Delayed, conservative, driven dissipative -- updated documentation

docs/_modules/teaspoon/MakeData/DynSysLib/medical_data.html

@@ -156,7 +156,7 @@ <h1>Source code for teaspoon.MakeData.DynSysLib.medical_data</h1><div class="hig
 <div class="viewcode-block" id="EEG"><a class="viewcode-back" href="../../../../modules/MakeData/DynSysLib/medical_data.html#teaspoon.MakeData.DynSysLib.medical_data.EEG">[docs]</a><span class="k">def</span> <span class="nf">EEG</span><span class="p">(</span><span class="n">SampleSize</span><span class="o">=</span><span class="mi">5000</span><span class="p">,</span> <span class="n">dynamic_state</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">):</span>
 
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    The EEG signal was taken from andrzejak et al. [1]. Speci cally, the first 5000 data points from the EEG data of a healthy patient from set A (file Z-093) was used and the first 5000 data points of a patient experiencing a seizure from set E (file S-056) was used (see figure below for case during seizure).</span>
+<span class="sd">    The EEG signal was taken from andrzejak et al. [1]_. Speci cally, the first 5000 data points from the EEG data of a healthy patient from set A (file Z-093) was used and the first 5000 data points of a patient experiencing a seizure from set E (file S-056) was used (see figure below for case during seizure).</span>
 
 <span class="sd">    .. figure:: ../../../figures/Human_Medical_Data/EEG_Data.png</span>
 <span class="sd">    </span>
@@ -191,7 +191,7 @@ <h1>Source code for teaspoon.MakeData.DynSysLib.medical_data</h1><div class="hig
 <div class="viewcode-block" id="ECG"><a class="viewcode-back" href="../../../../modules/MakeData/DynSysLib/medical_data.html#teaspoon.MakeData.DynSysLib.medical_data.ECG">[docs]</a><span class="k">def</span> <span class="nf">ECG</span><span class="p">(</span><span class="n">dynamic_state</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">):</span>
 
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    The Electrocardoagram (ECG) data was taken from SciPys misc.electrocardiogram data set. This ECG data was originally provided by the MIT-BIH Arrhythmia Database [1]. We used data points 3000 to 5500  during normal sinus rhythm and 8500 to 11000 during arrhythmia (arrhythmia case shown below in figure).</span>
+<span class="sd">    The Electrocardoagram (ECG) data was taken from SciPys misc.electrocardiogram data set. This ECG data was originally provided by the MIT-BIH Arrhythmia Database [2]_. We used data points 3000 to 5500  during normal sinus rhythm and 8500 to 11000 during arrhythmia (arrhythmia case shown below in figure).</span>
 
 <span class="sd">    .. figure:: ../../../figures/Human_Medical_Data/ECG_Data.png</span>
 <span class="sd">    </span>
@@ -203,7 +203,7 @@ <h1>Source code for teaspoon.MakeData.DynSysLib.medical_data</h1><div class="hig
 
 <span class="sd">    References</span>
 <span class="sd">    ----------</span>
-<span class="sd">    .. [1] George B Moody and Roger G Mark. Theimpact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3):4550, 2001.</span>
+<span class="sd">    .. [2] George B Moody and Roger G Mark. Theimpact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3):4550, 2001.</span>
 <span class="sd">    </span>
 <span class="sd">    &quot;&quot;&quot;</span>
 

docs/modules/MakeData/DynSysLib/medical_data.html

@@ -176,7 +176,7 @@ <h1><span class="section-number">2.1.2.1.7. </span>Medical Data<a class="headerl
 <dl class="py function">
 <dt class="sig sig-object py" id="teaspoon.MakeData.DynSysLib.medical_data.EEG">
 <span class="sig-prename descclassname"><span class="pre">teaspoon.MakeData.DynSysLib.medical_data.</span></span><span class="sig-name descname"><span class="pre">EEG</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">SampleSize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'normal'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/teaspoon/MakeData/DynSysLib/medical_data.html#EEG"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#teaspoon.MakeData.DynSysLib.medical_data.EEG" title="Permalink to this definition"></a></dt>
-<dd><p>The EEG signal was taken from andrzejak et al. [1]. Speci cally, the first 5000 data points from the EEG data of a healthy patient from set A (file Z-093) was used and the first 5000 data points of a patient experiencing a seizure from set E (file S-056) was used (see figure below for case during seizure).</p>
+<dd><p>The EEG signal was taken from andrzejak et al. <a class="footnote-reference brackets" href="#id2" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>. Speci cally, the first 5000 data points from the EEG data of a healthy patient from set A (file Z-093) was used and the first 5000 data points of a patient experiencing a seizure from set E (file S-056) was used (see figure below for case during seizure).</p>
 <figure class="align-default">
 <img alt="../../../_images/EEG_Data.png" src="../../../_images/EEG_Data.png" />
 </figure>
@@ -196,8 +196,8 @@ <h1><span class="section-number">2.1.2.1.7. </span>Medical Data<a class="headerl
 </dl>
 <p class="rubric">References</p>
 <aside class="footnote-list brackets">
-<aside class="footnote brackets" id="id1" role="note">
-<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<aside class="footnote brackets" id="id2" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
 <p>Ralph G Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E Elger. Indications of nonlinear deterministic and nite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6):061907, 2001.</p>
 </aside>
 </aside>
@@ -206,7 +206,7 @@ <h1><span class="section-number">2.1.2.1.7. </span>Medical Data<a class="headerl
 <dl class="py function">
 <dt class="sig sig-object py" id="teaspoon.MakeData.DynSysLib.medical_data.ECG">
 <span class="sig-prename descclassname"><span class="pre">teaspoon.MakeData.DynSysLib.medical_data.</span></span><span class="sig-name descname"><span class="pre">ECG</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'normal'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/teaspoon/MakeData/DynSysLib/medical_data.html#ECG"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#teaspoon.MakeData.DynSysLib.medical_data.ECG" title="Permalink to this definition"></a></dt>
-<dd><p>The Electrocardoagram (ECG) data was taken from SciPys misc.electrocardiogram data set. This ECG data was originally provided by the MIT-BIH Arrhythmia Database [1]. We used data points 3000 to 5500  during normal sinus rhythm and 8500 to 11000 during arrhythmia (arrhythmia case shown below in figure).</p>
+<dd><p>The Electrocardoagram (ECG) data was taken from SciPys misc.electrocardiogram data set. This ECG data was originally provided by the MIT-BIH Arrhythmia Database <a class="footnote-reference brackets" href="#id4" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>. We used data points 3000 to 5500  during normal sinus rhythm and 8500 to 11000 during arrhythmia (arrhythmia case shown below in figure).</p>
 <figure class="align-default">
 <img alt="../../../_images/ECG_Data.png" src="../../../_images/ECG_Data.png" />
 </figure>
@@ -223,8 +223,8 @@ <h1><span class="section-number">2.1.2.1.7. </span>Medical Data<a class="headerl
 </dl>
 <p class="rubric">References</p>
 <aside class="footnote-list brackets">
-<aside class="footnote brackets" id="id2" role="note">
-<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<aside class="footnote brackets" id="id4" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">2</a><span class="fn-bracket">]</span></span>
 <p>George B Moody and Roger G Mark. Theimpact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3):4550, 2001.</p>
 </aside>
 </aside>

teaspoon/teaspoon/MakeData/DynSysLib/conservative_flows.py

@@ -19,7 +19,7 @@ def conservative_flows(system, dynamic_state=None, L=None, fs=None,
 
     return t, ts
 
-def simplest_driven_chaotic_flow(fs = 50, SampleSize = 5000, L = 300.0, parameters=[1], InitialConditions=[0, 0]):
+def simplest_driven_chaotic_flow(fs = 50, SampleSize = 5000, L = 300.0, parameters=[1], InitialConditions=[0, 0], dynamic_state=None):
 
     """
     The simplest driven chaotic flow can be reproduced with the following equations
@@ -37,20 +37,28 @@ def simplest_driven_chaotic_flow(fs = 50, SampleSize = 5000, L = 300.0, paramete
         L (Optional[int]): Number of iterations.
         fs (Optional[int]): sampling rate for simulation.
         SampleSize (Optional[int]): length of sample at end of entire time series
-        parameters (Optional[floats]): list of values for [:math:`\\omega`].
-        InitialConditions (Optional[floats]): list of values for [:math:`x_0`, :math:`y_0`]    
+        parameters (Optional[floats]): array [:math:`\\omega`] or None if using the dynamic_state variable
+        InitialConditions (Optional[floats]): list of values for [:math:`x_0`, :math:`y_0`]
+        dynamic_state (Optional[str]): Set dynamic state as either 'periodic' or 'chaotic' if not supplying parameters.
+
     Returns:
         array: Array of the time indices as `t` and the simulation time series `ts`
 
     """
 
     t = np.linspace(0, L, int(L*fs))
 
-    if len(parameters) != 1:
-        print(
-            'Warning: needed 1 parameters. Defaulting to periodic solution parameters.')
-        print('Parameters needed are [omega].')
-        parameters = None
+    num_param = 1
+
+    if len(parameters) != num_param:
+        raise ValueError(f'Need {num_param} parameters as specified in documentation.')
+    elif dynamic_state != None:
+        if dynamic_state == 'periodic':
+            omega = 1
+        elif dynamic_state == 'chaotic':
+            omega = 1.88
+        else:
+            raise ValueError(f'dynamic_state needs to be either "periodic" or "chaotic" or provide an array of length {num_param} in parameters.')
     else:
         omega = parameters[0]
 
@@ -66,7 +74,7 @@ def simplest_driven_chaotic_flow(state, t):
     return t, ts
 
 
-def nose_hoover_oscillator(fs = 20, SampleSize = 5000, L = 500.0, parameters=[6], InitialConditions=[0, 5, 0]):
+def nose_hoover_oscillator(fs = 20, SampleSize = 5000, L = 500.0, parameters=[6], InitialConditions=[0, 5, 0], dynamic_state=None):
 
     """
     The Nose Hoover Oscillator is represented by the following equations
@@ -86,20 +94,28 @@ def nose_hoover_oscillator(fs = 20, SampleSize = 5000, L = 500.0, parameters=[6]
         L (Optional[int]): Number of iterations.
         fs (Optional[int]): sampling rate for simulation.
         SampleSize (Optional[int]): length of sample at end of entire time series
-        parameters (Optional[floats]): list of values for [:math:`a`].
+        parameters (Optional[floats]): list of values for [:math:`a`] or None if using the dynamic_state variable
         InitialConditions (Optional[floats]): list of values for [:math:`x_0`, :math:`y_0`, :math:`z_0`]    
+        dynamic_state (Optional[str]): Set dynamic state as either 'periodic' or 'chaotic' if not supplying parameters.
+
     Returns:
         array: Array of the time indices as `t` and the simulation time series `ts`
 
     """
 
     t = np.linspace(0, L, int(L*fs))
 
-    if len(parameters) != 1:
-        print(
-            'Warning: needed 1 parameters. Defaulting to periodic solution parameters.')
-        print('Parameters needed are [a].')
-        parameters = None
+    num_param = 1
+
+    if len(parameters) != num_param:
+        raise ValueError(f'Need {num_param} parameters as specified in documentation.')
+    elif dynamic_state != None:
+        if dynamic_state == 'periodic':
+            a = 6
+        elif dynamic_state == 'chaotic':
+            a = 1
+        else:
+            raise ValueError(f'dynamic_state needs to be either "periodic" or "chaotic" or provide an array of length {num_param} in parameters.')
     else:
         a = parameters[0]
 
@@ -116,7 +132,7 @@ def nose_hoover_oscillator(state, t):
     return t, ts
 
 
-def labyrinth_chaos(fs = 10, SampleSize = 5000, L = 2000.0, parameters=[1, 1, 1], InitialConditions=[0.1, 0, 0]):
+def labyrinth_chaos(fs = 10, SampleSize = 5000, L = 2000.0, parameters=[1, 1, 1], InitialConditions=[0.1, 0, 0], dynamic_state=None):
 
     """
     The Labyrinth Chaos Oscillator is represented by the following equations
@@ -136,20 +152,29 @@ def labyrinth_chaos(fs = 10, SampleSize = 5000, L = 2000.0, parameters=[1, 1, 1]
         L (Optional[int]): Number of iterations.
         fs (Optional[int]): sampling rate for simulation.
         SampleSize (Optional[int]): length of sample at end of entire time series
-        parameters (Optional[floats]): list of values for [:math:`a`, :math:`b`, :math:`c`].
-        InitialConditions (Optional[floats]): list of values for [:math:`x_0`, :math:`y_0`, :math:`z_0`]    
+        parameters (Optional[floats]): list of values for [:math:`a`, :math:`b`, :math:`c`] or None if using the dynamic_state variable
+        InitialConditions (Optional[floats]): list of values for [:math:`x_0`, :math:`y_0`, :math:`z_0`]
+        dynamic_state (Optional[str]): Set dynamic state as either 'periodic' or 'chaotic' if not supplying parameters.
+
     Returns:
         array: Array of the time indices as `t` and the simulation time series `ts`
 
     """
 
     t = np.linspace(0, L, int(L*fs))
 
-    if len(parameters) != 3:
-        print(
-            'Warning: needed 3 parameters. Defaulting to periodic solution parameters.')
-        print('Parameters needed are [a, b, c].')
-        parameters = None
+    num_param = 3
+
+    if len(parameters) != num_param:
+        raise ValueError(f'Need {num_param} parameters as specified in documentation.')
+    elif dynamic_state != None:
+        if dynamic_state == 'periodic':
+            print('We could not find a periodic response. Using the chaotic parameters.')
+            a, b, c = 1, 1, 1
+        elif dynamic_state == 'chaotic':
+            a, b, c = 1, 1, 1
+        else:
+            raise ValueError(f'dynamic_state needs to be either "periodic" or "chaotic" or provide an array of length {num_param} in parameters.')
     else:
         a, b, c = parameters[0], parameters[1], parameters[2]
 
@@ -166,7 +191,7 @@ def labyrinth_chaos(state, t):
     return t, ts
 
 
-def henon_heiles_system(fs = 20, SampleSize = 5000, L = 10000.0, parameters=[1], InitialConditions=[0.499, 0, 0, 0.03]):
+def henon_heiles_system(fs = 20, SampleSize = 5000, L = 10000.0, parameters=[1], InitialConditions=[0.499, 0, 0, 0.03], dynamic_state=None):
 
     """
     The Henon Heiles System is represented by the following equations
@@ -188,23 +213,33 @@ def henon_heiles_system(fs = 20, SampleSize = 5000, L = 10000.0, parameters=[1],
         L (Optional[int]): Number of iterations.
         fs (Optional[int]): sampling rate for simulation.
         SampleSize (Optional[int]): length of sample at end of entire time series
-        parameters (Optional[floats]): list of values for [:math:`a`].
+        parameters (Optional[floats]): list of values for [:math:`a`] or None if using the dynamic_state variable
         InitialConditions (Optional[floats]): list of values for [:math:`x_0`, :math:`px_0`, :math:`y_0`, :math:`py_0`]    
+        dynamic_state (Optional[str]): Set dynamic state as either 'periodic' or 'chaotic' if not supplying parameters.
+
     Returns:
         array: Array of the time indices as `t` and the simulation time series `ts`
 
     """
 
     t = np.linspace(0, L, int(L*fs))
 
-    if len(parameters) != 1:
-        print(
-            'Warning: needed 1 parameters. Defaulting to periodic solution parameters.')
-        print('Parameters needed are [a].')
-        parameters = None
+    num_param = 1
+
+    if len(parameters) != num_param:
+        raise ValueError(f'Need {num_param} parameters as specified in documentation.')
+    elif dynamic_state != None:
+        if dynamic_state == 'periodic':
+            print('We could not find a periodic response. Using the chaotic parameters.')
+            a = 1
+        elif dynamic_state == 'chaotic':
+            a = 1
+        else:
+            raise ValueError(f'dynamic_state needs to be either "periodic" or "chaotic" or provide an array of length {num_param} in parameters.')
     else:
         a = parameters[0]
 
+
     # defining simulation functions
     def henon_heiles_system(state, t):
         x, px, y, py = state  # unpack the state vector