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August 8, 2017 05:58
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 15, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Version:\t\u001b[1m1.5.1\u001b[0m\r\n", | |
"Config files:\t\u001b[1m/***********************/datacube.conf\u001b[0m\r\n", | |
"Host:\t\t\u001b[1m130.56.244.105:6432\u001b[0m\r\n", | |
"Database:\t\u001b[1mdatacube\u001b[0m\r\n", | |
"User:\t\t\u001b[1m************\u001b[0m\r\n", | |
"\r\n", | |
"Valid connection:\t\u001b[1mYES\u001b[0m\r\n", | |
"You have \u001b[1mUSER\u001b[0m privileges.\r\n" | |
] | |
} | |
], | |
"source": [ | |
"!datacube system check" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 16, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline\n", | |
"from matplotlib import pyplot as plt\n", | |
"import numpy as np\n", | |
"\n", | |
"from datacube.utils import geometry\n", | |
"from datacube.helpers import ga_pq_fuser\n", | |
"import datacube" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 17, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"dc = datacube.Datacube(app='dbg')\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 18, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"Geometry(POLYGON ((131.55 -13.8,131.55 -13.88,131.5 -13.88,131.5 -13.8,131.55 -13.8)), EPSG:4326)" | |
] | |
}, | |
"execution_count": 18, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"geom = geometry.box(131.55, -13.8,\n", | |
" 131.50, -13.88,\n", | |
" geometry.CRS('EPSG:4326'))\n", | |
"\n", | |
"start_of_epoch = '2014-01-01'\n", | |
"end_of_epoch = '2014-02-01'\n", | |
"\n", | |
"\n", | |
"geom" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 19, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"{'geopolygon': Geometry(POLYGON ((131.55 -13.8,131.55 -13.88,131.5 -13.88,131.5 -13.8,131.55 -13.8)), EPSG:4326),\n", | |
" 'product': 'ls8_pq_albers',\n", | |
" 'time': ('2014-01-01', '2014-02-01')}" | |
] | |
}, | |
"execution_count": 19, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"query = {\n", | |
" 'time': (start_of_epoch, end_of_epoch),\n", | |
" 'geopolygon': geom,\n", | |
" 'product': 'ls8_pq_albers',\n", | |
"}\n", | |
"query" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 20, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"[<matplotlib.lines.Line2D at 0x7f0b2a61bbe0>]" | |
] | |
}, | |
"execution_count": 20, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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/zbqarJdpNDtmLkJm9hH//OlRdC8q9GzIWp2LkJl9xMBe3fiHyWU89fJm/vRO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\nM0vN/w8nmBLIIR3n3gAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f0b2a330ac8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"dd = dc.find_datasets(**query)\n", | |
"\n", | |
"plt.plot([ds.center_time for ds in dd], '.-')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 21, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(<xarray.Dataset>\n", | |
" Dimensions: (time: 4, x: 221, y: 351)\n", | |
" Coordinates:\n", | |
" * time (time) datetime64[ns] 2014-01-09T01:18:17.500000 ...\n", | |
" * y (y) float64 -1.46e+06 -1.46e+06 -1.46e+06 -1.46e+06 ...\n", | |
" * x (x) float64 -5.476e+04 -5.474e+04 -5.471e+04 -5.469e+04 ...\n", | |
" Data variables:\n", | |
" pixelquality (time, y, x) int16 16383 16383 16383 16383 16383 16383 ...\n", | |
" Attributes:\n", | |
" crs: EPSG:3577, <xarray.Dataset>\n", | |
" Dimensions: (time: 2, x: 221, y: 351)\n", | |
" Coordinates:\n", | |
" * time (time) datetime64[ns] 2014-01-09T01:18:17.500000 ...\n", | |
" * y (y) float64 -1.46e+06 -1.46e+06 -1.46e+06 -1.46e+06 ...\n", | |
" * x (x) float64 -5.476e+04 -5.474e+04 -5.471e+04 -5.469e+04 ...\n", | |
" Data variables:\n", | |
" pixelquality (time, y, x) int16 16383 16383 16383 16383 16383 16383 ...\n", | |
" Attributes:\n", | |
" crs: EPSG:3577)" | |
] | |
}, | |
"execution_count": 21, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"query.update(dict(dask_chunks={'time':5})) # Without this line fusing works as expected\n", | |
"\n", | |
"vv = dc.load(**query)\n", | |
"vv2 = dc.load(group_by='solar_day', fuse_func=ga_pq_fuser, **query)\n", | |
"\n", | |
"vv,vv2" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 22, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.image.AxesImage at 0x7f0b120de828>" | |
] | |
}, | |
"execution_count": 22, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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Q9JikhZIuzqWfI2lR2ndELn1SSlsk6exc+q7pvaS/lXStpFHNqXlpbYnMko5NN6pf0kZL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\nAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f0b2a403080>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"a,b = [vv.pixelquality[i] for i in (0,1)]\n", | |
"\n", | |
"plt.figure(figsize=(14,7))\n", | |
"plt.subplot(221)\n", | |
"a.plot.imshow()\n", | |
"plt.subplot(222)\n", | |
"b.plot.imshow()\n", | |
"plt.subplot(223)\n", | |
"vv2.pixelquality[0].plot.imshow()\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.6.1" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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