Repository: UCL-Chimera/medical-waveforms (language: python)

Issue:
Design data structure for quality-control presets
A dict or similar in style of https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html

Beware that these should be immutable where used as default args (dicts are mutable).


Discussion / hints:
@eliza-brown Could just use a dict then convert to a frozendict (https://pypi.org/project/frozendict/) where we use them as default arguments?
Group decision to use dataclasses (with default values) to hold the presets.

Later, we could have an hierarchy where e.g. adult and paediatric classes inherit from a common base class with limits that make sense for adults and kids.

Also later, could also think about a function that generates paediatric classes with age-appropriate presets.  
> Later, we could have an hierarchy where e.g. adult and paediatric classes inherit from a common base class with limits that make sense for adults and kids.

We would need to be a bit careful with ordering if we do this. See e.g. https://stackoverflow.com/questions/51575931/class-inheritance-in-python-3-7-dataclasses 




Fix patch:
diff --git a/abpfeature_annotated.m b/abpfeature_annotated.m
deleted file mode 100644
index 8295e8f..0000000
--- a/abpfeature_annotated.m
+++ /dev/null
@@ -1,195 +0,0 @@
-function r = abpfeature(abp,OnsetTimes, Fs)
-    % ABPFEATURE  ABP waveform feature extractor.
-    %   r = ABPFEATURE(ABP,ONSETTIMES) extracts features from ABP waveform such
-    %   as systolic pressure, mean pressure, etc.
-    %
-    %   In:     ABP = signal (default 125Hz sampled)
-    %           ONSETTIMES = times of onset (in samples)
-    %   Out:    Beat-to-beat ABP features
-    %           Col 1:  Time of systole   [samples]
-    %               2:  Systolic BP       [mmHg]
-    %               3:  Time of diastole  [samples]
-    %               4:  Diastolic BP      [mmHg]
-    %               5:  Pulse pressure    [mmHg]
-    %               6:  Mean pressure     [mmHg]
-    %               7:  Beat Period       [samples]
-    %               8:  mean_dyneg
-    %               9:  End of systole time  0.3*sqrt(RR)  method
-    %              10:  Area under systole   0.3*sqrt(RR)  method
-    %              11:  End of systole time  1st min-slope method
-    %              12:  Area under systole   1st min-slope method
-    %              13:  Pulse             [samples]
-    % 
-    %   Usage:
-    %   - OnsetTimes must be obtained using wabp.m
-    %
-    %   Written by James Sun (xinsun@mit.edu) on Nov 19, 2005.
-    %   Updated by Alistair Johnson, 2014.
-    %
-    %   LICENSE:    
-    %       This software is offered freely and without warranty under 
-    %       the GNU (v3 or later) public license. See license file for
-    %       more information
-    
-    % NOTE THAT ONLY THE FOLLOWING FEATURES ARE USED DOWNSTREAM BY jSQI.m
-    % - Systolic BP
-    % - Diastolic BP
-    % - Pulse pressure
-    % - MAP
-    % - Beat duration
-    % - Mean dyneg
-    
-    
-    if length(OnsetTimes)<30 % don't process anything if too little onsets
-        r = [];
-        return
-    end
-    
-    %% P_sys, P_dias
-    if nargin<3
-    Fs        = 125; % Sampling Frequency
-    end
-    Window    = ceil(0.32*Fs);
-    OT        = OnsetTimes(1:end-1);  % exclude last element of vector
-    BeatQty   = length(OT);
-    
-    % [MinDomain,MaxDomain] = init(zeros(BeatQty,Window));
-    
-    % iterate over columns
-    % n_rows is number of beats
-    % n_columns is size of window, with increasing size each side on onset
-    for i=1:Window % Vectorized version (this isn't vectorized)
-        MinDomain(:,i) = OT-i+1;  % these are matrices created on fly
-        MaxDomain(:,i) = OT+i-1;
-    end
-    
-    %MinDomain = bsxfun(@minus,OT,(0:Window-1)); 
-    %MaxDomain = bsxfun(@plus,OT,(0:Window-1));
-    
-    % Ensures window doesn't go off edges of signal
-    MinDomain(MinDomain<1) = 1;  % Error protection
-    MaxDomain(MaxDomain<1) = 1;
-    MinDomain(MinDomain>length(abp)) = length(abp);
-    MaxDomain(MaxDomain>length(abp)) = length(abp);
-    
-    % Get min or max across an axis
-    % 2 selects column axis
-    % [] is default arg
-    % What is abp(...) doing here?
-    [P_dias Dindex]  = min(abp(MinDomain),[],2);% Get lowest value across 'Window' samples before beat onset
-    [P_sys  Sindex]  = max(abp(MaxDomain),[],2);% Get highest value across 'Window' samples after beat onset
-    
-    % Flattens indices
-    DiasTime         = MinDomain(sub2ind(size(MinDomain),(1:BeatQty)',Dindex)); % Map offset indices Dindex to original indices
-    SysTime          = MaxDomain(sub2ind(size(MaxDomain),(1:BeatQty)',Sindex)); % Map offset indices Sindex to original indices
-
-    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    % Pulse Pressure [mmHg]
-    PP         = P_sys - P_dias;
-    
-    % Beat Period [samples]
-    % Differences bnetween adjacent elements
-    BeatPeriod = diff(OnsetTimes);
-    
-    % Mean,StdDev, Median Deriv- (noise detector)
-    % This seems to be a homegrown noise metric
-    dyneg          = diff(abp);
-    dyneg(dyneg>0) = 0;
-    
-    % what does init do here? why assign 3 outputs?
-    % assume this inits 3 zero vectors of len BeatQty
-    % is init borrowed from the neural net library?
-    [MAP,stddev,mean_dyneg] = init(zeros(BeatQty,1));
-    
-    % shift last onset time if it is at the very end of the signal
-    if OnsetTimes(end)==numel(abp)
-        OnsetTimes(end) = numel(abp) - 1;
-    end
-    
-    
-    for i=1:BeatQty
-        interval       = abp(OnsetTimes(i):OnsetTimes(i+1));  % equivalent to get_cycles()
-        MAP(i)         = mean(interval);
-        stddev(i)      = std(interval);
-    
-        dyneg_interval = dyneg(OnsetTimes(i):OnsetTimes(i+1));
-        dyneg_interval(dyneg_interval==0) = [];  % chop out all the zeros...
-        if min(size(dyneg_interval))==0
-            dyneg_interval = 0;  % ...and if there's nothing left (if no negative) make it zero
-        end
-        mean_dyneg(i)  = mean(dyneg_interval);  % if artefact may have high magnitude
-    end
-    
-    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    % Systolic Area calculation using 0.3*sqrt(RR)
-    RR                  = BeatPeriod/Fs;  % RR time in seconds
-    sys_duration        = 0.3*sqrt(RR); % does the sqrt account for relatively greater shortening of diastole as HR increases
-    EndOfSys1           = round(OT + sys_duration*Fs);
-    
-    % This looks like an approximation for speed, not the actual integral
-    SysArea1            = localfun_area(abp,OT,EndOfSys1',P_dias);
-    
-    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    % Systolic Area calculation using 'first minimum slope' method
-    % Looks like this trying to find the dicrotic notch, then obtain the systolic integral that way
-    
-    SlopeWindow         = ceil(0.28*Fs);
-    ST                  = EndOfSys1; % start 12 samples after P_sys
-    
-    if ST(end) > (length(abp)-SlopeWindow)   % error protection
-        ST(end) = length(abp)-SlopeWindow;  % avoid going over edge of signal
-    end
-    
-    SlopeDomain = zeros(BeatQty,SlopeWindow);  % init matrix
-    for i=1:SlopeWindow  % loop over columns
-        SlopeDomain(:,i) = ST+i-1;
-    end
-    % y[n] = x[n]-x[n-1]
-    % abp is 2 dimensional, just with 1 row
-    
-    Slope              = diff(abp(SlopeDomain),1,2);  % 1 means adjacent diffs, 2 selects column axis
-    Slope(Slope>0)     = 0; % Set positive slopes to zero
-    
-    [MinSlope index]   = min(abs(Slope),[],2); % Find first positive slope
-    EndOfSys2          = SlopeDomain(sub2ind(size(SlopeDomain),(1:BeatQty)',index));
-    SysArea2           = localfun_area(abp,OT,EndOfSys2,P_dias);
-    Pulse              = 60./BeatPeriod;
-    
-    
-    % OUTPUT:
-    % Ensure that there is no concatenation error by using
-    % (:) indexing to force column vectors
-    r = [SysTime(:),P_sys(:),DiasTime(:),P_dias(:),PP(:),...
-        MAP(:),BeatPeriod(:),mean_dyneg(:),EndOfSys1(:),SysArea1(:),...
-        EndOfSys2(:),SysArea2(:),Pulse(:)];
-    end
-    
-    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    % Helper function:
-    
-    function SysArea = localfun_area(abp,onset,EndSys,P_dias)
-    %% Input:  abp,
-    %%         onset, P_dias, end of systole, beat duration in unit of samples, same length
-    %% Output: systolic area, warner correction factor
-
-    % Just approximates integral
-    
-    BeatQty = length(onset);
-    SysArea = init(zeros(BeatQty,1));
-    for i=1:BeatQty
-        SysArea(i) = sum(abp(onset(i):EndSys(i))); % faster than trapz below
-        %b(i) = trapz(abp(onset(i):EndSys(i)));
-    end
-    EndSys = EndSys(:); onset = onset(:); % force col vectors
-    SysPeriod  = EndSys     - onset;
-    
-    % Time scale and subtract the diastolic area under each systolic interval
-    SysArea = (SysArea - P_dias.*SysPeriod)/125; % Area [mmHg*sec]
-    end
-    
-    % for initializing a variable
-    function varargout = init(z)
-    for i=1:nargout
-        varargout(i) = {z};
-    end
-    end
\ No newline at end of file
diff --git a/examples/get_cycles_benchmarking.ipynb b/examples/get_cycles_benchmarking.ipynb
index 4bed58d..1a2215e 100644
--- a/examples/get_cycles_benchmarking.ipynb
+++ b/examples/get_cycles_benchmarking.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
     "from sidewinder import synthetic\n",
     "from sidewinder.waveforms import Waveforms\n",
     "from sidewinder.features.waveform import find_troughs\n",
-    "from sidewinder.features import cycle"
+    "from sidewinder.features import cycles"
    ]
   },
   {
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -77,7 +77,7 @@
        "240002"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,20 +106,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "25.2 ms ± 3.37 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "34.1 ms ± 437 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
-    "cycles = cycle.get_cycles(wf, 'pressure')"
+    "_ = cycles.get_cycles(wf, 'pressure')"
    ]
   },
   {
diff --git a/examples/signal_quality.ipynb b/examples/signal_quality.ipynb
new file mode 100644
index 0000000..ee5176d
--- /dev/null
+++ b/examples/signal_quality.ipynb
@@ -0,0 +1,851 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quality assessment of some real-world arterial pressure data\n",
+    "\n",
+    "Data are used under the terms of the [VitalDB Data Use Agreement](https://vitaldb.net/dataset/?query=overview&documentId=13qqajnNZzkN7NZ9aXnaQ-47NWy7kx-a6gbrcEsi-gak&sectionId=h.usmoena3l4rb). If you do not agree with this agreement, please close this file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "sys.path.append(os.pardir)  # Needed for sidewinder import before we install it\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sidewinder.features import cycles\n",
+    "from sidewinder.waveforms import Waveforms\n",
+    "from sidewinder.features.waveform import find_troughs\n",
+    "from sidewinder.features.cycles import get_cycles\n",
+    "from sidewinder import quality\n",
+    "from sidewinder.utils import get_root_directory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load and visualise data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = os.path.join(\n",
+    "    get_root_directory(), \"local_data\", \"abp_waveform_extract_125hz.csv\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "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>time</th>\n",
+       "      <th>pressure</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000</td>\n",
+       "      <td>-0.910027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.008</td>\n",
+       "      <td>-0.910027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.016</td>\n",
+       "      <td>0.077430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.024</td>\n",
+       "      <td>-0.910027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.032</td>\n",
+       "      <td>-0.910027</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    time  pressure\n",
+       "0  0.000 -0.910027\n",
+       "1  0.008 -0.910027\n",
+       "2  0.016  0.077430\n",
+       "3  0.024 -0.910027\n",
+       "4  0.032 -0.910027"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(data_path, index_col=0)\n",
+    "data = data.rename({\"Time\": \"time\", \"SNUADC/ART\": \"pressure\"}, axis=1)\n",
+    "data.time -= data.time.values[0]\n",
+    "\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data.loc[data.time < 1000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data into `sidewinder`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wf = Waveforms(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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D3PTkcgDufL3hev9D3fbpI3h6yTbOOWIQvTJTWVZQwrlHDuJv/9nIzZ+YxDNLtzFhYA6Th/Sqfc6anfvJTEsmIyWJbSXljB+Ywz8Xb+Ezx+RTUFxGTkYKuVlptftXBm9GaSktH8SptLKarLQUXly+nSOH5lJSVsXqHfuYMDCH55dt56pZY3h++XbyctI5dkQf3t9czMi+kb8FVeEw2WkpLC0o4biRfWpLgxZvKiY9Jan2e3J3fvniapZsKeHCKUN4bPEWjh3Rh16ZqSzaVMQLy7ZTHXZu/9xUrnrwPQCunDWaDbtLufjYfN5ZX8jnjh9GZmoyDvTJTuNXL33IzDH9mJKf2+rvXRpW8zvRnFDYOVBZTWV1mJdX7ODiY/PZUlRGz4xUemWlsrmwlP4907HglrVEXKO1u/azrKCEU8b3Z/X2feRkpPDhjn08vWQbN5w9gdLKEO+sL2RwbgbfeeQDbr1wMiVlVTywYBPfnD2GF5Zv5+szR5GRmsQPn1xO/5x0Xlyxg3EDcvjtJVO46/V1fPbYfBZtLKKkrIqrZ4+tvVGvo9hbXkV5VYj05GTufGMtJ4/NY2ReNn99awNLC/by+oe7Gnzeyyt38I8rTgTg7XV7uOTOt/nWqWO55vRxlJRWURUO0++Qf4o/3LGvdvnQ5PpQhfsrKS5tey9zdxJNicjCxrq/23xys5OB/cB9dRLsnwOF7j7XzOYAvd39ejM7B/gmkQT7eOC37n58U8fvbiUiizcV8ak/vsVR+bk8eeWM2o95aj7aSbSaeJ65eibn/k5lH9K9XXPaOJ58v4AfnT+Jeat3cvnJoyivCvPB5mJmjOnHul37Gdgrg7+8tYE/v7khpudOS0mqTfTj7Qfnfoxbn1lZu94jPYVnrp7JIws385UTRzLnsSXccuFkQiEnNzu1Xi9cItz5+lqy01OorA7zvy9+yHPfOol731zPOUcMYm9ZFW98tJtrTh/HlqJSctJT6Z2dSuGBSob3zWbnvnLSk5PplZXK8q0lTBzUs14P4v6KalKSjOQko6wq1Oj3+uLy7YzK68GY/j1q29bu2s+ba3Zzyrj+XHH/IsqqQmSnJ7OsoP7H7985fRy/eulDrjltHBv2HODx9wq4cMpgnnh/a4t+DrdeOJk7Xl3L7An9uXDqYDYXlnHh1CH19qn7Pa7YupeBvTJ4ZslWxg3Ioai0it+/8hEXH5vPff/ZyMRBPfnpJyfz6KItfGH6cF5asYO31u5m8uBezPnn0hbFFmsZqUnMv342+8qr6/XMttXu/RVUh5yBvTIorwpRHXaKDlRy05PL+PlFR/Lmmt1sLS5n9fZ9PPXBViYN7tlsjXJTFtx4Ktf94wPe+OhgH+iGuecy9vvPUhVy3pwzmyG5mQD87NmVUXc8NKRuOcahJZ+J8MmpQ/j1xVPa/bxNlYhEk2DPBXYT6VU+UNMeTKMei+BGAE/XSbBXA6e4+zYzGwS86u7jzexPwfLfD92vsWN3twR70cYiPn3HW0zJz+WJOgk2JKY26VAd4UUoIh1br8xUSsqq+MbJo4Ibh3tTHQrz1/9s4C9fPY5XVu1k9oT+bCspJycjhb7ZaUFZkVFSVsWu/RWMG5BDKOwkJxmrtu8lHIZfvLCKeat3cfWpY7nr9XX8+uKj2LW/kj/PX881p4/j2n980G7/dNT4/PHDeGDBJnqkpzBxUE/e2dDw22rNJzcSf/d+ZRpf+0skb6i5l+TKWaNZWrCXt9fu4dH/PoG/v7OJo4bmMn/Nbp5eso3XvzuLBev3MKR3JsP6ZPGNvy1ic2Epe8ur2zX2844cxNNL6qdEq289i/E/eB6AgT0zePvGU7n/7Y38IBgytjWuO2McV80eW7veEd7bL5gymN9eMrXdz9vWBLuhz3Pd3UfFKLgR1E+wi909N1g2oMjdc83saWCuu88Ptv0buN7dG82gu1uCvWDdHi6+820AVt5yFh/74fO125Rgi4iIdC+zxucxb/XBspLnv30SZ/3mjTYd850bT6V/z4za9Y7w3n725IHc8YVj2v28bbrJ0d1bNhZLDLm7m1mLBjo0s8uBywGGDRsWl7g6qv0VB/9brptci4iISPdTN7kG2pxcA/WS646iop0/fYpGswm2mX2poXZ3vy/24QCww8wG1SkRqRl2ogDIr7Pf0KDt0LjuBO6ESA92nGLskO54dW3zO4mIiIh0IRXVoUSHcJhobhM+ts7jJOBHwPlxjOkp4MvB8peBJ+u0f8kipgMlTdVfd0cLNxYlOgQRERHpoiYN7pnoEBpUUdUJe7Dd/Zt1180sF3goFic3s78DpwD9zGwLkTG35wKPmNnXgY3AZ4PdnyUygsgaIsP0fTUWMYiIiIhI8z5//PBEh9Cg0sqO14MdzUQzhzoAxKQu290vbWTTqQ3s68CVsTiviIiIiLTM9FEdazryGgcq23fElmhEU4P9L6CmljkJmAg8Es+gRERERKRjye+TlegQGnSgohMm2MAv6yxXAxvdvevPcSkiIiLSTfXrkc7u/RX12lKTO+YsrHVnDe4oovlJLQTecPfXgF3A0WaW2Km2JK7CYScc7lYDsIiIiEgdnzhqUKJDiNrkIb0SHcJhounBfh04ycx6Ay8C7wIXA5+PZ2ASvQ27D1BQXBaz453+69coKC5j1U/Ojtkx42143yx+dP4kqkPOoo1FTBvem8vui0wy9JuLp/Dth99PbIAiIiKdyMAONt51/5x0hvfNYl95NVWhMHk56by9rpARfbP4388cmejwDhNNgm3uXhqM6vFHd/+5mb0f57ikBU755asxPd7aDjIl75yzJ3Dv/PXs3FfR7L6vfXdW7fLpEwcAB2ev3FZy8J+PKfm59MxMpehAJZlpyXxz9hie/mAb/16187CPwtrb6RMH8D+njOa5ZdsZ2S+bd9YXsqyghFDY6ZOdRsid9zYVA5CTnsK+imqmj+rD4o3FVIYODlH020um0K9HOkWllSzcUMRf3trAlPxc3t9cnJhvTEREJHDdGeMAmDqsNwcqqtlbXs2EgTl8bFBPkpMMiEycN/nmF/jdpVPpm53G4NxMRvbLTmTYLRZVgm1mJxDpsf560JYcv5AkHj7YXMxR+bmJDqNRG+aeS0lZFSlJRnb6wV/Ly2aOZMz3nwNg/vWzOPs3b/DDT0xk5th+hB3mf7SLvJz0qM4xsGcGT1w547D2k8bmNfqcUNjZsbecnfsqmDy4J8VlVewvrya/TxZJBmZWb/9w2DEDd3hr7R7690xnwbo93PTk8sOO/dRVMzj/9jdr128852OM7JfN1GG9Abj0uLbPRHrekYP50fmTGtxWVhniQGU1fbPTCDskJxmlldWs3r6Pfj3SuX/BRo4ckssRQ3qxuaiUwbmZbC6MfHV3thSVMaJfNnv2V5CblUZJWSXpKclsKylnzc79TBiYwzNLtzGibxZFpVVs2H2A3tlpPPFeAdV1SpDy+2SyuTDyT1ByknHOEYPISEliRL9s/jhvDQcaGX7p88cP45YLJvPM0m1MGZrLliDGTYWlhMLO9r3lDM7N5IXl23lrzW7Kq8I4zo69FXzq6CH0z8ng/16rPzlTzbVrTp/sNAoPVEZ5FTq3CQNzWLV9HwCj87LpmZnKuP45PLxwc5PPO/fIQTyzRFMVxMPHBvVk5ba9AHzlxBEM6JlBj4wU/jx/PcP6ZjElP5d/LNxCRmoS/Xqks2B9IVOH5fKNk0exYU8p50weRGUoRHJSEs8v2860Eb05YkgvfvL0CgoPVDJuQA6bCkuZPqoPTy/ZxrKCEkb0y+a9TcWMH5DD6h2R34eTxvbjjY9214vtomOGkt87i16ZKZgZO/aWU1kdprw6xHNLt7MneN1MGJjD6P49WLfrABdPG0qfHumkpyRx9LDe7K+oZnBuBiWlVZSUVTEqrwdG5PW5r6KaPfsrGZybQUpSEu5OkhklZVWkpiSxvaSMFdv28YkjB9X+fQ6HnZ37KhjYK4PK6jAlZVXk5aSzfvcBHl+8hd+9sqb9Ll4rZaYmk5JkTBmWyzHDe/PO+kIuO2kksydEOpTufmMdtz6zEoDjRvbh9kun8r3HlvBqMJPjv66aySdun9+ic2akti7Ve/jy6Vx859sALP/xmWwqLKVnZioLNxRy/lGDD3vfbEiP9JTaTrLOyryZdxMz+zhwLfCmu99mZqOAb7v71e0RYFtMmzbNFy5cmOgw4m7EnGea3ad/TjrvfP+0Fh0v1r/cjcW58pazyExr+IUcCjujb3y2TfFsLS7jxLmvMLBnBm/feNgIkO3ix/9azp/f3FC7PrR3JvOvn13vZzLvulM63X/o3U3N30szw90pKasiNyutyedUVIeorA6Tk5GKu2Nm7NpXwfubizlhdF8WrNtDfp8slm4p4aj8XqQmJ7GtpJx+PdL5cMc+ZozuBwaV1WH69Uhr8M0pHI7E0ju78VjcnaLSKjJTk9lXHtk3FHaqw06P9NaM2BqdHXvLOf5n/yY12agKOU9dNYPNhWUcM7w3K7fv5cghvfhwx35Sk413NhSyvaSclKQkXv1wJ4N6ZVBaGeK9TcWcNWkgr324ix+fP4mfPruSkrKDNzV998zx/OKF1QCkJhsXHZPPMcN7c90/PuCIIb0orazm4+P6s2t/BQVFpcye0J/VO/bz2WlD+efiAkorq/nC9OEs37qXE0f3ZWjvLMLu9M1Oo6SsiorqMPvKqwEnKy2FfeXVhN1ZsqWYrLQUZk/oz8KNRQzrk0VZZYgBPdPJSE3mzTW7Gd2/B1lpyezZX0leTjortu6lMhTmxNF9eXH5DgoPVJKSbAzrk8X4gTmkJifRKzOVPQcqKa8KMTqvBwcqqnlr7R5OHN2XqlCYXpmpvLV2D8P6ZEU1qkPRgUpys1KjSmy6I3enoLiMz921gFDYGdAznaLSKtbvbvrT3M8cM5R1uw+wqJFJ3maM6Ys7bCkq46szRpCWksR7m4qZMaYvC9YVMnFwTwzok53OxwblkJ2eQkZKMr2yDv6taIk3PtrF0cN613ZSuTv/WLiFvJ7pzBrfn7LKELc9v4p5q3cy91NHcsLoviwrKKFnRio795Vz0f/9p/ZYsyf0Z9aE/tz0xLLatitnjea7Z0447LyHvrdvmHtu3PKIjsbMFrn7tAa3NZdgH3KgJKCHu++NVXDxpAS7vmh/0ds7wW7qPO7OyBvalmAXFJcxY+4rDOqVwX9uSEyCXV4VYmtxGR/u2M8V9y/ipWtOZuyAnHo/k8U3nU6fJhIkERGReKkOhQm5s2d/JX2y03j8vQJu+OfS2u3fnD2Ga88Yf9jzlGA3nGBHMw72g8AVQIjIDY49zey37v6L2IYpcrhY9rgksu8mIzWZUXk9GJXXg7U/O6e2zqzGOzeequRaREQSJiU5iRRgcG4mAMmHvP+ua6RH/43vzSI9JYmNhaUdckbFRIlmmL6JQY/1hcBzRGZx/GI8g5LO7Y5X1zL/kLq8RGrJpzTtoW5ynZmaTM+MFPp3sLu1RUSkezu0f+u5pQ3fU5HfJ4v+PTM4dkQfPj6u8XuauptoCu9Sg3GvLwRud/cqM+tYGYvERXlVqMU3OcxbtZPbnl8FNP/R0IOXHc/+KGZfuvXCyfRups61KTX5dUesP/zg5jMSHYKIiEizUpI65iQzHVU0CfafgA3AB8DrZjYc6BQ12NKw6lCY5Vv3NjuqyISbnm9x/dRX//Ju1PueOKZfVPt9YfrwFsXQmaSl6A+WiIh0fIeWNjbl9IkDyGpk8ILuotkE291/B/yuTtNGM5vV2P7S8c19bhV3z1/PjedM4PKTR9fbtnFP7MbArgqFa6dV7WhlGiIiIhK9liTYd32pwfv+upVmu8/MbICZ3WNmzwXrE4Evxz0yibmy4OaDu+evB+Bnz646bEr0WM4IefLP59UuhzT1uoiISKcVTUmnHBTN59N/AV4ABgfrHwLfjlM8EkcLNxYe1vbSyh2Ewt5oAlzWhjuCt5WU1y7/a8nWVh+nrQ7WYCcsBBERkU6lI9631JlEU4Pdz90fMbMbANy92swSNg6LmZ0F/JbIbJJ3u/vcRMXS2XzxnncOa/vG3xY1+Zxbnl6BGTy4YFOzx2+onnhLUSkrtu7l5ZU7ow80TvS3QkRERNpDNAn2ATPrCziAmU0HSuIaVSPMLBn4A3A6sAV418yecvcViYinO/j7O80n1jUqq8OHtc28bV4De7YvR+UpIiIi0n6iKRH5DvAUMNrM3gTuA74Z16gadxywxt3XuXsl8BBwQYJiabH7397Iax/uql13dw5UVLN8awmhsLNoY2GzU7Meqip0eFLblNkT+tdbnz6qT+3ynLMn8J3Tx7XoeJ1BbYlIQqeaERERke6iyR7soMf448FjPJHJ8Fa7e1U7xNaQIcDmOutbgOMTFEuDyqtCTLjp+USH0aDH/+dEpg7rXW9a0199dkrtrE1weCnIs1efxMTBPQ87VmllNYaRWWcYnsIDlRz9k5fiEHlsqEREREQkOnrLbJsme7DdPQRc6u7V7r7c3ZclMLmOipldbmYLzWzhrl27mn9CjHXk0eimDut9WFteTnq99ZRDhuHp16PhCV6y0lLqJdcAfbLT+MqJI+q1rfnp2WyYey4/Pn9SbdvF0/J576bTWxJ6m3TgSyIiIiJdUDQ12G+a2e3Aw0Bt/YK7L45bVI0rAPLrrA8N2mq5+53AnQDTpk1r99wqMy2ZtT87h6pQmEUbi8hMSyY3M5XyqjA79pVzyri8enfmukdG8EhJPvx/HXenKnTwWzCD0soQ7k5GajL7K6oJhZ3jf/bvFsX44jUnc8avX+eN782qHae6xhmTBvC9xw6ut3QK7x+dP4nvnTWeiT98AaDB72tM/x70zm79zIwtVTMGt/4bFxERaZ1R/bITHUKnEk2CPSX4ekudNgdmxzya5r0LjDWzkUQS60uAzyUgjiYlJxnJScnMOGSmwokcXmphZqQkN5z6mRlpKfW39co8mLC2dBrzGuMG5DQ6Q2PPjNTa5btbOVB8Vtrhv1Yzxx78WZx9xMBWHbe1av5F0ZBDIiIirXPcyD7N7yS1opnJscPM2hgMEXgVkXG5k4F73X15gsPqFKYOy41qv6Q6JSKnTRwQs/OPzutRuzy0d1bMjisiIiLx94XpwxMdQqfSbIIdDNF3MzCTSGfgfOAWd98T59ga5O7PAs8m4tydWd0EV0RERKQph37oO3lIr8QE0klFUyLyEPA68Olg/fNE6rFPi1dQEnvtffPlq9edQllVwuYjqufgMH0iIiIi8RdNgj3I3X9SZ/1WM7s4XgFJfPRIb129dmuN6Ig3QyjDFhERkXYQzUQzL5rZJWaWFDw+S6QGWjqR7501IdEhJJAG6hMREZH2E02C/V/Ag0BF8HgI+IaZ7TOzvfEMTmInOz2aDyu6JpWIiIiItIwG3mqbaEYRyWmPQETiTcP0iYiISHuIpgdbJGYSkeOqQERERETakxLsLuDZq09KdAhReeLKGbx5ffvPT6QSEREREWlP3bcwtwuZOPjwGSLb4s9fPTYu3b5T8nNjf9AoePDNqEJERERE2kOjCbaZNTknprsXxj4c6Qhmje+f6BDiwtSHLSIiEhW9Z7ZNUz3Yi4j0Yzb0E3ZgVFwiklY5dkRv3t1QlOgwOqTBuZkAfG3miMQGIiIi0kms27U/0SF0ao3WYLv7SHcfFXw99KHkuoP59cVT6q2v+9k5tctfnD68naPpWHpmpLJh7rlcfOywRIciIiLSKby4YkeiQ+jUorrJ0cx6m9lxZnZyzSPegUnLDO2dxY/Pn1S7XrfeOCdDpfYiIiIi7aXZzMvMLgO+BQwF3gemA/8B2n84CGmS+8E7EzXms4iIiLRWUWllokPo1KLpwf4WcCyw0d1nAVOB4ngGJa2j8Z5FREQkFnbsrUh0CJ1aNAl2ubuXA5hZuruvAsbHNywRERERkc4pmuLcLWaWCzwBvGRmRcDGeAYlrePqwhYRERFJuGZ7sN39k+5e7O4/Am4C7gEubMtJzewzZrbczMJmNu2QbTeY2RozW21mZ9ZpPytoW2Nmc9py/q5K+bWIiIjEwvC+WYkOoVNrNME2s57B1z41D2ApMB/o0cbzLgM+Bbx+yDknApcAk4CzgD+aWbKZJQN/AM4GJgKXBvtKHf16pCU6BBEREekCjhyam+gQOrWmSkQeBM6j/oQzdb+2eixsd18JDY50cQHwkLtXAOvNbA1wXLBtjbuvC573ULDvitbG0BX1SI9cztkTuuZMjCIiItI+QuFwokPo1BpNsN39PItkwB93903tFM8Q4O0661uCNoDNh7Qf39ABzOxy4HKAYcO618QiaSmRDyRqEm0RERGR1qgOqfC0LZqswfbIwMrPtObAZvaymS1r4HFBqyKNkrvf6e7T3H1aXl5ePE/V4cwc0485Z0/gJxdOTnQoIiIi0oldOHVI8ztJo6Lp6lxsZse6+7stObC7n9aKeAqA/DrrQ4M2mmiXgJlxxcdHJzoMERER6eTOOWIQG+aey4g5repn7faiGQf7eOA/ZrbWzJaY2VIzWxKneJ4CLjGzdDMbCYwF3gHeBcaa2UgzSyNyI+RTcYqhy9GkjiIiIiLtJ5oe7DOb36VlzOyTwO+BPOAZM3vf3c909+Vm9giRmxergSvdPRQ85yrgBSAZuNfdl8c6LhERERGRtmo2wXb3jWY2Exjr7n82szzaOEyfuz8OPN7Itp8CP22g/Vng2bacV0REREQk3potETGzm4HrgRuCplTg/ngGJSIiIiLSWUVTg/1J4HzgAIC7bwVy4hmUiIiIiEhnFU2CXRkM1+cAZpYd35Ak1lxDWYqIiIi0m2gS7EfM7E9Arpn9F/AycHd8wxIRERER6Zyiucnxl2Z2OrAXGA/80N1fintkIiIiIiKdULMJtpnd5u7XAy810CYiIiIiInVEUyJyegNtZ8c6EBERERGRrqDRHmwz+2/gf4DRh8zcmAO8Ge/AREREREQ6o6ZKRB4EngP+HzCnTvs+dy+Ma1QiIiIiIp1Uowm2u5eY2X5gqrtvbMeYREREREQ6rSZrsN09BKw2s2HtFI+IiIiISKfW7CgiQG9guZm9QzCbI4C7nx+3qEREREREOqloEuyb4h6FiIiIiEgXEc1EM6/VXTezmcClwGsNP0NEREREpPuKpgcbM5sKfA74DLAeeCyeQYmIiIiIdFaN3uRoZuPM7GYzWwX8HtgEmLvPcvfb23JSM/uFma0ysyVm9riZ5dbZdoOZrTGz1WZ2Zp32s4K2NWY2p8EDi4iIiIgkWFOjiKwCZgPnuftMd/89EIrReV8CJrv7kcCHwA0AZjYRuASYBJwF/NHMks0sGfgDkRkkJwKXBvuKiIiIiHQoTSXYnwK2AfPM7C4zOxWwWJzU3V909+pg9W1gaLB8AfCQu1e4+3pgDXBc8Fjj7uvcvRJ4KNhXRERERKRDaTTBdvcn3P0SYAIwD/g20N/M7jCzM2IYw9eIzBgJMATYXGfblqCtsXZpwqh+2QCcMWlggiMRERER6T6iGUXkAJFp0x80s95EbnS8HnixqeeZ2ctAQ5nd9939yWCf7wPVwAMtjLup814OXA4wbFj3nh/n4W+cwJPvF3DU0F6JDkVERESk24hqFJEa7l4E3Bk8mtv3tKa2m9lXgPOAU93dg+YCIL/ObkODNppoP/S8tfFNmzbNG9qnu8jLSeeyk0YlOgwRERHppD45dQiPv9dgyiVNsIO5bTue1Ows4FfAx919V532SUR6y48DBgP/BsYSqf3+EDiVSGL9LvA5d1/e1HmmTZvmCxcujMv3ICIiIiLdl5ktcvdpDW1rUQ92DN0OpAMvmRnA2+5+hbsvN7NHgBVESkeudPcQgJldBbwAJAP3Npdci4iIiIgkQkJ6sNuLerBFREREJB6a6sFuapg+ERERERFpISXYIiIiIiIxpARbRERERCSGunQNtpntAjYm6PT9gN0JOre0D13jrk/XuOvTNe76dI27vkRd4+HuntfQhi6dYCeSmS1srPBdugZd465P17jr0zXu+nSNu76OeI1VIiIiIiIiEkNKsEVEREREYkgJdvw0O528dHq6xl2frnHXp2vc9ekad30d7hqrBltEREREJIbUgy0iIiIiEkNKsGPMzM4ys9VmtsbM5iQ6HmkdM8s3s3lmtsLMlpvZt4L2Pmb2kpl9FHztHbSbmf0uuO5LzOzoxH4HEi0zSzaz98zs6WB9pJktCK7lw2aWFrSnB+trgu0jEhq4RMXMcs3sUTNbZWYrzewEvY67FjO7Jvg7vczM/m5mGXodd35mdq+Z7TSzZXXaWvzaNbMvB/t/ZGZfbq/4lWDHkJklA38AzgYmApea2cTERiWtVA1c6+4TgenAlcG1nAP8293HAv8O1iFyzccGj8uBO9o/ZGmlbwEr66zfBvza3ccARcDXg/avA0VB+6+D/aTj+y3wvLtPAI4icq31Ou4izGwIcDUwzd0nA8nAJeh13BX8BTjrkLYWvXbNrA9wM3A8cBxwc01SHm9KsGPrOGCNu69z90rgIeCCBMckreDu29x9cbC8j8ib8hAi1/OvwW5/BS4Mli8A7vOIt4FcMxvUvlFLS5nZUOBc4O5g3YDZwKPBLode45pr/yhwarC/dFBm1gs4GbgHwN0r3b0YvY67mhQg08xSgCxgG3odd3ru/jpQeEhzS1+7ZwIvuXuhuxcBL3F40h4XSrBjawiwuc76lqBNOrHgI8SpwAJggLtvCzZtBwYEy7r2ndNvgO8B4WC9L1Ds7tXBet3rWHuNg+0lwf7ScY0EdgF/DsqA7jazbPQ67jLcvQD4JbCJSGJdAixCr+OuqqWv3YS9ppVgizTBzHoAjwHfdve9dbd5ZAgeDcPTSZnZecBOd1+U6FgkblKAo4E73H0qcICDHykDeh13dsHH/RcQ+WdqMJBNO/VQSmJ19NeuEuzYKgDy66wPDdqkEzKzVCLJ9QPu/s+geUfNR8bB151Bu6595zMDON/MNhAp55pNpF43N/ioGepfx9prHGzvBexpz4ClxbYAW9x9QbD+KJGEW6/jruM0YL2773L3KuCfRF7beh13TS197SbsNa0EO7beBcYGdy+nEbnR4qkExyStENTk3QOsdPdf1dn0FFBzF/KXgSfrtH8puJN5OlBS52Ms6YDc/QZ3H+ruI4i8Vl9x988D84CLgt0OvcY11/6iYP8O23si4O7bgc1mNj5oOhVYgV7HXckmYLqZZQV/t2uusV7HXVNLX7svAGeYWe/g044zgra400QzMWZm5xCp60wG7nX3nyY2ImkNM5sJvAEs5WB97o1E6rAfAYYBG4HPunth8If9diIfTZYCX3X3he0euLSKmZ0CXOfu55nZKCI92n2A94AvuHuFmWUAfyNSj18IXOLu6xIUskTJzKYQuYk1DVgHfJVI55Jex12Emf0YuJjI6E/vAZcRqbPV67gTM7O/A6cA/YAdREYDeYIWvnbN7GtE3r8Bfuruf26X+JVgi4iIiIjEjkpERERERERiSAm2iIiIiEgMKcEWEREREYkhJdgiIiIiIjGkBFtEREREJIaUYIuIiIiIxJASbBGRTsrM+prZ+8Fju5kVBMv7zeyPcTrnt83sS01sP8/MbonHuUVEOguNgy0i0gWY2Y+A/e7+yzieIwVYDBzt7tWN7GPBPjPcvTResYiIdGTqwRYR6WLM7BQzezpY/pGZ/dXM3jCzjWb2KTP7uZktNbPnzSw12O8YM3vNzBaZ2QtmNqiBQ88GFtck12Z2tZmtMLMlZvYQQDDt9KvAee3yzYqIdEBKsEVEur7RRJLj84H7gXnufgRQBpwbJNm/By5y92OAe4GfNnCcGcCiOutzgKnufiRwRZ32hcBJMf8uREQ6iZREByAiInH3nLtXmdlSIBl4PmhfCowAxgOTgZciFR4kA9saOM4gYGWd9SXAA2b2BPBEnfadwODYhS8i0rkowRYR6foqANw9bGZVfvDmmzCR9wEDlrv7Cc0cpwzIqLN+LnAy8Ang+2Z2RFA+khHsKyLSLalEREREVgN5ZnYCgJmlmtmkBvZbCYwJ9kkC8t19HnA90AvoEew3DlgW96hFRDooJdgiIt2cu1cCFwG3mdkHwPvAiQ3s+hyRHmuIlJHcH5SdvAf8zt2Lg22zgGfiGbOISEemYfpERCRqZvY48D13/6iR7QOAB9391PaNTESk41CCLSIiUTOz8cAAd3+9ke3HAlXu/n67BiYi0oEowRYRERERiSHVYIuIiIiIxJASbBERERGRGFKCLSIiIiISQ0qwRURERERiSAm2iIiIiEgM/X8wAECmjmp0PAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 720x180 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 2.5), constrained_layout=True)\n",
+    "ax.plot(data.time, data.pressure)\n",
+    "ax.set(xlabel='Time (s)', ylabel='Arterial pressure (mmHg)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find troughs in the waveform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wf = find_troughs(wf, name='pressure')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x180 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 2.5), constrained_layout=True)\n",
+    "ax.plot(data.time, data.pressure)\n",
+    "ax.scatter(\n",
+    "    data.time[wf.features.waveform['pressure']['troughs']],\n",
+    "    data.pressure[wf.features.waveform['pressure']['troughs']],\n",
+    "    color='red',\n",
+    ")\n",
+    "ax.set(xlabel='Time (s)', ylabel='Arterial pressure (mmHg)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assess signal quality"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load some preset checks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ArterialPressureChecks(diastolic_pressure=CycleCheck(feature=<class 'sidewinder.features.cycles.MinimumValue'>, min=20.0, max=200.0, units='mmHg', description=None), systolic_pressure=CycleCheck(feature=<class 'sidewinder.features.cycles.MaximumValue'>, min=30.0, max=300.0, units='mmHg', description=None), mean_pressure=CycleCheck(feature=<class 'sidewinder.features.cycles.MeanValue'>, min=30.0, max=200.0, units='mmHg', description=None), heart_rate=CycleCheck(feature=<class 'sidewinder.features.cycles.CyclesPerMinute'>, min=20.0, max=200.0, units='beats per minute', description=None), pulse_pressure=CycleCheck(feature=<class 'sidewinder.features.cycles.MaximumMinusMinimumValue'>, min=20.0, max=250.0, units='mmHg', description=None), mean_dyneg=CycleCheck(feature=<class 'sidewinder.features.cycles.MeanNegativeFirstDifference'>, min=-3.0, max=0.0, units='mmHg', description=None), diastolic_pressure_diff=DiffCheck(feature=<class 'sidewinder.features.cycles.MinimumValue'>, threshold=20.0, units='mmHg', description=None), systolic_pressure_diff=DiffCheck(feature=<class 'sidewinder.features.cycles.MaximumValue'>, threshold=20.0, units='mmHg', description=None), beat_time_diff=DiffCheck(feature=<class 'sidewinder.features.cycles.Duration'>, threshold=0.5, units='seconds', description=None))"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "checks = quality.ArterialPressureChecks()\n",
+    "checks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tweak the presets a little:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "checks.diastolic_pressure.min = 30.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check each cycle:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "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>diastolic_pressure</th>\n",
+       "      <th>systolic_pressure</th>\n",
+       "      <th>mean_pressure</th>\n",
+       "      <th>heart_rate</th>\n",
+       "      <th>pulse_pressure</th>\n",
+       "      <th>mean_dyneg</th>\n",
+       "      <th>diastolic_pressure_diff</th>\n",
+       "      <th>systolic_pressure_diff</th>\n",
+       "      <th>beat_time_diff</th>\n",
+       "      <th>all</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   diastolic_pressure  systolic_pressure  mean_pressure  heart_rate  \\\n",
+       "0               False              False          False        True   \n",
+       "1               False              False          False        True   \n",
+       "2               False              False          False       False   \n",
+       "3               False              False          False        True   \n",
+       "4               False              False          False       False   \n",
+       "\n",
+       "   pulse_pressure  mean_dyneg  diastolic_pressure_diff  \\\n",
+       "0           False        True                     True   \n",
+       "1           False        True                     True   \n",
+       "2           False        True                     True   \n",
+       "3           False        True                     True   \n",
+       "4           False        True                     True   \n",
+       "\n",
+       "   systolic_pressure_diff  beat_time_diff    all  \n",
+       "0                    True            True  False  \n",
+       "1                    True           False  False  \n",
+       "2                    True           False  False  \n",
+       "3                    True           False  False  \n",
+       "4                    True           False  False  "
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "check_results = quality.check_cycles(wf, \"pressure\", checks)\n",
+    "check_results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x180 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 2.5), constrained_layout=True)\n",
+    "\n",
+    "for cycle_i, cycle in enumerate(get_cycles(wf, \"pressure\")):\n",
+    "    if check_results.loc[cycle_i, 'all'].squeeze():\n",
+    "        colour='tab:blue'\n",
+    "    else:\n",
+    "        colour='lightgray'\n",
+    "    ax.plot(cycle.time, cycle.pressure, c=colour)\n",
+    "\n",
+    "ax.set(xlabel='Time (s)', ylabel='Arterial pressure (mmHg)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 4), constrained_layout=True)\n",
+    "\n",
+    "for cycle_i, cycle in enumerate(get_cycles(wf, \"pressure\")):\n",
+    "    if check_results.loc[cycle_i, 'all'].squeeze():\n",
+    "        colour='tab:blue'\n",
+    "    else:\n",
+    "        colour='lightgray'\n",
+    "    ax.plot(cycle.time, cycle.pressure, c=colour)\n",
+    "\n",
+    "ax.set(\n",
+    "    xlabel='Time (s)',\n",
+    "    ylabel='Arterial pressure (mmHg)',\n",
+    "    xlim=(120, 240),\n",
+    "    ylim=(30, 260)\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Further check customisation\n",
+    "\n",
+    "We can add extra checks of our own:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "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>diastolic_pressure</th>\n",
+       "      <th>systolic_pressure</th>\n",
+       "      <th>mean_pressure</th>\n",
+       "      <th>heart_rate</th>\n",
+       "      <th>pulse_pressure</th>\n",
+       "      <th>mean_dyneg</th>\n",
+       "      <th>diastolic_pressure_diff</th>\n",
+       "      <th>systolic_pressure_diff</th>\n",
+       "      <th>beat_time_diff</th>\n",
+       "      <th>map_diff</th>\n",
+       "      <th>all</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   diastolic_pressure  systolic_pressure  mean_pressure  heart_rate  \\\n",
+       "0               False              False          False        True   \n",
+       "1               False              False          False        True   \n",
+       "2               False              False          False       False   \n",
+       "3               False              False          False        True   \n",
+       "4               False              False          False       False   \n",
+       "\n",
+       "   pulse_pressure  mean_dyneg  diastolic_pressure_diff  \\\n",
+       "0           False        True                     True   \n",
+       "1           False        True                     True   \n",
+       "2           False        True                     True   \n",
+       "3           False        True                     True   \n",
+       "4           False        True                     True   \n",
+       "\n",
+       "   systolic_pressure_diff  beat_time_diff  map_diff    all  \n",
+       "0                    True            True      True  False  \n",
+       "1                    True           False      True  False  \n",
+       "2                    True           False      True  False  \n",
+       "3                    True           False      True  False  \n",
+       "4                    True           False      True  False  "
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "checks_with_extra_diff = quality.ArterialPressureChecks(\n",
+    "    map_diff=quality.DiffCheck(\n",
+    "        feature=cycles.MeanValue,\n",
+    "        threshold=15.0,\n",
+    "        units='mmHg'\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "quality.check_cycles(wf, \"pressure\", checks_with_extra_diff).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also remove unwanted checks easily:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "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>diastolic_pressure</th>\n",
+       "      <th>systolic_pressure</th>\n",
+       "      <th>mean_pressure</th>\n",
+       "      <th>pulse_pressure</th>\n",
+       "      <th>mean_dyneg</th>\n",
+       "      <th>diastolic_pressure_diff</th>\n",
+       "      <th>systolic_pressure_diff</th>\n",
+       "      <th>beat_time_diff</th>\n",
+       "      <th>all</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   diastolic_pressure  systolic_pressure  mean_pressure  pulse_pressure  \\\n",
+       "0               False              False          False           False   \n",
+       "1               False              False          False           False   \n",
+       "2               False              False          False           False   \n",
+       "3               False              False          False           False   \n",
+       "4               False              False          False           False   \n",
+       "\n",
+       "   mean_dyneg  diastolic_pressure_diff  systolic_pressure_diff  \\\n",
+       "0        True                     True                    True   \n",
+       "1        True                     True                    True   \n",
+       "2        True                     True                    True   \n",
+       "3        True                     True                    True   \n",
+       "4        True                     True                    True   \n",
+       "\n",
+       "   beat_time_diff    all  \n",
+       "0            True  False  \n",
+       "1           False  False  \n",
+       "2           False  False  \n",
+       "3           False  False  \n",
+       "4           False  False  "
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "checks_with_check_removed = quality.ArterialPressureChecks()\n",
+    "del checks_with_check_removed.heart_rate\n",
+    "\n",
+    "quality.check_cycles(wf, \"pressure\", checks_with_check_removed).head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.9 ('sidewinder-lxfscp8q-py3.9')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "f2c1554fc2f673ff3983ee6916670176b140c542ca5d14124814c0e65b9d8c04"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb
index 223ffb5..52d13a2 100644
--- a/examples/tutorial.ipynb
+++ b/examples/tutorial.ipynb
@@ -27,18 +27,27 @@
     "sys.path.append(os.pardir)  # Needed for sidewinder import before we install it\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
+    "from matplotlib.lines import Line2D\n",
     "\n",
     "from sidewinder.synthetic import synthetic_arterial_pressure_data\n",
     "from sidewinder.waveforms import Waveforms\n",
     "from sidewinder.features.waveform import find_troughs\n",
-    "from sidewinder.features import cycle"
+    "from sidewinder.features import cycles\n",
+    "from sidewinder import quality"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Load some waveform data\n",
+    "## Per-cycle feature extraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load some waveform data\n",
     "\n",
     "We generate some synthetic arterial blood pressure data for demonstration purposes."
    ]
@@ -136,7 +145,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 720x180 with 1 Axes>"
       ]
@@ -158,7 +167,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Load the data into `sidewinder`"
+    "### Load the data into `sidewinder`"
    ]
   },
   {
@@ -174,9 +183,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Find troughs in a waveform\n",
+    "### Find troughs in a waveform\n",
     "\n",
-    "For example, we could use the troughs to split our arterial blood pressure data into separate heartbeats."
+    "In this case, we use the troughs to split our arterial blood pressure data into separate heartbeats."
    ]
   },
   {
@@ -195,7 +204,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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rvkcoGhfpmlraqGls6XIEG6CkOIv65lbW7a0LQcuUP8rqmvw6dsNzkslIjNXFcmGkssGXw+tfkLZ0ZzWuVnewm6X8VF7f3OU0NRyYqtY87PCxz1c5q4sgLTEuhvH5mocdTqqcLtrcxq/ObUmRZ+bWV1JTHc6fodJXgO3A/cCfOjxUF3wLdbq60IDnwwpo8f0wUl7n303eZhNKijP12IWRSu8IdmeL5HxmFGfS1OJm9R5dZBwu/D33Rg9MJSU+RtNEwsj+2iYy/Jj5A8/s36rdNbrIOEwciFk6n30A7dz6w58Au8kYc58x5mNjzKe+R9Bb1gf4NpnJ6aQKhc+AtAQGZyXqVHUYcTS2kHGErX6PZEZxFqXVjex2aG8+HPjKvHWVxwsHqlHouRceGppbcbra/Aqw7TZhyuAMXegYRvZ3UaKvoxnFmbrIOIyU1XVdHtNnZG4KqQkxet3shD8B9t9E5E4RmSUiU3yPoLesDyj3VqHw58MKnlFs3TghfDicLaQnHr1+eUczhnhnIHS6MyxUNriItQupCUddZtKuf3I8Q7KT9EYRJtrLY/rROQLPSNqmsnqqG1zBbJbyU7kfVSh8phZlYBN0BiJMtI9g+zlzO60oUxeId8KfAHs88G3gdxxID7k3mI3qK8q6kSICnpG0amcLm8u0tqTV3G6Dw+kivZ9/I9ijBqSSmhCjedhhorK+maykzuvPdzTDu2CnTTdOsFy5H5vMdKRT1eGl2tlCZheLi31SE2IZM0gXGYeLsm4OCk4vymRreUP7jKE6mD8B9iXAEGPMccaYE7yPE4PdsL6grLbzLUcPNaPYl4etFxur1btacRv8HsG224SS4izNww4TlfUuv/KvfUqKM6lramXDPl1kbLXybkxTA0zITyMuxqZBWpjwDEz4d90EKCnK0kXGYaK8rpnk+BgS47qe+YMDnVsttXhk/gTYq4H0ILejT3I0ukhPjOt0y9GOCjMTyU3V2pLhwNHQAkC6nznYADOHZLK90tleB1ZZp6LB5VcFEZ8Dmz1pB8lq/myT3lF8jJ1J+ek6gh0G2tyG2qZW0rpx3SwpzqS51c3KUkfwGqb84qk/7/+xG5+XRkKsbnl/NP4E2OnAehF5V8v0dY/D2dKtnryIZxR0oe4KaDlHoyefM8PPEWzoUA9b87AtV1nf3GUN7I7yMxLJS+/Hou262Mpq5XXN2G3i9wJj8ARpq/fU0qC7AlqqttEzMNGd6+b0ogxAZ27DgaMb6T0AcTE2JhVo5/Zo/Amw7wQuAH5DN8r0icijIlImIqs7PHeJiKwREbeITDvk928Xkc0iskFETuvef0Z4cjhbSOvGhQY8N4p9tboroNWqnb4RbP+P35hBnpJhX2ketuW6myICeEstaufWalUNLjISY/2e+QPP+pU2t2GpVqOwVLXTMzDRnetmVnI8w3N0J+Nw4Jt1746SokzW7qmlrqklSK2KXF0G2B3K8i0DVnV4dOVx4PRDnlsNXAh81vFJERkDXAaM9f7NgyLSdRHNMOdo7F4uGhzIw9aVudZyeG8UaX4ucgRPHva0ogzNw7aY09VKY0tbt1JEwJMmUlHfzLaKhiC1TPnD0dhCWjevm1MK07EJWq7PYg7vCLa/i8N9ZgzJZMmOalrbNA/bSt2ddQfPJnluA0t3OoLTqAjmz06O14vIPmAlsBhY4v3aKWPMZ0DVIc+tM8ZsOMKvnwc8Y4xpNsZsAzYDJX60P6x5yrx170IzLNuzK6DmglrL4ez+VCfAjCFZbC1vaF+NrUKvos67yUw3pjpBq1GEi5oeXDdTEmIZOyhNByYsVtODmT/QnYzDRU0PZt0nF6Zjt4nGLEfgT4rIrcA4Y0yRMWaIMabYGDMkwO3IA3Z1+L7U+1xEq+lGHWWf9tqSOhJjKV+A3d2RtBm6aYnlKhr832Smo6HZSWQlxWkuqMV6MvMHnhmIZTsdNLfqroBW8a1d6UmaAehOxlZqaXNT19za7dmHpPgYxg1KZdE2Tc86lD8B9hbAGeyG+Ms7or5YRBaXl5db3Zyj6umHFTxBmlajsJaj0UVKQgwxdn9OkQPG5aWRGGfXetgWqvJuk96dxTrgWWQ8XTu3luvJ2hWAkuIMmlvdrN6tW95bpdpXfambHSTfTsbaubWOb4FqdwcFwdO5XV6qndtD+RM93A58KSL/EpH7fI8At2M3UNDh+3zvc4cxxjxkjJlmjJmWnZ0d4GYETm8+rCU6Cmq57uzi2FGs3cbUwZqHbSVHeyWD7nduS4ozKa1uZI9ueW+ZGmdLjwYmDpRa1JE0qzgaWxCB1B7MQPh2MnbrZk+W6MnCfp+S4kxcrW5WlmrntiN/Aux/AR8BX+HJv/Y9Auk14DIRiReRYmA4sDDA7xFSjl4E2GMGppIUZ9cA20Ld2cXxUDOHZLFxv27dbJX2Baq96NxqHrY12mf+enDsspLjGZqdpLmgFqpxukhN6F4FGJ8ZQ7JwOFvYpDsZW6Km0bewv2cj2KCDgofyJ8CONcb82BjzmDHmCd+jqz8SkaeB+cBIESkVketE5AIRKQVmAW+KyLsAxpg1wHPAWuAd4CZjTETPNfQ0hxcgxm5jqrc3r6xR3cMRbICpgz11XZft0pE0K9Q0tmATSIn3bzeyjkYP9JRa1Klqa/Rm5g88HaTFO6p1y3uLOBp7ft08sJOxdpCs4HB2f3M1n4ykOC21eAT+BNhve/OeB4pIpu/R1R8ZYy43xgw0xsQaY/KNMY8YY172/jveGJNrjDmtw+/fY4wZaowZaYx5u1f/VWGgpoeLPXxmFGeyfl9d+2icCq2axu5XMvCZkJ+G3SYs3eEIbKOUXxzOFlL7xWLrwSia3SZMLcrQG4VFfDN/PRmYAN3y3mrVPSjz5pOf0Y+BaQnaubVIe4Ddw+M3vTiTpdq5PYg/AfblePOwOZAe0mWZvmjX08UePgemqnUU1ArVTle3S/T5JMbFMGZgKkt26LGzQk1jz2/y4Dn3NpfVU+ndsluFTm9G0UC3vLdajbP7G5X4eHYyztSdjC3Sm7RW8AwK1jW3sm5vbSCbFdH82Wim+AiPQJfp63N6+2GdkJ9GXIxNbxQWcLtNr4O0KYXprCh16MYJFnA0tpDWw5s8HCgZpp3b0Guf+evxKGgig9IS9NhZpDcpIuDp3JbXNbO9MmwKl0WNGqcLEU9N+Z7QPOzDHTXAFpG5nf2hiKSKyLjAN6lv6O2HNT7GzqSCdP2wWqCuqRVj6FWQNmVwBk5XG+t1qjrkapw9q6PsMz4/jfgYm557FnD0opKBT0lxJgu36yioFaobenfuHejc6rkXar4dVHuyQBVgUHo/8tL76bHroLMR7ItE5EsR+YWInCUiJSJyrIhcKyL/Bd4A+oWonRGntx9W8Ey5rN5TS0NzawBbprpS7c1772mKCHRY6LhTR9JCrSdbbXcUH2NncmG63igscCAPtOed2+k6CmqJNrehtqm1VwMTQ7OTSYmPYcUuR+AapvzSk23SD1VS7CnOoJ1bj6MG2MaYHwFnA3uBS4BfAT/GU0LvX8aYY40xi0LSyggUiA/r9KJM2tyG5XqxCanepvcA5KX3IyclXvOwLVDTy2lq8IykrdlTQ11TS4Bapfzhq6OcktD9CjA+7aOgOgMRUrXt9ed7fu7ZbMKEgjRWlDoC1Crlr96m1oEnZqmod7GtoiFArYpsneZgG2OqjDH/NsZcY4w5zRhzvjHmdmPMvFA1MFIF4sM6MT8dQC82IeYbwe7pYh3wLNiZOjiDJTqCHVKByJ8HmDw4A7eB1bt1wU4o1ThdpPWwAozPsJxkMpPiWKgzECEViIEJ8Nz31u+to6kloiv1RpzeptaBbpJ3qO7tA638FogPa1piLEVZiazcpbsjhVJNL8sV+UwuTGdXVaNWowihQOTPA0zISwNgpXZuQ8oRgM6RiDBtcAaLNcAOqfaBiV6k9wBMKkin1W1Ys0c7t6HU2wWqAEOzk8jSzm07DbCDJBAfVoDx+ems2q0Bdig5AjCCDTA+Lx1Aj18IOXqxG1lHWcnx5KX3Y6Ueu5ByOHuXP+8zsSCd7ZXO9s6yCr6aACxQBU+ADWhqZIgFIq1VRJgyOIPlOx2BaVSE0wA7SALxYQWYmJ/GbkcjFToKGjLVvdiFs6NxeakArCrVIC1UahoDM/sAMLEgTUewQywQqXXgKXMKsHqPnnuh4ujl5mo+OakJDExL0IWOIeRZoBqYc29SQTpbKxq0c4sfAbaIJIrIz0Xk397vh4vI2cFvWuQK5Id1gjcPW2/0oVPT2EJqQkyvKsCAp0TjkOwkHQUNoUCUefOZkO9J8alu0N1UQyUQqXUA49tTfPTcC5Xe7gTY0aSCdF17FEJ1TS0YE6CBCV071s6fEezHgGZglvf73cCvg9aiPqC2MXAf1rGDUrGJ3ihCqdrpIiOp950j8FxsdAQ7dAK10Ao65GFrBylkApVal54YR2FmIqv12IVMtdNTASY1QCk+OyqdVGnnNiQCOjBR4Llu6gyEfwH2UGPMH4AWAGOME+jd0F4f57vJZyT1/sOaFB/DsJxkDbBDKFDpPeAZSdtX20RZbVNAXk91rsabPx+Im/w4b5rBKh2JCYlAVYDxGZ+fxsrdjoC8lupajdNFakLv9n7w8eVh6yhoaARyYCI1IZah2Ul67PAvwHaJSD/AAIjIUDwj2uooHAFaTe0zPi+dlaUOLd4eIg6nKyDpPXAgF1QXOoaGLwc7EAvlUhNiGdI/iRXauQ2JQFWA8Rmfl6YpPiEUqNkHgHF5aYjo+pVQ8cUsaQGKWSYWpLN8V03Uxyz+BNh3Au8ABSLyJPAhcFtQWxXhfL3BtABdbCYWpFFR72JvjY6ChoKjsaVXmyV0NEZTfELK4WwhMc5OfIw9IK83IT9Nb/Ih0r5ILkAj2L4UH+3chkYgZ/6S42MYmp2sa49CpCaAI9jgmYGoqG9mT5THLJ0G2CJiAzKAC4FrgKeBacaYT4LesggWqDrKPrrQMbQCeaNIjItheE6KHrsQCUQd5Y7G56drik+IBDIPFGCsBtgh5XC6el1BpKMJ+WmsKNVR0FAI5AJVOLDQMdrL9XW1k6MbuM0YU2mMedMY84YxpiJEbYtYgaqj7DNqQAoxNtGp6hDwVYAJ5I1ifH4aq3brjSIUHM7AVO/xmZiv1ShCJZB5oOBJEyrun6QzECESyBQR8MxAlNc1s79WM1KDzRGg0rQ+owemEme3RX0etj8pIh+IyE9EpEBEMn2PoLcsgvluFKkJMQF5vYRYO6MG6ihoKLRXgAnkjSJfU3xCpabRRVq/wJx30DHFxxGw11RHFug8UPDkYesIdmgEcuYPYIIudAwZR6OLlPgYYuyB2RolLsbGmEGpUb9ZkD//N78G3AR8BizxPhYHs1GRzuFsISUhcB9W8KSJrNTpsqDzbfebEcgRbK3JGzKeKhSBO3aJcTGMyE3R2aMQCHQeKHg6t7pRV/AFcu8HnzEDU4mxic5AhECNsyVga8Z8JhV4StS2trkD+rqRpMsI0BhTfITHkFA0LlJ5ctEC+2GdkJdGXVMr2yudAX1ddbBAL1AFz3RZjE10FDQEHM7ATlODJ0jTKj7BF+hpavBUowDNww4238xfoBaHg2fm1tO5dQTsNdWRVQchZplYkEZjSxuby+sD+rqRxJ+dHK860iMUjYtUjgCPooEudAyVQC9QhQM3Cr3JB5cxxrvVdqAD7HSqnS2UVjcG9HXVwRzOFpLjY4gN4Mzf2EGpiMBqHQUNqkDnz/tM0PUrIRGMmKV9R8coThPx50o2vcPjGOAu4NwgtiniBWMUbURuMgmxNlbs0htFMAUjRQQ8vXlN8QmuphY3rlZ3QEdAQbf+DRVHoyvgxy7FW8tcd+MMrkDv/eAzIT8dh7OFXVXauQ2mYKSIFGUlkZoQE9V52P6kiNzc4fFtYAqQHPymRa6axsBWoQCIsdsYOyhNR7CDLNClwnzG56VT09jCzipN8QmW9hzeAN/kRw5IIc5u0xz6IKsJwsAEeII0zeMNrmBdN30bdWnnNrgCufeDj80m7RvORKuezMU1AMWBbkhf4nC6Appi4DM+L401e2qjetFAsDmcLkQ8u/gF0gQt9xZ07RuVBPhGERdjY/Sg1Kie6gyFQJd58xmfl6a1zIPswLkXhM5tjE0HloLI7TbemCWwxw48Cx037q/D6WoN+GtHAn9ysF8Xkde8jzeADcDLwW9aZHK7jXcEO/A3Cl00EHyOxhbS+sVis0lAX1dvFMEX6M0SOpqYn8bq3TW0uTXFJ1iCdZOf6C33tkw7SEETrHMv1m5jzMBUHZgIonpXK+4Al6b1mZifTpvbsGZPbcBfOxL4M4J9L/An7+O3wLHGmJ919Uci8qiIlInI6g7PZYrI+yKyyfs1w/u8iMh9IrJZRFaKyJQe/vdYrq7Z82ENdC4hdFjoGMVTLsFWHeBarj56owg+300+NUjnXoOrjW0V2rkNlpogLFAFz0LHWLtEdS5osDmcLZ6Zv6Cce9q5DaaaIFTv8ZlQ4E3xidJzz58AezHwuTHmU6AcmCIi/hyJx4HTD3nuZ8CHxpjhwIfe7wHOAIZ7H9cD//Dj9cNSexWKAE+VARRnJZESH6P5aEEU6O1+O9JR0OCqDVIlAziwo6MuMg4OY0zANyrxSYi1M2ZgatRv2xxMDqeL1IRY7AGe+YMDndutOnMbFI4gxiw5KQnkpfeL2tkjfwLsz4AEEckD3gOuxBM8d8oY8xlQdcjT5wFPeP/9BHB+h+f/Yzy+AtJFZKAfbQs71e2rqQN/o7DZhPH5aToKGkTBSu8BGK83iqAKVh4owJDsZJLi7JriEyQNrjZa3SZo596kgnRWljq0cxskwcqfB12/EmzBWrviM6kgXUewOyHGGCdwIfCgMeYSYGwP3y/XGLPX++99QK7333nArg6/V+p97vDGiFwvIotFZHF5eXkPmxE8waoH6jM+P431+2ppbm0LyutHu2qnK+Al+nwm6o0iqBzOFmJsQlKcPeCvbbcJ4/LSdEfHIAlWmTefSYWezu2msrqgvH60C9bsA8DQ7GQStXMbNMFcuwKetWOl1dG5m6pfAbaIzAKuAN70PtfrO5jxFATu9nCCMeYhY8w0Y8y07Ozs3jYj4NpvFEEL0tJpaTOs36s3imBwOFuCkosGnlHQ5PgYlu2qDsrrRzvfAlWRwE9Tg2ex3Nq9tbhatYpPoLXv4hi0UbQMAE0TCRJHEErT+vg6t1rLPDiCsXtxRxOjeJM8fwLsHwK3Ay8bY9aIyBDg4x6+335f6of3a5n3+d1AQYffy/c+F3FqgjyC7Zsu0wU7gdfa5qauqTVox85uEyYXprN4uwbYwRCsRXI+E/LTcLW62bBPO7eBdqCGeXCOX1FWIumJsXrdDBJHELba7mhCXhpr99TSoiVqA67GOygYrIGlcXlp2ISorIftz0YznxpjzjXG/F5EbECFMeb7PXy/14Crvf++Gni1w/NXeauJzARqOqSSRBRHEFfkAuSl92NgWgKLth+a3q56y3eTD1aKCMD0okw27K9rfy8VODVBnKYGmFzoGQVdvEPPvUAL5kIrABFhUkE6y3QEOyiCmSICntmj5lY36/ZGZ7m3YHI4W0iMsxMfE/jUOoCk+BhG5KZEZR62P3WwnxKRVBFJAlYDa0XkVj/+7mlgPjBSREpF5Drgd8ApIrIJONn7PcBbwFZgM/Bv4MYe/deEAYezheT4GGLtPdnDp2siQklxJgu2Vem22wFWHaTdyDqaVpSBMbB0p45iB5qjMXgVYMDTuc3P6MeCrRpgB1qwF1oBTC7IYGNZHbVN2rkNpDa3obaphbQgnnslxZkAeu4FgaMxuJ0j8C50LHVEXcziTxQ4xhhTi6fix9t4dnG8sqs/MsZcbowZaIyJNcbkG2MeMcZUGmNOMsYMN8acbIyp8v6uMcbcZIwZaowZb4xZ3Jv/KCs5Gl1BG732KSnOpLyume2Vuu12INUEsQqFz+SCDGJswqJteqMItGDmz/vMKM5i4Xbt3AZasGf+wHPdNAY99wKsrqkFYwj4Vtsd5aYmUNw/iQXbKoP2HtHK4Qxu5wg8MxAOZws7oixm8SfAjvXWvT4feM0Y00IPFidGixpn8MoV+cwozgJgoV5sAirYq6kB+sXZGZeXpik+QVDTGIIAe0gmVQ0uNpVpqcVAqmlsISHWRkJscKapAaYMTic+xsYXm/W6GUihmPkDmDnEM3OrpRYDq6bRFfQRbN9Cx2jbw8OfAPtfwHYgCfhMRAYDmgh1FMGsB+ozNDuJrKQ4nS4LsFDdKGYNzWLZTgd1OlUdMMFeoOoz09u5XbBVg7RACtY26R3Fx9iZXpTJl1sqgvo+0SbYJRZ9Zg7Joq6pVfOwA8wRgkHBEbnJ9Iu1s2RHdKVG+rPI8T5jTJ4x5kxvKscO4IQQtC0iVYfgRiEizB7Wn882lePW3nzABLvEos/xI7JpdRu+3KJBWqDUNrUCwZ19ACjI7Edeej8+36RBWiCF4iYPns7t+n11VEZhTd5gCfbeDz6+mVvtIAVWKAYFY+w2Zg3N4pMN5VGVXufPIsdcEXlERN72fj+GA5VA1CFCkSICcNKoHCrqXVE35RJMDmcLNoGU+Jigvs+UwRkkx8fwyYbw2ygpUoWqcyQinDgqh883VdDUops9BYojBOk9ALOHeoK0eZs1SAuUUJ17A9ISGDUghQ/WlnX9y8ovxhhqnC2kBXlQEOCEUTnsrHKypbwh6O8VLvxJEXkceBcY5P1+I57a2OoQxpiQ9AYBjh+ZjU3go/V6sQkUXxUKmy04G5X4xNptzBmWxacbyqKqNx9MvrKHoQjSTh6TS2NLG/N1BiJgQjUwMSE/neyUeN5ZvS/o7xUtQrF2xefUsQNYvKNKZyACpLGlDVebOyTn3omjcgD4ZEP0xCz+BNj9jTHPAW4AY0wroEM3R1Df3Eqb2wQ9RQQ8owXTBmfy4bro+bAGW7BruXZ0/Mgc9tQ0sU535AyIYO9G1tHMIZkkxdl5f93+oL9XtHA0Bj+1DjybPZ0xbgAfbyjD6WoN+vtFA4ezBRFIDUWAPSYXt4EPdWApIELZOcpL78fI3JSoiln8CbAbRCQLb+UQ30YwQW1VhAr2dr+HOnlMDmv31rK1vPOKBs2tbbq9sx885YpCc+xOGzuAGJvw2oo9nf6e221wulp1pLsLNSG8UcTH2Dl2RDbvrdlPaxc7yzW62rTqgR9ClYMNcOb4gTS1uPl4fecpWi1tbk0D8kNNYwupCbHYgzzzBzB2UCqD0hJ4b03nMxDGGBqa9brZFUeIFvb7nDo2lwXbKtlf29Tp7zW1tHV5bY0E/iSb/hjPTotDReQLIBu4OKitilDB3u73UOdNyuN3b6/n+SWl/PT0UYf9fOP+Ou54eRWLd1RjE+G4EdnccNzQ9qL96mCORhc5KQkhea/MpDiOHZHNS0tL+fEpI4iLObivu6+miQc/2cxLS3dT39xKYWYi35xTxOUlhUEtZRapQpUH6nPB5DzeXr2PjzeUc8qY3IN+ZozhqYU7+fdnW9le6aRfrJ1zJw7iu8cPpah/UkjaF0maWtpobnWHrHM7vSiT/snxvLxsN2dNGHjYz1fvruGBjzbzwbr9tLoNJUWZXDOniDPGDUAk+EFkpKkO8jbpHYkI50wcxMPztrG/tonc1IOv17VNLTz8+Tae/GoHlQ0u+ifHcXlJId+aOyRkn69I4mjfJj101837P9rMi0tLufH4YQf9zBjDR+vL+OsHm1i1u4ZYu3DSqFxuOH4okwrSQ9K+QOt0BFtE7MBx3sds4DvAWGPMyhC0LeIEe7vfQ+WmJnDy6FyeWrDzoJJvxhie+HI759w/j63lDXzvhGFcO6eIlaU1XPqv+dzx8iodmTmC6obQpYgAXDlrMGV1zbx+yCj255vKOfO+z3l64U5OHZPLraeNZEBqAne/vpZzH5inZaqOoKbRM92fmhDcBao+J4zKYUBqAv/+bOtBo2Q1jS18939LuePl1fRPjufW00Zy7sRBvLpiN6f99TMe+2Kbjqod4sA0dWium3abcHlJAR+u38+WDrN/xhgenbeNCx78gq+2VXLVrCJuPnEYFfXN3PjkUq57YjFVDa6QtDGShDK1DuDykkLcxvDoF9sOen79vlrOuX8e9324iSmDM7jt9JFMKsjggY83c+pfP+Wzjbqo/FC+1LqMpNAcvyHZycwemsUTX26nufVADNLoauNHzy7nuicW09Dcyg9OGs4VMwazcHsVFzz4Bb9+Yy0tETii3WmAbYxpAy43xrQaY9YYY1Z7N5pRRxCK7X4P9b0Th1HT2MIf390AQHldM9c+vog7X1vDrKFZvPPDY7nl1JHccdYYPr/tBL59TDFPLtjJZQ99pQtFDlHT2BKyzhHAccOzGZeXyh/f3UBVg4s2t+GvH2zkqkcXkp0czzs/PJY/f20SN50wjOdumMWj10yj2tnChQ9+yceag3iQaqeLlPgYYuz+ZL31Xqzdxo0nDGXh9qr2NJ/Vu2s45/55vL9uP3ecOZrnb5jFTScM4/cXT+DTW09g7rD+3P36Wm59YWVE3iyCxYrr5lWzikiKi+HOV9d4a6i38L2nlvHLN9Zy3IgcPv3JCfzinDHccupI3vvRsfz87DHM21TBOffPY7NuMnQQR4ivm0X9kzhv4iAe/2I7G/fXYYzhxSWlXPD3L2l0tfH8DbP491XTuPH4YTx89TRe/95c0vvFcc1jC3n8kKA82oW6cwvwvROGsb+2mfs+3ATAzkonF/3jS15dsYcfnTyCd390LD86ZQR3nTuWT289nq+XFPLwvG1c+cgCaiNs7wh/hnu+EJEHgGeB9voqxpilQWtVhKoOYR6oz4T8dK6dU8yjX2xj/b46Nuyro7GljbvPHctVswYfNKXZL87OHWeNYergTH747DKuenQhT317ZkgqL4Q7V6ub+ubgb1TSkc0m3HP+eC7913zOuX8eKQkxrN9XxwWT87jngnEkxh18ep44Kpc3v5/GtY8v4lv/Wcy/vjGVkw9JT4hWDqeL9BCNwvh8vaSQV5fv4ZbnVvD84lK+2lpJdko8z31nJlMHH5yGlZuawMNXT+OvH2zibx9uorXNzZ8vnRT0ijWRIJQLrXyyU+K546zR3P7SKs5/8Asq612U1TXzszNG8Z1jhxx03Yyx27hubjHTBmdw3ROLueyhr3jm+pkMy0kOWXvDWY3TRVFWYkjf8/YzRzNvcwWX/ms+Q/onsXSng5LiTB64fDI5h6SNjMtL4+WbZvODZ5Zz1+trMcA35xSHtL3hyorO7exh/bl4aj5//3gLS3c4WLW7BrtNePTq6ZzgrTTik5IQyz0XjGfq4Ax++uJKrn50If+5toSUhMiIWfwZ7pkEjAV+CfzJ+7g3iG2KWI6G0OaB+txx1mh+dPIIapwtzB6axRs3z+Xq2UVHzRc8fdwA/vmNqWzcX8e1jy/S1fR0yJ8PcZ7exIJ0nri2hLz0fsTH2vnL1yby50snHhZc++SkJPDM9bMYOyiVm55aymLdch3wdG4zQnzexdhtPHrNdC6ckkdZXROXlxTy5vePOSy49hERfnTKCG49bSSvLN/Dna+t0XQRQr843OfykkJ+f9F43G4Ymp3MM9fP5Ibjhh71ujmxIJ1nrp8BwNWPLqSsrvOFWtGiOsQpIuDpsD5z/SwmF6TjdLVxx5mjefrbMw8Lrn0S42L4xxVTOHVMLne/vpaXlpaGtL3hqsbZQnyMLeTren574Xi+e/xQKhuaOWFUDm/cPPew4LqjC6fk88DXp7CqtIYbn1waMTOAXY5gG2N010Y/VTtbSIqzH7ZgLdjsNuEHJw/nBycP9/tvjh+Zw98um8xNTy3lR88u5x9XTI3q0bSaRms6R+DZAvi5G2b5/fvJ8TE8ds10LvnnfK59fBEv3Tgn6kfTHE6XJccurV8sf7h4Yrf+5sbjh1LT2MJDn20lL6MfNxw3NEitiwxWnntfm17I16YX+v37w3JSeOya6Vz6r/l864nFPHP9zKN2hqNBm9tQ29RCmgXHblhOMo99s8Tv34+x27jv8slc+/gibn1hJQNSE5g9rH8QWxj+QrlAtaNYu42fnj7qiMUZjua0sQP47YXjufWFlfz8ldX89sLxYb/o2J+dHLNE5D4RWSoiS0Tkb96yfeoQVt3ke+rM8QO548zRvLtmP3/w5nBHKyvSe3ojKzmeJ64tIS7GxrWPL4r6fHrPCHZkHDsR4Wenj+KsCQP5/TvrebeLkmN9XaSde+Pz07j/8sms3l3D959eHtVlGOuaWjCGiDn3EmLt/PPKqQzpn8R3n1zKtoro2VXwSKyY+euNS6YVcPOJw3hm0S7+9dlWq5vTJX+GWp8ByoGL8JTnK8eTj60OUe10kZkUOR9WgOvmFnPFjEL++ekWnlu8y+rmWCbU9UADoSAzkYeumsb+2ia+898lETNtFgzVTldE3ShsNuFPl0xkQl4aP3xmOat3R+/WAtVOF3ExNhLjIqf85MljcvnF2WP4YN1+fvvWOqubY5lIvG6mJsTyyNXTsQlc9/ii9hr60ai6IbKumwA/PmUE50wcxO/eXs9bq/Za3ZxO+RNgDzTG/MoYs837+DWgK6uOoCqEmyUEiohw17ljmTusPz9/ZXXUrpCv9ubPR9rFZkphBn+4eAKLd1TzwEebrW6OJTxVIEK7QDUQEmLt/PuqaaQnxnLjk0ujdi2E5yYfG/bTvYe6Zk4xV80azMPztkXV9s8dVfvqz4ewCkUgFGYl8s9vTGVXtZPbXlwRtWshqp2ukJXoCxQR4Y8XT2ByYTo/fWElux2NVjfpqPwJsN8TkctExOZ9XAq8G+yGRSJHhI2i+cTabfz5axPpF2fnJ8+viMopzyrvjSLSZiDAs+HQhZPzeODjzawqjb6R0PZarhF47uWkJvDnSyexs8rZXmoz2lQ1RNY0dUf/d+ZoRuQmc9sLK9sXSkcTh0WLwwNhxpAsbjl1JO+u2c+bYT4SGiyOCEsR8UmItfO3r02mzRhueyF8O0j+BNjfBp4Cmr2PZ4DviEidiOiOFx34RmIiUU5KAnefO5bluxw8vXCn1c0JuUicpu7orvPGkpEYxy9eW407yjpIB3ZxjMxzb9bQLK6aNZjHv9welakikTowAZ4b/Z8umUR5fTN//WCj1c0JuVDvoBpo35pbzMT8NO58dU3UdZDcbhNxqXUdFWYlcvuZo/lic2X7XgThpssA2xiTYoyxGWNivQ+b97kUY0xqKBoZCVrb3NQ2tUbshQbg3ImDKCnO5M/vb4y6i011g4vMxLiIm6b2SU2I5WdnjGLZTgcvLdttdXNCqjrEO6gGw09OG0lmYhy/fH1t2I7GBEtVBK5d6Wh8fhqXlxTyn/k72Li/zurmhFRVg+fci9TjF2O3cc8F46l2uvjL+9HVQaprasVtIndgAjx7EYzPS+M3b62jvjn8UuxCW0+uD/NNlUXqhQY8uU13njOGaqerfZelaFHV0EJGBB87gAsn5zG5MJ3fvb0+4na86o0D+fORe6NITYjlllNHsnB7FW+tiq6qIg5nS8TlgR7qJ6eOJDk+hruirLZ5VUMzdpuQmhC5pQrH5aXxjZmD+c/87azZEz0zSJGcFuljtwl3nzeWsrpmPly33+rmHEYD7ACJ9Glqn7GD0rhsegFPfLk9qhY8eirARPaxs9mEX547jsqG5qha8BjJOdgdfW16AaMHpvKbt9bR1NJmdXNCwu02EZ0i4pOZFMePTxnBl1sqo6rsoi9/PlJn/nxuOWUkGYlx3Plq9HSQfAtUI/3cm1KYwYc/Po7zJuVZ3ZTDaIAdIL5p6kj/sALccupI+sXa+fWb0TNdXd0QWTXMj2Z8fhqXTi3gsS+2sbU8OjpIfaVza7cJvzh7DLsdjfw7Amq8BkJtUwtu0zeum1fMKGRkbgq/fjN6OkjVDZE/MAGeXUR/evooFu+o5qWl0ZFi1z7zF8Ej2D5DssNzo7WjBtgiktnZI5SNjASRWubtSPonx/ODk4fzyYZyPlofHeWnqp2eHOy+4CenjSQ+xs6v34yO+rzVzhZibEJyfOROU/vMGprFGeMG8OAnW9gTxuWnAqWq/SYf+UFajN3GneeMobS6kYc/j44OUlVDZOfPd3Tx1HwmFaTz27fXRcUapAODgpF/7oWrzkawlwCLvV8PfSzuzZuKyA9EZLWIrBGRH3qfyxSR90Vkk/drRm/eI9QiseB+Z66eXcSwnGR++cbaPj8a0+Y2OBojPwfbJzslnu+fNIyP1pfxcRTU5/XtoBrp09Q+d5w1Grcx3BMFG5j0lWlqn9nD+nP62AH8/eMt7K2Jgg5ShC9Q7chmE3513jgqG6JjwWOkV4CJBEcNsI0xxcaYId6vhz6G9PQNRWQcntJ/JcBE4GwRGQb8DPjQGDMc+ND7fcTwLRjoK0FarHc0Zkelk0fmbbO6OUFV0+jZ7jezj3SOAK6ZXUxx/yR+9cZaXK19e4fH6obI2SbdH/kZidx4/DDeXLmXLzdXWN2coKpu6DupdT53nDWaNmP4/dvrrW5K0EXiToCdGZ+fxtdLCvnP/O2s29u3qxBXNbgifoFquPMrB1tEMkSkRESO9T168Z6jgQXGGKcxphX4FLgQOA94wvs7TwDn9+I9Qq7a6SLObiMpQusoH8kxw7M5bWwuD3y0uU9PV1f1oVw0n7gYGz8/ezRbyxt4/Mu+3UGK5FquR/Od44aQn9GPu15fQ0tb3+0g9YVKBocqyEzkO8cO4ZXle1i0vcrq5gSNr45yXzp2ALeeNpK0frH84tXVfXoNUrWzJSJ3UI0kXQbYIvIt4DM8uzfe7f16Vy/eczVwjIhkiUgicCZQAOQaY3zbKe0jwrZjdzR4tknvax/W/3fWGAyGu15bY3VTgsbRx6apfU4clcvJo3P4y/ub2FXltLo5QeNwtvSZ1CyfhFg7vzh7DBv31/NoH55B6isLVA/13eOHkpfej/97aVWfnUGqafQsUO1rAXZ6Yhy3nT6KRdureWV5313w2NdmH8KRPyPYPwCmAzuMMScAkwFHT9/QGLMO+D3wHvAOsBxoO+R3DHDErqOIXC8ii0VkcXl5eU+bEXB9cRQNPKMxPzx5BO+t3c87q/tm+SnfCHZfu1EA3H3eOETo06MxlQ3NZCX3vWN3yphcThmTy18+2MjOyr7ZQapqaCHW3jcWqHaUGBfDr84fy6ayeh76bIvVzQmKvjj74PO1aQVMLEjnN2/13T0F+mrMEk78CbCbjDFNACISb4xZD4zszZsaYx4xxkw1xhwLVAMbgf0iMtD7PgOBI67OMsY8ZIyZZoyZlp2d3ZtmBFRlH1pNfajr5hYzemAqd762uk9ebKr7WP58R3np/bjl1JF8vKGcN1ft7foPIkyb21DV4KJ/crzVTQk4Ec+iqxibjTteWdUnO0h9bYFqRyeOyuWsCQO576PNfbJkZlUfqpx1KM+Cx7FU1Dfz1/f75qZrfXHmL9z4E2CXikg68Arwvoi8CuzozZuKSI73ayGe/OungNeAq72/cjXwam/eI9Qq6pvpn9L3bvLgWfD4uwvHU17XzB/f2WB1cwKufbvfPnijALhmdhHj89K4+/W1fa78VLXThdvQJwNsgAFpCfz09JF8vqmCl5f1venqqoa+Ux7zSO48ZwzxMTbueLnvzSD15Zk/gAn56Vw2vZAn5m9n/b6+t+Cxr878hZMuA2xjzAXGGIcx5i7g58Aj9H4B4osishZ4HbjJGOMAfgecIiKbgJO930eMirpm+vfhD+vEgnSumV3M/xbsYMmOvrVwp9rpIiHWRr8+tEC1I7tN+O2F46msb+b37/StygYV9c1A3w2wAa6YMZgphen86o217UFNX1HV4OoTNbCPJiclgZ+dMYr5Wyt5YUmp1c0JqOo+HmAD3HbaSFISYvhFH9vh0Tfzl92Hr5vhoLONZlK9XztuLrMKmAf0atscY8wxxpgxxpiJxpgPvc9VGmNOMsYMN8acbIyJmCiu0dVGg6uN7D46gu1zy6kjGJTWj9v72MKdvj6KBjAuL41r5xTz1IKdLO5DlQ0q6jw3+b7cubXZhN9dNIH65lZ+/cZaq5sTUOX1zWSnJFjdjKC6fHoh0wZncM9b66j0dgj7gso+nCLik5EUx62njWThtqo+1UGqbGj2zPz18ZjFap2NYD/l/dpxw5mOX5VXNIyiASTFexbubNxfz78+7TsLd8rqmvt85wjgR6eMIC/d00HqK5sH+c69rD5+7o3ITeGG44by0rLdfLYxfBZ391Z5XTM5ffzcs3lnkBqaW/lVH+ogVTe46Bdr77Mzfz6XTS+kpCiTX76+lt19pFytb2BCR7CDq7ONZs4Wz8qT4zpsONP+NYRtDHtldZ6bfDR8WA8s3NnEylKH1c0JiLLapj4/igaeDtI9F4xjU1k9v+sjm2D4AuxoOPduOmEYQ/on8bMXV7aXt4tkDc2tOKNg5g9geG4KNx4/jFeW7+HVPlL6rS9tk94Zu02495KJtBnDrc+vwO2O/FSRct91MwrOPSt1moPtLZf3ZojaErGiZQTb557zx5GdHM9NTy3tEzf6ivroGMEGOH5kDt+cU8TjX27nrT5QVaSi3rPBU2q/vlXm7UgSYu389bJJlNc3c8tzkX+jL4+igQmAm08cxtTBGdzx8mo2l0V+VZFomfkDKMxK5Odnj+HLLZXc/9Fmq5vTa75zL1piFqv4U0VkqYhMD3pLIlh7gJ3S93vz4CnEf//Xp7C/ppnrnlhMoyty0w1a29xUNrj6/DR1Rz87YxRTCtP50bPLI37BakW9ZyV8XyzzdiQT8tP5+dlj+HB9GXe+FtkLr6JtFC3GbuNvl00iIdbG1Y8uZH9tk9VN6pX9tU3kpkbHsQO4bHoBF07J4y8fbOTlZZGdj10RZeeeVfwJsGcA80Vki4isFJFVIrIy2A2LJL58pqyk6PmwTh2cwd8um8TSndVc8fBX7SdspKmod2FMdF1o4mPs/PuqaQxMS+DKRxbyyYYjlpyPCBX1zVE3CnPVrCK+c9wQ/vvVDv7v5VURu5V6+wh2FJ17+RmJPHrNdKqdLi76x5ds2l9ndZN6rKyumdzUvp9a5yPiyaWfOSSTHz+3gicX9KpasaXK65pJjLOT1Mc2eAo3/gTYpwFDgROBc4CzvV+VV0V9M+mJscTF+PO/s+84Y/xAHrh8Cmv21HL+37+IyFqhvpt8NI1gg2dR4HM3zKIoK4lrH1/EQ59ticjRUN8IdrT52emjuOmEoTy9cBdXP7qQGmfk1TePxgAbPLMQT397Jk0tbi78x5cRuWi1qaWNmsaWqLtuxsfYeeyaEo4fkc0dL6/m56+sjsiKWtE4MGEFf+pg7wAKgBO9/3b683fRJJo/rGdNGMhz35mFq9XNhQ9+ydsRltdbVueZps2JopEYn5yUBJ67YRanjR3Ab95az/eeWkZDc6vVzeqWyvq+uYtjV0SEW08bxZ8umcii7VVc8OAXbKtosLpZ3VJe14zdJn26zNvRTCxI55WbZpOX3o9rHlvIPz6JrA5uWa13YCIKr5v94jwzgNcf65lFuvzfX0Vcuk95FOXPW6nLQFlE7gR+CtzufSoW+F8wGxVpyvv4JjNdmViQzus3z2VEbgrffXIp9767IWIWYEXrKJpPcnwMD14xhZ+dMYq3V+/l/L9/ETHbOhtjojbA9rloaj5Pfmsm1U4X5z0wj48jKN2nvK6ZrKQ47LboyJ8/VH5GIi/dOJszxw/k9++s56anlkZMB9c3MBFNKSIdxdht/N+Zo3ng65NZt7eWs++fx6II2l8g2mOWUPFnJPoC4FygAcAYswdICWajIk00j2D75KYm8Ox3ZnLptHwe+Hgz3/rP4ojYlrusfTV19F5sRIQbjhvKf6+bQUV9M+c98AXvrdlndbO6VNvYiqvNHdXHDqCkOJPXvjeXvIxErn18EX/9YCNtEdDBLY+i6j1HkxgXw/2XT+b/zhzFO6v3cf7fI2MmYn9tdKbWHersCYN4+cY5JMXZufyhr/jP/O0RMRMRTZWzrORPgO3yluszACKSFNwmRZ6Kepd+WPHkp/3+ogn86ryxfLaxnLPu+5zluxxWN6tTZXVNpCfGEh/TtzdL8MecYf15/ea5FPVP4vr/LuHedzeEdaBWXu8ZRdNzDwoyE3npu7M5f1Ief/1gE994eEHYT1vrNLWHiHD9sUP5z7WeDu65988L+1S7aB/B7mjkgBRe/d5cjhuRzS9eXcMtz63A6QrfmYiWNjfVzhayk/XYBZs/AfZzIvIvIF1Evg18ADwc3GZFDqerlfrmVr1ReIkIV84q4rkbZmEMXPyPL3n4861h26uPhp3kuiM/I5Hnb5jF16YV8MDHm7nq0QVhWyFmb43nJj9Ab/KAJzf0z5dO5A8XT2D5Lgdn/u3zsK4QU17XHDU1sP0xd7ingzskJ5nvPrmUu15bQ3NreJZA3V/bTKxdyEiMtbopYSGtXyz/vmoaPzx5OC8v3835f/+CLWGaaldZ76l6Fi1lha3kzyLHe4EXgBeBkcAvjDH3BbthkWKPw3OTH5TWz+KWhJcphRm89f1jOGl0Dr9+cx3f+e+SsMwvjKbNEvyVEGvn9xdP4A8XTWDx9mrOuu9zFodhfqEvwB6Urueej4hw6bQCXr95Dtkp8Vzz2CL+/F74rYlwuw0V9c3kRFEdZX/kZyTy/HdmtW8Gdek/51Na7bS6WYcpq20iJyUhaurP+8NmE3548gie+GYJFfUuzr1/Hq+v2GN1sw7jm9nKiYLdi63mzyLH3xtj3jfG3GqM+Ykx5n0R+X0oGhcJ9tY0AjAwTT+sh0pLjOWf35jKL7wbY1z0jy/ZVRVeN4uy2ma90BzFpdMLeOnG2STE2rnsoa94/IttYTUTsdfhqwCjQdqhhuWk8MpNc7hkaj73fbSZ7z4ZXh3cioZmWt1GUwyOIC7Gxp3njOWf35jK1vIGzrl/HvM2VVjdrIOU1Wnn6GiOHZHNm9+fy6iBqdz89DJ+/cZaWsOoVv1uhydmydOBiaDzJ0XklCM8d0agGxKp9ng/rDqKdmQiwrVzi3nimyXscTRy7gPzmL+l0upmAdDmNuyvbdLOUSfGDkrj9ZvncvzIbO56fS23PLcibHbu3FvTSP/keM2fP4qEWDt/uHgCPz97DO+v3R9WHdzSar3Jd+X0cQN49XuemYirHl3Ag59sDpsO7v7aJnJ1YOKoBqb145nrZ3LN7CIenreNqx5dSGWYpNrt0QA7ZI4aYIvId0VkFTDKu4Oj77EN0J0cvfY4mhDRxR5dmTu8P69+by6ZSXFc+ciCsBgN3V/bRKvbkJ+RaGk7wl1qQiwPXTmNH58ygpeX7w6bQG1vjXaOuiIiXDe3mMe+WcJubwf3i83Wj4bu9gXYGXqT78yQ7GRevnEOZ4wfyB/e2cB3/7eUuibrqzPti7Jt0nsi1m7jrnPHcu8lE1m8o5pzH/iCVaU1VjeL3Y5GkuLspPbTXRyDrbMR7Kfw7Nj4qver7zHVGPONELQtIuytaSQ7OT7qdnHsieL+Sbx805z20dCfPL+SphbrRkN9o2j5epPvks0mfP+k4Tx69XRKq52cff88PrV4B7rdjkYGpWuA7Y/jRmTz2vfm0j85nisfWcC/PrV2YxOdpvZfUnwMD1w+mf931mjeX7ef8//+BZvLrFtAV+Nsoa6plYJMHZjwx8VT83nxhtkAXPTPL3lhSaml7dnjaGRQej/Nnw+Bo0aFxpgaYBcw2Rizo8Mj/FY7WWhnlVMDtG7wjYb+8OThvLi0lNP++hmfb7ImUPMtHtLj578TRuXw+s1zGZiWwDWPLeTP72+0pJPkdht2VjkZnKVVQ/1V3D+JV26aw+njBvDbt9dz+b+/YnNZnSVt2V3dSFq/WFIStAqFP0SEbx0zhP9dNwOHs4XzHpjHS0tLLVm8utM7e6UBtv/G56fx2vfmMG1wBj95fgW3v7SSqgaXJW3Z7Q2wVfB1OuxqjGkDNohIYYjaE3G2Vzgp6q83+e7wrbZ+6lszsIlw5SMLufrRhSGvVOGbptaLTfcMzkripRs9NZfv+3ATJ9z7CU8v3BnSQHtfbROuVjeDs/Qm3x1J8TH8/etT+M0F41m7p5Yz/vY5t7+0ku0h3txkV7VTR697YNbQLN74/lxGDEjhx8+t4Ly/f8HH68tCGmjvqPJ8Vgo1wO6WrOR4/nNtCd85bgjPLtrFcX/8mL9/vJkaZ+hSfowxnphFr5sh4U9eQwawRkQ+FJHXfI9gNywSNLra2FfbRLGOovXI7GH9efsHx3Db6SNZvbuGi/85n0v/OZ/XVuwJSf3XbZUNDEhNICFWF8l1V2JcDH/52iSeuX4mOakJ3P7SKub87iP+/P7G9so6wbS90nOTL9Jzr9tEhK/PKOSjnxzPZdMLeXHpbk780yfc9NRSvtxSEZLUka3lDQzNSQ76+/RFA9P68cINs/nzpROpanDxzccXcfJfPuW/X+0ISX62bwRbA+zui7HbuP2M0bz7w2OZXpTJH9/dwMzffsidr65m0/7gzyaV1zdT39xKsQ4KhoQ/We4/D3orIpTvJj9YP6w9lhBr58bjh/HN2cU8vXAnj325je8/vYzMpDgunprPZdMLGJIdnBvxpv31DM/Vm3xvzBySxSs3zmb+1koenbeN+z/axAMfbeLYEdlcMrWAk8fkBKXKx85Kvcn3Vv/keH51/jhuPnEYj8zbxtMLd/Lmyr0U90/i8pICLpqST1YQNoJpamljV7WTCybnBfy1o4XdJlw4JZ+zJwzi7dV7eWTeNn7+ymp+8+Y6zhw/kEum5TOjODMoebbbKxronxxPUrwukuup4bkpPHrNdNbtreXhz7fx1MKdPDF/B5ML07l0WgFnTxgYlPSpbeWemKU4SPdUdbAuzxBjzKcdvxeRucDlwKdH/ovosdX7YR2iAXav9Yuzc+3cYq6ZXcS8zRU8vXAnj87bxkOfbWXmkEzOnZjHqWNz6R+gG77bbdhcVs9lJQUBeb1oJiLMHtqf2UP7s7PSyfNLdvHCklJuemop6YmxnD8pj0unFTBmUGrA3nP9vjr6xdo1vScAclITuP3M0fzolBG8tWovTy/cyW/eWs8f393AqWMHcPHUfI4Z1p8Ye2AWcm+raMAYdAQ7AOJibJw3KY9zJw5iRWkNzy3exevL9/Di0lIGZyVyydR8Lpqaz8AAboS2fl8dowakBOz1otnogan86dKJ3H7mKF5ZtptnF+3i9pdW8cvX13Lm+IFcOi2fkgB2lLZWaMwSSn51QUVkMvB14BJgG55dHaPe6j01xNpFR0EDyGYTjh2RzbEjsimrbeL5JaU8v3gX//fyKv7fK6uYUZzFmeMHcNrYAeT0ojTibkcjjS1tDM/RG0UgFWYlcsupI/nhySOYt7mC5xbv4qkFO3n8y+2My0vl0mkFnDtxEOmJvdumd+2eWkYPTMFu05XwgZIQa+fCKflcOCWfTfvreHrhLl5aVsqbK/eSnRLP+ZMGcf7kPMYMTO3VDX+jdyp8aLbe5ANFRJhUkM6kgnR+ftYY3lmzl+cWlXLvexv58/sbOWZ4NpdO6/2MUmubmw376rhy5uAAtl71T47nW8cM4bq5xYd1lIqyErlkWgEXTM7r9YDC2j21JMXpwESoyNHy7URkBJ6R6suBCuBZ4CfGmLA5s6ZNm2YWL15s2ft/4+EFOBpdvHHzMZa1IRoYY1i/r463V+3lzVV72VLegAhMG5zBGeMGcsb4Ad0eoXlj5R6+99QyXrlpDpMK0oPTcAVAdYOLV5fv5tnFpazbW4vdJkwvyuDEUTkcOyKbkbkp3QrY3G7DhLvf44LJefzq/HFBbLlytbr5aH0ZLy0t5aP1ZbS6DYWZiZwyJpcTRuYwvTij2wHbXa+t4dlFu1h516nEBmhUXB3ZzkonLyzZxfNLStlb00RSnJ25w/tzwkjPudfdQGtzWR0n//kz/nTJRC6amh+kVivwrPF6e/Venlu8i6+2egoATMhP48RRORwzvD8T89O7Pat0zv3zSI6P4enrZwajyVFJRJYYY6Yd8WedBNhu4HPgOmPMZu9zW40xQ4LW0m6yMsBubXMz5Vfvc9aEQfz2wvGWtCFabdpfx1ur9vH26r2s31eHCJQUZXLB5DzOnTSIxLiuJ2Z+/spqXlxayoo79SYfSqt31/DWqr18uK6MDd6RzJyUeM6aMJBLpvqXRrJ+Xy2n//Vz/nDxBC6dpik+oVLV4OLdNft4d80+vtxciavNTWKcnTnD+nPRlHxOGp3j17l0zv3zSIq388z1s0LQagWeXWu/2FzBu2v28dH6MvbWNAEwIjeZ8yblccnUfL9mBP87fzs/f3UNH95yHEM1jzdkdlQ28NYqz7m3otSBMZASH8PZEwdy5cwiv66bTS1tjLvzXb597BB+evqoELQ6OvQ0wD4fuAyYA7wDPAM8bIwpDkCDfgR8CzDAKuCbwEDve2QBS4ArjTGdFoq0JMB+8km44w6+JJ2vX3YP/yxs4PQbLw1tG1S7LeX1vLFiL6+u2M3W8gZS4mO4YEoe35g5mBG5HdI/vMeNnTsxhYWceNV95A/O5b/XzbCu8VFub00jn2+s4KP1ZXy0vgxXm5uxg1K5ZGo+503KIyOpQxpJh+P3wOnf5t4J57Lw/07qVZqQ6jmnq5X5Wyr5ZEM5763dx/7aZrJT4rlkaj6XTS+ksGMZsA7HrmzEOGad/xtuOmkEPz5lhHX/AVHMGMPG/fV8ttFz7BZtr8ZuE04YmcPXphdwwsjsg0dGOxy/6674DRuLxvDZL8/RjUosUt3gYv7WSj5cV8YbK/fQ3Opmindx5FmHLo7scOzemXUONxxzPU9cW8JxI7Kt+w/oY3oUYHf44yTgPDypIicC/wFeNsa818PG5AHzgDHGmEYReQ54CzgTeMkY84yI/BNYYYz5R2evFfIA+8kn4frrwenkB2f/hPeGz2TpI9+i34MPwBVXhK4d6jDGGJbsqObJBZ5KCK42z0XnrAmDOHnjVxTe/C3E6ak88cXgiVxx2T38Ic/JpTdfYnHLFXhuGq+t2MPzS3axenctsXbh5NG5XDItn7mLPiDuBs9512Kzc+p1D5LmauCVU3L0vAsDrW1uPtlQztMLd/LxhjLcBuYMy+KSqQXMWfEp2Td9G7zn3gOzLuXeY6/i4xG1FF97ucUtVwBby+t5bnEpLywppaK+mZyUeC6ems+5kwYx4oPXsX3Hc+7tSs3hxOv/xZUr3+MXV83Rcy8MOJwuXlhSyjOLdrG5rJ6EWBtnjBvIuZMGUbLgfZK+e337uXftRb9g5aARfFUCMd/QYxcovQqwD3mhDDwLHb9mjDmph43JA74CJgK1wCvA/cCTwABjTKuIzALuMsac1tlrhTzALiqiZVcp98/+GvfN+To3zn+O2z77DwweDNu3h64dqlNVDS6eX7yLV5bvYd3eWgCy66uYXrqWYZU7eXbCaQiGT975NQlbNlncWnWodXtreWFJKa8s201lg4u4thZGlW1jwt5N7EnN5qNhJTz6/F2c2Fau512Y2VvTyPOLS3l20a727dCHVeykZNcaUpobeGzaeRyzfRmPLH5Cj12YaWlz8/H6Mp5dtKu9o5TeXM+0nWsYu38L746YxdbMPD596NsMzEzW4xdGjDEs3+Xg+SWlvL5iD3VNrdjdbYzft4nppWtxJKTw/IRTuO2Tx7lx3yI9dgEUsAA7UETkB8A9QCPwHvAD4CtjzDDvzwuAt40xh61gEpHrgesBCgsLp+7YsSNk7cZmo9lm55yr/8bY/Vv47Tv3k9DWAiLgdoeuHcpvW8vr+eK0y1icN5rF+aPZnZbL8Iod/PX1exlbvl2PWxhztbr5bGM5i276GSsHDGf1gGG4Eb43/1luWPCiZ4paj19YanMbVpY6WHDpt/mqYDyL88dQH5/IMduW8rfX7yWzqU6PXRjbX9vkOfd+cS+L8seyLTOPAXUV/OadBzhx62K954WxppY2Fm2v4qtv/YQFBWNZOWAERuDSle9z9/v/JAajxy6AwirA9o6Cvwh8DXAAzwMv4Bmx7jLA7siKEWx27KAurh8prg671ekIdnjzHjcAly2GOHer53k9bpHBe/zcCEYEu/HeHPT4hT/vsTNAq81OrNu7Q6seu8jgPX4uWwyx7lbas671+IU/77FrExsGiNHrZlB0FmBbUT7hZGCbMabcGNMCvIRnIWW6iPjKP+QDuy1oW+fuuQcSEw8OrhMTPc+r8OU9bsCB4FqPW+TwHj8b5kBwrccvMniPncCB4FqPXeTwHr+4jsG1Hr/I4D12duM+EFzrsQspKwLsncBMEUkUzzLkk4C1wMfAxd7fuRp41YK2de6KK+Chhzw9QBHP14ce0sUe4U6PW2TT4xe59NhFNj1+kUuPneWsysG+G0+KSCuwDE/Jvjw8Zfoyvc99wxjT3NnrWL3RjFJKKaWUik6dpYj4tVV6oBlj7gTuPOTprUCJBc1RSimllFIqYHQLO6WUUkoppQLIkhSRQBGRciCEdfoO0h+osOi9VfjQz4EC/RwoD/0cKNDPQTQZbIw54taYER1gW0lEFh8t70ZFD/0cKNDPgfLQz4EC/RwoD00RUUoppZRSKoA0wFZKKaWUUiqANMDuuYesboAKC/o5UKCfA+WhnwMF+jlQaA62UkoppZRSAaUj2EoppZRSSgWQBtjdJCKni8gGEdksIj+zuj3KGiLyqIiUichqq9uirCEiBSLysYisFZE1IvIDq9ukQk9EEkRkoYis8H4O7ra6Tco6ImIXkWUi8obVbVHW0gC7G0TEDvwdOAMYA1wuImOsbZWyyOPA6VY3QlmqFbjFGDMGmAncpNeDqNQMnGiMmQhMAk4XkZnWNklZ6AfAOqsboaynAXb3lACbjTFbjTEu4BngPIvbpCxgjPkMqLK6Hco6xpi9xpil3n/X4bmp5lnbKhVqxqPe+22s96GLm6KQiOQDZwEPW90WZT0NsLsnD9jV4ftS9IaqVNQTkSJgMrDA4qYoC3jTApYDZcD7xhj9HESnvwK3AW6L26HCgAbYSinVCyKSDLwI/NAYU2t1e1ToGWPajDGTgHygRETGWdwkFWIicjZQZoxZYnVbVHjQALt7dgMFHb7P9z6nlIpCIhKLJ7h+0hjzktXtUdYyxjiAj9H1GdFoDnCuiGzHkz56ooj8z9omKStpgN09i4DhIlIsInHAZcBrFrdJKWUBERHgEWCdMebPVrdHWUNEskUk3fvvfsApwHpLG6VCzhhzuzEm3xhThCc2+MgY8w2Lm6UspAF2NxhjWoHvAe/iWdD0nDFmjbWtUlYQkaeB+cBIESkVkeusbpMKuTnAlXhGqpZ7H2da3SgVcgOBj0VkJZ5BmPeNMVqiTakopzs5KqWUUkopFUA6gq2UUkoppVQAaYCtlFJKKaVUAGmArZRSSimlVABpgK2UUkoppVQAaYCtlFJKKaVUAGmArZRSSimlVABpgK2UUhFCRLI61NzeJyK7vf+uF5EHg/SePxSRqzr5+dki8stgvLdSSkUqrYOtlFIRSETuAuqNMfcG8T1igKXAFO9GW0f6HfH+zhxjjDNYbVFKqUiiI9hKKRXhROR4EXnD+++7ROQJEflcRHaIyIUi8gcRWSUi74hIrPf3porIpyKyRETeFZGBR3jpE4GlvuBaRL4vImtFZKWIPANgPKM0nwBnh+Q/VimlIoAG2Eop1fcMxRMcnwv8D/jYGDMeaATO8gbZ9wMXG2OmAo8C9xzhdeYASzp8/zNgsjFmAnBDh+cXA8cE/L9CKaUiVIzVDVBKKRVwbxtjWkRkFWAH3vE+vwooAkYC44D3PRke2IG9R3idgcC6Dt+vBJ4UkVeAVzo8XwYMClzzlVIqsmmArZRSfU8zgDHGLSIt5sBiGzee674Aa4wxs7p4nUYgocP3ZwHHAucAd4jIeG/6SIL3d5VSSqEpIkopFY02ANkiMgtARGJFZOwRfm8dMMz7OzagwBjzMfBTIA1I9v7eCGB10FutlFIRQgNspZSKMsYYF3Ax8HsRWQEsB2Yf4VffxjNiDZ40kv95006WAfcZYxzen50AvBnMNiulVCTRMn1KKaWOSkReBm4zxmw6ys9zgaeMMSeFtmVKKRW+NMBWSil1VCIyEsg1xnx2lJ9PB1qMMctD2jCllApjGmArpZRSSikVQJqDrZRSSimlVABpgK2UUkoppVQAaYCtlFJKKaVUAGmArZRSSimlVABpgK2UUkoppVQA/X/NLVIPrsM2GwAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 720x180 with 1 Axes>"
       ]
@@ -210,8 +219,8 @@
     "fig, ax = plt.subplots(figsize=(10, 2.5), constrained_layout=True)\n",
     "ax.plot(data.time, data.pressure)\n",
     "ax.scatter(\n",
-    "    data.time[wf.waveform_features['pressure']['troughs']],\n",
-    "    data.pressure[wf.waveform_features['pressure']['troughs']],\n",
+    "    data.time[wf.features.waveform['pressure']['troughs']],\n",
+    "    data.pressure[wf.features.waveform['pressure']['troughs']],\n",
     "    color='red',\n",
     ")\n",
     "ax.set(xlabel='Time (s)', ylabel='Arterial pressure (mmHg)')\n",
@@ -222,7 +231,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Extract per-cycle features"
+    "### Calculate per-heartbeat features"
    ]
   },
   {
@@ -232,12 +241,13 @@
    "outputs": [],
    "source": [
     "for feature_extractor in [\n",
-    "    cycle.Duration,\n",
-    "    cycle.MaximumValue,\n",
-    "    cycle.MinimumValue,\n",
-    "    cycle.MeanValue,\n",
-    "    cycle.MaximumMinusMinimumValue,\n",
-    "    cycle.MeanNegativeFirstDifference\n",
+    "    cycles.Duration,\n",
+    "    cycles.CyclesPerMinute,\n",
+    "    cycles.MaximumValue,\n",
+    "    cycles.MinimumValue,\n",
+    "    cycles.MeanValue,\n",
+    "    cycles.MaximumMinusMinimumValue,\n",
+    "    cycles.MeanNegativeFirstDifference\n",
     "]:\n",
     "    wf = feature_extractor().extract_feature(wf, 'pressure')"
    ]
@@ -251,6 +261,7 @@
      "data": {
       "text/plain": [
        "{'pressure': {'Duration': array([0.85 , 0.86 , 0.855, 0.86 , 0.855]),\n",
+       "  'CyclesPerMinute': array([70.58823529, 69.76744186, 70.1754386 , 69.76744186, 70.1754386 ]),\n",
        "  'MaximumValue': array([120.        , 119.97736746, 119.99709951, 119.99027587,\n",
        "         119.98921318]),\n",
        "  'MinimumValue': array([80.00332686, 80.00103817, 80.00103817, 80.00115878, 80.        ]),\n",
@@ -265,7 +276,350 @@
     }
    ],
    "source": [
-    "wf.cycle_features"
+    "wf.features.cycles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Signal quality assessment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make noisy synthetic data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we generate some synthetic arterial pressure data which is corrupted by a line flush-type artifact. We can use sidewinder to assess signal quality for each beat of these data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_with_flush = synthetic_arterial_pressure_data(\n",
+    "    systolic_pressure=120,\n",
+    "    diastolic_pressure=80,\n",
+    "    heart_rate=60,\n",
+    "    n_beats_target=8.3,\n",
+    "    hertz=100\n",
+    ")\n",
+    "\n",
+    "data_with_flush.loc[\n",
+    "    330:460,\n",
+    "    \"pressure\"\n",
+    "] += 300.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wf_with_flush = Waveforms(data_with_flush)\n",
+    "wf_with_flush = find_troughs(wf_with_flush, \"pressure\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x180 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 2.5), constrained_layout=True)\n",
+    "ax.plot(data_with_flush.time, data_with_flush.pressure)\n",
+    "ax.scatter(\n",
+    "    data_with_flush.time[wf_with_flush.features.waveform['pressure']['troughs']],\n",
+    "    data_with_flush.pressure[wf_with_flush.features.waveform['pressure']['troughs']],\n",
+    "    color='red',\n",
+    ")\n",
+    "ax.set(xlabel='Time (s)', ylabel='Arterial pressure (mmHg)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Assess signal quality"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load some preset checks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "checks = quality.ArterialPressureChecks()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check each cycle:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>diastolic_pressure</th>\n",
+       "      <th>systolic_pressure</th>\n",
+       "      <th>mean_pressure</th>\n",
+       "      <th>heart_rate</th>\n",
+       "      <th>pulse_pressure</th>\n",
+       "      <th>mean_dyneg</th>\n",
+       "      <th>diastolic_pressure_diff</th>\n",
+       "      <th>systolic_pressure_diff</th>\n",
+       "      <th>beat_time_diff</th>\n",
+       "      <th>all</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   diastolic_pressure  systolic_pressure  mean_pressure  heart_rate  \\\n",
+       "0                True               True           True        True   \n",
+       "1                True               True           True        True   \n",
+       "2                True               True           True        True   \n",
+       "3                True              False          False        True   \n",
+       "4                True              False          False        True   \n",
+       "5                True               True           True        True   \n",
+       "6                True               True           True        True   \n",
+       "7                True               True           True        True   \n",
+       "\n",
+       "   pulse_pressure  mean_dyneg  diastolic_pressure_diff  \\\n",
+       "0            True        True                     True   \n",
+       "1            True        True                     True   \n",
+       "2            True        True                     True   \n",
+       "3           False        True                     True   \n",
+       "4           False       False                     True   \n",
+       "5            True        True                     True   \n",
+       "6            True        True                     True   \n",
+       "7            True        True                     True   \n",
+       "\n",
+       "   systolic_pressure_diff  beat_time_diff    all  \n",
+       "0                    True            True   True  \n",
+       "1                    True            True   True  \n",
+       "2                    True            True   True  \n",
+       "3                   False            True  False  \n",
+       "4                   False            True  False  \n",
+       "5                   False            True  False  \n",
+       "6                    True            True   True  \n",
+       "7                    True            True   True  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "check_results = quality.check_cycles(wf_with_flush, \"pressure\", checks)\n",
+    "check_results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x180 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 2.5), constrained_layout=True)\n",
+    "\n",
+    "for cycle_i, cycle in enumerate(cycles.get_cycles(wf_with_flush, \"pressure\")):\n",
+    "    if check_results.loc[cycle_i, 'all'].squeeze():\n",
+    "        colour='tab:blue'\n",
+    "    else:\n",
+    "        colour='lightgray'\n",
+    "    ax.plot(cycle.time, cycle.pressure, c=colour)\n",
+    "\n",
+    "custom_lines = [Line2D([0], [0], color='tab:blue'),\n",
+    "                Line2D([0], [0], color='lightgray')]\n",
+    "ax.legend(custom_lines, ['Higher quality', 'Lower quality'])\n",
+    "\n",
+    "ax.set(xlabel='Time (s)', ylabel='Arterial pressure (mmHg)')\n",
+    "plt.show()"
    ]
   }
  ],
diff --git a/poetry.lock b/poetry.lock
index 8e42e1d..50ec554 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -104,7 +104,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [[package]]
 name = "coverage"
-version = "6.4.2"
+version = "6.4.3"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
@@ -178,6 +178,14 @@ ufo = ["fs (>=2.2.0,<3)"]
 unicode = ["unicodedata2 (>=14.0.0)"]
 woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"]
 
+[[package]]
+name = "frozendict"
+version = "2.3.4"
+description = "A simple immutable dictionary"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "iniconfig"
 version = "1.1.1"
@@ -567,6 +575,21 @@ category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
+[[package]]
+name = "pydantic"
+version = "1.9.2"
+description = "Data validation and settings management using python type hints"
+category = "main"
+optional = false
+python-versions = ">=3.6.1"
+
+[package.dependencies]
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+dotenv = ["python-dotenv (>=0.10.4)"]
+email = ["email-validator (>=1.0.3)"]
+
 [[package]]
 name = "pygments"
 version = "2.12.0"
@@ -787,7 +810,7 @@ python-versions = "*"
 [metadata]
 lock-version = "1.1"
 python-versions = ">=3.8,<3.11"
-content-hash = "eab78847818ed7a439ec304e8db2fce2801740438b38cbb8d7e74b157880725f"
+content-hash = "72ff2339704c5cd33487b344d5e0ba1cdcfd1371ab27c5e987a1e1029c37c1ce"
 
 [metadata.files]
 appnope = [
@@ -833,6 +856,7 @@ executing = [
     {file = "executing-0.8.3.tar.gz", hash = "sha256:c6554e21c6b060590a6d3be4b82fb78f8f0194d809de5ea7df1c093763311501"},
 ]
 fonttools = []
+frozendict = []
 iniconfig = [
     {file = "iniconfig-1.1.1-py2.py3-none-any.whl", hash = "sha256:011e24c64b7f47f6ebd835bb12a743f2fbe9a26d4cecaa7f53bc4f35ee9da8b3"},
     {file = "iniconfig-1.1.1.tar.gz", hash = "sha256:bc3af051d7d14b2ee5ef9969666def0cd1a000e121eaea580d4a313df4b37f32"},
@@ -914,6 +938,7 @@ pycparser = [
     {file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"},
     {file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
 ]
+pydantic = []
 pygments = [
     {file = "Pygments-2.12.0-py3-none-any.whl", hash = "sha256:dc9c10fb40944260f6ed4c688ece0cd2048414940f1cea51b8b226318411c519"},
     {file = "Pygments-2.12.0.tar.gz", hash = "sha256:5eb116118f9612ff1ee89ac96437bb6b49e8f04d8a13b514ba26f620208e26eb"},
diff --git a/pyproject.toml b/pyproject.toml
index 0d0c76a..8ea2496 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,6 +11,8 @@ numpy = "^1.22"
 scipy = "^1.7.3"
 matplotlib = "^3.5.1"
 pyampd = "^0.0.1"
+frozendict = "^2.3.4"
+pydantic = "^1.9.2"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"
diff --git a/sidewinder/features/cycle.py b/sidewinder/features/cycles.py
similarity index 77%
rename from sidewinder/features/cycle.py
rename to sidewinder/features/cycles.py
index dcc12fb..c4b0be2 100644
--- a/sidewinder/features/cycle.py
+++ b/sidewinder/features/cycles.py
@@ -22,13 +22,13 @@ def get_cycles(waveforms: Waveforms, name: str) -> List[pd.DataFrame]:
     """
     return [
         waveforms.waveforms.iloc[
-            waveforms.waveform_features[name]["troughs"][
+            waveforms.features.waveform[name]["troughs"][
                 cycle_i
-            ] : waveforms.waveform_features[name]["troughs"][cycle_i + 1]
+            ] : waveforms.features.waveform[name]["troughs"][cycle_i + 1]
             + 1
         ]
         for cycle_i in range(
-            waveforms.waveform_features[name]["troughs"].size - 1
+            waveforms.features.waveform[name]["troughs"].size - 1
         )
     ]
 
@@ -53,7 +53,7 @@ def extract_feature(self, waveforms: Waveforms, name: str) -> Waveforms:
 
         Returns:
             `waveforms` with new np.ndarray of shape (n_cycles,) at
-                `waveforms.cycle_features[`name`][`self.class_name`]`
+                `waveforms.features.cycle[`name`][`self.class_name`]`
                 where each element is the feature for the corresponding cycle
         """
         pass
@@ -68,7 +68,22 @@ def extract_feature(self, waveforms: Waveforms, name: str) -> Waveforms:
             - cycle[waveforms.time_column_name].values[0]
             for cycle in get_cycles(waveforms, name)
         ]
-        waveforms.cycle_features[name][self.class_name] = np.array(feature)
+        waveforms.features.cycles[name][self.class_name] = np.array(feature)
+        return waveforms
+
+
+class CyclesPerMinute(CycleFeatureExtractor):
+    """Calculates rate (cycles per minute) for each cycle in the waveform."""
+
+    def extract_feature(self, waveforms: Waveforms, name: str) -> Waveforms:
+        feature = [
+            cycle[waveforms.time_column_name].values[-1]
+            - cycle[waveforms.time_column_name].values[0]
+            for cycle in get_cycles(waveforms, name)
+        ]
+        waveforms.features.cycles[name][self.class_name] = 60 / np.array(
+            feature
+        )
         return waveforms
 
 
@@ -77,7 +92,7 @@ class MaximumValue(CycleFeatureExtractor):
 
     def extract_feature(self, waveforms: Waveforms, name: str) -> Waveforms:
         feature = [cycle[name].max() for cycle in get_cycles(waveforms, name)]
-        waveforms.cycle_features[name][self.class_name] = np.array(feature)
+        waveforms.features.cycles[name][self.class_name] = np.array(feature)
         return waveforms
 
 
@@ -86,7 +101,7 @@ class MinimumValue(CycleFeatureExtractor):
 
     def extract_feature(self, waveforms: Waveforms, name: str) -> Waveforms:
         feature = [cycle[name].min() for cycle in get_cycles(waveforms, name)]
-        waveforms.cycle_features[name][self.class_name] = np.array(feature)
+        waveforms.features.cycles[name][self.class_name] = np.array(feature)
         return waveforms
 
 
@@ -98,7 +113,7 @@ def extract_feature(self, waveforms: Waveforms, name: str) -> Waveforms:
             cycle[name].max() - cycle[name].min()
             for cycle in get_cycles(waveforms, name)
         ]
-        waveforms.cycle_features[name][self.class_name] = np.array(feature)
+        waveforms.features.cycles[name][self.class_name] = np.array(feature)
         return waveforms
 
 
@@ -107,7 +122,7 @@ class MeanValue(CycleFeatureExtractor):
 
     def extract_feature(self, waveforms: Waveforms, name: str) -> Waveforms:
         feature = [cycle[name].mean() for cycle in get_cycles(waveforms, name)]
-        waveforms.cycle_features[name][self.class_name] = np.array(feature)
+        waveforms.features.cycles[name][self.class_name] = np.array(feature)
         return waveforms
 
 
@@ -131,7 +146,7 @@ def extract_feature(self, waveforms: Waveforms, name: str) -> Waveforms:
         ]
         # TODO: See https://github.com/UCL-Chimera/sidewinder/issues/16
 
-        waveforms.cycle_features[name][self.class_name] = np.array(feature)
+        waveforms.features.cycles[name][self.class_name] = np.array(feature)
         return waveforms
 
     @staticmethod
diff --git a/sidewinder/features/diffs.py b/sidewinder/features/diffs.py
new file mode 100644
index 0000000..ece60ad
--- /dev/null
+++ b/sidewinder/features/diffs.py
@@ -0,0 +1,41 @@
+from typing import Type
+
+import numpy as np
+
+from ..waveforms import Waveforms
+from .cycles import CycleFeatureExtractor
+
+
+def calculate_absolute_diffs(
+    wf: Waveforms,
+    name: str,
+    feature_extractor: Type[CycleFeatureExtractor],
+) -> Waveforms:
+    """Calculates the absolute values of the differences between successive
+    values of cycle-level features.
+
+    waveforms.features.diffs[`name`][`feature_extractor.class_name`] is the same
+    length as waveforms.features.cycles[`name`][`feature_extractor.class_name`]
+    as always has first element 0.0
+
+    Args:
+        waveforms: `sidewinder.waveforms.Waveforms` instance holding your
+            data
+        name: Name of column in `waveforms` to extract feature from
+        feature_extractor: Extractor class for the cycle-level feature you want
+            to calculate the absolute differences for
+
+    Returns:
+        `waveforms` with the calculated absolute differences at
+            waveforms.features.diffs[`name`][`feature_extractor.class_name`]
+    """
+    fe = feature_extractor()
+    if not fe.class_name in wf.features.cycles[name].keys():
+        wf = fe.extract_feature(wf, name)
+    wf.features.diffs[name][fe.class_name] = np.zeros(
+        wf.features.cycles[name][fe.class_name].size
+    )
+    wf.features.diffs[name][fe.class_name][1:] = np.abs(
+        np.diff(wf.features.cycles[name][fe.class_name])
+    )
+    return wf
diff --git a/sidewinder/features/waveform.py b/sidewinder/features/waveform.py
index cbfaa9e..6046cfb 100644
--- a/sidewinder/features/waveform.py
+++ b/sidewinder/features/waveform.py
@@ -12,10 +12,10 @@ def find_troughs(waveforms: Waveforms, name: str) -> Waveforms:
 
     Returns:
         `waveforms` with trough indices added to
-            `waveforms.waveform_features[`name`]['troughs']`
+            `waveforms.features.waveform[`name`]['troughs']`
     """
     waveforms.waveforms[name] *= -1  # invert signal, so troughs become peaks
-    waveforms.waveform_features[name]["troughs"] = ampd.find_peaks(
+    waveforms.features.waveform[name]["troughs"] = ampd.find_peaks(
         waveforms.waveforms[name]
     )
     waveforms.waveforms[name] *= -1  # un-invert signal
diff --git a/sidewinder/quality.py b/sidewinder/quality.py
new file mode 100644
index 0000000..ed9cda7
--- /dev/null
+++ b/sidewinder/quality.py
@@ -0,0 +1,146 @@
+from typing import Optional, Type
+
+import numpy as np
+import pandas as pd
+from pydantic import BaseModel, Extra
+
+from sidewinder.features import cycles, diffs
+from sidewinder.waveforms import Waveforms
+
+
+class CycleCheck(BaseModel):
+    """A signal quality check to run on a feature of each cycle in a signal.
+
+    Args:
+        feature: The class that extracts the feature for each cycle
+        min: The minimum acceptable value of the feature in order for the check
+            to pass for that cycle
+        max: The maximum acceptable value of the feature in order for the check
+            to pass for that cycle
+        units: The units of `min` and `max` (optional and just for
+            documentation purposes)
+        description: A description of the check (optional and just for
+            documentation purposes)
+    """
+
+    feature: Type[cycles.CycleFeatureExtractor]
+    min: float = -np.inf
+    max: float = np.inf
+    units: Optional[str] = None
+    description: Optional[str] = None
+
+
+class DiffCheck(BaseModel):
+    """A signal quality check to run on the absolute differences between
+        features of adjacent cycles in a signal.
+
+    Args:
+        feature: The class that extracts the absolute differences feature
+        threshold: The minimum acceptable value of absolute difference in order
+            for the check to pass for that cycle
+        units: The units of `threshold` (optional and just for documentation
+            purposes)
+        description: A description of the check (optional and just for
+            documentation purposes)
+    """
+
+    feature: Type[cycles.CycleFeatureExtractor]
+    threshold: float
+    units: Optional[str] = None
+    description: Optional[str] = None
+
+
+class ArterialPressureChecks(BaseModel):
+    """Some preset checks for use with adult human arterial pressure signals."""
+
+    diastolic_pressure: Optional[CycleCheck] = CycleCheck(
+        feature=cycles.MinimumValue, min=20.0, max=200.0, units="mmHg"
+    )
+    systolic_pressure: Optional[CycleCheck] = CycleCheck(
+        feature=cycles.MaximumValue, min=30.0, max=300.0, units="mmHg"
+    )
+    mean_pressure: Optional[CycleCheck] = CycleCheck(
+        feature=cycles.MeanValue, min=30.0, max=200.0, units="mmHg"
+    )
+    heart_rate: Optional[CycleCheck] = CycleCheck(
+        feature=cycles.CyclesPerMinute,
+        min=20.0,
+        max=200.0,
+        units="beats per minute",
+    )
+    pulse_pressure: Optional[CycleCheck] = CycleCheck(
+        feature=cycles.MaximumMinusMinimumValue,
+        min=20.0,
+        max=250.0,
+        units="mmHg",
+    )
+    mean_dyneg: Optional[CycleCheck] = CycleCheck(
+        feature=cycles.MeanNegativeFirstDifference,
+        min=-3.0,
+        max=0.0,
+        units="mmHg",
+    )
+    diastolic_pressure_diff: Optional[DiffCheck] = DiffCheck(
+        feature=cycles.MinimumValue, threshold=20.0, units="mmHg"
+    )
+    systolic_pressure_diff: Optional[DiffCheck] = DiffCheck(
+        feature=cycles.MaximumValue, threshold=20.0, units="mmHg"
+    )
+    beat_time_diff: Optional[DiffCheck] = DiffCheck(
+        feature=cycles.Duration, threshold=0.5, units="seconds"
+    )
+
+    class Config:
+        """Allow users to define extra checks on instantiation."""
+
+        extra = Extra.allow
+
+
+def check_cycles(
+    waveforms: Waveforms, name: str, checks: Type[BaseModel]
+) -> pd.DataFrame:
+    """Runs signal quality checks for each cycle in a signal.
+
+    Args:
+        waveforms: `sidewinder.waveforms.Waveforms` instance holding your data
+        name: Name of column in `waveforms` to perform signal quality checks on
+        checks: The checks to run. This should subclass pydantic's `BaseModel`
+            and should have attributes which are instances of `CycleCheck`
+            and/or `DiffCheck`, each of which defines a check.
+
+    Returns:
+        DataFrame with one row for each cycle in the signal. Has one Boolean
+            column for each check, which is True if the check passed for that
+            cycle, else False. Also has an 'all' column which is positive if
+            all checks passed for that cycle.
+    """
+    checked = {}
+
+    for check_name, check in vars(checks).items():
+        if isinstance(check, CycleCheck):
+            # Flag unphysiological cycles
+            feature_extractor = check.feature()
+            waveforms = check.feature().extract_feature(waveforms, name)
+            checked[check_name] = (
+                waveforms.features.cycles[name][feature_extractor.class_name]
+                > check.min
+            ) & (
+                waveforms.features.cycles[name][feature_extractor.class_name]
+                < check.max
+            )
+
+        elif isinstance(check, DiffCheck):
+            # Flag unphysiological cycle-to-cycle changes
+            feature_extractor = check.feature()
+            waveforms = diffs.calculate_absolute_diffs(
+                waveforms, name, check.feature
+            )
+            checked[check_name] = (
+                waveforms.features.diffs[name][feature_extractor.class_name]
+                < check.threshold
+            )
+
+    checked_df = pd.DataFrame(checked)
+    checked_df["all"] = checked_df.all(axis=1)
+
+    return checked_df
diff --git a/sidewinder/waveforms.py b/sidewinder/waveforms.py
index e6bab1e..311f51b 100644
--- a/sidewinder/waveforms.py
+++ b/sidewinder/waveforms.py
@@ -20,8 +20,7 @@ def __init__(
         self.time_column_name = time_column_name
         self._validate_arguments()
         self.names = self._init_names()
-        self.waveform_features = self._init_features_container()
-        self.cycle_features = self._init_features_container()
+        self.features = FeaturesContainer(self.names)
 
     def _validate_arguments(self):
         assert isinstance(
@@ -41,7 +40,23 @@ def _init_names(self) -> Tuple[str, ...]:
             if name is not self.time_column_name
         )
 
-    def _init_features_container(self) -> Dict[str, Dict[str, np.ndarray]]:
+
+class FeaturesContainer:
+    """Holds features of the waveform data."""
+
+    def __init__(self, waveform_names: Tuple[str, ...]):
+        """
+        Args:
+            waveform_names: Names of each of the waveform columns
+        """
+        self.waveform = self._init_features_container(waveform_names)
+        self.cycles = self._init_features_container(waveform_names)
+        self.diffs = self._init_features_container(waveform_names)
+
+    @staticmethod
+    def _init_features_container(
+        waveform_names,
+    ) -> Dict[str, Dict[str, np.ndarray]]:
         """Makes a holder for features (the features themselves haven't been
         derived yet)"""
-        return {name: {} for name in self.names}
+        return {name: {} for name in waveform_names}


Test patch:
diff --git a/tests/test_features.py b/tests/test_features.py
index 0338dc3..99adbb1 100644
--- a/tests/test_features.py
+++ b/tests/test_features.py
@@ -4,7 +4,7 @@
 from numpy.testing import assert_allclose, assert_equal
 
 from sidewinder import synthetic, waveforms
-from sidewinder.features import cycle, waveform
+from sidewinder.features import cycles, diffs, waveform
 
 
 @pytest.fixture(scope="function")
@@ -27,13 +27,13 @@ def abp_waveforms_fixture(abp_data_fixture) -> waveforms.Waveforms:
 
 def test_find_troughs(abp_waveforms_fixture):
     assert_equal(
-        abp_waveforms_fixture.waveform_features["pressure"]["troughs"],
+        abp_waveforms_fixture.features.waveform["pressure"]["troughs"],
         np.array([0, 10, 20]),
     )
 
 
 def test_get_cycles(abp_waveforms_fixture):
-    expected = cycle.get_cycles(abp_waveforms_fixture, "pressure")
+    expected = cycles.get_cycles(abp_waveforms_fixture, "pressure")
     assert len(expected) == 2
     assert_equal(
         expected[0].pressure.values,
@@ -47,56 +47,67 @@ def test_get_cycles(abp_waveforms_fixture):
 
 class TestDuration:
     def test_extract_feature(self, abp_waveforms_fixture):
-        wf = cycle.Duration().extract_feature(
+        wf = cycles.Duration().extract_feature(
             abp_waveforms_fixture, "pressure"
         )
         assert_equal(
-            wf.cycle_features["pressure"]["Duration"], np.array([1.0, 1.0])
+            wf.features.cycles["pressure"]["Duration"], np.array([1.0, 1.0])
+        )
+
+
+class TestCyclesPerMinute:
+    def test_extract_feature(self, abp_waveforms_fixture):
+        wf = cycles.CyclesPerMinute().extract_feature(
+            abp_waveforms_fixture, "pressure"
+        )
+        assert_equal(
+            wf.features.cycles["pressure"]["CyclesPerMinute"],
+            np.array([60.0, 60.0]),
         )
 
 
 class TestMaximumValue:
     def test_extract_feature(self, abp_waveforms_fixture):
-        wf = cycle.MaximumValue().extract_feature(
+        wf = cycles.MaximumValue().extract_feature(
             abp_waveforms_fixture, "pressure"
         )
         assert_equal(
-            wf.cycle_features["pressure"]["MaximumValue"],
+            wf.features.cycles["pressure"]["MaximumValue"],
             np.array([120.0, 120.0]),
         )
 
 
 class TestMinimumValue:
     def test_extract_feature(self, abp_waveforms_fixture):
-        wf = cycle.MinimumValue().extract_feature(
+        wf = cycles.MinimumValue().extract_feature(
             abp_waveforms_fixture, "pressure"
         )
         assert_equal(
-            wf.cycle_features["pressure"]["MinimumValue"],
+            wf.features.cycles["pressure"]["MinimumValue"],
             np.array([80.0, 80.0]),
         )
 
 
 class TestMaximumMinusMinimumValue:
     def test_extract_feature(self, abp_waveforms_fixture):
-        wf = cycle.MaximumMinusMinimumValue().extract_feature(
+        wf = cycles.MaximumMinusMinimumValue().extract_feature(
             abp_waveforms_fixture, "pressure"
         )
         expected_pulse_pressure = 120.0 - 80.0
         assert_equal(
-            wf.cycle_features["pressure"]["MaximumMinusMinimumValue"],
+            wf.features.cycles["pressure"]["MaximumMinusMinimumValue"],
             np.array([expected_pulse_pressure, expected_pulse_pressure]),
         )
 
 
 class TestMeanValue:
     def test_extract_feature(self, abp_waveforms_fixture):
-        wf = cycle.MeanValue().extract_feature(
+        wf = cycles.MeanValue().extract_feature(
             abp_waveforms_fixture, "pressure"
         )
         approx_expected_map = 80.0 + (120.0 - 80.0) / 3
         assert_allclose(
-            wf.cycle_features["pressure"]["MeanValue"],
+            wf.features.cycles["pressure"]["MeanValue"],
             np.array([approx_expected_map, approx_expected_map]),
             atol=1.0,  # allow approximation to be wrong by <1mmHg
         )
@@ -111,10 +122,19 @@ def test_extract_feature(self):
         wf = waveforms.Waveforms(data)
 
         # Define manual troughs such that the data contains a single cycle
-        wf.waveform_features["signal"]["troughs"] = np.array([0, 3])
+        wf.features.waveform["signal"]["troughs"] = np.array([0, 3])
 
-        wf = cycle.MeanNegativeFirstDifference().extract_feature(wf, "signal")
+        wf = cycles.MeanNegativeFirstDifference().extract_feature(wf, "signal")
         assert_equal(
-            wf.cycle_features["signal"]["MeanNegativeFirstDifference"],
+            wf.features.cycles["signal"]["MeanNegativeFirstDifference"],
             np.array([-1]),
         )
+
+
+def test_calculate_diffs(abp_waveforms_fixture):
+    wf = diffs.calculate_absolute_diffs(
+        abp_waveforms_fixture, "pressure", cycles.Duration
+    )
+    assert_equal(
+        wf.features.diffs["pressure"]["Duration"], np.array([0.0, 0.0])
+    )
diff --git a/tests/test_quality.py b/tests/test_quality.py
new file mode 100644
index 0000000..581dd41
--- /dev/null
+++ b/tests/test_quality.py
@@ -0,0 +1,77 @@
+from lib2to3.pytree import Base
+
+import pandas as pd
+import pytest
+from pydantic import BaseModel
+
+from sidewinder import quality, synthetic, waveforms
+from sidewinder.features import cycles, waveform
+
+
+class TestArterialPressureChecks:
+    def test_instantiation(self):
+        """Test if the preset checks are defined validly. If we can instantiate
+        without raising an exception, all the pydantic checks must have
+        passed."""
+        try:
+            quality.ArterialPressureChecks()
+        except Exception as exc:
+            assert (
+                False
+            ), f"Instantiating ArterialPressureChecks raised an exception {exc}"
+
+    def custom_check(self):
+        """Test if user can add a custom check at instantiation."""
+        try:
+            quality.ArterialPressureChecks(
+                map_diff=quality.DiffCheck(
+                    feature=cycles.MeanValue, threshold=15.0, units="mmHg"
+                )
+            )
+        except Exception as exc:
+            assert False, (
+                "Instantiating ArterialPressureChecks with a custom check "
+                f"raised an exception {exc}"
+            )
+
+
+@pytest.fixture(scope="function")
+def abp_flush_data_fixture() -> pd.DataFrame:
+    # Simulate some arterial pressure data
+    data_with_flush = synthetic.synthetic_arterial_pressure_data(
+        systolic_pressure=120,
+        diastolic_pressure=80,
+        heart_rate=60,
+        n_beats_target=5.3,
+        hertz=100,
+    )
+
+    # Add a flush-type artifact affecting the 4th and 5th beat (near the
+    #  signal's end so that trough finding doesn't fail)
+    data_with_flush.loc[330:460, "pressure"] += 300.0
+
+    return data_with_flush
+
+
+@pytest.fixture(scope="function")
+def abp_flush_waveforms_fixture(abp_flush_data_fixture) -> waveforms.Waveforms:
+    w = waveforms.Waveforms(abp_flush_data_fixture)
+    w = waveform.find_troughs(w, name="pressure")
+    return w
+
+
+def test_check_cycles(abp_flush_waveforms_fixture):
+    class SystolicPressureCheck(BaseModel):
+        systolic_pressure: quality.CycleCheck = quality.CycleCheck(
+            feature=cycles.MaximumValue, min=30.0, max=300.0, units="mmHg"
+        )
+
+    check_results = quality.check_cycles(
+        abp_flush_waveforms_fixture, "pressure", SystolicPressureCheck()
+    )
+    expected_columns = ["systolic_pressure", "all"]
+
+    assert list(check_results.columns) == expected_columns
+    for column in expected_columns:
+        assert (check_results[column].values[:3] == True).all()
+        assert (check_results[column].values[3:] == False).all()
diff --git a/tests/test_waveforms.py b/tests/test_waveforms.py
index 444a85b..5b88af0 100644
--- a/tests/test_waveforms.py
+++ b/tests/test_waveforms.py
@@ -1,3 +1,5 @@
+import wave
+
 import pandas as pd
 import pytest
 
@@ -28,5 +30,6 @@ def test_names(self, example_waveforms):
         assert example_waveforms.names == ("signal",)
 
     def test_features(self, example_waveforms):
-        assert example_waveforms.waveform_features == {"signal": {}}
-        assert example_waveforms.cycle_features == {"signal": {}}
+        assert example_waveforms.features.waveform == {"signal": {}}
+        assert example_waveforms.features.cycles == {"signal": {}}
+        assert example_waveforms.features.diffs == {"signal": {}}