Fix shared libs loading + adding more advanced tests

docs/environment-cpp.yml

@@ -4,4 +4,3 @@ channels:
   - https://prefix.dev/conda-forge
 dependencies:
   - xeus-cpp
-  - symengine

docs/environment-r.yml

@@ -1,6 +1,7 @@
 name: xeus-r-kernel-docs
 channels:
-  - https://repo.mamba.pm/emscripten-forge
+  - https://prefix.dev/emscripten-forge-dev
   - https://prefix.dev/conda-forge
 dependencies:
   - xeus-r
+  - r-ggplot2

packages/xeus/src/worker.ts

@@ -178,7 +178,12 @@ export abstract class EmpackedXeusRemoteKernel extends XeusRemoteKernelBase {
     }
 
     // Load shared libs
-    await this._maybeLoadSharedLibs();
+    await loadSharedLibs({
+      sharedLibs: this._sharedLibs,
+      prefix: '/',
+      Module: this.Module,
+      logger: this.logger
+    });
   }
 
   /**
@@ -326,36 +331,14 @@ export abstract class EmpackedXeusRemoteKernel extends XeusRemoteKernelBase {
     this._paths = { ...newPath, ...paths };
     this._sharedLibs = sharedLibs;
 
-    await this._maybeLoadSharedLibs();
-
-    this._env = newEnv;
-  }
-
-  private async _maybeLoadSharedLibs() {
-    const toLoad: TSharedLibsMap = {};
-
-    // If we're running in the Python kernel, load all cpython shared libs beforehand,
-    // otherwise load everything but cpython shared libs
-    if (this._pythonVersion) {
-      for (const pkgName of Object.keys(this._sharedLibs)) {
-        toLoad[pkgName] = this._sharedLibs[pkgName].filter(sharedLib =>
-          sharedLib.includes('cpython-3')
-        );
-      }
-    } else {
-      for (const pkgName of Object.keys(this._sharedLibs)) {
-        toLoad[pkgName] = this._sharedLibs[pkgName].filter(
-          sharedLib => !sharedLib.includes('cpython-3')
-        );
-      }
-    }
-
     await loadSharedLibs({
-      sharedLibs: toLoad,
+      sharedLibs: this._sharedLibs,
       prefix: '/',
       Module: this.Module,
       logger: this.logger
     });
+
+    this._env = newEnv;
   }
 
   private _pythonVersion: number[] | undefined;

ui-tests/build.py

@@ -4,7 +4,7 @@
 import jupyterlab
 
 extra_labextensions_path = str(Path(jupyterlab.__file__).parent / "galata")
-cmd = f"jupyter lite build --FederatedExtensionAddon.extra_labextensions_path={extra_labextensions_path}"
+cmd = f"jupyter lite build --contents notebooks --FederatedExtensionAddon.extra_labextensions_path={extra_labextensions_path}"
 
 run(
     cmd,

ui-tests/env-cpp.yml

@@ -0,0 +1,9 @@
+name: env-cpp
+channels:
+  - https://prefix.dev/emscripten-forge-dev
+  - https://prefix.dev/conda-forge
+dependencies:
+  - xeus-cpp
+  - symengine
+  - xtensor-blas
+  - xsimd

ui-tests/env2.yml → ui-tests/env-default.yml

@@ -1,4 +1,4 @@
-name: env2
+name: env-default
 channels:
   - https://prefix.dev/emscripten-forge-dev
   - https://prefix.dev/conda-forge

ui-tests/env3.yml → ui-tests/env-javascript.yml

@@ -1,4 +1,4 @@
-name: env3
+name: env-javascript
 channels:
   - https://repo.mamba.pm/emscripten-forge
   - conda-forge

ui-tests/env1.yml → ui-tests/env-python.yml

@@ -1,11 +1,11 @@
-name: env1
+name: env-python
 channels:
   - https://prefix.dev/emscripten-forge-dev
   - https://prefix.dev/conda-forge
 dependencies:
   - xeus-python
-  - xeus-lua
-  - xeus-r
-  - xeus-cpp
-  - pandas
+  - scipy
+  - numpy
+  - matplotlib
   - bqplot
+  - ipywidgets

ui-tests/env-r.yml

@@ -0,0 +1,7 @@
+name: env-r
+channels:
+  - https://prefix.dev/emscripten-forge-dev
+  - https://prefix.dev/conda-forge
+dependencies:
+  - xeus-r
+  - r-ggplot2

ui-tests/jupyter-lite.json

@@ -2,7 +2,6 @@
   "jupyter-lite-schema-version": 0,
   "jupyter-config-data": {
     "appName": "JupyterLite UI Tests",
-    "defaultKernelName": "xlua",
     "exposeAppInBrowser": true
   }
 }

ui-tests/jupyter_lite_config.json

@@ -3,6 +3,12 @@
     "output_dir": "dist"
   },
   "XeusAddon": {
-    "environment_file": ["env1.yml", "env2.yml", "env3.yml"]
+    "environment_file": [
+      "env-default.yml",
+      "env-cpp.yml",
+      "env-javascript.yml",
+      "env-python.yml",
+      "env-r.yml"
+    ]
   }
 }

ui-tests/notebooks/Lorenz.ipynb

@@ -0,0 +1,155 @@
+{
+  "metadata": {
+    "kernelspec": {
+      "name": "xpython (env-python)",
+      "display_name": "Python 3.13 (XPython) [env-python]",
+      "language": "python"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "python",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8"
+    }
+  },
+  "nbformat_minor": 4,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": "# The Lorenz Differential Equations",
+      "metadata": {}
+    },
+    {
+      "cell_type": "markdown",
+      "source": "Before we start, we import some preliminary libraries.",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy import integrate\n\nfrom ipywidgets import interactive",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "source": "We will also define the actual solver and plotting routine.",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": "def solve_lorenz(sigma=10.0, beta=8./3, rho=28.0):\n    \"\"\"Plot a solution to the Lorenz differential equations.\"\"\"\n\n    max_time = 4.0\n    N = 30\n\n    fig = plt.figure(1)\n    ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n    ax.axis('off')\n\n    # prepare the axes limits\n    ax.set_xlim((-25, 25))\n    ax.set_ylim((-35, 35))\n    ax.set_zlim((5, 55))\n\n    def lorenz_deriv(x_y_z, t0, sigma=sigma, beta=beta, rho=rho):\n        \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n        x, y, z = x_y_z\n        return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n\n    # Choose random starting points, uniformly distributed from -15 to 15\n    np.random.seed(1)\n    x0 = -15 + 30 * np.random.random((N, 3))\n\n    # Solve for the trajectories\n    t = np.linspace(0, max_time, int(250*max_time))\n    x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t)\n                      for x0i in x0])\n\n    # choose a different color for each trajectory\n    colors = plt.cm.viridis(np.linspace(0, 1, N))\n\n    for i in range(N):\n        x, y, z = x_t[i,:,:].T\n        lines = ax.plot(x, y, z, '-', c=colors[i])\n        plt.setp(lines, linewidth=2)\n    angle = 104\n    ax.view_init(30, angle)\n    plt.show()\n\n    return t, x_t",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "source": "We explore the Lorenz system of differential equations:\n\n$$\n\\begin{aligned}\n\\dot{x} & = \\sigma(y-x) \\\\\n\\dot{y} & = \\rho x - y - xz \\\\\n\\dot{z} & = -\\beta z + xy\n\\end{aligned}\n$$\n\nLet's change (\\\\(\\sigma\\\\), \\\\(\\beta\\\\), \\\\(\\rho\\\\)) with ipywidgets and examine the trajectories.",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": "w=interactive(solve_lorenz,sigma=(0.0,50.0),rho=(0.0,50.0))\nw",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "source": "For the default set of parameters, we see the trajectories swirling around two points, called attractors. ",
+      "metadata": {}
+    },
+    {
+      "cell_type": "markdown",
+      "source": "The object returned by `interactive` is a `Widget` object and it has attributes that contain the current result and arguments:",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": "t, x_t = w.result",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "w.kwargs",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "source": "After interacting with the system, we can take the result and perform further computations. In this case, we compute the average positions in \\\\(x\\\\), \\\\(y\\\\) and \\\\(z\\\\).",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": "xyz_avg = x_t.mean(axis=1)",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "xyz_avg.shape",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "source": "Creating histograms of the average positions (across different trajectories) show that, on average, the trajectories swirl about the attractors.",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": "from matplotlib import pyplot as plt\n%matplotlib inline",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "plt.hist(xyz_avg[:,0])\nplt.title('Average $x(t)$');",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "plt.hist(xyz_avg[:,1])\nplt.title('Average $y(t)$');",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}