Fix for the TXT file reader (#238)

CHANGELOG.md

@@ -0,0 +1,3 @@
+# Version 1.3.2 (15 May 2025)
+
+Fixed loading of experiment data files in .txt format.

src/easyreflectometry/__version__.py

@@ -1 +1 @@
-__version__ = '1.3.1.post1'
+__version__ = '1.3.2'

src/easyreflectometry/data/measurement.py

@@ -74,13 +74,48 @@ def _load_txt(fname: Union[TextIO, str]) -> sc.DataGroup:
 
     :param fname: The path for the file to be read.
     """
-    f_data = np.loadtxt(fname)
-    data = {'R_0': sc.array(dims=['Qz_0'], values=f_data[:, 1], variances=np.square(f_data[:, 2]))}
+    # fname can have either a space or a comma as delimiter
+    # Determine the delimiter used in the file
+    delimiter = None
+    with open(fname, 'r') as f:
+        # find first non-comment and non-empty line
+        for line in f:
+            if line.strip() and not line.startswith('#'):
+                break
+        first_line = line
+    if ',' in first_line:
+        delimiter = ','
+
+    try:
+        # First load only the data to check column count
+        data = np.loadtxt(fname, delimiter=delimiter, comments='#')
+        if data.ndim == 1:
+            # Handle single row case
+            num_columns = len(data)
+        else:
+            num_columns = data.shape[1]
+
+        # Verify minimum column requirement
+        if num_columns < 3:
+            raise ValueError(f"File must contain at least 3 columns (found {num_columns})")
+
+        # Now unpack the data based on column count
+        if num_columns >= 4:
+            x, y, e, xe = np.loadtxt(fname, delimiter=delimiter, comments='#', unpack=True)
+        else:  # 3 columns
+            x, y, e = np.loadtxt(fname, delimiter=delimiter, comments='#', unpack=True)
+            xe = np.zeros_like(x)
+
+    except (ValueError, IOError) as error:
+        # Re-raise with more descriptive message
+        raise ValueError(f"Failed to load data from {fname}: {str(error)}") from error
+
+    data = {'R_0': sc.array(dims=['Qz_0'], values=y, variances=np.square(e))}
     coords = {
         data['R_0'].dims[0]: sc.array(
             dims=['Qz_0'],
-            values=f_data[:, 0],
-            variances=np.square(f_data[:, 3]),
+            values=x,
+            variances=np.square(xe),
             unit=sc.Unit('1/angstrom'),
         )
     }

tests/_static/ref_concat_1.txt

@@ -0,0 +1,193 @@
+# q(A-1), ref, err
+7.6135e-03, 9.7447e-01, 2.5462e-02
+7.7658e-03, 1.0139e+00, 2.5015e-02
+7.9211e-03, 1.0429e+00, 2.4555e-02
+8.0796e-03, 9.7839e-01, 2.2475e-02
+8.2411e-03, 9.7563e-01, 2.1450e-02
+8.4060e-03, 9.8295e-01, 2.0815e-02
+8.5741e-03, 1.0347e+00, 2.0846e-02
+8.7456e-03, 9.9588e-01, 1.9555e-02
+8.9205e-03, 9.8956e-01, 1.8711e-02
+9.0989e-03, 1.0045e+00, 1.8276e-02
+9.2809e-03, 9.8197e-01, 1.7210e-02
+9.4665e-03, 9.9510e-01, 1.6929e-02
+9.6558e-03, 9.9095e-01, 1.6384e-02
+9.8489e-03, 9.5620e-01, 1.5578e-02
+1.0046e-02, 7.4635e-01, 1.2730e-02
+1.0247e-02, 5.3653e-01, 1.0084e-02
+1.0452e-02, 3.4076e-01, 7.5298e-03
+1.0661e-02, 2.1115e-01, 5.5973e-03
+1.0874e-02, 1.5915e-01, 4.6622e-03
+1.1091e-02, 1.2474e-01, 3.9928e-03
+1.1313e-02, 1.0077e-01, 3.4676e-03
+1.1540e-02, 6.7939e-02, 2.7482e-03
+1.1770e-02, 6.1736e-02, 2.5467e-03
+1.2006e-02, 4.8561e-02, 2.2045e-03
+1.2246e-02, 4.1062e-02, 1.9733e-03
+1.2491e-02, 3.2515e-02, 1.7031e-03
+1.2741e-02, 2.7232e-02, 1.5269e-03
+1.2995e-02, 2.2746e-02, 1.3521e-03
+1.3255e-02, 1.7792e-02, 1.1778e-03
+1.3520e-02, 1.2697e-02, 9.7116e-04
+1.3791e-02, 1.2313e-02, 9.4167e-04
+1.4067e-02, 1.0335e-02, 8.7240e-04
+1.4348e-02, 7.9747e-03, 7.5829e-04
+1.4635e-02, 7.9688e-03, 7.5098e-04
+1.4928e-02, 5.5187e-03, 6.3000e-04
+1.5226e-02, 4.3436e-03, 5.7090e-04
+1.5531e-02, 3.7503e-03, 5.3663e-04
+1.5841e-02, 3.4079e-03, 5.2009e-04
+1.6158e-02, 1.5589e-03, 3.6834e-04
+1.6481e-02, 1.8615e-03, 3.9704e-04
+1.6811e-02, 8.4691e-04, 2.5540e-04
+1.7147e-02, 1.2625e-03, 3.0629e-04
+1.7490e-02, 1.0137e-03, 2.7099e-04
+1.7840e-02, 6.0481e-04, 2.0163e-04
+1.8197e-02, 7.6578e-04, 2.2110e-04
+1.8561e-02, 9.3992e-04, 2.4274e-04
+1.8932e-02, 4.2138e-04, 1.5928e-04
+1.9311e-02, 4.8892e-04, 3.9837e-05
+1.9697e-02, 4.4982e-04, 3.6981e-05
+2.0091e-02, 4.3653e-04, 3.5081e-05
+2.0493e-02, 3.9129e-04, 3.1923e-05
+2.0902e-02, 4.5988e-04, 3.3398e-05
+2.1320e-02, 4.7029e-04, 3.2732e-05
+2.1747e-02, 4.1789e-04, 2.9339e-05
+2.2182e-02, 5.5580e-04, 3.3223e-05
+2.2625e-02, 5.5040e-04, 3.2084e-05
+2.3078e-02, 5.9884e-04, 3.2446e-05
+2.3539e-02, 5.8326e-04, 3.0862e-05
+2.4010e-02, 6.4411e-04, 3.1161e-05
+2.4490e-02, 6.0736e-04, 2.9565e-05
+2.4980e-02, 6.7219e-04, 3.0222e-05
+2.5480e-02, 7.6819e-04, 3.1418e-05
+2.5989e-02, 7.7232e-04, 3.0737e-05
+2.6509e-02, 7.4941e-04, 2.9341e-05
+2.7039e-02, 7.2715e-04, 2.7959e-05
+2.7580e-02, 8.1394e-04, 2.9100e-05
+2.8132e-02, 7.9111e-04, 2.7939e-05
+2.8694e-02, 7.7717e-04, 2.6943e-05
+2.9268e-02, 7.7420e-04, 2.6248e-05
+2.9854e-02, 7.4343e-04, 2.5134e-05
+3.0451e-02, 7.6124e-04, 2.4730e-05
+3.1060e-02, 7.9219e-04, 2.4832e-05
+3.1681e-02, 7.3167e-04, 2.3434e-05
+3.2315e-02, 6.7479e-04, 2.2047e-05
+3.2961e-02, 6.4508e-04, 2.1919e-05
+3.3620e-02, 6.0626e-04, 2.0950e-05
+3.4293e-02, 6.0048e-04, 2.0702e-05
+3.4978e-02, 5.4601e-04, 1.9966e-05
+3.5678e-02, 5.1839e-04, 1.9762e-05
+3.6392e-02, 4.3221e-04, 1.8272e-05
+3.7119e-02, 4.7017e-04, 1.9439e-05
+3.7862e-02, 4.1933e-04, 1.9311e-05
+3.8619e-02, 3.6578e-04, 1.7446e-05
+3.9391e-02, 2.9619e-04, 1.4991e-05
+4.0179e-02, 2.6859e-04, 1.4041e-05
+4.0983e-02, 2.1978e-04, 1.2514e-05
+4.1802e-02, 1.5957e-04, 6.1469e-06
+4.2638e-02, 1.4973e-04, 5.6957e-06
+4.3491e-02, 1.3650e-04, 5.1658e-06
+4.4361e-02, 1.0894e-04, 4.3978e-06
+4.5248e-02, 8.4313e-05, 3.6870e-06
+4.6153e-02, 6.6209e-05, 3.1442e-06
+4.7076e-02, 4.7721e-05, 2.5218e-06
+4.8018e-02, 3.8550e-05, 2.1951e-06
+4.8978e-02, 2.4256e-05, 1.6738e-06
+4.9958e-02, 1.2668e-05, 1.1440e-06
+5.0957e-02, 9.8042e-06, 9.8695e-07
+5.1976e-02, 5.5653e-06, 7.2030e-07
+5.3016e-02, 4.6001e-06, 6.3347e-07
+5.4076e-02, 4.5340e-06, 6.0180e-07
+5.5157e-02, 6.0764e-06, 6.7701e-07
+5.6261e-02, 1.0837e-05, 8.8046e-07
+5.7386e-02, 1.5080e-05, 1.0136e-06
+5.8534e-02, 1.8215e-05, 1.0789e-06
+5.9704e-02, 2.4590e-05, 1.2253e-06
+6.0898e-02, 2.9010e-05, 1.3040e-06
+6.2116e-02, 3.4848e-05, 1.3756e-06
+6.3359e-02, 3.7815e-05, 1.4164e-06
+6.4626e-02, 3.6734e-05, 1.3531e-06
+6.5918e-02, 3.8991e-05, 1.3649e-06
+6.7237e-02, 3.7503e-05, 1.3006e-06
+6.8581e-02, 3.7938e-05, 1.2808e-06
+6.9953e-02, 3.5174e-05, 1.1989e-06
+7.1352e-02, 3.1424e-05, 1.1148e-06
+7.2779e-02, 2.5972e-05, 9.9229e-07
+7.4235e-02, 2.1881e-05, 8.9970e-07
+7.5719e-02, 1.8255e-05, 8.3013e-07
+7.7234e-02, 1.3071e-05, 6.9096e-07
+7.8778e-02, 9.7935e-06, 5.9432e-07
+8.0354e-02, 6.2634e-06, 4.8657e-07
+8.1961e-02, 2.9788e-06, 3.3668e-07
+8.3600e-02, 2.0870e-06, 2.8795e-07
+8.5272e-02, 1.4029e-06, 2.4111e-07
+8.6978e-02, 1.1454e-06, 2.2760e-07
+8.8717e-02, 2.3790e-06, 3.1299e-07
+9.0492e-02, 3.0687e-06, 3.4127e-07
+9.2301e-02, 5.3517e-06, 4.4876e-07
+9.4147e-02, 6.2487e-06, 4.7367e-07
+9.6030e-02, 7.6304e-06, 5.0400e-07
+9.7951e-02, 9.0528e-06, 5.4178e-07
+9.9910e-02, 9.6743e-06, 5.5215e-07
+1.0191e-01, 9.6967e-06, 5.4148e-07
+1.0395e-01, 8.7491e-06, 5.0450e-07
+1.0603e-01, 7.5989e-06, 4.5372e-07
+1.0815e-01, 5.2862e-06, 3.6156e-07
+1.1031e-01, 3.8920e-06, 2.9952e-07
+1.1251e-01, 2.9870e-06, 2.8059e-07
+1.1477e-01, 1.6669e-06, 1.8473e-07
+1.1706e-01, 8.6049e-07, 1.2721e-07
+1.1940e-01, 8.7034e-07, 1.2368e-07
+1.2179e-01, 1.0986e-06, 1.3499e-07
+1.2423e-01, 1.9180e-06, 1.7343e-07
+1.2671e-01, 2.1959e-06, 1.8012e-07
+1.2924e-01, 3.1488e-06, 2.1032e-07
+1.3183e-01, 3.6948e-06, 2.2170e-07
+1.3447e-01, 3.8395e-06, 2.2005e-07
+1.3716e-01, 3.5445e-06, 2.0548e-07
+1.3990e-01, 2.8194e-06, 1.7880e-07
+1.4270e-01, 2.1205e-06, 1.5114e-07
+1.4555e-01, 1.4023e-06, 1.1952e-07
+1.4846e-01, 1.0245e-06, 9.9819e-08
+1.5143e-01, 6.8465e-07, 7.9165e-08
+1.5446e-01, 9.5744e-07, 9.2363e-08
+1.5755e-01, 1.2631e-06, 1.0379e-07
+1.6070e-01, 1.4797e-06, 1.1061e-07
+1.6391e-01, 2.0772e-06, 1.3256e-07
+1.6719e-01, 1.8731e-06, 1.2463e-07
+1.7054e-01, 1.6925e-06, 1.1733e-07
+1.7395e-01, 1.4556e-06, 1.0970e-07
+1.7742e-01, 1.1049e-06, 9.7703e-08
+1.8097e-01, 6.5186e-07, 7.5546e-08
+1.8459e-01, 5.9841e-07, 7.3744e-08
+1.8828e-01, 8.0107e-07, 8.9328e-08
+1.9205e-01, 1.0381e-06, 1.0071e-07
+1.9589e-01, 1.1199e-06, 9.9764e-08
+1.9981e-01, 1.2928e-06, 1.0516e-07
+2.0381e-01, 8.6691e-07, 8.5084e-08
+2.0788e-01, 7.0954e-07, 7.4143e-08
+2.1204e-01, 5.3766e-07, 6.2839e-08
+2.1628e-01, 5.7456e-07, 6.4332e-08
+2.2061e-01, 7.8023e-07, 7.3591e-08
+2.2502e-01, 8.3610e-07, 7.4847e-08
+2.2952e-01, 9.8054e-07, 7.9847e-08
+2.3411e-01, 6.9297e-07, 6.5946e-08
+2.3879e-01, 8.1531e-07, 7.0975e-08
+2.4357e-01, 5.4552e-07, 5.7255e-08
+2.4844e-01, 6.1100e-07, 5.9889e-08
+2.5341e-01, 5.1126e-07, 5.4720e-08
+2.5847e-01, 7.3334e-07, 6.4703e-08
+2.6364e-01, 6.5027e-07, 6.1524e-08
+2.6892e-01, 7.0689e-07, 6.6486e-08
+2.7430e-01, 6.6355e-07, 6.3295e-08
+2.7978e-01, 6.1870e-07, 6.0932e-08
+2.8538e-01, 4.6847e-07, 5.3019e-08
+2.9108e-01, 5.1032e-07, 5.5877e-08
+2.9691e-01, 4.6251e-07, 5.3567e-08
+3.0284e-01, 3.6602e-07, 4.8650e-08
+3.0890e-01, 4.0211e-07, 5.2733e-08
+3.1508e-01, 3.6333e-07, 5.3747e-08
+3.2138e-01, 3.1715e-07, 5.3378e-08
+3.2781e-01, 3.4336e-07, 5.6772e-08
+3.3436e-01, 3.2231e-07, 5.7251e-08

tests/test_data.py

@@ -37,6 +37,14 @@ def test_load_with_txt(self):
         assert_almost_equal(er_data['data']['R_0'].variances, np.square(n_data[:, 2]))
         assert_almost_equal(er_data['coords']['Qz_0'].variances, np.square(n_data[:, 3]))
 
+    def test_load_with_txt_commas(self):
+        fpath = os.path.join(PATH_STATIC, 'ref_concat_1.txt')
+        er_data = load(fpath)
+        x, y, e = np.loadtxt(fpath, delimiter=',', comments='#', unpack=True)
+        assert_almost_equal(er_data['data']['R_0'].values, y)
+        assert_almost_equal(er_data['coords']['Qz_0'].values, x)
+        assert_almost_equal(er_data['data']['R_0'].variances, np.square(e))
+
     def test_orso1(self):
         fpath = os.path.join(PATH_STATIC, 'test_example1.ort')
         er_data = _load_orso(fpath)