{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Offline Plotting Tutorial\n", "\n", "The dataset comes with a tool for offline (i.e. not live as the data are coming in) plotting. This notebook explains how to use it and what it is capable of plotting. **NOTE**: This notebook only covers the plotting of numerical data. For categorical (string-valued) data, please see [Offline plotting with categorical data](Offline%20plotting%20with%20categorical%20data.ipynb).\n", "\n", "The function that is going to be used in this tutorial is the `plot_dataset`. For convenience it is also possible to directly plot the dataset from the `captured_run_id`. Apart from the first argument `plot_by_id` behaves exactly like the `plot_dataset`. All customizations shown below can also be applied to `plot_by_id`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from pathlib import Path\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from qcodes.dataset import (\n", " Measurement,\n", " initialise_or_create_database_at,\n", " load_or_create_experiment,\n", " plot_dataset,\n", ")\n", "from qcodes.parameters import Parameter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us first initialise our database and create an experiment which shall produce the data to be visualise." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "initialise_or_create_database_at(Path.cwd() / \"offline_plotting_example.db\")\n", "exp = load_or_create_experiment(\"offline_plotting_experiment\", \"nosample\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we make a handful of parameters to be used in the examples of this notebook.\n", "\n", "For those curious, setting `set_cmd=None` and `get_cmd=None` makes the `Parameters` settable and gettable without them being hooked up to any external/auxiliary action (in old QCoDeS versions, this was known as a `ManualParameter`)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Make a handful of parameters to be used in the examples\n", "\n", "x = Parameter(name=\"x\", label=\"Voltage\", unit=\"V\", set_cmd=None, get_cmd=None)\n", "t = Parameter(name=\"t\", label=\"Time\", unit=\"s\", set_cmd=None, get_cmd=None)\n", "y = Parameter(name=\"y\", label=\"Voltage\", unit=\"V\", set_cmd=None, get_cmd=None)\n", "y2 = Parameter(name=\"y2\", label=\"Current\", unit=\"A\", set_cmd=None, get_cmd=None)\n", "z = Parameter(\n", " name=\"z\", label=\"Majorana number\", unit=\"Anyonic charge\", set_cmd=None, get_cmd=None\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A single, simple 1D sweep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the sake of simplicity, let us perform a single, 1D sweep:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 743\n" ] } ], "source": [ "meas = Measurement(exp=exp)\n", "meas.register_parameter(x)\n", "meas.register_parameter(y, setpoints=(x,))\n", "\n", "xvals = np.linspace(-3.4, 4.2, 250)\n", "\n", "# Randomly shuffle the values in order to test the plot\n", "# that is to be created for this data is a correct line\n", "# that does not depend on the order of the data.\n", "np.random.shuffle(xvals)\n", "\n", "with meas.run() as datasaver:\n", " for xnum in xvals:\n", " noise = np.random.randn() * 0.1 # multiplicative noise yeah yeah\n", " datasaver.add_result(\n", " (x, xnum), (y, 2 * (xnum + noise) ** 3 - 5 * (xnum + noise) ** 2)\n", " )\n", "\n", "dataid = datasaver.run_id\n", "dataset = datasaver.dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let us plot that run. The function `plot_dataset` takes the `dataset` created by the run to plot as a positional argument. Furthermore, the user may specify the `matplotlib` axis object (or list of axis objects) to plot on." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If no axes are specified, the function creates new axis object(s). The function returns a tuple of a list of the axes and a list of the colorbar axes (just `None`s if there are no colorbars)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "axes, cbaxes = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the returned axis, we can change, among other things, the plot linewidth and color. We refer to the `matplotlib` documentation for details on `matplotlib` plot customization." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "my_ax = axes[0]\n", "line = my_ax.lines[0]\n", "line.set_color(\"#223344\")\n", "line.set_linewidth(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rescaling units and ticks\n", "\n", "The `plot_dataset` function can conveniently rescale the units and ticks of the plot. For example, if one of the axes is voltage in units of `V`, but the values are in the range of millivolts, then `plot_dataset` will rescale the ticks of the axis to show `5` instead of `0.005`, and the unit in the axis label will be adjusted from `V` to `mV`.\n", "\n", "This feature works with the relevant SI units, and some others. In case the units of the parameter are not from that list, or are simply not specified, ticks and labels are left intact.\n", "\n", "The feature can be explicitly turned off by passing `rescale_axes=False` to the function.\n", "\n", "The following plot demontrates the feature." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 744\n" ] } ], "source": [ "meas = Measurement(exp=exp)\n", "meas.register_parameter(t)\n", "meas.register_parameter(y, setpoints=(t,))\n", "\n", "with meas.run() as datasaver:\n", " for tnum in np.linspace(-3.4, 4.2, 50):\n", " noise = np.random.randn() * 0.1\n", " datasaver.add_result(\n", " (t, tnum * 1e-6),\n", " (y, (2 * (tnum + noise) ** 3 - 5 * (tnum + noise) ** 2) * 1e3),\n", " )\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([], [None])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Two interleaved 1D sweeps\n", "\n", "Now we make a run where two parameters are measured as a function of the same parameter." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 745\n" ] } ], "source": [ "meas = Measurement(exp=exp)\n", "meas.register_parameter(x)\n", "meas.register_parameter(y, setpoints=[x])\n", "meas.register_parameter(y2, setpoints=[x])\n", "\n", "xvals = np.linspace(-5, 5, 250)\n", "\n", "with meas.run() as datasaver:\n", " for xnum in xvals:\n", " datasaver.add_result((x, xnum), (y, xnum**2))\n", " datasaver.add_result((x, xnum), (y2, -(xnum**2)))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In such a situation, the `plot_dataset` by default creates a new axis for **each** dependent parameter. Sometimes this is not desirable; we'd rather have both plots on the same axis. If this is the case, then, we might pass the same axis twice to the `plot_dataset`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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7Rdymsa8C6NixY3AiM2GruLSMdTvzWLUtl1Xbc1m1LZfMvYfYkVvA4TbfFyHQLqERnVo1oU9SAn2TEuiTlEC31k2JssRjGqBQSDCVvf7zVz9ht2nsZwDS0tKshU5Tp0rLlGVZOcxJ38Pc9L0syNjLoSKnVBIXHUnPdvGM7ppIhxZxdGgRR3LzOJo3jiEhLoqEuGgaRUVS/vpyVcgvLnVKOAXF7DtYzLacfLL2HSJrXz4bsw/w2tzNFJY4b4SOj41iWOeWjOzSklFdEumblEBERJ28hdsYT4VCgsnil+8KL3+HtzEBVVBcyuyNu/lq5U6mr97J7gNFAPRo25Szh3YgLbUlfZMSSG3VhMhaHvBjoiJoFhdd5fCS0jIy9hxkxdZc5mfsZW76Hr5dswuAtgmxnNinLeP7tGNkl1bERFnpxtRPnjfX7767ex1wPLAVmA+cr6orq5omLS1NrS0yczhUlUWZObyzMItPlm4jr7CEprFRjOvVhhP7tGV011YkNg3Um6artyuvgFkbdvP1qp3MWJvNoaJSmsVFc/rAJCaldaB/cjNErGRjDo+ILFTVtKAu0+sEAyAip+C86zsSeEFV769ufEswprb25xfz5vxMps3bQvrug8RFR3Jy/3acPjCJUV1bERsV6XWIv1BQXMrM9bv5aOk2vly5g8KSMnq0bcrk4R2ZlJZC09hQuPhg6pOwTTC1ZQnG+Cs9+wAvzc7gnYVZHCoqZVhqCyalpXBK//b15iC9P7+YT5dt580FW1i6JYf42CjOGZbCpaNTSWnZ2OvwTD1hCcZPlmBMTdbtzOOxb9bz2fLtREdEcNrAJC4bk0q/5GZeh3ZEFmfu48VZGXy2fDtlqpwxKJnrjutGl9ZNvQ7NhDhLMH6yBGOqst5NLJ8u307j6EguGZ3KpWNSaRPfyOvQ6tT2/fm8MHMTr87dTFFJGWcMTuaG47qTmtjE69BMiLIE4ydLMKai7LxCHpm+jmnzMomLjuTSMalceVQXWjSJ8Tq0gMrOK+SZHzby6tzNlJQqF47sxI3Hd2/w621qzxKMnyzBmHIFxaW8MGsTU77bSEFxKReN6sT1x3WnZZgdYHflFfDY9PVMnZdJ09gobji+OxePSrVbnM3PLMH4yRKMAZi5fjd//mA5m/cc4sQ+bbnz5F5hXxexdkcef/90FT+u3023Nk154Kz+DEtt6XVYJgRYgvGTJZjwtvtAIfd/upr3F2+lc2IT7pvYj6O6J3odVkj5ds1O/t8HK9mak895w1K48+TeNGtc9YOfpuHzIsHUj/s0jcF5SPKjpdu456OVHCws4YbjunHNuG40ig6tZ1hCwXG92jLy5lY8Nn09z83cxPTVO7n/zP6c1Led16GZMGIlGFMv7DtYxN0fruDTZdsZ3LE5//rtALq3jfc6rHph5bb93PbOMlZuy+W3Qzpwz+l9SGhkpZlwYyUYYyoxY+0ubntnGfsOFfGnk3ry+2O6WOvDtdA3qRnvXzOG/367nidmbGRu+h4emjSQUV1beR2aaeDsV2pCVnFpGQ98tppLX5xPi8YxfHDtGK4d182Sy2GIiYrg5vE9eecPo4iJiuD85+byyNfrKC2rf1cwTP1hv1QTkrbm5HPu03N4+od0LhrZiQ+vG0PfpPr9FH4oGNyxBZ/ecBRnDe7AY9+s58LnfmJXXoHXYZkGyhKMCTnfrd3Fbx7/kXU7D/Df8wdz3xn9rCK/DjWOieLhcwby77MHsHjLPk55bCZzNu7xOizTAFmCMSFDVXlyxkYuf2k+7ZvF8fH1R3HqgCSvw2qwJqWl8NF1R9EsLooLn/+Jl2dnUB9v+jGhyxKMCQn5RaXcOG0JD36xht/0b897V4+ms7WrFXA92sbzwbVjGNezNfd8tJI73l1OYUmp12GZBsLuIjOe27G/gCtfmc/KbbncNqEnVx/b1V6sFUTxjaJ55qI0Hpm+jv98u4EN2Qd4+qKhnr14zTQcVoIxnlqzI5czp8wiY/chnrs4jWvGdrPk4oGICOGW8T154vwhrNy2n7OmzCY9+4DXYZl6zhKM8czM9buZ9OQcylR56/ejOL53W69DCnu/GdCeqb8bycHCEs56cjYLMvZ6HZKpxyzBGE+8uzCLS1+cR1LzON6/Zgx9khK8Dsm4BndswXvXjKZF4xjOf+4nPlu+3euQTD1lCcYE3QszN3HL20sZ3rklb189iqTmcV6HZCro1KoJ7149mn5JCVz7xiKmzsv0OiRTD1mCMUGjqvz32/Xc+8kqTurblhcvG2ZtYoWwlk1ieP3KkRzTvTV3vrecZ39I9zokU89YgjFBoao8+MVaHvpqHWcNTuaJ84cQG2UPT4a6uJhInr04jd/0b8/9n63m/75aa8/KGL/Zbcom4MrKlHs+WsmrczdzwYiO3DexHxERdqdYfRETFcHjkwfTNDaKx7/dwMGiUu7+TW+728/UyBKMCaiyMuX2d5fx9sIsrjqmC3ee3MsOTPVQZITwz9/2Jy4mkudnbiIyQuy7NDWyBGMCRlX58wfLeXthFjce352bTuhuB6R6TES457Q+lKnyzA/pRIhw+4Se9p2aKlmCMQGhqvzt41VMnbeF68Z1448n9vA6JFMHRIS/nd6X0jLlqe83EhkBt463JGMqZwnG1DlV5Z+fr+Gl2RlceVRnbhlvyaUhERHum9iPMlWe+G4jkSLcPL6n12GZEGQJxtS5R6av//k9Ln+2yuAGKSJCuP+M/pSVwePfbiA2OpJrx3XzOiwTYizBmDr15IyNPP7Nes5NS+Fvp/e15NKARUQID5zVn8KSUv795VpaNolh8vCOXodlQoglGFNn3lqwhQe/WMPEQUn846z+dityGIiIEP49aSA5+cX8+f3ltGgcw4R+7bwOy4QIe9DS1Inv1uzizveWc3T3RP599kAiLbmEjejICKZcMISBKc25Ydpiezum+ZklGHPElmzJ4ZrXF9G7fTxPXjiUmCjbrcJN45goXrx0GJ1aNuaqVxawctt+r0MyIcDTI4GIjBWR/SKyxO3+4mU8pvbSsw9w+UvzaR0fy4uXDqdprF11DVfNG8fwyhXDiW8UxSUvzCdzzyGvQzIeC4VTzR9VdZDb3et1MMZ/u/IKuPiFeQC8fPlwWsfbGxDDXftmcbxyxQhKysq47KV57M8v9jok46FQSDCmHsovKuXKlxew50ARL146jM6JTbwOyYSIbm2a8tSFQ8nce4hrX19EcWmZ1yEZj4RCghklIktF5HMR6VvVSCJylYgsEJEF2dnZwYzPVKCq3PrOUpZv3c/jkwczMKW51yGZEDOySyv+cWZ/Zm7YzT0frbQWmMOU1wlmEdBJVQcC/wE+qGpEVX1GVdNUNa1169ZBC9D82mPfrOfTZdu5fUIvTuxjrzk2lZuUlsLVY7vyxk+ZPD9zk9fhGA8EPcGIyLXllfpAU1U9AKCqnwHRIpIY7JiM/z5dtp1Hp6/nrCHJ/P6YLl6HY0Lcn8b35OR+7bj/s9VMX7XT63BMkAU9wajqE+WV+kCZuI96i8hwNx67iT5ELcvK4Za3l5DWqQUPnNXfntI3NYqIEP7vnEH0T27GDdMWs3p7rtchmSDy+hLZ2cAKEVkKPA6cp3axNiTtzC3gd68soFWTWJ66aKi9jdL4LS4mkucuTiO+URS/f3Uh+w/ZnWXhwtMEo6r/VdW+qjpQVUeq6mwv4zGVKyop4w+vLSSvoITnLkkjsandjmxqp01CI6ZcMJTt+/O58c3FlJXZeWQ48LoEY+qB+z9dxeLMHB6aNJDe7RO8DsfUU0M7teCe0/oyY202j05f53U4JggswZhqfbB4Ky/P2cyVR3XmlP7tvQ7H1HMXjOjIOWkdePzbDXy1cofX4ZgAswRjqrR6ey53vLeM4Z1bcvvJvbwOxzQAIsK9E/sxoEMzbn5rKRuzD3gdkgkgSzCmUvvzi7n6tYUkNIrmv+cPJjrSdhVTNxpFR/7cKOrvX13IwcISr0MyAWJHDfMrZWXKrW8vJWtfPk9cMIQ28Y28Dsk0MMnN4/jv+YNJzz7A//tghT3p30BZgjG/8tzMdL5etZO7TunNsNSWXodjGqjRXRO58fgevLd4K+8szPI6HBMAlmDMLyzZksO/vljLyf3acdmYVK/DMQ3cdcd1Y1SXVvzlw5Ws35nndTimjlmCMT/LLSjmhqmLaZvQiH+eNcCe1DcBFxkhPHbeIBrHRHLtG4vILyr1OiRThyzBGMBpIfnP769ga04+j08eRLPG0V6HZMJEm4RGPHLuINbtPMDfPl7pdTimDlmCMQC8vSCLj5du4+YTezC0k9W7mOA6pkdrrhnblWnzt/Dhkq1eh2PqiCUYw4Zdedzz0UpGd23FH47t6nU4JkzdfGIP0jq14K73ltvrlhsISzBhrqC4lOveWExcTCSPnDuIyAirdzHeiIqM4LHJg4mIEP741hJK7E2Y9Z4lmDD30JdrWbMjj4cnDaRtgj3vYryV3DyO+yb2Y+HmfTz1/UavwzFHyBJMGJuzcQ/Pz9rERSM7Ma5XG6/DMQaAiYOSOHVAex6dvp5lWTleh2OOgCWYMJVbUMytby8ltVUT7jzF2hkzoUNEuP+M/iQ2jeWmN5fYrcv1mCWYMHXvx6vYvj+fh88ZSOOYKK/DMeYXmjWO5uFzBpKefZAHPl/tdTjmMFmCCUNfrtzBOwuzuHZcN4Z0bOF1OMZUaky3RK44qjOvzNnMjLW7vA7HHAZLMGFm94FC7npvOX2TErj+uO5eh2NMtf50Uk96to3nT+8ss1ct10OWYMKIqnLHu8vJKyzhkXMHERNlX78JbY2iI3n4nIHsO1jEvZ+s8jocU0t2hAkj7y/eyvTVO7ntpJ70aBvvdTjG+KVfcjOuHtuVdxdl8e2anV6HY2rBEkyYyM4r5N5PVjG0UwsuH9PZ63CMqZXrjutGj7ZNueu9FeQW2KWy+sISTJi456MVHCos5cHfDiDCntY39UxsVCT/Pnsgu/IKuP8Tu6usvrAEEwa+WLGdz5bv4MYTutOtTVOvwzHmsAxMac5Vx3TlzQVb+GFdttfhGD9Ygmngcg4VcfcHK+nTPoGrjunidTjGHJGbTuhO19ZNuOPdZeTZpbKQZwmmgfv7p6vZd6iIf509gOhI+7pN/dYoOpJ/TxrIjtwCHvh8jdfhmBrYEacB+35dNu8szOIPx3ahX3Izr8Mxpk4M6ejcqPLGT5ksyNjrdTimGpZgGqiDhSXc9d5yurZuYg9Umgbnjyf2ILl5HHe+t5yiEmvWP1RZgmmgHvtmPVtz8nnwtwNoFB3pdTjG1KkmsVHcO7Ev63cd4Nkf070Ox1ShxgQjIh1E5FYR+VBE5ovIDyIyRUR+IyKWoELQqm25PD9zE5OHp5CWaq8/Ng3T8b3bckr/djz+zXoydh/0OhxTiWoThIi8CLwAFAEPApOBa4DpwARgpogcE+ggjf/KypQ/f7Cc5nHR3D7BmuE3Dds9p/UlOjKCuz9Ygap6HY6poKYSyMOqOl5VH1fV2aq6QVVXqOp7qno9MBbYVtNCRKSXiMwRkUIRubXCsAkislZENojIHYe/KgZg2vwtLM7M4c+/6U3zxjFeh2NMQLVNaMRtE3oyc8NuPlxS46HIBFm1CUZVV1TWX0RSRORPqlqkqhv8WM5e4AbgoQrziQSeAE4G+gCTRaSPX5GbX8nOK+Sfn69mZJeWnDk42etwjAmKC0Z0YlBKc+77ZBU5h4q8Dsf48LsORUQSReRqEfkBmAG09XdaVd2lqvOBik9GDQc2qGq6qhYB04CJ/s7X/NI/PltNfnEpfz+jPyLWHIwJD5ERwj/O7E9OfjH/tGdjQkpNdTDxInKxiHwBzAO6AV1Utauq3lrdtH5KBrb4fM5y+1UWy1UiskBEFmRnWzMRFc3esJv3F2/lD8d2teZgTNjpk5TA5WNSeXPBFpZsyfE6HOOqqQSzC7gCuB/oqqq34FT415XKTrMrralT1WdUNU1V01q3bl2HIdR/RSVl3P3BCjq1asy147p5HY4xnrjh+O60bhrLXz5cQVmZVfiHgpoSzF1AI+BJ4E4R6ervjEXkWhFZ4nZJVYyWBaT4fO6AHzcNmF96YdYm0ncf5K+n97VnXkzYim8UzV2n9GZZ1h40cSkAABu/SURBVH7eXLCl5glMwNVUyf+Iqo4ATscpbXwAJInI7SLSo4Zpn1DVQW5XVdKYD3QXkc4iEgOcB3xU+9UIXztzC/jPN+s5oXcbxvVs43U4xnhq4qAkhqe25F9frLEK/xDgVyW/Wwl/v6r2B4YBzYDP/V2IiLQTkSzgZuBuEckSkQRVLQGuA74EVgNvqerKWq9FGPvn52soLlP+36l2850xIsLfJvZlf34xD3+1zutwwl5UdQNFRLTC00uquhxYjnP5rNJxKlLVHTiXvyob9hnwWW2CNo4FGXt5f/FWrhvXjU6tmngdjjEhoXf7BC4elcorczI4d1iKNfTqoZpKMN+JyPUi0tG3p4jEiMhxIvIycEngwjNVKS1T7vloJe2bNeKacX5XjRkTFv54Yg9aNI6xCn+P1ZRgJgClwFQR2SYiq0QkHViP02zMI6r6UoBjNJWYNj+TldtyueuU3jSOqbYgakzYaeY2lbQoM4f3Fm/1OpywVe2RSVULgCnAFBGJBhKBfFW1G809lHOoiIe+XMuIzi05dUB7r8MxJiSdPbQDr8/L5F9frOHkfu1oEmsnYsHm95P8qlqsqtstuXjv/75ex/78Yv56el97Yt+YKkRECH85tTe78gp5+vuNXocTlqy5/Xpm/c48Xv8pkwtGdKJ3+wSvwzEmpA3t5JTyn/4hna05+V6HE3YswdQz//hsNY1jIvnjidU+hmSMcd1xci8U+NcX1k5ZsPmVYETkQX/6mcD6cX02363N5vrjutGyiTXFb4w/OrRozO+O7syHS7axOHOf1+GEFX9LMCdW0u/kugzEVK+0TLn/09WktIzjktGpXodjTL1y9dhutI6P5b5PVtmLyYKoptaUrxaR5UBPEVnm020ClgUnRAPwzsItrNmRxx0TehMbZe2NGVMbTWOjuHV8DxZl5vDxsu1ehxM2airBvAGchtM+2Gk+3VBVvTDAsRnXwcISHvpqHUM7teCU/u28DseYeunsoSn0aZ/Ag5+voaC41OtwwkJNjV3uV9UMVZ2M0/JxMU5z+k0rPt1vAufp7zeSnVfIn3/T225LNuYwRUYId5/am605+Tw/c5PX4YQFfyv5rwN2Al8Dn7rdJwGMy7i278/nmR/TOW1gEkM6tvA6HGPqtdFdEzmhdxuemrGRvQetteVA87eS/yagp6r2VdX+bjcgkIEZx0NfrqNM4baTenodijENwm0TenGwqIQnvtvgdSgNnr8JZguwP5CBmF9bsyOX9xZncdnoVFJaNvY6HGMahB5t4zl7aAdenbOZLXsPeR1Og+ZvgkkHZojInSJyc3kXyMAM/OuLtcTHRnH1WGst2Zi69McTeyACD3+11utQGjR/E0wmTv1LDBDv05kAmbdpL9+u2cXVY7vRvLE9VGlMXWrfLI7LxnTmgyXbWLHVLs4Eil/Ni6rq3wBEpImqHgxsSEZV+efnq2mbEMul9lClMQFx9diuTJufyYNfrOHVK0Z4HU6D5O9dZKNEZBXOa40RkYEiMiWgkYWxr1ftZFFmDjed0IO4GHuo0phAaBYXzXXjuvHj+t3MXL/b63AaJH8vkT0KnATsAVDVpcAxgQoqnJWWKf/+ci1dWjdh0tBK3zJtjKkjF47sRHLzOP75xWp782UA1OZ9MFsq9LJHYQPg3UVZrN91gD+N70lUpDV2bUwgNYqO5JbxPVixNZdPllsTMnXN79uURWQ0oCISIyK34l4uM3WnoLiUR79ex8CU5kzoZ03CGBMMZwxKpmfbeB79eh0lpWVeh9Og+Jtg/gBcCyTjNBkzyP1s6tCrczazbX8Bt0/oaU3CGBMkERHCH0/sQfrug7y3eKvX4TQoNd5FJiKRwEWqekEQ4glbBwpLmDJjA0d3T2R010SvwzEmrJzUty0DOjTjsenrmTgoyVosryM1lmBUtRSYGIRYwtpLszax71Axt4y3JmGMCTYR4ZbxPdmak89b8ytWN5vD5e8lslki8l8ROVpEhpR3AY0sjOzPL+aZH9I5oXcbBqU09zocY8LSMd0TGZbagv98u4H8IruHqS74m2BGA32Be4GH3e6hQAUVbp6fuYncghL+eGIPr0MxJmyJCLeO78muvEJenZvhdTgNgj91MBHAk6r6VhDiCTv7DhbxwsxNnNyvHX2TmnkdjjFhbUSXVhzdPZEnZ2zk/BGdaBrrV2Mnpgr+1MGUAdcFIZaw9PQP6RwsstKLMaHilvE92XeomBfspWRHzN9LZF+LyK0ikiIiLcu7gEYWBrLzCnl5dganD0yiR1trO9SYUDAopTkn9mnLsz+kk3PIXkp2JPxNMJfjPPfyA7DQ7RYEKqhw8dT3GyksKeXG47t7HYoxxsfNJ/Ygr7DESjFHyK8Eo6qdK+m6BDq4hmxnbgGvzd3MWUM60KV1U6/DMcb46N0+gQl92/HirAz2Hyr2Opx6y9/WlC+urPN3ISLSS0TmiEih28yM77AMEVkuIktEJGxKRU/O2EhpmVrpxZgQdcPx3ckrLOH5WVaKOVz+3iIxzOf/RsDxwCLgFT+n3wvcAJxRxfBxqho27WXvyi3gjXmZnD20g70K2ZgQ1ScpgZP6tuXFWZu44qjONIuL9jqkesffS2TX+3S/AwbjvN3SL6q6S1XnA1bWxLlzrLRMuWZsN69DMcZU44bju5NXUMKLVoo5LIfbHvwhoK6u7SjwlYgsFJGrqhpJRK4SkQUisiA7O7uOFh182XmFvP7TZs4YlEzHVlZ6MSaU9U1qxol92vLCzE3kFtj5cW35WwfzsYh85HafAGuBD+sohjGqOgQ4GbhWRCp9kZmqPqOqaaqa1rp16zpadPA992M6RSVlXDuuq9ehGGP8cOPx3cktKOGlWRleh1Lv+FsH49ssTAmwWVWzqptARK4Ffud+PEVVt1U2Xnl/Vd0lIu8Dw3Fuh25w9h4s4tW5mzl9YJLdOWZMPdEvuRkn9G7L8zM3cdmYVOIbWV2Mv6otwYhINxEZo6rf+3SzgM4iUu0puKo+oaqD3K7S5CIiTUQkvvx/YDyw4jDXJeQ9PzOd/OJSrjvO6l6MqU9uPL47+/OLeXl2hteh1Cs1XSJ7FMirpH++O8wvItJORLKAm4G7RSRLRBKAtsBMEVkKzAM+VdUv/J1vfZJzqIiXZ2/mlP7t6dbGnto3pj7p36EZx/dqw7M/buJAYYnX4dQbNSWYVFVdVrGnqi4AUv1diKruUNUOqpqgqs3d/3NVNV1VB7pdX1W9v5bx1xsvzMrgQGEJ11vpxZh66Qa3FPP63M1eh1Jv1JRgGlUzLK4uA2nIcguKeXHWJib0bUevdgleh2OMOQwDU5pzdPdEnv1xEwXF9r4Yf9SUYOaLyO8q9hSRK3DaIzN+eGV2BnkFJVb3Ykw9d83Ybuw+UMjbC6u9x8m4arqL7CbgfRG5gP8llDSchyzPDGRgDUV+USkvzspgXM/W9Eu2970YU5+N7NKSIR2b8/T3GzlvWArRkYf7KGF4qHbrqOpOVR0N/A3IcLu/qeooVd0R+PDqv7cXbmHPwSKutqf2jan3RIRrx3Uja18+Hy+t9OZY48Ov52BU9TvguwDH0uAUl5bx9PfpDO3UgmGpLbwOxxhTB47r1YZe7eKZMmMjZwxKJiJCvA4pZFn5LoA+WbaNrTn5XH1sV0RsJzSmIRARrhnXjQ27DvDVqp1ehxPSLMEESFmZ8uSMjfRsG89xvdp4HY4xpg79pn97Uls1ZsqMDaiq1+GELEswAfLd2l2s23mAP4ztYkVoYxqYyAjhD8d2ZVnWfmZuCJs3jdSaJZgAUFWmzNhIcvM4Th2Q5HU4xpgAOHNIMu0SGvHfbzd4HUrIsgQTAPMz9rFw8z5+f2wXu43RmAYqNiqSK4/uzE+b9rI4c5/X4YQkO/oFwJMzNtCqSQyThqZ4HYoxJoAmD+9IQqMonvkh3etQQpIlmDq2ensu363N5rIxqcTFRHodjjEmgJrERnHhyE58sXIHGbsPeh1OyLEEU8eenLGRJjGRXDQy1etQjDFBcOnoVKIjInh+pr1WuSJLMHUoc88hPlm2jQtHdqJZY3spkTHhoE1CI84YnMTbC7ew92CR1+GEFEswdejZH9OJiojg8qM6ex2KMSaIrjqmCwXFZbwyJ8PrUEKKJZg6knOoiLcXbmHioCTaJlT3lgNjTEPTrU08x/dqwytzNpNfZE35l7MEU0de/ymTguIyrjy6i9ehGGM8cNUxXdh7sIh3FllT/uUswdSBwpJSXpqdwdHdE+nZzl6HbEw4Gt65JQNTmvPcj+mUllnzMWAJpk58vHQ72XmF/M5KL8aELRHhqqO7sHnPIb5eZW8zAUswR0xVee7HdHq2jefo7oleh2OM8dCEfu3o2LIxT/+Qbo1gYgnmiM3csJs1O/K44ujO1iS/MWEuMkK48ujOLM7MYcFmaz7GEswReu7HTbSOj2XiIGvU0hgDk4am0KJxNE9/b83HWII5Aut25vH9umwuGdWJ2ChrFsYYA3ExkVw0KpXpq3eyMfuA1+F4yhLMEXjux3QaRUdwwYhOXodijAkhF43sRExkBC/PzvA6FE9ZgjlMu/IK+GDxNs4e2oEWTWK8DscYE0Jax8dy2sAk3lmYxf78Yq/D8YwlmMP02pzNFJeVcfkYaxbGGPNrl41J5VBRKW/N3+J1KJ6xBHMY8otKeXXuZo7v1ZYurZt6HY4xJgT1S27G8M4teWl2BiWlZV6H4wlLMIfhvcVZ7DtUzO+OttKLMaZql4/pzNacfKav3ul1KJ6wBFNLqsqLszLol5zA8M4tvQ7HGBPCTuzTlg4t4nhhZobXoXjCEkwtzdqwhw27DnDZaHuw0hhTvcgI4dLRqczL2MuKrfu9DifogpJgROQCEVnmdrNFZKDPsAkislZENojIHcGI50i8NHsTiU1jOHVge69DMcbUA5PSUmgcE8kLs8LvjZfBKsFsAo5V1QHAfcAzACISCTwBnAz0ASaLSJ8gxVRrmXsO8c2aXUwe3tEerDTG+KVZXDSThnbg46Xb2JVX4HU4QRWUBKOqs1W1vGGeuUAH9//hwAZVTVfVImAaMDEYMR2OV+ZkECliD1YaY2rlktGpFJcqr8/N9DqUoPKiDuYK4HP3/2TA9ybxLLffr4jIVSKyQEQWZGdnBzjEXztYWMKbC7YwoV872jWzN1YaY/zXpXVTjuvVhtd/2kxhSfi88TKoCUZExuEkmNvLe1UyWqVtXKvqM6qapqpprVu3DlSIVXp/8VbyCkq4bExq0JdtjKn/LhuTyu4DRXy8dLvXoQRNwBKMiFwrIkvcLklEBgDPARNVdY87WhaQ4jNZB2BboGI6XKrKy7OdW5OHdGzhdTjGmHroqG6JdG/TlBdmbgqbd8UELMGo6hOqOkhVBwFRwHvARaq6zme0+UB3EeksIjHAecBHgYrpcM3euIf1uw5wqd2abIw5TCLCZWM6s2p7LvM27fU6nKAI1iWyvwCtgCluiWYBgKqWANcBXwKrgbdUdWWQYvLbi7MyaNUkhlMH2K3JxpjDd+bgZOIbRfHq3M1ehxIUUcFYiKpeCVxZxbDPgM+CEcfh2LL3EN+s2cm1Y7vRKNpuTTbGHL64mEgmDU3hlTkZ7MotoE1Cw75hyJ7kr8ErczKIEOHCkXZrsjHmyF00qhMlZcq0MGhl2RJMNQ4VlfDmfLs12RhTdzonNuHo7om88VNmg29l2RJMNd5fvJXcghIuG53qdSjGmAbk4lGp7Mgt4OtVDbuVZUswVVBVXp2zmb5JCQztZLcmG2PqznG92pDcPK7BV/ZbgqnCgs37WLMjj4tGdrJbk40xdSoyQjh/REdmb9zDhl15XocTMJZgqvDa3M3EN4ri9EFJXodijGmAzh2WQkxkBK/OabilGEswldhzoJDPl+/gt0M60DgmKHdyG2PCTGLTWE7p3453F23lYGGJ1+EEhCWYSry9MIui0jIuGNHR61CMMQ3YRaNSOVBYwvuLt3odSkBYgqmgrEx546dMhnduSfe28V6HY4xpwIZ0bE7fpARem7u5QbZPZgmmgh/WZ5O595A9WGmMCTgR4eJRnVizI4/5GftqnqCesQRTwWtzM0lsGsOEvu28DsUYEwZOH5hMQqMoXpmT4XUodc4SjI+tOfl8u2Yn56SlEBNlm8YYE3hxMZFMSkvhixU7Gtwrle0o6mPavEwUmDzcKveNMcFz4UinfbK3Glj7ZJZgXMWlZUybv4VxPduQ0rKx1+EYY8JI58QmjOnWiqnztlBW1nAq+y3BuL5auZPsvEK7NdkY44nJwzuyNSefHzfs9jqUOmMJxvXa3M0kN49jbM82XodijAlD4/u0o1WTGKb+lOl1KHXGEgywYdcB5qTv4fwRHYmMsHbHjDHBFxMVwdlDO/D16p3sym0Ylf2WYIA3fsokOlI4Jy3F61CMMWHsvOEdKS1T3l6Y5XUodSLsE0xBcSnvLspifN92tI6P9TocY0wY65zYhNFdWzF1XmaDqOwP+wTzxYod7M8v5ny7NdkYEwImD+9I1r6GUdkf9glm2vxMOrZszKgurbwOxRhjOKlvw6nsD+sEs2n3Qeam7+XcYSlEWOW+MSYElFf2T28Alf1hnWDenL+FyAjh7KEdvA7FGGN+du6wFEoaQGV/2CaY4tIy3lmYxbiebWib0MjrcIwx5mddWjdlVJf6X9kftgnmm9W72H2gkMnD7dZkY0zoOX+EU9k/sx5X9odtgpk2P5O2CbEc26O116EYY8yvjO/blpZNYpg6r/5W9odlgtmak8/367I5Jy2FqMiw3ATGmBAXGxXpPNm/ame9bcY/LI+uby/Ygir25L4xJqSdV17Zv6B+VvaHXYIpdd+5cHT3RGuW3xgT0rq0bsqIzi3dk+L6V9kfdgnmx/XZbNtfwHnD7Ml9Y0zoO3dYChl7DvHTpr1eh1JrYZdgps3bQssmMZzQx5rlN8aEvpP7tSc+Noo36+HbLoOSYETkAhFZ5nazRWSgz7AMEVkuIktEZEEg48jOK2T66p38dkgysVGRgVyUMcbUibiYSCYOTuKz5dvZn1/sdTi1EqwSzCbgWFUdANwHPFNh+DhVHaSqaYEM4t1FWZSUKecOs8p9Y0z9cW5aRwpLyvhoyVavQ6mVoCQYVZ2tqvvcj3OBoLfNoqq8OX8Lw1Jb0K1NfLAXb4wxh61fcgK92yfw5oL6dZnMizqYK4DPfT4r8JWILBSRq6qaSESuEpEFIrIgOzu71gs9VFTKiM4tuXhUaq2nNcYYL4kIl41OZVBKcwpLSr0Ox28SzFvfRGQcMAU4SlX3uP2SVHWbiLQBvgauV9UfqptPWlqaLlgQ0OoaY4xpUERkYaCrISoKWAlGRK51K+6XiEiSiAwAngMmlicXAFXd5v7dBbwPDA9UTMYYY4InYAlGVZ9wK+4HAVHAe8BFqrqufBwRaSIi8eX/A+OBFYGKyRhjTPBEBWk5fwFaAVNEBKDELaq1Bd53+0UBb6jqF0GKyRhjTAAFJcGo6pXAlZX0TwcG/noKY4wx9V3YPclvjDEmOCzBGGOMCQhLMMYYYwLCEowxxpiACOqDlnVFRLKBzV7HUUuJQP19ufbhsXUOD7bO9UMnVQ3qO+LrZYKpj0RkQbCfovWarXN4sHU2VbFLZMYYYwLCEowxxpiAsAQTPBXfgRMObJ3Dg62zqZTVwRhjjAkIK8EYY4wJCEswxhhjAsISTBCJyDARKRWRs72OJdBE5AIRWeZ2s0WkQTdqKiITRGStiGwQkTu8jifQRCRFRL4TkdUislJEbvQ6pmARkUgRWSwin3gdS6izBBMkIhIJPAh86XUsQbIJOFZVBwD30YArRd3v9gngZKAPMFlE+ngbVcCVALeoam9gJHBtGKxzuRuB1V4HUR9Yggme64F3gV1eBxIMqjpbVfe5H+cCHbyMJ8CGAxtUNV1Vi4BpwESPYwooVd2uqovc//NwDrjJ3kYVeCLSAfgNztt5TQ0swQSBiCQDZwJPeR2LR64APvc6iABKBrb4fM4iDA625UQkFRgM/ORtJEHxKHAbUOZ1IPWBJZjgeBS4XVVLvQ4k2ERkHE6Cud3rWAJIKukXFvf/i0hTnJL5Taqa63U8gSQipwK7VHWh17HUF8F6ZXLYEZFrgd+5H5sB09xXQycCp4hIiap+4FV8gVBhnU/BWdfngJNVdY9ngQVeFpDi87kDsM2jWIJGRKJxksvrqvqe1/EEwRjgdBE5BWgEJIjIa6p6ocdxhSx70DLIROQl4BNVfcfrWAJJRDoC3wIXq+psr+MJJBGJAtYBxwNbgfnA+aq60tPAAkics6WXgb2qepPX8QSbiIwFblXVU72OJZRZCcYEyl+AVsAUt+RW0lBbn1XVEhG5DucOwUjghYacXFxjgIuA5SKyxO13l6p+5mFMJsRYCcYYY0xAWCW/McaYgLAEY4wxJiAswRhjjAkISzDGGGMCwhKMMcaYgLAEYxo0EZkhIidV6HeTiEypZppUEVnh/j/IfbAu4ETkURE5RkT+KiIPVBg2SERWu/9PF5EWwYjJmCNhCcY0dFOB8yr0O8/t749BOK0SBJSItARGquoPOLGdW2GU84A33P9fBa4JdEzGHClLMKahewc4VURi4eeGGZOAmeL4t4isEJHlIvKLg7qIxAD3AueKyBIROVdEhrvvt1ns/u3pjttYRN5y33/zpoj8JCJp7rDxIjJHRBaJyNtu+10VnQ18AaCqa4EcERnhM/wcnFaaAT4CJtfN5jEmcCzBmAbNbQNtHjDB7XUe8KY6TxifhVNCGQicAPxbRNr7TFuE0yLBm6o6SFXfBNYAx6jqYHfYP9zRrwH2+bz/ZiiAiCQCdwMnqOoQYAFwcyWhjgF8G1H8ueQlIiOBPaq63o1rHxArIq0Oe8MYEwSWYEw48L1M5nt57ChgqqqWqupO4HtgWA3zaga87dbRPAL09ZnXNABVXQEsc/uPxHkJ2Sy3SZVLgE6VzLc9kO3zeRpwtohEUPklvV04JTFjQpa1RWbCwQfA/4nIECCu/EVZVN7Mfk3uA75T1TPdy20zapiXAF+rak2XtPJxWugFQFW3iEgGcCzwW2BUhfEbudMYE7KsBGMaPFU9gJMIXuCXJYEfcOpXIkWkNXAMzuU0X3lAvM/nZjgtJgNc6tN/Jk49Ce6rg/u7/ecCY0SkmzussYj0qCTM1UC3Cv2m4pSSNqpqVnlPtyXjdkBGpStsTIiwBGPCxVScupZpPv3ex7mUtRTn1QK3qeqOCtN9B/Qpr+QH/gU8ICKzcFpOLjcFaC0iy3BerrYM2K+q2TiJaKo7bC7Qq5L4PgXGVuj3Ns4luGkV+g8F5qpqSU0rbYyXrDVlY+qAiEQC0apaICJdgW+AHu6NAv7OYyZwqqrm1DDeY8BHqvrNEQVtTIBZHYwxdaMx8J37lkcBrq5NcnHdAnQEqk0wwApLLqY+sBKMMcaYgLA6GGOMMQFhCcYYY0xAWIIxxhgTEJZgjDHGBIQlGGOMMQHx/wEz2M3zeP/vDwAAAABJRU5ErkJggg==", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "axes, cbaxes = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's do that now" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1)\n", "axes, cbaxes = plot_dataset(dataset, axes=[ax, ax])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Regular 2D rectangular sweep scan\n", "\n", "For 2D plots, a colorbar is usually present. As mentioned above, the `plot_dataset` function returns a colorbar." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 746\n" ] } ], "source": [ "meas = Measurement(exp=exp)\n", "\n", "meas.register_parameter(x)\n", "meas.register_parameter(t)\n", "meas.register_parameter(z, setpoints=(x, t))\n", "\n", "xvals = np.linspace(-4, 5, 50)\n", "tvals = np.linspace(-500, 1500, 25)\n", "\n", "with meas.run() as datasaver:\n", " for xv in xvals:\n", " for tv in tvals:\n", " # just some arbitrary semi good looking function\n", " zv = np.sin(2 * np.pi * xv) * np.cos(2 * np.pi * 0.001 * tv) + 0.001 * tv\n", " datasaver.add_result((x, xv), (t, tv), (z, zv))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "axes, colorbars = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A fairly normal situation is that the colorbar was somehow mislabelled. Using the returned colorbar, the label can be overwritten." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "colorbar = colorbars[0]\n", "colorbar.set_label(\"Correct science label\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Customisations\n", "\n", "In addition to tweaking the returned axes and colorbars, it is possible to customise the plot directly via the call to the `plot_dataset`. This is done by the `plot_dataset` via passing on the keyword arguments to the corresponding `matplotlib` function that runs under the hood. \n", "\n", "A few examples:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Changing the colormap\n", "\n", "Any `matplotlib` colormap can be used. The relevant keyword argument is `cmap`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cmap = matplotlib.colormaps.get_cmap(\"hot\")\n", "axes, colorbars = plot_dataset(dataset, cmap=cmap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Setting the colorscale to logarithmic\n", "\n", "To set the colorscale to logarithmic, we can use the keyword argument `norm` along with a `matplotlib` `Norm`." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib.colors import LogNorm\n", "\n", "norm = LogNorm()\n", "axes, colorbars = plot_dataset(dataset, norm=norm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Setting limits on the colorscale\n", "\n", "The keyword arguments `vmin` and `vmax` come in handy, if we need to set limits on the colorscale." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "axes, colorbars = plot_dataset(dataset, vmin=0.2, vmax=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Warped 2D rectangular sweep scan\n", "\n", "A nice feature of the `plot_dataset` is that the grid may be warped; it makes no difference.\n", "Here we warp the x axis of the previous scan to increase the resolution in the right half plane." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 747\n" ] } ], "source": [ "xvals = np.linspace(-4, 5, 50) + np.cos(-1 / 6 * np.pi * xvals)\n", "tvals = np.linspace(-500, 1500, 25)\n", "\n", "with meas.run() as datasaver:\n", " for xv in xvals:\n", " for tv in tvals:\n", " zv = np.sin(2 * np.pi * xv) * np.cos(2 * np.pi * 0.001 * tv) + 0.001 * tv\n", " datasaver.add_result((x, xv), (t, tv), (z, zv))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "axes, cbaxes = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interrupted 2D scans (a hole in the cheese)\n", "\n", "In case a sweep in interrupted, the entire grid will not be filled out. This is also supported,\n", "in fact, any single rectangular hole is allowed." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 748\n" ] } ], "source": [ "xvals = np.linspace(-4, 5, 50) + np.cos(2 / 9 * np.pi * xvals + np.pi / 4)\n", "tvals = np.linspace(-500, 1500, 25)\n", "\n", "# define two small forbidden range functions\n", "\n", "\n", "def no_x(xv):\n", " if xv > 0 and xv < 3:\n", " return True\n", " else:\n", " return False\n", "\n", "\n", "def no_t(tv):\n", " if tv > 0 and tv < 450:\n", " return True\n", " else:\n", " return False\n", "\n", "\n", "with meas.run() as datasaver:\n", " for xv in xvals:\n", " for tv in tvals:\n", " if no_x(xv) and no_t(tv):\n", " continue\n", " else:\n", " zv = (\n", " np.sin(2 * np.pi * xv) * np.cos(2 * np.pi * 0.001 * tv) + 0.001 * tv\n", " )\n", " datasaver.add_result((x, xv), (t, tv), (z, zv))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "axes, colorbars = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fancy plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a final example, let us combine several plots in one window.\n", "\n", "We first make a little grid of axes." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, figaxes = plt.subplots(2, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we make some runs (shamelessly copy-pasting from above)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 749\n", "Starting experimental run with id: 750\n" ] } ], "source": [ "# First run\n", "meas = Measurement(exp=exp)\n", "meas.register_parameter(x)\n", "meas.register_parameter(y, setpoints=(x,))\n", "\n", "xvals = np.linspace(-3.4, 4.2, 250)\n", "\n", "with meas.run() as datasaver:\n", " for xnum in xvals:\n", " noise = np.random.randn() * 0.1 # multiplicative noise yeah yeah\n", " datasaver.add_result(\n", " (x, xnum), (y, 2 * (xnum + noise) ** 3 - 5 * (xnum + noise) ** 2)\n", " )\n", "\n", "ds1 = datasaver.dataset\n", "\n", "# Second run\n", "\n", "meas = Measurement(exp=exp)\n", "\n", "meas.register_parameter(x)\n", "meas.register_parameter(t)\n", "meas.register_parameter(z, setpoints=(x, t))\n", "\n", "xvals = np.linspace(-4, 5, 50)\n", "tvals = np.linspace(-500, 1500, 25)\n", "\n", "with meas.run() as datasaver:\n", " for xv in xvals:\n", " for tv in tvals:\n", " # just some arbitrary semi good looking function\n", " zv = np.sin(2 * np.pi * xv) * np.cos(2 * np.pi * 0.001 * tv) + 0.001 * tv\n", " datasaver.add_result((x, xv), (t, tv), (z, zv))\n", "\n", "ds2 = datasaver.dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we put them just where we please." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "axes, colorbars = plot_dataset(ds1, figaxes[0, 0])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "axes, colorbars = plot_dataset(ds2, figaxes[1, 1], colorbars)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that if we want to replot on an axis with a colorbar we probably also want to reuse the colorbar." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "axes, colorbars = plot_dataset(ds2, figaxes[1, 1], colorbars)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rasterizing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default `matplotlib` renders each individual data point as a separate square in 2D plots when storing in a vector format (pdf, svg). This is not a problem for small data sets, but the time needed to generate a pdf increases rapidly with the number of data points. Therefore, the `plot_dataset` will automatically raster the data (lines, ticks and labels are still stored as text) if more than 5000 data points are plotted. The particular value of the rasterization threshold can be set in the `qcodesrc.json` config file.\n", "\n", "Alternatively the `rasterized` keyword can be passed to the `plot_dataset` function." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 751\n" ] } ], "source": [ "meas = Measurement(exp=exp)\n", "\n", "meas.register_parameter(x)\n", "meas.register_parameter(t)\n", "meas.register_parameter(z, setpoints=(x, t))\n", "\n", "xvals = np.linspace(-4, 5, 100)\n", "tvals = np.linspace(-500, 1500, 500)\n", "\n", "with meas.run() as datasaver:\n", " for xv in xvals:\n", " for tv in tvals:\n", " # just some arbitrary semi good looking function\n", " zv = np.sin(2 * np.pi * xv) * np.cos(2 * np.pi * 0.001 * tv) + 0.001 * tv\n", " datasaver.add_result((x, xv), (t, tv), (z, zv))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get a feeling for the time difference between rasterzing and not, we time the two approaches here." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 1 s\n" ] }, { "data": { "image/png": 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be601fcly+641VW6u/MGVV6jqrahhD77/Dnrlb1aLdwS++e0bP6OqD6nDWy/rS6VyPuERI9eKyMMIj0I4YHwnETmK7HHPO+y+Aw9/T5i2/fWL9wPgpl9tP9x3aWV0f9Jg5+Cce+01aiwdute3R8s7hfuHDtg4uiXl2sFoUsWXb9gFgI9eMbrtJDIt12OmuJGZ4kZmitt2rR7Tcj2m5XrMedVa15fmSWuq3Jx88Em17+e48jernPOZckNey3v8eLe6vPWyXlQqqnq1WT5DRN4qIrup6hVj+20CNgHc8o630oFmBaDCsNJqwU2xAyfTIa8ic724XWHmuD0/vznuQutMuDmtDZgCAwaF+3XdelGpiMjuhMclqIjckzBWVHifyWDc0XIFZnLZOtlAR03nm7KhqS3mkVSrxkkH2fYJ3jgrwfe4N5nhMI87MNtdbsu1ekzL9ZhJ7rxrrelLltt3rYXMGqYoW7Rc91eXrROVioicQnii524SHqB2LOHOeVT1BMKjoJ8pIiuEu+MP0604rW1YYHIFYrS82nDEY5kp7iyYlrveWmfNtNy+ay3ypa3JXW+ts/KlEWfRUmmFqer4Q+/Gtx/PlDeFKU7zVkw9VOBbqybiik1r6zCrpk5bjVFeymEjt4Q/R65tznvcVRtxedyWa/WYlusxk9yF1jW5Rb5kuX3XWsisYYrm9PXVOlGptMU0F+WMlqMjrurAbB+ZbfI3xbXO73FnwbTcWWv1mJY76/NruX3XWuRLTXLbrrUus8hSY0Z9srmuVCYcKNH1nouIMrMR15Zs+UZGz3W7wUQkN+myzxvjFjEtd4vJy+NGZpLbcq0e03I9ZpI771pr+pLl9l1rIbOGKfkxo77a/FYqKrWatzbtyGFt091GSQVdBxW4+cI5yR002IxfD60e03I95qy4fdda5EuW23etTTLd41hUKnNmKR9y1ttm9Jbs5YI3GSe1EdFqUcTjcRPHErlbzAsNPW4lZmL9emj1mJZbmzkFt/Naa/pSk9y2ay1k1jAlX5H11ea2UglzxjMPHDadzXbrW46j2ijmBt0IwI25dSPn3RK7DmxGuW6CckzLjcwUd4ttxnvclmv1mJbrMXNch1mV23mtNX3JcvuuNVluGjBFF91fc2fJ2STOjVFMRlT5OfB+4ViTW8C0XBtRedzSzBLc9dDqMS23ErMit/ta6/nSVNyOay1k1jGF1f7XKfNdqcToRTLntFGWFERcWwaeQxqHNc7pTccUUyBisiKm5eYL3yQ3NTWyK1o9puV6TMv1mFW5ndda05cst+9aZzulOD+7ra82t5WKe5/KkgkjrMM6M0rszJAbNLyu/feD0Wvbr9PR8o2DjVmahJNGbgHTcm8w+XvcyExyW67VY1qux0xy51xrXV+y3L5rLWTWMil8XEwfbG4rFWvDYYZE37s4fmCd44bMEW1/rXVez5FyXcNOf7THtHndMLCsSW7Kebui1WNabhVmVW7Xtdb1pWm4Xdc6y4t+GKhfVCq9tokmtY18TDtVlryIa+QcsUltndQ247cMNkykyUVUMf8Cps3DNuM9bmQmuS3X6jEt12PadC6zIrfrWuv6kuX2XWuq3DRh4T6VRaUyFzZ0VOOwsmQLz2SaFWdAMRf5mIjIG/zLFY5suYhpuanCEbmpAceuaPWYlluFWZXbda11fWkabte1znSgntnc+9I2m9tKRRET/YR1NspRrMMOsv3MvHbzMp/rBtvmviFfOG50o7zRsUSux0xxLcvj3phsqXRDq8e0yx7Tcj3mvGqt60vzpDVVbpqwRUtljmxpGGXZpvPIOZcKmvHDvvdEf60X/Szloqy4vDbTcnMsh5uKuLqi1WPm+JWY1bhd11rXl6bjdlvrLFsqiuRml/XV5rpSiTc3yfBCa7faZnY6LYwc0UY+1jlXBpM3jkmucJRj2jxs/h53xUSEHrftWj2m5XpMm85nVuN2XmtNX7LcvmtNlZumbNH91WfTycG/pWXz1FLTzl7OmtQ2SrLOd302NfGa1dELeKzz+l0Ho7wi12OmuNeb6ZAeN9391Q2tHtNyPablesx51VrXl+ZJ62y7vyQ3EaGvNr+VirHlpckLrYhfeKJZh7txNZzGG2V0OlM3WY0zLbeIabmRmeJ6TMttu1aPablVmFW5Xdda15em4XZda6rcNGEKuScK9NXmtlJRnJaKcUhz42/OkaOt2EdoZ9HNdSYKsoVn2HWQivKy5SKm5dqIyuPmmvFeS6XlWj2m5XpMu95jVuV2XWtdX7LcvmtNlZumrEsD9SKyA3CD6nTvQJ7bSgVGTrOU9cdusM1p41DLwyjMNqdHheP61dGA3zC9vclrdfJu5CXT9xy5HjPF9ZiWe4PZ7nHbrrUK0+brMVPcvmut60uW23etqXLThKnKTJ+CXNdEZAk4DDgc+BPgRmBbEfk1cAawSVV/XJTPXFcq0WJ0Yx3SBjy2nzaadbibBpOn0RaOlYJmfOQWMS3XY1qux7TctmudBXO9uOutta4vTcPtutZUuWnKmhr8F5F9gJOB3QlT3zap6nE1s/0S8HngxcB3VcOrMUXkFsD9gdeIyMdV9X1rZdKaSkVETgIeAVyuqndxth8OvCj7eS3wTFX9Vrbt58A1wCqwoqoHFfHy96lkF1rjkPZVpMtOP611vpsGk4Nv+Wb82g/ji9wipuV6TMuwEaHHbbvWKky73mPOitt2rXV9yXL7rjVVbpqwMFDf2CV3Bfh7VT1fRHYCvikin1PV79fI8wGqumV8par+BjgVOFVE/KaesdZUKsC7geMJta9nPwPup6q/FZGHApuAg832+6vqFVXAsW8255CO8+b6cG3EtTrZ35wrHE70Y/MachMFxuNGZoqbiri6otVj2vWVmAlu37XW9aWpuB3XOsuWSpMD9ap6GXBZtnyNiFwI7AVUrlRshSIi9wUOUNV3icitgB1V9WdepTNuralUVPUsEdlvje1fMz/PBvauz8yiG+fO6iJzI5rEcFbcnoviEnf+luXmoiiHa7d73LZrTUaMJbXWPb+5fHuutYhp9+m71lS5acpWy+e5m4icZ35vUtVN3o7ZdfNuwDdqHdwov2OBg4A7AO8CNgLvA+5TJn1rKpUp7W+BT5nfCnxWgpe9fY2TfxRwFMC2t95puD4O/qX6Yz3nsgNuZV+8YwcZ7bLHTTl05E7zsh+P23atVZiWVcRsktt2rbP2pWm4XdXahE15R/0VZbrxRWRHQtfU81T16jrHZ+yvCJXU+QCqemnWxVbKOlepiMj9CZXKfc3q+2TCbw18TkR+oKpnjafNKptNADvdYXeNg2bLEpxzQ8Eg32oiyorrUw49dH6Tf2SmuF7+qWPxuLkC53DbrjWlv6zWIqZl9F1rXV/KcXuuNVVumrJBg91r2fjGqcD7VfVjjWUMN6mqZkF6nFpc2jpVqYjIXYETgYeq6pVxvapemn1fLiIfB+4JTFQq1lRHjrZtNotku+UVd9/Yz3qDddhVM99+JZzG1YEdxByl37gU2tnbbhxFPtuamSse1/btetzITHEjM8Vtu1aPabke03KLmJbbd611fcly+641VW6aMIXGnv0l4W7PdwIXquobGsl0ZB8WkbcDu4jI04GnAu8om7gzlYqI7At8DHiSqv7IrN8BWMoGq3YAHgS8Ypq844CejXzsIJ/X92ojnuicK2YQ0KZfcvp+7brILWJari0QHtdj2vVt1+oxLbcKsyq381pr+tI03K5rTZWbJkyRJh9YeR/gScB3ROSCbN0/q+oZdTNW1deJyAOBqwnjKi9V1c+VTd+aSkVETgEOIQxQbQaOJQwQoaonAC8Fbgm8NXskQ5w6fBvg49m6DcAHVPXTZZix+bshdgnlmtYj7xzeeGUcTo3vRedcMVGYbVgvOdMtc6xhl9QkM8W1BcLjLpkC53HbrtVjWq7HtFyPmeL2XWtdX7LcvmtNlZsmTJXGbn5U1a+QfHxmI/l/DihdkVhrTaWiqk8o2P404GnO+ouAP56eKBMXle2WR7Pl8tMRJx3BOseWleCoqyvGSe3Ux+XJqak2/8gtYlpuZKa4kZnitl2rx7Rcj2m5HrMqt+ta6/qS5fZda6rcNGMykycfN20icg1MzFK4CjiPcG/MRWulb02lsp426hIaRVkbTRTk3TBlm9GD1bA8sE5q7vy1/cjjTMstYlpuZKa4HtNy267VY1puFWZVbte11vWlabhd15oqN02Y0lxLZcb2BuBS4AOE1tBhhDv3fwicROhRStpcVyrDJnXW5N2Y6CaJzex8c3rknBpnCRmHtfW8N8ff9gdvdLoObNPe46qdxeJwU/Ptu6LVY+a4DtOm85gpbt+11vUly+271pnfp9KNpxQ/RFXtjeWbRORsVX2FiPxzUeK5rlSiDR+yaGd+LI1mlly/NDn6Zx1uGP2YKEdN57EOJp0z92C8OLOmgGm5NuLyuB7Tctuu1WUabhVmZW7Htdb1pWm4XdeaKjdNmH00VMttICJ/DXw0+/04s61wJsPcVirKZD+tLRB2eaMT0dpmvEbn3GLWmVb4YHVyjr7NK7I8ZoqrtpnucAdmu8dtu1aXabge03I9Zorbd611fcly+641VW6aMAW2NPfsr1na4cBxwFsJh3028EQR2R44pihxJxTO2pYkdgnZPlrTjHein4F9ccQwoh05oXVHde6hWhLbjF/NfaeYOW4u4prkekzLbbtWl2mWqzArczuuta4vTcPtutZUuWnGpPXvUxGRZeBQVX1kYpevFOUxv5WKjqYZxogmdfFZdlp8dk10xCXjpPntsT96tM5GUV6B9Jg2X+v8Htc24z1u27V6TMv1mJbrMatyu661ri9Zbt+1pspNExZ6R9o9pqKqqyJyKPDGqnnMb6UCw+l9sZ/WNqe3WxpNV/SiH+t8EmeurPiDnHFw0E4ntH3DkVvEtFyxM1ccrh2Q9Lht1+oxc1yHabkesyq361rr+pLl9l1rqtw0ZW1vqWT2VRE5HvgQ8Pu4UlXPL5N4riuVaMPoPdcf7Ee30XIzQ7LoJ7ebrB3x5CKuYX/w2swc10RcHjcVZXVFq8fMcSswq3K7rrWuL03D7brWplsn48fQ9pZKZvfOvl9h1inwF2USz22loowcLfbT2qbzRhk5rPcIh5zzxSjI9seuTm7X3CDnaOfI9ZhJro34PG6uGT/JbbtWj5njOkzL9Zgpbt+11vYls0/vtSbKTRMWBuobe0zLzExV718n/dxWKtbiy35SUZYbcZlmcnTOXHPaOHxuPvwY0+ZfqqWScW2B8Lge03LbrtVj5rnTM6tyu661ri9Nw+261lS5acba/Y56ayLycODOwHZxnaq+Ip1iZHNcqUzOGbdRVm7AMRfSBLMRl8SI1vizLpmIKNs3NUUxcouYlis24nK49rl4HrftWj2m5XrMFHfetdb1Jcvtu9YiZh3TGeQ5CxORE4CbAfcnPBX+ccA5ZdPPcaUysuELgOxzhxLOObRcMz58WYfMjRcW3MQVuYVMy7URl8ctuPmx9VodpuVWYVbl9l5rkS9Nwe281hne/AiduaP+3qp6VxH5tqq+XEReT3hCfCmb60plfFBu2XjcUm55cvQu17ccLz7GYXMBifq8cW4R03Lt+4Ncrto0aWYZ7npo9ZiW6zFT3HnXWtuXDLf3WguYdaxDd9Rfn31fJyJ7AlcC+5dNPOeVythAvfGybYq6omwzPt64ZaIs+7Kh6PvJQc5suZBpuGJv3PK4tsB4A/Vt1+owLddjWq7HrMrtutbavmS4vdeaKDdN2aAbLZVPisguwH8QXimshG6wUjbXlcq4paKc6MjJh80N+2tHq2zTmgLn9Li28LjcXN+0w63AtNz11uoxLbcu03L7rrW2LzXIbb3WGbYkVGHLoP2Viqq+Mls8VUQ+CWynqleVTT+3lYrq2pHIUiriGWYwWhzuapN4TfuiQjLFmIokWMNxgCkirlZqTRR+HP5Caz1ukS/ZffqudRrmtBa6v9pfqQCIyL2B/cjqCBFBVU8uk3ZuKxWYnIlhm87LFDipuTNWvIjWSZ7qT43cYuaIK6mIqyS3/Vr9WUDiFP4i7kLr2ty6vmS586C1jnXhjnoReS9we+ACRnfwKLCoVGZq3iwW67CJ5ca4A9bIjAgAACAASURBVGedYc2Eabmz1pqYJYSjb6G1JrfAlxrltlxro0znEJqqqETkJOARwOWqehdn+yHAfwE/y1Z9rOx9JsBBwJ1Uq01V6E2lUnSSPZv2lKUcwnPIXN7D/uTpeGtxU4VvyMj1JzfDtKz11uoxq3LnXWuhLxlu77XWZK5tjXZ/vRs4nrVbD/+jqo+okPd3CW96vKxC2v5UKpQ7ya5V+qMdh0s2p519KztXWW6iQHRGa+L43W6S9Ti/TXIXWtujdYYtFaCxh1Sq6lkisl8jmWUmIp8gnIGdgO+LyDnAjYb5qDL59KZSqXKSpx2Iy0U+06QdRmHVHKoSNxeFTc9dd60VmFW5C63lub3XWpO5ZtYKWwaln/21m4icZ35vUtVNUyL/VES+RXjf/AtV9XsF+79uyvxd602lUsZE5CjgKIANt7r5cH2MHuxzeeydr150lG+mj32PLztmI5bILWLmuClWSW7btbrMFL8kszK341pn7UuW21etTdiUNz9eoaoH1cCdD9xWVa8VkYcBpwEHrHl8ql8GEJH9gctU9Ybs9/bAbcqCuzG/rSFT1U2qepCqHrS88w6oylTRyEBl+JnGRMMn8qq0kKblRmZV7nprrcK03CrMhdZibt+11i03hceElPrUNVW9WlWvzZbPADaKyG4lk3+E/JSG1WxdKZurlsq4xeBkNXMa+1hq+y7pGBElb9wqGdHaVasmr8j1mCmupFglua3XmhhQLYpox/9Ty0xx+6511r5kuX3V2oQps5mm7JmI7A78SlVVRO5JaEBcWTL5BlW9Kf5Q1ZtEZJuy7LmuVKINnKa7dd5ZvAFu4DTdZ8203L5r9ZjrxV1vrbP2Jcvtq9amrKnZXyJyCnAIYexlM3AssBFAVU8gPFn4mSKyQniW12FTTBH+tYg8SlVPz1iHAleUPbbeVCreSVbVdyYT6CiSGYx9Q/4ZPTH6qeywziDnwFn2mJW5iQHHedHqMVPcvmutzTTc3mud6UC9sNJQpaKqTyjYfjxhNmwVOxp4f/ZKYYDNwJPKJu5NpVJ0ktey6Hy26Z5vWoflwtkkklheg2m5HnNW3NZrTRVoGfuegpni9l3rrH3JcvuqtSnrwlOKVfWnwL1EZEdAVPWaadL3plKpZFlUEv9o+wiFXMSTba8duZgoyDpX5HrMWXH7rtVjprh91zprX7LcvmptwrbmmEoTFgf6p7W5rlSi060MJqOcwojW5uNFWc6ydfKVwWT+pVoq48wU1wZpDrfzWhP88f90Lda8aK3tS2a591oT5aYp61KlUtXmulKJFvs5bxxsHK6zr0WNjpx0MqdlX+Q7tm81cj1mkptgleV2XWsVZorbd62z9iXL7avWJqxDL+mqZXNdqeh495dx2HwUNNm0di0VBY3xxvOKXI85K27ntSZ2G/9PLTPF7bvWWfuS5fZVa1PW9tlpACLybOD9qvq77PeuwBNU9a1l0s91pRItOt+Ng9HpyEVcGiPatfPJTexwoiDrTtbhI9djVuXa8uRxO681EWXGRY+Z4vZda11fsty+a02VmyZMFVY68JIu4Omq+pb4Q1V/KyJPBxaVylqmmH5aJ8qyDhuf15OKMry+dy9IyvVHO1GWx0xxU33DZbld15oKQsf/U8tMcfuudda+ZLl91dqUdaT7a0lEJN7XIiLLwOLmx0JThp4UowdbIJbNrbc3ZY66mooystU28nEjvtwgp+0b3pBkJrkJlhfxedyua01F1OP/qWWmuH3XWtuXLLfvWhPlpgnr0JjKZ4APi8gJhCvl0cCnyyae30rFWHTUm6yTmvmEowFdP/1wkNH6cKpwjDEt12OmuLlBRo+b8N3eaK3ATHH7rrW2L03B7bzWGV/zZ9H6mYG9CHgG8EzCGfkscGLZxHNdqUSnGw4yGodcsoXDe0aUNefi4zenR8u5QcaM6zGT3EThKMvtutZ0N0mameL2Xeusfcly+6q1KevCQL2qDoC3ZZ+pba4rlWixmXzD6miKom2m3rC6IbffuHlRVr5pHbzTupPNK3I9ZoqbiihH3QijEuFxu67VY1qux0xx+661ri9Zbt+1pspNE6ba7jEVEfmwqv61iHwHJm/9VNW7lslnviuVGP0Mu4SW3d1ikzsZuWT9ubpkHMbzHRvl5Zrxk1zbzHe5pu+6CrfzWlNlc+w/TTEtt+9aZ+1LjXJbqrUZk/T4XTvs77LvKq8gHtp8VyqZrQ6CR9206l98tqw6N8nlnDP/Pb68FjPF3bJq+4YnuSlWWW7rtTrMFL8K03L7rnXWvjQVt6Nam7I2j6mo6mXZ9y/q5DPHlcro5Tuxb/a6lVFzOnfnbtakTj7OIxaI5cl1uWWT3vYHR67HTHFz+TvcXCFxuF3XmrwgjP2nlpni9l1rXV/KLfdca6rcNGFdefaXiDwG+Hfg1oQ2nACqqjuXST/HlQrD5u1gMOmwNqKITVYdJByioD94GJ3Zu5EHk4XTYya5BX3fubcROdyua3WZhusxU9y+a63tS5bbd62JctOIKTMZ/J+BvRZ4pKpeWCXxfFcqma2sLmXfxklNf+uo793vg40OmYry3KmRq0tmeTnJTHFt4XO5ietkZ7Q6TMutwkxx+661ti9Nwe281hk3JLow+4vwxshKFQrMc6WiI6cbZI64xTik7buNjjzVYx+cwpF7wZFhRa7HrMy1hdfhdl5r4uIz/p9aZorbd621fclwe681UW6aMG3/QH2080TkQ8BpwI1xpap+rEzi+a1UYKJJfdOW0ekQ0wzespI9zmPVdwhdLvDepbW7DiLXY6a4pZkJbt+1eswUt+9aazMtt+9aE+WmKetI99fOwHXAg8w6BRaVSlkbjjMYh7SFYxhd2O5eG8RE3x2MVuX7htNMy3WZKa4tLx43EWR1RavLNNwqzCS351pr+9IU3M5rnXHvVJtnf0VT1afUSd+qSkVEHgIcBywDJ6rqa8a2vxG4f/bzZsCtVXWXbNsq8J1s2y9V9VEliABo5oiDgX91Gaxm+6Ucwul795/wOlqpxvmHXIeZ5Cb6hv2nrU5yO6/VYVquy0xw+661ri9Zbt+1pspNE6a6hq9NaSJyEuF+kstV9S6NZDrKe2/gzcB9CFXzV4C/U9XNZdK3plLJnoT5FuCBwGbgXBE5XVW/H/dR1eeb/Z8D3M1kcb2qHliFPViJFxf/foYY8eRmk8ikc0qiQOSa1GPMJNe2wh2uTeJyHabltl6rw7Tc2kzD7b3Wur7UJLftWhPlpilrcErxu4HjgZObytDYu4APAP8n+/3EbN0DyyRuTaUC3BP4iapeBCAiHwQOBb6f2P8JwLG1iGMXFRtF2Gb8cH3CIST2oojvkEPnTTh8zN9lJrhiW/kON9fN4HD7rtVjJrk911qXmeP2XGuq3DRlTY2pqOpZIrJfM7lN2K1U9V3m97tF5HllE7epUtkLuNj83gwc7O0oIrcF9ge+aFZvJyLnASvAa1T1NCfdUcBRAMu33GW0YXhxMauKmr65zZrlX2bfMabhFjJzeY0O1uWmsuqKVoeZ5JZkVuX2X2sBcwpu57U21pCYNEVys9cKbLfsehZtk6pumsFheXaFiDwROCX7/QTgyrKJ21SpuC6U2Pcw4KOqumrW7auql4rI7YAvish3VPWnuczCn7IJYNv999ZhQYj9sDbysQ4bW+525siSs1x0IbcDg6sysd1jJrkFA5q5DDxu27V6zBTX0eoxU9y+a63tSzaTvmtNlJumbIqGyhWqelDzR1DKnkroWnsj4ZC/lq0rZW2qVDYD+5jfewOXJvY9DHi2XaGql2bfF4nImYTxlp9OJo0JGBWE2KRe9UvUqL/XRDm2bzY+wdUOAhrn1OicZmK82P5eh5vv753kWpbHVVsgPG7LtXpMu+wyDbeIabl911rXl3LcnmtNlZtGTMe659pr15ab6ORbmyqVc4EDRGR/4BJCxfE34zuJyB2AXYGvm3W7Atep6o0ishth1sJrS5OdC20u4orOt2wdYtI5bWRk08dJKrkoxSkcLjPBzRUOh2snxrjc1mv1C3/kesxcqsTFZx611vUly+271mS5acpmkmnj9g0RuQA4Cfh0fK1wWWtNpaKqKyJyDOFVlsvASar6PRF5BXCeqp6e7foE4INjQu8IvF1EBoSZ6K+xs8bSzPAtWXSyZDvTvIhi4J/bOPi3lIt8bDM7PhfJrDIR0ZCbimIcrh1wdLkmJPS4XdfqMS3XZaa4c6610JcMt/daE+WmKWtwSvEpwCGEsZfNwLGq+s5GMoc/BB5A1g2W3V3/blX9UZnEralUAFT1DOCMsXUvHfv9Mifd14A/qsqVbJqibPEHAaPD6gY/ol1aDp6+vDxwtvp39oqZGjnkOswUd8mwPG7qLvHuaJ1kWm4lZoLbe601fWkqbse1pspNE6bkb96slZfqExrJyM9bgc8BnxOR+wPvA54lIt8C/klVv75W+tqViohsR7gJ58+APYHrge8C/62q36ub/0wtBifx2zanTUQ0cJ7Qai02qa2TWufxpmPaZn7kuswE1zbjPW5qamRftLpMk95jJrk911rXl3Lcnmud6ZRiJd2Sa5GJyC0J96Y8CfgV8BzgdOBA4COEmbdJq1WpiMjLgEcCZwLfAC4HtiM0n16TVTh/r6rfrsOZmQ1ntGQOZZrTtnAMW8S5m6nMYubIy0t2lM9EPN7UFFt4VtdgJrj2fgCXm5qG0xWtDjPPdZiW6zCT3J5rre1Lltt3raly05B15NlfXwfeCzx67C7680TkhKLEdVsq53rdUZm9QURuDexbkzFzi865tGLWWX+TGIWZPlizOTrnxg0jL5dV08xfmjzNtkBErsdMcW2B8Lge03LbrtVjWm4VZorbe601fWkabte1pspNY9aNSuUOqcF5Vf33osS1zqCq/vf4OhFZAnZU1atV9XJC66WVNuwKilGOjXzsIGDiKR/DzbGbxEY5pmkdo6Nc091GdKv572mYKa6NyDxu57U6TMv1mFW5ndda05cst+9aU+WmGZOuTCk+QEReCOyHqSNU9S/KJG6kWhaRDwBHA6vAN4Gbi8gbVPU/msh/5uZ1CXlNa+tk1vm8bhJ756znR17XQaqbxuEmm/GRm/Ldrmh1mDluBWaK23ettX1pCm7ntc76mt+NlspHgBOAEwnX9KmsqbbenVT1ahE5nDB760WEyqW9lYoy/IPj1MSlLaPN1mE1nqXEzI04NXHb5VF7/EZzaoeObBzKToeMXJeZ4NrpkB7XFh6P23WtLtNwPWaS23OtdX0px+251lS5acSU9Kur22Urqvq2qombqlQ2ishG4NHA8aq6RZIPEWqLiRn8y9akmtbDu4n9nGLEYyOfZXNr7vBMJB4B4XZJ2b5ph5tjOdzc2Xe4XdfqMi3XYSa5Pdda15dy3J5rTZabxqzt10UAPiEizwI+Tv7Nj78pk7ipSuXtwM+BbwFnZQ98vKqhvGduo2m2o3W2ZbzqXIitvy7FOfAmytlgds5FP2NMy/WYKe6SOQKP6zEtt+1aPablVmGmuH3XWteXpuF2XWuq3DRm3ej+enL2/Q9mnQK3K5O4sUpFVf8z/hCRXxJaLe22scG/XJTjRbcJh4iNsg2mRK14TyNNDXLG5URE7c63NwFPFW7XtbpMk95lprg91zprX8ot91RrY9aBSkVVJ+5DEZFtyqZvqlL5mIgcqqqxk3J34JPAPRrKf7bmOKSdrhj7cfMDf6PF2IzexnT4Dsxzh9zoxyl8HjPFtU13j5uMuLqi1WFabhVmitt3rXV9aRpu17XOtKWidOLmx2gSxjDuT3gG4yOB25RJ11SlchrwURF5LOFJw6cDL2wo79nZWPSTc0gb8Qz73hMDujLZTWKX3buRTeEYXtw8ZoKbYsXl5B31PdHqMg039YyoudRa05dy3J5r7cpLumZpInIwoSL5K+AWhCfC/8OaiYw1Uqmo6juy5tFphLnNz8iex9UJc+fADya3p7tJnItPgUd68/2T0yHdZnyicJTkdl1rFWaS23Ots/alHKunWhuzFs/+EpFXA38N/JLwgq74MN/3TJNP3ce0vMD+JLRSLgDuJSL3UtU31Ml/5hb9yO0SMhFTvLfDRkHGvEFG2187dF7rt27XwSQzxU0NqEau3e5xu67VZVpusptk/rTW9aXcPj3Xmiw3Ddms5wHUtKOAHwJvAz6pqjdIhf7Aui2VncZ+fzyxvn2mDJvHoyjLFggmllNR0GiWUKJADNP4Dh+5hRG14eZnsUzPbb1Wh2m5LtNwPWaK23ets/Yly+2r1kZMmUlF1aDtDjyI8HqRN4nIl4DtRWSDGS8vtLqPaXl5nfStseyPThaIQX6/lKUKhFvZF3UdDPx9y3KTAca8aC3oJskt91xrXeY03K5rne2UYmm+omrQstezfwr4lHn6/M2AS0TkC6o68dJEz+p2f20C/lNVv+ts2wF4PHCjqr6/DmdWNj6+kI6CMkdI+Nuw7z0xB36cN8lag5ng5vqGK3C7rjV58SusNOdP66x9yS73VWtj1u6WytBU9Qbgo4QJWDsTBu1LWd3ur7cCLxWRPyK8Q+XXhEffHwDsTHgdZSsrFGAUtRc47Ph+Zcwd+LMFsuBCn9q3KW7XtSYHVp3jn3utM/Yly+qr1sYsMX7XBhORJwIfUNXcUarq1cB7ROT2wB6q+pW18qnb/XUB8NcisiNwELAH4SVdF6rqD+vkvVVNx76ZrhkdLdkfvBbTLFdhVub2XavDrMrtvNZZ+5JZ7q3WJkxpdfcXcEvg/4nINwnPboyNhD8A7gdcAfxTUSaNvDtTVa9V1TNV9RRVPa3pCkVEHiIiPxSRn4jIhCgROVJEfi0iF2Sfp00F0PAR80En1+eSqAw/lczL31mX4la2jmidBXNetc7al+ZCa0MmWu5TKq+Gr4uqehxwd8J04lsBf5n9vgR4kqo+VlV/XHRcrXpHvWcisgy8BXggsBk4V0ROV9Xvj+36IVU9phIjLpg7k/J9w9lyRaf0nDn/4DpNMytyUwVoXrR6zCS351rrMqfhdl3rzCuekhVGkc3qupgN1n8u+1Sy1lcqwD2Bn6jqRQAi8kHgUGD85E1tw4hAx34X7W/SVOKNpV8Pbuu1Vix8XvqF1sT2hdby6dtnM7su1rVGKxUR2UFVf99knsBewMXm92bgYGe/x4rInwM/Ap6vqheP7yAiRxFu8GF5110nc0g5bIFzxehmYKKcgYmpBkXRj3ehL8lMccsyq3LXW2sVZlVu57XW9KVpuF3XWsisaVNUVLuJyHnm9yZV3WR+N3ZdbNqaevPjvQlvCdsR2FdE/pjwqJZnNZG9s278r/kEcIqq3igiRwPvASZefZn9KZsAtt13Hx3eUBULRNHMlRzVcciiAmFvHLP5F81ccbgDhz8Nt/1aE/qKCv/Yf5pjprg91zprX8pxe6q1EVOmeUzLFap60BrbG7su5jINr4N/nKp+uOyBjlsjA/XAG4EHA1cCqOq3gD9vKO/NhMe/RNsbuNTuoKpXqmp8mcw7KPN0ZDWfsqaJj2MDleFHNev+rcJMcRM2zqzMbblWj6lNMudJa8LmUWvtclPlmKY4TmMzuS5m04krjU1Ha6z7S1UvHnvb49TvNk7YucABIrI/YRbCYYQnaA5NRPZQ1cuyn48CLmyIXcqGTfdEZDOLwb9c093Jf1YDjguts+Wuh9Yi5npx+8K01uA4zSyvi58TkRcCHwKGwxlb+82PF2ddYJo9rfi5NHRhV9UVETkG+AywDJykqt8TkfgEzdOB54rIo4AV4DfAkeUyH/susFTTejV7wc/KYHm4zr70Z+ioyab59NxVk7/HzRWOCtx11+owLddl2nQ1z6/l9l1rkS/luD3Xmiw3TVlDec70ughPzb6fPXbkW/XNj0cDxxEGjzYDnx07oFqmqmcAZ4yte6lZfjHw4qZ4gN9jmTuA0Q7DAd1Ef21pPyoTJGWs3ICjw53Kd9uo1WFaViVmRW7ftRb50lTcjmudRT2SP44Gs5rRddF78+M01tT7VK4ADm8ir61pEzdITTHCZF+2E5vRK7koaDLiSjV9q3Bt093j2sLjcduu1WNaViqiXWgtx7TcIl+y3L5rLWLWsWlubFxPE5GbAS8A9lXVo0TkAOAOqvrJMumbmv21P/Acwgu6hnmq6qOayH89LNcKLphnsepcfHL9tSUdqZBp8lpNFI4hdwrnbaVWh2m5VZhVuV3XWtuXpuB2XuusL/otfkmXsXcRHtNy7+z3ZuAjhFfEF1qTrxN+J2EKW4sfmTZm0YFk7BvIv1fUSzvaPux7N6HTFtNfO/CehivOchHTcHN9ww53kHpaa1e0OkzLdZmW6zErcvuutciXctyea02Wm4asCy0V4Paq+ngReQKAql4vUubiFKypSuUGVf3PhvLaejbe/ZWKsuKy2d86R3REG/lY59W4nOo68KIsu+xwrfN7XDXrPG7btXpMy3WZCe7ca63pSzluz7UWMWtbNyqVm0Rke7KjzZ5OfOPaSUbWVKVynIgcSxigH8JV9fyG8t86Zitj4/3qVdLGOWI/rI2ycoUjFqgibhHTcG3fr8e1fdduTm3X6jAt12MmuXOuta4vWW7ftRYy61hHxlSAY4FPA/uIyPuB+1B+5lhjlcofAU8i3K1p761d8+7N9TQ7aDaMsmyQsmx3djKwEc9q1nRfHSXakou4wr5LtkDZKCsGgUVMw43MFFfN8Xnc1mt1mJbrMS3XY1bldl5rTV+y3L5rTZWbxqwDlYqqfk5EzgfuRTijf5dNxiplTVUqfwXcTlVvaii/rWPRQ52m+zQDjsNZQsZh7fLQUW1GTv7TDNTnZrE4XB0kMuuK1tQsoYzlMm1mJfTNi9a6vpTj9lxrstw0ZNKdEef7AfclnLmNwMfLJmyqUvkWsAtweUP5rYvlIp8lp/BYH7MRW+aIqyaDwRR9s8Moz2MmuNb5XW5JZpK73lodpuVWYSa5fdda15em4HZeawdaErM2EXkr4cVcp2SrniEiD1DVUvceNlWp3Ab4gYicS35Mpd1TijX3lewmcefD21ksK1nTfcU0p83ysMlvC5TNyuk6SM7Bj7NYEqzhcmIWS2e0OkzLcpkG5jGrcruutbYvWW7ftc549ldHKq37AXdR1ThQ/x7gO2UTN1WpHNtQPutjsbyYKEvty36cVrBtxsY3OtvmtJ1lkmtSO3lGbhHTcu1bpD2uyzT5tl2rx7TcKsyq3M5rrelL03C7rjVZbpqw7gzU/xDYF/hF9nsf4NtlEzd1R/2Xm8hna9vwDx5eaEfbUsueDbtJBn6BjNFPzqFyhaM6M8k12z1u17V6zBzXYVbldl1rbV8y3N5rTZWbpqzFlYqIfIJwhDcHLhSRc7LfBwNfK5tPrUpFRL6iqvcVkWvIny4BVFV3rpP/1jJvjrsuy+R2Y/n59lmBWB3tODBRkDc4583nL2Jaru179ripAcGuaPWYlluFWZXbda11fWkabte1znwgvcWVCvC6JjKp21LZAUBVd2rgWLa+jbdUUtMhYxRk19lm+koW0W4ZZaDGYYnLqSjPmw5pozCHG5lJruV7LZW2a3WYlusyLddjJri911rXlyy371pT5aYBE9o9+2u8x0lEdqZCHVG3Uml3vbuWOfepmHuhcm8v8wcczXJ0WBNl5R5W43QdWIeP3EKm5drC6XETzfjOaHWYOa7DtFyPWZXbda21fcns03uts+z+6siYSvba9VcC1xPOjBDO4O3KpK9bqdxaRF6Q2qiqb6iZ/9YxJ8rJvanUa8abHcSJcuyA4JIXnTgRXRHTciUR0UWuy7Tclmt1mYZbhVmZ23GtdX1pGm7XtSbLTVPWgUoF+AfgztPc8GitbqWyTHgvfeKvbLnF6CcWCOOk9mkPw+jHrMs1Y2OT2iSSgbNsozxbIJbWYKa4K3blJMvyPW7btbpMk9BlGq7HTHJ7rrWuL+WWe641VW4as25UKj8FrquauG6lcpmqvqJmHutn4/20tj82UXhGK61Dhp1tf22+nT7Gs0zDLWSafMVM3XS5uZDR4bZdq8PMcT2m5SYi2nnUWtuXLLfvWlPlpiHrQvcX4cVeXxORb5C/7/C5ZRLXrVS62UIZt+hPpo82F1EVRLS5JnVcp/6+40zLdZkJrse03OSAYEe0zoKZ5PZda01fmobbda1zPvsr2tuBLxJueJz6jNStVP6yZvqcichDCK8lXgZOVNXXjG1/AfA0wjuXfw08VVV/kW1bZXTX5y/L3M0v49GNnU1ioyC7Pqa1hWMlrvObzpJFRJKK4mL+BUzLjcwUV0wU5nJbrtVj5rgO03JdZkVu17XW9SXL7bvWVLlpxLTZSqvoelnDVlQ1OVZeZLUqFVX9TZ301kRkGXgL8EDCm8bOFZHTVfX7Zrf/BxykqteJyDOB1wKPz7Zdr6oHVmEPW9zWIQub8ebYY/STcJjC+faRW6r7a4yZ4Bbep9J2rR4zwS3NrMrtuNa6vjQVt+Nau9JSKXm9rGpfymaAfYJ891ep631Tj2lpwu4J/ERVLwIQkQ8ChwLDk6SqXzL7nw08sRFyjHLMqtx/73T3WudbWnW2JzPLM+1mj5niRmaSm3Lejmj1mLntFZgpbu+11vWlKbid1zrj7qkGWz+F18sa9jfZ94vNOmUrTSlu0vYCLja/NxMeD5CyvwU+ZX5vJyLnEbrGXqOqp40nyGrfowA23HzX4fqhQyeinKInYEvmqNMMMOUc2uGWZVbl9l1rEXMabte1ztqXtiZ3vbQ2ZuUrld2y61m0Taq6yfye9npZ2lR1/zrp21SpeP+x+xeIyBOBgwhP04y2r6peKiK3A74oIt9R1Z/mMgt/yiaA7fbaR8dnlAwSFx8vusg1kweT+7mPsEgMInrcVEQz5Nq+aY9rIzaH23atHtPum3xchzP4Pu9aa/uSYfRea6rcNGHKNJXKFap60BrbS18vpzUROcJbr6onl0nfgg28ygAAIABJREFUpkplM+FpmNH2Bi4d30lEHgC8BLifqtr+vkuz74tE5EzgboT51r7pyKmGzmmb1vYvixeXRIEYNqltf7FFOY+wyBWI2CXlMFNc24z3uKlHk3RGa6KbZNg37jHNBo+Z4vZea01fynF7rrXU21crmtBo91ep62VF+xOzvB1hQtb5QOcqlXOBA0Rkf+AS4DBGfXsAiMjdCNPdHqKql5v1uwLXqeqNIrIb4Z3Kry1Njg5b4FCSiHKGTWrrxE765IW+4FWoHtc246tw267VZVpuIr178VtonWBabl1fynH7qrUha7BSKbxeVjVVfY79LSI3B95bNn1rKhVVXRGRY4DPEOZ1nKSq3xORVwDnqerpwH8Q7uD/iIS7YePU4TsCbxeRASF+eU2pWRBjM0Z02fzjuaZv5nG56YhmOU49tQ5rnX+MZ5k5rsdMcHNTIx1ubhKLw2271uTU08j1mIbrMlPc3mut50s5bs+1pspNY9ZQnqnrZTO5T9h1wAFld25NpQKgqmcAZ4yte6lZfkAi3deAP6oMjv3FyafZBk/Q3DMozK6Zcy4lCqQb8XjbHWaKawuEx01GWV3Rmohol5yLT2lmgtt7rTV9aSpux7XOonWSswYrKu962YSZ96pAqK7vBHy4bPpWVSpb3cYHB20UZJwzd5PUcN1oOT6EbslEQTaiGu6bGgSNrYcCps3LPvjO4+b6kz1uy7V6TMt1mYbrMatyu661ri/luD3Xmio3jZgZx2252feqrAC/UNXNZRPPbaWSGzSLEauNcnIO62TgNOOXtvhpBhuz/axD5Vg6kSY588RpxnvcyExxW6810U0SuR4zx3WYVbld11rXlyy371qT5aYp60ClUvdNvnNbqVjTYdM30fc+bJqPoiDrcLEZLat+GJfru53c7PdH55rmk1zbdPe4HtNy267VY+a4VZgpbt+11vSlqbgd15oqN03ZzO/Yb8BE5DHAvwO3Jou/ofybfOe7Uok+5XQJJftmYxJvuqJtThuH955rlO97LsfMcVPN+GWnG8Djtlxrcppv7CbxmJab6CaZR611fSnH7bnWWT/6viPdX68FHqmqF1ZJPN+VSjRvkM/27Xp+mou4NPsercvNVvSiE2/AuIhpuEsmyvK4RU8pbr1Wh2m5VZhVuV3XWteXpuF2XetMWxLKTCqqGdivqlYoMM+Vihk086bZilnW5cxTjcPmmvGx7/0mk96c2ZXVyaa/NzXSZSa4NsryuCvmwXket+1aPablekybLjX1dB611vUly+271lS5acy6UamcJyIfAk4j/0DJj5VJPL+VCkz+wammdUHENXpY3WjlwCTyZs643CKmySP/YL5JbuEslrZrdZiW6zJT3HnXWtOXcvv0XOssZ38Jnen+2plwb8qDzDoFFpVKaYtR1JL1eJ3YnktiN2fOaQf+cs3wggIx5BYw7S45lsctKPxt1+oxc/wqzIrczmut60vTcLuudcYXffvmybaaqj6lTvr5rlTi/ztsG482SWLq4tCcAb+lLb7DDCOi1HPFY39wEdNwc814h2ujMJfbdq2JAdXSWh1mVW7Xtdb1pRy351qT5aYJU7rS/VXL5rtSyWz0hFITuRjn9O6n8l57aiOfpaVEM36caTIrYlpu/rWsk9zCl3S1XGvqtbKRW4VZldt1rXV9aRpu17XOespvR7q/atlcVyrjf7BYJ00479ByhSMrUMYhNRfxpLOx3EKmzcs0o12u0zPgMUtx10Orw7Rcj5nKau611vUlu2/ftRYwa9uiUpkvs1GW2Oc5lIy4llZGaXRpaWJ7EbeIabm5AUeHW5ZZhrseWlMRbeRWYVbldl5rTV+ahtt1rYuWSjAReThwZ8Kj7wFQ1VeUSTvflUr8g2XsG1jKOeykJ3jN6FxzeouNiGIU5E93jMtFTMtNDThGbm5A0OO2XGuqm2LYTeIxLddhVuV2XWtdX8pxe641WW6asg5UKiJyAnAz4P7AicDjgHPKpp/fSkWZ+IMl4bDJ9HFXb5aQnUOvk2lcbhkfjhFXahZLvF9AJ9O4zDLc9dDqMHN8j5ngLrSuzS3ypRy351qLmLVMZ98Sasjurap3FZFvq+rLReT1lJxODPNcqXiW66O13u/s6zmfjXJs323R84SG/dEFTMuyfIdbllmKux5aU4U7siowK3O7rrWmL03D7brWWT77S6Ar3V/XZ9/XiciewJXA/mUTz3WlMrxLN7asrZPmdkynhdE0RDF9tN4sltzdyLYXIRbIAqbNw0599Liptxl2RavHtNzULKHx/9Qyq3I7r7WmL1lu37Wmyk1jpp2oVT4pIrsQXop4PqEqPrFs4rmuVGo1b52IS4zDqHWeuNyEPzkRl8vN8RtimuWZa01EtJHrMmfE7b3WIl+y3L5rbZLpWBdaKqr6ymzxVBH5JLCdql5VNv18VypjJokoyN3XON9weTAKc2RgZrEUOJI4rYciruV73LLMMtz10OoyDbcKszK351qLfGkabte1zvSir3RioB5ARO4N7EdWR4gIqnpymbS9qlRE5CHAcYR3Np+oqq9Zc3/NfxdaKsqKTercc4MmI55cd/M0zuVFz3Y+v8dNsDqj1WNabiKinPo/HUs/j1oLfcnu23etVfWVtC4M1IvIe4HbAxcwekONAvNVqYjIMvAW4IHAZuBcETldVb8/c/jQ4U1zeuZ9szb/Se7MIq6WaJ0503B7r7XAl7Ymd721zrp7qulKRUSEEEg/jPAQyCNV9XxnvzOBPRgNwj9IVS9PZHsQcCfN9UWWt95UKsA9gZ+o6kUAIvJB4FAgXamMnbLCU1gUxaSa9gneenLbrjVZuL1ujALuQqufZqG1mtbKppQ46KntocAB2edg4G3Zt2eHq+p5JfL8LrA7cFmVA+pTpbIXcLH5vZmxkysiRwFHAWzcadfRBtdhZc3tYzvnv9dabpqZWp6qcHZQawVmZW7Xtdb1pSm4ndc649lZM2gJHQqcnLUqzhaRXURkD1WtVCFkthvwfRE5h/z7VB5VJnGfKpWiWfGo6iZgE8D2t9lHJ1sqkkiYAXRyXW59wmGLbhyLXI+Z4ubXTXKLbuJqvdak/snCv9A6ycxtrulLqWP1uF3XWsSsbeXz3E1EbKtiU3b9GjcvmN4Lv5XxLhFZBU4FXrVG99bLSh+lY32qVDYD+5jfewOXpnYW/FqorFWJOJp46MN6cLvCXC/uQutsmevFbfohLVPe/HiFqh5UMttx8yiHq+olIrIToVJ5EomBd1X9cumjdGypeJfO2LnAASKyv4hsAxwGnF4moWj2Z6sMP2o+w/VVTZn4myPTcl1mVa6yJrfvWj3mvGqtzbRcY73Umig3jZgqMij3WctE5NkicoGIXEAInAuDaVW9JPu+BvgAYQw6lf+9RORcEblWRG4SkVURubqszN60VFR1RUSOAT5DmFJ8kqp+b+1E8TtruttHYZsXMwwbiXWbxrn0Jv/42G+POSNu67VWLdRj/6llJrk91zprX7Lc3mptyhrIU1XfQpjpGp8mfEw2Melg4Krx8RQR2QDsoqpXiMhG4BHA59dAHE8Iyj9CmAl2BGEiQCnrTaUCoKpnAGdMnzCmTxSIzGEbHWTLdec6BcIUzllwe6/VYaa4vdc6Y1+y3N5qbchmcExnEKYT/4QwpfgpQ5bIBap6ILAt8JmsQlkmVCjvWCtTVf2JiCyr6iphLOZrZQ+oV5XKtDYxeGgd1ruzt8gh8rf4pnnjecULfepu4llwu67VYebySuQ5j1pn7ktmubdamzAl/xDLJrIMg+3PTmw7MPv+PXCPKbK9LhtCuEBEXksY9N+hbOK5rlSGM0KyZvTAFAgdmMeVxuhnGiddg2eZlusyZ8Ttu1aPmeT2XeuMfcly+6q1MWth68mxJxHG248Bnk8Ys3ls2cTzXalEc6P3SedMPaLCHROcxmGHUZ5fIDxuchyyLLflWmfBTHF7r3XWvmSWe6u1IWtjl5y17Mkkr1bVJwI3AC+fNo/5rlTGHd0OMlonG+T3nzCn6Z5LHxcTDh+5LjPFTTTdh3nYMuJxO67VZZr0HjPJ7bvWur5kuX3Xmig3TVnRzK71NlVdFZFbicg2qnpTlTzmu1KJFguEfe5Q7r0OYbkoysgViKLJ2rZAxOcOOcxZcTuvtQIzxe271ln7kuX2VmsTpnSl++vnwFdF5HTg93Glqr6hTOL5rVTUNMljgVgxfbSm83apMKLNvpdTUVC2KtEfHbkuM8W1EZXDzb3MyOF2XqvDtFyPmeT2XGtdX7LcvmtNlZsmTIDks83aZZdmnyVgp2kTz2+lAk7312iTrNpmvOT2n8gmOuSScfIlcXY0+eea8WswE1wbZVXidlyryzTpPWaS23ets/Ylw+2r1sasA4++V9WXA2R336uqXjtN+vmuVKLFP9o6pHXYgsdia4x4bBO6YLpizrki12GmuGqjLI+bGnjsi9YqzAS371pr+9I03K5rnfGAfRdaKiJyF+C9wC2y31cARxTeTJ7ZXFcq403qJTsb0rSTZXW4cmi5WSQx4F02kY9x3qKug8h1mSmuDcgcblH3V9u1ekzL9ZiW6zGT3J5rretLdt++a51l91eHxlQ2AS9Q1S8BiMghhJsl710m8VxXKtGGzmOa7rnXkxYM6Mamc65A2Oa0E/zk8sq4HjPFtfm73ETA1RetVZgpbu+11vWlKbid1zrThkrxc71aYjvECgVAVc8UkcXNj6VsbHwhF+WYZnCMiFJvbYvRTc5h7XJcTPYHp5kpro2oPG4uInT7vs26Dmp1mZbrMRPcvmut60u5fXquNVlumrIOdH8BF4nIvxC6wACeCPysbOK5rlRiJBMdcWnFOJl9r4N9S7NnMcqyzekNNuKajIit80euy0xxbZTlcU3h9rhd1+oxLddjJrk911rblwy391oT5aYR03QA0zJ7KuGmx48R2m5nYZ4pVmRzXakMzYvebd9w0YBu5puprgP3Ll434ppkprhqBhk9bvLO4Z5orcb0uX3XWteXpuN2W2udtwOUsg60VFT1t8Bzq6af30rFDJoNLy6JKMKNaG0zethNYtYtOfsmBjk9bjLiiqxcM95ZTjTjO6PVYVqWyzTpi5g5bs+11vYly+271pl3f80gz4ZMRN6kqs8TkU8weaQK/AZ4u6qevVY+81upGBMvevcuxEUDuslBwDQzx00V2KIBR3fA0z/WvmitxExw+661ri9Nxe241lS5acpk0Or+rziG8rrE9t2Ak4A7rZXJfFcq0RHjOMMWs8k4l6xkC4mpp0OH3Jhw2JiXLXC2b3jLGswEN1f4HG6ucDjctmv1mJbrMg3XYya5Pdda15dy3J5rTZWbRkzzx9Q2U9VvZt9fTu0jIoXPA5vvSiUzL3r3IqKiWUKD3GMfLGCyo7Yo/9SxjDOT3NR7KfqitQKzzLF43K5rre1LU3A7r3WGNz8K2pWbHw8A/o3QItkurlfV26nqJ4rSz22lIoyax8MbAk2Uk4u4YoFJdZMsZ982MnIGHJM3jq2kmSluZKa4hTc/dlxrakDXvUluzrXW9SXL7bvWmd78CHRhoB54F3As8Ebg/oSZX6Vr21ZUKiIiwHGE12JeBxypqueP7XMzwjuTbw+sAp9Q1X/Kth0J/AdwSbb78ap6YiF4vPvLNs2tc3kRrdOMzwVGdntR10FsmjvMFNc24z1uYfdX27UmukmGXI9puB4zxe271rq+lOP2XOtMu7+gK5XK9qr6BRERVf0F8DIR+R9CRVNorahUgIcCB2Sfg4G3Zd/j9jpV/VL2qssviMhDVfVT2bYPqeoxVeBD58vdTOVsT3WTDB0xUZk7q3MOvzq5X26714zPDSh6gMShzIlWl5ni9lxrbeY03K5rnV3v10zGVETk/yO0LO4OvERVU4Ps09gNIrIE/FhEjiEE67cum7gtlcqhwMnZ+5bPFpFdRGQPVb0s7qCq1wFfypZvEpHzgb3rQGV4w5TTd5vbL/8NYwOOzoyR3PZh14G9MawcM8VNzVIZbs8146fnrrfWKkzL9Zgpbt+11vUlu9x3raly05TNYPbXbwj3lDy6wTyfB9wsy/eVwF8ATy6buC2Vyl7Axeb35mzdZd7OIrIL8EhCl1m0x4rInwM/Ap6vqhc76Y4CjgLYZoddh83bpdVsYXU8RTkbZP20UlQ4jY8OmRW5A9M37HFTj5uYF611mZbbd61FzBy351pn+5gWbbz7S1UvBy4XkYc3mOe52eK1THEnfbS2VCpe+OGefRHZAJwC/KeqXpSt/gRwiqreKCJHA+8h1K75DFU3EZ7AyQ677TPMf9hkLvq/E1HW8L0bqWTRkRKDnFW4ufdCrMVMcVuu1WOmknta655fy+271iJmjttzraly04gp01Qqu4nIeeb3puz6NTPL3vSYNFV9VJl81q1SEZFnA0/Pfp4L7GM2701485hnm4Afq+qb4gpVvdJsfwfw76WOYWzwL/lcnswPbZRjrfBu3OzJqUvmCaVFUx9tXh636M5j+7RWj9t5rQ7TcguZJq/ea63rS4bbe62JctOYle/9ukJVD2r+ANa0PyX0GJ0CfIOKI0zrVqmo6luAtwBkTbdjROSDhAH6q+x4SjQReRVwc+BpY+vt+MujgAunOZbhNNtVP0oZTle0UY512IL+2qElCsSQ6zCT3IK+6SJu27V6zGm4LjPB7bvWWftSjttTrU1ZE+M0YwH5w1Q1FYBPa7sDDwSeAPwN8N+EHqBSL+eK1pburzMI04l/QphSPOzHE5ELVPVAEdkbeAnwA+D8zGHi1OHnisijgBXCwNWRpahj/bRLN5kozPz3A+cRDuo4bK6/tqDLyRaIyPWYKa4tnB431U3QFa0e03JT90t4fe/zrrWuL+W4Pdc6VfdaFWugUrEBeZOmqqvAp4FPi8i2hMrlTBF5haq+uWw+rahUsllfz05sOzD73kyiOaaqLwZeXJU/vHdjxb/4RBskHmE6LDA2CrNBlBPx5Obbr0wWyCJu/mmrk1yPabmd11qBmeLOu9YiX5qG23WtqXLTiKnCarOzv0Rkd+A8YGdgICLPA+6kqldXzG9b4OGECmU/4D8Jj8Avba2oVNbLRmMqWfRuHcr2HQ/7e20Y5URhqRunhtMxzSobcUWux0xwk0+TjbvmpkZOcluv1WHmlh2m5brMFLfnWmv7kuH2Xmui3DRmzc/++l9q3loRTUTeA9wF+BTwclX9bpV85rpSGTWps++bRlFE/rWfwfvsYyFy2SznvwH/IYW5rgOznHE9Zoqbe9yEw01PKc4zU9zWa/WYhusxU9z+a63nSzluz7XO+ZsfnwT8HvhDwpBCXC+EDqWdy2Qy35VKZtEhxTanbcS1HNvGfvq4OuewxjmXnAHBXP6x68BjJrh2lcf1mJbRda1VmEluz7XW9aVpuF3Xmio3jZiSa8m1zVSbefD//FYqOjk4J6a/0/bXxleNpu68xukmyfXmel0HuaetDpLMJDfRjJeJBZ/bdq0e03I9pk3vMVPcvmut7UvmR++1zrT7S8k9xbKnNr+VCgzDlhjx5JrTK2Z5Y/A+SUQZ7oCuOMu2QJrCN+w6cJgpbnLAUca+E9zOa/WYhusxU9y+a63tS3a571pn2f2lND5Q30ab70ols1GXkHHS1VWzHNvmfnr34mO3O5OL8s34QZqZ4CYLxxpMy+27Vo+Z5PZca13mNNyua128o76+zW2lItgmdXahtQXCOqzTX+xFVFPNnMndmbyaZCa5hj/V7K+uaE1ErKVnCTnMFLfvWmv7ktmn91o7NvurjTa3lYq1OE3RTlfEFo7syaLpd5ln39YhrfOuwcwtO8wUN39j1yQ35bp90VqJmeD2XWtdX5qK23Gts73k66JS6b3p2AV2i4l8tozmI8rKxvCd6A4dOqp1WC/i0kSByLgeM8XNRVkeN9c37HA7rtVlmg0eM8Xtu9bavmS5fdeaKDeNmALNP/q+dTbflUq06LArZhL8itNlkpoO6HWTDCa3e8wc12OmuKlm/GByu8ftvNYqzBS351pr+9I03K5rXYyp1La5rlQm7tK1UcTAOGxcn+omcS4+3rsaUncjD/P3mAlu6mVHpbkd15p674d35/Xca521L1lGT7U2Y7qY/dVrU4ZONxzQ27JltD3XjPfuFjY2vPiMtqt5gbZ3N3Iur8h1mElurnBMclN3BvdFq8s0XJeZ4PZda11fynF7rnWmd9Qr6OI+lTmx4dsQzR9uZpYMu4xSTubNLClqRufeYDdIMpPc1CyastyOa63ETHB7r3XWvmS5fdXalLX4jvqmbK4rleHd5dERbdN51Vku6CZJ3rgVZ7bYu4Ft32rkeswEVwtYuamRHrfrWhP8if/UMlPcvmut60tmufdaE+WmMVuMqcyJRedcGTXddcU247NBwJRDOFGW5MIkJwyyBSJjecwkNxFljbiJ0KsnWqswU9y+a63vS+W53dc6wyaL6mL2V+8t+lH8n23TNNeMH+T3S1mRP1oft3lFrsecFbfvWj1mitt3rbP2Jcvtq9ambNFSmQ+LEY2aAqG5x00McvulMxotFvXR2rwiy2POitt1rVWYKW7ftc7alyyrr1qbMc0dc19triuVYYs3Ol+uv9Yse85ZIeDItezVyb+I2SS37VorBnQT/+n48jxqnbUv2eW+am3ClMVA/XqbhLfEHEd4f/11wJGqer6z35nAHsD12aoHqerla2aumIIwOXNFvVksRQ6Rm8TvhD7JQcbJKC95IWyK23WtHtPm5TCT3L5rnbUvGW5vtTZliynF624PBQ7IPgcDb8u+PTtcVc+rRImTgHI3TtnlGThXDjU8gNkyDbf3Wj3menHXW+uMfSnH7avWBkwBXbRU1t0OBU5WVQXOFpFdRGQPVb2sicwnmre2v3YwGZHlApsi33C2J9PEcQ6HOTNuy7VWYSbTzbvWWfuS4c6F1qqmumiptMD2Ai42vzdn67xK5V0isgqcCrwqq4jWtqGjOwXCi7iKskyN9unYt2HmuKkorwpX/eV50eoyU9y+a63LtNy+a02Um6ZsHgbqpcy1d71MRP4b+DdV/Ur2+wvAP6rqN8f220tVLxGRnQiVyvtU9WQnv6OAo7KfdwB+COwGXDFDGW2yhdZ+2kLr7O22qnqrOhmIyKcJx1/GrlDVh9ThrZe1rlIRkWcDT89+ngt8UVVPybb9EDhkre4vETkSOEhVjynJO09VD6p31N2whdZ+2kLrwtpkiWeCrp+p6ltU9UBVPRA4DThCgt0LuGq8QhGRDSKyW7a8EXgE8N2tfuALW9jCFraw1o+pnEGYTvwTwpTip8QNInJBVvFsC3wmq1CWgc8D71iHY13Ywha2sLm3Vlcq2WD7sxPbDsy+fw/cowZmU420XbOF1n7aQuvCWmOtG1NZ2MIWtrCFdddaN6aysIUtbGEL664tKpWFLWxhC1tYYzb3lYqI/ImIrIrI49b7WGZlInK4iHw7+3xNRP54vY9pViYiD/n/27vTECurOI7j31/TYlJJqZBpFhQtRqVWZkyYUbTYELSQU7RBr7IiyaiIiEhIKmh5kfRCpGiZmWwjWmmZCjONSNOJabESsk0xK4NCJn69OGfidrnOWN1nnjt3/p83c5/zLPyfO8P9zznnPv8j6TNJ6yXdUnY8RZF0oKRuSb2SPpF0fdkxFU1Si6TVkl4sO5awYyM6qUhqAe4GXis7loJ9DZxi+xhgIU062Zl/nw+RasZNAS6WNKXcqArTByywfSQwE7imie+13/VAb9lBhIGN6KQCXEd6An/gisbDnO0VtrfmzZXApDLjKdAMYL3tr2xvBzpJ9eOaju3v+yt2295G+rCdWG5UxZE0CTgHWFJ2LGFgIzapSJoInAc8XHYsQ+wq4JWygyjIjmrFNTVJBwPTgFXlRlKoB4CbGHw9x1CyEZtUSH+kN9tu/gpvmaRTSUnl5rJjKUitaohN/Z15SXuRetvzbf9adjxFkNQGbKqu+RcaU0M//FhvVXXFxgCdaR0wxgFzJPXZfr6s+Oqp6l7nkO5xCXC27S2lBVasjcCBFduTgO9KiqVwuYrEM8ATtp8tO54CtQLnSpoDjAL2kfS47UtLjivUEA8/ApIeAV60/XTZsRRB0mTgLeBy2yvKjqcoknYFPgdOA74lFSS9xPYnpQZWgLwq6qPAT7bnlx3PUJE0G7jRdlvZsYTaRlRPZQS7HRgLLM49s75mrPRqu0/StaRv87UAS5sxoWStwGXAOklrctuttl8uMaYQoqcSQgihfkbyRH0IIYQ6i6QSQgihbiKphBBCqJtIKiGEEOomkkoIIYS6iaQSGpqktyWdWdU2X9LiAc45WFJPfj01PzRXOEkPSJol6Q5Ji6r2TZXUm1+/IWnfoYgphKEWSSU0ug6gvaqtPbfvjKmkigKFkrQfMNP2u6TY5lYd0g48mV8/BswrOqYQyhBJJTS6p4E2SXvA38UTDwCWK7lXUo+kdZL+8UEuaXfgTmCupDWS5kqakdeUWZ1/Hp6PHS3pqbzmTJekVZKOz/vOkPS+pI8kLcv1tqpdCLwKYPsz4GdJJ1bsv4hUNRngBeDi+rw9ITSWSCqhoeU6ZR8AZ+WmdqDL6and80k9kWOB04F7JU2oOHc7qZpAl+2ptruAT4FZtqflfXflw+cBWyvWnDkOQNI44DbgdNvTgQ+BG2qE2gpUFjz8u4claSawxfYXOa6twB6Sxv7nNyaEBhVJJQwHlUNglUNfJwMdtv+0/SPwDnDCINcaAyzLcy73A0dVXKsTwHYPsDa3zyQt+PVeLodyBXBQjetOADZXbHcCF0rahdrDdZtIPa4QmkrU/grDwfPAfZKmA3v2L05F7VL3g1kIdNs+Lw+lvT3ItQS8bnuw4arfSRV0AbD9jaQNwCnABcBJVcePyueE0FSipxIanu3fSB/+S/nnf/zvkuZLWiSNB2aRhsoqbQP2rtgeQ6pgDHBlRfty0rxpDqDOAAAA/UlEQVQHeVneo3P7SqBV0qF532hJh9UIsxc4tKqtg9Qb+tL2xv7GXGF4f2BDzRsOYRiLpBKGiw7S3ElnRdtzpGGqj0ml/W+y/UPVed3AlP6JeuAeYJGk90iVjPstBsZLWktaxGwt8IvtzaTk05H3rQSOqBHfS8DsqrZlpOG1zqr244CVtvsGu+kQhpuoUhwCIKkF2M32H5IOAd4EDsuT/Tt7jeVAm+2fBznuQeAF22/+r6BDaEAxpxJCMhrozqspCrj63ySUbAEwGRgwqQA9kVBCs4qeSgghhLqJOZUQQgh1E0klhBBC3URSCSGEUDeRVEIIIdRNJJUQQgh18xeyldGP+7EOyAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%%time\n", "axeslist, _ = plot_dataset(dataset)\n", "axeslist[0].figure.savefig(f\"test_plot_dataset_{dataid}.pdf\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 8.26 s\n" ] }, { "data": { "image/png": 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", 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