diff --git a/nbs/00_config.ipynb b/nbs/00_config.ipynb
index 4b3ac9b..e2fa422 100644
--- a/nbs/00_config.ipynb
+++ b/nbs/00_config.ipynb
@@ -54,6 +54,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
+    "DEFAULT_PALETTE = 'muted'\n",
     "REF_COLOR = \"k\"\n",
     "FEMALE_COLOR = \"C1\"\n",
     "MALE_COLOR = \"C0\"\n",
@@ -62,6 +63,9 @@
     "GLUC_COLOR = \"C0\"\n",
     "FOOD_COLOR = \"C1\"\n",
     "\n",
+    "LEGEND_SHIFT = (1.05, 1.05)\n",
+    "TIME_FORMAT = '%d/%m\\n%H:%M'\n",
+    "\n",
     "DATASETS_PATH = '/home/ec2-user/studies/hpp_datasets/'\n",
     "COHORT = None\n",
     "EVENTS_DATASET = 'events'\n",
@@ -70,8 +74,7 @@
     "BULK_DATA_PATH = {}\n",
     "PREFERRED_LANGUAGE = 'english'\n",
     "\n",
-    "config_found = False\n",
-    "\n"
+    "config_found = False"
    ]
   },
   {
diff --git a/nbs/06_sleep_plots.ipynb b/nbs/06_sleep_plots.ipynb
index cd8364a..e3bb636 100644
--- a/nbs/06_sleep_plots.ipynb
+++ b/nbs/06_sleep_plots.ipynb
@@ -37,14 +37,13 @@
    "outputs": [],
    "source": [
     "#| export\n",
-    "from typing import Iterable, Optional\n",
+    "from typing import Iterable, Tuple, List\n",
     "\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
-    "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib.dates as mdates"
+    "\n",
+    "from pheno_utils.timeseries_plots import TimeSeriesFigure, plot_events_bars, get_events_period, format_xticks, prep_to_plot_timeseries, DEFAULT_PALETTE"
    ]
   },
   {
@@ -72,6 +71,7 @@
     "\n",
     "DEFAULT_CHANNELS = ['actigraph', 'pat_infra', 'body_position', 'snore_db', 'heart_rate', 'spo2']\n",
     "\n",
+    "# Color groups are designed to match events and raw channels\n",
     "COLOR_GROUPS= {\n",
     "    'actigraph': ['actigraph', 'sleep_stage'],\n",
     "    'general': ['body_position'],\n",
@@ -95,289 +95,521 @@
    "outputs": [],
    "source": [
     "#| export\n",
-    "\n",
-    "def plot_sleep(events: pd.DataFrame, channels: pd.DataFrame,\n",
-    "               array_index: Optional[int] = None,\n",
-    "               trim_to_events: Optional[bool] = True,\n",
-    "               add_events: Optional[pd.DataFrame] = None,\n",
-    "               event_filter: Optional[Iterable[str]] = None,\n",
-    "               channel_filter: Optional[Iterable[str]] = DEFAULT_CHANNELS,\n",
-    "               event_height: float=2, channel_height: float=0.45, width: float=10, aspect: float=0.2,\n",
-    "               style: str='whitegrid',\n",
-    "               xlim: Iterable[float]=None, **kwargs):\n",
+    "def plot_sleep(\n",
+    "    events: pd.DataFrame,\n",
+    "    channels: pd.DataFrame,\n",
+    "    participant_id: int=None,\n",
+    "    array_index: int=None,\n",
+    "    time_range: Tuple[str, str]=None,\n",
+    "    event_filter: Iterable[str]=None,\n",
+    "    channel_filter: Iterable[str]=DEFAULT_CHANNELS,\n",
+    "    event_height: float=1,\n",
+    "    channel_height: float=0.5,\n",
+    "    padding: float=-0.02,\n",
+    "    figsize: Tuple[float, float]=None,\n",
+    "    palette: str=DEFAULT_PALETTE,\n",
+    ") -> TimeSeriesFigure:\n",
     "    \"\"\"\n",
     "    Plot sleep events and channels data.\n",
     "\n",
     "    Args:\n",
-    "\n",
-    "        events (pd.DataFrame): A pandas dataframe containing sleep events data.\n",
-    "        channels (pd.DataFrame): A pandas dataframe containing raw channels data.\n",
-    "        array_index (int, optional): The index of the array. Defaults to None.\n",
-    "        trim_to_events (bool, optional): Whether to trim the plot to the start and end of the events. Defaults to True.\n",
-    "        add_events (pd.DataFrame, optional): Additional events data to include in the plot. Defaults to None.\n",
-    "        event_filter (Iterable[str], optional): A list of events to include in the plot. Defaults to None.\n",
-    "        channel_filter (Iterable[str], optional): A list of channels to include in the plot. Defaults to DEFAULT_CHANNELS.\n",
-    "        event_height (float, optional): The height of the event plot in inches. Defaults to 2.\n",
-    "        channel_height (float, optional): The height of each channel plot in inches. Defaults to 0.45.\n",
-    "        width (float, optional): The width of the plot in inches. Defaults to 10.\n",
-    "        aspect (float, optional): The aspect ratio of the plot. Defaults to 0.2.\n",
-    "        style (str, optional): The seaborn style to use. Defaults to 'whitegrid'.\n",
-    "        xlim (List[float], optional): The x-axis limits of the plot. Defaults to None.\n",
-    "        **kwargs: Additional arguments to be passed to plot_channels().\n",
+    "        events (pd.DataFrame): The sleep events dataframe.\n",
+    "        channels (pd.DataFrame): The sleep channels dataframe.\n",
+    "        participant_id (int): The participant id to filter the data.\n",
+    "        array_index (int): The array index to filter the data.\n",
+    "        time_range (Tuple[str, str]): The time range to filter the data.\n",
+    "        event_filter (Iterable[str]): The events to include in the plot.\n",
+    "        channel_filter (Iterable[str]): The channels to include in the plot.\n",
+    "        event_height (float): The relative height of the events subplot.\n",
+    "        channel_height (float): The relative height of each channel's subplot.\n",
+    "        padding (float): The padding between subplots.\n",
+    "        figsize (Tuple[float, float]): The size of the figure.\n",
+    "        palette (str): The color palette to use.\n",
     "\n",
     "    Returns:\n",
-    "\n",
-    "        None\n",
+    "        TimeSeriesFigure: The figure with the sleep events and channels data.\n",
     "    \"\"\"\n",
-    "    nC = min([len(channel_filter), channels.index.get_level_values('source').nunique()])\n",
-    "    if xlim is not None:\n",
-    "        trim_to_events = True\n",
-    "\n",
-    "    fig, ax = plt.subplots(nrows=nC+1, ncols=1, sharex=True, squeeze=False,\n",
-    "                           height_ratios=nC*[channel_height] + [event_height],\n",
-    "                           figsize=(width, width*aspect*(nC*channel_height + event_height)),\n",
-    "                           facecolor='white')\n",
-    "\n",
-    "    with sns.axes_style(style):\n",
-    "        try:\n",
-    "            plot_channels(channels, array_index=array_index, y_filter=channel_filter, ax=ax[:-1],\n",
-    "                     **kwargs)\n",
-    "            plot_events(events, array_index=array_index, y_include=event_filter, y_exclude=['Gross Trunc'],\n",
-    "                        ax=ax[-1,0],\n",
-    "                        set_xlim=trim_to_events, add_events=add_events,\n",
-    "                        xlim=xlim)\n",
-    "        except Exception as err:\n",
-    "            print(f'plot_channels failed due to:\\n{err}')\n",
-    "            fig.clf()\n",
-    "            plot_events(events, array_index, y_include=event_filter, y_exclude=['Gross Trunc'],\n",
-    "                        set_xlim=trim_to_events, add_events=add_events,\n",
-    "                        xlim=xlim)\n",
-    "\n",
-    "\n",
-    "def plot_events(events: pd.DataFrame, array_index: Optional[int]=None,\n",
-    "                x_start: str='collection_timestamp', x_end: str='event_end',\n",
-    "                y: str='event', color: str='channel', cmap: str='muted', set_xlim: bool=True,\n",
-    "                xlim: Iterable[float]=None, figsize: Iterable[float]=[10, 4],\n",
-    "                y_include: Optional[Iterable[str]] = None,\n",
-    "                y_exclude: Optional[Iterable[str]] = None,\n",
-    "                ax: plt.Axes=None,\n",
-    "                add_events: Optional[pd.DataFrame] = None,\n",
-    "                rename_channels: dict={'PAT Amplitude': 'PAT', 'PulseRate': 'Heart Rate'},\n",
-    "                rename_events: dict={}):\n",
-    "    \"\"\" plot an events timeline for a given participant and array_index \"\"\"\n",
-    "    # slice to night\n",
-    "    if (array_index is not None) and (('array_index' in events.columns) or ('array_index' in events.index.names)):\n",
-    "        plot_df = events.query('array_index == @array_index').copy()\n",
-    "    else:\n",
-    "        plot_df = events.copy()\n",
-    "    # extract start and end times\n",
-    "    if x_start in plot_df.index.names:\n",
-    "        plot_df = plot_df.reset_index(x_start)\n",
-    "    if x_end in plot_df.index.names:\n",
-    "        plot_df = plot_df.reset_index(x_end)\n",
-    "    # remove timezone for correct matplotlib labeling\n",
-    "    plot_df[x_start] = plot_df[x_start].dt.tz_localize(None)\n",
-    "    plot_df[x_end] = plot_df[x_end].dt.tz_localize(None)\n",
-    "\n",
-    "    # filter events\n",
-    "    if y_include is not None:\n",
-    "        plot_df = plot_df.query(f'{y} in {y_include}')\n",
-    "    if y_exclude is not None:\n",
-    "        plot_df = plot_df.query(f'{y} not in {y_exclude}')\n",
-    "    # additional user-provided events (application logging, etc.)\n",
-    "    if add_events is not None:\n",
-    "        tlim = plot_df[x_start].min(), plot_df[x_end].max()\n",
-    "        add_events = add_events.loc[\n",
-    "            (tlim[0] < add_events[x_end]) & (add_events[x_start] < tlim[1])]\n",
-    "        if len(add_events):\n",
-    "            add_events = add_events.set_index(plot_df.index[[0]])\n",
-    "        plot_df = pd.concat([plot_df, add_events[\n",
-    "            plot_df.columns.intersection(add_events.columns)]], axis=0)\n",
-    "\n",
-    "    # rename channels and events\n",
-    "    plot_df = plot_df.copy()\n",
-    "    plot_df['channel'] = plot_df['channel'].replace(rename_channels)\n",
-    "    plot_df['event'] = plot_df['event'].replace(rename_events)\n",
-    "\n",
-    "    # set x limits\n",
-    "    if xlim is not None:\n",
-    "        if type(xlim[0]) is str:\n",
-    "            xlim = (pd.to_datetime(xlim[0]), xlim[1])\n",
-    "        if type(xlim[0]) is not pd.Timestamp:\n",
-    "            xlim = plot_df.loc[plot_df['start'] < xlim[0], x_start].iloc[-1], xlim[1]\n",
-    "        if type(xlim[1]) is str:\n",
-    "            xlim = (xlim[0], pd.to_datetime(xlim[1]))\n",
-    "        if type(xlim[1]) is not pd.Timestamp:\n",
-    "            xlim = xlim[0], plot_df.loc[plot_df['end'] > xlim[1], x_end].iloc[0]\n",
-    "    else:\n",
-    "        xlim = plot_df[x_start].min(), plot_df[x_end].max()\n",
-    "\n",
-    "    if ax is None:\n",
-    "        fig, ax = plt.subplots(figsize=figsize)\n",
-    "\n",
-    "    # set colors\n",
-    "    colors = sorted(plot_df[color].unique())\n",
-    "    colors = pd.DataFrame({color: colors, 'color': sns.color_palette(cmap, len(colors))})\\\n",
-    "        .set_index(color)['color']\n",
-    "\n",
-    "    # plot events\n",
-    "    plot_df = plot_df.assign(diff=lambda x: x[x_end] - x[x_start]).sort_values([color, y])\n",
-    "    labels = []\n",
-    "    legend = []\n",
-    "    for i, (y_label, y) in enumerate(plot_df.groupby(y, observed=True, sort=False)):\n",
-    "        if len(y) == 0:\n",
-    "            continue\n",
-    "        labels.append(y_label)\n",
-    "        for c, r in y.groupby(color, observed=True):\n",
-    "            data = r[[x_start, 'diff']]\n",
-    "            if not len(data):\n",
-    "                continue\n",
-    "            h = ax.broken_barh(data.values, (i-0.4,0.8), color=colors[c], alpha=0.7)\n",
-    "            legend.append({'label': c, 'handle': h})\n",
-    "\n",
-    "    # format plot\n",
-    "    legend = pd.DataFrame.from_dict(legend).drop_duplicates(subset='label')\n",
-    "    ax.legend(legend['handle'], legend['label'],\n",
-    "              bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n",
-    "\n",
-    "    str_title = ''\n",
-    "    if 'participant_id' in events.index.names:\n",
-    "        str_title += events.index.get_level_values('participant_id')[0].astype(str)\n",
-    "    if array_index is not None:\n",
-    "        str_title += f' / {array_index}'\n",
-    "    plt.suptitle(str_title, fontsize=14, weight='bold')\n",
-    "    ax.set_yticks(np.arange(len(labels)), labels)\n",
-    "    plt.tight_layout()\n",
-    "    ax.set_xlabel('Time')\n",
-    "    if set_xlim:\n",
-    "        ax.set_xlim(*xlim)\n",
-    "    format_xticks(ax)\n",
-    "\n",
-    "    return ax\n",
-    "\n",
-    "\n",
-    "def plot_channels(channels: pd.DataFrame, array_index: Optional[int]=None,\n",
-    "             y_filter: Optional[Iterable[str]]=None, ax: plt.Axes=None,\n",
-    "             discrete_events: Optional[Iterable[str]]=['sleep_stage', 'body_position'],\n",
-    "             time_col='collection_timestamp', height=1.5, resample='1s', cmap='muted',\n",
-    "             rename_channels=CHANNELS, **kwargs):\n",
+    "    # Create figure\n",
+    "    if figsize is None:\n",
+    "        if 'source' in channels.index.names:\n",
+    "            nC = channels.index.get_level_values('source').nunique()\n",
+    "        else:\n",
+    "            nC = channels['source'].nunique()\n",
+    "        figsize = 2 * (nC * channel_height + event_height)\n",
+    "        figsize = (8 * 2 * channel_height, figsize)\n",
+    "    g = TimeSeriesFigure(figsize=figsize)\n",
+    "\n",
+    "    # Set colors\n",
+    "    channels, color_map = get_channels_colors(\n",
+    "        channels, events,\n",
+    "        participant_id=participant_id, array_index=array_index, time_range=time_range,\n",
+    "        event_filter=event_filter, palette=palette,\n",
+    "    )\n",
+    "\n",
+    "    # Plot\n",
+    "    plot_sleep_channels(\n",
+    "        channels,\n",
+    "        x='collection_timestamp', y='values', row='source', hue='channel_group',\n",
+    "        participant_id=participant_id, array_index=array_index, time_range=time_range,\n",
+    "        y_include=channel_filter,\n",
+    "        fig=g, height=channel_height,\n",
+    "        color_map=color_map, palette=palette,\n",
+    "    )\n",
+    "    g.plot(plot_events_bars,\n",
+    "        events,\n",
+    "        x_start='collection_timestamp', x_end='event_end', y='event', hue='channel',\n",
+    "        participant_id=participant_id, array_index=array_index, time_range=time_range,\n",
+    "        y_include=event_filter, y_exclude=['Gross Trunc'],\n",
+    "        palette=palette,\n",
+    "        name='sleep_events', height=event_height, sharex='sleep_channels',\n",
+    "    )\n",
+    "    g.set_axis_padding(padding)\n",
+    "\n",
+    "    return g\n",
+    "\n",
+    "\n",
+    "def plot_sleep_channels(\n",
+    "    channels: pd.DataFrame,\n",
+    "    x: str='collection_timestamp',\n",
+    "    y: str='values',\n",
+    "    row: str='source',\n",
+    "    hue: str='channel_group',\n",
+    "    participant_id: int=None,\n",
+    "    array_index: int=None,\n",
+    "    time_range: Tuple[str, str]=None,\n",
+    "    y_include: Iterable[str]=None,\n",
+    "    y_exclude: Iterable[str]=None,\n",
+    "    rename_channels: dict=CHANNELS,\n",
+    "    discrete_events: Iterable[str]=['sleep_stage', 'body_position'],\n",
+    "    resample: str='1s',\n",
+    "    color_map: pd.Series=None,\n",
+    "    palette: str=DEFAULT_PALETTE,\n",
+    "    fig: TimeSeriesFigure=None,\n",
+    "    ax: List[plt.Axes]=None,\n",
+    "    height=1,\n",
+    "    **kwargs\n",
+    ") -> List[plt.Axes]:\n",
     "    \"\"\" plot channels data for a given participant and array_index \"\"\"\n",
-    "    # set colors\n",
-    "    colors = get_legend_colors(cmap).explode('source')\n",
-    "    colors['source'] = pd.Categorical(colors['source'])\n",
-    "    colors = colors.set_index('source')\n",
-    "\n",
-    "    # filter data\n",
-    "    if (array_index is not None) and (('array_index' in channels.columns) or ('array_index' in channels.index.names)):\n",
-    "        data = channels.query('array_index == @array_index').copy()\n",
-    "    else:\n",
-    "        data = channels.copy()\n",
-    "    # extract time and channel name\n",
-    "    if time_col in channels.index.names:\n",
-    "        data = data.reset_index(time_col)\n",
-    "    if 'source' not in data.index.names:\n",
-    "        data = data.set_index('source')\n",
-    "    data[time_col] = data[time_col].dt.tz_localize(None)\n",
-    "\n",
-    "    # grouping and coloring sources by event \"channels\"\n",
-    "    data = data.join(colors[['channel']])\\\n",
-    "        .sort_values(['channel', time_col], ascending=[False, True])\n",
-    "\n",
-    "    if ax is None:\n",
-    "        n = data.index.unique().size\n",
-    "        fig, ax = plt.subplots(nrows=n, figsize=(10, n*height), sharex=True, squeeze=False)\n",
-    "\n",
-    "    # plot data\n",
-    "    ax_shift = 0\n",
-    "    for i, (source, d) in enumerate(data.groupby('source', observed=True, sort=False)):\n",
-    "        if (source not in CHANNELS) or (y_filter is not None and source not in y_filter):\n",
-    "            print(f'plot_channels: skipping {source}')\n",
-    "            ax_shift += 1\n",
-    "            continue\n",
-    "        iax = i - ax_shift\n",
-    "        if resample is not None:\n",
-    "            d = d.resample(resample, on=time_col).mean(numeric_only=True).reset_index()\n",
-    "        if source in colors.index:\n",
-    "            c = colors.loc[source, 'color']\n",
+    "    # Filter data and prepare channels\n",
+    "    channels, colors = prep_to_plot_timeseries(\n",
+    "        channels, x, x, row, row,\n",
+    "        participant_id, array_index, time_range,\n",
+    "        y_include, y_exclude,\n",
+    "        add_columns=[y, hue], palette=palette\n",
+    "        )\n",
+    "    if color_map is not None:\n",
+    "        colors = color_map\n",
+    "\n",
+    "    # Create axes if necessary\n",
+    "    n = channels[row].nunique()\n",
+    "    if ax is None and fig is None:\n",
+    "        fig, ax = plt.subplots(nrows=n, figsize=(12, n*height), sharex=True, squeeze=True)\n",
+    "    elif ax is None:\n",
+    "        ax = fig.add_axes(n_axes=n, height=height, name='sleep_channels')\n",
+    "\n",
+    "    # Plot data\n",
+    "    for i, (source, d) in enumerate(channels.groupby(row, observed=True, sort=False)):\n",
+    "        if colors is not None and hue is not None:\n",
+    "            c = colors.get(d[hue].iloc[0], 'grey')\n",
     "        else:\n",
-    "            c = 'grey'\n",
+    "            c = '#4c72b0'\n",
+    "        if resample is not None:\n",
+    "            d = d.resample(resample, on=x).mean(numeric_only=True).reset_index()\n",
+    "\n",
+    "        # Set channel value limits\n",
     "        if source in CHANNEL_LIMS:\n",
-    "            d = d.loc[(CHANNEL_LIMS[source][0] <= d['values']) & (d['values'] <= CHANNEL_LIMS[source][1])]\n",
-    "        ax[iax, 0].scatter(d[time_col].dt.tz_localize(None).values, d['values'].values, s=0.1, color=c)\n",
+    "            d = d.loc[\n",
+    "                (CHANNEL_LIMS[source][0] <= d[y]) &\n",
+    "                (d[y] <= CHANNEL_LIMS[source][1])\n",
+    "                ]\n",
+    "        ax[i].scatter(d[x].values, d[y].values, s=0.1, color=c)\n",
     "        if source not in CHANNEL_LIMS:\n",
-    "            ylim = d['values'].quantile([0.001, 0.999]).tolist()\n",
+    "            ylim = d[y].quantile([0.001, 0.999]).tolist()\n",
     "            ylim[0] = 0.95*ylim[0] if ylim[0] >= 0 else 1.1*ylim[0]\n",
     "            ylim[1] = 1.1*ylim[1] if ylim[1] >= 0 else 0.95*ylim[1]\n",
-    "            ax[iax,0].set_ylim(*ylim)\n",
+    "            ax[i].set_ylim(*ylim)\n",
+    "\n",
     "        if source in rename_channels:\n",
-    "            ax[iax, 0].set_ylabel(rename_channels[source], rotation=0, horizontalalignment='right')\n",
+    "            ax[i].set_ylabel(rename_channels[source], rotation=0, horizontalalignment='right')\n",
     "        else:\n",
-    "            ax[iax,0].set_ylabel(source, rotation=0, horizontalalignment='right')\n",
+    "            ax[i].set_ylabel(source, rotation=0, horizontalalignment='right')\n",
     "\n",
     "        if source in discrete_events:\n",
     "            if source in ENUMS:\n",
-    "                ax[iax, 0].set_yticks(ENUMS[source][0],labels=ENUMS[source][1])\n",
+    "                ax[i].set_yticks(ENUMS[source][0],labels=ENUMS[source][1])\n",
     "            else:\n",
-    "                ax[iax, 0].set_yticks(d['values'].drop_duplicates().sort_values().values)\n",
-    "                ylabels = ax[iax, 0].get_yticklabels()\n",
+    "                ax[i].set_yticks(d[y].drop_duplicates().sort_values().values)\n",
+    "                ylabels = ax[i, 0].get_yticklabels()\n",
     "                for label in ylabels[1:-1]:\n",
     "                    label.set_text('')\n",
-    "                ax[iax, 0].set_yticklabels(ylabels)\n",
-    "\n",
-    "    # format plot\n",
-    "    for i in range(len(ax)):\n",
-    "        ax[i,0].set_xlabel('')\n",
-    "        ax[i,0].set_xticklabels([])\n",
-    "    if ax is None:\n",
-    "        print('entered')\n",
-    "        ax[-1,0].set_xlabel('Time')\n",
-    "        ax[-1,0].set_xlim(data[time_col].min(), data[time_col].max())\n",
-    "        format_xticks(ax[-1,0])\n",
+    "                ax[i].set_yticklabels(ylabels)\n",
     "\n",
+    "    format_xticks(ax[-1])\n",
     "\n",
     "    return ax\n",
     "\n",
     "\n",
-    "def format_xticks(ax, format='%m/%d %H:%M'):\n",
-    "    \"\"\" format datestrings on x axis \"\"\"\n",
-    "    xticks = ax.get_xticks()\n",
-    "    ax.set_xticks(xticks)\n",
-    "    ax.set_xticklabels(xticks, rotation=25, ha='right')\n",
-    "    xfmt = mdates.DateFormatter(format)\n",
-    "    ax.xaxis.set_major_formatter(xfmt)\n",
+    "def get_channels_colors(\n",
+    "    channels: pd.DataFrame,\n",
+    "    events: pd.DataFrame,\n",
+    "    participant_id: int=None,\n",
+    "    array_index: int=None,\n",
+    "    time_range: Tuple[str, str]=None,\n",
+    "    event_filter: Iterable[str]=None,\n",
+    "    palette: str=DEFAULT_PALETTE,\n",
+    ") -> Tuple[pd.DataFrame, pd.Series]:\n",
+    "    # Group channels like events do\n",
+    "    channel_groups = pd.Series(COLOR_GROUPS, name='source')\\\n",
+    "        .reset_index()\\\n",
+    "        .rename(columns={'index': 'channel_group'})\\\n",
+    "        .explode('source').set_index('source')\n",
+    "    channels = channels.join(channel_groups)\n",
+    "    # Simulate events colors\n",
+    "    _, color_map = prep_to_plot_timeseries(\n",
+    "        events,\n",
+    "        x_start='collection_timestamp', x_end='event_end', label='event', hue='channel',\n",
+    "        participant_id=participant_id, array_index=array_index, time_range=time_range,\n",
+    "        y_include=event_filter, y_exclude=['Gross Trunc'],\n",
+    "        palette=palette)\n",
+    "\n",
+    "    return channels, color_map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "\n",
+    "def get_sleep_period(events: pd.DataFrame) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Get the sleep period from the sleep events dataframe.\n",
     "\n",
+    "    Args:\n",
+    "        events (pd.DataFrame): The sleep events dataframe.\n",
     "\n",
-    "def get_legend_colors(cmap='muted'):\n",
-    "    # the following dict keys should correspond to sleep event channels\n",
-    "    colors = pd.Series(COLOR_GROUPS, name='source').to_frame().reset_index()\\\n",
-    "        .rename(columns={'index': 'channel'}).sort_values('channel')\n",
-    "    colors['color'] = sns.color_palette(cmap, len(colors))\n",
+    "    Returns:\n",
+    "        pd.DataFrame: The sleep period dataframe.\n",
+    "    \"\"\"\n",
+    "    return events.groupby(['participant_id', 'research_stage', 'array_index'])\\\n",
+    "        .apply(get_events_period, 'Wake', 'Wake', 'Sleep',\n",
+    "               first_start=True, first_end=False, include_start=False, include_end=False)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This module provides functions for plotting sleep events and channels data.\n",
     "\n",
-    "    return colors\n"
+    "First, we will load time series DFs from the sleep monitoring dataset."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ec2-user/projects/pheno-utils/pheno_utils/pheno_loader.py:610: UserWarning: No date field found\n",
+      "  warnings.warn(f'No date field found')\n"
+     ]
+    }
+   ],
    "source": [
-    "#| hide\n",
-    "\n",
-    "# events = pd.read_parquet('examples/Sleep/10k/sleep_events.parquet')\n",
-    "# channels = pd.read_parquet('examples/Sleep/10k/sleep_channels.parquet')\n",
+    "#| eval: false\n",
+    "from pheno_utils import PhenoLoader\n",
     "\n",
-    "# plot_sleep(events, channels)"
+    "pl = PhenoLoader('sleep')\n",
+    "channels_df = pl.load_bulk_data('channels_time_series')  # contains: heart_rate, spo2, respiratory_movement\n",
+    "events_df = pl.load_bulk_data('events_time_series')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>participant_id</th>\n",
+       "      <th>cohort</th>\n",
+       "      <th>research_stage</th>\n",
+       "      <th>array_index</th>\n",
+       "      <th>collection_timestamp</th>\n",
+       "      <th>source</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">10k</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">00_00_visit</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">2020-06-23 00:40:29+03:00</th>\n",
+       "      <th>heart_rate</th>\n",
+       "      <td>62.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>respiratory_movement</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>spo2</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-23 00:40:29.200000+03:00</th>\n",
+       "      <th>respiratory_movement</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-23 00:40:29.400000+03:00</th>\n",
+       "      <th>respiratory_movement</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                        values\n",
+       "participant_id cohort research_stage array_index collection_timestamp             source                      \n",
+       "0              10k    00_00_visit    1           2020-06-23 00:40:29+03:00        heart_rate              62.0\n",
+       "                                                                                  respiratory_movement     0.0\n",
+       "                                                                                  spo2                     NaN\n",
+       "                                                 2020-06-23 00:40:29.200000+03:00 respiratory_movement     0.0\n",
+       "                                                 2020-06-23 00:40:29.400000+03:00 respiratory_movement     0.0"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#| hide\n",
-    "import nbdev; nbdev.nbdev_export()"
+    "#| eval: false\n",
+    "channels_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>event_end</th>\n",
+       "      <th>event</th>\n",
+       "      <th>channel</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>participant_id</th>\n",
+       "      <th>cohort</th>\n",
+       "      <th>research_stage</th>\n",
+       "      <th>array_index</th>\n",
+       "      <th>collection_timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">10k</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">00_00_visit</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
+       "      <th>2020-06-23 00:40:29+03:00</th>\n",
+       "      <td>2020-06-23 00:40:34+03:00</td>\n",
+       "      <td>Excluded</td>\n",
+       "      <td>heart_rate_raw</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-23 00:40:29+03:00</th>\n",
+       "      <td>2020-06-23 00:40:34+03:00</td>\n",
+       "      <td>Excluded</td>\n",
+       "      <td>spo2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-23 00:40:29+03:00</th>\n",
+       "      <td>2020-06-23 00:55:29+03:00</td>\n",
+       "      <td>Wake</td>\n",
+       "      <td>actigraph</td>\n",
+       "      <td>0</td>\n",
+       "      <td>900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-23 00:40:29+03:00</th>\n",
+       "      <td>2020-06-23 00:40:34+03:00</td>\n",
+       "      <td>Invalid Time</td>\n",
+       "      <td>general</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-23 00:40:29+03:00</th>\n",
+       "      <td>2020-06-23 00:40:32+03:00</td>\n",
+       "      <td>Artifact</td>\n",
+       "      <td>pat_infra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                           event_end  \\\n",
+       "participant_id cohort research_stage array_index collection_timestamp                                  \n",
+       "0              10k    00_00_visit    1           2020-06-23 00:40:29+03:00 2020-06-23 00:40:34+03:00   \n",
+       "                                                 2020-06-23 00:40:29+03:00 2020-06-23 00:40:34+03:00   \n",
+       "                                                 2020-06-23 00:40:29+03:00 2020-06-23 00:55:29+03:00   \n",
+       "                                                 2020-06-23 00:40:29+03:00 2020-06-23 00:40:34+03:00   \n",
+       "                                                 2020-06-23 00:40:29+03:00 2020-06-23 00:40:32+03:00   \n",
+       "\n",
+       "                                                                                   event  \\\n",
+       "participant_id cohort research_stage array_index collection_timestamp                      \n",
+       "0              10k    00_00_visit    1           2020-06-23 00:40:29+03:00      Excluded   \n",
+       "                                                 2020-06-23 00:40:29+03:00      Excluded   \n",
+       "                                                 2020-06-23 00:40:29+03:00          Wake   \n",
+       "                                                 2020-06-23 00:40:29+03:00  Invalid Time   \n",
+       "                                                 2020-06-23 00:40:29+03:00      Artifact   \n",
+       "\n",
+       "                                                                                   channel  \\\n",
+       "participant_id cohort research_stage array_index collection_timestamp                        \n",
+       "0              10k    00_00_visit    1           2020-06-23 00:40:29+03:00  heart_rate_raw   \n",
+       "                                                 2020-06-23 00:40:29+03:00            spo2   \n",
+       "                                                 2020-06-23 00:40:29+03:00       actigraph   \n",
+       "                                                 2020-06-23 00:40:29+03:00         general   \n",
+       "                                                 2020-06-23 00:40:29+03:00       pat_infra   \n",
+       "\n",
+       "                                                                            start  \\\n",
+       "participant_id cohort research_stage array_index collection_timestamp               \n",
+       "0              10k    00_00_visit    1           2020-06-23 00:40:29+03:00      0   \n",
+       "                                                 2020-06-23 00:40:29+03:00      0   \n",
+       "                                                 2020-06-23 00:40:29+03:00      0   \n",
+       "                                                 2020-06-23 00:40:29+03:00      0   \n",
+       "                                                 2020-06-23 00:40:29+03:00      0   \n",
+       "\n",
+       "                                                                            end  \n",
+       "participant_id cohort research_stage array_index collection_timestamp            \n",
+       "0              10k    00_00_visit    1           2020-06-23 00:40:29+03:00    5  \n",
+       "                                                 2020-06-23 00:40:29+03:00    5  \n",
+       "                                                 2020-06-23 00:40:29+03:00  900  \n",
+       "                                                 2020-06-23 00:40:29+03:00    5  \n",
+       "                                                 2020-06-23 00:40:29+03:00    3  "
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#| eval: false\n",
+    "events_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pheno_utils.timeseries_plots.TimeSeriesFigure>"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#| eval: false\n",
+    "import seaborn as sns\n",
+    "sns.set_style('whitegrid')\n",
+    "\n",
+    "plot_sleep(events_df, channels_df,\n",
+    "           channel_filter=['heart_rate', 'spo2', 'respiratory_movement'],\n",
+    "           figsize=(8, 8), padding=0.03)"
    ]
   },
   {
@@ -385,7 +617,10 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "#| hide\n",
+    "import nbdev; nbdev.nbdev_export()"
+   ]
   }
  ],
  "metadata": {
diff --git a/nbs/08_cgm_plots.ipynb b/nbs/08_cgm_plots.ipynb
index 1fe2865..f0828b6 100644
--- a/nbs/08_cgm_plots.ipynb
+++ b/nbs/08_cgm_plots.ipynb
@@ -69,7 +69,7 @@
     "        cgm_date_col: str = \"collection_timestamp\",\n",
     "        gluc_col: str = \"glucose\",\n",
     "        diet_date_col: str = \"collection_timestamp\",\n",
-    "        diet_text_col: str = \"shortname_eng\",\n",
+    "        diet_text_col: str = \"short_food_name\",\n",
     "        ax: Optional[plt.Axes] = None,\n",
     "        smooth: bool = False,\n",
     "        sleep_tuples: Optional[List[Tuple[pd.Timestamp, pd.Timestamp]]] = None,\n",
@@ -80,15 +80,15 @@
     "        Args:\n",
     "            cgm_df (pd.DataFrame): DataFrame containing the glucose measurements.\n",
     "            diet_df (Optional[pd.DataFrame], optional): DataFrame containing the diet data. Defaults to None.\n",
-    "            cgm_date_col (str, optional): Name of the date column in cgm_df. Defaults to \"Date\".\n",
+    "            cgm_date_col (str, optional): Name of the date column in cgm_df. Defaults to \"collection_timestamp\".\n",
     "            gluc_col (str, optional): Name of the glucose column in cgm_df. Defaults to \"glucose\".\n",
-    "            diet_date_col (str, optional): Name of the date column in diet_df. Defaults to \"Date\".\n",
-    "            diet_text_col (str, optional): Name of the text column in diet_df. Defaults to \"shortname_eng\".\n",
+    "            diet_date_col (str, optional): Name of the date column in diet_df. Defaults to \"collection_timestamp\".\n",
+    "            diet_text_col (str, optional): Name of the text column in diet_df. Defaults to \"short_food_name\".\n",
     "            ax (Optional[plt.Axes], optional): Matplotlib Axes object to plot on. Defaults to None.\n",
     "            smooth (bool, optional): Apply smoothing to the glucose curve. Defaults to False.\n",
     "            sleep_tuples (Optional[List[Tuple[pd.Timestamp, pd.Timestamp]]], optional): List of sleep start and end times. Defaults to None.\n",
     "        \"\"\"\n",
-    "        self.cgm_df = cgm_df\n",
+    "        self.cgm_df = cgm_df.reset_index()\n",
     "        self.diet_df = diet_df\n",
     "        self.cgm_date_col = cgm_date_col\n",
     "        self.gluc_col = gluc_col\n",
@@ -151,7 +151,7 @@
     "        for i, (food_datetime, group) in enumerate(\n",
     "            self.diet_df.groupby(self.diet_date_col)\n",
     "        ):\n",
-    "            food_str = \"\\n\".join(group[self.diet_text_col])\n",
+    "            food_str = \"\\n\".join(group[self.diet_text_col].dropna())\n",
     "\n",
     "            txt_x = food_datetime - pd.to_timedelta(7.5, \"m\")\n",
     "            if i % 2 == 0:\n",
@@ -231,55 +231,48 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
        "      <th>glucose</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>participant_id</th>\n",
        "      <th>collection_timestamp</th>\n",
-       "      <th>connection_id</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
-       "      <th>2020-05-25 10:48:00+03:00</th>\n",
-       "      <th>1000001</th>\n",
-       "      <td>111.6</td>\n",
+       "      <th>2020-06-22 00:14:00+03:00</th>\n",
+       "      <td>106.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2020-05-25 11:03:00+03:00</th>\n",
-       "      <th>1000001</th>\n",
-       "      <td>79.2</td>\n",
+       "      <th>2020-06-22 00:29:00+03:00</th>\n",
+       "      <td>100.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2020-05-25 11:18:00+03:00</th>\n",
-       "      <th>1000001</th>\n",
-       "      <td>84.6</td>\n",
+       "      <th>2020-06-22 00:44:00+03:00</th>\n",
+       "      <td>97.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2020-05-25 11:33:00+03:00</th>\n",
-       "      <th>1000001</th>\n",
-       "      <td>106.2</td>\n",
+       "      <th>2020-06-22 00:59:00+03:00</th>\n",
+       "      <td>95.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2020-05-25 11:48:00+03:00</th>\n",
-       "      <th>1000001</th>\n",
-       "      <td>102.6</td>\n",
+       "      <th>2020-06-22 01:14:00+03:00</th>\n",
+       "      <td>93.6</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                        glucose\n",
-       "participant_id collection_timestamp      connection_id         \n",
-       "0              2020-05-25 10:48:00+03:00 1000001          111.6\n",
-       "               2020-05-25 11:03:00+03:00 1000001           79.2\n",
-       "               2020-05-25 11:18:00+03:00 1000001           84.6\n",
-       "               2020-05-25 11:33:00+03:00 1000001          106.2\n",
-       "               2020-05-25 11:48:00+03:00 1000001          102.6"
+       "                                          glucose\n",
+       "participant_id collection_timestamp              \n",
+       "0              2020-06-22 00:14:00+03:00    106.2\n",
+       "               2020-06-22 00:29:00+03:00    100.8\n",
+       "               2020-06-22 00:44:00+03:00     97.2\n",
+       "               2020-06-22 00:59:00+03:00     95.4\n",
+       "               2020-06-22 01:14:00+03:00     93.6"
       ]
      },
      "execution_count": null,
@@ -319,14 +312,26 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th>collection_timestamp</th>\n",
-       "      <th>food_id</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>shortname_eng</th>\n",
+       "      <th>short_food_name</th>\n",
+       "      <th>food_category</th>\n",
+       "      <th>weight_g</th>\n",
+       "      <th>calories_kcal</th>\n",
+       "      <th>carbohydrate_g</th>\n",
+       "      <th>lipid_g</th>\n",
+       "      <th>protein_g</th>\n",
+       "      <th>sodium_mg</th>\n",
+       "      <th>alcohol_g</th>\n",
+       "      <th>dietary_fiber_g</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>participant_id</th>\n",
-       "      <th>cohort</th>\n",
+       "      <th>collection_timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
@@ -336,60 +341,122 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
-       "      <th>10k</th>\n",
-       "      <td>2020-05-25 08:15:00+03:00</td>\n",
-       "      <td>1007294</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>Coffee</td>\n",
+       "      <th>2020-06-21 16:06:00+03:00</th>\n",
+       "      <td>Quinoa</td>\n",
+       "      <td>Pasta, Grains and Side dishes_wholewheat</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>78.3104</td>\n",
+       "      <td>12.1744</td>\n",
+       "      <td>1.2992</td>\n",
+       "      <td>2.8000</td>\n",
+       "      <td>37.1504</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.4896</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10k</th>\n",
-       "      <td>2020-05-25 08:15:00+03:00</td>\n",
-       "      <td>1007417</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>Yellow Cheese</td>\n",
+       "      <th>2020-06-21 16:06:00+03:00</th>\n",
+       "      <td>Hummus Salad</td>\n",
+       "      <td>Pulses and products</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>215.2000</td>\n",
+       "      <td>9.4400</td>\n",
+       "      <td>16.8000</td>\n",
+       "      <td>6.5600</td>\n",
+       "      <td>377.6000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10k</th>\n",
-       "      <td>2020-05-25 08:15:00+03:00</td>\n",
-       "      <td>1008624</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>Almonds</td>\n",
+       "      <th>2020-06-21 16:06:00+03:00</th>\n",
+       "      <td>Meatballs</td>\n",
+       "      <td>Beef, veal, lamb, and other meat products</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>311.0940</td>\n",
+       "      <td>16.1280</td>\n",
+       "      <td>15.4440</td>\n",
+       "      <td>24.7320</td>\n",
+       "      <td>1164.5460</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.3904</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10k</th>\n",
-       "      <td>2020-05-25 08:15:00+03:00</td>\n",
-       "      <td>1011642</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>Brazil nuts</td>\n",
+       "      <th>2020-06-21 19:28:00+03:00</th>\n",
+       "      <td>Banana</td>\n",
+       "      <td>Fruits</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>113.9200</td>\n",
+       "      <td>25.9072</td>\n",
+       "      <td>0.4224</td>\n",
+       "      <td>1.3952</td>\n",
+       "      <td>1.2800</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.3280</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10k</th>\n",
-       "      <td>2020-05-25 10:05:00+03:00</td>\n",
-       "      <td>1007118</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>Hummus</td>\n",
+       "      <th>2020-06-21 21:07:00+03:00</th>\n",
+       "      <td>Bread</td>\n",
+       "      <td>Bread</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>162.6000</td>\n",
+       "      <td>28.3800</td>\n",
+       "      <td>2.1000</td>\n",
+       "      <td>5.2800</td>\n",
+       "      <td>367.8000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.6200</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                           collection_timestamp  food_id  weight  \\\n",
-       "participant_id cohort                                              \n",
-       "0              10k    2020-05-25 08:15:00+03:00  1007294    40.0   \n",
-       "               10k    2020-05-25 08:15:00+03:00  1007417    87.0   \n",
-       "               10k    2020-05-25 08:15:00+03:00  1008624     6.0   \n",
-       "               10k    2020-05-25 08:15:00+03:00  1011642    12.0   \n",
-       "               10k    2020-05-25 10:05:00+03:00  1007118   100.0   \n",
+       "                                         short_food_name  \\\n",
+       "participant_id collection_timestamp                        \n",
+       "0              2020-06-21 16:06:00+03:00          Quinoa   \n",
+       "               2020-06-21 16:06:00+03:00    Hummus Salad   \n",
+       "               2020-06-21 16:06:00+03:00       Meatballs   \n",
+       "               2020-06-21 19:28:00+03:00          Banana   \n",
+       "               2020-06-21 21:07:00+03:00           Bread   \n",
+       "\n",
+       "                                                                      food_category  \\\n",
+       "participant_id collection_timestamp                                                   \n",
+       "0              2020-06-21 16:06:00+03:00   Pasta, Grains and Side dishes_wholewheat   \n",
+       "               2020-06-21 16:06:00+03:00                        Pulses and products   \n",
+       "               2020-06-21 16:06:00+03:00  Beef, veal, lamb, and other meat products   \n",
+       "               2020-06-21 19:28:00+03:00                                     Fruits   \n",
+       "               2020-06-21 21:07:00+03:00                                      Bread   \n",
+       "\n",
+       "                                          weight_g  calories_kcal  \\\n",
+       "participant_id collection_timestamp                                 \n",
+       "0              2020-06-21 16:06:00+03:00      56.0        78.3104   \n",
+       "               2020-06-21 16:06:00+03:00      80.0       215.2000   \n",
+       "               2020-06-21 16:06:00+03:00     180.0       311.0940   \n",
+       "               2020-06-21 19:28:00+03:00     128.0       113.9200   \n",
+       "               2020-06-21 21:07:00+03:00      60.0       162.6000   \n",
+       "\n",
+       "                                          carbohydrate_g  lipid_g  protein_g  \\\n",
+       "participant_id collection_timestamp                                            \n",
+       "0              2020-06-21 16:06:00+03:00         12.1744   1.2992     2.8000   \n",
+       "               2020-06-21 16:06:00+03:00          9.4400  16.8000     6.5600   \n",
+       "               2020-06-21 16:06:00+03:00         16.1280  15.4440    24.7320   \n",
+       "               2020-06-21 19:28:00+03:00         25.9072   0.4224     1.3952   \n",
+       "               2020-06-21 21:07:00+03:00         28.3800   2.1000     5.2800   \n",
+       "\n",
+       "                                          sodium_mg  alcohol_g  \\\n",
+       "participant_id collection_timestamp                              \n",
+       "0              2020-06-21 16:06:00+03:00    37.1504        0.0   \n",
+       "               2020-06-21 16:06:00+03:00   377.6000        NaN   \n",
+       "               2020-06-21 16:06:00+03:00  1164.5460        0.0   \n",
+       "               2020-06-21 19:28:00+03:00     1.2800        0.0   \n",
+       "               2020-06-21 21:07:00+03:00   367.8000        0.0   \n",
        "\n",
-       "                       shortname_eng  \n",
-       "participant_id cohort                 \n",
-       "0              10k            Coffee  \n",
-       "               10k     Yellow Cheese  \n",
-       "               10k           Almonds  \n",
-       "               10k       Brazil nuts  \n",
-       "               10k            Hummus  "
+       "                                          dietary_fiber_g  \n",
+       "participant_id collection_timestamp                        \n",
+       "0              2020-06-21 16:06:00+03:00           1.4896  \n",
+       "               2020-06-21 16:06:00+03:00           0.0000  \n",
+       "               2020-06-21 16:06:00+03:00           2.3904  \n",
+       "               2020-06-21 19:28:00+03:00           3.3280  \n",
+       "               2020-06-21 21:07:00+03:00           1.6200  "
       ]
      },
      "execution_count": null,
@@ -408,12 +475,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "start_date = pd.to_datetime('2020-05-25', utc=True).tz_convert('Asia/Jerusalem')\n",
-    "end_date = pd.to_datetime('2020-05-27',utc=True).tz_convert('Asia/Jerusalem')\n",
+    "start_date = pd.to_datetime('2020-06-22', utc=True).tz_convert('Asia/Jerusalem')\n",
+    "end_date = pd.to_datetime('2020-06-24',utc=True).tz_convert('Asia/Jerusalem')\n",
     "\n",
     "\n",
-    "sample_days = cgm_df[(cgm_df.index.get_level_values('collection_timestamp') >= start_date) \\\n",
-    "                     & (cgm_df.index.get_level_values('collection_timestamp') <= end_date)]"
+    "sample_cgm = cgm_df[(cgm_df.index.get_level_values('collection_timestamp') >= start_date) \\\n",
+    "                     & (cgm_df.index.get_level_values('collection_timestamp') <= end_date)]\n",
+    "sample_diet = diet_df[(diet_df.index.get_level_values('collection_timestamp') >= start_date) \\\n",
+    "                     & (diet_df.index.get_level_values('collection_timestamp') <= end_date)]\n"
    ]
   },
   {
@@ -423,7 +492,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 1800x500 with 1 Axes>"
       ]
@@ -433,13 +502,11 @@
     }
    ],
    "source": [
-    "cgmplt = CGMPlot(cgm_df=sample_days.reset_index(),\n",
-    "                 cgm_date_col=\"collection_timestamp\",\n",
+    "cgmplt = CGMPlot(cgm_df=sample_cgm,\n",
     "                 gluc_col=\"glucose\",\n",
-    "                 diet_df=diet_df.iloc[9:],\n",
-    "                 diet_date_col=\"collection_timestamp\",\n",
+    "                 diet_df=sample_diet,\n",
     "                 smooth=True)\n",
-    "cgmplt.plot()"
+    "cgmplt.plot()\n"
    ]
   },
   {
@@ -526,7 +593,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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lA5blY3y8O4Xwi6XTCrJZFcvLJpaXDYQhI4P6bbSfdURERERERERE1FG+H8KyfGiatO1tVVWC54UDVRDtJ113YNsBMpnRHypaH6Jaqw3GzoA4jlEoWI3hnr3qyldVCfm8hmLRxsqKyQ7xPmMxnIiIiIiIiIiImtZMREqdIAgQBAGWNZjZ0b3keSHKZWfLnPVRk04rqFadgdgZoOsuikUL+by26dDXbpFlsVEQX101By46ZjdhMZyIiIiIiIiIqMc8LxlAadv+0BXGTNODJDVfTNQ0CYbh7fqIiFrNheuGSKdHvyu8LpWS4Thh3xdDHCfA6qqJVEru6LDMVsiyiLExFaurFkoluy/HQMDuWYoiIiIiIiIiIuqzOI5RKtkolWy4bghJSgbt7d2b7VuRrhVhGMGyfKjq9hEpdZomo1p14LohMpnB/xm7wfdDVCq7qyu8TlUlVCoO8vn+DAyNoiQexfdDTEyke/79L6YoEjIZBaurJjRNRi6n9vV4dqPdeQYiIiIiIiIiIuqDYtHC4qIBURQwNZVGNquiWLSxvGwMRZaw4wRwnKCpiJQ6URQQx4Dr7s7ccMcJUCzasO1gV3WF16VSMizL71tufLXqolJxMDam9eX7XyqVkiGKIpaXDXhe/+NjdhsWw4mIiIiIiIiIeqBWc7GyYiKbVRpFUVkWMT6uoVx2UCpZfT7C7TlOgDhGy5nLiiINzCDFXqpUHMzMVFAoJFnVu5EsiwjDGKbZ+7+/54UoFi2kUjIkaXDKoLmcCtsOUCxaQxeTNOwG51FARERERERERDSigiDC6qoFWZbWdVVLkohMRkGpZMO2B3fQZBzHqNVcaFrzESl1qirBcfyBGKTYK7ruYGGhCkkSMTWVHooYnG5Jp2Xoutvz3PhKxYFtB8hkBq8jP5/XUCrZu3KRqJ9277OQiIiIiIiIiKhHdN2BZXnIZjcuyqVSMnw/QqXi9PjImud5IVw3bCkvvE5RRPh+BNfdHcVw2/axvGxA0+RdGY1yKU2T4TgBbLt3USm27aNctjd9zvWbLItQVQmrq+auWiTqNxbDiYiIiIiIiIi6yPNClMs20mllywGCuZwKXXcGtjvcdUP4fgRFab0YXv+5HWcwf7ZOqg9sDIKYhfALRFGAIAg9i0qJ4xiVioMwjFrKt++1TEaBbQfQ9cFdBBs1LIYTEREREREREXVRrebCccJtC6OKIiEMYxjGYMYmOE7Qclb4xVRVgmmOfjHcMDxUqy7GxtR+H8pASaVk1GpuT7qgbTtApeIgmx3sv4EgCI2IpH4NGN1tWAwnIiIiIiIiIuqSMEyiT9Lp5rpTUykZuu4MXGxCFLWfF16nKBJcNxi4n62ToihGqWRBlsWBGtg4CDRNguOEXY9KSbrCbcQx2trF0Gv1iCR2h/cGn5VERERERERERF1imj4cJ0Aq1XwxvBcFw1Z5Xgjfby8vvK6eG+55o1sMtywfpukPfEdyPwiCAFkWUau5Xf0+SeyIi1xueP4G2ax6Ydjn6O+c6DcWw4mIiIiIiIiIuiCOY+i6A0kSt8wKv1ivCoatct0AQRDvqNtZEATEMUa6GK7rDkRR2FGczChLpWSYptfVx4CuO4jjGLI8PGVPVZUQBBGq1cF63o+i4XlUEBERERERERENEdcNYZpe0xEpdamUDMPw4LqD0x1u2z4kaecFXkURezZEsdccJ4BheMhkODRzM6oqwfOirnVAO04yjDKTGZ6u8Lp6d/ggPe9HEYvhRERERERE1FDPN15YqGFlxRzZohVRL9i2jyCIW84tVlUJvh8NzEC9KIphmv6OIlLqFEWC4wQIw6gDRzZYLMuH70dDkVPdT4oiolp1Ecdxx++7WnURBHFHHqu9Vu8OH7RdIaOGxXAiIiIiIiICAPh+iIWFGubmqqhWXayumpid1VEsWl0pWhCNsnpESrsDJ2VZhGEMxmKU54XwvJ3lhdfVC/2jFpUSRcnfu9ls+N2sHpXiup19DHheeKErfHg781MpGZXK4A3QHSUshhMRERERERHCMMLysglddzExkUI+r2FyMg1Nk7G0ZKBUsvt9iERDxXEC2HbzgzMvpWnShU7j/hfFXDdAGO4sL7xOFAWEYQzfH63O8OTv7be9+LGbKIqEIIg7HpVSL7AP84JEfYCuaXKQZrewGE5ERERERESoVBxUKg4mJlJrBr9pmox0WsHqKiNTiFph2zsrICfZymHHu2fb4ThBR/LC60RRGJgImE6xbR9RhI4sGOwGmiZB1x1EUWd2HUVRjEql/Z0Yg0IQBKiqhErF5o6sLuEzlIiIiIiIaJdz3QClko10Wl5TCK9LuuwErK6aI5nzS9RpcRyjWt1ZYU4QBAgCujZosFlRFMMwvI5mMCuKCMsancW15O/tDnVHcl2yANP9hYpUSoZl+R1bFLEsH5blI50e3oiUunRabvw81HkshhMREREREe1y1aoLzwu3LCKMjakwDB/VKgd7EW3HdUM4TgBN21lxVFVlGIbX1w5Rzwvh+2FHh0IqSpIbPggRMJ3gOMGFv/dwdyUvLxv48pefwUMPLXb9e0mSiDhGx3Yc6boDURQ2XNAdNvXdBYxK6Q4Ww4mIiIiIiHYxzwtRqTjbdtMJgoB0WkapZI9MAYu6w3UDzM9Xcf58BcWihSDY2W6CKIphWf5QDVx0nABBEEOWd1Z2qUel9PNn97ywIz/LxRRFvFBkH42dJo7TuUz1fpmfr+LrXz+HkydLPZsRkUrJqFbdHZ8jXDeAYXgj0RVel0opqFadHf9uaL3hfZYSERERERHRjrUycCyVkmHbPgxjdOINqLN8P8TiYg2VigPPC7G4aGB+vtp2Mdf3Q8zPV3HuXBnnz1eg606Hj7g7TNPrSPFYlkX4ftTX3HDHCTrebSsIAuIYI7OwZhgeFGV4S2yFgoX77puBbXs4fHgMrhv0ZDdCMiwy2HEcSDJoNupolE+/aZoExwmZHd4Fw/tMJSIiIiIioh1pdeCYIAhIpRSUSjazw2lD5bIDw/AxMZFCNqticjIFw/CwvGy0/JiJohjLyyaqVRdjYxpEUcDSkjHwObq+H8Ky/I5FZvRz2GQcxzDNzuaF14miMBDDQXfK90PYtr/jSJx+cZwA998/h0rFweHDeShK7wa3CoIASRJRq7ltF3zrr2OjkNd+MUEQkM0qjXMgdQ6L4URERERERLuU4wSw7dYGjqXTMmw7YJYprWPbPkolC7mcCkFIOokFQcD4eAq67qJSebarOwgilMs2KhUHUbRxEaxaTb4mn9cgSSLSaQVRFKNYtDb9mkHgukmsSacKyKoqwTT7kxvu+xE8L+xK1/OoDNF0nACeFw1tZ/gTT6xgZkbH5ZePQxCERoRNL4ZoAslrSq3mtl18t20fth2MXDEcADRNhqbJWF4e/EXAYTKcz1QiIiIiIiLaMcvyEMdoKQKhXizRdYdbt2mNemH70iKwKD6bN1+PX1hdNTE/X8XcXBWrq+a6x1IQRCgWLaRS8poc5lxORa3mrSsMRVF8Idu6/zsWHMeHIKCxILBT/czXdt0Avh91dHhmXX2IZr//Zjs9jzlO0NG/dy+trJh44olV7N2bbjzPVFVCEPSmMxxIHgdhGMMw2ut+rg+YHYXBmRvJZBSEYYyVFaPvz5VRMXrLJkRERERERLStKIqh625b3XTptALD8OA4wUgNLKP2OU4AXXeQzaobfj6dTuJ1ajUXqiqjXHaQz6cQxzEKBQuyLGJ6OtO4fa3mwrYDTE6m1tyPJIkQhKTwnsupCMMIuu6gXE4GzUmSgLExDZOT6b7kB8dxDMPwoKqdK7coioRazYPrBj3/mVw3KfR2gywnneG+H3Z0OGezTNNDpeLAdUOk0zLGx1PIZFo7n9X/3sMYkRLHMZ58cgW27ePAgVzj45KU5NQ7Tu86kVMpGZWKg4mJdEuPBd8PUa26I/86lM9rKJcdFIsW9u3LDuXCyyBhZzgREREREdEu5DgBXDdoq4gjyyKiKMkSJgKS4nUQxFt2EKfTMopFG6urJmRZhCyLUBQJ6bSCQsGCbSfFt4uz7Dcq+mQyCkzTQ7XqYmGhhoUFA3GcFNREUUShYGF+vtqXnO161nKni9aCgLaHkO6EafpdK8CLooAoQl863stlG7OzOqrVJKu6XHYwO6u3PKDV8zobidNLi4sGzpwpY//+7JqPJ8+53ua5J4M0w5aHM1uWD9cNO5bPP6gEQcDYmIpi0UatxtfdnWIxnIiIiIiIaBdK4ipai0i5WColQ9ddDtIkBEHSnZ1Ob72wkk4rkOWk6zSbfbaTM5WSEYYxikUbcRzDsnxY1uZZ9vV4jeVlA7ruYmIi6ehVFAmplIyJiRRsO8DSUg2+39sCsusmUS2d7nRWFKnnmcG+H17IC+9eoTEp8vd20aJadbG4WIOiSBgfTyGdVjAxkYIsi1hcrLVUkHXdJL6mH53tOxHHMZ55pgDfj5DJbLSbI+5ZZjiQFHs1TUKxaDX9mhLHMapVF4oi7opOaUWRoCgiVlfNviyMjZLherYSERERERFRRyRFhPaLXJomw3XDvnTf0mAxTQ+OEzYVuZNOK8jntXXFq7ExFdWqC8PwUKu5EARhy4WasTEVkiRiaiq97nbJ0E4NhuGjULB6mm1v235XCnOyLMJ1g55mBtdzyrs5GFJRJNh2784hnhdiddVs7Ei4WDqtQBRFLC8bTS+ieF73YmS6qVSycf68jr17Mxt+XhQFWFZvz+2ZjALbDprufHbdEJblj+TgzM1ksyocJ0Cx2Nvz2qhhMZyIiIiIiGiXSaIcdpY/nBQg4553q9Jg6VR3piSJEEUBpZINw/C27TKvd4FvRhCERs5uq9EL7Yqien505zupVVW6UJzuXUeo54WI47irXbeynAwH7dUOk1LJhuMEm2bbZ7NJQbZcbi4uxbKCrnbOd8u5cxVYlo9cTtvw80lOfXsDLdslCAJSKRnFotXU49y2/a4Ndx1kY2O9Pa+NIhbDiYiIiIiIdhnXDeD70Y5zbjVNRrXKqJTdzPM6152Zy6mwLB9hGHdkIKEsP1tgj6Lud1HWi9XdyI8WRQFx3NvccNP0uh7/oSgifD/sSce7bfuoVOxNC+FAUpDNZhWUy/a2u16CIILnBV3tnO8G2/Zx+nQJExOpTW+jKGJfiq3ptNzUYsTFcwV2m/q8hULB6ulOkVEyXM9YIiIiIiIi2rFORZtomgzHCRiVsotZVue6M0VRwPh4Cvn8xt2q7cjlVBiG15Nhr54XIghiSFJ3Si2CIPSsGB6GERyn+13PkiQiCOKeDNGsVBxEUbztYoWmyY0c/IvZto9y2Ua5bF/oSu5+pno3LCzUUCrZmJzcqhguwXF6G8sDJI/xXE5FoWCiWt28M71Wc2Gam88VGHXZrALTTB6P1LrdE6xDREREREREiOMYpul1pPO2ntVs2/6W3ZY0muI4Rq3mdaUTulNEUYAkiahUHORyalcjP2zbb3sgbTMURexZLJHnJYXefL77xUZBQNfjXxwnQLXqbjIscr1MRoGuO5iYSEFVJZRKdqMTN44BTZOQy6mIovaHEPfL+fOVC7FEmy/aJI+1AJ4XQJZ7e25XVQm+L2F52YAoJsXxi9VqLlZXTWiaNHS/+06p72AoFi1ksyoymd25KNAudoYTERERERHtIr4fwXXDjm3t1zQZhuFxmNculAyw605GdidlswpqNbergxqTRSa/q7+Ler52L7p1XTfsWaFXFAW4bnd3lxiG21I0lKbJ8LwIhuGhUnGwtGRAUSRMTqYxNZVudI5vl20/aHTdwfy8gampzbvCgaQz3PdDOE7vYnkuls2qiGNgbk7HyorZGKy7uFjD3FwVUYRdvwCraTKiCCgWrZ7EQI2S4XrWEhERERER0Y4kucYRxsY6U7RTVQmW5cF1w47kRtPwSGIU4oGPiZAkEXGcdJR2q4My6aQOmu48boeiSI14jm5nedu23/XvUVeP5OjWsM4wjKDrbsuF6/owxzhO/vvi89vYmAbHCYbunLe0ZMAwXOzbN7nl7epZ7t1epNhKLqfCdQOsrpoAgDhOFk6yWWXgzzm9MjamQtdd5PMuxse3XuCgZw3Xs5aIiIiIiIh2xHF8dLLeJMtJ5q/rDl9hiHamVnOHZnhgOp10h09NpbtSSKvnhXezgCyKAqIo2d2RTnft2yCKYliW37O/rSQJ8P0IYRhDljtfDLcsH44TbDkwciOZjNIo0l+aTS2KwlBGU8zM6NA0adtFB1EUEYZxX4vhQNL93IlIr1ElSSI0TUKhYCGT4SJBs4bjVYuIiIiIiIg6wrI6PxRPlkUYRvcHFNLg8LwQtu0PTaFK0yQ4Tti1qBTXDTq6yLQZQQA8r7sFymT3SNizLHhFkRAEUddyww3DgyAIbXWdp1LyyAxp1PUk7qXZRQFBEOC6/YlJoeZlMgpsO0Cl4mx/YwLAYjgREREREdGuEQQRPC/oeMenqibxDb3IMqbB4LpBSxnM/SYIAmRZhK53vmAUxzEMozeDRJOolO4Xw4MghiT1pmQkigLCMO7K+cP3QxiGN3TZ3t2wumrBNL0Wsrb73xlO26sP0yyVbNh2bwbsDjsWw4mIiIiIiHaJJNc47HhnuKZJcN3+5stSb1mW35V8525Kp2VYlt/xx6nvR115Xm2kPkSzmwPzHCfoyeDMiwkCulIMd5wAnte7LvdBNj9fhSyLTT9vJUmEabK4Ogw0TUYYRiiVbA6zbsLAFcPvvfdevPnNb8ahQ4cgCAK+8IUvND7n+z5+4zd+A9dffz2y2SwOHTqEt7/97VhYWFhzH67r4t3vfjf27NmDbDaLW265BXNzcz3+SYiIiIiIiAaL74eIInS80FUvrnBL/e4QRUkntKYNV4FRUST4fuejUnw/GUrbi4xtWRYvfL/WnmsrK2ZTXfG97HK/mCSJcJzOL6YZhgdRbC8iZZTYto+FhRryea3pr1EUEbUa46+GRS6nQdddRpY1YeCK4aZp4oYbbsDHPvaxdZ+zLAsPP/wwfud3fgcPP/ww/uEf/gHPPPMMbrnlljW3u/322/H5z38en/70p3HffffBMAy86U1vQhjywoyIiIiIiHYvxwkgSd0pCimKBNPkm/DdwHWTbtthyQu/mKJIqNXcjt6n54WIY/Sk4CrLyWBD32++i9pxApTLNhYXa9sWynqdF14nyyJsO+hoV2sQRDBNbygfp522umqiWnVbLIZLsCye04eFLIuQJAHFooUwZGTZVgbujPCGN7wBb3jDGzb83Pj4OO655541H/voRz+Kl73sZZiZmcHll18OXddx55134uMf/zhuuukmAMAnPvEJHDlyBF/96lfx+te/vus/AxERERFRr7huANcNIUkCMhll13e/0ebiOIZl+V2LclCUpLPT93sTF0H947pJTEevozQ6IZV6NiqlU0VS0/R60hVeF8etRYoYhgvfjyDLIopFC+m0vGkeeJIXnty2l+od72EYQ5Y787hynOT1cWJiNAZg7sTKioU4jiGKzf9dZVmE44Q8pw+RXE5FueygVvOaHpS6Gw1cZ3irdF2HIAiYmJgAADz00EPwfR8333xz4zaHDh3Cddddh6NHj256P67rolqtrvm3my0vG10ZLEJEREREnaPrDmZmdMzM6Dh/voKlJYPdQLQp34/g+2HXilyqKjWyk2m0mabX82JppyRRKVHHIjnCMLmvXhYLJUloOvc8DCPouot0WkYup6JW87bMgXbdAP1YU5VlEUEQtRz/shXH8SEIvenYH2RhGGF2toKxsea7wgFAVUX4fsRZEENEEAQIAro6U2AUDOer1wWO4+A3f/M38ba3vQ35fB4AsLS0BFVVMTk5uea2+/fvx9LS0qb39YEPfADj4+ONf0eOHOnqsQ86xwlQqTgM3iciIiIaUKbpYWnJgCAImJpKI5fTUCxaKBQsXsPRhpL4g+7lGguCgCiKmRs+4oIggm37Qz2QUJbFjuXq1p9Xvfx9JJEizQ02tO0AjpN0wYuiAFkWoesbv9ev54X3owtYFJPzR6eGaMZxjGrVg6oOXCBCz1UqDioVF7mc2tLXKYqEIAh5TqeRM7TFcN/38da3vhVRFOHP/uzPtr19HMdbrgb+1m/9FnRdb/ybnZ3t5OEOJcvyOz5YhIiIiGhYBUEEw/Bgml7fO27CMEKhYCGKYmQyyfZvWRaRz6dQKFgceEUbqtXcC11j3euSVBSRueEjrh7NNMzFcE2TYFl+R7qQfT9CGPY2MqaVLmrLSorm9ePLZBQYhrdhZ3x9Z0e/IjFajX/ZiuuG8LxgqB+nnVIoWHCcAOl0a3ExiiLB81gMp9EzlMVw3/dx22234ezZs7jnnnsaXeEAcODAAXieh3K5vOZrVlZWsH///k3vU9M05PP5Nf92M0kS4DgBhyUQERERIenCrkeRnD+vY36+2tdtw4bhwTC8dVueZVmEokgoFKyOFRRoNLhugGrVbSyedIuiSHDdgI+/Eea6vRsW2S2aJsPzwo5EpThO0PPsdFWV4DjBttGmYRihVnORSj3bHS3LIqIobhTJL9bt3SPbaSX+ZTvJeSge2jifTlpaMtr6mwqCgDiOGZNCI2fozgr1QvjJkyfx1a9+FdPT02s+/5KXvASKoqwZtLm4uIjHH38cr3zlK3t9uENNEAToutv3ziciIiKifnKcAIuLNXheiImJFPJ5DdWqi8XFWkezTZsVRTHKZRuKIm1YgMlmFViW37EIABoNSRds96McVDXpJGRu+OgyTW8kum0FQdiwINyKZCht738fgiAgk1FRKtlbFvQd59mIlItpmrzhe/160bNfCx2KInUsy92yfBbCkTwGFhaMlvPCL8bOcBo1OwpP8n0fKysrKBQKyGQy2Lt3b2OQZbsMw8CpU6ca/3/27FkcO3YMU1NTOHToEH7qp34KDz/8ML70pS8hDMNGDvjU1BRUVcX4+Dje9a534T3veQ+mp6cxNTWFX/u1X8P111+Pm266aUfHttukUjIcJ4Bt+8hmW8uWIiIiIhoFURRjddW8UAhPAwAEAZiYSKFcdlAoWDhwINfTwoFl+bCsAPn8xm9sBUGApkkolWyMjamQJBYDdrsoilGpONC07hfsRFFAHCcdpt3uQqfe8/3wwrDI4T+vaJoEw/CwZ0/U9nmyHivSj1zqVEpGueyjXLZx8ODYhrdxnABxjHULp/Wf3XXXRmeYpt+3iBQg6QwPgghh2P7fBEg64i3LH4nH6U6VyzYMw8Xhwxs/RprheewMp9HS8hn79OnTuOuuu/D1r38dDz30EHx/7UrqoUOH8OpXvxo/8RM/gVtvvRWy3Nq3ePDBB/Ga17ym8f933HEHAOAd73gH3v/+9+OLX/wiAOBFL3rRmq/7xje+gVe/+tUAgA9/+MOQZRm33XYbbNvG6173Otx1112QpOFfve4lURQQhmAxnIiIiHYtw/Cg6y4mJlJrPi4IAvJ5DeWyg1xOxdiYhjiOEYYxJKm7mcy1mgtgfXHjYpmMgnLZgWX5O+oGo9FQb3DJ51Pb37gDRFGAbfvrnjc0/OoxGqOw0JF0RztwnKDt97u+n/w+stn+FF2zWRW67mBiIrUuDzqOY9Rq7oaLYJIkIgzjNTnSvh/Cdfu70CHLYiNmaSfF8CTnOujZOW+QlUo2giCCLLdXD5MkEaa5sx0URIOm6Ur1/fffj/e+9734+te/jiiKoKoqXvjCF2L//v2YmpqCbdsolUp4+umn8clPfhKf+tSnsG/fPvzqr/4q7rjjDqTT6aa+z6tf/eoNpxrXbfW5ulQqhY9+9KP46Ec/2uyPR5tIpZLtU1NTmZ7noBERERH107NxJOKG10GyLEKSBBSLFkRRQKXiwLaTbdnT05muFKE9L4RheEint76MFwQBsiyiWnVZDCdYlrdhd2i3qGoynDCKejtUkLrPdUNEUTzUeeF19V0MOymG1+OA+vX7UFUJpumhWnXXFcPrgw8vzgu/9GtrNQ+Tk+nG7fu90FEv0gdBBG0HL13J47R357xBtrBQQyrVfmOooogcyk0jp6li+Fvf+lZ87nOfw759+/Dud78bt912G17ykpdAVTd+wZidncW//Mu/4BOf+ATe97734c///M/x8Y9/fE3HNw2H+mo5u8OJiIhot3HdAJblI5fb/Bool1NRLjvw/VojFsLzIszNVXHo0BjGxzvbleY4AVw3bOq6LJ2WG9vgL82Lpd0jimLourtpQawbFEWCZXnw/ZCPvREzKnnhdZomoVZzMTWVbqugbVl+36OoMhml0R1+8fOtXtweG9v475UM4fTh+yEUJcn6j+PBWOjY6QBe0/SYF45kl3+hYG15HbMdRZFgmm4Hj4qo/5o6Ozz00EO48847MTc3hw9/+MN4xStesWkhHACOHDmCd73rXfjGN76BEydO4KabbsJ3v/vdjh009U59JdW2uS2GiIiIdhfXDS/Enmx+ySwIAsbHNYiigMnJNDRNRi6nQlUlLC8bHb+GqtXcpt/gK4oE3492PCCOhluygNLbBRFZTro7OURztARBNDJ54XWaJsN12xv4GkXxQPw+NE2G50XrhibbdrBlZ7SiiPC8qDEc0TAGo4AsCMKOzh1hOHqP03aVSjZqNRe5XPtt9kl0TdSXgeFE3dLUFdGJEyfaztu+9tprcddddyEM+cQZVvWolMnJdN9XvYmIiIh6pdnOMkkSkU6vvV06raBSSQZsHj6c78hWbd8PYVl+S0MQVVVCtZpkng9Ctx/1nm37PY1IuRiL4aPF88KRG4wqyyJ8PymetrpglHReN7dTp9sufs+exL/EMIytu/gFQYAoAo7jI5WS4brBQHT9y7IIx2l/YGP9ccqIMKBcdrZd1N+OqoowjOBCnnz/Hx9EndDUM6KdQvjc3Bwee+yxHd0HDYb6arltc4IwERER7Q5BEMG2/R0VBvJ5DdWqu65br12OE8DzwpaOKZWSYdt+o/OPdpf6AL1+FLiSqBTuShglrhsgjvuXj90tkiS09Vj1/RBBsLNCY6ekUjIcJ2j8HPVC/XbPfUWRGnFartva60u3SFLSGR5F28+L24jnJbu6mBcOLC7uLC8cSB4jQRDyOoJGStfO2r/zO7+DG2+8sVt3Tz1UX1k2TQ5NICIiot2h3lm2k8KAKApQFAmlktX2m/qLWZYPQRBaKkTJsoggiHfUZUfDqz5Arx8FrmRrfYAw3Fn2Lw2GZGHFG8noCU2TYZpey49V1w0wKOsCl75nr+eFb7e7SVWlxmDmQVnoSGKWorZzwx0ngCT1/+fot3pe+E53LiS7J1gMp9Eyeq9k1BXptIJazd3xIAsiIiKiYeC6AaJo59ESmYwC0/R33FQQRcmW91YiUuoURUStxuFXu5HrJgWxfmxtV9Uks55RKaPBNH0YhjdSESl1mibBdcOWFw0tazBiRepSKbnxnr3ZQn0yODOZLTEoP0uyiBu1tZCWLAgMzs/ST5WKA8PwdjQ8EwBEUUQUxXBdLqrT6GAxnJpSv0DgVkciIiLaDSzL70gHpCgKkCQRuu4ijtvvDk+2sLc3BFHTkqgUFiV3H9P0+tYhKYoCoiiG77OZZtgZhoelpRokSRiISJBOq3dDt1IMrxecB2HgZF2S+x3Ctv1t88IvJghAteoimx2MhQ5BEBDHaKsRrx4Pw2zrJC88CCLIcmd+F7yGoFEyOGduGmiCkFz4sKuIiIiIRl0YJsPUOvVmOp2WYRjujrYYO077ner1Dl12de0uYTgY3Z6ex8fdMNN1B/PzVUQRuj6Q8PTpEmy7P81XqiqhVmt+0bLZTO5eqsdoVatuS/FIExMp7NmTGYiIlIu1WwxPCsAsda2sGB19fPIagkYJzxDUtFRKbgzXICIiIhpV9bzwTmXjKoqEMIxhGO03FRjGzrJ6BUHgMPRdJskLb283QackQzT5uBtGcRyjVLIwP1+FLIs7jlrYTrls49ixJZw6Verq99mMqiY7oZvtfk0GPA5GxvbFNE2C4wQtxSO1OouiFyRJbKvuwFpFwvNCLC+bO84Lr5MkEabJlAAaHSyGU9PqXUWMSiEiIqJR5nkhwjDuaByApsnQdbetQZpBkHSq76TDS9MkGIbXkUGeNBxcN+xI7v1OyLIIz+MQzWGTFMJtLC2ZSKUUpNPdj8+Yn69hdrYKx+lPFIOiJO91m41KcRx/IIc0alrSwJZO928RrBMkSWhr8LNldW5X1zDTdQem6SGX68xzV5ZFGMbOZp8QDZKmz5Df+973Wrrj1dXVlg+GBp+mSahUHIyPp/p6YU1ERETULa4bdvw6J5WSUa26sG2/5U4t1026/HYyuE5Vk2K454VIpYa7SELNsSy/71EBiiLCMJLH7yhmTY+iZwvhBjIZpSc7C4IgwqlTJfh+iKWlWte/32YkSYBl+RgfT215uziOB254Zp0oCgMZedIqSXp2iGaz544giOB5Qcd2dQ2zSsWB54VQ1c48f1VVYmc4jZSmnxkvf/nLWzqhxnE89CdgWi+dVlCturAsv+tb5YiIiIh6LSlyND94rFlJcT2GabZTDA8RRTu7tpYkEVEUw3UDFsN3gTCMYNudGQK7E5IkIgxj+D4XYYZFpeJgednsWSEcAFZXTRSLJi67LI9azYVlechkev9eU9NkmKa3bQG2nhc+qI/pUajDyLIIywoQBM0Xw30/ibnJ5wdjEGg/FQpWRxcgZVm8EL/D4aQ0Gpo+e7/97W8fiZMq7Uy9S6pWc1kMJyIiopGTdJZ1rpvqYpomo1ZzMT2dbulNqml2pjgvis11PdLwq+fed3vgYTMEAU3nMFN/VasulpYMaJrU06z5hYUaPC/CoUMpzM1Voetun4rhEioVH44TbLlo6fsRfD/C2BiLgt0iyyKCIEYQRNCaPI15Xog47m801CCIohiLi7Ud7Sa7lKomu3xclzE0NBqafoW76667ungYNEwymaQ7fGoq3deBPERERESdlnT8RchmO99Rq2lJVMp2hZaL1fPCO9Hhq6oSLMtHFMW7vlgw6uq594Pwd5ZlEbbN7fWDzrZ9LC8bkGWxJxnhdZ4X4uzZMvJ57cJOggjVqouDB8d6dgx19ea/7c7RnheCfYLdJwjJa2CzXDfsewPnE0+sYGoq3ZfHb52uO6jVPIyPd24xVFEkBIEL1w2Ry3Xsbon6hmFK1LL6IM1q1e33oRARERHtWBzHME0PKysmCgULcdydbeb1qJRWhpG7bnChU33nnViqKsHzQrhu60PJaLjY9uAM95NlsRH1Q4PJ90MsLxsIgqjlGKedWlkxUCrZmJxMdqxIkoBSye7pMVxMVSXUai7iePPHq2l67I7tEd9vbldJPeKsn9FQURTjqacKeOihxS0fP92m6w5s2+/oopaiSPC8oK2hpkSDiMVwaks2q6BYtLC6arb0ho6IiIhokERRjELBwsyMjtVV80LeaPeiJerd4c0WBuvxEp0oztfzmxlZMdqSxR1/YIb7JR2FUdNFLeqtKIqxumrCMPyunvs2MztbQxTFkOXk8ZrJqFheNvpWTNQ0GY4TwHU3frwGQQTXDfo+nHY3kGURntdcZ3hyjon6ukhhGMlstULB6uuCTqXiAOjsor4gCIhjAbbNYjiNhqYyLl772te2deeCIOBrX/taW19Lg03TZERRjJUVE6JoYXo6g337sv0+LCIiIqKmxXFSCF9ZMZHLqT0pHibFcAeOEzSV52lZfkeLLpIkwHECjI937C5pwHheiCCIehp1sZUk+zcpVDWb/Uu9Y5oeymUH4+NazyMmbNvHuXMVTE6mGx/LZGTUah4Mw+tL5n09q3qzYcMc0tg7kpQMbYzj7QdI+34y76OTOdkbqdVciKKw4Q6KWs2DbfsIwxjlsoPp6UxXj2Uzi4tml87/MTvDaWQ0VQz/5je/ueHHk9Wh9Su29Y/3O6+JuiudVpBOK7BtH7ruYGoqzRVyIiIiGhqW5aNYtHpWCAeSqJQ4TuJPtnvTHoYRbNvvaKebqkowTb9j0Ss0eOrF8EG7Lm8l+5d6I45jVCoOJElsaahvpywu1qDrNq68cqLxsXRawcqKhWrV7dsAWFkWYRjehsOGPS9EFHFIYy9IkoAgiBCGMWR5u2J42LWIs4sdPTqHWs3Fa15z5bpit64n8TqSlESV9INt+yiX7a4sCtSHcBONgqZe8aIoWvPPtm286U1vwnOf+1x8/OMfx7lz52DbNs6dO4e//du/xXOf+1y86U1vgmVZ3T5+GgCplMz8SSIiIhoq9SJQHKPnRWFFkVCtbp1JCyRFl04XrTVNhmX5mJmpoFbj/JdRNIgxOKIowHFYRBk0th3AMDxks/3pcj53rgJJEiGKz5YlRFFEHMfQ9f6dnzQtGTa8UbSP4wQDk8c/6mQ5GajazEKa64Y9WaAwTRfnz1cwO6uv+1y5bEOSRGiajEKhPzEplYoD0+zOczq5dulPkZ+o09pa/n3f+96H48eP4/vf/z5+9md/Fpdffjk0TcPll1+On/u5n8MDDzyAxx57DO973/s6fbw0gOqrr5wST0RERMPCdUPUam5fikCaJsFxgm2Llt3oQBRFAVNTaURR0pXJLq/RY1md3U3QCYoiNeIOaHAYhocoivvSFV4u25ibq2F6Or3uc7IsolDoX2NdfdjwpZEQg5bHP+rqcy62K4bXh2d2++9Sz5JPpWScP6+vmf0RxzFWVy2k0zLSaQWVit2XOQm67iIMo0YGfyepqgRdd/H00wWsrJgdv3+iXmrrVe9Tn/oU3vKWtyCXy234+Xw+j7e85S34u7/7ux0dHA2PVEpGoWD1bTsQERERUSsMw0UYxn0pGiqKBN+PNh3QVtfNDsRcTkUUASsrBsKQ8RWjIgwHc7ifJAnw/STugAaD74eoVp2+ZcvPzekXutLXZy9nMgpWVoymBw13Wr3Z69JiuOeF8P1w4BabRt12r1H1mQTdPu/Ztg/XDbFnTwalko1y2V7zOdP0kE7LSKVk2HaAWs3r6vFspFCwura4lc0qWFoy8PWvn8WXv3wS3//+PAcj09Bq61myuroK39+6iyQIAqysrLR1UDR80mkFiiJhcbEGw+j9SZ+IiIioWVGUbMHXtKbG53SFKApb7qrrRQfi2JgKw/BQqbCZYVQkxbpo4DpXFUW6ULBi4WRQWJYPxwmhab1/rPh+iJMnSxgbUzfMeM5kFJim39coJ02TYZremt0Mg5rHP8oEQdj2vOH70YVFim4XwwO4boDxcQ2OE6zZvVCtJsMz02kFqZQM1/VhGL19/IZhhKWlWtd2vGUyKp7znGlcc80UxsYUPPjgAr7//XkuqNNQautscc011+Bzn/scisXihp9fXV3FZz/7WVx77bU7OjgaLum0AlEUsbxsMD+ciIiIBla9uyuV6l8xXNMkGIa36ZvIXnQgCoKAdFpBuWwPZM40ta7efT1ow/1EUUAUbR93QL1Rn5mgKGLXBw5uZH6+itVVa8OIFCB5X+k4AarV/hXDFUWE6yaLS3V8/PaeJAnrOvQv1avhmZblI45jiKIIVZUwN1dtfM4wvAsLJRIEIRmU3evO8GrVhWn6XRmeeamxsRQOHszh+PEVPPXUate/H1GntVUMv/3227G0tIQXv/jF+JM/+RM89NBDmJ2dxUMPPYSPfOQjeMlLXoKVlRX85//8nzt9vDTgcjm1sUrar21tRERERFsxTR9AfwuGmibDdYNNo1J61YGYFJ1CDtMcEZ4XoA+1zaZdXFik5kRRDNP0UKu52xYFAVx4L2ZuWUi27QCm6fWkaHapOI5x8mQJkiRsmmucFBPjPhfDkzirixcKk+gqdoX3kiyL8Lxoy3kDnhf2ZFHn4iHA+byK5WUTlpUUvC8dLCnLIsrl3u660nWn0Z3eC5mMinxewyOPLGF52ejJ9yTqlLbaYX7xF38Ri4uL+P3f/33ccccdaz4XxzEkScL73/9+vPOd7+zIQdJwyec1VCoOslkVExOpfh8OERERUUMYRqjV+huRAtQ7ZQHXDTYsSPWyUzudllGpOBgfT3H7/5CzrGBg84wVJdkNMTmZ6ks38jBy3QArKyZqtWTQpaKImJ5OY2oqs+FinmF4WFqqwXFCSJIAQchjbEzb8HZxjL4UdpeWDMzO6ti7N7vl7RRFRLHYvyGaACAISQE0l1MRxzFsO+h6FAetJUkifD9ZHN7s3JYMDe7+36VadRvPmbExDefP6ygULFx+uYpi0V5zXZFOK1hdNRDHcc/Od/XIs16eX6enMzh3roxjx5bx2temB/b1h+hSbb8L+J3f+R287W1vwyc/+Uk89thj0HUd4+PjuOGGG/C2t70N11xzTSePk4aIJInQNBmFgolUSu7rFmQiIiKiizlOAMcJMD7e/wV7RREvFAfXRwWYpt+zN5WplIxy2YFpegPxe6H2BEEEzxvcYp2mSbAsr+8RRcPC80IsLNRgWT7yeQ2SJMJ1AywtmQiCGPv2ZdcUxG3bx9JSDVEETE2lUau5KBYtZDLKmqJ3PwdnJl3hRXheuO33z+U0zM8bOHu2jFxO3bZ43g2qKsE0fezZkzy/giCEqvKx20uyLMK2/U2L4WGY5IX3YiG3UnEbGfuimHy/lRULhw7lUSrZax7T6bQM0wxgWf6GQ2K7YWnJ7Mu59fDhPM6eLeHs2Qk897nTPf/+RO3Y0TPlmmuuwXvf+95tb2dZFjKZzE6+FQ2ZTEZBpWJjednAoUNjXCEkIiKigVAfWjkImcqqKsFxgnXZ4EEQwXV7V9QUBAGKIqJadZHPa+zaHVKeF8LzQuTzvS9yNkNRJNRqyZA5FsO3FoYRlpcNWJaPiYlnO+k1TYYkiSgULMiygD17so3br65a8LyosTM3m1Wh6w5MMymm19UHZ05N9aZAd7GVFROnT1ewf//2he3xcQ1nz1bw4IMLyGZVvPGNz+nBEa6lKBJcNzlH+36EIIiQzQ7mYtOoqs8bqFbdDRdQkuGZEVKp7p73gmD9rrJcTsXsbAVXXz15YZH92edZOq2gWLRRrbo9KYY7ToBy2ena8MytKIqEbFbF8ePLOHIk35eFNqJWde1MvrKygjvvvBO33HIL9u7d261vQwNsfDyFWs1DoWBtmfFFRERE1AtxHKNW87aMSAnDCNWq01Q2706pqgTPC9flhvdieOal0mkFpultmmFOg891A0TRYCz0bEZVk4I43xtsrVJxoOsuxsfXR8rIsohsVsHqqgXDqOcVu43FrDpRFCDLIioVp/H7jqIY5bINVe19o1IcxzhxogDPC5DNro9uuZQoikinZczM6LBtvy/zqBRFhO9HcN0kpiOKehtBQYlMRkGxaGF+vopazV0zeNr3w54MDbZtH54XNjrDAWBsTEWl4mJ2VofjBGsW+SRJvBDL1pshmrruwLJcZDK9X+QCgL17M1heNnHyZLEv35+oVR1dkn/66adx99134+6778YDDzyAKIpw6NAhvP3tb+/kt6EhIQgC8nkNpZKNTEbhtlsiIiLqK9cNL2R0b/xmcX6+ikceWUKplBSLfuAHpvGCF+zrWlG6XlRx3QC53LPH5Pthz4uasiwiCGJ27e6QZfkwDA+W5UGSRIyNaRgbU3uSzWwYXl+KnK3QNOmiohIfZxtxnKARb7LZOUDTZHheiELBhCQJFyIa5HW3z2QU1GoubDuZTWDb/rpO8V5ZWKjh1KlSU13hdYcO5TE+rsEwfBiGi3y+t+8nLz5HC4Iw0MNpR5mmyRAEAdWqi3LZQTotY2oqjbExDb4f9eTvYlk+XDfA9PSzsWaplALHqWFpyUAUxY3olDpBEBo53t2m6y58f/Nc9W4TRRGTkyk88UQBV189iVyu9+cYolbs+Ark6NGjjQL4yZMnEccxXvjCF+I3f/M3ceutt+KlL31pJ46ThpQsi1BVCYWChWxW5VAmIiIi6hvHCRAE8YbXI2fPlnHffTPwvADT0xnYdoCjR+dQq3l4+cuPdO0aJumUdRtD7lQ1KRZKUu+rLqoqQdedNbEM1JwoilEqWSgUrAuDDiW4bghdT7p19+/PdrX46/shHCcY+GJ4PSrFcQIWwzdRLtvw/WjbYlIup6JcdiDLNmw7wNTU+tkDkiQijgHT9JDJKKhWXQiC0PPBmb4f4vjxFURR3HLnajqtYGXFutD53vvmKlVNBr/Kssj3sn2kqhJUVWoMMp2fryGdtnv2d7EsH74fQZbXnmNVVcLiorHh7I90WsbystHy91pcrGH//lxLC+LFot2XgbgXm5pK4/TpMk6dKuFFLzrY12Mh2k7LVyCO4+Cee+7B3XffjX/8x39EoVCAKIp41atehV/+5V/Grbfeiquvvrobx0pDKpNRUC47qNXcDV8kiIiIiHrBNL0N3zQXixbuv38eURTjyJEJAEAmoyKTUfDkk6s4cmQcV1wx0ZVj0jQZpulhZcWA4wTYsycL0/T7UtRMpWR27bYhimKsrJgXmj+UNb+7KIqh686FHbP5rv1dXTfJC89kBj+rVZaTwbHcNbqeZfnQdWfNTpHNCIKAbFZBqWRvmROcSsmoVt1GMbwfj5HTp8s4e7aMyy8fb/lrRVFsRFz1Qz03PAwlFsMHgCAIyGQUpNMyHCdAreb2pAvZsvwNO9Cnp9NYXjZx8GBu3efSaaUxJ6HZHO1azcVDDy3ixS8+iEOHxpr6mjiOsbxsIJ3u7+u2IAiYnEzhqaeKuPbaKXaH00Br+tly11134e6778Y999wD27aRyWRw880349Zbb8Wb3vQmTE1NdfM4aYgJgoBUSkapZDcmoRMRERH1UhBEsO31ReYwjPDww4vQdRtXXTW55nPptAJBEHD2bLlrxXBZFhGGSaFHEATMz1cBoDEEr5eSqJRkGJnG97BNieMYxWLSEZ7Pa+uKZaIoYGIihXLZweqqiYMHx7oSf+O6Scb9MHT016NSgiBicfESycJJ3HTUgabJkGVxy/dXqZR8IYPcge9HGBvr7UJbuWzjkUeWkM9rbUc4CIIAw/A7fGTNSTrDXXhehLGx/uQx03qCICCdVno2rNE0/Q3P3ZmMiquu2vhxkc0qWFysbTr8cyPVqovVVROVitN0MbxWcy8sCvR/MbTeHX76dAk33MDucBpcTRfD3/nOd2L//v1429vehltvvRU33XQTNF4lU5PSaRnlssMuECIiIuoLxwnguiEmJta+WZyZ0XH2bBmHD+c3LCROT6cxM1NFqWRvGEPQCZmMAt8PMTamIQwjiKLQ16JmEETb32jARVGMMEyKrd38XdZqHlZXLeRym8cBCoKA8XEN5bKDbFbtykKHYXh9y4ptlapKqFR8OE7QVAf0bmHbSVd4Ntva72S7RqP6498wvJ53jgZBhEceWUSlYuPqqye3/4JNJBFOdgePrDWTk2l4Xm+HGtNgKZedlndMybIE349QrbrYv3995/hGdN1FpeJgZcXAC16wF0Cy2Dk/X4Xrhti7N4s9ezJrvqZadWFZPvbuzWx0lz21tjt8uuXzGVGvNP1sPnr0KF7+8pd381hohAmCAEVJppnn89pQdK0QERHR6KjV3AsD0J69BgmCCE88sQpZFjd9k5vLaVhaMjE/r3etGJ5KyY2hlf3eQSeKAhzHBzCczQtxHEPXXZRKNoIgiXvZsyfTlTfkrhtgddWEoojbxp9IkohUSkahYCKTUToal+L7SUTKoOeF19Wfg47jsxh+kWrVRRg23xXeikxGgWX5PY8tOHFiFU8/XcRll2282Nis+myFOI778j5SEARGR+1iYRhB1+22zrHJEE236duXSjaiKMLysgnPC2HbPr773TmcO1dGHCfXJC972SE873l7Gl9TqSTPjUsHePZLvTv8zJkSrr/+QL8Ph2hDTT9bWAinnapfhNl20O9DISIiol3E98MNuyIXFqpYWKhu27E1Nqbi9OkyfD/s5mEOBEWRYFkB4jju96G0LI5jrK5amJ+vIopiqKoMy/KxsFCDaXY2bziOY5RKyeDCZgvtmYwCxwmh605Hj8Xzwgtdq4NRCGlGfSjhMD7OusHzwpaiFFqlaXLPZzfNzel46KFFTE6md1xITqJ1kiGxRL1m2z5cN0Iq1XoxPJ2WsbRUa+q2URRjddXA9HQG1aqLuTkdR4/ONvL2r7lmCqII3H//XCNSDQBWV42B2rVQ3w311FMFWFZ/sv6JttPWq1IzAzJFUUQ+n8fznvc8/Nt/+29x2223tfOtaIRIkogoimFZ3lAM9yEiIqLRYNtJRMrFRcs4jnHqVOnC7rWt30ROTaWxsFDDyoqJw4fz3T7cvpJlEb4fIgiigXpz3Yxy2cbKiomxMbVx7PWO0qUlA5dfPt6xn8k0fZTLyTycViSD5ZOv61SnqeuGiOPhyAuv0zQOa72YaXpw3RBTU6PRKV8q2bj//jmEYdSRHTXJ8zhpqupVRjRRnWUF8Dwfqtr6jqlMRkGt5l6ogWz9/K5WHVSrHiYmUlhcNPD000WcPVvBlVeOQ5aT1669e7OYmangkUeWsG9fFgCwsmINXH1lejqNM2cqOHOmjOuu29/vwyFap632gSiK4Hkezp07h3PnzmFubg6O42Bubq7xMcdxcOrUKXzmM5/Bz/zMz+Dmm2+G53FVaLdLpWTouoswHP4sSiIiIhoOhuFCktYWCstlB7OzVezZs32hRlEkRFGMM2fKI9+ZeHExfJjYto/VVQvptLyu4J3LqbDtAOVyZzqyoygZmimKYssDIFMpGZ4XoVZrftv8dkzTG6qucKD+OIvguqO/22I7URSjUnGgacO1+LQZw3Bx9OgsCgWrY4uHmibD8wLYdn+GaNLuZlkefL+9BeJsVoVp+tD17c/5uu7Ctn2k0wrGxlScOVPGvn2ZRiG87tChMczPVzEzozfywrPZwSqGi6KI8XENTz65yu5wGkhtXTUdO3YMBw8exE033YTvfve7cF0XCwsLcF0XR48exete9zocOnQIMzMzeOaZZ/DGN74RX/va1/ChD32o08dPQyaVkmHbAQyDJ0QiIiLqPt8PYZp+I5O7bmGhCtP0kc0219m7b18Gx4+v4OjRmZGOdhBFAVEE+P7wFMOjKEahYCEM4w27RgVBQDaroFKx4bo7X8yo1VwYhtd23nU6LaNScToSuxOGEVw3GLoufiB5rLG4iUaM5Ch0PFuWh+98Zw4zMzquuGKiY7sVBEFAHINxm9QXltX+406SRIRhMkRzO5WK3djls2dPBgcOZJHPr+9Gl2UJqirixIkCymUHrhsM5A6bPXsyKBRsnDxZ7PehEK3TVjH8N37jN+C6Lr7yla/gh3/4hxsvcoIg4OUvfzm+8pWvwHEc/OZv/iauvfZafO5zn8MVV1yBT3/60x09eBo+giAglZKxumrCsnjxS0RERN3luiFcd+1wwTCMcPp0Gblc88WnbFbD4cNjOHu2gsVFoxuHOjAEAfC8jd/8x3EM1w0GKj/dMDxUqy7GxjYvTmuaDN/feUd2GEYolZJBaqLYXqEvlZLhOGFHroXreeHDMjzzYpqW5IZH0eguLjWj/phs9/E0KJJC+CxOny7hyivHuzAMOL4w3JeotwzD29HCjiQJKBatbW+3uGg2ZpuIooixsc1jWaanM1haMnD6dBmCMJgxWYIgYHIyhSefLHR8VgbRTrX1CnX33XfjjW9846bTaiVJwhvf+EbcfffdAIBUKoXXvva1OHXqVPtHSiMjk1Hg+xEWFqrsECciIqKusix/3RvFYtFGsWhhYqK1/M90WkEQhDh7ttzpwxwosixuGAcTBBGWlgycO1fBzIw+EJExURSjVLKgKNK2xbd6XN9OImAMw4Np+jvKZxUEAaoqoVy2d7zLwPNCRNFwFlJVVYLvJ8X83crzQtRq7rrhvsPGND3cd98sTp4s4YorxtfFOnSCJImo1fjesRnFosVY0g4qlex1u8takcmoWFoytlz4M00PpZLV9I6jVEqB54WYm9Mb2eGDaGoqjXLZwZNPFvp9KERrtFUMr1arqFarW95G13Xout74/z179rTzrWhEjY+nEIYxlpeNgeosIiIiotERRTFqNXfdm9ilpRpcN0Qq1XpBc2IijZmZ6kjHO8iyCNcN17xxj6IYKysGikX7QmdzgJUVs+9dvYbhwTCaK07Xj7vdjuwoilEuJ13hO+3CS6flRjzGTjhOsC4Pf1hIkoggiHd1Mdyyhn+IaLXq4FvfOo9Tp5KO8G5F9qiqxO7SJui6g6NHZzEzo29/Y9pWEnHi7Gj3TTaroFbzUK1u/vgtl23Uaq3Fb+3fn4WqSk3HvfWDIAg4cCCLEydWMT+/dQ2RqJfaKoa/4AUvwGc+8xmcP39+w8+fO3cOn/nMZ/CCF7yg8bGZmRns3bu3vaOkkVQfZtTMG5IgiGCaHgzDg237I53VSURERJ3husG6CIkoinH+vN52Z+/4uAZdt7G4WOvUYQ4cRZEQBNGaDupazUWp5GB8XIOiSBgfT6FWczs6CLJVcZwUpxVFbKozWhAEiKIA02yvuzS5Ft1ZV3idJImIY+xol2QcxzBNfyjzwuskaffmhsdxDF13oKrDWwgvlWx885vncf58BVde2Z2O8DpVlVCtuux43sb58xUsLNQQhny/3AmW5V9YPG//sZ1OK7BtH5XK5sXwYtFCFMWbpi9sJJfTsH9/ru3j6pVcTkMQRDh2bGkgdpQRAUBbr7y//du/jZ/6qZ/CDTfcgF/6pV/CK17xCuzduxerq6s4evQo/vqv/xq1Wg2//du/DQDwPA//8i//gptvvrmjB0/DTRAEKIqISsVBPq9t2GETBBEqFRuVitOYNi+KAjIZBXv2ZJDNtje4iIiIiEaf64YIw3hNfEal4qBUsjE+3l4nlSiKkCQR8/M1XH31VKcOdaBIkoAgiOD7yUJCEEQoFCykUnLjdymKAhRFQqlkY2xM60tMh2X5MAwP+Xzzf8tUSoZhePD9sKUichzHqFScpgvvzR5LreZiejrdVr6y7yd/o51s3+83VU1yw/fujYcy6mUn6rsU2h3E2m+LizUcPTqLQsHCVVdNtFTEa4emSTDNAI4T8D3gJuI4xpkzFRiGx6JjhyTF8ADT0+m276Ne59isGB7HMWZmqh1ZaB1Uhw+P4dw5HcePL+OHfujQQGac0+7S1pXTT/7kT+Kv//qvcfvtt+NDH/rQmgdyHMfI5XL4y7/8S/zkT/4kAMCyLNx555144Qtf2JmjppGRySgXur2DdSd/y/KxsmLAMHyk0zImJlIQBAFhGME0fczOJvlYk5NpnkyJiIhoHdP0IMtrCzSFggXL8nHgQPvdVPm8hrm5GmzbRzo9em9eBUFAHKPRGV6/VpucXJuxnskoqFSclgvSnVKtuhAEoaVCsqpKsCwfjhO0VAy3LB+1mouxsc79nKmUjErFgW0HbRVEPS9EEERD3RmuqhJM04PnDXdRvx2mmQwP7fygye47c6aE+++fh2V5uPLKiZ68F0ty9h1Yls9i+CZcN2zsou7EgF5KnqdBEO1410M6rWBx0cCLXrT+cztdpB8Gsixh//4MHn10CZOTaVx77Wg2E9DwaPuK453vfCfe8pa34O6778ajjz6KarWKfD6PG264AbfeeivGx8cbt52YmMCtt97akQOm0SJJIqIohmF4a4rh1aqLpaUaogiYnEytucCSJBH5vAbHCbC0ZCKOk2nKRERERHVhGMG2/XU5n0tLNSiKuKPizfi4hvPndaysmLjiiokdHulgEkXhwnDGJIpE09bnZIuiAFkWoesOxsbUnjYnuG6AWs1tuZOufoy2HbRU2K5WkziYThYuBUGAICSxLe0Uw30/xLAnB16cG76biuFhGEHX188zGHRhGOGJJ1bw0EOLEEUBl18+0bPvrSjJwNXdGqvTjGShL0Qup26ZT03Ns6zOdNhnswpKJRuW5SGTWXu+LxQs2PbOFumHwdhYCpYV4P7755BKSbjssvHtv4ioS3Z0NTc+Po63v/3t+NCHPoS/+qu/woc+9CG8/e1vX1MIb9W9996LN7/5zTh0KNk68YUvfGHN5//hH/4Br3/967Fnzx4IgoBjx46tuw/XdfHud78be/bsQTabxS233IK5ubm2j4m6K5NRUC7bjVVXXXewsFCFIAibxqcASTdNOi1jedngMBUiIqIhE4YRXDeA6wYXinqdreq5brguL9x1AywsGDvu7hVFEXEcY3nZ3OlhDixFES8MePRhWZt3wGcyCkyz91vyk8GDUVtDzepRKc0O//S8ELWa25VdAPWolIvz2ZtlWT4UZfi6ii8lisKui3Sw7STuY5iK4bbt4+jRWXz3u3PIZBQcPDjW0+9/8UIWbcy2fXief6EY7nLOVgdUq05HFkFzORWm6aFcXl+3OH++suNF+mGxf38Onhfg29+exdwch7xS/wzc1ZNpmrjhhhvwsY99bNPPv+pVr8If/uEfbnoft99+Oz7/+c/j05/+NO677z4YhoE3velNCMPdO6l8UFQqNsple83HNE1GGEaYndVx7lwZc3NVyLLU1PY3TZOhaUlBnFvBiIiIBlsUxahWXczPV3H2bHnNv5kZvaMFMdcNEEVYk0NcLNoXoi52vsU+n9cwM1OB73f2+vLkySJWV/tfZJdlEZ4XQtddCAI2zXOWZRFh2Nst+VGUDB7UtPa2rauq1Biu2owkxiOCpnW+cKlpElw3bPmxH0UxHCdYFwO0kTiOcepUCbOzOlx38+8TxzFqNRflst3UQkEUxThzptS4zzCMMD9fxbFji/jGN87i3nvP48knV2EYWw9ZreeG76bCXa3mNnYGDIPlZQNf/eoZHD++ggMHcpicbD8/eacsq/2hszvV6fN9p1mWjzhO3iO7btiYuUXtK5WcjixaiaLY2Gl1MV13sLho9PU51WuHD+fhOB6+8Y3zePrpQtML0ztR/93Pz1cxP1/lzglqPyYljmN88YtfxKOPPor5+Xn4/voLYEEQcOedd7Z0v294wxvwhje8YdPP//zP/zwA4Ny5cxt+Xtd13Hnnnfj4xz+Om266CQDwiU98AkeOHMFXv/pVvP71r2/peKhzbNvHN795DrWahxe9aD+uu25/4wJwYiKNMIwQBBHyebml1dd0WkG16mJ52cBll+WHOjeRiIhoVNm2j9VVC9WqC1kWoaoSMhkZggCEYRKZJkkWDh0a68ggvY26ZstlG2G48+xPICmGLy8bKBbtjm1tLpVs3H//PDIZGT/6o1cjn09t/0VdUs/WFkV/2yKwpsmoVl1MTqZ7MgSxPniw3Q5/SUoK+K67fWduFCWDM9stvG+nfi3c6iBF3w/h++G67fYbKRQsfOc7M3AcH9PTGTz/+XtwzTVTazrdXTfAI48s4eTJEqIowpEjefzQDx3a8jF47NgiHn10GS996WHk8yqeeKKA+XkdQRBB0yREUYzHH4+wd28GL3vZ4U0jhVRVutDRGnZlwWHQ+H4Iw/CQTg/+zxoEEU6cWMUjjyzDcTxcddVEXzPOVVVqRBb12vKygUcfXcZrX3tVU4tQ/WBZPgRBuGg2gj9Uuw8GTbIryGtrB9JGNE3G/HwN1123v/GxxUUDtZqHffuybd1nobCAc+eewuWXPxf79h3pyHF2myAIOHx4HKurJr71rXNYXjbxgz+4DxMTnV8QsG0fMzM6Tp0qoVCw4Dg+AAGZjIorrhjv2velwdfWmfH06dN485vfjKeffnrLFfx2iuE79dBDD8H3fdx8882Njx06dAjXXXcdjh49umkx3HVduO6zL6zVarXrx7rbPP10AQsLBiYnU/je9+ahqjKe97w9jc9Lktj2xdXYmIpy2YGuO9izp70XEiIiIuqO+iyQIIgxMZFaVzCVJECSNOi6g1xOxcTEzorA9bzwSxfIFxdrHStqqqoM349QKFgdK4Y/80wRtZoL03RRKtl9LYbXh2g6TrDt3yOVklGtOnCc9QPRu8E0PcTxzvK769Ec26U7JlExQVcHhNajUqan003/TJ4XIgzjpopyc3NVmKaPq66aQKlk4957Z/D000X84A/ux1VXTUIQgAcemMfjj69g794MFEXBiRNF2HaA17zmyg0L7qbp4cSJIizLx/HjyzAMD6KYbIG/uKAdxzEWF2v45jfP4d/8mytw5ZWT6+5LlkX4frSmGF5/jzksndOtsCwfrhsO/BBI1w1w773ncfp0CePjKVxxxfq/Xa+pqoRKpX/F8ELBhGG4A1s803WnsdjseSEch53hO1GruXCcAFNTnXktzuVUFItWIzc8CCI880wR2azS1rnu5Mlj+OAHfwlhmOzO+ZEfuRX//t+/b2jOm3v3ZpHNKnjiiRXMzel43vP24MorJzA1ld7xz+A4Ac6eLePJJ1exsmIilZIxOZlqXK8ZhosnnljByoqBf/Wvrhj5vHZar61i+K/8yq/gxIkT+I//8T/iZ37mZ3Dw4EHI8mCsOC4tLUFVVUxOrn2x3r9/P5aWljb9ug984AP43d/93W4f3siK43jLafYrKyaOH1/B9HQak5NpxHF8YXCC3JHBU4IgIJ2WUak4GB9P9a07PI6TLau+HyGOYyiKhFRK7kmXFBER0SBKtgDXIMsSJiY2L5RKkghNk1EsWshklB11Ynlekheezz/7/RwnwOqq3dECVColY3a2ihe+cO+O37iVSjZOnixi7940ikUbptn/+Ld6d+92P5soCoiipAOr28XwMIxQre588KCmSTBNH3G89c9XqyWFt25ey2maDF134LohMpnmiuG+31zGeBzHOHeujHxehSSJ2Ls3i+npNJaXTXz962dw5ZVT2Ls3jaeeWsHhw2ONbvGrrprA2bMVHDu2hFe84si639HiooFq1cFVV01gZcXEvn3ZDTPVBUHAoUN5LC7W8N3vziOfT2Fqan0hURCS52iy49NBteoiDGPkciomJ9Md68zstzhOoqKGIet9acnAmTNlXHZZHqo6GO/1k47nYN08iG6L4xjnz+uo1TyYpj+QxfA4jhuRHslg2ojDRndoedmE4/hIpzuTj5/LqZiZ0bG4aOCaa6Zw7lwFS0s1HDrU+v3HcYxPfeqPGoVwAPj2t+/Gy172elx33Ss6cry9kMmouPrqSZTLTmNR9sCBMVxxxTj27s1gejrT0v3VO8GfemoVi4sGcjkVV101AVFce84dG0shl9MwM6PjvvvO43Wvu3pXRdVQm8Xwb3/727jlllvwp3/6p50+nq7Z7kL3t37rt3DHHXc0/r9areLIkeHYZjIIHnlkEaurFl772qvWFaIdJ8CDDy7Asnzs35+suO3Zk8X8fBXf//78phfPrUqnkwnNhuH15UTm+yEKBQu67iAIYghCcmGfySiYmspgbEwdmlVaIiKiTjBND8vLBmRZaqpImskkr+WVitP2lmEgGZ55aV54uWzDMFwcPty5oW/5vIZCwUS16mJ8fGedY08/XYRheNi/fwqVioNarX+5uHWtLBwk3eFuRzq6tlIfPLjT3QOKIsE0Pdj25t3svYqzEMWkC7+VxQTL8prqCnecALa9Ng5GFEUcPDgGzwswM1PB6dMljI2pa67HJUnEwYNZPPVUAUeOjOPIkXHEcYxy2cH8vI6nnipAUSTIsoRDh/LbHseBAzmcPVvGww8v4LWvvXrd4kL9Ot6yfNRqHjRNgigKWF21YNs+jhwZ72s8R6e4bgjLGo7oioWFGgAMTCEcSBaOKhUHpulBVXv3fk/XHZRKNjwvHIiFyo24bgjb9huLBIKw+wbTdlKlYuP8eb2jz9Vk0V/Cd74zi+PHV1CreUil5LaeY08++QDOnXtq3cdPnnxkqIrhQPJYnZpKY2oqDdN0MT+v48yZEvJ5Da9+9ZU4fDh5jUmef15jwQcAgiCEbQcXYnNNnDtXQblsI5NRcOWVW8c6CYKAyy8fx9mzZXz/+wt4zWuuZOTuLtLWMzuXy+Haa6/t9LF0xIEDB+B5Hsrl8pru8JWVFbzyla/c9Os0TYOmdW/74yhbXKzh+PEVmKaPw4fzuO66fY3PBUGEhx5awLlzFVx55dp9qAcP5nD+fAXLy8aGWybbkUrJF7YVaz29YHacAEtLNRiGj7ExtXESjaJkoNTsrI6pqTT27MnwBEtERLuC74dYWTERRUAu1/yidzaroFy21xXnNhOGEZaXDQiCgMnJNFIpGbbtQ5LWFtsqFadjeeHPHquKpSUTS0vGjorhq6smnnmmgH37kg4oVZVRqdjbfNVg0TQJtZoHw/DazvJuRr3TcacFd1kWLxR3baTT8ob318s4i+T319xiQhTFcN2wqWK4bQdw3XDDv4mqJjs0gyDc8HmRzWoolx0cO7Z04XFeQ6mU7FoYH9daWlhKMmLzOHOmjKuvLuPqq6fWfD6VkhEEESzLx+RkqvE7SKVklMsODMPb8YLTILBtH74fYWxssN8PeF6ImRm9q/FA7aif3+szCnpF190L0Vti3zLLt5NkhAeYnk5+L4IAdoa3oVSy8dRTqzhzpoJazcVll3VuAR0ADh3Ko1KxLyzoiJiYaK3zue7YsXs3/Pjc3KmdHF7fZbMastnkvHPmTAmLi0ajGP7II0t46qlVKIrY6PSOoiRiy3UDCAKQz6c27ATfjCAIuOyyPM6cKeHIkTx+4Af2ducH20Z9IWsUFn2HRVu/6R/7sR/D0aNHO30sHfGSl7wEiqLgnnvuaXxscXERjz/++JbFcGqP74d49NFleF6AyckUvv/9BZw4UYDnhSiXbRw9OovHHlvG4cNj6y6ykxOUgLm5WseOJ52WYdsBDKN33VSuG2Bxsda4eL+42C2KAnI5FWNjGopFGwsLNa7QExHRyIvjuBH1MTbWWiFR02SEYYRy2d5yNk1dpeKgVEq69ubnq7AsH5blr9tCv7pqdnzoWT2m7cyZCqJo+2PdSBzHePLJVdi2j7GxpNiXSsnQdRdB0FwUxiBIZr8IKBSsRrRIp8VxfKFruDOdermchmrVhW2vvzbrdZyFqkpw3STeZzuelwzPbCYmIhlMGWx5260WiA4cyGF+voqHH15AuWxjfFzDtddOYe/ebNPFhjpNkyHLIk6cKCAM1z+2czkV4+OpNYsBgiBA0ySUSnbbz7FBEUUxdL17w1g7qVZzWx7q2gv1x0a53NvFwnoBPJWSUSwO5kKlZXlrcvcVRezauXgURVHyWvxP/3QSjz22jHRawjXXTCKV6nz018REGnv3ZjE52f5OqsXFsxt+fNiL4RcbG9MwM1NBEEQolWw880wBmiYhlZIgy4AsJ/WfPXsyuOqqSVx11RSmpzMtvzapqoxcTsXx4yuwrN7vyiuXbXzlK6fwrW+dY62oh9q6uvujP/ojLC4u4td//ddh2519MTAMA8eOHcOxY8cAAGfPnsWxY8cwMzMDACiVSjh27BiefPJJAMDTTz+NY8eONfLAx8fH8a53vQvvec978LWvfQ2PPPIIfu7nfg7XX389brrppo4eKwGnTpVw9mwZhw7lMT2dgaqK+Na3zuIf/uEpfOlLz+Dxx1dw6NDYpp1dExMpzM5WO7ZqXZ+eXS735oLZ90MsLtZg2/66i/eLybKIyckUTNPD/Hznfl4iIqJBZJo+SiWr7YiwXE6DrifFmK24boBSKensnZxMw3VDLC0Z8P1wzeJ0EERYXja70uE7NZXG4mIVy8tGW1+/sFDDqVOlNcObNE26EKfQ/6iUVuRyKlw3xOysjmLR6vj9J1nwWxd2W1HvDtd1Z93nXPfZ7di9oCgSfD+C625fDPf9ZHhmMx1kth0gjtvvpJdlCVdfPYWrrprEgQNjGw7TbMXevVnMz9ewtNT88yWdVmBZPkxzuJ4Pl3KcYGgiUqpVF67buedaJ6XTChYXzZ5+z6UlA5omI5VSUKnYA7lQaVnBmmhYRZFQrQ73c6ZXgiDCAw/M49vfPgdRFHDNNVPI5zd/bz8IlpbObfjxlZVZOE7nX3/7IZ/XLjQ82Dh9ugzD8DA9ncHYWArj42mMj6eRy2nQtI13d7Vi794sCgULp06VOnT0zXGcAN/73jyWlgw880wRi4udaxSlrbVVDN+/fz++8pWv4M4778SBAwfwQz/0Q3jta1+77t/rXve6lu/7wQcfxI033ogbb7wRAHDHHXfgxhtvxHvf+14AwBe/+EXceOON+PEf/3EAwFvf+lbceOON+Iu/+IvGfXz4wx/GT/zET+C2227Dq171KmQyGfzjP/4jJGnwXsyHWa3m4rHHlpHPa403nHv3ZnHZZeMQhGSb8zXXTG65xTmf11CrOSgUOnfCzmQUmGb3L5ijKMbKignD2LoQXicIAiYm0vC8EAsLtW3f4BMREQ2jMIxQKJiQJLHtaLCkSJl0fW/VHV6pJEMH69ca4+MaTDPpjru4C7xadWEY3RnumE4r8P0IJ0+2/gaqvsMujuM1RUZNk+G6wcBm025GEATk8xpSKeXCNVJnr8VcN0QQxB3t8M9k1Ebh72K27SMI4p7G20mS0NT1a7PDM4HBi0l4dufH+gWIzYiiAEkSoetuU7tFBpVpeohjDMU2+FrN3dEiSjdlMkmUVq8e254XolhMMojTaRmOE8AwBq/j2jBcXPznSoaN+vD97RfYdruFhSoef3wZ+/fnWh7Y2A+ua6NYXNr08/Pzp3t4NN2TXAuFOH9ex6lTpR3PCtmKIAgYH9dw4kSxZ+eWKIrx8MMLOH26jCuuGIcgJHMyqDfaWpZ+5JFH8KM/+qOoVCoAgIcffnjD27Xz4vnqV796y4ucX/iFX8Av/MIvbHkfqVQKH/3oR/HRj3605e9PzXviiVUUizauuWZt3reiSE1nuElS8mZ3acnAkSPj239BEy6+YM7luje0sly2US47GB/XWvoe4+Mp6LqDxcXall3zREREw8gwPBiGv+M3LblcUqScmEht2NHtugF03UE2++zrqCAImJ7OrNsdVqnYcBwfqVTu0rvpiL17MzhzpoznP39PS4M/T50q4fz5Ci6/fO01kCSJCMNoaBfOUyn5wmBxE+m03LHin+ME6wYv7pSqShces17jOCVJ6EucRb14FYbRlr8z2/abXhCoVt2OxwPtlCgKLT+202kZhuHCcdJDee0chhGqVbevXeG+H+LYsWWMjSl4/vO3zsVdWTGRSg1mI1kup2Juropy2enJY6FWS/LC9+zJQFUlOE6yUDkx0bvM8maUSvaaQYyqKsIwkhxxzqzaWn2myE53vvTK8vLMlp+fmzuJa665vkdH012plIT5+SrK5fV1p06bnk7j7NkKZmZ0PO95e7r6vQDgxIkCjh9fwaFDOciydOHcpuMlLzk4FIumw66t3/Dtt9+OSqWCP/zDP8TMzAx830cURev+hSFXIUdVpWLj5MkS9u7N7LjYPDam4ty5SiMjsRPbzjIZBdWq07U3kZblo1CwkMkobZ2oxsdTjQ7xQevYISIialcYJrmOmibtuGhZ7w7X9Y27w2s1F74fbZgffen31nUHgtC9LsdcToPj+DhxYrXpzlXL8vDoo2t32K2/zfBeI+RyKgzD79iwuTiOLwwc63xRJ52Wsbpq4vz5CpaWDJimD9sOel64rO8I2CoqJYpiOE7QdIG7UnEGLupCVSVUKs13hgNJs00YxkMblWJZ/XlM1fl+iPvvn8MDD8zhmWe23sUSBBGKRbsrWcmdUF8sbPUx1K5azYXjJJExgiAgjuOezqdqRhTFqFTWLuCpqgzPC5lB3ITFRbNjsyiaYVk1fOMb/wf33/9P8P3mHktB4OMb3/gcPvrRO/D+979ty9vOzZ3c9HOzs8/gs5/9CL7xjf8D3x+8HQ6XGh9P4cyZMvbt23ndaTuiKCKdlvHMM8UN51p00vx8FQ8+uIB8XmsswoyNqdB1t2fntt2urWf8Qw89hJ/+6Z/Gf/kv/6XTx0NDYn6+hlrNxb59O1+dm5hIYW6uivvvn4XvJxc2L3vZ4R11iidvEJKunkxG6eiJMwwjrK6aiKJ4Rxe09Q7xhQV2iBMR0WgwzWR4Zae2smazCqpVF5OTa7tBgyCCrjffZbm0ZHX9dfbAgRxOnizh6qsncdll21/D6LqLWs3FZZflN/y8oohD/YaoPviwXHaQz2s77nLy/QieF3almFh/bIRh3PidN5vJ3UmiKCCKkl0Pm0X6+H6IIIiaejwHQQTD8NZ0iw4CVZUuxHDELV2j1wfLTk6mh65rLtl5IPQldiQMI3z/+/M4fnwF2awMzwsQRfGmC5b1obKTk92LJNgpRZGwsmLgBS/YusO9E+qF7/rfThQF6PpgFREdx78QGfbsc12WRfg+i+Hbse1kxkku15v34lEU4iMf+U84efIYAOBHfuQn8M53vm/br/vkJz+Ib37z75v6HpsN0axUVvHf//t/QK1WBgDcf/+Xcfvt/xPpdHd2zXVCLqfh+c+f3nLQcyft2ZPB4qKBpSUDhw9vfH22mSiK8cQTK7jmmsktdxkYhovvf38eQRCumReTSilwnBpKJXso4nqGXVtXEfl8Hvv37+/0sdCQCMMIp0+Xkc12psgsyxLiGHj88VWcO1dGoWDhoYcWd9wxnc0q0PXkYq6TymUbtZqHfF7b8X1d3CE+rJ0uREREQPImoFy2Ictixwo+iiIhiuJ13cWm6TXdZWnbPioVG5lMdwuC9Tc+x44tN1V8MM2t4zBUVR7qYjiQ7NRLBh/uvMPd80L4ftS17f7ptIJcTsXYmIpi0epbB6+iiFvuCPD9CEEQNdUZbts+PC+Epg1W4VhVkwGxrRbpUim5MYRymHheiFqtfxEpTzyxgsceW8ahQzmMj6fgOOGW77OSTujBHvSZzapYXjYbO4u7qVCw1uyu0DS5KwOCd8I0kziUi7ubk9dhgcXwbdRqHizL61lEyjPPPNIohAPAt7/9BSwtnd/ya3zfw333fXHTz09NHVjz/3NzpzbcpXbvvZ9vFMLrx/JHf/TLMIxKcwffJ70qhAPJtVcUxZiZqbb8tZWKgxMnClsOiE5ywhexsGDg0KGxdZ+XZRGrq70dELxbtXVldOutt+LrX/86omjwpihT9xUKFgoFs6PdAldcMY6rrprAkSMTuOKKcSws1HD8+MqO7lNRJMRxvOn26naYpodCwerYQgCQFMR9P8LCQtJtT0RENIxs24dheB0fUplOK9B1pzHgMI6T4riiNFd07+Ub3UOHxjAzo+PRR5fW5ZZfartF8FRKgmH46wY7DhNBECDLYkeub1w3QC+aahVFwp49ma4MW22GqkoXhndu/D6rlWF4th3A84KB6wxXFKmt+AZBECCKwsBFVGwnWZTYONKp22ZndTz00CKmptLIZNQLUTxbL1BVq4M7PLMul1NgGF7XFwyjKEahYK3puE6lZFQqbk8K8XVJNIuLlRUTS0sGikVrzfOnPjh6/WJhzGL4NizL6+pC66UeffTb6z72jW98bsuvmZl5GkGw+XP2pS+9ac3/G0YFlcrqmo/FcYzvfOdL67723Lkn8Yd/+Ivrbr+bTUykcO5cBZbV2mtNsWhhedlEoWBvepvTp0s4caKAw4fHIIrry7G5nIqFhVpHooNpa20Vwz/4wQ9C0zT87M/+LObm5jp9TDTglpZqcN2wozlyoig2OqNEUcT+/Rk88cQq5udbX5G7WDarolJxOtIdHoYRCgULcYyOX8zWu8zrwyE6VbwnIiLqlWrVhSAIHY8v0DQZnhc1CqquG8I0vaa7FqtVt2dvdCVJxMGDWRw7toRjxxa3fDNTLFpbDmhU1SQ/eti6YC+VSskwDG/HRX3T9HtWrOhnEbDeNb3Z78txgqafY/Ui7KANz9M0CZ4XtHV9nkrJjYGGwyBpzEkW73rNND08+OA8gLgx7FFRJPh+tGWRZ3XV7Pnw2FapajKkt1zevOjUCZblwbbXxo+k0zIcx+/JokwYRjhzpoR/+Zcz+MIXnsY//uMJ/OM/Po0vfvFpfP7zJ3DPPafx1FOrOHOmjA3qapAkcegWj3qtEzuXWvHYY+uL4d/5zj/C8zZf2Dl9+viW93n99a+Cqq5tVLx0yObs7DNYWZnd8Ovn50/jv/23d2J1dX7L77NbjI9r0HUbi4ubd3hvZHHRgG37mJ/XN2yI0HUHDz+8iHRa2TTqLJNRUKt1bt4Kba6tV+UXvehFmJ+fx2c/+1lcccUVmJ6extVXX73u3zXXXNPp46U+C8MIZ89WkMt1t7tqbCyFIAjx6KNLO1p1T7rDk+naOy0wl0o2ajW3I/EoG8lmVSiKhIWFGlZWzK4PbSAiIuqUegzAxQWDTkqnk6zgIIgudM3GTRf4ymWnp8XNbFbD1FQaDzwwj69+9QyefHIVJ08WsbLy7LbXKIovDBrd/PeVSiUdnMNeDFfVpPi2k8aEIIjgukFfCoq9Vn+sbnT9G8cxbLv534Pj9KabvlWiKCKKkpzjVmmaDN+PMD9fHYpiQRLr4vV8NlAcx3jssRUsLpo4eHD9VvzNCoBBEGF1tfszFjpBlkUsL3c3TsAwfDiOv+ZcXR902+0dvdWqg29+8yy++tUzmJvTkcnIOHhwDEeO5LFnTwaSBMzN6fjmN8/hqacKG+Ybq6qElRUTjz++jNOntx6cultVqy4kqTcnypWVOSwsnF33cdOs4nvf+5dNv+7Mma2L4QcPXoX9+4+s+dil0SvPPPPwlvexujqHD3zgnVhcXH98u40oipBlEbOzetNf4/shFhdr2LMnjWp1/a6VOI5x/PgyikUb+/ZlN72f+rVftxf6qM1ieBRFUBQFl19+OS6//HLk83nEcbzuH2NURs/qqoVi0e7JQJXDh8dw/ryOZ54p7Oh+kqm8Dmq19lfFDaMej6J29Q11Pa9yddXC4qLR0+13RERE7bKsejZxd4rhqZQM2w5gGB503VmT37qdlRWj59m34+MpHDmSx8JCFffeew733HMG3/3ubGNh3rZ9OM7WwyAFQUAcY+iL4UCSg10fmNgOzwvheWFLf/dhJssbd3MmeeFhU3nhwPZRPP0Vt71AMjmZBiBgfr4KXR/sXH3L8hGGcdN/s05ZWKjhxIlVHDiQXbcVXxSFTd8XVasuLGvzAa6DJJtVsbhYayk6qFWG4SEMozWZxfX3gt3suC6VbHz96+fw9NOlCwXwceRyGmRZgiiK0DQZExNpHD48jmuumcK1105tGIeUSsmYn6/hq189g+PHl7n7eAPbLUx30kZd4XVbRaWcOfP4lvc7ObkP+/dfseZj64vhx9b8//Of/1Ls33/5mo+Vyyv4b//tnTh37sktv99uMDGRwvx88zG2lYoD0/SwZ0+2MavmYgsLNTz9dBGHDuW2rCcJQjJouVRiMbzb2nrWnzt3rsOHQcNiebl24c1I918wZFnCxEQKx4+v4LLL8o3tfa2SJBGKImF11UQ6Lbe8VdTzQqyumhBFoScvlIqS/NyVigPfD7F/f27TC9I4jgc6z4+IiEZfHMeoVOyuXhsIggBVlVAu27BtH7lcc7u0bDvZatrt4ZkbUVUZl102DgAwDBfVqgfD8DA2psEwPDiOj6mp7ZsLDGPwu1+3k0rJO1ow8bxw4DOMO0lVJThOsG5QZhBE8P0I2WxzhdVazR3YBQRBEGBZ7e8WyGQU2DawvGxAUaSBLN4GQYRKxen5YlwQRDh+fAVBEG14rtQ0eV2hpk7XnQvDM3PdPswdGxtLhmiWy86WnZY7sdmCkiyLKJe7txDz/e8vYGmphquvntgwV/hSm50bJyfTyOc12LaPWs27sMu5+01tw8L3Q9RqXs+K4ceObV4MP3PmcZw79xSuvPIH1ny8Wi1hdXXzaOLrr38lBEHAgQNri+HLy88Ww+M4xsmTazvDX/GKN+CGG/41/sf/+I+YmzvZ+LhhVPCBD7wLv/zL/w0vfvFrmvq5RtHYmIZCoYzlZRNjY9tfc5bLDlw3ucYRBAHFoo2rr04+FwQRHntsBXEcNzW/JpNRsLBQY62ny0Z/ryF1TBKRonc9IuViU1NplMsOjh9f2dFKdjarwLaDC5nfzd9PMjTFhGX5Pf25RVHA5GQKrhuu2wbqOAEKBRPnzpVx5kwZc3M6dN1hrAoREfWF4yTZv92KSKnLZBRYlo9USmm6yzIZnun3ZHjmVpJj9xqv54bhXSh0bl2o1DQJpdJgd742Q1EkBEHU9o43y/J73lnbT6oqNbrhL+b7rS0KVCqDWwxXVQm6vrPOt3RaQRQluz8GbdhYfZHQtoOeF8PPn6/g/PkyDh7cuKCtac/GTl2q3mk/DAUYVZXheWFXOyhLJXvD51A6rbT8vrJZjhOgVLIwPZ1uqhC+HUkSkc2qME2vqwX8YWSayTyLXmTk27aBp59+cMvb/PM/f3zdxy7tClfVFN785l9EOp3D4cPX4t/9u/8HANZ1eS8tPZsZvro6j0pl7W775zznRoyPT+M3fuOvcPXV16/5nOc5+NjH3oMvf/muXbuboD4Dp9kZdqurJmQ5OW9mMgrm52uN3PBz5yqYmSnjwIHmFhmzWaXRQEHds3uuKmnHikUbpZKF8fHuZGZvRBAEHDqUwzPPFHc0TFMQBOTz2oWfobkLpjiOUSxaKJUc5PNazy8K68csCMk20FLJRqFgYmamgqUlE0GQnFxN08fsbBWzs/qAb4clIqJRZFk+oiju+ODMSyULxemWCkvJ8Myw7wMERVFEHMeNYnizr9dJ0coZmTejjtN6J3AUxbBtf1fkhdfVI3IuHaLpuiFEsbnrUc8LYVnBQBfDO5G5PDamwjAGK1+1/h5iedlENqv09D2E54V44olVqKq86W6dVCpZbNloCOnystXz4v1OqKqIxcX23yNupb6gsVHXcDotwzS9rsRY1WouLMvvaG57/ZwySM+TQWCaPhynNwtWx48fRRA8+3iRJBmvf/3Pr7nNAw98BXNzp9Z87NK88KuuegF+8id/BR/72Dfx+7//WRw58lwAWNcZvro6hzBMXkMuzQsfG5ts3D6XG8ev/dqf4wd+4GVrbhPHMT73uT/B//yf/xmGUWnxpx0NExMa5udrWw4cBpKm0cXFGrLZpPFibExFteqiWnUunJNXtjwnX+rSBgrqjqauKn/1V38VCwsLbX+T//N//g/+7u/+ru2vp/5bWTHxyCNLcN0QqVTzL8xRFOJLX7oTv//7P48//uNfwWc/+yc4ceLBlvLk691cjz22sqNMOFkWkckoWFkxUSrZqFQcrK6aWFioYW5Ox+ysjpmZZ/+dP1/B8rKJXE7t+hv8rWQyCjRNxsJCDUtLJmRZwtRUGplMMoV4bEzD5GQKjhNidlZHubzzYaFERETNiKKkwNurLcatqtUGpwtOksTGgny5bDdVoNc0CbYdbFi0GjaalhSPWr1G8bwQvr978sLrFEVcV2izLK/pRQHb9uF5Oy+Gu66NQmEBrtvZIpqqinCcaF3Bv1WCICCbVVAq2QPzPDEMDysryayhXp8bz5+vYGGhiv37N48NqcfwXDpEM+lItpHNDl7kzGZyOQ1LS1ZbC23bqe96SqXWP4fq0U87mUm1mVrNvXDO6+xjJ5WSmEN8CdsOEMdxRzrwt/PII99c8//Pf/5L8aY3vROp1LPP1TiO8clPfnDN6+Tp02uL4fUublGU1iy0XVoMD8MAxeIiAODkyWNrPvec59y45mvT6SzuuONj+Ff/6pZ1x33s2Lfw3vf+NJ544v4mfsrRMjamoVp11gxA34iuuzAMv1EMT6eTYnal4lw4J9e2PCdfqt5AcekQTuqspp71f//3f49rrrkG73rXu/DNb36zqTteXl7GRz7yEVx//fX46Z/+aRiGsZPjpD7x/RCPPbaEf/7nUzh3rowjR9ZPqd5MFEX4q796L/7+7z+GM2cex/HjR/FP/3QXPvjBX8Jv/uatuPfezzddFD9wIIeZmQrOnCm3+6MASC5cVFXC4mINc3NVrK5ajUExth3AdZ/9FwQxxsbUgXjzlUrJmJpKY2pq4464ehe5okhYXDR4oUNERD2RFAv8nmwxbsfKij0wXY7JgryBMIxQLDZ3XJomw3XXF62GkaKIcN0Qvt9anIXnhQjD7u88GDSKIsG2/UYMXhBE6zLEt2LbwY6H2h49+iX8yq/8a/z6r/84/sN/eCXuuOP1+Iu/+C1897tfhu/vrGNNUSR4XtCRIqamyQjDaCAKB/UChigKPX8P4fshnnxyFamUvGUEkyxLCMNoXbdj8p7IbRR0hkGyM8BFsWh1/L4ty29kAF+q/jvsRoxBt7pBNU1GqWQ3ohuodwvmQeDjscfuW/OxF7/41cjlJtZ1h5848SC+9rVPA0jqKWfPPrHm85dGmtTlchPIZsfXfKw+RPOZZx5Z8/HnPvfGdV8vywre+c7346d+6t3rPlcur+B//I//iD/901/DwsKZDb//KBJFEYIgYHFx62J4EonlN67r6gsNy8smnnhiFZmMsm0s3qVUNZl5R93T1NXRqVOn8MEPfhAf/vCHcdddd2F6ehove9nL8OIXvxj79+/H5OQkbNtGqVTCyZMn8b3vfQ9PPPEEwjDEK17xCtx33314xSte0e2fhTqsVLLw1FNFnDlTwtRUGvv3b5xxdPTol/Dtb38RV1zxfPzUT70bspx0E9x33xdx//1f3vBrVlfn8P/+v7+Hb33rH/CLv/h7OHjwqi2PRVEkZLMqHntsGUeO5HeU/ZlOKx3ddjZI0ulkK+bysglJEjExwQEpRETUPbbtI4owkIVK3w9RLtsD85qfycgwDB8rKyZMM0A2u/1leFIwDLuyFb/XFEVCreZdGMTe/JtC1w2GIr+401RVQrWaDB1Np0X4frKQ0OwOTdv2EQRR28/Nc+eewv/+37/b2GYPJAWRBx74Ch544Cv41Kf+O2699f+D173urW39fTRNhudZsO0A4+PJwprrBhgfb+/aNZNRUau5mJpK93Wnim0HMAyvLwXlmRkdS0tG081Llw4w1XUHvh/1PVaqFbKczCMolWwcPtx801Yz6nnSm/0+BAFdWYApFu2uLDAn3ezJAvYwLXh0U6Xi9mQx/5lnHoZl1dZ87EUv+jcAgDe84e247767G13cAPDpT/8xJib24tChq2Hba5tKr7lm42I4ABw4cPmaTvKlpRlceWUJS0vn1tzuOc9ZXwwHkiLuj//4O3HZZc/BnXe+D7Xa2kbEBx/8Gh588Gu4/vpX4WUvuxk/8AMvxdTUgZF+jc7nNczN6fD9g5ueC0olB4KAS7rtFZw+XYauO7jiivENv24r6XSSaNDKIji1pqkrhWw2i9/7vd/De97zHvzt3/4t7rrrLvzTP/0TvvzlpNBZ/6PXt3NMTU3h53/+5/HLv/zL+OEf/uEuHTp109mzFdx773l4XojLLx/f9Il//vwJ/NVf/Q4A4MSJ70MURdx22+2wrBr+/u8/tu33OXPmcfz+778d/+E/fAA/+IP/asvb7t2bwZkzFZw4UcCLX3yo9R9ql0ilZMRxjKWlGmRZ7OngTyIi2j3qGdiD0nl9qVrNg20HmJ4ejIXh5I2Nhfn5GhzHb+q46tfYo9AZDiTFo1aGaMZxDNNsPhpklIiigCjChWK4giCIEEVx05nhjrOzRYTPfvYjawrhlzKMCj75yT/CZZc9B89//g+1fP+SJCIIIjhO8tg+fbqEubkabr756raOW1UlGEaSsbp3b//OSbWaiziOe168CMMITz9dhKKITXUgSpKwLrO9WLSbfnwNkkxGweysjuuu29fRolx9sWCz+0yllI53bsZxDF13urKgk0olneH9WqwZNHEco1y2Ox5Hs5FLI1KuvPIHMDW1HwCgaWm84x3/X/zxH/9q4/NhGOBP//TX193P5OQ+TE7u2/T77N9/xZpi+PLy+XURKZqWxhVXPG/L473hhh/B7/3eZ/BXf/U7ePLJB9Z9/vjx7+D48e8AADKZMRw8eBVuueWXtq3nDKN8XsPiYg2FgoWDB8c2vM3SkrGu8WLfviwqFQf792db7goHkvNaueygWk0WeanzWnqVHh8fx7vf/W489NBDKBaL+L//9//iL//yL/EHf/AH+OM//mN84hOfwKOPPopCoYD//b//NwvhQ+yZZ0owTRdXXjmxZXfAww9/Y83//9M//Q1c18bdd/8lqtXims8dOHAlDh26et192LaBj3zk/8F3v7txF3mdKIqYnk7jqacKjAHZRjqtQBRFLC8bO85iJCIi2ojjJBEHgxiRYhguzp+vwHX9gckzr2dAlko2wjBq+s2RKK4vWg0rRZFaGvbt+xE8r/8DUPtFkoRGDnYriwhAsoDSbl2wWi3hxInvN3XbY8fube+bIFkccZzk51peNrC0VNtRp202q6BYtDAzk8wCqlbdpiMhgiDC8rKBmRkdKysmDKO1fHvfD1EoJHOJ+rEbZXGxhoWFKvbtay6XVlWT4bx1URRjcbE6lEXSfF5FsWh3PF6kVvO2LK6n0zIqFbejeeXdfF1NuujjrkS7DCPb9jfNhO+kOI7XFcNvvPHVa/7/+utfhTe+8d9ve19bdYUDwP79l6/5/6Wl8zh5cm1EyjXX/CAkafvroomJvXjPe/4M73jHf0UuN7Hp7SyrhtOnH8NHP/oemGZ3htn2k6JI8P0IhcLGUUym6UHXHeRya8/7kiRiejqDXE5r6/umUjIcx0e12v/4r1HV9ruDiYkJvOENb+jksdCAUVV529X1jQYpfOELf4GvfvXTaz52ww3/Grff/ieI4xiPP/5dfOpT/33Ndp04jvDXf/1eaFoaL37xazb9fhMTKZw5U8ZTT63iVa+6fNPbEZDLqahUbKysmDh0aGwgt7ATEdHwcpxgYLOcH3lkCQsLtR13x3ZaNquiXLbhus0XNus5r6NAUUQ4TtD0tt9keGaEbHbwHmO9oKoSTNNHFMWwLL+lbmNdd9rOrD5+/DvrCsH/9b/+LWZnn8Hf/M3/b83Hz59/qq3vASTFcNtOonPqc3wKBQuTk+11wWmajCiK4boBoiiGrrsYH9ewZ09mywJ1EERYXKxB15PIhFrNhSAIGB/XsHdvdtvfY7XqYnXVhG0HjcH3vRTHMU6dKiGO46a7XDVNhq67CMMkSqdWc1Gtehgfb69w00+ZjIrlZROrq1bbMTsb0XV7y799MgciWTjp1A6pek55Pt+dv0Mcx0Mdu5WcC722C4wXM00fjhN0PVZ0dvYZFItLaz52aTEcAN7yll+Faer41rf+YdP7eulLb97ye106RHNh4fS6mJXNIlI2IooiXv3qt+CHfugm/PM/fwJf//pn1sW91AWBh7Nnn8B1141ePHImo2BmZuPdJ5WKA9P0MDXVehTKVgRBQBx3b4YAtdgZTnSpjSbLf+Urf4soevZNniwreNvbfg1A8qS+/vpX4nd/9+/wwz/8+jVfF0Uh/vzPf2PdxOSLCYKA/fuzeOaZEpaWOJR1O/l8CrrujsybaCIiGgxxHKNW8wZiyPSl4jhGsWhjZkZvukuyV/bty2JhodbSltdUSka16sL3W+sMHkSqmnRYNbtrzfdDxPHmMQWjLulIC7G0VGs5kqhaddt+fl7a7f3iF78G11xzPV796rfgP/2nD6/53LlzTyGKWhuKWifLSY68rifFBEkSsbCwcaGlWem0gmxWxdiYhomJFAzDw9xcFbrubNjpHYZJR7iuu5iYSCGbVTE5mUYup6JcdjA7q29aPEzONRbm5nREUbzpoPtuK5VsnD+vY3o60/TXpFJr5xFUKg5s2x+YGQutEAQBkiR2/L1hrbb1c0hVZXhe2NHOTcvy4Xmb55TvlKpKKJeH933hwkIV3/nOXEeGgJqm15OdRw899PU1/79nzyFcdtlz1t1OFEW84x3/FW9/+28jk1kfx/GqV70ZL33pj275vS677No1/1+pFNYN4NxoeOZ2crlxvOUtv4IPfegr+MVf/D28+MWv2fAYS6WlDb56+OXzKkqljXefJIuK3WkMSaVkrKxwiGa3sBhObYuisDGheCtveMM7sG/fkTUfU9UUfvmXP4Cbb/7ZNR8PAh9/+qe/jmq1tOn95XIaPC/AE08scxr2NkRRQC6nolCwuCWOiIg6xvNC2LY/kBEp1aqLatXFc54zhT17BqsYLggCXvjCfZsOJd+IpklwnGCou/nqBEFodOzOziZxFGG4eSF1t+aF18myCN+PYBge0mml6aKN6wZwnBCq2vrvzvfdRhZs3Q03/Ejjv6+88gVrPuc4JlZWZlv+PkBSmNN1F7ruwvNCTE+nsbBgNKJhdkoUBYyPpyAIAubmqigWrTUF8frCWbnsYHxcW5OXLcsipqbS8LwQi4u1DY+pVLIbWbH9jBc5d66C/z975x3mRnW18XfUe9vem3sD414wBgwBY3oJkBAgwEcLJaFDaAkxNXQIJCH0XmyqKQYb4wruXpe119uLpFVvI81IM98fY2k9q7KSVtpi9HsePzB32t1daebec895X5+PTilbViIRIhDofa7YbJwEwEhdeFKrpejocGVMsoT7DjFJfYcOl5sZKD4fDZYlsvZ3kMlEUd+DkYTHQ8PpJJN+RjAMi/p6E3y+6HnwQKSkkoVlWfz88ze8tqlTF8b9+xIEgeOPPx///OfXuPrqpfjNb36PE064AFdfvRRXXPFgv5+LkpIaaDSGuPuFQlG/UiuJkMkUmDfvdNxww5N4/vkfUVHBD+r3zYA/UlAoJPB4qJhSKRaLN2vjFLlcDKuVPCKSIYYjv97RZY4Bs23bjwgGEwdY8/KKcdppf4y5jyAIXHjhLTj22LN47Xa7CS+/fHfCLJPiYhWamx3o6HCm3O9fGxKJEAIBgZ4eb+5BmiNHjhw5MoLfHwRNM8NSy9luJ0dshmMsZDIRAoHgoJpohkJM1nTKw8EYr5eG2exBV5c7ZqZ4KMTA789ehuRIQSTijCZTyTgmSS67NB1juN27N/EqPwmCwNFHHxfZ1ukKoNHk8c5padmT8n0Abozq89Ho6fGCIAhoNFK43f6MVzQqFGIoFGIYjZyURjiZxu3mghtqtSRuVp9WKwNFhWA0eni67U6nHyaTZ0hkUQ6HJGk0NtpSlnrg9KOZSDC8qyvaAG4kodVK4XL5YbFkJovS7w8mlaEtk4lgMsXWEk4HTqc8Y5eLQiYTwucLZmzBabBxuym43VTS70ObjcSuXT1ob4/Wsna5AhAKsxsNb23dF5U82Lc6PhYymQKzZ5+KCy+8BZdcchdmzz41qQUSgiASGhpXVY2DVJoZM0aCIDB69DG8tiM1M5wgCAgERFSWdijEwGz2Zm0xVKEQwecLwu3OJTVmg1wwPEdabNy4As8/f0u/x1144S0JH7gEQeDSS++Jemjv2bMJ3333dtzz5HIxCAKor+8Z1AAvSdIwGj3o7naPKDMrlUoCr5eGzUaO2EyAHDly5MgxfPD56KxPItPF7abAMOyIzXDsi0AgiGhGDxZtbU789FNbVsZYCoUYeXkKaDRS6HRyuFwBdHS4on4+igqBokLDUopnMNFqZdDpUgte+P3p/+62buWX9I8adTQv05AgCFRXj+cd09KSnm64RCKIBJoVCjGEQgFCITZpqRSfj4LHk9x4XCoVQa2WwGz2HtL35hZjxGJBvwFPjUYKn4+OVDJw53ohkYiG3KC3s9MNm42EXp+e7jFJBuH1UnA4og3gRhJCIWdQbDRmJhhOkkFQVKjfv69CIYbd7k9a+qk/HI7EOuUDRSYTw++n4fGMzGC4w8F5bsTK9I6FyeSByeRGS4szag5stZKQybL7md+06WvedkFBGWpr08/MToYJE2bF3Td69NEZvVdeXjFvO5vBcIryY9++zfj++/fx7bfvYNOmr+Pql2cDjUaKzk4XgsHehE2XKwCPh4ZCkZ3PkVSaM9HMJkP79s4xIvnll+/w73//td/jJkyYhWnTTuz3OKFQhGuueQQPPHARHI6eSPuyZf/CsceeFVOPCgCKi9Vob3eitdWJUaPilwNlApKkUV/fg8ZGW2TQLZeLUVGhwbhx+SmVOw8FBEFArebkUoRCAkqlBDJZ/wapOXLkyJEjR1+CQQZeLzXkQaB42O3ZDSYMFU1Ndkilwn7NADOBxUKiu9sNi8WHkpLY47BMIBAQ0Ou5gHhnpwvFxSqo1ZzUA0WFwDDgSVfkSA6fj4oYI6ZCKBTEtm0/8tqmTYs2tq+uHo+dO9dGttM10VQoJGhudoJlEQnE6vVyNDbaMXFiARSK2Nl2DMPiwAErtm41QioV4NRTRyf1nRCLhVCrJejp8cHvDyIQCCVl1smZacrgcPghlXLZ7DTNZN14rz8YhjPOlEqFEAhSz3EjCAIeDwW7nYTHQyEvL7MGcIONSiVFa6sDRx1VNOCKEr+fTuo7FDbRdLkCKCgY2DuRYVg4HP6s6s6LxUJQFAOPJzDsPDX6g5PY8h+SDet/8YFlWbS2OiAWCw/5AvgjC4sUFYLbTWVV6o1hGGzaxJdImTnzN1mff8+efSo+++w/MQPTxxxzQkbv1TcYng2ZFKOxFZ9//h/8/PN3UaoEFRWjcc89r2cs2z0R4cVUm42MfHe4zyMNmSw7sSCCIEAQyGWGZ4lcZniOlNi9eyNefvlusCxfwqS6ejxOOOGCyLZMpsTvfnd70g97rTYPV1+9lHd8IEBi27bVcc8Ri4WQyUTYtcuYMX24WNjtJL7/vhm//NIJgQAoL9egokIDuVyIfft68PXXjdixo5u3SjgcEYuFkMu5EtHWVgc6OlxwuQIJtTpz5MiRI0eOvgQCQVAUMyz1whmGhcXiGxITu2yi1crQ1GTDV18dwOefN6C+3pS1yjiGYdHZ6YLTGUBPz+AYN2k0XAC8s9MFh4MzOhys6gOKCg3YtHG4YTL50gq4NDbugMfj4LXFCp5UVfEzw1tb96VlokkQBCorNXA4yEhmnU4nhdNJoqsrthmi3x/Ezz93Ys2aFtB0EGazNyWDsXBA3OOhIp+7ZBAICCiVYphM3pTPzRYWiw/d3e6UjDMPRyoVwuHww2rlpGPSCagPJ3Q6GWw2MiOGcySZ3NxSIhEhEAjB6Rx4xTBJ0vD7Q4Pybh2JHhTh3w/AJuWFZbf70dPjQ2mpCl4vxZNf8ngoBALBrC7qNzRsgd1u4rXNnn1q1u4XRiqV45ZbXoBOl89rP+64c9Myz0yEwdA3M9yUsSp0hgnho4+ewz33nIv167+MKc/b3n4A33//fkbu1x/h7/quXcaIzBBnzJxdrwWJRDhoY7FfG0fWTCFHVrHbzXjppbsQCvEHBwsXnouLL74dQqEINTUT0d3djFmzTkFpaW1K1x83bjqmTDkWO3b0Othv2vQN5s07Pe45RUVKNDc7cOCABZMnF8c9Ll08ngB++qkNXV0u1NToeNkBarUMarUMdjuJDRs64HZTmDGjbNhmygGcrp1MJjqU1UfD5QpAJhPBYJBDqZQckZl0OXLkyJEjs5AkDZYdnjIkPh8FrzcIpXL4vovTIS9Pgbw8BRiGgdVKYu3aNpjNXsyZU5HxLHGXyx8ZH7S2OjF5ctGg/K2VSglIkkZXlwteryxiGpltjEY3du40w2CQHxGLKIFAEG1tjrSCtVu3ruJtV1SMQUFBWdRxfU00SdIDs7kdxcVVKd9To5FBo+nNsBYIBCAIAlarL6rys6fHi82bu9HcbEdJiRJKpRTNzXZ0dXlQVaVL+p5isTCpjPC+SKUiiMXCYVOt0NbmhN8fTPt7IpWK4HT6QdOhrJX5DyZiMaeD3t3tQVmZZkDXIkk66eeeQEDAbh+4jIHXSyMQoNOWvEkWiUQAm23kyS5w0jVBqNXSpHwFenq88PloFBerIBD4YLGQqD0UnvD5uGB4Np/5q1Z9xNsuKxuF8vJRWbvf4ZSW1uLBB9/H8uUv4cCB7Zg8eR7OOee6jN+nbzCcpgNwu+0JTTyTwWrtxquv/g27d2/s99gPP3wGX331GvLySqBUaqDR6DFnzmk84+dE7Nu3GRs2fIXy8lFYsODshFnmFRUaNDTYEAgwmD+/AiZT9pMvwiaa6VR75UjMgP5y27Ztw7vvvot9+/bB5/Nh5cqVAIDW1lZs2rQJixYtgsGQXfmKHIMDw4Tw8st3R2WLHH/8+bjkkrsig4X5888Y0H1mzTqFFwzfs2cTaJqCWBy7TFIgEECnk6G+3oLKSh202swNHkIhBps3d6Gjw4XaWl3cbAm9nps87dxpRijEYs6cimEfVBaJBNBopGBZFiQZRGenGzKZEDodNyEZ7v3PkSNHjhxDA8tyGVnD9T3hdlPw+2nk52e/ZHYoEAgEKChQQqORYt8+K0IhFsceW5XRyZjHQ4EkaZSUKGGzkXA4/GkFDtNBLhfzAktq9WBkSAZht5ORBYCRTns7l11fUZFaMJBlWWzZwtcLP+aYaIkUANDrC6HRGOBy2Q677/60guGxUCrF6OpyHcpWJkBRIezfb8GOHSZ4PAFUV2shEnGfDY2Gk8aYOrV4UP5+wyUQ7vcH0dSUunHm4UilQpBkEH5/CGp1dgzgBhutllsgmTKlaEDvKaczAIkkucATZ87qjiwSMwwLtzsQyT5mGIAguPmXVCqCTCZEIBBCT48XJSXqSGa/xxMYFGNqmUwc8ZEajova8fD7OXkijYYzS+0vONjW5oREIgRBEFCpJOjsdIJhSiEQEPB6s7uo73D0RPkvLFhwdlbuFQ+NxoA//OHurN5Dp8uHQCAEw/RWqtlsxrSD4X6/D8uWvYgffvgAwWB09QJBCKIUCgDA63XC63VGtjdt+gYnnHABLrroVohE8Rf6vv/+fbz11iOR7a++eg0XXvgXzJp1SszjJRIRqqu1aG11gKJCIMlg1hcS5XIRnM4A3O5Ayv4hORKT9ojh9ttvxz//+c9IGcThDxKWZXHxxRfjn//8J2666aaB9zLHkPPNN2+hoWELr+3oo4/D739/Z0ZfIkcdNZ+3HQoF0d3djMrKsXHPMRjkOHjQjj17LJgzpzxqP8uy2LvXgqIiZUplhAcP2tHQYEF5ubrfskG5XIzycjV27+6BWCzAnDkVI2JwQRAEFAoxFArxIXNQL+x2P3Q6GbTaXFA8R44cOXLwCQRC8PuDcbV8hxqvNzmd15GOVCpCVZUWBw7YoFCIMWdORcaCdB4PDZbl9JxNJi8sFt+gBcOB3uzbwcLjoeBwBOB0+oethq7V6oNcLur3e+f1UtixwwiZTBQJFidLW1sDrNZuXlu8YDhBECgvH409ezZF2jo6GjFjxkkp3TMeKpUETmcAZrMXPh+F3bt70NHhgk4nQ00NP8ii08nQ1uaA0ehGdbU+I/cfCXR1uWCzkaiu1qV9DZlMhI4OLiGmsDA9qZXhhk4nQ3u7C0ajG5WVurSvwxlZJhcqUSrFcDgC6Opyw+kM4OBBG+x2P0iSQq9iBAGWZSESCSCRcBnsPh+NwkIlCguVEAgAk8mb8vc2HTjde2pAVQVDgd8fAsuykMtFcLsp+Hx0xGOiLx4P9/zQarn9KpUENpsfdjuJvDwFXC4qq3P1NWuW8arpJRIZ5s+PX+0+UhEKRdDpCnj65FarMap6KBmamurx8st3w2xuj3mfk0/+PU455RKoVFrcddfZMY87nB9++ABdXU247rrHoFb3vhuCQRqbN6/Exo1f85IwAW4R46WX7kJ9/Qb8/vd3xswSF4mEqK7WYe9eC5RK0YCrUPpDLu+V50o2GM6ynJ8ETTMwGHIB9HikNVN49dVX8cQTT2DJkiXYuXMn7rrrLt7+6upqzJw5E5999llGOpljaLFau7F8+Uu8try8YlxxxYMZ15ZTKNRR5ZhtbQ0JzyEIAsXFSuzbZ0F3d7TmY1ubExs3tqO+3px0P3w+bjKhUEiSdpmWycQoLlaiocEKk2nk6TrJ5WIYDHIIhQKYTJyuuMXizZomaY4cOXLkGHn4/UEEg9yEfjjidPoxAtaiM4JYLERpqQq7d5vR1GTP2HXdbgoEwY2vRCLBkOhpCwTEoGXgOhwk/H46qbL7oYBlWWza1In9+639Hrt7dw9MJi+Ki1M38/r5Z77RW0FBGSoqxsQ9vm+5f0fHgZTvGQ+5XAyfj8YPPzThu+8OwmLxoqpKGzOphVv4ItDe7srY/UcCLS0OCIWCAc3FRCIhxo3LR3W1fkQk8SSDSCQEQWBAnwe/Pwi/n0k6M1yplMDjCeCHH5rx448tsNlIaDQSVFfrUVtrOPRPj7o6A8rLNdDpZCgqUmLMmDwEgwxaWhw4eNAOigqlXNGRDjKZCCQZTEp3ezgR9giTSIQIBIIJdc/NZi/c7kAkWC6XixEI0LBauee8zUZmTZudpimsXv0xr2327FOhUGTPjHoo6WuiGcu4sz8sli7885/XxQxwa7X5uOOO/+CCC26CRmOAQCDEtdc+gurq8TGuxGffvs34xz8uQ09PJwDA6bTikUeuxMsv3x0VCD+ctWs/wz/+cRns9tjxI6FQgDFjDFCppEkvmqULQXALaS5Xct9XhmGxa5cJ69e3o7XVkdW+jXTS+su9+OKLGD9+PD7++GOIRCJIJNFZCuPGjYvIpuQY2bzzzuOgqF5dMYIgcPXVD0Olyo7jeEXFmMgDC+DKLvtDpZLCavVhxw4T8vMVkYwikqSxbVs3SDKIlhYHJk8mk1odO3DAip4eH2prdSn1XaWSoqfHh4YGLhN9JA4sw7rifn8QRqM3Uh6t1cqGbfAjR44cOXJkH4oKwW4nh3XVkMVCDmvvjkyjUEggl1PYurULRUXKuFlyqWC19mpgqtVSdHV50NhoQ3GxEirV0JsGZhKGYeFw+CEWC2A0eoalbIDLFYDF4kMoxOCoo4rj9s9s9mLv3h4UFChS/hkYhsGGDSt4bdOnL0p4nb6B8o6OxpTumQiCIFBbq4fL5UdFhbbfbFmDQYb2dicv+HUkY7eT6OhwIy8vl/EXC71ehtZWB6ZMKUrr80CSNAKBIBSK5CRoCIJATY0ObncAhYWJFxaEQgHk8t75VEHB4FejSCQi0HQIHg81JPdPF5IMHlqkFR7yv4ofHDQaPSAIgve3EIkEMBrdqK3Vw+Egk054S5U1a5ZFBVGPP/78rNxrONBXN9xqTT0Y/t1378Dn4y+8i0RiLFx4Hk4//coo2ZXq6gm4//53QNMBWK1GWCxdsNmM2L9/GzZs+Ion22IytWHp0stwwQV/xscfP5d0/9rb9+Ohhy7Fn//8XEytd4lEhIICEVwuGxobd6C2dhJ0uoKUf/ZkEAoJOBz9L9gzDItt27qxZUsXnE4/lMqRU/kxFKQV2dqzZw9OOukkiETxJxtFRUUwm5PPxM0xPPnhhw+izHSOO+4cjB59dNbu2Xdw3dbWfzAcAEpK1GhutuPgwd7sqPp6M7q6PKir08PrpZNaHfN4Atizxwq9XpZWtkVhoQLNzQ709PhSPnc4ETbWJAgC3d1utLY64HBw+mw5cuTIkePXRTDIwGj0wOejh+3gmqa5YP1IKvvOBIWFSlgsJHbtMg34WjQdgtPpPywYLoHb7cfWrV288dWRgt9PIxAIQaeTRcruhxtWqw9udwB2ux9OZ2zTO5ZlsWePGX5/MC3/nP37t8Ju539+5sw5LeE5fYMDPT0d8PtTG/vW12/AI49cgaefvgnd3c28fQRBQKuVJyUbodHI4HQGhqSKYSjo6nLD46GgVA5PuaqhJvx56OhILzs8bNSYSuawQCCAVisfdotpiUgUTB6OuFx+3mK8zxeMeRxFhdDe7ooyEVarpejs9MBq9cHvD0Imy/zCfiBA4vPP/8trGzv2mKSymEcqfYPhqWaGh0JBbNzIX4zV6wvx0EMf4Xe/uz2h/rhYLEVxcRUmTZqDBQvOxpVX/g333PN6VFDa4bDg3/++J24gfO7cJbj66qWQyfiLQzabEUuXXo69e3+Jed6WLT/gtttOw3PP/QX33ntB1HssU8jlYpjNvohEdSyCQc7vbvPmLhgM8iPGByKbpBUMF4lEoKjED8+uri6oVKmX6OUYPjgcRrz77hO8NrVaj/POuzGr9+2rD97evj/hFz+MRCKCRiPFtm1G2O0kTCYP9uzhMmQ4o00pDhywRUqs4tHcbIfdnlwGeSyUSilIkj5iJo1yuRh6vRwsC3R0uCKZN8n8TXLkyJEjx8iHZVlYrT64XAHodLJhO9nnzDODR4QJYiqE5eIaGmwx5eJSwevlfofhIBCXgceivd11RMrPcEGvEPR6OUiShtMZGOouRWE2+yASEfD5qLhSLt3dHjQ12VFUlF6W5/r1X/K2y8tHo6JidMJzSktrQRC9U0mWZdHZeTDpezocPXjhhVvR0LAVO3asweuvP5Rapw+DIAjIZCI0NtqO+KSNUIjBwYN2KJXiYfssHmrCnkj791vTknskSfqQeeuRWxErFgtgs8VeXBuuuN295t1CoQAuV+z+WyzceKVvMDxsvNnS4sjaWOGHHz6A02nhtZ199nUZv89wYqAyKfX1G3hmzABwyy0voqioMq3+1NZOxH33vYWysuhs7liMHTsNl112L2bPPhX33/921H1J0oMnn7wemzZ9HWljWRbt7Qfwr3/dEVFQ8HgceP/9p9Lqc3/I5SJ4vZy5eSwoKoRNmzqwZUsXCgsV0GjSN1b+NZHWE37y5MlYtWoVGCb2YMPn82HlypWYNm3agDqXY2j55ZfPolx8L7nkLiiV2dUy65sZ7vU6YbMll+2Un6+Aw0Fi+3YjduwwIRDozZAxGOSw2XwJswT8/iD27bNCq5UOaICZny/HwYO2uBk8I43woFKnkyEQ4Fbbu7rccR/IOXLkyJHjyMHjoWC1klCrJcM6+OLxBOD307+6YDjAybTRdBC7d5vBMOkvVnu9dFSQoK5OD41GAq/3yHvn+3ycHIJMJkIoxMDlGl7B8GCQQWenCyqVFASBmMErlmWxb18PgkEmLWNbivJj82a+tOWcOYv7PU8ikUUFDVLRDf/uu3d5meQNDVujyuRTIT9fge5uN4xGT9rXGAn09PjQ0+PJmaL1Q36+AiaTB52dqWeHc4lTw/ddlwmkUhFsNnLEJDfRNGfeHZbslMlEEf3vvlgsXgSDTJQRs1AoAMuyhyqdM7/YYbOZ8Nln/+G1TZw4B2PHDl1MrLPTldZ3IBX6BsNTlUlZv/4L3nZt7WSUldUNqE96fSHuuusVjB49Neb+qqrxOO+8G3DllX/Drbf+C2Ix9+4sLq7CPfe8hrq6Kbzjg0EaL710F7755k1s2PAVbrzxBNx33wU8k1QA2LHjJ7S27htQ32Mhl4tBkjTc7uiEZJ+Pwrp1bdixw4SSEhWUyiNfKixTpPUE+OMf/4iGhgZce+21URniLpcLl112GYxGI6666qqMdDLH4ENRfmzb9hWv7dhjz8yYS3wi8vNLIZfzqwqS0Q0Hwu72Guzda0Fjow2lpb1GFQKBAGKxEAcP2uO++Ds6XLBYfAPW4NNoZHC5AkecaYFAQECtlkKtlsDp9KOtzYmeHm7AkSNHjhw5jjxCIQYWiw8ikSBqYjncCE8ShnPAPpsUF6vQ3OxAe7sz7Wt4vTRYlh8kIAgCEokoafOmkQRJchNZgiAgFBKw2YaXxJ3D4YfLFYBaLYFcLkZ3tztqDGs0etDS4kBhYXpZ4Rs3rgBJ9gaQCYLArFmnJHVu3+zxZHXDOVPQr6PaB2LCKZWKDmVN2/o/eATT2ekCRTG/Km+EdBCLhRAKBWhosKW8QOjzBY/ISpjDkclE8PmofiumhwuBQBA03WtqKpUK4fHQoCh+5j/LsmhtdUKhiC2XVlioRFOTPStyam+//Sj8fi+v7Zxzhi4r3GYjIRIJYgZQM0lfmRSn0wKaTm5h2em0YNu2H3lt8+YtyUi/lEoNbr31RcyceTKvfe7cJbjnntdw2ml/xLx5p0Mk4n8W1Go9br/9ZUybdkLUNd9770n8+9/3wONxxL3v55//J+6+dBGJhKBpJupvabORWL26BXv3WlBZqUlrQfzXTNrB8Isuugj/+c9/kJ+fj1deeQUAMHPmTJSVleGjjz7CpZdeivPOOy+jnc0xeGza9DVIkr+KeOqplw3KvQmCiBpcJxsMB7jBcF2dHtXV2ih337w8Obq6uIB3XxiGxf79VshkwgGvFHM6h5wsSyAwMgYZqSAUCqDTySGRCGEyeXPSKTly5MhxhOJyBeD1Dl+d8MMJT/x+rchkYhAEsGePJW2pCJcrEHMxQSwWwOM58t7zh1e4JaPJOdj0atuKoVJJ4HQGonR+DxzgpCDSmQSzLIuVK9/ltU2cOCcq0y8efXXDkw1mNzXVw2rtjmpP1icoHgUFSjQ3O2KO848EAoEgmprs0GpzmX/JUFioRFubAx0dqS0QOp3D2yg6E8hkIpBkEB7PyFjk9PtDoOlgZFFeKhUhEAhGPQ8dDj9sNjLud0SlkqKuzoCyMnXM/emyadM3UT5rxx57JmprJ2X0PqngdPpRUKCERCLI6nutoKCct82yLMzmjqTO/fDDZ3mBc6FQFBW8HggSiQzXXvsobr31X1i8+DLcfPOzuPLKv0UywROdd911j+HEE3+b8j23bPmBJxnGsiyCQXrAfwOBgPt8h2ltdWDlyia0tTlRU6OLinvl6J+0f2Nvv/02Fi5ciOeffx719fVgWRabN2/G+PHjceONN+Lqq6/OZD9zDCIsy+KHH97ntU2cOAclJdWD1oeKijHYv39bZLutrSGl88NO031RKCTo7vagrc0Z5Z5tMnnQ3e1GQYEivU73wWCQo7XVgY4OF+rq4hs/jGSkUhEkEiG8XhptbU7k5SmQlydPOXswFGIiup0Mw5W1SaWiX2Wpe44cOXIMF4JBBjYbCZlMNOyzrVmWhcXiHbbmmSTpRXv7fpSXj4JCkdlJ+OEUF6vQ3s5lh1dX61M+32YjY5rGicVCUFQIgUAo5Xfzrl1GqNXStPqTbZxOf2TMolCI4fFQ8HopqFTDI9hoNHogFnMLPAqFGD09PthsZKR/FosPzc2OqDFtsjQ0bEF7Oz+AvWjRhUmfX14enRnOsmy/z4tYWeHc+elnhgNcoMtk8mLv3h7Mn1857J9bqWIyeWC3kygvz65k5WBgtXZj48YV8Pt9UKl0UCo1IAgiEjAKhYKg6QDkchXGj58Jg6EILS17UF+/AT6fB8XFVZg+/cSEz1OpVASBgEB9fQ9KStRJz0/c7sARHwyXSESgqBA8Hirt58dgEs4M7w2GCxEI0PD5aOj1vRXdVisJn49GcfHgedd1dh7Eq68+yGtTq/W44II/D1of+sKyLBiGRV6eAl1dLtB0KGvBUrlcCa02n6eVbjK19St10ti4A+vWfc5rmzPnNKhUuoz3ceLE2Zg4cXZK5wgEQvzud3fAYCjGhx8+k9K5y5f/C3V1U7B16yq0tTUgECAhFktRUzMBEybMwokn/jbln1MmE6OnxwuSpFFf34P6ek5GuKZGf8S96waLAX0jrrrqKlx11VUgSRJ2ux0ajSZnmnkEsG7dOrS384PPqQyMM0EsE81ModPJcPCgDRMmFPAmza2tTtB0CDJZZibSQqEAYrEAjY121NYeuQ8pgiCgUklA0yFYLD6QJI2CAiVUqv4zlBiGhdsdgMXi45XpsSyXhabRSGEwyHOloDly5MgxiASDDBiGhc9HgySD0OuHvxGP0+mHxzM8M9jtdjMeeeRKmM3tkMkUuPvu1/o1J0wXiYQL/jQ02FBZqYNAkPzYIxRi4HCQMd+5EomQp6+dLHY7ie3bTWBZbiI3mAGKZLDb/ZHgv1wuhtVKwuUaHsFwvz+Iri4P1GquL+GqRZPJh8pKHQDO9N3no1FSkt4Cy3ff8bPCCwsrMHnyvKTP7xsM93gccLvt0GjiJ4EwTAg///xtzH2ZGO8XFyvR2GhDba0eZWUjP2h8OO3tXNVurISfkYTTacFDD10Kh6Mn6XNUKl2UNMG77z6BCy/8CxYsODvuPKu4WIW2NgeamuwYOzYfwSADr5eCUEjE/J4HAkH4/b1yHEc6fTOrAc6Mr6fHi9JS9bCZv5JkECzbK4MmEAjAskSUl4XR6IFIRAxav0nSg+efvxWBAF+//KKLboNKpR2UPsSCW9SVoLxcjYYGCygqe8FwACgurowKhieCYUJ4++1HeW0ymRLnnfenrPQvXQiCwOLFl0GvL8T//vdAlJ9emL7Pp82bv8fmzd/zjqHpAPbv34b9+7dh27Yfcd99b0IgSP5ZLpeL4HAEsGLFARiN3pxRZgbIyFNeLpejtLQ0Fwg/Qnjuued42wUF5ZgyJfHA2OsNoLXVju5uN3y+gZdb9TXRNJvbeSY7A0Gvl8FmI9HV1WvS4/VSaG6281aWM0F4Nban58gs1zwcsVgIvV4Gigqhvd0Ji8WbUKMvEAiiu9uNjg4XGIaFTieDXi+HXi+HwSCHTMYZu7S1OeF0+odV2XKOHDlyHKk4nX60tjrQ0mKH1eqDVCocRpNhGp2dLuzbZ8HOncaIL4fbHcCBA3a43RSUyuGnl/juu0/AbG4HAPj9Pnz22ctZvV9YGiBV7XCvl4LfH4JcHj1hFosFEQOzVOBk1Cj4fBSvvHc4QFEh+HzBSAaoUChAKMTA7R4eJpo2GwmPh9MLD6NSSdDe7kAwyMDjCeDAAVvaRoqtrfuwdesPvLZFiy5MSSowP78EYjE/oNjV1ZTwnH37NvOCJoeTbGl9IpRKKUIhBtu2GZP+vLIsi54eL/bts8Bmi23KN9T4fBTa2pzQakd+8OO9955KKRAOIKZGr9/vxWuv/R0ffPB03HmCWCyESiXB1q1G/PBDEz75ZA+WL9+HVataYs5T/P4gKCo47D0yMoFEIogy5bXbSXz9dSPWrGmNa1A5FHDfZf7fiyBYeDy9z+tAIIjOThfU6sH5jrAsi1deuR9GYwuv/dhjz8Ts2cn5LmQLt5tCXp4chYVKiMUCUFR2/b0KC/lmykZja8Ljf/rpU7S07OW1nXXW1dBq8zPet0wwZ85i3HnnK5gwYWbUPqFQhDvu+A+EwuQXG1pb90ZppfeHSiWBzeaD0xlAba0uFwjPAGkFwzs7O7FmzRr4fL0BPoZh8Oijj2LevHk46aST8PXXscvfcgxvOjs78fHHH/PaTjzxgn5Xrex2P4qK1MjPV8Bu96OpaWBa2WVldSCI3o8ny7IDLp0MwxlpchnbNM2ZbrS3u2C3+6HTZfaholBI4PcH0daWvpnVSIIgOINNmUwEo9GL7m53lLEJAHg8FDo6XHA4/NBopFAqJVHBFi64LgdBEOjsdMNq9aVsgJMjR44cOZLH5Qqgq8t9qDqHk8UYLrIjNB3CypVN+PLL/Vi9uhk//dSGzZu74HCQ+P77JtTXm6FWR79Lhpp9+zbjl1++47Vt3vw9GCb63ZgpZDIxBAICu3aZI+OcZPB4aPj9dMzM73jmTYmgqBAOHLBBo5EMS3NKkqRBUUGeHIJAQEQFh4YKtzuAUIjhZQFrtVL09PjQ2upAa6sTDkd6Y1eWZaPKvhUKNebPPyOl6wgEwigZxf6C4Rs3roi7z+NxZCT5pbRUjbY2JzZv7kxo8h4KMRHd1S++2I+VK5uwcmUTWlsdwy4Jo6vLA4fDP+L1wtvaGrBx41cZvebXX7+Br756Le7+ggIlSJJCS4sDoRALsVgAm42EyxX9Xff7g4ckJY78YLhMJobV2pu85HYH8OOPrejsdMX9/QwVJBmMer9LJCLe4hXX5wA0msFZFF+x4nVs2cJfUKyuHo9LLrlryMciPh+NykothEIB1GopKCq7HmZFRfxgeKLMcK/XhY8+4idflpbW4MQTB1eJIFXq6ibjtttexssvb0BhYUWk/dhjz0F5+aiU9cW/++6dlI4XiYQYPToPZWWaAfvb5eBIq1bi3nvvxfLly2EymSJt//jHP3D//fdHtn/88UesX78e06dPH3gvcwwaL730EkKh3omTRCLD/Pln9nteKMSioECB2bPLYTJ5UV9vimSrpJNtLZHIUFJSzRtQt7fvx6hRR6V8rVgUFanQ1GQDQXCTNYfDD6VSnJUXV1iWZeLEgmETVMg2UqkIIpEADocfFBVCUZEKCoUYLMvC4fDDZPJAIBAk9dlQKMQQiQQwmbyHPmfKlMq+c+TIkSNHYliWhcdDwWTyQCwWQqHg3lXDSaLK7aZgsfhQVKSCXC4GTYfQ1eXGhg0d6OryoLBQEZGTGC4wTAjvvPN4zH3NzXtQVzc5a/cuKVGhrc2JlhYHRo/OS+ocr5cCw7BxJ1kikQBmswejRiXng9LZ6YLF4kVFhRZ2OwGTyXPo+sPjHR72Kjn8c86ZaHqS0r3ONoebe4aRSDg/lW3buiEUEmkvAP3887fYvXsjr+3UUy+FXJ56lW9JSQ3P26e7uznusTRNRZWO98Vq7e5Xa7Y/RCIhSktV2LXLDIYBpk4t5j0fuCoTN/bvt6Cz0wWCIJCfr0BpqRpdXW6sXNmEcePyMW5cPvLyer2EOKmBoQmStrU5IBYLRnwQ5PPP/xPVVlY2CmLx4XMkAkKhEAcP7oo6VqXSoaysFg0NW3ntH330LD799GVce+0jmDp1IW8fQRAoL++VrGBZFs3NdjidAeh0/LlIa6sTFMWMeCmaZFCpJLBafTCbvSgoUOCXXzrR3e1GTY0Ozc0OuFzDo0oGAFwuf9R3TyYTweHwR94rVqsPweDg/O327Pk5KqCrVGpx/fVPRFXLDDY0HYJIJIg8uzQaKbq7XVm9Z3FxFW+7s/Ng3PfosmUvRlV6XHzx7RCJRkacRCKR4bbbXsLnn3+I0tISHHUUZ/h5/vk3oaPjAPbs+Tly7PTpizB//hkoLCzHZ5/9h7cY3NCwBW1tDVHywDkGj7RmORs2bMCiRYsiLy2GYfDcc89h3Lhx+Pbbb2E0GrFo0SI88cQTeO+99zLa4RzZIxAI4N///jevbc6c06BU9q+3xzBMpJS6uFiF/HwFioqU2Ly5G35/MC0tw4qKMbxgeKommomQSEQoKVGhs9MFkUgAqVQIrTYzxpl90etlaG52oLPTnfQE8khAKBRAp5PB5Qqgs9OFwkIl/P4gLBYfZDJRSgsDEokQBCGJyM0UFiqHfJKaI0eOHCMdlmXh9dJwOPxwufyHMoiGn8wIwGWsUVSveaNYLARNh3DwoB3l5WooFMOv3xs3roirgbxnz6asBsNFIiHkcjF27TKhvFyT1DvX7Q4g0atVqRSjq8t9KNiQOCDHsiwaG20QCgUQiYRQKETweGh4vdSwWbQgSRqhEAOhsPdn4fpJgSTpIf9MuVyBmFINxcUqNDfbEQqxqKnRpXxdq9WIN99cymvT6wuxaNFFafWztLSGt50oM3zXrnUgSU9k+3DDxDAWS9eAg+EAV51ZWqpGfb0ZDocfo0cb4PHQcDpJ9PSQcDhIyGRClJSoeVq6ZWUaeL0B7NxpQmOjDdXVOtTW6qFSSbB2bRuqq7WYNKloUMehTqcfnZ2eEeHfkIjOzoNRiyHnnvsnLFlyRczjjcZWPProVRFJlbFjp+Hqq5dCry/EunWf45VX7ud9fmg6gBdfvAOPPfY59PrCuP3gPnecAW1lpRYUFYLNRqKlxYk9e0woLMzOnHC4IZeLD1XwWGGxeHHggBVlZWoIBALIZKJhJfPpdlMRM+EwUqkQXm8QPh8nkdbe7oop85VprFYjXnrpTrBsb9UJQRC45pqHkZ9fmvX794fLFYBWKzssGC4BTWdXJqW8fBRv2+NxwG43w2Ao4rW3t+/HDz98yGubNu2ElM0thxqZLA+LF1+BkhIVmprsADi5lN/+9mEcOLAWYjGDceOm8TLI//jHB7Bnz89wuayRtu++exdXXPHAIPc+R5i0nhbd3d04/fTTI9tbt26FxWLBgw8+iPLycpSXl+Oss87Cjz+mpoOTY2jZsmULnE6+nEey5R7hkuowIpEAkycXQ6mUYMOGDnR2OlFWlpqJRGXlWJ7bfCaD4QA3SB6MiY5AwAXbGxttqKrS/io06MIQBAGtVgafj0ZHhwssC6jVkrR+B2KxEGo1FxAXCgnk5SlyAfEcOXLkSBOPh4LdzpUUCwSckVh/Ac6hxO2morKMqqt1UeOP4UIwSGP58pfi7t+9eyNOP/3KrPahqEiJpiYHGhutmDy5uN/jrVYyocEWl0VIwun087JlY2Gx+NDR4UZ+PnecXC5GT48PLldg2ATDfb7ozGu5XAybzQ+XixryYLjTGYiZhUwQBGpq9KCoUMoZkCTpwbPP/hleLz9L8KKLboVMll4AsLS0lrfd1RU/M/znn7/hbY8ZMxU0TaGpqT7SZrV2p9WPWMjlYtTW6g4lpbggEBCQSARQKCSoqdHFzbJWKqWoq5PC4wmgocGC/fut0Gg4iRqz2QupVIwxY5KruMgERqMHHk8AhYX6QbtnNvjii1d42wqFGieccEHc44uLq7B06TJ0djZCqdSgpKR34WXevNPhcPREZecGgxR27VqHBQvOTtgXjUaKnTtNaG62g6ZZ+HwBUBSD/Hz5sNTira/fgK1bf0Bt7WTMmbM4JX3iRBQXcxUUQiGg0cggk3ELp9yzkARNh4b8HRv2q+jbj7C/lNdLg2FYWCwkNJrsvl9CoSBeeulOuN12XvvZZ1+HSZPmZPXeyeJ2BzB5clHk/RH+m2aTwsIKyOUq3mJnS8seXjCcZVm8/fajvEUEsViKCy+8Jev9yzR2ux+VlVpUV+vR2GgDy7Lo6nJDo1Hg6KMXweUKID+fH/sSiyU4/vjz8emnvWPDn3/+BhdffBvkcuVg/wg5kKZmeCgUAsP0foh/+uknEASBE044IdJWVlYGo9E48B7mGDTmzp2Ljo4OPPzwwzAYilFdfTQqKkb3f+IhYk2ia2sNOO64KshkYnR0pKabXVU1jrfd1rY/roPvcKegQImmJjvWrm3LiMHoSEOhEEOjkUKvlw1oQBU2wTGbfcPOiCtHjhw5RgokSaOrywWPh4JGI4VWKxvWgXCA0wLtmxUmEgmHfJIejzVrlqGnpzPu/sbGHQgEsmtOxsmRyVBf3wOHI/G9aJrLjEyUQS6TiREIBGG39//+bWlxwO/vza4WCARgGBZO5/Apu/f5ojVoRSLhsDDRpOkQvF46riQHQRApyxg5nRY88cS1aGvbx2ufMeNkTJ++KO2+Hh6gBACHowc+nzvquGCQxs6d63htM2f+Bnl5/ExKi6Ur7b7EQiAQoKZGh6oqLWpq9Cgr00KvlyclN6JSSVFdrUdpqRoMw6KqSguZTIRNmzphNHr6PT8TsCyLpiYHZDLRiE4CcblsUf4JixZdBIUicfWwXK7EqFFHRX3OAGDx4suxePFlUe2vvvo3fPjhs9i5cy0vZnE4BQVK6PUy0DQDgYCT0KyrM0CrTc+QNps0Nu7AU0/dgFWrPsIrr9yPpUv/CJfLlpFrhxeGyso0vEVOuVwEny+Ykk9EtujVcY8eAwSDDLxeClYrZzicbRPtTz99GY2NO3htRx+9AKed9ses3jdZGIYBy3JSaWFkMu49kk0fBIIgomI3ra38d83GjV9HyRstXnz5sMimTwWWZUFRIVRX62AwyA/Jq3kBALNnl+Hkk+tQUqJGc7Mj6vlz/PHn8rz4KMqPLVsSS4flyB5pzXwqKyvx88+9WjjLly9HSUkJxo7t1bsxGo3Q6XQD7mCOwSU/Px933nknHn74M5xzzj0pnEnEnUiXl2uxYAEXEO/sTF6vqqpqPG87GKQS6hAOZ6RSESoqNNi714JVq1pgtw8fd+7BQigUZGQQL5EIIZUKYTJ5hnyymiNHjhwjDZZlYbWSCAZZaLUynkTEcIXrs3dQspsyQSBA4rPP+Lq4FRWjeZl8oVAQDQ1bst4Xg0EOh8OPnTvNCU2onc4AvF4KSmXi37FAQPRbOu/1UmhstEf5gohEAlgsw6fs3uEg42ZeOxxDO77gzD1DkEoHvthDUX78+OMnuOee83gZ2ABnenbppfcMaHxWVFTJm9wDsXXD9+3bzMsaBIBjjjke+fklvLZMB8MBLiA+EB1hsViIvDwFpFIRCgqU8Ptp/PxzZ0xd90xjs5EwmUa+RMr69V8gFOo18ROLpTjppPSkecIQBIHzz78Js2adErXvq69exVNP3YD77vttVFAujEIhQV6eAnq9PGFVzFDz7bfv8EyXm5p24YknrgFJejNyfaFQEPXzy2QiBAL0sJhr+f3BQ3r9sf9GXi8Nk8kLgYDI6oJRQ8OWqOqGgoJyXHXVQ8NGy9/t5qqvCgt7M41lMjHEYgFCof6lUliWhdPpR2urAy0tdng8yf/9EwXDfT433n//Sd7+vLwSLF58adLXHy54PNzvuKhICZ1OBrWaq5qbPLkIlZU65OUpcNxxVSgqUqK7m//O02rzMXnyXF7bunVfDGb3cxxGWt/ac889F+vWrcP555+PSy65BGvXrsU555zDO6a+vh61tbVxrpBjuCMUiqBWp1L+xybMKisr0+DYYyshkQiTDoirVNqolcJ4g5mRgFQqQk2NDm1tTqxa1TxoGSVHInK5GAKBACaTZ1AmIjly5MhxpBAIhA5lT42MwDLAyVl4vcG0tUCbmnbj8cevPpQVm1nJtVh8//37cDotvLbf/e4O1NbyNcL37NmU9b4QBIGSEhUaGixobrbHPS5seN1ftrFSKUF3tyvhpLqtzQm7nYwK3ikUnDlloqD8YMGyLFyuQMxgs0wmgtk8tGM0ztwzmLZZo8/nxoYNK/DCC7fihhuOx2uv/R1eL79CU6PJw803P5uUN1AiRCIxiooqeW2xpFK2bVvN266tnQydriBqrJ9JmZRsUV6uQUeHC/X15qQCTAOhu9sDn2/oNewHAsuyWLNmOa9txoxFUKl0Gbl+Ismpzs5GPPTQpdi1a13cY4YalmWxdu1nePrpG/HJJy/wKitI0ovt26OlZ9vbD+Cttx7OWp/CuurDwUQzEAjG9aoQi7lF1s5OF1Sq7H1HaJrCa689xMuuFgpFuOaaR/qtbsgELMuis9OF5mY7enq8CAZDMY9zOAJRPipyuQgSiQgUFfucMCRJ4+BBO/z+EMaOzcO4cQVwuSg0N9vh9/c/366oGMPbNhpbIv+/bNm/osZFF110KySSkbfIZ7f7UVqqgkYjg0BAID9fiaoqLSZOLIgco9XKMHVqCWg6BJrm/97nzTudt71v3y9ZWQSm6QAoaui/v8OZtGYVt956K7799lt8/PHHAIDJkyfjgQceiOzfu3cvfvnlF9x1110Z6WSOkUF/JdYVFVrMn1+Jn35qQ1eXC6Wl/Q++q6rG8R4OK1a8Do3GgPHjZwy5U3M6CIUC1NbqIwHxOXPKUV09svX/hgqVSgKHww+TyYOyMs2wLZXPkSNHjuGE10shGGRH1DPT46Hg99MwGFKfNPl8bjz11J/g8TgAAA88cDEeeeRTFBaWZ7iXHIEAiRUrXue1TZo0F2PHTsOECbNw4MC2SPvu3dkPhgNc9qNEEsDmzV3Q6WRRet8sy8Js9iaVUadSSWC3++F0BmAwRMsJ0HQIDQ1WKJXiqOspFGI4nQG4XH7odEMrReD3BxEIBGMGwxUKEVyuAPz+YMSwdbAhSfpQ8Cf572kwSGP9+i+xefN32LPnZ14Wbl/y8krwl788j+Liqkx0FyUlNbxs8L4mmizLYts2fkBv6tTjIn05nGwEBTKNUChAcbESW7Z0gSAITJ+enTL/QCCIxkYbVKqRs3gZi8bGHVHVAv1peqdCaWktiourecG3wwkGKTz77F9w553/zapxcTr4fG688sr92Lp1FQBgx46fsGnTN7j//rehUKixffsa0HTsgNb69V9i9uzFUZmmmUIqFaKnJzPZ5wPB7w9FeYaEkck4bXOXK4C8vIG9VxgmhI8+eg579/6MyspxmDRpDoqKKsGyLN599/Goz9c551yP2tqJA7pnsnR3u6FUSjBpEpdY19HhglBIoKhIFcmYZxgGoRCDsjJ+jEUmE0EsFiIQCEERxxrCbudMhSdNKsDkyUWRyq4xY/KwaxenrS8WC1FSooqbBd93UdRi6UIoFERHRyO+//593r4pU+bjmGOOT+dXMaSwLItgkEFlpS7SNn16CQKBUJTMXHm5BgUFCthsJIqKemVrjj56ARQKNW/Ra8OGr2Iu6nFm5DtgsXRh4sTZ0GgMSfWzqakeb755Fzo7f4PFi/+b4k/56yGtzHCNRoONGzdi586d2LlzJ7Zu3QqDofcPI5fLsWzZMlx33XUZ62iO4QvDMBAKBUnpjVZV6XDssZUQibgM8f60q+rqjuJtd3U14amnbsCLL94RVwNuuEMQBCortQgEgvjxx1bs3duTVQ2vIxmtVgqvl4bZ7M16Zk6OHDlyjHQYhoXD4R+yAF+6eDxUwsDgtm2r8fe//wH3338Rdu/eGGkPBEg899wtkUA4ALAsgzvuOB3Ll7+UFR+SNWuW8e4HAOecw42HJ06cxWvv6DgQlSmVLYqLVXA4/Fi3rp0n1cYwLHbv7sGePeakAgkymQh+Px1Xg7yryw2TyRMxzjwcuVwMv58eFpmGJBlEIBC77F4uF8PnG1p5AJIMAki+3J9lWTz33F/w6qsPYteu9QkD4TNmnIz77nsryvhyIPS9Vt/AZ2vrXtjtJl7b1KkLASAqM9zlsmVdTz8TqFSc38KBA7asVSkePGhHd7cbBQUj21ztp5+W87aLi6swZswxGbs+QRC4+uqlGDt2Giorx2HOnNNQVlbHOyYYpPDCC7dFSfUMJdu2rcZf/3peJBAexmxux7Jl/wIAbN++OuE13nvvnzwJlUyiVkvQ1uZCU1Nm9MnTJVFWskwmRCAQBEkGByyl9s03b2PFitfR0rIXa9Ysw4sv3o77778QDzxwUZTWdU3NRJxyyiUDul8q+P1BTJpUiBkzynDGGWNx0kl1KCnRoLPTjc5OJ2g6BKczAK1WxtMLBziZJ4VCfEhuJgiSpBEIBCOxFKvVB4+Hwty5FZg/v4oncVZcrMKJJ9Zi0aJa6PVyNDU54HbH9g3pGwwPhYLo6enEG28sjTLN/N3v7hiRHghhE/Di4t5nskIhiZKFA7jf+6hRhijdfbFYipkzT+a1rV//BS8eRNMUNm36Gg89dCmWLr0c//73PbjjjtOxYsXroKj4vi0+nxsffPAM/vGPy2CzdeDjj/+HNWvWpPvjHvEMaDY0adKkmO3V1dWorq4eyKVzjCBCIRYCQXzN8L5UVekgEBBYu7YdbW1OVFZq4z4Mx46NPVDavv1HHDiwDWPHTku730MJQRAoLdXAavVh7dpWeL00jjqqaERl6g0HCIKAViuD3e6HSCRAYaFyRL5Yc+TIkWMw8Plo+P1BaLUjqyzV641v4LVq1Yd4442lke1nnrkZf/vb+5DJFHjqqRujzALDfPrpy2ho2IIbbngyYyXOwSCNr79+k9c2efI81NRwmWO1tZMgkynh9/dm2u3Z8zPmzFmckfsngjO30qK11YHvvjuICRMKIZeL0N7uREODFTqdDBpN/58LgiBAEIDV6kdfNUSWZXHggBUCARFzPDOcyu45Te7YMiQSiQg0zcDlCgxZENLrpZHKcKarqwk7d66Nu58gBBg79hicdtofMWnSnAz0kE9pKd/csK9MSl+JlMLCikgAvW9mOADYbMaYhonDDb1ehtZWJ7q73aitTS5jL1m4jEAbFArxiPB2iEcwSGPLlh94bccee1bGx+vV1eNx5529GZDBII1XX30Q69d/GWmz20345pu3cdZZV2f03umwevXHeP31h+Lu//nnb3HBBTdh1671vPa6usk4eHBXZLurqwlbt64akAluPLRaOUjSjfXrOyCViqIyjgcLj4eGUBj78yKVimA2+wbsr8AwIaxc+W5SxxKEAH/4wz1RXgnZIhwkVas56ROxWIiaGj0qKrRoa3Ngzx4LOjtdcLsDmDOnIqakUmmpGjYbCbPZC6GQM7SmqBAYhoVEIsC8eVUYNy4/5v0FAgK1tQaUlKhRX2/Gzp0meL00iov5YyeVSge5XMVbcHrzzYfR1LSLd9ySJX/MWnUeADidfojFgqxISzkcfowZkweVKjmFgrIyLZRKI7zeAJTK3nPmzTsdq1d/HNk2Glvx2GP/h6qqcejqasb+/VujFoX9fh8++OBpfPnlqzj++PNw+ulXRmRmDhzYjk2bvsbGjSvg9fZKErMsi8suuww7duyAWp19OZ+RxoDfrOvXr8eLL76Ihx9+GC+88ALWrRuYHteaNWtw+umno7S0FARBYPny5bz9LMvigQceQGlpKeRyORYuXIjdu3fzjgkEArjhhhuQn58PpVKJM844Ax0dHQPqV474hELMIWOa5D9OFRVaLFxYBZlMlDDzprJybFwtqQ0bVqTc16GGYUK8Vb+8PAUMBjl++aUT69a1JZzw54iNQEBArZagp8cHm234ZxLlyJEjx1DhcvlBEAQEgpG1aGix+OJqJ3/55f942zQdwLPP/hmPPHJl3EB4mH37NuPpp29MmGWTChs3roDNZuS1LVnyx8j/C4UijBs3nbd/796fMVgIBAJUV+sRCITw00+t+O67g9i/34riYlXMrKZ4KBQSdHVFV/f19PjQ3u5GQUGcOmxwJthm89CX3ZNkECyLuAE5guC+L0NFPHPPMC6XjZf9vW/f5qhjRCIxjjrqWFx++f14+unvcMcd/8lKIBxAVODaYunkfa/6BsOnTl0Y+d3LZIoo7eiRIJUCIDL/OXDAnvEKRafTD7udhFY78mQhD2f37o08OQAAMQ0vM41IJMYf//hAVOLUN9+8GVW9M9j4fG68++4TCY9xuaz49NN/R2WyX3/9E1HazH2luTJJcbEaFBXEunXtQzbPcrsDcSvDJBIRBAIMWCKlvn5D1Ps7HieddBGqq8cP6H6p4PcHIZWKoVTyg7sikQC1tQaccsoonHLKKMyfX4VRo2J7vk2bVoLTTx+DM84YhzPPHIszzxyH004bjZkzyzB3biXGju3fK04uF2P69FIsXFiNUIh7Rh0OQRBR2eF9vVGKiipx6qnZM82kqCAsFh9MpsyPM8IyNOXlyS8K6fWyyELE4dTVTYn6Xe3btxnffPMWdu1al7A6yut14osvXsFbbz0KgEvuWLr0cnz//fu8QHiYvLw82O3xPWN+zaQdDN+0aRPGjRuHY489FjfccAPuuece3HjjjViwYAHGjRuHDRs2pHVdr9eLo446Cs8//3zM/Y899hiefPJJPP/88/jll19QXFyMk046CW5370v25ptvxrJly/Dee+9h7dq18Hg8WLJkCUKh7JQQ/dphGBYiUfKZ4WFKStSordXDYvHFPUYkEsfN/t68+buslDhnGpZl8dNPn+Luu8/BFVdMx1//eh7a2/dH9qvVMpSXa7BnjwXff98Mk2n4lO+NFMLlX2azd1hknOXIkSPHcMPvD8LtpqBQjCztWYZhYbORUVqMAOB222G1Rk9eu7ubYTK1xbweQfDHKgcObMd77z2ZgX4yWLHiNV7b6NFHR0kBjB8/k7e9d+8vA753KnCTVRXq6gyorTWgulof83ebCJVKDJcrAI+Hv4Df1MSZbCXKxpLLxejp8UUZSg02JEkjkUKdVCqE2Tw0gR+WZeF2B2IGw10uG5555ibcdNOJuOGG47FixRtgWRYNDVt4xx199AI8++wPuPnmZ7FgwVlJ64ymS0lJNW9hgWVZGI2tAICenk60tx/gHX/MMQt52/n5I083PExhoRJtbXZ0dEQHIQaC1UqCJOmUv5+pwjAMli17EX/+80l49NH/g81m6v+kFPjll+9426NGHYW8vOKM3iMeQqEIv//9nbzPpt/vzWrwOBk2blwRtQg7YcKsqCqlvou9tbWTodcX4rTTLue1NzXVo7U18eJvGJL04ocfPsAXX7wCp9Oa1DllZRrYbD78/HMn/P74EkzZgGVZeDyxn4dhamoMA84C7itVAwBqNefrJRAIodEYUFc3BWeffS0uuODmAd0rVfx+zkA8nkGoSCRAZaUOs2aVo7AwdjUTQRDIy1MgP18BnU4Og0GO8nItpk0rxfjxBUlXahAEgbo6AyZPLkRPjy9KtraoqCLhuX/84wNZ9X0zm70oLeW+R5mW1I0nQ5MIgiBQU6MHRTG8/hAEgeOPP39A/dm06WvY7WZ88cUrMfcLBEJceunN2LBhAyorK2Me82snLZmUvXv3YtGiRfB6vfjNb36DhQsXori4GCaTCatXr8bXX3+N3/zmN9i4cSMmTJiQ0rVPPfVUnHrqqTH3sSyLp59+Gvfccw/OOeccAMDrr7+OoqIivPPOO7j66qvhdDrxyiuv4M0338SiRVy50FtvvYWKigqsXLkSv/nNb9L5kXMkIBRiIBSmHgwHgLo6A/bvtyYc7J111jVoaqqH1+vktXu9LuzcuXZYmy+QpAevvvp3/PLLt5G2rq4mPP74Nbj77lcjxkVSqQi1tTq0t7uwcmUTpk0rxZgxeSMue28okclEYBgWRqMbAgGRkqM4w3BmGKEQg1CIjWS7EQT3uRaLBSO6RDVHjhw5PJ4AaJqBWj2y5Li8XgokGYxpINfR0ZjUNQoKyvHnPz+LkpIaNDXtxjPP3ASXqzcIsGrVh5gwYeaAysy3b/8xShpi8eLLo46bMIEfDLdYumA2d2S1ZDjTyOVimM0+OBx+qNXcpNbtDqCpyRbTVPNwFAoxbDY/3G6q32OzicsVgFgc/70ul4tht5OgqFDCIEw2CARCIMno+7a378fTT98UyV4kSQ8++OApCIWiqGD41KkLIZcnP2EfKFKpHHl5JbwgdldXEyorx2LHDr5eqUqlw6hRfE+g/PxStLTsjWxbLN3Z7XAGkUpFEAgEaGiwoqJCm7Gxe1cXN57NpvxfKBTEG28sxZo1ywAADocF7733JK677tGMXJ+mqagg44wZJ8c5OjuUl4/CrFmnYOPG3orilSvfw6mnXhpVkTBYbNjwFW+7tnYybr31X/j88/9i2bIX454X1hmePn0RDIZneJnMa9YsxyWX3Nnvvd966+GIdMyWLT/g3nvfjGuIGCbsd9XUZIfBIMeMGaWDJktJUSH4/QwkkuzNw1iWjZKj+e1v/4xTTvlD1u6ZCn5/EIWFykF/FyViwoR8tLY6YDZ7eXIpxcXx5a1OPvl3GDNmatb6RJI0GIbFuHH58PmCcDoDKVW99YfT6cfEiYUpL7yUlamh08nhcPCNxxctuhANDVuiKqfCEASBMWOm4oQTfgu9vhAffvgMDhzYHtlPUX689dYjMZNDp09fhMmTf4uLLz4eItHI8gkaTNJ6qjz44IOgKArffPMNVqxYgTvuuAOXXnopbr/9dnz11Vf49ttvEQgE8Le//S2jnW1ubobRaMTJJ/e+RKVSKY477jisX889wLZs2QKapnnHlJaWYtKkSZFjYhEIBOByuXj/ciRHMMimLJMSpqhIifJyTUKn6traSXj88S/w8MPLMXr00bx9hw9shhsWSxceeuhSXiA8jNttxxNPXAOHoyfSJhAIUFWlAwCsWdOC9evb4PPlZFNSQaEQg2UBo9ENr5cCw7BgGBahEINgkDk0oArC66XgdPphsXjR3u5Ec7MdLS32Q/91oK3NibY2J1paHJE2k8kDr5fKmZ3myJFj2MCybOS5RlGhuM8nvz8Ih8MPuXzkDYg9Hgp+f2xjrI6OAzHO4KPRGHD77S9HZBxqayfi1ltfjJJg+9//Hkw6S64vLMtGZfCVlY3CUUcdG3VsWVldVJbuYEqlZAKBQBAxYw3T0mKHwxGATpdYd1wmEyEQoKPKqwcbpzNxpqFCIQZJDo2Jps9HHzL37O1fff0GLF16ecwy/k8/fQkuF9/gbig8dfpKpXR1NQEAduzga5kfddSxUVq7eXl8E02rdeRkhgNAfr4C7e3OjFV3ut0BtLc7oddnz98hECDx/PO3RALhYbZtWw2SzIzEwO7dG6NkPmbMyLy2dX+cddY1vM8cRfmxatVHg94PgPu9NzfzJV5PP/1KEASBGTNOSnhueL9QKML8+Wfw9m3atKLfimmn08rTUG9p2YPW1r0JzuhFJBKiuFiJXbuMaG11JHVOJgibPsYyO84UXV1NUc/WyZPnZe1+qeL3B2OaUg8lCoUEkycXweulEQz2Vnr1rfoJU1pai3POuT6rfTKZPKiu1mH06DyUlakyWi3OMAxYFpGs81SQy8WoqdFFGY8LhSJcf/3j+P3v78SsWadg2rQTMGvWKViy5Er83/8txdNPr8Sdd76CmTNPxujRR+Puu19FYSE/8z5WRcPSpctw/fWPIy8vfpZ+Do60guGrVq3Ceeedh5NOiv3AXrRoEc4991ysWhX9xxkIRiP3kCoqKuK1FxUVRfYZjUZIJBLo9fq4x8Ti4YcfhlarjfyrqMh9ePojFGLgcvkPGTGklxlOEARGjzaAYdiEJbNyuQrFxVVRJlNbt66C02lJ+b7ZpqVlL/7+90siE4FYWK1G/Otfd0QNXAoKlCgsVGLnTjNWrmxGd7c7zhVyxEKtliIYZCNB7nAwu2/Au73dBZPJC5Lkyv2kUhG0WhkMBjn0eu6fwSCPZL1ZrSRaWx3o6nKDJIe/PE+OHDmOXBiGhdPpR1tb73OuudmO1lYH7HYSwSBXihkKMejp8aKtzYFAIASZbOQFw71e+lAFWvQYo6/0Ql5eCS8bViKR4brrHkN+Pj/QVlExBr//PT+DjiQ9+PDDZ9Lq4969v6CpqZ7Xdtppl8fMnCMIAuPHz+hz/sgKhgNcULuriwty+f1B7N9vg0Yj6TdbMGyiOZTB8FCIOSRDEv/7IJEIEQgEh0R6LWzuGTaE2759DZ555ib4/bFlBfvqMet0BVET5sEgbIgZpru7GYEAGaVnHmuRaCTLpABcsCMYZNDcnBld1q4uN5zOQFKmtunQ1LQbDzxwMbZvXxO1LxikUF8fP4EsFbZv/5G3PXr0VOj1hRm5dioUFVVi5kx+dfjKle+Bpgf/+93cvJun9y8QCCNeEiUl1aioGB3zvDFjpsJg6I2BHHvsmbz9Xq8rqkKkL7t3R8vYtrU1JN13lUoKgUCArVu74fEMzu+OJINZr9Dp+z0wGIqjnmdDCcuyw9L4vKZGh4oKLW8RsLJybNRnWCqV4/rrH4/rA5cJfD4KYrEQEyYUQiAgUFamQTDIZCyJze0OQK2WoqgoPVPtigoNhEIBKIovMyQUinDiib/FNdc8jD/96Z+45pqHce6512POnFNjypstWHBWwvvccMOTKCmpTquPv0bSCoY7nU5UV1cnPKampgZOpzPhMenSd6DNsmy/g+/+jrnrrrvgdDoj/9rb2zPS1yMNluUm4M3NdrS1OUFRIZSXa1BVpUu7XKqsTIPCQlVC7fAwM2acBJGotzQlFApi1aqPE5wx+DQ17cbjj18dlaUjFEZPuvbv34aPPno2ql2hkKC2VgeTyYOVK5tQX2/KuDHPkYxGI43I7hAEEZE7kUpFUKulkUC3Xi+HSiWBXC6GWCyM+RkWCAjI5WLodDKoVFI4nX60tztht5NgmFyWeI4cOQYXvz+I7m432ttdkQC3Ws0982iaQWenG+3tTrhcARiNHphMHojFQuj18kEra84kiUwM+2aGL1p0IZYu/QSXXHIXrrzyb3j00c/iZsjOn38GZs3iB0fWrfucV4KaLH2zwgsKyiLl7LGIpRs+0qqOlEoxbDYSfn8QHR0u9PR4kZeXXOaaXC6C0Zhe5ilNhwZsbMkFmxlIpfGnQeGg/VBkhnPmnlzV5cGDu/D887ek5JEzduy0Ifmu952Ad3e3Ys+eTQgGe6schUIRJk6cHXVu3wUrqzWxTEogQMLh6OEFFYcag0GG5mbHgD+fDMPi4EEbZDJRxv+OLpcNb775MP7xj0thNLbEPW7nznUDvhfLstix4yde27RpmZO29HgCaG1NfvHh1FP5khcuFz9LerDYv38bb7uycgxkst5n5+zZi/ueAgBRwfz8/FLU1EzktW3evDLhvftKgQCIaPsnS0mJCkajFzt3mgblveX3x18QzxR9F22mTJk3bMZLDMOkLP85WHDB53xQFMML8v7mN5fwjrv00nuzvrjQ3e1BXZ0+ouddVKSERiPN2IK20xlAaakqbW36oiIVCgqUsNsH9n6oq5sSd59SqcWUKfMHdP1fG2k9VUpLS7Fx48aEx2zatAmlpaUJj0mV4mLObKNvhrfZbI5kixcXF4OiqCjH1MOPiYVUKoVGo+H9y8HH4SAPmSMFMWFCAU49dRTOOmsczj57PGbOLEv7umKxEGPH5sHrpft9qapUOsyezdeU//779+Jmyww2bW0NeOKJa6KydMrK6vDII5/imWe+jzKN+eabt1BfH71SH5ZNEYkEWLu2DWvXtg3aKvyRgFgshFwuhkwmgkwmglQqglgsHJCWo0gkgE4nh1AoQFeXGyaTJ5KBmSNHjhzZhKJCMJu5LG+Hww+tVgqVShJ5rolEAiiVEuj1MlBUCB0dTtjtfmi1MkilIy8jPIzVSsbsfygUjNIMr6gYA52uACeccAHmzTsdOl1B3OsSBIGLLroVMhk/y+eddx5LaYLf1FSPPXs28dpOOeUPMRfAw/QNhrtcNnR2Hkz6nsMBpVICrzcAm43E/v0WSKXCpIMVCoUYdrs/LSO2PXt68M03BwdUNcdlGvZfdi+TiWA2Z0YuIhUON/f89NOXowK+M2eeDK02P+75Y8ceE3dfNgn74IQxmdqiMi5Hjz46yiQQiJZJcTgsvKxdhglhx46f8Oqrf8Mdd5yBa66Ziz//+WTcfPNJUQaNQ4VGI4PTGUBHx8AqOnt6vGhu7kZb20Z8/fUb+P7799HYuGNAZnA9PZ14++1Hcdttp+GHHz4AwyQ2sI1nQJwKbW0NPDlIAJgyJboqIF2MRi8EAiLpas3KyrGYMGEWr+27794Z9IXI/fu38rZHj+ZrKM+ZszjK6FkikcX0tJg27UTe9tatq+L+bRmGiTnf/PrrNyLfpddff6jfz4ZAIEBJiRJ79/agvT07iY+H4/dn12zZ6bTi4MGdvLapUxdm9Z6pwJlniqFWD79gOABUVupQUaGBydT7rpw7dwmuuuohLFx4Lv785+cwZ05sP8BM4XT6oVRKMGFCYWQRQ6WSorxcw5NzSxeaDoFhWFRX69K+hkgkwKhRBrjdA5PATbSoMHPmyRCJsmu4fKSRVjD8zDPPxOrVq3HvvffC7+d/wPx+P+6//36sWrUKZ555ZpwrpEdNTQ2Ki4vx3Xe9gx6KovDjjz9i7ty5AIBp06ZBLBbzjunu7kZ9fX3kmByp4fEEcPCgDYEAg+nTS7FkyRgce2wVKit1A3ZuDlNZqYVeL09qteykky7q0z8HvvvunYz0IwxF+dHYuANeb/La8RZLF5566k9R2njjxs3A3Xe/ivz8Umg0Blx33RNRD6r//e/BqAB6mLw8BUpK1Ni9uycnmzJM4AYlUlitJDo7XQgEhk9mUo4cOY4sWJbTZm5r44yKwlne8QKPBEFArZZCpZJCr5eNaPPfYJCBzUbGNNg2mdqiStwrKsakdH2tNh9nnXUNr62lZW9MDcZ49M0K12jyosrX+1JQUIa8PL4sxJ49I0sqRSwWIhhkUF9vRmenGwUFyZcOKxRi+HxUyhlbDMNG/Dy2betOu2KOJGnQdKhfeT+5XAyrlRz0Re+wuSdF+aMkRubNOx1XX70Uo0bFzw4bCr1wIFozPBikovSo4wVD+8qkAJycYCgUxA8/fIA77jgDTz99I9asWQazubd61+Nx4L//vQ9ud2bkSQYCQRBQKsU4cMCa9meGZVk8/PDf8Nhj5+Lf/74D77//FN566xH84x+X4e67z8bmzd+ndL2Wlj148cU7cMcdZ2DlyvdAUdHzrJqaibjggpt5bRZLZ1r9P5y+WeGFhRVRCybp4nb7oVZLoNfL4fEkH1w65RR+xmpn58GItIjdbk7bNyJZaDoQlRne11BQry/E3LmnRbYJQoA//OFuaLV5UdebNu0E3rbLZUNj446Y925t3QuPxxFzn8PRA6fTgtWrP8Y337zV78+hVEpBEAS2bTNmXTqSJClkM0l7x441vAURmUwRtWA9GIR9rPouepFkEHK5CErl8AyGi0QCjB9fgFCIyw4PBrnA8dy5p+HSS/+a9UxllmVhsfgwblx+lK56RQUnlTKQhUQAsFh8KCpSoaxsYMmypaVqKJVieL3pJzdqNAZoNNHPAgBRcsI5+ietGdK9996L2tpaLF26FJWVlViyZAmuuOIKLFmyBFVVVfj73/+Ompoa3HvvvSlf2+PxYPv27di+fTsAzjRz+/btaGtrA0EQuPnmm7F06VIsW7YM9fX1uOyyy6BQKHDxxRcDALRaLa644grccsst+P7777Ft2zb8/ve/x+TJk7Fo0eAbdoxkfD4azc12OJ0UpkwpwmmnjcaMGWUZdeUNo1RKMHq0AXY72e+xlZVjcfTRC3htX375P1it8TXhU+HAge24/fbT8Y9/XIZ77jk3KS01kvTiqadugMPB1y+fNGku/vznZ3lZMLW1E3HhhbfwjrPbTXjvvSfjXl8uF6O2VgeLxYuVK5uwd29PTqJjiBGJBNDrZfB6KXR0uOD15sxOc+TIkVloOoTubjc6OlwACBgM8qSzvEUiwbAp800XjydwyDwzWiu077tZpyuIqa/YHyee+FsUF1fz2pYt+1dSk6fOzoNRgfNTTrkEYrE04XkEQWDChL5SKZviHD18CVdJCQRIydxMIhEdkjtJbUJot5OwWEhUV2vR2elCZ2d6Zvdhr5D+vh9yuQg+3+DrhjscfkgkQjQ0bOEt+AgEQlx88W0QCISorp4Q81yNxhAVlB4s1Gp9zKzvwznqqNiBEblcBaWSH2jYvHklHnzwd3jzzYcTaohTlB8//vhJ6h3OAnl5cpjN3rSNND/77FN8+OFLPGmZMCZTG1544Va88cY/EsrDsCyL3bs34vHHr8GDD/4Ov/zyLVg2+nkmFktx9tnX4u67X42SrnE4ekDTAxvX9g2GH3XUsRl7J9ntflRUaFBRoU1JymjixDlRAfkffvgAK1e+i9tuOw0337wIy5e/nJE+xuLAge287zRBCGIGXi+55C6ce+6fcOKJv8V9972JefNOj3m94uIqlJfz9ZljLZh4PE4899xfkurj6tUfJ5UtX1KiRmenG3v39vR77EBwu2mIRNnTC9+2bTVve9KkORCLBzfwzLIsrFY/8vLkaGpywOfr/e55PBRKStQDqmzONhUVWlRX63HwoAOdnW40NzuitLGzhc1GQqeTY9y46ABxcbEKOp0cTmf673CWZeH10hg9Og9i8cA+h3q9DGVlalit/ce7ElFeXhfVVlBQhlGjjhrQdX+NpBUMNxgM2LRpEy677DJ4vV589dVXePXVV/HVV1/B7Xbj8ssvx8aNG2EwpD4p2bx5M6ZOnYqpU7lV0r/85S+YOnUq7rvvPgDA7bffjptvvhnXXXcdpk+fjs7OTnz77bdQq3sHX0899RTOOussXHDBBZg3bx4UCgU+//xzCIXZe5AeaVBUEDKZCGPH5mHx4lGYN68SBkPmg+CHU12tg1wu5r0A4nHWWdfyBlSBAIn//ve+AWsH7ty5Fk88cW3ElNPptODFF29PKMPCsixeffXBKLPMsWOn4cYbn4xpFnH88edHTYR/+mk5Dh7cFfc+AoEAlZU6CIUE1q5tw88/dyY0Hc2RfQiCgE4nB00z6OpyD4nR1pFG2LDM4fDD7Q7kZGhy/GohSRqdnS7YbJwkikLx6yt99HrpQ8Hw6EBre/t+3nZl5di07iESiXHWWVfz2jo7G5OSXvjyy1d52wqFGgsXnpfUfceP55frNzRsHVb6x8lQXq6BXi9Debk25XMJgoDNltqEkDO9pqHVygEQaGy0pSVxkMw4E+BkUgIBesAa0KkQDDLweChIpSLs3s1fIKmrmxwJNscLho8Zc8yQLYIRBBG1sHQ4BQVlCQP1faVSPv74+ajveTx++OGDlHTVs4VEIgLDsGhudqR1/nvvvdvvMatWfYTXXvt7zM/+gQPb8dBDl+KJJ66Nkm8KIxAIMXfuEjz88HKcccb/QSQSR2m2sywLmy39JCOXy4bmZr6pcCzj1HRgWRbBIIPyci0KChSRtmQQCAQ4/vjzeW2//PId3n77scjz99NPX+JVH2SS3bv5MrM1NROiFoEAznBwyZIr8Pvf3xn3ux6mr1TKzp1redvbt/+Iu+8+G3a7Oak+ms3tSXlnCIUC5OXJsXt3D3p6sicn5XT6s2aeGQiQUc/ZoZBI8Xop6HRSzJpVjilTCtHT40Nnpws0HUIoxKCoSNX/RYYQkUiA2bPLMHVqEU4+uQ6jRxvQ2Zn9SnaGYeBw+DFpUn5Ms2GFQoLqat2ApFJcrgA0GinKyxMv9CYDQRCoqdGDpgeWrV5WNiqqbc6c00Z8AsxQkHbtrMFgwCuvvAKHw4EdO3bgp59+wo4dO+B0OvHKK68gPz++ll0iFi5cCJZlo/699tprALgP0QMPPIDu7m74/X78+OOPmDRpEu8aMpkMzz33HKxWK3w+Hz7//HNUVAy+q/pIZvr0UixaVIvjjqsetAdwXp4clZUa9PT0r/9dVTUOCxaczWvbt+8X/Pe/98csAUyGlSvfwzPP3Bx1vsnUhrfeeiTuQOu7796JmjSXltbixhufipsdJhAIcPnl90dplb799qP9Phzz85XIz5dj507joGi15egfjYb7O3d1uWC3k8PKCI1lWTBM77/hCknS6O52o6XFgfZ2Jzo6XGhtdaK11QGn0z+sfqc5cmQbj4dCZ6cLJBkc8VInA8HtpiJGgn1pa+MHyVKVSDmcGTNOjppcLF/+UsLgdGfnQWzcuILXtmjRRZDLk5MLGT9+Om+bJD1oadmbZI+HB0KhAHK5OK0JmEIhhtHoSfrZzrKcRIpczi2MFBQo0NrqSmrM2BdOhqT/4Er45xqoxmcqeL0UAoEQpFIh2tv51Q/jx8+I/H919fiY5w+VXniYRDIYU6bMT/hZKSzs33+osnIcLrzwL7jsMn71sd1uxpYtPyTf0SxiMMjQ2pq6kabH48WmTauj2mMtMKxd+xm++OKVyLbNZsJLL92JpUsvR1NT7MQamUyBk0/+HR577HNcddXfeR5GCoUacjl/vpcoG78/du5cGyU9MWZMZj6bbncAarUURUVK5OUpoFRKU6rOnD//jJiJSofz44/LEu5Pl74LFH01zNOhb7W0ydQGs7kdgQCJ119/CM88c3NMGaHCwgpMn74IZ555ddS+eAspfdHr5fD5aOzcaUxbtioRwSADn4/OWjB8+/Y1UdU3Q2FAaLeTKClRoaREjblzK7FoUS3y8hRob3dCqZQgPz+7CYmZQKORYcGCalRV6TBlShFkMlHWF5K7uz0oKVFj9Oj4ccfKSg0EAiLtTHWbzYeqKm3MYHs6lJaqk5YGjkffSpGKitE4+eTfDbRrv0oG7KgkFosxefLkTPQlxzCipEQFny84qCtMBEGgrs6AAwdsoOlQvxOV88+/CTt3roPdboq0bdz4FbZs+R5isQTFxdW49tpHorId+hIM0nj33Sfwww8fxD1m3brPkZ9fFpU91tbWgA8+eJrXplRqcPPNz/ZbKpqfX4ozz/w/vP/+U5G25ubdWL/+C8yff0bCc5VKKXp6fOjq8qC2NvUKjByZR6mUHArocqaaeXmKQStpCwYZ0HQIwSBz6P/Dum0MQiF+oIEgCEgkAshkIkgkIkgkQkilwoTfdZZlQdPctbnAFAGxWNiv3moyMAwLu52E1epDMMhAoZBArRZG7uvz0ejsdIOiQsjPV+RWvXMc8Xg8FLq6XGBZQKvNzOB7pOJw+OM+R/vKpKSbGQ5wC9RnnXUNXnjh1kib0diCTZu+xty5S2Ke88knL/CkB6RSeZSnSSJ0ugKUltbyqsr27v0ZdXW/jjG1UimGyxWA10tBpUosKwNw2YFmsxc6HXesQiFBd7cHLS0OFBYmr1cOAA5HAFJpcsEVqVQIk8mDyZOLUrpHung8FEiSRkGBPGrBp7JyXOT/VSpdzPOnTj0+m93rl5KS6rj7+gsyTZ48P64mdlnZKFx88W0YP35GZBywdu1nPH3kr756DTNnnjzk4wSNRoamJjs6O90pBVA+++xrBAL8aolnnvkearUe69d/gddee4gnn/LJJy+grKwOfr8Xb731aJRnUW9/8nDSSRfh+OPPj5mFHCY/v5SXiT/QYPjhTJgwO2PSEy5XABUVWqjVUrAsi7w8GXp6fEk9RwAu8D9nzuKE0jqbN6/EeefdkNHPksfjQGvrPl5bX3madKisHAuNxgCXyxZpW736Y+zatR4dHQeijheJJDjnnOtw6qmXRtpI0oNvv307sp1sRQbAxQwOHrSjqkqPykotxOLMSbT5/TQoioFKlR0T8E2bvuZtjx8/I+6zNVuwLAuKYlBVxd1XIOCyh8vLNZGkiJE2FiwqUmHcuHxs29adsSByX/x+zvtjypTCmNWDYYqLVSguVsFi8aG0NDXN70AgCKFQgNpa/UC7G0EuF6OuTo8tW7qQl6fo/4QYVFWNw113vYKtW1ehsnIsZs78Tc44M03SimTs2bMHzz77LHp6YmtEmc1mPPvss9i7d2RluOQYekpL1SgqUsFq7T/TR6nU4NprH4FIxB9c0XQAPp8bTU278Oyzf06Yae3xOPDUUzckDISH+fTTl/DZZ/+OZDrQNIX//vfeqMyxq656CAUF/We3AFwWWd8smo8+eg4k2X+5mUYjQ3u7C37/yCqrPpKRy8VQKMQwmbwwGt0Zl7FhGBYUFYLXS8Hh8MNk8qClxY7mZjtaWhxoa3Oiq8sNq9UHny+IYJD7rBIEERmYMgynfWYyeQ8ZkdnR2uqA3U5GGYFSVAh2O4nWVgeam3vvw/3Xjq4uF9zuQNoZ54FAEF1dbnR3eyASCaHTyXnZH5wZlQQKhRhmsxcWiy+XIZ7jiMbrpQ6ZJHMmmL92enq8Mc0znU4LXC6+0dlAguEAZ0R2eLARAL744n8xxxCNjTuitMJPPvl3KU+iD8/0Bbhg+K8FhUIMr5dKWsvTYiHh9VI843a9Xo6DB20pZYVSVAg+XzDpTEO5XAKrlRw0WbpwNYTHY48yuysv51cvzJhxMm97/vwzeNm+Q0G8zHC5XIVx46bH3BdmzpxTYxqDLV58GR588F1MmDCTF2Q76aSLece1te3Dli2pGUxmA4IgoFCI0dhoSzpb1u8PYtWqdby2UaOOgkZjAEEQmDfvdFx33aNRQcbnnvsL/vOfe2MGwgsLK/CHP9yDJ574EkuWXJEwEA4gKnnIYulOqu99CQZp1Ndv4LVlSiIFAPz+ECorOWkmgiBQUaFN2cRx8eLLE3o7mM3taGqqj7s/Hfbs+Zk3hpVIZKiri2+EmywCgQCTJs3hta1Y8XrMQPjRRy/Aww8v4wXCgej3ZzJ+WWFkMjGkUhF27DBixYoD+PnnzpgShx5PIOW/E0kGQVHBlDwpksXnc2PXLv53btasUzJ+n/7gFoQlUYu6YrEQ1dV6jB9fkJHFBYZh4XT6B+1dVl6ugVBIZKViAAC6utwYNcqA6urEgWqhUIAxY/JAksGUpUksFi9KS9UoLs6sSkJFhRYSiXBA5rNjxhyDCy+8BXPnLskFwgdAWsHwRx55BI8++ijy8mI7mebl5eHxxx/HY489NqDO5fj1IRYLMWZMHrxeOqmg1+jRR+NPf3oibrlbe/v+qAlrmAMHtuP++y+KWQp25plX49prH41qX7bsX/jXv+6Az+fG8uUvob2dP9A49dTLUhrwiUTiKDNNp9OCzz//b7/narVSuFz+rOq05UgdiUQIrVYKm82Pjg4uWOz1UvB4+P+8Xgo+Hw2S5DRxD/9HkjTc7kBEO7unx4uODmckIN3a6jykJUyCphmIRAKo1VLo9XLo9XJotTKoVFwQWS4XQyYTQSYTRYL14WMNBjkUCglomkFnJydR0t3thtsdgMXiRWsrZ4RC0wxkMhF0Ohn0ejk0Gk62wekMoK3NibY2Tsok2QEPy7JwuQJob3fC6fRDp5MlXNWXSIRQKiUwm70D0n3LkWM44/cHYTR6wDAsVKrBNW8ajvj9QXg8VBzzTH7WmlQqR2Fh+YDuRxAEzj77Wl5bd3cztm7lSy8wTAjvvPM4r02l0kUFF5Khb4n8gQM7eCXbRzICgQAsy0aC4T4fhTVrWuIu8Hd2uqIyDvV6Gex2/yGD2eQgSfpQcCW5YLhCIYLPRw+aJ4jTyVVDdHQ08tolEhkKCvif8cWLL4skhBgMxTj77OsGpY+JiKcJPn36if1KU4jFUlx4Ya/Jn1KpwU03PYPzz78JQmH0GGHatBNimN++CIYZej8dg0EOs9kDk6n/MTrLsti504R9+3bw2vuaoU2duhC//W3/JogKhRoXXXQb/vGPj3H88ef1a+gbpm8ij8XSmdR5fWls3BEVnJ8yZV5a1+oLSdKQy8XIz+/NqNTrZRAIiJQCXYWF5TjttMsTHrN+/Zdp9zMWfY0ax449JmPZ8pMmJf79SiQyXHbZvbjxxqdjVkz3DYZbLF3w+ZLXfC4qUsJk8qKry40dO4zo6ODLeNJ0CCtXNuPzzxvQ1ZX8df3+IGg6lJFK1L5s3bqK5zMgFIowbdoJGb9PfzidfhQWqrKa/R0OhEskQvh8g+OtYDDIoVLJsvLutFp9UKulOOqo4qSqsCsqNMjLk6fkUxIMhhAIhDBmTH7GpQoLC5UoLlan7JuSI/Oktcz2008/4cQTT4yp4QgAQqEQJ554ItasWTOgzuX4dVJZqYFWK4PD4Yde379G1lFHHYv77nsL77zzGPbsic6q+uyzl3HMMcdHPq8Mw2DFitfwySfRA2aRSIIrr3wwsjLs9TrxxhtLecf88st3MY21KipGR02kk+Goo47FlCnzeSWF3377FhYsOCuh9qJQyE0kjUYPKipSN6/KkT2EQgH0ehk8HgqtrU70XdBnWUTaCAJRK/6cV0LvNkEQEIkEEAoJyGQiiESZK0EUiQQQiSRQKsOZ4P6Iy7VcLoppnCsQEJBKRZBKOaMon49GR4cLSqUYer0cKpUk7sAhnG1utZIQiwVJfccBLiAeColgNnsjwfEcOY4UaDoEo9GNQCAEnW5klcNmi7BcRCwJjL5Za+XloyAQDFxT9KijjkVFxWjeQvcXX/wP06adGHnmfvfdO2hu3s07b8mSK6L0dpNh7NhpIAhBRG6FpgNobNwZlTF+pCIWC2CxeAEUwGolYTJ54XIFohZHSZJGV5c74s8RhiAIyOUiHDxow+jReUlNirlMwxCk0uSmQFKpCH5/EE6nP+2S5mRhGBZmM1cNsW8ff8GH+4zz36vV1ePxxBNfobV1L2prJw16eX8sCgtjezTFkxvqy5w5i1FSUo3u7lZMmTIvYTazUCjC2Wdfi3/9645IW1dXM3766VMcd9w5qXU8w0ilItA0g7Y2J0pLE8smdna6UF9vgtHIf67V1EyMOvbkk3+HhoYtUYHVMHPnnoYLL7wFanXqZf19DUzTlUnZuZOfbVtVNR46XUFa1+qL2x2AXi/ljR25oJsEbjeVUkDx+OPPx+ef/zeuN8TGjV/hggtuglQ6cL1mmg5g+3Z+XOToo48b8HXDTJw4CwRBxEwkKy2txQ03PJlwTllSUgORSMwLDre1NfRbzRFGIBCgrk4PgiDQ2spVqh6esdvd7YbJ5AbDAJs3d+Hkk+sSJsGECS+OZkP6aONGvkTK5Mnz+pU4zQYkGUJlZWryHanALTr7odFIIZOJklqgywRyuRjFxQo0NdmTnuslA02H4HT6MW9eRdLvZIVCgrFj87BuXQfy8pKT3Ozp8aG4WB2pQskkAgGBUaMMaG11gGGYuDHVHNknrd+80Wjs15CyrKwM3d3plVfl+HWjUklRW6tPabWsrKwOt932Mh5//EvMn38mb197+4FIZpfV2o1//vM6fPTRc1GBcK02H3fe+V9eidTxx5+Piy++rd+HplAowlVXPZT2Cv9FF93Ky3oJhYL48MNn+j1PrZairc05aCVPOZKHIDiZA4NBHsnYDv87vE2rlUGtlvL+abUy3vE6HZfpLZeLIRYn1vceCBKJEDqdDAYD18dY8gR9EQgIqFQSaLUyUBSD9nYXWlocsFi88HopUFQIFBUCSdKwWn1oa3Ogp8cHpVKcckBbLheDZQGz2Zv7zOc4YmAYFj09Xng8NLTanDRKGLc7AIqK7R/S11hwoBIpYQiCwGmnXcFra23di/r69QA4Y7JPPnmRt7+4uAonnHBBWvdTKjWoquJLs8Ra1D9SUSi4ip9QiIHD4YfN5ospedLT44XL5Y8KhgOc+Xp3twdmc3ITfJKkEQoxSWd6cTJjXPZetmlutsNo9ECjkUZlhpeXj455jlabhylT5g+LQDgAiMWSqMxToVCEsWOnJX2N6uoJmDPn1H5lPQBg+vRFUd//999/ClarMeF5a9d+hjvuOAP/93+z8eCDv8uKea1eL0dzswM+X3wZH4oKYft2E6zWTvh8/AqH2tpJUccTBIHLLrs3Ktgtkylx3XWP4aqrHkorEA4A+fklvO30g+F8vfBMZYUDgNdLo7xcy1v4UigkyMtTwONJzehWozFgzpzT4u73+dxYt+7ztPt6OLt3b4Tf3/uMIggio1nIGo0B1dUTotqPPnoB/vrX1xMGwgGuUrm0tI7XduDA9pT6EJ6b6HRStLdzlbEAF4xtbLRBIBCgulqHjg4X9u+3JHVNkqSRDXVEq9WIPXs28tpmzx58iZRwpUOqvhfJwrIs7HY/1GopiotVUColIAgMmuRkYaEKNJ05mRSWZdHR4UJNjQHjxqW2wFZXZ4BeL09KijcY5OatEycWZM28tbxcDZ1ODofj11ENOFxJKxiuVCphNpsTHmM2myGT5bKbcqRHTY0OMpkoZS2l/PxSXH75fSgtreW1L1/+Mn788RP89a/nx5RFGTduBh544J2YxlUnnXRxv4aYZ599HSoqxqTU18MpLq6K0j7cunUV9u79JeF5Wq0UDgcJi6X/B3uO4QlBEBAI+P+G2vwpHcJBcb2ee+6bTF60tjoj+uLNzXZ0d3tAEAQMBnm/BrnxUKsl8HppWK1kTj88xxGB3U7CZvNDq5WOyO9+tggHNmL9Tvqae1VUZCYYDgAzZixCUVElr+2LL16B1+vCc8/dAoriB0Uvv/z+AZW6jx8/k7edjm44Jz1lG3HPRIVCDI+HhtMZgNHogcdDw+uNHvdxmWxEzOwpmUwMigqhszM5qRSSTN1nRS4Xw2jM7jjL7Q5g69ZuSCRCyOXiKBm+iorYwfDhSF/94uuueyxrzzaBQIDzz7+J10aSHrz22t/jymasW/c5XnnlfpjN7aDpAFpa9mDp0suxYcNXGe2bTieDw0GiszO+LERjow3t7U643U28drVaj7y8kpjnaDQGXH31UshkXFZkaWkt7r33DcyYcdKA+tt3EcPh6AFNpxZgtlqN6OzkL+RMnpyZYDhXNcmioCA6G7SsTJ2Wh9KFF/4F1dXj4+7/6qvXeNnS6fLLLyt522PGTIVWmz/g6x7OySf/jre9ZMmVuOGGp5KuWhoz5mje9rp1n6essQxwnlYuVwDd3ZxUjtnMzQcKChSRytldu8yw2/tPenM6AxCLM581u3btp7z3pVyuymimfrK4XAHk5ckzmjkdhmFYOBzcInJJiQpisRASCfePogYnoUink4EgUpMwSoTZ7IVGI8OMGaUpzyNVKikmTcqHwxHoV9azu9uD8nJtxNQ0GygUEowapU/qe5Aje6QlkzJt2jQsX74cjz/+OHQ6XdR+u92OZcuW4Zhjjhlo/3L8SiksVKK0VIOODgcqKnQpnSsQCHDmmVfzyiY7Oxvx2mt/jzqWIAQ488z/w+mnX5mwxHrKlPl48MH38MYb/8CuXet5+yZMmIlTT/1DSn2MxemnX4m1az/jGSa9994/cf/9b0f1LRQKwu/3QanUgKIYmM0elJQMfmlXjhx94crWxYeyuFmEQtzkRaEQJFXCnsz1NRopbDYSCoU4ZqbgkQZNh+D3B8EwbESiJluZCjkGF6+XgsXig0Ihzrgm4Uinp8cb83MeCJDo7m7ltWUqMxwABAIhFi++HK+++mCkbf/+bfjTn6InyieccAHGjJk6oPtNmDATK1a8Ftlubt4NkvQkFcBgGAY//bQcn376b9jtJlRWjsO11z7SbxYgSXohFAr71XA+HL/fB5L0QKfLjJkXgEjSQ0+PFxYLNyF0ufiLDcEgJzWRSEdfq5WiqcmOSZMK+5U/8XrplPuvUIjgcJCRLL5MwzAstm0zwmLxobZWj1AoiK4ufnA0Xmb4cOTMM6+G02lFV1cTFiw4G8ccc3xW7zdp0hzMnbsE69d/EWmrr1+Pu+46C9OnnwibzQS32w6WZeH3+9DUtCvqGjQdwL//fQ9aWvbg/PNvQnv7fthsJni9Lvh8Lni9rsj/h0JBiMUyaDQGFBVVoLp6AqqqxkWN1QmCgEwmRGOjHXV1hqgxkM9Hob7eDLVagvXr+X2qq5uS8HM6ceJsPPbYF7BajaisHJuRMvtYWtJPPvknqFRchr5EIoNUKodKpYNGkweNxhD5JxSKEAiQ+PDDZ3nnK5XamIlG6eD1UlAqpTHl+/R6ecSsL5V3qVKpwZ13voLvv38fLpcNpaU1ePXVv0X2W63dWL36YyxadGHa/Q4G6ShZm+nTF6V9vXjMmnUKhEIRDh7ciWnTTsTo0UendP7YsdOwcuV7kW2TqQ3XX78ARx01H8cccwKmTl2Y1MIvQRCQSgXYubMJZWVyNDRYEQgEI+bHBoMcBw/asXdvD+bOrUx4LZcrkPHxLsMwWLv2M17brFm/yYgcTqp4PBQmTy7MyPzocMKBcK2WywgPB45FIkEkGC4dhOmTRiOFQiEGSdJQKgd2Q6eTBEUFMWdORcxnQDKMGZOP5mYnurrccSVmtcM/DwAA7/JJREFUvd4AABaTJxdmfa5VVaVDfb0ZPh/fHDzH4JFWMPz666/HWWedheOPPx7PPPMMFixYENn3448/4qabboLdbsef/vSnjHU0x68LgiAwerQezc22lAc2ADfIKC2tjZpMHI5eX4j/+79/JK2Hlp9fij//+Xls3rwSn3zyIozGFowZMxXXXPNIRrRKFQo1zj77Wrz55sORtra2Bqxf/yXmzz8DAGA0tmLFitexfv2XCIVoLFx4Hk488Vo0NzsxenQeZDJxxl+oOXKkC6d1nvnPo0gkgFgsQE+PFzLZkRsYZhgWdjsJu51EINCbxSEWCyJSOkfqz36kEwwycLsDsFp9YBg2Ke3MXxOhEAObjYwZeOzsPBjR2Aa450x5+aiU7+HzURAKBTGDp3Pnnobly1+C3W6Ke35ZWV1URmo6jB59NIRCUUS3lmFC2L9/W79m3D6fGy++eAd2794QaWtr24fHHvs//O1vH0Clij3RW7nyXbz//tMQCAS49NJ7ktJy3rHjJ7z00l3w+71QKNSoqZmE6dNPxPz5Z0AkSj84HJYgaW11wusNwGCQRTwrwthsJJxOPwoK4peR63QytLe7YDZ7+/VQcTrJlJ+bSqUEnZ1uOBz+rATDW1sdaGiwoKREBYIgYDK1IRjkZ+Sm8xkfKnS6Atx441ODes+LL74Ne/ZsgsPRE2kzm9vx1VevpXSdb799G99++3bK91cqtZg69TjMnn0qxo+fEZkXGAwKGI1u9PR4UVTEX+BqbLTBYvGhrk6PxsadvH2jRk3p955qtT5tSZRYKBRqKBRqnnHivn2JK1T7Y9KkORmZIwGA200hP18Rc2FMr+fkBD2e1HTDAc6AefHiywBw2edr137GkwhZtuxFzJixKO1M7m3bVkcZik6bdmJa10oEQRCYMeOktCsExoyJTmL0+73YtOkbbNr0DUpLa3DLLf+CwVCU8DrBII1PP30Ymzd/i7/+VYcLL3wIRx/d64NBEASKi5U4cMCGUaPy4kqEBIMMPB4KEknmxkfBII2vv34jSgLo2GPPztg9ku8LZwya6N2W3nUZuFwB6HQyFBUpozKouYosCsrsKLPwUKkkUKulcLn8AwqGezwBWK0kZs0qR11d+s88qVSEadNKsHJlE6xWX5TmeDAYQne3F1OnlmRFK7wvBQUKVFRo0NzsQFVVLhg+FKS1jHzGGWfg1ltvxY4dO3D88cdDoVCgtrYWCoUCJ5xwAnbu3Ilbb70VZ511Voa7m+PXRFmZBgUFyqS0nfoiEAgSmlnOn38GHnroo6QD4WHCA42lSz/Biy/+hLvu+l9GB6LHHXdOlMTLxx8/B6fTgvfeexL33HMu1qxZhmCQAsuyWLXqQ6xc+SI6O134/PP9+Oij3fj44z348sv9+PHHZmzd2oW9e3vQ3GxHVxc3GE+ljJBlWXg8AbjdgZTKrxmGjcruypEjkyiVEpBkEFarb8RJAyRDMMjAaHRHpGUO15AXi4WwWDj9dZcrte9mpmFZFoFAED4fDb8/2G/p4a+dsDZya6sDnZ1uEASR8sT914DHQ8HrpSGXR0+C+waNioqqks7oYlkWVqsPjY02OJ0BdHS4YsqxiUTihBVfXLDv6YhMwUCQSuVRga9Ycm6H43Ra8OijV/EC4WHsdjO+//69GGdx+95770kEgxQoyo/XX/9HVJCmL8Egjddffyiid+vzubF79wa8/vpDeOihS+F0WhOe3x9cACuAYJCBUimBx0MjEOgdp1gsPgQCiQ0vRSIhABYdHfHlKICwkVgAUmlqwTmRSIhQiIHTmTltT5KkUV9vQnOzHdu2dUMkEkQyw/pKpOh0BcNGE3y4olRqcOWVf8tY4DVVvF4n1q79DE88cS1uueUUvPfeP9HSshcyGWfA2tbm7HM8hT17LNDppKAoPzo6+H/zurr+g+HZID+/LKPXy6ReOEnSKCtTx8yYVygkMBhS1w3vC0EQOOec63ltPp8bL710V8qSMWFWr/6Itz169FTo9YVp9zFbaDSGKNmuw+nqasb337+f8Bosy+Ldd5/A5s3fAgDcbgeWLXs8ahFRpZKCJGns29cTdwzr99OgKAZS6cCrHliWxebNK3H33Wfj44+f5+2rqBiNmppovfVs43ZTUKmkGTVmDgSCEemVwzPCD0cqFWVFhz0WBEFg7Ng8+HxcBRiXdZ0adjsJs9mLadNKcdRRxQOuTCsr02D69FJ4vRQvxkTTIbS0OFFbq8fRRxcNimwhl/yZB5ZlQVGpyzyF8XoDOS+tNEl7qe2xxx7DwoUL8cILL+CXX35BR0cHdDodTjjhBFx//fU49dRTM9nPHL9CpFIRxowx4Kef2lFQwKb8UJo27USMGTMV+/dvi7TpdPm47LL7+s246g9OCiI5DbZUEApFuPDCv+DJJ3urKhwOC26+Of4q/+rVH8FgKMbcuRcBAEKhEHw+GiaTBzTNRAYZXJauAEqlBFVVOhQUKKBUiiGViiAQAAzTK8fg9dJwOv0wm71wu7nAe2GhEtXVOpSUqKDRxA/e0HQI27cbceCAFRUVWkyYUJDRF32OHGE0Ginsdj+USskRJZfCBcI9kRLHvpUxUqkIUqkIXi+Fjg4nCguVMBgUg1oVwrIsvF4adjsJn49GMMhAICAgkQgPGcFKspJBOVIJhRi43dzAmySDkMlE0OtlOY3wOHg8FPz+IIqKolOXDpdCAIDa2on9Xi8cBHc4AsjPl2Pu3HKUl2uxa5cJjY22mLqQCxacjS+++B9cLn6wt7S0Fjfe+BQKC8tT+6ESMH78TDQ0bI1sJ9INJ0kPHnvs6oSVb5s2fYMzzvi/qM/Xtm2rIxnoAEBRfmzf/hPmzIk/Zt+2bTXs9tg+Qa2te/HBB0/jqquiZeiSRaWSoKfHB7GYy9K32fzweulI8LujwwWZrP8Ap0YjQ2urA1OnFsettPD7gwgEQmlV1AiFXDXSuHGZ0fltbnbgxx9bIRQSoOkgmpq+xjffNGP+/DOizDNHkl54GJZlQdOhrJp+92XixNm49NJ7eDIXiVi06EJcdNFtWLv2U7z55sMZ0YYGuHH7N9+8hW++eQsajQF6fQmWLdPj5puvx+LFpyEQCGLLlm7YbCTq6vRoaNgChukNZAgEQtTU9P9cywajRk1BW9u+jFzLYCjOWAY0wzARz5l4lJWp0dxsH/C9xo2bjtmzT8XGjSsibfv2bcYTT1yDuXOXwO22o6ioEtOnL+r3s200tkaZIi9ceO6A+5gtLr/8PnzwwdPYvXtjzIXSjo4D2LTpa3zxxf8gEAgxbdoJmDv3NOTnl8Lh6MGHHz6D9eu/5J1jsbRi//6tUSa6RUVKHDzowNix+VFVEwDn70BRwZTkvGJht5vx5psPR0nVhDnuuHOHZCzm8QQwalTegCsDQyEGgQA3fxeLBSguTjwnEIsFEAqJiPRithk9Oi+iId/T44XD4QdJhiCXi6BSSaBSSWIqAJAkDbOZk8ubO7cCkyYVZay/EyYUQCAgsGVLNxobbRAKCbAsi1GjDJgzp3xQ5y9lZRqUlKjR0+NDWVn/ptF9CYUYtLW5IBYLMWqUIdLOsiwYhoVIlJNgTMSAvn2LFy/G4sWLM9WXHDmiqKrSYedOM1wuP7Ta1PShCILANdc8gpdeuhNdXc2YPv1EnHfejUm50w8lkyfPw+TJc6O0yRPxySfPw2AoxLx5p8c9hmEYhEIsXK4Adu40gmVZCIUCiEScljPDcIF0muYGnEIhZ0ylVHIvhK4uN5qa7FCrpSgvV6O8XAuDQQ61mnuJBQJBmM1e7NtnxcGDNuh0UtTXm9HS4sSUKQUYPz772ls5fl2IRNznt6fHC7lclLYp53CCYViYzVwgXKeTJRz4KZUSBAJBGI1ehEIsCgqUgzKwDQYZWK0+WK0+CAQCyOUiiEQCsCyXldLT44XN5otksg/F955lWQSDTGSwLxIJhmSywzAs3O4AbDYSXi+dC4InSbjioa8Obnv7frS27uW1zZ6deBzq9QbQ3e2FXi/HscdWoK7OEMnALSpSoaHBEvM8qVSOG298Cq+++iBMpnYUFpZj7twlOOmkiwY8Oe/L+PEzsXz5S5Ht9vYDcLls0GgMvOMYJoSXXrorYSAcALq7m9Hevj9KS33r1tVRx/7yy7cJg+H9ZQI2NdUn3N8fcrkYwSCDoiIlZDIRKCp4KFtfDq+XQk+PF2p1/4udOp0MbW1O9PTEl0ohySACgSDU6tQX6FUqCUwmD4JBZsCTS4oKoaGhBxqNFIWFSnz++X+xbNmLAIC1az+L+tyPJL3wMC0tdojFQgQCIVRVaQ9l72efBQvOhkKhxvvvPwW/34eqqvEoLx8FrTYfAoEADMNAIpFi/PgZkUrMBQvORllZHf73vwd53y2DoRgajR4KhQZKpRZKpRpKpRYikRiBAAmr1Yj29gYYja3xugOXywaXywYAuOaajXjllU8RCpWgqckeyXJubNzBO6eycsyQ6BcDwJIlV8BobEFr6z4UFJShqmo8NBoDBAIhKMoPkvTA7bbD5bLD7bbB6bTygqZ6fSGKiqowevRROPHECzP2c/h8NBQKSUKjQb1edmg+wwxYQ/3ii29DQ8NWnlTW/v3beAlWF1xwM0499dKE1+mbFa5S6TBjRub1wjNFQUEZrr/+8UMLyN3417/u5Gns79y5Fjt3ro1st7Xtw7JlL0IikUWZSx/O6tWfRAXDlUopTCYvGhqscYLhNGg6NKDn7caNK/DGG0vjVkDNmHEyjjvunLSvPxAoikFxceqJdQzDgqJCoGluzi4Ucl5CJSUqKJWSfoPrYRPNQCA4KEFfkUiAWbPKwbIs1qxpxc6dJkydWgyvNwir1Yv2dicYhkvcCwfpWZZL/Kmq0mHSpEKUlmbWF40gCIwfX4CiIhW6ulzw+0PQ62WorNQO+lxSJBJg/PgCrFx58JB0Tmr3t9lIlJaqDxmN92I2e5GXp0BNTeYUDI5EciKVOYY1Go0MtbV67NhhTDkYDnCDsrvu+l8WepZdfvvbv6C+fiNPFzWMSCSGXl+Enp4OXvsrr9wPAHED4gKBAAIBkJeniGRqh0IML2AkFBJxH8IqlRQsywV2DhywYe9eC+RyMWQyLrM8GOT2CQQEqqq4l0l+vhJ2O4n16zvQ3e3FzJllaZte5MgRC6VSDLvdD6uVRFGRcsQGGbksOgZOpx82G5cRnkxgWyoVHcpY5LSnCwuVWTVi5ILvHrjdFNRqCW/QSBCImKdSVAg9PT54PJzGp0YjHZS/DUnS8HopeDwUKCp0WDBcCI1GApVKOmj63D4fDavVF5FlyAXBk8duJ2N+jtet+5y3rdcXYuLEWTGvwTAMurrcYFng6KOLMGlSYVRVk0YjAUFwGUGx/jZ1dZPx0EMfRbVnmtraSZBK5QgEevWy9+3bjJkzT+Yd98knL/ICEQBQUFCOW255AY8/fjWsVmOkffXqj/GHP9wd2XY6LTH1f3ftWh/XsLO9/QAaGrbw2pRKLbzeXskHq7U77u8vGQiCQF0dP5spLHVgsfjg8QSQl6fr9zrhz4vR6EkQDKcPZRqmPtlVKsWwWjn98oFWu3Ea0j6Ul2tAEAQ2b/4uso9lmSi5qZEWDA8GQyAIAnPmVGDPnh7Y7Yk13zPN9OmLUjYorKubgoce+gjt7fvhdFpRVFSBwsKKpM61Wrvxyy/fYcOGFQmzqkOhIP7+9/txySWPo7q6d4Ggr/TTUEmkANwz9bbbXk7pnGCQPpRgI8qIkWcsPB4Ker08oZGuTieDUimB10tBrR7YgqVarcef/vRPPP741RGJqL6sXv1xwmC41+vCmjXLeW3z558BsXj4VzISBIH8/FKcc851eOKJ+LKjYRIFwgFg8+aV+N3vbouSeyosVKKpyY7x4/OjnhEkGYz0JRUYJoSenk589NFz2Lx5Zcxj6uqm4IILboqpkz4YBAJchWAyc2JOjjAEigodqsLkAtoqlQRKpQRSqfBQlXdyvyehUACVSgKrNbYvS7YgCAKjRhng89E45pgSKBQS+HwU7Hb/IWk8ChTFLTarVBIYDHLk52e36tVgkA+LuERlpRbFxVx2eElJ8oH/sPRbXZ2eJ/kSDIbg8VCYPr00qWSCXzNpzQjb2tqSPrayMrFLcI4c/VFTo8fevT0gSfpXU3ZfVlaH00+/Ap999h9e++jRU/HHP96P4uIqfPPNm3jvvScj+1iWxSuv3A+drgATJ85O6j5CoSClwBlBENBoZNBoZGBZFn5/8JBOMAuJRIDKSm3U9cKD15YWO5xOP2bOLMutUubIGNxnUnrIbE804vSXw3rbLlcAgUAQFMXELRmMh0gkgEYjjQyEshUQ9/lodHe7EQgE+w3sSiRCGAzyQ1IuLhgMcuTlKbKWJU6SnGSLyxVAMMgeMlblJgfhhQaj0QuxmDyUsS7LWvZHKMTAbidhsfjAsug3wz8HH5ZlYTb7oFDw3/fBII0NG77itc2duySmRjBJ0ujsdKO0VIVjjilBRYU25udVpeKyqEiSjmSLDwUikRhjxkzlVYTt3fszLxi+f/9WfPXVq7zzVCodbr/9ZeTnl2Lu3CX4/PP/RvZt2PAlzjvvBigU3MRq7drPeRIpYYJBCtu3r8GcOdEZ9j/8wM8K12jy8Ne/vo7bb+813aTpAFwua9rmcn0hCCISDO/p8QEgkg6wqdUStLY6cPTRxTG/3yQZBMumHlwBuEo5v98Nu33gwfCWFicIgtMiZxgmSiO8LyNNJsXloiLJLCaTB01NA5euGAwIgoiqpkiGvLwSnHLKH3DKKX9AV1cTNmxYgY0bv4oy6QOAgwc3w27fiaqqBQC4593Bg32D4Uel9wMMEQMx0U0Wn4/GhAmFCb+7KpUUer0MFosv5WA4y7Lw+SgoFJLIPWprJ+L221/GCy/cyltoDGM2t8PjccTV8//66zeiMpKHKgs5XQyG4oxcJxik8NNPn0V5cahUXHb4/v3WqGC410sh1Uc1SXrw5JN/iqq2CKNW63Hxxbdh1qxThjQ5we0OQK1OXOlAUZzsKcuykEqFUKt7g98SiXBA43yFQgyLJXVPtoES9oMLzwUUCsmQjr2GCxKJEBMnFuD775tSyg7nqoilGDXKgI4O16HFfhFMJi4poLpal92OHwGk9S2qrq5GTU1Nv/9qa2v7v1iOHP1QVKREebkWPT2xV+aPVM488xqcdNLFAACxWIozz7wad9zxbxQXVwEATj759zjlFP6ggmVZfPTRc4NiqMfppouh13NBLo1GFvfFLBYLUVOjh98fxOrVLdi1yxgpicqRY6CIRAJIJMKUDWKHEr8/CJPJEzFS5HRsuSyRdALGIpEAWq0MVqsPJpMn40aWXi+Fri4XKCoEnU6e9CRCqeSc5G02Eu3tzowbftJ0CGazF62tDjgcfsjlYhgMcigUYkgkQohEAojFQigU4sjv1mz2oqPDBa93YEZbsaCoELq63DAavZBKucWZXCA8NcKZ/X3NM3ftWheRGwgTqxLK56PQ3e3GpEmFOOmkOlRW6uJ+XlUqKeRyUSQDbSgZP56f4X64zixJevCf/9zL++4IhSLccMOTyM8vBRDWPe19B/v9Pnz77dsAAJqm8MMPH8S9988/fxPV5vO5o7RfFy48B3l5JRAK+X8bi6W7vx8vaSQSIWw2EgzDoqPDGZFqSwaNRgqnk6sUikUss9RUEAgI2O0DMwd3ufxob3dCp+OCIDZbdJDtcIRCEUpKagZ0z8HG5fKjvFwDqVQEvV4Oivr1GHuVltbi3HOvx2OPfYGHH16Oa655JOqYt956JFIF0tXVBI/Hwds/evTICoZnG5blZBMMhv4D3KWlqrS+562tTjgcAXR2unjtNTUT8dBDH+OCC/4cM2M/nkyUy2XDd9+9w2ubMePkyBxupGAwFCXcH15sDSOVynHssWfh8ce/xKxZp/D2ff/9uzEXZAsLFTh40M7LbAUApzOQ8nj4009fjhsInz17MZYu/QSzZ5865FV6bjeFsjJtXAkYmg7B66Wg18tQVaVDdbUeZWUa6HQyyOXiASe8SKUiiMWCIXk25yRTY1NdrUN5uRbd3YmNwMNwUkYkRo3KQ2mpGgqFGCQZBE2HQFEMJkzIz+mFJ0FameF/+MMfYj5EnE4nduzYgebmZhx33HGorq4eaP9y5DjktGtAc7M95mpZMMg9yAdLj3CwEAgEuPji23D66VdCKBRFDTgIgsAFF9wMgMs+CNPSsgcHDmwbstKveBAEgZIS9SHZlHZ4PEFMm1aSeynmyAgKhRgOB4meHi9KStTDdgBAUSE4HH7Y7SSCQQYKhRgqVWZK2IRCLiBus5FgWU4PORO/B4+HCy6GQmxamfcikQB6PZclzgWBZNDr5ZDLRWlPSDjJJk5PmCSDUKkkST1LpFIRJBIh3G4uY72oSAWtNjMSLiRJw2j0wOejc9ngA8DlouDzUcjL42dMrV3Ll0ipq5uCkpLqqPO5yYEB8+dX9vs3EIkEMBjkaG93JTxuMBg/fgZv22xuh9Xajby8EnzyyQtRWabnnHM9xoyZGtnOyyvGtGkn8MrCv/76DcyduwRbt/6QMOi6a9f6KI3ytWs/45W+CwRCLFx4HgQCAfLySmA2t0f2WSxdqKubnPoPHQOZTHRILoqE3R5IKgAWRiIRgaIYWCzemFqsLldgQM9EiUQIpzN2oD1ZOjrckbJmADCZElfb1tVNHpTM20wRNu0qLeV+/0rlrzPrjyAIFBdXobi4CjQdiEgZApysymef/Qfnn39jlAxRXl5xZIErB8f/s3fe8W3U9/9/nXSnvWVLsmR5xk6cOHtPSAirhdJCCy20lNVCSwctBUonpWWUlsK3lA66KKV0/AoUStkrEBJGEsheXrEtb+1xd7r1++MsJYpkW/KUg5+PRx7g0y3LNz6f1/v9fr0ZRvY2TgWQhsNmk6s2CrFuomkOFKXE0qUu7NrVg2CQzsjY1Wh0OPfcy3HuuZfjzjuvxNGjH6Q/a2nZjwUL1mXt88knf51he0UQCnziE9fldT7FhFqthcFgyQrYKBRK3H33U+l3QTQahFZrgMtVmX5enXHGJXjnnefT2/j9Pdi589Us+y+jUYPe3gCOHg1kVN3IYnj+UtWRI+/j5Zf/kbVcpdLgc5+7FevWfSzvfU0kqWekwzF0hVFqHFmIZUYhqFRKaLUUEgluZh5eJFCUEgsWOPDSS9F0lcpwBINyVnh9vQ1aLQWTSZ22lvN4TKiosCAaZSfp7KcvoxLDH3744SE/kyQJ9957L+655x788Y9/HO15zTBDBuXlJjidhiwvpViMRVdXDAoFkdFB91TCaBzaUoQgCHzqU1/Dzp2voL/fl17+9NO/x5VX/hAajQ5arWHCPPxGg9WqhVqtxPvvd4NhOKxaNbldm2c4dTGZNAiFGJCkAk6noajESEEQEYmw8PsTYBgBej01IT5uKUE8GGQgihKcTsOYBrrRKIvubtl32WQa2/nKjX1EhMMsIhEWBoMKJpMaKpUSFKWEUknkNXllWX5QKGNAUYqCvbhT1jqylUYEgmCAzZZ/tnsuIhEWfX0xcJwIi2XGG3wsRCIsRFHKyHyKRALYvfuNjPXWr78g5/bJpACv15z3/e9w6NHUFBh5xQmmomJ2lh/3wYPvoaJiNl55JTOru75+Mc4553NZ+7jggmuxc+cr6QxylqVxyy3Z2fMVFXPQ3d0KjpMnSoLAY9u2Z9DQsAJdXa0YGPDhiScezNhm6dJNsFodAICSkmwxfLzQaJSIxXi0t4fAMBy02sLEAJ2OwrFjYcybl22pEAwyY3oeqtXKdHXLaO5xnhfR1BSAXk+lt+/ubht2mwsumF4Cmlz+LzcGBQCtlhy3pobTlbVrz8fWrU9nCN8vvvgoTjvtQhw6lCmGn9xkcAZZGDQYKJjN+TXS1etlL2K9fuT1k0keAwMJOBx6NDbKWdBvvdUBjYbMOTepqWnMEMPb2vZnrbN//9t4/fXHM5atXXvetKvwSKHXm7LE8NWrP4LSUg8ApIM+JzNr1kLU1MzPaMD5wguPZonhAFBaqkVTUwCzZ9thtWrBMHIjZZUqv2fGwYPv4f/+7+tZmedarQG33vqnorKaSiSSMBhUKCnJLYYLgghJwoTbPhqNaoTDM2JpMeH1mlFfX4K9e/tQW2sdcpwhCCICARqrV5eng4QulwGtrSFQlAJz5sxkhefLuH9LBEHgW9/6FubNm4ebbrppvHc/w4cUilJizpwSxOMcRFEu/w8GafT3JzBrlg1aLYlEYvxL3qcDCoUSZ5xxScay/fvfxre+dS6+8pXTcO21q/GTn3wezz33SIZgPpXodCp4vSYcPDiALVuOIRIZW9nxDDMAcgm77J0tZ4gXgxVPqulsR0cYPl8UBEHAZtNCrZ64Jo5KpQIWiwaRCIuursiorQHCYQZdXfI5j5dwnzo3ebLKoaMjgra2EFpbg2htDaG9PQyfL4Kenij6+uIIBGiEQnJzHYbhEQrJFgN+P51uHjRa4VmrpaDTUejpicHvT4zaviUYpOHzRdKTlw+DEC4IIhhGLsccb+TmmZnf4dtvP5cxyaUoNZYvPzNrW5rmoNFQQ04yczFcQ7bJRKFQoKFhWcay/fvfwV//eldGM22KUuPqq3+U0yu9vHwWNmwY2ZP2s5+9BUuXbspY9s9/3ofbbvsMHnroO1lCOICMcYbdnpm56vePn02KWk2CYTj09MRBkoXfSyaTCoGA3D/gRHheRDyeHNOzl6KUYFkBLDu66767O4re3mhG1UNPT9uQ659++kWYO3fFqI41VYTDDFwuQ7riSa+nBv+mU29FNFUQBIHLL/9Ohr0Qz3P4978fwOHDOzLWnRHDs4nFknC5jHlZQ5hMaphMasRiQ497RFFET08UTU0B9PcnoFaTqK21QaEgMHeuA/PmOeDzRdOVxydSWdmQ8XNnZ6bffywWxh//eFvGMpVKgwsuuHbEcy9WclVgnXfe1SNuRxAEzjrrsoxlLS17ceTIrqx1TSZ5zJoKTNM0l7YPHIn9+9/Gffd9NSMTHwBWrToXDz74RlEJ4YBc/Wa1aodMMInHuUF/8IlNFNNoSJAkAZ4fX1vFGUYPQRBYtMgFl0uPzs6hKxZ9vgjKy02YM+d4rxY5MUi2HaqoyN1EfIZsJmw2vmzZMvzhD38YecUZZsiTigoTSkq0ab/GRCKJtWu9mDvXgVdeaUF7e+hD24Rh1aqP4F//+j+IYvbAjeeTaG7eg+bmPfjXv+5DVVUDli3bjGXLNsPpnLoGt2o1iaoqM1pbg2AYHuvWVRQkYMwwQy5IUgGjUYX+/gREUUJpqX7KouOJBIdAgEY4zAxahUyeUKpQEGlBvLMzgtJSPUwmdV7ZsqIoIRBIoK9P9r2eiMoN+e+kTh+P4wSIogSW5SGKsj9oqpRUbngHKJUEeF6CVkuOW/f3lDDW2xtPByqG+xuJopTxHYZCDLq7oxP2PRUjPl8E773nQyLBQ6EgoNdTKCszoqzMAJfLMCYvS0mS0Nsby3iXS5KErVufylhv6dIzsqzDADmr3GbTDNuU6mRMJg1UKmW68dBwiKJc4RGPc9DpqIKOkw8NDSuwY8cr6Z/ffvvZrHXOO+8qOBzeIfdxySXfwMGD72Zkbp/IOedcjrq6RRAEHm+//Vxe5+X11mVYr5WUlGV8Pp6Z4SSphCCIiEbZUVlI6XQq9PbGMTCQyMiso2kOyaQAo3H040SVSol4nBsMuhQ2hZIbJQYG93N8256eTJuU88+/BnV1i6BUUpgzJzM4UuxIkoRkUoTXe3wiLjd8I8GyAnQf4iGe212DzZs/jRdeeDS97L33Xsxab0YMz0YQxLznBwRBwO02oacnd/JPIpFEV1cMLpcey5Z54HDoodEo0+8cklRg+XI3aJpHc3MA1dWWjHdaeXmmsOr39yCRiEKnM4Jladx//9cQDPZmrHPxxTdMa+ubRYtOwwcfHK/MuuyyW/L2Pl+27AzY7a6MBqT/+c/vcPPNv8tYjyAI2O1aHDniR12dDYkEB5bloVYPX8nT1LQbv/zlN9JVTsfPeQOuuuq2oktOkCQJ8TiHRYuMOc+N50WIogSrdWzVivmgViuh0ZBgWR4k+eHUT4oRo1GNlSvL8eqrrejtjcHpzLR86++X52bLlrkz5h0WixYlJTpUVFhmrG8KYMLE8ObmZvD8hzcLYIbxR6dTob7ejjffbIdeT2HNmgrMmVMCgiBQU2NFc3Ng2pZhCoJYkIAgSRJ4XgRFyQ87s9mO+fPXZpWR56Kt7SDa2g7i3/9+AMuXn4VLLvkG7Pbx6RZeKCSpRHW1Be3tYbz6agtWr/ZmTKJOBQr9284wdihKmc4QTyYFlJbqodNNnlBJ0xxCIQahEJO2F5mKa4AgCJjNGtA0h87OCKzWkb26aZqD3y9nY6cy+iYahYLI6ziCIIIgiHG3vzlREE9lrp9IMik3MopEWPC8CKNRDaNRBY6Ts8uKVQiPx5MIBOR7QKeT/QQJQp5sJZMiGIYDwwhgGA40Lf9XkgCKUsBgUMFolMt4TxQkRVHCu+/6MDAQh9WqhShKCIUY+HwRKJUKeL1mNDaWwuMxjWoiF4slEY1y0OuPXw/Hjh1CR0dm9t369bm9P2OxJBYscBR0jRiNKmi1qkHvzNzXIU1z6O+Pg+dFmM1alJUZcOyY3NyR4wR0dkbh9ZrGHJBvaBg+C9jh8OLccz8/7DparR433/w73Hvv9ejubs34bO3a83HRRV8BIItutbXz0dy8N9du0iiVJD75ya9l/D1PFnb8/vETw1PEYmzWJDAfUs+I3t4YamuP2+fRND8Y8Bh9AEOtloMmiQRXcCDE76fR1hbOEvVOzgz3eGoxf/7aUZ8jgCkbC8fjcvn/iV64FKWE0ajKao73YeS8867B1q1PIx7PnfFnsZQOG+j6MMLzwqD9W/6BMbtdA1HM9g2PxVj09cWxcKETS5aUDfne1moprFlTDo4T0NYWQlXVcUG8rKwKSiWZUanU2dkEr7ceDz54E5qb92Tsa+7cFdi48VOF/MpFx7p1H0MiEUNLyx4sXnw61qw5L+9tlUoSZ5/9OTz22M/Syw4efBeHDu3ICvZZLBo0Nwdx+LAfNM0NJh8M/Rzz+Zpx//1fy+htAQDLlm3GtdfeWTS9FiRJSlfSxeMczGY1KiosOdeNRllYrZoJzwoHkK767OmJQa+f8MPNUAAejwlr1nixbVsnjh0LweHQQ6Eg0N8vz1PWrPHC7c5MCLFYNGhsdKCmZmh73RmyGddZriiK8Pl8ePjhh/HUU0/hjDPOGM/dzzADqqutaGsLo77ejoaG0vRyj8cEm02LQIBGScn0eaJLkoSuriiSSeGkEv2TJ/LS4DJ5HYKQsxeSSREulx56vRqf+9y30dXVXJAVynvvvYjdu9/ApZfehA0bPjElEXSFQoHKSgu6uqJ49dVWzJ1binnzSqd9lr/fn8ChQwPo6Ylh8WIXampOTU/7YoUkj1uFMIzctNFs1hSczZcvoiiBpjmEw0za81ivV6UDVlOJVksNNn7L9upWKAhIkty5PhZLIhyWvcbN5qkR8IdjIs9HrSYhihJ6eqJpux2eFxEKyd7kcrmu7Gve3x9HIJBIn1MxCuE8L+LVV1vR1RWFJEkZ5ykIInhe/pd67xAEMWhJQUAU5WArQciBnKoqC+rq7Cgt1aevbznAlPmMZlkenZ1hdHdH0djowMKFroKzU8JhBolEEqWlx4XGk7PC7XYX5sxZfvKmg0KnAg5HYQKqWk3CalWjtzcOiyXzM44T0NUVhUIBVFVZUFtrg8tlAEUp8dprrWhqCkCS5M86OyMZ4utocLkqYbGUIhTqz/n5ZZfdDIoaWRSy28tw222P4a23/oumpj2gKBWWLduMxsbV6XUIgsBnPvMt/Oxn12WUl5eWeuBweGEwWOByVWLVqnOzsgBPFsMHBrpH7aOdC7VaCZrOr0Q+F0ajGj5fFBwnpJ/BNM2B48QxNVtXKORx165d3UgmBVRX5zfplCQJR44MgKa5jAlsMslkNTZ1uapGfX6AnDXW2xtHfb1t1N/faAkGGVRUmLO8bm02Lbq6pr5J7VRjMJhx/vlfwD/+cW/Oz+fMWVZ0maxTTSLBQa+nsoLUw2GxaKHTUaBpLv2eomkOvb1xLFvmxpIlZSOOJ4xGNTZsqMDWre1obw+n73WSpOByVcHna0qv+8wzf0Bvb0dWNY7V6sDVV98+pUlaoijC76dRUqIb9bWlVJI499zLR30Op59+EZ599uGM99p//vMb3HLLHzLOiSAIlJRosXt3D3heQnn50P0i/P5u3Hvvl7MCSytXno0vfOEnGZZEU4EkSYhEmHT/HrWaglqthFZLYs6ckpzB1GiUhUZDwm4f/d+qULRaCgSRXfU4w9RTW2uDTkfhgw960dMTAyChpESPRYucOYMpCgWBefMck36e051RPSkUCsWwN6kkSbBYLPjZz3425DozzDAazGYNzj67NkvQ0mhI1NXZsX1756S+RMaCIIhobw/BYtFi/fpKKBRAMin7dpGkIv1yOtH3WKlUQKkk0iLWkSN+tLQEEQwy8Hhc+OlP/4tgsA8kSYEkKdB0HJ2dR7Fr12vYteu1rAYogDwZe/jhH+Pw4V244orvQaWa2IYduSAIAh6PCeEwjffe64LPF8Xy5W54PKZJP5exwvMiDh3qxwcf9CIaZaFWK7FlyzHEYknMm+coOoHxVCZlFcKyPPr7EwgGaej1KhgMKqhUSpCkAgoFkRaEAfn9JUkYtOc4btNx8n6VSnk7jhNB0xwiERaJBAeCkG0jikEEP5FUxrPsm8shHGahUMjLJUmCIMhClk5HfWjL67RaCqIo23QIgohQiEnbYdhsqoz1OE6AIEgTFlwZK36/bHPjdhuhVpPgeSHt2atQEKAo5eB7ZvixXCTCYPfuXhw9GsDcuaUwGOQMapcrW3BWq8nB7vUM3nuvC+Ewg9WrvdDr8w9shsNyc8KUeMBxSbz99vMZ66xde35OcSEYZGCzaVFaWnhAvLRUj2PHQhnL/P4EQiEG1dVWzJ/vgNudWda8cKELvb3xwcZrDoRCDMJhZkxNrwiCwNy5K7Bt2/+yPlu8+HQsWLAu732pVBps3PipYbMSa2sX4Pvf/ytaWvbBYDCjtnYBTKaRBX27PdMmJZlkEI0GsWfPVjzxxK+h0xlx5ZU/QG3t/LzP90TKyoxj6vlgNMpWKcEgk27kmEiMrnfCyajVShw+PACNhsxbDO/qiuLwYT+czsxrs7e3I6tXwVjs6zhOQDDIoKzMiHh86EqHiUC2SBFQWWnJeq6YTGoIwtT38CgGzjjjEuzd+xb2738767O5c1dOwRkVN/E4h5ISXUFBZ4tFA6NRNfj+VkEQRPh8cpA2HyE8hcmkweLFZejtjSORSKaFda+3LkMM37t3W9a2er0JN974a9hszrzPeyIIBhn4/TT0emrKkowoSo3zzrsajz56d3rZ4cO7sHPnq1i2LDNx0mLR4vDh7XjjjX/hwIF3MGfOMixbthkbN34y3ScjFgvh3nuvRzDYl7Ht/Plrcc01P55yITwYpOH30zCb1Zg3z4GyMkM6QJOav58IxwmIRpPQakm4XIZJqchModGQ0GgoMAw/YRW0NM2lx5ynAjwvQqkkJkVrKiszwuHQp8fGZrPmlPkei4VR3W0bNmzIeQEoFApYrVYsW7YMV155JZzOqX0BzHBqMtSAqKrKgn37+hGJsBPegXms0DQHny+KigozVq0qH7VXtstlQG2tFe++60NLSxCVleaMgZdOZ4Td7sLChetx+eXfweHDO7Fjx8t4550XkEhEM/a1ffv/4Pd34etf/7+cXqyTgdmsTWd0vfxyC5YuLcPcuYWVvE8liUQS773XhQMH+mGxaNJZgsEgjW3bOhAKsVi2rGzaZ71PN9RqEmo1CY4TkEjIQnDKgzolhgNI+1Sf7Fed8qw+EaWSgFKpAMcJkCTZS9Zs1hT9tUqSinTTnlSwTf4uZgZXgOxvG4mw6O6ODo5pcvu8U5QSVPElhKfp748jmRTSkyqSVMJgyA5ytLTsx4EDb8NksmHNmvMyyoplmx0tzGZtOlBZVmYYvB+Gvs6NRg3UahJHjgTAsgLWravI+53c3x/PCCR98MEWxOPhjHXWrh3eImU0EwWzWZMOeomiiI6OCDQaEuvWeTFnTmnO4JbDocf69RWw2eT31uzZJdixo2vQjmb0z4GGhmwxnCQpXHrpxDSl93hq4fHUFrSNxVKaZRXw+uv/xpNP/gYAEAz24i9/+Qluv/2fozonhUKBsSRTqlQkkkkBfn8iLYbHYty4TF4rKy2IxVj09MQQCtGwWIa3S+E4Abt394LnxSwP9JMtUqxWBzSa0Rtrd3VFUVtrgyiK6OuLj7un/XBEoyyMRjVcruxglE5HZQWVP6yQJIUbbvglnnji13jllX+kLR7q6hZjzZqPTvHZFR8Mkzv4OhwKBYGyMhP27u1Baak+3Wxu+XJ3wWMdeZ5lw/79fekxfWPj6mH7LRgMFtxwwy8Lfq5OBHKllT4dGJgqNmz4BJ599uGMSph//OPnmD9/DdTq48+pQ4d24He/+1Y6SHjo0I70vy9+8Q6IooD77/9algVYbe0CXH/9z6bUGkW2TItAp6OwcqUHs2bZhhz7CIKIZFJOUlAoZL90m007qUI4gMFKSDl4PBFiOMcJYFkeNM2PW6+fqYSmOTAMD0mSg26TMedTKhWnxHdXrIzqjnv99dfH+TRmmGHsWK1azJplwwcfdBe1GB4I0AiFaDQ2lmLZMveYBicEQaC83AyjUY3t2zvR0hJERYUpZzaQUkli7tyVmDt3JS688Hr8+9+/wpYtj2esc+TI+7jrrqvxzW/+Clbr1JTaKBSy76zfn8C2bR2IxzksWVJWdJm2JxMK0di2rRNtbSGUlxuh0RwfVFitWmg0JPbt60MoxGDFCk/Bg/sZxo4sYMrXUUrsFgQpwyqCII7/V6HIHflPZVKLogStlip6AXwoTgwEzHCclEXKdM2+kCQJx46Fh53YsCyNv/zlDmzfflx03bbtf/ja1+7LGQw1m7UwGNTo6YlmWJgMhUpForragra2ELZsOYb16ytGFOY4TsiakL35ZqZFyuzZS+FwlGdtm2poWFY2umoik0kNilIgEmHQ15dAebkpr+d0ZaUl/f/19TYcPepHKMSMSYRsaMi2gDn//GuKqgGbUknCZnNm2LKlhPAUHR1HEA77YTbbJ/v0AMgByq6uaNpSLxymx63qRa9Xoacnjvb28IhieHNzEMeOhVBRkd0PpafnWMbPY7FIiUYZkKQSCxc60dcXQ0dHeOSNxpFgkEF9vR0mU/b4W69XQaVSZNjWfJghSQoXX/x1nHfeVejsbEIySaK6un7KM1qLEVFEQRYpKUpLdRAECeEwDaVSiaVLh/YIHw6CIDBvXimOHQsjGKRhtWqxcuU5eOaZP2bdvwBQWzsfX/zinTnfU1NBMinC7VajoyM8qqqp8YKiVLj44hvw299+O73M7+/B00//Hp/61NcAyIHov/zljqxqGQDYseNlRCJ+EASR1efC7a7GDTf8MkNUn2zkAGkctbU2LFniGvK7liS5z4pCIVd5l5bqYTCohu3lM9Gk7ouJsEqJxZKw2bSIx5Ogaa4obQXzJeX9XlZmTNtKWiy5E2ZmmD5Mz5neDDMMQX29DQaDCuEwPfLKk4ycbRYGy/JYt64Ca9dWjFuU3mzW4LTTKjFnjh3t7REwzPDlwAaDBVdc8T1cf/3PoNVmTvY7O4/irruuyuj8PRXY7TqUluqwc2c3tm3rAE2PT4nzRDAwkMDrrx/DsWMhVFWZM4TwFFothZoaC3p7Y3jppRYcONCPQIDOOeibYeIhCDmzW6VSpjPHVSplupRPqRzaQkL2V1bkLHec4dQgHyGcYfgxWTnkw2ieD6EQg4GBxJANx4LBPtx999UZQjgAHD68E3fffQ3C4YGc2ymVCng8ZpjN+U04lUoFqqst6OqK4I03jiEYHP69HA7LVkMGgyp9nvv2bc9YZ9263FnhgUACLpdx1FVWFosGbrcJgQCNefNKsXlzdcEBS4tFi/p6O/z+sT3X7fYyLFq0If1zeXndiE0zp4KTrVJy0dq6P/3/sVgIzzzzR7z44t9A0/GJPDUAslVKX19iMItLQiQi25aNB3LVhDrd6G0oZJuhnsFgS/axu7vbMn4+2Zs9XyRJQm9vAnPmlMDtNsJkUqcrnCYDQRAhiiIqK3M3QJebMVNgWT7n57n219QUOOWbbup0RhgMtXA4atDTEy/qce5UkEzy0GjIUSU4ORw6mM0adHXF0k2dR4vdrsO8eSXw+2kIggiSpHDVVbdBozkueJaX1+Hqq3+E73znz0UjhMvnqoBeT034WCUfVqw4K6vfx3PPPYwDB94FALS1HciqljmRI0fex+HDuzKWWa1OfPObv4bBkPvZMxkEAjT6+xNYtsyNjRurhg06RKNJ6HQUKistqK62wuHQQ6ejplRQ1WopaDRk3s/nfBEEMW1XabfrkEjIjcgFQRzX40wWNM1Dq6VgNqvhchmg01GIRNipPq0ZxsiMGD7DKYXdrsOcOSXo7y8ukZGmObS0hGC367B5cw0aG53jbksgdz+vwLx5pejsjOY1qF62bDNuvfWPMJtLMpb39/twzz1fzPJjm2z0ejXKy43Yv78fr7zSir6+iZ9AF0pPTwyvvdaK3t4YqqstwzbnSjULJUkCW7a04emnD+OVV1oQiTBDbjMexGIs9uzpgc83cQ2sGIbHsWMh7NvXh/37++DzRabtgGeGGUbi6FE/nnzyEN54o62g65ymubyeYzTN4dVXW/DMM0fQ1hYs6NwGBhJDZuB0d7fijjs+j7a2gzm37eg4gjvuuDKrEVg47MeOHS9nNXbcuvVp3HXXVXjkkTtziugKhQJVVXKD5C1bjiEQGFoQD4UYMAyfLhN++umHIEnHv1uNRofly8/M2k4URbCsgFmzrKMOTqlUSpx+ehXOPLN2TIHqWbNsMBrVY54gfeELP8Ell3wDF130Fdxyy+/zapo52Tgc3hHXaW3dB0AWwn/848vx+OO/wt///nM89NB3J/r0YDCoEIux8PtlQZxhhHHth2C3a9Hfn0BzcyDn55IkYd++vnTzulx0dbVk/Ox2V4/qXAIBGlarBnPnymO5VLPo8RY3hju+zaZDWVluiz1ZbFGCZYW89tfeHobbbRxsPnfqjiNSmYZLlpShsdEBny+/sfuHhXhcfo+lrN0KwWTSoKGhBFVVFsybVzrmc2loKEV5uQnd3bLFZF3dItx55xO4+eaHcPfdT+H22/+Jdes+lva1LgZkn3MK5eUmUJRcmTGVEASBz372lowKCEmS8NBD30U4PIDOzqZhts5GpzPixhsfhN3uGu9TzRu/P4F4nMXatV4sW+Ye9h0jV6OKKCnRQacrnqpShUIO7qb6yowXNC37kGs0JEwmNUpLdTAYVOC46flMZxgOVqsmnUjldBqgVBIzz+xpTl71WJs2bRrVzgmCwCuvvDKqbWeYYbQ0NJSgrS2Enp7YkAPzycTvTyASYdDYWIqlS90FNRMrFI2GxOrVXpCkAnv29KKszDDipN7rrcd3v/sw7r33y+jtbU8v7+vrwD33XItvf/sPU1bmDAAaDYXqags6OsJ48UUGy5a5UV9vL4pBRGdnGG++2YF4nEVVVXbTqKGw23WwWjVgGB5HjwYQj3NYt64CdvvovUKHoqsrinfe6UBnZwwWixorV5ajrs42blkIPC+iuTmAffv6MDAQT/uCprJCly/3FLVt0QwzFEowSGPHjm4kEkkcPszA4zGhrm7kZ2QwSOP119tA0xw2bKhEeXnuTKZYjMVbb3WiuTkAjUaJ115rw/Llybz7J3R1RXM2x2xu3ov77/9azkbKJ9Lf34k77rgSN974ICoqZqO3tx233fYZMEwCSiWJT37yqzj77M9hz56t+OMffwhAztjaufMVfPWrv8CsWQsz9qdQyM+CY8fCePnlZsyb50BFhWzvdSJ+f2KwKS2LRx/9Kd5448mMz5cvPzNnGbQsxGlRXj62hstaLZV3Q8ShGC+7Np3OiHPOuRyiKCIeT47pnCaKVavOzfobnUxLy34IAo/f/OaWjADLBx9sQXPzHtTWLkgvY5gEDhx4B3q9CfX1S8b8jiJJJXheRDBIp4Vho3H83rEKhQIWixp79/bD7TZleXq2t4dx8OAAysr0OX8XURSzfG/d7pqCz0MURQSDDNat86avOZNJDZ2OQiLB56xUG2/CYRZr15YP6Xebyg7s6Bg5IN/fH4fRqMbSpWUQBBH9/Qk4naemrVwikYRer4LbbcCsWTYoFAT27OlFSYk27wqcU5lEgkN5uWnUQaz5852oq7ONSxWuVkth6dIyvPRSS9ouxWp1TJmlZD7QNA+DQQ232wSdTo14PDmirdNE4/HU4sILr8f/+3//l14WDg/gnnu+iMrKuVnrf+5zt+Kpp36HSCQz6EhR6in3ZQ8GaSQSSaxZU5G24xqORIKDXk9NqA4wWnQ6FQgiPq5WKckkD4dDP1iJS8DlMqK9PTzuovtkkErUSFUuAnIvDIfDgM7OCFQq5UzvpWlKXmL4aD3CZzx0ZpgKDAY1liwpw6uvtiIWY7MaFp1MqpklQWBcB6CCIKKzMwytVoX166swe7Z9Uh6UKpUSK1eWQ5Ik7NnTh+pqy4jHLS314Dvf+TN++tMvZGQq9fS04Z57vohbbvk9TCZbQechZ7zEkUhEwTByqavsxUxAqaSgVmuhUmmgVmvSmRRdXS3Ys2crOjub0N/fCZZlIEkiDAYzjEYb1GozXn/diYUL52DTpiWora2BRjP5QqskSWhuDmL79k7wPA+v11zw806hUECnUw2KRCG8/nob1q6tyFman5rQx2JJ8LxcdqbXq2CzaYecJPC8iEOH+rFrVw84TkB9vQ0DAwm8+WYbGIZDY6NzzAOeSITBe+/5cPRoAHq9ChUVx681lpWF/kSCx4YNFVM+AJ9hhvFAFCXs3t2DUIhGTY0VfX1x7NrVDYdDP6z4mcoS7emJQaVS4p13fNDrVVne0oGA3Gy3vT2MqiozSFKZXkbTcgbhcM9zmubQ1RXNyqTbu/ct/OpX30o3a0vhcHhx6aU34bHHfpYhVkYifvzwh5/G3LkrceDAO+nlgsDjn/+8D/39Xdiz582MfUUiAdxzz7W48sofwmAwoalpD4LBPtB0FHq9BdXV8wCsxJYtspDQ2FiC2bNLoVIpIYoSOjuDaG7eit/+9vdZmekUpcY551ye83sNhVisWVNeNF6UtbVWHDrUP5iVN/pJL01zaG8PQ6WSbZuKrfFyQ8NynH76RXj99ceHXOfo0ffxyCN3psvgT+T55x/B9df/HADA8xzuu+8rOHLkfQDAsmVn4Atf+AlUqrG93zUaEj5fDCSpBE3z45oZDsiB7ZaWIN5914d167zp8WZfXxw7dnRBoZAr3HIRCPRk3Y9lZYVnhsuZ53IQJgVFKWG3awfF54l990YiDIxGFbxey7Dr2WzaIbPoU9A0h3g8idNOq4LXa0Y8zuG111pRUiJOmdAgSRJ4XgRBYNjKv9EQDrPpdwdBEFi5shxaLYndu3sRiyVRVmaEYiydZKc5DMPD6Ry9zzVJKkacAxaCx2PCkiUuvPVWB9Rq5aQ9k5NJHizLQxQBilJAoyHzui5omkd1tRUaDQmbTYuurkhRjMXPOedyHDz4Hvbt25Ze1tXViq6uzODgeeddjU2bLsa8eatw773Xo7+/EwCg1Rpw3XV3oa5uUV7HE0U5qGa3a8ftHo7FWIRCDNau9eYlhEuShGRSgNOpL4pkrpPRaEhoNBQYhh+XRposK79vT96XRkMiFivOAP9wJBIcnE59lt2Z2axGIqFBMDi2XjEzTB15ieGnconaDKcmNTVW+P00duzogsejGHKSHIux6O2NY8ECB7RaCnv29CESCcPjGdsANJFIoqsrBq9XbsI12VktJKnA0qVudHXFEAoxeWUcm0w23Hzz73D33ddkNIXp6mrB7bd/FhdffAMaG1dnNFeTJAnRaBA9PcfQ3d2Knp5j6Ok5ht7edvT1dUAQ8ov+UpQaBEFkTQyH4rnngLvvlsV1t7scmzefjYsu+jQWLFgMijr+txYEAR0dx9DcfBRNTUfR1HQYR48eRjAYQH19Az72sQtx1lkfgVo99GCZZVn8+tf344kn/gm1WoPy8grodKXgOBMcDg9mzZqFZFKVzljkeQ4+XzN27nwFe/a8BZVKg/PPvxrz56/NuX/ZRsCK5uYe/PjH/8SxY9vAcQy+8IUv48wzP4G2tiCamoIIhxkkk8dLHElSAbNZg1mzbKirs2UM+Pv64ti9uwdNTQFYLJq0wF5aqkcoRGP79k6wrIDFi8tG3SSwtzeG7ds70tf5yRlharXcQK+1NYR33+3C6adXQaVSQhBEBAI0wmEWNM0hmRSgVBKgKCUMBhXMZnV6YjjDDMVGa2sQR48G4HYbQRAEHA49WlqCeP/9bqxfXwmlUgFRlBCPy4ErlUoJrZZCT08MTU0BuFx66HSqtIB22mlV0GhISJKEtrYQ3nvPh2CQQU2NJf0OkoNeCuzY0QWel7Bs2dANhQcGEohE2Azv3m3b/oc//em2rOdxdfU83HDDL2Ey2VBV1YB7770eHR1HMtY5UQg/kVdf/WfO5RzH4qGHvpPzsy1bHodarcW5516B1as/jTff7EBTUx9efvlBvPPOmxgYGADH5X4HXH75d3Jmzcr2EFrU1hYWrJ1ISkt18HpNaG0NobJydGJJOEzD76exaJELAwOJQZ/R4hLDAeCzn/021GodXnrpMYiigMrKBhw7dtyCh2XpIbPHd+58FX19HXA4vHjrrf+mhXAA2LHjFQSDfbjppt+NqSmawaCC3x8Hy/IT0qCMIAhUVprR0hIEw3CorbWBYQQcOTKAeDyZs2lmipMtUrRaAyyWwuwcRFFEOMxiw4bKrOvD6TTgyJEAeF5IZ/sN1wtjtAwMJLBggTMrM/5kDAY1hnMvlCQJPl8UjY2OdKVNdbUFR46Y0NUVhdc7eZ7AkiQNNruXG91RlPxc57hUMgIFi0WTs1n9iXR3R5FIcKipseb83mmaR0XF8apCklRg8eIylJTosGtXD1paQrBY1LDZtBMiiouiCI4T09cHRSmKRnxPWV2eXEE01cyb50A0msTu3b3weIwTEoSVJHkMEQzSSCZFqNVybxuFggBN8+jpiYGiFHA6DcNeg4IgpgW6sjI9WluHD0ZNFgqFAl/4wo9xxx1XZAW+TyQVHHQ6K3DbbY9h69anIUkSVqw4K++M/GSSR3t7BGazGn19cbjdY6sgS+2zpyeO5cvdmDs3v/NgGPkdVIxZ4cBxq5Tu7ti4iOE0zcNq1WQFoNVqZVHZ2OYDxwkgSSIjKzwFQRCw23WIx7lp3yD0wwohTbcrcpKIRCIwm80Ih8Mwmcb+4JxudHaGkUjwOW/86QLHCdi+vRP79vWhtDQz41sURfT2xpFMCli40IXFi12gKCU6O8PYsaMbXV1RWK0a2GzagicOsi0Ki8ZGB5YsGV338vHi3Xd9eP/97oJKv+XmatcMOUCxWh3QavXgeR6xWAiJRHS8TnfMqFQquFxuaDQaxGIxDAz0IZkcPgLtcnnw5S/fhI985JNQKpUgiONVLW1tTbj55mtx+PD+YfcBYDBznkA8Hs4ZBFi37gJcdtnN0GiyAxPvv/86/vKXO7I8d9et+xTWr/8CrFYdzGZ1xqCX5wWEQgxCIRalpTosXVoGg0GN5uYgjhwZAMNw8HhMOQfKx4NATqxY4RlSWBuK9vYQ3nqrE7EYC6/XNOzkieMEtLeHsXq1XEJ99KjcFItleUgSIG9KQJIkEAQBrZZCSYkW1dVWlJebJmUilDr2DJMDxwlpYWY6QdMcnnvuKKLRZIYFF8Nw6O6OYc0aLwgCOHo0gFgsCUGQBptXkSAIBfr7Y6islJ/FPC+grS2MxYvLUFtrxZEjAzh0aAAkqUBZmTHn9ZhIJNHdHRv2vt2xows7dvhQUyOLwy+88Ff84x+/yFqvsXENrr/+ZxnPo0Qiil/+8oasBlUTQUPDCnzpS/fgV7+6FUeObB9yPbVaiyuv/AFWrjwn6zNRFNHSEsKaNeVYtGjkZo6TSVtbEC+80Ayv11RQJpokSejpiYHnRSxZUoYFC5zYs6cHb7/tKyrB/2RisTDC4QGUlVXjRz+6DO3th/LabtOmS3DZZTfj1ls/nnPMcd55V+Oii74y6vMSRRFNTbLnvtdrmrDxWGpMmUhwIAi5marVOnxQ9+R7s7Z2Pr73vUcKOu7AQBwkqcB559VnieF9fXFs2dKGcJgZDNKJaa9WtZpMB/TGQjTKIBbjcO65dXA4hs/g9fkieOaZw6iqyi0Md3dHoddTOPvsWRnv/Y6OMF56qRkmkxom08RXAiaTPDo6IrBYNKivt8Pp1EOjoSAIsmVRIMCgoyOEQEAWKo1GFUwmdZYdTSKRRH9/AiaT7MV7sm0jTXMIBGicd159zoZ7NM3h6FE/Dh0aQCBAQ6VSQq+Xj1XouzPlTZ5IcOkkBEC2syNJBRQKYlDsFyAIElQqJWw2zZBVDZOB/P0wuOCC2SMGWiabZFLA2293Yv/+Ptjt2nHLtk4meQSDDKLRJIxGFcrKDKiosMBq1UCvp6BQKJBM8hgYSODIkQBaW4NDHj8VYP/IR+rg9Zpx7FgIzz13FNXVue+/E7cLBOghm/6OJ4FAL3760y8MOd/84Q//hqqqbOuUfOA4Id0/pa7OBpNJgx07uoYMTOVLqjJ4zhw71q+vzPs7CgRolJUZJsQOc7xgGB5tbUHodKpRJ0oBcgVlOMygstKSpSPF40m0tYWmVRZ1OMzAZFIP24Q3GKTh80VHfO9PNsEgjbIyY9E9Q4uJGTF8CGbE8OkvhgPyy3DPnl7s29eHeJyDWq2AIEgQRQmlpXosXOhCbW3mi5GmORw6NICDBwcQCsnln2Zz9kA3hSiKSCQ4RKNJxOMcjEYVli1zY/bskikvhersDOP555ug1VKIxVhIEiBJAEket9nIJWT6/d24++5rMDDQNQVnPTV4vfNw0UXfgc3mAUEA+/dvwRNP3I1kcuiGb4Xi8dTiK1+5Fy5XJQA5i/KRR+7C1q1PDbnNwoXrce21d0GrzT3RTIknHCdAqVSAYXiUlupGnDSmqhfmzSvFqlVDe32eTFNTANu3d4Dnxbwn035/AokEB5YVoNdTsFo1Oe8nSZKQSCQRDrNIJHhYLGrMmmVHXZ0tr4GT35/A/v194HkRXq8ZJpMaarUSCgUBQZDAsgJomkMiwSEWSx2HG4z6K2CxaAYntST0egpGozrd/CX1e/K8iFCIQVdXBM3NQajVJCwWeZKe8gLUakmoVEqQpAKSJE9wolF5YtzeHoLHY0R9fcmHKoNAFCW0t4fQ1BTAwAANhQLwei1oaLAXRdluPuza1YW33/ZlZG2nkK/xJFhWFkcMBnniKggiaJpHMinA4cgssYzHWfT1JaBSkWAYLq8eD6n7dtEiJ1asKM+YsAiCiKeeOgSa5mG3a/Hvf/8Szz33l6x9rFr1EVx99W0gyezrL5lk8Otf34zdu9/M+mw4qqoahmzKOVpqaubjqqt+OKQnaEdHGHa7DmefXVt09xLHCXj22aPw+aLQauUS9ZHOMfW3LSnRYtkyd1q06OwM43//O4qKCvO0CCD9v//3Szz77J+zlisUSlitDvj93ellKpUGX//6/fjZz67LuS+Hw4uf/vTpMZ9TMQY8//SnH+HNN/+T/nndugtw9dW35b19SpRZt86LBQtyN5BLJJKIRpOgKNk/nablserevb2QJAklJaO3oEgdf/HiMqxeXT7i+sEgjaeeOgyrVZN1L9A0h97eGDZtqsmwe0kdZ9eubrzzji9rvH4yiUQSwSCTbhwqSQBByA1F80luSQUp6+psWL7cPeS7iedFDAwk0N0dQVtbCKEQmz6mUklAoVCAZQXMn+9AWZkRW7e2g2Eym6wRBOB2m/DRj9YPO1egaQ6dnfJxBgZoRCIMJElKN3FTqZQZ26ea9HGciGRSGLz25f47Oh0Fu12L0lI99HoKWi01mHEMiKIshkUiLDo6wvD5omBZvuCA3ngRCNAgCODCCxsmXJQdDRwnYN++PuzZ0wuFghh1BTDDyPdkJMKCJBUoLdVj1iwbPB7jsONejhNw4EAfduzogk6nyhJZaZpDKMTgYx+bDatVi3CYwX/+cwhms3rIcYYkSWhtDUKvV4HnxWHFv/EiGOzDL3/5DbS1HchYTlFqPPDAa3lVBsmB8eDgfUAAkBMRSkr0mDu3FDU1ViQSSTz99BFotUoYjaMPqnV2hmGxaHDmmbV5J+swDA9BEFFZaRl3q67xRJIkdHZGkEhwY0pESiQ4KBQEqqosWc82luXR1haCVkuNSXCfLERRQiTCoqLCPKwmJggiOjrCYFmhqKpZZsTwkSlIDA8EAmAYBmVlZekBxbZt27B169asdRcsWIBzzsnO5JkuzIjhp4YYnkIetEYRCjFQqUjY7doRy9siEQZtbSG0tAQHB9fHB5UEgXS5J0EQ0OnkksmKChPcblPOLI+pQBQlvPOODy0tAVRXW1BWZgTLCggGaXR0RDAwkIDTqcs5MBgY6MK99345wzJlMrBYSjB//jp4PLXQ6+V7LxYLIRIJIhDoQX+/D/39nYhGg+N+bLVahyuu+AH6+jrw5JMPjvv+AbkU+tpr70Rt7Xz88pffxNGj74+4jddbjxtu+CVsNueQ69A0B0mSCiqlT/nlNzSUYNWq4T13JUnCwYMDeOedTpCkoqCBv2ynw8JgUOVdgit7ATPw+2mYzWo0NJSgoaE05zkKgojDh/14//1uRKPyhILjRKhUCpCkEgQhi+E8L6S9PxUKAiqVEhSlTGdFJZM8kkkRkiRBkmRvO7WahEqlgFpNQhRPzK7iYTSqBrcTIAhyxh1FKaBSkSBJJVK/Ks+LYBgOLCtCqyVB0zzcbiOWLHGhosKS9/c4XUkkktixoxuHDw+AIOSyZ1GUEAwyKCnRYs0a75DNJIsFvz+BZ59tgkqlGHKCGo0y0Ovzv8YB+bsBUNB9m8oQX7y4DMuXu9MCaV9fHM88cxg2mxp/+9sd2LbtmaxtzzrrMlxyyTeHPUee5/DII3fgzTczg3RKJYnzz78G77zzQkbjv/PPvwaf+MSX8dxzf8ETTzwIQeChVJKoqWmE1zsber0RPl8L3n//dUjSyLZ7NTWNOOecy7Fs2eYhxSu5cRWPzZurJ9U+oRCCQRrd3TG0tQXR3S1ne7tchqxnWDzOpgNEs2bZsWiRK8N/nqY5/Oc/h6FUYloEjgYGuvD9718Mhomnl1kspbjqqtvgdtfgllvOz6ie0uvNiMfDQ+7v5z9/Fnb76DL/JUnCoUM7oFAoMGvWQiiV+QV9J4M77rgCTU270z9ffPE3cO652b74QxEK0eB5EeedV19wxvT+/X3YsuVYXj1lhqK7OwqtlsI559TmdfxkUsDjjx/Muo4lSUJLSxANDSXYsKEqpzA8nJDOsjxCITmbVqul4HTq0g16CQLo7U2gqSmAYFButDucKNDaGoDXa8GmTdXQaPK7VgRBbmAaCjGDnudy5rVGQ2LBAic0GhKdneF0UIKiFFAq5f45st1K/vc0w/Do748jEpH9ilN2czwvpoV/hYKAWi0H9E0mDQwGCgaDCgaDCkajOi8BSpIk9PXFsXNnN9raQnC7Rw7Ujjc+XxherxmbN09dg8R8OHRoAK+91orq6uwg+Ykkkzx6e+OQJDkhSxTlSaRaTcJkUqOiwgy32win01CQSHjwYD/eeqt9MJnj+H14cjBBFCU89dRBJBL8kHNU+Z4m4XDocejQQLrCbKLheQ6vvPJPPP/8IwiF+gEAF154Pc4//5q8tg8GaYiihLlzS0GSClCUEkajCiUluoxAyquvtqC1NThif4OhiETk58zmzTUFjTvCYQYWiyarOqQYCYcZdHRExpThHAjQQwZzRFEOuAAouiSGXMRiSahUClRUZAv7JxONsujoiMBoVBVN4sKMGD4yeYvhgUAANTU1WLp0KV555ZX08h/96Ee4/fbbs/x/zGYzmpubYbMVb1nncMyI4aeWGD4WUgPdcFge6Kb89ShKke4sbDSqBgfexZV5BMhCXCzGZg24aZobzGrogUpF5hQ3OY7Fu+++iK1b/4ujR98f0gOcJCk4nRVwuSrhclXB5aqE01kBq9UBnc4IjUbuJi0/JyRwXBLJJAOWpcGydPr/dTojysvr8mwME0d/fyfef/91vP328+jpaRtyXb3eBJerCmVl1Sgrq4IoCtizZyuOHv1gxOOksNtdWLHiHCiVJPr7OzEw4EN/vy+rw/nx9ctA07Fxs5GxWErw9a//36hLBoci5adXV2fD2rXenBMeQRCxe3cPdu7shsGQnYEykZwoipeV6bF4cRkqK497bDIMjx07fNi3rw9msybj3HheAMfJ4rbsh5l/t+9UsxuG4cHzYkYDLa2WHHIQx/MCkkkBHCcg1W4j9axIDcpFUUR3dxQAgYULnZg/31mUmU/jQSTCYOvWjpwTakmS0NUVgVpN4rTTqiYlC2k0SJKEN944hgMH+guyqhBFccL8V+Nx2epo+XIPFi8ug0JB4MCBfrz00kE8//zd2LMnO0nhU5/6Os499/N5v6dSgdBkkkFfXwfKy+vgclVCEHgcPfoB/P5ulJfXoaJidnqfwWBf2i7j5Iyu5uY9uP/+ryMWC+U83sc/fh1OP/2TMJvtw55XMEgjEmGxapUH8+fnzogtJkRRrt45dKg/3UCQJBXpwJtWS8HjMWLOnBJ4PKacf58tW9rQ1OQf9SR+sunsbMJ7770ElqVRVdWAxYtPT18Pv//997Bt2//y3tdVV92G9esvGNV5PPLIHXjttX8DAE477UJcccX3R7Wf8UaSJHz1q6cjHo+kl91wwy+xcOH6vPfh80Xg9ZpGJRYyDI9nnz2CWCwJl6twgSYclu/B00+vLuiZ+N//HkYoxGSMNwMBGoIg4txz64acsPO8iMcfPwBRlNLveFEU0dERgUIh91WoqbHC4zHDbs/OAI/Hk2hq8mPv3n7E40mUl5uy3rkDA3FIEnD22bNQUlK8VgYnc6K4KjeoJ8ZtLkLTHHbt6saePb2oqDBP6jilpSWAtWsrMH/+0EkgxQDD8HjmmcNgWWFIkTk1zp41ywa9ngJFKaHTkdDpKJjNGlgsmlGLZ6nKiXff9cHrPW6N6PNF4PGYcNZZx58P27d3YO/eXlRVZVtnpmx9Nm6shlJJ4IUXmka0VBlvRFFET08bSFIFh2PkapMUra1BzJ/vwJo1FcOu19QUwMsvN4/q9xIEEa2tIaxY4cGyZe6Ctg0EaFRUmLMamxcjPC+irS2Ytq0slGRSQDLJo7LSMmTF8XTRmCRJTtopLzfBYhk54CvPaaIIh9m81p8MZsTwkck7ReKvf/0rotEo7rnnnpyf//nPx0sig8EgvvnNb+LRRx/F1772tbGf5QwzTCFKpQIlJbppNTg+Edn+IfshqNVSWLbMDatVi7ff7kwPnE6EotRYu/Z8rF17Pjguia6uFgSDfWBZGkqlEjqdCXa7CyUl7rwyrlKDD7VaC7VaC6Mxfy/z7PPXo6JiNioqZuOCC65FIhFFZ2cT4vEwkkkGarUOer0JTmcFjMbsgc9HP3oV9u59C48++tN0h/KhWLfuY4ONwrK/R5alMTDQhUCgBwqFEhqNDi5XVTqrva+vEw888E10dh4d9hgajQ6XXXYL1q49H0eO7MIDD9yYkS0XCg3grruuxlVX3YaVK8/O81saGZWKRGWlGUePBiCKEtas8WaUeCWTAnbu7MaePT2w2bQZGYuTAUEQsFq16cYur7zSgnnznFi40AmeF7FtWyeamwM5M5dIUjnq8l6CkLOr8rWPKeSYCoUCHo8Z4TCDd97xIRxmsXKlpygb5I2FcJjBG28cQ2dnJGcGIkEQ8HjM6OgIY9u2Dpx5Zk1RZr76fBE0NflRVpZfNUR3dysee+znOHJkFxoaluOzn/02SkoKmzyNhF6vRmmphJ07u0BRCsyf78TevU149NEb0dV1OGNdhUKJK6/8Adat+1hBx0jZOQFARcXs9P8rlSTmzFmWcxur1TFkY6va2gX4xjcewF13XQWeP24ZoFJpcP31P8OCBeuGPJfjfr00VCoSK1d6MG9ecYskKRQKAm63ES6XAdXVVnR1RcHzAnQ6NcxmNUpKdCNmYZWVGXHgQH9R2n3korx8FsrLZ+X87MwzLytIDD9w4N1RieGxWBhbthxv3rllyxM444xL4PXWF7yv8SYS8WcI4cDxZnH5wrKyHdJo0GhIzJ3rwGuvtUIQxIKEuGCQRjjMYMUKD2pqChvDlZTo0NMTS/8sinJD7bVry4edrJOkAna7Fh0d8ncm2zmE4PGYsHRp2YjZtHq9CgsXlqG83IwdO7rR3Cw3M041Hk8meYTDLE47rWrajfVTArhyAnRqrZbC0qVutLdHkEgkM3ovTSTHm2cW/5hIoyFRUWHB++93D5NxHUNNjRUbN1aNe0CBIAgsWOBEIECjuTmQFnpZlkdpaebfy27XpTPST0QUxbT9Wm2tFcEgA7WaGmz6OHnZuwqFImej7BNJVYIYDBT0evVgDxoM26w4hculh8mkRiTCFHwty018TWhszK9hZopkUoBKpci70mSqSVlG9vTER/W3TySSsFq1w86dNBoS4TA7ltOcFBiGh0ajhF6f3/eQmq9Goyw4Tjhlk5xONfK+M5999lk0NDRg6dKlOT///Oc/n/HzY489hv/9738zYvgMMxQxBEFg1iwb1Goltm5tR2dneEi7AopSobJyDior50zyWeaHTmdEff3ivNeXB5Dr8KMfLcZjj92DrVuzfUkJgsDFF9+As8/+3JAChFqthcdTO6SvrcNRju997y/4859/hHfeeSHnOlarAzfc8Mu04DR79lJ8//uP4P77v5ZhU5NMMvjtb7+N9vbDuOii66FQjM+LlqKUqKoyo6UlCI4TsXatF1arFolEEu+848PBgwNTUiZ7IrKAbEIsxmLXrm4EAjRomkNfXxxVVeYp8bQcK2azBhoNiYMHB5BIcFizxjutmsoMRyzGDj5TIiOWD5eXm9DSEsR773Vh48bqovIRjMeT2LWrBwCR1/Xf3d2Gn/zk8+mKkN2730Rr63585Sv3oq5u0biem9GoAc9LeO+9Lhw5chC33/5FhEK9GeuoVBp8+cv3FJRxejKhEI3+/gRmzbKBIAj4/QloteSongc1NY244YZf4j//+S2CwV64XFX49KdvHFI4BY4LX3IPARtmzy4p2iqC4VAoCFRXWwtqaJ3C5dLDaFQjGmUnpYngRFJV1YDa2vlobt6b8/OPfeyLePrph9I/Hzz47qiCAN3drRBFIWPZa6/9P1x++XcLP+lxxudrzvhZpdKgpKRwK5ixeN9WVVngcOgH7fJGDvSxLI+enhhUKiVWry7HvHnOgv8mJpMagnBcjOvtjcPl0qO+vmTEbR0OPZqa5MqKzs4InE491q+vKOidabfrsHFjFaxWDT74oBscJ8Jq1aKzU87ara8fvirlw4hGQ8JgoBAKMTBPkiMVw/BQq6lp86wrKdFCFKWcz6l4XLbumz/fMWHiGEUpsXSpG/39CQwMJNKi/MnPB7NZDYpSZAl1Pl8U5eUmLFrkAkEQMJnU0OtJJBJc0VhZSJKE3t4YaJqH06lHX59cyRGLJVFaasirwsVgUMPrNePgwf6CxPBYjIVCQWDJkrKCRW2GkS0Vi9kr/GQMBjUoii5Y0E3ZRY6UAa9SkcjPl2JqYRj5WivkO5Btc7Xw+xOnzHzuVCfvGefevXuxZs2avHe8aNEi7N2be6A7wwwzFBderxkbNlRCq6Xg8w3t3XkqotXqcdVVt+Gyy27OEJfVai2+8pV7cc45l485E0+t1uLaa+/ChRden/VZeXkdvve9RzIyLwHA6azAd7/7F8yZszxrm2ef/TPuv//rWZllY4EklaiqsqCjI4zXXmtFU1MAr73WhoMHB+D1mooma9lgUKOqyoyurghisSSqqy3TUghPoVaTqK62oL1d/t57e2Mjb1Tk0DSHbds60d4eHmygM/xQgyAIeL0mNDUFcOSIf5LOMj927+5BZ2cEHs/IEy1RFPHww7dnWSNFIgHcffc1ePrph4a0mhotZrMKW7b8Cd/4xoVZQrheb8JNN/12TEI4AASDDKxWLQYGEujsDEMUJfT2xuH3J0a1v3nzVuG7330YP//5c/jWt34zrBAOyCXGVqsW555bh02baqalED5WTCYNvF4T+vsTEMWRfdeLnU2bLs653O2uxurVH8lYFg4PoKurpeBjnOhrn+Ldd18qiu+vpWVfxs9ud3VBwe1YTBbYxpI5K2eHlyIaTQ75nYiiiGCQRmtrEL29MVRUWHDmmbWYP981qgbxcoadrILwvNzQev58Z16Cm9msAUEA/f1xqFRKrFo1uuCxSqXEsmVurFnjRTTKorU1CKNRjSVLyooqEFtM2O3adIPQyYCmeeh05LTIDAdkmx6djkr3ADmRnp44Zs2ywe2eWL9om02LRYtciERYxGLsYHP3k8VwDXQ6NeLx4+cZDNIgSQWWLi1Lj/NJUgGXy5Dz95kKGIZDS0sQGg2FTZuqcd55s7FggQs9PXHEYhzq6+1537uVlXJEh+OEEdaUka1b4pg7t3RUYw+eF4veDuRkNBrZxz6R4EZe+QTicbnxpk43/POcohSDTXuLVxFPJgWQpCJdPVQIFosGFKUAw0zeM3OG0ZP3W9/v98PhyC4N2bBhA77zne9kLXc4HAgEcnvpzjDDDMWHx2PC+vUVUKtJdHWNn8g6HSAIAps3fwa33fYYLr74Blxxxffx858/hyVLNo7rMc4//xp8+cv3wOHwwmCw4KyzLsN3v/vwkI0xDQYzbrzxQZx22oVZn+3d+xa+//2LsX//2+N2jkqlAjU1Vvj9NF5+uRk+n5zVW6hVyERDkkpUVMgNYSfKk3kyOfF7f/XVNhw7FprqUxo1HCfg3Xd9aGoKoKLCnHf5vUpFwmxWY/fuHgSD9ASfZX74/Qk0NQXhcOjyus6ee+5hHDmSuyGuKAp48snf4Ec/ugyHD+8cl/PjeQ4PPngzXnnlkawM2NLScnz3u3/BrFkLx3SMlB+t06lDLCY3gNuwoRIbNlSC5yUcOxaCIIhgWR40XdjEKV9CIQYNDfYPfZZNY6MDHo8JLS2hohEpRsvy5WfCYLBkLZ8zZ/lgv5HMd+LBg+8WfIzu7rasZfF4GD5fU8H7Gm9ObJwJyBZC+RIOM+jri8NoVI9ZLJSzw3UYGMgMbAmC3NeitTUEUQQWLHDi3HPrcOaZNWMS9fR6FShKCY4T0Nsbg8djQlWVJa9tnU4DrFYdQiEGixe7xnQeCgWBefNkj2GHQ4/Vq8sntRfKdMNk0oDnJy+IRNMc7HbttLEZMJs1qKw0o7s7nhFYCgZpGI1qzJvnmBR7q7o6G6qrrWhuDsJgUGWJ4RoNCbtdi3hcflcnkzwCARqLFjmzhN6SEj04buoDh+EwDZ8vioaGEpxzTi3q6mThe+FCJ8rLTXC7jQXZNZWVGeFwGLKeeUPR2xuH06kv2B4FkAVVipo+FiknkrLEzPe+F0UJgiDCYhm58abcSFiZd0BiKkgkOJhM6lH97TQacrC6emLGxDOML3n/hTUaDRKJ7AfHxo0bsXFjtmCUSCSgUk2vSNgMM3zYKS83Y+3aCrzxRju6u6PTovP1eOL11k+4n+jy5Wdi6dIzQBD5NTkiSQpXXPF9VFbOwd/+dk9GZmkw2Iuf//xL2LTpYnzsY1/EsWMH0dy8Fz5fE2KxEJJJFiqVBkajFStXnoNly84Y8XgEQaCy0jJtvGlzIQg8gsE+DAx0IZGIQhRFSJIIjUYHvd4Cg8EMg8EMrdZQNL8jQRCoqDCjuzuK115rw5IlLtTXl4zrIJrnRSQSSfC8BKWSgE5Hjetkk+dFvPeeD/v396O83Fjwvu12HVpagti3rw/r1lVM+d/myJEA4vEkXK6RLQSOHv0ATzzx6xHX6+g4grvvvgZz5izHpk2fQmPjGvT0tKGt7QA6O5sRDvcjHo9AoVCAotRQq7WwWp0oK6vCggXr0n7c4fAA/vznH2P37jeyjjFr1kJ89au/gMk09gbmiUQSer0Kixe74fVaQJIEvF45s8pq1eLdd31obQ0h9afyeIzQaMavrJpleahUyg/duygXdrsOmzdXY+fObhw40AeTSTNtBTyKUuPssz+Lxx//VcbyhoblIAgCc+euwFtv/Te9/MCBd7F582cKOkYuMRwADh/eNSW+4XJzrRbs2bMVu3e/mfFZIUGrgYEEFi50obLSPOaKLa2Wwpw5pXjjjWMoLZXf+bEYi56eONxuI1av9qK83DhulWF6PQWNhkQkwiKZFDFvXmne7wmNhsSsWVZYLBrMnj2yrcpIyNdZKerqbNNGdJ0qcnnmSpLcGNhkUkGvH9/GgAzDw+HI7b9djBAEgWXL3AiFGHR1RVFeboYkSfD7aaxc6Zm05nUUpcT8+Q50d0eh11M5s6XLygxobg5AkiR0dkZQW2vD3LnZQq/FooFCQRTcU2A8GRiIg6Y5rF5djsZGZ8bvo9VSWL++AoIgFTROpiglZs+247XX2kZscs4wHFiWx9q1FdDrC38GsiwPnY4quoSifNBq5QSVUIjJq5dPLJaE0ajO63siSQUoSgGeL87McNnuRRpTw1OLRYNwmAFNF4/V0Ay5yfvuLC8vL8j2ZM+ePfB4PKM6qRlmmGHqqKy0YN06CVu3Tj9BnGV5MAwHnpcndSqVAmo1WXQTndFkM2/c+Cm43TV48MGbEI0GMz579dV/4dVX/zXs9jt2vIzSUg9mz16KcHgAySQLURRhMJhhsZTCYimBxVIKs7kEVqsDFkspDAbZ4kL26mvH/v1v4+jR90EQCsyatRBlZdUwGi0QBAE8nwRNxxCLhRGLhQb/hdP/BQCTyQ6z2Y6SEjcaG1dnNOgbC6Io4ODB9/DBB1tw+PAu+HzNWZmyuVAqSej1JhgM5gyR3GCwQK8//v8EQUAQeDBMAuHwAILBPvT1dYJlC7eJoCg1SkvL4XCUw2IpgVZrgEajhyRJkCQRLEujt7cPL788AIKIQ5JCIEngwgsvwcc+ll0hcDKSJOEf//grXnzxWdTXz8Gll14BpdKK9vYwfL4oaJqHIIhQKAhoNCTKygzwes3weExj8jSUhfAu7N7dC7fbMCpBlCAIuFx6HD3qh9ttRHW1dVSl+ONBIECjqcmfVzO1np5jePDBmzKuOYJQ4MYbH8TRox/gv//9Q9b1eOjQezh06L2Cz8vtroFKpUZHx9EsyxWlksTZZ38WH//4daCo8REnwmEWDoceVqsma0Lvchlw5pk1aGryg2VFxGIsDhwYQE3NyNY4+R+fgc2mmVZN7URRAk1zYFkBKpVyXEukdToV1q6tgM2mxY4dXejsDMPjMU154Gg0nHvu59Hf78Mbb8hNLsvKqrFo0WkAkCWGHzq0A4LA59WoO0UumxQAOHx4JzZv/jTa2w/j2Wf/jJaWfSBJFRoaVuCjH71yyEqtYLAPH3ywBUeOfID+/k5oNDqcffbnMH/+0BaS4fAAjh79APv2bcfevdsQCPTkXC9fMTweZ6HTUZg92z5ugZDqagv27u1DIEBDoSAQiTBYvtyN+fOd457RqNVS0OlIHD0awLx5DlRUWArafsECF0RRGtcxXbGND4sROaNfAZ4X0rZ0NM1BFCUMDNCIxTg4HPpxeQ4JggiCGNl3uNgwGNRYvtyDl19uQTBIQxBkP/rJ9qF3u42YO7d0yGCCLHIDfX1xmM0aLFvmznkPWCxqGAwqxGLJdJbwZNLfH0cyKWLdukrU19tzXlujrRarqrKgpEQHv58esukpIDc+ra21orraMqrjcJw47a7jFMebQSbBsvywgj7PixBFCVarJu8xu1ZLIRCgARSfUJyv3ctwUJQSdrsOPl8UGg05LcdoHxbyHuWsX78ef/7zn9HW1oaqqqph121pacGWLVtw1VVXjfX8ZpjhQ0WqAUsKgiCmRAySu5EDb73VgY6OEMrLzUX7IJckCYEAjWCQgUajhFarAkkqBgUJHsEgDY6TMxv0egp6vWravphmz16K2277O/74xx/gwIHCy8b7+33o7/flvb5SSUKnM4KmY+D5zHKvt99+ruDjnwhBEDjttAtx2WW3gCRHN+CIxcJ4+eV/4PXX/41weKDg7QWBRyQSQCQy2ZZe7xS8xUsvPYdEgsGZZ34ciQQHmubBsjySSQEcJ4vbBMHj//7vVrz22jMAgBdffBYPPng/FizYjE2bvgCPxw2TSQWlUgFBEEHTPA4f9uPgwX44nUbMn+8YlQBN0xx27OjCvn19KCsbW6NVvV6NSCSJ119vw86dXbBYtHA6dbDbdSgt1U94hk2qIqKpKYBoNJluLBeJBBAOD8Dh8EKtlidgoihix46X8de/3oVYLJSxn4997IuYN28V5s1bhfnz1+Dhh3+Czs6jYz6/obyTVSoNvvGNBzBnzrIxH+NEEgkeFRWWIZ+XWi2F+fNdAGQf41CITWfGjQexWBJz55ZOWWZaIQiCiHicgyCI0OkoWK1aBAIJxGLJcRXEUxYPZrMa27d3orU1iMpKy7T4jk5EqSRx5ZU/wJo1H0Ug0IvFi09PvwsaGlZkrEvTMbS1HURt7fy89s1xSQwMdOX87MiRXdiy5Qk88sidGUGq7u5WbNv2DK677q4Mn/2+vk7897+/x/btz2YFoPbvfxu1tfNRV7cYlZVzsHDhBjBMHE8++WscOPAu/P7uEc/V7a6G3Z5f88xAgEZFhWVcM031ehVmz7bhrbc6oVIpsHp1OebPL7w5Zj4QBAG9Xg2jUY05c0oK9uie7p7ekiQhGk2C50VoNOSYxJbJRKsloVKRgz66snAaDDJwOg2or7dj584utLQE4XTqR+WxK0kSGIZHIsEhFGJQXm7KqyFiseHxmLBkiQtbt3ZAkoD1670wGidXDCUIAitXlg/5udmshk4ni9ybNlUP+SzR6VSw2XTo6YlOuhgeDNJgGB7r11dOSDBBq6Uwd24J3nijHTabNue7MxikodNRWLjQNap3azIpQKWanhYpKbRaCjabFr29cp+God4J0SgLi0VT0DhHrSaL0jO8ELuXkTCZ1AiHmXTW/AzFSd536Je+9CU89NBD+PSnP43nnnsOVmtuf6ZAIIBPf/rTEEUR11133bid6AwznEoIgpgWsVLdlwGAIJAhRImiBFGUBy+TPdGtqrKCopTYvr0DLS1BeL0mqFQjPzIkSUJfXxyxWBIajRI6nSw+U5QCBEFAkiQIggSeF5BMyt9DMsmnvelcrvzFNJbl0dkZgcWixdq15SgrM8FsVkOlUkIUJbAsj2g0iUiERX9/HN3dMYRCTNrbNlW+Jp+b/H1znASOEwb/iRnBCYWCQHm5aUobNtpsTtx442/w6qv/xP/7f79EMslM2LEEgc/KQh8vJEnC668/jqqquTk90YeD5zm8+OLf8N///h4MM7omftORb3/7BrS1qWGxVAAAJAlQKOTrkmES+Nvfvo2OjszGbJIkYvfuF3H48FZcdNFXccYZl6QHeDqdCna7DjwvoLc3jldeaYHXK2fMWCxqmExqmM2aIQfzoiihqyuCDz7oRXt7GB6PcVzKAcvKjEgkkqBpHj5fBM3NASiVBCwWLWpqrOmsnvFCkiTs2NGN9vYQRFECSRKIRJIoKZEnie+++yIeeui76cxUp9MLjcaAgQFfzkBKQ8MKfOxj16R/rq1dgNtuewyvv/44nnnmDwiFCg/cDIfBYMH11/9s3IXwSISBVkvmXa4uZ8a58fLLLejujsLlys+GSJIk+HwRaDQkSkqOH4tleVCUsugbZvK8iHg8CUmSYDSqYbFooNer0tUXXV0RxOPJUZVZD0d5uRlnnKHCtm0daGsLoabGOqFBXlEU0dYWgtOpH1dbhNmzl2Yts1odKCurzsju3rt3W95ieF9fx5DVQZFIAA8//OOcnzFMHA888E184xsPoKFhBV555R/4978fGPY929y8F83NcuWsRqMHw8TzOkcAcLmqcM01P87r7yaKIpJJcTBZYXz/zlVVVhw9GkBFhQWNjRMjhKfQaGTbo4qK8QmYTSficQ4UpYDDoUcwSCMQoHHiVy1JSP8sSXKjOa02t93FZKLVUlCplGBZAbrBVy/D8KisNGPWLBtKS3XYu7cXR48G0dsbHxRcKSgURHquY7Np09eVKIpIJDhEo0kkEhwIAlCr5aqB+nr7hFQlTBYNDQ709cURCrGorR27Vdl4I/caUMNm02LWrOHPz+MxorV1YuYAQ0HTckBk9eryCc2qnzXLjqamIHp6YlljDFEU4ffTWLXKM2zm+HAwDA+jUTUtLVJOxGrVIh6X59G5giKJBAeVSs6CLuS9kapELTZLzkSCSyfOjRWlUgG7XYeOjgh4Xpzy5/gMuSGkE5WeEfjWt76FX/ziF3A6nbjuuutw+umnw+12AwB8Ph9ee+01PPTQQ+jt7cUNN9yAX/ziFxN24hNNJBKB2WxGOByGyVTcE7GJoLMzjESCn3YdkIsZjhPAMPxgljIBlUoJrVb2TyRJBZRKYjC7k0gPhgVBQihEIxBgYDCoxmRjMFqCQRo7dnShuTkAjYZEaak+Z0kdzwsIBhmEwyxKS3Woq7Ohry8Bv59GMsmD5wWIYkq8U4AklVCpFNDpKJjNGlgsckbonj29qKoyjyg4JxJJdHXFMGeOHUuXuvPKXBAEEZEIi0iEhd+fQFdXDLFYEoIgDgqLRNpL2WRSwWhUQ60m0xHxpqYAWlqCqKw0F0VpbU/PMTz66P3Yv//19LLq6kbU1y9CSYkHKpUGDz98Owp4zE869fWLceutf8p7/c7OJvz2t7fm1QRNo9HBaLRBqVQCIMAwso3LyVnu0wmnswK33fZ3aDTHxWCajuO++76Co0c/GHH7+fPX4pprbs/pJ03THAIBGsmkAEmSQFEK6HTypMnh0EKnk6suJEm+/9rbI+jpiQKQM6ImMmAnCCJCIQahEAOdjkJtrRUNDaXjYhXQ1hbEyy+3QKMhoVQq0tkqJSU6hEL9uPXWj4Nl82vqWVXVgJtu+h10utxZbckki5df/g/effc5HDt2vIme3V6OWbMa4XR6oddbAEjgOBbxeAT9/Z04cOBdJBLRjH2RJIXVqz+KCy/8MiyW0lH97kPR3x9HIsFh8eIyLFlSVlC1QHNzALt2daO/PwGnUwejcehnsyRJaG8Pw2xWo6srhrlzj/8e3d1RWK1afPSjdUWZ9czzImKxJABp8B2mgU5HZU3sIhEWXV0RUJRyQrwjg0Eazz57FBSlyMvbc7T4fBHo9XJpc03NxIs8f//7z/Hii39L/1xVNRc//OHfhtniODt2vIIHH/zWqI+tUChRVlYFn6951PsYCqvVifnz12DVqnMxe/bSvC2FgkEaoghccEH9uHl4n0h3dxR2u27Cx5nRKItkUpi2fvejJTX+9HrNMJnUSCYFsKxcaXDiM0O2TJPXj8WSiMWSkKRUdvbQ2ZkTzTPPHEEwSMPpNIBleQwMJHDeefVpsVCSJPT3J3DsWAjHjoUQj3MQRUCtVoAgFAgGaciXOgGFQg7GWywalJcbYTKpYbFoYTKpTwnBKJFIIpHgi9beq68vnpfg19ERxrPPHkVlpXlSmtWLooiWlhAaGx1Yu9Y74e/9trYgXnmlBVarNqOiweeLwGrV4OyzZ436nS1X8ZinrU3KidA0B58vAkGQMjKcWVau5igvNxVcPcBxAlpbg1CpyCnRNnIhihLCYSb9jB4PUr0V/H560noHnEgwSKOszDglx54uFCSGS5KE73znO/j5z3+e0S35xM8VCgW++c1v4u67756UB+dEMSOGz4jh4wHPi2AYPt1RWqejBgVWJdRqMi+BQRDkCPXAQBwUpcw52Z5oeF7OCDtwoB+9vTHwvAiKUgwKR3K2klJJwGaT/fFqa23pQVYyKSCRSA5mwUtQKAiQpCKnnzfD8Hj55Rb09sbSTdpyEY0y6OtLYNGiMixbVjYmYTqVBZ4Sw1Uq5ZADsEQiibfe6sDRowF4vaYpjfjLg5MoystNsFgiGBhoBU07EYvpUF19XKjguCSefvohtLYegE5nhNks+3ZrtbL9QzQaRCjUj1BoYPC//YhEApCk7Ge8xVKC8vJ6qFQa9PS0IRTqRyIRhVJJgqLU0Gi0ae9to9GS4cMtSRLCYT/ee++lLN/Ue+99fkif1hPZvv1ZPPzwj4fM0quqasDixRsxZ85SlJVVp/2+T0SSJLAsnfYyj8fDWf7m8vLU5xEAAEmSIEk1zGY7LJZS2GwumM02AIXdi/F4GH19nejv70Q8HkYiEQPL0oOWSEqoVGoYDJb0v+3b/5e1j3PPvQIXX/x1AADDJHDffV/BkSPv530OdrsLX/va/aiomD3sehwnIB5PIh7n0pP2FJKEQSsIzbg2TMyHWIxFb28cBoMajY2laGgoHXUWmSRJeOGFZvh8kZzPnN/97jt5WwItXnw6vvCFn0CrzZ1JxDDyPWs2qzFvXimSyS50djbD4ahHU9PxDvS54HkOra370dvbDkHgYbM5MWvWwvR9PF7wvIDOzgj0ehWWLXMP6dU5EtEoi337+nH48AAYhkNJiQ5GozpjX4lEEt3dMTgcesyZU4J33+2CzaaBVktBkiQ0Nwdx+ulVGQJ5MSDfF1za13YoEfxEwmG5sVoqCD7evP12J95/v3vCMhETiWS6ceOePb0oKdFNeFOoAwfewc9+lllhev/9L8FsHrl54jPP/DGrOedQrFv3MSQSUeza9dqI61KUGkuWbEQg0IvW1v3g+eSI22g0OtTWLsD8+WvQ2LgGbnfNqO6p1tYg5s93YM2aioK3nWHqCQZpWK0alJUZ8/77S5Js95cqtZcD1ZiS5JitW9tx6FA/Kios6O+PQ6slccEFc3KOl1PVMnJzQyWSSQGdnXIzc42Ggl5PwWyWm+0VU1boDJnE40n85z+HoFYrYTJNvFVKZ2cYNpsOZ55ZM+6VVLmQqwK7sGNHF9xuubKRpjn09cVwxhk1ow76siwPnhdRVWUpisSp8SAWS6K3Nwaa5qFWK8Hz8hzR4dBnVH3kiyRJaGsLQRCkorGLisWSUKkU8HrN4xqIYVkeHR1hSBLG/bqmaS79XjhZ0rVYNAiFmBkxfAQKEsNTNDU14c9//jPefvtt9PT0QJIkOJ1OrFmzBp///OdRXz/5XdrHmxkxfEYMHy0p7zua5tNZxmazBlotOWrxNOUz2N8fB03L3amnooSQ50X09sYQDNIIh1kwDA+VSgmLRQOrVTMunr4+XwQvvNAEm02bM/spFKIRCjFYssSNxYtH5+U2FmiawzvvdOLgwYEx+yOPlnCYht9PY+5cB5YtK0ufQ19fHM89dzR9zY2WlJd2SuzWag0wm0tgs2WXTxda4sZxSXz962eApmPpZZ/+9Ddx9tmfG3a7//3vT/j3vx/IWk4QBFasOBsf/eiV8Hqn/7vnZDiOxZ13XoW2tgPpZQqFEt///l9RVlaF++77Cg4f3pWxjdFoxU03/Q4mkw3//vcD2Lr1qaz9qlQaXHPN7Vi+/MwJ/x0mimBQvg8qK81Ytsyd9vcuBL8/gaefPgKrVZ11Lx85sgt33XX1iPvweutx3nlXY/nyM4e8F/z+BCIRFvX1dixc6MoamO7Z04O33upARUX+VScpwVipJODxGPOysRqOcJhGfz+Nqiozli3z5G2PMhy9vTEcOeJHW1sY8TgLQC65TyQ4aDQkqqstWLTIBZNJg//97wgCATmLJRxmwLICzj+/fkqad+WCZXnQNAeFgkhbCBUSnA4GafT0xEAQckD4xM3G2seivz+OZ545mvM6HiupbL0FCxxYvdqLl19uQWdn7uDReMLzHL761Y0ZtiNXXvlDbNjw8RG3/f3vv4dt244HEkmSylkRtG7dx3DVVbdBkiT85jc3Y8eOV4bc58aNn8LHP35duqomkYjiyJFdaGrag8OHd6KpaXfWNl/4wk+watU5UCjGJojwvICOjgg+8pG6Cf/eZxh/4vEkCALwes2jHiNznIBkUg7EDQwkoFZPTGBtKPbu7cVbb7WjpsaGlpYAli51Y/lyz6Qdf4ap4YUXmtDVFZ1wuzK55wiDzZtrCm6sOxY4TsD27Z3Yv78vLR42NJRgw4aqUffsCoWYdODrVIJleYTDLBKJJChKCbO5MJ/wk+ntjSEQoItijCdJEoJBBl5v4Vnu+RCJsOjujkIQJBgMqnGpgAmHGVCUAiaTJl11TxDyvNjvly1EGYafEcNHYFRv5FmzZuGOO+4Y73OZYYZpDccJoGkegiA3xnG59OPWrJEg5Mm3VkumrQICARpaLTmpg2GSVMDjMU3ooMjtNmLWLDv27+/LynLz+xOIx5NYtaocjY3OKWkuqtVSWLOmAiqVEnv29MJu103qi7y/Pw6G4bB6tfwdnBgMcDj0aGgoxY4dXTCZ1KO+7pRKElarA1arY8R1Cz0GRamwdOkmbN36dHrZW289M6wY/vjjv8Izz/wxa7nXW4/Pf/57efvITkcoSo1rrrkdP/zhp9PN20RRwG9/+20YDBY0N+/JWN9otOLmmx9CefksAMDVV9+GlSvPxh/+8IOMJqPJJINf//pmfPrTN+Lssz87eb/QOGK1amE0quDzRfDSSwyWLy88k9nni4CmOXg8mZMWURTw6KM/zVimVJK49lq56R5Nx6HTGVFZOQcOh3fIY6b8sClKiXXrKjBnTknOAF5DQym6u+M4diyIqqrcPVlOJhJhBr3dNWhvD8PtHl1wjucF+HwRqFQkVq3yYO5cx7gFW51OA5xOAxYsYDAwkEAkwqC9PYL58x0oLzejtPS4z2RFhQmdnXIlRiBAo7HRURSTJI4TEI3KGUN2uw4mk3pU73WrVQuSVCAaTYJh+HQWjyRJY26wVFKiQ2WlCc3NQVRWjq8Y7vNF4HYbsGiRHHyurbWhtTUIQRAnNBhNkhQaG1dlCNR79ryZlxje3d2W8fMFF1yL11//N/z+41VJNpsLl112y6A1HYGrrroNLS37syqXjEYrrrvuLsyduzJjuU5nxKJFp2HRotMgSRJeffVf+Ne/7kcyycBqdeLqq2/DvHmrCv69cxEOs7BateMSoJphcpE9swWUl49eCAcAilKmq0NVKiV6e2OIRtlJa8yWmmuIogiCwMy1+CHB4zGhpWXifcN7e+NYtKhs0oN9FKXEmjVeWCxq7N8/ALtdiwULXKOeX4qilO4fcqqhVpNwOEgA43PvazQkBKE4bDwTCQ46HTVhSaApC6hgkEY0yoLnJRDE8d5PhVrcpRIz3G5Tzsx6nhfQ0xPHTOHNyExvV/8ZZphiRFHOAmcYHhSlgNEo+0zr9dSETBIpSonSUj0sFg1iMblsORRiYDaPXvgsNgiCwNy5pWhtDSIWY9M+bil7lrVrZUFpKn9flUqJlSvLodGQ2LWrGyzLw+EYX7uCk5EkCV1dUZCkAhs2VGHWLFvO76ChoQStrUEMDCRG3fhlolmx4uwMMbyj4wi++tWNWLr0DGzY8AnU1MxLf/bCC3/NKYRv3PhJfOYzN4GiTv3qFY+nFueddw2eeuq36WW9ve3o7W3PWM9gsOCmm36XFsJTNDauxu23/xO/+tW3cPRopp3KP/5xL6LRIC666CvT8hlCkkpUVlrR3x/HG2+0IRJhsXhxWV5ZFxwnoKkpAKMx+xp6/fXH0dFxJGPZpz/9zYIy6XlewLFjYTgceqxe7YXbPXSWEEUpsXChE93dUUSjzLA+2ylCIRazZ9uxcmU5du7sxt69PTCbtQVlgKSy66uqLFi82DVhmUxmsyYtbC9e7M65TkmJHkolgWhUznapqckvKDCRxONJ8LyI0lIdrFbtmK0JUs3LAHn8QBByU72OjjBYlh+1WEYQBGprbTh6NACOE8atNNvvT4CiSCxf7km/i8vLTbDbdQgE6Al/xyxcuCFDDN+//21wXHLY574oClle31VVc3HNNT/Gffd9FckkA5JU4YorvpfRe0GrNeDaa+/Effd9Jd2Y2eutx9e+dh9KSnJfsykIgsAZZ1yCNWs+iv5+HzyeWiiV4zfFCocZLF3qnvbN2D5spPoKOJ36nO+Z0UAQBCwWDShKgZ4euVLTYtFM+Ps71RAzGGRgMmlmxPAPCXa7HMQdz/fKySSTcpVxTU22teFkQJIKLFjgwqxZNrCsMKRdXT4wDA+NhoRWO/OsHgmKUkKhkMdCU5HclkK2zxRQXq6f0AC/TkdBqyXBsjokk0J6DChX/XN5JzfyvAia5uHxGIe0mDEa1fD7adA0n/PzGY4zc6fOMEOBiKIElpUFcIKQsyXcbuOkWpdQlBJWqxYaDYm+vjiCQQZGo+qU8SYrKdGhttaKffv6oNer0NUVAUkqsX595YjdzycLpVKBxYvLYDJp8M47nTh2LASv1zQhvRJSTeYMBjXWrfMOmzlhNKrR2OjAG28cg82mLcrGcw0Ny2EwWBCLhdLLYrEQtmx5HFu2PI4lSzbh85//Lg4efA//+Ed2I+bPfOYmnHXWpZN4xlPPeeddhffeewFdXa05PzcYLLj55t/B663L+bnJZMPNN/8OjzxyJ9588z8Zn/3vf39CNBrE5Zd/Z1wFnMmktFSPWIzEzp1dYFkeK1aUjyhc9vbKTW1OFqmj0SCeeOLBjGUezyxs3PipvM+H42QhvKrKgjVryvPK+nC5DJg9uwTvv98Ng2H4AKcoihAEEeXlcu+CVavKYTKpsHNnNzo7w/B4TMNuLwgiOjsjUKtJrFvnxZw5pVPexChVZXPsWBiLFrlGZXsznvC8CJ4X4XYbx1RpMxSpyZ/BoEJpqQ49PfHByeHojuN2G+F0GuD3J+ByjT2oIfsUs1i3rjKjGkyjIVFfb8Nbb3VOuBg+f/5aEASRzqJnmAQOHHgHCxeuH3Kb3t6OrL4SXm89zGY7brvtMRw58j6qq+fl7JlQX78Y9977PNraDkChUKKublFBz0St1jBiL4ZC4XkBSiUxbDBthtGREkEIAuMeaJAbZjKw23Ww23Xj/vzQ61UoLzcNCuJyUsxEjvdMJhW0WhV6e+NYvNg1qVWpM0wdqWqoSITNaHqbsgQdj+sgHGZhsWimvKmuTqeCboynINtSGIpy7lVsqFRytQvHCVMa6I3FkjAYqHELWA4HQRDQaMgMvUgUJfh80bx6yYmihEiERUmJDhbL0EkzajUJk0mNRCLbHm6GTKblnRqNRnHDDTegsrISWq0Wa9aswXvvvZf+XJIk3HbbbXC73dBqtTj99NOxf//+KTzjGaYz8mCZRyyWRDBIIxKRJ1kOhx6VlRZUVlpgs2mnxMNbq6Xg8ZhQWqpDPM6dUg+9ujo71GoKzc1B6PUqnH56VdEI4SkIgsCsWTZs3lwDp9OAlpYQEomRG2oVAs8LaGkJoqREi02bqvIqIZw1ywaPR54kFSMkSeETn/jSkJ/v2vUqvv71M/Db334767Mrr/zBh04IB+Tv7NJLb875mclkxy23/H5Ez3SSpHDllT/AJz/5tazP3njjSfzmN7eA49hxOd8T4TgW4bAfAwNdYFl63PefwmBQo6zMiD17+rB9e0dW08+TaW8PQxTFrCDi3/7203Tj1BSf/ewteYtiySSPY8fCqKuz4fTTKwsqf5w7twQWiwaBwPDfUyjEwmzWpjO5FQoCjY1OnHFGNSwWDZqbg6Dp3O8DmubQ2hqCy2XAWWfVYsEC15QL4YAssq5Y4UFNjQ0NDaVTmikEyFnhJpN6QoTwk7HZdLBaZb/SUbTyASAHyevr7YjFuFHvI0U4zCAQSGDpUjfmzMluWOn1WmAwqBCLjf/z4kTMZjtqaxdkLNu589Uh15ckCXv3bstYZjLJTaMBoKysGqedduGwgrVOZ8TcuSsxZ86yoggOykKRtmgrvaYzkQgLURTBcQICARo0PfZ7B5CDoeGwLB46HPoJe5ap1SQ8HhNsNg3CYTbd1G4i0OtVMBqptF3iDB8OVColvF4TotHjz3pRFHHsWAjd3dG0N/FYiMVYeL3mcfFRnkrk7HnFpDT/PBUgSQXUahIcN3HPrZHgeRGCIMFu101ZAMNkUsNoVCEWG14/kCQp7QZQUjJygNVkUkOtVkIQpu77nQ5M/ShvFFxzzTXYt28f/vrXv8LtduPRRx/F5s2bceDAAXg8Htxzzz34xS9+gYcffhj19fX4yU9+gjPPPBOHDx+G0TiTWVEssCyPREL2PFIqFVAoiAxvo1RnXNl/67gP11CkPjp5H6llFKWEVkvm9bDjeRGJBAdBENPbajQkSkp06YjeVE/UU5CkAg6HHhoNif7+OEIherCZQnGc32hxOPSorjYjFpP9sac6Y2A4nE4DNm+uxq5d3Th4sB8qFQuXyzBmAYWmOfh80YKySwF5gtTYWIqXX24ZU+n9RLJx46cgigL+85/fZgmPQ/GpT30dGzZ8YoLPrHiZN28VNm/+DF5++e/pZVarEzff/Du4XJV57YMgCHz0o1fCaLTi4Yd/DEk6PkjaufNV3Hff1/DVr/4CWu3ohReajuPVV/+FAwfeRnf3MQSDvRmfWyylmDNnGU4//SLMnr101MfJhVZLobzciP37+wEAq1d7cwq9sRiLtrZwVjns1q1P4513XshYtnz5WZgzZ1lex08mebS3R9DQUIJVq8oLzpoymzVoaCjB9u2dsNm0Qz5DwmEGCxdmZ+eVl5thNmuwa1c3Dh0agE5HweHQp/cTjTLo60ugsdGB5cvdRZfdV11thdttnPJnliRJEARpUoRwQA5mlJbqwbKyP7nJNDq/Ubn5kxqRCAOz+fi13dUVAccJcLtNw1aQSZI02JeCx/LlHixaVJbz97fZtKisNOPw4YG0fcpEsXTppozmlO+//zoE4bsZQnVr6368/vrj2LXrtYyKIwDTvrFyyiKlGAJWpxLxeBIUJQu7CgWBaFRu4BcMMlCp8p8vnAxNc2AYHqWlOpSWTpwQnoIkFXA65UzUgYEE9HrVhFwrBEHA4dAjGuXgcBTveHyG8cflMmD37l6Iolwt1d4egcdjhNttxHvvdcFq1Yy6KlYURUgSTolrKpHg0j1FZsgPnY6c8KD6cEQiLGy2sTUCHStKpQIlJTp0dITTNjsnI4qyEG40quB0GvIKHOl0FPR61ZQGG6YDhDQeIfBJhKZpGI1GPPXUU/joRz+aXr5o0SKcd955+PGPfwy3240bbrgBt9xyCwCAZVk4nU789Kc/xbXXXptzvyzLgmWP34yRSARerxfhcBgm04cvAt7ZGUYiwU/Yw4GmObCsgJISLQACySQPnhchnnC/pjrjKpXE4D9FhmguNz0auoGfJEkZgnokwiIeT0KS5Eg3SSrSA11JktIl0RwngiQJGAzqtPUIRSmmhQUJw/Do748jHGZgMKin/eQpZUUz1cJIvoiihNbWID74oAe9vXGUlmozBIl8kSQJfn8C0SiLuXMdWLascNFKEES8+morWlvzb8g3FSSTDLq6WrB37za8+OLfsoSMFKeffhEuv/y709LXejzheQ7/+tf9eOed59MNREtLPaPa165dr+E3v/k2eD4zG6G6eh6++c1fwWCwFLzPcNiPn/3sOvh8TXmtP2fOclx66U1D2ruMFobh0NkZxaJFTqxcWZ4lahw+PIBXXmlFba0VBEGAYRJ4/PEH8PLL/8hYT6834Sc/+TcsltIRj5myRmloKMHq1d5RT4ji8SSeeebIoFd1dlAimeTR0xPDuefWDZmhJ4oSmpsDeP/9bgwM0HC59BAEEcGgLKylmiHOkJtUhmhVlXVSs9USCQ4+nxwcHG122bZt7di7tw/V1fJzv78/DlGUUFKiw7FjIeh0FEpKdCDJzPFBLMaitzcOi0WDpUvdqKvL3ZciRUdHGM8/34SyMgNUqol7R/f3+3DzzedlLLv55ofQ0LAcAwNd+Nvf7sEHH2wZcvtzz/08Lr74hgk7v4lEEER0dITxkY/Uobx8chvLncpwnIBYLAmv15wReOI4AYkEh2CQRjwuV9ZotRTUauWIYw/ZH5xN9/aZ7F4+oiiPG/v64tDpqAkZNx854kd3dwSnnVY97vueoXiJx5N46qlDYFkeNC2gvt6GlSvLAQD/+c9h6PXkqIOikQgDlhXw8Y/PmdYZ1aIoIRxmUFlpmVJhdboRjbJob89OTJkM5Gp6CV7v2Jobjxd+fwI9PTEYjeqMcWeqibvZrIbTaShI25luOspUMO2+GZ7nIQgCNJpMnxytVoutW7eitbUVPT09OOuss9KfqdVqnHbaadi2bduQYvhdd92FH/3oRxN67jPIpEqt3G7jpA4WzWYNaJoDTXOIxzlwnIBkUgAgZ44rlQro9XIULd/Bb7Gh0ZDprLqBgQR4XhyyucJ0YLpF1xUKuYmZy2XAgQP9OHRoAP39Adjt2rwbHMXjLHp64jCbNdiwoQqzZ5eMKrNIqVSgsdGBzs5oRiPSYkOl0qCqai6qqubitNMuxG9+820cOvRexjqVlQ249NKbhvz+RFFEKMQiGmUhCCIUCgIGgwpWa3F5pkciDAIBGg6HHjrd6AbLsl3KTbj00pvGfD5LlmzEjTc+iP/7vxvAMPH08tbW/bjrrqtw442/gc3mLGifjzxyR95COAAcOvQebr/9s/jUp76OM8/8zLg9czUaCm63nM2kUpFYuNCZDmiKooSjRwPQakkQBIH9+9/Gww//GAMDXVn7ufTSm/MSwlPNMuvqbFi1qnxMzy69XoV580rxxhvtsNu1WRlXfj8Np9MAl2toT22FgkBdnR0ulwH79vXj8OEBcJyAlSs9mD/fNepsxUSCg1JJnPIDa4bhBwXjyX1+6HQUnE4DuroiBTVUOpHKSgv27+9HMskjHpfHPKedVoWqKgtaWoI4cKAPnZ0RCIIEkiQgivI9odNRmDfPgcZGR15NWMvKZI/ygYEE3O6JSxopLfWgomIO2tsPpZft2PEKgsFe/OUvd2T5g5/M4sWnT9i5TTShkNyssKRkxiJlvJAkCdFoEqWluiyPWIpSwmxWwmhUg6Y5xGLJwYxxDgQhN2wmyeNJOaIoV5AkkwIkSUr7Hk/F2FWhIFBSooNCQaC3V36fj/dzurbWiqoqy7juc4biR69XobRUj/b2EJYvd2PhQtlaTZIkmM3qdALWaIhEZIuU6SyEA3IAXa+npvWceypIJSfyvDip4y2eF8GyfLrvTjFgs2nBcSIGBhLpBqypnhalpXLviUK/o+mmo0wF0y4zHADWrFkDlUqFxx57DE6nE3//+99x+eWXo66uDn/+85+xdu1a+Hw+uN3Hu79/8YtfxLFjx/DCCy/k3OdMZngm3d1RBAL0uEbqBEFEOMxCqyXhchmm/MUnCLJPFHBcDJ/u1iIpJEnOhO/ri4PjxEnPUJlBJhCg0dQUQFNTAOEwA5VKAYNBDZ2OAkUpQBBEuiFrPM4hEmGh0VCorbXmLUiMxLZt7di9uxc1NdZpcQ0IAo8//elH2LbtGQByY8jvf/8ROBzenOv7/QkEgwysVi28XhNMJhUSCR4dHWH4/QkYjfl5q00kLMvD54tAp1MNessH4HTqiyZA0dZ2APfee31WVr7d7sK3vvXbvC1Y2toO4Ec/umzU57Fy5dm4+urbQVEq+HzNkCQJbnfNmJrSRqMM+vsTmD27BCtXeqDTqdDVFcVzzx2B3a7BM8/8Ds8993DObZcvPxNf+tJPR7x2RFFEa2sIVVVWnHZaxagDHSdC0xz++98jSCZ5OBzHRe/UsTZtqsbs2dlezkPR2xtDNJpETY111O+5SISFUkmAZQVYLNPfimsoUr6MU5nhFQ4z6OqKDto1FF4V9PLLLThyxA+djsKKFW40NjrT13EyKaC3N4ZQiEE8zkGpVMBkksUOqzW/oG2Kw4cH8OqrraiutkxI8+gUTz/9ezz55K/zXl+l0qCkxI2NGz+JzZs/M2HnNZFIkoTm5iCWL3dj+fLRVf/MkE00ykKtVqK8PD+PYp4XwTA8GIZDIsGD44S0baNCQUChOJ5Io9dTUz7OkiQJgQCNnp7YhGWInwrwvIhkUpgRL/NkYCCBREKupjjxGn/vvS7s2uVDdXXhPZ0kSUJLS7Dg8UyxIUkSgkFm0KZs6KaGM2QjSRJaW0PpgPxkEQzSsFo1KCszTvkz+0REUUIgkEA4LGuSBoMKBoNqyjWzU5lp+Yb861//iquuugoejwdKpRJLlizBpZdeil27dqXXOfnCliRp2ItdrVZDrS4OYaIYsNm0iMeTiMeT43IDMozsD261alBaqi8K+w7ZdmWqz2JiIAgCZrMGKpUSfX1xBIMMTCb1tG9OMt2w2bRYscKDuXNL0NUVQ2dnGL29CQwMJMBxwuBkSm4gYjRSmDOnBF6vGaWl4yfezpvnwLFjEfj9iWmRXaZUkrjmmtuxdOkm9Pf7sGLFWbBaHVnriaJcPq7RqLBunRezZtkyRMhFi1xpy5rW1iAqKsxZtgCTQSBAIxikUVdnw8KFLthsWuzapcHOnV0QRQkm09QPnKuq5uI73/kzfv7zLyEQ6Ekv9/t7cOedV+LGG3+Nyso5I+7nP//5bcbPCoUSn/vcraitnQ+zuQRKJYlAoBcHD76L559/BMFgX8b677zzAvr7fdDrzdi79y0AQE1NI7785Z/BbneN6nczGjVQqUgcPDgAmuaxdq0Xra1BhEJ+PPbYT7OqEACAotT4yEeuxHnnXTXifShJEo4dC8HjMWHt2vJxEcIBuTS/sdGB119vQ0mJmBYaU4GfiorCLBOcTgOchSX5ZyAH85RwOPTw+xNIJLhTthQ45dmYysyZCsxmDSQJ6OmJQpJQ0CRRqVRg+XIPRFFCXZ09bQWUQm6IZs6rGfNIVFSYYbdrEQjQE/p+Wb78zBHFcJPJhjPO+DRWrDgLDod3WHF+pDlBMZBqlFVXV1yNw6czyaQwaBmkz3s8TJKKtCAByGJFSgwnCNnGsZiuJYIgYLNpIUkSenriIAiiKOZcxUY0yoIg5Iz6mezJkSkp0QHI9vW22TRpS9JC74OUxuB0Fv/cZDgYhodWS84IlqOAIAjo9RT8fnrSxPBEgoNarYTdPrWJUrmQK3z0sNl06Z9nmFimZWZ4ing8jkgkgrKyMlxyySWIxWJ44IEHUFtbi127dmHx4sXpdS+44AJYLBb85S9/yWvfkUgEZrP5Q5sZDshZYD5fBGo1WfBAITVYFAQRNM2BJBWw23WwWrUzN/Ykw/Mi+vvj8Ptp6PUzWSJTTSoLnGF4SJIEpVIBrZaE0aiesHtj375ebN3aPmWC8HiTyibxeExYtaocDsfQA+lAgMbbb3eirS0Er3fyyuEkSYLPF4FSqcDSpWWYM6c0PfkWRQkffNCNHTu6YDZrpsQrLxd+fw/uvffL6O5uzViu1Rrw9a/fP2yzy5aWffjxjz+XseyTn/waPvrRK3Ouz3Esnnji13j++UdGPC+PZxZ+9KO/ZzTMKxSeF9DWFkJf304888wf0N19NOd6s2cvxZVX/gBOZ8WI+5QkCR0dstfhxo3V41LJcSIMw+PZZ48gFkvC5TKmM0XXri3HwoVl43qsoUhlSacsuLRaCsEgDZ8vOu6/b7EQCsnC7nDPlckiEmHTgrjRWFjCxmQJvrt3d+OttzqzRPfx5v77v4bdu9/M+dn69R/HJZd8A3r9yOP1lLe/QoGifSeOlBUuCGI6IFVsk/liJZW9WVqqg9M5tMXUqYIkSRgYkD3EDQbVtOh7NJnI1c+awQzRoZtVzzA8gQCNp546DJtNU3AVU0dHCBUVFmzeXDNtv3/5uULD4zEVzVh+uhGJyL7hkzGm5HkR0SiL8vKZLP4ZZKZ1mqher0dZWRmCwSBeeOEFXHDBBaiurobL5cJLL72UXi+ZTGLLli1Ys2bNFJ7t9MNkUsPlMgw2zOCGXZemuXQGZDBIIxplQdMceF6E2ayB12uG3a6bEcKngFSneZdLD5rmBhtGzDBVqNUkbDYt3G4jPB4TXC4DzOaJtRyory9BRYUZPl90wo4xmfT1xWG3a3HaaZUjClY2mxYbN1Zh7twSdHRERnyWjQeiKKKtLQSDQY1Nm6rR2OjMyEJTKAgsWlSGVavKEY2yGBiID7O3ycNud+Hb3/4DqqoaMpbTdAz33ns9du16bchtT84KNxgsOOOMS4Zcn6LUuOSSb+Ab33gAGk12ttGJ+HxN2L792Tx+g6FJJMJ4+ukf4ve/vyWnEK5Ukrjssptx880P5SWEA0BXVxR6vQpr1ngnZBCv0ZBobHQikeDA8wL8/gSsVi1qaycnU5Tn5YabBoMK5eWm9ERXoyGhVBLg+VOvQ72cH0IUTem8yaSG220CSSoQCjEoJH9lssSFmhobLBYNQqHhvbvHynnnXZ21jCQpXHXVbbjqqh/mJYSzLJ/29i8vN6e9lYuNkbLCEwkOoiiBYfhJPrPpSyLBQaslT9kg3skQhOwhXlqqRzSaBMcJY95nKslpuiP7ExOwWDQwGFSIRpMjb5QH0zi/cNSYTGoYjVTBc0tRFJFMimO2cEwFBkMhJq1BBIM0wmEGiQQ3Ltf9cMhZ4dQpWyk3Gci+4RM/pkxZyNps2ozGyTN8uJmWYvgLL7yA559/Hq2trXjppZewceNGzJ49G1deeSUIgsANN9yAO++8E08++ST27duHK664AjqdDpdeeulUn/q0w2KR/ZQEQUQgQCMWSyKRkAXVaJRFMEgjEKAhCCLKygzwes2oqrKgutqa/ufxmEbVBGqG8SNVduPxmCCKYrqJ6QwfDlQqJRYtKgNJKhEOT6xgMVZGmkzwvIB4PIlFi8ryjuprtRTWrKnAwoVOdHVFkUiMz8QnFxwnoKUlhLIyIzZvrhnSikChIDB/vhPr11eC40R0dUWKYiJlMtlw880PYc6c5RnLOY7FAw98E7///fcQDvszPjt69IO0rUmKj3zkihFFbgBYsGAdbr31T7BYhveLfOqp34HnRxfI4Lgk7rvvq1nnmMJqdeLWW/+IzZs/k7fvcXd3FCSpwNq1XpSVGUd1XvlQVWVBebkZPl8EoRCLxsaSCfealyQJ8XgSsVgSJSU6eDyZFRUpCxGWPfWEOIbhoVYrp9Qi5WQMBhU8HhN0OhLBIFN0YpTRqMbs2Xb4/fSEPsNmzVqIL33ppygpkfsBuVyV+Pa3/4j16y/Ia/tEIonOzggaGkqwbl0FqqosRXkNS5IEv5/G7NklsFhyC7ccJ8JkUoOmi+/8ixFBkP2hS0p0H6oM6eOCuA7RaHJMz45UlVDKy3Y6w7KyFZZOR8HhMEChwJgThWKxJPx+GsnkxIqvo0GSpAk7L5JUwOEwIB7Pf1wtCCLa28NwOOR56WjgeRGhEINoVO5lUloqj1XKy01wu42w2bRQKgkwDJ8hkI/n+1OSJNA0D5tN+6F6row3KpUSKhU5ofeOKEoIhxnodNSU95GaobgontF+AYTDYdx6663o7OyEzWbDRRddhDvuuAMUJQuuN998M2iaxpe//GUEg0GsXLkSL774IozGiZuwnqoQhBw512hIxGIsaJpPv0i0WhXUavkBplIpZzzXpgFmswYkqUBPTwzhsOwjPvNC+HDgdhsxf74D777rg15PFWVpeHd3FJEIi5oaa9bAUs6MpRGNJlFRYUZ1taWgfatUSqxY4QFBAHv29MLlMoybv3MKhuHQ2RnFrFk2rF5dPqKtAUEQmD27BBoNiW3bOnHsWAgVFeYJbUSXD1qtAd/85gP4zW++jffffz3js23b/oe9e7fh85//HpYu3QRRFPDooz/NWMdksmHTpovzPl5FxWx873uP4A9/+AEOHdqRc52BgS5s2fLEsNnmQ/H3v/8MbW0Hcn7W0LAC1113F0ym/LKtU/Y3KhWJtWu9qKiwFHw+haBSKTF/vgPBIIOKCh3q6ia2yVSqv4dWS6K83ASjMduGgSAIGI1qdHfHoJ96J5FxhWV5lJbqoVQWV66IRkPC4zGle4AYDKqi8gGur7fjyBE/AgEadvvIQbDRsmLFWVi+/ExEIn4Yjba8n5XBII1QiMHChU4sW+aBSqUcFEsU4HmhqN6HwSADi0UzZFZ4MilApVLAYtGApjnQNDeTcDIC0WgSZrOmYKuhUwE5GUY3mNTEjLr5cTSahE5HQRBEsCw/YZZzHCdAqVRMaLVkMimk/YJ1OgpOpwFdXVHQNEZ1L6WaS1utGkSjyaJ6NqdEQKWSAE1zEzLvczj02L+/b+QVIQclu7piqKgwY8UKT8HagRysT1Weq2GxyPYsua4XSZLAcfL1yrI8GEZAOCzfA+PxHcTjHPR6aibLeIwoFAQMBhX6++MTVpUXi6X86Q0zgYsZMpjWnuETyYxn+AynMgzDo6cniliMG/XAeIbpB8PwePXVVrS3h1BdPbH+roXCsjy6uqIoKzOguzuG6mpr+jNBENHaGoLbbYDHY0JNjXXUggvPi3j3XR927+5BWdn4CeLxOIvu7jjmzSvFihWegidU/f1xbN/eic7OCDweY1GIG4LA4+GHf4KtW5/K+fmqVR9Baakb//3vHzKWf+YzN+Gss0ZXidXV1YJgsA+zZy/FPfd8EUePfpD+TK834c47n8xbuAaALVuewMMP/zhjGUWpUV+/BsuWbcaGDWdDochvYJxM8ujoiKCkRIc1a7yjzmgqFEmS0N0dQ2npxGQ1SpJst0DTPFQqBWw2LcxmzbDHSiQ4tLUFYTKdOu+PlGhQVWUp2kZYoijB70+gvz8xmME+9c+JFHv39uDNNztQXW0pmmCCJEno6pLtwZYuLcO8eY70uTEMjyefPASFApPu9SqfVwQsK2T4lqe8wlet8mDJEnfObePxJChKgaoqK/z+BLq7Yx8a64/RkEwKYBgOFRWWorE/mgp4XkR3dxThMAurtTAxMGV7WV5uQiTCpgM24w1NH++nYzZrJuQ5IghydWxVlTXjegiFGHR3R0FRyryvk9Q7Q6MhUVZmhCjKDbXHS2wdK6lsfqNRDZtNi4GBOGIxDmazely/276+OP7738NwOvVQqYYWt/v744jHk2hsdGDx4rKC31+CICISYaHRkHA49AX3TEgmBfh8ETAMP2a/6JT3tNdrnhHDx4FoVPYNn4h3sXzPy4lUM3Y2M5xMcYxWZ5hhhklFboZmgtmsRijEnJLerzNko9GQWLWqHKWlenR0hIvCliNFd3cU1dVWLF8uZ4qEwzQA2VcwlTF9xhk1WL7cM6bMQ5JUYMUKDxYtKkN3dwyx2NhLfoNBGn19CSxZUoY1a7yjEqhKS/XYvLkajY0O9PTE0Nsbm/K/j1JJ4qqrfojPfOamnJYnb7/9bJYQ7vHUYtOmT436mG53DebNWwWSpHDhhddnfBaPR/DYYz9DNBrMa18ffPAGHnnkzoxlJKnGddf9Frfeej/mzj0NPt/I37PciCyOjo4IZs2yYfPmmkkTwgE5E9vtNo67EM5xwqDdmexF7XLpUVlpQUmJfsRjaTQk1OpTyyqFYfhBC5jiFcxSWZ5utxEcJyAWmzjLp0KpqytBebkJPl9kqk8FgJyB2NwchMkk925YsMCVIQBpNCScTt2UfIeRCAuVSm5K2919vJdHqi9AXZ19yG2TSSE9oTeZ1NBolJPSC2O6EoslYbVqP9RCOHC8f5DBQCEczr//gJxRy8PpNECvV8FoVEOSxt87nKY5JJMC3G4jzGYN4vGJuaZZVoBGQ2VlJFssmrSVZD7fD8vyCIXkCtvychN0OmrwvagsCquUVMNYo1GNsjJD2m7LatUgHGbH9ZlhtcpVF0N5r6fG8ZIEnHZaFVatKnyczHECwmEWFovch8xoLDzDXaVSwuk0gCQVBdm65CISYQd/7xlxdTxQq0lQlOL/t3fnUZKd5X34v3e/tdzaeqmu6u7ZkMRIowUhAVqR2CQMBrL8YgwxFtGJDwRkWYSDwRgCIQaZBOTYJJJPEg4hiEQkBmMlZpEUtEAQQkKDJSFZEtLs090z0921V931/f1xu0rTmt6qu6qruvr7OWcOqPrW7Vszb93leZ/3ebry3Wk2m47Ftvc1gJbGuhZE25SuK8jlLKiqjNnZet8tu6buyGQiuOKKSTzwwCEcP17e1KDecioVG5qm4oILRpHLWTj//FE88sgxmKaGqakyxsbiuPLKyY4tcQ4D4nmoqoTHH5+C6wbrykZ4KetQwpVXTuK880Y3lCUbjeq46qodGBuLY//+KbzwwjxGR6NIJDaWwRIEARoND64bABBQFBmapkDXlVUfJiRJwhvf+G686lVvwje/+QU88cRDK27/T//px6Gqnbnh3Lv3UrzmNdfh0Ufvab32yCM/wCOP/AC53G6cc87FyGSysO0G6vUKFEXBOee8GpaVxmOP3Yf77/8rBMHiG+sbb/wU3v/+6zE2FsexYyX8v/93GAcOzC/KzmwKgrCB5Px8A+l0BFdfvQOvfOXwll5i6fvhWLBtH5omIxoNl/jGYvqiJq+rkWUJiYSBkyerfR08bkej4WFsLNb3me7N8nWKImF6uoJSye6LzDTTVHHppXncf/8BHDpUQCZjIh7vTSm2mZkKbNtbyEAcW/bakctZeO652SV/1k2VioN83sK55w7j3ntfQK3mwDRVFAo2rr565WudEGiVqNA0BUNDURw7VoZpqn2RjdpP6nUXpqlseuZ/v9J1BWNj1kKGeAPJ5MoZzOGKIXeh0Xs4JmOxsFlgrea2fU/meWHt9iAIA82KIsEwwlrBtu0hn08glQrLOZZKNoJAdPx8bNsestmlz/OJhAFVlXHiRAXz83VEItoZ1zfH8VGrOVAUGWNjMaTTkdYkm6rKMAwVtZrbtTIya9HMWE8kwkB4855F08LnvkhExexs2POrGcTfCE1TkM9beOaZk2ckqziOh8OHS8jnLVx22QTGxuJt79+2PVSrLkZGwoawG7vPbpbFKa27xFSl4iASUVuldmjjwnK7GhoNr6OxiCAIS+Xkcv2xWoP6D4PhRNtYM1NE02TMzFQhhNbTGzjaHLmchauumsSDDx7Ciy/OwTRVxOP6ujItNkoIgenpKi6+ONdqRHjBBVnMz9fxzDOzyOViuOqqnR1/mFUUGZdckkckouLRR6dQqxWRy1lrrkFbr7s4fryM4eEoXvvacezcmerIccmyhHPOGcLYWAxPP30Kzz03i5Mn55BIGEgmjRWXoDbLXZz+R4jwYdIw1NZ7bduD69pwHH8h+0mCJIW/W5bDYEtztYgQgKbJ0LQofud3Po/HH78H3/3ubWg0qmf8/ssuexvOPfc1Z7y+Ee95z0fx5JP/74zfNzV1AFNTB87Y/r777lp2X+9734fx6U/f3HqQGh9P4E1v2oOf//w4Dh6cbzXUcl0ftZqLIBBIJiN43evGcfbZmQ1PSvTK6eNCliVEIiqGh6OIRDQYxuoTIsuJRDQIEe5/qz9k+H4ARZH6tjzKUiwrXOo+NVVGodBAMtn7HiD5vIVrr92Fp58+ienpKk6cmIcsSwsNshTIsgRVlZBILP9gKoTA3Fwd9boLRZGhqjI0TYauK9A0Baoqr/g5PS/8/r7+9Tuxd+/witsODYWlhxzHW/Hc+nKzszXEYhpMc30TQWHgz8LkZBLnnDOMp546sRCkieGss5bPCve8AKoqLQoWJBIGisUGKhVn1eBkc2l/PK5v6Um9tQiCsLnd+LjFRI/TNEt6TE83zxtLl7qqVMKGm2NjcWQykdb3SJIkZDIRHD5cbCtYXa+7sG0f0ajamni2bb8V9M7l4q3SK9FoGIRuNLyOZvQHgYAkYcXyeNGohomJJAqFOubnG5ibq0NVZQSBQBAIaJqMdDrSqlX9cvG4jlKpd01Gg0C0rgdjY2fWR5ZlCZlMFLGYjmLRxtxcDUKIDU9qj43F8dRTM4vuB5r1wc85J4PXvW71PjpLse2wj8nYWKxjwedEwkAQWDh+PFzF1M5nbzTC3mm5XILPyx1mWZ3/7oRZ4RrLo9CyWDN8GawZTtuJEGEd0unpatu1BGnrOnKkiMOHi6hUHJw8WUOpZGNoKNJ6INmMcXDqVBWSJONtbztrUQ2/RsPDc8+dwtiYhdHR7nbpO3y4gMceO47p6Wrr8y/32RsNFydOhIHZs88ewsUXj3U1SDo3V8fBg/M4cKCAYrHRWkIoSeG/jxACzau4JEnQdRWRiIpUysTwcBSJhIF4XEc0qsI0NUhSGBCxbb9VnzPMFA6zhT0vaDWzMQwVpqm0gqaGoUJVZZw8eQL/7t/9Cf7X/7qztZQ4k8njxhvvwO7dYx1vTPqjH/1PfOMbt25oH+985/+H22//6pL/ro7j48CBebzwwhzKZQeGoSKbjWF0NNbRuvKbzfcD1GphrVfTVJFMGohGdUQinckg9f0ABw8WAKyv6Vg/qVQc6LqMnTtTW+76V6+7mJ6uoFrtrx4g8/N1nDxZQ7Foo1iso1Jx4bo+HMdfWGIega6HjfKCQMC2w4xLxwmQSkWQz8fRaPio1Ww0GgE8z4fr+nDdoHXeaTbEOt3MTAWxmIbf/M1XrhoEdV0f3/3u38PzgjWX36rVwuul5wV4xSvW3r+gyfcDHDlSxNvffg7GxxMoFhv43vd+jUrFxhvesBtnnbX8Put1F5IkYffuxeO0XLZx5EgRsdjyQe5m2YREwkCpZPfVWOm00zNj83mrb2rY9xPH8TEzU0GxaCMSUVsrC5r1dXU9TJZZKoAZBAJHj5ZQq7lrWpXi+wGKRRvj49ai+6sgEKjVwvPCy++75ufrOHashEymcw15azUXiiJh587Umsa+4/ioVh3YtgdZlmEYSqtE2HKqVQeHDhV7MjnZ/HteLhC+lPDvubzh80Gx2MDddz+7sHLAwOxs+Exx4YVjuOSS3LompJp//9ls5wLhpysUGpieLkOW5TUFS18KzMe72ih6u6rXXRw8WEA8rnfknN28DkxMJDZcI54GF4Phy2AwnLab5gOabfsdK0dBW0ep1MCvfz2Hv//7UyiXbfg+ut7I0fN8HDpUxOtfvxP79o127fesRbXq4O///hSefXYWxWIDmiYjEtFaGUHNoLGuKxgfT+Dcc4cxOZnctGCC6/ooFBooFm00GmGGVRAAihIuLzSMMAgejeqbVvLo2Wefwbe+dSeCIMBv/dbv4eRJGc89N7uwFDvesQcXIQR+/OO/wc9+9n288MITcJzGmt+rqho+9KE/wEc/+kmo6upZPK7rr5p52u/CsRrWA43HDaRSJmIxrSsBoZmZCmZn611ppraZ5ubqmJhIbNnPcXpgq5+DnM2Gbs88cxJHjpRagW1ZlqBpYfAtl7MwPm4tytJvllGw7fB/PS/A7GwNjzxyDHv2LG4GfeDAHF7zmuUbUL7cT396GL/61ck1r+45cGAeu3alceJEBYahIJVqb9VSsViH6wr8w3+4t3V9/fWv51CrObjgguyK555isYFMJnLGBIAQAidOVHHyZG3ZhIZisYFIJMwKPn68DM8LttRKiLVqBkCatZIHPQN+I8LgaVgKLFxJBshyuOqkuXpoOZWKg8OHCytOwDQVCnUkEibyeWvN5ybH8XHoUAGKIm+4jEfT3FwduVx3A5mu6+PgwUKrFN1m8bywuWQmY2J0NL7msmfNpp8bPR8IIXDvvS/i17+eAxCOoYsvzuLcc0fWde8R9jRxOpoRvpRy2caJE1XU6x4sa+mxLMRLkzbZ7OKVEtQ5QoRj0XU7c21qJjlMTiY5IUrLYjB8GQyG03bUzC6Kx4226sfS4CiXbZRKNl54YR5PPXUCe/ak1lw6pF1HjxYxOhrHW96yp2+WG5bLNqamKpieLmN2tgbHCRbqI5vI5WIYHY1jdLT/6wr3QhAIHDw4j1/8YgqnTtWwY0ey44EIz3Nx+PBzeP75x/Hii0/B8zzougFJknHw4NOt0in5/B5cf/3b8MEPfhATEzs6egybxfMC+H6wUFpi+QC9EGIhWBhm9huGgkTCgGUZHcsCX065bOPQoeKWXlHkOD4cx8POnam+OQ+th+cFmJmpYH6+0dcB8aYwy9uH74tW7eB2HlhLpQbuvvs5mKayaHXOiy/O4c1vfsWKGdan+/Wv53DffS9g9+70qmO4VnMwP9/AW996Fo4eLeHRR4/jFa9Y/X2nO3asiImJJN7ylles+T1Nc3N1TE4uneXmeQGOHSuhWnXOCNA3m8VNTCQQiWgoFBo4erS0pb+3S2kGBNvJjKXw763R8BAEAqoaBp9XO38IITAzU8WpU8tPwABhNrYQApOTybaD2uEET7UjZfJcN1wNt2tXumPB9eUcPlxEo+FtammGQqGOdDqC0dFY24G/Tp0PisUGnn76JHRdwa5dqXVPOjQz3EdGohgdjXX9HOU4PmZnaygUGggCsXAtCn+n64Z17iMRFSMjsb7ozzHIwlXqlQ1/55vlgnbsSPLfjFa0de/6iajj4nEd6XQEs7N1ZDJsOHS65vLuZqmIQWVZYRAtk4lgbq6O48fLmJhIdvz3VKs2hAAuuijbVwGo5uc/55ywbmszGDlIAYNukWUJe/ZkkMlE8Oijx/H883PI5ztbZkRVNezZsw979uxb9LoQAidPVjE/X8TevUO46qpz+r50hxBhTVvPe6nsQ7MGNxA2F1NVGY4jWjXcw9fD0hJCvPS6riuwLH2hJI62aUGgSESDaSqwbb/rAYZuqdfDpf79dB5aj2YPEABbIiCuacqGxmkiYWJyMoHnnpttBcM9z4eirG3Je9PQUASRiIZ63V31XDUzU8UrXzmEsbE4LEvHwYMFnDhRPSNTeyWNho983lrz9k3NuvbLZZs2//2PHy9jfr6ORCKsKV+vu/D9APl8onVOjMfDckmNhtf358m1CAKBatVZKHcTwchIbKDv0zpNVdv7zgBhWbahoQiqVQflsrNkwMm2PTiOj/Fxa13XB8vSMTdXg+v6G76m1eseYjEdhtH9a2MspqFc3ry64a7rQ5abjZXbH/dh/wNlw+eDZNLE5ZdPrvv9QHNlR5jhPjLS/UA4gNZqxmTSbI1n3w/vrQxDwchIdFv0WegH0agGWZYWrnfrP4dXq07rnphoJVv7zp+IOqrZGKdaddbdZXsQNRv/RCIqajUHnvfSsu5mgy9FGayAaSSi4dWvHsN9972IcrkBy+pc+QAhBKamqrjooiwmJvp75Q2X1rUvlYrg9a/fiXjcwBNPTC00m+re5Jrn+ThypAjLMnH99ftw1lmZvg4CAi9lPoX13cMggizLkOVwzClKs2GgDN8XcN0w67v5v80geDQaPtw3mwtuNlWVEYvpmJ9vbMlguBACvi8GJnOoGRAVIsz26/eA+Ebt2pXCs8+eguf5UFWl1XDPstb+AJxKma3J35WC4ZWKDcNQce65I5AkCfG4gVe9agw/+tEBNBrumppphkE9eV3JBo7jt0piLcc0VYyPWzhxoopy2VnI9JWQy1mLxriqykilTExNVVq1onul0fCgKNKGzl/FYgOxmIZczoJl6QN1L9bPNC0MIh47VkKl4iwKPDUaHup1F9ns+rNpIxENyaS5MLm3/vERnucDJJObsxKi+R3drObSYWNSfd3PbJqmIJUyMT1d7flzX7PW/+hofFOvXZIkIRrVEI1qGB6OwvfDZqt8BthczUb29fr6V1b4fgDfF8hkIgN9/0OdsfWeXIioqwxDxdBQFMeOlWEYqy+V7Dbb9haCzb25IanXXThOmMmVSBiLapfW62Fzuub/CgFomgzDUDe1VmC3TE4mcf75WTz22PGFz9SZS8b0dAXZbAwXXrhyfVTaugxDxeteN45YTMWjjx6D6wYYGel8I9RKxcb0dBW7dqXw2teOY3i4/5saNWthNjOfVgsCNevC96t4XMfsbH3THvw7KcyECx++BoWqyhgbi7fqcw9yQDyXszA8HMXsbB3ZbHwh+1Nr699TkiTs2JHAsWOlFbebmaniwguzi7LA9+xJ49ixEp555tSammmWSjYsy1hX+QDH8df0b2kYKsbHE6jXXbhuAE2Tl6y/mkgYKBQaPc0OD4/RRxCIhUbN7d9jVKsOTDOshb7VV3dsRbGYjnw+gZmZCubm6tA0GZ4XtM5DG62v3BynzX2uR73uwTRVxGKbM86bE9muG2zKtdtxfIyMbOzeJ5EwW7XjezWxXSrZMM2wgXkvV3ZIkgRVHcxrZr+TJAnJpImjR4sA1hcMr1QcJJMGs8JpTXjXQERnSCZNVCoOymW7px2Ym4Fo3xc9qW0ZBr095PMvNVYzTXXRjWIQiFbGpusGqFRsVKsuKhUHsdj6Hu76hSRJeNWrxlAqNfDcc3PYtSsJVd3YjX21asN1fVx88RgbtQ44WZZwwQVZmKaGhx8+guPHS8jnO7MSoNkwzrY9XHppHhdemN0SmcmeF6BcdjAyEsXIyGDUnt/KpVLqdQ9jY+3XWO13zUAUEGaIJ5PGwH1GIAw67d07ggceOIjR0bDJ2e7dqbbvFYaHwzHQzDB/ufn5OizLwLnnjix6XVFk7N07ghdemFv2vaerVByce+7IugJkvi/WHOSXZWnVBmSapiCTifQs8SEIwjJRuVwcvh+ez9u9X2red01MJLb0vdZWFzbtTqJctluNxsNSPBsPPkejGhIJo9UcuF1CCDQaLsbHE5t2DtS0cGWX6/pdD4YHgYAsY8PjX9fD7PCZmWpPruO1mgtZBsbG4vwub3PRaPjsvJ6JGdv2IMsSm5zSmg3enTERbZgsSwvdu8MLSy80GmEgemwsjnhcQ7nsbOrv97wA1aqLbDa+4g24LIc1PGMxHamUiYmJJHbtSiGbjcFxfBSLDWzlPsW6ruDyyyexa1cKhw4VEQTB6m9ahuf5mJqqYt++LHbvTnfwKKlfSZKEc84ZwrXX7oJhqDh8uLDh74PvBzh4cB6qquDaa3fjNa/Jb4kgrO+Hzd0GKRAOhIFXyzLQaPTmWrFezZIV8fhgTsppmoJczkI6baJYbMB1/Q3tz3V9VKsOCoUG5ubqmJ+vo1hsoFZzN7zvjdi1K4nh4QhOnarB84J1Nd4aHo62Am4vJ4TA7Gwd5547vGR5E8vSYRjaquO/WeO/OUnRjma98E4HiRIJA/G4hkplc++vgDALNJEwkEqZSKVMRCJqW8chhEC57GBoKNJWWRzqDl1XMDQUxfh4AiMjsY6tNpAkCel0BLIcZkC3q7nyYTOzRMMySvqmnBdt24NhqB2phZ5MmjAMZdOv5WFteQ/ZbHzVSTwafLoeNoGv19223ieEQLXqIpOJ9LzcD20dDIYT0ZKiUQ1DQ1FUq+6mB3Mdx0ej4S08yEcwPBxDEASb9sAthECpZGNoKLKu2p6GEXYdn5xMIhJRUShs7YB4LKbjiismkMtZOHy4uK7PIoTAoUNF7NmTxsUXj3HGfpvZuTOFa6/dhXQ6goMHC63mRO2q110cOFDA5GQS1123B2edldkSY+nlTaEGJRDe1HyADYKtc56r1VxYlr4lJlLWq5khPjISQ6XioFp12jp/NxsTzs3VWxmfw8NR5PMWcjkLmUwEqiqh0fAwN1dHteps+hiIRnXs2zeKQqEBWZbWVZ/YMFRMTiaWbHp34kQVIyNR7N07vOR7IxGt1YxyJbWag1hMx/Dw+uuFdzrLVFFkDA/HIIRYV6Bxvep1F6oqYWQk2uqPMDoag+8Haw7EFYsNWJa+kLgxWOdTWiwa1ZBOh/2M2tFsUp1KmZveU8MwVGzGbb9t+4jH9Y5kveu6gnTaRK22ec994SSri9HRWE9XIlN/CRtAS2099zdXZK9nQpy2LwbDiWhZ6XQEkYiKarW92dmN8LwAlYqD0dFoKyM7FgsD85uVvVQq2YjHtQ13Mo9GNeTzCcTjOgqFxpYKFL1cKhXBlVdOIpWK4OjRUlsZ4p7n4+DBeYyNxXDZZeMDHXyi5eVyFt7wht0YH0/gwIFCW1kfQgjMzdUxNVXGBReM4g1v2L2uuru9UiyGpSo2uynUZgmXtW5+Rtl6+X7Y4yGRGPyHb0WRMToaw8REArIsYX6+gVLJXjFw7XnhKoZCoQFVlTE+bmHXrhR27EhidDSGTCacKM5m49i5M4Vdu1IYH7egqjIKhUbbQauNOuusDMbHE4jF9HU36xsbC8t1nB4EchwP1aqDiy4aWzZjsbkke6VVdEIIzM83MDQUWVfAx3HCgFc3zh3xuI7h4fD+qp1JyjDQ2P6qgLDvio/R0fii7D3LMjA2Fkej4a16HikWGzAMFdlsvKe1hWnzpNMRmKba1rmlWnURi2k9CbIahrpQN7y7k0zNevudkkyGqzTq9e5fy33/pbJxW+l+jrqv2Tx3rc/9YUnVAMPDUV4TqC2MSBDRslRVxshIDEePljbUvGatguD0jOyXsn2ayyTLZRu1mtvVZmeNRlhvbHS0Mw9Zuq4gn7cwPV1BsRg2M+tVFlNzmXYQCCiK3PbnGxmJ4aqrduDhh4/gxRcLSCYNDA1FIMtL76dedzE7W4PrBsjnE7jiijCYTttXJhPBG96wC48+egwvvDAP3w8wNBRBPG4s+b3wPB+FQgOFQrik/oorJrFv3+iWqn9cLodNoUZHe9sUqptkWUIqZWJqqrIlmlHWam7bjRa3smZTqlhMR7XqoFZz0Wh4KBQa0LSXrgW+L+C6PmQ5XOafSpmIRrUVv2+SJC0s01eRTJool22cOlVrNe/cDIah4oorJvHii/PrXmY/OhpDImGgVHqpV8rx42Xs3p3Bnj0rl/VKp008++zioFej4WJqqgIhBIQIg73nnDPU9vVfCAHf72zA6+UymQhc18f8fBhkjkTUZY/T88LsbcfxF5IlHKiqglhMW/WzNRoe6nUX2WwMyeSZkxaplIkgEJiZqcD3gzP+LcN7xPAYczmLE+vbiK4rC88jxTXVEnZdH57nI5frzYRJs2644/hdy0oPS6QoHf0eaFq4+ufIkRJ0XWn77y4s6bT6e3w/QLHYwNBQdMOJRzSYms/9q33fg0CgUnGQzcbYi4raxrsIIlqRZelIp03MztbXVTKkHc3syaXKCITLs8PAvGl2p9mT7weo1VzkcvGOBkk0TVnIOgsWPWhvFt8P6597XgBdlyHLEmzbg+eJtpt85vMW3va2s/HCC3N45plTOHiw0Kq7qygSPC/MFmvWMczlEjj77Ax27kxu+jJV2jjfD1oTKECYZapp8oYeXGIxHa9//S6cdVYGv/71HI4cKePkyfnW/mU5DLgIEf53KmXissvGsXt3esstf2xmv2+HplCxmA5Nk1slHfpVs+lxWId2ez2Aq6qMZNJEMmnC98NVWJWK08rEjURUjIxEW42i2/2ey3IYdNc0BYcPFxbqsm/OWBgdjWF0NLbu98fjBnK5OA4cmEcyaaJQqMMwVLzqVdlVA0IvrzsvhMDRo2Xs3TuE0dEYTFPDyEh03VnhhqEgEune+UNRZIyNWYhGdczOhhMZkiS1zvXNiXTfF1BVCZGIhmw2hmg0rJV+8mQN8/MNRCJLj5sgECiXbciyhFwujnR66eZmkhT2q9E0BSdPVjE3V4euKwv3FgFcN2jdIzIQvv0kEgay2ThmZipw3XC1xFLjqDneRkZim1or/HSSJMGyDExPVxBb/2lpRbWai+HhaMevt5YVJrrMztaQSJhrCog7jo9KxW4Fwlc617muj3LZQSYTwejo4JWNo84wTRVDQ1FMTVWg68qS4yQIBAqFBtJps+sxChpMvJMgohU1H06a2WTdyqRbS/ZkImEgmQwzt7qRcdbcbzcCbmFA3MLRo0VUKs6m3aA3MwCTSQPJpNmaSAjL0diYmalClqW2AhaRiIbzz8/irLOGcPRoCYcPF3DiRA2OEzb5Gh9PIJ+PY3Q0juHhKG90txjPCyeFwtUgElT1peB3s4keEGZjrndiSpYlTEwkMTGRRKnUwKlTtYWaxi6CQEDXVcTj4TLJbjzsbQbXDXsf5PPWtmgKZZoqLEtHoWD39b9X8zoWi22PrPDlKMpLgfFmaZBOZedFIiricQOVirOlJkEnJ5N49tlZeJ6PU6fquOyycWSzqze8jMXC7PlmVuTJk1UMD0fwmteMbzhTrV73kE53v+Zxc3VHPK6jXndRr4flSsLPpCCRCDNQm836mmNF0xREIhqKxcbCKp4wkB5eNwDXDSCEaAXY1nIuTCSMVkPNctmB5wWIx3XE4zosy+A9xTY2NBTeDzRXnyST5qLxEGYc20ilIhge7m09+WhUgyxLa86Wbofr+lCU9fVIWE24OjbM1j51qoZoVFt28qk5qQoA2WxY+mh6utxqkPtytVqYLDNojcSpO9LpCBoND/Pz4Uqzpb7rYQnC2JZaMUr9g8FwIlrV6VnZhqF0/ILTaHgQQiCbXTl7UpZfCsw3M487pVZzYRhKV4O3phnWuDx2rLSmZZ4bIYRAsdiApimYnEyc8QCpqjIMQ4HvC5w8WUM63X75FtNUcdZZGZx1VqZVry3M4uINyVbUrN8oSWEd2UTCaNW9lGUJQoTbuG64TL4Z/AhXBiydobUWiYQ5cLWbT89M26xSEf0gzKhtdOXhvxOCIGwUmM3ywel0nQ4YSVIYpGk2j94qS+Cz2RjicQPPPHMKr3zlMM47b3RN72uusLJtD5qmoFx2cO21uzYcCHddH5K0cpZlp6mqDMsy2jp2VZUxNBRmvjcn4JurDZJJFdGo1goMrpWmKUinI0inI1tqDFH3WVZ4bzIzU0Gh0Fi0esDzAmQyJkZH4z0/x5tmOPbrda/jCTDVqotUyuha+aRmrwlNkzE7W8P8vAvTVFuTcp4XwLa9hRJQOjKZlya6crmwNOTcXL2VMOH7AWzbh2kqGB9PIJlcujQe0emaEzNCAIVCoxWDcF0frhu0mtJvpUl36i8MhhPRmiQSBlIpE6VSo6N1n5tZqLlcfE03i9Fo2ExzZiZcNtWJm6nmTd3ERKLrS28tK1ziOzVVgaq2X7d7LZqz5ZalI5uNL/uZmln/tZqLSsXZ0IN7mAnKm5H1CgKBRsODpsk9uamr15srCMKlhkvVjJUkQJYVaJqCaFRDKhU2t5mbC5fIr5Q9tJ0IIRbqJfc+M22zRaMaEomwbvR6A3ieF64w6cbfW3ie01lXchNEImEGcVjmY2ucFxIJE+m0CcOQ8drXrr3ZcyymwzQV1Osepqcr2LMnnCTeqGrVaTUy3wpUVUYiYXQ8W3U7nUNpbZr9eCwrXC3quj6i0XA1Wbeazbar2avh6NEigM4Fwz0vACC6PknWTECKxXQUiw1UKk6r9JuqhiXsLMs4Y6IrFtOxY0cSxWIDpZIDIQQ0TUYmE4FlGX29coz6j6YpyOXiiMU0FAqNVtPYsTGDK4Vow7bG3RUR9ZwsSxgejqJWC2+GOpGNIMRLDTPbKU2STkdQrYbLZzf60HX6MXRjueFSMpkIHMfH7Gx9XRnZK/G8sC55Om0im129cVCzSeqRI8W+r/U7qJpL0sMMovD/W9b6M63bIYRAuexAlsN68C9fcrwSWQ6zP5s3qKdO1WDbHhKJ7Z3xUyzaiMW0bblsM2x2HAbD11MvutlgT4hwAraTk4Wu6yMIAgwNsXTTZtC0sKlisWhvmWA4AFx99Q4A7WVjhyVnIvi7v5vC5GQKl16a2/Ckpm17rZ4J2/l8SrSc5vcjlTL7dvVANKq1Gml26v66WnWQSJib1gA67CERx/BwsBCID58dVrq/CRtxxjA0FEUQCMhydya4aXtQFBnpdGThuw7ew1HHbK+nNCLaENNUMTISQ70e1pHcqGLRRjyutV2apBnABQRs29vQMZTLDmIxbVMzOCVJwshIDJYVZlt0iuP4KJVsjIxEMTa2eiC8KR4PlzhWKk6rdixtjkrFge8HmJhIYNeuFCYnkzAMBfPzjVbTym4JAoH5+XDZ4cREct0NBRUlXCI/OZlEJKJifr7RkfPDVlQu2zCMsGHudp1YisXC80m53N75xHX9hVVCFkZGoiiVOvcdaE76hFlu27tW+GayLANBILbUdaVZR71dkUhYBu2yy8YxNBTd0DEIIVCtugurdDheiVbTr4FWXVdadfg7wffDhua9mCRTFHmhZ4C65ol+SZKgKBtruk7UJEkSA+HUUQyGE1FbkslwGXGpZG9oP7WaC02Tkc3G15VBFYvpGB6Oolp11x14q9ddSBIW6uJtbuBKVeVWjfRm85mNaDQ8VKsOstkYRkbaz0htlsaoVjtzw06rq9VcAAL5fKLVGCYW01v1FJu1l7vB94OF5lMGxscTHckwikY1jI8nkE6bKBbDzODtpFy2IcsSxsbi2z6ANTQURTweNtVbSyDUcXyUyw5GRqJIp8Omqcmk2bHJwlIpnHjNZCJ8KN9EkYgGw1Ba9aMH2b59I3jjG3dj9+70hvdVqYST9N1o5k1EmyuRMBEEoiOTu5WKg2TS5KQuEVEHMBhORG1plksxzfUHcW3bg+N4rc7j65XJRFuBt3YzzxzHh237yGbjraYvm63ZUFMIsaGskbBhlYuxsfi6G4BqmoKRkVirARF1l+v6C438zqyVr+sKcjmrNbY7HRB3XR/Foo1MxkQuZ3U0g1nTwqzokZEoKhUHjjP4AfFmjXBFkZDPWz07n/QTVZWRy1mIRjXMzzdQrTrLnlc8L0Cl4mBsLJzIa2aSjY7GYJoqyuWNT7wqioTR0fVNvNL6hTWkzS0VDF9v0Kq5OmajbNtDEAiMjMS60lOEiDZXJKIiEtE2fB5sJhh0urwiEdF2xbssImqbYagYHY0hCIK2b+5c10e16iKbjW+4Rnezy7Rl6ZifX1sG4kvH4GB0NIpksreN1OLxsMmlbXtt/102650HQYDx8QSGhjZW6sWy9Fa9X+quSsVBJrN8nXpVlTE2FkcmE2bHdiogbtteKwN3bMzqSrClGcgcHY2hWnU2XMqon7muv9A8VMX4eIKB8NMYRvh3MjYWg6bJKJftM85xvh+0eja8vJa3Ybw0WbjeIEKt5sLzfIyNWZtWX5UWs6ywmd1WmGRtrpjp5qqclQRBWB5lZCS6pobiRNT/wnrHG58UrFQcpFKbVyuciGjQbZ2ONkTUVyzLQDYbx/HjZciytKbsUs8LWoG4TKYzy3/DLtMWgDLm58OyDyuVCGk0PNRqLkZHYxsOHndKs/nP1FQZQog1Zcs3G2XGYhqy2XhHbo4lSUImEzYn7VSTVDpTve7CMJRVSzYoSlhKBwDm5hqwLH1Dma31uotGw8PYWPfHviRJC3X4gZmZKgBsqSZ6q/H9ANWqiyAIMDwcxdBQhFnHSzi9idbcXB0nT1Zh2x4MQ4XvB7BtH+m02coIf7l4XMfISAxTUxWoqrzmyRshRGvlUi5nbVpzZDqTaaqIx3VUq25f/zsEgWitmAHCc+5mZ2CWyzaSSQOZzMZqjhNRf4nFdOi63Lr+tated6Hrq983EhHR2g3OkykRbbpUyoTnBZiZqSIe11cMiLvuSzVhlwt8rJeuK8jnLZw8WcXcXNgQMBrVFv2O5lJ8RZGQy8X77oYynQ6PZ2amglLJhmXpSx5fmDnmwPcFhoYiGB6OdjQIZxgqhodjOHq0BMNQ2aikC+p1D7nc2hosNgPiiiLj1KkaTFNte5Ki2TxQkoCJiQQSCWNTxr4kSa2g+8xMBa4bIBbT+up71y7H8VGvuxDipcazW/0zbYbmWDDNsMGqbXtQVblVG3yl80wmE4HrBjh1qtaqrb8Sx/FbNZdHR2PM1u8xSZKQSpkol8Nmwe32s9gMzVJHyaSB0dFwNYLj+CgWG0ilNqdutxBheZb1NjImov6l6wqSSROnTtXaDob7foB63cP4uDVQSQVERL3GMyoRrdvp2Z8nTtTgecGSGcq1mgvb9jA6Glt3TevVhLWKw3q9s7P1Vh3xZuUURZGQSBjIZCJ9u8QwlTKhaTJOnqxifr4BVZWhaWEX9iAQrfrLsVjYCC4eXzpgvlGJhIFk0ljIUjM7vv/trNHwYJpKWxmSzbIjuq7g5MkqCoUwS3wtQaXTVxD0IjDYXG2g6wpOnAjH9UYz3DdTMyhm2z48L4Cuywvfj3CpMoNW7YnFdMRiOnw/gCxLazp/Na8zruujUFg6W7dZSqVe96Bpcmv10VYZZ4MuFtNhWf2bHV4shg1Ws9l4a/XB2JiFY8dKrSB5tye8HMeHriswTT6aEQ2iRMLA/Hwdruu3dW0ql22kUibvx4mIOox3XES0Ic2MP01TFjKz6zAMBYoit5bBm6ayKRmpsiwhmTQRj+toNDy4boAgEFAUqfWQ2e8ZnLGYDtNUUa26rRq7QohW49JoVOt6EK75u+p1l+VSOqxedzEyEms7SCdJEtLpCExTxalTNZRKNlRVRjSqLRkUP72MRzdWELRDkiRYlgHTVDE3V8fsbA2yLC+7+qHXgkAsquGv6wosS0c8riMS0TracHS7ajc7WFXDFRK+H6BYbCCZNOH74USF43gQotmQONYaa9Q/ZDk8f5XLdtuBoG4rlWwYRjiZfvp32zRV5PMWZmYqmJ+vI5Xqbsa24/iIRjU2zSQaUJGIhmTSbJ1P1uL0VVScfCci6ixJrLXj3DZTKpWQTCZRLBaRSCR6fThEW4Lj+CiXbZTLNnw/DEJblgHLMhhAWqcgEK1g+GYHDmdna5iaqqypNAGtzvMCVKsOdu1KbWiCIQjCesiFQqNVMkeWJchyuIKgOQEUj+tIp/urjIcQYYO4U6eqqFRcRCLtl33pFtsOM4uFEDBNFZZlIBrVYBhKXwXvtrNGw8P0dBmVigNVlaHrKiwrnKSIRNS+LMFBISEEZmaqOHWqtum1uJdTrYZ15fN5a9lVM54X4NixEup1r6tZ7fPzdeTzFtLpzSnLQkSbr1ZzcfhwAaa5ton1ubk6stkYRkZim3B0RETbC1NniKhjdF3B0FAUQ0NRBIGAJKEvHni3sjAI3Zu/w3Q6gnrdQ7HY4AN6BzQaHqJRbcNZq7IclvyxrHAFhG37cBwfQRBAlmXougLD6M+VEJIkLWRYqygWG5ibq2Nurt6zoPjp5TV0XUY6bcKyDAZW+5RpqpiYSKJWc1vjvN/GOC0tXEUWQb3uolx2el4upV534fsBxscTK5aPUlUZQ0NRHD5cgOcFXcncbt4vsR4w0WCLRjWk0xGcPFlDJrPyfXWz2Xqvz5VERIOKd11E1BXMJN76ZFnCyEgUjYaLatVhI7oNcpywbn6ngneSJC1kxPZHZnU7FEVGJhNFPG6gVGqgUAgD45oWBvN1vbtBziAQqNdd2LaPSERFLhdHPK4zGLUFqKrM4MAWpWkKstk4jh8voVSye/bvWK+7cBwf+bwFy1r9GGKxMIA1N1fvysSwbXswDBWGwRUoRIMubChsr1iGMLxHCZut876EiKg7mPZERETLMgwV2Wwcnhe0aihT+5p1ciMRPtScTtcVDA/HsHNnCpOTCcRiGjwvQKHQQDequNm2h0KhjmIxbFA7MZHAzp0pDA1F+cBJtAmiUQ25nAVNkzE3V0e97nblu76ccDWNh7Gx+Job0p3eCLhedzt+TM164VyNQjT4DEPF8HAM9boHzwuW3KZUsmFZOlIpNs0kIuoWPvkREdGKLMvA6GgM09MVyLLE+u/rEGYgs/nicjRNQTKpIJk0YdsepqcrmJ9vdKy2sOP4qFQc6HpY8qDZDJMrWIg2XyymY3IyiVKpgWLRxvx8A8mk0fVgcLgaxEMu135tbsNQMTISw9GjJei60tFj9X3BlVdE20gyaaBej2B2toZk0lx0PqlUHGiajNHRGCfIiIi6iMFwIiJaVSYTQRCEDdBiMY1ZtG1yHL+jJVIGmWGoyOUsAGUUCg0kk+tv4CqEQLnsABAYGYkilTI5don6QHNVSCoVwdRUGdWq29WyKZWKgyAIkM8n1p1tmUwaqNVMzM11bqIuXDUks0QK0TYiSRJGR2MIAoH5+QZMU4WiSKjXPWiajFzO2pIl8IiIthI+ERIR0aokScLwcBSyLGFmpoIgEOu+UXfdsOGj4/iLXhcCkKQwSDJIWbu+H0BVpQ03ztxOdF1BPm9herqCQqEOyzKgaWsPFgWBQK0W1gWOxzWMjMSYeUnUh1RVRjJpolSyIYTo+ISh7wcol23ouoJcLrGmGuHLCQNYcbhugPn5BixLb52XPC9ApWJD05S2zjW27cM0Va4aItpmFKUZ9FYxP9+A7wdIJg1kMhEGwomINgGfzImIaE0kScLQUBSKImN6urywtFtbU/DCcXzYtgfXDaBpMkxTRTptQtfDbBhJkhAEAq7ro1hsoFhsLAQV1rb/fmbbPpujrYOmhQFxXVdw6lQNqiojFtNXnCRxHB/1uosgCMdmNhtDPK5zqTFRH4tGNZimikbDazsIFAQChUIDiiIhGtUWBafrdReuGyCVMjE8HO3IhKSqysjnLZw6VUOx2ECl4kAIQFEkWJaBQqHRVjDccXyMjES3/HWOiNonyxIymShSqXD1paryXoWIaLMwGE5ERG1JpUwoioQTJ6qYn28gElFhGOqiIGUQiFb2t+eFAfBoVEMiYbSy4JZ7+E8mTVQqDk6eDPefSq2/TEY/sG0P2SxLpKyHooR1M6NRDbOzYfBJksK69c0x0ZxE8X0BTZORSBhIJIxVA+dE1B/C7HADMzPVtoPh9bqLWEyDqsqo1VxUKg6AMDgdiWjI5SKIxzt7LtA0BbmchVTKbK1wal7TajW31TB5NZ4XrhpiFijR9ibLEu9XiIg2GYPhRETUNssKg9qlko1CoYFSqQEhXip1IssSNE2GZemIx3WYprrmWs2yLLWC5jMzFRQKWzcgLoQAAJgmgx3rJUlhxmUspqNadVCruajXXfh++HerqhLicRORiNbWOCOi/hGPGzh1qrbmQHKTbXsYGUkimTTgOD5cNwAQBtgNY/lJ106IRLRFgWwhwvJhjYa3ps9g297COYurhoiIiIg2E58YiYhoXTRNwdBQtJUd53kBhAiD2YoiQdOUDS35DGu8WpAkCYVC5xqWbSbH8aHrCoMdHSDLYVC8WfM3CETrdSLa2kxTRSJhoFCwkUqt7XzZaHgwDBXRaFhOKyxH1eUDXUE4caejXLbXtL1t+xgeZokUIiIios3GYDgREW2IosiIRLpT51BVZWSzMfh+gGKxgVQq0pXf0y2O4y+qY0udwyA40WBJJk0UCo2F8iGrX1NqNRfZbKyvmk8ahgpJCifrVjpHhRnwYfkwIiIiItpc7NJARER9TdMUZLNx6LqCatXp9eG0xXUDxONrb6ZGRLRdRaPaQs+I1TOrbduDrsutlSL9wjAUGIYK2/ZW3K5WcxGP6yzrRERERNQDDIYTEVHfM00Vo6NxuG4A1/V7fThrEmYGgsEOIqI1kCQJ6XQEiiKvGEwWQqBadZFOR2Ca/XV+VZSwGWi9vvzxB4FAEAgkEv0VyCciIiLaLhgMJyKiLcGydAwNRVAuO63GlP3Mtr2FGrb9s4SfiKifRaMaMpkIqlWn1Rfg5SoVB5GIilTK3OSjWxvLMqBpywf0azUXsZiGWIyrhoiIiIh6gcFwIiLaEiRJQiYTQSSiolp1e304q3LdANGoBkXhpZaIaK0ymQiSSRPFYuOMic963YUQAtlsvG97MRiGinQ6suR1KggEHMdHJhNl3wMiIiKiHuETOhERbRmapmBkJAbPC+B5Qa8PZ0WeF7A5GhFRmxRFxuhoDLGYhvn5Bmzbg+v6KJdtOI6PbDbe970YUilzYeJ2cZ+LUslGMmn0/fETERERDTIGw4mIaEuxLB2plIFyefUma73i+wEURYKu92fmIhFRPzMMFePjCYyMROF5ARoND4ahYGIigXQ60uvDW5WuhxO3ruu3stnLZRu6LmN4mFnhRERERL205YLhnufhU5/6FHbv3o1IJII9e/bgc5/7HILgpQxBIQQ++9nPIp/PIxKJ4Nprr8WvfvWrHh41ERF1SlguJQpNk9FoLN+krJccx4euK2yeSUS0TpqmIJuNY9euFHbvTmPnzhQsa+s0nUwkDOTzCQSBQKHQgKrKyOUsRCJcMURERETUS1vuKf2LX/wi/vIv/xJf//rXsW/fPjz22GP4Z//snyGZTOIP/uAPAAD/9t/+W9x22234r//1v+Kcc87Bn/zJn+Atb3kLnn32WViW1eNPQEREG2WaKjKZCKanqzAMBZLUX1l2juMjlTKZ/UdEtEH9Wht8LVIpE9GoBs8LoOsKVHXL5SERERERDRxJvLwzTZ/7zd/8TWSzWXz1q19tvfaP//E/RjQaxTe+8Q0IIZDP53HLLbfg4x//OADAtm1ks1l88YtfxAc+8IE1/Z5SqYRkMolisYhEItGVz0JEROvneQEOHSpACPRdbe65uTomJxNIJs1eHwoRERERERERLdhy6QlXXXUV/u///b947rnnAAB/93d/h5/85Cd429veBgA4cOAApqencd1117XeYxgGrrnmGvz0pz9ddr+2baNUKi36Q0RE/UtVZQwNRdFoeAiC/pnXZb1wIiIiIiIiov605cqkfPzjH0exWMTevXuhKAp838fnP/95vOc97wEATE9PAwCy2eyi92WzWRw6dGjZ/d5666341//6X3fvwImIqOMsS0c8rqFWcxGP670+HACsF05ERERERETUr7ZcZvi3vvUt3Hnnnfjv//2/4/HHH8fXv/51fOlLX8LXv/71Rdu9vH6sEGLFmrJ/9Ed/hGKx2Ppz5MiRrhw/ERF1jqLIyGSicF2/b7LDXTdANKqxXjgRERERERFRn9lyaWsf+9jH8IlPfAK//du/DQC44IILcOjQIdx666244YYbMDY2BiDMEM/lcq33nThx4oxs8dMZhgHD2Dod6omIKBSP67AsA5WKg0Si9+dxzwv6roY5EREREREREW3BzPBarQZZXnzYiqIgCAIAwO7duzE2NoZ777239XPHcfDggw/iiiuu2NRjJSKi7pNlCem0iSAQ8P2gp8cSBIL1womIiIiIiIj61JbLDH/HO96Bz3/+89ixYwf27duH/fv347bbbsONN94IICyPcsstt+ALX/gCzj77bJx99tn4whe+gGg0ive+9709PnoiIuqGMDtcR6XiIJk0e3Yctu2xXjgRERERERFRn9pyT+tf+cpX8OlPfxof+tCHcOLECeTzeXzgAx/Av/pX/6q1zR/+4R+iXq/jQx/6EObn5/G6170O99xzDyzL6uGRExFRt0iShHQ6glLJhu8HUJTeLHxy3QDJpMF64URERERERER9SBJC9EfHsT5TKpWQTCZRLBaRSCR6fThERLQKIQSOHSujXLZ7lh0+N1fH5GSip9npRERERERERLS0LVcznIiIaClhdrgJIQQ8b/Nrh7NeOBEREREREVF/YzCciIgGRjSqIZk0Ua06m/67WS+ciIiIiIiIqL8xGE5ERANDkiSkUr3JDnccH9GoxnrhRERERERERH2KwXAiIhoozezwSmVzs8N9XyAa1Tb1dxIRERERERHR2jEYTkREA6WZHS5J2LTscN8PoKoSS6QQERERERER9TEGw4mIaOCE2eHGpmWHO44PTVPYPJOIiIiIiIiojzEYTkREAyfMDo9AkgDX9bv++2zbh2UZrBdORERERERE1McYDCciooEUjWpIpTandngQCEQiLJFCRERERERE1M8YDCciooGVSplQVRm27XXtdziOD12XWS+ciIiIiIiIqM8xGE5ERAMrEtGQTkdQrbpd+x2O48M0NdYLJyIiIiIiIupzDIYTEdFAS6VMmKaCer07AXHX9WFZelf2TURERERERESdw2A4ERENNF1XMDQURb3uQQjR0X37fgBJAkyTJVKIiIiIiIiI+h2D4URENPASCQPxuNbxZpqO48MwVNYLJyIiIiIiItoCGAwnIqKBpygyhodj8P0Arut3bL+NhodEwoAsSx3bJxERERERERF1B4PhRES0LcRiGoaGoiiXnY6USwmCcB/RqLbhfRERERERERFR9zEYTkRE24IkSchkIojFNJTLGy+XYtseTFNlvXAiIiIiIiKiLYLBcCIi2jY0TcHoaAySBNTr7ob2Va97SCYNKAovpURERERERERbAZ/giYhoW4nFdIyOxtBoeHCc9dUP97wAiiIhFtM7fHRERERERERE1C1c201ERNtOKmXC9wPMzFQRBFrbpU5qNRfxuM4SKURERERERERbCJ/iiYho25EkCUNDUUiShJMnq3BdH5ZlrOm9QSDgeQFSKROSJHX5SImIiIiIiIioU1gmhYiItqVmQHxiIglZllCrra2GeLXqIB7XWCKFiIiIiIiIaIthMJyIiLa1eFxHNhuH43iwbW/FbT0vgOcFGBqKQpaZFU5ERERERES0lTAYTkRE214iYSCbjaNWc5cNiAshUCrZSKdNxOPMCiciIiIiIiLaalgznIiICEAmE4EQAidO1BAEApGI1vqZEAKFQgOxmIbh4RhrhRMRERERERFtQQyGExER4aUa4pqm4MSJKubm6tB1BQDgOD7icQ1jY1brNSIiIiIiIiLaWhgMJyIiWiBJEpJJE6apolJxUK2GTTWHhyNIJEyoKquLEREREREREW1VDIYTERG9jGGoMAwVQ0O9PhIiIiIiIiIi6hSmuBERERERERERERHRwGMwnIiIiIiIiIiIiIgGHoPhRERERERERERERDTwGAwnIiIiIiIiIiIiooHHYDgRERERERERERERDTwGw4mIiIiIiIiIiIho4DEYTkREREREREREREQDj8FwIiIiIiIiIiIiIhp4DIYTERERERERERER0cBjMJyIiIiIiIiIiIiIBh6D4UREREREREREREQ08BgMJyIiIiIiIiIiIqKBx2A4EREREREREREREQ08BsOJiIiIiIiIiIiIaOCpvT6AfiWEAACUSqUeHwkRERERERERERERrcayLEiStOzPGQxfRrlcBgBMTk72+EiIiIiIiIiIiIiIaDXFYhGJRGLZn0uimQJNiwRBgOPHj686mzCISqUSJicnceTIkRUHD1GncexRr3DsUS9w3FGvcOxRL3DcUa9w7FGvcOxRL3DcMTN83WRZxsTERK8Po6cSicS2/eJQb3HsUa9w7FEvcNxRr3DsUS9w3FGvcOxRr3DsUS9w3C2PDTSJiIiIiIiIiIiIaOAxGE5EREREREREREREA4/BcDqDYRj4zGc+A8Mwen0otM1w7FGvcOxRL3DcUa9w7FEvcNxRr3DsUa9w7FEvcNytjg00iYiIiIiIiIiIiGjgMTOciIiIiIiIiIiIiAYeg+FERERERERERERENPAYDCciIiIiIiIiIiKigcdgOBERERERERERERENPAbDiYiIiIiIiIiIiGjgMRi+Tdx+++3YvXs3TNPEJZdcgh//+Mcrbn/48GG84x3vQCwWw/DwMG6++WY4jrNomyeffBLXXHMNIpEIxsfH8bnPfQ5CiG5+DNoi7rjjDlx44YVIJBJIJBK4/PLL8f3vf3/F93DMUaccO3YMv/M7v4OhoSFEo1G86lWvwi9+8YsV38PxRxtVLpdxyy23YOfOnYhEIrjiiivw6KOPrvgejjtq10MPPYR3vOMdyOfzkCQJ3/3ud1s/c10XH//4x3HBBRcgFoshn8/jd3/3d3H8+PFV97uWcfbggw/ikksugWma2LNnD/7yL/+y0x+P+thKYw8A3v/+90OSpEV/LrvsslX3y7FHq1lt7FUqFdx0002YmJhAJBLBueeeizvuuGPV/XLs0UpuvfVWvOY1r4FlWRgdHcU/+Af/AM8+++yibb7zne/g+uuvx/DwMCRJwi9/+cs17Ztjj5azlnF3ug984AOQJAn//t//+1X3zXG3GIPh28C3vvUt3HLLLfjjP/5j7N+/H1dffTV+4zd+A4cPH15ye9/38fa3vx3VahU/+clPcNddd+Hb3/42PvrRj7a2KZVKeMtb3oJ8Po9HH30UX/nKV/ClL30Jt91222Z9LOpjExMT+NM//VM89thjeOyxx/DGN74R73rXu/CrX/1qye055qhT5ufnceWVV0LTNHz/+9/H008/jS9/+ctIpVLLvofjjzrhn//zf457770X3/jGN/Dkk0/iuuuuw5vf/GYcO3Zsye057mg9qtUqLrroIvyH//AfzvhZrVbD448/jk9/+tN4/PHH8Z3vfAfPPfcc3vnOd664z7WMswMHDuBtb3sbrr76auzfvx+f/OQncfPNN+Pb3/52xz8j9aeVxl7TW9/6VkxNTbX+fO9731txnxx7tBarjb2PfOQj+MEPfoA777wTzzzzDD7ykY/g93//9/E3f/M3y+6TY49W8+CDD+LDH/4wfvazn+Hee++F53m47rrrUK1WW9tUq1VceeWV+NM//dM175djj1aylnHX9N3vfhePPPII8vn8qvvluFuCoIH32te+Vnzwgx9c9NrevXvFJz7xiSW3/973vidkWRbHjh1rvfY//sf/EIZhiGKxKIQQ4vbbbxfJZFI0Go3WNrfeeqvI5/MiCIIufAra6tLptPgv/+W/LPkzjjnqlI9//OPiqquuaus9HH+0UbVaTSiKIv7P//k/i16/6KKLxB//8R8v+R6OO9ooAOKv//qvV9zm5z//uQAgDh06tOw2axlnf/iHfyj27t276H0f+MAHxGWXXbb+D0Bb1lJj74YbbhDvete72toPxx61a6mxt2/fPvG5z31u0WuvfvWrxac+9all98OxR+06ceKEACAefPDBM3524MABAUDs379/1f1w7FE7lht3R48eFePj4+Kpp54SO3fuFH/2Z3+24n447s7EzPAB5zgOfvGLX+C6665b9Pp1112Hn/70pwCAz372s9i1a1frZw8//DDOP//8RTNM119/PWzbbpUaePjhh3HNNdfAMIxF2xw/fhwHDx7s3geiLcf3fdx1112oVqu4/PLLAXDMUffcfffduPTSS/FP/sk/wejoKC6++GL85//8nxdtw/FHneZ5Hnzfh2mai16PRCL4yU9+AoDjjnqjWCxCkqRFq2Pe//7349prr23991rG2cMPP3zGveT111+Pxx57DK7rdvMj0BbywAMPYHR0FOeccw5+7/d+DydOnFj0c4496oarrroKd999N44dOwYhBO6//34899xzuP7661vbcOzRRhWLRQBAJpNp630ce7QRS427IAjwvve9Dx/72Mewb9++Jd/Hcbc6BsMH3KlTp+D7PrLZ7KLXs9kspqenAQDDw8N4xSte0frZ9PT0Gdun02nout56z1LbNP+7uQ1tb08++STi8TgMw8AHP/hB/PVf/zXOO+88ABxz1D0vvvgi7rjjDpx99tn44Q9/iA9+8IO4+eab8d/+239rbcPxR51mWRYuv/xy/Jt/829w/Phx+L6PO++8E4888gimpqYAcNzR5ms0GvjEJz6B9773vUgkEq3Xc7kcduzY0frvtYyz5bbxPA+nTp3q1kegLeQ3fuM38M1vfhM/+tGP8OUvfxmPPvoo3vjGN8K27dY2HHvUDX/xF3+B8847DxMTE9B1HW9961tx++2346qrrmptw7FHGyGEwL/8l/8SV111Fc4///y23suxR+u13Lj74he/CFVVcfPNNy/7Xo671am9PgDaHJIkLfpvIUTrtZtuugk33XTTitu//D3L7XO599L288pXvhK//OUvUSgU8O1vfxs33HADHnzwQZx33nkcc9Q1QRDg0ksvxRe+8AUAwMUXX4xf/epXuOOOO/C7v/u7AHjOo+74xje+gRtvvBHj4+NQFAWvfvWr8d73vhePP/44AI472lyu6+K3f/u3EQQBbr/99kU/u/XWW8/Yfi3jjGORVvLud7+79f/PP/98XHrppdi5cyf+9m//Fv/oH/0jABx71B1/8Rd/gZ/97Ge4++67sXPnTjz00EP40Ic+hFwuhze/+c0AOPZoY2666SY88cQTrdV+7eDYo/Vaatz94he/wJ//+Z/j8ccfX3EscNytjpnhA254eBiKopyRQXbixIkzZn2axsbGzth+fn4eruu23rPUNs2lkMvtl7YXXddx1lln4dJLL8Wtt96Kiy66CH/+53++5LYcc9QpuVyutQKh6dxzz122YTDA8Ued8YpXvAIPPvggKpUKjhw5gp///OdwXRe7d+9ecnuOO+oW13XxW7/1Wzhw4ADuvffeRVnhS1nLOFtuG1VVMTQ01MGjp0GRy+Wwc+dOPP/888tuw7FHG1Wv1/HJT34St912G97xjnfgwgsvxE033YR3v/vd+NKXvrTs+zj2aK1+//d/H3fffTfuv/9+TExMbHh/HHu0FsuNux//+Mc4ceIEduzYAVVVoaoqDh06hI9+9KOLyjG+HMfdmRgMH3C6ruOSSy7Bvffeu+j1e++9F1dcccWS77n88svx1FNPtZZ2A8A999wDwzBwySWXtLZ56KGH4DjOom3y+fyKX0LavoQQi5bKno5jjjrlyiuvxLPPPrvoteeeew47d+5c9j0cf9RJsVgMuVwO8/Pz+OEPf4h3vetdS27HcUfd0AyEP//887jvvvvW9PCylnF2+eWXn3Evec899+DSSy+Fpmkd/Qw0GGZnZ3HkyBHkcrllt+HYo41yXReu60KWF4c1FEVBEATLvo9jj1YjhMBNN92E73znO/jRj360bHJDuzj2aCWrjbv3ve99eOKJJ/DLX/6y9Sefz+NjH/sYfvjDHy67X467JWxaq07qmbvuuktomia++tWviqefflrccsstIhaLiYMHDwohhPjKV74i3vjGN7a29zxPnH/++eJNb3qTePzxx8V9990nJiYmxE033dTaplAoiGw2K97znveIJ598UnznO98RiURCfOlLX9r0z0f954/+6I/EQw89JA4cOCCeeOIJ8clPflLIsizuueceIQTHHHXPz3/+c6Gqqvj85z8vnn/+efHNb35TRKNRceedd7a24fijbvjBD34gvv/974sXX3xR3HPPPeKiiy4Sr33ta4XjOEIIjjvqjHK5LPbv3y/2798vAIjbbrtN7N+/Xxw6dEi4rive+c53iomJCfHLX/5STE1Ntf7Ytt3axyc+8Qnxvve9r/XfaxlnL774oohGo+IjH/mIePrpp8VXv/pVoWma+Ku/+qtN/fzUOyuNvXK5LD760Y+Kn/70p+LAgQPi/vvvF5dffrkYHx8XpVKptQ+OPVqPlcaeEEJcc801Yt++feL+++8XL774ovja174mTNMUt99+e2sfHHvUrn/xL/6FSCaT4oEHHlh0Pa3Vaq1tZmdnxf79+8Xf/u3fCgDirrvuEvv37xdTU1OtbTj2qB1rGXcvt3PnTvFnf/Zni17juFsdg+HbxH/8j/9R7Ny5U+i6Ll796leLBx98sPWzz3zmM2Lnzp2Ltj906JB4+9vfLiKRiMhkMuKmm24SjUZj0TZPPPGEuPrqq4VhGGJsbEx89rOfFUEQbMbHoT534403tsbbyMiIeNOb3tQKhAvBMUfd9b//9/8W559/vjAMQ+zdu1f8p//0nxb9nOOPuuFb3/qW2LNnj9B1XYyNjYkPf/jDolAotH7OcUedcP/99wsAZ/y54YYbxIEDB5b8GQBx//33t/Zxww03iGuuuWbRftcyzh544AFx8cUXC13Xxa5du8Qdd9yxCZ+Y+sVKY69Wq4nrrrtOjIyMCE3TxI4dO8QNN9wgDh8+vGgfHHu0HiuNPSGEmJqaEu9///tFPp8XpmmKV77yleLLX/7yonHEsUftWu56+rWvfa21zde+9rUlt/nMZz7T2oZjj9qxlnH3cksFwznuVicJsVARnYiIiIiIiIiIiIhoQLFmOBERERERERERERENPAbDiYiIiIiIiIiIiGjgMRhORERERERERERERAOPwXAiIiIiIiIiIiIiGngMhhMRERERERERERHRwGMwnIiIiIiIiIiIiIgGHoPhRERERERERERERDTwGAwnIiIiIiIiIiIiooHHYDgRERERERERERERDTwGw4mIiIiIiIiIiIho4DEYTkREREREREREREQD7/8HnbRK3z3Rx6YAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1800x500 with 1 Axes>"
       ]
diff --git a/nbs/15_timeseries_plots.ipynb b/nbs/15_timeseries_plots.ipynb
new file mode 100644
index 0000000..0351316
--- /dev/null
+++ b/nbs/15_timeseries_plots.ipynb
@@ -0,0 +1,921 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "output-file: timeseries_plots.html\n",
+    "title: Time series plots\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| default_exp timeseries_plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "from nbdev.showdoc import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "from typing import Callable, Iterable, Optional, Union, Tuple\n",
+    "import warnings\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "\n",
+    "from pheno_utils.config import DEFAULT_PALETTE, TIME_FORMAT, LEGEND_SHIFT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "class TimeSeriesFigure:\n",
+    "    def __init__(self, figsize: tuple = (10, 6), padding: float = 0.05):\n",
+    "        \"\"\"\n",
+    "        Initialize a TimeSeriesFigure instance. This class is used to create and manage\n",
+    "        a figure with multiple axes for time series data.\n",
+    "        \n",
+    "        Args:\n",
+    "            figsize (tuple): Size of the figure (width, height) in inches.\n",
+    "        \"\"\"\n",
+    "        self.fig = plt.figure(figsize=figsize)\n",
+    "        self.axes: Iterable[tuple] = []\n",
+    "        self.axis_names: dict = {}\n",
+    "        self.padding = padding\n",
+    "        self.custom_paddings = {}  # To store custom padding for specific axes\n",
+    "        self.shared_x_groups = []  # To keep track of shared x-axis groups\n",
+    "\n",
+    "    def plot(\n",
+    "        self, \n",
+    "        plot_function: Callable, \n",
+    "        *args, \n",
+    "        n_axes: int = 1, \n",
+    "        height: float = 1, \n",
+    "        sharex: Union[str, int, plt.Axes] = None, \n",
+    "        second_y: bool = False,\n",
+    "        name: str = None, \n",
+    "        ax: Union[str, int, plt.Axes] = None, \n",
+    "        adjust_time: Optional[str] = 'union',\n",
+    "        adjust_by_axis: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]] = None,\n",
+    "        **kwargs\n",
+    "    ) -> Union[plt.Axes, Iterable[plt.Axes]]:\n",
+    "        \"\"\"\n",
+    "        Plot using a dataset-specific function, creating a new axis if needed.\n",
+    "        The plot function should accept the axis object as the argument `ax`, or\n",
+    "        a list of axes if multiple axes are used.\n",
+    "        \n",
+    "        Args:\n",
+    "            plot_function (Callable): The dataset-specific function to plot the data.\n",
+    "            *args: Arguments to pass to the plot function.\n",
+    "            n_axes (int): The number of axes required. Default is 1.\n",
+    "            height (float): The proportional height of the axes relative to a single unit axis.\n",
+    "            sharex (str, int, or plt.Axes): Index or name of the axis to share the x-axis with. If None, the x-axis is independent.\n",
+    "            second_y (bool): If True, plot will be done on a secondary y-axis in the plot. Default is False.s\n",
+    "            name (str): Name or ID to assign to the axis.\n",
+    "            ax (plt.Axes, str, int): Pre-existing axis (object, name, or index) or list of axes to plot on.\n",
+    "            adjust_time (str, None): Method to adjust the time limits of all axes to match the data.\n",
+    "            adjust_by_axis (str, int, plt.Axes): Axes (single or multiple) to use as a reference for adjusting the time limits.\n",
+    "            **kwargs: Keyword arguments to pass to the plot function.\n",
+    "        \n",
+    "        Returns:\n",
+    "            Union[plt.Axes, Iterable[plt.Axes]]: A single axis object or a list of axis objects if multiple axes are used.\n",
+    "        \"\"\"\n",
+    "        if ax is None:\n",
+    "            ax = self.add_axes(height=height, n_axes=n_axes, sharex=sharex, name=name)\n",
+    "        else:\n",
+    "            ax = self.get_axes(ax, squeeze=True)\n",
+    "\n",
+    "        if second_y:\n",
+    "            ax.yaxis.grid(False)\n",
+    "            ax = ax.twinx()\n",
+    "\n",
+    "        plot_function(*args, ax=ax, **kwargs)\n",
+    "        if adjust_time:\n",
+    "            self.set_time_limits(None, None, method=adjust_time, reference_axis=adjust_by_axis)\n",
+    "        if second_y:\n",
+    "            ax.yaxis.grid(False)\n",
+    "            ax.yaxis.label.set_rotation(90)\n",
+    "            ax.yaxis.label.set_ha('center')\n",
+    "\n",
+    "        return ax\n",
+    "\n",
+    "    def add_axes(\n",
+    "        self, \n",
+    "        height: float = 1, \n",
+    "        n_axes: int = 1, \n",
+    "        sharex: Optional[Union[str, int, plt.Axes]] = None, \n",
+    "        name: Optional[str] = None,\n",
+    "    ) -> Union[plt.Axes, Iterable[plt.Axes]]:\n",
+    "        \"\"\"\n",
+    "        Add one or more axes with a specific proportional height to the figure.\n",
+    "        \n",
+    "        Args:\n",
+    "            height (float): The proportional height of each new axis relative to a single unit axis.\n",
+    "            n_axes (int): The number of axes to create.\n",
+    "            sharex (str, int, or plt.Axes): Index or name of the axis to share the x-axis with. If None, the x-axis is independent.\n",
+    "            name (Optional[str]): Name or ID to assign to the axis (only valid if num_axes=1).\n",
+    "        \n",
+    "        Returns:\n",
+    "            Union[plt.Axes, Iterable[plt.Axes]]: A single axis object or a list of axis objects if multiple axes are created.\n",
+    "        \"\"\"\n",
+    "        new_axes = []\n",
+    "        shared_group = []\n",
+    "        \n",
+    "        if sharex is not None:\n",
+    "            sharex = self.get_axes(sharex)[0]\n",
+    "            shared_group.append(sharex)\n",
+    "\n",
+    "        for _ in range(n_axes):\n",
+    "            ax = self.fig.add_subplot(len(self.axes) + 1, 1, len(self.axes) + 1, sharex=sharex)\n",
+    "            new_axes.append(ax)\n",
+    "            self.axes.append((ax, height))\n",
+    "            shared_group.append(ax)\n",
+    "            # When creating mulitple axes, always share their x-axis with the first one\n",
+    "            if sharex is None:\n",
+    "                sharex = ax\n",
+    "        \n",
+    "        if shared_group:\n",
+    "            self.shared_x_groups.append(shared_group)\n",
+    "\n",
+    "        if name is not None:\n",
+    "            self.axis_names[name] = new_axes\n",
+    "        \n",
+    "        self._adjust_axes()\n",
+    "\n",
+    "        return new_axes if n_axes > 1 else new_axes[0]\n",
+    "\n",
+    "    def _adjust_axes(self) -> None:\n",
+    "        \"\"\"\n",
+    "        Adjust the positions and sizes of all axes based on their proportional height and apply padding.\n",
+    "        \"\"\"\n",
+    "        total_height = sum(height for _, height in self.axes)\n",
+    "        total_padding = self.padding * (len(self.axes) - 1)\n",
+    "        bottom = 1 - total_padding  # Start from the top of the figure\n",
+    "\n",
+    "        for i, (ax, height) in enumerate(self.axes):\n",
+    "            ax_height = height / total_height * (1 - total_padding)\n",
+    "            # Adjust for any custom padding before this axis\n",
+    "            custom_pad = self.custom_paddings.get(i, 0)\n",
+    "            ax.set_position([0.1, bottom - ax_height, 0.8, ax_height])\n",
+    "            bottom -= ax_height + self.padding + custom_pad  # Move down, considering padding\n",
+    "\n",
+    "    def _get_axis_by_name(self, name: str) -> Optional[plt.Axes]:\n",
+    "        \"\"\"\n",
+    "        Retrieve an axis by its name or ID.\n",
+    "        \n",
+    "        Args:\n",
+    "            name (str): The name or ID of the axis to retrieve.\n",
+    "        \n",
+    "        Returns:\n",
+    "            Optional[plt.Axes]: The corresponding axis object if found, otherwise None.\n",
+    "        \"\"\"\n",
+    "        return self.axis_names.get(name, [])\n",
+    "\n",
+    "    def get_axes(self, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]]=None, squeeze=False) -> Iterable[plt.Axes]:\n",
+    "        \"\"\"\n",
+    "        Retrieve the axis object(s) based on the input type.\n",
+    "\n",
+    "        Args:\n",
+    "            ax: The axis object, index, name, or list of those to retrieve.\n",
+    "            squeeze (bool): Whether to return a single axis object if only one is found.\n",
+    "        \n",
+    "        Returns:\n",
+    "            Iterable[plt.Axes]: A list of axis objects.\n",
+    "        \"\"\"\n",
+    "        if ax is None:\n",
+    "            return [a for a, _ in self.axes]\n",
+    "        elif not isinstance(ax, list):\n",
+    "            ax = [ax]\n",
+    "        \n",
+    "        ax_list = []\n",
+    "        for a in ax:\n",
+    "            if isinstance(a, str):\n",
+    "                by_name = self._get_axis_by_name(a)\n",
+    "                if len(by_name) == 0:\n",
+    "                    warnings.warn(f\"No axis found with name '{a}'\")\n",
+    "                ax_list.extend(by_name)\n",
+    "            elif isinstance(a, int):\n",
+    "                ax_list.append(self.axes[a][0])\n",
+    "\n",
+    "        if squeeze and len(ax_list) == 1:\n",
+    "            return ax_list[0]\n",
+    "        else:\n",
+    "            return ax_list\n",
+    "\n",
+    "    def print_shared_axes(self):\n",
+    "        \"\"\"\n",
+    "        Print which axes in the figure share their x-axis.\n",
+    "\n",
+    "        Returns:\n",
+    "            None\n",
+    "        \"\"\"\n",
+    "        shared_groups = {}\n",
+    "        for i, (ax, _) in enumerate(self.axes):\n",
+    "            for j, (other_ax, _) in enumerate(self.axes):\n",
+    "                if i != j and ax.get_shared_x_axes().joined(ax, other_ax):\n",
+    "                    if i not in shared_groups:\n",
+    "                        shared_groups[i] = []\n",
+    "                    shared_groups[i].append(j)\n",
+    "\n",
+    "        for ax_idx, shared_with in shared_groups.items():\n",
+    "            print(f\"Axis {ax_idx} shares its x-axis with: {shared_with}\")\n",
+    "\n",
+    "    def get_axis_properties(self, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]]=None) -> dict:\n",
+    "        \"\"\"\n",
+    "        Get the properties of a specific axis or axes.\n",
+    "        \n",
+    "        Args:\n",
+    "            ax (str, int, plt.Axes, or a list of those): The axis or axes to get the properties for.\n",
+    "        \n",
+    "        Returns:\n",
+    "            dict: A dictionary of properties for the axis or axes.\n",
+    "        \"\"\"\n",
+    "        ax_list = self.get_axes(ax)\n",
+    "        properties = {}\n",
+    "        for a in ax_list:\n",
+    "            properties = {key: properties.get(key, []) + [value] for key, value in a.properties().items()}\n",
+    "\n",
+    "        for k, v in properties.items():\n",
+    "            if len(v) == 1:\n",
+    "                properties[k] = v[0]\n",
+    "\n",
+    "        return properties\n",
+    "\n",
+    "    def set_axis_properties(self, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]]=None, **kwargs) -> None:\n",
+    "        \"\"\"\n",
+    "        Set properties for a specific axis or axes.\n",
+    "        \n",
+    "        Args:\n",
+    "            ax (str, int, plt.Axes, or a list of those): The axis or axes to set the properties for.\n",
+    "            **kwargs: Additional keyword arguments to pass to the axis object.\n",
+    "        \"\"\"\n",
+    "        ax_list = self.get_axes(ax)\n",
+    "        for a in ax_list:\n",
+    "            a.set(**kwargs)\n",
+    "\n",
+    "    def set_axis_padding(self, padding: float, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]]=None, above: bool = True) -> None:\n",
+    "        \"\"\"\n",
+    "        Set custom padding for a specific axis.\n",
+    "        \n",
+    "        Args:\n",
+    "            padding (float): The amount of padding to add as a fraction of the figure height.\n",
+    "            \n",
+    "            above (bool): Whether to add padding above the axis (default) or below.\n",
+    "        \"\"\"\n",
+    "        ax_list = self.get_axes(ax)\n",
+    "        all_axes = [a for a, _ in self.axes]\n",
+    "\n",
+    "        for ax in ax_list:\n",
+    "            axis_index = all_axes.index(ax)\n",
+    "            if axis_index < 0:\n",
+    "                warnings.warn(\"Axis not found in the figure.\")\n",
+    "                continue\n",
+    "            if above:\n",
+    "                self.custom_paddings[axis_index] = padding\n",
+    "            elif axis_index == len(self.axes) - 1:\n",
+    "                continue\n",
+    "            else:\n",
+    "                self.custom_paddings[axis_index + 1] = padding\n",
+    "            self._adjust_axes()\n",
+    "\n",
+    "    def set_time_limits(\n",
+    "            self, start_time: Union[float, str, pd.Timestamp, None],\n",
+    "            end_time: Union[float, str, pd.Timestamp, None],\n",
+    "            method: str='union',\n",
+    "            reference_axis: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]] = None\n",
+    "    ) -> None:\n",
+    "        \"\"\"\n",
+    "        Set the time limits for all axes in the figure. Calling with None will adjust the limits to the data.\n",
+    "\n",
+    "        Args:\n",
+    "            start_time (Union[float, str, pd.Timestamp, None]): The start time for the x-axis.\n",
+    "            end_time (Union[float, str, pd.Timestamp, None]): The end time for the x-axis.\n",
+    "        \"\"\"\n",
+    "        # Default values\n",
+    "        xlim = np.array(self.get_axis_properties(reference_axis)['xlim']).reshape((-1, 2))\n",
+    "        if method == 'union':\n",
+    "            xlim = xlim[:, 0].min(), xlim[:, 1].max()\n",
+    "        elif method == 'intersect':\n",
+    "            xlim = xlim[:, 0].max(), xlim[:, 1].min()\n",
+    "        else:\n",
+    "            raise ValueError(f\"Invalid method: {method} not in ['union', 'intersect']\")\n",
+    "\n",
+    "        # Convert string inputs to pandas Timestamp objects\n",
+    "        if start_time is not None:\n",
+    "            start_time = pd.to_datetime(start_time)\n",
+    "        else:\n",
+    "            start_time = xlim[0]\n",
+    "        if end_time is not None:\n",
+    "            end_time = pd.to_datetime(end_time)\n",
+    "        else:\n",
+    "            end_time = xlim[1]\n",
+    "\n",
+    "        self.set_axis_properties(xlim=(start_time, end_time))\n",
+    "\n",
+    "    def set_periodic_ticks(\n",
+    "        self, \n",
+    "        interval: Union[str, pd.Timedelta], \n",
+    "        start_time: str = '2018-01-01 00:00',\n",
+    "        end_time: str = None,\n",
+    "        fmt=TIME_FORMAT,\n",
+    "        ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]] = None\n",
+    "    ) -> None:\n",
+    "        \"\"\"\n",
+    "        Set periodic x-ticks at a regular interval throughout the day.\n",
+    "\n",
+    "        Args:\n",
+    "            interval (Union[str, pd.Timedelta]): The interval between ticks (e.g., '1H' for hourly ticks, '30T' for 30 minutes).\n",
+    "            start_time (str): The time of day to start the ticks from (default is '00:00').\n",
+    "            end_time (str): The time of day to end the ticks at (default is None).\n",
+    "            fmt (str): The date format string to be used for the tick labels.\n",
+    "            ax (str, int, plt.Axes, or a list of those): The axis (or axes) to apply the ticks to. \n",
+    "                Can be an axis object, a list of axes, an index, or a name. If None, applies to all axes.\n",
+    "        \"\"\"\n",
+    "        # Convert interval to pandas Timedelta if it's a string\n",
+    "        if isinstance(interval, str):\n",
+    "            interval = pd.to_timedelta(interval)\n",
+    "\n",
+    "        # Convert start_time to a datetime object with today's date\n",
+    "        if start_time is not None:\n",
+    "            start_time = pd.to_datetime(start_time).tz_localize(None)\n",
+    "        if end_time is not None:\n",
+    "            end_time = pd.to_datetime(end_time).tz_localize(None)\n",
+    "\n",
+    "        # Determine which axes to apply this to\n",
+    "        axes = self.get_axes(ax)\n",
+    "\n",
+    "        for a in axes:\n",
+    "            if a is not None:\n",
+    "                # Get the x-axis limits\n",
+    "                min_x, max_x = a.get_xlim()\n",
+    "\n",
+    "                # Convert limits to datetime if they are in float format\n",
+    "                if isinstance(min_x, (float, int)):\n",
+    "                    min_x = mdates.num2date(min_x).replace(tzinfo=None)\n",
+    "                if isinstance(max_x, (float, int)):\n",
+    "                    max_x = mdates.num2date(max_x).replace(tzinfo=None)\n",
+    "\n",
+    "                # Set the ticks to align with the start_datetime\n",
+    "                ticks = pd.date_range(start=start_time if start_time else min_x,\n",
+    "                                      end=end_time if end_time else max_x,\n",
+    "                                      freq=interval)\n",
+    "\n",
+    "                # Make sure ticks are within the limits\n",
+    "                ticks = [tick for tick in ticks if min_x <= tick and tick <= max_x]\n",
+    "\n",
+    "                # Set the locator and formatter\n",
+    "                format_xticks(a, ticks, fmt)\n",
+    "\n",
+    "                plt.setp(a.get_xticklabels(), rotation=0, ha='center')\n",
+    "\n",
+    "    def add_legend(self, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]], **kwargs) -> None:\n",
+    "        \"\"\"\n",
+    "        Add a legend to a specific axis.\n",
+    "        \n",
+    "        Args:\n",
+    "            axis (str, int, plt.Axes, or a list of those): The axis to add the legend to.\n",
+    "        \"\"\"\n",
+    "        ax_list = self.get_axes(ax)\n",
+    "        for a in ax_list:\n",
+    "            a.legend(**kwargs)\n",
+    "\n",
+    "    def set_legend(self, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]]=None, bbox_to_anchor: tuple=None, **kwargs):\n",
+    "        \"\"\"\n",
+    "        Update the legend properties for all axes in the figure, or a subset of them, if the legend exists.\n",
+    "\n",
+    "        Args:\n",
+    "            axis (str, int, plt.Axes, or a list of those): The name or list of names of axes to update the legend for.\n",
+    "            bbox_to_anchor (tuple, optional): The bounding box coordinates for the legend.\n",
+    "            **kwargs: Additional keyword arguments passed to the legend object.\n",
+    "        \"\"\"\n",
+    "        ax_list = self.get_axes(ax)\n",
+    "\n",
+    "        for a in ax_list:\n",
+    "            legend = a.get_legend()\n",
+    "            if legend is None:\n",
+    "                continue\n",
+    "            if bbox_to_anchor is not None:\n",
+    "                legend.set_bbox_to_anchor(bbox_to_anchor)\n",
+    "            legend.set(**kwargs)\n",
+    "\n",
+    "    def show(self) -> None:\n",
+    "        \"\"\"\n",
+    "        Display the figure.\n",
+    "        \"\"\"\n",
+    "        plt.show()\n",
+    "\n",
+    "\n",
+    "def format_xticks(ax: plt.Axes, xticks: Iterable=None, format: str=TIME_FORMAT, **kwargs):\n",
+    "    \"\"\" format datestrings on x axis \"\"\"\n",
+    "    if xticks is None:\n",
+    "        xticks = ax.get_xticks()\n",
+    "    ax.set_xticks(xticks)\n",
+    "    ax.set_xticklabels(xticks, **kwargs)\n",
+    "    xfmt = mdates.DateFormatter(format)\n",
+    "    ax.xaxis.set_major_formatter(xfmt)\n",
+    "\n",
+    "\n",
+    "def format_timeseries(\n",
+    "    df: pd.DataFrame,\n",
+    "    participant_id: int=None,\n",
+    "    array_index: int=None,\n",
+    "    time_range: Tuple[str, str]=None,\n",
+    "    x_start: str='collection_timestamp',\n",
+    "    x_end: str='collection_timestamp',\n",
+    "    unique: bool=False,\n",
+    ") -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Reformat and filter a time series DataFrame based on participant ID, array index, and date range.\n",
+    "\n",
+    "    Args:\n",
+    "        df (pd.DataFrame): The DataFrame to filter.\n",
+    "        participant_id (int): The participant ID to filter by.\n",
+    "        array_index (int): The array index to filter by.\n",
+    "        time_range: The date range to filter by. Can be a tuple of two dates / times or two strings.\n",
+    "        x_start (str): The name of the column containing the start time.\n",
+    "        x_end (str): The name of the column containing the end time.\n",
+    "\n",
+    "    Returns:\n",
+    "        pd.DataFrame: The filtered DataFrame\n",
+    "    \"\"\"\n",
+    "    if participant_id is not None:\n",
+    "        df = df.query('participant_id == @participant_id')\n",
+    "    if array_index is not None:\n",
+    "        df = df.query('array_index == @array_index')\n",
+    "\n",
+    "    # Reset index to avoid issues with slicing and indexing\n",
+    "    x_ind = np.unique([c for c in [x_start, x_end] if c in df.index.names])\n",
+    "    if len(x_ind):\n",
+    "        if np.isin(x_ind, df.index.names).any():\n",
+    "            df = df.reset_index(x_ind)\n",
+    "        df[x_start] = df[x_start].dt.tz_localize(None)\n",
+    "        if x_start != x_end:\n",
+    "            df[x_end] = df[x_end].dt.tz_localize(None)\n",
+    "    if time_range is not None:\n",
+    "        time_range = pd.to_datetime(time_range)\n",
+    "        df = df.loc[(time_range[0] <= df[x_start]) & (df[x_end] <= time_range[1])]\n",
+    "    if unique:\n",
+    "        df = df.drop_duplicates()\n",
+    "\n",
+    "    return df.sort_values(x_start)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "def plot_events_bars(\n",
+    "    events: pd.DataFrame,\n",
+    "    x_start: str = 'collection_timestamp',\n",
+    "    x_end: str = 'event_end',\n",
+    "    y: str = 'event',\n",
+    "    hue: str = 'channel',\n",
+    "    participant_id: Optional[int] = None,\n",
+    "    array_index: Optional[int] = None,\n",
+    "    time_range: Optional[Tuple[str, str]] = None,\n",
+    "    y_include: Optional[Iterable[str]] = None,\n",
+    "    y_exclude: Optional[Iterable[str]] = None,\n",
+    "    legend: bool = True,\n",
+    "    palette: str = DEFAULT_PALETTE,\n",
+    "    alpha: Optional[float] = 0.7,\n",
+    "    ax: Optional[plt.Axes] = None,\n",
+    "    figsize: Tuple[float, float] = (12, 6),\n",
+    ") -> plt.Axes:\n",
+    "    \"\"\"\n",
+    "    Plot events as bars on a time series plot.\n",
+    "\n",
+    "    Args:\n",
+    "        events (pd.DataFrame): The events dataframe.\n",
+    "        x_start (str): The column name for the start time of the event.\n",
+    "        x_end (str): The column name for the end time of the event.\n",
+    "        y (str): The column name for the y-axis values.\n",
+    "        hue (str): The column name for the color of the event.\n",
+    "        participant_id (int): The participant ID to filter events by.\n",
+    "        array_index (int): The array index to filter events by.\n",
+    "        time_range (Tuple[str, str]): The time range to filter events by.\n",
+    "        y_include (Iterable[str]): The list of values to include in the plot.\n",
+    "        y_exclude (Iterable[str]): The list of values to exclude from the plot.\n",
+    "        legend (bool): Whether to show the legend.\n",
+    "        palette (str): The name of the colormap to use for coloring events.\n",
+    "        alpha (float): The transparency of the bars. Default is 0.7.\n",
+    "        ax (plt.Axes): The axis to plot on. If None, a new figure is created.\n",
+    "        figsize (Tuple[float, float]): The size of the figure (width, height) in inches.\n",
+    "    \"\"\"\n",
+    "    events, color_map = prep_to_plot_timeseries(\n",
+    "        events, x_start, x_end,\n",
+    "        hue, y,\n",
+    "        participant_id, array_index, time_range,\n",
+    "        y_include, y_exclude,\n",
+    "        palette=palette)\n",
+    "    if hue is None:\n",
+    "        hue = 'hue'\n",
+    "\n",
+    "    if ax is None:\n",
+    "        fig, ax = plt.subplots(figsize=figsize)\n",
+    "\n",
+    "    # Plot events\n",
+    "    events = events.assign(diff=lambda x: x[x_end] - x[x_start]).sort_values([hue, y])\n",
+    "    y_labels = []\n",
+    "    legend_dicts = []\n",
+    "    for i, (y_label, events) in enumerate(events.groupby(y, observed=True, sort=False)):\n",
+    "        if len(y) == 0:\n",
+    "            continue\n",
+    "        y_labels.append(y_label)\n",
+    "        for c, r in events.groupby(hue, observed=True):\n",
+    "            data = r[[x_start, 'diff']]\n",
+    "            if not len(data):\n",
+    "                continue\n",
+    "            h = ax.broken_barh(data.values, (i-0.4,0.8), color=color_map[c], alpha=alpha)\n",
+    "            legend_dicts.append({'label': c, 'handle': h})\n",
+    "\n",
+    "    # format plot\n",
+    "    if legend:\n",
+    "        legend_df = pd.DataFrame.from_dict(legend_dicts).drop_duplicates(subset='label')\n",
+    "        ax.legend(\n",
+    "            legend_df['handle'],\n",
+    "            legend_df['label'],\n",
+    "            loc='upper left', \n",
+    "            bbox_to_anchor=LEGEND_SHIFT)\n",
+    "\n",
+    "    ax.set_yticks(np.arange(len(y_labels)), y_labels)\n",
+    "    format_xticks(ax)\n",
+    "    ax.invert_yaxis()  # Invert y-axis to match the order of the legend\n",
+    "\n",
+    "    return ax\n",
+    "\n",
+    "\n",
+    "def plot_events_fill(\n",
+    "    events: pd.DataFrame,\n",
+    "    x_start: str = 'collection_timestamp',\n",
+    "    x_end: str = 'event_end',\n",
+    "    hue: str = 'channel',\n",
+    "    label: str = None,\n",
+    "    participant_id: Optional[int] = None,\n",
+    "    array_index: Optional[int] = None,\n",
+    "    time_range: Optional[Tuple[str, str]] = None,\n",
+    "    y_include: Optional[Iterable[str]] = None,\n",
+    "    y_exclude: Optional[Iterable[str]] = None,\n",
+    "    legend: bool = True,\n",
+    "    palette: str = DEFAULT_PALETTE,\n",
+    "    alpha: Optional[float] = 0.5,\n",
+    "    ax: Optional[plt.Axes] = None,\n",
+    "    figsize: Iterable[float] = [12, 6],\n",
+    ") -> plt.Axes:\n",
+    "    \"\"\"\n",
+    "    Plot events as filled regions on a time series plot.\n",
+    "\n",
+    "    Args:\n",
+    "        events (pd.DataFrame): The events dataframe.\n",
+    "        x_start (str): The column name for the start time of the event.\n",
+    "        x_end (str): The column name for the end time of the event.\n",
+    "        hue (str): The column name for the color of the event.\n",
+    "        label (str): The column name for the label of the event.\n",
+    "        participant_id (int): The participant ID to filter events by.\n",
+    "        array_index (int): The array index to filter events by.\n",
+    "        time_range (Iterable[str]): The time range to filter events by.\n",
+    "        y_include (Iterable[str]): The list of values to include in the plot.\n",
+    "        y_exclude (Iterable[str]): The list of values to exclude from the plot.\n",
+    "        legend (bool): Whether to show the legend.\n",
+    "        palette (str): The name of the palette to use for coloring events.\n",
+    "        alpha (float): The transparency of the filled regions.\n",
+    "        ax (plt.Axes): The axis to plot on. If None, a new figure is created.\n",
+    "        figsize (Tuple[float, float]): The size of the figure (width, height) in inches.\n",
+    "    \"\"\"\n",
+    "    events, color_map = prep_to_plot_timeseries(\n",
+    "        events, x_start, x_end,\n",
+    "        hue, label,\n",
+    "        participant_id, array_index, time_range,\n",
+    "        y_include, y_exclude,\n",
+    "        palette=palette)\n",
+    "    if hue is None:\n",
+    "        hue = 'hue'\n",
+    "\n",
+    "    if ax is None:\n",
+    "        fig, ax = plt.subplots(figsize=figsize)\n",
+    "    if type(ax) is not list:\n",
+    "        ax = [ax]\n",
+    "\n",
+    "    for a in ax:\n",
+    "        # Plotting events\n",
+    "        this_color = hue if hue is not None else '#4c72b0'\n",
+    "        for _, row in events.iterrows():\n",
+    "            if color_map is not None:\n",
+    "                this_color = color_map[row[hue]]\n",
+    "            # Plot the event as a filled region, with zorder to ensure it's behind other elements\n",
+    "            a.axvspan(\n",
+    "                row[x_start], row[x_end], 0, 1,\n",
+    "                color=this_color, alpha=alpha, zorder=0,\n",
+    "                transform=a.get_xaxis_transform())\n",
+    "\n",
+    "        # Add labels as xticks on the top secondary x-axis\n",
+    "        if label:\n",
+    "            secax = a.secondary_xaxis('top')\n",
+    "            secax.set_xticks(events[x_start])\n",
+    "            secax.set_xticklabels(events[label], rotation=0, ha='center')\n",
+    "\n",
+    "        # Add legend\n",
+    "        if legend:\n",
+    "            # Get existing handles from existing legends in the axes\n",
+    "            handles, labels = a.get_legend_handles_labels()\n",
+    "            if color_map is not None:\n",
+    "                handles += [plt.Rectangle((0, 0), 1, 1, color=c, alpha=alpha) for c in color_map]\n",
+    "                labels += color_map.index.tolist()\n",
+    "            else:\n",
+    "                handles += [plt.Rectangle((0, 0), 1, 1, color=this_color, alpha=alpha)]\n",
+    "                labels += ['events']\n",
+    "            a.legend(handles, labels, loc='upper left', bbox_to_anchor=LEGEND_SHIFT)\n",
+    "\n",
+    "        format_xticks(a)\n",
+    "\n",
+    "    return ax\n",
+    "\n",
+    "\n",
+    "def prep_to_plot_timeseries(\n",
+    "    data: pd.DataFrame,\n",
+    "    x_start: str,\n",
+    "    x_end: str,\n",
+    "    hue: str,\n",
+    "    label: str,\n",
+    "    participant_id: int,\n",
+    "    array_index: int,\n",
+    "    time_range: Tuple[str, str],\n",
+    "    y_include: Iterable[str],\n",
+    "    y_exclude: Iterable[str],\n",
+    "    add_columns: Iterable[str]=None,\n",
+    "    palette=DEFAULT_PALETTE,\n",
+    ") -> Tuple[pd.DataFrame, pd.DataFrame]:\n",
+    "    \"\"\"\n",
+    "    Prepare timeseries / events data for plotting.\n",
+    "\n",
+    "    Args:\n",
+    "        events (pd.DataFrame): The timeseries / events dataframe.\n",
+    "        x_start (str): The column name for the start time of the event.\n",
+    "        x_end (str): The column name for the end time of the event.\n",
+    "        hue (str): The column name for the color of the event.\n",
+    "        label (str): The column name for the label of the event.\n",
+    "        participant_id (int): The participant ID to filter events by.\n",
+    "        array_index (int): The array index to filter events by.\n",
+    "        time_range (Iterable[str]): The time range to filter events by.\n",
+    "        y_include (Iterable[str]): The list of values to include in the plot.\n",
+    "        y_exclude (Iterable[str]): The list of values to exclude from the plot.\n",
+    "        add_columns (Iterable[str]): Additional columns to include in the plot.\n",
+    "        palette (str): The name of the colormap to use for coloring events.\n",
+    "\n",
+    "    Returns:\n",
+    "        Tuple[pd.DataFrame, pd.DataFrame]: The filtered events dataframe and the color map.\n",
+    "    \"\"\"\n",
+    "    if type(add_columns) is str:\n",
+    "        add_columns = [add_columns]\n",
+    "\n",
+    "    data = format_timeseries(data, participant_id, array_index, time_range, x_start, x_end)\n",
+    "\n",
+    "    # Filter events based on y_include and y_exclude\n",
+    "    data = data.dropna(subset=[x_start, x_end])\n",
+    "    if hue is not None and hue in data.index.names:\n",
+    "        data = data.reset_index(hue)\n",
+    "    if label is not None and label in data.index.names:\n",
+    "        data = data.reset_index(label)\n",
+    "    if y_include is not None:\n",
+    "        ind = pd.Series(False, index=data.index)\n",
+    "        if hue is not None:\n",
+    "            ind |= data[hue].isin(y_include)\n",
+    "        if label is not None:\n",
+    "            ind |= data[label].isin(y_include)\n",
+    "        data = data.loc[ind]\n",
+    "    if y_exclude is not None:\n",
+    "        ind = pd.Series(False, index=data.index)\n",
+    "        if hue is not None:\n",
+    "            ind |= data[hue].isin(y_exclude)\n",
+    "        if label is not None:\n",
+    "            ind |= data[label].isin(y_exclude)\n",
+    "        data = data.loc[~ind]\n",
+    "    if hue is None:\n",
+    "        hue = 'hue'\n",
+    "        data[hue] = 'events'\n",
+    "\n",
+    "    col_list = [x_start, x_end, hue, label]\n",
+    "    if add_columns is not None:\n",
+    "        col_list += list(add_columns)\n",
+    "    col_list = pd.Series(col_list).dropna().drop_duplicates()\n",
+    "\n",
+    "    # Set colors\n",
+    "    if hue in data.columns:\n",
+    "        colors = get_color_map(data, hue, palette)\n",
+    "    else:\n",
+    "        colors = None\n",
+    "\n",
+    "    return data[col_list], colors\n",
+    "\n",
+    "\n",
+    "def get_events_period(\n",
+    "    events_filtered: pd.DataFrame,\n",
+    "    period_start: str,\n",
+    "    period_end: str,\n",
+    "    period_name: str,\n",
+    "    col: str = 'event',\n",
+    "    first_start: bool = True,\n",
+    "    first_end: bool = True,\n",
+    "    include_start: bool = True,\n",
+    "    include_end: bool = True,\n",
+    "    x_start: str = 'collection_timestamp',\n",
+    "    x_end: str = 'event_end',\n",
+    ") -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Get the period of time between the start and end events.\n",
+    "\n",
+    "    Args:\n",
+    "        events_filtered (pd.DataFrame): The events DataFrame.\n",
+    "        period_start (str): The label of the start event.\n",
+    "        period_end (str): The label of the end event.\n",
+    "        period_name (str): The label to assign to the period.\n",
+    "        col (str): The column name for the event labels. Default is 'event'.\n",
+    "        first_start (bool): If True, get the first start event. Default is True.\n",
+    "        first_end (bool): If True, get the first end event. Default is True.\n",
+    "        include_start (bool): If True, include the start event in the period. Default is True.\n",
+    "        include_end (bool): If True, include the end event in the period. Default is True.\n",
+    "        x_start (str): The column name for the start time of the event. Default is 'collection_timestamp'.\n",
+    "        x_end (str): The column name for the end time of the event. Default is 'event_end'.\n",
+    "\n",
+    "    Returns:\n",
+    "        pd.DataFrame: The period of events in the same format as the input DataFrame.\n",
+    "    \"\"\"\n",
+    "    events_filtered = format_timeseries(events_filtered, None, None, None, x_start, x_end)\n",
+    "\n",
+    "    start_time = events_filtered.loc[\n",
+    "        events_filtered[col] == period_start,\n",
+    "        x_start if include_start else x_end]\\\n",
+    "        .iloc[0 if first_start else -1]\n",
+    "    end_time = events_filtered.loc[\n",
+    "        events_filtered[col] == period_end,\n",
+    "        x_end if include_end else x_start]\\\n",
+    "        .iloc[0 if first_end else -1]\n",
+    "\n",
+    "    return pd.DataFrame({\n",
+    "        x_start: [start_time],\n",
+    "        x_end: [end_time],\n",
+    "        col: [period_name]\n",
+    "    })\n",
+    "\n",
+    "\n",
+    "def get_color_map(data: pd.DataFrame, hue: str, palette: str) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Get a color map for a specific column in the data.\n",
+    "\n",
+    "    Args:\n",
+    "        data (pd.DataFrame): The data to get the color map from.\n",
+    "        hue (str): The column name to use for the color map.\n",
+    "        palette (str): The name of the colormap to use.\n",
+    "\n",
+    "    Returns:\n",
+    "        pd.DataFrame: A DataFrame with the color map.\n",
+    "    \"\"\"\n",
+    "    colors = sorted(data[hue].unique())\n",
+    "    colors = pd.DataFrame({\n",
+    "        hue: colors,\n",
+    "        'color': sns.color_palette(palette, len(colors))\n",
+    "        }).set_index(hue)['color']\n",
+    "\n",
+    "    return colors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The class `TimeSeriesFigure` provides a user-friendly interface for plotting multiple channels of time series data.\n",
+    "\n",
+    "First, we will load time series DFs from the sleep monitoring dataset. The data includes sleep events, and sensor channels for heart rate, respiratory movement, and oxygen saturation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ec2-user/projects/pheno-utils/pheno_utils/pheno_loader.py:610: UserWarning: No date field found\n",
+      "  warnings.warn(f'No date field found')\n"
+     ]
+    }
+   ],
+   "source": [
+    "#| eval: false\n",
+    "from pheno_utils import PhenoLoader\n",
+    "\n",
+    "pl = PhenoLoader('sleep')\n",
+    "channels_df = pl.load_bulk_data('channels_time_series', pivot='source')\n",
+    "events_df = pl.load_bulk_data('events_time_series')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Any plotting function that accepts an `ax` argument can be used with `TimeSeriesFigure`. The pheno-utils package includes a number of functions that are useful for plotting time series data, such as `plot_events_bars` and `plot_events_fill`, however standard seaborn plotting functions (and others) can also be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#| eval: false\n",
+    "sns.set_style('whitegrid')\n",
+    "\n",
+    "g = TimeSeriesFigure()\n",
+    "\n",
+    "channels_df = format_timeseries(channels_df).set_index('collection_timestamp')\n",
+    "g.plot(sns.lineplot, channels_df, x='collection_timestamp', y='heart_rate',\n",
+    "       name='heart_rate')  # Named axis 'heart_rate'\n",
+    "\n",
+    "# You can also use the `sharex` argument to share the x-axis between plots\n",
+    "# Named axes, such as 'heart_rate', can be referred to by name\n",
+    "g.plot(sns.lineplot, channels_df, x='collection_timestamp', y='spo2',\n",
+    "       sharex='heart_rate')\n",
+    "\n",
+    "# You can increase the relative height of the plot by passing a `height` argument\n",
+    "g.plot(sns.lineplot, channels_df, x='collection_timestamp', y='respiratory_movement',\n",
+    "       sharex='heart_rate', height=1.5)\n",
+    "\n",
+    "# You may add a plot to an existing axes by passing an `ax` argument to the plotting function\n",
+    "# Named axes, such as 'heart_rate', can be referred to by name\n",
+    "stage_events = ['Wake', 'Light Sleep', 'Deep Sleep', 'REM']  # Include only sleep stage events\n",
+    "g.plot(plot_events_fill, events_df, hue='event', y_include=stage_events,\n",
+    "       ax='heart_rate')\n",
+    "\n",
+    "apnea_events = ['Resp. Event', 'Desaturation', 'A/H obstructive', 'A/H central', 'A/H unclassified']\n",
+    "g.plot(plot_events_bars, events_df, hue='event', y_include=apnea_events, height=1.5)\n",
+    "\n",
+    "# Control functions to conveniently modify all axes\n",
+    "g.set_periodic_ticks('1h')\n",
+    "g.set_axis_padding(0.05)\n",
+    "g.set_axis_properties(xlabel='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import nbdev; nbdev.nbdev_export()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/16_diet_plots.ipynb b/nbs/16_diet_plots.ipynb
new file mode 100644
index 0000000..a3d108f
--- /dev/null
+++ b/nbs/16_diet_plots.ipynb
@@ -0,0 +1,1105 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "output-file: diet_plots.html\n",
+    "title: Diet logging plots\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| default_exp diet_plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "from nbdev.showdoc import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "from typing import List, Tuple\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "import matplotlib.patches as mpatches\n",
+    "import matplotlib.lines as mlines\n",
+    "import matplotlib.patches as Patch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "from pheno_utils.timeseries_plots import format_timeseries, format_xticks, plot_events_bars\n",
+    "from pheno_utils.config import DEFAULT_PALETTE, LEGEND_SHIFT\n",
+    "\n",
+    "\n",
+    "def plot_nutrient_bars(\n",
+    "    diet_log: pd.DataFrame, \n",
+    "    x: str='collection_timestamp',\n",
+    "    label: str='short_food_name',\n",
+    "    participant_id: int=None, \n",
+    "    array_index: int = None,\n",
+    "    time_range: Tuple[str, str]=None, \n",
+    "    meals: bool=True,\n",
+    "    summary: bool=False,\n",
+    "    nut_include: List[str]=None,\n",
+    "    nut_exclude: List[str]=None,\n",
+    "    agg_units: dict={'kcal': 'sum', 'g': 'sum', 'mg': 'sum'},\n",
+    "    legend: bool=True,\n",
+    "    bar_width=np.timedelta64(15, 'm'),\n",
+    "    palette: str=DEFAULT_PALETTE,\n",
+    "    alpha: float=0.7,\n",
+    "    ax: plt.Axes=None,\n",
+    "    figsize: Tuple[float, float]=(14, 3),\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Plot a stacked bar chart representing nutrient intake for each meal over time.\n",
+    "\n",
+    "    Args:\n",
+    "        diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements.\n",
+    "        x (str): The name of the column in `diet_log` representing the x-axis variable, such as timestamps. Default is 'collection_timestamp'.\n",
+    "        label (str): The name of the column in `diet_log` representing the labels for each meal. Default is 'short_food_name'.\n",
+    "        participant_id (Optional[int]): The participant's ID to filter the diet log. If None, no filtering is done. Default is None.\n",
+    "        array_index (Optional[int]): The array index to filter the diet log. If None, no filtering is done. Default is None.\n",
+    "        time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be 'YYYY-MM-DD'. Default is None.\n",
+    "        meals (bool): If True, includes individual meals in the plot. Default is True.\n",
+    "        summary (bool): If True, includes a daily summary in the plot. Default is False.\n",
+    "        nut_include (List[str]): A list of nutrients to include in the plot. Default is None.\n",
+    "        nut_exclude (List[str]): A list of nutrients to exclude from the plot. Default is None.\n",
+    "        agg_units (dict): A dictionary mapping nutrient units to aggregation functions. Only nutrients with units in this dictionary are plotted.\n",
+    "        legend (bool): If True, includes a legend in the plot. Default is True.\n",
+    "        bar_width (np.timedelta64): The width of the bars representing each meal on the time axis. Default is 15 minutes.\n",
+    "        palette (str): The color palette to use for the stacked bars.\n",
+    "        alpha (float): The transparency of the stacked bars. Default is 0.7.\n",
+    "        ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the bar chart. If None, a new axis is created. Default is None.\n",
+    "        figsize (Tuple[float, float]): The size of the figure to create. Default is (14, 3).\n",
+    "\n",
+    "    Returns:\n",
+    "        None: The function creates a stacked bar chart on the specified or newly created axis.\n",
+    "    \"\"\"\n",
+    "    # Prepare the data for plotting\n",
+    "    df, grouped_nutrients = prepare_meals(\n",
+    "        diet_log,\n",
+    "        participant_id=participant_id,\n",
+    "        array_index=array_index,\n",
+    "        time_range=time_range,\n",
+    "        label=label,\n",
+    "        return_meals=meals,\n",
+    "        return_summary=summary,\n",
+    "        y_include=nut_include,\n",
+    "        y_exclude=nut_exclude,\n",
+    "        agg_units=agg_units,\n",
+    "        x_col=x,\n",
+    "        )\n",
+    "\n",
+    "    if ax is None:\n",
+    "        fig, ax = plt.subplots(\n",
+    "            len(grouped_nutrients), 1,\n",
+    "            figsize=(figsize[0], figsize[1] * len(grouped_nutrients)),\n",
+    "            sharex=True)\n",
+    "        if len(grouped_nutrients) == 1:\n",
+    "            ax = [ax]\n",
+    "\n",
+    "    colors = sns.color_palette(\n",
+    "        palette, sum([len(g) for g in grouped_nutrients.values()]))\n",
+    "\n",
+    "    # Calculate the width in time units\n",
+    "    bar_width_in_days = bar_width / np.timedelta64(1, 'D')\n",
+    "\n",
+    "    unit_list = [g for g in grouped_nutrients if g != 'kcal']\n",
+    "    if 'kcal' in grouped_nutrients:\n",
+    "        # kcal is last to keep colours synced with the lollipop plot\n",
+    "        unit_list.append('kcal')\n",
+    "\n",
+    "    # Stacked bar plots for grouped nutrients\n",
+    "    c = 0\n",
+    "    for idx, unit in enumerate(unit_list):\n",
+    "        bottom = pd.Series([0] * len(df))\n",
+    "        for nut in grouped_nutrients[unit]:\n",
+    "            if nut in ['weight_g']:\n",
+    "                continue\n",
+    "            ax[idx].bar(\n",
+    "                df[x], df[nut], bottom=bottom, width=bar_width_in_days,\n",
+    "                color=colors[c], alpha=alpha, label=nut)\n",
+    "            bottom += df[nut]\n",
+    "            c += 1\n",
+    "        ax[idx].set_ylabel(f'Nutrients ({unit})', rotation=0, horizontalalignment='right')\n",
+    "        if legend:\n",
+    "            ax[idx].legend(loc='upper left', bbox_to_anchor=LEGEND_SHIFT)\n",
+    "        ax[idx].grid(True)\n",
+    "\n",
+    "    # Set x-tick labels for the bottom and top axes\n",
+    "    format_xticks(ax[-1], df[x])\n",
+    "    if label is not None:\n",
+    "        secax = ax[0].secondary_xaxis('top')\n",
+    "        secax.set_xticks(df[x])\n",
+    "        secax.set_xticklabels(df[label], ha='center', fontsize=9)\n",
+    "\n",
+    "    return ax\n",
+    "\n",
+    "\n",
+    "def plot_nutrient_lollipop(\n",
+    "    diet_log: pd.DataFrame, \n",
+    "    x: str='collection_timestamp',\n",
+    "    y: str='calories_kcal',\n",
+    "    size: str='total_g', \n",
+    "    label: str='short_food_name',\n",
+    "    participant_id: int=None, \n",
+    "    array_index: int=None,\n",
+    "    time_range: Tuple[str, str]=None, \n",
+    "    meals: bool=True,\n",
+    "    summary: bool=False,\n",
+    "    nut_include: List[str]=None,\n",
+    "    nut_exclude: List[str]=None,\n",
+    "    legend: bool=True,\n",
+    "    size_scale: float=5,\n",
+    "    palette: str=DEFAULT_PALETTE,\n",
+    "    alpha: float=0.7,\n",
+    "    ax: plt.Axes=None,\n",
+    "    figsize: Tuple[float, float] = (12, 3),\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Plot a lollipop chart with pie charts representing nutrient composition for each meal.\n",
+    "\n",
+    "    NOTE: The y-axis is scaled to match the units of the x-axis, to avoid distortion of the pie charts.\n",
+    "    Due to scaling, if you intend to change `xlim` after plotting, you must also provide `date_range`.\n",
+    "    Use the `second_y` of g.plot() option to plot it with other y-axis data.\n",
+    "\n",
+    "    Args:\n",
+    "        diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements.\n",
+    "        x (str): The name of the column in `diet_log` representing the x-axis variable, such as timestamps. Default is 'collection_timestamp'.\n",
+    "        y (str): The name of the column in `diet_log` representing the y-axis variable, such as calories. Default is 'calories_kcal'.\n",
+    "        size (str): The name of the column in `diet_log` representing the size of the pie charts. Default is 'total_g'.\n",
+    "        label (str): The name of the column in `diet_log` representing the labels for each meal. Default is 'short_food_name'.\n",
+    "        participant_id (Optional[int]): The participant's ID to filter the diet log. If None, no filtering is done. Default is None.\n",
+    "        time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be 'YYYY-MM-DD'. Default is None.\n",
+    "        meals (bool): If True, includes individual meals in the plot. Default is True.\n",
+    "        summary (bool): If True, includes a daily summary in the plot. Default is False.\n",
+    "        nut_include (List[str]): A list of nutrients to include in the plot. Default is None.\n",
+    "        nut_exclude (List[str]): A list of nutrients to exclude from the plot. Default is None.\n",
+    "        legend (bool): If True, includes a legend in the plot. Default is True.\n",
+    "        size_scale (float): The scaling factor for the size of the pie charts. Default is 5.\n",
+    "        palette (str): The color palette to use for the pie slices. Default is DEFAULT_PALETTTE.\n",
+    "        alpha (float): The transparency of the pie slices. Default is 0.7.\n",
+    "        ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the lollipop chart. If None, a new axis is created. Default is None.\n",
+    "        figsize (Tuple[float, float]): The size of the figure to create. Default is (12, 6).\n",
+    "\n",
+    "    Returns:\n",
+    "        None: The function creates a lollipop plot with pie charts on the specified or newly created axis.\n",
+    "    \"\"\"\n",
+    "    # Prepare the data for plotting\n",
+    "    df, grouped_nutrients = prepare_meals(\n",
+    "        diet_log,\n",
+    "        participant_id=participant_id,\n",
+    "        array_index=array_index,\n",
+    "        time_range=time_range,\n",
+    "        return_meals=meals,\n",
+    "        return_summary=summary,\n",
+    "        y_include=nut_include,\n",
+    "        y_exclude=nut_exclude,\n",
+    "        x_col=x,\n",
+    "        )\n",
+    "\n",
+    "    if ax is None:\n",
+    "        fig, ax = plt.subplots(nrows=1, ncols=1, figsize=figsize)\n",
+    "\n",
+    "    # Convert nutrients in mg to grams\n",
+    "    for nut in grouped_nutrients['mg']:\n",
+    "        df[nut.replace('_mg', '_g')] = df[nut] / 1000\n",
+    "        grouped_nutrients['g'] += [nut.replace('_mg', '_g')]\n",
+    "\n",
+    "    pie_nuts = [nut for nut in grouped_nutrients['g']\n",
+    "                if nut not in ['weight_g']]\n",
+    "    df['total_g'] = df[pie_nuts].sum(axis=1)\n",
+    "\n",
+    "    # Calculate unknown component and ensure all values are non-negative\n",
+    "    df['other_g'] = (df['weight_g'] - df[pie_nuts].sum(axis=1)).clip(lower=0)\n",
+    "    # pie_nuts += ['other_g']\n",
+    "\n",
+    "    # Pre-set the x-axis limits based on the range of timestamps\n",
+    "    if time_range is None:\n",
+    "        min_x = mdates.date2num(df[x].min())\n",
+    "        max_x = mdates.date2num(df[x].max())\n",
+    "    else:\n",
+    "        min_x = mdates.date2num(pd.to_datetime(time_range[0]))\n",
+    "        max_x = mdates.date2num(pd.to_datetime(time_range[1]))\n",
+    "\n",
+    "    # Pre-set the y-axis limits based on the range of the y-axis column\n",
+    "    min_y = 0  # df[y_col].min()\n",
+    "    max_y = df[y].max()\n",
+    "\n",
+    "    # Calculate the aspect ratio between the x and y axes\n",
+    "    # This is necessary to avoid distortion of the (circular) pie charts\n",
+    "    x_range = max_x - min_x\n",
+    "    y_range = max_y - min_y\n",
+    "    aspect_ratio = x_range / y_range\n",
+    "    y_delta = 0.1 * y_range\n",
+    "\n",
+    "    # Scale the y-axis to match the aspect ratio of the x-axis\n",
+    "    ax.set_xlim(min_x, max_x)\n",
+    "    ax.set_ylim(min_y * aspect_ratio, (max_y + y_delta) * aspect_ratio)\n",
+    "\n",
+    "    # Custom formatter to adjust the y-ticks back to the original scale\n",
+    "    def ytick_formatter(y, pos):\n",
+    "        return f'{y / aspect_ratio:.0f}'\n",
+    "\n",
+    "    # Plotting the lollipop plot with pies using absolute figure coordinates\n",
+    "    for idx, row in df.iterrows():\n",
+    "        # Pie chart parameters\n",
+    "        size_value = np.sqrt(row[size]) * aspect_ratio * size_scale\n",
+    "        position = mdates.date2num(row[x])\n",
+    "        y_value = row[y] * aspect_ratio  # Scale y-value\n",
+    "\n",
+    "        # Plot the stem (lollipop stick)\n",
+    "        ax.plot([position, position], [0, y_value], color='gray', lw=1, zorder=1)\n",
+    "\n",
+    "        # Plot the pie chart in figure coordinates (no distortion)\n",
+    "        wedges = draw_pie_chart(ax, position, y_value, row[pie_nuts].fillna(0.).values, size_value, palette, alpha)\n",
+    "\n",
+    "    if legend:\n",
+    "        # Create a custom legend\n",
+    "        ax.legend(handles=wedges, labels= pie_nuts, loc='upper left', bbox_to_anchor=LEGEND_SHIFT)\n",
+    "\n",
+    "    # Format x-axis to display dates properly\n",
+    "    ax.set_ylabel(y.replace('_', ' ').title(), rotation=0, horizontalalignment='right')\n",
+    "    ax.grid(True)\n",
+    "\n",
+    "    # Set y-ticks and x-ticks\n",
+    "    ax.yaxis.set_major_formatter(FuncFormatter(ytick_formatter))\n",
+    "    ylim = ax.get_ylim()\n",
+    "    yticks = np.arange(0, ylim[1] / aspect_ratio, 100, dtype=int)\n",
+    "    ax.set_yticks(yticks * aspect_ratio)\n",
+    "    ax.set_yticklabels(yticks)\n",
+    "\n",
+    "    format_xticks(ax, df[x])\n",
+    "    if label is not None:\n",
+    "        secax = ax.secondary_xaxis('top')\n",
+    "        secax.set_xticks(df[x])\n",
+    "        secax.set_xticklabels(df[label], ha='center', fontsize=9)\n",
+    "\n",
+    "    return ax\n",
+    "\n",
+    "\n",
+    "def prepare_meals(\n",
+    "    diet_log: pd.DataFrame,\n",
+    "    participant_id: int=None,\n",
+    "    array_index: int=None,\n",
+    "    time_range: Tuple[str, str]=None,\n",
+    "    label: str='short_food_name',\n",
+    "    return_meals: bool = True,\n",
+    "    return_summary: bool = False,\n",
+    "    y_include: List[str] = None,\n",
+    "    y_exclude: List[str] = None,\n",
+    "    agg_units: dict={'kcal': 'sum', 'g': 'sum', 'mg': 'sum', 'unknown': 'first'},\n",
+    "    x_col: str='collection_timestamp'\n",
+    ") -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Prepare the diet log data for plotting meals and/or daily summaries.\n",
+    "\n",
+    "    Args:\n",
+    "        diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements.\n",
+    "        participant_id (Optional[int]): The participant's ID to filter the diet log. If None, no filtering is done. Default is None.\n",
+    "        array_index (Optional[int]): The array index to filter the diet log. If None, no filtering is done. Default is None.\n",
+    "        time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be 'YYYY-MM-DD'. Default is None.\n",
+    "        label (str): The name of the column in `diet_log` representing the labels for each meal. Default is 'short_food_name'.\n",
+    "        return_meals (bool): If True, includes individual meals in the plot. Default is True.\n",
+    "        return_summary (bool): If True, includes a daily summary in the plot. Default is False.\n",
+    "        y_include (List[str]): A list of nutrients (regex) to include in the plot. Default is None.\n",
+    "        y_exclude (List[str]): A list of nutrients (regex) to exclude from the plot. Default is None.\n",
+    "        agg_units (dict): A dictionary mapping nutrient units to aggregation functions.\n",
+    "        x_col (str): The name of the column in `diet_log` representing the x-axis variable, such as timestamps. Default is 'collection_timestamp'.\n",
+    "\n",
+    "    Returns:\n",
+    "        pd.DataFrame: A dataframe containing the prepared data for plotting.\n",
+    "    \"\"\"\n",
+    "    diet_log = format_timeseries(\n",
+    "        diet_log, participant_id, array_index, time_range,\n",
+    "        x_start=x_col, x_end=x_col, unique=True)\n",
+    "\n",
+    "    units = extract_units(diet_log.columns)\n",
+    "    grouped_nutrients = {}\n",
+    "    import re  # Add this line to import the re module\n",
+    "\n",
+    "    agg_dict = {}\n",
+    "    for nut, unit in units.items():\n",
+    "        if unit not in agg_units:\n",
+    "            continue\n",
+    "        if y_include is not None and not any([re.match(inc, nut) for inc in y_include]):\n",
+    "            continue\n",
+    "        if y_exclude is not None and any([re.match(exc, nut) for exc in y_exclude]):\n",
+    "            continue\n",
+    "        if unit not in grouped_nutrients:\n",
+    "            grouped_nutrients[unit] = []\n",
+    "        grouped_nutrients[unit].append(nut)\n",
+    "        agg_dict[nut] = agg_units[unit]\n",
+    "    nut_list = list(agg_dict.keys())\n",
+    "    if label is not None:\n",
+    "        agg_dict[label] = lambda x: '\\n'.join(x)\n",
+    "\n",
+    "    df = diet_log\\\n",
+    "        .dropna(subset=['short_food_name'])\\\n",
+    "        .drop_duplicates()\\\n",
+    "        .groupby([x_col])\\\n",
+    "        .agg(agg_dict)\\\n",
+    "        .reset_index()\n",
+    "\n",
+    "    if return_summary:\n",
+    "        # Add daily summary by grouping by date and summing up the nutrients\n",
+    "        daily_df = df.groupby(df[x_col].dt.date)[nut_list]\\\n",
+    "            .sum().reset_index()\n",
+    "        if label is not None:\n",
+    "            daily_df[label] = daily_df[x_col].astype('string') + '\\nDaily Summary'\n",
+    "        daily_df[x_col] = pd.to_datetime(daily_df[x_col] + pd.Timedelta(hours=24))\n",
+    "        if time_range is not None:\n",
+    "            daily_df = daily_df[(time_range[0] <= daily_df[x_col]) & (daily_df[x_col] <= time_range[1])]\n",
+    "        if return_meals:\n",
+    "            # God knows why, but the two refuse to concat without this\n",
+    "            df = pd.DataFrame(np.vstack([df, daily_df]), columns=df.columns)\n",
+    "        else:\n",
+    "            df = daily_df\n",
+    "\n",
+    "    return df, grouped_nutrients\n",
+    "\n",
+    "\n",
+    "def extract_units(column_names: List[str]) -> dict:\n",
+    "    units = {}\n",
+    "    for col in column_names:\n",
+    "        if '_' in col:\n",
+    "            unit = col.split('_')[-1]\n",
+    "            units[col] = unit\n",
+    "        else:\n",
+    "            units[col] = 'unknown'\n",
+    "    return units\n",
+    "\n",
+    "\n",
+    "def draw_pie_chart(\n",
+    "    ax: plt.Axes, \n",
+    "    x: float, \n",
+    "    y: float, \n",
+    "    data: List[float], \n",
+    "    size: float, \n",
+    "    palette: str = DEFAULT_PALETTE,\n",
+    "    alpha: float = 0.7,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Draw a pie chart as an inset (in absolute figure coordinates) within the given axes\n",
+    "    at the specified data coordinates.\n",
+    "    What this solves is the issue of y-axis and x-axis scaling being different, which\n",
+    "    distorts the pie chart when drawn directly on the axes.\n",
+    "\n",
+    "    Args:\n",
+    "        ax (plt.Axes): The axis on which to draw the pie chart.\n",
+    "        x (float): The x-coordinate in data coordinates where the pie chart's center will be placed.\n",
+    "        y (float): The y-coordinate in data coordinates where the pie chart's center will be placed.\n",
+    "        data (List[float]): The data values to be represented in the pie chart.\n",
+    "        size (float): The size (radius) of the pie chart in axes-relative coordinates.\n",
+    "        palette (str): The color palette to use for the pie slices.\n",
+    "\n",
+    "    Returns:\n",
+    "        List[plt.Patch]: A list of wedge objects representing the pie chart slices.\n",
+    "    \"\"\"\n",
+    "    # Convert the position from data coordinates to axes coordinates\n",
+    "    axes_coords = ax.transData.transform((x, y))\n",
+    "    axes_coords = ax.transAxes.inverted().transform(axes_coords)\n",
+    "\n",
+    "    # Create a new inset axis to draw the pie, using axes-relative coordinates\n",
+    "    inset_ax = ax.inset_axes([axes_coords[0] - size, axes_coords[1] - size, 2 * size, 2 * size])\n",
+    "\n",
+    "    # Plot the pie chart using the calculated position and scaled radius\n",
+    "    colors = [(r, g, b, alpha) for r, g, b in sns.color_palette(palette, len(data))]\n",
+    "    wedges, _ = inset_ax.pie(data, radius=1, startangle=90, wedgeprops=dict(edgecolor='none'), normalize=True,\n",
+    "                             colors=colors)\n",
+    "\n",
+    "    # Hide the axes for the inset (pie chart)\n",
+    "    inset_ax.set_axis_off()\n",
+    "\n",
+    "    return wedges\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "SHORT_FOOD_CATEGORIES = {\n",
+    "    'beef, veal, lamb, and other meat products': 'meat products',\n",
+    "    'milk, cream cheese and yogurts': 'milk products',\n",
+    "    'nuts, seeds, and products': 'nuts and seeds',\n",
+    "    'eggs and their products': 'eggs',\n",
+    "    'pulses and products': 'pulses',\n",
+    "    'fruit juices and soft drinks': 'juices and soft drinks',\n",
+    "    'low calories and diet drinks': 'low cal. drinks',\n",
+    "    'poultry and its products': 'poultry',\n",
+    "    'pasta, grains and side dishes': 'grains',\n",
+    "    'industrialized vegetarian food ready to eat': 'industrialized veg.',\n",
+    "}\n",
+    "\n",
+    "def plot_meals_hbars(\n",
+    "    diet_log: pd.DataFrame, \n",
+    "    x: str='collection_timestamp',\n",
+    "    y: str='short_food_category',\n",
+    "    size: str='weight_g', \n",
+    "    hue: str='short_food_category',\n",
+    "    participant_id: int=None, \n",
+    "    array_index: int=None,\n",
+    "    time_range: Tuple[str, str]=None, \n",
+    "    y_include: List[str] = None,\n",
+    "    y_exclude: List[str] = None,\n",
+    "    rename_categories: dict=SHORT_FOOD_CATEGORIES,\n",
+    "    legend: bool=True,\n",
+    "    size_legend: List[int]=[100, 200, 500],\n",
+    "    size_scale: float=5,\n",
+    "    palette: str=DEFAULT_PALETTE,\n",
+    "    alpha: float=0.7,\n",
+    "    ax: plt.Axes=None,\n",
+    "    figsize: Tuple[float, float] = (12, 6),\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Plot a diet chart with bars representing meals and their size over time.\n",
+    "\n",
+    "    Args:\n",
+    "        diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements.\n",
+    "        x (str): The name of the column in `diet_log` representing the x-axis variable, such as timestamps. Default is 'collection_timestamp'.\n",
+    "        y (str): The name of the column in `diet_log` representing the y-axis variable, such as food categories. Default is 'short_food_category'.\n",
+    "        size (str): The name of the column in `diet_log` representing the size of the bars. Default is 'weight_g'.\n",
+    "        hue (str): The name of the column in `diet_log` representing the color of the bars. Default is 'short_food_category'.\n",
+    "        participant_id (Optional[int]): The participant's ID to filter the diet log. If None, no filtering is done. Default is None.\n",
+    "        time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be 'YYYY-MM-DD'. Default is None.\n",
+    "        y_include (List[str]): A list of strings representing the categories to include in the plot. Default is None.\n",
+    "        y_exclude (List[str]): A list of strings representing the categories to exclude from the plot. Default is None.\n",
+    "        rename_categories (dict): A dictionary mapping original food categories to shorter names. Default is SHORT_FOOD_CATEGORIES.\n",
+    "        legend (bool): If True, includes a legend in the plot. Default is True.\n",
+    "        size_legend (List[int]): A list of integers representing the sizes to include in the size legend. Default is [100, 200, 500].\n",
+    "        size_scale (float): The scaling factor for the size of the bars. Default is 5.\n",
+    "        palette (str): The palette to use for the bars.\n",
+    "        alpha (float): The transparency of the bars. Default is 0.7.\n",
+    "        ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the lollipop chart. If None, a new axis is created. Default is None.\n",
+    "        figsize (Tuple[float, float]): The size of the figure to create. Default is (12, 6).\n",
+    "    \"\"\"\n",
+    "    diet_log = format_timeseries(\n",
+    "        diet_log, participant_id, array_index,\n",
+    "        time_range, x_start=x, x_end=x, unique=True)\n",
+    "\n",
+    "    diet_log['event_end'] = diet_log[x] \\\n",
+    "        + size_scale * pd.to_timedelta(diet_log[size], unit='s')\n",
+    "\n",
+    "    # Categories\n",
+    "    diet_log['short_food_category'] = diet_log['food_category'].str.lower()\n",
+    "    for s, t in rename_categories.items():\n",
+    "        diet_log['short_food_category'] = diet_log['short_food_category'].str.replace(s, t, regex=False)\n",
+    "    diet_log['short_food_category'] = diet_log['short_food_category']\\\n",
+    "        .str.replace(' and ', ' & ', regex=False)\\\n",
+    "        .str.replace('_wholewheat', ' (whole/w)', regex=False)\n",
+    "\n",
+    "    # User events plot to plot meals\n",
+    "    ax = plot_events_bars(\n",
+    "        diet_log,\n",
+    "        x_start=x, x_end='event_end',\n",
+    "        y=y, hue=hue,\n",
+    "        y_include=y_include, y_exclude=y_exclude, alpha=alpha,\n",
+    "        ax=ax, figsize=figsize, palette=palette, legend=legend)\n",
+    "\n",
+    "    format_xticks(ax, diet_log[x].drop_duplicates())\n",
+    "\n",
+    "    add_size_legend(ax, size_legend, size_scale, alpha)\n",
+    "\n",
+    "    return ax\n",
+    "\n",
+    "\n",
+    "def add_size_legend(ax: plt.Axes, sizes: List[int], size_scale: float, alpha: float, shift: int=0):\n",
+    "    \"\"\"\n",
+    "    Add a size legend to a plot_meals_hbars plot using broken_barh.\n",
+    "    \"\"\"\n",
+    "    if len(sizes) == 0:\n",
+    "        return\n",
+    "\n",
+    "    # Manually add size legend using broken_barh\n",
+    "    sec2day = 1 / (60 * 60 * 24)  # Convert seconds to days\n",
+    "    size_durations = [\n",
+    "        s * size_scale * sec2day\n",
+    "        for s in sizes]\n",
+    "    max_duration = max(size_durations)\n",
+    "\n",
+    "    # Calculate the xlim to place the legend bars right at the end\n",
+    "    xlim = ax.get_xlim()  # These are in days\n",
+    "    y_start_legend = ax.get_ylim()[0] - 1 - shift\n",
+    "    x_bar_start = \\\n",
+    "        xlim[1] - \\\n",
+    "        1.5 * max_duration\n",
+    "\n",
+    "    # Add a bounding box around the text and bars\n",
+    "    ax.add_patch(mpatches.Rectangle(\n",
+    "        (x_bar_start - 1.5*(max_duration + 10 * sec2day), y_start_legend - len(sizes) + 0.25),\n",
+    "         3 * (max_duration + 10 * sec2day), len(sizes) + 0.5,\n",
+    "         edgecolor='gray', facecolor='white', lw=1))\n",
+    "\n",
+    "    for i, (s, duration) in enumerate(zip(sizes, size_durations)):\n",
+    "        # Plot the bar\n",
+    "        ax.broken_barh(\n",
+    "            xranges=[(x_bar_start, duration)],\n",
+    "            yrange=(y_start_legend - i - 0.4, 0.8), \n",
+    "            facecolors='gray', alpha=alpha\n",
+    "        )\n",
+    "\n",
+    "        # Add text next to the bar\n",
+    "        ax.annotate(f'{s}g', \n",
+    "                    (x_bar_start - 10 * sec2day, y_start_legend - i),\n",
+    "                    va='center', ha='right', fontsize=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "from pheno_utils.timeseries_plots import TimeSeriesFigure, plot_events_fill\n",
+    "from pheno_utils.sleep_plots import plot_sleep_channels, get_sleep_period\n",
+    "\n",
+    "def plot_diet_cgm_sleep(\n",
+    "    diet: pd.DataFrame=None,\n",
+    "    cgm: pd.DataFrame=None,\n",
+    "    sleep_events: pd.DataFrame=None,\n",
+    "    sleep_channels: pd.DataFrame=None,\n",
+    "    cgm_grid: List[int] = [0, 54, 70, 100, 140, 180],\n",
+    "    channel_filter: List[str]=['heart_rate', 'actigraph', 'spo2'],\n",
+    "    participant_id=None,\n",
+    "    array_index=None,\n",
+    "    time_range: Tuple[str, str]=None,\n",
+    "    figsize=(14, 10),\n",
+    "    nutrient_kws: dict={},\n",
+    "    meals_kws: dict={},\n",
+    "    cgm_kws: dict={},\n",
+    "    events_kws: dict={},\n",
+    "    channels_kws: dict={},\n",
+    ") -> TimeSeriesFigure:\n",
+    "    \"\"\"\n",
+    "    Plot diet, CGM and sleep data together.\n",
+    "\n",
+    "    Arg:\n",
+    "        diet (pd.DataFrame): Diet logging data. Set to None to remove from figure.\n",
+    "        cgm (pd.DataFrame): CGM data. Set to None to remove from figure.\n",
+    "        sleep_events (pd.DataFrame): Sleep events data. Set to None to remove from figure.\n",
+    "        sleep_channels (pd.DataFrame): Sleep channels data. Set to None to remove from figure.\n",
+    "        cgm_grid (List[int]): CGM grid lines. Default: [0, 54, 70, 100, 140, 180].\n",
+    "        channel_filter (List[str]): Which sleep channels to include in the plot. Default: ['heart_rate', 'actigraph', 'spo2'].\n",
+    "        participant_id (int): Participant ID.\n",
+    "        array_index (int): Array index.\n",
+    "        time_range (Tuple[str, str]): Time range to plot.\n",
+    "        figsize (Tuple[int, int]): Figure size.\n",
+    "        nutrient_kws (dict): Keyword arguments for diet nutrients lollipop plot.\n",
+    "        meals_kws (dict): Keyword arguments for diet meals plot.\n",
+    "        cgm_kws (dict): Keyword arguments for CGM plot.\n",
+    "        events_kws (dict): Keyword arguments for sleep events plot.\n",
+    "        channels_kws (dict): Keyword arguments for sleep channels plot.\n",
+    "\n",
+    "    Returns:\n",
+    "        TimeSeriesFigure: Plot.\n",
+    "    \"\"\"\n",
+    "    g = TimeSeriesFigure(figsize=figsize)\n",
+    "\n",
+    "    # Add diet\n",
+    "    if diet is not None:\n",
+    "        g.plot(plot_nutrient_lollipop, diet,\n",
+    "            second_y=True if cgm is not None else False,\n",
+    "            participant_id=participant_id, array_index=array_index, time_range=time_range,\n",
+    "            size_scale=10, name='diet_glucose', height=1.5, **nutrient_kws)\n",
+    "        g.plot(plot_meals_hbars, diet,\n",
+    "            participant_id=participant_id, array_index=array_index, time_range=time_range,\n",
+    "            name='diet_bars', sharex='diet_glucose', height=3, **meals_kws)\n",
+    "\n",
+    "    # Add CGM\n",
+    "    if cgm is not None:\n",
+    "        if diet is None:\n",
+    "            g.add_axes(name='diet_glucose')\n",
+    "        cgm = format_timeseries(\n",
+    "            cgm,\n",
+    "            participant_id=participant_id, array_index=array_index, time_range=time_range,\n",
+    "            )\n",
+    "        ax = g.get_axes('diet_glucose', squeeze=True)\n",
+    "        ax.plot(cgm['collection_timestamp'], cgm['glucose'], label='glucose', color='#4c72b0', **cgm_kws)\n",
+    "        ax.scatter(cgm['collection_timestamp'], cgm['glucose'], s=10, color='#4c72b0', **cgm_kws)\n",
+    "        ax.set_ylabel('Glucose', rotation=0, horizontalalignment='right')\n",
+    "        ax.set_yticks(cgm_grid)\n",
+    "        ax.yaxis.grid(True)\n",
+    "\n",
+    "    # Add sleep\n",
+    "    if sleep_channels is not None:\n",
+    "        plot_sleep_channels(\n",
+    "            sleep_channels,\n",
+    "            x='collection_timestamp', y='values', row='source', hue=None,\n",
+    "            participant_id=participant_id, array_index=array_index, time_range=time_range,\n",
+    "            y_include=channel_filter,\n",
+    "            fig=g, height=1, **channels_kws,\n",
+    "        )\n",
+    "    if sleep_events is not None:\n",
+    "        g.plot(plot_events_fill, sleep_events,\n",
+    "            participant_id=participant_id, array_index=array_index, time_range=time_range,\n",
+    "            y_include=[\"Wake\", \"REM\", \"Light Sleep\", \"Deep Sleep\", \"Sleep\"],\n",
+    "            hue='event', ax=['sleep_channels'], sharex='sleep_channels', alpha=0.3, **events_kws)\n",
+    "        if cgm is not None or diet is not None:\n",
+    "            g.plot(plot_events_fill, get_sleep_period(sleep_events),\n",
+    "                participant_id=participant_id, array_index=array_index, time_range=time_range,\n",
+    "                y_include=[\"Wake\", \"REM\", \"Light Sleep\", \"Deep Sleep\", \"Sleep\"], legend=False,\n",
+    "                hue=None, palette='gray', label='event',\n",
+    "                ax=['diet_glucose'], sharex='sleep_channels', alpha=0.3, **events_kws)\n",
+    "\n",
+    "    # Tidy up\n",
+    "    g.set_axis_padding(0.03)\n",
+    "    if time_range is not None:\n",
+    "        g.set_time_limits(*time_range)\n",
+    "    g.set_periodic_ticks('2H', ax='sleep_channels')\n",
+    "\n",
+    "    return g"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This module provides functions for plotting diet data, as well as a function for plotting diet, CGM and sleep data together.\n",
+    "\n",
+    "First, we will load the time series data for diet, CGM and sleep. (See also the dedicated modules for sleep and CGM.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: index is not unique for diet_logging\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ec2-user/projects/pheno-utils/pheno_utils/pheno_loader.py:610: UserWarning: No date field found\n",
+      "  warnings.warn(f'No date field found')\n",
+      "/home/ec2-user/projects/pheno-utils/pheno_utils/pheno_loader.py:610: UserWarning: No date field found\n",
+      "  warnings.warn(f'No date field found')\n",
+      "/home/ec2-user/projects/pheno-utils/pheno_utils/pheno_loader.py:610: UserWarning: No date field found\n",
+      "  warnings.warn(f'No date field found')\n"
+     ]
+    }
+   ],
+   "source": [
+    "#| eval: false\n",
+    "from pheno_utils import PhenoLoader\n",
+    "\n",
+    "pl = PhenoLoader('sleep')\n",
+    "channels_df = pl.load_bulk_data('channels_time_series')  # contains: heart_rate, spo2, respiratory_movement\n",
+    "events_df = pl.load_bulk_data('events_time_series')\n",
+    "\n",
+    "diet_df = PhenoLoader('diet_logging').dfs['diet_logging']\n",
+    "cgm_df = PhenoLoader('cgm').dfs['cgm']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>short_food_name</th>\n",
+       "      <th>food_category</th>\n",
+       "      <th>weight_g</th>\n",
+       "      <th>calories_kcal</th>\n",
+       "      <th>carbohydrate_g</th>\n",
+       "      <th>lipid_g</th>\n",
+       "      <th>protein_g</th>\n",
+       "      <th>sodium_mg</th>\n",
+       "      <th>dietary_fiber_g</th>\n",
+       "      <th>alcohol_g</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>participant_id</th>\n",
+       "      <th>collection_timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
+       "      <th>2020-06-21 16:06:00+03:00</th>\n",
+       "      <td>Quinoa</td>\n",
+       "      <td>Pasta, Grains and Side dishes_wholewheat</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>78.3104</td>\n",
+       "      <td>12.1744</td>\n",
+       "      <td>1.2992</td>\n",
+       "      <td>2.8000</td>\n",
+       "      <td>37.1504</td>\n",
+       "      <td>1.4896</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-21 16:06:00+03:00</th>\n",
+       "      <td>Hummus Salad</td>\n",
+       "      <td>Pulses and products</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>215.2000</td>\n",
+       "      <td>9.4400</td>\n",
+       "      <td>16.8000</td>\n",
+       "      <td>6.5600</td>\n",
+       "      <td>377.6000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-21 16:06:00+03:00</th>\n",
+       "      <td>Meatballs</td>\n",
+       "      <td>Beef, veal, lamb, and other meat products</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>311.0940</td>\n",
+       "      <td>16.1280</td>\n",
+       "      <td>15.4440</td>\n",
+       "      <td>24.7320</td>\n",
+       "      <td>1164.5460</td>\n",
+       "      <td>2.3904</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-21 19:28:00+03:00</th>\n",
+       "      <td>Banana</td>\n",
+       "      <td>Fruits</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>113.9200</td>\n",
+       "      <td>25.9072</td>\n",
+       "      <td>0.4224</td>\n",
+       "      <td>1.3952</td>\n",
+       "      <td>1.2800</td>\n",
+       "      <td>3.3280</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-21 21:07:00+03:00</th>\n",
+       "      <td>Bread</td>\n",
+       "      <td>Bread</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>162.6000</td>\n",
+       "      <td>28.3800</td>\n",
+       "      <td>2.1000</td>\n",
+       "      <td>5.2800</td>\n",
+       "      <td>367.8000</td>\n",
+       "      <td>1.6200</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         short_food_name  \\\n",
+       "participant_id collection_timestamp                        \n",
+       "0              2020-06-21 16:06:00+03:00          Quinoa   \n",
+       "               2020-06-21 16:06:00+03:00    Hummus Salad   \n",
+       "               2020-06-21 16:06:00+03:00       Meatballs   \n",
+       "               2020-06-21 19:28:00+03:00          Banana   \n",
+       "               2020-06-21 21:07:00+03:00           Bread   \n",
+       "\n",
+       "                                                                      food_category  \\\n",
+       "participant_id collection_timestamp                                                   \n",
+       "0              2020-06-21 16:06:00+03:00   Pasta, Grains and Side dishes_wholewheat   \n",
+       "               2020-06-21 16:06:00+03:00                        Pulses and products   \n",
+       "               2020-06-21 16:06:00+03:00  Beef, veal, lamb, and other meat products   \n",
+       "               2020-06-21 19:28:00+03:00                                     Fruits   \n",
+       "               2020-06-21 21:07:00+03:00                                      Bread   \n",
+       "\n",
+       "                                          weight_g  calories_kcal  \\\n",
+       "participant_id collection_timestamp                                 \n",
+       "0              2020-06-21 16:06:00+03:00      56.0        78.3104   \n",
+       "               2020-06-21 16:06:00+03:00      80.0       215.2000   \n",
+       "               2020-06-21 16:06:00+03:00     180.0       311.0940   \n",
+       "               2020-06-21 19:28:00+03:00     128.0       113.9200   \n",
+       "               2020-06-21 21:07:00+03:00      60.0       162.6000   \n",
+       "\n",
+       "                                          carbohydrate_g  lipid_g  protein_g  \\\n",
+       "participant_id collection_timestamp                                            \n",
+       "0              2020-06-21 16:06:00+03:00         12.1744   1.2992     2.8000   \n",
+       "               2020-06-21 16:06:00+03:00          9.4400  16.8000     6.5600   \n",
+       "               2020-06-21 16:06:00+03:00         16.1280  15.4440    24.7320   \n",
+       "               2020-06-21 19:28:00+03:00         25.9072   0.4224     1.3952   \n",
+       "               2020-06-21 21:07:00+03:00         28.3800   2.1000     5.2800   \n",
+       "\n",
+       "                                          sodium_mg  dietary_fiber_g  \\\n",
+       "participant_id collection_timestamp                                    \n",
+       "0              2020-06-21 16:06:00+03:00    37.1504           1.4896   \n",
+       "               2020-06-21 16:06:00+03:00   377.6000           0.0000   \n",
+       "               2020-06-21 16:06:00+03:00  1164.5460           2.3904   \n",
+       "               2020-06-21 19:28:00+03:00     1.2800           3.3280   \n",
+       "               2020-06-21 21:07:00+03:00   367.8000           1.6200   \n",
+       "\n",
+       "                                          alcohol_g  \n",
+       "participant_id collection_timestamp                  \n",
+       "0              2020-06-21 16:06:00+03:00        0.0  \n",
+       "               2020-06-21 16:06:00+03:00        NaN  \n",
+       "               2020-06-21 16:06:00+03:00        0.0  \n",
+       "               2020-06-21 19:28:00+03:00        0.0  \n",
+       "               2020-06-21 21:07:00+03:00        0.0  "
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#| eval: false\n",
+    "diet_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>glucose</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>participant_id</th>\n",
+       "      <th>collection_timestamp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
+       "      <th>2020-06-22 00:14:00+03:00</th>\n",
+       "      <td>106.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-22 00:29:00+03:00</th>\n",
+       "      <td>100.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-22 00:44:00+03:00</th>\n",
+       "      <td>97.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-22 00:59:00+03:00</th>\n",
+       "      <td>95.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-06-22 01:14:00+03:00</th>\n",
+       "      <td>93.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          glucose\n",
+       "participant_id collection_timestamp              \n",
+       "0              2020-06-22 00:14:00+03:00    106.2\n",
+       "               2020-06-22 00:29:00+03:00    100.8\n",
+       "               2020-06-22 00:44:00+03:00     97.2\n",
+       "               2020-06-22 00:59:00+03:00     95.4\n",
+       "               2020-06-22 01:14:00+03:00     93.6"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#| eval: false\n",
+    "cgm_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we will use `plot_diet_cgm_sleep` to plot the data together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pheno_utils.timeseries_plots.TimeSeriesFigure>"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#| eval: false\n",
+    "import seaborn as sns\n",
+    "sns.set_style('whitegrid')\n",
+    "\n",
+    "plot_diet_cgm_sleep(diet_df, cgm_df, events_df, channels_df,\n",
+    "                    channel_filter=['heart_rate', 'respiratory_movement', 'spo2'],\n",
+    "                    time_range=('2020-06-22 08:00', '2020-06-23 10:00'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each of the types of diet plots can be plotted independently as well. We will use `TimeSeriesFigure`, `plot_nutrient_lollipop`, `plot_meals_hbars` and `plot_nutrient_bars` to plot them together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#| eval: false\n",
+    "from pheno_utils.timeseries_plots import TimeSeriesFigure\n",
+    "\n",
+    "g = TimeSeriesFigure(figsize=(14, 7))\n",
+    "\n",
+    "time_range = ('2020-06-22 06:00', '2020-06-23 15:00')\n",
+    "# Each call to the plot() methods adds a new time-synced subplot to the figure\n",
+    "g.plot(plot_nutrient_lollipop, diet_df, size_scale=15,\n",
+    "       time_range=time_range,\n",
+    "       name='diet_pie')\n",
+    "g.plot(plot_meals_hbars, diet_df,\n",
+    "       time_range=time_range,\n",
+    "       name='diet_meals', height=2)\n",
+    "g.plot(plot_nutrient_bars, diet_df,\n",
+    "       time_range=time_range,\n",
+    "       label=None, n_axes=2, nut_exclude=['sodium'],\n",
+    "       name='diet_bars')\n",
+    "g.set_axis_padding(0.03)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import nbdev; nbdev.nbdev_export()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/examples/cgm/cgm.parquet b/nbs/examples/cgm/cgm.parquet
index e65f5c5..1a59e82 100644
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diff --git a/nbs/examples/diet_logging/diet_logging.parquet b/nbs/examples/diet_logging/diet_logging.parquet
index ae2d2b4..370e4ab 100644
Binary files a/nbs/examples/diet_logging/diet_logging.parquet and b/nbs/examples/diet_logging/diet_logging.parquet differ
diff --git a/nbs/examples/diet_logging/metadata/diet_logging_data_dictionary.csv b/nbs/examples/diet_logging/metadata/diet_logging_data_dictionary.csv
index fd2ea07..2611d14 100644
--- a/nbs/examples/diet_logging/metadata/diet_logging_data_dictionary.csv
+++ b/nbs/examples/diet_logging/metadata/diet_logging_data_dictionary.csv
@@ -1,19 +1,13 @@
 tabular_field_name,field_string,description_string,parent_dataframe,relative_location,value_type,units,field_type,array,cohorts,data_type,debut,pandas_dtype,sampling_rate
 collection_timestamp,Collection timestamp,Collection timestamp,,diet_logging/diet_logging.parquet,Time,Time,Data,Single,10K,Time Series,2019-01-29,"datetime64[ns, Asia/Jerusalem]",
-collection_date,Date,Datetime column relecting the time food item was logged,,diet_logging/diet_logging.parquet,Time,Time,Data,Single,10K,Time Series,2019-09-01,datetime64[ns],
 food_id,Food ID,IDs in the diet logging app representing specific food ,,diet_logging/diet_logging.parquet,Categorical (single) ,None,Data,Single,10K,Time Series,2019-09-01,integer,
-logging_day,Logging day per participant,Integer indicating which day of logging period ,,diet_logging/diet_logging.parquet,Integer,None ,Data,Single,10K,Time Series,2019-09-01,float,
-weight,Weight,Weight of food item logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,
 short_food_name,Short food name,Classifcation of food item logged into a short food name category,,diet_logging/diet_logging.parquet,Categorical (single) ,None,Data,Single,10K,Time Series,2019-09-01,object,
 food_category,Food category,Classifcation of food item logged into a food category,,diet_logging/diet_logging.parquet,Categorical (single) ,None,Data,Single,10K,Time Series,2019-09-01,object,
-product_name,Product name ,Product name of food logged,,diet_logging/diet_logging.parquet,Categorical (single) ,None,Data,Single,10K,Time Series,2019-09-01,object,
-calories,Calories,Calories of food item logged,,diet_logging/diet_logging.parquet,Continuous,kcal,Data,Single,10K,Time Series,2019-09-01,float,
+weight_g,Weight,Weight of food item logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,
+calories_kcal,Calories,Calories of food item logged,,diet_logging/diet_logging.parquet,Continuous,kcal,Data,Single,10K,Time Series,2019-09-01,float,
 carbohydrate_g,Carbohydrate intake per food logged,Carbohydrate intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,
-llipid_g,Fat intake per food logged,Fat intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,
+lipid_g,Fat intake per food logged,Fat intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,
 protein_g,Protein intake per food logged,Protein intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,
-sodium_mg             ,Sodium intake per food logged,Sodium intake per food logged,,diet_logging/diet_logging.parquet,Continuous,mg,Data,Single,10K,Time Series,2019-09-01,float,
+sodium_mg,Sodium intake per food logged,Sodium intake per food logged,,diet_logging/diet_logging.parquet,Continuous,mg,Data,Single,10K,Time Series,2019-09-01,float,
 alcohol_g ,Alcohol intake per food logged,Alcohol intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,
 dietary_fiber_g,Dietary fiber intake per food logged,Dietary fiber intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,
-local_timestamp,Local timestamp,Local timestamp of food logging,,diet_logging/diet_logging.parquet,Time,Time,Data,Single,10K,Time Series,2019-09-01,datetime64[ns],
-eaten_in_restaurant,Eaten at restaurant indication,Indication if food was eatn at home or at a restaurant,,diet_logging/diet_logging.parquet,Boolean,None,Data,Single,10K,Time Series,2019-09-01,bool,
-total_logging_days,Total number of days logged,Total number of days diet was logged per research stage,,diet_logging/diet_logging.parquet,Integer,None,Data,Single,10K,Time Series,2019-09-01,integer,
diff --git a/nbs/examples/sleep/metadata/sleep_data_dictionary.csv b/nbs/examples/sleep/metadata/sleep_data_dictionary.csv
index f5d6693..55c9018 100644
--- a/nbs/examples/sleep/metadata/sleep_data_dictionary.csv
+++ b/nbs/examples/sleep/metadata/sleep_data_dictionary.csv
@@ -1,3 +1,5 @@
-tabular_field_name,field_string,description_string,parent_dataframe,relative_location,value_type,units,sampling_rate,item_type,array,cohorts,field_type,debut,pandas_dtype
-ahi,AHI,AHI (Apnea-Hypopnea Index),,sleep/sleep.parquet,Continuous,Events / Hour,,Data,Multiple,10K,Continuous,2020-01-15,float64
-total_sleep_time,Total sleep time,Total sleep time,,sleep/sleep.parquet,Integer,Seconds,,Data,Multiple,10K,Continuous,2020-01-15,float64
+tabular_field_name,field_string,description_string,parent_dataframe,relative_location,units,sampling_rate,array,cohorts,field_type,debut,pandas_dtype
+ahi,AHI,AHI (Apnea-Hypopnea Index),,sleep/sleep.parquet,Events / Hour,,Multiple,10K,Continuous,2020-01-15,float
+total_sleep_time,Total sleep time,Total sleep time,,sleep/sleep.parquet,Seconds,,Multiple,10K,Continuous,2020-01-15,int
+channels_time_series,Channels time series,Sensor and derived channels time series parquet files,,sleep/sleep.parquet,,,Multiple,10K,Time series file (individual),2020-01-15,string
+events_time_series,Events time series,"Events during sleep derived from the raw channels, such as sleep stages, respiratory events, pulse rate events, and others",,sleep/sleep.parquet,,Data,Multiple,10K,Time series file (group),2020-01-15,string
\ No newline at end of file
diff --git a/nbs/examples/sleep/sleep.parquet b/nbs/examples/sleep/sleep.parquet
index 02a77e3..0b1d5f3 100644
Binary files a/nbs/examples/sleep/sleep.parquet and b/nbs/examples/sleep/sleep.parquet differ
diff --git a/nbs/examples/sleep/time_series/channels.parquet b/nbs/examples/sleep/time_series/channels.parquet
new file mode 100644
index 0000000..74c77e8
Binary files /dev/null and b/nbs/examples/sleep/time_series/channels.parquet differ
diff --git a/nbs/examples/sleep/time_series/events.parquet b/nbs/examples/sleep/time_series/events.parquet
new file mode 100644
index 0000000..2e91199
Binary files /dev/null and b/nbs/examples/sleep/time_series/events.parquet differ
diff --git a/nbs/sidebar.yml b/nbs/sidebar.yml
index 1213d18..15712ba 100644
--- a/nbs/sidebar.yml
+++ b/nbs/sidebar.yml
@@ -15,6 +15,7 @@ website:
           - 01_basic_plots.ipynb
           - 02_blandaltman_plots.ipynb
           - 03_age_reference_plots.ipynb
+          - 15_timeseries_plots.ipynb
           - 04_date_plots.ipynb
           - 06_sleep_plots.ipynb
           - 08_cgm_plots.ipynb
diff --git a/pheno_utils/cgm_plots.py b/pheno_utils/cgm_plots.py
index 87f6131..8b6e8f9 100644
--- a/pheno_utils/cgm_plots.py
+++ b/pheno_utils/cgm_plots.py
@@ -28,7 +28,7 @@ def __init__(
         cgm_date_col: str = "collection_timestamp",
         gluc_col: str = "glucose",
         diet_date_col: str = "collection_timestamp",
-        diet_text_col: str = "shortname_eng",
+        diet_text_col: str = "short_food_name",
         ax: Optional[plt.Axes] = None,
         smooth: bool = False,
         sleep_tuples: Optional[List[Tuple[pd.Timestamp, pd.Timestamp]]] = None,
@@ -39,15 +39,15 @@ def __init__(
         Args:
             cgm_df (pd.DataFrame): DataFrame containing the glucose measurements.
             diet_df (Optional[pd.DataFrame], optional): DataFrame containing the diet data. Defaults to None.
-            cgm_date_col (str, optional): Name of the date column in cgm_df. Defaults to "Date".
+            cgm_date_col (str, optional): Name of the date column in cgm_df. Defaults to "collection_timestamp".
             gluc_col (str, optional): Name of the glucose column in cgm_df. Defaults to "glucose".
-            diet_date_col (str, optional): Name of the date column in diet_df. Defaults to "Date".
-            diet_text_col (str, optional): Name of the text column in diet_df. Defaults to "shortname_eng".
+            diet_date_col (str, optional): Name of the date column in diet_df. Defaults to "collection_timestamp".
+            diet_text_col (str, optional): Name of the text column in diet_df. Defaults to "short_food_name".
             ax (Optional[plt.Axes], optional): Matplotlib Axes object to plot on. Defaults to None.
             smooth (bool, optional): Apply smoothing to the glucose curve. Defaults to False.
             sleep_tuples (Optional[List[Tuple[pd.Timestamp, pd.Timestamp]]], optional): List of sleep start and end times. Defaults to None.
         """
-        self.cgm_df = cgm_df
+        self.cgm_df = cgm_df.reset_index()
         self.diet_df = diet_df
         self.cgm_date_col = cgm_date_col
         self.gluc_col = gluc_col
@@ -110,7 +110,7 @@ def plot_diet(self) -> None:
         for i, (food_datetime, group) in enumerate(
             self.diet_df.groupby(self.diet_date_col)
         ):
-            food_str = "\n".join(group[self.diet_text_col])
+            food_str = "\n".join(group[self.diet_text_col].dropna())
 
             txt_x = food_datetime - pd.to_timedelta(7.5, "m")
             if i % 2 == 0:
diff --git a/pheno_utils/config.py b/pheno_utils/config.py
index 4e87367..469731c 100644
--- a/pheno_utils/config.py
+++ b/pheno_utils/config.py
@@ -1,11 +1,11 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/00_config.ipynb.
 
 # %% auto 0
-__all__ = ['REF_COLOR', 'FEMALE_COLOR', 'MALE_COLOR', 'ALL_COLOR', 'GLUC_COLOR', 'FOOD_COLOR', 'DATASETS_PATH', 'COHORT',
-           'EVENTS_DATASET', 'ERROR_ACTION', 'CONFIG_FILES', 'BULK_DATA_PATH', 'PREFERRED_LANGUAGE', 'config_found',
-           'DICT_PROPERTY_PATH', 'DATA_CODING_PATH', 'copy_tre_config', 'get_dictionary_properties_file_path',
-           'get_data_coding_file_path', 'generate_synthetic_data', 'generate_synthetic_data_like',
-           'generate_categorical_synthetic_data']
+__all__ = ['DEFAULT_PALETTE', 'REF_COLOR', 'FEMALE_COLOR', 'MALE_COLOR', 'ALL_COLOR', 'GLUC_COLOR', 'FOOD_COLOR', 'LEGEND_SHIFT',
+           'TIME_FORMAT', 'DATASETS_PATH', 'COHORT', 'EVENTS_DATASET', 'ERROR_ACTION', 'CONFIG_FILES', 'BULK_DATA_PATH',
+           'PREFERRED_LANGUAGE', 'config_found', 'DICT_PROPERTY_PATH', 'DATA_CODING_PATH', 'copy_tre_config',
+           'get_dictionary_properties_file_path', 'get_data_coding_file_path', 'generate_synthetic_data',
+           'generate_synthetic_data_like', 'generate_categorical_synthetic_data']
 
 # %% ../nbs/00_config.ipynb 3
 import os
@@ -16,6 +16,7 @@
 from glob import glob
 
 # %% ../nbs/00_config.ipynb 4
+DEFAULT_PALETTE = 'muted'
 REF_COLOR = "k"
 FEMALE_COLOR = "C1"
 MALE_COLOR = "C0"
@@ -24,6 +25,9 @@
 GLUC_COLOR = "C0"
 FOOD_COLOR = "C1"
 
+LEGEND_SHIFT = (1.05, 1.05)
+TIME_FORMAT = '%d/%m\n%H:%M'
+
 DATASETS_PATH = '/home/ec2-user/studies/hpp_datasets/'
 COHORT = None
 EVENTS_DATASET = 'events'
@@ -34,8 +38,6 @@
 
 config_found = False
 
-
-
 # %% ../nbs/00_config.ipynb 5
 def copy_tre_config():
     default_config_found = False
diff --git a/pheno_utils/diet_plots.py b/pheno_utils/diet_plots.py
new file mode 100644
index 0000000..1ca0022
--- /dev/null
+++ b/pheno_utils/diet_plots.py
@@ -0,0 +1,633 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/16_diet_plots.ipynb.
+
+# %% auto 0
+__all__ = ['SHORT_FOOD_CATEGORIES', 'plot_nutrient_bars', 'plot_nutrient_lollipop', 'prepare_meals', 'extract_units',
+           'draw_pie_chart', 'plot_meals_hbars', 'add_size_legend', 'plot_diet_cgm_sleep']
+
+# %% ../nbs/16_diet_plots.ipynb 3
+from typing import List, Tuple
+
+import pandas as pd
+import numpy as np
+
+import seaborn as sns
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+from matplotlib.ticker import FuncFormatter
+import matplotlib.patches as mpatches
+import matplotlib.lines as mlines
+import matplotlib.patches as Patch
+
+# %% ../nbs/16_diet_plots.ipynb 4
+from .timeseries_plots import format_timeseries, format_xticks, plot_events_bars
+from .config import DEFAULT_PALETTE, LEGEND_SHIFT
+
+
+def plot_nutrient_bars(
+    diet_log: pd.DataFrame, 
+    x: str='collection_timestamp',
+    label: str='short_food_name',
+    participant_id: int=None, 
+    array_index: int = None,
+    time_range: Tuple[str, str]=None, 
+    meals: bool=True,
+    summary: bool=False,
+    nut_include: List[str]=None,
+    nut_exclude: List[str]=None,
+    agg_units: dict={'kcal': 'sum', 'g': 'sum', 'mg': 'sum'},
+    legend: bool=True,
+    bar_width=np.timedelta64(15, 'm'),
+    palette: str=DEFAULT_PALETTE,
+    alpha: float=0.7,
+    ax: plt.Axes=None,
+    figsize: Tuple[float, float]=(14, 3),
+):
+    """
+    Plot a stacked bar chart representing nutrient intake for each meal over time.
+
+    Args:
+        diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements.
+        x (str): The name of the column in `diet_log` representing the x-axis variable, such as timestamps. Default is 'collection_timestamp'.
+        label (str): The name of the column in `diet_log` representing the labels for each meal. Default is 'short_food_name'.
+        participant_id (Optional[int]): The participant's ID to filter the diet log. If None, no filtering is done. Default is None.
+        array_index (Optional[int]): The array index to filter the diet log. If None, no filtering is done. Default is None.
+        time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be 'YYYY-MM-DD'. Default is None.
+        meals (bool): If True, includes individual meals in the plot. Default is True.
+        summary (bool): If True, includes a daily summary in the plot. Default is False.
+        nut_include (List[str]): A list of nutrients to include in the plot. Default is None.
+        nut_exclude (List[str]): A list of nutrients to exclude from the plot. Default is None.
+        agg_units (dict): A dictionary mapping nutrient units to aggregation functions. Only nutrients with units in this dictionary are plotted.
+        legend (bool): If True, includes a legend in the plot. Default is True.
+        bar_width (np.timedelta64): The width of the bars representing each meal on the time axis. Default is 15 minutes.
+        palette (str): The color palette to use for the stacked bars.
+        alpha (float): The transparency of the stacked bars. Default is 0.7.
+        ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the bar chart. If None, a new axis is created. Default is None.
+        figsize (Tuple[float, float]): The size of the figure to create. Default is (14, 3).
+
+    Returns:
+        None: The function creates a stacked bar chart on the specified or newly created axis.
+    """
+    # Prepare the data for plotting
+    df, grouped_nutrients = prepare_meals(
+        diet_log,
+        participant_id=participant_id,
+        array_index=array_index,
+        time_range=time_range,
+        label=label,
+        return_meals=meals,
+        return_summary=summary,
+        y_include=nut_include,
+        y_exclude=nut_exclude,
+        agg_units=agg_units,
+        x_col=x,
+        )
+
+    if ax is None:
+        fig, ax = plt.subplots(
+            len(grouped_nutrients), 1,
+            figsize=(figsize[0], figsize[1] * len(grouped_nutrients)),
+            sharex=True)
+        if len(grouped_nutrients) == 1:
+            ax = [ax]
+
+    colors = sns.color_palette(
+        palette, sum([len(g) for g in grouped_nutrients.values()]))
+
+    # Calculate the width in time units
+    bar_width_in_days = bar_width / np.timedelta64(1, 'D')
+
+    unit_list = [g for g in grouped_nutrients if g != 'kcal']
+    if 'kcal' in grouped_nutrients:
+        # kcal is last to keep colours synced with the lollipop plot
+        unit_list.append('kcal')
+
+    # Stacked bar plots for grouped nutrients
+    c = 0
+    for idx, unit in enumerate(unit_list):
+        bottom = pd.Series([0] * len(df))
+        for nut in grouped_nutrients[unit]:
+            if nut in ['weight_g']:
+                continue
+            ax[idx].bar(
+                df[x], df[nut], bottom=bottom, width=bar_width_in_days,
+                color=colors[c], alpha=alpha, label=nut)
+            bottom += df[nut]
+            c += 1
+        ax[idx].set_ylabel(f'Nutrients ({unit})', rotation=0, horizontalalignment='right')
+        if legend:
+            ax[idx].legend(loc='upper left', bbox_to_anchor=LEGEND_SHIFT)
+        ax[idx].grid(True)
+
+    # Set x-tick labels for the bottom and top axes
+    format_xticks(ax[-1], df[x])
+    if label is not None:
+        secax = ax[0].secondary_xaxis('top')
+        secax.set_xticks(df[x])
+        secax.set_xticklabels(df[label], ha='center', fontsize=9)
+
+    return ax
+
+
+def plot_nutrient_lollipop(
+    diet_log: pd.DataFrame, 
+    x: str='collection_timestamp',
+    y: str='calories_kcal',
+    size: str='total_g', 
+    label: str='short_food_name',
+    participant_id: int=None, 
+    array_index: int=None,
+    time_range: Tuple[str, str]=None, 
+    meals: bool=True,
+    summary: bool=False,
+    nut_include: List[str]=None,
+    nut_exclude: List[str]=None,
+    legend: bool=True,
+    size_scale: float=5,
+    palette: str=DEFAULT_PALETTE,
+    alpha: float=0.7,
+    ax: plt.Axes=None,
+    figsize: Tuple[float, float] = (12, 3),
+):
+    """
+    Plot a lollipop chart with pie charts representing nutrient composition for each meal.
+
+    NOTE: The y-axis is scaled to match the units of the x-axis, to avoid distortion of the pie charts.
+    Due to scaling, if you intend to change `xlim` after plotting, you must also provide `date_range`.
+    Use the `second_y` of g.plot() option to plot it with other y-axis data.
+
+    Args:
+        diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements.
+        x (str): The name of the column in `diet_log` representing the x-axis variable, such as timestamps. Default is 'collection_timestamp'.
+        y (str): The name of the column in `diet_log` representing the y-axis variable, such as calories. Default is 'calories_kcal'.
+        size (str): The name of the column in `diet_log` representing the size of the pie charts. Default is 'total_g'.
+        label (str): The name of the column in `diet_log` representing the labels for each meal. Default is 'short_food_name'.
+        participant_id (Optional[int]): The participant's ID to filter the diet log. If None, no filtering is done. Default is None.
+        time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be 'YYYY-MM-DD'. Default is None.
+        meals (bool): If True, includes individual meals in the plot. Default is True.
+        summary (bool): If True, includes a daily summary in the plot. Default is False.
+        nut_include (List[str]): A list of nutrients to include in the plot. Default is None.
+        nut_exclude (List[str]): A list of nutrients to exclude from the plot. Default is None.
+        legend (bool): If True, includes a legend in the plot. Default is True.
+        size_scale (float): The scaling factor for the size of the pie charts. Default is 5.
+        palette (str): The color palette to use for the pie slices. Default is DEFAULT_PALETTTE.
+        alpha (float): The transparency of the pie slices. Default is 0.7.
+        ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the lollipop chart. If None, a new axis is created. Default is None.
+        figsize (Tuple[float, float]): The size of the figure to create. Default is (12, 6).
+
+    Returns:
+        None: The function creates a lollipop plot with pie charts on the specified or newly created axis.
+    """
+    # Prepare the data for plotting
+    df, grouped_nutrients = prepare_meals(
+        diet_log,
+        participant_id=participant_id,
+        array_index=array_index,
+        time_range=time_range,
+        return_meals=meals,
+        return_summary=summary,
+        y_include=nut_include,
+        y_exclude=nut_exclude,
+        x_col=x,
+        )
+
+    if ax is None:
+        fig, ax = plt.subplots(nrows=1, ncols=1, figsize=figsize)
+
+    # Convert nutrients in mg to grams
+    for nut in grouped_nutrients['mg']:
+        df[nut.replace('_mg', '_g')] = df[nut] / 1000
+        grouped_nutrients['g'] += [nut.replace('_mg', '_g')]
+
+    pie_nuts = [nut for nut in grouped_nutrients['g']
+                if nut not in ['weight_g']]
+    df['total_g'] = df[pie_nuts].sum(axis=1)
+
+    # Calculate unknown component and ensure all values are non-negative
+    df['other_g'] = (df['weight_g'] - df[pie_nuts].sum(axis=1)).clip(lower=0)
+    # pie_nuts += ['other_g']
+
+    # Pre-set the x-axis limits based on the range of timestamps
+    if time_range is None:
+        min_x = mdates.date2num(df[x].min())
+        max_x = mdates.date2num(df[x].max())
+    else:
+        min_x = mdates.date2num(pd.to_datetime(time_range[0]))
+        max_x = mdates.date2num(pd.to_datetime(time_range[1]))
+
+    # Pre-set the y-axis limits based on the range of the y-axis column
+    min_y = 0  # df[y_col].min()
+    max_y = df[y].max()
+
+    # Calculate the aspect ratio between the x and y axes
+    # This is necessary to avoid distortion of the (circular) pie charts
+    x_range = max_x - min_x
+    y_range = max_y - min_y
+    aspect_ratio = x_range / y_range
+    y_delta = 0.1 * y_range
+
+    # Scale the y-axis to match the aspect ratio of the x-axis
+    ax.set_xlim(min_x, max_x)
+    ax.set_ylim(min_y * aspect_ratio, (max_y + y_delta) * aspect_ratio)
+
+    # Custom formatter to adjust the y-ticks back to the original scale
+    def ytick_formatter(y, pos):
+        return f'{y / aspect_ratio:.0f}'
+
+    # Plotting the lollipop plot with pies using absolute figure coordinates
+    for idx, row in df.iterrows():
+        # Pie chart parameters
+        size_value = np.sqrt(row[size]) * aspect_ratio * size_scale
+        position = mdates.date2num(row[x])
+        y_value = row[y] * aspect_ratio  # Scale y-value
+
+        # Plot the stem (lollipop stick)
+        ax.plot([position, position], [0, y_value], color='gray', lw=1, zorder=1)
+
+        # Plot the pie chart in figure coordinates (no distortion)
+        wedges = draw_pie_chart(ax, position, y_value, row[pie_nuts].fillna(0.).values, size_value, palette, alpha)
+
+    if legend:
+        # Create a custom legend
+        ax.legend(handles=wedges, labels= pie_nuts, loc='upper left', bbox_to_anchor=LEGEND_SHIFT)
+
+    # Format x-axis to display dates properly
+    ax.set_ylabel(y.replace('_', ' ').title(), rotation=0, horizontalalignment='right')
+    ax.grid(True)
+
+    # Set y-ticks and x-ticks
+    ax.yaxis.set_major_formatter(FuncFormatter(ytick_formatter))
+    ylim = ax.get_ylim()
+    yticks = np.arange(0, ylim[1] / aspect_ratio, 100, dtype=int)
+    ax.set_yticks(yticks * aspect_ratio)
+    ax.set_yticklabels(yticks)
+
+    format_xticks(ax, df[x])
+    if label is not None:
+        secax = ax.secondary_xaxis('top')
+        secax.set_xticks(df[x])
+        secax.set_xticklabels(df[label], ha='center', fontsize=9)
+
+    return ax
+
+
+def prepare_meals(
+    diet_log: pd.DataFrame,
+    participant_id: int=None,
+    array_index: int=None,
+    time_range: Tuple[str, str]=None,
+    label: str='short_food_name',
+    return_meals: bool = True,
+    return_summary: bool = False,
+    y_include: List[str] = None,
+    y_exclude: List[str] = None,
+    agg_units: dict={'kcal': 'sum', 'g': 'sum', 'mg': 'sum', 'unknown': 'first'},
+    x_col: str='collection_timestamp'
+) -> pd.DataFrame:
+    """
+    Prepare the diet log data for plotting meals and/or daily summaries.
+
+    Args:
+        diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements.
+        participant_id (Optional[int]): The participant's ID to filter the diet log. If None, no filtering is done. Default is None.
+        array_index (Optional[int]): The array index to filter the diet log. If None, no filtering is done. Default is None.
+        time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be 'YYYY-MM-DD'. Default is None.
+        label (str): The name of the column in `diet_log` representing the labels for each meal. Default is 'short_food_name'.
+        return_meals (bool): If True, includes individual meals in the plot. Default is True.
+        return_summary (bool): If True, includes a daily summary in the plot. Default is False.
+        y_include (List[str]): A list of nutrients (regex) to include in the plot. Default is None.
+        y_exclude (List[str]): A list of nutrients (regex) to exclude from the plot. Default is None.
+        agg_units (dict): A dictionary mapping nutrient units to aggregation functions.
+        x_col (str): The name of the column in `diet_log` representing the x-axis variable, such as timestamps. Default is 'collection_timestamp'.
+
+    Returns:
+        pd.DataFrame: A dataframe containing the prepared data for plotting.
+    """
+    diet_log = format_timeseries(
+        diet_log, participant_id, array_index, time_range,
+        x_start=x_col, x_end=x_col, unique=True)
+
+    units = extract_units(diet_log.columns)
+    grouped_nutrients = {}
+    import re  # Add this line to import the re module
+
+    agg_dict = {}
+    for nut, unit in units.items():
+        if unit not in agg_units:
+            continue
+        if y_include is not None and not any([re.match(inc, nut) for inc in y_include]):
+            continue
+        if y_exclude is not None and any([re.match(exc, nut) for exc in y_exclude]):
+            continue
+        if unit not in grouped_nutrients:
+            grouped_nutrients[unit] = []
+        grouped_nutrients[unit].append(nut)
+        agg_dict[nut] = agg_units[unit]
+    nut_list = list(agg_dict.keys())
+    if label is not None:
+        agg_dict[label] = lambda x: '\n'.join(x)
+
+    df = diet_log\
+        .dropna(subset=['short_food_name'])\
+        .drop_duplicates()\
+        .groupby([x_col])\
+        .agg(agg_dict)\
+        .reset_index()
+
+    if return_summary:
+        # Add daily summary by grouping by date and summing up the nutrients
+        daily_df = df.groupby(df[x_col].dt.date)[nut_list]\
+            .sum().reset_index()
+        if label is not None:
+            daily_df[label] = daily_df[x_col].astype('string') + '\nDaily Summary'
+        daily_df[x_col] = pd.to_datetime(daily_df[x_col] + pd.Timedelta(hours=24))
+        if time_range is not None:
+            daily_df = daily_df[(time_range[0] <= daily_df[x_col]) & (daily_df[x_col] <= time_range[1])]
+        if return_meals:
+            # God knows why, but the two refuse to concat without this
+            df = pd.DataFrame(np.vstack([df, daily_df]), columns=df.columns)
+        else:
+            df = daily_df
+
+    return df, grouped_nutrients
+
+
+def extract_units(column_names: List[str]) -> dict:
+    units = {}
+    for col in column_names:
+        if '_' in col:
+            unit = col.split('_')[-1]
+            units[col] = unit
+        else:
+            units[col] = 'unknown'
+    return units
+
+
+def draw_pie_chart(
+    ax: plt.Axes, 
+    x: float, 
+    y: float, 
+    data: List[float], 
+    size: float, 
+    palette: str = DEFAULT_PALETTE,
+    alpha: float = 0.7,
+):
+    """
+    Draw a pie chart as an inset (in absolute figure coordinates) within the given axes
+    at the specified data coordinates.
+    What this solves is the issue of y-axis and x-axis scaling being different, which
+    distorts the pie chart when drawn directly on the axes.
+
+    Args:
+        ax (plt.Axes): The axis on which to draw the pie chart.
+        x (float): The x-coordinate in data coordinates where the pie chart's center will be placed.
+        y (float): The y-coordinate in data coordinates where the pie chart's center will be placed.
+        data (List[float]): The data values to be represented in the pie chart.
+        size (float): The size (radius) of the pie chart in axes-relative coordinates.
+        palette (str): The color palette to use for the pie slices.
+
+    Returns:
+        List[plt.Patch]: A list of wedge objects representing the pie chart slices.
+    """
+    # Convert the position from data coordinates to axes coordinates
+    axes_coords = ax.transData.transform((x, y))
+    axes_coords = ax.transAxes.inverted().transform(axes_coords)
+
+    # Create a new inset axis to draw the pie, using axes-relative coordinates
+    inset_ax = ax.inset_axes([axes_coords[0] - size, axes_coords[1] - size, 2 * size, 2 * size])
+
+    # Plot the pie chart using the calculated position and scaled radius
+    colors = [(r, g, b, alpha) for r, g, b in sns.color_palette(palette, len(data))]
+    wedges, _ = inset_ax.pie(data, radius=1, startangle=90, wedgeprops=dict(edgecolor='none'), normalize=True,
+                             colors=colors)
+
+    # Hide the axes for the inset (pie chart)
+    inset_ax.set_axis_off()
+
+    return wedges
+
+
+# %% ../nbs/16_diet_plots.ipynb 5
+SHORT_FOOD_CATEGORIES = {
+    'beef, veal, lamb, and other meat products': 'meat products',
+    'milk, cream cheese and yogurts': 'milk products',
+    'nuts, seeds, and products': 'nuts and seeds',
+    'eggs and their products': 'eggs',
+    'pulses and products': 'pulses',
+    'fruit juices and soft drinks': 'juices and soft drinks',
+    'low calories and diet drinks': 'low cal. drinks',
+    'poultry and its products': 'poultry',
+    'pasta, grains and side dishes': 'grains',
+    'industrialized vegetarian food ready to eat': 'industrialized veg.',
+}
+
+def plot_meals_hbars(
+    diet_log: pd.DataFrame, 
+    x: str='collection_timestamp',
+    y: str='short_food_category',
+    size: str='weight_g', 
+    hue: str='short_food_category',
+    participant_id: int=None, 
+    array_index: int=None,
+    time_range: Tuple[str, str]=None, 
+    y_include: List[str] = None,
+    y_exclude: List[str] = None,
+    rename_categories: dict=SHORT_FOOD_CATEGORIES,
+    legend: bool=True,
+    size_legend: List[int]=[100, 200, 500],
+    size_scale: float=5,
+    palette: str=DEFAULT_PALETTE,
+    alpha: float=0.7,
+    ax: plt.Axes=None,
+    figsize: Tuple[float, float] = (12, 6),
+):
+    """
+    Plot a diet chart with bars representing meals and their size over time.
+
+    Args:
+        diet_log (pd.DataFrame): The dataframe containing the diet log data, with columns for timestamps, nutrients, and other measurements.
+        x (str): The name of the column in `diet_log` representing the x-axis variable, such as timestamps. Default is 'collection_timestamp'.
+        y (str): The name of the column in `diet_log` representing the y-axis variable, such as food categories. Default is 'short_food_category'.
+        size (str): The name of the column in `diet_log` representing the size of the bars. Default is 'weight_g'.
+        hue (str): The name of the column in `diet_log` representing the color of the bars. Default is 'short_food_category'.
+        participant_id (Optional[int]): The participant's ID to filter the diet log. If None, no filtering is done. Default is None.
+        time_range (Optional[Tuple[str, str]]): A tuple of strings representing the start and end dates for filtering the data. Format should be 'YYYY-MM-DD'. Default is None.
+        y_include (List[str]): A list of strings representing the categories to include in the plot. Default is None.
+        y_exclude (List[str]): A list of strings representing the categories to exclude from the plot. Default is None.
+        rename_categories (dict): A dictionary mapping original food categories to shorter names. Default is SHORT_FOOD_CATEGORIES.
+        legend (bool): If True, includes a legend in the plot. Default is True.
+        size_legend (List[int]): A list of integers representing the sizes to include in the size legend. Default is [100, 200, 500].
+        size_scale (float): The scaling factor for the size of the bars. Default is 5.
+        palette (str): The palette to use for the bars.
+        alpha (float): The transparency of the bars. Default is 0.7.
+        ax (Optional[plt.Axes]): The Matplotlib axis on which to plot the lollipop chart. If None, a new axis is created. Default is None.
+        figsize (Tuple[float, float]): The size of the figure to create. Default is (12, 6).
+    """
+    diet_log = format_timeseries(
+        diet_log, participant_id, array_index,
+        time_range, x_start=x, x_end=x, unique=True)
+
+    diet_log['event_end'] = diet_log[x] \
+        + size_scale * pd.to_timedelta(diet_log[size], unit='s')
+
+    # Categories
+    diet_log['short_food_category'] = diet_log['food_category'].str.lower()
+    for s, t in rename_categories.items():
+        diet_log['short_food_category'] = diet_log['short_food_category'].str.replace(s, t, regex=False)
+    diet_log['short_food_category'] = diet_log['short_food_category']\
+        .str.replace(' and ', ' & ', regex=False)\
+        .str.replace('_wholewheat', ' (whole/w)', regex=False)
+
+    # User events plot to plot meals
+    ax = plot_events_bars(
+        diet_log,
+        x_start=x, x_end='event_end',
+        y=y, hue=hue,
+        y_include=y_include, y_exclude=y_exclude, alpha=alpha,
+        ax=ax, figsize=figsize, palette=palette, legend=legend)
+
+    format_xticks(ax, diet_log[x].drop_duplicates())
+
+    add_size_legend(ax, size_legend, size_scale, alpha)
+
+    return ax
+
+
+def add_size_legend(ax: plt.Axes, sizes: List[int], size_scale: float, alpha: float, shift: int=0):
+    """
+    Add a size legend to a plot_meals_hbars plot using broken_barh.
+    """
+    if len(sizes) == 0:
+        return
+
+    # Manually add size legend using broken_barh
+    sec2day = 1 / (60 * 60 * 24)  # Convert seconds to days
+    size_durations = [
+        s * size_scale * sec2day
+        for s in sizes]
+    max_duration = max(size_durations)
+
+    # Calculate the xlim to place the legend bars right at the end
+    xlim = ax.get_xlim()  # These are in days
+    y_start_legend = ax.get_ylim()[0] - 1 - shift
+    x_bar_start = \
+        xlim[1] - \
+        1.5 * max_duration
+
+    # Add a bounding box around the text and bars
+    ax.add_patch(mpatches.Rectangle(
+        (x_bar_start - 1.5*(max_duration + 10 * sec2day), y_start_legend - len(sizes) + 0.25),
+         3 * (max_duration + 10 * sec2day), len(sizes) + 0.5,
+         edgecolor='gray', facecolor='white', lw=1))
+
+    for i, (s, duration) in enumerate(zip(sizes, size_durations)):
+        # Plot the bar
+        ax.broken_barh(
+            xranges=[(x_bar_start, duration)],
+            yrange=(y_start_legend - i - 0.4, 0.8), 
+            facecolors='gray', alpha=alpha
+        )
+
+        # Add text next to the bar
+        ax.annotate(f'{s}g', 
+                    (x_bar_start - 10 * sec2day, y_start_legend - i),
+                    va='center', ha='right', fontsize=10)
+
+# %% ../nbs/16_diet_plots.ipynb 6
+from .timeseries_plots import TimeSeriesFigure, plot_events_fill
+from .sleep_plots import plot_sleep_channels, get_sleep_period
+
+def plot_diet_cgm_sleep(
+    diet: pd.DataFrame=None,
+    cgm: pd.DataFrame=None,
+    sleep_events: pd.DataFrame=None,
+    sleep_channels: pd.DataFrame=None,
+    cgm_grid: List[int] = [0, 54, 70, 100, 140, 180],
+    channel_filter: List[str]=['heart_rate', 'actigraph', 'spo2'],
+    participant_id=None,
+    array_index=None,
+    time_range: Tuple[str, str]=None,
+    figsize=(14, 10),
+    nutrient_kws: dict={},
+    meals_kws: dict={},
+    cgm_kws: dict={},
+    events_kws: dict={},
+    channels_kws: dict={},
+) -> TimeSeriesFigure:
+    """
+    Plot diet, CGM and sleep data together.
+
+    Arg:
+        diet (pd.DataFrame): Diet logging data. Set to None to remove from figure.
+        cgm (pd.DataFrame): CGM data. Set to None to remove from figure.
+        sleep_events (pd.DataFrame): Sleep events data. Set to None to remove from figure.
+        sleep_channels (pd.DataFrame): Sleep channels data. Set to None to remove from figure.
+        cgm_grid (List[int]): CGM grid lines. Default: [0, 54, 70, 100, 140, 180].
+        channel_filter (List[str]): Which sleep channels to include in the plot. Default: ['heart_rate', 'actigraph', 'spo2'].
+        participant_id (int): Participant ID.
+        array_index (int): Array index.
+        time_range (Tuple[str, str]): Time range to plot.
+        figsize (Tuple[int, int]): Figure size.
+        nutrient_kws (dict): Keyword arguments for diet nutrients lollipop plot.
+        meals_kws (dict): Keyword arguments for diet meals plot.
+        cgm_kws (dict): Keyword arguments for CGM plot.
+        events_kws (dict): Keyword arguments for sleep events plot.
+        channels_kws (dict): Keyword arguments for sleep channels plot.
+
+    Returns:
+        TimeSeriesFigure: Plot.
+    """
+    g = TimeSeriesFigure(figsize=figsize)
+
+    # Add diet
+    if diet is not None:
+        g.plot(plot_nutrient_lollipop, diet,
+            second_y=True if cgm is not None else False,
+            participant_id=participant_id, array_index=array_index, time_range=time_range,
+            size_scale=10, name='diet_glucose', height=1.5, **nutrient_kws)
+        g.plot(plot_meals_hbars, diet,
+            participant_id=participant_id, array_index=array_index, time_range=time_range,
+            name='diet_bars', sharex='diet_glucose', height=3, **meals_kws)
+
+    # Add CGM
+    if cgm is not None:
+        if diet is None:
+            g.add_axes(name='diet_glucose')
+        cgm = format_timeseries(
+            cgm,
+            participant_id=participant_id, array_index=array_index, time_range=time_range,
+            )
+        ax = g.get_axes('diet_glucose', squeeze=True)
+        ax.plot(cgm['collection_timestamp'], cgm['glucose'], label='glucose', color='#4c72b0', **cgm_kws)
+        ax.scatter(cgm['collection_timestamp'], cgm['glucose'], s=10, color='#4c72b0', **cgm_kws)
+        ax.set_ylabel('Glucose', rotation=0, horizontalalignment='right')
+        ax.set_yticks(cgm_grid)
+        ax.yaxis.grid(True)
+
+    # Add sleep
+    if sleep_channels is not None:
+        plot_sleep_channels(
+            sleep_channels,
+            x='collection_timestamp', y='values', row='source', hue=None,
+            participant_id=participant_id, array_index=array_index, time_range=time_range,
+            y_include=channel_filter,
+            fig=g, height=1, **channels_kws,
+        )
+    if sleep_events is not None:
+        g.plot(plot_events_fill, sleep_events,
+            participant_id=participant_id, array_index=array_index, time_range=time_range,
+            y_include=["Wake", "REM", "Light Sleep", "Deep Sleep", "Sleep"],
+            hue='event', ax=['sleep_channels'], sharex='sleep_channels', alpha=0.3, **events_kws)
+        if cgm is not None or diet is not None:
+            g.plot(plot_events_fill, get_sleep_period(sleep_events),
+                participant_id=participant_id, array_index=array_index, time_range=time_range,
+                y_include=["Wake", "REM", "Light Sleep", "Deep Sleep", "Sleep"], legend=False,
+                hue=None, palette='gray', label='event',
+                ax=['diet_glucose'], sharex='sleep_channels', alpha=0.3, **events_kws)
+
+    # Tidy up
+    g.set_axis_padding(0.03)
+    if time_range is not None:
+        g.set_time_limits(*time_range)
+    g.set_periodic_ticks('2H', ax='sleep_channels')
+
+    return g
diff --git a/pheno_utils/sleep_plots.py b/pheno_utils/sleep_plots.py
index c6411f8..50029ae 100644
--- a/pheno_utils/sleep_plots.py
+++ b/pheno_utils/sleep_plots.py
@@ -1,18 +1,17 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/06_sleep_plots.ipynb.
 
 # %% auto 0
-__all__ = ['CHANNELS', 'DEFAULT_CHANNELS', 'COLOR_GROUPS', 'ENUMS', 'CHANNEL_LIMS', 'plot_sleep', 'plot_events', 'plot_channels',
-           'format_xticks', 'get_legend_colors']
+__all__ = ['CHANNELS', 'DEFAULT_CHANNELS', 'COLOR_GROUPS', 'ENUMS', 'CHANNEL_LIMS', 'plot_sleep', 'plot_sleep_channels',
+           'get_channels_colors', 'get_sleep_period']
 
 # %% ../nbs/06_sleep_plots.ipynb 3
-from typing import Iterable, Optional
+from typing import Iterable, Tuple, List
 
-import numpy as np
 import pandas as pd
 
-import seaborn as sns
 import matplotlib.pyplot as plt
-import matplotlib.dates as mdates
+
+from .timeseries_plots import TimeSeriesFigure, plot_events_bars, get_events_period, format_xticks, prep_to_plot_timeseries, DEFAULT_PALETTE
 
 # %% ../nbs/06_sleep_plots.ipynb 4
 CHANNELS = {
@@ -32,6 +31,7 @@
 
 DEFAULT_CHANNELS = ['actigraph', 'pat_infra', 'body_position', 'snore_db', 'heart_rate', 'spo2']
 
+# Color groups are designed to match events and raw channels
 COLOR_GROUPS= {
     'actigraph': ['actigraph', 'sleep_stage'],
     'general': ['body_position'],
@@ -48,262 +48,197 @@
 CHANNEL_LIMS = {'spo2': [0, 100]}
 
 # %% ../nbs/06_sleep_plots.ipynb 5
-def plot_sleep(events: pd.DataFrame, channels: pd.DataFrame,
-               array_index: Optional[int] = None,
-               trim_to_events: Optional[bool] = True,
-               add_events: Optional[pd.DataFrame] = None,
-               event_filter: Optional[Iterable[str]] = None,
-               channel_filter: Optional[Iterable[str]] = DEFAULT_CHANNELS,
-               event_height: float=2, channel_height: float=0.45, width: float=10, aspect: float=0.2,
-               style: str='whitegrid',
-               xlim: Iterable[float]=None, **kwargs):
+def plot_sleep(
+    events: pd.DataFrame,
+    channels: pd.DataFrame,
+    participant_id: int=None,
+    array_index: int=None,
+    time_range: Tuple[str, str]=None,
+    event_filter: Iterable[str]=None,
+    channel_filter: Iterable[str]=DEFAULT_CHANNELS,
+    event_height: float=1,
+    channel_height: float=0.5,
+    padding: float=-0.02,
+    figsize: Tuple[float, float]=None,
+    palette: str=DEFAULT_PALETTE,
+) -> TimeSeriesFigure:
     """
     Plot sleep events and channels data.
 
     Args:
-
-        events (pd.DataFrame): A pandas dataframe containing sleep events data.
-        channels (pd.DataFrame): A pandas dataframe containing raw channels data.
-        array_index (int, optional): The index of the array. Defaults to None.
-        trim_to_events (bool, optional): Whether to trim the plot to the start and end of the events. Defaults to True.
-        add_events (pd.DataFrame, optional): Additional events data to include in the plot. Defaults to None.
-        event_filter (Iterable[str], optional): A list of events to include in the plot. Defaults to None.
-        channel_filter (Iterable[str], optional): A list of channels to include in the plot. Defaults to DEFAULT_CHANNELS.
-        event_height (float, optional): The height of the event plot in inches. Defaults to 2.
-        channel_height (float, optional): The height of each channel plot in inches. Defaults to 0.45.
-        width (float, optional): The width of the plot in inches. Defaults to 10.
-        aspect (float, optional): The aspect ratio of the plot. Defaults to 0.2.
-        style (str, optional): The seaborn style to use. Defaults to 'whitegrid'.
-        xlim (List[float], optional): The x-axis limits of the plot. Defaults to None.
-        **kwargs: Additional arguments to be passed to plot_channels().
+        events (pd.DataFrame): The sleep events dataframe.
+        channels (pd.DataFrame): The sleep channels dataframe.
+        participant_id (int): The participant id to filter the data.
+        array_index (int): The array index to filter the data.
+        time_range (Tuple[str, str]): The time range to filter the data.
+        event_filter (Iterable[str]): The events to include in the plot.
+        channel_filter (Iterable[str]): The channels to include in the plot.
+        event_height (float): The relative height of the events subplot.
+        channel_height (float): The relative height of each channel's subplot.
+        padding (float): The padding between subplots.
+        figsize (Tuple[float, float]): The size of the figure.
+        palette (str): The color palette to use.
 
     Returns:
-
-        None
+        TimeSeriesFigure: The figure with the sleep events and channels data.
     """
-    nC = min([len(channel_filter), channels.index.get_level_values('source').nunique()])
-    if xlim is not None:
-        trim_to_events = True
-
-    fig, ax = plt.subplots(nrows=nC+1, ncols=1, sharex=True, squeeze=False,
-                           height_ratios=nC*[channel_height] + [event_height],
-                           figsize=(width, width*aspect*(nC*channel_height + event_height)),
-                           facecolor='white')
-
-    with sns.axes_style(style):
-        try:
-            plot_channels(channels, array_index=array_index, y_filter=channel_filter, ax=ax[:-1],
-                     **kwargs)
-            plot_events(events, array_index=array_index, y_include=event_filter, y_exclude=['Gross Trunc'],
-                        ax=ax[-1,0],
-                        set_xlim=trim_to_events, add_events=add_events,
-                        xlim=xlim)
-        except Exception as err:
-            print(f'plot_channels failed due to:\n{err}')
-            fig.clf()
-            plot_events(events, array_index, y_include=event_filter, y_exclude=['Gross Trunc'],
-                        set_xlim=trim_to_events, add_events=add_events,
-                        xlim=xlim)
-
-
-def plot_events(events: pd.DataFrame, array_index: Optional[int]=None,
-                x_start: str='collection_timestamp', x_end: str='event_end',
-                y: str='event', color: str='channel', cmap: str='muted', set_xlim: bool=True,
-                xlim: Iterable[float]=None, figsize: Iterable[float]=[10, 4],
-                y_include: Optional[Iterable[str]] = None,
-                y_exclude: Optional[Iterable[str]] = None,
-                ax: plt.Axes=None,
-                add_events: Optional[pd.DataFrame] = None,
-                rename_channels: dict={'PAT Amplitude': 'PAT', 'PulseRate': 'Heart Rate'},
-                rename_events: dict={}):
-    """ plot an events timeline for a given participant and array_index """
-    # slice to night
-    if (array_index is not None) and (('array_index' in events.columns) or ('array_index' in events.index.names)):
-        plot_df = events.query('array_index == @array_index').copy()
-    else:
-        plot_df = events.copy()
-    # extract start and end times
-    if x_start in plot_df.index.names:
-        plot_df = plot_df.reset_index(x_start)
-    if x_end in plot_df.index.names:
-        plot_df = plot_df.reset_index(x_end)
-    # remove timezone for correct matplotlib labeling
-    plot_df[x_start] = plot_df[x_start].dt.tz_localize(None)
-    plot_df[x_end] = plot_df[x_end].dt.tz_localize(None)
-
-    # filter events
-    if y_include is not None:
-        plot_df = plot_df.query(f'{y} in {y_include}')
-    if y_exclude is not None:
-        plot_df = plot_df.query(f'{y} not in {y_exclude}')
-    # additional user-provided events (application logging, etc.)
-    if add_events is not None:
-        tlim = plot_df[x_start].min(), plot_df[x_end].max()
-        add_events = add_events.loc[
-            (tlim[0] < add_events[x_end]) & (add_events[x_start] < tlim[1])]
-        if len(add_events):
-            add_events = add_events.set_index(plot_df.index[[0]])
-        plot_df = pd.concat([plot_df, add_events[
-            plot_df.columns.intersection(add_events.columns)]], axis=0)
-
-    # rename channels and events
-    plot_df = plot_df.copy()
-    plot_df['channel'] = plot_df['channel'].replace(rename_channels)
-    plot_df['event'] = plot_df['event'].replace(rename_events)
-
-    # set x limits
-    if xlim is not None:
-        if type(xlim[0]) is str:
-            xlim = (pd.to_datetime(xlim[0]), xlim[1])
-        if type(xlim[0]) is not pd.Timestamp:
-            xlim = plot_df.loc[plot_df['start'] < xlim[0], x_start].iloc[-1], xlim[1]
-        if type(xlim[1]) is str:
-            xlim = (xlim[0], pd.to_datetime(xlim[1]))
-        if type(xlim[1]) is not pd.Timestamp:
-            xlim = xlim[0], plot_df.loc[plot_df['end'] > xlim[1], x_end].iloc[0]
-    else:
-        xlim = plot_df[x_start].min(), plot_df[x_end].max()
-
-    if ax is None:
-        fig, ax = plt.subplots(figsize=figsize)
-
-    # set colors
-    colors = sorted(plot_df[color].unique())
-    colors = pd.DataFrame({color: colors, 'color': sns.color_palette(cmap, len(colors))})\
-        .set_index(color)['color']
-
-    # plot events
-    plot_df = plot_df.assign(diff=lambda x: x[x_end] - x[x_start]).sort_values([color, y])
-    labels = []
-    legend = []
-    for i, (y_label, y) in enumerate(plot_df.groupby(y, observed=True, sort=False)):
-        if len(y) == 0:
-            continue
-        labels.append(y_label)
-        for c, r in y.groupby(color, observed=True):
-            data = r[[x_start, 'diff']]
-            if not len(data):
-                continue
-            h = ax.broken_barh(data.values, (i-0.4,0.8), color=colors[c], alpha=0.7)
-            legend.append({'label': c, 'handle': h})
-
-    # format plot
-    legend = pd.DataFrame.from_dict(legend).drop_duplicates(subset='label')
-    ax.legend(legend['handle'], legend['label'],
-              bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
-
-    str_title = ''
-    if 'participant_id' in events.index.names:
-        str_title += events.index.get_level_values('participant_id')[0].astype(str)
-    if array_index is not None:
-        str_title += f' / {array_index}'
-    plt.suptitle(str_title, fontsize=14, weight='bold')
-    ax.set_yticks(np.arange(len(labels)), labels)
-    plt.tight_layout()
-    ax.set_xlabel('Time')
-    if set_xlim:
-        ax.set_xlim(*xlim)
-    format_xticks(ax)
-
-    return ax
-
-
-def plot_channels(channels: pd.DataFrame, array_index: Optional[int]=None,
-             y_filter: Optional[Iterable[str]]=None, ax: plt.Axes=None,
-             discrete_events: Optional[Iterable[str]]=['sleep_stage', 'body_position'],
-             time_col='collection_timestamp', height=1.5, resample='1s', cmap='muted',
-             rename_channels=CHANNELS, **kwargs):
+    # Create figure
+    if figsize is None:
+        if 'source' in channels.index.names:
+            nC = channels.index.get_level_values('source').nunique()
+        else:
+            nC = channels['source'].nunique()
+        figsize = 2 * (nC * channel_height + event_height)
+        figsize = (8 * 2 * channel_height, figsize)
+    g = TimeSeriesFigure(figsize=figsize)
+
+    # Set colors
+    channels, color_map = get_channels_colors(
+        channels, events,
+        participant_id=participant_id, array_index=array_index, time_range=time_range,
+        event_filter=event_filter, palette=palette,
+    )
+
+    # Plot
+    plot_sleep_channels(
+        channels,
+        x='collection_timestamp', y='values', row='source', hue='channel_group',
+        participant_id=participant_id, array_index=array_index, time_range=time_range,
+        y_include=channel_filter,
+        fig=g, height=channel_height,
+        color_map=color_map, palette=palette,
+    )
+    g.plot(plot_events_bars,
+        events,
+        x_start='collection_timestamp', x_end='event_end', y='event', hue='channel',
+        participant_id=participant_id, array_index=array_index, time_range=time_range,
+        y_include=event_filter, y_exclude=['Gross Trunc'],
+        palette=palette,
+        name='sleep_events', height=event_height, sharex='sleep_channels',
+    )
+    g.set_axis_padding(padding)
+
+    return g
+
+
+def plot_sleep_channels(
+    channels: pd.DataFrame,
+    x: str='collection_timestamp',
+    y: str='values',
+    row: str='source',
+    hue: str='channel_group',
+    participant_id: int=None,
+    array_index: int=None,
+    time_range: Tuple[str, str]=None,
+    y_include: Iterable[str]=None,
+    y_exclude: Iterable[str]=None,
+    rename_channels: dict=CHANNELS,
+    discrete_events: Iterable[str]=['sleep_stage', 'body_position'],
+    resample: str='1s',
+    color_map: pd.Series=None,
+    palette: str=DEFAULT_PALETTE,
+    fig: TimeSeriesFigure=None,
+    ax: List[plt.Axes]=None,
+    height=1,
+    **kwargs
+) -> List[plt.Axes]:
     """ plot channels data for a given participant and array_index """
-    # set colors
-    colors = get_legend_colors(cmap).explode('source')
-    colors['source'] = pd.Categorical(colors['source'])
-    colors = colors.set_index('source')
-
-    # filter data
-    if (array_index is not None) and (('array_index' in channels.columns) or ('array_index' in channels.index.names)):
-        data = channels.query('array_index == @array_index').copy()
-    else:
-        data = channels.copy()
-    # extract time and channel name
-    if time_col in channels.index.names:
-        data = data.reset_index(time_col)
-    if 'source' not in data.index.names:
-        data = data.set_index('source')
-    data[time_col] = data[time_col].dt.tz_localize(None)
-
-    # grouping and coloring sources by event "channels"
-    data = data.join(colors[['channel']])\
-        .sort_values(['channel', time_col], ascending=[False, True])
-
-    if ax is None:
-        n = data.index.unique().size
-        fig, ax = plt.subplots(nrows=n, figsize=(10, n*height), sharex=True, squeeze=False)
-
-    # plot data
-    ax_shift = 0
-    for i, (source, d) in enumerate(data.groupby('source', observed=True, sort=False)):
-        if (source not in CHANNELS) or (y_filter is not None and source not in y_filter):
-            print(f'plot_channels: skipping {source}')
-            ax_shift += 1
-            continue
-        iax = i - ax_shift
-        if resample is not None:
-            d = d.resample(resample, on=time_col).mean(numeric_only=True).reset_index()
-        if source in colors.index:
-            c = colors.loc[source, 'color']
+    # Filter data and prepare channels
+    channels, colors = prep_to_plot_timeseries(
+        channels, x, x, row, row,
+        participant_id, array_index, time_range,
+        y_include, y_exclude,
+        add_columns=[y, hue], palette=palette
+        )
+    if color_map is not None:
+        colors = color_map
+
+    # Create axes if necessary
+    n = channels[row].nunique()
+    if ax is None and fig is None:
+        fig, ax = plt.subplots(nrows=n, figsize=(12, n*height), sharex=True, squeeze=True)
+    elif ax is None:
+        ax = fig.add_axes(n_axes=n, height=height, name='sleep_channels')
+
+    # Plot data
+    for i, (source, d) in enumerate(channels.groupby(row, observed=True, sort=False)):
+        if colors is not None and hue is not None:
+            c = colors.get(d[hue].iloc[0], 'grey')
         else:
-            c = 'grey'
+            c = '#4c72b0'
+        if resample is not None:
+            d = d.resample(resample, on=x).mean(numeric_only=True).reset_index()
+
+        # Set channel value limits
         if source in CHANNEL_LIMS:
-            d = d.loc[(CHANNEL_LIMS[source][0] <= d['values']) & (d['values'] <= CHANNEL_LIMS[source][1])]
-        ax[iax, 0].scatter(d[time_col].dt.tz_localize(None).values, d['values'].values, s=0.1, color=c)
+            d = d.loc[
+                (CHANNEL_LIMS[source][0] <= d[y]) &
+                (d[y] <= CHANNEL_LIMS[source][1])
+                ]
+        ax[i].scatter(d[x].values, d[y].values, s=0.1, color=c)
         if source not in CHANNEL_LIMS:
-            ylim = d['values'].quantile([0.001, 0.999]).tolist()
+            ylim = d[y].quantile([0.001, 0.999]).tolist()
             ylim[0] = 0.95*ylim[0] if ylim[0] >= 0 else 1.1*ylim[0]
             ylim[1] = 1.1*ylim[1] if ylim[1] >= 0 else 0.95*ylim[1]
-            ax[iax,0].set_ylim(*ylim)
+            ax[i].set_ylim(*ylim)
+
         if source in rename_channels:
-            ax[iax, 0].set_ylabel(rename_channels[source], rotation=0, horizontalalignment='right')
+            ax[i].set_ylabel(rename_channels[source], rotation=0, horizontalalignment='right')
         else:
-            ax[iax,0].set_ylabel(source, rotation=0, horizontalalignment='right')
+            ax[i].set_ylabel(source, rotation=0, horizontalalignment='right')
 
         if source in discrete_events:
             if source in ENUMS:
-                ax[iax, 0].set_yticks(ENUMS[source][0],labels=ENUMS[source][1])
+                ax[i].set_yticks(ENUMS[source][0],labels=ENUMS[source][1])
             else:
-                ax[iax, 0].set_yticks(d['values'].drop_duplicates().sort_values().values)
-                ylabels = ax[iax, 0].get_yticklabels()
+                ax[i].set_yticks(d[y].drop_duplicates().sort_values().values)
+                ylabels = ax[i, 0].get_yticklabels()
                 for label in ylabels[1:-1]:
                     label.set_text('')
-                ax[iax, 0].set_yticklabels(ylabels)
-
-    # format plot
-    for i in range(len(ax)):
-        ax[i,0].set_xlabel('')
-        ax[i,0].set_xticklabels([])
-    if ax is None:
-        print('entered')
-        ax[-1,0].set_xlabel('Time')
-        ax[-1,0].set_xlim(data[time_col].min(), data[time_col].max())
-        format_xticks(ax[-1,0])
+                ax[i].set_yticklabels(ylabels)
 
+    format_xticks(ax[-1])
 
     return ax
 
 
-def format_xticks(ax, format='%m/%d %H:%M'):
-    """ format datestrings on x axis """
-    xticks = ax.get_xticks()
-    ax.set_xticks(xticks)
-    ax.set_xticklabels(xticks, rotation=25, ha='right')
-    xfmt = mdates.DateFormatter(format)
-    ax.xaxis.set_major_formatter(xfmt)
-
-
-def get_legend_colors(cmap='muted'):
-    # the following dict keys should correspond to sleep event channels
-    colors = pd.Series(COLOR_GROUPS, name='source').to_frame().reset_index()\
-        .rename(columns={'index': 'channel'}).sort_values('channel')
-    colors['color'] = sns.color_palette(cmap, len(colors))
+def get_channels_colors(
+    channels: pd.DataFrame,
+    events: pd.DataFrame,
+    participant_id: int=None,
+    array_index: int=None,
+    time_range: Tuple[str, str]=None,
+    event_filter: Iterable[str]=None,
+    palette: str=DEFAULT_PALETTE,
+) -> Tuple[pd.DataFrame, pd.Series]:
+    # Group channels like events do
+    channel_groups = pd.Series(COLOR_GROUPS, name='source')\
+        .reset_index()\
+        .rename(columns={'index': 'channel_group'})\
+        .explode('source').set_index('source')
+    channels = channels.join(channel_groups)
+    # Simulate events colors
+    _, color_map = prep_to_plot_timeseries(
+        events,
+        x_start='collection_timestamp', x_end='event_end', label='event', hue='channel',
+        participant_id=participant_id, array_index=array_index, time_range=time_range,
+        y_include=event_filter, y_exclude=['Gross Trunc'],
+        palette=palette)
+
+    return channels, color_map
+
+# %% ../nbs/06_sleep_plots.ipynb 6
+def get_sleep_period(events: pd.DataFrame) -> pd.DataFrame:
+    """
+    Get the sleep period from the sleep events dataframe.
 
-    return colors
+    Args:
+        events (pd.DataFrame): The sleep events dataframe.
 
+    Returns:
+        pd.DataFrame: The sleep period dataframe.
+    """
+    return events.groupby(['participant_id', 'research_stage', 'array_index'])\
+        .apply(get_events_period, 'Wake', 'Wake', 'Sleep',
+               first_start=True, first_end=False, include_start=False, include_end=False)
+    
diff --git a/pheno_utils/timeseries_plots.py b/pheno_utils/timeseries_plots.py
new file mode 100644
index 0000000..22e1eb0
--- /dev/null
+++ b/pheno_utils/timeseries_plots.py
@@ -0,0 +1,763 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/15_timeseries_plots.ipynb.
+
+# %% auto 0
+__all__ = ['TimeSeriesFigure', 'format_xticks', 'format_timeseries', 'plot_events_bars', 'plot_events_fill',
+           'prep_to_plot_timeseries', 'get_events_period', 'get_color_map']
+
+# %% ../nbs/15_timeseries_plots.ipynb 3
+from typing import Callable, Iterable, Optional, Union, Tuple
+import warnings
+
+import numpy as np
+import pandas as pd
+
+import seaborn as sns
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+
+from .config import DEFAULT_PALETTE, TIME_FORMAT, LEGEND_SHIFT
+
+# %% ../nbs/15_timeseries_plots.ipynb 4
+class TimeSeriesFigure:
+    def __init__(self, figsize: tuple = (10, 6), padding: float = 0.05):
+        """
+        Initialize a TimeSeriesFigure instance. This class is used to create and manage
+        a figure with multiple axes for time series data.
+        
+        Args:
+            figsize (tuple): Size of the figure (width, height) in inches.
+        """
+        self.fig = plt.figure(figsize=figsize)
+        self.axes: Iterable[tuple] = []
+        self.axis_names: dict = {}
+        self.padding = padding
+        self.custom_paddings = {}  # To store custom padding for specific axes
+        self.shared_x_groups = []  # To keep track of shared x-axis groups
+
+    def plot(
+        self, 
+        plot_function: Callable, 
+        *args, 
+        n_axes: int = 1, 
+        height: float = 1, 
+        sharex: Union[str, int, plt.Axes] = None, 
+        second_y: bool = False,
+        name: str = None, 
+        ax: Union[str, int, plt.Axes] = None, 
+        adjust_time: Optional[str] = 'union',
+        adjust_by_axis: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]] = None,
+        **kwargs
+    ) -> Union[plt.Axes, Iterable[plt.Axes]]:
+        """
+        Plot using a dataset-specific function, creating a new axis if needed.
+        The plot function should accept the axis object as the argument `ax`, or
+        a list of axes if multiple axes are used.
+        
+        Args:
+            plot_function (Callable): The dataset-specific function to plot the data.
+            *args: Arguments to pass to the plot function.
+            n_axes (int): The number of axes required. Default is 1.
+            height (float): The proportional height of the axes relative to a single unit axis.
+            sharex (str, int, or plt.Axes): Index or name of the axis to share the x-axis with. If None, the x-axis is independent.
+            second_y (bool): If True, plot will be done on a secondary y-axis in the plot. Default is False.s
+            name (str): Name or ID to assign to the axis.
+            ax (plt.Axes, str, int): Pre-existing axis (object, name, or index) or list of axes to plot on.
+            adjust_time (str, None): Method to adjust the time limits of all axes to match the data.
+            adjust_by_axis (str, int, plt.Axes): Axes (single or multiple) to use as a reference for adjusting the time limits.
+            **kwargs: Keyword arguments to pass to the plot function.
+        
+        Returns:
+            Union[plt.Axes, Iterable[plt.Axes]]: A single axis object or a list of axis objects if multiple axes are used.
+        """
+        if ax is None:
+            ax = self.add_axes(height=height, n_axes=n_axes, sharex=sharex, name=name)
+        else:
+            ax = self.get_axes(ax, squeeze=True)
+
+        if second_y:
+            ax.yaxis.grid(False)
+            ax = ax.twinx()
+
+        plot_function(*args, ax=ax, **kwargs)
+        if adjust_time:
+            self.set_time_limits(None, None, method=adjust_time, reference_axis=adjust_by_axis)
+        if second_y:
+            ax.yaxis.grid(False)
+            ax.yaxis.label.set_rotation(90)
+            ax.yaxis.label.set_ha('center')
+
+        return ax
+
+    def add_axes(
+        self, 
+        height: float = 1, 
+        n_axes: int = 1, 
+        sharex: Optional[Union[str, int, plt.Axes]] = None, 
+        name: Optional[str] = None,
+    ) -> Union[plt.Axes, Iterable[plt.Axes]]:
+        """
+        Add one or more axes with a specific proportional height to the figure.
+        
+        Args:
+            height (float): The proportional height of each new axis relative to a single unit axis.
+            n_axes (int): The number of axes to create.
+            sharex (str, int, or plt.Axes): Index or name of the axis to share the x-axis with. If None, the x-axis is independent.
+            name (Optional[str]): Name or ID to assign to the axis (only valid if num_axes=1).
+        
+        Returns:
+            Union[plt.Axes, Iterable[plt.Axes]]: A single axis object or a list of axis objects if multiple axes are created.
+        """
+        new_axes = []
+        shared_group = []
+        
+        if sharex is not None:
+            sharex = self.get_axes(sharex)[0]
+            shared_group.append(sharex)
+
+        for _ in range(n_axes):
+            ax = self.fig.add_subplot(len(self.axes) + 1, 1, len(self.axes) + 1, sharex=sharex)
+            new_axes.append(ax)
+            self.axes.append((ax, height))
+            shared_group.append(ax)
+            # When creating mulitple axes, always share their x-axis with the first one
+            if sharex is None:
+                sharex = ax
+        
+        if shared_group:
+            self.shared_x_groups.append(shared_group)
+
+        if name is not None:
+            self.axis_names[name] = new_axes
+        
+        self._adjust_axes()
+
+        return new_axes if n_axes > 1 else new_axes[0]
+
+    def _adjust_axes(self) -> None:
+        """
+        Adjust the positions and sizes of all axes based on their proportional height and apply padding.
+        """
+        total_height = sum(height for _, height in self.axes)
+        total_padding = self.padding * (len(self.axes) - 1)
+        bottom = 1 - total_padding  # Start from the top of the figure
+
+        for i, (ax, height) in enumerate(self.axes):
+            ax_height = height / total_height * (1 - total_padding)
+            # Adjust for any custom padding before this axis
+            custom_pad = self.custom_paddings.get(i, 0)
+            ax.set_position([0.1, bottom - ax_height, 0.8, ax_height])
+            bottom -= ax_height + self.padding + custom_pad  # Move down, considering padding
+
+    def _get_axis_by_name(self, name: str) -> Optional[plt.Axes]:
+        """
+        Retrieve an axis by its name or ID.
+        
+        Args:
+            name (str): The name or ID of the axis to retrieve.
+        
+        Returns:
+            Optional[plt.Axes]: The corresponding axis object if found, otherwise None.
+        """
+        return self.axis_names.get(name, [])
+
+    def get_axes(self, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]]=None, squeeze=False) -> Iterable[plt.Axes]:
+        """
+        Retrieve the axis object(s) based on the input type.
+
+        Args:
+            ax: The axis object, index, name, or list of those to retrieve.
+            squeeze (bool): Whether to return a single axis object if only one is found.
+        
+        Returns:
+            Iterable[plt.Axes]: A list of axis objects.
+        """
+        if ax is None:
+            return [a for a, _ in self.axes]
+        elif not isinstance(ax, list):
+            ax = [ax]
+        
+        ax_list = []
+        for a in ax:
+            if isinstance(a, str):
+                by_name = self._get_axis_by_name(a)
+                if len(by_name) == 0:
+                    warnings.warn(f"No axis found with name '{a}'")
+                ax_list.extend(by_name)
+            elif isinstance(a, int):
+                ax_list.append(self.axes[a][0])
+
+        if squeeze and len(ax_list) == 1:
+            return ax_list[0]
+        else:
+            return ax_list
+
+    def print_shared_axes(self):
+        """
+        Print which axes in the figure share their x-axis.
+
+        Returns:
+            None
+        """
+        shared_groups = {}
+        for i, (ax, _) in enumerate(self.axes):
+            for j, (other_ax, _) in enumerate(self.axes):
+                if i != j and ax.get_shared_x_axes().joined(ax, other_ax):
+                    if i not in shared_groups:
+                        shared_groups[i] = []
+                    shared_groups[i].append(j)
+
+        for ax_idx, shared_with in shared_groups.items():
+            print(f"Axis {ax_idx} shares its x-axis with: {shared_with}")
+
+    def get_axis_properties(self, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]]=None) -> dict:
+        """
+        Get the properties of a specific axis or axes.
+        
+        Args:
+            ax (str, int, plt.Axes, or a list of those): The axis or axes to get the properties for.
+        
+        Returns:
+            dict: A dictionary of properties for the axis or axes.
+        """
+        ax_list = self.get_axes(ax)
+        properties = {}
+        for a in ax_list:
+            properties = {key: properties.get(key, []) + [value] for key, value in a.properties().items()}
+
+        for k, v in properties.items():
+            if len(v) == 1:
+                properties[k] = v[0]
+
+        return properties
+
+    def set_axis_properties(self, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]]=None, **kwargs) -> None:
+        """
+        Set properties for a specific axis or axes.
+        
+        Args:
+            ax (str, int, plt.Axes, or a list of those): The axis or axes to set the properties for.
+            **kwargs: Additional keyword arguments to pass to the axis object.
+        """
+        ax_list = self.get_axes(ax)
+        for a in ax_list:
+            a.set(**kwargs)
+
+    def set_axis_padding(self, padding: float, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]]=None, above: bool = True) -> None:
+        """
+        Set custom padding for a specific axis.
+        
+        Args:
+            padding (float): The amount of padding to add as a fraction of the figure height.
+            
+            above (bool): Whether to add padding above the axis (default) or below.
+        """
+        ax_list = self.get_axes(ax)
+        all_axes = [a for a, _ in self.axes]
+
+        for ax in ax_list:
+            axis_index = all_axes.index(ax)
+            if axis_index < 0:
+                warnings.warn("Axis not found in the figure.")
+                continue
+            if above:
+                self.custom_paddings[axis_index] = padding
+            elif axis_index == len(self.axes) - 1:
+                continue
+            else:
+                self.custom_paddings[axis_index + 1] = padding
+            self._adjust_axes()
+
+    def set_time_limits(
+            self, start_time: Union[float, str, pd.Timestamp, None],
+            end_time: Union[float, str, pd.Timestamp, None],
+            method: str='union',
+            reference_axis: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]] = None
+    ) -> None:
+        """
+        Set the time limits for all axes in the figure. Calling with None will adjust the limits to the data.
+
+        Args:
+            start_time (Union[float, str, pd.Timestamp, None]): The start time for the x-axis.
+            end_time (Union[float, str, pd.Timestamp, None]): The end time for the x-axis.
+        """
+        # Default values
+        xlim = np.array(self.get_axis_properties(reference_axis)['xlim']).reshape((-1, 2))
+        if method == 'union':
+            xlim = xlim[:, 0].min(), xlim[:, 1].max()
+        elif method == 'intersect':
+            xlim = xlim[:, 0].max(), xlim[:, 1].min()
+        else:
+            raise ValueError(f"Invalid method: {method} not in ['union', 'intersect']")
+
+        # Convert string inputs to pandas Timestamp objects
+        if start_time is not None:
+            start_time = pd.to_datetime(start_time)
+        else:
+            start_time = xlim[0]
+        if end_time is not None:
+            end_time = pd.to_datetime(end_time)
+        else:
+            end_time = xlim[1]
+
+        self.set_axis_properties(xlim=(start_time, end_time))
+
+    def set_periodic_ticks(
+        self, 
+        interval: Union[str, pd.Timedelta], 
+        start_time: str = '2018-01-01 00:00',
+        end_time: str = None,
+        fmt=TIME_FORMAT,
+        ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]] = None
+    ) -> None:
+        """
+        Set periodic x-ticks at a regular interval throughout the day.
+
+        Args:
+            interval (Union[str, pd.Timedelta]): The interval between ticks (e.g., '1H' for hourly ticks, '30T' for 30 minutes).
+            start_time (str): The time of day to start the ticks from (default is '00:00').
+            end_time (str): The time of day to end the ticks at (default is None).
+            fmt (str): The date format string to be used for the tick labels.
+            ax (str, int, plt.Axes, or a list of those): The axis (or axes) to apply the ticks to. 
+                Can be an axis object, a list of axes, an index, or a name. If None, applies to all axes.
+        """
+        # Convert interval to pandas Timedelta if it's a string
+        if isinstance(interval, str):
+            interval = pd.to_timedelta(interval)
+
+        # Convert start_time to a datetime object with today's date
+        if start_time is not None:
+            start_time = pd.to_datetime(start_time).tz_localize(None)
+        if end_time is not None:
+            end_time = pd.to_datetime(end_time).tz_localize(None)
+
+        # Determine which axes to apply this to
+        axes = self.get_axes(ax)
+
+        for a in axes:
+            if a is not None:
+                # Get the x-axis limits
+                min_x, max_x = a.get_xlim()
+
+                # Convert limits to datetime if they are in float format
+                if isinstance(min_x, (float, int)):
+                    min_x = mdates.num2date(min_x).replace(tzinfo=None)
+                if isinstance(max_x, (float, int)):
+                    max_x = mdates.num2date(max_x).replace(tzinfo=None)
+
+                # Set the ticks to align with the start_datetime
+                ticks = pd.date_range(start=start_time if start_time else min_x,
+                                      end=end_time if end_time else max_x,
+                                      freq=interval)
+
+                # Make sure ticks are within the limits
+                ticks = [tick for tick in ticks if min_x <= tick and tick <= max_x]
+
+                # Set the locator and formatter
+                format_xticks(a, ticks, fmt)
+
+                plt.setp(a.get_xticklabels(), rotation=0, ha='center')
+
+    def add_legend(self, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]], **kwargs) -> None:
+        """
+        Add a legend to a specific axis.
+        
+        Args:
+            axis (str, int, plt.Axes, or a list of those): The axis to add the legend to.
+        """
+        ax_list = self.get_axes(ax)
+        for a in ax_list:
+            a.legend(**kwargs)
+
+    def set_legend(self, ax: Union[str, int, plt.Axes, Iterable[Union[str, int, plt.Axes]]]=None, bbox_to_anchor: tuple=None, **kwargs):
+        """
+        Update the legend properties for all axes in the figure, or a subset of them, if the legend exists.
+
+        Args:
+            axis (str, int, plt.Axes, or a list of those): The name or list of names of axes to update the legend for.
+            bbox_to_anchor (tuple, optional): The bounding box coordinates for the legend.
+            **kwargs: Additional keyword arguments passed to the legend object.
+        """
+        ax_list = self.get_axes(ax)
+
+        for a in ax_list:
+            legend = a.get_legend()
+            if legend is None:
+                continue
+            if bbox_to_anchor is not None:
+                legend.set_bbox_to_anchor(bbox_to_anchor)
+            legend.set(**kwargs)
+
+    def show(self) -> None:
+        """
+        Display the figure.
+        """
+        plt.show()
+
+
+def format_xticks(ax: plt.Axes, xticks: Iterable=None, format: str=TIME_FORMAT, **kwargs):
+    """ format datestrings on x axis """
+    if xticks is None:
+        xticks = ax.get_xticks()
+    ax.set_xticks(xticks)
+    ax.set_xticklabels(xticks, **kwargs)
+    xfmt = mdates.DateFormatter(format)
+    ax.xaxis.set_major_formatter(xfmt)
+
+
+def format_timeseries(
+    df: pd.DataFrame,
+    participant_id: int=None,
+    array_index: int=None,
+    time_range: Tuple[str, str]=None,
+    x_start: str='collection_timestamp',
+    x_end: str='collection_timestamp',
+    unique: bool=False,
+) -> pd.DataFrame:
+    """
+    Reformat and filter a time series DataFrame based on participant ID, array index, and date range.
+
+    Args:
+        df (pd.DataFrame): The DataFrame to filter.
+        participant_id (int): The participant ID to filter by.
+        array_index (int): The array index to filter by.
+        time_range: The date range to filter by. Can be a tuple of two dates / times or two strings.
+        x_start (str): The name of the column containing the start time.
+        x_end (str): The name of the column containing the end time.
+
+    Returns:
+        pd.DataFrame: The filtered DataFrame
+    """
+    if participant_id is not None:
+        df = df.query('participant_id == @participant_id')
+    if array_index is not None:
+        df = df.query('array_index == @array_index')
+
+    # Reset index to avoid issues with slicing and indexing
+    x_ind = np.unique([c for c in [x_start, x_end] if c in df.index.names])
+    if len(x_ind):
+        if np.isin(x_ind, df.index.names).any():
+            df = df.reset_index(x_ind)
+        df[x_start] = df[x_start].dt.tz_localize(None)
+        if x_start != x_end:
+            df[x_end] = df[x_end].dt.tz_localize(None)
+    if time_range is not None:
+        time_range = pd.to_datetime(time_range)
+        df = df.loc[(time_range[0] <= df[x_start]) & (df[x_end] <= time_range[1])]
+    if unique:
+        df = df.drop_duplicates()
+
+    return df.sort_values(x_start)
+
+# %% ../nbs/15_timeseries_plots.ipynb 5
+def plot_events_bars(
+    events: pd.DataFrame,
+    x_start: str = 'collection_timestamp',
+    x_end: str = 'event_end',
+    y: str = 'event',
+    hue: str = 'channel',
+    participant_id: Optional[int] = None,
+    array_index: Optional[int] = None,
+    time_range: Optional[Tuple[str, str]] = None,
+    y_include: Optional[Iterable[str]] = None,
+    y_exclude: Optional[Iterable[str]] = None,
+    legend: bool = True,
+    palette: str = DEFAULT_PALETTE,
+    alpha: Optional[float] = 0.7,
+    ax: Optional[plt.Axes] = None,
+    figsize: Tuple[float, float] = (12, 6),
+) -> plt.Axes:
+    """
+    Plot events as bars on a time series plot.
+
+    Args:
+        events (pd.DataFrame): The events dataframe.
+        x_start (str): The column name for the start time of the event.
+        x_end (str): The column name for the end time of the event.
+        y (str): The column name for the y-axis values.
+        hue (str): The column name for the color of the event.
+        participant_id (int): The participant ID to filter events by.
+        array_index (int): The array index to filter events by.
+        time_range (Tuple[str, str]): The time range to filter events by.
+        y_include (Iterable[str]): The list of values to include in the plot.
+        y_exclude (Iterable[str]): The list of values to exclude from the plot.
+        legend (bool): Whether to show the legend.
+        palette (str): The name of the colormap to use for coloring events.
+        alpha (float): The transparency of the bars. Default is 0.7.
+        ax (plt.Axes): The axis to plot on. If None, a new figure is created.
+        figsize (Tuple[float, float]): The size of the figure (width, height) in inches.
+    """
+    events, color_map = prep_to_plot_timeseries(
+        events, x_start, x_end,
+        hue, y,
+        participant_id, array_index, time_range,
+        y_include, y_exclude,
+        palette=palette)
+    if hue is None:
+        hue = 'hue'
+
+    if ax is None:
+        fig, ax = plt.subplots(figsize=figsize)
+
+    # Plot events
+    events = events.assign(diff=lambda x: x[x_end] - x[x_start]).sort_values([hue, y])
+    y_labels = []
+    legend_dicts = []
+    for i, (y_label, events) in enumerate(events.groupby(y, observed=True, sort=False)):
+        if len(y) == 0:
+            continue
+        y_labels.append(y_label)
+        for c, r in events.groupby(hue, observed=True):
+            data = r[[x_start, 'diff']]
+            if not len(data):
+                continue
+            h = ax.broken_barh(data.values, (i-0.4,0.8), color=color_map[c], alpha=alpha)
+            legend_dicts.append({'label': c, 'handle': h})
+
+    # format plot
+    if legend:
+        legend_df = pd.DataFrame.from_dict(legend_dicts).drop_duplicates(subset='label')
+        ax.legend(
+            legend_df['handle'],
+            legend_df['label'],
+            loc='upper left', 
+            bbox_to_anchor=LEGEND_SHIFT)
+
+    ax.set_yticks(np.arange(len(y_labels)), y_labels)
+    format_xticks(ax)
+    ax.invert_yaxis()  # Invert y-axis to match the order of the legend
+
+    return ax
+
+
+def plot_events_fill(
+    events: pd.DataFrame,
+    x_start: str = 'collection_timestamp',
+    x_end: str = 'event_end',
+    hue: str = 'channel',
+    label: str = None,
+    participant_id: Optional[int] = None,
+    array_index: Optional[int] = None,
+    time_range: Optional[Tuple[str, str]] = None,
+    y_include: Optional[Iterable[str]] = None,
+    y_exclude: Optional[Iterable[str]] = None,
+    legend: bool = True,
+    palette: str = DEFAULT_PALETTE,
+    alpha: Optional[float] = 0.5,
+    ax: Optional[plt.Axes] = None,
+    figsize: Iterable[float] = [12, 6],
+) -> plt.Axes:
+    """
+    Plot events as filled regions on a time series plot.
+
+    Args:
+        events (pd.DataFrame): The events dataframe.
+        x_start (str): The column name for the start time of the event.
+        x_end (str): The column name for the end time of the event.
+        hue (str): The column name for the color of the event.
+        label (str): The column name for the label of the event.
+        participant_id (int): The participant ID to filter events by.
+        array_index (int): The array index to filter events by.
+        time_range (Iterable[str]): The time range to filter events by.
+        y_include (Iterable[str]): The list of values to include in the plot.
+        y_exclude (Iterable[str]): The list of values to exclude from the plot.
+        legend (bool): Whether to show the legend.
+        palette (str): The name of the palette to use for coloring events.
+        alpha (float): The transparency of the filled regions.
+        ax (plt.Axes): The axis to plot on. If None, a new figure is created.
+        figsize (Tuple[float, float]): The size of the figure (width, height) in inches.
+    """
+    events, color_map = prep_to_plot_timeseries(
+        events, x_start, x_end,
+        hue, label,
+        participant_id, array_index, time_range,
+        y_include, y_exclude,
+        palette=palette)
+    if hue is None:
+        hue = 'hue'
+
+    if ax is None:
+        fig, ax = plt.subplots(figsize=figsize)
+    if type(ax) is not list:
+        ax = [ax]
+
+    for a in ax:
+        # Plotting events
+        this_color = hue if hue is not None else '#4c72b0'
+        for _, row in events.iterrows():
+            if color_map is not None:
+                this_color = color_map[row[hue]]
+            # Plot the event as a filled region, with zorder to ensure it's behind other elements
+            a.axvspan(
+                row[x_start], row[x_end], 0, 1,
+                color=this_color, alpha=alpha, zorder=0,
+                transform=a.get_xaxis_transform())
+
+        # Add labels as xticks on the top secondary x-axis
+        if label:
+            secax = a.secondary_xaxis('top')
+            secax.set_xticks(events[x_start])
+            secax.set_xticklabels(events[label], rotation=0, ha='center')
+
+        # Add legend
+        if legend:
+            # Get existing handles from existing legends in the axes
+            handles, labels = a.get_legend_handles_labels()
+            if color_map is not None:
+                handles += [plt.Rectangle((0, 0), 1, 1, color=c, alpha=alpha) for c in color_map]
+                labels += color_map.index.tolist()
+            else:
+                handles += [plt.Rectangle((0, 0), 1, 1, color=this_color, alpha=alpha)]
+                labels += ['events']
+            a.legend(handles, labels, loc='upper left', bbox_to_anchor=LEGEND_SHIFT)
+
+        format_xticks(a)
+
+    return ax
+
+
+def prep_to_plot_timeseries(
+    data: pd.DataFrame,
+    x_start: str,
+    x_end: str,
+    hue: str,
+    label: str,
+    participant_id: int,
+    array_index: int,
+    time_range: Tuple[str, str],
+    y_include: Iterable[str],
+    y_exclude: Iterable[str],
+    add_columns: Iterable[str]=None,
+    palette=DEFAULT_PALETTE,
+) -> Tuple[pd.DataFrame, pd.DataFrame]:
+    """
+    Prepare timeseries / events data for plotting.
+
+    Args:
+        events (pd.DataFrame): The timeseries / events dataframe.
+        x_start (str): The column name for the start time of the event.
+        x_end (str): The column name for the end time of the event.
+        hue (str): The column name for the color of the event.
+        label (str): The column name for the label of the event.
+        participant_id (int): The participant ID to filter events by.
+        array_index (int): The array index to filter events by.
+        time_range (Iterable[str]): The time range to filter events by.
+        y_include (Iterable[str]): The list of values to include in the plot.
+        y_exclude (Iterable[str]): The list of values to exclude from the plot.
+        add_columns (Iterable[str]): Additional columns to include in the plot.
+        palette (str): The name of the colormap to use for coloring events.
+
+    Returns:
+        Tuple[pd.DataFrame, pd.DataFrame]: The filtered events dataframe and the color map.
+    """
+    if type(add_columns) is str:
+        add_columns = [add_columns]
+
+    data = format_timeseries(data, participant_id, array_index, time_range, x_start, x_end)
+
+    # Filter events based on y_include and y_exclude
+    data = data.dropna(subset=[x_start, x_end])
+    if hue is not None and hue in data.index.names:
+        data = data.reset_index(hue)
+    if label is not None and label in data.index.names:
+        data = data.reset_index(label)
+    if y_include is not None:
+        ind = pd.Series(False, index=data.index)
+        if hue is not None:
+            ind |= data[hue].isin(y_include)
+        if label is not None:
+            ind |= data[label].isin(y_include)
+        data = data.loc[ind]
+    if y_exclude is not None:
+        ind = pd.Series(False, index=data.index)
+        if hue is not None:
+            ind |= data[hue].isin(y_exclude)
+        if label is not None:
+            ind |= data[label].isin(y_exclude)
+        data = data.loc[~ind]
+    if hue is None:
+        hue = 'hue'
+        data[hue] = 'events'
+
+    col_list = [x_start, x_end, hue, label]
+    if add_columns is not None:
+        col_list += list(add_columns)
+    col_list = pd.Series(col_list).dropna().drop_duplicates()
+
+    # Set colors
+    if hue in data.columns:
+        colors = get_color_map(data, hue, palette)
+    else:
+        colors = None
+
+    return data[col_list], colors
+
+
+def get_events_period(
+    events_filtered: pd.DataFrame,
+    period_start: str,
+    period_end: str,
+    period_name: str,
+    col: str = 'event',
+    first_start: bool = True,
+    first_end: bool = True,
+    include_start: bool = True,
+    include_end: bool = True,
+    x_start: str = 'collection_timestamp',
+    x_end: str = 'event_end',
+) -> pd.DataFrame:
+    """
+    Get the period of time between the start and end events.
+
+    Args:
+        events_filtered (pd.DataFrame): The events DataFrame.
+        period_start (str): The label of the start event.
+        period_end (str): The label of the end event.
+        period_name (str): The label to assign to the period.
+        col (str): The column name for the event labels. Default is 'event'.
+        first_start (bool): If True, get the first start event. Default is True.
+        first_end (bool): If True, get the first end event. Default is True.
+        include_start (bool): If True, include the start event in the period. Default is True.
+        include_end (bool): If True, include the end event in the period. Default is True.
+        x_start (str): The column name for the start time of the event. Default is 'collection_timestamp'.
+        x_end (str): The column name for the end time of the event. Default is 'event_end'.
+
+    Returns:
+        pd.DataFrame: The period of events in the same format as the input DataFrame.
+    """
+    events_filtered = format_timeseries(events_filtered, None, None, None, x_start, x_end)
+
+    start_time = events_filtered.loc[
+        events_filtered[col] == period_start,
+        x_start if include_start else x_end]\
+        .iloc[0 if first_start else -1]
+    end_time = events_filtered.loc[
+        events_filtered[col] == period_end,
+        x_end if include_end else x_start]\
+        .iloc[0 if first_end else -1]
+
+    return pd.DataFrame({
+        x_start: [start_time],
+        x_end: [end_time],
+        col: [period_name]
+    })
+
+
+def get_color_map(data: pd.DataFrame, hue: str, palette: str) -> pd.DataFrame:
+    """
+    Get a color map for a specific column in the data.
+
+    Args:
+        data (pd.DataFrame): The data to get the color map from.
+        hue (str): The column name to use for the color map.
+        palette (str): The name of the colormap to use.
+
+    Returns:
+        pd.DataFrame: A DataFrame with the color map.
+    """
+    colors = sorted(data[hue].unique())
+    colors = pd.DataFrame({
+        hue: colors,
+        'color': sns.color_palette(palette, len(colors))
+        }).set_index(hue)['color']
+
+    return colors