def test_getRegion(self):
expected = [[0.0, 0.0], [10.0, 0.0], [10.0, 10.0], [0.0, 10.0],
[0.0, 0.0]]
pol = ee.Geometry.Polygon(expected)
feat = ee.Feature(pol)
region_geom = tools.getRegion(pol)
region_feat = tools.getRegion(feat)
self.assertEqual(region_geom, [expected])
self.assertEqual(region_feat, [expected])
def test_bap2016_0(self):
pmulti = scores.MultiYear(2016, self.temporada)
objbap = bap.Bap(year=2016,
# colgroup=self.coleccion,
season=self.temporada,
scores=(self.pindice, self.pmascpor, self.psat,
self.pout, self.pop, self.pdoy, pmulti),
masks=(self.nubes,),
filters=(self.filtro,),
)
# objbap.debug = True
sitio = self.sitio
unpix = objbap.bestpixel(sitio, indices=("ndvi",))
img = unpix.image
col = unpix.col
self.assertIsInstance(img, ee.Image)
self.assertIsInstance(col, ee.ImageCollection)
idict = img.getInfo()
cdict = col.getInfo()
self.assertIsInstance(idict, dict)
self.assertIsInstance(cdict, dict)
value = tools.get_value(img, self.centroid, 30, 'client')
print(value)
self.assertIsInstance(value, dict)
visual = img.visualize(bands=['NIR','SWIR','RED'], min=0, max=0.5)
url = visual.getThumbUrl({'region':tools.getRegion(img)})
print('Result:', url)
def export(image, folder, score, scale=30):
user = ee.data.getAssetRoots()[0]['id']
task = ee.batch.Export.image.toAsset(image,
assetId='{}/{}/{}'.format(
user, folder, score),
scale=30,
region=tools.getRegion(image))
task.start()
def test_generate_mask(self):
band = 'B1'
mask = cloud_mask.sentinel2(cloud_S2)
cld_score = self.score.generate_score(mask, band)
vis = cld_score.visualize(
min=0, max=1, palette=['b9936c', 'dac292', 'e6e2d3', 'c4b7a6'])
region = cloud_S2.geometry().centroid().buffer(5000)
url = vis.getThumbUrl({'region': tools.getRegion(region)})
print('\nCloud Distance Score: {}\n'.format(url))
self.assertEqual(cld_score.getInfo()['type'], 'Image')
def __init__(self,
timeseries,
fit_band,
area=None,
date_measure='year',
**kwargs):
self.timeSeries = timeseries
self.fit_band = fit_band
self.date_measure = date_measure
self.maxSegments = kwargs.get("maxSegments", 4)
self.spikeThreshold = kwargs.get("spikeThreshold", 0.9)
self.vertexCountOvershoot = kwargs.get("vertexCountOvershoot", 3)
self.preventOneYearRecovery = kwargs.get("preventOneYearRecovery",
False)
self.recoveryThreshold = kwargs.get("recoveryThreshold", 0.25)
self.pvalThreshold = kwargs.get("pvalThreshold", 0.1)
self.bestModelProportion = kwargs.get("bestModelProportion", 1.25)
self.minObservationsNeeded = kwargs.get("minObservationsNeeded", 6)
# Axes
self.year_axis = 1
self.band_axis = 0
if area:
self.area = area
else:
self.area = ee.Image(self.timeSeries.first()).geometry()
self.region = tools.getRegion(self.area)
self._core = None
self._breakdown = None
self._slope = None
self._statistics = None
self._date_range = None
self._date_range_bitreader = None
def col2gcs(col,
bucket,
fileNamePrefix=None,
scale=30,
dataType="float",
region=None,
**kwargs):
""" Upload all images from one collection to Google Drive. You can use the
same arguments as the original function ee.batch.export.image.toDrive
:param col: Collection to upload
:type col: ee.ImageCollection
:param region: area to upload. Defualt to the footprint of the first
image in the collection
:type region: ee.Geometry.Rectangle or ee.Feature
:param scale: scale of the image (side of one pixel). Defults to 30
(Landsat resolution)
:type scale: int
:param maxImgs: maximum number of images inside the collection
:type maxImgs: int
:param dataType: as downloaded images **must** have the same data type in all
bands, you have to set it here. Can be one of: "float", "double", "int",
"Uint8", "Int8" or a casting function like *ee.Image.toFloat*
:type dataType: str
:return: list of tasks
:rtype: list
"""
TYPES = {
'float': ee.Image.toFloat,
'int': ee.Image.toInt,
'Uint8': ee.Image.toUint8,
'int8': ee.Image.toInt8,
'double': ee.Image.toDouble
}
size = col.size().getInfo()
alist = col.toList(size)
tasklist = []
#region = ee.Image(alist.get(0)).geometry().getInfo()["coordinates"]
region = tools.getRegion(region)
for idx in range(0, size):
img = alist.get(idx)
img = ee.Image(img)
name = img.id().getInfo().split("/")[-1] + "_" + str(scale) + "m"
if dataType in TYPES:
typefunc = TYPES[dataType]
img = typefunc(img)
elif dataType in dir(ee.Image):
img = dataType(img)
else:
raise ValueError("specified data type is not found")
if fileNamePrefix is None:
fileNamePrefix = name
task = ee.batch.Export.image.toCloudStorage(
image=img,
description=name,
bucket=bucket,
fileNamePrefix=fileNamePrefix + name,
region=region,
scale=scale)
print("starting task {}/{} of {}".format(idx, size, name))
task.start()
tasklist.append(task)
return tasklist
def output(image, name, vis_params):
visimage = image.visualize(**vis_params)
url = visimage.getThumbUrl({'region': tools.getRegion(cloud_S2)})
print(name, url)