{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc7029d7",
   "metadata": {},
   "source": [
    "# Notebook. Demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f243cf4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Work from this notebook's folder (content/), also when the notebook is executed from the project root\n",
    "# (myst build --execute, NeuroLibre), so the local modules (helpers, stats_test, ...) and ../data/ resolve.\n",
    "import os\n",
    "import sys\n",
    "\n",
    "if os.path.isdir(\"content\") and not os.path.isfile(\"helpers.py\"):\n",
    "    os.chdir(\"content\")\n",
    "if os.getcwd() not in sys.path:\n",
    "    sys.path.insert(0, os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9dd80ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tableone import TableOne\n",
    "\n",
    "import helpers\n",
    "\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fbb88076",
   "metadata": {},
   "outputs": [],
   "source": [
    "tsv = helpers.data_path(\"participants.tsv\")\n",
    "df= pd.read_csv(tsv, sep='\\t').convert_dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "63a5a245",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the desired order of categories\n",
    "group_order = ['HC', 'LBD', 'FTD', 'AD']\n",
    "\n",
    "# Convert to ordered categorical type (the data files code healthy controls as 'Control'; the paper uses HC)\n",
    "df['group'] = df['group'].replace({'Control': 'HC'})\n",
    "df['group'] = pd.Categorical(df['group'], categories=group_order, ordered=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58cb5783",
   "metadata": {},
   "source": [
    "## Demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "51305fec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set style\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Create subplots\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15,5))\n",
    "\n",
    "# 1. Age distribution\n",
    "sns.histplot(df['age'], kde=True, ax=axes[0], color=\"skyblue\")\n",
    "axes[0].set_title(\"Age Distribution\")\n",
    "\n",
    "# 2. Sex counts\n",
    "sns.countplot(x=\"sex\", data=df, ax=axes[1], hue=\"sex\", palette=\"pastel\")\n",
    "axes[1].set_title(\"Sex Distribution\")\n",
    "\n",
    "# 3. Group counts\n",
    "sns.countplot(x=\"group\", data=df, ax=axes[2], hue=\"group\", palette=\"muted\")\n",
    "\n",
    "axes[2].set_title(\"Group Distribution\")\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33f8715",
   "metadata": {},
   "source": [
    "Total subjects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0718ef1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ad04a8",
   "metadata": {},
   "source": [
    "Total subject per group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7eca6d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "group\n",
      "FTD    17\n",
      "AD     16\n",
      "HC     13\n",
      "LBD    11\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"group\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee553500",
   "metadata": {},
   "source": [
    "Demographics table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "94d4b967",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Characteristic</th>\n",
       "      <th></th>\n",
       "      <th>Role</th>\n",
       "      <th>Overall</th>\n",
       "      <th>HC</th>\n",
       "      <th>LBD</th>\n",
       "      <th>FTD</th>\n",
       "      <th>AD</th>\n",
       "      <th>P-Value</th>\n",
       "      <th>Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>n</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>57</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>age, mean (SD)</td>\n",
       "      <td></td>\n",
       "      <td>Covariate</td>\n",
       "      <td>69.5 (8.1)</td>\n",
       "      <td>65.4 (7.1)</td>\n",
       "      <td>72.9 (3.4)</td>\n",
       "      <td>68.6 (8.3)</td>\n",
       "      <td>71.4 (9.6)</td>\n",
       "      <td>0.086</td>\n",
       "      <td>One-way ANOVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sex, n (%)</td>\n",
       "      <td>F</td>\n",
       "      <td>Covariate</td>\n",
       "      <td>25 (43.9)</td>\n",
       "      <td>7 (53.8)</td>\n",
       "      <td>2 (18.2)</td>\n",
       "      <td>7 (41.2)</td>\n",
       "      <td>9 (56.2)</td>\n",
       "      <td>0.211</td>\n",
       "      <td>Chi-squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sex, n (%)</td>\n",
       "      <td>M</td>\n",
       "      <td>Covariate</td>\n",
       "      <td>32 (56.1)</td>\n",
       "      <td>6 (46.2)</td>\n",
       "      <td>9 (81.8)</td>\n",
       "      <td>10 (58.8)</td>\n",
       "      <td>7 (43.8)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MoCA, mean (SD)</td>\n",
       "      <td></td>\n",
       "      <td>Descriptive only</td>\n",
       "      <td>18.9 (8.2)</td>\n",
       "      <td>28.8 (1.0)</td>\n",
       "      <td>18.3 (7.3)</td>\n",
       "      <td>18.0 (6.7)</td>\n",
       "      <td>12.5 (6.3)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>One-way ANOVA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/markdown": [
       "| Characteristic   |    | Role             | Overall    | HC         | LBD        | FTD        | AD         | P-Value   | Test          |\n",
       "|:-----------------|:---|:-----------------|:-----------|:-----------|:-----------|:-----------|:-----------|:----------|:--------------|\n",
       "| n                |    |                  | 57         | 13         | 11         | 17         | 16         |           |               |\n",
       "| age, mean (SD)   |    | Covariate        | 69.5 (8.1) | 65.4 (7.1) | 72.9 (3.4) | 68.6 (8.3) | 71.4 (9.6) | 0.086     | One-way ANOVA |\n",
       "| sex, n (%)       | F  | Covariate        | 25 (43.9)  | 7 (53.8)   | 2 (18.2)   | 7 (41.2)   | 9 (56.2)   | 0.211     | Chi-squared   |\n",
       "| sex, n (%)       | M  | Covariate        | 32 (56.1)  | 6 (46.2)   | 9 (81.8)   | 10 (58.8)  | 7 (43.8)   |           |               |\n",
       "| MoCA, mean (SD)  |    | Descriptive only | 18.9 (8.2) | 28.8 (1.0) | 18.3 (7.3) | 18.0 (6.7) | 12.5 (6.3) | <0.001    | One-way ANOVA |"
      ],
      "text/plain": [
       " Characteristic               Role    Overall         HC        LBD        FTD         AD P-Value          Test\n",
       "              n                            57         13         11         17         16                      \n",
       " age, mean (SD)          Covariate 69.5 (8.1) 65.4 (7.1) 72.9 (3.4) 68.6 (8.3) 71.4 (9.6)   0.086 One-way ANOVA\n",
       "     sex, n (%) F        Covariate  25 (43.9)   7 (53.8)   2 (18.2)   7 (41.2)   9 (56.2)   0.211   Chi-squared\n",
       "     sex, n (%) M        Covariate  32 (56.1)   6 (46.2)   9 (81.8)  10 (58.8)   7 (43.8)                      \n",
       "MoCA, mean (SD)   Descriptive only 18.9 (8.2) 28.8 (1.0) 18.3 (7.3) 18.0 (6.7) 12.5 (6.3)  <0.001 One-way ANOVA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| label: notebook_tbl_demographics_all\n",
    "\n",
    "columns = [\"age\", \"sex\", \"MoCA\"]\n",
    "categorical = [\"sex\"]\n",
    "\n",
    "#mean (SD) shown for all continuous vars, ANOVA/chi-square used for p-values. \n",
    "table_demographics = TableOne(\n",
    "    df,\n",
    "    columns=columns,\n",
    "    categorical=categorical,\n",
    "    groupby=\"group\",\n",
    "    pval=True,\n",
    "    htest_name=True\n",
    ")\n",
    "\n",
    "# Get Pandas dataframe\n",
    "table_demographics = table_demographics.tableone.copy()\n",
    "\n",
    "# Flatten the \"Grouped by group\" column header down to just the group names,\n",
    "# and drop the \"Missing\" column (always 0 here, so it adds nothing).\n",
    "table_demographics.columns = table_demographics.columns.get_level_values(1)\n",
    "table_demographics = table_demographics.drop(columns=\"Missing\")\n",
    "\n",
    "# Role per row, in the same order as table before\n",
    "table_demographics.insert(0, \"Role\", [\n",
    "    \"\",                   # n\n",
    "    \"Covariate\",          # age, mean (SD)\n",
    "    \"Covariate\",          # sex, n (%) -- F\n",
    "    \"Covariate\",          # sex, n (%) -- M\n",
    "    \"Descriptive only\",   # MoCA, mean (SD)\n",
    "    #\"Descriptive only\",   # years_diagnosis, mean (SD)\n",
    "])\n",
    "\n",
    "# remove warnings from table. An alternative approach was used to validate the results.\n",
    "table_demographics[\"Test\"] = table_demographics[\"Test\"].str.replace(\n",
    "    r\"Chi-squared \\(warning: expected count < 5\\)\", \"Chi-squared\", regex=True)\n",
    "\n",
    "helpers.display_table(table_demographics.reset_index().rename(columns={'level_0': 'Characteristic', 'level_1': ''}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0e0f567f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "bingroup": "x",
         "hovertemplate": "group=LBD<br>sex=M<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "LBD, M",
         "marker": {
          "color": "orange",
          "opacity": 0.8,
          "pattern": {
           "shape": ""
          }
         },
         "name": "LBD, M",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "QkxFS01ISUdI",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=LBD<br>sex=F<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "LBD, F",
         "marker": {
          "color": "orange",
          "opacity": 0.8,
          "pattern": {
           "shape": "/"
          }
         },
         "name": "LBD, F",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "TUo=",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=AD<br>sex=M<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "AD, M",
         "marker": {
          "color": "red",
          "opacity": 0.8,
          "pattern": {
           "shape": ""
          }
         },
         "name": "AD, M",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "SEVWSFNSPw==",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=AD<br>sex=F<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "AD, F",
         "marker": {
          "color": "red",
          "opacity": 0.8,
          "pattern": {
           "shape": "/"
          }
         },
         "name": "AD, F",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "PDhMUDVHRk1J",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=HC<br>sex=M<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "HC, M",
         "marker": {
          "color": "blue",
          "opacity": 0.8,
          "pattern": {
           "shape": ""
          }
         },
         "name": "HC, M",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "P0ZPSUQ3",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=HC<br>sex=F<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "HC, F",
         "marker": {
          "color": "blue",
          "opacity": 0.8,
          "pattern": {
           "shape": "/"
          }
         },
         "name": "HC, F",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "RDg+QUg6PQ==",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=FTD<br>sex=M<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "FTD, M",
         "marker": {
          "color": "green",
          "opacity": 0.8,
          "pattern": {
           "shape": ""
          }
         },
         "name": "FTD, M",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "RUk8MExGRktBQQ==",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=FTD<br>sex=F<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "FTD, F",
         "marker": {
          "color": "green",
          "opacity": 0.8,
          "pattern": {
           "shape": "/"
          }
         },
         "name": "FTD, F",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "P1NRQj9CSg==",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        }
       ],
       "layout": {
        "bargap": 0.05,
        "barmode": "stack",
        "height": 500,
        "legend": {
         "title": {
          "text": "Group / Sex"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermap": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermap"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0.0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1.0,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0.0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1.0,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Age Distribution by Group and Sex"
        },
        "width": 750,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0.0,
          1.0
         ],
         "title": {
          "text": "Age"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.0,
          1.0
         ],
         "title": {
          "text": "Count"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| label: notebook_fig_demographics_all\n",
    "\n",
    "# Build histogram\n",
    "fig = px.histogram(\n",
    "    df,\n",
    "    x=\"age\",\n",
    "    color=\"group\",\n",
    "    pattern_shape=\"sex\",   # distinguishes male/female inside groups\n",
    "    barmode=\"stack\",       # stack sex inside group\n",
    "    opacity=0.8,\n",
    "    nbins=10,\n",
    "    color_discrete_map={\n",
    "    \"AD\": \"red\",\n",
    "    \"LBD\": \"orange\",\n",
    "    \"FTD\": \"green\",\n",
    "    \"HC\": \"blue\"\n",
    "    },\n",
    "    width=750,\n",
    "    height=500\n",
    ")\n",
    "\n",
    "# Improve layout\n",
    "fig.update_layout(\n",
    "    title=\"Age Distribution by Group and Sex\",\n",
    "    xaxis_title=\"Age\",\n",
    "    yaxis_title=\"Count\",\n",
    "    bargap=0.05,\n",
    "    legend_title=\"Group / Sex\"\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da2950be",
   "metadata": {},
   "source": [
    "## Removing Subject with failed segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2eafb2e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  participant_id                                             reason\n",
      "0        sub-021  HippUnfold segmentation with a small leak arti...\n",
      "1        sub-024  HippUnfold segmentation slightly failed: SRLM ...\n",
      "2        sub-035  HippUnfold segmentation failed with an artifac...\n",
      "3        sub-045  HippUnfold segmentation slightly failed with s...\n",
      "4        sub-049  HippUnfold segmentation failed with subiculum ...\n",
      "5        sub-051  HippUnfold segmentation struggled with multipl...\n",
      "6        sub-057  HippUnfold segmentation failed with small leak...\n"
     ]
    }
   ],
   "source": [
    "# Exclusion list obtained from QC the segemntation and registration of the anatomical scans. Participants with a QC level of 0 were excluded from the analyses.\n",
    "exclusion_list = pd.read_csv(helpers.data_path(\"exclusion_list.csv\")) \n",
    "print(exclusion_list)\n",
    "exclusion_list = exclusion_list[\"participant_id\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b9b3579",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_removed = df.query(\"participant_id == @exclusion_list\")\n",
    "df = df.query(\"participant_id != @exclusion_list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bbaac80f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subjects exclude because failed segmentation (QC = 0):\n",
      "group\n",
      "FTD    4\n",
      "AD     2\n",
      "LBD    1\n",
      "HC     0\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Subjects exclude because failed segmentation (QC = 0):\")\n",
    "print(df_removed[\"group\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "48bcea63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set style\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Create subplots\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15,5))\n",
    "\n",
    "# 1. Age distribution\n",
    "sns.histplot(df['age'], kde=True, ax=axes[0], color=\"skyblue\")\n",
    "axes[0].set_title(\"Age Distribution\")\n",
    "\n",
    "# 2. Sex counts\n",
    "sns.countplot(x=\"sex\", data=df, ax=axes[1], hue=\"sex\", palette=\"pastel\")\n",
    "axes[1].set_title(\"Sex Distribution\")\n",
    "\n",
    "# 3. Group counts\n",
    "sns.countplot(x=\"group\", data=df, ax=axes[2], hue=\"group\", palette=\"muted\")\n",
    "\n",
    "axes[2].set_title(\"Group Distribution\")\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a39e8a68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total subject in the study after failed segementation removal\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0d325a31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "group\n",
      "AD     14\n",
      "HC     13\n",
      "FTD    13\n",
      "LBD    10\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Total subject in each group after failed segementation removal\n",
    "print(df[\"group\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1589e554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Characteristic</th>\n",
       "      <th></th>\n",
       "      <th>Role</th>\n",
       "      <th>Overall</th>\n",
       "      <th>HC</th>\n",
       "      <th>LBD</th>\n",
       "      <th>FTD</th>\n",
       "      <th>AD</th>\n",
       "      <th>P-Value</th>\n",
       "      <th>Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>n</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>age, mean (SD)</td>\n",
       "      <td></td>\n",
       "      <td>Covariate</td>\n",
       "      <td>68.7 (7.9)</td>\n",
       "      <td>65.4 (7.1)</td>\n",
       "      <td>72.9 (3.6)</td>\n",
       "      <td>67.0 (7.9)</td>\n",
       "      <td>70.2 (9.6)</td>\n",
       "      <td>0.097</td>\n",
       "      <td>One-way ANOVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sex, n (%)</td>\n",
       "      <td>F</td>\n",
       "      <td>Covariate</td>\n",
       "      <td>21 (42.0)</td>\n",
       "      <td>7 (53.8)</td>\n",
       "      <td>2 (20.0)</td>\n",
       "      <td>4 (30.8)</td>\n",
       "      <td>8 (57.1)</td>\n",
       "      <td>0.193</td>\n",
       "      <td>Chi-squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sex, n (%)</td>\n",
       "      <td>M</td>\n",
       "      <td>Covariate</td>\n",
       "      <td>29 (58.0)</td>\n",
       "      <td>6 (46.2)</td>\n",
       "      <td>8 (80.0)</td>\n",
       "      <td>9 (69.2)</td>\n",
       "      <td>6 (42.9)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MoCA, mean (SD)</td>\n",
       "      <td></td>\n",
       "      <td>Descriptive only</td>\n",
       "      <td>19.6 (8.0)</td>\n",
       "      <td>28.8 (1.0)</td>\n",
       "      <td>19.2 (7.0)</td>\n",
       "      <td>18.1 (5.7)</td>\n",
       "      <td>12.8 (6.6)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>One-way ANOVA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/markdown": [
       "| Characteristic   |    | Role             | Overall    | HC         | LBD        | FTD        | AD         | P-Value   | Test          |\n",
       "|:-----------------|:---|:-----------------|:-----------|:-----------|:-----------|:-----------|:-----------|:----------|:--------------|\n",
       "| n                |    |                  | 50         | 13         | 10         | 13         | 14         |           |               |\n",
       "| age, mean (SD)   |    | Covariate        | 68.7 (7.9) | 65.4 (7.1) | 72.9 (3.6) | 67.0 (7.9) | 70.2 (9.6) | 0.097     | One-way ANOVA |\n",
       "| sex, n (%)       | F  | Covariate        | 21 (42.0)  | 7 (53.8)   | 2 (20.0)   | 4 (30.8)   | 8 (57.1)   | 0.193     | Chi-squared   |\n",
       "| sex, n (%)       | M  | Covariate        | 29 (58.0)  | 6 (46.2)   | 8 (80.0)   | 9 (69.2)   | 6 (42.9)   |           |               |\n",
       "| MoCA, mean (SD)  |    | Descriptive only | 19.6 (8.0) | 28.8 (1.0) | 19.2 (7.0) | 18.1 (5.7) | 12.8 (6.6) | <0.001    | One-way ANOVA |"
      ],
      "text/plain": [
       " Characteristic               Role    Overall         HC        LBD        FTD         AD P-Value          Test\n",
       "              n                            50         13         10         13         14                      \n",
       " age, mean (SD)          Covariate 68.7 (7.9) 65.4 (7.1) 72.9 (3.6) 67.0 (7.9) 70.2 (9.6)   0.097 One-way ANOVA\n",
       "     sex, n (%) F        Covariate  21 (42.0)   7 (53.8)   2 (20.0)   4 (30.8)   8 (57.1)   0.193   Chi-squared\n",
       "     sex, n (%) M        Covariate  29 (58.0)   6 (46.2)   8 (80.0)   9 (69.2)   6 (42.9)                      \n",
       "MoCA, mean (SD)   Descriptive only 19.6 (8.0) 28.8 (1.0) 19.2 (7.0) 18.1 (5.7) 12.8 (6.6)  <0.001 One-way ANOVA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| label: notebook_tbl_demographics\n",
    "\n",
    "columns = [\"age\", \"sex\", \"MoCA\"]\n",
    "categorical = [\"sex\"]\n",
    "\n",
    "#mean (SD) shown for all continuous vars, ANOVA/chi-square used for p-values. \n",
    "table_demographics = TableOne(\n",
    "    df,\n",
    "    columns=columns,\n",
    "    categorical=categorical,\n",
    "    groupby=\"group\",\n",
    "    pval=True,\n",
    "    htest_name=True\n",
    ")\n",
    "\n",
    "# Get Pandas dataframe\n",
    "table_demographics = table_demographics.tableone.copy()\n",
    "\n",
    "# Flatten the \"Grouped by group\" column header down to just the group names,\n",
    "# and drop the \"Missing\" column (always 0 here, so it adds nothing).\n",
    "table_demographics.columns = table_demographics.columns.get_level_values(1)\n",
    "table_demographics = table_demographics.drop(columns=\"Missing\")\n",
    "\n",
    "# Role per row, in the same order as table before\n",
    "table_demographics.insert(0, \"Role\", [\n",
    "    \"\",                   # n\n",
    "    \"Covariate\",          # age, mean (SD)\n",
    "    \"Covariate\",          # sex, n (%) -- F\n",
    "    \"Covariate\",          # sex, n (%) -- M\n",
    "    \"Descriptive only\",   # MoCA, mean (SD)\n",
    "    #\"Descriptive only\",   # years_diagnosis, mean (SD)\n",
    "])\n",
    "\n",
    "# remove warnings from test table. An alternative approach was used to validate the results.\n",
    "table_demographics[\"Test\"] = table_demographics[\"Test\"].str.replace(\n",
    "    r\"Chi-squared \\(warning: expected count < 5\\)\", \"Chi-squared\", regex=True)\n",
    "\n",
    "helpers.display_table(table_demographics.reset_index().rename(columns={'level_0': 'Characteristic', 'level_1': ''}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8508e8a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "bingroup": "x",
         "hovertemplate": "group=LBD<br>sex=M<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "LBD, M",
         "marker": {
          "color": "orange",
          "opacity": 0.8,
          "pattern": {
           "shape": ""
          }
         },
         "name": "LBD, M",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "QkxFS01IR0g=",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=LBD<br>sex=F<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "LBD, F",
         "marker": {
          "color": "orange",
          "opacity": 0.8,
          "pattern": {
           "shape": "/"
          }
         },
         "name": "LBD, F",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "TUo=",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=AD<br>sex=M<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "AD, M",
         "marker": {
          "color": "red",
          "opacity": 0.8,
          "pattern": {
           "shape": ""
          }
         },
         "name": "AD, M",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "SEVWSFI/",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=AD<br>sex=F<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "AD, F",
         "marker": {
          "color": "red",
          "opacity": 0.8,
          "pattern": {
           "shape": "/"
          }
         },
         "name": "AD, F",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "PDhMUDVHRkk=",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=HC<br>sex=M<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "HC, M",
         "marker": {
          "color": "blue",
          "opacity": 0.8,
          "pattern": {
           "shape": ""
          }
         },
         "name": "HC, M",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "P0ZPSUQ3",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=HC<br>sex=F<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "HC, F",
         "marker": {
          "color": "blue",
          "opacity": 0.8,
          "pattern": {
           "shape": "/"
          }
         },
         "name": "HC, F",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "RDg+QUg6PQ==",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=FTD<br>sex=M<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "FTD, M",
         "marker": {
          "color": "green",
          "opacity": 0.8,
          "pattern": {
           "shape": ""
          }
         },
         "name": "FTD, M",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "RUk8MEZGS0FB",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "bingroup": "x",
         "hovertemplate": "group=FTD<br>sex=F<br>age=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "FTD, F",
         "marker": {
          "color": "green",
          "opacity": 0.8,
          "pattern": {
           "shape": "/"
          }
         },
         "name": "FTD, F",
         "nbinsx": 10,
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": {
          "bdata": "P1FCQg==",
          "dtype": "i1"
         },
         "xaxis": "x",
         "yaxis": "y"
        }
       ],
       "layout": {
        "bargap": 0.05,
        "barmode": "stack",
        "height": 500,
        "legend": {
         "title": {
          "text": "Group / Sex"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermap": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermap"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0.0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1.0,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0.0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1.0,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0.0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1.0,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Age Distribution by Group and Sex"
        },
        "width": 750,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0.0,
          1.0
         ],
         "title": {
          "text": "Age"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.0,
          1.0
         ],
         "title": {
          "text": "Count"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| label: notebook_fig_demographics\n",
    "\n",
    "# Build histogram\n",
    "fig = px.histogram(\n",
    "    df,\n",
    "    x=\"age\",\n",
    "    color=\"group\",\n",
    "    pattern_shape=\"sex\",   # distinguishes male/female inside groups\n",
    "    barmode=\"stack\",       # stack sex inside group\n",
    "    opacity=0.8,\n",
    "    nbins=10,\n",
    "    color_discrete_map={\n",
    "    \"AD\": \"red\",\n",
    "    \"LBD\": \"orange\",\n",
    "    \"FTD\": \"green\",\n",
    "    \"HC\": \"blue\"\n",
    "    },\n",
    "    width=750,\n",
    "    height=500\n",
    ")\n",
    "\n",
    "# Improve layout\n",
    "fig.update_layout(\n",
    "    title=\"Age Distribution by Group and Sex\",\n",
    "    xaxis_title=\"Age\",\n",
    "    yaxis_title=\"Count\",\n",
    "    bargap=0.05,\n",
    "    legend_title=\"Group / Sex\"\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "085225ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| label: notebook_figStatic_demographics\n",
    "# Static version of the demographics figure (analysed sample), used in the Word/PDF/LaTeX exports.\n",
    "# Same design as the interactive figure: age histogram stacked by group, hatched bars for male participants\n",
    "if helpers.STATIC_FIGURE_FORMATS:\n",
    "    from matplotlib.patches import Patch\n",
    "\n",
    "    helpers.static_theme()\n",
    "    group_colors = dict(zip(group_order, sns.color_palette(\"deep\")))\n",
    "    bins = np.histogram_bin_edges(df[\"age\"], bins=10)\n",
    "    fig, ax = plt.subplots(figsize=(helpers.TEXT_WIDTH_IN / 2, 2.4))\n",
    "    bottom = np.zeros(len(bins) - 1)\n",
    "    for group in group_order:\n",
    "        for sex, hatch in ((\"F\", None), (\"M\", \"////\")):\n",
    "            counts, _ = np.histogram(df.query(\"group == @group and sex == @sex\")[\"age\"], bins=bins)\n",
    "            ax.bar(bins[:-1], counts, width=np.diff(bins), bottom=bottom, align=\"edge\", color=group_colors[group],\n",
    "                   hatch=hatch, edgecolor=\"white\", linewidth=0.5)\n",
    "            bottom += counts\n",
    "    handles = ([Patch(facecolor=group_colors[g], label=g) for g in group_order]\n",
    "               + [Patch(facecolor=\"0.6\", edgecolor=\"white\", hatch=h, label=s)\n",
    "                  for s, h in ((\"F\", None), (\"M\", \"////\"))])\n",
    "    ax.legend(handles=handles, title=\"Group / Sex\", loc=\"upper left\", bbox_to_anchor=(1, 1), frameon=False)\n",
    "    ax.set_xlabel(\"Age (years)\")\n",
    "    ax.set_ylabel(\"Count\")\n",
    "    sns.despine(fig)\n",
    "    fig.tight_layout()\n",
    "    helpers.save_static_figure(fig, \"fig_demographics\")\n",
    "    plt.close(fig)\n",
    "    sns.set_theme()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "default",
   "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": "3.12.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}