def test_2_library_instances(bliss_tango_server, s1hg, s1f, s1b):
s1hg.dial(1); s1hg.position(1)
assert s1f.position() == 0.5
assert s1b.position() == 0.5
assert s1hg.position() == 1
dev_name, proxy = bliss_tango_server
tango_s1hg = DeviceProxy("tango://localhost:12345/id00/bliss_test/s1hg")
assert tango_s1hg.position == 1
assert tango_s1hg.offset == 0
s1f.velocity(0.1)
s1b.velocity(0.1)
eval_id = proxy.eval("(s1f.velocity(), s1b.velocity())")
gevent.sleep(0.1)
res = proxy.get_result(eval_id)
assert decode_tango_eval(res) == (0.1, 0.1)
# trigger move
tango_s1hg.position = 2
gevent.sleep(0.1)
assert s1hg.state() == "MOVING"
s1hg.wait_move()
assert pytest.approx(s1hg.position(), 2)
s1hg.rmove(1)
assert pytest.approx(tango_s1hg.position, 3)
def test_colorscale(file_3nob):
m = molecule.load("mae", file_3nob)
r = molrep.addrep(color="User2", selection="lipid", molid=m)
assert molrep.get_scaleminmax(m, r) == pytest.approx((0., 0.))
with pytest.raises(ValueError):
molrep.get_scaleminmax(m+1, 0)
with pytest.raises(ValueError):
molrep.get_scaleminmax(m, r+1)
molrep.set_scaleminmax(molid=m, rep=r, scale_min=-10., scale_max=200.)
assert molrep.get_scaleminmax(m, r) == pytest.approx((-10., 200.))
with pytest.raises(ValueError):
molrep.set_scaleminmax(m+1, 0, 0, 12)
with pytest.raises(ValueError):
molrep.set_scaleminmax(m, r+1, 12, 13)
with pytest.raises(RuntimeError):
molrep.set_scaleminmax(m, r, scale_min=100, scale_max=0)
# Test reset
molrep.reset_scaleminmax(molid=m, rep=r)
assert molrep.get_scaleminmax(m, r) == pytest.approx((-10., 200.))
with pytest.raises(ValueError):
molrep.reset_scaleminmax(m+1, 0)
with pytest.raises(ValueError):
molrep.reset_scaleminmax(m, r+1)
# Test changing with modrep
assert molrep.modrep(m, r, scaleminmax=(2.0, 3.0))
assert molrep.get_scaleminmax(m, r) == pytest.approx((2.0, 3.0))
assert molrep.modrep(m, r, scaleminmax=[-10., -5.])
assert molrep.get_scaleminmax(m, r) == pytest.approx((-10., -5.))
molecule.delete(m)
def test_drag_and_drop(session,
test_actions_page,
mouse_chain,
dx,
dy,
drag_duration):
drag_target = session.find.css("#dragTarget", all=False)
initial_rect = drag_target.rect
initial_center = get_inview_center(initial_rect, get_viewport_rect(session))
# Conclude chain with extra move to allow time for last queued
# coordinate-update of drag_target and to test that drag_target is "dropped".
mouse_chain \
.pointer_move(0, 0, origin=drag_target) \
.pointer_down() \
.pointer_move(dx, dy, duration=drag_duration, origin="pointer") \
.pointer_up() \
.pointer_move(80, 50, duration=100, origin="pointer") \
.perform()
# mouseup that ends the drag is at the expected destination
e = get_events(session)[1]
assert e["type"] == "mouseup"
assert pytest.approx(e["pageX"], initial_center["x"] + dx)
assert pytest.approx(e["pageY"], initial_center["y"] + dy)
# check resulting location of the dragged element
final_rect = drag_target.rect
assert initial_rect["x"] + dx == final_rect["x"]
assert initial_rect["y"] + dy == final_rect["y"]
def test_D8_D4_fill(d4_grid):
"""
Tests the functionality of D4 filling.
"""
d4_grid.lfD8.map_depressions(pits=None, reroute_flow=False)
d4_grid.lfD4.map_depressions(pits=None, reroute_flow=False)
assert d4_grid.lfD8.number_of_lakes == 1
assert d4_grid.lfD4.number_of_lakes == 3
correct_D8_lake_map = np.empty(7 * 7, dtype=int)
correct_D8_lake_map.fill(XX)
correct_D8_lake_map[d4_grid.lake_nodes] = 10
correct_D4_lake_map = correct_D8_lake_map.copy()
correct_D4_lake_map[d4_grid.lake_nodes[5:]] = 32
correct_D4_lake_map[d4_grid.lake_nodes[-2]] = 38
correct_D8_depths = np.zeros(7 * 7, dtype=float)
correct_D8_depths[d4_grid.lake_nodes] = 2.
correct_D4_depths = correct_D8_depths.copy()
correct_D4_depths[d4_grid.lake_nodes[5:]] = 4.
correct_D4_depths[d4_grid.lake_nodes[-2]] = 3.
assert_array_equal(d4_grid.lfD8.lake_map, correct_D8_lake_map)
assert_array_equal(d4_grid.lfD4.lake_map, correct_D4_lake_map)
assert d4_grid.mg1.at_node["depression__depth"] == approx(correct_D8_depths)
assert d4_grid.mg2.at_node["depression__depth"] == approx(correct_D4_depths)
def test_uses_named_inputs(self):
inputs = {
"premise": "I always write unit tests for my code.",
"hypothesis": "One time I didn't write any unit tests for my code."
}
archive = load_archive(self.FIXTURES_ROOT / 'decomposable_attention' / 'serialization' / 'model.tar.gz')
predictor = Predictor.from_archive(archive, 'textual-entailment')
result = predictor.predict_json(inputs)
# Label probs should be 3 floats that sum to one
label_probs = result.get("label_probs")
assert label_probs is not None
assert isinstance(label_probs, list)
assert len(label_probs) == 3
assert all(isinstance(x, float) for x in label_probs)
assert all(x >= 0 for x in label_probs)
assert sum(label_probs) == approx(1.0)
# Logits should be 3 floats that softmax to label_probs
label_logits = result.get("label_logits")
assert label_logits is not None
assert isinstance(label_logits, list)
assert len(label_logits) == 3
assert all(isinstance(x, float) for x in label_logits)
exps = [math.exp(x) for x in label_logits]
sumexps = sum(exps)
for e, p in zip(exps, label_probs):
assert e / sumexps == approx(p)
def test_activity_pv_head(next100pv):
pv = next100pv
activity_bi214 = pv.head_mass * pv.cv.head.material.mass_activity_bi214
activity_tl208 = pv.head_mass * pv.cv.head.material.mass_activity_tl208
assert pv.head_activity_bi214 == approx(activity_bi214, rel=1e-5)
assert pv.head_activity_tl208 == approx(activity_tl208, rel=1e-5)
def test_int(self):
within_1e6 = [(1000001, 1000000), (-1000001, -1000000)]
for a, x in within_1e6:
assert a == approx(x, rel=5e-6, abs=0)
assert a != approx(x, rel=5e-7, abs=0)
assert approx(x, rel=5e-6, abs=0) == a
assert approx(x, rel=5e-7, abs=0) != a
def test_stratified_splitter(test_specs, spark_dataset):
splits = spark_stratified_split(
spark_dataset, ratio=test_specs["ratio"], filter_by="user", min_rating=10
)
assert splits[0].count() / test_specs["number_of_rows"] == pytest.approx(
test_specs["ratio"], test_specs["tolerance"]
)
assert splits[1].count() / test_specs["number_of_rows"] == pytest.approx(
1 - test_specs["ratio"], test_specs["tolerance"]
)
# Test if both contains the same user list. This is because stratified split is stratified.
users_train = (
splits[0].select(DEFAULT_USER_COL).distinct().rdd.map(lambda r: r[0]).collect()
)
users_test = (
splits[1].select(DEFAULT_USER_COL).distinct().rdd.map(lambda r: r[0]).collect()
)
assert set(users_train) == set(users_test)
splits = spark_stratified_split(spark_dataset, ratio=test_specs["ratios"])
assert splits[0].count() / test_specs["number_of_rows"] == pytest.approx(
test_specs["ratios"][0], test_specs["tolerance"]
)
assert splits[1].count() / test_specs["number_of_rows"] == pytest.approx(
test_specs["ratios"][1], test_specs["tolerance"]
)
assert splits[2].count() / test_specs["number_of_rows"] == pytest.approx(
test_specs["ratios"][2], test_specs["tolerance"]
)
def test_pv_properties(next100pv):
pv = next100pv
assert pv.name == 'Next100PV'
assert pv.material_name == ti316.name
assert pv.radius / mm == approx(1360 / 2, rel=1e-3)
assert pv.body_thickness / mm == approx(10, rel=1e-3)
assert pv.head_thickness / mm == approx(12, rel=1e-3)
def test_radial_deprecate_origin_y():
with pytest.deprecated_call():
mg = RadialModelGrid(num_shells=1, dr=1.0, origin_y=10)
assert mg._xy_of_center == (0.0, 10.0)
pts, npts = mg._create_radial_points(1, 1, xy_of_center=mg._xy_of_center)
assert pts[0, 0] == approx(mg._xy_of_center[0])
assert pts[0, 1] == approx(mg._xy_of_center[1])
def test_random_splitter(test_specs, spark_dataset):
"""Test random splitter for Spark dataframes.
NOTE: some split results may not match exactly with the ratios, which may be owing to the
limited number of rows in
the testing data. A approximate match with certain level of tolerance is therefore used
instead for tests.
"""
splits = spark_random_split(
spark_dataset, ratio=test_specs["ratio"], seed=test_specs["seed"]
)
assert splits[0].count() / test_specs["number_of_rows"] == pytest.approx(
test_specs["ratio"], test_specs["spark_randomsplit_tolerance"]
)
assert splits[1].count() / test_specs["number_of_rows"] == pytest.approx(
1 - test_specs["ratio"], test_specs["spark_randomsplit_tolerance"]
)
splits = spark_random_split(
spark_dataset, ratio=test_specs["ratios"], seed=test_specs["seed"]
)
assert splits[0].count() / test_specs["number_of_rows"] == pytest.approx(
test_specs["ratios"][0], test_specs["spark_randomsplit_tolerance"]
)
assert splits[1].count() / test_specs["number_of_rows"] == pytest.approx(
test_specs["ratios"][1], test_specs["spark_randomsplit_tolerance"]
)
assert splits[2].count() / test_specs["number_of_rows"] == pytest.approx(
test_specs["ratios"][2], test_specs["spark_randomsplit_tolerance"]
)
def test_vec2d_magic_math():
import operator
params = (
(cy.Vec2d(2.1, 7), 3.2),
(cy.Vec2d(2.3, 7), (3., 5.7)),
(cy.Vec2d(-2.3, 7), cy.Vec2d(0.3, 5.7)),
)
ops = (
operator.add, operator.sub, operator.mul,
operator.div, operator.floordiv, operator.truediv,
operator.mod,
)
for left, right in params:
tright = right if hasattr(right, '__getitem__') else (right, right)
for op in ops:
# test op(left, right)
msg = "%s( %s , %s )" % (op.__name__, left, right)
tres = (op(left.x, tright[0]), op(left.y, tright[1]))
res = op(left, right)
assert tuple(res) == pytest.approx(tres), msg
# test op(right, left)
msg = "%s( %s , %s )" % (op.__name__, right, left)
tres = (op(tright[0], left.x), op(tright[1], left.y))
res = op(right, left)
assert tuple(res) == pytest.approx(tres), msg
def test_multidim_tendencies():
# Same test just repeated in two parallel columns
num_lat = 2
state = climlab.column_state(num_lev=num_lev, num_lat=num_lat)
state['q'] = state.Tatm * 0. #+ Q
state['U'] = state.Tatm * 0. #+ U
state['V'] = state.Tatm * 0. #+ V
for i in range(num_lat):
state.Tatm[i,:] = T
state['q'][i,:] += Q
state['U'][i,:] += U
state['V'][i,:] += V
assert hasattr(state, 'Tatm')
assert hasattr(state, 'q')
assert hasattr(state, 'U')
assert hasattr(state, 'V')
conv = emanuel_convection.EmanuelConvection(state=state, timestep=DELT)
conv.step_forward()
# Did we get all the correct output?
assert np.all(conv.IFLAG == 1)
# relative tolerance for these tests ...
tol = 1E-5
assert np.all(conv.CBMF == pytest.approx(3.10377218E-02, rel=tol))
tend = conv.tendencies
assert np.tile(FT,(num_lat,1)) == pytest.approx(tend['Tatm'], rel=tol)
assert np.tile(FQ,(num_lat,1)) == pytest.approx(tend['q'], rel=tol)
assert np.tile(FU,(num_lat,1)) == pytest.approx(tend['U'], rel=tol)
assert np.tile(FV,(num_lat,1)) == pytest.approx(tend['V'], rel=tol)
def test_decimal_width():
obj = Round(0.02, 0.005)
assert 0.005 == pytest.approx(obj( 0.005))
assert 0.025 == pytest.approx(obj( 0.025))
assert 0.065 == pytest.approx(obj( 0.081))
assert -0.055 == pytest.approx(obj( -0.048))
assert -0.015 == pytest.approx(obj( -0.015))
def test_formatters(Formatter, regex, direction, factor, values):
fmt = Formatter()
result = fmt(direction, factor, values)
prev_degree = prev_minute = prev_second = None
for tick, value in zip(result, values):
m = regex.match(tick)
assert m is not None, '"%s" is not an expected tick format.' % (tick, )
sign = sum(m.group(sign + '_sign') is not None
for sign in ('degree', 'minute', 'second'))
assert sign <= 1, \
'Only one element of tick "%s" may have a sign.' % (tick, )
sign = 1 if sign == 0 else -1
degree = float(m.group('degree') or prev_degree or 0)
minute = float(m.group('minute') or prev_minute or 0)
second = float(m.group('second') or prev_second or 0)
if Formatter == FormatterHMS:
# 360 degrees as plot range -> 24 hours as labelled range
expected_value = pytest.approx((value // 15) / factor)
else:
expected_value = pytest.approx(value / factor)
assert sign * dms2float(degree, minute, second) == expected_value, \
'"%s" does not match expected tick value.' % (tick, )
prev_degree = degree
prev_minute = minute
prev_second = second
def test_run_analytics_band_center_spectrum(expected_center, expected_wavelengths, expected_values):
spectra = phat.Spectra.from_file(get_path('SP_2C_02_02358_S138_E3586.spc'))
spectrum = spectra[spectra.columns[1]]
center, center_fit = analytics.run_analytics(spectrum, analytics.band_center, 512.6, 2587.9)
assert center_fit.mean() == pytest.approx(expected_center)
assert np.mean(center[0]) == pytest.approx(expected_wavelengths)
assert np.mean(center[1]) == expected_values
def test_seed():
rand = Random()
N = 10
min = -10
max = 10
first = []
second = []
third = []
tol = 1e-6
for i in range(N):
Random.setSeed(i)
first.append(Random.uniform(min, max));
second.append(Random.uniform(min, max));
third.append(Random.uniform(min, max));
for i in range(N):
Random.setSeed(i)
assert Random.getSeed() is i
assert Random.uniform(min, max) == pytest.approx(first[i], tol)
assert Random.uniform(min, max) == pytest.approx(second[i], tol)
assert Random.uniform(min, max) == pytest.approx(third[i], tol)
def test_element_wo():
# This test doesn't require an OpenMC run. We just need to make sure the
# element.expand() method expands elements with the proper nuclide
# compositions.
h_am = (NATURAL_ABUNDANCE['H1'] * atomic_mass('H1') +
NATURAL_ABUNDANCE['H2'] * atomic_mass('H2'))
o_am = (NATURAL_ABUNDANCE['O17'] * atomic_mass('O17') +
(NATURAL_ABUNDANCE['O16'] + NATURAL_ABUNDANCE['O18'])
* atomic_mass('O16'))
water_am = 2 * h_am + o_am
water = Material()
water.add_element('O', o_am / water_am, 'wo')
water.add_element('H', 2 * h_am / water_am, 'wo')
densities = water.get_nuclide_densities()
for nuc in densities.keys():
assert nuc in ('H1', 'H2', 'O16', 'O17')
if nuc in ('H1', 'H2'):
val = 2 * NATURAL_ABUNDANCE[nuc] * atomic_mass(nuc) / water_am
assert densities[nuc][1] == pytest.approx(val)
if nuc == 'O16':
val = (NATURAL_ABUNDANCE[nuc] + NATURAL_ABUNDANCE['O18']) \
* atomic_mass(nuc) / water_am
assert densities[nuc][1] == pytest.approx(val)
if nuc == 'O17':
val = NATURAL_ABUNDANCE[nuc] * atomic_mass(nuc) / water_am
assert densities[nuc][1] == pytest.approx(val)
def test_run_analytics_band_minima(expected_wavelengths, expected_values):
spectra = phat.Spectra.from_file(get_path('SP_2C_02_02358_S138_E3586.spc'))
minima = analytics.run_analytics(spectra, analytics.band_minima)
wavelengths = [np.mean(val[0]) for val in minima]
values = [val[1] for val in minima]
assert np.mean(wavelengths) == pytest.approx(expected_wavelengths)
assert np.mean(values) == pytest.approx(expected_values)
def test_voronoi_closedinternal():
"""Test routing on a (radial) voronoi, but with a closed interior node."""
vmg = RadialModelGrid(2, dr=2.)
z = np.full(20, 10., dtype=float)
all_bounds_but_one = np.array((0, 1, 2, 3, 4, 7, 11, 15, 16, 17, 18, 19))
vmg.status_at_node[all_bounds_but_one] = CLOSED_BOUNDARY
vmg.status_at_node[8] = CLOSED_BOUNDARY # new internal closed
z[12] = 0. # outlet
inner_elevs = (8., 7., 1., 6., 4., 5.)
z[vmg.core_nodes] = np.array(inner_elevs)
vmg.add_field("node", "topographic__elevation", z, units="-")
fr = FlowRouter(vmg)
cells_contributing = [
np.array([0]),
np.array([1]),
np.array([0, 1, 3, 4, 5, 6]),
np.array([1, 4]),
np.array([1, 4, 5, 6]),
np.array([1, 4, 6]),
]
A_target_internal = np.zeros(vmg.number_of_core_nodes, dtype=float)
for i in range(6):
A_target_internal[i] = vmg.area_of_cell[cells_contributing[i]].sum()
A_target_outlet = vmg.area_of_cell[vmg.cell_at_node[vmg.core_nodes]].sum()
fr.route_flow()
assert vmg.at_node["drainage_area"][vmg.core_nodes] == approx(A_target_internal)
assert vmg.at_node["drainage_area"][12] == approx(A_target_outlet)
def test_intensity():
from .. import toymodel
np.random.seed(0)
geom = CameraGeometry.from_name('LSTCam')
width = 0.05
length = 0.15
intensity = 50
# make a toymodel shower model
model = toymodel.generate_2d_shower_model(
centroid=(0.2, 0.3),
width=width, length=length,
psi='30d',
)
image, signal, noise = toymodel.make_toymodel_shower_image(
geom, model.pdf, intensity=intensity, nsb_level_pe=5,
)
# test if signal reproduces given cog values
assert np.average(geom.pix_x.value, weights=signal) == approx(0.2, rel=0.15)
assert np.average(geom.pix_y.value, weights=signal) == approx(0.3, rel=0.15)
# test if signal reproduces given width/length values
cov = np.cov(geom.pix_x.value, geom.pix_y.value, aweights=signal)
eigvals, eigvecs = np.linalg.eigh(cov)
assert np.sqrt(eigvals[0]) == approx(width, rel=0.15)
assert np.sqrt(eigvals[1]) == approx(length, rel=0.15)
# test if total intensity is inside in 99 percent confidence interval
assert poisson(intensity).ppf(0.05) <= signal.sum() <= poisson(intensity).ppf(0.95)
def test_uniform1():
""" Test uniform distribution """
for n in range(2, 10):
d = uniform(n)
assert d.outcomes == tuple(range(n))
assert d[0] == pytest.approx(1/n)
assert entropy(d) == pytest.approx(np.log2(n))
def test_ii3():
""" Test II and conditional II for xor """
d = Xor()
ii1 = interaction_information(d, [[0], [1], [2]], [2])
ii2 = interaction_information(d, [[0], [1]], [2])
assert ii1 == pytest.approx(0)
assert ii2 == pytest.approx(1)
def test_rotation_angle():
assert Affine.identity().rotation_angle == 0.0
assert Affine.scale(2).rotation_angle == 0.0
assert Affine.scale(2, 1).rotation_angle == 0.0
assert Affine.translation(32, -47).rotation_angle == pytest.approx(0.0)
assert Affine.rotation(30).rotation_angle == pytest.approx(30)
assert Affine.rotation(-150).rotation_angle == pytest.approx(-150)
def test_compute_rating_predictions(rating_true):
svd = surprise.SVD()
train_set = surprise.Dataset.load_from_df(
rating_true, reader=surprise.Reader()
).build_full_trainset()
svd.fit(train_set)
preds = compute_rating_predictions(svd, rating_true)
assert set(preds.columns) == {"userID", "itemID", "prediction"}
assert preds["userID"].dtypes == rating_true["userID"].dtypes
assert preds["itemID"].dtypes == rating_true["itemID"].dtypes
user = rating_true.iloc[0]["userID"]
item = rating_true.iloc[0]["itemID"]
assert preds[(preds["userID"] == user) & (preds["itemID"] == item)][
"prediction"
].values == pytest.approx(svd.predict(user, item).est, rel=TOL)
preds = compute_rating_predictions(
svd,
rating_true.rename(columns={"userID": "uid", "itemID": "iid"}),
usercol="uid",
itemcol="iid",
predcol="pred",
)
assert set(preds.columns) == {"uid", "iid", "pred"}
assert preds["uid"].dtypes == rating_true["userID"].dtypes
assert preds["iid"].dtypes == rating_true["itemID"].dtypes
user = rating_true.iloc[1]["userID"]
item = rating_true.iloc[1]["itemID"]
assert preds[(preds["uid"] == user) & (preds["iid"] == item)][
"pred"
].values == pytest.approx(svd.predict(user, item).est, rel=TOL)
def test_dial(s1hg, s1b, s1f):
s1hg.move(4)
s1hg.dial(0)
assert s1hg.position() == pytest.approx(4)
assert s1hg.dial() == pytest.approx(0)
assert s1b.position() == pytest.approx(0)
assert s1f.position() == pytest.approx(0)
def test_read_from_xtc(self):
"""
Tests for read_from_xtc()
"""
topology = os.path.join(here, "test_data/barstar_md_traj.gro")
traj = os.path.join(here, "test_data/barstar_md_traj.xtc")
universe = MDAnalysis.Universe(topology, traj)
selection = universe.select_atoms("backbone")
# First timeframe
atom = structure.Atom.read_from_xtc(selection[0])
assert atom.resid == 1
assert atom.name == "N"
for a, b in zip(atom.coords, [21.68, 33.87, 36.18]):
assert a == pytest.approx(b, abs=1e-3)
atom = structure.Atom.read_from_xtc(selection[-1])
assert atom.resid == 89
assert atom.name == "C"
for a, b in zip(atom.coords, [40.14, 38.75, 28.42]):
assert a == pytest.approx(b, abs=1e-3)
#Last one
ts = universe.trajectory[-1]
atom = structure.Atom.read_from_xtc(selection[0])
for a, b in zip(atom.coords, [20.63, 38.43, 32.09]):
assert a == pytest.approx(b, abs=1e-3)
atom = structure.Atom.read_from_xtc(selection[-1])
for a, b in zip(atom.coords, [39.14, 39.77, 25.60]):
assert a == pytest.approx(b, abs=1e-3)
def test_subside_point_load():
params = dict(eet=65000.0, youngs=7e10)
load = 1e9
loc = (5000.0, 2500.0)
x = np.arange(0, 10000, 1000.0)
y = np.arange(0, 5000, 1000.0)
(x, y) = np.meshgrid(x, y)
x.shape = (x.size,)
y.shape = (y.size,)
dz_one_load = subside_point_load(load, loc, (x, y), params=params)
n_loads = 16
dz = subside_point_load(
np.full(n_loads, load / n_loads),
np.full((n_loads, 2), loc).T,
(x, y),
params=params,
)
assert dz.mean() == approx(dz_one_load.mean())
assert dz.min() == approx(dz_one_load.min())
assert dz.max() == approx(dz_one_load.max())
def test_initial_routing(dans_grid3):
"""
Test the action of fr.run_one_step() on the grid.
"""
dans_grid3.fr.run_one_step()
assert dans_grid3.mg.at_node["flow__receiver_node"] == approx(dans_grid3.r_old)
assert dans_grid3.mg.at_node["drainage_area"] == approx(dans_grid3.A_old)
def test_jog(robz):
robz.velocity(10)
robz.jog(300)
assert robz.velocity() == 300
t = 1+robz.acctime()
start_time = time.time()
time.sleep(t)
elapsed_time = (time.time()-start_time) - robz.acctime()
assert robz._hw_position() == pytest.approx(300*elapsed_time+robz.acceleration()*0.5*robz.acctime()**2, 1e-2)
assert robz.state() == "MOVING"
robz.stop()
assert robz.state() == "READY"
assert robz._set_position() == robz.position()
robz.dial(0); robz.position(0)
assert robz.velocity() == 10
robz.jog(-300, reset_position=0)
assert robz.velocity() == 300
start_time = time.time()
time.sleep(t)
elapsed_time = (time.time()-start_time) - robz.acctime()
assert robz._hw_position() == pytest.approx(-300*elapsed_time-robz.acceleration()*0.5*robz.acctime()**2, 1e-2)
robz.stop()
assert robz.dial() == 0
assert robz.velocity() == 10
robz.jog(300, reset_position=Modulo())
time.sleep(t)
robz.stop()
assert robz.position() == pytest.approx(90, 0.1)
def test_shapExplainer(self, model):
explainer = model.shapExplainer()
assert explainer.expected_value[0] == pytest.approx(-0.22667938806360247)
def test_reasonable_defaults(self):
# Whatever the defaults are, they should work for numbers close to 1
# than have a small amount of floating-point error.
assert 0.1 + 0.2 == approx(0.3)
def test_nan_tolerance(self):
illegal_kwargs = [dict(rel=nan), dict(abs=nan), dict(rel=nan, abs=nan)]
for kwargs in illegal_kwargs:
with pytest.raises(ValueError):
1.1 == approx(1, **kwargs)
def test_opposite_sign(self):
examples = [(eq, 1e-100, -1e-100), (ne, 1e100, -1e100)]
for op, a, x in examples:
assert op(a, approx(x))
def test_can_get_atlas_center_in_shared_space(res, w, h, d):
atlas = Atlas(volume=np.empty((w, h, d)), resolution_um=res)
assert atlas.center == approx((w * res / 2, h * res / 2, d * res / 2))
def test_rgb_distance():
assert (
rgb_distance((0, 0, 0), (0, 255, 255)) ==
pytest.approx(360.62445840513923744443))
def test_score(self, model):
assert model.score(cutoff=0.9, method="accuracy") == pytest.approx(1.0)
assert model.score(cutoff=0.1, method="accuracy") == pytest.approx(1.0)
assert model.score(
cutoff=0.9, method="auc", pos_label="Train"
) == pytest.approx(1.0)
assert model.score(
cutoff=0.1, method="auc", pos_label="Train"
) == pytest.approx(1.0)
assert model.score(
cutoff=0.9, method="best_cutoff", pos_label="Train"
) == pytest.approx(0.999)
assert model.score(
cutoff=0.1, method="best_cutoff", pos_label="Train"
) == pytest.approx(0.999)
assert model.score(cutoff=0.9, method="bm", pos_label="Train") == pytest.approx(
0.0
)
assert model.score(cutoff=0.1, method="bm", pos_label="Train") == pytest.approx(
0.0
)
assert model.score(
cutoff=0.9, method="csi", pos_label="Train"
) == pytest.approx(0.0)
assert model.score(
cutoff=0.1, method="csi", pos_label="Train"
) == pytest.approx(0.0)
assert model.score(cutoff=0.9, method="f1", pos_label="Train") == pytest.approx(
0.0
)
assert model.score(cutoff=0.1, method="f1", pos_label="Train") == pytest.approx(
0.0
)
assert model.score(
cutoff=0.9, method="logloss", pos_label="Train"
) == pytest.approx(0.0)
assert model.score(
cutoff=0.1, method="logloss", pos_label="Train"
) == pytest.approx(0.0)
assert model.score(
cutoff=0.9, method="mcc", pos_label="Train"
) == pytest.approx(0.0)
assert model.score(
cutoff=0.1, method="mcc", pos_label="Train"
) == pytest.approx(0.0)
assert model.score(cutoff=0.9, method="mk", pos_label="Train") == pytest.approx(
0.0
)
assert model.score(cutoff=0.1, method="mk", pos_label="Train") == pytest.approx(
0.0
)
assert model.score(
cutoff=0.9, method="npv", pos_label="Train"
) == pytest.approx(0.0)
assert model.score(
cutoff=0.1, method="npv", pos_label="Train"
) == pytest.approx(0.0)
assert model.score(
cutoff=0.9, method="prc_auc", pos_label="Train"
) == pytest.approx(1.0)
assert model.score(
cutoff=0.1, method="prc_auc", pos_label="Train"
) == pytest.approx(1.0)
assert model.score(
cutoff=0.9, method="precision", pos_label="Train"
) == pytest.approx(0.0)
assert model.score(
cutoff=0.1, method="precision", pos_label="Train"
) == pytest.approx(0.0)
assert model.score(
cutoff=0.9, method="specificity", pos_label="Train"
) == pytest.approx(1.0)
assert model.score(
cutoff=0.1, method="specificity", pos_label="Train"
) == pytest.approx(1.0)
def bf10(x):
return approx(1 / x, rel=1e-5)
def test_rand(self):
t_psi = Dense1D.rand(7, dtype='complex64')
assert t_psi.shape == (2, ) * 7
assert t_psi.dtype == 'complex64'
assert (t_psi.H @ t_psi) == pytest.approx(1.0)
def test_classification_report(self, model):
cls_rep1 = model.classification_report().transpose()
assert cls_rep1["auc"][0] == pytest.approx(1.0)
assert cls_rep1["prc_auc"][0] == pytest.approx(1.0)
assert cls_rep1["accuracy"][0] == pytest.approx(1.0)
assert cls_rep1["log_loss"][0] == pytest.approx(0.0)
assert cls_rep1["precision"][0] == pytest.approx(1.0)
assert cls_rep1["recall"][0] == pytest.approx(1.0)
assert cls_rep1["f1_score"][0] == pytest.approx(1.0)
assert cls_rep1["mcc"][0] == pytest.approx(1.0)
assert cls_rep1["informedness"][0] == pytest.approx(1.0)
assert cls_rep1["markedness"][0] == pytest.approx(1.0)
assert cls_rep1["csi"][0] == pytest.approx(1.0)
assert cls_rep1["cutoff"][0] == pytest.approx(0.999)
cls_rep2 = model.classification_report(cutoff=0.2).transpose()
assert cls_rep2["cutoff"][0] == pytest.approx(0.2)
def test_slopes_at_patches(self):
rmg = RasterModelGrid((4, 5))
rmg.at_node["topographic__elevation"] = rmg.node_x.copy()
slopes_out = rmg.calc_slope_at_node()
assert np.all(slopes_out == approx(np.full(20, np.pi / 4.0, dtype=float)))
def test_envelope_rect(graph):
# Without a clock signal, generate a single pulse at t = 0
graph.sample_rate = 1000
env = EnvelopeRect(0.1)
graph.render_subgraph(env, reset=True)
assert env.output_buffer[0][0] == pytest.approx(1.0)
assert env.output_buffer[0][99] == pytest.approx(1.0)
assert env.output_buffer[0][100] == pytest.approx(0.0)
# Trigger 2x envelope pulses at t = 0.1, 0.2
env = EnvelopeRect(0.01, clock=OneTapDelay(Impulse(10.0), 0.1))
graph.render_subgraph(env, reset=True)
assert env.output_buffer[0][0] == pytest.approx(0.0)
assert env.output_buffer[0][99] == pytest.approx(0.0)
assert env.output_buffer[0][100] == pytest.approx(1.0)
assert env.output_buffer[0][109] == pytest.approx(1.0)
assert env.output_buffer[0][110] == pytest.approx(0.0)
assert env.output_buffer[0][199] == pytest.approx(0.0)
assert env.output_buffer[0][200] == pytest.approx(1.0)
assert env.output_buffer[0][209] == pytest.approx(1.0)
assert env.output_buffer[0][210] == pytest.approx(0.0)
def test_changing_slopes(dans_grid3):
"""
Test with the output from a successful run of fr.run_one_step.
"""
slope_old = np.array(
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
2.0,
2.0,
2.0,
2.0,
2.0,
0.0,
0.0,
2.0,
0.1,
0.0,
0.1,
2.0,
0.0,
0.0,
2.0,
0.14142136,
0.1,
0.14142136,
2.0,
0.0,
0.0,
2.0,
1.2,
1.0,
1.0,
2.0,
0.0,
0.0,
1.06066017,
1.1,
1.06066017,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
)
slope_new = np.array(
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
2.0,
2.0,
2.0,
2.0,
2.0,
0.0,
0.0,
2.0,
0.0,
0.0,
0.0,
2.0,
0.0,
0.0,
2.0,
0.0,
0.0,
0.1,
2.0,
0.0,
0.0,
2.0,
1.2,
1.0,
1.0,
2.0,
0.0,
0.0,
1.06066017,
1.1,
1.06066017,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
)
dans_grid3.fr.run_one_step()
assert dans_grid3.mg.at_node["topographic__steepest_slope"] == approx(slope_old)
dans_grid3.lf.map_depressions()
assert dans_grid3.mg.at_node["topographic__steepest_slope"] == approx(slope_new)
def test_swap_gating(self):
psi0 = MPS_rand_state(20, 5)
CNOT = qu.controlled('not')
psi0XX = psi0.gate(CNOT, (4, 13))
psi0XX_s = psi0.gate_with_auto_swap(CNOT, (4, 13))
assert psi0XX.H @ psi0XX_s == pytest.approx(1.0)
def test_composite_pits():
"""
A test to ensure the component correctly handles cases where there are
multiple pits, inset into each other.
"""
mg = RasterModelGrid((10, 10))
z = mg.add_field("topographic__elevation", mg.node_x.copy(), at="node")
# a sloping plane
# np.random.seed(seed=0)
# z += np.random.rand(100)/10000.
# punch one big hole
z.reshape((10, 10))[3:8, 3:8] = 0.0
# dig a couple of inset holes
z[57] = -1.0
z[44] = -2.0
z[54] = -10.0
# make an outlet
z[71] = 0.9
fr = FlowAccumulator(mg, flow_director="D8")
lf = DepressionFinderAndRouter(mg)
fr.run_one_step()
lf.map_depressions()
flow_sinks_target = np.zeros(100, dtype=bool)
flow_sinks_target[mg.boundary_nodes] = True
# no internal sinks now:
assert_array_equal(mg.at_node["flow__sink_flag"], flow_sinks_target)
# test conservation of mass:
assert mg.at_node["drainage_area"].reshape((10, 10))[1:-1, 1].sum() == approx(
8.0 ** 2
)
# ^all the core nodes
# test the actual flow field:
# nA = np.array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
# 8., 8., 7., 6., 5., 4., 3., 2., 1., 0.,
# 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,
# 1., 1., 1., 4., 2., 2., 8., 4., 1., 0.,
# 1., 1., 1., 8., 3., 15., 3., 2., 1., 0.,
# 1., 1., 1., 13., 25., 6., 3., 2., 1., 0.,
# 1., 1., 1., 45., 3., 3., 5., 2., 1., 0.,
# 50., 50., 49., 3., 2., 2., 2., 4., 1., 0.,
# 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,
# 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
nA = np.array(
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
8.0,
8.0,
7.0,
6.0,
5.0,
4.0,
3.0,
2.0,
1.0,
0.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.0,
1.0,
1.0,
1.0,
4.0,
2.0,
2.0,
6.0,
4.0,
1.0,
0.0,
1.0,
1.0,
1.0,
6.0,
3.0,
12.0,
3.0,
2.0,
1.0,
0.0,
1.0,
1.0,
1.0,
8.0,
20.0,
4.0,
3.0,
2.0,
1.0,
0.0,
1.0,
1.0,
1.0,
35.0,
5.0,
4.0,
3.0,
2.0,
1.0,
0.0,
50.0,
50.0,
49.0,
13.0,
10.0,
8.0,
6.0,
4.0,
1.0,
0.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
)
assert_array_equal(mg.at_node["drainage_area"], nA)
# the lake code map:
lc = np.array(
[
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
57,
57,
57,
57,
57,
XX,
XX,
XX,
XX,
XX,
57,
57,
57,
57,
57,
XX,
XX,
XX,
XX,
XX,
57,
57,
57,
57,
57,
XX,
XX,
XX,
XX,
XX,
57,
57,
57,
57,
57,
XX,
XX,
XX,
XX,
XX,
57,
57,
57,
57,
57,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
]
)
# test the remaining properties:
assert lf.lake_outlets.size == 1
assert lf.lake_outlets[0] == 72
outlets_in_map = np.unique(lf.depression_outlet_map)
assert outlets_in_map.size == 2
assert outlets_in_map[1] == 72
assert lf.number_of_lakes == 1
assert lf.lake_codes[0] == 57
assert_array_equal(lf.lake_map, lc)
assert lf.lake_areas[0] == approx(25.0)
assert lf.lake_volumes[0] == approx(63.0)
def test_works_seamlessly_for_hawki_package(self, capsys):
cmd = scopesim.UserCommands(use_instrument="HAWKI")
opt = scopesim.OpticalTrain(cmd)
opt["detector_1024_window"].include = False
opt["detector_array_list"].include = True
opt.update()
# test that the major values have been updated in rc.__currsys__
assert rc.__currsys__["!TEL.area"].value == approx(52.02, rel=1e-3)
assert rc.__currsys__["!TEL.etendue"].value == approx(0.58455, rel=1e-3)
assert rc.__currsys__["!INST.pixel_scale"] == approx(0.106, rel=1e-3)
# test that OpticalTrain builds properly
assert isinstance(opt, scopesim.OpticalTrain)
# test that we have a system throughput
wave = np.linspace(0.7, 2.5, 181) * u.um
tc = opt.optics_manager.surfaces_table.throughput
ec = opt.optics_manager.surfaces_table.emission
# ..todo:: something super wierd is going on here when running pytest in the top directory
# ..todo:: perhaps this is has to be relaxed due to different filter
# assert 0.5 < np.max(tc(wave)).value < 0.55
assert 0.5 < np.max(tc(wave)).value < 0.8
if PLOTS:
plt.plot(wave, tc(wave))
plt.show()
if PLOTS:
plt.plot(wave, ec(wave))
plt.show()
# test that we have the correct number of FOVs for Ks band
# assert len(opt.fov_manager.fovs) == 18
# Apparently this is 9 now?
assert len(opt.fov_manager.fovs) == 9
if PLOTS:
fovs = opt.fov_manager.fovs
from scopesim.optics.image_plane_utils import calc_footprint
plt.subplot(121)
for fov in fovs:
x, y = calc_footprint(fov.hdu.header)
plt.fill(x*3600, y*3600, alpha=0.1, c="b")
plt.title("Sky plane")
plt.xlabel("[arcsec]")
plt.subplot(122)
for fov in fovs:
x, y = calc_footprint(fov.hdu.header, "D")
plt.fill(x, y)
plt.title("Detector focal plane")
plt.xlabel("[mm]")
plt.show()
# test that the ImagePlane is large enough
assert opt.image_planes[0].header["NAXIS1"] > 4200
assert opt.image_planes[0].header["NAXIS2"] > 4200
assert np.all(opt.image_planes[0].data == 0)
# test assert there are 4 detectors, each 2048x2048 pixels
hdu = opt.readout()[0]
assert len(opt.detector_arrays[0].detectors) == 4
for detector in opt.detector_arrays[0].detectors:
assert detector.hdu.header["NAXIS1"] == 2048
assert detector.hdu.header["NAXIS2"] == 2048
if PLOTS:
for i in range(1, 5):
plt.subplot(2, 2, i)
plt.imshow(hdu[i].data)
plt.show()
dit = rc.__currsys__["!OBS.dit"]
ndit = rc.__currsys__["!OBS.ndit"]
assert np.average(hdu[1].data) == approx(ndit * dit * 0.1, abs=0.5)
def test_degenerate_drainage():
"""
This "hourglass" configuration should be one of the hardest to correctly
re-route.
"""
mg = RasterModelGrid((9, 5))
z_init = mg.node_x.copy() * 0.0001 + 1.0
lake_pits = np.array([7, 11, 12, 13, 17, 27, 31, 32, 33, 37])
z_init[lake_pits] = -1.0
z_init[22] = 0.0 # the common spill pt for both lakes
z_init[21] = 0.1 # an adverse bump in the spillway
z_init[20] = -0.2 # the spillway
mg.add_field("topographic__elevation", z_init, at="node")
fr = FlowAccumulator(mg, flow_director="D8")
lf = DepressionFinderAndRouter(mg)
fr.run_one_step()
lf.map_depressions()
# correct_A = np.array([ 0., 0., 0., 0., 0.,
# 0., 1., 3., 1., 0.,
# 0., 5., 1., 2., 0.,
# 0., 1., 10., 1., 0.,
# 21., 21., 1., 1., 0.,
# 0., 1., 9., 1., 0.,
# 0., 3., 1., 2., 0.,
# 0., 1., 1., 1., 0.,
# 0., 0., 0., 0., 0.])
correct_A = np.array(
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
1.0,
3.0,
1.0,
0.0,
0.0,
2.0,
4.0,
2.0,
0.0,
0.0,
1.0,
10.0,
1.0,
0.0,
21.0,
21.0,
1.0,
1.0,
0.0,
0.0,
1.0,
9.0,
1.0,
0.0,
0.0,
2.0,
2.0,
2.0,
0.0,
0.0,
1.0,
1.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
)
assert mg.at_node["drainage_area"] == approx(correct_A)
def test_D8_D4_route(d4_grid):
"""
Tests the functionality of D4 routing.
"""
d4_grid.frD8.run_one_step()
d4_grid.frD4.run_one_step()
d4_grid.lfD8.map_depressions()
d4_grid.lfD4.map_depressions()
assert d4_grid.lfD8.number_of_lakes == 1
assert d4_grid.lfD4.number_of_lakes == 3
# flow_recD8 = np.array([ 0, 1, 2, 3, 4, 5, 6, 7, 16, 10, 16, 10, 18,
# 13, 14, 14, 15, 16, 10, 18, 20, 21, 16, 16, 16, 18,
# 33, 27, 28, 28, 24, 24, 24, 32, 34, 35, 35, 38, 32,
# 32, 32, 41, 42, 43, 44, 45, 46, 47, 48])
flow_recD8 = np.array(
[
0,
1,
2,
3,
4,
5,
6,
7,
16,
10,
16,
10,
18,
13,
14,
14,
15,
16,
17,
18,
20,
21,
16,
16,
16,
18,
33,
27,
28,
28,
24,
24,
24,
32,
34,
35,
35,
38,
32,
32,
32,
41,
42,
43,
44,
45,
46,
47,
48,
]
)
# flow_recD4 = np.array([ 0, 1, 2, 3, 4, 5, 6, 7, 7, 10, 17, 10, 11,
# 13, 14, 14, 15, 16, 17, 18, 20, 21, 21, 16, 17, 18,
# 33, 27, 28, 28, 29, 24, 31, 32, 34, 35, 35, 36, 37,
# 32, 33, 41, 42, 43, 44, 45, 46, 47, 48])
flow_recD4 = np.array(
[
0,
1,
2,
3,
4,
5,
6,
7,
7,
10,
17,
10,
11,
13,
14,
14,
15,
16,
17,
18,
20,
21,
21,
16,
17,
18,
33,
27,
28,
28,
29,
38,
31,
32,
34,
35,
35,
36,
37,
32,
33,
41,
42,
43,
44,
45,
46,
47,
48,
]
)
assert_array_equal(d4_grid.mg1.at_node["flow__receiver_node"], flow_recD8)
assert_array_equal(d4_grid.mg2.at_node["flow__receiver_node"], flow_recD4)
assert d4_grid.mg1.at_node["drainage_area"].reshape((7, 7))[:, 0].sum() == approx(
d4_grid.mg2.at_node["drainage_area"].reshape((7, 7))[:, 0].sum()
)
def test_scaleindex(self):
dat = self.make_5d()
assert dat.scale_to_index(2, 1) == pytest.approx(1)
assert dat.scale_to_index(2, 3) == pytest.approx(1.5)
assert dat.index_to_scale(2, 2) == pytest.approx(5)
assert dat.index_to_scale(2, 2.5) == pytest.approx(7)
def test_three_pits():
"""
A test to ensure the component correctly handles cases where there are
multiple pits.
"""
mg = RasterModelGrid((10, 10))
z = mg.add_field("topographic__elevation", mg.node_x.copy(), at="node")
# a sloping plane
# np.random.seed(seed=0)
# z += np.random.rand(100)/10000.
# punch some holes
z[33] = 1.0
z[43] = 1.0
z[37] = 4.0
z[74:76] = 1.0
fr = FlowAccumulator(mg, flow_director="D8")
lf = DepressionFinderAndRouter(mg)
fr.run_one_step()
lf.map_depressions()
flow_sinks_target = np.zeros(100, dtype=bool)
flow_sinks_target[mg.boundary_nodes] = True
# no internal sinks now:
assert_array_equal(mg.at_node["flow__sink_flag"], flow_sinks_target)
# test conservation of mass:
assert mg.at_node["drainage_area"].reshape((10, 10))[1:-1, 1].sum() == approx(
8.0 ** 2
)
# ^all the core nodes
# test the actual flow field:
nA = np.array(
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
8.0,
8.0,
7.0,
6.0,
5.0,
4.0,
3.0,
2.0,
1.0,
0.0,
2.0,
2.0,
1.0,
1.0,
2.0,
1.0,
1.0,
1.0,
1.0,
0.0,
26.0,
26.0,
25.0,
15.0,
11.0,
10.0,
9.0,
8.0,
1.0,
0.0,
2.0,
2.0,
1.0,
9.0,
2.0,
1.0,
1.0,
1.0,
1.0,
0.0,
2.0,
2.0,
1.0,
1.0,
5.0,
4.0,
3.0,
2.0,
1.0,
0.0,
2.0,
2.0,
1.0,
1.0,
1.0,
1.0,
3.0,
2.0,
1.0,
0.0,
20.0,
20.0,
19.0,
18.0,
17.0,
12.0,
3.0,
2.0,
1.0,
0.0,
2.0,
2.0,
1.0,
1.0,
1.0,
1.0,
3.0,
2.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
)
assert_array_equal(mg.at_node["drainage_area"], nA)
# test a couple more properties:
lc = np.empty(100, dtype=int)
lc.fill(XX)
lc[33] = 33
lc[43] = 33
lc[37] = 37
lc[74:76] = 74
assert_array_equal(lf.lake_map, lc)
assert_array_equal(lf.lake_codes, [33, 37, 74])
assert lf.number_of_lakes == 3
assert lf.lake_areas == approx([2.0, 1.0, 2.0])
assert lf.lake_volumes == approx([2.0, 2.0, 4.0])
def test_LMPR_complexity3(n):
"""
Test that uniform Distirbutions have zero complexity.
"""
d = Distribution.from_distribution(uniform(n))
assert LMPR_complexity(d) == pytest.approx(0)
def test_edge_draining():
"""
This tests when the lake attempts to drain from an edge, where an issue
is suspected.
"""
# Create a 7x7 test grid with a well defined hole in it, AT THE EDGE.
mg = RasterModelGrid((7, 7))
z = mg.node_x.copy()
guard_sides = np.concatenate((np.arange(7, 14), np.arange(35, 42)))
edges = np.concatenate((np.arange(7), np.arange(42, 49)))
hole_here = np.array(([15, 16, 22, 23, 29, 30]))
z[guard_sides] = z[13]
z[edges] = -2.0 # force flow outwards from the tops of the guards
z[hole_here] = -1.0
A_new = np.array(
[
[
[
0.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.0,
0.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.0,
15.0,
5.0,
4.0,
3.0,
2.0,
1.0,
0.0,
0.0,
10.0,
4.0,
3.0,
2.0,
1.0,
0.0,
0.0,
1.0,
4.0,
3.0,
2.0,
1.0,
0.0,
0.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.0,
0.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.0,
]
]
]
).flatten()
depr_outlet_target = np.array(
[
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
14,
14,
XX,
XX,
XX,
XX,
XX,
14,
14,
XX,
XX,
XX,
XX,
XX,
14,
14,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
XX,
]
).flatten()
mg.add_field("topographic__elevation", z, at="node", units="-")
fr = FlowAccumulator(mg, flow_director="D8")
lf = DepressionFinderAndRouter(mg)
fr.run_one_step()
lf.map_depressions()
assert mg.at_node["drainage_area"] == approx(A_new)
assert lf.depression_outlet_map == approx(depr_outlet_target)
def test_disequilibrium4(n):
"""
Test that uniform Distributions have zero disequilibrium.
"""
d = Distribution.from_distribution(uniform(n))
assert disequilibrium(d) == pytest.approx(0)
def test_lsvi_without_bonus():
def lsvi_debug_gather_data(agent):
"""
Function to gather data sampling uniformly
states and actions
"""
N = agent.n_episodes * agent.horizon
count = 0
while count < N:
state = agent.env.observation_space.sample()
action = agent.env.action_space.sample()
next_state, reward, done, info = agent.env.sample(state, action)
#
#
feat = agent.feature_map.map(state, action)
outer_prod = np.outer(feat, feat)
inv = agent.lambda_mat_inv
#
agent.lambda_mat += np.outer(feat, feat)
# update inverse
agent.lambda_mat_inv -= (inv @ outer_prod @ inv) / (1 + feat @ inv.T @ feat)
# update history
agent.reward_hist[count] = reward
agent.state_hist.append(state)
agent.action_hist.append(action)
agent.nstate_hist.append(next_state)
#
tt = agent.total_time_steps
agent.feat_hist[tt, :] = agent.feature_map.map(state, action)
for aa in range(agent.env.action_space.n):
agent.feat_ns_all_actions[tt, aa, :] = agent.feature_map.map(
next_state, aa
)
# increments
agent.total_time_steps += 1
count += 1
env = GridWorld(nrows=2, ncols=2, walls=(), success_probability=0.95)
env.reseed(123)
def feature_map_fn(_env):
return OneHotFeatureMap(_env.observation_space.n, _env.action_space.n)
agent = LSVIUCBAgent(
env, feature_map_fn=feature_map_fn, horizon=20, gamma=0.99, reg_factor=1e-5
)
agent.reseed(123)
agent.n_episodes = 100
agent.reset()
lsvi_debug_gather_data(agent)
# estimated Q
S = env.observation_space.n
Q_est = agent._run_lsvi(bonus_factor=0.0)[0, :].reshape((S, -1))
# near optimal Q
agent_opt = ValueIterationAgent(env, gamma=0.99, horizon=20)
agent_opt.fit()
Q = agent_opt.Q[0, :, :]
print(Q)
print("---")
print(Q_est)
print("-------")
print(np.abs(Q - Q_est))
# Check error
assert Q_est == pytest.approx(Q, rel=0.01)
def test_LMPR_complexity4(n):
"""
Test that peaked ScalarDistributions have zero complexity.
"""
d = ScalarDistribution([1] + [0] * (n - 1))
assert LMPR_complexity(d) == pytest.approx(0)
def test_LMPR_complexity2(n):
"""
Test that uniform ScalarDistirbutions have zero complexity.
"""
d = uniform(n)
assert LMPR_complexity(d) == pytest.approx(0)
def test_market_in_focus(self):
li = get_market_in_focus()
assert len(li) == pytest.approx(10, 1)
def test_disequilibrium3(n):
"""
Test that uniform ScalarDistributions have zero disequilibrium.
"""
d = uniform(n)
assert disequilibrium(d) == pytest.approx(0)
def test_market_iex_percent(self):
li = get_market_iex_percent()
assert len(li) == pytest.approx(10, 1)
def test_list_sector_performance(self):
li = get_sector_performance()
assert len(li) == pytest.approx(10, 1)