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        "author": "Ilya Kamenshchikov",
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        "description": "# Pen'n'paper\n\nPen'n'paper is a package to easily collect the data about (noisy) processes and plot them for comparison. This package is not aiming at feature completeness. Instead it should give you an easy start during the phase of the project when you want to just concentrate on an experimental idea.\n\nInstallation:\n`pip install pennpaper`\n\n\nBy example:\n\n```python\n# We have a mysterious function that we would like to better understand on the interval [0.1, 5.].\n# Unfortunately the function is noisy.\n\nimport numpy as np\n\nX = np.arange(0.1, 5, step=0.01)\n\nimport random\n\n\ndef noisy_mapping(mapping):\n    def _(x):\n        y = mapping(x)\n        y += random.gauss(0, 1)\n        return y\n\n    return _\n\n\npow2 = noisy_mapping(lambda x: x ** 2)\n\n\n# lets record the pairs (x, f(x)) in a metric and make a plot:\nfrom pennpaper import Metric, plot_group, plot\n\nm1 = Metric(\"pow2\")\nfor x in X:\n    m1.add_record(x, pow2(x))\n\nplot(m1)\n\n# try again - see in how far it repeats itself.\nm2 = Metric(\"pow2_second_try\")\nfor x in X:\n    m2.add_record(x, pow2(x))\n\n# lets plot two metrics side-by-side\nplot_group([m1, m2])\n\n\n# Actually, m1 and m2 are metrics of the same process. \n# What if we create a new metric tracking the mean and stddev of this process?\nm3 = m1 + m2\nplot(m3)\n\n# the plot is too noisy to understand. We can smoothen it!\nplot(m3, smoothen=True)\n```\n\n\n",
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