Tidal power performance (#236)

Tidal power performance capabilities specified in IEC/TS 6200-200.

Also includes bugfixes, docstring clarifications, and useability edits for the Tidal module in general that I came across from a users standpoint. Changelog as follows:

Bugfixes:
- Standardized function inputs to accept both numpy.ndarrays and pandas.Series (some functions already have this implemented).
- Fixed assertions so that if a numpy array is supplied, the following assertions that rely on pandas don't fail (some functions already have this implemented).
- Added a (temporary) fix so that assertions can handle nans
- Fixed river.performance capture area calculation the multiplies by instead of dividing by 4.
- Fixed a misnamed variable in tidal.performance.principal_flow_directions.
- Corrected the "Returns" statement in the tidal.performance.principal_flow_directions docstring and added a "Notes" section.

API/useability
- Added 'ax' argument passthrough to tidal.graphics.plot_rose and tidal.graphics.plot_joint_prob_distribution to enable more complex matplotlib capabilities outside of the function.
- Added check and updated docstring in tidal.performance.principal_flow_directions to make sure directions are between 0 and 360 degrees, which the function requires.

Author: jmcvey3

.gitignore

@@ -9,6 +9,7 @@ $ cat .gitignore
 *.swp
 *.tws
 *~
+*.png
 
 # Directories
 **/__pycache__/

examples/adcp_example.ipynb

@@ -20,7 +20,7 @@ def circular(diameter):
     assert isinstance(diameter, (int,float)), 'diameter must be of type int or float'
 
     equivalent_diameter = diameter
-    projected_capture_area = 4.*np.pi*(equivalent_diameter**2.) 
+    projected_capture_area = (1/4)*np.pi*(equivalent_diameter**2) 
 
     return equivalent_diameter, projected_capture_area
 
@@ -44,7 +44,7 @@ def ducted(duct_diameter):
     assert isinstance(duct_diameter, (int,float)), 'duct_diameter must be of type int or float'
 
     equivalent_diameter = duct_diameter
-    projected_capture_area = 4.*np.pi*(equivalent_diameter**2.) 
+    projected_capture_area = (1/4)*np.pi*(equivalent_diameter**2) 
 
     return equivalent_diameter, projected_capture_area
 

examples/adv_example.ipynb

@@ -33,12 +33,12 @@ def tearDownClass(self):
     def test_circular(self):
         eq, ca = river.performance.circular(self.diameter) 
         self.assertEqual(eq, self.diameter)
-        self.assertEqual(ca, 4*np.pi*self.diameter**2.)
+        self.assertEqual(ca, 0.25*np.pi*self.diameter**2.)
 
     def test_ducted(self):
         eq, ca =river.performance.ducted(self.diameter) 
         self.assertEqual(eq, self.diameter)
-        self.assertEqual(ca, 4*np.pi*self.diameter**2.)
+        self.assertEqual(ca, 0.25*np.pi*self.diameter**2.)
 
     def test_rectangular(self):
         eq, ca = river.performance.rectangular(self.height, self.width)

examples/data/tidal/adcp.principal.a1.20200815.nc

@@ -0,0 +1,117 @@
+import unittest
+from os.path import abspath, dirname, join, isfile, normpath, relpath
+import os
+import numpy as np
+from numpy.testing import assert_allclose
+
+from mhkit.tidal import resource, graphics, performance
+from mhkit.dolfyn import load
+
+testdir = dirname(abspath(__file__))
+plotdir = join(testdir, 'plots')
+isdir = os.path.isdir(plotdir)
+if not isdir: os.mkdir(plotdir)
+datadir = normpath(join(testdir,relpath('../../../examples/data/tidal')))
+
+
+class TestResource(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(self):
+        filename = join(datadir, 'adcp.principal.a1.20200815.nc') 
+        self.ds = load(filename)
+        # Emulate power data
+        self.power = abs(self.ds['vel'][0,10]**3 * 1e5)
+
+    @classmethod
+    def tearDownClass(self):
+        pass
+
+    def test_power_curve(self,):
+        df93_circ = performance.power_curve(
+            power=self.power,
+            velocity=self.ds['vel'].sel(dir='streamwise'),
+            hub_height=4.2,
+            doppler_cell_size=0.5, 
+            sampling_frequency=1, 
+            window_avg_time=600,
+            turbine_profile='circular',
+            diameter=3,
+            height=None,
+            width=None)
+        test_circ = np.array([1.26250990e+00, 
+                              1.09230978e+00, 
+                              1.89122103e+05, 
+                              1.03223668e+04,
+                              2.04261423e+05, 
+                              1.72095731e+05])
+
+        df93_rect = performance.power_curve(
+            power=self.power,
+            velocity=self.ds['vel'].sel(dir='streamwise'),
+            hub_height=4.2,
+            doppler_cell_size=0.5, 
+            sampling_frequency=1, 
+            window_avg_time=600,
+            turbine_profile='rectangular',
+            diameter=None,
+            height=1,
+            width=3)
+        test_rect = np.array([1.15032239e+00, 
+                              3.75747621e-01, 
+                              1.73098627e+05, 
+                              3.04090212e+04,
+                              2.09073742e+05, 
+                              1.27430552e+05])
+        
+        assert_allclose(df93_circ.values[-2], test_circ, atol=1e-5)
+        assert_allclose(df93_rect.values[-3], test_rect, atol=1e-5)
+
+    def test_velocity_profiles(self):
+        df94 = performance.velocity_profiles(
+            velocity=self.ds['vel'].sel(dir='streamwise'), 
+            hub_height=4.2,
+            water_depth=10,
+            sampling_frequency=1, 
+            window_avg_time=600,
+            function='mean')
+        df95a = performance.velocity_profiles(
+            velocity=self.ds['vel'].sel(dir='streamwise'), 
+            hub_height=4.2,
+            water_depth=10,
+            sampling_frequency=1,
+            window_avg_time=600,
+            function='rms')
+        df95b = performance.velocity_profiles(
+            velocity=self.ds['vel'].sel(dir='streamwise'), 
+            hub_height=4.2, 
+            water_depth=10,
+            sampling_frequency=1, 
+            window_avg_time=600,
+            function='std')
+        
+        test_df94 = np.array([0.32782955, 0.69326691, 1.00948623])
+        test_df95a = np.array([0.3329345 , 0.69936798, 1.01762123])
+        test_df95b = np.array([0.05635571, 0.08671777, 0.12735139])
+
+        assert_allclose(df94.values[1], test_df94, atol=1e-5)
+        assert_allclose(df95a.values[1], test_df95a, atol=1e-5)
+        assert_allclose(df95b.values[1], test_df95b, atol=1e-5)
+        
+
+    def test_power_efficiency(self):
+        df97 = performance.device_efficiency(
+            self.power,
+            velocity=self.ds['vel'].sel(dir='streamwise'),
+            water_density=self.ds['water_density'],
+            capture_area=np.pi*1.5**2,
+            hub_height=4.2,
+            sampling_frequency=1,
+            window_avg_time=600)
+        
+        test_df97 = np.array(24.79197)
+        assert_allclose(df97.values[-1,-1], test_df97, atol=1e-5)
+
+
+if __name__ == '__main__':
+    unittest.main()