starting to add pareto front calculations

opal/analysis/pareto_fronts.py

+# Author:   Nicole Neveu 
+# Date:     May 2018
+
+from opal.datasets.filetype import FileType
+from opal.statistics import statistics as stat
+from opal.datasets.DatasetBase import DatasetBase
+from opal.analysis import impl_beam
+import numpy as np
+
+import json
+import pylab as pl
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.widgets import Slider, Button, RadioButtons
+
+from collections import OrderedDict
+from optPilot.Annotate import AnnoteFinder
+import pyOPALTools.optPilot.OptPilotJsonReader as jsonreader
+
+
+def scaleData(vals):
+    """
+    Scale 1D data array from 0 to 1.
+    Used to compare objectives with different units.
+
+    Parameters
+    ----------
+    vals    (numpy array)   1D array that holds any opal data
+
+    Returns
+    -------
+    sacaled_vals    (numpy array)   1D array scaled from 0 to 1
+    """
+    smax = np.max(vals)
+    smin = np.min(vals)
+    scaled_vals = (vals - smin)/smax
+    return (scaled_vals)
+
+def pareto(x, y, dvars=0):
+    """
+    Find Pareto points for 2 objectives given
+    all data recorded by optimization run. 
+    These points are calculated independent
+    of generation. i.e. best points from all 
+    generations are found and saved.
+
+    Parameters 
+    ----------
+    x   (numpy array)   array of first objective values
+    y   (numpy array)   array of second objective values
+    
+    Optionals
+    ---------
+    dvars
+    """
+    #Making holders for my pareto fronts            
+    pareto_y = []
+    pareto_x = []
+    pdvar    = []
+    w  = np.arange(0,1.001, 0.001)
+    sx = scaleData(x)
+    sy = scaleData(y)
+    #Finding best point with respect to all weights (w)
+    for i in range(0, len(w)):
+        fobj     = sy * w[i] + sx *(1-w[i])
+        wmins    = np.where(fobj==min(fobj))[0][0]
+        pareto_y = np.append(pareto_y, y[wmins])
+        pareto_x = np.append(pareto_x, x[wmins])
+
+    pareto_pts = delete_repeats(pareto_x, pareto_y)
+    ind        = np.array(pareto_pts.index.tolist())
+    pdvar      = dvars[ind, :]
+
+    return(pareto_pts.ix[:,0], pareto_pts.ix[:,1], pdvar) #pareto_x, pareto_y, pdvar)
+
+def delete_repeats(x, y): #, z):
+    df = pd.DataFrame({'x':x, 'y':y}) #, 'z':z})
+    return df.drop_duplicates(subset=['x', 'y'], keep='first')
+