diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index aaa70b69..b594f520 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -35,7 +35,7 @@ repos: - id: ruff-format - repo: https://github.com/astral-sh/uv-pre-commit - rev: "0.4.7" + rev: "0.4.12" hooks: - id: uv-lock diff --git a/CHANGELOG.md b/CHANGELOG.md index 5d768ae8..0a397ba3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -36,7 +36,10 @@ - Removed Broken Link "Introduction to NASA earthaccess" ([#779](https://github.com/nsidc/earthaccess/issues/779)) ([**@Sherwin-14**](https://github.com/Sherwin-14)) -* Remove the base class on `EarthAccessFile` to fix method resolution +- Restore automation for tidying notebooks used in documentation + ([#788](https://github.com/nsidc/earthaccess/issues/788)) + ([**@itcarroll**](https://github.com/itcarroll)) +- Remove the base class on `EarthAccessFile` to fix method resolution ([#610](https://github.com/nsidc/earthaccess/issues/610)) ([**@itcarroll**](https://github.com/itcarroll)) diff --git a/docs/howto/edl.ipynb b/docs/howto/edl.ipynb index 5949ddb5..e5031eb1 100644 --- a/docs/howto/edl.ipynb +++ b/docs/howto/edl.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ce723d6f-8b5f-43e1-9531-19ee08a056fe", "metadata": {}, "outputs": [], @@ -35,12 +35,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "f1f45172-c9c3-4f25-ad7e-acf94fba8b1d", "metadata": {}, "outputs": [], "source": [ - "#s3_credentials = auth.get_s3_credentials(\"NSIDC\")" + "# s3_credentials = auth.get_s3_credentials(\"NSIDC\")" ] }, { @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "a7bb1a93-6f85-40cd-93b4-ce821a03a8b5", "metadata": {}, "outputs": [], @@ -71,27 +71,16 @@ "nsidc_url = \"https://n5eil01u.ecs.nsidc.org/DP7/ATLAS/ATL06.005/2019.02.21/ATL06_20190221121851_08410203_005_01.h5\"\n", "lpcloud_url = \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20220903T163129_2224611_012/EMIT_L2A_RFL_001_20220903T163129_2224611_012.nc\"\n", "\n", - "# this is a Python requests session \n", + "# this is a Python requests session\n", "session = earthaccess.get_requests_https_session()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "c0f4e286-8ce9-4cde-b3a2-631b361a0d51", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'�HDF\\r\\n\\x1a\\n\\x00\\x00\\x00\\x00\\x00\\x08\\x08\\x00\\x04\\x00\\x10\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00���������HUn\\x00\\x00\\x00\\x00��������\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00`\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00OHDR\\x02'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "headers = {\"Range\": \"bytes=0-100\"}\n", "r = session.get(lpcloud_url, headers=headers)\n", @@ -108,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "1e5f5f7a-0643-42cb-b70c-4ac982824ff9", "metadata": {}, "outputs": [], @@ -118,21 +107,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "fbc7bc5f-b984-4324-b095-3648a437fba2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "b'\\x89HDF\\r\\n\\x1a\\n\\x00\\x00\\x00\\x00\\x00\\x08\\x08\\x00\\x04\\x00\\x10\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xd7HUn\\x00\\x00\\x00\\x00\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00`\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00OHDR'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "with fs.open(lpcloud_url) as f:\n", " data = f.read(100)\n", @@ -141,451 +119,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "07433a9f-f276-4d2b-9ed3-f556df2884af", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c76e3afc58a84118a04144f2f08ff6bc", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/1 [00:00\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset> Size: 3kB\n",
-       "Dimensions:           (bands: 285)\n",
-       "Dimensions without coordinates: bands\n",
-       "Data variables:\n",
-       "    wavelengths       (bands) float32 1kB ...\n",
-       "    fwhm              (bands) float32 1kB ...\n",
-       "    good_wavelengths  (bands) float32 1kB ...
" - ], - "text/plain": [ - " Size: 3kB\n", - "Dimensions: (bands: 285)\n", - "Dimensions without coordinates: bands\n", - "Data variables:\n", - " wavelengths (bands) float32 1kB ...\n", - " fwhm (bands) float32 1kB ...\n", - " good_wavelengths (bands) float32 1kB ..." - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "%%time \n", + "%%time\n", "\n", "import xarray as xr\n", "\n", @@ -613,7 +152,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/docs/tutorials/SSL.ipynb b/docs/tutorials/SSL.ipynb index 265f1fd7..bed7877e 100644 --- a/docs/tutorials/SSL.ipynb +++ b/docs/tutorials/SSL.ipynb @@ -28,7 +28,6 @@ "source": [ "import earthaccess\n", "\n", - "\n", "print(f\"using earthaccess v{earthaccess.__version__}\")\n", "\n", "auth = earthaccess.login()\n", @@ -64,17 +63,24 @@ "outputs": [], "source": [ "from pprint import pprint\n", + "\n", "# We'll get 4 collections that match with our keywords\n", "collections = earthaccess.search_datasets(\n", - " keyword = \"SEA SURFACE HEIGHT\",\n", - " cloud_hosted = True,\n", - " count = 4\n", + " keyword=\"SEA SURFACE HEIGHT\",\n", + " cloud_hosted=True,\n", + " count=4,\n", ")\n", "\n", "# Let's print 2 collections\n", "for collection in collections[0:2]:\n", " # pprint(collection.summary())\n", - " print(pprint(collection.summary()), collection.abstract(), \"\\n\", collection[\"umm\"][\"DOI\"], \"\\n\\n\")" + " print(\n", + " pprint(collection.summary()),\n", + " collection.abstract(),\n", + " \"\\n\",\n", + " collection[\"umm\"][\"DOI\"],\n", + " \"\\n\\n\",\n", + " )" ] }, { @@ -101,8 +107,8 @@ "outputs": [], "source": [ "granules = earthaccess.search_data(\n", - " short_name = \"SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205\",\n", - " count = 1\n", + " short_name=\"SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205\",\n", + " count=1,\n", ")" ] }, @@ -182,9 +188,9 @@ "for year in range(1999, 2019):\n", " print(f\"Retrieving data for year: {year}\")\n", " results = earthaccess.search_data(\n", - " doi = \"10.5067/SLREF-CDRV3\",\n", + " doi=\"10.5067/SLREF-CDRV3\",\n", " temporal=(f\"{year}-05\", f\"{year}-05\"),\n", - " count = 1,\n", + " count=1,\n", " )\n", " granules.append(results[0])\n", "print(f\"Total granules: {len(granules)}\")" @@ -213,7 +219,6 @@ "\n", "import xarray as xr\n", "\n", - "\n", "fileset = earthaccess.open(granules)\n", "\n", "print(f\" Using {type(fileset[0])} filesystem\")\n", @@ -239,7 +244,11 @@ }, "outputs": [], "source": [ - "ds.SLA.where((ds.SLA>=0) & (ds.SLA < 10)).std('Time').plot(figsize=(14,6), x='Longitude', y='Latitude')" + "(\n", + " ds.SLA.where((ds.SLA >= 0) & (ds.SLA < 10))\n", + " .std(\"Time\")\n", + " .plot(figsize=(14, 6), x=\"Longitude\", y=\"Latitude\")\n", + ")" ] }, { @@ -251,32 +260,35 @@ }, "outputs": [], "source": [ - "from pyproj import Geod\n", "import numpy as np\n", + "from pyproj import Geod\n", + "\n", "\n", "def ssl_area(lats):\n", - " \"\"\"\n", - " Calculate the area associated with a 1/6 by 1/6 degree box at latitude specified in 'lats'.\n", + " \"\"\"Calculate the area associated with a 1/6 by 1/6 degree box at latitude specified in \"lats\".\n", "\n", - " Parameter\n", - " ==========\n", - " lats: a list or numpy array of size N the latitudes of interest.\n", + " Parameters:\n", + " lats: a list or numpy array of size N the latitudes of interest.\n", "\n", - " Return\n", - " =======\n", - " out: Array (N) area values (unit: m^2)\n", + " Returns:\n", + " Array (N) area values (unit: m^2)\n", " \"\"\"\n", " # Define WGS84 as CRS:\n", - " geod = Geod(ellps='WGS84')\n", - " dx=1/12.0\n", - " # TODO: explain this\n", - " c_area=lambda lat: geod.polygon_area_perimeter(np.r_[-dx,dx,dx,-dx], lat+np.r_[-dx,-dx,dx,dx])[0]\n", - " out=[]\n", + " geod = Geod(ellps=\"WGS84\")\n", + " # Set offsets for a box that will be centered at 0, lat\n", + " dx = 1 / 12.0\n", + " lon_offset = np.array((-dx, dx, dx, -dx))\n", + " lat_offset = np.array((-dx, -dx, dx, dx))\n", + " out = []\n", " for lat in lats:\n", - " out.append(c_area(lat))\n", + " # Run a geodesic area calculator for the given box\n", + " c_area, *_ = geod.polygon_area_perimeter(lon_offset, lat + lat_offset)\n", + " out.append(c_area)\n", " return np.array(out)\n", + "\n", + "\n", "# note: they rotated the data in the last release, this operation used to be (1,-1)\n", - "ssh_area = ssl_area(ds.Latitude.data).reshape(-1,1)\n", + "ssh_area = ssl_area(ds.Latitude.data).reshape(-1, 1)\n", "print(ssh_area.shape)" ] }, @@ -297,42 +309,44 @@ { "cell_type": "code", "execution_count": null, - "id": "fb170068-cefb-4c16-b146-0ecbd2e603ad", - "metadata": { - "tags": [] - }, + "id": "76ef1122-8185-4d68-ab94-88e19a8173f7", + "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", - "import numpy as np\n", "from matplotlib import pyplot as plt\n", - "plt.rcParams[\"figure.figsize\"] = (16,4)\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (16, 4)\n", "\n", "img = plt.imread(\"underwater.jpeg\")\n", "\n", "fig, axs = plt.subplots()\n", "plt.grid(True)\n", "\n", + "\n", "def global_mean(SLA, **kwargs):\n", - " dout=((SLA*ssh_area).sum()/(SLA/SLA*ssh_area).sum())*1000\n", + " dout = ((SLA * ssh_area).sum() / (SLA / SLA * ssh_area).sum()) * 1000\n", " return dout\n", "\n", - "result = ds.SLA.groupby('Time').apply(global_mean)\n", "\n", - "plt.xlabel('Time (year)',fontsize=16)\n", - "plt.ylabel('Global Mean SLA (meter)',fontsize=12)\n", + "result = ds.SLA.groupby(\"Time\").apply(global_mean)\n", + "\n", + "plt.xlabel(\"Time (year)\", fontsize=16)\n", + "plt.ylabel(\"Global Mean SLA (meter)\", fontsize=12)\n", "# axs.imshow(img, aspect='auto')\n", "plt.grid(True)\n", "\n", - "historic_ts=xr.open_dataset(gmsl)\n", + "historic_ts = xr.open_dataset(gmsl)\n", "\n", - "result.plot(ax=axs, color=\"orange\", marker=\"o\", label='satellite record')\n", - "historic_ts['global_average_sea_level_change'].plot(ax=axs, label='Historical in-situ record', color=\"lightblue\")\n", + "result.plot(ax=axs, color=\"orange\", marker=\"o\", label=\"satellite record\")\n", + "historic_ts[\"global_average_sea_level_change\"].plot(\n", + " ax=axs, label=\"Historical in-situ record\", color=\"lightblue\"\n", + ")\n", "\n", - "x0,x1 = axs.get_xlim()\n", - "y0,y1 = axs.get_ylim()\n", - "axs.imshow(img, extent=[x0, x1, y0, y1], aspect='auto')\n", + "x0, x1 = axs.get_xlim()\n", + "y0, y1 = axs.get_ylim()\n", + "axs.imshow(img, extent=[x0, x1, y0, y1], aspect=\"auto\")\n", "\n", "plt.legend()\n", "plt.show()" @@ -355,7 +369,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/docs/tutorials/emit-earthaccess.ipynb b/docs/tutorials/emit-earthaccess.ipynb index e3b9f43a..56dfc3b8 100644 --- a/docs/tutorials/emit-earthaccess.ipynb +++ b/docs/tutorials/emit-earthaccess.ipynb @@ -29,11 +29,10 @@ }, "outputs": [], "source": [ + "from pprint import pprint\n", + "\n", "import earthaccess\n", - "import numpy as np\n", "import xarray as xr\n", - "import h5netcdf\n", - "from pprint import pprint\n", "\n", "print(f\"using earthaccess version {earthaccess.__version__}\")\n", "\n", @@ -59,11 +58,11 @@ }, "outputs": [], "source": [ - "results = earthaccess.search_datasets(short_name = \"EMITL2ARFL\", cloud_hosted=True)\n", + "results = earthaccess.search_datasets(short_name=\"EMITL2ARFL\", cloud_hosted=True)\n", "\n", "# Let's print our datasets\n", "for dataset in results:\n", - " print(pprint(dataset.summary()))" + " pprint(dataset.summary())" ] }, { @@ -84,9 +83,11 @@ "outputs": [], "source": [ "# ~Ecuador = -82.05,-3.17,-76.94,-0.52\n", - "granules = earthaccess.search_data(short_name=\"EMITL2ARFL\",\n", - " bounding_box=(-82.05,-3.17,-76.94,-0.52),\n", - " count=10)\n", + "granules = earthaccess.search_data(\n", + " short_name=\"EMITL2ARFL\",\n", + " bounding_box=(-82.05, -3.17, -76.94, -0.52),\n", + " count=10,\n", + ")\n", "print(len(granules))" ] }, @@ -155,10 +156,16 @@ "file_p = file_handlers[4]\n", "\n", "refl = xr.open_dataset(file_p)\n", - "wvl = xr.open_dataset(file_p, group='sensor_band_parameters')\n", - "loc = xr.open_dataset(file_p, group='location')\n", + "wvl = xr.open_dataset(file_p, group=\"sensor_band_parameters\")\n", + "loc = xr.open_dataset(file_p, group=\"location\")\n", "ds = xr.merge([refl, loc])\n", - "ds = ds.assign_coords({'downtrack':(['downtrack'], refl.downtrack.data),'crosstrack':(['crosstrack'],refl.crosstrack.data), **wvl.variables}) \n", + "ds = ds.assign_coords(\n", + " {\n", + " \"downtrack\": ([\"downtrack\"], refl.downtrack.data),\n", + " \"crosstrack\": ([\"crosstrack\"], refl.crosstrack.data),\n", + " **wvl.variables,\n", + " }\n", + ")\n", "\n", "ds" ] @@ -176,20 +183,27 @@ "\n", "```python\n", "\n", + "import holoviews as hv\n", "import hvplot.xarray\n", + "import numpy as np\n", "import panel as pn\n", - "import holoviews as hv\n", "\n", "pn.extension()\n", "\n", - "b850 = np.nanargmin(abs(ds['wavelengths'].values-850)) # Find band nearest to value of 850 nm (NIR)\n", - "image = ds.sel(bands=b850).reflectance_uncertainty.hvplot('crosstrack', 'downtrack', cmap='viridis')\n", + "# Find band nearest to value of 850 nm (NIR)\n", + "b850 = np.nanargmin(abs(ds[\"wavelengths\"].values - 850))\n", + "ref_unc = ds[\"reflectance_uncertainty\"]\n", + "image = ref_unc.sel(bands=b850).hvplot(\"crosstrack\", \"downtrack\", cmap=\"viridis\")\n", "stream = hv.streams.Tap(source=image, x=255, y=484)\n", "\n", + "\n", "def wavelengths_histogram(x, y):\n", - " histo = ds['reflectance_uncertainty'].sel(crosstrack=x, downtrack=y, method='nearest').hvplot(x=\"wavelengths\", color='green')\n", + " histo = ref_unc.sel(crosstrack=x, downtrack=y, method=\"nearest\").hvplot(\n", + " x=\"wavelengths\", color=\"green\"\n", + " )\n", " return histo\n", "\n", + "\n", "tap_dmap = hv.DynamicMap(wavelengths_histogram, streams=[stream])\n", "pn.Column(image, tap_dmap)\n", "\n", @@ -214,7 +228,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/docs/tutorials/file-access.ipynb b/docs/tutorials/file-access.ipynb index f5011c6f..fa5b0711 100644 --- a/docs/tutorials/file-access.ipynb +++ b/docs/tutorials/file-access.ipynb @@ -38,7 +38,7 @@ " short_name=\"ATL06\",\n", " cloud_hosted=False,\n", " temporal=(\"2019-01\", \"2019-02\"),\n", - " polygon=[(-100, 40), (-110, 40), (-105, 38), (-100, 40)]\n", + " polygon=[(-100, 40), (-110, 40), (-105, 38), (-100, 40)],\n", ")\n", "results[0]" ] @@ -97,7 +97,7 @@ "metadata": {}, "outputs": [], "source": [ - "%%time \n", + "%%time\n", "\n", "import xarray as xr\n", "\n", @@ -124,7 +124,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/docs/tutorials/restricted-datasets.ipynb b/docs/tutorials/restricted-datasets.ipynb index 285af550..9dc9241b 100644 --- a/docs/tutorials/restricted-datasets.ipynb +++ b/docs/tutorials/restricted-datasets.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "2acb1926-3442-446a-8a35-fcbba8868d4d", "metadata": {}, "outputs": [], @@ -54,19 +54,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "a0a5f73d-b4b9-4dad-ac72-12c7ae6278d1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You're now authenticated with NASA Earthdata Login\n", - "Using token with expiration date: 07/24/2022\n" - ] - } - ], + "outputs": [], "source": [ "auth.login(strategy=\"environment\")\n", "# are we authenticated?\n", @@ -113,68 +104,32 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "d0dd51cc-b394-47cd-b606-d54750ea629e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collections found: 1\n" - ] - }, - { - "data": { - "text/plain": [ - "[{\n", - " \"meta\": {\n", - " \"concept-id\": \"C2144439155-NSIDC_ECS\",\n", - " \"granule-count\": 123484,\n", - " \"provider-id\": \"NSIDC_ECS\"\n", - " },\n", - " \"umm\": {\n", - " \"ShortName\": \"ATL06\",\n", - " \"Version\": \"005\"\n", - " }\n", - " }]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# The first step is to create a DataCollections query \n", + "# The first step is to create a DataCollections query\n", "Query = DataCollections()\n", "\n", "# Use chain methods to customize our query\n", - "Query.short_name(\"ATL06\").version(\"005\")\n", + "Query.short_name(\"ATL06\").version(\"006\")\n", "\n", - "print(f'Collections found: {Query.hits()}')\n", + "print(f\"Collections found: {Query.hits()}\")\n", "\n", "# filtering what UMM fields to print, to see the full record we omit the fields filters\n", - "# meta is always included as \n", - "collections = Query.fields(['ShortName','Version']).get(5)\n", + "# meta is always included as\n", + "collections = Query.fields([\"ShortName\", \"Version\"]).get(5)\n", "# Inspect some results printing just the ShortName and Abstract\n", "collections" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "14eee26c-e9d9-4f2f-8b04-06379c29c9d8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "earthaccess generated a token for CMR with expiration on: 07/24/2022\n" - ] - } - ], + "outputs": [], "source": [ "if not auth.refresh_tokens():\n", " print(\"Something went wrong, we may need to regenerate our tokens manually\")" @@ -182,59 +137,20 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "a17a8afe-0a68-4adf-a6d1-5f5bda6becc9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collections found: 1\n" - ] - }, - { - "data": { - "text/plain": [ - "[{\n", - " \"meta\": {\n", - " \"concept-id\": \"C2144439155-NSIDC_ECS\",\n", - " \"granule-count\": 123484,\n", - " \"provider-id\": \"NSIDC_ECS\"\n", - " },\n", - " \"umm\": {\n", - " \"ShortName\": \"ATL06\",\n", - " \"Version\": \"005\"\n", - " }\n", - " },\n", - " {\n", - " \"meta\": {\n", - " \"concept-id\": \"C2153572614-NSIDC_CPRD\",\n", - " \"granule-count\": 123484,\n", - " \"provider-id\": \"NSIDC_CPRD\"\n", - " },\n", - " \"umm\": {\n", - " \"ShortName\": \"ATL06\",\n", - " \"Version\": \"005\"\n", - " }\n", - " }]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Query = DataCollections(auth)\n", "\n", "# Use chain methods to customize our query\n", - "Query.short_name(\"ATL06\").version(\"005\")\n", + "Query.short_name(\"ATL06\").version(\"006\")\n", "\n", "# This will say 1, even though we get 2 back.\n", - "print(f'Collections found: {Query.hits()}')\n", + "print(f\"Collections found: {Query.hits()}\")\n", "\n", - "collections = Query.fields(['ShortName','Version']).get()\n", + "collections = Query.fields([\"ShortName\", \"Version\"]).get()\n", "# Inspect some results printing just the ShortName and Abstract\n", "collections" ] @@ -257,21 +173,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "9478b3ef-7652-4943-9946-9ce49c085eb6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Granules found with hits(): 0\n", - "Actual number found: 4\n" - ] - } - ], + "outputs": [], "source": [ - "Query = DataGranules(auth).concept_id(\"C2153572614-NSIDC_CPRD\").bounding_box(-134.7,58.9,-133.9,59.2).temporal(\"2020-03-01\", \"2020-03-30\")\n", + "Query = (\n", + " DataGranules(auth)\n", + " .concept_id(\"C2153572614-NSIDC_CPRD\")\n", + " .bounding_box(-134.7, 58.9, -133.9, 59.2)\n", + " .temporal(\"2020-03-01\", \"2020-03-30\")\n", + ")\n", "\n", "# Unfortunately the hits() methods will behave the same for granule queries\n", "print(f\"Granules found with hits(): {Query.hits()}\")\n", @@ -291,6 +203,14 @@ "store = Store(auth)\n", "files = store.get(cloud_granules, \"./data/C2153572614-NSIDC_CPRD/\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9726088c-b7eb-4c3c-9012-9874dd4aa3f8", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -309,7 +229,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/notebooks/DatasetSearch.ipynb b/notebooks/DatasetSearch.ipynb index c6bcd6e0..633b3361 100644 --- a/notebooks/DatasetSearch.ipynb +++ b/notebooks/DatasetSearch.ipynb @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "2e674100-f041-4620-bd33-0ec29f0a8fd8", "metadata": {}, "outputs": [], @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "197dece4-02dc-4622-964c-41ca6a275cae", "metadata": {}, "outputs": [], @@ -160,7 +160,7 @@ "metadata": {}, "outputs": [], "source": [ - "query = DataCollections().cloud_hosted(True).bounding_box(-25.31,63.23,-11.95,66.65)\n", + "query = DataCollections().cloud_hosted(True).bounding_box(-25.31, 63.23, -11.95, 66.65)\n", "\n", "query.hits()" ] @@ -172,7 +172,12 @@ "metadata": {}, "outputs": [], "source": [ - "for c in DataCollections().cloud_hosted(True).short_name(\"ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4\").get(40):\n", + "query = (\n", + " DataCollections()\n", + " .cloud_hosted(True)\n", + " .short_name(\"ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4\")\n", + ")\n", + "for c in query.get(40):\n", " print(c.summary(), \"\\n\")" ] } @@ -193,7 +198,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/notebooks/Demo.ipynb b/notebooks/Demo.ipynb index 62e9dfd8..9c4b54a2 100644 --- a/notebooks/Demo.ipynb +++ b/notebooks/Demo.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "2acb1926-3442-446a-8a35-fcbba8868d4d", "metadata": {}, "outputs": [], @@ -91,7 +91,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8d5bf4c9-571b-4c93-af94-e66bd51cb584", + "id": "52cb48b4-5cc6-4203-8734-8fde89431e7b", "metadata": {}, "outputs": [], "source": [ @@ -99,13 +99,17 @@ "# Query = earthaccess.collection_query().keyword('fire').temporal(\"2016-01-01\", \"2020-12-12\")\n", "# Query = earthaccess.collection_query().keyword('GEDI').bounding_box(-134.7,58.9,-133.9,59.2)\n", "\n", - "Query = earthaccess.collection_query().keyword('elevation').bounding_box(-134.7,58.9,-133.9,59.2)\n", + "Query = (\n", + " earthaccess.collection_query()\n", + " .keyword(\"elevation\")\n", + " .bounding_box(-134.7, 58.9, -133.9, 59.2)\n", + ")\n", "\n", - "print(f'Collections found: {Query.hits()}')\n", + "print(f\"Collections found: {Query.hits()}\")\n", "\n", "# filtering what UMM fields to print, to see the full record we omit the fields filters\n", - "# meta is always included as \n", - "collections = Query.fields(['ShortName','Abstract']).get(3)\n", + "# meta is always included as\n", + "collections = Query.fields([\"ShortName\", \"Abstract\"]).get(3)\n", "# Inspect 3 results printing just the ShortName and Abstract\n", "collections[0:3]" ] @@ -153,8 +157,8 @@ "# We can now search for collections using a pythonic API client for CMR.\n", "Query = earthaccess.collection_query().daac(\"PODAAC\")\n", "\n", - "print(f'Collections found: {Query.hits()}')\n", - "collections = Query.fields(['ShortName']).get(20)\n", + "print(f\"Collections found: {Query.hits()}\")\n", + "collections = Query.fields([\"ShortName\"]).get(20)\n", "# Printing 3 collections\n", "collections[0:3]" ] @@ -169,8 +173,8 @@ "# What if we want cloud collections\n", "Query = earthaccess.collection_query().daac(\"PODAAC\").cloud_hosted(True)\n", "\n", - "print(f'Collections found: {Query.hits()}')\n", - "collections = Query.fields(['ShortName']).get(20)\n", + "print(f\"Collections found: {Query.hits()}\")\n", + "collections = Query.fields([\"ShortName\"]).get(20)\n", "# Printing 3 collections\n", "collections[0:3]" ] @@ -196,7 +200,7 @@ "The DataGranules client, accessed via `earthaccess.granule_query()`, provides similar functionality as the collection class. To query for granules in a more reliable way, concept-id would be the main key.\n", "You can search data granules using a short name but that could (and more likely will) return multiple versions of the same data granules. \n", "\n", - "In this example we're querying for 20 data grnaules from ICESat-2 [ATL03](https://nsidc.org/data/ATL03/versions/) version `005` dataset. " + "In this example we're querying for 20 data grnaules from ICESat-2 [ATL03](https://nsidc.org/data/ATL03/versions/) version `006` dataset. " ] }, { @@ -206,9 +210,14 @@ "metadata": {}, "outputs": [], "source": [ - "Query = earthaccess.granule_query().short_name('ATL03').version(\"005\").bounding_box(-134.7,58.9,-133.9,59.2)\n", + "Query = (\n", + " earthaccess.granule_query()\n", + " .short_name(\"ATL03\")\n", + " .version(\"006\")\n", + " .bounding_box(-134.7, 58.9, -133.9, 59.2)\n", + ")\n", "granules = Query.get(20)\n", - "print(granules[0:2])" + "granules[0:2]" ] }, { @@ -251,7 +260,13 @@ "metadata": {}, "outputs": [], "source": [ - "Query = earthaccess.granule_query().short_name(\"ATL03\").temporal(\"2020-03-01\", \"2020-03-30\").bounding_box(-134.7,58.9,-133.9,59.2).version(\"005\")\n", + "Query = (\n", + " earthaccess.granule_query()\n", + " .short_name(\"ATL03\")\n", + " .temporal(\"2020-03-01\", \"2020-03-30\")\n", + " .bounding_box(-134.7, 58.9, -133.9, 59.2)\n", + " .version(\"006\")\n", + ")\n", "# Always inspects the hits before retrieving the granule metadata, just because it's very verbose.\n", "print(f\"Granules found: {Query.hits()}\")" ] @@ -265,7 +280,7 @@ "source": [ "# Now we can print some info about these granules using the built-in methods\n", "granules = Query.get(4)\n", - "data_links = [{'links': g.data_links(), 'size (MB):': g.size()} for g in granules]\n", + "data_links = [{\"links\": g.data_links(), \"size (MB):\": g.size()} for g in granules]\n", "data_links" ] }, @@ -282,7 +297,12 @@ "# C1968980609-POCLOUD: Sentinel-6A MF Jason-CS L2 P4 Altimeter Low Resolution (LR) STC Ocean Surface Topography\n", "# C1575731655-LPDAAC_ECS: ASTER Global Digital Elevation Model NetCDF V003\n", "# Query = earthaccess.granule_query().concept_id(\"C1575731655-LPDAAC_ECS\").bounding_box(-134.7,58.9,-133.9,59.2)\n", - "Query = earthaccess.granule_query().short_name('ATL03').version(\"005\").bounding_box(-134.7,58.9,-133.9,59.2)\n", + "Query = (\n", + " earthaccess.granule_query()\n", + " .short_name(\"ATL03\")\n", + " .version(\"006\")\n", + " .bounding_box(-134.7, 58.9, -133.9, 59.2)\n", + ")\n", "\n", "print(f\"Granules found: {Query.hits()}\")" ] @@ -318,7 +338,7 @@ "outputs": [], "source": [ "# Size in MB\n", - "data_links = [{'links': g.data_links(), 'size (MB):': g.size()} for g in granules]\n", + "data_links = [{\"links\": g.data_links(), \"size (MB):\": g.size()} for g in granules]\n", "data_links" ] }, @@ -348,7 +368,12 @@ "metadata": {}, "outputs": [], "source": [ - "Query = earthaccess.granule_query().short_name(\"ATL06\").bounding_box(-134.7,54.9,-100.9,69.2).debug(True)\n", + "Query = (\n", + " earthaccess.granule_query()\n", + " .short_name(\"ATL06\")\n", + " .bounding_box(-134.7, 54.9, -100.9, 69.2)\n", + " .debug(True)\n", + ")\n", "print(f\"Granule hits: {Query.hits()}\")\n", "# getting more than 6,000 metadata records for demo purposes is going to slow us down a bit so let's get only 100\n", "granules = Query.get(10)" @@ -384,6 +409,7 @@ "source": [ "# since the response is an array of dictionaries we can do pythonic things like ordering the granules by size\n", "import operator\n", + "\n", "granules_by_size = sorted(granules, key=operator.itemgetter(\"size\"))\n", "# now our array is sorted by size from less to more. Let's print the first 3\n", "granules_by_size[0:3]" @@ -418,7 +444,12 @@ "metadata": {}, "outputs": [], "source": [ - "Query = earthaccess.granule_query().short_name(\"ATL06\").cloud_hosted(True).bounding_box(-134.7,54.9,-100.9,69.2)\n", + "Query = (\n", + " earthaccess.granule_query()\n", + " .short_name(\"ATL06\")\n", + " .cloud_hosted(True)\n", + " .bounding_box(-134.7, 54.9, -100.9, 69.2)\n", + ")\n", "print(f\"Granule hits: {Query.hits()}\")\n", "cloud_granules = Query.get(10)\n", "# is this a cloud hosted data granule?\n", @@ -445,6 +476,7 @@ "source": [ "# Let's order them by size again.\n", "import operator\n", + "\n", "cloud_granules_by_size = sorted(cloud_granules, key=operator.itemgetter(\"size\"))\n", "# now our array is sorted by size from less to more. Let's print the first 3\n", "cloud_granules_by_size[0:3]" @@ -518,7 +550,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/notebooks/EarthdataLogin.ipynb b/notebooks/EarthdataLogin.ipynb index 04aafba6..dd4728d8 100644 --- a/notebooks/EarthdataLogin.ipynb +++ b/notebooks/EarthdataLogin.ipynb @@ -13,36 +13,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "adc12546-827a-4d14-b92e-cf10e3823231", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No .netrc found in /home/beto\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Enter your Earthdata Login username: betolink\n", - "Enter your Earthdata password: ········\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You're now authenticated with NASA Earthdata Login\n", - "Using token with expiration date: 05/13/2023\n", - "Using user provided credentials for EDL\n", - "Using earthaccess version 0.5.1\n" - ] - } - ], + "outputs": [], "source": [ "import earthaccess\n", "\n", @@ -53,57 +27,30 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "901a1c21-29c1-4534-97bb-e4a75e63a477", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "We are already authenticated with NASA EDL\n" - ] - } - ], + "outputs": [], "source": [ "auth = earthaccess.login(strategy=\"environment\")" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "668d4bce-3a26-4e54-be30-6be2bc82bd44", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "We are already authenticated with NASA EDL\n" - ] - } - ], + "outputs": [], "source": [ "auth = earthaccess.login(strategy=\"netrc\")" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "6d9726cd-47f2-464f-80f9-d51fb006c545", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'II*\\x00\\x08\\x00\\x00\\x00\\x13\\x00\\x00\\x01\\x03\\x00\\x01\\x00\\x00\\x00\\x11\\x0e\\x00\\x00\\x01\\x01\\x03\\x00\\x01\\x00\\x00\\x00\\x11\\x0e\\x00\\x00\\x02\\x01\\x03\\x00\\x01\\x00\\x00\\x00\\x10\\x00\\x00\\x00\\x03\\x01\\x03\\x00\\x01\\x00\\x00\\x00\\x05\\x00\\x00\\x00\\x06\\x01\\x03\\x00\\x01\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x15\\x01\\x03\\x00\\x01\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x1c\\x01\\x03\\x00\\x01\\x00\\x00\\x00\\x01\\x00\\x00\\x00=\\x01\\x03\\x00\\x01\\x00\\x00'" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# using earthaccess authenticated session to fetch restricted files behind EDL\n", "req_session = earthaccess.get_requests_https_session()\n", @@ -112,7 +59,7 @@ "restricted_gesdisc_resource = \"https://data.gesdisc.earthdata.nasa.gov/data/MERGED_IR/GPM_MERGIR.1/1998/002/merg_1998010217_4km-pixel.nc4\"\n", "restricted_lpdaac_resource = \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/ASTGTM.003/ASTGTMV003_N01E024_dem.tif\"\n", "# reading the first 100 bytes\n", - "data = req_session.get(restricted_lpdaac_resource, headers = {\"Range\": \"bytes=0-100\"})\n", + "data = req_session.get(restricted_lpdaac_resource, headers={\"Range\": \"bytes=0-100\"})\n", "data.text" ] }, @@ -144,39 +91,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "9ecafd00-d5d5-4c10-99a8-5e48f2614407", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'cloud-info': {'Region': 'us-west-2',\n", - " 'S3BucketAndObjectPrefixNames': ['nsidc-cumulus-prod-protected/GLAS/GLAH12/034',\n", - " 'nsidc-cumulus-prod-public/GLAS/GLAH12/034'],\n", - " 'S3CredentialsAPIDocumentationURL': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME',\n", - " 'S3CredentialsAPIEndpoint': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentials'},\n", - " 'concept-id': 'C2153549818-NSIDC_CPRD',\n", - " 'file-type': \"[{'FormatType': 'Native', 'Format': 'HDF', 'FormatDescription': \"\n", - " \"'FTP 15 GB'}]\",\n", - " 'get-data': ['https://search.earthdata.nasa.gov/search?q=GLAH12+V034'],\n", - " 'short-name': 'GLAH12',\n", - " 'version': '034'}\n", - "{'cloud-info': {'Region': 'us-west-2',\n", - " 'S3BucketAndObjectPrefixNames': ['nsidc-cumulus-prod-protected/GLAS/GLAH13/034',\n", - " 'nsidc-cumulus-prod-public/GLAS/GLAH13/034'],\n", - " 'S3CredentialsAPIDocumentationURL': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME',\n", - " 'S3CredentialsAPIEndpoint': 'https://data.nsidc.earthdatacloud.nasa.gov/s3credentials'},\n", - " 'concept-id': 'C2153549910-NSIDC_CPRD',\n", - " 'file-type': \"[{'FormatType': 'Native', 'Format': 'HDF', 'FormatDescription': \"\n", - " \"'FTP 18 GB'}]\",\n", - " 'get-data': ['https://search.earthdata.nasa.gov/search?q=GLAH13+V034'],\n", - " 'short-name': 'GLAH13',\n", - " 'version': '034'}\n" - ] - } - ], + "outputs": [], "source": [ "from pprint import pprint\n", "\n", @@ -190,63 +108,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "2044634d-2d60-4890-a357-8dcbc9f03fd2", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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"id": "159b710c-531b-4c65-ae44-d7c2a0f3c9bb", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Getting 2 granules, approx download size: 0.05 GB\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c5a1ffd648254c4f98dfe04410532948", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/2 [00:00=6.0.1", ] docs = [ - "jupyterlab >=3", - "ipywidgets >=7.7.0", - "widgetsnbextension >=3.6.0", - # Prevent install of 1.6.0 because the module mkdocs.tests - # is no longer exported, which causes mkdocs-jupyter to - # break with a ModuleNotFoundError. - # for notebook integrations and documentation examples - "mkdocs >=1.2,!=1.6.0", - "mkdocs-material >=7.1,<10.0", - "mkdocs-redirects >=1.2.1", - "markdown-callouts >=0.2.0", - "markdown-include >=0.6", - "mkdocs-jupyter >=0.19.0", - "mkdocstrings[python] >=0.19.0", - "pymdown-extensions >=9.2", - "pygments 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