2024-heraklion-comp-arch/exercise-my-solution.ipynb

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2024-08-21 12:34:31 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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"shell.execute_reply": "2024-03-04T09:40:28.967Z"
}
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-04T10:02:39.062Z",
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}
},
"outputs": [],
"source": [
"n_series = 32\n",
"len_one_series = 5*2**20\n",
"time_series = np.random.rand(n_series, len_one_series)\n",
"gap = 16*2**10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-04T10:02:41.027Z",
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"shell.execute_reply": "2024-03-04T10:02:41.040Z"
},
"scrolled": true
},
"outputs": [],
"source": [
"print(f'Size of one time series: {int(time_series[0].nbytes/2**20)} M')\n",
"print(f'Size of collection: {int(time_series.nbytes/2**20)} M')\n",
"print(f'Gap size: {int(gap*8/2**10)} K')\n",
"print(f'Gapped series size: {int(time_series[0, ::gap].nbytes/2**10)} K')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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"shell.execute_reply": "2024-03-04T10:06:08.468Z"
}
},
"outputs": [],
"source": [
"# compute a Taylor-like series\n",
"def taylor(time_series, mean, gap):\n",
" for row, ts in enumerate(time_series):\n",
" for pwr in range(1,20):\n",
" mean[row] += (ts[::gap]**pwr).sum()\n",
" return mean\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-04T10:06:08.461Z",
"iopub.status.busy": "2024-03-04T10:06:08.459Z",
"iopub.status.idle": "2024-03-04T10:06:08.466Z",
"shell.execute_reply": "2024-03-04T10:06:08.468Z"
}
},
"outputs": [],
"source": [
"def taylor_improved(time_series, mean, gap):\n",
" y = time_series[:,::gap].copy()\n",
" for row, ts in enumerate(y):\n",
" for pwr in range(1,20):\n",
" mean[row] += (ts**pwr).sum()\n",
" return mean"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# verify that they yield the same results\n",
"out1 = taylor(time_series, np.zeros(n_series), gap)\n",
"out2 = taylor_improved(time_series, np.zeros(n_series), gap)\n",
"np.testing.assert_allclose(out1, out2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-04T10:06:14.959Z",
"iopub.status.busy": "2024-03-04T10:06:14.956Z",
"iopub.status.idle": "2024-03-04T10:06:17.437Z",
"shell.execute_reply": "2024-03-04T10:06:17.443Z"
}
},
"outputs": [],
"source": [
"mean = np.zeros(n_series, dtype='float64')\n",
"%timeit taylor(time_series, mean, gap)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-04T10:06:20.056Z",
"iopub.status.busy": "2024-03-04T10:06:20.053Z",
"iopub.status.idle": "2024-03-04T10:06:21.695Z",
"shell.execute_reply": "2024-03-04T10:06:21.700Z"
}
},
"outputs": [],
"source": [
"mean = np.zeros(n_series, dtype='float64')\n",
"%timeit taylor_improved(time_series, mean, gap)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"file_extension": ".py",
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"name": "python",
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