{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Auto Color Scale\n", "(back to overview [offline](../Main.ipynb),[online](https://nbviewer.jupyter.org/github/QCoDeS/Qcodes/tree/master/docs/examples/Main.ipynb))\n", "\n", "\n", "[read on nbviewer](https://nbviewer.jupyter.org/github/QCoDeS/Qcodes/tree/master/docs/examples/plotting/auto_color_scale.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TL;DR\n", " * use `plot_by_id(run_id, auto_color_scale=True, cutoff_percentile=(0.5, 0.6)` to enable automatic color scaling\n", " * this maximally cuts 0.5% of the low valued data points and 0.6% of the high valued data points.\n", " * You can also use it for matplotlib with the supplied auto scaling function. \n", " * Set your defaults in `qcodesrc.json`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents\n", "1. [Introduction to the problem and risks](#Introduction-to-the-problem-and-risks)\n", "2. [Using the automatic color scale](#using)\n", " 1. [Example 1: outliers in homogeneous data](#case1)\n", "3. [Details of the calculation](#details)\n", "4. [Cutoff Percentiles](#percentiles)\n", " 1. [Example 2: data on noisy background with outliers](#case2)\n", "4. [Defaults and customizing the auto color scaling](#customizing)\n", "5. [Using auto color scaling in custom plotting](#custom_plotting)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction to the problem and risks\n", "It is a fairly common issue that a heat map plot does not properly show the right details due to some outliers that push the range of the color scale beyond the desired limits, as it is shown in the image below:\n", "![](./files/auto_color_scale_swamped_small.png)\n", "Here there are two pixels (black and white) corresponding to high and low outliers. The remaining data is uniformly displayed as red and the actual structure of it is drowned. \n", "One can of course specify the limits of the color bar manually to view the full structure as shown here:\n", "![](./files/auto_color_scale_clean_small.png)\n", "But for measurements that takes a lot of time to perform, manual intervention becomes an unwieldy task.\n", "\n", "\n", "In this notebook an *automatic color scaling* is presented that tries to empower the experimenter to circumvent this difficulty, while keeping possible risks in mind.\n", "\n", "\n", "The risk narrowing the color scale is clearly that the image representing the data saturates in some regions and is no longer fully representative of the actual data. Especially for automatic color scaling this can become risky because without a theoretical model describing the specific measurement one cannot scientifically argue for the choice of the new limits of the color scale and possible meaningful features might be disregarded.\n", "\n", "\n", "For this reason automatic color scaling is deactivated by default and has to be activated manually." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using the automatic color scale\n", "The following section presents an example of the usage of the automatic color scaling with the new `DataSet` and the `plot_dataset` function for plotting. Let's first make the necessary imports:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# store data in a separate db in order not to spam the actual measurement db\n", "from qcodes import initialise_or_create_database_at, new_data_set, new_experiment\n", "from qcodes.dataset.plotting import plot_dataset\n", "initialise_or_create_database_at('../example.db')\n", "\n", "# create an experiment for this tutorial\n", "new_experiment(name='tutorial_auto_color_scale', sample_name=\"outliers\")\n", "\n", "# import methods for creating datasets with outliers:\n", "from qcodes.tests.dataset_generators import dataset_with_outliers_generator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exampe 1: outliers in homogeneous data\n", "Here we basically reproduce the images of the introduction, where we have some smoothly structured data with some outliers that lie far outside of the range of the remaining data:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# create a new dataset with outliers\n", "ds = dataset_with_outliers_generator(new_data_set('data_with_outliers'),\n", " low_outlier=-3, high_outlier=3,\n", " background_noise=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "by plotting it simply by `plot_dataset` we get the full range" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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p9ZXGzTWSTif4wXi5QiSkqg7NHMdxesoCLGkbRBrvyZvZnk3L4DiOU0RwLzmYCjyFxpW84zjOoDOovfQUXMk7juO0wYC5Q+z+xZX8CFBxLK46ZZ7/OmQqan/ewG1R4opy5Q6SlhiRz5WrhEy5rSr1AKQvMMrNXbGu1MFYyB+QzcvfKwxzc43jOM7IYjB/eHW8K3nHcZx2hBWvw4srecdxnLaI+QOx8KU7XMmXpcwClTrq6uNiptQFQtXbX9CAHAFqWyCVU0S+Rb3Iw1maDMXjd2njB3nth/RrUNV2DgX285z667DTQ7mFU70gDLy6knccxxlJwjx5V/KO4zgjS5GLh2HAlbzjOE4bvCc/ovR17nmh16s66kqdz92D+lPNpLXMUa/Lpp1/PNWZWRnbcV67iub557arRNCRWua5F7Q1va5qDs56hSHmN+/mq2tcyTuO43TAzTWO4zgjiiGes/FNi9E1ruQdx3HaEBZDubnGcRxnZPGBV2cgyRv3qmfguNoCoWISo0j1oE3Jg5w1DRJXdRCWv5grfeFaqaoSB+8LFzP1ceFULzAT88178o7jOCNLnT8ideNK3nEcpw1h4HV4VeXwSu44jtMHfODVKUd6HIlqZdZUbv4Cn97XEwquWGyJxUTJdRUGKMkrMt0mXjnoSFUHZSUGcPLqSpe/fw7Oesl8nyfvOI4zmviKV8dxnBFngc+ucRzHGU2CgzJX8sPNUNnJawjaUdc891qCniTOnS9bbuI883IOxvJEajboSHF8lhLPVd5YRymber8cnPUGQ8x1twaO4zijiRlDvRhqeCV3HMfpC2JB4ta2FGmypEsk3SzpRkkf6Yf03pN3HMdpg9Gznvw84Egzu1bSssA1ki4ys5t6UXgRruQdx3E60IuBVzO7H7g//v2EpJuB1QFX8k7vqMNpWeG4Zx2D13U47aL6YqLKzsxyyi0TWSr/HlRbDFZ14VSxrNUcnOVRd2SoXgcNkTQFeA3wl54WnIMrecdxnDYYMDfdd80kSTMy+9PMbFo2gaRlgF8DHzWzf/dGymJcyTuO47RFZfzJzzazTQtLkhYnKPhfmNlveiFdJ1zJO47jtMHozYpXSQJ+AtxsZt+uXGAiruR7QR0LlKAWm3Qti7wq2p4LV44ll5u+6KaIWoKGJDp4g4Lxi0Q7fcjfvzGF5IVTle3YBQFOcoqtO9B2jyJDvQHYF/i7pOvisc+Y2Xm9KLwIV/KO4zhtMFNPevJm9id6EsesHLUuhpK0o6RbJc2U9Kmc82vGxQF/lfQ3STvXKY/jOE5ZwsDr+KRtEKmtJy9pPHA8sD0wC5gu6ZwxE/8/C5xpZidI2gg4D5hSl0yO4zjl8RivRWwOzDSz2wEk/RLYjYUn/huwXPx7eeC+GuUZXOpyZlaVimMNuQ666ogD0YsyKwcNSZwnXiboSKKdvrjY9ODgVQOslHFwltusAQoQkldb3Tb/OqlTya8O3JPZnwW8bkyao4ELJR0OTAS2yytI0qHAoQDjV1yx54I6juO0Y5hdDdcpeco0ij2BU8xsDWBn4FRJi8hkZtPMbFMz23T8MhNrENVxHCef1orXlK0OJG0h6fg4bvmwpLslnSfpQ5KW75S/TiU/C5ic2V+DRc0xBwFnApjZlcAEYFKNMjmO45RmAeOStl4j6XzgYOACYEdgVWAjwnjmBOB3knZtV0ad5prpwPqS1gbuBfYA9hqT5m5gW+AUSa8gCP1wjTI5juOUwgzmLmjMXLOvmc0ec2wOcG3cviWpbce4NiVvZvMkHUb4BRoPnGRmN0r6IjDDzM4BjgR+JOm/CKacA8yKY984BVSM7FQiAFBBwqKCU/NXjExVboVRchmlIkPVkn9RCgeukxdOpS8wqrpwqoyDs6qDsWUidpUlmGuaUfI5Cr50mloXQ8WVXOeNOfb5zN83EVaBOY7jDCw9WvHaNZLeCXwdWIXwmyjAzGy5thnxFa+O4zhtGZAplMcBbzOzm8tmdCXvOI7TlubMNRke7EbBgyv50lT2w9S007E6HIw1vUCqVCermjOzQjt1VQdhyfnzj1d1cFZugVGJhVO5NVV06NdAr7pMAJOamCHpDOBs4NnWwRR3xa7kHcdx2hBm1zTul2Y54Clgh8wxA1zJO47jVKGO8H+lZTB7X7d5Gzc0OY7jDDoLUNJWF5LWkPRbSQ9JelDSryWtkZLXe/KjTEX7efPjD6nBKUqUWVh/7kTv5PxVg45Uz5+TtKKDs8pBw2taU5B7tMae9oDMrjkZOA14T9zfJx7bvlNG78k7juN0YIGNS9pq5CVmdrKZzYvbKcBLUjK6knccx2mDmZhn45K2GpktaR9J4+O2D/BISkZX8o7jOB1o0gtl5EDgvcADwP3Au+OxjrhN3nEcpw0DYpOfY2ZtvU0W4Uq+Lqo63YIeLPypUE+Zuiq2teoCn8rXFCpHhkp10FU12lK5/DnJKjs4yx8QTR2MLSKvXbmLpgoLqNev4QAo+b9Iug44CfhjGUeObq5xHMdpQ9NBQyIbANOA/YCZkr4iaYOUjK7kHcdxOtD0PHkLXGRmexKCiOwPXC3pMklbtMvr5hrHcZw2mMG85oKGACBpZcLc+H2BB4HDgXOAjYGzgLWL8rqSHwWq2plL0M8FUn11ZpYcoSN9gU+ZQBpVA3GkOggrF18lfeFUmdpSA5RUHb/oJQNgk78SOBV4u5nNyhyfIemH7TK6knccx2nDIPiuAV5eNNhqZl9vl9GVvOM4Tgfq/lJIYH1JHwOmkNHbZvaWThldyTuO43RgAPzJnwX8EPgxML9MRlfyRfTRzt3fukrMM2/awVgd8/QrX+sSRv1S4w81ODgr0f6qQcPzrnVx7zfVql9t/KFXmPXOJi/pJOCtwENmNrVE1nlmdkI3dfoUSsdxnLaI+QvGJW0JnALs2IUQv5f0n5JWlbRSa0vJ6D15x3GcDvTKJm9ml0ua0kXW/eP/H88WB6zTKaMrecdxnDaU9F0zSdKMzP40M5tWWQazRebBS1oiJa8recdxnHZYsX+lHGab2aZ1iSJJwDbAXsDbgJd2yuNKvgdUXiBUhsoLfHooS6vIosG8GhyMVXW6VWqQOY8ykaVSB2MLstfi4KxUtKZ0lCdAYWSpNLmKFWv/Z7o0PbtG0usIiv0dwErAh1jYdFOID7w6juO0wXo78FoKScdKug34CvB34DXAw2b2UzN7LKUMV/KO4zgdMEvbOiHpdIKLgpdLmiXpoA5ZDiX4qjkB+LmZPUK5jyw31ziO43Sih7Nr9iyZ5WXADsCewHclXQIsJWkxM5uXUoAr+X5TNehFHVRdTNTw+EPloCNQbuFUbv7Ug310cJZbTUH+igvHcsdKSjwY5d6A/i+GasqtgZnNB84Hzpc0gbCQamngXkkXm9lencpwJe84jtOBAXBQhpk9A/wK+JWk5QiDsB1xm7zjOE4HemWTL4ukfSQtoqfN7N9m9lNJ60raql0Z3pN3HMdpgyEWNBc0ZGXgr5KuAa4BHgYmAOsBbwZmA59qV4Ar+UEm1f5dxulWH23qyesHahoTqGVOfS12+oKCqzo4K+WgrFrQ7fwy06ljnn0vaWrUzMy+J+n7wFuANwCvAp4Gbgb2NbO7O5XhSt5xHKcdDQ68wvODrxfFrTS1foNI2lHSrZJmSsr9pJD0Xkk3SbpR0ml1yuM4jtMVlrgNILX15CWNB44HtgdmAdMlnWNmN2XSrA98GniDmT0maZW65HEcx+mWAYgM1TV19uQ3B2aa2e1m9hzwS2C3MWkOAY5vLc81s4dqlMdxHKc0BixYoKStDiSNk/TebvPXqeRXB+7J7M+Kx7JsAGwg6QpJV0nKdaYv6VBJMyTNmD/nyZrEdRbBtOiWm65gy0G26NbP+ktNfUutv50MyZ/zytlK1JOX1LTIVkr+oqSp5Va8L7nXv2DLk6lnve8ystSAmS0ADus2f50DrylzOxYD1ge2BtYA/lfSVDP710KZgj/maQBLrjl5QC1fjuOMKnXMgS/JRTGQ9xnA8z1dM3u0U8Y6lfwsYHJmfw3gvpw0V5nZXOAOSbcSlP70GuVyHMcpR/NK/sD4/4cyx4xeRIaStAWwD/BGYFXCHM0bgD8QvKI9XpB1OrC+pLWBe4E9CP6Qs5xNcLxziqRJBPPN7Z1kchzH6R89NP10SV5kqFTaKnlJ5xN6378DjgUeIqy22oAQneR3kr5tZufkCDVP0mHABcB44CQzu1HSF4EZMc8FwA6SbgLmAx+PrjQrUyqQRRkSF6hUDqRRVEiZxUy1LKaqGIijjmtVsX7I/xzvq4Oz3DJLPKyJ9oQ6Fj3VVW7lxVS9pOGevKSlgSOANc3s0Dgz8eVmdm6nvJ168vua2ewxx+YA18btW7EHnouZnQecN+bY5zN/WxT8iE6COo7jNIKB1TRzpgQnE9wabBn3ZwFnAR2VfNvZNS0FL2liy0mOpA0k7Spp8Wwax3Gc0SVv9lPijKjesK6ZHQfMBTCzp1MrTJ1CeTkwQdLqwMXA+4BTysvpOI4zhDS/4vU5SUu1apG0LvBsSsZUJS8zewp4J/A/ZvYOYKNuJHUcxxk6mlfyXwD+CEyW9AtCZ/sTKRlTp1AqzrLZG2jFJHTnZs5g0IvB0NRoR3V5sazsMTSxgFLtrxhFqiaPmWWig/WE1mKoBjGziyRdC7yecAU+kmoqT1XUHyH4mPltnCGzDnBJV9I6juMMGQOwGAqC//itCD87iwO/TcmUpOTN7HKCXb61fzvw4fIyOo7jDCENz66R9ANCoJDT46H3S9rOzD7UJhvQeZ78NIIN/u855yYCuwPPmtkvyovtOI4zHNQckySFNwNT47RzJP0UWEQv59GpJ/8D4HOS/oOwyrUVemp9YDngJMAV/DBRy2KqehZzJUeWKuCOXX68yLG1/3BwfuJEuYo+25MXTlVduFXGpl+mgBJKLHXhU9OLqXpG/YOqKdwKrAncFfcnA39LydhWyZvZdcB7JS0DbMoLbg1uNrNbuxbXcRxnaKjPw2THmqXfE35ilgdulnR13H8d8OeUMlJt8nOAS7sT03EcZ8hprif/zaoF+DRIx3GcTixoplozuyy7L2k5SuptV/J9ppSduY6511UpZdPvvfxlrt/a5x5SUHBeXVXnfuckbdrBWW49ZR6KauMHZTw31mW/7wkDME9e0qHAlwjm8gWEK57karhUZKg4o8ZxHOdFRV5Es1JRzlrlSDtKulXSTEmfKiHCx4FXmtkUM1vHzNY2s44KHhKVvKQtozvgm+P+q+O8TcdxnNGnB24NJI0Hjgd2IriF2VNSqnuYfwJPdSF5srnmO8D/Ac4BMLPrJb2pmwodx3FepGwOzIyLSZH0S2A34KaEvJ8G/izpL2Qck5lZx0WpyTZ5M7tHC9vz5qfmdRzHGWZKLIaaJGlGZn9ajFENsDpwT+bcLMJUyBROBP4fYQFUqWHgVCV/j6QtAZO0BMGlwc1lKhppSjmS6iO1LLCpSNORpYqoYZC4soOz3HqaXkxVQBnnLomDtLlOy6D/0xmNMm4NZpvZpgXnytyRscwzs66CK6UOvH6AEEB2dcKvz8YsHFDWcRxndOmNq+FZhJWqLdYghFdN4RJJh0paVdJKrS0lY+piqNkEN8OO4zgvOnrku2Y6sL6ktYF7gT2AvRLzttJ9OnMsaQplkpKPQh0OTMnmMbNdEwV0HMcZXnqg5M1snqTDgAuA8cBJZnZjYt61u6031SZ/NvAT4Pc0tvbLAcrZtCvXlXOsDpv+gDo4q2P8oJTpehAXU0Hf7PelnJbV7SayR8Wb2XnAeWXzSdqvoLyfdcqbquSfMbP/LiWV4zjOCJC60KlmNsv8PQHYFrgW6JmS/56kLwAXsvAczWtLCOk4jjOcNBw0xMwOz+5LWh44NSVvqpL/D2Bf4C28YK6xuO84jjPSDEBPfixPEeJ6dCRVyb8DWMfMnutaJKeQnszzTqWfc/qr1tUnB2dlqq/NJj4s8+yL0ubWVfrEmHoGyGlZw0o+41cewtT3jYAzU/KmKvnrgRWAh0pL5ziOM8wMhk0+61d+HnCXmc1KyZiq5F8K3CJpOgvb5H0KpeM4o0/DSn6sX/kypCr5L3RbgeM4zrCjhieOS3on8HVgFYK9S4CZ2b9L/E8AABK3SURBVHKd8qaueO36V8RxHMepzHHA28ystM+wtkpe0p/MbCtJT7DwB0vyr8iLlqadfkG6IbGMrJUHU6vm7/0CpSIqR/FKLRSaX0yVSzWnY1UHnmtzkNYNzdvkH+xGwUPnnvxEADNbtpvCHcdxhp7BGHidIekMgveB7Ljobzpl7KTkm2+a4zhO0zSvCZcjzI3fIXPMgMpKfhVJhT6MzezbSeI5juMMM83Prnlft3k7KfnxwDJ0aUmWtCPwvVjOj83sawXp3g2cBWxmZjPy0rwY6auDrSpUdlCWnn/9D/5lkWO3nVAQXKeGBUqVF641vJiqMHs/7fdVqWq/76K6pmfXVKGTkr/fzL7YTcGZoLXbE5zlT5d0jpndNCbdsoRIU4u+vY7jOE0zGDb5rukUGarKz+PzQWujO4RW0NqxfIkwPeiZCnU5juPUR28iQzVCp578thXK7hi0VtJrgMlmdq6kjxUVJOlQ4FCA8SuuWEEkx3GcLhgABS5pF+CVBFfDAKRYWtoqeTN7tIpMeUU+f1IaB3wHOKBTQTHa+TSAJdecPACX+0VC43PiFz1UaH/PzV/VQ1nOsapLEvo5zz43f0H2ftrv+xngpEc0ba6R9ENgaWAb4MfAu4GrU/KmBvLuhk5Ba5cFpgKXSroTeD1wjqSiSOeO4zjN0Ly5Zksz2w94zMyOAbZgYf1aSKrvmm5oG7TWzB4HJrX2JV0KfMxn1ziOM1DYQMyueTr+/5Sk1YBHgKS4r7X15M1sHtAKWnszcKaZ3Sjpi5Lce6XjOMND8z35cyWtAHyDEPbvTsJklo7U2ZPPDVprZp8vSLt1nbI4juN0S9M2eTP7Uvzz15LOBSZEa0hHalXyg0jlBUZV6afTrNz6+zcY2bf8RTQ9QNjPyFS59fdefkgfpO1rFKu6GYCBYUlbAlOIelsSZtazQN6O4zgvTgZgDrykU4F1geuA+fGwAa7kHcdxqiCaN9cAmwIbmZX3qVznFErHcZyRQJa21cgNwMu6yeg9+RGgr06zcvMXHK/qoKxKmUXlNj1+UcSw2+/zqq+WHdXodKw0zffkJwE3SbqaknG2Xck7juN0onklf3S3GV3JO47jtKMGU4ykDYGTgU2Ao8zsm21FqBBn223yjuM4nej9YqhHCS7W2yr3FpJeL2m6pDmSnpM0X9K/U/J6T34QqGrTLso+iHPq83gxzbMvouK16puDtCIaDnpSN712a2BmDwEPRc+SKXyf4BrmLMJMm/2A9VMyupJ3HMfpQInf60mSsv63pkUvupUxs5mSxpvZfOBkSX9OyedK3nEcpx3lTDGzzawOT7pPSVoCuE7SccD9wMSUjG6TdxzH6UQPbPKSPiTpuritVlKCfQn6+jDgSYKb4XelZPSevOM4Tht6teLVzI4nxL0uV3+Il32sme1DCJN6TJn8ruQHmaqDjHnZmx6MzS2z4PggLqYqU24dg7R9XGBVKELVtuaXWi17zeumtKC3o8CSXgbMAJYDFkj6KMFtwSIzZsxsvqSXSFoixssuhSt5x3GcdtTgoMzMHiBEy0vlTuAKSecQzDWtcr7dKaMrecdxnA4MgIOy++I2jhA6NRlX8o7jOJ1oPmjIMQCSlg27Nic1ryt5BiCQSBn6ZKeHHjg4K1NZbrmpZaYX2bj9flAXI+WWmV5VGQbSpt+BpnvykqYCpwIrxf3ZwH5mdmOnvD6F0nEcpxPNx3idBhxhZmuZ2VrAkcCPUjJ6T95xHKcd1nu3Bl0w0cwuae2Y2aWSkhZDuZJ3HMdpw4BEhrpd0ucIJhuAfYA7UjK6kncKqWWsYhDn2Zctt0pdtY0JVAyOnVtmifpz66ouQh712PQ7Vdq4lj+QsAjqN4QreznwvpSMruQdx3E60HRP3sweI7gmLo0recdxnHbUP6haiKTvmtlHJf0+Rwoj+KU/0cyuKirDlbzjOE4HGhx4bdngi4KLTAJOAjYqKsCVvOM4TgeaUvJmdk38vzD8n6S2/mxcyY8CdQ3m5WXv12BsGQEKyy0vysJ1lUjb9MKt1LrqGqAcosVYpTEaH3iVtD7wVUKPfULruJmtY2a/b5fXF0M5juN0QJa21cjJwAnAPGAb4Ge8YMppiyt5x3GcTjS/4nUpM7sYkJndZWZHA29JyejmGsdxnDYMyGKoZySNA26TdBhwL7BKSkZX8gVUdto1qPTRwVlu9YNqv8+tq0TaOhZDVamn73XV8WI0r1kBMOt50JAu+CiwNGGu/JcIvfj9UzK6knccx+lE84uhpsc/55C40rWFK3nHcZwONGWuiZGgCjGzXTuV4UrecRynHQY0Z67ZArgHOB34C10YV2udXSNpR0m3Spop6VM554+QdJOkv0m6WNJadcrjFNDHmQKpU9F60nMyVdtK1VVx61c9Zeqvq67k+ivev16OEzQ3u+ZlwGeAqcD3gO2B2WZ2WbsFUllqU/KSxgPHAzsRJvDvKWns0tu/Apua2auAXwHH1SWP4zhOtzQ1T97M5pvZH81sf+D1wEzgUkmHp5ZRp7lmc2Cmmd0OIOmXwG7ATa0EWSf4wFUEH8mO4zgDRZOzayQtCewC7AlMAf6b4HI4iTqV/OoEW1KLWcDr2qQ/CDg/74SkQ4FDAcavuGKv5HMcx+lM/QudCpH0U4Kp5nzgGDO7oWwZdSr5XM8ZuQmlfYBNgTfnnTezaYQYhyy55uTGJ6w6jvPiISyGakzt7As8CWwAfFh6Xq0KMDNbrlMBdSr5WcDkzP4awH1jE0naDjgKeLOZPVujPD2hFgddg0rqc91Hn1dl6OvCqzz66Uwtt/4SaUex/l7SnBfKyuOmdSr56cD6ktYmLMHdA9grm0DSa4ATgR3N7KEaZXEcx+maBnvylaltdo2ZzQMOAy4AbgbONLMbJX1RUmsC/zeAZYCzJF3XaeK/4zhO36ljKmwfqXUxlJmdB5w35tjnM39vV2f9juM41em97xpJewOfjLtzgA+a2fU9rSTiK157wIvKTp9H1effbfrpNG3nLyJ5msWQ0ntzzR2EccjHJO1EmFjSbvZh17iSdxzHaYf1Pvyfmf05s3sVYWJKLbiSdxzH6US9A6+Fa4R6gSt5x3GcTqTr+EmSZmT2p8V1PrlI2oag5LfqXrj2uJJ3HMfpgBYk22tmm9mmuWVIHwIOibs7A5OAHwM7mdkjlYUswJV8TYxsZKk6GNGB2zwGcjC3iEEd5M2jzsti9GQxlJkdT3DaiKQ1Cf5n9jWzf1QvvRhX8o7jOG0QVsdiqM8DKwM/iK4K5hV9AVTFlbzjOE4neqzkzexg4OCeFlqAK3nHcZxODLFbA1fyfeZFv3CqDup6/xpey1SVobL/5zEoYwI9ssk3hSt5x3GcDpSYXTNwuJJ3HMdpi7m5xnEcZ2QxXMk71ShlenT7ff9o2sFX1SKHSC/lPteD9LAPr7XGlbzjOE4nhjloiCt5x3GcTriSdxzHGVHMYP7w2mtcyTuO43TCe/JOv2g82pFTD8OrQwIVn6uBHyR2Je84jjOiGNDjGK/9xJW84zhOWwzMbfKO4zijieEDr87wMJCBMJzhZ3itGWm4Td5xHGeEcSXvOI4zqriDMsdxnNHFAHc17LyYGfg5zl3iYw3O83hP3nEcZ1RxtwaO4ziji4H5PHnHcZwRxle8Oo7jjDBuk3ec0WNUB5Sdkpj57BrHcZyRZoh78uOaFkDSjpJulTRT0qealsdxHGdhDJs/P2kbRBpV8pLGA8cDOwEbAXtK2qhJmRzHcRai5Wo4ZRtAmu7Jbw7MNLPbzew54JfAbg3L5DiOszC2IG0bQJpW8qsD92T2Z8VjjuM4A4EBtsCStlQk7Sbpb5KukzRD0lZ1yd/0wGvewvFFrpSkQ4FDAcavuGLdMjmO47yA1RI05GLgHDMzSa8CzgQ27HUl0LySnwVMzuyvAdw3NpGZTQOmASy55uTBNHw5jjOy9HpQ1czmZHYnUqNHflmDU4MkLQb8A9gWuBeYDuxlZje2yfMwcFfcnQTMrlvOPjJq7YHRa9OotQdGr03Z9qxlZi+pUpikP8YyU5gAPJPZnxY7qXnlvgP4KrAKsIuZXVlFziIaVfIAknYGvguMB04ys2NL5J1hZpvWJlyfGbX2wOi1adTaA6PXpmFrj6Q3AZ83s+3qKL9pcw1mdh5wXtNyOI7j1ImkDwGHxN2dzew+ADO7XNK6kiaZWc+/qJqeXeM4jvOiwMyON7ONzWxjYGlJApC0CbAE8Egd9Tbek69Irq1riBm19sDotWnU2gOj16ZhaM+7gP0kzQWeBna3mmznjdvkHcdxnPpwc43jOM4I40recRxnhBlKJT/snis7yS/pCEk3xWXPF0taqwk5U0m9H5LeLckkDfz0tpQ2SXpvvE83Sjqt3zKWIeGZW1PSJZL+Gp+7nZuQswqSTpL0kKQbmpZloDCzodoI8+n/CaxDGJG+Htioabl6KT+wDbB0/PuDwBlNy131fgDLApcDVwGbNi13D+7R+sBfgRXj/ipNy12xPdOAD8a/NwLubFruLtr5JmAT4IamZRmkbRh78sPuubKj/GZ2iZk9FXevIrh7GFRS78eXgONYeDXgoJLSpkOA483sMQAze6jPMpYhpT0GLBf/Xp4c9yKDjpldDjzatByDxjAq+WH3XFlW/oOA82uVqBod2yPpNcBkMzu3n4JVIOUebQBsIOkKSVdJ2rFv0pUnpT1HA/tImkVYnHh4f0Rz6mYY58knea4cYJLll7QPsCnw5lolqkbb9kgaB3wHOKBfAvWAlHu0GMFkszXhS+t/JU01s3/VLFs3pLRnT+AUM/uWpC2AU2N7BtNJupPMMPbkkzxXDjBJ8kvaDjgK2NXMnu2TbN3QqT3LAlOBSyXdCbweOGfAB19T7tEs4HdmNtfM7gBuJSj9QSSlPQcR3N1iwVHWBNKdcjkDzDAq+enA+pLWlrQEsAdwTsMylaGj/NG8cSJBwQ+yrRc6tMfMHjezSWY2xcymEMYYdjWzGc2Im0TKM3Y2YYAcSZMI5pvb+yplOintuZvgDRZJryAo+Yf7KqVTC0On5M1sHnAYcAFwM3CmtXFNPGgUyS/pi5J2jcm+ASwDnBUjxwzsj1hie4aKxDZdADwi6SbgEuDjZlaL75GqJLbnSOAQSdcDpwMHWJyyMixIOh24Eni5pFmSDmpapkHA3Ro4juOMMEPXk3ccx3HScSXvOI4zwriSdxzHGWFcyTuO44wwruQdx3FGGFfyTikkzY/TOlvbFElbS+qJywJJS0m6TNL4hLSLSfqDpNmSpo459w1Jt0SPir+VtMKY89dIWkLSnIKyT5H07grtWELS5ZKGcVW5M0K4knfK8rTFOJVxu7PH5R8I/MbM5iekPYGw0nQ34AxJWUduFwFTzexVwD+AT7dOSJoC3BuddfUcSeNj2RcDu9dRh+Ok4kre6SmSjpb0scz+DbG3v1nsVU+QNDH6YJ+aU8TewO8y+T8uaXrMe0zm+BeAx83sCDO7AjgYOF3S8gBmdmFcBASLevLcCfhjpqxjJV0fHY29NJNuO0n/K+kfkt4a006Jx66N25bx+NbRH/tpwN9j/rNjexynMfxT0inLUpKui3/fYWbvSMlkZtPjyt0vA0sBPzezhYI7xCX367S+DiTtQPAHsznBydY5kt5kZpeb2TFjyr8SeGNB9QcCZ2T2dwT+K/49EbjKzI6SdBzBhfCX47kpBOdw6wKXSFoPeAjY3syekbQ+YXVoyw/P5oSvhzvi/g3AZh0vjuPUiCt5pyxPm9nGXeb9IsGPyjPAh3POTwKyXhx3iNtf4/4yBKV/eWqFko4C5gG/iPtLAGuYWcvPzHNAazzhGmD7TPYzoxfG2yTdDmwI3AF8X9LGwHyCz5oWV2cUPGY2X9JzkpY1sydSZXacXuJK3uk181jYDDgh8/dKBEW9eDz+5Ji8T49JL+CrZnZiN4JI2h94K7Btxg/LG4E/ZZLNzZybz8LvxFifH0b4AngQeDWhndkgKGPbA7AkwxEoxRlR3Cbv9Jo7CSHYkLQJsHbm3DTgc4Re9dfHZoxRlsZLain6C4ADJS0Ty1td0iopQsQgHp8keLx8KnNqR9KDsLxH0jhJ6xJC591KiJp0f+zh70sIrVckw8rAw2Y2N7E+x+k5ruSdXvNrYKVot/8gYWYLkvYD5pnZacDXgM0kvSUn/4XAVhAGT4HTgCsl/R34FcE/fQrfj2kvilM9fxiPbw1clljGrTHt+cAHzOwZ4AfA/pKuIphq8nrvLbYhRFlynMZwL5TOQBF96R9hZvvWUPYawI/MbKdel11Q32+AT5vZrf2oz3Hy8J68M1CY2V8JM1k6LobqouxZfVTwSwBnu4J3msZ78o7jOCOM9+Qdx3FGGFfyjuM4I4wrecdxnBHGlbzjOM4I40recRxnhPn//SatT0owGRsAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, cb = plot_dataset(ds);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By adding the keyword `auto_color_scale=True` one activates the scaling:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, cb = plot_dataset(ds, auto_color_scale=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the actual structure of the data becomes visible.\n", "\n", "Please note two further details:\n", " - The triangles that appeared at the top and the bottom of the color bar: These indicate that the color bar does not cover the full range of the data spectrum.\n", " - The clipped regions are marked with colors that are clearly not part of the color scale in order to clearly separate those regions where we cannot make any claim about any structure.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " ## Details of the calculation\n", " \n", "The new limits are calculated in three steps:\n", " - Determine the inter quartile range (IQR), i.e. the distance between the first (q1) and the third (q3) quartile (see image below).\n", " - Expand the region spanned by [q1, q3] by 1.5 x the IQR (yet not beyond the original limits of the min/max of the data).\n", " - Limit the amount of data points clipped on each side to an amount that is described by the lower (pl) and upper (pu) cutoff percentiles. E.g. for pu=pl=0.5%, no more the white and gray areas may not take up more than half a percent each of the total area.\n", " \n", "To understand how this works lets consider the histogram of the previous example:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "data = cb[0].mappable.get_array()\n", "pl, q3, q1, pu = np.percentile(data, [97,75, 25, 3])\n", "plt.hist(data, bins=100);\n", "# IQR\n", "plt.axvline(q3, color='k')\n", "plt.axvline(q1, color='k')\n", "# cut off\n", "plt.axvline(q1-1.5*(q3-q1), color='b')\n", "plt.axvline(q3+1.5*(q3-q1), color='b')\n", "# limit through percentiles\n", "plt.axvline(pl, color='r')\n", "plt.axvline(pu, color='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The black lines mark q1 and q3 and the blue lines correspond to the interval widened by 1.5 x IQR. The red lines present the lines of the percentile cutoff, in this case 3% on each side. The red lines limit how much of the data may be cut away through the blue lines. In this specific case of 3% they are not limiting the cutoff through the IQR estimation. Please also note the barely filled bins at +-10, that represent the outliers. The new color scale therefore roughly corresponds to a threefold increase of the steepness of color gradient." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cuttoff Percentiles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Limiting the effect of an automatic color scale by the cutoff percentiles can be very important. To understand that consider the following example:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 2: data on noisy background with outliers" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# create a new dataset with outliers\n", "ds = dataset_with_outliers_generator(new_data_set('data_outside_iqr'),\n", " data_offset=2, low_outlier=-2,\n", " high_outlier=3)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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vVFK1xyB3WrOunm+3CVDcbJ37OnWyLRvv/0jKms2mrOfixXY/h/0HTI745f15+M0z/C+8h9wt86+1NvOD4NRd9A77Fl190iVu8/Ie+4MgPc5glKfFt91ZiDHfWcG3ZYctA2oHHzVlhYWn24r7coIwp9sIfN1hnwsAOdWRSne2W9ndPzg8a39OY7pyWvYDkYu77LFW1m106/7f2jdc3Z5m2Luvyp0/nN9Q3cKpa46p6qV532dLsS8UkWmkEernqmpbrRgdN9cEQRCMZBSoNfhfw9tUPQDcCjyvTd1+nFExyIvIdSJyt4jc3V0zulVBEARtQ1F6tNrQZyCyKPRp2b/Hk0p0PNju/nfcXNMImU3rBoCppVmxHCgIgmGlmVn6AJwKfDGzyyfAN1S1KSmKE2FUDPJ9EIFSXxtycaPVg6qc3rguWmGSFWOsJjkvOUfsm0TiONhqntMMoMfeLIVfuWJ/VA9b+3/hFGsP7R7n2y6ru2yUe8Hpq+zxHWTVo9bJW9pj+yT7D5kyAD1k61YPO7Zr9W3yjf+a53nohjYfKM6zvp68uVpt1ZqGtikF/1rJEpvXozbOPp66/2BD+wHoPnWKKSvv2e/Wra6zpmu9zLede8+b52QunLfE79hjdlFCz8kTTVn5QT97X3XvPlOWnJ8Tf7TcL24GRam2YIGKqq4g1aofVjpurhGRr5LqYCyRNBPSUAXNgiAIWkoNbegzEun4TF5Vr+l0H4IgCPJI5SVH5gDeCB0f5IMgCEY6I3WW3ggxyAdBEAyAAj2jWP5l1A3y2lWk58y+TtXiAccZejzHmegEbRRm2cxlyYocR9p8xxk3xYkC3eLnDU4e3GDKKhf2TzaVUlptM7jpY0dsvZ3WwQkgs62TtrrDBt0kS5ygGSDxoigdp1llgRMcAxQO23wNxV3WadYuPL9rboCUU7f7TJvFL/EiW4FkqnVy6un2Xkm2+ZLv1dXW+S7n2cCzytN9v13Xentda7evsmVexDNQmOssVHjIzyDYfW7/pFSQ/MRJQ7B4ni3DX2hQHG+DjSpn5EjAO0FWSV50bQtQNMw1QRAEYxYFJ/h81BCDfBAEwQCkEa+jlxjkgyAIBkSoDqcyXosZnYN8PyeIF4zTvdgPhiovtvZnTyBMHCEvgOoaa1OvPtmKmZUm2eAOAC9XdvLLnFwSjq+gus8GsxR2+kJStWPWfl69ygp9JgccZUtAnGOoOWJuzDnJbe+d18pZ1k6dZ+70Hqvm3prtFiRnC56tvnjInr9kjx+M5G1V73/IFp7sB37J+WfZMkdFNM8noI46ZmWhI1yX40Asr7XCZ9rtC8wVb7d6WnKO7b/ebX0CAHqerZs8av1qOt1/hpKJ9lirUxpUtjwBUsdrDPJBEARjknSdfAzyQRAEY5ZazOSDIAjGJjGTH24OH0V+vqJv2al2PXP53rVu8+qj1n4vTmakwqKcteOOnVq7nfXkE/wMQrUZdu04p+esB95kE5xwhbX/9+QkVyjtsuvnS479PS9piZ5k135zsk0ukSew5mUQkqV2jfVIJdllYyqqs6e7dQvlsi0r2uOvLLT3KpAK7/Vvf9RmgSrc7SvT9ly+zJSVV1uBL+3JySw11/arus25/4CCY3+XPU4ykhy/VG35alPWc9X5dj93+L6qniutX6m8wUm60iIUodp5ma8TZvQN8kEQBMNMmGuCIAjGKIrQrf7b8mggBvkgCIIBSIOhwlwTBEEwZgnH6zAiSUIyvq8gmE63DsLuJb4zs3yXE6DiZOvRnMxQlXMXmrLSRhsgdDwnGKt4yAaYJA9tdOt6YSu1ku1X+WFfDK02yzoJk702mEccITLACMEBlB7easo0J8Cmcs5Cu6+cYB4XZ7PtCJBKS52tONm9krW+aBcz7bnuWWIFumo5Tu6uzVa4rbLOBt7JZdbxDlB+2HGSjuuy+z/Nd/wWHrHZnhLHmQygZTts9CyxQW7lnX7GsGSOI2jnCAomM3wntxfQpU6AVKtQFaoaM/kgCIIxSy1m8kEQBGOT1PE6eofK0dvzIAiCYSAcr8NNqUjSL/ipe7q1x5Xv8wN0up9kM8gXf7rClFVPmeS2Lx60wUSV06yQmNR8S3FtnLX/e4E0AD3LTjNlpb2PmbLqfD9phziJT6rn2AQlha2+wFlpixVDqyyyttfCQZvIBKC0yW63Z749V7lG9Q6/Ietee/w9F57p1i3tsYFnxeU2IK/nEpsIBKD7NCvyVthkg5lqXf4jW5jgCHTtd8TUZvticj2LrQ+rtNsJhgM4bIXbyvvtfVmb4Yv8uWJkk2zinbz72gvok4rvV2oV1VgnHwRBMDaJiNcgCIIxTi1W1wRBEIxNUoGyGOSHj0oV3dN3TXH5wKO23nQrpAVQOG5tdwWnbsURjAJIdtj1zDUnOXbpPGv7B5CtTsLl+Tlrl5019eIkmyw84tvUmXuqKVIvEXeeaNYvf2X3tccmUfb8HADl7bZfpdXWHsuLreAV0PAC+CZycze1FXEExnJjEpwE8Zxj7fflLU494Lhjk6881Yp2le9+2G3PSY7wXZf19STO9QdIvLXn2+zaefCTyegRe13zkmtX5lm/THGDXeevOc+wrttoymrn+/dgK1CEnpA1CIIgGJuoEsFQQRAEYxeJYKggCIKxihIz+SAIgjFNOF6HER1fpueCM/qUlVc7olFOViKA4jYb4NLtBB0Vj/gZdLrPskEj5S4rBFVZYbPfABSmWgeZlnynTmGrdXJ64kyekBhAabcN0NHxVnSruNMJmgE4c6Hd12wbIFO49T63uZw8w5RVdllnrOR4SIc1/sTrQ9Fel9pBx8kP1C52guw2OU72nMxS5T02mEiO2XuwOyeYyhN+Kz6w0Vb0MpMBctjJGLbQBr4BsGOPLXNE7vSQvf8AauMcR78TzFR72A9oVGdfnuO4VSgSSUOCIAjGKgr0hHZNEATBWEVCTz4IgmCsokTE6/By+CjJbf1swLNONtW0y9qeAdht7dxSs3bSwlqbHAMg8RJsnGKDO/SpF7ntq57tNM8mXrN1PZ9A8ZAf4KI7rP1bl8w3ZZVTfDutR8mxHXu2e/ADfEo5SSsapgnTa1MJRrzKXjKZbt9XU3BEu3ASWSQ5Ym4ctTZxjttguHITtufufr4rAE38GWnZudc0J8EJsx1BvoOO/+dgTtKQbidxjOP/4JKz/fb3O8J7E3Ke9xYxmmfyo/fnKQiCYBhQFWqaNPQZCBE5TUR+LCKrRWSliPzRcPS/rYO8iDxPRB4SkbUi8qfO9/Ozg75PRFaIyAva2Z8gCIJmSR2vhYY+g1AB3q2qy4ArgLeKiP+60kLaNsiLSAG4Hng+cDZwjXNA7wO+oaoXAa8EPt2u/gRBEJwYaY7XRj4DoarbVfXe7N+HgNVAzjrV1tFOm/xlwFpVXQ8gIl8DXgKsqqujQO/C66mAr/5Uh0wYR7K0729F1Vtnfu+Dbnu91P5wesmpq05yDPATcVROtglG8myf4gmfPebbaauO/bpwzBF9yrHTareXNHyz7dN4m7ABQE92RLOmW3GqYo7/ousBu6ZcT7X+k7wF8V5y7aEuV85dk++UVXfade7Vp17gti88ZG9dL5G6rLPnH6B2jrWfF3bYmA4cITDwYyXKW60YWnXDJn//F9nnQhw7PYA6xyDT7b1SW7rAbV9YYZOpcIq9L7x7FYCJNkGK/Ox+v24LSB2vDd94M0Xk7rq/b1DVG/pXEpGFwEXAHUPt32C0c5CfC9RHKW0BLu9X54PAf4vI24GJwLO9DYnIdcB1AOPKjTsJgyAIWkETEa97VPXSgSqIyCTg34F3qqofXddC2mmTb2RxwzXATao6D3gB8GURMX1S1RtU9VJVvbRctCsWgiAI2kVvxGsjn8EQkRLpAP9vqvofjexfRK4Ukeszv+VuEdksIreIyFtFZNBZbzsH+S1AvV7APKw55k3ANwBU9XZgHOAkAQ2CIOgcNZKGPgMhqa32X4DVqvqJRvYrIt8H3gz8EHgecCqpj/N9pOPlf4rIiwfaRjvNNXcBi0XkdGArqWP1Vf3qbAaeBdwkIstIO52TASMIgmD4UYWeWkvmw08GrgV+JSLLs7I/V9VbBmhzrar2Fws6DNybfT4uIgNOjNs2yKtqRUTeRvoLVABuVNWVIvKXwN2qejPwbuBzIvK/SE05r1cdONpDCwk90/o6Xro2O9macgTKvCxK6jg+8zLo6DLrICtttoJNtT22TwA9ly8zZbLQFxjj7pWmqDDTin7pKbYMQJwgMbxsPV4gDrgOXS3YV9LupfPc5qUVG2zhZpsBCKzTLg/PcdqUNFUzjlsnGKdw2A+Gqmzf4dR1AsecACsAvfMBW7Z0kSmTvY4zFv+6eA59ccT0AKrjbL9KG/17uOY8A7rBcb7fYx3XALJood3mpi2mrOI8K+AH5BVzzit+F5oiNdcMfZBX1Z/R3B2IM8A3XaetEa/ZL9Qt/creX/fvVaS/bkEQBCOWTke8ishvA38DzCL9oRBAVdXKwvZj9MkaBEEQDCNNLqFsFx8DXqSqvob5AMQgHwRBMCCtMdcMkZ0nMsDDKBzkpbtC16a+ImPHF1ibdFdOpnicwJ+eRU4gyQN+0IjMtPbj6qmOwJm/d4o/XWHrzvFFu3SSDbKqOYkY8hImuGJa80+1ZTW7HwDZZv0XxY02wEqmT3PbV5bZYJjCaue8NqMalhfN5DFEMbPCRmtnr82b5bf3gol2OvbznPsyOX+pKatOLJuy4gF/WXXx9lWmTM9aaCse8MXwiqvsdek+73S3ruerKc2x5yXJEXOrbbBJfmqX2OMvr7HnH4Aue17yhONaxQjI8Xq3iHwd+A7wuMOwkWWYo26QD4IgGE7S1TWD6tK0mynAEeC5dWUKxCAfBEEwFEZC+j9VfcOJtu24oSkIgmCkU0Ma+rQLEZknIt8WkV0islNE/l1E/LXL/Rh1M3ktFeiZ29cuXrrD+iNqVV9cqXKlI1C2zxF9muzbqSsPrzdlyYWOPXWWv/bbe+nrme/HMsjPrehVMs5JGu4kQgEoLjnTFm51Fg7n2NQ5yUZMV5xE3l6cAEDhkF1/L1P889owzozKEzLLqTrAdm2Rl3S8lhMT4LkKqhOtr6W0wV+4rV32zvCSyehxP35DHNEunAQ1XsJtgNoie1zlbb79XsfZBB263rGzH/GF9xLHf1HYb+vqo37SkZ6LbTLzwszJbt1WyH+NkNU1XwC+Avxu9vdrsrLnDNYwZvJBEASD0IqkIUPkZFX9gqpWss9NgBPtaIlBPgiCYABUhYomDX3ayB4ReY2IFLLPawD/Fb4fMcgHQRAMQqtUKIfAG4GXAzuA7cDLsrJBGXU2+SAIguFkhNjkD6vqgGqTeYy6QV4qNUq7+jpkqo4zSi70xY3Kj9gAFd3lOA4nOI4soHDeEtvey/Z0nw1OAaida9uX1vlBH1Unu1Qy1wYzJQX/hay6xjqJC45oWXWDn4FHznMcXPc8ZNv3+AE+hcesk1Zn2cC1ZrI1NbOAoSkxM2e7xQWnmbJKt+/QL65zBLpqjsDbXD+YysuCdPxiR6DsNN+h7y4ecMTgEueYAGoPrLP7zxEI61qx0ZT1XGYXHxR+6mdrqo23wUziZHfT85yFA0DByQJVefqFbt1WMQIG+Tsy5cobgR8MJuRYT5hrgiAIBqCVSUOGwFnADcBrgbUi8lEROauRhjHIB0EQDEKn18lryo9U9RrSJCKvA+4UkZ+IyJUDtR115pogCILhRBUqrUkacsKIyAzStfHXAjuBtwM3AxcC3wRyhIZG4SCvhYTqtL55XgunO1nhD/uJMKpO0IQ8Yu2peYEcsv+AKUvmOwEyi/xz3j3VBjOVd/h23sJ0a3+tTfF9BW77hfNte+dYC7N9OzFbnQQrTtKJZFZOYprDTtIKJ5FFUzRjqHeM8s3Y/2s77PEXpuYEczkBZdV1VvQr6bYCbwDiCOeVd9lgoNpEP+lHbZx9lAvONmtbvaQtIGVrJy8dzEkm4wiElbdb4bSqk3QFgF8sN0WFMxb6dR26n3y+7dK6QXNrDIkRYJO/Hfgy8FJVrc+wcreI/PNADUfdIB8EQTCcjATtGmBJnrNVVf9moIYxyAdBEAyCdn6QXywi7wEWUjduq5zWrW4AACAASURBVOozB2sYg3wQBMEgjAA9+W8C/wx8HvAFiHIYdYO8qCL9hJe0yx5GdeoEUwZQK1sHSmmpXY9bm+TbPgubrMDUcWftcuG4fx3Km6ztsLogJ2nIXTaRd+IlHT/Vb+/5HwrH7Tr544v9ROJJjz2G0m6btMSzPQOIk1zZE1jLW7zuPVbuhKpNSUdqx61NutjtxwToVhvroJedY8q6HSGytFtOIo6dzrmeYMXBABLnflMnkXhysp/0XZ1kNF5yEIDKAuvDKa51fD05CeJ1mV35Vxtvj0tyzjVO/AhHnTiBFqE6ImzyFVX9zIk0HHWDfBAEwfAiVDu8ugb4roj8IfBt+maG2jdYwxjkgyAIBmEE2ORfl/3/j+vKFDhjsIYxyAdBEAzASNCuUVWzJltE7FpWh46/gwRBEIxoNLXLN/JpN5LyTBH5PGAztTiMupm8IkYQLNlvg0aKu3xTVe2gDdroucI6yMqbBzV1PY44QlTJfVbIC4AFNnCqsM86yACOP/0iU1Zct9OU1Sb5AVLJESfwxskg1OWItgFUp0+0+9q4xZQlZ1shLQA56DjzSr7jsFFc0bGmMkD5lfOyS/WnunajW54ss8775J4HbcWLrEAdQHGbIw3uZDcrrvfF5NTJ+CROkJoe9LMt1RwnbeFoj7+vX62xZc7xyz77rKUbdoT3vHv1iO9MLTsLAipnzvH35Wv/NU2nV9eIyOXAq4DfAqYDb6Wv6SaXmMkHQRAMgGaO10Y+rUZEPiIia4CPAr8CLgJ2q+oXVdWfnfVj1M3kgyAIhpvhMMXkcB3wEPAZ4HuqekykiXXAxCAfBEEwKB1cXTMbeC5wDfD3IvJjYLyIFFU1J5CgL6NukJdajeRo3yQhlW1WdKl29cVu+/Jq66tInOQGlSutCBL4wVTJrffaelf5SQySvY6d2vETAMg8R/Rqtg28EsdGCdAzzQaElTda0S1vm+kXdsLgBgjt9t8ajy+da/d/n01OIeoHYzWqRdaSpCPOQ1yc69h5vWAugKPWply9xCbSKO7OsYnvtjZ5OdMKzMl0m4gFgLJ9lGvO9FPWWZ8KQOKImamzTfD9D1UneLC407fpi+NDU0+4bp4f5Nc/GBKgcChHTK0FpE7VzgzyqloFvg98X0TGAb8JTAC2isj/qOqrBtvGqBvkgyAIhptOL6EEUNVjwLeAb4nIFFIn7KCE4zUIgmAQOrWEUkReIyJmnFbVR1X1iyJypog8ZaBtxEw+CIJgABSh1jlZgxnAfSJyD3APsBsYBywCrgb2AH860AZG3yDfU4Htfe3KnhBW3hrfyi4nEcR51naqeevcj9mk4VxxgS27fYXf3knEXT3dlgEU91s7Y3LcHlfFWc8OUH7Qsb9OcUTLtvsxAeokyJAzbTKUyroNbvsu57roqTkJShrFmy01tU4+p9zZhifwxZScpCHeGOBN7XJEu1hmo9N1pfVfJAudBDUAG61AmEx17Pc5AmWVWbZucsx/hnqmNpbgJNd/4SW+99hmxQABtNvpV3lo8ReD0anFNar6KRH5J+CZwJOB84GjwGrgWlX1AyfqGH2DfBAEwXDSQseriNxI6jzdparnNrT71Pn6o+zTNG19BxGR54nIQyKyVkTcVwoRebmIrBKRlSLylXb2JwiC4ITQBj+DcxPwvHZ0MY+2zeRFpABcDzwH2ALcJSI3q+qqujqLgT8Dnqyq+0VkiO/yQRAEradVM3lVvU1EFrZkYw3Szpn8ZcBaVV2vqt3A14CX9KvzFuD63vBcVfWNcEEQBB1CgVpNGvoAM0Xk7rrPdUPdv4gkIvLyE23fTpv8XPqqpG0BLu9X5ywAEfk5UAA+qKo/6L+h7ERdB9DVNY2e8/o6/8prnAz0u3LEkRbbQA72HjRF4jgoAWoX2qw2yT2rbfuJfmaqylbb12LB/62tTbN9cAWyHHEqgJ6nWIew/PQ+u/9FviS1lqzjtOo46Ap7prrtuxfZYJbCz37l1LRCWtCEGNlQM0vlbKO6bKEpK3hCWkBt5cOmLLnAceh7GZiApOgFM9mgn7wsXFxs91V41HHy9vhBksWtNhirsskXOSxPs0F64gRTVXIynknNCWZynNS1nTaLGkCt216D4gwbeAeAo/vWNEozKnh7VPXSFuz1id2r1kTkbcA3TqR9Owf5Rh7HIrAYeDowD/ipiJyrqgf6NFK9AbgBYMrkeZ1TkQiC4NeSDmrX9PKjLJH314HHl311OjPUFuC0ur/nAducOr9U1R5gg4g8RDro39XGfgVBEDRH5wf5N2b/f2tdWUOZoQa1yYvIlSJyvYisEJHdIrJZRG4RkbeKiP+ennIXsFhETs8ymLwSuLlfne8Az8j2M5PUfLN+sD4FQRAMH4JqY59BtyTyVeB2YImIbBGRNzXSA1U93fkMOsDDIDN5Efk+6ez7P4GPALtIo63OIh2c/1NEPqGq/QdvVLWS2ZF+SGpvv1FVV4rIXwJ3Z21+CDxXRFYBVeCPVXVgK5qqyUxfWWhtf3mBHMlOK6bVvdgGIxVvf8BtL3fa7uklZ9t6eQkXnEAS1vmZDbTLCSZ6kt1XstzagwGSBx2bquOT0L3+G1/iiEbp3JNtRcfGClDa6difvQQjTQQoNZU0xKnbhD4ZxR0HbGGPf12rT7X+j4IjfJcs9p9Lz9dSWLTQlNUm+Bnfkp22r55PR9fkzKEusvdVYYofZOf2wfFLJXNyfC3HHDE3x1dVWHCaKQNIvMCnnAQjLaNFM3lVveZE2onIBOBdwHxVvS5bmbhEVb83WNvBzDXXqmp/78dh4N7s8/FsBu6iqrcAt/Qre3/dvzXr+LsG62gQBEFHUNBaxwXKvkAqa3BV9vcW4JvAoIP8gOaa3gFeRCb2iuSIyFki8mIRKdXXCYIgGLtIg5+2caaqfgzoAVDVo43usNF18rcB40RkLvA/wBtII7eCIAjGPq2LeD1RukVkfO9eRORMwBHSsjQ6yIuqHgF+G/hHVf0twBrxgiAIxiKdH+Q/APwAOE1E/o10sv0njTRsdAmliMiVwKuBXm9wR8TNpKYk/ZyaiePIYY+frah2yKrllZxsO3ruYrd9YYfjpNziWKxyHHRFL/Cn4geoiJOZiXusQzjPmcdhJ9vOcfvjL9NzMkPtt0FiyVrrzK16ao1AoWjnENLdUMaylAYVJ3MzQw0xcKp6il08Vh3v3/auk3WSVaysTnIc70DtSqtVVS3YXpUfdgL/gMoCRxHklzbwLDnfBk0BJJv87XoUptnzok62qNwxz1ONddRZtct3Mssh535zgrFaRnPBUO3pguqPRORe4ArS2/WPGjWVNzpQ/xGpxsy3sxUyZwA/PqHeBkEQjDJGQDAUpPrxTyH92SkB326kUUODvKreRmqX7/17PfCO5vsYBEEwCunw6hoR+TRpopCvZkW/JyLPVtW3DtAMGHyd/A2kNnjz3iciE4FXAMdV9d+a73YQBMHoIM8kOIxcDZybLTtHRL4IeEJQhsFm8p8G/kJEzgMe4InUU4uBKcCNwPAO8DUlOdLXrlx1MiMVnAxKgJ8B/hFrj6zty7Hp23SLcMV5dv+rNrrtda0VmKoe9QM5kjnWzqpPsrbb43l24lttZqHiIpvZqWe2k0EIKHlZjBz7uyT+LKc62dpJpdsRU2siGKqZzFAPveEzpmzJF/7Ar+xst7DWZlsqTPPPVcW5LrLfnivPpwGQOH4ZmWft1LW9/n1ZdHxANSczkxdgB6COcFnP+fZeASje4QQ+ORmnJCdb0/HTbd2Sky0qOeBkmwJw7jct+8fVEtrvVG2Eh4D5QO8AchqQk36uLwMO8qq6HHi5iEwCLgVOJUs9pao5+fGCIAjGEtIxx6uIfJf0J2YqsFpE7sz+vhz4RSPbaNQmfxi49cS6GQRBMMrp3Ez+74a6gcjxGgRBMBi+PFPbUdWf1P8tIlNoctwefYN8tQoH+q7fLhyw67ndjO5AMs5Ze1uytsPiHGsPBV/MLLltuSmr5dmpHSGr8uotbl2c9f+J42sorM5JqDXV2o9rnhCUUwagp8+zhVPs2u/CXn89M14iirnWHptLo/b3nFnW0hut/b0ZgbLKYnv8Sc46/+JeK8bWPdcRaT3Vt+mXt1iBse5TrMBYctISt70ctvEPtQecmI47fV+dXnyO7dNmX7hOHVt7bddu26cu6xMAcEsPWvu7F9MCIJOsD6526nS3bksYAevks8RJHyY1l9dIb+WGpIabGuRFZKKq+pEvQRAEY5QRsLrmj4FzTkQrrCFZAxG5KpMDXp39fUG2bjMIgmDs03lZg3WAE8I+OI3O5D8J/AZZ0g9VvV9EnnYiOwyCIAia5s+AX4jIHdQJk6nqoEGpDZtrVPUR6buW1c8eHQRBMMYYAeaazwL/jzQAqik3cKOD/CMichWgWSq/d5CZboYb7SpRWdQ3M3ut5Ahh1fyrUtrjuBQq9vdKjzuiZ0B5g+PknO2IQ+U4neT2VXZfJf8y1PZYx2Vh4XxTVl220G1f3Oz0db4jBPWgDZoCkIONuV/yzlXVccZ52YZEJ/jbHWIwVDN1vYe4eMB5O3buFcDNTFRaYR2X3Rfl+Mmc9uUH7IICin7QT22evQeLc2abssrW/mmWU7yMaXmiX57jE89Jv8FfUKAT7LPRc5p1nJZ2OZnFAEr2HCQr25g1VOm4rAFQUdUTSq7UqNTw75MmkJ1LmpHkQvomlA2CIBi7dN4m/2MRuU5EThWR6b2fRho2Ggy1h1RmOAiC4NeOEWCueVX2/z+rK2vdEkoROR14O7Cwvo2qvrjhLgZBEIxWOjzIq6ovJNQAjdrkvwP8C/BdOhb7lSLdPRQ37uxTpl7SihwB6Fq3tR/rhTbApNblnxr56X227tMuMmXlrTa4BUArNphJq76dNxk/3pRVZ9hgpLwAndrJTjKQNRttWU6ClNpKa6sXT/Rq0Wluex3v3JcHfDE2D2/21EwikGYCp9y6ux2b+jnWJwJQutf6P7xgoMJj/rU6vmyOKevabO+h2hR7TwBQsY+lHnzU9ulSK6YHUFu51pT1XOUnfyvtcWz1q217OXOh2149m/px+wxIzR9q1PE16VkL3Lrc4xc3TYcHeRF5rVeuql8arG2jg/wxVf2HpnoVBEEwBhAdEeaaJ9X9exzwLOBeoGWD/KdE5APAf9N3jea9TXQyCIJgdNLh1TWq+vb6v0VkKvDlRto2OsifB1wLPJMnzDWa/R0EQTCmGQEz+f4cIc3rMSiNDvK/BZyhqv6C6GFESyWqc0/uU5asd5JTz3ISZgPirHOueQmzc2z6hXPOMmXJIbt/T8QJQMpWzCuZbO3sADrLrpDyknvLepvcAkDPdAS2vH0dzImWPsWeQ51o17RXJvkCZUXvvKz3kmY0LlrWsJ0ehrymvnbEnpek4t8XPZfa+6K80YlzOOwkYgH4pZOe4RS79j13zbOzzl4XzjVlhe22TwDdly8zZV2r/TX1esweQ4+TiDzxEsTklBc37bQVc54h79lMdvk+sJbReZt8r648pLfB2cA3Gmnb6CB/PzANyJE7DIIgGKOMDJt8va58BdikqjnytX1pdJA/BXhQRO6ir00+llAGQTD26fwSyp8MXsun0UH+Aye6gyAIgtGOdHThOIjIbwN/A8wiNToKoKrqJyioo9GI1xP+FQmCIAiGzMeAF6lq05phAw7yIvIzVX2KiByi7wtLw78irUaOHUP6B24UHNGmQzniWo7oUvKY47jt8Z1GlelWnKm433FcPuI7rWrH7L6S6U7QEiB7rUBVz1k2aKZ43G4TQI44fvKiveS1bTv89mfYICdPtKy40XOmQuIIZJFzrEMhz17aVOCUQ+I4Pjnqrz0orLLXu3a6vVbJbkd0DCicbx2f1VU2wChx7h/AF55bt9nWcxz/AMVfPGALZ/oO8Z7znCA351wXt+cEBDrCe3gLJY76TmqZ6Ui2VPwgs5bReZv8zhMZ4GHwmfxEAFW1eciCIAh+HRgZjte7ReTrpOoD9X7R/xis4WCDfOcPLQiCoNN0fiScQro2/rl1ZQoMeZCfJSK5Gsaq+omGuhcEQTCa6fzqmjecaNvBBvkCMIn8EJIBEZHnAZ/KtvN5Vf3rnHovA74JPElV7x5om6qgPX3tb54QlE7PcRd4QU4bbTCRnpkjRPWIFa3SKTZAqOcSGxwDULzd2j57zjjFrVu4Y6UpK++0lrOeS6zAGkDhTpughMULTVGSY8/U7TbpBydNs/Uc0TfAtZN6olnNiIbN/8AvTNnmD13lN2/iwfTs99Utjl9l5lJ/A2fYwKNkq3P+nGAygOoKa25NLrACYbUHnKApoOD4kHSptZ1LTuCbTLX3VW2G/wx5YmLJvY65+OScgMSpdruVWVNtvbv9ID+XS6xPA4CNjW8iD6Hzq2uGwmCD/HZV/csT2bCIFIDrgeeQJhq5S0RuVtVV/epNJs00dceJ7CcIgqCttNAm3+jEt5UMlhlqKKo8lwFrVXV9JofwNeAlTr0Pky4Pyon3DoIg6DAtyAxVN/F9PqkswTUi4us5t5DBZvLPGsK25wL1a+u2AJfXVxCRi4DTVPV7IvKevA2JyHXAdQDj8F93gyAI2kZrZvKPT3wBRKR34uvYVS0i8kLgHFKp4bRbDVhaBhzkVdUaoBtnwFXKIpIAnwReP9iGVPUG4AaAqaVZWjipr/2udpq1acvDm/xOTbBJF7xEIslhP7lF9RErF5GcdaYpK+3PSY4xwf5I1Yr+C1Vh2SJTVplo1znXnCQMACXHJtp9shUoK3sCbYAcs+ele661yZf2Okmgge6FJ5uy0sFDbl0Xp1t59veh4r2OV59sE2yUfrXBba8L7Dp1SlZgq7rJjymoXX2xKSt4iawv9G3PusHar3susJnhZLKfYL507xpTlrcmX6bbe0BOd3xYBxz/C9C91ArnlR924gzO9/1assau/69Ke6WAmzDXzBSRer/iDdn4BQ1MfHP3L/LPwATgGcDngZcBdzbStlFZgxNhC1AfTTMPqL+Sk4FzgVslvUCzgZtF5MWDOV+DIAiGlcYH+T2qemnOd0MJz7tKVc8XkRWq+iER+TgNLJ+E9g7ydwGLs/ywW4FX8kQyWlT1IPD4VFNEbgXeEwN8EAQjCm3Z6prBJr4D0WsaOCIic4C9QEN5XwdzvJ4wqloB3gb8EFgNfENVV4rIX4pIqFcGQTB6aIHjlbqJr4iUSSe+NzfYg++JyDTgb0nT/m0kXcwyKO2cyaOqtwC39Ct7f07dp7ezL0EQBCdKK5ZQqmpFRHonvgXgRlW1wTB+2w9n//x3EfkeMC6zhgxKWwf5dqDjSnQv6yucVXDElWSSFRIDwCkXL6tOTlaawgwrjlRdY51xhZzMVJ6Tt+sh/42te7F15knVvjd2PbzdbV/ZardbetQ6PtUTeANkmg1QKRxxAqfm+cFcpX028EacbFPtEhgb0gJgoPCz++0m51nRMQB1Xudrp1gxNv9MA0d7TFF1ml0kUHxkj9u8+qh10pbvftiUeQsPAJjiBEPtzxEYW2Cvd+HRxldAS81eRJ1mA6RyM54ttk7eWlfumW0NLVon7018G0VErgIWko3bIoKqtiyRdxAEwa8njZli2oqIfBk4E1gO9IYcKxCDfBAEwVAQRoQK5aXA2ao5yacHoG2O1yAIgrGCaGOfNvIA6TLzphl1M3k5cpzSff2SKTiiT9LlJ0fgsE164QlsyXY/Z3nVaS+O/b6y3ck+D8gl59jCfX6AkBdQVXWCoapz/eQO3sU9fpa185f25QR+rXjQlBUm2wAV2ecHvZDYOUR1h3deneQc+A+Na6fPw3vommhf8JJm5CTdEEfkrudCGyRXECfhBVArW5ty4V5rU++52A8QKlWdJDfOc+EFswGU1/h+HQ8vIE/v/JUpKy6xwXwAyVHr16k9vM6UFab5fa122Tvb85+0lM7P5GcCq0TkTprMsz3qBvkgCIJhp/OD/AdPtGEM8kEQBAMxAjJDDSXPdtjkgyAIBqM1wVAnjIhcISJ3ichhEekWkaqI5NhJ+zL6ZvLjymadbM9Um5y7a6WTxBionm5t0oXtjmiTs5YXoHKJtbN2PWjtmbXzrTgXQGGbTWKsjp0fQKfZNf21kvO7nLNGWJx1zl0POcd13E/6IWdbm6o8avt6fIm/dry8w/oaCmWbXKOZpCFDTQQy1AexNskX+KrMcezvx6ztuTrJj79IfrLcli1wEqmv8oX3ao5AmpeMvnjIFx3TGTYmIpnsx5p0rdho259jfQW6yY//SBbae0CL9ryoF3yAHyuSTLLCewD4S/2bZgQkDfkn0gjZb5KutHktsLiRhqNvkA+CIBhmOm2uAVDVtSJSUNUq8AURsWnSHGKQD4IgGIgREAxFKkxWBpaLyMeA7UBOWH9fwiYfBEEwGB22yQPXko7XbwMeI1Wz/J1GGsZMPgiCYAA6HfGapQ38iKq+hjRN6oeaaT/qBnk9cozavX2F27rmOY6c2b5AWGGnFW5TL1uRF1wCdK2zp6znDCvYlBu04zg5u50MPgBd22xfy486gUsVv6/MdbIVNRg0A9A9y4pWlR0xs9IvfCG9ypOWmjIt5jjIPIYYzDTkYCpPzM5xpgKUj9iFDrrTiokVxttFAgAVx8moe21itjyBsYrjEC5ttRm75IjveMXLApWzr9oCG3jpOXk15xlKjjsO6YuW2LKJ/vBUPGifIT3TZpsC4B6/uFk8UbXhQlWrInKyiJSzfNlNMeoG+SAIgmFlZNjkNwI/F5GbSc01AKjqJwZrGIN8EATBIIyA1TXbsk9Cmjq1YWKQD4IgGIzOR7x+CEBEJqd/qpPl3WfUDfIyrovCor6BF90zrZ03+cm9bvvimTYtoi6yQSda9Bce6R0rbNlCK2SVdPvRE/qYTaSROPZMAPbZSA43mGuLn0iicqoVeHKPK0e9tPhT51gdm3zVsb0DlLZYm7BOduy86tuph5T2OKd9MzOyysk2kUVxnZ/IouoEI1Xm2QApqfgdSJxEHNU7HNGvOX6CFvnpfaZMHYEvmWETmQCu8FqumNkmG9BXm2Enl7Jogb+vg974ZJ/h8n3r3ebiJe7Z4Ac/topOz+RF5Fzgy8D07O89wGsbySwVSyiDIAgGo/NLKG8A3qWqC1R1AfBu4HONNBx1M/kgCIJhRUeErMFEVf1x7x+qequINBQMFYN8EATBAHR6nXzGehH5C1KTDcBrAJtc2mFMDPLldTZBh+YIFtUmWZuwHHGShuTt7Dxrfy6scM71PD+Ji8y15ckx3yavPXY9sRx31iNPt7Zj8O3vpQ02aUd1jp/IgkuWmaJaYs9MaeNuv72XTMVJTp3LENfJtyPpt87MOVf3rTJF5WWOwNtRf5lzbdsOU1a5+kJbcYN/rpNLzjVlWrHTT9nh+2/USWaP42sCoGbvQW+dfG21TQQCwMX2GZKa7Wt1iU3YDVDcZWMSCrP8uBh8F0rzNJ91r9W8kTQI6j9I79bbgDc00nBMDPJBEATtpNMzeVXdD7zjRNrGIB8EQTAQHQyGEpG/V9V3ish3nV4osA/4rKr+Mm8bMcgHQRAMQgcdr702+L/L+X4mcCNwdt4GYpAPgiAYhE4N8qp6T/b/3PR/IjKgns3oG+RFIOnrUKxssd6VZIIvuiVOthqZ4jhpPcEmcING6LLiUNWVD7vNC04gR5LkBF6dbjMuJXudjF/iew1L2x0nW5ftv+fMBZD1j9hCJxiKaTarEADdPaZIC814Th3a4EzN225xzRan0M/ClUy1zm91zp8utcF4ALX1Ofdb/3rT/Yj2xBGuq022QWa1Rx0xPoCzbL/Ka+2CBoDqbHsPy8q1tsxxvAN4CZ/EcWwWtthFAgDdi+zihbITeNcylI47XkVkMfBXpDP2xy+sqp6hqt8dqG0EQwVBEAyCaGOfNvIF4DNABXgG8CWeMOUMSAzyQRAEg9H5iNfxqvo/gKjqJlX9IPDMRhqOPnNNEATBMDJCgqGOiUgCrBGRt5FGAMxqpOGoG+T16DFqq9b0KSvOsEEblcV+EoHCIWu77J7hRAc7QT+QY6esOEFLF9pAIoDaKhsgoo4QGUDtShvgImts3do5ftKRWtkGiBSXW9tp1Ul6AlCa7ghUVa1BtbLJsd0DhXNsMvnSTitOJXkCZQ5NJf0Y6oPp3AOVM5xELEBxo70v5GTnvrxvtb+r822AUNG7144ec9v3nG0Dh0qbrU+mkudrGu/Yz2f4vhZx7uGeK+zijuIR65MBSB7cZMpqS62YmWd7Byj+0tHkOskXU2sJqh1NGpLxTmAC6Vr5D5PO4l/XSMNRN8gHQRAMO50Phror++dhGox07SUG+SAIgkHolLkmywSVi6q+eLBtxCAfBEEwEAp0zlxzJfAI8FXgDppbGAy0eZAXkecBnwIKwOdV9a/7ff8u4M2ky4J2A29UVWuwq2fCeDinr/2vUrZrlwsrcwTanKQL5Z127XB1mr/Onh7Hzlhy7Jm/8tfJq3OzFBbapCUA8pgjnOYkVy4cdMSlgIJjP9f51qaclyClttsmh9CzbSKMYiFnkdZem4j8+FKbdL0Zhjqjasamr6eebMqK22xybcCNCeCAjWkoLrHnD6Bnso1fSLY7Catn+aJhpXVW4Ky6y9rki/P9899TtCdGx/nDQ8FJcJ44Ymjc7eezECeZiZf4Rnb7YmrixMDU9vt+rZbROXPNbOA5wDXAq4D/Ar7aSLKQXtq2hFJECsD1wPNJF/BfIyL9vTP3AZeq6vnAt4CPtas/QRAEJ0qr18mLyFIRuV1EjovIe/LqqWpVVX+gqq8DrgDWAreKyNsb3Vc7Z/KXAWtVdT2AiHwNeAnwuCZrvQg+8EtSjeQgCIIRRRtW1+wjXSnz0kH3LdIFvJB0Nr8Q+AdSyeGGaOcgP5fUltTLFuDyAeq/Cfi+94WIXAdcBzCunBNCHwRB0A7aEOikqruAXSLywoHqicgXgXNJAOibqgAAEdpJREFUx8YPqeoDze6rnYN8w2mYReQ1wKXA1d73qnoDaY5Dpkyc2/EFq0EQ/PqQBkM1POzMFJG76/6+IRu/TpRrgceAs4B3yBM6VQKoqvoZg+po5yC/Baj3KM4DjDqYiDwbeC9wtaoOqtIkWiM51tchJbseM/U0zxm402bWUcdBWZ3tO8gKjphY91nWmZks9AOMkvsesoWHvOz1kBx0xMgcMbDu2b5oVXm9FXiqTT3JlBXv8AN0qpfaAJ3kdjuRUMcRB6Bn2oA092Fpl+iY17yZfXl99QTagNp853o7wnG6wnfIJ1Od4DnnvOYNNl52sMoSR+Buj31WwH9eCut9gbLj59jAqyYGQZ9xVuQv71x7wn09eRnH/t9QOlVH4yqUe1T10hbtFVUdst+0ndo1dwGLReR0ESkDrwT6rPkUkYuAzwIvzl5fgiAIRhyi2tBnwG2IvFVElmcf+0vVJto2k1fVSqax8EPSJZQ3qupKEflL4G5VvRn4W2AS8M3sNWRzI4v7gyAIho0W2eRV9XrSFYfDSlvXyavqLcAt/creX/fvZ7dz/0EQBEOn9do1IjIbuBuYAtRE5J3A2arq2GiHxuiLeBVBu/oFH01xbJdegBKg452kGfvsefUCpACYaAMxSvesMWWeOBWAFu0przhBKwDVp19kt+v4DzzbOwDHnWCakmN7neLb9Kuu/dzuX5xEJAD6gBVDKy/0hOMaFygb8oyqGZt+zQkmm+j3VR37cbLWCrclOfcFa23im9o8a+dPtvnXuuoEDnUtssJ1lbXr3fbFi8+xhU7gHUDXJhskV9mw2ZTVnnKB277qXIPyJtv/JCcZTeW+Vaasa7HvQ2sZLU4aoqo7SP2UbWf0DfJBEATDiXY0x+uQiUE+CIJgMDqc/m8oxCAfBEEwGKN3jI9BPgiCYDDE8c+MFkbfIF+pkuzuqzjXvdCqBZZyxAJlv3WoesFMxYN+Bp5ks91w9bANMCmWfWdkzyU2W5J4Cn5A10MmdszNQsVJvoOqZ4GjonjAKlbqTD+rTnHvEVNWrVZNWW2en4UscQJcKid5AT5u84ZpV7ao7lnWId318Ha3bmGSo1o6255/9u73d+a0TxwVTyb6gWfJHOca7HGUGa/0naF6l3VmVi5znLFAcbMNKPTUNatO4BxAsszWrW23gVfJfN8vWfScrO0chJVmgqFGHKNvkA+CIBhGhMEDnUYyMcgHQRAMRgzyQRAEY5gY5IeRag19tK+gV/EOJ6vMzOkNb1IL1qgrPdb2DNDtiDOVN1l7qh70g6lK+6xNvGd6jriSd2M5wVSeEBZA0RGjkl1Otqfjvi5c7ezTTVlhqrX/ywFf9IpjdrvF7Z6zxLczN0q78m+WHOE2XbTAr7zJ+k90kc34lRzLOdZuJ3DN85Vs9n0CiSMwVjvFitEl99vAPQA5e5Hd/zEn2xVQmW99DZ6MVunobLe9esewzO6/Z4ojWgYUV9iArp6LcoKhbDxe84RNPgiCYGwTq2uCIAjGLBrmmiAIgjGLEoP8cKLjy/Sc39dWXDzk2JT359iJ+4ub4dvJPRsrQPnoTKdT9gbQBb5cdLLfiqEVS35yhOpp1vZZnWD7X3zUt6knR6yd10NyhKAKjzqxAqfa9di1zVZcC6B24VmmrLjNWSc+nKJjzWz2DGtTlwM5wnXeeVlubfq1c5e4zSvTrHBZ+RHHf5Hja6pt22HKdKa1cydO0hsAedTGROg23/4vTqxEcZH139Sm+8J3iRdDss4KnJVyBNKqS61fpLzKisG1lNFrrRl9g3wQBMFwE+vkgyAIxjIxyAdBEIxRVMHJ4zBaiEE+CIJgMGImP4yIQNLP0/bgBlOt6gSXAIjj9BEnwEVm+A6unjk2QKVWtM6ssiPuBVCZYx1shYet0wkgmeCJXjn9qvo3oG6xjrPquTZopLjOd5zqVBu4U3vInuvC/Llue5Y/bLeZF0w0FNr0/FWnWMdfIedh17X2GhYWWMetbvTPdfGodf57YnCFU/0Ao2SuFdljiw0S1NP8BQG604qOyXjf8Zk4wmuVh22AkidaBoATuCWn2G12z7PBXGllZ5OOGF5LiUE+CIJgjKJAi3O8DicxyAdBEAyIurmNRwsxyAdBEAyEEo7X4UR6qpS39wsoOsnayY8v8W2PXiIOVzDJEwIDSg87ttM9VvSrmvN6V3ASfEjOvnSytYkXDjpBK2U/mErmWvttcZdNRFHZ42dYSebb9slE6yfomevbTpNNNkAlOXjYVtQc2+tQGWKQVNGxaec97JXLltnCx6xfSPY5iTyAZLYTTDVjiq2YE+BWnerYz6c7988j1vYOgGd/7/IT33jCc4WlNvCqOmWc21w8X815NplOeYcNHAToPsUJsupxkum0krDJB0EQjGFikA+CIBirhEBZEATB2EVpbw7ZNjPqBnktJCYZdNFJmlHekSMkNWWSKaqcaW3P3tp3gK41VggqOccKcWlOIg/xkjM766EBek6xfS0ctbbH6rgc/8Eex/69zfoPaldf6Lan297YxWnWTlz8lV07D9DzFCdp9LE2207rGWqCcCfxi0y21wSgtMnaunvmWzE7OXuh2973tdjrqmv8mArZ4Nj/l5xhK/b4iUDcBOF5ywYdH1L1JOurKW60ybkBKhdbkTbvHq5O8O/r8hZH5K4r1snnMeoG+SAIguElZA2CIAjGLgoa6+SDIAjGMBHxGgRBMIYJm/zwIaokPX0dlV5mop4rneAUoGuLdXxqwXHc3uunea+d4YhxrbR1NeeXv3LZOaYsuedBt27yk12mTC6yx1Xe4TiigOpWx0m81DrjEsfBCiAVxyE83ga46HbfweYFs6iTmUjUd2Z2GnWclHkJnWt7bZBTYaYT+NbjO9lllxOQ9qC9rpIjUFbb7lxrZ5vVM3IEyvqL/gHFfX52tdo4GyTl1nWC+QCSex+y27zc3tfFX6502zPLipnptDbeQ6qxuiYIgmBMM4pn8v46wWFERJ4nIg+JyFoR+dNO9ycIgqAvilarDX1GIh0d5EWkAFwPPB84G7hGRM7uZJ+CIAj60Cs13MhnBNJpc81lwFpVXQ8gIl8DXgKsymugiVAd37fbydk2OUH5bt+mziRrJyzeaUXLyEluURtv7ZHJeU4w1D0PuO09+z9nO0ErQPLQRlu2ydpeu8+Z77YvHbA2cTnqCFytXOO2927ZmpN0JZnqCGkB3bNteXmVF8xjg4ZGAolj+82TnPUEysobrE+lus33X3iJV5KZNkFMbb0fDFWYZkX6mGKFvAo5/hvEzvdqO23/AWqOQFn1yefbfd3pP8aVK6xfqniX9Uslc5xEKED1kS12X2WboKWljOIllJ0218wF6qUKt2RlQRAEIwIlXUjRyKdRROTVIrIi+/xCRJzw8NbQ6Zm8F/tvzpSIXAdcBzCuy65YCIIgaBvalqQhG4CrVXW/iDwfuAG4vNU7gc4P8luA+veseYCxnajqDaQngSmT545Mw1cQBGOWVjtVVfUXdX/+knTsawuiHVwaJCJF4GHgWcBW4C7gVaqas0AWRGQ3sCn7cybgZHYYtYy144Gxd0xj7Xhg7B1T/fEsUFXHudI4IvIDGnccjQOO1f19QzZJHWj77wGWquqbT7CLA9LRmbyqVkTkbcAPgQJw40ADfNbm8QsmIner6qVt7uawMdaOB8beMY2144Gxd0ytPh5VfV6rttUfEXkG8CbgKe3aR6fNNajqLcAtne5HEARBOxGRtwJvyf58AenbweeB56uq1QBvEZ1eXRMEQfBrgaper6oXquqFpBPs/wCuVVWb9LaFdHwmP0QGtHWNQsba8cDYO6axdjww9o5pNBzP+4EZwKclTTBUaZfJrKOO1yAIgqC9hLkmCIJgDBODfBAEwRhmVA7yo125crD+i8i7RGRVFvL8PyLiC+mMEBq9HiLyMhFRERnxy/UaOSYReXl2nVaKyFeGu4/N0MA9N19Efiwi92X33Qs60c+hICI3isguEfGFo35dUdVR9SFdT78OOAMoA/cDZ3e6X63sP/AMYEL27z8Avt7pfg/1egCTgdtIo/su7XS/W3CNFgP3ASdlf8/qdL+HeDw3AH+Q/ftsYGOn+30Cx/k04GLggU73ZSR9RuNM/nHlSlXtBnqVK0cLg/ZfVX+sqkeyP9sa8twCGr0eHwY+Rt9owJFKI8f0FuB6Vd0PoKq+ZOPIoJHjUaBXNnQqjrzISEdVbwOcFFu/3ozGQX60K1c22/83Ad9va4+GxqDHIyIXAaep6veGs2NDoJFrdBZwloj8XER+KSJti4psAY0czweB14jIFtLgxLcPT9eCdjMa18k3pFw5gmm4/yLyGuBS4Oq29mhoDHg8IpIAnwReP1wdagGNXKMiqcnm6aRvWj8VkXNV1SZ77TyNHM81wE2q+nERuRL4cnY8o1dIPQBG50y+IeXKEUxD/ReRZwPvBV6sqjZLw8hhsOOZDJwL3CoiG4ErgJtHuPO1kWu0BfhPVe1R1Q3AQ6SD/kikkeN5E/ANAFW9nVRoa2RmcwmaYjQO8ncBi0XkdBEpA68Ebu5wn5ph0P5n5o3Pkg7wI9nWC4Mcj6oeVNWZqrpQVReS+hherKr/v727C9GiiuM4/v1lbS5uFCZ1kRdbFhUsJcF6ERnbi6IgRVB0EWZsBXUTZEmIRClGL95Krzdd1IpWZkGUSohbsZKZilKtgSthRFkX0csuusu/i3MemH3axdn12bfh94GBZ2bOOXNm2f3vmXlm/uebqeluKWV+x3aQviBH0jzS7Zvjk9rL8sqcz0+kbLBIup4U5E9Nai9tQsy4IB8Rg0Atc+X3wLY4S+bK6WS0/kvaIOmuXGwT0AK8J+mQpGn7T6zk+cwoJc9pJ/CHpO+APcCamMAkU+ei5Pk8BTwq6TCwBXgo8iMrM4WkLUAPcK2kk5Ienuo+TQdOa2BmVmEzbiRvZmblOcibmVWYg7yZWYU5yJuZVZiDvJlZhTnI25hIGsqPddaWVkkdkhqSskBSs6S9kmaVKHu+pE8k/S6prW7fJkk/5IyKH0q6pG7/AUlNkv4epe23Jd17DufRJKlb0kx8q9wqxEHexqo/8jyVeTnR4PY7ge0RMVSi7GukN03vBrZKKiZy2w20RcQNwDFgbW2HpFbg55ysq+Ekzcptfw7cPxHHMCvLQd4aStLzkp4urB/No/32PKqeLWlOzsHeNkITDwAfFeqvkbQ/111f2P4c8GdErI6Ir4BHgC2SLgaIiF35JSD4fybP5cBnhbZekHQ4Jxq7vFDuTklfSDomaUUu25q3fZuXm/P2jpyPvQs4kuvvyOdjNmV8KWlj1SzpUP7cFxH3lKkUEfvzm7sbgWbgnYgYNrlDfuX+qtrVgaSlpHwwi0hJtj6WdGtEdEfE+rr2e4DFoxy+E9haWF8GPJk/zwH2RcQ6Sa+QUghvzPtaScnhFgB7JF0N/AYsiYgBSdeQ3g6t5eFZRLp66MvrR4H2s/5wzCaQg7yNVX9ELBxn3Q2kPCoDwBMj7J8HFLM4Ls3LwbzeQgr63WUPKGkdMAi8m9ebgPkRUcszcxqofZ9wAFhSqL4tZ2H8UdJx4DqgD9gsaSEwRMpZU/N1IcATEUOSTku6KCL+Kttns0ZykLdGG2T4bcDZhc9zSYH6grz9n7q6/XXlBbwYEW+MpyOSVgErgDsKeVgWA18Wip0p7Bti+N9Efc6PIF0B/ArcSDrP4iQo9ecDcCEzY6IUqyjfk7dGO0Gagg1JNwFXFva9CTxLGlW/XF8xz7I0S1It0O8EOiW15PaukHRZmU7kSTyeIWW8/LewaxnlJ2G5T9J5khaQps7rJc2a9Ese4a8kTa03Wh8uBU5FxJmSxzNrOAd5a7QPgLn5vv3jpCdbkPQgMBgRXcBLQLuk20eovwu4BdKXp0AX0CPpCPA+KT99GZtz2d35Uc/X8/YOYG/JNnpz2U+BxyJiAHgVWCVpH+lWzUij95rbSLMsmU0ZZ6G0aSXn0l8dESsnoO35wFsRsbzRbY9yvO3A2ojonYzjmY3EI3mbViLiIOlJlrO+DDWOtk9OYoBvAnY4wNtU80jezKzCPJI3M6swB3kzswpzkDczqzAHeTOzCnOQNzOrsP8AULLEddJcjsMAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, cb = plot_dataset(ds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example dataset the region of interest shall be represented by the center region. Again there are outliers that render the color grading almost useless.\n", "\n", "\n", "Looking at the same plot with an automatic color scale without limiting cutoff percentiles (by setting the cutoff to 50% we basically deactivate them) does not give a better result:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, cb = plot_dataset(ds, auto_color_scale=True, cutoff_percentile=50);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here all the relevant region is considered as outliers. To make this clearer let's draw the histogram again:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "data = cb[0].mappable.get_array()\n", "pl, q3, q1, pu = np.percentile(data, [99.5,75, 25, 0.5])\n", "plt.hist(data, bins=100);\n", "# IQR\n", "plt.axvline(q3, color='k')\n", "plt.axvline(q1, color='k')\n", "# cut off\n", "plt.axvline(q1-1.5*(q3-q1), color='b')\n", "plt.axvline(q3+1.5*(q3-q1), color='b')\n", "# limit through percentiles\n", "plt.axvline(pl, color='r')\n", "plt.axvline(pu, color='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The region of interest is represented by the right hand side structure around 0. The IQR induced limits (blue) does not capture these values. The percentile limits (0.5% here) however save the day:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, cb = plot_dataset(ds, auto_color_scale=True, cutoff_percentile=0.5);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is some undesired clipping in the top right corner of the ROI but the structure within the ROI is relatively well resolved." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Asymmetric cutoffs\n", "Asymmetric cutoffs can be simply defined via a tuples. So for the data of the first example we can disregard the lower outliers that take up up to five percent of the data but not allow any clipping on the upper end:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds = dataset_with_outliers_generator(new_data_set('data_with_outliers'),\n", " low_outlier=-3,\n", " high_outlier=3,\n", " background_noise=False)\n", "ax, cb = plot_dataset(ds, auto_color_scale=True, cutoff_percentile=(0,5));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defaults and customizing the auto color scaling\n", "The defaults used by `plot_dataset` can be set in the `qcodesrc.json` files. The system default is described in `qcodes/config/qcodesrc.json` ([online](https://github.com/QCoDeS/Qcodes/blob/master/qcodes/config/qcodesrc.json))\n", "To override the default edit these values in your custom `qcodesrc.json` file in your home directory.\n", "\n", "The defaults are (for detailed description see the schema file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "\"auto_color_scale\":{\n", " \"enabled\": false,\n", " \"cutoff_percentile\": [0.5, 0.5],\n", " \"color_over\": \"white\",\n", " \"color_under\": \"gray\"\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because of the possible risks due to auto color scaling it is deactivated by default. Please remember to change the colors marking the outliers in case you should use a color map the includes white and gray." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using auto color scaling in custom plotting\n", "If you are using matplotlib but do not want to rely on `plot_dataset`, you can simply call:\n", "`qcodes.utils.plotting.apply_auto_color_scale(colorbar, ...)` and provide any matplotlib color bar to achieve the described effects.\n", "\n", "If you want to use the qcodes config system for defaults call\n", "`qcodes.utils.plotting.auto_color_scale_from_config(colorbar,...)` instead." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "1225px", "left": "1336px", "top": "111.133px", "width": "227px" }, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }