Merge pull request #856 from BradleySappington/pep8speaks

Additional Pep8speaks updates

.pep8speaks.yml

@@ -12,7 +12,7 @@ message:  # Customize the comment made by the bot
     no_errors: "There are no PEP8 issues in this Pull Request."
 
 scanner:
-    diff_only: False  # If True, errors caused by only the patch are shown
+    diff_only: True  # If True, errors caused by only the patch are shown
 
 pycodestyle:
     max-line-length: 125  # Default is 79 in PEP8

dev_utils/compute_psf_library.py

@@ -16,7 +16,7 @@
 
 if not os.environ.get('WEBBPSF_PATH'):
     os.environ['WEBBPSF_PATH'] = '/grp/jwst/ote/webbpsf-data'
-import webbpsf # noqa
+import webbpsf  # noqa
 
 N_PROCESSES = 16
 

dev_utils/field_dependence/basis.py

@@ -22,20 +22,19 @@
 
 os.environ['PINT_ARRAY_PROTOCOL_FALLBACK'] = '0'
 
-import pint # noqa
+import pint  # noqa
 
 units = pint.UnitRegistry()
 Q_ = units.Quantity
 
 # Silence NEP 18 warning
-import warnings # noqa
+import warnings  # noqa
 
 with warnings.catch_warnings():
     warnings.simplefilter('ignore')
     Q_([])
 
 
-
 def embed(n, m):
     # Return vector of indices needed to pull an array of length m from the center of an array of (larger) length n, or
     # conversely insert an array of length m into an array of length n.
@@ -216,7 +215,7 @@ def numpolys(self):
     def numpolys(self, new_numpolys):
         polynomials_copy = self._polynomials
         self._polynomials = np.zeros((new_numpolys, self._pts_x, self._pts_y))
-        self._polynomials[0 : self._numpolys, :, :] = polynomials_copy
+        self._polynomials[0: self._numpolys, :, :] = polynomials_copy  # noqa
         self._numpolys = new_numpolys
 
     def project(self, in_data):
@@ -480,7 +479,8 @@ def __init__(self, *args, **kwargs):
     #                 next_order_start = (self._term_order[index] + 1) * (self._term_order[index] + 2) / 2
     #                 radial_term_index = order_start + self._term_order[index] / 2
     #                 if index < radial_term_index:
-    #                     self._polynomial_map[index] = next_order_start - (index - order_start) * 2 - (self._term_order[index] - 1) / 2
+    #                     self._polynomial_map[index] =
+    #                         next_order_start - (index - order_start) * 2 - (self._term_order[index] - 1) / 2
     #                 elif index > radial_term_index:
     #                     self._polynomial_map[index] =
     #

dev_utils/field_dependence/read_codev_dat_fits.py

@@ -45,12 +45,13 @@ def main():
     ]
 
     # Zernike fitting order for each instrument
-    order= {'fgs': 15,
-            'nircam': 15,
-            'miri': 15,
-            'nirspec': 15,
-            'niriss': 15
-            }
+    order = {
+        'fgs': 15,
+        'nircam': 15,
+        'miri': 15,
+        'nirspec': 15,
+        'niriss': 15
+    }
 
     # Define the origin of the CodeV coordinate system in V2/V3 space
     v2_origin_degrees = 0

dev_utils/wfe_benchmark.py

@@ -9,11 +9,11 @@
 
 inst = webbpsf.NIRCam()
 inst.filter = "F430M"
-inst.detector_position = (1024,1024)
+inst.detector_position = (1024, 1024)
 
 # Baseline test: No SI WFE, no distortion
 inst.include_si_wfe = False
-%timeit psf = inst.calc_psf(add_distortion=False, monochromatic=4.3e-6) # noqa
+%timeit psf = inst.calc_psf(add_distortion=False, monochromatic=4.3e-6)  # noqa
 # Result: 911 ms ± 5.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
 %timeit psf = inst.calc_psf(add_distortion=False, nlambda=ncores)  # noqa
 # Result: 5.62 s ± 177 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
@@ -26,8 +26,8 @@
 # Result: 6.1 s ± 96.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
 
 # Use pixel (0,0) to force extrapolation algorithm
-inst.detector_position = (0,0)
-%timeit psf = inst.calc_psf(add_distortion=False, monochromatic=4.3e-6) # noqa
+inst.detector_position = (0, 0)
+%timeit psf = inst.calc_psf(add_distortion=False, monochromatic=4.3e-6)  # noqa
 # Result: 1.8 s ± 12.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
-%timeit psf = inst.calc_psf(add_distortion=False, nlambda=ncores) # noqa
+%timeit psf = inst.calc_psf(add_distortion=False, nlambda=ncores)  # noqa
 # Result: 6.53 s ± 85.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

docs/conf.py

@@ -118,7 +118,7 @@
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = "sphinx"
 
-intersphinx_mapping.update( # noqa - defined in star import
+intersphinx_mapping.update(  # noqa - defined in star import
     {
         "poppy": ("http://poppy-optics.readthedocs.io/", None),
     }

docs/exts/numfig.py

@@ -1,11 +1,12 @@
 from docutils.nodes import SkipNode, Text, caption, figure, raw, reference
 from sphinx.roles import XRefRole
 
-# Element classes
 
+# Element classes
 class page_ref(reference):
     pass
 
+
 class num_ref(reference):
     pass
 
@@ -15,10 +16,12 @@ class num_ref(reference):
 def skip_page_ref(self, node):
     raise SkipNode
 
+
 def latex_visit_page_ref(self, node):
     self.body.append("\\pageref{%s:%s}" % (node['refdoc'], node['reftarget']))
     raise SkipNode
 
+
 def latex_visit_num_ref(self, node):
     fields = node['reftarget'].split('#')
     if len(fields) > 1:
@@ -57,7 +60,6 @@ def doctree_resolved(app, doctree, docname):
 
         i += 1
 
-
     # replace numfig nodes with links
     if app.builder.name != 'latex':
         for ref_info in doctree.traverse(num_ref):
@@ -75,16 +77,17 @@ def doctree_resolved(app, doctree, docname):
                 target_doc = app.builder.env.figid_docname_map[target]
                 link = "%s#%s" % (app.builder.get_relative_uri(docname, target_doc),
                                   target)
-                html = '<a class="pageref" href="%s">%s</a>' % (link, labelfmt %(figids[target]))
+                html = '<a class="pageref" href="%s">%s</a>' % (link, labelfmt % (figids[target]))
                 ref_info.replace_self(raw(html, html, format='html'))
             else:
                 ref_info.replace_self(Text(labelfmt % (figids[target])))
 
 
 def clean_env(app):
-    app.builder.env.i=1
+    app.builder.env.i = 1
     app.builder.env.figid_docname_map = {}
 
+
 def setup(app):
     app.add_config_value('number_figures', True, True)
     app.add_config_value('figure_caption_prefix', "Figure", True)

webbpsf/constants.py

@@ -400,4 +400,4 @@
 # See Gaspar et al. 2021 for illustrative figures.
 # This is a rough approximation of a detector-position-dependent phenomenon
 MIRI_CRUCIFORM_INNER_RADIUS_PIX = 12
-MIRI_CRUCIFORM_RADIAL_SCALEFACTOR = 0.005   # Brightness factor for the diffuse circular halo
+MIRI_CRUCIFORM_RADIAL_SCALEFACTOR = 0.005   # Brightness factor for the diffuse circular halo

webbpsf/detectors.py

@@ -1,15 +1,16 @@
 import copy
-
 import os
+
+import astropy.convolution
 import numpy as np
 import scipy
-import webbpsf
-from webbpsf import utils, constants
-from astropy.convolution.kernels import CustomKernel
+import scipy.signal as signal
 from astropy.convolution import convolve
+from astropy.convolution.kernels import CustomKernel
 from astropy.io import fits
-import astropy.convolution
-import scipy.signal as signal
+
+import webbpsf
+from webbpsf import constants, utils
 
 
 def get_detector_ipc_model(inst, header):
@@ -60,7 +61,8 @@ def get_detector_ipc_model(inst, header):
 
         # PPC effect
         # read the SCA extension for the detector
-        ## TODO: This depends on detector coordinates, and which readout amplifier. if in subarray, then the PPC effect is always like in amplifier 1
+        # TODO: This depends on detector coordinates, and which readout amplifier.
+        # If in subarray, then the PPC effect is always like in amplifier 1
         sca_path_ppc = os.path.join(utils.get_webbpsf_data_path(), 'NIRCam', 'IPC', 'KERNEL_PPC_CUBE.fits')
         kernel_ppc = CustomKernel(fits.open(sca_path_ppc)[det2sca[det]].data[0])  # we read the first slice in the cube
 
@@ -167,7 +169,8 @@ def apply_detector_ipc(psf_hdulist, extname='DET_DIST'):
 
     """
 
-    # In cases for which the user has asked for the IPC to be applied to a not-present extension, we have nothing to add this to
+    # In cases for which the user has asked for the IPC
+    # to be applied to a not-present extension, we have nothing to add this to
     if extname not in psf_hdulist:
         webbpsf.webbpsf_core._log.debug(f'Skipping IPC simulation since ext {extname} is not found')
         return
@@ -283,9 +286,11 @@ def oversample_ipc_model(kernel, oversample):
 
 # Functions for applying MIRI Detector Scattering Effect
 
-# Lookup tables of shifts of the cruciform, estimated roughly from F560W ePSFs (ePSFs by Libralatto, shift estimate by Perrin)
-cruciform_xshifts = scipy.interpolate.interp1d([0, 357, 1031], [1.5,0.5,-0.9], kind='linear', fill_value='extrapolate')
-cruciform_yshifts = scipy.interpolate.interp1d([0, 511, 1031], [1.6,0,-1.6], kind='linear', fill_value='extrapolate')
+# Lookup tables of shifts of the cruciform
+# Estimated roughly from F560W ePSFs (ePSFs by Libralatto, shift estimate by Perrin)
+cruciform_xshifts = scipy.interpolate.interp1d([0, 357, 1031], [1.5, 0.5, -0.9], kind='linear', fill_value='extrapolate')
+cruciform_yshifts = scipy.interpolate.interp1d([0, 511, 1031], [1.6, 0, -1.6], kind='linear', fill_value='extrapolate')
+
 
 def _make_miri_scattering_kernel_2d(in_psf, kernel_amp, oversample=1, wavelength=5.5, detector_position=(0, 0)):
     """Improved / more complex model of the MIRI cruciform, with parameterization to model
@@ -310,13 +315,13 @@ def _make_miri_scattering_kernel_2d(in_psf, kernel_amp, oversample=1, wavelength
     """
     # make output array
     npix = in_psf.shape[0]
-    cen = (npix-1) // 2
-    kernel_2d = np.zeros( (npix, npix), float)
+    cen = (npix - 1) // 2
+    kernel_2d = np.zeros((npix, npix), float)
 
-    ### make 1d kernels for the main cruciform bright lines 
+    # make 1d kernels for the main cruciform bright lines
     # Compute 1d indices
     x = np.arange(npix, dtype=float)
-    x -= (npix-1)/2
+    x -= (npix - 1) / 2
     x /= oversample
     y = x  # we're working in 1d in this part, but clarify let's have separate coords for each axis
 
@@ -330,19 +335,22 @@ def _make_miri_scattering_kernel_2d(in_psf, kernel_amp, oversample=1, wavelength
 
     # Add in the offset copies of the main 1d kernels
     # Empirically, the 'center' of the cruciform shifts inwards towards the center of the detector
-    # i.e. for the upper right corner, the cruciform shifts down and left a bit, etc. 
+    # i.e. for the upper right corner, the cruciform shifts down and left a bit, etc.
     yshift = cruciform_yshifts(detector_position[1])
     xshift = cruciform_xshifts(detector_position[0])
-    kernel_2d[cen + int(round(yshift*oversample))] = kernel_x
-    kernel_2d[:, cen + int(round(xshift*oversample))] = kernel_y
+    kernel_2d[cen + int(round(yshift * oversample))] = kernel_x
+    kernel_2d[:, cen + int(round(xshift * oversample))] = kernel_y
 
-    ### create and add in the more diffuse radial term
+    # create and add in the more diffuse radial term
     # Model this as an expoential falloff outside the inner radius, times some scale factor relative to the above
     y, x = np.indices(kernel_2d.shape)
-    r = np.sqrt((x-cen)**2 + (y-cen)**2) / oversample
-    radial_term  = np.exp(-r/2/webbpsf.constants.MIRI_CRUCIFORM_INNER_RADIUS_PIX) * kernel_amp \
-                   * (r > webbpsf.constants.MIRI_CRUCIFORM_INNER_RADIUS_PIX) \
-                   * webbpsf.constants.MIRI_CRUCIFORM_RADIAL_SCALEFACTOR
+    r = np.sqrt((x - cen) ** 2 + (y - cen) ** 2) / oversample
+    radial_term = (
+        np.exp(-r / 2 / webbpsf.constants.MIRI_CRUCIFORM_INNER_RADIUS_PIX)
+        * kernel_amp
+        * (r > webbpsf.constants.MIRI_CRUCIFORM_INNER_RADIUS_PIX)
+        * webbpsf.constants.MIRI_CRUCIFORM_RADIAL_SCALEFACTOR
+    )
 
     kernel_2d += radial_term
 
@@ -373,9 +381,9 @@ def _apply_miri_scattering_kernel_2d(in_psf, kernel_2d, oversample):
     """
 
     # Convolve the input PSF with the kernel for scattering
-    im_conv = astropy.convolution.convolve_fft(in_psf, kernel_2d, boundary='fill', fill_value=0.0,
-                                                 normalize_kernel=False, nan_treatment='fill', allow_huge = True)
-
+    im_conv = astropy.convolution.convolve_fft(
+        in_psf, kernel_2d, boundary='fill', fill_value=0.0, normalize_kernel=False, nan_treatment='fill', allow_huge=True
+    )
 
     # Normalize.
     # Note, it appears we do need to correct the amplitude for the sampling factor. Might as well do that here.
@@ -489,9 +497,13 @@ def apply_miri_scattering(hdulist_or_filename=None, kernel_amp=None, old_method=
     in_psf = psf[ext].data
 
     # create cruciform model using improved method using a 2d convolution kernel, attempting to model more physics.
-    kernel_2d = _make_miri_scattering_kernel_2d(in_psf, kernel_amp, oversample,
-        detector_position= (hdu_list[0].header['DET_X'], hdu_list[0].header['DET_Y']),
-        wavelength = hdu_list[0].header['WAVELEN']*1e6 )
+    kernel_2d = _make_miri_scattering_kernel_2d(
+        in_psf,
+        kernel_amp,
+        oversample,
+        detector_position=(hdu_list[0].header['DET_X'], hdu_list[0].header['DET_Y']),
+        wavelength=hdu_list[0].header['WAVELEN'] * 1e6,
+    )
     im_conv_both = _apply_miri_scattering_kernel_2d(in_psf, kernel_2d, oversample)
 
     # Add this 2D scattered light output to the PSF
@@ -511,25 +523,22 @@ def apply_miri_scattering(hdulist_or_filename=None, kernel_amp=None, old_method=
     return psf
 
 
-def _show_miri_cruciform_kernel(filt, npix=101, oversample=4, detector_position=(512,512), ax=None):
-    """ utility function for viewing/visualizing the cruciform kernel
-    """
+def _show_miri_cruciform_kernel(filt, npix=101, oversample=4, detector_position=(512, 512), ax=None):
+    """utility function for viewing/visualizing the cruciform kernel"""
     import matplotlib
 
-    placeholder = np.zeros((npix*oversample, npix*oversample))
+    placeholder = np.zeros((npix * oversample, npix * oversample))
     kernel_amp = get_miri_cruciform_amplitude(filt)
-    extent =[-npix/2, npix/2, -npix/2, npix/2]
+    extent = [-npix / 2, npix / 2, -npix / 2, npix / 2]
 
-    kernel_2d = _make_miri_scattering_kernel_2d(placeholder, kernel_amp, oversample,
-        detector_position= detector_position)
+    kernel_2d = _make_miri_scattering_kernel_2d(placeholder, kernel_amp, oversample, detector_position=detector_position)
     norm = matplotlib.colors.LogNorm(1e-6, 1)
     cmap = matplotlib.cm.viridis
     cmap.set_bad(cmap(0))
     if ax is None:
         ax = matplotlib.pyplot.gca()
     ax.imshow(kernel_2d, norm=norm, cmap=cmap, extent=extent, origin='lower')
-    ax.set_title(f"MIRI cruciform model for {filt}, position {detector_position}, oversample {oversample}")
-    ax.plot(0,0,marker='+', color='yellow')
+    ax.set_title(f'MIRI cruciform model for {filt}, position {detector_position}, oversample {oversample}')
+    ax.plot(0, 0, marker='+', color='yellow')
 
     matplotlib.pyplot.colorbar(mappable=ax.images[0])
-

webbpsf/gridded_library.py

@@ -579,7 +579,6 @@ def tuple_to_int(t):
         if isinstance(t, tuple):
             return (int(t[0]), int(t[1]))
 
-
     def show_grid_helper(grid, data, title='Grid of PSFs', vmax=0, vmin=0, scale='log'):
         npsfs = grid.data.shape[0]
         n = int(np.sqrt(npsfs))