[skip ci] Updates

Author: ci-build

_downloads/029881e221949484b26146d507a804ad/example_depth.zip

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime, timedelta\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cmocean\nfrom opendrift.models.oceandrift import OceanDrift\nfrom opendrift.models.physics_methods import wind_drift_factor_from_trajectory, distance_between_trajectories, skillscore_liu_weissberg"
+        "import trajan as _\nfrom datetime import datetime, timedelta\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cmocean\nfrom opendrift.models.oceandrift import OceanDrift\nfrom opendrift.models.physics_methods import wind_drift_factor_from_trajectory, distance_between_trajectories, skillscore_liu_weissberg"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "drifter_lons = ot.result.lon.squeeze()\ndrifter_lats = ot.result.lat.squeeze()\ndrifter_times = pd.to_datetime(ot.result.time).to_pydatetime()\ndrifter={'lon': drifter_lons, 'lat': drifter_lats,\n        'time': drifter_times, 'linewidth': 2, 'color': 'b', 'label': 'Synthetic drifter'}\n\no = OceanDrift(loglevel=50)\no.add_readers_from_list([o.test_data_folder() +\n    '16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc',\n    o.test_data_folder() + '16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc'], lazy=False)\nt = o.env.get_variables_along_trajectory(variables=['x_sea_water_velocity', 'y_sea_water_velocity', 'x_wind', 'y_wind'],\n        lons=drifter_lons, lats=drifter_lats, times=drifter_times)\n\nwind_drift_factor, azimuth = wind_drift_factor_from_trajectory(t)\n\no.seed_elements(lon=4, lat=60, number=1, time=ot.env.readers[list(ot.env.readers)[0]].start_time,\n                wind_drift_factor=0.033)"
+        "drifter_lons = ot.result.lon.squeeze()\ndrifter_lats = ot.result.lat.squeeze()\ndrifter_times = pd.to_datetime(ot.result.time).to_pydatetime()\ndrifter={'lon': drifter_lons, 'lat': drifter_lats,\n        'time': drifter_times, 'linewidth': 2, 'color': 'b', 'label': 'Synthetic drifter'}\n\no = OceanDrift(loglevel=50)\no.add_readers_from_list([o.test_data_folder() +\n    '16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc',\n    o.test_data_folder() + '16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc'], lazy=False)\nt = o.env.get_variables_along_trajectory(variables=['x_sea_water_velocity', 'y_sea_water_velocity', 'x_wind', 'y_wind'],\n        lons=drifter_lons, lats=drifter_lats, times=drifter_times)\n\nwind_drift_factor, azimuth = wind_drift_factor_from_trajectory(t)\n\no.seed_elements(lon=4, lat=60, number=1, time=ot.start_time,\n                wind_drift_factor=0.033)"
       ]
     },
     {
@@ -195,7 +195,25 @@
       },
       "outputs": [],
       "source": [
-        "wdf = np.linspace(0.0, 0.05, 100)\no.seed_elements(lon=4, lat=60, time=ot.env.readers[list(ot.env.readers)[0]].start_time,\n                wind_drift_factor=wdf, number=len(wdf))\no.run(duration=timedelta(hours=12), time_step=600)\no.plot(linecolor='wind_drift_factor', drifter=drifter)"
+        "wdf = np.linspace(0.0, 0.05, 100)\no.seed_elements(lon=4, lat=60, time=ot.start_time,\n                wind_drift_factor=wdf, number=len(wdf))\no.run(duration=timedelta(hours=12), time_step=600)\no.plot(linecolor='wind_drift_factor', drifter=drifter)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Using TrajAn to calculate skillscore\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "skillscore = o.result.traj.skill(ot.result, tolerance_threshold=1)"
       ]
     },
     {
@@ -213,7 +231,7 @@
       },
       "outputs": [],
       "source": [
-        "skillscore = o.skillscore_trajectory(drifter_lons, drifter_lats, drifter_times, tolerance_threshold=1)\nind = np.argmax(skillscore)\nplt.plot(wdf, skillscore)\nplt.xlabel('Wind drift factor  [fraction]')\nplt.ylabel('Liu-Weissberg skillscore')\nplt.title('Maximum skillscore %.3f for wdf=%.3f' % (skillscore[ind], wdf[ind]))\nplt.show()"
+        "ind = skillscore.argmax()\nplt.plot(wdf, skillscore)\nplt.xlabel('Wind drift factor  [fraction]')\nplt.ylabel('Liu-Weissberg skillscore')\nplt.title('Maximum skillscore %.3f for wdf=%.3f' % (skillscore[ind], wdf[ind]))\nplt.show()"
       ]
     },
     {

_downloads/033af0d93f9b09e836d807ac3b9561f7/example_relative.zip

@@ -4,6 +4,7 @@
 ===========================================
 """
 
+import trajan as _
 from datetime import datetime, timedelta
 import pandas as pd
 import numpy as np
@@ -55,7 +56,7 @@
 
 wind_drift_factor, azimuth = wind_drift_factor_from_trajectory(t)
 
-o.seed_elements(lon=4, lat=60, number=1, time=ot.env.readers[list(ot.env.readers)[0]].start_time,
+o.seed_elements(lon=4, lat=60, number=1, time=ot.start_time,
                 wind_drift_factor=0.033)
 
 #%% 
@@ -114,15 +115,18 @@
 #%%
 # Calulating trajectories for 100 different wind_drift_factors between 0 and 0.05
 wdf = np.linspace(0.0, 0.05, 100)
-o.seed_elements(lon=4, lat=60, time=ot.env.readers[list(ot.env.readers)[0]].start_time,
+o.seed_elements(lon=4, lat=60, time=ot.start_time,
                 wind_drift_factor=wdf, number=len(wdf))
 o.run(duration=timedelta(hours=12), time_step=600)
 o.plot(linecolor='wind_drift_factor', drifter=drifter)
 
+#%%
+# Using TrajAn to calculate skillscore
+skillscore = o.result.traj.skill(ot.result, tolerance_threshold=1)
+
 #%%
 # Plotting and finding the wind_drift_factor which maximises the skillscore
-skillscore = o.skillscore_trajectory(drifter_lons, drifter_lats, drifter_times, tolerance_threshold=1)
-ind = np.argmax(skillscore)
+ind = skillscore.argmax()
 plt.plot(wdf, skillscore)
 plt.xlabel('Wind drift factor  [fraction]')
 plt.ylabel('Liu-Weissberg skillscore')