plottting_disulfide.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "t = np.arange(1000)/100.\n",
    "x = np.sin(2*np.pi*10*t)\n",
    "y = np.cos(2*np.pi*10*t)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = plt.subplot(211)\n",
    "#plt.plot(t,x)\n",
    "ax2 = plt.subplot(212)\n",
    "#plt.plot(t,y)\n",
    "\n",
    "# some code to share both x axes\n",
    "axes = fig.get_axes()\n",
    "#axes[0].get_shared_x_axes().join(axes[0], axes[1])\n",
    "axes[0].get_shared_y_axes().join(axes[0], axes[1])\n",
    "axes[0].autoscale()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, 1.0)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 39 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "\n",
    "s1 = np.sin(2 * np.pi * t)\n",
    "s2 = np.exp(-t)\n",
    "s3 = s1 * s2\n",
    "\n",
    "fig, axs = plt.subplots(3, 13,figsize=(10,10), sharex='row',sharey=True)\n",
    "# Remove horizontal space between axes\n",
    "fig.subplots_adjust(wspace=0,hspace=0)\n",
    "\n",
    "# Plot each graph, and manually set the y tick values\n",
    "axs[0,0].plot(t, s1)\n",
    "axs[0,0].set_yticks(np.arange(-0.9, 1.0, 0.4))\n",
    "axs[0,0].set_ylim(-1, 1)\n",
    "\n",
    "axs[0,1].plot(t, s2)\n",
    "axs[0,1].set_yticks(np.arange(0.1, 1.0, 0.2))\n",
    "axs[0,1].set_ylim(0, 1)\n",
    "\n",
    "axs[0,2].plot(t, s3)\n",
    "axs[0,2].set_yticks(np.arange(-0.9, 1.0, 0.4))\n",
    "axs[0,2].set_ylim(-1, 1)\n",
    "\n",
    "axs[1,0].plot(t, s1)\n",
    "axs[1,0].set_yticks(np.arange(-0.9, 1.0, 0.4))\n",
    "axs[1,0].set_ylim(-1, 1)\n",
    "\n",
    "axs[1,1].plot(t, s2)\n",
    "axs[1,1].set_yticks(np.arange(0.1, 1.0, 0.2))\n",
    "axs[1,1].set_ylim(0, 1)\n",
    "\n",
    "axs[1,2].plot(t, s3)\n",
    "axs[1,2].set_yticks(np.arange(-0.9, 1.0, 0.4))\n",
    "axs[1,2].set_ylim(-1, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "\n",
    "s1 = np.sin(2 * np.pi * t)\n",
    "s2 = np.exp(-t)\n",
    "s3 = s1 * s2\n",
    "\n",
    "fig, axs = plt.subplots(1, 13,figsize=(15,10), sharex='row',sharey=True)\n",
    "# Remove horizontal space between axes\n",
    "fig.subplots_adjust(wspace=0,hspace=0)\n",
    "\n",
    "# Plot each graph, and manually set the y tick values\n",
    "axs[0].scatter(4.5, 's1',label='sim13')\n",
    "axs[1].scatter(9.5, 's1',label='sim14')\n",
    "#axs[0].set_yticks(np.arange(-0.9, 1.0, 0.4))\n",
    "#axs[0].set_ylim(-1, 1)\n",
    "\n",
    "# axs[0,1].plot(t, s2)\n",
    "# axs[0,1].set_yticks(np.arange(0.1, 1.0, 0.2))\n",
    "# axs[0,1].set_ylim(0, 1)\n",
    "\n",
    "# axs[0,2].plot(t, s3)\n",
    "# axs[0,2].set_yticks(np.arange(-0.9, 1.0, 0.4))\n",
    "# axs[0,2].set_ylim(-1, 1)\n",
    "\n",
    "# axs[1,0].plot(t, s1)\n",
    "# axs[1,0].set_yticks(np.arange(-0.9, 1.0, 0.4))\n",
    "# axs[1,0].set_ylim(-1, 1)\n",
    "\n",
    "# axs[1,1].plot(t, s2)\n",
    "# axs[1,1].set_yticks(np.arange(0.1, 1.0, 0.2))\n",
    "# axs[1,1].set_ylim(0, 1)\n",
    "\n",
    "# axs[1,2].plot(t, s3)\n",
    "# axs[1,2].set_yticks(np.arange(-0.9, 1.0, 0.4))\n",
    "# axs[1,2].set_ylim(-1, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(12,8)) # Notice the equal aspect ratio\n",
    "ax = [fig.add_subplot(1,6,i+1,sharey=ax[0]) for i in range(6)]\n",
    "\n",
    "# for a in ax:\n",
    "#       a.set_xticklabels([])\n",
    "#       a.set_yticklabels([])\n",
    "#       #a.set_aspect('equal')\n",
    "fig.subplots_adjust(wspace=0)\n",
    "\n",
    "\n",
    "#ax[0].set_ylim(-1, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}