update quickstart docs

Author: chaoming0625

docs/auto_generater.py

@@ -257,7 +257,7 @@ def generate_datasets_docs(path='apis/auto/datasets/'):
                header='Chaotic Systems')
   write_module(module_name='brainpy.datasets.vision',
                filename=os.path.join(path, 'vision.rst'),
-               header='Chaotic Systems')
+               header='Vision Datasets')
 
 
 def generate_dyn_docs(path='apis/auto/dyn/'):

docs/index.rst

@@ -38,7 +38,6 @@ The code of BrainPy is open-sourced at GitHub:
 
    quickstart/installation
    quickstart/simulation
-   quickstart/rate_model
    quickstart/training
    quickstart/analysis
 
@@ -66,9 +65,6 @@ The code of BrainPy is open-sourced at GitHub:
    tutorial_toolbox/synaptic_connections
    tutorial_toolbox/synaptic_weights
    tutorial_toolbox/optimizers
-   tutorial_toolbox/runners
-   tutorial_toolbox/inputs
-   tutorial_toolbox/monitors
    tutorial_toolbox/saving_and_loading
 
 

docs/quickstart/analysis.ipynb

@@ -35,6 +35,14 @@ of BrainPy, you can use:
    pip install --pre brainpy
 
 
+To install ``brainpylib`` (needed in dedicated operators), you can use:
+
+.. code-block:: bash
+
+    pip install brainpylib
+
+
+
 Installation from source
 ------------------------
 
@@ -157,13 +165,7 @@ packages:
 
     conda install numba
 
-- brainpylib: needed in dedicated operators
-
-.. code-block:: bash
 
-    pip install brainpylib
-    
- 
 - `matplotlib`_: required in some visualization functions, but now it is recommended that users explicitly import matplotlib for visualization
 
 .. code-block:: bash
@@ -174,15 +176,6 @@ packages:
 
     conda install matplotlib
 
-- `NetworkX`_: needed in the visualization of network training
-
-.. code-block:: bash
-
-    pip install networkx
-
-    # or
-
-    conda install networkx
 
 .. _NumPy: https://numpy.org/
 .. _Matplotlib: https://matplotlib.org/
@@ -194,4 +187,3 @@ packages:
 .. _Numba: https://numba.pydata.org/
 .. _CUDA: https://developer.nvidia.com/cuda-downloads
 .. _CuDNN: https://developer.nvidia.com/CUDNN
-.. _NetworkX: https://networkx.org/

docs/quickstart/installation.rst

@@ -3,15 +3,23 @@
   {
    "cell_type": "markdown",
    "id": "9f5ef59c",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "# Runners"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "95e252ca",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "@[Chaoming Wang](https://github.com/chaoming0625)\n",
     "@[Xiaoyu Chen](mailto:[email protected])"
@@ -20,15 +28,23 @@
   {
    "cell_type": "markdown",
    "id": "4a593794",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## Runners for Dynamical Systems"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "ff45c9c2",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "The convenient simulation interfaces for dynamical systems in BrainPy are implemented in ``brainpy.simulation.runner``. Currently, we implement two kinds of runner: ``DSRunner`` and ``ReportRunner``. They have their respective advantages. "
    ]
@@ -37,7 +53,11 @@
    "cell_type": "code",
    "execution_count": 41,
    "id": "c79f1bb6",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import brainpy as bp\n",
@@ -49,15 +69,23 @@
   {
    "cell_type": "markdown",
    "id": "8addcec8",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## Initializing a runner"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "a9e04882",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Generally, we can initialize a runner with the format of:\n",
     "\n",
@@ -73,7 +101,11 @@
   {
    "cell_type": "markdown",
    "id": "1a4205d5",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "In which\n",
     "- ``target`` specifies the model to be simulated. It must an instance of [brainpy.DynamicalSystem](../apis/auto/simulation/generated/brainpy.simulation.brainobjects.DynamicalSystem.rst). \n",
@@ -86,7 +118,11 @@
   {
    "cell_type": "markdown",
    "id": "94806315",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Here we define an E/I balanced network as the simulation model. "
    ]
@@ -95,7 +131,11 @@
    "cell_type": "code",
    "execution_count": 42,
    "id": "06017318",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "class EINet(bp.Network):\n",
@@ -123,23 +163,35 @@
   {
    "cell_type": "markdown",
    "id": "f00418dd",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Then we will wrap it in different runners for dynamic simulation."
    ]
   },
   {
    "cell_type": "markdown",
    "id": "1cbdeac2",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## ``brainpy.DSRunner``"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "23e41c2d",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "``brainpy.DSRunner`` aims to provide model simulation with an outstanding performance. It takes advantage of the [structural loop primitive](../tutorial_math/control_flows.ipynb) to lower the model onto the XLA devices. "
    ]
@@ -148,7 +200,11 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "e0d0127e",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -190,7 +246,10 @@
    "execution_count": 4,
    "id": "7190e822",
    "metadata": {
-    "scrolled": false
+    "scrolled": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [
     {
@@ -213,31 +272,47 @@
   {
    "cell_type": "markdown",
    "id": "b8b45777",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Note that if the parameter ``jit`` is set to ``True``, then all the variables will be JIT compiled and thus the system cannot be debugged by Python debugging tools. For debugging, users can set ``jit=False``."
    ]
   },
   {
    "cell_type": "markdown",
    "id": "3d9e82a9",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## ``brainpy.ReportRunner``"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "eaab18b7",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "``brainpy.ReportRunner`` aims to provide a Pythonic interface for model debugging. Users can use the standard Python debugging tools when simulating the model with ``ReportRunner``."
    ]
   },
   {
    "cell_type": "markdown",
    "id": "cb659ddd",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "The drawback of the ``brainpy.ReportRunner`` is that it is relatively slow. It iterates the loop along times during the simulation."
    ]
@@ -246,7 +321,11 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "a6c62e4b",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -286,7 +365,11 @@
   {
    "cell_type": "markdown",
    "id": "d5b1aa9c",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "We can see from the output that the time spent for simulation through ``ReportRunner`` is longer than that through ``DSRunner``."
    ]
@@ -296,7 +379,10 @@
    "execution_count": 5,
    "id": "d6036bd0",
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [
     {
@@ -319,7 +405,11 @@
   {
    "cell_type": "markdown",
    "id": "3551f214",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## Runners for Neural Network Training"
    ]
@@ -328,7 +418,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "26d3e6e1",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": []
   }
@@ -385,4 +479,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}