""" This function implements a log transformation on the data. """
# Copy the original sample
new_sample = original_sample.copy()
new_data = new_sample.data
# Our transformation goes here
new_data['Y2-A'] = log(new_data['Y2-A'])
new_data = new_data.dropna() # Removes all NaN entries
new_sample.data = new_data
return new_sample
# Load data
sample = FCMeasurement(ID='Test Sample', datafile=datafile)
# Transform using our own custom method
custom_transform_sample = sample.apply(transform_using_this_method)
###
# To do this with a collection (a plate):
# compensated_plate = plate.apply(transform_using_this_method,
# output_format='collection')
# Plot
custom_transform_sample.plot(['Y2-A'], color='green', alpha=0.9)
grid(True)
title('Custom log transformation')
# show() # <-- Uncomment when running as a script.
""" This function implements a log transformation on the data. """
# Copy the original sample
new_sample = original_sample.copy()
new_data = new_sample.data
# Our transformation goes here
new_data['Y2-A'] = log(new_data['Y2-A'])
new_data = new_data.dropna() # Removes all NaN entries
new_sample.data = new_data
return new_sample
# Load data
sample = FCMeasurement(ID='Test Sample', datafile=datafile)
# Transform using our own custom method
custom_transform_sample = sample.apply(transform_using_this_method)
###
# To do this with a collection (a plate):
# compensated_plate = plate.apply(transform_using_this_method,
# output_format='collection')
# Plot
custom_transform_sample.plot(['Y2-A'], color='green', alpha=0.9);
grid(True)
title('Custom log transformation')
# show() # <-- Uncomment when running as a script.
new_sample = original_sample.copy()
new_data = new_sample.data
original_data = original_sample.data
# Our transformation goes here
new_data['Y2-A'] = original_data['Y2-A'] - 0.15 * original_data['FSC-A']
new_data['FSC-A'] = original_data['FSC-A'] - 0.32 * original_data['Y2-A']
new_data = new_data.dropna() # Removes all NaN entries
new_sample.data = new_data
return new_sample
# Load data
sample = FCMeasurement(ID='Test Sample', datafile=datafile)
sample = sample.transform('hlog')
compensated_sample = sample.apply(custom_compensate)
###
# To do this with a collection (a plate):
# compensated_plate = plate.apply(compensate, output_format='collection')
#
# Plot
sample.plot(['Y2-A', 'FSC-A'], kind='scatter', color='gray', alpha=0.6, label='Original');
compensated_sample.plot(['Y2-A', 'FSC-A'], kind='scatter', color='green', alpha=0.6, label='Compensated');
legend(loc='best')
grid(True)
#show() # <-- Uncomment when running as a script.
new_data = new_sample.data
original_data = original_sample.data
# Our transformation goes here
new_data['Y2-A'] = original_data['Y2-A'] - 0.15 * original_data['FSC-A']
new_data['FSC-A'] = original_data['FSC-A'] - 0.32 * original_data['Y2-A']
new_data = new_data.dropna() # Removes all NaN entries
new_sample.data = new_data
return new_sample
# Load data
sample = FCMeasurement(ID='Test Sample', datafile=datafile)
sample = sample.transform('hlog')
compensated_sample = sample.apply(custom_compensate)
###
# To do this with a collection (a plate):
# compensated_plate = plate.apply(compensate, output_format='collection')
#
# Plot
sample.plot(['Y2-A', 'FSC-A'],
kind='scatter',
color='gray',
alpha=0.6,
label='Original')
compensated_sample.plot(['Y2-A', 'FSC-A'],
kind='scatter',
color='green',
