plot_module.py

#!/usr/bin/python
# -*- coding: utf-8 -*-
# Filename: gmode_func.py
# G-mode plotting methods

# Escrevendo em python3 e usando python2.6:
from __future__ import print_function, unicode_literals, absolute_import, division

##### IMPORT ######

from os import path
#import scipy.stats as stats
from numpy import array, amin, amax, degrees, sqrt, arctan2, zeros, median, std, insert
from numpy import insert as ainsert
from numpy.linalg import eigh
from numpy.linalg.linalg import LinAlgError
from itertools import tee, izip, product
import matplotlib.pyplot as plt
from matplotlib.patches import Arc
from collections import deque
from support import config

pathjoin = path.join

option = config('config.cfg', 'PlotConfig')

ylim   = map(float, option["ylim"])

try:
   norm  = [int(option["norm"][0]), float(option["norm"][1])]
except ValueError:
   norm = None

axis   = map(float, option["axis"])
label  = option["label"]
xtitle = option["xtitle"]
ytitle = option["ytitle"]
grid_size = int(*option["grid_size"])

def pairwise(iterable):
    "s -> (s0,s1), (s1,s2), (s2, s3), ..."
    a, b = tee(iterable)
    next(b, None)
    return izip(a, b)

#
# Plot clusters
#

def plot_distribution(data,*zones):

    plt.figure(figsize=(6,9),dpi=80)
    data = array(data)
    Na = data.shape[1]
    
    for n in xrange(Na):
        i = int(Na*100+10+n+1)
        plt.subplot(i)
        
        #x = linspace(0.5,amax(data, axis=0)[n],50)
        #dist = stats.gaussian_kde(data[:,n])
        
        #plt.plot(x, dist.evaluate(x))
        plt.hist(data[:,n],bins=50)
        if len(zones) != 0: plt.plot(zones[0][n],(1,1),'r*')
        plt.xlim(amin(data, axis=0)[n],amax(data, axis=0)[n])
        
    plt.show()

def plot_cluster(cov, ct, q1, ax=None, **kwargs):
    """
    Plots an `nstd` sigma error ellipse based on the specified covariance
    matrix (`cov`). Additional keyword arguments are passed on to the 
    ellipse patch artist.

    Parameters
    ----------
        cov : The 2x2 covariance matrix to base the ellipse on
        ct  : The location of the center of the ellipse. Expects a 2-element
            sequence of [x0, y0].
        nstd : The radius of the ellipse in numbers of standard deviations.
            Defaults to 2 standard deviations.
        ax : The axis that the ellipse will be plotted on. Defaults to the 
            current axis.
        Additional keyword arguments are pass on to the ellipse patch.

    Returns
    -------
        A matplotlib ellipse artist
    """
    def eigsorted(cov):
        vals, vecs = eigh(cov)
        order = vals.argsort()[::-1]
        return vals[order], vecs[:,order]

    if ax is None:
       ax = plt.gca()

    vals, vecs = eigsorted(cov)
    theta = degrees(arctan2(*vecs[:,0][::-1]))

    # Width and height are "full" widths, not radius
    width, height = 2 * q1 * sqrt(vals)
    ellip = Arc(xy=ct, width=width, height=height, angle=theta, **kwargs)

    ax.add_artist(ellip)
    return ellip

def plot_map(Nc, clump, seed, data, q1, ct, cov, link):
    ''' Density distribution of sample superposed with initial seed and identified cluster.'''    
    
    plt.figure(figsize=(6,10))
    
    data  = array(data)
    clump = array(clump)
    seed  = array(seed)
    M = data.shape[1]    

    plt.title('Cluster '+str(Nc))
    
    for n in xrange(1,M):
        i = int((M-1)*100+10+n)
        plt.subplot(i)

        plt.hexbin(data[:,n-1],data[:,n],gridsize=grid_size,bins='log',mincnt=1)
        if seed.size != 0:
           plt.plot(data[clump,n-1], data[clump,n], 'ro', data[seed,n-1], data[seed,n], 'go', markersize=2.5)
           plot_cluster(cov[n-1:n+1, n-1:n+1], ct[n-1:n+1], 1e0, linestyle='solid', linewidth=1)          
           plot_cluster(cov[n-1:n+1, n-1:n+1], ct[n-1:n+1], q1, linestyle='solid', linewidth=2)
            
        plt.xlim(ylim[0], ylim[1])
        plt.ylim(ylim[0], ylim[1])
        
        if label != None:
           plt.ylabel(label[n])
           plt.xlabel(label[n-1])

    cb=plt.colorbar(orientation='horizontal',fraction=0.10,pad=0.3,drawedges=False)
    cb.set_label('log10(N)')
    
    try:
       plt.savefig(pathjoin("TESTS",link,"maps",str(Nc)+'.png'),format='png', dpi=70)
    except LinAlgError:
       pass
    plt.clf()

def plot_spectral(n, stats, data, link):
    ''' The spectral variable plot of central tendency and members of a given cluster.'''
    
    #from scipy.stats import scoreatpercentile

    max_points = amax(data, axis=0)
    min_points = amin(data, axis=0)
    data = ainsert(data, norm[0], norm[1], axis=1)
    
    x, y = deque(), deque()
    x_out, y_out = deque(), deque()
    
    plt.figure(figsize=(10,9))
    plt.ylim(ylim[0],ylim[1])
    plt.xlim(axis[0]-0.1, axis[-1]+0.1)
    plt.xlabel(*xtitle)
    plt.ylabel(*ytitle)
    plt.title('Clump '+str(n)+' Na='+str(data.shape[0]))

    for item in data:
            
        if any([True for I, Q in izip(item,max_points) if I == Q]) or any([True for I, Q in izip(item,min_points) if I == Q]):
            for xy in izip(pairwise(item),pairwise(axis)):
               y_out.extend(xy[0])
               y_out.append(None)
               x_out.extend(xy[1])
               x_out.append(None)
        else:
           for xy in izip(pairwise(item),pairwise(axis)):
              y.extend(xy[0])
              y.append(None)
              x.extend(xy[1])
              x.append(None)

    plt.plot(x, y, 'k-', alpha=0.5)
    plt.plot(x_out, y_out, 'go-', linewidth=2)
    #plt.plot(axis, min_points, 'k-')

    #Cluster boxplot:
    ct, dev = stats[0], stats[1]

    axis_box = list()
    axis_box.extend(axis)
    axis_box.pop(norm[0])
    
    plt.errorbar(axis_box,ct,yerr = dev,fmt='o',color='r',ecolor='r', linewidth= 2, elinewidth=3)
    #plt.boxplot(data, notch=True, positions=axis_box) #, usermedians=stats[0])
    

    # Save figures:
    if n == 0:
       plt.show()
    else:
       try:
          plt.savefig(pathjoin("TESTS",link,"plots",'clump'+str(n)+'_'+link+'.png'),format='png', dpi=60)
          #plt.savefig(pathjoin("TESTS",label,'Graph'+str(n)+'.png'),format='png')
       except OverflowError:
          pass
    
    plt.clf()

#
# Cluster profile mosaic
#

def mosaic(cluster_members, data, link):
    ''' Make a boxplot of central tendency and members of each cluster.'''
  
    import matplotlib.gridspec as gridspec
    from math import sqrt, ceil
    
    kwargs = {'markersize':0.7, 'linestyle':'-','linewidth':0.5} 

    data = ainsert(data, norm[0], norm[1], axis=1)
    
    grid = int(ceil(sqrt(len(cluster_members))))
    
    fig = plt.figure(figsize=(10,10))
    plt.subplots_adjust(wspace=0.0, hspace=0.0)
    gs = gridspec.GridSpec(grid, grid)
    xy = list(product(range(grid), range(grid)))
    
    for n, members in enumerate(cluster_members):
        sub = plt.subplot(gs[xy[n][0],xy[n][1]])

        meas = data[members]
        
        x, y = deque(), deque()
        for item in meas:
            for line in izip(pairwise(item),pairwise(axis)):
                y.extend(line[0])
                y.append(None)
                x.extend(line[1])
                x.append(None)       

        plt.plot(x, y, 'k-', alpha=0.5, **kwargs)
        plt.errorbar(axis, median(meas, axis=0), fmt='bo-', yerr=std(meas, axis=0), label=str(len(members)), **kwargs)
        
        if xy[n][0] == int((grid-1)/2) and xy[n][1] == 0:
           plt.ylabel('Normalized Reflectance',fontsize=10,fontweight='black')         

        if xy[n][1] == 0:
           vis1 = True
        else:
           vis1 = False
        
        plt.setp(sub.get_yticklabels(), fontsize=6, visible=vis1)
        plt.setp(sub.get_xticklabels(), fontsize=6, visible=True)
        plt.ylim(ylim[0],ylim[1])
        plt.xlim(axis[0]-0.1, axis[-1]+0.1)
        plt.legend(loc=4, title=str(n+1), prop={'size':5,'weight':'black'}, numpoints=1,frameon=False)

    plt.suptitle('Wavelength ($microns$)',fontsize=10,fontweight='black')

    try:
        plt.savefig(pathjoin("TESTS",link,"plots","mosaic_"+link+".png"),format='png', dpi=300)
    except OverflowError:
        pass

#
# Cluster Size Distribution
#
    
def histogram(Y, cluster_sizes, link):  
    ''' Plot histogram of cluster sizes and integrated standard deviation.'''
    
    from collections import OrderedDict as ordict
    from mpl_toolkits.axes_grid1 import host_subplot
    import mpl_toolkits.axisartist as AA
    
    cl = ordict(sorted(cluster_sizes.iteritems(), key=lambda x: x[1]))
    #Y  = ordict(sorted(Y.iteritems(), key=lambda x: cl[x[0]]))
    #X  = ordict(zip( range(len(cluster_sizes)), cl.itervalues() ))

    fig = plt.figure(figsize=(15,8),dpi=80)
    #ax  = fig.add_subplot(1,1,1)

    host = host_subplot(111, axes_class=AA.Axes)
    plt.subplots_adjust(right=0.75)
    test = host.twinx()
    
    bottom = zeros(len(cl.keys()))

    size = host.bar(cl.keys(), cl.values(), align='center', width=0.4, color='black', label="Cluster Size", bottom=bottom)
    var  = test.plot(Y.keys(), Y.values(), color="red", marker='o', linestyle='-', linewidth=2, label="Cluster $\sigma$")

    host.set_xlabel("Identified Cluster")
    host.set_ylabel("Cluster Size")
    test.set_ylabel("Integrated Cluster $\sigma$")
    #test.set_ylim(0.0, 1.0)
    host.set_yscale('log') 
    
    #host.set_axisbelow(True)
    #host.set_xticks(X.keys(), cl.keys())

    host.legend(title=link)

    plt.savefig(pathjoin("TESTS",link,"plots",'hist_'+link+'.png'),format='png', dpi=90)
    #plt.show()
    plt.close("all")    
        
#
# Dendograms
#

def dendrogram(D, link):
    ''' Plot dendrogram of cluster's parity.'''
  
    import scipy.cluster.hierarchy as sch

    lbl = [str(n+1) for n in xrange(D.shape[0])]

    # Compute and plot dendrogram.
    fig = plt.figure(figsize=(8,14),dpi=80)
    ax1 = fig.add_axes([0.04,0.04,0.9,0.9])
    Y = sch.median(D)
    Z1 = sch.dendrogram(Y, orientation='right',labels=lbl)
    ax1.set_xticks([])
    ax1.set_ylabel("Identified Cluster")
    ax1.set_xlabel("Cluster Parity")
    plt.title(link)

    #plt.show()
    plt.savefig(pathjoin("TESTS",link,"plots",'dendrogram_'+link+'.png'),format='png', dpi=100)
    #plt.show()
    plt.close("all")


# END