From 8cc5a23ed2219c6bfd385db2ae3ef73748ba89b6 Mon Sep 17 00:00:00 2001 From: betolink Date: Wed, 15 Nov 2023 12:51:08 -0600 Subject: [PATCH] experimenting with interactive maps --- earthaccess/__init__.py | 3 +- earthaccess/api.py | 7 + earthaccess/formatters.py | 2 +- earthaccess/results.py | 11 +- earthaccess/search.py | 12 +- earthaccess/widgets.py | 272 +++ notebooks/bosque_primavera.json | 8 + notebooks/custom-maps.ipynb | 202 +++ notebooks/explore.ipynb | 491 ++++++ poetry.lock | 2802 ++++++++++++++++++------------- pyproject.toml | 2 + 11 files changed, 2656 insertions(+), 1156 deletions(-) create mode 100644 notebooks/bosque_primavera.json create mode 100644 notebooks/custom-maps.ipynb create mode 100644 notebooks/explore.ipynb diff --git a/earthaccess/__init__.py b/earthaccess/__init__.py index 4e71e69d..8c4348f5 100644 --- a/earthaccess/__init__.py +++ b/earthaccess/__init__.py @@ -1,7 +1,6 @@ import logging import threading from importlib.metadata import version -from typing import Any from .api import ( auth_environ, @@ -16,6 +15,7 @@ open, search_data, search_datasets, + search_widget ) from .auth import Auth from .search import DataCollections, DataGranules @@ -39,6 +39,7 @@ "DataCollections", "Auth", "Store", + "search_widget", "auth_environ", ] diff --git a/earthaccess/api.py b/earthaccess/api.py index 4ef8598d..e7de0e76 100644 --- a/earthaccess/api.py +++ b/earthaccess/api.py @@ -5,6 +5,8 @@ import s3fs from fsspec import AbstractFileSystem +from .widgets import SearchWidget + from .auth import Auth from .results import DataGranule from .search import CollectionQuery, DataCollections, DataGranules, GranuleQuery @@ -335,6 +337,11 @@ def get_edl_token() -> str: return token +def search_widget(projection: str = "global", map: Any = None) -> Any: + sw = SearchWidget(projection=projection, map=map) + return sw + + def auth_environ() -> Dict[str, str]: auth = earthaccess.__auth__ if not auth.authenticated: diff --git a/earthaccess/formatters.py b/earthaccess/formatters.py index d37d4e22..54f754c2 100644 --- a/earthaccess/formatters.py +++ b/earthaccess/formatters.py @@ -21,7 +21,7 @@ def _repr_collection_html() -> str: def _repr_granule_html(granule: Any) -> str: css_styles = _load_static_files() css_inline = f"""""" style = "max-height: 120px;" dataviz_img = "".join( diff --git a/earthaccess/results.py b/earthaccess/results.py index f0bb2167..7facef4a 100644 --- a/earthaccess/results.py +++ b/earthaccess/results.py @@ -207,7 +207,16 @@ def __init__( self.cloud_hosted = cloud_hosted # TODO: maybe add area, start date and all that as an instance value self["size"] = self.size() - self.uuid = str(uuid.uuid4()) + if "ConceptId" in self["umm"]["CollectionReference"]: + self["dataset-id"] = self["umm"]["CollectionReference"]["ConceptId"] + elif "ShortName" in self["umm"]["CollectionReference"]: + self["dataset-id"] = self["umm"]["CollectionReference"]["ShortName"] + elif "EntryTitle" in self["umm"]["CollectionReference"]: + self["dataset-id"] = self["umm"]["CollectionReference"]["EntryTitle"] + else: + self["dataset-id"] = self["meta"]["provider-id"] + self["meta"]["native-id"][0:4] + + self.render_dict: Any if fields is None: self.render_dict = self diff --git a/earthaccess/search.py b/earthaccess/search.py index 0fd8e60b..2d888981 100644 --- a/earthaccess/search.py +++ b/earthaccess/search.py @@ -1,6 +1,7 @@ import datetime as dt from inspect import getmembers, ismethod from typing import Any, Dict, List, Optional, Tuple, Type +from uuid import uuid4 import dateutil.parser as parser # type: ignore from cmr import CollectionQuery, GranuleQuery # type: ignore @@ -404,12 +405,7 @@ def cloud_hosted(self, cloud_hosted: bool = True) -> Type[CollectionQuery]: if not isinstance(cloud_hosted, bool): raise TypeError("cloud_hosted must be of type bool") - if "short_name" in self.params: - provider = find_provider_by_shortname( - self.params["short_name"], cloud_hosted - ) - if provider is not None: - self.params["provider"] = provider + self.params["cloud_hosted"] = cloud_hosted return self def granule_name(self, granule_name: str) -> Type[CollectionQuery]: @@ -527,6 +523,7 @@ def get(self, limit: int = 2000) -> list: results: List = [] page = 1 headers: Dict[str, str] = {} + while len(results) < limit: params = {"page_size": page_size} # TODO: should be in a logger @@ -556,7 +553,8 @@ def get(self, limit: int = 2000) -> list: else: cloud = False latest = list( - DataGranule(granule, cloud_hosted=cloud) + DataGranule(granule, + cloud_hosted=cloud) for granule in response.json()["items"] ) else: diff --git a/earthaccess/widgets.py b/earthaccess/widgets.py index e69de29b..37b06643 100644 --- a/earthaccess/widgets.py +++ b/earthaccess/widgets.py @@ -0,0 +1,272 @@ +from typing import List, Any, Tuple, Union, Dict +import geopandas +import pandas + +import re + +from shapely import geometry +from shapely.geometry.polygon import orient +from shapely.geometry import Polygon +import shapely + +import matplotlib.pyplot as plt +import matplotlib + +from .results import DataGranule + +import ipyleaflet +import ipywidgets + + +class SearchWidget: + + def __init__(self, params: Dict[str, Any] = {}, projection: str="global", map: Any = None): + self.current_geometry = None + self.active_layers = [] + self.roi = [] + self.overview = None + self.map = map + self.default_fields = ["size", + "concept_id", + "dataset-id", + "native-id", + "provider-id", + "_related_urls", + "_beginning_date_time", + "_ending_date_time", + "geometry"] + self.html = ipywidgets.HTML( + """ +

File Info

+ Hover over a file footprint + """ + ) + self.html.layout.margin = "0px 20px 20px 20px" + + self._build_widget(params, projection) + + def _build_widget(self, params: Dict[str, Any]={}, projection: str ="global") -> None: + self.map_projection = projection + self.search_params = params + + if "polygon" not in self.search_params: + self.search_params["polygon"] = "" + self.dc = ipyleaflet.DrawControl(marker={"shapeOptions": {"color": "#0000FF"}}, + circlemarker={}, + rectangle={"shapeOptions": {"color": "#0000FF"}},) + self.dc.on_draw(self._handle_draw) + proj_dict = { + "global": { + "basemap": ipyleaflet.basemaps.Esri.WorldImagery, + "crs": ipyleaflet.projections.EPSG3857, + "map_center": [0, 0] + }, + "south": { + "basemap": ipyleaflet.basemaps.NASAGIBS.BlueMarble3031, + "crs": ipyleaflet.projections.EPSG3031.NASAGIBS, + "map_center": [-90, 0] + + }, + "north": { + "basemap": ipyleaflet.basemaps.NASAGIBS.BlueMarble3413, + "crs": ipyleaflet.projections.EPSG3413.NASAGIBS, + "map_center": [90, 0] + + } + } + if self.map is None: + + self.m = ipyleaflet.Map( + center=proj_dict[self.map_projection]["map_center"], + zoom=3, + prefer_canvas=True, + basemap=proj_dict[self.map_projection]["basemap"], + crs=proj_dict[self.map_projection]["crs"], + ) + else: + self.m = self.map + self.m.clear_controls() + self.m.add(ipyleaflet.ZoomControl()) + + self.m.add(ipyleaflet.LayersControl()) + self.m.add(ipyleaflet.FullScreenControl()) + self.m.add_control( + ipyleaflet.MeasureControl( + position="topleft", + active_color="orange", + primary_length_unit="kilometers", + ) + ) + self.m.layout.height = '600px' + + self.m.add(self.dc) + + return None + + def _flattent_column_names(self, df: pandas.DataFrame) -> pandas.DataFrame: + df.columns = [re.sub('([A-Z]+)', r'_\1', col.split(".")[-1]).lower() for col in df.columns] + return df + + + def to_geopandas(self, results: List[DataGranule], fields: List[str] = []) -> geopandas.GeoDataFrame: + + results_df = pandas.json_normalize(list(results), errors="ignore") + # results_df = results_df.loc[:,~results_df.columns.duplicated()].copy() + results_df = self._flattent_column_names(results_df) + if len(fields) == 0: + fields = self.default_fields + + results_df = results_df.drop(columns=[col for col in results_df.columns if col not in fields]) + + # results_df["_related_urls"] = results_df["_related_urls"].apply( lambda r: [l["URL"] for l in r._related_urls if l["Type"] == "GET DATA"]) + results_df["_related_urls"] = results_df["_related_urls"].apply( lambda r: [l for l in r if l["Type"] in ["GET DATA", "GET DATA VIA DIRECT ACCESS", "GET RELATED VISUALIZATION"]]) + + # Create shapely polygons for result + geometries = [self._get_shapely_object(results[index]) for index in results_df.index.to_list()] + # Convert to GeoDataframe + gdf = geopandas.GeoDataFrame(results_df, geometry=geometries, crs="EPSG:4326") + return gdf + + + + def _orient_polygon(self, coords) -> List[Tuple[int, int]]: + polygon = orient(Polygon(coords)) + return list(polygon.exterior.coords) + + def _extract_geometry_info(self, geometry) -> Any: + geometry_type = geometry['type'] + coordinates = geometry['coordinates'] + + if geometry_type in ['Polygon']: + coords = self._orient_polygon(coordinates[0]) + self.search_params["polygon"]= coords + return coords + elif geometry_type in ['Point']: + self.search_params["point"] = coordinates + return coordinates + elif geometry_type in ['LineString']: + self.search_params["line"] = coordinates + return coordinates + else: + print("Unsupported geometry type:", geometry_type) + return None + + + def _handle_draw(self, target, action, geo_json): + for l in self.active_layers: + self.m.remove_layer(l) + + self.active_layers = [] + + self.dc.clear() + if self.current_geometry: + self.m.remove_layer(self.current_geometry) + + self.current_geometry = ipyleaflet.GeoJSON(name="ROI", + data=geo_json, + style={ + "color": "red", + "opacity": 0.9, + "fillOpacity": 0.1 + } + ) + + self.roi = self._extract_geometry_info(geo_json["geometry"]) + + self.m.add(self.current_geometry) + + + def _get_shapely_object(self, result: DataGranule) -> Union[shapely.Geometry, None]: + shape = None + try: + geo = result['umm']['SpatialExtent']['HorizontalSpatialDomain']['Geometry'] + keys = geo.keys() + if 'BoundingRectangles' in keys: + bounding_rectangle = geo['BoundingRectangles'][0] + # Create bbox tuple + bbox_coords = (bounding_rectangle['WestBoundingCoordinate'],bounding_rectangle['SouthBoundingCoordinate'], + bounding_rectangle['EastBoundingCoordinate'],bounding_rectangle['NorthBoundingCoordinate']) + # Create shapely geometry from bbox + shape = geometry.box(*bbox_coords, ccw=True) + elif 'GPolygons' in keys: + points = geo['GPolygons'][0]['Boundary']['Points'] + # Create shapely geometry from polygons + shape = geometry.Polygon([[p['Longitude'],p['Latitude']] for p in points]) + else: + raise ValueError('Provided result does not contain bounding boxes/polygons or is incompatible.') + + except Exception as e: + print(e) + pass + + return shape + + def display(self): + return self.m + + def _calculate_bbox_center(self, bbox): + # Calculate the center coordinates + center_x = (bbox[0] + bbox[2]) / 2 + center_y = (bbox[1] + bbox[3]) / 2 + + return [center_x, center_y] + + def update_html(self, feature, **kwargs): + native_id = feature["properties"]["native-id"] + start = feature["properties"]["_beginning_date_time"] + end = feature["properties"]["_ending_date_time"] + date_range = f"Start: {start} <> End: {end}" + size = feature["properties"]["size"] + url = [f"link" for link in feature["properties"]["_related_urls"] if link["Type"] == "GET DATA" and link["URL"].startswith("https")] + preview = [f"" for link in feature["properties"]["_related_urls"] if link["Type"] == "GET RELATED VISUALIZATION" and link["URL"].startswith("https")] + if len(preview)>1: + browse = "".join(preview[0:2]) + elif len(preview) == 1: + browse = preview + else: + browse = "" + + self.html.value = """ +

{}

+ Size: {} MB
+ Start: {}
+ End: {}
+ Url: {}
+
{}
+ """.format( + native_id, round(size, 2), start, end, url, browse + ) + if self.overview: + self.m.remove_control(self.overview) + self.overview = ipyleaflet.WidgetControl(widget=self.html, position="bottomright") + self.m.add(self.overview) + + + def explore(self, results: List[DataGranule]) -> Any: + gdf = self.to_geopandas(results) + dataset_ids = list(gdf["dataset-id"].unique()) + colors = [matplotlib.colors.to_hex(c) for c in plt.cm.tab10.colors] + + + for p, c in zip(dataset_ids, colors): + df = gdf.loc[gdf['dataset-id'] == p] + total_size = round(df["size"].sum() / 1024, 2) + total_granules = len(df) + g = ipyleaflet.GeoData( + geo_dataframe=df, + style={ + "color": c, + "fillColor": c, + "opacity": 0.15, + "weight": 0.04, + "fillOpacity": 0.1, + "stroke-width": 0.05, + }, + hover_style={"fillColor": "red", "fillOpacity": 0.6}, + name=f"{p} [Count: {total_granules:,} | Size: {total_size} GB]", + ) + g.on_hover(self.update_html) + self.active_layers.append(g) + self.m.add(g) + + return self.m diff --git a/notebooks/bosque_primavera.json b/notebooks/bosque_primavera.json new file mode 100644 index 00000000..0081d140 --- /dev/null +++ b/notebooks/bosque_primavera.json @@ -0,0 +1,8 @@ +{ +"type": "FeatureCollection", +"name": "bosque_primavera", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "objectid": 61, "nombre": "La Primavera", "cat_decret": "Zona de Protección Forestal y Refugio de la Fauna Silvestre", "cat_manejo": "APFyF", "estados": "Jalisco", "municipios": "Tala, Zapopan, El Arenal y Tlajomulco de Zuñiga", "region": "Occidente y Pacífico Centro", "superficie": 30500.0, "s_terres": 30500.0, "s_marina": 0.0, "prim_dec": "1980-03-06T00:00:00Z", "ult_dof": "2000-06-07T00:00:00Z", "pcm1": "2001-05-22T00:00:00Z", "tipo": "Federal", "clave": "5402", "nom_polig": " ", "cat_uicn": " ", "fuente": " ", "instrume": " ", "vigencia": " ", "tipo_prop": " ", "globalid": 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-103.489602, 20.712381, 0.0 ], [ -103.489528, 20.712552, 0.0 ], [ -103.489478, 20.712724, 0.0 ], [ -103.489489, 20.712895, 0.0 ], [ -103.489512, 20.713079, 0.0 ], [ -103.489523, 20.71325, 0.0 ], [ -103.489558, 20.713434, 0.0 ], [ -103.489581, 20.713628, 0.0 ], [ -103.489677, 20.713858, 0.0 ], [ -103.494899, 20.715344, 0.0 ] ] ] } } +] +} diff --git a/notebooks/custom-maps.ipynb b/notebooks/custom-maps.ipynb new file mode 100644 index 00000000..558901ac --- /dev/null +++ b/notebooks/custom-maps.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "d6949dd5-4c06-4bfe-928a-3ff86020a938", + "metadata": {}, + "outputs": [], + "source": [ + "import earthaccess\n", + "import ipyleaflet\n", + "from ipywidgets import Dropdown\n", + "\n", + "auth = earthaccess.login()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f7df3940-0c94-4530-b5b0-5c6a89ab1f61", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# ArcticDEM\n", + "# note that we need to use the same projection for the image service layer and the map.\n", + "m1 = ipyleaflet.Map(\n", + " center=(90, 0),\n", + " zoom=4,\n", + " basemap=ipyleaflet.basemaps.Esri.ArcticOceanBase,\n", + " crs=ipyleaflet.projections.EPSG5936.ESRIBasemap,\n", + ")\n", + "# add arctic ocean reference basemap\n", + "tl1 = ipyleaflet.basemap_to_tiles(ipyleaflet.basemaps.Esri.ArcticOceanReference)\n", + "m1.add(tl1)\n", + "\n", + "# create a widget control for the raster function\n", + "raster_functions = [\n", + " \"Aspect Map\",\n", + " \"Contour 25\",\n", + " \"Hillshade Elevation Tinted\",\n", + " \"Hillshade Gray\",\n", + " \"Height Ellipsoidal\",\n", + " \"Height Orthometric\",\n", + " \"Slope Map\"]\n", + "raster_dropdown1 = Dropdown(\n", + " value=raster_functions[3],\n", + " options=raster_functions,\n", + " description=\"Raster:\",\n", + ")\n", + "\n", + "# add image service layer with ArcticDEM\n", + "url = 'https://elevation2.arcgis.com/arcgis/rest/services/Polar/ArcticDEM/ImageServer'\n", + "rendering_rule = {\"rasterFunction\": raster_dropdown1.value}\n", + "image_layer = ipyleaflet.ImageService(\n", + " name=\"CustomBaseLayer\",\n", + " url=url,\n", + " format='jpgpng', rendering_rule=rendering_rule,\n", + " attribution='Esri, PGC, UMN, NSF, NGA, DigitalGlobe',\n", + " crs=ipyleaflet.projections.EPSG5936.ESRIBasemap)\n", + "\n", + "m1.add(image_layer) \n", + "\n", + "# add control for raster function\n", + "dropdown_control = ipyleaflet.WidgetControl(widget=raster_dropdown1, position=\"topright\")\n", + "m1.add(dropdown_control)\n", + "\n", + "# set the rendering rule\n", + "def set_raster_function(sender):\n", + " image_layer.rendering_rule = {\"rasterFunction\": raster_dropdown1.value}\n", + " # force redrawing of map by removing and adding layer\n", + " m1.remove(image_layer)\n", + " m1.add(image_layer)\n", + "\n", + "\n", + "# watch raster function widget for changes\n", + "raster_dropdown1.observe(set_raster_function)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "eaf798ac-3fd2-46ef-a8f1-2201665d1f1d", + "metadata": {}, + "outputs": [], + "source": [ + "sw = earthaccess.search_widget(map=m1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0611e720-d8df-4795-9731-59d7d7784f4f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 2241\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f546505055d24289bac2946c810fd44d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(bottom=2348.0, center=[90, 0], controls=(LayersControl(options=['position']), ZoomControl(options=['positi…" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "polygon = [(-83.837578, 45.82529), (-80.167794, 45.82529), (-80.167794, 46.021921), (-83.837578, 46.021921), (-83.837578, 45.82529)]\n", + "\n", + "if 'sw' in vars():\n", + " if len(sw.roi) > 0:\n", + " polygon = sw.roi\n", + "\n", + "params = {\n", + " \"concept_id\" : [\"C2596864127-NSIDC_CPRD\", \"C2076090826-LPCLOUD\", \"C2237824918-ORNL_CLOUD\"],\n", + " \"temporal\": (\"2021-01\", \"2023-09\"),\n", + " # \"cloud_cover\": (0, 20),\n", + " \"polygon\": polygon\n", + "}\n", + "results = earthaccess.search_data(**params)\n", + "m = sw.explore(results)\n", + "# explore will inject its own controls so we add the base layer dropdown back\n", + "try:\n", + " sw.map.add(dropdown_control)\n", + "except Exception:\n", + " pass\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "5da379fd-d352-422a-a4fe-624aa6493223", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eaf5706e-da3d-42ee-ba8c-a258ae3087f3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "from shapely.geometry import shape\n", + "from shapely.geometry.polygon import Polygon\n", + "\n", + "geojson = requests.get(\"https://www.glims.org/geoserver/GLIMS/wms?SERVICE=WMS&VERSION=1.3.0&REQUEST=GetFeatureInfo&FORMAT=application/json&TRANSPARENT=true&QUERY_LAYERS=GLIMS_GLACIERS&LAYERS=GLIMS_GLACIERS&INFO_FORMAT=application%2Fjson&FEATURE_COUNT=20&I=50&J=50&CRS=EPSG%3A3857&WIDTH=101&HEIGHT=101&BBOX=1509769.8507459976%2C5231269.001494909%2C1510734.8682280981%2C5232234.018977009\").json()\n", + "\n", + "polygon: Polygon = shape(geojson[\"features\"][0][\"geometry\"])\n", + "polygon" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/explore.ipynb b/notebooks/explore.ipynb new file mode 100644 index 00000000..7b1f05b4 --- /dev/null +++ b/notebooks/explore.ipynb @@ -0,0 +1,491 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "56ba2387-54e8-4aa5-aedb-c7d90644536f", + "metadata": {}, + "source": [ + "# earthaccess a NASA Earthdata API Client 🌍 in Python\n", + "\n", + "## Overview\n", + "\n", + "**TL;DR**: *earthaccess* is uses NASA APIs to search, preview and access NASA datasets on-prem and in the cloud with 4 lines of Python.\n", + "\n", + "There are many ways to access NASA datasets, we can use the NASA's Earthdata search portal. We can use DAAC specific websites or tools.\n", + "We could even use data.gov! These web portals are great but... they are not designed for programmatic access and reproducible workflows. \n", + "This is extremely important in the age of the cloud and reproducible open science. In this context, **earthaccess** aims to be a simple \n", + "library that can deal with the important parts of the metadata so we can access or download data without having to worry if a given dataset is on-prem or in the cloud.\n", + "\n", + "The core function of auth is to deal with cloud credentials and remote file sessions (fsspec or requests).\n", + "essentially, anything that requires you to log in to Earthdata. Most of this will happen behind-the-scenes for you once you have been authenticated." + ] + }, + { + "cell_type": "markdown", + "id": "ef7372e6-4495-4191-a377-dbcee15065f0", + "metadata": {}, + "source": [ + "### NASA EDL and the Auth class\n", + "\n", + "* **Step 1**. We need to open an account with [NASA Eardtada](https://urs.earthdata.nasa.gov/), this credentials will allow us to access NASA datasets.\n", + "\n", + "Once we have our account we can use it with *earthaccess* " + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "067af873-c063-4fa4-9f1e-3869d1fbe2a4", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import earthaccess\n", + "import shapely\n", + "import ipyleaflet\n", + "\n", + "auth = earthaccess.login()" + ] + }, + { + "cell_type": "markdown", + "id": "9d8a1f0b-f1af-4ed4-bf06-1d68122cc4d5", + "metadata": {}, + "source": [ + "## Searching for data using a region of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "2a1e465c-390a-4606-8f31-140fa9ffdfd2", + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"bosque_primavera.json\") as f:\n", + " geojson = json.load(f)\n", + "\n", + "geojosn_geom = geojson['features'][0]['geometry']" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "3ff24f91-7742-4ce2-9602-3d125ae65e27", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# polygon = shapely.geometry.box(*bbox, ccw=True)\n", + "polygon = shapely.geometry.shape(geojosn_geom)\n", + "polygon" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "8c39f611-7090-48db-8e63-11eadecbde09", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1527" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(polygon.boundary.coords)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "bf859d08-a315-4e80-8138-297dba76f442", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = polygon.simplify(0.01)\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "90746e8b-3723-49b4-b7c3-99150d4c2ca1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(p.boundary.coords)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "1847a536-f37c-45d9-a80f-a9c932c6354d", + "metadata": {}, + "outputs": [], + "source": [ + "polygon_coords = [(coord[0], coord[1]) for coord in p.boundary.coords]" + ] + }, + { + "cell_type": "markdown", + "id": "3817feda-6a24-4624-94b5-c6458cfc5447", + "metadata": {}, + "source": [ + "## Search and Access with earthaccess\n", + "\n", + "earthaccess uses NASA's [search API](https://cmr.earthdata.nasa.gov/search/site/docs/search/api.html) to search for data from the different Distributed Archive Centers, the data can be hosted by the DAACs or in AWS, with earthaccess we don't need to think about this because it will handle the authentication for us. For reproducible workflows we just need to use the dataset (or collection as NASA calls them) `concept_id`. \n", + "\n", + "The `concept_id` of a collection can be found with *earthaccess* or using NASA Earthdata [search portal](https://search.earthdata.nasa.gov/search).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "8c6547fb-193b-45b0-b01c-6308cbe86bb7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 49\n" + ] + } + ], + "source": [ + "if len(sw.roi) > 0:\n", + " polygon_coords = sw.roi\n", + "\n", + "results = earthaccess.search_data(\n", + " concept_id = [\"C2613553260-NSIDC_CPRD\", \"C2237824918-ORNL_CLOUD\"],\n", + " temporal= (\"2021-01\", \"2023-09\"),\n", + " polygon=polygon_coords\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "e5e45722-e68c-47d8-8174-be36a3a3fc12", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "57a1af31b0c8449289d4f8df077f2017", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[0, 0], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_text'…" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sw = earthaccess.search_widget()\n", + "sw.m.add(ipyleaflet.GeoJSON(name=\"ROI\",\n", + " data=geojosn_geom,\n", + " style={\n", + " \"color\": \"red\",\n", + " \"opacity\": 0.9,\n", + " \"fillOpacity\": 0.1\n", + " }\n", + " ))\n", + "sw.explore(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "161802e2-df86-4ba2-ac6d-4aeb04ed37aa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sizedataset-idnative-idprovider-id_related_urls_beginning_date_time_ending_date_timegeometry
055.712407ATLAS/ICESat-2 L3A Land and Vegetation Height ...ATL08_20210103194147_01651007_006_01.h5NSIDC_CPRD[{'URL': 'https://data.nsidc.earthdatacloud.na...2021-01-03T19:41:47.222Z2021-01-03T19:44:08.049ZPOLYGON ((-102.74055 27.04262, -102.86583 27.0...
151.302756ATLAS/ICESat-2 L3A Land and Vegetation Height ...ATL08_20210129062030_05541001_006_01.h5NSIDC_CPRD[{'URL': 'https://data.nsidc.earthdatacloud.na...2021-01-29T06:25:09.419Z2021-01-29T06:27:32.313ZPOLYGON ((-103.37694 17.82757, -103.25939 17.8...
221.235174ATLAS/ICESat-2 L3A Land and Vegetation Height ...ATL08_20210227045631_09961001_006_01.h5NSIDC_CPRD[{'URL': 'https://data.nsidc.earthdatacloud.na...2021-02-27T05:01:09.321Z2021-02-27T05:03:33.211ZPOLYGON ((-103.20873 17.77707, -103.09164 17.7...
359.943995ATLAS/ICESat-2 L3A Land and Vegetation Height ...ATL08_20210430020019_05541101_006_02.h5NSIDC_CPRD[{'URL': 'https://data.nsidc.earthdatacloud.na...2021-04-30T02:04:59.091Z2021-04-30T02:07:21.085ZPOLYGON ((-103.47879 17.87801, -103.36156 17.8...
454.423923ATLAS/ICESat-2 L3A Land and Vegetation Height ...ATL08_20210529003623_09961101_006_01.h5NSIDC_CPRD[{'URL': 'https://data.nsidc.earthdatacloud.na...2021-05-29T00:41:01.353Z2021-05-29T00:43:25.227ZPOLYGON ((-103.20843 17.77693, -103.09139 17.7...
\n", + "
" + ], + "text/plain": [ + " size dataset-id \\\n", + "0 55.712407 ATLAS/ICESat-2 L3A Land and Vegetation Height ... \n", + "1 51.302756 ATLAS/ICESat-2 L3A Land and Vegetation Height ... \n", + "2 21.235174 ATLAS/ICESat-2 L3A Land and Vegetation Height ... \n", + "3 59.943995 ATLAS/ICESat-2 L3A Land and Vegetation Height ... \n", + "4 54.423923 ATLAS/ICESat-2 L3A Land and Vegetation Height ... \n", + "\n", + " native-id provider-id \\\n", + "0 ATL08_20210103194147_01651007_006_01.h5 NSIDC_CPRD \n", + "1 ATL08_20210129062030_05541001_006_01.h5 NSIDC_CPRD \n", + "2 ATL08_20210227045631_09961001_006_01.h5 NSIDC_CPRD \n", + "3 ATL08_20210430020019_05541101_006_02.h5 NSIDC_CPRD \n", + "4 ATL08_20210529003623_09961101_006_01.h5 NSIDC_CPRD \n", + "\n", + " _related_urls \\\n", + "0 [{'URL': 'https://data.nsidc.earthdatacloud.na... \n", + "1 [{'URL': 'https://data.nsidc.earthdatacloud.na... \n", + "2 [{'URL': 'https://data.nsidc.earthdatacloud.na... \n", + "3 [{'URL': 'https://data.nsidc.earthdatacloud.na... \n", + "4 [{'URL': 'https://data.nsidc.earthdatacloud.na... \n", + "\n", + " _beginning_date_time _ending_date_time \\\n", + "0 2021-01-03T19:41:47.222Z 2021-01-03T19:44:08.049Z \n", + "1 2021-01-29T06:25:09.419Z 2021-01-29T06:27:32.313Z \n", + "2 2021-02-27T05:01:09.321Z 2021-02-27T05:03:33.211Z \n", + "3 2021-04-30T02:04:59.091Z 2021-04-30T02:07:21.085Z \n", + "4 2021-05-29T00:41:01.353Z 2021-05-29T00:43:25.227Z \n", + "\n", + " geometry \n", + "0 POLYGON ((-102.74055 27.04262, -102.86583 27.0... \n", + "1 POLYGON ((-103.37694 17.82757, -103.25939 17.8... \n", + "2 POLYGON ((-103.20873 17.77707, -103.09164 17.7... \n", + "3 POLYGON ((-103.47879 17.87801, -103.36156 17.8... \n", + "4 POLYGON ((-103.20843 17.77693, -103.09139 17.7... " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.head(ipyleaflet.Geo)\n", + "gdf = sw.to_geopandas(results)" + ] + }, + { + "cell_type": "markdown", + "id": "f7c90c43-6e17-42f5-8bf5-95fdd3cb0dce", + "metadata": {}, + "source": [ + "## On-prem access 📡\n", + "\n", + "DAAC hosted data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "434466a3-602b-4dff-a260-f7db6901514a", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# accessing the data on prem means downloading it if we are in a local environment or \"uploading them\" if we are in the cloud.\n", + "files = earthaccess.download(results[0:2], \"./data\")\n", + "files" + ] + }, + { + "cell_type": "markdown", + "id": "4fe45fff-68ea-4f01-94c7-416d79cfd84c", + "metadata": {}, + "source": [ + "## Cloud access ☁️\n", + "\n", + "Same API, just a different origin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2a294f1-b1f9-4cd4-8751-dfc32feacec1", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "files = earthaccess.download(results[0:2], \"./data\")\n", + "files" + ] + }, + { + "cell_type": "markdown", + "id": "d779e877-2f0c-4da2-92d5-6cc299204956", + "metadata": {}, + "source": [ + "### Related links\n", + "\n", + "**CMR** API documentation: https://cmr.earthaccess.nasa.gov/search/site/docs/search/api.html\n", + "\n", + "**EDL** API documentation: https://urs.earthaccess.nasa.gov/\n", + "\n", + "NASA OpenScapes: https://nasa-openscapes.github.io/earthaccess-cloud-cookbook/\n", + "\n", + "NSIDC: https://nsidc.org" + ] + }, + { + "cell_type": "markdown", + "id": "293954e8-a101-4654-a79e-bd3389b6faab", + "metadata": {}, + "source": [ + "## Subsetting and Reading Icesat-2 Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51402508-5f26-41fb-833a-5f168ec6088b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + 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