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[testing] harder then expected to test image files against each other....

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mzwiessele 2015-10-04 18:22:21 +01:00
parent 7f393c76f2
commit 0402cf98e9
39 changed files with 115188 additions and 14 deletions
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60
GPy/testing/plotting_tests.py
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@ -30,15 +30,16 @@
import numpy as np
import GPy, os, sys
from nose import SkipTest
import unittest
try:
from matplotlib import cbook
from matplotlib import cbook, pyplot as plt
import matplotlib
matplotlib.rcParams['text.usetex'] = False
except:
raise SkipTest("Matplotlib not installed, not testing plots")
extensions = ['pdf']
extensions = ['svg', 'pdf']
def _image_directories(func):
"""
@ -62,13 +63,44 @@ def _image_directories(func):
return baseline_dir, result_dir
import matplotlib.testing.decorators
matplotlib.testing.decorators._image_directories = _image_directories
from matplotlib.testing.decorators import image_comparison
import matplotlib.pyplot as plt
@image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error']], extensions=extensions)
def testPlot():
def sequenceEqual(a, b):
assert len(a) == len(b), "Sequences not same length"
for i, [x, y], in enumerate(zip(a, b)):
assert x == y, "element not matching {}".format(i)
def notFound(path):
raise IOError('File {} not in baseline')
class test_image_comparison(object):
def __init__(self, baseline_images=[], extensions=['pdf','svg','ong']):
self.baseline_images = baseline_images
self.extensions = extensions
self.f = None
def __call__(self, func):
self.baseline_dir, self.result_dir = _image_directories(func)
def test_wrap():
func()
for num, base in zip(plt.get_fignums(), self.baseline_images):
for ext in self.extensions:
fig = plt.figure(num)
fig.axes[0].set_axis_off()
fig.set_frameon(False)
fig.savefig(os.path.join(self.result_dir, "{}.{}".format(base, ext)), frameon=False)
print os.path.join(self.result_dir, "{}.{}".format(base, ext))
with open(os.path.join(self.result_dir, "{}.{}".format(base, ext)), 'r') as f:
try:
with open(os.path.join(self.baseline_dir, "{}.{}".format(base, ext)), 'r') as b:
yield sequenceEqual, f.read(), b.read()
except:
yield notFound, os.path.join(self.baseline_dir, "{}.{}".format(base, ext))
#plt.close(num)
return test_wrap
@test_image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error']], extensions=extensions)
def Plot(self=None):
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))
f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
@ -81,8 +113,8 @@ def testPlot():
m.plot_density()
m.plot_errorbars_trainset()
@image_comparison(baseline_images=['sparse_gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error', 'inducing']], extensions=extensions)
def testPlotSparse():
@test_image_comparison(baseline_images=['sparse_gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error', 'inducing']], extensions=extensions)
def PlotSparse(self=None):
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))
f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
@ -96,8 +128,8 @@ def testPlotSparse():
m.plot_errorbars_trainset()
m.plot_inducing()
@image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions)
def testPlotClassification():
@test_image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions)
def PlotClassification(self=None):
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))
f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
@ -109,8 +141,8 @@ def testPlotClassification():
m.plot(plot_raw=False, apply_link=True)
m.plot(plot_raw=True, apply_link=True)
@image_comparison(baseline_images=['sparse_gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions)
def testPlotSparseClassification():
@test_image_comparison(baseline_images=['sparse_gp_class_{}'.format(sub) for sub in ["", "raw", 'link', 'raw_link']], extensions=extensions)
def PlotSparseClassification(self=None):
np.random.seed(11111)
X = np.random.uniform(0, 1, (40, 1))
f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)