Numpy performance example

Numpy performance.ipynb

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   "source": [
    "import numpy as np"
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   "source": [
    "# Generate individual arrays then concatenate once"
   ]
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     "text": [
      "100 loops, best of 3: 3.52 ms per loop\n"
     ]
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   "source": [
    "%%timeit\n",
    "l = []\n",
    "for i in range(100):\n",
    "    l.append(np.random.normal(size=1000))\n",
    "a = np.concatenate(l)"
   ]
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   "source": [
    "# Build empty array then fill it in"
   ]
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     "text": [
      "100 loops, best of 3: 3.65 ms per loop\n"
     ]
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   "source": [
    "%%timeit\n",
    "b = np.empty(100000)\n",
    "for i in range(100):\n",
    "    b[i*1000:(i+1)*1000] = np.random.normal(size=1000)"
   ]
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   "cell_type": "markdown",
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   "source": [
    "# Concatenate arrays repeatedly"
   ]
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     "text": [
      "10 loops, best of 3: 30.3 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "c = np.random.normal(size=1000)\n",
    "for i in range(1,100):\n",
    "    c = np.concatenate([c, np.random.normal(size=1000)])"
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