fix plots aspect ratio, fits saving of reduction parameters
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@@ -16,6 +16,7 @@ from astropy.io import fits
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from astropy.wcs import WCS
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from .convex_hull import clean_ROI
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from .utils import wcs_PA
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def get_obs_data(infiles, data_folder="", compute_flux=False):
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@@ -57,22 +58,20 @@ def get_obs_data(infiles, data_folder="", compute_flux=False):
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if new_wcs.wcs.has_cd() or (new_wcs.wcs.cdelt[:2] == np.array([1.0, 1.0])).all():
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# Update WCS with relevant information
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if new_wcs.wcs.has_cd():
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old_cd = new_wcs.wcs.cd
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del new_wcs.wcs.cd
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keys = list(new_wcs.to_header().keys()) + ["CD1_1", "CD1_2", "CD1_3", "CD2_1", "CD2_2", "CD2_3", "CD3_1", "CD3_2", "CD3_3"]
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for key in keys:
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header.remove(key, ignore_missing=True)
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new_cdelt = np.linalg.eig(old_cd)[0]
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elif (new_wcs.wcs.cdelt == np.array([1.0, 1.0])).all() and (new_wcs.array_shape in [(512, 512), (1024, 512), (512, 1024), (1024, 1024)]):
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old_cd = new_wcs.wcs.pc
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new_wcs.wcs.pc = np.dot(old_cd, np.diag(1.0 / new_cdelt))
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new_cdelt = np.linalg.eigvals(wcs.wcs.cd)
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new_cdelt.sort()
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new_wcs.wcs.pc = wcs.wcs.cd.dot(np.diag(1.0 / new_cdelt))
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new_wcs.wcs.cdelt = new_cdelt
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for key, val in new_wcs.to_header().items():
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header[key] = val
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try:
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_ = header["ORIENTAT"]
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except KeyError:
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header["ORIENTAT"] = -np.arccos(new_wcs.wcs.pc[0, 0]) * 180.0 / np.pi
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header["ORIENTAT"] = wcs_PA(new_wcs.wcs.pc[1, 0], np.diag(new_wcs.wcs.pc).mean())
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# force WCS for POL60 to have same pixel size as POL0 and POL120
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is_pol60 = np.array([head["filtnam1"].lower() == "pol60" for head in headers], dtype=bool)
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@@ -130,7 +129,6 @@ def save_Stokes(
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Only returned if return_hdul is True.
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"""
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# Create new WCS object given the modified images
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exp_tot = header_stokes['exptime']
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new_wcs = WCS(header_stokes).deepcopy()
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if data_mask.shape != (1, 1):
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@@ -140,23 +138,23 @@ def save_Stokes(
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new_wcs.wcs.crpix = np.array(new_wcs.wcs.crpix) - vertex[0::-2]
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header = new_wcs.to_header()
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header["TELESCOP"] = (header_stokes["telescop"] if "TELESCOP" in list(header_stokes.keys()) else "HST", "telescope used to acquire data")
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header["INSTRUME"] = (header_stokes["instrume"] if "INSTRUME" in list(header_stokes.keys()) else "FOC", "identifier for instrument used to acuire data")
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header["PHOTPLAM"] = (header_stokes["photplam"], "Pivot Wavelength")
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header["PHOTFLAM"] = (header_stokes["photflam"], "Inverse Sensitivity in DN/sec/cm**2/Angst")
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header["EXPTIME"] = (exp_tot, "Total exposure time in sec")
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header["PROPOSID"] = (header_stokes["proposid"], "PEP proposal identifier for observation")
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header["TARGNAME"] = (header_stokes["targname"], "Target name")
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header["ORIENTAT"] = (np.arccos(new_wcs.wcs.pc[0, 0]) * 180.0 / np.pi, "Angle between North and the y-axis of the image")
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header["FILENAME"] = (filename, "Original filename")
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header["TELESCOP"] = (header_stokes["TELESCOP"] if "TELESCOP" in list(header_stokes.keys()) else "HST", "telescope used to acquire data")
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header["INSTRUME"] = (header_stokes["INSTRUME"] if "INSTRUME" in list(header_stokes.keys()) else "FOC", "identifier for instrument used to acuire data")
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header["PHOTPLAM"] = (header_stokes["PHOTPLAM"], "Pivot Wavelength")
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header["PHOTFLAM"] = (header_stokes["PHOTFLAM"], "Inverse Sensitivity in DN/sec/cm**2/Angst")
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header["EXPTIME"] = (header_stokes["EXPTIME"], "Total exposure time in sec")
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header["PROPOSID"] = (header_stokes["PROPOSID"], "PEP proposal identifier for observation")
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header["TARGNAME"] = (header_stokes["TARGNAME"], "Target name")
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header["ORIENTAT"] = (header_stokes["ORIENTAT"], "Angle between North and the y-axis of the image")
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header["FILENAME"] = (filename, "ORIGINAL FILENAME")
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header["BKG_TYPE"] = (header_stokes["BKG_TYPE"], "Bkg estimation method used during reduction")
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header["BKG_SUB"] = (header_stokes["BKG_SUB"], "Amount of bkg subtracted from images")
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header["SMOOTH"] = (header_stokes["SMOOTH"], "Smoothing method used during reduction")
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header["SAMPLING"] = (header_stokes["SAMPLING"], "Resampling performed during reduction")
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header["P_INT"] = (header_stokes["P_int"], "Integrated polarization degree")
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header["sP_INT"] = (header_stokes["sP_int"], "Integrated polarization degree error")
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header["PA_INT"] = (header_stokes["PA_int"], "Integrated polarization angle")
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header["sPA_INT"] = (header_stokes["sPA_int"], "Integrated polarization angle error")
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header["SMOOTH"] = (header_stokes["SMOOTH"] if "SMOOTH" in list(header_stokes.keys()) else "None", "Smoothing method used during reduction")
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header["SAMPLING"] = (header_stokes["SAMPLING"] if "SAMPLING" in list(header_stokes.keys()) else "None", "Resampling performed during reduction")
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header["P_INT"] = (header_stokes["P_INT"], "Integrated polarization degree")
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header["sP_INT"] = (header_stokes["sP_INT"], "Integrated polarization degree error")
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header["PA_INT"] = (header_stokes["PA_INT"], "Integrated polarization angle")
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header["sPA_INT"] = (header_stokes["sPA_INT"], "Integrated polarization angle error")
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# Crop Data to mask
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if data_mask.shape != (1, 1):
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@@ -182,9 +182,11 @@ def plot_Stokes(Stokes, savename=None, plots_folder=""):
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wcs = WCS(Stokes[0]).deepcopy()
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# Plot figure
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plt.rcParams.update({"font.size": 10})
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fig, (axI, axQ, axU) = plt.subplots(ncols=3, figsize=(20, 6), subplot_kw=dict(projection=wcs))
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fig.subplots_adjust(hspace=0, wspace=0.75, bottom=0.01, top=0.99, left=0.08, right=0.95)
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plt.rcParams.update({"font.size": 12})
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ratiox = max(int(stkI.shape[1]/stkI.shape[0]),1)
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ratioy = max(int(stkI.shape[0]/stkI.shape[1]),1)
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fig, (axI, axQ, axU) = plt.subplots(ncols=3, figsize=(20*ratiox, 8*ratioy), subplot_kw=dict(projection=wcs))
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fig.subplots_adjust(hspace=0, wspace=0.50, bottom=0.01, top=0.99, left=0.07, right=0.97)
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fig.suptitle("I, Q, U Stokes parameters")
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imI = axI.imshow(stkI, origin="lower", cmap="inferno")
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@@ -320,9 +322,11 @@ def polarization_map(
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print("No pixel with polarization information above requested SNR.")
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# Plot the map
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plt.rcParams.update({"font.size": 10})
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plt.rcParams.update({"font.size": 12})
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plt.rcdefaults()
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fig, ax = plt.subplots(figsize=(10, 10), layout="constrained", subplot_kw=dict(projection=wcs))
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ratiox = max(int(stkI.shape[1]/stkI.shape[0]),1)
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ratioy = max(int(stkI.shape[0]/stkI.shape[1]),1)
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fig, ax = plt.subplots(figsize=(10*ratiox, 10*ratioy), layout="constrained", subplot_kw=dict(projection=wcs))
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ax.set(aspect="equal", fc="k")
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# fig.subplots_adjust(hspace=0, wspace=0, left=0.102, right=1.02)
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@@ -439,17 +443,17 @@ def polarization_map(
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ax.transAxes,
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"E",
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"N",
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length=-0.08,
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fontsize=0.025,
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length=-0.05,
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fontsize=0.02,
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loc=1,
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aspect_ratio=-1,
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aspect_ratio=-(stkI.shape[1]/stkI.shape[0]),
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sep_y=0.01,
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sep_x=0.01,
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back_length=0.0,
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head_length=10.0,
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head_width=10.0,
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angle=-Stokes[0].header["orientat"],
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text_props={"ec": "k", "fc": "w", "alpha": 1, "lw": -0.2},
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text_props={"ec": "k", "fc": "w", "alpha": 1, "lw": 0.4},
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arrow_props={"ec": "k", "fc": "w", "alpha": 1, "lw": 1},
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)
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@@ -666,7 +670,7 @@ class align_maps(object):
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length=-0.08,
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fontsize=0.03,
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loc=1,
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aspect_ratio=-1,
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aspect_ratio=-(self.map_data.shape[1]/self.map_data.shape[0]),
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sep_y=0.01,
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sep_x=0.01,
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angle=-self.map_header["orientat"],
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@@ -724,7 +728,7 @@ class align_maps(object):
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length=-0.08,
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fontsize=0.03,
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loc=1,
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aspect_ratio=-1,
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aspect_ratio=-(self.other_data.shape[1]/self.other_data.shape[0]),
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sep_y=0.01,
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sep_x=0.01,
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angle=-self.other_header["orientat"],
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@@ -988,7 +992,7 @@ class overplot_radio(align_maps):
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length=-0.08,
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fontsize=0.03,
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loc=1,
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aspect_ratio=-1,
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aspect_ratio=-(stkI.shape[1]/stkI.shape[0]),
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sep_y=0.01,
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sep_x=0.01,
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angle=-self.Stokes_UV[0].header["orientat"],
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@@ -1190,7 +1194,7 @@ class overplot_chandra(align_maps):
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length=-0.08,
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fontsize=0.03,
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loc=1,
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aspect_ratio=-1,
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aspect_ratio=-(stkI.shape[1]/stkI.shape[0]),
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sep_y=0.01,
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sep_x=0.01,
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angle=-self.Stokes_UV[0].header["orientat"],
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@@ -1329,7 +1333,6 @@ class overplot_pol(align_maps):
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else:
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self.scale_vec = scale_vec
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step_vec = 1
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px_scale = np.abs(self.wcs_UV.wcs.get_cdelt()[0] / self.other_wcs.wcs.get_cdelt()[0])
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self.X, self.Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
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self.U, self.V = pol * np.cos(np.pi / 2.0 + pang * np.pi / 180.0), pol * np.sin(np.pi / 2.0 + pang * np.pi / 180.0)
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self.Q = self.ax_overplot.quiver(
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@@ -1339,7 +1342,7 @@ class overplot_pol(align_maps):
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self.V[::step_vec, ::step_vec],
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units="xy",
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angles="uv",
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scale=px_scale / self.scale_vec,
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scale=1. / self.scale_vec,
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scale_units="xy",
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pivot="mid",
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headwidth=0.0,
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@@ -1385,7 +1388,7 @@ class overplot_pol(align_maps):
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length=-0.08,
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fontsize=0.03,
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loc=1,
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aspect_ratio=-1,
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aspect_ratio=-(stkI.shape[1]/stkI.shape[0]),
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sep_y=0.01,
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sep_x=0.01,
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angle=-self.Stokes_UV[0].header["orientat"],
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@@ -1395,7 +1398,7 @@ class overplot_pol(align_maps):
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self.ax_overplot.add_artist(north_dir)
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pol_sc = AnchoredSizeBar(
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self.ax_overplot.transData,
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self.scale_vec / px_scale,
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self.scale_vec,
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r"$P$= 100%",
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4,
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pad=0.5,
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@@ -1550,7 +1553,7 @@ class align_pol(object):
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length=-0.08,
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fontsize=0.025,
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loc=1,
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aspect_ratio=-1,
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aspect_ratio=-(stkI.shape[1]/stkI.shape[0]),
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sep_y=0.01,
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sep_x=0.01,
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back_length=0.0,
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@@ -1814,6 +1817,8 @@ class crop_map(object):
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# Write cropped map to new HDUList
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self.header_crop = deepcopy(header)
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self.header_crop.update(self.wcs_crop.to_header())
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if self.header_crop["FILENAME"][-4:] != "crop":
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self.header_crop["FILENAME"] += "_crop"
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self.hdul_crop = fits.HDUList([fits.PrimaryHDU(self.data_crop, self.header_crop)])
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self.rect_selector.clear()
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@@ -1936,6 +1941,8 @@ class crop_Stokes(crop_map):
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)
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for dataset in self.hdul_crop:
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if dataset.header["FILENAME"][-4:] != "crop":
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dataset.header["FILENAME"] += "_crop"
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dataset.header["P_int"] = (P_diluted, "Integrated polarization degree")
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dataset.header["sP_int"] = (np.ceil(P_diluted_err * 1000.0) / 1000.0, "Integrated polarization degree error")
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dataset.header["PA_int"] = (PA_diluted, "Integrated polarization angle")
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@@ -2797,10 +2804,10 @@ class pol_map(object):
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ax.transAxes,
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"E",
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"N",
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length=-0.08,
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fontsize=0.025,
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length=-0.05,
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fontsize=0.02,
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loc=1,
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aspect_ratio=-1,
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aspect_ratio=-(self.I.shape[1]/self.I.shape[0]),
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sep_y=0.01,
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sep_x=0.01,
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back_length=0.0,
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