Change np.fromfunction() example and fix typo in explanation, fixes #403
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@ -36,7 +36,7 @@
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@ -59,7 +59,7 @@
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@ -75,7 +75,7 @@
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@ -100,7 +100,7 @@
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@ -110,7 +110,7 @@
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"execution_count": 5,
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@ -119,7 +119,7 @@
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@ -128,7 +128,7 @@
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 7,
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@ -145,7 +145,7 @@
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 8,
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@ -162,7 +162,7 @@
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"execution_count": 10,
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"execution_count": 9,
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"source": [
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@ -181,7 +181,7 @@
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -198,7 +198,7 @@
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 11,
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"outputs": [],
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@ -215,7 +215,7 @@
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 12,
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"metadata": {
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"scrolled": true
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@ -234,7 +234,7 @@
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 13,
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@ -251,7 +251,7 @@
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 14,
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"metadata": {
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"scrolled": true
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@ -269,7 +269,7 @@
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 15,
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"source": [
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@ -285,7 +285,7 @@
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -301,7 +301,7 @@
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 17,
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@ -320,7 +320,7 @@
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -338,7 +338,7 @@
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -354,7 +354,7 @@
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -370,7 +370,7 @@
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -404,12 +404,12 @@
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"def my_function(z, y, x):\n",
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" return x * y + z\n",
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" return x + 10 * y + 100 * z\n",
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"\n",
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"np.fromfunction(my_function, (3, 2, 10))"
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]
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@ -418,7 +418,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"NumPy first creates three `ndarrays` (one per dimension), each of shape `(2, 10)`. Each array has values equal to the coordinate along a specific axis. For example, all elements in the `z` array are equal to their z-coordinate:\n",
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"NumPy first creates three `ndarrays` (one per dimension), each of shape `(3, 2, 10)`. Each array has values equal to the coordinate along a specific axis. For example, all elements in the `z` array are equal to their z-coordinate:\n",
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"\n",
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" [[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
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" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
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@ -429,7 +429,7 @@
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" [[ 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]\n",
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" [ 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]]]\n",
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"\n",
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"So the terms x, y and z in the expression `x * y + z` above are in fact `ndarray`s (we will discuss arithmetic operations on arrays below). The point is that the function `my_function` is only called *once*, instead of once per element. This makes initialization very efficient."
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"So the terms `x`, `y` and `z` in the expression `x + 10 * y + 100 * z` above are in fact `ndarray`s (we will discuss arithmetic operations on arrays below). The point is that the function `my_function` is only called *once*, instead of once per element. This makes initialization very efficient."
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]
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},
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{
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