From 818dcd93f0f99d8dac8ebc85b7439a65e3514723 Mon Sep 17 00:00:00 2001 From: "Ryan C. Cooper" Date: Tue, 25 Aug 2020 14:29:12 +0000 Subject: [PATCH] updated data for Marble densities --- ME3263_lab-00.ipynb | 137 +++++++++++++++++--------------------------- apt.txt | 7 --- pretty_plots.py | 1 - 3 files changed, 51 insertions(+), 94 deletions(-) delete mode 100644 apt.txt diff --git a/ME3263_lab-00.ipynb b/ME3263_lab-00.ipynb index 0dc78d1..a7eed58 100644 --- a/ME3263_lab-00.ipynb +++ b/ME3263_lab-00.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "slideshow": { "slide_type": "slide" @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -82,13 +82,13 @@ "Text(0, 0.5, 'Probability of\\\\\\\\ Measurement (\\\\%)')" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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EmQ4v/emxohRmjoJKbCCoNaHiPLjJyKiIKCVHarCrQs2q6BEdhfuvG2Vyi4IfEbUZq3pj2xE0NLWY2CIRjDye8MvMtVAVzwt1woPbm/+aLUTwWb3F3pu6bepQJPbvZWJrQsdXJ12BYbq0VPXFRqwvPe7mGSLSeFWZgpkXM/NGx/1Sa09EusNnL+LDvadt249ISrrHukdH4aGZSbbtV7ZY0CpllYRBp0oo6fWjPiCiKiJqgSqFVEVE73tTDkmIcPHHTw6D9XvrTeMSMHZwf3MbFGLumjYS/Xup8TzL2YvYtP+MyS0SwcTrQEVEv4RaPyoLaizqsL7FQdXvKyKil70853JP5mcR0R1E9JS3bRbCn843NKGg5Jht++HrpTflrX49u+HuaSNt23/cWm5eY0TQ8SpQEdGdULX56gDkMHMUM6fqWxTUulLnACwiotu9OPVSeDY/6y6oZA4hgkZByTFcbFQJAGMS+2Fm6kCTWxSa7ps+CtZyf1sOnkWZTAAWmre5s4ug0s7TmLnc8UFmLiCiUgCH9LHvOjuJLpOU57B7MRFN6+C1Y6GK0tZ2cIwQAdXaynjd8On/geuSpLhqJ42I74PMCYNRqMf63thajp9/8yqTWyWCgbeBKgNAqbMgZcXMFh2srungPPFQRWhtT4Pn87McA5wQpvnoYCXKq9QE3/69uuH2qcNMblFoe/C6JFugKig5hqduHof+vbqb3CphNrOW+ThMRNagRFA9sFwAK908tVrS3kUwMfamFmSMQN+eMsG3K65LGYgxif1w8MwFXGxsQUHJMTw0c7T7J4qw5m0yRQmANCJyOZNRX9ZLg1pXyiW9BP1hvTZVEdT8rMNubhKkRNCwVF7AZsMKvvfPSDK3QWGAiPDAdUm27de3lkuquvA6UOVC9YAKiWiy44NENAUqQDGAfE9PyszzmHm9l20RwlRvbLMv5TFnXCJGDuxjYmvCx+1Th9lS1cur6vHRwUqTWyTM5tV1Cp0ssQ7AnQBKiagW9p5TBuyr7+Yz8yvenJuIkqASMNzl9jIz3+XNuYXwtQuXm9ukpD9omLAquqZvz25YkDECr+hq6q9vLcdN4xJNbpUwk9cX1Jk5h4iyodLER0PNp7KyAFjKzOu8OScRTYU94LlLmWKoNHUhTLOu5BguXG4GAKQk9MX1qYNMblF4uX9GEl7Vk6g376+EpfICkhP6md0sYZJOVaZg5gJDsdoUACmGOVVeBSltBVSAWgcV+FI6uKV2ps1C+IpjSvqDkpLucyMH9sHc8fZelPEyq4g8XU5RYmZfrHaWAaCMmef74FxC+NWWQ2dhOXsRANC/ZzfckSZrTvnDA9cloWiffa2qp24eh36SVRmROtWj8oNYAKVmN0IIT/zxE/tns+yM4ZKS7ifXpw5CSkJfAGpMcJ1hTFBElmAJVBugUtqFCGqHz17EJkNK+gOSku43RIQHjanq2yRVPVIFS6BaCiCFiH5odkOE6MibhrGSG8cmIGlQXxNbE/7uSBuO/rrHaqm8iC2HzprcImGGYLlm0QoVrFYS0QIAa6EyCJ2SOVfCDPWNzcgvqbBtGyemCv/o27MbsjOG47VPygEAb24rx+yxCaa2SQResASqUqi0c4JKrMjQ245I748OXNOEUP6y8wTON6iU9KSBfXDDGHnDDIT7ZyTZAtWGL8+goroeI+JlcnUk8SpQEdEcZyv8+sAyOA9MQgQFZsYb28pt29+aPgpRUZKSHgijB/XFrDGDsOXgWTADf/7sKJbdMt7sZokA8rZHVURE1VAVzNcy8y5fNIKZ3RWjFcJUxUdq8OWp8wCAXt2jkJM+wuQWRZb7ZyRhy0E1PrVmx1E8kTkGvbrLhZVI4W0yxQaoJTqWASghogNE9ENd/shniGgAESUR0QBfnleIzjJOOL1tyjDE9JGlJwJpzvhEDIvtDQCoqW/Ce3tOmtwiEUheBSpmti4/vxjARqgqEc8CKCOi7UT0cFeCCxE9RURVAGoAlAFYqPd/QEQPd/a8QnTFmfMNeP/f9jfG+2a4XDxA+El0FOHe6fal6t/4VCpVRBKv09OZuY6Z8xyC1i6oBIjVAGp0YPFmKXoQ0QdQpZTiABxG25p/0wDkEdH73rZXiK56Z3sFmlrUEGrGqDhMHBpjcosi04KMEegRrd6ydlfUYs8xWew7UnRpHpUhaKVDBZiVUAEmE0ABEbUQ0TtEdFNH5yGiR6Fq/O0EkMzMjvX8RkP14LKkZyUCqamlFW99dtS2Lb0p8wzs1xNfu/oK27bU/4scXZ7wq8eTHoGa+/Qj626oXlEdgPlQSRjbOzjNIqisvznOlrln5lrdg6uD6sEJERCFe0/j1LkGAMCgfj1xy1VXuHmG8CfjB4X/230C1RcbTWyNCJROBSqd6PAUEe2AGk/KheoRHYbqVaXoSurxAOZBXRpMJ6JfuDhlGoBSZj7n5qWL4X69KiF8xpiSfve0EejRLViKuUSmKSNiMWmYuvTa2NyKtcUVbp4hwoFX/3VEtJyIDkIlOqwEkA51uW4Z7MFpmbGiOjMXAZgL1cvKcnJaoIMqFA4kSImAOXD6PD61VANQg/n3XDvSzTOEvxFRm17Vnz49ghap/xf2vP14uBRqTaid+us4Zs5g5mc7Wu6DmWuheltFLg7ZCSCNiFwOABDRaKhAVezqGCF8yVjXL2vCYFwR09vE1girb0weilg9PeBYzSVs3n/G5BYJf/M2UC1C2+BU5+kT9UKLT7t4eDlUj6uQiCYbnwYARDQFKkAxVGagEH51vqEJ60vty0rcL0kUQaNX92jMz7BPuJakivDnbaBKhrrc1yEiuoOInvL0pMxcCnX5MBVAqZ5LxQB+rL8ugc4q9FMJJyHaeHfncVxsbAEApCb2w4yUgSa3SBh969pRsC6q/NGBSpTrhSxFeOrMpb+pHhx3F7zs+egySqlQaeikb3H6fgOA9A56ZEL4DDO3uex33/RRstR8kBk5sA9uNFRR/5NMAA5rHdb6I6IYqLp+RouJaFoHT4uFmkfl9Ww8ZrbAkHBBRDHeXF4Uwhe2lVXh4JkLAIC+PaJxR9owk1sknLn/uiTbIpZriyvw5Lyx6NMjWBaEEL7k7rcaDyDHsM1QyRQpHpzbMcB5TYKUMMPr28ptX9+ZPhz9e0ldv2A0e0wCRg3sgyNV9TjX0Iy/7jqBu6dJZmY46vDSn87kswamVKjLcLmGfa5ucXKZToSi47WXULj3tG1bkiiCV1QU4b7p9t/P61vLwSyp6uHIbT/ZmHZOREUACjtKRe8sndm3AO7nSjEz3+Xr1xcCAP786RFYp+XMTB2I1MT+5jZIdCgnfQR+/eEBXGpqwZenzmNHeQ2mjY43u1nCx7y6oMvM8/zRCCK6E6oEE9C2GK3TZkAlawjhUw1NLXhnh73Swf0zksxrjPBITJ/uuG3qMLy9XdVjfH1buQSqMOQumWKO/rKYmc/pdac8rg7hRSr501ABKg9APjyvVCGEz7y356Stdtyw2N6YOz7R5BYJTzxw3ShboPrg36dwqq4BQ2J6mdwq4UvuelRFUD2YLKi08RyotHNPLwR7ugRnGoASZpaCs8I0jkvNd4uWun6hYPyQAbh2dDw+O1yN5lbGW9uP4smssWY3S/iQu0D1LFRQsvZwSqFq/PmaBdKLEibaVVGL3cdUkmmPblFYcI0sNR9KHrguCZ8dVnUZ3/rsKL57U6oUEA4jHQYqZl7qsL0BavKtr20AkE1E/Zn5vB/OL0SH3thabvv6G5OHIr5vD/MaI7yWdeVgDBnQC6fONeDshcv4579P4ptTZP5buAiKjxzMvAiqaO0Gh1p/Qvjd2QuX8fc99qXmH5AkipDTPToK9xqq279u+OAhQp+nyRSd4mVdPguAbKhaf25Oy15lKxJRNlTCRjJU5YxSAGt02SZnx6fp4zP1riIAy3VNQhFm3tl+FI0trQCAqSNjMWm4LDUfiu6aNhIvbDyIphZG6dFafH6sTn6XYcLTZIrO8iiZgoh+CeBOvVkHoLoLr+l47nyoAAjYx8LSoJYVWcTMKQ7HZwIoNBwP/fxsIsph5gJftU2Yr7mlFX/61L7UvPSmQldC/564ddIV+MuuEwBUcsyzOXKBJhx4mkzhb9ZAkunL6ug66GRD1R2ca+0REVEsVBp8JhHl6kuP1v3WIJWlF300Bq98IorT62uJMOC41PxXJ8lS86HsgeuSbIHqr7tP4MdfnYA4GW8MeV4lU/hRMlTFC18v4bFI3y81XrZj5loiygFQA2Ch4biF+n6lNUjp44uIKE8/vhD+yXwUJjDW9btHlpoPeVNGxOLq4THYc6wOjc2tWFNcgcWzPSlNKoJZsPxX+mvsJ03ft1tZWPeKLICtJwXYK7evcXKuXIdjRIj78tQ5h6Xmpa5fqCOiNhVF3tx2BM16/FGErmCpTPFLAGuJaDIz7/b0/B7I0e1oN0dLB6dk/bj1Up51u13gZGZrkofH378Ibn/4l71k5VcmDpFqBmHia1dfgeX/2Ieqi404XnsJH+49LZd0Q1ywVKY4BFU+qZSIcqF6WC4TKph5vScndZWlp4OUdT6Y8TKeJ0HI6TFEZL0siJEjZamBYHf2wmXbWAYAfPv60Sa2RvhSr+7RuPfakXhh4yEA6gOJBKrQFiyVKUr16xCAxXAdCEk/5mkAbH8ClRiRCxVwipyMw3WUKFELld7eDjPnQa/BlZGRIWsNBLm3PjuKxmZ1SWjyiFikjXT6axUh6lvTR2HVR2VoamEUH6nB7opaTB4hv+NQFSyVKZbBz9mFRJQMFaCsc6NWukgW6eivWf7Sw8Dl5ha8aVi6/Nszk2Sp+TCTOKAXvn71UKzfeRwA8Nonh/H8XVNNbpXorE6v20xEA6B6JdZLYaXMXN6Zc7maeOsrRLQE6pIlABRAZQE6qy1ogfvLf1KTMMS9t+ckKs9fBgAMHiAp6eHqoZmjbYHq73tOYtktE2QcMkR5nfVHRAOIaBVUancJ1HykfABlRFRFRD/saqP0ayTpYNjVc+VCBSkLgHRmznERpAB7FmCa4wOGfTKHKoQxM141JFHcPyMJ3aVKeliaNDwG05LU2lTNrYw3Py03t0Gi0zrzH1oKlTRAUJcBn9W3jQDiAKwkovc70xgieoqIqqCCYJl+HRDRB0T0cCfOZ01wKGLmFA9KIOXr+wVOHrPuy3XymAgR2w9X44sT5wAAPbtF4Z5pkvgSzoxJMm99dhSXGltMbI3oLK8ClS51lAwVrOKYeR4zL9O3LADxAHYByPK2Z0VEH0D1fOKgCtQaBw2mAcjrRAC0jkHleHKwTogAgCXGXpX+eonDMSIE/eETe2/qjrThUrUgzGVdORjD43oDAGrqm/CXXcdNbpHoDG97VJlQSQ9zmbnO8UE9H2kuVJBx1itxiogehUqB3wkgmZlTHQ4ZDdVjy/K0Z2WcJwXgMBHVuLo5PNVapaKEiAqJqBDqEqfxMRGCjlbV48O9p23b356ZZF5jREBERxEevC7Jtv2Hfx0GsyTlhhpvA1UaVNLEOVcH6GBVCiDdi/MuggqAc5wlZDBzre6x1UGlr3vCmBQR6+ZmfK08qKBZChWYM/XXWdKbCm2vbyuH9T1q1phBGDO4v6ntEYEx/5oR6NtDzWg5eOYCthw8a3KLhLe8DVSeZMUB6s3fm6QDtwFQK/bw9cHMpcxMntycPLeImdMNx6Qba/+J0HO+oQlrdlTYth+WCb4RY0Cv7sjJsK/YbLz8K0KDt4FqHYA4Irrd1QFEdCdUMPGm9+FpyreULxKdUlByDBcuNwMAUhL64oYxCSa3SATSQzOTYJ0qt3l/JQ6duWBug4RXvApUeoJsEYACIvqFrv0HANDp5MsBrAVQwsxPe3HqnVDrQ7msCkpEo6ECVbE3bRaipZXx2ifltu2HZo5GVJRM8I0kowb2ReaEwbbt16RXFVI6DFRE1OJ4gz1ZYinU3Cnr/jKozDgCEEtEj3jRjuX6eYUOS9GzbscUqADFsE/cFcIjhXtP42h1PQAgpnd33JE2zOQWCTN8e6b9cu+60mOovthoYmuEN9z1qMqhUsUdbxYX+62PefVxVc9vWgYgFaowbRVUUPqx/roEeo6WH9asEmGMmfH7j8ps23dPG4k+PTpdkDgF1MAAACAASURBVEWEsOnJ8bjyClVDoKGpFa9vLTe3QcJj7mr9BWzFMWZeSUQFUBNq06GCXRxUUsYGqLJHOwPVHhEePjtcjV0VKq+nR3SUpKRHMCLCotnJ+I93dgFQWaCLZifLB5cQ4JfaMUT0CBG97O3zmNnCzFnMHM/MUVCTiuP1xGIJUsJrxt7UnenDkDhAar1FslsnXWGbAFxb3zYTVASvTgUqnTgxx8XtDqjLeB5NjiWiGD3O1a7qhLNJxUJ4au+Jc9i8vxIAQAQ8OkuSRiNdt+goLLzB/nfwypbDaJIVgIOeV31eIoqByvprV7TV8VA4Wf7dGWauI6JyAAG7zCgiQ+7H9t7ULVcNQXJCPxNbI4JFTvoIPF90ENV6BeC/7T6BO9KGm90s0QFve1RPQ40fHYYqRGu9HGctTLseKkjlM/M8L86bA2CgLyqvCwEAFdX1+Ntu+wq+i2fL5yCh9O4RjYcMZZVyP7JIWaUg5+0oYjYAttbi08VadwB4h5l36X0LASwnov7MfN7D81ZDBat8IroLqje2w9XBni5FLyLX6i0WtOr3nutSBuLq4bLmpbC7b4ZaAbi+sQX7T5/Hpv1nMGf8YPdPFKbwNlAlAyi0bjBzKamlUdOhqqaDmfMMCxV+x8PzWmBfij5d35x9xOnyUvQi/J29cLnNIPljN0pvSrQV26cH7p420rY22arNZRKogpi3gcpZ/b5SABkAXnXYl+XFef2+FL2IHK9vLcflZjVAPnHoAFyfOsjkFolg9PD1o/H61nI0tzJ2lNeg5Eg10kfFm90s4YS3gcoCFZSMiqEqjDvyOMXK30vRi8hx8XIz3th2xLa9eHYKiKRckmhvaGxv3DZ1GApKjgEAVm224JUHJFAFI2+TKdZCFaV9X5c1AlTvKdlaqFbX5MuG54VmhfCZt7cfRd2lJgDAqIF9cMtVQ0xukQhmi2fbP08X7TuNA6c9HVYXgeRVj0pXj1gAYB7U2lALAKwB8HuoQrXWZUC8qsmni9l6qlDKKAlnGptb8coWe7HRR2clo1u0X+a0izCRmtgfmRMGo2ifWlAz9yMLfj1/sptniUDzunYIM6frFXmr9XYdEc2DKn2UAjWOlcfMr3hx2qVuHjeOX1VBrfYrRBt/2XUcp841AAAG9euB7HSZGyPce+zGFFug+uuu43hy3lgMi+1tcquEUaeKXDHzaoftInRtwm5HiRfJAOZDVW1fwsy/6sLriDDV1NKKFzcesm0/NHM0enWX5FDhXvqoOExLisf28mo0tzJe2nQIv7h9ktnNEgadrsZIRAOggoj1Im+ps2XkPcHMGzp4eAOA1Xp+1ioiKmHmTZ15HRG+1pcea7OUx/0zXC5tJkQ73587Bt969TMAwNodFXhsdgpGxPcxuVXCyusL+EQ0gIhWAaiBWn4jX9/KiKjKX9UlmDkPqhKGrEcl2mhsbsULG+y9qYU3JKN/r+4mtkiEmpmpAzEtSWX8Nbdym965MF9nRppLASyEmny7AfbySRuh14xyVmDWRyxQk4GFsCkoOYbjtZcAAPF9e+ABQ3kcITxBRPhB1ljbdkHpMRypumhii4SRV4GKiH4JdamvFGoJjnnMvEzfsgDEQ1WoyPJTzyoNzicdiwh1ubkFL248aNtedEMy+vWU9YWE92akDMSM5IEAgJZWxu+kVxU0vO1RZUJl4M11tgQHM9fCvlT9Ak9PSkRT3NzmENEaAKOhJhgLAUCNJ5yos2f63SdjU6ILjL2q9aXHcPis9KqCgbcfPdMAlDDzOVcHMHMtEZXCu0t0pXBfQslaXsBdKruIEA1NLXhxk/1T7+LZKbJaq+iSaaPjMWvMIGw5eBatDLyw4SCeWzDF/ROFX3WmhJInpZFi4d0lutVwH6jKABQw82E3x4kI8fb2ozh97jIAIKF/T3xruvSmRNc9kTkWWw6eBaDmVT1+UwpSE/ub3KrI5m2gWgfgR0R0OzO/6+wAIroTKph5nJ3HzB6tBiyE1aXGFry82b4w4uM3psi8KeET6aPicOO4BGzeX4lWBn674RB+d/dUs5sV0bwao2LmpVBrRRUQ0S+IKMn6mF6efjlUPcASZn7alw0VwujPnx1B5XnVmxoyoBfumjbS5BaJcPKDTPtY1d/3nMD+U1ID0EwdBioianG8wZ4ssRRq7pR1fxmAJfqxWCJ6xNvGENEdhmK31iSLg/o1thORFOESqG9sxipjb2pOqvSmhE9NHhGLzAmJAABm4LcbDpjcosjm7tJfOTq3TpRX6yoQUQzU5OHRUAFwl963EWq8C1DLi5QQUWpnK2CI8PDGtiOoutgIABgW2xvzM6Smn/C9JzLHomjfGQDAPz4/hb0nzuHKoQNMblVk6rBHxcwpzJzayZs3RWlXQI1rbYC6tAio+n6xAHKZOUpvR0H12kSEqrnYiJcNmX7fnZOKnt2kNyV876phMbh5on3V3+X/3AdmWd/VDD5ZA0HX/euKTAA1egLxLr0vB4blQpi5ACrr0JuVg0WYeb7oAM41NAMARg/qizvTpDcl/OfJrHGI0teHthw8i837K81tUITqVKDSY0cf6Np+LQBq9NfvE9FNnThlMtpP5M0AYHG4zOdperwIQ4fOnMefPjtq2376lvHo0U3WmxL+M25I/zaJOs+8txdNLa0mtigydaYo7XKo8aQsqNp+h/UtDmpBxSIietnL01qgyi9ZX2Mu1GW/IofjkiEllCLWL/7xJVpa1aWXGckDkXXlYDfPEKLrnswaayvLVVZ5EW9vP+rmGcLXvK31dydUskMdgBxmjjKMSVnHkc4BWGRdmt5DOwGkGXpjS6Eu++UbXnsuVKCSJe4j0JaDldj4pRrYJgJ++rUJIPIqZ0eIThnUrycevynVtv1c4QHU1TeZ2KLI422PahFUAElj5nWOD+pxpHSorD9vJvEu188pMqTAW6xLzus6fx/q1/Zm2XoRBppbWvHM3/fZtnPSh2Pi0BgTWyQizUMzkzA8Tq36W1PfhN8ZCiEL//M2UGXAzQKJzGyBqt13jacnZeZSqMuGu6AC1k69bXxdArCMmdd72WYR4tYWH8P+02rCZZ8e0Xhq3jiTWyQiTa/u0Xj6lgm27de3laNcCtYGTNCMRDNzETOn68uJGQ41/TL1/mdNa6AwxfmGJvymcL9t+7HZKUgc0MvEFolI9dVJQ5AxKg4A0NTCWP7PfW6eIXzF20BVAjWW5LL6p56omwYfLsdhDVpE9IheXVhEiJc2leHsBTW5d2hMLzx6gyR9CnMQEf7za1fatj/44jS2lVWZ2KLI4W2gyoW6BFforJyRLn9UDIdECE/peoFzXNzuALAManVhEQEqquvxh3/ZO9ZLbxkvpZKEqSaPiMXtU4fZtp95by9aW2USsL95VT2dmQuIaB2AOwGUElEt7D2nDKiUcgKQ701lCt0LK4LqiXV4KNqnrIsw9ct/folGPWdl8ohYfP3qoSa3SAjgRzePwz//fRINTa344sQ5FJQew/yMEWY3K6x5PUbFzDlQaejlUHOnstB2TlUOM3u8uq/2NFS24GEAz0IlU0B//SyA9bAHwHlOzyDCyqYvz+C9z0/atv/f1yYgKkrS0YX5hsb2xsJZ9kvQy/+xD1UXLpvYovDXqWQKZi7QdQCjAKQASDHMqWqXtu6BbHVaTmVm4+W9d5h5mQ6OiwFkEpGsYBbmLlxuxk/e/dy2ffvUYUgfFd/BM4QIrEWzUzAs1p6u/t9/32tyi8Kbx4GKiGL0chvvG/cz82EfrLqbDMMlPZ2uTjAsZ8/MeQBq4MWCjCI0/eqD/ThR1wAAiO/bo80AthDBoG/Pbnjm9qts23/ddQKb9IR04XseBypmroO63Jfih3Y4K4tUCjXu5bhPitKGsZIjNXh9W7lt+/997UrE9+1hWnuEcOWmcYm4bYp93PQn736OC5ebTWxR+PL20l8OgIFE9EMft8OC9kGpGKqquiPJTw5Tl5tbsHTdHlhXUrhxXAK+OUUSKETw+s+vXYm4Pt0BACfqGvCrD/a7eYboDG8DVTVUsPoJEe0gouV6VV6nNy/OuxZAnK6+bl3htxRAsrVmIBGNhhrLklp/YWrV5jIcOnMBgKpA8cxtV0k9PxHUBvbriZ99faJt+/Vt5Sg5UmNeg8IUebMQGBG1Qs2RMr57ODsBQSVHeDzphYhKAEyFyuxboFPWa/T5jct7LPJyUcaAy8jI4OJin813jggHTp/HrS9sQVOL+nP62devxEMzR5vcKiHcY2Y89McdtrWqxiT2w9+/f70s6NkJRFTCzI5X17ybRwU14dYvs9uYOZ2IHoXqtYGZ64hoHtQk4xSocay8YA9SwnstrYyl6/bYgtTUkbG4f0aSuY0SwkNEhGduuwrznvsY9Y0tOHjmAlZtLsMTmWPNblrY8HbC70p/NUSff7XDdhH8k7whgsib28qx86jKp+keTVhx59WIljlTIoQMj+uDH908Dj//m0pTf2nTIXx10hUYO1hm0/hC0BSlNSKiAbqcUleXuBdBrvzsRaw0DEA/dmOq/HOLkHT/jCRMGRELQBWt/VH+bjQ2y2rAvuBRoNKBY44uCjvHXwGEiJ4ioiqosaky6Im/etn7h/3xmsI8DU0tePytUtQ3tgAAUhP74fGbpAMtQlN0FGFl9tXoHq2uBuw+VodnP/jS5FaFB7eBSmfvHQZQCDVeVAig2ssVfN0iog+gJvNaSzEZr/1MA5DnONlYhLZf/GMfvjhxDgDQIzoKz82fIgPQIqSNHdwfP7rZvl7a6i2HsWHfaRNbFB46DFRENBVAAezBY4O+jwJQ4KyCemfoJIosqBp/ycyc6nDIaAAbAWRJzyo8/PPzk3hj2xHb9k9unYBJw2XVXhH6Hrk+GXPGJ9q2f5i/GydqL5nYotDnrkf1tL7P1XX45ukgshqqx+OrckbWJe7nOFs9mJlrmTkLQB1UzT8Rwo5W1WPJuj227a9MHIL7Z7hc4kyIkBIVRfhVzmQM0Qt81tY34ftv70RTi4xXdZa7QJUGNR/qMeNOZl6kv/R4uXkPXqeUmc+5Oa4YUpkipDU2t+J7b5fifIMqNTM8rjdWZF8tE3tFWInv2wO/u2eqLXu1+EgNflN4wORWhS53gSoZritBWKDWn/IFT6tNSJAKcSve/xK7j9UBUKnoL96Thpje3U1ulRC+d01SPJ7Mss+lWrW5DB8dqDSxRaHLk6w/ZwVjO9rfGTvhfon70VCBSko+hKjCvafxqnHF3q+Mt6XzChGOHpudglljBtm2n1yzC6fPNZjYotAULPOolsP5EvcMtFviXpb5CEFHq+rxVP5u23bmhEQ8fL2USBLhLSqK8NyCKUjs3xMAUHWxEd97e6fMr/JSUAQqvf7UMgCpUEvcV0EFpR/rr0ugMg9XMvNG81oqOqPmYiMefG076i41AQCGxvTCr3Imy7iUiAiD+vXEb++aCmuxle2Hq7Fs3R54U2c10gVFoAJs5ZlSodLQSd/i9P0GAOnM/LTrM4hg1NDUgkfeKIbl7EUAQI9uUfjdPWmI7SNrTInIMSNlIH44zz6/av3O4/j1h5Jc4SlPav2lEVGLqwc7eIyZ2dtaghYYFkYkohi9YKMIQS2tjCfe2WVb9oAIeH7BFKSPijO5ZUIE3nduTEFFdT3e2VEBAHhx0yEMje2Ne64daXLLgp8nPSrq5K3LvTUJUqHtmff24v0vTtm2f3rrlfjqpCtMbJEQ5rFWWb9pXIJt30//8jk2fimVK9zpsMfDzH65NEhEq7rwdGbm7/isMcIvXtliwWuflNu2vz1ztCRPiIjXLToKL96ThrvyPsXnx+vQysDjf96JNYum4+rhkgHrilcLJ/rsRe0LMAJta/p5wqsFGc0Q6QsnvrfnJB5/q9S2fctVQ/DSPWmIkqU7hAAAnDnfgDte3opjNaq00qB+PbD+sZkYObCPyS0zl68WTvQlggpWhQDyoWoIihC39dBZ/GDtLtt2xqg4PLdgigQpIQwS+/fCHx+ahjtXbUXdpSacvaAyY99ZOB2JuvSSsDOrR5UNtYRHpt5lDFj5HpRSCmqR2qMq2nsa33mr1DZHJDmhL9Ytvg5xfSXDTwhndpRX495XPrP9z4wa2Ad/evhajIiPzJ6Vqx6VKenpzFygC9xGAZgPlZI+D6rYbQ0RvU9ED8vCiaHj/3afwOI/ldj+4QYP6InXH5omQUqIDlyTFI/fLphiqwl4pKoe83O3oazygsktCy6mz6PSQStLglboeuuzo/iPd3aiuVX1zkfG90HB4usi9lOhEN64ZdIVWHVvGnpEq7fjk3UNmP/7bfjihCQ9W5keqIycBK31aBu0ZKXfIJP3cRl+/O7nsF5BHpPYD/mLZ0iQEsIL8yYOwR8evAa9u6s8saqLjbgr71OUHKk2uWXBIagClZEOWjk6aOVAVafIglplWJiMmfGbD/fjF/+wL7U9aVgM1iyagcEyGCyE164fMwh/euRaDOilctzONzTjW69sx5aDUnE9aAOVFRHdAdW7mqt3BX2bw11DUwueXv85Xth4yLZvWlI83nr0WsTLmJQQnZY+Kg5vL5yOgfr/6FJTCx7+YzHWFleY3DJzBeWbPhHdQURrdHmmfKge1U6olYCl/o6JKqrrkf37rbYyMAAwe2wCXv/2NPTvJetKCdFVE4fGYO3iGbgiRl2ZaGxpxZKCPVhasAcNTS6r2YW1oAlURDTHRXBaBiCOmTOYebWUVTJP0d7TuPWFLfj3cfvsgW9OGYrV92egd4+gnoMtREhJSVBjvWMS+9n2rSmuwB0vb8WRqosmtswcpsyjsr040RyoXlK2dReAUgBrAOSFalAKt3lUzS2t+HXhAazaXGbb1z2a8NNbr8T9M0bJch1C+El9YzN+vP5z/GXXCdu+/r264Vc5k3HzxCEmtsw/XM2jMmvC7yqocadYtA1OBcwc8hUqwilQnTnfgO+/vROfWuzZR0NjeuGle9MwdaRchRXC35gZf/7sKP77b3vR2GJfcHHhDcn40c3j0D06aC6MdVmwBSpjrb8iqEDlKWbmH/u+Vb4TDoGqtZWxtrgCv3z/S9TWN9n23zA2Ac8vmCJJE0IE2O6KWnznz6U4XnvJtm/8kP7439snhc3SOcEYqDpLitL62b6T5/CTdz9H6dFa2z4i4Im5Y/HdOam2WfRCiMCqudiIJ9fuwqb9bVPW7542Aku/Mj7kFyQNtkB1Z1eez8zrfNUWfwjVQHXxcjOeLzqAP3xSjpZW+9/F8Lje+OUdV+P6MYNMbJ0QAlBXO175lwW/KTyAhib7Z/74vj3w469OwJ1pw0J23DioAlW4C7VA1drK+Me/T+J/39uHk3UNtv3dowkLb0jGd28aI1l9QgSZiup6/PxvX6Bo35k2+6eNjsfPvn4lJg6NMallnSeBKoBCJVA1t7Ti73tO4qVNh3DwTNsimNOT4/HMbVchNbG/Sa0TQnjiwy9O4b/+7wucMHzIBIC54xPx+JxUpIVQ0pMEqgAK9kDV2NyKd3cew8uby3Ckqr7NYwP79sBPbp2A26eG7uUDISJNfWMzfrvhIF7dcthWHNpqZupAfPemMZieHB/0/9MSqAIoWANVbX0j1pcexytbLO0+ffXtEY1vzRiF78xORUwfqTAhRCg6cPo8ni86gH/++xQc39rTR8Vh4Q3JmDM+MWhT2iVQBVAwBaqmllZ8fKAS60qPoWjvmTbzMAAgpnd3PDQzCQ9elxTyGUNCCOXQmfN4eVMZ/rr7RJvEKEAlXXxzylDcmTYcE4cOCKpelgSqADI7UDEz9p08j/Wlx/CXXcdx9kJju2MG9euBR2Yl41vTR6Ffz24mtFII4W9Hqi7i9x+VoaDkGJpa2r/Xjx/SH9npw/GNyUORGASrHkigCiAzAtX5hiZsLavC5v2V+PhAZZtJgUaTh8cgO2MEstOGSyafEBHiZN0lvLntCN7debxNZq/RVcMG4MaxiZg9LgFTR8SimwmXByVQeYGI0gA8DSBT7yoCsJyZPaqgEYhAdamxBV+cqMOO8hp8dOAMistr2g2iWiX274nb04YhO204xgyWLD4hIlVLK2NbWRUKSirw/hen2szDMurfqxtmjRmEWWMSkDYyDqmJ/QIy0V8ClYeIKBNAod606PtkfZ/DzAXuzuHrQNXU0oqyygvYXVGLXRV12F1Ri/2nz7e79mzUv2c3zB6XgOz04bg+dZApn46EEMHrfEMT/vH5Sby78zh2lNd0+H7Sp0c0Jg2LwZQRsZg8IhZXD4/BsNjePh/fkkDlASKKBVCjN7OYuUjvNwavOGaudfZ8q84Gqtr6RpRVXkBZ5UV1f+YiLGcv4GhVvcvektHEoQMwe2wCZo9NQNqouKDN7BFCBJdzDU3YeugsNu+vxEcHKl1eHjTq0yMayQl9kZLQD8mD+iElUX09elBf9OreuWEFV4FKRtHbWqjvV1qDFAAwcxER5enHFwJY6esXfmWLBc+8t8/j44nUmjWTh8dienI8Zo9NCIrBUCFE6BnQqzu+ctUV+MpVV4CZceD0BduQwq6KWpw5f7ndc+obW/Dv4+farE8HqPem9//jBowb4rthBglUbWXp+zVOHsuFClJZ8EOgGh7Xu8PHh8b0wqThMZg8IhZThsfiquExGCAr6gohfIyIMG5If4wb0h8Lb1D7TtU1YFdFLXYfq8Xuilp8ceIc6i41OX0+MzAivuP3M29JoGorGQCcJU0wc6m+Hpvs+JgvpCT0Q89uUUhO6IeUhL62+5SEfkhO6Is+PeRXJYQwx5CYXvhKzBB85Sq1WCMzo/piI8oqL8JSecE2ZGGpvIAWZp+/X8kYlQERMQAws9MRQnePW3VmjKpVj0FFyRIaQogQ1tLKnc4QdDVGJaPt7XWUKOHyMSJaSETFRFRcWVnp6jCXoqJIgpQQIuT5I41dAlV7sZ15jJnzmDmDmTMSEhL80CwhhIhMEqjasrg/xKNjhBBC+IgEqrYsgK0yRRuGfR3OoRJCCOFbEqjaytf3C5w8Zt2XG6C2CCGEgASqNpg5T3+5xNir0l8vcThGCCFEAEigam+Rvi8hokIiKgRQ4vCYEEKIAJF5VE7o2n4rAFh7VaUAlhrLKrl5fiWAI35qXigZBOCs2Y0QbcjvJDjJ70UZxczt0qYlUAm/IaJiZ5P3hHnkdxKc5PfSMbn0J4QQIqhJoBJCCBHUJFAJf5IMyeAjv5PgJL+XDsgYlRBCiKAmPSohhBBBTQKVEEKIoCaBSgghTEBE2URUQkQ1RMT66yVmtysYSaASIswRURoR5es3xBr9dbvCyyJwiCgfqrZoGoBqqKICaQBWEFGZmW0LRhKoRMDIJ8jA01VWSgBkQ70hVuuvS4go28y2RSr9O8mGWokhnZlTmDkdQByAIgDJRCTFrw0k608EhP4EaX1jtED9k1o/1VuYOcWUhoUxIooFUKM3s6wlwPQbZaHeH8fMsnRNABn+FxY5Frk2/s6YWZb81qRHJfxOPkGaZqG+X2msU6m/znM4RgSO9QNau9qh+kODdV28jlYbjygSqEQgWKvOL2XmUutO/U+ZozflDdP3svT9GieP5TocIwInB+oDW7vVwnVwSgZs/x8CEqhEYMgnSHNY3/BKHR8w7EsOaIsEmLnU2e9E//1v0JsrA9uq4CZjVMLvrBlmHfxzyjV5PyAiBlz/XN09LgJHXx7PhfrgUMTM0tM1kB6V8Dv5BGmqji4fyaUlkxFRsl6ctRAqSK2UINWe9KiEKeQTpP9Jjyq46akZK/RmAdQYbrtxKyE9KhFg8gkyoDx505M3RhPoLNcVUD//dGbOkSDlWjezGyCCXydmytfq9HPH88gnyMCyQKX+pzleejVUppDLfwFGRAuhslzlSoKHJFAJt3wxGVd/glwI9eaZ42zMSvhcPoBMAAugSvQYLdD3Mn8t8Jbq+5wOjxI2MkYl/E5/gsyFfIIMOOs4FNTlpVK9Lw2qrJKMTwWYQ7WQDnuzzBzn/xaFBulRiUCQT5DmWQT1IaGEiKzz2DINj4nAMs5bk3mDHpIelfAr+QRpPp1huQL2idelUOOD7SZgCxGMJFAJvzJeZnJHLkMJIZyRQCWEECKoyTwqIYQQQU0ClfAbvVAi65vbNGhvjxfBTU/uloQB0WUSqESgzPfgGMlCCxNElAygDMBqs9siQp8EKhEosTr7rCPuHhdCRCAJVCIQrGnQLntMRJTtcGzYksthQnhHApUIhFJ9y+7gGGsQy3d1ABGlEVE+EZXpcawSXfXC1fFL9DE1+vgyIlrhLFA4OXcZEeU6Hqv3saFWnvGxTP3YCsO+JXpfsn6NMtjnlXn1fRnOlabH80qM35dDG43ncvpz9+bn6fDaaURUqLdr9NdphmMLoS77AYB13HGFs/N6w/D9tut5d/SYCH0SqESgrAFsk0+dyYTqTTktUqsL2pZABbtaqMCXBiBXvzE6Hl8CNck1GUCxPncygCWwr4FlPTbT4dxFAOKhahN6NAfMA8mG17X1Gr39vrRFUAG9GobvSwedEqjxwFLDufIdA2snXxdQv6cS/ZoFug2ZUJUvrFUXcgHk6a8tUJVJOjqnp5br+6XGnUSUr9u+SCYxhylmlpvc/HKDehNk2AMGA8jv4LiFUG96DCDX8Lj1uWUAkg37Y6HeNBnAEsP+hXpfoZPXKtOPGc9jPUemw7GFen+2YV+u3pfm5NzWtq8w7Fui99UY29jJ78t6rjavb3hd1s+LddLeFZ19XSev7fhYfgev0e733cW/qRrj78/Qrlxfvo4P29vmZ2z4OdcYf09y6/gmPSoREKyW83B1+c962W+ti6dbU9XbrNnDzLWw1w982nB8NfQyIk7OVaDvjTXX0vT5HD+NL9U3XyxFYmFmx1WMvf2+rPLYUH1et9tanupR/Xwr66VU4/fb2dcFgNIOvo9kx4P9wNarMpSGKmLmNuOf+nKkqVMc9CXXZKi/R6P5UEFKlljxc7Rz5QAABIVJREFUkBSlFYG0BkAaEWUzc4FhfybUm00tkdMqShlQa1y1WxqEmS1EZF13KZaZa/W5CxyP1Ze/nF16LNXtKoTqFRTpc1svn/mCs0tSXn1fhoedXY6shnrzczyXsyDb2dcF9CVcD17DKX3JcaAHh+5w+BuxyoMKTtY1nUrZeUX+UhdtDaQsqA8ojj/DLPju7yoiSKASgWR9k1kAHUgMA/0ukyigq0wblqxwJR66Z6GTIOYDSId6Y26X/GCQA3WZLxNAJhHVQo1r5QNY66NPvjuc7PP6+9IcP6F7q7OvC3S9d/k0PKsa7vTDhv4wkwcVpGoBzHX2ZGZ21psONOu4q7P9eU72CxckUImA0W8yjpf/rAv4Ob3sZ8i6q4X9so8r1fo5mbAP3lug3ixyoYLPAqhxDWO7LABS9PNyoAOWvq0gornOeh9d0ZnvK5Rf14q7WCFft9/WK3b2IUL/Hhcxs9tlZchHq1e7aGcyHJJIdMJJrON+0TEJVCLQHC//ZUNf9nN2sA5utfprx7ERV4xjMG0+lRPRAifHW1+rCPoTsH5DWQr1yT0fgCerHHs8RtPJ76vLzHpdH9oA9XO2Xp50vIwMqEtrHv0u2AerV7tgDaaOPapM/bqSnegFSaYQgWa95LHAw8t+gOoJxbqYuxRrnctj2J0MNTbgbIyjzRuYnt9UplOcbZjZYhigd/amF+9kn7cLQ3r7ffmKWa8Lss9pc3dr9zdhTENHx0kfaVDfo5myoHpfjh/A0qHHp/TPWiZ/e0B6VCKgHC7/Wf9JXWX7WS2FSiDYQETpxkw1qCAXi7bBrhbq03ay8Vg9mdUaHGN1eyy695RMRJnGT7qGQGq87Ge9VLQIbedDWVPrveHt9+UrgXxdxzfi5fAwmcK4oZMwsqEyHvP0PmsSTJrDpdkM2HvVZsmE8/G8+bD/vc+3fi/CDbPz4+UWvjcY5lE57LfOc2o31wlO5lHp/SsMzymDusZf4+IcxmPz9c1aESLfcI6F+vglTs5dZthnnLOU7HBsPgxzjxy/X8O+bBc/I2++L5fnsrbXyX6n85m8eV0PXrvda0AFKOPvwOn37+XfkePPI7uD103u7Ov54O/e9r07+RmWQc9Dc/y/kJvrm1z6E2Yw9qA8+uTOKosrC/aqERlQn1gXsUN6sj52kX48G+pSUBGA0awG2K3VHNL18SuhLiWV6nNbe0Z5AFK47Zwli36etR3WXleWPo9XVRi8+b58yd+vy+qS10qo3q11PpHX9OXJfN22NpdWWV3arYUq02TtuVnHgHwx962zrH8/K0mVs1qie4R5UH+X1+hts3t9IUNW+BVChA1dUzCTPcjM82MbcnUb/JWoEXGkRyWECCfBkEjhav6U6CQJVEKIcJIB3xUS9pqr+VOiayTrTwgRNriLE4p9IEPfS4/Kh6RHJYQQvlMMlVgjBWd9SJIphBBCBDXpUQkhhAhqEqiEEEIENQlUQgghgpoEKiGEEEFNApUQQoigJoFKCCFEUJNAJYQQIqhJoBJCCBHUJFAJIYQIahKohBBCBLX/DxeFBHyPGIGpAAAAAElFTkSuQmCC\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -125,13 +125,13 @@ "Text(0, 0.5, 'Probability \\\\\\\\ Measurement $" ] @@ -161,18 +161,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "data mean=9.778, standard deviation=0.916\n", - "data mean=9.936, standard deviation=0.832\n", - "data mean=9.656, standard deviation=0.864\n", - "data mean=9.894, standard deviation=0.916\n", - "data mean=9.688, standard deviation=1.425\n" + "data mean=10.207, standard deviation=0.945\n", + "data mean=9.369, standard deviation=1.253\n", + "data mean=10.015, standard deviation=1.038\n", + "data mean=9.877, standard deviation=0.938\n", + "data mean=10.186, standard deviation=1.194\n" ] } ], @@ -202,17 +202,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Whoops, try again\n" - ] - } - ], + "outputs": [], "source": [ "data # enter your work here\n", "pts=p.check_p01(data)\n" @@ -242,18 +234,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "data confidence interval is 10.264 +/- 0.507\n", - "data confidence interval is 9.788 +/- 0.482\n", - "data confidence interval is 9.519 +/- 0.505\n", - "data confidence interval is 9.963 +/- 0.468\n", - "data confidence interval is 9.980 +/- 0.460\n" + "data confidence interval is 9.687 +/- 0.427\n", + "data confidence interval is 9.986 +/- 0.417\n", + "data confidence interval is 9.796 +/- 0.579\n", + "data confidence interval is 9.995 +/- 0.475\n", + "data confidence interval is 10.086 +/- 0.545\n" ] } ], @@ -271,21 +263,19 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ef8542775f774fa19a2facaaf70114a2", - "version_major": 2, - "version_minor": 0 - }, + "image/png": 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\n", "text/plain": [ - "interactive(children=(IntSlider(value=505, description='N', max=1000, min=10), Output()), _dom_classes=('widge…" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -334,21 +324,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "invalid index to scalar variable.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mavg\u001b[0m \u001b[0;31m# =... enter your work here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;31m# =... enter your work here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mconf_interval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtstat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_p02\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mavg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mconf_interval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mIndexError\u001b[0m: invalid index to scalar variable." - ] - } - ], + "outputs": [], "source": [ "N=100\n", "data # =... enter your work here\n", @@ -388,14 +366,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "t=1.81\n", + "t=1.38\n", "df=200\n", "| p=0.05 | p=0.025 | p=0.01 | p=0.005 |\n", "| --- | --- | --- | --- |\n", @@ -454,22 +432,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'N' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdata1\u001b[0m \u001b[0;31m#=\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdata2\u001b[0m \u001b[0;31m#=\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_p03\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Documents/UConn/ME3263/me3263_F2018/experiment_00/check_lab00.py\u001b[0m in \u001b[0;36mcheck_p03\u001b[0;34m(data1, data2)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mN1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mN2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mmu1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mmu2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0msigma1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0msigma2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'N' is not defined" - ] - } - ], + "outputs": [], "source": [ "data1 #=\n", "data2 #=\n", @@ -488,21 +453,21 @@ "\n", "|mass(g) A |mass(g) B|mass(g) C|\n", "|---|---|---|\n", - "|43.8| 54.1| 41.9|\n", - "|41.1| 47.1| 40.9|\n", - "|49.4| 46.8| 44.6|\n", - "|41.4| 45.9| 42.4|\n", - "|45.5| 49.5| 50.2|\n", - "|53.5| 42.7| 44.2|\n", - "|43.7| 40.3| 45.8|\n", - "|41.0| 46.9| 40.2|\n", - "|44.2| 53.8| 43.8|\n", - "|35.2| 52.3| 44.4|\n", - "|48.5| 53.0| 46.2|\n", - "|47.7| 41.6| 42.2|\n", - "|38.5| 43.8| 37.1|\n", - "|37.8| 48.7| 42.2|\n", - "|40.8| 46.7| 46.9|" + "|34.6| 40.3| 56.1|\n", + "|40.7| 35.0| 59.3|\n", + "|37.5| 43.7| 51.4|\n", + "|45.8| 43.2| 50.2|\n", + "|41.4| 41.1| 46.4|\n", + "|44.2| 42.3| 56.1|\n", + "|44.5| 39.8| 43.1|\n", + "|51.8| 39.2| 55.1|\n", + "|47.5| 49.4| 46.9|\n", + "|45.4| 43.2| 39.9|\n", + "|36.4| 44.4| 51.8|\n", + "|46.2| 44.8| 46.2|\n", + "|43.0| 44.5| 49.7|\n", + "|43.3| 50.1| 54.9|\n", + "|42.0| 47.1| 64.5|" ] }, { @@ -588,9 +553,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/apt.txt b/apt.txt deleted file mode 100644 index 64aabeb..0000000 --- a/apt.txt +++ /dev/null @@ -1,7 +0,0 @@ -texlive-latex-base -texlive-latex-recommended -texlive-science -texlive-latex-extra -texlive-fonts-recommended -dvipng -ghostscript diff --git a/pretty_plots.py b/pretty_plots.py index eb78b6f..4956ea0 100644 --- a/pretty_plots.py +++ b/pretty_plots.py @@ -2,7 +2,6 @@ import matplotlib.pyplot as plt plt.rcParams.update({'font.size': 22}) plt.rcParams['lines.linewidth'] = 3 plt.rcParams['text.usetex'] = True -plt.rcParams['text.latex.unicode'] = True plt.rcParams['text.latex.preamble'] = [ r'\usepackage{siunitx}', # I need upright \micro symbols, but you need... r'\sisetup{detect-all}', # ...this to force siunitx to actually use your fonts