* add `crs` property in `rio_tiler.io.base.SpatialMixin` (https://github.com/cogeotiff/rio-tiler/pull/429)
* add `geographic_bounds` in `rio_tiler.io.base.SpatialMixin` to return bounds in WGS84 (https://github.com/cogeotiff/rio-tiler/pull/429)
python
from rio_tiler.io import COGReader
with COGReader("https://rio-tiler-dev.s3.amazonaws.com/data/fixtures/cog.tif") as cog:
print(cog.bounds)
>> (373185.0, 8019284.949381611, 639014.9492102272, 8286015.0)
print(cog.crs)
>> "EPSG:32621"
print(cog.geographic_bounds)
>> (-61.28762442711404, 72.22979795551834, -52.301598718454485, 74.66298001264106)
* Allow errors to be ignored when trying to find `zooms` for dataset in `rio_tiler.io.COGReader`. If we're not able to find the zooms in selected TMS, COGReader will defaults to the min/max zooms of the TMS (https://github.com/cogeotiff/rio-tiler/pull/429)
python
from pyproj import CRS
from morecantile import TileMatrixSet
from rio_tiler.io import COGReader
For a non-earth dataset there is no available transformation from its own CRS and the default WebMercator TMS CRS.
with COGReader("https://rio-tiler-dev.s3.amazonaws.com/data/fixtures/cog_nonearth.tif") as cog:
>> UserWarning: Cannot determine min/max zoom based on dataset information, will default to TMS min/max zoom.
print(cog.minzoom)
>> 0
print(cog.maxzoom)
>> 24
if we use a `compatible TMS` then we don't get warnings
europa_crs = CRS.from_authority("ESRI", 104915)
europa_tms = TileMatrixSet.custom(
crs=europa_crs,
extent=europa_crs.area_of_use.bounds,
matrix_scale=[2, 1],
)
with COGReader(
"https://rio-tiler-dev.s3.amazonaws.com/data/fixtures/cog_nonearth.tif",
tms=europa_tms,
) as cog:
print(cog.minzoom)
>> 4
print(cog.maxzoom)
>> 6
* compare dataset bounds and tile bounds in TMS crs in `rio_tiler.io.base.SpatialMixin.tile_exists` method to allow dataset and TMS not compatible with WGS84 crs (https://github.com/cogeotiff/rio-tiler/pull/429)
* use `httpx` package instead of requests (author rodrigoalmeida94, https://github.com/cogeotiff/rio-tiler/pull/431)
* allow **half pixel** `tile_buffer` around the tile (e.g 0.5 -> 257x257, 1.5 -> 259x259) (author bstadlbauer, https://github.com/cogeotiff/rio-tiler/pull/405)
* add support for **intervals** colormap (https://github.com/cogeotiff/rio-tiler/pull/439))
python
from rio_tiler.colormap import apply_cmap, apply_intervals_cmap
data = numpy.random.randint(0, 255, size=(1, 256, 256))
cmap = [
([min, max], [r, g, b, a])
([0, 1], [0, 0, 0, 0]),
([1, 10], [255, 255, 255, 255]),
([10, 100], [255, 0, 0, 255]),
([100, 256], [255, 255, 0, 255]),
]
data, mask = apply_intervals_cmap(data, cmap)
or
data, mask = apply_cmap(data, cmap)
**breaking changes**
* update morecantile requirement to version >=3.0 (https://github.com/cogeotiff/rio-tiler/pull/418)
* remove python 3.6 support (https://github.com/cogeotiff/rio-tiler/pull/418)
* remove `max_size` defaults for `COGReader.part` and `COGReader.feature`, which will now default to full resolution reading.
python
before
with COGReader("my.tif") as cog:
img = cog.part(*cog.dataset.bounds, dst_crs=cog.dataset.crs, bounds_crs=cog.dataset.crs)
by default image should be max 1024x1024
assert max(img.width, 1024) by default image should be max 1024x1024
assert max(img.height, 1024)
now (there is no more max_size default)
with COGReader("my.tif") as cog:
img = cog.part(*cog.dataset.bounds, dst_crs=cog.dataset.crs, bounds_crs=cog.dataset.crs)
assert img.width == cog.dataset.width
assert img.height == cog.dataset.height
* add `.statistics` method in base classes (https://github.com/cogeotiff/rio-tiler/pull/427)
* remove `rio_tiler.io.base.SpatialMixin.spatial_info` and `rio_tiler.io.base.SpatialMixin.center` properties (https://github.com/cogeotiff/rio-tiler/pull/429)
* Reader's `.bounds` property should now be in dataset's CRS, not in `WGS84` (https://github.com/cogeotiff/rio-tiler/pull/429)
python
before
with COGReader("my.tif") as cog:
print(cog.bounds)
>>> (-61.287001876638215, 15.537756794450583, -61.27877967704677, 15.542486503997608)
now
with COGReader("my.tif") as cog:
print(cog.bounds)
>>> (683715.3266400001, 1718548.5702, 684593.2680000002, 1719064.90736)
print(cog.crs)
>>> EPSG:32620
print(cog.geographic_bounds)
>>> (-61.287001876638215, 15.537756794450583, -61.27877967704677, 15.542486503997608)
* Use `RIO_TILER_MAX_THREADS` environment variable instead of `MAX_THREADS` (author rodrigoalmeida94, https://github.com/cogeotiff/rio-tiler/pull/432)
* remove `band_expression` in `rio_tiler.io.base.MultiBandReader` (https://github.com/cogeotiff/rio-tiler/pull/433)
* change `asset_expression` input type from `str` to `Dict[str, str]` in `rio_tiler.io.base.MultiBaseReader` (https://github.com/cogeotiff/rio-tiler/pull/434)
python
before
with STACReader("mystac.json") as stac:
img = stac.preview(
assets=("data1", "data2"),
asset_expression="b1*2", expression was applied to each asset
)
now
with STACReader("mystac.json") as stac:
img = stac.preview(
assets=("data1", "data2"),
asset_expression={"data1": "b1*2", "data2": "b2*100"}, we can now pass per asset expression
)
* add `asset_indexes` in `rio_tiler.io.base.MultiBaseReader`, which replaces `indexes`. (https://github.com/cogeotiff/rio-tiler/pull/434)
python
before
with STACReader("mystac.json") as stac:
img = stac.preview(
assets=("data1", "data2"),
indexes=(1,), indexes was applied to each asset
)
now
with STACReader("mystac.json") as stac:
img = stac.preview(
assets=("data1", "data2"),
asset_indexes={"data1": 1, "data2": 2}, we can now pass per asset indexes
)
**removed**
* `rio_tiler.io.BaseReader.metadata` and `rio_tiler.io.BaseReader.stats` base class methods (https://github.com/cogeotiff/rio-tiler/pull/425)
* `rio_tiler.reader.stats` function (https://github.com/cogeotiff/rio-tiler/pull/440)
* `rio_tiler.reader.metadata` function (https://github.com/cogeotiff/rio-tiler/pull/440)
* `rio_tiler.utils._stats` function (https://github.com/cogeotiff/rio-tiler/pull/440)