def it_gets_flat_test_radmat():
flat_test_radmat = result.flat_test_radmat()
assert flat_test_radmat.shape == (
n_peps * n_test_samples,
n_channels * n_cycles,
)
expected = npf([
[4.0, 2.0, 1.0, 1.0, 1.0, 0.0],
[5.0, 2.0, 1.0, 1.0, 1.0, 1.0],
[6.0, 3.0, 0.9, 1.0, 0.0, 1.0],
[0.4, 0.2, 0.1, 0.3, 0.2, 0.1],
[0.5, 0.2, 0.1, 0.3, 0.2, 0.1],
[0.6, 0.3, 2.9, 0.3, 0.2, 0.1],
])
assert np_array_same(flat_test_radmat, expected)
def it_can_edman_degrade():
# Degrade = Edman success; thus p_edman_failure=0.0
result = sim_v1_worker._step_3a_cycle_edman(samples,
is_mock=False,
p_edman_failure=0.0)
expected = npf([
[[n, 1, 0], [n, 1, 0]],
[[n, 1, 1], [n, 0, 1]],
])
assert np_array_same(expected, result)
def it_makes_a_copy():
# The original should not have changed
assert samples[0, 0, 0] == 1.0
zest()
def zest_step_6_compact_flu():
flu = npf([
[0, 0, 1, 0, 0, 0, 1, 0],
[1, 0, 0, 1, 0, 0, 1, 1],
])
compact_flu, remainder_flu = sim_v1_worker._step_6_compact_flu(
flu, n_edmans=4, n_channels=2)
n = np.nan
def it_makes_a_flu():
assert np_array_same(compact_flu, [1, n, 0, 1])
def it_makes_remainder():
assert np_array_same(remainder_flu, [1, 2])
zest()
def it_proceeds_with_no_error():
evolution = sim_v1_worker._step_3_evolve_cycles(samples, sim_params)
# fmt: off
n = np.nan
expected = npf([
[
[[1, 1, 0], [0, 1, 0]],
[[0, 1, 1], [0, 0, 1]],
],
[
[[n, 1, 0], [n, 1, 0]],
[[n, 1, 1], [n, 0, 1]],
],
])
# fmt: on
if not np_array_same(evolution, expected):
debug(evolution)
debug(sim_params)
assert np_array_same(evolution, expected)
def zest_step_3c_detach():
n = np.nan
samples = npf([
[[1, 1, 0], [0, 1, 0]],
[[n, 1, 1], [n, 0, 1]],
])
def it_detach():
result = sim_v1_worker._step_3c_detach(samples, 1.0)
expected = np.full_like(samples, n)
assert np_array_same(expected, result)
def it_make_a_copy():
# The original should not have changed
assert samples[0, 0, 0] == 1.0
zest()
def it_fail_to_detach():
result = sim_v1_worker._step_3c_detach(samples, 0.0)
assert np_array_same(samples, result)
zest()
def zest_all_df():
props = Munch(
signal_radmat=npf(
[
[[3.0, 2.0, 1.0], [1.0, 0.0, 1.0]],
[[4.0, 2.0, 1.0], [1.0, 1.0, 0.0]],
[[5.0, 2.0, 1.0], [1.0, 1.0, 1.0]],
[[6.0, 3.0, 0.9], [1.0, 0.0, 1.0]],
]
),
noise_radmat=npf(
[
[[0.3, 0.2, 0.1], [0.3, 0.2, 0.1]],
[[0.4, 0.2, 0.1], [0.3, 0.2, 0.1]],
[[0.5, 0.2, 0.1], [0.3, 0.2, 0.1]],
[[0.6, 0.3, 2.9], [0.3, 0.2, 0.1]],
]
),
localbg_radmat=npf(
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
]
),
peak_df=pd.DataFrame(
[
(0, 0, 0, 1.0, 11.0),
(0, 0, 1, 2.0, 12.0),
(0, 1, 0, 3.0, 13.0),
(0, 1, 1, 4.0, 14.0),
],
columns=["peak_i", "field_i", "field_peak_i", "aln_x", "aln_y"],
),
field_df=pd.DataFrame(
[
(0, 0, 0, 1, 2, 0.1, 100.0, 2, 10, 5, 10, 500, 20, 490),
(0, 0, 1, 2, 3, 0.2, 110.0, 2, 10, 5, 10, 500, 20, 490),
(0, 0, 2, 3, 4, 0.3, 120.0, 2, 10, 5, 10, 500, 20, 490),
(0, 1, 0, 4, 5, 0.4, 130.0, 2, 10, 5, 10, 500, 20, 490),
(0, 1, 1, 5, 6, 0.5, 140.0, 2, 10, 5, 10, 500, 20, 490),
(0, 1, 2, 6, 7, 0.6, 150.0, 2, 10, 5, 10, 500, 20, 490),
(1, 0, 0, 7, 8, 0.7, 160.0, 2, 10, 6, 20, 510, 30, 480),
(1, 0, 1, 8, 9, 0.8, 170.0, 2, 10, 6, 20, 510, 30, 480),
(1, 0, 2, 9, 0, 0.9, 180.0, 2, 10, 6, 20, 510, 30, 480),
(1, 1, 0, 0, 1, 0.0, 190.0, 2, 10, 6, 20, 510, 30, 480),
(1, 1, 1, 1, 2, 0.1, 200.0, 2, 10, 6, 20, 510, 30, 480),
(1, 1, 2, 2, 3, 0.2, 210.0, 2, 10, 6, 20, 510, 30, 480),
],
columns=[
"field_i",
"channel_i",
"cycle_i",
"shift_y",
"shift_x",
"aln_score",
"bg_median",
"n_mask_rects",
"mask_area",
"border_size",
"aligned_roi_l",
"aligned_roi_r",
"aligned_roi_b",
"aligned_roi_t",
],
),
mask_rects_df=pd.DataFrame(
[
(0, 0, 0, 1, 2, 3, 4),
(0, 0, 1, 2, 2, 3, 4),
(0, 0, 2, 3, 2, 3, 4),
(0, 1, 0, 4, 2, 3, 4),
(0, 1, 1, 5, 2, 3, 4),
(0, 1, 2, 6, 2, 3, 4),
(1, 0, 0, 7, 2, 3, 4),
(1, 0, 1, 8, 2, 3, 4),
(1, 0, 2, 9, 2, 3, 4),
(1, 1, 0, 0, 2, 3, 4),
(1, 1, 1, 1, 2, 3, 4),
(1, 1, 2, 2, 2, 3, 4),
],
columns=["field_i", "channel_i", "cycle_i", "l", "r", "w", "h",],
),
)
def _mock_load_field_prop(inst, field_i, prop):
return props[prop]
zest.stack_mock(
SigprocV1Result._load_field_prop,
substitute_fn=_mock_load_field_prop,
reset_before_each=False,
)
res = SigprocV1Result(
is_loaded_result=True, field_files=[""], n_peaks=4, n_channels=2, n_cycles=3
)
def it_np_signal_radmat():
assert np_array_same(res.signal_radmat(), props.signal_radmat)
def it_np_noise_radmat():
assert np_array_same(res.noise_radmat(), props.noise_radmat)
# All of the following are testing the DataFrames
def it_fields():
assert res.fields().equals(props.field_df)
def it_radmats():
rad_df = res.radmats()
check.df_t(rad_df, SigprocV1Result.radmat_df_schema)
assert len(rad_df) == 4 * 2 * 3
# Sanity check a few
assert (
rad_df[
(rad_df.peak_i == 1) & (rad_df.channel_i == 1) & (rad_df.cycle_i == 1)
].signal.values[0]
== 1.0
)
assert (
rad_df[
(rad_df.peak_i == 2) & (rad_df.channel_i == 0) & (rad_df.cycle_i == 0)
].signal.values[0]
== 5.0
)
def it_mask_rects():
rects_df = res.mask_rects()
check.df_t(rects_df, SigprocV1Result.mask_rects_df_schema)
assert len(rects_df) == 2 * 2 * 3
# TASK: Lots of work left here
# def it_peaks():
# assert res.peaks()[["field_i", "field_peak_i", "aln_x", "aln_y"]].equals(
# props.peak_df[["field_i", "field_peak_i", "aln_x", "aln_y"]]
# )
# assert np.all(res.peaks().peak_i.values == np.arange(4))
# def it_radmats__peaks():
# df = res.radmats__peaks()
#
# # Sanity check a few
# assert df[(df.peak_i == 1) & (df.channel_i == 0) & (df.cycle_i == 0)].signal.values[0] == 4.0
# assert df[(df.peak_i == 3) & (df.channel_i == 0) & (df.cycle_i == 2)].signal.values[0] == 0.9
# assert np.all(df[df.peak_i == 3].aln_x.values == 4.0)
# assert np.all(df[df.peak_i == 3].aln_y.values == 14.0)
#
# def it_n_peaks():
# df = res.n_peaks()
# assert np.all(df.n_peaks.values == 2)
#
# def it_field__n_peaks__peaks():
# df = res.field__n_peaks__peaks()
# raise NotImplementedError
#
# def it_fields__n_peaks__radmat__peaks():
# df = res.fields__n_peaks__radmat__peaks()
# debug(df)
# raise NotImplementedError
zest()
def zest_v2_all_df():
props = Munch(
radmat=npf(
[
[
[[3.0, 2.0, 1.0], [1.0, 0.0, 1.0]],
[[4.0, 2.0, 1.0], [1.0, 1.0, 0.0]],
[[5.0, 2.0, 1.0], [1.0, 1.0, 1.0]],
[[6.0, 3.0, 0.9], [1.0, 0.0, 1.0]],
],
[
[[0.3, 0.2, 0.1], [0.3, 0.2, 0.1]],
[[0.4, 0.2, 0.1], [0.3, 0.2, 0.1]],
[[0.5, 0.2, 0.1], [0.3, 0.2, 0.1]],
[[0.6, 0.3, 2.9], [0.3, 0.2, 0.1]],
],
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
],
[
[[1.1, 1.2, 1.3], [1.4, 1.5, 1.6]],
[[1.1, 1.2, 1.3], [1.4, 1.5, 1.6]],
[[1.1, 1.2, 1.3], [1.4, 1.5, 1.6]],
[[1.1, 1.2, 1.3], [1.4, 1.5, 1.6]],
],
],
),
peak_df=pd.DataFrame(
[
(0, 0, 0, 1.0, 11.0),
(0, 0, 1, 2.0, 12.0),
(0, 1, 0, 3.0, 13.0),
(0, 1, 1, 4.0, 14.0),
],
columns=["peak_i", "field_i", "field_peak_i", "aln_x", "aln_y"],
),
field_df=pd.DataFrame(
[
(0, 0, 0, 1, 2, 0.1, 100.0, 2, 10, 5, 10, 500, 20, 490),
(0, 0, 1, 2, 3, 0.2, 110.0, 2, 10, 5, 10, 500, 20, 490),
(0, 0, 2, 3, 4, 0.3, 120.0, 2, 10, 5, 10, 500, 20, 490),
(0, 1, 0, 4, 5, 0.4, 130.0, 2, 10, 5, 10, 500, 20, 490),
(0, 1, 1, 5, 6, 0.5, 140.0, 2, 10, 5, 10, 500, 20, 490),
(0, 1, 2, 6, 7, 0.6, 150.0, 2, 10, 5, 10, 500, 20, 490),
(1, 0, 0, 7, 8, 0.7, 160.0, 2, 10, 6, 20, 510, 30, 480),
(1, 0, 1, 8, 9, 0.8, 170.0, 2, 10, 6, 20, 510, 30, 480),
(1, 0, 2, 9, 0, 0.9, 180.0, 2, 10, 6, 20, 510, 30, 480),
(1, 1, 0, 0, 1, 0.0, 190.0, 2, 10, 6, 20, 510, 30, 480),
(1, 1, 1, 1, 2, 0.1, 200.0, 2, 10, 6, 20, 510, 30, 480),
(1, 1, 2, 2, 3, 0.2, 210.0, 2, 10, 6, 20, 510, 30, 480),
],
columns=[
"field_i",
"channel_i",
"cycle_i",
"shift_y",
"shift_x",
"aln_score",
"bg_median",
"n_mask_rects",
"mask_area",
"border_size",
"aligned_roi_l",
"aligned_roi_r",
"aligned_roi_b",
"aligned_roi_t",
],
),
mask_rects_df=pd.DataFrame(
[
(0, 0, 0, 1, 2, 3, 4),
(0, 0, 1, 2, 2, 3, 4),
(0, 0, 2, 3, 2, 3, 4),
(0, 1, 0, 4, 2, 3, 4),
(0, 1, 1, 5, 2, 3, 4),
(0, 1, 2, 6, 2, 3, 4),
(1, 0, 0, 7, 2, 3, 4),
(1, 0, 1, 8, 2, 3, 4),
(1, 0, 2, 9, 2, 3, 4),
(1, 1, 0, 0, 2, 3, 4),
(1, 1, 1, 1, 2, 3, 4),
(1, 1, 2, 2, 2, 3, 4),
],
columns=["field_i", "channel_i", "cycle_i", "l", "r", "w", "h",],
),
)
# ramar got refactored and I just reorder it here
props["radmat"] = np.moveaxis(props["radmat"], 0, 3)
def _mock_load_field_prop(inst, field_i, prop):
return props[prop]
zest.stack_mock(
SigprocV2Result._load_field_prop,
substitute_fn=_mock_load_field_prop,
reset_before_each=False,
)
res = SigprocV2Result(
is_loaded_result=True,
field_files=[""],
n_peaks=4,
n_channels=2,
n_cycles=3,
n_fields=1,
)
def it_np_signal_radmat():
assert np_array_same(res.sig(), props.radmat[:, :, :, 0])
def it_np_noise_radmat():
assert np_array_same(res.noi(), props.radmat[:, :, :, 1])
# All of the following are testing the DataFrames
def it_fields():
assert res.fields().equals(props.field_df)
def it_radmats():
rad_df = res.radmats()
check.df_t(rad_df, SigprocV2Result.radmat_df_schema)
assert len(rad_df) == 4 * 2 * 3
# Sanity check a few
assert (
rad_df[
(rad_df.peak_i == 1) & (rad_df.channel_i == 1) & (rad_df.cycle_i == 1)
].signal.values[0]
== 1.0
)
assert (
rad_df[
(rad_df.peak_i == 2) & (rad_df.channel_i == 0) & (rad_df.cycle_i == 0)
].signal.values[0]
== 5.0
)
zest()
def it_sums():
# fmt: off
# Using a huge array here to make sure all my axes
# are getting set properly
evolution = npf([
[
[[1, 1, 0], [0, 1, 0]],
[[0, 1, 1], [0, 0, 1]],
[[1, 1, 1], [1, 0, 1]],
[[1, 1, 1], [1, 0, 1]],
],
[
[[n, 1, 0], [n, 1, 0]],
[[n, 1, 1], [n, 0, 1]],
[[n, n, 1], [n, n, 1]],
[[n, 1, 1], [n, 0, 1]],
],
[
[[n, 1, 0], [n, 1, 0]],
[[n, 1, 1], [n, 0, 1]],
[[n, n, 1], [n, n, 1]],
[[n, 1, 1], [n, 0, 1]],
],
[
[[n, 1, 0], [n, 1, 0]],
[[n, 1, 1], [n, 0, 1]],
[[n, n, 1], [n, n, 1]],
[[n, 1, 1], [n, 0, 1]],
],
[
[[n, 3, 0], [n, 4, 0]],
[[n, 1, 5], [n, 0, 1]],
[[n, n, 9], [n, n, 1]],
[[n, 1, 1], [n, 0, 2]],
],
])
dyemat = sim_v1_worker._step_4_make_dyemat(evolution)
# The axes are now rearranged so that there's
# 4 samples, 2 channels, 5 cycles
# I've thrown enough noise in to make sure I've got
# the dimensions as I want
expected = npf([
[
[2, 1, 1, 1, 3],
[1, 1, 1, 1, 4],
],
[
[2, 2, 2, 2, 6],
[1, 1, 1, 1, 1],
],
[
[3, 1, 1, 1, 9],
[2, 1, 1, 1, 1],
],
[
[3, 2, 2, 2, 2],
[2, 1, 1, 1, 2],
],
])
# fmt: on
assert np_array_same(dyemat, expected)
def zest_step_3a_cycle_edman():
n = np.nan
samples = npf([
[[1, 1, 0], [0, 1, 0]],
[[0, 1, 1], [0, 0, 1]],
])
def it_can_edman_fail():
# Failure means Edman did not happen; p_edman_failure=1.0 and we no change
result = sim_v1_worker._step_3a_cycle_edman(samples,
is_mock=False,
p_edman_failure=1.0)
assert np_array_same(samples, result)
def it_can_edman_degrade():
# Degrade = Edman success; thus p_edman_failure=0.0
result = sim_v1_worker._step_3a_cycle_edman(samples,
is_mock=False,
p_edman_failure=0.0)
expected = npf([
[[n, 1, 0], [n, 1, 0]],
[[n, 1, 1], [n, 0, 1]],
])
assert np_array_same(expected, result)
def it_makes_a_copy():
# The original should not have changed
assert samples[0, 0, 0] == 1.0
zest()
def it_degrades_at_first_non_nan():
samples = npf([
[[n, 1, 0], [n, 1, 0]],
[[0, 1, 1], [0, 0, 1]],
])
result = sim_v1_worker._step_3a_cycle_edman(samples,
is_mock=False,
p_edman_failure=0.0)
expected = npf([
[[n, n, 0], [n, n, 0]],
[[n, 1, 1], [n, 0, 1]],
])
assert np_array_same(expected, result)
def it_can_mock():
# A mock cycle is the same as a edman_failure no matter what (p_edman_failure=0.0)
# and yet I should still see failure behavior
result = sim_v1_worker._step_3a_cycle_edman(samples,
is_mock=True,
p_edman_failure=0.0)
assert np_array_same(samples, result)
def it_can_degrade_one_sample_and_not_another():
samples = npf([
[[n, 1, 0], [n, 1, 0]],
[[0, 1, 1], [0, 0, 1]],
])
n_samples = 2
with zest.mock(sim_v1_worker._rand1,
returns=np.full((n_samples, ), [0.0, 1.0])):
result = sim_v1_worker._step_3a_cycle_edman(samples,
is_mock=False,
p_edman_failure=0.5)
expected = npf([
[[n, 1, 0], [n, 1, 0]],
[[n, 1, 1], [n, 0, 1]],
])
assert np_array_same(expected, result)
zest()