special case for fit trace with all 0 columns, some pep8 fixes (#257)

Author: cshanahan1

CHANGES.rst

@@ -9,6 +9,8 @@ API Changes
 
 Bug Fixes
 ^^^^^^^^^
+- When all-zero bin encountered in fit_trace with peak_method=gaussian, the bin peak
+  will be set to NaN in this caseto work better with DogBoxLSQFitter. [#257]
 
 Other changes
 ^^^^^^^^^^^^^

specreduce/tests/test_tracing.py

@@ -174,6 +174,23 @@ def test_fit_trace():
         FitTrace(img, bins=ncols + 1)
 
 
+def test_fit_trace_gaussian_all_zero():
+    """
+    Test fit_trace when peak_method is 'gaussian', which uses DogBoxLSQFitter
+    for the fit for each bin peak and does not work well with all-zero columns.
+    In this case, an all zero bin should fall back to NaN to for its'
+    peak to be filtered out in the final fit for the trace.
+    """
+    img = mk_img(ncols=100)
+    # add some all-zero columns so there is an all-zero bin
+    img[:, 10:20] = 0
+
+    t = FitTrace(img, bins=10, peak_method='gaussian')
+
+    # this is a pretty flat trace, so make sure the fit reflects that
+    assert np.all((t.trace >= 99) & (t.trace <= 101))
+
+
 @pytest.mark.filterwarnings("ignore:The fit may be unsuccessful")
 @pytest.mark.filterwarnings("ignore:Model is linear in parameters")
 class TestMasksTracing:

specreduce/tracing.py

@@ -413,6 +413,13 @@ def _fit_trace(self, img):
 
             if self.peak_method == "gaussian":
 
+                # if bin is fully 0, set bin peak to nan so it doesn't bias the
+                # all-bin fit. DogBoxLSQFitter, which is always used for the bin
+                # center fits when peak_method is gaussian, does not like all zeros.
+                if np.all(z_i == 0.0):
+                    y_bins[i] = np.nan
+                    continue
+
                 peak_y_i = ilum2[z_i.argmax()]
 
                 yy_i_above_half_max = np.sum(z_i > (z_i.max() / 2))