From 10344932395d3cf4a4ec2ead18cb10a0c3405b5c Mon Sep 17 00:00:00 2001
From: Scott Henderson <scottyhq@gmail.com>
Date: Fri, 22 Nov 2024 20:56:08 +0100
Subject: [PATCH] Refactor sliderule io (#26)

* refactor sliderule io

* better maxar authentication handling

* add docs

* fix maxar test logic

* add docs
---
 .pre-commit-config.yaml          |    1 +
 docs/api.rst                     |   13 +
 docs/conf.py                     |    1 +
 docs/examples/index.md           |    1 +
 docs/examples/sliderule.ipynb    |  483 +++
 pixi.lock                        | 5590 ++++++++++++++++++++++--------
 pyproject.toml                   |    5 +-
 src/coincident/datasets/maxar.py |    1 -
 src/coincident/io/__init__.py    |    4 +-
 src/coincident/io/sliderule.py   |  188 +-
 src/coincident/search/main.py    |    5 +
 src/coincident/search/stac.py    |   18 +
 src/coincident/search/wesm.py    |    2 +
 tests/test_search.py             |   17 +-
 14 files changed, 4733 insertions(+), 1596 deletions(-)
 create mode 100644 docs/examples/sliderule.ipynb

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index cf0abb4..b3a0484 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -15,6 +15,7 @@ repos:
     rev: "v4.6.0"
     hooks:
       - id: check-added-large-files
+        args: ["--maxkb=1000"]
       - id: check-case-conflict
       - id: check-merge-conflict
       - id: check-symlinks
diff --git a/docs/api.rst b/docs/api.rst
index 520d9f1..b86119a 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -43,3 +43,16 @@ Datasets
    general.Dataset
    maxar.open_browse
    maxar.plot_browse
+
+
+IO
+--
+
+.. currentmodule:: coincident.io
+
+.. autosummary::
+   :toctree: generated/
+
+   sliderule.subset_gedi02a
+   sliderule.subset_atl06
+   sliderule.sample_3dep
diff --git a/docs/conf.py b/docs/conf.py
index 7007742..aafffbd 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -88,3 +88,4 @@
 always_document_param_types = True
 # autodoc_typehints = "none"
 nb_execution_mode = "auto"  # off, on
+nb_execution_excludepatterns = ["sliderule.ipynb"]
diff --git a/docs/examples/index.md b/docs/examples/index.md
index 1f95832..6f8f952 100644
--- a/docs/examples/index.md
+++ b/docs/examples/index.md
@@ -8,4 +8,5 @@ This section contains Jupyter Notebooks with narrative workflows using the
 
 quickstart
 cascading_search
+sliderule
 ```
diff --git a/docs/examples/sliderule.ipynb b/docs/examples/sliderule.ipynb
new file mode 100644
index 0000000..b076944
--- /dev/null
+++ b/docs/examples/sliderule.ipynb
@@ -0,0 +1,483 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sliderule data I/O\n",
+    "\n",
+    "This notebook will highlight analyzing various coincident elevation measurements. We will find regions with and use sliderule to retrieve ICESat-2 and GEDI point elevation measurements.\n",
+    "\n",
+    "```{note}\n",
+    "Keep in mind, these measurements are from different sensor types, close in time, but not at exactly the same time. All measurements also have uncertainties, so we do not expect perfect agreement.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import coincident\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# For testing\n",
+    "# sliderule.init(url='slideruleearth.io', verbose=True)\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Identify a primary dataset\n",
+    "\n",
+    "Start by loading a full resolution polygon of a 3DEP LiDAR workunit which has a known start_datetime and end_datatime:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>workunit</th>\n",
+       "      <th>workunit_id</th>\n",
+       "      <th>project</th>\n",
+       "      <th>project_id</th>\n",
+       "      <th>start_datetime</th>\n",
+       "      <th>end_datetime</th>\n",
+       "      <th>ql</th>\n",
+       "      <th>spec</th>\n",
+       "      <th>p_method</th>\n",
+       "      <th>dem_gsd_meters</th>\n",
+       "      <th>...</th>\n",
+       "      <th>seamless_category</th>\n",
+       "      <th>seamless_reason</th>\n",
+       "      <th>lpc_link</th>\n",
+       "      <th>sourcedem_link</th>\n",
+       "      <th>metadata_link</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>collection</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>dayofyear</th>\n",
+       "      <th>duration</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CO_WestCentral_2019</td>\n",
+       "      <td>175984</td>\n",
+       "      <td>CO_WestCentral_2019_A19</td>\n",
+       "      <td>175987</td>\n",
+       "      <td>2019-08-21</td>\n",
+       "      <td>2019-09-19</td>\n",
+       "      <td>QL 2</td>\n",
+       "      <td>USGS Lidar Base Specification 1.3</td>\n",
+       "      <td>linear-mode lidar</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Meets</td>\n",
+       "      <td>Meets 3DEP seamless DEM requirements</td>\n",
+       "      <td>https://rockyweb.usgs.gov/vdelivery/Datasets/S...</td>\n",
+       "      <td>http://prd-tnm.s3.amazonaws.com/index.html?pre...</td>\n",
+       "      <td>http://prd-tnm.s3.amazonaws.com/index.html?pre...</td>\n",
+       "      <td>MULTIPOLYGON (((-106.13571 38.4146, -106.1702 ...</td>\n",
+       "      <td>3DEP</td>\n",
+       "      <td>2019-09-04 12:00:00</td>\n",
+       "      <td>247</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              workunit  workunit_id                  project  project_id  \\\n",
+       "0  CO_WestCentral_2019       175984  CO_WestCentral_2019_A19      175987   \n",
+       "\n",
+       "  start_datetime end_datetime    ql                               spec  \\\n",
+       "0     2019-08-21   2019-09-19  QL 2  USGS Lidar Base Specification 1.3   \n",
+       "\n",
+       "            p_method  dem_gsd_meters  ... seamless_category  \\\n",
+       "0  linear-mode lidar             1.0  ...             Meets   \n",
+       "\n",
+       "                        seamless_reason  \\\n",
+       "0  Meets 3DEP seamless DEM requirements   \n",
+       "\n",
+       "                                            lpc_link  \\\n",
+       "0  https://rockyweb.usgs.gov/vdelivery/Datasets/S...   \n",
+       "\n",
+       "                                      sourcedem_link  \\\n",
+       "0  http://prd-tnm.s3.amazonaws.com/index.html?pre...   \n",
+       "\n",
+       "                                       metadata_link  \\\n",
+       "0  http://prd-tnm.s3.amazonaws.com/index.html?pre...   \n",
+       "\n",
+       "                                            geometry collection  \\\n",
+       "0  MULTIPOLYGON (((-106.13571 38.4146, -106.1702 ...       3DEP   \n",
+       "\n",
+       "             datetime dayofyear duration  \n",
+       "0 2019-09-04 12:00:00       247       29  \n",
+       "\n",
+       "[1 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "workunit = \"CO_WestCentral_2019\"\n",
+    "df_wesm = coincident.search.wesm.read_wesm_csv()\n",
+    "gf_lidar = coincident.search.wesm.load_by_fid(\n",
+    "    df_wesm[df_wesm.workunit == workunit].index\n",
+    ")\n",
+    "gf_lidar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Search secondary datasets\n",
+    "\n",
+    "Provide a list that will be searched in order. The list contains tuples of dataset aliases and the temporal pad in days to search before the primary dataset start and end dates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ICESat2(alias='icesat-2', has_stac_api=True, collections=['ATL06_006'], search='https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS', start='2018-10-13', end=None, type='altimeter', provider='nasa', stac_kwargs={'limit': 1000})\n"
+     ]
+    }
+   ],
+   "source": [
+    "# For more control, modify the dataset object being searched\n",
+    "# For example, search for ATL06 instead of default ATL03\n",
+    "is2 = coincident.datasets.nasa.ICESat2()\n",
+    "is2.collections = [\"ATL06_006\"]\n",
+    "print(is2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "secondary_datasets = [\n",
+    "    (\"gedi\", 40),  # +/- 40 days from lidar\n",
+    "    (is2, 60),  # +/- 60 days from lidar\n",
+    "]\n",
+    "\n",
+    "gf_gedi, gf_is2 = coincident.search.cascading_search(\n",
+    "    gf_lidar,\n",
+    "    secondary_datasets,\n",
+    "    min_overlap_area=30,  # km^2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get ICESat-2 ATL06 Data\n",
+    "\n",
+    "We've identified 7 granules of icesat-2 data to examine, but there is no need to work with the entire granule, which spans a huge geographic extent. Instead we'll retrive a subset of elevation values in the area of interest for each granule."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 452,
+       "width": 236
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Hone in on single granule as a pandas series\n",
+    "i = 0\n",
+    "granule_gdf = gf_is2.iloc[[i]]\n",
+    "granule = gf_is2.iloc[i]\n",
+    "granule_gdf.plot()  # needs to be a geodataframe\n",
+    "plt.title(granule.id);\n",
+    "# The icesat-2 track trends N-S, and is crossed by multiple GEDI tracks, resulting in the crosshatched appearance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_is2 = coincident.io.sliderule.subset_atl06(granule_gdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 832,
+       "width": 362
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot this data and overlay footprint\n",
+    "fig, ax = plt.subplots(figsize=(8, 10))\n",
+    "plt.scatter(\n",
+    "    x=data_is2.geometry.x,\n",
+    "    y=data_is2.geometry.y,\n",
+    "    c=data_is2.h_li,\n",
+    "    s=10,\n",
+    ")\n",
+    "granule_gdf.dissolve().boundary.plot(ax=ax, color=\"magenta\")\n",
+    "cb = plt.colorbar()\n",
+    "cb.set_label(\"elevation_hr (m)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "`sliderule` gets data from the envelope of multipolygon, not only data within intersecting GEDI tracks. Along-track gaps are where there is missing data.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here, we specify aoi=granule_gdf to only return GEDI data\n",
+    "# in our icesat-2 track of interest\n",
+    "data_gedi = coincident.io.sliderule.subset_gedi02a(\n",
+    "    gf_gedi,\n",
+    "    aoi=granule_gdf,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 831,
+       "width": 362
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot this data and overlay footprint\n",
+    "fig, ax = plt.subplots(figsize=(8, 10))\n",
+    "plt.scatter(\n",
+    "    x=data_gedi.geometry.x,\n",
+    "    y=data_gedi.geometry.y,\n",
+    "    c=data_gedi.elevation_hr,\n",
+    "    s=10,\n",
+    ")\n",
+    "granule_gdf.dissolve().boundary.plot(ax=ax, color=\"magenta\")\n",
+    "cb = plt.colorbar()\n",
+    "cb.set_label(\"elevation_hr (m)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE** Like ICESat-2, it's possible to have points falling outside the estimated footprints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Spatial join: nearest points\n",
+    "\n",
+    "NOTE: we will not worry about the difference in time of acquisition between adjacent points for now"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 126 ms, sys: 8 ms, total: 134 ms\n",
+      "Wall time: 139 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "utm_crs = granule_gdf.estimate_utm_crs()\n",
+    "subset_is2 = data_is2.to_crs(utm_crs)\n",
+    "subset_gedi = data_gedi.to_crs(utm_crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 8.72 ms, sys: 1.21 ms, total: 9.93 ms\n",
+      "Wall time: 9.18 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "469"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "# find nearest IS2 point to each GEDI\n",
+    "result = subset_gedi.sjoin_nearest(\n",
+    "    subset_is2,\n",
+    "    how=\"left\",\n",
+    "    max_distance=100,  # at most 100m apart\n",
+    "    distance_col=\"distances\",\n",
+    ")\n",
+    "# index is GEDI subset (with _left added), with _right apprended to is2 columns + Distances in meters\n",
+    "result = result[result[\"distances\"].notna()]\n",
+    "len(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 454,
+       "width": 581
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "plt.scatter(result.h_li, result.elevation_lm)\n",
+    "ax.axline((0, 0), slope=1, color=\"k\", transform=ax.transAxes)\n",
+    "plt.xlabel(\"ATL06 elevation (m)\")\n",
+    "plt.ylabel(\"GEDI L2A elevation_lm (m)\")\n",
+    "plt.title(\"Points within 100m\");"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pixi.lock b/pixi.lock
index 2324a29..6ff6967 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -11,20 +11,21 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-24.2.0-pyh71513ae_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.31-hcdce11a_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.7.4-hd3f4568_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.31-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.19-hf20e7d7_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.0-h72d8268_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.8.10-h6bb76cc_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.20-h389d861_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.10.7-had056f2_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.0-hc85afc5_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.19-hf20e7d7_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.20-hf20e7d7_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.0-h07ed512_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.407-h9c41b47_6.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.8.0-hb88c0a9_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.8.0-hecf86a2_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.10.3-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.0-hf42f96a_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.0-h1ffe551_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.9.1-hab05fe4_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.15.2-hdeadb07_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.11.0-h7bd072d_8.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.1-h3a84f74_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.1-hf42f96a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.2-hf42f96a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.5-h21d7256_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.449-hdaa582e_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.14.0-h5cfcd09_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.10.0-h113e628_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.13.0-h3cf044e_1.conda
@@ -36,7 +37,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py312h2ec8cdc_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.2-heb4867d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.3-heb4867d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py312h06ac9bb_0.conda
@@ -45,11 +46,12 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.7-unix_pyh707e725_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/click-plugins-1.1.1-py_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/cligj-0.7.2-pyhd8ed1ab_1.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.0-py312h68727a3_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.1-py312h68727a3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/deltalake-0.21.0-py312h258d185_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/folium-0.18.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.54.1-py312h178313f_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.55.0-py312h178313f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freexl-2.0.0-h743c826_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.0.1-pyhd8ed1ab_1.conda
@@ -64,9 +66,12 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/json-c-0.18-h6688a6e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.23.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2024.10.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.7-py312h68727a3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda
@@ -75,10 +80,11 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240722.0-cxx17_h5888daf_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libarchive-3.7.4-hfca40fe_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-18.0.0-ha5db6c2_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-18.0.0-h5888daf_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-18.0.0-h5888daf_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-18.0.0-he882d9a_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-18.0.0-h94eee4b_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-18.0.0-h5888daf_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-18.0.0-h5888daf_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-18.0.0-h5c8f2c3_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.1.1-h1909e37_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-25_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_2.conda
@@ -86,32 +92,33 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-25_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.10.1-hbbe4b11_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libde265-1.0.15-h00ab1b0_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.22-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.3-h5888daf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.4-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.2.0-h77fa898_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.2.0-h69a702a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.9.3-hd5b9bfb_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.10.0-hef9eae6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.2.0-h69a702a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-14.2.0-h69a702a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.2.0-hd5240d6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.30.0-h438788a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.30.0-h0121fbd_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.65.5-hf5c653b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.31.0-h804f50b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.31.0-h0121fbd_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.67.1-hc2c308b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libheif-1.18.2-gpl_hffcb242_100.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libkml-1.3.0-hf539b9f_1021.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-25_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.64.0-h161d5f1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-18.0.0-h6bd9018_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-18.0.0-h6bd9018_8_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.44-hadc24fc_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.27.5-h5b01275_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.28.2-h5b01275_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2024.07.02-hbbce691_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/librttopo-1.1.0-h97f6797_17.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libspatialite-5.1.0-h1b4f908_11.conda
@@ -126,65 +133,70 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.4-hb346dea_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.5-hb346dea_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lzo-2.10-hd590300_1001.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/mapclassify-2.8.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.2-py312hd3ec401_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.2-py312hd3ec401_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/minizip-4.0.7-h401b404_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-he02047a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.1.2-py312h58c1407_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.1.3-py312h58c1407_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.2-h488ebb8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.2-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.2-h690cf93_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/orjson-3.10.10-py312h12e396e_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.4.0-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.3-he039a57_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/orjson-3.10.11-py312h12e396e_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhff2d567_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py312h1d6d2e6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-hba22ea6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-11.0.0-py312h7b63e92_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pkgutil-resolve-name-1.3.10-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/planetary-computer-1.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/proj-9.5.0-h12925eb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-18.0.0-py312h9cebb41_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-18.0.0-py312h01725c0_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-18.0.0-py312h7900ff3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-18.0.0-py312h01725c0_1_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyarrow-hotfix-0.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.9.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.23.4-py312h12e396e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.10.1-pyh10f6f8f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.27.1-py312h12e396e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.10.0-py312he8b4914_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.10.0-py312h02b19dd_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.7.0-py312he630544_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/pystac-1.11.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pystac-client-0.8.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.12.7-hc5c86c4_0_cpython.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-dotenv-1.0.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-5_cp312.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2024.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.2-py312h66e93f0_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.4.2-py312h8456570_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.4.2-py312h8cae83d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2024.07.02-h77b4e00_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.35.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/rioxarray-0.17.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.5-h3931f03_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.21.0-py312h12e396e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.9-h0fd0ee4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.5.2-py312h7a48858_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.14.1-py312h62794b6_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.3.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.6.0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/shapely-2.0.6-py312h391bc85_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-ha2e4443_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/snuggs-1.4.7-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.47.0-h9eae976_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/stac-geoparquet-0.6.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.3.0-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.12.2-hd8ed1ab_0.conda
@@ -193,6 +205,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-15.1.0-py312h66e93f0_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/uriparser-0.9.8-hac33072_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.3-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/x265-3.5-h924138e_3.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2024.10.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.2.5-h988505b_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hb9d3cd8_1.conda
@@ -200,15 +213,16 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2024.9.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312hef9b889_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
       - pypi: https://files.pythonhosted.org/packages/a4/57/6402242dde160d9ef9903487b4277443dc3da04615f6c4d3b48564a8ab57/aiobotocore-2.15.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/f7/d8/120cd0fe3e8530df0539e71ba9683eade12cae103dd7543e50d15f737917/aiohappyeyeballs-2.4.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/bf/fe/1332409d845ca601893bbf2d76935e0b93d41686e5f333841c7d7a4a770d/aiohttp-3.10.10-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/91/93/ad77782c5edfa17aafc070bef978fbfb8459b2f150595ffb01b559c136f9/aiohttp-3.11.7-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/42/0e/c2500f07a6b8e79983ecda6acf43505c7f12cc53e6e940e8e46afc3f7a5a/aiohttp_oauth2_client-1.0.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/13/1f/ec9b0dfcbad7b48f2ca06e0ba5e6d9932eb9f27108f36628131b52d961d9/aiohttp_retry-2.9.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/1a/99/84ba7273339d0f3dfa57901b846489d2e5c2cd731470167757f1935fffbd/aiohttp_retry-2.9.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/85/13/58b70a580de00893223d61de8fea167877a3aed97d4a5e1405c9159ef925/aioitertools-0.12.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/52/6b/8b126c2e1c07fae33185544ea974de67027afc905bd072feef9fbbd38d3d/boto3-1.35.36-py3-none-any.whl
@@ -220,7 +234,7 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/cb/39/cef2ccdfd984ae3cf93878d050c1b7c9354dd9493ce83fd9bb33a41f7a33/lark-0.12.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/0a/6e/94537acfb5b8f18235d13186d247bca478fea5e87d224644e0fe907df976/lxml-5.3.0-cp312-cp312-manylinux_2_28_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/96/a2/08928cfa4a2b0dd228e79dd41af7fda0d509436ae1646bca8a3ce55a5250/maxar_platform-1.0.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/e5/d0/6dcc39e2c367b9fb91c4a0fd2c7c7b565d1f1bef0cbcb8cbda032ebb3bce/maxar_platform-1.0.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/d3/c8/529101d7176fe7dfe1d99604e48d69c5dfdcadb4f06561f465c8ef12b4df/multidict-6.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/7e/80/cab10959dc1faead58dc8384a781dfbf93cb4d33d50988f7a69f1b7c9bbe/oauthlib-3.2.2-py3-none-any.whl
@@ -229,33 +243,34 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/3c/e9/1b54b7e26f50b3e0497cd13d3483d781d284452c2c50dd2a615a92a087a3/propcache-0.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/bd/7f/3ae203c863da7c3cf1dbdd7891754df19f0ca917e5bff3490414574177bd/pygeofilter-0.2.4-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/2b/af/a173f5db8a431a23b4f8f52000964f94b6d649a79ae9185762601b958650/pygeoif-1.5.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/29/52/84662b6636061277cb857f658518aa7db6672bc6d1a3f503ccd5aefc581e/regex-2024.9.11-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/fb/13/e3b075031a738c9598c51cfbc4c7879e26729c53aa9cca59211c44235314/regex-2024.11.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/3b/5d/63d4ae3b9daea098d5d6f5da83984853c1bbacd5dc826764b249fe119d24/requests_oauthlib-2.0.0-py2.py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/9a/e2/10e9819cf4a20bd8ea2f5dabafc2e6bf4a78d6a0965daeb60a4b34d1c11f/rich-13.9.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/e5/c0/b0fba8259b61c938c9733da9346b9f93e00881a9db22aafdd72f6ae0ec05/s3transfer-0.10.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/4e/78/b5d239e4a9284fd0564729bbdae44877b15622b571bc848c2fea3a82df44/stac_asset-0.4.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/19/71/39c7c0d87f8d4e6c020a393182060eaefeeae6c01dab6a84ec346f2567df/rich-13.9.4-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/66/05/7957af15543b8c9799209506df4660cba7afc4cf94bfb60513827e96bed6/s3transfer-0.10.4-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/ac/ad/173f9fc405b787b819c5285192d8d44e683b648d29cee5252567c05fe470/stac_asset-0.4.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a1/cb/93bc0e51bea4e171a85151dbba3c3b3f612b50b953cd3076f5b4f0db9e14/time_machine-2.16.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/97/3f/c4c51c55ff8487f2e6d0e618dba917e3c3ee2caae6cf0fbb59c9b1876f2e/tzlocal-5.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/62/62/30ca2405de6a20448ee557ab2cd61ab9c5900be7cbd18a2639db595f0b98/wrapt-1.16.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-      - pypi: https://files.pythonhosted.org/packages/c3/c0/cd8e94618983c1b811af082e1a7ad7764edb3a6af2bc6b468e0e686238ba/yarl-1.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/d2/50/dbef1a651578a3520d4534c1e434989e3620380c1ad97e309576b47f0ada/wrapt-1.17.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/9a/9f/63864f43d131ba8c8cdf1bde5dd3f02f0eff8a7c883a5d7fad32f204fda5/yarl-1.18.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: .
       osx-arm64:
       - conda: https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aom-3.9.1-h7bae524_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-24.2.0-pyh71513ae_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.7.31-hee1f4ab_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.7.4-hfd083d3_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-common-0.9.31-h7ab814d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-compression-0.2.19-hfd083d3_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-event-stream-0.5.0-h33c80d7_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-http-0.8.10-h4a91a90_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-io-0.14.20-h5fdde16_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-mqtt-0.10.7-hd821a15_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-s3-0.7.0-hc6bcb7c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-sdkutils-0.1.19-hfd083d3_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-checksums-0.1.20-hfd083d3_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.29.0-h45f4ed5_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-sdk-cpp-1.11.407-h0a0d3c4_6.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.8.0-h9b725a8_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.8.0-h5d7ee29_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-common-0.10.3-h5505292_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-compression-0.3.0-h5d7ee29_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-event-stream-0.5.0-h13ead76_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-http-0.9.1-hf483d09_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-io-0.15.2-h39f8ad8_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-mqtt-0.11.0-h68a0d7e_8.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-s3-0.7.1-h840aca7_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-sdkutils-0.2.1-h5d7ee29_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-checksums-0.2.2-h5d7ee29_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.29.5-h6832833_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-sdk-cpp-1.11.449-h8577fd2_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-core-cpp-1.14.0-hd50102c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-identity-cpp-1.10.0-hc602bab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-storage-blobs-cpp-12.13.0-h7585a09_1.conda
@@ -267,7 +282,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-bin-1.1.0-hd74edd7_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-python-1.1.0-py312hde4cb15_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-h99b78c6_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.2-h7ab814d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.3-h5505292_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ca-certificates-2024.8.30-hf0a4a13_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cffi-1.17.1-py312h0fad829_0.conda
@@ -276,11 +291,12 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.7-unix_pyh707e725_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/click-plugins-1.1.1-py_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/cligj-0.7.2-pyhd8ed1ab_1.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.0-py312h4721b07_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.1-py312hb23fbb9_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/dav1d-1.2.1-hb547adb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/deltalake-0.21.0-py312h30a1e44_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/folium-0.18.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.54.1-py312ha0ccf2a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.55.0-py312h998013c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/freetype-2.12.1-hadb7bae_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/freexl-2.0.0-hfbad9fb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.0.1-pyhd8ed1ab_1.conda
@@ -295,19 +311,23 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-75.1-hfee45f7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/json-c-0.18-he4178ee_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.23.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2024.10.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/kiwisolver-1.4.7-py312h6142ec9_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/krb5-1.21.3-h237132a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lcms2-2.16-ha0e7c42_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lerc-4.0.0-h9a09cb3_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libabseil-20240722.0-cxx17_hf9b8971_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarchive-3.7.4-h83d404f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-18.0.0-h6fea68a_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-18.0.0-h286801f_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-18.0.0-h286801f_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-18.0.0-hdcc9e87_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-18.0.0-hb943b0e_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-18.0.0-h286801f_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-18.0.0-h286801f_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-18.0.0-h6a6e5c5_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libavif16-1.1.1-h45b7238_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-25_osxarm64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlicommon-1.1.0-hd74edd7_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlidec-1.1.0-hd74edd7_2.conda
@@ -315,28 +335,30 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-25_osxarm64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.10.1-h13a7ad3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.3-ha82da77_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.4-ha82da77_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libde265-1.0.15-h2ffa867_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libdeflate-1.22-hd74edd7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20191231-hc8eb9b7_2.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libev-4.33-h93a5062_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.6.3-hf9b8971_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.6.4-h286801f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.4.2-h3422bc3_5.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.9.3-hb8ac103_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.10.0-h1554e7d_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-5.0.0-13_2_0_hd922786_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-13.2.0-hf226fd6_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.30.0-h2e6cea1_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.30.0-h90fd6fa_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.65.5-h3d9cf25_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.31.0-h8d8be31_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.31.0-h7081f7f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.67.1-hc70892a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libheif-1.18.2-gpl_he913df3_100.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libiconv-1.17-h0d3ecfb_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libjpeg-turbo-3.0.0-hb547adb_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libkml-1.3.0-he250239_1021.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-25_osxarm64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libnghttp2-1.64.0-h6d7220d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.28-openmp_h517c56d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-18.0.0-hda0ea68_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.28-openmp_hf332438_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-18.0.0-hda0ea68_8_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libpng-1.6.44-hc14010f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libprotobuf-5.27.5-h53f8970_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libprotobuf-5.28.2-h8f0b736_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libre2-11-2024.07.02-h2348fd5_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/librttopo-1.1.0-ha2cf0f4_17.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libspatialite-5.1.0-hffd3212_11.conda
@@ -347,65 +369,70 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libutf8proc-2.8.0-h1a8c8d9_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libwebp-base-1.4.0-h93a5062_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxcb-1.17.0-hdb1d25a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.13.4-h8424949_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.13.5-hbbdcc80_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.3.1-h8359307_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-19.1.3-hb52a8e5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-19.1.4-hdb05f8b_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-c-1.9.4-hb7217d7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lzo-2.10-h93a5062_1001.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/mapclassify-2.8.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/markupsafe-3.0.2-py312ha0ccf2a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.9.2-py312h9bd0bc6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.9.2-py312h9bd0bc6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/minizip-4.0.7-h27ee973_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h7bae524_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.1.2-py312h801f5e3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.1.3-py312h94ee1e1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openjpeg-2.5.2-h9f1df11_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.3.2-h8359307_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.2-h4a9587e_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orjson-3.10.10-py312heca05bc_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.4.0-h39f12f2_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.3-h121fd32_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orjson-3.10.11-py312hcd83bfe_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhff2d567_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-2.2.2-py312h8ae5369_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pcre2-10.44-h297a79d_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pillow-11.0.0-py312haf37ca6_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pkgutil-resolve-name-1.3.10-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/planetary-computer-1.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/proj-9.5.0-h61a8e3e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pthread-stubs-0.4-hd74edd7_1002.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-18.0.0-py312ha814d7c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-18.0.0-py312hc40f475_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-18.0.0-py312h1f38498_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-18.0.0-py312hc40f475_1_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyarrow-hotfix-0.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.9.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pydantic-core-2.23.4-py312he431725_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.10.1-pyh10f6f8f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pydantic-core-2.27.1-py312hcd83bfe_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyogrio-0.10.0-py312hf9e36c7_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyogrio-0.10.0-py312hfd5e53c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyproj-3.7.0-py312h1ab748d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/pystac-1.11.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pystac-client-0.8.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.12.7-h739c21a_0_cpython.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-dotenv-1.0.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python_abi-3.12-5_cp312.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2024.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyyaml-6.0.2-py312h024a12e_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/qhull-2020.2-h420ef59_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rasterio-1.4.2-py312h72e867f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rasterio-1.4.2-py312h02264c4_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rav1e-0.6.6-h69fbcac_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/re2-2024.07.02-hcd0e937_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.2-h92ec313_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.35.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/rioxarray-0.17.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.21.0-py312hcd83bfe_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scikit-learn-1.5.2-py312h387f99c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.14.1-py312h20deb59_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.3.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.6.0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/shapely-2.0.6-py312h3a6007a_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.1-hd02b534_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/snuggs-1.4.7-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/sqlite-3.47.0-hcd14bea_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/stac-geoparquet-0.6.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/svt-av1-2.3.0-hf24288c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h5083fa2_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.12.2-hd8ed1ab_0.conda
@@ -414,6 +441,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/unicodedata2-15.1.0-py312h0bf5046_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/uriparser-0.9.8-h00cdb27_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.3-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/x265-3.5-hbc6ce65_3.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2024.10.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xerces-c-3.2.5-h92fc2f4_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.11-hd74edd7_1.conda
@@ -421,15 +449,16 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2024.9.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xz-5.2.6-h57fd34a_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h3422bc3_2.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py312h15fbf35_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
       - pypi: https://files.pythonhosted.org/packages/a4/57/6402242dde160d9ef9903487b4277443dc3da04615f6c4d3b48564a8ab57/aiobotocore-2.15.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/f7/d8/120cd0fe3e8530df0539e71ba9683eade12cae103dd7543e50d15f737917/aiohappyeyeballs-2.4.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/e1/77/0aa8660dcf11fa65d61712dbb458c4989de220a844bd69778dff25f2d50b/aiohttp-3.10.10-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/28/fa/f4d98db1b7f8f0c3f74bdbd6d0d98cfc89984205cd33f1b8ee3f588ee5ad/aiohttp-3.11.7-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/42/0e/c2500f07a6b8e79983ecda6acf43505c7f12cc53e6e940e8e46afc3f7a5a/aiohttp_oauth2_client-1.0.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/13/1f/ec9b0dfcbad7b48f2ca06e0ba5e6d9932eb9f27108f36628131b52d961d9/aiohttp_retry-2.9.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/1a/99/84ba7273339d0f3dfa57901b846489d2e5c2cd731470167757f1935fffbd/aiohttp_retry-2.9.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/85/13/58b70a580de00893223d61de8fea167877a3aed97d4a5e1405c9159ef925/aioitertools-0.12.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/52/6b/8b126c2e1c07fae33185544ea974de67027afc905bd072feef9fbbd38d3d/boto3-1.35.36-py3-none-any.whl
@@ -441,7 +470,7 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/cb/39/cef2ccdfd984ae3cf93878d050c1b7c9354dd9493ce83fd9bb33a41f7a33/lark-0.12.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/eb/6d/d1f1c5e40c64bf62afd7a3f9b34ce18a586a1cccbf71e783cd0a6d8e8971/lxml-5.3.0-cp312-cp312-macosx_10_9_universal2.whl
       - pypi: https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/96/a2/08928cfa4a2b0dd228e79dd41af7fda0d509436ae1646bca8a3ce55a5250/maxar_platform-1.0.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/e5/d0/6dcc39e2c367b9fb91c4a0fd2c7c7b565d1f1bef0cbcb8cbda032ebb3bce/maxar_platform-1.0.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a2/12/adb6b3200c363062f805275b4c1e656be2b3681aada66c80129932ff0bae/multidict-6.1.0-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/7e/80/cab10959dc1faead58dc8384a781dfbf93cb4d33d50988f7a69f1b7c9bbe/oauthlib-3.2.2-py3-none-any.whl
@@ -450,15 +479,15 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/0b/17/308acc6aee65d0f9a8375e36c4807ac6605d1f38074b1581bd4042b9fb37/propcache-0.2.0-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/bd/7f/3ae203c863da7c3cf1dbdd7891754df19f0ca917e5bff3490414574177bd/pygeofilter-0.2.4-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/2b/af/a173f5db8a431a23b4f8f52000964f94b6d649a79ae9185762601b958650/pygeoif-1.5.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/8a/ea/909d8620329ab710dfaf7b4adee41242ab7c9b95ea8d838e9bfe76244259/regex-2024.9.11-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/60/85/cebcc0aff603ea0a201667b203f13ba75d9fc8668fab917ac5b2de3967bc/regex-2024.11.6-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/3b/5d/63d4ae3b9daea098d5d6f5da83984853c1bbacd5dc826764b249fe119d24/requests_oauthlib-2.0.0-py2.py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/9a/e2/10e9819cf4a20bd8ea2f5dabafc2e6bf4a78d6a0965daeb60a4b34d1c11f/rich-13.9.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/e5/c0/b0fba8259b61c938c9733da9346b9f93e00881a9db22aafdd72f6ae0ec05/s3transfer-0.10.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/4e/78/b5d239e4a9284fd0564729bbdae44877b15622b571bc848c2fea3a82df44/stac_asset-0.4.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/19/71/39c7c0d87f8d4e6c020a393182060eaefeeae6c01dab6a84ec346f2567df/rich-13.9.4-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/66/05/7957af15543b8c9799209506df4660cba7afc4cf94bfb60513827e96bed6/s3transfer-0.10.4-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/ac/ad/173f9fc405b787b819c5285192d8d44e683b648d29cee5252567c05fe470/stac_asset-0.4.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/4a/f4/603a84e7ae6427a53953db9f61b689dc6adf233e03c5f5ca907a901452fd/time_machine-2.16.0-cp312-cp312-macosx_10_9_universal2.whl
       - pypi: https://files.pythonhosted.org/packages/97/3f/c4c51c55ff8487f2e6d0e618dba917e3c3ee2caae6cf0fbb59c9b1876f2e/tzlocal-5.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/6a/d7/cfcd73e8f4858079ac59d9db1ec5a1349bc486ae8e9ba55698cc1f4a1dff/wrapt-1.16.0-cp312-cp312-macosx_11_0_arm64.whl
-      - pypi: https://files.pythonhosted.org/packages/e3/a2/b65447626227ebe36f18f63ac551790068bf42c69bb22dfa3ae986170728/yarl-1.17.1-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/85/82/518605474beafff11f1a34759f6410ab429abff9f7881858a447e0d20712/wrapt-1.17.0-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/eb/e1/3081b578a6f21961711b9a1c49c2947abb3b0d0dd9537378fb06777ce8ee/yarl-1.18.0-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: .
   dev:
     channels:
@@ -471,22 +500,23 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/astroid-3.3.5-py312h7900ff3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-2.4.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-24.2.0-pyh71513ae_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.31-hcdce11a_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.7.4-hd3f4568_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.31-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.19-hf20e7d7_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.0-h72d8268_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.8.10-h6bb76cc_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.20-h389d861_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.10.7-had056f2_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.0-hc85afc5_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.19-hf20e7d7_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.20-hf20e7d7_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.0-h07ed512_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.407-h9c41b47_6.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.8.0-hb88c0a9_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.8.0-hecf86a2_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.10.3-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.0-hf42f96a_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.0-h1ffe551_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.9.1-hab05fe4_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.15.2-hdeadb07_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.11.0-h7bd072d_8.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.1-h3a84f74_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.1-hf42f96a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.2-hf42f96a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.5-h21d7256_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.449-hdaa582e_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.14.0-h5cfcd09_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.10.0-h113e628_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.13.0-h3cf044e_1.conda
@@ -498,11 +528,14 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py312h2ec8cdc_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.2-heb4867d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.3-heb4867d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hebfffa5_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/capnproto-1.0.2-h766bdaa_3.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py312h06ac9bb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cfitsio-4.4.1-ha728647_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ciso8601-2.3.1-py312h66e93f0_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.7-unix_pyh707e725_0.conda
@@ -510,9 +543,11 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/cligj-0.7.2-pyhd8ed1ab_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.0-py312h68727a3_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.1-py312h68727a3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.7-py312h2ec8cdc_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.27-h54b06d7_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.9-py312h2ec8cdc_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.1.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/deltalake-0.21.0-py312h258d185_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/dill-0.3.9-pyhd8ed1ab_0.conda
@@ -520,10 +555,20 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.16.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fiona-1.10.1-py312hf31e026_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fmt-11.0.2-h434a139_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/folium-0.18.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.54.1-py312h178313f_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.55.0-py312h178313f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freexl-2.0.0-h743c826_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gdal-3.10.0-py312h22e8dc9_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.0.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-base-1.0.1-pyha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/geos-3.13.0-h5888daf_0.conda
@@ -532,22 +577,28 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.15-h2a13503_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.14.3-nompi_hdf9ad27_105.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.0.0-pyh9f0ad1d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.5.0-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-6.29.5-pyh3099207_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-8.29.0-pyh707e725_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/isort-5.13.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/json-c-0.18-h6688a6e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.23.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2024.10.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.6.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.7.2-pyh31011fe_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/kealib-1.5.3-hf8d3e68_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.7-py312h68727a3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda
@@ -555,11 +606,13 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240722.0-cxx17_h5888daf_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libaec-1.1.3-h59595ed_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libarchive-3.7.4-hfca40fe_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-18.0.0-ha5db6c2_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-18.0.0-h5888daf_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-18.0.0-h5888daf_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-18.0.0-he882d9a_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-18.0.0-h94eee4b_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-18.0.0-h5888daf_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-18.0.0-h5888daf_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-18.0.0-h5c8f2c3_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.1.1-h1909e37_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-25_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_2.conda
@@ -567,32 +620,51 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-25_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.10.1-hbbe4b11_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libde265-1.0.15-h00ab1b0_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.22-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.3-h5888daf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.4-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.2.0-h77fa898_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.2.0-h69a702a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.9.3-hd5b9bfb_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-3.10.0-ha770c72_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.10.0-hef9eae6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-fits-3.10.0-he1674de_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-grib-3.10.0-ha360943_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-hdf4-3.10.0-h380f24e_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-hdf5-3.10.0-hefe6d7a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-jp2openjpeg-3.10.0-h9fdfae1_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-kea-3.10.0-h38e673a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-netcdf-3.10.0-hba670d9_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-pdf-3.10.0-h697c966_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-pg-3.10.0-h5cc4e75_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-postgisraster-3.10.0-h5cc4e75_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-tiledb-3.10.0-hec57c18_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-xls-3.10.0-h1e14832_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.2.0-h69a702a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-14.2.0-h69a702a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.2.0-hd5240d6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.82.2-h2ff4ddf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.30.0-h438788a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.30.0-h0121fbd_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.65.5-hf5c653b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.31.0-h804f50b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.31.0-h0121fbd_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.67.1-hc2c308b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libheif-1.18.2-gpl_hffcb242_100.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libkml-1.3.0-hf539b9f_1021.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-25_linux64_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.9.2-nompi_h135f659_114.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.64.0-h161d5f1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-18.0.0-h6bd9018_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.4-h7f98852_1002.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-18.0.0-h6bd9018_8_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.44-hadc24fc_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.27.5-h5b01275_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libpq-17.2-h04577a9_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.28.2-h5b01275_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2024.07.02-hbbce691_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/librttopo-1.1.0-h97f6797_17.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.20-h4ab18f5_0.conda
@@ -608,13 +680,15 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.4-hb346dea_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.5-hb346dea_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.39-h76b75d6_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libzip-1.11.2-h6991a6a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lzo-2.10-hd590300_1001.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/mapclassify-2.8.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.2-py312hd3ec401_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.2-py312hd3ec401_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.1.7-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/mccabe-0.7.0-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/minizip-4.0.7-h401b404_0.conda
@@ -623,23 +697,31 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-he02047a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/nodeenv-1.9.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.1.2-py312h58c1407_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/nspr-4.36-h5888daf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/nss-3.107-hdf54f9c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.1.3-py312h58c1407_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.2-h488ebb8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.2-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.2-h690cf93_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/orjson-3.10.10-py312h12e396e_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.8-hedd0468_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.4.0-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.3-he039a57_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/orjson-3.10.11-py312h12e396e_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhff2d567_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py312h1d6d2e6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-hba22ea6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-py_1003.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-11.0.0-py312h7b63e92_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pkgutil-resolve-name-1.3.10-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/planetary-computer-1.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/poppler-24.08.0-h47131b8_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/poppler-data-0.4.12-hd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/postgresql-17.2-h1122569_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pre-commit-3.8.0-pyha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/proj-9.5.0-h12925eb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.48-pyha770c72_0.conda
@@ -647,15 +729,15 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd3deb0d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-18.0.0-py312h9cebb41_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-18.0.0-py312h01725c0_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-18.0.0-py312h7900ff3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-18.0.0-py312h01725c0_1_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyarrow-hotfix-0.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.9.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.23.4-py312h12e396e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.10.1-pyh10f6f8f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.27.1-py312h12e396e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pylint-3.3.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.10.0-py312he8b4914_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.10.0-py312h02b19dd_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.7.0-py312he630544_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2
@@ -663,7 +745,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/pystac-client-0.8.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.12.7-hc5c86c4_0_cpython.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-dotenv-1.0.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-5_cp312.conda
@@ -671,30 +753,38 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.2-py312h66e93f0_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-26.2.0-py312hbf22597_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.4.2-py312h8456570_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.4.2-py312h8cae83d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2024.07.02-h77b4e00_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.35.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/rioxarray-0.17.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.5-h3931f03_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.21.0-py312h12e396e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.9-h0fd0ee4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.5.2-py312h7a48858_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.14.1-py312h62794b6_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.3.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.6.0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/shapely-2.0.6-py312h391bc85_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/sliderule-4.8.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-ha2e4443_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/snuggs-1.4.7-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/spdlog-1.14.1-hed91bc2_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.47.0-h9eae976_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/stac-geoparquet-0.6.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.3.0-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/tiledb-2.26.2-h33a2f6d_11.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.1.0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/tomlkit-0.13.2-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4.1-py312h66e93f0_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.12.2-hd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/tzcode-2024b-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2024b-hc8b5060_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ukkonen-1.0.1-py312h68727a3_5.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-15.1.0-py312h66e93f0_1.conda
@@ -702,37 +792,44 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.27.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.13-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/x265-3.5-h924138e_3.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2024.10.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.2.5-h988505b_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hb9d3cd8_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.4-he73a12e_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.10-h4f16b4b_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hb9d3cd8_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.6-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hb9d3cd8_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-xorgproto-2024.1-hb9d3cd8_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2024.9.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312hef9b889_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
       - pypi: https://files.pythonhosted.org/packages/a4/57/6402242dde160d9ef9903487b4277443dc3da04615f6c4d3b48564a8ab57/aiobotocore-2.15.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/f7/d8/120cd0fe3e8530df0539e71ba9683eade12cae103dd7543e50d15f737917/aiohappyeyeballs-2.4.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/bf/fe/1332409d845ca601893bbf2d76935e0b93d41686e5f333841c7d7a4a770d/aiohttp-3.10.10-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/91/93/ad77782c5edfa17aafc070bef978fbfb8459b2f150595ffb01b559c136f9/aiohttp-3.11.7-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/42/0e/c2500f07a6b8e79983ecda6acf43505c7f12cc53e6e940e8e46afc3f7a5a/aiohttp_oauth2_client-1.0.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/13/1f/ec9b0dfcbad7b48f2ca06e0ba5e6d9932eb9f27108f36628131b52d961d9/aiohttp_retry-2.9.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/1a/99/84ba7273339d0f3dfa57901b846489d2e5c2cd731470167757f1935fffbd/aiohttp_retry-2.9.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/85/13/58b70a580de00893223d61de8fea167877a3aed97d4a5e1405c9159ef925/aioitertools-0.12.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/52/6b/8b126c2e1c07fae33185544ea974de67027afc905bd072feef9fbbd38d3d/boto3-1.35.36-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/2a/60/056d58b606731f94fe395266c604ea9efcecc10e6857ceb9b10e6831d746/botocore-1.35.36-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/1f/6e/b64600156934dab14cc8b403095a9ea8bd722aad2e775673c68346b76220/cloudpathlib-0.20.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/c0/70/6b0627e5bd68204ee580126ed3513140b2298995c1233bd67404b4e44d0e/coverage-7.6.4-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/60/a2/23eb11eb60f825a84397cb94701d6f41d2e8e88ad7d0ba2b4339f38435fb/coverage-7.6.7-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/a4/29/db12aa4dda81580be1999824a689bd52aa40061fc12c9ccdc3feab5ea718/dateparser-1.2.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/af/f2/64b73a9bb86f5a89fb55450e97cd5c1f84a862d4ff90d9fd1a73ab0f64a5/frozenlist-1.5.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/31/b4/b9b800c45527aadd64d5b442f9b932b00648617eb5d63d2c7a6587b7cafc/jmespath-1.0.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/cb/39/cef2ccdfd984ae3cf93878d050c1b7c9354dd9493ce83fd9bb33a41f7a33/lark-0.12.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/0a/6e/94537acfb5b8f18235d13186d247bca478fea5e87d224644e0fe907df976/lxml-5.3.0-cp312-cp312-manylinux_2_28_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/96/a2/08928cfa4a2b0dd228e79dd41af7fda0d509436ae1646bca8a3ce55a5250/maxar_platform-1.0.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/e5/d0/6dcc39e2c367b9fb91c4a0fd2c7c7b565d1f1bef0cbcb8cbda032ebb3bce/maxar_platform-1.0.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/d3/c8/529101d7176fe7dfe1d99604e48d69c5dfdcadb4f06561f465c8ef12b4df/multidict-6.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/7e/80/cab10959dc1faead58dc8384a781dfbf93cb4d33d50988f7a69f1b7c9bbe/oauthlib-3.2.2-py3-none-any.whl
@@ -742,36 +839,37 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/bd/7f/3ae203c863da7c3cf1dbdd7891754df19f0ca917e5bff3490414574177bd/pygeofilter-0.2.4-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/2b/af/a173f5db8a431a23b4f8f52000964f94b6d649a79ae9185762601b958650/pygeoif-1.5.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/29/52/84662b6636061277cb857f658518aa7db6672bc6d1a3f503ccd5aefc581e/regex-2024.9.11-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/fb/13/e3b075031a738c9598c51cfbc4c7879e26729c53aa9cca59211c44235314/regex-2024.11.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/3b/5d/63d4ae3b9daea098d5d6f5da83984853c1bbacd5dc826764b249fe119d24/requests_oauthlib-2.0.0-py2.py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/9a/e2/10e9819cf4a20bd8ea2f5dabafc2e6bf4a78d6a0965daeb60a4b34d1c11f/rich-13.9.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/e5/c0/b0fba8259b61c938c9733da9346b9f93e00881a9db22aafdd72f6ae0ec05/s3transfer-0.10.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/4e/78/b5d239e4a9284fd0564729bbdae44877b15622b571bc848c2fea3a82df44/stac_asset-0.4.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/19/71/39c7c0d87f8d4e6c020a393182060eaefeeae6c01dab6a84ec346f2567df/rich-13.9.4-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/66/05/7957af15543b8c9799209506df4660cba7afc4cf94bfb60513827e96bed6/s3transfer-0.10.4-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/ac/ad/173f9fc405b787b819c5285192d8d44e683b648d29cee5252567c05fe470/stac_asset-0.4.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a1/cb/93bc0e51bea4e171a85151dbba3c3b3f612b50b953cd3076f5b4f0db9e14/time_machine-2.16.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/97/3f/c4c51c55ff8487f2e6d0e618dba917e3c3ee2caae6cf0fbb59c9b1876f2e/tzlocal-5.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/62/62/30ca2405de6a20448ee557ab2cd61ab9c5900be7cbd18a2639db595f0b98/wrapt-1.16.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-      - pypi: https://files.pythonhosted.org/packages/c3/c0/cd8e94618983c1b811af082e1a7ad7764edb3a6af2bc6b468e0e686238ba/yarl-1.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/d2/50/dbef1a651578a3520d4534c1e434989e3620380c1ad97e309576b47f0ada/wrapt-1.17.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/9a/9f/63864f43d131ba8c8cdf1bde5dd3f02f0eff8a7c883a5d7fad32f204fda5/yarl-1.18.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: .
       osx-arm64:
       - conda: https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aom-3.9.1-h7bae524_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/astroid-3.3.5-py312h81bd7bf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-2.4.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-24.2.0-pyh71513ae_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.7.31-hee1f4ab_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.7.4-hfd083d3_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-common-0.9.31-h7ab814d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-compression-0.2.19-hfd083d3_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-event-stream-0.5.0-h33c80d7_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-http-0.8.10-h4a91a90_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-io-0.14.20-h5fdde16_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-mqtt-0.10.7-hd821a15_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-s3-0.7.0-hc6bcb7c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-sdkutils-0.1.19-hfd083d3_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-checksums-0.1.20-hfd083d3_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.29.0-h45f4ed5_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-sdk-cpp-1.11.407-h0a0d3c4_6.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.8.0-h9b725a8_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.8.0-h5d7ee29_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-common-0.10.3-h5505292_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-compression-0.3.0-h5d7ee29_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-event-stream-0.5.0-h13ead76_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-http-0.9.1-hf483d09_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-io-0.15.2-h39f8ad8_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-mqtt-0.11.0-h68a0d7e_8.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-s3-0.7.1-h840aca7_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-sdkutils-0.2.1-h5d7ee29_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-checksums-0.2.2-h5d7ee29_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.29.5-h6832833_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-sdk-cpp-1.11.449-h8577fd2_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-core-cpp-1.14.0-hd50102c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-identity-cpp-1.10.0-hc602bab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-storage-blobs-cpp-12.13.0-h7585a09_1.conda
@@ -783,11 +881,14 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-bin-1.1.0-hd74edd7_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-python-1.1.0-py312hde4cb15_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-h99b78c6_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.2-h7ab814d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.3-h5505292_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ca-certificates-2024.8.30-hf0a4a13_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cairo-1.18.0-hb4a6bf7_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/capnproto-1.0.2-h221ca0e_3.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cffi-1.17.1-py312h0fad829_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cfitsio-4.4.1-hd313823_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ciso8601-2.3.1-py312h024a12e_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.7-unix_pyh707e725_0.conda
@@ -795,9 +896,11 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/cligj-0.7.2-pyhd8ed1ab_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.0-py312h4721b07_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.1-py312hb23fbb9_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.7-py312hde4cb15_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cyrus-sasl-2.1.27-h60b93bd_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/dav1d-1.2.1-hb547adb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.9-py312hd8f9ff3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.1.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/deltalake-0.21.0-py312h30a1e44_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/dill-0.3.9-pyhd8ed1ab_0.conda
@@ -805,10 +908,20 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.16.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fiona-1.10.1-py312hbbf7dfe_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fmt-11.0.2-h420ef59_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/folium-0.18.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.54.1-py312ha0ccf2a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fontconfig-2.15.0-h1383a14_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.55.0-py312h998013c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/freetype-2.12.1-hadb7bae_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/freexl-2.0.0-hfbad9fb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gdal-3.10.0-py312h9e5d920_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.0.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-base-1.0.1-pyha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/geos-3.13.0-hf9b8971_0.conda
@@ -817,32 +930,40 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/giflib-5.2.2-h93a5062_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/glog-0.7.1-heb240a5_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/hdf4-4.2.15-h2ee6834_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/hdf5-1.14.3-nompi_hec07895_105.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.0.0-pyh9f0ad1d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-75.1-hfee45f7_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.5.0-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-6.29.5-pyh57ce528_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-8.29.0-pyh707e725_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/isort-5.13.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/json-c-0.18-he4178ee_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.23.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2024.10.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.6.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.7.2-pyh31011fe_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/kealib-1.5.3-h8edbb62_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/kiwisolver-1.4.7-py312h6142ec9_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/krb5-1.21.3-h237132a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lcms2-2.16-ha0e7c42_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lerc-4.0.0-h9a09cb3_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libabseil-20240722.0-cxx17_hf9b8971_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libaec-1.1.3-hebf3989_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarchive-3.7.4-h83d404f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-18.0.0-h6fea68a_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-18.0.0-h286801f_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-18.0.0-h286801f_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-18.0.0-hdcc9e87_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-18.0.0-hb943b0e_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-18.0.0-h286801f_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-18.0.0-h286801f_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-18.0.0-h6a6e5c5_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libavif16-1.1.1-h45b7238_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-25_osxarm64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlicommon-1.1.0-hd74edd7_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlidec-1.1.0-hd74edd7_2.conda
@@ -850,28 +971,48 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-25_osxarm64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.10.1-h13a7ad3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.3-ha82da77_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.4-ha82da77_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libde265-1.0.15-h2ffa867_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libdeflate-1.22-hd74edd7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20191231-hc8eb9b7_2.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libev-4.33-h93a5062_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.6.3-hf9b8971_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.6.4-h286801f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.4.2-h3422bc3_5.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.9.3-hb8ac103_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-3.10.0-hce30654_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.10.0-h1554e7d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-fits-3.10.0-haf2d7a4_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-grib-3.10.0-h4b7ce82_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-hdf4-3.10.0-h624721c_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-hdf5-3.10.0-hf20c56d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-jp2openjpeg-3.10.0-h0d2a31d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-kea-3.10.0-hcaf0198_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-netcdf-3.10.0-hb1be66d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-pdf-3.10.0-h14aca81_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-pg-3.10.0-h84049b1_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-postgisraster-3.10.0-h84049b1_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-tiledb-3.10.0-h391dfa7_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-xls-3.10.0-h3aa7eb4_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-5.0.0-13_2_0_hd922786_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-13.2.0-hf226fd6_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.30.0-h2e6cea1_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.30.0-h90fd6fa_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.65.5-h3d9cf25_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libglib-2.82.2-h07bd6cf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.31.0-h8d8be31_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.31.0-h7081f7f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.67.1-hc70892a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libheif-1.18.2-gpl_he913df3_100.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libiconv-1.17-h0d3ecfb_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libintl-0.22.5-h8414b35_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libjpeg-turbo-3.0.0-hb547adb_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libkml-1.3.0-he250239_1021.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-25_osxarm64_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libnetcdf-4.9.2-nompi_he469be0_114.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libnghttp2-1.64.0-h6d7220d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.28-openmp_h517c56d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-18.0.0-hda0ea68_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libntlm-1.4-h3422bc3_1002.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.28-openmp_hf332438_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-18.0.0-hda0ea68_8_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libpng-1.6.44-hc14010f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libprotobuf-5.27.5-h53f8970_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libpq-17.2-h9b1ab17_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libprotobuf-5.28.2-h8f0b736_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libre2-11-2024.07.02-h2348fd5_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/librttopo-1.1.0-ha2cf0f4_17.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsodium-1.0.20-h99b78c6_0.conda
@@ -883,14 +1024,16 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libutf8proc-2.8.0-h1a8c8d9_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libwebp-base-1.4.0-h93a5062_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxcb-1.17.0-hdb1d25a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.13.4-h8424949_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.13.5-hbbdcc80_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxslt-1.1.39-h223e5b9_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libzip-1.11.2-h1336266_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.3.1-h8359307_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-19.1.3-hb52a8e5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-19.1.4-hdb05f8b_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-c-1.9.4-hb7217d7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lzo-2.10-h93a5062_1001.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/mapclassify-2.8.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/markupsafe-3.0.2-py312ha0ccf2a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.9.2-py312h9bd0bc6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.9.2-py312h9bd0bc6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.1.7-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/mccabe-0.7.0-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/minizip-4.0.7-h27ee973_0.conda
@@ -899,23 +1042,31 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h7bae524_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/nodeenv-1.9.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.1.2-py312h801f5e3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/nspr-4.36-h5833ebf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/nss-3.107-hc555b47_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.1.3-py312h94ee1e1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openjpeg-2.5.2-h9f1df11_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.3.2-h8359307_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.2-h4a9587e_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orjson-3.10.10-py312heca05bc_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openldap-2.6.8-h50f2afc_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.4.0-h39f12f2_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.3-h121fd32_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orjson-3.10.11-py312hcd83bfe_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhff2d567_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-2.2.2-py312h8ae5369_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pcre2-10.44-h297a79d_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-py_1003.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pillow-11.0.0-py312haf37ca6_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pixman-0.43.4-hebf3989_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pkgutil-resolve-name-1.3.10-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/planetary-computer-1.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/poppler-24.08.0-h37b219d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/poppler-data-0.4.12-hd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/postgresql-17.2-h393ceee_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pre-commit-3.8.0-pyha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/proj-9.5.0-h61a8e3e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.48-pyha770c72_0.conda
@@ -923,15 +1074,15 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pthread-stubs-0.4-hd74edd7_1002.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd3deb0d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-18.0.0-py312ha814d7c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-18.0.0-py312hc40f475_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-18.0.0-py312h1f38498_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-18.0.0-py312hc40f475_1_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyarrow-hotfix-0.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.9.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pydantic-core-2.23.4-py312he431725_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.10.1-pyh10f6f8f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pydantic-core-2.27.1-py312hcd83bfe_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pylint-3.3.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyogrio-0.10.0-py312hf9e36c7_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyogrio-0.10.0-py312hfd5e53c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyproj-3.7.0-py312h1ab748d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2
@@ -939,7 +1090,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/pystac-client-0.8.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.12.7-h739c21a_0_cpython.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-dotenv-1.0.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python_abi-3.12-5_cp312.conda
@@ -947,29 +1098,37 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyyaml-6.0.2-py312h024a12e_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyzmq-26.2.0-py312hf8a1cbd_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/qhull-2020.2-h420ef59_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rasterio-1.4.2-py312h72e867f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rasterio-1.4.2-py312h02264c4_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rav1e-0.6.6-h69fbcac_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/re2-2024.07.02-hcd0e937_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.2-h92ec313_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.35.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/rioxarray-0.17.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.21.0-py312hcd83bfe_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scikit-learn-1.5.2-py312h387f99c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.14.1-py312h20deb59_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.3.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.6.0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/shapely-2.0.6-py312h3a6007a_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/sliderule-4.8.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.1-hd02b534_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/snuggs-1.4.7-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/spdlog-1.14.1-h6d8af72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/sqlite-3.47.0-hcd14bea_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/stac-geoparquet-0.6.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/svt-av1-2.3.0-hf24288c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tiledb-2.26.2-h4762ebe_11.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h5083fa2_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.1.0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/tomlkit-0.13.2-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.4.1-py312h024a12e_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.12.2-hd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tzcode-2024b-hd74edd7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2024b-hc8b5060_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ukkonen-1.0.1-py312h6142ec9_5.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/unicodedata2-15.1.0-py312h0bf5046_1.conda
@@ -977,6 +1136,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.27.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.13-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/x265-3.5-hbc6ce65_3.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2024.10.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xerces-c-3.2.5-h92fc2f4_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.11-hd74edd7_1.conda
@@ -984,30 +1144,30 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2024.9.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xz-5.2.6-h57fd34a_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h3422bc3_2.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h9f5b81c_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-hc1bb282_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py312h15fbf35_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
       - pypi: https://files.pythonhosted.org/packages/a4/57/6402242dde160d9ef9903487b4277443dc3da04615f6c4d3b48564a8ab57/aiobotocore-2.15.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/f7/d8/120cd0fe3e8530df0539e71ba9683eade12cae103dd7543e50d15f737917/aiohappyeyeballs-2.4.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/e1/77/0aa8660dcf11fa65d61712dbb458c4989de220a844bd69778dff25f2d50b/aiohttp-3.10.10-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/28/fa/f4d98db1b7f8f0c3f74bdbd6d0d98cfc89984205cd33f1b8ee3f588ee5ad/aiohttp-3.11.7-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/42/0e/c2500f07a6b8e79983ecda6acf43505c7f12cc53e6e940e8e46afc3f7a5a/aiohttp_oauth2_client-1.0.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/13/1f/ec9b0dfcbad7b48f2ca06e0ba5e6d9932eb9f27108f36628131b52d961d9/aiohttp_retry-2.9.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/1a/99/84ba7273339d0f3dfa57901b846489d2e5c2cd731470167757f1935fffbd/aiohttp_retry-2.9.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/85/13/58b70a580de00893223d61de8fea167877a3aed97d4a5e1405c9159ef925/aioitertools-0.12.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/52/6b/8b126c2e1c07fae33185544ea974de67027afc905bd072feef9fbbd38d3d/boto3-1.35.36-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/2a/60/056d58b606731f94fe395266c604ea9efcecc10e6857ceb9b10e6831d746/botocore-1.35.36-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/1f/6e/b64600156934dab14cc8b403095a9ea8bd722aad2e775673c68346b76220/cloudpathlib-0.20.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/27/03/932c2c5717a7fa80cd43c6a07d3177076d97b79f12f40f882f9916db0063/coverage-7.6.4-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/88/8b/e28f86412317b9514692fd6f9d8ac6faa12494c3f470c3c63f202e10c756/coverage-7.6.7-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/a4/29/db12aa4dda81580be1999824a689bd52aa40061fc12c9ccdc3feab5ea718/dateparser-1.2.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/d0/9a/8e479b482a6f2070b26bda572c5e6889bb3ba48977e81beea35b5ae13ece/frozenlist-1.5.0-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/31/b4/b9b800c45527aadd64d5b442f9b932b00648617eb5d63d2c7a6587b7cafc/jmespath-1.0.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/cb/39/cef2ccdfd984ae3cf93878d050c1b7c9354dd9493ce83fd9bb33a41f7a33/lark-0.12.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/eb/6d/d1f1c5e40c64bf62afd7a3f9b34ce18a586a1cccbf71e783cd0a6d8e8971/lxml-5.3.0-cp312-cp312-macosx_10_9_universal2.whl
       - pypi: https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/96/a2/08928cfa4a2b0dd228e79dd41af7fda0d509436ae1646bca8a3ce55a5250/maxar_platform-1.0.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/e5/d0/6dcc39e2c367b9fb91c4a0fd2c7c7b565d1f1bef0cbcb8cbda032ebb3bce/maxar_platform-1.0.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a2/12/adb6b3200c363062f805275b4c1e656be2b3681aada66c80129932ff0bae/multidict-6.1.0-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/7e/80/cab10959dc1faead58dc8384a781dfbf93cb4d33d50988f7a69f1b7c9bbe/oauthlib-3.2.2-py3-none-any.whl
@@ -1017,15 +1177,15 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/bd/7f/3ae203c863da7c3cf1dbdd7891754df19f0ca917e5bff3490414574177bd/pygeofilter-0.2.4-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/2b/af/a173f5db8a431a23b4f8f52000964f94b6d649a79ae9185762601b958650/pygeoif-1.5.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/8a/ea/909d8620329ab710dfaf7b4adee41242ab7c9b95ea8d838e9bfe76244259/regex-2024.9.11-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/60/85/cebcc0aff603ea0a201667b203f13ba75d9fc8668fab917ac5b2de3967bc/regex-2024.11.6-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/3b/5d/63d4ae3b9daea098d5d6f5da83984853c1bbacd5dc826764b249fe119d24/requests_oauthlib-2.0.0-py2.py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/9a/e2/10e9819cf4a20bd8ea2f5dabafc2e6bf4a78d6a0965daeb60a4b34d1c11f/rich-13.9.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/e5/c0/b0fba8259b61c938c9733da9346b9f93e00881a9db22aafdd72f6ae0ec05/s3transfer-0.10.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/4e/78/b5d239e4a9284fd0564729bbdae44877b15622b571bc848c2fea3a82df44/stac_asset-0.4.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/19/71/39c7c0d87f8d4e6c020a393182060eaefeeae6c01dab6a84ec346f2567df/rich-13.9.4-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/66/05/7957af15543b8c9799209506df4660cba7afc4cf94bfb60513827e96bed6/s3transfer-0.10.4-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/ac/ad/173f9fc405b787b819c5285192d8d44e683b648d29cee5252567c05fe470/stac_asset-0.4.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/4a/f4/603a84e7ae6427a53953db9f61b689dc6adf233e03c5f5ca907a901452fd/time_machine-2.16.0-cp312-cp312-macosx_10_9_universal2.whl
       - pypi: https://files.pythonhosted.org/packages/97/3f/c4c51c55ff8487f2e6d0e618dba917e3c3ee2caae6cf0fbb59c9b1876f2e/tzlocal-5.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/6a/d7/cfcd73e8f4858079ac59d9db1ec5a1349bc486ae8e9ba55698cc1f4a1dff/wrapt-1.16.0-cp312-cp312-macosx_11_0_arm64.whl
-      - pypi: https://files.pythonhosted.org/packages/e3/a2/b65447626227ebe36f18f63ac551790068bf42c69bb22dfa3ae986170728/yarl-1.17.1-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/85/82/518605474beafff11f1a34759f6410ab429abff9f7881858a447e0d20712/wrapt-1.17.0-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/eb/e1/3081b578a6f21961711b9a1c49c2947abb3b0d0dd9537378fb06777ce8ee/yarl-1.18.0-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: .
   docs:
     channels:
@@ -1038,21 +1198,22 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-2.4.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-24.2.0-pyh71513ae_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.31-hcdce11a_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.7.4-hd3f4568_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.31-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.19-hf20e7d7_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.0-h72d8268_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.8.10-h6bb76cc_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.20-h389d861_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.10.7-had056f2_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.0-hc85afc5_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.19-hf20e7d7_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.20-hf20e7d7_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.0-h07ed512_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.407-h9c41b47_6.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.8.0-hb88c0a9_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.8.0-hecf86a2_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.10.3-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.0-hf42f96a_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.0-h1ffe551_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.9.1-hab05fe4_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.15.2-hdeadb07_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.11.0-h7bd072d_8.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.1-h3a84f74_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.1-hf42f96a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.2-hf42f96a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.5-h21d7256_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.449-hdaa582e_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.14.0-h5cfcd09_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.10.0-h113e628_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.13.0-h3cf044e_1.conda
@@ -1064,7 +1225,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py312h2ec8cdc_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.2-heb4867d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.3-heb4867d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py312h06ac9bb_0.conda
@@ -1074,15 +1235,16 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/click-plugins-1.1.1-py_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/cligj-0.7.2-pyhd8ed1ab_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.0-py312h68727a3_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.1-py312h68727a3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.7-py312h2ec8cdc_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.9-py312h2ec8cdc_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.1.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/deltalake-0.21.0-py312h258d185_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/folium-0.18.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.54.1-py312h178313f_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.55.0-py312h178313f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freexl-2.0.0-h743c826_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.0.1-pyhd8ed1ab_1.conda
@@ -1098,12 +1260,15 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.5.0-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-6.29.5-pyh3099207_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-8.29.0-pyh707e725_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/json-c-0.18-h6688a6e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.23.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2024.10.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.6.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.7.2-pyh31011fe_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2
@@ -1114,10 +1279,11 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240722.0-cxx17_h5888daf_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libarchive-3.7.4-hfca40fe_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-18.0.0-ha5db6c2_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-18.0.0-h5888daf_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-18.0.0-h5888daf_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-18.0.0-he882d9a_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-18.0.0-h94eee4b_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-18.0.0-h5888daf_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-18.0.0-h5888daf_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-18.0.0-h5c8f2c3_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.1.1-h1909e37_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-25_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_2.conda
@@ -1125,32 +1291,33 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-25_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.10.1-hbbe4b11_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libde265-1.0.15-h00ab1b0_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.22-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.3-h5888daf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.4-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.2.0-h77fa898_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.2.0-h69a702a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.9.3-hd5b9bfb_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.10.0-hef9eae6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.2.0-h69a702a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-14.2.0-h69a702a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.2.0-hd5240d6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.30.0-h438788a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.30.0-h0121fbd_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.65.5-hf5c653b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.31.0-h804f50b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.31.0-h0121fbd_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.67.1-hc2c308b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libheif-1.18.2-gpl_hffcb242_100.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libkml-1.3.0-hf539b9f_1021.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-25_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.64.0-h161d5f1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-18.0.0-h6bd9018_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-18.0.0-h6bd9018_8_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.44-hadc24fc_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.27.5-h5b01275_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.28.2-h5b01275_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2024.07.02-hbbce691_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/librttopo-1.1.0-h97f6797_17.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.20-h4ab18f5_0.conda
@@ -1166,31 +1333,32 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.4-hb346dea_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.5-hb346dea_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lzo-2.10-hd590300_1001.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/mapclassify-2.8.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.2-py312hd3ec401_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.2-py312hd3ec401_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.1.7-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/minizip-4.0.7-h401b404_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-he02047a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.1.2-py312h58c1407_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.1.3-py312h58c1407_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.2-h488ebb8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.2-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.2-h690cf93_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/orjson-3.10.10-py312h12e396e_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.4.0-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.3-he039a57_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/orjson-3.10.11-py312h12e396e_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhff2d567_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py312h1d6d2e6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-hba22ea6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-py_1003.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-11.0.0-py312h7b63e92_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pkgutil-resolve-name-1.3.10-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/planetary-computer-1.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/proj-9.5.0-h12925eb_0.conda
@@ -1199,21 +1367,21 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd3deb0d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-18.0.0-py312h9cebb41_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-18.0.0-py312h01725c0_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-18.0.0-py312h7900ff3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-18.0.0-py312h01725c0_1_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyarrow-hotfix-0.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.9.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.23.4-py312h12e396e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.10.1-pyh10f6f8f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.27.1-py312h12e396e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.10.0-py312he8b4914_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.10.0-py312h02b19dd_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.7.0-py312he630544_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/pystac-1.11.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pystac-client-0.8.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.12.7-hc5c86c4_0_cpython.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-dotenv-1.0.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-5_cp312.conda
@@ -1221,15 +1389,18 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.2-py312h66e93f0_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-26.2.0-py312hbf22597_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.4.2-py312h8456570_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.4.2-py312h8cae83d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2024.07.02-h77b4e00_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.35.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/rioxarray-0.17.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.5-h3931f03_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.21.0-py312h12e396e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.9-h0fd0ee4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.5.2-py312h7a48858_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.14.1-py312h62794b6_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.3.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.6.0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/shapely-2.0.6-py312h391bc85_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-ha2e4443_0.conda
@@ -1237,6 +1408,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.47.0-h9eae976_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/stac-geoparquet-0.6.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.3.0-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4.1-py312h66e93f0_1.conda
@@ -1248,6 +1420,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/uriparser-0.9.8-hac33072_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.13-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/x265-3.5-h924138e_3.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2024.10.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.2.5-h988505b_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hb9d3cd8_1.conda
@@ -1255,8 +1428,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2024.9.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312hef9b889_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
@@ -1264,9 +1437,9 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/a4/57/6402242dde160d9ef9903487b4277443dc3da04615f6c4d3b48564a8ab57/aiobotocore-2.15.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/f7/d8/120cd0fe3e8530df0539e71ba9683eade12cae103dd7543e50d15f737917/aiohappyeyeballs-2.4.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/bf/fe/1332409d845ca601893bbf2d76935e0b93d41686e5f333841c7d7a4a770d/aiohttp-3.10.10-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/91/93/ad77782c5edfa17aafc070bef978fbfb8459b2f150595ffb01b559c136f9/aiohttp-3.11.7-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/42/0e/c2500f07a6b8e79983ecda6acf43505c7f12cc53e6e940e8e46afc3f7a5a/aiohttp_oauth2_client-1.0.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/13/1f/ec9b0dfcbad7b48f2ca06e0ba5e6d9932eb9f27108f36628131b52d961d9/aiohttp_retry-2.9.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/1a/99/84ba7273339d0f3dfa57901b846489d2e5c2cd731470167757f1935fffbd/aiohttp_retry-2.9.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/85/13/58b70a580de00893223d61de8fea167877a3aed97d4a5e1405c9159ef925/aioitertools-0.12.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl
@@ -1282,13 +1455,11 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/f1/66/033e58a50fd9ec9df00a8671c74f1f3a320564c6415a4ed82a1c651654ba/greenlet-3.1.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/31/b4/b9b800c45527aadd64d5b442f9b932b00648617eb5d63d2c7a6587b7cafc/jmespath-1.0.1-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/69/4a/4f9dbeb84e8850557c02365a0eee0649abe5eb1d84af92a25731c6c0f922/jsonschema-4.23.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/d1/0f/8910b19ac0670a0f80ce1008e5e751c4a57e14d2c4c13a482aa6079fa9d6/jsonschema_specifications-2024.10.1-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/f8/2f/0bb8eacdd1102a20fecc759fb8ace695b9a1048563499a6dff8fa8da32a7/jupyter_cache-1.0.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/64/6b/67b87da9d36bff9df7d0efbd1a325fa372a43be7158effaf43ed7b22341d/jupyter_cache-1.0.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/cb/39/cef2ccdfd984ae3cf93878d050c1b7c9354dd9493ce83fd9bb33a41f7a33/lark-0.12.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/0a/6e/94537acfb5b8f18235d13186d247bca478fea5e87d224644e0fe907df976/lxml-5.3.0-cp312-cp312-manylinux_2_28_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/96/a2/08928cfa4a2b0dd228e79dd41af7fda0d509436ae1646bca8a3ce55a5250/maxar_platform-1.0.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/e5/d0/6dcc39e2c367b9fb91c4a0fd2c7c7b565d1f1bef0cbcb8cbda032ebb3bce/maxar_platform-1.0.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a7/f7/7782a043553ee469c1ff49cfa1cdace2d6bf99a1f333cf38676b3ddf30da/mdit_py_plugins-0.4.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/d3/c8/529101d7176fe7dfe1d99604e48d69c5dfdcadb4f06561f465c8ef12b4df/multidict-6.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
@@ -1303,12 +1474,10 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/ba/92/38f384061e1361fac7092c35e932c0e08026fb9080bf3fbf05f4c3bb6bda/pydata_sphinx_theme-0.16.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/bd/7f/3ae203c863da7c3cf1dbdd7891754df19f0ca917e5bff3490414574177bd/pygeofilter-0.2.4-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/2b/af/a173f5db8a431a23b4f8f52000964f94b6d649a79ae9185762601b958650/pygeoif-1.5.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/b7/59/2056f61236782a2c86b33906c025d4f4a0b17be0161b63b70fd9e8775d36/referencing-0.35.1-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/29/52/84662b6636061277cb857f658518aa7db6672bc6d1a3f503ccd5aefc581e/regex-2024.9.11-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/fb/13/e3b075031a738c9598c51cfbc4c7879e26729c53aa9cca59211c44235314/regex-2024.11.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/3b/5d/63d4ae3b9daea098d5d6f5da83984853c1bbacd5dc826764b249fe119d24/requests_oauthlib-2.0.0-py2.py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/9a/e2/10e9819cf4a20bd8ea2f5dabafc2e6bf4a78d6a0965daeb60a4b34d1c11f/rich-13.9.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/c8/1b/f23015cb293927c93bdb4b94a48bfe77ad9d57359c75db51f0ff0cf482ff/rpds_py-0.20.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-      - pypi: https://files.pythonhosted.org/packages/e5/c0/b0fba8259b61c938c9733da9346b9f93e00881a9db22aafdd72f6ae0ec05/s3transfer-0.10.3-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/19/71/39c7c0d87f8d4e6c020a393182060eaefeeae6c01dab6a84ec346f2567df/rich-13.9.4-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/66/05/7957af15543b8c9799209506df4660cba7afc4cf94bfb60513827e96bed6/s3transfer-0.10.4-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/d1/c2/fe97d779f3ef3b15f05c94a2f1e3d21732574ed441687474db9d342a7315/soupsieve-2.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/26/60/1ddff83a56d33aaf6f10ec8ce84b4c007d9368b21008876fceda7e7381ef/sphinx-8.1.3-py3-none-any.whl
@@ -1321,32 +1490,33 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/27/83/859ecdd180cacc13b1f7e857abf8582a64552ea7a061057a6c716e790fce/sphinxcontrib_qthelp-2.0.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/8a/ab/81d4514527c068670cb1d7ab62a81a185df53a7c379bd2a5636e83d09ede/SQLAlchemy-2.0.36-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-      - pypi: https://files.pythonhosted.org/packages/4e/78/b5d239e4a9284fd0564729bbdae44877b15622b571bc848c2fea3a82df44/stac_asset-0.4.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/ac/ad/173f9fc405b787b819c5285192d8d44e683b648d29cee5252567c05fe470/stac_asset-0.4.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a1/cb/93bc0e51bea4e171a85151dbba3c3b3f612b50b953cd3076f5b4f0db9e14/time_machine-2.16.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/97/3f/c4c51c55ff8487f2e6d0e618dba917e3c3ee2caae6cf0fbb59c9b1876f2e/tzlocal-5.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/62/62/30ca2405de6a20448ee557ab2cd61ab9c5900be7cbd18a2639db595f0b98/wrapt-1.16.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-      - pypi: https://files.pythonhosted.org/packages/c3/c0/cd8e94618983c1b811af082e1a7ad7764edb3a6af2bc6b468e0e686238ba/yarl-1.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/d2/50/dbef1a651578a3520d4534c1e434989e3620380c1ad97e309576b47f0ada/wrapt-1.17.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/9a/9f/63864f43d131ba8c8cdf1bde5dd3f02f0eff8a7c883a5d7fad32f204fda5/yarl-1.18.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: .
       osx-arm64:
       - conda: https://conda.anaconda.org/conda-forge/noarch/affine-2.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aom-3.9.1-h7bae524_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-2.4.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-24.2.0-pyh71513ae_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.7.31-hee1f4ab_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.7.4-hfd083d3_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-common-0.9.31-h7ab814d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-compression-0.2.19-hfd083d3_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-event-stream-0.5.0-h33c80d7_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-http-0.8.10-h4a91a90_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-io-0.14.20-h5fdde16_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-mqtt-0.10.7-hd821a15_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-s3-0.7.0-hc6bcb7c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-sdkutils-0.1.19-hfd083d3_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-checksums-0.1.20-hfd083d3_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.29.0-h45f4ed5_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-sdk-cpp-1.11.407-h0a0d3c4_6.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.8.0-h9b725a8_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.8.0-h5d7ee29_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-common-0.10.3-h5505292_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-compression-0.3.0-h5d7ee29_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-event-stream-0.5.0-h13ead76_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-http-0.9.1-hf483d09_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-io-0.15.2-h39f8ad8_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-mqtt-0.11.0-h68a0d7e_8.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-s3-0.7.1-h840aca7_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-sdkutils-0.2.1-h5d7ee29_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-checksums-0.2.2-h5d7ee29_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.29.5-h6832833_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-sdk-cpp-1.11.449-h8577fd2_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-core-cpp-1.14.0-hd50102c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-identity-cpp-1.10.0-hc602bab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-storage-blobs-cpp-12.13.0-h7585a09_1.conda
@@ -1358,7 +1528,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-bin-1.1.0-hd74edd7_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-python-1.1.0-py312hde4cb15_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-h99b78c6_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.2-h7ab814d_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.3-h5505292_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ca-certificates-2024.8.30-hf0a4a13_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cffi-1.17.1-py312h0fad829_0.conda
@@ -1368,15 +1538,16 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/click-plugins-1.1.1-py_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/cligj-0.7.2-pyhd8ed1ab_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.0-py312h4721b07_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.1-py312hb23fbb9_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.7-py312hde4cb15_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/dav1d-1.2.1-hb547adb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.9-py312hd8f9ff3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.1.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/deltalake-0.21.0-py312h30a1e44_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/folium-0.18.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.54.1-py312ha0ccf2a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.55.0-py312h998013c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/freetype-2.12.1-hadb7bae_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/freexl-2.0.0-hfbad9fb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.0.1-pyhd8ed1ab_1.conda
@@ -1392,12 +1563,15 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-75.1-hfee45f7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.5.0-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-6.29.5-pyh57ce528_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-8.29.0-pyh707e725_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/json-c-0.18-he4178ee_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.23.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2024.10.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.6.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.7.2-pyh31011fe_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/kiwisolver-1.4.7-py312h6142ec9_0.conda
@@ -1406,10 +1580,11 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lerc-4.0.0-h9a09cb3_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libabseil-20240722.0-cxx17_hf9b8971_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarchive-3.7.4-h83d404f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-18.0.0-h6fea68a_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-18.0.0-h286801f_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-18.0.0-h286801f_0_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-18.0.0-hdcc9e87_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-18.0.0-hb943b0e_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-18.0.0-h286801f_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-18.0.0-h286801f_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-18.0.0-h6a6e5c5_8_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libavif16-1.1.1-h45b7238_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-25_osxarm64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlicommon-1.1.0-hd74edd7_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlidec-1.1.0-hd74edd7_2.conda
@@ -1417,28 +1592,30 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-25_osxarm64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.10.1-h13a7ad3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.3-ha82da77_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.4-ha82da77_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libde265-1.0.15-h2ffa867_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libdeflate-1.22-hd74edd7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20191231-hc8eb9b7_2.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libev-4.33-h93a5062_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.6.3-hf9b8971_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.6.4-h286801f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.4.2-h3422bc3_5.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.9.3-hb8ac103_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.10.0-h1554e7d_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-5.0.0-13_2_0_hd922786_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-13.2.0-hf226fd6_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.30.0-h2e6cea1_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.30.0-h90fd6fa_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.65.5-h3d9cf25_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.31.0-h8d8be31_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.31.0-h7081f7f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.67.1-hc70892a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libheif-1.18.2-gpl_he913df3_100.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libiconv-1.17-h0d3ecfb_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libjpeg-turbo-3.0.0-hb547adb_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libkml-1.3.0-he250239_1021.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-25_osxarm64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libnghttp2-1.64.0-h6d7220d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.28-openmp_h517c56d_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-18.0.0-hda0ea68_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.28-openmp_hf332438_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-18.0.0-hda0ea68_8_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libpng-1.6.44-hc14010f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libprotobuf-5.27.5-h53f8970_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libprotobuf-5.28.2-h8f0b736_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libre2-11-2024.07.02-h2348fd5_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/librttopo-1.1.0-ha2cf0f4_17.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsodium-1.0.20-h99b78c6_0.conda
@@ -1450,32 +1627,33 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libutf8proc-2.8.0-h1a8c8d9_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libwebp-base-1.4.0-h93a5062_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxcb-1.17.0-hdb1d25a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.13.4-h8424949_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.13.5-hbbdcc80_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.3.1-h8359307_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-19.1.3-hb52a8e5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-19.1.4-hdb05f8b_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-c-1.9.4-hb7217d7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lzo-2.10-h93a5062_1001.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/mapclassify-2.8.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/markupsafe-3.0.2-py312ha0ccf2a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.9.2-py312h9bd0bc6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.9.2-py312h9bd0bc6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.1.7-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/minizip-4.0.7-h27ee973_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h7bae524_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.1.2-py312h801f5e3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.1.3-py312h94ee1e1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openjpeg-2.5.2-h9f1df11_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.3.2-h8359307_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.2-h4a9587e_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orjson-3.10.10-py312heca05bc_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.4.0-h39f12f2_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.3-h121fd32_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orjson-3.10.11-py312hcd83bfe_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhff2d567_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-2.2.2-py312h8ae5369_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pcre2-10.44-h297a79d_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-py_1003.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pillow-11.0.0-py312haf37ca6_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pkgutil-resolve-name-1.3.10-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/planetary-computer-1.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/proj-9.5.0-h61a8e3e_0.conda
@@ -1484,21 +1662,21 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pthread-stubs-0.4-hd74edd7_1002.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd3deb0d_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-18.0.0-py312ha814d7c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-18.0.0-py312hc40f475_0_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-18.0.0-py312h1f38498_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-18.0.0-py312hc40f475_1_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyarrow-hotfix-0.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.9.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pydantic-core-2.23.4-py312he431725_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.10.1-pyh10f6f8f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pydantic-core-2.27.1-py312hcd83bfe_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyogrio-0.10.0-py312hf9e36c7_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyogrio-0.10.0-py312hfd5e53c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyproj-3.7.0-py312h1ab748d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/pystac-1.11.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pystac-client-0.8.5-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.12.7-h739c21a_0_cpython.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-dotenv-1.0.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python_abi-3.12-5_cp312.conda
@@ -1506,14 +1684,17 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyyaml-6.0.2-py312h024a12e_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyzmq-26.2.0-py312hf8a1cbd_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/qhull-2020.2-h420ef59_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rasterio-1.4.2-py312h72e867f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rasterio-1.4.2-py312h02264c4_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rav1e-0.6.6-h69fbcac_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/re2-2024.07.02-hcd0e937_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.2-h92ec313_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.35.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/rioxarray-0.17.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.21.0-py312hcd83bfe_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scikit-learn-1.5.2-py312h387f99c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.14.1-py312h20deb59_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.3.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.6.0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/shapely-2.0.6-py312h3a6007a_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.1-hd02b534_0.conda
@@ -1521,6 +1702,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/sqlite-3.47.0-hcd14bea_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/stac-geoparquet-0.6.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/svt-av1-2.3.0-hf24288c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h5083fa2_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.4.1-py312h024a12e_1.conda
@@ -1532,6 +1714,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/uriparser-0.9.8-h00cdb27_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.13-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/x265-3.5-hbc6ce65_3.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2024.10.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xerces-c-3.2.5-h92fc2f4_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.11-hd74edd7_1.conda
@@ -1539,8 +1722,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2024.9.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xz-5.2.6-h57fd34a_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h3422bc3_2.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h9f5b81c_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-hc1bb282_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py312h15fbf35_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
@@ -1548,9 +1731,9 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/a4/57/6402242dde160d9ef9903487b4277443dc3da04615f6c4d3b48564a8ab57/aiobotocore-2.15.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/f7/d8/120cd0fe3e8530df0539e71ba9683eade12cae103dd7543e50d15f737917/aiohappyeyeballs-2.4.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/e1/77/0aa8660dcf11fa65d61712dbb458c4989de220a844bd69778dff25f2d50b/aiohttp-3.10.10-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/28/fa/f4d98db1b7f8f0c3f74bdbd6d0d98cfc89984205cd33f1b8ee3f588ee5ad/aiohttp-3.11.7-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/42/0e/c2500f07a6b8e79983ecda6acf43505c7f12cc53e6e940e8e46afc3f7a5a/aiohttp_oauth2_client-1.0.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/13/1f/ec9b0dfcbad7b48f2ca06e0ba5e6d9932eb9f27108f36628131b52d961d9/aiohttp_retry-2.9.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/1a/99/84ba7273339d0f3dfa57901b846489d2e5c2cd731470167757f1935fffbd/aiohttp_retry-2.9.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/85/13/58b70a580de00893223d61de8fea167877a3aed97d4a5e1405c9159ef925/aioitertools-0.12.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl
@@ -1565,13 +1748,11 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/d0/9a/8e479b482a6f2070b26bda572c5e6889bb3ba48977e81beea35b5ae13ece/frozenlist-1.5.0-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/31/b4/b9b800c45527aadd64d5b442f9b932b00648617eb5d63d2c7a6587b7cafc/jmespath-1.0.1-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/69/4a/4f9dbeb84e8850557c02365a0eee0649abe5eb1d84af92a25731c6c0f922/jsonschema-4.23.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/d1/0f/8910b19ac0670a0f80ce1008e5e751c4a57e14d2c4c13a482aa6079fa9d6/jsonschema_specifications-2024.10.1-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/f8/2f/0bb8eacdd1102a20fecc759fb8ace695b9a1048563499a6dff8fa8da32a7/jupyter_cache-1.0.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/64/6b/67b87da9d36bff9df7d0efbd1a325fa372a43be7158effaf43ed7b22341d/jupyter_cache-1.0.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/cb/39/cef2ccdfd984ae3cf93878d050c1b7c9354dd9493ce83fd9bb33a41f7a33/lark-0.12.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/eb/6d/d1f1c5e40c64bf62afd7a3f9b34ce18a586a1cccbf71e783cd0a6d8e8971/lxml-5.3.0-cp312-cp312-macosx_10_9_universal2.whl
       - pypi: https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/96/a2/08928cfa4a2b0dd228e79dd41af7fda0d509436ae1646bca8a3ce55a5250/maxar_platform-1.0.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/e5/d0/6dcc39e2c367b9fb91c4a0fd2c7c7b565d1f1bef0cbcb8cbda032ebb3bce/maxar_platform-1.0.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a7/f7/7782a043553ee469c1ff49cfa1cdace2d6bf99a1f333cf38676b3ddf30da/mdit_py_plugins-0.4.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a2/12/adb6b3200c363062f805275b4c1e656be2b3681aada66c80129932ff0bae/multidict-6.1.0-cp312-cp312-macosx_11_0_arm64.whl
@@ -1586,12 +1767,10 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/ba/92/38f384061e1361fac7092c35e932c0e08026fb9080bf3fbf05f4c3bb6bda/pydata_sphinx_theme-0.16.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/bd/7f/3ae203c863da7c3cf1dbdd7891754df19f0ca917e5bff3490414574177bd/pygeofilter-0.2.4-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/2b/af/a173f5db8a431a23b4f8f52000964f94b6d649a79ae9185762601b958650/pygeoif-1.5.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/b7/59/2056f61236782a2c86b33906c025d4f4a0b17be0161b63b70fd9e8775d36/referencing-0.35.1-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/8a/ea/909d8620329ab710dfaf7b4adee41242ab7c9b95ea8d838e9bfe76244259/regex-2024.9.11-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/60/85/cebcc0aff603ea0a201667b203f13ba75d9fc8668fab917ac5b2de3967bc/regex-2024.11.6-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/3b/5d/63d4ae3b9daea098d5d6f5da83984853c1bbacd5dc826764b249fe119d24/requests_oauthlib-2.0.0-py2.py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/9a/e2/10e9819cf4a20bd8ea2f5dabafc2e6bf4a78d6a0965daeb60a4b34d1c11f/rich-13.9.3-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/d2/e4/61144f3790e12fd89e6153d77f7915ad26779735fef8ee9c099cba6dfb4a/rpds_py-0.20.1-cp312-cp312-macosx_11_0_arm64.whl
-      - pypi: https://files.pythonhosted.org/packages/e5/c0/b0fba8259b61c938c9733da9346b9f93e00881a9db22aafdd72f6ae0ec05/s3transfer-0.10.3-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/19/71/39c7c0d87f8d4e6c020a393182060eaefeeae6c01dab6a84ec346f2567df/rich-13.9.4-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/66/05/7957af15543b8c9799209506df4660cba7afc4cf94bfb60513827e96bed6/s3transfer-0.10.4-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/d1/c2/fe97d779f3ef3b15f05c94a2f1e3d21732574ed441687474db9d342a7315/soupsieve-2.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/26/60/1ddff83a56d33aaf6f10ec8ce84b4c007d9368b21008876fceda7e7381ef/sphinx-8.1.3-py3-none-any.whl
@@ -1604,12 +1783,12 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/27/83/859ecdd180cacc13b1f7e857abf8582a64552ea7a061057a6c716e790fce/sphinxcontrib_qthelp-2.0.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/94/65/f109d5720779a08e6e324ec89a744f5f92c48bd8005edc814bf72fbb24e5/SQLAlchemy-2.0.36-cp312-cp312-macosx_11_0_arm64.whl
-      - pypi: https://files.pythonhosted.org/packages/4e/78/b5d239e4a9284fd0564729bbdae44877b15622b571bc848c2fea3a82df44/stac_asset-0.4.5-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/ac/ad/173f9fc405b787b819c5285192d8d44e683b648d29cee5252567c05fe470/stac_asset-0.4.6-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/4a/f4/603a84e7ae6427a53953db9f61b689dc6adf233e03c5f5ca907a901452fd/time_machine-2.16.0-cp312-cp312-macosx_10_9_universal2.whl
       - pypi: https://files.pythonhosted.org/packages/97/3f/c4c51c55ff8487f2e6d0e618dba917e3c3ee2caae6cf0fbb59c9b1876f2e/tzlocal-5.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/6a/d7/cfcd73e8f4858079ac59d9db1ec5a1349bc486ae8e9ba55698cc1f4a1dff/wrapt-1.16.0-cp312-cp312-macosx_11_0_arm64.whl
-      - pypi: https://files.pythonhosted.org/packages/e3/a2/b65447626227ebe36f18f63ac551790068bf42c69bb22dfa3ae986170728/yarl-1.17.1-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/85/82/518605474beafff11f1a34759f6410ab429abff9f7881858a447e0d20712/wrapt-1.17.0-cp312-cp312-macosx_11_0_arm64.whl
+      - pypi: https://files.pythonhosted.org/packages/eb/e1/3081b578a6f21961711b9a1c49c2947abb3b0d0dd9537378fb06777ce8ee/yarl-1.18.0-cp312-cp312-macosx_11_0_arm64.whl
       - pypi: .
 packages:
 - kind: conda
@@ -1682,12 +1861,12 @@ packages:
   url: https://files.pythonhosted.org/packages/a4/57/6402242dde160d9ef9903487b4277443dc3da04615f6c4d3b48564a8ab57/aiobotocore-2.15.2-py3-none-any.whl
   sha256: d4d3128b4b558e2b4c369bfa963b022d7e87303adb82eec623cec8aa77ae578a
   requires_dist:
-  - botocore<1.35.37,>=1.35.16
-  - aiohttp<4.0.0,>=3.9.2
-  - wrapt<2.0.0,>=1.10.10
-  - aioitertools<1.0.0,>=0.5.1
-  - awscli<1.35.3,>=1.34.16 ; extra == 'awscli'
-  - boto3<1.35.37,>=1.35.16 ; extra == 'boto3'
+  - botocore>=1.35.16,<1.35.37
+  - aiohttp>=3.9.2,<4.0.0
+  - wrapt>=1.10.10,<2.0.0
+  - aioitertools>=0.5.1,<1.0.0
+  - awscli>=1.34.16,<1.35.3 ; extra == 'awscli'
+  - boto3>=1.35.16,<1.35.37 ; extra == 'boto3'
   requires_python: '>=3.8'
 - kind: pypi
   name: aiofiles
@@ -1703,38 +1882,40 @@ packages:
   requires_python: '>=3.8'
 - kind: pypi
   name: aiohttp
-  version: 3.10.10
-  url: https://files.pythonhosted.org/packages/bf/fe/1332409d845ca601893bbf2d76935e0b93d41686e5f333841c7d7a4a770d/aiohttp-3.10.10-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-  sha256: a45d85cf20b5e0d0aa5a8dca27cce8eddef3292bc29d72dcad1641f4ed50aa16
+  version: 3.11.7
+  url: https://files.pythonhosted.org/packages/28/fa/f4d98db1b7f8f0c3f74bdbd6d0d98cfc89984205cd33f1b8ee3f588ee5ad/aiohttp-3.11.7-cp312-cp312-macosx_11_0_arm64.whl
+  sha256: f7c58a240260822dc07f6ae32a0293dd5bccd618bb2d0f36d51c5dbd526f89c0
   requires_dist:
   - aiohappyeyeballs>=2.3.0
   - aiosignal>=1.1.2
+  - async-timeout>=4.0,<6.0 ; python_full_version < '3.11'
   - attrs>=17.3.0
   - frozenlist>=1.1.1
-  - multidict<7.0,>=4.5
-  - yarl<2.0,>=1.12.0
-  - async-timeout<5.0,>=4.0 ; python_full_version < '3.11'
-  - brotlicffi ; platform_python_implementation != 'CPython' and extra == 'speedups'
-  - brotli ; platform_python_implementation == 'CPython' and extra == 'speedups'
+  - multidict>=4.5,<7.0
+  - propcache>=0.2.0
+  - yarl>=1.17.0,<2.0
   - aiodns>=3.2.0 ; (sys_platform == 'darwin' and extra == 'speedups') or (sys_platform == 'linux' and extra == 'speedups')
-  requires_python: '>=3.8'
+  - brotli ; platform_python_implementation == 'CPython' and extra == 'speedups'
+  - brotlicffi ; platform_python_implementation != 'CPython' and extra == 'speedups'
+  requires_python: '>=3.9'
 - kind: pypi
   name: aiohttp
-  version: 3.10.10
-  url: https://files.pythonhosted.org/packages/e1/77/0aa8660dcf11fa65d61712dbb458c4989de220a844bd69778dff25f2d50b/aiohttp-3.10.10-cp312-cp312-macosx_11_0_arm64.whl
-  sha256: 5c6a5b8c7926ba5d8545c7dd22961a107526562da31a7a32fa2456baf040939f
+  version: 3.11.7
+  url: https://files.pythonhosted.org/packages/91/93/ad77782c5edfa17aafc070bef978fbfb8459b2f150595ffb01b559c136f9/aiohttp-3.11.7-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+  sha256: 9ee6a4cdcbf54b8083dc9723cdf5f41f722c00db40ccf9ec2616e27869151129
   requires_dist:
   - aiohappyeyeballs>=2.3.0
   - aiosignal>=1.1.2
+  - async-timeout>=4.0,<6.0 ; python_full_version < '3.11'
   - attrs>=17.3.0
   - frozenlist>=1.1.1
-  - multidict<7.0,>=4.5
-  - yarl<2.0,>=1.12.0
-  - async-timeout<5.0,>=4.0 ; python_full_version < '3.11'
-  - brotlicffi ; platform_python_implementation != 'CPython' and extra == 'speedups'
-  - brotli ; platform_python_implementation == 'CPython' and extra == 'speedups'
+  - multidict>=4.5,<7.0
+  - propcache>=0.2.0
+  - yarl>=1.17.0,<2.0
   - aiodns>=3.2.0 ; (sys_platform == 'darwin' and extra == 'speedups') or (sys_platform == 'linux' and extra == 'speedups')
-  requires_python: '>=3.8'
+  - brotli ; platform_python_implementation == 'CPython' and extra == 'speedups'
+  - brotlicffi ; platform_python_implementation != 'CPython' and extra == 'speedups'
+  requires_python: '>=3.9'
 - kind: pypi
   name: aiohttp-oauth2-client
   version: 1.0.2
@@ -1758,9 +1939,9 @@ packages:
   requires_python: '>=3.8'
 - kind: pypi
   name: aiohttp-retry
-  version: 2.9.0
-  url: https://files.pythonhosted.org/packages/13/1f/ec9b0dfcbad7b48f2ca06e0ba5e6d9932eb9f27108f36628131b52d961d9/aiohttp_retry-2.9.0-py3-none-any.whl
-  sha256: 7661af92471e9a96c69d9b8f32021360272073397e6a15bc44c1726b12f46056
+  version: 2.9.1
+  url: https://files.pythonhosted.org/packages/1a/99/84ba7273339d0f3dfa57901b846489d2e5c2cd731470167757f1935fffbd/aiohttp_retry-2.9.1-py3-none-any.whl
+  sha256: 66d2759d1921838256a05a3f80ad7e724936f083e35be5abb5e16eed6be6dc54
   requires_dist:
   - aiohttp
   requires_python: '>=3.7'
@@ -1815,6 +1996,38 @@ packages:
   - pkg:pypi/annotated-types?source=hash-mapping
   size: 18235
   timestamp: 1716290348421
+- kind: conda
+  name: aom
+  version: 3.9.1
+  build: h7bae524_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aom-3.9.1-h7bae524_0.conda
+  sha256: ec238f18ce8140485645252351a0eca9ef4f7a1c568a420f240a585229bc12ef
+  md5: 7adba36492a1bb22d98ffffe4f6fc6de
+  depends:
+  - __osx >=11.0
+  - libcxx >=16
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 2235747
+  timestamp: 1718551382432
+- kind: conda
+  name: aom
+  version: 3.9.1
+  build: hac33072_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda
+  sha256: b08ef033817b5f9f76ce62dfcac7694e7b6b4006420372de22494503decac855
+  md5: 346722a0be40f6edc53f12640d301338
+  depends:
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 2706396
+  timestamp: 1718551242397
 - kind: conda
   name: appnope
   version: 0.1.4
@@ -1904,521 +2117,525 @@ packages:
   timestamp: 1722977241383
 - kind: conda
   name: aws-c-auth
-  version: 0.7.31
-  build: hcdce11a_5
-  build_number: 5
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.31-hcdce11a_5.conda
-  sha256: f2a0f4ea442315232166ea9b7b85be36d10066507029a7ffd9bdee7c4da4ea1c
-  md5: 5e8936e89bae15461d8a0d2b8920f181
+  version: 0.8.0
+  build: h9b725a8_10
+  build_number: 10
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.8.0-h9b725a8_10.conda
+  sha256: 63cb8c25e0a26be4261d4271de525e7e33aefe9d9b001b8abfa5c9ac69c3dab3
+  md5: 17c90d9eb8c6842fd739dc5445ce9962
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - aws-c-cal >=0.7.4,<0.7.5.0a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - aws-c-http >=0.8.10,<0.8.11.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
-  - aws-c-sdkutils >=0.1.19,<0.1.20.0a0
-  - libgcc >=13
+  - __osx >=11.0
+  - aws-c-cal >=0.8.0,<0.8.1.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - aws-c-http >=0.9.1,<0.9.2.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
+  - aws-c-sdkutils >=0.2.1,<0.2.2.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 107177
-  timestamp: 1729645226835
+  size: 92355
+  timestamp: 1731733738919
 - kind: conda
   name: aws-c-auth
-  version: 0.7.31
-  build: hee1f4ab_5
-  build_number: 5
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.7.31-hee1f4ab_5.conda
-  sha256: c4a5583292972810a24ac062ec8ff5ea16d2c9d4300daa7c895757da3dff1a2d
-  md5: 262580840e8d3007fd6a55340a518fa8
+  version: 0.8.0
+  build: hb88c0a9_10
+  build_number: 10
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.8.0-hb88c0a9_10.conda
+  sha256: d2837a84e6bd7d993a83e79f9e240e1465e375f3d57149ea5b1927c6a4133bcc
+  md5: 409b7ee6d3473cc62bda7280f6ac20d0
   depends:
-  - __osx >=11.0
-  - aws-c-cal >=0.7.4,<0.7.5.0a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - aws-c-http >=0.8.10,<0.8.11.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
-  - aws-c-sdkutils >=0.1.19,<0.1.20.0a0
+  - __glibc >=2.17,<3.0.a0
+  - aws-c-cal >=0.8.0,<0.8.1.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - aws-c-http >=0.9.1,<0.9.2.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
+  - aws-c-sdkutils >=0.2.1,<0.2.2.0a0
+  - libgcc >=13
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 92423
-  timestamp: 1729645339177
+  size: 107163
+  timestamp: 1731733534767
 - kind: conda
   name: aws-c-cal
-  version: 0.7.4
-  build: hd3f4568_4
-  build_number: 4
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.7.4-hd3f4568_4.conda
-  sha256: ea8910baaeecdb05f86ee41cf6ea679745677fe320626d347047fce6c04dec02
-  md5: 933b666a736387d5a618ae2173364635
+  version: 0.8.0
+  build: h5d7ee29_2
+  build_number: 2
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.8.0-h5d7ee29_2.conda
+  sha256: 2a8c09b33400cf2b7d658e63fd5a6f9b6e9626458f6213b904592fc15220bc92
+  md5: 92734dad83d22314205ba73b679710d2
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - libgcc >=13
+  - __osx >=11.0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
   - openssl >=3.3.1,<4.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 47689
-  timestamp: 1729595594849
+  size: 39966
+  timestamp: 1731678721786
 - kind: conda
   name: aws-c-cal
-  version: 0.7.4
-  build: hfd083d3_4
-  build_number: 4
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.7.4-hfd083d3_4.conda
-  sha256: 29f767fd1e7f47b3cedddd04ff3f190ab3ee9c96255dde7234e2b04485595af9
-  md5: 335d26e89405e0078c5b43b04c08993c
+  version: 0.8.0
+  build: hecf86a2_2
+  build_number: 2
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.8.0-hecf86a2_2.conda
+  sha256: 220a37955c120bf2f565fbd5320a82fc4c8b550b2449294bc0509c296cfcb9fa
+  md5: c54459d686ad9d0502823cacff7e8423
   depends:
-  - __osx >=11.0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
+  - __glibc >=2.17,<3.0.a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - libgcc >=13
   - openssl >=3.3.1,<4.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 39866
-  timestamp: 1729595783840
+  size: 47477
+  timestamp: 1731678510949
 - kind: conda
   name: aws-c-common
-  version: 0.9.31
-  build: h7ab814d_0
+  version: 0.10.3
+  build: h5505292_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-common-0.9.31-h7ab814d_0.conda
-  sha256: b79d2bccd06dec9a54243d617fb6e2436a930707666ba186bbbe047c46b84064
-  md5: 37eded160015046030d7a68cb44fb3d2
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-common-0.10.3-h5505292_0.conda
+  sha256: bb2c1038726d31ffd2d35a5764f80bcd670b6a1c753aadfd261aecb9f88db6d8
+  md5: 4150339e3b08db33fe4c436340b1d7f6
   depends:
   - __osx >=11.0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 219971
-  timestamp: 1729561861114
+  size: 221524
+  timestamp: 1731567512057
 - kind: conda
   name: aws-c-common
-  version: 0.9.31
+  version: 0.10.3
   build: hb9d3cd8_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.31-hb9d3cd8_0.conda
-  sha256: 31057d023a4ab78996f15dfefa9b2576da3282953623eeba28934c93baf132bc
-  md5: 75f7776e1c9af78287f055ca34797517
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.10.3-hb9d3cd8_0.conda
+  sha256: 90bd2ff40b65acb62f11e2500ee7b7e85ac77d2e332429002f4c1da949bec27f
+  md5: ff3653946d34a6a6ba10babb139d96ef
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 235865
-  timestamp: 1729561746720
+  size: 237137
+  timestamp: 1731567278052
 - kind: conda
   name: aws-c-compression
-  version: 0.2.19
-  build: hf20e7d7_4
-  build_number: 4
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.19-hf20e7d7_4.conda
-  sha256: 48076dd2faa3e7baef0a4e532555ec46c64a0db897d30b1ee505a2c63e70e5e6
-  md5: 7035bf89ef7848fbdd1a0df681651dbd
+  version: 0.3.0
+  build: h5d7ee29_2
+  build_number: 2
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-compression-0.3.0-h5d7ee29_2.conda
+  sha256: a52ea62bf08aed3af079e16d1738f3d2a7fcdd1d260289ae27ae96298e15d12a
+  md5: 15566c36b0cf5f314e3bee7f7cc796b5
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - libgcc >=13
+  - __osx >=11.0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 19070
-  timestamp: 1729595656962
+  size: 18204
+  timestamp: 1731678916439
 - kind: conda
   name: aws-c-compression
-  version: 0.2.19
-  build: hfd083d3_4
-  build_number: 4
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-compression-0.2.19-hfd083d3_4.conda
-  sha256: 46635284267648e2b291b73feaac316a9ab2621cfb1ea37190daabb2226f77e9
-  md5: 1fbd6d35286563704d3d121be73cc3b2
+  version: 0.3.0
+  build: hf42f96a_2
+  build_number: 2
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.0-hf42f96a_2.conda
+  sha256: 210ba4fff1c9500fe02de1dae311ce723bfa313a2d21b72accd745f736f56fce
+  md5: 257f4ae92fe11bd8436315c86468c39b
   depends:
-  - __osx >=11.0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
+  - __glibc >=2.17,<3.0.a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - libgcc >=13
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 18190
-  timestamp: 1729595822426
+  size: 19034
+  timestamp: 1731678703956
 - kind: conda
   name: aws-c-event-stream
   version: 0.5.0
-  build: h33c80d7_0
+  build: h13ead76_7
+  build_number: 7
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-event-stream-0.5.0-h33c80d7_0.conda
-  sha256: c5318cdaa132e524e59bda10058b97d804758494ba5617a289b1e3dd1c5f434f
-  md5: fe41af1ea3a037d48c250f6cbdead72b
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-event-stream-0.5.0-h13ead76_7.conda
+  sha256: 386965fab5f0bed4a6109cdba32579f16bee1b0f76ce1db840ce6f7070188f9f
+  md5: 55a901b6d4fb9ce1bc8328925b229f0b
   depends:
   - __osx >=11.0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
-  - aws-checksums >=0.1.20,<0.1.21.0a0
-  - libcxx >=17
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
+  - aws-checksums >=0.2.2,<0.2.3.0a0
+  - libcxx >=18
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 47002
-  timestamp: 1729717479380
+  size: 47528
+  timestamp: 1731714690911
 - kind: conda
   name: aws-c-event-stream
   version: 0.5.0
-  build: h72d8268_0
+  build: h1ffe551_7
+  build_number: 7
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.0-h72d8268_0.conda
-  sha256: 2b7515d53020bde5a8fcd76c0f0b8cbba396f8482fa879f96c8e6ce914b7aa3a
-  md5: a2d73df9aa3ab6eafc1c8dc0642d532f
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.0-h1ffe551_7.conda
+  sha256: 3b780d6483baa889e8df5aa66ab3c439a9c81331cf2a4799e373f4174768ddd9
+  md5: 7cce4dfab184f4bbdfc160789251b3c5
   depends:
   - __glibc >=2.17,<3.0.a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
-  - aws-checksums >=0.1.20,<0.1.21.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
+  - aws-checksums >=0.2.2,<0.2.3.0a0
   - libgcc >=13
   - libstdcxx >=13
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 53685
-  timestamp: 1729717317804
-- kind: conda
-  name: aws-c-http
-  version: 0.8.10
-  build: h4a91a90_5
-  build_number: 5
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-http-0.8.10-h4a91a90_5.conda
-  sha256: 5a6a382998e3f7f91a909d5c0d5faed19ed2b05a8f7334b6dfabcc1b0f72aaec
-  md5: 8508d0f9a832dba72601771fb1bff339
-  depends:
-  - __osx >=11.0
-  - aws-c-cal >=0.7.4,<0.7.5.0a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - aws-c-compression >=0.2.19,<0.2.20.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
-  license: Apache-2.0
-  license_family: Apache
-  purls: []
-  size: 152140
-  timestamp: 1729624809388
+  size: 53500
+  timestamp: 1731714597524
 - kind: conda
   name: aws-c-http
-  version: 0.8.10
-  build: h6bb76cc_5
-  build_number: 5
+  version: 0.9.1
+  build: hab05fe4_2
+  build_number: 2
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.8.10-h6bb76cc_5.conda
-  sha256: 16b2b1c1498c0b1a2143b418e18ec4ccd40e776837f8a176c351aada48b818b5
-  md5: 243b3e5ef92b277b04b1490213c21ca7
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.9.1-hab05fe4_2.conda
+  sha256: 90a325b6f5371dd2203b643de646967fe57a4bcbbee8c91086abbf9dd733d59a
+  md5: fb409f7053fa3dbbdf6eb41045a87795
   depends:
   - __glibc >=2.17,<3.0.a0
-  - aws-c-cal >=0.7.4,<0.7.5.0a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - aws-c-compression >=0.2.19,<0.2.20.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
+  - aws-c-cal >=0.8.0,<0.8.1.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - aws-c-compression >=0.3.0,<0.3.1.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
   - libgcc >=13
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 197365
-  timestamp: 1729624546275
+  size: 196945
+  timestamp: 1731714483279
 - kind: conda
-  name: aws-c-io
-  version: 0.14.20
-  build: h389d861_2
+  name: aws-c-http
+  version: 0.9.1
+  build: hf483d09_2
   build_number: 2
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.20-h389d861_2.conda
-  sha256: 1f345fac0112b1a7b34a3c9f7c4952c28080ef793ca188d3a10694091f112c53
-  md5: 79adfaf8508472f5fbffe6df841d3d8c
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-http-0.9.1-hf483d09_2.conda
+  sha256: fca9ed0f0895bab9bf737c8d8a3314556cb893d45c40f0656f21a93502db3089
+  md5: d880c40b8fc7d07374c036f93f1359d2
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - aws-c-cal >=0.7.4,<0.7.5.0a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - libgcc >=13
-  - s2n >=1.5.5,<1.5.6.0a0
+  - __osx >=11.0
+  - aws-c-cal >=0.8.0,<0.8.1.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - aws-c-compression >=0.3.0,<0.3.1.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 159514
-  timestamp: 1729608940267
+  size: 153315
+  timestamp: 1731714621306
 - kind: conda
   name: aws-c-io
-  version: 0.14.20
-  build: h5fdde16_2
+  version: 0.15.2
+  build: h39f8ad8_2
   build_number: 2
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-io-0.14.20-h5fdde16_2.conda
-  sha256: a7dbeccb720b1afcad782c6f987cb73d3330d0e132f09b0f6b2742d6e80cd68c
-  md5: 9126fa7621e270452608acd95e21c263
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-io-0.15.2-h39f8ad8_2.conda
+  sha256: b14e32f024f6be1610dccfdb6371e101cba204d24f37c2a63d9b6380ac74ac17
+  md5: 3b49f1dd8f20bead8b222828cfdad585
   depends:
   - __osx >=11.0
-  - aws-c-cal >=0.7.4,<0.7.5.0a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
+  - aws-c-cal >=0.8.0,<0.8.1.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 137534
-  timestamp: 1729608966952
+  size: 137610
+  timestamp: 1731702839896
 - kind: conda
-  name: aws-c-mqtt
-  version: 0.10.7
-  build: had056f2_5
-  build_number: 5
+  name: aws-c-io
+  version: 0.15.2
+  build: hdeadb07_2
+  build_number: 2
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.10.7-had056f2_5.conda
-  sha256: d2e6e45502253646f9f78e2ac034ff15bc4fd7ae5898707f24f91c3039c8ceda
-  md5: 575798408145288d75bf0fd36bed5aa1
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.15.2-hdeadb07_2.conda
+  sha256: 1636136a5d882b4aaa13ea8b7de8cf07038a6878872e3c434df9daf478cee594
+  md5: 461a1eaa075fd391add91bcffc9de0c1
   depends:
   - __glibc >=2.17,<3.0.a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - aws-c-http >=0.8.10,<0.8.11.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
+  - aws-c-cal >=0.8.0,<0.8.1.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
   - libgcc >=13
+  - s2n >=1.5.9,<1.5.10.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 194676
-  timestamp: 1729646037940
+  size: 159368
+  timestamp: 1731702542973
 - kind: conda
   name: aws-c-mqtt
-  version: 0.10.7
-  build: hd821a15_5
-  build_number: 5
+  version: 0.11.0
+  build: h68a0d7e_8
+  build_number: 8
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-mqtt-0.10.7-hd821a15_5.conda
-  sha256: a9ba03c5f143d0d792261c9b0c2cc500b49e7b617164e090ddcbf5974a0c617a
-  md5: 8cd5a4acf5aa0d20d30781faaf74d7ad
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-mqtt-0.11.0-h68a0d7e_8.conda
+  sha256: 837c24c105624e16ace94b4b566ffe45231ff275339c523571ebd45946926156
+  md5: 9e3ac70d27e7591b1310a690768cfe27
   depends:
   - __osx >=11.0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - aws-c-http >=0.8.10,<0.8.11.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - aws-c-http >=0.9.1,<0.9.2.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 135540
-  timestamp: 1729645603895
+  size: 134573
+  timestamp: 1731734281038
 - kind: conda
-  name: aws-c-s3
-  version: 0.7.0
-  build: hc6bcb7c_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-s3-0.7.0-hc6bcb7c_0.conda
-  sha256: a77487a570970d35b63268808e283ff64e4482b3a2a6c641ba0a11dd2a189093
-  md5: 1334e8b8532d5b462eba6bfc1cca59a7
+  name: aws-c-mqtt
+  version: 0.11.0
+  build: h7bd072d_8
+  build_number: 8
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.11.0-h7bd072d_8.conda
+  sha256: 51d3d87a47c642096e2ce389a169aec2e26958042e9130857552a12d65a19045
+  md5: 0e9d67838114c0dbd267a9311268b331
   depends:
-  - __osx >=11.0
-  - aws-c-auth >=0.7.31,<0.7.32.0a0
-  - aws-c-cal >=0.7.4,<0.7.5.0a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - aws-c-http >=0.8.10,<0.8.11.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
-  - aws-checksums >=0.1.20,<0.1.21.0a0
+  - __glibc >=2.17,<3.0.a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - aws-c-http >=0.9.1,<0.9.2.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
+  - libgcc >=13
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 96959
-  timestamp: 1729717328952
+  size: 194447
+  timestamp: 1731734668760
 - kind: conda
   name: aws-c-s3
-  version: 0.7.0
-  build: hc85afc5_0
+  version: 0.7.1
+  build: h3a84f74_3
+  build_number: 3
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.0-hc85afc5_0.conda
-  sha256: 4cb865c093e33e5463bccdca1ee0e986d5467507f7e3353076960124e3d19a4c
-  md5: 7824d1b3e9570ab637f4baf0144cdeaf
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.1-h3a84f74_3.conda
+  sha256: 274c9ec3c173a2979b949ccc10a6013673c4391502a4a71e07070d6c50eabc60
+  md5: e7a54821aaa774cfd64efcd45114a4d7
   depends:
   - __glibc >=2.17,<3.0.a0
-  - aws-c-auth >=0.7.31,<0.7.32.0a0
-  - aws-c-cal >=0.7.4,<0.7.5.0a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - aws-c-http >=0.8.10,<0.8.11.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
-  - aws-checksums >=0.1.20,<0.1.21.0a0
+  - aws-c-auth >=0.8.0,<0.8.1.0a0
+  - aws-c-cal >=0.8.0,<0.8.1.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - aws-c-http >=0.9.1,<0.9.2.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
+  - aws-checksums >=0.2.2,<0.2.3.0a0
   - libgcc >=13
-  - openssl >=3.3.2,<4.0a0
+  - openssl >=3.4.0,<4.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 114367
-  timestamp: 1729717110736
+  size: 113837
+  timestamp: 1731745115080
 - kind: conda
-  name: aws-c-sdkutils
-  version: 0.1.19
-  build: hf20e7d7_6
-  build_number: 6
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.19-hf20e7d7_6.conda
-  sha256: d09020368d88fe8db7c6d7f61c79bc729f3fb0993b1eba9665e9775152c30369
-  md5: e5885a040165a8775ea8558058b87555
+  name: aws-c-s3
+  version: 0.7.1
+  build: h840aca7_3
+  build_number: 3
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-s3-0.7.1-h840aca7_3.conda
+  sha256: a75dce44667327d365abdcd68c525913c7dd948ea26d4709386acd58717307fc
+  md5: 540af65a722c5e490012153673793df5
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
-  - libgcc >=13
+  - __osx >=11.0
+  - aws-c-auth >=0.8.0,<0.8.1.0a0
+  - aws-c-cal >=0.8.0,<0.8.1.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - aws-c-http >=0.9.1,<0.9.2.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
+  - aws-checksums >=0.2.2,<0.2.3.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 55815
-  timestamp: 1729602473258
+  size: 96830
+  timestamp: 1731745236535
 - kind: conda
   name: aws-c-sdkutils
-  version: 0.1.19
-  build: hfd083d3_6
-  build_number: 6
+  version: 0.2.1
+  build: h5d7ee29_1
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-sdkutils-0.1.19-hfd083d3_6.conda
-  sha256: dac95362fca87b19bdfd13c48266a22d39fee2192a759868a0736d6b29e855e5
-  md5: b00b00335e3c5ea91acb2619ecc5d9ce
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-sdkutils-0.2.1-h5d7ee29_1.conda
+  sha256: ed3b272b9a345142e62f0cf9ab2a9fa909c92e09691f6a06e98ff500a1f8a303
+  md5: 0f1e5bc57d4567c9d9bec8d8982828ed
   depends:
   - __osx >=11.0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 49775
-  timestamp: 1729602625619
+  size: 50276
+  timestamp: 1731687215375
 - kind: conda
-  name: aws-checksums
-  version: 0.1.20
-  build: hf20e7d7_3
-  build_number: 3
+  name: aws-c-sdkutils
+  version: 0.2.1
+  build: hf42f96a_1
+  build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.20-hf20e7d7_3.conda
-  sha256: 7bfd6394646231b0e967e6de27f0cb03587883256e512a22b98fa8203915f0d5
-  md5: 8b9d7eb23651b31d4db8b50236be9d25
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.1-hf42f96a_1.conda
+  sha256: f6e38c79b124c34edb048c28ec58fdfc4ea8f7a218dc493195afbada48ba063b
+  md5: bbdd20fb1994a9f0ba98078fcb6c12ab
   depends:
   - __glibc >=2.17,<3.0.a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
   - libgcc >=13
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 72880
-  timestamp: 1729602448721
+  size: 55738
+  timestamp: 1731687063424
 - kind: conda
   name: aws-checksums
-  version: 0.1.20
-  build: hfd083d3_3
-  build_number: 3
+  version: 0.2.2
+  build: h5d7ee29_1
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-checksums-0.1.20-hfd083d3_3.conda
-  sha256: 776aaf074ca90d0b9b8f73f4c402ce580a6b30261fdc7a143aca7deb3ca474d3
-  md5: cd06e766af6df7063db6cb0ad6bb590b
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-checksums-0.2.2-h5d7ee29_1.conda
+  sha256: eb7ebe309b33a04329b3e51a7f10bb407815389dc37cc047f7d41f9c91f0d1b0
+  md5: db1ed95988a8fe6c1ce0d94abdfc8e72
   depends:
   - __osx >=11.0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  license: Apache-2.0
+  license_family: Apache
+  purls: []
+  size: 70184
+  timestamp: 1731687342560
+- kind: conda
+  name: aws-checksums
+  version: 0.2.2
+  build: hf42f96a_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.2-hf42f96a_1.conda
+  sha256: da802ace5448481c968cfec7e7a4f79f686f42df9de8e3f78c09a925c2882a79
+  md5: d908d43d87429be24edfb20e96543c20
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
+  - libgcc >=13
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 70091
-  timestamp: 1729602726939
+  size: 72744
+  timestamp: 1731687193373
 - kind: conda
   name: aws-crt-cpp
-  version: 0.29.0
-  build: h07ed512_0
+  version: 0.29.5
+  build: h21d7256_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.0-h07ed512_0.conda
-  sha256: 266b700186acaf5002624f097c52350a1b83dee5ae3e9bf064a7d9b2404a24be
-  md5: 4122cbb9952f750ef4728df6f3dafcb3
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.5-h21d7256_0.conda
+  sha256: 54a9f37ba03e20b29215a9e301846ee22368bd6efb9e9afce7c6ad3e64426219
+  md5: b2468de19999ee8452757f12f15a9b34
   depends:
   - __glibc >=2.17,<3.0.a0
-  - aws-c-auth >=0.7.31,<0.7.32.0a0
-  - aws-c-cal >=0.7.4,<0.7.5.0a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
+  - aws-c-auth >=0.8.0,<0.8.1.0a0
+  - aws-c-cal >=0.8.0,<0.8.1.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
   - aws-c-event-stream >=0.5.0,<0.5.1.0a0
-  - aws-c-http >=0.8.10,<0.8.11.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
-  - aws-c-mqtt >=0.10.7,<0.10.8.0a0
-  - aws-c-s3 >=0.7.0,<0.7.1.0a0
-  - aws-c-sdkutils >=0.1.19,<0.1.20.0a0
+  - aws-c-http >=0.9.1,<0.9.2.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
+  - aws-c-mqtt >=0.11.0,<0.11.1.0a0
+  - aws-c-s3 >=0.7.1,<0.7.2.0a0
+  - aws-c-sdkutils >=0.2.1,<0.2.2.0a0
   - libgcc >=13
   - libstdcxx >=13
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 346552
-  timestamp: 1729770541326
+  size: 354933
+  timestamp: 1732136158458
 - kind: conda
   name: aws-crt-cpp
-  version: 0.29.0
-  build: h45f4ed5_0
+  version: 0.29.5
+  build: h6832833_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.29.0-h45f4ed5_0.conda
-  sha256: 5be298cc2e36920271babf7054013168c7333bd4782dbe0ddc9c340657c7a9cd
-  md5: 10a7b87e5b99107476b148d27f7345ac
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.29.5-h6832833_0.conda
+  sha256: e42172ffef36e2e5793532ab5b8d8c499fe8c4eaf154404501bd3f97a424e701
+  md5: 546159ec6849eaf2b632eb79e3ab16f7
   depends:
   - __osx >=11.0
-  - aws-c-auth >=0.7.31,<0.7.32.0a0
-  - aws-c-cal >=0.7.4,<0.7.5.0a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
+  - aws-c-auth >=0.8.0,<0.8.1.0a0
+  - aws-c-cal >=0.8.0,<0.8.1.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
   - aws-c-event-stream >=0.5.0,<0.5.1.0a0
-  - aws-c-http >=0.8.10,<0.8.11.0a0
-  - aws-c-io >=0.14.20,<0.14.21.0a0
-  - aws-c-mqtt >=0.10.7,<0.10.8.0a0
-  - aws-c-s3 >=0.7.0,<0.7.1.0a0
-  - aws-c-sdkutils >=0.1.19,<0.1.20.0a0
-  - libcxx >=17
+  - aws-c-http >=0.9.1,<0.9.2.0a0
+  - aws-c-io >=0.15.2,<0.15.3.0a0
+  - aws-c-mqtt >=0.11.0,<0.11.1.0a0
+  - aws-c-s3 >=0.7.1,<0.7.2.0a0
+  - aws-c-sdkutils >=0.2.1,<0.2.2.0a0
+  - libcxx >=18
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 229280
-  timestamp: 1729770878998
+  size: 236424
+  timestamp: 1732136497320
 - kind: conda
   name: aws-sdk-cpp
-  version: 1.11.407
-  build: h0a0d3c4_6
-  build_number: 6
+  version: 1.11.449
+  build: h8577fd2_3
+  build_number: 3
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-sdk-cpp-1.11.407-h0a0d3c4_6.conda
-  sha256: 2991388a278df6ea16ef6029be269dc6de1db3b90fed26b9046db1d347ec2e67
-  md5: 0b1563943346dbbe64feba305a28fac7
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-sdk-cpp-1.11.449-h8577fd2_3.conda
+  sha256: ddd7aaa925ac3d569aa3dc1fe0239fa5c57034a1360683c41d310d6805f0d5bd
+  md5: 3c789cd7093639a2662b14b87f11b04c
   depends:
   - __osx >=11.0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
   - aws-c-event-stream >=0.5.0,<0.5.1.0a0
-  - aws-checksums >=0.1.20,<0.1.21.0a0
-  - aws-crt-cpp >=0.29.0,<0.29.1.0a0
+  - aws-checksums >=0.2.2,<0.2.3.0a0
+  - aws-crt-cpp >=0.29.5,<0.29.6.0a0
   - libcurl >=8.10.1,<9.0a0
-  - libcxx >=17
+  - libcxx >=18
   - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.3.2,<4.0a0
+  - openssl >=3.4.0,<4.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 2717498
-  timestamp: 1729832162744
+  size: 2737395
+  timestamp: 1732184718613
 - kind: conda
   name: aws-sdk-cpp
-  version: 1.11.407
-  build: h9c41b47_6
-  build_number: 6
+  version: 1.11.449
+  build: hdaa582e_3
+  build_number: 3
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.407-h9c41b47_6.conda
-  sha256: 534bad638e17c88b17c32618b51fe34d0861ad50f7a84e136d48b18723049176
-  md5: 29bb91b9dcb9af1a5aa9d657bb325711
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.449-hdaa582e_3.conda
+  sha256: a6fdba49b87ad3b92c219f60ac31e0d0b4fea7e651efe6d668288e5a0f7a1755
+  md5: 0dca4b37cf80312f8ef84b649e6ad3a3
   depends:
   - __glibc >=2.17,<3.0.a0
-  - aws-c-common >=0.9.31,<0.9.32.0a0
+  - aws-c-common >=0.10.3,<0.10.4.0a0
   - aws-c-event-stream >=0.5.0,<0.5.1.0a0
-  - aws-checksums >=0.1.20,<0.1.21.0a0
-  - aws-crt-cpp >=0.29.0,<0.29.1.0a0
+  - aws-checksums >=0.2.2,<0.2.3.0a0
+  - aws-crt-cpp >=0.29.5,<0.29.6.0a0
   - libcurl >=8.10.1,<9.0a0
   - libgcc >=13
   - libstdcxx >=13
   - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.3.2,<4.0a0
+  - openssl >=3.4.0,<4.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 2920673
-  timestamp: 1729831505960
+  size: 2951998
+  timestamp: 1732184141
 - kind: conda
   name: azure-core-cpp
   version: 1.14.0
@@ -2684,10 +2901,10 @@ packages:
   url: https://files.pythonhosted.org/packages/52/6b/8b126c2e1c07fae33185544ea974de67027afc905bd072feef9fbbd38d3d/boto3-1.35.36-py3-none-any.whl
   sha256: 33735b9449cd2ef176531ba2cb2265c904a91244440b0e161a17da9d24a1e6d1
   requires_dist:
-  - botocore<1.36.0,>=1.35.36
-  - jmespath<2.0.0,>=0.7.1
-  - s3transfer<0.11.0,>=0.10.0
-  - botocore[crt]<2.0a0,>=1.21.0 ; extra == 'crt'
+  - botocore>=1.35.36,<1.36.0
+  - jmespath>=0.7.1,<2.0.0
+  - s3transfer>=0.10.0,<0.11.0
+  - botocore[crt]>=1.21.0,<2.0a0 ; extra == 'crt'
   requires_python: '>=3.8'
 - kind: pypi
   name: botocore
@@ -2695,10 +2912,10 @@ packages:
   url: https://files.pythonhosted.org/packages/2a/60/056d58b606731f94fe395266c604ea9efcecc10e6857ceb9b10e6831d746/botocore-1.35.36-py3-none-any.whl
   sha256: 64241c778bf2dc863d93abab159e14024d97a926a5715056ef6411418cb9ead3
   requires_dist:
-  - jmespath<2.0.0,>=0.7.1
-  - python-dateutil<3.0.0,>=2.1
-  - urllib3<1.27,>=1.25.4 ; python_full_version < '3.10'
-  - urllib3!=2.2.0,<3,>=1.25.4 ; python_full_version >= '3.10'
+  - jmespath>=0.7.1,<2.0.0
+  - python-dateutil>=2.1,<3.0.0
+  - urllib3>=1.25.4,<1.27 ; python_full_version < '3.10'
+  - urllib3>=1.25.4,!=2.2.0,<3 ; python_full_version >= '3.10'
   - awscrt==0.22.0 ; extra == 'crt'
   requires_python: '>=3.8'
 - kind: conda
@@ -2876,35 +3093,35 @@ packages:
   timestamp: 1720974522888
 - kind: conda
   name: c-ares
-  version: 1.34.2
-  build: h7ab814d_0
+  version: 1.34.3
+  build: h5505292_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.2-h7ab814d_0.conda
-  sha256: 24d53d27397f9c2f0c168992690b5ec1bd62593fb4fc1f1e906ab91b10fd06c3
-  md5: 8a8cfc11064b521bc54bd2d8591cb137
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.3-h5505292_0.conda
+  sha256: e9e0f737286f9f4173c76fb01a11ffbe87cfc2da4e99760e1e18f47851d7ae06
+  md5: d0155a4f41f28628c7409ea000eeb19c
   depends:
   - __osx >=11.0
   license: MIT
   license_family: MIT
   purls: []
-  size: 177487
-  timestamp: 1729006763496
+  size: 178951
+  timestamp: 1731182071026
 - kind: conda
   name: c-ares
-  version: 1.34.2
+  version: 1.34.3
   build: heb4867d_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.2-heb4867d_0.conda
-  sha256: c2a515e623ac3e17a56027c06098fbd5ab47afefefbd386b4c21289f2ec55139
-  md5: 2b780c0338fc0ffa678ac82c54af51fd
+  url: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.3-heb4867d_0.conda
+  sha256: 1015d731c05ef7de298834833d680b08dea58980b907f644345bd457f9498c99
+  md5: 09a6c610d002e54e18353c06ef61a253
   depends:
   - __glibc >=2.28,<3.0.a0
   - libgcc >=13
   license: MIT
   license_family: MIT
   purls: []
-  size: 205797
-  timestamp: 1729006575652
+  size: 205575
+  timestamp: 1731181837907
 - kind: conda
   name: ca-certificates
   version: 2024.8.30
@@ -2929,6 +3146,102 @@ packages:
   purls: []
   size: 158482
   timestamp: 1725019034582
+- kind: conda
+  name: cairo
+  version: 1.18.0
+  build: hb4a6bf7_3
+  build_number: 3
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/cairo-1.18.0-hb4a6bf7_3.conda
+  sha256: f7603b7f6ee7c6e07c23d77302420194f4ec1b8e8facfff2b6aab17c7988a102
+  md5: 08bd0752f3de8a2d8a35fd012f09531f
+  depends:
+  - __osx >=11.0
+  - fontconfig >=2.14.2,<3.0a0
+  - fonts-conda-ecosystem
+  - freetype >=2.12.1,<3.0a0
+  - icu >=75.1,<76.0a0
+  - libcxx >=16
+  - libglib >=2.80.3,<3.0a0
+  - libpng >=1.6.43,<1.7.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - pixman >=0.43.4,<1.0a0
+  - zlib
+  license: LGPL-2.1-only or MPL-1.1
+  purls: []
+  size: 899126
+  timestamp: 1721139203735
+- kind: conda
+  name: cairo
+  version: 1.18.0
+  build: hebfffa5_3
+  build_number: 3
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hebfffa5_3.conda
+  sha256: aee5b9e6ef71cdfb2aee9beae3ea91910ca761c01c0ef32052e3f94a252fa173
+  md5: fceaedf1cdbcb02df9699a0d9b005292
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - fontconfig >=2.14.2,<3.0a0
+  - fonts-conda-ecosystem
+  - freetype >=2.12.1,<3.0a0
+  - icu >=75.1,<76.0a0
+  - libgcc-ng >=12
+  - libglib >=2.80.3,<3.0a0
+  - libpng >=1.6.43,<1.7.0a0
+  - libstdcxx-ng >=12
+  - libxcb >=1.16,<2.0.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - pixman >=0.43.2,<1.0a0
+  - xorg-libice >=1.1.1,<2.0a0
+  - xorg-libsm >=1.2.4,<2.0a0
+  - xorg-libx11 >=1.8.9,<2.0a0
+  - xorg-libxext >=1.3.4,<2.0a0
+  - xorg-libxrender >=0.9.11,<0.10.0a0
+  - zlib
+  license: LGPL-2.1-only or MPL-1.1
+  purls: []
+  size: 983604
+  timestamp: 1721138900054
+- kind: conda
+  name: capnproto
+  version: 1.0.2
+  build: h221ca0e_3
+  build_number: 3
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/capnproto-1.0.2-h221ca0e_3.conda
+  sha256: 41e81f2d739d968b6166a723ed3f15372562c0ae4af32f9315ad0d3c15c6c5b2
+  md5: 77b5500817183990757074a1fdc106c0
+  depends:
+  - __osx >=11.0
+  - libcxx >=18
+  - libzlib >=1.3.1,<2.0a0
+  - openssl >=3.3.2,<4.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 2862262
+  timestamp: 1730914758286
+- kind: conda
+  name: capnproto
+  version: 1.0.2
+  build: h766bdaa_3
+  build_number: 3
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/capnproto-1.0.2-h766bdaa_3.conda
+  sha256: fecb35e58b73a3dcb50725305964839ad08c0973592ba4d4ee0964360609fd12
+  md5: 7ea5f8afe8041beee8bad281dee62414
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
+  - libzlib >=1.3.1,<2.0a0
+  - openssl >=3.3.2,<4.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 4037969
+  timestamp: 1730914760842
 - kind: conda
   name: certifi
   version: 2024.8.30
@@ -3004,6 +3317,47 @@ packages:
   - pkg:pypi/cfgv?source=hash-mapping
   size: 10788
   timestamp: 1629909423398
+- kind: conda
+  name: cfitsio
+  version: 4.4.1
+  build: ha728647_2
+  build_number: 2
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/cfitsio-4.4.1-ha728647_2.conda
+  sha256: 985f6dd1346b3cdc6658ec9b17d794d8a5859f966e8e5b2a886a2bdc84c39975
+  md5: dab65ce7f9da0b25f53f0ec0d37ee09c
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - bzip2 >=1.0.8,<2.0a0
+  - libcurl >=8.10.1,<9.0a0
+  - libgcc >=13
+  - libgfortran
+  - libgfortran5 >=13.3.0
+  - libzlib >=1.3.1,<2.0a0
+  license: LicenseRef-fitsio
+  purls: []
+  size: 908041
+  timestamp: 1729563306012
+- kind: conda
+  name: cfitsio
+  version: 4.4.1
+  build: hd313823_2
+  build_number: 2
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/cfitsio-4.4.1-hd313823_2.conda
+  sha256: df2eb73c631b4993f5f21d99bfb78d86a7fd83642e7d787284d29a27ffe5ff8c
+  md5: cdd112ea3fdf3ae6aa4afc6460dc2036
+  depends:
+  - __osx >=11.0
+  - bzip2 >=1.0.8,<2.0a0
+  - libcurl >=8.10.1,<9.0a0
+  - libgfortran 5.*
+  - libgfortran5 >=13.2.0
+  - libzlib >=1.3.1,<2.0a0
+  license: LicenseRef-fitsio
+  purls: []
+  size: 794923
+  timestamp: 1729563447297
 - kind: conda
   name: charset-normalizer
   version: 3.4.0
@@ -3133,24 +3487,24 @@ packages:
   requires_python: '>=3.8'
 - kind: pypi
   name: coincident
-  version: 0.1.dev34+gd35fb3f.d20241101
+  version: 0.1.dev36+g02ec20b.d20241122
   path: .
-  sha256: a63116f47c1734a666e812f4002a1e91949d0229437b571ba9ae4e913c2c5a0b
+  sha256: 1fde1e4bd7e1104b7674555efff1d760099aef9ce1d8d27e7eb5ccf28b31799c
   requires_dist:
-  - cloudpathlib[s3]<0.21,>=0.20.0
-  - geopandas<2,>=1.0.1
-  - maxar-platform<2,>=1.0.2
-  - planetary-computer<2,>=1.0.0
-  - pyarrow<19,>=18.0.0
-  - pystac-client<0.9,>=0.8.3
-  - requests<3,>=2.32.3
-  - rioxarray<0.18,>=0.17.0
-  - stac-asset<0.5,>=0.4.3
-  - stac-geoparquet<0.7,>=0.6.0
-  - ipykernel<7,>=6.29.5 ; extra == 'dev'
-  - mypy<2,>=1.11.2 ; extra == 'dev'
-  - pre-commit<4,>=3.8.0 ; extra == 'dev'
-  - pylint<4,>=3.3.1 ; extra == 'dev'
+  - cloudpathlib[s3]>=0.20.0,<0.21
+  - geopandas>=1.0.1,<2
+  - maxar-platform>=1.0.2,<2
+  - planetary-computer>=1.0.0,<2
+  - pyarrow>=18.0.0,<19
+  - pystac-client>=0.8.3,<0.9
+  - requests>=2.32.3,<3
+  - rioxarray>=0.17.0,<0.18
+  - stac-asset>=0.4.3,<0.5
+  - stac-geoparquet>=0.6.0,<0.7
+  - ipykernel>=6.29.5,<7 ; extra == 'dev'
+  - mypy>=1.11.2,<2 ; extra == 'dev'
+  - pre-commit>=3.8.0,<4 ; extra == 'dev'
+  - pylint>=3.3.1,<4 ; extra == 'dev'
   - pytest-cov>=3 ; extra == 'dev'
   - pytest>=6 ; extra == 'dev'
   - folium ; extra == 'docs'
@@ -3158,7 +3512,7 @@ packages:
   - matplotlib ; extra == 'docs'
   - myst-nb ; extra == 'docs'
   - myst-parser>=0.13 ; extra == 'docs'
-  - pydata-sphinx-theme<0.17,>=0.16.0 ; extra == 'docs'
+  - pydata-sphinx-theme>=0.16.0,<0.17 ; extra == 'docs'
   - sphinx-autodoc-typehints ; extra == 'docs'
   - sphinx-copybutton ; extra == 'docs'
   - sphinx>=7.0 ; extra == 'docs'
@@ -3201,61 +3555,59 @@ packages:
   timestamp: 1710320435158
 - kind: conda
   name: contourpy
-  version: 1.3.0
-  build: py312h4721b07_2
-  build_number: 2
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.0-py312h4721b07_2.conda
-  sha256: 1452b38f34d4b7803c21d6d908793fbb2975da591e780d099c25bc773e9f0da3
-  md5: 3bb511fda97b3dbe459a2e67e5d185d6
+  version: 1.3.1
+  build: py312h68727a3_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.1-py312h68727a3_0.conda
+  sha256: e977af50b844b5b8cfec358131a4e923f0aa718e8334321cf8d84f5093576259
+  md5: f5fbba0394ee45e9a64a73c2a994126a
   depends:
-  - __osx >=11.0
-  - libcxx >=17
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
   - numpy >=1.23
   - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/contourpy?source=hash-mapping
-  size: 250676
-  timestamp: 1729602748580
+  size: 276332
+  timestamp: 1731428454756
 - kind: conda
   name: contourpy
-  version: 1.3.0
-  build: py312h68727a3_2
-  build_number: 2
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.0-py312h68727a3_2.conda
-  sha256: 777ff055866872f45f0f8d2ad17a0c42f3c63463f8c1da9d75fa5b1652940b50
-  md5: ff28f374b31937c048107521c814791e
+  version: 1.3.1
+  build: py312hb23fbb9_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.1-py312hb23fbb9_0.conda
+  sha256: fa1f8505f45eac22f25c48cd46809da0d26bcb028c37517b3474bacddd029b0a
+  md5: f4408290387836e05ac267cd7ec80c5c
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - libgcc >=13
-  - libstdcxx >=13
+  - __osx >=11.0
+  - libcxx >=18
   - numpy >=1.23
   - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/contourpy?source=hash-mapping
-  size: 276004
-  timestamp: 1727293728397
+  size: 245638
+  timestamp: 1731428781337
 - kind: pypi
   name: coverage
-  version: 7.6.4
-  url: https://files.pythonhosted.org/packages/27/03/932c2c5717a7fa80cd43c6a07d3177076d97b79f12f40f882f9916db0063/coverage-7.6.4-cp312-cp312-macosx_11_0_arm64.whl
-  sha256: 2b6b4c83d8e8ea79f27ab80778c19bc037759aea298da4b56621f4474ffeb117
+  version: 7.6.7
+  url: https://files.pythonhosted.org/packages/60/a2/23eb11eb60f825a84397cb94701d6f41d2e8e88ad7d0ba2b4339f38435fb/coverage-7.6.7-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+  sha256: 3c42ec2c522e3ddd683dec5cdce8e62817afb648caedad9da725001fa530d354
   requires_dist:
   - tomli ; python_full_version <= '3.11' and extra == 'toml'
   requires_python: '>=3.9'
 - kind: pypi
   name: coverage
-  version: 7.6.4
-  url: https://files.pythonhosted.org/packages/c0/70/6b0627e5bd68204ee580126ed3513140b2298995c1233bd67404b4e44d0e/coverage-7.6.4-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-  sha256: 5915fcdec0e54ee229926868e9b08586376cae1f5faa9bbaf8faf3561b393d52
+  version: 7.6.7
+  url: https://files.pythonhosted.org/packages/88/8b/e28f86412317b9514692fd6f9d8ac6faa12494c3f470c3c63f202e10c756/coverage-7.6.7-cp312-cp312-macosx_11_0_arm64.whl
+  sha256: 49ed5ee4109258973630c1f9d099c7e72c5c36605029f3a91fe9982c6076c82b
   requires_dist:
   - tomli ; python_full_version <= '3.11' and extra == 'toml'
   requires_python: '>=3.9'
@@ -3276,29 +3628,96 @@ packages:
   - pkg:pypi/cycler?source=hash-mapping
   size: 13458
   timestamp: 1696677888423
-- kind: pypi
-  name: dateparser
-  version: 1.2.0
-  url: https://files.pythonhosted.org/packages/a4/29/db12aa4dda81580be1999824a689bd52aa40061fc12c9ccdc3feab5ea718/dateparser-1.2.0-py2.py3-none-any.whl
-  sha256: 0b21ad96534e562920a0083e97fd45fa959882d4162acc358705144520a35830
-  requires_dist:
-  - python-dateutil
-  - pytz
-  - regex!=2019.2.19,!=2021.8.27
-  - tzlocal
+- kind: conda
+  name: cyrus-sasl
+  version: 2.1.27
+  build: h54b06d7_7
+  build_number: 7
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.27-h54b06d7_7.conda
+  sha256: d2ea5e52da745c4249e1a818095a28f9c57bd4df22cbfc645352defa468e86c2
+  md5: dce22f70b4e5a407ce88f2be046f4ceb
+  depends:
+  - krb5 >=1.21.1,<1.22.0a0
+  - libgcc-ng >=12
+  - libntlm
+  - libstdcxx-ng >=12
+  - openssl >=3.1.1,<4.0a0
+  license: BSD-3-Clause-Attribution
+  license_family: BSD
+  purls: []
+  size: 219527
+  timestamp: 1690061203707
+- kind: conda
+  name: cyrus-sasl
+  version: 2.1.27
+  build: h60b93bd_7
+  build_number: 7
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/cyrus-sasl-2.1.27-h60b93bd_7.conda
+  sha256: befd4d6e8b542d0c30aff47b098d43bbbe1bbf743ba6cd87a100d8a8731a6e03
+  md5: 80a3b015d05a7d235db1bf09911fe08e
+  depends:
+  - krb5 >=1.21.1,<1.22.0a0
+  - libcxx >=15.0.7
+  - libntlm
+  - openssl >=3.1.1,<4.0a0
+  license: BSD-3-Clause-Attribution
+  license_family: BSD
+  purls: []
+  size: 210957
+  timestamp: 1690061457834
+- kind: pypi
+  name: dateparser
+  version: 1.2.0
+  url: https://files.pythonhosted.org/packages/a4/29/db12aa4dda81580be1999824a689bd52aa40061fc12c9ccdc3feab5ea718/dateparser-1.2.0-py2.py3-none-any.whl
+  sha256: 0b21ad96534e562920a0083e97fd45fa959882d4162acc358705144520a35830
+  requires_dist:
+  - python-dateutil
+  - pytz
+  - regex!=2019.2.19,!=2021.8.27
+  - tzlocal
   - hijri-converter ; extra == 'calendars'
   - convertdate ; extra == 'calendars'
   - fasttext ; extra == 'fasttext'
   - langdetect ; extra == 'langdetect'
   requires_python: '>=3.7'
+- kind: conda
+  name: dav1d
+  version: 1.2.1
+  build: hb547adb_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/dav1d-1.2.1-hb547adb_0.conda
+  sha256: 93e077b880a85baec8227e8c72199220c7f87849ad32d02c14fb3807368260b8
+  md5: 5a74cdee497e6b65173e10d94582fae6
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 316394
+  timestamp: 1685695959391
+- kind: conda
+  name: dav1d
+  version: 1.2.1
+  build: hd590300_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda
+  sha256: 22053a5842ca8ee1cf8e1a817138cdb5e647eb2c46979f84153f6ad7bde73020
+  md5: 418c6ca5929a611cbd69204907a83995
+  depends:
+  - libgcc-ng >=12
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 760229
+  timestamp: 1685695754230
 - kind: conda
   name: debugpy
-  version: 1.8.7
+  version: 1.8.9
   build: py312h2ec8cdc_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.7-py312h2ec8cdc_0.conda
-  sha256: e03c74ba23342f580f4cc822e46623561206da4857fd47c84c482f36a121095d
-  md5: 13e4b568d8f94e2a38f9acd192149516
+  url: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.9-py312h2ec8cdc_0.conda
+  sha256: cf79cac70773567382910fcaf7b10bb0f5242d159f8dd93296d8451cd542af9a
+  md5: c522fd70ca7a0c2fe1a861dd13987a57
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
@@ -3309,19 +3728,19 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/debugpy?source=hash-mapping
-  size: 2640727
-  timestamp: 1728594265044
+  size: 2605093
+  timestamp: 1732236790708
 - kind: conda
   name: debugpy
-  version: 1.8.7
-  build: py312hde4cb15_0
+  version: 1.8.9
+  build: py312hd8f9ff3_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.7-py312hde4cb15_0.conda
-  sha256: 65f015c6c2a2c5f52f91cfb1622f9e82499e9d7f3c4ccb4fa255b16ae575c9a2
-  md5: 1985200ccb082e68d47b4fdd0bacfe97
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.9-py312hd8f9ff3_0.conda
+  sha256: d588943ac0392300f31115d9852a2ff4213ec22856c382ef56f5650576523ec6
+  md5: 51085e5bb7f21019186cc88fd9a03164
   depends:
   - __osx >=11.0
-  - libcxx >=17
+  - libcxx >=18
   - python >=3.12,<3.13.0a0
   - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
@@ -3329,8 +3748,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/debugpy?source=hash-mapping
-  size: 2555984
-  timestamp: 1728594350396
+  size: 2512030
+  timestamp: 1732236996277
 - kind: conda
   name: decorator
   version: 5.1.1
@@ -3368,6 +3787,7 @@ packages:
   constrains:
   - __glibc >=2.17
   license: Apache-2.0
+  license_family: APACHE
   purls:
   - pkg:pypi/deltalake?source=hash-mapping
   size: 23935587
@@ -3390,6 +3810,7 @@ packages:
   constrains:
   - __osx >=11.0
   license: Apache-2.0
+  license_family: APACHE
   purls:
   - pkg:pypi/deltalake?source=hash-mapping
   size: 19112434
@@ -3497,6 +3918,97 @@ packages:
   - pkg:pypi/filelock?source=hash-mapping
   size: 17357
   timestamp: 1726613593584
+- kind: conda
+  name: fiona
+  version: 1.10.1
+  build: py312hbbf7dfe_2
+  build_number: 2
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/fiona-1.10.1-py312hbbf7dfe_2.conda
+  sha256: 8aaf3ea6b37d44bb9ea2fd3a4d8578bd396adc492f7eef02ae06081abab9547a
+  md5: cf56999f17fb2b64a7f33218ebe496e2
+  depends:
+  - __osx >=11.0
+  - attrs >=19.2.0
+  - click >=8.0,<9.dev0
+  - click-plugins >=1.0
+  - cligj >=0.5
+  - gdal
+  - libcxx >=18
+  - libgdal >=3.10.0,<3.11.0a0
+  - libgdal-core >=3.10.0,<3.11.0a0
+  - pyparsing
+  - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
+  - python_abi 3.12.* *_cp312
+  - shapely
+  license: BSD-3-Clause
+  purls:
+  - pkg:pypi/fiona?source=hash-mapping
+  size: 1022417
+  timestamp: 1731977690992
+- kind: conda
+  name: fiona
+  version: 1.10.1
+  build: py312hf31e026_2
+  build_number: 2
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/fiona-1.10.1-py312hf31e026_2.conda
+  sha256: a824be98e0b7c55ea2ea935bb13319946445fdc5c3520409fed43ff952d994c7
+  md5: 9e62cbffdeadb245182ad0167c4bad00
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - attrs >=19.2.0
+  - click >=8.0,<9.dev0
+  - click-plugins >=1.0
+  - cligj >=0.5
+  - gdal
+  - libgcc >=13
+  - libgdal >=3.10.0,<3.11.0a0
+  - libgdal-core >=3.10.0,<3.11.0a0
+  - libstdcxx >=13
+  - pyparsing
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - shapely
+  license: BSD-3-Clause
+  purls:
+  - pkg:pypi/fiona?source=hash-mapping
+  size: 1173174
+  timestamp: 1731977406960
+- kind: conda
+  name: fmt
+  version: 11.0.2
+  build: h420ef59_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/fmt-11.0.2-h420ef59_0.conda
+  sha256: 62e6508d5bbde4aa36f7b7658ce2d8fdd0e509c0d1661735c1bd1bed00e070c4
+  md5: 0e44849fd4764e9f85ed8caa9f24c118
+  depends:
+  - __osx >=11.0
+  - libcxx >=16
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 179582
+  timestamp: 1723046771323
+- kind: conda
+  name: fmt
+  version: 11.0.2
+  build: h434a139_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/fmt-11.0.2-h434a139_0.conda
+  sha256: c620e2ab084948985ae9b8848d841f603e8055655513340e04b6cf129099b5ca
+  md5: 995f7e13598497691c1dc476d889bc04
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 198533
+  timestamp: 1723046725112
 - kind: conda
   name: folium
   version: 0.18.0
@@ -3520,14 +4032,145 @@ packages:
   size: 79126
   timestamp: 1729664648900
 - kind: conda
-  name: fonttools
-  version: 4.54.1
-  build: py312h178313f_1
+  name: font-ttf-dejavu-sans-mono
+  version: '2.37'
+  build: hab24e00_0
+  subdir: noarch
+  noarch: generic
+  url: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
+  sha256: 58d7f40d2940dd0a8aa28651239adbf5613254df0f75789919c4e6762054403b
+  md5: 0c96522c6bdaed4b1566d11387caaf45
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 397370
+  timestamp: 1566932522327
+- kind: conda
+  name: font-ttf-inconsolata
+  version: '3.000'
+  build: h77eed37_0
+  subdir: noarch
+  noarch: generic
+  url: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
+  sha256: c52a29fdac682c20d252facc50f01e7c2e7ceac52aa9817aaf0bb83f7559ec5c
+  md5: 34893075a5c9e55cdafac56607368fc6
+  license: OFL-1.1
+  license_family: Other
+  purls: []
+  size: 96530
+  timestamp: 1620479909603
+- kind: conda
+  name: font-ttf-source-code-pro
+  version: '2.038'
+  build: h77eed37_0
+  subdir: noarch
+  noarch: generic
+  url: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
+  sha256: 00925c8c055a2275614b4d983e1df637245e19058d79fc7dd1a93b8d9fb4b139
+  md5: 4d59c254e01d9cde7957100457e2d5fb
+  license: OFL-1.1
+  license_family: Other
+  purls: []
+  size: 700814
+  timestamp: 1620479612257
+- kind: conda
+  name: font-ttf-ubuntu
+  version: '0.83'
+  build: h77eed37_3
+  build_number: 3
+  subdir: noarch
+  noarch: generic
+  url: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
+  sha256: 2821ec1dc454bd8b9a31d0ed22a7ce22422c0aef163c59f49dfdf915d0f0ca14
+  md5: 49023d73832ef61042f6a237cb2687e7
+  license: LicenseRef-Ubuntu-Font-Licence-Version-1.0
+  license_family: Other
+  purls: []
+  size: 1620504
+  timestamp: 1727511233259
+- kind: conda
+  name: fontconfig
+  version: 2.15.0
+  build: h1383a14_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/fontconfig-2.15.0-h1383a14_1.conda
+  sha256: f79d3d816fafbd6a2b0f75ebc3251a30d3294b08af9bb747194121f5efa364bc
+  md5: 7b29f48742cea5d1ccb5edd839cb5621
+  depends:
+  - __osx >=11.0
+  - freetype >=2.12.1,<3.0a0
+  - libexpat >=2.6.3,<3.0a0
+  - libzlib >=1.3.1,<2.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 234227
+  timestamp: 1730284037572
+- kind: conda
+  name: fontconfig
+  version: 2.15.0
+  build: h7e30c49_1
   build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.54.1-py312h178313f_1.conda
-  sha256: 45f405d6d1ef528226c20b681373db0bdb79cc70fec1e495003247b0d3d00140
-  md5: bbbf5fa5cab622c33907bc8d7eeea9f7
+  url: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda
+  sha256: 7093aa19d6df5ccb6ca50329ef8510c6acb6b0d8001191909397368b65b02113
+  md5: 8f5b0b297b59e1ac160ad4beec99dbee
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - freetype >=2.12.1,<3.0a0
+  - libexpat >=2.6.3,<3.0a0
+  - libgcc >=13
+  - libuuid >=2.38.1,<3.0a0
+  - libzlib >=1.3.1,<2.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 265599
+  timestamp: 1730283881107
+- kind: conda
+  name: fonts-conda-ecosystem
+  version: '1'
+  build: '0'
+  subdir: noarch
+  noarch: generic
+  url: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
+  sha256: a997f2f1921bb9c9d76e6fa2f6b408b7fa549edd349a77639c9fe7a23ea93e61
+  md5: fee5683a3f04bd15cbd8318b096a27ab
+  depends:
+  - fonts-conda-forge
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 3667
+  timestamp: 1566974674465
+- kind: conda
+  name: fonts-conda-forge
+  version: '1'
+  build: '0'
+  subdir: noarch
+  noarch: generic
+  url: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
+  sha256: 53f23a3319466053818540bcdf2091f253cbdbab1e0e9ae7b9e509dcaa2a5e38
+  md5: f766549260d6815b0c52253f1fb1bb29
+  depends:
+  - font-ttf-dejavu-sans-mono
+  - font-ttf-inconsolata
+  - font-ttf-source-code-pro
+  - font-ttf-ubuntu
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 4102
+  timestamp: 1566932280397
+- kind: conda
+  name: fonttools
+  version: 4.55.0
+  build: py312h178313f_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.55.0-py312h178313f_0.conda
+  sha256: 2a8d4fe8968828584057f8b07f3e102e326d8ec08d0e30e4ecc21f35031239a0
+  md5: f404f4fb99ccaea68b00c1cc64fc1e68
   depends:
   - __glibc >=2.17,<3.0.a0
   - brotli
@@ -3540,17 +4183,16 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/fonttools?source=hash-mapping
-  size: 2827447
-  timestamp: 1729530553018
+  size: 2843090
+  timestamp: 1731643626471
 - kind: conda
   name: fonttools
-  version: 4.54.1
-  build: py312ha0ccf2a_1
-  build_number: 1
+  version: 4.55.0
+  build: py312h998013c_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.54.1-py312ha0ccf2a_1.conda
-  sha256: 7db92b5980ac60c362d2c7fa9c75c21578b0ebe58a23ba6e123f47d4d3baca53
-  md5: 1d203fe9d62f4d2fea1c955d77bc642a
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.55.0-py312h998013c_0.conda
+  sha256: 427d75267cfeee820498efeea59477790f7e28cdbe0f18a8484f23dae9a85cce
+  md5: b009bb8037e769ff4fd6439642268ecb
   depends:
   - __osx >=11.0
   - brotli
@@ -3563,8 +4205,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/fonttools?source=hash-mapping
-  size: 2730304
-  timestamp: 1729530557674
+  size: 2752240
+  timestamp: 1731643678207
 - kind: conda
   name: freetype
   version: 2.12.1
@@ -3645,6 +4287,56 @@ packages:
   url: https://files.pythonhosted.org/packages/d0/9a/8e479b482a6f2070b26bda572c5e6889bb3ba48977e81beea35b5ae13ece/frozenlist-1.5.0-cp312-cp312-macosx_11_0_arm64.whl
   sha256: 7948140d9f8ece1745be806f2bfdf390127cf1a763b925c4a805c603df5e697e
   requires_python: '>=3.8'
+- kind: conda
+  name: gdal
+  version: 3.10.0
+  build: py312h22e8dc9_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/gdal-3.10.0-py312h22e8dc9_1.conda
+  sha256: 0be653461d72532e1ab000d8c28af815d50b85dfbb24505e84658e21b272ae4c
+  md5: 1692d27b55984c9cd88c6dee205feb54
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libgdal-core 3.10.0.*
+  - libkml >=1.3.0,<1.4.0a0
+  - libstdcxx >=13
+  - libxml2 >=2.13.5,<3.0a0
+  - numpy >=1.19,<3
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/gdal?source=hash-mapping
+  size: 1698464
+  timestamp: 1731504294985
+- kind: conda
+  name: gdal
+  version: 3.10.0
+  build: py312h9e5d920_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/gdal-3.10.0-py312h9e5d920_1.conda
+  sha256: c36aa329a197f923196274a515dc51a1fd16388e3c4b10078a01f90ec7f90a2d
+  md5: 8b6768f5186a1d4a14245f8ef28c66e4
+  depends:
+  - __osx >=11.0
+  - libcxx >=18
+  - libgdal-core 3.10.0.*
+  - libkml >=1.3.0,<1.4.0a0
+  - libxml2 >=2.13.5,<3.0a0
+  - numpy >=1.19,<3
+  - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
+  - python_abi 3.12.* *_cp312
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/gdal?source=hash-mapping
+  size: 1670118
+  timestamp: 1731505249886
 - kind: conda
   name: geopandas
   version: 1.0.1
@@ -3894,6 +4586,90 @@ packages:
   - pkg:pypi/h2?source=hash-mapping
   size: 46754
   timestamp: 1634280590080
+- kind: conda
+  name: hdf4
+  version: 4.2.15
+  build: h2a13503_7
+  build_number: 7
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.15-h2a13503_7.conda
+  sha256: 0d09b6dc1ce5c4005ae1c6a19dc10767932ef9a5e9c755cfdbb5189ac8fb0684
+  md5: bd77f8da987968ec3927990495dc22e4
+  depends:
+  - libgcc-ng >=12
+  - libjpeg-turbo >=3.0.0,<4.0a0
+  - libstdcxx-ng >=12
+  - libzlib >=1.2.13,<2.0.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 756742
+  timestamp: 1695661547874
+- kind: conda
+  name: hdf4
+  version: 4.2.15
+  build: h2ee6834_7
+  build_number: 7
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/hdf4-4.2.15-h2ee6834_7.conda
+  sha256: c3b01e3c3fe4ca1c4d28c287eaa5168a4f2fd3ffd76690082ac919244c22fa90
+  md5: ff5d749fd711dc7759e127db38005924
+  depends:
+  - libcxx >=15.0.7
+  - libjpeg-turbo >=3.0.0,<4.0a0
+  - libzlib >=1.2.13,<2.0.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 762257
+  timestamp: 1695661864625
+- kind: conda
+  name: hdf5
+  version: 1.14.3
+  build: nompi_hdf9ad27_105
+  build_number: 105
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.14.3-nompi_hdf9ad27_105.conda
+  sha256: 2278fa07da6f96e807d402cd55480624d67d2dee202191aaaf278ce5ab23605a
+  md5: 7e1729554e209627636a0f6fabcdd115
+  depends:
+  - libaec >=1.1.3,<2.0a0
+  - libcurl >=8.8.0,<9.0a0
+  - libgcc-ng >=12
+  - libgfortran-ng
+  - libgfortran5 >=12.3.0
+  - libstdcxx-ng >=12
+  - libzlib >=1.2.13,<2.0a0
+  - openssl >=3.3.1,<4.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 3911675
+  timestamp: 1717587866574
+- kind: conda
+  name: hdf5
+  version: 1.14.3
+  build: nompi_hec07895_105
+  build_number: 105
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/hdf5-1.14.3-nompi_hec07895_105.conda
+  sha256: 5d87a1b63862e7da78c7bd9c17dea3526c0462c11df9004943cfa4569cc25dd3
+  md5: f9c8c7304d52c8846eab5d6c34219812
+  depends:
+  - __osx >=11.0
+  - libaec >=1.1.3,<2.0a0
+  - libcurl >=8.8.0,<9.0a0
+  - libcxx >=16
+  - libgfortran 5.*
+  - libgfortran5 >=12.3.0
+  - libgfortran5 >=13.2.0
+  - libzlib >=1.2.13,<2.0a0
+  - openssl >=3.3.1,<4.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 3445248
+  timestamp: 1717587775787
 - kind: conda
   name: hpack
   version: 4.0.0
@@ -3962,13 +4738,13 @@ packages:
   timestamp: 1720853997952
 - kind: conda
   name: identify
-  version: 2.6.1
+  version: 2.6.2
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.1-pyhd8ed1ab_0.conda
-  sha256: dc752392f327e64e32bc3122758b2d8951aec9d6e6aa888463c73d18a10e3c56
-  md5: 43f629202f9eec21be5f71171fb5daf8
+  url: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.2-pyhd8ed1ab_0.conda
+  sha256: 4e3f1c381ad65b476a98d03c0f6c73df04ae4095b501f51129ba6f2a7660179c
+  md5: 636950f839e065401e2031624a414f0b
   depends:
   - python >=3.6
   - ukkonen
@@ -3976,8 +4752,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/identify?source=hash-mapping
-  size: 78078
-  timestamp: 1726369674008
+  size: 78376
+  timestamp: 1731187862708
 - kind: conda
   name: idna
   version: '3.10'
@@ -4019,6 +4795,26 @@ packages:
   - pkg:pypi/importlib-metadata?source=hash-mapping
   size: 28646
   timestamp: 1726082927916
+- kind: conda
+  name: importlib_resources
+  version: 6.4.5
+  build: pyhd8ed1ab_0
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.5-pyhd8ed1ab_0.conda
+  sha256: 2cb9db3e40033c3df72d3defc678a012840378fd55a67e4351363d4b321a0dc1
+  md5: c808991d29b9838fb4d96ce8267ec9ec
+  depends:
+  - python >=3.8
+  - zipp >=3.1.0
+  constrains:
+  - importlib-resources >=6.4.5,<6.4.6.0a0
+  license: Apache-2.0
+  license_family: APACHE
+  purls:
+  - pkg:pypi/importlib-resources?source=hash-mapping
+  size: 32725
+  timestamp: 1725921462405
 - kind: conda
   name: iniconfig
   version: 2.0.0
@@ -4146,22 +4942,21 @@ packages:
   timestamp: 1702518633821
 - kind: conda
   name: jedi
-  version: 0.19.1
-  build: pyhd8ed1ab_0
+  version: 0.19.2
+  build: pyhff2d567_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.1-pyhd8ed1ab_0.conda
-  sha256: 362f0936ef37dfd1eaa860190e42a6ebf8faa094eaa3be6aa4d9ace95f40047a
-  md5: 81a3be0b2023e1ea8555781f0ad904a2
+  url: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhff2d567_0.conda
+  sha256: d37dad14c00d06d33bfb99c378d0abd7645224a9491c433af5028f24863341ab
+  md5: 11ead81b00e0f7cc901fceb7ccfb92c1
   depends:
   - parso >=0.8.3,<0.9.0
-  - python >=3.6
-  license: MIT
-  license_family: MIT
+  - python >=3.9
+  license: Apache-2.0 AND MIT
   purls:
   - pkg:pypi/jedi?source=hash-mapping
-  size: 841312
-  timestamp: 1696326218364
+  size: 842916
+  timestamp: 1731317305873
 - kind: conda
   name: jinja2
   version: 3.1.4
@@ -4235,50 +5030,54 @@ packages:
   purls: []
   size: 73715
   timestamp: 1726487214495
-- kind: pypi
+- kind: conda
   name: jsonschema
   version: 4.23.0
-  url: https://files.pythonhosted.org/packages/69/4a/4f9dbeb84e8850557c02365a0eee0649abe5eb1d84af92a25731c6c0f922/jsonschema-4.23.0-py3-none-any.whl
-  sha256: fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566
-  requires_dist:
-  - attrs>=22.2.0
-  - importlib-resources>=1.4.0 ; python_full_version < '3.9'
-  - jsonschema-specifications>=2023.3.6
-  - pkgutil-resolve-name>=1.3.10 ; python_full_version < '3.9'
-  - referencing>=0.28.4
-  - rpds-py>=0.7.1
-  - fqdn ; extra == 'format'
-  - idna ; extra == 'format'
-  - isoduration ; extra == 'format'
-  - jsonpointer>1.13 ; extra == 'format'
-  - rfc3339-validator ; extra == 'format'
-  - rfc3987 ; extra == 'format'
-  - uri-template ; extra == 'format'
-  - webcolors>=1.11 ; extra == 'format'
-  - fqdn ; extra == 'format-nongpl'
-  - idna ; extra == 'format-nongpl'
-  - isoduration ; extra == 'format-nongpl'
-  - jsonpointer>1.13 ; extra == 'format-nongpl'
-  - rfc3339-validator ; extra == 'format-nongpl'
-  - rfc3986-validator>0.1.0 ; extra == 'format-nongpl'
-  - uri-template ; extra == 'format-nongpl'
-  - webcolors>=24.6.0 ; extra == 'format-nongpl'
-  requires_python: '>=3.8'
-- kind: pypi
+  build: pyhd8ed1ab_0
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.23.0-pyhd8ed1ab_0.conda
+  sha256: 7d0c4c0346b26be9f220682b7c5c0d84606d48c6dbc36fc238e4452dda733aff
+  md5: da304c192ad59975202859b367d0f6a2
+  depends:
+  - attrs >=22.2.0
+  - importlib_resources >=1.4.0
+  - jsonschema-specifications >=2023.03.6
+  - pkgutil-resolve-name >=1.3.10
+  - python >=3.8
+  - referencing >=0.28.4
+  - rpds-py >=0.7.1
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/jsonschema?source=hash-mapping
+  size: 74323
+  timestamp: 1720529611305
+- kind: conda
   name: jsonschema-specifications
   version: 2024.10.1
-  url: https://files.pythonhosted.org/packages/d1/0f/8910b19ac0670a0f80ce1008e5e751c4a57e14d2c4c13a482aa6079fa9d6/jsonschema_specifications-2024.10.1-py3-none-any.whl
-  sha256: a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf
-  requires_dist:
-  - referencing>=0.31.0
-  requires_python: '>=3.9'
-- kind: pypi
-  name: jupyter-cache
-  version: 1.0.0
-  url: https://files.pythonhosted.org/packages/f8/2f/0bb8eacdd1102a20fecc759fb8ace695b9a1048563499a6dff8fa8da32a7/jupyter_cache-1.0.0-py3-none-any.whl
-  sha256: 594b1c4e29b488b36547e12477645f489dbdc62cc939b2408df5679f79245078
-  requires_dist:
-  - attrs
+  build: pyhd8ed1ab_0
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2024.10.1-pyhd8ed1ab_0.conda
+  sha256: 82f8bed0f21dc0b3aff40dd4e39d77e85b93b0417bc5659b001e0109341b8b98
+  md5: 720745920222587ef942acfbc578b584
+  depends:
+  - python >=3.8
+  - referencing >=0.31.0
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/jsonschema-specifications?source=hash-mapping
+  size: 16165
+  timestamp: 1728418976382
+- kind: pypi
+  name: jupyter-cache
+  version: 1.0.1
+  url: https://files.pythonhosted.org/packages/64/6b/67b87da9d36bff9df7d0efbd1a325fa372a43be7158effaf43ed7b22341d/jupyter_cache-1.0.1-py3-none-any.whl
+  sha256: 9c3cafd825ba7da8b5830485343091143dff903e4d8c69db9349b728b140abf6
+  requires_dist:
+  - attrs
   - click
   - importlib-metadata
   - nbclient>=0.2
@@ -4302,7 +5101,7 @@ packages:
   - nbformat>=5.1 ; extra == 'testing'
   - numpy ; extra == 'testing'
   - pandas ; extra == 'testing'
-  - pytest>=6,<8 ; extra == 'testing'
+  - pytest>=6 ; extra == 'testing'
   - pytest-cov ; extra == 'testing'
   - pytest-regressions ; extra == 'testing'
   - sympy ; extra == 'testing'
@@ -4351,6 +5150,43 @@ packages:
   - pkg:pypi/jupyter-core?source=hash-mapping
   size: 57671
   timestamp: 1727163547058
+- kind: conda
+  name: kealib
+  version: 1.5.3
+  build: h8edbb62_2
+  build_number: 2
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/kealib-1.5.3-h8edbb62_2.conda
+  sha256: 29fef9ff99514a34d8026da4be5289bc4d2526974df459b63e92445fca7fd55e
+  md5: d5c581103f5433dd862acbf24facdf9b
+  depends:
+  - __osx >=11.0
+  - hdf5 >=1.14.3,<1.14.4.0a0
+  - libcxx >=17
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 142261
+  timestamp: 1725399546359
+- kind: conda
+  name: kealib
+  version: 1.5.3
+  build: hf8d3e68_2
+  build_number: 2
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/kealib-1.5.3-hf8d3e68_2.conda
+  sha256: a45cb038fce2b6fa154cf0c71485a75b59cb1d8d6b0465bdcb23736aca6bf2ac
+  md5: ffe68c611ae0ccfda4e7a605195e22b3
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - hdf5 >=1.14.3,<1.14.4.0a0
+  - libgcc >=13
+  - libstdcxx >=13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 180005
+  timestamp: 1725399272056
 - kind: conda
   name: keyutils
   version: 1.6.1
@@ -4576,6 +5412,37 @@ packages:
   purls: []
   size: 1179072
   timestamp: 1727295571173
+- kind: conda
+  name: libaec
+  version: 1.1.3
+  build: h59595ed_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libaec-1.1.3-h59595ed_0.conda
+  sha256: 2ef420a655528bca9d269086cf33b7e90d2f54ad941b437fb1ed5eca87cee017
+  md5: 5e97e271911b8b2001a8b71860c32faa
+  depends:
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 35446
+  timestamp: 1711021212685
+- kind: conda
+  name: libaec
+  version: 1.1.3
+  build: hebf3989_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libaec-1.1.3-hebf3989_0.conda
+  sha256: 896189b7b48a194c46a3556ea04943ef81cbe0498521231f8eb25816a68bc8ed
+  md5: 6f0b8e56d2e7bae12a18fc5b2cd9f310
+  depends:
+  - libcxx >=16
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 28451
+  timestamp: 1711021498493
 - kind: conda
   name: libarchive
   version: 3.7.4
@@ -4626,208 +5493,249 @@ packages:
 - kind: conda
   name: libarrow
   version: 18.0.0
-  build: h6fea68a_0_cpu
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-18.0.0-h6fea68a_0_cpu.conda
-  sha256: ddd556d066216a1e3f157eaa0cedd811105bae706f98feaeef064569e889f40f
-  md5: 64ff84a32d9fa037380459f0440f3d8e
+  build: h94eee4b_8_cpu
+  build_number: 8
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-18.0.0-h94eee4b_8_cpu.conda
+  sha256: aeb31e3713767d5e1d2f29bc05ba04e4cbd48fd72edf8ae66867ac5b22b94160
+  md5: 8d5436adb1b35ba955c5600806d52dbc
   depends:
-  - __osx >=11.0
-  - aws-crt-cpp >=0.29.0,<0.29.1.0a0
-  - aws-sdk-cpp >=1.11.407,<1.11.408.0a0
+  - __glibc >=2.17,<3.0.a0
+  - aws-crt-cpp >=0.29.5,<0.29.6.0a0
+  - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
   - azure-core-cpp >=1.14.0,<1.14.1.0a0
   - azure-identity-cpp >=1.10.0,<1.10.1.0a0
   - azure-storage-blobs-cpp >=12.13.0,<12.13.1.0a0
   - azure-storage-files-datalake-cpp >=12.12.0,<12.12.1.0a0
   - bzip2 >=1.0.8,<2.0a0
+  - gflags >=2.2.2,<2.3.0a0
   - glog >=0.7.1,<0.8.0a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libcxx >=18
-  - libgoogle-cloud >=2.30.0,<2.31.0a0
-  - libgoogle-cloud-storage >=2.30.0,<2.31.0a0
+  - libgcc >=13
+  - libgoogle-cloud >=2.31.0,<2.32.0a0
+  - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
   - libre2-11 >=2024.7.2
+  - libstdcxx >=13
   - libutf8proc >=2.8.0,<3.0a0
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
-  - orc >=2.0.2,<2.0.3.0a0
+  - orc >=2.0.3,<2.0.4.0a0
   - re2
   - snappy >=1.2.1,<1.3.0a0
   - zstd >=1.5.6,<1.6.0a0
   constrains:
+  - parquet-cpp <0.0a0
   - arrow-cpp <0.0a0
   - apache-arrow-proc =*=cpu
-  - parquet-cpp <0.0a0
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 5448966
-  timestamp: 1730155187081
+  size: 8712917
+  timestamp: 1732208188022
 - kind: conda
   name: libarrow
   version: 18.0.0
-  build: ha5db6c2_0_cpu
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-18.0.0-ha5db6c2_0_cpu.conda
-  sha256: a997e60707f8c36aa6adadbe1dad4a92b02b6b7a8c58042c12ed1e8102887429
-  md5: 55f4011e75175bfbbc10f8e5998345d4
+  build: hb943b0e_8_cpu
+  build_number: 8
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-18.0.0-hb943b0e_8_cpu.conda
+  sha256: ec1e48caf98615d4e6cb5bac539d6ddcdff37fd7a722dab599131974b2a97343
+  md5: e1b2b2f162540ebea865e908c865bb9c
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - aws-crt-cpp >=0.29.0,<0.29.1.0a0
-  - aws-sdk-cpp >=1.11.407,<1.11.408.0a0
+  - __osx >=11.0
+  - aws-crt-cpp >=0.29.5,<0.29.6.0a0
+  - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
   - azure-core-cpp >=1.14.0,<1.14.1.0a0
   - azure-identity-cpp >=1.10.0,<1.10.1.0a0
   - azure-storage-blobs-cpp >=12.13.0,<12.13.1.0a0
   - azure-storage-files-datalake-cpp >=12.12.0,<12.12.1.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - gflags >=2.2.2,<2.3.0a0
   - glog >=0.7.1,<0.8.0a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
   - libbrotlienc >=1.1.0,<1.2.0a0
-  - libgcc >=13
-  - libgoogle-cloud >=2.30.0,<2.31.0a0
-  - libgoogle-cloud-storage >=2.30.0,<2.31.0a0
+  - libcxx >=18
+  - libgoogle-cloud >=2.31.0,<2.32.0a0
+  - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
   - libre2-11 >=2024.7.2
-  - libstdcxx >=13
   - libutf8proc >=2.8.0,<3.0a0
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
-  - orc >=2.0.2,<2.0.3.0a0
+  - orc >=2.0.3,<2.0.4.0a0
   - re2
   - snappy >=1.2.1,<1.3.0a0
   - zstd >=1.5.6,<1.6.0a0
   constrains:
-  - parquet-cpp <0.0a0
-  - apache-arrow-proc =*=cpu
   - arrow-cpp <0.0a0
+  - apache-arrow-proc =*=cpu
+  - parquet-cpp <0.0a0
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 8719515
-  timestamp: 1730155543609
+  size: 5459847
+  timestamp: 1732208317959
 - kind: conda
   name: libarrow-acero
   version: 18.0.0
-  build: h286801f_0_cpu
+  build: h286801f_8_cpu
+  build_number: 8
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-18.0.0-h286801f_0_cpu.conda
-  sha256: 93014da94788f24710be8e457c49609cf8dc17cd91e5fb80285ce28cefce6b57
-  md5: deab7a5984465e46176d289377025757
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-18.0.0-h286801f_8_cpu.conda
+  sha256: b74c5a10cd40e33db50392263cff9aacd6ead0f6d42107e01e92d1e57af1daf1
+  md5: 7208462cfc8f9610060695ffab85ab39
   depends:
   - __osx >=11.0
-  - libarrow 18.0.0 h6fea68a_0_cpu
+  - libarrow 18.0.0 hb943b0e_8_cpu
   - libcxx >=18
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 491557
-  timestamp: 1730155291137
+  size: 491510
+  timestamp: 1732208453361
 - kind: conda
   name: libarrow-acero
   version: 18.0.0
-  build: h5888daf_0_cpu
+  build: h5888daf_8_cpu
+  build_number: 8
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-18.0.0-h5888daf_0_cpu.conda
-  sha256: 62cefa335403df349ddf91f2a2c0ff8f967edbdb5a4c0ca7e9c5bc13c47ed163
-  md5: 8771a1fcc6d8bf2fd18cc57d778f90a3
+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-18.0.0-h5888daf_8_cpu.conda
+  sha256: 6a33ef82a569d02b2b4664bdcc4cb6aea624dbf258921ab59afadd655e13953d
+  md5: 3ac00dbba52c89287214f5e8f0795e7e
   depends:
   - __glibc >=2.17,<3.0.a0
-  - libarrow 18.0.0 ha5db6c2_0_cpu
+  - libarrow 18.0.0 h94eee4b_8_cpu
   - libgcc >=13
   - libstdcxx >=13
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 620074
-  timestamp: 1730155586601
+  size: 619612
+  timestamp: 1732208230617
 - kind: conda
   name: libarrow-dataset
   version: 18.0.0
-  build: h286801f_0_cpu
+  build: h286801f_8_cpu
+  build_number: 8
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-18.0.0-h286801f_0_cpu.conda
-  sha256: b204bb8d3c5d5a2ab74b9375086ebee91c0a500e2146aed01e8915a4eae2f140
-  md5: 719055efe1941ef666b3882e6a85a9bb
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-18.0.0-h286801f_8_cpu.conda
+  sha256: 7b503c2179880d8d755e4f81e8e058ca869227c1958c172af5ab4c62d637571d
+  md5: 08b882378a3e10b0be0218e5867638e3
   depends:
   - __osx >=11.0
-  - libarrow 18.0.0 h6fea68a_0_cpu
-  - libarrow-acero 18.0.0 h286801f_0_cpu
+  - libarrow 18.0.0 hb943b0e_8_cpu
+  - libarrow-acero 18.0.0 h286801f_8_cpu
   - libcxx >=18
-  - libparquet 18.0.0 hda0ea68_0_cpu
+  - libparquet 18.0.0 hda0ea68_8_cpu
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 497503
-  timestamp: 1730156406678
+  size: 498580
+  timestamp: 1732209786094
 - kind: conda
   name: libarrow-dataset
   version: 18.0.0
-  build: h5888daf_0_cpu
+  build: h5888daf_8_cpu
+  build_number: 8
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-18.0.0-h5888daf_0_cpu.conda
-  sha256: e90edc2e0982c00f75130d7d2837de7402453ee033adc1030b1475f33746a8b4
-  md5: 5c121a2d50b068076ff4f2b6d68dbca5
+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-18.0.0-h5888daf_8_cpu.conda
+  sha256: 3234ede6af0403cc29258aaaca45fe426e00259c154a4c857087cd805e16f7db
+  md5: 84e996f59d99626426a73bd0977ef7f3
   depends:
   - __glibc >=2.17,<3.0.a0
-  - libarrow 18.0.0 ha5db6c2_0_cpu
-  - libarrow-acero 18.0.0 h5888daf_0_cpu
+  - libarrow 18.0.0 h94eee4b_8_cpu
+  - libarrow-acero 18.0.0 h5888daf_8_cpu
   - libgcc >=13
-  - libparquet 18.0.0 h6bd9018_0_cpu
+  - libparquet 18.0.0 h6bd9018_8_cpu
+  - libstdcxx >=13
+  license: Apache-2.0
+  purls: []
+  size: 594655
+  timestamp: 1732208308056
+- kind: conda
+  name: libarrow-substrait
+  version: 18.0.0
+  build: h5c8f2c3_8_cpu
+  build_number: 8
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-18.0.0-h5c8f2c3_8_cpu.conda
+  sha256: ceee34b069762aa81521f8aba1cf5f613250f59db7a2e1570865332ad2da8555
+  md5: faa0b78b5daac5dab1651c610204401d
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libabseil * cxx17*
+  - libabseil >=20240722.0,<20240723.0a0
+  - libarrow 18.0.0 h94eee4b_8_cpu
+  - libarrow-acero 18.0.0 h5888daf_8_cpu
+  - libarrow-dataset 18.0.0 h5888daf_8_cpu
+  - libgcc >=13
+  - libprotobuf >=5.28.2,<5.28.3.0a0
   - libstdcxx >=13
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 594320
-  timestamp: 1730155664725
+  size: 527756
+  timestamp: 1732208344211
 - kind: conda
   name: libarrow-substrait
   version: 18.0.0
-  build: hdcc9e87_0_cpu
+  build: h6a6e5c5_8_cpu
+  build_number: 8
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-18.0.0-hdcc9e87_0_cpu.conda
-  sha256: 6ea9df616248191a06fb4d078486f282b1807bd8eab3e4f380f04df46264cea2
-  md5: dd51b0ba8e9dc24f04362cca5a93569d
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-18.0.0-h6a6e5c5_8_cpu.conda
+  sha256: 1d7ef3812071a036942cc9bc6742b60354aee1e168338bd18a8596bbd696e43c
+  md5: 7acbdff23cd797bb9ada6a3de2d7502a
   depends:
   - __osx >=11.0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
-  - libarrow 18.0.0 h6fea68a_0_cpu
-  - libarrow-acero 18.0.0 h286801f_0_cpu
-  - libarrow-dataset 18.0.0 h286801f_0_cpu
+  - libarrow 18.0.0 hb943b0e_8_cpu
+  - libarrow-acero 18.0.0 h286801f_8_cpu
+  - libarrow-dataset 18.0.0 h286801f_8_cpu
   - libcxx >=18
-  - libprotobuf >=5.27.5,<5.27.6.0a0
+  - libprotobuf >=5.28.2,<5.28.3.0a0
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 457812
-  timestamp: 1730156602117
+  size: 459728
+  timestamp: 1732209986506
 - kind: conda
-  name: libarrow-substrait
-  version: 18.0.0
-  build: he882d9a_0_cpu
+  name: libavif16
+  version: 1.1.1
+  build: h1909e37_2
+  build_number: 2
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-18.0.0-he882d9a_0_cpu.conda
-  sha256: 0d4458a0ccbfa19031fecf94a5e610eda9be81ee342d8a0a2685e076f6b14881
-  md5: 1d73c2c8cabb70f9bf1dd36222ef7b25
+  url: https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.1.1-h1909e37_2.conda
+  sha256: e06da844b007a64a9ac35d4e3dc4dbc66583f79b57d08166cf58f2f08723a6e8
+  md5: 21e468ed3786ebcb2124b123aa2484b7
   depends:
   - __glibc >=2.17,<3.0.a0
-  - libabseil * cxx17*
-  - libabseil >=20240722.0,<20240723.0a0
-  - libarrow 18.0.0 ha5db6c2_0_cpu
-  - libarrow-acero 18.0.0 h5888daf_0_cpu
-  - libarrow-dataset 18.0.0 h5888daf_0_cpu
+  - aom >=3.9.1,<3.10.0a0
+  - dav1d >=1.2.1,<1.2.2.0a0
   - libgcc >=13
-  - libprotobuf >=5.27.5,<5.27.6.0a0
-  - libstdcxx >=13
-  license: Apache-2.0
-  license_family: APACHE
+  - rav1e >=0.6.6,<1.0a0
+  - svt-av1 >=2.3.0,<2.3.1.0a0
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 116202
+  timestamp: 1730268687453
+- kind: conda
+  name: libavif16
+  version: 1.1.1
+  build: h45b7238_2
+  build_number: 2
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libavif16-1.1.1-h45b7238_2.conda
+  sha256: c671365e8c822d29b53f20c4573fdbc70f18b50ff9a4b5b2b6b3c8f7ad2ac2a9
+  md5: 7571064a60bc193ff5c25f36ed23394a
+  depends:
+  - __osx >=11.0
+  - aom >=3.9.1,<3.10.0a0
+  - dav1d >=1.2.1,<1.2.2.0a0
+  - rav1e >=0.6.6,<1.0a0
+  - svt-av1 >=2.3.0,<2.3.1.0a0
+  license: BSD-2-Clause
+  license_family: BSD
   purls: []
-  size: 528788
-  timestamp: 1730155701400
+  size: 96781
+  timestamp: 1730268761553
 - kind: conda
   name: libblas
   version: 3.9.0
@@ -5091,19 +5999,50 @@ packages:
   timestamp: 1726659794676
 - kind: conda
   name: libcxx
-  version: 19.1.3
+  version: 19.1.4
   build: ha82da77_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.3-ha82da77_0.conda
-  sha256: 6d062760c6439e75b9a44d800d89aff60fe3441998d87506c62dc94c50412ef4
-  md5: bf691071fba4734984231617783225bc
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.4-ha82da77_0.conda
+  sha256: 342896ebc1d6acbf022ca6df006a936b9a472579e91e3c502cb1f52f218b78e9
+  md5: a2d3d484d95889fccdd09498d8f6bf9a
   depends:
   - __osx >=11.0
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
   purls: []
-  size: 520771
-  timestamp: 1730314603920
+  size: 520678
+  timestamp: 1732060258949
+- kind: conda
+  name: libde265
+  version: 1.0.15
+  build: h00ab1b0_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libde265-1.0.15-h00ab1b0_0.conda
+  sha256: 7cf7e294e1a7c8219065885e186d8f52002fb900bf384d815f159b5874204e3d
+  md5: 407fee7a5d7ab2dca12c9ca7f62310ad
+  depends:
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  license: LGPL-3.0-or-later
+  license_family: LGPL
+  purls: []
+  size: 411814
+  timestamp: 1703088639063
+- kind: conda
+  name: libde265
+  version: 1.0.15
+  build: h2ffa867_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libde265-1.0.15-h2ffa867_0.conda
+  sha256: 13747fa634f7f16d7f222b7d3869e3c1aab9d3a2791edeb2fc632a87663950e0
+  md5: 7c718ee6d8497702145612fa0898a12d
+  depends:
+  - libcxx >=15
+  license: LGPL-3.0-or-later
+  license_family: LGPL
+  purls: []
+  size: 277861
+  timestamp: 1703089176970
 - kind: conda
   name: libdeflate
   version: '1.22'
@@ -5233,39 +6172,39 @@ packages:
   timestamp: 1685725977222
 - kind: conda
   name: libexpat
-  version: 2.6.3
-  build: h5888daf_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.3-h5888daf_0.conda
-  sha256: 4bb47bb2cd09898737a5211e2992d63c555d63715a07ba56eae0aff31fb89c22
-  md5: 59f4c43bb1b5ef1c71946ff2cbf59524
+  version: 2.6.4
+  build: h286801f_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.6.4-h286801f_0.conda
+  sha256: e42ab5ace927ee7c84e3f0f7d813671e1cf3529f5f06ee5899606630498c2745
+  md5: 38d2656dd914feb0cab8c629370768bf
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - libgcc >=13
+  - __osx >=11.0
   constrains:
-  - expat 2.6.3.*
+  - expat 2.6.4.*
   license: MIT
   license_family: MIT
   purls: []
-  size: 73616
-  timestamp: 1725568742634
+  size: 64693
+  timestamp: 1730967175868
 - kind: conda
   name: libexpat
-  version: 2.6.3
-  build: hf9b8971_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.6.3-hf9b8971_0.conda
-  sha256: 5cbe5a199fba14ade55457a468ce663aac0b54832c39aa54470b3889b4c75c4a
-  md5: 5f22f07c2ab2dea8c66fe9585a062c96
+  version: 2.6.4
+  build: h5888daf_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.4-h5888daf_0.conda
+  sha256: 56541b98447b58e52d824bd59d6382d609e11de1f8adf20b23143e353d2b8d26
+  md5: db833e03127376d461e1e13e76f09b6c
   depends:
-  - __osx >=11.0
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
   constrains:
-  - expat 2.6.3.*
+  - expat 2.6.4.*
   license: MIT
   license_family: MIT
   purls: []
-  size: 63895
-  timestamp: 1725568783033
+  size: 73304
+  timestamp: 1730967041968
 - kind: conda
   name: libffi
   version: 3.4.2
@@ -5332,15 +6271,71 @@ packages:
   purls: []
   size: 54142
   timestamp: 1729027726517
+- kind: conda
+  name: libgdal
+  version: 3.10.0
+  build: ha770c72_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-3.10.0-ha770c72_1.conda
+  sha256: 83594983999b15912a29fd4ae6e3d4d7e6c3f4eb6b2094e4334c7ae364bc71bb
+  md5: f32b9e97d0394dcc2f6f5758dc18afa1
+  depends:
+  - libgdal-core 3.10.0.*
+  - libgdal-fits 3.10.0.*
+  - libgdal-grib 3.10.0.*
+  - libgdal-hdf4 3.10.0.*
+  - libgdal-hdf5 3.10.0.*
+  - libgdal-jp2openjpeg 3.10.0.*
+  - libgdal-kea 3.10.0.*
+  - libgdal-netcdf 3.10.0.*
+  - libgdal-pdf 3.10.0.*
+  - libgdal-pg 3.10.0.*
+  - libgdal-postgisraster 3.10.0.*
+  - libgdal-tiledb 3.10.0.*
+  - libgdal-xls 3.10.0.*
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 423447
+  timestamp: 1731505795398
+- kind: conda
+  name: libgdal
+  version: 3.10.0
+  build: hce30654_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-3.10.0-hce30654_1.conda
+  sha256: 53833755e83870cff13e4236885d909acc12aae4e4510f06f47012f7123671c5
+  md5: 8a598e7be90a6a298aa3b76674e627a4
+  depends:
+  - libgdal-core 3.10.0.*
+  - libgdal-fits 3.10.0.*
+  - libgdal-grib 3.10.0.*
+  - libgdal-hdf4 3.10.0.*
+  - libgdal-hdf5 3.10.0.*
+  - libgdal-jp2openjpeg 3.10.0.*
+  - libgdal-kea 3.10.0.*
+  - libgdal-netcdf 3.10.0.*
+  - libgdal-pdf 3.10.0.*
+  - libgdal-pg 3.10.0.*
+  - libgdal-postgisraster 3.10.0.*
+  - libgdal-tiledb 3.10.0.*
+  - libgdal-xls 3.10.0.*
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 423949
+  timestamp: 1731509356372
 - kind: conda
   name: libgdal-core
-  version: 3.9.3
-  build: hb8ac103_2
-  build_number: 2
+  version: 3.10.0
+  build: h1554e7d_1
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.9.3-hb8ac103_2.conda
-  sha256: 20689dcd48fb76e0f21db1d1876d3dab354c191d8c6d4bc922a847f42aa571c5
-  md5: 1e2a7da651b747b86708f42b0ea8f3b8
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.10.0-h1554e7d_1.conda
+  sha256: 601f5178992a0e1a0248fc38adabea52a58803181bb60a566a546807825d5aaa
+  md5: 5fefd6201f8af5441ab43567a283fd0c
   depends:
   - __osx >=11.0
   - blosc >=1.21.6,<2.0a0
@@ -5351,9 +6346,10 @@ packages:
   - lerc >=4.0.0,<5.0a0
   - libarchive >=3.7.4,<3.8.0a0
   - libcurl >=8.10.1,<9.0a0
-  - libcxx >=17
+  - libcxx >=18
   - libdeflate >=1.22,<1.23.0a0
-  - libexpat >=2.6.3,<3.0a0
+  - libexpat >=2.6.4,<3.0a0
+  - libheif >=1.18.2,<1.19.0a0
   - libiconv >=1.17,<2.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libkml >=1.3.0,<1.4.0a0
@@ -5362,31 +6358,31 @@ packages:
   - libsqlite >=3.47.0,<4.0a0
   - libtiff >=4.7.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
-  - libxml2 >=2.12.7,<3.0a0
+  - libxml2 >=2.13.5,<3.0a0
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
-  - openssl >=3.3.2,<4.0a0
+  - openssl >=3.4.0,<4.0a0
   - pcre2 >=10.44,<10.45.0a0
   - proj >=9.5.0,<9.6.0a0
   - xerces-c >=3.2.5,<3.3.0a0
   - xz >=5.2.6,<6.0a0
   - zstd >=1.5.6,<1.6.0a0
   constrains:
-  - libgdal 3.9.3.*
+  - libgdal 3.10.0.*
   license: MIT
   license_family: MIT
   purls: []
-  size: 8279267
-  timestamp: 1730220682729
+  size: 8478111
+  timestamp: 1731504801049
 - kind: conda
   name: libgdal-core
-  version: 3.9.3
-  build: hd5b9bfb_2
-  build_number: 2
+  version: 3.10.0
+  build: hef9eae6_1
+  build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.9.3-hd5b9bfb_2.conda
-  sha256: ac788fcbb7ae09f7d771f08ecfd4a985af8e608e8ae7b04a878dced496cd0d48
-  md5: b70c6b3de9d4779d40dc3194f3958889
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.10.0-hef9eae6_1.conda
+  sha256: 2d42f719cf5e70ef0e5ccf1334a3cda9c1e8d483623cc9266a5d77cf74a7279c
+  md5: 6271d1929f8c1964f5f1d56a7f996b19
   depends:
   - __glibc >=2.17,<3.0.a0
   - blosc >=1.21.6,<2.0a0
@@ -5398,8 +6394,9 @@ packages:
   - libarchive >=3.7.4,<3.8.0a0
   - libcurl >=8.10.1,<9.0a0
   - libdeflate >=1.22,<1.23.0a0
-  - libexpat >=2.6.3,<3.0a0
+  - libexpat >=2.6.4,<3.0a0
   - libgcc >=13
+  - libheif >=1.18.2,<1.19.0a0
   - libiconv >=1.17,<2.0a0
   - libjpeg-turbo >=3.0.0,<4.0a0
   - libkml >=1.3.0,<1.4.0a0
@@ -5410,271 +6407,868 @@ packages:
   - libtiff >=4.7.0,<4.8.0a0
   - libuuid >=2.38.1,<3.0a0
   - libwebp-base >=1.4.0,<2.0a0
-  - libxml2 >=2.12.7,<3.0a0
+  - libxml2 >=2.13.5,<3.0a0
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
-  - openssl >=3.3.2,<4.0a0
+  - openssl >=3.4.0,<4.0a0
   - pcre2 >=10.44,<10.45.0a0
   - proj >=9.5.0,<9.6.0a0
   - xerces-c >=3.2.5,<3.3.0a0
   - xz >=5.2.6,<6.0a0
   - zstd >=1.5.6,<1.6.0a0
   constrains:
-  - libgdal 3.9.3.*
+  - libgdal 3.10.0.*
   license: MIT
   license_family: MIT
   purls: []
-  size: 10427151
-  timestamp: 1730219530936
+  size: 10805350
+  timestamp: 1731503989671
 - kind: conda
-  name: libgfortran
-  version: 5.0.0
-  build: 13_2_0_hd922786_3
-  build_number: 3
+  name: libgdal-fits
+  version: 3.10.0
+  build: haf2d7a4_1
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-5.0.0-13_2_0_hd922786_3.conda
-  sha256: 44e541b4821c96b28b27fef5630883a60ce4fee91fd9c79f25a199f8f73f337b
-  md5: 4a55d9e169114b2b90d3ec4604cd7bbf
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-fits-3.10.0-haf2d7a4_1.conda
+  sha256: 5f07ccfed8a82e81e1c9c21be5bda5fc09468ff93874528545bb6fdef0da8813
+  md5: 7883831c83bbaf4a32bc48913007a1f6
   depends:
-  - libgfortran5 13.2.0 hf226fd6_3
-  license: GPL-3.0-only WITH GCC-exception-3.1
-  license_family: GPL
+  - __osx >=11.0
+  - cfitsio >=4.4.1,<4.4.2.0a0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  license: MIT
+  license_family: MIT
   purls: []
-  size: 110233
-  timestamp: 1707330749033
+  size: 466141
+  timestamp: 1731506929069
 - kind: conda
-  name: libgfortran
-  version: 14.2.0
-  build: h69a702a_1
+  name: libgdal-fits
+  version: 3.10.0
+  build: he1674de_1
   build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.2.0-h69a702a_1.conda
-  sha256: fc9e7f22a17faf74da904ebfc4d88699013d2992e55505e4aa0eb01770290977
-  md5: f1fd30127802683586f768875127a987
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-fits-3.10.0-he1674de_1.conda
+  sha256: 83761f3a6d93bedae4b2745b66393b0b96da415c90c9392b5aad5efd31f4f809
+  md5: 415c6f3d27f39731b38f89db57f785f7
   depends:
-  - libgfortran5 14.2.0 hd5240d6_1
-  constrains:
-  - libgfortran-ng ==14.2.0=*_1
-  license: GPL-3.0-only WITH GCC-exception-3.1
-  license_family: GPL
+  - __glibc >=2.17,<3.0.a0
+  - cfitsio >=4.4.1,<4.4.2.0a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libstdcxx >=13
+  license: MIT
+  license_family: MIT
   purls: []
-  size: 53997
-  timestamp: 1729027752995
+  size: 478652
+  timestamp: 1731505043220
 - kind: conda
-  name: libgfortran-ng
-  version: 14.2.0
-  build: h69a702a_1
+  name: libgdal-grib
+  version: 3.10.0
+  build: h4b7ce82_1
   build_number: 1
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-14.2.0-h69a702a_1.conda
-  sha256: 423f1e2403f0c665748e42d335e421e53fd03c08d457cfb6f360d329d9459851
-  md5: 0a7f4cd238267c88e5d69f7826a407eb
-  depends:
-  - libgfortran 14.2.0 h69a702a_1
-  license: GPL-3.0-only WITH GCC-exception-3.1
-  license_family: GPL
-  purls: []
-  size: 54106
-  timestamp: 1729027945817
-- kind: conda
-  name: libgfortran5
-  version: 13.2.0
-  build: hf226fd6_3
-  build_number: 3
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-13.2.0-hf226fd6_3.conda
-  sha256: bafc679eedb468a86aa4636061c55966186399ee0a04b605920d208d97ac579a
-  md5: 66ac81d54e95c534ae488726c1f698ea
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-grib-3.10.0-h4b7ce82_1.conda
+  sha256: e3f5565fc96494f9d4a75465a2a50988f41a6830e91cde89d5001ebea60e950c
+  md5: c2f9d2e735d44f35ef753f017795ec97
   depends:
-  - llvm-openmp >=8.0.0
-  constrains:
-  - libgfortran 5.0.0 13_2_0_*_3
-  license: GPL-3.0-only WITH GCC-exception-3.1
-  license_family: GPL
+  - __osx >=11.0
+  - libaec >=1.1.3,<2.0a0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  license: MIT
+  license_family: MIT
   purls: []
-  size: 997381
-  timestamp: 1707330687590
+  size: 654421
+  timestamp: 1731507122904
 - kind: conda
-  name: libgfortran5
-  version: 14.2.0
-  build: hd5240d6_1
+  name: libgdal-grib
+  version: 3.10.0
+  build: ha360943_1
   build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.2.0-hd5240d6_1.conda
-  sha256: d149a37ca73611e425041f33b9d8dbed6e52ec506fe8cc1fc0ee054bddeb6d5d
-  md5: 9822b874ea29af082e5d36098d25427d
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-grib-3.10.0-ha360943_1.conda
+  sha256: ce9fcf5f4f602d7463f889b94e95d4a3c82d731ff7ee215956acf839d9296162
+  md5: c8ec329a2a0e09deae512b24bebba974
   depends:
-  - libgcc >=14.2.0
-  constrains:
-  - libgfortran 14.2.0
-  license: GPL-3.0-only WITH GCC-exception-3.1
-  license_family: GPL
+  - __glibc >=2.17,<3.0.a0
+  - libaec >=1.1.3,<2.0a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libstdcxx >=13
+  license: MIT
+  license_family: MIT
   purls: []
-  size: 1462645
-  timestamp: 1729027735353
+  size: 726550
+  timestamp: 1731505103485
 - kind: conda
-  name: libgomp
-  version: 14.2.0
-  build: h77fa898_1
+  name: libgdal-hdf4
+  version: 3.10.0
+  build: h380f24e_1
   build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda
-  sha256: 1911c29975ec99b6b906904040c855772ccb265a1c79d5d75c8ceec4ed89cd63
-  md5: cc3573974587f12dda90d96e3e55a702
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-hdf4-3.10.0-h380f24e_1.conda
+  sha256: 52b3dfa752dafde428001c1ab2bcb485ef1a42115c263a1a62af5d0ba531ca9d
+  md5: 06b598afa8b4d73818d6ae5fbf57cce1
   depends:
-  - _libgcc_mutex 0.1 conda_forge
-  license: GPL-3.0-only WITH GCC-exception-3.1
-  license_family: GPL
+  - __glibc >=2.17,<3.0.a0
+  - hdf4 >=4.2.15,<4.2.16.0a0
+  - libaec >=1.1.3,<2.0a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libstdcxx >=13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 580175
+  timestamp: 1731505162987
+- kind: conda
+  name: libgdal-hdf4
+  version: 3.10.0
+  build: h624721c_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-hdf4-3.10.0-h624721c_1.conda
+  sha256: d6e260a74b7f3b2dd6d9dbb0aeb79ba8b015061884c154ea66736af6fdd74f24
+  md5: 41932ee6f6d0be947150e44156e2c62a
+  depends:
+  - __osx >=11.0
+  - hdf4 >=4.2.15,<4.2.16.0a0
+  - libaec >=1.1.3,<2.0a0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 580426
+  timestamp: 1731507318786
+- kind: conda
+  name: libgdal-hdf5
+  version: 3.10.0
+  build: hefe6d7a_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-hdf5-3.10.0-hefe6d7a_1.conda
+  sha256: 3ae04d93346a1046fc943e3d58b6d5cf4ba43e9ed3488a9bf0ae6a30e0cebfed
+  md5: ff882b327028dd49f9db1eb0c4ca4225
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - hdf5 >=1.14.3,<1.14.4.0a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libstdcxx >=13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 645554
+  timestamp: 1731505230854
+- kind: conda
+  name: libgdal-hdf5
+  version: 3.10.0
+  build: hf20c56d_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-hdf5-3.10.0-hf20c56d_1.conda
+  sha256: 6f0bd22f717ab00733032fe0e1856e8b74206c78d7555a2d508c5e42d5fe6780
+  md5: a5c63bcc7ad7b52f980429affeb51624
+  depends:
+  - __osx >=11.0
+  - hdf5 >=1.14.3,<1.14.4.0a0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 594449
+  timestamp: 1731507566135
+- kind: conda
+  name: libgdal-jp2openjpeg
+  version: 3.10.0
+  build: h0d2a31d_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-jp2openjpeg-3.10.0-h0d2a31d_1.conda
+  sha256: c4e48b9e411517b1418440357439427b058d1cae757c2dd570fcf08c55fa51d7
+  md5: a68dbe24fd7185b2d68feec8332da6a3
+  depends:
+  - __osx >=11.0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - openjpeg >=2.5.2,<3.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 464091
+  timestamp: 1731508004383
+- kind: conda
+  name: libgdal-jp2openjpeg
+  version: 3.10.0
+  build: h9fdfae1_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-jp2openjpeg-3.10.0-h9fdfae1_1.conda
+  sha256: 4943126f43e0164acaf9e0b037127679e6e1a2309af10f6f076b9f0affde74c2
+  md5: b5284debccc01a949a6d744e4e793b2d
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libstdcxx >=13
+  - openjpeg >=2.5.2,<3.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 469676
+  timestamp: 1731505331411
+- kind: conda
+  name: libgdal-kea
+  version: 3.10.0
+  build: h38e673a_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-kea-3.10.0-h38e673a_1.conda
+  sha256: 76d8ecd43ab916a8b28d46e06ce8e3e5b75d75e39f88a72f8a91d4dcbba44660
+  md5: 1b4358b735ef045fbd87d2ec3341a6a2
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - hdf5 >=1.14.3,<1.14.4.0a0
+  - kealib >=1.5.3,<1.6.0a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libgdal-hdf5 3.10.0.*
+  - libkml >=1.3.0,<1.4.0a0
+  - libstdcxx >=13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 481230
+  timestamp: 1731505718036
+- kind: conda
+  name: libgdal-kea
+  version: 3.10.0
+  build: hcaf0198_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-kea-3.10.0-hcaf0198_1.conda
+  sha256: 50a94f31cadc44b8a4a41c0cd6c9e5a1ddf5b87ff22713737a42322ec5e9e427
+  md5: f1fc499efb7be0a4ce89e179b2a257e1
+  depends:
+  - __osx >=11.0
+  - hdf5 >=1.14.3,<1.14.4.0a0
+  - kealib >=1.5.3,<1.6.0a0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libgdal-hdf5 3.10.0.*
+  - libkml >=1.3.0,<1.4.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 470164
+  timestamp: 1731509175246
+- kind: conda
+  name: libgdal-netcdf
+  version: 3.10.0
+  build: hb1be66d_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-netcdf-3.10.0-hb1be66d_1.conda
+  sha256: 9a13b60a1fe1b2ffcbb9053e1020a09bdd1b8c967df4c91e05d09178f65d8770
+  md5: 95237f0a8a57cab65867d12c2dff0c25
+  depends:
+  - __osx >=11.0
+  - hdf4 >=4.2.15,<4.2.16.0a0
+  - hdf5 >=1.14.3,<1.14.4.0a0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libgdal-hdf4 3.10.0.*
+  - libgdal-hdf5 3.10.0.*
+  - libkml >=1.3.0,<1.4.0a0
+  - libnetcdf >=4.9.2,<4.9.3.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 669558
+  timestamp: 1731509350059
+- kind: conda
+  name: libgdal-netcdf
+  version: 3.10.0
+  build: hba670d9_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-netcdf-3.10.0-hba670d9_1.conda
+  sha256: fba073505abfcab1148241e11f520c8a5548b8c5c1da5eb9f38247b06687b8dd
+  md5: 85699d0969e7c92ba75c7bb0e7cbed19
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - hdf4 >=4.2.15,<4.2.16.0a0
+  - hdf5 >=1.14.3,<1.14.4.0a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libgdal-hdf4 3.10.0.*
+  - libgdal-hdf5 3.10.0.*
+  - libkml >=1.3.0,<1.4.0a0
+  - libnetcdf >=4.9.2,<4.9.3.0a0
+  - libstdcxx >=13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 738107
+  timestamp: 1731505793561
+- kind: conda
+  name: libgdal-pdf
+  version: 3.10.0
+  build: h14aca81_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-pdf-3.10.0-h14aca81_1.conda
+  sha256: 5d9a066f9cf74b3469efc9a93092c4c5c246fb25d021d3fe91b413bf3e84873e
+  md5: f14d8d752ec62de789b7843a7182b842
+  depends:
+  - __osx >=11.0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - poppler
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 602811
+  timestamp: 1731508293610
+- kind: conda
+  name: libgdal-pdf
+  version: 3.10.0
+  build: h697c966_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-pdf-3.10.0-h697c966_1.conda
+  sha256: 13e5757cc60dad0a0eb49e0e401581c5aa32ba10ba2640b1409c955362ce1a51
+  md5: 0b8c19eaf166d18bb1f10d759038825f
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libstdcxx >=13
+  - poppler
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 669963
+  timestamp: 1731505409362
+- kind: conda
+  name: libgdal-pg
+  version: 3.10.0
+  build: h5cc4e75_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-pg-3.10.0-h5cc4e75_1.conda
+  sha256: 5cef217e59e0d14669fb5581e2449ec2e0e5ec0e958caf83478231e1ededfbeb
+  md5: 4749862355fee05f7a5a7c41a2cfac7d
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libpq >=17.0,<18.0a0
+  - libstdcxx >=13
+  - postgresql
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 527366
+  timestamp: 1731505468179
+- kind: conda
+  name: libgdal-pg
+  version: 3.10.0
+  build: h84049b1_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-pg-3.10.0-h84049b1_1.conda
+  sha256: c9f87b1e5fa581715765b600d63d4407c5457f78c09799e7daaf283e818bd36e
+  md5: 25bd1b0a0d3085c06d4c63881b0b3221
+  depends:
+  - __osx >=11.0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libpq >=17.0,<18.0a0
+  - postgresql
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 504944
+  timestamp: 1731508463443
+- kind: conda
+  name: libgdal-postgisraster
+  version: 3.10.0
+  build: h5cc4e75_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-postgisraster-3.10.0-h5cc4e75_1.conda
+  sha256: 783c652a21f9710eb1a7c7c107f0dc207236f83b8bab36c20d29a2539bcfc713
+  md5: 8d3f0806eb386761975cff7345858c6c
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libpq >=17.0,<18.0a0
+  - libstdcxx >=13
+  - postgresql
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 480893
+  timestamp: 1731505528157
+- kind: conda
+  name: libgdal-postgisraster
+  version: 3.10.0
+  build: h84049b1_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-postgisraster-3.10.0-h84049b1_1.conda
+  sha256: 7e370c461cf69c1b0eb4ffd15e68c11b62ecc3c4e2c3d759ed8c6404b8cc04dc
+  md5: d7aa54cbbb5d7013aa79336d536b3a92
+  depends:
+  - __osx >=11.0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libpq >=17.0,<18.0a0
+  - postgresql
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 469379
+  timestamp: 1731508636941
+- kind: conda
+  name: libgdal-tiledb
+  version: 3.10.0
+  build: h391dfa7_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-tiledb-3.10.0-h391dfa7_1.conda
+  sha256: fbfe23773e1761fb17b9cc96e7e991c106f85e9141032cdfb77ee4c29bf60491
+  md5: a66e32e8ff766cfb665da8342d15721f
+  depends:
+  - __osx >=11.0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - tiledb >=2.26.2,<2.27.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 624454
+  timestamp: 1731508823524
+- kind: conda
+  name: libgdal-tiledb
+  version: 3.10.0
+  build: hec57c18_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-tiledb-3.10.0-hec57c18_1.conda
+  sha256: 2654472d2070b3f6f608da3ad86acd296fcba4a3a862a3ef93294517a8a6d1cb
+  md5: eb39051813bc34137bff2e4ad8dfe64e
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libstdcxx >=13
+  - tiledb >=2.26.2,<2.27.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 696199
+  timestamp: 1731505608248
+- kind: conda
+  name: libgdal-xls
+  version: 3.10.0
+  build: h1e14832_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgdal-xls-3.10.0-h1e14832_1.conda
+  sha256: d2ba6f6be2d3043e94fae011fcfef8ec7552759fb65fd2fdcdd27e0cd650e312
+  md5: 5419c1134aabe809f67059808be80195
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - freexl >=2.0.0,<3.0a0
+  - libgcc >=13
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  - libstdcxx >=13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 434755
+  timestamp: 1731505660709
+- kind: conda
+  name: libgdal-xls
+  version: 3.10.0
+  build: h3aa7eb4_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-xls-3.10.0-h3aa7eb4_1.conda
+  sha256: 2117da93e9bc0c8d4e57c4fb8b062a6b77b0e9d46b7d12cc2c23e1ec67d60773
+  md5: ef82721e7c36508b35a5ea31d031259e
+  depends:
+  - __osx >=11.0
+  - freexl >=2.0.0,<3.0a0
+  - libcxx >=18
+  - libgdal-core >=3.10
+  - libkml >=1.3.0,<1.4.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 433326
+  timestamp: 1731508973136
+- kind: conda
+  name: libgfortran
+  version: 5.0.0
+  build: 13_2_0_hd922786_3
+  build_number: 3
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-5.0.0-13_2_0_hd922786_3.conda
+  sha256: 44e541b4821c96b28b27fef5630883a60ce4fee91fd9c79f25a199f8f73f337b
+  md5: 4a55d9e169114b2b90d3ec4604cd7bbf
+  depends:
+  - libgfortran5 13.2.0 hf226fd6_3
+  license: GPL-3.0-only WITH GCC-exception-3.1
+  license_family: GPL
+  purls: []
+  size: 110233
+  timestamp: 1707330749033
+- kind: conda
+  name: libgfortran
+  version: 14.2.0
+  build: h69a702a_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.2.0-h69a702a_1.conda
+  sha256: fc9e7f22a17faf74da904ebfc4d88699013d2992e55505e4aa0eb01770290977
+  md5: f1fd30127802683586f768875127a987
+  depends:
+  - libgfortran5 14.2.0 hd5240d6_1
+  constrains:
+  - libgfortran-ng ==14.2.0=*_1
+  license: GPL-3.0-only WITH GCC-exception-3.1
+  license_family: GPL
+  purls: []
+  size: 53997
+  timestamp: 1729027752995
+- kind: conda
+  name: libgfortran-ng
+  version: 14.2.0
+  build: h69a702a_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-14.2.0-h69a702a_1.conda
+  sha256: 423f1e2403f0c665748e42d335e421e53fd03c08d457cfb6f360d329d9459851
+  md5: 0a7f4cd238267c88e5d69f7826a407eb
+  depends:
+  - libgfortran 14.2.0 h69a702a_1
+  license: GPL-3.0-only WITH GCC-exception-3.1
+  license_family: GPL
+  purls: []
+  size: 54106
+  timestamp: 1729027945817
+- kind: conda
+  name: libgfortran5
+  version: 13.2.0
+  build: hf226fd6_3
+  build_number: 3
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-13.2.0-hf226fd6_3.conda
+  sha256: bafc679eedb468a86aa4636061c55966186399ee0a04b605920d208d97ac579a
+  md5: 66ac81d54e95c534ae488726c1f698ea
+  depends:
+  - llvm-openmp >=8.0.0
+  constrains:
+  - libgfortran 5.0.0 13_2_0_*_3
+  license: GPL-3.0-only WITH GCC-exception-3.1
+  license_family: GPL
+  purls: []
+  size: 997381
+  timestamp: 1707330687590
+- kind: conda
+  name: libgfortran5
+  version: 14.2.0
+  build: hd5240d6_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.2.0-hd5240d6_1.conda
+  sha256: d149a37ca73611e425041f33b9d8dbed6e52ec506fe8cc1fc0ee054bddeb6d5d
+  md5: 9822b874ea29af082e5d36098d25427d
+  depends:
+  - libgcc >=14.2.0
+  constrains:
+  - libgfortran 14.2.0
+  license: GPL-3.0-only WITH GCC-exception-3.1
+  license_family: GPL
+  purls: []
+  size: 1462645
+  timestamp: 1729027735353
+- kind: conda
+  name: libglib
+  version: 2.82.2
+  build: h07bd6cf_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libglib-2.82.2-h07bd6cf_0.conda
+  sha256: 101fb31c509d6a69ac5d612b51d4088ddbc675fca18cf0c3589cfee26cd01ca0
+  md5: 890783f64502fa6bfcdc723cfbf581b4
+  depends:
+  - __osx >=11.0
+  - libffi >=3.4,<4.0a0
+  - libiconv >=1.17,<2.0a0
+  - libintl >=0.22.5,<1.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - pcre2 >=10.44,<10.45.0a0
+  constrains:
+  - glib 2.82.2 *_0
+  license: LGPL-2.1-or-later
+  purls: []
+  size: 3635416
+  timestamp: 1729191799117
+- kind: conda
+  name: libglib
+  version: 2.82.2
+  build: h2ff4ddf_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.82.2-h2ff4ddf_0.conda
+  sha256: 49ee9401d483a76423461c50dcd37f91d070efaec7e4dc2828d8cdd2ce694231
+  md5: 13e8e54035ddd2b91875ba399f0f7c04
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libffi >=3.4,<4.0a0
+  - libgcc >=13
+  - libiconv >=1.17,<2.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - pcre2 >=10.44,<10.45.0a0
+  constrains:
+  - glib 2.82.2 *_0
+  license: LGPL-2.1-or-later
+  purls: []
+  size: 3931898
+  timestamp: 1729191404130
+- kind: conda
+  name: libgomp
+  version: 14.2.0
+  build: h77fa898_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda
+  sha256: 1911c29975ec99b6b906904040c855772ccb265a1c79d5d75c8ceec4ed89cd63
+  md5: cc3573974587f12dda90d96e3e55a702
+  depends:
+  - _libgcc_mutex 0.1 conda_forge
+  license: GPL-3.0-only WITH GCC-exception-3.1
+  license_family: GPL
   purls: []
   size: 460992
   timestamp: 1729027639220
 - kind: conda
   name: libgoogle-cloud
-  version: 2.30.0
-  build: h2e6cea1_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.30.0-h2e6cea1_0.conda
-  sha256: 2c58299d8275cfcf575166ba59baa9ac2b32c0c5a2677ee7a51e1d67b2d28f92
-  md5: be857dc2a7d747d9aa191ed6c701bde7
+  version: 2.31.0
+  build: h804f50b_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.31.0-h804f50b_0.conda
+  sha256: b2de99c83516236ff591d30436779f8345bcc11bb0ec76a7ca3a38a3b23b6423
+  md5: 35ab838423b60f233391eb86d324a830
   depends:
-  - __osx >=11.0
+  - __glibc >=2.17,<3.0.a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
   - libcurl >=8.10.1,<9.0a0
-  - libcxx >=17
-  - libgrpc >=1.65.5,<1.66.0a0
-  - libprotobuf >=5.27.5,<5.27.6.0a0
+  - libgcc >=13
+  - libgrpc >=1.67.1,<1.68.0a0
+  - libprotobuf >=5.28.2,<5.28.3.0a0
+  - libstdcxx >=13
   - openssl >=3.3.2,<4.0a0
   constrains:
-  - libgoogle-cloud 2.30.0 *_0
+  - libgoogle-cloud 2.31.0 *_0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 845094
-  timestamp: 1728021687922
+  size: 1248705
+  timestamp: 1731122589027
 - kind: conda
   name: libgoogle-cloud
-  version: 2.30.0
-  build: h438788a_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.30.0-h438788a_0.conda
-  sha256: 506a0997b586536a6bbe8fd260bd50b625a541850507486fa66abc5a99104bce
-  md5: ab8466a39822527f7786b0d0b2aac223
+  version: 2.31.0
+  build: h8d8be31_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.31.0-h8d8be31_0.conda
+  sha256: 184d650d55453a40935c128ea309088ae52e15a68cd87ab17ae7c77704251168
+  md5: a338736f1514e6f999db8726fe0965b1
   depends:
-  - __glibc >=2.17,<3.0.a0
+  - __osx >=11.0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
   - libcurl >=8.10.1,<9.0a0
-  - libgcc >=13
-  - libgrpc >=1.65.5,<1.66.0a0
-  - libprotobuf >=5.27.5,<5.27.6.0a0
-  - libstdcxx >=13
+  - libcxx >=18
+  - libgrpc >=1.67.1,<1.68.0a0
+  - libprotobuf >=5.28.2,<5.28.3.0a0
   - openssl >=3.3.2,<4.0a0
   constrains:
-  - libgoogle-cloud 2.30.0 *_0
+  - libgoogle-cloud 2.31.0 *_0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 1200100
-  timestamp: 1728022256338
+  size: 873497
+  timestamp: 1731121684939
 - kind: conda
   name: libgoogle-cloud-storage
-  version: 2.30.0
+  version: 2.31.0
   build: h0121fbd_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.30.0-h0121fbd_0.conda
-  sha256: 9fad535d14a204f3646a29f9884c024b69d84120bea5489e14e7dc895b543646
-  md5: ad86b6c98964772688298a727cb20ef8
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.31.0-h0121fbd_0.conda
+  sha256: 3c38b0a80441f82323dc5a72b96c0dd7476bd5184fbfcdf825a8e15249c849af
+  md5: 568d6a09a6ed76337a7b97c84ae7c0f8
   depends:
   - __glibc >=2.17,<3.0.a0
   - libabseil
   - libcrc32c >=1.1.2,<1.2.0a0
   - libcurl
   - libgcc >=13
-  - libgoogle-cloud 2.30.0 h438788a_0
+  - libgoogle-cloud 2.31.0 h804f50b_0
   - libstdcxx >=13
   - libzlib >=1.3.1,<2.0a0
   - openssl
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 782269
-  timestamp: 1728022391174
+  size: 782150
+  timestamp: 1731122728715
 - kind: conda
   name: libgoogle-cloud-storage
-  version: 2.30.0
-  build: h90fd6fa_0
+  version: 2.31.0
+  build: h7081f7f_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.30.0-h90fd6fa_0.conda
-  sha256: 1c531f3f5867c5ec9d3d8a7f0babee5ca106f6bf39510b277503d9aea55afeae
-  md5: 34381339cf47d7af329026d1474f30ff
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.31.0-h7081f7f_0.conda
+  sha256: 01f5156584b816d34270a60a61f6b6561f2a01cb3b4eeb455a4e1808d763d486
+  md5: 548fd1d31741ee6b13df4124db4a9f5f
   depends:
   - __osx >=11.0
   - libabseil
   - libcrc32c >=1.1.2,<1.2.0a0
   - libcurl
-  - libcxx >=17
-  - libgoogle-cloud 2.30.0 h2e6cea1_0
+  - libcxx >=18
+  - libgoogle-cloud 2.31.0 h8d8be31_0
   - libzlib >=1.3.1,<2.0a0
   - openssl
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 538215
-  timestamp: 1728022502810
+  size: 526858
+  timestamp: 1731122580689
 - kind: conda
   name: libgrpc
-  version: 1.65.5
-  build: h3d9cf25_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.65.5-h3d9cf25_0.conda
-  sha256: a92096af0fa67bb03fe2d40dfb11e7746603842a78fddce9f06e3ced9d93b61e
-  md5: b829a3509f5d89b21fa481ebc8edd953
+  version: 1.67.1
+  build: hc2c308b_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.67.1-hc2c308b_0.conda
+  sha256: 870550c1faf524e9a695262cd4c31441b18ad542f16893bd3c5dbc93106705f7
+  md5: 4606a4647bfe857e3cfe21ca12ac3afb
   depends:
-  - __osx >=11.0
-  - c-ares >=1.33.1,<2.0a0
+  - __glibc >=2.17,<3.0.a0
+  - c-ares >=1.32.3,<2.0a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
-  - libcxx >=17
-  - libprotobuf >=5.27.5,<5.27.6.0a0
-  - libre2-11 >=2023.9.1
+  - libgcc >=13
+  - libprotobuf >=5.28.2,<5.28.3.0a0
+  - libre2-11 >=2024.7.2
+  - libstdcxx >=13
   - libzlib >=1.3.1,<2.0a0
   - openssl >=3.3.2,<4.0a0
   - re2
   constrains:
-  - grpc-cpp =1.65.5
+  - grpc-cpp =1.67.1
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 4614162
-  timestamp: 1727200966365
+  size: 7362336
+  timestamp: 1730236333879
 - kind: conda
   name: libgrpc
-  version: 1.65.5
-  build: hf5c653b_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.65.5-hf5c653b_0.conda
-  sha256: d279abd46262e817c7a00aeb4df9b5ed4de38130130b248e2c50875e982f30fa
-  md5: 3b0048cabc6815a4d8874a0240519d32
+  version: 1.67.1
+  build: hc70892a_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.67.1-hc70892a_0.conda
+  sha256: d2393fcd3c3584e5d58da4122f48bcf297567d2f6f14b3d1fcbd34fdd5040694
+  md5: 624e27571fde34f8acc2afec840ac435
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - c-ares >=1.32.3,<2.0a0
+  - __osx >=11.0
+  - c-ares >=1.34.2,<2.0a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
-  - libgcc >=13
-  - libprotobuf >=5.27.5,<5.27.6.0a0
-  - libre2-11 >=2023.9.1
-  - libstdcxx >=13
+  - libcxx >=17
+  - libprotobuf >=5.28.2,<5.28.3.0a0
+  - libre2-11 >=2024.7.2
   - libzlib >=1.3.1,<2.0a0
   - openssl >=3.3.2,<4.0a0
   - re2
   constrains:
-  - grpc-cpp =1.65.5
+  - grpc-cpp =1.67.1
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 7229891
-  timestamp: 1727200905306
+  size: 4882208
+  timestamp: 1730236299095
+- kind: conda
+  name: libheif
+  version: 1.18.2
+  build: gpl_he913df3_100
+  build_number: 100
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libheif-1.18.2-gpl_he913df3_100.conda
+  sha256: 34a70c5889989013b199c6266a30362539af9e24211a6963a0cb0d7ba786f12d
+  md5: 29911afbc2ec42a42914d5255dea52e6
+  depends:
+  - __osx >=11.0
+  - aom >=3.9.1,<3.10.0a0
+  - dav1d >=1.2.1,<1.2.2.0a0
+  - libavif16 >=1.1.1,<2.0a0
+  - libcxx >=16
+  - libde265 >=1.0.15,<1.0.16.0a0
+  - x265 >=3.5,<3.6.0a0
+  license: LGPL-3.0-or-later
+  license_family: LGPL
+  purls: []
+  size: 351064
+  timestamp: 1723121589940
+- kind: conda
+  name: libheif
+  version: 1.18.2
+  build: gpl_hffcb242_100
+  build_number: 100
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libheif-1.18.2-gpl_hffcb242_100.conda
+  sha256: 82af131dc112f4f36ca9226f30a7b1b3e05ed4fb3f85003e8f1af72b6a8e44bc
+  md5: 76ac2c07b62d45c192940f010eea11fa
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - aom >=3.9.1,<3.10.0a0
+  - dav1d >=1.2.1,<1.2.2.0a0
+  - libavif16 >=1.1.1,<2.0a0
+  - libde265 >=1.0.15,<1.0.16.0a0
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  - x265 >=3.5,<3.6.0a0
+  license: LGPL-3.0-or-later
+  license_family: LGPL
+  purls: []
+  size: 428886
+  timestamp: 1723121455966
 - kind: conda
   name: libiconv
   version: '1.17'
@@ -5703,6 +7297,22 @@ packages:
   purls: []
   size: 705775
   timestamp: 1702682170569
+- kind: conda
+  name: libintl
+  version: 0.22.5
+  build: h8414b35_3
+  build_number: 3
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libintl-0.22.5-h8414b35_3.conda
+  sha256: 7c1d238d4333af385e594c89ebcb520caad7ed83a735c901099ec0970a87a891
+  md5: 3b98ec32e91b3b59ad53dbb9c96dd334
+  depends:
+  - __osx >=11.0
+  - libiconv >=1.17,<2.0a0
+  license: LGPL-2.1-or-later
+  purls: []
+  size: 81171
+  timestamp: 1723626968270
 - kind: conda
   name: libjpeg-turbo
   version: 3.0.0
@@ -5816,6 +7426,64 @@ packages:
   purls: []
   size: 15823
   timestamp: 1729643275943
+- kind: conda
+  name: libnetcdf
+  version: 4.9.2
+  build: nompi_h135f659_114
+  build_number: 114
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.9.2-nompi_h135f659_114.conda
+  sha256: 055572a4c8a1c3f9ac60071ee678f5ea49cfd7ac60a636d817988a6f9d6de6ae
+  md5: a908e463c710bd6b10a9eaa89fdf003c
+  depends:
+  - blosc >=1.21.5,<2.0a0
+  - bzip2 >=1.0.8,<2.0a0
+  - hdf4 >=4.2.15,<4.2.16.0a0
+  - hdf5 >=1.14.3,<1.14.4.0a0
+  - libaec >=1.1.3,<2.0a0
+  - libcurl >=8.8.0,<9.0a0
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  - libxml2 >=2.12.7,<3.0a0
+  - libzip >=1.10.1,<2.0a0
+  - libzlib >=1.2.13,<2.0a0
+  - openssl >=3.3.1,<4.0a0
+  - zlib
+  - zstd >=1.5.6,<1.6.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 849172
+  timestamp: 1717671645362
+- kind: conda
+  name: libnetcdf
+  version: 4.9.2
+  build: nompi_he469be0_114
+  build_number: 114
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libnetcdf-4.9.2-nompi_he469be0_114.conda
+  sha256: aeac591ba859f9cf775993e8b7f21e50803405d41ef363dc4981d114e8df88a8
+  md5: 8fd3ce6d910ed831c130c391c4364d3f
+  depends:
+  - __osx >=11.0
+  - blosc >=1.21.5,<2.0a0
+  - bzip2 >=1.0.8,<2.0a0
+  - hdf4 >=4.2.15,<4.2.16.0a0
+  - hdf5 >=1.14.3,<1.14.4.0a0
+  - libaec >=1.1.3,<2.0a0
+  - libcurl >=8.8.0,<9.0a0
+  - libcxx >=16
+  - libxml2 >=2.12.7,<3.0a0
+  - libzip >=1.10.1,<2.0a0
+  - libzlib >=1.2.13,<2.0a0
+  - openssl >=3.3.1,<4.0a0
+  - zlib
+  - zstd >=1.5.6,<1.6.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 681051
+  timestamp: 1717671966211
 - kind: conda
   name: libnghttp2
   version: 1.64.0
@@ -5874,85 +7542,115 @@ packages:
   purls: []
   size: 33408
   timestamp: 1697359010159
+- kind: conda
+  name: libntlm
+  version: '1.4'
+  build: h3422bc3_1002
+  build_number: 1002
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libntlm-1.4-h3422bc3_1002.tar.bz2
+  sha256: d0047d4d967e4e3e1d0ad0dd0e45ed4b0effdd0ae57ec88b4850122b0635d8fe
+  md5: 02fb3eb7be85f98c084bcee20cf925f1
+  license: LGPL-2.1-or-later
+  purls: []
+  size: 32219
+  timestamp: 1661533625744
+- kind: conda
+  name: libntlm
+  version: '1.4'
+  build: h7f98852_1002
+  build_number: 1002
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.4-h7f98852_1002.tar.bz2
+  sha256: 63244b73156033ea3b7c2a1581526e79b4670349d64b15f645dcdb12de441d1a
+  md5: e728e874159b042d92b90238a3cb0dc2
+  depends:
+  - libgcc-ng >=9.3.0
+  license: LGPL-2.1-or-later
+  purls: []
+  size: 33201
+  timestamp: 1609781914458
 - kind: conda
   name: libopenblas
   version: 0.3.28
-  build: openmp_h517c56d_0
+  build: openmp_hf332438_1
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.28-openmp_h517c56d_0.conda
-  sha256: 43d69d072f1a3774994d31f9d3241cfa0f1c5560b536989020d7cde30fbef956
-  md5: 9306fd5b6b39b2b7e13c1d50c3fee354
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.28-openmp_hf332438_1.conda
+  sha256: 62bb669c37a845129096f73d446cdb6bb170e4927f2fea2b661329680dbbc373
+  md5: 40803a48d947c8639da6704e9a44d3ce
   depends:
   - __osx >=11.0
   - libgfortran 5.*
-  - libgfortran5 >=12.3.0
-  - llvm-openmp >=16.0.6
+  - libgfortran5 >=13.2.0
+  - llvm-openmp >=18.1.8
   constrains:
   - openblas >=0.3.28,<0.3.29.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 2934061
-  timestamp: 1723931625423
+  size: 4165774
+  timestamp: 1730772154295
 - kind: conda
   name: libopenblas
   version: 0.3.28
-  build: pthreads_h94d23a6_0
+  build: pthreads_h94d23a6_1
+  build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_0.conda
-  sha256: 1e41a6d63e07be996238a1e840a426f86068956a45e0c0bb24e49a8dad9874c1
-  md5: 9ebc9aedafaa2515ab247ff6bb509458
+  url: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_1.conda
+  sha256: 99ba271d8a80a1af2723f2e124ffd91d850074c0389c067e6d96d72a2dbfeabe
+  md5: 62857b389e42b36b686331bec0922050
   depends:
   - __glibc >=2.17,<3.0.a0
-  - libgcc-ng >=14
-  - libgfortran-ng
-  - libgfortran5 >=14.1.0
+  - libgcc >=14
+  - libgfortran
+  - libgfortran5 >=14.2.0
   constrains:
   - openblas >=0.3.28,<0.3.29.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 5572213
-  timestamp: 1723932528810
+  size: 5578513
+  timestamp: 1730772671118
 - kind: conda
   name: libparquet
   version: 18.0.0
-  build: h6bd9018_0_cpu
+  build: h6bd9018_8_cpu
+  build_number: 8
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libparquet-18.0.0-h6bd9018_0_cpu.conda
-  sha256: e6bc15680d5c0bad21d7292e1c1f1a6cd82c2226cba652df3f765f460e33e015
-  md5: f9efb8ef19962dc9d87b29e667a13287
+  url: https://conda.anaconda.org/conda-forge/linux-64/libparquet-18.0.0-h6bd9018_8_cpu.conda
+  sha256: 3183fa77b6fd965160deb512ac6035c3be2df8af9f6529af20cb2d053d177afd
+  md5: e2718d24a8af33752dfe0b75e3043b75
   depends:
   - __glibc >=2.17,<3.0.a0
-  - libarrow 18.0.0 ha5db6c2_0_cpu
+  - libarrow 18.0.0 h94eee4b_8_cpu
   - libgcc >=13
   - libstdcxx >=13
   - libthrift >=0.21.0,<0.21.1.0a0
-  - openssl >=3.3.2,<4.0a0
+  - openssl >=3.4.0,<4.0a0
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 1212570
-  timestamp: 1730155645262
+  size: 1211318
+  timestamp: 1732208288781
 - kind: conda
   name: libparquet
   version: 18.0.0
-  build: hda0ea68_0_cpu
+  build: hda0ea68_8_cpu
+  build_number: 8
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-18.0.0-hda0ea68_0_cpu.conda
-  sha256: 2b691ea4f0150dd1abbbd0321d3ec92315be9ad07d1e9f575175f042fbdddbe1
-  md5: b24b66fb60eacddddaa69532a7f37776
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-18.0.0-hda0ea68_8_cpu.conda
+  sha256: 4d91a04771b0fcb9830b5db2c67d77ee001790df7ed8c0bd4057564c5483fb00
+  md5: 74f66533d553fc03cdb21af7a5d4d84e
   depends:
   - __osx >=11.0
-  - libarrow 18.0.0 h6fea68a_0_cpu
+  - libarrow 18.0.0 hb943b0e_8_cpu
   - libcxx >=18
   - libthrift >=0.21.0,<0.21.1.0a0
-  - openssl >=3.3.2,<4.0a0
+  - openssl >=3.4.0,<4.0a0
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 882091
-  timestamp: 1730156351893
+  size: 882388
+  timestamp: 1732209712346
 - kind: conda
   name: libpng
   version: 1.6.44
@@ -5985,46 +7683,81 @@ packages:
   size: 263385
   timestamp: 1726234714421
 - kind: conda
-  name: libprotobuf
-  version: 5.27.5
-  build: h53f8970_2
-  build_number: 2
+  name: libpq
+  version: '17.2'
+  build: h04577a9_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libpq-17.2-h04577a9_0.conda
+  sha256: d8ed60436b8f1484d74f68b01f98301d6c8174df1d77a3e89ba42f033dcb43c5
+  md5: 52dd46162c6fb2765b49e6fd06adf8d5
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - icu >=75.1,<76.0a0
+  - krb5 >=1.21.3,<1.22.0a0
+  - libgcc >=13
+  - openldap >=2.6.8,<2.7.0a0
+  - openssl >=3.4.0,<4.0a0
+  license: PostgreSQL
+  purls: []
+  size: 2588868
+  timestamp: 1732204566030
+- kind: conda
+  name: libpq
+  version: '17.2'
+  build: h9b1ab17_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libprotobuf-5.27.5-h53f8970_2.conda
-  sha256: 787d86c041c03d33b24e28df5f881f47c74c3fe9053b791f14616dc51f32a687
-  md5: e9d021f82c48bb08b0b2c321b2f7778c
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libpq-17.2-h9b1ab17_0.conda
+  sha256: 3df7defd514d989ead1280cabb13618e03f7cdace828e6b7a307f2e47845123d
+  md5: 0ba6b6772a08c40de13427c957ceaf67
   depends:
   - __osx >=11.0
+  - icu >=75.1,<76.0a0
+  - krb5 >=1.21.3,<1.22.0a0
+  - openldap >=2.6.8,<2.7.0a0
+  - openssl >=3.4.0,<4.0a0
+  license: PostgreSQL
+  purls: []
+  size: 2603320
+  timestamp: 1732204754944
+- kind: conda
+  name: libprotobuf
+  version: 5.28.2
+  build: h5b01275_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.28.2-h5b01275_0.conda
+  sha256: 5e8fd4aa00193c85602ce6101dd28fe31306dff85c9725048f6dc828dfa7c421
+  md5: ab0bff36363bec94720275a681af8b83
+  depends:
+  - __glibc >=2.17,<3.0.a0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
-  - libcxx >=17
+  - libgcc >=13
+  - libstdcxx >=13
   - libzlib >=1.3.1,<2.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 2375066
-  timestamp: 1727423411355
+  size: 2945348
+  timestamp: 1728565355702
 - kind: conda
   name: libprotobuf
-  version: 5.27.5
-  build: h5b01275_2
-  build_number: 2
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.27.5-h5b01275_2.conda
-  sha256: 79ac9726cd0a1cb1ba335f7fc7ccac5f679a66d71d9553ca88a805b8787d55ce
-  md5: 66ed3107adbdfc25ba70454ba11e6d1e
+  version: 5.28.2
+  build: h8f0b736_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libprotobuf-5.28.2-h8f0b736_0.conda
+  sha256: f732a6fa918428e2d5ba61e78fe11bb44a002cc8f6bb74c94ee5b1297fefcfd8
+  md5: d2cb5991f2fb8eb079c80084435e9ce6
   depends:
-  - __glibc >=2.17,<3.0.a0
+  - __osx >=11.0
   - libabseil * cxx17*
   - libabseil >=20240722.0,<20240723.0a0
-  - libgcc >=13
-  - libstdcxx >=13
+  - libcxx >=17
   - libzlib >=1.3.1,<2.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 2940269
-  timestamp: 1727424395109
+  size: 2374965
+  timestamp: 1728565334796
 - kind: conda
   name: libre2-11
   version: 2024.07.02
@@ -6502,45 +8235,111 @@ packages:
   timestamp: 1702724383534
 - kind: conda
   name: libxml2
-  version: 2.13.4
-  build: h8424949_2
-  build_number: 2
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.13.4-h8424949_2.conda
-  sha256: 51048cd9d4d7ab3ab440bac01d1db8193ae1bd3e9502cdf6792a69c792fec2e5
-  md5: 3f0764c38bc02720231d49d6035531f2
+  version: 2.13.5
+  build: hb346dea_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.5-hb346dea_0.conda
+  sha256: 8c9d6a3a421ac5bf965af495d1b0a08c6fb2245ba156550bc064a7b4f8fc7bd8
+  md5: c81a9f1118541aaa418ccb22190c817e
   depends:
-  - __osx >=11.0
+  - __glibc >=2.17,<3.0.a0
   - icu >=75.1,<76.0a0
+  - libgcc >=13
   - libiconv >=1.17,<2.0a0
   - libzlib >=1.3.1,<2.0a0
   - xz >=5.2.6,<6.0a0
   license: MIT
   license_family: MIT
   purls: []
-  size: 572400
-  timestamp: 1730356085177
+  size: 689626
+  timestamp: 1731489608971
 - kind: conda
   name: libxml2
-  version: 2.13.4
-  build: hb346dea_2
-  build_number: 2
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.4-hb346dea_2.conda
-  sha256: a111cb7f2deb6e20ebb475e8426ce5291451476f55f0dec6c220aa51e5a5784f
-  md5: 69b90b70c434b916abf5a1d5ee5d55fb
+  version: 2.13.5
+  build: hbbdcc80_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.13.5-hbbdcc80_0.conda
+  sha256: 936de9c0e91cb6f178c48ea14313cf6c79bdb1f474c785c117c41492b0407a98
+  md5: 967d4a9dadd710415ee008d862a07c99
   depends:
-  - __glibc >=2.17,<3.0.a0
+  - __osx >=11.0
   - icu >=75.1,<76.0a0
-  - libgcc >=13
   - libiconv >=1.17,<2.0a0
   - libzlib >=1.3.1,<2.0a0
   - xz >=5.2.6,<6.0a0
   license: MIT
   license_family: MIT
   purls: []
-  size: 690019
-  timestamp: 1730355770718
+  size: 583082
+  timestamp: 1731489765442
+- kind: conda
+  name: libxslt
+  version: 1.1.39
+  build: h223e5b9_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libxslt-1.1.39-h223e5b9_0.conda
+  sha256: 2f1d99ef3fb960f23a63f06cf65ee621a5594a8b4616f35d9805be44617a92af
+  md5: 560c9cacc33e927f55b998eaa0cb1732
+  depends:
+  - libxml2 >=2.12.1,<3.0.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 225705
+  timestamp: 1701628966565
+- kind: conda
+  name: libxslt
+  version: 1.1.39
+  build: h76b75d6_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.39-h76b75d6_0.conda
+  sha256: 684e9b67ef7b9ca0ca993762eeb39705ec58e2e7f958555c758da7ef416db9f3
+  md5: e71f31f8cfb0a91439f2086fc8aa0461
+  depends:
+  - libgcc-ng >=12
+  - libxml2 >=2.12.1,<3.0.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 254297
+  timestamp: 1701628814990
+- kind: conda
+  name: libzip
+  version: 1.11.2
+  build: h1336266_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libzip-1.11.2-h1336266_0.conda
+  sha256: 507599a77c1ce823c2d3acaefaae4ead0686f183f3980467a4c4b8ba209eff40
+  md5: 7177414f275db66735a17d316b0a81d6
+  depends:
+  - __osx >=11.0
+  - bzip2 >=1.0.8,<2.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - openssl >=3.3.2,<4.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 125507
+  timestamp: 1730442214849
+- kind: conda
+  name: libzip
+  version: 1.11.2
+  build: h6991a6a_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/libzip-1.11.2-h6991a6a_0.conda
+  sha256: 991e7348b0f650d495fb6d8aa9f8c727bdf52dabf5853c0cc671439b160dce48
+  md5: a7b27c075c9b7f459f1c022090697cba
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - bzip2 >=1.0.8,<2.0a0
+  - libgcc >=13
+  - libzlib >=1.3.1,<2.0a0
+  - openssl >=3.3.2,<4.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 109043
+  timestamp: 1730442108429
 - kind: conda
   name: libzlib
   version: 1.3.1
@@ -6580,20 +8379,20 @@ packages:
   timestamp: 1727963148474
 - kind: conda
   name: llvm-openmp
-  version: 19.1.3
-  build: hb52a8e5_0
+  version: 19.1.4
+  build: hdb05f8b_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-19.1.3-hb52a8e5_0.conda
-  sha256: 49a8940e727aa82ee034fa9a60b3fcababec41b3192d955772aab635a5374b82
-  md5: dd695d23e78d1ca4fecce969b1e1db61
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-19.1.4-hdb05f8b_0.conda
+  sha256: dfdcd8de37899d984326f9734b28f46f80b88c068e44c562933a8b3117f2401a
+  md5: 76ca179ec970bea6e275e2fa477c2d3c
   depends:
   - __osx >=11.0
   constrains:
-  - openmp 19.1.3|19.1.3.*
+  - openmp 19.1.4|19.1.4.*
   license: Apache-2.0 WITH LLVM-exception
   purls: []
-  size: 280488
-  timestamp: 1730364082380
+  size: 281554
+  timestamp: 1732102484807
 - kind: pypi
   name: lxml
   version: 5.3.0
@@ -6778,12 +8577,12 @@ packages:
 - kind: conda
   name: matplotlib-base
   version: 3.9.2
-  build: py312h9bd0bc6_1
-  build_number: 1
+  build: py312h9bd0bc6_2
+  build_number: 2
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.9.2-py312h9bd0bc6_1.conda
-  sha256: b3289cea8de29ba5b9fb437d3e4e32d2cbf88998890378a4e729c5be08e1ba41
-  md5: b6a861da93e2f4fcecdb01ff7b8fc160
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.9.2-py312h9bd0bc6_2.conda
+  sha256: 78ffc8f58af8faa583867afb303e18a423d2c6087fc58da0033c35e02c2184d6
+  md5: faf7592748a40887a1a80424f136bf86
   depends:
   - __osx >=11.0
   - certifi >=2020.06.20
@@ -6807,17 +8606,17 @@ packages:
   license_family: PSF
   purls:
   - pkg:pypi/matplotlib?source=hash-mapping
-  size: 7790076
-  timestamp: 1726165022207
+  size: 7786050
+  timestamp: 1731025378750
 - kind: conda
   name: matplotlib-base
   version: 3.9.2
-  build: py312hd3ec401_1
-  build_number: 1
+  build: py312hd3ec401_2
+  build_number: 2
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.2-py312hd3ec401_1.conda
-  sha256: 3efd50d9b7b0f1b30611585810d4ae7566d7c860c101f47ec9372f6d4a80d040
-  md5: 2f4f3854f23be30de29e9e4d39758349
+  url: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.2-py312hd3ec401_2.conda
+  sha256: f199be5149f45a14c88d465d9cb83cfba5efe17c45a0233354ef62cdcb7eab9e
+  md5: 2380c9ba933ffaac9ad16d8eac8e3318
   depends:
   - __glibc >=2.17,<3.0.a0
   - certifi >=2020.06.20
@@ -6842,8 +8641,8 @@ packages:
   license_family: PSF
   purls:
   - pkg:pypi/matplotlib?source=hash-mapping
-  size: 7892651
-  timestamp: 1726164930325
+  size: 7965171
+  timestamp: 1731025360821
 - kind: conda
   name: matplotlib-inline
   version: 0.1.7
@@ -6864,9 +8663,9 @@ packages:
   timestamp: 1713250613726
 - kind: pypi
   name: maxar-platform
-  version: 1.0.5
-  url: https://files.pythonhosted.org/packages/96/a2/08928cfa4a2b0dd228e79dd41af7fda0d509436ae1646bca8a3ce55a5250/maxar_platform-1.0.5-py3-none-any.whl
-  sha256: 0eb36ba700644cff715a941e43df4334411128a31f039829471c8bea98c274b1
+  version: 1.0.6
+  url: https://files.pythonhosted.org/packages/e5/d0/6dcc39e2c367b9fb91c4a0fd2c7c7b565d1f1bef0cbcb8cbda032ebb3bce/maxar_platform-1.0.6-py3-none-any.whl
+  sha256: c4751006f7a8f4751bcedc51bdc22fd8e194fb6e5d8ed3c018e535f779f1b166
   requires_dist:
   - owslib>=0.29.0,<0.30.0
   - affine>=2.4.0,<3.0.0
@@ -7148,7 +8947,7 @@ packages:
   sha256: f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f
   requires_dist:
   - jupyter-client>=6.1.12
-  - jupyter-core!=5.0.*,>=4.12
+  - jupyter-core>=4.12,!=5.0.*
   - nbformat>=5.1
   - traitlets>=5.4
   - pre-commit ; extra == 'dev'
@@ -7167,7 +8966,7 @@ packages:
   - nbconvert>=7.0.0 ; extra == 'test'
   - pytest-asyncio ; extra == 'test'
   - pytest-cov>=4.0 ; extra == 'test'
-  - pytest<8,>=7.0 ; extra == 'test'
+  - pytest>=7.0,<8 ; extra == 'test'
   - testpath ; extra == 'test'
   - xmltodict ; extra == 'test'
   requires_python: '>=3.8.0'
@@ -7179,7 +8978,7 @@ packages:
   requires_dist:
   - fastjsonschema>=2.15
   - jsonschema>=2.6
-  - jupyter-core!=5.0.*,>=4.12
+  - jupyter-core>=4.12,!=5.0.*
   - traitlets>=5.1
   - myst-parser ; extra == 'docs'
   - pydata-sphinx-theme ; extra == 'docs'
@@ -7242,26 +9041,26 @@ packages:
 - kind: conda
   name: networkx
   version: 3.4.2
-  build: pyhd8ed1ab_1
-  build_number: 1
+  build: pyh267e887_2
+  build_number: 2
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyhd8ed1ab_1.conda
-  sha256: ad3ac7c22d4f68a5a50ae584ae259af91fbf96f688bf2955750bbdb61bb88fc1
-  md5: 1d4c088869f206413c59acdd309908b7
+  url: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda
+  sha256: 39625cd0c9747fa5c46a9a90683b8997d8b9649881b3dc88336b13b7bdd60117
+  md5: fd40bf7f7f4bc4b647dc8512053d9873
   depends:
   - python >=3.10
+  - python
   constrains:
+  - numpy >=1.24
   - scipy >=1.10,!=1.11.0,!=1.11.1
   - matplotlib >=3.7
   - pandas >=2.0
-  - numpy >=1.24
   license: BSD-3-Clause
   license_family: BSD
-  purls:
-  - pkg:pypi/networkx?source=hash-mapping
-  size: 1198024
-  timestamp: 1730311574645
+  purls: []
+  size: 1265008
+  timestamp: 1731521053408
 - kind: conda
   name: nodeenv
   version: 1.9.1
@@ -7280,14 +9079,84 @@ packages:
   - pkg:pypi/nodeenv?source=hash-mapping
   size: 34489
   timestamp: 1717585382642
+- kind: conda
+  name: nspr
+  version: '4.36'
+  build: h5833ebf_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/nspr-4.36-h5833ebf_0.conda
+  sha256: 71f790d3dafe309e46c2214a6354d8d1818d646d637b2f5f9f84c5aa5c315a42
+  md5: 026a08bd5b6a2a2f240c00c32446156d
+  depends:
+  - __osx >=11.0
+  - libcxx >=17
+  license: MPL-2.0
+  license_family: MOZILLA
+  purls: []
+  size: 202873
+  timestamp: 1729545964601
+- kind: conda
+  name: nspr
+  version: '4.36'
+  build: h5888daf_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/nspr-4.36-h5888daf_0.conda
+  sha256: a87471d9265a7c02a98c20debac8b13afd80963968ed7b1c1c2ac7b80955ce31
+  md5: de9cd5bca9e4918527b9b72b6e2e1409
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
+  license: MPL-2.0
+  license_family: MOZILLA
+  purls: []
+  size: 230204
+  timestamp: 1729545773406
+- kind: conda
+  name: nss
+  version: '3.107'
+  build: hc555b47_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/nss-3.107-hc555b47_0.conda
+  sha256: b97ad2d2df9ccf29c28b4f05cf97a311bec07fde038d14774f98a3d75e4fb47d
+  md5: 3f07767cb955cacd06bf95ca01fe1f09
+  depends:
+  - __osx >=11.0
+  - libcxx >=18
+  - libsqlite >=3.47.0,<4.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - nspr >=4.36,<5.0a0
+  license: MPL-2.0
+  purls: []
+  size: 1820732
+  timestamp: 1732240021200
+- kind: conda
+  name: nss
+  version: '3.107'
+  build: hdf54f9c_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/nss-3.107-hdf54f9c_0.conda
+  sha256: 4a901b96cc8d371cc71ab5cf1e3184c234ae7e74c4d50b3789d4bdadcd0f3c40
+  md5: 294b7009fe9010b35c25bb683f663bc3
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libsqlite >=3.47.0,<4.0a0
+  - libstdcxx >=13
+  - libzlib >=1.3.1,<2.0a0
+  - nspr >=4.36,<5.0a0
+  license: MPL-2.0
+  purls: []
+  size: 2002459
+  timestamp: 1732239827455
 - kind: conda
   name: numpy
-  version: 2.1.2
+  version: 2.1.3
   build: py312h58c1407_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.1.2-py312h58c1407_0.conda
-  sha256: 598603f9aba1a5f06d11c45fe3d25ffa5d19eb44e99244310693fdaed3538865
-  md5: b7e9a46277a1ee0afc6311e7760df0c3
+  url: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.1.3-py312h58c1407_0.conda
+  sha256: e4c14f71588a5627a6935d3e7d9ca78a8387229ec8ebc91616b0988ce57ba0dc
+  md5: dfdbc12e6d81889ba4c494a23f23eba8
   depends:
   - __glibc >=2.17,<3.0.a0
   - libblas >=3.9.0,<4.0a0
@@ -7303,21 +9172,21 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=hash-mapping
-  size: 8447869
-  timestamp: 1728240376876
+  size: 8388631
+  timestamp: 1730588649810
 - kind: conda
   name: numpy
-  version: 2.1.2
-  build: py312h801f5e3_0
+  version: 2.1.3
+  build: py312h94ee1e1_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.1.2-py312h801f5e3_0.conda
-  sha256: 7e6840963d5395a59cb0aca32d43fd4e539c00a0677fcad16e4a3ab1f5c7aab7
-  md5: 3f9101c02190f155b6525490238fc794
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.1.3-py312h94ee1e1_0.conda
+  sha256: cd287b6c270ee8af77d200c46d56fdfe1e2a9deeff68044439718b8d073214dd
+  md5: a2af54c86582e08718805c69af737897
   depends:
   - __osx >=11.0
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
-  - libcxx >=17
+  - libcxx >=18
   - liblapack >=3.9.0,<4.0a0
   - python >=3.12,<3.13.0a0
   - python >=3.12,<3.13.0a0 *_cpython
@@ -7328,8 +9197,8 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=hash-mapping
-  size: 6436873
-  timestamp: 1728240394809
+  size: 6398123
+  timestamp: 1730588490904
 - kind: pypi
   name: oauthlib
   version: 3.2.2
@@ -7339,7 +9208,7 @@ packages:
   - cryptography>=3.0.0 ; extra == 'rsa'
   - blinker>=1.4.0 ; extra == 'signals'
   - cryptography>=3.0.0 ; extra == 'signedtoken'
-  - pyjwt<3,>=2.0.0 ; extra == 'signedtoken'
+  - pyjwt>=2.0.0,<3 ; extra == 'signedtoken'
   requires_python: '>=3.6'
 - kind: conda
   name: openjpeg
@@ -7378,30 +9247,68 @@ packages:
   purls: []
   size: 316603
   timestamp: 1709159627299
+- kind: conda
+  name: openldap
+  version: 2.6.8
+  build: h50f2afc_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/openldap-2.6.8-h50f2afc_0.conda
+  sha256: f04e9522b971b96b306752dd55f8046634cb6d95a2c271672c02e658dc1eb7c8
+  md5: d7d7451d23b52d99eadad211de640ff4
+  depends:
+  - __osx >=11.0
+  - cyrus-sasl >=2.1.27,<3.0a0
+  - krb5 >=1.21.2,<1.22.0a0
+  - libcxx >=16
+  - openssl >=3.3.0,<4.0a0
+  license: OLDAP-2.8
+  license_family: BSD
+  purls: []
+  size: 848751
+  timestamp: 1716378259578
+- kind: conda
+  name: openldap
+  version: 2.6.8
+  build: hedd0468_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.8-hedd0468_0.conda
+  sha256: 902652f7a106caa6ea9db2c44118078e23a499bf091ce8ea01d8498c156e8219
+  md5: dcd0ed5147d8876b0848a552b416ce76
+  depends:
+  - cyrus-sasl >=2.1.27,<3.0a0
+  - krb5 >=1.21.2,<1.22.0a0
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  - openssl >=3.3.0,<4.0a0
+  license: OLDAP-2.8
+  license_family: BSD
+  purls: []
+  size: 780492
+  timestamp: 1716377814828
 - kind: conda
   name: openssl
-  version: 3.3.2
-  build: h8359307_0
+  version: 3.4.0
+  build: h39f12f2_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.3.2-h8359307_0.conda
-  sha256: 940fa01c4dc6152158fe8943e05e55a1544cab639df0994e3b35937839e4f4d1
-  md5: 1773ebccdc13ec603356e8ff1db9e958
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.4.0-h39f12f2_0.conda
+  sha256: bd1d58ced46e75efa3b842c61642fd12272c69e9fe4d7261078bc082153a1d53
+  md5: df307bbc703324722df0293c9ca2e418
   depends:
   - __osx >=11.0
   - ca-certificates
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 2882450
-  timestamp: 1725410638874
+  size: 2935176
+  timestamp: 1731377561525
 - kind: conda
   name: openssl
-  version: 3.3.2
+  version: 3.4.0
   build: hb9d3cd8_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.2-hb9d3cd8_0.conda
-  sha256: cee91036686419f6dd6086902acf7142b4916e1c4ba042e9ca23e151da012b6d
-  md5: 4d638782050ab6faa27275bed57e9b4e
+  url: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.4.0-hb9d3cd8_0.conda
+  sha256: 814b9dff1847b132c676ee6cc1a8cb2d427320779b93e1b6d76552275c128705
+  md5: 23cc74f77eb99315c0360ec3533147a9
   depends:
   - __glibc >=2.17,<3.0.a0
   - ca-certificates
@@ -7409,21 +9316,20 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 2891789
-  timestamp: 1725410790053
+  size: 2947466
+  timestamp: 1731377666602
 - kind: conda
   name: orc
-  version: 2.0.2
-  build: h4a9587e_1
-  build_number: 1
+  version: 2.0.3
+  build: h121fd32_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.2-h4a9587e_1.conda
-  sha256: ee0100b8b449be287d24fffce69444232a47142ca95bbc3d0cdc38ede9d690fb
-  md5: 47749df556fda8cc1848804bf6011645
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.3-h121fd32_0.conda
+  sha256: 4759fd0c3f06c035146100e22ee36a312c9a8226654bd2973e9ca9ac5de5cf1f
+  md5: 39995f7406b949c1bef74f0c7277afb3
   depends:
   - __osx >=11.0
-  - libcxx >=17
-  - libprotobuf >=5.27.5,<5.27.6.0a0
+  - libcxx >=18
+  - libprotobuf >=5.28.2,<5.28.3.0a0
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
   - snappy >=1.2.1,<1.3.0a0
@@ -7432,21 +9338,20 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 445128
-  timestamp: 1727242589123
+  size: 438254
+  timestamp: 1731665228473
 - kind: conda
   name: orc
-  version: 2.0.2
-  build: h690cf93_1
-  build_number: 1
+  version: 2.0.3
+  build: he039a57_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.2-h690cf93_1.conda
-  sha256: ce023f259ffd93b4678cc582fc4b15a8a991a7b8edd9def8b6838bf7e7962bec
-  md5: 0044701dd48af57d3d5467a704ef9ebd
+  url: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.3-he039a57_0.conda
+  sha256: 9657ae19d6541fe67a61ef0c26ba1012ec508920b49afa897962c7d4b263ba35
+  md5: 052499acd6d6b79952197a13b23e2600
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  - libprotobuf >=5.27.5,<5.27.6.0a0
+  - libprotobuf >=5.28.2,<5.28.3.0a0
   - libstdcxx >=13
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.9.3,<1.10.0a0
@@ -7456,16 +9361,17 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 1184634
-  timestamp: 1727242386732
+  size: 1187593
+  timestamp: 1731664886527
 - kind: conda
   name: orjson
-  version: 3.10.10
-  build: py312h12e396e_0
+  version: 3.10.11
+  build: py312h12e396e_1
+  build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/orjson-3.10.10-py312h12e396e_0.conda
-  sha256: 58192ed8e66d0c856f760057723f306563202efeb30ebad3496415e4f51a4d46
-  md5: 4dd3d2c0ebe3d33ee01f753184f36dde
+  url: https://conda.anaconda.org/conda-forge/linux-64/orjson-3.10.11-py312h12e396e_1.conda
+  sha256: b7ad96c8533ee22f5d9caa895c5b67c96f2a5e47a8bba3cd5501ea99139537ec
+  md5: 2014708fe67fbe926bf5d2688d8793e1
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
@@ -7477,16 +9383,17 @@ packages:
   license_family: Apache
   purls:
   - pkg:pypi/orjson?source=hash-mapping
-  size: 310644
-  timestamp: 1729643816480
+  size: 306636
+  timestamp: 1731002220222
 - kind: conda
   name: orjson
-  version: 3.10.10
-  build: py312heca05bc_0
+  version: 3.10.11
+  build: py312hcd83bfe_1
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/orjson-3.10.10-py312heca05bc_0.conda
-  sha256: c13f0759cf97a89d9c43e8702ab3680e8dd4edf19ad2de6278bc6f0779e62bf2
-  md5: 7b4641510c2d804d95ac102549dc0d4f
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/orjson-3.10.11-py312hcd83bfe_1.conda
+  sha256: b0a375d53ffda0121499795698dcaacdd8f5a14fa824161082d770bf8aa35ff4
+  md5: 7a442fb74361200b15a250f3d73deaab
   depends:
   - __osx >=11.0
   - python >=3.12,<3.13.0a0
@@ -7498,8 +9405,8 @@ packages:
   license_family: Apache
   purls:
   - pkg:pypi/orjson?source=hash-mapping
-  size: 270784
-  timestamp: 1729644031010
+  size: 265834
+  timestamp: 1731002576719
 - kind: pypi
   name: owslib
   version: 0.29.3
@@ -7515,21 +9422,22 @@ packages:
   requires_python: '>=3.6'
 - kind: conda
   name: packaging
-  version: '24.1'
-  build: pyhd8ed1ab_0
+  version: '24.2'
+  build: pyhff2d567_1
+  build_number: 1
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda
-  sha256: 36aca948219e2c9fdd6d80728bcc657519e02f06c2703d8db3446aec67f51d81
-  md5: cbe1bb1f21567018ce595d9c2be0f0db
+  url: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhff2d567_1.conda
+  sha256: 74843f871e5cd8a1baf5ed8c406c571139c287141efe532f8ffbdafa3664d244
+  md5: 8508b703977f4c4ada34d657d051972c
   depends:
   - python >=3.8
   license: Apache-2.0
   license_family: APACHE
   purls:
   - pkg:pypi/packaging?source=hash-mapping
-  size: 50290
-  timestamp: 1718189540074
+  size: 60380
+  timestamp: 1731802602808
 - kind: conda
   name: pandas
   version: 2.2.2
@@ -7746,6 +9654,54 @@ packages:
   - pkg:pypi/pillow?source=hash-mapping
   size: 41737424
   timestamp: 1729065920347
+- kind: conda
+  name: pixman
+  version: 0.43.2
+  build: h59595ed_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda
+  sha256: 366d28e2a0a191d6c535e234741e0cd1d94d713f76073d8af4a5ccb2a266121e
+  md5: 71004cbf7924e19c02746ccde9fd7123
+  depends:
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 386826
+  timestamp: 1706549500138
+- kind: conda
+  name: pixman
+  version: 0.43.4
+  build: hebf3989_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/pixman-0.43.4-hebf3989_0.conda
+  sha256: df0ba2710ccdea5c909b63635529797f6eb3635b6fb77ae9cb2f183d08818409
+  md5: 0308c68e711cd295aaa026a4f8c4b1e5
+  depends:
+  - libcxx >=16
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 198755
+  timestamp: 1709239846651
+- kind: conda
+  name: pkgutil-resolve-name
+  version: 1.3.10
+  build: pyhd8ed1ab_1
+  build_number: 1
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/pkgutil-resolve-name-1.3.10-pyhd8ed1ab_1.conda
+  sha256: fecf95377134b0e8944762d92ecf7b0149c07d8186fb5db583125a2705c7ea0a
+  md5: 405678b942f2481cecdb3e010f4925d9
+  depends:
+  - python >=3.6
+  license: MIT AND PSF-2.0
+  purls:
+  - pkg:pypi/pkgutil-resolve-name?source=hash-mapping
+  size: 10778
+  timestamp: 1694617398467
 - kind: conda
   name: planetary-computer
   version: 1.0.0
@@ -7805,6 +9761,143 @@ packages:
   - pkg:pypi/pluggy?source=hash-mapping
   size: 23815
   timestamp: 1713667175451
+- kind: conda
+  name: poppler
+  version: 24.08.0
+  build: h37b219d_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/poppler-24.08.0-h37b219d_1.conda
+  sha256: a6b5abfcb9b44049f80e85d91fd1de2cfb2c18c9831c8f9efef9923bcac6051d
+  md5: 7926153cd183b32ba82966ab548611ab
+  depends:
+  - __osx >=11.0
+  - cairo >=1.18.0,<2.0a0
+  - fontconfig >=2.14.2,<3.0a0
+  - fonts-conda-ecosystem
+  - freetype >=2.12.1,<3.0a0
+  - lcms2 >=2.16,<3.0a0
+  - libcurl >=8.9.1,<9.0a0
+  - libcxx >=17
+  - libglib >=2.80.3,<3.0a0
+  - libiconv >=1.17,<2.0a0
+  - libintl >=0.22.5,<1.0a0
+  - libjpeg-turbo >=3.0.0,<4.0a0
+  - libpng >=1.6.43,<1.7.0a0
+  - libtiff >=4.6.0,<4.8.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - nspr >=4.35,<5.0a0
+  - nss >=3.103,<4.0a0
+  - openjpeg >=2.5.2,<3.0a0
+  - poppler-data
+  license: GPL-2.0-only
+  license_family: GPL
+  purls: []
+  size: 1514138
+  timestamp: 1724660307740
+- kind: conda
+  name: poppler
+  version: 24.08.0
+  build: h47131b8_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/poppler-24.08.0-h47131b8_1.conda
+  sha256: b32fe787525236908e21885fef8d77e8ebdbbe6694b2fb89ed799444ebda3178
+  md5: 0854b9ff0cc10a1f6f67b0f352b8e75a
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - cairo >=1.18.0,<2.0a0
+  - fontconfig >=2.14.2,<3.0a0
+  - fonts-conda-ecosystem
+  - freetype >=2.12.1,<3.0a0
+  - lcms2 >=2.16,<3.0a0
+  - libcurl >=8.9.1,<9.0a0
+  - libgcc-ng >=13
+  - libglib >=2.80.3,<3.0a0
+  - libiconv >=1.17,<2.0a0
+  - libjpeg-turbo >=3.0.0,<4.0a0
+  - libpng >=1.6.43,<1.7.0a0
+  - libstdcxx-ng >=13
+  - libtiff >=4.6.0,<4.8.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - nspr >=4.35,<5.0a0
+  - nss >=3.103,<4.0a0
+  - openjpeg >=2.5.2,<3.0a0
+  - poppler-data
+  license: GPL-2.0-only
+  license_family: GPL
+  purls: []
+  size: 1907007
+  timestamp: 1724659640508
+- kind: conda
+  name: poppler-data
+  version: 0.4.12
+  build: hd8ed1ab_0
+  subdir: noarch
+  noarch: generic
+  url: https://conda.anaconda.org/conda-forge/noarch/poppler-data-0.4.12-hd8ed1ab_0.conda
+  sha256: 2f227e17b3c0346112815faa605502b66c1c4511a856127f2899abf15a98a2cf
+  md5: d8d7293c5b37f39b2ac32940621c6592
+  license: BSD-3-Clause AND (GPL-2.0-only OR GPL-3.0-only)
+  license_family: OTHER
+  purls: []
+  size: 2348171
+  timestamp: 1675353652214
+- kind: conda
+  name: postgresql
+  version: '17.2'
+  build: h1122569_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/postgresql-17.2-h1122569_0.conda
+  sha256: ccfd1577944940f1f643cfbe71046ac0c18f3262496bed71699b24856debe1dd
+  md5: 848402b976b31bfecb3e476ea85cb285
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - icu >=75.1,<76.0a0
+  - krb5 >=1.21.3,<1.22.0a0
+  - libgcc >=13
+  - libpq 17.2 h04577a9_0
+  - libxml2 >=2.13.5,<3.0a0
+  - libxslt >=1.1.39,<2.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - lz4-c >=1.9.3,<1.10.0a0
+  - openldap >=2.6.8,<2.7.0a0
+  - openssl >=3.4.0,<4.0a0
+  - readline >=8.2,<9.0a0
+  - tzcode
+  - tzdata
+  - zstd >=1.5.6,<1.6.0a0
+  license: PostgreSQL
+  purls: []
+  size: 5578100
+  timestamp: 1732204587837
+- kind: conda
+  name: postgresql
+  version: '17.2'
+  build: h393ceee_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/postgresql-17.2-h393ceee_0.conda
+  sha256: 0ec1eda2b5611887d00a819bab94559004182a6bd16f42369db1b23fff1e85ee
+  md5: 75be3b77c1bc7ef0bedaee64cb60bbcb
+  depends:
+  - __osx >=11.0
+  - icu >=75.1,<76.0a0
+  - krb5 >=1.21.3,<1.22.0a0
+  - libpq 17.2 h9b1ab17_0
+  - libxml2 >=2.13.5,<3.0a0
+  - libxslt >=1.1.39,<2.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - lz4-c >=1.9.3,<1.10.0a0
+  - openldap >=2.6.8,<2.7.0a0
+  - openssl >=3.4.0,<4.0a0
+  - readline >=8.2,<9.0a0
+  - tzcode
+  - tzdata
+  - zstd >=1.5.6,<1.6.0a0
+  license: PostgreSQL
+  purls: []
+  size: 4583025
+  timestamp: 1732205016578
 - kind: conda
   name: pre-commit
   version: 3.8.0
@@ -8012,97 +10105,99 @@ packages:
 - kind: conda
   name: pyarrow
   version: 18.0.0
-  build: py312h9cebb41_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-18.0.0-py312h9cebb41_0.conda
-  sha256: 70d1c68eea0c5e0bf06dcab2bdb9330293cb9a16f7ce5c5da64b16bad6da3f14
-  md5: e110b1f861e749bc1dd48ad5467adab8
+  build: py312h1f38498_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-18.0.0-py312h1f38498_1.conda
+  sha256: c411c8bf7c22113a1d4ceac1c8df638a223ffcec9b4e5fc528631b64f3df7ccd
+  md5: 4510221533398449a8f707bda652dd27
   depends:
   - libarrow-acero 18.0.0.*
   - libarrow-dataset 18.0.0.*
   - libarrow-substrait 18.0.0.*
   - libparquet 18.0.0.*
-  - numpy >=1.19,<3
-  - pyarrow-core 18.0.0 *_0_*
+  - pyarrow-core 18.0.0 *_1_*
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 25247
-  timestamp: 1730164987752
+  size: 25409
+  timestamp: 1731058762728
 - kind: conda
   name: pyarrow
   version: 18.0.0
-  build: py312ha814d7c_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-18.0.0-py312ha814d7c_0.conda
-  sha256: 6ffedb4731bf86d46f03e46c1bf2a7141b3c5cd6801234a5db3fff7ead6f896a
-  md5: 5f3093c17c87a707769dd10578fc3f52
+  build: py312h7900ff3_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-18.0.0-py312h7900ff3_1.conda
+  sha256: 948514cde269fb6874a3945c8b2c26666588ac7835eb19fa7ec11c0547250b8d
+  md5: ea33ac754057779cd2df785661486310
   depends:
   - libarrow-acero 18.0.0.*
   - libarrow-dataset 18.0.0.*
   - libarrow-substrait 18.0.0.*
   - libparquet 18.0.0.*
-  - numpy >=1.19,<3
-  - pyarrow-core 18.0.0 *_0_*
+  - pyarrow-core 18.0.0 *_1_*
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 25458
-  timestamp: 1730165326841
+  size: 25161
+  timestamp: 1731058699977
 - kind: conda
   name: pyarrow-core
   version: 18.0.0
-  build: py312h01725c0_0_cpu
+  build: py312h01725c0_1_cpu
+  build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-18.0.0-py312h01725c0_0_cpu.conda
-  sha256: 60c9f2412994806ee91b8447c8dd4886c09c128051e8a7e17f68d85ea0c25fac
-  md5: 9100ae6cdd482666b38fa20e7819b385
+  url: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-18.0.0-py312h01725c0_1_cpu.conda
+  sha256: 240ab4328ebbfd81fe4f93cacd24fc44cd9e58edf9a95acc492e1025525f9a82
+  md5: c8ae967c39337603035d59c8994c23f9
   depends:
   - __glibc >=2.17,<3.0.a0
   - libarrow 18.0.0.* *cpu
   - libgcc >=13
   - libstdcxx >=13
   - libzlib >=1.3.1,<2.0a0
-  - numpy >=1.19,<3
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
   constrains:
   - apache-arrow-proc =*=cpu
+  - numpy >=1.21,<3
   license: Apache-2.0
   license_family: APACHE
   purls:
   - pkg:pypi/pyarrow?source=hash-mapping
-  size: 4553744
-  timestamp: 1730164968064
+  size: 4578590
+  timestamp: 1731058358731
 - kind: conda
   name: pyarrow-core
   version: 18.0.0
-  build: py312hc40f475_0_cpu
+  build: py312hc40f475_1_cpu
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-18.0.0-py312hc40f475_0_cpu.conda
-  sha256: 62b7a7f8b298eec3ad255d0d8b7bf1de4515ff83adb5e9cbafb6701e4e868969
-  md5: 188e7d741b5e3fe99a10639a00f1ee1d
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-18.0.0-py312hc40f475_1_cpu.conda
+  sha256: afa1d9cfb76ab37ae837c6a68f9a79e0a25f96da826c373be9728fed152eaec9
+  md5: 801f7771b21af9ca4016d9c2f9ff2a08
   depends:
   - __osx >=11.0
   - libarrow 18.0.0.* *cpu
   - libcxx >=18
   - libzlib >=1.3.1,<2.0a0
-  - numpy >=1.19,<3
   - python >=3.12,<3.13.0a0
   - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
   constrains:
   - apache-arrow-proc =*=cpu
+  - numpy >=1.21,<3
   license: Apache-2.0
   license_family: APACHE
   purls:
   - pkg:pypi/pyarrow?source=hash-mapping
-  size: 3888974
-  timestamp: 1730165298176
+  size: 3915622
+  timestamp: 1731058726842
 - kind: conda
   name: pyarrow-hotfix
   version: '0.6'
@@ -8140,32 +10235,33 @@ packages:
   timestamp: 1711811634025
 - kind: conda
   name: pydantic
-  version: 2.9.2
-  build: pyhd8ed1ab_0
+  version: 2.10.1
+  build: pyh10f6f8f_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.9.2-pyhd8ed1ab_0.conda
-  sha256: 1b7b0dc9f6af4da156bf22b0263be70829364a08145c696d3670facff2f6441a
-  md5: 1eb533bb8eb2199e3fef3e4aa147319f
+  url: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.10.1-pyh10f6f8f_0.conda
+  sha256: b1872231d26ee3ded32bd1fe3ee1b3c7d9834ad72bf28aed70e5cd1235341584
+  md5: c15343c9dbdb30766a07e5b70e46c7d3
   depends:
   - annotated-types >=0.6.0
-  - pydantic-core 2.23.4
-  - python >=3.7
+  - pydantic-core 2.27.1
+  - python >=3.9
   - typing-extensions >=4.6.1
+  - typing_extensions >=4.12.2
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/pydantic?source=hash-mapping
-  size: 300649
-  timestamp: 1726601202431
+  size: 315991
+  timestamp: 1732286519715
 - kind: conda
   name: pydantic-core
-  version: 2.23.4
+  version: 2.27.1
   build: py312h12e396e_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.23.4-py312h12e396e_0.conda
-  sha256: 365fde689865087b2a9da636f36678bd59617b324ce7a538b4806e90602b20f1
-  md5: 0845ab52d4ea209049129a6a91bc74ba
+  url: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.27.1-py312h12e396e_0.conda
+  sha256: c89741f4eff395f8de70975f42e1f20591f0e0870929d440af35b13399976b09
+  md5: 114030cb28527db2c385f07038e914c8
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
@@ -8175,19 +10271,18 @@ packages:
   constrains:
   - __glibc >=2.17
   license: MIT
-  license_family: MIT
   purls:
   - pkg:pypi/pydantic-core?source=hash-mapping
-  size: 1611784
-  timestamp: 1726525286507
+  size: 1635156
+  timestamp: 1732254225040
 - kind: conda
   name: pydantic-core
-  version: 2.23.4
-  build: py312he431725_0
+  version: 2.27.1
+  build: py312hcd83bfe_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/pydantic-core-2.23.4-py312he431725_0.conda
-  sha256: d6edd3d0f9e701c8299519d412ad3dc900c7d893a134f2582203cf43585decca
-  md5: 3148052477686acc581b20a34b478eeb
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/pydantic-core-2.27.1-py312hcd83bfe_0.conda
+  sha256: 5bba8de2bbbbdb39390abb1e2aff310e8cfd49646ae5a0e0ea4d6582bd1d52ba
+  md5: 3847a96eaf24a877b6091150ff9c4955
   depends:
   - __osx >=11.0
   - python >=3.12,<3.13.0a0
@@ -8197,11 +10292,10 @@ packages:
   constrains:
   - __osx >=11.0
   license: MIT
-  license_family: MIT
   purls:
   - pkg:pypi/pydantic-core?source=hash-mapping
-  size: 1431747
-  timestamp: 1726525575527
+  size: 1449057
+  timestamp: 1732254359451
 - kind: pypi
   name: pydata-sphinx-theme
   version: 0.16.0
@@ -8360,15 +10454,16 @@ packages:
 - kind: conda
   name: pyogrio
   version: 0.10.0
-  build: py312he8b4914_0
+  build: py312h02b19dd_1
+  build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.10.0-py312he8b4914_0.conda
-  sha256: cb0647597ae54d0007e698149f6ca519d80c389daee0f4fef149fe9902a0b31b
-  md5: 309f7524c82d168cc055e7b136713693
+  url: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.10.0-py312h02b19dd_1.conda
+  sha256: b1a2754f1ddda7f098dc1f6712153c8a184bf31e11e71ee8b6ca95d9791c2147
+  md5: 6ebb12bd1833a52e08e63297b8621903
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  - libgdal-core >=3.9.2,<3.10.0a0
+  - libgdal-core >=3.10.0,<3.11.0a0
   - libstdcxx >=13
   - numpy
   - packaging
@@ -8378,20 +10473,21 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/pyogrio?source=hash-mapping
-  size: 639343
-  timestamp: 1727771812389
+  size: 640043
+  timestamp: 1732013500715
 - kind: conda
   name: pyogrio
   version: 0.10.0
-  build: py312hf9e36c7_0
+  build: py312hfd5e53c_1
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/pyogrio-0.10.0-py312hf9e36c7_0.conda
-  sha256: 632019ab944cc1d9989fea02386c92132c3b9e7b0786ffc4516e3140ca3898ca
-  md5: 3955ea435842be121db2da0762accb76
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/pyogrio-0.10.0-py312hfd5e53c_1.conda
+  sha256: af738bd24e6f2fcf763a0dbc22fb088222e7f52b99b7b5f49333d9da1320c8ea
+  md5: 19550465d36f2f513be5c62def9edfd9
   depends:
   - __osx >=11.0
-  - libcxx >=17
-  - libgdal-core >=3.9.2,<3.10.0a0
+  - libcxx >=18
+  - libgdal-core >=3.10.0,<3.11.0a0
   - numpy
   - packaging
   - python >=3.12,<3.13.0a0
@@ -8401,8 +10497,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/pyogrio?source=hash-mapping
-  size: 562964
-  timestamp: 1727771822945
+  size: 564110
+  timestamp: 1732013713458
 - kind: conda
   name: pyparsing
   version: 3.2.0
@@ -8620,22 +10716,22 @@ packages:
   timestamp: 1728059777603
 - kind: conda
   name: python-dateutil
-  version: 2.9.0
-  build: pyhd8ed1ab_0
+  version: 2.9.0.post0
+  build: pyhff2d567_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda
-  sha256: f3ceef02ac164a8d3a080d0d32f8e2ebe10dd29e3a685d240e38b3599e146320
-  md5: 2cf4264fffb9e6eff6031c5b6884d61c
+  url: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_0.conda
+  sha256: 3888012c5916efaef45d503e3e544bbcc571b84426c1bb9577799ada9efefb54
+  md5: b6dfd90a2141e573e4b6a81630b56df5
   depends:
-  - python >=3.7
+  - python >=3.9
   - six >=1.5
   license: Apache-2.0
   license_family: APACHE
   purls:
   - pkg:pypi/python-dateutil?source=hash-mapping
-  size: 222742
-  timestamp: 1709299922152
+  size: 221925
+  timestamp: 1731919374686
 - kind: conda
   name: python-dotenv
   version: 1.0.1
@@ -8843,11 +10939,12 @@ packages:
 - kind: conda
   name: rasterio
   version: 1.4.2
-  build: py312h72e867f_0
+  build: py312h02264c4_1
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/rasterio-1.4.2-py312h72e867f_0.conda
-  sha256: 2400328666a83bd712ec0d5aeb723c1ccf735f6c90e933138173b79d7a8b960d
-  md5: ba08fe3fdef68d656356378ef8532fd1
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/rasterio-1.4.2-py312h02264c4_1.conda
+  sha256: 477c36aaf8b68967f7afaa027a6af8a1563111a8dad67ce46ff70e3e2b568848
+  md5: 623571800a67501761e4e9c48e8e43c9
   depends:
   - __osx >=11.0
   - affine
@@ -8856,8 +10953,8 @@ packages:
   - click >=4
   - click-plugins
   - cligj >=0.5
-  - libcxx >=17
-  - libgdal-core >=3.9.3,<3.10.0a0
+  - libcxx >=18
+  - libgdal-core >=3.10.0,<3.11.0a0
   - numpy >=1.21,<3
   - proj >=9.5.0,<9.6.0a0
   - python >=3.12,<3.13.0a0
@@ -8868,16 +10965,17 @@ packages:
   license: BSD-3-Clause
   purls:
   - pkg:pypi/rasterio?source=hash-mapping
-  size: 8212824
-  timestamp: 1730321793830
+  size: 7461860
+  timestamp: 1731967289492
 - kind: conda
   name: rasterio
   version: 1.4.2
-  build: py312h8456570_0
+  build: py312h8cae83d_1
+  build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.4.2-py312h8456570_0.conda
-  sha256: 3edaec6559866681744456380660b4e5f0bc5c1327aad43a0a7a34888f0f1ed5
-  md5: d81d9f238ce6441b59460a7a53e7c431
+  url: https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.4.2-py312h8cae83d_1.conda
+  sha256: a13ed2ca5b3bea05a8b17e09c67ab5bb1cef6795d81f4359da7f3fca9215ecc1
+  md5: 4c4bd2fcc48250420ec628e67bf27980
   depends:
   - __glibc >=2.17,<3.0.a0
   - affine
@@ -8887,7 +10985,7 @@ packages:
   - click-plugins
   - cligj >=0.5
   - libgcc >=13
-  - libgdal-core >=3.9.3,<3.10.0a0
+  - libgdal-core >=3.10.0,<3.11.0a0
   - libstdcxx >=13
   - numpy >=1.21,<3
   - proj >=9.5.0,<9.6.0a0
@@ -8898,8 +10996,38 @@ packages:
   license: BSD-3-Clause
   purls:
   - pkg:pypi/rasterio?source=hash-mapping
-  size: 8007235
-  timestamp: 1730321394455
+  size: 8359040
+  timestamp: 1731967182688
+- kind: conda
+  name: rav1e
+  version: 0.6.6
+  build: h69fbcac_2
+  build_number: 2
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/rav1e-0.6.6-h69fbcac_2.conda
+  sha256: be6174970193cb4d0ffa7d731a93a4c9542881dbc7ab24e74b460ef312161169
+  md5: e309ae86569b1cd55a0285fa4e939844
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 1526706
+  timestamp: 1694329743011
+- kind: conda
+  name: rav1e
+  version: 0.6.6
+  build: he8a937b_2
+  build_number: 2
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda
+  sha256: 91b3c1ced90d04ee2eded1f72cf3cbc19ff05a25e41876ef0758266a5bab009f
+  md5: 77d9955b4abddb811cb8ab1aa7d743e4
+  depends:
+  - libgcc-ng >=12
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 15423721
+  timestamp: 1694329261357
 - kind: conda
   name: re2
   version: 2024.07.02
@@ -8965,26 +11093,36 @@ packages:
   purls: []
   size: 250351
   timestamp: 1679532511311
-- kind: pypi
+- kind: conda
   name: referencing
   version: 0.35.1
-  url: https://files.pythonhosted.org/packages/b7/59/2056f61236782a2c86b33906c025d4f4a0b17be0161b63b70fd9e8775d36/referencing-0.35.1-py3-none-any.whl
-  sha256: eda6d3234d62814d1c64e305c1331c9a3a6132da475ab6382eaa997b21ee75de
-  requires_dist:
-  - attrs>=22.2.0
-  - rpds-py>=0.7.0
-  requires_python: '>=3.8'
+  build: pyhd8ed1ab_0
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/referencing-0.35.1-pyhd8ed1ab_0.conda
+  sha256: be8d6d9e86b1a3fef5424127ff81782f8ca63d3058980859609f6f1ecdd34cb3
+  md5: 0fc8b52192a8898627c3efae1003e9f6
+  depends:
+  - attrs >=22.2.0
+  - python >=3.8
+  - rpds-py >=0.7.0
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/referencing?source=hash-mapping
+  size: 42210
+  timestamp: 1714619625532
 - kind: pypi
   name: regex
-  version: 2024.9.11
-  url: https://files.pythonhosted.org/packages/29/52/84662b6636061277cb857f658518aa7db6672bc6d1a3f503ccd5aefc581e/regex-2024.9.11-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-  sha256: 85ab7824093d8f10d44330fe1e6493f756f252d145323dd17ab6b48733ff6c0a
+  version: 2024.11.6
+  url: https://files.pythonhosted.org/packages/60/85/cebcc0aff603ea0a201667b203f13ba75d9fc8668fab917ac5b2de3967bc/regex-2024.11.6-cp312-cp312-macosx_11_0_arm64.whl
+  sha256: 805e6b60c54bf766b251e94526ebad60b7de0c70f70a4e6210ee2891acb70bf2
   requires_python: '>=3.8'
 - kind: pypi
   name: regex
-  version: 2024.9.11
-  url: https://files.pythonhosted.org/packages/8a/ea/909d8620329ab710dfaf7b4adee41242ab7c9b95ea8d838e9bfe76244259/regex-2024.9.11-cp312-cp312-macosx_11_0_arm64.whl
-  sha256: 5b513b6997a0b2f10e4fd3a1313568e373926e8c252bd76c960f96fd039cd28d
+  version: 2024.11.6
+  url: https://files.pythonhosted.org/packages/fb/13/e3b075031a738c9598c51cfbc4c7879e26729c53aa9cca59211c44235314/regex-2024.11.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+  sha256: 70b7fa6606c2881c1db9479b0eaa11ed5dfa11c8d60a474ff0e095099f39d98e
   requires_python: '>=3.8'
 - kind: conda
   name: requests
@@ -9021,9 +11159,9 @@ packages:
   requires_python: '>=3.4'
 - kind: pypi
   name: rich
-  version: 13.9.3
-  url: https://files.pythonhosted.org/packages/9a/e2/10e9819cf4a20bd8ea2f5dabafc2e6bf4a78d6a0965daeb60a4b34d1c11f/rich-13.9.3-py3-none-any.whl
-  sha256: 9836f5096eb2172c9e77df411c1b009bace4193d6a481d534fea75ebba758283
+  version: 13.9.4
+  url: https://files.pythonhosted.org/packages/19/71/39c7c0d87f8d4e6c020a393182060eaefeeae6c01dab6a84ec346f2567df/rich-13.9.4-py3-none-any.whl
+  sha256: 6049d5e6ec054bf2779ab3358186963bac2ea89175919d699e378b99738c2a90
   requires_dist:
   - ipywidgets>=7.5.1,<9 ; extra == 'jupyter'
   - markdown-it-py>=2.2.0
@@ -9053,43 +11191,73 @@ packages:
   - pkg:pypi/rioxarray?source=hash-mapping
   size: 51306
   timestamp: 1721412091165
-- kind: pypi
+- kind: conda
   name: rpds-py
-  version: 0.20.1
-  url: https://files.pythonhosted.org/packages/c8/1b/f23015cb293927c93bdb4b94a48bfe77ad9d57359c75db51f0ff0cf482ff/rpds_py-0.20.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-  sha256: ecd27a66740ffd621d20b9a2f2b5ee4129a56e27bfb9458a3bcc2e45794c96cb
-  requires_python: '>=3.8'
-- kind: pypi
+  version: 0.21.0
+  build: py312h12e396e_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.21.0-py312h12e396e_0.conda
+  sha256: 6a2c3808b0922e453b450cc092f5e5da9d2466f48acce224da90432a94146c12
+  md5: 37f4ad7cb4214c799f32e5f411c6c69f
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  constrains:
+  - __glibc >=2.17
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/rpds-py?source=hash-mapping
+  size: 336759
+  timestamp: 1730922756033
+- kind: conda
   name: rpds-py
-  version: 0.20.1
-  url: https://files.pythonhosted.org/packages/d2/e4/61144f3790e12fd89e6153d77f7915ad26779735fef8ee9c099cba6dfb4a/rpds_py-0.20.1-cp312-cp312-macosx_11_0_arm64.whl
-  sha256: 142c0a5124d9bd0e2976089484af5c74f47bd3298f2ed651ef54ea728d2ea42c
-  requires_python: '>=3.8'
+  version: 0.21.0
+  build: py312hcd83bfe_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.21.0-py312hcd83bfe_0.conda
+  sha256: a3d885b49b03259ff7306855466933f9ba06e3f4c327cd0122e9a43b68910555
+  md5: 8ea53395d5403ae5ec1adabb1a74719a
+  depends:
+  - __osx >=11.0
+  - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
+  - python_abi 3.12.* *_cp312
+  constrains:
+  - __osx >=11.0
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/rpds-py?source=hash-mapping
+  size: 295817
+  timestamp: 1730922974629
 - kind: conda
   name: s2n
-  version: 1.5.5
-  build: h3931f03_0
+  version: 1.5.9
+  build: h0fd0ee4_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.5-h3931f03_0.conda
-  sha256: a6fa0afa836f8f26dea0abc180ca2549bb517932d9a88a121e707135d4bcb715
-  md5: 334dba9982ab9f5d62033c61698a8683
+  url: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.9-h0fd0ee4_0.conda
+  sha256: f2c8e55d6caa8d87a482b1f133963c184de1ccb2303b77cc8ca86c794253f151
+  md5: f472432f3753c5ca763d2497e2ea30bf
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  - openssl >=3.3.2,<4.0a0
+  - openssl >=3.4.0,<4.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 353081
-  timestamp: 1728534228471
+  size: 355568
+  timestamp: 1731541963573
 - kind: pypi
   name: s3transfer
-  version: 0.10.3
-  url: https://files.pythonhosted.org/packages/e5/c0/b0fba8259b61c938c9733da9346b9f93e00881a9db22aafdd72f6ae0ec05/s3transfer-0.10.3-py3-none-any.whl
-  sha256: 263ed587a5803c6c708d3ce44dc4dfedaab4c1a32e8329bab818933d79ddcf5d
+  version: 0.10.4
+  url: https://files.pythonhosted.org/packages/66/05/7957af15543b8c9799209506df4660cba7afc4cf94bfb60513827e96bed6/s3transfer-0.10.4-py3-none-any.whl
+  sha256: 244a76a24355363a68164241438de1b72f8781664920260c48465896b712a41e
   requires_dist:
-  - botocore<2.0a0,>=1.33.2
-  - botocore[crt]<2.0a0,>=1.33.2 ; extra == 'crt'
+  - botocore>=1.33.2,<2.0a0
+  - botocore[crt]>=1.33.2,<2.0a0 ; extra == 'crt'
   requires_python: '>=3.8'
 - kind: conda
   name: scikit-learn
@@ -9203,21 +11371,21 @@ packages:
   timestamp: 1729481826601
 - kind: conda
   name: setuptools
-  version: 75.3.0
-  build: pyhd8ed1ab_0
+  version: 75.6.0
+  build: pyhff2d567_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.3.0-pyhd8ed1ab_0.conda
-  sha256: a36d020b9f32fc3f1a6488a1c4a9c13988c6468faf6895bf30ca69521a61230e
-  md5: 2ce9825396daf72baabaade36cee16da
+  url: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.6.0-pyhff2d567_0.conda
+  sha256: eeec4645f70ce0ed03348397dced9d218a650a42df98592419af61d2689163ed
+  md5: 68d7d406366926b09a6a023e3d0f71d7
   depends:
-  - python >=3.8
+  - python >=3.9
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/setuptools?source=hash-mapping
-  size: 779561
-  timestamp: 1730382173961
+  size: 774304
+  timestamp: 1732216189406
 - kind: conda
   name: shapely
   version: 2.0.6
@@ -9279,6 +11447,30 @@ packages:
   - pkg:pypi/six?source=hash-mapping
   size: 14259
   timestamp: 1620240338595
+- kind: conda
+  name: sliderule
+  version: 4.8.6
+  build: pyhd8ed1ab_0
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/sliderule-4.8.6-pyhd8ed1ab_0.conda
+  sha256: 38b8ff778f309b8ee9409e39b01f14aed4756132b0a7bdb9cd014ef293d71c28
+  md5: dfd8ada8d3feef4cea904cbc9583dc83
+  depends:
+  - fiona
+  - geopandas
+  - numpy
+  - pandas
+  - pyarrow
+  - python >=3.8
+  - requests
+  - shapely
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/sliderule?source=hash-mapping
+  size: 82423
+  timestamp: 1730112917158
 - kind: conda
   name: snappy
   version: 1.2.1
@@ -9342,6 +11534,43 @@ packages:
   url: https://files.pythonhosted.org/packages/d1/c2/fe97d779f3ef3b15f05c94a2f1e3d21732574ed441687474db9d342a7315/soupsieve-2.6-py3-none-any.whl
   sha256: e72c4ff06e4fb6e4b5a9f0f55fe6e81514581fca1515028625d0f299c602ccc9
   requires_python: '>=3.8'
+- kind: conda
+  name: spdlog
+  version: 1.14.1
+  build: h6d8af72_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/spdlog-1.14.1-h6d8af72_1.conda
+  sha256: f981d4f3555125cb913be49397892f43c6b747705c0d72cba3676f7d98709f92
+  md5: 4af518b01539da8e4af17aee5fb92639
+  depends:
+  - __osx >=11.0
+  - fmt >=11.0.1,<12.0a0
+  - libcxx >=16
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 164011
+  timestamp: 1722238482313
+- kind: conda
+  name: spdlog
+  version: 1.14.1
+  build: hed91bc2_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/spdlog-1.14.1-hed91bc2_1.conda
+  sha256: 0c604fe3f78ddb2b612841722bd9b5db24d0484e30ced89fac78c0a3f524dfd6
+  md5: 909188c8979846bac8e586908cf1ca6a
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - fmt >=11.0.1,<12.0a0
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 195665
+  timestamp: 1722238295031
 - kind: pypi
   name: sphinx
   version: 8.1.3
@@ -9508,8 +11737,8 @@ packages:
   - typing-extensions!=3.10.0.1 ; extra == 'aiosqlite'
   - greenlet!=0.4.17 ; extra == 'asyncio'
   - greenlet!=0.4.17 ; extra == 'asyncmy'
-  - asyncmy!=0.2.4,!=0.2.6,>=0.2.3 ; extra == 'asyncmy'
-  - mariadb!=1.1.10,!=1.1.2,!=1.1.5,>=1.0.1 ; extra == 'mariadb-connector'
+  - asyncmy>=0.2.3,!=0.2.4,!=0.2.6 ; extra == 'asyncmy'
+  - mariadb>=1.0.1,!=1.1.2,!=1.1.5,!=1.1.10 ; extra == 'mariadb-connector'
   - pyodbc ; extra == 'mssql'
   - pymssql ; extra == 'mssql-pymssql'
   - pyodbc ; extra == 'mssql-pyodbc'
@@ -9547,8 +11776,8 @@ packages:
   - typing-extensions!=3.10.0.1 ; extra == 'aiosqlite'
   - greenlet!=0.4.17 ; extra == 'asyncio'
   - greenlet!=0.4.17 ; extra == 'asyncmy'
-  - asyncmy!=0.2.4,!=0.2.6,>=0.2.3 ; extra == 'asyncmy'
-  - mariadb!=1.1.10,!=1.1.2,!=1.1.5,>=1.0.1 ; extra == 'mariadb-connector'
+  - asyncmy>=0.2.3,!=0.2.4,!=0.2.6 ; extra == 'asyncmy'
+  - mariadb>=1.0.1,!=1.1.2,!=1.1.5,!=1.1.10 ; extra == 'mariadb-connector'
   - pyodbc ; extra == 'mssql'
   - pymssql ; extra == 'mssql-pymssql'
   - pyodbc ; extra == 'mssql-pyodbc'
@@ -9609,9 +11838,9 @@ packages:
   timestamp: 1730208324005
 - kind: pypi
   name: stac-asset
-  version: 0.4.5
-  url: https://files.pythonhosted.org/packages/4e/78/b5d239e4a9284fd0564729bbdae44877b15622b571bc848c2fea3a82df44/stac_asset-0.4.5-py3-none-any.whl
-  sha256: ae3e5b99deb61ca400d0505817083dca97b53d0c04fc1f299ca5c76d87a9afe0
+  version: 0.4.6
+  url: https://files.pythonhosted.org/packages/ac/ad/173f9fc405b787b819c5285192d8d44e683b648d29cee5252567c05fe470/stac_asset-0.4.6-py3-none-any.whl
+  sha256: 901f721589f58dcc6c1dd0dbf6baa8dc3b216f948602b770f1d7d2eb75781acd
   requires_dist:
   - aiobotocore>=2.5.0
   - aiofiles>=23.1.0
@@ -9675,6 +11904,39 @@ packages:
   - pkg:pypi/stack-data?source=hash-mapping
   size: 26205
   timestamp: 1669632203115
+- kind: conda
+  name: svt-av1
+  version: 2.3.0
+  build: h5888daf_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.3.0-h5888daf_0.conda
+  sha256: df30a9be29f1a8b5a2e314dd5b16ccfbcbd1cc6a4f659340e8bc2bd4de37bc6f
+  md5: 355898d24394b2af353eb96358db9fdd
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 2746291
+  timestamp: 1730246036363
+- kind: conda
+  name: svt-av1
+  version: 2.3.0
+  build: hf24288c_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/svt-av1-2.3.0-hf24288c_0.conda
+  sha256: ab876ed8bdd20e22a868dcb8d03e9ce9bbba7762d7e652d49bfff6af768a5b8f
+  md5: 114c33e9eec335a379c9ee6c498bb807
+  depends:
+  - __osx >=11.0
+  - libcxx >=17
+  license: BSD-2-Clause
+  license_family: BSD
+  purls: []
+  size: 1387330
+  timestamp: 1730246134730
 - kind: pypi
   name: tabulate
   version: 0.9.0
@@ -9700,6 +11962,81 @@ packages:
   - pkg:pypi/threadpoolctl?source=hash-mapping
   size: 23548
   timestamp: 1714400228771
+- kind: conda
+  name: tiledb
+  version: 2.26.2
+  build: h33a2f6d_11
+  build_number: 11
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/tiledb-2.26.2-h33a2f6d_11.conda
+  sha256: a7b405bc0af1a3770236c4e411869db01fc55673cd7e69c619baa22bc7f58163
+  md5: c9b0bf5bac79dbce00b5acc28dd6e59d
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - aws-crt-cpp >=0.29.5,<0.29.6.0a0
+  - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
+  - azure-core-cpp >=1.14.0,<1.14.1.0a0
+  - azure-identity-cpp >=1.10.0,<1.10.1.0a0
+  - azure-storage-blobs-cpp >=12.13.0,<12.13.1.0a0
+  - azure-storage-common-cpp >=12.8.0,<12.8.1.0a0
+  - bzip2 >=1.0.8,<2.0a0
+  - capnproto >=1.0.2,<1.0.3.0a0
+  - fmt >=11.0.2,<12.0a0
+  - libabseil * cxx17*
+  - libabseil >=20240722.0,<20240723.0a0
+  - libcurl >=8.10.1,<9.0a0
+  - libgcc >=13
+  - libgoogle-cloud >=2.31.0,<2.32.0a0
+  - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
+  - libstdcxx >=13
+  - libwebp-base >=1.4.0,<2.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - lz4-c >=1.9.3,<1.10.0a0
+  - openssl >=3.4.0,<4.0a0
+  - spdlog >=1.14.1,<1.15.0a0
+  - zstd >=1.5.6,<1.6.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 4358157
+  timestamp: 1732207838896
+- kind: conda
+  name: tiledb
+  version: 2.26.2
+  build: h4762ebe_11
+  build_number: 11
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/tiledb-2.26.2-h4762ebe_11.conda
+  sha256: 5a1cc22fec3ed07ed5eb9ebf583e755eeb1c78153ccac604487a5ed6d61abcd4
+  md5: 6fe15e83acc616d0a44045fe9d87b97a
+  depends:
+  - __osx >=11.0
+  - aws-crt-cpp >=0.29.5,<0.29.6.0a0
+  - aws-sdk-cpp >=1.11.449,<1.11.450.0a0
+  - azure-core-cpp >=1.14.0,<1.14.1.0a0
+  - azure-identity-cpp >=1.10.0,<1.10.1.0a0
+  - azure-storage-blobs-cpp >=12.13.0,<12.13.1.0a0
+  - azure-storage-common-cpp >=12.8.0,<12.8.1.0a0
+  - bzip2 >=1.0.8,<2.0a0
+  - capnproto >=1.0.2,<1.0.3.0a0
+  - fmt >=11.0.2,<12.0a0
+  - libabseil * cxx17*
+  - libabseil >=20240722.0,<20240723.0a0
+  - libcurl >=8.10.1,<9.0a0
+  - libcxx >=17
+  - libgoogle-cloud >=2.31.0,<2.32.0a0
+  - libgoogle-cloud-storage >=2.31.0,<2.32.0a0
+  - libwebp-base >=1.4.0,<2.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - lz4-c >=1.9.3,<1.10.0a0
+  - openssl >=3.4.0,<4.0a0
+  - spdlog >=1.14.1,<1.15.0a0
+  - zstd >=1.5.6,<1.6.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 3407179
+  timestamp: 1732208771419
 - kind: pypi
   name: time-machine
   version: 2.16.0
@@ -9751,21 +12088,21 @@ packages:
   timestamp: 1699202167581
 - kind: conda
   name: tomli
-  version: 2.0.2
-  build: pyhd8ed1ab_0
+  version: 2.1.0
+  build: pyhff2d567_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.2-pyhd8ed1ab_0.conda
-  sha256: 5e742ba856168b606ac3c814d247657b1c33b8042371f1a08000bdc5075bc0cc
-  md5: e977934e00b355ff55ed154904044727
+  url: https://conda.anaconda.org/conda-forge/noarch/tomli-2.1.0-pyhff2d567_0.conda
+  sha256: 354b8a64d4f3311179d85aefc529ca201a36afc1af090d0010c46be7b79f9a47
+  md5: 3fa1089b4722df3a900135925f4519d9
   depends:
-  - python >=3.7
+  - python >=3.9
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/tomli?source=hash-mapping
-  size: 18203
-  timestamp: 1727974767524
+  size: 18741
+  timestamp: 1731426862834
 - kind: conda
   name: tomlkit
   version: 0.13.2
@@ -9873,6 +12210,37 @@ packages:
   - pkg:pypi/typing-extensions?source=hash-mapping
   size: 39888
   timestamp: 1717802653893
+- kind: conda
+  name: tzcode
+  version: 2024b
+  build: hb9d3cd8_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/tzcode-2024b-hb9d3cd8_0.conda
+  sha256: 20c72e7ba106338d51fdc29a717a54fcd52340063232e944dcd1d38fb6348a28
+  md5: db124840386e1f842f93372897d1b857
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 69349
+  timestamp: 1725600364789
+- kind: conda
+  name: tzcode
+  version: 2024b
+  build: hd74edd7_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/tzcode-2024b-hd74edd7_0.conda
+  sha256: cd765bc7b8637b90a66771af99ea9eef1d532630643c7e4e05afbffb0e9de00c
+  md5: 2b216eef43a512307c43cf9ddcbb8a87
+  depends:
+  - __osx >=11.0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 62805
+  timestamp: 1725600435622
 - kind: conda
   name: tzdata
   version: 2024b
@@ -10076,16 +12444,49 @@ packages:
   timestamp: 1704731373922
 - kind: pypi
   name: wrapt
-  version: 1.16.0
-  url: https://files.pythonhosted.org/packages/62/62/30ca2405de6a20448ee557ab2cd61ab9c5900be7cbd18a2639db595f0b98/wrapt-1.16.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-  sha256: 98b5e1f498a8ca1858a1cdbffb023bfd954da4e3fa2c0cb5853d40014557248b
-  requires_python: '>=3.6'
+  version: 1.17.0
+  url: https://files.pythonhosted.org/packages/85/82/518605474beafff11f1a34759f6410ab429abff9f7881858a447e0d20712/wrapt-1.17.0-cp312-cp312-macosx_11_0_arm64.whl
+  sha256: 89fc28495896097622c3fc238915c79365dd0ede02f9a82ce436b13bd0ab7569
+  requires_python: '>=3.8'
 - kind: pypi
   name: wrapt
-  version: 1.16.0
-  url: https://files.pythonhosted.org/packages/6a/d7/cfcd73e8f4858079ac59d9db1ec5a1349bc486ae8e9ba55698cc1f4a1dff/wrapt-1.16.0-cp312-cp312-macosx_11_0_arm64.whl
-  sha256: 9090c9e676d5236a6948330e83cb89969f433b1943a558968f659ead07cb3b36
-  requires_python: '>=3.6'
+  version: 1.17.0
+  url: https://files.pythonhosted.org/packages/d2/50/dbef1a651578a3520d4534c1e434989e3620380c1ad97e309576b47f0ada/wrapt-1.17.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+  sha256: 18b956061b8db634120b58f668592a772e87e2e78bc1f6a906cfcaa0cc7991c1
+  requires_python: '>=3.8'
+- kind: conda
+  name: x265
+  version: '3.5'
+  build: h924138e_3
+  build_number: 3
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/x265-3.5-h924138e_3.tar.bz2
+  sha256: 76c7405bcf2af639971150f342550484efac18219c0203c5ee2e38b8956fe2a0
+  md5: e7f6ed84d4623d52ee581325c1587a6b
+  depends:
+  - libgcc-ng >=10.3.0
+  - libstdcxx-ng >=10.3.0
+  license: GPL-2.0-or-later
+  license_family: GPL
+  purls: []
+  size: 3357188
+  timestamp: 1646609687141
+- kind: conda
+  name: x265
+  version: '3.5'
+  build: hbc6ce65_3
+  build_number: 3
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/x265-3.5-hbc6ce65_3.tar.bz2
+  sha256: 2fed6987dba7dee07bd9adc1a6f8e6c699efb851431bcb6ebad7de196e87841d
+  md5: b1f7f2780feffe310b068c021e8ff9b2
+  depends:
+  - libcxx >=12.0.1
+  license: GPL-2.0-or-later
+  license_family: GPL
+  purls: []
+  size: 1832744
+  timestamp: 1646609481185
 - kind: conda
   name: xarray
   version: 2024.10.0
@@ -10165,6 +12566,60 @@ packages:
   purls: []
   size: 1648243
   timestamp: 1727733890754
+- kind: conda
+  name: xorg-libice
+  version: 1.1.1
+  build: hb9d3cd8_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hb9d3cd8_1.conda
+  sha256: ec276da68d1c4a3d34a63195b35ca5b248d4aff0812464dcd843d74649b5cec4
+  md5: 19608a9656912805b2b9a2f6bd257b04
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 58159
+  timestamp: 1727531850109
+- kind: conda
+  name: xorg-libsm
+  version: 1.2.4
+  build: he73a12e_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.4-he73a12e_1.conda
+  sha256: 70e903370977d44c9120a5641ab563887bd48446e9ef6fc2a3f5f60531c2cd6c
+  md5: 05a8ea5f446de33006171a7afe6ae857
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libuuid >=2.38.1,<3.0a0
+  - xorg-libice >=1.1.1,<2.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 27516
+  timestamp: 1727634669421
+- kind: conda
+  name: xorg-libx11
+  version: 1.8.10
+  build: h4f16b4b_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.10-h4f16b4b_0.conda
+  sha256: c4650634607864630fb03696474a0535f6fce5fda7d81a6462346e071b53dfa7
+  md5: 0b666058a179b744a622d0a4a0c56353
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libxcb >=1.17.0,<2.0a0
+  - xorg-xorgproto
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 838308
+  timestamp: 1727356837875
 - kind: conda
   name: xorg-libxau
   version: 1.0.11
@@ -10229,6 +12684,59 @@ packages:
   purls: []
   size: 18487
   timestamp: 1727795205022
+- kind: conda
+  name: xorg-libxext
+  version: 1.3.6
+  build: hb9d3cd8_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.6-hb9d3cd8_0.conda
+  sha256: da5dc921c017c05f38a38bd75245017463104457b63a1ce633ed41f214159c14
+  md5: febbab7d15033c913d53c7a2c102309d
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - xorg-libx11 >=1.8.10,<2.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 50060
+  timestamp: 1727752228921
+- kind: conda
+  name: xorg-libxrender
+  version: 0.9.11
+  build: hb9d3cd8_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hb9d3cd8_1.conda
+  sha256: f1217e902c0b1d8bc5d3ce65e483ebf38b049c823c9117b7198cfb16bd2b9143
+  md5: a7a49a8b85122b49214798321e2e96b4
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - xorg-libx11 >=1.8.10,<2.0a0
+  - xorg-xorgproto
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 37780
+  timestamp: 1727529943015
+- kind: conda
+  name: xorg-xorgproto
+  version: '2024.1'
+  build: hb9d3cd8_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-xorgproto-2024.1-hb9d3cd8_1.conda
+  sha256: 1316680be6edddee0156b86ec1102fc8286f51c1a5440366ed1db596a2dc3731
+  md5: 7c21106b851ec72c037b162c216d8f05
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 565425
+  timestamp: 1726846388217
 - kind: conda
   name: xyzservices
   version: 2024.9.0
@@ -10304,9 +12812,9 @@ packages:
   timestamp: 1641346969816
 - kind: pypi
   name: yarl
-  version: 1.17.1
-  url: https://files.pythonhosted.org/packages/c3/c0/cd8e94618983c1b811af082e1a7ad7764edb3a6af2bc6b468e0e686238ba/yarl-1.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
-  sha256: 7f6595c852ca544aaeeb32d357e62c9c780eac69dcd34e40cae7b55bc4fb1147
+  version: 1.18.0
+  url: https://files.pythonhosted.org/packages/9a/9f/63864f43d131ba8c8cdf1bde5dd3f02f0eff8a7c883a5d7fad32f204fda5/yarl-1.18.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+  sha256: 9b4c90c5363c6b0a54188122b61edb919c2cd1119684999d08cd5e538813a28e
   requires_dist:
   - idna>=2.0
   - multidict>=4.0
@@ -10314,9 +12822,9 @@ packages:
   requires_python: '>=3.9'
 - kind: pypi
   name: yarl
-  version: 1.17.1
-  url: https://files.pythonhosted.org/packages/e3/a2/b65447626227ebe36f18f63ac551790068bf42c69bb22dfa3ae986170728/yarl-1.17.1-cp312-cp312-macosx_11_0_arm64.whl
-  sha256: c73df5b6e8fabe2ddb74876fb82d9dd44cbace0ca12e8861ce9155ad3c886139
+  version: 1.18.0
+  url: https://files.pythonhosted.org/packages/eb/e1/3081b578a6f21961711b9a1c49c2947abb3b0d0dd9537378fb06777ce8ee/yarl-1.18.0-cp312-cp312-macosx_11_0_arm64.whl
+  sha256: 01be8688fc211dc237e628fcc209dda412d35de7642453059a0553747018d075
   requires_dist:
   - idna>=2.0
   - multidict>=4.0
@@ -10325,12 +12833,12 @@ packages:
 - kind: conda
   name: zeromq
   version: 4.3.5
-  build: h3b0a872_6
-  build_number: 6
+  build: h3b0a872_7
+  build_number: 7
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_6.conda
-  sha256: e67288b1c98a31ee58a5c07bdd873dbe08e75f752e1ad605d5e8c0697339903e
-  md5: 113506c8d2d558e733f5c38f6bf08c50
+  url: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_7.conda
+  sha256: a4dc72c96848f764bb5a5176aa93dd1e9b9e52804137b99daeebba277b31ea10
+  md5: 3947a35e916fcc6b9825449affbf4214
   depends:
   - __glibc >=2.17,<3.0.a0
   - krb5 >=1.21.3,<1.22.0a0
@@ -10340,44 +12848,44 @@ packages:
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 335528
-  timestamp: 1728364029042
+  size: 335400
+  timestamp: 1731585026517
 - kind: conda
   name: zeromq
   version: 4.3.5
-  build: h9f5b81c_6
-  build_number: 6
+  build: hc1bb282_7
+  build_number: 7
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h9f5b81c_6.conda
-  sha256: 5c5061c976141eccbbb2aec21483ddd10fd1df4fd9bcf638e3fd57b2bd85721f
-  md5: 84121ef1717cdfbecedeae70142706cc
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-hc1bb282_7.conda
+  sha256: 9e585569fe2e7d3bea71972cd4b9f06b1a7ab8fa7c5139f92a31cbceecf25a8a
+  md5: f7e6b65943cb73bce0143737fded08f1
   depends:
   - __osx >=11.0
   - krb5 >=1.21.3,<1.22.0a0
-  - libcxx >=17
+  - libcxx >=18
   - libsodium >=1.0.20,<1.0.21.0a0
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 280870
-  timestamp: 1728363954972
+  size: 281565
+  timestamp: 1731585108039
 - kind: conda
   name: zipp
-  version: 3.20.2
+  version: 3.21.0
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.2-pyhd8ed1ab_0.conda
-  sha256: 1e84fcfa41e0afdd87ff41e6fbb719c96a0e098c1f79be342293ab0bd8dea322
-  md5: 4daaed111c05672ae669f7036ee5bba3
+  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda
+  sha256: 232a30e4b0045c9de5e168dda0328dc0e28df9439cdecdfb97dd79c1c82c4cec
+  md5: fee389bf8a4843bd7a2248ce11b7f188
   depends:
   - python >=3.8
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/zipp?source=hash-mapping
-  size: 21409
-  timestamp: 1726248679175
+  size: 21702
+  timestamp: 1731262194278
 - kind: conda
   name: zlib
   version: 1.3.1
diff --git a/pyproject.toml b/pyproject.toml
index ac527ca..3879fdb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -63,6 +63,7 @@ docs = [
   "myst-nb",
   "myst_parser>=0.13",
   "pydata-sphinx-theme>=0.16.0,<0.17",
+  #"rpds-py>=0.21.0,<0.22",
   "sphinx>=7.0",
   "sphinx_autodoc_typehints",
   "sphinx_copybutton",
@@ -120,7 +121,7 @@ report.exclude_also = [
 ]
 
 [tool.codespell]
-skip="pixi.lock"
+skip="pixi.lock,docs/examples/sliderule.ipynb"
 
 [tool.mypy]
 files = ["src", "tests"]
@@ -220,6 +221,7 @@ rioxarray = "*"
 #s3fs = "*"
 stac-geoparquet = "*"
 pyarrow = "*"
+jsonschema = ">=4.23.0,<5"
 #nbconvert = ">=7.16.4,<8"
 #cloudpathlib-s3 = ">=0.20.0,<0.21"
 #matplotlib-base = ">=3.9.2,<4"
@@ -236,6 +238,7 @@ mypy = "*"
 # Testing additional dependencies
 #rich = ">=13.8.1,<14" # Optional. convenient for rich.print(dataset)
 #xvec = ">=0.3.0,<0.4"
+sliderule = ">=4.7.1,<5"
 
 [tool.pixi.pypi-dependencies]
 coincident = { path = ".", editable = false }
diff --git a/src/coincident/datasets/maxar.py b/src/coincident/datasets/maxar.py
index 73afd8f..0595533 100644
--- a/src/coincident/datasets/maxar.py
+++ b/src/coincident/datasets/maxar.py
@@ -79,7 +79,6 @@ async def download_item(
     return item  # noqa: RET504
 
 
-@depends_on_optional("matplotlib")
 def open_browse(item: pystac.Item, overview_level: int = 0) -> xr.DataArray:
     """
     Open a browse image from a STAC item using the specified overview level.
diff --git a/src/coincident/io/__init__.py b/src/coincident/io/__init__.py
index f7e8831..89139a1 100644
--- a/src/coincident/io/__init__.py
+++ b/src/coincident/io/__init__.py
@@ -4,6 +4,6 @@
 
 from __future__ import annotations
 
-from coincident.io.sliderule import load_gedi, load_icesat2
+from coincident.io import sliderule
 
-__all__ = ["load_icesat2", "load_gedi"]
+__all__ = ["sliderule"]
diff --git a/src/coincident/io/sliderule.py b/src/coincident/io/sliderule.py
index 4a419ea..0c75643 100644
--- a/src/coincident/io/sliderule.py
+++ b/src/coincident/io/sliderule.py
@@ -1,17 +1,18 @@
 """
-Read ICESat-2 & GEDI using Sliderule
+Subset and Sample using Sliderule
+https://slideruleearth.io
 """
 
 from __future__ import annotations
 
 import warnings
+from pathlib import Path
 from typing import Any
 
 import geopandas as gpd
-import pandas as pd
 
 try:
-    from sliderule import gedi, icesat2, toregion
+    from sliderule import earthdata, gedi, icesat2, raster, toregion
 except ImportError:
     warnings.warn(
         "'sliderule' package not found. Install for GEDI & ICESat2 functionality: https://slideruleearth.io/web/rtd/getting_started/Install.html",
@@ -19,52 +20,139 @@
     )
 from coincident._utils import depends_on_optional
 
-# TODO: make these configurable?
-default_params = {
-    "ats": 20.0,
-    "cnt": 5,
-    "len": 40.0,
-    "res": 20.0,
-    "maxi": 6,
-}
-
-
-@depends_on_optional("sliderule.gedi")
-def load_gedi(
-    aoi: gpd.GeoDataFrame,
-    start_datetime: pd.Timestamp,
-    end_datetime: pd.Timestamp,
-    quality_flags: bool = True,
-    **kwargs: Any,
+
+def _gdf_to_sliderule_polygon(gf: gpd.GeoDataFrame) -> list[dict[str, float]]:
+    # Ignore type necessary I think b/c sliderule doesn't have type hints?
+    return toregion(gf[["geometry"]].dissolve())["poly"]  # type: ignore[no-any-return]
+
+
+def _gdf_to_sliderule_params(gf: gpd.GeoDataFrame) -> dict[str, Any]:
+    # Sliderule expects single 'geometry' column
+    # Dissolve creates a single multipolygon from all geometries
+    sliderule_aoi = _gdf_to_sliderule_polygon(gf)
+    params = {}
+    params["poly"] = sliderule_aoi
+    params["t0"] = gf.start_datetime.min().tz_localize(None).isoformat()
+    params["t1"] = gf.end_datetime.max().tz_localize(None).isoformat()
+    return params
+
+
+def _granule_from_assets(assets: gpd.GeoDataFrame) -> str:
+    # NOTE: change to while loop in case tons of assets?
+    for _k, v in assets.items():
+        if v.get("roles") == "data":
+            granule = Path(v.get("href")).name
+
+    return granule
+
+
+@depends_on_optional("sliderule")
+def subset_gedi02a(
+    gf: gpd.GeoDataFrame,
+    aoi: gpd.GeoDataFrame = None,
+    # NOTE: investigate B006 best-practice
+    sliderule_params: dict[str, Any] = {},  # noqa: B006
 ) -> gpd.GeoDataFrame:
-    # NOTE: allow passing private cluster?
-    gedi.init("slideruleearth.io")
-    params = default_params.copy()
-    if quality_flags:
-        params["degrade_flag"] = 0
-        params["l2_quality_flag"] = 1
-    params["poly"] = toregion(aoi)["poly"]
-    params["t0"] = start_datetime.tz_localize(None).isoformat()
-    params["t1"] = end_datetime.tz_localize(None).isoformat()
-    params.update(kwargs)
-    return gedi.gedi02ap(params)
-
-
-@depends_on_optional("sliderule.icesat2")
-def load_icesat2(
-    aoi: gpd.GeoDataFrame,
-    start_datetime: pd.Timestamp,
-    end_datetime: pd.Timestamp,
-    **kwargs: Any,
+    """
+    Subsets GEDI L2A data based on a given GeoDataFrame and optional area of interest (AOI).
+
+    Parameters
+    ----------
+    gf : gpd.GeoDataFrame
+        A GeoDataFrame of results from coincident.search.search(dataset='gedi')
+
+    aoi : gpd.GeoDataFrame, optional
+        A GeoDataFrame with a POLYGON to subset. If not provided the union of geometries in gf is used.
+
+    sliderule_params : dict[Any], optional
+        A dictionary of additional parameters to be passed to the sliderule `gedi.gedi02ap`.
+
+    Returns
+    -------
+    gpd.GeoDataFrame
+        A GeoDataFrame containing the subsetted GEDI L2A data.
+
+    Notes
+    -----
+    The function sets `degrade_filter=True` and `l2_quality_filter=True` by default.
+    """
+    params = _gdf_to_sliderule_params(gf)
+
+    granule_names = gf.assets.apply(_granule_from_assets).to_list()
+
+    if aoi is not None:
+        params["poly"] = _gdf_to_sliderule_polygon(aoi)
+
+    params.update(
+        {
+            "degrade_filter": True,
+            "l2_quality_filter": True,
+        }
+    )
+
+    # User-provided parameters take precedence
+    params.update(sliderule_params)
+
+    return gedi.gedi02ap(params, resources=granule_names)
+
+
+@depends_on_optional("sliderule")
+def subset_atl06(
+    gf: gpd.GeoDataFrame,
+    aoi: gpd.GeoDataFrame = None,
+    dropna: bool = True,
+    sliderule_params: dict[str, Any] = {},  # noqa: B006
 ) -> gpd.GeoDataFrame:
-    # NOTE: allow passing private cluster?
-    # NOTE: deal with kwargs
-    icesat2.init("slideruleearth.io")
-    params = default_params.copy()
-    params["srt"] = icesat2.SRT_LAND
-    params["cnf"] = icesat2.CNF_SURFACE_HIGH
-    params["poly"] = toregion(aoi)["poly"]
-    params["t0"] = start_datetime.tz_localize(None).isoformat()
-    params["t1"] = end_datetime.tz_localize(None).isoformat()
-    params.update(kwargs)
-    return icesat2.atl06p(params)
+    params = _gdf_to_sliderule_params(gf)
+
+    # Note necessary but avoids another CMR search
+    granule_names = gf.assets.apply(_granule_from_assets).to_list()
+
+    if aoi is not None:
+        params["poly"] = _gdf_to_sliderule_polygon(aoi)
+
+    # User-provided parameters take precedence
+    params.update(sliderule_params)
+
+    data = icesat2.atl06sp(params, resources=granule_names)
+
+    # NOTE: add server-side filtering for NaNs in sliderule.atl06sp?
+    if dropna:
+        return data.dropna(subset=["h_li"])
+    return data
+
+
+@depends_on_optional("sliderule")
+def sample_worldcover(gf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
+    points = [[x, y] for x, y in zip(gf.geometry.x, gf.geometry.y, strict=False)]
+    return raster.sample("esa-worldcover-10meter", points)
+
+
+@depends_on_optional("sliderule")
+def sample_3dep(gf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
+    """
+    Sample 3DEP 1-meter DEM with POINT geometries in GeoDataFrame.
+
+    Points should be (longitude, latitude) not UTM
+
+    Parameters
+    ----------
+    gf : gpd.GeoDataFrame
+        A GeoDataFrame containing POINT geometries
+
+    Returns
+    -------
+    gpd.GeoDataFrame
+        A GeoDataFrame with sampled elevation data from the 3DEP 1-meter DEM.
+    """
+    points = [[x, y] for x, y in zip(gf.geometry.x, gf.geometry.y, strict=False)]
+
+    poly = _gdf_to_sliderule_polygon(gf)
+    geojson = earthdata.tnm(
+        short_name="Digital Elevation Model (DEM) 1 meter", polygon=poly
+    )
+
+    # NOTE: make sliderule aware of 3dep polygons to not sample NaN regions for 1m grid?
+    data_3dep = raster.sample("usgs3dep-1meter-dem", points, {"catalog": geojson})
+
+    return data_3dep[data_3dep.value.notna()]
diff --git a/src/coincident/search/main.py b/src/coincident/search/main.py
index 9669812..3a298f3 100644
--- a/src/coincident/search/main.py
+++ b/src/coincident/search/main.py
@@ -147,6 +147,11 @@ def search(
             **kwargs,
         )
 
+    # Keep track of dataset alias in geodataframe metadata
+    # NOTE: attrs not always retrained and not saved to file
+    # https://github.com/geopandas/geopandas/issues/3320
+    gf.attrs["dataset"] = dataset.alias
+
     return gf
 
 
diff --git a/src/coincident/search/stac.py b/src/coincident/search/stac.py
index 6378696..410cfbb 100644
--- a/src/coincident/search/stac.py
+++ b/src/coincident/search/stac.py
@@ -25,6 +25,21 @@
 except maxar_platform.session.NoSessionCredentials:
     msg_noauth = "Unable to authenticate with Maxar API. Please set MAXAR_API_KEY environment variable."
     warnings.warn(msg_noauth, stacklevel=2)
+except maxar_platform.exceptions.UnAuthorizedException:
+    msg_noauth = (
+        "Unable to authenticate with Maxar API. Please check MAXAR_API_KEY is valid."
+    )
+    warnings.warn(msg_noauth, stacklevel=2)
+
+
+def _filter_assets(assets: gpd.GeoDataFrame) -> dict[str, str]:
+    """Remove key:None pairs from assets"""
+    keep_keys = []
+    for k, v in assets.items():
+        if v is not None:
+            keep_keys.append(k)
+
+    return {key: assets[key] for key in keep_keys}
 
 
 def to_geopandas(
@@ -59,6 +74,9 @@ def to_geopandas(
     record_batch_reader = stac_geoparquet.arrow.parse_stac_items_to_arrow(collection)
     gf = gpd.GeoDataFrame.from_arrow(record_batch_reader)  # doesn't keep arrow dtypes
 
+    # Workaround stac-geoparquet limitation https://github.com/stac-utils/stac-geoparquet/issues/82
+    gf["assets"] = gf["assets"].apply(_filter_assets)
+
     # Additional columns for convenience
     # NOTE: these become entries under STAC properties
     gf["dayofyear"] = gf["datetime"].dt.dayofyear
diff --git a/src/coincident/search/wesm.py b/src/coincident/search/wesm.py
index 5f69e91..314a8ac 100644
--- a/src/coincident/search/wesm.py
+++ b/src/coincident/search/wesm.py
@@ -58,6 +58,8 @@ def stacify_column_names(gf: GeoDataFrame) -> GeoDataFrame:
         "collect_end": "end_datetime",
     }
     gf = gf.rename(columns=name_map)
+    # Add collection name to match STAC (used to identify geodataframe contents in other functions)
+    gf["collection"] = "3DEP"
     gf["start_datetime"] = pd.to_datetime(gf["start_datetime"])
     gf["end_datetime"] = pd.to_datetime(gf["end_datetime"])
     duration = gf.end_datetime - gf.start_datetime
diff --git a/tests/test_search.py b/tests/test_search.py
index b7611e7..ea1d46f 100644
--- a/tests/test_search.py
+++ b/tests/test_search.py
@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import typing
+
 import geopandas as gpd
 import pytest
 from geopandas.testing import assert_geodataframe_equal
@@ -9,6 +11,16 @@
 # Decorate tests requiring internet (slow & flaky)
 network = pytest.mark.network
 
+try:
+    import maxar_platform.discovery  # noqa: F401
+
+    not_authenticated = False
+except:  # noqa: E722
+    not_authenticated = True
+maxar_authenticated = pytest.mark.skipif(
+    not_authenticated, reason="tests for linux only"
+)
+
 
 @pytest.fixture
 def aoi():
@@ -24,11 +36,12 @@ def large_aoi():
     return gpd.read_file(aoi_url)
 
 
+@typing.no_type_check
 def test_no_dataset_specified():
     with pytest.raises(
         TypeError, match="missing 1 required positional argument: 'dataset'"
     ):
-        coincident.search.search(intersects="-120, 40, -121, 41")  # type: ignore[call-arg]
+        coincident.search.search(intersects="-120, 40, -121, 41")
 
 
 def test_unknown_dataset_specified():
@@ -75,6 +88,7 @@ def test_cascading_search(aoi):
 
 # TODO: add more assertions / tests for this section
 @network
+@maxar_authenticated
 @pytest.mark.filterwarnings("ignore:Server does not conform")
 def test_maxar_search(aoi):
     gf = coincident.search.search(
@@ -91,6 +105,7 @@ def test_maxar_search(aoi):
 
 
 @network
+@maxar_authenticated
 def test_maxar_large_aoi(large_aoi):
     gf = coincident.search.search(
         dataset="maxar",