def test_import_fpho_data_errors(self):
with self.assertRaises(SystemExit) as cm:
fpho_setup.import_fpho_data(input_filename='TestData/1Fdlkjf',
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)
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()
# 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_column_assignments_one_fiber(self):
fTime, f1Red, f1Green, f2Red, f2Green = fpho_setup.import_fpho_data(
'1FiberTest.csv')
test_time = [
'57887085.8496', '57887107.8144', '57887132.3904',
'57887157.6448', '57887182.5664', '57887207.8336',
'57887232.704', '57887257.5616', '57887282.7008',
'57887308.032', '57887333.1712', '57887358.0288',
'57887382.9632', '57887407.9232', '57887433.3952',
'57887458.0352', '57887483.136', '57887508.9536',
'57887533.4016', '57887558.3104'
]
self.assertEqual(fTime, test_time)
test_red1 = [
'644.744558543487', '678.416060082934', '647.206535086939',
'644.814607841392', '678.270266422253', '647.262916229155',
'644.723486803467', '678.379611667764', '647.031696595419',
'644.656285037997', '678.365943512075', '647.200270515581',
'644.59420883091', '678.557867198206', '647.119970100909',
'644.794675114346', '678.310701382833', '647.334674046522',
'644.818024880315', '678.212746267063'
]
self.assertEqual(f1Red, test_red1)
test_green1 = [
'712.658962090541', '706.853029076187', '714.173102686787',
'712.524225248436', '706.683430253957', '714.162738314317',
'712.601486934118', '706.846433566434', '714.076996687523',
'712.630695620169', '706.940655134339', '714.133529628267',
'712.506323150534', '706.897313213103', '714.293706293706',
'712.506323150534', '706.771056312109', '714.288995215311',
'712.807832167832', '706.860566801619'
]
self.assertEqual(f1Green, test_green1)
test_red2 = [
'661.240182347449', '702.872228630014', '663.797440544845',
'661.14175866425', '703.062045733326', '663.734168177075',
'661.214990571392', '703.00814704967', '663.732410611303',
'661.227293531792', '703.05911645704', '663.754087255817',
'661.062668204537', '703.013419746984', '663.687299756504',
'661.326888925505', '702.928470734699', '663.968510279929',
'661.102506362022', '703.291700994123'
]
self.assertEqual(f2Red, test_red2)
test_green2 = [
'479.767687953734', '456.4006398425', '515.287149829785',
'479.69746934088', '456.525983347689', '515.158525081006',
'479.75784422296', '456.437389770723', '514.962962962963',
'479.951437594848', '456.417702309175', '514.988556662975',
'480.10959353595', '456.319265001435', '514.76740084492',
'480.127968500062', '456.411139821992', '515.082400229687',
'479.812969115295', '456.644108116976'
]
self.assertEqual(f2Green, test_green2)
def test_plot_fitted_exp(self):
df_test = fpho_setup.import_fpho_data(input_filename='Python/'
'SampleData/1fiberSignal.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.plot_fitted_exp(fpho_dataframe=df_test,
output_filename='my_file_name')
self.assertTrue(path.exists('my_file_name_f1GreenNormExp.png'))
self.assertTrue(path.exists('my_file_name_f1RedNormExp.png'))
def test_plot_isosbestic_norm(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.plot_isosbestic_norm(fpho_dataframe=df_test,
output_filename='my_file_name')
self.assertTrue(path.exists('my_file_name_f1GreenNormIso.png'))
self.assertTrue(path.exists('my_file_name_f1RedNormIso.png'))
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 test_import_fpho_data(self):
df = fpho_setup.import_fpho_data(input_filename='Python/TestData'
'/1FiberTesting.csv',
output_filename='my_file_name',
n_fibers=1,
f1greencol=4,
animal_ID='vole1',
exp_date='2020-09-01',
exp_desc="testing",
f2greencol=None,
write_xlsx=False)
test_green_iso = [1640.48859543818, 1640.90076030412]
self.assertEqual(df['f1GreenIso'].values[0], test_green_iso)
test_green_green = [1511.23969587835, 1511.07482993197]
self.assertEqual(df['f1GreenGreen'].values[0], test_green_green)
test_red_iso = [1536.2730469085, 1536.54499614098]
self.assertEqual(df['f1RedIso'].values[0], test_red_iso)
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 test_column_assignments_one_fiber(self):
fTime, f1Red, f1Green = fpho_setup.import_fpho_data('1FiberTest.csv')
test_time = [
'52111503.5648', '52111528.4224', '52111553.6768', '52111579.1104',
'52111603.6736', '52111628.608', '52111653.696', '52111678.848',
'52111704.256', '52111729.792', '52111754.176', '52111779.0592',
'52111804.0832', '52111829.1584', '52111855.296', '52111879.296',
'52111904.3712', '52111929.4336', '52111954.9312', '52111979.5456'
]
self.assertEqual(fTime, test_time)
test_red = [
'1360.02593259583', '1543.68695652174', '1888.49759025813',
'1379.57643426807', '1544.10860132064', '1883.40917588543',
'1379.09578938341', '1543.43963639482', '1881.64479890232',
'1379.13064059686', '1543.77490781237', '1881.50361032501',
'1378.9618729097', '1543.00722065003', '1880.44435297144',
'1379.31223737244', '1542.8058657062', '1880.79807906698',
'1379.1495755081', '1542.52636995112'
]
self.assertEqual(f1Red, test_red)
test_green = [
'1510.66266506603', '1649.64105642257', '1540.86554621849',
'1511.04921968788', '1649.37254901961', '1539.86634653862',
'1510.93917567027', '1648.68547418968', '1540.050020008',
'1510.9443777511', '1648.91356542617', '1540.14965986395',
'1511.02521008403', '1648.08083233293', '1539.74429771909',
'1510.95918367347', '1647.96798719488', '1540.39575830332',
'1511.10444177671', '1647.51860744298'
]
self.assertEqual(f1Green, test_green)
def test_column_assignments_one_fiber(self):
fTime, f1Red, f1Green, f2Red, f2Green = fpho_setup.import_fpho_data(
'1FiberTest.csv')
test_time = [
'57887085.8496', '57887107.8144', '57887132.3904',
'57887157.6448', '57887182.5664', '57887207.8336',
'57887232.704', '57887257.5616', '57887282.7008',
'57887308.032', '57887333.1712', '57887358.0288',
'57887382.9632', '57887407.9232', '57887433.3952',
'57887458.0352', '57887483.136', '57887508.9536',
'57887533.4016', '57887558.3104'
]
self.assertEqual(fTime, test_time)
test_red1 = [
'644.744558543487', '678.416060082934', '647.206535086939',
'644.814607841392', '678.270266422253', '647.262916229155',
'644.723486803467', '678.379611667764', '647.031696595419',
'644.656285037997', '678.365943512075', '647.200270515581',
'644.59420883091', '678.557867198206', '647.119970100909',
'644.794675114346', '678.310701382833', '647.334674046522',
'644.818024880315', '678.212746267063'
]
self.assertEqual(f1Red, test_red1)
test_green1 = [
'712.658962090541', '706.853029076187', '714.173102686787',
'712.524225248436', '706.683430253957', '714.162738314317',
'712.601486934118', '706.846433566434', '714.076996687523',
'712.630695620169', '706.940655134339', '714.133529628267',
'712.506323150534', '706.897313213103', '714.293706293706',
'712.506323150534', '706.771056312109', '714.288995215311',
'712.807832167832', '706.860566801619'
]
self.assertEqual(f1Green, test_green1)
test_red2 = [
'661.240182347449', '702.872228630014', '663.797440544845',
'661.14175866425', '703.062045733326', '663.734168177075',
'661.214990571392', '703.00814704967', '663.732410611303',
'661.227293531792', '703.05911645704', '663.754087255817',
'661.062668204537', '703.013419746984', '663.687299756504',
'661.326888925505', '702.928470734699', '663.968510279929',
'661.102506362022', '703.291700994123'
]
self.assertEqual(f2Red, test_red2)
test_green2 = [
'479.767687953734', '456.4006398425', '515.287149829785',
'479.69746934088', '456.525983347689', '515.158525081006',
'479.75784422296', '456.437389770723', '514.962962962963',
'479.951437594848', '456.417702309175', '514.988556662975',
'480.10959353595', '456.319265001435', '514.76740084492',
'480.127968500062', '456.411139821992', '515.082400229687',
'479.812969115295', '456.644108116976'
]
self.assertEqual(f2Green, test_green2)
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)
