Small coding style improvements

hexrd/cli/fit_grains.py

@@ -335,7 +335,6 @@ def execute(args, parser):
         gid_list = None
         if args.grains is not None:
             gid_list = [int(i) for i in args.grains.split(',')]
-            pass
 
         fit_results = fit_grains(
             cfg,

hexrd/config/utils.py

@@ -8,11 +8,10 @@
                             "sfacmin", "sfacmax", "pintmin", "pintmax"]
 )
 
-
+# Converting to none threw errors, maybe change later
 class Null():
     pass
 
-
 null = Null()
 
 

hexrd/convolution/utils.py

@@ -1,7 +1,5 @@
 # Based on code from astropy
 # Licensed under a 3-clause BSD style license
-import ctypes
-import numpy as np
 
 class DiscretizationError(Exception):
     """
@@ -18,8 +16,7 @@ class KernelSizeError(Exception):
 def has_even_axis(array):
     if isinstance(array, (list, tuple)):
         return not len(array) % 2
-    else:
-        return any(not axes_size % 2 for axes_size in array.shape)
+    return any(not axes_size % 2 for axes_size in array.shape)
 
 
 def raise_even_kernel_exception():

hexrd/distortion/registry.py

@@ -13,12 +13,10 @@ def __init__(cls, name, bases, attrs):
 
 class Registry(object):
     """Registry for imageseries adapters"""
-    distortion_registry = dict()
+    distortion_registry = {}
 
     @classmethod
     def register(cls, acls):
         """Register adapter class"""
         if acls.__name__ != 'DistortionBase':
             cls.distortion_registry[acls.maptype] = acls
-
-    pass  # end class

hexrd/findorientations.py

@@ -105,12 +105,6 @@ def generate_orientation_fibers(cfg, eta_ome):
     del_ome = eta_ome.omegas[1] - eta_ome.omegas[0]
     del_eta = eta_ome.etas[1] - eta_ome.etas[0]
 
-    params = dict(
-        bMat=bMat,
-        chi=chi,
-        csym=csym,
-        fiber_ndiv=fiber_ndiv)
-
     # =========================================================================
     # Labeling of spots from seed hkls
     # =========================================================================
@@ -131,9 +125,12 @@ def generate_orientation_fibers(cfg, eta_ome):
                 ome_c = eta_ome.omeEdges[0] + (0.5 + coms[i][ispot][0])*del_ome
                 eta_c = eta_ome.etaEdges[0] + (0.5 + coms[i][ispot][1])*del_eta
                 input_p.append(np.hstack([this_hkl, this_tth, eta_c, ome_c]))
-                pass
-            pass
-        pass
+
+    params = dict(
+        bMat=bMat,
+        chi=chi,
+        csym=csym,
+        fiber_ndiv=fiber_ndiv)
 
     # do the mapping
     start = timeit.default_timer()
@@ -352,8 +349,6 @@ def quat_distance(x, y):
             qbar[:, i] = rot.quatAverageCluster(
                 qfib_r[:, cl == i + 1], qsym
             ).flatten()
-            pass
-        pass
 
     if algorithm in ('dbscan', 'ort-dbscan') and qbar.size/4 > 1:
         logger.info("\tchecking for duplicate orientations...")
@@ -374,10 +369,7 @@ def quat_distance(x, y):
                 tmp[:, i] = rot.quatAverageCluster(
                     qbar[:, cl == i + 1].reshape(4, npts), qsym
                 ).flatten()
-                pass
             qbar = tmp
-            pass
-        pass
 
     logger.info("clustering took %f seconds", timeit.default_timer() - start)
     logger.info(

hexrd/fitgrains.py

@@ -185,7 +185,7 @@ def fit_grain_FF_reduced(grain_id):
             num_refl_tot += len(valid_refl_ids)
             num_refl_valid += sum(valid_refl_ids)
 
-            pass  # now we have culled data
+            # now we have culled data
 
         # CAVEAT: completeness from pullspots only; incl saturated and overlaps
         # <JVB 2015-12-15>
@@ -220,8 +220,8 @@ def fit_grain_FF_reduced(grain_id):
                     plane_data.latVecOps['B'], plane_data.wavelength,
                     ome_period,
                     simOnly=False, return_value_flag=2)
-            pass  # end conditional on fit
-        pass  # end tolerance looping
+            # end conditional on fit
+        # end tolerance looping
 
     if refit is not None:
         # first get calculated x, y, ome from previous solution
@@ -279,7 +279,6 @@ def fit_grain_FF_reduced(grain_id):
             ]
 
             num_refl_valid += sum(idx_new)
-            pass
 
         # only execute fit if left with enough reflections
         if num_refl_valid > 12:
@@ -297,8 +296,7 @@ def fit_grain_FF_reduced(grain_id):
                     plane_data.latVecOps['B'], plane_data.wavelength,
                     ome_period,
                     simOnly=False, return_value_flag=2)
-            pass
-        pass  # close refit conditional
+        # close refit conditional
     return grain_id, completeness, chisq, grain_params
 
 

hexrd/fitting/calibration/laue.py

@@ -209,7 +209,6 @@ def autopick_points(self, raw_img_dict, tth_tol=5., eta_tol=5.,
             valid_hkls = hkls[gid][:, valid_refl]
             valid_angs = angles[gid][valid_refl, :]
             valid_energy = energy[gid][valid_refl]
-            # pass
 
             # make patches
             refl_patches = xrdutil.make_reflection_patches(
@@ -291,7 +290,6 @@ def autopick_points(self, raw_img_dict, tth_tol=5., eta_tol=5.,
                         #
                     else:
                         closest_peak_idx = 0
-                        pass   # end multipeak conditional
                     #
                     coms = coms[closest_peak_idx]
                     #
@@ -338,7 +336,6 @@ def autopick_points(self, raw_img_dict, tth_tol=5., eta_tol=5.,
                             gaussian_2d_int,
                             [[0., 2.*y0], [0., 2.*x0]],
                             args=fit_par)
-                        pass
                     com_angs = np.hstack([
                         tth_edges[0] + (0.5 + coms[1])*delta_tth,
                         eta_edges[0] + (0.5 + coms[0])*delta_eta
@@ -382,14 +379,13 @@ def autopick_points(self, raw_img_dict, tth_tol=5., eta_tol=5.,
                     #
                     spot_intensity = np.nan
                     max_intensity = np.nan
-                    pass
                 reflInfoList.append([peakId, valid_hkls[:, iRefl],
                                      (spot_intensity, max_intensity),
                                      valid_energy[iRefl],
                                      valid_angs[iRefl, :],
                                      meas_angs[iRefl, :],
                                      meas_xy[iRefl, :]])
-                pass
+
             reflInfo = np.array(
                 [tuple(i) for i in reflInfoList],
                 dtype=reflInfo_dtype)

hexrd/fitting/fitpeak.py

@@ -28,9 +28,7 @@
 import numpy as np
 # from numpy.polynomial import chebyshev
 
-from scipy import integrate
-from scipy import ndimage as imgproc
-from scipy import optimize
+from scipy import integrate, optimize, ndimage as imgproc
 
 from hexrd import constants
 from hexrd.imageutil import snip1d
@@ -54,33 +52,14 @@
 alpha0, alpha1, beta0, beta1 = np.r_[14.4, 0., 3.016, -7.94]
 
 
-def cnst_fit_obj(x, b):
-    return np.ones_like(x)*b
-
-
-def cnst_fit_jac(x, b):
-    return np.vstack([np.ones_like(x)]).T
-
-
 def lin_fit_obj(x, m, b):
     return m*np.asarray(x) + b
 
 
-def lin_fit_jac(x, m, b):
+def lin_fit_jac(x, _m, _b):
     return np.vstack([x, np.ones_like(x)]).T
 
 
-def quad_fit_obj(x, a, b, c):
-    x = np.asarray(x)
-    return a*x**2 + b*x + c
-
-
-def quad_fit_jac(x, a, b, c):
-    x = np.asarray(x)
-    return a*x**2 + b*x + c
-    return np.vstack([x**2, x, np.ones_like(x)]).T
-
-
 def _amplitude_guess(x, x0, y, fwhm):
     pt_l = np.argmin(np.abs(x - (x0 - 0.5*fwhm)))
     pt_h = np.argmin(np.abs(x - (x0 + 0.5*fwhm)))
@@ -198,13 +177,13 @@ def fit_pk_parms_1d(p0, x, f, pktype='pvoigt'):
     weight = np.max(f)*10.  # hard coded should be changed
     fitArgs = (x, f, pktype)
     if pktype == 'gaussian':
-        p, outflag = optimize.leastsq(
+        p, _ = optimize.leastsq(
             fit_pk_obj_1d, p0,
             args=fitArgs, Dfun=eval_pk_deriv_1d,
             ftol=ftol, xtol=xtol
         )
     elif pktype == 'lorentzian':
-        p, outflag = optimize.leastsq(
+        p, _ = optimize.leastsq(
             fit_pk_obj_1d, p0,
             args=fitArgs, Dfun=eval_pk_deriv_1d,
             ftol=ftol, xtol=xtol
@@ -214,7 +193,7 @@ def fit_pk_parms_1d(p0, x, f, pktype='pvoigt'):
         ub = [p0[0]*2.0, np.max(x), 4.*p0[2], 1., 2.*p0[4], None]
 
         fitArgs = (x, f, pktype, weight, lb, ub)
-        p, outflag = optimize.leastsq(
+        p, _ = optimize.leastsq(
             fit_pk_obj_1d_bnded, p0,
             args=fitArgs,
             ftol=ftol, xtol=xtol
@@ -223,13 +202,13 @@ def fit_pk_parms_1d(p0, x, f, pktype='pvoigt'):
         lb = [p0[0]*0.5, np.min(x), 0., 0., 0., 0., 0., None]
         ub = [p0[0]*2.0, np.max(x), 4.*p0[2], 4.*p0[2], 1., 1., 2.*p0[4], None]
         fitArgs = (x, f, pktype, weight, lb, ub)
-        p, outflag = optimize.leastsq(
+        p, _ = optimize.leastsq(
             fit_pk_obj_1d_bnded, p0,
             args=fitArgs,
             ftol=ftol, xtol=xtol
         )
     elif pktype == 'tanh_stepdown':
-        p, outflag = optimize.leastsq(
+        p, _ = optimize.leastsq(
             fit_pk_obj_1d, p0,
             args=fitArgs,
             ftol=ftol, xtol=xtol)
@@ -403,7 +382,7 @@ def estimate_mpk_parms_1d(
     p0tmp_ub = np.zeros([num_pks, npp])
 
     # case processing
-    # !!! used to use (f[pt] - min_val) for ampl
+    # !!! used to use (f[pt] - min_val) for amplitude guess
     if pktype == 'gaussian' or pktype == 'lorentzian':
         # x is just 2theta values
         # make guess for the initital parameters

hexrd/fitting/grains.py

@@ -218,7 +218,6 @@ def objFuncFitGrain(gFit, gFull, gFlag,
             meas_omes = meas_xyo[:, 2]
             xy_unwarped = panel.distortion.apply(meas_xyo[:, :2])
             meas_xyo = np.vstack([xy_unwarped.T, meas_omes]).T
-            pass
 
         # append to meas_omes
         meas_xyo_all.append(meas_xyo)
@@ -234,7 +233,7 @@ def objFuncFitGrain(gFit, gFull, gFlag,
         gHat_c = mutil.unitVector(np.dot(rMat_c.T, gVec_s))
 
         # !!!: check that this operates on UNWARPED xy
-        match_omes, calc_omes = matchOmegas(
+        _, calc_omes = matchOmegas(
             meas_xyo, hkls, chi, rMat_c, bMat, wavelength,
             vInv=vInv_s, beamVec=bVec, etaVec=eVec,
             omePeriod=omePeriod)
@@ -251,7 +250,6 @@ def objFuncFitGrain(gFit, gFull, gFlag,
 
         # append to xy dict
         calc_xy_dict[det_key] = calc_xy
-        pass
 
     # stack results to concatenated arrays
     calc_omes_all = np.hstack([calc_omes_dict[k] for k in det_keys_ordered])

hexrd/imageutil.py

@@ -49,7 +49,7 @@ def fast_snip1d(y, w=4, numiter=2):
     zfull = _scale_image_snip(y, min_val, invert=False)
     for k, z in enumerate(zfull):
         b = z
-        for i in range(numiter):
+        for _ in range(numiter):
             for p in range(w, 0, -1):
                 kernel = np.zeros(p*2 + 1)
                 kernel[0] = 0.5

hexrd/material/crystallography.py

@@ -1383,14 +1383,13 @@ def getHKLs(self, *hkl_ids, **kwargs):
         """
         # kwarg parsing
         opts = dict(asStr=False, thisTTh=None, allHKLs=False)
-        if len(kwargs) > 0:
-            # check keys
-            for k, v in kwargs.items():
-                if k not in opts:
-                    raise TypeError(
-                        f"getHKLs() got an unexpected keyword argument '{k}'"
-                    )
-            opts.update(kwargs)
+        # check keys
+        for k in kwargs.keys():
+            if k not in opts:
+                raise TypeError(
+                    f"getHKLs() got an unexpected keyword argument '{k}'"
+                )
+        opts.update(kwargs)
 
         hkls = []
         if len(hkl_ids) == 0:

hexrd/nf_config/experiment.py

@@ -31,35 +31,42 @@ def max_tth(self):
 
     @property
     def comp_thresh(self):
-        key = 'experiment:comp_thresh'
-        temp = self._cfg.get(key, None)
-        if temp is None:
-            return temp
-        elif np.logical_and(temp <= 1.0, temp > 0.0):
-            return temp
-        else:
-            raise RuntimeError('comp_thresh must be None or a number between 0 and 1')
-
+        temp = self._cfg.get('experiment:comp_thresh')
+        if temp is not None and np.any(
+            np.logical_or(temp > 1.0, temp <= 0.0)
+        ):
+            raise RuntimeError(
+                'comp_thresh must be None or a number between 0 and 1'
+            )
+
+        return temp
+
     @property
     def chi2_thresh(self):
-        key = 'experiment:chi2_thresh'
-        temp = self._cfg.get(key, None)
-        if temp is None:
-            return temp
-        elif np.logical_and(temp <= 1.0, temp > 0.0):
-            return temp
-        else:
-            raise RuntimeError('chi2_thresh must be None or a number between 0 and 1')
+        temp = self._cfg.get('experiment:chi2_thresh')
+        if temp is not None and np.any(
+            np.logical_or(temp > 1.0, temp <= 0.0)
+        ):
+            raise RuntimeError(
+                'chi2_thresh must be None or a number between 0 and 1'
+            )
+
+        return temp
 
 
     @property
     def misorientation(self):
         key = self._cfg.get('experiment:misorientation:use_misorientation')
-        if key is True:
-            parms = dict(misorientation_bnd='experiment:bnd',
-            misorientation_spacing='experiment:spacing')
-            return parms
-        else:
-            parms = dict(misorientation_bnd=None,
-            misorientation_spacing=None) #won't look at spacing if bnd is 0
-            return parms
+        if key not in [True, False, None]:
+            raise ValueError(
+                'use_misorientation must be of type bool'
+            )
+        if key:
+            return {
+                'misorientation_bnd': 'experiment:bnd',
+                'misorientation_spacing': 'experiment:spacing',
+            }
+        return {
+            'misorientation_bnd': None,
+            'misorientation_spacing': None,
+        }