def cli(slack_url, version: bool = False):
"""
Check all dead queues which the user is allowed to
"""
if version:
click.echo(__version__)
log = setup_logging()
check_deadletter_queues(slack_url=slack_url, log=log)
def cli(tile_string, workdir, s3_bucket, update_metadata, s3_path):
"""
Example command:
download-alos-palsar --tile-string 2020/N10E010 -w /tmp/download -s example-bucket -p alos_palsar_mosaic
"""
log = setup_logging()
s3_destination = s3_bucket.rstrip("/").lstrip(
"s3://") + "/" + s3_path.rstrip("/")
run_one(tile_string, Path(workdir), s3_destination, update_metadata, log)
def send_messages(
idx: int,
queue_name: str,
max_workers: int = 2,
limit: int = None,
slack_url: str = None,
) -> None:
"""
Publish a list of missing scenes to an specific queue and by the end of that it's able to notify slack the result
:param limit: (int) optional limit of messages to be read from the report
:param max_workers: (int) total number of pods used for the task. This number is used to split the number of scenes
equally among the PODS
:param idx: (int) sequential index which will be used to define the range of scenes that the POD will work with
:param queue_name: (str) queue to be sens to
:param slack_url: (str) Optional slack URL in case of you want to send a slack notification
"""
log = setup_logging()
latest_report = find_latest_report(
report_folder_path=S3_BUCKET_PATH, not_contains="orphaned"
)
if "update" in latest_report:
log.info("FORCED UPDATE FLAGGED!")
log.info(f"Limited: {int(limit) if limit else 'No limit'}")
log.info(f"Number of workers: {max_workers}")
files = read_report_missing_scenes(report_path=latest_report, limit=limit)
log.info(f"Number of scenes found {len(files)}")
log.info(f"Example scenes: {files[0:10]}")
# Split scenes equally among the workers
split_list_scenes = split_list_equally(
list_to_split=files, num_inter_lists=int(max_workers)
)
# In case of the index being bigger than the number of positions in the array, the extra POD isn' necessary
if len(split_list_scenes) <= idx:
log.warning(f"Worker {idx} Skipped!")
sys.exit(0)
log.info(f"Executing worker {idx}")
messages = prepare_message(scene_paths=split_list_scenes[idx], log=log)
queue = get_queue(queue_name=queue_name)
batch = []
failed = 0
sent = 0
error_list = []
for message in messages:
try:
batch.append(message)
if len(batch) == 10:
publish_messages(queue=queue, messages=batch)
batch = []
sent += 10
except Exception as exc:
failed += 1
error_list.append(exc)
batch = []
if len(batch) > 0:
publish_messages(queue=queue, messages=batch)
sent += len(batch)
environment = "DEV" if "dev" in queue_name else "PDS"
error_flag = ":red_circle:" if failed > 0 else ""
message = dedent(
f"{error_flag}*Sentinel 2 GAP Filler - {environment}*\n"
f"Sent Messages: {sent}\n"
f"Failed Messages: {failed}\n"
)
if slack_url is not None:
send_slack_notification(slack_url, "S2 Gap Filler", message)
log.info(message)
if failed > 0:
sys.exit(1)
def create_mosaic(
dc: Datacube,
product: str,
out_product: str,
time: Tuple[str, str],
time_str: str,
bands: Tuple[str],
s3_output_root: str,
split_bands: bool = False,
resolution: int = 120,
overwrite: bool = False,
):
log = setup_logging()
log.info(f"Creating mosaic for {product} over {time}")
client = start_local_dask()
assets = {}
data = dc.load(
product=product,
time=time,
resolution=(-resolution, resolution),
dask_chunks={"x": 2048, "y": 2048},
measurements=bands,
)
# This is a bad idea, we run out of memory
# data.persist()
if not split_bands:
log.info("Creating a single tif file")
out_file = _get_path(s3_output_root, out_product, time_str, "tif")
exists = s3_head_object(out_file) is not None
skip_writing = not (not exists or overwrite)
try:
asset, _ = _save_opinionated_cog(
data,
out_file,
skip_writing=skip_writing,
)
except ValueError:
log.exception(
"Failed to create COG, please check that you only have one timestep in the period."
)
exit(1)
assets[bands[0]] = asset
if skip_writing:
log.info(f"File exists, and overwrite is False. Not writing {out_file}")
else:
log.info(f"Finished writing: {asset.href}")
else:
log.info("Creating multiple tif files")
for band in bands:
out_file = _get_path(
s3_output_root, out_product, time_str, "tif", band=band
)
exists = s3_head_object(out_file) is not None
skip_writing = not (not exists or overwrite)
try:
asset, band = _save_opinionated_cog(
data=data,
out_file=out_file,
band=band,
skip_writing=skip_writing,
)
except ValueError:
log.exception(
"Failed to create COG, please check that you only have one timestep in the period."
)
exit(1)
assets[band] = asset
if skip_writing:
log.info(f"File exists, and overwrite is False. Not writing {out_file}")
else:
log.info(f"Finished writing: {asset.href}")
# Aggressively heavy handed, but we get memory leaks otherwise
client.restart()
out_stac_file = _get_path(s3_output_root, out_product, time_str, "stac-item.json")
item = create_stac_item(
assets[bands[0]].href,
id=f"{product}_{time_str}",
assets=assets,
with_proj=True,
properties={
"odc:product": out_product,
"start_datetime": f"{time[0]}T00:00:00Z",
"end_datetime": f"{time[1]}T23:59:59Z",
},
)
item.set_self_href(out_stac_file)
log.info(f"Writing STAC: {out_stac_file}")
client = s3_client(aws_unsigned=False)
s3_dump(
data=json.dumps(item.to_dict(), indent=2),
url=item.self_href,
ACL="bucket-owner-full-control",
ContentType="application/json",
s3=client,
)
def download_gls(year: str, s3_dst: str, workdir: Path, overwrite: bool = False):
log = setup_logging()
assets = {}
out_stac = URL(s3_dst) / year / f"{PRODUCT_NAME}_{year}.stac-item.json"
if s3_head_object(str(out_stac)) is not None and not overwrite:
log.info(f"{out_stac} exists, skipping")
return
# Download the files
for name, file in FILES.items():
# Create a temporary directory to work with
with TemporaryDirectory(prefix=workdir) as tmpdir:
log.info(f"Working on {file}")
url = URL(
BASE_URL.format(
record_id=YEARS[year][1], year_key=YEARS[year][0], file=file
)
)
dest_url = URL(s3_dst) / year / f"{PRODUCT_NAME}_{year}_{name}.tif"
if s3_head_object(str(dest_url)) is None or overwrite:
log.info(f"Downloading {url}")
try:
local_file = Path(tmpdir) / str(url.name)
# Download the file
download_file(url, local_file)
log.info(f"Downloaded file to {local_file}")
local_file_small = translate_file_deafrica_extent(local_file)
log.info(f"Clipped Africa out and saved to {local_file_small}")
resampling = "nearest" if name in DO_NEAREST else "bilinear"
# Create a COG in memory and upload to S3
with MemoryFile() as mem_dst:
# Creating the COG, with a memory cache and no download. Shiny.
cog_translate(
local_file_small,
mem_dst.name,
cog_profiles.get("deflate"),
in_memory=True,
nodata=255,
overview_resampling=resampling,
)
mem_dst.seek(0)
s3_dump(mem_dst, str(dest_url), ACL="bucket-owner-full-control")
log.info(f"File written to {dest_url}")
except Exception:
log.exception(f"Failed to process {url}")
exit(1)
else:
log.info(f"{dest_url} exists, skipping")
assets[name] = pystac.Asset(
href=str(dest_url), roles=["data"], media_type=pystac.MediaType.COG
)
# Write STAC document from the last-written file
source_doc = f"https://zenodo.org/record/{YEARS[year][1]}"
item = create_stac_item(
str(dest_url),
id=str(odc_uuid("Copernicus Global Land Cover", "3.0.1", [source_doc])),
assets=assets,
with_proj=True,
properties={
"odc:product": PRODUCT_NAME,
"start_datetime": f"{year}-01-01T00:00:00Z",
"end_datetime": f"{year}-12-31T23:59:59Z",
},
)
item.add_links(
[
pystac.Link(
target=source_doc,
title="Source",
rel=pystac.RelType.DERIVED_FROM,
media_type="text/html",
)
]
)
s3_dump(
json.dumps(item.to_dict(), indent=2),
str(out_stac),
ContentType="application/json",
ACL="bucket-owner-full-control",
)
log.info(f"STAC written to {out_stac}")
import boto3
import click as click
from datetime import datetime
from datetime import timedelta
from deafrica.utils import setup_logging
from kubernetes import config, client
# Set log level to info
log = setup_logging()
def delete_volumes(cluster_name, dryrun):
"""
Cleanup sandbox unused Volumes, k8s PVs & PVCs by
looking into CloudTrail "AttachVolume" events over the past 90 days
"""
# configure kubernetes API client
try:
config.load_incluster_config()
except config.ConfigException:
try:
config.load_kube_config()
except config.ConfigException:
log.exception("Could not configure kubernetes python client")
k8s_api = client.CoreV1Api()
k8s_namespace = "sandbox"
# configure boto3 client
ec2_resource = boto3.resource("ec2")
ct_client = boto3.client("cloudtrail")
def generate_buckets_diff(
bucket_name: str,
update_stac: bool = False,
notification_url: str = None,
) -> None:
"""
Compare Sentinel-2 buckets in US and Africa and detect differences
A report containing missing keys will be written to s3://deafrica-sentinel-2/status-report
:param bucket_name: (str) Bucket where the gap report is
:param update_stac: (bool) Define if the report will contain all scenes from the source for an update
:param notification_url: (str) Optional slack URL in case of you want to send a slack notification
"""
log = setup_logging()
log.info("Task started")
# defines where the report will be saved
s2_status_report_path = URL(f"s3://{bucket_name}/status-report/")
environment = "DEV" if "dev" in bucket_name else "PDS"
log.info(f"Environment {environment}")
date_string = datetime.now().strftime("%Y-%m-%d")
# Retrieve keys from inventory bucket
source_keys = get_and_filter_cogs_keys()
output_filename = "No missing scenes were found"
if update_stac:
log.info("FORCED UPDATE ACTIVE!")
missing_scenes = set(f"s3://sentinel-cogs/{key}"
for key in source_keys)
orphaned_keys = set()
else:
destination_keys = set(ns.Key for ns in list_inventory(
manifest=f"{SENTINEL_2_INVENTORY_PATH}",
prefix=BASE_FOLDER_NAME,
contains=".json",
n_threads=200,
))
# Keys that are missing, they are in the source but not in the bucket
missing_scenes = set(f"s3://sentinel-cogs/{key}" for key in source_keys
if key not in destination_keys)
# Keys that are lost, they are in the bucket but not found in the source
orphaned_keys = destination_keys.difference(source_keys)
s2_s3 = s3_client(region_name=SENTINEL_2_REGION)
if len(missing_scenes) > 0 or len(orphaned_keys) > 0:
output_filename = (f"{date_string}_gap_report.json" if not update_stac
else URL(f"{date_string}_gap_report_update.json"))
log.info(
f"File will be saved in {s2_status_report_path}/{output_filename}")
missing_orphan_scenes_json = json.dumps({
"orphan": list(orphaned_keys),
"missing": list(missing_scenes)
})
s3_dump(
data=missing_orphan_scenes_json,
url=str(URL(s2_status_report_path) / output_filename),
s3=s2_s3,
ContentType="application/json",
)
report_http_link = f"https://{bucket_name}.s3.{SENTINEL_2_REGION}.amazonaws.com/status-report/{output_filename}"
message = dedent(f"*SENTINEL 2 GAP REPORT - {environment}*\n"
f"Missing Scenes: {len(missing_scenes)}\n"
f"Orphan Scenes: {len(orphaned_keys)}\n"
f"Report: {report_http_link}\n")
log.info(message)
if not update_stac and (len(missing_scenes) > 200
or len(orphaned_keys) > 200):
if notification_url is not None:
send_slack_notification(notification_url, "S2 Gap Report", message)
raise Exception(f"More than 200 scenes were found \n {message}")
def download_cci_lc(year: str,
s3_dst: str,
workdir: str,
overwrite: bool = False):
log = setup_logging()
assets = {}
cci_lc_version = get_version_from_year(year)
name = f"{PRODUCT_NAME}_{year}_{cci_lc_version}"
out_cog = URL(s3_dst) / year / f"{name}.tif"
out_stac = URL(s3_dst) / year / f"{name}.stac-item.json"
if s3_head_object(str(out_stac)) is not None and not overwrite:
log.info(f"{out_stac} exists, skipping")
return
workdir = Path(workdir)
if not workdir.exists():
workdir.mkdir(parents=True, exist_ok=True)
# Create a temporary directory to work with
tmpdir = mkdtemp(prefix=str(f"{workdir}/"))
log.info(f"Working on {year} in the path {tmpdir}")
if s3_head_object(str(out_cog)) is None or overwrite:
log.info(f"Downloading {year}")
try:
local_file = Path(tmpdir) / f"{name}.zip"
if not local_file.exists():
# Download the file
c = cdsapi.Client()
# We could also retrieve the object metadata from the CDS.
# e.g. f = c.retrieve("series",{params}) | f.location = URL to download
c.retrieve(
"satellite-land-cover",
{
"format": "zip",
"variable": "all",
"version": cci_lc_version,
"year": str(year),
},
local_file,
)
log.info(f"Downloaded file to {local_file}")
else:
log.info(
f"File {local_file} exists, continuing without downloading"
)
# Unzip the file
log.info(f"Unzipping {local_file}")
unzipped = None
with zipfile.ZipFile(local_file, "r") as zip_ref:
unzipped = local_file.parent / zip_ref.namelist()[0]
zip_ref.extractall(tmpdir)
# Process data
ds = xr.open_dataset(unzipped)
# Subset to Africa
ulx, uly, lrx, lry = AFRICA_BBOX
# Note: lats are upside down!
ds_small = ds.sel(lat=slice(uly, lry), lon=slice(ulx, lrx))
ds_small = assign_crs(ds_small, crs="epsg:4326")
# Create cog (in memory - :mem: returns bytes object)
mem_dst = write_cog(
ds_small.lccs_class,
":mem:",
nodata=0,
overview_resampling="nearest",
)
# Write to s3
s3_dump(mem_dst, str(out_cog), ACL="bucket-owner-full-control")
log.info(f"File written to {out_cog}")
except Exception:
log.exception(f"Failed to process {name}")
exit(1)
else:
log.info(f"{out_cog} exists, skipping")
assets["classification"] = pystac.Asset(href=str(out_cog),
roles=["data"],
media_type=pystac.MediaType.COG)
# Write STAC document
source_doc = (
"https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover"
)
item = create_stac_item(
str(out_cog),
id=str(
odc_uuid("Copernicus Land Cover", cci_lc_version,
[source_doc, name])),
assets=assets,
with_proj=True,
properties={
"odc:product": PRODUCT_NAME,
"start_datetime": f"{year}-01-01T00:00:00Z",
"end_datetime": f"{year}-12-31T23:59:59Z",
},
)
item.add_links([
pystac.Link(
target=source_doc,
title="Source",
rel=pystac.RelType.DERIVED_FROM,
media_type="text/html",
)
])
s3_dump(
json.dumps(item.to_dict(), indent=2),
str(out_stac),
ContentType="application/json",
ACL="bucket-owner-full-control",
)
log.info(f"STAC written to {out_stac}")
def fill_the_gap(
landsat: str,
sync_queue_name: str,
scenes_limit: Optional[int] = None,
notification_url: str = None,
) -> None:
"""
Function to retrieve the latest gap report and create messages to the filter queue process.
:param landsat:(str) satellite name
:param sync_queue_name:(str) Queue name
:param scenes_limit:(int) limit of how many scenes will be filled
:param notification_url:(str) Slack notification URL
:return:(None)
"""
log = setup_logging()
log.info(f"Satellite: {landsat}")
log.info(f"Queue: {sync_queue_name}")
log.info(f"Limit: {scenes_limit if scenes_limit else 'No limit'}")
log.info(f"Notification URL: {notification_url}")
environment = "DEV" if "dev" in sync_queue_name else "PDS"
latest_report = find_latest_report(report_folder_path=S3_BUCKET_PATH,
contains=landsat)
if not latest_report:
raise RuntimeError("Report not found!")
update_stac = False
if "update" in latest_report:
log.info("FORCED UPDATE FLAGGED!")
update_stac = True
log.info(f"Reading missing scenes from the report {latest_report}")
missing_scene_paths = read_report_missing_scenes(report_path=latest_report,
limit=scenes_limit)
log.info(f"Number of scenes found {len(missing_scene_paths)}")
log.info(f"Example scenes: {missing_scene_paths[0:10]}")
returned = build_messages(missing_scene_paths=missing_scene_paths,
update_stac=update_stac)
messages_to_send = returned["message_list"]
log.info("Publishing messages")
result = post_messages(message_list=messages_to_send,
queue_name=sync_queue_name)
error_flag = (":red_circle:" if result["failed"] > 0
or len(returned["failed"]) > 0 else "")
extra_issues = "\n".join(returned["failed"])
message = dedent(
f"{error_flag}*Landsat GAP Filler - {environment}*\n"
f"Sent Messages: {result['sent']}\n"
f"Failed Messages: {int(result['failed']) + len(returned['failed'])}\n"
f"Failed sending: {int(result['failed'])}\n"
f"Other issues presented: {extra_issues}")
log.info(message)
if notification_url is not None and result["sent"] > 0:
send_slack_notification(notification_url, "Landsat Gap Filler",
message)
if (int(result["failed"]) + len(returned["failed"])) > 0:
sys.exit(1)
def generate_buckets_diff(
bucket_name: str,
satellites: str,
file_name: str,
update_stac: bool = False,
notification_url: str = None,
):
"""
Compare USGS bulk files and Africa inventory bucket detecting differences
A report containing missing keys will be written to AFRICA_S3_BUCKET_PATH
"""
log = setup_logging()
start_timer = time.time()
log.info("Task started")
landsat_status_report_path = URL(f"s3://{bucket_name}/status-report/")
landsat_status_report_url = URL(
f"https://{bucket_name}.s3.af-south-1.amazonaws.com/status-report/")
environment = "DEV" if "dev" in bucket_name else "PDS"
title = " & ".join(satellites).replace("ls", "Landsat ")
log.info(f"Environment {environment}")
log.info(f"Bucket Name {bucket_name}")
log.info(f"Satellites {satellites}")
log.info(f"File Name {file_name}")
log.info(f"Update all ({update_stac})")
log.info(f"Notification URL ({notification_url})")
# Create connection to the inventory S3 bucket
log.info(f"Retrieving keys from inventory bucket {LANDSAT_INVENTORY_PATH}")
dest_paths = get_and_filter_keys(satellites=satellites)
log.info(f"INVENTORY bucket number of objects {len(dest_paths)}")
log.info(f"INVENTORY 10 first {list(dest_paths)[0:10]}")
date_string = datetime.now().strftime("%Y-%m-%d")
# Download bulk file
log.info("Download Bulk file")
file_path = download_file_to_tmp(url=str(BASE_BULK_CSV_URL),
file_name=file_name)
# Retrieve keys from the bulk file
log.info("Filtering keys from bulk file")
source_paths = get_and_filter_keys_from_files(file_path)
log.info(f"BULK FILE number of objects {len(source_paths)}")
log.info(f"BULK 10 First {list(source_paths)[0:10]}")
output_filename = "No missing scenes were found"
if update_stac:
log.info("FORCED UPDATE ACTIVE!")
missing_scenes = source_paths
orphaned_scenes = []
else:
# collect missing scenes
# missing scenes = keys that are in the bulk file but missing in PDS sync bucket and/or in source bucket
log.info("Filtering missing scenes")
missing_scenes = [
str(USGS_S3_BUCKET_PATH / path)
for path in source_paths.difference(dest_paths)
]
# collect orphan scenes
# orphan scenes = keys that are in PDS sync bucket but missing in the bulk file and/or in source bucket
log.info("Filtering orphan scenes")
orphaned_scenes = [
str(URL(f"s3://{bucket_name}") / path)
for path in dest_paths.difference(source_paths)
]
log.info(f"Found {len(missing_scenes)} missing scenes")
log.info(f"missing_scenes 10 first keys {list(missing_scenes)[0:10]}")
log.info(f"Found {len(orphaned_scenes)} orphaned scenes")
log.info(
f"orphaned_scenes 10 first keys {list(orphaned_scenes)[0:10]}")
landsat_s3 = s3_client(region_name="af-south-1")
if len(missing_scenes) > 0 or len(orphaned_scenes) > 0:
output_filename = (
(f"{title}_{date_string}_gap_report.json" if not update_stac
else URL(f"{date_string}_gap_report_update.json")).replace(
" ", "_").replace("_&", ""))
log.info(
f"Report file will be saved in {landsat_status_report_path / output_filename}"
)
missing_orphan_scenes_json = json.dumps({
"orphan": orphaned_scenes,
"missing": missing_scenes
})
s3_dump(
data=missing_orphan_scenes_json,
url=str(landsat_status_report_path / output_filename),
s3=landsat_s3,
ContentType="application/json",
)
report_output = (str(landsat_status_report_url /
output_filename) if len(missing_scenes) > 0
or len(orphaned_scenes) > 0 else output_filename)
message = dedent(f"*{title} GAP REPORT - {environment}*\n "
f"Missing Scenes: {len(missing_scenes)}\n"
f"Orphan Scenes: {len(orphaned_scenes)}\n"
f"Report: {report_output}\n")
log.info(message)
log.info(
f"File {file_name} processed and sent in {time_process(start=start_timer)}"
)
if not update_stac and (len(missing_scenes) > 200
or len(orphaned_scenes) > 200):
if notification_url is not None:
send_slack_notification(notification_url,
f"{satellites} Gap Report", message)
raise Exception(f"More than 200 scenes were found \n {message}")