moser/pyplot_histogram_on_axes.ipynb

## pyplot_histogram_on_axes.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import numpy as np\n",
    "x = np.random.geometric(0.001, size=500)\n",
    "y = np.random.normal(size=500)",
    "plt.figure()\n",
    "\n",
    "gs = gridspec.GridSpec(2, 2,\n",
    "                       width_ratios=[1, 3],\n",
    "                       height_ratios=[3, 1])\n",
    "\n",
    "ax_vertical_hist = plt.subplot(gs[0])\n",
    "ax_main_plot = plt.subplot(gs[1])\n",
    "ax_horizontal_hist = plt.subplot(gs[3])\n",
    "\n",
    "ax_vertical_hist.hist(y, orientation='horizontal')\n",
    "ax_main_plot.scatter(x, y)\n",
    "ax_horizontal_hist.hist(x)\n",
    "\n",
    "ax_main_plot.set_axis_off()\n",
    "plt.tight_layout(w_pad=0, h_pad=0)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 25,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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el3G86bTzuqbX0tK3XyFKUIlLy/hTodlghKd4j1OAksaEYqu91m+a8BLLHX5dmyfeFMokxZUZqawh6kR/STTy63Yo24lGSL/M+d7pjE6n6c66lhFbRsWM7UTZk7n7aRXcj2pSjPx6nKb4pBGhA/3osNsR1H2zsKcEJyg3VXWM7A7lI/Serly1rlT2PqUapoNqm17LqZjRsz2jUGi2ISO/HldLgDKzi4gqLk8BX3D3uTrOK2NlF+3Z6j2rXl2/UgvAkn4japabyQ7gRdbSUjudop11TetHI33j7aiv16QY+fW4ylN8ZjYF3ARcDGwF3m9mW6ueV8bOyLxrS9FrnerktCm2lOmmYxnf/0ui9aYqAbxqp5M1erutMzu/lNxePs0oT6+Nw3pNipFfj6tjBHU+cNDdfwxgZl8FLgUer+HcMj7aMnrqR6+2ryMjdTs+gsnI6jtKNEKqktnYd6dTIA2+O3orNKIa4em1xlPyB22UNn7MUkeAOhP4Sezrp4HfSL7IzHYAOwCmTtlYw2VFWmXFhns5JXyIHT9CfxsvHg6fN9CjPFCyM0q85ghwCsWnPEe6w+5h5Ndr0ozwGwagwSQJd98N7Iao1FFT15XWGMZNs03rjjJ+k5X3L8W3hY/rp+xSalmjImtIKa8pUwi3a6Q77Bwjv14zjupIM38GODv29VnhmEjczcNuQEO6WXdp00U3snKdIy84vUS59YMiKcVl7svKMq4d9siv14yjOgLU94EtZnauma0FLgPuruG8MkZCGvbDw25HQ7JGitOUCxDxDvMw0U27uzKSFopMUVUd/Yxthz3KCR7jrPIUn7u/ambXAvcQ/cfc4+6PVW6ZjKN/MewGVJBVLy9NHdOZJyeudwrwYZbv4UpO4WXtXfVU4nG/yRjHGPMOu8h6zRimordaLZUk3P2b7v7P3P2fuPuf1nFOGUujuAa1BHxgYW7mlIKvXySazkybLsraWr6IrGret4UKHWntO8rKEU/aNNZRotGZs5x8kWbNpHfEY5qK3moqdSRNyroPqM0s1jHnBZi8GnqHwtd13yAMUdD/aMa5X4oHlYxprKsW5mY2LszNrFmYm9lIdpAa17WnMka+dNCoUamjBmlLDB5geLX4FsNH2cy1eMd8lPRpvv9HNNKKZ9BtZ+VOtwvUX2KpKyvZYkPyQIFprOtoYTWIlkytjWUqeptpBCVNelPOc4O89eAY0QjmzpLftwjs7MzOX96ZnX+B7OB2Ir3fWZfpxA5TrJp3L/3WwMtNFgi/j4WiFSaqatHUWtPV4SeeRlDSpLxOepCVJqaIRjTJTr+XW4hSw/u5XwhW/rxFExQWiUYxkBgxhGO3UWwtr+9RT94oq8Y9n8poS5WHka41OIo0gpImZRVVzVLnqGo95QLNS0RrO72+Z5Fi6zZ7Wf3zLBLVsUyuVe0Cbg+v+eDC3ExnYW7myyEAXEGxQDuojLthrMO0YmpNqejN0whKGhHeeZ9a8tuGWb+vTEr5naze+fYV4KTO7PwSyyWF4j+PEwWj7xAlVQCcRNQBdhMeVo1OEuWSskZkhwbYaQ4jWLSmykMTpYNast7WChpBSVN2Mfw3RL+k/rWuk4mCUzxrr5u2PU0UlKZZnWVnRDvhxtdW0l63anQSRlMd4AM0X/1gGOswE1PloUXrbYUMej1SAUqa0oZMp9cymFHZeuCSMBXX/T9VJGOvaGWJvL2emp5yajxYTNjU2siksjcRTM29+bqt6zZt8U1Xfqrx6w5b02nmZrbf3bc1etEMPbLgxoWTXdGhimPAFW3pkDUFNThhSjjtTZTH3vy0Qrh1Im3qNbWgcT+GPeUiMk6603R1m2LwmXKFjfoWDi3XmvW2Aga+HtmqiCxjbdVNo5LqKNFNv0mtnOaR2o3SetvA1yMVoKQpbXwHCNXq4w3CVWSvk7VhHU8GaMTW2wYeTDXFJ03ZCdxR4vWvMJjadV0OfHZhbubanHn/Ig5T37TeoZBGnpVC3tYgP1EGvQY3KlOoTWwprwAljQj/mL9AlElXxCCDE0Q3wHb/I/W7DcUx0mvX9SP+zlMVC1pqSJU0WmvQwbTSFJ+Z/Qcze8zMlsysFdli0monDrsBwfHU1dDhnN7neW4O/0FvYblS+xLwauJ1vbbaOEw0rdPNjLqd5QoVbZ/mmTQjkwY+DqquQT0K/Dvg2zW0RcZfmSmqw0TTfINgRDvTdt8NF60a0XUMuClMD15OdKNutz7emvD8iuBCfqHXl4HfZPVNu9NEaetK426PVpRdmhSVpvjc/QkAs2FWpJERspeovl2vfzDxgqlVirVCFCTSrrc5nLvM1Fza/R1p76jXAc+F/ZUA6MzO3062zWT/XqaZ4CmkFhqlNPCR19galJntAHYATJ2yscerh0P7NQ1ObKRR5N1MfB8liKa8+n0XtER29e8ygc+Bc8IUXHxEk/mOOrGYntcOyP/5hlG5W9JpfbBBPQOUmd0PvCHlqRvc/a6iF3L33cBuiCpJFG6hjIu0kUaaw4mRwi6qlSdaQ9SBVE1i6Lbh+KJ4D0dY2ZFV3e5eU0gt0ETmmizrGaDc/V1NNETGXtEO9rrE11U7ZiNKYrim4nni4oviqWVpYq8rI2s6EspvVSIDMipp4ONAaebSlCKp3CtG1mGKrNfUWBFXECUr9JNKnqVX4CxbOcOBzwJ/iG6gV70/Aaqnmf9bM3saeBswb2b31NMsGUNpd50nGaEacizDLi04OeW2zTgZOFDi9UXk3dz7FNmL5mnbuR+/aTjnnBNTKmrUtpyQwakUoNz9r9z9LHdf5+5nuPu762qYjJeM+4XSgsx64GZ6r1ml1avL884ez5ddF80a1XUXzLPKwFzH6lI2HwzBCYaz31Lb6F4jATTFJw0J734/zMr7hbKcTP69SUb5ab8ir4+XLVqi/Bu4Q8SmonospmdNV6VliWVlEI4r3WskgAKUNOdGim3iNyjHyA9ST8XvcUopaQPLo6ys/Xo68QP9LKanZInFrzcpZXV0r5EAWoyV5jS1WeGrrJ6uWySaNsxaA1t1H0tGVekPkt1JLtW1RhLb0v0pVgfDSZjqGqUtJ2SANIKScfMaoqm6l0lMrXVm57/Dyhtn15CTIZY2Agqjm7TisIU2FSyZnTaRU12610i6FKCkKf2s6fRrQ7zMUFcdHV/sHLexesowt+JDH5WwJ3aqS/caCWiKT5rTZMHG1A48BIg9rExf3lN2ai50nln/d/JGN2Wz0zTVVVK4RWGhMzu/FD4rNX2EKUBJU5p615/XgaclaqwD/nsfHVk/6eClpuxGbHfVodP9U+NHU3zSlLT0aVjeJ6nslhdd8anDJaINEXdlTJtlJWqsoXx23F7Syyftzfme0lN2muoqJW+Eqt/hCNIIShqRcqPuMeBeonWcfoLTMaL9ouL/htfQ/zvnstlxl5Q8DpqyG7SJTCoZZyMxgtI2GKMvBIurWU4smAIuzHj5MaJgk1eHbyrnOUh/5xy/ETfN5s7s/BLFkidKd4ajlJ02orXwJjapZFyNRICSsXAjsLbga9cszM2sCcGiimSwuI5oFJfXju4I7PawyWBW59xXZzgKU3Z9ZBu2hfZqGjOa4pOmlLlRt9vJV91iYkWwCJ3rdqKRVC9G/nThOE/XjWQtPCWVjB+NoKRtFoGdISCcXuE8TkrCQncEk5jC6pUCv2q6cJSm6/owsms5ozBCleIUoKQpWes/LwE/Y3XVhxeoNsI3YHtndv47vapEhCKsvfaKWtU5j3FnqLUcaYVKAcrM/gz4N0TZVD8Ctrv7z+tomIyd64huko3fh3QU+EjGqKOO2n1FU4yzUuDj+u6cRzDhQGs50gpV16DuA37N3d8M/BC4vnqTZByFDvkqVq4PXNVAR91zWipl7SKt2GxfnfMo3jyqtRxpi0ojKHe/N/bld4H3VmuOjLOSU2J5KeEvsfreKSd9LanQyCcx5VfniGegN48OanQ2xtOXMkLqXIO6Crgz60kz2wHsAJg6ZVUdT5GktCnB41ujp3TMe4ky9CpPS9XcOQ8s4WCE08FFCukZoMzsfuANKU/d4O53hdfcQLQPzx1Z53H33cBugHWbtpTdXlsmTK8suYytML6T9fohGmTCgUr7yFjrGaDc/V15z5vZfyQq7/JOd1fgkdpkjWSyprVaOi01yISDkU0HFymiUpKEmV0EfAL4XXfP2q1UpDajlnQw4ISDfiqqi4yMqmtQnyFaI7jPzAC+6+4fqdwqkWwjN601wJGd0sFlrFXN4vundTVEJk+fGWgDndYapXuWxryahYgqSUizEgEAllPDi2agDSzpYBSz4lq67iZSCwUoaUxKAEhKnapLBLUjRBUo4unndU1rjdz0ocg4a0WA0n5PEyMtACStmKpLCWrTRKW1DgMbqHdaS1lxIi3SigAlE6NIR5+cqksLamuBlxfmZuq+41tFUkVaRAFKmpQVALoWgb2hunh30b/QqKam5AZlxYm0iDYslCalbfLXLc56iGi32+2svMcpy/FRTV33RqlIqki7aAQljYmlRd/IciHYI8B14bkFVk/nGasLwSZHNbUlNygrTqQ9FKBkGOLBZJqQyk3+GtUhsqfvlNwgMoYUoKRpeaOdzCSFhbmZTs45ldwgMoa0BiVNyxvtpK1RFUlS6Pf7RKTFFKCkaZkFTvtNUlByg8h40hSfNC03lbvfJAUlN4iMH42gpFEa7YhIURpBSeM02hGRIqpuWPifzexhM3vIzO41s39cV8NERGSyVZ3i+zN3f7O7vwXYC/xRDW0SERGpFqDc/RexL08iWlMQERGprPIalJn9KXAF8CLwr3JetwPYATB1ykZtsSEiIrnMPX/QY2b3A29IeeoGd78r9rrrgRPd/Y97XXTdpi1+9Nkny7ZVSjKz/e6+bdjtEBHpR88RlLu/q+C57gC+CfQMUCIiIr1UzeLbEvvyUuDvqzVHREQkUnUNas7M3gQsEd1w+ZHqTRIREakYoNz939fVEBERkTiVOhIRkVZSgBIRkVZSgBIRkVZSgBIRkVZSgBIRkVZSgBIRkVbSflAiferMzl8O7ALOIdrKfqc2XhSpj0ZQIn0IwenzwGbAwufPh+MiUgMFKJH+7ALWJ46tD8dFpAZDCVC/fubrh3FZkTqdU/K4iJSkEZRIf54qeVxESlKAEunPTmAxcWwxHBeRGihAifQhZOtdTVTF38Pnq5XFJ1KfWtLMzezjwH8FNrr74TrOKdJ2IRgpIIkMSOURlJmdDVyI5t5FRKRGdUzx/TfgE0TTHCIiIrWouuX7pcAz7v6DmtojIiICFFiDMrP7gTekPHUDUcbShUUuZGY7gB3hy6Nm9mjRRo6JaaDp9bnNDV9PRKQ25t7fzJyZ/TrwAMuptmcB/xc4392f6/G9+9x9W18XHlGT+DOLiFTRdxafuz8C/KPu12a2AGxTFp+IiNRB90GJiEgr1bbdhrt3Srx8d13XHSGT+DOLiPSt7zUoERGRQdIUn4iItJIClIiItFKjAcrMLjKzA2Z20Mxmm7z2oJnZHjN7Pn5/l5mdbmb3mdmT4fNp4biZ2afD7+FhMztveC0XEWmnxgKUmU0BNwEXA1uB95vZ1qau34AvARcljs0CD7j7FqJ7xrpB+WJgS/jYAXyuoTaKiIyMJkdQ5wMH3f3H7v4K8FXg0gavP1Du/m3gZ4nDlwK3hse3Ar8XO36bR74LnGpmm5ppqYjIaGgyQJ0J/CT29dPh2Dg7w92fDY+fA84IjyfxdyEiUoqSJBriUT6/cvpFRApqMkA9A5wd+/qscGyc/bQ7dRc+Px+OT+LvQkSklCYD1PeBLWZ2rpmtBS4D7m7w+sNwN3BleHwlcFfs+BUhm+8C4MXYVKCIiFBjqaNe3P1VM7sWuAeYAva4+2NNXX/QzOwrwG8D02b2NPDHwBzwNTP7EHAI+P3w8m8C7wEOElWD3954g0VEWk6ljkREpJWUJCEiIq2kACUiIq2kACUiIq3UWJKEjK/p6WnvdDrDboaIjIj9+/cfdveNvV6nACWVdTod9u3bN+xmiMiIMLNDRV6nKT4REWkljaCkcZ3Z+YGef2FuZqDnF5FmaAQlIiKtpAAlIiKtpAAlIiKtpAAlIiKtpAA15szsbDP7lpk9bmaPmdnh+DYsAAAHl0lEQVR14fjpZnafmT0ZPp8WjpuZfdrMDprZw2Z23nB/AhGZVApQ4+9V4OPuvhW4ALjGzLYCs8AD7r4FeCB8DXAxsCV87AA+13yTRUQUoMaeuz/r7n8bHr8EPEG0vfylwK3hZbcCvxceXwrc5pHvAqd2N10UEWmSAtQEMbMO8FbgQeCM2CaJzwFnhMdnAj+JfdvT4VjyXDvMbJ+Z7XvhhRcG1mYRmVwKUBPCzF4HfB34mLv/Iv6cR5uCldoYzN13u/s2d9+2cWPPkloiIqUpQE0AMzuBKDjd4e7fCId/2p26C5+fD8efAc6OfftZ4ZiISKMUoMacmRnwReAJd//z2FN3A1eGx1cCd8WOXxGy+S4AXoxNBYqINEa1+Mbf24E/AB4xs4fCsZ3AHPA1M/sQcAj4/fDcN4H3AAeBRWB7s80VEYkoQI05d/8bwDKefmfK6x24ZqCNEhEpQFN8IiLSSgpQIiLSSgpQIiLSSgpQIiLSSgpQIiLSSgpQIiLSSgpQIiLSSgpQIiLSSgpQIiLSSgpQIiLSSip1JGOnMzs/8GsszM0M/Boik04jKBERaSUFKBERaSUFKBERaSUFKBERaSUFKBERaSUFKBERaSUFKBERaSUFKBERaSUFKBERaSUFKBERaSUFKBERaSUFKBERaSUVixXpw6AL0qoYrYhGUCIi0lIKUCIi0koKUCIi0koKUCIi0koKUCIi0koKUCIi0koKUCIi0koKUCIi0koKUCIi0kqqJCHSQoOuVAGqViHtpxGUiIi0kgKUiIi0kgKUiIi0ktagZBUzuwi4EZgCvuDuc0NukgyAKrJL2ylAyQpmNgXcBPxr4Gng+2Z2t7s/PtyWyahRoodUpQAlSecDB939xwBm9lXgUkABSlqniSA4aIMOsqP8RkEBSpLOBH4S+/pp4DeSLzKzHcCO8OU/mNmBEteYBg733cLBUbvKUbvKSW2XfXIILVmp8u+rj59hc5EXKUBJX9x9N7C7n+81s33uvq3mJlWmdpWjdpWjdpWnLD5JegY4O/b1WeGYiEijFKAk6fvAFjM718zWApcBdw+5TSIygTTFJyu4+6tmdi1wD1Ga+R53f6zmy/Q1NdgAtasctasctaskc/dht0FERGQVTfGJiEgrKUCJiEgrKUBJY8zsIjM7YGYHzWy2gevtMbPnzezR2LHTzew+M3syfD4tHDcz+3Ro28Nmdl7se64Mr3/SzK6soV1nm9m3zOxxM3vMzK5rQ9vM7EQz+56Z/SC060/C8XPN7MFw/TtD8gxmti58fTA834md6/pw/ICZvbtKu2LnnDKzvzOzvW1pl5ktmNkjZvaQme0Lx9rwb+xUM/tLM/t7M3vCzN7WhnaV5u760MfAP4gSLn4EvBFYC/wA2Drga/4WcB7waOzYfwFmw+NZ4JPh8XuA/wUYcAHwYDh+OvDj8Pm08Pi0iu3aBJwXHp8M/BDYOuy2hfO/Ljw+AXgwXO9rwGXh+M3AH4bHHwVuDo8vA+4Mj7eGv+864Nzwd5+q4e/5n4AvA3vD10NvF7AATCeOteHf2K3Ah8PjtcCpbWhX6Z+jyYvpY3I/gLcB98S+vh64voHrdlgZoA4Am8LjTcCB8PgvgPcnXwe8H/iL2PEVr6upjXcR1T5sTduA9cDfElUROQy8Jvl3JMr0fFt4/JrwOkv+beOvq9Ces4AHgN8B9obrtKFdC6wOUEP9OwKvB/4PIQmuLe3q50NTfNKUtBJKZw6hHWe4+7Ph8XPAGeFxVvsG2u4w/fRWotHK0NsWptEeAp4H7iMaZfzc3V9Nucbx64fnXwQ2DKJdwKeATwBL4esNLWmXA/ea2X6Lyn/B8P+O5wIvALeEKdEvmNlJLWhXaQpQMrE8els4tPsszOx1wNeBj7n7L+LPDatt7n7M3d9CNGI5H/jnTbchycwuAZ539/3DbkuKd7j7ecDFwDVm9lvxJ4f0d3wN0dT259z9rcDLRFN6w25XaQpQ0pS2lFD6qZltAgifnw/Hs9o3kHab2QlEwekOd/9Gm9oG4O4/B75FNHV2qpl1b+qPX+P49cPzrweODKBdbwd+18wWgK8STfPd2IJ24e7PhM/PA39FFNSH/Xd8Gnja3R8MX/8lUcAadrtKU4CSprSlhNLdQDcb6Uqi9Z/u8StCRtMFwIthOuQe4EIzOy1kPV0YjvXNzAz4IvCEu/95W9pmZhvN7NTw+LVE62JPEAWq92a0q9ve9wJ/Hd6Z3w1cFrLpzgW2AN/rt13ufr27n+XuHaJ/N3/t7h8YdrvM7CQzO7n7mOj3/yhD/ju6+3PAT8zsTeHQO4m2yxn6v/3Smlzw0sdkfxBlC/2QaF3jhgau9xXgWeBXRO8qP0S0FvEA8CRwP3B6eK0RbdT4I+ARYFvsPFcBB8PH9hra9Q6i6ZWHgYfCx3uG3TbgzcDfhXY9CvxROP5Goo78IPA/gHXh+Inh64Ph+TfGznVDaO8B4OIa/6a/zXIW31DbFa7/g/DxWPff9LD/juF8bwH2hb/l/yTKwht6u8p+qNSRiIi0kqb4RESklRSgRESklRSgRESklRSgRESklRSgRESklRSgRESklRSgRESklf4/37RmY/7GKQwAAAAASUVORK5CYII=\n",
	"text/plain": [
	"<Figure size 432x288 with 3 Axes>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"from matplotlib import pyplot as plt\n",
	"import matplotlib.gridspec as gridspec\n",
	"import numpy as np\n",
	"x = np.random.geometric(0.001, size=500)\n",
	"y = np.random.normal(size=500)",
	"plt.figure()\n",
	"\n",
	"gs = gridspec.GridSpec(2, 2,\n",
	" width_ratios=[1, 3],\n",
	" height_ratios=[3, 1])\n",
	"\n",
	"ax_vertical_hist = plt.subplot(gs[0])\n",
	"ax_main_plot = plt.subplot(gs[1])\n",
	"ax_horizontal_hist = plt.subplot(gs[3])\n",
	"\n",
	"ax_vertical_hist.hist(y, orientation='horizontal')\n",
	"ax_main_plot.scatter(x, y)\n",
	"ax_horizontal_hist.hist(x)\n",
	"\n",
	"ax_main_plot.set_axis_off()\n",
	"plt.tight_layout(w_pad=0, h_pad=0)\n",
	"\n",
	"plt.show()"
	]
	}
	],
	"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": "3.6.3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}