diff --git a/build_wasm.sh b/build_wasm.sh
index a621f59..e8fd2da 100755
--- a/build_wasm.sh
+++ b/build_wasm.sh
@@ -9,13 +9,16 @@ fi
 rm -rf docs/lib
 mkdir -p docs/lib
 
-# Generate test_numpy.js from test source
+# Generate test_sources.js from all test files
 python3 -c "
-import json
-with open('tests/test_numpy.py') as f:
-    src = f.read()
-print('var TEST_SOURCE = ' + json.dumps(src) + ';')
-" > docs/test_numpy.js
+import json, glob, os
+sources = {}
+for path in sorted(glob.glob('tests/*.py')):
+    name = os.path.basename(path)
+    with open(path) as f:
+        sources[name] = f.read()
+print('var TEST_SOURCES = ' + json.dumps(sources) + ';')
+" > docs/test_sources.js
 
 # Common flags
 DEFINES="-DPK_ENABLE_OS=0 -DPK_ENABLE_THREADS=0 -DPK_ENABLE_DETERMINISM=0 \
diff --git a/docs/index.html b/docs/index.html
index 8c4ee56..b3f76ac 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -76,6 +76,17 @@
       min-height: 50px;
       line-height: 1.4;
     }
+    #file-select {
+      font-family: monospace;
+      font-size: 14px;
+      padding: 4px 8px;
+      margin-bottom: 6px;
+      background-color: #282a36;
+      color: #f8f8f2;
+      border: 1px solid #444;
+      border-radius: 4px;
+      outline: none;
+    }
     .pass { color: #4ec9b0; font-weight: bold; }
     .fail { color: #f44747; font-weight: bold; }
   </style>
@@ -83,6 +94,7 @@
 <body>
   <h1>pocket-numpy</h1>
   GitHub: <a href="https://github.com/cubao/xtensor-numpy" style="color: #ffff00; text-decoration: none;" target="_blank">cubao/xtensor-numpy</a>
+  <select id="file-select"></select>
   <div class="editor-container">
     <div class="line-numbers" id="line-numbers">1</div>
     <textarea id="editor" spellcheck="false"></textarea>
@@ -92,7 +104,7 @@ <h1>pocket-numpy</h1>
   <div id="status">Loading WASM module...</div>
   <pre id="output"></pre>
 
-  <script src="test_numpy.js"></script>
+  <script src="test_sources.js"></script>
   <script>
     var editorEl = document.getElementById('editor');
     var outputEl = document.getElementById('output');
@@ -100,8 +112,23 @@ <h1>pocket-numpy</h1>
     var runBtn = document.getElementById('run-btn');
     var ready = false;
 
-    // Fill editor with bundled test source
-    editorEl.value = TEST_SOURCE;
+    // Populate file selector dropdown
+    var fileSelect = document.getElementById('file-select');
+    var fileNames = Object.keys(TEST_SOURCES);
+    for (var i = 0; i < fileNames.length; i++) {
+      var opt = document.createElement('option');
+      opt.value = fileNames[i];
+      opt.textContent = fileNames[i];
+      fileSelect.appendChild(opt);
+    }
+
+    function loadSelectedFile() {
+      editorEl.value = TEST_SOURCES[fileSelect.value];
+      updateLineNumbers();
+      autoResizeEditor();
+    }
+
+    fileSelect.addEventListener('change', loadSelectedFile);
 
     // Tab key inserts a tab instead of moving focus
     editorEl.addEventListener('keydown', function (e) {
@@ -157,9 +184,11 @@ <h1>pocket-numpy</h1>
       document.getElementById('line-numbers').scrollTop = this.scrollTop;
     });
 
-    // Initial line numbers and auto-resize
-    updateLineNumbers();
-    autoResizeEditor();
+    // Load initial file and set up editor
+    if (fileNames.indexOf('test_numpy.py') !== -1) {
+      fileSelect.value = 'test_numpy.py';
+    }
+    loadSelectedFile();
 
     // Re-calc on window resize
     window.addEventListener('resize', function() {
diff --git a/docs/lib/pocketpy.wasm b/docs/lib/pocketpy.wasm
index a6064e4..4942050 100755
Binary files a/docs/lib/pocketpy.wasm and b/docs/lib/pocketpy.wasm differ
diff --git a/docs/test_numpy.js b/docs/test_numpy.js
deleted file mode 100644
index 238aa18..0000000
--- a/docs/test_numpy.js
+++ /dev/null
@@ -1 +0,0 @@
-var TEST_SOURCE = "import math\nimport numpy as np\n\ndef assert_equal(a, b):\n    assert (a == b).all()\n\n\n# test dtypes\nassert hasattr(np, 'int8')\nassert hasattr(np, 'int16')\nassert hasattr(np, 'int32')\nassert hasattr(np, 'int64')\nassert hasattr(np, 'int_')\nassert hasattr(np, 'float32')\nassert hasattr(np, 'float64')\nassert hasattr(np, 'float_')\nassert hasattr(np, 'bool_')\n\n\n# test array int\narr1 = np.array([])\narr2 = np.array(10)\narr3 = np.array([-2, -1, 0, 1, 2])\narr4 = np.array([[1, 2], [2, 1]])\narr5 = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])\narr6 = np.array([[[[[1], [10], [100], [1000], [10000]]]]])\narr7 = np.array([[[2147483647]]])\n\narr8 = np.array([1, 2, 3, 4, 5], dtype='int8')\narr8 = np.array([1, 2, 3, 4, 5], np.int8)\n\narr9 = np.array([1, 2, 3, 4, 5], dtype='int16')\narr9 = np.array([1, 2, 3, 4, 5], np.int16)\n\narr10 = np.array([1, 2, 3, 4, 5], dtype='int32')\narr10 = np.array([1, 2, 3, 4, 5], np.int32)\n\narr11 = np.array([1, 2, 3, 4, 5], dtype='int64')\narr11 = np.array([1, 2, 3, 4, 5], np.int64)\n\n\n# test array bool\narr1 = np.array([True, False, True, False])\narr2 = np.array([[True, False], [False, True]])\narr3 = np.array([[[True, False, True], [False, True, False]], [[True, False, True], [False, True, False]]])\n\narr4 = np.array([[1, 0], [0, 1]], dtype='bool')\narr4 = np.array([[1, 0], [0, 1]], np.bool_)\n\n\n# test array float\narr1 = np.array([0.123456789])\narr2 = np.array([-2.0, -1.0, 0.0, 1.0, 2.0])\narr3 = np.array([[1.0, 2.0], [2.0, 1.0]])\narr4 = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]])\narr5 = np.array([[[[[1.0], [10.0], [100.0], [1000.0], [10000.0]]]]])\narr6 = np.array([[[3.141592653589793]]])\n\narr7 = np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype='float32')\narr7 = np.array([1.0, 2.0, 3.0, 4.0, 5.0], np.float32)\n\narr8 = np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype='float64')\narr8 = np.array([1.0, 2.0, 3.0, 4.0, 5.0], np.float64)\n\n\n# test array creation\nassert_equal(np.ones([1, 1]), np.array([[1.0]]))\nassert_equal(np.ones([1, 1, 2, 2]), np.array([[[[1.0, 1.0], [1.0, 1.0]]]]))\n\nassert_equal(np.zeros([1, 1]), np.array([[0.0]]))\nassert_equal(np.zeros([1, 1, 2, 2]), np.array([[[[0.0, 0.0], [0.0, 0.0]]]]))\n\nassert_equal(np.full([1, 1], -1e9), np.array([[-1.000000e+09]]))\nassert_equal(np.full([1, 1, 2, 2], 3.14), np.array([[[[3.14, 3.14], [3.14, 3.14]]]]))\n\nassert_equal(np.identity(1), np.array([[1.0]]))\nassert_equal(np.identity(3), np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]))\n\nassert_equal(np.arange(10), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]))\nassert_equal(np.arange(1, 10), np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]))\nassert_equal(np.arange(1, 10, 2), np.array([1, 3, 5, 7, 9]))\n\nassert_equal(np.linspace(0, 1, 5), np.array([0.0, 0.25, 0.5, 0.75, 1.0]))\nassert np.allclose(np.linspace(0, 1, 5, False), np.array([0.0, 0.2, 0.4, 0.6, 0.8]))\nassert np.allclose(np.linspace(0, 1, 20, True),\n                   np.array([0.0, 0.05263158, 0.10526316,\n                             0.15789474, 0.21052632, 0.26315789,\n                             0.31578947, 0.36842105, 0.42105263,\n                             0.47368421, 0.52631579, 0.57894737,\n                             0.63157895, 0.68421053, 0.73684211,\n                             0.78947368, 0.84210526, 0.89473684,\n                             0.94736842, 1.0]))\n\n\n# test array properties\narr1 = np.array([1, 2, 3])\nassert arr1.size == 3\nassert arr1.ndim == 1\nassert arr1.shape == (3,)\nassert arr1.dtype == 'int64'\n\narr2 = np.array([[1, 2], [3, 4]])\nassert arr2.size == 4\nassert arr2.ndim == 2\nassert arr2.shape == (2, 2)\nassert arr2.dtype == np.int64\n\narr3 = np.array([[1, 2, 2, 1], [3, 4, 4, 3], [5, 6, 6, 5]], np.int32)\nassert arr3.size == 12\nassert arr3.ndim == 2\nassert arr3.shape == (3, 4)\nassert arr3.dtype == 'int32'\n\narr4 = np.array([[[[[1.5, 2.5], [3.5, 4.5], [5.5, 6.5]]]]])\nassert arr4.size == 6\nassert arr4.ndim == 5\nassert arr4.shape == (1, 1, 1, 3, 2)\nassert arr4.dtype == np.float64\n\n\n# test boolean functions\narr1 = np.array([0.0])\nassert arr1.all() == False\nassert arr1.any() == False\n\narr2 = np.array([1.0])\nassert arr2.all() == True\nassert arr2.any() == True\n\narr3 = np.array([[1, 0], [0, 1]])\nassert arr3.all() == False\nassert arr3.any() == True\n\narr4 = np.array([[[True, False, True], [False, True, False]], [[True, False, True], [False, True, False]]])\nassert arr4.all() == False\nassert arr4.any() == True\n\n\n# test array sum\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.sum(), 6.0)\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.sum() == 45\nassert arr1.sum(0) == 45\n\narr2 = np.array([[1], [2], [3]])\nassert arr2.sum() == 6\nassert_equal(arr2.sum(0), np.array([6]))\nassert_equal(arr2.sum(1), np.array([1, 2, 3]))\nassert arr2.sum((0, 1)) == 6\n\narr3 = np.array([[[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]])\nassert arr3.sum() == 40.5\nassert_equal(arr3.sum(0), np.array([[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]))\nassert_equal(arr3.sum(1), np.array([[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]))\nassert_equal(arr3.sum(2), np.array([[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]))\nassert_equal(arr3.sum(3), np.array([[[[10.5, 13.5, 16.5]]]]))\nassert_equal(arr3.sum(4), np.array([[[[7.5, 13.5, 19.5]]]]))\n\nassert_equal(arr3.sum((0, 1)), np.array([[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]))\nassert_equal(arr3.sum((3, 4)), np.array([[[40.5]]]))\n\n\n# test array prod\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.prod() == 362880\nassert arr1.prod(0) == 362880\n\narr2 = np.array([[1], [2], [3]])\nassert arr2.prod() == 6\nassert_equal(arr2.prod(0), np.array([6]))\nassert_equal(arr2.prod(1), np.array([1, 2, 3]))\nassert arr2.prod((0, 1)) == 6\n\narr3 = np.array([[[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]])\nassert arr3.prod() == 304845.556640625\nassert_equal(arr3.prod(0), np.array([[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]))\nassert_equal(arr3.prod(1), np.array([[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]))\nassert_equal(arr3.prod(4), np.array([[[[13.125, 86.625, 268.125]]]]))\nassert_equal(arr3.prod((0, 3)), np.array([[[28.875, 73.125, 144.375]]]))\n\n\n# test array min\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.min(), 1.0)\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.min() == 1\n\narr2 = np.array([[-1], [-2], [-3]])\nassert arr2.min() == -3\n\narr3 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert arr3.min() == -5.5\nassert_equal(arr3.min(0), np.array([[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]))\nassert_equal(arr3.min(4), np.array([[[[-1.5, -3.5, -5.5]]]]))\nassert_equal(arr3.min((0, 3)), np.array([[[1.5, -5.5]]]))\nassert_equal(arr3.min((0, 4)), np.array([[[-1.5, -3.5, -5.5]]]))\n\n\n# test array max\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.max(), 3.0)\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.max() == 9\n\narr2 = np.array([[-1], [-2], [-3]])\nassert arr2.max() == -1\n\narr3 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert arr3.max() == 5.5\nassert_equal(arr3.max(0), np.array([[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]))\n\nassert_equal(arr3.max(4), np.array([[[[1.5, 3.5, 5.5]]]]))\nassert_equal(arr3.max((0, 3)), np.array([[[5.5, -1.5]]]))\nassert_equal(arr3.max((0, 4)), np.array([[[1.5, 3.5, 5.5]]]))\n\n# test array mean\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.mean(), 2.0)\n\narr = np.array([[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]])\nassert arr.mean() == 4.5\nassert_equal(arr.mean(0), np.array([[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]))\nassert_equal(arr.mean(1), np.array([[3.5, 4.5, 5.5]]))\nassert_equal(arr.mean(2), np.array([[2.5, 4.5, 6.5]]))\n\n\n# test array std\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.std(), 0.816496580927726)\n\narr = np.array([[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]])\nassert arr.std() == 1.8257418583505538\nassert_equal(arr.std(0), np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]))\nassert np.allclose(arr.std(1), np.array([[1.632993, 1.632993, 1.632993]]))\nassert np.allclose(arr.std(2), np.array([[0.81649658, 0.81649658, 0.81649658]]))\n\n\n# test array var\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.var(), 0.6666666666666666)\n\narr = np.array([[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]])\nassert arr.var() == 3.3333333333333335\nassert_equal(arr.var(0), np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]))\nassert np.allclose(arr.var(1), np.array([[2.66666667, 2.66666667, 2.66666667]]))\nassert np.allclose(arr.var(2), np.array([[0.66666667, 0.66666667, 0.66666667]]))\n\n\n# test array argmin\na = np.array([3, 1, 2])\nassert a.argmin() == 1\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.argmin() == 0\n\narr2 = np.array([[-1], [-2], [-3]])\nassert arr2.argmin() == 2\nassert_equal(arr2.argmin(0), np.array([2]))\n\narr3 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert arr3.argmin() == 5\nassert_equal(arr3.argmin(0), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmin(1), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmin(2), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmin(3), np.array([[[[0, 2]]]]))\nassert_equal(arr3.argmin(4), np.array([[[[1, 1, 1]]]]))\n\n\n# test array argmax\na = np.array([3, 1, 2])\nassert a.argmax() == 0\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.argmax() == 8\n\narr2 = np.array([[-1], [-2], [-3]])\nassert arr2.argmax() == 0\nassert_equal(arr2.argmax(0), np.array([0]))\n\narr3 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert arr3.argmax() == 4\nassert_equal(arr3.argmax(0), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmax(1), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmax(2), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmax(3), np.array([[[[2, 0]]]]))\nassert_equal(arr3.argmax(4), np.array([[[[0, 0, 0]]]]))\n\n\n# test array argsort\na = np.array([3, 1, 2])\nassert_equal(a.argsort(), np.array([1, 2, 0]))\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert_equal(arr1.argsort(), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8]))\n\narr2 = np.array([9, 8, 7, 6, 5, 4, 3, 2, 1])\nassert_equal(arr2.argsort(), np.array([8, 7, 6, 5, 4, 3, 2, 1, 0]))\n\narr3 = np.array([[-1], [-2], [-3]])\nassert_equal(arr3.argsort(), np.array([[0], [0], [0]]))\nassert_equal(arr3.argsort(0), np.array([[2], [1], [0]]))\n\narr4 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert_equal(arr4.argsort(), np.array([[[[[1, 0], [1, 0], [1, 0]]]]]))\nassert_equal(arr4.argsort(0), np.array([[[[[0, 0], [0, 0], [0, 0]]]]]))\nassert_equal(arr4.argsort(1), np.array([[[[[0, 0], [0, 0], [0, 0]]]]]))\nassert_equal(arr4.argsort(2), np.array([[[[[0, 0], [0, 0], [0, 0]]]]]))\nassert_equal(arr4.argsort(3), np.array([[[[[0, 2], [1, 1], [2, 0]]]]]))\nassert_equal(arr4.argsort(4), np.array([[[[[1, 0], [1, 0], [1, 0]]]]]))\n\n\n# test array sort\na = np.array([3, 1, 2])\na.sort()\nassert_equal(a, np.array([1, 2, 3]))\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\narr1.sort()\nassert_equal(arr1, np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]))\n\narr2 = np.array([9, 8, 7, 6, 5, 4, 3, 2, 1])\narr2.sort()\nassert_equal(arr2, np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]))\n\narr3 = np.array([[-1], [-2], [-3]])\narr3.sort(0)\nassert_equal(arr3, np.array([[-3], [-2], [-1]]))\n\narr4 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\narr4.sort(3)\nassert_equal(arr4, np.array([[[[[1.5, -5.5], [3.5, -3.5], [5.5, -1.5]]]]]))\n\n\n# test array reshape\na = np.array([[1, 2], [3, 4]])\nassert_equal(a.reshape([1, 4]), np.array([[1, 2, 3, 4]]))\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert_equal(arr1.reshape([3, 3]), np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))\n\narr2 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\nassert_equal(arr2.reshape([9]), np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]))\n\narr3 = np.array([1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5])\nassert_equal(arr3.reshape([3, 4]), np.array([[1.5, 2.5, 3.5, 4.5], [5.5, 6.5, 7.5, 8.5], [9.5, 10.5, 11.5, 12.5]]))\nassert_equal(arr3.reshape([2, 3, 2]), np.array([[[1.5, 2.5], [3.5, 4.5], [5.5, 6.5]],\n                                                [[7.5, 8.5], [9.5, 10.5], [11.5, 12.5]]]))\nassert_equal(arr3.reshape([1, 1, 2, 2, 3]), np.array([[[[[1.5, 2.5, 3.5], [4.5, 5.5, 6.5]],\n                                                        [[7.5, 8.5, 9.5], [10.5, 11.5, 12.5]]]]]))\n\n\n# test array resize\na = np.array([[1, 2], [3, 4]])\na.resize([1, 4])\nassert_equal(a, np.array([[1, 2, 3, 4]]))\n\n\n# test array squeeze\na = np.array([[1, 2, 3, 4]])\nassert_equal(a.squeeze(), np.array([1, 2, 3, 4]))\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert_equal(arr1.squeeze(), np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]))\n\narr2 = np.array([[-1], [-2], [-3]])\nassert_equal(arr2.squeeze(), np.array([-1, -2, -3]))\n\narr3 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert_equal(arr3.squeeze(), np.array([[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]))\nassert_equal(arr3.squeeze(0), np.array([[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]))\n\n\n# test array transpose\na = np.array([[1, 2, 3, 4]])\nassert_equal(a.transpose(), np.array([[1], [2], [3], [4]]))\n\narr1 = np.array([[1], [2], [3]])\nassert_equal(arr1.transpose(), np.array([[1, 2, 3]]))\n\narr2 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\nassert_equal(arr2.transpose(), np.array([[1, 4, 7], [2, 5, 8], [3, 6, 9]]))\nassert_equal(arr2.transpose(0, 1), np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))\nassert_equal(arr2.transpose(1, 0), np.array([[1, 4, 7], [2, 5, 8], [3, 6, 9]]))\n\narr3 = np.array([[[[[1.5, -5.5], [3.5, -3.5], [5.5, -1.5]]]]])\nassert_equal(arr3.transpose(), np.array([[[[[1.5]]], [[[3.5]]], [[[5.5]]]],\n                                         [[[[-5.5]]], [[[-3.5]]], [[[-1.5]]]]]))\nassert_equal(arr3.transpose(0, 1, 2, 3, 4), np.array([[[[[1.5, -5.5],\n                                                         [3.5, -3.5],\n                                                         [5.5, -1.5]]]]]))\nassert_equal(arr3.transpose((3, 4, 0, 2, 1)), np.array([[[[[1.5]]], [[[-5.5]]]],\n                                                        [[[[3.5]]], [[[-3.5]]]],\n                                                        [[[[5.5]]], [[[-1.5]]]]]))\n\n\n# test array repeat\na = np.array([[1, 2, 3, 4]])\nassert_equal(a.repeat(2), np.array([[1, 1, 2, 2, 3, 3, 4, 4]]))\n\narr1 = np.array([[1, 2], [3, 4]])\nassert_equal(arr1.repeat(2, 0), np.array([[1, 2], [1, 2], [3, 4], [3, 4]]))\n\narr2 = np.array([[[[1, 2, 3], [4, 5, 6]]]])\nassert_equal(arr2.repeat(4, 3), np.array([[[[1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n                                            [4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6]]]]))\nassert_equal(arr2.repeat([1, 2, 3], 3), np.array([[[[1, 2, 2, 3, 3, 3],\n                                                    [4, 5, 5, 6, 6, 6]]]]))\n\n\n# test array flatten\na = np.array([[1, 2, 3, 4]])\nassert_equal(a.transpose().flatten(), np.array([1, 2, 3, 4]))\n\nassert_equal(a.repeat(2), np.array([[1, 1, 2, 2, 3, 3, 4, 4]]))\narr1 = np.array([[1, 2], [3, 4]])\nassert_equal(arr1.flatten(), np.array([1, 2, 3, 4]))\n\narr2 = np.array([[[1., 2.], [3., 4.], [5., 6.]], [[7., 8.], [9., 10.], [11., 12.]]])\nassert_equal(arr2.flatten(), np.array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.]))\n\n\n# test array copy\na = np.array([1.1, 2.2, 3.3])\nassert_equal(a.copy(), a)\n\narr1 = np.array([[1, 2], [3, 4]])\narr2 = arr1\narr3 = arr1.copy()\n\narr1[0] = [10, 20]\nassert_equal(arr2, np.array([[10, 20], [3, 4]])) # Shallow copy\nassert_equal(arr3, np.array([[1, 2], [3, 4]]))   # Deep copy\n\n\n# test array astype\na = np.array([1.1, 2.2, 3.3])\nassert_equal(a.astype(np.int32), np.array([1, 2, 3]))\n\narr1 = np.array([1, 2, 2.5])\n\nassert_equal(arr1.astype('int_'), np.array([1, 2, 2]))\nassert arr1.dtype == 'float64'\n\nassert_equal(arr1.astype('float64'), np.array([1.0, 2.0, 2.5]))\nassert arr1.dtype == 'float64'\n\n\n# test array round\na = np.array([1.1, 2.2, 3.3])\nassert_equal(a.round(), np.array([1., 2., 3.]))\n\nassert_equal(np.round(np.array([40, 20, 30, 10])), np.array([40, 20, 30, 10]))\nassert_equal(np.round(np.array([0.37, 1.64])), np.array([0.0, 2.0]))\nassert_equal(np.round(np.array([.5, 1.5, 2.5, 3.5, 4.5])),np.array([1., 2., 3., 4., 5.]))\n\n\n# test array floor\nassert_equal(np.floor(np.array([40, 20, 30, 10])), np.array([40, 20, 30, 10]))\nassert_equal(np.floor(np.array([0.37, 1.64])), np.array([0., 1.]))\nassert_equal(np.floor(np.array([.5, 1.5, 2.5, 3.5, 4.5])), np.array([0., 1., 2., 3., 4.]))\n\n\n# test array ceil\nassert_equal(np.ceil(np.array([40, 20, 30, 10])), np.array([40, 20, 30, 10]))\nassert_equal(np.ceil(np.array([0.37, 1.64])), np.array([1., 2.]))\nassert_equal(np.ceil(np.array([.5, 1.5, 2.5, 3.5, 4.5])), np.array([1., 2., 3., 4., 5.]))\n\n\n# test array abs\nassert_equal(np.abs(np.array([[-1.2, 1.2], [-10, 10]])), np.array([[1.2, 1.2], [10, 10]]))\nassert_equal(np.abs(np.linspace(0, -10)), np.linspace(0, 10))\n\n\n# test __repr__ __str__\na = np.array([[1, 2], [3, 4]])\nassert repr(a) == '''\narray([[1, 2],\n       [3, 4]])\n'''.strip()\n\n\n# test array add\na = np.ones([2, 3])\nassert_equal(a + 1, np.array([[2., 2., 2.], [2., 2., 2.]]))\n\narr1 = np.array([1, 2, 3, 4, 5])\n\nassert_equal(arr1 + 1, np.array([2, 3, 4, 5, 6]))\nassert_equal(arr1 + 2.5, np.array([3.5, 4.5, 5.5, 6.5, 7.5]))\nassert_equal(1 + arr1, np.array([2, 3, 4, 5, 6]))\nassert_equal(2.5 + arr1, np.array([3.5, 4.5, 5.5, 6.5, 7.5]))\nassert_equal(arr1 + arr1, np.array([2, 4, 6, 8, 10]))\nassert_equal(arr1 + np.array([-1, -2, -3, -4, -5]), np.array([0, 0, 0, 0, 0]))\n\narr2 = np.array([[1.33, 2.66], [3.99, 5.33]])\n\nassert_equal(arr2 + 1, np.array([[2.33, 3.66], [4.99, 6.33]]))\nassert_equal(arr2 + 1.66, np.array([[2.99, 4.32], [5.65, 6.99]]))\nassert_equal(1 + arr2, np.array([[2.33, 3.66], [4.99, 6.33]]))\nassert_equal(1.66 + arr2, np.array([[2.99, 4.32], [5.65, 6.99]]))\nassert_equal(arr2 + arr2, np.array([[2.66, 5.32], [7.98, 10.66]]))\n\narray1 = np.ones([2, 2, 2, 2, 2])\narray2 = np.array([[[[[1.1, 1.2], [1.3, 1.4]], [[1.5, 1.6], [1.7, 1.8]]],\n                    [[[2.1, 2.2], [2.3, 2.4]], [[2.5, 2.6], [2.7, 2.8]]]],\n                   [[[[3.1, 3.2], [3.3, 3.4]], [[3.5, 3.6], [3.7, 3.8]]],\n                    [[[4.1, 4.2], [4.3, 4.4]], [[4.5, 4.6], [4.7, 4.8]]]]])\n\nassert_equal(array1 + array2, np.array([[[[[2.1, 2.2], [2.3, 2.4]],\n                                          [[2.5, 2.6], [2.7, 2.8]]],\n                                         [[[3.1, 3.2], [3.3, 3.4]],\n                                          [[3.5, 3.6], [3.7, 3.8]]]],\n                                        [[[[4.1, 4.2], [4.3, 4.4]],\n                                          [[4.5, 4.6], [4.7, 4.8]]],\n                                         [[[5.1, 5.2], [5.3, 5.4]],\n                                          [[5.5, 5.6], [5.7, 5.8]]]]]))\n\n\n# test array sub\na = np.ones([2, 3])\nassert_equal(a - 1, np.array([[0., 0., 0.], [0., 0., 0.]]))\n\narr1 = np.array([1, 2, 3, 4, 5])\n\nassert_equal(arr1 - 1, np.array([0, 1, 2, 3, 4]))\nassert_equal(arr1 - 2.5, np.array([-1.5, -0.5, 0.5, 1.5, 2.5]))\nassert_equal(1 - arr1, np.array([0, -1, -2, -3, -4]))\nassert_equal(2.5 - arr1, np.array([1.5, 0.5, -0.5, -1.5, -2.5]))\nassert_equal(arr1 - arr1, np.array([0, 0, 0, 0, 0]))\n\narr2 = np.array([[1.33, 2.66], [3.99, 5.33]])\n\nassert np.allclose(arr2 - 1, np.array([[0.33, 1.66], [2.99, 4.33]]))\nassert np.allclose(arr2 - 1.66, np.array([[-0.33, 1.], [2.33, 3.67]]))\nassert np.allclose(1 - arr2, np.array([[-0.33, -1.66], [-2.99, -4.33]]))\nassert np.allclose(1.66 - arr2, np.array([[0.33, -1.], [-2.33, -3.67]]))\nassert_equal(arr2 - arr2, np.array([[0, 0], [0, 0]]))\n\narray1 = np.ones([2, 2, 2, 2, 2])\narray2 = np.array([[[[[1.1, 1.2], [1.3, 1.4]], [[1.5, 1.6], [1.7, 1.8]]],\n                    [[[2.1, 2.2], [2.3, 2.4]], [[2.5, 2.6], [2.7, 2.8]]]],\n                   [[[[3.1, 3.2], [3.3, 3.4]], [[3.5, 3.6], [3.7, 3.8]]],\n                    [[[4.1, 4.2], [4.3, 4.4]], [[4.5, 4.6], [4.7, 4.8]]]]])\n\nassert np.allclose(array1 - array2, np.array([[[[[-0.1, -0.2], [-0.3, -0.4]],\n                                                [[-0.5, -0.6], [-0.7, -0.8]]],\n                                               [[[-1.1, -1.2], [-1.3, -1.4]],\n                                                [[-1.5, -1.6], [-1.7, -1.8]]]],\n                                              [[[[-2.1, -2.2], [-2.3, -2.4]],\n                                                [[-2.5, -2.6], [-2.7, -2.8]]],\n                                               [[[-3.1, -3.2], [-3.3, -3.4]],\n                                                [[-3.5, -3.6], [-3.7, -3.8]]]]]))\n\n\n# test array mul\na = np.ones([2, 3])\nassert_equal(a * 2, np.array([[2., 2., 2.], [2., 2., 2.]]))\n\narr1 = np.array([1, 2, 3, 4, 5])\n\nassert_equal(arr1 * 2, np.array([2, 4, 6, 8, 10]))\nassert_equal(arr1 * 2.5, np.array([2.5, 5.0, 7.5, 10.0, 12.5]))\nassert_equal(4 * arr1, np.array([4, 8, 12, 16, 20]))\nassert_equal(5.0 * arr1, np.array([5.0, 10.0, 15.0, 20.0, 25.0]))\nassert_equal(arr1 * arr1, np.array([1, 4, 9, 16, 25]))\n\narr2 = np.array([[1.33, 2.66], [3.99, 5.33]])\n\nassert np.allclose(arr2 * 2, np.array([[2.66, 5.32], [7.98, 10.66]]))\nassert np.allclose(arr2 * 2.5, np.array([[3.325, 6.65], [9.975, 13.325]]))\nassert np.allclose(4 * arr2, np.array([[5.32, 10.64], [15.96, 21.32]]))\nassert np.allclose(5.0 * arr2, np.array([[6.65, 13.3], [19.95, 26.65]]))\nassert np.allclose(arr2 * arr2, np.array([[1.7689, 7.0756], [15.9201, 28.4089]]))\n\n\n# test array div\na = np.ones([2, 3])\nassert_equal(a / 2, np.array([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]))\narr1 = np.array([1, 2, 3, 4, 5])\n\nassert np.allclose(arr1 / 2, np.array([0.5, 1.0, 1.5, 2.0, 2.5]))\nassert np.allclose(arr1 / 2.5, np.array([0.4, 0.8, 1.2, 1.6, 2.0]))\nassert np.allclose(4 / arr1, np.array([4.0, 2.0, 1.3333333333333333, 1.0, 0.8]))\nassert np.allclose(5.0 / arr1, np.array([5.0, 2.5, 1.6666666666666667, 1.25, 1.0]))\n\narr2 = np.array([[1.33, 2.66], [3.99, 5.33]])\n\nassert np.allclose(arr2 / 2, np.array([[0.665, 1.33], [1.995, 2.665]]))\nassert np.allclose(arr2 / 1.33, np.array([[1.0, 2.0], [3.0, 4.007519]]))\nassert np.allclose(4 / arr2, np.array([[3.0075188, 1.5037594], [1.00250627, 0.75046904]]))\nassert np.allclose(1.33 / arr2, np.array([[1.0, 0.5], [0.33333333, 0.24953096]]))\nassert np.allclose(arr2 / arr2, np.array([[1.0, 1.0], [1.0, 1.0]]))\n\n\n# test array matmul\na = np.ones([2, 3])\nassert_equal(a @ np.ones([3, 4]), np.ones([2, 4]) * 3)\n\narr1 = np.array([[1, 2], [3, 4]])\narr2 = np.array([[5, 6], [7, 8]])\n\nassert_equal(arr1 @ arr2, np.array([[19, 22], [43, 50]]))\nassert_equal(arr2 @ arr1, np.array([[23, 34], [31, 46]]))\n\narr3 = np.array([[1.0, 2.0], [2.0, 3.0], [4.0, 5.0]])\narr4 = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])\n\nassert_equal(arr3 @ arr4, np.array([[9.0, 12.0, 15.0],\n                                    [14.0, 19.0, 24.0],\n                                    [24.0, 33.0, 42.0]]))\nassert_equal(arr4 @ arr3, np.array([[17.0, 23.0],\n                                    [38.0, 53.0]]))\n\n\n# test array len\narr1 = np.array([1, 2, 3, 4, 5])\nassert len(arr1) == 5\n\narr2 = np.array([[1, 2], [3, 4], [5, 6]])\nassert len(arr2) == 3\n\narr3 = np.array([[1, 2], [3, 4]])\nassert len(arr3) == 2\n\narr4 = np.array([[[[1.5, -1.5, 2.5], [3.5, -3.5, 4.5], [5.5, -5.5, 6.5]]]])\nassert len(arr4) == 1\n\n\n# test array pow\nassert_equal(np.ones([2, 3]) ** 2, np.array([[1., 1., 1.], [1., 1., 1.]]))\n\na = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\nb = np.array([[[2, 2], [2, 2]], [[3, 3], [3, 3]]])\n\nassert np.allclose(a ** b, np.array([[[1, 4], [9, 16]], [[125, 216], [343, 512]]]))\n\narr1 = np.array([1, 2, 3, 4, 5])\nassert_equal(arr1 ** 2, np.array([1, 4, 9, 16, 25]))\nassert np.allclose(arr1 ** 2.5, np.array([1.0, 5.656854, 15.588457, 32.0, 55.901699]))\nassert_equal(2 ** arr1, np.array([2, 4, 8, 16, 32]))\nassert np.allclose(2.5 ** arr1, np.array([2.5, 6.25, 15.625, 39.0625, 97.65625]))\n\narr2 = np.array([[1.33, 2.66], [3.99, 5.33]])\nassert np.allclose(arr2 ** 2, np.array([[1.7689, 7.0756], [15.9201, 28.4089]]))\nassert np.allclose(arr2 ** 2.5, np.array([[2.039995, 11.53995437], [31.80037484, 65.58703869]]))\nassert np.allclose(4 ** arr2, np.array([[6.32033049, 39.94657756],\n                                        [252.47557235, 1618.0046067]]))\nassert np.allclose(5.0 ** arr2, np.array([[8.50413422, 72.32029875],\n                                          [615.0215271, 5315.08388464]]))\n\n\n# test array binary\na = np.array([[1, 0], [0, 1]], dtype=np.bool_)\n'''\narray([[ True, False],\n       [False,  True]])\n'''\n\nassert_equal(a & True, np.array([[1, 0], [0, 1]], dtype=np.bool_))\nassert_equal(a | True, np.array([[1, 1], [1, 1]], dtype=np.bool_))\nassert_equal(a ^ True, np.array([[0, 1], [1, 0]], dtype=np.bool_))\nassert_equal(~a, np.array([[False,  True], [ True, False]]))\n\n\n# test array trigonometry\narr1 = np.array([np.pi / 6, np.pi / 4, np.pi / 3, np.pi / 2, np.pi])\nassert np.allclose(np.sin(arr1), np.array([0.5, 0.707107, 0.866025, 1.0, 0.0]))\nassert np.allclose(np.cos(arr1), np.array([0.866025, 0.707107, 0.5, 0.0, -1.0]))\nassert np.allclose(np.tan(arr1), np.array([0.57735, 1.0, 1.73205, np.inf, 0.0]))\n\narr2 = np.array([0.5, 0.707107, 0.866025, 1.0, 0.0])\nassert np.allclose(np.arcsin(arr2), np.array([np.pi / 6, np.pi / 4, np.pi / 3, np.pi / 2, 0.0]))\nassert np.allclose(np.arccos(arr2), np.array([np.pi / 3, np.pi / 4, np.pi / 6, 0.0, np.pi / 2]))\nassert np.allclose(np.arctan(arr2), np.array([0.463648, 0.61548, 0.713724, 0.785398, 0.0]))\n\n\n# test array exponential\narr1 = np.array([0.0, 1.0, 2.0, 3.0, 4.0])\n\nassert np.allclose(np.exp(arr1), np.array([1.0, 2.718282, 7.389056, 20.085537, 54.598150]))\nassert np.allclose(np.log(arr1), np.array([-np.inf, 0.0, 0.693147, 1.098612, 1.386294]))\nassert np.allclose(np.log2(arr1), np.array([-np.inf, 0.0, 1.0, 1.584963, 2.0]))\nassert np.allclose(np.log10(arr1), np.array([-np.inf, 0.0, 0.30103, 0.477121, 0.60206]))\n\n\n# test array getitem\narr1 = np.arange(30).reshape([3, 2, 5])\n\nassert_equal(arr1[0], np.array([[0, 1, 2, 3, 4],\n                                [5, 6, 7, 8, 9]]))\nassert_equal(arr1[1], np.array([[10, 11, 12, 13, 14],\n                                [15, 16, 17, 18, 19]]))\nassert_equal(arr1[2], np.array([[20, 21, 22, 23, 24],\n                                [25, 26, 27, 28, 29]]))\nassert_equal(arr1[-1], np.array([[20, 21, 22, 23, 24],\n                                 [25, 26, 27, 28, 29]]))\nassert_equal(arr1[-2], np.array([[10, 11, 12, 13, 14],\n                                 [15, 16, 17, 18, 19]]))\n\nassert_equal(arr1[0, 0], np.array([0, 1, 2, 3, 4]))\nassert_equal(arr1[1, 1], np.array([15, 16, 17, 18, 19]))\nassert_equal(arr1[2, 0], np.array([20, 21, 22, 23, 24]))\n\nassert_equal(arr1[(0,)], np.array([[0, 1, 2, 3, 4],\n                                   [5, 6, 7, 8, 9]]))\nassert_equal(arr1[(0, 1)], np.array([5, 6, 7, 8, 9]))\nassert_equal(arr1[(1, 0)], np.array([10, 11, 12, 13, 14]))\nassert_equal(arr1[(-1,)], np.array([[20, 21, 22, 23, 24],\n                                    [25, 26, 27, 28, 29]]))\nassert_equal(arr1[(-3, -1)], np.array([5, 6, 7, 8, 9]))\nassert arr1[(0, 1, 2)] == 7\nassert arr1[(2, 1, 0)] == 25\nassert arr1[(-3, -2, -1)] == 4\nassert arr1[(-1, -2, -3)] == 22\n\nassert_equal(arr1[[0, ]], np.array([[[0, 1, 2, 3, 4],\n                                     [5, 6, 7, 8, 9]]]))\nassert_equal(arr1[[0, 1]], np.array([[[0, 1, 2, 3, 4],\n                                      [5, 6, 7, 8, 9]],\n                                     [[10, 11, 12, 13, 14],\n                                      [15, 16, 17, 18, 19]]]))\nassert_equal(arr1[[1, 2]], np.array([[[10, 11, 12, 13, 14],\n                                      [15, 16, 17, 18, 19]],\n                                     [[20, 21, 22, 23, 24],\n                                      [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[[2, 1]], np.array([[[20, 21, 22, 23, 24],\n                                      [25, 26, 27, 28, 29]],\n                                     [[10, 11, 12, 13, 14],\n                                      [15, 16, 17, 18, 19]]]))\nassert_equal(arr1[[2, 2]], np.array([[[20, 21, 22, 23, 24],\n                                      [25, 26, 27, 28, 29]],\n                                     [[20, 21, 22, 23, 24],\n                                      [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[[0, 1, 2]], np.array([[[0, 1, 2, 3, 4],\n                                         [5, 6, 7, 8, 9]],\n                                        [[10, 11, 12, 13, 14],\n                                         [15, 16, 17, 18, 19]],\n                                        [[20, 21, 22, 23, 24],\n                                         [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[[2, 1, 0]], np.array([[[20, 21, 22, 23, 24],\n                                         [25, 26, 27, 28, 29]],\n                                        [[10, 11, 12, 13, 14],\n                                         [15, 16, 17, 18, 19]],\n                                        [[0, 1, 2, 3, 4],\n                                         [5, 6, 7, 8, 9]]]))\n\nassert_equal(arr1[0:1], np.array([[[0, 1, 2, 3, 4],\n                                   [5, 6, 7, 8, 9]]]))\nassert_equal(arr1[2:3], np.array([[[20, 21, 22, 23, 24],\n                                   [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[0:2], np.array([[[0, 1, 2, 3, 4],\n                                   [5, 6, 7, 8, 9]],\n                                  [[10, 11, 12, 13, 14],\n                                   [15, 16, 17, 18, 19]]]))\nassert_equal(arr1[1:3], np.array([[[10, 11, 12, 13, 14],\n                                   [15, 16, 17, 18, 19]],\n                                  [[20, 21, 22, 23, 24],\n                                   [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[1:3:3], np.array([[[10, 11, 12, 13, 14],\n                                     [15, 16, 17, 18, 19]]]))\nassert_equal(arr1[0:3:2], np.array([[[0, 1, 2, 3, 4],\n                                     [5, 6, 7, 8, 9]],\n                                    [[20, 21, 22, 23, 24],\n                                     [25, 26, 27, 28, 29]]]))\n\nassert_equal(arr1[1:], np.array([[[10, 11, 12, 13, 14],\n                                   [15, 16, 17, 18, 19]],\n                                  [[20, 21, 22, 23, 24],\n                                   [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[:2], np.array([[[0, 1, 2, 3, 4],\n                                   [5, 6, 7, 8, 9]],\n                                  [[10, 11, 12, 13, 14],\n                                   [15, 16, 17, 18, 19]]]))\n\nassert_equal(arr1[::2], np.array([[[0, 1, 2, 3, 4],\n                                   [5, 6, 7, 8, 9]],\n                                  [[20, 21, 22, 23, 24],\n                                   [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[-2:3:1], np.array([[[10, 11, 12, 13, 14],\n                                      [15, 16, 17, 18, 19]],\n                                     [[20, 21, 22, 23, 24],\n                                      [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[3::-2], np.array([[[20, 21, 22, 23, 24],\n                                     [25, 26, 27, 28, 29]],\n                                    [[0, 1, 2, 3, 4],\n                                     [5, 6, 7, 8, 9]]]))\nassert_equal(arr1[::-1], np.array([[[20, 21, 22, 23, 24],\n                                    [25, 26, 27, 28, 29]],\n                                   [[10, 11, 12, 13, 14],\n                                    [15, 16, 17, 18, 19]],\n                                   [[0, 1, 2, 3, 4],\n                                    [5, 6, 7, 8, 9]]]))\nassert_equal(arr1[::], np.array([[[0, 1, 2, 3, 4],\n                                  [5, 6, 7, 8, 9]],\n                                 [[10, 11, 12, 13, 14],\n                                  [15, 16, 17, 18, 19]],\n                                 [[20, 21, 22, 23, 24],\n                                  [25, 26, 27, 28, 29]]]))\n\n\n# test array setitem\narr1 = np.arange(30).reshape([3, 2, 5])\n\narr1[0] = 10\nassert_equal(arr1[0], np.array([[10, 10, 10, 10, 10],\n                                [10, 10, 10, 10, 10]]))\n\narr1[1] = [1, 2, 3, 4, 5]\nassert_equal(arr1[1], np.array([[1, 2, 3, 4, 5],\n                                [1, 2, 3, 4, 5]]))\n\narr1[2] = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]\nassert_equal(arr1[2], np.array([[1, 2, 3, 4, 5],\n                                [6, 7, 8, 9, 10]]))\n\narr1[-1] = 0\nassert_equal(arr1[-1], np.array([[0, 0, 0, 0, 0],\n                                 [0, 0, 0, 0, 0]]))\n\narr1[0, 0] = 5\nassert_equal(arr1[0, 0], np.array([5, 5, 5, 5, 5]))\n\narr1[1, 1] = [1, 2, 3, 4, 5]\nassert_equal(arr1[1, 1], np.array([1, 2, 3, 4, 5]))\n\narr1[2, 0] = [2.5]\nassert_equal(arr1[2, 0], np.array([2, 2, 2, 2, 2]))\n\narr1[(0,)] = 10.5\nassert_equal(arr1[(0,)], np.array([[10, 10, 10, 10, 10],\n                                   [10, 10, 10, 10, 10]]))\n\narr1[(0, 1)] = 0\nassert_equal(arr1[(0, 1)], np.array([0, 0, 0, 0, 0]))\n\narr1[(-3, -1)] = 1\nassert_equal(arr1[(-3, -1)], np.array([1, 1, 1, 1, 1]))\n\narr1[(-1, -2, -3)] = 3.14159\nassert arr1[(-1, -2, -3)] == 3\n\narr1[[0, ]] = 0\nassert_equal(arr1[[0, ]], np.array([[[0, 0, 0, 0, 0],\n                                     [0, 0, 0, 0, 0]]]))\n\narr1[[0, 1]] = 1\nassert_equal(arr1[[0, 1]], np.array([[[1, 1, 1, 1, 1],\n                                      [1, 1, 1, 1, 1]],\n                                     [[1, 1, 1, 1, 1],\n                                      [1, 1, 1, 1, 1]]]))\n\narr1[[2, 2]] = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]\nassert_equal(arr1[[2, 2]], np.array([[[1, 2, 3, 4, 5],\n                                      [6, 7, 8, 9, 10]],\n                                     [[1, 2, 3, 4, 5],\n                                      [6, 7, 8, 9, 10]]]))\n\narr1[0:1] = 0\nassert_equal(arr1[0:1], np.array([[[0, 0, 0, 0, 0],\n                                   [0, 0, 0, 0, 0]]]))\n\narr1[2:3] = 1\nassert_equal(arr1[2:3], np.array([[[1, 1, 1, 1, 1],\n                                   [1, 1, 1, 1, 1]]]))\n\narr1[::2] = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]\nassert_equal(arr1[::2], np.array([[[1, 2, 3, 4, 5],\n                                   [6, 7, 8, 9, 10]],\n                                  [[1, 2, 3, 4, 5],\n                                   [6, 7, 8, 9, 10]]]))\n\narr1[-2:3:1] = [[[1, 2, 3, 4, 5],\n                 [6, 7, 8, 9, 10]],\n                [[11, 12, 13, 14, 15],\n                 [16, 17, 18, 19, 20]]]\nassert_equal(arr1[-2:3:1], np.array([[[1, 2, 3, 4, 5],\n                                      [6, 7, 8, 9, 10]],\n                                     [[11, 12, 13, 14, 15],\n                                      [16, 17, 18, 19, 20]]]))\n\nprint(\"ALL TESTS PASSED\")";
diff --git a/docs/test_sources.js b/docs/test_sources.js
new file mode 100644
index 0000000..e39922a
--- /dev/null
+++ b/docs/test_sources.js
@@ -0,0 +1 @@
+var TEST_SOURCES = {"primes.py": "UPPER_BOUND = 5000000\nPREFIX = 32338\n\nimport time\n# exit(0)\n\nclass Node:\n    def __init__(self):\n        self.children = {}\n        self.terminal = False\n\n\nclass Sieve:\n    def __init__(self, limit):\n        self.limit = limit\n        self.prime = [False] * (limit + 1)\n\n    def to_list(self):\n        result = [2, 3]\n        for p in range(5, self.limit + 1):\n            if self.prime[p]:\n                result.append(p)\n        return result\n\n    def omit_squares(self):\n        r = 5\n        while r * r < self.limit:\n            if self.prime[r]:\n                i = r * r\n                while i < self.limit:\n                    self.prime[i] = False\n                    i = i + r * r\n            r += 1\n        return self\n\n    def step1(self, x, y):\n        n = (4 * x * x) + (y * y)\n        if n <= self.limit and (n % 12 == 1 or n % 12 == 5):\n            self.prime[n] = not self.prime[n]\n\n    def step2(self, x, y):\n        n = (3 * x * x) + (y * y)\n        if n <= self.limit and n % 12 == 7:\n            self.prime[n] = not self.prime[n]\n\n    def step3(self, x, y):\n        n = (3 * x * x) - (y * y)\n        if x > y and n <= self.limit and n % 12 == 11:\n            self.prime[n] = not self.prime[n]\n\n    def loop_y(self, x):\n        y = 1\n        while y * y < self.limit:\n            self.step1(x, y)\n            self.step2(x, y)\n            self.step3(x, y)\n            y += 1\n\n    def loop_x(self):\n        x = 1\n        while x * x < self.limit:\n            self.loop_y(x)\n            x += 1\n\n    def calc(self):\n        self.loop_x()\n        return self.omit_squares()\n\n\ndef generate_trie(l):\n    root = Node()\n    for el in l:\n        head = root\n        for ch in str(el):\n            if ch not in head.children:\n                head.children[ch] = Node()\n            head = head.children[ch]\n        head.terminal = True\n    return root\n\n\ndef find(upper_bound, prefix):\n    primes = Sieve(upper_bound).calc()\n    str_prefix = str(prefix)\n    head = generate_trie(primes.to_list())\n    for ch in str_prefix:\n        head = head.children.get(ch)\n        if head is None:    # either ch does not exist or the value is None\n            return None\n\n    queue, result = [(head, str_prefix)], []\n    while queue:\n        top, prefix = queue.pop()\n        if top.terminal:\n            result.append(int(prefix))\n        for ch, v in top.children.items():\n            queue.insert(0, (v, prefix + ch))\n\n    result.sort()\n    return result\n\n\ndef verify():\n    left = [2, 23, 29]\n    right = find(100, 2)\n    if left != right:\n        print(f\"{left} != {right}\")\n        exit(1)\n\nverify()\nt0 = time.time()\nresults = find(UPPER_BOUND, PREFIX)\nt1 = time.time()\nassert results == [323381, 323383, 3233803, 3233809, 3233851, 3233863, 3233873, 3233887, 3233897]\nprint(t1 - t0)\n", "test_numpy.py": "import math\nimport numpy as np\n\ndef assert_equal(a, b):\n    assert (a == b).all()\n\n\n# test dtypes\nassert hasattr(np, 'int8')\nassert hasattr(np, 'int16')\nassert hasattr(np, 'int32')\nassert hasattr(np, 'int64')\nassert hasattr(np, 'int_')\nassert hasattr(np, 'float32')\nassert hasattr(np, 'float64')\nassert hasattr(np, 'float_')\nassert hasattr(np, 'bool_')\n\n\n# test array int\narr1 = np.array([])\narr2 = np.array(10)\narr3 = np.array([-2, -1, 0, 1, 2])\narr4 = np.array([[1, 2], [2, 1]])\narr5 = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])\narr6 = np.array([[[[[1], [10], [100], [1000], [10000]]]]])\narr7 = np.array([[[2147483647]]])\n\narr8 = np.array([1, 2, 3, 4, 5], dtype='int8')\narr8 = np.array([1, 2, 3, 4, 5], np.int8)\n\narr9 = np.array([1, 2, 3, 4, 5], dtype='int16')\narr9 = np.array([1, 2, 3, 4, 5], np.int16)\n\narr10 = np.array([1, 2, 3, 4, 5], dtype='int32')\narr10 = np.array([1, 2, 3, 4, 5], np.int32)\n\narr11 = np.array([1, 2, 3, 4, 5], dtype='int64')\narr11 = np.array([1, 2, 3, 4, 5], np.int64)\n\n\n# test array bool\narr1 = np.array([True, False, True, False])\narr2 = np.array([[True, False], [False, True]])\narr3 = np.array([[[True, False, True], [False, True, False]], [[True, False, True], [False, True, False]]])\n\narr4 = np.array([[1, 0], [0, 1]], dtype='bool')\narr4 = np.array([[1, 0], [0, 1]], np.bool_)\n\n\n# test array float\narr1 = np.array([0.123456789])\narr2 = np.array([-2.0, -1.0, 0.0, 1.0, 2.0])\narr3 = np.array([[1.0, 2.0], [2.0, 1.0]])\narr4 = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]])\narr5 = np.array([[[[[1.0], [10.0], [100.0], [1000.0], [10000.0]]]]])\narr6 = np.array([[[3.141592653589793]]])\n\narr7 = np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype='float32')\narr7 = np.array([1.0, 2.0, 3.0, 4.0, 5.0], np.float32)\n\narr8 = np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype='float64')\narr8 = np.array([1.0, 2.0, 3.0, 4.0, 5.0], np.float64)\n\n\n# test array creation\nassert_equal(np.ones([1, 1]), np.array([[1.0]]))\nassert_equal(np.ones([1, 1, 2, 2]), np.array([[[[1.0, 1.0], [1.0, 1.0]]]]))\n\nassert_equal(np.zeros([1, 1]), np.array([[0.0]]))\nassert_equal(np.zeros([1, 1, 2, 2]), np.array([[[[0.0, 0.0], [0.0, 0.0]]]]))\n\nassert_equal(np.full([1, 1], -1e9), np.array([[-1.000000e+09]]))\nassert_equal(np.full([1, 1, 2, 2], 3.14), np.array([[[[3.14, 3.14], [3.14, 3.14]]]]))\n\nassert_equal(np.identity(1), np.array([[1.0]]))\nassert_equal(np.identity(3), np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]))\n\nassert_equal(np.arange(10), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]))\nassert_equal(np.arange(1, 10), np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]))\nassert_equal(np.arange(1, 10, 2), np.array([1, 3, 5, 7, 9]))\n\nassert_equal(np.linspace(0, 1, 5), np.array([0.0, 0.25, 0.5, 0.75, 1.0]))\nassert np.allclose(np.linspace(0, 1, 5, False), np.array([0.0, 0.2, 0.4, 0.6, 0.8]))\nassert np.allclose(np.linspace(0, 1, 20, True),\n                   np.array([0.0, 0.05263158, 0.10526316,\n                             0.15789474, 0.21052632, 0.26315789,\n                             0.31578947, 0.36842105, 0.42105263,\n                             0.47368421, 0.52631579, 0.57894737,\n                             0.63157895, 0.68421053, 0.73684211,\n                             0.78947368, 0.84210526, 0.89473684,\n                             0.94736842, 1.0]))\n\n\n# test array properties\narr1 = np.array([1, 2, 3])\nassert arr1.size == 3\nassert arr1.ndim == 1\nassert arr1.shape == (3,)\nassert arr1.dtype == 'int64'\n\narr2 = np.array([[1, 2], [3, 4]])\nassert arr2.size == 4\nassert arr2.ndim == 2\nassert arr2.shape == (2, 2)\nassert arr2.dtype == np.int64\n\narr3 = np.array([[1, 2, 2, 1], [3, 4, 4, 3], [5, 6, 6, 5]], np.int32)\nassert arr3.size == 12\nassert arr3.ndim == 2\nassert arr3.shape == (3, 4)\nassert arr3.dtype == 'int32'\n\narr4 = np.array([[[[[1.5, 2.5], [3.5, 4.5], [5.5, 6.5]]]]])\nassert arr4.size == 6\nassert arr4.ndim == 5\nassert arr4.shape == (1, 1, 1, 3, 2)\nassert arr4.dtype == np.float64\n\n\n# test boolean functions\narr1 = np.array([0.0])\nassert arr1.all() == False\nassert arr1.any() == False\n\narr2 = np.array([1.0])\nassert arr2.all() == True\nassert arr2.any() == True\n\narr3 = np.array([[1, 0], [0, 1]])\nassert arr3.all() == False\nassert arr3.any() == True\n\narr4 = np.array([[[True, False, True], [False, True, False]], [[True, False, True], [False, True, False]]])\nassert arr4.all() == False\nassert arr4.any() == True\n\n\n# test array sum\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.sum(), 6.0)\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.sum() == 45\nassert arr1.sum(0) == 45\n\narr2 = np.array([[1], [2], [3]])\nassert arr2.sum() == 6\nassert_equal(arr2.sum(0), np.array([6]))\nassert_equal(arr2.sum(1), np.array([1, 2, 3]))\nassert arr2.sum((0, 1)) == 6\n\narr3 = np.array([[[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]])\nassert arr3.sum() == 40.5\nassert_equal(arr3.sum(0), np.array([[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]))\nassert_equal(arr3.sum(1), np.array([[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]))\nassert_equal(arr3.sum(2), np.array([[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]))\nassert_equal(arr3.sum(3), np.array([[[[10.5, 13.5, 16.5]]]]))\nassert_equal(arr3.sum(4), np.array([[[[7.5, 13.5, 19.5]]]]))\n\nassert_equal(arr3.sum((0, 1)), np.array([[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]))\nassert_equal(arr3.sum((3, 4)), np.array([[[40.5]]]))\n\n\n# test array prod\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.prod() == 362880\nassert arr1.prod(0) == 362880\n\narr2 = np.array([[1], [2], [3]])\nassert arr2.prod() == 6\nassert_equal(arr2.prod(0), np.array([6]))\nassert_equal(arr2.prod(1), np.array([1, 2, 3]))\nassert arr2.prod((0, 1)) == 6\n\narr3 = np.array([[[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]])\nassert arr3.prod() == 304845.556640625\nassert_equal(arr3.prod(0), np.array([[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]))\nassert_equal(arr3.prod(1), np.array([[[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]]]))\nassert_equal(arr3.prod(4), np.array([[[[13.125, 86.625, 268.125]]]]))\nassert_equal(arr3.prod((0, 3)), np.array([[[28.875, 73.125, 144.375]]]))\n\n\n# test array min\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.min(), 1.0)\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.min() == 1\n\narr2 = np.array([[-1], [-2], [-3]])\nassert arr2.min() == -3\n\narr3 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert arr3.min() == -5.5\nassert_equal(arr3.min(0), np.array([[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]))\nassert_equal(arr3.min(4), np.array([[[[-1.5, -3.5, -5.5]]]]))\nassert_equal(arr3.min((0, 3)), np.array([[[1.5, -5.5]]]))\nassert_equal(arr3.min((0, 4)), np.array([[[-1.5, -3.5, -5.5]]]))\n\n\n# test array max\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.max(), 3.0)\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.max() == 9\n\narr2 = np.array([[-1], [-2], [-3]])\nassert arr2.max() == -1\n\narr3 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert arr3.max() == 5.5\nassert_equal(arr3.max(0), np.array([[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]))\n\nassert_equal(arr3.max(4), np.array([[[[1.5, 3.5, 5.5]]]]))\nassert_equal(arr3.max((0, 3)), np.array([[[5.5, -1.5]]]))\nassert_equal(arr3.max((0, 4)), np.array([[[1.5, 3.5, 5.5]]]))\n\n# test array mean\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.mean(), 2.0)\n\narr = np.array([[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]])\nassert arr.mean() == 4.5\nassert_equal(arr.mean(0), np.array([[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]))\nassert_equal(arr.mean(1), np.array([[3.5, 4.5, 5.5]]))\nassert_equal(arr.mean(2), np.array([[2.5, 4.5, 6.5]]))\n\n\n# test array std\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.std(), 0.816496580927726)\n\narr = np.array([[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]])\nassert arr.std() == 1.8257418583505538\nassert_equal(arr.std(0), np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]))\nassert np.allclose(arr.std(1), np.array([[1.632993, 1.632993, 1.632993]]))\nassert np.allclose(arr.std(2), np.array([[0.81649658, 0.81649658, 0.81649658]]))\n\n\n# test array var\na = np.array([1.0, 2.0, 3.0])\nassert math.isclose(a.var(), 0.6666666666666666)\n\narr = np.array([[[1.5, 2.5, 3.5], [3.5, 4.5, 5.5], [5.5, 6.5, 7.5]]])\nassert arr.var() == 3.3333333333333335\nassert_equal(arr.var(0), np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]))\nassert np.allclose(arr.var(1), np.array([[2.66666667, 2.66666667, 2.66666667]]))\nassert np.allclose(arr.var(2), np.array([[0.66666667, 0.66666667, 0.66666667]]))\n\n\n# test array argmin\na = np.array([3, 1, 2])\nassert a.argmin() == 1\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.argmin() == 0\n\narr2 = np.array([[-1], [-2], [-3]])\nassert arr2.argmin() == 2\nassert_equal(arr2.argmin(0), np.array([2]))\n\narr3 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert arr3.argmin() == 5\nassert_equal(arr3.argmin(0), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmin(1), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmin(2), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmin(3), np.array([[[[0, 2]]]]))\nassert_equal(arr3.argmin(4), np.array([[[[1, 1, 1]]]]))\n\n\n# test array argmax\na = np.array([3, 1, 2])\nassert a.argmax() == 0\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert arr1.argmax() == 8\n\narr2 = np.array([[-1], [-2], [-3]])\nassert arr2.argmax() == 0\nassert_equal(arr2.argmax(0), np.array([0]))\n\narr3 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert arr3.argmax() == 4\nassert_equal(arr3.argmax(0), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmax(1), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmax(2), np.array([[[[0, 0], [0, 0], [0, 0]]]]))\nassert_equal(arr3.argmax(3), np.array([[[[2, 0]]]]))\nassert_equal(arr3.argmax(4), np.array([[[[0, 0, 0]]]]))\n\n\n# test array argsort\na = np.array([3, 1, 2])\nassert_equal(a.argsort(), np.array([1, 2, 0]))\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert_equal(arr1.argsort(), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8]))\n\narr2 = np.array([9, 8, 7, 6, 5, 4, 3, 2, 1])\nassert_equal(arr2.argsort(), np.array([8, 7, 6, 5, 4, 3, 2, 1, 0]))\n\narr3 = np.array([[-1], [-2], [-3]])\nassert_equal(arr3.argsort(), np.array([[0], [0], [0]]))\nassert_equal(arr3.argsort(0), np.array([[2], [1], [0]]))\n\narr4 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert_equal(arr4.argsort(), np.array([[[[[1, 0], [1, 0], [1, 0]]]]]))\nassert_equal(arr4.argsort(0), np.array([[[[[0, 0], [0, 0], [0, 0]]]]]))\nassert_equal(arr4.argsort(1), np.array([[[[[0, 0], [0, 0], [0, 0]]]]]))\nassert_equal(arr4.argsort(2), np.array([[[[[0, 0], [0, 0], [0, 0]]]]]))\nassert_equal(arr4.argsort(3), np.array([[[[[0, 2], [1, 1], [2, 0]]]]]))\nassert_equal(arr4.argsort(4), np.array([[[[[1, 0], [1, 0], [1, 0]]]]]))\n\n\n# test array sort\na = np.array([3, 1, 2])\na.sort()\nassert_equal(a, np.array([1, 2, 3]))\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\narr1.sort()\nassert_equal(arr1, np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]))\n\narr2 = np.array([9, 8, 7, 6, 5, 4, 3, 2, 1])\narr2.sort()\nassert_equal(arr2, np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]))\n\narr3 = np.array([[-1], [-2], [-3]])\narr3.sort(0)\nassert_equal(arr3, np.array([[-3], [-2], [-1]]))\n\narr4 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\narr4.sort(3)\nassert_equal(arr4, np.array([[[[[1.5, -5.5], [3.5, -3.5], [5.5, -1.5]]]]]))\n\n\n# test array reshape\na = np.array([[1, 2], [3, 4]])\nassert_equal(a.reshape([1, 4]), np.array([[1, 2, 3, 4]]))\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert_equal(arr1.reshape([3, 3]), np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))\n\narr2 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\nassert_equal(arr2.reshape([9]), np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]))\n\narr3 = np.array([1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5])\nassert_equal(arr3.reshape([3, 4]), np.array([[1.5, 2.5, 3.5, 4.5], [5.5, 6.5, 7.5, 8.5], [9.5, 10.5, 11.5, 12.5]]))\nassert_equal(arr3.reshape([2, 3, 2]), np.array([[[1.5, 2.5], [3.5, 4.5], [5.5, 6.5]],\n                                                [[7.5, 8.5], [9.5, 10.5], [11.5, 12.5]]]))\nassert_equal(arr3.reshape([1, 1, 2, 2, 3]), np.array([[[[[1.5, 2.5, 3.5], [4.5, 5.5, 6.5]],\n                                                        [[7.5, 8.5, 9.5], [10.5, 11.5, 12.5]]]]]))\n\n\n# test array resize\na = np.array([[1, 2], [3, 4]])\na.resize([1, 4])\nassert_equal(a, np.array([[1, 2, 3, 4]]))\n\n\n# test array squeeze\na = np.array([[1, 2, 3, 4]])\nassert_equal(a.squeeze(), np.array([1, 2, 3, 4]))\n\narr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\nassert_equal(arr1.squeeze(), np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]))\n\narr2 = np.array([[-1], [-2], [-3]])\nassert_equal(arr2.squeeze(), np.array([-1, -2, -3]))\n\narr3 = np.array([[[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]])\nassert_equal(arr3.squeeze(), np.array([[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]))\nassert_equal(arr3.squeeze(0), np.array([[[[1.5, -1.5], [3.5, -3.5], [5.5, -5.5]]]]))\n\n\n# test array transpose\na = np.array([[1, 2, 3, 4]])\nassert_equal(a.transpose(), np.array([[1], [2], [3], [4]]))\n\narr1 = np.array([[1], [2], [3]])\nassert_equal(arr1.transpose(), np.array([[1, 2, 3]]))\n\narr2 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\nassert_equal(arr2.transpose(), np.array([[1, 4, 7], [2, 5, 8], [3, 6, 9]]))\nassert_equal(arr2.transpose(0, 1), np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))\nassert_equal(arr2.transpose(1, 0), np.array([[1, 4, 7], [2, 5, 8], [3, 6, 9]]))\n\narr3 = np.array([[[[[1.5, -5.5], [3.5, -3.5], [5.5, -1.5]]]]])\nassert_equal(arr3.transpose(), np.array([[[[[1.5]]], [[[3.5]]], [[[5.5]]]],\n                                         [[[[-5.5]]], [[[-3.5]]], [[[-1.5]]]]]))\nassert_equal(arr3.transpose(0, 1, 2, 3, 4), np.array([[[[[1.5, -5.5],\n                                                         [3.5, -3.5],\n                                                         [5.5, -1.5]]]]]))\nassert_equal(arr3.transpose((3, 4, 0, 2, 1)), np.array([[[[[1.5]]], [[[-5.5]]]],\n                                                        [[[[3.5]]], [[[-3.5]]]],\n                                                        [[[[5.5]]], [[[-1.5]]]]]))\n\n\n# test array repeat\na = np.array([[1, 2, 3, 4]])\nassert_equal(a.repeat(2), np.array([[1, 1, 2, 2, 3, 3, 4, 4]]))\n\narr1 = np.array([[1, 2], [3, 4]])\nassert_equal(arr1.repeat(2, 0), np.array([[1, 2], [1, 2], [3, 4], [3, 4]]))\n\narr2 = np.array([[[[1, 2, 3], [4, 5, 6]]]])\nassert_equal(arr2.repeat(4, 3), np.array([[[[1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n                                            [4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6]]]]))\nassert_equal(arr2.repeat([1, 2, 3], 3), np.array([[[[1, 2, 2, 3, 3, 3],\n                                                    [4, 5, 5, 6, 6, 6]]]]))\n\n\n# test array flatten\na = np.array([[1, 2, 3, 4]])\nassert_equal(a.transpose().flatten(), np.array([1, 2, 3, 4]))\n\nassert_equal(a.repeat(2), np.array([[1, 1, 2, 2, 3, 3, 4, 4]]))\narr1 = np.array([[1, 2], [3, 4]])\nassert_equal(arr1.flatten(), np.array([1, 2, 3, 4]))\n\narr2 = np.array([[[1., 2.], [3., 4.], [5., 6.]], [[7., 8.], [9., 10.], [11., 12.]]])\nassert_equal(arr2.flatten(), np.array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.]))\n\n\n# test array copy\na = np.array([1.1, 2.2, 3.3])\nassert_equal(a.copy(), a)\n\narr1 = np.array([[1, 2], [3, 4]])\narr2 = arr1\narr3 = arr1.copy()\n\narr1[0] = [10, 20]\nassert_equal(arr2, np.array([[10, 20], [3, 4]])) # Shallow copy\nassert_equal(arr3, np.array([[1, 2], [3, 4]]))   # Deep copy\n\n\n# test array astype\na = np.array([1.1, 2.2, 3.3])\nassert_equal(a.astype(np.int32), np.array([1, 2, 3]))\n\narr1 = np.array([1, 2, 2.5])\n\nassert_equal(arr1.astype('int_'), np.array([1, 2, 2]))\nassert arr1.dtype == 'float64'\n\nassert_equal(arr1.astype('float64'), np.array([1.0, 2.0, 2.5]))\nassert arr1.dtype == 'float64'\n\n\n# test array round\na = np.array([1.1, 2.2, 3.3])\nassert_equal(a.round(), np.array([1., 2., 3.]))\n\nassert_equal(np.round(np.array([40, 20, 30, 10])), np.array([40, 20, 30, 10]))\nassert_equal(np.round(np.array([0.37, 1.64])), np.array([0.0, 2.0]))\nassert_equal(np.round(np.array([.5, 1.5, 2.5, 3.5, 4.5])),np.array([1., 2., 3., 4., 5.]))\n\n\n# test array floor\nassert_equal(np.floor(np.array([40, 20, 30, 10])), np.array([40, 20, 30, 10]))\nassert_equal(np.floor(np.array([0.37, 1.64])), np.array([0., 1.]))\nassert_equal(np.floor(np.array([.5, 1.5, 2.5, 3.5, 4.5])), np.array([0., 1., 2., 3., 4.]))\n\n\n# test array ceil\nassert_equal(np.ceil(np.array([40, 20, 30, 10])), np.array([40, 20, 30, 10]))\nassert_equal(np.ceil(np.array([0.37, 1.64])), np.array([1., 2.]))\nassert_equal(np.ceil(np.array([.5, 1.5, 2.5, 3.5, 4.5])), np.array([1., 2., 3., 4., 5.]))\n\n\n# test array abs\nassert_equal(np.abs(np.array([[-1.2, 1.2], [-10, 10]])), np.array([[1.2, 1.2], [10, 10]]))\nassert_equal(np.abs(np.linspace(0, -10)), np.linspace(0, 10))\n\n\n# test __repr__ __str__\na = np.array([[1, 2], [3, 4]])\nassert repr(a) == '''\narray([[1, 2],\n       [3, 4]])\n'''.strip()\n\n\n# test array add\na = np.ones([2, 3])\nassert_equal(a + 1, np.array([[2., 2., 2.], [2., 2., 2.]]))\n\narr1 = np.array([1, 2, 3, 4, 5])\n\nassert_equal(arr1 + 1, np.array([2, 3, 4, 5, 6]))\nassert_equal(arr1 + 2.5, np.array([3.5, 4.5, 5.5, 6.5, 7.5]))\nassert_equal(1 + arr1, np.array([2, 3, 4, 5, 6]))\nassert_equal(2.5 + arr1, np.array([3.5, 4.5, 5.5, 6.5, 7.5]))\nassert_equal(arr1 + arr1, np.array([2, 4, 6, 8, 10]))\nassert_equal(arr1 + np.array([-1, -2, -3, -4, -5]), np.array([0, 0, 0, 0, 0]))\n\narr2 = np.array([[1.33, 2.66], [3.99, 5.33]])\n\nassert_equal(arr2 + 1, np.array([[2.33, 3.66], [4.99, 6.33]]))\nassert_equal(arr2 + 1.66, np.array([[2.99, 4.32], [5.65, 6.99]]))\nassert_equal(1 + arr2, np.array([[2.33, 3.66], [4.99, 6.33]]))\nassert_equal(1.66 + arr2, np.array([[2.99, 4.32], [5.65, 6.99]]))\nassert_equal(arr2 + arr2, np.array([[2.66, 5.32], [7.98, 10.66]]))\n\narray1 = np.ones([2, 2, 2, 2, 2])\narray2 = np.array([[[[[1.1, 1.2], [1.3, 1.4]], [[1.5, 1.6], [1.7, 1.8]]],\n                    [[[2.1, 2.2], [2.3, 2.4]], [[2.5, 2.6], [2.7, 2.8]]]],\n                   [[[[3.1, 3.2], [3.3, 3.4]], [[3.5, 3.6], [3.7, 3.8]]],\n                    [[[4.1, 4.2], [4.3, 4.4]], [[4.5, 4.6], [4.7, 4.8]]]]])\n\nassert_equal(array1 + array2, np.array([[[[[2.1, 2.2], [2.3, 2.4]],\n                                          [[2.5, 2.6], [2.7, 2.8]]],\n                                         [[[3.1, 3.2], [3.3, 3.4]],\n                                          [[3.5, 3.6], [3.7, 3.8]]]],\n                                        [[[[4.1, 4.2], [4.3, 4.4]],\n                                          [[4.5, 4.6], [4.7, 4.8]]],\n                                         [[[5.1, 5.2], [5.3, 5.4]],\n                                          [[5.5, 5.6], [5.7, 5.8]]]]]))\n\n\n# test array sub\na = np.ones([2, 3])\nassert_equal(a - 1, np.array([[0., 0., 0.], [0., 0., 0.]]))\n\narr1 = np.array([1, 2, 3, 4, 5])\n\nassert_equal(arr1 - 1, np.array([0, 1, 2, 3, 4]))\nassert_equal(arr1 - 2.5, np.array([-1.5, -0.5, 0.5, 1.5, 2.5]))\nassert_equal(1 - arr1, np.array([0, -1, -2, -3, -4]))\nassert_equal(2.5 - arr1, np.array([1.5, 0.5, -0.5, -1.5, -2.5]))\nassert_equal(arr1 - arr1, np.array([0, 0, 0, 0, 0]))\n\narr2 = np.array([[1.33, 2.66], [3.99, 5.33]])\n\nassert np.allclose(arr2 - 1, np.array([[0.33, 1.66], [2.99, 4.33]]))\nassert np.allclose(arr2 - 1.66, np.array([[-0.33, 1.], [2.33, 3.67]]))\nassert np.allclose(1 - arr2, np.array([[-0.33, -1.66], [-2.99, -4.33]]))\nassert np.allclose(1.66 - arr2, np.array([[0.33, -1.], [-2.33, -3.67]]))\nassert_equal(arr2 - arr2, np.array([[0, 0], [0, 0]]))\n\narray1 = np.ones([2, 2, 2, 2, 2])\narray2 = np.array([[[[[1.1, 1.2], [1.3, 1.4]], [[1.5, 1.6], [1.7, 1.8]]],\n                    [[[2.1, 2.2], [2.3, 2.4]], [[2.5, 2.6], [2.7, 2.8]]]],\n                   [[[[3.1, 3.2], [3.3, 3.4]], [[3.5, 3.6], [3.7, 3.8]]],\n                    [[[4.1, 4.2], [4.3, 4.4]], [[4.5, 4.6], [4.7, 4.8]]]]])\n\nassert np.allclose(array1 - array2, np.array([[[[[-0.1, -0.2], [-0.3, -0.4]],\n                                                [[-0.5, -0.6], [-0.7, -0.8]]],\n                                               [[[-1.1, -1.2], [-1.3, -1.4]],\n                                                [[-1.5, -1.6], [-1.7, -1.8]]]],\n                                              [[[[-2.1, -2.2], [-2.3, -2.4]],\n                                                [[-2.5, -2.6], [-2.7, -2.8]]],\n                                               [[[-3.1, -3.2], [-3.3, -3.4]],\n                                                [[-3.5, -3.6], [-3.7, -3.8]]]]]))\n\n\n# test array mul\na = np.ones([2, 3])\nassert_equal(a * 2, np.array([[2., 2., 2.], [2., 2., 2.]]))\n\narr1 = np.array([1, 2, 3, 4, 5])\n\nassert_equal(arr1 * 2, np.array([2, 4, 6, 8, 10]))\nassert_equal(arr1 * 2.5, np.array([2.5, 5.0, 7.5, 10.0, 12.5]))\nassert_equal(4 * arr1, np.array([4, 8, 12, 16, 20]))\nassert_equal(5.0 * arr1, np.array([5.0, 10.0, 15.0, 20.0, 25.0]))\nassert_equal(arr1 * arr1, np.array([1, 4, 9, 16, 25]))\n\narr2 = np.array([[1.33, 2.66], [3.99, 5.33]])\n\nassert np.allclose(arr2 * 2, np.array([[2.66, 5.32], [7.98, 10.66]]))\nassert np.allclose(arr2 * 2.5, np.array([[3.325, 6.65], [9.975, 13.325]]))\nassert np.allclose(4 * arr2, np.array([[5.32, 10.64], [15.96, 21.32]]))\nassert np.allclose(5.0 * arr2, np.array([[6.65, 13.3], [19.95, 26.65]]))\nassert np.allclose(arr2 * arr2, np.array([[1.7689, 7.0756], [15.9201, 28.4089]]))\n\n\n# test array div\na = np.ones([2, 3])\nassert_equal(a / 2, np.array([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]))\narr1 = np.array([1, 2, 3, 4, 5])\n\nassert np.allclose(arr1 / 2, np.array([0.5, 1.0, 1.5, 2.0, 2.5]))\nassert np.allclose(arr1 / 2.5, np.array([0.4, 0.8, 1.2, 1.6, 2.0]))\nassert np.allclose(4 / arr1, np.array([4.0, 2.0, 1.3333333333333333, 1.0, 0.8]))\nassert np.allclose(5.0 / arr1, np.array([5.0, 2.5, 1.6666666666666667, 1.25, 1.0]))\n\narr2 = np.array([[1.33, 2.66], [3.99, 5.33]])\n\nassert np.allclose(arr2 / 2, np.array([[0.665, 1.33], [1.995, 2.665]]))\nassert np.allclose(arr2 / 1.33, np.array([[1.0, 2.0], [3.0, 4.007519]]))\nassert np.allclose(4 / arr2, np.array([[3.0075188, 1.5037594], [1.00250627, 0.75046904]]))\nassert np.allclose(1.33 / arr2, np.array([[1.0, 0.5], [0.33333333, 0.24953096]]))\nassert np.allclose(arr2 / arr2, np.array([[1.0, 1.0], [1.0, 1.0]]))\n\n\n# test array matmul\na = np.ones([2, 3])\nassert_equal(a @ np.ones([3, 4]), np.ones([2, 4]) * 3)\n\narr1 = np.array([[1, 2], [3, 4]])\narr2 = np.array([[5, 6], [7, 8]])\n\nassert_equal(arr1 @ arr2, np.array([[19, 22], [43, 50]]))\nassert_equal(arr2 @ arr1, np.array([[23, 34], [31, 46]]))\n\narr3 = np.array([[1.0, 2.0], [2.0, 3.0], [4.0, 5.0]])\narr4 = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])\n\nassert_equal(arr3 @ arr4, np.array([[9.0, 12.0, 15.0],\n                                    [14.0, 19.0, 24.0],\n                                    [24.0, 33.0, 42.0]]))\nassert_equal(arr4 @ arr3, np.array([[17.0, 23.0],\n                                    [38.0, 53.0]]))\n\n\n# test array len\narr1 = np.array([1, 2, 3, 4, 5])\nassert len(arr1) == 5\n\narr2 = np.array([[1, 2], [3, 4], [5, 6]])\nassert len(arr2) == 3\n\narr3 = np.array([[1, 2], [3, 4]])\nassert len(arr3) == 2\n\narr4 = np.array([[[[1.5, -1.5, 2.5], [3.5, -3.5, 4.5], [5.5, -5.5, 6.5]]]])\nassert len(arr4) == 1\n\n\n# test array pow\nassert_equal(np.ones([2, 3]) ** 2, np.array([[1., 1., 1.], [1., 1., 1.]]))\n\na = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\nb = np.array([[[2, 2], [2, 2]], [[3, 3], [3, 3]]])\n\nassert np.allclose(a ** b, np.array([[[1, 4], [9, 16]], [[125, 216], [343, 512]]]))\n\narr1 = np.array([1, 2, 3, 4, 5])\nassert_equal(arr1 ** 2, np.array([1, 4, 9, 16, 25]))\nassert np.allclose(arr1 ** 2.5, np.array([1.0, 5.656854, 15.588457, 32.0, 55.901699]))\nassert_equal(2 ** arr1, np.array([2, 4, 8, 16, 32]))\nassert np.allclose(2.5 ** arr1, np.array([2.5, 6.25, 15.625, 39.0625, 97.65625]))\n\narr2 = np.array([[1.33, 2.66], [3.99, 5.33]])\nassert np.allclose(arr2 ** 2, np.array([[1.7689, 7.0756], [15.9201, 28.4089]]))\nassert np.allclose(arr2 ** 2.5, np.array([[2.039995, 11.53995437], [31.80037484, 65.58703869]]))\nassert np.allclose(4 ** arr2, np.array([[6.32033049, 39.94657756],\n                                        [252.47557235, 1618.0046067]]))\nassert np.allclose(5.0 ** arr2, np.array([[8.50413422, 72.32029875],\n                                          [615.0215271, 5315.08388464]]))\n\n\n# test array binary\na = np.array([[1, 0], [0, 1]], dtype=np.bool_)\n'''\narray([[ True, False],\n       [False,  True]])\n'''\n\nassert_equal(a & True, np.array([[1, 0], [0, 1]], dtype=np.bool_))\nassert_equal(a | True, np.array([[1, 1], [1, 1]], dtype=np.bool_))\nassert_equal(a ^ True, np.array([[0, 1], [1, 0]], dtype=np.bool_))\nassert_equal(~a, np.array([[False,  True], [ True, False]]))\n\n\n# test array trigonometry\narr1 = np.array([np.pi / 6, np.pi / 4, np.pi / 3, np.pi / 2, np.pi])\nassert np.allclose(np.sin(arr1), np.array([0.5, 0.707107, 0.866025, 1.0, 0.0]))\nassert np.allclose(np.cos(arr1), np.array([0.866025, 0.707107, 0.5, 0.0, -1.0]))\nassert np.allclose(np.tan(arr1), np.array([0.57735, 1.0, 1.73205, np.inf, 0.0]))\n\narr2 = np.array([0.5, 0.707107, 0.866025, 1.0, 0.0])\nassert np.allclose(np.arcsin(arr2), np.array([np.pi / 6, np.pi / 4, np.pi / 3, np.pi / 2, 0.0]))\nassert np.allclose(np.arccos(arr2), np.array([np.pi / 3, np.pi / 4, np.pi / 6, 0.0, np.pi / 2]))\nassert np.allclose(np.arctan(arr2), np.array([0.463648, 0.61548, 0.713724, 0.785398, 0.0]))\n\n\n# test array exponential\narr1 = np.array([0.0, 1.0, 2.0, 3.0, 4.0])\n\nassert np.allclose(np.exp(arr1), np.array([1.0, 2.718282, 7.389056, 20.085537, 54.598150]))\nassert np.allclose(np.log(arr1), np.array([-np.inf, 0.0, 0.693147, 1.098612, 1.386294]))\nassert np.allclose(np.log2(arr1), np.array([-np.inf, 0.0, 1.0, 1.584963, 2.0]))\nassert np.allclose(np.log10(arr1), np.array([-np.inf, 0.0, 0.30103, 0.477121, 0.60206]))\n\n\n# test array getitem\narr1 = np.arange(30).reshape([3, 2, 5])\n\nassert_equal(arr1[0], np.array([[0, 1, 2, 3, 4],\n                                [5, 6, 7, 8, 9]]))\nassert_equal(arr1[1], np.array([[10, 11, 12, 13, 14],\n                                [15, 16, 17, 18, 19]]))\nassert_equal(arr1[2], np.array([[20, 21, 22, 23, 24],\n                                [25, 26, 27, 28, 29]]))\nassert_equal(arr1[-1], np.array([[20, 21, 22, 23, 24],\n                                 [25, 26, 27, 28, 29]]))\nassert_equal(arr1[-2], np.array([[10, 11, 12, 13, 14],\n                                 [15, 16, 17, 18, 19]]))\n\nassert_equal(arr1[0, 0], np.array([0, 1, 2, 3, 4]))\nassert_equal(arr1[1, 1], np.array([15, 16, 17, 18, 19]))\nassert_equal(arr1[2, 0], np.array([20, 21, 22, 23, 24]))\n\nassert_equal(arr1[(0,)], np.array([[0, 1, 2, 3, 4],\n                                   [5, 6, 7, 8, 9]]))\nassert_equal(arr1[(0, 1)], np.array([5, 6, 7, 8, 9]))\nassert_equal(arr1[(1, 0)], np.array([10, 11, 12, 13, 14]))\nassert_equal(arr1[(-1,)], np.array([[20, 21, 22, 23, 24],\n                                    [25, 26, 27, 28, 29]]))\nassert_equal(arr1[(-3, -1)], np.array([5, 6, 7, 8, 9]))\nassert arr1[(0, 1, 2)] == 7\nassert arr1[(2, 1, 0)] == 25\nassert arr1[(-3, -2, -1)] == 4\nassert arr1[(-1, -2, -3)] == 22\n\nassert_equal(arr1[[0, ]], np.array([[[0, 1, 2, 3, 4],\n                                     [5, 6, 7, 8, 9]]]))\nassert_equal(arr1[[0, 1]], np.array([[[0, 1, 2, 3, 4],\n                                      [5, 6, 7, 8, 9]],\n                                     [[10, 11, 12, 13, 14],\n                                      [15, 16, 17, 18, 19]]]))\nassert_equal(arr1[[1, 2]], np.array([[[10, 11, 12, 13, 14],\n                                      [15, 16, 17, 18, 19]],\n                                     [[20, 21, 22, 23, 24],\n                                      [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[[2, 1]], np.array([[[20, 21, 22, 23, 24],\n                                      [25, 26, 27, 28, 29]],\n                                     [[10, 11, 12, 13, 14],\n                                      [15, 16, 17, 18, 19]]]))\nassert_equal(arr1[[2, 2]], np.array([[[20, 21, 22, 23, 24],\n                                      [25, 26, 27, 28, 29]],\n                                     [[20, 21, 22, 23, 24],\n                                      [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[[0, 1, 2]], np.array([[[0, 1, 2, 3, 4],\n                                         [5, 6, 7, 8, 9]],\n                                        [[10, 11, 12, 13, 14],\n                                         [15, 16, 17, 18, 19]],\n                                        [[20, 21, 22, 23, 24],\n                                         [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[[2, 1, 0]], np.array([[[20, 21, 22, 23, 24],\n                                         [25, 26, 27, 28, 29]],\n                                        [[10, 11, 12, 13, 14],\n                                         [15, 16, 17, 18, 19]],\n                                        [[0, 1, 2, 3, 4],\n                                         [5, 6, 7, 8, 9]]]))\n\nassert_equal(arr1[0:1], np.array([[[0, 1, 2, 3, 4],\n                                   [5, 6, 7, 8, 9]]]))\nassert_equal(arr1[2:3], np.array([[[20, 21, 22, 23, 24],\n                                   [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[0:2], np.array([[[0, 1, 2, 3, 4],\n                                   [5, 6, 7, 8, 9]],\n                                  [[10, 11, 12, 13, 14],\n                                   [15, 16, 17, 18, 19]]]))\nassert_equal(arr1[1:3], np.array([[[10, 11, 12, 13, 14],\n                                   [15, 16, 17, 18, 19]],\n                                  [[20, 21, 22, 23, 24],\n                                   [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[1:3:3], np.array([[[10, 11, 12, 13, 14],\n                                     [15, 16, 17, 18, 19]]]))\nassert_equal(arr1[0:3:2], np.array([[[0, 1, 2, 3, 4],\n                                     [5, 6, 7, 8, 9]],\n                                    [[20, 21, 22, 23, 24],\n                                     [25, 26, 27, 28, 29]]]))\n\nassert_equal(arr1[1:], np.array([[[10, 11, 12, 13, 14],\n                                   [15, 16, 17, 18, 19]],\n                                  [[20, 21, 22, 23, 24],\n                                   [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[:2], np.array([[[0, 1, 2, 3, 4],\n                                   [5, 6, 7, 8, 9]],\n                                  [[10, 11, 12, 13, 14],\n                                   [15, 16, 17, 18, 19]]]))\n\nassert_equal(arr1[::2], np.array([[[0, 1, 2, 3, 4],\n                                   [5, 6, 7, 8, 9]],\n                                  [[20, 21, 22, 23, 24],\n                                   [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[-2:3:1], np.array([[[10, 11, 12, 13, 14],\n                                      [15, 16, 17, 18, 19]],\n                                     [[20, 21, 22, 23, 24],\n                                      [25, 26, 27, 28, 29]]]))\nassert_equal(arr1[3::-2], np.array([[[20, 21, 22, 23, 24],\n                                     [25, 26, 27, 28, 29]],\n                                    [[0, 1, 2, 3, 4],\n                                     [5, 6, 7, 8, 9]]]))\nassert_equal(arr1[::-1], np.array([[[20, 21, 22, 23, 24],\n                                    [25, 26, 27, 28, 29]],\n                                   [[10, 11, 12, 13, 14],\n                                    [15, 16, 17, 18, 19]],\n                                   [[0, 1, 2, 3, 4],\n                                    [5, 6, 7, 8, 9]]]))\nassert_equal(arr1[::], np.array([[[0, 1, 2, 3, 4],\n                                  [5, 6, 7, 8, 9]],\n                                 [[10, 11, 12, 13, 14],\n                                  [15, 16, 17, 18, 19]],\n                                 [[20, 21, 22, 23, 24],\n                                  [25, 26, 27, 28, 29]]]))\n\n\n# test array setitem\narr1 = np.arange(30).reshape([3, 2, 5])\n\narr1[0] = 10\nassert_equal(arr1[0], np.array([[10, 10, 10, 10, 10],\n                                [10, 10, 10, 10, 10]]))\n\narr1[1] = [1, 2, 3, 4, 5]\nassert_equal(arr1[1], np.array([[1, 2, 3, 4, 5],\n                                [1, 2, 3, 4, 5]]))\n\narr1[2] = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]\nassert_equal(arr1[2], np.array([[1, 2, 3, 4, 5],\n                                [6, 7, 8, 9, 10]]))\n\narr1[-1] = 0\nassert_equal(arr1[-1], np.array([[0, 0, 0, 0, 0],\n                                 [0, 0, 0, 0, 0]]))\n\narr1[0, 0] = 5\nassert_equal(arr1[0, 0], np.array([5, 5, 5, 5, 5]))\n\narr1[1, 1] = [1, 2, 3, 4, 5]\nassert_equal(arr1[1, 1], np.array([1, 2, 3, 4, 5]))\n\narr1[2, 0] = [2.5]\nassert_equal(arr1[2, 0], np.array([2, 2, 2, 2, 2]))\n\narr1[(0,)] = 10.5\nassert_equal(arr1[(0,)], np.array([[10, 10, 10, 10, 10],\n                                   [10, 10, 10, 10, 10]]))\n\narr1[(0, 1)] = 0\nassert_equal(arr1[(0, 1)], np.array([0, 0, 0, 0, 0]))\n\narr1[(-3, -1)] = 1\nassert_equal(arr1[(-3, -1)], np.array([1, 1, 1, 1, 1]))\n\narr1[(-1, -2, -3)] = 3.14159\nassert arr1[(-1, -2, -3)] == 3\n\narr1[[0, ]] = 0\nassert_equal(arr1[[0, ]], np.array([[[0, 0, 0, 0, 0],\n                                     [0, 0, 0, 0, 0]]]))\n\narr1[[0, 1]] = 1\nassert_equal(arr1[[0, 1]], np.array([[[1, 1, 1, 1, 1],\n                                      [1, 1, 1, 1, 1]],\n                                     [[1, 1, 1, 1, 1],\n                                      [1, 1, 1, 1, 1]]]))\n\narr1[[2, 2]] = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]\nassert_equal(arr1[[2, 2]], np.array([[[1, 2, 3, 4, 5],\n                                      [6, 7, 8, 9, 10]],\n                                     [[1, 2, 3, 4, 5],\n                                      [6, 7, 8, 9, 10]]]))\n\narr1[0:1] = 0\nassert_equal(arr1[0:1], np.array([[[0, 0, 0, 0, 0],\n                                   [0, 0, 0, 0, 0]]]))\n\narr1[2:3] = 1\nassert_equal(arr1[2:3], np.array([[[1, 1, 1, 1, 1],\n                                   [1, 1, 1, 1, 1]]]))\n\narr1[::2] = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]\nassert_equal(arr1[::2], np.array([[[1, 2, 3, 4, 5],\n                                   [6, 7, 8, 9, 10]],\n                                  [[1, 2, 3, 4, 5],\n                                   [6, 7, 8, 9, 10]]]))\n\narr1[-2:3:1] = [[[1, 2, 3, 4, 5],\n                 [6, 7, 8, 9, 10]],\n                [[11, 12, 13, 14, 15],\n                 [16, 17, 18, 19, 20]]]\nassert_equal(arr1[-2:3:1], np.array([[[1, 2, 3, 4, 5],\n                                      [6, 7, 8, 9, 10]],\n                                     [[11, 12, 13, 14, 15],\n                                      [16, 17, 18, 19, 20]]]))\n\nprint(\"ALL TESTS PASSED\")", "test_numpy_simple.py": "import numpy as np\n\nassert np.array([1, 2, 3]).sum() == 6\n", "test_rdp.py": "from pocket_numpy import rdp\nimport numpy as np\n\nret = rdp([[0, 0], [1, 1], [2, 2], [3, 3]])\nassert isinstance(ret, np.ndarray)\nassert ret.tolist() == [[0, 0], [3, 3]]\n\narr = [[0, 0], [5, 1], [10, 0]]\nret = rdp(arr, epsilon=1.0)\nassert ret.tolist() == [[0, 0], [10, 0]]\nret = rdp(arr, epsilon=1.0 - 1e-6)\nassert ret.tolist() == arr\n\narr = [[xy[0], xy[1], 0.0] for xy in arr]\nret = rdp(arr, epsilon=1.0)\nassert ret.tolist() == [[0, 0, 0], [10, 0, 0]]\nret = rdp(arr, epsilon=1.0 - 1e-6)\nassert ret.tolist() == arr"};
diff --git a/src/numpy.cpp b/src/numpy.cpp
index 0271b9f..bfa5427 100644
--- a/src/numpy.cpp
+++ b/src/numpy.cpp
@@ -2,6 +2,7 @@
 #include <pybind11/stl.h>
 #include <numpy.hpp>
 #include <typeinfo>
+#include "rdp.hpp"
 
 namespace py = pybind11;
 
@@ -2868,6 +2869,88 @@ void array_creation_registry(py::module_& m) {
     register_array_float<5>(m);
 }
 
+// --- RDP helpers: bridge ndarray/list <-> Eigen types ---
+
+// Helper: extract Nx2 or Nx3 Eigen matrix from ndarray_base
+static rdp::RowVectors ndarray_to_eigen(const ndarray_base& base, int& input_cols) {
+    int dims = base.ndim();
+    if (dims != 2) throw std::invalid_argument("rdp: expected 2D array");
+    auto shape_vec = base.shape();
+    int rows = shape_vec[0].cast<int>();
+    int cols = shape_vec[1].cast<int>();
+    if (cols != 2 && cols != 3) throw std::invalid_argument("rdp: expected Nx2 or Nx3 array");
+    input_cols = cols;
+
+    if (auto* p = dynamic_cast<const ndarray<float64>*>(&base)) {
+        const double* ptr = p->data.get_array().data();
+        if (cols == 3) {
+            return Eigen::Map<const rdp::RowVectors>(ptr, rows, 3);
+        } else {
+            rdp::RowVectors result(rows, 3);
+            result.setZero();
+            Eigen::Map<const rdp::RowVectorsNx2> map2(ptr, rows, 2);
+            result.leftCols(2) = map2;
+            return result;
+        }
+    }
+    if (auto* p = dynamic_cast<const ndarray<int_>*>(&base)) {
+        const int_* iptr = p->data.get_array().data();
+        rdp::RowVectors result(rows, 3);
+        result.setZero();
+        for (int i = 0; i < rows; ++i)
+            for (int j = 0; j < cols; ++j)
+                result(i, j) = static_cast<double>(iptr[i * cols + j]);
+        return result;
+    }
+    throw std::invalid_argument("rdp: unsupported ndarray dtype");
+}
+
+// Helper: convert vector<vector<double>> to Eigen Nx3 (zero-pad 2D to 3D)
+static rdp::RowVectors vec2d_to_eigen_d(const std::vector<std::vector<double>>& coords, int& input_cols) {
+    int rows = (int)coords.size();
+    if (rows == 0) throw std::invalid_argument("rdp: empty coords");
+    int cols = (int)coords[0].size();
+    if (cols != 2 && cols != 3) throw std::invalid_argument("rdp: expected Nx2 or Nx3");
+    input_cols = cols;
+    rdp::RowVectors result(rows, 3);
+    result.setZero();
+    for (int i = 0; i < rows; ++i)
+        for (int j = 0; j < cols; ++j)
+            result(i, j) = coords[i][j];
+    return result;
+}
+
+// Helper: convert vector<vector<int_>> to Eigen Nx3
+static rdp::RowVectors vec2d_to_eigen_i(const std::vector<std::vector<int_>>& coords, int& input_cols) {
+    int rows = (int)coords.size();
+    if (rows == 0) throw std::invalid_argument("rdp: empty coords");
+    int cols = (int)coords[0].size();
+    if (cols != 2 && cols != 3) throw std::invalid_argument("rdp: expected Nx2 or Nx3");
+    input_cols = cols;
+    rdp::RowVectors result(rows, 3);
+    result.setZero();
+    for (int i = 0; i < rows; ++i)
+        for (int j = 0; j < cols; ++j)
+            result(i, j) = static_cast<double>(coords[i][j]);
+    return result;
+}
+
+// Helper: Eigen RowVectors result -> ndarray_base (return Nx2 or Nx3)
+static std::unique_ptr<ndarray_base> eigen_to_ndarray(const rdp::RowVectors& mat, int output_cols) {
+    int N = (int)mat.rows();
+    std::vector<std::vector<double>> data(N, std::vector<double>(output_cols));
+    for (int i = 0; i < N; ++i)
+        for (int j = 0; j < output_cols; ++j)
+            data[i][j] = mat(i, j);
+    return std::unique_ptr<ndarray_base>(new ndarray<float64>(data));
+}
+
+// Helper: Eigen VectorXi mask -> ndarray_base (1D int array)
+static std::unique_ptr<ndarray_base> mask_to_ndarray(const Eigen::VectorXi& mask) {
+    std::vector<int_> data(mask.size());
+    for (int i = 0; i < mask.size(); ++i) data[i] = mask[i];
+    return std::unique_ptr<ndarray_base>(new ndarray<int_>(data));
+}
 
 PYBIND11_MODULE(numpy, m) {
     m.doc() = "Python bindings for pkpy::numpy::ndarray using pybind11";
@@ -3535,4 +3618,65 @@ PYBIND11_MODULE(numpy, m) {
         py::arg("arr2"),
         py::arg("rtol") = 1e-5,
         py::arg("atol") = 1e-8);
+
+    // --- RDP bindings ---
+
+    // rdp from list<list<double>>
+    m.def("rdp", [](std::vector<std::vector<double>> coords, double epsilon, bool recursive) -> std::unique_ptr<ndarray_base> {
+        int input_cols;
+        auto eigen_coords = vec2d_to_eigen_d(coords, input_cols);
+        auto result = rdp::douglas_simplify(eigen_coords, epsilon, recursive);
+        return eigen_to_ndarray(result, input_cols);
+    }, py::arg("coords"), py::arg("epsilon") = 0.0, py::arg("recursive") = true);
+
+    // rdp from list<list<int>>
+    m.def("rdp", [](std::vector<std::vector<int_>> coords, double epsilon, bool recursive) -> std::unique_ptr<ndarray_base> {
+        int input_cols;
+        auto eigen_coords = vec2d_to_eigen_i(coords, input_cols);
+        auto result = rdp::douglas_simplify(eigen_coords, epsilon, recursive);
+        return eigen_to_ndarray(result, input_cols);
+    }, py::arg("coords"), py::arg("epsilon") = 0.0, py::arg("recursive") = true);
+
+    // rdp from ndarray
+    m.def("rdp", [](const ndarray_base& coords, double epsilon, bool recursive) -> std::unique_ptr<ndarray_base> {
+        int input_cols;
+        auto eigen_coords = ndarray_to_eigen(coords, input_cols);
+        auto result = rdp::douglas_simplify(eigen_coords, epsilon, recursive);
+        return eigen_to_ndarray(result, input_cols);
+    }, py::arg("coords"), py::arg("epsilon") = 0.0, py::arg("recursive") = true);
+
+    // rdp_mask from list<list<double>>
+    m.def("rdp_mask", [](std::vector<std::vector<double>> coords, double epsilon, bool recursive) -> std::unique_ptr<ndarray_base> {
+        int input_cols;
+        auto eigen_coords = vec2d_to_eigen_d(coords, input_cols);
+        auto mask = rdp::douglas_simplify_mask(eigen_coords, epsilon, recursive);
+        return mask_to_ndarray(mask);
+    }, py::arg("coords"), py::arg("epsilon") = 0.0, py::arg("recursive") = true);
+
+    // rdp_mask from list<list<int>>
+    m.def("rdp_mask", [](std::vector<std::vector<int_>> coords, double epsilon, bool recursive) -> std::unique_ptr<ndarray_base> {
+        int input_cols;
+        auto eigen_coords = vec2d_to_eigen_i(coords, input_cols);
+        auto mask = rdp::douglas_simplify_mask(eigen_coords, epsilon, recursive);
+        return mask_to_ndarray(mask);
+    }, py::arg("coords"), py::arg("epsilon") = 0.0, py::arg("recursive") = true);
+
+    // rdp_mask from ndarray
+    m.def("rdp_mask", [](const ndarray_base& coords, double epsilon, bool recursive) -> std::unique_ptr<ndarray_base> {
+        int input_cols;
+        auto eigen_coords = ndarray_to_eigen(coords, input_cols);
+        auto mask = rdp::douglas_simplify_mask(eigen_coords, epsilon, recursive);
+        return mask_to_ndarray(mask);
+    }, py::arg("coords"), py::arg("epsilon") = 0.0, py::arg("recursive") = true);
+
+    // Create pocket_numpy module that re-exports rdp/rdp_mask from numpy
+    {
+        py_GlobalRef pocket_numpy_mod = py_newmodule("pocket_numpy");
+        if (py_getattr(m.ptr(), py_name("rdp"))) {
+            py_setattr(pocket_numpy_mod, py_name("rdp"), py_retval());
+        }
+        if (py_getattr(m.ptr(), py_name("rdp_mask"))) {
+            py_setattr(pocket_numpy_mod, py_name("rdp_mask"), py_retval());
+        }
+    }
 }
diff --git a/tests/test_rdp.py b/tests/test_rdp.py
index a861036..e2a7f0f 100644
--- a/tests/test_rdp.py
+++ b/tests/test_rdp.py
@@ -11,7 +11,7 @@
 ret = rdp(arr, epsilon=1.0 - 1e-6)
 assert ret.tolist() == arr
 
-arr = [[*xy, 0.0] for xy in arr]
+arr = [[xy[0], xy[1], 0.0] for xy in arr]
 ret = rdp(arr, epsilon=1.0)
 assert ret.tolist() == [[0, 0, 0], [10, 0, 0]]
 ret = rdp(arr, epsilon=1.0 - 1e-6)
