MAINT: better tests for CrossRecurrencePlot

- testing against issue pik-copan#128
- for all three possible metrics
  - intitialize CrossRecurrencePlot
  - assert two calculations of distance matrix are np.allclose
- remove redundant tests

tests/test_timeseries/test_timeseries.py

@@ -35,7 +35,6 @@
 def create_test_data():
     # Create test time series
     tdata = Data.SmallTestData().observable()
-    n_index, n_times = tdata.shape
     # subtract means form the input data
     tdata -= np.mean(tdata, axis=1)[:, None]
     # normalize the data
@@ -47,45 +46,28 @@ def create_test_data():
 # cross_recurrence_plot
 # -----------------------------------------------------------------------------
 
-
-def testCrossRecurrencePlot():
-    tdata = create_test_data()
-    CrossRecurrencePlot(x=tdata, y=tdata, threshold=0.2)
-
-
-def testDistanceMatrix():
+def testSupremumDistanceMatrix():
     tdata = create_test_data()
     crp = CrossRecurrencePlot(x=tdata, y=tdata, threshold=1.0)
-    crp.distance_matrix(tdata.T, tdata.T, metric='manhattan')
-
+    supr_dist_1 = crp.distance_matrix(tdata, tdata, metric=crp.metric)
+    supr_dist_2 = crp.distance_matrix(tdata, tdata, metric=crp.metric)
+    assert np.allclose(supr_dist_1, supr_dist_2, atol=1e-04)
 
 def testManhattanDistanceMatrix():
     tdata = create_test_data()
-    n_index, n_times = tdata.shape
-    crp = CrossRecurrencePlot(x=tdata, y=tdata, threshold=1.0)
-    manh_dist_1 = crp.manhattan_distance_matrix(tdata.T, tdata.T)
-    manh_dist_2 = crp.manhattan_distance_matrix(tdata.T, tdata.T)
+    crp = CrossRecurrencePlot(x=tdata, y=tdata, threshold=1.0, metric='manhattan')
+    manh_dist_1 = crp.distance_matrix(tdata, tdata, metric=crp.metric)
+    manh_dist_2 = crp.distance_matrix(tdata, tdata, metric=crp.metric)
     assert np.allclose(manh_dist_1, manh_dist_2, atol=1e-04)
 
-
 def testEuclideanDistanceMatrix():
     tdata = create_test_data()
-    n_index, n_times = tdata.shape
-    crp = CrossRecurrencePlot(x=tdata, y=tdata, threshold=1.0)
-    eucl_dist_1 = crp.euclidean_distance_matrix(tdata.T, tdata.T)
-    eucl_dist_2 = crp.euclidean_distance_matrix(tdata.T, tdata.T)
+    crp = CrossRecurrencePlot(x=tdata, y=tdata, threshold=1.0, metric='euclidean')
+    eucl_dist_1 = crp.distance_matrix(tdata, tdata, metric=crp.metric)
+    eucl_dist_2 = crp.distance_matrix(tdata, tdata, metric=crp.metric)
     assert np.allclose(eucl_dist_1, eucl_dist_2, atol=1e-04)
 
 
-def testSupremumDistanceMatrix():
-    tdata = create_test_data()
-    n_index, n_times = tdata.shape
-    crp = CrossRecurrencePlot(x=tdata, y=tdata, threshold=1.0)
-    supr_dist_1 = crp.supremum_distance_matrix(tdata.T, tdata.T)
-    supr_dist_2 = crp.supremum_distance_matrix(tdata.T, tdata.T)
-    assert np.allclose(supr_dist_1, supr_dist_2, atol=1e-04)
-
-
 # -----------------------------------------------------------------------------
 # surrogates
 # -----------------------------------------------------------------------------