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Tiziano Zito 2025-08-13 13:57:19 +02:00
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-04T09:40:28.904Z",
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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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}
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"outputs": [],
"source": [
"# create a collection of time series\n",
"# in real life, this data comes from an experiment/simulation\n",
"n_series = 128\n",
"len_one_series = 2**20 # ➔ 2^20 = 1,048,576 items (8 B x 2^20 = 8,388,608 B = 8 M)\n",
"time_series = np.zeros((n_series, len_one_series), dtype='float64')"
]
},
{
"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"
}
},
"outputs": [],
"source": [
"# how much memory does one time series need?\n",
"ts_size = time_series[0].nbytes/2**20 # -> 2^20 is 1 M\n",
"total_size = time_series.nbytes/2**20\n",
"print(f'Size of one time series: {int(ts_size)} M')\n",
"print(f'Size of collection: {int(total_size)} M')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-04T10:06:08.461Z",
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},
"outputs": [],
"source": [
"# let's load the collection in one big array\n",
"def load_data_row(x, time_series):\n",
" \"\"\"Store one time series per raw\"\"\"\n",
" for row, ts in enumerate(time_series):\n",
" x[row,:] = ts\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-04T10:06:10.280Z",
"iopub.status.busy": "2024-03-04T10:06:10.277Z",
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"shell.execute_reply": "2024-03-04T10:06:10.288Z"
}
},
"outputs": [],
"source": [
"# let's load the collection in one big array\n",
"def load_data_column(x, time_series):\n",
" \"\"\"Store one time series per column\"\"\"\n",
" for column, ts in enumerate(time_series):\n",
" x[:,column] = ts\n",
" return x"
]
},
{
"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": [
"x = np.zeros((n_series, len_one_series), dtype='float64')\n",
"%timeit load_data_row(x, time_series)"
]
},
{
"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": [
"x = np.zeros((len_one_series, n_series), dtype='float64')\n",
"%timeit load_data_column(x, time_series)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# on my machine: 31 ms vs 1240 ms ≈ 40x slowdown!!!"
]
}
],
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"display_name": "Python 3 (ipykernel)",
"language": "python",
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