# possibleFreqLabels = ["{0:.1f}".format(freq) for freq in np.unique(thalPopStat)]
ytickLabels = [4, 8, 16, 32, 64, 128]
yticks = np.log(ytickLabels)
acPopStat = np.log(acPopStat)
thalPopStat = np.log(thalPopStat)
axSummary = plt.subplot(gs[0, 5])
spacing = 0.07
plt.sca(axSummary)
# pos = jitter(np.ones(len(thalPopStat))*0, 0.20)
# axSummary.plot(pos, thalPopStat, 'o', mec = colorATh, mfc = 'None', alpha=0.5)
plt.hold(1)
markers = extraplots.spread_plot(0, thalPopStat, spacing)
plt.setp(markers, mec=colorD1, mfc='None')
plt.setp(markers, ms=dataMS)
plt.hold(1)
medline(np.median(thalPopStat), 0, 0.5)
plt.hold(1)
# pos = jitter(np.ones(len(acPopStat))*1, 0.20)
# axSummary.plot(pos, acPopStat, 'o', mec = colorAC, mfc = 'None', alpha=0.5)
markers = extraplots.spread_plot(1, acPopStat, spacing)
plt.setp(markers, mec=colornD1, mfc='None')
plt.setp(markers, ms=dataMS)
plt.hold(1)
medline(np.median(acPopStat), 1, 0.5)
#plt.hold(1)
#
#plt.hold(True)
# ======================= Beginning of plotting for threshold ================================
if PANELS[3]:
popStatCol = 'thresholdFRA'
D1PopStat = D1[popStatCol][pd.notnull(D1[popStatCol])]
nD1PopStat = nD1[popStatCol][pd.notnull(nD1[popStatCol])]
plt.sca(axThresh)
spacing = 0.05
markers = extraplots.spread_plot(0, nD1PopStat, spacing)
plt.setp(markers, mec=colornD1, mfc='None')
medline(axThresh, np.median(nD1PopStat), 0, 0.5)
markers = extraplots.spread_plot(1, D1PopStat, spacing)
plt.setp(markers, mec=colorD1, mfc='None')
medline(axThresh, np.median(D1PopStat), 1, 0.5)
axThresh.set_ylabel('Threshold (dB SPL)', fontsize=fontSizeLabels)
tickLabels = ['nD1:Str\nn={}'.format(len(nD1PopStat)), 'D1:Str\nn={}'.format(len(D1PopStat))]
axThresh.set_xticks(range(2))
axThresh.set_xlim([-0.5, 1.5])
axThresh.set_ylim([0, 71])
extraplots.boxoff(axThresh)
extraplots.set_ticks_fontsize(axThresh, fontSizeTicks)
'''
Test plot where many samples are plotted at quantized levels.
'''
from jaratoolbox import extraplots
reload(extraplots)
from matplotlib import pyplot as plt
import numpy as np
plt.clf()
plt.hold(True)
y = np.array([1, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 6, 6])
x = 0
hp = extraplots.spread_plot(x, y, spacing=0.03)
plt.setp(hp, mec='r', mew=2)
'''
possibleOffsetsOdd = 0.05*np.array([0,-1,1,-2,2,-3,3])
possibleOffsetsEven = 0.05*np.array([-0.5,0.5,-1.5,1.5,-2.5,2.5])
spacing = 0.03
uniqueY = np.unique(y)
for oneY in uniqueY:
nVals = np.sum(y==oneY)
#possibleOffsets = possibleOffsetsOdd if nVals%2 else possibleOffsetsEven
possibleOffsets = spacing * np.arange(-nVals/2.0+0.5, nVals/2.0, 1)
"""
if nVals%2:
possibleOffsets = spacing * np.arange(-nVals/2.0+0.5, nVals/2.0, 1)
else: