnHMIyIiIioOvOoiIiIyAF9fX1SsWBEA8PLlS4SFhWHLli3YunUr5syZg7FjxxZzCw1DcQNDCFHMLSHSz/r16zF79myYmZlpLF+5cuUbbtH7qWXLloiNjYWFhQV8fHzQtGlTPHnyBKGhoQgLC8OKFStw9OhRuLu767U8uVwOIQSMjY21zpOVlQVTU1NDbYJSSEgImjdvDn9//3fiRrhUKkX79u0RFRUFHx8ftGnTBsbGxqhdu3ZxN42IiIiISA3fkCAiIjKAYcOGISgoCEFBQdixYwfu3buHgQMHQgiBb775Bnfu3CnuJr4RDRo0wK1bt7BmzZribgoRfHx88Pz5c+zcuVNj+ZkzZxAREYH69eu/4Za9f7y9vbFixQo8e/YMJ0+exMaNG3H06FHcunUL1apVw/379zFo0CC9l7d8+XL4+Pjg9OnTamVSqRQTJkxAzZo1kZmZacCteDdduHABUVFRaNy4MS5cuIC1a9ciKCgIXbp0Ke6mERERERGpYUKCiIioCFhYWGDRokWwtraGTCbD9u3bi7tJb4SVlRWqVKmi91PQREVpyJAhALS/BbFixQqV+f5rDNml25EjRzBkyBDY2NioTC9fvjwWL14MADh27BgePXqk1/J69uyJjz76CK1bt8bAgQMRHx8PANi8eTOqVKmCAwcOYP78+VrffPkviY6OBgBUqlSpmFtCRERERJQ3JiSIiIiKiI2NDby9vQHkjOeQ2507dzB8+HB4eXnBwsICdnZ2aNq0KdatW6dxWQ8fPsTMmTPRokULuLu7w9zcHPb29vDz88OSJUsgl8vz1bbU1FR07twZEokEzZs3x8uXL3XOr+gPXeH1cTMU26dtDInc41nI5XIsWLAANWvWhJWVFcqUKYMRI0YgMTERAJCRkYHp06ejSpUqsLS0hKurK8aMGQOpVKq1fRcvXkS/fv2U+8bR0RFt2rTB3r1787Vf6P1So0YN+Pj44ODBg4iNjVUpS0lJwZYtW1C2bFm0bt1a53Kys7OxfPlyNGvWDI6OjjA3N4enpyc+++wzxMTEaKyzfft2DBs2DNWrV4eDgwMsLCzg6emJIUOG4Pbt2xrrDBo0CBKJBEFBQYiMjMSAAQPg4uICc3NzeHl5YdKkScjIyCjYzihGderUUf5f2/56nYODA2bNmoU7d+7A3NwckydPxsOHDzF58mTMmTMHFy9eRNu2bfVuw927dzFkyBB4enrC3NwcNjY28PDwQPv27bFq1SrlfM2aNUPz5s0BAMePH1c5zr2evElLS8PUqVNRqVIlmJubo0yZMggMDFQmCArqwIED6NChA0qVKgUzMzO4urqid+/eCAsLU5lPcbwNDAwEAKxevVqlvfl1/PhxtG7dGo6OjrCyskKDBg2wdu1ajfM+e/YMCxYsQEBAADw9PWFpaYkSJUrAx8cHM2fORHp6utb1pKam4tdff4Wfnx8cHBxgbm4ODw8PdOzYERs2bNC7vatWrYKZmRkcHBxw7NgxlbKHDx9i0KBBcHFxgYWFBSpVqoQpU6YgPT1d67gUuaefPHkSHTt2RMmSJWFkZISgoCDlfI8ePcLo0aNRqVIl5fnb19cXS5YsgUwmU2tnXuOJaDtv5p6elZWFmTNnolq1arC0tISTkxO6deuGW7duad0/oaGhaNeuHezt7WFjYwMfHx92UUdERERvBY4hQUREBSKEQFpqVnE3o8AsrUzfyICOr169AgCYm5srp23duhUDBw5Eeno6qlSpgoCAACQlJeHcuXMYMGAAjh49qnbTYO3atZg8eTI8PT1RuXJl+Pr64vHjxwgNDcXp06dx8OBBbNu2Ta9tevLkCTp06ICwsDD0798fK1asyPMp49q1ayMwMBCrV68GAOUNMIXXn4rWpX///tixYwf8/f3h5eWFM2fOYMmSJTh//jxOnjyJtm3b4urVq2jWrBkqVaqEkydPYsGCBbh7967GBMNvv/2GsWPHQi6Xo3bt2mjYsCHi4+MREhKCgwcPYtq0afj+++/1bh+9X4YMGYKwsDAEBQVh4sSJyulbtmxBSkoKxowZAyMj7c/oJCcno1OnTggJCYGNjQ3q1auHkiVL4tq1a1i8eDG2bt2KQ4cOqdx0B4BevXrB3NwcVatWRYsWLZCdnY3r169j1apV2LJlCw4ePIjGjRtrXOfly5cxZswYODg4wN/fH4mJiTh9+jR+/PFH3LhxA8HBwYbZOW/I3bt3lf8vU6ZMvuoaGRmpjCOhGHw8P8fv69evw9fXF69evYK3tzc6dOgAY2NjPHr0CCdOnEBsbCwGDx4MAGjbti0sLCxw4MABlC5dWiXp4ezsrPx/amoqWrZsibNnz8La2hqtW7eGpaUlDhw4gD179qB9+/b52k6FyZMnY8aMGZBIJGjcuDHc3d1x69YtbNmyBX/99ReWLl2qfKPHxcUFgYGBuHfvHk6fPg0vLy/4+fkVaL3BwcFYuHAhqlSpgjZt2iAuLg6nTp3CwIEDcfnyZcydO1dl/gMHDmDMmDFwc3NDxYoV0ahRIzx79gznzp3D+PHjsXPnThw7dkzl3AfkJKTatm2LmzdvwsrKCr6+vnByckJsbCxOnjyJa9eu4eOPP86zvd9//z2mT5+O8uXLY8+ePahataqy7ObNm/D390dCQgJcXV3RuXNnSKVSzJ07F0ePHs0zgb9161YsXrwYVapUQatWrZCYmKjcjgsXLqBt27ZITEyEu7s7unTpgqSkJISEhODMmTMIDg7Grl27DPrmTlZWFgICAnDmzBk0bdoUH3zwAc6fP4/g4GAcO3YMly5dUkuWbd26FX379oVMJkP16tVRo0YNxMTEYNiwYbhx44bB2kZERERUIIKIiEiDtLQ0cfPmTZGWlqaxXJqSIVxL/fDO/khTMgyynzw8PAQAsWrVKrWyK1euCCMjIwFArFy5UgghxNWrV4W5ubmwsLAQf/31l8r8UVFRokaNGgKAWL16tUrZ+fPnxbVr19TWERsbK2rVqiUAiC1btqiURUZGCgDCw8NDOe3GjRvKNk+aNCnf2wtA6Pr6cOzYMQFA+Pv7a2wLAOHl5SWioqKUZQkJCaJSpUoCgKhRo4Zo0KCBSEhIUJY/ePBAODg4CADi1KlTKsvdv3+/kEgkwtnZWRw/flyl7OrVq6Js2bICgAgJCcn3tr7LXi1eIuLq+oi4uj4i/fQZlbKshw+VZS8mqn8GEgIHKctfl7Jps7Isdc9elTJZcrKy7PnIUWp1E//3pbI8+3liIbdQN8Vn/OTJk+Lly5fC0tJSVKxYUWUeX19fIZFIxP3795WfT2NjY7VlffzxxwKA6NChg3jy5IlK2fz58wUAUalSJZGdna1StmnTJpGSkqIyTS6Xi0WLFgkAolq1akIul6uUBwYGKuNk4sSJKsu8du2asLa2FgDEmTOqf9OCev34UFR69+4tAIi6devqXefly5diwoQJwtraWvTv31/8/PPPwsPDQ2zcuFGULVtWNGjQQBw9elSvZQ0ePFgAEDNmzFArS01NVTt2aDuO5fbVV18JAKJKlSoiNjZWOV0qlYrOnTsr/45TpkzRq41CCLFv3z4BQFhYWIiDBw+qlC1fvlwAEKampuL69esqZatWrRIARGBgoN7rUvD391e29aefflIpCwkJEZaWlgKA2L9/v0rZzZs3RWhoqNryEhMTRevWrQUAMWvWLJUymUwmfHx8BADRunVr8fTpU5XytLQ0sWfPHo3tO3bsmBBCiIyMDNGvXz8BQPj4+Ij4+Hi1NtStW1cA/2/v3qOius42gD/DwMBwVSCIGJlyUWKKGoJarNEBaRhBxAQVKBCBSogYrTGNfpW0i1qT1cQkGrEqJstLbPDSamg1AbVommjIxUvbWC1GmqnRgBqRkgkMF2F/f9hzZJwZmEEQtc9vLdfSs8/eZ8+5jbPfc94NkZaWJpqbm+XlFy5cEGFhYfLnldq0tC/Wrl1r1m5zc7N8b5k7d65obW2Vy/71r3+J733vewKAKCgoMKlXWFjY5blg7XyTlgMQERERora21mRf6XQ6AUDk5eWZ1KutrRUeHh4CgFi5cqVJWUVFhXBxcen2u5yI7g3d/Z4jIuovTNlERETUyxoaGlBWVobk5GR0dHQgICAAKSkpAIAXX3wRLS0teOGFF5CcnGxST6PRyDnti4qKTMrGjh2L8PBws20FBARgxYoVAK4/EdmVQ4cOYcKECaipqcGmTZuwfPnyHn/GW1FUVASNRiP/28fHB/n5+QCuP8m8ceNG+Pj4yOVBQUHIzMwEcD1PfWeFhYUQQqC4uBiTJk0yKRs5ciRWrlwJAFizZk2ffJY7lfjuO3RcvIiOixchWm9K8dPeLpd1NDSY1W2vuyqXm7VrbLrRrtF4U6G40e5/02911lFff6NdYV+KsVvh5eWF5ORkVFdX44MPPgAAnDlzBh999BG0Wi2Cg4Ot1v3nP/+J7du3IyAgANu2bYOfn59J+TPPPIOEhAScPXsW5eXlJmWpqalwc3MzWaZQKDBv3jyMHz8ep06dsppuJTIyEsuXLzd5MyA8PBxPPPEEAKCiosL2HdDPtmzZgp07d0KpVGL16tU219uxYwfKy8uxf/9+/O53v4O/vz8AIC0tDVVVVYiJicHcuXNtmtT60qVLAICEhASzMrVabXbv6I7RaMSGDRsAAKtWrUJAQIBc5urqiuLiYri4uNjVJgC8+uqrAIB58+bh0UcfNSmbM2cOEhMT0dbWZtd+tFVERASWLl1qskyr1WLevHkAYPaGxIgRIxAVFWXWzsCBA+X77c3fSXv37sWxY8cwePBg7N69G/fdd59JuYuLi8VjJKmvr0dcXBxKSkqQlJSEDz74AIMGDTJZ5/Dhwzhx4gTc3d2xdu1akzc0hgwZYvY5LJk8ebL8uTv7wx/+gHPnziEgIACvv/46nJyc5LLg4GD5+K1Zs6bLlFX2UigU2Lx5s3wNANf31bJlywCY3w82btwIg8GAqKgoLFq0yKQsNjYWTz31VK/1jYiIiKgnmLKJiIh6RO3qhLNf/l9/d6PH1K5O3a9kh5ycHDnlR2chISHYvXs33Nzc0NHRIQ9apqamWmxnzJgxcHd3x1//+lc0NzebDGq1tLTgwIEDOHr0KC5fvoyWlhYIIWAwGADAal564Hpu8SeffBJqtRrvvfee2WDX7eLo6GgxX780GWtgYKDFwItUXlNTIy+7cuUKPvvsM6jVakybNs3i9qSc3JWVlbfa9buKwt0dDv8dvFKoTFOmQKmUyxy8vMzqKn280d5p4MukXbXrjXbV6psKFTfa9fY2q+swcKBcDsXtfSbmJz/5CUpKSrBp0yZotVo5JVp3k1mXlZVBCIH4+Hh4eHhYXCc6OhplZWWorKxEYmKiSVl1dTX27duH6upqGAwGOb+8NEB+5swZk1QzksTERIspiUaMGAEAZvNhdCc7O9tq2ZUrVyyW+/r6ygOsPXXw4EF58HPFihV2pRN68sknkZubaxKUkbi5ueGll17C8uXLTQaFrRk3bhzKysqQn5+PZcuWQavV9ihgIDlx4gQMBgN8fX0tzmPh7++PuLg47Nmzx+Y2r127ho8++giA9eM1Z84cvPvuu2bzJfSG2bNnW1yelZWF1157DUeOHEF7e7vJ8Whvb5dTFdXW1sJoNEIIASEEAPPvpH379gEA0tPT7UrzBwB6vR75+fmoqqrC/PnzsXr1aoup1qSg45QpU+Bt4T40depUDBgwoMt5k2bOnGlxuTTnRFpamlkqKgBITk7GwIEDUV9fj+PHj2PChAk2fLLuBQYGYvTo0WbLrd0PpH5mZGRYbC8rK6tPglpEREREtmJAgoiIekShUMDVrfdyJN/tJkyYgNDQUACASqWCn58foqKiMGXKFDg6Xv+6raurk+eUGDp0aLdt1tXVYciQIQCATz75BKmpqV1Oliq1fbMLFy7IA1yHDh3qcY7x3jB48GB5f3QmDU4FBgZarCcNBnd+6lSv11+fy8RotDg41Nk333zT0y7flTyeyoPHU3kWyxwDAzH4+FGrdX22bLZa5paaArfUFItlDu7uXbY7cNVKq2V9LSYmBkFBQdi1axdef/11bN26FZ6enlYHHiVffvklgOtPHEtvL1nT+Rxrb2/H/PnzsWHDBnlw1hJr16y168DT0xMA7H76Wpr7xZLGxkaL5RqN5pYCEkeOHMH06dPR2tqKwsJCPPvss3bV72peD4ktwQgAWLx4MY4cOYKKigpMmTIFTk5OGD16NCZNmoS0tDSMHTvWrr5duHABAMzy9ncWFBRkV5t1dXXycbVWNyQkBID9ASlbWNumtNxoNKKurk5+S+js2bN4/PHHu5yP4Obz+9y5cwCABx54wO7+5eXl4dq1a8jNze3yjTdbjo1Go+kyIGGtrrTfre0rhUKBoKAg1NfX9+ox6u5+cPNE99I+6O6YEhEREfUXBiSIiIh6QW5ubpdPIQMwmUjz5kmhLZEG2ZuamvDYY4/h0qVLyMnJQX5+PkJDQ+Hp6QmlUokvvvgCYWFhVgc+/fz88NBDD6G8vBzPPPMM9u/fb5IS6XbqbpDRlkFIibQ/3d3dMWPGjFvqF93bFAoFsrOzUVhYiKysLFy8eBF5eXlQ3/yWx02kc+yhhx6y+IRyZz/4wQ/kv69evRrFxcXw9/fHypUr8cMf/hCDBg2Sn8pPT0/H9u3brV6z9lwHtrC2HYVCAY1Gg3//+9+9ur3KykokJCSgsbERzz//PH71q1/dcpvZ2dnd3mOtcXV1xZ///GccPXoU+/btQ2VlJSorK3Hs2DGsXLkS8+bNw9q1a2+5j/e6zufRzJkzcerUKSQmJmLJkiV48MEH4enpCScnJ7S2tnYbJLZXZmYmtm7dipKSEiQnJyM+Pr7L9bua9Ly7CdG7uy/0tu4m2e7t+wERERFRf2NAgoiI6Dbx9fWFWq2G0WjEq6++Cl9fX5vqffjhh7h06RIefvhhOdVMZ2fPnu2yvkqlwp/+9Cekp6dj165d0Gq1qKioMMlHfTeS3jJRKBTYtGkTB22oS9nZ2Vi2bBn27t0LoPt0TcCNc2zChAn47W9/a/O2fv/73wMANmzYgKSkJLPy7q7Zu9knn3yCKVOmwGAwoKCgAC+88EJ/d0k2duxY+W2Ia9eu4Y9//CNmz56NdevWYebMmYiJibGpHenNta4COfYGeXx8fODs7IyWlhZ8+eWXGDVqlNk60hs70vZ7k16vt7hc+hwuLi5yILuqqgqff/45/Pz8UFpaavbWm7XzW3rSv6qqyu7+ZWVlIT4+HpmZmXjsscewbds2i4FoW46N9KaGvaS2peNgibQfOx8jler626RSesXe6o81Q4YMQVVVldV90NsBSCIiIiJ78Zc7ERHRbaJUKuW5G6QBS1tc/e8EwdbSNrz99tvdtuHk5IQdO3YgOzsbp06dwsSJE3s0CCKlSLl27ZrddXtbQEAARo0aBYPBIOcmJ7ImMDAQ06dPh4+PD6KiokzeaLBGegp7z549dqVJkq7ZzpO3S06dOoW//e1vNrd1N/nss8+g0+nkYMSLL77Y312yytHRETNnzoROpwMAk2MiDSBbu89FRkbC3d0dV65cwYEDB8zKL126ZHF5d/2R0ult2bLF4jpSQNrWwIk9rH2PbN26FQDwyCOPyIEH6fwOCAiwmILPWlvSfBvbt29HY2Oj3X1MSUlBaWkpHBwckJqaKvetM2mC8n379qG+vt6svLy83OJyW0hzEu3cudPi/aC0tBT19fXw8PBAZGSkvFwKTlibxP69997rUX+s0Wq1AICSkhKL5Zb2GxEREdHtxIAEERHRbVRYWAiVSoXFixfjrbfespiq4R//+Afeeecd+d/SxJUHDx7E6dOnTdZ94403sHPnTpu2rVQqsWnTJsyfPx/V1dWYOHEivvjiC7v6f//99wNAl3nDbyfp6eucnBz5yffOhBD49NNP7R4cpHvTO++8gytXruDjjz+2af2IiAjMmDED58+fR3JyssUnixsbG1FSUiJPVA3cuGbXrl1rco3X1tZi9uzZd0RAr7cdO3YMcXFx+Pbbb++4YMS6devMJlgGgIsXL+LYsWMATINH0n3u7NmzaGtrM6unVquRl3d9jpZFixahtrZWLjMajcjPz4fRaLS7nz/72c8AAOvXr8fBgwdNyrZs2YI9e/bAyckJCxcutLvt7hw/fhwrVqwwWXbkyBE5ldWiRYvk5cOHD4dSqcTJkyflCZQle/fuxapVqyxuIykpCREREaipqcGsWbNQV1dnUt7c3Izy8vIu+zl16lSUlZVBrVYjOzsb69atMymfNGkSRo8eDYPBgAULFqC1tVUuq6mpkfdxT8yaNQuBgYGoqanBs88+a3Id6/V6ue0FCxaYTJo+efJkODg4YP/+/fKk28D176eioiLs3r27x32yZM6cOXB3d8fHH3+MoqIik7K//OUvKC4u7tXtEREREdmLAQkiIqLb6OGHH5afHs3OzoZGo4FOp0NmZiYSEhIwdOhQjBw50uQNioiICEyfPh0GgwERERHQ6XT48Y9/jBEjRmDu3LkoKCiwefsKhQJr1qzB0qVLcf78eUyaNAknT560ub6UIuNHP/oRUlNTkZubi9zcXLOBpdtl2rRpWL16Na5evYqkpCQMGzYMiYmJyMjIQFxcHPz9/REVFYVDhw71S//o7rd582bExsaivLwcYWFhGDduHFJTU5GSkoJx48bB29sbmZmZJk9dFxQUQKVS4c0330RYWBhSU1MRHx+PkJAQtLS04PHHH+/HT9Q34uLi0NDQgAEDBuDrr7+W53y4+U9P0vXcqjfeeAMPPPAAgoODkZSUhMzMTOh0OgQHB+PChQuYPHmySWqtwMBAjBkzBpcvX8bIkSORmZmJ3Nxc/PznP5fX+fWvf41x48bh9OnTGD58OJKSkpCSkoLg4GB8+OGHmD17tt39jI+Pxy9+8Qs0Nzfj0UcfxcSJE5GRkYHIyEjk5ORAqVSiuLgY3//+93tlv3T205/+FEuXLkV4eDjS09MRHR0NrVaLpqYmLFy4EAkJCfK6vr6+mD9/Ptrb2xEbG4vo6Gikp6cjMjISSUlJWLx4scVtODg4oLS0FGFhYSgvL0dgYCB0Oh3S09Oh1Wrh7++P/Pz8bvsaExODiooKDBgwAE8//TRefvlluUyhUODtt9+Gt7c3SkpKEBwcjNTUVEybNg3Dhw+Ht7c3xo8fD+DGmzC2cnZ2xq5du+Dt7Y3169cjNDQUaWlpmDp1Kh588EHo9XrodDoUFhaa1Bs6dCgWLFiAjo4OxMbGIiYmBjNmzMCwYcPw3HPPmZxXvSEgIABvvvkmlEolFi5ciFGjRsn7ePLkyZg7d26vbo+IiIjIboKIiMgCo9EoTp8+LYxGY3935Y6m0WgEALF582a76un1erFo0SIRHh4u3NzchIuLi9BoNCI6Olq89NJLorq62mT91tZW8corr4iRI0cKV1dX4e3tLeLi4sSBAweEXq8XAIRGozHbhqXlkt/85jcCgBg4cKD49NNPbeq30WgUS5YsEaGhoUKlUgkAAoDQ6/VCCCHef/99AUBotVq7+mKtnmTz5s0CgMjKyrJYfvLkSZGXlyeGDRsmXFxchKurqwgODhY6nU4UFRWJr7/+2qbPR/cG6bo8fPiwTetL56dSqbRY3t7eLrZt2yYSEhLEoEGDhJOTk/Dx8RHh4eEiJydHlJaWitbWVpM6n3/+uUhKShKDBw8WLi4uYtiwYWLJkiXi22+/FVlZWRbvG9aWS7q7DuzV1TXZk7Zs+fP+++/3yvbs8e6774r8/HwREREh7rvvPqFSqcT9998voqOjxVtvvWV27IQQ4ty5cyI9PV0MHjxYODo6WtxXjY2N4pe//KUICQkRKpVKDBo0SGRkZAi9Xi8KCwsFAFFYWGh3f8vLy0VCQoLw8fERjo6Owt/fX8yaNcvqffpWzgutVisfl4MHD4rY2Fjh5eUl1Gq1GDNmjNiyZYvFeh0dHWLjxo0iMjJSuLu7Cy8vL/HII4+IHTt2CCFunA+WGAwG8fLLL4uxY8cKDw8P4ezsLDQajUhKSpLrW+rfzf7+978LPz8/AUA8//zzJmV6vV488cQTws/PT6hUKhESEiIKCgpEU1OTCA4OFgDEmTNnbN5WZ1999ZV4+umnRXBwsFCpVMLDw0OMHz9erF+/XrS1tVndX6+99poYMWKEUKlUwtvbW0ybNk0cP37c6vdfd9+LQnS9nw8fPix0Op3w9PQUrq6uIiIiQmzYsKHbekR07+DvOSK6UymEEKKvgh1ERHT3am5uhl6vR1BQkEnqASIiIqK7kV6vR2hoKDw8PHD16lU4ODBhABHdu/h7jojuVPwfGBEREREREd0TGhsbLc5zdO7cOWRkZKCjowNZWVkMRhARERH1E8f+7gARERERERFRb/jmm28QHh6OkJAQDB8+HJ6envjqq69w4sQJtLS0YPTo0Vi+fHl/d5OIiIjofxYDEkRERERERHRP8PX1xXPPPYdDhw7h6NGj+M9//gNXV1eMGjUKM2bMwIIFC+Dq6trf3SQiIiL6n8WABBEREREREd0T3N3d8corr/R3N4iIiIjICibOJCIiIiIiIiIiIiKiPseABBERERERERERERER9TkGJIiIiIiIiIiIiIiIqM8xIEFERF0SQvR3F4iIiIiIiMgO/B1HRHcqBiSIiMgiR0dHAEBLS0s/94SIiIiIiIjs0dbWBgBQKpX93BMiIlMMSBARkUWOjo5wc3PD1atX0d7e3t/dISIiIiIiIhsIIdDQ0ABnZ2c4OTn1d3eIiEwoBN/hIiIiK5qamnD+/HkolUp4eXlBrVZDqVRCoVD0d9eIiIiIiIioEyEE2tra0NDQgO+++w5DhgyBp6dnf3eLiMgEAxJERNSl1tZWXL58GU1NTXxTgoiIiIiI6A7n7OwMX19fBiOI6I7EgAQREdlEetqmo6Ojv7tCREREREREFiiVSqZpIqI7GgMSRERERERERERERETU5zipNRERERERERERERER9TkGJIiIiIiIiIiIiIiIqM8xIEFERERERERERERERH2OAQkiIiIiIiIiIiIiIupzDEgQEREREREREREREVGfY0CCiIiIiIiIiIiIiIj6HAMSRERERERERERERETU5/4fdf/eDAJzJmEAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -340,8 +341,8 @@
     }
    ],
    "source": [
-    "flux, onset_stats, onset_found, peak_flux, peak_time, fig, bg_mean = Event_onset.analyse(viewing=w.view_drop.value, background_range=background_range, channels=channels,\n",
-    "                                                                                         resample_period=averaging, yscale='log', cusum_window=30, xlim=plot_range)\n",
+    "flux, onset_stats, onset_found, peak_flux, peak_time, fig, bg_mean = Event_onset.find_onset(viewing=w.view_drop.value, background_range=background_range, channels=channels,\n",
+    "                                                                                            resample_period=averaging, yscale='log', cusum_window=30, xlim=plot_range)\n",
     "onset = onset_stats[-1]\n",
     "peak_flux = peak_flux.values[0]\n",
     "output = Event_onset.output"
@@ -503,7 +504,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.9.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e3b113a080b82eb2d3a809b09472aaf632ea244e00139a5388ab3f89cf23b31b"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/sep_analysis_tools/onset_functions.py b/notebooks/sep_analysis_tools/onset_functions.py
deleted file mode 100644
index dfb6a9e..0000000
--- a/notebooks/sep_analysis_tools/onset_functions.py
+++ /dev/null
@@ -1,2071 +0,0 @@
-
-import os
-import datetime
-import warnings
-import matplotlib.pyplot as plt
-import matplotlib.ticker as ticker
-import matplotlib.colors as cl
-import numpy as np
-import pandas as pd
-import astropy.units as u
-import astropy.constants as const
-from sunpy.coordinates import get_horizons_coord
-from matplotlib.dates import DateFormatter
-from matplotlib.ticker import ScalarFormatter
-from matplotlib.offsetbox import AnchoredText
-from pandas.tseries.frequencies import to_offset
-from seppy.loader.psp import calc_av_en_flux_PSP_EPIHI, calc_av_en_flux_PSP_EPILO, psp_isois_load
-from seppy.loader.soho import calc_av_en_flux_ERNE, soho_load
-from seppy.loader.solo import epd_load
-from seppy.loader.stereo import calc_av_en_flux_HET as calc_av_en_flux_ST_HET
-from seppy.loader.stereo import calc_av_en_flux_SEPT, stereo_load
-from seppy.loader.wind import wind3dp_load
-
-from IPython.core.display import display
-
-# This is to get rid of this specific warning:
-# /home/user/xyz/serpentine/notebooks/sep_analysis_tools/read_swaves.py:96: UserWarning: The input coordinates to pcolormesh are interpreted as 
-# cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which 
-# case, please supply explicit cell edges to pcolormesh. 
-# colormesh = ax.pcolormesh( time_arr, freq[::-1], data_arr[::-1], vmin = 0, vmax = 0.5*np.max(data_arr), cmap = 'inferno' )
-warnings.filterwarnings("ignore", category=UserWarning)
-
-class Event:
-
-    def __init__(self, start_date, end_date, spacecraft, sensor,
-                 species, data_level, data_path, radio_spacecraft=None, 
-                 threshold=None):
-
-        if spacecraft == "Solar Orbiter":
-            spacecraft = "solo"
-        if spacecraft == "STEREO-A":
-            spacecraft = "sta"
-        if spacecraft == "STEREO-B":
-            spacecraft = "stb"
-
-        if sensor in ["ERNE-HED"]:
-            sensor = "ERNE"
-
-        if species in ("protons", "ions"):
-            species = 'p'
-        if species == "electrons":
-            species = 'e'
-
-        self.start_date = start_date
-        self.end_date = end_date
-        self.spacecraft = spacecraft.lower()
-        self.sensor = sensor.lower()
-        self.species = species.lower()
-        self.data_level = data_level.lower()
-        self.data_path = data_path + os.sep
-        self.threshold = threshold
-        self.radio_spacecraft = radio_spacecraft # this is a 2-tuple, e.g., ("ahead", "STEREO-A")
-        self.viewing = None
-
-        self.radio_files = None
-
-        # placeholding class attributes
-        self.flux_series = None
-        self.onset_stats = None
-        self.onset_found = None
-        self.onset = None
-        self.peak_flux = None
-        self.peak_time = None
-        self.fig = None
-        self.bg_mean = None
-        self.output = {"flux_series": self.flux_series,
-                       "onset_stats": self.onset_stats,
-                       "onset_found": self.onset_found,
-                       "onset": self.onset,
-                       "peak_flux": self.peak_flux,
-                       "peak_time": self.peak_time,
-                       "fig": self.fig,
-                       "bg_mean": self.bg_mean
-                       }
-
-        self.load_all_viewing()
-
-        # Download radio cdf files ONLY if asked to
-        if self.radio_spacecraft is not None:
-            from read_swaves import get_swaves
-            self.radio_files = get_swaves(start_date, end_date)
-
-
-    def update_onset_attributes(self, flux_series, onset_stats, onset_found, peak_flux, peak_time, fig, bg_mean):
-        """
-        Method to update onset-related attributes, that are None by default and only have values after analyse() has been run.
-        """
-        self.flux_series = flux_series
-        self.onset_stats = onset_stats
-        self.onset_found = onset_found
-        self.onset = onset_stats[-1]
-        self.peak_flux = peak_flux
-        self.peak_time = peak_time
-        self.fig = fig
-        self.bg_mean = bg_mean
-
-        # also remember to update the dictionary, it won't update automatically
-        self.output = {"flux_series": self.flux_series,
-                       "onset_stats": self.onset_stats,
-                       "onset_found": self.onset_found,
-                       "onset": self.onset,
-                       "peak_flux": self.peak_flux,
-                       "peak_time": self.peak_time,
-                       "fig": self.fig,
-                       "bg_mean": self.bg_mean
-                       }
-
-    def update_viewing(self, viewing):
-        self.viewing = viewing
-
-    def load_data(self, spacecraft, sensor, viewing, data_level,
-                  autodownload=True, threshold=None):
-
-        if(self.spacecraft == 'solo'):
-            df_i, df_e, energs = epd_load(sensor=sensor,
-                                          viewing=viewing,
-                                          level=data_level,
-                                          startdate=self.start_date,
-                                          enddate=self.end_date,
-                                          path=self.data_path,
-                                          autodownload=autodownload)
-
-            self.update_viewing(viewing)
-            return df_i, df_e, energs
-
-        if(self.spacecraft[:2].lower() == 'st'):
-            if(self.sensor == 'sept'):
-                if self.species in ["p", "i"]:
-                    df_i, channels_dict_df_i = stereo_load(instrument=self.sensor,
-                                                           startdate=self.start_date,
-                                                           enddate=self.end_date,
-                                                           spacecraft=self.spacecraft,
-                                                           # sept_species=self.species,
-                                                           sept_species='p',
-                                                           sept_viewing=viewing,
-                                                           resample=None,
-                                                           path=self.data_path)
-                    df_e, channels_dict_df_e = [], []
-
-                    self.update_viewing(viewing)
-                    return df_i, df_e, channels_dict_df_i, channels_dict_df_e
-
-                if self.species == "e":
-                    df_e, channels_dict_df_e = stereo_load(instrument=self.sensor,
-                                                           startdate=self.start_date,
-                                                           enddate=self.end_date,
-                                                           spacecraft=self.spacecraft,
-                                                           # sept_species=self.species,
-                                                           sept_species='e',
-                                                           sept_viewing=viewing,
-                                                           resample=None,
-                                                           path=self.data_path)
-
-                    df_i, channels_dict_df_i = [], []
-
-                    self.update_viewing(viewing)
-                    return df_i, df_e, channels_dict_df_i, channels_dict_df_e
-
-            if(self.sensor == 'het'):
-                df, meta = stereo_load(instrument=self.sensor,
-                                       startdate=self.start_date,
-                                       enddate=self.end_date,
-                                       spacecraft=self.spacecraft,
-                                       resample=None,
-                                       pos_timestamp='center',
-                                       path=self.data_path)
-
-                self.update_viewing(viewing)
-                return df, meta
-
-        if(self.spacecraft.lower() == 'soho'):
-            if(self.sensor == 'erne'):
-                df, meta = soho_load(dataset="SOHO_ERNE-HED_L2-1MIN",
-                                     startdate=self.start_date,
-                                     enddate=self.end_date,
-                                     path=self.data_path,
-                                     resample=None,
-                                     pos_timestamp='center')
-
-                self.update_viewing(viewing)
-                return df, meta
-
-            if(self.sensor == 'ephin'):
-                df, meta = soho_load(dataset="SOHO_COSTEP-EPHIN_L2-1MIN",
-                                     startdate=self.start_date,
-                                     enddate=self.end_date,
-                                     path=self.data_path,
-                                     resample=None,
-                                     pos_timestamp='center')
-
-                self.update_viewing(viewing)
-                return df, meta
-
-        if(self.spacecraft.lower() == 'wind'):
-            if(self.sensor == '3dp'):
-                df_i, meta_i = wind3dp_load(dataset="WI_SOPD_3DP",
-                                            startdate=self.start_date,
-                                            enddate=self.end_date,
-                                            resample=None,
-                                            multi_index=False,
-                                            path=self.data_path,
-                                            threshold=self.threshold)
-
-                df_e, meta_e = wind3dp_load(dataset="WI_SFPD_3DP",
-                                            startdate=self.start_date,
-                                            enddate=self.end_date,
-                                            resample=None,
-                                            multi_index=False,
-                                            path=self.data_path,
-                                            threshold=self.threshold)
-
-                self.update_viewing(viewing)
-                return df_i, df_e, meta_i, meta_e
-
-        if(self.spacecraft.lower() == 'psp'):
-            if(self.sensor.lower() == 'isois-epihi'):
-                df, meta = psp_isois_load(dataset='PSP_ISOIS-EPIHI_L2-HET-RATES60',
-                                          startdate=self.start_date,
-                                          enddate=self.end_date,
-                                          path=self.data_path,
-                                          resample=None)
-
-                self.update_viewing(viewing)
-                return df, meta
-            if(self.sensor.lower() == 'isois-epilo'):
-                df, meta = psp_isois_load(dataset='PSP_ISOIS-EPILO_L2-PE',
-                                          startdate=self.start_date,
-                                          enddate=self.end_date,
-                                          path=self.data_path,
-                                          resample=None,
-                                          epilo_channel='F',
-                                          epilo_threshold=self.threshold)
-
-                self.update_viewing(viewing)
-                return df, meta
-
-        if(self.spacecraft.lower() == 'bepi'):
-            df, meta = bepi_sixs_load(startdate=self.start_date,
-                                      enddate=self.end_date,
-                                      side=viewing,
-                                      path=self.data_path)
-            df_i = df[[f"P{i}" for i in range(1, 10)]]
-            df_e = df[[f"E{i}" for i in range(1, 8)]]
-            return df_i, df_e, meta
-
-    def load_all_viewing(self):
-
-        if(self.spacecraft == 'solo'):
-
-            if(self.sensor in ['het', 'ept']):
-
-                self.df_i_sun, self.df_e_sun, self.energies_sun =\
-                    self.load_data(self.spacecraft, self.sensor,
-                                   'sun', self.data_level)
-
-                self.df_i_asun, self.df_e_asun, self.energies_asun =\
-                    self.load_data(self.spacecraft, self.sensor,
-                                   'asun', self.data_level)
-
-                self.df_i_north, self.df_e_north, self.energies_north =\
-                    self.load_data(self.spacecraft, self.sensor,
-                                   'north', self.data_level)
-
-                self.df_i_south, self.df_e_south, self.energies_south =\
-                    self.load_data(self.spacecraft, self.sensor,
-                                   'south', self.data_level)
-
-            elif(self.sensor == 'step'):
-
-                self.df_step, self.energies_step =\
-                    self.load_data(self.spacecraft, self.sensor, 'None',
-                                   self.data_level)
-
-        if(self.spacecraft[:2].lower() == 'st'):
-
-            if(self.sensor == 'sept'):
-
-                self.df_i_sun, self.df_e_sun, self.energies_i_sun, self.energies_e_sun =\
-                    self.load_data(self.spacecraft, self.sensor,
-                                   'sun', self.data_level)
-
-                self.df_i_asun, self.df_e_asun, self.energies_i_asun, self.energies_e_asun =\
-                    self.load_data(self.spacecraft, self.sensor,
-                                   'asun', self.data_level)
-
-                self.df_i_north, self.df_e_north, self.energies_i_north, self.energies_e_north =\
-                    self.load_data(self.spacecraft, self.sensor,
-                                   'north', self.data_level)
-
-                self.df_i_south, self.df_e_south, self.energies_i_south, self.energies_e_south =\
-                    self.load_data(self.spacecraft, self.sensor,
-                                   'south', self.data_level)
-
-            elif(self.sensor == 'het'):
-
-                self.df_het, self.meta_het =\
-                    self.load_data(self.spacecraft, self.sensor, 'None',
-                                   self.data_level)
-                self.current_df_i = self.df_het.filter(like='Proton')
-                self.current_df_e = self.df_het.filter(like='Electron')
-                self.current_energies = self.meta_het
-
-        if(self.spacecraft.lower() == 'soho'):
-
-            if(self.sensor.lower() == 'erne'):
-
-                self.df, self.meta =\
-                    self.load_data(self.spacecraft, self.sensor, 'None',
-                                   self.data_level)
-                self.current_df_i = self.df.filter(like='PH_')
-                # self.current_df_e = self.df.filter(like='Electron')
-                self.current_energies = self.meta
-
-            if(self.sensor.lower() == 'ephin'):
-                self.df, self.meta =\
-                    self.load_data(self.spacecraft, self.sensor, 'None',
-                                   self.data_level)
-                # self.current_df_i = self.df.filter(like='PH_')
-                self.current_df_e = self.df.filter(like='E')
-                self.current_energies = self.meta
-
-        if(self.spacecraft.lower() == 'wind'):
-            if(self.sensor.lower() == '3dp'):
-                self.df_i, self.df_e, self.meta_i, self.meta_e = \
-                    self.load_data(self.spacecraft, self.sensor, 'None', self.data_level, threshold=self.threshold)
-                # self.df_i = self.df_i.filter(like='FLUX')
-                # self.df_e = self.df_e.filter(like='FLUX')
-                self.current_i_energies = self.meta_i
-                self.current_e_energies = self.meta_e
-
-        if(self.spacecraft.lower() == 'psp'):
-            if(self.sensor.lower() == 'isois-epihi'):
-                # Note: load_data(viewing='all') doesn't really has an effect, but for PSP/ISOIS-EPIHI all viewings are always loaded anyhow.
-                self.df, self.meta = self.load_data(self.spacecraft, self.sensor, 'all', self.data_level)
-                self.df_e = self.df.filter(like='Electrons_Rate_')
-                self.current_e_energies = self.meta
-                self.df_i = self.df.filter(like='H_Flux_')
-                self.current_i_energies = self.meta
-            if(self.sensor.lower() == 'isois-epilo'):
-                # Note: load_data(viewing='all') doesn't really has an effect, but for PSP/ISOIS-EPILO all viewings are always loaded anyhow.
-                self.df, self.meta = self.load_data(self.spacecraft, self.sensor, 'all', self.data_level, threshold=self.threshold)
-                self.df_e = self.df.filter(like='Electron_CountRate_')
-                self.current_e_energies = self.meta
-                # protons not yet included in PSP/ISOIS-EPILO dataset
-                # self.df_i = self.df.filter(like='H_Flux_')
-                # self.current_i_energies = self.meta
-
-        if(self.spacecraft.lower() == 'bepi'):
-            self.df_i_0, self.df_e_0, self.energies_0 =\
-                self.load_data(self.spacecraft, self.sensor, viewing='0', data_level='None')
-            self.df_i_1, self.df_e_1, self.energies_1 =\
-                self.load_data(self.spacecraft, self.sensor, viewing='1', data_level='None')
-            self.df_i_2, self.df_e_2, self.energies_2 =\
-                self.load_data(self.spacecraft, self.sensor, viewing='2', data_level='None')
-            # side 3 and 4 should not be used for SIXS, but they can be activated by uncommenting the following lines
-            # self.df_i_3, self.df_e_3, self.energies_3 =\
-            #     self.load_data(self.spacecraft, self.sensor, viewing='3', data_level='None')
-            # self.df_i_4, self.df_e_4, self.energies_4 =\
-            #     self.load_data(self.spacecraft, self.sensor, viewing='4', data_level='None')
-
-    def choose_data(self, viewing):
-
-        self.update_viewing(viewing)
-
-        if(self.spacecraft == 'solo'):
-            if(viewing == 'sun'):
-
-                self.current_df_i = self.df_i_sun
-                self.current_df_e = self.df_e_sun
-                self.current_energies = self.energies_sun
-
-            elif(viewing == 'asun'):
-
-                self.current_df_i = self.df_i_asun
-                self.current_df_e = self.df_e_asun
-                self.current_energies = self.energies_asun
-
-            elif(viewing == 'north'):
-
-                self.current_df_i = self.df_i_north
-                self.current_df_e = self.df_e_north
-                self.current_energies = self.energies_north
-
-            elif(viewing == 'south'):
-
-                self.current_df_i = self.df_i_south
-                self.current_df_e = self.df_e_south
-                self.current_energies = self.energies_south
-
-        if(self.spacecraft[:2].lower() == 'st'):
-            if(self.sensor == 'sept'):
-                if(viewing == 'sun'):
-
-                    self.current_df_i = self.df_i_sun
-                    self.current_df_e = self.df_e_sun
-                    self.current_i_energies = self.energies_i_sun
-                    self.current_e_energies = self.energies_e_sun
-
-                elif(viewing == 'asun'):
-
-                    self.current_df_i = self.df_i_asun
-                    self.current_df_e = self.df_e_asun
-                    self.current_i_energies = self.energies_i_asun
-                    self.current_e_energies = self.energies_e_asun
-
-                elif(viewing == 'north'):
-
-                    self.current_df_i = self.df_i_north
-                    self.current_df_e = self.df_e_north
-                    self.current_i_energies = self.energies_i_north
-                    self.current_e_energies = self.energies_e_north
-
-                elif(viewing == 'south'):
-
-                    self.current_df_i = self.df_i_south
-                    self.current_df_e = self.df_e_south
-                    self.current_i_energies = self.energies_i_south
-                    self.current_e_energies = self.energies_e_south
-
-        if(self.spacecraft.lower() == 'wind'):
-            if(self.sensor.lower() == '3dp'):
-                col_list_i = [col for col in self.df_i.columns if col.endswith(str(viewing))]
-                col_list_e = [col for col in self.df_e.columns if col.endswith(str(viewing))]
-                self.current_df_i = self.df_i[col_list_i]
-                self.current_df_e = self.df_e[col_list_e]
-
-        if(self.spacecraft.lower() == 'psp'):
-            if(self.sensor.lower() == 'isois-epihi'):
-                # viewing = 'A' or 'B'
-                self.current_df_e = self.df_e[self.df_e.columns[self.df_e.columns.str.startswith(viewing.upper())]]
-                self.current_df_i = self.df_i[self.df_i.columns[self.df_i.columns.str.startswith(viewing.upper())]]
-            if(self.sensor.lower() == 'isois-epilo'):
-                # viewing = '0' to '7'
-                self.current_df_e = self.df_e[self.df_e.columns[self.df_e.columns.str.endswith(viewing)]]
-                # protons not yet included in PSP/ISOIS-EPILO dataset
-                # self.current_df_i = self.df_i[self.df_i.columns[self.df_i.columns.str.endswith(viewing)]]
-
-        if(self.spacecraft.lower() == 'bepi'):
-            if(viewing == '0'):
-                self.current_df_i = self.df_i_0
-                self.current_df_e = self.df_e_0
-                self.current_energies = self.energies_0
-            elif(viewing == '1'):
-                self.current_df_i = self.df_i_1
-                self.current_df_e = self.df_e_1
-                self.current_energies = self.energies_1
-            elif(viewing == '2'):
-                self.current_df_i = self.df_i_2
-                self.current_df_e = self.df_e_2
-                self.current_energies = self.energies_2
-            # side 3 and 4 should not be used for SIXS, but they can be activated by uncommenting the following lines
-            # elif(viewing == '3'):
-            #     self.current_df_i = self.df_i_3
-            #     self.current_df_e = self.df_e_3
-            #     self.current_energies = self.energies_3
-            # elif(viewing == '4'):
-            #     self.current_df_i = self.df_i_4
-            #     self.current_df_e = self.df_e_4
-            #     self.current_energies = self.energies_4
-
-    def calc_av_en_flux_HET(self, df, energies, en_channel):
-
-        """This function averages the flux of several
-        energy channels of SolO/HET into a combined energy channel
-        channel numbers counted from 0
-
-        Parameters
-        ----------
-        df : pd.DataFrame DataFrame containing HET data
-            DataFrame containing HET data
-        energies : dict
-            Energy dict returned from epd_loader (from Jan)
-        en_channel : int or list
-            energy channel or list with first and last channel to be used
-        species : string
-            'e', 'electrons', 'p', 'i', 'protons', 'ions'
-
-        Returns
-        -------
-        pd.DataFrame
-            flux_out: contains channel-averaged flux
-
-        Raises
-        ------
-        Exception
-            [description]
-        """
-
-        species = self.species
-
-        try:
-
-            if species not in ['e', 'electrons', 'p', 'protons', 'H']:
-
-                raise ValueError("species not defined. Must by one of 'e',\
-                                 'electrons', 'p', 'protons', 'H'")
-
-        except ValueError as error:
-
-            print(repr(error))
-            raise
-
-        if species in ['e', 'electrons']:
-
-            en_str = energies['Electron_Bins_Text']
-            bins_width = 'Electron_Bins_Width'
-            flux_key = 'Electron_Flux'
-
-        if species in ['p', 'protons', 'H']:
-
-            en_str = energies['H_Bins_Text']
-            bins_width = 'H_Bins_Width'
-            flux_key = 'H_Flux'
-
-            if flux_key not in df.keys():
-
-                flux_key = 'H_Flux'
-
-        if type(en_channel) == list:
-
-            # An IndexError here is caused by invalid channel choice
-            try:
-                en_channel_string = en_str[en_channel[0]][0].split()[0] + ' - '\
-                    + en_str[en_channel[-1]][0].split()[2] + ' ' +\
-                    en_str[en_channel[-1]][0].split()[3]
-
-            except IndexError:
-                raise Exception(f"{en_channel} is an invalid channel or a combination of channels!")
-
-            if len(en_channel) > 2:
-
-                raise Exception('en_channel must have len 2 or less!')
-
-            if len(en_channel) == 2:
-
-                DE = energies[bins_width]
-
-                for bins in np.arange(en_channel[0], en_channel[-1] + 1):
-
-                    if bins == en_channel[0]:
-
-                        I_all = df[flux_key].values[:, bins] * DE[bins]
-
-                    else:
-
-                        I_all = I_all + df[flux_key].values[:, bins] * DE[bins]
-
-                DE_total = np.sum(DE[(en_channel[0]):(en_channel[-1] + 1)])
-                flux_av_en = pd.Series(I_all/DE_total, index=df.index)
-                flux_out = pd.DataFrame({'flux': flux_av_en}, index=df.index)
-
-            else:
-
-                en_channel = en_channel[0]
-                flux_out = pd.DataFrame({'flux':
-                                        df[flux_key].values[:, en_channel]},
-                                        index=df.index)
-
-        else:
-
-            flux_out = pd.DataFrame({'flux':
-                                    df[flux_key].values[:, en_channel]},
-                                    index=df.index)
-            en_channel_string = en_str[en_channel]
-
-        return flux_out, en_channel_string
-
-    def calc_av_en_flux_EPT(self, df, energies, en_channel):
-
-        """This function averages the flux of several energy
-        channels of EPT into a combined energy channel
-        channel numbers counted from 0
-
-        Parameters
-        ----------
-        df : pd.DataFrame DataFrame containing EPT data
-            DataFrame containing EPT data
-        energies : dict
-            Energy dict returned from epd_loader (from Jan)
-        en_channel : int or list
-            energy channel number(s) to be used
-        species : string
-            'e', 'electrons', 'p', 'i', 'protons', 'ions'
-
-        Returns
-        -------
-        pd.DataFrame
-            flux_out: contains channel-averaged flux
-
-        Raises
-        ------
-        Exception
-            [description]
-        """
-
-        species = self.species
-
-        try:
-
-            if species not in ['e', 'electrons', 'p', 'i', 'protons', 'ions']:
-
-                raise ValueError("species not defined. Must by one of 'e',"
-                                 "'electrons', 'p', 'i', 'protons', 'ions'")
-
-        except ValueError as error:
-            print(repr(error))
-            raise
-
-        if species in ['e', 'electrons']:
-
-            bins_width = 'Electron_Bins_Width'
-            flux_key = 'Electron_Flux'
-            en_str = energies['Electron_Bins_Text']
-
-        if species in ['p', 'i', 'protons', 'ions']:
-
-            bins_width = 'Ion_Bins_Width'
-            flux_key = 'Ion_Flux'
-            en_str = energies['Ion_Bins_Text']
-
-            if flux_key not in df.keys():
-
-                flux_key = 'H_Flux'
-
-        if type(en_channel) == list:
-
-            # An IndexError here is caused by invalid channel choice
-            try:
-                en_channel_string = en_str[en_channel[0]][0].split()[0] + ' - '\
-                    + en_str[en_channel[-1]][0].split()[2] + ' '\
-                    + en_str[en_channel[-1]][0].split()[3]
-
-            except IndexError:
-                raise Exception(f"{en_channel} is an invalid channel or a combination of channels!")
-
-            if len(en_channel) > 2:
-
-                raise Exception('en_channel must have len 2 or less!')
-
-            if len(en_channel) == 2:
-
-                DE = energies[bins_width]
-
-                for bins in np.arange(en_channel[0], en_channel[-1]+1):
-
-                    if bins == en_channel[0]:
-
-                        I_all = df[flux_key].values[:, bins] * DE[bins]
-
-                    else:
-
-                        I_all = I_all + df[flux_key].values[:, bins] * DE[bins]
-
-                DE_total = np.sum(DE[(en_channel[0]):(en_channel[-1]+1)])
-                flux_av_en = pd.Series(I_all/DE_total, index=df.index)
-                flux_out = pd.DataFrame({'flux': flux_av_en}, index=df.index)
-
-            else:
-
-                en_channel = en_channel[0]
-                flux_out = pd.DataFrame({'flux':
-                                        df[flux_key].values[:, en_channel]},
-                                        index=df.index)
-
-        else:
-
-            flux_out = pd.DataFrame({'flux':
-                                    df[flux_key].values[:, en_channel]},
-                                    index=df.index)
-            en_channel_string = en_str[en_channel]
-
-        return flux_out, en_channel_string
-
-    def resample(self, df_flux, resample_period):
-
-        df_flux_out = df_flux.resample(resample_period, label='left').mean()
-        df_flux_out.index = df_flux_out.index\
-            + to_offset(pd.Timedelta(resample_period)/2)
-
-        return df_flux_out
-
-    def print_info(self, title, info):
-
-        title_string = "##### >" + title + "< #####"
-        print(title_string)
-        print(info)
-        print('#'*len(title_string) + '\n')
-
-    def mean_value(self, tb_start, tb_end, flux_series):
-
-        """
-        This function calculates the classical mean of the background period
-        which is used in the onset analysis.
-        """
-
-        # replace date_series with the resampled version
-        date = flux_series.index
-        background = flux_series.loc[(date >= tb_start) & (date < tb_end)]
-        mean_value = np.nanmean(background)
-        sigma = np.nanstd(background)
-
-        return [mean_value, sigma]
-
-    def onset_determination(self, ma_sigma, flux_series, cusum_window, bg_end_time):
-
-        flux_series = flux_series[bg_end_time:]
-
-        # assert date and the starting index of the averaging process
-        date = flux_series.index
-        ma = ma_sigma[0]
-        sigma = ma_sigma[1]
-        md = ma + self.x_sigma*sigma
-
-        # k may get really big if sigma is large in comparison to mean
-        try:
-
-            k = (md-ma)/(np.log(md)-np.log(ma))
-            k_round = round(k/sigma)
-
-        except ValueError:
-
-            # First ValueError I encountered was due to ma=md=2.0 -> k = "0/0"
-            k_round = 1
-
-        # choose h, the variable dictating the "hastiness" of onset alert
-        if k < 1.0:
-
-            h = 1
-
-        else:
-
-            h = 2
-
-        alert = 0
-        cusum = np.zeros(len(flux_series))
-        norm_channel = np.zeros(len(flux_series))
-
-        # set the onset as default to be NaT (Not a Date)
-        onset_time = pd.NaT
-
-        for i in range(1, len(cusum)):
-
-            # normalize the observed flux
-            norm_channel[i] = (flux_series[i]-ma)/sigma
-
-            # calculate the value for ith cusum entry
-            cusum[i] = max(0, norm_channel[i] - k_round + cusum[i-1])
-
-            # check if cusum[i] is above threshold h,
-            # if it is -> increment alert
-            if cusum[i] > h:
-
-                alert = alert + 1
-
-            else:
-
-                alert = 0
-
-            # cusum_window(default:30) subsequent increments to alert
-            # means that the onset was found
-            if alert == cusum_window:
-
-                onset_time = date[i - alert]
-                break
-
-        # ma = mu_a = background average
-        # md = mu_d = background average + 2*sigma
-        # k_round = integer value of k, that is the reference value to
-        # poisson cumulative sum
-        # h = 1 or 2,describes the hastiness of onset alert
-        # onset_time = the time of the onset
-        # S = the cusum function
-
-        return [ma, md, k_round, norm_channel, cusum, onset_time]
-
-    def onset_analysis(self, df_flux, windowstart, windowlen, windowrange, channels_dict,
-                       channel='flux', cusum_window=30, yscale='log',
-                       ylim=None, xlim=None):
-
-        self.print_info("Energy channels", channels_dict)
-        spacecraft = self.spacecraft.upper()
-        sensor = self.sensor.upper()
-
-        color_dict = {
-            'onset_time': '#e41a1c',
-            'bg_mean':    '#e41a1c',
-            'flux_peak':  '#1a1682',
-            'bg':         '#de8585'
-        }
-
-        if(self.spacecraft == 'solo'):
-            flux_series = df_flux[channel]
-        if(self.spacecraft[:2].lower() == 'st'):
-            flux_series = df_flux  # [channel]'
-        if(self.spacecraft.lower() == 'soho'):
-            flux_series = df_flux  # [channel]
-        if(self.spacecraft.lower() == 'wind'):
-            flux_series = df_flux  # [channel]
-        if(self.spacecraft.lower() == 'psp'):
-            flux_series = df_flux[channel]
-        if(self.spacecraft.lower() == 'bepi'):
-            flux_series = df_flux  # [channel]
-        date = flux_series.index
-
-        if ylim is None:
-
-            ylim = [np.nanmin(flux_series[flux_series > 0]),
-                    np.nanmax(flux_series) * 3]
-
-        # windowrange is by default None, and then we define the start and stop with integer hours
-        if windowrange is None:
-            # dates for start and end of the averaging processes
-            avg_start = date[0] + datetime.timedelta(hours=windowstart)
-            # ending time is starting time + a given timedelta in hours
-            avg_end = avg_start + datetime.timedelta(hours=windowlen)
-
-        else:
-            avg_start, avg_end = windowrange[0], windowrange[1]
-
-        if xlim is None:
-
-            xlim = [date[0], date[-1]]
-
-        else:
-
-            df_flux = df_flux[xlim[0]:xlim[-1]]
-
-        # onset not yet found
-        onset_found = False
-        background_stats = self.mean_value(avg_start, avg_end, flux_series)
-        onset_stats =\
-            self.onset_determination(background_stats, flux_series,
-                                     cusum_window, avg_end)
-
-        if not isinstance(onset_stats[-1], pd._libs.tslibs.nattype.NaTType):
-
-            onset_found = True
-
-        if(self.spacecraft == 'solo'):
-            df_flux_peak = df_flux[df_flux[channel] == df_flux[channel].max()]
-        if(self.spacecraft[:2].lower() == 'st'):
-            df_flux_peak = df_flux[df_flux == df_flux.max()]
-        if(self.spacecraft == 'soho'):
-            df_flux_peak = df_flux[df_flux == df_flux.max()]
-        if(self.spacecraft == 'wind'):
-            df_flux_peak = df_flux[df_flux == df_flux.max()]
-        if(self.spacecraft == 'psp'):
-            # df_flux_peak = df_flux[df_flux == df_flux.max()]
-            df_flux_peak = df_flux[df_flux[channel] == df_flux[channel].max()]
-        if(self.spacecraft == 'bepi'):
-            df_flux_peak = df_flux[df_flux == df_flux.max()]
-            # df_flux_peak = df_flux[df_flux[channel] == df_flux[channel].max()]
-        self.print_info("Flux peak", df_flux_peak)
-        self.print_info("Onset time", onset_stats[-1])
-        self.print_info("Mean of background intensity",
-                        background_stats[0])
-        self.print_info("Std of background intensity",
-                        background_stats[1])
-
-        plt.rcParams['axes.linewidth'] = 1.5
-        plt.rcParams['font.size'] = 16
-        fig, ax = plt.subplots()
-        ax.plot(flux_series.index, flux_series.values, ds='steps-mid')
-
-        # CUSUM and norm datapoints in plots.
-        '''
-        ax.scatter(flux_series.index, onset_stats[-3], s=1,
-                   color='darkgreen', alpha=0.7, label='norm')
-        ax.scatter(flux_series.index, onset_stats[-2], s=3,
-                   c='maroon', label='CUSUM')
-        '''
-
-        # onset time
-        if onset_found:
-
-            # Onset time line
-            ax.axvline(onset_stats[-1], linewidth=1.5,
-                       color=color_dict['onset_time'], linestyle='-',
-                       label="Onset time")
-
-        # Flux peak line (first peak only, if there's multiple)
-        try:
-            ax.axvline(df_flux_peak.index[0], linewidth=1.5,
-                       color=color_dict['flux_peak'], linestyle='-',
-                       label="Peak time")
-
-        except IndexError:
-            exceptionmsg = "IndexError! Maybe you didn't adjust background_range or plot_range correctly?"
-            raise Exception(exceptionmsg)
-
-        # background mean
-        ax.axhline(onset_stats[0], linewidth=2,
-                   color=color_dict['bg_mean'], linestyle='--',
-                   label="Mean of background")
-
-        # background mean + 2*std
-        ax.axhline(onset_stats[1], linewidth=2,
-                   color=color_dict['bg_mean'], linestyle=':',
-                   label=f"Mean + {str(self.x_sigma)} * std of background")
-
-        # Background shaded area
-        ax.axvspan(avg_start, avg_end, color=color_dict['bg'],
-                   label="Background")
-
-        ax.set_xlabel("Time (HH:MM \nYYYY-mm-dd)", fontsize=16)
-        ax.set_ylabel(r"Intensity [1/(cm$^{2}$ sr s MeV)]", fontsize=16)
-        ax.yaxis.set_major_locator(plt.MaxNLocator(4))
-
-        # figure limits and scale
-        plt.ylim(ylim)
-        plt.xlim(xlim[0], xlim[1])
-        plt.yscale(yscale)
-        plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.2),
-                   fancybox=True, shadow=False, ncol=3, fontsize=16)
-
-        # tickmarks, their size etc...
-        plt.tick_params(which='major', length=5, width=1.5, labelsize=16)
-        plt.tick_params(which='minor', length=4, width=1)
-
-        # date tick locator and formatter
-        ax.xaxis_date()
-        ax.xaxis.set_major_locator(ticker.MaxNLocator(9))
-        utc_dt_format1 = DateFormatter('%H:%M \n%Y-%m-%d')
-        ax.xaxis.set_major_formatter(utc_dt_format1)
-
-        if self.species == 'e':
-
-            s_identifier = 'electrons'
-
-        if self.species in ['p', 'i']:
-
-            if((spacecraft == 'sta' and sensor == 'sept') or (spacecraft == 'solo' and sensor == 'ept')):
-
-                s_identifier = 'ions'
-
-            else:
-
-                s_identifier = 'protons'
-
-        self.print_info("Particle species", s_identifier)
-
-        if(self.viewing_used != '' and self.viewing_used != None):
-
-            plt.title(f"{spacecraft}/{sensor} {channels_dict} {s_identifier}\n"
-                      f"{self.averaging_used} averaging, viewing: "
-                      f"{self.viewing_used.upper()}")
-
-        else:
-
-            plt.title(f"{spacecraft}/{sensor} {channels_dict} {s_identifier}\n"
-                      f"{self.averaging_used} averaging")
-
-        fig.set_size_inches(16, 8)
-
-        # Onset label
-        if(onset_found):
-
-            if(self.spacecraft == 'solo' or self.spacecraft == 'psp'):
-                plabel = AnchoredText(f"Onset time: {str(onset_stats[-1])[:19]}\n"
-                                      f"Peak flux: {df_flux_peak['flux'][0]:.2E}",
-                                      prop=dict(size=13), frameon=True,
-                                      loc=(4))
-            # if(self.spacecraft[:2].lower() == 'st' or self.spacecraft == 'soho' or self.spacecraft == 'wind'):
-            else:
-                plabel = AnchoredText(f"Onset time: {str(onset_stats[-1])[:19]}\n"
-                                      f"Peak flux: {df_flux_peak.values[0]:.2E}",
-                                      prop=dict(size=13), frameon=True,
-                                      loc=(4))
-
-        else:
-
-            plabel = AnchoredText("No onset found",
-                                  prop=dict(size=13), frameon=True,
-                                  loc=(4))
-
-        plabel.patch.set_boxstyle("round, pad=0., rounding_size=0.2")
-        plabel.patch.set_linewidth(2.0)
-
-        # Background label
-        blabel = AnchoredText(f"Background:\n{avg_start} - {avg_end}",
-                              prop=dict(size=13), frameon=True,
-                              loc='upper left')
-        blabel.patch.set_boxstyle("round, pad=0., rounding_size=0.2")
-        blabel.patch.set_linewidth(2.0)
-
-        # Energy and species label
-        '''
-        eslabel = AnchoredText(f"{channels_dict} {s_identifier}",
-                               prop=dict(size=13), frameon=True,
-                               loc='lower left')
-        eslabel.patch.set_boxstyle("round, pad=0., rounding_size=0.2")
-        eslabel.patch.set_linewidth(2.0)
-        '''
-
-        ax.add_artist(plabel)
-        ax.add_artist(blabel)
-        # ax.add_artist(eslabel)
-        plt.tight_layout()
-        plt.show()
-
-        return flux_series, onset_stats, onset_found, df_flux_peak, df_flux_peak.index[0], fig, background_stats[0]
-
-    def analyse(self, viewing, bg_start=None, bg_length=None, background_range=None, resample_period=None,
-                channels=[0, 1], yscale='log', cusum_window=30, xlim=None, x_sigma=2):
-        """
-        This method runs Poisson-CUSUM onset analysis for the Event object.
-
-        Parameters:
-        -----------
-        viewing : str
-                        The viewing direction of the sensor.
-        bg_start : int or float, default None
-                        The start of background averaging from the start of the time series data in hours.
-        bg_length : int or float, default None
-                        The length of  the background averaging period in hours.
-        background_range : tuple or list of datetimes with len=2, default None
-                        The time range of background averaging. If defined, takes precedence over bg_start and bg_length.
-        resample_period : str, default None
-                        Pandas-compatible time string to average data. e.g. '10s' for 10 seconds or '2min' for 1 minutes.
-        channels : int or list of 2 ints, default [0,1]
-                        Index or a combination of indices to plot a channel or combination of channels.
-        yscale : str, default 'log'
-                        Matplotlib-compatible string for the scale of the y-axis. e.g. 'log' or 'linear'
-        cusum_window : int, default 30
-                        The amount of consecutive data points above the threshold before identifying an onset.
-        xlim : tuple or list, default None
-                        Panda-compatible datetimes or strings to assert the left and right boundary of the x-axis of the plot.
-        x_sigma : int, default 2
-                        The multiplier of m_d in the definition of the control parameter k in Poisson-CUSUM method.
-        """
-
-        # This check was initially transforming the 'channels' integer to a tuple of len==1, but that
-        # raised a ValueError with solo/ept. However, a list of len==1 is somehow okay.
-        if isinstance(channels, int):
-            channels = [channels]
-
-        if (background_range is not None) and (xlim is not None):
-         # Check if background is separated from plot range by over a day, issue a warning if so, but don't 
-            if (background_range[0] < xlim[0] - datetime.timedelta(days=1) and background_range[0] < xlim[1] - datetime.timedelta(days=1) ) or \
-                (background_range[1] > xlim[0] + datetime.timedelta(days=1) and background_range[1] > xlim[1] + datetime.timedelta(days=1) ):
-                background_warning = "NOTICE that your background_range is separated from plot_range by over a day.\nIf this was intentional you may ignore this warning."
-                warnings.warn(message=background_warning)
-
-
-        if (self.spacecraft[:2].lower() == 'st' and self.sensor == 'sept') \
-                or (self.spacecraft.lower() == 'psp' and self.sensor.startswith('isois')) \
-                or (self.spacecraft.lower() == 'solo' and self.sensor == 'ept') \
-                or (self.spacecraft.lower() == 'solo' and self.sensor == 'het') \
-                or (self.spacecraft.lower() == 'wind' and self.sensor == '3dp') \
-                or (self.spacecraft.lower() == 'bepi'):
-            self.viewing_used = viewing
-            self.choose_data(viewing)
-        elif (self.spacecraft[:2].lower() == 'st' and self.sensor == 'het'):
-            self.viewing_used = ''
-        elif (self.spacecraft.lower() == 'soho' and self.sensor == 'erne'):
-            self.viewing_used = ''
-        elif (self.spacecraft.lower() == 'soho' and self.sensor == 'ephin'):
-            self.viewing_used = ''
-
-        self.averaging_used = resample_period
-        self.x_sigma = x_sigma
-
-        if(self.spacecraft == 'solo'):
-
-            if(self.sensor == 'het'):
-
-                if(self.species in ['p', 'i']):
-
-                    df_flux, en_channel_string =\
-                        self.calc_av_en_flux_HET(self.current_df_i,
-                                                 self.current_energies,
-                                                 channels)
-                elif(self.species == 'e'):
-
-                    df_flux, en_channel_string =\
-                        self.calc_av_en_flux_HET(self.current_df_e,
-                                                 self.current_energies,
-                                                 channels)
-
-            elif(self.sensor == 'ept'):
-
-                if(self.species in ['p', 'i']):
-
-                    df_flux, en_channel_string =\
-                        self.calc_av_en_flux_EPT(self.current_df_i,
-                                                 self.current_energies,
-                                                 channels)
-                elif(self.species == 'e'):
-
-                    df_flux, en_channel_string =\
-                        self.calc_av_en_flux_EPT(self.current_df_e,
-                                                 self.current_energies,
-                                                 channels)
-
-            else:
-                invalid_sensor_msg = "Invalid sensor!"
-                raise Exception(invalid_sensor_msg)
-
-        if(self.spacecraft[:2] == 'st'):
-
-            # Super ugly implementation, but easiest to just wrap both sept and het calculators
-            # in try block. KeyError is caused by an invalid channel choice.
-            try:
-
-                if(self.sensor == 'het'):
-
-                    if(self.species in ['p', 'i']):
-
-                        df_flux, en_channel_string =\
-                            calc_av_en_flux_ST_HET(self.current_df_i,
-                                                self.current_energies['channels_dict_df_p'],
-                                                channels,
-                                                species='p')
-                    elif(self.species == 'e'):
-
-                        df_flux, en_channel_string =\
-                            calc_av_en_flux_ST_HET(self.current_df_e,
-                                                self.current_energies['channels_dict_df_e'],
-                                                channels,
-                                                species='e')
-
-                elif(self.sensor == 'sept'):
-
-                    if(self.species in ['p', 'i']):
-
-                        df_flux, en_channel_string =\
-                            calc_av_en_flux_SEPT(self.current_df_i,
-                                                self.current_i_energies,
-                                                channels)
-                    elif(self.species == 'e'):
-
-                        df_flux, en_channel_string =\
-                            calc_av_en_flux_SEPT(self.current_df_e,
-                                                self.current_e_energies,
-                                                channels)
-            
-            except KeyError:
-                raise Exception(f"{channels} is an invalid channel or a combination of channels!")
-
-
-        if(self.spacecraft == 'soho'):
-
-            # A KeyError here is caused by invalid channel
-            try:
-
-                if(self.sensor == 'erne'):
-
-                    if(self.species in ['p', 'i']):
-
-                        df_flux, en_channel_string =\
-                            calc_av_en_flux_ERNE(self.current_df_i,
-                                                self.current_energies['channels_dict_df_p'],
-                                                channels,
-                                                species='p',
-                                                sensor='HET')
-
-                if(self.sensor == 'ephin'):
-                    # convert single-element "channels" list to integer
-                    if type(channels) == list:
-                        if len(channels) == 1:
-                            channels = channels[0]
-                        else:
-                            print("No multi-channel support for SOHO/EPHIN included yet! Select only one single channel.")
-                    if(self.species == 'e'):
-                        df_flux = self.current_df_e[f'E{channels}']
-                        en_channel_string = self.current_energies[f'E{channels}']
-
-            except KeyError:
-                raise Exception(f"{channels} is an invalid channel or a combination of channels!")
-
-        if(self.spacecraft == 'wind'):
-            if(self.sensor == '3dp'):
-                # convert single-element "channels" list to integer
-                if type(channels) == list:
-                    if len(channels) == 1:
-                        channels = channels[0]
-                    else:
-                        print("No multi-channel support for Wind/3DP included yet! Select only one single channel.")
-                if(self.species in ['p', 'i']):
-                    df_flux = self.current_df_i.filter(like=f'FLUX_E{channels}')
-                    # extract pd.Series for further use:
-                    df_flux = df_flux[df_flux.columns[0]]
-                    # change flux units from '#/cm2-ster-eV-sec' to '#/cm2-ster-MeV-sec'
-                    df_flux = df_flux*1e6
-                    en_channel_string = self.current_i_energies['channels_dict_df']['Bins_Text'][f'ENERGY_{channels}']
-                elif(self.species == 'e'):
-                    df_flux = self.current_df_e.filter(like=f'FLUX_E{channels}')
-                    # extract pd.Series for further use:
-                    df_flux = df_flux[df_flux.columns[0]]
-                    # change flux units from '#/cm2-ster-eV-sec' to '#/cm2-ster-MeV-sec'
-                    df_flux = df_flux*1e6
-                    en_channel_string = self.current_e_energies['channels_dict_df']['Bins_Text'][f'ENERGY_{channels}']
-
-        if(self.spacecraft.lower() == 'bepi'):
-            if type(channels) == list:
-                if len(channels) == 1:
-                    # convert single-element "channels" list to integer
-                    channels = channels[0]
-                    if(self.species == 'e'):
-                        df_flux = self.current_df_e[f'E{channels}']
-                        en_channel_string = self.current_energies['Energy_Bin_str'][f'E{channels}']
-                    if(self.species in ['p', 'i']):
-                        df_flux = self.current_df_i[f'P{channels}']
-                        en_channel_string = self.current_energies['Energy_Bin_str'][f'P{channels}']
-                else:
-                    if(self.species == 'e'):
-                        df_flux, en_channel_string = calc_av_en_flux_sixs(self.current_df_e, channels, self.species)
-                    if(self.species in ['p', 'i']):
-                        df_flux, en_channel_string = calc_av_en_flux_sixs(self.current_df_i, channels, self.species)
-
-
-        if(self.spacecraft.lower() == 'psp'):
-            if(self.sensor.lower() == 'isois-epihi'):
-                if(self.species in ['p', 'i']):
-                    # We're using here only the HET instrument of EPIHI (and not LET1 or LET2)
-                    df_flux, en_channel_string =\
-                        calc_av_en_flux_PSP_EPIHI(df=self.current_df_i,
-                                                  energies=self.current_i_energies,
-                                                  en_channel=channels,
-                                                  species='p',
-                                                  instrument='het',
-                                                  viewing=viewing.upper())
-                if(self.species == 'e'):
-                    # We're using here only the HET instrument of EPIHI (and not LET1 or LET2)
-                    df_flux, en_channel_string =\
-                        calc_av_en_flux_PSP_EPIHI(df=self.current_df_e,
-                                                  energies=self.current_e_energies,
-                                                  en_channel=channels,
-                                                  species='e',
-                                                  instrument='het',
-                                                  viewing=viewing.upper())
-            if(self.sensor.lower() == 'isois-epilo'):
-                if(self.species == 'e'):
-                    # We're using here only the F channel of EPILO (and not E or G)
-                    df_flux, en_channel_string =\
-                        calc_av_en_flux_PSP_EPILO(df=self.current_df_e,
-                                                  en_dict=self.current_e_energies,
-                                                  en_channel=channels,
-                                                  species='e',
-                                                  mode='pe',
-                                                  chan='F',
-                                                  viewing=viewing)
-
-        if(resample_period is not None):
-
-            df_averaged = self.resample(df_flux, resample_period)
-
-        else:
-
-            df_averaged = df_flux
-
-        flux_series, onset_stats, onset_found, peak_flux, peak_time, fig, bg_mean =\
-            self.onset_analysis(df_averaged, bg_start, bg_length, background_range,
-                                en_channel_string, yscale=yscale, cusum_window=cusum_window, xlim=xlim)
-
-        # At least in the case of solo/ept the peak_flux is a pandas Dataframe, but it should be a Series
-        if isinstance(peak_flux, pd.core.frame.DataFrame):
-            peak_flux = pd.Series(data=peak_flux.values[0])
-
-        # update class attributes before returning variables:
-        self.update_onset_attributes(flux_series, onset_stats, onset_found, peak_flux.values[0], peak_time, fig, bg_mean)
-
-        return flux_series, onset_stats, onset_found, peak_flux, peak_time, fig, bg_mean
-
-
-    def dynamic_spectrum(self, view, cmap:str='magma', xlim:tuple=None, resample:str=None, save:bool=False) -> None:
-        """
-        Shows all the different energy channels in a single 2D plot, and color codes the corresponding intensity*energy^2 by a colormap.
-
-        Parameters:
-        -----------
-        view : str or None
-                The viewing direction of the sensor
-        cmap : str, default='magma'
-                The colormap for the dynamic spectrum plot
-        xlim : 2-tuple of datetime strings (str, str)
-                Pandas-compatible datetime strings for the start and stop of the figure
-        resample : str
-                Pandas-compatibe resampling string, e.g. '10min' or '30s'
-        save : bool
-                Saves the image
-        """
-
-        # Event attributes
-        spacecraft = self.spacecraft
-        instrument = self.sensor
-        species = self.species
-
-        self.choose_data(view)
-
-        if self.spacecraft == "solo":
-            if species in ["electron", 'e']:
-                particle_data = self.current_df_e["Electron_Flux"]
-                s_identifier = "electrons"
-            else:
-                try:
-                    particle_data = self.current_df_i["Ion_Flux"]
-                    s_identifier = "ions"
-                except KeyError:
-                    particle_data = self.current_df_i["H_Flux"]
-                    s_identifier = "protons"
-
-        if self.spacecraft[:2] == "st":
-            if species in ["electron", 'e']:
-                if instrument == "sept":
-                    particle_data = self.current_df_e[[ch for ch in self.current_df_e.columns if ch[:2] == "ch"]]
-                else:
-                    particle_data = self.current_df_e[[ch for ch in self.current_df_e.columns if "Flux" in ch]]
-                s_identifier = "electrons"
-            else:
-                if instrument == "sept":
-                    particle_data = self.current_df_i[[ch for ch in self.current_df_i.columns if ch[:2] == "ch"]]
-                    s_identifier = "ions"
-                else:
-                    particle_data = self.current_df_i[[ch for ch in self.current_df_i.columns if "Flux" in ch]]
-                s_identifier = "protons"
-
-        if self.spacecraft == "soho":
-            if instrument.lower() == "erne":
-                particle_data = self.current_df_i
-                s_identifier = "protons"
-            if instrument.lower() == "ephin":
-                particle_data = self.current_df_e
-                s_identifier = "electrons"
-                warnings.warn('SOHO/EPHIN data is not fully implemented yet!')
-
-        # These particle instruments will have keVs on their y-axis
-        LOW_ENERGY_SENSORS = ("sept", "ept")
-
-        if instrument in LOW_ENERGY_SENSORS:
-            y_multiplier = 1e-3 # keV
-            y_unit = "keV"
-        else:
-            y_multiplier = 1e-6 # MeV
-            y_unit = "MeV"
-
-        # Resample only if requested
-        if resample is not None:
-            particle_data = particle_data.resample(resample).mean()
-
-        if xlim is None:
-            df = particle_data[:]
-            t_start, t_end = df.index[0], df.index[-1]
-        else:
-            # td is added to the end to avert white pixels at the end of the plot
-            td_str = resample if resample is not None else '0s'
-            td = pd.Timedelta(value=td_str)
-            t_start, t_end = pd.to_datetime(xlim[0]), pd.to_datetime(xlim[1])
-            df = particle_data.loc[(particle_data.index >= t_start) & (particle_data.index <= (t_end+td))]
-
-
-        # In practice this seeks the date on which the highest flux is observed
-        date_of_event = df.iloc[np.argmax(df[df.columns[0]])].name.date()
-
-        # Assert time and channel bins
-        time = df.index
-
-        # The low and high ends of each energy channel
-        e_lows, e_highs = self.get_channel_energy_values()  # this function return energy in eVs
-
-        # The mean energy of each channel in eVs
-        mean_energies = np.sqrt(np.multiply(e_lows, e_highs))
-
-        # Energy boundaries of plotted bins in keVs are the y-axis:
-        y_arr = np.append(e_lows,e_highs[-1]) * y_multiplier
-
-        # Set image pixel length and height
-        image_len = len(time)
-        image_hei = len(y_arr)-1
-
-        # Init the grid
-        grid = np.zeros((image_len, image_hei))
-
-        # Display energy in MeVs -> multiplier squared is 1e-6*1e-6 = 1e-12
-        energy_multiplier_squared = 1e-12
-
-        # Assign grid bins -> intensity * energy^2
-        for i, channel in enumerate(df):
-
-            grid[:,i] = df[channel]*(mean_energies[i]*mean_energies[i]*energy_multiplier_squared) # Intensity*Energy^2, and energy is in eV -> tranform to keV or MeV
-
-
-        # Finally cut the last entry and transpose the grid so that it can be plotted correctly
-        grid = grid[:-1,:]
-        grid = grid.T
-
-        maskedgrid = np.where(grid==0, 0, 1)
-        maskedgrid = np.ma.masked_where(maskedgrid==1, maskedgrid)
-    
-        # ---only plotting_commands from this point----->
-
-        # Some visual parameters
-        plt.rcParams['axes.linewidth'] = 2.8
-        plt.rcParams['font.size'] = 28 if self.radio_spacecraft is None else 20
-        plt.rcParams['axes.titlesize'] = 32
-        plt.rcParams['axes.labelsize'] = 28 if self.radio_spacecraft is None else 26
-        plt.rcParams['xtick.labelsize'] = 28 if self.radio_spacecraft is None else 26
-        plt.rcParams['ytick.labelsize'] = 20 if self.radio_spacecraft is None else 18
-        plt.rcParams['pcolor.shading'] = 'auto'
-
-        normscale = cl.LogNorm()
-
-        # Init the figure and axes
-        if self.radio_spacecraft is None:
-            figsize=(27,14)
-            fig, ax = plt.subplots(figsize=figsize)
-            ax = np.array([ax])
-            DYN_SPEC_INDX = 0
-
-        else:
-            figsize = (27,18)
-            fig, ax = plt.subplots(nrows=2, figsize=figsize, sharex=True)
-            DYN_SPEC_INDX = 1
-
-            from read_swaves import plot_swaves
-            ax[0], colormesh = plot_swaves(downloaded_files=self.radio_files, spacecraft=self.radio_spacecraft[0], start_time=t_start, end_time=t_end, ax=ax[0], cmap=cmap)
-
-            fig.tight_layout(pad=9.5, w_pad=-0.5, h_pad=-0.5)
-            # plt.subplots_adjust(wspace=-1, hspace=-1.8)
-
-            # Colorbar
-            cb = fig.colorbar(colormesh, orientation='vertical', ax=ax[0])
-            clabel = "Intensity"
-            cb.set_label(clabel)
-
-        # Colormesh
-        cplot = ax[DYN_SPEC_INDX].pcolormesh(time, y_arr, grid, shading='auto', cmap=cmap, norm=normscale)
-        greymesh = ax[DYN_SPEC_INDX].pcolormesh(time, y_arr, maskedgrid, shading='auto', cmap='Greys', vmin=-1, vmax=1)
-
-        # Colorbar
-        cb = fig.colorbar(cplot, orientation='vertical', ax=ax[DYN_SPEC_INDX])
-        clabel = r"Intensity $\cdot$ $E^{2}$" + "\n" + r"[MeV/(cm$^{2}$ sr s)]"
-        cb.set_label(clabel)
-
-        # y-axis settings
-        ax[DYN_SPEC_INDX].set_yscale('log')
-        ax[DYN_SPEC_INDX].set_ylim(np.nanmin(y_arr), np.nanmax(y_arr))
-        ax[DYN_SPEC_INDX].set_yticks([yval for yval in y_arr])
-        ax[DYN_SPEC_INDX].yaxis.set_major_formatter(ScalarFormatter(useMathText=True))
-
-        # gets rid of minor ticks and labels
-        ax[DYN_SPEC_INDX].yaxis.set_tick_params(length=0, width=0, which='minor', labelsize=0.)
-        ax[DYN_SPEC_INDX].yaxis.set_tick_params(length=9., width=1.5, which='major')
-
-        ax[DYN_SPEC_INDX].set_ylabel(f"Energy [{y_unit}]")
-
-        # x-axis settings
-        ax[DYN_SPEC_INDX].set_xlabel("Time [HH:MM \nm-d]")
-        ax[DYN_SPEC_INDX].xaxis_date()
-        ax[DYN_SPEC_INDX].set_xlim(t_start, t_end)
-        #ax[DYN_SPEC_INDX].xaxis.set_major_locator(mdates.HourLocator(interval = 1))
-        utc_dt_format1 = DateFormatter('%H:%M \n%m-%d')
-        ax[DYN_SPEC_INDX].xaxis.set_major_formatter(utc_dt_format1)
-        #ax.xaxis.set_minor_locator(mdates.MinuteLocator(interval = 5))
-
-        # Title
-        if view is not None:
-            title = f"{spacecraft.upper()} {instrument.upper()} ({view}) {s_identifier}, {date_of_event}"
-        else:
-            title = f"{spacecraft.upper()} {instrument.upper()} {s_identifier}, {date_of_event}"
-
-        if self.radio_spacecraft is None:
-            ax[0].set_title(title)
-        else:
-            ax[0].set_title(f"Radio + Dynamic Spectrum, {title}")
-
-        # saving of the figure
-        if save:
-            plt.savefig(f'plots/{spacecraft}_{instrument}_{date_of_event}_dynamic_spectra.png', transparent=False, 
-                    facecolor='white', bbox_inches='tight')
-
-        self.fig = fig
-        plt.show()
-            
-
-
-    def tsa_plot(self, view, selection=None, xlim=None, resample=None):
-        """
-        Makes an interactive time-shift plot
-
-        Parameters:
-        ----------
-        view : str or None 
-                    Viewing direction for the chosen sensor
-        selection : 2-tuple
-                    The indices of the channels one wishes to plot. End-exclusive.
-        xlim : 2-tuple
-                    The start and end point of the plot as pandas-compatible datetimes or strings
-        resample : str
-                    Pandas-compatible resampling time-string, e.g. "2min" or "50s"
-        """
-
-        import ipywidgets as widgets
-
-        # inits
-        spacecraft = self.spacecraft
-        instrument = self.sensor
-        species = self.species
-
-
-        # This here is an extremely stupid thing, but we must convert spacecraft input name back
-        # to its original version so that sunpy.get_horizon_coords() understands it
-        if spacecraft == "solo":
-            spacecraft_input_name = "Solar Orbiter"
-        elif spacecraft == "sta":
-            spacecraft_input_name = "STEREO-A"
-        elif spacecraft == "stb":
-            spacecraft_input_name = "STEREO-B"
-        else:
-            spacecraft_input_name = spacecraft.upper()
-
-        # get (lon, lat, radius) in (deg, deg, AU) in Stonyhurst coordinates:
-        # e.g. 'Solar Orbiter', 'STEREO-A', 'STEREO-B', 'SOHO', 'PSP'
-        position = get_horizons_coord(spacecraft_input_name, self.start_date)
-        radial_distance_value = np.round(position.radius.value,2)
-
-        METERS_PER_AU = 1 * u.AU.to(u.m)
-
-        self.choose_data(view)
-
-        if self.spacecraft == "solo":
-            if species in ["electron", 'e']:
-                particle_data = self.current_df_e["Electron_Flux"]
-                s_identifier = "electrons"
-            else:
-                try:
-                    particle_data = self.current_df_i["Ion_Flux"]
-                    s_identifier = "ions"
-                except KeyError:
-                    particle_data = self.current_df_i["H_Flux"]
-                    s_identifier = "protons"
-            sc_identifier = "Solar Orbiter"
-
-        if self.spacecraft[:2] == "st":
-            if species in ["electron", 'e']:
-                if instrument == "sept":
-                    particle_data = self.current_df_e[[ch for ch in self.current_df_e.columns if ch[:2] == "ch"]]
-                else:
-                    particle_data = self.current_df_e[[ch for ch in self.current_df_e.columns if "Flux" in ch]]
-                s_identifier = "electrons"
-            else:
-                if instrument == "sept":
-                    particle_data = self.current_df_i[[ch for ch in self.current_df_i.columns if ch[:2] == "ch"]]
-                else:
-                    particle_data = self.current_df_i[[ch for ch in self.current_df_i.columns if "Flux" in ch]]
-                s_identifier = "protons"
-            sc_identifier = "STEREO-A" if spacecraft[-1] == "a" else "STEREO-B"
-
-        if self.spacecraft == "soho":
-            # ERNE-HED (only protons)
-            if instrument.lower() == "erne":
-                particle_data = self.current_df_i
-                s_identifier = "protons"
-            # EPHIN, as of now only electrons, could be extended to protons in the future
-            if instrument.lower() == "ephin":
-                particle_data = self.current_df_e
-                s_identifier = "electrons"
-            sc_identifier = spacecraft.upper()
-
-
-        # make a copy to make sure original data is not altered
-        dataframe = particle_data.copy()
-
-        particle_speeds = self.calculate_particle_speeds()
-
-        # t_0 = t - L/v -> L/v is the coefficient that shifts the x-axis
-        shift_coefficients = [METERS_PER_AU/v for v in particle_speeds]
-
-        stepsize = 0.05
-        min_slider_val, max_slider_val = 0.0, 2.55
-
-        # Only the selected channels will be plotted
-        if selection is not None:
-
-            # len==3 means that we only choose every selection[2]:th channel 
-            if len(selection) == 3:
-                channel_indices = [i for i in range(selection[0],selection[1],selection[2])]
-                selected_channels = [channel for channel in dataframe.columns[channel_indices]]
-            else:
-                channel_indices = [i for i in range(selection[0],selection[1])]
-                selected_channels = dataframe.columns[selection[0]:selection[1]]
-        else:
-            selected_channels = dataframe.columns
-            channel_indices = [i for i in range(len(selected_channels))]
-
-        # Change 0-values to nan purely for plotting purposes, since we're not doing any
-        # calculations with them
-        dataframe[dataframe[selected_channels] == 0] = np.nan
-
-        # Get the channel numbers (not the indices!)
-        channel_nums = [int(name.split('_')[-1]) for name in selected_channels]
-
-        # Channel energy values as strings:
-        channel_energy_strs = self.get_channel_energy_values("str")
-        if selection is not None:
-            if len(selection) == 3:
-                channel_energy_strs = channel_energy_strs[slice(selection[0], selection[1], selection[2])]
-            else:
-                channel_energy_strs = channel_energy_strs[slice(selection[0], selection[1])]
-
-        # creation of the figure
-        fig, ax = plt.subplots(figsize=(9, 6))
-
-        # settings of title
-        ax.set_title(f"{sc_identifier} {instrument.upper()}, {s_identifier}")
-
-        ax.grid(True)
-
-        # settings for y and x axes
-        ax.set_yscale("log")
-        ax.set_ylabel(r"Intensity" + "\n" + r"[1/(cm$^{2}$ sr s MeV)$^{-1}$]")
-
-        ax.set_xlabel(r"$t_{0} = t - L/v$")
-        ax.xaxis_date()
-        ax.xaxis.set_major_formatter(DateFormatter('%H:%M\n%m-%d'))
-
-        if xlim is None:
-            ax.set_xlim(dataframe.index[0], dataframe.index[-1])
-        else:
-            try:
-                ax.set_xlim(xlim[0], xlim[1])
-            except ValueError:
-                ax.set_xlim(pd.to_datetime(xlim[0]), pd.to_datetime(xlim[1]))
-
-        # cosmetic settings
-        plt.rcParams['axes.linewidth'] = 1.5
-        plt.rcParams['font.size'] = 12
-
-        # housekeeping lists
-        series_natural = []
-        series_norm = []
-        plotted_natural = []
-        plotted_norm = []
-
-        # go through the selected channels to create individual series and plot them
-        for i, channel in enumerate(selected_channels):
-
-            # construct series and its normalized counterpart
-            series = flux2series(dataframe[channel], dataframe.index, resample)
-            series_normalized = flux2series(series.values/np.nanmax(series.values), series.index, resample)
-
-            # store all series to arrays for later referencing
-            series_natural.append(series)
-            series_norm.append(series_normalized)
-
-            # save the plotted lines, NOTICE that they come inside a list of len==1
-            p1 = ax.step(series.index, series.values, c=f"C{i}", visible=True, label = f"{channel_nums[i]}: {channel_energy_strs[i]}")
-            p2 = ax.step(series_normalized.index, series_normalized.values, c=f"C{i}", visible=False) # normalized lines are initially hidden
-
-            # store plotted line objects for later referencing
-            plotted_natural.append(p1[0])
-            plotted_norm.append(p2[0])
-
-        plt.legend(loc=1, bbox_to_anchor=(1.0, 0.25), fancybox=True, shadow=False, ncol=1, fontsize=9)
-
-        # widget objects, slider and button
-        style = {'description_width': 'initial'}
-        slider = widgets.FloatSlider(value = min_slider_val,
-                                     min = min_slider_val,
-                                     max = max_slider_val,
-                                     step = stepsize,
-                                     continuous_update = True,
-                                     description = "Path length L [AU]: ",
-                                     style = style
-                                     )
-
-        # button = widgets.Checkbox(value = False,
-        #                           description = "Normalize",
-        #                           indent = True
-        #                           )
-        button = widgets.RadioButtons(value='original data', 
-                                      description='Intensity:',
-                                      options=['original data', 'normalized'],
-                                      disabled=False
-                                      )
-
-        # A box for the path length
-        path_label = f"R={radial_distance_value:.2f} AU\nL = {slider.value} AU"
-        text = plt.text(0.02,0.03, path_label, transform=ax.transAxes, bbox=dict(boxstyle="square",
-                                                                 ec=(0., 0., 0.),
-                                                                 fc=(1., 1.0, 1.0),
-                                                                 ))
-
-
-        # timeshift connects the slider to the shifting of the plotted curves
-        def timeshift(sliderobject):
-
-            # shift the x-values (times) by the timedelta
-            for i, line in enumerate(plotted_natural):
-
-                # calculate the timedelta in seconds corresponding to the change in the path length
-                # The relevant part here is sliderobject["old"]! It saves the previous value of the slider!
-                timedelta_sec = shift_coefficients[i]*(slider.value - sliderobject["old"])
-
-                # Update the time value
-                line.set_xdata(line.get_xdata() - pd.Timedelta(seconds=timedelta_sec))
-
-            for i, line in enumerate(plotted_norm):
-
-                # calculate the timedelta in seconds corresponding to the change in the path length
-                # The relevant part here is sliderobject["old"]! It saves the previous value of the slider!
-                timedelta_sec = shift_coefficients[i]*(slider.value - sliderobject["old"])
-
-                # Update the time value
-                line.set_xdata(line.get_xdata() - pd.Timedelta(seconds=timedelta_sec))
-
-            # Update the path label artist
-            text.set_text(f"R={radial_distance_value:.2f} AU\nL = {slider.value} AU")
-
-            # Effectively this refreshes the figure
-            fig.canvas.draw_idle()
-
-
-        # this function connects the button to switching visibility of natural / normed curves
-        def normalize_axes(button):
-
-            # flip the truth values of natural and normed intensity visibility
-            for line in plotted_natural:
-                line.set_visible(not line.get_visible())
-
-            for line in plotted_norm:
-                line.set_visible(not line.get_visible())
-
-            # Reset the y-axis label
-            if plotted_natural[0].get_visible():
-                ax.set_ylabel(r"Intensity" + "\n" + r"[1/(cm$^{2}$ sr s MeV)$^{-1}$]")
-            else:
-                ax.set_ylabel("Intensity normalized")
-
-            # Effectively this refreshes the figure
-            fig.canvas.draw_idle()
-
-
-
-        slider.observe(timeshift, names="value")
-        display(slider)
-        
-        button.observe(normalize_axes)
-        display(button)
-
-
-    def get_channel_energy_values(self, returns:str="num") -> list:
-        """
-        A class method to return the energies of each energy channel in either str or numerical form.
-
-        Parameters:
-        -----------
-        returns: str, either 'str' or 'num'
-
-        Returns:
-        ---------
-        energy_ranges : list of energy ranges as strings
-        or
-        lower_bounds : list of lower bounds of each energy channel in eVs
-        higher_bounds : list of higher bounds of each energy channel in eVs
-        """
-
-        # First check by spacecraft, then by sensor
-        if self.spacecraft == "solo":
-
-            # All solo energies are in the same object
-            energy_dict = self.current_energies
-
-            if self.species == 'e':
-                energy_ranges = energy_dict["Electron_Bins_Text"]
-            else:
-                try:
-                    energy_ranges = energy_dict["Ion_Bins_Text"]
-                except KeyError:
-                    energy_ranges = energy_dict["H_Bins_Text"]
-
-            # Each element in the list is also a list with len==1, so fix that
-            energy_ranges = [element[0] for element in energy_ranges]
-
-        if self.spacecraft[:2] == "st":
-
-            # STEREO/SEPT energies come in two different objects
-            if self.sensor == "sept":
-                if self.species == 'e':
-                    energy_df = self.current_e_energies
-                else:
-                    energy_df = self.current_i_energies
-
-                energy_ranges = energy_df["ch_strings"].values
-
-            # STEREO/HET energies all in the same dictionary
-            else:
-                energy_dict = self.current_energies
-
-                if self.species == 'e':
-                    energy_ranges = energy_dict["Electron_Bins_Text"]
-                else:
-                    energy_ranges = energy_dict["Proton_Bins_Text"]
-
-                # Each element in the list is also a list with len==1, so fix that
-                energy_ranges = [element[0] for element in energy_ranges]
-
-        if self.spacecraft == "soho":
-            if self.sensor.lower() == "erne":
-                energy_ranges = self.current_energies["channels_dict_df_p"]["ch_strings"].values
-            if self.sensor.lower() == "ephin":
-                # Choose only the 4 first channels / descriptions, since I only know of 
-                # E150, E300, E1300 and E3000. The rest are unknown to me. 
-                energy_ranges = [val for val in self.current_energies.values()][:4]
-
-        # Check what to return before running calculations
-        if returns == "str":
-            return energy_ranges
-
-        lower_bounds, higher_bounds = [], []
-        for energy_str in energy_ranges:
-
-            # Sometimes there is no hyphen, but then it's not a range of energies
-            try:
-                lower_bound, temp = energy_str.split('-')
-            except ValueError:
-                lower_bounds.append(np.nan)
-                higher_bounds.append(np.nan)
-                continue
-
-            try:
-                higher_bound, energy_unit = temp.split(' ')
-
-            # It could be that the strings are not in a standard format, so check if
-            # there is an empty space before the second energy value
-            except ValueError:
-
-                try:
-                    _, higher_bound, energy_unit = temp.split(' ')
-
-                # It could even be that for some godforsaken reason there are empty spaces
-                # between the numbers themselves, so take care of that too
-                except ValueError:
-                    higher_bound, energy_unit = temp.split(' ')[1], temp.split(' ')[2]
-
-            lower_bounds.append(float(lower_bound))
-            higher_bounds.append(float(higher_bound))
-
-        # Transform lists to numpy arrays for performance and convenience
-        lower_bounds, higher_bounds = np.asarray(lower_bounds), np.asarray(higher_bounds)
-
-        # Finally before returning the lists, make sure that the unit of energy is eV
-        if energy_unit == "keV":
-            lower_bounds, higher_bounds = lower_bounds * 1e3, higher_bounds * 1e3
-
-        elif energy_unit == "MeV":
-            lower_bounds, higher_bounds = lower_bounds * 1e6, higher_bounds * 1e6
-
-        # This only happens with ephin, which has MeV as the unit of energy
-        else:
-            lower_bounds, higher_bounds = lower_bounds * 1e6, higher_bounds * 1e6
-
-        return lower_bounds, higher_bounds
-
-
-    def calculate_particle_speeds(self):
-        """
-        Calculates average particle speeds by input channel energy boundaries.
-        """
-
-        if self.species in ["electron", 'e']:
-            m_species = const.m_e.value
-        if self.species in ['p', "ion", 'H']:
-            m_species = const.m_p.value
-
-        C_SQUARED = const.c.value*const.c.value
-
-        # E=mc^2, a fundamental property of any object with mass
-        mass_energy = m_species*C_SQUARED  # e.g. 511 keV/c for electrons
-
-        # Get the energies of each energy channel, to calculate the mean energy of particles and ultimately
-        # To get the dimensionless speeds of the particles (beta)
-        e_lows, e_highs = self.get_channel_energy_values()  # get_energy_channels() returns energy in eVs
-
-        mean_energies = np.sqrt(np.multiply(e_lows, e_highs))
-
-        # Transform kinetic energy from electron volts to joules
-        e_Joule = [((En*u.eV).to(u.J)).value for En in mean_energies]
-
-        # Beta, the unitless speed (v/c)
-        beta = [np.sqrt(1-((e_J/mass_energy + 1)**(-2))) for e_J in e_Joule]
-
-        return np.array(beta)*const.c.value
-
-    
-    def print_energies(self):
-        """
-        Prints out the channel name / energy range pairs 
-        """
-
-        # This has to be run first, otherwise self.current_df does not exist
-        self.choose_data(self.viewing)
-
-        if self.species in ['e', "electron"]:
-            channel_names = self.current_df_e.columns
-            SOLO_EPT_CHANNELS_AMOUNT = 34
-            SOLO_HET_CHANNELS_AMOUNT = 4
-        if self.species in ['p', 'i', 'H', "proton", "ion"]:
-            channel_names = self.current_df_i.columns
-            SOLO_EPT_CHANNELS_AMOUNT = 64
-            SOLO_HET_CHANNELS_AMOUNT = 36
-
-        # Extract only the numbers from channel names
-        if self.spacecraft == "solo":
-            if self.sensor == "ept":
-                channel_names = [name[1] for name in channel_names[:SOLO_EPT_CHANNELS_AMOUNT]]
-                channel_numbers = [name.split('_')[-1] for name in channel_names]
-            if self.sensor == "het":
-                channel_names = [name[1] for name in channel_names[:SOLO_HET_CHANNELS_AMOUNT]]
-                channel_numbers = [name.split('_')[-1] for name in channel_names]
-
-        if self.spacecraft in ["sta", "stb"] or self.sensor == "erne":
-            channel_numbers = [name.split('_')[-1] for name in channel_names]
-
-        if self.sensor == "ephin":
-            channel_numbers = [name.split('E')[-1] for name in channel_names]
-
-        energy_strs = self.get_channel_energy_values("str")
-        
-        print(f"{self.spacecraft}, {self.sensor}:\n")
-        print("Channel number | Energy range")
-        for i, energy_range in enumerate(energy_strs):
-            print(f" {channel_numbers[i]}  :  {energy_range}")
-
-
-def flux2series(flux, dates, cadence=None):
-    """
-    Converts an array of observed particle flux + timestamps into a pandas series
-    with the desired cadence.
-
-    Parameters:
-    -----------
-    flux: an array of observed particle fluxes
-    dates: an array of corresponding dates/times
-    cadence: str - desired spacing between the series elements e.g. '1s' or '5min'
-
-    Returns:
-    ----------
-    flux_series: Pandas Series object indexed by the resampled cadence
-    """
-
-    # from pandas.tseries.frequencies import to_offset
-
-    # set up the series object
-    flux_series = pd.Series(flux, index=dates)
-
-    # if no cadence given, then just return the series with the original
-    # time resolution
-    if cadence is not None:
-        try:
-            flux_series = flux_series.resample(cadence, origin='start').mean()
-            flux_series.index = flux_series.index + pd.tseries.frequencies.to_offset(pd.Timedelta(cadence)/2)
-        except ValueError:
-            raise Warning(f"Your 'resample' option of [{cadence}] doesn't seem to be a proper Pandas frequency!")
-
-    return flux_series
-
-
-def bepicolombo_sixs_stack(path, date, side):
-    # def bepicolombo_sixs_stack(path, date, side, species):
-
-    # side is the index of the file here
-    try:
-        try:
-            filename = f"{path}/sixs_phys_data_{date}_side{side}.csv"
-            df = pd.read_csv(filename)
-        except FileNotFoundError:
-            # try alternative file name format
-            filename = f"{path}/{date.strftime('%Y%m%d')}_side{side}.csv"
-            df = pd.read_csv(filename)
-            times = pd.to_datetime(df['TimeUTC'])
-
-        # list comprehension because the method can't be applied onto the array "times"
-        times = [t.tz_convert(None) for t in times]
-        df.index = np.array(times)
-        df = df.drop(columns=['TimeUTC'])
-
-        # choose the subset of desired particle species
-        # if species=="ion":
-        #     df = df[[f"P{i}" for i in range(1,10)]]
-        # if species=="ele":
-        #     df = df[[f"E{i}" for i in range(1,8)]]
-
-    except FileNotFoundError:
-        print(f'Unable to open {filename}')
-        df = pd.DataFrame()
-        filename = ''
-
-    return df, filename
-
-
-def bepi_sixs_load(startdate, enddate, side, path):
-    dates = pd.date_range(startdate, enddate)
-
-    # read files into Pandas dataframes:
-    df, file = bepicolombo_sixs_stack(path, startdate, side=side)
-    if len(dates) > 1:
-        for date in dates[1:]:
-            t_df, file = bepicolombo_sixs_stack(path, date.date(), side=side)
-            df = pd.concat([df, t_df])
-
-    channels_dict = {"Energy_Bin_str": {'E1': '71 keV', 'E2': '106 keV', 'E3': '169 keV', 'E4': '280 keV', 'E5': '960 keV', 'E6': '2240 keV', 'E7': '8170 keV',
-                                        'P1': '1.1 MeV', 'P2': '1.2 MeV', 'P3': '1.5 MeV', 'P4': '2.3 MeV', 'P5': '4.0 MeV', 'P6': '8.0 MeV', 'P7': '15.0 MeV', 'P8': '25.1 MeV', 'P9': '37.3 MeV'},
-                     "Electron_Bins_Low_Energy": np.array([55, 78, 134, 235, 1000, 1432, 4904]),
-                     "Electron_Bins_High_Energy": np.array([92, 143, 214, 331, 1193, 3165, 10000]),
-                     "Ion_Bins_Low_Energy": np.array([0.001, 1.088, 1.407, 2.139, 3.647, 7.533, 13.211, 22.606, 29.246]),
-                     "Ion_Bins_High_Energy": np.array([1.254, 1.311, 1.608, 2.388, 4.241, 8.534, 15.515, 28.413, 40.0])}
-    return df, channels_dict
-
-
-def calc_av_en_flux_sixs(df, channel, species):
-    """
-    This function averages the flux of two energy channels of BepiColombo/SIXS into a combined energy channel
-    channel numbers counted from 1
-
-    Parameters
-    ----------
-    df : pd.DataFrame
-        DataFrame containing HET data
-    channel : int or list
-        energy channel or list with first and last channel to be used
-    species : string
-        'e', 'electrons', 'p', 'protons'
-
-    Returns
-    -------
-    flux: pd.DataFrame
-        channel-averaged flux
-    en_channel_string: str
-        string containing the energy information of combined channel
-    """
-
-    # define constant geometric factors
-    GEOMFACTOR_PROT8 = 5.97E-01
-    GEOMFACTOR_PROT9 = 4.09E+00
-    GEOMFACTOR_ELEC5 = 1.99E-02
-    GEOMFACTOR_ELEC6 = 1.33E-01
-    GEOMFACTOR_PROT_COMB89 = 3.34
-    GEOMFACTOR_ELEC_COMB56 = 0.0972
-
-    if species in ['p', 'protons']:
-        if channel == [8, 9]:
-            countrate = df['P8'] * GEOMFACTOR_PROT8 + df['P9'] * GEOMFACTOR_PROT9
-            flux = countrate / GEOMFACTOR_PROT_COMB89
-            en_channel_string = '37 MeV'
-        else:
-            print('No valid channel combination selected.')
-            flux = pd.Series()
-            en_channel_string = ''
-
-    if species in ['e', 'electrons']:
-        if channel == [5, 6]:
-            countrate = df['E5'] * GEOMFACTOR_ELEC5 + df['E6'] * GEOMFACTOR_ELEC6
-            flux = countrate / GEOMFACTOR_ELEC_COMB56
-            en_channel_string = '1.4 MeV'
-        else:
-            print('No valid channel combination selected.')
-            flux = pd.Series()
-            en_channel_string = ''
-
-    return flux, en_channel_string
diff --git a/notebooks/sep_analysis_tools/onset_widgets.py b/notebooks/sep_analysis_tools/onset_widgets.py
deleted file mode 100644
index 77c34dc..0000000
--- a/notebooks/sep_analysis_tools/onset_widgets.py
+++ /dev/null
@@ -1,186 +0,0 @@
-"""
-A library to run the interactive user interface in SEP event onset determination notebooks.
-
-@ Author: Christian Palmroos <chospa@utu.fi>
-@Last updated: 2022-09-30
-"""
-
-
-from importlib.resources import path
-import ipywidgets as widgets
-
-# a list of available spacecraft:
-# list_of_sc = ["STEREO-A", "STEREO-B", "Solar Orbiter", "Bepicolombo", "SOHO"]
-list_of_sc = ["STEREO-A", "STEREO-B", "Solar Orbiter", "SOHO"]
-
-stereo_instr = ["SEPT", "HET"] #["LET", "SEPT", "HET"]
-solo_instr = ["EPT", "HET"]
-bepi_instr = ["SIXS-P"]
-soho_instr = ["ERNE-HED", "EPHIN"]
-
-sensor_dict = {
-    "STEREO-A" : stereo_instr,
-    "STEREO-B" : stereo_instr,
-    "Solar Orbiter" : solo_instr,
-    "Bepicolombo" : bepi_instr,
-    "SOHO" : soho_instr
-}
-
-view_dict = {
-    ("STEREO-A", "SEPT") : ["sun", "asun", "north", "south"],
-    ("STEREO-B", "SEPT") : ["sun", "asun", "north", "south"],
-    ("Solar Orbiter", "EPT") : ["sun", "asun", "north", "south"],
-    ("Solar Orbiter", "HET") : ["sun", "asun", "north", "south"],
-    ("Bepicolombo", "SIXS-P") : [0, 1, 2, 3, 4]
-}
-
-species_dict = {
-    ("STEREO-A", "LET") : ['protons', 'electrons'],
-    ("STEREO-A", "SEPT") : ['ions', 'electrons'],
-    ("STEREO-A", "HET") : ['protons', 'electrons'],
-    ("STEREO-B", "LET") : ['protons', 'electrons'],
-    ("STEREO-B", "SEPT") : ['ions', 'electrons'],
-    ("STEREO-B", "HET") : ['protons', 'electrons'],
-    ("Solar Orbiter", "EPT") : ['ions', 'electrons'],
-    ("Solar Orbiter", "HET") : ['protons', 'electrons'],
-    ("Bepicolombo", "SIXS-P") : ['protons', 'electrons'],
-    ("SOHO", "ERNE-HED") : ['protons'],
-    ("SOHO", "EPHIN") : ['electrons']
-}
-
-radio_dict = {
-    "None" : None,
-    "STEREO-A" : ("ahead", "STEREO-A"),
-    "STEREO-B" : ("behind", "STEREO-B"),
-    "WIND (Coming soon!)" : ("wind", "WIND")
-}
-
-# Drop-downs for dynamic particle spectrum:
-spacecraft_drop = widgets.Dropdown(
-                                options = list_of_sc,
-                                description = "Spacecraft:",
-                                disabled = False,
-                                )
-
-sensor_drop = widgets.Dropdown(
-                                options = sensor_dict[spacecraft_drop.value],
-                                description = "Sensor:",
-                                disabled = False,
-                                )
-
-view_drop = widgets.Dropdown(
-                                options = view_dict[(spacecraft_drop.value, sensor_drop.value)],
-                                description = "Viewing:",
-                                disabled = False
-                                )
-
-species_drop = widgets.Dropdown(
-                                options = species_dict[(spacecraft_drop.value, sensor_drop.value)],
-                                description = "Species:",
-                                disabled = False,
-                                )
-
-
-# A button to enable radio spectrum (Leave this out for now, sincde it doesn't work in the server as of 2022-09-30)
-radio_button = widgets.Checkbox(
-                                value=False,
-                                description='Radio Spectrum',
-                                disabled=True,
-                                indent=False
-                                )
-
-# The drop-drown for radio options
-radio_drop_style = {'description_width': 'initial'}
-radio_drop = widgets.Dropdown(
-                                options = radio_dict,
-                                value = None,
-                                description = "Plot radio spectrum for:",
-                                disabled = False,
-                                style=radio_drop_style
-                              )
-
-# this function updates the options in sensor_drop menu
-def update_sensor_options(val):
-    sensor_drop.options = sensor_dict[spacecraft_drop.value]
-
-
-# updates the options and availability of view_drop menu
-def update_view_options(val):
-    try:
-        view_drop.disabled = False
-        view_drop.options = view_dict[(spacecraft_drop.value, sensor_drop.value)]
-        view_drop.value = view_drop.options[0]
-    except KeyError:
-        view_drop.disabled = True
-        view_drop.value = None
-
-
-def update_species_options(val):
-    try:
-        species_drop.options = species_dict[(spacecraft_drop.value, sensor_drop.value)]
-    except KeyError:
-        pass
-
-
-def update_radio_options(val):
-    radio_drop.disabled = not radio_button.value
-    if radio_drop.disabled:
-        radio_drop.value = None
-    else:
-        radio_drop.value = radio_drop.options[0]
-
-
-def confirm_input(event_date : int, data_path : str, plot_path : str):
-
-    print("You've chosen the following options:")
-    print(f"Spacecraft: {spacecraft_drop.value}")
-    print(f"Sensor: {sensor_drop.value}")
-    print(f"Species: {species_drop.value}")
-    print(f"Viewing: {view_drop.value}")
-    print(f"Event_date: {event_date}")
-    print(f"Data_path: {data_path}")
-    print(f"Plot_path: {plot_path}")
-
-    if spacecraft_drop.value == "Solar Orbiter":
-        spacecraft_drop_value = "solo"
-    elif spacecraft_drop.value == "STEREO-A":
-        spacecraft_drop_value = "sta"
-    elif spacecraft_drop.value == "STEREO-B":
-        spacecraft_drop_value = "stb"
-    else:
-        spacecraft_drop_value = spacecraft_drop.value
-    
-    if sensor_drop.value in ["ERNE-HED"]:
-        sensor_drop_value = "ERNE"
-    else:
-        sensor_drop_value = sensor_drop.value
-    
-    if species_drop.value == "protons":
-        species_drop_value = 'p'
-    else:
-        species_drop_value = 'e'
-
-    # this is to be fed into Event class as input
-    global input_dict
-
-    input_dict = {
-        "Spacecraft" : spacecraft_drop_value,
-        "Sensor" : sensor_drop_value,
-        "Species" : species_drop_value,
-        "Viewing" : view_drop.value,
-        "Event_date" : event_date,
-        "Data_path" : data_path,
-        "Plot_path" : plot_path
-    }
-
-# makes spacecraft_drop run these functions every time it is accessed by user
-spacecraft_drop.observe(update_sensor_options)
-spacecraft_drop.observe(update_view_options)
-sensor_drop.observe(update_view_options)
-
-# does the same but for sensor menu
-spacecraft_drop.observe(update_species_options)
-sensor_drop.observe(update_species_options)
-
-# also observe the radio menu
-# radio_button.observe(update_radio_options)
diff --git a/notebooks/sep_analysis_tools/read_swaves.py b/notebooks/sep_analysis_tools/read_swaves.py
deleted file mode 100644
index 6fa16b3..0000000
--- a/notebooks/sep_analysis_tools/read_swaves.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import datetime
-
-import cdflib
-import numpy as np
-from matplotlib import dates
-from sunpy.net import Fido
-from sunpy.net import attrs as a
-
-# # define start and end date
-# start_time="2012-5-26 10:30"
-# end_time="2012-5-28 15:40"
-# # specify spacecraft 'ahead'/'behind'
-# spacecraft = 'ahead'
-
-
-def get_swaves(start_time, end_time, path=None):
-
-    #######################
-    # downloading the files
-    #######################
-
-    dataset = 'STEREO_LEVEL2_SWAVES'
-    cda_dataset = a.cdaweb.Dataset(dataset)
-
-    trange = a.Time(start_time, end_time)
-
-    # always add 1 day to enddate because the enddate itself should be included in the data (which isn't the case)
-    trange = a.Time(start_time, trange.end.to_datetime().date()+datetime.timedelta(days=1))
-
-    result = Fido.search(trange, cda_dataset)
-    downloaded_files = Fido.fetch(result, path=path)  # use Fido.fetch(result, path='/ThisIs/MyPath/to/Data/{file}')  to use a specific local folder for saving data files
-    downloaded_files.sort()
-    # print(downloaded_files)
-
-    return downloaded_files
-
-
-def plot_swaves(downloaded_files, spacecraft, start_time, end_time, ax, cmap='inferno'):
-
-    ###################
-    # reading the files
-    ###################
-    data_all = []
-    time_all = []
-
-    for i in downloaded_files:
-        cdf_file = cdflib.CDF(i)
-
-        data = cdf_file.varget("avg_intens_" + spacecraft)
-        data_all.append(data)
-
-        freq = cdf_file.varget('frequency')/1000  # in MHz
-
-        time = cdf_file.varget('Epoch')
-        time_all.append(cdflib.epochs.CDFepoch.to_datetime(time))
-
-    # full time array for plotting
-    time_arr = np.array(time_all).flatten()
-
-    # full data array for plotting
-    data_all = np.array(data_all)
-    # if there are more than one 1-day file downloaded
-    if data_all.shape[0] > 1:
-        data_arr = np.concatenate((data_all[0], data_all[1]))
-        for i in range(1, data_all.shape[0] - 1):
-            data_arr = np.concatenate((data_arr, data_all[i+1]))
-        # switching frequency axis
-        data_arr = data_arr.T
-    else:
-        # switching frequency axis
-        # one must choose the first entry of data_all here, because it's a list with len==1
-        data_arr = data_all[0].T
-
-    if isinstance(start_time, str):
-        start = datetime.datetime.strptime(start_time, '%Y-%m-%d %H:%M')
-        end = datetime.datetime.strptime(end_time, '%Y-%m-%d %H:%M')
-    else:
-        start, end = start_time, end_time
-
-    ######################
-    # plotting the spectra
-    ######################
-
-    colormesh = ax.pcolormesh(time_arr, freq[::-1], data_arr[::-1], vmin=0, vmax=0.5*np.max(data_arr), cmap=cmap)
-
-    ax.set_ylabel('Frequency (MHz)')
-    ax.set_xlabel('Date and time (UT)')
-    ax.set_yscale('log')
-    ax.set_ylim(freq[-1], freq[0])
-    ax.set_yticks([0.01, 0.1, 1, 10])
-    ax.set_yticklabels(['0.01', '0.1', '1', '10'])
-    ax.set_xlim(start, end)
-
-    ax.xaxis_date()
-    ax.xaxis.set_major_formatter(dates.DateFormatter('%d/%m %H:%M'))
-    # plt.show()
-
-    return ax, colormesh
diff --git a/notebooks/sep_analysis_tools/requirements.txt b/notebooks/sep_analysis_tools/requirements.txt
index b5e57d9..cca52d2 100644
--- a/notebooks/sep_analysis_tools/requirements.txt
+++ b/notebooks/sep_analysis_tools/requirements.txt
@@ -7,6 +7,6 @@ matplotlib>=3.4.3
 matplotlib-inline==0.1.3
 numpy>=1.20.3
 pandas>=1.3.4
-seppy
+seppy>=0.1.0
 solo_epd_loader>=0.1.10
 sunpy>=4.0
diff --git a/notebooks/sep_analysis_tools/time_shift_analysis.ipynb b/notebooks/sep_analysis_tools/time_shift_analysis.ipynb
index 38a8f23..cd1d131 100644
--- a/notebooks/sep_analysis_tools/time_shift_analysis.ipynb
+++ b/notebooks/sep_analysis_tools/time_shift_analysis.ipynb
@@ -7,8 +7,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from onset_functions import *\n",
-    "import onset_widgets as w"
+    "from seppy.tools import Event\n",
+    "import seppy.tools.widgets as w\n",
+    "import datetime, os"
    ]
   },
   {