[skip ci] Updates

Author: ci-build

_downloads/029881e221949484b26146d507a804ad/example_depth.zip

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime, timedelta, date\nfrom opendrift.readers import reader_netCDF_CF_generic\nfrom opendrift.models.openoil import OceanDrift\nfrom opendrift.readers import reader_timeseries\nfrom opendrift.readers.operators import ops\nimport opendrift.models.physics_methods as phy\nimport xarray as xr\nimport numpy as np\nimport pyproj\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\n# Preparation of figsize, dpi, fontsize and font style\n# Could also be done using mpl style sheet\n# https://matplotlib.org/stable/users/explain/customizing.html\nmpl.rcParams['figure.figsize'] = (5, 5)\nmpl.rcParams['savefig.dpi'] = 300\nmpl.rcParams['font.family'] = \"sans-serif\"\nmpl.rcParams['font.sans-serif'] = \"DejaVu Sans\"\nmpl.rcParams['font.size'] = 22\nmpl.rcParams['mathtext.fontset'] = \"dejavusans\""
+        "from datetime import datetime, timedelta, date\nfrom opendrift.readers import reader_netCDF_CF_generic\nfrom opendrift.models.openoil import OceanDrift\nfrom opendrift.readers import reader_timeseries\nfrom opendrift.readers.operators import ops\nimport opendrift.models.physics_methods as phy\nimport xarray as xr\nimport trajan as _\nimport numpy as np\nimport pyproj\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\n# Preparation of figsize, dpi, fontsize and font style\n# Could also be done using mpl style sheet\n# https://matplotlib.org/stable/users/explain/customizing.html\nmpl.rcParams['figure.figsize'] = (5, 5)\nmpl.rcParams['savefig.dpi'] = 300\nmpl.rcParams['font.family'] = \"sans-serif\"\nmpl.rcParams['font.sans-serif'] = \"DejaVu Sans\"\nmpl.rcParams['font.size'] = 22\nmpl.rcParams['mathtext.fontset'] = \"dejavusans\""
       ]
     },
     {
@@ -148,7 +148,25 @@
       },
       "outputs": [],
       "source": [
-        "radius = 100\nnumber = 100\n  \no = OceanDrift(loglevel=20)\no.add_reader([reader_norkyst])\no.seed_elements(lon=lon, lat=lat, radius=radius, number=number, time=time_start, wind_drift_factor=wind_drift_factor)\no.run(end_time=time_end, time_step=time_step, time_step_output=time_step_output)\n\no2 = OceanDrift(loglevel=20)\no2.set_config('drift:max_speed', 10) #Necessary for the moment because of a bug to be solved\no2.add_reader([c, reader_norkyst])\no2.seed_elements(lon=lon, lat=lat, radius=radius, number=number, time=time_start, wind_drift_factor=wind_drift_factor)\no2.run(end_time=time_end, time_step=time_step, time_step_output=time_step_output)\n\no.plot(fast = False, compare=o2, background=['x_sea_water_velocity', 'y_sea_water_velocity'], legend=['Norkyst only', 'Gaussian measurement'], buffer = .023, markersize = 70, linewidth = 1, title = \"\", xlocs = mpl.ticker.MaxNLocator(5), ylocs = mpl.ticker.MaxNLocator(5), clabel = r\"Wind speed $\\mathrm{(m.s^{-1})}$\", cpad = 0.08, text=text)\n\nskillscores = phy.skillscore_liu_weissberg(o.result.lon, o.result.lat, o2.result.lon, o2.result.lat)\n\nplt.figure()\nplt.hist(skillscores, bins = 100, range = (0, 1), facecolor = 'none', edgecolor = 'C0')\nplt.xlabel(\"Skillscore\")\nplt.ylabel(\"Histogram (percentages)\")\nplt.show()\n\nplt.figure()\nplt.hist(skillscores, bins = 100, range = (0.9, 0.95), facecolor = 'none', edgecolor = 'C0')\nplt.xlabel(\"Skillscore\")\nplt.ylabel(\"Histogram (percentages)\")\nplt.show()"
+        "radius = 100\nnumber = 100\n  \no = OceanDrift(loglevel=20)\no.add_reader([reader_norkyst])\no.seed_elements(lon=lon, lat=lat, radius=radius, number=number, time=time_start, wind_drift_factor=wind_drift_factor)\no.run(end_time=time_end, time_step=time_step, time_step_output=time_step_output)\n\no2 = OceanDrift(loglevel=20)\no2.set_config('drift:max_speed', 10) #Necessary for the moment because of a bug to be solved\no2.add_reader([c, reader_norkyst])\no2.seed_elements(lon=lon, lat=lat, radius=radius, number=number, time=time_start, wind_drift_factor=wind_drift_factor)\no2.run(end_time=time_end, time_step=time_step, time_step_output=time_step_output)\n\no.plot(fast = False, compare=o2, background=['x_sea_water_velocity', 'y_sea_water_velocity'], legend=['Norkyst only', 'Gaussian measurement'], buffer = .023, markersize = 70, linewidth = 1, title = \"\", xlocs = mpl.ticker.MaxNLocator(5), ylocs = mpl.ticker.MaxNLocator(5), clabel = r\"Wind speed $\\mathrm{(m.s^{-1})}$\", cpad = 0.08, text=text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calculating skillscores with TrajAn\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "skillscores = o.result.traj.skill(o2.result, method='liu-weissberg', tolerance_threshold=1)\n\nplt.figure()\nplt.hist(skillscores, bins = 100, range = (0, 1), facecolor = 'none', edgecolor = 'C0')\nplt.xlabel(\"Skillscore\")\nplt.ylabel(\"Histogram (percentages)\")\nplt.show()\n\nplt.figure()\nplt.hist(skillscores, bins = 100, range = (0.9, 0.95), facecolor = 'none', edgecolor = 'C0')\nplt.xlabel(\"Skillscore\")\nplt.ylabel(\"Histogram (percentages)\")\nplt.show()"
       ]
     }
   ],

_downloads/033af0d93f9b09e836d807ac3b9561f7/example_relative.zip

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "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"
+        "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"
       ]
     },
     {

_downloads/053b8b00e60c7fece7f7aff0a68521f8/example_oil_verticalmixing.zip

@@ -11,7 +11,7 @@
 import matplotlib.pyplot as plt
 import cmocean
 from opendrift.models.oceandrift import OceanDrift
-from opendrift.models.physics_methods import wind_drift_factor_from_trajectory, distance_between_trajectories, skillscore_liu_weissberg
+from opendrift.models.physics_methods import wind_drift_factor_from_trajectory
 
 #%%
 # A very simple drift model is: current + wind_drift_factor*wind

_downloads/0f924af619246b5eb8bbb3b1416da11f/example_checkerboard.zip

@@ -11,6 +11,7 @@
 from opendrift.readers.operators import ops
 import opendrift.models.physics_methods as phy
 import xarray as xr
+import trajan as _
 import numpy as np
 import pyproj
 import matplotlib.pyplot as plt
@@ -161,7 +162,9 @@
 
 o.plot(fast = False, compare=o2, background=['x_sea_water_velocity', 'y_sea_water_velocity'], legend=['Norkyst only', 'Gaussian measurement'], buffer = .023, markersize = 70, linewidth = 1, title = "", xlocs = mpl.ticker.MaxNLocator(5), ylocs = mpl.ticker.MaxNLocator(5), clabel = r"Wind speed $\mathrm{(m.s^{-1})}$", cpad = 0.08, text=text)
 
-skillscores = phy.skillscore_liu_weissberg(o.result.lon, o.result.lat, o2.result.lon, o2.result.lat)
+#%%
+# Calculating skillscores with TrajAn
+skillscores = o.result.traj.skill(o2.result, method='liu-weissberg', tolerance_threshold=1)
 
 plt.figure()
 plt.hist(skillscores, bins = 100, range = (0, 1), facecolor = 'none', edgecolor = 'C0')

_downloads/12323c17693ac6b1a46fd45c87dba9f4/example_river_runoff.zip

@@ -118,7 +118,7 @@ <h1>Source code for opendrift.models.oceandrift</h1><div class="highlight"><pre>
 <span class="kn">import</span><span class="w"> </span><span class="nn">logging</span><span class="p">;</span> <span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">opendrift.models.basemodel</span><span class="w"> </span><span class="kn">import</span> <span class="n">OpenDriftSimulation</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">opendrift.elements</span><span class="w"> </span><span class="kn">import</span> <span class="n">LagrangianArray</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">opendrift.models.physics_methods</span><span class="w"> </span><span class="kn">import</span> <span class="n">verticaldiffusivity_Large1994</span><span class="p">,</span> <span class="n">verticaldiffusivity_Sundby1983</span><span class="p">,</span> <span class="n">gls_tke</span><span class="p">,</span> <span class="n">skillscore_liu_weissberg</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">opendrift.models.physics_methods</span><span class="w"> </span><span class="kn">import</span> <span class="n">verticaldiffusivity_Large1994</span><span class="p">,</span> <span class="n">verticaldiffusivity_Sundby1983</span><span class="p">,</span> <span class="n">gls_tke</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">opendrift.config</span><span class="w"> </span><span class="kn">import</span> <span class="n">CONFIG_LEVEL_ESSENTIAL</span><span class="p">,</span> <span class="n">CONFIG_LEVEL_BASIC</span><span class="p">,</span> <span class="n">CONFIG_LEVEL_ADVANCED</span>
 
 <span class="c1"># Defining the oil element properties</span>