# abffile = "2019_08_08_0037.abf"
masterdf = pd.read_excel(os.path.join(analysisdatapath,masterfile),sheet_name=0,header=0,use_cols=4)
print(masterdf)
yy = []
for row in range(0,masterdf.shape[0]):
abfname = str(masterdf.loc[row,"abffile"])
cell = str(masterdf.loc[row,"cell"])
date = str(masterdf.loc[row,"date"])
nsweeps = int(masterdf.loc[row,"nsweeps"])
goodsweeps = [int(item) for item in str(masterdf.loc[row,"sweeps"]).split(',')]
badsweeps = [elm for elm in np.arange(0,nsweeps) if np.isin(elm,goodsweeps,invert=True)]
print("goodsweeps: {}".format(goodsweeps))
print("badsweeps: {}".format(badsweeps))
abfnamefull = os.path.join(rawdatapath,date,cell,abfname)
print(abfnamefull)
ephys = EphysClass(abfnamefull,loaddata=True,badsweeps=badsweeps)
# ephys.show([],[]) # [channels],[sweeps]
ephys.extract_stim_props(trgchannel)
ephys.extract_res_props(reschannel,trgchannel)
ephys.extract_indices_holdingsteps(vclampchannel,NstepsNeg=1,NstepsPos=1)
ephys.info()
t,y = ephys.extract_response(reschannel,trgchannel,tpre=-0.0,tpost=0.15)
yy.append(y)
# ---------------
yy = np.concatenate(yy,axis=1)
print(yy.shape)
yya = []
# align traces to peak
ipeaks = np.zeros(yy.shape[1],dtype=np.int)
for icol in np.arange(0,yy.shape[1]):
ipeaksall,_ = find_peaks(-yy[:,icol],prominence=(0.5,None),width=(100,None),height=(15,None))
# extract date
expdate = re.search('[0-9]{8,8}?', abffile)[0]
# extract spineid
spineid = re.sub("\/", "_",
re.search('\/[0-9]{8,8}?\/.*?\/',
abffile)[0][1:-1]) + "S" + str(spinecount)
# filename
# abffname = re.search('[^/]+.\.*$',abffile)[0] # filename without extension
# abffname = re.sub(' ','_',abffname)
print('ABF filename:\t', abffile)
print('Expdate:\t', expdate)
print('Spineid:\t', spineid)
print('Clamp:\t', clamp)
if (not re.search(".*CC.*", clamp)):
continue
ephys1 = EphysClass(abffile, loaddata=True)
ephys1.get_stimprops(trgchannel)
ephys1.get_signal_props(reschannel, trgchannel)
sweeps = np.setdiff1d(np.arange(0, ephys1.nsweeps), badsweeps)
print("Bad sweeps:\t", badsweeps)
print("Sweeps:\t", sweeps)
t = np.arange(0, timewindow, ephys1.si)
y = ephys1.extract_response(reschannel, trgchannel, timewindow, min_isi)
plotcolors = ['red', 'green', 'blue', 'gray']
if ((len(y) > 0) and len(sweeps) > 0):
y = y - y[0, :]
print(y.shape)
yy.append(y[:, sweeps])
# fh = plt.figure()
# ah1 = plt.subplot(111)
# ah1.set_title(re.search("[^/]+.abf",abffile)[0])
# generate template trace
template_fileids = list(
dfspine[dfspine["isi"].isin(isis_template)]["fileid"])
template = []
for itfile in range(len(template_fileids)):
templateid = template_fileids[itfile]
fileid = "".join((list(dfspine.loc[dfspine["fileid"] == templateid,
"fileid"].astype(str))[0], ".abf"))
cellfolder = list(dfspine.loc[dfspine["fileid"] == templateid,
"cellfolder"].astype(str))[0]
expdatefolder = list(
dfspine.loc[dfspine["fileid"] == templateid,
"expdatefolder"].astype(int).astype(str))[0]
print(templateid, fileid, cellfolder, expdatefolder)
filename = os.path.join(path_ephysdata, expdatefolder, cellfolder,
fileid)
print(filename)
ephys = EphysClass(filename, loaddata=True)
print(ephys.goodsweeps)
ephys.extract_indices_holdingsteps(clampchannel, 1, 1)
ephys.extract_stim_props(stimchannel)
ephys.extract_res_props(reschannel, stimchannel)
print(ephys.stimprop)
# input()
# ephys.show([0,1,3],[]) # [channels],[sweeps]
ephys.extract_response(reschannel, stimchannel, -0.005, 0.06)
# fh = plt.figure()
# ah = fh.add_subplot(111)
# ah.plot(t,y)
# plt.show()
cellfolder = str(masterdf.loc[row, "cellfolder"])
ephysfile = str(masterdf.loc[row, "ephysfile"])
abffile = os.path.join(ephysdatapath, datefolder, cellfolder, ephysfile)
# ophysfile = os.path.join(ophysdatapath,datefolder,cellfolder,str(masterdf.loc[row,"imagefile"]))
print(ephysfile)
neuronid = int(masterdf.loc[row, "neuronid"])
dendriteid = int(masterdf.loc[row, "dendriteid"])
badsweeps = masterdf.loc[row, "badsweeps"]
if (math.isnan(badsweeps)):
badsweeps = []
print("badsweeps: ", badsweeps)
spineid = masterdf.loc[row, "spineid"]
clampmode = str(masterdf.loc[row, "clampmode"])
stimpower = masterdf.loc[row, "laserstimpower"]
dob = masterdf.loc[row, "dob"]
ephys = EphysClass(abffile, loaddata=True, badsweeps=badsweeps)
ephys.info()
ephys.extract_stim_props(trgch)
ephys.extract_res_props(resch, trgch)
# input()
if ((clampmode == "vc") or (selectfile == 0)):
continue
if (clampmode == "cc"):
ephys.get_clampprop(resch, clampch)
tau, accessres = ephys.estimate_tau_access_res_from_epsp(
resch, clampch)
peaks, fh, ah = ephys.deconvolve_crop_reconvolve(resch, clampch, trgch)
isi = ephys.isi
nstims = int(ephys.nstim)
nsweeps = int(ephys.nsweeps)
masterdf = pd.read_excel(masterdfpath,header=0,use_cols=10)
masterdf["datefolder"] = masterdf["datefolder"].astype(int)
masterdf["date"] = pd.to_datetime(masterdf["datefolder"],format="%Y%m%d")
masterdf["dob"] = pd.to_datetime(masterdf["dob"],format="%Y%m%d")
masterdf["age"] = (masterdf["date"]-masterdf["dob"]).dt.days
print(masterdf)
channels = ['ImLEFT', 'IN1', 'IN7']
resch = "ImLEFT"
clampch = "IN1"
trgch = "IN7"
for row in range(0,masterdf.shape[0]):
# for row in range(20,30):
datefolder = str(masterdf.loc[row,"datefolder"])
cellfolder = str(masterdf.loc[row,"cellfolder"])
ephysfile = os.path.join(ephysdatapath,datefolder,cellfolder,str(masterdf.loc[row,"ephysfile"]))
ophysfile = os.path.join(ophysdatapath,datefolder,cellfolder,str(masterdf.loc[row,"imagefile"]))
print(ephysfile,"\n",ophysfile)
neuronid = masterdf.loc[row,"neuronid"]
branchid = masterdf.loc[row,"neuronid"]
spineid = masterdf.loc[row,"neuronid"]
clampmode = masterdf.loc[row,"clampmode"]
stimmode = masterdf.loc[row,"stim-mode"]
ephys = EphysClass(ephysfile,loaddata=True)
# ephys.info()
ephys.extract_stim_props(trgch)
ephys.extract_res_props(resch,trgch)
if(clampmode == "cc"):
# ephys.estimate_tau_access_res_from_epsp(resch,clampch)
ephys.deconvolve_crop_reconvolve(resch,clampch,trgch)
input()
print("sweep labels: ",labels)
# extract successes alone (can contain '1','0','b' for success, failure and bad
isuccess = [label == '1' for label in labels]
# get success sweeps
success_sweeps = np.arange(1,len(labels)+1)[isuccess]
print("success sweeps: ",success_sweeps)
else:
print("!!!! Label file not found !!!!!!")
break
try:
badsweeps = np.array([float(elm)-1 for elm in badsweeps],dtype=np.int)
except:
badsweeps = None
print("Opening: ",abffullpath)
print(badsweeps)
ephys = EphysClass(abffullpath,loaddata=True,badsweeps=badsweeps,vclamp = int(vclamp), cclamp = None,fid=fileid)
ephys.extract_stim_props(trgchannel)
ephys.extract_res_props(reschannel,trgchannel)
ephys.extract_indices_holdingsteps(vclampchannel,NstepsNeg=1,NstepsPos=1)
ephys.info()
# ephys.show([0],[]) # [channels],[sweeps]
# create folder for each cell
figpath = mainpath + os.path.splitext(os.path.basename(abfname))[0] +'/'
print("Figure path: ",figpath)
if(not os.path.exists(figpath)):
os.mkdir(figpath)
# ----------------
lags,betas,peaks = ephys.findpeaks_template_match(reschannel,trgchannel,success_sweeps,figpath)
nsweeps = len(ephys.sweeps)
isi = ephys.isi
for isweep in np.arange(0,betas.shape[0]):
continue
# extract date
# expdate = datetime.datetime.strptime(re.search('[0-9]{8,8}?',abffile)[0],"%Y%m%d")
expdate = re.search('[0-9]{8,8}?', abffile)[0]
# extract spineid
spineid = re.sub("\/", "_",
re.search('\/[0-9]{8,8}?\/.*?\/',
abffile)[0][1:-1]) + "S" + str(spinecount)
# abffname = re.search('[^/]+.\.*$',abffile)[0] # filename without extension
# abffname = re.sub(' ','_',abffname)
print('ABF filename:\t', abffile)
print('Expdate:\t', expdate)
print('Spineid:\t', spineid)
print('Clamp:\t', clamp)
# create ephys object
ephys1 = EphysClass(abffile, loaddata=True, badsweeps=badsweeps)
ephys1.info()
ephys1.get_stimprops(trgchannel)
ephys1.get_signal_props(reschannel, trgchannel)
fh = None
fht = None
# skip if no good sweeps found
if (len(ephys1.sweeps) == 0):
print('No good sweeps in the file!')
continue
if (re.search(".*CC.*", clamp)):
ephys1.seriesres_currentclamp(reschannel, clampchannel)
[tlags, ypeaks, fh, ah] = ephys1.find_peaks(reschannel,
trgchannel,
plotdata=False,
peakdir="+")
from ephys_class import EphysClass
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
import os
import re
datapath="/home/anup/gdrive-beiquelabdata/Ephys Data/Olympus 2P/Anup/"
expdate = "20201218"
cellid = "C1"
channels = ['ImLEFT', 'IN1', 'IN7']
reschannel = "ImLEFT"
clampchannel = "IN1"
print(os.listdir(datapath))
print(os.listdir(os.path.join(datapath,expdate)))
files = os.listdir(os.path.join(datapath,expdate,cellid))
abffiles = [f for f in files if re.search(".*.abf",f)]
abffiles.sort()
print(abffiles)
for abffile in abffiles:
print("Opening %s"%abffile)
ephys = EphysClass(os.path.join(datapath,expdate,cellid,abffile),loaddata=True)
EphysClass.seriesres_voltageclamp(ephys,reschannel,clampchannel)
ephys.info()
ephys.show([0,1,3],[]) # [channels],[sweeps]
# input()
from ephys_class import EphysClass
import os
import re
datapath = "/home/pi/ephys/"
expdate = "2021-02-16"
cellid = "C1"
# channels = ['ImLEFT', 'IN1', 'IN7'] # olympus Rig
channels = ['ImRightP', 'VmRightS', 'IN6'] # LSM880
reschannel = [channels[1]]
clampchannel = [channels[0]]
print(os.listdir(datapath))
print(os.listdir(os.path.join(datapath, expdate)))
files = os.listdir(os.path.join(datapath, expdate))
abffiles = [f for f in files if re.search(".*.abf", f)]
abffiles.sort()
print(abffiles)
for abffile in abffiles:
print("Opening %s" % abffile)
ephys = EphysClass(os.path.join(datapath, expdate, abffile), loaddata=True)
# EphysClass.seriesres_voltageclamp(ephys,'ImRightP',clampchannel)
# ephys.info()
ephys.show([0, 2], []) # [channels],[sweeps]
# input()
exp = str(masterdf.loc[row, "exp"])
badsweeps = str(masterdf.loc[row, "badsweeps"]).split(",")
fileid = re.search(
".*[^.abf]", abfname
)[0] + '_' + "neuron" + neuron + '_' + "vc" + vclamp + '_' + "exp" + exp
print('fileid: ', fileid)
try:
badsweeps = np.array([float(elm) - 1 for elm in badsweeps],
dtype=np.int)
except:
badsweeps = None
# ------------------
print("Opening: ", abffullpath)
ephys = EphysClass(abffullpath,
loaddata=True,
badsweeps=None,
vclamp=int(vclamp),
cclamp=None,
fid=fileid)
ephys.extract_stim_props(trgchannel)
ephys.extract_res_props(reschannel, trgchannel)
ephys.extract_indices_holdingsteps(vclampchannel, NstepsNeg=1, NstepsPos=1)
ephys.info()
# ephys.show([],[1,2,3,4,5]) # [channels],[sweeps]
t, y = ephys.extract_response(reschannel,
trgchannel,
-0.01,
0.1,
min_isi=0)
# fh = plt.figure()
# ah = fh.add_subplot(111)
# ah.plot(t,y)