tests

.coveragerc

@@ -0,0 +1,7 @@
+[report]
+
+omit = toasty/*tests/*
+exclude_lines =
+              pragma: no cover
+              if __name__ == .__main__.:
+              raise NotImplementedError

toasty/norm.py

@@ -1,3 +1,32 @@
+# This file and its tests is adapted from Glue (http://glueviz.org).
+
+# Copyright (c) 2013, Glue Developers
+#
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without modification,
+# are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+# * Redistributions in binary form must reproduce the above copyright notice, this
+#   list of conditions and the following disclaimer in the documentation and/or
+#   other materials provided with the distribution.
+# * Neither the name of the Glue project nor the names of its contributors may be
+#   used to endorse or promote products derived from this software without
+#   specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
 import numpy as np
 
 
@@ -86,7 +115,7 @@ def asinh_warp(x, vmin, vmax, bias, contrast):
                squared=squared_warp,
                arcsinh=asinh_warp)
 
-def normalize(value, vmin, vmax, bias, contrast, stretch):
+def normalize(value, vmin, vmax, bias=.5, contrast=1, stretch='linear'):
     inverted = vmax <= vmin
 
     hi, lo = max(vmin, vmax), min(vmin, vmax)

toasty/tests/test_norm.py

@@ -0,0 +1,91 @@
+import numpy as np
+import pytest
+
+from ..norm import *
+
+
+def test_log_warp():
+    x = np.array([0, 1, 10, 100, 101])
+    y = log_warp(x, 1, 100, .5, 1)
+    yexp = np.array([0, 0, .654, 1, 1])
+    np.testing.assert_array_almost_equal(y, yexp, 3)
+
+
+def test_sqrt_warp():
+    x = np.array([0, 1, 10, 100, 101])
+    y = sqrt_warp(x, 1, 100, .5, 1)
+    yexp = np.array([0, 0, .3015, 1, 1])
+    np.testing.assert_array_almost_equal(y, yexp, 3)
+
+
+def test_pow_warp():
+    x = np.array([0, 1, 10, 100, 101])
+    y = pow_warp(x, 1, 100, .5, 1)
+    yexp = np.array([0, 0, .00087, 1, 1])
+    np.testing.assert_array_almost_equal(y, yexp, 3)
+
+
+def test_squared_warp():
+    x = np.array([0, 1, 10, 100, 101])
+    y = squared_warp(x, 1, 100, .5, 1)
+    yexp = np.array([0, 0, .008264, 1, 1])
+    np.testing.assert_array_almost_equal(y, yexp, 3)
+
+
+def test_asinh_warp():
+    x = np.array([0, 1, 10, 100, 101])
+    y = asinh_warp(x, 1, 100, .5, 1)
+    yexp = np.array([0, 0, .27187, 1, 1])
+    np.testing.assert_array_almost_equal(y, yexp, 3)
+
+
+def test_linear_warp():
+    x = np.array([0, 1, 10, 100, 101])
+    y = linear_warp(x, 1, 100, .5, 1)
+    yexp = np.array([0, 0, 9. / 99., 1, 1])
+    np.testing.assert_array_almost_equal(y, yexp, 3)
+
+
+def test_bias():
+    x = np.array([0, .4, .5, .6, 1])
+
+    y = cscale(x.copy(), .5, 1)
+    np.testing.assert_array_almost_equal(x, y)
+
+    y = cscale(x.copy(), .5, 2)
+    yexp = np.array([0, .3, .5, .7, 1])
+    np.testing.assert_array_almost_equal(y, yexp)
+
+    y = cscale(x.copy(), .5, 0)
+    yexp = np.array([.5, .5, .5, .5, .5])
+    np.testing.assert_array_almost_equal(y, yexp)
+
+    y = cscale(x.copy(), .5, 0)
+    yexp = np.array([.5, .5, .5, .5, .5])
+    np.testing.assert_array_almost_equal(y, yexp)
+
+    y = cscale(x.copy(), .4, 1)
+    yexp = np.array([.1, .5, .6, .7, 1])
+    np.testing.assert_array_almost_equal(y, yexp)
+
+    y = cscale(x.copy(), .6, 1)
+    yexp = np.array([0, .3, .4, .5, .9])
+    np.testing.assert_array_almost_equal(y, yexp)
+
+
+class TestNormalize(object):
+
+    def test_input_unmodified(self):
+        x = np.array([1, 2, 3])
+        y = normalize(x, 1, 3, contrast=100)
+        assert np.abs(x - y).max() > .1  #they are different
+        np.testing.assert_array_almost_equal(x, [1, 2, 3]) # x is not
+
+    def test_call_default(self):
+        x = np.array([1, 2, 3])
+        np.testing.assert_array_almost_equal(normalize(x, 1, 3), [0, 127, 255])
+
+    def test_call_invert(self):
+        x = np.array([1, 2, 3])
+        y = normalize(x, vmin=3, vmax=1)
+        np.testing.assert_array_almost_equal(y, [255, 127, 0])