170 lines
4.5 KiB
Plaintext
170 lines
4.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-03-04T09:40:28.904Z",
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"iopub.status.busy": "2024-03-04T09:40:28.896Z",
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"iopub.status.idle": "2024-03-04T09:40:28.978Z",
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"shell.execute_reply": "2024-03-04T09:40:28.967Z"
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}
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},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-03-04T10:02:39.062Z",
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"iopub.status.busy": "2024-03-04T10:02:39.057Z",
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"iopub.status.idle": "2024-03-04T10:02:39.068Z",
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"shell.execute_reply": "2024-03-04T10:02:39.071Z"
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}
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},
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"outputs": [],
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"source": [
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"# create a collection of time series\n",
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"# in real life, this data comes from an experiment/simulation\n",
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"n_series = 128\n",
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"len_one_series = 2**20 # ➔ 2^20 = 1,048,576 items (8 B x 2^20 = 8,388,608 B = 8 M)\n",
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"time_series = np.zeros((n_series, len_one_series), dtype='float64')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-03-04T10:02:41.027Z",
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"iopub.status.busy": "2024-03-04T10:02:41.020Z",
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"iopub.status.idle": "2024-03-04T10:02:41.036Z",
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"shell.execute_reply": "2024-03-04T10:02:41.040Z"
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}
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},
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"outputs": [],
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"source": [
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"# how much memory does one time series need?\n",
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"ts_size = time_series[0].nbytes/2**20 # -> 2^20 is 1 M\n",
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"total_size = time_series.nbytes/2**20\n",
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"print(f'Size of one time series: {int(ts_size)} M')\n",
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"print(f'Size of collection: {int(total_size)} M')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-03-04T10:06:08.461Z",
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"iopub.status.busy": "2024-03-04T10:06:08.459Z",
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"iopub.status.idle": "2024-03-04T10:06:08.466Z",
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"shell.execute_reply": "2024-03-04T10:06:08.468Z"
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}
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},
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"outputs": [],
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"source": [
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"# let's load the collection in one big array\n",
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"def load_data_row(x, time_series):\n",
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" \"\"\"Store one time series per raw\"\"\"\n",
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" for row, ts in enumerate(time_series):\n",
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" x[row,:] = ts\n",
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" return x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-03-04T10:06:10.280Z",
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"iopub.status.busy": "2024-03-04T10:06:10.277Z",
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"iopub.status.idle": "2024-03-04T10:06:10.284Z",
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"shell.execute_reply": "2024-03-04T10:06:10.288Z"
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}
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},
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"outputs": [],
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"source": [
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"# let's load the collection in one big array\n",
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"def load_data_column(x, time_series):\n",
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" \"\"\"Store one time series per column\"\"\"\n",
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" for column, ts in enumerate(time_series):\n",
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" x[:,column] = ts\n",
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" return x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-03-04T10:06:14.959Z",
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"iopub.status.busy": "2024-03-04T10:06:14.956Z",
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"iopub.status.idle": "2024-03-04T10:06:17.437Z",
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"shell.execute_reply": "2024-03-04T10:06:17.443Z"
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}
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},
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"outputs": [],
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"source": [
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"x = np.zeros((n_series, len_one_series), dtype='float64')\n",
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"%timeit load_data_row(x, time_series)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-03-04T10:06:20.056Z",
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"iopub.status.busy": "2024-03-04T10:06:20.053Z",
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"iopub.status.idle": "2024-03-04T10:06:21.695Z",
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"shell.execute_reply": "2024-03-04T10:06:21.700Z"
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}
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},
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"outputs": [],
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"source": [
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"x = np.zeros((len_one_series, n_series), dtype='float64')\n",
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"%timeit load_data_column(x, time_series)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# on my machine: 31 ms vs 1240 ms ≈ 40x slowdown!!!"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.5"
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},
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"nteract": {
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"version": "0.28.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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