Updated documentation

Author: Your Name

.github/workflows/blank.yml

@@ -2,7 +2,7 @@
 
 name: Simulink DLL Test
 
-# Controls when the action will run. 
+# Controls when the action will run.
 on:
   # Triggers the workflow on push or pull request events but only for the main branch
   push:
@@ -35,4 +35,4 @@ jobs:
       - name: Run pytest
         run: |
           cd Example2
-          pytest
+          pytest

Example1/README.md

@@ -3,4 +3,3 @@
 For demonstrating minimal dll functionality and the steps required to run a model in Python.
 
 ![](dllModel.png)
-

Example1/dllModel.ipynb

@@ -46,6 +46,7 @@
    "source": [
     "class B_dllModel_T(ctypes.Structure):\n",
     "    \"\"\"Simulink.Parameter SimulinkGlobals\"\"\"\n",
+    "\n",
     "    _fields_ = [\n",
     "        (\"SimulationSignal1\", real_T),\n",
     "        (\"SignalOut3\", real32_T),\n",
@@ -55,21 +56,25 @@
     "\n",
     "class ExtU_dllModel_T(ctypes.Structure):\n",
     "    \"\"\"SimulinkGlobal Inputs to Model\"\"\"\n",
+    "\n",
     "    _fields_ = [(\"SignalIin2\", uint16_T)]\n",
     "\n",
     "\n",
     "class ExtY_dllModel_T(ctypes.Structure):\n",
     "    \"\"\"SimulinkGlobal Outputs from Model\"\"\"\n",
+    "\n",
     "    _fields_ = [(\"OutputPort2\", real32_T)]\n",
     "\n",
     "\n",
     "class P_dllModel_T(ctypes.Structure):\n",
     "    \"\"\"SimulinkGlobal Parameters\"\"\"\n",
+    "\n",
     "    _fields_ = [(\"K2\", uint16_T)]\n",
     "\n",
     "\n",
     "class Timing(ctypes.Structure):\n",
     "    \"\"\" Timing STructure\"\"\"\n",
+    "\n",
     "    _fields_ = [\n",
     "        (\"clockTick0\", uint32_T),\n",
     "        (\"clockTickH0\", uint32_T),\n",
@@ -78,6 +83,7 @@
     "\n",
     "class tag_RTM_dllModel_T(ctypes.Structure):\n",
     "    \"\"\" tag RTM \"\"\"\n",
+    "\n",
     "    _fields_ = [\n",
     "        (\"errorStatus\", ctypes.c_char_p),\n",
     "        (\"Timing\", Timing),\n",

Example2/README.md

@@ -4,11 +4,11 @@
 
 A simple discrete transfer function. Compiled with a 1st order low pass filter.
 
-There are two example notebooks for Example 2. 
+There are two example notebooks for Example 2.
 
 1. [Simple Example](https://nbviewer.jupyter.org/github/dapperfu/python_SimulinkDLL/blob/master/Example2/discrete_tf.ipynb) - A simple low-level ctypes wrapper.
 2. [Pythonic Example](https://nbviewer.jupyter.org/github/dapperfu/python_SimulinkDLL/blob/master/Example2/discrete_tf-python_class.ipynb) - Use Python syntactic sugar to create a high level [TransferTF python](https://github.com/dapperfu/python_SimulinkDLL/blob/master/Example2/discretetf.py) class to interact with the model. Adds datalogging and pandas integration.
 
 Example 2 also contains sample `pytest` tests in the [`tests`](https://github.com/dapperfu/python_SimulinkDLL/tree/master/Example2/tests) directory. This demonstrates how you can use `pytest` to test Simulink models. Sample test results are shown shown in [test_results.md.](test_results.md)
 
-- For the tests example the model shared library is presented to tests as a `pytest` fixture defined in [`conftest.py`](tests/conftest.py)
+- For the tests example the model shared library is presented to tests as a `pytest` fixture defined in [`conftest.py`](tests/conftest.py)

Example2/discrete_tf-python_class.ipynb

@@ -13,7 +13,9 @@
     "- Simulate a sine-wave input through the Simulink model & compare it to `python-control` forced response.\n",
     "- Configure the model with the transfer function $\\frac{1}{1s+1}$ and plot a step response. Compare the step response to the `python-control` forced-response with the same input.\n",
     "\n",
-    "![](discrete_tf.png)"
+    "![](discrete_tf.png)\n",
+    "\n",
+    "- [Pythonic discrete_tf](https://nbviewer.jupyter.org/github/dapperfu/python_SimulinkDLL/blob/master/Example2/discrete_tf-python_class.ipynb) Pythonize the code below into a DiscreteTF class, uses additional Python tools like pandas"
    ]
   },
   {
@@ -791,7 +793,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.LineCollection at 0x7f7cce6f8700>"
+       "<matplotlib.collections.LineCollection at 0x7fcca96b2880>"
       ]
      },
      "execution_count": 18,
@@ -847,6 +849,13 @@
     "Using the characteristics of a first order transfer function test that the transfer function matches."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Find the index of where the step occurs."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -868,6 +877,13 @@
     "step_idx"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate where the response crosses $63.2\\%$ & $86.4\\%$"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -878,6 +894,13 @@
     "tau2_idx = np.where(df.output < (1 - np.exp(-2 * tau)))[0][-1]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Subtract off the time of the step and compare to $\\tau$ and $2\\tau$"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 21,

Example2/discrete_tf.ipynb

@@ -4,7 +4,7 @@ Adapted from [Mathworks's Simulation of a Bouncing Ball](https://www.mathworks.c
 
 ![](bouncing_ball.png)
 
-Running a Simulation in Simulink also has some overhead. By compiling the model to a shared library and executing it, this overhead is eliminated. 
+Running a Simulation in Simulink also has some overhead. By compiling the model to a shared library and executing it, this overhead is eliminated.
 
 `bouncing_ball_benchmark.m` benchmarks the model by testing increasingly smaller time steps. The model was then compiled and tested in Python and the corresponding times are recorded below.
 
@@ -13,4 +13,4 @@ Running a Simulation in Simulink also has some overhead. By compiling the model
 | 1e-4      | 0.5905                | 0.06                |
 | 1e-5      | 1.0461                | 0.61                |
 | 1e-6      | 8.1991                | 6.08                |
-| 1e-7      | 78.9901               | 60.18               |
+| 1e-7      | 78.9901               | 60.18               |

Example3/README.md

@@ -27,11 +27,10 @@
     "import ctypes\n",
     "import os\n",
     "\n",
+    "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "from rtwtypes import *\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "dll_path = os.path.abspath(\"bouncing_ball_win64.dll\")\n",
     "dll = ctypes.windll.LoadLibrary(dll_path)\n",
     "# Model entry point functions\n",
@@ -103,10 +102,10 @@
     "\n",
     "df.plot(x=\"time\", y=[\"ball_position\"])\n",
     "plt.title(\"Ball Position (m)\")\n",
-    "plt.grid('on')\n",
+    "plt.grid(\"on\")\n",
     "plt.title(\"Ball Velocity (m/s)\")\n",
     "df.plot(x=\"time\", y=[\"ball_velocity\"])\n",
-    "plt.grid('on')"
+    "plt.grid(\"on\")"
    ]
   },
   {
@@ -157,7 +156,7 @@
     "df = pd.DataFrame(rows)\n",
     "\n",
     "df.plot(x=\"time\", y=[\"ball_position\", \"ball_velocity\"])\n",
-    "plt.grid('on')"
+    "plt.grid(\"on\")"
    ]
   },
   {
@@ -194,7 +193,7 @@
     "df = pd.DataFrame(rows)\n",
     "\n",
     "df.plot(x=\"time\", y=[\"ball_position\", \"ball_velocity\"])\n",
-    "plt.grid('on')"
+    "plt.grid(\"on\")"
    ]
   }
  ],

Example3/bouncing_ball.ipynb

@@ -21,7 +21,7 @@
 idx=1;
 results=zeros(4,10);
 for Ts = [1e-4, 1e-5, 1e-6, 1e-7]
-    
+
     for i = 1:10
         tic
         sim('bouncing_ball', 25)

Example3/bouncing_ball_benchmark.m

@@ -53,7 +53,7 @@ For a given library or model configuring the Python should only need done when t
 
 A simple discrete transfer function. Compiled with a 1st order low pass filter.
 
-There are two example notebooks for Example 2. 
+There are two example notebooks for Example 2.
 
 1. [Simple Example](https://nbviewer.jupyter.org/github/dapperfu/python_SimulinkDLL/blob/master/Example2/discrete_tf.ipynb) - A simple low-level ctypes wrapper.
 2. [Pythonic Example](https://nbviewer.jupyter.org/github/dapperfu/python_SimulinkDLL/blob/master/Example2/discrete_tf-python_class.ipynb) - Use Python syntactic sugar to create a high level [TransferTF python](https://github.com/dapperfu/python_SimulinkDLL/blob/master/Example2/discretetf.py) class to interact with the model. Adds datalogging and pandas integration.
@@ -72,7 +72,7 @@ Adapted from [Mathworks's Simulation of a Bouncing Ball](https://www.mathworks.c
 
 ![](Example3/bouncing_ball.png)
 
-Running a Simulation in Simulink also has some overhead. By compiling the model to a shared library and executing it, this overhead is eliminated. 
+Running a Simulation in Simulink also has some overhead. By compiling the model to a shared library and executing it, this overhead is eliminated.
 
 `bouncing_ball_benchmark.m` benchmarks the model by testing increasingly smaller time steps. The model was then compiled and tested in Python and the corresponding times are recorded below.
 
@@ -87,7 +87,7 @@ Running a Simulation in Simulink also has some overhead. By compiling the model
 
 This project also serves as a proof of concept for using [CI/CD devops techniques](https://www.atlassian.com/continuous-delivery/principles/continuous-integration-vs-delivery-vs-deployment) with Simulink Models. There is a [`Jenkinsfile` ](Jenkinsfile) that will build each of the examples and archive the artifacts:
 
-- shared library (`.dll`) 
+- shared library (`.dll`)
 - header files (`.h`)
 
 Jenkins Pipeline screenshot: