Turn a STAC catalog into a dask-based xarray

Last update: Dec 19, 2022

Overview

StackSTAC

Turn a list of STAC items into a 4D xarray DataArray (dims: time, band, y, x), including reprojection to a common grid. The array is a lazy Dask array, so loading and processing the data in parallel—locally or on a cluster—is just a compute() call away.

For more information and examples, please see the documentation.

import stackstac
import satsearch

stac_items = satsearch.Search(
    url="https://earth-search.aws.element84.com/v0",
    intersects=dict(type="Point", coordinates=[-105.78, 35.79]),
    collections=["sentinel-s2-l2a-cogs"],
    datetime="2020-04-01/2020-05-01"
).items()

stack = stackstac.stack(stac_items)
print(stack)

<xarray.DataArray 'stackstac-f350f6bfc3213d7eee2e6cb159246d88' (time: 13, band: 17, y: 10980, x: 10980)>
dask.array<fetch_raster_window, shape=(13, 17, 10980, 10980), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray>
Coordinates: (12/23)
  * time                        (time) datetime64[ns] 2020-04-01T18:04:04 ......
    id                          (time) <U24 'S2B_13SDV_20200401_0_L2A' ... 'S...
  * band                        (band) <U8 'overview' 'visual' ... 'WVP' 'SCL'
  * x                           (x) float64 4e+05 4e+05 ... 5.097e+05 5.098e+05
  * y                           (y) float64 4e+06 4e+06 ... 3.89e+06 3.89e+06
    eo:cloud_cover              (time) float64 29.24 1.16 27.26 ... 87.33 5.41
    ...                          ...
    data_coverage               (time) object 33.85 100 33.9 ... 32.84 100 34.29
    platform                    (time) <U11 'sentinel-2b' ... 'sentinel-2b'
    sentinel:sequence           <U1 '0'
    proj:epsg                   int64 32613
    sentinel:data_coverage      (time) float64 33.85 100.0 33.9 ... 100.0 34.29
    title                       (band) object None ... 'Scene Classification ...
Attributes:
    spec:        RasterSpec(epsg=32613, bounds=(399960.0, 3890220.0, 509760.0...
    crs:         epsg:32613
    transform:   | 10.00, 0.00, 399960.00|\n| 0.00,-10.00, 4000020.00|\n| 0.0...
    resolution:  10.0

Once in xarray form, many operations become easy. For example, we can compute a low-cloud weekly mean-NDVI timeseries:

lowcloud = stack[stack["eo:cloud_cover"] < 40]
nir, red = lowcloud.sel(band="B08"), lowcloud.sel(band="B04")
ndvi = (nir - red) / (nir + red)
weekly_ndvi = ndvi.resample(time="1w").mean(dim=("time", "x", "y")).rename("NDVI")
# Call `weekly_ndvi.compute()` to process ~25GiB of raster data in parallel. Might want a dask cluster for that!

Installation

pip install stackstac

Things `stackstac` does for you:

Figure out the geospatial parameters from the STAC metadata (if possible): a coordinate reference system, resolution, and bounding box.
Transfer the STAC metadata into xarray coordinates for easy indexing, filtering, and provenance of metadata.
Efficiently generate a Dask graph for loading the data in parallel.
Mediate between Dask's parallelism and GDAL's aversion to it, allowing for fast, multi-threaded reads when possible, and at least preventing segfaults when not.
Mask nodata and rescale by dataset-level scales/offsets.

Limitations:

Raster data only! We are currently ignoring other types of assets you might find in a STAC (XML/JSON metadata, point clouds, video, etc.).
Single-band raster data only! Each band has to be a separate STAC asset—a separate red, green, and blue asset on each Item is great, but a single RGB asset containing a 3-band GeoTIFF is not supported yet.
COGs work best. "Normal" GeoTIFFs that aren't internally tiled, or don't have overviews, will see much worse performance. Sidecar files (like .msk files) are ignored for performace. JPEG2000 will probably work, but probably be slow unless you buy kakadu. Formats make a big difference.
BYOBlocksize. STAC doesn't offer any metadata about the internal tiling scheme of the data. Knowing it can make IO more efficient, but actually reading the data to figure it out is slow. So it's on you to set this parameter. (But if you don't, things should be fine for any reasonable COG.)
Doesn't make geospatial data any easier to work with in xarray. Common operations (picking bands, clipping to bounds, etc.) are tedious to type out. Real geospatial operations (shapestats on a GeoDataFrame, reprojection, etc.) aren't supported at all. rioxarray might help with some of these, but it has limited support for Dask, so be careful you don't kick off a huge computation accidentally.
I haven't even written tests yet! Don't use this in production. Or do, I guess. Up to you.

Roadmap:

Short-term:

Write tests and add CI (including typechecking)
Support multi-band assets
Easier access to s3://-style URIs (right now, you'll need to pass in gdal_env=stackstac.DEFAULT_GDAL_ENV.updated(always=dict(session=rio.session.AWSSession(...))))
Utility to guess blocksize (open a few assets)
Support item assets to provide more useful metadata with collections that use it (like S2 on AWS)
Rewrite dask graph generation once the Blockwise IO API settles

Long term (if anyone uses this thing):

Support other readers (aiocogeo?) that may perform better than GDAL for specific formats
Interactive mapping with xarray_leaflet, made performant with some Dask graph-rewriting tricks to do the initial IO at coarser resolution for lower zoom levels (otherwize zooming out could process terabytes of data)
Improve ergonomics of xarray for raster data (in collaboration with rioxarray)
Implement core geospatial routines (warp, vectorize, vector stats, GeoPandas/spatialpandas interop) in Dask

Comments

Incorrect bounds and shape when stacking 3DEP items from Planetary Computer

Hi @gjoseph92, I'm using stackstac.stack with two adjacent COGs from the Planetary Computer 3dep-seamless collection, and the output bounds and shape are incorrect. If I load them manually with xarray and concat them, the bounds are correct.

Reproducing

import planetary_computer
from pystac_client import Client
import stackstac
import xarray as xr

catalog = Client.open("https://planetarycomputer.microsoft.com/api/stac/v1")
collection = "3dep-seamless"
# This bbox spans two items that are adjacent in x dimension
bbox = [-124.210979, 43.336502, -123.657496, 43.798309]

search = catalog.search(
    collections=[collection],
    bbox=bbox,
    # 3DEP contains both 10m and 30m items
    filter={"eq": [{"property": "gsd"}, 30]},
)

signed = planetary_computer.sign(search)

# Stack the two adjacent items with stackstac (doesn't work as expected)
da_stackstac = stackstac.stack(signed, assets=["data"])

# Stack the two items manually by opening and concatenating with xarray (works as expected)
da_list = [xr.open_rasterio(item.assets["data"].href, chunks=1024) for item in signed]
da_xarray = xr.concat(da_list, dim="time")

Issues

The bounds for the two arrays are much different and the stackstac version doesn't cover the the search bbox:

# Bounds don't cover the bbox: (-125.00167, 43.965540000000004, -123.96554, 44.001670000000004)
print(stackstac.array_bounds(da_stackstac))

# Bounds do cover the bbox: (-125.00152777777778, 42.99819444444364, -122.99819444444364, 44.001527777777774)
print(stackstac.array_bounds(da_xarray))

The array shapes (ignoring the time and band dims) are also much different:

# (3613, 103613)
print(da_stackstac.shape[2:])

# (3612, 7224)
print(da_xarray.shape[2:])

Checking the proj:shape property for the items indicates their shape should be 3612 x 3612, so it looks like the stackstac version is off by 1 in the y dimension and 100k in the x dimension.

Any thoughts on what's going on here? Thanks!

For reference, I'm running stackstac=0.4.1 and xarray=2022.3.0.

EDIT: This looked like it might be related to #132, but the proj:transform of the items seem to be in the correct order:

>> signed[0].properties

{'gsd': 30,
 'datetime': '2013-01-01T00:00:00Z',
 'proj:epsg': 5498,
 'proj:shape': [3612, 3612],
 'end_datetime': '2013-11-01T00:00:00Z',
 'proj:transform': [1e-05,
  0.0,
  -125.0016666667,
  0.0,
  -1e-05,
  44.00166666666,
  0.0,
  0.0,
  1.0],
 'start_datetime': '1999-02-01T00:00:00Z',
 'threedep:region': 'n40w130'}

opened by aazuspan 12

Remove calls to rio.parse_path (maintain compat with rasterio==1.3)

rasterio.path (and therefore, rasterio.parse_path) is being deprecated in rasterio 1.3 and removed in 1.4.

It is called internally anyway by DatasetBase so shouldn't be necessary to call it while initing.

Have checked that this works for my use case, YMMV.

opened by carderne 10
Fix for Pystac ItemCollections

Follow on to #64 to work with Pystac ItemCollection https://pystac.readthedocs.io/en/latest/api.html#itemcollection.

Partially addresses #65, but did not update all the docs and examples

Not sure if I should add the poetry.lock file that I updated to test this out locally

opened by scottyhq 10
Time coordinates are sometimes integers, not datetime64
Noticed this in https://gist.github.com/rmg55/b144cb273d9ccfdf979e9843fdf5e651, and I've had it happen before myself:

Coordinates: * time (time) object 1594404158627000000 ... 1614276155393000000

Pretty sure stackstac is correctly making it into a pandas DatetimeIndex: https://github.com/gjoseph92/stackstac/blob/b652a07f9b2ae27235aea4db4ef0f1f594fd8941/stackstac/prepare.py#L354-L358

but something is going weird when xarray receives that, and it reinterprets it as an object array.
needs-future-test
opened by gjoseph92 9
500 Internal Server Error when requesting tiles from within docker container
This is a separate issue to discuss the separate problem I was running into last week (partially discussed in #96 and #97). I am running stackstac.show from within a JupyterLab notebook running in a docker container.

When running show, I can see the checkerboard pattern, but not the data itself. When looking at developer tools, I can see that the tiles for the data itself is giving a 500 error:

Unfortunately, the content of the response is just:

500 Internal Server Error Server got itself in trouble

Do you have any suggestions as to how we could debug this further, and get more information out of the server?
needs-future-test
opened by robintw 8
Missing file handling

While looking at data in various areas, have come across missing bands, etc, yesterday this while testing stackstac:

File "/home/ubuntu/anaconda3/envs/richard/lib/python3.8/site-packages/stackstac/rio_reader.py", line 393, in read reader = self.dataset File "/home/ubuntu/anaconda3/envs/richard/lib/python3.8/site-packages/stackstac/rio_reader.py", line 389, in dataset self._dataset = self._open() File "/home/ubuntu/anaconda3/envs/richard/lib/python3.8/site-packages/stackstac/rio_reader.py", line 330, in _open ds = SelfCleaningDatasetReader(rio.parse_path(self.url), sharing=False) File "rasterio/_base.pyx", line 262, in rasterio._base.DatasetBase.init rasterio.errors.RasterioIOError: HTTP response code: 404

so perhaps can wrap this and report which one it failed on so can remove that and try again - and find out perhaps if a bad url or missing for the data provider end?
good first issue

opened by RichardScottOZ 8
Coordinate computation does not take into accound inverted axis in `center` mode

To be compatible with rioxarray one needs to use stackstac.stack(..., xy_coords="center") when computing X/Y coordinate values. When using this mode on data that contains "inverted Y axis", a most common scenario, Y axis coordinates are offset by 1 pixel size into positive direction.

I have made a small reproducer. Data is a global synthetic image with 1 degree per pixel in EPSG:4326, when loading it with xy_coords="center" you would expect Y coordinate to span from -89.5 to 89.5, but instead it goes from 90.5 to -88.5.

https://nbviewer.org/gist/Kirill888/b3dad8afdc10b37cd21af4aea8f417e3/stackstac-xy_coords-error-report.ipynb https://gist.github.com/Kirill888/b3dad8afdc10b37cd21af4aea8f417e3

This causes issue reported earlier here: #68

Code that performs computation of the coordinate just offsets "top-left" coordinate by positive half pixel size, but instead should offset by sign(coord[1] - coord[0])*abs(resolution)*0.5
needs-future-test

opened by Kirill888 7
Cannot pick a common CRS, since assets have multiple CRSs: asset 'overview'

lib/python3.9/site-packages/stackstac/prepare.py in prepare_items(items, assets, epsg, resolution, bounds, bounds_latlon, snap_bounds) 142 out_epsg = asset_epsg 143 elif out_epsg != asset_epsg: --> 144 raise ValueError( 145 f"Cannot pick a common CRS, since assets have multiple CRSs: asset {id!r} of item " 146 f"{item_i} {item['id']!r} is in EPSG:{asset_epsg}, "

ValueError: Cannot pick a common CRS, since assets have multiple CRSs: asset 'overview' of item 1 'S2B_11UQS_20210501_0_L2A' is in EPSG:32611, but assets before it were in EPSG:32612.

opened by goriliukasbuxton 7

"Assets must have exactly 1 band"

I tried stackstac for the first time today. I started by just running the Basic example from the documentation. Here is the code I ran

import pystac_client
import stackstac

URL = "https://earth-search.aws.element84.com/v0"
catalog = pystac_client.Client.open(URL)
lon, lat = -105.78, 35.79

items = catalog.search(
    intersects=dict(type="Point", coordinates=[lon, lat]),
    collections=["sentinel-s2-l2a-cogs"],
    datetime="2020-04-01/2020-05-01"
).get_all_items()

stack = stackstac.stack(items)

So far so good. Then I tried to actually compute something

stack[0, 0].compute()

This gave the following error

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In [4], line 1
----> 1 stack[0, 0].compute()

File /srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/core/dataarray.py:993, in DataArray.compute(self, **kwargs)
    974 """Manually trigger loading of this array's data from disk or a
    975 remote source into memory and return a new array. The original is
    976 left unaltered.
   (...)
    990 dask.compute
    991 """
    992 new = self.copy(deep=False)
--> 993 return new.load(**kwargs)

File /srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/core/dataarray.py:967, in DataArray.load(self, **kwargs)
    949 def load(self: T_DataArray, **kwargs) -> T_DataArray:
    950     """Manually trigger loading of this array's data from disk or a
    951     remote source into memory and return this array.
    952 
   (...)
    965     dask.compute
    966     """
--> 967     ds = self._to_temp_dataset().load(**kwargs)
    968     new = self._from_temp_dataset(ds)
    969     self._variable = new._variable

File /srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/core/dataset.py:733, in Dataset.load(self, **kwargs)
    730 import dask.array as da
    732 # evaluate all the dask arrays simultaneously
--> 733 evaluated_data = da.compute(*lazy_data.values(), **kwargs)
    735 for k, data in zip(lazy_data, evaluated_data):
    736     self.variables[k].data = data

File /srv/conda/envs/notebook/lib/python3.9/site-packages/dask/base.py:600, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
    597     keys.append(x.__dask_keys__())
    598     postcomputes.append(x.__dask_postcompute__())
--> 600 results = schedule(dsk, keys, **kwargs)
    601 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])

File /srv/conda/envs/notebook/lib/python3.9/site-packages/dask/threaded.py:89, in get(dsk, keys, cache, num_workers, pool, **kwargs)
     86     elif isinstance(pool, multiprocessing.pool.Pool):
     87         pool = MultiprocessingPoolExecutor(pool)
---> 89 results = get_async(
     90     pool.submit,
     91     pool._max_workers,
     92     dsk,
     93     keys,
     94     cache=cache,
     95     get_id=_thread_get_id,
     96     pack_exception=pack_exception,
     97     **kwargs,
     98 )
    100 # Cleanup pools associated to dead threads
    101 with pools_lock:

File /srv/conda/envs/notebook/lib/python3.9/site-packages/dask/local.py:511, in get_async(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)
    509         _execute_task(task, data)  # Re-execute locally
    510     else:
--> 511         raise_exception(exc, tb)
    512 res, worker_id = loads(res_info)
    513 state["cache"][key] = res

File /srv/conda/envs/notebook/lib/python3.9/site-packages/dask/local.py:319, in reraise(exc, tb)
    317 if exc.__traceback__ is not tb:
    318     raise exc.with_traceback(tb)
--> 319 raise exc

File /srv/conda/envs/notebook/lib/python3.9/site-packages/dask/local.py:224, in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
    222 try:
    223     task, data = loads(task_info)
--> 224     result = _execute_task(task, data)
    225     id = get_id()
    226     result = dumps((result, id))

File /srv/conda/envs/notebook/lib/python3.9/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk)
    115     func, args = arg[0], arg[1:]
    116     # Note: Don't assign the subtask results to a variable. numpy detects
    117     # temporaries by their reference count and can execute certain
    118     # operations in-place.
--> 119     return func(*(_execute_task(a, cache) for a in args))
    120 elif not ishashable(arg):
    121     return arg

File /srv/conda/envs/notebook/lib/python3.9/site-packages/dask/core.py:119, in <genexpr>(.0)
    115     func, args = arg[0], arg[1:]
    116     # Note: Don't assign the subtask results to a variable. numpy detects
    117     # temporaries by their reference count and can execute certain
    118     # operations in-place.
--> 119     return func(*(_execute_task(a, cache) for a in args))
    120 elif not ishashable(arg):
    121     return arg

File /srv/conda/envs/notebook/lib/python3.9/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk)
    115     func, args = arg[0], arg[1:]
    116     # Note: Don't assign the subtask results to a variable. numpy detects
    117     # temporaries by their reference count and can execute certain
    118     # operations in-place.
--> 119     return func(*(_execute_task(a, cache) for a in args))
    120 elif not ishashable(arg):
    121     return arg

File /srv/conda/envs/notebook/lib/python3.9/site-packages/dask/optimization.py:990, in SubgraphCallable.__call__(self, *args)
    988 if not len(args) == len(self.inkeys):
    989     raise ValueError("Expected %d args, got %d" % (len(self.inkeys), len(args)))
--> 990 return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))

File /srv/conda/envs/notebook/lib/python3.9/site-packages/dask/core.py:149, in get(dsk, out, cache)
    147 for key in toposort(dsk):
    148     task = dsk[key]
--> 149     result = _execute_task(task, cache)
    150     cache[key] = result
    151 result = _execute_task(out, cache)

File /srv/conda/envs/notebook/lib/python3.9/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk)
    115     func, args = arg[0], arg[1:]
    116     # Note: Don't assign the subtask results to a variable. numpy detects
    117     # temporaries by their reference count and can execute certain
    118     # operations in-place.
--> 119     return func(*(_execute_task(a, cache) for a in args))
    120 elif not ishashable(arg):
    121     return arg

File /srv/conda/envs/notebook/lib/python3.9/site-packages/stackstac/to_dask.py:185, in fetch_raster_window(reader_table, slices, dtype, fill_value)
    178 # Only read if the window we're fetching actually overlaps with the asset
    179 if windows.intersect(current_window, asset_window):
    180     # NOTE: when there are multiple assets, we _could_ parallelize these reads with our own threadpool.
    181     # However, that would probably increase memory usage, since the internal, thread-local GDAL datasets
    182     # would end up copied to even more threads.
    183 
    184     # TODO when the Reader won't be rescaling, support passing `output` to avoid the copy?
--> 185     data = reader.read(current_window)
    187     if all_empty:
    188         # Turn `output` from a broadcast-trick array to a real array, so it's writeable
    189         if (
    190             np.isnan(data)
    191             if np.isnan(fill_value)
    192             else np.equal(data, fill_value)
    193         ).all():
    194             # Unless the data we just read is all empty anyway

File /srv/conda/envs/notebook/lib/python3.9/site-packages/stackstac/rio_reader.py:385, in AutoParallelRioReader.read(self, window, **kwargs)
    384 def read(self, window: Window, **kwargs) -> np.ndarray:
--> 385     reader = self.dataset
    386     try:
    387         result = reader.read(
    388             window=window,
    389             masked=True,
   (...)
    392             **kwargs,
    393         )

File /srv/conda/envs/notebook/lib/python3.9/site-packages/stackstac/rio_reader.py:381, in AutoParallelRioReader.dataset(self)
    379 with self._dataset_lock:
    380     if self._dataset is None:
--> 381         self._dataset = self._open()
    382     return self._dataset

File /srv/conda/envs/notebook/lib/python3.9/site-packages/stackstac/rio_reader.py:340, in AutoParallelRioReader._open(self)
    338 if ds.count != 1:
    339     ds.close()
--> 340     raise RuntimeError(
    341         f"Assets must have exactly 1 band, but file {self.url!r} has {ds.count}. "
    342         "We can't currently handle multi-band rasters (each band has to be "
    343         "a separate STAC asset), so you'll need to exclude this asset from your analysis."
    344     )
    346 # Only make a VRT if the dataset doesn't match the spatial spec we want
    347 if self.spec.vrt_params != {
    348     "crs": ds.crs.to_epsg(),
    349     "transform": ds.transform,
    350     "height": ds.height,
    351     "width": ds.width,
    352 }:

RuntimeError: Assets must have exactly 1 band, but file 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/13/S/DV/2020/4/S2B_13SDV_20200401_0_L2A/L2A_PVI.tif' has 3. We can't currently handle multi-band rasters (each band has to be a separate STAC asset), so you'll need to exclude this asset from your analysis.

The relevant code is quite old:

https://github.com/gjoseph92/stackstac/blob/d53f3dc94e49b95b950201f9e53539a92b1458b6/stackstac/rio_reader.py#L338-L344

so this doesn't seem like a recent change. It is very common for STAC assets (e.g. COGs) to have multiple bands, so I really don't understand this limitation. All the other examples I tried fail in the same way.

What am I missing here?

I ran this on https://us-central1-b.gcp.pangeo.io/ with the following packages:

stackstac: 0.4.3
rasterio: 1.3.2

opened by rabernat 6

Using DigitalEarthAfrica data

Hi @gjoseph92

(solved some of my issues myself, thats why I edit again)

I would like to use Sentinel 1 RTC from Digital Earth Africa on AWS.

I try the following:

import stackstac
import pystac_client
import rasterio

URL = "https://explorer.digitalearth.africa/stac/"
catalog = pystac_client.Client.open(URL)

bbox = (-3, 15.25, -2.9, 15.35)

items = catalog.search(
    bbox = bbox,
    collections=["s1_rtc"],
    datetime="2021-11-01/2021-12-01"
).get_all_items()

with rasterio.Env(aws_unsigned = True,AWS_S3_ENDPOINT= 's3.af-south-1.amazonaws.com'):
     stack = stackstac.stack(items)
     stack.compute()

When I do this I get the error

[NotImplementedError: Cannot automatically compute the resolution, since asset 'vh' on item 0 '620fe0bb-2e29-5ac3-86c1-7bcc9698da29' has a non-rectilinear geotrans (its data is is not axis-aligned, or "north-up"): [-4.0, 0.0002, 0.0, 16.0, 0.0, -0.0002, 0.0, 0.0, 1.0]. We should be able to handle this but just don't want to deal with it right now.

Please specify the `resolution=` argument.]()

However when opening such Sentinel 1 tif manually:

test = xr.open_rasterio("https://deafrica-sentinel-1.s3.af-south-1.amazonaws.com/s1_rtc/N14W009/2018/01/06/022245/s1_rtc_022245_N14W009_2018_01_06_VH.tif")

I get test.transform == (0.0002, 0.0, -9.0, 0.0, -0.0002, 15.0), which to me seems like a legit Geotransform..

Do you have any idea?

Thanks(:

opened by vitusbenson 6

stackstac.show gives empty map

I'm running into a problem with stackstac.show. I've got a simple notebook that grabs some data from Planetary Computer, mosaics it, and then shows it on a map using stackstac.show. This works fine when I run it on Microsoft Planetary Computer, but when I run it locally, I don't get any data (or any checkerboard pattern) on the map - I just get the background OpenStreetMap data.

The key parts of the code are below, and the full ipynb file is available here.

search = stac_client.search(
        collections=["cop-dem-glo-30"],
        bbox=bounds)

items = list(search.get_items())

data = stackstac.stack([item.to_dict() for item in items], bounds_latlon=bounds)

mosaic = data.max(dim='time').squeeze()

mosaic = mosaic.persist()

stackstac.show(mosaic, checkerboard=True)

Am I doing something wrong here when running locally, or have I discovered a bug? When running the show call I can see various Dask jobs being run when looking at the Dask dashboard - which suggests that something is happening - but the result never seems to be displayed.

In case it is relevant, I'm running on Windows 10, with the following in my conda enviroment:

# packages in environment at C:\Users\rwilson3\Documents\mambaforge\envs\anglo:
#
# Name                    Version                   Build  Channel
abseil-cpp                20210324.2           h0e60522_0    conda-forge
affine                    2.3.0                      py_0    conda-forge
aiohttp                   3.8.1                    pypi_0    pypi
aiosignal                 1.2.0                    pypi_0    pypi
alabaster                 0.7.12                     py_0    conda-forge
alembic                   1.7.3                    pypi_0    pypi
altair                    4.1.0                    pypi_0    pypi
anyio                     3.3.2                    pypi_0    pypi
appdirs                   1.4.4              pyh9f0ad1d_0    conda-forge
argon2-cffi               20.1.0           py39hb82d6ee_2    conda-forge
arrow-cpp                 5.0.0           py39he0f88eb_8_cpu    conda-forge
asciitree                 0.3.3                      py_2    conda-forge
asgiref                   3.4.1                    pypi_0    pypi
astor                     0.8.1                    pypi_0    pypi
async-timeout             4.0.1                    pypi_0    pypi
async_generator           1.10                       py_0    conda-forge
asyncio                   3.4.3                    pypi_0    pypi
asyncpg                   0.22.0                   pypi_0    pypi
atomicwrites              1.4.0                    pypi_0    pypi
attrs                     21.2.0             pyhd8ed1ab_0    conda-forge
aws-c-cal                 0.5.11               he19cf47_0    conda-forge
aws-c-common              0.6.2                h8ffe710_0    conda-forge
aws-c-event-stream        0.2.7               h70e1b0c_13    conda-forge
aws-c-io                  0.10.5               h2fe331c_0    conda-forge
aws-checksums             0.1.11               h1e232aa_7    conda-forge
aws-sdk-cpp               1.8.186              hb0612c5_3    conda-forge
azure-core                1.20.1                   pypi_0    pypi
azure-storage-blob        12.9.0                   pypi_0    pypi
babel                     2.9.1              pyh44b312d_0    conda-forge
backcall                  0.2.0              pyh9f0ad1d_0    conda-forge
backports                 1.0                        py_2    conda-forge
backports-entry-points-selectable 1.1.0                    pypi_0    pypi
backports.functools_lru_cache 1.6.4              pyhd8ed1ab_0    conda-forge
base58                    2.1.0                    pypi_0    pypi
basemap                   1.2.2            py39h381b4b0_3    conda-forge
beautifulsoup4            4.10.0                   pypi_0    pypi
black                     21.9b0                   pypi_0    pypi
blas                      2.111                       mkl    conda-forge
blas-devel                3.9.0              11_win64_mkl    conda-forge
bleach                    4.1.0              pyhd8ed1ab_0    conda-forge
blinker                   1.4                      pypi_0    pypi
blosc                     1.21.0               h0e60522_0    conda-forge
bokeh                     2.4.0            py39hcbf5309_0    conda-forge
boost-cpp                 1.74.0               h5b4e17d_4    conda-forge
boto3                     1.18.53                  pypi_0    pypi
botocore                  1.21.53                  pypi_0    pypi
braceexpand               0.1.7                    pypi_0    pypi
branca                    0.4.2              pyhd8ed1ab_0    conda-forge
brotli                    1.0.9                    pypi_0    pypi
brotli-asgi               1.1.0                    pypi_0    pypi
brotli-bin                1.0.9                h8ffe710_5    conda-forge
brotlipy                  0.7.0           py39hb82d6ee_1001    conda-forge
bs4                       0.0.1                    pypi_0    pypi
buildpg                   0.3                      pypi_0    pypi
bzip2                     1.0.8                h8ffe710_4    conda-forge
c-ares                    1.17.2               h8ffe710_0    conda-forge
ca-certificates           2021.10.8            h5b45459_0    conda-forge
cachetools                4.2.4              pyhd8ed1ab_0    conda-forge
cachey                    0.2.1              pyh9f0ad1d_0    conda-forge
cairo                     1.16.0            hb19e0ff_1008    conda-forge
cartopy                   0.20.0           py39h381b4b0_0    conda-forge
certifi                   2021.10.8        py39hcbf5309_1    conda-forge
cffi                      1.14.6           py39h0878f49_1    conda-forge
cfgv                      3.3.1                    pypi_0    pypi
cfitsio                   3.470                h0af3d06_7    conda-forge
cftime                    1.5.1            py39h5d4886f_0    conda-forge
chardet                   4.0.0            py39hcbf5309_1    conda-forge
charls                    2.2.0                h39d44d4_0    conda-forge
charset-normalizer        2.0.6                    pypi_0    pypi
click                     7.1.2              pyh9f0ad1d_0    conda-forge
click-plugins             1.1.1                      py_0    conda-forge
cligj                     0.6.0              pyh9f0ad1d_0    conda-forge
cloudpickle               2.0.0              pyhd8ed1ab_0    conda-forge
cogeo-mosaic              3.0.2                    pypi_0    pypi
colorama                  0.4.4              pyh9f0ad1d_0    conda-forge
colorcet                  2.0.6              pyhd8ed1ab_0    conda-forge
colorlog                  6.6.0                    pypi_0    pypi
coverage                  6.0.2                    pypi_0    pypi
cramjam                   2.4.0                    pypi_0    pypi
cryptography              3.4.7            py39hd8d06c1_0    conda-forge
cudatoolkit               10.2.89              hb195166_9    conda-forge
curl                      7.79.1               h789b8ee_1    conda-forge
cycler                    0.10.0                     py_2    conda-forge
cython                    0.29.24                  pypi_0    pypi
cytoolz                   0.11.0           py39hb82d6ee_3    conda-forge
dask                      2021.9.1           pyhd8ed1ab_0    conda-forge
dask-core                 2021.9.1           pyhd8ed1ab_0    conda-forge
dask-image                0.6.0                    pypi_0    pypi
datacube                  1.8.6              pyhd8ed1ab_0    conda-forge
datashader                0.13.0             pyh6c4a22f_0    conda-forge
datashape                 0.5.4                      py_1    conda-forge
debugpy                   1.4.1            py39h415ef7b_0    conda-forge
decorator                 5.1.0              pyhd8ed1ab_0    conda-forge
defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge
distlib                   0.3.3                    pypi_0    pypi
distributed               2021.9.1         py39hcbf5309_0    conda-forge
docstring-parser          0.7.3                    pypi_0    pypi
docstring_parser          0.12               pyhd8ed1ab_0    conda-forge
docutils                  0.17.1           py39hcbf5309_0    conda-forge
easyprocess               0.3                      pypi_0    pypi
entrypoint2               0.2.4                    pypi_0    pypi
entrypoints               0.3             py39hde42818_1002    conda-forge
expat                     2.4.1                h39d44d4_0    conda-forge
falcon                    2.0.0                    pypi_0    pypi
fastapi                   0.67.0                   pypi_0    pypi
fastapi-utils             0.2.1                    pypi_0    pypi
fasteners                 0.16               pyhd8ed1ab_0    conda-forge
filelock                  3.3.0                    pypi_0    pypi
fiona                     1.8.20           py39hea8b339_1    conda-forge
folium                    0.12.1                   pypi_0    pypi
font-ttf-dejavu-sans-mono 2.37                 hab24e00_0    conda-forge
font-ttf-inconsolata      3.000                h77eed37_0    conda-forge
font-ttf-source-code-pro  2.038                h77eed37_0    conda-forge
font-ttf-ubuntu           0.83                 hab24e00_0    conda-forge
fontconfig                2.13.1            h1989441_1005    conda-forge
fonts-conda-ecosystem     1                             0    conda-forge
fonts-conda-forge         1                             0    conda-forge
freetype                  2.10.4               h546665d_1    conda-forge
freetype-py               2.2.0              pyh9f0ad1d_0    conda-forge
freexl                    1.0.6                ha8e266a_0    conda-forge
frozenlist                1.2.0                    pypi_0    pypi
fsspec                    2021.10.0          pyhd8ed1ab_0    conda-forge
gdal                      3.3.2            py39h7c9a9b1_2    conda-forge
geoalchemy2               0.7.0                    pypi_0    pypi
geojson-pydantic          0.3.1                    pypi_0    pypi
geopandas                 0.10.0             pyhd8ed1ab_0    conda-forge
geopandas-base            0.10.0             pyha770c72_0    conda-forge
geos                      3.9.1                h39d44d4_2    conda-forge
geotiff                   1.7.0                ha8a8a2d_0    conda-forge
gettext                   0.19.8.1          ha2e2712_1008    conda-forge
gflags                    2.2.2             ha925a31_1004    conda-forge
ghp-import                2.0.2                    pypi_0    pypi
giflib                    5.2.1                h8d14728_2    conda-forge
gitdb                     4.0.7                    pypi_0    pypi
gitpython                 3.1.24                   pypi_0    pypi
glog                      0.5.0                h4797de2_0    conda-forge
greenlet                  1.1.2            py39h415ef7b_0    conda-forge
grpc-cpp                  1.40.0               h2431d41_2    conda-forge
h11                       0.12.0                   pypi_0    pypi
hdf4                      4.2.15               h0e5069d_3    conda-forge
hdf5                      1.12.1          nompi_h2a0e4a3_100    conda-forge
heapdict                  1.0.1                      py_0    conda-forge
holoviews                 1.14.6             pyhd8ed1ab_0    conda-forge
hsluv                     5.0.2              pyh44b312d_0    conda-forge
httpcore                  0.13.7                   pypi_0    pypi
httpx                     0.20.0                   pypi_0    pypi
hug                       2.6.1                    pypi_0    pypi
hvplot                    0.7.3              pyh6c4a22f_0    conda-forge
icu                       68.1                 h0e60522_0    conda-forge
identify                  2.3.0                    pypi_0    pypi
idna                      3.2                      pypi_0    pypi
imagecodecs               2021.6.8         py39h166567b_0    conda-forge
imageio                   2.9.0                      py_0    conda-forge
imagesize                 1.2.0                      py_0    conda-forge
importlib-metadata        4.8.1            py39hcbf5309_0    conda-forge
importlib_metadata        4.8.1                hd8ed1ab_0    conda-forge
iniconfig                 1.1.1                    pypi_0    pypi
intel-openmp              2021.3.0          h57928b3_3372    conda-forge
ipykernel                 6.4.1            py39h832f523_0    conda-forge
ipyleaflet                0.13.6                   pypi_0    pypi
ipyspin                   0.1.5                    pypi_0    pypi
ipython                   7.28.0           py39h832f523_0    conda-forge
ipython_genutils          0.2.0                      py_1    conda-forge
ipyurl                    0.1.2                    pypi_0    pypi
ipywidgets                7.6.5              pyhd8ed1ab_0    conda-forge
iso-639                   0.4.5                    pypi_0    pypi
iso3166                   2.0.2                    pypi_0    pypi
isodate                   0.6.0                    pypi_0    pypi
jbig                      2.1               h8d14728_2003    conda-forge
jedi                      0.18.0           py39hcbf5309_2    conda-forge
jinja2                    2.11.3                   pypi_0    pypi
jmespath                  0.10.0             pyh9f0ad1d_0    conda-forge
joblib                    1.0.1              pyhd8ed1ab_0    conda-forge
jpeg                      9d                   h8ffe710_0    conda-forge
json5                     0.9.6                    pypi_0    pypi
jsonschema                4.0.1              pyhd8ed1ab_0    conda-forge
jupyter                   1.0.0            py39hcbf5309_6    conda-forge
jupyter-server            1.11.1                   pypi_0    pypi
jupyter_client            6.1.12             pyhd8ed1ab_0    conda-forge
jupyter_console           6.4.0              pyhd8ed1ab_1    conda-forge
jupyter_core              4.8.1            py39hcbf5309_0    conda-forge
jupyterlab                3.1.17                   pypi_0    pypi
jupyterlab-server         2.8.2                    pypi_0    pypi
jupyterlab_pygments       0.1.2              pyh9f0ad1d_0    conda-forge
jupyterlab_widgets        1.0.2              pyhd8ed1ab_0    conda-forge
jxrlib                    1.1                  h8ffe710_2    conda-forge
kealib                    1.4.14               h8995ca9_3    conda-forge
kiwisolver                1.3.2            py39h2e07f2f_0    conda-forge
krb5                      1.19.2               hbae68bd_2    conda-forge
lark-parser               0.12.0             pyhd8ed1ab_0    conda-forge
lcms2                     2.12                 h2a16943_0    conda-forge
lerc                      2.2.1                h0e60522_0    conda-forge
libaec                    1.0.6                h39d44d4_0    conda-forge
libblas                   3.9.0              11_win64_mkl    conda-forge
libbrotlicommon           1.0.9                h8ffe710_5    conda-forge
libbrotlidec              1.0.9                h8ffe710_5    conda-forge
libbrotlienc              1.0.9                h8ffe710_5    conda-forge
libcblas                  3.9.0              11_win64_mkl    conda-forge
libclang                  11.1.0          default_h5c34c98_1    conda-forge
libcurl                   7.79.1               h789b8ee_1    conda-forge
libdeflate                1.7                  h8ffe710_5    conda-forge
libffi                    3.4.2                h0e60522_4    conda-forge
libgdal                   3.3.2                hfb14b67_2    conda-forge
libglib                   2.68.4               h3be07f2_1    conda-forge
libiconv                  1.16                 he774522_0    conda-forge
libkml                    1.3.0             h9859afa_1014    conda-forge
liblapack                 3.9.0              11_win64_mkl    conda-forge
liblapacke                3.9.0              11_win64_mkl    conda-forge
libnetcdf                 4.8.1           nompi_h1cc8e9d_101    conda-forge
libpng                    1.6.37               h1d00b33_2    conda-forge
libpq                     13.3                 hfcc5ef8_0    conda-forge
libprotobuf               3.18.1               h7755175_0    conda-forge
librttopo                 1.1.0                hb340de5_6    conda-forge
libsodium                 1.0.18               h8d14728_1    conda-forge
libspatialindex           1.9.3                h39d44d4_4    conda-forge
libspatialite             5.0.1                h762a7f4_6    conda-forge
libssh2                   1.10.0               h680486a_2    conda-forge
libthrift                 0.15.0               h636ae23_1    conda-forge
libtiff                   4.3.0                h0c97f57_1    conda-forge
libutf8proc               2.6.1                hcb41399_0    conda-forge
libuv                     1.42.0               h8ffe710_0    conda-forge
libwebp-base              1.2.1                h8ffe710_0    conda-forge
libxml2                   2.9.12               hf5bbc77_0    conda-forge
libzip                    1.8.0                hfed4ece_1    conda-forge
libzlib                   1.2.11            h8ffe710_1013    conda-forge
libzopfli                 1.0.3                h0e60522_0    conda-forge
llvmlite                  0.37.0           py39ha0cd8c8_0    conda-forge
locket                    0.2.0                      py_2    conda-forge
loguru                    0.5.3                    pypi_0    pypi
lxml                      4.6.4                    pypi_0    pypi
lz4-c                     1.9.3                h8ffe710_1    conda-forge
m2w64-gcc-libgfortran     5.3.0                         6    conda-forge
m2w64-gcc-libs            5.3.0                         7    conda-forge
m2w64-gcc-libs-core       5.3.0                         7    conda-forge
m2w64-gmp                 6.1.0                         2    conda-forge
m2w64-libwinpthread-git   5.0.0.4634.697f757               2    conda-forge
magicgui                  0.3.2              pyhd8ed1ab_0    conda-forge
mako                      1.1.5                    pypi_0    pypi
mapclassify               2.4.3              pyhd8ed1ab_0    conda-forge
markdown                  3.3.4              pyhd8ed1ab_0    conda-forge
markupsafe                2.0.1            py39hb82d6ee_0    conda-forge
matplotlib-base           3.4.3            py39h581301d_1    conda-forge
matplotlib-inline         0.1.3              pyhd8ed1ab_0    conda-forge
mercantile                1.2.1                    pypi_0    pypi
mergedeep                 1.3.4                    pypi_0    pypi
mistune                   0.8.4           py39hb82d6ee_1004    conda-forge
mkdocs                    1.2.2                    pypi_0    pypi
mkdocs-material           7.3.2                    pypi_0    pypi
mkdocs-material-extensions 1.0.3                    pypi_0    pypi
mkl                       2021.3.0           hb70f87d_564    conda-forge
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mkl-include               2021.3.0           hb70f87d_564    conda-forge
monotonic                 1.5                        py_0    conda-forge
morecantile               2.1.4                    pypi_0    pypi
msgpack-python            1.0.2            py39h2e07f2f_1    conda-forge
msrest                    0.6.21                   pypi_0    pypi
mss                       6.1.0                    pypi_0    pypi
msys2-conda-epoch         20160418                      1    conda-forge
multidict                 5.2.0                    pypi_0    pypi
multipledispatch          0.6.0                      py_0    conda-forge
munch                     2.5.0                      py_0    conda-forge
mutagen                   1.45.1                   pypi_0    pypi
mypy-extensions           0.4.3                    pypi_0    pypi
napari                    0.4.12             pyhd8ed1ab_0    conda-forge
napari-console            0.0.4              pyhd8ed1ab_0    conda-forge
napari-plugin-engine      0.2.0            py39hcbf5309_0    conda-forge
napari-svg                0.1.5              pyhd8ed1ab_0    conda-forge
nbclassic                 0.3.2                    pypi_0    pypi
nbclient                  0.5.4              pyhd8ed1ab_0    conda-forge
nbconvert                 6.2.0            py39hcbf5309_0    conda-forge
nbformat                  5.1.3              pyhd8ed1ab_0    conda-forge
nest-asyncio              1.5.1              pyhd8ed1ab_0    conda-forge
netcdf4                   1.5.7           nompi_py39hf113b1f_103    conda-forge
networkx                  2.5                        py_0    conda-forge
nodeenv                   1.6.0                    pypi_0    pypi
notebook                  6.4.4              pyha770c72_0    conda-forge
numba                     0.54.0           py39hb8cd55e_0    conda-forge
numcodecs                 0.9.1            py39h415ef7b_1    conda-forge
numexpr                   2.7.3                    pypi_0    pypi
numpy                     1.20.0           py39h6635163_0    conda-forge
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odc-algo                  0.2.0a4                  pypi_0    pypi
odc-io                    0.2.0a1                  pypi_0    pypi
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py                        1.10.0                   pypi_0    pypi
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pycryptodome              3.11.0                   pypi_0    pypi
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pyct                      0.4.6                      py_0    conda-forge
pyct-core                 0.4.6                      py_0    conda-forge
pydantic                  1.8.2            py39hb82d6ee_0    conda-forge
pydeck                    0.7.0                    pypi_0    pypi
pyee                      8.2.2                    pypi_0    pypi
pygeos                    0.10.2                   pypi_0    pypi
pygments                  2.10.0             pyhd8ed1ab_0    conda-forge
pymdown-extensions        9.0                      pypi_0    pypi
pyopengl                  3.1.5                      py_0    conda-forge
pyopenssl                 21.0.0             pyhd8ed1ab_0    conda-forge
pyparsing                 2.4.7              pyh9f0ad1d_0    conda-forge
pypgstac                  0.3.4                    pypi_0    pypi
pyppeteer                 0.2.6                    pypi_0    pypi
pyproj                    3.2.1            py39ha996c60_2    conda-forge
pyqt                      5.12.3           py39hcbf5309_7    conda-forge
pyqt-impl                 5.12.3           py39h415ef7b_7    conda-forge
pyqt5-sip                 4.19.18          py39h415ef7b_7    conda-forge
pyqtchart                 5.12             py39h415ef7b_7    conda-forge
pyqtwebengine             5.12.1           py39h415ef7b_7    conda-forge
pyrsistent                0.17.3           py39hb82d6ee_2    conda-forge
pyscreenshot              3.0                      pypi_0    pypi
pyshp                     2.1.3              pyh44b312d_0    conda-forge
pysocks                   1.7.1            py39hcbf5309_3    conda-forge
pystac                    1.2.0                    pypi_0    pypi
pystac-client             0.3.0                    pypi_0    pypi
pytest                    6.2.5                    pypi_0    pypi
pytest-asyncio            0.16.0                   pypi_0    pypi
pytest-cov                3.0.0                    pypi_0    pypi
python                    3.9.7           h7840368_3_cpython    conda-forge
python-dateutil           2.8.2              pyhd8ed1ab_0    conda-forge
python-dotenv             0.19.0                   pypi_0    pypi
python-snappy             0.6.0            py39h1d87f24_0    conda-forge
python_abi                3.9                      2_cp39    conda-forge
pytorch                   1.10.0          py3.9_cuda10.2_cudnn7_0    pytorch
pytorch-mutex             1.0                        cuda    pytorch
pytz                      2021.1             pyhd8ed1ab_0    conda-forge
pytz-deprecation-shim     0.1.0.post0              pypi_0    pypi
pyviz_comms               2.1.0              pyhd8ed1ab_0    conda-forge
pywavelets                1.1.1            py39h5d4886f_3    conda-forge
pywin32                   301              py39hb82d6ee_0    conda-forge
pywinpty                  1.1.4            py39h99910a6_0    conda-forge
pyyaml                    5.4.1            py39hb82d6ee_1    conda-forge
pyyaml-env-tag            0.1                      pypi_0    pypi
pyzmq                     22.3.0           py39he46f08e_0    conda-forge
qt                        5.12.9               h5909a2a_4    conda-forge
qtconsole                 5.1.1              pyhd8ed1ab_0    conda-forge
qtpy                      1.11.2             pyhd8ed1ab_0    conda-forge
rasterio                  1.2.8            py39h85efae1_0    conda-forge
re2                       2021.09.01           h0e60522_0    conda-forge
regex                     2021.9.30                pypi_0    pypi
requests                  2.26.0             pyhd8ed1ab_0    conda-forge
requests-oauthlib         1.3.0                    pypi_0    pypi
requests-unixsocket       0.2.0                    pypi_0    pypi
retrying                  1.3.3                      py_2    conda-forge
rfc3986                   1.5.0                    pypi_0    pypi
rio-cogeo                 2.3.1                    pypi_0    pypi
rio-color                 1.0.4                    pypi_0    pypi
rio-mucho                 1.0.0                    pypi_0    pypi
rio-stac                  0.3.1                    pypi_0    pypi
rio-tiler                 2.1.3                    pypi_0    pypi
rio-tiler-pds             0.5.2                    pypi_0    pypi
rio-toa                   0.3.0                    pypi_0    pypi
rio-viz                   0.7.2                    pypi_0    pypi
rioxarray                 0.7.1              pyhd8ed1ab_0    conda-forge
rtree                     0.9.7            py39h09fdee3_2    conda-forge
s3transfer                0.5.0              pyhd8ed1ab_0    conda-forge
scikit-image              0.18.3                   pypi_0    pypi
scikit-learn              1.0              py39h74df8f2_1    conda-forge
scipy                     1.7.1            py39hc0c34ad_0    conda-forge
send2trash                1.8.0              pyhd8ed1ab_0    conda-forge
setuptools                58.0.4           py39hcbf5309_2    conda-forge
shapely                   1.7.1            py39haadaec5_5    conda-forge
simplejpeg                1.6.2                    pypi_0    pypi
simplejson                3.17.5                   pypi_0    pypi
six                       1.16.0             pyh6c4a22f_0    conda-forge
slicerator                1.0.0                    pypi_0    pypi
smart-open                4.2.0                    pypi_0    pypi
smmap                     4.0.0                    pypi_0    pypi
snappy                    1.1.8                ha925a31_3    conda-forge
sniffio                   1.2.0                    pypi_0    pypi
snowballstemmer           2.1.0              pyhd8ed1ab_0    conda-forge
snuggs                    1.4.7                      py_0    conda-forge
sortedcontainers          2.4.0              pyhd8ed1ab_0    conda-forge
soupsieve                 2.2.1                    pypi_0    pypi
spatialpandas             0.4.3              pyhd8ed1ab_0    conda-forge
sphinx                    4.2.0              pyh6c4a22f_0    conda-forge
sphinxcontrib-applehelp   1.0.2                      py_0    conda-forge
sphinxcontrib-devhelp     1.0.2                      py_0    conda-forge
sphinxcontrib-htmlhelp    2.0.0              pyhd8ed1ab_0    conda-forge
sphinxcontrib-jsmath      1.0.1                      py_0    conda-forge
sphinxcontrib-qthelp      1.0.3                      py_0    conda-forge
sphinxcontrib-serializinghtml 1.1.5              pyhd8ed1ab_0    conda-forge
sqlakeyset                1.0.1629029818           pypi_0    pypi
sqlalchemy                1.3.23                   pypi_0    pypi
sqlite                    3.36.0               h8ffe710_2    conda-forge
stac-fastapi-api          2.1.1                     dev_0    <develop>
stac-fastapi-extensions   2.1.1                     dev_0    <develop>
stac-fastapi-pgstac       2.1.1                     dev_0    <develop>
stac-fastapi-sqlalchemy   2.1.1                     dev_0    <develop>
stac-fastapi-types        2.1.1                     dev_0    <develop>
stac-nb                   0.4.0                    pypi_0    pypi
stac-pydantic             2.0.1                    pypi_0    pypi
stackstac                 0.2.1              pyhd8ed1ab_0    conda-forge
stacterm                  0.1.0                    pypi_0    pypi
starlette                 0.14.2                   pypi_0    pypi
starlette-cramjam         0.1.0                    pypi_0    pypi
streamlink                2.4.0                    pypi_0    pypi
streamlit                 1.0.0                    pypi_0    pypi
streamlit-folium          0.4.0                    pypi_0    pypi
supermercado              0.2.0                    pypi_0    pypi
superqt                   0.2.4              pyhd8ed1ab_0    conda-forge
tbb                       2021.3.0             h2d74725_0    conda-forge
tblib                     1.7.0              pyhd8ed1ab_0    conda-forge
terminado                 0.12.1           py39hcbf5309_0    conda-forge
termtables                0.2.4                    pypi_0    pypi
testpath                  0.5.0              pyhd8ed1ab_0    conda-forge
threadpoolctl             3.0.0              pyh8a188c0_0    conda-forge
tifffile                  2021.8.30                pypi_0    pypi
tiledb                    2.3.4                h78dabda_0    conda-forge
titiler-application       0.3.11                    dev_0    <develop>
titiler-core              0.3.11                    dev_0    <develop>
titiler-mosaic            0.3.11                    dev_0    <develop>
titiler-pgstac            0.1.0a1                   dev_0    <develop>
tk                        8.6.11               h8ffe710_1    conda-forge
toml                      0.10.2                   pypi_0    pypi
tomli                     1.2.1                    pypi_0    pypi
toolz                     0.11.1                     py_0    conda-forge
torchvision               0.11.1               py39_cu102    pytorch
tornado                   6.1              py39hb82d6ee_1    conda-forge
tqdm                      4.62.3             pyhd8ed1ab_0    conda-forge
traitlets                 5.1.0              pyhd8ed1ab_0    conda-forge
traittypes                0.2.1                    pypi_0    pypi
typer                     0.3.2                    pypi_0    pypi
typing-extensions         3.10.0.2             hd8ed1ab_0    conda-forge
typing_extensions         3.10.0.2           pyha770c72_0    conda-forge
tzdata                    2021.2.post0             pypi_0    pypi
tzlocal                   4.0                      pypi_0    pypi
ucrt                      10.0.20348.0         h57928b3_0    conda-forge
urllib3                   1.26.7             pyhd8ed1ab_0    conda-forge
uvicorn                   0.15.0                   pypi_0    pypi
validators                0.18.2                   pypi_0    pypi
vc                        14.2                 hb210afc_5    conda-forge
vidgear                   0.2.3                    pypi_0    pypi
virtualenv                20.8.1                   pypi_0    pypi
vispy                     0.9.2            py39h5d4886f_0    conda-forge
vs2015_runtime            14.29.30037          h902a5da_5    conda-forge
watchdog                  2.1.6                    pypi_0    pypi
wcwidth                   0.2.5              pyh9f0ad1d_2    conda-forge
webencodings              0.5.1                      py_1    conda-forge
websocket-client          1.2.1                    pypi_0    pypi
websockets                9.1                      pypi_0    pypi
wheel                     0.37.0             pyhd8ed1ab_1    conda-forge
widgetsnbextension        3.5.1            py39hcbf5309_4    conda-forge
win32-setctime            1.0.3                    pypi_0    pypi
win_inet_pton             1.1.0            py39hcbf5309_2    conda-forge
winpty                    0.4.3                         4    conda-forge
wrapt                     1.13.2           py39hb82d6ee_0    conda-forge
xarray                    0.19.0             pyhd8ed1ab_1    conda-forge
xarray-leaflet            0.1.15                   pypi_0    pypi
xarray-spatial            0.2.9              pyhd8ed1ab_0    conda-forge
xerces-c                  3.2.3                h0e60522_2    conda-forge
xyzservices               2021.9.1           pyhd8ed1ab_0    conda-forge
xz                        5.2.5                h62dcd97_1    conda-forge
yaml                      0.2.5                he774522_0    conda-forge
yarl                      1.7.2                    pypi_0    pypi
yt-dlp                    2021.11.10.1             pypi_0    pypi
zarr                      2.10.1             pyhd8ed1ab_0    conda-forge
zeromq                    4.3.4                h0e60522_1    conda-forge
zfp                       0.5.5                h0e60522_7    conda-forge
zict                      2.0.0                      py_0    conda-forge
zipp                      3.6.0              pyhd8ed1ab_0    conda-forge
zlib                      1.2.11            h8ffe710_1013    conda-forge
zstd                      1.5.0                h6255e5f_0    conda-forge

opened by robintw 6

Issues with numpy 1.24.0

Hi @gjoseph92 , I am getting an error when trying to stack data when the latest numpy version 1.24.0 is installed. All works fine with 1.23.5.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[48], line 1
----> 1 stack = stackstac.stack(itms.get_all_items(), resolution=10)

File ~/anaconda3/envs/ag-ml/lib/python3.8/site-packages/stackstac/stack.py:311, in stack(items, assets, epsg, resolution, bounds, bounds_latlon, snap_bounds, resampling, chunksize, dtype, fill_value, rescale, sortby_date, xy_coords, properties, band_coords, gdal_env, errors_as_nodata, reader)
    287 asset_table, spec, asset_ids, plain_items = prepare_items(
    288     plain_items,
    289     assets=assets,
   (...)
    294     snap_bounds=snap_bounds,
    295 )
    296 arr = items_to_dask(
    297     asset_table,
    298     spec,
   (...)
    306     errors_as_nodata=errors_as_nodata,
    307 )
    309 return xr.DataArray(
    310     arr,
--> 311     *to_coords(
    312         plain_items,
    313         asset_ids,
    314         spec,
    315         xy_coords=xy_coords,
    316         properties=properties,
    317         band_coords=band_coords,
    318     ),
    319     attrs=to_attrs(spec),
    320     name="stackstac-" + dask.base.tokenize(arr),
    321 )

File ~/anaconda3/envs/ag-ml/lib/python3.8/site-packages/stackstac/prepare.py:500, in to_coords(items, asset_ids, spec, xy_coords, properties, band_coords)
    496     except KeyError:
    497         pass
    499 coords.update(
--> 500     accumulate_metadata.metadata_to_coords(
    501         flattened_metadata_by_asset,
    502         "band",
    503         skip_fields={"href"},
    504         # skip_fields={"href", "title", "description", "type", "roles"},
    505     )
    506 )
    507 if any(eo_by_asset):
    508     coords.update(
    509         accumulate_metadata.metadata_to_coords(
    510             eo_by_asset,
   (...)
    513         )
    514     )

File ~/anaconda3/envs/ag-ml/lib/python3.8/site-packages/stackstac/accumulate_metadata.py:29, in metadata_to_coords(items, dim_name, fields, skip_fields)
     23 def metadata_to_coords(
     24     items: Iterable[Mapping[str, object]],
     25     dim_name: str,
     26     fields: Union[str, Sequence[str], Literal[True]] = True,
     27     skip_fields: Container[str] = (),
     28 ) -> Dict[str, xr.Variable]:
---> 29     return dict_to_coords(
     30         accumulate_metadata(
     31             items,
     32             fields=[fields] if isinstance(fields, str) else fields,
     33             skip_fields=skip_fields,
     34         ),
     35         dim_name,
     36     )

File ~/anaconda3/envs/ag-ml/lib/python3.8/site-packages/stackstac/accumulate_metadata.py:168, in dict_to_coords(metadata, dim_name)
    163     except TypeError:
    164         # if it's not set-able, just give up
    165         break
    167 props_arr = np.squeeze(
--> 168     np.array(
    169         props,
    170         # Avoid DeprecationWarning creating ragged arrays when elements are lists/tuples of different lengths
    171         dtype="object"
    172         if (
    173             isinstance(props, _ourlist)
    174             and len(set(len(x) for x in props if isinstance(x, (list, tuple))))
    175             > 1
    176         )
    177         else None,
    178     )
    179 )
    181 if (
    182     props_arr.ndim > 1
    183     or props_arr.ndim == 1
   (...)
    187     # our "bands", "y", and "x" dimensions, and xarray won't let us use unrelated
    188     # dimensions. so just skip it for now.
    189     continue

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (17,) + inhomogeneous part.

opened by julianblue 5

Enable opening datasets with gcps even if no valid crs is present

Some datasets like Sentinel-1 GRD files don't have a coordinate reference system (crs), but they may have ground control points (gcps) and can still be opened by rasterio.

Test this branch using pip install git+https://github.com/weiji14/[email protected]_vrt_with_gcps

Fixes #181.

opened by weiji14 6

AttributeError: 'NoneType' object has no attribute 'to_epsg'

Hi there, just encountering some issues while trying to stack Sentinel-1 GRD data from Planetary Computer at https://github.com/weiji14/zen3geo/pull/62, and I'm wondering if it's an issue on the STAC metadata side (c.f. #152), or if it's something to fix in stackstac.

Here's a MCWE, using pystac=1.4.0, planetary-computer=0.4.7, stackstac=0.4.3:

import stackstac
import pystac
import planetary_computer
import xarray as xr

# %%
item_urls: list = [
    "https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20220320T230514_20220320T230548_042411_050E99",
    "https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20220308T230513_20220308T230548_042236_0508AF",
]

# Load the individual item metadata and sign the assets
items: list[pystac.Item] = [pystac.Item.from_file(item_url) for item_url in item_urls]
signed_items: list[pystac.Item] = [planetary_computer.sign(item) for item in items]

# Stack Sentinel-1 GRD files
dataarray: xr.DataArray = stackstac.stack(
    items=signed_items,
    epsg=32647,
    resolution=30,
    bounds_latlon=[99.933681, -0.009951, 100.065765, 0.147054],  # W, S, E, N
)
assert dataarray.crs == "epsg:32647"
print(dataarray)

The output xarray repr looks ok like so. Dimensions are (time:2, band:2, y:579, x:491).

<xarray.DataArray 'stackstac-950602eb423dd1d439106f6794699f05' (time: 2,
                                                                band: 2,
                                                                y: 579, x: 491)>
dask.array<fetch_raster_window, shape=(2, 2, 579, 491), dtype=float64, chunksize=(1, 1, 579, 491), chunktype=numpy.ndarray>
Coordinates: (12/37)
  * time                                   (time) datetime64[ns] 2022-03-08T2...
    id                                     (time) <U62 'S1A_IW_GRDH_1SDV_2022...
  * band                                   (band) <U2 'vh' 'vv'
  * x                                      (x) float64 6.039e+05 ... 6.186e+05
  * y                                      (y) float64 1.626e+04 ... -1.08e+03
    end_datetime                           (time) <U32 '2022-03-08 23:05:48.2...
    ...                                     ...
    s1:resolution                          <U4 'high'
    s1:product_timeliness                  <U8 'Fast-24h'
    sat:relative_orbit                     int64 164
    description                            (band) <U145 'Amplitude of signal ...
    title                                  (band) <U41 'VH: vertical transmit...
    epsg                                   int64 32647
Attributes:
    spec:        RasterSpec(epsg=32647, bounds=(603870, -1110, 618600, 16260)...
    crs:         epsg:32647
    transform:   | 30.00, 0.00, 603870.00|\n| 0.00,-30.00, 16260.00|\n| 0.00,...
    resolution:  30

but when I try to run dataarray.compute(), this AttributeError: 'NoneType' object has no attribute 'to_epsg' message popped up. Here's the full traceback:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [11], in <cell line: 1>()
----> 1 dataarray.compute()

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/xarray/core/dataarray.py:993, in DataArray.compute(self, **kwargs)
    974 """Manually trigger loading of this array's data from disk or a
    975 remote source into memory and return a new array. The original is
    976 left unaltered.
   (...)
    990 dask.compute
    991 """
    992 new = self.copy(deep=False)
--> 993 return new.load(**kwargs)

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/xarray/core/dataarray.py:967, in DataArray.load(self, **kwargs)
    949 def load(self: T_DataArray, **kwargs) -> T_DataArray:
    950     """Manually trigger loading of this array's data from disk or a
    951     remote source into memory and return this array.
    952 
   (...)
    965     dask.compute
    966     """
--> 967     ds = self._to_temp_dataset().load(**kwargs)
    968     new = self._from_temp_dataset(ds)
    969     self._variable = new._variable

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/xarray/core/dataset.py:733, in Dataset.load(self, **kwargs)
    730 import dask.array as da
    732 # evaluate all the dask arrays simultaneously
--> 733 evaluated_data = da.compute(*lazy_data.values(), **kwargs)
    735 for k, data in zip(lazy_data, evaluated_data):
    736     self.variables[k].data = data

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/dask/base.py:602, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
    599     keys.append(x.__dask_keys__())
    600     postcomputes.append(x.__dask_postcompute__())
--> 602 results = schedule(dsk, keys, **kwargs)
    603 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/dask/threaded.py:89, in get(dsk, keys, cache, num_workers, pool, **kwargs)
     86     elif isinstance(pool, multiprocessing.pool.Pool):
     87         pool = MultiprocessingPoolExecutor(pool)
---> 89 results = get_async(
     90     pool.submit,
     91     pool._max_workers,
     92     dsk,
     93     keys,
     94     cache=cache,
     95     get_id=_thread_get_id,
     96     pack_exception=pack_exception,
     97     **kwargs,
     98 )
    100 # Cleanup pools associated to dead threads
    101 with pools_lock:

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/dask/local.py:511, in get_async(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)
    509         _execute_task(task, data)  # Re-execute locally
    510     else:
--> 511         raise_exception(exc, tb)
    512 res, worker_id = loads(res_info)
    513 state["cache"][key] = res

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/dask/local.py:319, in reraise(exc, tb)
    317 if exc.__traceback__ is not tb:
    318     raise exc.with_traceback(tb)
--> 319 raise exc

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/dask/local.py:224, in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
    222 try:
    223     task, data = loads(task_info)
--> 224     result = _execute_task(task, data)
    225     id = get_id()
    226     result = dumps((result, id))

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk)
    115     func, args = arg[0], arg[1:]
    116     # Note: Don't assign the subtask results to a variable. numpy detects
    117     # temporaries by their reference count and can execute certain
    118     # operations in-place.
--> 119     return func(*(_execute_task(a, cache) for a in args))
    120 elif not ishashable(arg):
    121     return arg

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/dask/optimization.py:990, in SubgraphCallable.__call__(self, *args)
    988 if not len(args) == len(self.inkeys):
    989     raise ValueError("Expected %d args, got %d" % (len(self.inkeys), len(args)))
--> 990 return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/dask/core.py:149, in get(dsk, out, cache)
    147 for key in toposort(dsk):
    148     task = dsk[key]
--> 149     result = _execute_task(task, cache)
    150     cache[key] = result
    151 result = _execute_task(out, cache)

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk)
    115     func, args = arg[0], arg[1:]
    116     # Note: Don't assign the subtask results to a variable. numpy detects
    117     # temporaries by their reference count and can execute certain
    118     # operations in-place.
--> 119     return func(*(_execute_task(a, cache) for a in args))
    120 elif not ishashable(arg):
    121     return arg

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/stackstac/to_dask.py:185, in fetch_raster_window(reader_table, slices, dtype, fill_value)
    178 # Only read if the window we're fetching actually overlaps with the asset
    179 if windows.intersect(current_window, asset_window):
    180     # NOTE: when there are multiple assets, we _could_ parallelize these reads with our own threadpool.
    181     # However, that would probably increase memory usage, since the internal, thread-local GDAL datasets
    182     # would end up copied to even more threads.
    183 
    184     # TODO when the Reader won't be rescaling, support passing `output` to avoid the copy?
--> 185     data = reader.read(current_window)
    187     if all_empty:
    188         # Turn `output` from a broadcast-trick array to a real array, so it's writeable
    189         if (
    190             np.isnan(data)
    191             if np.isnan(fill_value)
    192             else np.equal(data, fill_value)
    193         ).all():
    194             # Unless the data we just read is all empty anyway

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/stackstac/rio_reader.py:385, in AutoParallelRioReader.read(self, window, **kwargs)
    384 def read(self, window: Window, **kwargs) -> np.ndarray:
--> 385     reader = self.dataset
    386     try:
    387         result = reader.read(
    388             window=window,
    389             masked=True,
   (...)
    392             **kwargs,
    393         )

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/stackstac/rio_reader.py:381, in AutoParallelRioReader.dataset(self)
    379 with self._dataset_lock:
    380     if self._dataset is None:
--> 381         self._dataset = self._open()
    382     return self._dataset

File ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/stackstac/rio_reader.py:348, in AutoParallelRioReader._open(self)
    340     raise RuntimeError(
    341         f"Assets must have exactly 1 band, but file {self.url!r} has {ds.count}. "
    342         "We can't currently handle multi-band rasters (each band has to be "
    343         "a separate STAC asset), so you'll need to exclude this asset from your analysis."
    344     )
    346 # Only make a VRT if the dataset doesn't match the spatial spec we want
    347 if self.spec.vrt_params != {
--> 348     "crs": ds.crs.to_epsg(),
    349     "transform": ds.transform,
    350     "height": ds.height,
    351     "width": ds.width,
    352 }:
    353     with self.gdal_env.open_vrt:
    354         vrt = WarpedVRT(
    355             ds,
    356             sharing=False,
    357             resampling=self.resampling,
    358             **self.spec.vrt_params,
    359         )

AttributeError: 'NoneType' object has no attribute 'to_epsg'

So I verified that the stacked Sentinel-1 dataarray does have a crs attribute like 'epsg:32647', but it seems like ds is something else? I did try to step through the code at https://github.com/gjoseph92/stackstac/blob/9106708bcc20daec4c5975e9c1240de38c38f2f1/stackstac/rio_reader.py#L347-L362

but am having trouble understaing what ds is representing, or how ds.crs can be set to a proper coordinate reference system value. My guess that the crs needs to be set in the STAC Item metadata? Or perhaps the if-statement needs to be revised.

Oh, and just to make sure the Sentinel-1 GRD STAC Items are readable, I did try reading it using rioxarray=0.11.1:

import rioxarray

url: str = signed_items[1].assets["vv"].href
dataarray = rioxarray.open_rasterio(filename=url, overview_level=5)
dataarray = dataarray.compute()
print(dataarray)
# <xarray.DataArray (band: 1, y: 361, x: 396)>
# array([[[  0, 224, 259, ...,  45,   0,   0],
#         [  0, 243, 286, ...,  44,   0,   0],
#         [  0, 274, 248, ...,  43,   0,   0],
#         ...,
#         [  0,   0,   0, ...,  34,  36,  36],
#         [  0,   0,   0, ...,  33,  35,  34],
#         [  0,   0,   0, ...,  17,  17,  17]]], dtype=uint16)
# Coordinates:
#   * band         (band) int64 1
#     spatial_ref  int64 0
# Dimensions without coordinates: y, x
# Attributes:
#     _FillValue:    0.0
#     scale_factor:  1.0
#     add_offset:    0.0

Here's the output from rioxarray.show_version() for completeness to show my GDAL and other geo-library versions:

rioxarray (0.11.1) deps:
  rasterio: 1.3.0
    xarray: 2022.3.0
      GDAL: 3.5.0
      GEOS: 3.10.2
      PROJ: 9.0.0
 PROJ DATA: ~/mambaforge/envs/zen3geo/lib/python3.10/site-packages/rasterio/proj_data
 GDAL DATA: None

Other python deps:
     scipy: 1.9.0
    pyproj: 3.3.1

System:
    python: 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:36:39) [GCC 10.4.0]
executable: ~/mambaforge/envs/zen3geo/bin/python
   machine: Linux-5.17.0-1016-oem-x86_64-with-glibc2.35

P.S. Thanks for this amazing library, really like the design and it's been a pleasure to use :smile:

opened by weiji14 7

Unrecognized STAC collection type

I was following the basic example from the docs https://stackstac.readthedocs.io/en/latest/basic.html but it falls over at stackstac.stack(items):

TypeError: Unrecognized STAC collection type <class 'pystac.item_collection.ItemCollection'>: <pystac.item_collection.ItemCollection object at 0x000001C57CB0BCD0>

opened by letmaik 4

Possibly switch back to Poetry?

I just switched to PDM, because Poetry was taking multiple hours to lock dependencies (due to Coiled being a dependency with way too many transitive deps, and https://github.com/python-poetry/poetry/issues/5121).

Testing with Poetry 1.2.0 though, the situation doesn't seem as bad.

Poetry is still 2x slower than PDM—170 seconds instead of 42. But 170s is way, way better than 3h. It's within the range of tolerability. And since Poetry is more widely used and mature than PDM, and a bit more straightforward to work with in virtualenvs, I'm tempted to stick with it for now, even with the longer lock times. It still doesn't have https://github.com/python-poetry/poetry/issues/697, which could end up being critical at some point, but so far hasn't been too much of an issue.

Probably won't take any action on this for now, just noting this in case other compelling reasons come up one way or another.

gabe dev/stackstac ‹fc326d0› » time pdm add -dG docs sphinx-paramlinks
Adding packages to docs dev-dependencies: sphinx-paramlinks
Virtualenv /Users/gabe/dev/stackstac/.venv is reused.
🔒 Lock successful
Changes are written to pdm.lock.
Changes are written to pyproject.toml.
All packages are synced to date, nothing to do.

🎉 All complete!

pdm add -dG docs sphinx-paramlinks  42.10s user 1.82s system 105% cpu 41.601 total
gabe dev/stackstac ‹fc326d0*› » 
gabe dev/stackstac ‹fc326d0*› » 
gabe dev/stackstac ‹c67944b› » time poetry add sphinx-paramlinks
Using version ^0.5.4 for sphinx-paramlinks

Updating dependencies
Resolving dependencies... Downloading https://files.pythonhosted.org/packages/2f/be/7d6e073a3eb740ebeba43a69f5de2b141fea50b801e24e0ae024ac94d4ac/matplotlib-3.5.2.tar.gz  28% (2
Resolving dependencies... Downloading https://files.pythonhosted.org/packages/2f/be/7d6e073a3eb740ebeba43a69f5de2b141fea50b801e24e0ae024ac94d4ac/matplotlib-3.5.2.tar.gz  58% (2
Resolving dependencies... Downloading https://files.pythonhosted.org/packages/2f/be/7d6e073a3eb740ebeba43a69f5de2b141fea50b801e24e0ae024ac94d4ac/matplotlib-3.5.2.tar.gz  88% (2
Resolving dependencies... Downloading https://files.pythonhosted.org/packages/03/c6/14a17e10813b8db20d1e800ff9a3a898e65d25f2b0e9d6a94616f1e3362c/numpy-1.23.0.tar.gz  28% (42.5s
Resolving dependencies... (76.1s)

Writing lock file

Package operations: 1 install, 49 updates, 0 removals

  • Updating attrs (22.1.0 -> 21.4.0)
  • Updating fastjsonschema (2.16.1 -> 2.15.3)
  • Updating jsonschema (4.15.0 -> 4.6.1)
  • Updating jupyter-core (4.11.1 -> 4.10.0)
  • Updating pyzmq (23.2.1 -> 23.2.0)
  • Updating tornado (6.1 -> 6.2)
  • Updating mistune (2.0.4 -> 0.8.4)
  • Updating nbclient (0.6.7 -> 0.6.6)
  • Updating pygments (2.13.0 -> 2.12.0)
  • Updating sniffio (1.3.0 -> 1.2.0)
  • Updating matplotlib-inline (0.1.6 -> 0.1.3)
  • Updating nbconvert (7.0.0 -> 6.5.0)
  • Updating prompt-toolkit (3.0.31 -> 3.0.30)
  • Updating websocket-client (1.4.1 -> 1.3.3)
  • Updating charset-normalizer (2.1.1 -> 2.1.0)
  • Updating debugpy (1.6.3 -> 1.6.0)
  • Updating frozenlist (1.3.1 -> 1.3.0)
  • Updating psutil (5.9.2 -> 5.9.1)
  • Updating pytz (2022.2.1 -> 2022.1)
  • Updating urllib3 (1.26.12 -> 1.26.9)
  • Updating zipp (3.8.1 -> 3.8.0)
  • Updating ipykernel (6.15.2 -> 6.15.0)
  • Updating json5 (0.9.10 -> 0.9.8)
  • Updating toolz (0.12.0 -> 0.11.2)
  • Updating yarl (1.8.1 -> 1.7.2)
  • Updating cloudpickle (2.2.0 -> 2.1.0)
  • Updating docutils (0.19 -> 0.18.1)
  • Updating fsspec (2022.8.2 -> 2022.5.0)
  • Updating jupyterlab-server (2.15.1 -> 2.15.0)
  • Updating nbclassic (0.4.3 -> 0.4.2)
  • Updating numpy (1.23.2 -> 1.23.0)
  • Updating partd (1.3.0 -> 1.2.0)
  • Updating dask (2022.9.0 -> 2022.6.1)
  • Updating jupyterlab (3.4.6 -> 3.4.3)
  • Updating pystac (1.6.1 -> 1.4.0)
  • Updating sphinx (5.1.1 -> 5.0.2)
  • Updating distributed (2022.9.0 -> 2022.6.1)
  • Updating exceptiongroup (1.0.0rc9 -> 1.0.0rc8)
  • Updating keyring (23.9.1 -> 23.6.0)
  • Updating pathspec (0.10.1 -> 0.9.0)
  • Updating py-spy (0.3.13 -> 0.3.12)
  • Updating readme-renderer (37.1 -> 35.0)
  • Installing tqdm (4.64.0)
  • Updating viztracer (0.15.4 -> 0.12.3)
  • Updating xarray (2022.6.0 -> 2022.3.0)
  • Updating black (22.8.0 -> 22.6.0)
  • Updating graphviz (0.20.1 -> 0.16)
  • Updating hypothesis (6.54.5 -> 6.49.1)
  • Updating rasterio (1.3.2 -> 1.3.0)
  • Updating twine (4.0.1 -> 3.8.0)
poetry add sphinx-paramlinks  170.55s user 25.80s system 160% cpu 2:02.03 total

opened by gjoseph92 4

Releases(v0.4.3)

v0.4.3(Sep 14, 2022)
Changelog: https://github.com/gjoseph92/stackstac/blob/main/CHANGELOG.md#043-2022-09-14

What's Changed

Support sequence of Items by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/164

Switch to PDM for dependency management by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/169

Add param links to docs by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/170

Automatically hide pdm.lock in GitHub diffs by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/171

Fix docs build for visualization functions by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/172

Fix docs build for PDM again by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/175

Remove deprecated skip_equivalent pyproj arg by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/174

Release v0.4.3 by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/176

Full Changelog: https://github.com/gjoseph92/stackstac/compare/v0.4.2...v0.4.3
Source code(tar.gz)
Source code(zip)
v0.4.2(Jul 7, 2022)
Changelog: https://github.com/gjoseph92/stackstac/blob/main/CHANGELOG.md#042-2022-07-06

What's Changed

Ignore axis if dim is given by @aazuspan in https://github.com/gjoseph92/stackstac/pull/149

Update sphinx by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/158

Use poetry lockfile in docs build by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/159

Remove calls to rio.parse_path (maintain compat with rasterio==1.3) by @carderne in https://github.com/gjoseph92/stackstac/pull/155

Remove rasterio windows.from_bounds workaround by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/160

Update notebooks for release by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/161

Release 0.4.2 by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/162

New Contributors

@aazuspan made their first contribution in https://github.com/gjoseph92/stackstac/pull/149

@carderne made their first contribution in https://github.com/gjoseph92/stackstac/pull/155

Full Changelog: https://github.com/gjoseph92/stackstac/compare/v0.4.1...v0.4.2
Source code(tar.gz)
Source code(zip)
v0.4.1(Apr 15, 2022)
Changelog: https://github.com/gjoseph92/stackstac/blob/main/CHANGELOG.md#041-2022-04-15

What's Changed

Pandas compat by @TomAugspurger in https://github.com/gjoseph92/stackstac/pull/143

Better error when forgetting mosaic custom nodata by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/144

Release 0.4.1 by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/145

Full Changelog: https://github.com/gjoseph92/stackstac/compare/v0.4.0...v0.4.1
Source code(tar.gz)
Source code(zip)
v0.4.0(Mar 17, 2022)
Changelog: https://github.com/gjoseph92/stackstac/blob/main/CHANGELOG.md#040-2022-03-16

What's Changed

Support chunking the time/band dimensions by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/116

Better errors for pystac/satstac import issues by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/125

Use tree-reduction in mosaic by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/126

Update Coiled senv script and dev dependencies by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/127

Allow new releases of xarray using "calver" versioning by @scottyhq in https://github.com/gjoseph92/stackstac/pull/137

Pillow 9.0.1 by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/141

Release 0.4.0 by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/142

Full Changelog: https://github.com/gjoseph92/stackstac/compare/v0.3.1...v0.4.0
Source code(tar.gz)
Source code(zip)
v0.3.1(Jan 20, 2022)
Changelog: https://github.com/gjoseph92/stackstac/blob/main/CHANGELOG.md#031-2022-01-20

What's Changed

Support nodata= in mosaic by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/121

Catch some mosaic usage errors with nodata=nan by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/123

Release 0.3.1 by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/122

Full Changelog: https://github.com/gjoseph92/stackstac/compare/v0.3.0...v0.3.1
Source code(tar.gz)
Source code(zip)
v0.3.0(Jan 20, 2022)
Changelog: https://github.com/gjoseph92/stackstac/blob/main/CHANGELOG.md#030-2022-01-20

What's Changed

Relax NumPy requirement by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/101

Update base URL regex to make show with paths with 'notebook' in them by @robintw in https://github.com/gjoseph92/stackstac/pull/104

No fill_value=None; use fill value out-of-bounds by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/115

Support 2022 Dask by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/119

Release 0.3.0 by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/120

New Contributors

@robintw made their first contribution in https://github.com/gjoseph92/stackstac/pull/104

Full Changelog: https://github.com/gjoseph92/stackstac/compare/v0.2.2...v0.3.0
Source code(tar.gz)
Source code(zip)
v0.2.2(Dec 3, 2021)
Changelog: https://github.com/gjoseph92/stackstac/blob/main/CHANGELOG.md#022-2021-12-03

What's Changed

fixed upper right corner bounds transformation by @g2giovanni in https://github.com/gjoseph92/stackstac/pull/60

Support pystac.ItemCollection by @TomAugspurger in https://github.com/gjoseph92/stackstac/pull/64

Ignore assets that don't have a type, if filtering by mimetype by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/71

Fix for Pystac ItemCollections by @scottyhq in https://github.com/gjoseph92/stackstac/pull/69

Scripts: only make coiled env in one region by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/72

Add __iter__ to PystacItemCollection stub by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/73

Allow reading .tiff extensions from storage by @JamesOConnor in https://github.com/gjoseph92/stackstac/pull/75

Switch examples back to satsearch by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/80

Nit: remove default args from Reader protocol by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/82

Switch examples back to pystac again by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/81

Bump planetary-computer requirement by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/89

Fix accumulate_metadata replacing coordinates with Nones by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/88

Fix one-pixel shift with xy_coords="center" by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/94

Remove thread-unsafe event logging code by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/95

Switch examples to Binder by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/98

Release 0.2.2 by @gjoseph92 in https://github.com/gjoseph92/stackstac/pull/99

New Contributors

@g2giovanni made their first contribution in https://github.com/gjoseph92/stackstac/pull/60

@TomAugspurger made their first contribution in https://github.com/gjoseph92/stackstac/pull/64

@JamesOConnor made their first contribution in https://github.com/gjoseph92/stackstac/pull/75

Full Changelog: https://github.com/gjoseph92/stackstac/compare/v0.2.1...v0.2.2
Source code(tar.gz)
Source code(zip)
v0.2.1(May 7, 2021)

Source code(tar.gz)
Source code(zip)
v0.2.0(May 5, 2021)

Source code(tar.gz)
Source code(zip)
v0.1.1(Apr 16, 2021)

Source code(tar.gz)
Source code(zip)
v0.1.0(Mar 11, 2021)

Source code(tar.gz)
Source code(zip)

Turn a STAC catalog into a dask-based xarray

Related tags

Overview

StackSTAC

Installation

Things stackstac does for you:

Limitations:

Roadmap:

Comments

Reproducing

Issues

Releases(v0.4.3)

v0.4.3(Sep 14, 2022)

What's Changed

v0.4.2(Jul 7, 2022)

What's Changed

New Contributors

v0.4.1(Apr 15, 2022)

What's Changed

v0.4.0(Mar 17, 2022)

What's Changed

v0.3.1(Jan 20, 2022)

What's Changed

v0.3.0(Jan 20, 2022)

What's Changed

New Contributors

v0.2.2(Dec 3, 2021)

What's Changed

New Contributors

v0.2.1(May 7, 2021)

v0.2.0(May 5, 2021)

v0.1.1(Apr 16, 2021)

v0.1.0(Mar 11, 2021)

Owner

Gabe Joseph

The open-source tool for building high-quality datasets and computer vision models

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Things `stackstac` does for you: