From a5bca7b92b532fc16bee41a399f9bb9136adb4dc Mon Sep 17 00:00:00 2001 From: clausmichele Date: Wed, 16 Feb 2022 10:19:48 +0100 Subject: [PATCH] Removed square root for intensity --- SInCohMap_openEO_examples.ipynb | 15061 ++---------------------------- 1 file changed, 785 insertions(+), 14276 deletions(-) diff --git a/SInCohMap_openEO_examples.ipynb b/SInCohMap_openEO_examples.ipynb index 0f808df..e36bcf9 100644 --- a/SInCohMap_openEO_examples.ipynb +++ b/SInCohMap_openEO_examples.ipynb @@ -7,7 +7,7 @@ "source": [ "## SInCohMap data access and processing examples\n", "### Author michele.claus@eurac.edu\n", - "### Date: 2021/12/20" + "### Date: 2022/02/22" ] }, { @@ -21,6 +21,9 @@ "\n", "openEO main website: https://openeo.org/\n", "\n", + "openEO Web Editor (graphical interface): https://editor.openeo.org\n", + "connect to EURAC using https://openeo.eurac.edu\n", + "\n", "openEO Python Client documentation: https://open-eo.github.io/openeo-python-client/index.html\n", "\n", "Getting started guide for openEO with python: https://openeo.org/documentation/1.0/python/\n", @@ -36,7 +39,16 @@ "\n", "**Q: I receive a 500 error, what does it mean?**\n", "\n", - "A: It is a server error: something went wrong processing your request. Please check carefully that the area and time range you are requesting are available in the datacube (you can use `conn.describe_collection('COLLECTION_NAME')`) Currently the error logs are not passed if you run your request as a synchronous call (i.e. using `.download()`). If you run you process as a batch job you will get a more informative error message." + "A: It is a server error: something went wrong processing your request. Please check carefully that the area and time range you are requesting are available in the datacube (you can use `conn.describe_collection('COLLECTION_NAME')`) Currently the error logs are not passed if you run your request as a synchronous call (i.e. using `.download()`). If you run you process as a batch job you will get a more informative error message.\n", + "\n", + "**Q: I receive a 502 error, what does it mean?**\n", + "\n", + "A: If you get an error similar to: _[502] unknown: Received 502 Proxy Error. This typically happens if an OpenEO request takes too long and is killed. Consider using batch jobs instead of doing synchronous processing._\n", + "The message is already explaining you the problem: you are using a synchronous call (.download()) to run a process which is taking too much to complete. You need to use a batch job in this case.\n", + "\n", + "If you already started a batch job, please try to list the jobs with:\n", + "`conn.list_jobs()`\n", + "and check if it's actually running of it has been stopped due to an error." ] }, { @@ -49,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "0205484c-3eff-4825-bdae-78357b775897", "metadata": { "scrolled": true @@ -16035,7 +16047,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "id": "a4fa0ed5-6678-4ca7-9442-c8521cfa0c90", "metadata": { "scrolled": true @@ -16073,7 +16085,7 @@ { "data": { "text/plain": [ - "'0.9.1'" + "'0.9.2'" ] }, "execution_count": 3, @@ -16128,7 +16140,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 5, "id": "53a63f29-33a3-4703-8b96-510299a8fd93", "metadata": { "scrolled": true, @@ -16154,7 +16166,7 @@ " }\n", " \n", " \n", - " \n", + " \n", " \n", " " ], @@ -16278,14 +16290,14 @@ " 45.03419013978915,\n", " 11.710544147585775,\n", " 46.046265455836064]]},\n", - " 'temporal': {'interval': [['2015-07-04T23:59:00Z',\n", - " '2021-05-28T23:59:00Z']]}},\n", + " 'temporal': {'interval': [['2015-07-04T10:10:06Z',\n", + " '2021-11-24T10:12:39Z']]}},\n", " 'links': [{'rel': 'license',\n", " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", " 'type': 'text/html',\n", " 'title': 'License link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['2015-07-04T23:59:00+00:00', '2021-05-28T23:59:00+00:00']},\n", + " 'extent': ['2015-07-04T10:10:06+00:00', '2021-11-24T10:12:39+00:00']},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", " 'extent': [10.27007567177171, 11.710544147585775],\n", @@ -16310,21 +16322,19 @@ " 11.755584916051987,\n", " 46.94616825350529]]},\n", " 'temporal': {'interval': [['2015-07-04T10:10:06Z',\n", - " '2021-05-31T10:15:59Z']]}},\n", + " '2021-11-24T10:12:39Z']]}},\n", " 'links': [{'rel': 'license',\n", " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", " 'type': 'text/html',\n", " 'title': 'License link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['2015-07-04T10:10:06+00:00', '2021-05-31T10:15:59+00:00']},\n", + " 'extent': ['2015-07-04T10:10:06+00:00', '2021-11-24T10:12:39+00:00']},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", - " 'extent': [10.290484068721153, 11.755584916051987],\n", - " 'reference_system': 32632},\n", + " 'extent': [10.290484068721153, 11.755584916051987]},\n", " 'Y': {'type': 'spatial',\n", " 'axis': 'y',\n", - " 'extent': [45.933496504418216, 46.94616825350529],\n", - " 'reference_system': 32632},\n", + " 'extent': [45.933496504418216, 46.94616825350529]},\n", " 'bands': {'type': 'bands', 'values': ['LAI']}}},\n", " {'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", @@ -16340,14 +16350,14 @@ " 46.83262831878177,\n", " 11.802846682912097,\n", " 47.84592105161653]]},\n", - " 'temporal': {'interval': [['2015-07-04T23:59:00Z',\n", - " '2021-04-08T23:59:00Z']]}},\n", + " 'temporal': {'interval': [['2015-07-04T10:10:06Z',\n", + " '2021-11-24T10:12:39Z']]}},\n", " 'links': [{'rel': 'license',\n", " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", " 'type': 'text/html',\n", " 'title': 'License link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['2015-07-04T23:59:00+00:00', '2021-04-08T23:59:00+00:00']},\n", + " 'extent': ['2015-07-04T10:10:06+00:00', '2021-11-24T10:12:39+00:00']},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", " 'extent': [10.311886885505961, 11.802846682912097],\n", @@ -16371,14 +16381,14 @@ " 45.8957352514674,\n", " 13.065657132764626,\n", " 46.92357305783379]]},\n", - " 'temporal': {'interval': [['2015-07-04T23:59:00Z',\n", - " '2020-09-13T23:59:00Z']]}},\n", + " 'temporal': {'interval': [['2015-07-04T10:10:06Z',\n", + " '2021-11-24T10:12:39Z']]}},\n", " 'links': [{'rel': 'license',\n", " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", " 'type': 'text/html',\n", " 'title': 'License link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['2015-07-04T23:59:00+00:00', '2020-09-13T23:59:00+00:00']},\n", + " 'extent': ['2015-07-04T10:10:06+00:00', '2021-11-24T10:12:39+00:00']},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", " 'extent': [11.579459455543509, 13.065657132764626],\n", @@ -16402,14 +16412,14 @@ " 46.79367068772221,\n", " 13.13525847536845,\n", " 47.822607559518644]]},\n", - " 'temporal': {'interval': [['2015-07-04T23:59:00Z',\n", - " '2021-04-11T23:59:00Z']]}},\n", + " 'temporal': {'interval': [['2015-07-04T10:10:06Z',\n", + " '2021-11-24T10:12:39Z']]}},\n", " 'links': [{'rel': 'license',\n", " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", " 'type': 'text/html',\n", " 'title': 'License link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['2015-07-04T23:59:00+00:00', '2021-04-11T23:59:00+00:00']},\n", + " 'extent': ['2015-07-04T10:10:06+00:00', '2021-11-24T10:12:39+00:00']},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", " 'extent': [11.62219565550015, 13.13525847536845],\n", @@ -16482,6 +16492,54 @@ " 'reference_system': 3035},\n", " 'bands': {'type': 'bands', 'values': ['classes']}}},\n", " {'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'S2_L1C_T32TPS',\n", + " 'title': 'S2_L1C_T32TPS',\n", + " 'description': 'Sentinel-2 Level 1 C data for the 32TPS tile. It includes the FMASK layer, generated using FMASK 4.3 https://github.com/GERSL/Fmask.',\n", + " 'deprecated': False,\n", + " 'license': 'CC-BY-4.0',\n", + " 'providers': [{'name': 'Eurac EO ODC',\n", + " 'url': 'http://www.eurac.edu/',\n", + " 'roles': ['producer', 'host']}],\n", + " 'extent': {'spatial': {'bbox': [[10.290484068721153,\n", + " 45.933496504418216,\n", + " 11.755584916051987,\n", + " 46.94602219651639]]},\n", + " 'temporal': {'interval': [['2015-07-04T10:11:51.9745Z',\n", + " '2022-01-18T10:15:50.299942Z']]}},\n", + " 'links': [{'rel': 'license',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2015-07-04T10:11:51.974500+00:00',\n", + " '2022-01-18T10:15:50.299942+00:00']},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [10.290484068721153, 11.755584916051987],\n", + " 'reference_system': 32632},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [45.933496504418216, 46.94602219651639],\n", + " 'reference_system': 32632},\n", + " 'bands': {'type': 'bands',\n", + " 'values': ['B01',\n", + " 'B02',\n", + " 'B03',\n", + " 'B04',\n", + " 'B05',\n", + " 'B06',\n", + " 'B07',\n", + " 'B08',\n", + " 'B09',\n", + " 'B10',\n", + " 'B11',\n", + " 'B12',\n", + " 'B8A',\n", + " 'PVI',\n", + " 'TCI',\n", + " 'FMASK']}}},\n", + " {'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube', 'scientific'],\n", " 'id': 'S2_L2A_ALPS',\n", " 'title': 'Sentinel-2 L2A over the Alps',\n", @@ -16726,36 +16784,51 @@ " 'summaries': {'rows': 7751, 'columns': 44250}},\n", " {'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", - " 'id': 'SAR2Cube_L0_168_DES_ST_2016_2020',\n", - " 'title': 'SAR2Cube_L0_168_DES_ST_2016_2020',\n", - " 'description': 'SAR2Cube Sentinel-1 Data Level-0',\n", + " 'id': 'SAR2Cube_SInCohMap_S1_L0_117_ASC_SOUTH_TYROL',\n", + " 'title': 'SAR2Cube_SInCohMap_S1_L0_117_ASC_SOUTH_TYROL',\n", + " 'description': 'Sentinel-1 SLC Data. SAR2Cube Level-0 preprocessing.',\n", " 'deprecated': False,\n", " 'license': 'CC-BY-4.0',\n", " 'providers': [{'name': 'Eurac EO ODC',\n", " 'url': 'http://www.eurac.edu/',\n", " 'roles': ['producer', 'host']}],\n", - " 'extent': {'spatial': {'bbox': [[10.003181457519531,\n", - " 46.521915435791016,\n", - " 12.363468170166016,\n", - " 47.2063102722168]]},\n", - " 'temporal': {'interval': [['2016-09-12T23:59:59Z',\n", - " '2020-11-14T23:59:59Z']]}},\n", + " 'extent': {'spatial': {'bbox': [[9.528703689575195,\n", + " 45.31370162963867,\n", + " 13.141668319702148,\n", + " 47.318817138671875]]},\n", + " 'temporal': {'interval': [['2016-09-08T23:59:59Z',\n", + " '2020-11-10T23:59:59Z']]}},\n", " 'links': [{'rel': 'license',\n", " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", " 'type': 'text/html',\n", " 'title': 'License link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['2016-09-12T23:59:59+00:00', '2020-11-14T23:59:59+00:00']},\n", + " 'extent': ['2016-09-08T23:59:59+00:00', '2020-11-10T23:59:59+00:00']},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", - " 'extent': [10.003181457519531, 12.363468170166016],\n", + " 'extent': [9.528703689575195, 13.141668319702148],\n", " 'reference_system': 32632},\n", " 'Y': {'type': 'spatial',\n", " 'axis': 'y',\n", - " 'extent': [46.521915435791016, 47.2063102722168],\n", + " 'extent': [45.31370162963867, 47.318817138671875],\n", " 'reference_system': 32632},\n", " 'bands': {'type': 'bands',\n", - " 'values': ['i_VH', 'i_VV', 'q_VH', 'q_VV', 'grid_lat', 'grid_lon']}}},\n", + " 'values': ['DEM',\n", + " 'LIA',\n", + " 'i_VH',\n", + " 'i_VV',\n", + " 'q_VH',\n", + " 'q_VV',\n", + " 'grid_lat',\n", + " 'grid_lon',\n", + " 'i_ifg_VH',\n", + " 'i_ifg_VV',\n", + " 'q_ifg_VH',\n", + " 'q_ifg_VV',\n", + " 'i_ifg_VH_nocorrect',\n", + " 'i_ifg_VV_nocorrect',\n", + " 'q_ifg_VH_nocorrect',\n", + " 'q_ifg_VV_nocorrect']}}},\n", " {'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'SAR2Cube_SInCohMap_S1_L0_147_ASC_DONYANA',\n", @@ -16852,6 +16925,147 @@ " 'q_ifg_VV_nocorrect']}}},\n", " {'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", + " 'id': 'SAR2Cube_SInCohMap_S1_L0_168_DSC_SOUTH_TYROL',\n", + " 'title': 'SAR2Cube_SInCohMap_S1_L0_168_DSC_SOUTH_TYROL',\n", + " 'description': 'SAR2Cube Sentinel-1 Data Level-0',\n", + " 'deprecated': False,\n", + " 'license': 'CC-BY-4.0',\n", + " 'providers': [{'name': 'Eurac EO ODC',\n", + " 'url': 'http://www.eurac.edu/',\n", + " 'roles': ['producer', 'host']}],\n", + " 'extent': {'spatial': {'bbox': [[9.718852043151855,\n", + " 45.19416809082031,\n", + " 12.394761085510254,\n", + " 47.370574951171875]]},\n", + " 'temporal': {'interval': [['2016-09-12T23:59:59Z',\n", + " '2020-11-14T23:59:59Z']]}},\n", + " 'links': [{'rel': 'license',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2016-09-12T23:59:59+00:00', '2020-11-14T23:59:59+00:00']},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [9.718852043151855, 12.394761085510254],\n", + " 'reference_system': 32632},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [45.19416809082031, 47.370574951171875],\n", + " 'reference_system': 32632},\n", + " 'bands': {'type': 'bands',\n", + " 'values': ['DEM',\n", + " 'LIA',\n", + " 'i_VH',\n", + " 'i_VV',\n", + " 'q_VH',\n", + " 'q_VV',\n", + " 'grid_lat',\n", + " 'grid_lon',\n", + " 'i_ifg_VH',\n", + " 'i_ifg_VV',\n", + " 'q_ifg_VH',\n", + " 'q_ifg_VV',\n", + " 'i_ifg_VH_nocorrect',\n", + " 'i_ifg_VV_nocorrect',\n", + " 'q_ifg_VH_nocorrect',\n", + " 'q_ifg_VV_nocorrect']}}},\n", + " {'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'SAR2Cube_SInCohMap_S1_L0_80_DSC_FINLAND_AOI1',\n", + " 'title': 'SAR2Cube_SInCohMap_S1_L0_80_DSC_FINLAND_AOI1',\n", + " 'description': 'Sentinel-1 SLC Data. SAR2Cube Level-0 preprocessing.',\n", + " 'deprecated': False,\n", + " 'license': 'CC-BY-4.0',\n", + " 'providers': [{'name': 'Eurac EO ODC',\n", + " 'url': 'http://www.eurac.edu/',\n", + " 'roles': ['producer', 'host']}],\n", + " 'extent': {'spatial': {'bbox': [[26.32445526123047,\n", + " 64.42527770996094,\n", + " 30.129379272460938,\n", + " 65.43559265136719]]},\n", + " 'temporal': {'interval': [['2017-11-06T23:59:59Z',\n", + " '2018-11-25T23:59:59Z']]}},\n", + " 'links': [{'rel': 'license',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2017-11-06T23:59:59+00:00', '2018-11-25T23:59:59+00:00']},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [26.32445526123047, 30.129379272460938],\n", + " 'reference_system': 32632},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [64.42527770996094, 65.43559265136719],\n", + " 'reference_system': 32632},\n", + " 'bands': {'type': 'bands',\n", + " 'values': ['DEM',\n", + " 'LIA',\n", + " 'i_VH',\n", + " 'i_VV',\n", + " 'q_VH',\n", + " 'q_VV',\n", + " 'grid_lat',\n", + " 'grid_lon',\n", + " 'i_ifg_VH',\n", + " 'i_ifg_VV',\n", + " 'q_ifg_VH',\n", + " 'q_ifg_VV',\n", + " 'i_ifg_VH_nocorrect',\n", + " 'i_ifg_VV_nocorrect',\n", + " 'q_ifg_VH_nocorrect',\n", + " 'q_ifg_VV_nocorrect']}}},\n", + " {'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'SAR2Cube_SInCohMap_S1_L0_80_DSC_FINLAND_AOI2',\n", + " 'title': 'SAR2Cube_SInCohMap_S1_L0_80_DSC_FINLAND_AOI2',\n", + " 'description': 'Sentinel-1 SLC Data. SAR2Cube Level-0 preprocessing.',\n", + " 'deprecated': False,\n", + " 'license': 'CC-BY-4.0',\n", + " 'providers': [{'name': 'Eurac EO ODC',\n", + " 'url': 'http://www.eurac.edu/',\n", + " 'roles': ['producer', 'host']}],\n", + " 'extent': {'spatial': {'bbox': [[25.738134384155273,\n", + " 62.790409088134766,\n", + " 29.317707061767578,\n", + " 63.7945556640625]]},\n", + " 'temporal': {'interval': [['2017-11-06T23:59:59Z',\n", + " '2018-11-25T23:59:59Z']]}},\n", + " 'links': [{'rel': 'license',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2017-11-06T23:59:59+00:00', '2018-11-25T23:59:59+00:00']},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [25.738134384155273, 29.317707061767578],\n", + " 'reference_system': 32632},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [62.790409088134766, 63.7945556640625],\n", + " 'reference_system': 32632},\n", + " 'bands': {'type': 'bands',\n", + " 'values': ['DEM',\n", + " 'LIA',\n", + " 'i_VH',\n", + " 'i_VV',\n", + " 'q_VH',\n", + " 'q_VV',\n", + " 'grid_lat',\n", + " 'grid_lon',\n", + " 'i_ifg_VH',\n", + " 'i_ifg_VV',\n", + " 'q_ifg_VH',\n", + " 'q_ifg_VV',\n", + " 'i_ifg_VH_nocorrect',\n", + " 'i_ifg_VV_nocorrect',\n", + " 'q_ifg_VH_nocorrect',\n", + " 'q_ifg_VV_nocorrect']}}},\n", + " {'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", " 'id': 'SInCohMap_S2_L1C_T29SQB',\n", " 'title': 'SInCohMap_S2_L1C_T29SQB',\n", " 'description': 'Sentinel-2 L1C Data with FMASK layer',\n", @@ -17260,68 +17474,119 @@ " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", + " 'id': 'ADO_LST_MODIS_231m_3035',\n", + " 'title': 'Land Surface Temperature - 231m 8 day mean',\n", + " 'description': 'The Land Surface Temperature (LST) is based on MODIS satellite data. The LST is based on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m after regridding from the original 1000 m resolution. The LST is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The Land Surface Temperature is expressed in degree Celsius.',\n", + " 'keywords': ['land surface temperature', 'lst', 'modis'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'CC BY 4.0',\n", + " 'sci:citation': 'N/A',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Eurac Research',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['Processor']},\n", + " {'name': 'NASA',\n", + " 'url': 'https://modis.gsfc.nasa.gov/',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[3.99537296130427,\n", + " 42.87349365119738,\n", + " 17.523924303829347,\n", + " 50.32636203215727]]},\n", + " 'temporal': {'interval': [['2001-01-01T00:00:00Z',\n", + " '2021-01-03T00:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2001-01-01T00:00:00.000Z', '2021-01-03T00:00:00.000Z'],\n", + " 'step': 'P8D'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [3.99537296130427, 17.523924303829347],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.87349365119738, 50.32636203215727],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['8d_lst_celsius_linint']}},\n", + " 'summaries': {'constellation': ['Terra'],\n", + " 'platform': ['Terra'],\n", + " 'rows': 3606,\n", + " 'columns': 4430,\n", + " 'instruments': ['MODIS'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': '8d_lst_celsius_linint',\n", + " 'center_wavelength': 0.0,\n", + " 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_NDVI_MODIS_231m_3035',\n", - " 'title': '4 Day Maximum Value Composite NDVI based on MODIS Reflectance',\n", - " 'description': 'NDVI derived from daily MODIS observations (Aqua and Terra) over vegetated surfaces in the alps. Data is provided as a 4 Day Maximum Value Composite at 231 m spatial resolution. The time series is starting from 2000. It has 4 Bands: - 1) NDVI: The maximum NDVI value of the 4d window [unitless; -1 - 1; 254 = no vegetation, 255 = not processed due to insufficient quality], - 2) DOY: The day of year corresponding to the chosen value [days; 1-365; 254 = no vegetation, 255 not processed due to insufficient quality], -3) PLATFORM: The MODIS Platform [1 = Terra, 2 = Aqua], - 4) QU: The Quality Flag containing the values [unitless; 1 = good quality, 2 = bad MODIS QA Flag, 8 = reflectance out of range, 10 = 2 and 8, 65 = bad acquisition geometry, 66 = 65 and 2, 72 = 65 and 8, 74 = 65 and 2 and 8, 128 = no vegetation].',\n", - " 'keywords': ['NDVI', 'MODIS', 'ADO Project'],\n", + " 'title': 'Normalized Difference Vegetation Index - 231m 8 day Maximum Value Composite',\n", + " 'description': 'The Normalized Difference Vegetation Index (NDVI) is based on MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The NDVI is calculated using the formula NDVI = (NIR - Red) / (NIR + Red). The NDVI expresses the vitality of vegetation. The data is provided as 8 day measures. The time series is starting from 2001. The NDVI values range from -1 - 1, whereas high values correspond to healthy vegetation.',\n", + " 'keywords': ['normalized difference vegetation index', 'ndvi', 'modis'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", - " 'license': 'CC-BY-4.0',\n", - " 'sci:citation': 'Asam, S.; Callegari, M.; Matiu, M.; Fiore, G.; De Gregorio, L.; Jacob, A.; Menzel, A.; Zebisch, M.; Notarnicola, C. Relationship between Spatiotemporal Variations of Climate, Snow Cover and Plant Phenology over the Alps—An Earth Observation-Based Analysis. Remote Sens. 2018, 10, 1757.',\n", + " 'license': 'CC BY 4.0',\n", + " 'sci:citation': 'N/A',\n", " 'providers': [{'name': 'Eurac EO WCS',\n", " 'url': 'http://www.eurac.edu',\n", " 'roles': ['host']},\n", - " {'name': 'United States Geological Survey (USGS)',\n", - " 'url': 'https://lpdaac.usgs.gov/data/',\n", - " 'roles': ['Producer']},\n", - " {'name': 'Peter Zellner',\n", - " 'url': 'http://www.eurac.edu/en/research/mountains/remsen/staff/Pages/staffdetails.aspx?persId=50076',\n", - " 'roles': ['Processor']}],\n", - " 'extent': {'spatial': {'bbox': [[4.216446958382827,\n", - " 42.81715267605496,\n", - " 18.99055634337,\n", - " 48.601848359989106]]},\n", - " 'temporal': {'interval': [['2000-02-23T00:00:00Z',\n", - " '2020-11-13T00:00:00Z']]}},\n", + " {'name': 'Eurac Research',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['Processor']},\n", + " {'name': 'NASA',\n", + " 'url': 'https://modis.gsfc.nasa.gov/',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[3.99537296130427,\n", + " 42.87349365119738,\n", + " 17.523924303829347,\n", + " 50.32636203215727]]},\n", + " 'temporal': {'interval': [['2001-01-01T00:00:00Z',\n", + " '2021-01-03T00:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['2000-02-23T00:00:00.000Z', '2020-11-13T00:00:00.000Z'],\n", - " 'step': 'P4D'},\n", + " 'extent': ['2001-01-01T00:00:00.000Z', '2021-01-03T00:00:00.000Z'],\n", + " 'step': 'P8D'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", - " 'extent': [4.216446958382827, 18.99055634337],\n", + " 'extent': [3.99537296130427, 17.523924303829347],\n", " 'reference_system': 3035},\n", " 'Y': {'type': 'spatial',\n", " 'axis': 'y',\n", - " 'extent': [42.81715267605496, 48.601848359989106],\n", + " 'extent': [42.87349365119738, 50.32636203215727],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['NDVI', 'DOY', 'PLATFORM', 'QU']}},\n", - " 'summaries': {'constellation': ['Aqua, Terra'],\n", - " 'platform': ['Aqua, Terra'],\n", - " 'rows': 2867,\n", - " 'columns': 4901,\n", + " 'bands': {'type': 'bands', 'values': ['8d_ndvi_linint']}},\n", + " 'summaries': {'constellation': ['Terra'],\n", + " 'platform': ['Terra'],\n", + " 'rows': 3606,\n", + " 'columns': 4430,\n", " 'instruments': ['MODIS'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'NDVI', 'center_wavelength': 0.0, 'gsd': 0.0},\n", - " {'name': 'DOY', 'center_wavelength': 0.0, 'gsd': 0.0},\n", - " {'name': 'PLATFORM', 'center_wavelength': 0.0, 'gsd': 0.0},\n", - " {'name': 'QU', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': '8d_ndvi_linint',\n", + " 'center_wavelength': 0.0,\n", + " 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_REL_RR_12_ERA5_QM',\n", " 'title': 'Precipitation Anomalies - ERA5_QM REL_RR-12',\n", - " 'description': 'Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2010).',\n", + " 'description': 'Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).',\n", " 'keywords': ['RR anomalies',\n", " 'relative precipitation anomalies',\n", " 'precipitation anomalies',\n", - " 'ERA5',\n", - " 'ADO Project'],\n", + " 'ERA5'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'License to use Copernicus Products',\n", @@ -17339,14 +17604,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -17356,7 +17621,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['REL_RR-12']}},\n", + " 'bands': {'type': 'bands', 'values': ['REL_RR_12']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -17364,19 +17629,18 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'REL_RR-12', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'REL_RR_12', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_REL_RR_1_ERA5_QM',\n", " 'title': 'Precipitation Anomalies - ERA5_QM REL_RR-1',\n", - " 'description': 'Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2010).',\n", + " 'description': 'Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).',\n", " 'keywords': ['RR anomalies',\n", " 'relative precipitation anomalies',\n", " 'precipitation anomalies',\n", - " 'ERA5',\n", - " 'ADO Project'],\n", + " 'ERA5'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'License to use Copernicus Products',\n", @@ -17394,14 +17658,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -17411,7 +17675,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['REL_RR-1']}},\n", + " 'bands': {'type': 'bands', 'values': ['REL_RR_1']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -17419,19 +17683,18 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'REL_RR-1', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'REL_RR_1', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_REL_RR_2_ERA5_QM',\n", " 'title': 'Precipitation Anomalies - ERA5_QM REL_RR-2',\n", - " 'description': 'Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2010).',\n", + " 'description': 'Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).',\n", " 'keywords': ['RR anomalies',\n", " 'relative precipitation anomalies',\n", " 'precipitation anomalies',\n", - " 'ERA5',\n", - " 'ADO Project'],\n", + " 'ERA5'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'License to use Copernicus Products',\n", @@ -17449,14 +17712,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -17466,7 +17729,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['REL_RR-2']}},\n", + " 'bands': {'type': 'bands', 'values': ['REL_RR_2']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -17474,19 +17737,18 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'REL_RR-2', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'REL_RR_2', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_REL_RR_3_ERA5_QM',\n", " 'title': 'Precipitation Anomalies - ERA5_QM REL_RR-3',\n", - " 'description': 'Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2010).',\n", + " 'description': 'Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).',\n", " 'keywords': ['RR anomalies',\n", " 'relative precipitation anomalies',\n", " 'precipitation anomalies',\n", - " 'ERA5',\n", - " 'ADO Project'],\n", + " 'ERA5'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'License to use Copernicus Products',\n", @@ -17504,14 +17766,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -17521,7 +17783,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['REL_RR-3']}},\n", + " 'bands': {'type': 'bands', 'values': ['REL_RR_3']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -17529,19 +17791,18 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'REL_RR-3', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'REL_RR_3', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_REL_RR_6_ERA5_QM',\n", " 'title': 'Precipitation Anomalies - ERA5_QM REL_RR-6',\n", - " 'description': 'Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2010).',\n", + " 'description': 'Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2020).',\n", " 'keywords': ['RR anomalies',\n", " 'relative precipitation anomalies',\n", " 'precipitation anomalies',\n", - " 'ERA5',\n", - " 'ADO Project'],\n", + " 'ERA5'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'License to use Copernicus Products',\n", @@ -17559,14 +17820,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -17576,7 +17837,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['REL_RR-6']}},\n", + " 'bands': {'type': 'bands', 'values': ['REL_RR_6']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -17584,7 +17845,7 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'REL_RR-6', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'REL_RR_6', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", @@ -17650,12 +17911,11 @@ " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_SPEI_12_ERA5_QM',\n", - " 'title': 'Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-12',\n", - " 'description': 'The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2010). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.',\n", + " 'title': 'Standardised Precipitation-Evapotranspiration Index - ERA5_QM',\n", + " 'description': 'The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.',\n", " 'keywords': ['SPEI',\n", " 'standardised precipitation-evapotranspiration index,surface water balance anomalies',\n", - " 'ERA5',\n", - " 'ADO Project'],\n", + " 'ERA5'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'Licence to use Copernicus Products',\n", @@ -17673,14 +17933,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -17690,7 +17950,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['SPEI-12']}},\n", + " 'bands': {'type': 'bands', 'values': ['SPEI_12']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -17698,18 +17958,17 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'SPEI-12', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'SPEI_12', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_SPEI_1_ERA5_QM',\n", - " 'title': 'Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-1',\n", - " 'description': 'The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2010). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.',\n", + " 'title': 'Standardised Precipitation-Evapotranspiration Index - ERA5_QM',\n", + " 'description': 'The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.',\n", " 'keywords': ['SPEI',\n", " 'standardised precipitation-evapotranspiration index,surface water balance anomalies',\n", - " 'ERA5',\n", - " 'ADO Project'],\n", + " 'ERA5'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'Licence to use Copernicus Products',\n", @@ -17727,14 +17986,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -17744,7 +18003,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['SPEI-1']}},\n", + " 'bands': {'type': 'bands', 'values': ['SPEI_1']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -17752,18 +18011,17 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'SPEI-1', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'SPEI_1', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_SPEI_2_ERA5_QM',\n", - " 'title': 'Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-2',\n", - " 'description': 'The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2010). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.',\n", + " 'title': 'Standardised Precipitation-Evapotranspiration Index - ERA5_QM',\n", + " 'description': 'The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.',\n", " 'keywords': ['SPEI',\n", " 'standardised precipitation-evapotranspiration index,surface water balance anomalies',\n", - " 'ERA5',\n", - " 'ADO Project'],\n", + " 'ERA5'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'Licence to use Copernicus Products',\n", @@ -17781,14 +18039,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -17798,7 +18056,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['SPEI-2']}},\n", + " 'bands': {'type': 'bands', 'values': ['SPEI_2']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -17806,18 +18064,17 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'SPEI-2', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'SPEI_2', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_SPEI_3_ERA5_QM',\n", - " 'title': 'Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-3',\n", - " 'description': 'The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2010). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.',\n", + " 'title': 'Standardised Precipitation-Evapotranspiration Index - ERA5_QM',\n", + " 'description': 'The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.',\n", " 'keywords': ['SPEI',\n", " 'standardised precipitation-evapotranspiration index,surface water balance anomalies',\n", - " 'ERA5',\n", - " 'ADO Project'],\n", + " 'ERA5'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'Licence to use Copernicus Products',\n", @@ -17835,14 +18092,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -17852,7 +18109,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['SPEI-3']}},\n", + " 'bands': {'type': 'bands', 'values': ['SPEI_3']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -17860,18 +18117,17 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'SPEI-3', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'SPEI_3', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_SPEI_6_ERA5_QM',\n", - " 'title': 'Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-6',\n", - " 'description': 'The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2010). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.',\n", + " 'title': 'Standardised Precipitation-Evapotranspiration Index - ERA5_QM',\n", + " 'description': 'The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.',\n", " 'keywords': ['SPEI',\n", " 'standardised precipitation-evapotranspiration index,surface water balance anomalies',\n", - " 'ERA5',\n", - " 'ADO Project'],\n", + " 'ERA5'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'Licence to use Copernicus Products',\n", @@ -17889,14 +18145,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -17906,7 +18162,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['SPEI-6']}},\n", + " 'bands': {'type': 'bands', 'values': ['SPEI_6']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -17914,14 +18170,14 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'SPEI-6', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'SPEI_6', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_SPI_12_ERA5_QM',\n", " 'title': 'Standardised Precipitation Index - ERA5_QM SPI-12',\n", - " 'description': 'The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2010). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping',\n", + " 'description': 'The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping',\n", " 'keywords': ['SPI',\n", " 'standardised precipitation index',\n", " 'precipitation anomalies',\n", @@ -17943,14 +18199,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -17960,7 +18216,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['SPI-12']}},\n", + " 'bands': {'type': 'bands', 'values': ['SPI_12']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -17968,18 +18224,17 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'SPI-12', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'SPI_12', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_SPI_1_ERA5_QM',\n", " 'title': 'Standardised Precipitation Index - ERA5_QM SPI-1',\n", - " 'description': 'The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2010). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping',\n", + " 'description': 'The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.',\n", " 'keywords': ['SPI',\n", " 'standardised precipitation index',\n", - " 'precipitation anomalies',\n", - " 'ADO project'],\n", + " 'precipitation anomalies'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'Licence to use Copernicus Products',\n", @@ -17997,14 +18252,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -18014,7 +18269,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['SPI-1']}},\n", + " 'bands': {'type': 'bands', 'values': ['SPI_1']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -18022,18 +18277,17 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'SPI-1', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'SPI_1', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_SPI_2_ERA5_QM',\n", " 'title': 'Standardised Precipitation Index - ERA5_QM SPI-2',\n", - " 'description': 'The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2010). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping',\n", + " 'description': 'The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping',\n", " 'keywords': ['SPI',\n", " 'standardised precipitation index',\n", - " 'precipitation anomalies',\n", - " 'ADO project'],\n", + " 'precipitation anomalies'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'Licence to use Copernicus Products',\n", @@ -18051,14 +18305,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -18068,7 +18322,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['SPI-2']}},\n", + " 'bands': {'type': 'bands', 'values': ['SPI_2']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -18076,18 +18330,17 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'SPI-2', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'SPI_2', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_SPI_3_ERA5_QM',\n", " 'title': 'Standardised Precipitation Index - ERA5_QM SPI-3',\n", - " 'description': 'The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2010). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping',\n", + " 'description': 'The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping',\n", " 'keywords': ['SPI',\n", " 'standardised precipitation index',\n", - " 'precipitation anomalies',\n", - " 'ADO project'],\n", + " 'precipitation anomalies'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'Licence to use Copernicus Products',\n", @@ -18105,14 +18358,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -18122,7 +18375,7 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['SPI-3']}},\n", + " 'bands': {'type': 'bands', 'values': ['SPI_3']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -18130,18 +18383,17 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'SPI-3', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'SPI_3', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_SPI_6_ERA5_QM',\n", " 'title': 'Standardised Precipitation Index - ERA5_QM SPI-6',\n", - " 'description': 'The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2010). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping',\n", + " 'description': 'The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping',\n", " 'keywords': ['SPI',\n", " 'standardised precipitation index',\n", - " 'precipitation anomalies',\n", - " 'ADO project'],\n", + " 'precipitation anomalies'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'Licence to use Copernicus Products',\n", @@ -18159,14 +18411,14 @@ " 42.85381219788743,\n", " 17.360182784195487,\n", " 50.310634900383555]]},\n", - " 'temporal': {'interval': [['1979-01-01T00:00:00Z',\n", - " '2018-12-31T00:00:00Z']]}},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'TBD',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['1979-01-01T00:00:00.000Z', '2018-12-31T00:00:00.000Z'],\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", " 'step': '1-day'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", @@ -18176,7 +18428,109 @@ " 'axis': 'y',\n", " 'extent': [42.85381219788743, 50.310634900383555],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['SPI-6']}},\n", + " 'bands': {'type': 'bands', 'values': ['SPI_6']}},\n", + " 'summaries': {'constellation': ['N/A'],\n", + " 'platform': ['N/A'],\n", + " 'rows': 167,\n", + " 'columns': 202,\n", + " 'instruments': ['N/A'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': 'SPI_6', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'ADO_SSPI_10d_SNOWGRID',\n", + " 'title': 'Standardised Snow Pack Index - ERA5_QM SSPI-10',\n", + " 'description': 'The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data.',\n", + " 'keywords': ['SSPI', 'standardised snow pack index', 'ERA5', 'SNOWGRID'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'Licence to use Copernicus Products',\n", + " 'sci:citation': 'Olefs, Marc, Roland Koch, Wolfgang Schöner, and Thomas Marke. (2020): Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961–2020—A Model Based Approach Atmosphere 11, no. 12: 1330. https://doi.org/10.3390/atmos11121330. EDO Indicator Fact Sheet: Standardised Snow Pack Index: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_sspi.pdf',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Slovenian Environment Agency - ARSO',\n", + " 'url': 'https://www.arso.gov.si/',\n", + " 'roles': ['Producer']},\n", + " {'name': 'Zentralanstalt für Meteorologie und Geodynamik - ZAMG',\n", + " 'url': 'https://www.zamg.ac.at/cms/de/aktuell',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[4.0563685981112565,\n", + " 42.85381219788743,\n", + " 17.360182784195487,\n", + " 50.310634900383555]]},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'TBD',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", + " 'step': 'P1D'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [4.0563685981112565, 17.360182784195487],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.85381219788743, 50.310634900383555],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['SSPI10']}},\n", + " 'summaries': {'constellation': ['N/A'],\n", + " 'platform': ['N/A'],\n", + " 'rows': 167,\n", + " 'columns': 202,\n", + " 'instruments': ['N/A'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': 'SSPI10', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'ADO_SSPI_30d_SNOWGRID',\n", + " 'title': 'Standardised Snow Pack Index - ERA5_QM SSPI-30',\n", + " 'description': 'The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data.',\n", + " 'keywords': ['SSPI', 'standardised snow pack index', 'ERA5', 'SNOWGRID'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'Licence to use Copernicus Products',\n", + " 'sci:citation': 'Olefs, Marc, Roland Koch, Wolfgang Schöner, and Thomas Marke. (2020): Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961–2020—A Model Based Approach Atmosphere 11, no. 12: 1330. https://doi.org/10.3390/atmos11121330. EDO Indicator Fact Sheet: Standardised Snow Pack Index: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_sspi.pdf',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Slovenian Environment Agency - ARSO',\n", + " 'url': 'https://www.arso.gov.si/',\n", + " 'roles': ['Producer']},\n", + " {'name': 'Zentralanstalt für Meteorologie und Geodynamik - ZAMG',\n", + " 'url': 'https://www.zamg.ac.at/cms/de/aktuell',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[4.0563685981112565,\n", + " 42.85381219788743,\n", + " 17.360182784195487,\n", + " 50.310634900383555]]},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'TBD',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", + " 'step': 'P1D'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [4.0563685981112565, 17.360182784195487],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.85381219788743, 50.310634900383555],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['SSPI30']}},\n", " 'summaries': {'constellation': ['N/A'],\n", " 'platform': ['N/A'],\n", " 'rows': 167,\n", @@ -18184,7 +18538,7 @@ " 'instruments': ['N/A'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'SPI-6', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': 'SSPI30', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", @@ -18255,16 +18609,61 @@ " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", + " 'id': 'ADO_TCI_MODIS_231m_3035',\n", + " 'title': 'Temperature Condition Index - 231 m 8 days',\n", + " 'description': 'The Temperature Condition Index (TCI) is based on the Land Surface Temperature (LST) MODIS satellite data. The LST is based on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m after regridding from the original 1000 m resolution. The LST is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The TCI is calculated using the formula TCIi = (LSTmax,i - LSTi)/(LSTmax,i - LSTmin,i) * 100. The TCI expresses anomalies of the LST. The data is provided as 8 day measures. The time series is starting from 2001. The TCI values range from 0-100, whereas high values correspond to optimal vegetation conditions and low values indicate unfavorable vegetation conditions. ',\n", + " 'keywords': ['temperature condition index', 'tci', 'modis'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'CC BY 4.0',\n", + " 'sci:citation': 'Kogan, F. N. (eds) (1995): Application of vegetation index and brightness temperature for drought detection. In: Advances in Space Research, 15 (11), 91–100. Kogan, F. N. (eds) (1997): Global Drought Watch from Space. In: Bulletin of the American Meteorological Society, 78 (4), 621–636. Kogan, F. N. (eds) (2000): Satellite-Observed Sensitivity of World Land Ecosystems to El Nino/La Nina. In: Remote Sensing of Environment, 74, 445–462.',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Eurac Research',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['Processor']},\n", + " {'name': 'NASA',\n", + " 'url': 'https://modis.gsfc.nasa.gov/',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[3.99537296130427,\n", + " 42.87349365119738,\n", + " 17.523924303829347,\n", + " 50.32636203215727]]},\n", + " 'temporal': {'interval': [['2001-01-01T00:00:00Z',\n", + " '2021-01-03T00:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2001-01-01T00:00:00.000Z', '2021-01-03T00:00:00.000Z'],\n", + " 'step': 'P8D'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [3.99537296130427, 17.523924303829347],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.87349365119738, 50.32636203215727],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['8d_tci']}},\n", + " 'summaries': {'constellation': ['Terra'],\n", + " 'platform': ['Terra'],\n", + " 'rows': 3606,\n", + " 'columns': 4430,\n", + " 'instruments': ['MODIS'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': '8d_tci', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_VCI_MODIS_231m_3035',\n", - " 'title': 'Vegetation Condition Index - 231m',\n", - " 'description': 'The Vegetation Condition Index (VCI) is based on a the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite data. The NDVI is calculated from daily MOD09GQ products using the formula: VCIi = (NDVIi - NDVImin,i)/(NDVImax,i - NDVImin,i) * 100. The VCI expresses anomalies of the NDVI. The data is provided as aggregated four day measures. The time series is starting from 2001. The VCI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.',\n", - " 'keywords': ['vegetation condition index',\n", - " 'vci',\n", - " 'ndvi',\n", - " 'ndvi anomalies',\n", - " 'vegetation health',\n", - " 'modis',\n", - " 'ADO Project'],\n", + " 'title': 'Vegetation Condition Index - 231 m 8 days',\n", + " 'description': 'The Vegetation Condition Index (VCI) is based on the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VCI is calculated using the formula VCIi = (NDVIi - NDVImin,i)/(NDVImax,i - NDVImin,i) * 100. The VCI expresses anomalies of the NDVI. The data is provided as 8 day measures. The time series is starting from 2001. The VCI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.',\n", + " 'keywords': ['vegetation condition index', 'vci', 'modis'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'CC BY 4.0',\n", @@ -18274,88 +18673,91 @@ " 'roles': ['host']},\n", " {'name': 'Eurac Research',\n", " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['Processor']},\n", + " {'name': 'NASA',\n", + " 'url': 'https://modis.gsfc.nasa.gov/',\n", " 'roles': ['Producer']}],\n", - " 'extent': {'spatial': {'bbox': [[4.209735424695982,\n", - " 42.885944027609625,\n", - " 17.2793598042509,\n", - " 48.72744873588148]]},\n", - " 'temporal': {'interval': [['2001-01-04T00:00:00Z',\n", - " '2020-11-11T00:00:00Z']]}},\n", + " 'extent': {'spatial': {'bbox': [[3.99537296130427,\n", + " 42.87349365119738,\n", + " 17.523924303829347,\n", + " 50.32636203215727]]},\n", + " 'temporal': {'interval': [['2001-01-01T00:00:00Z',\n", + " '2021-01-03T00:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['2001-01-04T00:00:00.000Z', '2020-11-11T00:00:00.000Z'],\n", - " 'step': '4-days'},\n", + " 'extent': ['2001-01-01T00:00:00.000Z', '2021-01-03T00:00:00.000Z'],\n", + " 'step': 'P8D'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", - " 'extent': [4.209735424695982, 17.2793598042509],\n", + " 'extent': [3.99537296130427, 17.523924303829347],\n", " 'reference_system': 3035},\n", " 'Y': {'type': 'spatial',\n", " 'axis': 'y',\n", - " 'extent': [42.885944027609625, 48.72744873588148],\n", + " 'extent': [42.87349365119738, 50.32636203215727],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['VCI']}},\n", - " 'summaries': {'constellation': ['Aqua, Terra'],\n", - " 'platform': ['Aqua, Terra'],\n", - " 'rows': 2834,\n", - " 'columns': 4354,\n", + " 'bands': {'type': 'bands', 'values': ['8d_vci']}},\n", + " 'summaries': {'constellation': ['Terra'],\n", + " 'platform': ['Terra'],\n", + " 'rows': 3606,\n", + " 'columns': 4430,\n", " 'instruments': ['MODIS'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'VCI', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': '8d_vci', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", " 'id': 'ADO_VHI_MODIS_231m_3035',\n", - " 'title': 'Vegetation Health Index - 231m',\n", - " 'description': 'The Vegetation Health index (VHI) is based on a combination of products extracted from vegetation signals, namely the Normalized Difference Vegetation Index (NDVI) and from the land surface temperature, both derived from MODIS satellite data. The NDVI is based on daily MOD09GQ and the Land Surface Temperature on daily MOD11A1 products. The VHI relies on a strong inverse correlation between NDVI and land surface temperature, since increasing land temperatures are assumed to act negatively on vegetation vigour and consequently to cause stress. The data is provided as aggregated four day measures. The time series is starting from 2001. The VHI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation. ',\n", - " 'keywords': ['vegetation health index,vhi,tci,vci,land surface temperature,ndvi,vegetation health,temperature condition index',\n", - " 'vegetation condition index',\n", - " 'modis',\n", - " 'ADO Project'],\n", + " 'title': 'Vegetation Health Index - 231 m 8 days',\n", + " 'description': 'The Vegetation Health Index (VHI) is based on a combination of products extracted from vegetation signals, namely the Normalized Difference Vegetation Index (NDVI) and the land surface temperature, both derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance and the land surface temperature (LST) on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m, therefore the original 1000 m resolution of the MOD11A2 LST is downscaled to 231 m of the MOD09Q1 reflectance. Both products are masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VHI relies on a strong inverse correlation between NDVI and land surface temperature, since increasing land temperatures are assumed to act negatively on vegetation vigour and consequently to cause stress. The data is provided as 8 day measures. The time series is starting from 2001. The VHI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.',\n", + " 'keywords': ['vegetation health index', 'vhi', 'modis'],\n", " 'version': 'v1',\n", " 'deprecated': False,\n", " 'license': 'CC BY 4.0',\n", - " 'sci:citation': 'Kogan, F. N. (eds) (1995): Application of vegetation index and brightness temperature for drought detection. In: Advances in Space Research, 15 (11), 91–100.',\n", + " 'sci:citation': 'Kogan, F. N. (eds) (1995): Application of vegetation index and brightness temperature for drought detection. In: Advances in Space Research, 15 (11), 91–100. Kogan, F. N. (eds) (1997): Global Drought Watch from Space. In: Bulletin of the American Meteorological Society, 78 (4), 621–636. Kogan, F. N. (eds) (2000): Satellite-Observed Sensitivity of World Land Ecosystems to El Nino/La Nina. In: Remote Sensing of Environment, 74, 445–462.',\n", " 'providers': [{'name': 'Eurac EO WCS',\n", " 'url': 'http://www.eurac.edu',\n", " 'roles': ['host']},\n", " {'name': 'Eurac Research',\n", " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['Processor']},\n", + " {'name': 'NASA',\n", + " 'url': 'https://modis.gsfc.nasa.gov/',\n", " 'roles': ['Producer']}],\n", - " 'extent': {'spatial': {'bbox': [[4.209735424695982,\n", - " 42.885944027609625,\n", - " 17.2793598042509,\n", - " 48.72744873588148]]},\n", - " 'temporal': {'interval': [['2001-01-04T00:00:00Z',\n", - " '2020-11-11T00:00:00Z']]}},\n", + " 'extent': {'spatial': {'bbox': [[3.99537296130427,\n", + " 42.87349365119738,\n", + " 17.523924303829347,\n", + " 50.32636203215727]]},\n", + " 'temporal': {'interval': [['2001-01-01T00:00:00Z',\n", + " '2021-01-03T00:00:00Z']]}},\n", " 'links': [{'rel': 'licence',\n", " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", " 'type': 'text/html',\n", " 'title': 'License Link'}],\n", " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", - " 'extent': ['2001-01-04T00:00:00.000Z', '2020-11-11T00:00:00.000Z'],\n", - " 'step': '4-days'},\n", + " 'extent': ['2001-01-01T00:00:00.000Z', '2021-01-03T00:00:00.000Z'],\n", + " 'step': 'P8D'},\n", " 'X': {'type': 'spatial',\n", " 'axis': 'x',\n", - " 'extent': [4.209735424695982, 17.2793598042509],\n", + " 'extent': [3.99537296130427, 17.523924303829347],\n", " 'reference_system': 3035},\n", " 'Y': {'type': 'spatial',\n", " 'axis': 'y',\n", - " 'extent': [42.885944027609625, 48.72744873588148],\n", + " 'extent': [42.87349365119738, 50.32636203215727],\n", " 'reference_system': 3035},\n", - " 'bands': {'type': 'bands', 'values': ['VHI']}},\n", - " 'summaries': {'constellation': ['Aqua, Terra'],\n", - " 'platform': ['Aqua, Terra'],\n", - " 'rows': 2834,\n", - " 'columns': 4354,\n", + " 'bands': {'type': 'bands', 'values': ['8d_vhi']}},\n", + " 'summaries': {'constellation': ['Terra'],\n", + " 'platform': ['Terra'],\n", + " 'rows': 3606,\n", + " 'columns': 4430,\n", " 'instruments': ['MODIS'],\n", " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", " 'gsd': [],\n", - " 'eo:bands': [{'name': 'VHI', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'eo:bands': [{'name': '8d_vhi', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", " 'assets': {}},\n", " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", @@ -19534,6 +19936,59 @@ " {'engine': 'WCPS',\n", " 'stac_version': '0.9.0',\n", " 'stac_extensions': ['datacube'],\n", + " 'id': 'RESCALE_ADO_NDVI_MODIS_231m_3035',\n", + " 'title': 'SCALED NDVI TEST',\n", + " 'description': 'The Normalized Difference Vegetation Index (NDVI) is based on MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The NDVI is calculated using the formula NDVI = (NIR - Red) / (NIR + Red). The NDVI expresses the vitality of vegetation. The data is provided as 8 day measures. The time series is starting from 2001. The NDVI values range from -1 - 1, whereas high values correspond to healthy vegetation.',\n", + " 'keywords': ['normalized difference vegetation index', 'ndvi', 'modis'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'CC BY 4.0',\n", + " 'sci:citation': 'N/A',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Eurac Research',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['Processor']},\n", + " {'name': 'NASA',\n", + " 'url': 'https://modis.gsfc.nasa.gov/',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[3.99537296130427,\n", + " 42.87349365119738,\n", + " 17.523924303829347,\n", + " 50.32636203215727]]},\n", + " 'temporal': {'interval': [['2020-12-26T00:00:00Z',\n", + " '2021-01-03T00:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2020-12-26T00:00:00.000Z', '2021-01-03T00:00:00.000Z'],\n", + " 'step': 'P8D'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [3.99537296130427, 17.523924303829347],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.87349365119738, 50.32636203215727],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['8d_ndvi_linint']}},\n", + " 'summaries': {'constellation': ['Terra'],\n", + " 'platform': ['Terra'],\n", + " 'rows': 3606,\n", + " 'columns': 4430,\n", + " 'instruments': ['MODIS'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': '8d_ndvi_linint',\n", + " 'center_wavelength': 0.0,\n", + " 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", " 'id': 'S2_Cloudless_32631_10m_L1C',\n", " 'title': 'Sentinel-2 Cloudless Data S2_Cloudless_32631_10m_L1C',\n", " 'description': 'The Copernicus Sentinel-2 Cloudless dataset.',\n", @@ -22180,7 +22635,7 @@ " 'assets': {}}]" ] }, - "execution_count": 153, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -22224,7 +22679,7 @@ " }\n", " \n", " \n", - " \n", + " \n", " \n", " " ], @@ -23416,23 +23871,28 @@ " 'y': 'Sentinel-2B'}}}}}}}]},\n", " {'engine': '[ODC_DASK]',\n", " 'id': 'radar_mask',\n", - " 'summary': 'Compute Radar Mask from DEM and LIA',\n", - " 'description': 'Computes the Radar Mask taking into account Layover, Foreshortening and Shadow',\n", - " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'summary': 'Compute Radar Mask from DEM (Digital Elevation Model) and LIA (Local Incidence Angle)',\n", + " 'description': 'Computes the Radar Mask taking into account Layover, Foreshortening and Shadow. It returns an array with the new bands as integers: 1:layover_mask, 2:foreshortening_mask and 3:shadow_mask. This process must be used in an apply_dimension process, since it acts on pixel level but modifies the existing number of dimensions (bands). You need to use rename_labels to assign the band labels afterwards.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", " 'name': 'data',\n", - " 'description': 'A raster data cube with exactly two horizontal spatial dimensions and two bands called DEM and LIA, no other dimensions allowed (no temporal dimension)'},\n", + " 'description': 'An array of numbers with two bands (DEM and LIA), expected to be indexed in that order. If the given array is a labeled array the bands should be called DEM and LIA, no other dimensions allowed (no temporal dimension)'},\n", " {'schema': {'minItems': 1, 'type': 'float'},\n", " 'default': 0.2,\n", - " 'name': 'threshold',\n", - " 'description': 'The threshold to apply to the final mask, between 0 and 1.'},\n", + " 'name': 'foreshortening_th',\n", + " 'description': 'The foreshortening threshold, between 0 and 1.'},\n", + " {'schema': {'minItems': 1, 'type': 'float'},\n", + " 'default': 1.0,\n", + " 'name': 'layover_th',\n", + " 'description': 'The layover threshold, must be greater than 0.'},\n", " {'schema': [{'type': 'string', 'enum': ['ASC', 'DSC']}],\n", " 'default': 0,\n", - " 'name': 'orbit',\n", + " 'name': 'orbit_direction',\n", " 'description': 'The Sentinel-1 orbit direction.',\n", " 'optional': False}],\n", " 'categories': ['cubes'],\n", - " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", - " 'description': 'A boolean mask with zeros for the masked pixels and ones for the valid pixels.'},\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'An array with with zeros for the masked pixels and ones for the valid pixels.'},\n", " 'exceptions': {}},\n", " {'engine': '[WCPS]',\n", " 'id': 'tanh',\n", @@ -24392,14 +24852,14 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 7, "id": "8711d5ce-4958-4fc1-a457-06dd3d5e1b88", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b716dd2af4654151b335d2942f43ed78", + "model_id": "2ae6d5a174f04b30950d69c5189e70c2", "version_major": 2, "version_minor": 0 }, @@ -24421,21 +24881,22 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 8, "id": "68ebc058-7c34-4362-82eb-076f969b641b", "metadata": { "scrolled": true }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coordinates selected from map: \n", - " west 11.009592 \n", - " east 11.284933 \n", - " south 46.595706 \n", - " north 46.736598\n" + "ename": "IndexError", + "evalue": "tuple index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meoMap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetBbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Coordinates selected from map:\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'\\n west'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'\\n east'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'\\n south'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'\\n north'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/SAR2Cube/SInCohMap/SInCohMap/eo_utils.py\u001b[0m in \u001b[0;36mgetBbox\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0mmapBox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mmapBox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmapBox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmapBox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmapBox\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: tuple index out of range" ] } ], @@ -24446,7 +24907,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "3dfa661f-c493-4e85-8a71-dfe70eecb6fe", "metadata": {}, "outputs": [], @@ -24460,16 +24921,31 @@ "metadata": {}, "source": [ "## Doñana Datacubes:\n", - "\n", - "### Sentinel-1:\n", - "\n", + "Sentinel-1:\n", + "```\n", "SAR2Cube_SInCohMap_S1_L0_147_ASC_DONYANA\n", - "\n", "SAR2Cube_SInCohMap_S1_L0_154_DSC_DONYANA\n", - "\n", - "### Sentinel-2:\n", - "\n", - "SInCohMap_S2_L1C_T29SQB" + "```\n", + "Sentinel-2:\n", + "```\n", + "SInCohMap_S2_L1C_T29SQB\n", + "```\n", + "## South Tyrol Datacubes\n", + "Sentinel-1:\n", + "```\n", + "SAR2Cube_SInCohMap_S1_L0_117_ASC_SOUTH_TYROL\n", + "SAR2Cube_SInCohMap_S1_L0_168_DSC_SOUTH_TYROL\n", + "```\n", + "Sentinel-2:\n", + "```\n", + "S2_L1C_T32TPS\n", + "```\n", + "## Finland Datacubes\n", + "Sentinel-1:\n", + "```\n", + "SAR2Cube_SInCohMap_S1_L0_80_DSC_FINLAND_AOI1\n", + "SAR2Cube_SInCohMap_S1_L0_80_DSC_FINLAND_AOI2\n", + "```" ] }, { @@ -24517,7 +24993,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "b9ccdb2b-ae0f-43a7-86ef-44f783e1ae55", "metadata": { "scrolled": true @@ -24526,7 +25002,7 @@ "source": [ "i_VH = S1_slant_range.band('i_VH')\n", "q_VH = S1_slant_range.band('q_VH')\n", - "S1_INT = (i_VH**2+q_VH**2)**0.5\n", + "S1_INT = i_VH**2+q_VH**2\n", "S1_INT_VH = S1_INT.add_dimension(name=\"bands\",label=\"VH\")" ] }, @@ -24547,7 +25023,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "id": "50a82163-049a-4cac-bea5-ad9af93dab88", "metadata": { "scrolled": true @@ -24573,7 +25049,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "id": "b3efabc2-4119-491c-bf68-dfe2283ee68f", "metadata": { "scrolled": true @@ -24593,7 +25069,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "id": "fd6bb9aa-f4a0-4e13-8d84-335d9625a066", "metadata": { "scrolled": true @@ -24613,21 +25089,12 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "id": "90f4b51b-808f-487e-8c46-31bda55c1dc0", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 39.1 ms, sys: 3.84 ms, total: 42.9 ms\n", - "Wall time: 1min 3s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "S1_INT_PNG = S1_INT_ML_VH_MEAN_0_255.save_result(format=\"PNG\")\n", @@ -24645,33 +25112,10 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": null, "id": "152381ad-e2d4-46ca-bd8c-faac8959b283", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 165, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_args = {'cmap':'Greys_r','vmin':0,'vmax':0.7}\n", "fig, ax = plt.subplots(1,2,figsize=(25,10))\n", @@ -24704,7 +25148,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "id": "654c7555-5afb-460c-bd65-60e14c1f9d83", "metadata": { "scrolled": true @@ -24731,7 +25175,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": null, "id": "9ccbdf27-682d-4c91-9479-be105dd024ed", "metadata": { "scrolled": true @@ -24740,7 +25184,7 @@ "source": [ "i_VV = S1_slant_range.band('i_VV')\n", "q_VV = S1_slant_range.band('q_VV')\n", - "S1_INT = (i_VV**2+q_VV**2)**0.5\n", + "S1_INT = i_VV**2+q_VV**2\n", "S1_INT_VV = S1_INT.add_dimension(name=\"bands\",label=\"VV\")" ] }, @@ -24754,7 +25198,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": null, "id": "centered-shore", "metadata": {}, "outputs": [], @@ -24765,7 +25209,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "id": "f829da3a-8f0e-4b01-8bb6-4a921698fe27", "metadata": { "scrolled": true @@ -24787,7 +25231,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": null, "id": "ae5c12f7-2ef3-47f6-a1b6-9e80b84c5d0d", "metadata": { "scrolled": true @@ -24799,7 +25243,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": null, "id": "266374a2-2a53-49a1-bd5c-79cb2ac560a2", "metadata": { "scrolled": true @@ -24820,7 +25264,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": null, "id": "f52d7315-6d6c-4031-803a-8d018071248f", "metadata": { "scrolled": true @@ -24840,7 +25284,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": null, "id": "be520fde-27b8-4435-a91f-924e707161bf", "metadata": { "scrolled": true @@ -24862,7 +25306,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": null, "id": "89fe593a-d10e-4799-adfa-e2123ce5fe94", "metadata": { "scrolled": true @@ -24883,21 +25327,12 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": null, "id": "d02a24ab-62fa-43e6-9172-9a7aa281321d", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 282 ms, sys: 86.2 ms, total: 369 ms\n", - "Wall time: 1min 28s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "S1_INT_ML_VV_GEOCODED.download(\"./data/S1_INT_VV_4x19_GEOCODED_ASC_DB_DONYANA3.tiff\",format='GTiff')" @@ -24928,7 +25363,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": null, "id": "51022216-c147-431b-8aca-6cb9d15760cb", "metadata": {}, "outputs": [], @@ -24938,23 +25373,10 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": null, "id": "8969cea8-68eb-4bfe-a4ab-286dc8e39611", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_args = {'cmap':'Greys_r','add_colorbar':False,'vmin':-15,'vmax':0}\n", "fig, ax = plt.subplots(1,1,figsize=(10,10))\n", @@ -24975,7 +25397,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": null, "id": "round-fountain", "metadata": {}, "outputs": [], @@ -24990,13 +25412,13 @@ "\n", "i_VV = S1_slant_range.band('i_VV')\n", "q_VV = S1_slant_range.band('q_VV')\n", - "S1_INT = (i_VV**2+q_VV**2)**0.5\n", + "S1_INT = i_VV**2+q_VV**2\n", "S1_INT_VV = S1_INT.add_dimension(name=\"bands\",label=\"VV\")" ] }, { "cell_type": "code", - "execution_count": 137, + "execution_count": null, "id": "possible-external", "metadata": {}, "outputs": [], @@ -25048,7 +25470,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": null, "id": "4e07bc47-f848-4a7d-bd20-70e5b72b5262", "metadata": {}, "outputs": [], @@ -25058,42 +25480,10 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": null, "id": "6b5e1560-8379-4130-b7ac-2507120c494b", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "job" ] @@ -25110,42 +25500,10 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": null, "id": "8698677c-832c-41ce-ad73-374dc0a76a7d", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 180, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result = job.get_results()\n", "result" @@ -25161,28 +25519,17 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": null, "id": "c3817cd7-2b38-41a5-8a2b-db09ad6072c5", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[PosixPath('job_result/process.json'), PosixPath('job_result/result.tiff')]" - ] - }, - "execution_count": 181, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result.download_files(\"./job_result/\")" ] }, { "cell_type": "code", - "execution_count": 182, + "execution_count": null, "id": "f6a277cc-6156-4e19-a038-4821232d3a1c", "metadata": {}, "outputs": [], @@ -25192,23 +25539,10 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": null, "id": "48a16be3-b350-413d-994b-1d857a16d7db", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_args = {'cmap':'Greys_r','add_colorbar':False,'vmin':0,'vmax':60}\n", "fig, ax = plt.subplots(1,1,figsize=(10,10))\n", @@ -25227,13838 +25561,13 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": null, "id": "cc6fecbb-6bed-4c92-8222-f071eaeb2b9c", "metadata": { "scrolled": true, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " " - ], - "text/plain": [ - "[{'id': '438e2da5-9fdb-4396-abf0-62f03118efd0',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_coherence_VV_VH',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'23': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': 'coh_imag_VH'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_q_VH']}},\n", - " '24': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '23'},\n", - " 'cube1': {'from_node': '20'}}},\n", - " '27': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '32'}, 'format': 'GTiff'}},\n", - " '28': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2018-01-06T00:00:00Z', '2018-01-08T23:59:59Z'],\n", - " 'data': {'from_node': '1'}}},\n", - " '29': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2018-01-12T00:00:00Z', '2018-01-14T23:59:59Z'],\n", - " 'data': {'from_node': '1'}}},\n", - " 'INTENSITY': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '30'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'9ueocha1t': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ig51rtyk7'}}},\n", - " 'z1tq33ah0': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '9ueocha1t'},\n", - " 'y': {'from_node': 'll3ls24ps'}}},\n", - " 'd5616wu8k': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'COH_q_VH'}},\n", - " 'ig51rtyk7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'COH_i_VH'}},\n", - " 'z3nxzpnt0': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'z1tq33ah0'}}},\n", - " 'll3ls24ps': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'd5616wu8k'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'coh_imag_VV': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'9hzgoy0zv': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV_i_0'}},\n", - " 'cbyq53c6c': {'process_id': 'sqrt',\n", - " 'arguments': {'x': {'from_node': 'xcpvz9epu'}}},\n", - " 'iiivdkt5x': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '9hzgoy0zv'}}},\n", - " '63d5swqus': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '0hj07zkit'}}},\n", - " 'u8eq67uxb': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': '0hj07zkit'},\n", - " 'y': {'from_node': 'ui1pbzemv'}}},\n", - " 'xcpvz9epu': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': '5pu09eb8e'},\n", - " 'y': {'from_node': 'jb6qd2h9k'}}},\n", - " 'zi33vbeuo': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV_i_1'}},\n", - " 'qfksu50g5': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': '9hzgoy0zv'},\n", - " 'y': {'from_node': 'zi33vbeuo'}}},\n", - " '5pu09eb8e': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'iiivdkt5x'},\n", - " 'y': {'from_node': '63d5swqus'}}},\n", - " 'tnps8jufq': {'result': True,\n", - " 'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'jeq2b2s1r'},\n", - " 'y': {'from_node': 'cbyq53c6c'}}},\n", - " 'ui1pbzemv': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV_q_1'}},\n", - " 'jeq2b2s1r': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'qfksu50g5'},\n", - " 'y': {'from_node': 'u8eq67uxb'}}},\n", - " '8pnot4zwn': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'zi33vbeuo'}}},\n", - " '0hj07zkit': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV_q_0'}},\n", - " '5csoq19ox': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ui1pbzemv'}}},\n", - " 'jb6qd2h9k': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '8pnot4zwn'},\n", - " 'y': {'from_node': '5csoq19ox'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '30': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '24'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '32': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '43'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'coh_imag_VH': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'x17nmeq76': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '9hrjuuu46'},\n", - " 'y': {'from_node': 'medq50yx5'}}},\n", - " 'xti1go1gi': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'owebvn9b1'},\n", - " 'y': {'from_node': 'x17nmeq76'}}},\n", - " 'keci70mi8': {'process_id': 'sqrt',\n", - " 'arguments': {'x': {'from_node': 'xti1go1gi'}}},\n", - " 'k9yy04vf1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VH_q_1'}},\n", - " 'q1zuo485g': {'result': True,\n", - " 'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': '0fip2i0kl'},\n", - " 'y': {'from_node': 'keci70mi8'}}},\n", - " 'medq50yx5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'k9yy04vf1'}}},\n", - " 'vgww9vbei': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VH_q_0'}},\n", - " 'x9pc7wk1l': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'vgww9vbei'}}},\n", - " 'lm9ip7huh': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': '945wif9kj'},\n", - " 'y': {'from_node': 'zabq7xjj0'}}},\n", - " 'owebvn9b1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'v4ku3sgqf'},\n", - " 'y': {'from_node': 'x9pc7wk1l'}}},\n", - " 'husyxdrvx': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'vgww9vbei'},\n", - " 'y': {'from_node': 'k9yy04vf1'}}},\n", - " '945wif9kj': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VH_i_0'}},\n", - " '9hrjuuu46': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'zabq7xjj0'}}},\n", - " '0fip2i0kl': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'lm9ip7huh'},\n", - " 'y': {'from_node': 'husyxdrvx'}}},\n", - " 'v4ku3sgqf': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '945wif9kj'}}},\n", - " 'zabq7xjj0': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'VH_i_1'}}}},\n", - " 'dimension': 'bands'}},\n", - " '33': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': 'INTENSITY'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VH']}},\n", - " '34': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '17'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '35': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '34'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '36': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '42'},\n", - " 'cube1': {'from_node': '33'}}},\n", - " '37': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': 'coh_imag_VV'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_q_VV']}},\n", - " '38': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': 'coh_real_VV'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_i_VV']}},\n", - " '17': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '28'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}},\n", - " 'parameters': [{'schema': {}, 'name': 'data', 'description': ''}]},\n", - " 'dimension': 't'}},\n", - " '39': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '38'},\n", - " 'cube1': {'from_node': '37'}}},\n", - " '18': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '29'},\n", - " 'reducer': {'process_graph': {'2': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}},\n", - " 'parameters': [{'schema': {}, 'name': 'data', 'description': ''}]},\n", - " 'dimension': 't'}},\n", - " '19': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '4'}, 'cube1': {'from_node': '3'}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'description': 'Loading the data; The order of the specified bands is important for the following reduce operation.',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T00:00:00Z',\n", - " '2018-01-31T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.297440023010255,\n", - " 'south': 46.59899914769812,\n", - " 'north': 46.749746742497905,\n", - " 'west': 11.03722948132324},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " 'coh_real_VV': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mfkyoq4rg': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'jhcvywrpt'},\n", - " 'y': {'from_node': '1g0dcj4kf'}}},\n", - " '1zffvdv5z': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '0rvyuvdg7'},\n", - " 'y': {'from_node': 'hhz5fv5ul'}}},\n", - " 'hhz5fv5ul': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'g66vafxms'}}},\n", - " 'gilmg9wzc': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'q23tvwm7w'},\n", - " 'y': {'from_node': 'g66vafxms'}}},\n", - " 'hnz19yge1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '1g0dcj4kf'}}},\n", - " '587jfrewp': {'process_id': 'sqrt',\n", - " 'arguments': {'x': {'from_node': 'igogz8cka'}}},\n", - " 'glabu12hc': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'q23tvwm7w'}}},\n", - " 'lvwva51no': {'result': True,\n", - " 'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'g0wz7qpkn'},\n", - " 'y': {'from_node': '587jfrewp'}}},\n", - " 'g66vafxms': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV_q_1'}},\n", - " 'jhcvywrpt': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV_i_1'}},\n", - " '0rvyuvdg7': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'jhcvywrpt'}}},\n", - " 'igogz8cka': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': '81fc4jsnc'},\n", - " 'y': {'from_node': '1zffvdv5z'}}},\n", - " '81fc4jsnc': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'glabu12hc'},\n", - " 'y': {'from_node': 'hnz19yge1'}}},\n", - " '1g0dcj4kf': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV_q_0'}},\n", - " 'q23tvwm7w': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV_i_0'}},\n", - " 'g0wz7qpkn': {'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_node': 'mfkyoq4rg'},\n", - " 'y': {'from_node': 'gilmg9wzc'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '3': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '17'},\n", - " 'source': ['VH_q', 'VH_i', 'VV_q', 'VV_i'],\n", - " 'dimension': 'bands',\n", - " 'target': ['VH_q_0', 'VH_i_0', 'VV_q_0', 'VV_i_0']}},\n", - " '4': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '18'},\n", - " 'source': ['VH_q', 'VH_i', 'VV_q', 'VV_i'],\n", - " 'dimension': 'bands',\n", - " 'target': ['VH_q_1', 'VH_i_1', 'VV_q_1', 'VV_i_1']}},\n", - " '40': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '39'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '41': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '40'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'f9v7n1bbj': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'COH_q_VV'}},\n", - " '47rsavv9k': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'f9v7n1bbj'}}},\n", - " 'xsv6eiyjw': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'rjapk49zk'}}},\n", - " 'rjapk49zk': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'COH_i_VV'}},\n", - " 'j5bvtpqpk': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'z0pfwl9ni'}}},\n", - " 'z0pfwl9ni': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'xsv6eiyjw'},\n", - " 'y': {'from_node': '47rsavv9k'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '20': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': 'coh_real_VH'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_i_VH']}},\n", - " '42': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '41'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VV']}},\n", - " '43': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '35'},\n", - " 'cube1': {'from_node': '36'}}},\n", - " 'coh_real_VH': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'afr88roxu': {'result': True,\n", - " 'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'dk43onzuh'},\n", - " 'y': {'from_node': '20vtk8xhh'}}},\n", - " 'bmga5sqhs': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VH_q_1'}},\n", - " '7exd4sekm': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'w5su4vmnl'},\n", - " 'y': {'from_node': 'bmga5sqhs'}}},\n", - " '7fwlqs67p': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'wsa0xkjpt'},\n", - " 'y': {'from_node': '55qihwjss'}}},\n", - " '55qihwjss': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'c4gj8kbkf'},\n", - " 'y': {'from_node': '8ub9tnwfv'}}},\n", - " '8ub9tnwfv': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'bmga5sqhs'}}},\n", - " 'c4gj8kbkf': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '2n33py0vu'}}},\n", - " '20vtk8xhh': {'process_id': 'sqrt',\n", - " 'arguments': {'x': {'from_node': '7fwlqs67p'}}},\n", - " '2n33py0vu': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VH_i_1'}},\n", - " 'rmjdqfeqe': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'h1iii5ls0'}}},\n", - " 't227i8bpg': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': '2n33py0vu'},\n", - " 'y': {'from_node': 'h1iii5ls0'}}},\n", - " 'h1iii5ls0': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VH_q_0'}},\n", - " 'wsa0xkjpt': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '4wyk7m886'},\n", - " 'y': {'from_node': 'rmjdqfeqe'}}},\n", - " 'w5su4vmnl': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VH_i_0'}},\n", - " 'dk43onzuh': {'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_node': 't227i8bpg'},\n", - " 'y': {'from_node': '7exd4sekm'}}},\n", - " '4wyk7m886': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'w5su4vmnl'}}}}},\n", - " 'dimension': 'bands'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-06T08:49:17.500Z',\n", - " 'updated': '2021-05-06T08:49:17.500Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': 'f87eb794-bc07-410a-b7ba-5027a6aeae17',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_Intensity_geocoding',\n", - " 'description': 'Compute the intensity and geocode the data.',\n", - " 'process': {'process_graph': {'11': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '9'}, 'cube1': {'from_node': '12'}}},\n", - " '12': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VV']}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-04-01T00:00:00Z',\n", - " '2019-04-07T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.56079864501953,\n", - " 'south': 46.38252805006732,\n", - " 'north': 46.637713867804024,\n", - " 'west': 11.095252990722656},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '3'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}}}},\n", - " 'dimension': 'bands'}},\n", - " '6': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '8'}, 'format': 'GTIFF'}},\n", - " '8': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '32'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " '9': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '10'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '30': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VH'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VH'}}}},\n", - " 'dimension': 'bands'}},\n", - " '20': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '30'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '31': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '20'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VH']}},\n", - " '10': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '32': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '31'},\n", - " 'cube1': {'from_node': '11'}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-07T14:21:59.042Z',\n", - " 'updated': '2021-05-07T14:21:59.042Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': 'df36bb61-7316-4d9e-84bb-e19f8b4c787a',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': None,\n", - " 'description': None,\n", - " 'process': {'process_graph': {'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01', '2018-12-31'],\n", - " 'spatial_extent': {'east': 11.278145,\n", - " 'south': 46.492858,\n", - " 'north': 46.492858,\n", - " 'west': 11.278145},\n", - " 'id': 'ADO_NDVI_MODIS_231m_3035'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'format': 'netCDF',\n", - " 'options': {}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-11T10:18:38.836Z',\n", - " 'updated': '2021-05-11T10:18:38.836Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': 'ce06e260-f61d-4e1b-8e00-1d05f1b27226',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': None,\n", - " 'description': None,\n", - " 'process': {'process_graph': {'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01', '2018-12-31'],\n", - " 'spatial_extent': {'east': 11.236253,\n", - " 'south': 46.496592,\n", - " 'north': 46.496592,\n", - " 'west': 11.236253},\n", - " 'id': 'ADO_NDVI_MODIS_231m_3035'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'format': 'JSON',\n", - " 'options': {}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-11T12:00:09.712Z',\n", - " 'updated': '2021-05-11T12:00:09.712Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': 'db233068-5e3c-45b3-972e-5fb96dcc8954',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'wetsnow_usecase',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'25': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': 1, 'y': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': '36'}}},\n", - " '28': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '1002'}, 'format': 'GTIFF'}},\n", - " '29': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'process_id': 'lt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': -2}},\n", - " '2': {'result': True,\n", - " 'process_id': 'if',\n", - " 'arguments': {'reject': 2,\n", - " 'value': {'from_node': '1'},\n", - " 'accept': 1}}}},\n", - " 'data': {'from_node': '39'}}},\n", - " '1000': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'eq',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 2}}}},\n", - " 'data': {'from_node': '110'}}},\n", - " '110': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': '109'},\n", - " 'method': 'near',\n", - " 'target': {'from_node': '6'}}},\n", - " '999': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '107'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '30': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '15'},\n", - " 'dimension': 'bands',\n", - " 'target': ['max_VV']}},\n", - " '31': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '14'},\n", - " 'dimension': 'bands',\n", - " 'target': ['max_VH']}},\n", - " '10': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '6'}, 'bands': ['VH']}},\n", - " '32': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'reducer': {'process_graph': {'kqsiotxw2': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_node': 'm2g6nh19b'},\n", - " 'y': {'from_node': 'a93hugbbx'}}},\n", - " 'a93hugbbx': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'max_VH'}},\n", - " 'm2g6nh19b': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VH'}}}},\n", - " 'dimension': 'bands'}},\n", - " '11': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '6'}, 'bands': ['VV']}},\n", - " '33': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'reducer': {'process_graph': {'rnvwk5q0c': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_node': 'f71yhw2an'},\n", - " 'y': {'from_node': 'fslizwnng'}}},\n", - " 'f71yhw2an': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV'}},\n", - " 'fslizwnng': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'max_VV'}}}},\n", - " 'dimension': 'bands'}},\n", - " '99': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " '34': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '32'},\n", - " 'dimension': 'bands',\n", - " 'target': ['rat_VH']}},\n", - " '35': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '33'},\n", - " 'dimension': 'bands',\n", - " 'target': ['rat_VV']}},\n", - " '14': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '10'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'max',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " '36': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '17'},\n", - " 'dimension': 'bands',\n", - " 'target': ['cond_0']}},\n", - " '15': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '11'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'max',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " '37': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '3'},\n", - " 'reducer': {'process_graph': {'iad7ssa2a': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'rat_VH'}},\n", - " 'ragp1am89': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'iad7ssa2a'},\n", - " 'y': {'from_node': 'j0egyfsx8'}}},\n", - " 'j0egyfsx8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'cond_0'}}}},\n", - " 'dimension': 'bands'}},\n", - " '38': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '4'},\n", - " 'reducer': {'process_graph': {'0768wzsbd': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'rat_VV'}},\n", - " '9apfnxh0i': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': '0768wzsbd'},\n", - " 'y': {'from_node': 'jmjopn4gf'}}},\n", - " 'jmjopn4gf': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'cond_0'}}}},\n", - " 'dimension': 'bands'}},\n", - " '17': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'process_id': 'subtract',\n", - " 'arguments': {'x': 45, 'y': {'from_parameter': 'x'}}},\n", - " '2': {'process_id': 'multiply',\n", - " 'arguments': {'x': 0.04, 'y': {'from_node': '1'}}},\n", - " '3': {'process_id': 'add',\n", - " 'arguments': {'x': 1, 'y': {'from_node': '2'}}},\n", - " '4': {'process_id': 'multiply',\n", - " 'arguments': {'x': 0.5, 'y': {'from_node': '3'}}},\n", - " '6': {'process_id': 'if',\n", - " 'arguments': {'reject': {'from_node': '4'},\n", - " 'value': {'from_node': '7'},\n", - " 'accept': 1}},\n", - " '7': {'process_id': 'lt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 20}},\n", - " '8': {'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 45}},\n", - " '9': {'result': True,\n", - " 'process_id': 'if',\n", - " 'arguments': {'reject': {'from_node': '6'},\n", - " 'value': {'from_node': '8'},\n", - " 'accept': 0.5}}}},\n", - " 'data': {'from_node': '99'}}},\n", - " '39': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '5'},\n", - " 'reducer': {'process_graph': {'harm4a1fs': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'ratVVW'}},\n", - " '0p4xm5jm7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'ratVHW'}},\n", - " 'tx5mzhvna': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '0p4xm5jm7'},\n", - " 'y': {'from_node': 'harm4a1fs'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '31'},\n", - " 'cube1': {'from_node': '10'}}},\n", - " '2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '30'},\n", - " 'cube1': {'from_node': '11'}}},\n", - " '3': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '36'},\n", - " 'cube1': {'from_node': '34'}}},\n", - " '4': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '25'},\n", - " 'cube1': {'from_node': '35'}}},\n", - " '5': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '41'},\n", - " 'cube1': {'from_node': '40'}}},\n", - " '104': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'lt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 25}}}},\n", - " 'data': {'from_node': '99'}}},\n", - " '6': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2015-11-06T00:00:00.000Z',\n", - " '2016-09-27T00:00:00.000Z'],\n", - " 'spatial_extent': {'east': 10.777416229248047,\n", - " 'south': 46.78148963659169,\n", - " 'north': 46.85244345762143,\n", - " 'west': 10.570392608642578},\n", - " 'id': 'Backscatter_Sentinel1_Track015'}},\n", - " '105': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '29'},\n", - " 'replacement': 3,\n", - " 'mask': {'from_node': '104'}}},\n", - " '1002': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1001'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '106': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 75}}}},\n", - " 'data': {'from_node': '99'}}},\n", - " '1001': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '999'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': '1000'}}},\n", - " '107': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '105'},\n", - " 'replacement': 4,\n", - " 'mask': {'from_node': '106'}}},\n", - " '9': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2014-12-04T00:00:00.000Z',\n", - " '2014-12-08T00:00:00.000Z'],\n", - " 'spatial_extent': {'east': 10.777416229248047,\n", - " 'south': 46.78148963659169,\n", - " 'north': 46.85244345762143,\n", - " 'west': 10.570392608642578},\n", - " 'id': 'LIA_Sentinel1_Track015',\n", - " 'bands': ['LIA']}},\n", - " '108': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2015-11-06T00:00:00.000Z',\n", - " '2016-09-27T00:00:00.000Z'],\n", - " 'spatial_extent': {'east': 10.777416229248047,\n", - " 'south': 46.78148963659169,\n", - " 'north': 46.85244345762143,\n", - " 'west': 10.570392608642578},\n", - " 'id': 'Modis_snow_eurac'}},\n", - " '109': {'process_id': 'resample_spatial',\n", - " 'arguments': {'data': {'from_node': '108'}, 'resolution': [20, -20]}},\n", - " '40': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '37'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['ratVHW']}},\n", - " '41': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '38'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['ratVVW']}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-12T13:14:38.439Z',\n", - " 'updated': '2021-05-12T13:14:38.439Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '4d75f0d8-17d3-4072-ade9-b388c86fdf24',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': None,\n", - " 'description': None,\n", - " 'process': {'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-08-06T00:00:00.000Z',\n", - " '2016-09-27T00:00:00.000Z'],\n", - " 'spatial_extent': {'east': 11.148033142089846,\n", - " 'south': 46.54327732556237,\n", - " 'north': 46.742213379963886,\n", - " 'west': 10.666694641113281},\n", - " 'id': 'Backscatter_Sentinel1_Track015'}},\n", - " '2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-08-06T00:00:00.000Z',\n", - " '2016-09-27T00:00:00.000Z'],\n", - " 'spatial_extent': {'east': 11.148033142089846,\n", - " 'south': 46.54327732556237,\n", - " 'north': 46.742213379963886,\n", - " 'west': 10.666694641113281},\n", - " 'id': 'Modis_snow_eurac'}},\n", - " '4': {'process_id': 'resample_cube_spatial',\n", - " 'arguments': {'data': {'from_node': '6'},\n", - " 'method': 'near',\n", - " 'target': {'from_node': '1'}}},\n", - " '5': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '4'}, 'format': 'NETCDF'}},\n", - " '6': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'target': {'from_node': '1'}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-12T13:18:36.107Z',\n", - " 'updated': '2021-05-12T14:06:07.820Z',\n", - " 'plan': None,\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '2a3e10d8-2386-4318-8820-bef367d30ea4',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'agg_spat_wind_test',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-07-01T00:00:00Z',\n", - " '2018-07-30T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.388015747070312,\n", - " 'south': 46.460565545785386,\n", - " 'north': 46.52343797415179,\n", - " 'west': 11.29669189453125},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m'],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " '3': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '2'},\n", - " 'size': [3, 3],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '4': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '5'}, 'format': 'PNG'}},\n", - " '5': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 1800,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '3'},\n", - " 'context': ''}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-13T07:36:38.451Z',\n", - " 'updated': '2021-05-13T07:36:38.451Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '7de2363e-753b-4aac-888a-218d44dfed5e',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_coherence_timeseries_netcdf',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '22'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '22': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '12': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'vhijhs38e': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'tzyahop5h'}}},\n", - " 'ks6xripd0': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'u5eaqduq0'},\n", - " 'y': {'from_node': 'vhijhs38e'}}},\n", - " '2z56jc7uv': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'tzyahop5h': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'nwvcyuxcu': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'ks6xripd0'}}},\n", - " 'u5eaqduq0': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '2z56jc7uv'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '23': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '16'},\n", - " 'cube1': {'from_node': '11'}}},\n", - " '13': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '12'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VV']}},\n", - " '14': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '15'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VH']}},\n", - " '15': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'nghhsofj2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VH'}},\n", - " 'l07q6wffb': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'jzcz3p7jb'}}},\n", - " 'nbtjw74xu': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '6tmemct1n'}}},\n", - " '6tmemct1n': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '0qyftco41'},\n", - " 'y': {'from_node': 'l07q6wffb'}}},\n", - " 'jzcz3p7jb': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VH'}},\n", - " '0qyftco41': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'nghhsofj2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '16': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '14'},\n", - " 'cube1': {'from_node': '13'}}},\n", - " '19': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T23:59:59Z',\n", - " '2018-01-14T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.202020645141603,\n", - " 'south': 46.64813992180177,\n", - " 'north': 46.687955702208626,\n", - " 'west': 11.122198104858398},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'coherence',\n", - " 'arguments': {'data': {'from_node': '1'}}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '20': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '23'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " '21': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '20'}, 'format': 'NETCDF'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-21T08:00:56.823Z',\n", - " 'updated': '2021-05-21T08:00:56.823Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '727a8a98-d10c-45ac-92bc-60da8fc574ac',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_coerence_PNG',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '22'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '22': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '12': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'vhijhs38e': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'tzyahop5h'}}},\n", - " 'ks6xripd0': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'u5eaqduq0'},\n", - " 'y': {'from_node': 'vhijhs38e'}}},\n", - " '2z56jc7uv': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'tzyahop5h': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'nwvcyuxcu': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'ks6xripd0'}}},\n", - " 'u5eaqduq0': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '2z56jc7uv'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '23': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '13'},\n", - " 'cube1': {'from_node': '11'}}},\n", - " '13': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '12'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VV']}},\n", - " '24': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '25'}, 'format': 'PNG'}},\n", - " '25': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 1,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '26'},\n", - " 'context': ''}},\n", - " '26': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '20'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '19': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T23:59:59Z',\n", - " '2018-01-08T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.328681090761718,\n", - " 'south': 46.60451988036186,\n", - " 'north': 46.763726670682246,\n", - " 'west': 11.01078180017578},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'coherence',\n", - " 'arguments': {'data': {'from_node': '1'}}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '20': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '23'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}}}},\n", - " 'status': 'queued',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-21T12:30:10.273Z',\n", - " 'updated': '2021-07-01T10:28:53.241Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': 'd09ae57c-a4c1-45c9-900b-bd2a1eb08c78',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_intensity_VV_PNG',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '9'}, 'cube1': {'from_node': '12'}}},\n", - " '33': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '8'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-04-01T00:00:00Z',\n", - " '2019-04-07T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.277122497558594,\n", - " 'south': 46.619732651929496,\n", - " 'north': 46.74691863249299,\n", - " 'west': 11.04572296142578},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '12': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VV']}},\n", - " '34': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 200,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '33'},\n", - " 'context': ''}},\n", - " '2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '3'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}}}},\n", - " 'dimension': 'bands'}},\n", - " '6': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '34'}, 'format': 'PNG'}},\n", - " '8': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '11'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " '9': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '10'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '10': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-21T12:42:18.485Z',\n", - " 'updated': '2021-05-21T12:42:18.485Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '3a0f7bd9-a5da-4475-8fb8-8fa338c01d9e',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_radar_mask',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T23:59:59Z',\n", - " '2018-01-06T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.506118774414064,\n", - " 'south': 46.45299704748291,\n", - " 'north': 46.771849614677336,\n", - " 'west': 10.86753845214844},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '25': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 1,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '28'},\n", - " 'context': ''}},\n", - " '26': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '25'}, 'format': 'PNG'}},\n", - " '28': {'process_id': 'radar_mask',\n", - " 'arguments': {'data': {'from_node': '29'},\n", - " 'threshold': '0.7',\n", - " 'orbit': 'ASC'}},\n", - " '29': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '20'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat', 'LIA', 'DEM']}},\n", - " '20': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '11'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-21T13:35:36.103Z',\n", - " 'updated': '2021-05-21T13:35:36.103Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '0417562a-8886-484c-babf-ba0bbfe95d10',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_coherence_PNG',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '22'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '22': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '12': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'vhijhs38e': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'tzyahop5h'}}},\n", - " 'ks6xripd0': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'u5eaqduq0'},\n", - " 'y': {'from_node': 'vhijhs38e'}}},\n", - " '2z56jc7uv': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'tzyahop5h': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'nwvcyuxcu': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'ks6xripd0'}}},\n", - " 'u5eaqduq0': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '2z56jc7uv'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '23': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '13'},\n", - " 'cube1': {'from_node': '11'}}},\n", - " '13': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '12'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VV']}},\n", - " '24': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '25'}, 'format': 'PNG'}},\n", - " '25': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 1,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '26'},\n", - " 'context': ''}},\n", - " '26': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '20'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '19': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-03-15T23:59:59Z',\n", - " '2019-03-31T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.328681090761718,\n", - " 'south': 46.60451988036186,\n", - " 'north': 46.763726670682246,\n", - " 'west': 11.01078180017578},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'coherence',\n", - " 'arguments': {'data': {'from_node': '1'}}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '20': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '23'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-21T14:39:19.257Z',\n", - " 'updated': '2021-05-21T14:39:19.257Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '38ff7b60-f313-48b4-b47f-ff8202a312b1',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_coherence_VV_PNG_24days',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '22'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '22': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '12': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'vhijhs38e': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'tzyahop5h'}}},\n", - " 'ks6xripd0': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'u5eaqduq0'},\n", - " 'y': {'from_node': 'vhijhs38e'}}},\n", - " '2z56jc7uv': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'tzyahop5h': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'nwvcyuxcu': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'ks6xripd0'}}},\n", - " 'u5eaqduq0': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '2z56jc7uv'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '23': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '13'},\n", - " 'cube1': {'from_node': '11'}}},\n", - " '13': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '12'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VV']}},\n", - " '24': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '25'}, 'format': 'PNG'}},\n", - " '25': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 1,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '26'},\n", - " 'context': ''}},\n", - " '26': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '20'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '19': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-03-15T23:59:59Z',\n", - " '2019-04-09T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.328681090761718,\n", - " 'south': 46.60451988036186,\n", - " 'north': 46.763726670682246,\n", - " 'west': 11.01078180017578},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'coherence',\n", - " 'arguments': {'data': {'from_node': '1'}, 'timedelta': 24}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '20': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '23'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-25T07:02:08.722Z',\n", - " 'updated': '2021-05-25T07:02:08.722Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': 'f2fd641b-dc9b-4650-802c-6e3b5976f915',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_intensity_VV_masked',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '33'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '33': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '32'},\n", - " 'cube1': {'from_node': '30'}}},\n", - " '34': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['i_VV', 'q_VV']}},\n", - " '35': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '28'},\n", - " 'cube1': {'from_node': '29'}}},\n", - " '25': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 100,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '36'},\n", - " 'context': ''}},\n", - " '36': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '35'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'au2ocs5uq': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV'}},\n", - " '8wu772c7s': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'au2ocs5uq'},\n", - " 'y': {'from_node': 'nxxrzc7co'}}},\n", - " 'nxxrzc7co': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'mask'}}}},\n", - " 'dimension': 'bands'}},\n", - " '26': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '25'}, 'format': 'PNG'}},\n", - " '28': {'process_id': 'radar_mask',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'threshold': '0.7',\n", - " 'orbit': 'ASC'}},\n", - " '29': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '20'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T23:59:59Z',\n", - " '2018-01-06T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.506118774414064,\n", - " 'south': 46.45299704748291,\n", - " 'north': 46.771849614677336,\n", - " 'west': 10.86753845214844},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '29'},\n", - " 'wavelengths': [],\n", - " 'bands': ['LIA', 'DEM']}},\n", - " '30': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat', 'DEM', 'LIA']}},\n", - " '20': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '11'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " '31': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '34'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'ahqr0jh7x': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " '55c3zx5pn': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ahqr0jh7x'}}},\n", - " 'zaj71vw49': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'csaow0o83'}}},\n", - " 'csaow0o83': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'wdl15kt3r': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'zaj71vw49'},\n", - " 'y': {'from_node': '55c3zx5pn'}}},\n", - " '76t2raqmh': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'wdl15kt3r'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '32': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '31'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VV']}}}},\n", - " 'status': 'queued',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-25T07:15:58.829Z',\n", - " 'updated': '2021-07-02T12:09:24.968Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': 'eab37ddc-63d8-4a3f-b67c-d6e8010dcaa5',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_intensity_VV_Slant_Range_PNG',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'33': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '12'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-04-01T00:00:00Z',\n", - " '2019-04-07T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.277122497558594,\n", - " 'south': 46.619732651929496,\n", - " 'north': 46.74691863249299,\n", - " 'west': 11.04572296142578},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '12': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VV']}},\n", - " '34': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 200,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '33'},\n", - " 'context': ''}},\n", - " '2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '3'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}}}},\n", - " 'dimension': 'bands'}},\n", - " '6': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '34'}, 'format': 'PNG'}}}},\n", - " 'status': 'queued',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-25T08:23:47.110Z',\n", - " 'updated': '2021-11-12T13:16:33.175Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': 'cfb110af-2d57-42eb-877c-02e32e5b3d0c',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'resampling_and_cloudmasking',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-01-01T10:13:37.922Z',\n", - " '2019-12-31T10:17:58.42057Z'],\n", - " 'spatial_extent': {'east': 11.113047748804092,\n", - " 'south': 46.021239092915835,\n", - " 'north': 46.02206976327898,\n", - " 'west': 11.112058013677597},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m'],\n", - " 'properties': {}}},\n", - " '4': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '7'}, 'format': 'NETCDF'}},\n", - " '5': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-01-01T10:13:37.922Z',\n", - " '2019-12-31T10:17:58.42057Z'],\n", - " 'spatial_extent': {},\n", - " 'id': 's2cloudless_alps',\n", - " 'properties': {'crs': 'epsg:32632'}}},\n", - " '6': {'process_id': 'resample_cube_spatial',\n", - " 'arguments': {'data': {'from_node': '5'}, 'target': {'from_node': '1'}}},\n", - " '7': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '1'}, 'cube1': {'from_node': '8'}}},\n", - " '8': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': '6'},\n", - " 'target': {'from_node': '1'}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-27T08:35:09.041Z',\n", - " 'updated': '2021-05-27T08:35:09.041Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': 'c7f93c20-0ca9-4c67-a1d2-5e26df252048',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'RGB_geoJSON_PNG',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'linearscalerange1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 2500,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': 'reducedimension2'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-31T23:59:59Z'],\n", - " 'spatial_extent': {'coordinates': [[[11.077, 46.136],\n", - " [11.074, 46.141],\n", - " [11.074, 46.144],\n", - " [11.084, 46.155],\n", - " [11.09, 46.156],\n", - " [11.092, 46.154],\n", - " [11.094, 46.147],\n", - " [11.101, 46.153],\n", - " [11.091, 46.157],\n", - " [11.094, 46.157],\n", - " [11.11, 46.164],\n", - " [11.112, 46.167],\n", - " [11.112, 46.176],\n", - " [11.13, 46.178],\n", - " [11.137, 46.181],\n", - " [11.135, 46.172],\n", - " [11.125, 46.163],\n", - " [11.118, 46.149],\n", - " [11.119, 46.139],\n", - " [11.102, 46.133],\n", - " [11.09, 46.131],\n", - " [11.09, 46.128],\n", - " [11.083, 46.125],\n", - " [11.077, 46.136]]],\n", - " 'type': 'Polygon'},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m'],\n", - " 'properties': {}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'min1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'format': 'PNG',\n", - " 'options': {'red': 'B04_10m', 'green': 'B03_10m', 'blue': 'B02_10m'}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-27T12:54:29.532Z',\n", - " 'updated': '2021-05-27T12:54:29.532Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '62a9e28a-261f-4874-8d71-a49126b7fc8a',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': '2018_coherence_VV_VH_field',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '22'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '22': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '12': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'vhijhs38e': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'tzyahop5h'}}},\n", - " 'ks6xripd0': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'u5eaqduq0'},\n", - " 'y': {'from_node': 'vhijhs38e'}}},\n", - " '2z56jc7uv': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'tzyahop5h': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'nwvcyuxcu': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'ks6xripd0'}}},\n", - " 'u5eaqduq0': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '2z56jc7uv'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '23': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '16'},\n", - " 'cube1': {'from_node': '11'}}},\n", - " '13': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '12'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VV']}},\n", - " '14': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '15'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VH']}},\n", - " '15': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'nghhsofj2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VH'}},\n", - " 'l07q6wffb': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'jzcz3p7jb'}}},\n", - " 'nbtjw74xu': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '6tmemct1n'}}},\n", - " '6tmemct1n': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '0qyftco41'},\n", - " 'y': {'from_node': 'l07q6wffb'}}},\n", - " 'jzcz3p7jb': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VH'}},\n", - " '0qyftco41': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'nghhsofj2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '16': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '14'},\n", - " 'cube1': {'from_node': '13'}}},\n", - " '19': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-06-01T00:00:00Z',\n", - " '2018-07-31T23:59:59Z'],\n", - " 'spatial_extent': {'east': 10.470767319202421,\n", - " 'south': 46.705303346845824,\n", - " 'north': 46.705825713322355,\n", - " 'west': 10.470010936260222},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'coherence',\n", - " 'arguments': {'data': {'from_node': '1'}}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '20': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '23'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " '21': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '20'}, 'format': 'NETCDF'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-31T08:25:04.600Z',\n", - " 'updated': '2021-05-31T08:25:04.600Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '6a5b7bbc-3811-448a-867b-f8e9ebf3c5ae',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': '2018_intensity_VV_VH_field',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '9'}, 'cube1': {'from_node': '12'}}},\n", - " '12': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VV']}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-06-01T00:00:00Z',\n", - " '2018-07-31T23:59:59Z'],\n", - " 'spatial_extent': {'east': 10.470767319202421,\n", - " 'south': 46.705303346845824,\n", - " 'north': 46.705825713322355,\n", - " 'west': 10.470010936260222},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '3'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}}}},\n", - " 'dimension': 'bands'}},\n", - " '6': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '8'}, 'format': 'NETCDF'}},\n", - " '8': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '32'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " '9': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '10'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '30': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VH'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VH'}}}},\n", - " 'dimension': 'bands'}},\n", - " '20': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '30'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '31': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '20'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VH']}},\n", - " '10': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '32': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '31'},\n", - " 'cube1': {'from_node': '11'}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-31T08:26:44.955Z',\n", - " 'updated': '2021-05-31T08:26:44.955Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '7f6fc6bb-6ea1-42b2-a81c-5d2d76743893',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': '2018_intensity_VV_VH_field_2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '9'}, 'cube1': {'from_node': '12'}}},\n", - " '12': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VV']}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-06-01T00:00:00Z',\n", - " '2018-06-15T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.423410177230835,\n", - " 'south': 46.89092078353778,\n", - " 'north': 46.89226984970733,\n", - " 'west': 11.421672105789186},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '3'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}}}},\n", - " 'dimension': 'bands'}},\n", - " '6': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '8'}, 'format': 'NETCDF'}},\n", - " '8': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '32'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " '9': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '10'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '30': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VH'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VH'}}}},\n", - " 'dimension': 'bands'}},\n", - " '20': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '30'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '31': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '20'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VH']}},\n", - " '10': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '32': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '31'},\n", - " 'cube1': {'from_node': '11'}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-05-31T13:03:32.392Z',\n", - " 'updated': '2021-05-31T13:03:32.392Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': 'af4ef7e7-530c-47b6-9023-0d8dba2d43fa',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'Lavis_geoJSON_RGB',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'linearscalerange1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 2500,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': 'reducedimension2'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-31T23:59:59Z'],\n", - " 'spatial_extent': {'coordinates': [[[11.077, 46.136],\n", - " [11.074, 46.141],\n", - " [11.074, 46.144],\n", - " [11.084, 46.155],\n", - " [11.09, 46.156],\n", - " [11.092, 46.154],\n", - " [11.094, 46.147],\n", - " [11.101, 46.153],\n", - " [11.091, 46.157],\n", - " [11.094, 46.157],\n", - " [11.11, 46.164],\n", - " [11.112, 46.167],\n", - " [11.112, 46.176],\n", - " [11.13, 46.178],\n", - " [11.137, 46.181],\n", - " [11.135, 46.172],\n", - " [11.125, 46.163],\n", - " [11.118, 46.149],\n", - " [11.119, 46.139],\n", - " [11.102, 46.133],\n", - " [11.09, 46.131],\n", - " [11.09, 46.128],\n", - " [11.083, 46.125],\n", - " [11.077, 46.136]]],\n", - " 'type': 'Polygon'},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m'],\n", - " 'properties': {}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'min1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'format': 'PNG',\n", - " 'options': {'red': 'B04_10m', 'green': 'B03_10m', 'blue': 'B02_10m'}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-06-03T15:20:42.021Z',\n", - " 'updated': '2021-06-03T15:20:42.021Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '7f05ba8d-b114-489a-9f75-945d29e9084c',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'Cloud_free_RGB_composite',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-31T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.198158264160154,\n", - " 'south': 46.08799557293045,\n", - " 'north': 46.228778039111916,\n", - " 'west': 10.998344421386717},\n", - " 'id': 's2cloudless_alps'}},\n", - " '6': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'mask': {'from_node': '7'}}},\n", - " '7': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'linearscalerange1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 2500,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': 'reducedimension2'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-31T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.198158264160154,\n", - " 'south': 46.08799557293045,\n", - " 'north': 46.228778039111916,\n", - " 'west': 10.998344421386717},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m'],\n", - " 'properties': {}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '6'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'median',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'format': 'PNG',\n", - " 'options': {'red': 'B04_10m', 'green': 'B03_10m', 'blue': 'B02_10m'}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-06-08T09:56:44.234Z',\n", - " 'updated': '2021-06-08T09:56:44.234Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': 'bc9b22cd-61f7-48fb-a140-bd4957159471',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'NDVI_Bozen',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'3': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2009-05-01T00:00:00Z',\n", - " '2009-05-31T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.45341873168945,\n", - " 'south': 46.447556539566904,\n", - " 'north': 46.53642884481414,\n", - " 'west': 11.223049163818358},\n", - " 'id': 'ADO_NDVI_MODIS_231m_3035',\n", - " 'properties': {}}},\n", - " '4': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '5'}, 'format': 'GTIFF'}},\n", - " '5': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '3'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'2': {'result': True,\n", - " 'process_id': 'max',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-06-17T08:51:21.978Z',\n", - " 'updated': '2021-06-17T08:51:21.978Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': None},\n", - " {'id': '3a2f947c-21a5-4f41-b80e-ce61b32498d1',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_coherence_PNG_job',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '22'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '22': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '12': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'vhijhs38e': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'tzyahop5h'}}},\n", - " 'ks6xripd0': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'u5eaqduq0'},\n", - " 'y': {'from_node': 'vhijhs38e'}}},\n", - " '2z56jc7uv': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'tzyahop5h': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'nwvcyuxcu': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'ks6xripd0'}}},\n", - " 'u5eaqduq0': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '2z56jc7uv'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '23': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '13'},\n", - " 'cube1': {'from_node': '11'}}},\n", - " '13': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '12'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VV']}},\n", - " '24': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '25'}, 'format': 'PNG'}},\n", - " '25': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 1,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '26'},\n", - " 'context': ''}},\n", - " '26': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '20'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '19': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T23:59:59Z',\n", - " '2018-01-08T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.328681090761718,\n", - " 'south': 46.60451988036186,\n", - " 'north': 46.763726670682246,\n", - " 'west': 11.01078180017578},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'coherence',\n", - " 'arguments': {'data': {'from_node': '1'}}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '20': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '23'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-01T12:22:18.461Z',\n", - " 'updated': '2021-07-01T12:22:56.002Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'e5fcd603-dbab-4d64-9fc5-81cdd8911668',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'batch_coherence',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '22'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '22': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '12': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'vhijhs38e': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'tzyahop5h'}}},\n", - " 'ks6xripd0': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'u5eaqduq0'},\n", - " 'y': {'from_node': 'vhijhs38e'}}},\n", - " '2z56jc7uv': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'tzyahop5h': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'nwvcyuxcu': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'ks6xripd0'}}},\n", - " 'u5eaqduq0': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '2z56jc7uv'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '23': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '16'},\n", - " 'cube1': {'from_node': '11'}}},\n", - " '13': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '12'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VV']}},\n", - " '14': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '15'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['COH_VH']}},\n", - " '15': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '19'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'nghhsofj2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VH'}},\n", - " 'l07q6wffb': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'jzcz3p7jb'}}},\n", - " 'nbtjw74xu': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '6tmemct1n'}}},\n", - " '6tmemct1n': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '0qyftco41'},\n", - " 'y': {'from_node': 'l07q6wffb'}}},\n", - " 'jzcz3p7jb': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VH'}},\n", - " '0qyftco41': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'nghhsofj2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '16': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '14'},\n", - " 'cube1': {'from_node': '13'}}},\n", - " '19': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T23:59:59Z',\n", - " '2018-01-14T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.202020645141603,\n", - " 'south': 46.64813992180177,\n", - " 'north': 46.687955702208626,\n", - " 'west': 11.122198104858398},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'coherence',\n", - " 'arguments': {'data': {'from_node': '1'}}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '20': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '23'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " '21': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '20'}, 'format': 'NETCDF'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-02T07:16:43.456Z',\n", - " 'updated': '2021-07-02T07:17:21.004Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '5ee2c69a-f213-40d0-9ecb-b929e3865c05',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_int_VV_masked',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '33'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '33': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '32'},\n", - " 'cube1': {'from_node': '30'}}},\n", - " '34': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['i_VV', 'q_VV']}},\n", - " '35': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '28'},\n", - " 'cube1': {'from_node': '29'}}},\n", - " '25': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 100,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '36'},\n", - " 'context': ''}},\n", - " '36': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '35'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'au2ocs5uq': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV'}},\n", - " '8wu772c7s': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'au2ocs5uq'},\n", - " 'y': {'from_node': 'nxxrzc7co'}}},\n", - " 'nxxrzc7co': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'mask'}}}},\n", - " 'dimension': 'bands'}},\n", - " '26': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '25'}, 'format': 'PNG'}},\n", - " '28': {'process_id': 'radar_mask',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'threshold': '0.7',\n", - " 'orbit': 'ASC'}},\n", - " '29': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '20'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T23:59:59Z',\n", - " '2018-01-06T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.506118774414064,\n", - " 'south': 46.45299704748291,\n", - " 'north': 46.771849614677336,\n", - " 'west': 10.86753845214844},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '29'},\n", - " 'wavelengths': [],\n", - " 'bands': ['LIA', 'DEM']}},\n", - " '30': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat', 'DEM', 'LIA']}},\n", - " '20': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '11'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " '31': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '34'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'ahqr0jh7x': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " '55c3zx5pn': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ahqr0jh7x'}}},\n", - " 'zaj71vw49': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'csaow0o83'}}},\n", - " 'csaow0o83': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'wdl15kt3r': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'zaj71vw49'},\n", - " 'y': {'from_node': '55c3zx5pn'}}},\n", - " '76t2raqmh': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'wdl15kt3r'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '32': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '31'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VV']}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-02T12:10:55.245Z',\n", - " 'updated': '2021-07-02T12:11:51.589Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'e5adb210-e779-4fbf-b3f1-541b33a2affc',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_cloud_masked',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-07-04T00:00:00Z',\n", - " '2018-07-08T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.207084655761717,\n", - " 'south': 46.08561439703058,\n", - " 'north': 46.251098875803336,\n", - " 'west': 10.834236145019531},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B04_10m']}},\n", - " '12': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '11'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'median',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " '13': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '12'}, 'mask': {'from_node': '10'}}},\n", - " '14': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 1800,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '15'},\n", - " 'context': ''}},\n", - " '15': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '13'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '6': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'2': {'result': True,\n", - " 'process_id': 'max',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-07-04T00:00:00Z',\n", - " '2018-07-08T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.207084655761717,\n", - " 'south': 46.08561439703058,\n", - " 'north': 46.251098875803336,\n", - " 'west': 10.834236145019531},\n", - " 'id': 's2cloudless_alps',\n", - " 'bands': ['CLOUD_10m'],\n", - " 'properties': {}}},\n", - " '9': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '6'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'max',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '14'}, 'format': 'PNG'}},\n", - " '10': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': '9'},\n", - " 'context': ''}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-06T10:20:37.068Z',\n", - " 'updated': '2021-07-06T10:22:56.670Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'd6821c0b-a1f4-4b69-90d4-8a6469028d1c',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'Burundi_PNG',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'linearscalerange1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 2500,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': 'reducedimension2'},\n", - " 'context': ''}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-31T23:59:59Z'],\n", - " 'spatial_extent': {'east': 29.349770419513128,\n", - " 'south': -3.47422713166398,\n", - " 'north': -3.470478926391209,\n", - " 'west': 29.340017890682635},\n", - " 'id': 's2_l2a',\n", - " 'bands': ['B02', 'B03', 'B04'],\n", - " 'properties': {}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'min1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'format': 'PNG',\n", - " 'options': {'red': 'B04', 'green': 'B03', 'blue': 'B02'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-09T09:02:38.189Z',\n", - " 'updated': '2021-07-09T09:04:16.690Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'fb2a79fa-36fa-41a0-b33e-5e272ea181e1',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'Burundi_SCL_mask_PNG',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'SCL'}},\n", - " '3': {'result': True,\n", - " 'process_id': 'eq',\n", - " 'arguments': {'x': {'from_node': '1'}, 'y': 9}}}},\n", - " 'dimension': 'bands'}},\n", - " '3': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '5'},\n", - " 'format': 'PNG',\n", - " 'options': {}}},\n", - " '5': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 1,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '1'},\n", - " 'context': ''}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-03T23:59:59Z'],\n", - " 'spatial_extent': {'east': 29.488240242004387,\n", - " 'south': -2.6076627178704967,\n", - " 'north': -2.526719872041312,\n", - " 'west': 29.372025489807125},\n", - " 'id': 's2_l2a',\n", - " 'bands': ['B04', 'SCL'],\n", - " 'properties': {}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-09T12:28:31.534Z',\n", - " 'updated': '2021-07-09T12:46:37.936Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '8e30027b-b3c1-4139-87fe-4b034de4ba21',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'Burundi_SCL_mask_netCDF',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'SCL'}},\n", - " '3': {'result': True,\n", - " 'process_id': 'eq',\n", - " 'arguments': {'x': {'from_node': '1'}, 'y': 9}}}},\n", - " 'dimension': 'bands'}},\n", - " '3': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '6'},\n", - " 'format': 'NETCDF',\n", - " 'options': {}}},\n", - " '6': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '7'}, 'mask': {'from_node': '1'}}},\n", - " '7': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['B02', 'B03', 'B04']}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-03T23:59:59Z'],\n", - " 'spatial_extent': {'east': 29.488240242004387,\n", - " 'south': -2.6076627178704967,\n", - " 'north': -2.526719872041312,\n", - " 'west': 29.372025489807125},\n", - " 'id': 's2_l2a',\n", - " 'bands': ['B04', 'SCL', 'B02', 'B03'],\n", - " 'properties': {}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-07-09T12:49:19.681Z',\n", - " 'updated': '2021-07-09T12:49:19.681Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '13a6f748-8d74-4298-8ec6-1d0e419e60a0',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'Burundi_SCL_mask_PNG',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'SCL'}},\n", - " '3': {'result': True,\n", - " 'process_id': 'eq',\n", - " 'arguments': {'x': {'from_node': '1'}, 'y': 9}}}},\n", - " 'dimension': 'bands'}},\n", - " '3': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'format': 'PNG',\n", - " 'options': {}}},\n", - " '6': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '7'}, 'mask': {'from_node': '1'}}},\n", - " '7': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['B02', 'B03', 'B04']}},\n", - " '8': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '6'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-03T23:59:59Z'],\n", - " 'spatial_extent': {'east': 29.488240242004387,\n", - " 'south': -2.6076627178704967,\n", - " 'north': -2.526719872041312,\n", - " 'west': 29.372025489807125},\n", - " 'id': 's2_l2a',\n", - " 'bands': ['B04', 'SCL', 'B02', 'B03'],\n", - " 'properties': {}}},\n", - " '9': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 2500,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '8'},\n", - " 'context': ''}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-09T12:51:42.626Z',\n", - " 'updated': '2021-07-09T12:52:13.594Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '3f64a466-3a0a-4411-b0cb-f52a1bf60624',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'ODC_job_S2',\n", - " 'description': 'ODC_job_S2',\n", - " 'process': {'process_graph': {'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'netCDF',\n", - " 'options': {}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T00:00:00Z',\n", - " '2018-01-31T00:00:00Z'],\n", - " 'spatial_extent': {'east': 10.616183,\n", - " 'south': 46.694774,\n", - " 'north': 46.695473,\n", - " 'west': 10.615008},\n", - " 'id': 'S2_L2A_T32TPS'}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-15T08:39:02.033Z',\n", - " 'updated': '2021-07-15T08:39:17.566Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '21ef6656-fdc8-4fcb-9f0e-666b9fc8131e',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.460878,\n", - " 'south': 46.366001,\n", - " 'north': 46.370443,\n", - " 'west': 11.452341},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.460878,\n", - " 'south': 46.366001,\n", - " 'north': 46.370443,\n", - " 'west': 11.452341},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-15T10:09:54.518Z',\n", - " 'updated': '2021-07-15T10:10:28.033Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'fd71809d-996b-428a-9f4c-e33508c790b2',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_small',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.414793,\n", - " 'south': 46.34074,\n", - " 'north': 46.341348,\n", - " 'west': 11.414106},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.414793,\n", - " 'south': 46.34074,\n", - " 'north': 46.341348,\n", - " 'west': 11.414106},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-15T10:46:11.386Z',\n", - " 'updated': '2021-07-15T10:46:29.559Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '5d75770a-a394-46d9-aced-191e099a0ddd',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_small2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.414793,\n", - " 'south': 46.34074,\n", - " 'north': 46.341348,\n", - " 'west': 11.414106},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.414793,\n", - " 'south': 46.34074,\n", - " 'north': 46.341348,\n", - " 'west': 11.414106},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-15T10:47:03.529Z',\n", - " 'updated': '2021-07-15T10:47:20.148Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'd68290e4-1b3c-49a4-a2c6-e8a5f2593357',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_small',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.414793,\n", - " 'south': 46.34074,\n", - " 'north': 46.341348,\n", - " 'west': 11.414106},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.414793,\n", - " 'south': 46.34074,\n", - " 'north': 46.341348,\n", - " 'west': 11.414106},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'fitcurve1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'cos2': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply5'}}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'multiply8': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement6'},\n", - " 'y': {'from_node': 'sin2'}}},\n", - " 'multiply5': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply6': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement5'},\n", - " 'y': {'from_node': 'cos2'}}},\n", - " 'add4': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'multiply8'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement4'},\n", - " 'y': {'from_node': 'multiply6'}}},\n", - " 'multiply7': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'sin2': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply7'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement11'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement11': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement10': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement9': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add8'}, 'y': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'add8': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add7'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add7': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add6'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement9'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement10'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}}}},\n", - " 'dimension': 'bands'}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-07-15T10:47:41.595Z',\n", - " 'updated': '2021-07-15T10:47:41.862Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '8c2c96fb-0b42-4116-8d55-0ae460f417b2',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_tiny',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.411249,\n", - " 'south': 46.342991,\n", - " 'north': 46.343076,\n", - " 'west': 11.411158},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.411249,\n", - " 'south': 46.342991,\n", - " 'north': 46.343076,\n", - " 'west': 11.411158},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-15T10:54:55.442Z',\n", - " 'updated': '2021-07-15T10:55:11.407Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'fa9a8a2e-29b3-44c5-9d1a-8c2a90723979',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_tiny',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.411249,\n", - " 'south': 46.342991,\n", - " 'north': 46.343076,\n", - " 'west': 11.411158},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.411249,\n", - " 'south': 46.342991,\n", - " 'north': 46.343076,\n", - " 'west': 11.411158},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'fitcurve1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'cos2': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply5'}}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'multiply8': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement6'},\n", - " 'y': {'from_node': 'sin2'}}},\n", - " 'multiply5': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply6': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement5'},\n", - " 'y': {'from_node': 'cos2'}}},\n", - " 'add4': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'multiply8'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement4'},\n", - " 'y': {'from_node': 'multiply6'}}},\n", - " 'multiply7': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'sin2': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply7'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement11'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement11': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement10': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement9': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add8'}, 'y': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'add8': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add7'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add7': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add6'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement9'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement10'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}}}},\n", - " 'dimension': 'bands'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-15T10:55:25.748Z',\n", - " 'updated': '2021-07-15T11:09:42.039Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'eb96fcc5-55de-4369-9924-71264f7b18ad',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_16072021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.412585,\n", - " 'south': 46.345026,\n", - " 'north': 46.345634,\n", - " 'west': 11.411812},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.412585,\n", - " 'south': 46.345026,\n", - " 'north': 46.345634,\n", - " 'west': 11.411812},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-16T07:52:01.281Z',\n", - " 'updated': '2021-07-16T07:52:21.351Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'b0880a9d-0f0c-439c-b58b-65bdcefd1f4d',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_16072021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.412585,\n", - " 'south': 46.345026,\n", - " 'north': 46.345634,\n", - " 'west': 11.411812},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.412585,\n", - " 'south': 46.345026,\n", - " 'north': 46.345634,\n", - " 'west': 11.411812},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'eb96fcc5-55de-4369-9924-71264f7b18ad'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 3}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-16T07:57:13.078Z',\n", - " 'updated': '2021-07-16T07:57:46.827Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'be9d4396-3d0d-4805-9daf-6cb268760cc3',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_16072021_0',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-06-01', '2019-07-01'],\n", - " 'spatial_extent': {'east': 11.412585,\n", - " 'south': 46.345026,\n", - " 'north': 46.345634,\n", - " 'west': 11.411812},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-06-01', '2019-07-01'],\n", - " 'spatial_extent': {'east': 11.412585,\n", - " 'south': 46.345026,\n", - " 'north': 46.345634,\n", - " 'west': 11.411812},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'eb96fcc5-55de-4369-9924-71264f7b18ad'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'ae94a2ed-ab02-446a-ab7a-53760bf69bc9'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 3}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-07-16T08:31:46.827Z',\n", - " 'updated': '2021-07-16T08:31:47.143Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'fa4882ac-3ebe-47da-87fa-b5caacb1b836',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_16072021_1',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-06-01', '2019-07-01'],\n", - " 'spatial_extent': {'east': 11.412585,\n", - " 'south': 46.345026,\n", - " 'north': 46.345634,\n", - " 'west': 11.411812},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-06-01', '2019-07-01'],\n", - " 'spatial_extent': {'east': 11.412585,\n", - " 'south': 46.345026,\n", - " 'north': 46.345634,\n", - " 'west': 11.411812},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'eb96fcc5-55de-4369-9924-71264f7b18ad'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'b0880a9d-0f0c-439c-b58b-65bdcefd1f4d'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 3}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-16T08:35:46.844Z',\n", - " 'updated': '2021-07-16T08:35:56.697Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'f66b3d4a-d45a-463f-a79e-30e93075175d',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_16072021_2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.490927,\n", - " 'south': 46.351109,\n", - " 'north': 46.36882,\n", - " 'west': 11.459255},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.490927,\n", - " 'south': 46.351109,\n", - " 'north': 46.36882,\n", - " 'west': 11.459255},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-16T09:02:43.158Z',\n", - " 'updated': '2021-07-16T09:06:27.674Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '00c53ee8-7851-4b2a-b915-8354d65505af',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_16072021_2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.490927,\n", - " 'south': 46.351109,\n", - " 'north': 46.36882,\n", - " 'west': 11.459255},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.490927,\n", - " 'south': 46.351109,\n", - " 'north': 46.36882,\n", - " 'west': 11.459255},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'f66b3d4a-d45a-463f-a79e-30e93075175d'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 3}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-16T09:45:29.511Z',\n", - " 'updated': '2021-07-16T09:46:23.554Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'a66ab346-5139-44f7-8b0e-30520870b971',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_16072021_3',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-09-01', '2019-09-01'],\n", - " 'spatial_extent': {'east': 11.490927,\n", - " 'south': 46.351109,\n", - " 'north': 46.36882,\n", - " 'west': 11.459255},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-09-01', '2019-09-01'],\n", - " 'spatial_extent': {'east': 11.490927,\n", - " 'south': 46.351109,\n", - " 'north': 46.36882,\n", - " 'west': 11.459255},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'f66b3d4a-d45a-463f-a79e-30e93075175d'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '00c53ee8-7851-4b2a-b915-8354d65505af'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 3}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-16T09:51:51.626Z',\n", - " 'updated': '2021-07-16T09:52:25.810Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'ad7624bb-a666-4a06-9c17-65a6b2dc6c24',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'dashboardtestfitting',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.413905,\n", - " 'south': 46.341515,\n", - " 'north': 46.343144,\n", - " 'west': 11.410299},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.413905,\n", - " 'south': 46.341515,\n", - " 'north': 46.343144,\n", - " 'west': 11.410299},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-19T09:55:05.077Z',\n", - " 'updated': '2021-07-19T09:55:25.476Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '241f60ca-7623-4a86-a3d7-a0199eb57233',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'dashboardtest2fitting',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.413905,\n", - " 'south': 46.341515,\n", - " 'north': 46.343144,\n", - " 'west': 11.410299},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.413905,\n", - " 'south': 46.341515,\n", - " 'north': 46.343144,\n", - " 'west': 11.410299},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-19T09:55:46.083Z',\n", - " 'updated': '2021-07-19T09:56:04.781Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '7628c410-ce6a-416d-a1e1-45f184ddbc65',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'dashboardtest2predicting',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.413905,\n", - " 'south': 46.341515,\n", - " 'north': 46.343144,\n", - " 'west': 11.410299},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.413905,\n", - " 'south': 46.341515,\n", - " 'north': 46.343144,\n", - " 'west': 11.410299},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '241f60ca-7623-4a86-a3d7-a0199eb57233'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-19T10:04:16.160Z',\n", - " 'updated': '2021-07-19T10:04:56.129Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '37a162a2-ede4-41ab-b870-65ab5a1da60e',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'dashboardtest2alarms',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-09-02', '2018-12-31'],\n", - " 'spatial_extent': {'east': 11.413905,\n", - " 'south': 46.341515,\n", - " 'north': 46.343144,\n", - " 'west': 11.410299},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-09-02', '2018-12-31'],\n", - " 'spatial_extent': {'east': 11.413905,\n", - " 'south': 46.341515,\n", - " 'north': 46.343144,\n", - " 'west': 11.410299},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '241f60ca-7623-4a86-a3d7-a0199eb57233'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '7628c410-ce6a-416d-a1e1-45f184ddbc65'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-19T12:28:33.768Z',\n", - " 'updated': '2021-07-19T12:28:49.725Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'c51ef6de-81af-4a69-b29f-f6a4bcabaff6',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'dashboardtestbigfitting',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-19T14:27:48.443Z',\n", - " 'updated': '2021-07-19T14:36:06.890Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '1e63aa62-40f3-435e-a3ab-31e88a888015',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'dashboardtest2predicting',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'c51ef6de-81af-4a69-b29f-f6a4bcabaff6'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-07-19T14:37:50.787Z',\n", - " 'updated': '2021-07-19T14:37:50.787Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '3dcb3319-c798-4dac-803e-259d64d674c3',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'dashboardtestbigpredicting',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'c51ef6de-81af-4a69-b29f-f6a4bcabaff6'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-19T14:38:16.434Z',\n", - " 'updated': '2021-07-19T14:38:53.358Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'dae0c836-baff-4031-82ec-3f6b27da2158',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'dashboardtestbigalarms',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-09-02', '2019-09-02'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-09-02', '2019-09-02'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'c51ef6de-81af-4a69-b29f-f6a4bcabaff6'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '3dcb3319-c798-4dac-803e-259d64d674c3'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-19T14:39:21.529Z',\n", - " 'updated': '2021-07-20T13:40:34.614Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'dd231993-fd28-495d-9300-e54c98aa042b',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'dashboardtestbig_35alarms',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-09-01'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-09-01'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'c51ef6de-81af-4a69-b29f-f6a4bcabaff6'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '3dcb3319-c798-4dac-803e-259d64d674c3'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-20T06:59:34.419Z',\n", - " 'updated': '2021-07-20T07:00:07.845Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '8d06d177-0e68-4793-b8be-3dea09eef78f',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'dashboardtestbig_4alarms',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-09-01'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-09-01'],\n", - " 'spatial_extent': {'east': 11.441025,\n", - " 'south': 46.314462,\n", - " 'north': 46.349546,\n", - " 'west': 11.403593},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'c51ef6de-81af-4a69-b29f-f6a4bcabaff6'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '3dcb3319-c798-4dac-803e-259d64d674c3'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-07-20T07:07:46.153Z',\n", - " 'updated': '2021-07-20T07:08:16.505Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '8e75b8e7-b832-42b5-8eac-76a4d23a7777',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'interpolate_DRI2_test',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-01-01T23:59:00Z',\n", - " '2021-12-31T23:59:00Z'],\n", - " 'spatial_extent': {'east': 11.356929036541166,\n", - " 'south': 46.47268237721809,\n", - " 'north': 46.476750588150765,\n", - " 'west': 11.35105959886099},\n", - " 'id': 'DRI2_T32TPS',\n", - " 'bands': ['LAI'],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'aggregate_temporal_period',\n", - " 'arguments': {'period': 'day',\n", - " 'data': {'from_node': '1'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '3': {'process_id': 'apply_dimension',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'array_interpolate_linear',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'data': {'from_node': '2'},\n", - " 'dimension': 't'}},\n", - " '4': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '3'}, 'format': 'NETCDF'}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-08-23T12:22:45.254Z',\n", - " 'updated': '2021-08-23T12:23:45.094Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'adaceab5-65ca-4870-af8d-69bd1f7b6321',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'T32TPS_interp_5days',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-01-01T23:59:00Z',\n", - " '2019-01-05T23:59:00Z'],\n", - " 'id': 'DRI2_T32TPS',\n", - " 'bands': ['LAI'],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'aggregate_temporal_period',\n", - " 'arguments': {'period': 'day',\n", - " 'data': {'from_node': '1'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '3': {'process_id': 'apply_dimension',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'array_interpolate_linear',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'data': {'from_node': '2'},\n", - " 'dimension': 't'}},\n", - " '4': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '3'}, 'format': 'NETCDF'}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-08-23T15:15:18.392Z',\n", - " 'updated': '2021-08-23T15:16:50.941Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'f47f1e7e-8bb9-43f1-9ffb-d796a9a81ddb',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'burundi_data',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'3': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'format': 'NETCDF',\n", - " 'options': {}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T00:00:00Z',\n", - " '2019-01-01T23:59:00Z'],\n", - " 'spatial_extent': {'east': 29.12313134772924,\n", - " 'south': -2.649926094230338,\n", - " 'north': -2.638237547148421,\n", - " 'west': 29.107081997227795},\n", - " 'id': 's2_l2a',\n", - " 'bands': ['B04'],\n", - " 'properties': {}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-08-26T13:24:44.343Z',\n", - " 'updated': '2021-08-26T13:39:36.124Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '82b54a66-5312-4815-b5c3-f7711c92f3cd',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'DRI2_T32TPR_2015-01-01',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'aggregatetemporalperiod1': {'process_id': 'aggregate_temporal_period',\n", - " 'arguments': {'period': 'day',\n", - " 'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'applydimension1': {'process_id': 'apply_dimension',\n", - " 'arguments': {'process': {'process_graph': {'arrayinterpolatelinear1': {'result': True,\n", - " 'process_id': 'array_interpolate_linear',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'data': {'from_node': 'aggregatetemporalperiod1'},\n", - " 'dimension': 'DATE'}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2015-01-01', '2016-01-01'],\n", - " 'spatial_extent': {'east': 11.509209,\n", - " 'south': 46.310593,\n", - " 'north': 46.373875,\n", - " 'west': 11.394196},\n", - " 'id': 'DRI2_T32TPR'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'applydimension1'},\n", - " 'format': 'netCDF',\n", - " 'options': {}}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-08-27T08:04:49.916Z',\n", - " 'updated': '2021-08-27T08:16:54.725Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '6b10311f-0682-4ae1-89cb-d0639e34ee8b',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_30082021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-08-30T09:35:40.507Z',\n", - " 'updated': '2021-08-30T09:39:03.784Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'e6891a59-fad8-4d0d-95f6-b1482897d7dd',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_16072021_2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '6b10311f-0682-4ae1-89cb-d0639e34ee8b'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-08-30T09:40:31.023Z',\n", - " 'updated': '2021-08-30T09:40:31.023Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'faa11273-9eea-4d4c-ad41-aa58622cc576',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_30082021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '6b10311f-0682-4ae1-89cb-d0639e34ee8b'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-08-30T09:40:39.616Z',\n", - " 'updated': '2021-08-30T09:41:04.462Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'de1bfb73-7d55-4ceb-8864-317410de5c42',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_30082021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-09-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-09-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '6b10311f-0682-4ae1-89cb-d0639e34ee8b'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'faa11273-9eea-4d4c-ad41-aa58622cc576'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 3}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-08-30T09:42:29.378Z',\n", - " 'updated': '2021-08-30T09:43:25.783Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'e8c521e4-365f-43e6-b06e-dcf3fdff554e',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_30082021_2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-09-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-09-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '6b10311f-0682-4ae1-89cb-d0639e34ee8b'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'faa11273-9eea-4d4c-ad41-aa58622cc576'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 3}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-08-30T10:02:01.218Z',\n", - " 'updated': '2021-08-30T10:02:31.679Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '4508d2e5-a4da-4120-a6e7-44816d1fcb81',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_30082021_3',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.437197,\n", - " 'south': 46.334291,\n", - " 'north': 46.3462,\n", - " 'west': 11.404495},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '6b10311f-0682-4ae1-89cb-d0639e34ee8b'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'faa11273-9eea-4d4c-ad41-aa58622cc576'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 3}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-08-30T10:12:17.886Z',\n", - " 'updated': '2021-08-30T10:12:34.619Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '986435ce-0820-4fce-a6b3-b1a90f2f2ca2',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_hail_nets',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-01-01', '2020-12-01'],\n", - " 'spatial_extent': {'east': 11.121039,\n", - " 'south': 46.006416,\n", - " 'north': 46.016311,\n", - " 'west': 11.108251},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'apply1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-01-01', '2020-12-01'],\n", - " 'spatial_extent': {'east': 11.121039,\n", - " 'south': 46.006416,\n", - " 'north': 46.016311,\n", - " 'west': 11.108251},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-08-31T12:29:44.804Z',\n", - " 'updated': '2021-08-31T12:46:47.884Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '97609bca-881e-47dc-83c1-df71dd8e5818',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'kernel_test',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 1,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '3'}}},\n", - " '2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'max',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " '3': {'process_id': 'apply_kernel',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'kernel': [['1', '1', '1', '1', '1'],\n", - " ['1', '1', '1', '1', '1'],\n", - " ['1', '1', '1', '1', '1'],\n", - " ['1', '1', '1', '1', '1'],\n", - " ['1', '1', '1', '1', '1']]}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'loadcollection2'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'format': 'GTIFF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-01-01', '2016-01-15'],\n", - " 'spatial_extent': {'east': 11.347865554808674,\n", - " 'south': 45.96996854267825,\n", - " 'north': 46.142443868758896,\n", - " 'west': 11.125607577791113},\n", - " 'id': 's2cloudless_alps',\n", - " 'bands': [],\n", - " 'properties': {}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-02T07:23:23.061Z',\n", - " 'updated': '2021-09-02T07:23:31.972Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '5ad5d032-eaf5-4d83-b291-f50b62d62465',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_02092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.437111,\n", - " 'south': 46.329198,\n", - " 'north': 46.347686,\n", - " 'west': 11.403809},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.437111,\n", - " 'south': 46.329198,\n", - " 'north': 46.347686,\n", - " 'west': 11.403809},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-02T08:23:03.074Z',\n", - " 'updated': '2021-09-02T08:27:01.366Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '8f4c4178-7478-454d-95e6-165cf8fa4043',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_02092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.437111,\n", - " 'south': 46.329198,\n", - " 'north': 46.347686,\n", - " 'west': 11.403809},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.437111,\n", - " 'south': 46.329198,\n", - " 'north': 46.347686,\n", - " 'west': 11.403809},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '5ad5d032-eaf5-4d83-b291-f50b62d62465'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add5'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add4'},\n", - " 'y': {'from_node': 'power4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add3'},\n", - " 'y': {'from_node': 'power3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-02T09:14:18.991Z',\n", - " 'updated': '2021-09-02T09:14:49.375Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '1b7a04b5-17ad-479d-9606-86217c311023',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_02092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.437111,\n", - " 'south': 46.329198,\n", - " 'north': 46.347686,\n", - " 'west': 11.403809},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadcollection3': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439686,\n", - " 'south': 46.328463,\n", - " 'north': 46.347662,\n", - " 'west': 11.40295},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439686,\n", - " 'south': 46.328463,\n", - " 'north': 46.347662,\n", - " 'west': 11.40295},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '5ad5d032-eaf5-4d83-b291-f50b62d62465'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection3'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '8f4c4178-7478-454d-95e6-165cf8fa4043'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply1'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'resamplecubetemporal1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply2'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-02T09:46:35.252Z',\n", - " 'updated': '2021-09-02T09:46:40.469Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'b79fb5e4-a261-4263-97d8-0fe951ea6657',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_02092021_2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.437111,\n", - " 'south': 46.329198,\n", - " 'north': 46.347686,\n", - " 'west': 11.403809},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.437111,\n", - " 'south': 46.329198,\n", - " 'north': 46.347686,\n", - " 'west': 11.403809},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '5ad5d032-eaf5-4d83-b291-f50b62d62465'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '8f4c4178-7478-454d-95e6-165cf8fa4043'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-02T09:58:26.095Z',\n", - " 'updated': '2021-09-02T09:58:43.473Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'cf06f986-adb8-4eda-bf4c-bf5a0099bdc3',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_02092021_3',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 4000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 2.5}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.437111,\n", - " 'south': 46.329198,\n", - " 'north': 46.347686,\n", - " 'west': 11.403809},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.437111,\n", - " 'south': 46.329198,\n", - " 'north': 46.347686,\n", - " 'west': 11.403809},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '5ad5d032-eaf5-4d83-b291-f50b62d62465'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '8f4c4178-7478-454d-95e6-165cf8fa4043'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-02T10:08:23.655Z',\n", - " 'updated': '2021-09-02T10:08:45.095Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '4772bb6a-fabc-46c4-b064-d6b976bbe403',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_data_for_load_resut',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2020-01-01T00:00:00Z',\n", - " '2020-01-31T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.493759155273436,\n", - " 'south': 46.35166725060853,\n", - " 'north': 46.60322365618339,\n", - " 'west': 11.129837036132809},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B04_10m'],\n", - " 'properties': {}}},\n", - " '2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '1'}, 'format': 'NETCDF'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-06T09:28:35.492Z',\n", - " 'updated': '2021-09-06T09:28:45.213Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'c5264098-9a6a-44c1-b1aa-a9445ea65b7d',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_loadresult_filterbbox',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '3'}, 'format': 'NETCDF'}},\n", - " '2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '4772bb6a-fabc-46c4-b064-d6b976bbe403'}},\n", - " '3': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.376171431405425,\n", - " 'south': 46.48144875488205,\n", - " 'north': 46.50213211917301,\n", - " 'west': 11.317115460433833},\n", - " 'data': {'from_node': '2'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-06T09:29:23.745Z',\n", - " 'updated': '2021-09-06T09:29:28.515Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'b10da6ba-a6b8-481b-ae2d-e5af31800cc3',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_06092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.416082,\n", - " 'south': 46.339527,\n", - " 'north': 46.346163,\n", - " 'west': 11.40574},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.416082,\n", - " 'south': 46.339527,\n", - " 'north': 46.346163,\n", - " 'west': 11.40574},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-06T13:39:33.533Z',\n", - " 'updated': '2021-09-06T13:41:31.355Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '6dab3891-973e-4a01-888b-78fd492282c4',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_06092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.416082,\n", - " 'south': 46.339527,\n", - " 'north': 46.346163,\n", - " 'west': 11.40574},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.416082,\n", - " 'south': 46.339527,\n", - " 'north': 46.346163,\n", - " 'west': 11.40574},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'b10da6ba-a6b8-481b-ae2d-e5af31800cc3'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-06T14:24:58.577Z',\n", - " 'updated': '2021-09-06T14:25:17.069Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '51058420-4aba-4276-90ec-f515e4b19ce1',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_06092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.416082,\n", - " 'south': 46.339527,\n", - " 'north': 46.346163,\n", - " 'west': 11.40574},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.416082,\n", - " 'south': 46.339527,\n", - " 'north': 46.346163,\n", - " 'west': 11.40574},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'b10da6ba-a6b8-481b-ae2d-e5af31800cc3'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '6dab3891-973e-4a01-888b-78fd492282c4'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-06T14:25:56.279Z',\n", - " 'updated': '2021-09-06T14:26:23.029Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '8c31d4e7-a384-4f4e-bdc9-46e545972cf3',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_06092021_2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 2.5}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.416082,\n", - " 'south': 46.339527,\n", - " 'north': 46.346163,\n", - " 'west': 11.40574},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.416082,\n", - " 'south': 46.339527,\n", - " 'north': 46.346163,\n", - " 'west': 11.40574},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'b10da6ba-a6b8-481b-ae2d-e5af31800cc3'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '6dab3891-973e-4a01-888b-78fd492282c4'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-06T14:30:05.603Z',\n", - " 'updated': '2021-09-06T14:30:19.880Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'f8ea2429-2309-42df-ab05-e67f45bf925b',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_08092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-08T19:44:17.863Z',\n", - " 'updated': '2021-09-08T19:49:44.853Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'f5c9152e-ac44-4260-ac20-b531f6500a69',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_06092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'f8ea2429-2309-42df-ab05-e67f45bf925b'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-09-08T19:53:57.529Z',\n", - " 'updated': '2021-09-08T19:53:57.529Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'cdcd1c8a-f2db-423f-b878-224e50bfd108',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_08092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'f8ea2429-2309-42df-ab05-e67f45bf925b'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-08T19:54:03.362Z',\n", - " 'updated': '2021-09-08T19:54:21.457Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '3b07c4e4-2d91-4fe0-8df6-5e6ec43e3c7e',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_08092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'f8ea2429-2309-42df-ab05-e67f45bf925b'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'cdcd1c8a-f2db-423f-b878-224e50bfd108'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-08T19:59:46.190Z',\n", - " 'updated': '2021-09-08T20:00:27.979Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '11519f08-790e-4bdf-9977-ca1ea513bc90',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_09092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 2}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'f8ea2429-2309-42df-ab05-e67f45bf925b'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'cdcd1c8a-f2db-423f-b878-224e50bfd108'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-09T06:37:19.767Z',\n", - " 'updated': '2021-09-09T06:37:34.282Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '8c22b5c3-be93-41f1-95e0-51e055094a5b',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_09092021_1_5',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 1.5}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-03-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'f8ea2429-2309-42df-ab05-e67f45bf925b'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'cdcd1c8a-f2db-423f-b878-224e50bfd108'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-09T06:42:01.654Z',\n", - " 'updated': '2021-09-09T06:42:15.972Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'e4c164f3-ec86-407b-822d-3ce7348eb4d4',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_curve_fitting_09092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'filterbbox1': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'data': {'from_node': 'filtertemporal1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'filterbbox1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '84e0fd74-da48-4662-9587-45f33da9568b'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2016-09-01', '2018-09-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-09T08:58:19.789Z',\n", - " 'updated': '2021-09-09T08:58:44.219Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'a3ec6e4b-ddc6-4ef4-a481-f228b495a78e',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_curve_predicting_09092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'filterbbox1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult2'}}},\n", - " 'filterbbox1': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'data': {'from_node': 'filtertemporal1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'renamelabels1': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'dimension': 'bands',\n", - " 'target': ['VV', 'VH']}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'predictcurve1'},\n", - " 'cube1': {'from_node': 'filterbbox1'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'renamelabels1'},\n", - " 'reducer': {'process_graph': {'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add3'}, 'y': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '84e0fd74-da48-4662-9587-45f33da9568b'}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'd3e2b43d-a03e-4a07-9ae9-f944c912942b'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2016-09-01', '2018-09-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-09T08:59:38.547Z',\n", - " 'updated': '2021-09-09T08:59:42.482Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'ac570d83-3916-490b-a5d6-b8f735f76203',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_alarms_09092021_sigma15',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'filterbbox1': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'data': {'from_node': 'filtertemporal1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 1.5}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'renamelabels1': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'dimension': 'bands',\n", - " 'target': ['VV', 'VH']}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'filterbbox1'},\n", - " 'cube1': {'from_node': 'filterbbox1'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'renamelabels1'},\n", - " 'reducer': {'process_graph': {'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add1'}, 'y': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '84e0fd74-da48-4662-9587-45f33da9568b'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply1'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'a3ec6e4b-ddc6-4ef4-a481-f228b495a78e'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2018-10-01', '2019-03-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-09T09:02:21.840Z',\n", - " 'updated': '2021-09-09T09:02:23.254Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '33cf060f-2d3a-4ce6-82d3-46ad002c5b1d',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_alarms_09092021_sigma15_2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'loadresult3': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'a3ec6e4b-ddc6-4ef4-a481-f228b495a78e'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'filterbbox1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult2'}}},\n", - " 'filterbbox1': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'data': {'from_node': 'filtertemporal1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 1.5}}}},\n", - " 'data': {'from_node': 'loadresult3'}}},\n", - " 'renamelabels1': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'dimension': 'bands',\n", - " 'target': ['VV', 'VH']}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'predictcurve1'},\n", - " 'cube1': {'from_node': 'filterbbox1'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'renamelabels1'},\n", - " 'reducer': {'process_graph': {'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add3'}, 'y': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '84e0fd74-da48-4662-9587-45f33da9568b'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply1'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'e4c164f3-ec86-407b-822d-3ce7348eb4d4'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2018-10-01', '2019-03-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-09T09:08:23.529Z',\n", - " 'updated': '2021-09-09T09:08:25.243Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'f3cb90c5-044f-4611-8b97-9c93c9c8afb0',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_alarms_09092021_3',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'loadresult3': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'a3ec6e4b-ddc6-4ef4-a481-f228b495a78e'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'filterbbox1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult2'}}},\n", - " 'filterbbox1': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'data': {'from_node': 'filtertemporal1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult3'}}},\n", - " 'renamelabels1': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'dimension': 'bands',\n", - " 'target': ['VV', 'VH']}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'predictcurve1'},\n", - " 'cube1': {'from_node': 'filterbbox1'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'renamelabels1'},\n", - " 'reducer': {'process_graph': {'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add3'}, 'y': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '84e0fd74-da48-4662-9587-45f33da9568b'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply1'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'e4c164f3-ec86-407b-822d-3ce7348eb4d4'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2018-10-01', '2019-03-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-09T09:09:51.161Z',\n", - " 'updated': '2021-09-09T09:09:52.593Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '5952e95a-6bda-4d16-88a6-dc4aff575777',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_alarms_09092021_4',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'loadresult3': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'a3ec6e4b-ddc6-4ef4-a481-f228b495a78e'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'filterbbox1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult2'}}},\n", - " 'filterbbox1': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'data': {'from_node': 'filtertemporal1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult3'}}},\n", - " 'renamelabels1': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'dimension': 'bands',\n", - " 'target': ['VV', 'VH']}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'predictcurve1'},\n", - " 'cube1': {'from_node': 'filterbbox1'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'renamelabels1'},\n", - " 'reducer': {'process_graph': {'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add3'}, 'y': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '84e0fd74-da48-4662-9587-45f33da9568b'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply1'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'e4c164f3-ec86-407b-822d-3ce7348eb4d4'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2018-10-01', '2019-09-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-09T09:13:09.289Z',\n", - " 'updated': '2021-09-09T09:13:11.352Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'be065f6f-ee5e-4ba4-8e15-59a6e09bb06d',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_alarms_09092021_sigma2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'loadresult3': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'a3ec6e4b-ddc6-4ef4-a481-f228b495a78e'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'filterbbox1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult2'}}},\n", - " 'filterbbox1': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'data': {'from_node': 'filtertemporal1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 2}}}},\n", - " 'data': {'from_node': 'loadresult3'}}},\n", - " 'renamelabels1': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'dimension': 'bands',\n", - " 'target': ['VV', 'VH']}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'predictcurve1'},\n", - " 'cube1': {'from_node': 'filterbbox1'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'renamelabels1'},\n", - " 'reducer': {'process_graph': {'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add3'}, 'y': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '84e0fd74-da48-4662-9587-45f33da9568b'}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply1'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'e4c164f3-ec86-407b-822d-3ce7348eb4d4'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2018-10-01', '2019-09-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-09T10:04:26.865Z',\n", - " 'updated': '2021-09-09T10:04:28.624Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '75249f51-4120-4953-93ba-5ec3196c9167',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_09092021_may',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply5': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-05-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-05-01'],\n", - " 'spatial_extent': {'east': 11.439514,\n", - " 'south': 46.327301,\n", - " 'north': 46.34816,\n", - " 'west': 11.404152},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'f8ea2429-2309-42df-ab05-e67f45bf925b'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'cdcd1c8a-f2db-423f-b878-224e50bfd108'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-09T10:09:35.382Z',\n", - " 'updated': '2021-09-09T10:09:59.332Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '17af286a-ef98-446f-acdb-be52f4b9f3a3',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'DRI2_black_triangle',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-15T23:59:00Z',\n", - " '2017-07-17T23:59:00Z'],\n", - " 'spatial_extent': {'east': 11.755584916051987,\n", - " 'south': 45.933496504418216,\n", - " 'north': 46.94616825350529,\n", - " 'west': 10.290484068721153},\n", - " 'id': 'DRI2_T32TPS',\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 8,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '5'}}},\n", - " '3': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'format': 'PNG',\n", - " 'options': {}}},\n", - " '4': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'size': [11, 11],\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '5': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '4'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'max',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-09-17T14:14:25.883Z',\n", - " 'updated': '2021-09-17T14:14:25.883Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '1da407c8-3e66-40b7-948d-2843f6bfd6f5',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'DRI2_masked',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-15T23:59:00Z',\n", - " '2017-07-17T23:59:00Z'],\n", - " 'spatial_extent': {'east': 11.755584916051987,\n", - " 'south': 45.933496504418216,\n", - " 'north': 46.94616825350529,\n", - " 'west': 10.290484068721153},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['AOT_10m'],\n", - " 'properties': {}}},\n", - " '3': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '6'},\n", - " 'format': 'NETCDF',\n", - " 'options': {}}},\n", - " '6': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '7'}, 'mask': {'from_node': '8'}}},\n", - " '7': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-15T23:59:00Z',\n", - " '2017-07-17T23:59:00Z'],\n", - " 'id': 'DRI2_T32TPS',\n", - " 'properties': {}}},\n", - " '8': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'eq',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': '9'}}},\n", - " '9': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'max',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-17T14:33:11.452Z',\n", - " 'updated': '2021-09-17T14:34:03.656Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '4695c5b6-7699-420c-9f37-e85d50f29f42',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'DRI2_masked2',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-15T23:59:00Z',\n", - " '2017-07-17T23:59:00Z'],\n", - " 'spatial_extent': {'east': 11.755584916051987,\n", - " 'south': 45.933496504418216,\n", - " 'north': 46.94616825350529,\n", - " 'west': 10.290484068721153},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['AOT_10m'],\n", - " 'properties': {}}},\n", - " '3': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '6'},\n", - " 'format': 'NETCDF',\n", - " 'options': {}}},\n", - " '6': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '7'}, 'mask': {'from_node': '8'}}},\n", - " '7': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-15T23:59:00Z',\n", - " '2017-07-17T23:59:00Z'],\n", - " 'id': 'DRI2_T32TPS',\n", - " 'properties': {}}},\n", - " '8': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'eq',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': '9'}}},\n", - " '9': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'max',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-09-17T14:34:21.324Z',\n", - " 'updated': '2021-09-17T14:34:40.473Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '90c6ef07-9ba7-466d-86e5-05e3dc3fca49',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_12102021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'apply1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439171,\n", - " 'south': 46.33115,\n", - " 'north': 46.348393,\n", - " 'west': 11.400461},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439171,\n", - " 'south': 46.33115,\n", - " 'north': 46.348393,\n", - " 'west': 11.400461},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply3'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'x'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply3'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply2'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-10-12T14:42:00.426Z',\n", - " 'updated': '2021-10-12T14:46:22.862Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '051edcc7-6b81-4b19-adca-ea5ebc87a474',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_12102021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439171,\n", - " 'south': 46.33115,\n", - " 'north': 46.348393,\n", - " 'west': 11.400461},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.439171,\n", - " 'south': 46.33115,\n", - " 'north': 46.348393,\n", - " 'west': 11.400461},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '90c6ef07-9ba7-466d-86e5-05e3dc3fca49'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'x'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply3'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-10-12T15:15:25.777Z',\n", - " 'updated': '2021-10-12T15:15:52.961Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '5459a6ef-4015-4f97-b604-4d445118584f',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_alarms_12102021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply3': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply4': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 3}}}},\n", - " 'data': {'from_node': 'loadresult2'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-05-01'],\n", - " 'spatial_extent': {'east': 11.439171,\n", - " 'south': 46.33115,\n", - " 'north': 46.348393,\n", - " 'west': 11.400461},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-10-01', '2019-05-01'],\n", - " 'spatial_extent': {'east': 11.439171,\n", - " 'south': 46.33115,\n", - " 'north': 46.348393,\n", - " 'west': 11.400461},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '90c6ef07-9ba7-466d-86e5-05e3dc3fca49'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '051edcc7-6b81-4b19-adca-ea5ebc87a474'}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'x'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply3'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'apply3'},\n", - " 'cube1': {'from_node': 'reducedimension1'},\n", - " 'overlap_resolver': {'process_graph': {'gt2': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-10-12T15:46:00.442Z',\n", - " 'updated': '2021-10-12T15:46:15.153Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '27547327-d6a9-45d8-ab2d-cda4350cca5a',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_predicting_08092021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.438313,\n", - " 'south': 46.32983,\n", - " 'north': 46.346778,\n", - " 'west': 11.405354},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.438313,\n", - " 'south': 46.32983,\n", - " 'north': 46.346778,\n", - " 'west': 11.405354},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'mask2': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '90c6ef07-9ba7-466d-86e5-05e3dc3fca49'}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}},\n", - " 'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'x'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply3'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult1'}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'reducer': {'process_graph': {'sd1': {'result': True,\n", - " 'process_id': 'sd',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'mask2'},\n", - " 'cube1': {'from_node': 'apply2'},\n", - " 'overlap_resolver': {'process_graph': {'subtract1': {'result': True,\n", - " 'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_parameter': 'x'},\n", - " 'y': {'from_parameter': 'y'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement8'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'divide1'}}},\n", - " 'arrayelement8': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},\n", - " 'arrayelement7': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 1}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'divide1': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'add6'}, 'y': 5}},\n", - " 'arrayelement6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'arrayelement5': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'add6': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'add5'}}},\n", - " 'add5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power2'},\n", - " 'y': {'from_node': 'add4'}}},\n", - " 'power3': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement6'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement7'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'add4': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power3'},\n", - " 'y': {'from_node': 'add3'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement5'}}},\n", - " 'add3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-10-14T12:24:22.103Z',\n", - " 'updated': '2021-10-14T12:24:22.103Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '76c22979-cb0d-4597-86b4-181402310cc1',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_12102021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.443119,\n", - " 'south': 46.324069,\n", - " 'north': 46.348365,\n", - " 'west': 11.404324},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.443119,\n", - " 'south': 46.324069,\n", - " 'north': 46.348365,\n", - " 'west': 11.404324},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'x'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply3'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-10-15T09:28:42.952Z',\n", - " 'updated': '2021-10-15T09:28:42.952Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '6b73070f-42c2-4cbd-9b25-706ce2a7ff3c',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fitting_15102021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'clip1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 5000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': 'mask1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'gt1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': 'resamplecubetemporal1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.443119,\n", - " 'south': 46.324069,\n", - " 'north': 46.348365,\n", - " 'west': 11.404324},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'B8A_20m']}},\n", - " 'mask1': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'replacement': 0,\n", - " 'mask': {'from_node': 'applykernel1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadcollection2': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01', '2018-09-01'],\n", - " 'spatial_extent': {'east': 11.443119,\n", - " 'south': 46.324069,\n", - " 'north': 46.348365,\n", - " 'west': 11.404324},\n", - " 'id': 's2cloudless_alps'}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'apply2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'x'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply3'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'applykernel1': {'process_id': 'apply_kernel',\n", - " 'arguments': {'border': 0,\n", - " 'data': {'from_node': 'apply1'},\n", - " 'replace_invalid': 0,\n", - " 'kernel': [[1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0],\n", - " [1.0, 1.0, 1.0, 1.0, 1.0]],\n", - " 'factor': 1.0}},\n", - " 'resamplecubetemporal1': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection2'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': 'loadcollection1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-10-15T09:29:03.229Z',\n", - " 'updated': '2021-10-15T09:35:02.019Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '05fcbe32-3716-482d-8c2c-21b6b80d4957',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_curve_fit_test',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'11': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01T00:00:00Z',\n", - " '2018-09-01T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.441515445709229,\n", - " 'south': 46.32649604673932,\n", - " 'north': 46.348777106498574,\n", - " 'west': 11.40143251419067},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B04_10m', 'B02_10m', 'B08_10m'],\n", - " 'properties': {}}},\n", - " '22': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'clip',\n", - " 'arguments': {'min': 0, 'max': 3000, 'x': {'from_parameter': 'x'}}}}},\n", - " 'data': {'from_node': '13'},\n", - " 'context': ''}},\n", - " '13': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '11'}, 'mask': {'from_node': '10'}}},\n", - " '18': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': '22'},\n", - " 'function': {'process_graph': {'32frj455b': {'process_id': 'pi',\n", - " 'arguments': {}},\n", - " '9k6vt7qcn': {'process_id': 'multiply',\n", - " 'arguments': {'x': 2, 'y': {'from_node': '2sjyaa699'}}},\n", - " 'b4mf181yp': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'xb4c1hk1f'},\n", - " 'y': {'from_node': '0v09jn699'}}},\n", - " '1ipvki94n': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'lyjcuq5vd'}, 'y': 31557600}},\n", - " 'wz26aglyi': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'p42lrxmbq'},\n", - " 'y': {'from_parameter': 'x'}}},\n", - " 'kryhimf6r': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'v81bsalku': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'wz26aglyi'}}},\n", - " '0p7xlqeyo': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'ya3hbxpot'}}},\n", - " 'xb4c1hk1f': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'kryhimf6r'},\n", - " 'y': {'from_node': 'jhus2gz74'}}},\n", - " 'jxs4umqsh': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " '2sjyaa699': {'process_id': 'pi', 'arguments': {}},\n", - " '8jjjztmya': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'jhus2gz74': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'jxs4umqsh'},\n", - " 'y': {'from_node': 'v81bsalku'}}},\n", - " '0v09jn699': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': '8jjjztmya'},\n", - " 'y': {'from_node': '0p7xlqeyo'}}},\n", - " 'lyjcuq5vd': {'process_id': 'multiply',\n", - " 'arguments': {'x': 2, 'y': {'from_node': '32frj455b'}}},\n", - " 'p42lrxmbq': {'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': '9k6vt7qcn'}, 'y': 31557600}},\n", - " 'ya3hbxpot': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': '1ipvki94n'},\n", - " 'y': {'from_parameter': 'x'}}}}},\n", - " 'parameters': [1, 1, 1],\n", - " 'dimension': 't'}},\n", - " '19': {'process_id': 'resample_cube_temporal',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'method': 'nearest',\n", - " 'target': {'from_node': '11'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01T00:00:00Z',\n", - " '2018-09-01T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.441515445709229,\n", - " 'south': 46.32649604673932,\n", - " 'north': 46.348777106498574,\n", - " 'west': 11.40143251419067},\n", - " 'id': 's2cloudless_alps',\n", - " 'bands': ['CLOUD_10m'],\n", - " 'properties': {}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '18'}, 'format': 'NETCDF'}},\n", - " '10': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'gt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0}}}},\n", - " 'data': {'from_node': '19'},\n", - " 'context': ''}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-10-21T13:35:00.107Z',\n", - " 'updated': '2021-10-21T13:35:05.966Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'bef96799-e1a5-419f-84be-9e784c810872',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_curve_fitting_27102021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'filterbbox1': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.438656,\n", - " 'south': 46.32593,\n", - " 'north': 46.347619,\n", - " 'west': 11.405182},\n", - " 'data': {'from_node': 'filtertemporal1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'filterbbox1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '84e0fd74-da48-4662-9587-45f33da9568b'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2016-09-01', '2018-09-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-10-27T09:01:20.447Z',\n", - " 'updated': '2021-10-27T09:01:21.026Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'e9139694-edd9-4aab-9b6c-aa697a5bf837',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_forest_change_data',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'11': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '9'}, 'cube1': {'from_node': '12'}}},\n", - " '12': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VV']}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2016-09-01T00:00:00Z',\n", - " '2019-09-01T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.438656,\n", - " 'south': 46.32593,\n", - " 'north': 46.347619,\n", - " 'west': 11.405182},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '3'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}}}},\n", - " 'dimension': 'bands'}},\n", - " '6': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '8'}, 'format': 'NETCDF'}},\n", - " '8': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '32'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " '9': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '10'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '30': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VH'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VH'}}}},\n", - " 'dimension': 'bands'}},\n", - " '20': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '30'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '31': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '20'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VH']}},\n", - " '10': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '32': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '31'},\n", - " 'cube1': {'from_node': '11'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-10-27T09:07:19.963Z',\n", - " 'updated': '2021-10-27T10:02:37.471Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '13a7ae44-c693-473b-b8e6-68eb7ad5cb6c',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_curve_fitting_27102021_2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'filterbbox1': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.424408,\n", - " 'south': 46.330553,\n", - " 'north': 46.349396,\n", - " 'west': 11.400204},\n", - " 'data': {'from_node': 'filtertemporal1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'filterbbox1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': '84e0fd74-da48-4662-9587-45f33da9568b'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2016-09-01', '2018-09-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-10-27T10:01:49.300Z',\n", - " 'updated': '2021-10-27T10:01:49.543Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '0b2a101a-09f7-4a0d-9495-009ba8f2517f',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_curve_fitting_27102021_3',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'filterbbox1': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.424408,\n", - " 'south': 46.330553,\n", - " 'north': 46.349396,\n", - " 'west': 11.400204},\n", - " 'data': {'from_node': 'filtertemporal1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'filterbbox1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'e9139694-edd9-4aab-9b6c-aa697a5bf837'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2016-09-01', '2018-09-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-10-27T10:02:59.624Z',\n", - " 'updated': '2021-10-27T10:02:59.776Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'a6e6ebd0-ad84-49cb-a3d5-7b6a7a1e62e9',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_curve_fitting_27102021_4',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'filtertemporal2': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2016-09-01', '2018-09-01'],\n", - " 'data': {'from_node': 'filterbbox1'}}},\n", - " 'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'filterbbox1': {'process_id': 'filter_bbox',\n", - " 'arguments': {'extent': {'east': 11.424408,\n", - " 'south': 46.330553,\n", - " 'north': 46.349396,\n", - " 'west': 11.400204},\n", - " 'data': {'from_node': 'filtertemporal1'}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'filtertemporal2'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'e9139694-edd9-4aab-9b6c-aa697a5bf837'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2016-09-01', '2018-09-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-10-27T10:03:19.190Z',\n", - " 'updated': '2021-10-27T10:03:19.315Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '97327732-a8fd-414b-9eb5-1b7e5a3dafa8',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_curve_fitting_27102021_5',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'fitcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'fitcurve1': {'process_id': 'fit_curve',\n", - " 'arguments': {'data': {'from_node': 'filtertemporal1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': [1, 1, 1]}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'e9139694-edd9-4aab-9b6c-aa697a5bf837'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2016-09-01', '2018-09-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-10-27T10:03:53.377Z',\n", - " 'updated': '2021-10-27T10:04:21.419Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'a94cbeb8-be8b-4f32-b152-7649ca215088',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_curve_predicting_netcdf_27102021',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'filtertemporal1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult2'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'e9139694-edd9-4aab-9b6c-aa697a5bf837'}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '97327732-a8fd-414b-9eb5-1b7e5a3dafa8'}},\n", - " 'filtertemporal1': {'process_id': 'filter_temporal',\n", - " 'arguments': {'extent': ['2016-09-01', '2018-09-01'],\n", - " 'data': {'from_node': 'loadresult1'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-10-27T10:08:15.934Z',\n", - " 'updated': '2021-10-27T10:08:19.022Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '4501c6b5-94cb-4804-96cb-5890fb9caaf1',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S1_curve_predicting_netcdf_27102021_2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'predictcurve1': {'process_id': 'predict_curve',\n", - " 'arguments': {'data': {'from_node': 'loadresult1'},\n", - " 'function': {'process_graph': {'multiply1': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'multiply2': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement2'},\n", - " 'y': {'from_node': 'cos1'}}},\n", - " 'multiply3': {'process_id': 'multiply',\n", - " 'arguments': {'x': 1.991021277657232e-07,\n", - " 'y': {'from_parameter': 'data'}}},\n", - " 'cos1': {'process_id': 'cos',\n", - " 'arguments': {'x': {'from_node': 'multiply1'}}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 2}},\n", - " 'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 1}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'parameters'}, 'index': 0}},\n", - " 'sin1': {'process_id': 'sin',\n", - " 'arguments': {'x': {'from_node': 'multiply3'}}},\n", - " 'multiply4': {'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'arrayelement3'},\n", - " 'y': {'from_node': 'sin1'}}},\n", - " 'add2': {'result': True,\n", - " 'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'add1'},\n", - " 'y': {'from_node': 'multiply4'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'arrayelement1'},\n", - " 'y': {'from_node': 'multiply2'}}}}},\n", - " 'dimension': 't',\n", - " 'parameters': {'from_node': 'loadresult2'}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'predictcurve1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}},\n", - " 'loadresult1': {'process_id': 'load_result',\n", - " 'arguments': {'id': 'e9139694-edd9-4aab-9b6c-aa697a5bf837'}},\n", - " 'loadresult2': {'process_id': 'load_result',\n", - " 'arguments': {'id': '97327732-a8fd-414b-9eb5-1b7e5a3dafa8'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-10-27T10:15:10.197Z',\n", - " 'updated': '2021-10-27T10:15:13.602Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '2a12421e-9045-46b0-9280-daa05484979e',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'simple_NDVI_mask',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'2': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'lt',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'y': 0.5}}}},\n", - " 'data': {'from_node': '4'}}},\n", - " '3': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '4'}, 'mask': {'from_node': '2'}}},\n", - " '4': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'reducer': {'process_graph': {'hvakxw8sn': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'B08_10m'}},\n", - " 'tzju0ipgt': {'process_id': 'subtract',\n", - " 'arguments': {'x': {'from_node': 'hvakxw8sn'},\n", - " 'y': {'from_node': 'fsj8v5kfh'}}},\n", - " 'fsj8v5kfh': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'B04_10m'}},\n", - " 'oo2qnh658': {'result': True,\n", - " 'process_id': 'divide',\n", - " 'arguments': {'x': {'from_node': 'tzju0ipgt'},\n", - " 'y': {'from_node': 'ioaureau5'}}},\n", - " 'itp72p6o4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'B08_10m'}},\n", - " 'ioaureau5': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'itp72p6o4'},\n", - " 'y': {'from_node': 'q97vgtnwn'}}},\n", - " 'q97vgtnwn': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'},\n", - " 'label': 'B04_10m'}}}},\n", - " 'dimension': 'bands'}},\n", - " 'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'linearscalerange1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 1,\n", - " 'inputMin': -1,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '3'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-31T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.13503196568716,\n", - " 'south': 46.12454364652113,\n", - " 'north': 46.16782900697793,\n", - " 'west': 11.080602927325524},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B04_10m', 'B08_10m'],\n", - " 'properties': {}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'min1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'apply1'}, 'format': 'PNG'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-10-28T15:19:06.220Z',\n", - " 'updated': '2021-10-28T15:19:06.220Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'd4a4d8d6-5cf8-4a34-8752-884e1fe5f562',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_donyana_FMASK_test',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2020-10-01T00:00:00Z',\n", - " '2020-10-10T00:00:00Z'],\n", - " 'id': 'SInCohMap_S2_L1C_T29SQB',\n", - " 'bands': ['B02', 'B03', 'B04', 'FMASK'],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['B02', 'B03', 'B04']}},\n", - " '4': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'FMASK'}},\n", - " '2': {'process_id': 'eq',\n", - " 'arguments': {'x': {'from_node': '1'}, 'y': 4}},\n", - " '3': {'process_id': 'eq',\n", - " 'arguments': {'x': {'from_node': '1'}, 'y': 2}},\n", - " '4': {'result': True,\n", - " 'process_id': 'or',\n", - " 'arguments': {'x': {'from_node': '2'}, 'y': {'from_node': '3'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '5': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '2'}, 'mask': {'from_node': '4'}}},\n", - " '6': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '10'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " '8': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 2500,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '6'},\n", - " 'context': ''}},\n", - " '9': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '8'}, 'format': 'PNG'}},\n", - " '10': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '5'},\n", - " 'size': [9, 9],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-11-03T14:50:32.640Z',\n", - " 'updated': '2021-11-03T14:51:24.568Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'b93df5c4-600b-4d97-aee7-5d1fbe36d5bd',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_VV_INT_geocode',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-06-01T00:00:00Z',\n", - " '2018-06-30T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.484146,\n", - " 'south': 46.393927,\n", - " 'north': 46.545261,\n", - " 'west': 11.164169},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'adddimension1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-11-23T10:11:31.912Z',\n", - " 'updated': '2021-11-23T10:11:58.602Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '5e67fa8c-77b9-41ab-b966-1a37a6f011f4',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'ALPS_SNOW_MODIS_map',\n", - " 'description': 'ALPS_SNOW_MODIS_map',\n", - " 'process': {'process_graph': {'saveresult2': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'saveresult1'},\n", - " 'format': 'netCDF',\n", - " 'options': {}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2020-07-03T12:00:00Z',\n", - " '2021-03-28T12:00:00Z'],\n", - " 'spatial_extent': {'east': 15.99,\n", - " 'south': 45.817,\n", - " 'north': 45.817,\n", - " 'west': 15.99},\n", - " 'id': 'ALPS_SNOW_MODIS_map'}},\n", - " 'saveresult1': {'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'format': 'NetCDF',\n", - " 'options': {}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-11-26T12:53:03.690Z',\n", - " 'updated': '2021-11-26T12:53:07.672Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'WCPS'},\n", - " {'id': 'cae682f6-0115-4d5a-8112-f0f8523a56ef',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'sample_job_RGB',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'linearscalerange1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 2500,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': 'reducedimension2'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-31T23:59:59Z'],\n", - " 'spatial_extent': {'coordinates': [[[11.077, 46.136],\n", - " [11.074, 46.141],\n", - " [11.074, 46.144],\n", - " [11.084, 46.155],\n", - " [11.09, 46.156],\n", - " [11.092, 46.154],\n", - " [11.094, 46.147],\n", - " [11.101, 46.153],\n", - " [11.091, 46.157],\n", - " [11.094, 46.157],\n", - " [11.11, 46.164],\n", - " [11.112, 46.167],\n", - " [11.112, 46.176],\n", - " [11.13, 46.178],\n", - " [11.137, 46.181],\n", - " [11.135, 46.172],\n", - " [11.125, 46.163],\n", - " [11.118, 46.149],\n", - " [11.119, 46.139],\n", - " [11.102, 46.133],\n", - " [11.09, 46.131],\n", - " [11.09, 46.128],\n", - " [11.083, 46.125],\n", - " [11.077, 46.136]]],\n", - " 'type': 'Polygon'},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m'],\n", - " 'properties': {}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'min1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'format': 'PNG',\n", - " 'options': {'red': 'B04_10m', 'green': 'B03_10m', 'blue': 'B02_10m'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-11-30T10:47:56.030Z',\n", - " 'updated': '2021-11-30T10:48:29.489Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'dbacabe3-c288-4137-8023-3b68f7e6b4ae',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'sample_job_2',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'linearscalerange1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 2500,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': 'reducedimension2'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-31T23:59:59Z'],\n", - " 'spatial_extent': {'coordinates': [[[11.077, 46.136],\n", - " [11.074, 46.141],\n", - " [11.074, 46.144],\n", - " [11.084, 46.155],\n", - " [11.09, 46.156],\n", - " [11.092, 46.154],\n", - " [11.094, 46.147],\n", - " [11.101, 46.153],\n", - " [11.091, 46.157],\n", - " [11.094, 46.157],\n", - " [11.11, 46.164],\n", - " [11.112, 46.167],\n", - " [11.112, 46.176],\n", - " [11.13, 46.178],\n", - " [11.137, 46.181],\n", - " [11.135, 46.172],\n", - " [11.125, 46.163],\n", - " [11.118, 46.149],\n", - " [11.119, 46.139],\n", - " [11.102, 46.133],\n", - " [11.09, 46.131],\n", - " [11.09, 46.128],\n", - " [11.083, 46.125],\n", - " [11.077, 46.136]]],\n", - " 'type': 'Polygon'},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m'],\n", - " 'properties': {}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'min1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'format': 'PNG',\n", - " 'options': {'red': 'B04_10m', 'green': 'B03_10m', 'blue': 'B02_10m'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-12-01T13:57:43.385Z',\n", - " 'updated': '2021-12-01T13:57:49.743Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'c944bb51-f3dc-4320-b103-7b1cfaaf53fc',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'corine_load_save',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'spatial_extent': {'east': 11.831085643073234,\n", - " 'south': 46.01525640961307,\n", - " 'north': 46.94745701464103,\n", - " 'west': 10.138294089852007},\n", - " 'id': 'ADO_CORINE_100m_3035',\n", - " 'properties': {}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'format': 'GTIFF',\n", - " 'options': {'red': 'B04_10m', 'green': 'B03_10m', 'blue': 'B02_10m'}}}}},\n", - " 'status': 'queued',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-01T13:59:06.617Z',\n", - " 'updated': '2021-12-01T13:59:10.035Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'WCPS'},\n", - " {'id': 'e76e64e2-75a8-4a0d-9b76-a5baa73f427b',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'sample_job_3',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'linearscalerange1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 2500,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': 'reducedimension2'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2017-07-01T00:00:00Z',\n", - " '2017-07-31T23:59:59Z'],\n", - " 'spatial_extent': {'coordinates': [[[11.077, 46.136],\n", - " [11.074, 46.141],\n", - " [11.074, 46.144],\n", - " [11.084, 46.155],\n", - " [11.09, 46.156],\n", - " [11.092, 46.154],\n", - " [11.094, 46.147],\n", - " [11.101, 46.153],\n", - " [11.091, 46.157],\n", - " [11.094, 46.157],\n", - " [11.11, 46.164],\n", - " [11.112, 46.167],\n", - " [11.112, 46.176],\n", - " [11.13, 46.178],\n", - " [11.137, 46.181],\n", - " [11.135, 46.172],\n", - " [11.125, 46.163],\n", - " [11.118, 46.149],\n", - " [11.119, 46.139],\n", - " [11.102, 46.133],\n", - " [11.09, 46.131],\n", - " [11.09, 46.128],\n", - " [11.083, 46.125],\n", - " [11.077, 46.136]]],\n", - " 'type': 'Polygon'},\n", - " 'id': 'S2_L2A_T32TPS',\n", - " 'bands': ['B02_10m', 'B03_10m', 'B04_10m'],\n", - " 'properties': {}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'min1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'format': 'PNG',\n", - " 'options': {'red': 'B04_10m', 'green': 'B03_10m', 'blue': 'B02_10m'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-12-01T14:28:14.798Z',\n", - " 'updated': '2021-12-01T14:28:22.458Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'b1ec1024-0950-4b87-8a8a-459cbd866470',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': None,\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': 10, 'y': {'from_node': 'log1'}}},\n", - " 'log1': {'process_id': 'log',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'base': 10}}}},\n", - " 'data': {'from_node': 'reducedimension3'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-06-01T00:00:00Z',\n", - " '2018-06-30T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.544571,\n", - " 'south': 46.414523,\n", - " 'north': 46.568633,\n", - " 'west': 11.089325},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add2'}}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'add2': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement3'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'reducedimension3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'adddimension2'},\n", - " 'cube1': {'from_node': 'adddimension1'}}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'mergecubes1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " 'adddimension2': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'name': 'bands',\n", - " 'label': 'VH'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-03T08:42:49.776Z',\n", - " 'updated': '2021-12-03T08:42:49.776Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '5e9265d6-337a-4b8f-8a79-1fcc37c8a7bf',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': None,\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': 10, 'y': {'from_node': 'log1'}}},\n", - " 'log1': {'process_id': 'log',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'base': 10}}}},\n", - " 'data': {'from_node': 'reducedimension3'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-06-01T00:00:00Z',\n", - " '2018-06-30T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.544571,\n", - " 'south': 46.414523,\n", - " 'north': 46.568633,\n", - " 'west': 11.089325},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add2'}}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'add2': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement3'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'reducedimension3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'adddimension2'},\n", - " 'cube1': {'from_node': 'adddimension1'}}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'mergecubes1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " 'adddimension2': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'name': 'bands',\n", - " 'label': 'VH'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-03T08:42:59.651Z',\n", - " 'updated': '2021-12-03T08:42:59.651Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '848935c6-e921-470a-aecd-b4c63a2ee612',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': None,\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': 10, 'y': {'from_node': 'log1'}}},\n", - " 'log1': {'process_id': 'log',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'base': 10}}}},\n", - " 'data': {'from_node': 'reducedimension3'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-06-01T00:00:00Z',\n", - " '2018-06-30T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.544571,\n", - " 'south': 46.414523,\n", - " 'north': 46.568633,\n", - " 'west': 11.089325},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add2'}}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'add2': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement3'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'reducedimension3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'adddimension2'},\n", - " 'cube1': {'from_node': 'adddimension1'}}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'mergecubes1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " 'adddimension2': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'name': 'bands',\n", - " 'label': 'VH'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-03T08:52:15.189Z',\n", - " 'updated': '2021-12-03T08:52:15.189Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '88751d6d-be8c-4356-8c0c-e780b68b9958',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': None,\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': 10, 'y': {'from_node': 'log1'}}},\n", - " 'log1': {'process_id': 'log',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'base': 10}}}},\n", - " 'data': {'from_node': 'reducedimension3'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-06-01T00:00:00Z',\n", - " '2018-06-30T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.544571,\n", - " 'south': 46.414523,\n", - " 'north': 46.568633,\n", - " 'west': 11.089325},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add2'}}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'add2': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement3'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'reducedimension3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'adddimension2'},\n", - " 'cube1': {'from_node': 'adddimension1'}}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'mergecubes1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " 'adddimension2': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'name': 'bands',\n", - " 'label': 'VH'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-03T08:52:29.472Z',\n", - " 'updated': '2021-12-03T08:52:29.472Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '14c0d7e9-efad-432c-a9d7-78c59752f979',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'sar2cube_db',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': 10, 'y': {'from_node': 'log1'}}},\n", - " 'log1': {'process_id': 'log',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'base': 10}}}},\n", - " 'data': {'from_node': 'reducedimension3'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-06-01T00:00:00Z',\n", - " '2018-06-30T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.544571,\n", - " 'south': 46.414523,\n", - " 'north': 46.568633,\n", - " 'west': 11.089325},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'apply1'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add2'}}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'add2': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement3'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'reducedimension3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'adddimension2'},\n", - " 'cube1': {'from_node': 'adddimension1'}}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'mergecubes1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " 'adddimension2': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'name': 'bands',\n", - " 'label': 'VH'}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-03T08:53:23.233Z',\n", - " 'updated': '2021-12-03T08:53:26.124Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '22905d67-e341-4998-a2c2-af0fcd7d8517',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'Coh_Meran_CLASS',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-06-05T00:00:00Z',\n", - " '2018-06-15T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.38748860450466,\n", - " 'south': 46.52800431272808,\n", - " 'north': 46.7665963542475,\n", - " 'west': 10.946533860104717},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'coherence1': {'process_id': 'coherence',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'}, 'timedelta': 6}},\n", - " '1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'NETCDF',\n", - " 'options': {}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'aggregatespatialwindow1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add2'}}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VH'}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VH'}},\n", - " 'add2': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement3'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'reducedimension3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'filterbands1'},\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'reducedimension3'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'coherence1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'aggregatespatialwindow1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'adddimension2'},\n", - " 'cube1': {'from_node': 'adddimension1'}}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'mergecubes1'}}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV_coh'}},\n", - " 'adddimension2': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'name': 'bands',\n", - " 'label': 'VH_coh'}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-03T15:10:39.134Z',\n", - " 'updated': '2021-12-03T15:10:39.134Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '6a276a1e-30c6-4ff9-a21b-7b2a7dc24180',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'coh_2018_meran_12days',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-03-05T00:00:00Z',\n", - " '2018-12-31T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.38748860450466,\n", - " 'south': 46.52800431272808,\n", - " 'north': 46.7665963542475,\n", - " 'west': 10.946533860104717},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'coherence1': {'process_id': 'coherence',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'}, 'timedelta': 12}},\n", - " '1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'NETCDF',\n", - " 'options': {}}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'aggregatespatialwindow1'},\n", - " 'reducer': {'process_graph': {'power5': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement4'}}},\n", - " 'power6': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add2'}}},\n", - " 'arrayelement4': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VH'}},\n", - " 'arrayelement3': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VH'}},\n", - " 'add2': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power4'},\n", - " 'y': {'from_node': 'power5'}}},\n", - " 'power4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement3'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'reducedimension3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'filterbands1'},\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'reducedimension3'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'coherence1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'aggregatespatialwindow1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'adddimension2'},\n", - " 'cube1': {'from_node': 'adddimension1'}}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'mergecubes1'}}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV_coh'}},\n", - " 'adddimension2': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'name': 'bands',\n", - " 'label': 'VH_coh'}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-03T15:12:31.814Z',\n", - " 'updated': '2021-12-03T15:18:47.675Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '08e4777f-bf44-4f16-b803-c3f90ef23aeb',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'int_db_vv_2018_meran',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': '2'}, 'y': 10}},\n", - " '2': {'process_id': 'log',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'base': 10}}}},\n", - " 'data': {'from_node': 'reducedimension1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-03-01T00:00:00Z',\n", - " '2018-12-01T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.38748860450466,\n", - " 'south': 46.52800431272808,\n", - " 'north': 46.7665963542475,\n", - " 'west': 10.946533860104717},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'NETCDF',\n", - " 'options': {}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'adddimension1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-06T08:36:24.251Z',\n", - " 'updated': '2021-12-06T08:36:26.707Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '70dae9bf-74a7-476c-afda-e5e358e52b55',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': None,\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': 10, 'y': {'from_node': 'log1'}}},\n", - " 'log1': {'process_id': 'log',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'base': 10}}}},\n", - " 'data': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T00:00:00Z',\n", - " '2019-01-01T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.697916,\n", - " 'south': 46.253694,\n", - " 'north': 46.65107,\n", - " 'west': 11.041464},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [11, 3],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'adddimension1'},\n", - " 'size': [11, 3],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'apply1'}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-07T08:37:08.479Z',\n", - " 'updated': '2021-12-07T08:37:08.479Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'd7f62479-fd1c-4c3f-b5e9-e78bf16c5ba6',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': None,\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': 10, 'y': {'from_node': 'log1'}}},\n", - " 'log1': {'process_id': 'log',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'base': 10}}}},\n", - " 'data': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T00:00:00Z',\n", - " '2019-01-01T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.697916,\n", - " 'south': 46.253694,\n", - " 'north': 46.65107,\n", - " 'west': 11.041464},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [11, 3],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'adddimension1'},\n", - " 'size': [11, 3],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'apply1'}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-07T08:54:38.529Z',\n", - " 'updated': '2021-12-07T08:54:38.529Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '8d69ffb6-d115-4728-b352-d646e1ba36bf',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': None,\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': 10, 'y': {'from_node': 'log1'}}},\n", - " 'log1': {'process_id': 'log',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'base': 10}}}},\n", - " 'data': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T00:00:00Z',\n", - " '2019-01-01T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.697916,\n", - " 'south': 46.253694,\n", - " 'north': 46.65107,\n", - " 'west': 11.041464},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [11, 3],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'adddimension1'},\n", - " 'size': [11, 3],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'apply1'}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-07T09:58:05.545Z',\n", - " 'updated': '2021-12-07T09:58:05.546Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '60d5e1a4-ea6f-4faf-b001-8953aed181d6',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': None,\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': 10, 'y': {'from_node': 'log1'}}},\n", - " 'log1': {'process_id': 'log',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'base': 10}}}},\n", - " 'data': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T00:00:00Z',\n", - " '2019-01-01T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.697916,\n", - " 'south': 46.253694,\n", - " 'north': 46.65107,\n", - " 'west': 11.041464},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [11, 3],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'adddimension1'},\n", - " 'size': [11, 3],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'apply1'}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}}}},\n", - " 'status': 'error',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-07T09:58:59.637Z',\n", - " 'updated': '2021-12-07T10:07:35.410Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '89bbdd5c-4c7f-4005-bb83-087d68673037',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_VV_MASKED',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-06-01T00:00:00Z',\n", - " '2018-06-10T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.697916,\n", - " 'south': 46.253694,\n", - " 'north': 46.65107,\n", - " 'west': 11.041464},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes2'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'filterbands2': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands2'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 4}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'filterbands1'},\n", - " 'cube1': {'from_node': 'adddimension1'}}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'mergecubes1'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes2': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VH'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['DEM', 'LIA']}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-07T14:20:45.191Z',\n", - " 'updated': '2021-12-07T14:20:45.191Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '8265abca-4fea-4870-b14f-5c507786598a',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_VV_mask_netcdf',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T23:59:59Z',\n", - " '2018-01-06T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.506118774414064,\n", - " 'south': 46.45299704748291,\n", - " 'north': 46.771849614677336,\n", - " 'west': 10.86753845214844},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '34': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'reducer': {'process_graph': {'u61739s9l': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'k024nph6i'}}},\n", - " '6dcr1ok9c': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '1pk3qj0s2'},\n", - " 'y': {'from_node': 'u61739s9l'}}},\n", - " '1pk3qj0s2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '3pppq5byr'}}},\n", - " '3pppq5byr': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'k024nph6i': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'fkgxt98l9': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '6dcr1ok9c'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '26': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '31'},\n", - " 'format': 'NETCDF',\n", - " 'options': {'path': '/mnt/large_drive/work_spaces/mclaus/outputmask.nc'}}},\n", - " '28': {'process_id': 'radar_mask',\n", - " 'arguments': {'data': {'from_node': '29'},\n", - " 'threshold': '0.8',\n", - " 'orbit': 'ASC'}},\n", - " '39': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': '41'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " '29': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '11'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['LIA', 'DEM']}},\n", - " '40': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': '28'},\n", - " 'name': 'bands',\n", - " 'label': 'mask'}},\n", - " '41': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '34'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '31': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '39'},\n", - " 'cube1': {'from_node': '40'}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-07T15:14:04.273Z',\n", - " 'updated': '2021-12-07T15:14:04.273Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '90104d45-56f3-411d-986f-c936893e0e64',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_VV_masked_PNG',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '44': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '43'},\n", - " 'format': 'PNG',\n", - " 'options': {}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T23:59:59Z',\n", - " '2018-01-06T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.506118774414064,\n", - " 'south': 46.45299704748291,\n", - " 'north': 46.771849614677336,\n", - " 'west': 10.86753845214844},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '34': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'reducer': {'process_graph': {'u61739s9l': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'k024nph6i'}}},\n", - " '6dcr1ok9c': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '1pk3qj0s2'},\n", - " 'y': {'from_node': 'u61739s9l'}}},\n", - " '1pk3qj0s2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '3pppq5byr'}}},\n", - " '3pppq5byr': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'k024nph6i': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'fkgxt98l9': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '6dcr1ok9c'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '45': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '41'}, 'mask': {'from_node': '28'}}},\n", - " '28': {'process_id': 'radar_mask',\n", - " 'arguments': {'data': {'from_node': '29'},\n", - " 'threshold': '0.8',\n", - " 'orbit': 'ASC'}},\n", - " '29': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '11'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['LIA', 'DEM']}},\n", - " '41': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '34'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '43': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 255,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '45'}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-12-07T15:17:36.946Z',\n", - " 'updated': '2021-12-07T15:56:57.771Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': 'bc7ad98e-d97c-498d-9c94-abdec1da3926',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_VV_masked_gtiff',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '44': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '48'},\n", - " 'format': 'GTIFF',\n", - " 'options': {}}},\n", - " '34': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'reducer': {'process_graph': {'u61739s9l': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'k024nph6i'}}},\n", - " '6dcr1ok9c': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '1pk3qj0s2'},\n", - " 'y': {'from_node': 'u61739s9l'}}},\n", - " '1pk3qj0s2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '3pppq5byr'}}},\n", - " '3pppq5byr': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'k024nph6i': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'fkgxt98l9': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '6dcr1ok9c'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '45': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '29'},\n", - " 'wavelengths': [],\n", - " 'bands': ['LIA', 'DEM']}},\n", - " '46': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '29'},\n", - " 'wavelengths': [],\n", - " 'bands': ['grid_lon', 'grid_lat']}},\n", - " '47': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '46'},\n", - " 'cube1': {'from_node': '49'}}},\n", - " '48': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': '50'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " '49': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': '42'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " '28': {'process_id': 'radar_mask',\n", - " 'arguments': {'data': {'from_node': '45'},\n", - " 'threshold': '0.8',\n", - " 'orbit': 'ASC'}},\n", - " '39': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': '41'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " '29': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '11'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T23:59:59Z',\n", - " '2018-01-06T23:59:59Z'],\n", - " 'spatial_extent': {'east': 11.506118774414064,\n", - " 'south': 46.45299704748291,\n", - " 'north': 46.771849614677336,\n", - " 'west': 10.86753845214844},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['LIA', 'DEM', 'grid_lon', 'grid_lat']}},\n", - " '50': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '47'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " '40': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': '28'},\n", - " 'name': 'bands',\n", - " 'label': 'mask'}},\n", - " '41': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '34'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '31': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': '39'},\n", - " 'cube1': {'from_node': '40'}}},\n", - " '42': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '31'},\n", - " 'reducer': {'process_graph': {'zg73x70uf': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'mask'}},\n", - " '8tkxk2y84': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': {'from_node': 'zxjbiobr9'},\n", - " 'y': {'from_node': 'eaoz8tf2j'}}},\n", - " 'zxjbiobr9': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'VV'}},\n", - " 'eaoz8tf2j': {'process_id': 'subtract',\n", - " 'arguments': {'x': 1, 'y': {'from_node': 'zg73x70uf'}}}}},\n", - " 'dimension': 'bands'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-12-07T15:36:35.393Z',\n", - " 'updated': '2021-12-09T13:56:22.682Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '98e8303a-6d34-4de1-9424-daa39a8e8e83',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_Donyana_DSC_PNG',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'33': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '12'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-04-01T00:00:00Z',\n", - " '2019-04-20T23:59:59Z'],\n", - " 'id': 'SAR2Cube_SInCohMap_S1_L0_154_DSC_DONYANA',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '12': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VV']}},\n", - " '34': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 0.8,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '33'},\n", - " 'context': ''}},\n", - " '2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '3'},\n", - " 'size': [190, 40],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}}}},\n", - " 'dimension': 'bands'}},\n", - " '6': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '34'}, 'format': 'PNG'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-12-09T14:27:06.038Z',\n", - " 'updated': '2021-12-09T14:28:13.385Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '88da0bc9-737e-4584-acda-c131ed73eea4',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_Donyana_ASC_PNG',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'33': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '12'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2019-04-01T00:00:00Z',\n", - " '2019-04-20T23:59:59Z'],\n", - " 'id': 'SAR2Cube_SInCohMap_S1_L0_147_ASC_DONYANA',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '12': {'process_id': 'rename_labels',\n", - " 'arguments': {'data': {'from_node': '2'},\n", - " 'source': [],\n", - " 'dimension': 'bands',\n", - " 'target': ['VV']}},\n", - " '34': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 0.8,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '33'},\n", - " 'context': ''}},\n", - " '2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': '3'},\n", - " 'size': [190, 40],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '3': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'mxa7px21t': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " '4aapejbd3': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '3wcedunm4'},\n", - " 'y': {'from_node': 'uhjusxjsz'}}},\n", - " 'luxed5v2o': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '4aapejbd3'}}},\n", - " '3wcedunm4': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'ux15vkub6'}}},\n", - " 'uhjusxjsz': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'mxa7px21t'}}},\n", - " 'ux15vkub6': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}}}},\n", - " 'dimension': 'bands'}},\n", - " '6': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '34'}, 'format': 'PNG'}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-12-09T14:29:18.577Z',\n", - " 'updated': '2021-12-09T14:30:18.832Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '736a0ae3-8259-4f2c-ae1c-da0856a49a7c',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'S2_Donyana_FMASK',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2020-08-01T00:00:00Z',\n", - " '2020-08-08T00:00:00Z'],\n", - " 'id': 'SInCohMap_S2_L1C_T29SQB',\n", - " 'bands': ['B02', 'B03', 'B04', 'FMASK'],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['B02', 'B03', 'B04']}},\n", - " '4': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'FMASK'}},\n", - " '2': {'process_id': 'eq',\n", - " 'arguments': {'x': {'from_node': '1'}, 'y': 4}},\n", - " '3': {'process_id': 'eq',\n", - " 'arguments': {'x': {'from_node': '1'}, 'y': 2}},\n", - " '4': {'result': True,\n", - " 'process_id': 'or',\n", - " 'arguments': {'x': {'from_node': '2'}, 'y': {'from_node': '3'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '5': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '2'}, 'mask': {'from_node': '4'}}},\n", - " '6': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '10'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " '8': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 2500,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '6'},\n", - " 'context': ''}},\n", - " '9': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '8'}, 'format': 'PNG'}},\n", - " '10': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '5'},\n", - " 'size': [9, 9],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-12-09T15:06:56.240Z',\n", - " 'updated': '2021-12-09T15:07:31.992Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '09d6d4bc-bb8b-408a-bc25-160f5777d9c2',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 's2_testjob',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2020-08-01T00:00:00Z',\n", - " '2020-08-08T00:00:00Z'],\n", - " 'id': 'SInCohMap_S2_L1C_T29SQB',\n", - " 'bands': ['B02', 'B03', 'B04', 'FMASK'],\n", - " 'properties': {}}},\n", - " '2': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['B02', 'B03', 'B04']}},\n", - " '4': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'FMASK'}},\n", - " '2': {'process_id': 'eq',\n", - " 'arguments': {'x': {'from_node': '1'}, 'y': 4}},\n", - " '3': {'process_id': 'eq',\n", - " 'arguments': {'x': {'from_node': '1'}, 'y': 2}},\n", - " '4': {'result': True,\n", - " 'process_id': 'or',\n", - " 'arguments': {'x': {'from_node': '2'}, 'y': {'from_node': '3'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '5': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '2'}, 'mask': {'from_node': '4'}}},\n", - " '6': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '10'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'min',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " '8': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 2500,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '6'},\n", - " 'context': ''}},\n", - " '9': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '8'}, 'format': 'PNG'}},\n", - " '10': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '5'},\n", - " 'size': [9, 9],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-13T10:35:40.638Z',\n", - " 'updated': '2021-12-13T10:35:40.638Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '195277b0-4693-43e7-b9da-3eacecc8adf6',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_Donyana_masked',\n", - " 'description': None,\n", - " 'process': {'parameters': [],\n", - " 'process_graph': {'11': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '9'},\n", - " 'size': [19, 4],\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '44': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': '43'},\n", - " 'format': 'PNG',\n", - " 'options': {}}},\n", - " '1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T23:59:59Z',\n", - " '2018-01-15T23:59:59Z'],\n", - " 'spatial_extent': {'east': -5.8063037629650065,\n", - " 'south': 36.94762868470435,\n", - " 'north': 37.52219491350871,\n", - " 'west': -6.707213529620971},\n", - " 'id': 'SAR2Cube_SInCohMap_S1_L0_147_ASC_DONYANA',\n", - " 'bands': [],\n", - " 'properties': {}}},\n", - " '34': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '46'},\n", - " 'reducer': {'process_graph': {'u61739s9l': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'k024nph6i'}}},\n", - " '6dcr1ok9c': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': '1pk3qj0s2'},\n", - " 'y': {'from_node': 'u61739s9l'}}},\n", - " '1pk3qj0s2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': '3pppq5byr'}}},\n", - " '3pppq5byr': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'i_VV'}},\n", - " 'k024nph6i': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'label': 'q_VV'}},\n", - " 'fkgxt98l9': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': '6dcr1ok9c'}}}}},\n", - " 'dimension': 'bands'}},\n", - " '45': {'process_id': 'mask',\n", - " 'arguments': {'data': {'from_node': '41'}, 'mask': {'from_node': '28'}}},\n", - " '46': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 't'}},\n", - " '28': {'process_id': 'radar_mask',\n", - " 'arguments': {'data': {'from_node': '29'},\n", - " 'threshold': '0.8',\n", - " 'orbit': 'ASC'}},\n", - " '29': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': '11'},\n", - " 'context': '',\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'temporal'}},\n", - " '9': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': '1'},\n", - " 'wavelengths': [],\n", - " 'bands': ['LIA', 'DEM']}},\n", - " '41': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'data': {'from_node': '34'},\n", - " 'size': [19, 4],\n", - " 'reducer': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " '43': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'1': {'result': True,\n", - " 'process_id': 'linear_scale_range',\n", - " 'arguments': {'inputMax': 0.8,\n", - " 'inputMin': 0,\n", - " 'x': {'from_parameter': 'x'},\n", - " 'outputMax': 255}}}},\n", - " 'data': {'from_node': '45'}}}}},\n", - " 'status': 'created',\n", - " 'progress': 0.0,\n", - " 'created': '2021-12-17T12:15:36.192Z',\n", - " 'updated': '2021-12-17T12:16:10.129Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'},\n", - " {'id': '8bd3844b-2f2e-4aa6-96bc-2841e98315fc',\n", - " 'ownerPrincipal': 'michele.claus@eurac.edu',\n", - " 'title': 'SAR2Cube_South_Tyrol_1_year_average',\n", - " 'description': None,\n", - " 'process': {'process_graph': {'apply1': {'process_id': 'apply',\n", - " 'arguments': {'process': {'process_graph': {'multiply1': {'result': True,\n", - " 'process_id': 'multiply',\n", - " 'arguments': {'x': 10, 'y': {'from_node': 'log1'}}},\n", - " 'log1': {'process_id': 'log',\n", - " 'arguments': {'x': {'from_parameter': 'x'}, 'base': 10}}}},\n", - " 'data': {'from_node': 'aggregatespatialwindow1'}}},\n", - " 'loadcollection1': {'process_id': 'load_collection',\n", - " 'arguments': {'temporal_extent': ['2018-01-01T00:00:00Z',\n", - " '2019-01-01T00:00:00Z'],\n", - " 'spatial_extent': {'east': 11.697916,\n", - " 'south': 46.253694,\n", - " 'north': 46.65107,\n", - " 'west': 11.041464},\n", - " 'id': 'SAR2Cube_L0_117_ASC_ST_2016_2020_IFG_LIA_DEM'}},\n", - " 'reducedimension2': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'mergecubes1'},\n", - " 'reducer': {'process_graph': {'mean3': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}},\n", - " 'dimension': 'DATE'}},\n", - " 'aggregatespatialwindow2': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'filterbands1'},\n", - " 'size': [5, 1],\n", - " 'reducer': {'process_graph': {'mean2': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'saveresult1': {'result': True,\n", - " 'process_id': 'save_result',\n", - " 'arguments': {'data': {'from_node': 'geocode1'},\n", - " 'format': 'GTiff',\n", - " 'options': {}}},\n", - " 'reducedimension1': {'process_id': 'reduce_dimension',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'reducer': {'process_graph': {'arrayelement2': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 5}},\n", - " 'arrayelement1': {'process_id': 'array_element',\n", - " 'arguments': {'data': {'from_parameter': 'data'}, 'index': 3}},\n", - " 'power3': {'result': True,\n", - " 'process_id': 'power',\n", - " 'arguments': {'p': 0.5, 'base': {'from_node': 'add1'}}},\n", - " 'add1': {'process_id': 'add',\n", - " 'arguments': {'x': {'from_node': 'power1'},\n", - " 'y': {'from_node': 'power2'}}},\n", - " 'power1': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement1'}}},\n", - " 'power2': {'process_id': 'power',\n", - " 'arguments': {'p': 2, 'base': {'from_node': 'arrayelement2'}}}}},\n", - " 'dimension': 'bands'}},\n", - " 'aggregatespatialwindow1': {'process_id': 'aggregate_spatial_window',\n", - " 'arguments': {'boundary': 'trim',\n", - " 'data': {'from_node': 'adddimension1'},\n", - " 'size': [5, 1],\n", - " 'reducer': {'process_graph': {'mean1': {'result': True,\n", - " 'process_id': 'mean',\n", - " 'arguments': {'data': {'from_parameter': 'data'}}}}}}},\n", - " 'mergecubes1': {'process_id': 'merge_cubes',\n", - " 'arguments': {'cube2': {'from_node': 'aggregatespatialwindow2'},\n", - " 'cube1': {'from_node': 'apply1'}}},\n", - " 'geocode1': {'process_id': 'geocode',\n", - " 'arguments': {'data': {'from_node': 'reducedimension2'},\n", - " 'crs': 32632,\n", - " 'resolution': 20}},\n", - " 'adddimension1': {'process_id': 'add_dimension',\n", - " 'arguments': {'data': {'from_node': 'reducedimension1'},\n", - " 'name': 'bands',\n", - " 'label': 'VV'}},\n", - " 'filterbands1': {'process_id': 'filter_bands',\n", - " 'arguments': {'data': {'from_node': 'loadcollection1'},\n", - " 'bands': ['grid_lon', 'grid_lat']}}}},\n", - " 'status': 'finished',\n", - " 'progress': 100.0,\n", - " 'created': '2021-12-17T14:48:24.470Z',\n", - " 'updated': '2021-12-17T14:57:47.997Z',\n", - " 'plan': 'free',\n", - " 'costs': None,\n", - " 'budget': None,\n", - " 'engine': 'ODC_DASK'}]" - ] - }, - "execution_count": 148, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "conn.list_jobs()" ]