"""
"""
from __future__ import division, absolute_import, print_function
#from ..core.image_manager.image import Image
import numpy as np
from math import fabs
__author__ = 'andrea tramacere'
#from skimage import exposure
#from ..image_processing.filters import normalize
try:
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pylab as plt
#from scipy import ndimag
from matplotlib.patches import Ellipse, Circle
#from matplotlib.pyplot.arrow as arrow
import matplotlib.gridspec as gridspec
#from scipy import ndimage
import matplotlib.cm as cm
#from matplotlib.colors import Normalize
from matplotlib.colors import LogNorm, PowerNorm
except:
print("no pylab found")
[docs]def show_image(image_array,plot=False,ax=None,clim=None,cmap=None):
y_size,x_size=image_array.shape
if ax is None:
fig, ax = plt.subplots()
im=ax.imshow(image_array,interpolation='nearest',cmap=cmap)
ax.format_coord = Formatter(im)
ax.set_ylim([0,y_size-1])
ax.set_xlim([0,x_size-1])
if clim is not None:
im.set_clim(image_array.min(),clim)
if plot==True:
plt.show()
return im,ax
[docs]def plot_contour(ax,x_contour,y_contour,color,delta_max=5):
if len(x_contour)>3:
dx=fabs(x_contour[0]-x_contour[-1])
dy=fabs(y_contour[0]-y_contour[-1])
if dx<=delta_max and dy<=delta_max:
ax.plot([x_contour[0],x_contour[-1]],[y_contour[0],y_contour[-1]],'-',c=color,lw=1)#linestyle='None')
for ID in xrange(x_contour.size-1):
dx=fabs(x_contour[ID]-x_contour[ID+1])
dy=fabs(y_contour[ID]-y_contour[ID+1])
if dx<=delta_max and dy<=delta_max:
ax.plot([x_contour[ID],x_contour[ID+1]],[y_contour[ID],y_contour[ID+1]],'-',c=color,lw=1)#linestyle='None')
[docs]def do_detection_plot(ax,image,cluster_list,sex_srcs_array=None,plot_core_points=True,plot_cnt=True,arrows=True,plot_ID=True,plot_cl_centroid=True,title='detection plot',font_size=10,plot_image_center=False):
ax.set_title(title,fontsize=font_size)
if image is not None:
if hasattr(image,'array'):
image_array=image.array
else:
image_array=image
imgplot,ax=show_image(image_array,ax=ax)
image_center_y=np.float(image.shape[0])/2.0
image_center_x=np.float(image.shape[1])/2.0
if plot_image_center==True:
ax.scatter(image_center_x,image_center_y,marker='+', linewidths=1,c='black')
clip_flux=np.zeros(len(cluster_list))
clip_size=np.zeros(len(cluster_list))
for ID,cluster in enumerate(cluster_list):
c = cm.hsv(float(cluster.ID)/10,1)
clip_flux[ID]=cluster.pts_weight.mean()+2.0*cluster.pts_weight.std()
clip_size[ID]=cluster.pts_weight.size
if plot_ID==True:
ax.annotate('C.ID=%d'%(cluster.ID), xy=(cluster.x_c,cluster.y_c), color='w' )
if plot_core_points==True:
ax.plot(cluster.cartesian.x,cluster.cartesian.y,'s',mew=0)
if plot_cl_centroid==True:
ax.scatter(cluster.x_c,cluster.y_c,marker='+',s=100, linewidths=1,c='black')
ellipse=Ellipse(xy=(cluster.x_c,cluster.y_c), width=cluster.sig_x*2.0, height=cluster.sig_y*2.0, angle=cluster.semi_major_angle,edgecolor='y',fc='None')
ax.add_patch(ellipse)
if plot_cnt==True and cluster.contour_shape is not None:
if cluster.contour_shape is not None:
plot_contour(ax,cluster.contour_shape[:,0],cluster.contour_shape[:,1],color='w')
if arrows==True:
delta_x=cluster.sig_x*np.cos(np.deg2rad(cluster.semi_major_angle))
delta_y=cluster.sig_x*np.sin(np.deg2rad(cluster.semi_major_angle))
ax.arrow(cluster.x_c,cluster.y_c,delta_x ,delta_y ,head_width=0.05, head_length=0.1)
delta_x=image_center_x-cluster.x_c
delta_y=image_center_y-cluster.y_c
ax.arrow(cluster.x_c,cluster.y_c,delta_x ,delta_y ,head_width=0.05, head_length=0.1)
clip_flux_level=None
if len(cluster_list)>0:
clip_flux_level=clip_flux[np.argmax(clip_size)]*1.5
if image is not None:
imgplot.set_clim(image_array.min(),clip_flux_level)
if sex_srcs_array is not None:
for sex_src in sex_srcs_array:
ellipse=Ellipse(xy=(sex_src['X_IMAGE']-1,sex_src['Y_IMAGE']-1), width=sex_src['A_IMAGE'], height=sex_src['B_IMAGE'], angle=sex_src['THETA_IMAGE'],edgecolor='white',fc='None')
ax.add_patch(ellipse)
return clip_flux_level
[docs]def analysis_plot(image,selected_coords,fluxes,cluster_list,title='fig',plot=False,gal_id=None,threshold=None,K=None,only_detection=False,fig_name='detection_plot',msk=None):
if only_detection==True:
fig,((ax)) = plt.subplots(1, 1)
do_detection_plot(ax,image,cluster_list,sex_srcs_array=None)
if plot==True:
plt.show()
return
fig,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)
fig.canvas.set_window_title(fig_name)
if gal_id!=None:
fig.suptitle('gal ID=%d'%gal_id,fontsize=10)
clip_flux_level=do_detection_plot(ax3,image,cluster_list,sex_srcs_array=None,plot_core_points=False,arrows=False)
ax=ax1
ax.set_title('original image',fontsize=10)
show_image(image.array,ax=ax1,clim=clip_flux_level)
ax=ax2
ax.set_title('bool mask',fontsize=10)
show_image(image.array,ax=ax2,clim=clip_flux_level)
ax.scatter(selected_coords[:,0],selected_coords[:,1],marker='o', edgecolor='white', linewidth='0',facecolor='white',s=2)
if threshold!=None:
bins=max(10,int(fluxes.size/100))
ax4.hist( np.log10(fluxes[fluxes>0]),bins=bins)
ax4.plot([np.log10(threshold),np.log10(threshold)],[0,ax4.axis()[3]])
else:
ax4.hist( image.flatten(),bins=50)
#ax=ax6
#ax.hist(fluxes,bins=20,)
#if K!=None:
# ax.plot( [K*threshold,K*threshold],[0,ax.axis()[3]],lw=3)
#ax=ax4
#ax.set_title('Final Image',fontsize=10)
#if image is not None:
# show_image(image.array,ax=ax4)
plt.tight_layout()
if plot==True:
plt.show()
plt.close()
return fig
[docs]def ring_detection_plot(image,ring_image_array,new_coords_cartesian,cluster_list,x_c,y_c,x_hist, y_hist,x_c_fit=None,y_c_fit=None,R_c_fit=None,title='fig',plot=False,gal_id=None,fig_name='detection_plot',msk=None):
fig,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)
fig.canvas.set_window_title(fig_name)
if gal_id!=None:
fig.suptitle('gal ID=%d'%gal_id,fontsize=10)
clip_flux_level=do_detection_plot(ax3,image,cluster_list,sex_srcs_array=None,plot_core_points=False,arrows=False,plot_ID=False)
ax=ax1
ax.set_title('original image',fontsize=10)
show_image(image.array,ax=ax1,clim=clip_flux_level)
ax2.set_title('ring mask',fontsize=10)
img=np.copy(image.array)
img[~ring_image_array]=0
show_image(img,ax=ax2,cmap='gray')
if new_coords_cartesian is not None:
ax2.plot(new_coords_cartesian.x,new_coords_cartesian.y,'.',ms=5,c='y')
if x_hist is not None:
#for r in x_hist:
ellipse = Ellipse(xy=(x_c,y_c), width=2*x_hist.min(), height=2*x_hist.min(), edgecolor='w', fc='None',linewidth=1.0)
ax2.add_patch(ellipse)
ellipse = Ellipse(xy=(x_c, y_c), width=2 * x_hist.max() , height=2 * x_hist.max(), edgecolor='w',fc='None', linewidth=1.0)
ax2.add_patch(ellipse)
ax4.plot(x_hist, y_hist)
ax4.set_xlim(0, x_hist.max())
if x_c_fit is not None:
ellipse = Ellipse(xy=(x_c_fit,y_c_fit), width=2 * R_c_fit, height=2 * R_c_fit, edgecolor='r', fc='None',
linewidth=1.0,linestyle='--')
ax2.add_patch(ellipse)
ellipse = Ellipse(xy=(x_c_fit, y_c_fit), width=2 * R_c_fit, height=2 * R_c_fit, edgecolor='r', fc='None',
linewidth=1.0, linestyle='--')
ax1.add_patch(ellipse)
#ax=ax6
#ax.hist(fluxes,bins=20,)
#if K!=None:
# ax.plot( [K*threshold,K*threshold],[0,ax.axis()[3]],lw=3)
#ax=ax4
#ax.set_title('Final Image',fontsize=10)
#if image is not None:
# show_image(image.array,ax=ax4)
plt.tight_layout()
if plot==True:
plt.show()
plt.close()
return fig
