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fwd_alg_graph.py
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fwd_alg_graph.py
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#!/usr/bin/env python
import numpy as np # operations on numerical arrays
import csv # file I/O
import math as m
import sys # for command line args
import operator # for sorting list of class instances
import numpy as np
from scipy import stats
import datetime as dt
from dateutil import parser
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from matplotlib.colors import BoundaryNorm
from matplotlib.ticker import MaxNLocator
from abt_constants import *
def approx(a,b):
if abs(a-b) < abs(0.00001*a):
return True
return False
def figure_output(plt, task, modelstring, ratiostring='all'):
print 'Enter a filename for this plot: (.png will be added)'
rs = ratiostring.replace(' ','')
rs = rs.replace('=','-')
rs = rs.replace('.','p')
ms = modelstring.replace(' ','')
ms = ms.replace('Ratio','R_')
ms = ms.replace('-stateModel','')
fname = 'res_'+task+'_'+ ms +'_'+rs+'.png'
#fname.replace(' ','')
print 'proposed file name: (CR to accept)', fname
pfname = raw_input('new name:')
if(pfname == ''):
pfname = fname
plt.savefig(pfname)
return
if len(sys.argv) < 2:
print ' usage: fwd_alg_graph.py FILENAME'
quit()
else:
inputfilename = str(sys.argv[1])
print 'Input file: ', inputfilename
if '16' in inputfilename:
NST = 16
else:
NST = 6
#################################################
#
# Basic graph params
plotH = 800
plotV = 900
Xticklabs = []
RatioL = []
pert0 = []
pert1 = []
pert25 = []
pert50 = []
nrow = 0
allrows = []
perts = [0, 0.1, 0.25, 0.50]
headrow = False
with open(inputfilename,'r') as f:
d1 = csv.reader(f,delimiter=',',quotechar='"')
for row in d1:
print '---------------------------------'
print row
if not headrow:
print row
allrows.append(row)
#print row
nrow += 1
Xticklabs.append(row[0])
RatioL.append(float(row[0]))
pert0.append(float(row[1]))
pert1.append(float(row[2]))
pert25.append(float(row[3]))
pert50.append(float(row[4]))
headrow = False
N = len(pert0)
p0 = np.array(pert0)
p1 = np.array(pert1)
p25 = np.array(pert25)
p50 = np.array(pert50)
print pert0
print p0
#########################################################
#
# Basic lineplot
#
figno = 1
modelstring = 'ABT-like HMM'
ymax = 0.3
stXlabel = 'Output Ratio'
stYlabel = 'Log Probability per sequence'
stTitle = 'Forward LogP vs. Output Ratio ('+str(NST)+'-state model)'
listXticks = Xticklabs
ymax = 0
ymin = -40
#########################################################
#
# LogP Final vs Output Ratio 0.1 2.5
#
#figno = 1
#modelstring = comment[0]
#d1 = []
#d2 = []
#print 'going through ', len(Ratios), 'data'
#for i in range(len(Ratios)):
#d = logPs[i]
#if approx(pert[i],0.2):
#d1.append(d)
#elif approx(pert[i],0.5):
#d2.append(d)
#box_data = [d1, d2]
#ymax = 0
#ymin = -2000
#stXlabel = 'Model Perturbation'
#stYlabel = 'Final LogP'
#stTitle = 'BW Final LogP vs. Model Perturbation (all ratios)'
#listXticks = ['0.2','0.5']
#########################################################
#
# Improvement vs perturbation
#
#
# Plot 1
fig1 = plt.figure(figno)
#figno += 1
#bp = plt.boxplot(data, notch=True,vert=True ,patch_artist=True)
#bp = plt.boxplot(box_data, notch=True,vert=True ,patch_artist=True)
bp = plt.plot(RatioL, pert0, RatioL, pert1, RatioL, pert25, RatioL, pert50, marker='s')
#standardize graph size
#figptr = plt.gcf()
figptr = fig1
DPI = figptr.get_dpi()
figptr.set_size_inches(plotH/float(DPI),plotV/float(DPI))
#for b in bp['boxes']:
#b.set_facecolor('lightblue')
#plt.xlabel('Initial and Final RMS A-matrix Error')
#plt.ylabel('RMS Error')
#plt.ylim(0.0, ymax)
#plt.title('BW Parameter Estimation: A-matrix Improvement, '+modelstring)
#plt.xlabel('Perturbation in RMS A-matrix')
#plt.ylabel('Delta RMS Error')
#plt.ylim(-ymax, ymax)
#plt.title('BW Parameter Estimation: A-matrix Improvement, '+modelstring)
#locs, labels = plt.xticks()
#plt.xticks(locs, ['0.1','0.3','0.5'])
plt.xlabel(stXlabel)
plt.ylabel(stYlabel)
plt.ylim(ymin, ymax)
plt.title(stTitle)
#locs, labels = plt.xticks()
#plt.xticks(locs, listXticks)
if NST == 6:
plt.annotate('pert = 0.0', (3.2, -6.6))
plt.annotate('pert = 0.1', (3.2, -7.1))
plt.annotate('pert = 0.25', (3.2, -7.5))
plt.annotate('pert = 0.50', (3.2, -9))
else:
plt.annotate('pert = 0.0', (3.2, -28))
plt.annotate('pert = 0.1', (3.2, -34))
plt.annotate('pert = 0.25', (3.2, -36))
plt.annotate('pert = 0.50', (3.2, -38.5))
plt.grid(color='lightgray', which='both')
plt.show(block=False)
figure_output(plt, 'Forward_Alg_LogP_vs_output_ratio', '', str(NST))