def test_predict():
ds = mock_dataset(num_channels=2, hz=1000)
ds.add_event(0, 10, 11, {"type": "standard", "x": 1})
ds.add_event(0, 20, 21, {"type": "standard", "x": 2})
ds.add_event(0, 30, 31, {"type": "target", "x": 3})
ds.add_event(0, 40, 41, {"type": "target", "x": 4})
rerp = ds.rerp("has type", 0, 10, "type + x")
for predictors in [{"type": ["standard"], "x": [5]},
{"type": "standard", "x": 5},
pandas.DataFrame({"type": ["standard"], "x": [5]}),
]:
prediction = rerp.predict(predictors)
assert isinstance(prediction, pandas.DataFrame)
assert np.all(prediction.index == rerp.betas.major_axis)
assert np.all(prediction.columns == rerp.betas.minor_axis)
assert np.allclose(prediction,
rerp.betas["Intercept"] + 5 * rerp.betas["x"])
prediction = rerp.predict(predictors, which_terms="0 + x")
assert isinstance(prediction, pandas.DataFrame)
assert np.all(prediction.index == rerp.betas.major_axis)
assert np.all(prediction.columns == rerp.betas.minor_axis)
assert np.allclose(prediction, 5 * rerp.betas["x"])
prediction = rerp.predict(predictors, which_terms="0 + type + x")
assert isinstance(prediction, pandas.DataFrame)
assert np.allclose(prediction, 5 * rerp.betas["x"])
assert_raises(ValueError, rerp.predict, {"type": ["standard", "standard"],
"x": [1, 2]})
prediction = rerp.predict_many({"type": ["standard", "target"],
"x": [4, 5]})
assert isinstance(prediction, pandas.Panel)
assert np.all(prediction.items == [0, 1])
assert np.all(prediction.major_axis == rerp.betas.major_axis)
assert np.all(prediction.minor_axis == rerp.betas.minor_axis)
assert np.allclose(prediction[0],
rerp.betas["Intercept"] + 4 * rerp.betas["x"])
assert np.allclose(prediction[1],
rerp.betas["Intercept"]
+ rerp.betas["type[T.target]"]
+ 5 * rerp.betas["x"])
# non-trivial index on input
predictors = pandas.DataFrame({"type": ["standard", "target"],
"x": [4, 5]},
index=[10, 20])
prediction2 = rerp.predict_many(predictors)
assert np.all(prediction2.items == [10, 20])
assert np.all(np.asarray(prediction2) == np.asarray(prediction))
# broadcasting
prediction = rerp.predict_many({"type": "standard", "x": [4, 5]})
assert isinstance(prediction, pandas.Panel)
assert np.all(prediction.items == [0, 1])
assert np.all(prediction.major_axis == rerp.betas.major_axis)
assert np.all(prediction.minor_axis == rerp.betas.minor_axis)
assert np.allclose(prediction[0],
rerp.betas["Intercept"] + 4 * rerp.betas["x"])
assert np.allclose(prediction[1],
rerp.betas["Intercept"] + 5 * rerp.betas["x"])
# all scalars okay (well, this behaviour is kind of unfortunate in fact,
# maybe we should return a dataframe in this case instead of a panel, see:
# https://github.com/pydata/patsy/issues/24
# but in the mean time, oh well, let's test it):
prediction = rerp.predict_many({"type": "standard", "x": 4})
assert isinstance(prediction, pandas.Panel)
assert np.all(prediction.items == [0])
assert np.all(prediction.major_axis == rerp.betas.major_axis)
assert np.all(prediction.minor_axis == rerp.betas.minor_axis)
assert np.allclose(prediction[0],
rerp.betas["Intercept"] + 4 * rerp.betas["x"])
# subsetting
prediction = rerp.predict_many({"type": "standard", "x": [4, 5]},
which_terms=["x"])
assert np.all(prediction.items == [0, 1])
assert np.all(prediction.major_axis == rerp.betas.major_axis)
assert np.all(prediction.minor_axis == rerp.betas.minor_axis)
assert np.allclose(prediction[0], 4 * rerp.betas["x"])
assert np.allclose(prediction[1], 5 * rerp.betas["x"])
def test_rerp_overlap():
# A very simple case where overlap correction can be worked out by hand:
# event type A: |-- 1 --| |-- 2 --|
# event type B: |-- 3 --|
# The rerp for event type A will be:
# the average of 1 & 2 in the part where the epoch 2 has no overlap
# just the values from 1 in the part where epoch 2 has overlap
# The rerp for event type B will be:
# the difference between the values in 3 and 1 in the part where 2 and 3
# overlap
# just the values from 3 in the part where 3 does not overlap
HALF_EPOCH = 1
EPOCH = 2 * HALF_EPOCH
ds = mock_dataset(num_channels=2, ticks_per_recspan=10 * EPOCH, hz=1000)
ds.add_event(0, 0, 1, {"type": "A"})
ds.add_event(0, 4 * HALF_EPOCH, 4 * HALF_EPOCH + 1, {"type": "A"})
ds.add_event(0, 5 * HALF_EPOCH, 5 * HALF_EPOCH + 1, {"type": "B"})
epoch1 = np.asarray(ds[0].iloc[0:EPOCH, :])
epoch2 = np.asarray(ds[0].iloc[4 * HALF_EPOCH:6 * HALF_EPOCH, :])
epoch3 = np.asarray(ds[0].iloc[5 * HALF_EPOCH:7 * HALF_EPOCH, :])
expected_A = np.empty((EPOCH, 2))
expected_A[:HALF_EPOCH, :] = ((epoch1 + epoch2) / 2)[:HALF_EPOCH, :]
expected_A[HALF_EPOCH:, :] = epoch1[HALF_EPOCH:, :]
expected_B = np.empty((EPOCH, 2))
# Notice that the indexes here are different for the different arrays:
expected_B[:HALF_EPOCH, :] = epoch3[:HALF_EPOCH, :] - epoch1[HALF_EPOCH:, :]
expected_B[HALF_EPOCH:, :] = epoch3[HALF_EPOCH:, :]
for (regression_strategy, overlap_correction, parimap_mode) in product(
["auto", "by-epoch", "continuous"],
[True, False],
["serial", "multiprocess"]):
pyrerp.parimap.configure(mode=parimap_mode)
if overlap_correction and regression_strategy == "by-epoch":
assert_raises(ValueError,
ds.rerp, "True", 0, EPOCH - 1, "0 + type",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
else:
rerp = ds.rerp("True", 0, EPOCH - 1, "0 + type",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
if overlap_correction:
assert np.allclose(rerp.betas["type[A]"], expected_A)
assert np.allclose(rerp.betas["type[B]"], expected_B)
assert rerp.regression_strategy == "continuous"
s = rerp.global_stats
assert s.ticks.requested == 5 * HALF_EPOCH
assert s.ticks.accepted == 5 * HALF_EPOCH
assert s.event_ticks.requested == 3 * EPOCH
assert s.event_ticks.accepted == 3 * EPOCH
# all of epoch 1, plus half of epoch 2 and half of epoch 3:
assert s.no_overlap_ticks.requested == 4 * HALF_EPOCH
assert s.no_overlap_ticks.accepted == 4 * HALF_EPOCH
else:
assert np.allclose(rerp.betas["type[A]"],
(epoch1 + epoch2) / 2)
assert np.allclose(rerp.betas["type[B]"], epoch3)
if regression_strategy == "auto":
assert rerp.regression_strategy == "by-epoch"
else:
assert rerp.regression_strategy == regression_strategy
s = rerp.global_stats
assert s.ticks.requested == 3 * EPOCH
assert s.ticks.accepted == 3 * EPOCH
assert s.event_ticks.requested == 3 * EPOCH
assert s.event_ticks.accepted == 3 * EPOCH
assert s.no_overlap_ticks.requested == 3 * EPOCH
assert s.no_overlap_ticks.accepted == 3 * EPOCH
def test_multi_rerp():
ds = mock_dataset(num_channels=2, hz=1000)
ds.add_event(0, 10, 11, {"type": "standard"})
ds.add_event(0, 20, 21, {"type": "standard"})
ds.add_event(0, 30, 31, {"type": "target"})
ds.add_event(0, 40, 41, {"type": "target"})
ds.add_event(0, 50, 51, {"type": "target"})
ds.add_event(0, 51, 53, {"maybe_artifact": True})
for (regression_strategy, overlap_correction, parimap_mode) in product(
["auto", "by-epoch", "continuous"],
[True, False],
["multiprocess", "serial"]):
pyrerp.parimap.configure(mode=parimap_mode)
assert ds.multi_rerp([],
regression_strategy=regression_strategy,
overlap_correction=overlap_correction) == []
standard_req = rERPRequest("type == 'standard'", -2, 4, "~ 1")
target_req = rERPRequest("type == 'target'", -2, 4, "~ 1")
erps = ds.multi_rerp([standard_req, target_req],
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
standard_erp, target_erp = erps
assert standard_erp.event_query == "type == 'standard'"
assert target_erp.event_query == "type == 'target'"
for i, erp in enumerate([standard_erp, target_erp]):
assert erp.start_time == -2
assert erp.stop_time == 4
assert erp.formula == "~ 1"
assert erp.bad_event_query is None
assert erp.all_or_nothing == False
assert erp.data_format is ds.data_format
assert erp.design_info.column_names == ["Intercept"]
assert erp.start_tick == -2
assert erp.stop_tick == 5
assert erp.ticks == 7
assert erp.this_rerp_index == i
assert erp.total_rerps == 2
assert erp.global_stats.epochs.fully_accepted == 5
assert erp.global_stats.ticks.requested == 5 * 7
assert erp.this_rerp_stats.epochs.fully_accepted in (2, 3)
if regression_strategy == "auto":
assert erp.regression_strategy == "by-epoch"
else:
assert erp.regression_strategy == regression_strategy
assert erp.overlap_correction == overlap_correction
assert isinstance(erp.betas, pandas.Panel)
assert erp.betas.shape == (1, 7, 2)
assert np.all(erp.betas.items == ["Intercept"])
assert np.all(erp.betas.major_axis == [-2, -1, 0, 1, 2, 3, 4])
assert np.all(erp.betas.minor_axis == ["MOCK0", "MOCK1"])
standard_epoch0 = np.asarray(ds[0].iloc[10 - 2:10 + 5, :])
standard_epoch1 = np.asarray(ds[0].iloc[20 - 2:20 + 5, :])
target_epoch0 = np.asarray(ds[0].iloc[30 - 2:30 + 5, :])
target_epoch1 = np.asarray(ds[0].iloc[40 - 2:40 + 5, :])
target_epoch2 = np.asarray(ds[0].iloc[50 - 2:50 + 5, :])
assert np.allclose(standard_erp.betas["Intercept"],
(standard_epoch0 + standard_epoch1) / 2.0)
assert np.allclose(target_erp.betas["Intercept"],
(target_epoch0 + target_epoch1 + target_epoch2)
/ 3.0)
################
both_req = rERPRequest("has type", -2, 4, formula="~ type")
erps = ds.multi_rerp([both_req],
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
both_erp, = erps
assert both_erp.event_query == "has type"
assert both_erp.start_time == -2
assert both_erp.stop_time == 4
assert both_erp.formula == "~ type"
assert both_erp.bad_event_query is None
assert both_erp.all_or_nothing == False
assert both_erp.data_format is ds.data_format
assert both_erp.design_info.column_names == ["Intercept",
"type[T.target]"]
assert both_erp.start_tick == -2
assert both_erp.stop_tick == 5
assert both_erp.ticks == 7
assert both_erp.this_rerp_index == 0
assert both_erp.total_rerps == 1
assert both_erp.global_stats.epochs.fully_accepted == 5
assert both_erp.global_stats.ticks.requested == 5 * 7
assert both_erp.this_rerp_stats.epochs.fully_accepted == 5
if regression_strategy == "auto":
assert both_erp.regression_strategy == "by-epoch"
else:
assert both_erp.regression_strategy == regression_strategy
assert both_erp.overlap_correction == overlap_correction
assert isinstance(both_erp.betas, pandas.Panel)
assert both_erp.betas.shape == (2, 7, 2)
assert np.all(both_erp.betas.items == ["Intercept", "type[T.target]"])
assert np.all(both_erp.betas.major_axis == [-2, -1, 0, 1, 2, 3, 4])
assert np.all(both_erp.betas.minor_axis == ["MOCK0", "MOCK1"])
standard_epoch0 = np.asarray(ds[0].iloc[10 - 2:10 + 5, :])
standard_epoch1 = np.asarray(ds[0].iloc[20 - 2:20 + 5, :])
target_epoch0 = np.asarray(ds[0].iloc[30 - 2:30 + 5, :])
target_epoch1 = np.asarray(ds[0].iloc[40 - 2:40 + 5, :])
target_epoch2 = np.asarray(ds[0].iloc[50 - 2:50 + 5, :])
standard_avg = (standard_epoch0 + standard_epoch1) / 2.0
target_avg = (target_epoch0 + target_epoch1 + target_epoch2) / 3.0
assert np.allclose(both_erp.betas["Intercept"], standard_avg)
assert np.allclose(both_erp.betas["type[T.target]"],
target_avg - standard_avg)
################
both_req2 = rERPRequest("has type", -2, 4, formula="~ 0 + type")
erps = ds.multi_rerp([both_req2],
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
both_erp2, = erps
assert both_erp2.design_info.column_names == ["type[standard]",
"type[target]"]
assert np.allclose(both_erp2.betas["type[standard]"], standard_avg)
assert np.allclose(both_erp2.betas["type[target]"], target_avg)
################
# regular artifact (check accounting)
if regression_strategy == "by-epoch":
assert_raises(ValueError, ds.multi_rerp, [both_req2],
artifact_query="has maybe_artifact",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
else:
both_erp3, = ds.multi_rerp([both_req2],
artifact_query="has maybe_artifact",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
assert both_erp3.regression_strategy == "continuous"
assert both_erp3.global_stats.epochs.requested == 5
assert both_erp3.global_stats.epochs.fully_accepted == 4
assert both_erp3.global_stats.epochs.partially_accepted == 1
assert both_erp3.global_stats.ticks.rejected == 2
assert np.allclose(both_erp3.betas["type[standard]"],
standard_avg)
target_2avg = (target_epoch0 + target_epoch1) / 2.0
target_art_avg = target_avg.copy()
# starts 1 tick past timelock event, which itself is 2 ticks into
# epoch, and continues for 2 ticks
assert both_erp3.start_tick == -2
art_span = slice(2 + 1, 2 + 1 + 2)
target_art_avg[art_span, :] = target_2avg[art_span, :]
assert np.allclose(both_erp3.betas["type[target]"],
target_art_avg)
################
# all or nothing
both_req4 = rERPRequest("has type", -2, 4, formula="~ 0 + type",
all_or_nothing=True)
both_erp4, = ds.multi_rerp([both_req4],
artifact_query="has maybe_artifact",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
if regression_strategy == "auto":
assert both_erp4.regression_strategy == "by-epoch"
assert np.allclose(both_erp4.betas["type[standard]"],
standard_avg)
assert np.allclose(both_erp4.betas["type[target]"],
(target_epoch0 + target_epoch1) / 2.0)
################
# bad_event_query
both_req5 = rERPRequest("has type", -2, 4, formula="~ 0 + type",
bad_event_query="_START_TICK == 20")
if regression_strategy == "by-epoch":
assert_raises(ValueError, ds.multi_rerp, [both_req5],
artifact_query="has maybe_artifact",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
else:
both_erp5, = ds.multi_rerp([both_req5],
artifact_query="has maybe_artifact",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
assert both_erp5.regression_strategy == "continuous"
# standard_epoch1 is knocked out by bad_event_query
assert np.allclose(both_erp5.betas["type[standard]"],
standard_epoch0)
# plus there's an artifact knocking out part of target_epoch2
assert np.allclose(both_erp5.betas["type[target]"],
target_art_avg)
def test_predict():
ds = mock_dataset(num_channels=2, hz=1000)
ds.add_event(0, 10, 11, {"type": "standard", "x": 1})
ds.add_event(0, 20, 21, {"type": "standard", "x": 2})
ds.add_event(0, 30, 31, {"type": "target", "x": 3})
ds.add_event(0, 40, 41, {"type": "target", "x": 4})
rerp = ds.rerp("has type", 0, 10, "type + x")
for predictors in [
{
"type": ["standard"],
"x": [5]
},
{
"type": "standard",
"x": 5
},
pandas.DataFrame({
"type": ["standard"],
"x": [5]
}),
]:
prediction = rerp.predict(predictors)
assert isinstance(prediction, pandas.DataFrame)
assert np.all(prediction.index == rerp.betas.major_axis)
assert np.all(prediction.columns == rerp.betas.minor_axis)
assert np.allclose(prediction,
rerp.betas["Intercept"] + 5 * rerp.betas["x"])
prediction = rerp.predict(predictors, which_terms="0 + x")
assert isinstance(prediction, pandas.DataFrame)
assert np.all(prediction.index == rerp.betas.major_axis)
assert np.all(prediction.columns == rerp.betas.minor_axis)
assert np.allclose(prediction, 5 * rerp.betas["x"])
prediction = rerp.predict(predictors, which_terms="0 + type + x")
assert isinstance(prediction, pandas.DataFrame)
assert np.allclose(prediction, 5 * rerp.betas["x"])
assert_raises(ValueError, rerp.predict, {
"type": ["standard", "standard"],
"x": [1, 2]
})
prediction = rerp.predict_many({
"type": ["standard", "target"],
"x": [4, 5]
})
assert isinstance(prediction, pandas.Panel)
assert np.all(prediction.items == [0, 1])
assert np.all(prediction.major_axis == rerp.betas.major_axis)
assert np.all(prediction.minor_axis == rerp.betas.minor_axis)
assert np.allclose(prediction[0],
rerp.betas["Intercept"] + 4 * rerp.betas["x"])
assert np.allclose(
prediction[1], rerp.betas["Intercept"] + rerp.betas["type[T.target]"] +
5 * rerp.betas["x"])
# non-trivial index on input
predictors = pandas.DataFrame({
"type": ["standard", "target"],
"x": [4, 5]
},
index=[10, 20])
prediction2 = rerp.predict_many(predictors)
assert np.all(prediction2.items == [10, 20])
assert np.all(np.asarray(prediction2) == np.asarray(prediction))
# broadcasting
prediction = rerp.predict_many({"type": "standard", "x": [4, 5]})
assert isinstance(prediction, pandas.Panel)
assert np.all(prediction.items == [0, 1])
assert np.all(prediction.major_axis == rerp.betas.major_axis)
assert np.all(prediction.minor_axis == rerp.betas.minor_axis)
assert np.allclose(prediction[0],
rerp.betas["Intercept"] + 4 * rerp.betas["x"])
assert np.allclose(prediction[1],
rerp.betas["Intercept"] + 5 * rerp.betas["x"])
# all scalars okay (well, this behaviour is kind of unfortunate in fact,
# maybe we should return a dataframe in this case instead of a panel, see:
# https://github.com/pydata/patsy/issues/24
# but in the mean time, oh well, let's test it):
prediction = rerp.predict_many({"type": "standard", "x": 4})
assert isinstance(prediction, pandas.Panel)
assert np.all(prediction.items == [0])
assert np.all(prediction.major_axis == rerp.betas.major_axis)
assert np.all(prediction.minor_axis == rerp.betas.minor_axis)
assert np.allclose(prediction[0],
rerp.betas["Intercept"] + 4 * rerp.betas["x"])
# subsetting
prediction = rerp.predict_many({
"type": "standard",
"x": [4, 5]
},
which_terms=["x"])
assert np.all(prediction.items == [0, 1])
assert np.all(prediction.major_axis == rerp.betas.major_axis)
assert np.all(prediction.minor_axis == rerp.betas.minor_axis)
assert np.allclose(prediction[0], 4 * rerp.betas["x"])
assert np.allclose(prediction[1], 5 * rerp.betas["x"])
def test_rerp_overlap():
# A very simple case where overlap correction can be worked out by hand:
# event type A: |-- 1 --| |-- 2 --|
# event type B: |-- 3 --|
# The rerp for event type A will be:
# the average of 1 & 2 in the part where the epoch 2 has no overlap
# just the values from 1 in the part where epoch 2 has overlap
# The rerp for event type B will be:
# the difference between the values in 3 and 1 in the part where 2 and 3
# overlap
# just the values from 3 in the part where 3 does not overlap
HALF_EPOCH = 1
EPOCH = 2 * HALF_EPOCH
ds = mock_dataset(num_channels=2, ticks_per_recspan=10 * EPOCH, hz=1000)
ds.add_event(0, 0, 1, {"type": "A"})
ds.add_event(0, 4 * HALF_EPOCH, 4 * HALF_EPOCH + 1, {"type": "A"})
ds.add_event(0, 5 * HALF_EPOCH, 5 * HALF_EPOCH + 1, {"type": "B"})
epoch1 = np.asarray(ds[0].iloc[0:EPOCH, :])
epoch2 = np.asarray(ds[0].iloc[4 * HALF_EPOCH:6 * HALF_EPOCH, :])
epoch3 = np.asarray(ds[0].iloc[5 * HALF_EPOCH:7 * HALF_EPOCH, :])
expected_A = np.empty((EPOCH, 2))
expected_A[:HALF_EPOCH, :] = ((epoch1 + epoch2) / 2)[:HALF_EPOCH, :]
expected_A[HALF_EPOCH:, :] = epoch1[HALF_EPOCH:, :]
expected_B = np.empty((EPOCH, 2))
# Notice that the indexes here are different for the different arrays:
expected_B[:HALF_EPOCH, :] = epoch3[:HALF_EPOCH, :] - epoch1[
HALF_EPOCH:, :]
expected_B[HALF_EPOCH:, :] = epoch3[HALF_EPOCH:, :]
for (regression_strategy, overlap_correction,
parimap_mode) in product(["auto", "by-epoch", "continuous"],
[True, False], ["serial", "multiprocess"]):
pyrerp.parimap.configure(mode=parimap_mode)
if overlap_correction and regression_strategy == "by-epoch":
assert_raises(ValueError,
ds.rerp,
"True",
0,
EPOCH - 1,
"0 + type",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
else:
rerp = ds.rerp("True",
0,
EPOCH - 1,
"0 + type",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
if overlap_correction:
assert np.allclose(rerp.betas["type[A]"], expected_A)
assert np.allclose(rerp.betas["type[B]"], expected_B)
assert rerp.regression_strategy == "continuous"
s = rerp.global_stats
assert s.ticks.requested == 5 * HALF_EPOCH
assert s.ticks.accepted == 5 * HALF_EPOCH
assert s.event_ticks.requested == 3 * EPOCH
assert s.event_ticks.accepted == 3 * EPOCH
# all of epoch 1, plus half of epoch 2 and half of epoch 3:
assert s.no_overlap_ticks.requested == 4 * HALF_EPOCH
assert s.no_overlap_ticks.accepted == 4 * HALF_EPOCH
else:
assert np.allclose(rerp.betas["type[A]"],
(epoch1 + epoch2) / 2)
assert np.allclose(rerp.betas["type[B]"], epoch3)
if regression_strategy == "auto":
assert rerp.regression_strategy == "by-epoch"
else:
assert rerp.regression_strategy == regression_strategy
s = rerp.global_stats
assert s.ticks.requested == 3 * EPOCH
assert s.ticks.accepted == 3 * EPOCH
assert s.event_ticks.requested == 3 * EPOCH
assert s.event_ticks.accepted == 3 * EPOCH
assert s.no_overlap_ticks.requested == 3 * EPOCH
assert s.no_overlap_ticks.accepted == 3 * EPOCH
def test_multi_rerp():
ds = mock_dataset(num_channels=2, hz=1000)
ds.add_event(0, 10, 11, {"type": "standard"})
ds.add_event(0, 20, 21, {"type": "standard"})
ds.add_event(0, 30, 31, {"type": "target"})
ds.add_event(0, 40, 41, {"type": "target"})
ds.add_event(0, 50, 51, {"type": "target"})
ds.add_event(0, 51, 53, {"maybe_artifact": True})
for (regression_strategy, overlap_correction,
parimap_mode) in product(["auto", "by-epoch", "continuous"],
[True, False], ["multiprocess", "serial"]):
pyrerp.parimap.configure(mode=parimap_mode)
assert ds.multi_rerp([],
regression_strategy=regression_strategy,
overlap_correction=overlap_correction) == []
standard_req = rERPRequest("type == 'standard'", -2, 4, "~ 1")
target_req = rERPRequest("type == 'target'", -2, 4, "~ 1")
erps = ds.multi_rerp([standard_req, target_req],
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
standard_erp, target_erp = erps
assert standard_erp.event_query == "type == 'standard'"
assert target_erp.event_query == "type == 'target'"
for i, erp in enumerate([standard_erp, target_erp]):
assert erp.start_time == -2
assert erp.stop_time == 4
assert erp.formula == "~ 1"
assert erp.bad_event_query is None
assert erp.all_or_nothing == False
assert erp.data_format is ds.data_format
assert erp.design_info.column_names == ["Intercept"]
assert erp.start_tick == -2
assert erp.stop_tick == 5
assert erp.ticks == 7
assert erp.this_rerp_index == i
assert erp.total_rerps == 2
assert erp.global_stats.epochs.fully_accepted == 5
assert erp.global_stats.ticks.requested == 5 * 7
assert erp.this_rerp_stats.epochs.fully_accepted in (2, 3)
if regression_strategy == "auto":
assert erp.regression_strategy == "by-epoch"
else:
assert erp.regression_strategy == regression_strategy
assert erp.overlap_correction == overlap_correction
assert isinstance(erp.betas, pandas.Panel)
assert erp.betas.shape == (1, 7, 2)
assert np.all(erp.betas.items == ["Intercept"])
assert np.all(erp.betas.major_axis == [-2, -1, 0, 1, 2, 3, 4])
assert np.all(erp.betas.minor_axis == ["MOCK0", "MOCK1"])
standard_epoch0 = np.asarray(ds[0].iloc[10 - 2:10 + 5, :])
standard_epoch1 = np.asarray(ds[0].iloc[20 - 2:20 + 5, :])
target_epoch0 = np.asarray(ds[0].iloc[30 - 2:30 + 5, :])
target_epoch1 = np.asarray(ds[0].iloc[40 - 2:40 + 5, :])
target_epoch2 = np.asarray(ds[0].iloc[50 - 2:50 + 5, :])
assert np.allclose(standard_erp.betas["Intercept"],
(standard_epoch0 + standard_epoch1) / 2.0)
assert np.allclose(target_erp.betas["Intercept"],
(target_epoch0 + target_epoch1 + target_epoch2) /
3.0)
################
both_req = rERPRequest("has type", -2, 4, formula="~ type")
erps = ds.multi_rerp([both_req],
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
both_erp, = erps
assert both_erp.event_query == "has type"
assert both_erp.start_time == -2
assert both_erp.stop_time == 4
assert both_erp.formula == "~ type"
assert both_erp.bad_event_query is None
assert both_erp.all_or_nothing == False
assert both_erp.data_format is ds.data_format
assert both_erp.design_info.column_names == [
"Intercept", "type[T.target]"
]
assert both_erp.start_tick == -2
assert both_erp.stop_tick == 5
assert both_erp.ticks == 7
assert both_erp.this_rerp_index == 0
assert both_erp.total_rerps == 1
assert both_erp.global_stats.epochs.fully_accepted == 5
assert both_erp.global_stats.ticks.requested == 5 * 7
assert both_erp.this_rerp_stats.epochs.fully_accepted == 5
if regression_strategy == "auto":
assert both_erp.regression_strategy == "by-epoch"
else:
assert both_erp.regression_strategy == regression_strategy
assert both_erp.overlap_correction == overlap_correction
assert isinstance(both_erp.betas, pandas.Panel)
assert both_erp.betas.shape == (2, 7, 2)
assert np.all(both_erp.betas.items == ["Intercept", "type[T.target]"])
assert np.all(both_erp.betas.major_axis == [-2, -1, 0, 1, 2, 3, 4])
assert np.all(both_erp.betas.minor_axis == ["MOCK0", "MOCK1"])
standard_epoch0 = np.asarray(ds[0].iloc[10 - 2:10 + 5, :])
standard_epoch1 = np.asarray(ds[0].iloc[20 - 2:20 + 5, :])
target_epoch0 = np.asarray(ds[0].iloc[30 - 2:30 + 5, :])
target_epoch1 = np.asarray(ds[0].iloc[40 - 2:40 + 5, :])
target_epoch2 = np.asarray(ds[0].iloc[50 - 2:50 + 5, :])
standard_avg = (standard_epoch0 + standard_epoch1) / 2.0
target_avg = (target_epoch0 + target_epoch1 + target_epoch2) / 3.0
assert np.allclose(both_erp.betas["Intercept"], standard_avg)
assert np.allclose(both_erp.betas["type[T.target]"],
target_avg - standard_avg)
################
both_req2 = rERPRequest("has type", -2, 4, formula="~ 0 + type")
erps = ds.multi_rerp([both_req2],
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
both_erp2, = erps
assert both_erp2.design_info.column_names == [
"type[standard]", "type[target]"
]
assert np.allclose(both_erp2.betas["type[standard]"], standard_avg)
assert np.allclose(both_erp2.betas["type[target]"], target_avg)
################
# regular artifact (check accounting)
if regression_strategy == "by-epoch":
assert_raises(ValueError,
ds.multi_rerp, [both_req2],
artifact_query="has maybe_artifact",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
else:
both_erp3, = ds.multi_rerp([both_req2],
artifact_query="has maybe_artifact",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
assert both_erp3.regression_strategy == "continuous"
assert both_erp3.global_stats.epochs.requested == 5
assert both_erp3.global_stats.epochs.fully_accepted == 4
assert both_erp3.global_stats.epochs.partially_accepted == 1
assert both_erp3.global_stats.ticks.rejected == 2
assert np.allclose(both_erp3.betas["type[standard]"], standard_avg)
target_2avg = (target_epoch0 + target_epoch1) / 2.0
target_art_avg = target_avg.copy()
# starts 1 tick past timelock event, which itself is 2 ticks into
# epoch, and continues for 2 ticks
assert both_erp3.start_tick == -2
art_span = slice(2 + 1, 2 + 1 + 2)
target_art_avg[art_span, :] = target_2avg[art_span, :]
assert np.allclose(both_erp3.betas["type[target]"], target_art_avg)
################
# all or nothing
both_req4 = rERPRequest("has type",
-2,
4,
formula="~ 0 + type",
all_or_nothing=True)
both_erp4, = ds.multi_rerp([both_req4],
artifact_query="has maybe_artifact",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
if regression_strategy == "auto":
assert both_erp4.regression_strategy == "by-epoch"
assert np.allclose(both_erp4.betas["type[standard]"], standard_avg)
assert np.allclose(both_erp4.betas["type[target]"],
(target_epoch0 + target_epoch1) / 2.0)
################
# bad_event_query
both_req5 = rERPRequest("has type",
-2,
4,
formula="~ 0 + type",
bad_event_query="_START_TICK == 20")
if regression_strategy == "by-epoch":
assert_raises(ValueError,
ds.multi_rerp, [both_req5],
artifact_query="has maybe_artifact",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
else:
both_erp5, = ds.multi_rerp([both_req5],
artifact_query="has maybe_artifact",
regression_strategy=regression_strategy,
overlap_correction=overlap_correction)
assert both_erp5.regression_strategy == "continuous"
# standard_epoch1 is knocked out by bad_event_query
assert np.allclose(both_erp5.betas["type[standard]"],
standard_epoch0)
# plus there's an artifact knocking out part of target_epoch2
assert np.allclose(both_erp5.betas["type[target]"], target_art_avg)
