def expand_on_bends(df_trl,
Fs=500,
tPre_ms=100,
bendThr=10,
minLat_ms=5,
maxGap_ms=100):
"""Takes dataframe where each row contains single trial information and
expands such that each row contains single bend information
Parameters
----------
df_trl: pandas dataframe, (nTrlsInTotal, nVariables)
Fs: int
Sampling frequency when collecting data(images)
nPre_ms: scalar
"""
import apCode.SignalProcessingTools as spt
minPkDist = int((10e-3) * Fs)
nPre = tPre_ms * 1e-3 * Fs
minLat = minLat_ms * 1e-3 * Fs
maxGap = maxGap_ms * 1e-3 * Fs
df_bend = []
for iTrl in np.unique(df_trl.trlIdx_glob):
df_now = df_trl.loc[df_trl.trlIdx_glob == iTrl]
y = df_now.iloc[0]['tailAngles'][-1]
y = spt.chebFilt(y, 1 / Fs, (5, 60), btype='bandpass')
pks = spt.findPeaks(y,
thr=bendThr,
thrType='rel',
pol=0,
minPkDist=minPkDist)[0]
if len(pks) > 3:
dpks = np.diff(pks)
tooSoon = np.where(pks < (nPre + minLat))[0]
tooSparse = np.where(dpks > maxGap)[0] + 1
inds_del = np.union1d(tooSoon, tooSparse)
pks = np.delete(pks, inds_del, axis=0)
if len(pks) > 3:
nBends = len(pks)
bendIdx = np.arange(nBends)
bendSampleIdxInTrl = pks
bendAmp = y[pks]
bendAmp_abs = np.abs(bendAmp)
bendAmp_rel = np.insert(np.abs(np.diff(bendAmp)), 0,
np.abs(bendAmp[0]))
bendInt_ms = np.gradient(pks) * (1 / Fs) * 1000
onset_ms = (pks[0] - nPre + 1) * (1 / Fs) * 1000
else:
nBends = 0
bendIdx, bendAmp, bendAmp_abs, bendAmp_rel, bendInt_ms =\
[np.nan for _ in range(5)]
bendsampleIdxInTrl, onset_ms = [np.nan for _ in range(2)]
dic = dict(trlIdx_glob=iTrl,
nBends=nBends,
bendIdx=bendIdx,
bendSampleIdxInTrl=bendSampleIdxInTrl,
bendAmp=bendAmp,
bendAmp_abs=bendAmp_abs,
bendAmp_rel=bendAmp_rel,
bendInt_ms=bendInt_ms,
onset_ms=onset_ms)
df_now = pd.DataFrame(dic)
df_bend.append(df_now)
df_bend = pd.concat(df_bend, ignore_index=True)
return pd.merge(df_trl, df_bend, on='trlIdx_glob')
#%% Save img stack in specified dir
# saveDir = r'S:\Avinash\Ablations and behavior\GrpData\Session 20170121\blah'
saveDir = os.path.join(inputDir, 'proc')
imgName = 'K-means clustering of Alx cells_clstrs unord' + '.tif'
tff.imsave(os.path.join(saveDir, imgName),
np.transpose(imgStack, [0, 3, 1, 2]))
#%% REGRESSION
import apCode.AnalyzeEphysData as aed
# Chose # 2 after visual inspection (Note: Run Kmeans with k-means++
# init and unordered clusters before runing this)
centroid_M = centroids[0, :]
dt = data['time'][2] - data['time'][1]
centroid_M = spt.zscore(spt.chebFilt(centroid_M, dt, 0.01, btype='high'))
thr_Ca = volt.getGlobalThr(centroid_M)
pks = spt.findPeaks(centroid_M, thr=thr_Ca, minPkDist=30)
plt.figure(figsize=(16, 6))
plt.style.use('dark_background')
plt.subplot(131)
plt.plot(data['time'], centroid_M)
plt.plot(data['time'][pks[0]], centroid_M[pks[0]], 'ro')
plt.xlim(100, 500)
plt.xlabel('Time (s)')
plt.ylabel('dF/F')
plt.title('Some stim-elicited responses in chosen centroid'
'\n Shown peaks used to average responses')
centroid_M_seg = aed.SegmentDataByEvents(centroid_M, pks[0], 20, 50, axis=0)
trlLens = np.array([len(trl) for trl in centroid_M_seg])
#%% Save img stack in specified dir
# saveDir = r'S:\Avinash\Ablations and behavior\GrpData\Session 20170121\blah'
saveDir = os.path.join(inputDir, 'proc')
imgName = 'K-means clustering of Alx cells_clstrs unord' + '.tif'
tff.imsave(os.path.join(saveDir,imgName),np.transpose(imgStack,[0,3,1,2]))
#%% REGRESSION
import apCode.AnalyzeEphysData as aed
# Chose # 2 after visual inspection (Note: Run Kmeans with k-means++
# init and unordered clusters before runing this)
centroid_M = centroids[0,:]
dt = data['time'][2] - data['time'][1]
centroid_M = spt.zscore(spt.chebFilt(centroid_M,dt,0.01,btype='high'))
thr_Ca = volt.getGlobalThr(centroid_M)
pks = spt.findPeaks(centroid_M,thr=thr_Ca, minPkDist=30)
plt.figure(figsize = (16,6))
plt.style.use('dark_background')
plt.subplot(131)
plt.plot(data['time'],centroid_M)
plt.plot(data['time'][pks[0]],centroid_M[pks[0]],'ro')
plt.xlim(100,500)
plt.xlabel('Time (s)')
plt.ylabel('dF/F')
plt.title('Some stim-elicited responses in chosen centroid'
'\n Shown peaks used to average responses')
centroid_M_seg = aed.SegmentDataByEvents(centroid_M,pks[0],20,50,axis =0)
trlLens = np.array([len(trl) for trl in centroid_M_seg])
