def test_posneg_offset():
# make sure that you can use a positive or a negative offset
ds, es = pandas_dfs_from_asc(paths['mono250'])
fixations = es.EFIX[3:-1]
ext = extract_events(ds, fixations, duration=500, offset=-250, units=TIME_UNITS)
ext = extract_events(ds, fixations, duration=500, offset=250, units=TIME_UNITS)
assert(True)
def test_extracted__bino250_cols():
ds, es = pandas_dfs_from_asc(paths['bino250'])
rs = es.ESACC[:-3]
ext = extract_events(ds, rs, duration=40, offset=0, units=TIME_UNITS)
test_cols = ['x_l', 'y_l', 'pup_l', 'x_r',
'y_r', 'pup_r', 'samp_warns', 'orig_idx']
assert_array_equal(test_cols, ds.columns.tolist())
def test_binoRemote250_cols():
ds, es = pandas_dfs_from_asc(paths['binoRemote250'])
test_cols = [
'x_l', 'y_l', 'pup_l', 'x_r', 'y_r', 'pup_r', 'samp_warns', 'targ_x',
'targ_y', 'targ_dist', 'remote_warns'
]
assert_array_equal(test_cols, ds.columns.tolist())
def test_esacc_fields():
ds, es = pandas_dfs_from_asc(paths['binoRemote500'])
test_fields = [
'name', 'eye', 'last_onset', 'duration', 'x_start', 'y_start', 'x_end',
'y_end', 'vis_angle', 'peak_velocity'
]
assert_array_equal(es.ESACC.columns.values, test_fields)
def test_timeunit_extract_underbounds():
ds, es = pandas_dfs_from_asc(paths['mono1000'])
ext = extract_events(ds,
es.EFIX,
offset=-10000,
duration=3000,
units=TIME_UNITS)
def test_extracted_monoRemote500_cols():
ds, es = pandas_dfs_from_asc(paths['monoRemote500'])
rs = es.ESACC[:-3]
ext = extract_events(ds, rs, duration=500, offset=0, units=TIME_UNITS)
test_cols = ['x_l', 'y_l', 'pup_l', 'samp_warns', 'targ_x', 'targ_y', 'targ_dist',
'remote_warns', 'orig_idx']
assert_array_equal(test_cols, ds.columns.tolist())
def test_extracted_bino1000_cols():
ds, es = pandas_dfs_from_asc(paths['bino1000'])
rs = es.ESACC[:-3]
ext = extract_events(ds, rs, duration=500, offset=0, units=TIME_UNITS)
test_cols = [
'x_l', 'y_l', 'pup_l', 'x_r', 'y_r', 'pup_r', 'samp_warns', 'orig_idx'
]
assert_array_equal(test_cols, ds.columns.tolist())
def get_0_percentage(asc_path):
from cili.util import pandas_dfs_from_asc
import pandas as pd
# grab the data
ds, _ = pandas_dfs_from_asc(asc_path)
p_fields = [f for f in ds.columns if f in PUP_FIELDS]
if len(p_fields) == 0:
return 1. # if you can't find a pupil field, we'll call that "bad"
return (float(len(ds[ds[p_fields[0]] == 0])) / float(len(ds)))
def test_extracted_monoRemote250_cols():
ds, es = pandas_dfs_from_asc(paths['monoRemote250'])
rs = es.EFIX[:-3]
ext = extract_events(ds, rs, duration=500, offset=0, units=TIME_UNITS)
test_cols = [
'x_l', 'y_l', 'pup_l', 'samp_warns', 'targ_x', 'targ_y', 'targ_dist',
'remote_warns', 'orig_idx'
]
assert_array_equal(test_cols, ds.columns.tolist())
def get_0_percentage(asc_path):
from cili.util import pandas_dfs_from_asc
import pandas as pd
# grab the data
ds, _ = pandas_dfs_from_asc(asc_path)
p_fields = [f for f in ds.columns if f in PUP_FIELDS]
if len(p_fields) == 0:
return 1. # if you can't find a pupil field, we'll call that "bad"
return (float(len(ds[ds[p_fields[0]] == 0])) / float(len(ds)))
def test_borrowed_fields():
ds, es = pandas_dfs_from_asc(paths['binoRemote500'])
rs = es.ESACC[:-3]
ext = extract_events(ds,
rs,
duration=500,
offset=0,
units=TIME_UNITS,
borrow_attributes=['peak_velocity'])
assert_true('peak_velocity' in ext.columns)
def test_posneg_offset():
# make sure that you can use a positive or a negative offset
ds, es = pandas_dfs_from_asc(paths['mono250'])
fixations = es.EFIX[3:-1]
ext = extract_events(ds,
fixations,
duration=500,
offset=-250,
units=TIME_UNITS)
ext = extract_events(ds,
fixations,
duration=500,
offset=250,
units=TIME_UNITS)
assert (True)
def test_esacc_fields():
ds, es = pandas_dfs_from_asc(paths["binoRemote500"])
test_fields = [
"name",
"eye",
"last_onset",
"duration",
"x_start",
"y_start",
"x_end",
"y_end",
"vis_angle",
"peak_velocity",
]
assert_array_equal(es.ESACC.columns.values, test_fields)
def test_binoRemote250_cols():
ds, es = pandas_dfs_from_asc(paths["binoRemote250"])
test_cols = [
"x_l",
"y_l",
"pup_l",
"x_r",
"y_r",
"pup_r",
"samp_warns",
"targ_x",
"targ_y",
"targ_dist",
"remote_warns",
]
assert_array_equal(test_cols, ds.columns.tolist())
def load_eyelink_dataset(file_name):
""" Parses eyelink data to return samples and events.
For now, we can only parse events from .asc files. If you hand us a .txt,
we'll parse out the samples, but not the events.
Parameters
----------
file_name (string)
The .asc or .txt file you'd like to parse.
Returns
-------
(Samples object, Events object (or None))
"""
root, ext = os.path.splitext(file_name)
if ext == '.asc':
s, e = pandas_dfs_from_asc(file_name)
elif ext in ['.txt']:
s = load_tdf(file_name)
e = None
else:
raise ValueError("only .asc and .txt files supported at the moment...")
return s, e
def load_eyelink_dataset(file_name):
""" Parses eyelink data to return samples and events.
For now, we can only parse events from .asc files. If you hand us a .txt,
we'll parse out the samples, but not the events.
Parameters
----------
file_name (string)
The .asc or .txt file you'd like to parse.
Returns
-------
(Samples object, Events object (or None))
"""
root, ext = os.path.splitext(file_name)
if ext == '.asc':
s, e = pandas_dfs_from_asc(file_name)
elif ext in ['.txt']:
s = load_tdf(file_name)
e = None
else:
raise ValueError("only .asc and .txt files supported at the moment...")
return s, e
def test_eblink_fields():
ds, es = pandas_dfs_from_asc(paths["binoRemote500"])
test_fields = ["name", "eye", "last_onset", "duration"]
assert_array_equal(es.EBLINK.columns.values, test_fields)
def test_efix_fields():
ds, es = pandas_dfs_from_asc(paths["binoRemote500"])
test_fields = ["name", "eye", "last_onset", "duration", "x_pos", "y_pos", "p_size"]
assert_array_equal(es.EFIX.columns.values, test_fields)
def test_load_asc_binoRemote250():
pandas_dfs_from_asc(paths['binoRemote250'])
def test_efix_fields():
ds, es = pandas_dfs_from_asc(paths['binoRemote500'])
test_fields = [
'name', 'eye', 'last_onset', 'duration', 'x_pos', 'y_pos', 'p_size'
]
assert_array_equal(es.EFIX.columns.values, test_fields)
def test_load_asc_monoRemote500():
pandas_dfs_from_asc(paths["monoRemote500"])
def test_load_asc_binoRemote250():
pandas_dfs_from_asc(paths["binoRemote250"])
def test_msg_fields():
ds, es = pandas_dfs_from_asc(paths["binoRemote500"])
test_fields = ["name", "label", "content"]
assert_array_equal(es.MSG.columns.values, test_fields)
def test_msg_fields():
ds, es = pandas_dfs_from_asc(paths['binoRemote500'])
test_fields = ['name', 'label', 'content']
assert_array_equal(es.MSG.columns.values, test_fields)
def test_binoRemote250_cols():
ds, es = pandas_dfs_from_asc(paths['binoRemote250'])
test_cols = ['x_l', 'y_l', 'pup_l', 'x_r', 'y_r', 'pup_r',
'samp_warns', 'targ_x', 'targ_y', 'targ_dist', 'remote_warns']
assert_array_equal(test_cols, ds.columns.tolist())
def test_timeunit_extract_samplecount_250():
ds, es = pandas_dfs_from_asc(paths['mono250'])
fixations = es.EFIX[:-2]
sc_time_test(ds, fixations, 250, 400)
def test_bino1000_cols():
ds, es = pandas_dfs_from_asc(paths['bino1000'])
test_cols = ['x_l', 'y_l', 'pup_l', 'x_r', 'y_r', 'pup_r', 'samp_warns']
assert_array_equal(test_cols, ds.columns.tolist())
def test_load_asc_monoRemote500():
pandas_dfs_from_asc(paths['monoRemote500'])
def test_load_asc_mono250():
pandas_dfs_from_asc(paths['mono250'])
def test_start_fields():
ds, es = pandas_dfs_from_asc(paths["binoRemote500"])
test_fields = ["name", "eye", "types"]
assert_array_equal(es.START.columns.values, test_fields)
def test_mono2000_idx():
ds, es = pandas_dfs_from_asc(paths['mono2000'])
diffs = np.diff(ds.index)
diffs = np.unique(diffs[diffs < 100])
assert_equal(diffs, 4)
def test_eblink_fields():
ds, es = pandas_dfs_from_asc(paths['binoRemote500'])
test_fields = ['name', 'eye', 'last_onset', 'duration']
assert_array_equal(es.EBLINK.columns.values, test_fields)
def test_start_fields():
ds, es = pandas_dfs_from_asc(paths['binoRemote500'])
test_fields = ['name', 'eye', 'types']
assert_array_equal(es.START.columns.values, test_fields)
def test_load_asc_bino1000():
pandas_dfs_from_asc(paths["bino1000"])
def test_timeunit_extract_samplecount_1000():
ds, es = pandas_dfs_from_asc(paths['mono1000'])
fixations = es.EFIX[:-1]
sc_time_test(ds, fixations, 1000, 500)
sc_time_test(ds, fixations, 1000, 1000)
def test_load_asc_mono250():
pandas_dfs_from_asc(paths["mono250"])
def test_load_asc_monoRemote250():
pandas_dfs_from_asc(paths['monoRemote250'])
def test_bino1000_cols():
ds, es = pandas_dfs_from_asc(paths["bino1000"])
test_cols = ["x_l", "y_l", "pup_l", "x_r", "y_r", "pup_r", "samp_warns"]
assert_array_equal(test_cols, ds.columns.tolist())
def test_end_fields():
ds, es = pandas_dfs_from_asc(paths['binoRemote500'])
test_fields = ['name', 'types', 'x_res', 'y_res']
assert_array_equal(es.END.columns.values, test_fields)
def test_negative_duration():
ds, es = pandas_dfs_from_asc(paths['mono250'])
fixations = es.EFIX[:-1]
ext = extract_events(ds, fixations, duration=-1000, units=TIME_UNITS)
def test_load_asc_bino1000():
pandas_dfs_from_asc(paths['bino1000'])
def test_bino1000_cols():
ds, es = pandas_dfs_from_asc(paths['bino1000'])
test_cols = ['x_l', 'y_l', 'pup_l', 'x_r', 'y_r', 'pup_r', 'samp_warns']
assert_array_equal(test_cols, ds.columns.tolist())
def test_borrowed_fields():
ds, es = pandas_dfs_from_asc(paths['binoRemote500'])
rs = es.ESACC[:-3]
ext = extract_events(ds, rs, duration=500, offset=0, units=TIME_UNITS,
borrow_attributes=['peak_velocity'])
assert_true('peak_velocity' in ext.columns)
def test_sampleunit_extract_samplecount_2000():
ds, es = pandas_dfs_from_asc(paths['mono2000'])
fixations = es.EFIX[:-1]
sc_samp_test(ds, fixations, 350)
sc_samp_test(ds, fixations, 700)
def test_negative_duration():
ds, es = pandas_dfs_from_asc(paths['mono250'])
fixations = es.EFIX[:-1]
ext = extract_events(ds, fixations, duration=-1000, units=TIME_UNITS)
def test_sampleunit_extract_samplecount_2000():
ds, es = pandas_dfs_from_asc(paths['mono2000'])
fixations = es.EFIX[:-1]
sc_samp_test(ds, fixations, 350)
sc_samp_test(ds, fixations, 700)
def test_load_asc_mono2000():
pandas_dfs_from_asc(paths['mono2000'])
def test_mono2000_idx():
ds, es = pandas_dfs_from_asc(paths["mono2000"])
diffs = np.diff(ds.index)
diffs = np.unique(diffs[diffs < 100])
assert_equal(diffs, 4)
def test_event_types():
# make sure the full list of events is there
ds, es = pandas_dfs_from_asc(paths['binoRemote500'])
test_evs = ['END', 'EFIX', 'EBLINK', 'START', 'ESACC', 'MSG']
assert_array_equal(list(es.dframes.keys()), test_evs)
def test_timeunit_extract_samplecount_1000():
ds, es = pandas_dfs_from_asc(paths['mono1000'])
fixations = es.EFIX[:-1]
sc_time_test(ds, fixations, 1000, 500)
sc_time_test(ds, fixations, 1000, 1000)
def test_load_asc_bino250():
pandas_dfs_from_asc(paths['bino250'])
def test_timeunit_extract_overbounds():
ds, es = pandas_dfs_from_asc(paths['mono1000'])
ext = extract_events(ds, es.EFIX, duration=3000, units=TIME_UNITS)
def test_end_fields():
ds, es = pandas_dfs_from_asc(paths["binoRemote500"])
test_fields = ["name", "types", "x_res", "y_res"]
assert_array_equal(es.END.columns.values, test_fields)
def test_event_types():
# make sure the full list of events is there
ds, es = pandas_dfs_from_asc(paths["binoRemote500"])
test_evs = ["END", "EFIX", "EBLINK", "START", "ESACC", "MSG"]
assert_array_equal(es.dframes.keys(), test_evs)
def test_load_asc_binoRemote500():
pandas_dfs_from_asc(paths['binoRemote500'])
