def test_spc_format():
filename = load_spc_path()
R = Request(filename)
F = formats['SPC']
assert F.can_read(R)
reader = F.get_reader(R)
assert reader.get_length() == 1
assert reader.get_meta_data()['dat_fmt'] == 'x-y'
spec = reader.get_data()
assert spec.spectrum.shape == (1911, )
assert spec.wavelength.shape == (1911, )
spec = reader.get_data(0)
assert spec.spectrum.shape == (1911, )
assert spec.wavelength.shape == (1911, )
""" ==================================================== Read SPC Galactic Industries Corporation binary file ==================================================== This example shows how to read SPC file and plot the results. """ from __future__ import print_function import matplotlib.pyplot as plt from specio import specread from specio.datasets import load_spc_path # Find the path to the SPC toy data spc_filename = load_spc_path() print(spc_filename) # Read the data spectra = specread(spc_filename) # Plot the first spectra plt.plot(spectra.wavelength, spectra.spectrum[0]) plt.xlabel(spectra.meta['xlabel']) plt.ylabel(spectra.meta['ylabel']) plt.show()
def test_load_spc_path():
path_data = load_spc_path()
assert 'datasets/data/spectra.spc' in path_data
"""Test that all spectra files are retrieved."""
# Copyright (c) 2017
# Authors: Guillaume Lemaitre <*****@*****.**>
# License: BSD 3 clause
from os.path import join
import pytest
from specio.datasets import load_fsm_path
from specio.datasets import load_mzml_path
from specio.datasets import load_sp_path
from specio.datasets import load_spc_path
from specio.datasets import load_csv_path
@pytest.mark.parametrize("path_data,extension", [(load_fsm_path(), 'fsm'),
(load_mzml_path(), 'mzml'),
(load_sp_path(), 'sp'),
(load_spc_path(), 'spc'),
(load_csv_path(), 'csv')])
def test_load_spectra_path(path_data, extension):
assert join('datasets', 'data', 'spectra.' + extension) in path_data
@pytest.mark.parametrize("spectrum,wavelength,metadata", [(np.ones(
(1, 10)), np.ones((10, )), {
'kind': 'random'
}), (np.ones((10, )), np.ones((10, )), {
'kind': 'random'
})])
def test_spectrum(spectrum, wavelength, metadata):
spec = Spectrum(spectrum, wavelength, metadata)
assert_allclose(spec.amplitudes, spectrum)
assert_allclose(spec.wavelength, wavelength)
assert spec.meta == {'kind': 'random'}
@pytest.mark.parametrize("filename",
[(os.path.join(module_path, 'data', '*.spc')),
(load_spc_path())])
def test_spectrum_to_dataframe(filename):
spec = specread(filename)
df_spec = spec.to_dataframe()
if isinstance(spec.meta, tuple):
expected_index = np.array([meta['filename'] for meta in spec.meta])
else:
expected_index = np.array(spec.meta['filename'])
assert_array_equal(df_spec.index.values, expected_index)
assert_allclose(df_spec.columns.values, spec.wavelength)
assert_allclose(df_spec.values, np.atleast_2d(spec.amplitudes))
@pytest.mark.parametrize("filename",
[(os.path.join(module_path, 'data', '*.spc')),
(load_spc_path())])
for m in spec.meta:
if filename_cmp:
assert m['filename'] == basename(filename)
else:
assert m['filename']
else:
if filename_cmp:
assert spec.meta['filename'] == basename(filename)
else:
assert spec.meta['filename']
@pytest.mark.parametrize(
"filename,filename_cmp,spectrum_shape,wavelength_shape",
[(load_fsm_path(), True, (7998, 1641), (1641,)),
(load_spc_path(), True, (1911,), (1911,)),
(load_sp_path(), True, (3301,), (3301,)),
pytest.param(load_mzml_path(), True, (282,), (282,),
marks=skip_windows_py27()),
(load_csv_path(), False, (84, 1732,), (1732,))])
def test_specread(filename, filename_cmp, spectrum_shape, wavelength_shape):
spec = specread(filename)
if isinstance(spec, Spectrum):
_check_spectra(spec, filename, filename_cmp, spectrum_shape,
wavelength_shape)
elif isinstance(spec, list):
assert all([isinstance(sp, Spectrum) for sp in spec])
sp = spec[0]
_check_spectra(sp, filename, filename_cmp, spectrum_shape,
wavelength_shape)
else:
