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Release v0.8.4 (#742)
* release: v0.8.4 * fix: keep hvplot.pandas import * fix: another import
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‎.pre-commit-config.yaml

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repos:
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- repo: https://github.com/charliermarsh/ruff-pre-commit
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rev: "v0.5.0"
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rev: "v0.6.9"
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hooks:
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- id: ruff
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args: [--fix, --exit-non-zero-on-fix]
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- repo: https://github.com/psf/black
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rev: 24.4.2
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rev: 24.10.0
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hooks:
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- id: black
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- repo: https://github.com/codespell-project/codespell
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args: [--ignore-words=.codespellignore]
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types_or: [jupyter, markdown, python, shell]
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- repo: https://github.com/PyCQA/doc8
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rev: v1.1.1
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rev: v1.1.2
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hooks:
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- id: doc8
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args: [--ignore=D004]
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additional_dependencies:
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- importlib_metadata < 5; python_version == "3.7"
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v1.10.1
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rev: v1.12.0
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hooks:
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- id: mypy
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files: ".*\\.py$"

‎CHANGELOG.md

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@@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
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## [Unreleased]
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## [v0.8.4] - 2024-10-16
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### Added
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- Support for collection search via `CollectionSearch` class and associated client methods [#735](https://github.com/stac-utils/pystac-client/pull/735)
@@ -393,7 +395,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
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Initial release.
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[Unreleased]: https://github.com/stac-utils/pystac-client/compare/v0.8.3...main
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[Unreleased]: https://github.com/stac-utils/pystac-client/compare/v0.8.4...main
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[v0.8.4]: https://github.com/stac-utils/pystac-client/compare/v0.8.3...v0.8.4
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[v0.8.3]: https://github.com/stac-utils/pystac-client/compare/v0.8.2...v0.8.3
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[v0.8.2]: https://github.com/stac-utils/pystac-client/compare/v0.8.1...v0.8.2
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[v0.8.1]: https://github.com/stac-utils/pystac-client/compare/v0.8.0...v0.8.1

‎docs/tutorials/aoi-coverage.ipynb

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@@ -17,13 +17,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from pystac_client import Client\n",
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"from typing import Any, Dict\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"from shapely.geometry import shape\n",
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"from pystac_client import Client\n",
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"from pystac.item import Item\n",
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"from typing import Dict, Any"
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"from shapely.geometry import shape\n",
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"\n",
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"from pystac_client import Client"
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]
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},
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{
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"outputs": [],
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"source": [
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"def intersection_percent(item: Item, aoi: Dict[str, Any]) -> float:\n",
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" '''The percentage that the Item's geometry intersects the AOI. An Item that\n",
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" \"\"\"The percentage that the Item's geometry intersects the AOI. An Item that\n",
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" completely covers the AOI has a value of 100.\n",
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" '''\n",
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" \"\"\"\n",
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" geom_item = shape(item.geometry)\n",
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" geom_aoi = shape(aoi)\n",
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"\n",
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"\n",
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"\n",
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"# STAC API root URL\n",
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"URL = 'https://planetarycomputer.microsoft.com/api/stac/v1'\n",
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"URL = \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n",
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"\n",
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"# geometry of the AOI to search over\n",
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"intersects_geometry = {\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"print([f\"{intersection_percent(item, intersects_geometry):.2f}\" for item in item_search.items()])"
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"print(\n",
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" [\n",
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" f\"{intersection_percent(item, intersects_geometry):.2f}\"\n",
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" for item in item_search.items()\n",
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" ]\n",
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")"
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]
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},
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{
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"# Render the AOI and Item results\n",
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"# The green shape is the AOI\n",
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"# The blue shapes are the Item geometries\n",
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"# If there are no blue shapes, adjust the intersection percent filter above until there are\n",
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"# If there are no blue shapes, adjust the intersection percent filter above\n",
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"# until there are\n",
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"\n",
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"cm = plt.get_cmap('RdBu')\n",
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"cm = plt.get_cmap(\"RdBu\")\n",
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"fig, axs = plt.subplots()\n",
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"axs.set_aspect('equal', 'datalim')\n",
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"axs.set_aspect(\"equal\", \"datalim\")\n",
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"\n",
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"for item in items_gt_5_percent:\n",
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" xs, ys = shape(item.geometry).exterior.xy\n",
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" axs.fill(xs, ys, alpha=0.5, fc='b', ec='none')\n",
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" xs, ys = shape(item.geometry).exterior.xy\n",
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" axs.fill(xs, ys, alpha=0.5, fc=\"b\", ec=\"none\")\n",
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"\n",
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"geom_intersects = shape(intersects_geometry)\n",
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"xs, ys = geom_intersects.exterior.xy\n",
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"axs.fill(xs, ys, alpha=0.5, fc='g', ec='none')\n",
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"axs.fill(xs, ys, alpha=0.5, fc=\"g\", ec=\"none\")\n",
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"\n",
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"plt.show()"
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]

‎docs/tutorials/cql2-filter.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"from pystac_client import Client\n",
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"\n",
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"# set pystac_client logger to DEBUG to see API calls\n",
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"import logging\n",
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"logging.basicConfig()\n",
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"logger = logging.getLogger('pystac_client')\n",
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"logger.setLevel(logging.INFO)\n",
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"\n",
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"# function for creating GeoDataFrame from Items\n",
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"from copy import deepcopy\n",
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"\n",
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"import geopandas as gpd\n",
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"import pandas as pd\n",
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"from shapely.geometry import shape\n",
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"\n",
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"from pystac_client import Client\n",
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"\n",
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"logging.basicConfig()\n",
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"logger = logging.getLogger(\"pystac_client\")\n",
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"logger.setLevel(logging.INFO)\n",
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"\n",
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"\n",
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"# convert a list of STAC Items into a GeoDataFrame\n",
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"def items_to_geodataframe(items):\n",
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" _items = []\n",
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" for i in items:\n",
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" _i = deepcopy(i)\n",
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" _i['geometry'] = shape(_i['geometry'])\n",
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" _i[\"geometry\"] = shape(_i[\"geometry\"])\n",
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" _items.append(_i)\n",
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" gdf = gpd.GeoDataFrame(pd.json_normalize(_items))\n",
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" for field in ['properties.datetime', 'properties.created', 'properties.updated']:\n",
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" for field in [\"properties.datetime\", \"properties.created\", \"properties.updated\"]:\n",
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" if field in gdf:\n",
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" gdf[field] = pd.to_datetime(gdf[field])\n",
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" gdf.set_index('properties.datetime', inplace=True)\n",
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" gdf.set_index(\"properties.datetime\", inplace=True)\n",
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" return gdf"
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]
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},
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"outputs": [],
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"source": [
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"# STAC API root URL\n",
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"URL = 'https://planetarycomputer.microsoft.com/api/stac/v1'\n",
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"URL = \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n",
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"\n",
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"# custom headers\n",
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"headers = []\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"import hvplot.pandas # noqa: F401\n",
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"\n",
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"# AOI around Delfzijl, in the north of The Netherlands\n",
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"geom = {\n",
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" \"type\": \"Polygon\",\n",
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" \"coordinates\": [\n",
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" [\n",
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" [\n",
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" 6.42425537109375,\n",
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" 53.174765470134616\n",
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" ],\n",
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" [\n",
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" 7.344360351562499,\n",
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" 53.174765470134616\n",
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" ],\n",
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" [\n",
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" 7.344360351562499,\n",
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" 53.67393435835391\n",
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" ],\n",
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" [\n",
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" 6.42425537109375,\n",
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" 53.67393435835391\n",
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" ],\n",
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" [\n",
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" 6.42425537109375,\n",
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" 53.174765470134616\n",
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" [6.42425537109375, 53.174765470134616],\n",
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" [7.344360351562499, 53.174765470134616],\n",
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" [7.344360351562499, 53.67393435835391],\n",
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" [6.42425537109375, 53.67393435835391],\n",
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" [6.42425537109375, 53.174765470134616],\n",
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" ]\n",
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" ]\n",
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" ]\n",
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" ],\n",
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"}\n",
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"\n",
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"params = {\n",
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" \"datetime\": \"2018-01-01/2020-12-31\",\n",
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"}\n",
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"\n",
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"import hvplot.pandas\n",
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"import json\n",
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"\n",
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"# reusable search function\n",
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"def search_fetch_plot(params, filt):\n",
126114
" # limit sets the # of items per page so we can see multiple pages getting fetched\n",
127-
" params['filter'] = filt\n",
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" params[\"filter\"] = filt\n",
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" search = cat.search(**params)\n",
129-
" items = list(search.items_as_dicts()) # safe b/c we set max_items = 100\n",
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" items = list(search.items_as_dicts()) # safe b/c we set max_items = 100\n",
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" # DataFrame\n",
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" items_df = pd.DataFrame(items_to_geodataframe(items))\n",
132120
" print(f\"{len(items_df.index)} items found\")\n",
133-
" field = 'properties.eo:cloud_cover'\n",
134-
" return items_df.hvplot(y=field, label=json.dumps(filt), frame_height=500, frame_width=800) "
121+
" field = \"properties.eo:cloud_cover\"\n",
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" return items_df.hvplot(\n",
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" y=field, label=json.dumps(filt), frame_height=500, frame_width=800\n",
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" )"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"filt = {\n",
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" \"op\": \"lte\",\n",
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" \"args\": [{\"property\": \"eo:cloud_cover\"}, 10]\n",
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"}\n",
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"filt = {\"op\": \"lte\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 10]}\n",
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"\n",
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"search_fetch_plot(params, filt)"
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"filt = {\n",
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" \"op\": \"gte\",\n",
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" \"args\" : [{\"property\": \"eo:cloud_cover\"}, 80]\n",
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"}\n",
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"filt = {\"op\": \"gte\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 80]}\n",
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"\n",
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"search_fetch_plot(params, filt)"
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"filt = { \n",
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"filt = {\n",
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" \"op\": \"and\",\n",
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" \"args\": [\n",
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" { \n",
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" \"op\":\"lte\", \n",
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" \"args\": [{\"property\": \"eo:cloud_cover\"}, 60]\n",
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" },\n",
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" { \n",
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" \"op\": \"gte\", \n",
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" \"args\": [{\"property\": \"eo:cloud_cover\"}, 40]\n",
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" }\n",
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" ]\n",
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" {\"op\": \"lte\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 60]},\n",
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" {\"op\": \"gte\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 40]},\n",
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" ],\n",
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"}\n",
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"\n",
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"search_fetch_plot(params, filt)"

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