def plot_randomized_speed_profiles(avgSpeeds, trialTypes):
# Set Plotting Attributes
color1 = (0.0, 0.0, 0.0, 0.1)
color2 = (1.0, 0.6, 0.0, 0.1)
color1b = (0.0, 0.0, 0.0, 1.0)
color2b = (1.0, 0.6, 0.0, 1.0)
traceColors = [color1, color2]
boldColors = [color1b, color2b]
# Plot Average Speeds in bins
plt.figure()
numTrials = np.size(trialTypes)
for t in range(0, numTrials):
if trialTypes[t] == 0:
plt.plot(avgSpeeds[t, :], color=color1)
else:
plt.plot(avgSpeeds[t, :], color=color2)
stableTrials = np.where(trialTypes == 0)
unstableTrials = np.where(trialTypes == 1)
mSt = stats.nanmean(avgSpeeds[stableTrials, :], 1)
mUn = stats.nanmean(avgSpeeds[unstableTrials, :], 1)
eSt = stats.nanstd(avgSpeeds[stableTrials, :],
1) / np.sqrt(np.size(stableTrials) - 1)
eUn = stats.nanstd(avgSpeeds[unstableTrials, :],
1) / np.sqrt(np.size(unstableTrials) - 1)
# eSt = stats.nanstd(avgSpeeds[stableTrials, :], 1)
# eUn = stats.nanstd(avgSpeeds[unstableTrials, :], 1)
mSt = mSt[0]
mUn = mUn[0]
eSt = eSt[0]
eUn = eUn[0]
plt.plot(mUn, color=color2b, linewidth=7)
plt.plot(mSt, color=color1b, linewidth=7)
# plt.plot(mSt + eSt, color=color1b, linewidth = 0.5)
# plt.plot(mSt - eSt, color=color1b, linewidth = 0.5)
# plt.plot(mUn + eUn, color=color2b, linewidth = 0.5)
# plt.plot(mUn - eUn, color=color2b, linewidth = 0.5)
#pltutils.fix_font_size()
plt.xlabel('crossing extent (cm)')
plt.ylabel('normalized horizontal speed')
pltutils.fix_font_size()
plt.axis([0, 39, 0, 3])
def plot_randomized_group_average_speed_profiles(avgProfiles,
labelx=True,
labely=True,
legend=True,
title=None):
# Set Plotting Attributes
color1b = (0.0, 0.0, 0.0, 1.0)
color2b = (1.0, 0.6, 0.0, 1.0)
# Unpack average profile structure
(stAvg, stErr), (unAvg, unErr) = avgProfiles
# Prepare Bulk Arrays
stAvg = np.array(stAvg)
unAvg = np.array(unAvg)
stErr = np.array(stErr)
unErr = np.array(unErr)
# Plot Averge Speed Profiles
a1 = plt.plot(np.mean(unAvg, 0),
color=color2b,
linewidth=3,
label='unstable')
a2 = plt.plot(np.mean(stAvg, 0),
color=color1b,
linewidth=3,
label='stable')
if labelx:
plt.xlabel('crossing extent (cm)')
if labely:
plt.ylabel('normalized horizontal speed')
pltutils.fix_font_size()
plt.axis([0, numBins, 0.5, 2.0])
plt.yticks([0.75, 1.0, 1.25, 1.5, 1.75])
if legend:
plt.legend((a2[0], a1[0]), ('stable', 'unstable'), loc='upper left')
if title is not None:
ax = plt.gca()
ax.text(0.5,
0.9,
title,
horizontalalignment='center',
transform=ax.transAxes)
offsetscale = 2
plt.close('all')
fig = plt.figure('conditions average tip speed across sessions')
ax = fig.gca()
ax.set_color_cycle(group_cycle)
[
plot_average_tip_speed('conditions', [
mclesionsham[group_order[i]][s] for s in ([0, 1, 2] if i > 0 else [0])
],
crop=valid_positions,
label='lesion' if group_cycle[i] == 'r' else 'sham',
offset=i * 2,
scale=46,
trial_slices=slice(1, None)) for i in range(0, 14)
]
pltutils.fix_font_size()
handles, labels = ax.get_legend_handles_labels()
plt.legend((handles[0], handles[7]), (labels[0], labels[7]))
plt.title('average nose speed across assay conditions')
tickoffset = 6.5 * offsetscale
ax.set_xticks([tickoffset, scale + tickoffset, scale * 2 + tickoffset])
ax.set_xticklabels(['stable', 'center unstable', 'all released'])
plt.xlabel('')
plt.draw()
######## I.d Nose Speed Distribution Across Conditions Across Groups ##########
def barplot(data, offset, barwidth, color):
plt.bar(offset,
np.mean(data),
width=barwidth,
def plot_comparison_measure(group_order,
group_cycle,
mclesionsham,
bracket_offset=5,
bracket_tickheight=1):
scale = 46
offsetscale = 2
plt.close('all')
fig = plt.figure('stable sessions average trial times', figsize=(4, 7))
ax = fig.gca()
ax.set_color_cycle(group_cycle)
measures = [
plot_average_trial_times(
'stable sessions',
mclesionsham[group_order[i]][1:5],
label='lesion' if group_cycle[i] == 'r' else 'control',
offset=i * 2,
scale=46) for i in range(14)
]
plt.ylabel('time between rewards (s)')
plt.xlabel('sessions')
lesionoffset = 3 * offsetscale
lesioncenter = [scale * i + lesionoffset for i in range(4)]
lesion_times = [[
get_average_trial_time(mclesionsham[i][s]) for i in group_order[0:7]
] for s in range(1, 5)]
lesion_mean = [np.mean(x) for x in lesion_times]
lesion_std = [np.std(x) for x in lesion_times]
plt.errorbar(lesioncenter,
lesion_mean,
lesion_std,
fmt='--o',
color=group_cycle[0],
ecolor='k',
linewidth=2,
capthick=2,
markersize=0)
controloffset = 10 * offsetscale
controlcenter = [scale * i + controloffset for i in range(4)]
control_times = [[
get_average_trial_time(mclesionsham[i][s]) for i in group_order[7:14]
] for s in range(1, 5)]
control_mean = [np.mean(x) for x in control_times]
control_std = [np.std(x) for x in control_times]
plt.errorbar(controlcenter,
control_mean,
control_std,
fmt='--o',
color=group_cycle[7],
ecolor='k',
linewidth=2,
capthick=2,
markersize=0)
pltutils.fix_font_size()
handles, labels = ax.get_legend_handles_labels()
plt.legend((handles[0], handles[7]), (labels[0], labels[7]))
tickoffset = 6.5 * offsetscale
xticks = [scale * i + tickoffset for i in range(4)]
ax.set_xticks(xticks)
ax.set_xticklabels([1, 2, 3, 4])
### BRACKETS (BETWEEN GROUPS) ###
maxstd = []
minstd = []
significance = 0.05
maxrange = len(measures)
for i in range(len(xticks)):
sigtest = stats.ttest_ind(lesion_times[i], control_times[i])[1]
print sigtest, "groups"
testlabel = str.format("*",
sigtest) if sigtest < significance else 'n.s.'
maxstd.append(
get_bracket_y(
measures,
range(maxrange) if i < 1 else range(0, 7) + range(8, 14), i))
minstd.append(
get_negative_bracket_y(
measures,
range(maxrange) if i < 1 else range(0, 7) + range(8, 14), i))
minstd = min(minstd)
for i in range(len(xticks)):
pltutils.hbracket(xticks[i],
minstd - bracket_offset,
1.4,
label=testlabel,
tickheight=-10 * bracket_tickheight)
#################################
plt.draw()
def plot_randomized_speed_profile_difference_comparison(controls, lesions):
controlMeans = np.mean(controls, 0)
controlMeanAll = np.mean(controlMeans)
controlError = np.std(controlMeans) / np.sqrt(7 - 1)
lesionMeans = np.mean(lesions, 0)
lesionMeanAll = np.mean(lesionMeans)
lesionlError = np.std(lesionMeans) / np.sqrt(6 - 1)
significance = 0.05
sigtest = stats.ttest_ind(controlMeans, lesionMeans)[1]
testlabel = str.format("*", sigtest) if sigtest < significance else 'n.s.'
print sigtest
cX = [1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1]
lX = [1.9, 1.9, 1.9, 1.9, 1.9, 1.9]
plt.plot([0, 3], [0, 0], color=[0.25, 0.25, 0.25, 1], linewidth=1)
plt.plot(cX, controlMeans, 'bo')
plt.plot(lX, lesionMeans, 'ro')
plt.plot(1.9, lesionMeans[5], 'o', color=[1.0, 0.75, 0.75, 1.0])
#plt.bar(1.2, controlMeanAll, 0.2, color=[1.0,1.0,1.0,0.0])
plt.errorbar(1.3,
controlMeanAll,
controlError,
marker='s',
mfc='blue',
ecolor='black',
mec='black',
ms=1,
mew=1,
capsize=5,
elinewidth=2)
plt.plot([1.0, 1.4], [controlMeanAll, controlMeanAll],
color=[0.25, 0.25, 1.0, 1],
linewidth=2)
#plt.bar(1.6, lesionMeanAll, 0.2, color=[1.0,1.0,1.0,0.0])
plt.errorbar(1.7,
lesionMeanAll,
lesionlError,
marker='s',
mfc='red',
ecolor='black',
mec='black',
ms=1,
mew=1,
capsize=5,
elinewidth=2)
plt.plot([1.6, 2.0], [lesionMeanAll, lesionMeanAll],
color=[1.00, 0.25, 0.25, 1],
linewidth=2)
pltutils.hbracket(1.5, 0.17, 4.5, label=testlabel, tickheight=0.01)
ax = plt.gca()
ax.set_xticks([1.1, 1.9])
ax.set_xticklabels(['controls', 'lesions'])
plt.ylabel('normalized speed difference')
pltutils.fix_font_size()
plt.axis([0.75, 2.25, -0.2, 0.2])
def plot_trial_measures(group_cycle,
group_order,
trial_measures,
bracket_offset=0.5,
bracket_tickheight=1,
ylims=None,
xticklabels=None):
scale = 46
offsetscale = 2
num_sessions = len(trial_measures[0])
ax = plt.gca()
ax.set_color_cycle(group_cycle)
measures = [[
trial_measures[group_order[i]][s]
for s in (range(num_sessions) if i != 7 else [0])
] for i in range(len(trial_measures))]
[
plot_epoch_average(
measures[i],
label='lesion' if group_cycle[i] == 'r' else 'control',
offset=i * offsetscale,
scale=scale) for i in range(len(trial_measures))
]
plt.ylabel('time to cross obstacles (s)')
plt.xlabel('x')
offset1 = 3 * offsetscale
center1 = [scale * i + offset1 for i in range(num_sessions)]
group1_times = [[np.mean(trial_measures[i][s]) for i in group_order[0:7]]
for s in range(num_sessions)]
group1_mean = [np.mean(x) for x in group1_times]
group1_std = [np.std(x) for x in group1_times]
plt.errorbar(center1,
group1_mean,
group1_std,
fmt='o',
color=group_cycle[0],
ecolor='k',
linewidth=2,
capthick=2,
markersize=0)
offset2 = 10 * offsetscale
center2 = [scale * i + offset2 for i in range(num_sessions)]
group2_times = [[
np.mean(trial_measures[i][s])
for i in (group_order[7:14] if s < 1 else group_order[8:14])
] for s in range(num_sessions)]
group2_mean = [np.mean(x) for x in group2_times]
group2_std = [np.std(x) for x in group2_times]
plt.errorbar(center2,
group2_mean,
group2_std,
fmt='o',
color=group_cycle[7],
ecolor='k',
linewidth=2,
capthick=2,
markersize=0)
pltutils.fix_font_size()
#handles, labels = ax.get_legend_handles_labels()
#plt.legend((handles[0],handles[7]),(labels[0],labels[7]))
tickoffset = 6.5 * offsetscale
xticks = [scale * i + tickoffset for i in range(num_sessions)]
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels)
### BRACKETS (BETWEEN GROUPS) ###
maxstd = []
minstd = []
significance = 0.01
maxrange = len(measures)
for i in range(len(xticks)):
sigtest = stats.ttest_ind(group1_times[i], group2_times[i])[1]
print sigtest, "groups"
testlabel = str.format("*",
sigtest) if sigtest < significance else 'n.s.'
maxstd.append(
get_bracket_y(
measures,
range(maxrange) if i < 1 else range(0, 7) + range(8, 14), i))
minstd.append(
get_negative_bracket_y(
measures,
range(maxrange) if i < 1 else range(0, 7) + range(8, 14), i))
maxstd = max(maxstd)
for i in range(len(xticks)):
pltutils.hbracket(xticks[i],
maxstd + bracket_offset,
2,
label=testlabel,
tickheight=bracket_tickheight)
##################################
### BRACKETS (BETWEEN CONDITIONS) ###
minstd = min(minstd)
sigtest = stats.ttest_ind(group1_times[0] + group2_times[0],
group1_times[-1] + group2_times[-1])[1]
print sigtest, "conditions"
testlabel = str.format("*", sigtest) if sigtest < significance else 'n.s.'
#pltutils.hbracket(xticks[1],minstd-bracket_offset,5.5,label=testlabel,tickheight=-1.5*bracket_tickheight)
pltutils.hbracket((xticks[-1] - xticks[0]) / 2 + xticks[0],
minstd - bracket_offset,
9.5,
label=testlabel,
tickheight=-1.5 * bracket_tickheight)
#####################################
### SEPARATORS ###
ylims = plt.ylim() if ylims is None else ylims
for i in range(len(xticks) - 1):
separatorxi = (xticks[i] + xticks[i + 1]) / 2
ax.plot((separatorxi, separatorxi), ylims, 'k--')
plt.ylim(ylims)
##################
plt.xlabel('')
plt.draw()