mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-11-14 02:23:32 +01:00
378 lines
12 KiB
Python
378 lines
12 KiB
Python
# from wappy import *
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from earsim import *
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from atmocal import *
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import matplotlib.pyplot as plt
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from scipy.signal import hilbert
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from scipy import signal
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from scipy.interpolate import interp1d
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from scipy.optimize import curve_fit,minimize
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import pandas as pd
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import os
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try:
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from joblib import Parallel, delayed
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except:
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Parallel = None
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delayed = lambda x: x
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plt.rcParams.update({'font.size': 16})
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atm = AtmoCal()
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from matplotlib import cm
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def set_pol_and_bp(e,low=0.03,high=0.08):
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for ant in e.antennas:
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E = [np.dot(e.uAxB,[ex,ey,ez]) for ex,ey,ez in zip(ant.Ex,ant.Ey,ant.Ez)]
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dt = ant.t[1] -ant.t[0]
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E = block_filter(E,dt,low,high)
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ant.E_AxB = E
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ant.t_AxB = ant.t
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def pow_and_time(test_loc,ev,dt=1.0):
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t_ = []
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a_ = []
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t_min = 1e9
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t_max = -1e9
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for ant in ev.antennas:
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#propagate to test location
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aloc = [ant.x,ant.y,ant.z]
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delta,dist = atm.light_travel_time(test_loc,aloc)
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delta = delta*1e9
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t__ = np.subtract(ant.t_AxB,delta)
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t_.append(t__)
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a_.append(ant.E_AxB)
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if t__[0] < t_min:
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t_min = t__[0]
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if t__[-1] > t_max:
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t_max = t__[-1]
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t_sum = np.arange(t_min+1,t_max-1,dt)
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a_sum = np.zeros(len(t_sum))
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#interpolation
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for t_r,E_ in zip (t_,a_):
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f = interp1d(t_r,E_,assume_sorted=True,bounds_error=False,fill_value=0.)
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a_int = f(t_sum)
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a_sum = np.add(a_sum,a_int)
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if len(a_sum) != 0:
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P = np.sum(np.square(np.absolute(np.fft.fft(a_sum))))
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else:
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print("ERROR, a_sum lenght = 0",
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"tmin ",t_min,
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"t_max ",t_max,
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"dt",dt)
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P = 0
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return P,t_,a_,a_sum,t_sum
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def shower_axis_slice(e,Xb=200,Xe=1200,dX=2,zgr=0):
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zgr = zgr + e.core[2]
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N = int((Xe-Xb)/dX)
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Xs = np.array(np.linspace(Xb,Xe,N+1))
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ds = np.array([atm.distance_to_slant_depth(np.deg2rad(e.zenith),X,zgr) for X in Xs])
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locs = []
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for d_ in ds:
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xc = np.sin(np.deg2rad(e.zenith))*np.cos(np.deg2rad(e.azimuth))* d_
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yc = np.sin(np.deg2rad(e.zenith))*np.sin(np.deg2rad(e.azimuth))* d_
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zc = np.cos(np.deg2rad(e.zenith))* d_
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locs.append([xc,yc,zc])
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p = []
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for loc in locs:
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P,t_,pulses_,wav,twav = pow_and_time(loc,e)
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p.append(P)
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p = np.asanyarray(p)
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return ds,Xs,locs,p
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def shower_plane_slice(e,X=750.,Nx=10,Ny=10,wx=1e3,wy=1e3,xoff=0,yoff=0,zgr=0,n_jobs=None):
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zgr = zgr + e.core[2]
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dX = atm.distance_to_slant_depth(np.deg2rad(e.zenith),X,zgr)
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x = np.linspace(-wx,wx,Nx)
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y = np.linspace(-wy,wy,Ny)
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def loop_func(x_, y_, xoff=xoff, yoff=yoff):
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loc = (x_+xoff)* e.uAxB + (y_+yoff)*e.uAxAxB + dX *e.uA
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locs.append(loc)
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P,t_,pulses_,wav,twav = pow_and_time(loc,e)
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return x_+xoff, y_+yoff, P, locs
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res = ( delayed(loop_func)(x_, y_) for x_ in x for y_ in y)
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if Parallel:
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#if n_jobs is None change with `with parallel_backend`
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res = Parallel(n_jobs=n_jobs)(res)
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# unpack loop results
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xx, yy, p, locs = zip(*res)
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return xx,yy,p,locs[np.argmax(p)]
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def slice_figure(e,X,xx,yy,p,mode='horizontal'):
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fig, axs = plt.subplots(1,figsize=(10,8))
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fig.suptitle(r'E = %.1f EeV, $\theta$ = %.1f$^\circ$, $\phi$ = %.1f$^\circ$ X = %.f'%(e.energy,e.zenith,e.azimuth,X))
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sc = axs.scatter(xx/1e3,yy/1e3,c=p,cmap='Spectral_r',alpha=0.6)
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fig.colorbar(sc,ax=axs)
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zgr = 0 + e.core[2]
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dX = atm.distance_to_slant_depth(np.deg2rad(e.zenith),X,zgr)
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xc = np.sin(np.deg2rad(e.zenith))*np.cos(np.deg2rad(e.azimuth))* dX
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yc = np.sin(np.deg2rad(e.zenith))*np.sin(np.deg2rad(e.azimuth))* dX
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if mode == 'horizontal':
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axs.plot(xc/1e3,yc/1e3,'r+',ms=30)
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axs.set_xlabel('x (km)')
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axs.set_ylabel('y (km)')
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elif mode == "sp":
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axs.plot(0,0,'r+',ms=30)
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axs.set_xlabel('-v x B (km)')
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axs.set_ylabel(' vxvxB (km)')
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im = np.argmax(p)
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axs.plot(xx[im]/1e3,yy[im]/1e3,'bx',ms=30)
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fig.tight_layout()
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return fig,axs
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def dist_to_line(xp,core,u):
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xp = np.array(xp)
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xp_core = xp-core
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c2 = np.dot(xp_core,xp_core)
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a2 = np.dot((np.dot(xp_core,u)*u),(np.dot(xp_core,u)*u))
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d = (np.abs(c2 - a2))**0.5
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return d
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def dist_to_line_sum(param,data,weights):
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#distance line point: a = xp-core is D= | (a)^2-(a dot n)n |
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#where ux is direction of line and x0 is a point in the line (like t = 0)
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x0 = param[0]
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y0 = param[1]
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theta = param[2]
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phi = param[3]
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core = np.array([x0, y0, 0.])
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u = np.array([np.cos(phi)*np.sin(theta),np.sin(phi)*np.sin(theta),np.cos(theta)])
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dsum = 0
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for xp,w in zip(data,weights):
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dsum += dist_to_line(xp,core,u)*w**2
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# print('%.2e %.2e %.2e %.2e %.2e'%(x0,y0,theta,phi,dsum))
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return dsum/len(data)
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def get_axis_points(e,savefig=True,path="",zgr=0,Xlow=300, Xhigh=1000, N_X=15, n_jobs=None):
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Xsteps = np.linspace(Xlow, Xhigh, N_X)
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zgr=zgr+e.core[2] #not exact
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dXref = atm.distance_to_slant_depth(np.deg2rad(e.zenith),750,zgr)
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scale2d = dXref*np.tan(np.deg2rad(2.))
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scale4d = dXref*np.tan(np.deg2rad(4.))
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scale0_2d=dXref*np.tan(np.deg2rad(0.2))
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def loop_func(X):
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print("Starting", X)
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x,y,p,loc_max = shower_plane_slice(e,X,21,21,scale2d,scale2d)
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if savefig:
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fig,axs = slice_figure(e,X,x,y,p,'sp')
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fig.savefig(path+'X%d_a.pdf'%(X))
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plt.close(fig)
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im = np.argmax(p)
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if np.abs(x[im]) == np.max(x) or np.abs(y[im]) == (np.max(y)):
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x,y,p,loc_max = shower_plane_slice(e,X,21,21,scale4d,scale4d)
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if savefig:
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fig,axs = slice_figure(e,X,x,y,p,'sp')
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fig.savefig(path+'X%d_c.pdf'%(X))
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plt.close(fig)
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im = np.argmax(p)
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x,y,p,loc_max = shower_plane_slice(e,X,21,21,scale0_2d,scale0_2d,x[im],y[im])
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if savefig:
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fig,axs = slice_figure(e,X,x,y,p,'sp')
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fig.savefig(path+'X%d_b.pdf'%(X))
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plt.close(fig)
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print("Finished", X)
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return np.max(p), loc_max
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res = (delayed(loop_func)(X) for X in Xsteps)
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if Parallel:
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#if n_jobs is None change with `with parallel_backend`
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res = Parallel(n_jobs=n_jobs)(res)
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# unpack loop results
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max_vals, axis_points = zip(*res)
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return Xsteps,axis_points,max_vals
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def fit_track(e,axis_points,vals,nscale=1e0):
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weights = vals/np.max(vals)
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data=axis_points[:]
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data = [d/nscale for d in data] #km, to have more comparable step sizes
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x0=[0,0,np.deg2rad(e.zenith),np.deg2rad(e.azimuth)]
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res = minimize(dist_to_line_sum,args=(data,weights),x0=x0)
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zen_r = np.rad2deg(res.x[2])
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azi_r = np.rad2deg(res.x[3])
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print(res,zen_r,e.zenith,azi_r,e.azimuth)
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return zen_r,azi_r,[res.x[0]*nscale,res.x[1]*nscale,0]
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def update_event(e,core,theta,phi,axp=None):
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#recalculate
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e.zenith = theta
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e.azimuth = phi
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theta = np.deg2rad(theta)
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phi = np.deg2rad(phi)
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e.core = e.core+core
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e.uA = np.array([np.cos(phi)*np.sin(theta),np.sin(phi)*np.sin(theta),np.cos(theta)])
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e.uAxB = np.cross(e.uA,e.uB)
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e.uAxB = e.uAxB/(np.dot(e.uAxB,e.uAxB))**0.5
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e.uAxAxB = np.cross(e.uA,e.uAxB)
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#antenna position
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for a in e.antennas:
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a.x -= core[0]
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a.y -= core[1]
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a.z -= core[2]
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if axp != None:
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for ap in axp:
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ap[0] -= core[0]
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ap[1] -= core[1]
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ap[2] -= core[2]
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def longitudinal_figure(dist,Xs,p,mode='grammage'):
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fig, axs = plt.subplots(1,figsize=(6,5))
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if mode=='grammage':
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axs.plot(Xs,p/np.max(p),'o-')
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axs.set_xlabel('X (g/cm$^2$)')
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if mode=='distance':
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axs.plot(dist/1e3,p/np.max(p),'o-')
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axs.set_xlabel('distance from ground (km)')
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axs.grid()
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fig.tight_layout()
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return fig
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def time_residuals(e,tlable=True):
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ds,tp,nsum,ssum,swidth,azi,x,y,sid = lateral_parameters(e,True,[0,0,0])
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fig, axs = plt.subplots(1,figsize=(6,5),sharex=True)
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tp = tp-np.min(tp)
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cut_outlier = ~((ds<200)&(tp > 10))
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axs.plot(ds,tp,'o')
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if tlable:
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for d,t,s in zip(ds,tp,sid):
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plt.text(d,t,s)
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# axs.text(ds,tp,sid)
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axs.set_xlabel('distance (m)')
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axs.set_ylabel('$\Delta t (ns)$')
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axs.grid()
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z = np.polyfit(ds[cut_outlier],tp[cut_outlier],3)
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pfit = np.poly1d(z)
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xfit = np.linspace(np.min(ds),np.max(ds),100)
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yfit = pfit(xfit)
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tres = tp - pfit(ds)
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sigma =np.std(tres[cut_outlier])
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axs.plot(xfit,yfit,label=r'pol3 fit, $\sigma=%.2f$ (ns)'%(sigma))
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axs.legend()
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fig.tight_layout()
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return fig,tres
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def figure_3D(axis_points,max_vals,zen,azi,core,res = 0):
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fig = plt.figure(figsize=(5,9))
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# fig, axs = plt.subplots(1,2,figsize=(12,8))
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ax = fig.add_subplot(2,1,1,projection='3d')
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xp = [ap[0]/1e3 for ap in axis_points]
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yp = [ap[1]/1e3 for ap in axis_points]
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zp = [ap[2]/1e3 for ap in axis_points]
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max_vals = np.asarray(max_vals)
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ax.scatter(xp, yp, zp,c=max_vals,s=150*(max_vals/np.max(max_vals))**2,cmap='Spectral_r')
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ax = fig.add_subplot(2,1,2)
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core = np.array(core)
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theta = np.deg2rad(zen)
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phi = np.deg2rad(azi)
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u = np.array([np.cos(phi)*np.sin(theta),np.sin(phi)*np.sin(theta),np.cos(theta)])
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residuals = [dist_to_line(ap,core,u) for ap in axis_points]
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dist = [np.sum((ap-core)**2)**0.5 for ap in axis_points]
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ax.scatter(dist,residuals,c=max_vals,cmap='Spectral_r')
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ax.grid()
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# ax.plot(xl,yl,zl,'-')
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# ax.set_zlim(0,18)
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# ax.view_init(15, 10)
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fig.tight_layout()
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if res != 0:
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res.track_dis.append(dist)
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res.track_res.append(residuals)
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res.track_val.append(max_vals)
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return fig
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class RITResult():
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"""docstring for RITResult."""
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def __init__(self):
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super(RITResult, self).__init__()
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self.xmax_rit = []
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self.xmax = []
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self.profile_rit = []
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self.dX = []
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self.dl = []
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self.zenith_ini = []
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self.azimuth_ini = []
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self.core_ini = []
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self.dcore_rec = []
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self.zenith_rec = []
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self.azimuth_rec = []
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self.index = []
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self.isMC = []
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self.track_dis = []
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self.track_res =[]
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self.track_val =[]
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self.station_ids =[]
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self.station_x =[]
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self.station_y =[]
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self.station_z =[]
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self.station_maxE = []
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self.has_pulse = []
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def fill_stations_propeties(e,res):
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x = np.array([a.x for a in e.antennas])
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y = np.array([a.y for a in e.antennas])
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z = np.array([a.z for a in e.antennas])
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ids = [a.name for a in e.antennas]
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maxE = np.array([np.max(a.E_AxB) for a in e.antennas])
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#has_pulse = np.array([np.max(a.has_pulse) for a in e.antennas])
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res.station_x.append(x)
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res.station_y.append(y)
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res.station_z.append(z)
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res.station_ids.append(ids)
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#res.has_pulse.append(has_pulse)
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def reconstruction(e,outfile='', slice_outdir=None, Xlow=300, Xhigh=1000, N_X=15):
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res = RITResult()
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res.isMC.append(True)
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res.zenith_ini.append(e.zenith)
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res.azimuth_ini.append(e.azimuth)
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res.core_ini.append(e.core)
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set_pol_and_bp(e)
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#only use signal that have a signal in data
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fill_stations_propeties(e,res)
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Xs,axis_points,max_vals = get_axis_points(e,savefig=(slice_outdir is not None), path=slice_outdir, Xlow=Xlow, Xhigh=Xhigh, N_X=N_X)
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zen,azi,core = fit_track(e,axis_points,max_vals,1e2)
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fig = figure_3D(axis_points,max_vals,zen,azi,core,res)
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fig.savefig(outfile)
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update_event(e,core,zen,azi)
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ds,Xs,locs,p = shower_axis_slice(e)
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#result
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res.dX.append(Xs)
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res.dl.append(ds)
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res.profile_rit.append(p)
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res.xmax_rit.append(Xs[np.argmax(p)])
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res.azimuth_rec.append(e.azimuth)
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res.zenith_rec.append(e.zenith)
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res.dcore_rec.append(core)
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return res
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if __name__ == "__main__":
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file = '../ZH_airshower/mysim.sry'
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ev = REvent(file)
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set_pol_and_bp(ev)
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X = 750
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dXref = atm.distance_to_slant_depth(np.deg2rad(ev.zenith),X,0)
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scale2d = dXref*np.tan(np.deg2rad(2.))
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xx,yy,p,km= shower_plane_slice(ev,X,21,21,scale2d,scale2d)
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slice_figure(ev,X,xx,yy,p,mode='sp')
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#plt.scatter(xx,yy,c=p)
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#plt.colorbar()
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plt.show()
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