updates

brainpy/_src/math/compat_numpy.py

@@ -92,7 +92,7 @@
 
   # unique
   'array2string', 'asanyarray',
-  'ascontiguousarray', 'asfarray', 'asscalar', 'common_type', 'disp', 'genfromtxt',
+  'ascontiguousarray', 'asfarray', 'asscalar', 'common_type', 'genfromtxt',
   'loadtxt', 'info', 'issubclass_', 'place', 'polydiv', 'put', 'putmask', 'safe_eval',
   'savetxt', 'savez_compressed', 'show_config', 'typename', 'copyto', 'matrix', 'asmatrix', 'mat',
 
@@ -725,8 +725,6 @@ def common_type(*arrays):
     return array_type[0][precision]
 
 
-disp = np.disp
-
 genfromtxt = lambda *args, **kwargs: asarray(np.genfromtxt(*args, **kwargs))
 loadtxt = lambda *args, **kwargs: asarray(np.loadtxt(*args, **kwargs))
 

brainpy/math/compat_numpy.py

@@ -327,16 +327,12 @@
   sort_complex as sort_complex,
   unpackbits as unpackbits,
   delete as delete,
-  add_docstring as add_docstring,
-  add_newdoc as add_newdoc,
-  add_newdoc_ufunc as add_newdoc_ufunc,
   array2string as array2string,
   asanyarray as asanyarray,
   ascontiguousarray as ascontiguousarray,
   asfarray as asfarray,
   asscalar as asscalar,
   common_type as common_type,
-  disp as disp,
   genfromtxt as genfromtxt,
   loadtxt as loadtxt,
   info as info,

docs/quickstart/analysis.ipynb

@@ -78,7 +78,8 @@
    ],
    "source": [
     "bp.__version__"
-   ]
+   ],
+   "id": "b9f06f10049d4a8c"
   },
   {
    "cell_type": "markdown",
@@ -108,7 +109,8 @@
     "\\tau {\\dot {V}}= - (V - V_\\mathrm{rest}) + \\Delta_T \\exp(\\frac{V - V_T}{\\Delta_T}) + RI \\\\\n",
     "\\mathrm{if}\\, \\, V > \\theta, \\quad  V \\gets  V_\\mathrm{reset}\n",
     "$$"
-   ]
+   ],
+   "id": "1ecf04d3eaf72477"
   },
   {
    "cell_type": "markdown",
@@ -355,7 +357,8 @@
    },
    "source": [
     "## Slow point analysis of a high-dimensional system"
-   ]
+   ],
+   "id": "f8c9850a60c8a2b0"
   },
   {
    "cell_type": "markdown",
@@ -366,7 +369,8 @@
     "BrainPy is also capable of performing fixed/slow point analysis of high-dimensional systems. Moreover, it can perform automatic linearization analysis around the fixed point.\n",
     "\n",
     "In the following, we use a gap junction coupled FitzHugh–Nagumo (FHN) network as an example to demonstrate how to find fixed/slow points of a high-dimensional system."
-   ]
+   ],
+   "id": "a42b23306515127c"
   },
   {
    "cell_type": "markdown",
@@ -375,7 +379,8 @@
    },
    "source": [
     "We first define the gap junction coupled FHN network as the normal ``DynamicalSystem`` class."
-   ]
+   ],
+   "id": "24471089c2a5247d"
   },
   {
    "cell_type": "code",
@@ -424,7 +429,8 @@
     "    self.V.value = self.int_V(self.V, t, self.w, self.Iext, dt)\n",
     "    self.w.value = self.int_w(self.w, t, self.V, dt)\n",
     "    self.Iext[:] = 0."
-   ]
+   ],
+   "id": "f9fa252ff8617c22"
   },
   {
    "cell_type": "markdown",
@@ -433,7 +439,8 @@
    },
    "source": [
     "Through simulation, we can easily find that this system has a limit cycle attractor, implying that an unstable fixed point exists."
-   ]
+   ],
+   "id": "158d89a270070b5d"
   },
   {
    "cell_type": "code",
@@ -485,7 +492,8 @@
     "bp.visualize.line_plot(runner.mon.ts, runner.mon.V, legend='V',\n",
     "                       plot_ids=list(range(model.num)),\n",
     "                       show=True)"
-   ]
+   ],
+   "id": "58e6a900ff39ff43"
   },
   {
    "cell_type": "markdown",
@@ -494,7 +502,8 @@
    },
    "source": [
     "Let's try to optimize the fixed points for this system. Note that we only take care of the variables ``V`` and ``w``. Different from the low-dimensional analyzer, we should provide the candidate fixed points or initial fixed points when using the high-dimensional analyzer."
-   ]
+   ],
+   "id": "22084ff723d5331"
   },
   {
    "cell_type": "code",
@@ -582,7 +591,8 @@
     "\n",
     "# remove the duplicate fixed points\n",
     "finder.keep_unique()"
-   ]
+   ],
+   "id": "90ce07cf2a0a8e3c"
   },
   {
    "cell_type": "code",
@@ -617,7 +627,8 @@
    "source": [
     "print('fixed points:', )\n",
     "finder.fixed_points"
-   ]
+   ],
+   "id": "45b81a4cdac07efa"
   },
   {
    "cell_type": "code",
@@ -651,7 +662,8 @@
    "source": [
     "print('fixed point losses:', )\n",
     "finder.losses"
-   ]
+   ],
+   "id": "adc5767da75ea8b2"
   },
   {
    "cell_type": "markdown",
@@ -660,7 +672,8 @@
    },
    "source": [
     "Let's perform the linearization analysis of the found fixed points, and visualize its decomposition results."
-   ]
+   ],
+   "id": "511b2ac23cb0c586"
   },
   {
    "cell_type": "code",
@@ -694,7 +707,8 @@
    ],
    "source": [
     "_ = finder.compute_jacobians(finder.fixed_points, plot=True)"
-   ]
+   ],
+   "id": "49e773e8700380ce"
   },
   {
    "cell_type": "markdown",
@@ -703,7 +717,8 @@
    },
    "source": [
     "This is an unstable fixed point, because one of its eigenvalues has the real part bigger than 1."
-   ]
+   ],
+   "id": "a1a7df686a3bb68f"
   },
   {
    "cell_type": "markdown",
@@ -712,7 +727,8 @@
    },
    "source": [
     "## Further reading"
-   ]
+   ],
+   "id": "b9e1a94399ffa6a1"
   },
   {
    "cell_type": "markdown",
@@ -723,7 +739,8 @@
     "- For more details about how to perform bifurcation analysis and phase plane analysis, please see the tutorial of [Low-dimensional Analyzers](../tutorial_analysis/lowdim_analysis.ipynb).\n",
     "- A good example of phase plane analysis and bifurcation analysis is the decision-making model, please see the tutorial in [Analysis of a Decision-making Model](../tutorial_analysis/decision_making_model.ipynb)\n",
     "- If you want to how to analyze the slow points (or fixed points) of your high-dimensional dynamical models, please see the tutorial of [High-dimensional Analyzers](../tutorial_analysis/highdim_analysis.ipynb)"
-   ]
+   ],
+   "id": "7a4f436e37606967"
   }
  ],
  "metadata": {

docs/tutorial_toolbox/synaptic_weights.ipynb

@@ -71,7 +71,8 @@
    ],
    "source": [
     "bp.__version__"
-   ]
+   ],
+   "id": "23c6c2fe06bbd897"
   },
   {
    "cell_type": "markdown",