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giswqs/rainfall.ipynb

Created August 21, 2020 01:53
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daily rainfall
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rainfall.ipynb
{
"cells": [
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import ee\nimport geemap",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Map = geemap.Map()\nMap",
"execution_count": 2,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "Map(center=[40, -100], controls=(WidgetControl(options=['position'], widget=HBox(children=(ToggleButton(value=…",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f20d3b8ed26547519b208d46cccfc44a"
}
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "roi = ee.FeatureCollection(\"users/WaterSystemResearch/TBW_DWPA\")\nMap.addLayer(roi, {}, 'ROI')",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def date_sequence(start, end, unit, date_format='YYYY-MM-dd'):\n \"\"\"Creates a date sequence.\n\n Args:\n start (str): The start date, e.g., '2000-01-01'.\n end (str): The end date, e.g., '2000-12-31'.\n unit (str): One of 'year', 'month' 'week', 'day', 'hour', 'minute', or 'second'.\n date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.\n\n Returns:\n ee.List: A list of date sequence.\n \"\"\"\n start_date = ee.Date(start)\n end_date = ee.Date(end)\n count = ee.Number(end_date.difference(start_date, unit)).toInt()\n num_seq = ee.List.sequence(0, count)\n date_seq = num_seq.map(lambda d: start_date.advance(d, unit).format(date_format))\n return date_seq",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "start_date = ee.Date('2014-03-01')\nend_date = ee.Date('2014-03-31')\ndate_seq = date_sequence(start_date, end_date, 'day')",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "collection = ee.ImageCollection('NASA/GPM_L3/IMERG_V06') \\\n .filterDate(start_date, end_date) \\\n .select('precipitationCal')\n\ndef daily_image(start):\n start = ee.Date(start)\n end = start.advance(1, 'day')\n img = ee.Image(collection.filterDate(start, end).sum())\n return img\n\nimages = date_seq.map(daily_image)\nimage = ee.ImageCollection(images).toBands()",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "images.size().getInfo()",
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 7,
"data": {
"text/plain": "31"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Map.addLayer(image, {}, 'Rainfall')",
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "values = geemap.image_stats(image, region=roi).get('mean')\nvalues = ee.Dictionary(values).values()",
"execution_count": 9,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "y_values = values.getInfo()",
"execution_count": 10,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "x_values = date_seq.getInfo()[:-1]",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "x_values",
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 12,
"data": {
"text/plain": "['2014-03-01',\n '2014-03-02',\n '2014-03-03',\n '2014-03-04',\n '2014-03-05',\n '2014-03-06',\n '2014-03-07',\n '2014-03-08',\n '2014-03-09',\n '2014-03-10',\n '2014-03-11',\n '2014-03-12',\n '2014-03-13',\n '2014-03-14',\n '2014-03-15',\n '2014-03-16',\n '2014-03-17',\n '2014-03-18',\n '2014-03-19',\n '2014-03-20',\n '2014-03-21',\n '2014-03-22',\n '2014-03-23',\n '2014-03-24',\n '2014-03-25',\n '2014-03-26',\n '2014-03-27',\n '2014-03-28',\n '2014-03-29',\n '2014-03-30']"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "y_values",
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 13,
"data": {
"text/plain": "[0,\n 5.198448379815091e-05,\n 2.6075538351426073,\n 0.010660424207162579,\n 0,\n 0,\n 0.018552314075714095,\n 133.8765305949266,\n 3.3739757353439925,\n 0,\n 0,\n 0,\n 0.0007823675172403455,\n 0.09297811666619964,\n 0.7279145105807402,\n 28.96086519006958,\n 20.408764219137087,\n 0,\n 11.67541336764698,\n 15.24818054912903,\n 123.16892555568775,\n 0.09484591217296838,\n 0.002322763430129271,\n 0.13231867477297785,\n 3.29685334550959,\n 81.23060573176409,\n 0.6007589291257318,\n 0.01049478018976515,\n 0,\n 0.0002678348675544839]"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import matplotlib.pyplot as plt",
"execution_count": 14,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "plt.plot(x_values, y_values)",
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 15,
"data": {
"text/plain": "[<matplotlib.lines.Line2D at 0x7fcda9d37280>]"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
"image/png": 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\n"
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