Mercurial > pylearn
annotate _test_filetensor.py @ 324:ce79bf5fa463
- the cut and paste between file and dir conditions is always a bad thing
- i made one function (hg_version) to basically call and parse hg
- i made a function to include the cases of what might be returned by
imp.find_modules (_input_id)
- the check for a .hg folder was insufficient. Lots of things could go wrong.
Instead I use the return code from the Popen process. The return code
catches this and any other problem that hg runs into.
- its easier to offer more rcs support in future (cvs,svn,git)
author | James Bergstra <bergstrj@iro.umontreal.ca> |
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date | Thu, 12 Jun 2008 20:54:49 -0400 |
parents | b55c829695f1 |
children | 040cb796f4e0 |
rev | line source |
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1 from filetensor import * |
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2 import filetensor |
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3 |
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4 import unittest |
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5 import os |
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6 |
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7 class T(unittest.TestCase): |
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8 fname = '/tmp/some_mat' |
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9 |
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10 def setUp(self): |
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11 #TODO: test that /tmp/some_mat does not exist |
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12 try: |
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13 os.stat(self.fname) |
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14 except OSError: |
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15 return #assume file was not found |
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16 raise Exception('autotest file "%s" exists!' % self.fname) |
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17 |
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18 def tearDown(self): |
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19 os.remove(self.fname) |
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20 |
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21 def test_file(self): |
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22 gen = numpy.random.rand(1) |
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23 f = file(self.fname, 'w'); |
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24 write(f, gen) |
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25 f.flush() |
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26 f = file(self.fname, 'r'); |
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27 mat = read(f, None, debug=False) #load from filename |
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28 self.failUnless(gen.shape == mat.shape) |
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29 self.failUnless(numpy.all(gen == mat)) |
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30 |
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31 def test_filename(self): |
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32 gen = numpy.random.rand(1) |
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33 write(self.fname, gen) |
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34 mat = read(self.fname, None, debug=False) #load from filename |
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35 self.failUnless(gen.shape == mat.shape) |
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36 self.failUnless(numpy.all(gen == mat)) |
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37 |
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38 def testNd(self): |
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39 """shape and values are stored correctly for tensors of rank 0 to 5""" |
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40 whole_shape = [5, 6, 7, 8, 9] |
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41 for i in xrange(5): |
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42 gen = numpy.asarray(numpy.random.rand(*whole_shape[:i])) |
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43 f = file(self.fname, 'w'); |
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44 write(f, gen) |
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45 f.flush() |
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46 f = file(self.fname, 'r'); |
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47 mat = read(f, None, debug=False) #load from filename |
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48 self.failUnless(gen.shape == mat.shape) |
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49 self.failUnless(numpy.all(gen == mat)) |
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50 |
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51 def test_dtypes(self): |
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52 """shape and values are stored correctly for all dtypes """ |
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53 for dtype in filetensor._dtype_magic: |
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54 gen = numpy.asarray( |
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55 numpy.random.rand(4, 5, 2, 1) * 100, |
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56 dtype=dtype) |
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57 f = file(self.fname, 'w'); |
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58 write(f, gen) |
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59 f.flush() |
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60 f = file(self.fname, 'r'); |
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61 mat = read(f, None, debug=False) #load from filename |
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62 self.failUnless(gen.dtype == mat.dtype) |
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63 self.failUnless(gen.shape == mat.shape) |
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64 self.failUnless(numpy.all(gen == mat)) |
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65 |
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66 def test_dtype_invalid(self): |
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67 gen = numpy.zeros((3,4), dtype='uint16') #an unsupported dtype |
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68 f = file(self.fname, 'w') |
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69 passed = False |
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70 try: |
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71 write(f, gen) |
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72 except TypeError, e: |
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73 if e[0].startswith('Invalid ndarray dtype'): |
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74 passed = True |
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75 f.close() |
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76 self.failUnless(passed) |
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77 |
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78 |
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79 if __name__ == '__main__': |
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80 unittest.main() |
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81 |
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82 #a small test script, starts by reading sys.argv[1] |
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83 #print 'rval', rval.shape, rval.size |
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84 |
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85 if 0: |
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86 write(f, rval) |
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87 print '' |
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88 f.close() |
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89 f = file('/tmp/some_mat', 'r'); |
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90 rval2 = read(f) #load from file handle |
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91 print 'rval2', rval2.shape, rval2.size |
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92 |
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93 assert rval.dtype == rval2.dtype |
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94 assert rval.shape == rval2.shape |
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95 assert numpy.all(rval == rval2) |
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96 print 'ok' |
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97 |
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98 def _unused(): |
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99 f.seek(0,2) #seek to end |
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100 f_len = f.tell() |
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101 f.seek(f_data_start,0) #seek back to where we were |
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102 |
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103 if debug: print 'length:', f_len |
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104 |
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105 |
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106 f_data_bytes = (f_len - f_data_start) |
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107 |
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108 if debug: print 'data bytes according to header: ', dim_size * elsize |
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109 if debug: print 'data bytes according to file : ', f_data_bytes |
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110 |
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111 if debug: print 'reading data...' |
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112 sys.stdout.flush() |
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113 |
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114 def read_ndarray(f, dim, dtype): |
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115 return numpy.fromfile(f, dtype=dtype, count=_prod(dim)).reshape(dim) |
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116 |