diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 17b24bc..071a43f 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -5,13 +5,13 @@ ci:
 
 repos:
   - repo: https://github.com/adamchainz/blacken-docs
-    rev: "1.18.0"
+    rev: "1.19.1"
     hooks:
       - id: blacken-docs
         additional_dependencies: [black==24.*]
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: "v4.6.0"
+    rev: "v5.0.0"
     hooks:
       - id: check-added-large-files
       - id: check-case-conflict
@@ -26,22 +26,22 @@ repos:
       - id: requirements-txt-fixer
       - id: trailing-whitespace
 
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: "v3.1.0"
+  - repo: https://github.com/rbubley/mirrors-prettier
+    rev: "v3.5.3"
     hooks:
       - id: prettier
         types_or: [yaml, markdown, html, css, scss, javascript, json]
         args: [--prose-wrap=always]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.5.6"
+    rev: "v0.11.9"
     hooks:
       - id: ruff
         args: ["--fix", "--show-fixes"]
       - id: ruff-format
 
   - repo: https://github.com/codespell-project/codespell
-    rev: "v2.3.0"
+    rev: "v2.4.1"
     hooks:
       - id: codespell
 
@@ -51,13 +51,13 @@ repos:
       - id: shellcheck
 
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: "v0.18"
+    rev: "v0.24.1"
     hooks:
       - id: validate-pyproject
         additional_dependencies: ["validate-pyproject-schema-store[all]"]
 
   - repo: https://github.com/python-jsonschema/check-jsonschema
-    rev: "0.29.1"
+    rev: "0.33.0"
     hooks:
       - id: check-dependabot
       - id: check-github-workflows
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
index e72fe50..0f1df00 100644
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -2,7 +2,9 @@
 
 ## 1. Our Commitment
 
-We are dedicated to fostering a respectful environment for everyone contributing to this project. We expect all participants to treat each other with respect, professionalism, and kindness.
+We are dedicated to fostering a respectful environment for everyone contributing
+to this project. We expect all participants to treat each other with respect,
+professionalism, and kindness.
 
 ## 2. Expected Behavior
 
@@ -21,15 +23,20 @@ The following behaviors will not be tolerated:
 - Sharing inappropriate content.
 
 ## 4. Reporting Violations
-If you experience or witness any behavior that violates this Code of Conduct, please report it by contacting the project maintainers. All reports will be reviewed confidentially.
+
+If you experience or witness any behavior that violates this Code of Conduct,
+please report it by contacting the project maintainers. All reports will be
+reviewed confidentially.
 
 ## 5. Enforcement
-Violations of this Code of Conduct may result in actions such as warnings, temporary bans, or permanent exclusion from participation at the discretion of the maintainers.
+
+Violations of this Code of Conduct may result in actions such as warnings,
+temporary bans, or permanent exclusion from participation at the discretion of
+the maintainers.
 
 ## Contact Info
-Email: <nsidc@nsidc.org>
-Organization: National Snow and Ice Data Center¹
-Website: <https://nsidc.org/>
-Date last modified: 01-22-2025
+
+Email: <nsidc@nsidc.org> Organization: National Snow and Ice Data Center¹
+Website: <https://nsidc.org/> Date last modified: 01-22-2025
 
 ¹Work performed under NASA contract 80GSFC23CA035.
diff --git a/docs/iceflow-example.ipynb b/docs/iceflow-example.ipynb
index 9801a02..b94522b 100644
--- a/docs/iceflow-example.ipynb
+++ b/docs/iceflow-example.ipynb
@@ -32,29 +32,34 @@
    "id": "e29f9cf9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:39.880850Z",
-     "iopub.status.busy": "2024-12-09T17:35:39.880618Z",
-     "iopub.status.idle": "2024-12-09T17:35:40.959362Z",
-     "shell.execute_reply": "2024-12-09T17:35:40.959042Z"
+     "iopub.execute_input": "2025-05-12T20:02:45.054739Z",
+     "iopub.status.busy": "2025-05-12T20:02:45.054510Z",
+     "iopub.status.idle": "2025-05-12T20:02:46.182739Z",
+     "shell.execute_reply": "2025-05-12T20:02:46.182173Z"
     }
    },
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "import datetime as dt\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "from nsidc.iceflow import (\n",
-    "    find_iceflow_data,\n",
-    "    download_iceflow_results,\n",
-    "    read_iceflow_datafiles,\n",
-    "    DatasetSearchParameters,\n",
     "    BoundingBox,\n",
+    "    DatasetSearchParameters,\n",
     "    ILATM1BDataset,\n",
+    "    download_iceflow_results,\n",
+    "    find_iceflow_data,\n",
+    "    read_iceflow_datafiles,\n",
     ")\n",
-    "from pathlib import Path\n",
-    "import datetime as dt\n",
-    "import matplotlib.pyplot as plt\n",
+    "\n",
     "# note: use `inline` to save the resulting image as an embedded png (nice for sharing). \\\n",
     "# Use `widget` to obtain interactive controls to explore the data in depth!\n",
     "%matplotlib inline\n",
-    "# %matplotlib widget\n"
+    "# %matplotlib widget"
    ]
   },
   {
@@ -73,10 +78,10 @@
    "id": "f45d86d9-404a-4720-9ad0-9959114aa629",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:40.961329Z",
-     "iopub.status.busy": "2024-12-09T17:35:40.961091Z",
-     "iopub.status.idle": "2024-12-09T17:35:40.963836Z",
-     "shell.execute_reply": "2024-12-09T17:35:40.963457Z"
+     "iopub.execute_input": "2025-05-12T20:02:46.184443Z",
+     "iopub.status.busy": "2025-05-12T20:02:46.184174Z",
+     "iopub.status.idle": "2025-05-12T20:02:46.187210Z",
+     "shell.execute_reply": "2025-05-12T20:02:46.186710Z"
     }
    },
    "outputs": [],
@@ -87,8 +92,13 @@
     "# Define the dataset that we want to search for.\n",
     "atm1b_v1_dataset = ILATM1BDataset(version=\"1\")\n",
     "\n",
-    "# Define a bounding box for our area of interest. \n",
-    "BBOX = BoundingBox(lower_left_lon=-103.125559, lower_left_lat=-75.180563, upper_right_lon=-102.677327, upper_right_lat=-74.798063)\n",
+    "# Define a bounding box for our area of interest.\n",
+    "BBOX = BoundingBox(\n",
+    "    lower_left_lon=-103.125559,\n",
+    "    lower_left_lat=-75.180563,\n",
+    "    upper_right_lon=-102.677327,\n",
+    "    upper_right_lat=-74.798063,\n",
+    ")\n",
     "\n",
     "# We will define a short date range in 2009 to search for data.\n",
     "date_range = (dt.date(2009, 11, 1), dt.date(2009, 12, 31))"
@@ -108,10 +118,10 @@
    "id": "c440ba2d-0c4a-47ff-b41e-a7486709d3e0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:40.965375Z",
-     "iopub.status.busy": "2024-12-09T17:35:40.965208Z",
-     "iopub.status.idle": "2024-12-09T17:35:45.058227Z",
-     "shell.execute_reply": "2024-12-09T17:35:45.057626Z"
+     "iopub.execute_input": "2025-05-12T20:02:46.188544Z",
+     "iopub.status.busy": "2025-05-12T20:02:46.188403Z",
+     "iopub.status.idle": "2025-05-12T20:03:25.254201Z",
+     "shell.execute_reply": "2025-05-12T20:03:25.253134Z"
     }
    },
    "outputs": [
@@ -151,10 +161,10 @@
    "id": "e3b3b3bc-2d2e-49d3-bafc-3f9a4269ee57",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:45.061192Z",
-     "iopub.status.busy": "2024-12-09T17:35:45.060953Z",
-     "iopub.status.idle": "2024-12-09T17:35:46.823554Z",
-     "shell.execute_reply": "2024-12-09T17:35:46.823171Z"
+     "iopub.execute_input": "2025-05-12T20:03:25.257987Z",
+     "iopub.status.busy": "2025-05-12T20:03:25.257590Z",
+     "iopub.status.idle": "2025-05-12T20:03:25.279401Z",
+     "shell.execute_reply": "2025-05-12T20:03:25.279070Z"
     }
    },
    "outputs": [
@@ -162,18 +172,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-12-09 10:35:45.061\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 1 granules to downloaded-data/ILATM1B_1.\u001b[0m\n"
+      "\u001b[32m2025-05-12 14:03:25.259\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 2 granules to downloaded-data/ILATM1B_1.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b44e7f88a7ec45bfb9e3191d93f9ef0b",
+       "model_id": "8eda72595f644f829148b5b0fd867ddb",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+       "QUEUEING TASKS | :   0%|          | 0/2 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -182,12 +192,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "687a6e75fcfc447c913563b1431b3e8f",
+       "model_id": "1d2bd3ac9d1b4e19a1a473d674325121",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+       "PROCESSING TASKS | :   0%|          | 0/2 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -196,12 +206,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dd782e76e307437bb90a4afa7bac4827",
+       "model_id": "277f85f4b61f44639a1414879fe8d777",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+       "COLLECTING RESULTS | :   0%|          | 0/2 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -234,10 +244,10 @@
    "id": "15c0633c-1812-45d7-a91f-36d627fdb3b5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:46.826168Z",
-     "iopub.status.busy": "2024-12-09T17:35:46.825807Z",
-     "iopub.status.idle": "2024-12-09T17:35:48.628664Z",
-     "shell.execute_reply": "2024-12-09T17:35:48.628054Z"
+     "iopub.execute_input": "2025-05-12T20:03:25.280676Z",
+     "iopub.status.busy": "2025-05-12T20:03:25.280383Z",
+     "iopub.status.idle": "2025-05-12T20:03:27.243084Z",
+     "shell.execute_reply": "2025-05-12T20:03:27.242712Z"
     }
    },
    "outputs": [
@@ -481,10 +491,10 @@
    "id": "99e03fbd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:48.630798Z",
-     "iopub.status.busy": "2024-12-09T17:35:48.630364Z",
-     "iopub.status.idle": "2024-12-09T17:35:48.638029Z",
-     "shell.execute_reply": "2024-12-09T17:35:48.637720Z"
+     "iopub.execute_input": "2025-05-12T20:03:27.244571Z",
+     "iopub.status.busy": "2025-05-12T20:03:27.244342Z",
+     "iopub.status.idle": "2025-05-12T20:03:27.251587Z",
+     "shell.execute_reply": "2025-05-12T20:03:27.251226Z"
     }
    },
    "outputs": [
@@ -598,10 +608,10 @@
    "id": "6a0cd1e6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:48.639480Z",
-     "iopub.status.busy": "2024-12-09T17:35:48.639330Z",
-     "iopub.status.idle": "2024-12-09T17:35:50.311249Z",
-     "shell.execute_reply": "2024-12-09T17:35:50.310872Z"
+     "iopub.execute_input": "2025-05-12T20:03:27.252981Z",
+     "iopub.status.busy": "2025-05-12T20:03:27.252873Z",
+     "iopub.status.idle": "2025-05-12T20:03:28.896193Z",
+     "shell.execute_reply": "2025-05-12T20:03:28.895659Z"
     }
    },
    "outputs": [
@@ -715,10 +725,10 @@
    "id": "b64693c0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:50.313326Z",
-     "iopub.status.busy": "2024-12-09T17:35:50.313162Z",
-     "iopub.status.idle": "2024-12-09T17:35:50.319005Z",
-     "shell.execute_reply": "2024-12-09T17:35:50.318689Z"
+     "iopub.execute_input": "2025-05-12T20:03:28.897704Z",
+     "iopub.status.busy": "2025-05-12T20:03:28.897539Z",
+     "iopub.status.idle": "2025-05-12T20:03:28.903303Z",
+     "shell.execute_reply": "2025-05-12T20:03:28.902846Z"
     }
    },
    "outputs": [
@@ -727,14 +737,16 @@
      "output_type": "stream",
      "text": [
       "Max difference in latitude: 1.8509084043216717e-08\n",
-      "Max difference in longitude: 7.9922898521545e-08\n",
+      "Max difference in longitude: 7.992288431069028e-08\n",
       "Max difference in elevation: 0.00764224398881197\n"
      ]
     }
    ],
    "source": [
     "for variable in [\"latitude\", \"longitude\", \"elevation\"]:\n",
-    "    print(f\"Max difference in {variable}: {abs(itrf2014_df[variable] - iceflow_df[variable]).max()}\")"
+    "    print(\n",
+    "        f\"Max difference in {variable}: {abs(itrf2014_df[variable] - iceflow_df[variable]).max()}\"\n",
+    "    )"
    ]
   },
   {
@@ -763,10 +775,10 @@
    "id": "14e431b0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:50.320426Z",
-     "iopub.status.busy": "2024-12-09T17:35:50.320327Z",
-     "iopub.status.idle": "2024-12-09T17:35:52.054693Z",
-     "shell.execute_reply": "2024-12-09T17:35:52.054037Z"
+     "iopub.execute_input": "2025-05-12T20:03:28.905024Z",
+     "iopub.status.busy": "2025-05-12T20:03:28.904902Z",
+     "iopub.status.idle": "2025-05-12T20:03:30.544172Z",
+     "shell.execute_reply": "2025-05-12T20:03:30.543826Z"
     }
    },
    "outputs": [
@@ -883,10 +895,10 @@
    "id": "8c562732",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:52.056409Z",
-     "iopub.status.busy": "2024-12-09T17:35:52.056299Z",
-     "iopub.status.idle": "2024-12-09T17:35:52.062172Z",
-     "shell.execute_reply": "2024-12-09T17:35:52.061727Z"
+     "iopub.execute_input": "2025-05-12T20:03:30.545655Z",
+     "iopub.status.busy": "2025-05-12T20:03:30.545470Z",
+     "iopub.status.idle": "2025-05-12T20:03:30.552283Z",
+     "shell.execute_reply": "2025-05-12T20:03:30.551902Z"
     }
    },
    "outputs": [
@@ -896,13 +908,15 @@
      "text": [
       "Max difference in latitude: 4.895740346455568e-07\n",
       "Max difference in longitude: 5.5522629338611296e-06\n",
-      "Max difference in elevation: 0.00772995688021183\n"
+      "Max difference in elevation: 0.007729958742856979\n"
      ]
     }
    ],
    "source": [
     "for variable in [\"latitude\", \"longitude\", \"elevation\"]:\n",
-    "    print(f\"Max difference in {variable}: {abs(itrf2014_epoch_2019_7_df[variable] - iceflow_df[variable]).max()}\")"
+    "    print(\n",
+    "        f\"Max difference in {variable}: {abs(itrf2014_epoch_2019_7_df[variable] - iceflow_df[variable]).max()}\"\n",
+    "    )"
    ]
   },
   {
@@ -921,15 +935,17 @@
    "id": "5bafd254",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:52.064144Z",
-     "iopub.status.busy": "2024-12-09T17:35:52.064007Z",
-     "iopub.status.idle": "2024-12-09T17:35:52.073202Z",
-     "shell.execute_reply": "2024-12-09T17:35:52.072754Z"
+     "iopub.execute_input": "2025-05-12T20:03:30.553651Z",
+     "iopub.status.busy": "2025-05-12T20:03:30.553508Z",
+     "iopub.status.idle": "2025-05-12T20:03:30.562551Z",
+     "shell.execute_reply": "2025-05-12T20:03:30.562122Z"
     }
    },
    "outputs": [],
    "source": [
-    "filter_condition = (iceflow_df.reset_index().index > 50) & ( iceflow_df.reset_index().index < 60)\n",
+    "filter_condition = (iceflow_df.reset_index().index > 50) & (\n",
+    "    iceflow_df.reset_index().index < 60\n",
+    ")\n",
     "sampled_iceflow_df = iceflow_df[filter_condition]\n",
     "sampled_itrf2014_df = itrf2014_df[filter_condition]\n",
     "sampled_itrf2014_epoch_2019_7_df = itrf2014_epoch_2019_7_df[filter_condition]"
@@ -949,16 +965,16 @@
    "id": "0ffce6d2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:52.074587Z",
-     "iopub.status.busy": "2024-12-09T17:35:52.074475Z",
-     "iopub.status.idle": "2024-12-09T17:35:52.195075Z",
-     "shell.execute_reply": "2024-12-09T17:35:52.194349Z"
+     "iopub.execute_input": "2025-05-12T20:03:30.564106Z",
+     "iopub.status.busy": "2025-05-12T20:03:30.563998Z",
+     "iopub.status.idle": "2025-05-12T20:03:30.665278Z",
+     "shell.execute_reply": "2025-05-12T20:03:30.664765Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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+       "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_3dfe95989e294e67a81a6d2e226ed140",
+       "placeholder": "​",
+       "style": "IPY_MODEL_8ce60c7fbc364665908419311d780c56",
+       "tabbable": null,
+       "tooltip": null,
+       "value": " 2/2 [00:00&lt;00:00, 630.06it/s]"
       }
      },
-     "eb2aa718055f4037afea898c891bac39": {
+     "cd0790fd494e4a418946f4f5d270c9f3": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
+      "model_name": "HTMLModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
+       "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_394ec19d5074403e9c8b532298d0072f",
+       "placeholder": "​",
+       "style": "IPY_MODEL_a6d08979fcd84e76a48a6baf6c900f31",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "PROCESSING TASKS | : 100%"
+      }
+     },
+     "da30e7fdac1641a9809b5fd2c6be79bb": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
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+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_ba85e4271a2c4b9eb3277a494e7205e4",
+       "placeholder": "​",
+       "style": "IPY_MODEL_66eb7d4cc3ad4067b5107d5799a557ab",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "COLLECTING RESULTS | : 100%"
+      }
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+     "eb48f817f1974284b583a33ac75f8e82": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_207b1caad91b42ff904eef4ab7e872be",
+       "max": 2.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_20ea8a3efab643b08593452606e9fc10",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 2.0
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+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_67e29f7c8aeb49da9173fceb697ef79e",
+       "max": 2.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_73b2fa994e404a0c82ade246f335458e",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 2.0
       }
      },
-     "ee19d40b63aa4ef691061b855ac3b64e": {
+     "f44ce32c12344937939e56dd3617abe4": {
       "model_module": "@jupyter-widgets/base",
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diff --git a/docs/iceflow-with-icepyx.ipynb b/docs/iceflow-with-icepyx.ipynb
index b1cf1ae..2292543 100644
--- a/docs/iceflow-with-icepyx.ipynb
+++ b/docs/iceflow-with-icepyx.ipynb
@@ -47,10 +47,10 @@
    "id": "2ad99b0d-4e15-466c-b9aa-7bfa75ef5f26",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:54.192543Z",
-     "iopub.status.busy": "2024-12-09T17:35:54.192247Z",
-     "iopub.status.idle": "2024-12-09T17:35:56.824194Z",
-     "shell.execute_reply": "2024-12-09T17:35:56.823710Z"
+     "iopub.execute_input": "2025-05-12T20:03:32.732207Z",
+     "iopub.status.busy": "2025-05-12T20:03:32.732023Z",
+     "iopub.status.idle": "2025-05-12T20:03:37.400199Z",
+     "shell.execute_reply": "2025-05-12T20:03:37.399730Z"
     }
    },
    "outputs": [
@@ -58,7 +58,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_163498/2248149546.py:6: FutureWarning: icepyx v1.x is being deprecated; the back-end systems on which it relies\n",
+      "/tmp/ipykernel_3050068/1992169708.py:8: FutureWarning: icepyx v1.x is being deprecated; the back-end systems on which it relies\n",
       "will be shut down as of late 2024. At that time, upgrade to icepyx v2.x, which uses the\n",
       "new NASA Harmony back-end, will be required. Please see\n",
       "<https://icepyx.readthedocs.io/en/latest/user_guide/changelog/v1.3.0.html> for more\n",
@@ -100,7 +100,7 @@
        "  }\n",
        "\n",
        "  const force = true;\n",
-       "  const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
+       "  const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
        "  const reloading = false;\n",
        "  const Bokeh = root.Bokeh;\n",
        "\n",
@@ -260,7 +260,7 @@
        "    document.body.appendChild(element);\n",
        "  }\n",
        "\n",
-       "  const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.0.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/panel.min.js\"];\n",
+       "  const js_urls = [\"https://cdn.holoviz.org/panel/1.6.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.6.3/dist/panel.min.js\"];\n",
        "  const js_modules = [];\n",
        "  const js_exports = {};\n",
        "  const css_urls = [];\n",
@@ -341,7 +341,7 @@
        "  setTimeout(load_or_wait, 100)\n",
        "}(window));"
       ],
-      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n  const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = false;\n  const Bokeh = root.Bokeh;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.holoviz.org/panel/1.5.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.0.min.js\", \"https://cdn.holoviz.org/panel/1.5.2/dist/panel.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
+      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n  const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = false;\n  const Bokeh = root.Bokeh;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.holoviz.org/panel/1.6.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.6.3/dist/panel.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -420,7 +420,7 @@
        "            }\n",
        "            return messages.next().then(processIteratorResult);\n",
        "          }\n",
-       "        }) \n",
+       "        })\n",
        "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
        "          return comm_promise.then((comm) => {\n",
        "            comm.send(data, metadata, buffers, disposeOnDone);\n",
@@ -589,7 +589,7 @@
        "  }\n",
        "}\n"
       ],
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else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    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+      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -598,12 +598,12 @@
      "data": {
       "application/vnd.holoviews_exec.v0+json": "",
       "text/html": [
-       "<div id='p1002'>\n",
-       "  <div id=\"fd5fa07b-2a41-4632-a2ba-e551570685fd\" data-root-id=\"p1002\" style=\"display: contents;\"></div>\n",
+       "<div id='74630eba-1d1a-4d04-b6f6-b4beba09c190'>\n",
+       "  <div id=\"bc45f746-2296-4bbf-a9ac-f2bdba304838\" data-root-id=\"74630eba-1d1a-4d04-b6f6-b4beba09c190\" style=\"display: contents;\"></div>\n",
        "</div>\n",
        "<script type=\"application/javascript\">(function(root) {\n",
-       "  var docs_json = {\"c07d0974-a8ec-4e01-bb71-fc1684d0464b\":{\"version\":\"3.6.0\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"b98e7aa8a42346c6bd9d4cadf60c53f4\",\"client_comm_id\":\"69d7d2094d434330ad2c82a2c47d576f\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\"},{\"type\":\"model\",\"name\":\"JSComponent1\"},{\"type\":\"model\",\"name\":\"ReactComponent1\"},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\"},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
-       "  var render_items = [{\"docid\":\"c07d0974-a8ec-4e01-bb71-fc1684d0464b\",\"roots\":{\"p1002\":\"fd5fa07b-2a41-4632-a2ba-e551570685fd\"},\"root_ids\":[\"p1002\"]}];\n",
+       "  var docs_json = {\"d837e76e-7e11-46fe-8911-f52eddaae134\":{\"version\":\"3.7.3\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"74630eba-1d1a-4d04-b6f6-b4beba09c190\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"ab41d2ac-f1de-4e6c-8beb-8eb1e49f94fc\",\"attributes\":{\"plot_id\":\"74630eba-1d1a-4d04-b6f6-b4beba09c190\",\"comm_id\":\"9f1a30b2a96245d2b592f18d3a24166e\",\"client_comm_id\":\"3396ee58e8834348aff91926649819c6\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"max_notifications\",\"kind\":\"Any\",\"default\":5},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"d837e76e-7e11-46fe-8911-f52eddaae134\",\"roots\":{\"74630eba-1d1a-4d04-b6f6-b4beba09c190\":\"bc45f746-2296-4bbf-a9ac-f2bdba304838\"},\"root_ids\":[\"74630eba-1d1a-4d04-b6f6-b4beba09c190\"]}];\n",
        "  var docs = Object.values(docs_json)\n",
        "  if (!docs) {\n",
        "    return\n",
@@ -672,7 +672,7 @@
      },
      "metadata": {
       "application/vnd.holoviews_exec.v0+json": {
-       "id": "p1002"
+       "id": "74630eba-1d1a-4d04-b6f6-b4beba09c190"
       }
      },
      "output_type": "display_data"
@@ -770,8 +770,10 @@
    ],
    "source": [
     "# Imports\n",
-    "from pathlib import Path\n",
+    "from __future__ import annotations\n",
+    "\n",
     "import datetime as dt\n",
+    "from pathlib import Path\n",
     "\n",
     "import dask.dataframe as dd\n",
     "import icepyx as ipx\n",
@@ -781,12 +783,12 @@
     "import xarray as xr\n",
     "\n",
     "from nsidc.iceflow import (\n",
-    "    make_iceflow_parquet,\n",
-    "    DatasetSearchParameters,\n",
     "    BoundingBox,\n",
-    "    find_iceflow_data,\n",
+    "    DatasetSearchParameters,\n",
+    "    IceflowDataFrame,\n",
     "    download_iceflow_results,\n",
-    "    IceflowDataFrame\n",
+    "    find_iceflow_data,\n",
+    "    make_iceflow_parquet,\n",
     ")"
    ]
   },
@@ -808,18 +810,18 @@
    "id": "d7143806-d23d-421a-8b7a-3ca15b05f7cf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:56.825775Z",
-     "iopub.status.busy": "2024-12-09T17:35:56.825623Z",
-     "iopub.status.idle": "2024-12-09T17:35:56.828247Z",
-     "shell.execute_reply": "2024-12-09T17:35:56.827900Z"
+     "iopub.execute_input": "2025-05-12T20:03:37.402122Z",
+     "iopub.status.busy": "2025-05-12T20:03:37.401988Z",
+     "iopub.status.idle": "2025-05-12T20:03:37.404728Z",
+     "shell.execute_reply": "2025-05-12T20:03:37.404361Z"
     }
    },
    "outputs": [],
    "source": [
     "# Constants\n",
     "\n",
-    "# All of our data will be downloaded to this location. \n",
-    "OUTPUT_DIR =  Path(\"./downloaded-data/\")\n",
+    "# All of our data will be downloaded to this location.\n",
+    "OUTPUT_DIR = Path(\"./downloaded-data/\")\n",
     "\n",
     "# ICESat2 data products use ITRF2014 (e.g., see https://nsidc.org/data/atl06/versions/6):\n",
     "# > WGS 84 ellipsoid, ITRF2014 reference frame\n",
@@ -828,7 +830,12 @@
     "ICESAT2_ITRF = \"ITRF2014\"\n",
     "\n",
     "# This bounding box covers an area near Sermeq Kujalleq (Jakobshavn Isbrae)\n",
-    "BBOX = BoundingBox(lower_left_lon=-49.149, lower_left_lat=69.186, upper_right_lon=-48.949, upper_right_lat=69.238)\n",
+    "BBOX = BoundingBox(\n",
+    "    lower_left_lon=-49.149,\n",
+    "    lower_left_lat=69.186,\n",
+    "    upper_right_lon=-48.949,\n",
+    "    upper_right_lat=69.238,\n",
+    ")\n",
     "\n",
     "# Range of dates we want to evaluate\n",
     "DATE_RANGE = (dt.date(2007, 1, 1), dt.date(2024, 10, 28))"
@@ -850,10 +857,10 @@
    "id": "ee315618-32ad-4710-9629-0a3d2e2ed26b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:35:56.829407Z",
-     "iopub.status.busy": "2024-12-09T17:35:56.829265Z",
-     "iopub.status.idle": "2024-12-09T17:36:13.320654Z",
-     "shell.execute_reply": "2024-12-09T17:36:13.320060Z"
+     "iopub.execute_input": "2025-05-12T20:03:37.406552Z",
+     "iopub.status.busy": "2025-05-12T20:03:37.406386Z",
+     "iopub.status.idle": "2025-05-12T20:06:00.032573Z",
+     "shell.execute_reply": "2025-05-12T20:06:00.031840Z"
     }
    },
    "outputs": [
@@ -861,7 +868,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-12-09 10:36:09.114\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_find_iceflow_data\u001b[0m:\u001b[36m49\u001b[0m - \u001b[33m\u001b[1mFound no results for dataset.short_name='ILVIS2' dataset.version='1' with bounding_box=BoundingBox(lower_left_lon=-49.149, lower_left_lat=69.186, upper_right_lon=-48.949, upper_right_lat=69.238) temporal=(datetime.date(2007, 1, 1), datetime.date(2024, 10, 28))\u001b[0m\n"
+      "\u001b[32m2025-05-12 14:05:23.556\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_find_iceflow_data\u001b[0m:\u001b[36m49\u001b[0m - \u001b[33m\u001b[1mFound no results for dataset.short_name='ILVIS2' dataset.version='1' with bounding_box=BoundingBox(lower_left_lon=-49.149, lower_left_lat=69.186, upper_right_lon=-48.949, upper_right_lat=69.238) temporal=(datetime.date(2007, 1, 1), datetime.date(2024, 10, 28))\u001b[0m\n"
      ]
     },
     {
@@ -901,10 +908,10 @@
    "id": "84e53851-53a8-49e0-bce3-2789253498b6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:36:13.323061Z",
-     "iopub.status.busy": "2024-12-09T17:36:13.322806Z",
-     "iopub.status.idle": "2024-12-09T17:37:50.625700Z",
-     "shell.execute_reply": "2024-12-09T17:37:50.625180Z"
+     "iopub.execute_input": "2025-05-12T20:06:00.034779Z",
+     "iopub.status.busy": "2025-05-12T20:06:00.034525Z",
+     "iopub.status.idle": "2025-05-12T20:06:00.141046Z",
+     "shell.execute_reply": "2025-05-12T20:06:00.140680Z"
     }
    },
    "outputs": [
@@ -912,18 +919,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-12-09 10:36:13.324\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 4 granules to downloaded-data/ILATM1B_1.\u001b[0m\n"
+      "\u001b[32m2025-05-12 14:06:00.035\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 8 granules to downloaded-data/ILATM1B_1.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "53df9e177168414490ccff53eb90e7de",
+       "model_id": "e85dfe68fc454fee8be1dfe88f0e153a",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/4 [00:00<?, ?it/s]"
+       "QUEUEING TASKS | :   0%|          | 0/8 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -932,12 +939,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f67f65d982d74f4fb6532b8660ada12c",
+       "model_id": "eeb95fc8e2b04689a8c2535a0f686d97",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "PROCESSING TASKS | :   0%|          | 0/8 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -946,12 +953,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d488657e901d40fbbc87f151a4a4529e",
+       "model_id": "23fd18adf2fd4d548e412df3fed11939",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "COLLECTING RESULTS | :   0%|          | 0/8 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -961,18 +968,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-12-09 10:36:30.946\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 14 granules to downloaded-data/ILATM1B_2.\u001b[0m\n"
+      "\u001b[32m2025-05-12 14:06:00.064\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 28 granules to downloaded-data/ILATM1B_2.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3ecb098dfac34ee0bcfabe422b3aba88",
+       "model_id": "533017f24f17466da9081242d29c6658",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/14 [00:00<?, ?it/s]"
+       "QUEUEING TASKS | :   0%|          | 0/28 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -981,12 +988,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d365480879ee46e68db80d46cb40444e",
+       "model_id": "8ed31cec100242aebbaf1f7d9400aa2c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "PROCESSING TASKS | :   0%|          | 0/28 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -995,12 +1002,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "72f5a56fadaa40b2a8904fe86e3f333d",
+       "model_id": "0f02dad7d4574a9db43558e8dd2630d0",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/14 [00:00<?, ?it/s]"
+       "COLLECTING RESULTS | :   0%|          | 0/28 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1010,18 +1017,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-12-09 10:36:47.008\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 13 granules to downloaded-data/BLATM1B_1.\u001b[0m\n"
+      "\u001b[32m2025-05-12 14:06:00.091\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 26 granules to downloaded-data/BLATM1B_1.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d44038cacc8d42e8abfd86d0a4e826c6",
+       "model_id": "cd3c721bd10a4b26beabc626055d4cf5",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/13 [00:00<?, ?it/s]"
+       "QUEUEING TASKS | :   0%|          | 0/26 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1030,12 +1037,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8ac62732d56b427b8b117d0bbe51aa98",
+       "model_id": "823feef92dd747b498b5e4b6ec285e77",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/13 [00:00<?, ?it/s]"
+       "PROCESSING TASKS | :   0%|          | 0/26 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1044,12 +1051,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5c543e0ea83243e58bcb9469a0dacedb",
+       "model_id": "d68f2e35aa154f30bb83348214d93989",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/13 [00:00<?, ?it/s]"
+       "COLLECTING RESULTS | :   0%|          | 0/26 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1059,18 +1066,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-12-09 10:37:28.317\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 2 granules to downloaded-data/ILVIS2_2.\u001b[0m\n"
+      "\u001b[32m2025-05-12 14:06:00.110\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 4 granules to downloaded-data/ILVIS2_2.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1804ec2ca7fa4c5195d439dada4eba1c",
+       "model_id": "3a484b42b08546f795a3e86abb8256ab",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/2 [00:00<?, ?it/s]"
+       "QUEUEING TASKS | :   0%|          | 0/4 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1079,12 +1086,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b5449ac16c6e4fb7b1945fda779f515b",
+       "model_id": "0351ccc5f78b48b3bf8797a9c4d72429",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/2 [00:00<?, ?it/s]"
+       "PROCESSING TASKS | :   0%|          | 0/4 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1093,12 +1100,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c21cbd2f6343466eb15bc229b4fd245f",
+       "model_id": "6f14c20a99534ffca7be40b9c0260f61",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/2 [00:00<?, ?it/s]"
+       "COLLECTING RESULTS | :   0%|          | 0/4 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1108,18 +1115,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-12-09 10:37:46.713\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 8 granules to downloaded-data/GLAH06_034.\u001b[0m\n"
+      "\u001b[32m2025-05-12 14:06:00.125\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mDownloading 12 granules to downloaded-data/GLAH06_034.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "93b5a434ba33408fb7284a1a5a268b60",
+       "model_id": "36362a5e83bd47f1a1ebacfae3cccd22",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/8 [00:00<?, ?it/s]"
+       "QUEUEING TASKS | :   0%|          | 0/12 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1128,12 +1135,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb935d04a3fa4d70b17eecf880d6f78e",
+       "model_id": "f912ecc954da498e905e88f13a912ab7",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/8 [00:00<?, ?it/s]"
+       "PROCESSING TASKS | :   0%|          | 0/12 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1142,12 +1149,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fd5ccd1dca8a4bf5aff667fe5e46d41e",
+       "model_id": "e17f49240f1847b2a737199aaa7d0a72",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/8 [00:00<?, ?it/s]"
+       "COLLECTING RESULTS | :   0%|          | 0/12 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1176,13 +1183,21 @@
    "id": "17e03a49-405a-4d00-a08d-74e5890564da",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:37:50.627629Z",
-     "iopub.status.busy": "2024-12-09T17:37:50.627365Z",
-     "iopub.status.idle": "2024-12-09T17:42:56.593466Z",
-     "shell.execute_reply": "2024-12-09T17:42:56.592058Z"
+     "iopub.execute_input": "2025-05-12T20:06:00.142398Z",
+     "iopub.status.busy": "2025-05-12T20:06:00.142264Z",
+     "iopub.status.idle": "2025-05-12T20:11:54.974177Z",
+     "shell.execute_reply": "2025-05-12T20:11:54.965170Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-05-12 14:06:00.142\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.api\u001b[0m:\u001b[36mmake_iceflow_parquet\u001b[0m:\u001b[36m85\u001b[0m - \u001b[1mRemoving existing iceflow.parquet\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
     "parquet_path = make_iceflow_parquet(\n",
     "    data_dir=OUTPUT_DIR,\n",
@@ -1205,10 +1220,10 @@
    "id": "e29f9cf9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:42:56.613781Z",
-     "iopub.status.busy": "2024-12-09T17:42:56.613570Z",
-     "iopub.status.idle": "2024-12-09T17:43:01.607748Z",
-     "shell.execute_reply": "2024-12-09T17:43:01.607275Z"
+     "iopub.execute_input": "2025-05-12T20:11:54.997969Z",
+     "iopub.status.busy": "2025-05-12T20:11:54.997473Z",
+     "iopub.status.idle": "2025-05-12T20:12:02.457532Z",
+     "shell.execute_reply": "2025-05-12T20:12:02.456576Z"
     }
    },
    "outputs": [
@@ -1304,7 +1319,7 @@
    "source": [
     "iceflow_df = dd.read_parquet(parquet_path)\n",
     "\n",
-    "# Ensure that our index is set as a datetime object \n",
+    "# Ensure that our index is set as a datetime object\n",
     "iceflow_df = iceflow_df.reset_index()\n",
     "iceflow_df[\"utc_datetime\"] = dd.to_datetime(iceflow_df[\"utc_datetime\"])\n",
     "iceflow_df = iceflow_df.set_index(\"utc_datetime\")\n",
@@ -1330,17 +1345,17 @@
    "id": "bda00a2c-51dd-4d01-8ba4-4dcb0985931d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:43:01.615992Z",
-     "iopub.status.busy": "2024-12-09T17:43:01.615838Z",
-     "iopub.status.idle": "2024-12-09T17:43:02.136180Z",
-     "shell.execute_reply": "2024-12-09T17:43:02.135900Z"
+     "iopub.execute_input": "2025-05-12T20:12:02.466716Z",
+     "iopub.status.busy": "2025-05-12T20:12:02.466105Z",
+     "iopub.status.idle": "2025-05-12T20:12:02.952111Z",
+     "shell.execute_reply": "2025-05-12T20:12:02.951428Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<icepyx.core.query.Query at 0x7299a33de930>"
+       "<icepyx.core.query.Query at 0x7c2963d91790>"
       ]
      },
      "execution_count": 7,
@@ -1379,10 +1394,10 @@
    "id": "0c146fa2-fdb9-4774-a6bf-481e2498948a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:43:02.137768Z",
-     "iopub.status.busy": "2024-12-09T17:43:02.137627Z",
-     "iopub.status.idle": "2024-12-09T17:43:35.631894Z",
-     "shell.execute_reply": "2024-12-09T17:43:35.631432Z"
+     "iopub.execute_input": "2025-05-12T20:12:02.954029Z",
+     "iopub.status.busy": "2025-05-12T20:12:02.953846Z",
+     "iopub.status.idle": "2025-05-12T20:12:38.930580Z",
+     "shell.execute_reply": "2025-05-12T20:12:38.930184Z"
     }
    },
    "outputs": [
@@ -1390,7 +1405,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total number of data order requests is  1  for  85  granules.\n",
+      "Total number of data order requests is  1  for  88  granules.\n",
       "Data request  1  of  1  is submitting to NSIDC\n"
      ]
     },
@@ -1398,7 +1413,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "order ID:  5000005880685\n"
+      "order ID:  5000006224221\n"
      ]
     },
     {
@@ -1471,6 +1486,8 @@
       " 'Granule 306923340 contained no data within the spatial and/or temporal '\n",
       " 'subset constraints to be processed',\n",
       " 'Granule 309516749 contained no data within the spatial and/or temporal '\n",
+      " 'subset constraints to be processed',\n",
+      " 'Granule 315303352 contained no data within the spatial and/or temporal '\n",
       " 'subset constraints to be processed']\n",
       "Beginning download of zipped output...\n"
      ]
@@ -1479,7 +1496,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Data request 5000005880685 of  1  order(s) is downloaded.\n"
+      "Data request 5000006224221 of  1  order(s) is downloaded.\n"
      ]
     },
     {
@@ -1512,10 +1529,10 @@
    "id": "081376b5-308b-43c3-bc6d-b9c4342586d0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:43:35.633698Z",
-     "iopub.status.busy": "2024-12-09T17:43:35.633528Z",
-     "iopub.status.idle": "2024-12-09T17:43:53.899294Z",
-     "shell.execute_reply": "2024-12-09T17:43:53.898753Z"
+     "iopub.execute_input": "2025-05-12T20:12:38.932139Z",
+     "iopub.status.busy": "2025-05-12T20:12:38.931922Z",
+     "iopub.status.idle": "2025-05-12T20:13:29.811650Z",
+     "shell.execute_reply": "2025-05-12T20:13:29.810925Z"
     }
    },
    "outputs": [
@@ -1570,6 +1587,13 @@
       "file=PosixPath('downloaded-data/ATL06/processed_ATL06_20200208080743_06660605_006_01.h5') contains an error and will not be read\n"
      ]
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "file=PosixPath('downloaded-data/ATL06/processed_ATL06_20240930230647_02242505_006_02.h5') contains an error and will not be read\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -1581,7 +1605,7 @@
     {
      "data": {
       "text/plain": [
-       "49"
+       "50"
       ]
      },
      "execution_count": 9,
@@ -1597,7 +1621,7 @@
     "        reader.vars.append(var_list=[\"h_li\", \"latitude\", \"longitude\"])\n",
     "        ds = reader.load()\n",
     "        datasets.append(ds)\n",
-    "    except:\n",
+    "    except Exception:\n",
     "        print(f\"{file=} contains an error and will not be read\")\n",
     "        continue\n",
     "\n",
@@ -1610,10 +1634,10 @@
    "id": "dc7fc4b2-37ee-4f44-a21e-d6225eccc0f8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:43:53.901216Z",
-     "iopub.status.busy": "2024-12-09T17:43:53.901069Z",
-     "iopub.status.idle": "2024-12-09T17:43:53.980260Z",
-     "shell.execute_reply": "2024-12-09T17:43:53.979962Z"
+     "iopub.execute_input": "2025-05-12T20:13:29.812918Z",
+     "iopub.status.busy": "2025-05-12T20:13:29.812786Z",
+     "iopub.status.idle": "2025-05-12T20:13:29.898204Z",
+     "shell.execute_reply": "2025-05-12T20:13:29.897723Z"
     }
    },
    "outputs": [
@@ -1650,14 +1674,14 @@
        "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "html[data-theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
        "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-border-color: #1f1f1f;\n",
        "  --xr-disabled-color: #515151;\n",
        "  --xr-background-color: #111111;\n",
        "  --xr-background-color-row-even: #111111;\n",
@@ -1712,6 +1736,7 @@
        ".xr-section-item input {\n",
        "  display: inline-block;\n",
        "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -1748,7 +1773,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -1759,7 +1784,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -1831,15 +1856,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -2002,8 +2027,8 @@
        "    cycle_number         (gran_idx) int8 5B 8 15 2 19 5\n",
        "    rgt                  (gran_idx) int16 10B 1222 666 1222 224 224\n",
        "    atlas_sdp_gps_epoch  (gran_idx) datetime64[ns] 40B 2018-01-01T00:00:18 .....\n",
-       "    data_start_utc       (gran_idx) datetime64[ns] 40B 2020-09-13T09:02:02.96...\n",
-       "    data_end_utc         (gran_idx) datetime64[ns] 40B 2020-09-13T09:07:14.25...\n",
+       "    data_start_utc       (gran_idx) datetime64[us] 40B 2020-09-13T09:02:02.96...\n",
+       "    data_end_utc         (gran_idx) datetime64[us] 40B 2020-09-13T09:07:14.25...\n",
        "    h_li                 (spot, gran_idx, photon_idx) float32 500B nan ... nan\n",
        "    latitude             (spot, gran_idx, photon_idx) float64 1kB nan ... nan\n",
        "    longitude            (spot, gran_idx, photon_idx) float64 1kB nan ... nan\n",
@@ -2011,12 +2036,12 @@
        "Attributes:\n",
        "    data_product:  ATL06\n",
        "    Description:   The land_ice_height group contains the primary set of deri...\n",
-       "    data_rate:     Data within this group are sparse.  Data values are provid...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c05e0464-bb6a-4500-9b4a-b23c95eeb519' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c05e0464-bb6a-4500-9b4a-b23c95eeb519' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>gran_idx</span>: 5</li><li><span class='xr-has-index'>spot</span>: 5</li><li><span class='xr-has-index'>photon_idx</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5cb26bfe-b4ee-423e-a512-1ad33ed0d5c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5cb26bfe-b4ee-423e-a512-1ad33ed0d5c4' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>photon_idx</span></div><div class='xr-var-dims'>(photon_idx)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-aad1955a-bfaf-471a-87c7-95eade336bd4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aad1955a-bfaf-471a-87c7-95eade336bd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21768111-2029-4ef2-bab0-b9b3d729e77a' class='xr-var-data-in' type='checkbox'><label for='data-21768111-2029-4ef2-bab0-b9b3d729e77a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>spot</span></div><div class='xr-var-dims'>(spot)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-8056edb7-575b-40a5-a828-c0adf13035ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8056edb7-575b-40a5-a828-c0adf13035ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a9fafdc-86ff-4ccf-bb1d-7db29eb0ac99' class='xr-var-data-in' type='checkbox'><label for='data-0a9fafdc-86ff-4ccf-bb1d-7db29eb0ac99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gran_idx</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>122207 66614 122201 22418 22404</div><input id='attrs-eefa7514-2cf6-4089-b0b8-0813c87574d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eefa7514-2cf6-4089-b0b8-0813c87574d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e03c6d8-c67a-431f-b65f-df18d2dfe9ed' class='xr-var-data-in' type='checkbox'><label for='data-6e03c6d8-c67a-431f-b65f-df18d2dfe9ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([122207,  66614, 122201,  22418,  22404], dtype=uint64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>source_file</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>&lt;U71</div><div class='xr-var-preview xr-preview'>&#x27;downloaded-data/ATL06/processed...</div><input id='attrs-d660cc46-5ead-4508-8c92-57abc32365bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d660cc46-5ead-4508-8c92-57abc32365bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee5678ef-2f6c-4b45-b4b1-b2b19a7a99f5' class='xr-var-data-in' type='checkbox'><label for='data-ee5678ef-2f6c-4b45-b4b1-b2b19a7a99f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;downloaded-data/ATL06/processed_ATL06_20200913090149_12220803_006_02.h5&#x27;,\n",
+       "    data_rate:     Data within this group are sparse.  Data values are provid...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-97939e09-f23a-4527-a514-1b039cabdc13' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-97939e09-f23a-4527-a514-1b039cabdc13' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>gran_idx</span>: 5</li><li><span class='xr-has-index'>spot</span>: 5</li><li><span class='xr-has-index'>photon_idx</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-97357ff9-a700-4c1b-ac73-343da7465159' class='xr-section-summary-in' type='checkbox'  checked><label for='section-97357ff9-a700-4c1b-ac73-343da7465159' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>photon_idx</span></div><div class='xr-var-dims'>(photon_idx)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-c9476def-c9ba-433e-ba34-99f31352050f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9476def-c9ba-433e-ba34-99f31352050f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66143ba3-96ed-47c2-af3f-1210fa044380' class='xr-var-data-in' type='checkbox'><label for='data-66143ba3-96ed-47c2-af3f-1210fa044380' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>spot</span></div><div class='xr-var-dims'>(spot)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-718e32d0-6137-44d4-b4e2-771b3c3f6e65' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-718e32d0-6137-44d4-b4e2-771b3c3f6e65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20ab1265-62be-4965-937f-2e6bbefd904a' class='xr-var-data-in' type='checkbox'><label for='data-20ab1265-62be-4965-937f-2e6bbefd904a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gran_idx</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>122207 66614 122201 22418 22404</div><input id='attrs-4dcee810-9f45-469b-b166-9daecb9f1fb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4dcee810-9f45-469b-b166-9daecb9f1fb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecaec701-b408-44ba-81f3-0dc5cd9eb82d' class='xr-var-data-in' type='checkbox'><label for='data-ecaec701-b408-44ba-81f3-0dc5cd9eb82d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([122207,  66614, 122201,  22418,  22404], dtype=uint64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>source_file</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>&lt;U71</div><div class='xr-var-preview xr-preview'>&#x27;downloaded-data/ATL06/processed...</div><input id='attrs-263819cf-a153-4423-9e38-2ca893e59aa8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-263819cf-a153-4423-9e38-2ca893e59aa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b0fda69-e8dc-4c4d-8ba5-296963c7d9d0' class='xr-var-data-in' type='checkbox'><label for='data-2b0fda69-e8dc-4c4d-8ba5-296963c7d9d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;downloaded-data/ATL06/processed_ATL06_20200913090149_12220803_006_02.h5&#x27;,\n",
        "       &#x27;downloaded-data/ATL06/processed_ATL06_20220505170624_06661505_006_01.h5&#x27;,\n",
        "       &#x27;downloaded-data/ATL06/processed_ATL06_20190318110313_12220203_006_02.h5&#x27;,\n",
        "       &#x27;downloaded-data/ATL06/processed_ATL06_20230405010947_02241905_006_02.h5&#x27;,\n",
        "       &#x27;downloaded-data/ATL06/processed_ATL06_20191011135158_02240505_006_02.h5&#x27;],\n",
-       "      dtype=&#x27;&lt;U71&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta_time</span></div><div class='xr-var-dims'>(gran_idx, photon_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-09-13T09:04:22.172388928 .....</div><input id='attrs-cfb8cb82-2486-4f8d-b54d-ce741e5eca30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfb8cb82-2486-4f8d-b54d-ce741e5eca30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38c5488d-c812-473c-877e-aee985bcd927' class='xr-var-data-in' type='checkbox'><label for='data-38c5488d-c812-473c-877e-aee985bcd927' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>referenceInformation</dd><dt><span>description :</span></dt><dd>Number of GPS seconds since the ATLAS SDP epoch. The ATLAS Standard Data Products (SDP) epoch offset is defined within /ancillary_data/atlas_sdp_gps_epoch as the number of GPS seconds between the GPS epoch (1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP epoch. By adding the offset contained within atlas_sdp_gps_epoch to delta time parameters, the time in gps_seconds relative to the GPS epoch can be computed.</dd><dt><span>long_name :</span></dt><dd>Elapsed GPS seconds</dd><dt><span>source :</span></dt><dd>section 4.4</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2020-09-13T09:04:22.172388928&#x27;, &#x27;2020-09-13T09:04:22.175207936&#x27;,\n",
+       "      dtype=&#x27;&lt;U71&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta_time</span></div><div class='xr-var-dims'>(gran_idx, photon_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-09-13T09:04:22.172388928 .....</div><input id='attrs-20feea3a-78bd-4430-9811-e314c2fad934' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20feea3a-78bd-4430-9811-e314c2fad934' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8532f22-1df6-4aad-8560-075e303ebd47' class='xr-var-data-in' type='checkbox'><label for='data-b8532f22-1df6-4aad-8560-075e303ebd47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>referenceInformation</dd><dt><span>description :</span></dt><dd>Number of GPS seconds since the ATLAS SDP epoch. The ATLAS Standard Data Products (SDP) epoch offset is defined within /ancillary_data/atlas_sdp_gps_epoch as the number of GPS seconds between the GPS epoch (1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP epoch. By adding the offset contained within atlas_sdp_gps_epoch to delta time parameters, the time in gps_seconds relative to the GPS epoch can be computed.</dd><dt><span>long_name :</span></dt><dd>Elapsed GPS seconds</dd><dt><span>source :</span></dt><dd>section 4.4</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2020-09-13T09:04:22.172388928&#x27;, &#x27;2020-09-13T09:04:22.175207936&#x27;,\n",
        "        &#x27;2020-09-13T09:04:22.178032944&#x27;, &#x27;2020-09-13T09:04:22.180859024&#x27;,\n",
        "        &#x27;2020-09-13T09:04:22.183685712&#x27;],\n",
        "       [&#x27;2022-05-05T17:09:19.400209344&#x27;, &#x27;2022-05-05T17:09:19.403033312&#x27;,\n",
@@ -2030,13 +2055,13 @@
        "        &#x27;2023-04-05T01:12:36.659772672&#x27;],\n",
        "       [&#x27;2019-10-11T13:54:48.679390608&#x27;, &#x27;2019-10-11T13:54:48.738590504&#x27;,\n",
        "        &#x27;2019-10-11T13:54:48.741417288&#x27;, &#x27;2019-10-11T13:54:48.755482168&#x27;,\n",
-       "                                  &#x27;NaT&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4edbc1ee-2eda-4684-9a6e-2ac5bace2fee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4edbc1ee-2eda-4684-9a6e-2ac5bace2fee' class='xr-section-summary' >Data variables: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sc_orient</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>0 0 0 0 1</div><input id='attrs-a033c5cb-9bb5-41ab-bbc6-ebfc7c93b286' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a033c5cb-9bb5-41ab-bbc6-ebfc7c93b286' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-087393dc-618c-4884-979c-966a3a4caa64' class='xr-var-data-in' type='checkbox'><label for='data-087393dc-618c-4884-979c-966a3a4caa64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 1], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cycle_number</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>8 15 2 19 5</div><input id='attrs-5f318f22-ecc2-4a52-a267-db0516b3ef46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f318f22-ecc2-4a52-a267-db0516b3ef46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f72fe1e2-24fe-4d95-bc50-867c3f5d4871' class='xr-var-data-in' type='checkbox'><label for='data-f72fe1e2-24fe-4d95-bc50-867c3f5d4871' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 8, 15,  2, 19,  5], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rgt</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>1222 666 1222 224 224</div><input id='attrs-a4961e42-0ec4-4d97-af07-71e25d0bb7d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4961e42-0ec4-4d97-af07-71e25d0bb7d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8a08ad3-426b-490d-abc5-328fd49d10aa' class='xr-var-data-in' type='checkbox'><label for='data-c8a08ad3-426b-490d-abc5-328fd49d10aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1222,  666, 1222,  224,  224], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atlas_sdp_gps_epoch</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01T00:00:18 ... 2018-01-...</div><input id='attrs-3c1af49d-a042-47c6-bc1e-a00cc1a88215' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c1af49d-a042-47c6-bc1e-a00cc1a88215' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30326f61-964b-4cd4-b883-8742ff59e562' class='xr-var-data-in' type='checkbox'><label for='data-30326f61-964b-4cd4-b883-8742ff59e562' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:00:18.000000000&#x27;, &#x27;2018-01-01T00:00:18.000000000&#x27;,\n",
+       "                                  &#x27;NaT&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-31c39271-f937-4c83-8863-c595ea0bb8b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31c39271-f937-4c83-8863-c595ea0bb8b1' class='xr-section-summary' >Data variables: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sc_orient</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>0 0 0 0 1</div><input id='attrs-45d0b0af-8b15-440b-beb9-2ce3b221d970' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45d0b0af-8b15-440b-beb9-2ce3b221d970' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d7defc7-2547-4c5e-a450-13b5e9830ee5' class='xr-var-data-in' type='checkbox'><label for='data-7d7defc7-2547-4c5e-a450-13b5e9830ee5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 1], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cycle_number</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>8 15 2 19 5</div><input id='attrs-5912ca07-675d-4a71-a789-11455644c04b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5912ca07-675d-4a71-a789-11455644c04b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1080065-8372-458c-8b60-92970b7170ed' class='xr-var-data-in' type='checkbox'><label for='data-c1080065-8372-458c-8b60-92970b7170ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 8, 15,  2, 19,  5], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rgt</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>1222 666 1222 224 224</div><input id='attrs-057995a5-6060-40a6-bab0-814c2438ea0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-057995a5-6060-40a6-bab0-814c2438ea0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d438e16-7ebe-4ccd-8e5f-f3b32e3067c9' class='xr-var-data-in' type='checkbox'><label for='data-8d438e16-7ebe-4ccd-8e5f-f3b32e3067c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1222,  666, 1222,  224,  224], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atlas_sdp_gps_epoch</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01T00:00:18 ... 2018-01-...</div><input id='attrs-9c47e6d2-ec25-4e75-bb26-f080b97caf6e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c47e6d2-ec25-4e75-bb26-f080b97caf6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad466717-f7ec-4070-a9da-e82940be3c97' class='xr-var-data-in' type='checkbox'><label for='data-ad466717-f7ec-4070-a9da-e82940be3c97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:00:18.000000000&#x27;, &#x27;2018-01-01T00:00:18.000000000&#x27;,\n",
        "       &#x27;2018-01-01T00:00:18.000000000&#x27;, &#x27;2018-01-01T00:00:18.000000000&#x27;,\n",
-       "       &#x27;2018-01-01T00:00:18.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_start_utc</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-09-13T09:02:02.965879 ... 2...</div><input id='attrs-3d386509-f7bb-4f1a-ae5c-b9fb8f9f0d44' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d386509-f7bb-4f1a-ae5c-b9fb8f9f0d44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68a8a1c8-74a1-4e9e-a0f4-b1d5ce4b896e' class='xr-var-data-in' type='checkbox'><label for='data-68a8a1c8-74a1-4e9e-a0f4-b1d5ce4b896e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-09-13T09:02:02.965879000&#x27;, &#x27;2022-05-05T17:06:28.197561000&#x27;,\n",
-       "       &#x27;2019-03-18T11:03:27.347613000&#x27;, &#x27;2023-04-05T01:09:45.201298000&#x27;,\n",
-       "       &#x27;2019-10-11T13:51:57.388971000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_end_utc</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-09-13T09:07:14.250132 ... 2...</div><input id='attrs-5219aae4-e700-480c-bc53-01b28704861c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5219aae4-e700-480c-bc53-01b28704861c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6207463-cf99-4f45-add3-1017eb4aea5f' class='xr-var-data-in' type='checkbox'><label for='data-d6207463-cf99-4f45-add3-1017eb4aea5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-09-13T09:07:14.250132000&#x27;, &#x27;2022-05-05T17:11:08.953275000&#x27;,\n",
-       "       &#x27;2019-03-18T11:08:38.956207000&#x27;, &#x27;2023-04-05T01:14:22.410260000&#x27;,\n",
-       "       &#x27;2019-10-11T13:56:34.156353000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_li</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-3ad45cac-fe4a-4173-8372-e4b34fcfcbce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ad45cac-fe4a-4173-8372-e4b34fcfcbce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7eddfc83-9a97-4b47-bd91-9892ebfbeb21' class='xr-var-data-in' type='checkbox'><label for='data-7eddfc83-9a97-4b47-bd91-9892ebfbeb21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Standard land-ice segment height determined by land ice algorithm, corrected for first-photon bias, representing the median- based height of the selected PEs</dd><dt><span>long_name :</span></dt><dd>Land Ice height</dd><dt><span>source :</span></dt><dd>section 4.4</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([[[      nan,       nan,       nan,       nan,       nan],\n",
+       "       &#x27;2018-01-01T00:00:18.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_start_utc</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[us]</div><div class='xr-var-preview xr-preview'>2020-09-13T09:02:02.965879 ... 2...</div><input id='attrs-0ad9ba96-cbe1-4815-b796-62488a1e19c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ad9ba96-cbe1-4815-b796-62488a1e19c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd7f29a0-280c-4ea5-986b-46d37f06e848' class='xr-var-data-in' type='checkbox'><label for='data-bd7f29a0-280c-4ea5-986b-46d37f06e848' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-09-13T09:02:02.965879&#x27;, &#x27;2022-05-05T17:06:28.197561&#x27;,\n",
+       "       &#x27;2019-03-18T11:03:27.347613&#x27;, &#x27;2023-04-05T01:09:45.201298&#x27;,\n",
+       "       &#x27;2019-10-11T13:51:57.388971&#x27;], dtype=&#x27;datetime64[us]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_end_utc</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[us]</div><div class='xr-var-preview xr-preview'>2020-09-13T09:07:14.250132 ... 2...</div><input id='attrs-b82810ed-f3fc-4b66-bee1-ead544305b64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b82810ed-f3fc-4b66-bee1-ead544305b64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd7c83c9-498e-45dc-a2d7-e8e98ed47c8c' class='xr-var-data-in' type='checkbox'><label for='data-bd7c83c9-498e-45dc-a2d7-e8e98ed47c8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-09-13T09:07:14.250132&#x27;, &#x27;2022-05-05T17:11:08.953275&#x27;,\n",
+       "       &#x27;2019-03-18T11:08:38.956207&#x27;, &#x27;2023-04-05T01:14:22.410260&#x27;,\n",
+       "       &#x27;2019-10-11T13:56:34.156353&#x27;], dtype=&#x27;datetime64[us]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_li</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-7b174cf7-bb90-448e-8f03-c6aacb62625d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b174cf7-bb90-448e-8f03-c6aacb62625d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cbbabe5-4823-4fce-a3cd-17d4cedcc884' class='xr-var-data-in' type='checkbox'><label for='data-5cbbabe5-4823-4fce-a3cd-17d4cedcc884' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Standard land-ice segment height determined by land ice algorithm, corrected for first-photon bias, representing the median- based height of the selected PEs</dd><dt><span>long_name :</span></dt><dd>Land Ice height</dd><dt><span>source :</span></dt><dd>section 4.4</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([[[      nan,       nan,       nan,       nan,       nan],\n",
        "        [      nan,       nan,       nan,       nan,       nan],\n",
        "        [      nan,       nan,       nan,       nan,       nan],\n",
        "        [791.1915 , 791.431  , 791.43225, 791.37   , 791.3978 ],\n",
@@ -2065,7 +2090,7 @@
        "        [815.0689 , 810.5152 , 806.9575 , 806.5851 , 808.393  ],\n",
        "        [      nan,       nan,       nan,       nan,       nan],\n",
        "        [      nan,       nan,       nan,       nan,       nan]]],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-89b0df19-106f-41e9-a6bd-c84130294547' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89b0df19-106f-41e9-a6bd-c84130294547' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f30d2872-bc7e-41d8-840f-112b81757cbf' class='xr-var-data-in' type='checkbox'><label for='data-f30d2872-bc7e-41d8-840f-112b81757cbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Latitude of segment center, WGS84, North=+,</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>source :</span></dt><dd>section 3.10</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan,         nan,\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-346450db-d6b9-4ac5-8392-f2142621adab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-346450db-d6b9-4ac5-8392-f2142621adab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c80acb0c-8814-4b11-bbbf-b063e37c171d' class='xr-var-data-in' type='checkbox'><label for='data-c80acb0c-8814-4b11-bbbf-b063e37c171d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Latitude of segment center, WGS84, North=+,</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>source :</span></dt><dd>section 3.10</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan,         nan,\n",
        "                 nan],\n",
        "        [        nan,         nan,         nan,         nan,\n",
        "                 nan],\n",
@@ -2105,7 +2130,7 @@
        "        [        nan,         nan,         nan,         nan,\n",
        "                 nan],\n",
        "        [        nan,         nan,         nan,         nan,\n",
-       "                 nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-3164896e-e401-40f7-96a5-8a69e88222df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3164896e-e401-40f7-96a5-8a69e88222df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-380f809d-2273-4062-9c0a-7696fe569709' class='xr-var-data-in' type='checkbox'><label for='data-380f809d-2273-4062-9c0a-7696fe569709' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Longitude of segment center, , WGS84, East=+</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>source :</span></dt><dd>section 3.10</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan,          nan,\n",
+       "                 nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-dd8569de-ee3b-4a30-b237-223d10a3cfbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd8569de-ee3b-4a30-b237-223d10a3cfbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d948d07e-e700-4137-84d1-fc12f5a4a8eb' class='xr-var-data-in' type='checkbox'><label for='data-d948d07e-e700-4137-84d1-fc12f5a4a8eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Longitude of segment center, , WGS84, East=+</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>source :</span></dt><dd>section 3.10</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan,          nan,\n",
        "                  nan],\n",
        "        [         nan,          nan,          nan,          nan,\n",
        "                  nan],\n",
@@ -2145,11 +2170,11 @@
        "        [         nan,          nan,          nan,          nan,\n",
        "                  nan],\n",
        "        [         nan,          nan,          nan,          nan,\n",
-       "                  nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gt</span></div><div class='xr-var-dims'>(gran_idx, spot)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>nan nan &#x27;gt2l&#x27; ... nan nan nan</div><input id='attrs-d7375128-e2ad-4457-a215-e5cf07595c37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7375128-e2ad-4457-a215-e5cf07595c37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2b2d331-2f21-4031-9b07-501c60ccb0ba' class='xr-var-data-in' type='checkbox'><label for='data-c2b2d331-2f21-4031-9b07-501c60ccb0ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, &#x27;gt2l&#x27;, &#x27;gt2r&#x27;, &#x27;gt3l&#x27;],\n",
+       "                  nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gt</span></div><div class='xr-var-dims'>(gran_idx, spot)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>nan nan &#x27;gt2l&#x27; ... nan nan nan</div><input id='attrs-930b7128-3f34-4a99-a50e-1c79a4541acc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-930b7128-3f34-4a99-a50e-1c79a4541acc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad20cf5d-d17e-461f-99a2-74a37edfcfc5' class='xr-var-data-in' type='checkbox'><label for='data-ad20cf5d-d17e-461f-99a2-74a37edfcfc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, &#x27;gt2l&#x27;, &#x27;gt2r&#x27;, &#x27;gt3l&#x27;],\n",
        "       [nan, nan, &#x27;gt2l&#x27;, &#x27;gt2r&#x27;, &#x27;gt3l&#x27;],\n",
        "       [nan, nan, nan, nan, &#x27;gt3l&#x27;],\n",
        "       [&#x27;gt1l&#x27;, &#x27;gt1r&#x27;, nan, nan, nan],\n",
-       "       [nan, nan, nan, nan, nan]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2cbc249f-df24-43ea-ba19-842623d5e788' class='xr-section-summary-in' type='checkbox'  ><label for='section-2cbc249f-df24-43ea-ba19-842623d5e788' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>photon_idx</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b456f067-3899-43a2-8b75-4b400cc0c4a2' class='xr-index-data-in' type='checkbox'/><label for='index-b456f067-3899-43a2-8b75-4b400cc0c4a2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;photon_idx&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>spot</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fd648681-679d-4f8d-bfe4-6a3da7278b07' class='xr-index-data-in' type='checkbox'/><label for='index-fd648681-679d-4f8d-bfe4-6a3da7278b07' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5], dtype=&#x27;uint8&#x27;, name=&#x27;spot&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gran_idx</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ea1e17a4-4a4c-4f63-9033-eb2d53975907' class='xr-index-data-in' type='checkbox'/><label for='index-ea1e17a4-4a4c-4f63-9033-eb2d53975907' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([122207, 66614, 122201, 22418, 22404], dtype=&#x27;uint64&#x27;, name=&#x27;gran_idx&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e62617f-9351-4ca9-ba20-16b28b1c8206' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e62617f-9351-4ca9-ba20-16b28b1c8206' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data_product :</span></dt><dd>ATL06</dd><dt><span>Description :</span></dt><dd>The land_ice_height group contains the primary set of derived ATL06 products. This includes geolocation, height, and standard error and quality measures for each segment. This group is sparse, meaning that parameters are provided only for pairs of segments for which at least one beam has a valid surface-height measurement.</dd><dt><span>data_rate :</span></dt><dd>Data within this group are sparse.  Data values are provided only for those ICESat-2 20m segments where at least one beam has a valid land ice height measurement.</dd></dl></div></li></ul></div></div>"
+       "       [nan, nan, nan, nan, nan]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-50b732f4-2370-4ed4-be13-0a2b60b81539' class='xr-section-summary-in' type='checkbox'  ><label for='section-50b732f4-2370-4ed4-be13-0a2b60b81539' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>photon_idx</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bcd4b572-f1e3-4942-83ea-5c474d75c0c2' class='xr-index-data-in' type='checkbox'/><label for='index-bcd4b572-f1e3-4942-83ea-5c474d75c0c2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;photon_idx&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>spot</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7df86c7d-8a57-4c64-b55b-bea031deec19' class='xr-index-data-in' type='checkbox'/><label for='index-7df86c7d-8a57-4c64-b55b-bea031deec19' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5], dtype=&#x27;uint8&#x27;, name=&#x27;spot&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gran_idx</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4a96975f-46fe-4319-9e26-66bb2dfa565c' class='xr-index-data-in' type='checkbox'/><label for='index-4a96975f-46fe-4319-9e26-66bb2dfa565c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([122207, 66614, 122201, 22418, 22404], dtype=&#x27;uint64&#x27;, name=&#x27;gran_idx&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f573ad01-28f2-458a-9c3d-d07dbfbc604b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f573ad01-28f2-458a-9c3d-d07dbfbc604b' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data_product :</span></dt><dd>ATL06</dd><dt><span>Description :</span></dt><dd>The land_ice_height group contains the primary set of derived ATL06 products. This includes geolocation, height, and standard error and quality measures for each segment. This group is sparse, meaning that parameters are provided only for pairs of segments for which at least one beam has a valid surface-height measurement.</dd><dt><span>data_rate :</span></dt><dd>Data within this group are sparse.  Data values are provided only for those ICESat-2 20m segments where at least one beam has a valid land ice height measurement.</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 5kB\n",
@@ -2165,8 +2190,8 @@
        "    cycle_number         (gran_idx) int8 5B 8 15 2 19 5\n",
        "    rgt                  (gran_idx) int16 10B 1222 666 1222 224 224\n",
        "    atlas_sdp_gps_epoch  (gran_idx) datetime64[ns] 40B 2018-01-01T00:00:18 .....\n",
-       "    data_start_utc       (gran_idx) datetime64[ns] 40B 2020-09-13T09:02:02.96...\n",
-       "    data_end_utc         (gran_idx) datetime64[ns] 40B 2020-09-13T09:07:14.25...\n",
+       "    data_start_utc       (gran_idx) datetime64[us] 40B 2020-09-13T09:02:02.96...\n",
+       "    data_end_utc         (gran_idx) datetime64[us] 40B 2020-09-13T09:07:14.25...\n",
        "    h_li                 (spot, gran_idx, photon_idx) float32 500B nan ... nan\n",
        "    latitude             (spot, gran_idx, photon_idx) float64 1kB nan ... nan\n",
        "    longitude            (spot, gran_idx, photon_idx) float64 1kB nan ... nan\n",
@@ -2203,10 +2228,10 @@
    "id": "09a51188-be09-486d-be10-a7bede9d02ce",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:43:53.982508Z",
-     "iopub.status.busy": "2024-12-09T17:43:53.982391Z",
-     "iopub.status.idle": "2024-12-09T17:43:53.984774Z",
-     "shell.execute_reply": "2024-12-09T17:43:53.984480Z"
+     "iopub.execute_input": "2025-05-12T20:13:29.901247Z",
+     "iopub.status.busy": "2025-05-12T20:13:29.900905Z",
+     "iopub.status.idle": "2025-05-12T20:13:29.905154Z",
+     "shell.execute_reply": "2025-05-12T20:13:29.904727Z"
     }
    },
    "outputs": [],
@@ -2228,10 +2253,10 @@
    "id": "422d0a32-b676-4fe2-89ba-14f31bba6e80",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:43:53.986401Z",
-     "iopub.status.busy": "2024-12-09T17:43:53.986299Z",
-     "iopub.status.idle": "2024-12-09T17:43:54.090326Z",
-     "shell.execute_reply": "2024-12-09T17:43:54.090006Z"
+     "iopub.execute_input": "2025-05-12T20:13:29.907026Z",
+     "iopub.status.busy": "2025-05-12T20:13:29.906593Z",
+     "iopub.status.idle": "2025-05-12T20:13:30.052563Z",
+     "shell.execute_reply": "2025-05-12T20:13:30.052160Z"
     },
     "scrolled": true
    },
@@ -2360,8 +2385,8 @@
     "# Drop rows where lat, lon, or elev are missing.\n",
     "df = df.dropna(subset=[\"latitude\", \"longitude\", \"elevation\"], how=\"any\")\n",
     "df = df.set_index(\"utc_datetime\")\n",
-    "# Cast the df as an IceflowDataFrame. \n",
-    "# The `atl06_df` can now be used with e.g., `iceflow`'s ITRF conversion function \n",
+    "# Cast the df as an IceflowDataFrame.\n",
+    "# The `atl06_df` can now be used with e.g., `iceflow`'s ITRF conversion function\n",
     "# to perform plate motion model adjustments if desired.\n",
     "atl06_df = IceflowDataFrame(df)\n",
     "atl06_df.head()"
@@ -2381,10 +2406,10 @@
    "id": "826ace9e-0638-4240-8769-5b0a1385b173",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:43:54.092439Z",
-     "iopub.status.busy": "2024-12-09T17:43:54.092322Z",
-     "iopub.status.idle": "2024-12-09T17:43:54.095901Z",
-     "shell.execute_reply": "2024-12-09T17:43:54.095616Z"
+     "iopub.execute_input": "2025-05-12T20:13:30.054425Z",
+     "iopub.status.busy": "2025-05-12T20:13:30.054262Z",
+     "iopub.status.idle": "2025-05-12T20:13:30.057966Z",
+     "shell.execute_reply": "2025-05-12T20:13:30.057702Z"
     }
    },
    "outputs": [
@@ -2419,10 +2444,10 @@
    "id": "fb60aa6c-4b9d-4592-95be-4687636ccd1b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:43:54.097460Z",
-     "iopub.status.busy": "2024-12-09T17:43:54.097358Z",
-     "iopub.status.idle": "2024-12-09T17:43:54.116308Z",
-     "shell.execute_reply": "2024-12-09T17:43:54.115790Z"
+     "iopub.execute_input": "2025-05-12T20:13:30.059553Z",
+     "iopub.status.busy": "2025-05-12T20:13:30.059440Z",
+     "iopub.status.idle": "2025-05-12T20:13:30.080426Z",
+     "shell.execute_reply": "2025-05-12T20:13:30.079908Z"
     }
    },
    "outputs": [
@@ -2514,10 +2539,10 @@
    "id": "c5a76ba1-d2b1-49fd-96f7-bf41264c26a6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:43:54.118442Z",
-     "iopub.status.busy": "2024-12-09T17:43:54.118334Z",
-     "iopub.status.idle": "2024-12-09T17:44:22.375600Z",
-     "shell.execute_reply": "2024-12-09T17:44:22.375277Z"
+     "iopub.execute_input": "2025-05-12T20:13:30.083031Z",
+     "iopub.status.busy": "2025-05-12T20:13:30.082765Z",
+     "iopub.status.idle": "2025-05-12T20:14:08.311700Z",
+     "shell.execute_reply": "2025-05-12T20:14:08.310745Z"
     }
    },
    "outputs": [
@@ -2602,11 +2627,13 @@
     "\n",
     "iceflow_df_sampled = iceflow_df_sampled[iceflow_df_sampled.elevation > 0]\n",
     "\n",
-    "iceflow_avg = iceflow_df_sampled.resample(\"W\").agg({\n",
-    "    \"elevation\": \"mean\",\n",
-    "    \"dataset\": lambda x: \", \".join(x.astype(\"str\").unique()),   \n",
-    "})\n",
-    "iceflow_avg = iceflow_avg.replace([np.inf, -np.inf], np.nan) \n",
+    "iceflow_avg = iceflow_df_sampled.resample(\"W\").agg(\n",
+    "    {\n",
+    "        \"elevation\": \"mean\",\n",
+    "        \"dataset\": lambda x: \", \".join(x.astype(\"str\").unique()),\n",
+    "    }\n",
+    ")\n",
+    "iceflow_avg = iceflow_avg.replace([np.inf, -np.inf], np.nan)\n",
     "iceflow_avg = iceflow_avg.dropna(how=\"any\")\n",
     "iceflow_avg = iceflow_avg.compute()\n",
     "\n",
@@ -2629,17 +2656,17 @@
    "id": "5e733fe6-cc28-4696-aadf-272fe755ad2c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-09T17:44:22.377566Z",
-     "iopub.status.busy": "2024-12-09T17:44:22.377381Z",
-     "iopub.status.idle": "2024-12-09T17:44:22.537385Z",
-     "shell.execute_reply": "2024-12-09T17:44:22.536628Z"
+     "iopub.execute_input": "2025-05-12T20:14:08.316358Z",
+     "iopub.status.busy": "2025-05-12T20:14:08.316128Z",
+     "iopub.status.idle": "2025-05-12T20:14:08.652845Z",
+     "shell.execute_reply": "2025-05-12T20:14:08.651741Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7299a0717da0>"
+       "<matplotlib.legend.Legend at 0x7c2956a6b770>"
       ]
      },
      "execution_count": 16,
@@ -2648,7 +2675,7 @@
     },
     {
      "data": {
-      "image/png": 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///03Lly4gJYtWwIAfvnlF/Ts2ROLFy+Go6MjatasiZo1a8rHV69eHQEBATh58qRGzdnZ2fDy8sLs2bNx8uTJXEfvihunERIRERER5SO/Ea2itNPFkydP8M8//2DChAkaQetFkiQVuj8fHx94enrCysoKQ4YMgbe3d5HqCg4ORmZmJrp37y5vq1evHqpVq4agoCAAwOHDh6FSqfDff/+hfv36cHJyQv/+/XHv3j0AgKWlJXr16iWPUKlt3rwZffr0Qfny5REUFARra2s5aAFA9+7doVAocO7cuVxru3XrFv7++2906tRJY/ucOXNQuXJljB49ukjXXBQMW0REREREpdStW7cghICrq6vG9kqVKsHc3Bzm5uaYOnVqofpSqVTw9fXFkCFDAAADBw7EqVOncn1wtpOTk9y/+hUdHS3vj42NhYmJCaytrTWOq1KlCmJjYwHkTH9UT+VbtmwZduzYgfj4eLz77rvIyMgAkDNFcM+ePfJ9Z0lJSfjzzz/l58zFxsaicuXKGucwMjKCjY2NfB61du3awczMDHXq1EGHDh0wZ84ced+pU6fg7e2NtWvXFuqzKi4MW0REREREZcz58+cREhKChg0bIj09vVDHHD58GCkpKejZsyeAnMD27rvvYv369VptT548iZCQEI2Xo6OjTjWqVCpkZmZixYoVcHd3R5s2bbBlyxbcvHkTx48fBwD07NkTxsbG2LdvHwBg586dsLS01BgxKyx/f39cunQJfn5++PPPP7F48WIAOSOOQ4cOxdq1a1GpUiWd+30VvGeLiIiIiKiUql27NiRJQnh4uMZ29T1K5cqVK3Rf3t7eiI+P1zhGpVIhNDQUs2fPhkLxf+MwLi4uWqNWRkb/Fx3s7e2RkZGBhIQEjXZxcXGwt7cHADg4OAAAGjRoIO+3s7NDpUqV5FEyExMTfPTRR/Dz88PAgQPh5+eHAQMGyOeyt7fHw4cPNerIyspCfHy8fB41Z2dn+XzZ2dkYO3YsvvzyS9y+fRtRUVHo3bu3xnWrryk8PBy1atUqxCeoO45sERERERGVUra2tnj33Xfx66+/IiUlpcj9PHnyBHv37sXWrVs1RqsuX76Mp0+f4p9//tGpvxYtWsDY2BhHjx6Vt4WHhyM6Ohpt27YFALRv317erhYfH4/Hjx+jevXq8jYvLy/8/fffuHbtGo4dOyZPIQSAtm3bIiEhAcHBwfK2Y8eOQaVSoXXr1nnWpx5VU6lUqFevHq5evapx3e+99x66dOmCkJAQOaTpA0e2iIiIiIhKsd9++w3t27dHy5YtMWvWLDRp0gQKhQIXLlzAjRs30KJFC7ntf//9h5CQEI3jq1evjk2bNsHW1hb9+/fXWlCjZ8+e8Pb2hoeHR6FrsrKywujRozFlyhTY2NjA0tISn332Gdq2bYs2bdoAAOrWrYv3338fn3/+OdasWQNLS0tMmzYN9erVQ5cuXeS+OnbsCHt7e3h5ecHFxUUjRNWvXx8eHh4YM2YMVq9ejczMTEycOBEDBw6UpzVu3rwZxsbG8pL4Fy9exLRp0zBgwAAYGxvD2NgYjRo10qhfPRr38vbixrBFRERERFSK1apVC5cvX8b8+fMxbdo03L9/H6ampmjQoAG++uorfPrpp3LbxYsXy/cqqW3atAnr16/HBx98kOvKhX379sXQoUPx+PFjner6+eefoVAo0LdvX6Snp8Pd3R2//fabRpuNGzdi8uTJ8PT0hEKhQKdOnfD333/D2NhYbiNJEgYNGoSffvoJM2fO1DrP5s2bMXHiRHTr1k0+34oVK+T9RkZG+PHHHxEREQEhBKpXr46JEydi8uTJOl2PPkhCl4X531BJSUmwsrJCYmIiLC0tDV0OEREREekoLS0NkZGRcHFx0fkhvpcuXdIYPcpLcHAwmjdvXtQSqRTJ78+LLtmA92wREREREeXDwsKiWNvRm4PTCImIiIiI8lGnTh1ERETk+9BiCwsL1KlTpwSrorKAYYuIiIiIqAAMUlQUnEZIRERERESkBwxbREREREREesCwRUREREREpAcMW0RERERERHrAsEVERERERKQHDFtERERERER6wLBFRERERKSDrKwsZGRkGLoMKgMYtoiIiIiICunJkydo1qwZGjZsiJiYGL2fb8SIEZAkSX7Z2trCw8MDoaGhchtJkrBnz54C+1qwYAGUSiUWLVokb6tRo4ZG/y+/RowYIZ9DkiScPXtWo8/09HTY2tpCkiQEBATI2+fNm4d27dqhfPnysLa21qolKipK4zwmJiaoXbs2fvjhBwghCv35pKWlYcSIEWjcuDGMjIzQp0+fQh9bEhi2iIiIiIgK4cmTJ+jUqROuX7+OqKgodOjQoUQCl4eHB2JiYhATE4OjR4/CyMgIvXr10rmf9evX45tvvsH69evlbRcuXJD73rlzJwAgPDxc3rZ8+XK5rbOzM3x8fDT63L17N8zNzbXOlZGRgX79+mH8+PH51nTkyBHExMTg5s2bmD17NubNm6dRX0Gys7NRrlw5TJo0Cd27dy/0cSWFYYuIiIiIqADqoHXjxg1kZ2cjKysLd+/eLZHAZWpqCnt7e9jb2+Ott97Ct99+i3v37uHRo0eF7uPEiRN4/vw55syZg6SkJJw5cwYAYGdnJ/dtY2MDAKhcubK8zcrKSu5j+PDh2Lp1K54/fy5vW79+PYYPH651vtmzZ2Py5Mlo3LhxvnXZ2trC3t4e1atXh5eXF9q3b49Lly4BAP755x+YmZkhISFB45jPP/8cXbt2BQBUqFABq1atwpgxY2Bvb1/oz6OkMGwREREREeXj5aClVpKBSy05ORl//PEHateuDVtb20If5+3tjUGDBsHY2BiDBg2Ct7e3zudu0aIFatSoIY+ARUdHIzAwEEOHDtW5r9xcvHgRwcHBaN26NQCgW7dusLa2ls8H5Ixk+fv7w8vLq1jOqW8MW0REREREecgraKmVROA6cOAAzM3NYW5uDgsLC+zbtw/+/v5QKAr3VT4pKQk7duzAkCFDAABDhgzBtm3bkJycrHMto0aNkqf5+fr6omfPnrCzs9O5H7V27drB3NwcJiYmaNWqFfr3749hw4YBAJRKJQYOHAg/Pz+5/dGjR5GQkIC+ffsW+ZwliWGLiIiIiCgXBQUtNX0Hri5duiAkJAQhISE4f/483N3d0aNHD9y9e7dQx2/ZsgW1atVC06ZNAQBvvfUWqlevDn9/f51rGTJkCIKCgnDnzh34+vpi1KhROvfxIn9/f4SEhODKlSvYtm0b9u7di2+//Vbe7+XlhYCAADx48AAAsHnzZnh6eua66EZpxLBFRERERJSL9evX49q1a/kGLbWsrCzcvn0bv/32W7HXUaFCBdSuXRu1a9dGq1atsG7dOqSkpGDt2rWFOt7b2xvXrl2DkZGR/Pr33391WohCzdbWFr169cLo0aORlpaGHj166NzHi5ydnVG7dm3Ur18f/fr1wxdffIElS5YgLS0NANCqVSvUqlVLvlds9+7dZWYKIQAYGboAIiIiIqLSaNy4cdi1axcuXLhQYOBSKpVo2LAhJk+erPe6JEmCQqHQWKgiL1evXsXFixcREBAgL4ABAPHx8ejcuTNu3LiBevXq6XT+UaNGoWfPnpg6dSqUSqXO9edHqVTKzzEzMzMDkDO6tXnzZjg5OUGhUMDT07NYz6lPDFtERERERLmwsLDAP//8Azc3t3wDlzpoHT9+XCPQFJf09HTExsYCAJ4+fYpff/0VycnJ6N27t9wmMjISISEhGsfVqVMH3t7eePvtt9GxY0etflu1agVvb2+N524VhoeHBx49egRLS8s820RHRyM+Ph7R0dHIzs6Wa6tdu7bGUvFPnjxBbGwssrKycPXqVSxfvhxdunTR6NvLywuzZs3CvHnz8NFHH8HU1FTjXP/++y8yMjIQHx+PZ8+eyed66623dLoufWDYIiIiIiLKQ0GBS99BCwD+/vtvODg4yPXUq1cP27dvR+fOneU2U6ZM0TruxIkT+OOPPzB16tRc++3bty+WLFmC+fPnw9jYuND1SJKESpUq5dtm5syZ2LBhg/y+WbNmAIDjx49r1K1+NpZSqYSDgwN69uyJefPmafRVu3ZtvP322zh//jyWLVumda6ePXtq3L+mPpcuD0fWF0mUhipKuaSkJFhZWSExMTHfBE9EREREpVNaWhoiIyPh4uIiT0/TxbNnz7QCV0kELTKM/P686JINuEAGEREREVEB1CNcrVq1glKpZNCiQuE0QiIiIiKiQlAHrp49eyI1NRWHDx9m0KJ8MWwRERERERWShYUFAgMDIYQo9EOF6c3FsEVEREREpANJkiBJkqHLoDKAcZyIiIiIiEgPGLaIiIiIiIj0gGGLiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYYuIiIiIqJQaMWIE+vTpo/Vzfu7fvw8TExM0atRI3jZr1ix5yfq8XupzSJKEcePGafU7YcIESJKEESNGyNsCAwPRu3dvODo6QpIk7NmzR+u4zp07a5ynSpUq6NevH+7evavTZ7FmzRp07twZlpaWkCQJCQkJOh1vCAxbREREREQFyO+LfWn70u/r64v+/fsjKSkJ586dAwB89dVXiImJkV9OTk6YM2eOxjY1Z2dnbN26Fc+fP5e3paWlwc/PD9WqVdM4V0pKCpo2bYqVK1fmW9OYMWMQExODBw8eYO/evbh37x6GDBmi03WlpqbCw8MD06dP1+k4Q2LYIiIiIiLKx/bt21G3bl2EhoZq7YuIiEDDhg2xfv16A1SmTQgBHx8fDB06FIMHD4a3tzcAwNzcHPb29vJLqVTCwsJCY5ta8+bN4ezsjF27dsnbdu3ahWrVqqFZs2Ya5+vRowd++OEHfPDBB/nWVb58edjb28PBwQFt2rTBxIkTcenSJQCASqWCk5MTVq1apXHM5cuXoVAo5BGwL774At9++y3atGlT9A+ohDFsERERERHlo3v37nB2dkbXrl01AldERAQ6d+4Ma2treHp6GrDC/3P8+HGkpqaie/fuGDJkCLZu3YqUlBSd+xk1ahR8fHzk9+vXr8fIkSOLpcb4+Hhs27YNrVu3BgAoFAoMGjQIfn5+Gu02b96M9u3bo3r16sVyXkNg2CIiIiIiykfFihVx5MgRVK9eXQ5c6qBVsWJFHDt2DFWqVDF0mQAAb29vDBw4EEqlEo0aNULNmjWxfft2nfsZMmQITp06hbt37+Lu3bs4ffq0ztP+XvTbb7/B3NwcFSpUgK2tLcLDwzVGA728vHD69GlER0cDyBnt2rp1K7y8vIp8ztKAYYuIiIiIqAAvBq6mTZvC1dW11AWthIQE7Nq1SyMUDRkyRJ5KqAs7Ozt4enrC19cXPj4+8PT0RKVKlYpcm5eXF0JCQnDlyhWcOnUKtWvXhpubG549ewYAeOutt1C/fn15dOvEiRN4+PAh+vXrV+RzlgZGhi6AiIiIiKgsqFixIn777Tf5nqFly5aVmqAFAH5+fkhLS5On5wE593CpVCpERESgbt26OvU3atQoTJw4EQAKXACjIFZWVqhduzYAoHbt2vD29oaDgwP8/f3x8ccfA8gJZH5+fvj222/h5+cHDw8P2NravtJ5DY0jW0REREREhRAREYEPPvgADg4OcHJywqBBg3JdNMNQvL298eWXXyIkJER+XblyBR06dCjSAh4eHh7IyMhAZmYm3N3di7VWpVIJABorHg4ePBhhYWEIDg7Gjh07yvwUQoAjW0REREREBXr5Hi0TExN0794dXbt2xbFjx9CkSZMSqSMxMREhISEa22xtbfHkyRNcunQJmzdvRr169TT2Dxo0CHPmzMEPP/wAI6PCf/1XKpW4fv26/HNukpOTcevWLfl9ZGQkQkJCYGNjo7FMfGpqKmJjYwEAcXFxmDt3LszMzODm5ia3qVGjBtq1a4fRo0cjOzsb7733nsa5YmNjERsbK5/v6tWrsLCwQLVq1WBjY1Po6ypJHNkiIiIiIspHboth5LZoRkkICAhAs2bNNF6zZ8+Gt7c3GjRooBW0AOCDDz7Aw4cP8ddff+l8PktLS1haWua5/+LFi3IdADBlyhQ0a9YMM2fO1Gi3du1aODg4wMHBAV26dMHjx4/x119/wdXVVaOdl5cXrly5gg8++ADlypXT2Ld69Wo0a9YMY8aMAQB07NgRzZo1w759+3S+rpIiCSGEoYso7ZKSkmBlZYXExMR8/7ARERERUemUlpaGyMhIuLi4wMzMTKdjAwIC8PXXX+PAgQNa92g9ffoUnp6e+O6779CzZ8/iLJkMKL8/L7pkA04jJCIiIiLKR+fOnXHu3DkoFNqTwipWrIhTp07luo+IfyqIiIiIiAqQX5hi0KK88E8GERERERGRHjBsERERERER6QHDFhERERERkR4YNGwFBgaid+/ecHR0hCRJ2LNnj8Z+SZJyfS1atEhuU6NGDa39Cxcu1OgnNDQUHTp0gJmZGZydnfHTTz+VxOUREREREdEbzKBhKyUlBU2bNsXKlStz3R8TE6PxWr9+PSRJQt++fTXazZkzR6PdZ599Ju9LSkqCm5sbqlevjuDgYCxatAizZs3CmjVr9HptRERERET0ZjPo0u89evRAjx498txvb2+v8X7v3r3o0qULatasqbHdwsJCq63a5s2bkZGRgfXr18PExAQNGzZESEgIli5dirFjx776RRAREREREeWizNyzFRcXhz///BOjR4/W2rdw4ULY2tqiWbNmWLRoEbKysuR9QUFB6NixI0xMTORt7u7uCA8Px9OnT3M9V3p6OpKSkjReREREREREuigzDzXesGEDLCws8OGHH2psnzRpEpo3bw4bGxucOXMG06ZNQ0xMDJYuXQoAiI2NhYuLi8Yx6id/x8bGomLFilrnWrBgAWbPnq2nKyEiIiIiojdBmRnZWr9+Pby8vGBmZqaxfcqUKejcuTOaNGmCcePGYcmSJfjll1+Qnp5e5HNNmzYNiYmJ8uvevXuvWj4RERERkc5GjBiBPn36aP2cn/v378PExASNGjWSt82aNSvPxefUL/U5JEnCuHHjtPqdMGECJEnCiBEj5G0FLXgHAJ07d9Y4T5UqVdCvXz/cvXu30J9DfHw8PvvsM7i6uqJcuXKoVq0aJk2ahMTExEL3YQhlImydPHkS4eHh+Pjjjwts27p1a2RlZSEqKgpAzn1fcXFxGm3U7/O6z8vU1BSWlpYaLyIiIiJ6swkhcPbsWSxYsADz589HYGAghBCGLkuLr68v+vfvj6SkJJw7dw4A8NVXX2ksKOfk5KS1yJyas7Mztm7diufPn8vb0tLS4Ofnh2rVqmmcq6AF79TGjBmDmJgYPHjwAHv37sW9e/cwZMiQQl/TgwcP8ODBAyxevBhhYWHw9fXF33//nestRqVJmZhG6O3tjRYtWqBp06YFtg0JCYFCoUDlypUBAG3btsX//vc/ZGZmwtjYGABw+PBhuLq65jqFkIiIiIjoZffu3cOHH36IixcvQqlUAgCys7PRuHFj7N69G7Vq1TJwhTmEEPDx8cFvv/0GJycneHt7o3Xr1jA3N4e5ubncTqlU5rnIXPPmzXH79m3s2rULXl5eAIBdu3ahWrVqWrfnFLTgnVr58uXlczk4OGDixIn45JNPAAAqlQrVqlXD//73P4wfP14+5vLly2jRogUiIyPRqFEj7Ny5U95Xq1YtzJs3D0OGDEFWVhaMjEpnrDHoyFZycjJCQkIQEhICAIiMjERISAiio6PlNklJSdi+fXuuo1pBQUFYtmwZrly5gjt37mDz5s2YPHkyhgwZIgepwYMHw8TEBKNHj8a1a9fg7++P5cuXY8qUKSVyjURERERUtiUnJ6NTp07yd9bs7GxkZ2cDAK5fv44OHTogPj7egBX+n+PHjyM1NRXdu3fHkCFDsHXrVqSkpOjcz6hRo+Dj4yO/X79+PUaOHFksNcbHx2Pbtm1o3bo1AEChUGDQoEHw8/PTaLd582a0b98e1atXz7WfxMREWFpaltqgBRg4bF28eBHNmjVDs2bNAOTcf9WsWTPMnDlTbrN161YIITBo0CCt401NTbF161Z06tQJDRs2xLx58zB58mSNZ2hZWVnhn3/+QWRkJFq0aIEvv/wSM2fO5LLvRERERFQomzZtQlRUlMaK12pZWVmIi4vD2rVrDVCZNm9vbwwcOBBKpRKNGjVCzZo1sX37dp37GTJkCE6dOoW7d+/i7t27OH36tE7T/l7222+/wdzcHBUqVICtrS3Cw8Oxfv16eb+XlxdOnz4tD7qoVCps3bpVHll72ePHjzF37txS/53eoDGwc+fOBc5zHTt2bJ4fYvPmzXH27NkCz9OkSROcPHmySDUSERER0Ztt8+bN+e5XqVT4448/MHXq1BKqKHcJCQnYtWsXTp06JW8bMmQIvL29NRa1KAw7Ozt4enrC19cXQgh4enqiUqVKRa7Ny8sL//vf/wDkrJ8wf/58uLm5ITg4GBYWFnjrrbdQv359+Pn54dtvv8WJEyfw8OFD9OvXT6uvpKQkeHp6okGDBpg1a1aRayoJpXfMjYiIiIioFHjy5EmBAwSlYRqhn58f0tLS5Ol5QM49XCqVChEREahbt65O/Y0aNQoTJ04EgAIXwCiIlZUVateuDQCoXbs2vL294eDgAH9/f/l2IS8vLzls+fn5wcPDA7a2thr9PHv2DB4eHrCwsMDu3bvlNRlKqzKxGiERERERkaHUrVtXXhQjNwqFQucgow/e3t748ssv5TURQkJCcOXKFXTo0EFjyl5heXh4ICMjA5mZmXB3dy/WWtWf54srHg4ePBhhYWEIDg7Gjh07tKYQJiUlwc3NDSYmJti3b5/WI6FKI45sERERERHl45NPPsG+ffvy3K9SqeSV9fQtMTFRXqhDzdbWFk+ePMGlS5ewefNm1KtXT2P/oEGDMGfOHPzwww86LSahVCpx/fp1+efcJCcn49atW/J79YJ3NjY2GsvEp6amIjY2FkDONMK5c+fCzMwMbm5ucpsaNWqgXbt2GD16NLKzs/Hee+/J+9RBKzU1FX/88QeSkpKQlJQEIGfKY35h2JAYtoiIiIiI8tGjRw/0798f27dv15pOqFAo4O7ujo8++qhEagkICJAXl1MbPXo0ypUrhwYNGmgFLQD44IMPMHHiRPz1118aAaYwCnre7MWLF9GlSxf5vXrF7+HDh8PX11fevnbtWnkRkYoVK6JJkyb466+/4OrqqtGfl5cXPv30UwwbNgzlypWTt1+6dEl+Zph6OqJaZGQkatSoodN1lRRJlMYnsZUySUlJsLKykpeXJCIiIqKyJS0tDZGRkXBxcSnS9LOsrCwsXLgQy5Ytw5MnTwDk3Ic0ceJEzJw5EyYmJsVdMhlQfn9edMkGHNkiIiIiIiqAkZERvvvuO3zzzTe4fv06hBCoV69embhviAyHYYuIiIiIqJBMTEzQtGlTQ5dBZQRXIyQiIiIiItIDhi0iIiIiIiI9YNgiIiIiIiLSA4YtIiIiIiIiPWDYIiIiIiIi0gOGLSIiIiIiIj1g2CIiIiIiItIDhi0iIiIiIiI9YNgiIiIiIiqlRowYgT59+mj9rLZkyRJUrFgRaWlpWsempqbC0tISK1asAADUqFEDy5Ytk/dfuXIF7733HipXrgwzMzPUqFEDAwYMwMOHD+X9gwYNgrOzM8qVK4f69etj+fLlOtVfHH2UZQxbREREREQ6OHPmDI4fP27oMgAAQ4cORUpKCnbt2qW1b8eOHcjIyMCQIUO09j169AjdunWDjY0NDh06hOvXr8PHxweOjo5ISUkBAAQHB6Ny5cr4448/cO3aNfzvf//DtGnT8Ouvvxa6vuLooywzMnQBRERERERlxfPnz/H+++8jPT0d9+7dg5WVlUHrqVy5Mnr37o3169dj8ODBGvvWr1+PPn36wMbGRuu406dPIzExEevWrYORUU4kcHFxQZcuXeQ2o0aN0jimZs2aCAoKwq5duzBx4kRERETA1dUV169fR7169eR2P//8M3799Vfcvn27wD5edxzZIiIiIiIqpDVr1uDJkydISUmRp+cZ2ujRo3Hs2DHcvXtX3nbnzh0EBgZi9OjRuR5jb2+PrKws7N69G0KIQp8rMTFRDm9169ZFy5YtsXnzZo02mzdv1gp+efXxumPYIiIiIiIqhOfPn+OHH36AEAIqlQqLFi1CYmKiocuCu7s7HB0d4ePjI2/z9fWFs7MzunXrlusxbdq0wfTp0zF48GBUqlQJPXr0wKJFixAXF5fnec6cOQN/f3+MHTtW3ubl5YUtW7bI7yMiIhAcHAwvL69C9/E6Y9giIiIiIioE9aiWWmkZ3VIqlRg+fDh8fX3lILhhwwaMHDkSCkXeX/fnzZuH2NhYrF69Gg0bNsTq1atRr149XL16VattWFgY3n//fXz//fdwc3OTtw8cOBBRUVE4e/YsgJxRrebNm2tMKyyoj9cZwxYRERERUQFeHNVSK02jW6NGjUJ0dDSOHTuGo0eP4t69exg5cmSBx9na2qJfv35YvHgxrl+/DkdHRyxevFijzb///otu3bph7Nix+O677zT22dvbo2vXrvDz8wMA+Pn55TqqlV8frzOGLSIiIiKiArw8qqVWWka3atWqhU6dOmH9+vXw8fFB9+7dUb16dZ36MDExQa1ateTVCAHg2rVr6NKlC4YPH4558+blepyXlxf8/f0RFBSEO3fuYODAgRr7C9PH64qrERIRERER5SO3US019ejWpEmTSmRlwsTERISEhGhss7W1hbOzM0aPHo0xY8YAyLlnKz8HDhzA1q1bMXDgQNStWxdCCOzfvx9//fWXfO9XWFgYunbtCnd3d0yZMgWxsbEAcqYt2tnZyX19+OGHGD9+PMaPH48uXbrA0dFR3lfYPl5XHNkiIiIiIsrHmjVr8Pjx4zz3P3v2rMRGtwICAtCsWTON1+zZswEAffv2hampKcqXL6/18OOXNWjQAOXLl8eXX36Jt956C23atMG2bduwbt06DB06FEDOc7oePXqEP/74Aw4ODvKrVatWGn1ZWFigd+/euHLlitYUwsL28bqShC5rPb6hkpKSYGVlhcTERFhaWhq6HCIiIiLSUVpaGiIjI+Hi4gIzMzOdjvXw8MChQ4fybdOmTRsEBQW9SolUiuT350WXbMBphERERERE+dixYwfu3buXb5uqVauWUDVUljBsERERERHlw9zcHPXr1zd0GVQG8Z4tIiIiIiIiPWDYIiIiIiIi0gOGLSIiIiIiIj1g2CIiIiIiItIDhi0iIiIiIiI9YNgiIiIiIiLSA4YtIiIiIiIiPWDYIiIiIiIi0gOGLSIiIiKiUi4oKAhKpRKenp4AgBEjRkCSpDxfNWrUAAB07twZX3zxRZ79xsfHw8vLC5aWlrC2tsbo0aORnJys0UYIgcWLF6Nu3bowNTVF1apVMW/ePJ2vwdfXF02aNIGZmRkqV66MCRMm6NxHWWNk6AKIiIiIiMqSQ4cOIS0tDe+//36JndPb2xufffYZvL298eDBAyxfvhwLFy6U9zs4OMDHxwceHh4AAKVSWah+vby8EBMTg8OHDyMzMxMjR47E2LFj4efnJ7f5/PPP8c8//2Dx4sVo3Lgx4uPjER8fr1P9S5cuxZIlS7Bo0SK0bt0aKSkpiIqK0qmPsohhi4iIiIiokIQQmDBhApKTk9G7d28oFPqfKJacnAx/f39cvHgRsbGx8PX1xfTp02FlZaXRztraGvb29oXu9/r16/j7779x4cIFtGzZEgDwyy+/oGfPnli8eDEcHR1x/fp1rFq1CmFhYXB1dQUAuLi4yH1ERETA1dUV169fR7169eTtP//8M3799Vfcvn0bT58+xXfffYf9+/ejW7ducpsmTZoU6fMoSziNkIiIiIiokP7991/cvn0bcXFxOH/+fImcc9u2bahXrx5cXV0xZMgQrF+/HkKIV+43KCgI1tbWctACgO7du0OhUODcuXMAgP3796NmzZo4cOAAXFxcUKNGDXz88cfyyFbdunXRsmVLbN68WaPvzZs3Y/DgwQCAw4cPQ6VS4b///kP9+vXh5OSE/v374969e698DaUdwxYRERERUT52796NLl26oHPnzujbty+USiWMjIwwdOhQdO7cGZ07d8Yff/yht/N7e3tjyJAhAAAPDw8kJibixIkTr9xvbGwsKleurLHNyMgINjY2iI2NBQDcuXMHd+/exfbt27Fx40b4+voiODgYH330kXyMl5cXtmzZIr+PiIhAcHAwvLy85D5UKhXmz5+PZcuWYceOHYiPj8e7776LjIyMV76O0oxhi4iIiIgoH8bGxjhz5gxOnDiB8PBwZGdnIysrC7du3cKJEydw+vRpGBsb6+Xc4eHhOH/+PAYNGgQgJwwNGDAA3t7eejnfy1QqFdLT07Fx40Z06NABnTt3hre3N44fP47w8HAAwMCBAxEVFYWzZ88CyBnVat68uTytUKVSITMzEytWrIC7uzvatGmDLVu24ObNmzh+/HiJXIehMGwREREREeWjV69euHTpEurVqwdJkuTtCoUCLi4uOHfuHAYMGKCXc3t7eyMrKwuOjo4wMjKCkZERVq1ahZ07dyIxMfGV+ra3t8fDhw81tmVlZSE+Pl6+98vBwQFGRkaoW7eu3KZ+/foAgOjoaLmfrl27yotq+Pn5yaNa6j4AoEGDBvI2Ozs7VKpUSe7jdcWwRURERERUgIYNG2LEiBEQQsjLqwshMGjQIDRv3lwv58zKysLGjRuxZMkShISEyK8rV67A0dFRY+peUbRt2xYJCQkIDg6Wtx07dgwqlQqtW7cGALRv3x5ZWVm4ffu23CYiIgIAUL16dXmbl5cX/P39ERQUhDt37mDgwIHyvvbt2wOAPBIG5Cw5//jxY40+XkeSKI67615zSUlJsLKyQmJiIiwtLQ1dDhERERHpKC0tDZGRkXBxcYGZmVmR+mjWrBlCQkJQvnx5GBkZISkpCbVq1cLNmzc1RryKy549ezBgwAA8fPhQa+XBqVOn4tixY7hw4QIAQJIk7N69G3369NFo17lzZ1StWhVff/21xnYHBwdUqVIFPXr0QFxcHFavXi0v/d6yZUt5lEqlUqFVq1YwNzfHsmXLoFKpMGHCBFhaWuKff/6R+3v27BmqVKmCunXrolKlSjhy5IjG+fr06YNbt25hzZo1sLS0xLRp03Dnzh2EhITobQrmq8jvz4su2YAjW0REREREBUhJSUFISAhatGiBq1evIiwsDO3bt5dXJtQHb29vdO/eXStoAUDfvn1x8eJFhIaGFtiPn58fmjVrpvFau3YtgJz7q+rVq4du3bqhZ8+eeOedd7BmzRr5WIVCgf3796NSpUro2LEjPD09Ub9+fWzdulXjHBYWFujduzeuXLmiMYVQbePGjWjdujU8PT3RqVMnGBsb4++//y6VQas4cWSrEDiyRURERFS2FcfI1qVLl9C4cWM5IGRnZyMkJATNmzfXy8gWGU5xjWzxocZERERERIXw8r1ZSqUSLVq0MFA1VBZwGiEREREREZEeMGwRERERERHpAcMWERERERGRHjBsERERERER6QHDFhERERG9MbgQNxVGcf054WqERERERPTaMzY2hiRJePToEezs7LhUO+VJCIFHjx5BkqRXfg4YwxYRERERvfaUSiWcnJxw//59REVFGbocKuUkSYKTkxOUSuUr9cOwRURERERvBHNzc9SpUweZmZmGLoVKOWNj41cOWgDDFhERERG9QZRKZbF8iSYqDC6QQUREREREpAcMW0RERERERHrAsEVERERERKQHDFtERERERER6wLBFRERERESkBwxbREREREREemDQsBUYGIjevXvD0dERkiRhz549GvtHjBgBSZI0Xh4eHhpt4uPj4eXlBUtLS1hbW2P06NFITk7WaBMaGooOHTrAzMwMzs7O+Omnn/R9aURERERE9IYzaNhKSUlB06ZNsXLlyjzbeHh4ICYmRn5t2bJFY7+XlxeuXbuGw4cP48CBAwgMDMTYsWPl/UlJSXBzc0P16tURHByMRYsWYdasWVizZo3erouIiIiIiMigDzXu0aMHevTokW8bU1NT2Nvb57rv+vXr+Pvvv3HhwgW0bNkSAPDLL7+gZ8+eWLx4MRwdHbF582ZkZGRg/fr1MDExQcOGDRESEoKlS5dqhDIiIiIiIqLiVOrv2QoICEDlypXh6uqK8ePH48mTJ/K+oKAgWFtby0ELALp37w6FQoFz587JbTp27AgTExO5jbu7O8LDw/H06dNcz5meno6kpCSNFxERERERkS5Kddjy8PDAxo0bcfToUfz44484ceIEevTogezsbABAbGwsKleurHGMkZERbGxsEBsbK7epUqWKRhv1e3Wbly1YsABWVlbyy9nZubgvjYiIiIiIXnMGnUZYkIEDB8o/N27cGE2aNEGtWrUQEBCAbt266e2806ZNw5QpU+T3SUlJDFxERERERKSTUj2y9bKaNWuiUqVKuHXrFgDA3t4eDx8+1GiTlZWF+Ph4+T4ve3t7xMXFabRRv8/rXjBTU1NYWlpqvIiIiIiIiHRRpsLW/fv38eTJEzg4OAAA2rZti4SEBAQHB8ttjh07BpVKhdatW8ttAgMDkZmZKbc5fPgwXF1dUbFixZK9ACIiIiIiemMYNGwlJycjJCQEISEhAIDIyEiEhIQgOjoaycnJ+Prrr3H27FlERUXh6NGjeP/991G7dm24u7sDAOrXrw8PDw+MGTMG58+fx+nTpzFx4kQMHDgQjo6OAIDBgwfDxMQEo0ePxrVr1+Dv74/ly5drTBMkIiIiIiIqbpIQQhjq5AEBAejSpYvW9uHDh2PVqlXo06cPLl++jISEBDg6OsLNzQ1z587VWPAiPj4eEydOxP79+6FQKNC3b1+sWLEC5ubmcpvQ0FBMmDABFy5cQKVKlfDZZ59h6tSpha4zKSkJVlZWSExM5JRCIiIiIqI3mC7ZwKBhq6xg2CIiIiIiIkC3bFCm7tkiIiIiIiIqKxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYYuIiIiIiEgPGLaIiIiIiIj0gGGLiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYYuIiIiIiEgPGLaIiIiIiIj0gGGLiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYYuIiIiIiEgPGLaIiIiIiIj0gGGLiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYYuIiIiIiEgPGLaIiIiIiIj0gGGLiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYYuIiIiIiEgPGLaIiIiIiIj0gGGLiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYYuIiIiIiEgPGLaIiIiIiIj0gGGLiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYYuIiIiIiEgPGLaIiIiIiIj0gGGLiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYYuIiIiIiEgPGLaIiIiIiIj0gGGLiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYYuIiIiIiEgPGLaIiIiIiIj0gGGLiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYYuIiIiIiEgPGLaIiIiIiIj0gGGLiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8MGrYCAwPRu3dvODo6QpIk7NmzR96XmZmJqVOnonHjxqhQoQIcHR0xbNgwPHjwQKOPGjVqQJIkjdfChQs12oSGhqJDhw4wMzODs7Mzfvrpp5K4PCIiIiIieoMZNGylpKSgadOmWLlypda+1NRUXLp0CTNmzMClS5ewa9cuhIeH47333tNqO2fOHMTExMivzz77TN6XlJQENzc3VK9eHcHBwVi0aBFmzZqFNWvW6PXaiIiIiIjozWZkyJP36NEDPXr0yHWflZUVDh8+rLHt119/xdtvv43o6GhUq1ZN3m5hYQF7e/tc+9m8eTMyMjKwfv16mJiYoGHDhggJCcHSpUsxduzY4rsYIiIiIiKiF5Spe7YSExMhSRKsra01ti9cuBC2trZo1qwZFi1ahKysLHlfUFAQOnbsCBMTE3mbu7s7wsPD8fTp01zPk56ejqSkJI0XERERERGRLgw6sqWLtLQ0TJ06FYMGDYKlpaW8fdKkSWjevDlsbGxw5swZTJs2DTExMVi6dCkAIDY2Fi4uLhp9ValSRd5XsWJFrXMtWLAAs2fP1uPVEBERERHR665MhK3MzEz0798fQgisWrVKY9+UKVPkn5s0aQITExN88sknWLBgAUxNTYt0vmnTpmn0m5SUBGdn56IVT0REREREb6RSH7bUQevu3bs4duyYxqhWblq3bo2srCxERUXB1dUV9vb2iIuL02ijfp/XfV6mpqZFDmpERERERERAKb9nSx20bt68iSNHjsDW1rbAY0JCQqBQKFC5cmUAQNu2bREYGIjMzEy5zeHDh+Hq6prrFEIiIiIiIqLiYNCRreTkZNy6dUt+HxkZiZCQENjY2MDBwQEfffQRLl26hAMHDiA7OxuxsbEAABsbG5iYmCAoKAjnzp1Dly5dYGFhgaCgIEyePBlDhgyRg9TgwYMxe/ZsjB49GlOnTkVYWBiWL1+On3/+2SDXTEREREREbwZJCCEMdfKAgAB06dJFa/vw4cMxa9YsrYUt1I4fP47OnTvj0qVL+PTTT3Hjxg2kp6fDxcUFQ4cOxZQpUzSmAYaGhmLChAm4cOECKlWqhM8++wxTp04tdJ1JSUmwsrJCYmJigdMYiYiIiIjo9aVLNjBo2CorGLaIiIiIiAjQLRuU6nu2iIiIiIiIyiqGLSIiIiIiIj1g2CIiIiIiItIDhi0iIiIiIiI9YNgiIiIiIiLSA4YtIiIiIiIiPWDYIiIiIiIi0gOGLSIiIiIiIj1g2CIiIiIiItIDhi0iIiIiIiI9YNgiIiIiIiLSA4YtIiIiIiIiPWDYIiIiIiIi0gOGLSIiIiIiIj1g2CIiIiIiItIDhi0iIiIiIiI9YNgiIiIiIiLSA4YtIiIiIiI9evjwIb7//ntkZWUZuhQqYQxbRERERER6tGbNGsyZMweBgYGGLoVKGMMWEREREZEe+fv7AwB27txp4EqopBkZugAiIiIiotdJbGwsRo4ciZSUFKhUKoSFhQEAfH19cfXqVQBAtWrVsGnTJkiSZMhSSc8YtoiIiIiIipGpqSkiIyMRHh6usT01NRUnT54EAHzwwQeGKI1KGKcREhEREREVo4oVK+Ly5csYN26cxnZJkmBsbIxffvkFO3fu5KjWG0ASQghDF1HaJSUlwcrKComJibC0tDR0OURERERUBty7dw/VqlUDACgUCqhUKjg6OuL+/fsMWmWYLtmAI1tERERERHqwa9cu+WdnZ2cAwIMHD3Dx4kVDlUQljGGLiIiIiEgPAgMDYW5ujm3btuH27duYPXs2FAoFAgICDF0alRBOIywETiMkIiIiIl3du3cPxsbGsLe3l7eFh4fDwcGB3ynLMF2yAVcjJCIiIiLSA/XUwRe5uroaoBIylCKHrZs3b+L48eN4+PAhVCqVxr6ZM2e+cmFERERERERlWZHC1tq1azF+/HhUqlQJ9vb2GqupSJLEsEVERERERG+8IoWtH374AfPmzcPUqVOLux4iIiLKRUZGBnbv3o2zZ89CqVTCzc0N3bt3h0LBta6IiEqrIoWtp0+fol+/fsVdCxEREeXi7NmzeP/99/Hw4UMYGxtDCIElS5agQYMG+PPPP1GjRg1Dl0hERLko0j+H9evXD//8809x10JEREQviYqKwrvvvosnT54AADIzM5GVlQUAiIiIQJcuXZCammrIEomIKA9FGtmqXbs2ZsyYgbNnz6Jx48YwNjbW2D9p0qRiKY6IiOhNt2LFCjx//hzZ2dla+7KyshAVFYWtW7di1KhRBqiOiIjyU6TnbLm4uOTdoSThzp07r1RUacPnbBERkaE4OjoiJiYmz/2SJMHNzQ1///13CVZFRPTm0vtztiIjI4tUGBEREekmOTk53/1CCCQlJZVQNUREpItXXsJICIEiDI4RERFRIdSvXz/fFQeNjIzQqFGjEqyIiIgKq8hha+PGjWjcuDHKlSuHcuXKoUmTJti0aVNx1kZE9FrLyMiQ77UZNmwYVq5cyREK0jJhwgSoVKo892dlZeGTTz4pwYqIiKiwijSNcOnSpZgxYwYmTpyI9u3bAwBOnTqFcePG4fHjx5g8eXKxFklE9Lq5fv063N3dce/ePRgZGUEIgT/++ANTp07Fzp074e7ubugSqZQYPHgwduzYgQMHDmjMJJEkCUIIfPPNN2jRooUBKyQiorwUeYGM2bNnY9iwYRrbN2zYgFmzZr1293RxgQwiKk7JycmoW7cuHj58qLXCnEKhgLGxMUJCQlCvXj0DVUilTWZmJpYsWYIVK1bIi2XUq1cPU6dOxfDhwyFJkoErJCJ6c+iSDYo0jTAmJgbt2rXT2t6uXbt8V0wiIiLgjz/+QGxsbK5LeatUKmRnZ2P58uUGqIxKK2NjY3z77be4d+8eoqOj8eDBA/z7778YMWIEgxYRUSlWpLBVu3ZtbNu2TWu7v78/6tSp88pFERG9zvbs2ZPv/qysLOzYsaNkiqEyRalUwtnZGQ4ODgxZRERlQJHu2Zo9ezYGDBiAwMBA+Z6t06dP4+jRo7mGMCIi+j8pKSkFruKalpZWQtUQERGRvhRpZKtv3744d+4cKlWqhD179mDPnj2oVKkSzp8/jw8++KC4ayQieq00b94cRkZ5/1uXQqFA06ZNS7AiIiIi0ociLZDxpuECGURUnP799180bNgw3zZ+fn4YNGhQCVVEREREhaWXBTJefPZLUlJSvi8iIspbgwYN8NNPPwHIuQdHTX0PzuDBgzFgwACD1EZERETFp9Bhq2LFinj48CEAwNraGhUrVtR6qbcTEVH+vv76a+zfv19jZVdXV1esXr0amzZtgkJR5GfOExERUSlR6AUyjh07BhsbGwDA8ePH9VYQEdGbolevXujVqxfS09ORnZ2N8uXLG7okIiIiKkaFDludOnWSf3ZxcYGzs7PWsrNCCNy7d6/4qiMiegOYmpoaugQiIiLSgyLNU3FxccGjR4+0tsfHx8PFxeWViyIiIiIiIirrihS2hBC5PkwxOTkZZmZmr1wUERERERFRWafTQ42nTJkCIGfFrBkzZmjcX5CdnY1z587hrbfeKtYCiYiIiIiIyiKdwtbly5cB5IxsXb16FSYmJvI+ExMTNG3aFF999VXxVkhERERERFQG6RS21KsQjhw5EsuXL+cDfomIiIiIiPKgU9hS8/HxKe46iIiIiIiIXitFClsAcPHiRWzbtg3R0dHIyMjQ2Ldr165XLoyIiKiwbt++jRUrVmDHjh1IS0tD06ZNMXHiRHzwwQe5LuhERERUEoq0GuHWrVvRrl07XL9+Hbt370ZmZiauXbuGY8eOwcrKqrhrJCIiylNAQAAaN26MlStX4sGDB4iPj0dgYCD69u2LUaNGQaVSGbpEIiJ6QxUpbM2fPx8///wz9u/fDxMTEyxfvhw3btxA//79Ua1ateKukYiIKFfJycno06cP0tPTkZ2dLW9X/+zr6wtvb29DlUdERG+4IoWt27dvw9PTE0DOKoQpKSmQJAmTJ0/GmjVrirVAIiKivGzevBlJSUl5jl5JkoSff/65hKsiIiLKUaSwVbFiRTx79gwAULVqVYSFhQEAEhISkJqaWnzVERER5SMoKAhKpTLP/UIIXL9+HcnJySVYFRERUY4iha2OHTvi8OHDAIB+/frh888/x5gxYzBo0CB069atWAskIiqqhISEPPclJycjKyur5IohvcgvaBWlHRERUXEqUtj69ddfMXDgQADA//73P0yZMgVxcXHo27cv58YTUalw48YN1KxZE/7+/lr7nj17Bnd3d4wbN84AlVFx6t69e76hWaFQoHXr1ihXrlwJVkVERJSjSEu/29jYyD8rFAp8++23xVYQEVFxqFOnDnr16oXBgwcDAAYMGAAgJ2h5eHggLCyM9/K8Bj788EM4OjoiLi5OY4EMNZVKha+//toAleUtMzMTvr6+WLlyJSIiImBubo6BAwfiiy++QM2aNQ1dHhERFaMijWx1794dvr6+SEpKeqWTBwYGonfv3nB0dIQkSdizZ4/GfiEEZs6cCQcHB5QrVw7du3fHzZs3NdrEx8fDy8sLlpaWsLa2xujRo7Xm5oeGhqJDhw4wMzODs7Mzfvrpp1eqm4hKP6VSCR8fH3h5eWHw4MHw9/fXCFqHDx/G22+/begy6RWZmpri0KFDsLW11XielpFRzr8lzpkzB3379jVUeVrS09Ph6emJsWPHIjQ0FM+fP8ejR4+watUqNGnSBEFBQYYukYiIilGRwlbDhg0xbdo02Nvbo1+/fti7dy8yMzN17iclJQVNmzbFypUrc93/008/YcWKFVi9ejXOnTuHChUqwN3dHWlpaXIbLy8vXLt2DYcPH8aBAwcQGBiIsWPHyvuTkpLg5uaG6tWrIzg4GIsWLcKsWbO4aiLRK9i/f3+e/81fu3YN4eHhJVxR7l4MXAMHDoSlpSWDlo7u37+Pb7/9FjVq1ICdnR06d+6M7du3l6pnVzVq1AgRERFYtmwZOnXqhFatWmHkyJG4dOkSZsyYYejyNPz44484evQogJx/UFTLyspCWloa+vTpg4yMDEOVR0RExU0UUXZ2tjh06JAYPny4sLS0FBUrVhRjxowRAQEBReoPgNi9e7f8XqVSCXt7e7Fo0SJ5W0JCgjA1NRVbtmwRQgjx77//CgDiwoULcpuDBw8KSZLEf//9J4QQ4rfffhMVK1YU6enpcpupU6cKV1fXQteWmJgoAIjExMQiXRvR6yQyMlKYmJiIDz/8UGRkZGjsCwsLE3Z2dqJHjx4Gqi53T58+FQAEADFlyhRDl1NmnD9/XlhaWgqlUil/fuqf+/fvL7KysgxdYpmSmZkpKlWqJH+Web38/f0NXSoREeVDl2xQpJEtIOdeLTc3N/j6+iIuLg6///47zp8/j65du75C9Ps/kZGRiI2NRffu3eVtVlZWaN26tTzNIigoCNbW1mjZsqXcpnv37lAoFDh37pzcpmPHjjAxMZHbuLu7Izw8HE+fPs313Onp6UhKStJ4EVGOGjVqYOfOndi/fz8GDhwoj3Bdu3YNXbp0gaOjIzZu3GjgKv/Ps2fP4OnpCXNzc7i6umLZsmW5LppBmjIyMtC7d2+kpKTk+rDg7du3Y8WKFYYqr0y6f/8+Hj9+nG8bY2NjnD9/voQqIiIifSty2FKLjY3F6tWr8eOPPyI0NBStWrUqjroQGxsLAKhSpYrG9ipVqsj7YmNjUblyZY39RkZGsLGx0WiTWx8vnuNlCxYsgJWVlfxydnZ+9Qsieo306tULu3btkgNXSEiIHLSOHDmCSpUqGbpEAJqLYRw9ehTXrl3TuIeL8rZr1648F50AcqbALVu2rFRNJyzt1PeR5UcIUah2RERUNhQpbCUlJcHHxwfvvvsunJ2dsWrVKrz33nu4efMmzp49W9w1lrhp06YhMTFRft27d8/QJRGVOurAtWvXLjRr1gx2dnalNmip79HKbdEMyt2ZM2dgbGycb5vo6GjExcWVUEVlX9WqVVG3bl2NhTxelpWVBQ8PjxKsioiI9KlI/3xWpUoVVKxYEQMGDMCCBQs0pvEVF3t7ewBAXFwcHBwc5O1xcXF466235DYPHz7UOC4rKwvx8fHy8fb29lpfBtTv1W1eZmpqClNT02K5DqLXmYuLi/yznZ0drKysDFiNpoSEBKSmpmothqEOXEDOs7god4V9CLBC8coTJN4YkiTh22+/xahRo3Ldb2RkhIYNG6JTp04lXBkREelLkf6W3LdvH+7fv4+ff/5ZL0ELyPkSZ29vL6/aBOSMqJ07dw5t27YFALRt2xYJCQkIDg6W2xw7dgwqlQqtW7eW2wQGBmqsnHb48GG4urqiYsWKeqmd6E2gvkeradOm8PX1xZkzZzTu4TI0Z2dnBAcH57rqoFKphK+vL77//nsDVFY2dOvWLd/fpSRJqFevntZUbsrfiBEjMG3aNAD/N61QHVhdXFxw4MCBfEe+iIiobClS2Hr33XehUqlw5MgR/P7773j27BkA4MGDB1rPuMpPcnIyQkJCEBISAiBnUYyQkBBER0dDkiR88cUX+OGHH7Bv3z5cvXoVw4YNg6OjI/r06QMAqF+/Pjw8PDBmzBicP38ep0+fxsSJEzFw4EA4OjoCAAYPHgwTExOMHj0a165dg7+/P5YvX44pU6YU5dKJCJqLYRw5cgTDhw/XuIertASu/EZdOCKTvx49eqBWrVp5jnAJIfD1118zGOhIkiTMnz8fly9fxpgxY9CpUyf06tULf/zxB65evQonJydDl0hERMWpKMsdRkVFiXr16ony5csLpVIpbt++LYQQYtKkSeKTTz4pdD/Hjx/Pddnb4cOHCyFyln+fMWOGqFKlijA1NRXdunUT4eHhGn08efJEDBo0SJibmwtLS0sxcuRI8ezZM402V65cEe+8844wNTUVVatWFQsXLtTpern0O9H/uXv3rrCzsxNNmzYVjx490ti3f/9+YWxsLLy8vAxUHRWn8PBw4ejoKCRJEpIkCQDCyMhIXkJfpVIZukQiIqISp0s2kIR44amKhdSnTx9YWFjA29sbtra2uHLlCmrWrImAgACMGTMGN2/eLMY4aHhJSUmwsrJCYmIiLC0tDV0OkUGpVCrMmzcP48ePz3UxjAMHDsDExARubm4GqI6KW1JSEjZt2oRt27YhKSkJTZo0wbhx4+Tp3EREVPYJIdC3b18MGzZMnkFGedMlGxQpbNna2uLMmTNwdXWFhYWFHLaioqLQoEEDpKamFrn40ohhi4iIiIheV1evXkWTJk3QuXNnHD9+3NDllHq6ZIMi3bSgUqlyffbK/fv3YWFhUZQuiYiIiIiohAghoFKpoFKpsGPHDgBAYGAgHj58KG+nV1eksOXm5oZly5bJ7yVJQnJyMr7//nv07NmzuGojIiIiIiI96NGjB5RKJZRKJebMmQMgZ0ClSpUqUCqVMDY2xp9//mngKsu+IoWtJUuW4PTp02jQoAHS0tIwePBg1KhRA//99x9+/PHH4q6RSC/Onz+PhQsX5rovJSUFkyZNQkJCQskWlYuMjIx8HxzLh24TERGRrj777LN8p8B17doV7dq1K8GKXk9FCltOTk64cuUKpk+fjsmTJ6NZs2ZYuHAhLl++zGeuUJlx+vRpTJs2DbNmzdLYnpKSgp49e8LHxwe3bt0yTHEv+Pzzz/HOO+/gv//+09q3YcMG1K5dGxcuXDBAZURERFRWeXp6IiwsDI0aNdJ6zMfs2bNx6NAhPpO2GBgV+UAjIwwZMqQ4ayEqUZMnT0ZaWhqmT58OAJg1a5YctC5duoRDhw7p7aHduvj666/x119/oXPnzggICEDVqlUB5AStkSNH4uOPP0aLFi0MXCURERGVNc7OzlAoFMjOzoZCoYAQAkIIuLi48HmUxaTQYWvfvn2F7vS9994rUjFEJW3atGkAgOnTpyMlJQXnz5+Xg1ZpGTqvWbMmjh8/ji5dusiB68iRI3LQWr16Nf8PkYiIiHQWGRmJ0NBQAMAHH3yAM2fOICYmBjt27MDQoUMNXN3rodBhq7Br7kuSlOtKhUSl1bRp05CSkoJ58+YByJleWFqCltqLgcvJyQkAMGbMGAYtIiIiKrL79++jYsWKWLFiBYYMGYKnT5/i448/RnR0tKFLe20U+luaegnIgl4MWlTWpKSk4OTJk/L7f/75x4DV5K1mzZoYOXKk/H7GjBkMWkRERFRkHTp0wJMnT+RbgypWrIidO3fi/PnzBq7s9aHTN7WePXsiMTFRfr9w4UKN1dqePHmCBg0aFFtxRPr24j1ap0+fxvz58zF79mytRTNKgw0bNmDOnDno3r07nJyc0LVr11wXzSAiIiIqLEmSCrWNikansPX3338jPT1dfj9//nzEx8fL77OyshAeHl581RHp0cuLYbRr1w7Tpk0rlYHrxcUwDh06hBMnTiAjIwOdO3dm4CIiIiIqpV5pDpIQorjqICpxM2bMyHUxjBcD19GjRw1YYY4Xg5b6Hi31PVwMXERUGgkhkJmZaegyiIgMjjd80Btr9uzZOHHiRK6LYUybNg0BAQHo2rWrASrTNnbsWK3FMNSBq3r16gasjIhI25gxY/DOO+9ApVIZuhQiIoOShA7DU0qlErGxsbCzswMAWFhYIDQ0FC4uLgCAuLg4ODo6vnaLZCQlJcHKygqJiYn5PmmbiIjoTXf9+nU0bNgQQgjs2rULH3zwgaFLIiIqVrpkA50eaiyEwIgRI2BqagoASEtLw7hx41ChQgUA0Lifi4iIiN48c+bMgVKphEqlwowZM/D+++8X+8qpN27cQFBQEBQKBbp06YJq1aoVa/9ERMVFp5GtF5edzo+Pj0+RCyqNOLJFRERUsBdHtdSKc3TrwYMHGDZsmMb9tJIkoW/fvli3bh2srKyK5TxERPnRJRvoFLbeVAxbRET0Jrh06RJWrVqFkJAQVKhQAR999BGGDRtW6L/7Bg0ahB07diArKwsAoFAoUL9+fYSGhr7y6FZiYiKaN2+O6OhouX81pVKJFi1a4NSpUzA2Nn6l8xARFUSXbMAFMoiIiAjff/89WrRoAV9fX1y8eBGBgYGYNGkS6tati+vXrxd4/PXr1+Hv768RhFQqFa5du4a9e/e+cn1r165FVFSUVtACgOzsbJw/fx67d+9+5fMQERUnhi0iIqI33LZt2zBnzhwAkMOMEAJCCDx+/BgeHh4FLuWuvlfrZQqFAjNmzHjllQnXr1+fbx9KpRK+vr6vdA4iouLGsEUGFRUVhXfeeQd+fn6GLoWI6I31448/5jnNLzs7G9HR0dizZ0+ex+c2qqVWXKNbcXFx+e7Pzs5GTEzMK52DiEqvI0eO4Pvvvzd0GTpj2CKDUQetM2fOYMiQIfjjjz8MXRIR0Rvn2bNnuHTpUr6jRkZGRjhy5Eie+xcvXgwhBJRKJYyMjLReALBgwYJXqtPZ2RmSJOW5X6lU8rmDRK+xH374AXPnzkVsbKyhS9GJTku/ExUXddCKi4uTV60aNmwYAGDIkCGGLI2I6I1S2Gdj5tfO09MTBa231axZM53qetnYsWMxceLEPPdnZ2fj448/fqVzvCwjIwOzZ8/GlClTYGtrq9Oxjx8/xokTJ5CVlYUWLVqgdu3axVob0Zvk8ePHOHnyJIQQ2L17N8aPH2/okgqNqxEWAlcjLF4vBq2Xp5xIkoSNGzcycBERlRAhBOrWrYvbt2/nG5i8vb0xatSoEqxMU2pqKtq1a4ewsDCt4KdQKPDuu+/izz//zPW+saLav38/3nvvPSxevBhffvlloY5JS0vD5MmT4e3trXGfm5ubG9avX4+qVasWW31Er7NDhw5h3LhxyMrKQnp6Oh49egRJklC+fHlUrFgRANCnTx/88ssvJV4bVyOkUiu/oAXk/KU/bNgwTikkIiohkiRh8uTJeQYthUKBihUrYuDAgSVcmaby5csjICAAgwYNkqcmAoCZmRkmTJiAPXv2FGvQAoCdO3cCAPz9/QvVXqVSoW/fvlizZo3WgiLHjh1D+/btER8fX6w1Er2uateuDUmScP/+fTx69AhAzvfElJQU3L9/H3FxcWjVqpWBqywYwxaVmIKClhoDFxFRyRo3bpw8o+DFwGJkZAQzMzPs3bsX5cuXN1R5Mmtra2zatAn//fcf/vzzTxw8eBCxsbFYsWIFzMzMXrn/nTt3omvXrmjXrh3atWuHrVu3AgAuXLiAtm3bol27dnB3d8fjx49zPf7YsWP466+/cr3/LSsrC/fu3cPKlSu19j1+/BhBQUEF1hceHo5vv/22wCmbhZWRkYFff/0V6enpxdIfACQkJBRbffRmq1WrFkJDQzF48GCN7QqFAjVr1sSFCxfkW1BKNUEFSkxMFABEYmKioUsp01q2bCkkSRIACvVSKBTi5s2bhi6biOiNkJ2dLbZv3y46d+4sbGxshJOTk5g8ebK4ffu2oUsrERkZGcLCwkJYWVnl+3dT06ZNRVJSUq59DBkyRCiVynyPr1atmtZx48ePF6ampuLZs2f51vjpp58KAOLixYvFcs1bt24VAISfn1+x9JeUlCQsLCzEmjVr8m2nUqmEu7u72LhxY4F9jhkzRqxYsaJY6tPFlStXxNq1a0v8vKRt48aN8n8/RkZGQpIk4eHhYdCadMkGHNmiEjNmzBid/rWrQYMGqFatmh4rIiIiNYVCgY8++gjHjx/HkydPcO/ePSxduhQ1a9Y0dGklIiAgAM+ePUNiYiJmzZqlMVVR7csvv8S5c+dgYWGRax/3798vcMGRl5ewz87OxrZt25Ceno6DBw/meZxKpcL27dsB/N/0xle1Y8cOjf99VX/++SeePXuGTZs25dsuLCwMhw4dKvBem0ePHmHdunVYsmRJsY2WrVmzBlOmTCmw3axZs/DJJ5/gyZMnBdao/r0UByEEgoKCXovRwdTUVKSlpb1yP+rP19zcHPXr14cQAkeOHEFiYuIr910SuEBGIXCBjOLTv3//Qv+fkkKhgJ+fHwYMGKDnqoiI6E20YcMGOWjcvHkTt27dgkqlQvPmzREaGorMzExIkgQhBCRJQkxMDKpUqZJrXzdv3sSwYcNw9uzZfM9pbm6O69ev4/fff0d2djaePHmCNWvWQJIkNGvWDO7u7gCAVq1aoV69ehg2bBjS0tKQmZmJ8PBwAECFChXg4uICAGjatGmhp91nZmbi888/x9OnTwEAu3fvRnp6OkxMTPDhhx8CyJmquXz5cpiYmBSqz+vXr+Pu3bsAch4BcPToUUiShF27dsHMzAwmJibo0KEDJElCcnIyAOCnn36SHwVw48YNVKlSBQqFApaWlhBCICMjAwDg4+Mjrzp3/vx5NGnSBABgampaqNpeJoRAzZo15XuArK2tc22XmpoKGxsbpKenY/369Rg5cmSefU6dOhU//fQToqKiiuXRAwcPHkTPnj1x9OhRdO3a9ZX704cPPvgA7dq1w9dff51vOzc3N5ibm2PXrl2vdD5nZ2dUrVoV/v7+cHR0xIwZM/Djjz/i1KlTaN++/Sv1XVQ6ZQO9ja+9RjiNsHg8e/ZMKBSKQk8jBCAsLS2FSqUydOlERPQa+vXXXwuc3u7g4CD/vHr16lz7iY2NFcbGxmLYsGEF/r02atQocfToUfnvQ4VCIU89NDIyktt99NFHIjY2VtSrVy/f/r755ptCX29qaqpGf+prf/EzcHV1FSkpKYXu09XVVaumlz/TEydOiHfffbfAdjt27BCjR4/WuqXg5amZv/32W6Hre9GVK1fkPl6ewnjx4kXh7u4uunfvLt5++225vqpVq4ru3buL7t27iwULFmgco1KpRLVq1QQAsXTp0iLV9LKRI0cKAGLcuHHF0l9xi46OlqfD5vf97OHDh0KSJGFkZPTK358fP34ssrKytPo3JF2yAcNWITBsFZ9ly5YVOmhJkiS2bNli6JKJiOg1duTIEWFnZ6fxhV4dAr788kuRnZ0t/Pz8RIUKFcTgwYNz7WPVqlUCgGjfvr3o0aNHnn+vOTk5ifj4eCGEEKdOnRKOjo4a51UqlUKSJDF9+nSRkZEhhMgJSOPGjdPoR6lUCktLS7Fnzx6drzclJUWMGTMm1/o+/vhjkZycrFN/4eHhonHjxlp9KRQKYWpqKtauXStUKpUIDAwUlStXzvOzef/990ViYqI4f/68qFq1ap7t3n77bXH//v1C17dmzRphZGQklEqlHNzUn7P65wkTJojLly8La2vrfL+XzJw5U9y/f1+MHz9ejB49WgwePFjeV61aNTF69GgxevRosWzZskLXl52dLdasWSMWLVokFi1aJCwtLQUAUbFiRXnbunXrDPoPzxkZGSImJkbExMSIBQsWyNd8/PhxERMTI2JjY4VKpRJxcXHi4MGD4uDBg2Lq1Knyf0fff/+9vP3x48cGu47ixLBVzBi2ildhAheDFhERlZRJkyYJhUKhMdLyww8/aLSJiYkRDx48kN8fPXpUrFu3Tqxbt040a9ZM/rtrxYoVolatWlp/r1WpUkX8999/Gn0eO3ZMK0T17dtXq77z589rtAEg6tWrV+TrTUtLE+XLl9c4d7ly5URaWlqhjn/8+LEYOnSoHBzT0tJE8+bNtUagTp8+rXHco0ePRKtWrbTa/fjjjxph4unTp6Jr165an+HEiRNFZmamTtd6584d0bBhwzy/b9jZ2YlTp04JIYS4f/++6NChg9bvxMrKSg62165dE+bm5vLvWz1C+eI19e7du9D1PX36VFhYWGj9fl/sz9rausDFU/Rp4MCBWt/RXv4drly5Unz++edan93L7aZPn26w6yhODFvFjGGr+OUXuBi0iIiopGRnZwt7e3sBQFhYWMhT+bp3757vcblN73t5qnzFihXldqamplqjRl9//bXG330AhLm5uUhPT9doN3XqVLn/Bg0ayMeEh4cX6Zr//PNPuQ/1SAoAceDAgUIdv2LFCgFA/P7770KInLBVoUIFrWvZsGGD1rFNmjSRr0X9ean7eVGvXr20vrDPnTu3SNf7/PlzMWrUKK3f1zvvvKM1HW3//v0av0+FQiHef/99jTZ37twRrVq10gjn6rA+Z84crSlvBbl586Zo3ry51rRKSZLE22+/Le7cuVOk6y4uoaGhonbt2nl+b+vRo4d48uSJePbsmRg+fHiu3+sAiE8++USnKaqlGcNWMWPY0o/cAheDFhERlaSIiAj5i/e9e/fE5cuXRa1atYSxsbHIzs7O87gHDx6Izp07a/09pl6aukuXLiIpKUlkZWWJ+fPnCzMzM3Hjxg3xySefiKNHjwohcu53MjIyEitWrBBhYWGifv36AsiZnvWiDh06CEdHR3Hq1CmRlZUl5syZIxQKhfjtt9/EtWvXCrzGmzdvinnz5smjR5MnTxaSJIkZM2aI58+fi5kzZwqFQiE+//zzQn1m77zzjkYgVYc3a2trsXHjRnlUqlevXhrH3blzR/6cRo4cKX+B7969u4iLi5PrS0pKEsbGxgKA6Nmzpzwy1ahRo0LVl5sffvhBI7xJkiQ++ugjrXbqsKBQKISdnZ0AIExMTLRGlkJDQ7W+v7Rr167I9T1//lyYmZlp9GlhYSFPJzW05ORk4e7urhUIv/vuO/HHH3+IyMhIue2XX36p9d/FzJkzDVe8HjBsFTOGLf15MXAxaBERUUnLzs4WJ06c0BiNSE5OFkFBQYU69q233tIa0QoNDdVqm5mZKa5fvy4HCCGE2LJli7hy5Yrc5vnz52LlypVa97XExMRofdmPiooS48ePFxYWFgVO/xs/frwAIMLCwoQQQty+fVtcunRJo83ly5fFrVu3cj0+ISFBNGnSRDg5OQknJyeNhTWcnJxE5cqVRcWKFUVERIT8uSxZskTMnj1bo58LFy6I6tWri927dwshcj7nMWPGiDZt2ojy5cuLdevWyfVVqlRJ/Prrr0KlUonnz5+LSZMmFbgoQ37U95XVqlVLnipoZmYmUlNTNdpVq1ZNDrZPnz4V/fr1EwDkgKw2Z84c+fetDkmSJBV54YYTJ07kOmp07ty5IvWnD126dJGDqHq08ZtvvhGSJImPP/5Ybufp6Sl/Huo/KwMHDjRg5cWPYauYMWzp17Jly4SJiQmDFhERlSkJCQkaKwiqX+vXr8+1vXp0pThWaMvOzhaVKlUqcPrfi+3mzJlTpHNlZWWJvn375jmNTP1lOr+RwPz4+voKAKJz587yttxClUqlEllZWToHLpVKJWxtbcWoUaNEcnKyPNpYoUIFMX78eI37865evSoSEhI0jj179qxYunSp+OKLL+Tt7dq1E6ampmLNmjXi0aNHomfPngIo+gOiv/rqKwFAeHp6imvXrgk3NzcBQPzvf/8rUn95SUxMFO+9957GSFRhPH78WP5HheHDh8srNqpX67S2thaZmZni2bNnwsTEREiSJGbNmiW+/fZbIUmSqFChQqHvCSwLdMkGfM5WIfA5W/qXkZFR6Gd6EBERlQb+/v4YOHAgXFxc4OPjg99//x1btmxB7969sW/fPgDAgAED8M8//wAAUlJSkJmZCSDneVtGRkYwMjKCv79/oZ6pFBsbCz8/P6hUKsTFxWHx4sWQJAkdOnRA7969AQAtWrSAg4MDxo0bh4yMDKSnp+PSpUsAcp6hVb9+fQBA8+bN8euvvxb6WoUQWLduHT799FNkZWXJ242NjbFmzRoMHz4ckiQVur+QkBD5+VxLlizByZMnoVAosGXLFpiamsLU1BRdu3bV+G6QnZ0NV1dXfP755/jss88KfS71sUqlUmNbSkoK7OzsYGJigsePH+f6IGsg59qdnZ0RFxeHx48fw8rKCqdOnULlypVRt25duc2+ffvwzjvvwNbWVqfaAOD48eOIjo7GsGHDIEkSVCoVfHx8ULlyZdy4cQNffvklFAqFzv2+bOPGjRg+fDi+//57zJo1q9DH3blzBx4eHpg7dy4ePHiAoKAgXLt2Dbdu3UJmZiaEEOjZsyfKlSuHixcvYv78+Rg8eDAA4MiRI/D29saGDRtem+96fM5WMePIFhER0Ztt06ZNWvdShYaGiunTp8tT/FQqldi8ebNYu3at3Gbjxo3CxMQkzxGhFi1aiNjY2ELVsG/fPo0FGXJbua5Pnz4iOjpafv5TXq9JkyYV+trj4uKEjY2N2L9/v3wu9SiHsbGxSEpKKnRfarktuPDydMyXp3IGBgYKAKJp06Y6n09NPTqWlZUldu/eLZ/rn3/+kbe/2FalUmmsBrlp0yZ5e0ksx66erpjbyo6urq5a00ELol545FVWsxw7dqzW4hcv3stlbm4uLl++XOT+ywJOIyxmDFtERERvLvVqey1btizS8VevXhUuLi5ay2BPmTJF5wUQ9u7dK6ytrTWmL6pDyoQJE8Tz58+FEDnfXV5eslupVIoKFSroPG1f/RyxF5dF79Spk/xzUW4DuHXrlmjevHmugat8+fLydLxHjx6J4OBgERwcLEaMGCG3O3TokAgODhYhISE6TV+cPn26xvmMjIy0poLOmzdP/PLLL1qf3cu/v88++0zn69aVenGQyZMna2z//fffCxWaMzIyRN26dYWFhYWwsLDQCkUWFhaiatWqWo8lyI9KpRKrV68WJiYmWs+ne/vtt3WeolgWMWwVM4YtIiKiN9eBAwfkL5T37t0rUh8tW7aUv7Srv/Du3LmzSH39/fffWoGhX79+Wu3OnTunFciaNWtWqHPExMSIsLAwERYWJq8+aGJiIqysrMRvv/0m7ty5I/bs2SOsra3FV199VaTrSE9PF02bNtUKMcHBwXKb9u3bFzgCtm3btkKfUx188xrxc3V1FTdu3BCRkZG5PqxZ/XJ0dBTnz58v0nXnJyYmRvTt21f06NFDuLu7y+eztLQUbm5uomvXrmLUqFHyYhX29vYiLi5OPHz4MM/vqepHB+T1GjRokM7PLxNC5Lo6YWBg4Kt+BGUCw1YxY9giIiJ6s/z666+iffv2ol27dsLJyUkOSfXq1RPt2rUT7du3FwcPHixUX/fu3ZO/jHbs2FF+tlVRV2j75ptvtL4w29jYaD3fSd1OkiRRo0YNuW1UVFS+/WdnZwsrK6t8v6ADEDdu3BBJSUlaK/oVVkpKisZy5+ov7hs3bpTbXLt2TV4S/8WXuu3UqVN1Hh189uyZeO+997T6HDhwoMZzoNLS0sTIkSO12nXr1k1+oHNxi4uLK3AKaF4vpVIpbt++nWu/Bw8elB/G/GJo9fX1LdJ0yBcXh3nxWW0vj8C9rnTJBq9+px3RG+7mzZu4dOlSnq+bN28aukQiItKRmZkZgoKCcObMGdy/fx/Z2dkQQuDGjRs4c+YMQkJCUL58+UL1df36dRgbG+Pnn39GQEAAwsLC0K5dO1y7dq1Ite3evRsKhQI//PADTp48CScnJ8THx+PChQsa7QIDA2FnZ4cjR47gxo0bmDJlCgDg5MmT+favUCiwdetWVKxYUWufJEkwMjLCsmXLULduXVhYWKBcuXJFuo5Dhw4hLS0NlSpVwvbt2+Hm5gYA2LVrl9ymQYMGCA4ORseOHTUWuBBCYPPmzVi4cCGMjY11Oq+5uTkqV64MADAyMpL7tbe31/idmpqawtnZGQCgVCqhVCohSRIqVaqU62dTHCpXroyrV6/Ki0uoSZIEhUKR5yIekiRh+vTpcHFxyXV/x44d5YVN1AuZqFQqdOrUSaeFTdROnjyJrKws9OrVC7dv38bu3bthaWmJQ4cO6dzXa0/v0e81wJEtyov6YZgFvdTPHiEiorLjzJkzwsnJSWPamiRJolWrVuLOnTs69ZWcnKzxXqVSaYyi6GLVqlUai0ckJCSIuXPniri4OI12kZGR4unTpxrbrl27VugluB88eCAcHR21RpRenOb3Ki5cuCDGjBkjP5sqOztb/PLLL+Knn37SaKdSqTRGe9S/j19++aVI583MzBTW1tYCgGjbtq08xdPBwUFrlMfV1VUAEPXr15fvUytXrpx8b5y+3L17V2sUr0aNGuLff/+Vl/J/8fN4efGWl+3atUtu/95778lTNxcvXlyk+pKTk8WhQ4c0Pq///vuvUM+nex1wZIuohDx79qxY2xERUenRtm1beHp6QgghL7sthMCoUaPyHEHIS4UKFTTeS5JU6JGxl40bNw5t2rSR31tZWeG7776TR2vUatSoAWtra41tDRo0gKmpaaHOU758eTx8+FCjZiEEUlNTi1T3y1q2bIk1a9bAzs4OQM6I2sSJE/H1119rtLty5Qqio6NhZGSEefPmoV27dgCAbdu2Fem8iYmJAIBZs2YhMDAQZ86cwbfffov09HSkpKTI7VQqFZ48eYJPP/0Uly5dwrFjx+SRtLi4uCKdu7BeHN1T/16joqKQkJAg1/9inVZWVvn2d+PGDdja2uKvv/7C3r17ERQUBBcXF9y4caNI9VWoUAFubm4ao2KOjo4afy4pB8MWERERUS6ys7Oxfft2CCFgY2MDMzMzAMD27dsNXFnJOHDgALKyslChQgUsWrRInlK3Y8eOEq1DoVCgS5cuOHfuHKZPn46AgADMmzcPtWrVKlJ/tra2iI+Px/fffw8jIyMYGxtjwYIFePz4MczNzTXO+/DhQ6xcuRJmZmZQKBSYOnUqEhISUL169eK6vFwdP34c5cuXx6ZNmxAdHY0vv/wSALB69WpkZmbC2NgYX3/9tTydcefOnfn298033+DBgwfo0aMHAKBVq1a4efMmVq5cqdfrICD3iZ9EREREb7iIiAjEx8ejZ8+e2LBhAx4/fox+/frh9OnTEEIU6V6XssTMzAweHh5YtWoVatSogbFjx2Ly5MkFjqIUtyZNmuDYsWPye6VSienTp79Sn7n97l5lW3FbsmQJzMzM4OTkBABYvHgxhgwZgpMnT+LatWvw8fFB48aNMXnyZIwaNQppaWn59qe+56ygbVT8JCGEMHQRpZ1OT4mmN8qlS5fQokWLAtsFBwejefPmJVAREREVFyEErly5gqZNm8pfsNPT03H79m00aNDAwNURkaHokg04skVERESUC0mS8NZbb2lsMzU1ZdAiokLjPVtERERERER6wLBFRERERESkBwxbREREREREesCwRfQKLCwsirUdERFRUTx48ADr1q0rsF1ERATee+89jedJvQohBHx9fYu1v6ZNm2LVqlXF0h8A3Lt3D//991+x9VdYz58/R0hISKHapqenF9gmLS0Ny5cvR0ZGRr7thBDYvXt3ge304ezZs7h7926B7f7++28EBATov6BSgGGL6BXUqVMHERERCA4OzvMVERGBOnXqGLpUIiJ6jS1duhRjxoxBREREvu3WrVuH/fv346+//iqW8546dQojR46Ej49PsfR34cIFhIaGYs2aNcXSHwB88MEHGDBgQIHtLl26hEOHDhXbeRcvXoy3334bT548ybfdwYMHUalSpQIflLxt2zZ88cUX2L9/f77tzpw5gw8//BB//PGHzjW/iszMTPTo0QNffPFFvu1UKhVGjhyJ8ePHl0xhBsawRfSK6tSpg+bNm+f5YtAiIiJ9EkJg69atAPJ/uK0QAv7+/gByfzDxs2fP4OPjA12eCqQ+37Zt23QpWeu8ERERiIiIgK+vLwAgJCQEp0+fRkREBG7fvq1TTS+6e/cugoODcebMGcTExOTb9rPPPsPgwYORnZ1dpHO9bMuWLcjMzMTevXvzbbdx40YkJydj9+7d+bZTP0y7oIdKq/eX9MO3T5w4gYSEBBw8eDDfkc4LFy4gNjYWN27cKPAfB14HfM5WIfA5W0RERFSa3L17F9OnT0dGRgbS0tJw4MABAECVKlXQoUMHAEDjxo0xdOhQtG3bFsnJyQAgfwmWJAnly5cHAFSrVg1hYWFYvnw5pkyZgqCgILRp0ybX8wohMHPmTHla3p49e/D06VNIkoThw4dDkiRUqFAB8+bNK/R3pk6dOiEwMFBjm0KhgEqlkt/7+/ujf//+heovLCxMHqG6ePEitm3bBiEEvLy85KX8e/bsifr168vHxMbGwtHREUIIHD9+HJ07dy7UuV6UkJCADRs2IDMzE6mpqfj+++8BAM2aNcPgwYMBAPXq1YOnpyeuXLmC58+fQ6VSwc3NDampqWjXrh0WL14MIOf36ODggEmTJiExMRFAzmedmZkJMzMz9O7dGwBgY2ODX375BcuWLUNsbCyAnPD2+PFjGBkZ4bPPPpN/J9OmTUO5cuV0vq78HDhwQH7gdFBQEM6fPw+VSoV+/frByckJkiTh448/xtWrV7Fp0yYAQGRkJMLDwyGEQOPGjeUHN48bNw6enp7FWp++6JQNBBUoMTFRABCJiYmGLoWIiIhI3Lx5U9ja2goAGi9JkuSfBwwYINLT08VHH32k1e7F9nPmzBFCCNG6dWsBQHz11Vd5njcrK0u0aNFCPl6pVAoAQqFQyOeuXLmyiI2NLfS1XL58WdSsWTPPGseNGyeeP39e6P6+//57+Vh1XQqFQigUCnn7vHnzxMmTJ8WAAQPEgAEDxDvvvCMkSRJKpVI0btxY3r5ly5ZCnzcoKEj+DNR9vfgZARBt27YVYWFheV6r+mVjYyOePXsmXF1dtX63L/6OmzRpIpKTk0WdOnXkbUZGRvJ51W2tra11+p0U1ueff651XoVCIV+zJEli9+7dwsfHR6Pul19GRkZix44dxV6fvuiSDRi2CoFhi4iIiEqbmJgY0a1bN40vrUqlUhgZGYlly5YJlUolhBBCpVKJdevWaX3ZtbS0FO+9954YPny4GDp0qMYX/eHDh4vhw4eLGTNmaJ33+fPn4rPPPsv1S3OPHj3Ew4cPdb6WZ8+eiXfeeUcjmAAQa9eu1bmv7OxssXjxYmFkZKTRn/qzWb58uVCpVGLfvn1yQHjxs3nx58WLF+t07oCAAGFvb69xXnV/gwcPFomJiUKlUom1a9cKU1PTXAOIo6OjOHXqlBBCiOTkZPHxxx/n+llPnDhRDqEJCQli4MCBubbr0KGDuHfvns6fY2GoVCqxfPnyXD/rypUri2PHjsltT506JRwdHTXaKRQKUbNmTREcHKyX+vSFYauYMWwRERFRaXTlyhWtkaq3335bq92///6r9eW/fPnyws7OTt6mHvl5cQSodevWIjs7W6s/lUolnJyctM795MmTIl9L7dq1Nb6EAxC//vprkfubO3euVpj56aefNNpcvHhR1KhRQ+OalUqlsLa2Fvv27SvSeS9fvqwVgDt06KDVLjQ0VOvzq169uoiPj9dol5KSIkxNTTXaWlhYiIyMDI12KpVKODg4aLRTKBQl8v31k08+0QrK/v7+Wu2+++47IUmSxu9l0aJFeq+vuOmSDbhABhEREVEZtWvXLvlna2trCCFw/vx5PHjwQKOdeiELY2NjvP/++wCA1NRUrFixAh4eHhBCyPdIif9/O/+XX36JwMBAKBTaXxdDQ0Nx//59AJDvAxJCFLhSXl6uX7+OW7duQZIkfPvtt2jevDmAV1t448iRI1oLaxw9elTjfYsWLfDdd99p3B8mhMCIESPk+6J0pb6HSS07Oxvnzp2T75tTUy/YIUmSfN7//vtP6/M+dOiQvDS8tbU1gJxFRY4fP67RLjQ0VO5TqVQCyFn5rzhXWMyNSqXC3r17kZ2dLZ8XAP7880+ttv7+/hBCoFy5cjA1NQXwar/jsoBhi4iIiKiMOnLkCCpUqIAtW7YgOjoaXl5eALS/8IeHh6Nu3bq4ePEidu/ejX379sHGxgZxcXHYsGGDRlshBGrUqIFFixbBxMQk1/Oqv8CPGjUKcXFxmDdvHhQKRZG/2GdkZKBVq1YIDAzEggULcObMGUyfPh22trZF6i8xMRGnTp1ChQoVsHXrVmzatAnlypXD0aNHtVbKU6/ep1AoUK5cOahUKmzfvr3IKyDu2bMHQE5YvXjxIlxdXZGRkaEV9NQBuF69eli9ejWsra2RlZUlL3aiduTIESgUCsybNw9xcXGYMWMGJEnCkSNHNNodPHgQAPDRRx/hv//+w5QpUwCg2Jb5z8u1a9cQGxuLOnXq4NKlS/D394e5ubnWdcTExODmzZto2bIlrl69ikuXLqFBgwa4cOECUlNT9VqjQeltfO01wmmEREREVBpdvnxZREdHa2wLCAgQCQkJGtvS09O1pgOqt61Zs0ae0lW+fHn557CwsDzPe/fuXXHkyBGtWi5duvSKV1Q80tPTxZw5c0RkZKS87datW2Lu3LkiMzNT3qZSqYSJiYmoWrWqOHPmjLh165Zo3ry5ACAiIiKKdO6NGzeKw4cPy+9TU1PFwoULNWoRQoh58+aJKVOmyPdd3b9/X3z00Ufir7/+0mh3+/ZtERoaqrHt8uXLIioqSmPb9evXxa5du+R79YTI+bNw+vTpIl1HYaWkpIh169aJlJQUeVtUVJTW4iIqlUqcOHFCY/rj8+fPxcmTJzVqLgt0yQZc+r0QuPQ7ERERva769euHPXv2YMGCBfjkk0/w+eefw8fHB0uWLJFHR15nR44cQcuWLeUpehkZGThy5Ajc3NxgZGRk2OKoVNIlGzBsFQLDFhEREb2uLl++DGNjYzRq1EjeduzYMbi6uqJq1aoGrIyodNIlG5T6e7Zq1KgBSZK0XhMmTAAAdO7cWWvfuHHjNPqIjo6Gp6cnypcvj8qVK+Prr79GVlaWIS6HiIiIqFRp1qyZRtACgK5du5b6oDVgwAD5IcBEpVWpHxu9cOECsrOz5fdhYWF499130a9fP3nbmDFjMGfOHPm9+onoQM4KMJ6enrC3t8eZM2cQExODYcOGwdjYGPPnzy+ZiyAiIiKiYvPgwQNs374dQUFB+PLLL+UV/YhKm1Iftuzs7DTeL1y4ELVq1UKnTp3kbeXLl4e9vX2ux//zzz/4999/ceTIEVSpUgVvvfUW5s6di6lTp2LWrFl5rrJDRERERKVHVlYWEhISAABbtmyBEAL37t3DyZMn0aBBA0iSBBsbGwYvKlVK/TTCF2VkZOCPP/7AqFGjNP5D2rx5MypVqoRGjRph2rRpGstHBgUFoXHjxqhSpYq8zd3dHUlJSbh27VqJ1k9ERERERTNixAjY2dnBzs4OX331FRQKBZRKJTp16gQ7OztUqlQJv/32m6HLJNJQ6ke2XrRnzx4kJCRgxIgR8rbBgwejevXqcHR0RGhoKKZOnYrw8HD5IX+xsbEaQQuA/D42NjbX86Snp8sPjwNyboIjIiIiIsP55ptvEBQUhDt37gCAxoOIAaB79+7o37+/IUojylOZClve3t7o0aMHHB0d5W1jx46Vf27cuDEcHBzQrVs33L59G7Vq1SrSeRYsWIDZs2e/cr1EREREVDyaNGmCK1euoG/fvjh8+LDGQ4enTp2K+fPnQ6EoU5O26A1QZv5E3r17F0eOHMHHH3+cb7vWrVsDAG7dugUAsLe3R1xcnEYb9fu87vOaNm0aEhMT5de9e/detXwiIiIiekXm5uYQQkAIIU8jBHLu32fQotKozPyp9PHxQeXKleHp6Zlvu5CQEACAg4MDAKBt27a4evUqHj58KLc5fPgwLC0t0aBBg1z7MDU1haWlpcaLiIiIiAzr6dOnOHbsGICcpd+bN28OAPD39zdkWUR5KhNhS6VSwcfHB8OHD9d4kvft27cxd+5cBAcHIyoqCvv27cOwYcPQsWNHNGnSBADg5uaGBg0aYOjQobhy5QoOHTqE7777DhMmTICpqamhLomIiIiIdJSQkIBq1aph48aN8PPzw+nTpzFt2jR5hIuotJHEixNeS6l//vkH7u7uCA8PR926deXt9+7dw5AhQxAWFoaUlBQ4Ozvjgw8+wHfffacxGnX37l2MHz8eAQEBqFChAoYPH46FCxdqBLf86PKUaCIiIiIien3pkg3KRNgyNIYtIiIiIiICdMsGZWIaIRERERERUVnDsEVERERERKQHDFtERERERER6wLBFRERERESkBwxbREREREREesCwRUREREREpAcMW0RERERERHrAsEVERERERKQHDFtERERERER6wLBFRERERESkBwxbREREREREesCwRUREREREpAcMW0RERERERHrAsEVERERERKQHDFtERERERER6wLBFRERERESkB0aGLoAKdvPmTTx79izfNhYWFqhTp04JVURERERERAVh2Crlbt68ibp16xaqbUREBAMXEREREVEpwWmEpVxBI1pFbUtERERERPrFsEVERERERKQHDFtERERERER6wLBFRERERESkBwxbREREREREesCwRUREREREpAcMW0RERERERHrAsFXKWVhY6KUtERERERHpFx9qXMrVqVMHERERBT5Dy8LCgg80JiIiIiIqRRi2ygCGKCIiIiKisofTCImIiIhKqbCwMKxdu9bQZRBRETFsEREREZVSs2fPxrhx4/Do0SNDl0JERcCwRURERFQKPX/+HAcOHIBKpcK+ffsMXQ4RFQHv2SIiIiIqJS5duoT//e9/yMrKwrNnz5CWlgZJkjBz5kxs3boVANClSxdMnz7dwJUSUWEwbBERERGVEkqlEmfPnkVCQoK8TQiBBw8e4MGDBwCANm3aGKg6ItIVpxESERERlRJNmzZFWFgYOnbsqLFdqVTC0tISu3fvxty5cw1UHRHpimGLiIiIqBSpWrUqpk6dKr9XKBRQqVTo2LEj+vTpY7jCiEhnDFtEREREpczOnTsBAJIkwdbWFkIIHDp0CM+ePTNwZUSkC4YtIiIiolLm2LFjcHR0RGBgIG7evIn+/fsjMzMTZ8+eNXRpRKQDSQghDF1EaZeUlAQrKyskJibC0tLS0OUQERHRay4sLAzOzs6wsrICkLNIxvnz59G8eXMYGxsbuDqiN5su2YCrERIRERGVMo0aNdJ4L0kSWrdubaBqiKioOI2QiIiIiIhIDxi2iIiIiIiI9IBhi4iIiIiISA8YtoiIiIiIiPSAYev/tXfv0VGU9x/HP0mWRALZIJgbyLUSNHJHwYigKBA1UFFsKSqBgFAwaQUUKZZjgB6NIl5KC9qLJFpBBC9VKaKYAB4heIncQYggBms2IG0SEA0keX5/+GPrSlAg++xusu/XOXMOM/PszPP9npXl487MAgAAAIAFhC0AAAAAsICwBQAAAAAWELYAAAAAwALCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELAAAAACwgbAEAAACABYQtAAAAALCAsAUAAAAAFhC2AAAAAMACwhYAAAAAWEDYAgAAAAALCFsAAAAAYAFhCwAAAAAsIGwBAAAAgAWELQAAAACwgLAFAAAAABYQtgAAAADAgoAOW7NmzVJISIjHcvHFF7v3f/vtt8rIyFCLFi3UtGlTDR8+XKWlpR7HKC4uVmpqqiIjIxUbG6tp06apqqrK16UAAAAACDIOf0/gp1x66aV655133OsOx/+mPGXKFP3rX//S8uXLFR0drczMTN1yyy1av369JKm6ulqpqamKj4/Xhg0bVFJSorS0NDVq1EgPPfSQz2sBAAAAEDwCPmw5HA7Fx8efsr28vFzPPPOMlixZomuvvVaSlJOTo0suuUQbN27UFVdcobfffls7d+7UO++8o7i4OHXv3l1/+MMfNH36dM2aNUvh4eG+LgcAAABAkAjoywglqaioSC1btlSHDh10++23q7i4WJJUWFioEydOaODAge6xF198sdq0aaOCggJJUkFBgbp06aK4uDj3mJSUFFVUVGjHjh2+LQQAAABAUAnob7b69Omj3NxcderUSSUlJZo9e7b69eun7du3y+VyKTw8XM2aNfN4TVxcnFwulyTJ5XJ5BK2T+0/uO53KykpVVla61ysqKrxUEQAAAIBgEdBh64YbbnD/uWvXrurTp4/atm2rZcuWqXHjxtbOm52drdmzZ1s7PgAAAICGL+AvI/y+Zs2aKTExUZ9++qni4+N1/PhxlZWVeYwpLS113+MVHx9/ytMJT67Xdh/YSTNmzFB5ebl7OXDggHcLAQAAANDg1auwdfToUe3du1cJCQnq1auXGjVqpLy8PPf+3bt3q7i4WMnJyZKk5ORkbdu2TQcPHnSPWb16tZxOp5KSkk57noiICDmdTo8FAAAAAM5GQF9GeO+992ro0KFq27atvvzyS2VlZSksLEwjR45UdHS0xo0bp6lTp6p58+ZyOp36zW9+o+TkZF1xxRWSpMGDByspKUmjRo3S3Llz5XK5NHPmTGVkZCgiIsLP1QEAAABoyAI6bH3xxRcaOXKkDh8+rJiYGF111VXauHGjYmJiJElPPPGEQkNDNXz4cFVWViolJUULFy50vz4sLEwrVqzQpEmTlJycrCZNmmj06NGaM2eOv0oCAAAAECRCjDHG35MIdBUVFYqOjlZ5eTmXFAIAAABB7GyyQb26ZwsAAAAA6gvCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELAAAAACwgbAEAAACABYQtAAAAALAgoH/UGPYVFRXpyJEjp90fFRWljh07+nBGAAAAQMNA2ApiRUVFSkxM/Mlxe/bsIXABAAAAZ4nLCIPYj32jdS7jAAAAAPwPYQsAAAAALCBsAQAAAIAFhC0AAAAAsICwBQAAAAAWELYAAAAAwALCFgAAAABYQNgKYlFRUV4dBwAAAOB/+FHjINaxY0ft2bPnR39HKyoqih80BgAAAM4BYSvIEaQAAAAAO7iMEAAAAAAsIGwBAAAAgAWELQAAAACwgLAFAAAAABYQtgAAAADAAsIWAAAAAFhA2AIAAAAACwhbAAAAAGABYQsAAAAALCBsAQAAAIAFhC0AAAAAsICwBQAAAAAWELYAAAAAwALCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELAAAAACwgbAEAAACABYQtAAAAALCAsAUAAAAAFhC2AAAAAMACwhYAAAAAWEDYAgAAAAALCFsAAAAAYAFhCwAAAAAsIGwBAAAAgAWELQAAAACwgLAFAAAAABYQtgAAAADAAsIWAAAAAFhA2AIAAAAACwhbAAAAAGABYQsAAAAALCBsAQAAAIAFhC0AAAAAsICwBQAAAAAWELYAAAAAwALCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELAAAAACwgbAEAAACABYQtAAAAALCAsAUAAAAAFgR02MrOztbll1+uqKgoxcbGatiwYdq9e7fHmGuuuUYhISEey8SJEz3GFBcXKzU1VZGRkYqNjdW0adNUVVXly1IAAAAABBmHvyfwY9atW6eMjAxdfvnlqqqq0v3336/Bgwdr586datKkiXvc+PHjNWfOHPd6ZGSk+8/V1dVKTU1VfHy8NmzYoJKSEqWlpalRo0Z66KGHfFoPAAAAgOARYowx/p7EmTp06JBiY2O1bt069e/fX9J332x1795dTz75ZK2vefPNNzVkyBB9+eWXiouLkyQ9/fTTmj59ug4dOqTw8PCfPG9FRYWio6NVXl4up9PptXoAAAAA1C9nkw0C+jLCHyovL5ckNW/e3GP74sWLdcEFF6hz586aMWOGjh075t5XUFCgLl26uIOWJKWkpKiiokI7duyo9TyVlZWqqKjwWAAAAADgbAT0ZYTfV1NTo8mTJ6tv377q3Lmze/ttt92mtm3bqmXLltq6daumT5+u3bt365VXXpEkuVwuj6Alyb3ucrlqPVd2drZmz55tqRIAAAAAwaDehK2MjAxt375d7733nsf2CRMmuP/cpUsXJSQk6LrrrtPevXv1s5/97JzONWPGDE2dOtW9XlFRodatW5/bxAEAAAAEpXpxGWFmZqZWrFihNWvW6MILL/zRsX369JEkffrpp5Kk+Ph4lZaWeow5uR4fH1/rMSIiIuR0Oj0WAAAAADgbAR22jDHKzMzUq6++qvz8fLVv3/4nX7N582ZJUkJCgiQpOTlZ27Zt08GDB91jVq9eLafTqaSkJCvzBgAAAICAvowwIyNDS5Ys0WuvvaaoqCj3PVbR0dFq3Lix9u7dqyVLlujGG29UixYttHXrVk2ZMkX9+/dX165dJUmDBw9WUlKSRo0apblz58rlcmnmzJnKyMhQRESEP8sDAAAA0IAF9KPfQ0JCat2ek5OjMWPG6MCBA7rjjju0fft2ff3112rdurVuvvlmzZw50+PSv88//1yTJk3S2rVr1aRJE40ePVoPP/ywHI4zy5o8+h0AAACAdHbZIKDDVqAgbAEAAACQGvDvbAEAAABAfUHYAgAAAAALCFsAAAAAYAFhCwAAAAAsIGwBAAAAgAWELQAAAACwgLAFAAAAABYQtgAAAADAAsIWAAAAAFjg8PcEUH8VFRXpyJEjp90fFRWljh07+nBGAAAAQOAgbOGcFBUVKTEx8SfH7dmzh8AFAACAoMRlhDgnP/aN1rmMAwAAABoawhYAAAAAWEDYAgAAAAALCFsAAAAAYAFhCwAAAAAsIGwBAAAAgAWELQAAAACwgLCFcxIVFeXVcQAAAEBDw48a45x07NhRe/bs+dHf0YqKiuIHjQEAABC0CFs4ZwQpAAAA4PS4jBAAAAAALCBsAQAAAIAFhC0AAAAAsICwBQAAAAAWELYAAAAAwALCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELAAAAACwgbAEAAACABYQtAAAAALCAsAUAAAAAFhC2AAAAAMACwhYAAAAAWEDYAgAAAAALCFsAAAAAYAFhC/AiY4yysrKUm5tb6/6NGzdq1KhROn78uG8nBgAAAJ8jbAFe5nK5NHbs2FMC18aNG5WSkqL9+/cTtgAAAIKAw98TABqSkJAQPfXUU5KksWPHSpLGjBnjDlpdu3bVypUr1bRpU39OEwAAAD5A2AK8LDQ01CNwvf/++1qyZIk7aEVFRfl5hgAAAPCFEGOM8fckAl1FRYWio6NVXl4up9Pp7+mgnqipqVHPnj21ZcsWSd+9jwhaAAAA9dvZZAPu2QIs+eCDD7R37173+ssvv+zH2QAAAMDXCFuABSfv0erevbvKy8s1YcKEWh+aAQAAgIaLe7YAL/vhwzCioqJqfWgGAAAAGjbu2ToD3LOFM2WMUe/evXXeeeed8jCMmpoaTZo0ScuWLdPevXvVvHlzP84UAAAA5+JssgFh6wwQtnA2SkpK1LRp01ofhlFTU6N9+/bpoosu8sPMAAAAUFdnkw24jBDwsoSEhNPuCw0NJWgBAAAECR6QAQAAAAAWELYAAAAAwALCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELAAAAACwgbAEAAACABYQtAAAAALCAsAUAAAAAFhC2AAAAAMACwhYAAAAAWEDYAgAAAAALCFsAAAAAYAFhCwAAAAAsIGwBAAAAgAVBFbYWLFigdu3a6bzzzlOfPn30wQcf+HtKAAAAABqooAlbL774oqZOnaqsrCx9/PHH6tatm1JSUnTw4EF/Tw0AAABAAxQ0Yevxxx/X+PHjlZ6erqSkJD399NOKjIzUokWL/D01AAAAAA1QUISt48ePq7CwUAMHDnRvCw0N1cCBA1VQUHDK+MrKSlVUVHgsAAAAAHA2giJsffXVV6qurlZcXJzH9ri4OLlcrlPGZ2dnKzo62r20bt3aV1MFAAAA0EAERdg6WzNmzFB5ebl7OXDggL+nBAAAAKCecfh7Ar5wwQUXKCwsTKWlpR7bS0tLFR8ff8r4iIgIRUREuNeNMZLE5YQAAABAkDuZCU5mhB8TFGErPDxcvXr1Ul5enoYNGyZJqqmpUV5enjIzM3/y9UeOHJEkLicEAAAAIOm7jBAdHf2jY4IibEnS1KlTNXr0aF122WXq3bu3nnzySX399ddKT0//yde2bNlSBw4cUFRUlEJCQnww28BWUVGh1q1b68CBA3I6nf6eTr1ED+uOHtYdPaw7elh39LDu6GHd0cO6C6YeGmN05MgRtWzZ8ifHBk3YGjFihA4dOqQHHnhALpdL3bt316pVq055aEZtQkNDdeGFF/pglvWL0+ls8P8x2UYP644e1h09rDt6WHf0sO7oYd3Rw7oLlh7+1DdaJwVN2JKkzMzMM7psEAAAAADqiqcRAgAAAIAFhC2ctYiICGVlZXk8sRFnhx7WHT2sO3pYd/Sw7uhh3dHDuqOHdUcPaxdizuSZhQAAAACAs8I3WwAAAABgAWELAAAAACwgbAEAAACABYQtAAAAALCAsBWEsrOzdfnllysqKkqxsbEaNmyYdu/e7THm22+/VUZGhlq0aKGmTZtq+PDhKi0t9RhTXFys1NRURUZGKjY2VtOmTVNVVZXHmMWLF6tbt26KjIxUQkKCxo4dq8OHD1uv0TZv9fC3v/2tevXqpYiICHXv3r3Wc23dulX9+vXTeeedp9atW2vu3Lm2yvIpX/Vw7dq1uummm5SQkKAmTZqoe/fuWrx4sc3SfMaX78OTPv30U0VFRalZs2ZersY/fNlDY4zmzZunxMRERUREqFWrVnrwwQdtleYzvuzhW2+9pSuuuEJRUVGKiYnR8OHDtX//fkuV+ZY3+rhlyxaNHDlSrVu3VuPGjXXJJZfoj3/84ynnWrt2rXr27KmIiAhddNFFys3NtV2eT/iqh6+88ooGDRqkmJgYOZ1OJScn66233vJJjbb58n140vr16+VwOH7y86e+ImwFoXXr1ikjI0MbN27U6tWrdeLECQ0ePFhff/21e8yUKVP0xhtvaPny5Vq3bp2+/PJL3XLLLe791dXVSk1N1fHjx7VhwwY9++yzys3N1QMPPOAes379eqWlpWncuHHasWOHli9frg8++EDjx4/3ab02eKOHJ40dO1YjRoyo9TwVFRUaPHiw2rZtq8LCQj366KOaNWuW/vrXv1qrzVd81cMNGzaoa9euevnll7V161alp6crLS1NK1assFabr/iqhyedOHFCI0eOVL9+/bxei7/4sod33323/v73v2vevHn65JNP9Prrr6t3795W6vIlX/Xws88+00033aRrr71Wmzdv1ltvvaWvvvqq1uPUR97oY2FhoWJjY/X8889rx44d+v3vf68ZM2boz3/+s3vMZ599ptTUVA0YMECbN2/W5MmTdeeddzaIsOCrHr777rsaNGiQVq5cqcLCQg0YMEBDhw7Vpk2bfFqvDb7q4UllZWVKS0vTdddd55P6/MIg6B08eNBIMuvWrTPGGFNWVmYaNWpkli9f7h6za9cuI8kUFBQYY4xZuXKlCQ0NNS6Xyz3mqaeeMk6n01RWVhpjjHn00UdNhw4dPM41f/5806pVK9sl+dy59PD7srKyTLdu3U7ZvnDhQnP++ee7e2qMMdOnTzedOnXyfhF+ZquHtbnxxhtNenq6V+YdSGz38L777jN33HGHycnJMdHR0d6efkCw1cOdO3cah8NhPvnkE2tzDxS2erh8+XLjcDhMdXW1e9vrr79uQkJCzPHjx71fiJ/VtY8n3XXXXWbAgAHu9fvuu89ceumlHmNGjBhhUlJSvFyB/9nqYW2SkpLM7NmzvTPxAGK7hyNGjDAzZ848q8/w+oZvtqDy8nJJUvPmzSV9938kTpw4oYEDB7rHXHzxxWrTpo0KCgokSQUFBerSpYvi4uLcY1JSUlRRUaEdO3ZIkpKTk3XgwAGtXLlSxhiVlpbqpZde0o033uir0nzmXHp4JgoKCtS/f3+Fh4e7t6WkpGj37t3673//66XZBwZbPTzduU6epyGx2cP8/HwtX75cCxYs8N6EA5CtHr7xxhvq0KGDVqxYofbt26tdu3a688479Z///Me7BQQAWz3s1auXQkNDlZOTo+rqapWXl+sf//iHBg4cqEaNGnm3iADgrT7+8O+7goICj2NI332u1PXv1UBkq4c/VFNToyNHjvC5cpY9zMnJ0b59+5SVlWVh5oGDsBXkampqNHnyZPXt21edO3eWJLlcLoWHh59yT0ZcXJxcLpd7zPeD1sn9J/dJUt++fbV48WKNGDFC4eHhio+PV3R0dIP7x9q59vBMnEmfGwKbPfyhZcuW6cMPP1R6enpdphxwbPbw8OHDGjNmjHJzc+V0Or057YBis4f79u3T559/ruXLl+u5555Tbm6uCgsLdeutt3qzBL+z2cP27dvr7bff1v3336+IiAg1a9ZMX3zxhZYtW+bNEgKCt/q4YcMGvfjii5owYYJ72+k+VyoqKvTNN994txA/stnDH5o3b56OHj2qX/7yl16bfyCw2cOioiL97ne/0/PPPy+Hw2GthkBA2ApyGRkZ2r59u5YuXer1Y+/cuVN33323HnjgARUWFmrVqlXav3+/Jk6c6PVz+ZPNHgYLX/VwzZo1Sk9P19/+9jddeumlVs/lazZ7OH78eN12223q37+/148dSGz2sKamRpWVlXruuefUr18/XXPNNXrmmWe0Zs2aU24+r89s9tDlcmn8+PEaPXq0PvzwQ61bt07h4eG69dZbZYzx+vn8yRt93L59u2666SZlZWVp8ODBXpxd/eCrHi5ZskSzZ8/WsmXLFBsbe87nCkS2elhdXa3bbrtNs2fPVmJioremG7AIW0EsMzNTK1as0Jo1a3ThhRe6t8fHx+v48eMqKyvzGF9aWqr4+Hj3mB8+Serk+skx2dnZ6tu3r6ZNm6auXbsqJSVFCxcu1KJFi1RSUmKxMt+pSw/PxJn0ub6z3cOT1q1bp6FDh+qJJ55QWlpaXacdUGz3MD8/X/PmzZPD4ZDD4dC4ceNUXl4uh8OhRYsWeasMv7Ldw4SEBDkcDo9/WFxyySWSvnuya0Ngu4cLFixQdHS05s6dqx49eqh///56/vnnlZeXp/fff99bZfidN/q4c+dOXXfddZowYYJmzpzpse90nytOp1ONGzf2bjF+YruHJy1dulR33nmnli1bdsqlmfWdzR4eOXJEH330kTIzM92fK3PmzNGWLVvkcDiUn59vtTZfI2wFIWOMMjMz9eqrryo/P1/t27f32N+rVy81atRIeXl57m27d+9WcXGxkpOTJX13P9a2bdt08OBB95jVq1fL6XQqKSlJknTs2DGFhnq+xcLCwtxzqM+80cMzkZycrHfffVcnTpxwb1u9erU6deqk888/v+6F+JGveih995jj1NRUPfLIIz96KUh946seFhQUaPPmze5lzpw5ioqK0ubNm3XzzTd7rR5/8FUP+/btq6qqKu3du9e9bc+ePZKktm3b1rEK//JVD3/sM6WmpqYOFQQGb/Vxx44dGjBggEaPHl3rTwskJyd7HEP67nPlbP9eDUS+6qEkvfDCC0pPT9cLL7yg1NRUOwX5gS966HQ6tW3bNo/PlYkTJ6pTp07avHmz+vTpY7dIX/PPczngT5MmTTLR0dFm7dq1pqSkxL0cO3bMPWbixImmTZs2Jj8/33z00UcmOTnZJCcnu/dXVVWZzp07m8GDB5vNmzebVatWmZiYGDNjxgz3mJycHONwOMzChQvN3r17zXvvvWcuu+wy07t3b5/Wa4M3emiMMUVFRWbTpk3m17/+tUlMTDSbNm0ymzZtcj99sKyszMTFxZlRo0aZ7du3m6VLl5rIyEjzl7/8xaf12uCrHubn55vIyEgzY8YMj/McPnzYp/Xa4Kse/lBDehqhr3pYXV1tevbsafr3728+/vhj89FHH5k+ffqYQYMG+bReG3zVw7y8PBMSEmJmz55t9uzZYwoLC01KSopp27atx7nqK2/0cdu2bSYmJsbccccdHsc4ePCge8y+fftMZGSkmTZtmtm1a5dZsGCBCQsLM6tWrfJpvTb4qoeLFy82DofDLFiwwGNMWVmZT+u1wVc9/KGG/DRCwlYQklTrkpOT4x7zzTffmLvuusucf/75JjIy0tx8882mpKTE4zj79+83N9xwg2ncuLG54IILzD333GNOnDjhMWb+/PkmKSnJNG7c2CQkJJjbb7/dfPHFF74o0ypv9fDqq6+u9TifffaZe8yWLVvMVVddZSIiIkyrVq3Mww8/7KMq7fJVD0ePHl3r/quvvtp3xVriy/fh9zWksOXLHv773/82t9xyi2natKmJi4szY8aMaRCh35c9fOGFF0yPHj1MkyZNTExMjPn5z39udu3a5aNK7fJGH7Oysmo9Rtu2bT3OtWbNGtO9e3cTHh5uOnTo4HGO+sxXPTzde3X06NG+K9YSX74Pv68hh60QY+r59VwAAAAAEIC4ZwsAAAAALCBsAQAAAIAFhC0AAAAAsICwBQAAAAAWELYAAAAAwALCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELABB0xowZo5CQEIWEhKhRo0aKi4vToEGDtGjRItXU1JzxcXJzc9WsWTN7EwUA1GuELQBAULr++utVUlKi/fv3680339SAAQN09913a8iQIaqqqvL39AAADQBhCwAQlCIiIhQfH69WrVqpZ8+euv/++/Xaa6/pzTffVG5uriTp8ccfV5cuXdSkSRO1bt1ad911l44ePSpJWrt2rdLT01VeXu7+lmzWrFmSpMrKSt17771q1aqVmjRpoj59+mjt2rX+KRQA4DeELQAA/t+1116rbt266ZVXXpEkhYaGav78+dqxY4eeffZZ5efn67777pMkXXnllXryySfldDpVUlKikpIS3XvvvZKkzMxMFRQUaOnSpdq6dat+8Ytf6Prrr1dRUZHfagMA+F6IMcb4exIAAPjSmDFjVFZWpn/+85+n7PvVr36lrVu3aufOnafse+mllzRx4kR99dVXkr67Z2vy5MkqKytzjykuLlaHDh1UXFysli1burcPHDhQvXv31kMPPeT1egAAgcnh7wkAABBIjDEKCQmRJL3zzjvKzs7WJ598ooqKClVVVenbb7/VsWPHFBkZWevrt23bpurqaiUmJnpsr6ysVIsWLazPHwAQOAhbAAB8z65du9S+fXvt379fQ4YM0aRJk/Tggw+qefPmeu+99zRu3DgdP378tGHr6NGjCgsLU2FhocLCwjz2NW3a1BclAAACBGELAID/l5+fr23btmnKlCkqLCxUTU2NHnvsMYWGfneL87JlyzzGh4eHq7q62mNbjx49VF1drYMHD6pfv34+mzsAIPAQtgAAQamyslIul0vV1dUqLS3VqlWrlJ2drSFDhigtLU3bt2/XiRMn9Kc//UlDhw7V+vXr9fTTT3sco127djp69Kjy8vLUrVs3RUZGKjExUbfffrvS0tL02GOPqUePHjp06JDy8vLUtWtXpaam+qliAICv8TRCAEBQWrVqlRISEtSuXTtdf/31WrNmjebPn6/XXntNYWFh6tatmx5//HE98sgj6ty5sxYvXqzs7GyPY1x55ZWaOHGiRowYoZiYGM2dO1eSlJOTo7S0NN1zzz3q1KmThg0bpg8//FBt2rTxR6kAAD/haYQAAAAAYAHfbAEAAACABYQtAAA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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
@@ -2675,9 +2702,23 @@
     "for dataset, marker in iceflow_marker_map.items():\n",
     "    subset = iceflow_avg[iceflow_avg.dataset == dataset]\n",
     "    if subset.elevation.any():\n",
-    "        plt.scatter(subset.index, subset.elevation, marker=marker, label=dataset, linestyle=\"\", color=\"black\")\n",
+    "        plt.scatter(\n",
+    "            subset.index,\n",
+    "            subset.elevation,\n",
+    "            marker=marker,\n",
+    "            label=dataset,\n",
+    "            linestyle=\"\",\n",
+    "            color=\"black\",\n",
+    "        )\n",
     "\n",
-    "plt.scatter(atl06_avg_df.index, atl06_avg_df.elevation, color=\"black\", marker=\"*\", label=\"ATL06v6\", linestyle=\"\")\n",
+    "plt.scatter(\n",
+    "    atl06_avg_df.index,\n",
+    "    atl06_avg_df.elevation,\n",
+    "    color=\"black\",\n",
+    "    marker=\"*\",\n",
+    "    label=\"ATL06v6\",\n",
+    "    linestyle=\"\",\n",
+    ")\n",
     "\n",
     "plt.xlabel(\"Date\")\n",
     "plt.ylabel(\"Elevation\")\n",
@@ -2720,64 +2761,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.10"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "021908baf2db408698e5345a8a7dc741": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "FloatProgressModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "FloatProgressModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_ea20a0a062984094885095a68938074c",
-       "max": 2.0,
-       "min": 0.0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_ffdd290fffb943b8b76793f28e17a0b8",
-       "tabbable": null,
-       "tooltip": null,
-       "value": 2.0
-      }
-     },
-     "02c79c10bf5247e08a75f687330e4c33": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "FloatProgressModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
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diff --git a/notebooks/iceflow-example.ipynb b/notebooks/iceflow-example.ipynb
index 76b01b9..f339d74 100644
--- a/notebooks/iceflow-example.ipynb
+++ b/notebooks/iceflow-example.ipynb
@@ -33,21 +33,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "import datetime as dt\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "from nsidc.iceflow import (\n",
-    "    find_iceflow_data,\n",
-    "    download_iceflow_results,\n",
-    "    read_iceflow_datafiles,\n",
-    "    DatasetSearchParameters,\n",
     "    BoundingBox,\n",
+    "    DatasetSearchParameters,\n",
     "    ILATM1BDataset,\n",
+    "    download_iceflow_results,\n",
+    "    find_iceflow_data,\n",
+    "    read_iceflow_datafiles,\n",
     ")\n",
-    "from pathlib import Path\n",
-    "import datetime as dt\n",
-    "import matplotlib.pyplot as plt\n",
+    "\n",
     "# note: use `inline` to save the resulting image as an embedded png (nice for sharing). \\\n",
     "# Use `widget` to obtain interactive controls to explore the data in depth!\n",
     "%matplotlib inline\n",
-    "# %matplotlib widget\n"
+    "# %matplotlib widget"
    ]
   },
   {
@@ -73,8 +78,13 @@
     "# Define the dataset that we want to search for.\n",
     "atm1b_v1_dataset = ILATM1BDataset(version=\"1\")\n",
     "\n",
-    "# Define a bounding box for our area of interest. \n",
-    "BBOX = BoundingBox(lower_left_lon=-103.125559, lower_left_lat=-75.180563, upper_right_lon=-102.677327, upper_right_lat=-74.798063)\n",
+    "# Define a bounding box for our area of interest.\n",
+    "BBOX = BoundingBox(\n",
+    "    lower_left_lon=-103.125559,\n",
+    "    lower_left_lat=-75.180563,\n",
+    "    upper_right_lon=-102.677327,\n",
+    "    upper_right_lat=-74.798063,\n",
+    ")\n",
     "\n",
     "# We will define a short date range in 2009 to search for data.\n",
     "date_range = (dt.date(2009, 11, 1), dt.date(2009, 12, 31))"
@@ -202,7 +212,9 @@
    "outputs": [],
    "source": [
     "for variable in [\"latitude\", \"longitude\", \"elevation\"]:\n",
-    "    print(f\"Max difference in {variable}: {abs(itrf2014_df[variable] - iceflow_df[variable]).max()}\")"
+    "    print(\n",
+    "        f\"Max difference in {variable}: {abs(itrf2014_df[variable] - iceflow_df[variable]).max()}\"\n",
+    "    )"
    ]
   },
   {
@@ -257,7 +269,9 @@
    "outputs": [],
    "source": [
     "for variable in [\"latitude\", \"longitude\", \"elevation\"]:\n",
-    "    print(f\"Max difference in {variable}: {abs(itrf2014_epoch_2019_7_df[variable] - iceflow_df[variable]).max()}\")"
+    "    print(\n",
+    "        f\"Max difference in {variable}: {abs(itrf2014_epoch_2019_7_df[variable] - iceflow_df[variable]).max()}\"\n",
+    "    )"
    ]
   },
   {
@@ -277,7 +291,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filter_condition = (iceflow_df.reset_index().index > 50) & ( iceflow_df.reset_index().index < 60)\n",
+    "filter_condition = (iceflow_df.reset_index().index > 50) & (\n",
+    "    iceflow_df.reset_index().index < 60\n",
+    ")\n",
     "sampled_iceflow_df = iceflow_df[filter_condition]\n",
     "sampled_itrf2014_df = itrf2014_df[filter_condition]\n",
     "sampled_itrf2014_epoch_2019_7_df = itrf2014_epoch_2019_7_df[filter_condition]"
diff --git a/notebooks/iceflow-with-icepyx.ipynb b/notebooks/iceflow-with-icepyx.ipynb
index 1ba8d65..31c2ebf 100644
--- a/notebooks/iceflow-with-icepyx.ipynb
+++ b/notebooks/iceflow-with-icepyx.ipynb
@@ -49,8 +49,10 @@
    "outputs": [],
    "source": [
     "# Imports\n",
-    "from pathlib import Path\n",
+    "from __future__ import annotations\n",
+    "\n",
     "import datetime as dt\n",
+    "from pathlib import Path\n",
     "\n",
     "import dask.dataframe as dd\n",
     "import icepyx as ipx\n",
@@ -60,12 +62,12 @@
     "import xarray as xr\n",
     "\n",
     "from nsidc.iceflow import (\n",
-    "    make_iceflow_parquet,\n",
-    "    DatasetSearchParameters,\n",
     "    BoundingBox,\n",
-    "    find_iceflow_data,\n",
+    "    DatasetSearchParameters,\n",
+    "    IceflowDataFrame,\n",
     "    download_iceflow_results,\n",
-    "    IceflowDataFrame\n",
+    "    find_iceflow_data,\n",
+    "    make_iceflow_parquet,\n",
     ")"
    ]
   },
@@ -90,8 +92,8 @@
    "source": [
     "# Constants\n",
     "\n",
-    "# All of our data will be downloaded to this location. \n",
-    "OUTPUT_DIR =  Path(\"./downloaded-data/\")\n",
+    "# All of our data will be downloaded to this location.\n",
+    "OUTPUT_DIR = Path(\"./downloaded-data/\")\n",
     "\n",
     "# ICESat2 data products use ITRF2014 (e.g., see https://nsidc.org/data/atl06/versions/6):\n",
     "# > WGS 84 ellipsoid, ITRF2014 reference frame\n",
@@ -100,7 +102,12 @@
     "ICESAT2_ITRF = \"ITRF2014\"\n",
     "\n",
     "# This bounding box covers an area near Sermeq Kujalleq (Jakobshavn Isbrae)\n",
-    "BBOX = BoundingBox(lower_left_lon=-49.149, lower_left_lat=69.186, upper_right_lon=-48.949, upper_right_lat=69.238)\n",
+    "BBOX = BoundingBox(\n",
+    "    lower_left_lon=-49.149,\n",
+    "    lower_left_lat=69.186,\n",
+    "    upper_right_lon=-48.949,\n",
+    "    upper_right_lat=69.238,\n",
+    ")\n",
     "\n",
     "# Range of dates we want to evaluate\n",
     "DATE_RANGE = (dt.date(2007, 1, 1), dt.date(2024, 10, 28))"
@@ -195,7 +202,7 @@
    "source": [
     "iceflow_df = dd.read_parquet(parquet_path)\n",
     "\n",
-    "# Ensure that our index is set as a datetime object \n",
+    "# Ensure that our index is set as a datetime object\n",
     "iceflow_df = iceflow_df.reset_index()\n",
     "iceflow_df[\"utc_datetime\"] = dd.to_datetime(iceflow_df[\"utc_datetime\"])\n",
     "iceflow_df = iceflow_df.set_index(\"utc_datetime\")\n",
@@ -290,7 +297,7 @@
     "        reader.vars.append(var_list=[\"h_li\", \"latitude\", \"longitude\"])\n",
     "        ds = reader.load()\n",
     "        datasets.append(ds)\n",
-    "    except:\n",
+    "    except Exception:\n",
     "        print(f\"{file=} contains an error and will not be read\")\n",
     "        continue\n",
     "\n",
@@ -364,8 +371,8 @@
     "# Drop rows where lat, lon, or elev are missing.\n",
     "df = df.dropna(subset=[\"latitude\", \"longitude\", \"elevation\"], how=\"any\")\n",
     "df = df.set_index(\"utc_datetime\")\n",
-    "# Cast the df as an IceflowDataFrame. \n",
-    "# The `atl06_df` can now be used with e.g., `iceflow`'s ITRF conversion function \n",
+    "# Cast the df as an IceflowDataFrame.\n",
+    "# The `atl06_df` can now be used with e.g., `iceflow`'s ITRF conversion function\n",
     "# to perform plate motion model adjustments if desired.\n",
     "atl06_df = IceflowDataFrame(df)\n",
     "atl06_df.head()"
@@ -434,11 +441,13 @@
     "\n",
     "iceflow_df_sampled = iceflow_df_sampled[iceflow_df_sampled.elevation > 0]\n",
     "\n",
-    "iceflow_avg = iceflow_df_sampled.resample(\"W\").agg({\n",
-    "    \"elevation\": \"mean\",\n",
-    "    \"dataset\": lambda x: \", \".join(x.astype(\"str\").unique()),   \n",
-    "})\n",
-    "iceflow_avg = iceflow_avg.replace([np.inf, -np.inf], np.nan) \n",
+    "iceflow_avg = iceflow_df_sampled.resample(\"W\").agg(\n",
+    "    {\n",
+    "        \"elevation\": \"mean\",\n",
+    "        \"dataset\": lambda x: \", \".join(x.astype(\"str\").unique()),\n",
+    "    }\n",
+    ")\n",
+    "iceflow_avg = iceflow_avg.replace([np.inf, -np.inf], np.nan)\n",
     "iceflow_avg = iceflow_avg.dropna(how=\"any\")\n",
     "iceflow_avg = iceflow_avg.compute()\n",
     "\n",
@@ -479,9 +488,23 @@
     "for dataset, marker in iceflow_marker_map.items():\n",
     "    subset = iceflow_avg[iceflow_avg.dataset == dataset]\n",
     "    if subset.elevation.any():\n",
-    "        plt.scatter(subset.index, subset.elevation, marker=marker, label=dataset, linestyle=\"\", color=\"black\")\n",
+    "        plt.scatter(\n",
+    "            subset.index,\n",
+    "            subset.elevation,\n",
+    "            marker=marker,\n",
+    "            label=dataset,\n",
+    "            linestyle=\"\",\n",
+    "            color=\"black\",\n",
+    "        )\n",
     "\n",
-    "plt.scatter(atl06_avg_df.index, atl06_avg_df.elevation, color=\"black\", marker=\"*\", label=\"ATL06v6\", linestyle=\"\")\n",
+    "plt.scatter(\n",
+    "    atl06_avg_df.index,\n",
+    "    atl06_avg_df.elevation,\n",
+    "    color=\"black\",\n",
+    "    marker=\"*\",\n",
+    "    label=\"ATL06v6\",\n",
+    "    linestyle=\"\",\n",
+    ")\n",
     "\n",
     "plt.xlabel(\"Date\")\n",
     "plt.ylabel(\"Elevation\")\n",
diff --git a/pyproject.toml b/pyproject.toml
index 8a1700e..5b2d90b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,14 +24,14 @@ classifiers = [
   "Typing :: Typed",
 ]
 dependencies = [
-  "numpy >=2.0.1",
+  "numpy ~=2.0.1",
   "earthaccess >=0.10.0",
   "pandas >=2.2",
   "h5py >=3.11",
   "gps-timemachine >=1.1.4",
-  "pyproj >=3.6.1",
+  "pyproj ~=3.6.1",
   "shapely >=2.0.5",
-  "pandera[mypy] ~= 0.20.3",
+  "pandera[mypy] ~=0.23.0",
   "pydantic >=2.8.2",
   "loguru >=0.7.2",
   "dask[dataframe] >=2024.10.0",
@@ -47,7 +47,7 @@ Changelog = "https://github.com/NSIDC/iceflow/releases"
 dev = [
   "bump-my-version",
   "invoke",
-  "mypy >=1.11.1",
+  "mypy >=1.15.0",
   "pandas-stubs >=2.2",
   "pre-commit",
   "pytest",
@@ -83,6 +83,7 @@ addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"]
 xfail_strict = true
 filterwarnings = [
   "error",
+  "ignore:The 'shapely.geos' module is deprecated",
 ]
 log_cli_level = "INFO"
 
diff --git a/src/nsidc/iceflow/__init__.py b/src/nsidc/iceflow/__init__.py
index e934c77..e1aca73 100644
--- a/src/nsidc/iceflow/__init__.py
+++ b/src/nsidc/iceflow/__init__.py
@@ -38,18 +38,18 @@
 
 
 __all__ = [
-    "__version__",
-    "make_iceflow_parquet",
-    "download_iceflow_results",
-    "find_iceflow_data",
-    "read_iceflow_datafiles",
-    "transform_itrf",
-    "DatasetSearchParameters",
-    "BoundingBox",
     "ALL_DATASETS",
     "BLATM1BDataset",
+    "BoundingBox",
+    "DatasetSearchParameters",
     "GLAH06Dataset",
     "ILATM1BDataset",
     "ILVIS2Dataset",
     "IceflowDataFrame",
+    "__version__",
+    "download_iceflow_results",
+    "find_iceflow_data",
+    "make_iceflow_parquet",
+    "read_iceflow_datafiles",
+    "transform_itrf",
 ]
diff --git a/src/nsidc/iceflow/api.py b/src/nsidc/iceflow/api.py
index 989e98c..0ba969b 100644
--- a/src/nsidc/iceflow/api.py
+++ b/src/nsidc/iceflow/api.py
@@ -108,16 +108,16 @@ def make_iceflow_parquet(
         short_name, version = subdir.name.split("_")
         iceflow_df["dataset"] = [f"{short_name}v{version}"] * len(iceflow_df.latitude)
         common_columns = ["latitude", "longitude", "elevation", "dataset"]
-        common_dask_df = dd.from_pandas(iceflow_df[common_columns])  # type: ignore[attr-defined]
+        common_dask_df = dd.from_pandas(iceflow_df[common_columns])
         if parquet_subdir.exists():
-            dd.to_parquet(  # type: ignore[attr-defined]
+            dd.to_parquet(
                 df=common_dask_df,
                 path=parquet_subdir,
                 append=True,
                 ignore_divisions=True,
             )
         else:
-            dd.to_parquet(  # type: ignore[attr-defined]
+            dd.to_parquet(
                 df=common_dask_df,
                 path=parquet_subdir,
             )
diff --git a/tests/integration/test_e2e.py b/tests/integration/test_e2e.py
index 83bd93a..d2db54c 100644
--- a/tests/integration/test_e2e.py
+++ b/tests/integration/test_e2e.py
@@ -213,7 +213,7 @@ def test_create_iceflow_parquet(tmp_path):
         target_itrf=target_itrf,
     )
 
-    df = dd.read_parquet(parquet_path)  # type: ignore[attr-defined]
+    df = dd.read_parquet(parquet_path)
 
     # Assert that the parquet data has the expected columns
     expected_columns = sorted(["latitude", "longitude", "elevation", "dataset"])