def main():
"""Runs functions in fpho_setup, processes config.yml
Parameters
----------
config.yml
To use this driver, update the config.yml file, then
run the following bash command:
python fpho_config.py --config config.yml
Returns
-------
Pandas dataframe of parsed fiber photometry data
Writes an output CSV/XLXS to specified file name
Outputs specified plots and analysis
"""
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, required=True)
args = parser.parse_args()
# Opens and reads config file to process
f = open(args.config, 'r')
config = yaml.load(f, Loader=yaml.FullLoader)
f.close()
# Generate the dataframe with data
fpho_df = fpho_setup.import_fpho_data(
input_filename=(config['input_filename']),
output_filename=(config['output_filename']),
n_fibers=(config['n_fibers']),
f1greencol=(config['f1greencol']),
f2greencol=(config['f2greencol']),
animal_ID=(config['animal_ID']),
exp_date=(config['exp_date']),
exp_desc=(config['exp_desc']),
write_xlsx=(config['write_xlsx']))
# Plot raw signal if specified
if config['plot_raw_signal'] is True:
fpho_setup.raw_signal_trace(fpho_df, config['output_filename'])
# Plots isosbestic fit if specified
if config['plot_iso_fit'] is True:
fpho_setup.plot_isosbestic_norm(fpho_df, config['output_filename'])
# Plots fitted exponent if specified
if config['plot_fit_exp'] is True:
fpho_setup.plot_fitted_exp(fpho_df, config['output_filename'])
# Imports behavior data if specified
behaviorData = pd.DataFrame()
if config['import_behavior'] is True:
behaviorData = behavior_setup.import_behavior_data(
config['BORIS_file'], config['timestamp_file'])
# Plots z-score analysis of behavior if specified
if config['plot_zscore'] is True:
behavior_setup.plot_zscore(behaviorData, config['output_filename'])
def test_raw_signal_trace(self):
df_test = fpho_setup.import_fpho_data(input_filename='Python/TestData'
'/1FiberTesting.csv',
output_filename='my_file_name',
n_fibers=1,
f1greencol=3,
animal_ID='vole1',
exp_date='2020-09-01',
exp_desc="testing",
f2greencol=None,
write_xlsx=False)
fpho_setup.raw_signal_trace(fpho_dataframe=df_test,
output_filename='testing_unit',
data_row_index=0)
self.assertTrue(path.exists('testing_unit_RawSignal_f1Red.png'))
def test_raw_signal_trace_errors(self):
df_test = fpho_setup.import_fpho_data(input_filename='Python/TestData'
'/1FiberTesting.csv',
output_filename='my_file_name',
n_fibers=1,
f1greencol=3,
animal_ID='vole1',
exp_date='2020-09-01',
exp_desc="testing",
f2greencol=None,
write_xlsx=False)
with self.assertRaises(SystemExit) as cm:
# Use the user input: userinputfailure
fpho_setup.raw_signal_trace(fpho_dataframe=df_test,
output_filename='testing_unit.png',
data_row_index=0)
self.assertEqual(cm.exception.code, 1)
def main():
"""Runs functions in fpho_setup, processes config.yml
Parameters
----------
config.yml
To use this driver, update the config.yml file, then
run the following bash command:
python fpho_config.py --config config.yml
Returns
-------
Pandas dataframe of parsed fiber photometry data
Writes an output CSV/XLXS to specified file name
Outputs specified plots and analysis
"""
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, required=True)
args = parser.parse_args()
# Opens and reads config file to process
f = open(args.config, 'r')
config = yaml.load(f, Loader=yaml.FullLoader)
f.close()
#initializes a dictionary, that will hold a dataframe for each fiberphotometry file
all_data = {}
# Generate the dataframe with new data
if config['import_new'] is True:
fpho_df = fpho_setup.import_fpho_data(
input_filename=(config['input_filename']),
output_filename=(config['output_filename']),
f1greencol=(config['f1greencol']),
f1redcol=(config['f1redcol']),
f2greencol=(config['f2greencol']),
f2redcol=(config['f2redcol']),
animal_ID=(config['animal_ID']),
exp_date=(config['exp_date']),
exp_desc=(config['exp_desc']))
if config['write_xlsx'] is True:
output_xlsx = config['output_filename'] + '_Summary.csv'
if path.exists(output_xlsx):
answer = input('Are you sure you want to overwrite' +
output_xlsx + '(y or n)')
if answer != 'y':
print('Did not overwrite' + output_xlsx)
print(
'Change output_filename or write_xlsx value and rerun')
sys.exit()
fpho_df.to_csv(output_xlsx, index=False)
print('Summary CSV file has been saved to ' +
config['output_filename'] + '_Summary.csv')
#Imports behavior data associated with the newly imported file if specified
if config['import_behavior'] is True:
fpho_df = behavior_setup.import_behavior_data(
config['BORIS_file'], fpho_df)
if config['write_xlsx'] is True:
output_xlsx = key
if path.exists(output_xlsx):
answer = input('Are you sure you want to overwrite' +
output_xlsx + '(y or n)')
if answer != 'y':
print('Did not overwrite' + output_xlsx)
print(
'Change output_filename or write_xlsx value and rerun'
)
sys.exit()
else:
fpho_df.to_csv(output_xlsx, index=False)
print('Behavior data has been added to the summary file')
all_data['output_xlsx'] = fpho_df
#reads in one or more dataframes and assigns them to a dictionary using the file name as the key
if config['reload_data'] is True:
for file in config['reload_filenames']:
fpho_df = pd.read_csv(file)
all_data[file] = fpho_df
print('data was reloaded from', file)
#Runs plots and analyses as specified on all fiberpho data sets
for key in all_data:
fpho_df = all_data[key]
output_xlsx = key
# Plot raw signal if specified
if config['plot_raw_signal'] is True:
fpho_setup.raw_signal_trace(fpho_df, key)
# Normalizes signals of interest and plots normalization process
if config['normalize_data'] is True:
fpho_df = fpho_setup.plot_fitted_exp(
fpho_df,
key,
signals=config['all_signals'],
references=config['all_references'])
if config['write_xlsx'] is True:
fpho_df.to_csv(output_xlsx, index=False)
print(key, 'has been updated to include normalized data')
if config['plot_behavior'] is True:
behavior_setup.plot_behavior(fpho_df, key, config['all_signals'])
#Plot the discrete fourier transform of you're channels of interest
if config['fourier_transform'] is True:
correlation_setup.plot_FFT(fpho_df, key, config['channels'])
# Plots z-score analysis of behavior if specified
if config['plot_zscore'] is True:
behavior_setup.plot_zscore(fpho_df, key, config['all_signals'],
config['zscore_behs'])
if config['within_trial_pearsons'] is True:
print(
correlation_setup.within_trial_pearsons(
fpho_df, key, config['channels']))
if config['behavior_specific_pearsons'] is True:
print(
correlation_setup.behavior_specific_pearsons(
fpho_df, key, config['channels'], config['behaviors']))
all_data[key] = fpho_df
def main():
"""Runs functions in fpho_setup, asks for what analysis to perform
Parameters
----------
input_filename: string
Name of input file containing fiber photometry data
output_filename: string
Name you'd like for the output CSV file. Should include
file path if different than current file
animal_ID: int
Number of the animal corresponding to fluoresence data
exp_date: YYYY-MM-DD
Date of exp/date data was gathered
exp_desc: string
Brief explantation of experiment/what
type of information data contains
plot_raw_signal: boolean
optional plot of raw data for a
particular fiber
plot_iso_fit: boolean
optional isosbestic plot
plot_fit_exp: boolean
optional fitted exponent plot
Returns
-------
Pandas dataframe of parsed fiber photometry data
Optional output:
- Excel file of dataframe (.xlsx)
- Raw signal plot (.png)
- Isobestic plot (.png)
- Fitted exponent plot (.png)
"""
# use with config.txt
# run bash command: python fpho_driver.py @config.txt
parser = argparse.ArgumentParser(fromfile_prefix_chars='@')
parser = argparse.ArgumentParser(
description=('Parse fiber photometry data' +
'to prepare for analyses'))
parser.add_argument('--input_filename',
dest='input_filename',
type=str,
required=True,
help='Name of input file as string')
parser.add_argument('--output_filename',
dest='output_filename',
type=str,
required=True,
help='Name for output file as string')
parser.add_argument('--n_fibers',
dest='n_fibers',
type=int,
required=True,
help='Number of fibers in input data')
parser.add_argument('--f1greencol',
dest='f1greencol',
type=int,
required=True,
help='column index of f1green')
parser.add_argument('--f2greencol',
dest='f2greencol',
type=int,
default=None,
required=False,
help='column index of f1green')
parser.add_argument('--animal_ID',
dest='animal_ID',
type=int,
required=True,
help='Animal number for fluroesence data')
parser.add_argument('--exp_date',
dest='exp_date',
type=str,
required=True,
help='Date of experiment as YYYY-MM-DD')
parser.add_argument('--exp_desc',
dest='exp_desc',
type=str,
required=True,
help='Brief description for context')
parser.add_argument('--write_xlsx',
dest='write_xlsx',
type=bool,
default=False,
help="add --write_xlsx to command line")
parser.add_argument('--plot_raw_signal',
dest='plot_raw_signal',
action='store_true',
help='add --plot_raw_signal to command line')
parser.add_argument('--plot_iso_fit',
dest='plot_iso_fit',
action='store_true',
help='add --plot_iso_fit to command line')
parser.add_argument('--plot_fit_exp',
dest='plot_fit_exp',
action='store_true',
help='add --plot_fit_exp to command line')
args = parser.parse_args()
# Generate the dataframe with data
fpho_df = fpho_setup.import_fpho_data(input_filename=args.input_filename,
output_filename=args.output_filename,
write_xlsx=args.write_xlsx,
n_fibers=args.n_fibers,
f1greencol=args.f1greencol,
f2greencol=args.f2greencol,
animal_ID=args.animal_ID,
exp_date=args.exp_date,
exp_desc=args.exp_desc)
# Plot raw signal if specified in command line
if args.plot_raw_signal:
sys.stdin = open("Python/fpho_ftest_driver_input.txt")
fpho_setup.raw_signal_trace(fpho_dataframe=fpho_df,
output_filename=args.output_filename)
# Prints isosbestic fit if specified
if args.plot_iso_fit:
sys.stdin = open("Python/fpho_ftest_driver_input.txt")
fpho_setup.plot_isosbestic_norm(fpho_dataframe=fpho_df,
output_filename=args.output_filename)
# Prints fitted exponent if specified
if args.plot_fit_exp:
sys.stdin = open("Python/fpho_ftest_driver_input.txt")
fpho_setup.plot_fitted_exp(fpho_dataframe=fpho_df,
output_filename=args.output_filename)
def main():
"""Runs functions in fpho_setup, asks for what analysis to perform
Parameters
----------
input_filename: string
Name of input file containing fiber photometry data
output_filename: string
Name you'd like for the output CSV file. Should include
file path if different than current file
animal_ID: int
Number of the animal corresponding to fluoresence data
exp_date: YYYY-MM-DD
Date of exp/date data was gathered
exp_desc: string
Brief explantation of experiment/what
type of information data contains
plot_raw_signal: boolean
optional plot of raw data for a
particular fiber and color
plot_iso_fit: boolean
optional isosbestic plot
plot_fit_exp: boolean
optional fitted exponent plot
Returns
-------
Pandas dataframe of parsed fiber photometry data
Writes an output CSV to specified file name
"""
# use with config.txt
# run bash command: python fpho_driver.py @config.txt
parser = argparse.ArgumentParser(fromfile_prefix_chars='@')
parser = argparse.ArgumentParser(description=('Parse fiber photometry data'
+ 'to prepare for analyses'))
parser.add_argument('--input_filename',
dest='input_filename',
type=str,
required=True,
help='Name of input file as string')
parser.add_argument('--output_filename',
dest='output_filename',
type=str,
required=True,
help='Name for output file as string')
parser.add_argument('--animal_ID',
dest='animal_ID',
type=int,
required=True,
help='Animal number for fluroesence data')
parser.add_argument('--exp_date',
dest='exp_date',
type=str,
required=True,
help='Date of experiment as YYYY-MM-DD')
parser.add_argument('--exp_desc',
dest='exp_desc',
type=str,
required=True,
help='Brief description for context')
parser.add_argument('--plot_raw_signal',
dest='plot_raw_signal',
action='store_true',
help='Type 1 to plot raw signal trace')
parser.add_argument('--plot_iso_fit',
dest='plot_iso_fit',
action='store_true',
help='Type 1 to plot iso fitted trace')
parser.add_argument('--plot_fit_exp',
dest='plot_fit_exp',
action='store_true',
help='Type 1 to plot fitted exponent')
args = parser.parse_args()
# Generate the dataframe with data
fpho_df = fpho_setup.import_fpho_data(input_filename=args.input_filename,
output_filename=args.output_filename,
animal_ID=args.animal_ID,
exp_date=args.exp_date,
exp_desc=args.exp_desc)
# Plot raw signal if specified in commandline
if args.plot_raw_signal:
fpho_setup.raw_signal_trace(fpho_df, args.output_filename)
# Prints isosbestic fit if specified
if args.plot_iso_fit:
fpho_setup.plot_1fiber_norm_iso(fpho_df)
# Prints fitted exponent if specified
if args.plot_fit_exp:
fpho_setup.plot_1fiber_norm_fitted(fpho_df)
def main():
"""Runs functions in fpho_setup, asks for what analysis to perform
Parameters
----------
input_filename: string
Name of input file containing fiber photometry data
output_filename: string
Name you'd like for the output CSV file. Should include
file path if different than current file
animal_number: int
Number of the animal corresponding to fluoresence data
Returns
-------
Pandas dataframe of parsed fiber photometry data
Writes an output CSV to specified file name
"""
parser = argparse.ArgumentParser(
description=('Parse fiber photometry data' +
'to prepare for analyses'))
parser.add_argument('--input_filename',
dest='input_filename',
type=str,
required=True,
help='Name of input file as string')
parser.add_argument('--output_filename',
dest='output_filename',
type=str,
required=True,
help='Name for output file as string')
parser.add_argument(
'--animal_num',
dest='animal_num',
type=int,
required=False, # make required
help='Animal number for fluroesence data')
parser.add_argument('--plot_raw_signal',
dest='plot_raw_signal',
type=bool,
required=False,
help='Type 1 to plot raw signal trace')
parser.add_argument('--plot_iso_fit',
dest='plot_iso_fit',
type=bool,
required=False,
help='Type 1 to plot iso fitted trace')
args = parser.parse_args()
print(args.output_filename)
fpho_df = fpho_setup.import_fpho_data(input_filename=args.input_filename,
output_filename=args.output_filename)
# prints raw signal
if args.plot_raw_signal:
fpho_setup.raw_signal_trace(fpho_df, args.output_filename)
# prints isosbestic fit
if args.plot_iso_fit:
fpho_setup.plot_1fiber_norm_iso(fpho_df)
