some code formatting

package/lib/cross_correlation.py

@@ -48,20 +48,16 @@ def _upsampled_dft(data, upsampled_region_size, upsample_factor=1, axis_offsets=
     """
     # if people pass in an integer, expand it to a list of equal-sized sections
     if not hasattr(upsampled_region_size, "__iter__"):
-        upsampled_region_size = [
+        upsampled_region_size = [upsampled_region_size] * data.ndim
-            upsampled_region_size,
-        ] * data.ndim
     else:
         if len(upsampled_region_size) != data.ndim:
-            raise ValueError("shape of upsampled region sizes must be equal " "to input data's number of dimensions.")
+            raise ValueError("shape of upsampled region sizes must be equal to input data's number of dimensions.")
 
     if axis_offsets is None:
-        axis_offsets = [
+        axis_offsets = [0] * data.ndim
-            0,
-        ] * data.ndim
     else:
         if len(axis_offsets) != data.ndim:
-            raise ValueError("number of axis offsets must be equal to input " "data's number of dimensions.")
+            raise ValueError("number of axis offsets must be equal to input data's number of dimensions.")
 
     im2pi = 1j * 2 * np.pi
 

package/lib/query.py

@@ -173,11 +173,7 @@ def get_product_list(target=None, proposal_id=None, instrument="foc"):
 
     observations = Observations.query_criteria(obs_id=list(results["Dataset"][b]))
     products = Observations.filter_products(
-        Observations.get_product_list(observations),
+        Observations.get_product_list(observations), productType=["SCIENCE"], dataproduct_type=dataproduct_type, calib_level=[2], description=description
-        productType=["SCIENCE"],
-        dataproduct_type=dataproduct_type,
-        calib_level=[2],
-        description=description,
     )
 
     products["proposal_id"] = Column(products["proposal_id"], dtype="U35")

package/lib/reduction.py

@@ -191,8 +191,8 @@ def bin_ndarray(ndarray, new_shape, operation="sum"):
 
     Example
     -------
-    >>> m = np.arange(0,100,1).reshape((10,10))
+    >>> m = np.arange(0, 100, 1).reshape((10, 10))
-    >>> n = bin_ndarray(m, new_shape=(5,5), operation='sum')
+    >>> n = bin_ndarray(m, new_shape=(5, 5), operation="sum")
     >>> print(n)
 
     [[ 22  30  38  46  54]
@@ -279,9 +279,7 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5, nu
     if null_val is None:
         null_val = [1.00 * error.mean() for error in error_array]
     elif type(null_val) is float:
-        null_val = [
+        null_val = [null_val] * error_array.shape[0]
-            null_val,
-        ] * error_array.shape[0]
 
     vertex = np.zeros((data_array.shape[0], 4), dtype=int)
     for i, image in enumerate(data_array):  # Get vertex of the rectangular convex hull of each image
@@ -348,10 +346,7 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5, nu
                 headers,
                 vmin=convert_flux * data_array[data_array > 0.0].mean() / 5.0,
                 vmax=convert_flux * data_array[data_array > 0.0].max(),
-                rectangle=[
+                rectangle=[rectangle] * len(headers),
-                    rectangle,
-                ]
-                * len(headers),
                 savename=savename + "_crop_region",
                 plots_folder=plots_folder,
             )
@@ -629,12 +624,7 @@ def rebin_array(data_array, error_array, headers, pxsize=2, scale="px", operatio
 
         # Compute binning ratio
         if scale.lower() in ["px", "pixel"]:
-            Dxy_arr[i] = np.array(
+            Dxy_arr[i] = np.array([pxsize] * 2)
-                [
-                    pxsize,
-                ]
-                * 2
-            )
             scale = "px"
         elif scale.lower() in ["arcsec", "arcseconds"]:
             Dxy_arr[i] = np.array(pxsize / np.abs(w.wcs.cdelt) / 3600.0)
@@ -938,12 +928,7 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.5, scale="pi
                 dist_rc = np.where(data_mask, np.sqrt((r - xx) ** 2 + (c - yy) ** 2), fmax)
                 # Catch expected "OverflowWarning" as we overflow values that are not in the image
                 with warnings.catch_warnings(record=True) as w:
-                    g_rc = np.array(
+                    g_rc = np.array([np.exp(-0.5 * (dist_rc / stdev) ** 2) / (2.0 * np.pi * stdev**2)] * data_array.shape[0])
-                        [
-                            np.exp(-0.5 * (dist_rc / stdev) ** 2) / (2.0 * np.pi * stdev**2),
-                        ]
-                        * data_array.shape[0]
-                    )
                     # Apply weighted combination
                     smoothed[r, c] = np.where(data_mask[r, c], np.sum(data_array * weight * g_rc) / np.sum(weight * g_rc), data_array.mean(axis=0)[r, c])
                     error[r, c] = np.where(
@@ -1438,9 +1423,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
         all_Q_stokes = np.zeros((np.unique(rotate).size, data_array.shape[1], data_array.shape[2]))
         all_U_stokes = np.zeros((np.unique(rotate).size, data_array.shape[1], data_array.shape[2]))
         all_Stokes_cov = np.zeros((np.unique(rotate).size, 3, 3, data_array.shape[1], data_array.shape[2]))
-        all_header_stokes = [
+        all_header_stokes = [{}] * np.unique(rotate).size
-            {},
-        ] * np.unique(rotate).size
 
         for i, rot in enumerate(np.unique(rotate)):
             rot_mask = rotate == rot

package/src/emission_center.py

@@ -23,12 +23,7 @@ def main(infile, P_cut=0.99, target=None, display="pf", output_dir=None):
     stkI = Stokes["I_STOKES"].data
     QN, UN, QN_ERR, UN_ERR = np.full((4, stkI.shape[0], stkI.shape[1]), np.nan)
     for sflux, nflux in zip(
-        [
+        [Stokes["Q_STOKES"].data, Stokes["U_STOKES"].data, np.sqrt(Stokes["IQU_COV_MATRIX"].data[1, 1]), np.sqrt(Stokes["IQU_COV_MATRIX"].data[2, 2])],
-            Stokes["Q_STOKES"].data,
-            Stokes["U_STOKES"].data,
-            np.sqrt(Stokes["IQU_COV_MATRIX"].data[1, 1]),
-            np.sqrt(Stokes["IQU_COV_MATRIX"].data[2, 2]),
-        ],
         [QN, UN, QN_ERR, UN_ERR],
     ):
         nflux[stkI > 0.0] = sflux[stkI > 0.0] / stkI[stkI > 0.0]
@@ -49,25 +44,10 @@ def main(infile, P_cut=0.99, target=None, display="pf", output_dir=None):
         target = Stokes[0].header["TARGNAME"]
 
     fig = figure(figsize=(8, 8.5), layout="constrained")
-    fig, ax = polarization_map(
+    fig, ax = polarization_map(Stokes, P_cut=P_cut, step_vec=1, scale_vec=5, display=display, fig=fig, width=0.33, linewidth=0.5)
-        Stokes,
-        P_cut=P_cut,
-        step_vec=1,
-        scale_vec=5,
-        display=display,
-        fig=fig,
-        width=0.33,
-        linewidth=0.5,
-    )
 
     ax.plot(*Stokescenter, marker="+", color="k", lw=3)
-    ax.plot(
+    ax.plot(*Stokescenter, marker="+", color="red", lw=1.5, label="Best confidence for center: {0}".format(Stokespos.to_string("hmsdms")))
-        *Stokescenter,
-        marker="+",
-        color="red",
-        lw=1.5,
-        label="Best confidence for center: {0}".format(Stokespos.to_string("hmsdms")),
-    )
     ax.contour(Stokescentconf, [0.01], colors="k", linewidths=3)
     confcentcont = ax.contour(Stokescentconf, [0.01], colors="red")
     # confcont = ax.contour(Stokesconf, [0.9905], colors="r")
@@ -82,14 +62,7 @@ def main(infile, P_cut=0.99, target=None, display="pf", output_dir=None):
     # handles.append(Rectangle((0, 0), 1, 1, fill=False, ls="--", ec=snr3cont.get_edgecolor()[0]))
     # labels.append(r"$SNR_P \geq$ 4  contour")
     # handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=snr4cont.get_edgecolor()[0]))
-    ax.legend(
+    ax.legend(handles=handles, labels=labels, bbox_to_anchor=(-0.05, -0.02, 1.10, 0.01), loc="upper left", mode="expand", borderaxespad=0.0)
-        handles=handles,
-        labels=labels,
-        bbox_to_anchor=(-0.05, -0.02, 1.10, 0.01),
-        loc="upper left",
-        mode="expand",
-        borderaxespad=0.0,
-    )
 
     if output_dir is not None:
         filename = pathjoin(output_dir, "%s_center.pdf" % target)
@@ -103,57 +76,11 @@ if __name__ == "__main__":
     import argparse
 
     parser = argparse.ArgumentParser(description="Look for the center of emission for a given reduced observation")
-    parser.add_argument(
+    parser.add_argument("-t", "--target", metavar="targetname", required=False, help="the name of the target", type=str, default=None)
-        "-t",
+    parser.add_argument("-f", "--file", metavar="path", required=False, help="The full or relative path to the data product", type=str, default=None)
-        "--target",
+    parser.add_argument("-c", "--pcut", metavar="pcut", required=False, help="The polarization cut for the data mask", type=float, default=0.99)
-        metavar="targetname",
+    parser.add_argument("-d", "--display", metavar="display", required=False, help="The map on which to display info", type=str, default="pf")
-        required=False,
+    parser.add_argument("-o", "--output_dir", metavar="directory_path", required=False, help="output directory path for the plots", type=str, default="./data")
-        help="the name of the target",
-        type=str,
-        default=None,
-    )
-    parser.add_argument(
-        "-f",
-        "--file",
-        metavar="path",
-        required=False,
-        help="The full or relative path to the data product",
-        type=str,
-        default=None,
-    )
-    parser.add_argument(
-        "-c",
-        "--pcut",
-        metavar="pcut",
-        required=False,
-        help="The polarization cut for the data mask",
-        type=float,
-        default=0.99,
-    )
-    parser.add_argument(
-        "-d",
-        "--display",
-        metavar="display",
-        required=False,
-        help="The map on which to display info",
-        type=str,
-        default="pf",
-    )
-    parser.add_argument(
-        "-o",
-        "--output_dir",
-        metavar="directory_path",
-        required=False,
-        help="output directory path for the plots",
-        type=str,
-        default="./data",
-    )
     args = parser.parse_args()
-    exitcode = main(
+    exitcode = main(infile=args.file, P_cut=args.pcut, target=args.target, display=args.display, output_dir=args.output_dir)
-        infile=args.file,
-        P_cut=args.pcut,
-        target=args.target,
-        display=args.display,
-        output_dir=args.output_dir,
-    )
     print("Written to: ", exitcode)

package/src/overplot_NGC1068.py

@@ -15,17 +15,7 @@ Radio = fits.open("./data/NGC1068/MERLIN-VLA/Combined_crop.fits")
 
 levels = np.logspace(-0.5, 1.99, 7) / 100.0 * Stokes_UV[0].data.max() * Stokes_UV[0].header["photflam"]
 A = overplot_pol(Stokes_UV, Radio, norm=LogNorm())
-A.plot(
+A.plot(levels=levels, P_cut=0.99, SNRi_cut=1.0, scale_vec=3, step_vec=1, norm=LogNorm(5e-5, 1e-1), cmap="inferno_r", width=0.8, linewidth=1.2)
-    levels=levels,
-    P_cut=0.99,
-    SNRi_cut=1.0,
-    scale_vec=3,
-    step_vec=1,
-    norm=LogNorm(5e-5, 1e-1),
-    cmap="inferno_r",
-    width=0.8,
-    linewidth=1.2,
-)
 A.add_vector(
     A.other_wcs.celestial.wcs.crpix - (1.0, 1.0),
     pol_deg=0.124,

package/src/readfos.py

@@ -271,18 +271,9 @@ class specpol(object):
         """
         data_dump = np.loadtxt(join_path(data_dir, filename), skiprows=1).T
         self.zero(data_dump.shape[1])
-        (
+        (self.wav, self.wav_err[:, 0], self.I, self.IQUV_cov[0, 0], self.Q, self.IQUV_cov[1, 1], self.U, self.IQUV_cov[2, 2], self.V, self.IQUV_cov[3, 3]) = (
-            self.wav,
+            data_dump[:10]
-            self.wav_err[:, 0],
+        )
-            self.I,
-            self.IQUV_cov[0, 0],
-            self.Q,
-            self.IQUV_cov[1, 1],
-            self.U,
-            self.IQUV_cov[2, 2],
-            self.V,
-            self.IQUV_cov[3, 3],
-        ) = data_dump[:10]
         self.wav_err[:, 1] = deepcopy(self.wav_err[:, 0])
         self.bin_edges[:-1], self.bin_edges[-1] = deepcopy(self.wav - self.wav_err[:, 0]), deepcopy(self.wav[-1] + self.wav_err[-1, 1])
         for i in range(4):
@@ -314,11 +305,7 @@ class specpol(object):
                 self.PA_err,
             ]
         ).T
-        np.savetxt(
+        np.savetxt(join_path(output_dir, filename + ".txt"), data_dump, header=header)
-            join_path(output_dir, filename + ".txt"),
-            data_dump,
-            header=header,
-        )
         return join_path(output_dir, filename)
 
     def plot(self, fig=None, ax=None, savename=None, plots_folder=""):