def igor_to_pandas(path_to_file):
'''This function opens an igor binary file (.ibw), extracts the time
series data, and returns a pandas DataFrame'''
data_raw = IgorIO(filename=path_to_file)
data_neo = data_raw.read_block()
data_neo_array = data_neo.segments[0].analogsignals[0]
data_df = pd.DataFrame(data_neo_array.as_array().squeeze())
return data_df
def igor_to_pandas(file, data_dir):
'''This function opens an igor binary file (.ibw), extracts the time
series data, and returns a pandas DataFrame'''
file_path = os.path.join(data_dir, file)
data_raw = IgorIO(filename=file_path)
data_neo = data_raw.read_block()
data_neo_array = data_neo.segments[0].analogsignals[0]
data_df = pd.DataFrame(data_neo_array.as_array())
return data_df
def neo_IO_function(fullPath_read, out_format, out_folder, file_i=0):
input_format = ''
file_header = {}
# Open and Read the file, add more here if needed
if fullPath_read.lower().endswith("abf"):
r = AxonIO(filename=fullPath_read)
input_format = "abf"
try:
file_header = r.read_header()
except AttributeError:
pass
elif fullPath_read.lower().endswith("wcp"):
r = WinWcpIO(filename=fullPath_read)
input_format = "wcp"
file_header = process_winwcp_header.read_bin_header(fullPath_read)
elif fullPath_read.lower().endswith("ibw"):
r = IgorIO(filename=fullPath_read)
input_format = "ibw"
file_header = process_igor_header.get_header_dict(fullPath_read)
bl = r.read_block(
lazy=False,
cascade=True,
)
if len(bl.segments) > 1:
zfill_max_int = int(ceil(log10(len(bl.segments))) + 1)
# Iterate through each segment and analogsignal list
for i in range(0, len(bl.segments)):
folderName, tail_name = path.split(fullPath_read)
fileName = path.splitext(path.basename(fullPath_read))[0]
if len(bl.segments) <= 1:
txtFileName = fileName + out_format
else:
txtFileName = fileName + "_" + str(i + 1).zfill(
zfill_max_int) + out_format
fullPathtxt = path.join(folderName, out_folder, txtFileName)
analogSignals = bl.segments[i].analogsignals
write_ATF(analogSignals, fullPathtxt, file_header, input_format,
tail_name)
print("Wrote {} to {}".format(tail_name, txtFileName))
file_i += 1
yield file_i
def open_data(path):
"""Take a 'path' to an .ibw file and returns a neo.core.AnalogSignal."""
igor_io = IgorIO(filename=path)
analog_signal = igor_io.read_analogsignal()
return analog_signal
def open_data(path):
"""Take a 'path' to an .ibw file and returns a neo.core.AnalogSignal."""
igor_io = IgorIO(filename=path)
analog_signal = igor_io.read_analogsignal()
return analog_signal
condition = 'TTX+4-AP'
else:
condition = 'control'
# %%
'''
This cell sorts through the cell_id directory and sorts files only noise vm
files into two lits; 1 for drugs and 1 for contorl. Then it opens the correct
file type for the given file_name, and concatenates vm traces if necessary.
Open igor binary file into neo, read neo.block, convert to np.array, convert
to pd.dataframe, concat with empty data frame noise_vmdf and repeat if concat.
'''
os.chdir('../ETC/Data/')
currdf = pd.DataFrame()
temp_curr_raw = IgorIO(filename=file_name)
temp_curr_neo = temp_curr_raw.read_block()
temp_currarray = temp_curr_neo.segments[0].analogsignals[0]
temp_currdf = pd.DataFrame(temp_currarray.as_array())
currdf = pd.concat([currdf, temp_currdf], axis=1)
temp_noise_vm_raw = None
temp_noise_vm_block = None
temp_noise_vmarray = None
os.chdir('../..')
for item in depolarized_sweeps:
noise_vmdf[item] = np.nan
# %%
'''
#Plot all the traces on one plot
plt.figure()