Analysis.py

__author__ = 'cyberbeast'
__author__ = 'Sandesh'

from CustomClasses.Chromosome import *
import glob
from bokeh.plotting import figure, output_file, show
from mpl_toolkits.mplot3d import Axes3D
import matplotlib as mpl
import matplotlib.pyplot as plt
from numpy.random import rand
from multiprocessing import Pool, Process, Manager
import numpy
import pandas as pd

filelist = []

y_val_imag = []
y_val_real = []
y_val = []
x_val = []
x1_val = [x for x in range(22)]


def plot1():
    mpl.rcParams['legend.fontsize'] = 10
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    ax.scatter(y_val_real, y_val_imag, x_val, c='r', marker='o')

    ax.set_xlabel("Real")
    ax.set_ylabel("Imaginary")
    ax.set_zlabel("Chromosome")

    ax.set_zlim3d(1, 22)
    plt.show()

for name in glob.glob('GenomeDataset/Processing/*pChromosome'):
    filelist.append(name)

if len(filelist) == 0:
    for name in glob.glob('GenomeDataset/Processing/*pTREE'):
        filelist.append(name)


df_vis1 = pd.DataFrame(columns=('Real', 'Imaginary'))
df_vis2 = pd.DataFrame()

rows = []

i = 1
with open('Visualizer/output-file.txt', 'w') as outf:
    for infile in filelist:
        if "pChromosome" not in infile:
            ch = Chromosome()
            ch.analyze(infile)
        else:
            with open(infile, 'rb') as in_fh:
                ch = Chromosome()
                ch = pickle.load(in_fh)

        y_val = ch.calculate_eigen_values()
        y_val_real.append(y_val.real)
        y_val_imag.append(y_val.imag)
        x_val.append([i for _ in range(len(y_val))])

        y_r = [y.real for y in y_val.tolist()]  # List of all real values from EV calculation for current chromosome
        y_i = [y.imag for y in y_val.tolist()]  # List of all imaginary values from EV calculation for current chromosome

        # Populate intermediate list to store Analysis 1 DataFrame data
        for rr, ri in zip(y_r, y_i):
            rows.append((str(rr), str(ri)))

        y_r_square = [real**2 for real in y_r]
        y_i_square = [imag**2 for imag in y_i]

        # Define and populate list of data for Analysis 2 DataFrame
        y_sqrt_of_sum_of_R_I_list = []

        for rr2, ri2 in zip(y_r_square, y_i_square):
            y_sqrt_of_sum_of_R_I_list.append(np.sqrt(rr2 + ri2))

        # Populate Analysis 2 DataFrame with current chromosome
        df_vis2["C" + str(i)] = y_sqrt_of_sum_of_R_I_list

        i += 1

# Analysis 1 DataFrame Operations
for row in rows:
    df_vis1.loc[len(df_vis1)] = row

df_vis1.to_csv('Visualizer/real-vs-imaginary.csv')
# ---------------------------------------------------------------------------------------------------------------------

# Analysis 2 DataFrame Operations
df_vis2.to_csv('Visualizer/squareroot-of-sum-of-squares-of-real-and-imaginary.csv')
# ---------------------------------------------------------------------------------------------------------------------

# Uncomment next line for 3D plot of preliminary results
# plot1()

# ********************************************************************************************************************
# ********************************************************************************************************************
# ********************************************************************************************************************
# ********************************************************************************************************************
# ********************************************************************************************************************
# ********************************************************************************************************************
# Alpha Build Code from here onwards (18/09/2015)

data_collection_vis2 = pd.read_csv(open("Visualizer/squareroot-of-sum-of-squares-of-real-and-imaginary.csv"),)
y = data_collection_vis2.as_matrix
x = np.array([val1 for val1 in range(0, 340)])
# y_temp = np.array(np.sqrt(np.square(y_val_real) + np.square(y_val_imag)))
# y = y_temp[0]
# y = y_temp
# print(len(y))
# x = temp_x.flatten()
# print(x.shape)
p = np.poly2d(np.polyfit(x, y, 50))
p30 = np.poly2d(np.polyfit(x, y, 100))
xp = np.linspace(0, 340)
print(p)
_ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
plt.show()