MAINT: Fixup daf187e

Resolves #173.

src/pyunicorn/timeseries/joint_recurrence_plot.py

@@ -127,11 +127,18 @@ def __init__(self, x, y, metric=("supremum", "supremum"),
         threshold_std = kwds.get("threshold_std")
         recurrence_rate = kwds.get("recurrence_rate")
 
+        assert len(metric) == 2
+        assert all(m in ("manhattan", "euclidean", "supremum") for m in metric)
+
+        # initialise base class using dummy values (bad OO design)
         RecurrencePlot.__init__(
             self, np.empty((2, 0)), metric=metric[0], normalize=normalize,
             threshold=threshold[0] if threshold else 0,
             recurrence_rate=recurrence_rate, silence_level=silence_level)
 
+        #  Store type of metric
+        self.metric = metric
+
         self._distance_matrix_cached = False
         self.JR = None
         """The joint recurrence matrix."""

src/pyunicorn/timeseries/recurrence_plot.py

@@ -149,6 +149,7 @@ def __init__(self, time_series, metric="supremum", normalize=False,
         self.time_series.shape = (self.time_series.shape[0], -1)
 
         #  Store type of metric
+        assert metric in ("manhattan", "euclidean", "supremum")
         self.metric = metric
         """The metric used for measuring distances in phase space."""
 
@@ -282,7 +283,6 @@ def distance_matrix(self, embedding, metric):
         :rtype: 2D square array
         :return: the phase space distance matrix :math:`D`
         """
-
         if not self._distance_matrix_cached:
             #  Return distance matrix according to chosen metric:
             if metric == "manhattan":
@@ -294,6 +294,8 @@ def distance_matrix(self, embedding, metric):
             elif metric == "supremum":
                 self._distance_matrix = \
                     RecurrencePlot.supremum_distance_matrix(self, embedding)
+            else:
+                raise ValueError(f"unknown metric: {metric}")
 
             self._distance_matrix_cached = True
 

tests/test_timeseries/test_joint_recurrence_plot.py

@@ -0,0 +1,37 @@
+# This file is part of pyunicorn.
+# Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors
+# URL: <http://www.pik-potsdam.de/members/donges/software>
+# License: BSD (3-clause)
+#
+# Please acknowledge and cite the use of this software and its authors
+# when results are used in publications or published elsewhere.
+#
+# You can use the following reference:
+# J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng,
+# L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra,
+# and J. Kurths, "Unified functional network and nonlinear time series analysis
+# for complex systems science: The pyunicorn package"
+
+"""
+Simple tests for the JointRecurrencePlot class.
+"""
+
+import numpy as np
+
+import pytest
+
+from pyunicorn.timeseries import JointRecurrencePlot
+from pyunicorn.funcnet import CouplingAnalysis
+
+
+@pytest.mark.parametrize("met", ["supremum", "euclidean", "manhattan"])
+@pytest.mark.parametrize("n", [2, 10, 50])
+def test_recurrence(met: str, n: int):
+    ts = CouplingAnalysis.test_data()[:n, 0]
+    jrp = JointRecurrencePlot(ts, ts, threshold=(.1, .1), metric=(met, met))
+    dist = {
+        i: jrp.distance_matrix(getattr(jrp, f"{i}_embedded"), metric=met)
+        for i in "xy"}
+    assert all(d.shape == (n, n) for d in dist.values())
+    assert np.allclose(*dist.values())
+    assert jrp.recurrence_matrix().shape == (n, n)