<jupyter_start><jupyter_text>Exporting collection to Cloud Storage toCloudStorage(collection, bucket="opencrops", folder=None, namePattern='{id}', scale=30, dataType="float", region=None, datePattern=None):Export an image clipped by features (Polygons). You can use the same arguments as the original function ee.batch.export.image.toDrive**Parameters**- collection: image collection- bucket: GCS bucket- folder: same as ee.Export- name: name of the resulting image. If `None` uses image's ID- scale: same as ee.Export. Default to 1000- 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*- region: geometry to clip export- kwargs: keyword arguments that will be passed to ee.batch.export.image.toCloudStorageReturn a list of all tasks (for further processing/checking)<jupyter_code>import ee
import sys
sys.path.append("../..")
ee.Initialize()
from geetools import batch<jupyter_output><empty_output><jupyter_text>Get an Image Collection<jupyter_code>p1 = ee.Geometry.Point([-71,-42])
feat1 = ee.Feature(p1.buffer(1000), {'site': 1})
fc = ee.FeatureCollection([feat1])
collection = ee.ImageCollection('COPERNICUS/S2').filterBounds(fc.geometry()).filterDate("2015-01-01","2015-12-01")
# please ensure that you have a default project enabled
# and change this bucket with a bucket you have write access
BUCKET = "opencrops"

batch.imagecollection.toCloudStorage(collection, bucket=BUCKET, folder="geetools", scale=30, verbose=True)
# check the storage bucket via the gsutil tool
!gsutil ls gs://opencrops/geetools<jupyter_output>gs://opencrops/geetools/20150825T143316_20150825T144048_T19GCP.tif
gs://opencrops/geetools/20150825T143316_20161001T010844_T19GCP.tif
gs://opencrops/geetools/20150825T144048_20160417T022845_T19GCP.tif
gs://opencrops/geetools/20151123T142942_20170221T180430_T19GCP.tif
{'fraction_non_alphanumeric': 0.114460909555442, 'fraction_numerical': 0.08175779253960143, 'mean_word_length': 6.3059701492537314, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '264604', 'license': 'MIT', 'n_tokens_mistral': 748, 'n_tokens_neox': 642, 'n_words': 176, 'path': 'notebooks/batch/exportToGCS.ipynb', 'repo_name': 'hubert-thieriot/gee_tools', 'size': '1957'}