From dd50bc0c6678bd6685b97d1b06903850e2410222 Mon Sep 17 00:00:00 2001 From: Simon Liu Date: Wed, 16 Aug 2023 12:56:44 -0700 Subject: [PATCH] updated test notebook --- CHANGELOG.md | 2 + .../harmony_concise_api_test.ipynb | 225 +++++++++++++----- 2 files changed, 171 insertions(+), 56 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index b58acbd9..9d078b13 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -10,6 +10,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - PODAAC-5424 - Update concise test in uat to test only POCLOUD collections ### Changed + - Issue-68 + - Updated jupyter notebook ### Deprecated ### Removed ### Fixed diff --git a/jupyter_notebooks/harmony_concise_api_test.ipynb b/jupyter_notebooks/harmony_concise_api_test.ipynb index 4ff691cf..f9a2de83 100644 --- a/jupyter_notebooks/harmony_concise_api_test.ipynb +++ b/jupyter_notebooks/harmony_concise_api_test.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "3c95100d", "metadata": {}, "outputs": [], @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "0c9b70ea", "metadata": { "tags": [ @@ -67,7 +67,7 @@ }, "outputs": [], "source": [ - "collection_id = 'C1940473819-POCLOUD'\n", + "collection_id = 'C2205121485-POCLOUD'\n", "max_results = 2\n", "venue = \"OPS\"" ] @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "5ccd035f", "metadata": {}, "outputs": [], @@ -109,10 +109,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "b11675ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "if venue == \"UAT\":\n", " harmony_client = Client(auth=(username, password), env=Environment.UAT)\n", @@ -144,10 +155,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "36ce8016", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "curl -X GET -H 'Accept: */*' -H 'Accept-Encoding: gzip, deflate' -H 'Authorization: *****' -H 'Connection: keep-alive' -H 'Cookie: urs_user_already_logged=yes; state=s%3A1c51c3d53a38e6af99f0366288cd18bf.p404RcPg%2BvidV0pdWb9gqeHVcz4Hld5s5Q5PexRzbXs; token=*****; _urs-gui_session=a61daf78fa6987f16d1ffc770d775b69' -H 'User-Agent: CPython/3.8.12 harmony-py/0.4.2 Darwin/22.6.0 python-requests/2.27.1' 'https://harmony.earthdata.nasa.gov/C2205121485-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&format=application%2Fx-netcdf4&maxResults=2&concatenate=true&skipPreview=true'\n" + ] + } + ], "source": [ "print(harmony_client.request_as_curl(request))\n", "\n", @@ -166,10 +185,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "0d13a446", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "0790ef60-4c3b-4189-b9ac-d9dad2aafafe\n", + "{'status': 'running', 'message': 'CMR query identified 1308 granules, but the request has been limited to process only the first 2 granules because you requested 2 maxResults.', 'progress': 0, 'created_at': datetime.datetime(2023, 8, 16, 17, 32, 22, 669000, tzinfo=tzutc()), 'updated_at': datetime.datetime(2023, 8, 16, 17, 32, 22, 669000, tzinfo=tzutc()), 'created_at_local': '2023-08-16T10:32:22-07:00', 'updated_at_local': '2023-08-16T10:32:22-07:00', 'data_expiration': datetime.datetime(2023, 9, 15, 17, 32, 22, 669000, tzinfo=tzutc()), 'data_expiration_local': '2023-09-15T10:32:22-07:00', 'request': 'https://harmony.earthdata.nasa.gov/C2205121485-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&format=application%2Fx-netcdf4&maxResults=2&concatenate=true&skipPreview=true', 'num_input_granules': 2}\n", + "\n", + "Waiting for the job to finish\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " [ Processing: 100% ] |###################################################| [|]\n" + ] + } + ], "source": [ "print(f'\\n{job1_id}')\n", "\n", @@ -189,10 +227,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "168e7b11", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Downloading results:\n", + "\n", + "C2205121485-POCLOUD_merged.nc4\n", + "\n", + "Done downloading.\n" + ] + } + ], "source": [ "print('\\nDownloading results:')\n", "filename = None\n", @@ -217,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "04db3295", "metadata": { "scrolled": true @@ -226,30 +277,19 @@ "source": [ "#some collections time variabe has a time dimension which can cause an exception when we concatenate and makes time two dimension\n", "dt = nc.Dataset(filename,'r')\n", - "group = list(dt.groups)\n", + "groups = list(dt.groups)\n", "dt.close()\n", - "\n", - "if len(group) == 0:\n", - " group = None\n", - "else:\n", - " group = group[0]\n", " \n", "drop_variables = [\n", " 'time',\n", " 'sample',\n", " 'meas_ind',\n", " 'wvf_ind',\n", - " 'ddm'\n", + " 'ddm',\n", + " 'averaged_l1'\n", "]\n", - " \n", - "try:\n", - " ds = xr.open_dataset(filename, group=group, decode_times=False)\n", - "except xr.core.variable.MissingDimensionsError:\n", - " ds = xr.open_dataset(filename, group=group, decode_times=False, drop_variables=drop_variables)\n", - "\n", - "print(list(ds.variables))\n", - " \n", - "assert len(ds.coords['subset_index']) == max_results" + "if len(groups) == 0:\n", + " groups = [None]" ] }, { @@ -262,36 +302,96 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "a733274d", - "metadata": {}, - "outputs": [], + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['subset_files', 'ddm_source', 'spacecraft_id', 'spacecraft_num', 'prn_code', 'sv_num', 'antenna', 'sample_time', 'lat', 'lon', 'sc_lat', 'sc_lon', 'sc_alt', 'wind_speed', 'fds_nbrcs_wind_speed', 'fds_les_wind_speed', 'wind_speed_uncertainty', 'azimuth_angle', 'sc_roll', 'commanded_sc_roll', 'mean_square_slope', 'mean_square_slope_uncertainty', 'incidence_angle', 'nbrcs_mean', 'les_mean', 'range_corr_gain', 'fresnel_coeff', 'merra2_wind_speed', 'num_ddms_utilized', 'sample_flags', 'fds_sample_flags', 'sum_neg_brcs_value_used_for_nbrcs_flags', 'ddm_obs_utilized_flag', 'ddm_num_averaged_l1', 'ddm_channel', 'ddm_les', 'ddm_nbrcs', 'ddm_sample_index', 'ddm_averaged_l1_utilized_flag']\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "variable = None\n", - "variables = list(ds.variables)\n", + "for group in groups:\n", + "\n", + " try:\n", + " ds = xr.open_dataset(filename, group=group, decode_times=False)\n", + " except xr.core.variable.MissingDimensionsError:\n", + " ds = xr.open_dataset(filename, group=group, decode_times=False, drop_variables=drop_variables)\n", "\n", - "for v in variables:\n", - " if v not in ['subset_files', 'lat', 'lon', 'latitude', 'longitude', 'beam_clat', 'beam_clon']:\n", - " variable = v\n", - " break;\n", + " assert len(ds.coords['subset_index']) == max_results\n", "\n", - "if \"lon\" in variables:\n", - " x = \"lon\"\n", - "elif \"longitude\" in variables:\n", - " x = \"longitude\"\n", - "elif \"beam_clon\" in variables:\n", - " x = \"beam_clon\"\n", - "elif \"sp_lon\" in variables:\n", - " x = \"sp_lon\"\n", + " variable = None\n", + " variables = list(ds.variables)\n", + " print(variables)\n", " \n", - "if \"lat\" in variables:\n", - " y = \"lon\"\n", - "elif \"latitude\" in variables:\n", - " y = \"longitude\"\n", - "elif \"beam_clat\" in variables:\n", - " y = \"beam_clat\"\n", - "elif \"sp_lat\" in variables:\n", - " y = \"sp_lat\"\n", + " for v in variables:\n", + " if v not in ['subset_files', 'lat', 'lon', 'latitude', 'longitude', 'beam_clat', 'beam_clon']:\n", + " variable = v\n", + " break;\n", + "\n", + " if \"lon\" in variables:\n", + " x = \"lon\"\n", + " elif \"longitude\" in variables:\n", + " x = \"longitude\"\n", + " elif \"beam_clon\" in variables:\n", + " x = \"beam_clon\"\n", + " elif \"sp_lon\" in variables:\n", + " x = \"sp_lon\"\n", + " elif \"cellon\" in variables:\n", + " x = \"cellon\"\n", + " else:\n", + " x = None\n", + "\n", + " if \"lat\" in variables:\n", + " y = \"lon\"\n", + " elif \"latitude\" in variables:\n", + " y = \"longitude\"\n", + " elif \"beam_clat\" in variables:\n", + " y = \"beam_clat\"\n", + " elif \"sp_lat\" in variables:\n", + " y = \"sp_lat\"\n", + " elif \"cellat\" in variables:\n", + " y = \"cellat\"\n", + " else:\n", + " y = None\n", + " \n", + " if y is not None and x is not None:\n", + " break\n", + " \n", + " ds.close()\n", + "\n", + "if x is None or y is None:\n", + " raise Exception(\"Lon and Lat variables are not found\")\n", " \n", "for index in range(0, max_results):\n", " \n", @@ -321,10 +421,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "748cdb33", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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