Release 0.2.0 (#48)

Author: gjoseph92

CHANGELOG.md

@@ -1,5 +1,16 @@
 # Changelog
 
+## 0.2.0 (2021-05-05)
+Call [`stackstac.show`](https://stackstac.readthedocs.io/en/latest/api/main/stackstac.show.html) to render DataArrays in interactive ipyleaflet maps in your notebook! See [this example](https://stackstac.readthedocs.io/en/latest/examples/show.html) for more.
+
+- [`mosaic`](https://stackstac.readthedocs.io/en/latest/api/main/stackstac.mosaic.html) function
+- Exposed some assorted [handy geospatial ops](https://stackstac.readthedocs.io/en/latest/api/main.html#operations)
+- More robustly get band metadata (if one Item is missing some asset metadata, just ignore it instead of dropping the whole thing)
+- Fixed occasional `ValueError: conflicting sizes for dimension`
+- Resolved issue where timestamps became integers
+- Support [pystac-client](https://github.com/stac-utils/pystac-client) ItemCollections
+- Minimum Python version is now (accurately) 3.8. Stackstac would have failed upon import on 3.7 before.
+
 ## 0.1.1 (2021-04-16)
 - Passing a `gdal_env` now works when using a distributed cluster (before, you got a pickle error when calling `.compute()`)
 - Many typos fixed, thanks [@kylebarron](https://github.com/kylebarron) and [@RichardScottOZ](https://github.com/RichardScottOZ)!

README.md

@@ -62,8 +62,7 @@ pip install stackstac
 
 Windows notes:
 
-It is a good idea to use anaconda to handle installing rasterio on Windows.  There is considerably more pain involved with GDAL-type installations using pip.  
-Then `pip install stackstac`.
+It's a good idea to use `conda` to handle installing rasterio on Windows. There's considerably more pain involved with GDAL-type installations using pip. Then `pip install stackstac`.
 
 ## Things `stackstac` does for you:
 
@@ -72,6 +71,7 @@ Then `pip install stackstac`.
 * 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.
+* Display data in interactive maps in a notebook, computed on the fly by Dask.
 
 ## Limitations:
 

examples/show.ipynb

@@ -45,6 +45,15 @@
     "import distributed"
    ]
   },
+  {
+   "source": [
+    "You _definitely_ will need a cluster near the data for this (or to run on a beefy VM in `us-west-2`).\n",
+    "\n",
+    "You can sign up for a Coiled account and run clusters for free at https://cloud.coiled.io/ — no credit card or username required, just sign in with your GitHub or Google account."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -1145,4 +1154,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

pyproject.toml

@@ -6,7 +6,7 @@ license = "MIT"
 name = "stackstac"
 readme = "README.md"
 repository = "https://github.com/gjoseph92/stackstac"
-version = "0.1.1"
+version = "0.2.0"
 
 [tool.poetry.dependencies]
 Bottleneck = {version = "^1.3.2", optional = true}