def plotContrasts(self,
y,
colorBy,
compareBy,
groupBy='Temperature',
plot_kwargs=dict()):
resultsDf = self.resultsDf
resultsDf[
'newPlotColumn'] = resultsDf[groupBy] + ' ' + resultsDf[compareBy]
listIdx = tuple(
np.unique(resultsDf[groupBy])[0] + ' ' +
np.unique(resultsDf[compareBy])[::-1])
for i in range(1, len(np.unique(resultsDf[groupBy]))):
listIdx = (listIdx,
tuple(
np.unique(resultsDf[groupBy])[i] + ' ' +
np.unique(resultsDf[compareBy])[::-1]))
print(listIdx)
customPalette = locoPlotters.espressoCreatePalette(resultsDf[colorBy])
setFont('Source Sans Bold', 10)
dabestContrastData = dabest.load(
resultsDf,
x='newPlotColumn', # the default for this test config is to group flies by genotype
y=y,
idx=listIdx,
paired=False)
fig = dabestContrastData.mean_diff.plot(color_col=colorBy,
custom_palette=customPalette,
**plot_kwargs)
if len(np.unique(resultsDf[groupBy])) == 1:
flatListIdxC = [item.split(' ')[1] for item in listIdx]
flatListIdxG = [item.split(' ')[0] for item in listIdx]
else:
flatListIdxC = [item.split(' ')[1] for t in listIdx for item in t]
flatListIdxG = [item.split(' ')[0] for t in listIdx for item in t]
fig.axes[0].set_xticklabels(flatListIdxC, rotation=45, ha="right")
ylim = fig.axes[0].get_ylim()
for i in range(0, len(np.unique(resultsDf[groupBy]))):
# fig.axes[0].text(0.5, ylim[1], flatListIdxG[0], ha="center")
fig.axes[0].text(0.5 + 2 * i,
ylim[1] * 1.1,
flatListIdxG[2 * i],
ha="center")
locoUtilities.espressoSaveFig(fig, y + '_contrast',
self.metaDataDf.Date[0],
self.outputFolder)
return fig
def plotBoundedSpeedLines(self,
colorBy,
row=None,
col=None,
rp='200s',
YLim=None):
setFont('Source Sans Pro', 14)
T = self.countLogDf.iloc[:, 0] / 3600
VV = self.countLogDf.filter(regex='_V')
self.resultsDf['averageSpeed'] = np.nanmean(VV, axis=0)
listOfPlots, gp, custom_palette = locoPlotters.subplotRowColColor(
self.metaDataDf, colorBy, row, col)
nr, nc = listOfPlots[-1][0][0:2]
figure, axes = plt.subplots(nrows=nr + 1,
ncols=nc + 1,
squeeze=False,
figsize=(5 * (nc + 1), 5 * (nr + 1)))
maxYlim = [0] * len(listOfPlots)
plotNames = []
for i in range(0, len(listOfPlots)):
# print(listOfPlots[i])
ro, co = listOfPlots[i][0][0:2]
name = listOfPlots[i][1]
ind = gp[name]
locoPlotters.plotBoundedLine(T,
VV.iloc[:, ind],
ax=axes[ro, co],
c=custom_palette[name[-1]],
resamplePeriod=rp)
maxYlim[i] = plt.gca().get_ylim()[1]
axes[ro, co].set_ylabel('Average Speed (mm/s)')
axes[ro, co].set_xlabel('Time (hour)')
axes[ro, co].set_title(name[0] + ' ' + name[1])
plotNames.append([name[-1]])
for i in range(0, len(listOfPlots)):
ro, co = listOfPlots[i][0][0:2]
if YLim:
axes[ro, co].set_ylim([0, YLim])
else:
axes[ro, co].set_ylim([0, np.max(maxYlim)])
axes[ro, co].legend(plotNames, loc='upper right')
plt.show()
locoUtilities.espressoSaveFig(figure, 'splitTS',
self.metaDataDf.Date[0],
self.outputFolder)
return figure, axes
def plotMeanHeatMaps(self, binSize=0.8, row=None, col=None, verbose=False):
heatMapOutputDir = self.outputFolder
if verbose:
meanHeatmapFig, axes, Hall, resultsDf, smallHeatmapFigs = locoPlotters.espressoPlotMeanHeatmaps(
self, binSize, verbose)
locoUtilities.espressoSaveFig(smallHeatmapFigs,
'smallHeatmapFigs',
self.metaDataDf.Date[0],
heatMapOutputDir,
pngDPI=200)
else:
meanHeatmapFig, axes, Hall, resultsDf = locoPlotters.espressoPlotMeanHeatmaps(
self, binSize, row, col, verbose)
self.resultsDf = resultsDf
self.heatmapMatrix = Hall
self.heatmapAxes = axes
self.meanHeatmapFig = meanHeatmapFig
def plotChamberSmallMultiples(self):
dates = np.unique(self.metaDataDf.Date)
for i in range(0, len(dates)):
print(dates[i])
submeta = self.metaDataDf.loc[self.metaDataDf.Date == dates[i]]
subcount = self.countLogDf.filter(regex=dates[i])
chamberSmallsTrack, axarrT = locoPlotters.putThingsInToChamberSubplot(
subcount, submeta)
chamberSmallsHeat, axarrH = locoPlotters.putThingsInToChamberSubplot(
subcount, submeta, locoPlotters.espressoPlotHeatmap)
OutputDir = self.outputFolder + 'chamberPlots/'
locoUtilities.espressoSaveFig(chamberSmallsTrack,
'chamberSmallsTrack',
dates[i],
OutputDir,
pngDPI=200)
locoUtilities.espressoSaveFig(chamberSmallsHeat,
'chamberSmallsHeat',
dates[i],
OutputDir,
pngDPI=200)
plt.xlabel('Zeitgeber Time (hr)')
plt.fill_between(eightOClocks[2:4],
0, [.1, .1],
color=wes_colors['midnight'],
alpha=0.1)
plt.fill_between(eightOClocks[4:6],
0, [.1, .1],
color=wes_colors['midnight'],
alpha=0.1)
# plt.plot(np.mean(volumePivottedResampled.filter(regex = '_0'), axis = 1), 'r')
# plt.plot(np.mean(volumePivottedResampled.filter(regex = '_1'), axis = 1), 'b')
locoUtilities.espressoSaveFig(f,
'FeedV',
Long1.metaDataDf.Date[0],
Long1.outputFolder,
pngDPI=300,
tp=False)
#%%
durationPivotted = durationDf.pivot_table(values='Duration-ms',
index=pd.to_datetime(
durationDf.index),
columns='FlyID-Choice',
aggfunc='first')
durationPivotted = durationPivotted.fillna(0)
durationPivottedResampled = durationPivotted.resample('600S').sum()
durationPivottedResampled['Seconds'] = (
durationPivottedResampled.index -
durationPivottedResampled.index[0]).total_seconds()
def espressoPlotMeanHeatmaps(espLocoObj,
binSize,
row=None,
col=None,
verbose=False):
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
setFont('Source Sans Pro', 12)
XX = espLocoObj.countLogDf.filter(regex='_X')
YY = espLocoObj.countLogDf.filter(regex='_Y')
H = []
xedges = np.arange(-1, 14, binSize)
yedges = np.arange(-3, 19, binSize)
numlist = list(range(0, len(espLocoObj.metaDataDf)))
smallHeatmapFigs = plt.figure(
num=1, figsize=[5, np.ceil((len(numlist) + 1) / 5) * 0.8])
n = 1
for j in numlist:
# plotFunc(X.iloc[:, j-1], Y.iloc[:, j-1], flyGenotype, colorPalette)
# print(str(j) + ' ' + espLocoObj.metaDataDf.Genotype[j])
X = XX.iloc[:, j]
Y = YY.iloc[:, j]
h, xedges, yedges = np.histogram2d(X[~np.isnan(X)],
Y[~np.isnan(Y)],
bins=[xedges, yedges])
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
if verbose:
plt.subplot(np.ceil((len(numlist) + 1) / 5), 5, n)
plt.imshow(h.T, extent=extent, origin='lower', cmap='bone')
n += 1
H.append(h)
Hall = np.dstack(H)
Hall = Hall / 50 / 60 #originally resampled at 50ms and to convert to minute from second/60
listOfPlots, gp, custom_palette = subplotRowColColor(
espLocoObj.metaDataDf, None, row, col)
nr, nc = listOfPlots[-1][0][0:2]
meanHeatmapFig, axes = plt.subplots(nrows=nr + 1,
ncols=nc + 1,
figsize=(3 * (nc + 1), 4 * (nr + 1)),
squeeze=False)
images = []
for i in range(0, len(listOfPlots)):
# print(listOfPlots[i])
ro, co = listOfPlots[i][0][0:2]
# print(listOfPlots[i][0][0:2])
name = listOfPlots[i][1]
# print(name)
ind = gp[name]
Hmean = np.mean(Hall[:, :, ind], axis=2)
setRCParamsAxesTicks(False)
plt.plot([10, 12], [-1, -1], color='w', linewidth=2)
plt.text(11, -2, '2 mm', color='w', ha='center')
images.append(axes[ro, co].imshow(Hmean.T,
extent=extent,
origin='lower',
cmap='bone'))
axes[ro, co].set_title(name[0] + '\n' + name[1])
axes[ro, co].label_outer()
left = np.sum(np.sum(Hall[4:9, 21:25, :], axis=0), axis=0)
right = np.sum(np.sum(Hall[10:15, 21:25, :], axis=0), axis=0)
bottom = np.sum(np.sum(Hall[4:15, 4:7, :], axis=0), axis=0)
resultsDf = espLocoObj.resultsDf
resultsDf['left'] = left
resultsDf['right'] = right
resultsDf['bottom'] = bottom
resultsDf['LR Preference'] = (resultsDf['left'] - resultsDf['right']) / (
resultsDf['right'] + resultsDf['left'])
resultsDf['TB Preference'] = (
resultsDf['right'] + resultsDf['left'] - resultsDf['bottom']) / (
resultsDf['bottom'] + resultsDf['right'] + resultsDf['left'])
vmin = min(image.get_array().min() for image in images)
vmax = max(image.get_array().max() for image in images)
norm = colors.Normalize(vmin=vmin, vmax=vmax)
for im in images:
im.set_norm(norm)
axins = inset_axes(
axes[-1, -1],
width="5%", # width = 5% of parent_bbox width
height="100%", # height : 50%
loc='lower left',
bbox_to_anchor=(1.05, 0., 1, 1),
bbox_transform=axes[-1, -1].transAxes,
borderpad=0,
)
meanHeatmapFig.colorbar(images[-1], cax=axins, ticks=[0, 5, 10, 15, 20])
def update(changed_image):
for im in images:
if (changed_image.get_cmap() != im.get_cmap()
or changed_image.get_clim() != im.get_clim()):
im.set_cmap(changed_image.get_cmap())
im.set_clim(changed_image.get_clim())
for im in images:
im.callbacksSM.connect('changed', update)
plt.show()
meanHeatmapFileName = 'meanHeatmapFig' + '_' + str(col) + '_' + str(
row) + str(espLocoObj.startMin) + '-' + str(espLocoObj.endMin) + 'min'
locoUtilities.espressoSaveFig(meanHeatmapFig, meanHeatmapFileName,
espLocoObj.metaDataDf.Date[0],
espLocoObj.outputFolder)
# locoUtilities.espressoWriteDictToCSV(espLocoObj.outputFolder+meanHeatmapFileName+'_ConditionsTable.csv', gp)
if verbose:
return meanHeatmapFig, axes, Hall, resultsDf, smallHeatmapFigs
else:
return meanHeatmapFig, axes, Hall, resultsDf