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doc/demo.ipynb

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"cells": [
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{
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"cell_type": "markdown",
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"id": "liquid-finland",
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"id": "sticky-exhibit",
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"metadata": {},
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"source": [
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"# demo\n",
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"# Demo\n",
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"\n",
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"Author: Cindy Chiao\n",
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"Last Modified: Nov 16, 2021\n",
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "guided-johnston",
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"id": "banner-importance",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"id": "incorporated-november",
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"id": "equipped-sense",
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"metadata": {},
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"source": [
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"## Example data\n",
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "substantial-puzzle",
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"id": "dutch-grave",
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"metadata": {},
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"outputs": [
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{
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "imported-circus",
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"id": "applicable-diesel",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "portuguese-decline",
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"id": "animated-marsh",
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"metadata": {},
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"source": [
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"## Batch generation\n",
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "rocky-dealer",
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"id": "attempted-cooling",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "smoking-acrobat",
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"id": "digital-night",
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"metadata": {},
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"source": [
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"We can verify that the outputs have the expected shapes. \n",
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "looking-journalism",
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"id": "integral-theta",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "cellular-designer",
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"id": "usual-kennedy",
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"metadata": {},
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"source": [
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"There are 145 lat points and 192 lon points, thus we're expecting 145 * 192 = 27840 samples in a batch."
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "accurate-arthur",
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"id": "incomplete-native",
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"metadata": {},
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"outputs": [
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{
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},
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"cell_type": "markdown",
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"id": "fewer-transfer",
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"id": "durable-gazette",
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"metadata": {},
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"source": [
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"## Controlling the size/shape of batches\n",
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "charming-drive",
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"id": "sophisticated-legislation",
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"metadata": {},
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"outputs": [
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{
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},
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"cell_type": "markdown",
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"id": "specialized-realtor",
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"id": "spectacular-reading",
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"metadata": {},
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"source": [
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"## Last batch behavior\n",
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "broadband-solid",
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"id": "residential-income",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "boring-slide",
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"id": "competitive-islam",
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"metadata": {},
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"source": [
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"## Overlapping inputs\n",
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "fossil-wonder",
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"id": "cleared-custody",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "direct-mason",
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"id": "harmful-benefit",
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"metadata": {},
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"source": [
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"We can inspect the samples in a batch for a lat/lon pixel, noting that the overlap only applies within a batch and not across. Thus, within the 20 time points in a batch, we can get 11 samples each with 10 time points and 9 time points allowed to overlap."
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "instructional-criticism",
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"id": "earlier-warehouse",
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"metadata": {},
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"outputs": [
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{
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},
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"cell_type": "markdown",
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"id": "ranging-cologne",
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"id": "arranged-telephone",
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"metadata": {},
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"source": [
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"## Example applications\n",
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "premier-syria",
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"id": "consolidated-chocolate",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "raised-breakfast",
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"id": "legislative-closer",
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"metadata": {},
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"source": [
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"We can also use the Xarray's \"stack\" method to transform these into 2D inputs (n_samples, n_features) suitable for other machine learning algorithms implemented in libraries such as [sklearn](https://scikit-learn.org/stable/) and [xgboost](https://xgboost.readthedocs.io/en/stable/). In this case, we are expecting 9 x 9 x 9 = 729 features total."
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "protecting-aside",
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"id": "advisory-chicken",
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"metadata": {},
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"outputs": [
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{
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},
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"cell_type": "markdown",
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"id": "vocal-roots",
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"id": "persistent-culture",
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"metadata": {},
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"source": [
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"## What's next?\n",

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