interactive_align.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/matt-breeden/colab-public/blob/main/interactive_align.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "01hC2bszdvog"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from google.colab import output, files\n",
        "output.enable_custom_widget_manager()\n",
        "import matplotlib.pyplot as plt\n",
        "from ipywidgets import Output, widgets, FloatSlider, Dropdown, Layout"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "two types of patients: aligner (a) and brackets (b)\n",
        "\n",
        "total patients P\n",
        "aligner patients p_a\n",
        "brackets patients p_b\n",
        "P = p_a + p_b\n",
        "\n",
        "visits per aligner patient per year vpp_a\n",
        "visits per brackets patient per year vpp_b\n",
        "\n",
        "total visits V\n",
        "aligner visits v_a\n",
        "brackets visits v_b\n",
        "V = v_a + v_b\n",
        "v_a = p_a * vpp_a\n",
        "v_b = p_b * vpp_b\n",
        "\n",
        "contract amount per aligner patient cpp_a\n",
        "contract amount per brackets patient cpp_b\n",
        "\n",
        "total contract revenue C\n",
        "aligner contract revenue c_a\n",
        "brackets contract revenue c_b\n",
        "C = c_a + c_b\n",
        "c_a = p_a * cpp_a\n",
        "c_b = p_b * cpp_b\n",
        "\n",
        "SOC_visits = v_a / V * 100\n",
        "SOC_patients = p_a / P * 100\n",
        "\"\"\"\n",
        "\n",
        "def get_SOC_patients_from_SOC_visits(SOC_visits, vpp_a, vpp_b):\n",
        "  return 100 * (SOC_visits * vpp_b) / (SOC_visits * vpp_b + 100 * vpp_a - SOC_visits * vpp_a)\n",
        "\n",
        "def get_SOC_visits_from_SOC_patients(SOC_patients, vpp_a, vpp_b):\n",
        "  return (-100 * SOC_patients * vpp_a) / (SOC_patients * (vpp_b - vpp_a) - 100 * vpp_b)\n",
        "\n",
        "def calculate_revenue_soc(SOC_visits, V, vpp_a, vpp_b, cpp_a, cpp_b):\n",
        "    visits_a = SOC_visits * V / 100\n",
        "    visits_b = (1 - SOC_visits/100) * V\n",
        "    p_a = visits_a / vpp_a\n",
        "    p_b = visits_b / vpp_b\n",
        "    c_a = p_a * cpp_a\n",
        "    c_b = p_b * cpp_b\n",
        "    return c_a, c_b\n",
        "\n",
        "def calculate_patients_soc(SOC_visits, V, vpp_a, vpp_b, cpp_a, cpp_b):\n",
        "    visits_a = SOC_visits * V / 100\n",
        "    visits_b = (1 - SOC_visits/100) * V\n",
        "    p_a = visits_a / vpp_a\n",
        "    p_b = visits_b / vpp_b\n",
        "    return p_a, p_b\n"
      ],
      "metadata": {
        "id": "CerPFdQyoBA2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_V = 1000\n",
        "test_vpp_a = 10\n",
        "test_vpp_b = 18\n",
        "test_cpp_a = 5000\n",
        "test_cpp_b = 5500\n",
        "test_SOC_patients = 50\n",
        "test_SOC_visits = get_SOC_visits_from_SOC_patients(test_SOC_patients, test_vpp_a, test_vpp_b)\n",
        "\n",
        "title = \"Share of Chair Effects\"\n",
        "areaLabels = ['Aligner', 'Brackets']\n",
        "yLabel = 'Share of Chair (SOC)'\n",
        "xAxisLabels = {'Revenue': 'Total Contract Revenue ($K)', 'Patients': 'Maximum Total Patients'}\n",
        "\n",
        "def plot_stacked_area_chart(SOC_patients, V, vpp_a, vpp_b, cpp_a, cpp_b, dependent_variable):\n",
        "    print(\"\\n\")\n",
        "\n",
        "    x = list(range(101))\n",
        "\n",
        "    SOC_visits = get_SOC_visits_from_SOC_patients(SOC_patients, vpp_a, vpp_b)\n",
        "\n",
        "    if (dependent_variable == 'Revenue'):\n",
        "      y_values = np.array([calculate_revenue_soc(get_SOC_visits_from_SOC_patients(SOC, vpp_a, vpp_b), V, vpp_a, vpp_b, cpp_a, cpp_b) for SOC in x]) / 1000\n",
        "      actual_y_a, actual_y_b = calculate_revenue_soc(SOC_visits, V, vpp_a, vpp_b, cpp_a, cpp_b)\n",
        "      actual_y = (actual_y_a + actual_y_b) / 1000\n",
        "    else:\n",
        "      y_values = np.array([calculate_patients_soc(get_SOC_visits_from_SOC_patients(SOC, vpp_a, vpp_b), V, vpp_a, vpp_b, cpp_a, cpp_b) for SOC in x])\n",
        "      actual_y_a, actual_y_b = calculate_patients_soc(SOC_visits, V, vpp_a, vpp_b, cpp_a, cpp_b)\n",
        "      actual_y = actual_y_a + actual_y_b\n",
        "\n",
        "    y1 = y_values[:, 0]\n",
        "    y2 = y_values[:, 1]\n",
        "\n",
        "    plt.stackplot(x, y1, y2, labels=areaLabels, alpha=0.7)\n",
        "    plt.axhline(y=y1[-1], color='gray', linestyle='--', alpha=0.4)\n",
        "    plt.axhline(y=y2[0], color='gray', linestyle='--', alpha=0.4)\n",
        "    plt.scatter([SOC_patients], [actual_y], color='black', marker='o')\n",
        "    plt.axvline(x=SOC_patients, color='purple', linestyle='--')\n",
        "    plt.axhline(y=actual_y, color='black', linestyle='--')\n",
        "    plt.xlabel(yLabel)\n",
        "    plt.ylabel(xAxisLabels[dependent_variable])\n",
        "    plt.title(title)\n",
        "    plt.legend(loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "SOC_patients_slider = FloatSlider(value=test_SOC_patients, min=0, max=100, step=1, description=\"Share of Chair (patients):\", layout=Layout(width='50%', padding='10px'))\n",
        "V_slider = FloatSlider(value=test_V, min=0, max=10000, step=100, description='Visit Capacity V:', layout=Layout(width='50%', padding='10px'))\n",
        "vpp_a_slider = FloatSlider(value=test_vpp_a, min=0, max=30, step=1, description='Aligner Visits per Patient:', layout=Layout(width='50%', padding='10px'))\n",
        "vpp_b_slider = FloatSlider(value=test_vpp_b, min=0, max=30, step=1, description='Brackets Visits per Patient:', layout=Layout(width='50%', padding='10px'))\n",
        "cpp_a_slider = FloatSlider(value=test_cpp_a, min=0, max=10000, step=100, description='Aligner Contract per Patient:', layout=Layout(width='50%', padding='10px'))\n",
        "cpp_b_slider = FloatSlider(value=test_cpp_b, min=0, max=10000, step=100, description='Brackets Contract per Patient:', layout=Layout(width='50%', padding='10px'))\n",
        "dependent_variable_dropdown = Dropdown(options=['Revenue', 'Patients'], value='Revenue', description='Dependent Variable:', layout=Layout(width='30%', padding='10px'))\n",
        "\n",
        "SOC_patients_slider.style.description_width = '200px'\n",
        "V_slider.style.description_width = '200px'\n",
        "vpp_a_slider.style.description_width = '200px'\n",
        "vpp_b_slider.style.description_width = '200px'\n",
        "cpp_a_slider.style.description_width = '200px'\n",
        "cpp_b_slider.style.description_width = '200px'\n",
        "dependent_variable_dropdown.style.description_width = '200px'\n",
        "\n",
        "interactive_plot = widgets.interactive(plot_stacked_area_chart,\n",
        "                                        SOC_patients=SOC_patients_slider,\n",
        "                                        V=V_slider,\n",
        "                                        vpp_a=vpp_a_slider,\n",
        "                                        vpp_b=vpp_b_slider,\n",
        "                                        cpp_a=cpp_a_slider,\n",
        "                                        cpp_b=cpp_b_slider,\n",
        "                                        dependent_variable=dependent_variable_dropdown,\n",
        "                                        continuous_update=False)\n",
        "\n",
        "interactive_plot"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=50.0, description='Share of Chair (patients):', layout=Layout(padding=…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71bb8cfc610842d6866105bfd9aabf44"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CfBbWeV1frjl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}