, message=None, created=datetime.datetime(2025, 11, 10, 15, 0, 17, 657120, tzinfo=TzInfo(UTC)), started=datetime.datetime(2025, 11, 10, 15, 0, 17, 657646, tzinfo=TzInfo(UTC)), finished=datetime.datetime(2025, 11, 10, 15, 0, 19, 536983, tzinfo=TzInfo(UTC)), updated=None, progress=None, links=None, traceback=None), name='JobInfoPanel00371')"
]
},
"execution_count": 9,
"metadata": {
"application/vnd.holoviews_exec.v0+json": {
- "id": "c31c7bb9-dbf3-4fd3-886a-6db516b3223c"
+ "id": "7e8c262c-a2ef-4b0d-b840-abc9dbf5c519"
}
},
"output_type": "execute_result"
}
],
"source": [
- "client.show_job(\"job_0\")"
+ "client.show_job(\"job_3\")"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"id": "133234e0-e393-4384-972c-de5cbaa3d91c",
"metadata": {},
"outputs": [
@@ -1297,7 +1297,7 @@
"data": {
"application/json": {
"return_value": {
- "href": "file:///C:/Users/norma/Projects/s2gos-controller/test.zarr",
+ "href": "file:///C:/Users/Norman/Projects/s2gos-controller/notebooks/test.zarr",
"hreflang": null,
"rel": null,
"title": null,
@@ -1305,14 +1305,14 @@
}
},
"text/plain": [
- "{'return_value': {'href': 'file:///C:/Users/norma/Projects/s2gos-controller/test.zarr',\n",
+ "{'return_value': {'href': 'file:///C:/Users/Norman/Projects/s2gos-controller/notebooks/test.zarr',\n",
" 'rel': None,\n",
" 'type': 'application/zarr',\n",
" 'hreflang': None,\n",
" 'title': None}}"
]
},
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {
"application/json": {
"root": "Results:"
@@ -1327,7 +1327,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"id": "cea797e0-9977-4727-98df-b4d6a3cccf65",
"metadata": {},
"outputs": [
@@ -1777,25 +1777,29 @@
" filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
" stroke-width: 0.8px;\n",
"}\n",
- "<xarray.Dataset> Size: 388kB\n",
- "Dimensions: (time: 16, lat: 24, lon: 42)\n",
+ "<xarray.Dataset> Size: 538kB\n",
+ "Dimensions: (time: 16, lat: 25, lon: 56)\n",
"Coordinates:\n",
- " * lon (lon) float64 336B -14.26 -13.76 -13.26 ... 5.327 5.829 6.331\n",
- " * lat (lat) float64 192B 34.42 34.92 35.42 35.92 ... 44.95 45.45 45.95\n",
+ " * lon (lon) float64 448B -4.206 -3.704 -3.202 ... 22.41 22.92 23.42\n",
+ " * lat (lat) float64 200B 44.55 45.06 45.56 46.07 ... 55.66 56.16 56.67\n",
" * time (time) datetime64[ns] 128B 2025-01-01 2025-01-02 ... 2025-01-16\n",
"Data variables:\n",
- " a (time, lat, lon) float64 129kB ...\n",
- " c (time, lat, lon) float64 129kB ...\n",
- " b (time, lat, lon) float64 129kB ... Dimensions:
Coordinates: (3)
lon
(lon)
float64
-14.26 -13.76 ... 5.829 6.331
array([-14.2625 , -13.760213, -13.257927, -12.75564 , -12.253354, -11.751067,\n",
- " -11.24878 , -10.746494, -10.244207, -9.741921, -9.239634, -8.737348,\n",
- " -8.235061, -7.732774, -7.230488, -6.728201, -6.225915, -5.723628,\n",
- " -5.221341, -4.719055, -4.216768, -3.714482, -3.212195, -2.709909,\n",
- " -2.207622, -1.705335, -1.203049, -0.700762, -0.198476, 0.303811,\n",
- " 0.806098, 1.308384, 1.810671, 2.312957, 2.815244, 3.31753 ,\n",
- " 3.819817, 4.322104, 4.82439 , 5.326677, 5.828963, 6.33125 ]) lat
(lat)
float64
34.42 34.92 35.42 ... 45.45 45.95
array([34.415813, 34.917229, 35.418645, 35.920061, 36.421478, 36.922894,\n",
- " 37.42431 , 37.925726, 38.427142, 38.928558, 39.429974, 39.93139 ,\n",
- " 40.432807, 40.934223, 41.435639, 41.937055, 42.438471, 42.939887,\n",
- " 43.441303, 43.942719, 44.444136, 44.945552, 45.446968, 45.948384]) time
(time)
datetime64[ns]
2025-01-01 ... 2025-01-16
array(['2025-01-01T00:00:00.000000000', '2025-01-02T00:00:00.000000000',\n",
+ " a (time, lat, lon) float64 179kB ...\n",
+ " c (time, lat, lon) float64 179kB ...\n",
+ " b (time, lat, lon) float64 179kB ... Dimensions:
Coordinates: (3)
lon
(lon)
float64
-4.206 -3.704 ... 22.92 23.42
array([-4.20605 , -3.703777, -3.201505, -2.699232, -2.196959, -1.694686,\n",
+ " -1.192414, -0.690141, -0.187868, 0.314405, 0.816677, 1.31895 ,\n",
+ " 1.821223, 2.323495, 2.825768, 3.328041, 3.830314, 4.332586,\n",
+ " 4.834859, 5.337132, 5.839405, 6.341677, 6.84395 , 7.346223,\n",
+ " 7.848495, 8.350768, 8.853041, 9.355314, 9.857586, 10.359859,\n",
+ " 10.862132, 11.364405, 11.866677, 12.36895 , 12.871223, 13.373495,\n",
+ " 13.875768, 14.378041, 14.880314, 15.382586, 15.884859, 16.387132,\n",
+ " 16.889405, 17.391677, 17.89395 , 18.396223, 18.898495, 19.400768,\n",
+ " 19.903041, 20.405314, 20.907586, 21.409859, 21.912132, 22.414405,\n",
+ " 22.916677, 23.41895 ]) lat
(lat)
float64
44.55 45.06 45.56 ... 56.16 56.67
array([44.551055, 45.055828, 45.560601, 46.065373, 46.570146, 47.074919,\n",
+ " 47.579692, 48.084465, 48.589238, 49.09401 , 49.598783, 50.103556,\n",
+ " 50.608329, 51.113102, 51.617875, 52.122647, 52.62742 , 53.132193,\n",
+ " 53.636966, 54.141739, 54.646512, 55.151284, 55.656057, 56.16083 ,\n",
+ " 56.665603]) time
(time)
datetime64[ns]
2025-01-01 ... 2025-01-16
array(['2025-01-01T00:00:00.000000000', '2025-01-02T00:00:00.000000000',\n",
" '2025-01-03T00:00:00.000000000', '2025-01-04T00:00:00.000000000',\n",
" '2025-01-05T00:00:00.000000000', '2025-01-06T00:00:00.000000000',\n",
" '2025-01-07T00:00:00.000000000', '2025-01-08T00:00:00.000000000',\n",
@@ -1803,48 +1807,54 @@
" '2025-01-11T00:00:00.000000000', '2025-01-12T00:00:00.000000000',\n",
" '2025-01-13T00:00:00.000000000', '2025-01-14T00:00:00.000000000',\n",
" '2025-01-15T00:00:00.000000000', '2025-01-16T00:00:00.000000000'],\n",
- " dtype='datetime64[ns]') Data variables: (3)
Indexes: (3)
PandasIndex
PandasIndex(Index([ -14.2625, -13.760213414634146, -13.257926829268293,\n",
- " -12.755640243902437, -12.253353658536584, -11.75106707317073,\n",
- " -11.248780487804877, -10.746493902439024, -10.244207317073169,\n",
- " -9.741920731707317, -9.239634146341462, -8.737347560975609,\n",
- " -8.235060975609755, -7.732774390243901, -7.230487804878048,\n",
- " -6.728201219512194, -6.22591463414634, -5.723628048780487,\n",
- " -5.221341463414634, -4.719054878048778, -4.216768292682925,\n",
- " -3.7144817073170717, -3.2121951219512184, -2.709908536585365,\n",
- " -2.2076219512195117, -1.7053353658536565, -1.2030487804878032,\n",
- " -0.7007621951219498, -0.19847560975609646, 0.3038109756097569,\n",
- " 0.806097560975612, 1.3083841463414654, 1.8106707317073187,\n",
- " 2.312957317073174, 2.8152439024390254, 3.3175304878048806,\n",
- " 3.819817073170732, 4.322103658536587, 4.824390243902442,\n",
- " 5.326676829268294, 5.828963414634149, 6.33125],\n",
- " dtype='float64', name='lon')) PandasIndex
PandasIndex(Index([ 34.415813, 34.917229130434784, 35.41864526086957,\n",
- " 35.920061391304344, 36.42147752173913, 36.92289365217391,\n",
- " 37.424309782608695, 37.92572591304348, 38.42714204347826,\n",
- " 38.928558173913046, 39.42997430434782, 39.93139043478261,\n",
- " 40.43280656521739, 40.934222695652174, 41.43563882608696,\n",
- " 41.937054956521735, 42.43847108695652, 42.9398872173913,\n",
- " 43.441303347826086, 43.94271947826087, 44.444135608695646,\n",
- " 44.94555173913044, 45.44696786956521, 45.948384],\n",
- " dtype='float64', name='lat')) PandasIndex
PandasIndex(DatetimeIndex(['2025-01-01', '2025-01-02', '2025-01-03', '2025-01-04',\n",
+ " dtype='datetime64[ns]') Data variables: (3)
Indexes: (3)
PandasIndex
PandasIndex(Index([ -4.20605, -3.703777272727273, -3.2015045454545454,\n",
+ " -2.6992318181818185, -2.196959090909091, -1.6946863636363636,\n",
+ " -1.1924136363636366, -0.6901409090909092, -0.18786818181818177,\n",
+ " 0.3144045454545452, 0.8166772727272731, 1.31895,\n",
+ " 1.821222727272727, 2.323495454545455, 2.825768181818182,\n",
+ " 3.3280409090909098, 3.8303136363636368, 4.332586363636364,\n",
+ " 4.834859090909091, 5.3371318181818195, 5.8394045454545465,\n",
+ " 6.341677272727273, 6.84395, 7.346222727272727,\n",
+ " 7.848495454545454, 8.350768181818182, 8.853040909090911,\n",
+ " 9.355313636363636, 9.857586363636365, 10.35985909090909,\n",
+ " 10.862131818181819, 11.364404545454548, 11.866677272727273,\n",
+ " 12.368950000000002, 12.871222727272727, 13.373495454545456,\n",
+ " 13.87576818181818, 14.37804090909091, 14.880313636363638,\n",
+ " 15.382586363636364, 15.884859090909092, 16.387131818181818,\n",
+ " 16.889404545454546, 17.391677272727275, 17.89395,\n",
+ " 18.39622272727273, 18.898495454545454, 19.400768181818183,\n",
+ " 19.903040909090908, 20.405313636363637, 20.907586363636366,\n",
+ " 21.40985909090909, 21.91213181818182, 22.414404545454545,\n",
+ " 22.916677272727274, 23.41895],\n",
+ " dtype='float64', name='lon')) PandasIndex
PandasIndex(Index([ 44.551055, 45.05582783333333, 45.560600666666666,\n",
+ " 46.0653735, 46.570146333333334, 47.07491916666667,\n",
+ " 47.579691999999994, 48.08446483333333, 48.58923766666666,\n",
+ " 49.094010499999996, 49.59878333333333, 50.103556166666664,\n",
+ " 50.608329, 51.11310183333333, 51.617874666666665,\n",
+ " 52.1226475, 52.62742033333333, 53.13219316666667,\n",
+ " 53.636966, 54.141738833333335, 54.64651166666667,\n",
+ " 55.151284499999996, 55.65605733333333, 56.16083016666666,\n",
+ " 56.665603],\n",
+ " dtype='float64', name='lat')) PandasIndex
PandasIndex(DatetimeIndex(['2025-01-01', '2025-01-02', '2025-01-03', '2025-01-04',\n",
" '2025-01-05', '2025-01-06', '2025-01-07', '2025-01-08',\n",
" '2025-01-09', '2025-01-10', '2025-01-11', '2025-01-12',\n",
" '2025-01-13', '2025-01-14', '2025-01-15', '2025-01-16'],\n",
- " dtype='datetime64[ns]', name='time', freq=None)) Attributes: (0)
"
+ " dtype='datetime64[ns]', name='time', freq=None)) Attributes: (0)