#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, getopt, os
from numpy import *
from matplotlib.pyplot import *
from copy import deepcopy
from scipy.stats.mstats import mquantiles
set_printoptions(precision=4,linewidth=500)
class Data:
""" Class containing all data, names, values"""
def __init__(self,X,Y, ShotNo, names, norm):
"""construct a new Dir class with the specified directory"""
self.X = double(X)
self.Y = Y
self.shots = ShotNo
self.names = array(names)
self.norm = array(norm)
self.N = len(self.X)
self.Ndim = shape(X)[1]
self.remove_wrong()
def __getitem__(self, vars):
return self.X[:,self.ind(vars)].ravel()
def remove_dim(self,ind):
if type(ind[0]) == str:
ind = in1d(self.names, [ind])
if all(~ind):
return
self.X = self.X[:,~ind]
self.names = self.names[~ind]
if len(self.norm) == self.Ndim :
self.norm = self.norm[~ind]
self.Ndim = shape(self.X)[1]
def ind(self,vars):
ind = where(in1d(self.names, array(vars)))[0]
if len(ind) == 1:
return int(ind)
else:
return ind
def remove_data(self,ind):
ind = ravel(ind)
if len(ind) == 0:
return
if dtype(ind[0]) == int:
ind = in1d(self.shots, ind)
if sum(ind) < 2:
return
self.X = self.X[~ind,:]
self.Y = self.Y[~ind]
self.shots = self.shots[~ind]
self.N = len(self.X)
def add_data(self,X,Y):
self.X = vstack([self.X, X])
self.Y = concatenate([self.Y, Y])
self.shots = concatenate([self.shots, nan*ones(shape(Y))])
self.N = len(self.X)
def normalize(self, norm):
""" renormalize data in all dimensions to be similar """
self.norm = norm
self.X -= self.norm[0,:]
self.X /= self.norm[1,:]
#for i in range(self.Ndim):
#self.X[self.X[:,i] > 0,i] /= self.norm[1,i]
#self.X[self.X[:,i] < 0,i] /= self.norm[2,i]
def denormalize(self):
""" renormalize data in all dimensions to be similar """
self.X *= self.norm[1,:]
self.X += self.norm[0,:]
self.norm[0,:] = 0
self.norm[[1,2],:] = 1
def get_norm(self):
norm = zeros((3,self.Ndim))
for i in arange(self.Ndim):
d = self.X[:,i]
if median(d) != 0:
m = median(d)
else:
m = mean(d)
var = d-m;
sig1 = mean( abs(var[var >= 0]))
sig2 = mean( abs(var[var < 0]))
norm[:,i] = [m,sig1, sig2]
return norm
#self.norm = norm;
def remove_wrong(self):
""" remove wrong shots (semi manual way ...) """
wrong = list()
wrong.append(array([ 3111, 3112, 3971, 1151, 1108, 2135, 2134, 2133 , 2699, 3046, 2544 , 3706, 4169, 4146, 3943, 3940, 3930,3905, 3768, 3767, 4201, 4300, 2138,2919, 4366, 4360, 1162, 3165 ] )) #something wrong
wrong.append(arange(4374, 4398)) #missing diagnistics
wrong.append(arange(4443, 4447)) #stabilozace
wrong.append(concatenate([arange(3040, 3068), arange(1227, 1272), arange(1152, 1159)])) #reverse field
wrong.append(concatenate([arange(3800, 4020), arange(4476,4481)]))
wrong = concatenate(wrong)
self.remove_data(wrong)
def __str__(self):
print "========DATA OBJECT START======"
print 'N', self.N, 'Ndim', self.Ndim
print "size X", shape(self.X)
print "size Y", shape(self.Y)
print "size shots", shape(self.shots)
print "size norm", shape(self.norm)
print "names", self.names
print "mean", mean(self.X,0)
print " max", amax(abs(self.X),0)
return "========DATA OBJECT END======"
