From 0f90b51d080f146a5eaa284b8850dfdffdc8ed32 Mon Sep 17 00:00:00 2001 From: Stephan Meighen-Berger Date: Thu, 24 Jun 2021 14:03:40 +0200 Subject: [PATCH] Paper fixes --- examples/example_analysis.ipynb | 4 +- examples/example_basics.ipynb | 19 ++- examples/example_density_analysis.ipynb | 46 +---- .../probabilistic_model_constructions.ipynb | 159 +----------------- .../probabilistic_model_plot.ipynb | 30 ++-- 5 files changed, 40 insertions(+), 218 deletions(-) diff --git a/examples/example_analysis.ipynb b/examples/example_analysis.ipynb index 44dce32..c49fdce 100644 --- a/examples/example_analysis.ipynb +++ b/examples/example_analysis.ipynb @@ -162,7 +162,7 @@ "fontsize = 10.\n", "lw=1.\n", "h_length=0.2\n", - "export_dpi = 1000 # Dpi for the image export" + "export_dpi = 500 # Dpi for the image export" ] }, { @@ -172,7 +172,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/examples/example_basics.ipynb b/examples/example_basics.ipynb index 0da7a13..bf1fb2a 100644 --- a/examples/example_basics.ipynb +++ b/examples/example_basics.ipynb @@ -986,7 +986,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -995,12 +995,12 @@ "fontsize = 10. # Fontsize in the plot\n", "lw=0.5 # Linewidth\n", "h_length=0.2 # Handle length for the legends\n", - "export_dpi = 1000 # Dpi for the image export" + "export_dpi = 500 # Dpi for the image export" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1012,14 +1012,21 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" + ] + }, { "data": { - "image/png": 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\n", + "image/png": 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1DMoD+oRI0qQpsW1HkhruelI0g0/f+EHC4TA//OEPz7iM6cZYSfpNIcROIcRvhBAPMs0k9Tq5z9Cx0NF1bcJTqHjqbke/06k8SaooCoqAUKgEYFiVN52Icd1Va7j28vfxi3//tzzSeZ8VVUUA1TWDSVqoktQ3TknqXRfy+xBCpTwc5NSpU6NcNTwsy0IIhf6k7UhSM2uToumUhXzccsst/OY3vxlXhNh0Yqwk/YGUcraU8iYp5UeBj05mpUZDxibVNSTKpEhSy7JACI67JDVN0yWpikAQDDmSdDiSmqaJqqp8/P/8PCc6TvLKK68Mqr+maggEDbVOEH8hS9IMSX0To+4amk5Q16iucBw747mfoigYetYmNQyPpAaYJl/+8pcRQvCTn/zkjMqYbozVu/uMEOKzriS9TUq5ebIrNhK8F+33SKrrpCZ6CsZySBYw8qdgVFVFtaMEXEk6nLprWTaapnPV1X+Frqk8+uijmd8yHk5NRQhobKgHzg5J6p8gx5GqayiKQn1VBCnlkHb7WGC5JoiiqKTTaSzLQnenzYTuQ9gmzc3NfOxjH+Pee++lu7t7lDsWHsZEUiHEZ3HCAr8JbBZC3DaptRoFXgf2GzoSMSmS1JkkVzIN5JFUUyTB2MuUBR2vqydJ9+7dy7Zt2zLXm5YTRF5aWsqq887hkYd/l/kt4zzRNARQVxnB8PkKSpLeeuutfPvb385899rXP06bNDtPqoOi0FjlBK8dOXKEvXv38uijj7Jt27Yxe7U9dVdRVRKJBICz6BtAMxCmiQS+9rWvEY1G+fnPf35G9Z5OjFXd3SSlfMZ1HD0DFIQk9Rk6CBVN00glJ9hxZFmo6SQtbz2WKdObk4uFV1MdcEbkvr4+Dhw4QHt7OwsXLsxeb1oZL+M119/Ijl27M8EPmWkITUMICBkaldU1BSVJf/rTn/K9730v8z0jSX0TI0l1XQeh0FhVBsBVV11Fe3s71157LQsXLuTqq68eE1Fty3knQd0kEY87986xSYXlkPScc85h9erV/OQnP5mUFLCTibGSdIUQ4hwhRJsbHrhsMis1GrJ2jYquqhiGNmnBDGa4MvM94zhSVIJBJzi8r6+PD35w8JSxaVmZ6Zs1VzuJmp999lnnN7ejluhpVCuKrqlUVlUXFEkHIus4mqB5Uk0DodBQ7ZC0s7OTr33ta7z44ot88Ytf5I9//OOYFtZbloVfSRHxxTKS1MsWSGk14shBpO2Q/etf/zpHjx7l/vvvP6O6TxfGapPeS3audI2U8s5JrdUoyDpeVDRVRdcnQ921UBXhOIqEwLIsxxmkKAghKCkJAk7AwrvvvgtAc0tL5npHkjqdZe7cuTRUlfPM+vV59a/wJVB6nZU+AyXpdKu7HjxyZm1Sf97300VuWCBCUBMpY/7Chfzd3/0dd955J+973/u44447KCkp4Vvf+tao0tSybYSqIRUjUydPgxHhSuzGduTOPwOwZs0alixZwp133lkwQSJjwZjXk0op/1lKeQXwoBCidBLrNCqyge4quqah6xqp9OQEMwCZiXIv6F4IKC0rxefz8cc//hFwVuTkdlzTMlE1Z45VCMHl77uQZ59Zj23bGWnS1Zugo8cZ/QtN3fXgxRR7g+BESVJVVUEoqIrg5U2b+P73v585p7S0lG984xs8++yzo+auytikSjbYPy2hM+qotObcqxH7NiDNNEIIbrvtNrZu3cr69aOuliwYjNVxdLn32fXsrhjh9ElHboC9rmgY+iSou6aFogi3HCXr3UXie+IuNDSuufYannjiCQAWLlmSsYnAUXczkS/ApVd+mI7OU+zevTs/V+xJE2mbVFbXcOLEiQw5C0WSelkRPTsu4Kr5E6LuKgpIQA4ekLyF9R0dHSPeLxO+qWW7slRUthztxZYSITTk3GXILc5uAddddx0weQnPJwMjklQIcYMQ4h7gn7xABjeYYc3UVG9o5JLUp6to+sQ7jmzbQhEKEpElqW2j2hbWhTdiHNjN9Tdckzn/nOXnZmwip452Rt0FqK5vAhwb1uuoiiLpSZWC2UVldTWWZWUm9gtFknoLAeLuABQcJ0kHqrsOSwc/a3V1NTA6SU3LGUzVnO0NS/w+ZlWXsK8zhkBgNV2KPLENaZmEw2HKy8vPqsCGEQPspZT/JYRYD7RP99xoLvJyHBmGq+7GJrQM03JsUgBVyaq7iqpCRT3EYqx6vzNWNTQ0UFNbSyKRQEqJEGKQJPW5XtFUKpWNOFIUYlYI0j1UVmWjjqqqqgqGpN7A4/0fL0kHqrtIgWCwfThWkto5zjwPQZ9BQ5mfV/aeoqE8gMRAzF6I3LIesfRK2traxpWyZaoxqrrrLk/bnBvMMBUVGwleB1ZVlaChOVMwE+7dNVFVzybNUXdd4lqqj7Bh88ILL/CXv/yFQMDpvF49TMtC07OSNHdxem5H9QVKifb3UDUgNLBQ1N2BJA2FJlDdFQpICdImbUuORpPs7YpxJJqktMLxqo9NkiqZdwXOPKkQAkU4S7YkEtGyCnliK9IyaWtrO6sk6VkZzJAriRRFw9DViffuWjZCKIBA09TsFIwQ3PGrzXSXz0I9uoOVK1dS39iY8Xp60jR3nhSyJE0mkzkkVagur6A/2ktdbT5JC1aSTlgwQ9YmlVjsORXjRHeckKbS1ZekVziDwWhrQS3LyXGk5JBUzzEzHIqCUIOIWfOQW56hpaVlXBkKpxpndTCDqjrTI36/j5hrO01cGY4klYCiKq6660Qhfem6hWyI1aMec4ITLFsSdCVpIpHIep9zJOlQ6q6qqlSWBYin0jS4a04LQZLmlp1LUp9PzzzTxKi7AqSksydBUBUsbYpQE/axsL6U/qRNpLx8dO+u7dmkWXVXc9eT+jSVtCXxZltE8yrkiS3UVFcTjUaJxSbWRJosnJXBDLnqLkBVVYSOjo4J7di2ZbnOCKcDOJLURCiCylI/sbQA24lCsiQEXUkaj8dzOuJgSZqr7iqKQkXYRyxpUVddha7rQ0rSqZaquVqJ59V1SGqgqRNDUk/dtWxJbyJJc3lw0Lmz58zhjTeGTaUFOBqPqqp5klTTnLYO+zX6k1bG4hVaCDFrLtUpR4UeTZUuFJzVwQweSWtrKrEsa0Ib3bN1nHIULNt2bVLFtXcEVnkzdGyjP21i+LLqbqYj6kOru1mVTyXk10ibNiFDpSIn6ih3wJno/E2jIbe8fElqZOZ+J0Ld7YqZHO9NMLt6MEEB3vf+S3n11VeHXcTg3M9ECJGXFM0zM8oCOj3xFFZO4IJovpRqzbnf2bIt4mkHM0gp/3a6gxlybVKA6hon7/ZEblFoWaY7OmenYLyF4F6jpdovhV0v0x1Lgys1E4lEfhC5i1x1N1eSCuE4ogID4ndzpedU7wo2MkknTt19t6OfqpBByBjcDc9pilA/awGmaY6Y8M2yvFUwOZLUVcmrQgad/SnHN+USVWhhaiqd7vueIqkQYpkQ4h/dv/8bJ//upOLgwYPDJvoaKEnraqqAiSWplypSkuvdNVEVgcsrZEk1Mt6HJSU+f9YmzUrSob27mfq7HcuWCgIrT5LmknSqA8JzB4Vckvp9vnGTNKMhCAVVUdAV18M7AH5dRdWy2sfw9zNRhJInSXXduU4IARL8qiBuZduzproNeI+RFCdr/ZTmODpx4gQLFizgkUceGfTbQJu0oc5x10+oJDWz6q6mqtlVMK70ky5ThRGExCl0w5GU+SQ1MvcbyrvbVONkWI+bPrBieZI0V92dTkmab5PqGY2g/wwddd6zH4+maakIuprE0Da316YjkTSTLTBPkmbNjEhQJ540SVrZgaC20ukvZ7qGdapRsDmOFixYQHt7O9dddx233npr3osaKEkng6S2ZeWru6aF5aq7eWi/iPLDL+MbyiYdJpjB+70s5ByLmT6w+jOhgd5+KR4KQZLG43F8PoOq6kpKSkrYsWPHGd3be/ZTcZPaUj8SMWRYIDCmxOfOtJiCouXapFkNpqU8yMGuOKpwvPAAwdIyFEUZNdtjoWC0sMCnhBC/AT4/1TmOAoEAL730En/zN3/DT3/6U376059mfhtok5YEA5RFyide3XUHgdwA+4EkFY1LCJzaR4nqrC/N9e7qo6i73oifkn7MVC+V1dWkUim6u7sLRpIOtEn9us6s2bN4550zS5OZHWA1FCHwwg2GguEbiyS1BknSo10pOnvd4AufRixlEdRU+k2nbGEECPiNTKhjoWM0SfoDL6/RdOQ48vl8/PjHP6ahoSGvU3gv+tnNR+mPpzFUQXXt4P1UxgNvhAbyHEfqQJIKhT5ZQ1hzdu7q7+/Pnwt0MWQwg+J0Tp+/lHisl4qcrIHT6TgaWpIm8PsM/IbGzDmz87JQnA68Z2/ITLkML0l1Yyw2qRcWmH0vKUvw2z9ltwARbuSR5TmQNN97h6RubqPSgd7cqY7jbWpqytt5y+vAm3efYtuBLnRVoaq2fsJJmpGkikPSrAqcj0SwipbKCkpKSvjzn/+cH1XjQlVVFEXkS9LIOQCUlpSSSPRTWenEqw4k6VSru0NL0jg+v0HQ0Jm/eDGHDx8+o6idLEmdHFEj2qT66CTN5N3NUXcXzaymrS7M/uOOOltX6ud4XwJDFaRsidB9BHzvEZK6+77sA/YKIa6fkhoNgcbGRg4fPpz57nXyltowe4/2IYSguqZ2Qnf7tm0b4cbpZsICbSsvsgWckbm3Zg7Brg7WXHkVjzzySKZT5ZIUnDzBuTappjt2bEWpn0TSpDYna2ChqLves8QTCQJ+Hz5N5bwLLwTgueeeO+17e88VdLMOShhVko5qkyoDHEeawfsX1/HyVscx1FAW4HB3nKCqEDNtcEmau2qpkDGautsupayQUlYClVNRoaEwUJJ6LzrkN4gnnQ5fVVvHsWPHJiw6x5GkDskUNRu7Kwaou5aEWLAaEe3lw9dczYkTJ3jxxRcBUOPbkKf+lJmjM9z8wAMlbXnYRzxlUpdD0kJyHJ08eZLDhw456TyFwpx5c6iqqsqkgzkdZO11bwCbAEmas2cPgGYYhPw6saSzRaOhKZiWszpJFWBqBgGf/t6QpDhB9YM+T2QwgxBi6PRtfSeRJ50Fx42NjfT29mYiTzL7k7qxuwD1jU2kUqkJm/vKVXc1RcGyHe9u7ogtkdhSYkuw0Viz5lJ0Xee3v3WmkbWSdgi0QsJRCw13tc5AmzVS4iORsqivrUZV1YKRpLqm8swzz9DU1EQ8HmfJotmAk01h1apVPPfcc6edhsSTXlktY3w2qe0uesgLsHfJPbuxjJ2He51jqkLKtAnrKnF0AsZ7h6Qz3JjdcwZ8/tvRbiyEWD7g+1ohxGohxO0Dzhk6EWrAj9z+InLfm5m1hd6KiGwG+Gz1G5ud/EITtU7QlhKhCOJJJ9A+u55UcevuzMFbUmLbknSwknLZywc+8IGMhNE0A/ytkHBUdZ+benSgJFUVgZRQYmhUVFUVjCRNu97QRYsW8Z8P/o7P3nyDu1DbZvXq1Rw8eHDY3diHw/bt22mrq8jmIRIKw3l3fWPx7krHC6/mTcE4JF0xp5rXdjihog1lfo70xJ05bsOHz9DeMyT9GA4hv4WzP+m33L/VI13k7uh9b8735QBSyvVAtxBiuRAiwnAEBdBCiAXNyOgxyo45nl1vzxCvk+uaht9QSVs2DRNMUsuy6E/anOpLIhR3ntTNYA+gCIFPCLqSFtKWxGrnoB1+hzVrskkrVE1zJL0QSGljaFqed3egzRo0VGrrGzlw4EBBhAX+zxsv51vf+hYbN27kAx+8Cq3nFLKnCxXJh6+/kZKSEv76r//6tBJOb9myhfmt2V3mRrJJtTGou54JouUG2LsSOOjXSKSctq4p9XO8z7lPSSCIX3/vkPSzOVMwmT/glpEucsmYu8HHTWQJuQeH5CuACNDu7hw+AAIiFyGay6kIOyrtQJIaqqCuPEA0btLY3AxMoCS1bAxdp7zEQOJkC5RuPh1wBIoiIKg5YYJ9oRmo3Ydob88+ijei46uH5JFMLqahpmhSlo4h0rS0z2THjh156u50SdLPfOgivv+97znZEqVE6++D/j78mkAYQe677z527NjBL3/5y1Hv+cYbb3D11VezdetWFrTlbgXpSGYpJTJ5DL9OdHsAACAASURBVNn3NjK2ByntTFjlSIOUdMM382zSnEivkF+nL5ZGUwS2G8ygBEoI6Mp7g6TDTbWcwRRMhHzSVrpE3uP+NiSEUBFl51Ne6uy7MjD/z6rd/0Gj1kd/PE1ZuJSKisqJI2lOCKCqqo5NalkIkZWktiXxCSf+1BQ6YNOSk9Yz01n8TZA4hM8l6VBTNHHTABmnrX0W+/fvz1vrONWS1BsMdVVBuk4dW0qEbYGUhHRB3LK44YYbWLZsGd/+9rf58Y9/PMhbGo/H2bx5M7fddhvLli3jD3/4AwDnz2/LOUuATEPXn8HsdWx4ocKJR9AVE13XR5GkNmJA+hRd92U+L5tVxeZdJzPPkzJthGYQ0LX3jHd3UiGl7JZSrpFS7hnpvIoaRzoNlKRH686jIiCIJU38ukJjS8vEqbu2hVBUhCJQFJFNRJYjSS1bYto2hqbw6rsdyHAFLWXZKCMvPE0IDaSV2f1tKEkat3zIdJTWmbOQUuaF3U2lJJVS8s///M9ESgLUV4Qz0seWOCS1LEp8GknTIe/999/P7Nmz+epXv0pdXR0PPPBA5j4333wzy5cvH7Tt4MrFuYqTgN43oXQ5IjQHoZUiAq0QXoqfXnw+36jeXdXdC8aD53ACmNlQyu4jPQDUl/k52utIz/eSd3ei0A1UuJ8jQOdoF3R0dLBixQpWrFjBv//bvwGDSWoGKig1IJEyCWgqDc0TR1LblpmNgHKDGTxiKUJg2ZK05bj4O3sTpFuWU96VjYzKVbvQKwj5Dfr6+oaUpLpRQizeS2v7LADefffdjJ01lZJ037597Nq1i//1ySsoDQaQOHW1bImwbbBtFJHNdjB37lw2bNjAPffcQ09PDx//+Mdpb2/n85//PA888ACzZ8/m8ccfJxaL8fzzz3PXXXcRDvqzBQoFys5HaGGi8TRv7u6kP54GvZwAvRijktRCM1N5C+xzVx95aVltW1Jb6udYr3Mvv99XJOkA/Abwhs92YNTMxNXV1WzatIlNmzbx99/+NoahD1J3VcOHIk3HM6or1De1sH///gnJTu7lzhGKgvAkqZctECdSxnYlqSoE0UQas2I+9GSDLjI2KYC/mbpIkOPHjw8pSQPBUhLxPlpdm/b48eOY7vKqqZSkL730EgCXLp2FkBLpOnVsXElq266jR2K570EIwec+9zm2b9/OqlWr2Lt3L+vWrePTn/407777LldeeSWBQIBLL72UL33pS3nlCaGS9s/mdy/s5e5Ht7H3WC/3P7eL/3z2GGFxfNRd3G3bJtx5AJ+ZVV0VNT/gZFZjGbuO9KIpIhNkHziLSDpiSs8zhRBiLU7KlbVSyoeklK8LIVa4Xt9uKeVpZSYWmkZ5WUmGpJkpGMMHZpq0reHXLBqaW0kkEmzZsoXFixeP6xmc3DkqQlFRhTMFY9vSnTLIqrtpCYmkiW3j2FI5A0TuiC60EmorQhzbuH1ISVoWLiGZjBEMhQfVZSol6bPPPkskEmHhjHpAYEtJ3LQRQmTUXZD4VIWOWJq6kqz9N2fOHJ577jn+4R/+gcOHD/Ozn/1s8KqhIfD7F/exoLWcay9uzZgTj23Yz6lUBYYxsk1qWzZCVdFzAyJEfpnLZ1fx/72ynzlNZQQNlVjKJOB3JLTneyhkTErtXGKWSykfyjm2Tkq5Xkq57rRvqGi0Ndawc+dOwAsFEyi6D6yUsx7TjrHmmuuIRCJ5u4GdLrZu3ep4Gm3pTKEoSo5NOpS6aztB/oYrYSPV1FY7i9BVVc+7d215mGg0Sk+PYyPldo6KcIB40kQgMuT9xa3XIYQYspN6ibTi8XheNNZ4EIvFePzxx7niiiucoA0hiKZS7OuK0dETp7snRldPHJA0hv10x4dO6/Kd73yHf/3Xf81kUMxFb8rM03RSpk0ybbFoRkVeWs4ZdWF6EgKfYYxtnjS3Kw8gacivE0s42ktbRZB9nTF8OdkdCx2FPYS4EKrGknltvPnmm0gpM6k1ha4jzbSzHtPsp6q6hhtvvJEnn3wyL/5USsnrr7/OPffcw8aNG4fcsKevr48rr7ySRYsW8eCDDzobASmqG3Lm7PNiu6k6wJOkNqYt6etPEfS5UnHGeTRVOtJwYDa6uionKMOLMRbbX0Ye2AJARamPeMpCVwQb397Guzt2cv35c4fde/Xqq6/mE5/4BBdccAHNzc3cf//9PPbYY1x44YWZFUNPPvnkoNUq3nP39PTw9ttvs3XrVnbu3Mkdd9zBhz70IY4fP87NN98MOJrt/pNR6kIGF7ZVkIynOdndD9Im6NNIW6cfgpkw7UxmC4Bdh3v40Pktg85rqwvTG7fx+XT27NkzrAljWRZCVdFyNdzcAlyUhQy6okkiQYPueJqZLc7GzW+99Rb9/f2k02m+853vcPfdd3Pbbbdxzz33uG1gs379+kwKl+nY6GlS1N2JgCVlJhs8qsbSua3c+8CT7Nmzh8cff5y2ukqE5gOrB5MIZqoPiHDVVVdx77338uUvf5mPfexjXHrppdx1113ceuutefe/+OKLsSyLO+64g8OHDxMKhdiwYQPgvLhkKk0wGERRFWbPbOH3jz0PZO1IgeOMSEtJNJYm4HOPV8zh9o9ewk3f3Utra2tembWVju/sl7/8JZqmIhJ9yI79iJZF+HQVy5KENYVwQzM1QZ3EX2z8PoMXXniBhQsX8tBDDzF37lwAXn311Tyb6hOf+ETm89q1a7nrrru48soriUQi/OpXv6K/vx8pJZ/61KcIBALDBiDcfPPNfPCDHyS5fis9cZOl9WE0n4G002gKJBJpvAihkKGyvytG6xCZ/oaDaUt3HamDZNqismywxA35dWIp2LLFGXC8qa3//b//N/39/SiKwvXXX+9k0NB0tFxiisGy57x5NWza3sGac5sQAi49fwlCCC666CIALr/88kGxyAcPHuSee+7JmFk33nhjJuRzSiFdMhTa3/Jzz5U9ybS0bVvae9+UO576F4m7vQ8gf3rrjfL1N3ZK642n5bOb9squwy/Lt4/1StM05Q033JA577LLLsu77nT+/vF735EHnntEvvzUv8vS0lIJyA+vvkRKKeUDr+yTe470yLcOd8tfPLld/vzp7bIrkZZSSmk+82NpPvNDORD7f/+TvPtbLz8krRceyPy+edNj8mhPXO451S8TKVP2/cfX5DVXvT9z/qpVq2RDQ4MMhUJn/Ewj/X3xi1+U+/fvl1JK2ffEnTL1zL9IO9UlpZTSNmPy0APfl289cLe0ozucY7YtNx/ulgnTHPSsQ+FUPCX3d8dk7EmnbXYe6pY7H/q3Yc9/etMmefU1Hxy13v/j6pXyzcfWZb7bsd5B97JtW/774+9IKaXccqRH9m9+SH7+0zfl3eeSSy6Rb7zxhvzCF76Qd1wIMbDMTafTl8f7V7DqrsCx+7pSJr22YGZjFV/5ylcyv1990SIU3QArTaSshEQyjhBgI1i3bh0f+chHgOxyqsWLF+c5aj7ykY9w3333cffddzN//nyuu+46Nm/Oj9GYMaMdoShURMp48hnnPj19Tm4fRYBp2RzvTTi5BXJHcsUAe7C91lRby+t/dsz0RYvmOE6mUATZ79ioEgjpAtOWGW/q//zsWubNmwfA888/z5EjRwblF9qwYQMPPPAAX/nKV/jud7/LXXfdxde//nUefvjhQXX43ve+x69//Wv+9m+z4dehUIhvf/vb/OxnP8tILCmlG6fsenetNDYq0gLbctR4IQRVQYMDPSPbdSnL5lg0ydtHekkkTRQhkFLy1KZDzKgb7CjLQGjcc/c/k0wmueuuu2hqcja9uuaaa/JOu3zFYvyawtJl57jXDVZ3vTSspmVTU+Ijbiv8P//wNd56661M+1577bUsXbqUf/mXf8lM93ntu2vXLi699NIRn3OyULDqLkCJrgIqaUMnGTW584c/ZMWKFezatYvycJCobkA0TVWpn9gpE7VE0J8yqaio4He/+x3gLI06evQozc3NJJNJli5dyve//33Wrl2bKefzn/985vPy5csz2+K1trWB7EUgWXrOUu774f9i5uz5gPPSTVtSFtAZtIpVqAy5/ErRWLpgJm+//TYKB6A3hqidASf2woxzSFoGATVN2gJsG2FbzJ3RyDvvvINpmqxcuZIdO3bwox/9iBdffJHLL7+cvr4+LrjgAi644AJuuummQUX+/d//PZqmsXLlSs4991xKS0sRQvDxj3+cf/zHfwScaZelS5dmrumOpVz1MbtCJZVKIBWdoGYQ7eumzF0H1RQJsOVoL8eiSSIBHX+O88eyJSdiKdKmzcHuGKqUxNMWigJb9nWxbFYVomcwoTLNqGiAiabrfOlLX6KhoYGPfvSjrFu3jq1bt7J9+3YWzqpkgdVDt23z+z88inj1/iHVXYAFbeVs29/FwrYKjkmVsJ1m8eLzeOGFF9i4cSOXXXZZ5txIJMIVV1zBU089xZIlS/D7/TzxxBM0NzePuvXFRKOgSepB03SkbWNJmbG9uv94J6orSavK/Ow+YlKiKvSnbCKB3Gs1mt24Xp/Pl9mVezg8++yzbN26lXee/iX+QBDiUZDOfiKXXbAU0+8sq1UUxzOp6VmPhRCO1FBRhg4aVzWwUyxadA57dh2H3hhUNCK3PI+YcQ4J04dKHFv6nR1MbNsJIHCf4/HHH+fEiRPMnTuXz3zmM2Nqu+9+97vD/hYOh+nr6xtkOx/ojjPPTcLm2Z+pdBKhqFSEfXRFk5TlnD+/NszGA120VoUod4lq2pIdHVESpkVfLM3y5ghbj/aSSNsoQvDO/i4+fGELjDAZJxUdaWft7uuvv5502tkMuK6ujg984APsfP0p7FMpRDKGpqv43TWvQ2FpeyUPPr+HJe2VWIqGnXa8xlVVVVx11VWDzv+v//ovDh06lPFS+/1+Nm/enOlPU4WzgqSoGkgbO8exJpFORI6UBHwapiXx6yqx9Jnlg/VQVlbGxRdfzKzkNo5mtudzC7ZtUDzHkSBtSfx+d3tEIfALiFk24eFIKjSw8usn/CFk0lEfLREgGY8CfmzpOFhEzmqY8vJyysvLx/V8ufjTn/7Eo48+SiSSHz6tqCKze1xWkiZRFJXSkMHB/lTWqYez1O78lnK2dkSRQlAT0OlOppECWiMBQjVh/LqKpigk3eVv8aRJyK8Ps9zbgRAa0jbzFrKJAaqssExsI4CS6HPqKuWQ6i6AoasZj7Sm+zBHCRIpKSnJqMIePJV7KnF2kFTREJY96IWqA/INleiC7sTEuMilbSFUZ54UjygyO/GtKIK0ZRP2tkdUhDPPL3CJPMS+NIo2pK3qIRgsJdrfDWoVUppIoToBBJOEZcuWsWzZ4G19MkTIyT+UdiWpIgTdyRCYXaBXZK5RFMGsiiB/2deJXVdKX8KkrSxAyJftYoam0Jd0nn8sb0lRNKRlOicPoxULO4XU/QjLdqdHhicpgN9QiadMfP4A6a7ht68oJBSs4ygPqoaQ9qBE55qafRlx00dQSeTt+zEeCGmjaFpmUbLTT2yk6gXNOyRVhbNUTSjkzBsOnZUdVR+apG6nCofLSMT7AEnMtLFzB4gpQiJtZYLqHZvU+ZxOpzLTT6cSFfR07ht0bUBXWdlexaGT/fREk3kEBTDcVShSyrz1n8NC1ZB2ekRCK7aFNAIIaeVI0uHvPbc5wo9++xaaYZBOTe0SwDPFWUNSLMulShZaTsROXzqIsPuG5MYZwXa2OURRENJ2Bmhpo7odQBGQtm1s2ybg01CFwDRtVEVgSiUTPpgHRQXpSsbcimoGMp2ksqyURDKBQNBv2UhlciXpUDgRS1FqDM4/ZJnpTBD71SsXceDI4SGv11SF89sqOL+tYtBvhubYqvGkxZymsiGuzoeu6Ug58vMLOw26PxNnLEYh6TkzK/nkmjnsPBpDmlO7BPBMcXaQVHEkaa5NigQ1R5JKtZT+qJMqQ04AUwU2itDdjW49m9RRgcH17loS07QJ+TUnXafl5NCx5FAbzJNn3+p93VDR4BwvrYK+k1SW+ogn0wR0BWlboKh5NulUoC9p0pAJLnC8u7LnNTTZhaI4WoSuq9hSIOXQ9r/iTncMhKEK/JqgP2Eyv2V021rXnK0RR5SklgmaATjTOkh7RHVXUQQtNSX0JgTKCKZHIeHsIKmqgmVnVmSAY6Z4jiOARbNaOXbiqLNF4QRIU2lLFF3PEEviSFKRExZo2pJU2iLk1x0J6q6x1FTN7cQDKqJkA/B9XccRDXOce5VWQc9JDF3FtiX1YT8RXcFGQdjWlIWimZYzEOqeKioEmN1g9mLQl7d9w9H+SojtzaubNHuHrKt3zKekaDH2kzQVwkF90HmZ8086sciaoriq9/DPr1gmwiOps8phkHNpKKTRENb4nIxThbODpIrmEMV2GtV76aoqIFiK7DhAY3WYnphEWCkS5pmriN69Fenk2BU5klRgZ6SJEI4NmkzblAT0jCMJQFOcrfh6Uhb96Zy6CMfGlFKixmMQdrOkVrUgd/4FmegnZev4VRO/tDFRnY2Kh+j4pi0nzP720BVPEzLUPKIlu7bwvT+E+MXGdgKG4mRxPPgOplZLvHcvRLdmb9D7hkNqF9Ls48TJo/zdv2+ktz9FQJ6kWj2IaecvJcuc75Zrr/9XZMd+J0EbIzuZhPQkqRNsP1bMbKkiER9bcH08ZWaWuE0Hzg6SqhrClpkFyEhn92ZNURBtS5Ad+wHoTpYQ0fvpSw0mqZSSmJuUSsaH36in/7UnnQ+2hWr4HdtS2k5HsbOStKbER3fSIpk0HXVXZCUpigIo2EDalhmSCU/dzXz3dmbzIy6+EbntzxyLVSBje7DNJJZmIGybk73JQRIqlrZIWU6Af3/aoieeJm3ZeZ1J2lZmzWc0ObzUiKYdad0TS+LXVba89QzxeIz9x/t5YluQr39sOZ89V0etbkZ2HkHu2si5c2v5j42NnDh5nHf2OVtUppJROjv2E42nkXYKejbReegv/I8PNbF71yYC8dcxZBTdyI/VtV96EBntwn7sJ8gjOxGLL0O+9SyKnXZVWJBm1hNr2TLznIplIlRP3XXWuY4F7Y2VGKSQ0iKVtujqS9KfdgZEmU4g3TlUKSX3PrmDt/edGuWOk4ezZApGRdrSsdOAjdsO047qeHdLKiDahUzFaYh1cmLrCea2WiSND3OiX6ExEiBlS17f30VXf4LLZ1fT/dgv0K/6HLYi0CSUB3XXW2uhv/kku9tWEpE2qq4jhYISjyN2vUK5cpRe1yatChp0J9PsOJSkvS6cJ0nRfNimJBVPo2oKCVWgCoElwd/bg31s96CuJErKkYl+upIzkS/+DnXx+zFVP5pt8Ysn3uWWjyymIsdb+uqzLzJvZi0VRoJD0R76kxFEZQO14TgGcSor5mI99x/0zFlJRVM7z+/o4OL2SiJ9hzjuqyMa7aPv2CGWzNE5nGjAlzyEX7UIqks4acXojdv0xBT+6iPXIFJxfJ3vIC5ai3zzaRCChsoQK+ZW83aHQbP/FTZuXUL/yTix5A7awx3gP8X6XfXMrwtweetuTPUQJ5N1dJ7YRVM4lH1w24RkDPnmekjFkXvfQFx8AzTOo+6lR+ltrUWmEtgnnyIeuYyQX7Bpdw/xqM2qFTMQlonUfSAE/uO7GXauZmB7C4WwdZITR3ey7VQZ7xzq5spFERrsbvS3n0Q0ziQ142L29krmWbuI7eugOxEg3jn1e5oWLEmlhHXP7uSyeTWYioCjMSr3PMum0CVYxzdzMthMua4iFA2ZTiLfeIo59nFSqQP0HT2Hg8kNHDJnI6gmZCSo2v8Scw+/zKb0p5kf7eDVvaeY3VDKhrf3s2ZukLLKBqwN69AF/OXdQ1yBa5spKr5EH8qbf0St1jJL1XRNIZ2yONzRn1F3+xImT2w9yvKUgtaT4Dcv7aMm4ueC+bXohoqWsDB2bIVEwNnybwj4SCL3H8Cw/0hMaaUs3c/MsjTxniPsiMMzT77B9Te8n5niEMl39yB83dTE+knEbTr8rZSGThJdspKTHUfxnTqIcvAFejY+TEPbFTz9dj/XvPEjolRgzZtF+543SYtZBNQKKnds5tiyDyD1JKWqwDKaaKgohVd+i0wlHA+qoiDKasEfQu74CytaFiJ3bcWqP4+jz/2CulkziPZE0ba9yMbZH+L9yxfSWtaNPPgUzx25jKBPZ1VTN6GcZYT0nESs+DD2hodRlq5GzDrPOR6phdYl9B47QO+en1NelsTu3cfBGXNp7zlB5+Fe3qj5FA1mCukLIARUbnqMaOMQiSeHhIIo9ZF+4wmaGy/mQCxJ7eN309fShlh6PuVP3w/bX0YNL2WW3Uvg1D5i8SC+/o4z79RniIIlaU88RcKyeWpHBzOqQlTbpTS/8TL+5hCz07v5tfUB5rlRMfLYbsTcCxGaj9iuk5zo6aKpJE3Zvh3ExGIC9k4qdm0npMeZ0f0SodRJwke30He0l7VHX6DreDnp8kpiSUloxgwu2/JviOYaNE3BVFWMZD9SCFKmltnpWlUEPkVw2yfPRQhBUFfZ3xun63g/SSXIkn6NmrYA0ViaQx1RNr59jFZOslrzkzq6m6gWoXqI524Tp3g7XsFCW+VA1QVcuuUnnNtaScXm9RxsWMVHIm+T3tRHlb2fuAgQS6cJxPs5XD+H9ugBjN0HEEIQsP5E76JlhPe+Rr/tp657J3NqItiLFxBJ9sPhvehVEWLHEtSmN9Mpa7FffIlk6ynqe3YS8FUgVn4MOg8hT+xFtDiZLsR5V0PnYey3nkHs2ojs6UALhGmM1MOh40SqW9gfWcTM2RfTWu1HbtmAfOMtFrUsJrpnG1WnjiPq5maeV1xwLaKqGVHZCOX1eW1RPX8ZxsaHSS6aS6rrBL6u3VQ1+iDag9TSJP/y/4KuIVQN01eBVVWOEe0ZWwdTVERzFVXPbqEnHuWy6ga6qxbxtu883m9tYUdwLq2RPmTXUV6p/xArO58lkDxAODn2HMMThYK1Sc20zcywn2DKpr8/xcxyjXRtC+GTb5M0FVYtysabipJyxPyViKWrkXMuZ+fhPui0INlJ3caHYMdBjs8/h8RFq6k5uR3FrzAntZcW4yiJyByMVD+HMDg251p6gxVUpjrxWykMVSBKqwj3dpKobcTqjGZSopT4dc5tLs/YlU0RP7qEgKFyXKsirQW4ZF4NddUhNuzo4Fh/EttfTXzWAkpjxzlZsXDI515hHMJ34bU8Fl1BU0sDorSc6OKLSeJj8d7nqWjViUc7SYoSQkGJ1Pxoi5YSamhCK4lgVrRRcmw/KVIQDGDIJHpAo6R7B/5n/pPO8FziTYswtVL26bNJpf30Go287pvP0/EllJzYiiGdPEbC8CPqZ6EsXYMod3LlisomqG5FVDYhll2JqJuJ7D6GcsknELMuRCy9itb2Vlrto3B4O3LjE1BSxpLkFlasmI9yzdcRM87JvrsqJw5WXHAdVDTmtYVQNQ5f+kX+nFjC/mM2B9QZaH1d6HY/FcEUFelewnYUVfFhLr+GbW3Xki4fa1ytCrrOsXNXoft0/FLl9f75WFoF8USKjmUf57C2gHD6OM2RBMfmvp+0KCelTVxY5lhRsJI0mbY4Z2YVf/jLAY4f7aNMj7PNqqVU2YdJOfNbs40lrrglQ5aKJRdx5LndlOoqO1sW05vuo2XbqyizlxKsqUWNJdjXvorKGRW8fKqJKxf4SW3+Awfm3sCylgo69tbTvXANJb078OkqJQ0NWMkY8doZVB7ehx50FjiHAjrvW1CbqcPchjI2bjuBogg+s2oeWm8EJRLkpT2dGIbKitnVXL6wjnLfDDoPn8CqnTXomcXcixCJKAsa57LgQpDSxt6/kDa5gcOta6jnHaxwiNDBTRzvr6dt6QX4TB2ttI/qdJKe8hspbetAP/wEp6pnEoulCNlBSgMK8WMdnJx9Dr1lc6isqifYvIbUWy/hO3CIN+d8nK6OOCUhCPZtRl35UXjjz8O/nEgt4n03IowAcv/bEI8ifEHE/Pe5v9dhP3E3YsElKNf8DTLox/BX4DPclQ/u1FPes+fkys1FS0MNezssrO4+Os/5K2pPPEWoDGw1RKcSoKS9DkURzK4u4e6NpcxY9emRulVOgQq2MDii1lHu70KYGidlOebxOP7W80gfNYlqpagxjZnlIPu3oNfMJnrwzPZlHQ8KlqSNVSEaq0NUhH1ctqwB8e4e9h0tI6CUcVFlvus8d15MUQSLL78csaSe+l3rqY5Uo/d2MW/uMrAT2FqY9iWXICK1rIgkEEEN3/kfYXm5Mx1SXdtEjyzDevcAPtVJji2a5tMw80LEvDVODt0hoKoKOw718PHLZ2HoKlQ6UqHMr3P+OY201pS49fTTueyvEUPkBxKVAySJUFAu/T8oSRxknq+J/sPb6ZUVBEoEwdr5qDVLQEqEauFP9eA3qhDVdSjBPTRFLuLwgQ3Yah2qdorOdJAX0peQ3hbnykug1K9jllcgd8Qx/AGqIwo+CeH3fw78zbC8ZNh3I4QAj3ChCBzZkX9C22LEsiuh8xCibuYYXTlDI+zXqY342VF/Kddfcj7yVCNW9HlSnWWoJ/expWQN5/ucjbuWtkYoCwxN9sEPoSCFQXPrUnRhoex/i5kzKlm5yCF9cs8BOqwAYcWHr2wu5U1LkPve4mT/1Ku7BUtSD2svdRwBctmVaPZRhAbBRSPvwnjJUqezN5alIDQTPvh50HRAIiK1EHAWGteE3amAXFtIDRMOH6a3IauOKhd/FGGMrubccvV8WmvzFzGvnFk1KIY1oKv0JsY2kS6EQASchdg6Sfb0Bnnf8nMRNZcAuKtRVDR/1sKVRhXCV0tj+weRvc+A3kTT+0u5rm0pT206RIXP2aPGV1JGjxog7NdpKQ9SE/YhvKV31YPzDg1Zv4oGh6h5dVagqhnZN2p65TGhJhLgVNs8t7xGbLse7FpESQ+79p5i5UynP7xvUf1ItxkAlYAvSHNFU3stIQAABcdJREFUOTLWgDx1kEuWZK8P6Co7o4IVtdWUhX3OnG1lA1Xx3gl5ptNBwZPUg9ANFsyocJZv+Ycf5fOg+MCodSNSAASybWlWCgwFoxKFKMk5azISQIyxww4kKDCIoOB0gDORLgom/WYIys7LHBsyuibiqJ1C9UNlE6g6evMCdLKDHoA/XEZfMMyMyhA+TcHQTt9FIRrnDv1D7QyEPzT0b6eJ1towLTXZdy4jK9H0PkRtO9tfOzVkCOJoEEIgFXeQrm5FDBhoIiU+0iJEZMb8TIJtUVaNWlp15g9yhjhrSApDk2BElC5HaPmEVhatGvESofig5q9QhklXORHw6+pI4aXDQlEM6ssjCN9QfuEs8ojbumTY89RQGbXzl6H4J74bCF8Qatom7n45z+TTDCirxJi1jKpd28/8pqozWAtfEHz5ydTKSgzqI2GURfntN5aQw4nGWUXS04UwznxzcmUS30XQUCn1Dx+7OhwUPcTi6tMbyUfqVEJREHMvPO16FArCQZ1ZjePYz9o/vIbUWhumtnwEjWsKUbBTMNONM1GhxgpdVZh7uloBgFY2LSN5ocLQVT6ycsYZXy+Cwwc+qIogdAYD6WSgSNJh4GX4KSiUnT/dNShiGlAk6TAoNdSCk1pCHXsS6iLeOyiSdBhMprpbRBGngyJJiyiiwFEkaRFFFDiKJC2iiALHtM2TCiHagQiwGnhISrlnuupSRBGFjEmTpEKI5QO+rxVCrBZC3O4eWi6dHb/XA2sH3aCIIooAJomkQojVwL0535cDSCnXA91CiOUyuwv4auChwXcpoogiYJJI6pIxN3PTTYC3xmcPDjE9Mq8fStXt6Jj6NBVTiXXr1k13FSYN7+VnczGlUfZT5TiKkE/aSpeg3wA+J8T/397Z3bYNA3H8f299NIxOIG/gOhvYGzjtBs4IGaGoN2g3KOINmg0aeIN4gQKBnwz06frAo0NYlD9QW6So/w8IIB8EhIfTieR9UFJb7rb9ebm2KflBLlk343iHw5VJFjiy2fY51f8npCu05aRbAP7jIAMAJ7uBd7vdXxEJD9D9A6Ck6fWjiJSkT0jJugFAQxPtbWjLSX8CmNh1hTNmUFX9cOoeQvrAraK7cwATv9e0VIsPFG39b1IGQVotlmqLysj53GQmtfTK6kBWiyaIyMIuR6r6aLI53PJ4rKrLJlkXsJcSAMxK1A/Y63hn1/tUm4hUYa48lHXlJS0i31T1UUQW/vlNYb9kZYFB+uUHgMretLV8akyWasyXYOOc2bjHTbp0Vb8GYqm2aPqtIyxE5BVu3NF8fxv2S1m7W+HdYBv7XYyRVXXtZ08Alc0exegHuIfWHk5PLdXWIOsK96o6CnRMYr+UKZhw+TuGCy59QllG9vu1B/tZ2kNc/5x3WYyt8d8vY5PYL/lBZLY8+KWq69xOQrgGqroUkScReUk9lmsSmUWB5lTbRem3XAj2nLMgvtA6yZ0UwDTYbBdj5GCvsoZbBi1QkH5wcYQquParoViq7aL0Ww4EmYkVnE0qJLJfUie1qJl/W01RkJHh9iY+ijkA8Btu7EXo5xskLEI/MNlaRCaHqbaYrANs7A8ARgC+A3hBAvvJ4Rek28KM9gS3nh/CbdKfzegbuGCLD3vXZLkjIgMAn+H0m6nqg8mL0K8PmF3e4OyyDGSt2i+ZkxJCzoPHpxCSOXRSQjKHTkpI5tBJCckcOmlPsE6UWl1pWDNN8oROWhCxY2gChrEcpVUNTSL3k0ygkxaC5WVnqcdBrk8OZYHkOlRwjfbTSE3tHlvaDuGqhLbH7iV5wJm0EHyN8BlO98XuX+G97I1kDJ20f3yFO0b1FVZzS/KGTlogJ6K1U1W9h+vd7UyDeZ/hnrQsNta4cKx39c76djfg5z06AZ20IILjWv7rHpIXXO4Skjl00v7w1lBxNMfx5TFJDPtJCckczqSEZA6dlJDMoZMSkjl0UkIyh05KSObQSQnJnH80d9/382NImwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -1088,7 +1095,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [ { diff --git a/examples/example_density_analysis.ipynb b/examples/example_density_analysis.ipynb index 359f9bb..178a908 100644 --- a/examples/example_density_analysis.ipynb +++ b/examples/example_density_analysis.ipynb @@ -45,8 +45,6 @@ "source": [ "from matplotlib import rc\n", "rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n", - "## for Palatino and other serif fonts use:\n", - "#rc('font',**{'family':'serif','serif':['Palatino']})\n", "rc('text', usetex=True)" ] }, @@ -1019,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1070,7 +1068,7 @@ "fontsize = 10.\n", "lw=1.\n", "h_length=1.\n", - "export_dpi = 1000 # Dpi for the image export\n", + "export_dpi = 500 # Dpi for the image export\n", "# Plot figure with subplots of different sizes\n", "fig, ax1 = plt.subplots(1, 1, figsize=(std_size, std_size * 6. / 8.), sharex=True)\n", "# ---------------------------------------------------------------------------------------------------\n", @@ -1125,46 +1123,6 @@ " bbox_inches='tight', dpi=export_dpi)" ] }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "418.396006407" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1e-1 * 4258.96006407 - 7.5" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "17.101260920333335" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(1044.65740762 * 1e-1 - 1.85817524) / 6" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/probabilistic_modelling/probabilistic_model_constructions.ipynb b/probabilistic_modelling/probabilistic_model_constructions.ipynb index 1fa2807..ec1946f 100644 --- a/probabilistic_modelling/probabilistic_model_constructions.ipynb +++ b/probabilistic_modelling/probabilistic_model_constructions.ipynb @@ -92,9 +92,9 @@ "# Scenario Settings\n", "# These are general settings pertaining to the simulation run\n", "config['scenario']['population size'] = 1 # The starting population size\n", - "config['scenario']['duration'] = 600 * 1 # Total simulation time in seconds\n", + "config['scenario']['duration'] = 600 * 5 # Total simulation time in seconds\n", "config['scenario']['exclusion'] = True # If an exclusion zone should be used (the detector)\n", - "config['scenario']['injection']['rate'] = 1e0 # Injection rate in per second, a distribution is constructed from this value\n", + "config['scenario']['injection']['rate'] = 2e-1 # Injection rate in per second, a distribution is constructed from this value\n", "pos_det_y = 0.5 * 15. - 0.15\n", "config['scenario']['injection']['y range'] = [pos_det_y - 5., pos_det_y + 5.] # The y-range of injection\n", "config['scenario']['light prop'] = { # Where the emitted light should be propagated to (typically the detector location)\n", @@ -146,8 +146,8 @@ "# Properties of the current model\n", "config['water']['model']['name'] = 'custom' # Use a custom (non analytic) model\n", "config['water']['model']['off set'] = np.array([0., 0.]) # Offset of the custom model\n", - "config['water']['model']['directory'] = \"../data/current/Parabola_5mm/run_10cm_npy/\" # The files used by the custom model\n", - "config['water']['model']['time step'] = 1. # in Seconds" + "config['water']['model']['directory'] = \"../data/current/Parabola_5mm/run_2cm_npy/\" # The files used by the custom model\n", + "config['water']['model']['time step'] = 5. # in Seconds" ] }, { @@ -159,12 +159,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████| 1000/1000 [34:16:59<00:00, 123.42s/it]\n" + "100%|████████████████████████████████████████████████████████████████████████████| 100/100 [15:53:47<00:00, 572.27s/it]\n" ] } ], "source": [ - "runs = 1000\n", + "runs = 100\n", "alpha = 1e1\n", "# Storage\n", "x_all = []\n", @@ -222,152 +222,7 @@ "metadata": {}, "outputs": [], "source": [ - "pickle.dump([x_all, y_all, x_flash, y_flash], open(\"probability_model/offcenter_10cm_raw_v4.pkl\", \"wb\" ) )" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# For the binning\n", - "xedges = np.linspace(0., 30., 91)\n", - "yedges = np.linspace(0., 15., 46)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Flattening and touching up\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mx_all\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0melem\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0melem\u001b[0m \u001b[1;32min\u001b[0m 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np.concatenate(x_all).ravel()\n", - "y_all = np.concatenate(y_all).ravel()\n", - "x_flash = np.concatenate(x_flash).ravel()\n", - "y_flash = np.concatenate(y_flash).ravel()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Binning\n", - "H_all, xedges, yedges = np.histogram2d(x_all, y_all, bins=(xedges, yedges))\n", - "H_flash, xedges, yedges = np.histogram2d(x_flash, y_flash, bins=(xedges, yedges))\n", - "pickle.dump([xedges, yedges, H_all, H_flash], open(\"probability_model/offcenter_10cm_v4.pkl\", \"wb\" ) )" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Style\n", - "from matplotlib import rc\n", - "rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n", - "rc('text', usetex=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Plotting standards\n", - "std_size = 20.\n", - "fontsize = 20.\n", - "lw=1.\n", - "h_length=1." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "ename": "OSError", - "evalue": "[Errno 22] Invalid argument: '../pics/Organism_Flashes.png'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[0max1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'both'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhich\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'minor'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabelsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[1;33m,\u001b[0m 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encoding)\u001b[0m\n\u001b[0;32m 430\u001b[0m \u001b[0mfh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbz2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 431\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m \u001b[0mfh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 433\u001b[0m \u001b[0mopened\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 434\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'seek'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mOSError\u001b[0m: [Errno 22] Invalid argument: '../pics/Organism_Flashes.png'" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting\n", - "# -------------------------------------------------------------------------------------------\n", - "# figure, (ax1) = plt.subplots(1, 1, figsize=(std_size, std_size * 6. / 8.), sharex=True)\n", - "# ax1.set_title('All')\n", - "# X, Y = np.meshgrid(xedges, yedges)\n", - "# ax1.pcolormesh(X, Y, H_all.T)\n", - "# ax1.set_aspect('equal')\n", - "# figure.savefig(PICS + \"Organism_Paths.png\",\n", - "# bbox_inches='tight')\n", - "# -------------------------------------------------------------------------------------------\n", - "figure, (ax1) = plt.subplots(1, 1, figsize=(std_size, std_size * 6. / 8.), sharex=True)\n", - "ax1.set_title('Flashes')\n", - "X, Y = np.meshgrid(xedges, yedges)\n", - "ax1.pcolormesh(X, Y, H_flash.T)\n", - "ax1.set_aspect('equal')\n", - "ax1.tick_params(axis = 'both', which = 'major', labelsize=fontsize, direction='in')\n", - "ax1.tick_params(axis = 'both', which = 'minor', labelsize=fontsize, direction='in')\n", - "figure.savefig(PICS + \"Organism_Flashes.png\",\n", - " bbox_inches='tight')\n", - "# -------------------------------------------------------------------------------------------\n", - "figure, (ax1) = plt.subplots(1, 1, figsize=(std_size, std_size * 6. / 8.), sharex=True)\n", - "ax1.set_title('Probability')\n", - "X, Y = np.meshgrid(xedges, yedges)\n", - "ax1.pcolormesh(X, Y, np.nan_to_num(H_flash.T / H_all.T))\n", - "ax1.set_aspect('equal')\n", - "ax1.tick_params(axis = 'both', which = 'major', labelsize=fontsize, direction='in')\n", - "ax1.tick_params(axis = 'both', which = 'minor', labelsize=fontsize, direction='in')\n", - "figure.savefig(PICS + \"Organism_Probability.png\",\n", - " bbox_inches='tight')" + "pickle.dump([x_all, y_all, x_flash, y_flash], open(\"probability_model/offcenter_2cm_raw_v2.pkl\", \"wb\" ) )" ] }, { diff --git a/probabilistic_modelling/probabilistic_model_plot.ipynb b/probabilistic_modelling/probabilistic_model_plot.ipynb index 1fec2e5..f8ec6a0 100644 --- a/probabilistic_modelling/probabilistic_model_plot.ipynb +++ b/probabilistic_modelling/probabilistic_model_plot.ipynb @@ -61,12 +61,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# New approach\n", - "data = pickle.load( open(\"probability_model/offcenter_10cm_raw_v4.pkl\", \"rb\" ) )\n", + "data = pickle.load( open(\"probability_model/offcenter_2cm_raw_v2.pkl\", \"rb\" ) )\n", "x_all = np.array(data[0])\n", "y_all = np.array(data[1])\n", "x_flash = np.array(data[2])\n", @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -147,25 +147,25 @@ "fontsize = 10.\n", "lw=1.\n", "h_length=1.\n", - "export_dpi=1000" + "export_dpi=500" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\steph\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in true_divide\n", - " \n" + "C:\\Users\\steph\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in true_divide\n", + " if __name__ == '__main__':\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -182,9 +182,11 @@ "# -------------------------------------------------------------------------------------\n", "# Plot mesh\n", "X, Y = np.meshgrid(xedges, yedges)\n", + "# X, Y = np.meshgrid((xedges[1:] + xedges[:-1])/2., (yedges[1:] + yedges[:-1])/2.)\n", "# -------------------------------------------------------------------------------------\n", "# Plot\n", "ax1.pcolormesh(X, Y, np.nan_to_num(H_flash.T / H_all.T), vmax=0.05)\n", + "# ax1.contourf(X, Y, np.nan_to_num(H_flash.T / H_all.T), np.linspace(0., 0.5, 20))\n", "ax1.set_aspect('equal')\n", "# -------------------------------------------------------------------------------------\n", "# Axis\n", @@ -197,7 +199,7 @@ "figure.tight_layout()\n", "# -------------------------------------------------------------------------------------\n", "# Storing\n", - "figure.savefig(PICS + \"Organism_Flashes.png\",\n", + "figure.savefig(PICS + \"Organism_Flashes_10cm.png\",\n", " bbox_inches='tight', dpi=export_dpi)" ] },