def test_proximal_join(self):
s1 = Spectrum(name='s1',
measurement=pd.Series([1, 2, 3, 4],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'),
metadata={'gps_time_tgt': 1})
s2 = Spectrum(name='s2',
measurement=pd.Series([10, 11, 12, 13],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'),
metadata={'gps_time_tgt': 5})
s3 = Spectrum(name='s3',
measurement=pd.Series([100, 200, 300, 400],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'),
metadata={'gps_time_tgt': 10})
base = Collection(name='base', spectra=[s1, s2, s3])
s4 = Spectrum(name='s4',
measurement=pd.Series([1, 2, 3, 4],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'),
metadata={'gps_time_tgt': 2})
s5 = Spectrum(name='s5',
measurement=pd.Series([10, 11, 12, 13],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'),
metadata={'gps_time_tgt': 5})
s6 = Spectrum(name='s6',
measurement=pd.Series([100, 200, 300, 400],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'),
metadata={'gps_time_tgt': 9})
rover = Collection(name='rover', spectra=[s4, s5, s6])
proximal = proximal_join(base, rover)
proximal_df_correct = pd.DataFrame(
{
's4': [1.0, 1.0, 1.0, 1.0],
's5': [1.0, 1.0, 1.0, 1.0],
's6': [1.0, 1.0, 1.0, 1.0]
},
index=pd.Index([1, 2, 3, 4], name='wavelength'))
pdt.assert_frame_equal(proximal.data, proximal_df_correct)
def test_collection_data_with_meta(self):
m1 = OrderedDict()
m2 = OrderedDict()
m3 = OrderedDict()
for i, meta in enumerate([m1, m2, m3]):
meta['file'] = 'f{}'.format(i)
meta['instrument_type'] = 'asd'
meta['integration_time'] = '15'
meta['measurement_type'] = 'pct_reflect'
meta['gps_time'] = (100 + i, 200 + i)
meta['wavelength_range'] = (1, 4)
s1 = Spectrum(name='s1',
measurement=pd.Series([1, 2, 3, 4],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'),
metadata=m1)
s2 = Spectrum(name='s2',
measurement=pd.Series([10, 11, 12, 13],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'),
metadata=m2)
s3 = Spectrum(name='s3',
measurement=pd.Series([100, 200, 300, 400],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'),
metadata=m3)
c1 = Collection(name='c1', spectra=[s1, s2, s3])
c1.data_with_meta()
def read_dir(self):
directory = filedialog.askdirectory()
if not directory:
return
self.collectionList.add_collection(
Collection(name="collection" +
str(self.collectionList.listbox.size()),
directory=directory))
def setUp(self):
# total 36 spectra
self.c = Collection(name='For Groups')
for a in ('A', 'B', 'C'):
for b in ('a', 'b', 'c'):
for c in ('0', '1'):
for d in ('0001', '0002', '0003', '0004'):
self.c.append(Spectrum('_'.join([a, b, c, d])))
def test_collection_assert_unique_name1(self):
s1 = Spectrum(name='s1',
measurement= pd.Series([1, 2, 3, 4],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'))
s2 = Spectrum(name='s1',
measurement= pd.Series([1, 2, 3, 4],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'))
c1 = Collection(name='c1')
c1.append(s1)
self.assertRaises(AssertionError, c1.append, s2)
def test_collection_data_unstitched(self):
s1 = Spectrum(name='s1',
measurement=pd.Series([1, 2, 3, 4],
index=pd.Index([1, 2, 3, 4],
name='wavelength'),
name='pct_reflect'))
s2 = Spectrum(name='s2',
measurement=pd.Series([10, 11, 12, 13],
index=pd.Index([0.8, 2.2, 3.7, 5],
name='wavelength'),
name='pct_reflect'))
s3 = Spectrum(name='s3',
measurement=pd.Series([100, 200, 300, 400, 500],
index=pd.Index([1, 2, 3, 3, 4],
name='wavelength'),
name='pct_reflect'))
c1 = Collection(name='c1', spectra=[s1, s2, s3])
self.assertRaises(ValueError, lambda: c1.data)
c1.stitch()
def read_test_data():
path = '~/data/specdal/aidan_data2/ASD'
c = Collection("Test Collection", directory=path)
for i in range(30):
c.flag(c.spectra[i].name)
return c