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Copy pathnew_surround_modulation.py
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new_surround_modulation.py
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import __main__
import numpy
import pylab
import os.path
import scipy.stats
from math import pi, sqrt, exp, pow
from topo.plotting.plotfilesaver import *
from topo.command.pylabplot import cyclic_tuning_curve, matrixplot
from topo.command.analysis import save_plotgroup
from matplotlib.ticker import MaxNLocator
from param import normalize_path
import pickle
import matplotlib.gridspec as gridspec
from matplotlib import rc
rc('text', usetex=True)
rc('mathtext',default='regular')
pylab.rc(('xtick.major','xtick.minor','ytick.major','ytick.minor'), pad=8)
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
fontProperties = {'family':'sans-serif','sans-serif':['Helvetica'],
'weight' : 'normal', 'size' : 0}
rc('xtick', labelsize=20)
rc('ytick', labelsize=20)
rc('legend',fontsize=20)
#rc('legend',linewidth=2)
rc('legend',labelspacing=0.25)
prefix = '/media/DATA/LESI/CCLESISM_CCLESI52_A0_2nd_HC=200_LimSurr=True_duration=16.0_LHI=2.0_uniform=True_center_size=13_Max=True_num_sizes=20_LC=100_repetitions=1_NUM_PHASE=16_NEW/OUT/'
prefix_out = '/media/DATA/LESI/CCLESISM_CCLESI52_A0_2nd_HC=200_LimSurr=True_duration=16.0_LHI=2.0_uniform=True_center_size=13_Max=True_num_sizes=20_LC=100_repetitions=1_NUM_PHASE=16_NEW/OUT/out2.0'
normalize_path.prefix = prefix_out
def circular_dist(a, b, period):
"""
Returns the distance between a and b (scalars) in a domain with `period` period.
"""
return numpy.minimum(numpy.abs(a - b), period - numpy.abs(a - b))
def release_fig(filename=None):
fullname=filename+".png"
pylab.savefig(prefix_out + '/' + fullname,dpi=100)
def disable_top_right_axis(ax):
for loc, spine in ax.spines.iteritems():
if loc in ['right','top']:
spine.set_color('none') # don't draw spine
for tick in ax.yaxis.get_major_ticks():
tick.tick2On = False
for tick in ax.yaxis.get_minor_ticks():
tick.tick2On = False
for tick in ax.xaxis.get_major_ticks():
tick.tick2On = False
for tick in ax.xaxis.get_minor_ticks():
tick.tick2On = False
def disable_bottom_axis(ax):
for loc, spine in ax.spines.iteritems():
if loc in ['bottom']:
spine.set_color('none') # don't draw spine
for tick in ax.xaxis.get_major_ticks():
tick.tick1On = False
def disable_left_axis(ax):
for loc, spine in ax.spines.iteritems():
if loc in ['left']:
spine.set_color('none') # don't draw spine
for tick in ax.yaxis.get_major_ticks():
tick.tick1On = False
def remove_x_tick_labels():
pylab.xticks([],[])
def remove_y_tick_labels():
pylab.yticks([],[])
class SurroundModulationPlotting():
low_contrast=100
high_contrast=200
def __init__(self):
import pylab
f = open(prefix+'data_dict.pickle','rb')
(self.OR,self.OS,self.MR,self.data_dict) = pickle.load(f)
f.close()
#self.OR = self.data_dict.OR
#self.calculate_autocorrelation(self.OR)
self.recalculate_orientation_contrast_supression()
self.recalculate_size_tuning_measures()
print "Number of measured neurons: " , len(self.data_dict.keys())
if True:
self.lhi = compute_local_homogeneity_index(self.OR*pi,2.0)
f = open(prefix_out+'/lhi2.0.pickle','wb')
pickle.dump(self.lhi,f)
f.close()
else:
f = open(prefix_out+'/lhi2.0.pickle','rb')
self.lhi = pickle.load(f)
# determine pinwheels and domain centers
pinwheels = []
centers = []
or_sup_pinwheels = 0
or_sup_domains = 0
for coords in self.data_dict.keys():
if self.lhi[coords] < 0.5:
pinwheels.append(coords)
or_sup_pinwheels += self.data_dict[coords]['OCT']['Contrastsurround' + " = " + str(self.high_contrast) + "%"]["measures"]["or_suppression"]
if self.lhi[coords] > 0.5:
or_sup_domains += self.data_dict[coords]['OCT']['Contrastsurround' + " = " + str(self.high_contrast) + "%"]["measures"]["or_suppression"]
centers.append(coords)
print or_sup_pinwheels/len(pinwheels)
print or_sup_domains/len(centers)
self.plot_average_size_tuning_curve(independent=True)
self.plot_average_oct(keys=pinwheels,independent=True,string="pinwheels")
self.plot_average_oct(keys=centers,independent=True,string="domains")
self.plot_fullfield_optimal_or_pref_correlation(pinwheels)
self.plot_fullfield_optimal_or_pref_correlation(centers)
for coords in self.data_dict.keys():
xindex,yindex = coords
#self.plot_size_tunning(xindex,yindex,independent=False)
self.plot_orientation_contrast_tuning(xindex,yindex,str(self.lhi[xindex,yindex]) + " "+ str(self.data_dict[(xindex,yindex)]["OCT"]["Contrastsurround = " + str(self.high_contrast) + "%"]["measures"]["or_suppression"]),independent=True)
#return
#self.plot_histograms_of_measures()
#print('1')
#self.plot_average_size_tuning_curve()
#print('2')
#self.plot_average_oct()
#print('3')
#self.Figure6()
#print('4')
#self.Figure8()
#print('5')
raster_plots_lc,raster_plots_hc = self.plot_map_feature_to_surround_modulation_feature_correlations(self.lhi,"Local Homogeneity Index")
self.correlations_figure(raster_plots_lc)
#self.plot_map_feature_to_surround_modulation_feature_correlations(self.OS,"OrientationSelectivity")
print len(self.data_dict)
def recalculate_orientation_contrast_supression(self):
hc_curve_name = "Contrast = " + str(self.high_contrast) + "%";
for (xindex,yindex) in self.data_dict.keys():
measurment = self.data_dict[(xindex,yindex)]["OCT"]
for curve_label in measurment.keys():
if curve_label != 'ORTC':
if True:
peak_or_response = measurment['ORTC']['data'][measurment["ORTC"]["info"]["pref_or"]].view()[0][xindex][yindex]
if False:
z = measurment['ORTC']['data']
ar = []
ors = []
for o in sorted(z.keys()):
ar.append(z[o].view()[0][xindex][yindex])
ors.append(o)
peak_or_response = max(ar)
assert ors[numpy.argmax(ar)] == measurment["ORTC"]["info"]["pref_or"]
#curve = self.data_dict[(xindex,yindex)]["ST"][hc_curve_name]["data"]
#assert peak_or_response == [curve[key].view()[0][xindex, yindex] for key in curve.keys()][self.data_dict[(xindex,yindex)]["ST"][hc_curve_name]["measures"]["peak_supression_index"]]
if False:
curve = self.data_dict[(xindex,yindex)]["ST"][hc_curve_name]["data"]
x_values = sorted(curve.keys())
y_values = [curve[key].view()[0][xindex, yindex] for key in x_values]
peak_or_response = y_values[self.data_dict[(xindex,yindex)]["ST"][hc_curve_name]["measures"]["peak_near_facilitation_index"]]
curve = measurment[curve_label]["data"]
orr = measurment["ORTC"]["info"]["pref_or"]
#orr = self.OR[xindex,yindex]*numpy.pi
surr_ors = numpy.array(curve.keys())
#orr_ort = orr + numpy.pi/2.0
orr = surr_ors[numpy.argmin(circular_dist(surr_ors % numpy.pi,orr % numpy.pi, numpy.pi))]
orr_ort = surr_ors[numpy.argmin(circular_dist(surr_ors % numpy.pi,(orr + numpy.pi/2) % numpy.pi, numpy.pi))]
cont_or_resp=curve[orr_ort].view()[0][xindex][yindex]
pref_or_resp=curve[orr].view()[0][xindex][yindex]
if peak_or_response != 0:
measurment[curve_label]["measures"]["or_suppression"]=(cont_or_resp-pref_or_resp)/peak_or_response
else:
measurment[curve_label]["measures"]["or_suppression"]=0.0
#if measurment[curve_label]["measures"]["or_suppression"] > 1.0:
# measurment[curve_label]["measures"]["or_suppression"] = 1
#if measurment[curve_label]["measures"]["or_suppression"] < -1.0:
# measurment[curve_label]["measures"]["or_suppression"] = -1
def plot_fullfield_optimal_or_pref_correlation(self,coords):
optimal = []
full = []
for (xindex,yindex) in coords:
measurment = self.data_dict[(xindex,yindex)]["OCT"]
optimal.append(measurment["ORTC"]["info"]["pref_or"])
full.append(self.OR[(xindex,yindex)]*numpy.pi)
pylab.figure()
pylab.plot(optimal,full,'bo')
pylab.xlabel('optimal')
pylab.ylabel('full')
def recalculate_size_tuning_measures(self):
for (xindex,yindex) in self.data_dict.keys():
curve_data = self.data_dict[(xindex,yindex)]["ST"]
hc_curve_name = "Contrast = " + str(self.high_contrast) + "%";
lc_curve_name = "Contrast = " + str(self.low_contrast) + "%";
for curve_label in [hc_curve_name,lc_curve_name]:
curve = curve_data[curve_label]["data"]
x_values = sorted(curve.keys())
y_values = [curve[key].view()[0][xindex, yindex] for key in x_values]
#compute critical indexes in the size tuning curves
curve_data[curve_label]["measures"]["peak_near_facilitation_index"] = numpy.argmax(y_values)
curve_data[curve_label]["measures"]["peak_near_facilitation"] = x_values[curve_data[curve_label]["measures"]["peak_near_facilitation_index"]]
curve_data[curve_label]["measures"]["peak_supression_index"] = curve_data[curve_label]["measures"]["peak_near_facilitation_index"] + numpy.argmin(y_values[curve_data[curve_label]["measures"]["peak_near_facilitation_index"] + 1:]) + 1
curve_data[curve_label]["measures"]["peak_supression"] = x_values[curve_data[curve_label]["measures"]["peak_supression_index"]]
curve_data[curve_label]["measures"]["suppresion_index"] = (y_values[curve_data[curve_label]["measures"]["peak_near_facilitation_index"]] - y_values[-1]) / y_values[curve_data[curve_label]["measures"]["peak_near_facilitation_index"]]
#curve_data[curve_label]["measures"]["suppresion_index"] = (y_values[curve_data[curve_label]["measures"]["peak_near_facilitation_index"]] - y_values[curve_data[curve_label]["measures"]["peak_supression_index"]]) / y_values[curve_data[curve_label]["measures"]["peak_near_facilitation_index"]]
if len(y_values[curve_data[curve_label]["measures"]["peak_supression_index"] + 1:]) > 0:
curve_data[curve_label]["measures"]["peak_far_facilitation_index"] = curve_data[curve_label]["measures"]["peak_supression_index"] + numpy.argmax(y_values[curve_data[curve_label]["measures"]["peak_supression_index"] + 1:]) + 1
else:
curve_data[curve_label]["measures"]["peak_far_facilitation_index"] = len(y_values)-1
curve_data[curve_label]["measures"]["peak_far_facilitation"] = x_values[curve_data[curve_label]["measures"]["peak_far_facilitation_index"]]
curve_data[curve_label]["measures"]["counter_suppresion_index"] = (y_values[curve_data[curve_label]["measures"]["peak_far_facilitation_index"]] - y_values[curve_data[curve_label]["measures"]["peak_supression_index"]])/ y_values[curve_data[curve_label]["measures"]["peak_near_facilitation_index"]]
def plot_size_tunning(self, xindex, yindex,independent=True):
if independent:
fig = pylab.figure()
colors=['red','blue','green','purple','orange','black','yellow']
measurment = self.data_dict[(xindex,yindex)]["ST"]
i = 0
#m = 0
for curve_label in measurment.keys():
curve = measurment[curve_label]["data"]
x_values = sorted(curve.keys())
y_values = [curve[key].view()[0][xindex, yindex] for key in x_values]
pylab.plot(x_values, y_values, lw=3, color=colors[i],label=curve_label)
m = max(0,max(y_values))
pylab.annotate('', xy=(measurment[curve_label]["measures"]["peak_near_facilitation"], y_values[measurment[curve_label]["measures"]["peak_near_facilitation_index"]]), xycoords='data',
xytext=(-1, 20), textcoords='offset points', arrowprops=dict(facecolor='green', shrink=0.05))
if measurment[curve_label]["measures"].has_key("peak_supression"):
pylab.annotate('', xy=(measurment[curve_label]["measures"]["peak_supression"], y_values[measurment[curve_label]["measures"]["peak_supression_index"]]), xycoords='data',
xytext=(-1, 20), textcoords='offset points', arrowprops=dict(facecolor='red', shrink=0.05))
if measurment[curve_label]["measures"].has_key("peak_far_facilitation"):
pylab.annotate('', xy=(measurment[curve_label]["measures"]["peak_far_facilitation"], y_values[measurment[curve_label]["measures"]["peak_far_facilitation_index"]]), xycoords='data',
xytext=(-1, 20), textcoords='offset points', arrowprops=dict(facecolor='blue', shrink=0.05))
i+=1
disable_top_right_axis(pylab.gca())
ax = pylab.gca()
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax.set_xticklabels(ax.get_xticks(), fontProperties)
ax.set_yticklabels(ax.get_yticks(), fontProperties)
ax.set_xlim(0,2)
ax.set_xticks([0,1,2])
m = numpy.ceil(m)
ax.set_yticks([0,m/2,m])
ax.set_ylim(0,m)
if independent:
release_fig("STC[" + str(xindex) + "," + str(yindex) + "]")
def calculate_octc_selectivity(self,angles,responses):
c = 0
n = 0
for a,r in zip(angles,responses):
c = c + r * complex(numpy.cos(a),numpy.sin(a))
n = n + numpy.abs(r)
return numpy.abs(c)/n
def plot_orientation_contrast_tuning(self, xindex, yindex,string,independent=True):
if independent:
fig = pylab.figure()
colors=['red','blue','green','purple','orange','black','yellow']
orientation = self.data_dict[(xindex,yindex)]["OCT"]["ORTC"]["info"]["pref_or"]
measurment = self.data_dict[(xindex,yindex)]["OCT"]
i = 0
m = 0
for curve_label in measurment.keys():
#if curve_label != 'Contrastsurround = 100%':
curve = measurment[curve_label]["data"]
x_values = sorted(curve.keys())
y_values = []
for k in x_values:
y_values.append(curve[k].view()[0][xindex, yindex])
if curve_label != 'ORTC':
z = []
ks = sorted(measurment["ORTC"]["data"].keys())
for k in ks:
z.append(measurment["ORTC"]["data"][k].view()[0][xindex, yindex])
ssi = self.calculate_octc_selectivity(2*x_values,numpy.max(z)-y_values)
self.data_dict[(xindex,yindex)]["OCT"][curve_label]["measures"]["SSI"]=ssi
#self.data_dict[(xindex,yindex)]["OCT"]['Contrastsurround = 100%']["measures"]["SSI"]=ssi
x_values=numpy.array(x_values)-orientation
for j in xrange(0,len(x_values)):
if x_values[j] > numpy.pi/2.0:
x_values[j] -= numpy.pi
if x_values[j] < -numpy.pi/2.0:
x_values[j] += numpy.pi
for j in xrange(0,len(x_values)):
if x_values[j] > numpy.pi/2.0:
x_values[j] -= numpy.pi
if x_values[j] < -numpy.pi/2.0:
x_values[j] += numpy.pi
inds = numpy.argsort(x_values)
y_values = numpy.take(y_values, inds)
x_values = sorted(x_values)
#numpy.append(y_values,y_values[0])
#numpy.append(x_values,x_values[0]+numpy.pi)
if x_values[0] == -numpy.pi/2:
y_values = numpy.append(y_values,[y_values[0]])
x_values = numpy.append(x_values,[numpy.pi/2])
else:
y_values = numpy.append([y_values[-1]],y_values)
x_values = numpy.append([-numpy.pi/2],x_values)
m = max(m,max(y_values))
pylab.plot(x_values, y_values, lw=3, color=colors[i],label=curve_label)
i+=1
disable_top_right_axis(pylab.gca())
ax = pylab.gca()
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax.set_xlim(-numpy.pi/2-0.2,numpy.pi/2.0+0.2)
ax.set_xticks([-numpy.pi/2,0,numpy.pi/2.0])
#ax.set_xticklabels([r'-$\frac{\mathrm{\mathsf{\pi}}{2}$',r'0',r'$\frac{\mathrm{\mathsf{\pi}}{2}$'], fontProperties)
# get ceil at first decimal point
m = numpy.ceil(m)
ax.set_yticks([0.1,m/2,m])
ax.set_yticklabels(ax.get_yticks(), fontProperties)
#ax.set_ylim(0.1,m)
#pylab.show()
if independent:
release_fig("OCTC[" + str(xindex) + "," + str(yindex) + "] " + string)
def plot_average_size_tuning_curve(self,independent=True):
if independent:
fig = pylab.figure()
average_curves={}
sem_curves={}
curves={}
for k in self.data_dict.keys():
# find maximum for the curves
m = []
for curve_label in self.data_dict[k]["ST"].keys():
xindex, yindex = k
curve = self.data_dict[k]["ST"][curve_label]["data"]
x_values = sorted(curve.keys())
m.append(numpy.max([curve[key].view()[0][xindex, yindex] for key in x_values]))
m = numpy.max(m)
for curve_label in self.data_dict[k]["ST"].keys():
xindex, yindex = k
curve = self.data_dict[k]["ST"][curve_label]["data"]
x_values = sorted(curve.keys())
y_values = [curve[key].view()[0][xindex, yindex] for key in x_values]
# normalize curve
y_values = y_values / m
if curves.has_key(curve_label):
curves[curve_label].append(numpy.array(y_values))
else:
curves[curve_label]=[numpy.array(y_values)]
for curve_label in curves:
average_curves[curve_label]=numpy.mean(numpy.array(curves[curve_label]),axis=0)
sem_curves[curve_label]=scipy.stats.sem(numpy.array(curves[curve_label]),axis=0)
colors=['red','blue']
i=0
for curve_label in average_curves:
pylab.plot(x_values, average_curves[curve_label]*100, lw=3, color=colors[i])
pylab.errorbar(x_values, average_curves[curve_label]*100, lw=1, ecolor=colors[i], yerr=sem_curves[curve_label]*100, fmt=None)
pylab.xticks([0,numpy.max(x_values)/2,numpy.max(x_values)])
pylab.yticks([0,50,100])
pylab.gca().set_yticklabels(['0%','50%','100%'])
pylab.xlim(-0.1,numpy.max(x_values)+0.1)
pylab.ylim(0,100)
i=i+1
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels(pylab.gca().get_xticks(), fontProperties)
pylab.gca().set_yticklabels(pylab.gca().get_yticks(), fontProperties)
if independent:
release_fig("AverageSTC")
def plot_average_oct(self,keys=None,independent=True,string=""):
if independent:
fig = pylab.figure()
average_curves={}
sem_curves={}
curves={}
if keys == None:
keys = self.data_dict.keys()
for k in keys:
xindex, yindex = k
curve = self.data_dict[k]["OCT"]["ORTC"]["data"]
x_values = sorted(curve.keys())
m = numpy.max([curve[l].view()[0][xindex, yindex] for l in x_values])
orientation = self.data_dict[(xindex,yindex)]["OCT"]["ORTC"]["info"]["pref_or"]
for curve_label in self.data_dict[k]["OCT"].keys():
if curve_label != 'Contrastsurround = 100%':
curve = self.data_dict[k]["OCT"][curve_label]["data"]
x_values = sorted(curve.keys())
y_values = [curve[l].view()[0][xindex, yindex] for l in x_values]
x_values=numpy.array(x_values)-orientation
for j in xrange(0,len(x_values)):
if x_values[j] > numpy.pi/2.0:
x_values[j] -= numpy.pi
if x_values[j] < -numpy.pi/2.0:
x_values[j] += numpy.pi
for j in xrange(0,len(x_values)):
if x_values[j] > numpy.pi/2.0:
x_values[j] -= numpy.pi
if x_values[j] < -numpy.pi/2.0:
x_values[j] += numpy.pi
inds = numpy.argsort(x_values)
y_values = numpy.take(y_values, inds)
x_values = sorted(x_values)
if x_values[0] == -numpy.pi/2:
y_values = numpy.append(y_values,[y_values[0]])
x_values = numpy.append(x_values,[numpy.pi/2])
else:
y_values = numpy.append([y_values[-1]],y_values)
x_values = numpy.append([-numpy.pi/2],x_values)
# normalize curve
y_values = y_values / m
if curves.has_key(curve_label):
curves[curve_label].append(numpy.array(y_values))
else:
curves[curve_label]=[numpy.array(y_values)]
for curve_label in curves:
average_curves[curve_label]=numpy.mean(numpy.array(curves[curve_label]),axis=0)
sem_curves[curve_label]=scipy.stats.sem(numpy.array(curves[curve_label]),axis=0)
colors=['red','blue','green','purple','orange','black','yellow']
i=0
for curve_label in average_curves.keys():
pylab.plot(x_values, average_curves[curve_label]*100, lw=3, color=colors[i])
ax = pylab.gca()
ax.errorbar(x_values, average_curves[curve_label]*100, lw=1, ecolor=colors[i], yerr=sem_curves[curve_label]*100, fmt=None)
ax.set_xlim(-numpy.pi/2-0.2,numpy.pi/2.0+0.2)
ax.set_xticks([-numpy.pi/2,0,numpy.pi/2.0])
ax.set_yticks([0,50,100])
ax.set_yticklabels(['0%','50%','100%'])
ax.set_ylim(0,110)
i=i+1
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels([r'-$\frac{\pi}{2}$',r'0',r'$\frac{\pi}{2}$'], fontProperties)
pylab.gca().set_yticklabels(pylab.gca().get_yticks(), fontProperties)
if independent:
release_fig("AverageOCTC" + string)
def plot_histograms_of_measures(self):
histograms_lc = {}
histograms_hc = {}
for (xcoord,ycoord) in self.data_dict.keys():
for curve_type in self.data_dict[(xcoord,ycoord)].keys():
if curve_type == "ST":
curve_label = "Contrast"
else:
curve_label = "Contrastsurround"
print self.data_dict[(xcoord,ycoord)][curve_type].keys()
for measure_name in self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.high_contrast) + "%"]["measures"].keys():
if not histograms_hc.has_key(curve_type + "_" + measure_name):
histograms_hc[curve_type + "_" + measure_name]=[]
histograms_hc[curve_type + "_" + measure_name].append(self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.high_contrast) + "%"]["measures"][measure_name])
for measure_name in self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.low_contrast) + "%"]["measures"].keys():
if not histograms_lc.has_key(curve_type + "_" + measure_name):
histograms_lc[curve_type + "_" + measure_name]=[]
histograms_lc[curve_type + "_" + measure_name].append(self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.low_contrast) + "%"]["measures"][measure_name])
for key in histograms_lc.keys():
if ((len(histograms_lc[key]) != 0) and (len(histograms_hc[key]) != 0)):
fig = pylab.figure()
#pylab.title(str(numpy.mean(histograms_lc[key])) + "+/-" + str(numpy.std(histograms_lc[key])/ (len(histograms_lc[key])*len(histograms_lc[key]))) + "MeanHC: " + str(numpy.mean(histograms_hc[key])) + "+/-" + str(numpy.std(histograms_hc[key])/ (len(histograms_hc[key])*len(histograms_hc[key]))) , fontsize=12)
f = fig.add_subplot(111)
mmax = numpy.max([numpy.max(histograms_lc[key]),numpy.max(histograms_hc[key])])
mmin = numpy.min([numpy.min(histograms_lc[key]),numpy.min(histograms_hc[key])])
bins = numpy.arange(mmin-0.01,mmax+0.01,(mmax+0.01-(mmin-0.01))/10.0)
if key == 'ST_contrast_dependent_shift':
f.hist(histograms_hc[key],bins=bins,color='black',normed=False)
else:
histograms_lc[key]
histograms_hc[key]
f.hist((histograms_lc[key],histograms_hc[key]),bins=bins,color=['white','black'],normed=True,label=['Low contrast','High contrast'])
f.legend(loc = "upper left")
ax = pylab.gca()
disable_left_axis(ax)
disable_top_right_axis(ax)
remove_y_tick_labels()
ax.xaxis.set_major_locator(MaxNLocator(4))
pylab.setp(pylab.getp(ax, 'xticklabels'), fontsize=20)
#f.axvline(x=numpy.mean(histograms_lc[key]),linewidth=4, color='r')
release_fig("Histogram<" + key + ">")
print key + "LC mean :" + str(numpy.mean(histograms_lc[key]))
print key + "HC mean :" + str(numpy.mean(histograms_hc[key]))
else:
print "Histogram ", key , " empty!"
def Figure6(self):
self.fig = pylab.figure(facecolor='w',figsize=(14, 12),dpi=800)
gs = gridspec.GridSpec(57, 36)
gs.update(left=0.05, right=0.95, top=0.95, bottom=0.05,wspace=0.2,hspace=0.2)
picked_stcs = [(57,57), (55,51) , (49,51) , (47,55) , (53,59) , (47,61)]
ax = pylab.subplot(gs[1:19,9:27])
self.plot_average_size_tuning_curve(independent=False)
pylab.ylabel('Normalized response', fontsize=20)
ax = pylab.subplot(gs[22:32,1:11])
self.plot_size_tunning(picked_stcs[0][0], picked_stcs[0][1],independent=False)
remove_x_tick_labels()
pylab.ylabel('Response', fontsize=20)
ax = pylab.subplot(gs[22:32,13:23])
self.plot_size_tunning(picked_stcs[1][0], picked_stcs[1][1],independent=False)
disable_left_axis(pylab.gca())
remove_x_tick_labels()
remove_y_tick_labels()
ax = pylab.subplot(gs[22:32,25:35])
self.plot_size_tunning(picked_stcs[2][0], picked_stcs[2][1],independent=False)
disable_left_axis(pylab.gca())
remove_x_tick_labels()
remove_y_tick_labels()
ax = pylab.subplot(gs[34:44,1:11])
self.plot_size_tunning(picked_stcs[3][0], picked_stcs[3][1],independent=False)
pylab.ylabel('Response', fontsize=20)
ax = pylab.subplot(gs[34:44,13:23])
self.plot_size_tunning(picked_stcs[4][0], picked_stcs[4][1],independent=False)
disable_left_axis(pylab.gca())
remove_y_tick_labels()
ax = pylab.subplot(gs[34:44,25:35])
self.plot_size_tunning(picked_stcs[5][0], picked_stcs[5][1],independent=False)
disable_left_axis(pylab.gca())
remove_y_tick_labels()
# PLOT EXPERIMENTAL DATA
x,y = zip(*[(-0.0118, 10.3038),(0.0398, 5.0943),(0.1025, 4.8631),(0.2222, 10.5367),(0.3872, 22.694),(0.6212, 21.1903),(0.9463, 25.9388),(1.4487, 16.2168),(2.219, 17.9579),(3.3435, 9.6286),(4.9921, 21.2156)])
ox,oy = zip(*[(0.0447, 25.4706),(0.1181, 49.6678),(0.2268, 41.2169),(0.3803, 55.4581),(0.6206, 39.3668),(0.963, 36.5901),(1.4654, 29.8782),(2.2245, 23.9781),(3.3429, 25.1424),(4.9977, 23.7626)])
x = numpy.array(x)
y = numpy.array(y)
ox = numpy.array(ox)
oy = numpy.array(oy)
ax = pylab.subplot(gs[47:57,1:11])
ax.plot(x,y,lw=3,color='r')
pylab.hold('on')
ax.plot(ox,oy,lw=3,color='b')
ax.set_xlim(0,5)
ax.set_xticks([0,1,2,3,4,5])
ax.set_ylim(0,80)
ax.set_yticks([0,40,80])
ax.set_ylabel('Response', fontsize=20)
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels(pylab.gca().get_xticks(), fontProperties)
pylab.gca().set_yticklabels(pylab.gca().get_yticks(), fontProperties)
lc_x,lc_y = zip(*[(-0.0487, -0.0419),(1.3979, 6.7485),(2.0126, 8.1943),(2.8208, 10.3224),(3.9922, 4.6368),(5.6557, 3.5735),(7.947, 5.1138),(11.0084, 4.837)])
mc_x,mc_y = zip(*[(-0.0487, -0.0419),(1.347, 13.4836),(1.9927, 15.4864),(2.7962, 13.3683),(4.0149, 8.2393),(5.6128, 7.3489),(7.9576, 7.1724),(11.0201, 6.5096)])
hc_x,hc_y = zip(*[(-0.0487, -0.0419),(1.3515, 17.7727),(1.9832, 12.9988),(2.8384, 9.8502),(4.0052, 5.8375),(5.6352, 11.0801),(7.9759, 12.4049),(10.9943, 9.9844)])
lc_x = numpy.array(lc_x)
lc_y = numpy.array(lc_y)
mc_x = numpy.array(mc_x)
mc_y = numpy.array(mc_y)
hc_x = numpy.array(hc_x)
hc_y = numpy.array(hc_y)
ax = pylab.subplot(gs[47:57,13:23])
ax.plot(lc_x,lc_y,lw=3,color='r')
ax.plot(lc_x,mc_y,lw=3,color='g')
ax.plot(lc_x,hc_y,lw=3,color='b')
ax.set_xlim(0,12)
ax.set_xticks([0,5,10])
ax.set_ylim(0,20)
ax.set_yticks([0,10,20])
ax.set_ylabel('Response', fontsize=20)
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels(pylab.gca().get_xticks(), fontProperties)
pylab.gca().set_yticklabels(pylab.gca().get_yticks(), fontProperties)
lc_x,lc_y = zip(*[(0.665, 14.9452),(1.0443, 22.295),(1.5573, 27.5117),(2.2633, 32.0173),(3.3754, 37.7079),(5.2272, 28.233),(7.5956, 30.1319),(11.9241, 32.2685),(17.7866, 40.8027),(26.8654, 43.6499)])
mc_x,mc_y = zip(*[(0.6909, 11.628),(1.0305, 15.8968),(1.5377, 30.3552),(2.2968, 60.2165),(3.5141, 63.0637),(5.2352, 51.9297),(8.1111, 49.5638),(12.0944, 52.4108),(18.0381, 58.8123),(27.2453, 61.6595)])
hc_x,hc_y = zip(*[(0.6827, 25.3719),(1.0312, 27.745),(1.5594, 48.6018),(2.3, 82.0174),(3.5202, 89.604),(5.3143, 84.6312),(8.0208, 75.867),(12.1168, 81.0838),(18.5382, 83.4571),(27.2836, 83.4604)])
lc_x = numpy.array(lc_x)
lc_y = numpy.array(lc_y)
mc_x = numpy.array(mc_x)
mc_y = numpy.array(mc_y)
hc_x = numpy.array(hc_x)
hc_y = numpy.array(hc_y)
ax = pylab.subplot(gs[47:57,25:35])
ax.semilogx(lc_x,lc_y,lw=3,color='r')
ax.semilogx(lc_x,mc_y,lw=3,color='g')
ax.semilogx(lc_x,hc_y,lw=3,color='b')
ax.set_xlim(0.6,30)
ax.set_xticks([0.5,1.0,3.0,10,50])
ax.set_ylim(0,100)
ax.set_yticks([0.0,50.0,100])
ax.set_ylabel('Response', fontsize=20)
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels(pylab.gca().get_xticks(), fontProperties)
pylab.gca().set_yticklabels(pylab.gca().get_yticks(), fontProperties)
release_fig("Figure6")
def Figure8(self):
self.fig = pylab.figure(facecolor='w',figsize=(14, 12),dpi=800)
gs = gridspec.GridSpec(57, 36)
gs.update(left=0.05, right=0.95, top=0.95, bottom=0.05,wspace=0.2,hspace=0.2)
picked_stcs = [(53,47),(53,49),(55,47),(51,63),(49,53),(47,63)]
ax = pylab.subplot(gs[1:19,9:27])
self.plot_average_oct(independent=False)
pylab.ylabel('Normalized response', fontsize=20)
ax = pylab.subplot(gs[22:32,1:11])
self.plot_orientation_contrast_tuning(picked_stcs[0][0], picked_stcs[0][1],'',independent=False)
remove_x_tick_labels()
pylab.ylabel('Response', fontsize=20)
ax = pylab.subplot(gs[22:32,13:23])
self.plot_orientation_contrast_tuning(picked_stcs[1][0], picked_stcs[1][1],'',independent=False)
disable_left_axis(pylab.gca())
remove_x_tick_labels()
remove_y_tick_labels()
ax = pylab.subplot(gs[22:32,25:35])
self.plot_orientation_contrast_tuning(picked_stcs[2][0], picked_stcs[2][1],'',independent=False)
disable_left_axis(pylab.gca())
remove_x_tick_labels()
remove_y_tick_labels()
ax = pylab.subplot(gs[34:44,1:11])
self.plot_orientation_contrast_tuning(picked_stcs[3][0], picked_stcs[3][1],'',independent=False)
pylab.ylabel('Response', fontsize=20)
ax = pylab.subplot(gs[34:44,13:23])
self.plot_orientation_contrast_tuning(picked_stcs[4][0], picked_stcs[4][1],'',independent=False)
disable_left_axis(pylab.gca())
remove_y_tick_labels()
ax = pylab.subplot(gs[34:44,25:35])
self.plot_orientation_contrast_tuning(picked_stcs[5][0], picked_stcs[5][1],'',independent=False)
disable_left_axis(pylab.gca())
remove_y_tick_labels()
# PLOT EXPERIMENTAL DATA
x,y = zip(*[(-88.694, 45.5367),(-81.8258, 45.0061),(-74.9654, 44.2076),(-68.0972, 43.677),(-61.2444, 42.6107),(-54.3686, 42.3479),(-47.5158, 41.2816),(-40.7245, 38.0724),(-34.0868, 29.5059),(-27.4415, 21.2073),(-20.7654, 13.9801),(-13.9357, 12.1102),(-7.006, 13.7225),(0.1156, 22.0313),(7.0299, 23.1078),(13.9364, 23.9165),(20.8891, 26.3325),(27.9186, 31.427),(34.9404, 36.2537),(41.9622, 41.0803),(48.9224, 43.7641),(55.7906, 43.2335),(62.6588, 42.7029),(69.5346, 42.4401),(76.4028, 41.9095), (83.2631, 41.1111),(90.1314, 40.5805)])
ox,oy = zip(*[(-89.7405, 9.108),(-82.8946, 9.3988),(-76.0487, 9.6896),(-69.1876, 10.5161),(-62.3417, 10.8069),(-55.4958, 11.0977),(-48.6423, 11.6562),(-41.7506, 13.5544),(-34.7597, 18.935),(-27.7841, 23.7797),(-20.8008, 28.8923),(-13.8557, 32.6657),(-6.8343, 39.1177),(0.1566, 44.4983),(6.8652, 39.9672),(13.505, 33.0251),(20.183, 27.4224),(26.8686, 22.0877),(33.539, 16.2172),(40.2475, 11.6861),(46.956, 7.155),(53.7942, 7.1779),(60.6172, 6.665),(67.4402, 6.1521),(74.2709, 5.9072),(81.1015, 5.6622),(87.2407, 5.147)])
x = numpy.array(x)
y = numpy.array(y)
oy = numpy.array(oy)
x = x/360.0*numpy.pi*2
ax = pylab.subplot(gs[47:57,5:15])
ax.plot(x,y,lw=3,color='r')
ax.plot(x,oy,lw=3,color='b')
ax.set_xlim(-numpy.pi/2-0.2,numpy.pi/2.0+0.2)
ax.set_xticks([-numpy.pi/2,0,numpy.pi/2.0])
ax.set_ylim(0,50)
ax.set_yticks([0,25,50])
ax.set_ylabel('Response', fontsize=20)
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels([r'-$\frac{\pi}{2}$',r'0',r'$\frac{\pi}{2}$'], fontProperties)
ax.set_yticklabels(ax.get_yticks(), fontProperties)
x,y = zip(*[(-90.5455, 0.6447),(-67.5208, 0.6732),(-45.9417, 0.572),(-22.6714, 0.4924),(-0.5531, 0.3234),(22.8077, 0.5215),(45.0244, 0.6539),(67.7793, 0.7153),(89.9339, 0.6573)])
x = numpy.array(x)
y = numpy.array(y)
x = x/360.0*numpy.pi*2
ax = pylab.subplot(gs[47:57,19:29])
ax.plot(x,y,lw=3,color='r')
ax.set_xlim(-numpy.pi/2-0.2,numpy.pi/2.0+0.2)
ax.set_xticks([-numpy.pi/2,0,numpy.pi/2.0])
ax.set_ylim(0,1.0)
ax.set_yticks([0,0.5,1.0])
ax.set_ylabel('Relative response', fontsize=20)
disable_top_right_axis(pylab.gca())
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
pylab.gca().set_xticklabels([r'-$\frac{\pi}{2}$',r'0',r'$\frac{\pi}{2}$'], fontProperties)
ax.set_yticklabels(ax.get_yticks(), fontProperties)
release_fig("Figure8")
def correlations_figure(self,raster_plots):
self.fig = pylab.figure(facecolor='w',figsize=(15, 18),dpi=800)
gs = gridspec.GridSpec(3,2)
gs.update(left=0.07, right=0.95, top=0.95, bottom=0.05,wspace=0.2,hspace=0.2)
ax = pylab.subplot(gs[0,0])
m,b = numpy.polyfit(raster_plots["or_suppression"][1],raster_plots["or_suppression"][0],1)
correlation,pval = scipy.stats.pearsonr(raster_plots["or_suppression"][1],raster_plots["or_suppression"][0])
pylab.scatter(raster_plots["or_suppression"][1],raster_plots["or_suppression"][0],s=18, facecolor = 'r',lw = 0)
pylab.plot(raster_plots["or_suppression"][1],m*numpy.array(raster_plots["or_suppression"][1])+b,'-k',linewidth=2)
disable_top_right_axis(ax)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.ylabel('Orientation-contrast suppression', fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax = pylab.subplot(gs[0,1])
m,b = numpy.polyfit(raster_plots["SSI"][1],raster_plots["SSI"][0],1)
correlation,pval = scipy.stats.pearsonr(raster_plots["SSI"][1],raster_plots["SSI"][0])
pylab.scatter(raster_plots["SSI"][1],raster_plots["SSI"][0],s=18, facecolor = 'r',lw = 0)
pylab.plot(raster_plots["SSI"][1],m*numpy.array(raster_plots["SSI"][1])+b,'-k',linewidth=2)
disable_top_right_axis(ax)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.ylabel('Surround selectivity index', fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
print raster_plots.keys()
ax = pylab.subplot(gs[1,0])
m,b = numpy.polyfit(raster_plots["suppresion_index"][1],raster_plots["suppresion_index"][0],1)
correlation,pval = scipy.stats.pearsonr(raster_plots["suppresion_index"][1],raster_plots["suppresion_index"][0])
pylab.scatter(raster_plots["suppresion_index"][1],raster_plots["suppresion_index"][0],s=18, facecolor = 'r',lw = 0)
pylab.plot(raster_plots["suppresion_index"][1],m*numpy.array(raster_plots["suppresion_index"][1])+b,'-k',linewidth=2)
disable_top_right_axis(ax)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.ylabel('Suppression index', fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax = pylab.subplot(gs[1,1])
m,b = numpy.polyfit(raster_plots["counter_suppresion_index"][1],raster_plots["counter_suppresion_index"][0],1)
correlation,pval = scipy.stats.pearsonr(raster_plots["counter_suppresion_index"][1],raster_plots["counter_suppresion_index"][0])
pylab.scatter(raster_plots["counter_suppresion_index"][1],raster_plots["counter_suppresion_index"][0],s=18, facecolor = 'r',lw = 0)
pylab.plot(raster_plots["counter_suppresion_index"][1],m*numpy.array(raster_plots["counter_suppresion_index"][1])+b,'-k',linewidth=2)
disable_top_right_axis(ax)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.ylabel('Counter suppression index', fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax = pylab.subplot(gs[2,0])
pylab.scatter(self.lhi.ravel(),self.MR.ravel()*2,s=3, facecolor = 'r',lw = 0)
#xx,z = running_average(self.lhi.ravel(),self.MR.ravel()*2)
#pylab.plot(xx,z,'k',lw=3.0)
disable_top_right_axis(ax)
pylab.xlabel('Local homogeneity index', fontsize=20)
pylab.ylabel('Modulation ratio', fontsize=20)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
ax = pylab.subplot(gs[2,1])
pylab.scatter(self.lhi.ravel(),self.OS.ravel(),s=3, facecolor = 'r',lw = 0)
#xx,z = running_average(self.lhi.ravel(),self.OS.ravel())
#pylab.plot(xx,z,'k',lw=3.0)
disable_top_right_axis(ax)
ax.xaxis.set_major_locator(MaxNLocator(4))
ax.yaxis.set_major_locator(MaxNLocator(4))
pylab.xlabel('Local homogeneity index', fontsize=20)
pylab.ylabel('Orientation selectivity', fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'xticklabels'), fontsize=20)
pylab.setp(pylab.getp(pylab.gca(), 'yticklabels'), fontsize=20)
print scipy.stats.pearsonr(self.lhi.ravel(),self.OS.ravel())
print scipy.stats.pearsonr(self.lhi.ravel(),self.MR.ravel()*2)
release_fig("CorrelationFigure")
def plot_map_feature_to_surround_modulation_feature_correlations(self,map_feature,map_feature_name):
from numpy import polyfit
raster_plots_lc={}
raster_plots_hc={}
for (xcoord,ycoord) in self.data_dict.keys():
for curve_type in self.data_dict[(xcoord,ycoord)].keys():
print curve_type
if curve_type == "ST":
curve_label = "Contrast"
else:
curve_label = "Contrastsurround"
if self.data_dict[(xcoord,ycoord)][curve_type].has_key(curve_label + " = " + str(self.high_contrast) + "%"):
for measure_name in self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.high_contrast) + "%"]["measures"].keys():
if not raster_plots_hc.has_key(measure_name):
raster_plots_hc[measure_name]=[[],[]]
raster_plots_hc[measure_name][0].append(self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.high_contrast) + "%"]["measures"][measure_name])
raster_plots_hc[measure_name][1].append(map_feature[xcoord,ycoord])
if self.data_dict[(xcoord,ycoord)][curve_type].has_key(curve_label + " = " + str(self.low_contrast) + "%"):
for measure_name in self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.low_contrast) + "%"]["measures"].keys():
if not raster_plots_lc.has_key(measure_name):
raster_plots_lc[measure_name]=[[],[]]
raster_plots_lc[measure_name][0].append(self.data_dict[(xcoord,ycoord)][curve_type][curve_label + " = " + str(self.low_contrast) + "%"]["measures"][measure_name])
raster_plots_lc[measure_name][1].append(map_feature[xcoord,ycoord])
for key in raster_plots_hc.keys():
fig = pylab.figure()
f = fig.add_subplot(111)
f.set_xlabel(str(key).replace('_',' '))
f.set_ylabel(map_feature_name.replace('_',' '))
try:
#correlation = numpy.corrcoef(raster_plots_hc[key][0],raster_plots_hc[key][1])[0,1]
import scipy.stats
correlation = scipy.stats.pearsonr(raster_plots_hc[key][0],raster_plots_hc[key][1])[0]
pval= scipy.stats.pearsonr(raster_plots_hc[key][0],raster_plots_hc[key][1])[1]
except FloatingPointError:
correlation = 0
m,b = numpy.polyfit(raster_plots_hc[key][0],raster_plots_hc[key][1],1)
f.plot(raster_plots_hc[key][0],raster_plots_hc[key][1],'ro')
f.plot(raster_plots_hc[key][0],m*numpy.array(raster_plots_hc[key][0])+b,'-k',linewidth=2)
release_fig("RasterHC<" + map_feature_name + ","+ key + " Corr:"+ str(correlation) + '|'+ str(pval) + ">")
for key in raster_plots_lc.keys():
fig = pylab.figure()
f = fig.add_subplot(111)
f.set_xlabel(str(key).replace('_',' '))
f.set_ylabel(map_feature_name.replace('_',' '))
m,b = numpy.polyfit(raster_plots_lc[key][0],raster_plots_lc[key][1],1)
try:
#correlation = numpy.corrcoef(raster_plots_lc[key][0],raster_plots_lc[key][1])[0,1]
import scipy.stats
correlation = scipy.stats.pearsonr(raster_plots_lc[key][0],raster_plots_lc[key][1])[0]
pval= scipy.stats.pearsonr(raster_plots_lc[key][0],raster_plots_lc[key][1])[1]
except FloatingPointError:
correlation = 0
f.plot(raster_plots_lc[key][0],raster_plots_lc[key][1],'ro')
f.plot(raster_plots_lc[key][0],m*numpy.array(raster_plots_lc[key][0])+b,'-k',linewidth=2)
release_fig("RasterLC<" + map_feature_name + ","+ key + " Corr:"+ str(correlation)+ '|'+ str(pval) + ">")
return (raster_plots_lc,raster_plots_hc)
def calculate_autocorrelation(self,ormap):
import scipy.signal
pylab.figure()
pylab.subplot(2,1,1)
pylab.imshow(ormap,cmap='hsv')
pylab.axis('off')
pylab.xlabel('OR map')
pylab.subplot(2,1,2)
pylab.imshow (scipy.signal.correlate2d(ormap,ormap),cmap='autumn')
pylab.axis('off')
pylab.xlabel('Autocorrelation')
def running_average(x,y):
s = 0.1
z = []
xx = []
for i in numpy.arange(0.01,1.0,0.1):
w = []
for (j,h) in zip(x,y):
if abs(j-i) < 0.05:
w.append(h)
z.append(numpy.mean(w))
xx.append(i)
return xx,z
def compute_local_homogeneity_index(or_map,sigma):
(xsize,ysize) = or_map.shape
lhi = numpy.zeros(or_map.shape)
for sx in xrange(0,xsize):
for sy in xrange(0,ysize):
lhi_current=[0,0]
for tx in xrange(0,xsize):