[docs] Add colab link for documentation notebooks (#614)

* Add colab link for documentation notebooks * [math] Fix can not import `jax.in1d` * Update requirements
brainpy · Feb 8, 2024 · 9990f05 · 9990f05
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diff --git a/brainpy/_src/math/compat_numpy.py b/brainpy/_src/math/compat_numpy.py
@@ -205,6 +205,23 @@ def asfarray(a, dtype=np.float_):
     dtype = np.float_
   return asarray(a, dtype=dtype)
 
+def in1d(ar1, ar2, assume_unique: bool = False, invert: bool = False) -> Array:
+  del assume_unique
+  ar1_flat = ravel(ar1)
+  ar2_flat = ravel(ar2)
+  # Note: an algorithm based on searchsorted has better scaling, but in practice
+  # is very slow on accelerators because it relies on lax control flow. If XLA
+  # ever supports binary search natively, we should switch to this:
+  #   ar2_flat = jnp.sort(ar2_flat)
+  #   ind = jnp.searchsorted(ar2_flat, ar1_flat)
+  #   if invert:
+  #     return ar1_flat != ar2_flat[ind]
+  #   else:
+  #     return ar1_flat == ar2_flat[ind]
+  if invert:
+    return asarray((ar1_flat[:, None] != ar2_flat[None, :]).all(-1))
+  else:
+    return asarray((ar1_flat[:, None] == ar2_flat[None, :]).any(-1))
 
 # Others
 # ------
@@ -237,7 +254,6 @@ def asfarray(a, dtype=np.float_):
 histogram_bin_edges = _compatible_with_brainpy_array(jnp.histogram_bin_edges)
 histogramdd = _compatible_with_brainpy_array(jnp.histogramdd)
 i0 = _compatible_with_brainpy_array(jnp.i0)
-in1d = _compatible_with_brainpy_array(jnp.in1d)
 indices = _compatible_with_brainpy_array(jnp.indices)
 insert = _compatible_with_brainpy_array(jnp.insert)
 intersect1d = _compatible_with_brainpy_array(jnp.intersect1d)

diff --git a/docs/core_concept/brainpy_dynamical_system.ipynb b/docs/core_concept/brainpy_dynamical_system.ipynb
@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Concept 2: Dynamical System"
+    "# Concept 2: Dynamical System\n",
+    "\n",
+    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/brainpy/blob/master/docs/core_concept/brainpy_dynamical_system.ipynb)"
    ]
   },
   {

diff --git a/docs/core_concept/brainpy_transform_concept.ipynb b/docs/core_concept/brainpy_transform_concept.ipynb
@@ -9,7 +9,9 @@
     }
    },
    "source": [
-    "# Concept 1: Object-oriented Transformation"
+    "# Concept 1: Object-oriented Transformation\n",
+    "\n",
+    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/examples/blob/main/docs/core_concept/brainpy_transform_concept.ipynb)"
    ]
   },
   {

diff --git a/docs/quickstart/analysis.ipynb b/docs/quickstart/analysis.ipynb
diff --git a/docs/quickstart/simulation.ipynb b/docs/quickstart/simulation.ipynb
diff --git a/docs/quickstart/training.ipynb b/docs/quickstart/training.ipynb
diff --git a/docs/tutorial_FAQs/brainpy_ecosystem.ipynb b/docs/tutorial_FAQs/brainpy_ecosystem.ipynb
@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# BrainPy Ecosystem for Brain Dynamics Modeling"
+    "# BrainPy Ecosystem for Brain Dynamics Modeling\n",
+    "\n",
+    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/brainpy/blob/master/docs/tutorial_FAQs/brainpy_ecosystem.ipynb)"
    ]
   },
   {
@@ -54,17 +56,20 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "## 《神经计算建模实战》\n",
     "\n",
     "[《神经计算建模实战》 (Neural Modeling in Action)](https://github.com/c-xy17/NeuralModeling) is a book for brain dynamics modeling based on BrainPy. It introduces the basic concepts and methods of brain dynamics modeling, and provides comprehensive examples for brain dynamics modeling with BrainPy. \n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "## 神经计算建模与编程培训班\n",
     "\n",
@@ -76,10 +81,7 @@
     "\n",
     "This course is based on the textbook [《神经计算建模实战》 (Neural Modeling in Action)](https://github.com/c-xy17/NeuralModeling), supplemented by BrainPy, and based on the theory of \"theory+practice\" combination of teaching and learning. Through this course, students will master the basic concepts, methods and techniques of neural computation modelling, as well as how to use Python programming language to achieve convenient modelling and efficient simulation of neural systems, laying a solid foundation for future research in the field of neural computation or in the field of brain-like intelligence.\n",
     "\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   }
  ],
  "metadata": {