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"""
Partially taken and adapted from: https://github.com/jwcarr/eyekit/blob/1db1913411327b108b87e097a00278b6e50d0751/eyekit/measure.py
Functions for calculating common reading measures, such as gaze duration or
initial landing position.
"""
import pandas as pd
def fix_in_ia(fix_x, fix_y, ia_x_min, ia_x_max, ia_y_min, ia_y_max):
in_x = ia_x_min <= fix_x <= ia_x_max
in_y = ia_y_min <= fix_y <= ia_y_max
if in_x and in_y:
return True
else:
return False
def fix_in_ia_default(fixation, ia_row, prefix):
return fix_in_ia(
fixation.x,
fixation.y,
ia_row[f"{prefix}_xmin"],
ia_row[f"{prefix}_xmax"],
ia_row[f"{prefix}_ymin"],
ia_row[f"{prefix}_ymax"],
)
def number_of_fixations_own(trial, dffix, prefix="word"):
"""
Given an interest area and fixation sequence, return the number of
fixations on that interest area.
"""
ia_df = pd.DataFrame(trial[f"{prefix}s_list"])
counts = []
for cidx, ia_row in ia_df.iterrows():
count = 0
for idx, fixation in dffix.iterrows():
if fix_in_ia(
fixation.x,
fixation.y,
ia_row[f"{prefix}_xmin"],
ia_row[f"{prefix}_xmax"],
ia_row[f"{prefix}_ymin"],
ia_row[f"{prefix}_ymax"],
):
count += 1
counts.append(
{
f"{prefix}_index": cidx,
prefix: ia_row[f"{prefix}"],
"number_of_fixations": count,
}
)
return pd.DataFrame(counts)
def initial_fixation_duration_own(trial, dffix, prefix="word"):
"""
Given an interest area and fixation sequence, return the duration of the
initial fixation on that interest area for each word.
"""
ia_df = pd.DataFrame(trial[f"{prefix}s_list"])
durations = []
for cidx, ia_row in ia_df.iterrows():
initial_duration = 0
for idx, fixation in dffix.iterrows():
if fix_in_ia_default(fixation, ia_row, prefix):
initial_duration = fixation.duration
break # Exit the loop after finding the initial fixation for the word
durations.append(
{
f"{prefix}_index": cidx,
prefix: ia_row[f"{prefix}"],
"initial_fixation_duration": initial_duration,
}
)
return pd.DataFrame(durations)
def first_of_many_duration_own(trial, dffix, prefix="word"):
ia_df = pd.DataFrame(trial[f"{prefix}s_list"])
durations = []
for cidx, ia_row in ia_df.iterrows():
fixation_durations = []
for idx, fixation in dffix.iterrows():
if fix_in_ia_default(fixation, ia_row, prefix):
fixation_durations.append(fixation.duration)
if len(fixation_durations) > 1:
durations.append(
{
f"{prefix}_index": cidx,
prefix: ia_row[f"{prefix}"],
"first_of_many_duration": fixation_durations[0],
}
)
if durations:
return pd.DataFrame(durations)
else:
return pd.DataFrame()
def total_fixation_duration_own(trial, dffix, prefix="word"):
"""
Given an interest area and fixation sequence, return the sum duration of
all fixations on that interest area.
"""
ia_df = pd.DataFrame(trial[f"{prefix}s_list"])
durations = []
for cidx, ia_row in ia_df.iterrows():
total_duration = 0
for idx, fixation in dffix.iterrows():
if fix_in_ia_default(fixation, ia_row, prefix):
total_duration += fixation.duration
durations.append(
{
f"{prefix}_index": cidx,
prefix: ia_row[f"{prefix}"],
"total_fixation_duration": total_duration,
}
)
return pd.DataFrame(durations)
def gaze_duration_own(trial, dffix, prefix="word"):
"""
Given an interest area and fixation sequence, return the gaze duration on
that interest area. Gaze duration is the sum duration of all fixations
inside an interest area until the area is exited for the first time.
"""
ia_df = pd.DataFrame(trial[f"{prefix}s_list"])
durations = []
for cidx, ia_row in ia_df.iterrows():
duration = 0
in_ia = False
for idx, fixation in dffix.iterrows():
if fix_in_ia_default(fixation, ia_row, prefix):
duration += fixation.duration
in_ia = True
elif in_ia:
break
durations.append(
{
f"{prefix}_index": cidx,
prefix: ia_row[f"{prefix}"],
"gaze_duration": duration,
}
)
return pd.DataFrame(durations)
def go_past_duration_own(trial, dffix, prefix="word"):
"""
Given an interest area and fixation sequence, return the go-past time on
that interest area. Go-past time is the sum duration of all fixations from
when the interest area is first entered until when it is first exited to
the right, including any regressions to the left that occur during that
time period (and vice versa in the case of right-to-left text).
"""
ia_df = pd.DataFrame(trial[f"{prefix}s_list"])
results = []
for cidx, ia_row in ia_df.iterrows():
entered = False
go_past_time = 0
for idx, fixation in dffix.iterrows():
if fix_in_ia_default(fixation, ia_row, prefix):
if not entered:
entered = True
go_past_time += fixation.duration
elif entered:
if ia_row[f"{prefix}_xmax"] < fixation.x: # Interest area has been exited to the right
break
go_past_time += fixation.duration
results.append({f"{prefix}_index": cidx, prefix: ia_row[f"{prefix}"], "go_past_duration": go_past_time})
return pd.DataFrame(results)
def second_pass_duration_own(trial, dffix, prefix="word"):
"""
Given an interest area and fixation sequence, return the second pass
duration on that interest area for each word.
"""
ia_df = pd.DataFrame(trial[f"{prefix}s_list"])
durations = []
for cidx, ia_row in ia_df.iterrows():
current_pass = None
next_pass = 1
pass_duration = 0
for idx, fixation in dffix.iterrows():
if fix_in_ia_default(fixation, ia_row, prefix):
if current_pass is None: # first fixation in a new pass
current_pass = next_pass
if current_pass == 2:
pass_duration += fixation.duration
elif current_pass == 1: # first fixation to exit the first pass
current_pass = None
next_pass += 1
elif current_pass == 2: # first fixation to exit the second pass
break
durations.append(
{
f"{prefix}_index": cidx,
prefix: ia_row[f"{prefix}"],
"second_pass_duration": pass_duration,
}
)
return pd.DataFrame(durations)
def initial_landing_position_own(trial, dffix, prefix="word"):
"""
Given an interest area and fixation sequence, return the initial landing
position (expressed in character positions) on that interest area.
Counting is from 1. If the interest area represents right-to-left text,
the first character is the rightmost one. Returns `None` if no fixation
landed on the interest area.
"""
ia_df = pd.DataFrame(trial[f"{prefix}s_list"])
if prefix == "word":
chars_df = pd.DataFrame(trial[f"chars_list"])
else:
chars_df = None
results = []
for cidx, ia_row in ia_df.iterrows():
landing_position = None
for idx, fixation in dffix.iterrows():
if fix_in_ia_default(fixation, ia_row, prefix):
if prefix == "char":
landing_position = 1
else:
prefix_temp = "char"
matched_chars_df = chars_df.loc[
(chars_df.char_xmin >= ia_row[f"{prefix}_xmin"])
& (chars_df.char_xmax <= ia_row[f"{prefix}_xmax"])
& (chars_df.char_ymin >= ia_row[f"{prefix}_ymin"])
& (chars_df.char_ymax <= ia_row[f"{prefix}_ymax"]),
:,
] # need to find way to count correct letter number
for char_idx, (rowidx, char_row) in enumerate(matched_chars_df.iterrows()):
if fix_in_ia_default(fixation, char_row, prefix_temp):
landing_position = char_idx + 1 # starts at 1
break
break
results.append(
{
f"{prefix}_index": cidx,
prefix: ia_row[f"{prefix}"],
"initial_landing_position": landing_position,
}
)
return pd.DataFrame(results)
def initial_landing_distance_own(trial, dffix, prefix="word"):
"""
Given an interest area and fixation sequence, return the initial landing
distance on that interest area. The initial landing distance is the pixel
distance between the first fixation to land in an interest area and the
left edge of that interest area (or, in the case of right-to-left text,
the right edge). Technically, the distance is measured from the text onset
without including any padding. Returns `None` if no fixation landed on the
interest area.
"""
ia_df = pd.DataFrame(trial[f"{prefix}s_list"])
distances = []
for cidx, ia_row in ia_df.iterrows():
initial_distance = None
for idx, fixation in dffix.iterrows():
if fix_in_ia_default(fixation, ia_row, prefix):
distance = abs(ia_row[f"{prefix}_xmin"] - fixation.x)
if initial_distance is None:
initial_distance = distance
break
distances.append(
{
f"{prefix}_index": cidx,
prefix: ia_row[f"{prefix}"],
"initial_landing_distance": initial_distance,
}
)
return pd.DataFrame(distances)
def landing_distances_own(trial, dffix, prefix="word"):
"""
Given an interest area and fixation sequence, return a dataframe with
landing distances for each word in the interest area.
"""
ia_df = pd.DataFrame(trial[f"{prefix}s_list"])
distances = []
for cidx, ia_row in ia_df.iterrows():
landing_distances = []
for idx, fixation in dffix.iterrows():
if fix_in_ia_default(fixation, ia_row, prefix):
landing_distance = abs(ia_row[f"{prefix}_xmin"] - fixation.x)
landing_distances.append(round(landing_distance, ndigits=2))
distances.append({f"{prefix}_index": cidx, prefix: ia_row[f"{prefix}"], "landing_distances": landing_distances})
return pd.DataFrame(distances)
def number_of_regressions_in_own(trial, dffix, prefix="word"):
"""
Given an interest area and fixation sequence, return the number of
regressions back to that interest area after the interest area was read
for the first time. In other words, find the first fixation to exit the
interest area and then count how many times the reader returns to the
interest area from the right (or from the left in the case of
right-to-left text).
"""
ia_df = pd.DataFrame(trial[f"{prefix}s_list"])
counts = []
for cidx, ia_row in ia_df.iterrows():
entered_interest_area = False
first_exit_index = None
count = 0
prev_fixation = None
regression_counted = False
for fixidx, (rowidx, fixation) in enumerate(dffix.iterrows()):
if (
entered_interest_area
and first_exit_index is not None
and fix_in_ia_default(fixation, ia_row, prefix)
and not regression_counted
):
if prev_fixation.x > fixation.x:
count += 1
regression_counted = True
if fix_in_ia_default(fixation, ia_row, prefix):
entered_interest_area = True
elif entered_interest_area and first_exit_index is None:
first_exit_index = fixidx
else:
regression_counted = False
prev_fixation = fixation
counts.append(
{
f"{prefix}_index": cidx,
prefix: ia_row[f"{prefix}"],
"number_of_regressions_in": count,
}
)
return pd.DataFrame(counts)
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