Current direction test.

ioos_qartod/qc_tests/qc.py

@@ -224,7 +224,8 @@ def flat_line_check(arr, low_reps, high_reps, eps, prev_qc=None):
 
 
 @add_qartod_ident(16, 'Time Series Flat Line Test')
-def time_series_flat_line_check(arr, low_reps=3, high_reps=5, eps=None, prev_qc=None):
+def time_series_flat_line_check(arr, low_reps=3, high_reps=5, eps=None,
+                                prev_qc=None):
     """
     Check for invariate observations and can be applied to all bulk wave
     parameters.
@@ -281,6 +282,19 @@ def attenuated_signal_check(arr, times, min_var_warn, min_var_fail,
     return flag_arr
 
 
+@add_qartod_ident(11, 'Current Direction')
+def current_direction_check(arr, sensor_min=0.0, sensor_max=360.0):
+    """
+    Ensure current direction values fall between 0 and 360 degrees.
+
+    """
+    flag_arr = np.ones_like(arr, dtype='uint8')
+
+    flag_arr[(arr < sensor_min) |
+             (arr > sensor_max)] = QCFlags.BAD_DATA
+    return flag_arr
+
+
 def qc_compare(vectors):
     """
     Returns an array of flags that represent the aggregate of all the vectors.

test/test_qartod_qc.py

@@ -1,4 +1,5 @@
 import numpy as np
+import numpy.ma as ma
 import numpy.testing as npt
 import pandas as pd
 from ioos_qartod.qc_tests import qc
@@ -200,3 +201,34 @@ def test_qc_compare(self):
         primary_flags = qc.qc_compare([range_flags, spike_flags, grdtn_flags])
         np.testing.assert_array_equal(primary_flags,
                                       np.array([1, 3, 3, 4, 3, 1, 2, 9]))
+
+    def test_current_direction_check(self):
+        """
+        See if user and current direction fall between a valid range for
+        float input.
+
+        """
+        vals = np.array([0, -1, 5, 110, 360, 250, 360.01, 1e3, 1],
+                        dtype=float)
+        expected = np.array([1, 4, 1, 1, 1, 1, 4, 4, 1])
+        npt.assert_array_equal(qc.current_direction_check(vals), expected)
+
+    def test_current_direction_check_ints(self):
+        """
+        See if user and current direction fall between a valid range for
+        integer input.
+
+        """
+        vals = np.array([0, -1, 5, 110, 360, 250, 360, 1000, 1], dtype=int)
+        expected = np.array([1, 4, 1, 1, 1, 1, 1, 4, 1])
+        npt.assert_array_equal(qc.current_direction_check(vals), expected)
+
+    def test_current_direction_check_invalid(self):
+        """
+        See if user and current direction fall between a valid range for
+        invalid input. (Assumes NaNs should pass as "valid.")
+
+        """
+        vals = np.array([0, np.NaN, 5, -np.inf, 360, 250, 360.5, np.inf, 1])
+        expected = np.array([1, 1, 1, 4, 1, 1, 4, 4, 1])
+        npt.assert_array_equal(qc.current_direction_check(vals), expected)