def calc_corr_mouse(dataDB,
mc,
mousename,
nDropPCA=1,
dropChannels=None,
strategy='mean',
estimator='corr',
**kwargs):
# NOTE: zscore channels for each session to avoid session-wise effects
dataRSPLst = dataDB.get_neuro_data({'mousename': mousename},
zscoreDim='rs',
**kwargs)
dataRSP = np.concatenate(dataRSPLst, axis=0)
channelLabels = np.array(dataDB.get_channel_labels())
if strategy == 'mean':
dataRP = np.mean(dataRSP, axis=1)
if nDropPCA is not None:
dataRP = drop_PCA(dataRP, nDropPCA)
if dropChannels is not None:
nChannels = dataRP.shape[1]
channelMask = np.ones(nChannels).astype(bool)
channelMask[dropChannels] = 0
dataRP = dataRP[:, channelMask]
channelLabels = channelLabels[channelMask]
mc.set_data(dataRP, 'rp')
metricSettings = {'havePVal': False, 'estimator': estimator}
rez2D = mc.metric3D('corr', '', metricSettings=metricSettings)
return channelLabels, rez2D
elif strategy == 'sweep':
if nDropPCA is not None:
dataRSP = drop_PCA_3D(dataRSP, nDropPCA)
if dropChannels is not None:
nChannels = dataRSP.shape[2]
channelMask = np.ones(nChannels).astype(bool)
channelMask[dropChannels] = 0
dataRSP = dataRSP[:, :, channelMask]
channelLabels = channelLabels[channelMask]
mc.set_data(dataRSP, 'rsp')
metricSettings = {'havePVal': False, 'estimator': estimator}
rezS2D = mc.metric3D('corr', 's', metricSettings=metricSettings)
rez2D = np.mean(rezS2D, axis=0)
return channelLabels, rez2D
else:
raise ValueError('Unexpected strategy', strategy)
def preprocess_data(dataRSPLst, nDropPCA=None, nBin=None, timeAvg=False):
dataRSP = np.concatenate(dataRSPLst,
axis=0) # Concatenate trials and sessions
if timeAvg:
dataRP = np.mean(dataRSP, axis=1) # Average out time
else:
dataRP = numpy_merge_dimensions(dataRSP, 0, 2)
if nDropPCA is not None:
dataRP = drop_PCA(dataRP, nDropPCA)
if nBin is not None:
dataRP = bin_data(dataRP, nBin,
axis=1) # Bin data separately for each channel
return dataRP if timeAvg else dataRP.reshape(dataRSP.shape)
def plot_corr_consistency_l1_phase(dataDB,
nDropPCA=None,
dropChannels=None,
performance=None,
datatype=None):
mice = sorted(dataDB.mice)
phases = dataDB.get_interval_names()
nPhases = len(phases)
dfColumns = ['mousename', 'trialtype', 'consistency']
dfConsistency = pd.DataFrame(columns=dfColumns)
for iMouse, mousename in enumerate(mice):
for trialType in dataDB.get_trial_type_names():
fnameSuffix = '_'.join(
[datatype, mousename, trialType,
str(performance)])
print(fnameSuffix)
corrLst = []
for intervName in phases:
kwargs = {
'datatype': datatype,
'intervName': intervName,
'trialType': trialType
}
if performance is not None:
kwargs['performance'] = performance
dataRSPLst = dataDB.get_neuro_data({'mousename': mousename},
zscoreDim='rs',
**kwargs)
dataRSP = np.concatenate(dataRSPLst, axis=0)
dataRP = np.mean(dataRSP, axis=1)
# dataRP = zscore(dataRP, axis=0)
if dropChannels is not None:
channelMask = np.ones(dataRP.shape[1]).astype(bool)
channelMask[dropChannels] = 0
dataRP = dataRP[:, channelMask]
if nDropPCA is not None:
dataRP = drop_PCA(dataRP, nDropPCA)
corrLst += [tril_1D(np.corrcoef(dataRP.T))]
# fig, ax = plt.subplots(nrows=nMice, ncols=nMice, figsize=(4 * nMice, 4 * nMice))
pairDict = {}
rezMat = np.zeros((nPhases, nPhases))
for idxNamei, iName in enumerate(phases):
pairDict[iName] = corrLst[idxNamei]
for idxNamej, jName in enumerate(phases):
rezMat[idxNamei][idxNamej] = np.corrcoef(
corrLst[idxNamei], corrLst[idxNamej])[0, 1]
pPlot = sns.pairplot(data=pd.DataFrame(pairDict),
vars=phases,
kind='kde')
prefixPath = 'pics/consistency/corr/phase/dropPCA_' + str(
nDropPCA) + '/scatter/'
make_path(prefixPath)
plt.savefig(prefixPath + 'scatter_' + fnameSuffix + '.svg')
plt.close()
fig, ax = plt.subplots()
imshow(fig,
ax,
rezMat,
haveColorBar=True,
limits=[0, 1],
xTicks=phases,
yTicks=phases,
cmap='jet')
prefixPath = 'pics/consistency/corr/phase/dropPCA_' + str(
nDropPCA) + '/metric/'
make_path(prefixPath)
plt.savefig(prefixPath + 'metric_' + fnameSuffix + '.svg')
plt.close()
avgConsistency = np.round(np.mean(offdiag_1D(rezMat)), 2)
dfConsistency = pd_append_row(
dfConsistency, [mousename, trialType, avgConsistency])
fig, ax = plt.subplots()
dfPivot = pd_pivot(dfConsistency, *dfColumns)
sns.heatmap(data=dfPivot, ax=ax, annot=True, vmin=0, vmax=1, cmap='jet')
prefixPath = 'pics/consistency/corr/phase/dropPCA_' + str(nDropPCA) + '/'
make_path(prefixPath)
fig.savefig(prefixPath + datatype + '_' + str(performance) + '.svg')
plt.close()
def plot_corr_consistency_l1_mouse(
dataDB,
nDropPCA=None,
dropChannels=None,
exclQueryLst=None,
**kwargs): # performances=None, trialTypes=None,
assert 'intervName' in kwargs.keys(), 'Requires phases'
dps = DataParameterSweep(dataDB,
exclQueryLst,
mousename='auto',
intervName='auto',
datatype='auto',
**kwargs)
mice = sorted(dataDB.mice)
nMice = len(mice)
for paramExtraVals, dfTmp in dps.sweepDF.groupby(
dps.invert_param(['mousename', 'datatype', 'intervName'])):
plotExtraSuffix = param_vals_to_suffix(paramExtraVals)
dfColumns = ['datatype', 'phase', 'consistency']
dfConsistency = pd.DataFrame(columns=dfColumns)
for paramVals, dfMouse in dfTmp.groupby(dps.invert_param(['mousename'
])):
plotSuffix = param_vals_to_suffix(paramVals)
print(plotSuffix)
corrLst = []
for idx, row in dfMouse.iterrows():
plotSuffix = '_'.join([str(s) for s in row.values])
print(plotSuffix)
kwargsThis = pd_row_to_kwargs(row,
parseNone=True,
dropKeys=['mousename'])
dataRSPLst = dataDB.get_neuro_data(
{
'mousename': row['mousename']
}, # NOTE: zscore channels for each session to avoid session-wise effects
zscoreDim='rs',
**kwargsThis)
dataRSP = np.concatenate(dataRSPLst, axis=0)
dataRP = np.mean(dataRSP, axis=1)
# dataRP = zscore(dataRP, axis=0)
if dropChannels is not None:
channelMask = np.ones(dataRP.shape[1]).astype(bool)
channelMask[dropChannels] = 0
dataRP = dataRP[:, channelMask]
if nDropPCA is not None:
dataRP = drop_PCA(dataRP, nDropPCA)
corrLst += [tril_1D(np.corrcoef(dataRP.T))]
# fig, ax = plt.subplots(nrows=nMice, ncols=nMice, figsize=(4 * nMice, 4 * nMice))
pairDict = {}
rezMat = np.zeros((nMice, nMice))
for iMouse, mousename in enumerate(mice):
# ax[iMouse][0].set_ylabel(mice[iMouse])
# ax[-1][iMouse].set_xlabel(mice[iMouse])
pairDict[mousename] = corrLst[iMouse]
for jMouse in range(nMice):
# rezMat[iMouse][jMouse] = 1 - rmae(corrLst[iMouse], corrLst[jMouse])
rezMat[iMouse][jMouse] = np.corrcoef(
corrLst[iMouse], corrLst[jMouse])[0, 1]
# cci = offdiag_1D(corrLst[iMouse])
# ccj = offdiag_1D(corrLst[jMouse])
# ax[iMouse][jMouse].plot(cci, ccj, '.')
pPlot = sns.pairplot(data=pd.DataFrame(pairDict),
vars=mice,
kind='kde')
prefixPath = 'pics/consistency/corr/mouse/dropPCA_' + str(
nDropPCA) + '/scatter/'
make_path(prefixPath)
plt.savefig(prefixPath + 'scatter_' + plotSuffix + '.svg')
plt.close()
fig, ax = plt.subplots()
imshow(fig,
ax,
rezMat,
haveColorBar=True,
limits=[0, 1],
xTicks=mice,
yTicks=mice,
cmap='jet')
prefixPath = 'pics/consistency/corr/mouse/dropPCA_' + str(
nDropPCA) + '/metric/'
make_path(prefixPath)
plt.savefig(prefixPath + 'metric_' + plotSuffix + '.svg')
plt.close()
avgConsistency = np.round(np.mean(offdiag_1D(rezMat)), 2)
dfConsistency = pd_append_row(
dfConsistency,
[row['datatype'], row['intervName'], avgConsistency])
fig, ax = plt.subplots()
dfPivot = pd_pivot(dfConsistency, *dfColumns)
sns.heatmap(data=dfPivot,
ax=ax,
annot=True,
vmin=0,
vmax=1,
cmap='jet')
prefixPath = 'pics/consistency/corr/mouse/dropPCA_' + str(
nDropPCA) + '/'
make_path(prefixPath)
fig.savefig(prefixPath + plotExtraSuffix + '.svg')
plt.close()
def hypotheses_calc_plot_info3D(dataDB,
hDict,
datatypes=None,
nBin=4,
nDropPCA=None,
**kwargs):
if datatypes is None:
datatypes = dataDB.get_data_types()
for datatype in datatypes:
for hLabel, (intervName, sources, targets) in hDict.items():
print(hLabel)
dataLabel = '_'.join(['PID', datatype, hLabel, intervName])
# Find combinations of all source pairs
nSrc = len(sources)
sourcePairs = matiter.sample_list(
sources, matiter.iter_g_2D(nSrc)) # Pairs of sources
for s1Label, s2Label in sourcePairs:
for labelTrg in targets:
nMice = len(dataDB.mice)
fig, ax = plt.subplots(nrows=nMice,
ncols=nBin,
figsize=(4 * nBin, 4 * nMice),
tight_layout=True)
for iMouse, mousename in enumerate(sorted(dataDB.mice)):
channelNames = dataDB.get_channel_labels(mousename)
s1Idx = channelNames.index(s1Label)
s2Idx = channelNames.index(s2Label)
targetIdx = channelNames.index(labelTrg)
dataLst = dataDB.get_neuro_data(
{'mousename': mousename},
datatype=datatype,
intervName=intervName,
**kwargs)
dataRSP = np.concatenate(
dataLst, axis=0) # Concatenate all sessions
dataRP = np.mean(dataRSP, axis=1) # Average out time
if nDropPCA is not None:
dataRP = drop_PCA(dataRP, nDropPCA)
dataBin = bin_data(
dataRP, nBin,
axis=1) # Binarize data over channels
h3d = np.histogramdd(
dataBin[:, [targetIdx, s1Idx, s2Idx]],
bins=(nBin, nBin, nBin))[0]
h3d /= np.sum(h3d) # Normalize
for iTrgBin in range(nBin):
img = ax[iMouse][iTrgBin].imshow(h3d[iTrgBin],
vmin=0,
vmax=10 / nBin**3,
cmap='jet')
ax[iMouse][iTrgBin].set_ylabel(
s1Label) # First label is rows a.k.a Y-AXIS!!!
ax[iMouse][iTrgBin].set_xlabel(s2Label)
ax[iMouse][iTrgBin].set_title(labelTrg + '=' +
str(iTrgBin))
imshow_add_color_bar(fig, ax[iMouse][iTrgBin], img)
plt.savefig('pics/info3D_' + dataLabel + '_' + s1Label +
'_' + s2Label + '_' + labelTrg + '.pdf')
plt.close(fig)