# Display the first 20 unfiltered MOUSE_MOVE events
print '** KEY_PRESS Events (first 20):'
print
print exp_data.KEYBOARD_PRESS.head(20)
print
# Create an Event based Interest Period. Interest Periods define a start and
# end time; any events that have a time >= an IP start time and <= an IP
# end time would be kept after filtering with the IP. Here the interest
# period start time is based on MESSAGE events that have the text of
# 'TRIAL_START'. The IP end time is based on MESSAGE events
# with a text field equal to 'TRIAL_END'.
#
ip = EventBasedIP(name='trial_ip',
start_source_df=exp_data.MESSAGE,
start_criteria={'text': 'TRIAL_START'},
end_source_df=exp_data.MESSAGE,
end_criteria={'text': 'TRIAL_END'})
# Display the first 20 IP 's found using the criteria specified when creating
# the EventBasedIP
#
print '** MESSAGE Interest Periods (TRIAL_START to TRIAL_END):'
print
print ip.ip_df
print
# Now we can filter out events from any event dataframe using the IP created.
# Any events that do not occur within one of the interest periods found in the
# data will be removed.
#
# Display the first 20 unfiltered MOUSE_MOVE events
print '** KEY_PRESS Events (first 20):'
print
print exp_data.KEYBOARD_PRESS.head(20)
print
# Create an Event based Interest Period. Interest Periods define a start and
# end time; any events that have a time >= an IP start time and <= an IP
# end time would be kept after filtering with the IP. Here the interest
# period start time is based on MESSAGE events that have the text of
# 'TRIAL_START'. The IP end time is based on MESSAGE events
# with a text field equal to 'TRIAL_END'.
#
ip=EventBasedIP(name='trial_ip',
start_source_df=exp_data.MESSAGE,
start_criteria={'text':'TRIAL_START'},
end_source_df=exp_data.MESSAGE,
end_criteria={'text':'TRIAL_END'})
# Display the first 20 IP 's found using the criteria specified when creating
# the EventBasedIP
#
print '** MESSAGE Interest Periods (TRIAL_START to TRIAL_END):'
print
print ip.ip_df
print
# Now we can filter out events from any event dataframe using the IP created.
# Any events that do not occur within one of the interest periods found in the
# data will be removed.
#
# Display the first 20 unfiltered MOUSE_MOVE events
print '** KEY_PRESS Events (first 20):'
print
print exp_data.KEYBOARD_PRESS.head(20)
print
# Create an Event based Interest Period. Interest Periods define a start and
# end time; any events that have a time >= an IP start time and <= an IP
# end time would be kept after filtering with the IP. Here the interest
# period start time is based on MESSAGE events that have the text of
# 'TRIAL_START'. The IP end time is based on MESSAGE events
# with a text field equal to 'TRIAL_END'.
#
ip = EventBasedIP(name='trial_ip',
start_source_df=exp_data.MESSAGE,
start_criteria={'text': 'TRIAL_START'},
end_source_df=exp_data.MESSAGE,
end_criteria={'text': 'TRIAL_END'})
# Display the first 20 IP 's found using the criteria specified when creating
# the EventBasedIP
#
print '** MESSAGE Interest Periods (TRIAL_START to TRIAL_END):'
print
print ip.ip_df
print
# Now we can filter out events from any event dataframe using the IP created.
# Any events that do not occur within one of the interest periods found in the
# data will be removed.
#
# Display the first 20 unfiltered MOUSE_MOVE events
print '** KEY_PRESS Events (first 20):'
print
print exp_data.KEYBOARD_PRESS.head(20)
print
# Create an Event based Interest Period. Interest Periods define a start and
# end time; any events that have a time >= an IP start time and <= an IP
# end time would be kept after filtering with the IP. Here the interest
# period start time is based on MESSAGE events that have the text of
# 'TRIAL_START'. The IP end time is based on MESSAGE events
# with a text field equal to 'TRIAL_END'.
#
ip = EventBasedIP(name='trial_ip',
start_source_df=exp_data.MESSAGE,
start_criteria={'text': 'TRIAL_START'},
end_source_df=exp_data.MESSAGE,
end_criteria={'text': 'TRIAL_END'})
# Display the first 20 IP 's found using the criteria specified when creating
# the EventBasedIP
#
print '** MESSAGE Interest Periods (TRIAL_START to TRIAL_END):'
print
print ip.ip_df
print
# Now we can filter out events from any event dataframe using the IP created.
# Any events that do not occur within one of the interest periods found in the
# data will be removed.
#
