def makeBehavFigs(x):
# Initialize figure
behavFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(5, 2)
gs1.update(left=0.10, right=0.9, wspace=0.5, hspace=0.7)
plt.suptitle('Rat ' + x.rat + ': Session ' + x.session + ' (' +
x.contents + ')')
# Make figures of lick data from each bottle
ax = plt.subplot(gs1[0, :])
jmfig.sessionlicksFig(ax, x.lickData)
ax = plt.subplot(gs1[1, 0])
jmfig.licklengthFig(ax, x.lickData)
ax = plt.subplot(gs1[1, 1])
jmfig.iliFig(ax, x.lickData)
ax = plt.subplot(gs1[2, 0])
jmfig.burstlengthFig(ax, x.lickData, color3rdbar=True)
ax = plt.subplot(gs1[2, 1])
jmfig.ibiFig(ax, x.lickData)
#plt.tight_layout()
pdf_pages.savefig(behavFig)
def ps_mcc1_fig(df, keys):
data = []
for key in keys:
data.append(df[key])
f, ax = plt.subplots(figsize=(5, 3))
f.subplots_adjust(left=0.1, bottom=0.2)
colors = [color_scheme[0]] * 2 + [color_scheme[2]] * 5
print(colors)
jmfig.barscatter(data,
paired=True,
barwidth=.8,
barfacecoloroption='individual',
barfacecolor=colors,
barlabels=keys,
ax=ax)
ax.set_ylabel('Pellets consumed')
ax.set_yticks([0, 10, 20, 30])
ax.set_xlabel('Number of pellets given')
ax.xaxis.set_label_coords(0.5, -0.13)
return f
def cond_photobar_fig(ax, df, diet, keys):
scattersize = 50
if diet == 'NR':
cols = [col['np_cas'], col['np_cas'], col['np_malt'], col['np_malt']]
title = 'Non-restricted'
else:
cols = [col['lp_cas'], col['lp_cas'], col['lp_malt'], col['lp_malt']]
title = 'Protein restricted'
x = [[df.xs(diet, level=1)[keys[0][0]],
df.xs(diet, level=1)[keys[0][1]]],
[df.xs(diet, level=1)[keys[1][0]],
df.xs(diet, level=1)[keys[1][1]]]]
jmfig.barscatter(
x,
paired=True,
unequal=True,
barfacecoloroption='individual',
barfacecolor=cols,
scatteredgecolor=['xkcd:charcoal'],
scatterlinecolor='xkcd:charcoal',
grouplabel=['Cas', 'Malt'],
barlabels=['1', '2', '1', '2'],
scattersize=scattersize,
# ylim=[-5,50],
ax=ax)
def sacc_behav_fig(df):
f, ax = plt.subplots(figsize=(7.2, 2.5), ncols=2)
scattersize = 50
x = [[
df.xs('NR', level=1)['latx1'],
df.xs('NR', level=1)['latx2'],
df.xs('NR', level=1)['latx3']
],
[
df.xs('PR', level=1)['latx1'],
df.xs('PR', level=1)['latx2'],
df.xs('PR', level=1)['latx3']
]]
jmfig.barscatter(x,
paired=True,
unequal=True,
barfacecoloroption='individual',
barfacecolor=[
col['np_cas'], col['np_malt'], col['lp_cas'],
col['lp_malt']
],
scatteredgecolor=['xkcd:charcoal'],
scatterlinecolor='xkcd:charcoal',
grouplabel=['NR', 'PR'],
barlabels=['Cas', 'Malt', 'Cas', 'Malt'],
scattersize=scattersize,
ylim=[-1, 20],
ax=ax[0])
x = [[
df.xs('NR', level=1)['missed1'],
df.xs('NR', level=1)['missed2'],
df.xs('NR', level=1)['missed3']
],
[
df.xs('PR', level=1)['missed1'],
df.xs('PR', level=1)['missed2'],
df.xs('PR', level=1)['missed3']
]]
jmfig.barscatter(x,
paired=True,
unequal=True,
barfacecoloroption='individual',
barfacecolor=[
col['np_cas'], col['np_malt'], col['lp_cas'],
col['lp_malt']
],
scatteredgecolor=['xkcd:charcoal'],
scatterlinecolor='xkcd:charcoal',
grouplabel=['NR', 'PR'],
barlabels=['Cas', 'Malt', 'Cas', 'Malt'],
scattersize=scattersize,
ylim=[-5, 50],
ax=ax[1])
return f
def makeGrantFigs(x):
if x.leftTrials == True and x.rightTrials == True:
grantLFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(9, 7)
gs1.update(left=0.125, right=0.9, wspace=0.4, hspace=0.8)
plt.suptitle('Rat ' + x.rat + ': Session ' + x.session)
jmfig.trialstiledFig(gs1, x.lickLTrials)
pdf_pages.savefig(grantLFig)
grantRFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1.update(left=0.125, right=0.9, wspace=0.4, hspace=0.8)
plt.suptitle('Rat ' + x.rat + ': Session ' + x.session)
jmfig.trialstiledFig(gs1, x.lickRTrials)
pdf_pages.savefig(grantRFig)
elif x.rightTrials == False:
grantLFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(9, 7)
gs1.update(left=0.125, right=0.9, wspace=0.4, hspace=0.8)
plt.suptitle('Rat ' + x.rat + ': Session ' + x.session)
jmfig.trialstiledFig(gs1, x.lickLTrials)
pdf_pages.savefig(grantLFig)
elif x.leftTrials == False:
grantRFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(9, 7)
gs1.update(left=0.125, right=0.9, wspace=0.4, hspace=0.8)
plt.suptitle('Rat ' + x.rat + ': Session ' + x.session)
jmfig.trialstiledFig(gs1, x.lickRTrials)
pdf_pages.savefig(grantRFig)
def photoFigsCol(gs1, col, pps, cues, cuesUV, cuesnoise, licks, licksUV,
licksnoise):
ax = plt.subplot(gs1[1, col])
jmfig.trialsFig(ax,
cues,
pps,
noiseindex=cuesnoise,
eventText='Cue',
ylabel='Delta F / F0')
ax = plt.subplot(gs1[2, col])
jmfig.trialsMultShadedFig(ax, [cuesUV, cues],
pps,
noiseindex=cuesnoise,
eventText='Cue')
ax = plt.subplot(gs1[3, col])
jmfig.trialsFig(ax,
licks,
pps,
noiseindex=licksnoise,
eventText='First Lick',
ylabel='Delta F / F0')
ax = plt.subplot(gs1[4, col])
jmfig.trialsMultShadedFig(ax, [licksUV, licks],
pps,
noiseindex=licksnoise,
eventText='First Lick')
def makeBehavFigs(x):
# Initialize figure
behavFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(5, 2)
gs1.update(left=0.10, right=0.9, wspace=0.5, hspace=0.7)
# Make figures of lick data from each bottle
ax = plt.subplot(gs1[0, :])
x.sessionLicksFig(ax)
if len(x.lickDataA['licks']) > 1:
ax = plt.subplot(gs1[1, 0])
jmfig.licklengthFig(ax, x.lickDataA, contents=x.bottleA)
ax = plt.subplot(gs1[2, 0])
jmfig.iliFig(ax, x.lickDataA, contents=x.bottleA)
ax = plt.subplot(gs1[3, 0])
jmfig.burstlengthFig(ax, x.lickDataA, contents=x.bottleA)
ax = plt.subplot(gs1[4, 0])
jmfig.ibiFig(ax, x.lickDataA, contents=x.bottleA)
else:
print('No licks for Bottle A in this session.')
#plt.tight_layout()
pdf_pages.savefig(behavFig)
def makePhotoFigs(x):
# Initialize photometry figure
photoFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(6, 2)
gs1.update(left=0.125, right=0.9, wspace=0.4, hspace=0.8)
plt.suptitle('Rat ' + x.rat + ': Session ' + x.session)
ax = plt.subplot(gs1[0, :])
x.sessionFig(ax)
if x.leftTrials == True:
photoFigsCol(gs1, 0, x.pps, x.cueLTrials, x.cueLUVTrials, x.cueLnoise,
x.lickLTrials, x.lickLUVTrials, x.lickLnoise)
if x.rightTrials == True:
photoFigsCol(gs1, 1, x.pps, x.cueRTrials, x.cueRUVTrials, x.cueRnoise,
x.lickRTrials, x.lickRUVTrials, x.lickRnoise)
if x.leftTrials == True and x.rightTrials == True:
diffcueL = jmf.findphotodiff(x.cueLTrials, x.cueLUVTrials, x.cueLnoise)
diffcueR = jmf.findphotodiff(x.cueRTrials, x.cueRUVTrials, x.cueRnoise)
ax = plt.subplot(gs1[5, 0])
jmfig.trialsMultShadedFig(ax, [diffcueL, diffcueR],
x.pps,
linecolor=['orange', 'g'],
eventText='Cue')
difflickL = jmf.findphotodiff(x.lickLTrials, x.lickLUVTrials,
x.lickLnoise)
difflickR = jmf.findphotodiff(x.lickRTrials, x.lickRUVTrials,
x.lickRnoise)
ax = plt.subplot(gs1[5, 1])
jmfig.trialsMultShadedFig(ax, [difflickL, difflickR],
x.pps,
linecolor=['orange', 'g'],
eventText='Lick')
plt.savefig('R:/DA_and_Reward/jem64/1706_THPH1-lp/output/' + x.rat +
'.eps',
format='eps',
dpi=1000)
pdf_pages.savefig(photoFig)
def makeBehavFigs(x):
# Initialize figure
behavFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(5, 2)
gs1.update(left=0.10, right=0.9, wspace=0.5, hspace=0.7)
plt.suptitle('Rat ' + x.rat + ': Session ' + x.session)
# ax = plt.subplot(gs1[0, :])
# jmfig.sessionlicksFig(ax, x.lickData)
if x.leftTrials == True:
behavFigsCol(gs1, 0, x.lickDataL, x.cuesL)
if x.rightTrials == True:
behavFigsCol(gs1, 1, x.lickDataR, x.cuesR)
ax = plt.subplot(gs1[4, 0])
jmfig.latencyFig(ax, x)
pdf_pages.savefig(behavFig)
def makeBehavFigs(x, pdf_pages):
# Initialize figure
behavFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(5, 2)
gs1.update(left=0.10, right= 0.9, wspace=0.5, hspace = 0.7)
plt.suptitle('Rat ' + x.rat + ': Session ' + x.session)
ax = plt.subplot(gs1[0, :])
sessionlicksFig(x, ax)
if x.left['exist'] == True:
behavFigsCol(gs1, 0, x.left)
if x.right['exist'] == True:
behavFigsCol(gs1, 1, x.right)
ax = plt.subplot(gs1[4, 0])
jmfig.latencyFig(ax, x)
pdf_pages.savefig(behavFig)
def makePhotoFigs(x):
# Initialize photometry figure
photoFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(4, 2)
gs1.update(left=0.125, right=0.9, wspace=0.4, hspace=0.8)
ax = plt.subplot(gs1[0, :])
x.sessionFig(ax)
if len(x.lickDataA['licks']) > 1:
ax = plt.subplot(gs1[2, 0])
jmfig.trialsFig(ax, x.LiATrials, x.pps, eventText='LickA')
ax = plt.subplot(gs1[2, 1])
jmfig.trialsMultShadedFig(ax, [x.LiATrials, x.LiAUVTrials],
x.pps,
eventText='LickA')
# plt.savefig(x.rat + '.eps', format='eps', dpi=1000)
pdf_pages.savefig(photoFig)
def peakbargraph(ax,
df,
diet,
keys,
peaktype='average',
epoch=[100, 149],
sc_color='w',
colorgroup='control',
ylabel=True,
ylim=[-0.05, 0.1],
grouplabeloffset=0):
if colorgroup == 'control':
bar_colors = ['xkcd:silver', 'w']
else:
bar_colors = [green, light_green]
epochrange = range(epoch[0], epoch[1])
df = df.xs(diet, level=1)
if peaktype == 'average':
a1 = [np.mean(rat[epochrange]) for rat in df[keys[0]]]
a2 = [np.mean(rat[epochrange]) for rat in df[keys[1]]]
ylab = 'Mean Z-Score'
elif peaktype == 'auc':
a1 = [np.trapz(rat[epochrange]) / 10 for rat in df[keys[0]]]
a2 = [np.trapz(rat[epochrange]) / 10 for rat in df[keys[1]]]
ylab = 'AUC'
a = [a1, a2]
x = jmf.data2obj1D(a)
ax, x, _, _ = jmfig.barscatter(x,
paired=True,
barfacecoloroption='individual',
barfacecolor=bar_colors,
scatteredgecolor=[almost_black],
scatterlinecolor=almost_black,
scatterfacecolor=[sc_color],
grouplabel=['Cas', 'Malt'],
grouplabeloffset=grouplabeloffset,
scattersize=50,
xfontsize=6,
ax=ax)
# ax.set_yticks([-0.05,0,0.05, 0.1])
# ax.set_yticklabels(['5%', '0%', '5%', '10%'])
# ax.set_ylim(ylim)
if ylabel:
ax.set_ylabel(ylab)
def ps_myers_figB(df, keys):
data = [df[keys[0]], df[keys[1]], df[keys[2]]]
f, ax = plt.subplots(figsize=(3, 3))
f.subplots_adjust(left=0.2)
colors = color_scheme
jmfig.barscatter(data,
paired=True,
barwidth=.7,
barfacecoloroption='individual',
barfacecolor=colors,
barlabels=keys,
xlim=[0.25, 3.75],
ax=ax)
# ax.set_xlim([0.2,2.8])
ax.set_ylabel('Food consumed (g)')
ax.set_yticks([0, 10, 20, 30])
return f
def makePhotoFigs(x, pdf_pages):
# Initialize photometry figure
photoFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(7, 4)
gs1.update(left=0.125, right= 0.9, wspace=0.4, hspace = 0.8)
plt.suptitle('Rat ' + x.rat + ': Session ' + x.session)
ax = plt.subplot(gs1[0, :])
sessionFig(x, ax, filtered=False)
ax = plt.subplot(gs1[1, :])
sessionFig(x, ax)
if x.left['exist'] == True:
photoFigsCol(gs1, 0, x.pps,
x.left['snips_sipper'],
x.left['snips_licks'])
if x.right['exist'] == True:
photoFigsCol(gs1, 2, x.pps,
x.right['snips_sipper'],
x.right['snips_licks'])
if x.left['exist'] == True and x.right['exist'] == True:
ax = plt.subplot(gs1[6, 0])
jmfig.trialsMultShadedFig(ax, [x.left['snips_sipper']['diff'], x.right['snips_sipper']['diff']],
x.pps,
linecolor=[x.left['color'], x.right['color']],
eventText = 'Sipper')
ax = plt.subplot(gs1[6, 2])
jmfig.trialsMultShadedFig(ax, [x.left['snips_licks']['diff'], x.right['snips_licks']['diff']],
x.pps,
linecolor=[x.left['color'], x.right['color']],
eventText = 'Lick')
# plt.savefig(userhome + '/Dropbox/Python/photometry/output-thph1-lp/' + x.rat + '.eps', format='eps', dpi=1000)
pdf_pages.savefig(photoFig)
def behavFigsCol(gs1, col, lickdata, cues):
ax = plt.subplot(gs1[1, col])
jmfig.licklengthFig(ax, lickdata)
ax = plt.subplot(gs1[2, col])
jmfig.iliFig(ax, lickdata)
ax = plt.subplot(gs1[3, col])
jmfig.cuerasterFig(ax, cues, lickdata['licks'])
def behavFigsCol(gs1, col, side):
ax = plt.subplot(gs1[1, col])
jmfig.licklengthFig(ax, side['lickdata'], color=side['color'])
ax = plt.subplot(gs1[2, col])
jmfig.iliFig(ax, side['lickdata'], color=side['color'])
ax = plt.subplot(gs1[3, col])
jmfig.cuerasterFig(ax, side['sipper'], side['lickdata']['licks'])
def cond_licks_fig(ax, df, diet):
scattersize = 50
if diet == 'NR':
cols = [col['np_cas'], col['np_cas'], col['np_malt'], col['np_malt']]
title = 'Non-restricted'
else:
cols = [col['lp_cas'], col['lp_cas'], col['lp_malt'], col['lp_malt']]
title = 'Protein restricted'
x = [[
df.xs(diet, level=1)['cond1-cas1-licks'],
df.xs(diet, level=1)['cond1-cas2-licks']
],
[
df.xs(diet, level=1)['cond1-malt1-licks'],
df.xs(diet, level=1)['cond1-malt2-licks']
]]
jmfig.barscatter(x,
paired=True,
unequal=True,
barfacecoloroption='individual',
barfacecolor=cols,
scatteredgecolor=['xkcd:charcoal'],
scatterlinecolor='xkcd:charcoal',
grouplabel=[],
barlabels=['1', '2', '1', '2'],
barlabeloffset=0.025,
scattersize=scattersize,
ax=ax)
ax.set_yticks([0, 1000, 2000, 3000, 4000])
ax.set_yticklabels([0, 1000, 2000, 3000, 4000], fontsize=8)
ax.set_title(title, fontsize=10)
def ps_mcc2_fig(df, keys):
data = []
for key in keys:
data.append(df[key])
f, ax = plt.subplots(figsize=(3, 3))
f.subplots_adjust(left=0.2, bottom=0.2)
colors = [color_scheme[2]] * 4
jmfig.barscatter(data,
paired=True,
barwidth=.8,
barfacecoloroption='individual',
barfacecolor=colors,
barlabels=keys,
ax=ax)
ax.set_ylabel('Peanut butter consumed (g)')
ax.set_yticks([0, 1, 2, 3])
ax.set_xlabel('Amount of peanut butter given (g)')
ax.xaxis.set_label_coords(0.5, -0.13)
return f
def summary_subfig_bars(ax, df, keys):
df_NR = df.xs('NR', level=1)
df_PR = df.xs('PR', level=1)
a = [[df_NR[keys[0]], df_NR[keys[1]], df_NR[keys[2]]],
[df_PR[keys[0]], df_PR[keys[1]], df_PR[keys[2]]]]
x = jmf.data2obj2D(a)
cols = ['xkcd:silver', green]
xlabels = ['NR \u2192 PR', 'PR \u2192 NR']
jmfig.barscatter(
x,
paired=True,
barfacecoloroption='individual',
barfacecolor=[cols[0], cols[1], cols[1], cols[1], cols[0], cols[0]],
scatteredgecolor=[almost_black],
scatterlinecolor=almost_black,
scattersize=50,
grouplabel=xlabels,
ax=ax)
def nplp2Dfig(df, factor1, factor2, ax):
dietmsk = df.diet == 'NR'
a = [[df[factor1][dietmsk], df[factor2][dietmsk]],
[df[factor1][~dietmsk], df[factor2][~dietmsk]]]
ax, x, _, _ = jmfig.barscatter(a, paired=True,
barfacecoloroption = 'individual',
barfacecolor = [col['np_cas'], col['np_malt'], col['lp_cas'], col['lp_malt']],
scatteredgecolor = ['xkcd:charcoal'],
scatterlinecolor = 'xkcd:charcoal',
grouplabel=['NR', 'PR'],
grouplabeloffset=0.1,
barlabels=['Cas', 'Malt', 'Cas', 'Malt'],
barlabeloffset=0.025,
scattersize = 80,
ax=ax)
def makeHeatmapFigs(x):
heatmapsFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(2, 1)
gs1.update(left=0.125, right=0.9, wspace=0.4, hspace=0.8)
ax = plt.subplot(gs1[0, 0])
jmfig.heatmapFig(ax, x.LiATrials, pps=x.pps)
data = jmf.nearestevents(x.lickDataA['rStart'], x.lickDataA['licks'])
nLicks = [len(i) for i in data]
sortOrder = np.argsort(nLicks)
ax = plt.subplot(gs1[1, 0])
jmfig.heatmapFig(ax, x.LiATrials, pps=x.pps, sortEvs=nLicks)
sortedData = []
for val in sortOrder:
sortedData.append(data[val])
jmfig.addevent2heatmap(ax, sortedData, pps=x.pps)
pdf_pages.savefig(heatmapsFig)
def makeHeatmapFigs(x):
heatmapsFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(3, 2)
gs1.update(left=0.125, right=0.9, wspace=0.4, hspace=0.8)
ax = plt.subplot(gs1[0, 0])
jmfig.heatmapFig(ax, x.siTrials, pps=x.pps)
data = jmf.nearestevents(x.output.Sir.onset, x.lickDataA['licks'])
jmfig.addevent2heatmap(ax, data, pps=x.pps)
ax = plt.subplot(gs1[0, 1])
data = jmf.nearestevents(x.output.Sir.onset, x.lickDataA['licks'])
sortEv = jmf.findfirst(data)
jmfig.heatmapFig(ax, x.siTrials, pps=x.pps, sortEvs=sortEv)
jmfig.addevent2heatmap(ax, data, pps=x.pps)
ax = plt.subplot(gs1[1, 0])
jmfig.heatmapFig(ax, x.LiATrials, pps=x.pps)
data = jmf.nearestevents(x.lickDataA['rStart'], x.output.Sir.onset)
jmfig.addevent2heatmap(ax, data, pps=x.pps)
pdf_pages.savefig(heatmapsFig)
def makePhotoFigs(x):
# Initialize photometry figure
photoFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(5, 2)
gs1.update(left=0.125, right=0.9, wspace=0.4, hspace=0.8)
ax = plt.subplot(gs1[0, :])
x.sessionFig(ax)
# if x.siTrials != None:
try:
ax = plt.subplot(gs1[1, 0])
jmfig.trialsFig(ax, x.siTrials, x.pps, eventText='Sipper Out')
ax = plt.subplot(gs1[1, 1])
jmfig.trialsShadedFig(ax, x.siTrials, x.pps, eventText='Sipper Out')
except:
print('No no')
if len(x.lickDataA['licks']) > 1:
ax = plt.subplot(gs1[2, 0])
jmfig.trialsFig(ax, x.LiATrials, x.pps, eventText=x.bottleA)
ax = plt.subplot(gs1[2, 1])
jmfig.trialsShadedFig(ax, x.LiATrials, x.pps, eventText=x.bottleA)
if len(x.lickDataB['licks']) > 1:
ax = plt.subplot(gs1[3, 0])
jmfig.trialsFig(ax, x.LiBTrials, x.pps, eventText=x.bottleB)
ax = plt.subplot(gs1[3, 1])
jmfig.trialsShadedFig(ax, x.LiBTrials, x.pps, eventText=x.bottleB)
if x.ATrials == True and x.BTrials == True:
ax = plt.subplot(gs1[4, 0])
jmfig.trialsMultShadedFig(ax, [x.LiATrials, x.LiBTrials], x.pps)
#
# ax = plt.subplot(gs1[3, :])
# rats[i].sessions[j].trialsHeatMap(ax)
# plt.savefig(userhome + '\\Dropbox\\Python\\figformasa.eps', format='eps', dpi=1000)
pdf_pages.savefig(photoFig)
for key in keys:
NR[key] = []
PR[key] = []
dietmsk = df.diet == 'NR'
for x in quinconcs:
quinmsk = df.quin == x
for groupdict, msk in zip([NR, PR], [dietmsk, ~dietmsk]):
groupdict[keys[0]].append(list(df['pref'][msk & quinmsk]))
groupdict[keys[1]].append(list(df['cas_pal'][msk & quinmsk]))
groupdict[keys[2]].append(list(df['malt_pal'][msk & quinmsk]))
#jmfig.barscatter([nr_data, pr_data], transpose=False)
#_,_,_,_ = jmfig.barscatter(PR['pref'])
_, _, _, _ = jmfig.barscatter(PR['caspal'])
#_,_,_,_ = jmfig.barscatter(PR['maltpal'])
#_,_,_,_ = jmfig.barscatter([PR['caspal'], PR['maltpal']], transpose=True, paired=True)
figQPPpref, ax = plt.subplots()
jmfig.barscatter(PR['pref'],
barwidth=0.8,
barfacecolor=['xkcd:kelly green'],
scatteredgecolor=['xkcd:charcoal'],
barlabels=[str(x) for x in quinconcs],
barlabeloffset=2500,
ax=ax)
ax.set_ylabel('Casein preference')
ax.set_xlabel('Quinine concentration (mM)', labelpad=30)
try:
def averagetrace(ax,
df,
diet,
keys,
event='',
fullaxis=True,
colorgroup='control',
ylabel=True):
if colorgroup == 'control':
color = [almost_black, 'xkcd:bluish grey']
errorcolors = ['xkcd:silver', 'xkcd:silver']
else:
color = [green, light_green]
errorcolors = ['xkcd:silver', 'xkcd:silver']
# Selects diet group to plot
df = df.xs(diet, level=1)
# Plots casein and maltodextrin shaded erros
jmfig.shadedError(ax,
df[keys[0]],
linecolor=color[0],
errorcolor=errorcolors[0],
linewidth=2)
jmfig.shadedError(ax,
df[keys[1]],
linecolor=color[1],
errorcolor=errorcolors[1],
linewidth=2)
#ax.legend(['Casein', 'Maltodextrin'], fancybox=True)
if fullaxis == False:
ax.axis('off')
# Adds y scale bar
y = [y for y in ax.get_yticks() if y > 0][:2]
l = y[1] - y[0]
scale_label = '{0:.0f}% \u0394F'.format(l * 100)
ax.plot([50, 50], [y[0], y[1]], c=almost_black)
ax.text(40, y[0] + (l / 2), scale_label, va='center', ha='right')
# Adds x scale bar
y = ax.get_ylim()[0]
ax.plot([251, 300], [y, y], c=almost_black, linewidth=2)
ax.annotate('5 s',
xy=(276, y),
xycoords='data',
xytext=(0, -5),
textcoords='offset points',
ha='center',
va='top')
else:
ax.set_xticks([0, 100, 200, 300])
ax.set_xticklabels(['-10', '0', '10', '20'])
ax.set_xlabel('Time from first lick (s)')
if ylabel:
ax.set_ylabel('Z-Score')
def cond_photo_fig(ax,
df,
diet,
keys,
event='',
color=[almost_black, 'xkcd:bluish grey'],
errorcolors=['xkcd:silver', 'xkcd:silver'],
yerror=True):
if diet == 'NR':
color = [almost_black, 'xkcd:bluish grey']
errorcolors = ['xkcd:silver', 'xkcd:silver']
title = 'Non-restricted'
else:
color = [green, light_green]
errorcolors = ['xkcd:silver', 'xkcd:silver']
title = 'Protein restricted'
df = df.xs(diet, level=1)
# Plots casein and maltodextrin shaded erros
jmfig.shadedError(ax,
df[keys[0]],
linecolor=color[0],
errorcolor=errorcolors[0])
jmfig.shadedError(ax,
df[keys[1]],
linecolor=color[1],
errorcolor=errorcolors[1])
#ax.legend(['Casein', 'Maltodextrin'], fancybox=True)
ax.axis('off')
# Marks location of event on graph with arrow
arrow_y = ax.get_ylim()[1]
ax.plot([100, 150], [arrow_y, arrow_y], color='xkcd:silver', linewidth=3)
ax.annotate(event,
xy=(125, arrow_y),
xytext=(0, 5),
textcoords='offset points',
ha='center',
va='bottom')
# Adds y scale bar
if yerror:
y = [y for y in ax.get_yticks() if y > 0][:2]
l = y[1] - y[0]
scale_label = '{0:.0f}% \u0394F'.format(l * 100)
ax.plot([50, 50], [y[0], y[1]], c=almost_black)
ax.text(40, y[0] + (l / 2), scale_label, va='center', ha='right')
# Adds x scale bar
y = ax.get_ylim()[0]
ax.plot([251, 300], [y, y], c=almost_black, linewidth=2)
ax.annotate('5 s',
xy=(276, y),
xycoords='data',
xytext=(0, -5),
textcoords='offset points',
ha='center',
va='top')
def makePhotoFigs(x):
# Initialize photometry figure
photoFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(5, 2)
gs1.update(left=0.125, right=0.9, wspace=0.4, hspace=0.8)
plt.suptitle('Rat ' + x.rat + ': Session ' + x.session + ' (' +
x.contents + ')')
ax = plt.subplot(gs1[0, :])
x.sessionFig(ax)
ax = plt.subplot(gs1[1, 0])
jmfig.trialsFig(ax,
x.LiTrials,
x.pps,
eventText='First lick',
ylabel='Delta F / F0')
ax = plt.subplot(gs1[1, 1])
jmfig.trialsMultShadedFig(ax, [x.LiUVTrials, x.LiTrials],
x.pps,
eventText='First lick')
ax = plt.subplot(gs1[2, 0])
jmfig.heatmapFig(ax, x.LiTrials, pps=x.pps)
ax = plt.subplot(gs1[2, 1])
data = jmf.nearestevents(x.lickData['rStart'], x.lickData['licks'])
nLicks = [len(i) for i in data]
sortOrder = np.argsort(nLicks)
jmfig.heatmapFig(ax, x.LiTrials, pps=x.pps, sortEvs=nLicks)
if hasattr(x, 'distractedArray'):
ax = plt.subplot(gs1[3, 0])
jmfig.trialsMultShadedFig(
ax,
[x.siUVTrials[x.distractedArray], x.siTrials[x.distractedArray]],
x.pps,
eventText='',
title='Distracted trials (n=%i)' % np.sum(x.distractedArray))
ax = plt.subplot(gs1[3, 1])
jmfig.trialsMultShadedFig(
ax,
[x.siUVTrials[~x.distractedArray], x.siTrials[~x.distractedArray]],
x.pps,
eventText='',
title='Non-distracted trials')
ax = plt.subplot(gs1[4, 0])
jmfig.heatmapFig(ax, x.siTrials, pps=x.pps)
ax = plt.subplot(gs1[4, 1])
jmfig.heatmapFig(ax, x.siTrials, pps=x.pps, sortEvs=x.firstlick)
# plt.savefig(x.rat + '.eps', format='eps', dpi=1000)
pdf_pages.savefig(photoFig)
def makeBehavFigs(x):
# Initialize figure
behavFig = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(5, 2)
gs1.update(left=0.10, right=0.9, wspace=0.5, hspace=0.7)
plt.suptitle('Rat ' + x.rat + ': Session ' + x.session + ' (' +
x.contents + ')')
ax = plt.subplot(gs1[0, :])
jmfig.sessionlicksFig(ax, x.lickData)
ax = plt.subplot(gs1[1, 0])
jmfig.licklengthFig(ax, x.lickData)
ax = plt.subplot(gs1[1, 1])
jmfig.iliFig(ax, x.lickData)
ax = plt.subplot(gs1[2, 0])
jmfig.burstlengthFig(ax, x.lickData, color3rdbar=True)
ax = plt.subplot(gs1[2, 1])
jmfig.ibiFig(ax, x.lickData)
ax = plt.subplot(gs1[3, 0])
jmfig.distractionrasterFig(ax,
x.distractors,
x.lickData['licks'],
post=1.1)
ax.set_title('%i distractors' % len(x.distractors))
ax = plt.subplot(gs1[3, 1])
jmfig.distractionrasterFig(ax,
x.distractors,
x.lickData['licks'],
post=1.1,
sortevents=x.firstlick,
sortdirection='backwards')
ax.set_title('Sorted by first lick after')
ax = plt.subplot(gs1[4, 0])
jmfig.cumulativelickFig(ax, x.firstlick)
#plt.tight_layout()
pdf_pages.savefig(behavFig)
longsnipUV, _ = jmf.snipper(x.dataUV, [EOI],
t2sMap=x.t2sMap,
fs=x.fs,
bins=longbins,
preTrial=longlength / 2,
trialLength=longlength)
f = plt.figure(figsize=(8.27, 11.69), dpi=100)
gs1 = gridspec.GridSpec(3, 2)
gs1.update(left=0.125, right=0.9, wspace=0.4, hspace=0.8)
ax = plt.subplot(gs1[0, :])
#ax.vlines(triallicks, 1, 2)
#ax.set_xlim([EOI-longlength/2, EOI+longlength/2])
ax.hist(triallicksnorm, range(0, longlength, 1))
ax = plt.subplot(gs1[1, :])
jmfig.trialsMultFig(ax, [longsnipUV, longsnip],
longpps,
preTrial=longlength / 2,
scale=60,
eventText='lick')
ax = plt.subplot(gs1[2, 1])
jmfig.trialsMultFig(ax, [shortsnipUV, shortsnip], shortpps)
plt.savefig(userhome + '/Dropbox/Python/photometry/output-thph1-lp/' + x.rat +
'.eps',
format='eps',
dpi=1000)
def photoFigsCol(gs1, col, pps, snips_sipper, snips_licks):
ax = plt.subplot(gs1[2, col])
jmfig.trialsFig(ax, snips_sipper['blue'], pps, noiseindex = snips_sipper['noise'],
eventText = 'Sipper',
ylabel = 'Delta F / F0')
ax = plt.subplot(gs1[3, col])
jmfig.trialsMultShadedFig(ax, [snips_sipper['uv'], snips_sipper['blue']],
pps, noiseindex = snips_sipper['noise'],
eventText = 'Sipper')
ax = plt.subplot(gs1[2, col+1])
jmfig.shadedError(ax, snips_sipper['blue_z'])
ax = plt.subplot(gs1[4, col])
jmfig.trialsFig(ax, snips_licks['blue'], pps, noiseindex=snips_licks['noise'],
eventText = 'First Lick',
ylabel = 'Delta F / F0')
ax = plt.subplot(gs1[5, col])
jmfig.trialsMultShadedFig(ax, [snips_licks['uv'], snips_licks['blue']],
pps, noiseindex=snips_licks['noise'],
eventText = 'First Lick')
ax = plt.subplot(gs1[4, col+1])
jmfig.shadedError(ax, snips_licks['blue_z'])