ref: FILMGLS needs its own test because of api changes in FSL 5.0.5

nipype/interfaces/fsl/tests/test_FILMGLS.py

@@ -0,0 +1,51 @@
+def test_filmgls():
+    input_map = dict(args = dict(argstr='%s',),
+                     autocorr_estimate_only = dict(xor=['autocorr_estimate_only', 'fit_armodel', 'tukey_window', 'multitaper_product', 'use_pava', 'autocorr_noestimate'],argstr='-ac',),
+                    autocorr_noestimate = dict(xor=['autocorr_estimate_only', 'fit_armodel', 'tukey_window', 'multitaper_product', 'use_pava', 'autocorr_noestimate'],argstr='-noest',),
+                    brightness_threshold = dict(argstr='-epith %d',),
+                    design_file = dict(argstr='%s',),
+                    environ = dict(usedefault=True,),
+                    fit_armodel = dict(xor=['autocorr_estimate_only', 'fit_armodel', 'tukey_window', 'multitaper_product', 'use_pava', 'autocorr_noestimate'],argstr='-ar',),
+                    full_data = dict(argstr='-v',),
+                    ignore_exception = dict(usedefault=True,),
+                    in_file = dict(mandatory=True,argstr='%s',),
+                    mask_size = dict(argstr='-ms %d',),
+                    multitaper_product = dict(xor=['autocorr_estimate_only', 'fit_armodel', 'tukey_window', 'multitaper_product', 'use_pava', 'autocorr_noestimate'],argstr='-mt %d',),
+                    output_pwdata = dict(argstr='-output_pwdata',),
+                    output_type = dict(),
+                    results_dir = dict(usedefault=True,argstr='-rn %s',),
+                    smooth_autocorr = dict(argstr='-sa',),
+                    threshold = dict(argstr='%f',),
+                    tukey_window = dict(xor=['autocorr_estimate_only', 'fit_armodel', 'tukey_window', 'multitaper_product', 'use_pava', 'autocorr_noestimate'],argstr='-tukey %d',),
+                    use_pava = dict(argstr='-pava',),
+                    )
+    input_map2 = dict(args = dict(argstr='%s',),
+                     autocorr_estimate_only = dict(xor=['autocorr_estimate_only', 'fit_armodel', 'tukey_window', 'multitaper_product', 'use_pava', 'autocorr_noestimate'],argstr='--ac',),
+                     autocorr_noestimate = dict(xor=['autocorr_estimate_only', 'fit_armodel', 'tukey_window', 'multitaper_product', 'use_pava', 'autocorr_noestimate'],argstr='--noest',),
+                     brightness_threshold = dict(argstr='--epith=%d',),
+                     design_file = dict(argstr='--pd=%s',),
+                     environ = dict(usedefault=True,),
+                     fit_armodel = dict(xor=['autocorr_estimate_only', 'fit_armodel', 'tukey_window', 'multitaper_product', 'use_pava', 'autocorr_noestimate'],argstr='--ar',),
+                     full_data = dict(argstr='-v',),
+                     ignore_exception = dict(usedefault=True,),
+                     in_file = dict(mandatory=True,argstr='--in=%s',),
+                     mask_size = dict(argstr='--ms=%d',),
+                     multitaper_product = dict(xor=['autocorr_estimate_only', 'fit_armodel', 'tukey_window', 'multitaper_product', 'use_pava', 'autocorr_noestimate'],argstr='--mt=%d',),
+                     output_pwdata = dict(argstr='--outputPWdata',),
+                     output_type = dict(),
+                     results_dir = dict(argstr='--rn=%s',usedefault=True,),
+                     smooth_autocorr = dict(argstr='--sa',),
+                     terminal_output = dict(mandatory=True,),
+                     threshold = dict(usedefault=True,argstr='--thr=%f',),
+    tukey_window = dict(xor=['autocorr_estimate_only', 'fit_armodel', 'tukey_window', 'multitaper_product', 'use_pava', 'autocorr_noestimate'],argstr='--tukey=%d',),
+    use_pava = dict(argstr='--pava',),
+    )
+    instance = fsl.FILMGLS()
+    if isinstance(instance.inputs, fsl.FILMGLSInputSpec):
+        for key, metadata in input_map.items():
+            for metakey, value in metadata.items():
+                yield assert_equal, getattr(instance.inputs.traits()[key], metakey), value
+    else:
+        for key, metadata in input_map2.items():
+            for metakey, value in metadata.items():
+                yield assert_equal, getattr(instance.inputs.traits()[key], metakey), value