Computational_Seismology_Igel/rsf_widgets_dashboard.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 10,
        "hidden": true,
        "row": 28,
        "width": 12
       },
       "report_default": {}
      }
     }
    }
   },
   "source": [
    "<div style='background-image: url(\"../share/images/header.svg\") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>\n",
    "    <div style=\"float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; width: 50% ; height: 150px\">\n",
    "        <div style=\"position: relative ; top: 50% ; transform: translatey(-50%)\">\n",
    "            <div style=\"font-size: xx-large ; font-weight: 900 ; color: rgba(0 , 0 , 0 , 0.8) ; line-height: 100%\">Rate and State Friction Toolkit</div>\n",
    "            <div style=\"font-size: large ; padding-top: 20px ; color: rgba(0 , 0 , 0 , 0.5)\">Dashboard</div>\n",
    "        </div>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
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        "col": 0,
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        "row": 28,
        "width": 12
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   },
   "source": [
    "Seismo-Live: http://seismo-live.org\n",
    "\n",
    "##### Authors:\n",
    "* John Leeman ([@jrleeman](https://github.com/jrleeman), http://www.johnrleeman.com/)\n",
    "\n",
    "---\n",
    "\n",
    "<div style=\"background-color: #FBC02D; padding: 10px\">\n",
    "This is a dashboard demoing the functionality of the [Rate and State Friction Toolkit](https://github.com/jrleeman/rsfmodel). <br><br>\n",
    "\n",
    "**Two steps to get it going: (1) \"Cell -> Run All\" (wait until it finishes) and (2) \"View -> Dashboard Preview\".**\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display, clear_output\n",
    "import numpy as np\n",
    "from rsfmodel import rsf, staterelations, plot\n",
    "\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def RunModel():\n",
    "    # Set model initial conditions\n",
    "    model.mu0 = 0.6 # Friction initial (at the reference velocity)\n",
    "    model.a = a_floatBox.value # Empirical coefficient for the direct effect\n",
    "    model.k = k_floatBox.value # Normalized System stiffness (friction/micron)\n",
    "\n",
    "    if sv1_dropdown.value == \"Aging Relation\":\n",
    "        state1 = staterelations.DieterichState()\n",
    "        state1.b = b1_floatBox.value  # Empirical coefficient for the evolution effect\n",
    "        state1.Dc = Dc1_floatBox.value  # Critical slip distance\n",
    "\n",
    "    elif sv1_dropdown.value == \"Slowness Relation\":\n",
    "        state1 = staterelations.RuinaState()\n",
    "        state1.b = b1_floatBox.value  # Empirical coefficient for the evolution effect\n",
    "        state1.Dc = Dc1_floatBox.value  # Critical slip distance\n",
    "\n",
    "    else:\n",
    "        # We shouldn't be here!\n",
    "        pass\n",
    "\n",
    "    if sv2_dropdown.value == \"Aging Relation\":\n",
    "        state2 = staterelations.DieterichState()\n",
    "        state2.b = b1_floatBox.value  # Empirical coefficient for the evolution effect\n",
    "        state2.Dc = Dc1_floatBox.value  # Critical slip distance\n",
    "\n",
    "    elif sv2_dropdown.value == \"Slowness Relation\":\n",
    "        state2 = staterelations.RuinaState()\n",
    "        state2.b = b1_floatBox.value  # Empirical coefficient for the evolution effect\n",
    "        state2.Dc = Dc1_floatBox.value  # Critical slip distance\n",
    "\n",
    "    else:\n",
    "        # None\n",
    "        pass\n",
    "\n",
    "    model.state_relations = [state1] # Which state relation we want to use\n",
    "\n",
    "    if sv2_dropdown.value != \"None\":\n",
    "        model.state_relations.append(state2)\n",
    "\n",
    "    time, velocity = parse_sequence(step_sequence_string.value)\n",
    "\n",
    "    model.time = time\n",
    "    # Set the model load point velocity, must be same shape as model.model_time\n",
    "    model.loadpoint_velocity = velocity\n",
    "\n",
    "    model.v = velocity[0] # Initial slider velocity, generally is vlp(t=0)\n",
    "    model.vref = velocity[0] # Reference velocity, generally vlp(t=0)\n",
    "\n",
    "    # Run the model!\n",
    "    model.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def update_dispalcement_plot(model):\n",
    "    #clear_output(wait=True)\n",
    "    line_lp_mu.set_xdata(model.results.loadpoint_displacement)\n",
    "    line_lp_mu.set_ydata(model.results.friction)\n",
    "\n",
    "    line_lp_vs.set_xdata(model.results.loadpoint_displacement)\n",
    "    line_lp_vs.set_ydata(model.results.slider_velocity)\n",
    "\n",
    "    ax1.set_xlim(0, np.max(model.results.loadpoint_displacement))\n",
    "\n",
    "    ax1.relim()\n",
    "    ax1.autoscale_view(False, False, True)\n",
    "\n",
    "    ax1b.relim()\n",
    "    ax1b.autoscale_view(False, False, True)\n",
    "    fig_vars.canvas.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def update_time_plot(model):\n",
    "    #clear_output(wait=True)\n",
    "\n",
    "    line_time_mu.set_xdata(model.results.time)\n",
    "    line_time_mu.set_ydata(model.results.friction)\n",
    "\n",
    "    line_time_vs.set_xdata(model.results.time)\n",
    "    line_time_vs.set_ydata(model.results.slider_velocity)\n",
    "\n",
    "    ax2.set_xlim(0, np.max(model.results.time))\n",
    "\n",
    "    ax2.relim()\n",
    "    ax2.autoscale_view(False, False, True)\n",
    "\n",
    "    ax2b.relim()\n",
    "    ax2b.autoscale_view(False, False, True)\n",
    "    fig_vars.canvas.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def update_phase_plot(model):\n",
    "    ax3.cla()\n",
    "    phase_line, = ax3.plot([], [], color=tableau20[4], linewidth=2)\n",
    "\n",
    "    ax3.set_xlabel(r'ln$\\frac{V}{V_{ref}}$', fontsize=16, labelpad=20)\n",
    "    ax3.set_ylabel(r'$\\mu$', fontsize=16)\n",
    "\n",
    "    v_ratio = np.log(model.results.slider_velocity/model.vref)\n",
    "    phase_line.set_xdata(v_ratio)\n",
    "    phase_line.set_ydata(model.results.friction)\n",
    "\n",
    "    #xlims = ax2.get_xlims()#[np.min(v_ratio), np.max(v_ratio)]\n",
    "    #ylims = ax3.get_xlims()#[np.min(model.results.friction), np.max(model.results.friction)]\n",
    "    ax3.relim()\n",
    "    ax3.autoscale_view(False, True, True)\n",
    "    ylims = ax3.get_ylim()\n",
    "    xlims = ax3.get_xlim()\n",
    "    # Plot lines of constant a that are in the view\n",
    "    y_line = model.a * np.array(xlims)\n",
    "    for mu in np.arange(0, ylims[1]*2, 0.005):\n",
    "        y_line_plot = y_line + mu\n",
    "        if max(y_line_plot) > ylims[0]:\n",
    "            ax3.plot([xlims[0], xlims[1]], y_line_plot, color='k', linestyle='--')\n",
    "\n",
    "    # Plot a line of rate dependence \"Steady State Line\"\n",
    "    state_b_sum = 0\n",
    "    for state in model.state_relations:\n",
    "        state_b_sum += state.b\n",
    "        mu_rate_dependence = model.mu0 + (model.a - state_b_sum)*np.array(xlims)\n",
    "        ax3.plot(xlims, mu_rate_dependence, color='k', linestyle='--')\n",
    "\n",
    "    ax3.set_xlim(xlims)\n",
    "    ax3.set_ylim(ylims)\n",
    "    #ax3.relim()\n",
    "    #ax3.autoscale_view(False, True, True)\n",
    "    fig_phase.canvas.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def stiffness_text_update(model):\n",
    "    kc = (model.state_relations[0].b - model.a)/model.state_relations[0].Dc\n",
    "    kc_label.value = \"k$_c$ [$\\mu$m$^{-1}$]: %f\" %kc\n",
    "    kappa_label.value = \"k/k$_c$: %f\" %(model.k/kc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def parse_sequence(seqString):\n",
    "    steps = seqString.split(',')\n",
    "    if len(steps)%2 != 0:\n",
    "        # Odd number of steps is an error!\n",
    "        step_sequence_string.border_color = 'red'\n",
    "    else:\n",
    "        step_sequence_string.border_color = 'green'\n",
    "        steps = np.array(steps)\n",
    "        steps = steps.astype(int)\n",
    "\n",
    "        steps = steps.reshape((int(len(steps)/2),2))\n",
    "\n",
    "        dt = 0.01\n",
    "\n",
    "        if simType_buttons.value == 'Velocity-Displacement':\n",
    "            velocity = np.array([])\n",
    "            time = np.arange(0, np.sum(steps[:,1]/steps[:,0]), dt)\n",
    "            for step_velocity, step_displacement in steps:\n",
    "                step_time = step_displacement/step_velocity\n",
    "                velocity = np.hstack((velocity, np.ones(int(step_time/dt)) * step_velocity))\n",
    "\n",
    "        if simType_buttons.value == 'Velocity-Time':\n",
    "            velocity = np.array([])\n",
    "            time = np.arange(0, np.sum(steps[:,1]), dt)\n",
    "            for step_velocity, step_time in steps:\n",
    "                velocity = np.hstack((velocity, np.ones(int(step_time/dt)) * step_velocity))\n",
    "\n",
    "        return time, velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def on_calculate_clicked(button):\n",
    "    RunModel()\n",
    "    update_dispalcement_plot(model)\n",
    "    update_time_plot(model)\n",
    "    update_phase_plot(model)\n",
    "    stiffness_text_update(model)\n",
    "    clear_output(wait=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def on_reset(button):\n",
    "    a_floatBox.value = 0.005\n",
    "    b1_floatBox.value = 0.01\n",
    "    Dc1_floatBox.value = 10.\n",
    "    b2_floatBox.value = 0.01\n",
    "    Dc2_floatBox.value = 10.\n",
    "    k_floatBox.value = 1e-3\n",
    "    sv1_dropdown.value = \"Aging Relation\"\n",
    "    sv2_dropdown.value = \"None\"\n",
    "    RunModel()\n",
    "    update_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "# These are the \"Tableau 20\" colors as RGB.\n",
    "tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),\n",
    "             (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),\n",
    "             (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),\n",
    "             (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),\n",
    "             (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
    "\n",
    "# Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts.\n",
    "for i in range(len(tableau20)):\n",
    "    r, g, b = tableau20[i]\n",
    "    tableau20[i] = (r / 255., g / 255., b / 255.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "model = rsf.Model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "a_floatBox = widgets.BoundedFloatText(\n",
    "    value=0.005,\n",
    "    min=0,\n",
    "    max=1,\n",
    "    description='a:',\n",
    ")\n",
    "\n",
    "b1_floatBox = widgets.BoundedFloatText(\n",
    "    value=0.01,\n",
    "    min=0,\n",
    "    max=1,\n",
    "    description='b:',\n",
    ")\n",
    "\n",
    "b2_floatBox = widgets.BoundedFloatText(\n",
    "    value=0.01,\n",
    "    min=0,\n",
    "    max=1,\n",
    "    description='b:',\n",
    ")\n",
    "\n",
    "Dc1_floatBox = widgets.BoundedFloatText(\n",
    "    value=10.0,\n",
    "    min=0,\n",
    "    max=30000.0,\n",
    "    description='Dc:',\n",
    ")\n",
    "\n",
    "Dc2_floatBox = widgets.BoundedFloatText(\n",
    "    value=10.0,\n",
    "    min=0,\n",
    "    max=30000.0,\n",
    "    description='Dc:',\n",
    ")\n",
    "\n",
    "k_floatBox = widgets.BoundedFloatText(\n",
    "    value=1e-3,\n",
    "    min=0.,\n",
    "    max=100,\n",
    "    description='k:',\n",
    ")\n",
    "\n",
    "sv1_dropdown = widgets.Dropdown(\n",
    "    options=['Aging Relation', 'Slowness Relation'],\n",
    "    value='Aging Relation',\n",
    "    decription='State Relation:')\n",
    "\n",
    "sv2_dropdown = widgets.Dropdown(\n",
    "    options=['None', 'Aging Relation', 'Slowness Relation'],\n",
    "    value='None',\n",
    "    decription='State Relation:')\n",
    "\n",
    "simType_buttons = widgets.ToggleButtons(\n",
    "    description='Simulation Type',\n",
    "    options=['Velocity-Displacement', 'Velocity-Time'])\n",
    "\n",
    "calculate_button = widgets.Button(\n",
    "    description=\"Calculate\")\n",
    "\n",
    "reset_button = widgets.Button(\n",
    "    description=\"Reset\")\n",
    "\n",
    "step_sequence_string = widgets.Text(\n",
    "    description='Sequence:',\n",
    "    value='1,10,10,200',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "sv1_label = widgets.Label(\n",
    "    value=\"First State Variable\",\n",
    ")\n",
    "\n",
    "sv2_label = widgets.Label(\n",
    "    value=\"Second State Variable\",\n",
    ")\n",
    "\n",
    "kc_label = widgets.Label(\n",
    "    value=\"k$_c$ [$\\mu$m$^{-1}$]: \"\n",
    ")\n",
    "\n",
    "kappa_label = widgets.Label(\n",
    "    value=\"k/k$_c$: \"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 0,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5abb2b38afc40f08d151edca879b24f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Label(value='k$_c$ [$\\\\mu$m$^{-1}$]: ')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4641a891292d45a3a89aadb0f4932ac9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Label(value='k/k$_c$: ')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(kc_label, kappa_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
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       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 0,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ab34fca4b384130a75f1a1c50ece73b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Calculate', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e36af630eec842a4bcfcd007841de17c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Reset', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(calculate_button, reset_button)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
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       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def state_of_sv2():\n",
    "    if sv2_dropdown.value == \"None\":\n",
    "        b2_floatBox.disabled =  True\n",
    "        Dc2_floatBox.disabled =  True\n",
    "    else:\n",
    "        b2_floatBox.disabled = False\n",
    "        Dc2_floatBox.disabled = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lion/miniconda3/envs/seismo_live/lib/python3.7/site-packages/ipykernel_launcher.py:3: DeprecationWarning: on_trait_change is deprecated in traitlets 4.1: use observe instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "calculate_button.on_click(on_calculate_clicked)\n",
    "reset_button.on_click(on_reset)\n",
    "sv2_dropdown.on_trait_change(state_of_sv2)\n",
    "b2_floatBox.disabled = True\n",
    "Dc2_floatBox.disabled = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 8,
        "height": 4,
        "hidden": false,
        "row": 0,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3380618ea27d40beaddb2c6c02a5ccec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "BoundedFloatText(value=0.001, description='k:')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e53c0755ad24f759ac7494549e26ce3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "BoundedFloatText(value=0.005, description='a:', max=1.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(k_floatBox, a_floatBox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 5,
        "hidden": false,
        "row": 4,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce05847a60cb4460964b9e8a9a35a10e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Label(value='First State Variable')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1351fc823a244a7db5242f8d3519c140",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(options=('Aging Relation', 'Slowness Relation'), value='Aging Relation')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b15e83b379b14e7e8d0f31f1f2c400a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "BoundedFloatText(value=0.01, description='b:', max=1.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41b865f100d646a8a43555f666fb748e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "BoundedFloatText(value=10.0, description='Dc:', max=30000.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(sv1_label, sv1_dropdown, b1_floatBox, Dc1_floatBox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 5,
        "hidden": false,
        "row": 4,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23b18c3e954f4bd38cf04db269b976e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Label(value='Second State Variable')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df944a6e7f61485189a3731867d3bbee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(options=('None', 'Aging Relation', 'Slowness Relation'), value='None')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cccbcfbb53ad438ea739553f61fcf2b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "BoundedFloatText(value=0.01, description='b:', disabled=True, max=1.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b301fbf83edd40bd825296c57335ffcf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "BoundedFloatText(value=10.0, description='Dc:', disabled=True, max=30000.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(sv2_label, sv2_dropdown, b2_floatBox, Dc2_floatBox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 8,
        "height": 4,
        "hidden": false,
        "row": 4,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e8da344f88a4c0680ea87a5ddd638ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ToggleButtons(description='Simulation Type', options=('Velocity-Displacement', 'Velocity-Time'), value='Veloci…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e8f7025183b4fc2a473825a0e7088e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='1,10,10,200', description='Sequence:')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(simType_buttons)\n",
    "display(step_sequence_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 18,
        "hidden": false,
        "row": 9,
        "width": 5
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\mu$')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup figure and axes\n",
    "# Generally plots is ~1.33x width to height (10,7.5 or 12,9)\n",
    "fig_phase = plt.figure(figsize=(5,5))\n",
    "ax3 = plt.subplot(111)\n",
    "\n",
    "phase_line, = ax3.plot([], [], color=tableau20[4], linewidth=2)\n",
    "\n",
    "ax3.set_xlabel(r'ln$\\frac{V}{V_{ref}}$', fontsize=16, labelpad=20)\n",
    "ax3.set_ylabel(r'$\\mu$', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 5,
        "height": 19,
        "hidden": false,
        "row": 9,
        "width": 7
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup figure and axes\n",
    "# Generally plots is ~1.33x width to height (10,7.5 or 12,9)\n",
    "fig_vars = plt.figure()\n",
    "ax1 = plt.subplot(211)\n",
    "ax1b = ax1.twinx()\n",
    "\n",
    "ax2 = plt.subplot(212)\n",
    "ax2b = ax2.twinx()\n",
    "\n",
    "# Set labels and tick sizes\n",
    "ax1.set_xlabel(r'Load Point Displacement')\n",
    "ax1.set_ylabel(r'Friction')\n",
    "ax1b.set_ylabel(r'Slider Velocity')\n",
    "\n",
    "# Plotting\n",
    "line_lp_mu, = ax1.plot([], [], color=tableau20[0], linewidth=2)\n",
    "line_lp_vs, = ax1b.plot([], [], color=tableau20[2], linewidth=1, linestyle='--')\n",
    "\n",
    "ax1.grid()\n",
    "\n",
    "# Set labels and tick sizes\n",
    "ax2.set_xlabel(r'Time')\n",
    "ax2.set_ylabel(r'Friction')\n",
    "ax2b.set_ylabel(r'Slider Velocity')\n",
    "\n",
    "# Plotting\n",
    "line_time_mu, = ax2.plot([], [], color=tableau20[0], linewidth=2)\n",
    "line_time_vs, = ax2b.plot([], [], color=tableau20[2], linewidth=1, linestyle='--')\n",
    "\n",
    "ax2.grid()\n",
    "\n",
    "plt.subplots_adjust(wspace=0.4, hspace=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "# Run the defaults to have some output showing when we load\n",
    "on_calculate_clicked(calculate_button)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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    "version": 3
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   "nbconvert_exporter": "python",
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