/
mlt_comparator.py
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/
mlt_comparator.py
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import emfisis
import ephemeris
import matplotlib
matplotlib.use('GTKAgg') # do this before importing pylab
from matplotlib import pyplot as plt
from datetime import datetime, timedelta
from matplotlib.dates import date2num, num2date
import numpy as np
from smooth import smooth
from scipy.optimize import leastsq
import matplotlib
from densitymodels import *
import matplotlib.dates as mdates
from matplotlib.widgets import Slider
from matplotlib.patches import Rectangle
import themisdata
class mlt_comparator(object):
def __init__(self,fig):
times,L,MLT,MLAT,InvLat,density=get_density_and_time('rbspa',datetime(2012,10,6),datetime(2012,10,10))
otimes=date2num(times)
timesb,Lb,MLTb,MLATb,InvLatb,densityb=get_density_and_time('rbspb',datetime(2012,10,8),datetime(2012,10,9))
otimesb=date2num(timesb)
self.L=L
self.times=times
self.otimes=otimes
self.density=density
self.emfisis_fit=emfisis_fit_model('rbspa')
fitdensity,fituncert,inds=self.emfisis_fit(times,L,MLT,MLAT,InvLat,returnFull=True)
self.fitdensity=fitdensity
self.fig=fig
self.ax1=plt.subplot(2,1,1)
self.ax2=plt.subplot(2,1,2)
self.ax2.plot(L,density,linestyle='',marker='.')
self.Lline=None
self.Llim=np.array([1.5,2])
self.pressed=False
self.uinds=np.unique(inds)
self.uinds=self.uinds[self.uinds<self.emfisis_fit.fitcoeffs.shape[0]]
self.fig.canvas.mpl_connect('motion_notify_event',self.select_L)
self.fig.canvas.mpl_connect('button_press_event',self.select_Lstart)
self.fig.canvas.mpl_connect('button_release_event',self.on_release)
def update_L(self,Llim):
self.ax1.clear()
lmin,lmax=np.sort(Llim)
otimes=self.otimes
L=self.L
x1avg=np.zeros((len(self.uinds)/2))
x2avg=np.zeros((len(self.uinds)/2))
x1std=np.zeros((len(self.uinds)/2))
x2std=np.zeros((len(self.uinds)/2))
for ind in self.uinds:
if ind%2==0: continue
inL=(L>lmin)*(L<lmax)
thispass=(otimes>self.emfisis_fit.fitcoeffs[ind,0])*(otimes<>self.emfisis_fit.fitcoeffs[ind,1])
nextpass=(otimes>self.emfisis_fit.fitcoeffs[ind+1,0])*(otimes<>self.emfisis_fit.fitcoeffs[ind+1,1])
ind1=np.where(thispass*inL)
ind2=np.where(nextpass*inL)
x1=self.density[ind1]/self.fitdensity[ind1]
x2=self.density[ind2]/self.fitdensity[ind2]
if len(ind1)==0:
x1avg[ind/2]=None
else:
x1avg[ind/2]=np.ma.average(x1)
if len(ind2)==0:
x2avg[ind/2]=None
else:
x2avg[ind/2]=np.ma.average(x2)
x1std[ind/2]=np.ma.std(x1)
x2std[ind/2]=np.ma.std(x2)
self.ax1.plot(x1avg,x2avg,linestyle='',marker='.',color='b')
#self.ax1.errorbar(x1avg,x2avg,x1std,x2std,linestyle='',marker='.',color='b')
def show_L(self,Llim):
lmin,lmax=np.sort(Llim)
if self.Lline is not None:
self.Lline.remove()
self.Lline=self.ax2.axvspan(lmin,lmax,facecolor='y',alpha=0.5)
def select_L(self,event):
'on mouse movement'
if event.inaxes is not self.ax2: return
if event.button == 1 and self.pressed:
L,n = event.xdata, event.ydata
self.Llim[1]=L
self.show_L(self.Llim)
self.fig.canvas.draw()
else:
return
def on_release(self,event):
if event.inaxes is not self.ax2: return
if event.button == 1:
self.pressed=False
L,n = event.xdata, event.ydata
self.Llim[1]=L
self.show_L(self.Llim)
self.update_L(self.Llim)
self.fig.canvas.draw()
else:
return
def select_Lstart(self,event):
'on mouse press'
if event.inaxes is not self.ax2: return
if event.button == 1:
self.pressed=True
L,n = event.xdata, event.ydata
self.Llim[0]=L
else:
return
fig=plt.figure()
mlt_comparator(fig)
plt.show()