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# This program is public domain
# Author Paul Kienzle
"""
2-D correlation histograms
Generate 2-D correlation histograms and display them in a figure.
Uses false color plots of density.
"""
__all__ = ["Corr2d"]
import numpy as np
from numpy import inf
from matplotlib import cm, colors, image, artist
from matplotlib.font_manager import FontProperties
from matplotlib.ticker import MaxNLocator
try:
COLORMAP = colors.LinearSegmentedColormap.from_list("density", ("w", "y", "g", "b", "r"))
except Exception:
COLORMAP = cm.gist_earth_r
class Corr2d(object):
"""
Generate and manage 2D correlation histograms.
"""
def __init__(self, data, labels=None, **kw):
if labels is None:
labels = ["P" + str(i + 1) for i, _ in enumerate(data)]
self.N = len(data)
self.labels = labels
self.data = data
self.hists = _hists(data, **kw)
# for k, v in self.hists.items():
# print k, (v[1][0], v[1][-1]), (v[2][0], v[2][-1])
self.ax = None # will be set on plot
def R(self):
return np.corrcoef(self.data)
def __getitem__(self, key):
"""
Retrieve correlation histogram for data[i] X data[j].
Returns bin i edges, bin j edges, and histogram
"""
i, j = key
return self.hists[i, j]
def plot(self, title=None, fig=None):
"""
Plot the correlation histograms on the specified figure
"""
import pylab
if fig is None:
pylab.clf()
fig = pylab.gcf()
if title is not None:
fig.text(0.5, 0.95, title, horizontalalignment="center", fontproperties=FontProperties(size=16))
self.ax = _plot(fig, self.hists, self.labels, self.N)
def _hists(data, ranges=None, **kw):
"""
Generate pair-wise correlation histograms
"""
n = len(data)
if ranges is None:
low, high = np.min(data, axis=1), np.max(data, axis=1)
ranges = [(l, h) for l, h in zip(low, high)]
return dict(
((i, j), np.histogram2d(data[i], data[j], range=[ranges[i], ranges[j]], **kw))
for i in range(0, n)
for j in range(i + 1, n)
)
def _plot(fig, hists, labels, n, show_ticks=None):
"""
Plot pair-wise correlation histograms
"""
if n <= 1:
fig.text(0.5, 0.5, "No correlation plots when only one variable", ha="center", va="center")
return
vmin, vmax = inf, -inf
for data, _, _ in hists.values():
positive = data[data > 0]
if len(positive) > 0:
vmin = min(vmin, np.amin(positive))
vmax = max(vmax, np.amax(positive))
norm = colors.LogNorm(vmin=vmin, vmax=vmax, clip=False)
# norm = colors.Normalize(vmin=vmin, vmax=vmax)
mapper = image.FigureImage(fig)
mapper.set_array(np.zeros((1, 1)))
mapper.set_cmap(cmap=COLORMAP)
mapper.set_norm(norm)
if show_ticks is None:
show_ticks = n < 3
ax = {}
Nr = Nc = n - 1
for i in range(0, n - 1):
for j in range(i + 1, n):
sharex = ax.get((0, j), None)
sharey = ax.get((i, i + 1), None)
a = fig.add_subplot(Nr, Nc, (Nr - i - 1) * Nc + j, sharex=sharex, sharey=sharey)
ax[(i, j)] = a
a.xaxis.set_major_locator(MaxNLocator(4, steps=[1, 2, 4, 5, 10]))
a.yaxis.set_major_locator(MaxNLocator(4, steps=[1, 2, 4, 5, 10]))
data, x, y = hists[(i, j)]
data = np.clip(data, vmin, vmax)
a.pcolorfast(y, x, data, cmap=COLORMAP, norm=norm)
# Show labels or hide ticks
if i != 0:
artist.setp(a.get_xticklabels(), visible=False)
if i == n - 2 and j == n - 1:
a.set_xlabel(labels[j])
# a.xaxis.set_label_position("top")
# a.xaxis.set_offset_position("top")
if not show_ticks:
a.xaxis.set_ticks([])
if j == i + 1:
a.set_ylabel(labels[i])
else:
artist.setp(a.get_yticklabels(), visible=False)
if not show_ticks:
a.yaxis.set_ticks([])
a.zoomable = True
# Adjust subplots and add the colorbar
fig.subplots_adjust(left=0.07, bottom=0.07, top=0.9, right=0.85, wspace=0.0, hspace=0.0)
cax = fig.add_axes([0.88, 0.2, 0.04, 0.6])
fig.colorbar(mapper, cax=cax, orientation="vertical")
return ax
def zoom(event, step):
ax = event.inaxes
if not hasattr(ax, "zoomable"):
return
# TODO: test logscale
step *= 3
if ax.zoomable is not True and "mapper" in ax.zoomable:
mapper = ax.zoomable["mapper"]
if event.ydata is not None:
lo, hi = mapper.get_clim()
pt = event.ydata * (hi - lo) + lo
lo, hi = _rescale(lo, hi, pt, step)
mapper.set_clim((lo, hi))
if ax.zoomable is True and event.xdata is not None:
lo, hi = ax.get_xlim()
lo, hi = _rescale(lo, hi, event.xdata, step)
ax.set_xlim((lo, hi))
if ax.zoomable is True and event.ydata is not None:
lo, hi = ax.get_ylim()
lo, hi = _rescale(lo, hi, event.ydata, step)
ax.set_ylim((lo, hi))
ax.figure.canvas.draw_idle()
def _rescale(lo, hi, pt, step):
scale = float(hi - lo) * step / (100 if step > 0 else 100 - step)
bal = float(pt - lo) / (hi - lo)
new_lo = lo - bal * scale
new_hi = hi + (1 - bal) * scale
return new_lo, new_hi
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