2550 lines
122 KiB
Python
Executable File
2550 lines
122 KiB
Python
Executable File
#!/usr/bin/python
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"""
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Library functions for displaying informations using matplotlib
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prototypes :
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- plot_obs(data_array, headers, shape, vmin, vmax, rectangle, savename, plots_folder)
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Plots whole observation raw data in given display shape.
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- plot_Stokes(Stokes, savename, plots_folder)
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Plot the I/Q/U maps from the Stokes HDUList.
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- polarization_map(Stokes, data_mask, rectangle, SNRp_cut, SNRi_cut, step_vec, savename, plots_folder, display) -> fig, ax
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Plots polarization map of polarimetric parameters saved in an HDUList.
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class align_maps(map, other_map, **kwargs)
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Class to interactively align maps with different WCS.
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class overplot_radio(align_maps)
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Class inherited from align_maps to overplot radio data as contours.
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class overplot_chandra(align_maps)
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Class inherited from align_maps to overplot chandra data as contours.
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class overplot_pol(align_maps)
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Class inherited from align_maps to overplot UV polarization vectors on other maps.
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class crop_map(hdul, fig, ax)
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Class to interactively crop a region of interest of a HDUList.
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class crop_Stokes(crop_map)
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Class inherited from crop_map to work on polarization maps.
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class image_lasso_selector(img, fig, ax)
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Class to interactively select part of a map to work on.
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class aperture(img, cdelt, radius, fig, ax)
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Class to interactively simulate aperture integration.
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class pol_map(Stokes, SNRp_cut, SNRi_cut, selection)
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Class to interactively study polarization maps making use of the cropping and selecting tools.
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"""
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from copy import deepcopy
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import numpy as np
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from os.path import join as path_join
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import matplotlib.pyplot as plt
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from matplotlib.patches import Rectangle, Circle, FancyArrowPatch
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from matplotlib.path import Path
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from matplotlib.widgets import RectangleSelector, LassoSelector, Button, Slider, TextBox
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from matplotlib.colors import LogNorm
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import matplotlib.font_manager as fm
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import matplotlib.patheffects as pe
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from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
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from astropy.wcs import WCS
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from astropy.io import fits
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from astropy.coordinates import SkyCoord
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from scipy.ndimage import zoom as sc_zoom
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try:
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from .utils import rot2D, princ_angle, sci_not
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except ImportError:
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from utils import rot2D, princ_angle, sci_not
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def plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, wcs, convert, step_vec=1, vec_scale=2., adaptive_binning=False):
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def adaptive_binning(I_stokes, Q_stokes, U_stokes, Stokes_cov):
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shape = I_stokes.shape
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assert shape[0] == shape[1], "Only square images are supported"
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assert shape[0] % 2 == 0, "Image size must be a power of 2"
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n = int(np.log2(shape[0]))
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bin_map = np.zeros(shape)
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bin_num = 0
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for level in range(n):
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grid_size = 2**level
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temp_I = I_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
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temp_Q = Q_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
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temp_U = U_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
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temp_cov = Stokes_cov.reshape(3, 3, int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(3).sum(4)
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temp_bin_map = bin_map.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
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temp_P = (temp_Q**2 + temp_U**2)**0.5 / temp_I
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temp_P_err = (1 / temp_I) * np.sqrt((temp_Q**2 * temp_cov[1,1,:,:] + temp_U**2 * temp_cov[2,2,:,:] + 2. * temp_Q * temp_U * temp_cov[1,2,:,:]) / (temp_Q**2 + temp_U**2) + \
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((temp_Q / temp_I)**2 + (temp_U / temp_I)**2) * temp_cov[0,0,:,:] - \
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2. * (temp_Q / temp_I) * temp_cov[0,1,:,:] - \
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2. * (temp_U / temp_I) * temp_cov[0,2,:,:])
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for i in range(int(shape[0]/grid_size)):
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for j in range(int(shape[1]/grid_size)):
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if (temp_P[i,j] / temp_P_err[i,j] > 3) and (temp_bin_map[i,j] == 0): # the default criterion is 3 sigma in P
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bin_num += 1
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bin_map[i*grid_size:(i+1)*grid_size,j*grid_size:(j+1)*grid_size] = bin_num
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return bin_map, bin_num
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if adaptive_binning:
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bin_map, bin_num = adaptive_binning(stkI, stkQ, stkU, stk_cov)
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for i in range(1, bin_num+1):
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bin = np.where(bin_map==i)
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x_center, y_center = np.mean(bin, axis=1)
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bin_I = np.sum(stkI[bin])
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bin_Q = np.sum(stkQ[bin])
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bin_U = np.sum(stkU[bin])
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bin_cov = np.zeros((3,3))
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for i in range(3):
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for j in range(3):
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bin_cov[i,j] = np.sum(stk_cov[i,j][bin])
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poldata = np.sqrt(bin_Q**2 + bin_U**2) / bin_I
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pangdata = 0.5 * np.arctan2(bin_U, bin_Q)
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pangdata_err = (1 / (2. *(bin_Q**2 + bin_U**2))) * \
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np.sqrt(bin_U**2 * bin_cov[1,1] + bin_Q**2 * bin_cov[2,2] - 2. * bin_Q * bin_U * bin_cov[1,2])
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ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata), poldata * np.sin(np.pi/2.+pangdata), units='xy', angles='uv', scale=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, linewidth=0.5, color='white', edgecolor='white')
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ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata+3*pangdata_err), poldata * np.sin(np.pi/2.+pangdata+3*pangdata_err), units='xy', angles='uv', scale=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, linewidth=0.5, color='black', edgecolor='black', ls='dashed')
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ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata-3*pangdata_err), poldata * np.sin(np.pi/2.+pangdata-3*pangdata_err), units='xy', angles='uv', scale=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, linewidth=0.5, color='black', edgecolor='black', ls='dashed')
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else:
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X, Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
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U, V = poldata*np.cos(np.pi/2.+pangdata*np.pi/180.), poldata*np.sin(np.pi/2.+pangdata*np.pi/180.)
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ax.quiver(X[::step_vec, ::step_vec], Y[::step_vec, ::step_vec], U[::step_vec, ::step_vec], V[::step_vec, ::step_vec], units='xy', angles='uv', scale=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.5, linewidth=0.75, color='w', edgecolor='k')
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def plot_obs(data_array, headers, rectangle=None, savename=None, plots_folder="", **kwargs):
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"""
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Plots raw observation imagery with some information on the instrument and
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filters.
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----------
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Inputs:
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data_array : numpy.ndarray
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Array of images (2D floats, aligned and of the same shape) of a
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single observation with multiple polarizers of an instrument
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headers : header list
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List of headers corresponding to the images in data_array
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vmin : float, optional
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Min pixel value that should be displayed.
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Defaults to 0.
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vmax : float, optional
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Max pixel value that should be displayed.
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Defaults to 6.
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rectangle : numpy.ndarray, optional
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Array of parameters for matplotlib.patches.Rectangle objects that will
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be displayed on each output image. If None, no rectangle displayed.
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Defaults to None.
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savename : str, optional
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Name of the figure the map should be saved to. If None, the map won't
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be saved (only displayed).
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Defaults to None.
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plots_folder : str, optional
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Relative (or absolute) filepath to the folder in wich the map will
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be saved. Not used if savename is None.
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Defaults to current folder.
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"""
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plt.rcParams.update({'font.size': 10})
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nb_obs = np.max([np.sum([head['filtnam1'] == curr_pol for head in headers]) for curr_pol in ['POL0', 'POL60', 'POL120']])
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shape = np.array((3, nb_obs))
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fig, ax = plt.subplots(shape[0], shape[1], figsize=(3*shape[1], 3*shape[0]), dpi=200, layout='constrained',
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sharex=True, sharey=True)
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r_pol = dict(pol0=0, pol60=1, pol120=2)
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c_pol = dict(pol0=0, pol60=0, pol120=0)
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for i, (data, head) in enumerate(zip(data_array, headers)):
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instr = head['instrume']
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rootname = head['rootname']
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exptime = head['exptime']
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filt = head['filtnam1']
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convert = head['photflam']
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r_ax, c_ax = r_pol[filt.lower()], c_pol[filt.lower()]
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c_pol[filt.lower()] += 1
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if shape[1] != 1:
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ax_curr = ax[r_ax][c_ax]
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else:
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ax_curr = ax[r_ax]
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# plots
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if ('vmin' in kwargs.keys() or 'vmax' in kwargs.keys()):
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vmin, vmax = kwargs['vmin'], kwargs['vmax']
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del kwargs['vmin'], kwargs['vmax']
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else:
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vmin, vmax = convert*data[data > 0.].min()/10., convert*data[data > 0.].max()
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for key, value in [["cmap", [["cmap", "gray"]]], ["norm", [["norm", LogNorm(vmin, vmax)]]]]:
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try:
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_ = kwargs[key]
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except KeyError:
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for key_i, val_i in value:
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kwargs[key_i] = val_i
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# im = ax[r_ax][c_ax].imshow(convert*data, origin='lower', **kwargs)
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data[data*convert < vmin*10.] = vmin*10./convert
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im = ax_curr.imshow(convert*data, origin='lower', **kwargs)
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if rectangle is not None:
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x, y, width, height, angle, color = rectangle[i]
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ax_curr.add_patch(Rectangle((x, y), width, height, angle=angle, edgecolor=color, fill=False))
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# position of centroid
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ax_curr.plot([data.shape[1]/2, data.shape[1]/2], [0, data.shape[0]-1], '--', lw=1, color='grey', alpha=0.5)
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ax_curr.plot([0, data.shape[1]-1], [data.shape[1]/2, data.shape[1]/2], '--', lw=1, color='grey', alpha=0.5)
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ax_curr.annotate(instr+":"+rootname, color='white', fontsize=5, xy=(0.01, 1.00), xycoords='axes fraction', verticalalignment='top', horizontalalignment='left')
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ax_curr.annotate(filt, color='white', fontsize=10, xy=(0.01, 0.01), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='left')
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ax_curr.annotate(exptime, color='white', fontsize=5, xy=(1.00, 0.01), xycoords='axes fraction', verticalalignment='bottom', horizontalalignment='right')
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# fig.subplots_adjust(hspace=0.01, wspace=0.01, right=1.02)
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fig.colorbar(im, ax=ax, location='right', shrink=0.75, aspect=50, pad=0.025, label=r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
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if not (savename is None):
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# fig.suptitle(savename)
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if not savename[-4:] in ['.png', '.jpg', '.pdf']:
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savename += '.pdf'
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fig.savefig(path_join(plots_folder, savename), bbox_inches='tight')
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plt.show()
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return 0
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def plot_Stokes(Stokes, savename=None, plots_folder=""):
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"""
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Plots I/Q/U maps.
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----------
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Inputs:
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Stokes : astropy.io.fits.hdu.hdulist.HDUList
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HDUList containing I, Q, U, P, s_P, PA, s_PA (in this particular order)
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for one observation.
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savename : str, optional
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Name of the figure the map should be saved to. If None, the map won't
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be saved (only displayed).
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Defaults to None.
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plots_folder : str, optional
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Relative (or absolute) filepath to the folder in wich the map will
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be saved. Not used if savename is None.
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Defaults to current folder.
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"""
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# Get data
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stkI = Stokes['I_stokes'].data.copy()
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stkQ = Stokes['Q_stokes'].data.copy()
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stkU = Stokes['U_stokes'].data.copy()
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data_mask = Stokes['Data_mask'].data.astype(bool)
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for dataset in [stkI, stkQ, stkU]:
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dataset[np.logical_not(data_mask)] = np.nan
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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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fig.suptitle("I, Q, U Stokes parameters")
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imI = axI.imshow(stkI, origin='lower', cmap='inferno')
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fig.colorbar(imI, ax=axI, aspect=50, shrink=0.50, pad=0.025, label='counts/sec')
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axI.set(xlabel="RA", ylabel='DEC', title=r"$I_{stokes}$")
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imQ = axQ.imshow(stkQ, origin='lower', cmap='inferno')
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fig.colorbar(imQ, ax=axQ, aspect=50, shrink=0.50, pad=0.025, label='counts/sec')
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axQ.set(xlabel="RA", ylabel='DEC', title=r"$Q_{stokes}$")
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imU = axU.imshow(stkU, origin='lower', cmap='inferno')
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fig.colorbar(imU, ax=axU, aspect=50, shrink=0.50, pad=0.025, label='counts/sec')
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axU.set(xlabel="RA", ylabel='DEC', title=r"$U_{stokes}$")
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if not (savename is None):
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# fig.suptitle(savename+"_IQU")
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if not savename[-4:] in ['.png', '.jpg', '.pdf']:
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savename += '_IQU.pdf'
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else:
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savename = savename[:-4]+"_IQU"+savename[-4:]
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fig.savefig(path_join(plots_folder, savename), bbox_inches='tight')
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plt.show()
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return 0
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def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_cut=3.,
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flux_lim=None, step_vec=1, vec_scale=2., savename=None, plots_folder="", display="default", **kwargs):
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"""
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Plots polarization map from Stokes HDUList.
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----------
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Inputs:
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Stokes : astropy.io.fits.hdu.hdulist.HDUList
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HDUList containing I, Q, U, P, s_P, PA, s_PA (in this particular order)
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for one observation.
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rectangle : numpy.ndarray, optional
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Array of parameters for matplotlib.patches.Rectangle objects that will
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be displayed on each output image. If None, no rectangle displayed.
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Defaults to None.
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SNRp_cut : float, optional
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Cut that should be applied to the signal-to-noise ratio on P.
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Any SNR < SNRp_cut won't be displayed.
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Defaults to 3.
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SNRi_cut : float, optional
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Cut that should be applied to the signal-to-noise ratio on I.
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Any SNR < SNRi_cut won't be displayed.
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Defaults to 30. This value implies an uncertainty in P of 4.7%
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flux_lim : float list, optional
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Limits that should be applied to the flux colorbar.
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Defaults to None, limits are computed on the background value and the
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maximum value in the cut.
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step_vec : int, optional
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Number of steps between each displayed polarization vector.
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If step_vec = 2, every other vector will be displayed.
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Defaults to 1
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vec_scale : float, optional
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Pixel length of displayed 100% polarization vector.
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If vec_scale = 2, a vector of 50% polarization will be 1 pixel wide.
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Defaults to 2.
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savename : str, optional
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Name of the figure the map should be saved to. If None, the map won't
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be saved (only displayed).
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Defaults to None.
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plots_folder : str, optional
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Relative (or absolute) filepath to the folder in wich the map will
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be saved. Not used if savename is None.
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Defaults to current folder.
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display : str, optional
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Choose the map to display between intensity (default), polarization
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degree ('p', 'pol', 'pol_deg') or polarization degree error ('s_p',
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'pol_err', 'pol_deg_err').
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Defaults to None (intensity).
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----------
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Returns:
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fig, ax : matplotlib.pyplot object
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The figure and ax created for interactive contour maps.
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"""
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# Get data
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stkI = Stokes['I_stokes'].data.copy()
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stk_cov = Stokes['IQU_cov_matrix'].data.copy()
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pol = Stokes['Pol_deg_debiased'].data.copy()
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pol_err = Stokes['Pol_deg_err'].data.copy()
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pang = Stokes['Pol_ang'].data.copy()
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try:
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if data_mask is None:
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data_mask = Stokes['Data_mask'].data.astype(bool).copy()
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except KeyError:
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data_mask = np.ones(stkI.shape).astype(bool)
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for dataset in [stkI, pol, pol_err, pang]:
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dataset[np.logical_not(data_mask)] = np.nan
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for i in range(3):
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for j in range(3):
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stk_cov[i][j][np.logical_not(data_mask)] = np.nan
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pivot_wav = Stokes[0].header['photplam']
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convert_flux = Stokes[0].header['photflam']
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wcs = WCS(Stokes[0]).deepcopy()
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# Plot Stokes parameters map
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if display is None or display.lower() in ['default']:
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plot_Stokes(Stokes, savename=savename, plots_folder=plots_folder)
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# Compute SNR and apply cuts
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poldata, pangdata = pol.copy(), pang.copy()
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maskP = pol_err > 0
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SNRp = np.ones(pol.shape)*np.nan
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SNRp[maskP] = pol[maskP]/pol_err[maskP]
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maskI = stk_cov[0, 0] > 0
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SNRi = np.ones(stkI.shape)*np.nan
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SNRi[maskI] = stkI[maskI]/np.sqrt(stk_cov[0, 0][maskI])
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mask = (SNRp > SNRp_cut) * (SNRi > SNRi_cut)
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poldata[np.logical_not(mask)] = np.nan
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pangdata[np.logical_not(mask)] = np.nan
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# Look for pixel of max polarization
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if np.isfinite(pol).any():
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p_max = np.max(pol[np.isfinite(pol)])
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x_max, y_max = np.unravel_index(np.argmax(pol == p_max), pol.shape)
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else:
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print("No pixel with polarization information above requested SNR.")
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# Plot the map
|
|
plt.rcParams.update({'font.size': 10})
|
|
plt.rcdefaults()
|
|
fig, ax = plt.subplots(figsize=(10, 10), layout='constrained', subplot_kw=dict(projection=wcs))
|
|
ax.set(aspect='equal', fc='k')
|
|
# fig.subplots_adjust(hspace=0, wspace=0, left=0.102, right=1.02)
|
|
|
|
if display.lower() in ['intensity']:
|
|
# If no display selected, show intensity map
|
|
display = 'i'
|
|
if flux_lim is None:
|
|
if mask.sum() > 0.:
|
|
vmin, vmax = 1./2.*np.median(np.sqrt(stk_cov[0, 0][mask])*convert_flux), np.max(stkI[stkI > 0.]*convert_flux)
|
|
else:
|
|
vmin, vmax = 1./2.*np.median(np.sqrt(stk_cov[0, 0][stkI > 0.])*convert_flux), np.max(stkI[stkI > 0.]*convert_flux)
|
|
else:
|
|
vmin, vmax = flux_lim
|
|
im = ax.imshow(stkI*convert_flux, norm=LogNorm(vmin, vmax), aspect='equal', cmap='inferno', alpha=1.)
|
|
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
|
|
levelsI = np.array([0.8, 2., 5., 10., 20., 50.])/100.*vmax
|
|
print("Total flux contour levels : ", levelsI)
|
|
ax.contour(stkI*convert_flux, levels=levelsI, colors='grey', linewidths=0.5)
|
|
elif display.lower() in ['pol_flux']:
|
|
# Display polarization flux
|
|
display = 'pf'
|
|
if flux_lim is None:
|
|
if mask.sum() > 0.:
|
|
vmin, vmax = 1./2.*np.median(np.sqrt(stk_cov[0, 0][mask])*convert_flux), np.max(stkI[stkI > 0.]*convert_flux)
|
|
else:
|
|
vmin, vmax = 1./2.*np.median(np.sqrt(stk_cov[0, 0][stkI > 0.])*convert_flux), np.max(stkI[stkI > 0.]*convert_flux)
|
|
else:
|
|
vmin, vmax = flux_lim
|
|
im = ax.imshow(stkI*convert_flux*pol, norm=LogNorm(vmin, vmax), aspect='equal', cmap='inferno', alpha=1.)
|
|
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
|
|
levelsPf = np.linspace(vmax*0.01, vmax*0.99, 10)
|
|
print("Polarized flux contour levels : ", levelsPf)
|
|
ax.contour(stkI*convert_flux*pol, levels=levelsPf, colors='grey', linewidths=0.5)
|
|
elif display.lower() in ['p', 'pol', 'pol_deg']:
|
|
# Display polarization degree map
|
|
display = 'p'
|
|
vmin, vmax = 0., 100.
|
|
im = ax.imshow(pol*100., vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
|
|
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$P$ [%]")
|
|
elif display.lower() in ['pa', 'pang', 'pol_ang']:
|
|
# Display polarization degree map
|
|
display = 'pa'
|
|
vmin, vmax = 0., 180.
|
|
im = ax.imshow(princ_angle(pang), vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
|
|
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$\theta_P$ [°]")
|
|
elif display.lower() in ['s_p', 'pol_err', 'pol_deg_err']:
|
|
# Display polarization degree error map
|
|
display = 's_p'
|
|
if (SNRp > SNRp_cut).any():
|
|
vmin, vmax = 0., np.max([pol_err[SNRp > SNRp_cut].max(), 1.])*100.
|
|
im = ax.imshow(pol_err*100., vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno_r', alpha=1.)
|
|
else:
|
|
vmin, vmax = 0., 100.
|
|
im = ax.imshow(pol_err*100., vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno_r', alpha=1.)
|
|
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$\sigma_P$ [%]")
|
|
elif display.lower() in ['s_i', 'i_err']:
|
|
# Display intensity error map
|
|
display = 's_i'
|
|
if (SNRi > SNRi_cut).any():
|
|
vmin, vmax = 1./2.*np.median(np.sqrt(stk_cov[0, 0][stk_cov[0, 0] > 0.]) *
|
|
convert_flux), np.max(np.sqrt(stk_cov[0, 0][stk_cov[0, 0] > 0.])*convert_flux)
|
|
im = ax.imshow(np.sqrt(stk_cov[0, 0])*convert_flux, norm=LogNorm(vmin, vmax), aspect='equal', cmap='inferno_r', alpha=1.)
|
|
else:
|
|
im = ax.imshow(np.sqrt(stk_cov[0, 0])*convert_flux, aspect='equal', cmap='inferno', alpha=1.)
|
|
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$\sigma_I$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
|
|
elif display.lower() in ['snr', 'snri']:
|
|
# Display I_stokes signal-to-noise map
|
|
display = 'snri'
|
|
vmin, vmax = 0., np.max(SNRi[np.isfinite(SNRi)])
|
|
if vmax*0.99 > SNRi_cut:
|
|
im = ax.imshow(SNRi, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
|
|
levelsSNRi = np.linspace(SNRi_cut, vmax*0.99, 5)
|
|
print("SNRi contour levels : ", levelsSNRi)
|
|
ax.contour(SNRi, levels=levelsSNRi, colors='grey', linewidths=0.5)
|
|
else:
|
|
im = ax.imshow(SNRi, aspect='equal', cmap='inferno', alpha=1.)
|
|
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$I_{Stokes}/\sigma_{I}$")
|
|
elif display.lower() in ['snrp']:
|
|
# Display polarization degree signal-to-noise map
|
|
display = 'snrp'
|
|
vmin, vmax = 0., np.max(SNRp[np.isfinite(SNRp)])
|
|
if vmax*0.99 > SNRp_cut:
|
|
im = ax.imshow(SNRp, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
|
|
levelsSNRp = np.linspace(SNRp_cut, vmax*0.99, 5)
|
|
print("SNRp contour levels : ", levelsSNRp)
|
|
ax.contour(SNRp, levels=levelsSNRp, colors='grey', linewidths=0.5)
|
|
else:
|
|
im = ax.imshow(SNRp, aspect='equal', cmap='inferno', alpha=1.)
|
|
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$P/\sigma_{P}$")
|
|
else:
|
|
# Defaults to intensity map
|
|
if mask.sum() > 0.:
|
|
vmin, vmax = 1.*np.mean(np.sqrt(stk_cov[0, 0][mask])*convert_flux), np.max(stkI[stkI > 0.]*convert_flux)
|
|
else:
|
|
vmin, vmax = 1.*np.mean(np.sqrt(stk_cov[0, 0][stkI > 0.])*convert_flux), np.max(stkI[stkI > 0.]*convert_flux)
|
|
im = ax.imshow(stkI*convert_flux, norm=LogNorm(vmin, vmax), aspect='equal', cmap='inferno', alpha=1.)
|
|
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA$]")
|
|
|
|
# Get integrated values from header
|
|
I_diluted = stkI[data_mask].sum()
|
|
I_diluted_err = np.sqrt(np.sum(stk_cov[0, 0][data_mask]))
|
|
|
|
P_diluted = Stokes[0].header['P_int']
|
|
P_diluted_err = Stokes[0].header['P_int_err']
|
|
PA_diluted = Stokes[0].header['PA_int']
|
|
PA_diluted_err = Stokes[0].header['PA_int_err']
|
|
|
|
plt.rcParams.update({'font.size': 12})
|
|
px_size = wcs.wcs.get_cdelt()[0]*3600.
|
|
px_sc = AnchoredSizeBar(ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
|
|
north_dir = AnchoredDirectionArrows(ax.transAxes, "E", "N", length=-0.08, fontsize=0.025, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, back_length=0., head_length=10., head_width=10.,
|
|
angle=-Stokes[0].header['orientat'], text_props={'ec': 'k', 'fc': 'w', 'alpha': 1, 'lw': -0.2}, arrow_props={'ec': 'k', 'fc': 'w', 'alpha': 1, 'lw': 1})
|
|
|
|
if display.lower() in ['i', 's_i', 'snri', 'pf', 'p', 'pa', 's_p', 'snrp']:
|
|
if step_vec == 0:
|
|
poldata[np.isfinite(poldata)] = 1./2.
|
|
step_vec = 1
|
|
vec_scale = 2.
|
|
X, Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
|
|
U, V = poldata*np.cos(np.pi/2.+pangdata*np.pi/180.), poldata*np.sin(np.pi/2.+pangdata*np.pi/180.)
|
|
ax.quiver(X[::step_vec, ::step_vec], Y[::step_vec, ::step_vec], U[::step_vec, ::step_vec], V[::step_vec, ::step_vec], units='xy', angles='uv',
|
|
scale=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.5, linewidth=0.75, color='w', edgecolor='k')
|
|
pol_sc = AnchoredSizeBar(ax.transData, vec_scale, r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
|
|
|
|
ax.add_artist(pol_sc)
|
|
ax.add_artist(px_sc)
|
|
ax.add_artist(north_dir)
|
|
|
|
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav, sci_not(I_diluted*convert_flux, I_diluted_err*convert_flux, 2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_diluted*100., P_diluted_err *
|
|
100.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_diluted, PA_diluted_err), color='white', xy=(0.01, 1.00), xycoords='axes fraction', path_effects=[pe.withStroke(linewidth=0.5, foreground='k')], verticalalignment='top', horizontalalignment='left')
|
|
else:
|
|
if display.lower() == 'default':
|
|
ax.add_artist(px_sc)
|
|
ax.add_artist(north_dir)
|
|
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav, sci_not(I_diluted*convert_flux, I_diluted_err*convert_flux, 2)),
|
|
color='white', xy=(0.01, 1.00), xycoords='axes fraction', path_effects=[pe.withStroke(linewidth=0.5, foreground='k')], verticalalignment='top', horizontalalignment='left')
|
|
|
|
# Display instrument FOV
|
|
if not (rectangle is None):
|
|
x, y, width, height, angle, color = rectangle
|
|
x, y = np.array([x, y]) - np.array(stkI.shape)/2.
|
|
ax.add_patch(Rectangle((x, y), width, height, angle=angle,
|
|
edgecolor=color, fill=False))
|
|
|
|
# ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
|
|
ax.coords[0].set_axislabel('Right Ascension (J2000)')
|
|
ax.coords[0].set_axislabel_position('t')
|
|
ax.coords[0].set_ticklabel_position('t')
|
|
ax.set_ylabel('Declination (J2000)', labelpad=-1)
|
|
|
|
if savename is not None:
|
|
if savename[-4:] not in ['.png', '.jpg', '.pdf']:
|
|
savename += '.pdf'
|
|
fig.savefig(path_join(plots_folder, savename), bbox_inches='tight', dpi=200)
|
|
|
|
plt.show()
|
|
return fig, ax
|
|
|
|
|
|
class align_maps(object):
|
|
"""
|
|
Class to interactively align maps with different WCS.
|
|
"""
|
|
|
|
def __init__(self, map, other_map, **kwargs):
|
|
self.aligned = False
|
|
|
|
self.map = map
|
|
self.other = other_map
|
|
self.map_path = self.map.fileinfo(0)['filename']
|
|
self.other_path = self.other.fileinfo(0)['filename']
|
|
|
|
self.map_header = fits.getheader(self.map_path)
|
|
self.other_header = fits.getheader(self.other_path)
|
|
self.map_data = fits.getdata(self.map_path)
|
|
self.other_data = fits.getdata(self.other_path)
|
|
|
|
self.map_wcs = WCS(self.map_header).celestial.deepcopy()
|
|
if len(self.map_data.shape) == 4:
|
|
self.map_data = self.map_data[0, 0]
|
|
elif len(self.map_data.shape) == 3:
|
|
self.map_data = self.map_data[0]
|
|
|
|
self.other_wcs = WCS(self.other_header).celestial.deepcopy()
|
|
if len(self.other_data.shape) == 4:
|
|
self.other_data = self.other_data[0, 0]
|
|
elif len(self.other_data.shape) == 3:
|
|
self.other_data = self.other_data[0]
|
|
|
|
self.map_convert, self.map_unit = (float(self.map_header['photflam']), r"$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$") if "PHOTFLAM" in list(
|
|
self.map_header.keys()) else (1., self.map_header['bunit'] if 'BUNIT' in list(self.map_header.keys()) else "Arbitray Units")
|
|
self.other_convert, self.other_unit = (float(self.other_header['photflam']), r"$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$") if "PHOTFLAM" in list(
|
|
self.other_header.keys()) else (1., self.other_header['bunit'] if 'BUNIT' in list(self.other_header.keys()) else "Arbitray Units")
|
|
self.map_observer = "/".join([self.map_header['telescop'], self.map_header['instrume']]
|
|
) if "INSTRUME" in list(self.map_header.keys()) else self.map_header['telescop']
|
|
self.other_observer = "/".join([self.other_header['telescop'], self.other_header['instrume']]
|
|
) if "INSTRUME" in list(self.other_header.keys()) else self.other_header['telescop']
|
|
|
|
plt.rcParams.update({'font.size': 10})
|
|
fontprops = fm.FontProperties(size=16)
|
|
self.fig_align = plt.figure(figsize=(20, 10))
|
|
self.map_ax = self.fig_align.add_subplot(121, projection=self.map_wcs)
|
|
self.other_ax = self.fig_align.add_subplot(122, projection=self.other_wcs)
|
|
|
|
# Plot the UV map
|
|
other_kwargs = deepcopy(kwargs)
|
|
vmin, vmax = self.map_data[self.map_data > 0.].max()/1e3*self.map_convert, self.map_data[self.map_data > 0.].max()*self.map_convert
|
|
for key, value in [["cmap", [["cmap", "inferno"]]], ["norm", [["norm", LogNorm(vmin, vmax)]]]]:
|
|
try:
|
|
_ = kwargs[key]
|
|
except KeyError:
|
|
for key_i, val_i in value:
|
|
kwargs[key_i] = val_i
|
|
self.map_ax.imshow(self.map_data*self.map_convert, aspect='equal', **kwargs)
|
|
|
|
if kwargs['cmap'] in ['inferno', 'magma', 'Greys_r', 'binary_r', 'gist_yarg_r', 'gist_gray', 'gray', 'bone', 'pink', 'hot', 'afmhot', 'gist_heat', 'copper', 'gist_earth', 'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'nipy_spectral', 'gist_ncar', 'viridis']:
|
|
self.map_ax.set_facecolor('black')
|
|
self.other_ax.set_facecolor('black')
|
|
font_color = "white"
|
|
else:
|
|
self.map_ax.set_facecolor('white')
|
|
self.other_ax.set_facecolor('white')
|
|
font_color = "black"
|
|
px_size1 = self.map_wcs.wcs.get_cdelt()[0]*3600.
|
|
px_sc1 = AnchoredSizeBar(self.map_ax.transData, 1./px_size1, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5,
|
|
frameon=False, size_vertical=0.005, color=font_color, fontproperties=fontprops)
|
|
self.map_ax.add_artist(px_sc1)
|
|
|
|
if 'PHOTPLAM' in list(self.map_header.keys()):
|
|
self.map_ax.annotate(r"$\lambda$ = {0:.0f} $\AA$".format(self.map_header['photplam']), color=font_color, fontsize=12, xy=(
|
|
0.01, 0.93), xycoords='axes fraction', path_effects=[pe.withStroke(linewidth=0.5, foreground='k')])
|
|
if 'ORIENTAT' in list(self.map_header.keys()):
|
|
north_dir1 = AnchoredDirectionArrows(self.map_ax.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01,
|
|
sep_x=0.01, angle=-self.map_header['orientat'], color=font_color, arrow_props={'ec': 'k', 'fc': 'w', 'alpha': 1, 'lw': 0.5})
|
|
self.map_ax.add_artist(north_dir1)
|
|
|
|
self.cr_map, = self.map_ax.plot(*(self.map_wcs.wcs.crpix-(1., 1.)), 'r+')
|
|
|
|
self.map_ax.set_title("{0:s} observation\nClick on selected point of reference.".format(self.map_observer))
|
|
self.map_ax.set_xlabel(label="Right Ascension (J2000)")
|
|
self.map_ax.set_ylabel(label="Declination (J2000)", labelpad=-1)
|
|
|
|
# Plot the other map
|
|
vmin, vmax = self.other_data[self.other_data > 0.].max()/1e3*self.other_convert, self.other_data[self.other_data > 0.].max()*self.other_convert
|
|
for key, value in [["cmap", [["cmap", "inferno"]]], ["norm", [["norm", LogNorm(vmin, vmax)]]]]:
|
|
try:
|
|
_ = other_kwargs[key]
|
|
except KeyError:
|
|
for key_i, val_i in value:
|
|
other_kwargs[key_i] = val_i
|
|
self.other_ax.imshow(self.other_data*self.other_convert, aspect='equal', **other_kwargs)
|
|
|
|
px_size2 = self.other_wcs.wcs.get_cdelt()[0]*3600.
|
|
px_sc2 = AnchoredSizeBar(self.other_ax.transData, 1./px_size2, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5,
|
|
frameon=False, size_vertical=0.005, color=font_color, fontproperties=fontprops)
|
|
self.other_ax.add_artist(px_sc2)
|
|
|
|
if 'PHOTPLAM' in list(self.other_header.keys()):
|
|
self.other_ax.annotate(r"$\lambda$ = {0:.0f} $\AA$".format(self.other_header['photplam']), color='white', fontsize=12, xy=(
|
|
0.01, 0.93), xycoords='axes fraction', path_effects=[pe.withStroke(linewidth=0.5, foreground='k')])
|
|
if 'ORIENTAT' in list(self.other_header.keys()):
|
|
north_dir2 = AnchoredDirectionArrows(self.map_ax.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01,
|
|
sep_x=0.01, angle=-self.other_header['orientat'], color=font_color, arrow_props={'ec': 'k', 'fc': 'w', 'alpha': 1, 'lw': 0.5})
|
|
self.other_ax.add_artist(north_dir2)
|
|
|
|
self.cr_other, = self.other_ax.plot(*(self.other_wcs.wcs.crpix-(1., 1.)), 'r+')
|
|
|
|
self.other_ax.set_title("{0:s} observation\nClick on selected point of reference.".format(self.other_observer))
|
|
self.other_ax.set_xlabel(label="Right Ascension (J2000)")
|
|
self.other_ax.set_ylabel(label="Declination (J2000)", labelpad=-1)
|
|
|
|
# Selection button
|
|
self.axapply = self.fig_align.add_axes([0.80, 0.01, 0.1, 0.04])
|
|
self.bapply = Button(self.axapply, 'Apply reference')
|
|
self.bapply.label.set_fontsize(8)
|
|
self.axreset = self.fig_align.add_axes([0.60, 0.01, 0.1, 0.04])
|
|
self.breset = Button(self.axreset, 'Leave as is')
|
|
self.breset.label.set_fontsize(8)
|
|
self.enter = self.fig_align.canvas.mpl_connect('key_press_event', self.on_key)
|
|
|
|
def on_key(self, event):
|
|
if event.key.lower() == "enter":
|
|
self.on_close_align(event)
|
|
|
|
def get_aligned_wcs(self):
|
|
return self.map_wcs, self.other_wcs
|
|
|
|
def onclick_ref(self, event) -> None:
|
|
if self.fig_align.canvas.manager.toolbar.mode == '':
|
|
if (event.inaxes is not None) and (event.inaxes == self.map_ax):
|
|
x = event.xdata
|
|
y = event.ydata
|
|
|
|
self.cr_map.set(data=[x, y])
|
|
self.fig_align.canvas.draw_idle()
|
|
|
|
if (event.inaxes is not None) and (event.inaxes == self.other_ax):
|
|
x = event.xdata
|
|
y = event.ydata
|
|
|
|
self.cr_other.set(data=[x, y])
|
|
self.fig_align.canvas.draw_idle()
|
|
|
|
def reset_align(self, event):
|
|
self.map_wcs.wcs.crpix = WCS(self.map_header).wcs.crpix[:2]
|
|
self.other_wcs.wcs.crpix = WCS(self.other_header).wcs.crpix[:2]
|
|
self.fig_align.canvas.draw_idle()
|
|
|
|
if self.aligned:
|
|
plt.close()
|
|
|
|
self.aligned = True
|
|
|
|
def apply_align(self, event=None):
|
|
if np.array(self.cr_map.get_data()).shape == (2, 1):
|
|
self.map_wcs.wcs.crpix = np.array(self.cr_map.get_data())[:, 0]+(1., 1.)
|
|
else:
|
|
self.map_wcs.wcs.crpix = np.array(self.cr_map.get_data())+(1., 1.)
|
|
if np.array(self.cr_other.get_data()).shape == (2, 1):
|
|
self.other_wcs.wcs.crpix = np.array(self.cr_other.get_data())[:, 0]+(1., 1.)
|
|
else:
|
|
self.other_wcs.wcs.crpix = np.array(self.cr_other.get_data())+(1., 1.)
|
|
self.map_wcs.wcs.crval = np.array(self.map_wcs.pixel_to_world_values(*self.map_wcs.wcs.crpix))
|
|
self.other_wcs.wcs.crval = self.map_wcs.wcs.crval
|
|
self.fig_align.canvas.draw_idle()
|
|
|
|
if self.aligned:
|
|
plt.close()
|
|
|
|
self.aligned = True
|
|
|
|
def on_close_align(self, event):
|
|
if not self.aligned:
|
|
self.aligned = True
|
|
self.apply_align()
|
|
|
|
def align(self):
|
|
self.fig_align.canvas.draw()
|
|
self.fig_align.canvas.mpl_connect('button_press_event', self.onclick_ref)
|
|
self.bapply.on_clicked(self.apply_align)
|
|
self.breset.on_clicked(self.reset_align)
|
|
self.fig_align.canvas.mpl_connect('close_event', self.on_close_align)
|
|
plt.show(block=True)
|
|
return self.get_aligned_wcs()
|
|
|
|
def write_map_to(self, path="map.fits", suffix="aligned", data_dir="."):
|
|
new_head = deepcopy(self.map_header)
|
|
new_head.update(self.map_wcs.to_header())
|
|
new_hdul = fits.HDUList(fits.PrimaryHDU(self.map_data, new_head))
|
|
new_hdul.writeto("_".join([path[:-5], suffix])+".fits", overwrite=True)
|
|
return 0
|
|
|
|
def write_other_to(self, path="other_map.fits", suffix="aligned", data_dir="."):
|
|
new_head = deepcopy(self.other_header)
|
|
new_head.update(self.other_wcs.to_header())
|
|
new_hdul = fits.HDUList(fits.PrimaryHDU(self.other_data, new_head))
|
|
new_hdul.writeto("_".join([path[:-5], suffix])+".fits", overwrite=True)
|
|
return 0
|
|
|
|
def write_to(self, path1="map.fits", path2="other_map.fits", suffix="aligned", data_dir="."):
|
|
self.write_map_to(path=path1, suffix=suffix, data_dir=data_dir)
|
|
self.write_other_to(path=path2, suffix=suffix, data_dir=data_dir)
|
|
return 0
|
|
|
|
|
|
class overplot_radio(align_maps):
|
|
"""
|
|
Class to overplot maps from different observations.
|
|
Inherit from class align_maps in order to get the same WCS on both maps.
|
|
"""
|
|
|
|
def overplot(self, levels=None, SNRp_cut=3., SNRi_cut=3., vec_scale=2, savename=None, **kwargs):
|
|
self.Stokes_UV = self.map
|
|
self.wcs_UV = self.map_wcs
|
|
# Get Data
|
|
obj = self.Stokes_UV[0].header['targname']
|
|
stkI = self.Stokes_UV['I_STOKES'].data
|
|
stk_cov = self.Stokes_UV['IQU_COV_MATRIX'].data
|
|
pol = deepcopy(self.Stokes_UV['POL_DEG_DEBIASED'].data)
|
|
pol_err = self.Stokes_UV['POL_DEG_ERR'].data
|
|
pang = self.Stokes_UV['POL_ANG'].data
|
|
|
|
other_data = self.other_data
|
|
self.other_convert = 1.
|
|
if self.other_unit.lower() == 'jy/beam':
|
|
self.other_unit = r"mJy/Beam"
|
|
self.other_convert = 1e3
|
|
other_freq = self.other_header['crval3'] if 'CRVAL3' in list(self.other_header.keys()) else 1.
|
|
|
|
self.map_convert = self.Stokes_UV[0].header['photflam']
|
|
|
|
# Compute SNR and apply cuts
|
|
pol[pol == 0.] = np.nan
|
|
SNRp = pol/pol_err
|
|
SNRp[np.isnan(SNRp)] = 0.
|
|
pol[SNRp < SNRp_cut] = np.nan
|
|
SNRi = stkI/np.sqrt(stk_cov[0, 0])
|
|
SNRi[np.isnan(SNRi)] = 0.
|
|
pol[SNRi < SNRi_cut] = np.nan
|
|
|
|
plt.rcParams.update({'font.size': 16})
|
|
self.fig_overplot, self.ax_overplot = plt.subplots(figsize=(10, 10), subplot_kw=dict(projection=self.wcs_UV))
|
|
self.fig_overplot.subplots_adjust(hspace=0, wspace=0, bottom=0.1, left=0.1, top=0.8, right=1)
|
|
|
|
# Display UV intensity map with polarization vectors
|
|
vmin, vmax = stkI[np.isfinite(stkI)].max()/1e3*self.map_convert, stkI[np.isfinite(stkI)].max()*self.map_convert
|
|
for key, value in [["cmap", [["cmap", "inferno"]]], ["norm", [["norm", LogNorm(vmin, vmax)]]]]:
|
|
try:
|
|
_ = kwargs[key]
|
|
except KeyError:
|
|
for key_i, val_i in value:
|
|
kwargs[key_i] = val_i
|
|
if kwargs['cmap'] in ['inferno', 'magma', 'Greys_r', 'binary_r', 'gist_yarg_r', 'gist_gray', 'gray', 'bone', 'pink', 'hot', 'afmhot', 'gist_heat', 'copper', 'gist_earth', 'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'nipy_spectral', 'gist_ncar', 'viridis']:
|
|
self.ax_overplot.set_facecolor('black')
|
|
font_color = "white"
|
|
else:
|
|
self.ax_overplot.set_facecolor('white')
|
|
font_color = "black"
|
|
self.im = self.ax_overplot.imshow(stkI*self.map_convert, aspect='equal', label="{0:s} observation".format(self.map_observer), **kwargs)
|
|
self.cbar = self.fig_overplot.colorbar(self.im, ax=self.ax_overplot, aspect=50, shrink=0.75, pad=0.025,
|
|
label=r"$F_{{\lambda}}$ [{0:s}]".format(self.map_unit))
|
|
|
|
# Display full size polarization vectors
|
|
if vec_scale is None:
|
|
self.vec_scale = 2.
|
|
pol[np.isfinite(pol)] = 1./2.
|
|
else:
|
|
self.vec_scale = vec_scale
|
|
step_vec = 1
|
|
self.X, self.Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
|
|
self.U, self.V = pol*np.cos(np.pi/2.+pang*np.pi/180.), pol*np.sin(np.pi/2.+pang*np.pi/180.)
|
|
self.Q = self.ax_overplot.quiver(self.X[::step_vec, ::step_vec], self.Y[::step_vec, ::step_vec], self.U[::step_vec, ::step_vec], self.V[::step_vec, ::step_vec], units='xy', angles='uv', scale=1./self.vec_scale,
|
|
scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.5, linewidth=0.75, color='white', edgecolor='black', label="{0:s} polarization map".format(self.map_observer))
|
|
self.ax_overplot.autoscale(False)
|
|
|
|
# Display other map as contours
|
|
if levels is None:
|
|
levels = np.logspace(0., 1.9, 5)/100.*other_data[other_data > 0.].max()
|
|
other_cont = self.ax_overplot.contour(
|
|
other_data*self.other_convert, transform=self.ax_overplot.get_transform(self.other_wcs.celestial), levels=levels*self.other_convert, colors='grey')
|
|
self.ax_overplot.clabel(other_cont, inline=True, fontsize=5)
|
|
|
|
self.ax_overplot.set_xlabel(label="Right Ascension (J2000)")
|
|
self.ax_overplot.set_ylabel(label="Declination (J2000)", labelpad=-1)
|
|
self.fig_overplot.suptitle("{0:s} polarization map of {1:s} overplotted with {2:s} {3:.2f}GHz map in {4:s}.".format(
|
|
self.map_observer, obj, self.other_observer, other_freq*1e-9, self.other_unit), wrap=True)
|
|
|
|
# Display pixel scale and North direction
|
|
fontprops = fm.FontProperties(size=16)
|
|
px_size = self.wcs_UV.wcs.get_cdelt()[0]*3600.
|
|
px_sc = AnchoredSizeBar(self.ax_overplot.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5,
|
|
frameon=False, size_vertical=0.005, color=font_color, fontproperties=fontprops)
|
|
self.ax_overplot.add_artist(px_sc)
|
|
north_dir = AnchoredDirectionArrows(self.ax_overplot.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01,
|
|
sep_x=0.01, angle=-self.Stokes_UV[0].header['orientat'], color=font_color, arrow_props={'ec': 'k', 'fc': 'w', 'alpha': 1, 'lw': 0.5})
|
|
self.ax_overplot.add_artist(north_dir)
|
|
pol_sc = AnchoredSizeBar(self.ax_overplot.transData, self.vec_scale, r"$P$= 100%", 4, pad=0.5, sep=5, borderpad=0.5,
|
|
frameon=False, size_vertical=0.005, color=font_color, fontproperties=fontprops)
|
|
self.ax_overplot.add_artist(pol_sc)
|
|
|
|
self.cr_map, = self.ax_overplot.plot(*(self.map_wcs.celestial.wcs.crpix-(1., 1.)), 'r+')
|
|
self.cr_other, = self.ax_overplot.plot(*(self.other_wcs.celestial.wcs.crpix-(1., 1.)), 'g+', transform=self.ax_overplot.get_transform(self.other_wcs))
|
|
|
|
handles, labels = self.ax_overplot.get_legend_handles_labels()
|
|
handles[np.argmax([li == "{0:s} polarization map".format(self.map_observer) for li in labels])
|
|
] = FancyArrowPatch((0, 0), (0, 1), arrowstyle='-', fc='w', ec='k', lw=2)
|
|
labels.append("{0:s} contour".format(self.other_observer))
|
|
handles.append(Rectangle((0, 0), 1, 1, fill=False, lw=2, ec=other_cont.collections[0].get_edgecolor()[0]))
|
|
self.legend = self.ax_overplot.legend(handles=handles, labels=labels, bbox_to_anchor=(
|
|
0., 1.02, 1., .102), loc='lower left', mode="expand", borderaxespad=0.)
|
|
|
|
if not (savename is None):
|
|
if not savename[-4:] in ['.png', '.jpg', '.pdf']:
|
|
savename += '.pdf'
|
|
self.fig_overplot.savefig(savename, bbox_inches='tight', dpi=200)
|
|
|
|
self.fig_overplot.canvas.draw()
|
|
|
|
def plot(self, levels=None, SNRp_cut=3., SNRi_cut=3., savename=None, **kwargs) -> None:
|
|
while not self.aligned:
|
|
self.align()
|
|
self.overplot(levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename, **kwargs)
|
|
plt.show(block=True)
|
|
|
|
|
|
class overplot_chandra(align_maps):
|
|
"""
|
|
Class to overplot maps from different observations.
|
|
Inherit from class align_maps in order to get the same WCS on both maps.
|
|
"""
|
|
|
|
def overplot(self, levels=None, SNRp_cut=3., SNRi_cut=3., vec_scale=2, zoom=1, savename=None, **kwargs):
|
|
self.Stokes_UV = self.map
|
|
self.wcs_UV = self.map_wcs
|
|
# Get Data
|
|
obj = self.Stokes_UV[0].header['targname']
|
|
stkI = self.Stokes_UV['I_STOKES'].data
|
|
stk_cov = self.Stokes_UV['IQU_COV_MATRIX'].data
|
|
pol = deepcopy(self.Stokes_UV['POL_DEG_DEBIASED'].data)
|
|
pol_err = self.Stokes_UV['POL_DEG_ERR'].data
|
|
pang = self.Stokes_UV['POL_ANG'].data
|
|
|
|
other_data = deepcopy(self.other_data)
|
|
other_wcs = self.other_wcs.deepcopy()
|
|
if zoom != 1:
|
|
other_data = sc_zoom(other_data, zoom)
|
|
other_wcs.wcs.crpix *= zoom
|
|
other_wcs.wcs.cdelt /= zoom
|
|
self.other_unit = 'counts'
|
|
|
|
# Compute SNR and apply cuts
|
|
pol[pol == 0.] = np.nan
|
|
SNRp = pol/pol_err
|
|
SNRp[np.isnan(SNRp)] = 0.
|
|
pol[SNRp < SNRp_cut] = np.nan
|
|
SNRi = stkI/np.sqrt(stk_cov[0, 0])
|
|
SNRi[np.isnan(SNRi)] = 0.
|
|
pol[SNRi < SNRi_cut] = np.nan
|
|
|
|
plt.rcParams.update({'font.size': 16})
|
|
self.fig_overplot, self.ax_overplot = plt.subplots(figsize=(11, 10), subplot_kw=dict(projection=self.wcs_UV))
|
|
self.fig_overplot.subplots_adjust(hspace=0, wspace=0, bottom=0.1, left=0.1, top=0.8, right=1)
|
|
|
|
# Display UV intensity map with polarization vectors
|
|
vmin, vmax = stkI[np.isfinite(stkI)].max()/1e3*self.map_convert, stkI[np.isfinite(stkI)].max()*self.map_convert
|
|
for key, value in [["cmap", [["cmap", "inferno"]]], ["norm", [["norm", LogNorm(vmin, vmax)]]]]:
|
|
try:
|
|
_ = kwargs[key]
|
|
except KeyError:
|
|
for key_i, val_i in value:
|
|
kwargs[key_i] = val_i
|
|
if kwargs['cmap'] in ['inferno', 'magma', 'Greys_r', 'binary_r', 'gist_yarg_r', 'gist_gray', 'gray', 'bone', 'pink', 'hot', 'afmhot', 'gist_heat', 'copper', 'gist_earth', 'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'nipy_spectral', 'gist_ncar', 'viridis']:
|
|
self.ax_overplot.set_facecolor('black')
|
|
font_color = "white"
|
|
else:
|
|
self.ax_overplot.set_facecolor('white')
|
|
font_color = "black"
|
|
self.im = self.ax_overplot.imshow(stkI*self.map_convert, aspect='equal', **kwargs)
|
|
self.cbar = self.fig_overplot.colorbar(self.im, ax=self.ax_overplot, aspect=50, shrink=0.75, pad=0.025,
|
|
label=r"$F_{{\lambda}}$ [{0:s}]".format(self.map_unit))
|
|
|
|
# Display full size polarization vectors
|
|
if vec_scale is None:
|
|
self.vec_scale = 2.
|
|
pol[np.isfinite(pol)] = 1./2.
|
|
else:
|
|
self.vec_scale = vec_scale
|
|
step_vec = 1
|
|
self.X, self.Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
|
|
self.U, self.V = pol*np.cos(np.pi/2.+pang*np.pi/180.), pol*np.sin(np.pi/2.+pang*np.pi/180.)
|
|
self.Q = self.ax_overplot.quiver(self.X[::step_vec, ::step_vec], self.Y[::step_vec, ::step_vec], self.U[::step_vec, ::step_vec], self.V[::step_vec, ::step_vec], units='xy', angles='uv', scale=1./self.vec_scale,
|
|
scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.5, linewidth=0.75, color='white', edgecolor='black', label="{0:s} polarization map".format(self.map_observer))
|
|
self.ax_overplot.autoscale(False)
|
|
|
|
# Display other map as contours
|
|
if levels is None:
|
|
levels = np.logspace(np.log(3)/np.log(10), 2., 5)/100.*other_data[other_data > 0.].max()*self.other_convert
|
|
elif zoom != 1:
|
|
levels *= other_data.max()/self.other_data.max()
|
|
other_cont = self.ax_overplot.contour(other_data*self.other_convert, transform=self.ax_overplot.get_transform(other_wcs), levels=levels, colors='grey')
|
|
self.ax_overplot.clabel(other_cont, inline=True, fontsize=8)
|
|
|
|
self.ax_overplot.set_xlabel(label="Right Ascension (J2000)")
|
|
self.ax_overplot.set_ylabel(label="Declination (J2000)", labelpad=-1)
|
|
self.fig_overplot.suptitle("{0:s} polarization map of {1:s} overplotted\nwith {2:s} contour in counts.".format(
|
|
self.map_observer, obj, self.other_observer), wrap=True)
|
|
|
|
# Display pixel scale and North direction
|
|
fontprops = fm.FontProperties(size=16)
|
|
px_size = self.wcs_UV.wcs.get_cdelt()[0]*3600.
|
|
px_sc = AnchoredSizeBar(self.ax_overplot.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5,
|
|
frameon=False, size_vertical=0.005, color=font_color, fontproperties=fontprops)
|
|
self.ax_overplot.add_artist(px_sc)
|
|
north_dir = AnchoredDirectionArrows(self.ax_overplot.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01,
|
|
sep_x=0.01, angle=-self.Stokes_UV[0].header['orientat'], color=font_color, arrow_props={'ec': 'k', 'fc': 'w', 'alpha': 1, 'lw': 0.5})
|
|
self.ax_overplot.add_artist(north_dir)
|
|
pol_sc = AnchoredSizeBar(self.ax_overplot.transData, self.vec_scale, r"$P$= 100%", 4, pad=0.5, sep=5, borderpad=0.5,
|
|
frameon=False, size_vertical=0.005, color=font_color, fontproperties=fontprops)
|
|
self.ax_overplot.add_artist(pol_sc)
|
|
|
|
self.cr_map, = self.ax_overplot.plot(*(self.map_wcs.celestial.wcs.crpix-(1., 1.)), 'r+')
|
|
self.cr_other, = self.ax_overplot.plot(*(other_wcs.celestial.wcs.crpix-(1., 1.)), 'g+', transform=self.ax_overplot.get_transform(other_wcs))
|
|
handles, labels = self.ax_overplot.get_legend_handles_labels()
|
|
handles[np.argmax([li == "{0:s} polarization map".format(self.map_observer) for li in labels])
|
|
] = FancyArrowPatch((0, 0), (0, 1), arrowstyle='-', fc='w', ec='k', lw=2)
|
|
labels.append("{0:s} contour in counts".format(self.other_observer))
|
|
handles.append(Rectangle((0, 0), 1, 1, fill=False, lw=2, ec=other_cont.collections[0].get_edgecolor()[0]))
|
|
self.legend = self.ax_overplot.legend(handles=handles, labels=labels, bbox_to_anchor=(
|
|
0., 1.02, 1., .102), loc='lower left', mode="expand", borderaxespad=0.)
|
|
|
|
if not (savename is None):
|
|
if not savename[-4:] in ['.png', '.jpg', '.pdf']:
|
|
savename += '.pdf'
|
|
self.fig_overplot.savefig(savename, bbox_inches='tight', dpi=200)
|
|
|
|
self.fig_overplot.canvas.draw()
|
|
|
|
def plot(self, levels=None, SNRp_cut=3., SNRi_cut=3., zoom=1, savename=None, **kwargs) -> None:
|
|
while not self.aligned:
|
|
self.align()
|
|
self.overplot(levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, zoom=zoom, savename=savename, **kwargs)
|
|
plt.show(block=True)
|
|
|
|
|
|
class overplot_pol(align_maps):
|
|
"""
|
|
Class to overplot maps from different observations.
|
|
Inherit from class align_maps in order to get the same WCS on both maps.
|
|
"""
|
|
|
|
def overplot(self, levels=None, SNRp_cut=3., SNRi_cut=3., vec_scale=2., savename=None, **kwargs):
|
|
self.Stokes_UV = self.map
|
|
self.wcs_UV = self.map_wcs
|
|
# Get Data
|
|
obj = self.Stokes_UV[0].header['targname']
|
|
stkI = self.Stokes_UV['I_STOKES'].data
|
|
stk_cov = self.Stokes_UV['IQU_COV_MATRIX'].data
|
|
pol = deepcopy(self.Stokes_UV['POL_DEG_DEBIASED'].data)
|
|
pol_err = self.Stokes_UV['POL_DEG_ERR'].data
|
|
pang = self.Stokes_UV['POL_ANG'].data
|
|
|
|
other_data = self.other_data
|
|
|
|
# Compute SNR and apply cuts
|
|
pol[pol == 0.] = np.nan
|
|
SNRp = pol/pol_err
|
|
SNRp[np.isnan(SNRp)] = 0.
|
|
pol[SNRp < SNRp_cut] = np.nan
|
|
SNRi = stkI/np.sqrt(stk_cov[0, 0])
|
|
SNRi[np.isnan(SNRi)] = 0.
|
|
pol[SNRi < SNRi_cut] = np.nan
|
|
|
|
plt.rcParams.update({'font.size': 16})
|
|
self.fig_overplot, self.ax_overplot = plt.subplots(figsize=(11, 10), subplot_kw=dict(projection=self.other_wcs))
|
|
self.fig_overplot.subplots_adjust(hspace=0, wspace=0, bottom=0.1, left=0.1, top=0.80, right=1.02)
|
|
|
|
self.ax_overplot.set_xlabel(label="Right Ascension (J2000)")
|
|
self.ax_overplot.set_ylabel(label="Declination (J2000)", labelpad=-1)
|
|
self.fig_overplot.suptitle("{0:s} observation from {1:s} overplotted with polarization vectors and Stokes I contours from {2:s}".format(
|
|
obj, self.other_observer, self.map_observer), wrap=True)
|
|
|
|
# Display "other" intensity map
|
|
vmin, vmax = other_data[other_data > 0.].max()/1e3*self.other_convert, other_data[other_data > 0.].max()*self.other_convert
|
|
for key, value in [["cmap", [["cmap", "inferno"]]], ["norm", [["vmin", vmin], ["vmax", vmax]]]]:
|
|
try:
|
|
_ = kwargs[key]
|
|
except KeyError:
|
|
for key_i, val_i in value:
|
|
kwargs[key_i] = val_i
|
|
if kwargs['cmap'] in ['inferno', 'magma', 'Greys_r', 'binary_r', 'gist_yarg_r', 'gist_gray', 'gray', 'bone', 'pink', 'hot', 'afmhot', 'gist_heat', 'copper', 'gist_earth', 'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'nipy_spectral', 'gist_ncar', 'viridis']:
|
|
self.ax_overplot.set_facecolor('black')
|
|
font_color = "white"
|
|
else:
|
|
self.ax_overplot.set_facecolor('white')
|
|
font_color = "black"
|
|
self.im = self.ax_overplot.imshow(other_data*self.other_convert, alpha=1., label="{0:s} observation".format(self.other_observer), **kwargs)
|
|
self.cbar = self.fig_overplot.colorbar(self.im, ax=self.ax_overplot, aspect=80, shrink=0.75, pad=0.025,
|
|
label=r"$F_{{\lambda}}$ [{0:s}]".format(self.other_unit))
|
|
|
|
# Display full size polarization vectors
|
|
if vec_scale is None:
|
|
self.vec_scale = 2.
|
|
pol[np.isfinite(pol)] = 1./2.
|
|
else:
|
|
self.vec_scale = vec_scale
|
|
step_vec = 1
|
|
px_scale = np.abs(self.wcs_UV.wcs.get_cdelt()[0]/self.other_wcs.wcs.get_cdelt()[0])
|
|
self.X, self.Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
|
|
self.U, self.V = pol*np.cos(np.pi/2.+pang*np.pi/180.), pol*np.sin(np.pi/2.+pang*np.pi/180.)
|
|
self.Q = self.ax_overplot.quiver(self.X[::step_vec, ::step_vec], self.Y[::step_vec, ::step_vec], self.U[::step_vec, ::step_vec], self.V[::step_vec, ::step_vec], units='xy', angles='uv', scale=px_scale/self.vec_scale, scale_units='xy', pivot='mid',
|
|
headwidth=0., headlength=0., headaxislength=0., width=0.5, linewidth=0.75, color='white', edgecolor='black', transform=self.ax_overplot.get_transform(self.wcs_UV), label="{0:s} polarization map".format(self.map_observer))
|
|
|
|
# Display Stokes I as contours
|
|
if levels is None:
|
|
levels = np.array([2., 5., 10., 20., 90.])/100.*np.max(stkI[stkI > 0.])*self.map_convert
|
|
cont_stkI = self.ax_overplot.contour(stkI*self.map_convert, levels=levels, colors='grey', alpha=0.75,
|
|
transform=self.ax_overplot.get_transform(self.wcs_UV))
|
|
# self.ax_overplot.clabel(cont_stkI, inline=True, fontsize=5)
|
|
|
|
# Display pixel scale and North direction
|
|
fontprops = fm.FontProperties(size=16)
|
|
px_size = self.other_wcs.wcs.get_cdelt()[0]*3600.
|
|
px_sc = AnchoredSizeBar(self.ax_overplot.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5,
|
|
frameon=False, size_vertical=0.005, color=font_color, fontproperties=fontprops)
|
|
self.ax_overplot.add_artist(px_sc)
|
|
north_dir = AnchoredDirectionArrows(self.ax_overplot.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01,
|
|
sep_x=0.01, angle=-self.Stokes_UV[0].header['orientat'], color=font_color, arrow_props={'ec': 'k', 'fc': 'w', 'alpha': 1, 'lw': 0.5})
|
|
self.ax_overplot.add_artist(north_dir)
|
|
pol_sc = AnchoredSizeBar(self.ax_overplot.transData, self.vec_scale/px_scale, r"$P$= 100%", 4, pad=0.5, sep=5,
|
|
borderpad=0.5, frameon=False, size_vertical=0.005, color=font_color, fontproperties=fontprops)
|
|
self.ax_overplot.add_artist(pol_sc)
|
|
|
|
self.cr_map, = self.ax_overplot.plot(*(self.map_wcs.celestial.wcs.crpix-(1., 1.)), 'r+', transform=self.ax_overplot.get_transform(self.wcs_UV))
|
|
self.cr_other, = self.ax_overplot.plot(*(self.other_wcs.celestial.wcs.crpix-(1., 1.)), 'g+')
|
|
|
|
if "PHOTPLAM" in list(self.other_header.keys()):
|
|
self.legend_title = r"{0:s} image at $\lambda$ = {1:.0f} $\AA$".format(self.other_observer, float(self.other_header['photplam']))
|
|
elif "CRVAL3" in list(self.other_header.keys()):
|
|
self.legend_title = "{0:s} image at {1:.2f} GHz".format(self.other_observer, float(self.other_header['crval3'])*1e-9)
|
|
else:
|
|
self.legend_title = r"{0:s} image".format(self.other_observer)
|
|
|
|
handles, labels = self.ax_overplot.get_legend_handles_labels()
|
|
handles[np.argmax([li == "{0:s} polarization map".format(self.map_observer) for li in labels])
|
|
] = FancyArrowPatch((0, 0), (0, 1), arrowstyle='-', fc='w', ec='k', lw=2)
|
|
labels.append("{0:s} Stokes I contour".format(self.map_observer))
|
|
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=cont_stkI.collections[0].get_edgecolor()[0]))
|
|
self.legend = self.ax_overplot.legend(handles=handles, labels=labels, bbox_to_anchor=(
|
|
0., 1.02, 1., .102), loc='lower left', mode="expand", borderaxespad=0.)
|
|
|
|
if not (savename is None):
|
|
if not savename[-4:] in ['.png', '.jpg', '.pdf']:
|
|
savename += '.pdf'
|
|
self.fig_overplot.savefig(savename, bbox_inches='tight', dpi=200)
|
|
|
|
self.fig_overplot.canvas.draw()
|
|
|
|
def plot(self, levels=None, SNRp_cut=3., SNRi_cut=3., vec_scale=2., savename=None, **kwargs) -> None:
|
|
while not self.aligned:
|
|
self.align()
|
|
self.overplot(levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, vec_scale=vec_scale, savename=savename, **kwargs)
|
|
plt.show(block=True)
|
|
|
|
def add_vector(self, position='center', pol_deg=1., pol_ang=0., **kwargs):
|
|
if position == 'center':
|
|
position = np.array(self.X.shape)/2.
|
|
if isinstance(position, SkyCoord):
|
|
position = self.other_wcs.world_to_pixel(position)
|
|
|
|
u, v = pol_deg*np.cos(np.radians(pol_ang)+np.pi/2.), pol_deg*np.sin(np.radians(pol_ang)+np.pi/2.)
|
|
for key, value in [["scale", [["scale", self.vec_scale]]], ["width", [["width", 0.1]]], ["color", [["color", 'k']]]]:
|
|
try:
|
|
_ = kwargs[key]
|
|
except KeyError:
|
|
for key_i, val_i in value:
|
|
kwargs[key_i] = val_i
|
|
new_vec = self.ax_overplot.quiver(*position, u, v, units='xy', angles='uv', scale_units='xy',
|
|
pivot='mid', headwidth=0., headlength=0., headaxislength=0., **kwargs)
|
|
self.legend.remove()
|
|
self.legend = self.ax_overplot.legend(title=self.legend_title, bbox_to_anchor=(0., 1.02, 1., .102), loc='lower left', mode="expand", borderaxespad=0.)
|
|
self.fig_overplot.canvas.draw()
|
|
return new_vec
|
|
|
|
|
|
class align_pol(object):
|
|
def __init__(self, maps, **kwargs):
|
|
order = np.argsort(np.array([curr[0].header['mjd-obs'] for curr in maps]))
|
|
maps = np.array(maps)[order]
|
|
self.ref_map, self.other_maps = maps[0], maps[1:]
|
|
|
|
self.wcs = WCS(self.ref_map[0].header).celestial.deepcopy()
|
|
self.wcs_other = np.array([WCS(map[0].header).celestial.deepcopy() for map in self.other_maps])
|
|
|
|
self.aligned = np.zeros(self.other_maps.shape[0], dtype=bool)
|
|
|
|
self.kwargs = kwargs
|
|
|
|
def single_plot(self, curr_map, wcs, v_lim=None, ax_lim=None, SNRp_cut=3., SNRi_cut=3., savename=None, **kwargs):
|
|
# Get data
|
|
stkI = curr_map['I_STOKES'].data
|
|
stk_cov = curr_map['IQU_COV_MATRIX'].data
|
|
pol = deepcopy(curr_map['POL_DEG_DEBIASED'].data)
|
|
pol_err = curr_map['POL_DEG_ERR'].data
|
|
pang = curr_map['POL_ANG'].data
|
|
try:
|
|
data_mask = curr_map['DATA_MASK'].data.astype(bool)
|
|
except KeyError:
|
|
data_mask = np.ones(stkI.shape).astype(bool)
|
|
|
|
convert_flux = curr_map[0].header['photflam']
|
|
|
|
# Compute SNR and apply cuts
|
|
maskpol = np.logical_and(pol_err > 0., data_mask)
|
|
SNRp = np.zeros(pol.shape)
|
|
SNRp[maskpol] = pol[maskpol]/pol_err[maskpol]
|
|
|
|
maskI = np.logical_and(stk_cov[0, 0] > 0, data_mask)
|
|
SNRi = np.zeros(stkI.shape)
|
|
SNRi[maskI] = stkI[maskI]/np.sqrt(stk_cov[0, 0][maskI])
|
|
|
|
mask = (SNRp > SNRp_cut) * (SNRi > SNRi_cut) * (pol >= 0.)
|
|
pol[mask] = np.nan
|
|
|
|
# Plot the map
|
|
plt.rcParams.update({'font.size': 10})
|
|
plt.rcdefaults()
|
|
fig = plt.figure(figsize=(10, 10))
|
|
ax = fig.add_subplot(111, projection=wcs)
|
|
ax.set(xlabel="Right Ascension (J2000)", ylabel="Declination (J2000)", facecolor='k',
|
|
title="target {0:s} observed on {1:s}".format(curr_map[0].header['targname'], curr_map[0].header['date-obs']))
|
|
fig.subplots_adjust(hspace=0, wspace=0, right=0.102)
|
|
|
|
if ax_lim is not None:
|
|
lim = np.concatenate([wcs.world_to_pixel(ax_lim[i]) for i in range(len(ax_lim))])
|
|
x_lim, y_lim = lim[0::2], lim[1::2]
|
|
ax.set(xlim=x_lim, ylim=y_lim)
|
|
|
|
if v_lim is None:
|
|
vmin, vmax = 0., np.max(stkI[stkI > 0.]*convert_flux)
|
|
else:
|
|
vmin, vmax = v_lim*convert_flux
|
|
|
|
for key, value in [["cmap", [["cmap", "inferno"]]], ["norm", [["vmin", vmin], ["vmax", vmax]]]]:
|
|
try:
|
|
test = kwargs[key]
|
|
if isinstance(test, LogNorm):
|
|
kwargs[key] = LogNorm(vmin, vmax)
|
|
except KeyError:
|
|
for key_i, val_i in value:
|
|
kwargs[key_i] = val_i
|
|
|
|
im = ax.imshow(stkI*convert_flux, aspect='equal', **kwargs)
|
|
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
|
|
|
|
px_size = wcs.wcs.get_cdelt()[0]*3600.
|
|
px_sc = AnchoredSizeBar(ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
|
|
ax.add_artist(px_sc)
|
|
|
|
north_dir = AnchoredDirectionArrows(ax.transAxes, "E", "N", length=-0.08, fontsize=0.025, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, back_length=0., head_length=10., head_width=10.,
|
|
angle=curr_map[0].header['orientat'], color='white', text_props={'ec': None, 'fc': 'w', 'alpha': 1, 'lw': 0.4}, arrow_props={'ec': None, 'fc': 'w', 'alpha': 1, 'lw': 1})
|
|
ax.add_artist(north_dir)
|
|
|
|
step_vec = 1
|
|
X, Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
|
|
U, V = pol*np.cos(np.pi/2.+pang*np.pi/180.), pol*np.sin(np.pi/2.+pang*np.pi/180.)
|
|
ax.quiver(X[::step_vec, ::step_vec], Y[::step_vec, ::step_vec], U[::step_vec, ::step_vec], V[::step_vec, ::step_vec], units='xy',
|
|
angles='uv', scale=0.5, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.5, linewidth=0.75, color='w')
|
|
pol_sc = AnchoredSizeBar(ax.transData, 2., r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
|
|
ax.add_artist(pol_sc)
|
|
|
|
if 'PHOTPLAM' in list(curr_map[0].header.keys()):
|
|
ax.annotate(r"$\lambda$ = {0:.0f} $\AA$".format(curr_map[0].header['photplam']), color='white', fontsize=12, xy=(
|
|
0.01, 0.93), xycoords='axes fraction', path_effects=[pe.withStroke(linewidth=0.5, foreground='k')])
|
|
|
|
if savename is not None:
|
|
if savename[-4:] not in ['.png', '.jpg', '.pdf']:
|
|
savename += '.pdf'
|
|
fig.savefig(savename, bbox_inches='tight', dpi=300)
|
|
|
|
plt.show(block=True)
|
|
return fig, ax
|
|
|
|
def align(self):
|
|
for i, curr_map in enumerate(self.other_maps):
|
|
curr_align = align_maps(self.ref_map, curr_map, **self.kwargs)
|
|
self.wcs, self.wcs_other[i] = curr_align.align()
|
|
self.aligned[i] = curr_align.aligned
|
|
|
|
def plot(self, SNRp_cut=3., SNRi_cut=3., savename=None, **kwargs):
|
|
while not self.aligned.all():
|
|
self.align()
|
|
eps = 1e-35
|
|
vmin = np.min([np.min(curr_map[0].data[curr_map[0].data > SNRi_cut*np.max([eps*np.ones(curr_map[0].data.shape),
|
|
np.sqrt(curr_map[3].data[0, 0])], axis=0)]) for curr_map in self.other_maps])/2.5
|
|
vmax = np.max([np.max(curr_map[0].data[curr_map[0].data > SNRi_cut*np.max([eps*np.ones(curr_map[0].data.shape),
|
|
np.sqrt(curr_map[3].data[0, 0])], axis=0)]) for curr_map in self.other_maps])
|
|
vmin = np.min([vmin, np.min(self.ref_map[0].data[self.ref_map[0].data > SNRi_cut *
|
|
np.max([eps*np.ones(self.ref_map[0].data.shape), np.sqrt(self.ref_map[3].data[0, 0])], axis=0)])])/2.5
|
|
vmax = np.max([vmax, np.max(self.ref_map[0].data[self.ref_map[0].data > SNRi_cut *
|
|
np.max([eps*np.ones(self.ref_map[0].data.shape), np.sqrt(self.ref_map[3].data[0, 0])], axis=0)])])
|
|
v_lim = np.array([vmin, vmax])
|
|
|
|
fig, ax = self.single_plot(self.ref_map, self.wcs, v_lim=v_lim, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename+'_0', **kwargs)
|
|
x_lim, y_lim = ax.get_xlim(), ax.get_ylim()
|
|
ax_lim = np.array([self.wcs.pixel_to_world(x_lim[i], y_lim[i]) for i in range(len(x_lim))])
|
|
|
|
for i, curr_map in enumerate(self.other_maps):
|
|
self.single_plot(curr_map, self.wcs_other[i], v_lim=v_lim, ax_lim=ax_lim, SNRp_cut=SNRp_cut,
|
|
SNRi_cut=SNRi_cut, savename=savename+'_'+str(i+1), **kwargs)
|
|
|
|
|
|
class crop_map(object):
|
|
"""
|
|
Class to interactively crop a map to desired Region of Interest
|
|
"""
|
|
|
|
def __init__(self, hdul, fig=None, ax=None, **kwargs):
|
|
# Get data
|
|
self.cropped = False
|
|
self.hdul = hdul
|
|
self.header = deepcopy(self.hdul[0].header)
|
|
self.wcs = WCS(self.header).celestial.deepcopy()
|
|
|
|
self.data = deepcopy(self.hdul[0].data)
|
|
try:
|
|
self.map_convert = self.header['photflam']
|
|
except KeyError:
|
|
self.map_convert = 1.
|
|
try:
|
|
self.kwargs = kwargs
|
|
except AttributeError:
|
|
self.kwargs = {}
|
|
|
|
# Plot the map
|
|
plt.rcParams.update({'font.size': 12})
|
|
if fig is None:
|
|
self.fig = plt.figure(figsize=(15, 15))
|
|
self.fig.suptitle("Click and drag to crop to desired Region of Interest.")
|
|
else:
|
|
self.fig = fig
|
|
if ax is None:
|
|
self.ax = self.fig.add_subplot(111, projection=self.wcs)
|
|
self.mask_alpha = 1.
|
|
# Selection button
|
|
self.axapply = self.fig.add_axes([0.80, 0.01, 0.1, 0.04])
|
|
self.bapply = Button(self.axapply, 'Apply')
|
|
self.axreset = self.fig.add_axes([0.60, 0.01, 0.1, 0.04])
|
|
self.breset = Button(self.axreset, 'Reset')
|
|
self.embedded = False
|
|
else:
|
|
self.ax = ax
|
|
self.mask_alpha = 0.75
|
|
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
|
|
button=[1])
|
|
self.embedded = True
|
|
self.display(self.data, self.wcs, self.map_convert, **self.kwargs)
|
|
|
|
self.extent = np.array([0., self.data.shape[0], 0., self.data.shape[1]])
|
|
self.center = np.array(self.data.shape)/2
|
|
self.RSextent = deepcopy(self.extent)
|
|
self.RScenter = deepcopy(self.center)
|
|
|
|
def display(self, data=None, wcs=None, convert_flux=None, **kwargs):
|
|
if data is None:
|
|
data = self.data
|
|
if wcs is None:
|
|
wcs = self.wcs
|
|
if convert_flux is None:
|
|
convert_flux = self.map_convert
|
|
if kwargs is None:
|
|
kwargs = self.kwargs
|
|
else:
|
|
kwargs = {**self.kwargs, **kwargs}
|
|
|
|
vmin, vmax = np.min(data[data > 0.]*convert_flux), np.max(data[data > 0.]*convert_flux)
|
|
for key, value in [["cmap", [["cmap", "inferno"]]], ["origin", [["origin", "lower"]]], ["aspect", [["aspect", "equal"]]], ["alpha", [["alpha", self.mask_alpha]]], ["norm", [["vmin", vmin], ["vmax", vmax]]]]:
|
|
try:
|
|
_ = kwargs[key]
|
|
except KeyError:
|
|
for key_i, val_i in value:
|
|
kwargs[key_i] = val_i
|
|
if hasattr(self, 'im'):
|
|
self.im.remove()
|
|
self.im = self.ax.imshow(data*convert_flux, **kwargs)
|
|
if hasattr(self, 'cr'):
|
|
self.cr[0].set_data(*wcs.wcs.crpix)
|
|
else:
|
|
self.cr = self.ax.plot(*wcs.wcs.crpix, 'r+')
|
|
self.fig.canvas.draw_idle()
|
|
return self.im
|
|
|
|
@property
|
|
def crpix_in_RS(self):
|
|
crpix = self.wcs.wcs.crpix
|
|
x_lim, y_lim = self.RSextent[:2], self.RSextent[2:]
|
|
if (crpix[0] > x_lim[0] and crpix[0] < x_lim[1]):
|
|
if (crpix[1] > y_lim[0] and crpix[1] < y_lim[1]):
|
|
return True
|
|
return False
|
|
|
|
def reset_crop(self, event):
|
|
self.ax.reset_wcs(self.wcs)
|
|
if hasattr(self, 'hdul_crop'):
|
|
del self.hdul_crop, self.data_crop
|
|
self.display()
|
|
|
|
if self.fig.canvas.manager.toolbar.mode == '':
|
|
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
|
|
button=[1])
|
|
|
|
self.RSextent = deepcopy(self.extent)
|
|
self.RScenter = deepcopy(self.center)
|
|
self.ax.set_xlim(*self.extent[:2])
|
|
self.ax.set_ylim(*self.extent[2:])
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def onselect_crop(self, eclick, erelease) -> None:
|
|
# Obtain (xmin, xmax, ymin, ymax) values
|
|
self.RSextent = np.array(self.rect_selector.extents)
|
|
self.RScenter = np.array(self.rect_selector.center)
|
|
if self.embedded:
|
|
self.apply_crop(erelease)
|
|
|
|
def apply_crop(self, event):
|
|
if hasattr(self, 'hdul_crop'):
|
|
header = self.header_crop
|
|
data = self.data_crop
|
|
wcs = self.wcs_crop
|
|
else:
|
|
header = self.header
|
|
data = self.data
|
|
wcs = self.wcs
|
|
|
|
vertex = self.RSextent.astype(int)
|
|
shape = vertex[1::2] - vertex[0::2]
|
|
|
|
extent = np.array(self.im.get_extent())
|
|
shape_im = extent[1::2] - extent[0::2]
|
|
if (shape_im.astype(int) != shape).any() and (self.RSextent != self.extent).any():
|
|
# Update WCS and header in new cropped image
|
|
crpix = np.array(wcs.wcs.crpix)
|
|
self.wcs_crop = wcs.deepcopy()
|
|
self.wcs_crop.array_shape = shape
|
|
if self.crpix_in_RS:
|
|
self.wcs_crop.wcs.crpix = np.array(self.wcs_crop.wcs.crpix) - self.RSextent[::2]
|
|
else:
|
|
self.wcs_crop.wcs.crval = wcs.wcs_pix2world([self.RScenter], 1)[0]
|
|
self.wcs_crop.wcs.crpix = self.RScenter-self.RSextent[::2]
|
|
|
|
# Crop dataset
|
|
self.data_crop = deepcopy(data[vertex[2]:vertex[3], vertex[0]:vertex[1]])
|
|
|
|
# Write cropped map to new HDUList
|
|
self.header_crop = deepcopy(header)
|
|
self.header_crop.update(self.wcs_crop.to_header())
|
|
self.hdul_crop = fits.HDUList([fits.PrimaryHDU(self.data_crop, self.header_crop)])
|
|
|
|
self.rect_selector.clear()
|
|
self.ax.reset_wcs(self.wcs_crop)
|
|
self.display(data=self.data_crop, wcs=self.wcs_crop)
|
|
|
|
xlim, ylim = self.RSextent[1::2]-self.RSextent[0::2]
|
|
self.ax.set_xlim(0, xlim)
|
|
self.ax.set_ylim(0, ylim)
|
|
|
|
if self.fig.canvas.manager.toolbar.mode == '':
|
|
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
|
|
button=[1])
|
|
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def on_close(self, event) -> None:
|
|
if not hasattr(self, 'hdul_crop'):
|
|
self.hdul_crop = self.hdul
|
|
self.rect_selector.disconnect_events()
|
|
self.cropped = True
|
|
|
|
def crop(self) -> None:
|
|
if self.fig.canvas.manager.toolbar.mode == '':
|
|
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
|
|
button=[1])
|
|
self.bapply.on_clicked(self.apply_crop)
|
|
self.breset.on_clicked(self.reset_crop)
|
|
self.fig.canvas.mpl_connect('close_event', self.on_close)
|
|
plt.show()
|
|
|
|
def write_to(self, filename):
|
|
self.hdul_crop.writeto(filename, overwrite=True)
|
|
|
|
|
|
class crop_Stokes(crop_map):
|
|
"""
|
|
Class to interactively crop a polarization map to desired Region of Interest.
|
|
Inherit from crop_map.
|
|
"""
|
|
|
|
def apply_crop(self, event):
|
|
"""
|
|
Redefine apply_crop method for the Stokes HDUList.
|
|
"""
|
|
if hasattr(self, 'hdul_crop'):
|
|
hdul = self.hdul_crop
|
|
data = self.data_crop
|
|
wcs = self.wcs_crop
|
|
else:
|
|
hdul = self.hdul
|
|
data = self.data
|
|
wcs = self.wcs
|
|
|
|
vertex = self.RSextent.astype(int)
|
|
shape = vertex[1::2] - vertex[0::2]
|
|
|
|
extent = np.array(self.im.get_extent())
|
|
shape_im = extent[1::2] - extent[0::2]
|
|
if (shape_im.astype(int) != shape).any() and (self.RSextent != self.extent).any():
|
|
# Update WCS and header in new cropped image
|
|
self.hdul_crop = deepcopy(hdul)
|
|
crpix = np.array(wcs.wcs.crpix)
|
|
self.wcs_crop = wcs.deepcopy()
|
|
self.wcs_crop.array_shape = shape
|
|
if self.crpix_in_RS:
|
|
self.wcs_crop.wcs.crpix = np.array(self.wcs_crop.wcs.crpix) - self.RSextent[::2]
|
|
else:
|
|
self.wcs_crop.wcs.crval = wcs.wcs_pix2world([self.RScenter], 1)[0]
|
|
self.wcs_crop.wcs.crpix = self.RScenter-self.RSextent[::2]
|
|
|
|
# Crop dataset
|
|
for dataset in self.hdul_crop:
|
|
if dataset.header['datatype'] == 'IQU_cov_matrix':
|
|
stokes_cov = np.zeros((3, 3, shape[1], shape[0]))
|
|
for i in range(3):
|
|
for j in range(3):
|
|
stokes_cov[i, j] = deepcopy(dataset.data[i, j][vertex[2]:vertex[3], vertex[0]:vertex[1]])
|
|
dataset.data = stokes_cov
|
|
else:
|
|
dataset.data = deepcopy(dataset.data[vertex[2]:vertex[3], vertex[0]:vertex[1]])
|
|
dataset.header.update(self.wcs_crop.to_header())
|
|
|
|
self.data_crop = self.hdul_crop[0].data
|
|
self.rect_selector.clear()
|
|
if not self.embedded:
|
|
self.ax.reset_wcs(self.wcs_crop)
|
|
self.display(data=self.data_crop, wcs=self.wcs_crop)
|
|
|
|
xlim, ylim = self.RSextent[1::2]-self.RSextent[0::2]
|
|
self.ax.set_xlim(0, xlim)
|
|
self.ax.set_ylim(0, ylim)
|
|
else:
|
|
self.on_close(event)
|
|
|
|
if self.fig.canvas.manager.toolbar.mode == '':
|
|
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
|
|
button=[1])
|
|
# Update integrated values
|
|
mask = np.logical_and(self.hdul_crop['data_mask'].data.astype(bool), self.hdul_crop[0].data > 0)
|
|
I_diluted = self.hdul_crop['i_stokes'].data[mask].sum()
|
|
Q_diluted = self.hdul_crop['q_stokes'].data[mask].sum()
|
|
U_diluted = self.hdul_crop['u_stokes'].data[mask].sum()
|
|
I_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[0, 0][mask]))
|
|
Q_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[1, 1][mask]))
|
|
U_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[2, 2][mask]))
|
|
IQ_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[0, 1][mask]**2))
|
|
IU_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[0, 2][mask]**2))
|
|
QU_diluted_err = np.sqrt(np.sum(self.hdul_crop['iqu_cov_matrix'].data[1, 2][mask]**2))
|
|
|
|
P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted
|
|
P_diluted_err = (1./I_diluted)*np.sqrt((Q_diluted**2*Q_diluted_err**2 + U_diluted**2*U_diluted_err**2 + 2.*Q_diluted*U_diluted*QU_diluted_err)/(Q_diluted**2 + U_diluted **
|
|
2) + ((Q_diluted/I_diluted)**2 + (U_diluted/I_diluted)**2)*I_diluted_err**2 - 2.*(Q_diluted/I_diluted)*IQ_diluted_err - 2.*(U_diluted/I_diluted)*IU_diluted_err)
|
|
|
|
PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted, Q_diluted))
|
|
PA_diluted_err = (90./(np.pi*(Q_diluted**2 + U_diluted**2)))*np.sqrt(U_diluted**2*Q_diluted_err **
|
|
2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err)
|
|
|
|
for dataset in self.hdul_crop:
|
|
dataset.header['P_int'] = (P_diluted, 'Integrated polarization degree')
|
|
dataset.header['P_int_err'] = (np.ceil(P_diluted_err*1000.)/1000., 'Integrated polarization degree error')
|
|
dataset.header['PA_int'] = (PA_diluted, 'Integrated polarization angle')
|
|
dataset.header['PA_int_err'] = (np.ceil(PA_diluted_err*10.)/10., 'Integrated polarization angle error')
|
|
self.fig.canvas.draw_idle()
|
|
|
|
@property
|
|
def data_mask(self):
|
|
return self.hdul_crop['data_mask'].data.astype(int)
|
|
|
|
|
|
class image_lasso_selector(object):
|
|
def __init__(self, img, fig=None, ax=None):
|
|
"""
|
|
img must have shape (X, Y)
|
|
"""
|
|
self.selected = False
|
|
self.img = img
|
|
self.vmin, self.vmax = 0., np.max(self.img[self.img > 0.])
|
|
plt.ioff() # see https://github.com/matplotlib/matplotlib/issues/17013
|
|
if fig is None:
|
|
self.fig = plt.figure(figsize=(15, 15))
|
|
else:
|
|
self.fig = fig
|
|
if ax is None:
|
|
self.ax = self.fig.gca()
|
|
self.mask_alpha = 1.
|
|
self.embedded = False
|
|
else:
|
|
self.ax = ax
|
|
self.mask_alpha = 0.1
|
|
self.embedded = True
|
|
self.displayed = self.ax.imshow(self.img, vmin=self.vmin, vmax=self.vmax, aspect='equal', cmap='inferno', alpha=self.mask_alpha)
|
|
plt.ion()
|
|
|
|
lineprops = {'color': 'grey', 'linewidth': 1, 'alpha': 0.8}
|
|
self.lasso = LassoSelector(self.ax, self.onselect, props=lineprops, useblit=False)
|
|
self.lasso.set_visible(True)
|
|
|
|
pix_x = np.arange(self.img.shape[0])
|
|
pix_y = np.arange(self.img.shape[1])
|
|
xv, yv = np.meshgrid(pix_y, pix_x)
|
|
self.pix = np.vstack((xv.flatten(), yv.flatten())).T
|
|
|
|
self.fig.canvas.mpl_connect('close_event', self.on_close)
|
|
plt.show()
|
|
|
|
def on_close(self, event=None) -> None:
|
|
if not hasattr(self, 'mask'):
|
|
self.mask = np.zeros(self.img.shape[:2], dtype=bool)
|
|
self.lasso.disconnect_events()
|
|
self.selected = True
|
|
|
|
def onselect(self, verts):
|
|
self.verts = verts
|
|
p = Path(verts)
|
|
self.indices = p.contains_points(self.pix, radius=0).reshape(self.img.shape[:2])
|
|
self.update_mask()
|
|
|
|
def update_mask(self):
|
|
self.displayed.remove()
|
|
self.displayed = self.ax.imshow(self.img, vmin=self.vmin, vmax=self.vmax, aspect='equal', cmap='inferno', alpha=self.mask_alpha)
|
|
array = self.displayed.get_array().data
|
|
|
|
self.mask = np.zeros(self.img.shape[:2], dtype=bool)
|
|
self.mask[self.indices] = True
|
|
if hasattr(self, 'cont'):
|
|
for coll in self.cont.collections:
|
|
coll.remove()
|
|
self.cont = self.ax.contour(self.mask.astype(float), levels=[0.5], colors='white', linewidths=1)
|
|
if not self.embedded:
|
|
self.displayed.set_data(array)
|
|
self.fig.canvas.draw_idle()
|
|
else:
|
|
self.on_close()
|
|
|
|
|
|
class slit(object):
|
|
def __init__(self, img, cdelt=np.array([1., 1.]), width=1., height=2., angle=0., fig=None, ax=None):
|
|
"""
|
|
img must have shape (X, Y)
|
|
"""
|
|
self.selected = False
|
|
self.img = img
|
|
self.vmin, self.vmax = 0., np.max(self.img[self.img > 0.])
|
|
plt.ioff() # see https://github.com/matplotlib/matplotlib/issues/17013
|
|
if fig is None:
|
|
self.fig = plt.figure(figsize=(15, 15))
|
|
else:
|
|
self.fig = fig
|
|
if ax is None:
|
|
self.ax = self.fig.gca()
|
|
self.mask_alpha = 1.
|
|
self.embedded = False
|
|
else:
|
|
self.ax = ax
|
|
self.mask_alpha = 0.1
|
|
self.embedded = True
|
|
|
|
self.displayed = self.ax.imshow(self.img, vmin=self.vmin, vmax=self.vmax, aspect='equal', cmap='inferno', alpha=self.mask_alpha)
|
|
plt.ion()
|
|
|
|
xx, yy = np.indices(self.img.shape)
|
|
self.pix = np.vstack((xx.flatten(), yy.flatten())).T
|
|
|
|
self.x0, self.y0 = np.array(self.img.shape)/2.
|
|
|
|
self.cdelt = cdelt
|
|
self.width = width/np.abs(self.cdelt).max()/3600.
|
|
self.height = height/np.abs(self.cdelt).max()/3600.
|
|
self.angle = angle
|
|
|
|
self.rect_center = (self.x0, self.y0)-np.dot(rot2D(self.angle), (self.width/2, self.height/2))
|
|
self.rect = Rectangle(self.rect_center, self.width, self.height, angle=self.angle, alpha=0.8, ec='grey', fc='none')
|
|
self.ax.add_patch(self.rect)
|
|
|
|
self.fig.canvas.mpl_connect('button_press_event', self.on_press)
|
|
self.fig.canvas.mpl_connect('button_release_event', self.on_release)
|
|
self.fig.canvas.mpl_connect('motion_notify_event', self.on_move)
|
|
self.fig.canvas.mpl_connect('close_event', self.on_close)
|
|
self.x0, self.y0 = self.rect.xy
|
|
self.pressevent = None
|
|
plt.show()
|
|
|
|
def on_close(self, event=None) -> None:
|
|
if not hasattr(self, 'mask'):
|
|
self.mask = np.zeros(self.img.shape[:2], dtype=bool)
|
|
self.selected = True
|
|
|
|
def on_press(self, event):
|
|
if event.inaxes != self.ax:
|
|
return
|
|
|
|
if not self.rect.contains(event)[0]:
|
|
return
|
|
|
|
self.pressevent = event
|
|
|
|
def on_release(self, event):
|
|
self.pressevent = None
|
|
self.x0, self.y0 = self.rect.xy
|
|
self.update_mask()
|
|
|
|
def on_move(self, event):
|
|
if self.pressevent is None or event.inaxes != self.pressevent.inaxes:
|
|
return
|
|
|
|
dx = event.xdata - self.pressevent.xdata
|
|
dy = event.ydata - self.pressevent.ydata
|
|
self.rect.xy = self.x0 + dx, self.y0 + dy
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_width(self, width):
|
|
self.width = width/np.abs(self.cdelt).max()/3600
|
|
self.rect.set_width(self.width)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_height(self, height):
|
|
self.height = height/np.abs(self.cdelt).max()/3600
|
|
self.rect.set_height(self.height)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_angle(self, angle):
|
|
self.angle = angle
|
|
self.rect.set_angle(self.angle)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_mask(self):
|
|
if hasattr(self, 'displayed'):
|
|
try:
|
|
self.displayed.remove()
|
|
except ValueError:
|
|
return
|
|
self.displayed = self.ax.imshow(self.img, vmin=self.vmin, vmax=self.vmax, aspect='equal', cmap='inferno', alpha=self.mask_alpha)
|
|
array = self.displayed.get_array().data
|
|
|
|
self.mask = np.zeros(array.shape, dtype=bool)
|
|
for p in self.pix:
|
|
self.mask[tuple(p)] = (np.abs(np.dot(rot2D(-self.angle), p-self.rect.get_center()[::-1])) < (self.height/2., self.width/2.)).all()
|
|
if hasattr(self, 'cont'):
|
|
for coll in self.cont.collections:
|
|
try:
|
|
coll.remove()
|
|
except AttributeError:
|
|
return
|
|
self.cont = self.ax.contour(self.mask.astype(float), levels=[0.5], colors='white', linewidths=1)
|
|
if not self.embedded:
|
|
self.displayed.set_data(array)
|
|
self.fig.canvas.draw_idle()
|
|
else:
|
|
self.on_close()
|
|
|
|
|
|
class aperture(object):
|
|
def __init__(self, img, cdelt=np.array([1., 1.]), radius=1., fig=None, ax=None):
|
|
"""
|
|
img must have shape (X, Y)
|
|
"""
|
|
self.selected = False
|
|
self.img = img
|
|
self.vmin, self.vmax = 0., np.max(self.img[self.img > 0.])
|
|
plt.ioff() # see https://github.com/matplotlib/matplotlib/issues/17013
|
|
if fig is None:
|
|
self.fig = plt.figure(figsize=(15, 15))
|
|
else:
|
|
self.fig = fig
|
|
if ax is None:
|
|
self.ax = self.fig.gca()
|
|
self.mask_alpha = 1.
|
|
self.embedded = False
|
|
else:
|
|
self.ax = ax
|
|
self.mask_alpha = 0.1
|
|
self.embedded = True
|
|
|
|
self.displayed = self.ax.imshow(self.img, vmin=self.vmin, vmax=self.vmax, aspect='equal', cmap='inferno', alpha=self.mask_alpha)
|
|
plt.ion()
|
|
|
|
xx, yy = np.indices(self.img.shape)
|
|
self.pix = np.vstack((xx.flatten(), yy.flatten())).T
|
|
|
|
self.x0, self.y0 = np.array(self.img.shape)/2.
|
|
if np.abs(cdelt).max() != 1.:
|
|
self.cdelt = cdelt
|
|
self.radius = radius/np.abs(self.cdelt).max()/3600.
|
|
|
|
self.circ = Circle((self.x0, self.y0), self.radius, alpha=0.8, ec='grey', fc='none')
|
|
self.ax.add_patch(self.circ)
|
|
|
|
self.fig.canvas.mpl_connect('button_press_event', self.on_press)
|
|
self.fig.canvas.mpl_connect('button_release_event', self.on_release)
|
|
self.fig.canvas.mpl_connect('motion_notify_event', self.on_move)
|
|
self.fig.canvas.mpl_connect('close_event', self.on_close)
|
|
self.x0, self.y0 = self.circ.center
|
|
self.pressevent = None
|
|
plt.show()
|
|
|
|
def on_close(self, event=None) -> None:
|
|
if not hasattr(self, 'mask'):
|
|
self.mask = np.zeros(self.img.shape[:2], dtype=bool)
|
|
self.selected = True
|
|
|
|
def on_press(self, event):
|
|
if event.inaxes != self.ax:
|
|
return
|
|
|
|
if not self.circ.contains(event)[0]:
|
|
return
|
|
|
|
self.pressevent = event
|
|
|
|
def on_release(self, event):
|
|
self.pressevent = None
|
|
self.x0, self.y0 = self.circ.center
|
|
self.update_mask()
|
|
|
|
def on_move(self, event):
|
|
if self.pressevent is None or event.inaxes != self.pressevent.inaxes:
|
|
return
|
|
|
|
dx = event.xdata - self.pressevent.xdata
|
|
dy = event.ydata - self.pressevent.ydata
|
|
self.circ.center = self.x0 + dx, self.y0 + dy
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_radius(self, radius):
|
|
self.radius = radius/np.abs(self.cdelt).max()/3600
|
|
self.circ.set_radius(self.radius)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_mask(self):
|
|
if hasattr(self, 'displayed'):
|
|
try:
|
|
self.displayed.remove()
|
|
except ValueError:
|
|
return
|
|
self.displayed = self.ax.imshow(self.img, vmin=self.vmin, vmax=self.vmax, aspect='equal', cmap='inferno', alpha=self.mask_alpha)
|
|
array = self.displayed.get_array().data
|
|
|
|
yy, xx = np.indices(self.img.shape[:2])
|
|
x0, y0 = self.circ.center
|
|
self.mask = np.sqrt((xx-x0)**2+(yy-y0)**2) < self.radius
|
|
if hasattr(self, 'cont'):
|
|
for coll in self.cont.collections:
|
|
try:
|
|
coll.remove()
|
|
except AttributeError:
|
|
return
|
|
self.cont = self.ax.contour(self.mask.astype(float), levels=[0.5], colors='white', linewidths=1)
|
|
if not self.embedded:
|
|
self.displayed.set_data(array)
|
|
self.fig.canvas.draw_idle()
|
|
else:
|
|
self.on_close()
|
|
|
|
|
|
class pol_map(object):
|
|
"""
|
|
Class to interactively study polarization maps.
|
|
"""
|
|
|
|
def __init__(self, Stokes, SNRp_cut=3., SNRi_cut=3., flux_lim=None, selection=None):
|
|
|
|
if isinstance(Stokes, str):
|
|
Stokes = fits.open(Stokes)
|
|
self.Stokes = deepcopy(Stokes)
|
|
self.SNRp_cut = SNRp_cut
|
|
self.SNRi_cut = SNRi_cut
|
|
self.flux_lim = flux_lim
|
|
self.SNRi = deepcopy(self.SNRi_cut)
|
|
self.SNRp = deepcopy(self.SNRp_cut)
|
|
self.region = None
|
|
self.data = None
|
|
self.display_selection = selection
|
|
self.vec_scale = 2.
|
|
|
|
# Get data
|
|
self.targ = self.Stokes[0].header['targname']
|
|
self.pivot_wav = self.Stokes[0].header['photplam']
|
|
self.map_convert = self.Stokes[0].header['photflam']
|
|
|
|
# Create figure
|
|
plt.rcParams.update({'font.size': 10})
|
|
self.fig, self.ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(projection=self.wcs))
|
|
self.fig.subplots_adjust(hspace=0, wspace=0, right=1.02)
|
|
self.ax_cosmetics()
|
|
|
|
# Display selected data (Default to total flux)
|
|
self.display()
|
|
# Display polarization vectors in SNR_cut
|
|
self.pol_vector()
|
|
# Display integrated values in ROI
|
|
self.pol_int()
|
|
|
|
# Set axes for sliders (SNRp_cut, SNRi_cut)
|
|
ax_I_cut = self.fig.add_axes([0.120, 0.080, 0.230, 0.01])
|
|
ax_P_cut = self.fig.add_axes([0.120, 0.055, 0.230, 0.01])
|
|
ax_vec_sc = self.fig.add_axes([0.240, 0.030, 0.110, 0.01])
|
|
ax_snr_reset = self.fig.add_axes([0.080, 0.020, 0.05, 0.02])
|
|
SNRi_max = np.max(self.I[self.IQU_cov[0, 0] > 0.]/np.sqrt(self.IQU_cov[0, 0][self.IQU_cov[0, 0] > 0.]))
|
|
SNRp_max = np.max(self.P[self.s_P > 0.]/self.s_P[self.s_P > 0.])
|
|
s_I_cut = Slider(ax_I_cut, r"$SNR^{I}_{cut}$", 1., int(SNRi_max*0.95), valstep=1, valinit=self.SNRi_cut)
|
|
s_P_cut = Slider(ax_P_cut, r"$SNR^{P}_{cut}$", 1., int(SNRp_max*0.95), valstep=1, valinit=self.SNRp_cut)
|
|
s_vec_sc = Slider(ax_vec_sc, r"Vectors scale", 1., 6., valstep=1, valinit=self.vec_scale)
|
|
b_snr_reset = Button(ax_snr_reset, "Reset")
|
|
b_snr_reset.label.set_fontsize(8)
|
|
|
|
def update_snri(val):
|
|
self.SNRi = val
|
|
self.pol_vector()
|
|
self.pol_int()
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_snrp(val):
|
|
self.SNRp = val
|
|
self.pol_vector()
|
|
self.pol_int()
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_vecsc(val):
|
|
self.vec_scale = val
|
|
self.pol_vector()
|
|
self.ax_cosmetics()
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def reset_snr(event):
|
|
s_I_cut.reset()
|
|
s_P_cut.reset()
|
|
s_vec_sc.reset()
|
|
|
|
s_I_cut.on_changed(update_snri)
|
|
s_P_cut.on_changed(update_snrp)
|
|
s_vec_sc.on_changed(update_vecsc)
|
|
b_snr_reset.on_clicked(reset_snr)
|
|
|
|
# Set axe for ROI selection
|
|
ax_select = self.fig.add_axes([0.375, 0.070, 0.05, 0.02])
|
|
ax_roi_reset = self.fig.add_axes([0.430, 0.070, 0.05, 0.02])
|
|
b_select = Button(ax_select, "Select")
|
|
b_select.label.set_fontsize(8)
|
|
self.selected = False
|
|
b_roi_reset = Button(ax_roi_reset, "Reset")
|
|
b_roi_reset.label.set_fontsize(8)
|
|
|
|
def select_roi(event):
|
|
if self.data is None:
|
|
self.data = self.Stokes[0].data
|
|
if self.selected:
|
|
self.selected = False
|
|
self.region = deepcopy(self.select_instance.mask.astype(bool))
|
|
self.select_instance.displayed.remove()
|
|
for coll in self.select_instance.cont.collections:
|
|
coll.remove()
|
|
self.select_instance.lasso.set_active(False)
|
|
self.set_data_mask(deepcopy(self.region))
|
|
self.pol_int()
|
|
else:
|
|
self.selected = True
|
|
self.region = None
|
|
self.select_instance = image_lasso_selector(self.data, fig=self.fig, ax=self.ax)
|
|
self.select_instance.lasso.set_active(True)
|
|
k = 0
|
|
while not self.select_instance.selected and k < 60:
|
|
self.fig.canvas.start_event_loop(timeout=1)
|
|
k += 1
|
|
select_roi(event)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def reset_roi(event):
|
|
self.region = None
|
|
self.pol_int()
|
|
self.fig.canvas.draw_idle()
|
|
|
|
b_select.on_clicked(select_roi)
|
|
b_roi_reset.on_clicked(reset_roi)
|
|
|
|
# Set axe for Aperture selection
|
|
ax_aper = self.fig.add_axes([0.375, 0.040, 0.05, 0.02])
|
|
ax_aper_reset = self.fig.add_axes([0.430, 0.040, 0.05, 0.02])
|
|
ax_aper_radius = self.fig.add_axes([0.375, 0.020, 0.10, 0.01])
|
|
self.selected = False
|
|
b_aper = Button(ax_aper, "Aperture")
|
|
b_aper.label.set_fontsize(8)
|
|
b_aper_reset = Button(ax_aper_reset, "Reset")
|
|
b_aper_reset.label.set_fontsize(8)
|
|
s_aper_radius = Slider(ax_aper_radius, r"$R_{aper}$", np.ceil(self.wcs.wcs.cdelt.max()/1.33*3.6e5)/1e2, 3.5, valstep=1e-2, valinit=1.)
|
|
|
|
def select_aperture(event):
|
|
if self.data is None:
|
|
self.data = self.Stokes[0].data
|
|
if self.selected:
|
|
self.selected = False
|
|
self.select_instance.update_mask()
|
|
self.region = deepcopy(self.select_instance.mask.astype(bool))
|
|
self.select_instance.displayed.remove()
|
|
for coll in self.select_instance.cont.collections[:]:
|
|
coll.remove()
|
|
self.select_instance.circ.set_visible(False)
|
|
self.set_data_mask(deepcopy(self.region))
|
|
self.pol_int()
|
|
else:
|
|
self.selected = True
|
|
self.region = None
|
|
self.select_instance = aperture(self.data, fig=self.fig, ax=self.ax, cdelt=self.wcs.wcs.cdelt, radius=s_aper_radius.val)
|
|
self.select_instance.circ.set_visible(True)
|
|
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_aperture(val):
|
|
if hasattr(self, 'select_instance'):
|
|
if hasattr(self.select_instance, 'radius'):
|
|
self.select_instance.update_radius(val)
|
|
else:
|
|
self.selected = True
|
|
self.select_instance = aperture(self.data, fig=self.fig, ax=self.ax, cdelt=self.wcs.wcs.cdelt, radius=val)
|
|
else:
|
|
self.selected = True
|
|
self.select_instance = aperture(self.data, fig=self.fig, ax=self.ax, cdelt=self.wcs.wcs.cdelt, radius=val)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def reset_aperture(event):
|
|
self.region = None
|
|
s_aper_radius.reset()
|
|
self.pol_int()
|
|
self.fig.canvas.draw_idle()
|
|
|
|
b_aper.on_clicked(select_aperture)
|
|
b_aper_reset.on_clicked(reset_aperture)
|
|
s_aper_radius.on_changed(update_aperture)
|
|
|
|
# Set axe for Slit selection
|
|
ax_slit = self.fig.add_axes([0.55, 0.080, 0.05, 0.02])
|
|
ax_slit_reset = self.fig.add_axes([0.605, 0.080, 0.05, 0.02])
|
|
ax_slit_width = self.fig.add_axes([0.55, 0.060, 0.10, 0.01])
|
|
ax_slit_height = self.fig.add_axes([0.55, 0.040, 0.10, 0.01])
|
|
ax_slit_angle = self.fig.add_axes([0.55, 0.020, 0.10, 0.01])
|
|
self.selected = False
|
|
b_slit = Button(ax_slit, "Slit")
|
|
b_slit.label.set_fontsize(8)
|
|
b_slit_reset = Button(ax_slit_reset, "Reset")
|
|
b_slit_reset.label.set_fontsize(8)
|
|
s_slit_width = Slider(ax_slit_width, r"$W_{slit}$", np.ceil(self.wcs.wcs.cdelt.max()/1.33*3.6e5)/1e2, 7., valstep=1e-2, valinit=1.)
|
|
s_slit_height = Slider(ax_slit_height, r"$H_{slit}$", np.ceil(self.wcs.wcs.cdelt.max()/1.33*3.6e5)/1e2, 7., valstep=1e-2, valinit=1.)
|
|
s_slit_angle = Slider(ax_slit_angle, r"$\theta_{slit}$", 0., 90., valstep=1., valinit=0.)
|
|
|
|
def select_slit(event):
|
|
if self.data is None:
|
|
self.data = self.Stokes[0].data
|
|
if self.selected:
|
|
self.selected = False
|
|
self.select_instance.update_mask()
|
|
self.region = deepcopy(self.select_instance.mask.astype(bool))
|
|
self.select_instance.displayed.remove()
|
|
for coll in self.select_instance.cont.collections[:]:
|
|
coll.remove()
|
|
self.select_instance.rect.set_visible(False)
|
|
self.set_data_mask(deepcopy(self.region))
|
|
self.pol_int()
|
|
else:
|
|
self.selected = True
|
|
self.region = None
|
|
self.select_instance = slit(self.data, fig=self.fig, ax=self.ax, cdelt=self.wcs.wcs.cdelt,
|
|
width=s_slit_width.val, height=s_slit_height.val, angle=s_slit_angle.val)
|
|
self.select_instance.rect.set_visible(True)
|
|
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_slit_w(val):
|
|
if hasattr(self, 'select_instance'):
|
|
if hasattr(self.select_instance, 'width'):
|
|
self.select_instance.update_width(val)
|
|
else:
|
|
self.selected = True
|
|
self.select_instance = slit(self.data, fig=self.fig, ax=self.ax, cdelt=self.wcs.wcs.cdelt,
|
|
width=val, height=s_slit_height.val, angle=s_slit_angle.val)
|
|
else:
|
|
self.selected = True
|
|
self.select_instance = slit(self.data, fig=self.fig, ax=self.ax, cdelt=self.wcs.wcs.cdelt,
|
|
width=val, height=s_slit_height.val, angle=s_slit_angle.val)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_slit_h(val):
|
|
if hasattr(self, 'select_instance'):
|
|
if hasattr(self.select_instance, 'height'):
|
|
self.select_instance.update_height(val)
|
|
else:
|
|
self.selected = True
|
|
self.select_instance = slit(self.data, fig=self.fig, ax=self.ax, cdelt=self.wcs.wcs.cdelt,
|
|
width=s_slit_width.val, height=val, angle=s_slit_angle.val)
|
|
else:
|
|
self.selected = True
|
|
self.select_instance = slit(self.data, fig=self.fig, ax=self.ax, cdelt=self.wcs.wcs.cdelt,
|
|
width=s_slit_width.val, height=val, angle=s_slit_angle.val)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def update_slit_a(val):
|
|
if hasattr(self, 'select_instance'):
|
|
if hasattr(self.select_instance, 'angle'):
|
|
self.select_instance.update_angle(val)
|
|
else:
|
|
self.selected = True
|
|
self.select_instance = slit(self.data, fig=self.fig, ax=self.ax, cdelt=self.wcs.wcs.cdelt,
|
|
width=s_slit_width.val, height=s_slit_height.val, angle=val)
|
|
else:
|
|
self.selected = True
|
|
self.select_instance = slit(self.data, fig=self.fig, ax=self.ax, cdelt=self.wcs.wcs.cdelt,
|
|
width=s_slit_width.val, height=s_slit_height.val, angle=val)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def reset_slit(event):
|
|
self.region = None
|
|
s_slit_width.reset()
|
|
s_slit_height.reset()
|
|
s_slit_angle.reset()
|
|
self.pol_int()
|
|
self.fig.canvas.draw_idle()
|
|
|
|
b_slit.on_clicked(select_slit)
|
|
b_slit_reset.on_clicked(reset_slit)
|
|
s_slit_width.on_changed(update_slit_w)
|
|
s_slit_height.on_changed(update_slit_h)
|
|
s_slit_angle.on_changed(update_slit_a)
|
|
|
|
# Set axe for crop Stokes
|
|
ax_crop = self.fig.add_axes([0.70, 0.070, 0.05, 0.02])
|
|
ax_crop_reset = self.fig.add_axes([0.755, 0.070, 0.05, 0.02])
|
|
b_crop = Button(ax_crop, "Crop")
|
|
b_crop.label.set_fontsize(8)
|
|
self.cropped = False
|
|
b_crop_reset = Button(ax_crop_reset, "Reset")
|
|
b_crop_reset.label.set_fontsize(8)
|
|
|
|
def crop(event):
|
|
if self.cropped:
|
|
self.cropped = False
|
|
self.crop_instance.im.remove()
|
|
self.crop_instance.cr.pop(0).remove()
|
|
self.crop_instance.rect_selector.set_active(False)
|
|
self.Stokes = self.crop_instance.hdul_crop
|
|
self.region = deepcopy(self.data_mask.astype(bool))
|
|
self.pol_int()
|
|
self.ax.reset_wcs(self.wcs)
|
|
self.ax_cosmetics()
|
|
self.display()
|
|
self.ax.set_xlim(0, self.I.shape[1])
|
|
self.ax.set_ylim(0, self.I.shape[0])
|
|
self.pol_vector()
|
|
else:
|
|
self.cropped = True
|
|
self.crop_instance = crop_Stokes(self.Stokes, fig=self.fig, ax=self.ax)
|
|
self.crop_instance.rect_selector.set_active(True)
|
|
k = 0
|
|
while not self.crop_instance.cropped and k < 60:
|
|
self.fig.canvas.start_event_loop(timeout=1)
|
|
k += 1
|
|
crop(event)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
def reset_crop(event):
|
|
self.Stokes = deepcopy(Stokes)
|
|
self.region = None
|
|
self.pol_int()
|
|
self.ax.reset_wcs(self.wcs)
|
|
self.ax_cosmetics()
|
|
self.display()
|
|
self.pol_vector()
|
|
|
|
b_crop.on_clicked(crop)
|
|
b_crop_reset.on_clicked(reset_crop)
|
|
|
|
# Set axe for saving plot
|
|
ax_save = self.fig.add_axes([0.850, 0.070, 0.05, 0.02])
|
|
b_save = Button(ax_save, "Save")
|
|
b_save.label.set_fontsize(8)
|
|
ax_text_save = self.fig.add_axes([0.3, 0.020, 0.5, 0.025], visible=False)
|
|
text_save = TextBox(ax_text_save, "Save to:", initial='')
|
|
|
|
def saveplot(event):
|
|
ax_text_save.set(visible=True)
|
|
ax_snr_reset.set(visible=False)
|
|
ax_vec_sc.set(visible=False)
|
|
ax_save.set(visible=False)
|
|
ax_dump.set(visible=False)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
b_save.on_clicked(saveplot)
|
|
|
|
def submit_save(expression):
|
|
ax_text_save.set(visible=False)
|
|
if expression != '':
|
|
save_fig, save_ax = plt.subplots(figsize=(12, 10), layout='constrained', subplot_kw=dict(projection=self.wcs))
|
|
self.ax_cosmetics(ax=save_ax)
|
|
self.display(fig=save_fig, ax=save_ax)
|
|
self.pol_vector(fig=save_fig, ax=save_ax)
|
|
self.pol_int(fig=save_fig, ax=save_ax)
|
|
save_fig.suptitle(r"{0:s} with $SNR_{{p}} \geq$ {1:d} and $SNR_{{I}} \geq$ {2:d}".format(self.targ, int(self.SNRp), int(self.SNRi)))
|
|
if not expression[-4:] in ['.png', '.jpg', '.pdf']:
|
|
expression += '.pdf'
|
|
save_fig.savefig(expression, bbox_inches='tight', dpi=200)
|
|
plt.close(save_fig)
|
|
text_save.set_val('')
|
|
ax_snr_reset.set(visible=True)
|
|
ax_vec_sc.set(visible=True)
|
|
ax_save.set(visible=True)
|
|
ax_dump.set(visible=True)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
text_save.on_submit(submit_save)
|
|
|
|
# Set axe for data dump
|
|
ax_dump = self.fig.add_axes([0.850, 0.045, 0.05, 0.02])
|
|
b_dump = Button(ax_dump, "Dump")
|
|
b_dump.label.set_fontsize(8)
|
|
ax_text_dump = self.fig.add_axes([0.3, 0.020, 0.5, 0.025], visible=False)
|
|
text_dump = TextBox(ax_text_dump, "Dump to:", initial='')
|
|
|
|
def dump(event):
|
|
ax_text_dump.set(visible=True)
|
|
ax_snr_reset.set(visible=False)
|
|
ax_vec_sc.set(visible=False)
|
|
ax_save.set(visible=False)
|
|
ax_dump.set(visible=False)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
shape = np.array(self.I.shape)
|
|
center = (shape/2).astype(int)
|
|
cdelt_arcsec = self.wcs.wcs.cdelt*3600
|
|
xx, yy = np.indices(shape)
|
|
x, y = (xx-center[0])*cdelt_arcsec[0], (yy-center[1])*cdelt_arcsec[1]
|
|
|
|
P, PA = np.zeros(shape), np.zeros(shape)
|
|
P[self.cut] = self.P[self.cut]
|
|
PA[self.cut] = self.PA[self.cut]
|
|
dump_list = []
|
|
for i in range(shape[0]):
|
|
for j in range(shape[1]):
|
|
dump_list.append([x[i, j], y[i, j], self.I[i, j]*self.map_convert, self.Q[i, j] *
|
|
self.map_convert, self.U[i, j]*self.map_convert, P[i, j], PA[i, j]])
|
|
self.data_dump = np.array(dump_list)
|
|
|
|
b_dump.on_clicked(dump)
|
|
|
|
def submit_dump(expression):
|
|
ax_text_dump.set(visible=False)
|
|
if expression != '':
|
|
if not expression[-4:] in ['.txt', '.dat']:
|
|
expression += '.txt'
|
|
np.savetxt(expression, self.data_dump)
|
|
text_dump.set_val('')
|
|
ax_snr_reset.set(visible=True)
|
|
ax_vec_sc.set(visible=True)
|
|
ax_save.set(visible=True)
|
|
ax_dump.set(visible=True)
|
|
self.fig.canvas.draw_idle()
|
|
|
|
text_dump.on_submit(submit_dump)
|
|
|
|
# Set axes for display buttons
|
|
ax_tf = self.fig.add_axes([0.925, 0.105, 0.05, 0.02])
|
|
ax_pf = self.fig.add_axes([0.925, 0.085, 0.05, 0.02])
|
|
ax_p = self.fig.add_axes([0.925, 0.065, 0.05, 0.02])
|
|
ax_pa = self.fig.add_axes([0.925, 0.045, 0.05, 0.02])
|
|
ax_snri = self.fig.add_axes([0.925, 0.025, 0.05, 0.02])
|
|
ax_snrp = self.fig.add_axes([0.925, 0.005, 0.05, 0.02])
|
|
b_tf = Button(ax_tf, r"$F_{\lambda}$")
|
|
b_pf = Button(ax_pf, r"$F_{\lambda} \cdot P$")
|
|
b_p = Button(ax_p, r"$P$")
|
|
b_pa = Button(ax_pa, r"$\theta_{P}$")
|
|
b_snri = Button(ax_snri, r"$I / \sigma_{I}$")
|
|
b_snrp = Button(ax_snrp, r"$P / \sigma_{P}$")
|
|
|
|
def d_tf(event):
|
|
self.display_selection = 'total_flux'
|
|
self.display()
|
|
self.pol_int()
|
|
b_tf.on_clicked(d_tf)
|
|
|
|
def d_pf(event):
|
|
self.display_selection = 'pol_flux'
|
|
self.display()
|
|
self.pol_int()
|
|
b_pf.on_clicked(d_pf)
|
|
|
|
def d_p(event):
|
|
self.display_selection = 'pol_deg'
|
|
self.display()
|
|
self.pol_int()
|
|
b_p.on_clicked(d_p)
|
|
|
|
def d_pa(event):
|
|
self.display_selection = 'pol_ang'
|
|
self.display()
|
|
self.pol_int()
|
|
b_pa.on_clicked(d_pa)
|
|
|
|
def d_snri(event):
|
|
self.display_selection = 'snri'
|
|
self.display()
|
|
self.pol_int()
|
|
b_snri.on_clicked(d_snri)
|
|
|
|
def d_snrp(event):
|
|
self.display_selection = 'snrp'
|
|
self.display()
|
|
self.pol_int()
|
|
b_snrp.on_clicked(d_snrp)
|
|
|
|
plt.show()
|
|
|
|
@property
|
|
def wcs(self):
|
|
return WCS(self.Stokes[0].header).celestial.deepcopy()
|
|
|
|
@property
|
|
def I(self):
|
|
return self.Stokes['I_STOKES'].data
|
|
|
|
@property
|
|
def Q(self):
|
|
return self.Stokes['Q_STOKES'].data
|
|
|
|
@property
|
|
def U(self):
|
|
return self.Stokes['U_STOKES'].data
|
|
|
|
@property
|
|
def IQU_cov(self):
|
|
return self.Stokes['IQU_COV_MATRIX'].data
|
|
|
|
@property
|
|
def P(self):
|
|
return self.Stokes['POL_DEG_DEBIASED'].data
|
|
|
|
@property
|
|
def s_P(self):
|
|
return self.Stokes['POL_DEG_ERR'].data
|
|
|
|
@property
|
|
def PA(self):
|
|
return self.Stokes['POL_ANG'].data
|
|
|
|
@property
|
|
def data_mask(self):
|
|
return self.Stokes['DATA_MASK'].data
|
|
|
|
def set_data_mask(self, mask):
|
|
self.Stokes[np.argmax([self.Stokes[i].header['datatype'] == 'Data_mask' for i in range(len(self.Stokes))])].data = mask.astype(float)
|
|
|
|
@property
|
|
def cut(self):
|
|
s_I = np.sqrt(self.IQU_cov[0, 0])
|
|
SNRp_mask, SNRi_mask = np.zeros(self.P.shape).astype(bool), np.zeros(self.I.shape).astype(bool)
|
|
SNRp_mask[self.s_P > 0.] = self.P[self.s_P > 0.] / self.s_P[self.s_P > 0.] > self.SNRp
|
|
SNRi_mask[s_I > 0.] = self.I[s_I > 0.] / s_I[s_I > 0.] > self.SNRi
|
|
return np.logical_and(SNRi_mask, SNRp_mask)
|
|
|
|
def ax_cosmetics(self, ax=None):
|
|
if ax is None:
|
|
ax = self.ax
|
|
ax.set(aspect='equal', fc='black')
|
|
|
|
ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
|
|
ax.coords[0].set_axislabel('Right Ascension (J2000)')
|
|
ax.coords[0].set_axislabel_position('t')
|
|
ax.coords[0].set_ticklabel_position('t')
|
|
ax.set_ylabel('Declination (J2000)', labelpad=-1)
|
|
|
|
# Display scales and orientation
|
|
fontprops = fm.FontProperties(size=14)
|
|
px_size = self.wcs.wcs.cdelt[0]*3600.
|
|
if hasattr(self, 'px_sc'):
|
|
self.px_sc.remove()
|
|
self.px_sc = AnchoredSizeBar(ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5,
|
|
frameon=False, size_vertical=0.005, color='white', fontproperties=fontprops)
|
|
ax.add_artist(self.px_sc)
|
|
if hasattr(self, 'pol_sc'):
|
|
self.pol_sc.remove()
|
|
self.pol_sc = AnchoredSizeBar(ax.transData, self.vec_scale, r"$P$= 100%", 4, pad=0.5, sep=5, borderpad=0.5,
|
|
frameon=False, size_vertical=0.005, color='white', fontproperties=fontprops)
|
|
ax.add_artist(self.pol_sc)
|
|
if hasattr(self, 'north_dir'):
|
|
self.north_dir.remove()
|
|
self.north_dir = AnchoredDirectionArrows(ax.transAxes, "E", "N", length=-0.08, fontsize=0.025, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, back_length=0., head_length=10., head_width=10.,
|
|
angle=-self.Stokes[0].header['orientat'], color='white', text_props={'ec': None, 'fc': 'w', 'alpha': 1, 'lw': 0.4}, arrow_props={'ec': None, 'fc': 'w', 'alpha': 1, 'lw': 1})
|
|
ax.add_artist(self.north_dir)
|
|
|
|
def display(self, fig=None, ax=None, flux_lim=None):
|
|
norm = None
|
|
if self.display_selection is None:
|
|
self.display_selection = "total_flux"
|
|
if flux_lim is None:
|
|
flux_lim = self.flux_lim
|
|
if self.display_selection.lower() in ['total_flux']:
|
|
self.data = self.I*self.map_convert
|
|
if flux_lim is None:
|
|
vmin, vmax = 1./2.*np.median(self.data[self.data > 0.]), np.max(self.data[self.data > 0.])
|
|
else:
|
|
vmin, vmax = flux_lim
|
|
norm = LogNorm(vmin, vmax)
|
|
label = r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]"
|
|
elif self.display_selection.lower() in ['pol_flux']:
|
|
self.data = self.I*self.map_convert*self.P
|
|
if flux_lim is None:
|
|
vmin, vmax = 1./2.*np.median(self.I[self.I > 0.]*self.map_convert), np.max(self.I[self.I > 0.]*self.map_convert)
|
|
else:
|
|
vmin, vmax = flux_lim
|
|
norm = LogNorm(vmin, vmax)
|
|
label = r"$P \cdot F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]"
|
|
elif self.display_selection.lower() in ['pol_deg']:
|
|
self.data = self.P*100.
|
|
vmin, vmax = 0., np.max(self.data[self.P > self.s_P])
|
|
label = r"$P$ [%]"
|
|
elif self.display_selection.lower() in ['pol_ang']:
|
|
self.data = princ_angle(self.PA)
|
|
vmin, vmax = 0, 180.
|
|
label = r"$\theta_{P}$ [°]"
|
|
elif self.display_selection.lower() in ['snri']:
|
|
s_I = np.sqrt(self.IQU_cov[0, 0])
|
|
SNRi = np.zeros(self.I.shape)
|
|
SNRi[s_I > 0.] = self.I[s_I > 0.]/s_I[s_I > 0.]
|
|
self.data = SNRi
|
|
vmin, vmax = 0., np.max(self.data[self.data > 0.])
|
|
label = r"$I_{Stokes}/\sigma_{I}$"
|
|
elif self.display_selection.lower() in ['snrp']:
|
|
SNRp = np.zeros(self.P.shape)
|
|
SNRp[self.s_P > 0.] = self.P[self.s_P > 0.]/self.s_P[self.s_P > 0.]
|
|
self.data = SNRp
|
|
vmin, vmax = 0., np.max(self.data[self.data > 0.])
|
|
label = r"$P/\sigma_{P}$"
|
|
|
|
if fig is None:
|
|
fig = self.fig
|
|
if ax is None:
|
|
ax = self.ax
|
|
if hasattr(self, 'cbar'):
|
|
self.cbar.remove()
|
|
if hasattr(self, 'im'):
|
|
self.im.remove()
|
|
if norm is not None:
|
|
self.im = ax.imshow(self.data, norm=norm, aspect='equal', cmap='inferno')
|
|
else:
|
|
self.im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
|
|
plt.rcParams.update({'font.size': 14})
|
|
self.cbar = fig.colorbar(self.im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=label)
|
|
plt.rcParams.update({'font.size': 10})
|
|
fig.canvas.draw_idle()
|
|
return self.im
|
|
else:
|
|
if norm is not None:
|
|
im = ax.imshow(self.data, norm=norm, aspect='equal', cmap='inferno')
|
|
else:
|
|
im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
|
|
ax.set_xlim(0, self.data.shape[1])
|
|
ax.set_ylim(0, self.data.shape[0])
|
|
plt.rcParams.update({'font.size': 14})
|
|
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=label)
|
|
plt.rcParams.update({'font.size': 10})
|
|
fig.canvas.draw_idle()
|
|
return im
|
|
|
|
def pol_vector(self, fig=None, ax=None):
|
|
P_cut = np.ones(self.P.shape)*np.nan
|
|
P_cut[self.cut] = self.P[self.cut]
|
|
X, Y = np.meshgrid(np.arange(self.I.shape[1]), np.arange(self.I.shape[0]))
|
|
XY_U, XY_V = P_cut*np.cos(np.pi/2. + self.PA*np.pi/180.), P_cut*np.sin(np.pi/2. + self.PA*np.pi/180.)
|
|
|
|
if fig is None:
|
|
fig = self.fig
|
|
if ax is None:
|
|
ax = self.ax
|
|
if hasattr(self, 'quiver'):
|
|
self.quiver.remove()
|
|
self.quiver = ax.quiver(X, Y, XY_U, XY_V, units='xy', scale=1./self.vec_scale, scale_units='xy', pivot='mid', headwidth=0.,
|
|
headlength=0., headaxislength=0., width=0.5, linewidth=0.75, color='white', edgecolor='black')
|
|
fig.canvas.draw_idle()
|
|
return self.quiver
|
|
else:
|
|
ax.quiver(X, Y, XY_U, XY_V, units='xy', scale=1./self.vec_scale, scale_units='xy', pivot='mid', headwidth=0.,
|
|
headlength=0., headaxislength=0., width=0.5, linewidth=0.75, color='white', edgecolor='black')
|
|
fig.canvas.draw_idle()
|
|
|
|
def pol_int(self, fig=None, ax=None):
|
|
if self.region is None:
|
|
s_I = np.sqrt(self.IQU_cov[0, 0])
|
|
I_reg = self.I.sum()
|
|
I_reg_err = np.sqrt(np.sum(s_I**2))
|
|
P_reg = self.Stokes[0].header['P_int']
|
|
P_reg_err = self.Stokes[0].header['P_int_err']
|
|
PA_reg = self.Stokes[0].header['PA_int']
|
|
PA_reg_err = self.Stokes[0].header['PA_int_err']
|
|
|
|
s_I = np.sqrt(self.IQU_cov[0, 0])
|
|
s_Q = np.sqrt(self.IQU_cov[1, 1])
|
|
s_U = np.sqrt(self.IQU_cov[2, 2])
|
|
s_IQ = self.IQU_cov[0, 1]
|
|
s_IU = self.IQU_cov[0, 2]
|
|
s_QU = self.IQU_cov[1, 2]
|
|
|
|
I_cut = self.I[self.cut].sum()
|
|
Q_cut = self.Q[self.cut].sum()
|
|
U_cut = self.U[self.cut].sum()
|
|
I_cut_err = np.sqrt(np.sum(s_I[self.cut]**2))
|
|
Q_cut_err = np.sqrt(np.sum(s_Q[self.cut]**2))
|
|
U_cut_err = np.sqrt(np.sum(s_U[self.cut]**2))
|
|
IQ_cut_err = np.sqrt(np.sum(s_IQ[self.cut]**2))
|
|
IU_cut_err = np.sqrt(np.sum(s_IU[self.cut]**2))
|
|
QU_cut_err = np.sqrt(np.sum(s_QU[self.cut]**2))
|
|
|
|
P_cut = np.sqrt(Q_cut**2+U_cut**2)/I_cut
|
|
P_cut_err = np.sqrt((Q_cut**2*Q_cut_err**2 + U_cut**2*U_cut_err**2 + 2.*Q_cut*U_cut*QU_cut_err)/(Q_cut**2 + U_cut**2) +
|
|
((Q_cut/I_cut)**2 + (U_cut/I_cut)**2)*I_cut_err**2 - 2.*(Q_cut/I_cut)*IQ_cut_err - 2.*(U_cut/I_cut)*IU_cut_err)/I_cut
|
|
|
|
PA_cut = princ_angle((90./np.pi)*np.arctan2(U_cut, Q_cut))
|
|
PA_cut_err = (90./(np.pi*(Q_cut**2+U_cut**2)))*np.sqrt(U_cut**2*Q_cut_err**2 + Q_cut**2*U_cut_err**2 - 2.*Q_cut*U_cut*QU_cut_err)
|
|
|
|
else:
|
|
s_I = np.sqrt(self.IQU_cov[0, 0])
|
|
s_Q = np.sqrt(self.IQU_cov[1, 1])
|
|
s_U = np.sqrt(self.IQU_cov[2, 2])
|
|
s_IQ = self.IQU_cov[0, 1]
|
|
s_IU = self.IQU_cov[0, 2]
|
|
s_QU = self.IQU_cov[1, 2]
|
|
|
|
I_reg = self.I[self.region].sum()
|
|
Q_reg = self.Q[self.region].sum()
|
|
U_reg = self.U[self.region].sum()
|
|
I_reg_err = np.sqrt(np.sum(s_I[self.region]**2))
|
|
Q_reg_err = np.sqrt(np.sum(s_Q[self.region]**2))
|
|
U_reg_err = np.sqrt(np.sum(s_U[self.region]**2))
|
|
IQ_reg_err = np.sqrt(np.sum(s_IQ[self.region]**2))
|
|
IU_reg_err = np.sqrt(np.sum(s_IU[self.region]**2))
|
|
QU_reg_err = np.sqrt(np.sum(s_QU[self.region]**2))
|
|
|
|
P_reg = np.sqrt(Q_reg**2+U_reg**2)/I_reg
|
|
P_reg_err = np.sqrt((Q_reg**2*Q_reg_err**2 + U_reg**2*U_reg_err**2 + 2.*Q_reg*U_reg*QU_reg_err)/(Q_reg**2 + U_reg**2) +
|
|
((Q_reg/I_reg)**2 + (U_reg/I_reg)**2)*I_reg_err**2 - 2.*(Q_reg/I_reg)*IQ_reg_err - 2.*(U_reg/I_reg)*IU_reg_err)/I_reg
|
|
|
|
PA_reg = princ_angle((90./np.pi)*np.arctan2(U_reg, Q_reg))
|
|
PA_reg_err = (90./(np.pi*(Q_reg**2+U_reg**2)))*np.sqrt(U_reg**2*Q_reg_err**2 + Q_reg**2*U_reg_err**2 - 2.*Q_reg*U_reg*QU_reg_err)
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new_cut = np.logical_and(self.region, self.cut)
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I_cut = self.I[new_cut].sum()
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Q_cut = self.Q[new_cut].sum()
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U_cut = self.U[new_cut].sum()
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I_cut_err = np.sqrt(np.sum(s_I[new_cut]**2))
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Q_cut_err = np.sqrt(np.sum(s_Q[new_cut]**2))
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U_cut_err = np.sqrt(np.sum(s_U[new_cut]**2))
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IQ_cut_err = np.sqrt(np.sum(s_IQ[new_cut]**2))
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IU_cut_err = np.sqrt(np.sum(s_IU[new_cut]**2))
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QU_cut_err = np.sqrt(np.sum(s_QU[new_cut]**2))
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P_cut = np.sqrt(Q_cut**2+U_cut**2)/I_cut
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P_cut_err = np.sqrt((Q_cut**2*Q_cut_err**2 + U_cut**2*U_cut_err**2 + 2.*Q_cut*U_cut*QU_cut_err)/(Q_cut**2 + U_cut**2) +
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((Q_cut/I_cut)**2 + (U_cut/I_cut)**2)*I_cut_err**2 - 2.*(Q_cut/I_cut)*IQ_cut_err - 2.*(U_cut/I_cut)*IU_cut_err)/I_cut
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PA_cut = princ_angle((90./np.pi)*np.arctan2(U_cut, Q_cut))
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PA_cut_err = (90./(np.pi*(Q_cut**2+U_cut**2)))*np.sqrt(U_cut**2*Q_cut_err**2 + Q_cut**2*U_cut_err**2 - 2.*Q_cut*U_cut*QU_cut_err)
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if hasattr(self, 'cont'):
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for coll in self.cont.collections:
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try:
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coll.remove()
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except AttributeError:
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del coll
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del self.cont
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if fig is None:
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fig = self.fig
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if ax is None:
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ax = self.ax
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if hasattr(self, 'an_int'):
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self.an_int.remove()
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self.str_int = r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav, sci_not(I_reg*self.map_convert, I_reg_err*self.map_convert, 2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100., np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg, np.ceil(PA_reg_err*10.)/10.)
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self.str_cut = ""
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# self.str_cut = "\n"+r"$F_{{\lambda}}^{{cut}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav, sci_not(I_cut*self.map_convert, I_cut_err*self.map_convert, 2))+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100., np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut, np.ceil(PA_cut_err*10.)/10.)
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self.an_int = ax.annotate(self.str_int+self.str_cut, color='white', fontsize=12, xy=(0.01, 1.00), xycoords='axes fraction', path_effects=[pe.withStroke(linewidth=0.5, foreground='k')], verticalalignment='top', horizontalalignment='left')
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if self.region is not None:
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self.cont = ax.contour(self.region.astype(float), levels=[0.5], colors='white', linewidths=0.8)
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|
fig.canvas.draw_idle()
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|
return self.an_int
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|
else:
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|
str_int = r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav, sci_not(I_reg*self.map_convert, I_reg_err*self.map_convert, 2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100., np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg, np.ceil(PA_reg_err*10.)/10.)
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|
str_cut = ""
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|
# str_cut = "\n"+r"$F_{{\lambda}}^{{cut}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav, sci_not(I_cut*self.map_convert, I_cut_err*self.map_convert, 2))+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100., np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut, np.ceil(PA_cut_err*10.)/10.)
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|
ax.annotate(str_int+str_cut, color='white', fontsize=12, xy=(0.01, 1.00), xycoords='axes fraction', path_effects=[pe.withStroke(linewidth=0.5, foreground='k')], verticalalignment='top', horizontalalignment='left')
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|
if self.region is not None:
|
|
ax.contour(self.region.astype(float), levels=[0.5], colors='white', linewidths=0.8)
|
|
fig.canvas.draw_idle()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import argparse
|
|
|
|
parser = argparse.ArgumentParser(description='Interactively plot the pipeline products')
|
|
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('-p', '--snrp', metavar='snrp_cut', required=False, help='the cut in signal-to-noise for the polarization degree', type=float, default=3.)
|
|
parser.add_argument('-i', '--snri', metavar='snri_cut', required=False, help='the cut in signal-to-noise for the intensity', type=float, default=3.)
|
|
parser.add_argument('-l', '--lim', metavar='flux_lim', nargs=2, required=False, help='limits for the intensity map', default=None)
|
|
args = parser.parse_args()
|
|
|
|
if args.file is not None:
|
|
Stokes_UV = fits.open(args.file, mode='readonly')
|
|
p = pol_map(Stokes_UV, SNRp_cut=args.snrp, SNRi_cut=args.snri, flux_lim=args.lim)
|
|
|
|
else:
|
|
print("python3 plots.py -f <path_to_reduced_fits> -p <SNRp_cut> -i <SNRi_cut> -l <flux_lim>")
|