Test python clusterer's plotting methods (#301)

Author: sbaldu

CLUEstering/CLUEstering.py

@@ -760,17 +760,6 @@ def n_clusters(self) -> int:
         """
         return self.clust_prop.n_clusters
 
-    @property
-    def n_seeds(self) -> int:
-        """
-        Number of seeds found.
-
-        :return: Number of seeds.
-        :rtype: int
-        """
-
-        return self.clust_prop.n_seeds
-
     @property
     def clusters(self) -> np.ndarray:
         """
@@ -1088,8 +1077,6 @@ def cluster_plotter(self, filepath: Union[str, None] = None, plot_title: str = '
             # Customization of axis ticks
             if x_ticks is not None:
                 plt.xticks(x_ticks)
-            if y_ticks is not None:
-                plt.yticks(y_ticks)
 
             if filepath is not None:
                 plt.savefig(filepath)
@@ -1098,8 +1085,7 @@ def cluster_plotter(self, filepath: Union[str, None] = None, plot_title: str = '
         elif self.clust_data.n_dim == 2:
             data = {'x0': self.coords[0],
                     'x1': self.coords[1],
-                    'cluster_ids': self._cluster_ids,
-                    'isSeed': self._is_seed}
+                    'cluster_ids': self.cluster_ids}
             df_ = pd.DataFrame(data)
 
             max_clusterid = max(df_["cluster_ids"])
@@ -1109,8 +1095,6 @@ def cluster_plotter(self, filepath: Union[str, None] = None, plot_title: str = '
             for i in range(0, max_clusterid+1):
                 dfi = df_[df_.cluster_ids == i] # ith cluster
                 plt.scatter(dfi.x0, dfi.x1, s=pt_size, marker='.')
-            df_seed = df_[df_.isSeed == 1] # Only Seeds
-            plt.scatter(df_seed.x0, df_seed.x1, s=seed_size, color='r', marker='*')
 
             # Customization of the plot title
             plt.title(plot_title, fontsize=title_size)

tests/test_plotters.py

@@ -0,0 +1,122 @@
+'''
+Test the clusterer's plotting methods
+'''
+
+import matplotlib
+import os
+import sys
+import pandas as pd
+import pytest
+from check_result import check_result
+sys.path.insert(1, '../CLUEstering/')
+import CLUEstering as clue
+matplotlib.use("Agg")
+
+
+@pytest.fixture
+def dataset1d():
+    '''
+    Returns a 1d dataset for testing
+    '''
+
+    data = {'x': [1, 2, 1.5, 8, 9, 8.5, 50, 51, 49.5], 'weight': [1]*9}
+    return pd.DataFrame(data)
+
+
+@pytest.fixture
+def dataset2d():
+    '''
+    Returns a 2d dataset for testing
+    '''
+
+    data = {'x': [1, 2, 1.5, 8, 9, 8.5, 50, 51, 49.5],
+            'y': [1, 2, 1.5, 8, 9, 8.5, 50, 51, 49.5],
+            'weight': [1]*9}
+    return pd.DataFrame(data)
+
+
+@pytest.fixture
+def dataset3d():
+    '''
+    Returns a generic dataset for testing
+    '''
+
+    data = {'x': [1, 2, 1.5, 8, 9, 8.5, 50, 51, 49.5],
+            'y': [1, 2, 1.5, 8, 9, 8.5, 50, 51, 49.5],
+            'z': [1, 2, 1.5, 8, 9, 8.5, 50, 51, 49.5],
+            'weight': [1]*9}
+    return pd.DataFrame(data)
+
+
+def test_input_plotter(dataset1d, dataset2d, dataset3d):
+    '''
+    Tests the input plotter method of the clusterer
+    '''
+
+    c = clue.clusterer(3., 2., 3.)
+
+    xticks = [0, 10, 20, 30, 40, 50, 60]
+    yticks = [0, 10, 20, 30, 40, 50, 60]
+    zticks = [0, 10, 20, 30, 40, 50, 60]
+
+    filename = 'dataset1d.png'
+    c.read_data(dataset1d)
+    assert c.n_dim == 1
+    c.input_plotter()
+    c.input_plotter(grid=True, xticks=xticks)
+    c.input_plotter(filename)
+    assert os.path.isfile(filename)
+
+    filename = 'dataset2d.png'
+    c.read_data(dataset2d)
+    assert c.n_dim == 2
+    c.input_plotter()
+    c.input_plotter(grid=True, xticks=xticks, yticks=yticks)
+    c.input_plotter(filename)
+    assert os.path.isfile(filename)
+
+    filename = 'dataset3d.png'
+    c.read_data(dataset3d)
+    assert c.n_dim == 3
+    c.input_plotter()
+    c.input_plotter(grid=True, xticks=xticks, yticks=yticks, zticks=zticks)
+    c.input_plotter(filename)
+    assert os.path.isfile(filename)
+
+def test_output_plotter(dataset1d, dataset2d, dataset3d):
+    '''
+    Tests the output plotter method of the clusterer
+    '''
+
+    c = clue.clusterer(3., 2., 3.)
+
+    xticks = [0, 10, 20, 30, 40, 50, 60]
+    yticks = [0, 10, 20, 30, 40, 50, 60]
+    zticks = [0, 10, 20, 30, 40, 50, 60]
+
+    filename = 'output1d.png'
+    c.read_data(dataset1d)
+    assert c.n_dim == 1
+    c.run_clue()
+    c.cluster_plotter()
+    c.cluster_plotter(grid=True, xticks=xticks)
+    c.cluster_plotter(filename)
+    assert os.path.isfile(filename)
+
+    filename = 'output2d.png'
+    c.read_data(dataset2d)
+    assert c.n_dim == 2
+    c.run_clue()
+    c.cluster_plotter()
+    c.cluster_plotter(grid=True, xticks=xticks, yticks=yticks)
+    c.cluster_plotter(filename)
+    assert os.path.isfile(filename)
+
+    filename = 'output3d.png'
+    c.read_data(dataset3d)
+    assert c.n_dim == 3
+    c.run_clue()
+    c.cluster_plotter()
+    c.cluster_plotter(grid=True, xticks=xticks, yticks=yticks, zticks=zticks)
+    c.cluster_plotter(filename)
+    assert os.path.isfile(filename)