def test_integer_levels(self):
data = 1 + np.random.RandomState(3).rand(100)
cmap_params = _determine_cmap_params(data, levels=5, vmin=0, vmax=5,
cmap='Blues')
self.assertEqual(cmap_params['vmin'], cmap_params['levels'][0])
self.assertEqual(cmap_params['vmax'], cmap_params['levels'][-1])
self.assertEqual(cmap_params['cmap'].name, 'Blues')
self.assertEqual(cmap_params['extend'], 'neither')
self.assertEqual(cmap_params['cmap'].N, 5)
self.assertEqual(cmap_params['cnorm'].N, 6)
cmap_params = _determine_cmap_params(data, levels=5,
vmin=0.5, vmax=1.5)
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'max')
def test_list_levels(self):
data = 1 + np.random.RandomState(3).rand(100)
orig_levels = [0, 1, 2, 3, 4, 5]
# vmin and vmax should be ignored if levels are explicitly provided
cmap_params = _determine_cmap_params(data, levels=orig_levels,
vmin=0, vmax=3)
self.assertEqual(cmap_params['vmin'], 0)
self.assertEqual(cmap_params['vmax'], 5)
self.assertEqual(cmap_params['cmap'].N, 5)
self.assertEqual(cmap_params['cnorm'].N, 6)
for wrap_levels in [list, np.array, pd.Index, DataArray]:
cmap_params = _determine_cmap_params(
data, levels=wrap_levels(orig_levels))
self.assertArrayEqual(cmap_params['levels'], orig_levels)
def test_robust(self):
data = np.random.RandomState(1).rand(100)
vmin, vmax, cmap, extend = _determine_cmap_params(data, robust=True)
self.assertEqual(vmin, np.percentile(data, 2))
self.assertEqual(vmax, np.percentile(data, 98))
self.assertEqual(cmap.name, 'viridis')
self.assertEqual(extend, 'both')
def test_center(self):
data = np.random.RandomState(2).rand(100)
cmap_params = _determine_cmap_params(data, center=0.5)
self.assertEqual(cmap_params['vmax'] - 0.5, 0.5 - cmap_params['vmin'])
self.assertEqual(cmap_params['cmap'], 'RdBu_r')
self.assertEqual(cmap_params['extend'], 'neither')
self.assertIsNone(cmap_params['levels'])
self.assertIsNone(cmap_params['cnorm'])
def test_center(self):
data = np.random.RandomState(2).rand(100)
cmap_params = _determine_cmap_params(data, center=0.5)
self.assertEqual(cmap_params['vmax'] - 0.5, 0.5 - cmap_params['vmin'])
self.assertEqual(cmap_params['cmap'], 'RdBu_r')
self.assertEqual(cmap_params['extend'], 'neither')
self.assertIsNone(cmap_params['levels'])
self.assertIsNone(cmap_params['cnorm'])
def test_robust(self):
data = np.random.RandomState(1).rand(100)
cmap_params = _determine_cmap_params(data, robust=True)
self.assertEqual(cmap_params['vmin'], np.percentile(data, 2))
self.assertEqual(cmap_params['vmax'], np.percentile(data, 98))
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'both')
self.assertIsNone(cmap_params['levels'])
self.assertIsNone(cmap_params['cnorm'])
def test_robust(self):
data = np.random.RandomState(1).rand(100)
cmap_params = _determine_cmap_params(data, robust=True)
self.assertEqual(cmap_params['vmin'], np.percentile(data, 2))
self.assertEqual(cmap_params['vmax'], np.percentile(data, 98))
self.assertEqual(cmap_params['cmap'].name, 'viridis')
self.assertEqual(cmap_params['extend'], 'both')
self.assertIsNone(cmap_params['levels'])
self.assertIsNone(cmap_params['cnorm'])
def test_center(self):
data = np.random.RandomState(2).rand(100)
vmin, vmax, cmap, extend = _determine_cmap_params(data, center=0.5)
self.assertEqual(vmax - 0.5, 0.5 - vmin)
self.assertEqual(cmap, 'RdBu_r')
self.assertEqual(extend, 'neither')
