def test_pick_analysis(hdf5_ds_2, tmpdir_factory, lt_ctx):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'local'}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
analysis = [{
"analysisType": 'PICK_FRAME',
"parameters": {
'x': 4,
'y': 4,
}
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
out = ep.preprocess(nb, {"metadata": {"path": datadir}})
data_path = os.path.join(datadir, 'pick_result.npy')
results = np.load(data_path)
analysis = lt_ctx.create_pick_analysis(
dataset=hdf5_ds_2,
x=4,
y=4,
)
expected = lt_ctx.run(analysis)
assert np.allclose(
results,
expected['intensity'].raw_data,
)
def test_ring_tcp_cluster(lt_ctx, random_hdf5_1):
conn = {"connection": {"type": "TCP", "address": "tcp://scheduler:8786"}}
ds = random_hdf5_1
ds_path = ds.path
tmp_dir = os.path.dirname(ds_path)
dataset = _get_hdf5_params(ds_path)
analysis = [{
"analysisType": "APPLY_RING_MASK",
"parameters": {
'shape': 'ring',
'cx': 8,
'cy': 8,
'ri': 5,
'ro': 8,
}
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
out = ep.preprocess(nb, {"metadata": {"path": tmp_dir}})
data_path = os.path.join(tmp_dir, 'ring_result.npy')
results = np.load(data_path)
analysis = lt_ctx.create_ring_analysis(dataset=ds, cx=8, cy=8, ri=5, ro=8)
expected = lt_ctx.run(analysis)
assert np.allclose(
results,
expected['intensity'].raw_data,
)
def test_fem_analysis(hdf5_ds_2, tmpdir_factory, lt_ctx, local_cluster_url):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'tcp', 'address': local_cluster_url}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
params = {
'shape': 'ring',
'cx': 1,
'cy': 1,
'ri': 0,
'ro': 1,
}
analysis = [{"analysisType": 'FEM', "parameters": params}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
ep.preprocess(nb, {"metadata": {"path": datadir}})
data_path = os.path.join(datadir, 'fem_result.npy')
results = np.load(data_path)
analysis = FEMUDF(center=(1, 1), rad_in=0, rad_out=1)
expected = lt_ctx.run_udf(dataset=hdf5_ds_2, udf=analysis)
assert np.allclose(
results,
expected['intensity'],
)
def test_pick_fft_analysis(hdf5_ds_2, tmpdir_factory, lt_ctx,
local_cluster_url):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'tcp', 'address': local_cluster_url}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
params = {
'x': 4,
'y': 4,
'real_rad': 4,
'real_centerx': 8,
'real_centery': 8,
}
analysis = [{
"analysisType": 'PICK_FFT_FRAME',
"parameters": params,
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
ep.preprocess(nb, {"metadata": {"path": datadir}})
data_path = os.path.join(datadir, 'pickfft_result.npy')
results = np.load(data_path)
analysis = PickFFTFrameAnalysis(dataset=hdf5_ds_2, parameters=params)
expected = lt_ctx.run(analysis)
assert np.allclose(
results,
expected['intensity'].raw_data,
)
def test_fft_analysis(hdf5_ds_2, tmpdir_factory, lt_ctx):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'local'}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
params = {
'rad_in': 4,
'rad_out': 8,
'real_rad': 4,
'real_centerx': 8,
'real_centery': 8
}
analysis = [{
"analysisType": 'APPLY_FFT_MASK',
"parameters": params,
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
out = ep.preprocess(nb, {"metadata": {"path": datadir}})
data_path = os.path.join(datadir, 'fft_result.npy')
results = np.load(data_path)
analysis = ApplyFFTMask(dataset=hdf5_ds_2, parameters=params)
expected = lt_ctx.run(analysis)
assert np.allclose(
results,
expected['intensity'].raw_data,
)
def test_sd_default(hdf5_ds_2, tmpdir_factory, lt_ctx, local_cluster_url):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'tcp', 'address': local_cluster_url}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
params = {"roi": {}}
analysis = [{
"analysisType": 'SD_FRAMES',
"parameters": params,
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
ep.preprocess(nb, {"metadata": {"path": datadir}})
data_path = os.path.join(datadir, 'sd_result.npy')
results = np.load(data_path)
udf = StdDevUDF()
expected = lt_ctx.run_udf(dataset=hdf5_ds_2, udf=udf)
assert np.allclose(
results,
expected['varsum'],
)
def test_disk_default(hdf5_ds_2, tmpdir_factory, lt_ctx):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'local'}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
analysis = [{
"analysisType": 'APPLY_DISK_MASK',
"parameters": {
'shape': 'disk',
'cx': 8,
'cy': 8,
'r': 5,
}
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
out = ep.preprocess(nb, {"metadata": {"path": datadir}})
data_path = os.path.join(datadir, 'disk_result.npy')
results = np.load(data_path)
disk_analysis = lt_ctx.create_disk_analysis(dataset=hdf5_ds_2,
cx=8,
cy=8,
r=5)
expected = lt_ctx.run(disk_analysis)
assert np.allclose(
results,
expected['intensity'].raw_data,
)
def test_point_default(hdf5_ds_2, tmpdir_factory, lt_ctx, local_cluster_url):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'tcp', 'address': local_cluster_url}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
analysis = [{
"analysisType": "APPLY_POINT_SELECTOR",
"parameters": {
'shape': 'point',
'cx': 8,
'cy': 8,
}
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
ep.preprocess(nb, {"metadata": {"path": datadir}})
data_path = os.path.join(datadir, 'point_result.npy')
results = np.load(data_path)
analysis = lt_ctx.create_point_analysis(dataset=hdf5_ds_2, x=8, y=8)
expected = lt_ctx.run(analysis)
assert np.allclose(
results,
expected['intensity'].raw_data,
)
async def test_clust_default(hdf5_ds_2, tmpdir_factory, inline_executor):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'local'}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
analysis = [{
"analysisType": 'CLUST',
"parameters": {
'n_peaks': 42,
'n_clust': 7,
'cy': 3,
'cx': 3,
'ri': 1,
'ro': 5,
'delta': 0.05,
'min_dist': 1,
}
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
out = ep.preprocess(nb, {"metadata": {"path": datadir}})
data_path = os.path.join(datadir, 'cluster_result.npy')
results = np.load(data_path)
executor = AsyncAdapter(wrapped=inline_executor)
analysis = ClusterAnalysis(dataset=hdf5_ds_2,
parameters={
'n_peaks': 42,
'n_clust': 7,
'cy': 3,
'cx': 3,
'ri': 1,
'ro': 5,
'delta': 0.05,
'min_dist': 1,
})
uuid = 'bd3b39fb-0b34-4a45-9955-339da6501bbb'
res_container = ResultContainer()
async def send_results(results, finished):
pass
await analysis.controller(
cancel_id=uuid,
executor=executor,
job_is_cancelled=lambda: False,
send_results=res_container,
)
expected = res_container.results
assert np.allclose(results, expected['intensity'].raw_data)
def test_com_default(hdf5_ds_2, tmpdir_factory, lt_ctx):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'local'}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
analysis = [{
"analysisType": 'CENTER_OF_MASS',
"parameters": {
'shape': 'com',
'cx': 0,
'cy': 0,
'r': 8,
}
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
out = ep.preprocess(nb, {"metadata": {"path": datadir}})
channels = [
"field",
"magnitude",
"divergence",
"curl",
"x",
"y"
]
results = {}
for channel in channels:
data_path = os.path.join(datadir, f"com_result_{channel}.npy")
results[channel] = np.load(data_path)
com_analysis = lt_ctx.create_com_analysis(
dataset=hdf5_ds_2,
cx=0,
cy=0,
mask_radius=8
)
expected = lt_ctx.run(com_analysis)
assert np.allclose(results["field"], expected["field"].raw_data)
assert np.allclose(results["magnitude"], expected["magnitude"].raw_data)
assert np.allclose(results["divergence"], expected["divergence"].raw_data)
assert np.allclose(results["curl"], expected["curl"].raw_data)
assert np.allclose(results["x"], expected["x"].raw_data)
assert np.allclose(results["y"], expected["y"].raw_data)
def test_radial_fourier_default(hdf5_ds_2, tmpdir_factory, lt_ctx,
local_cluster_url):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'tcp', 'address': local_cluster_url}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
analysis = [{
"analysisType": "RADIAL_FOURIER",
"parameters": {
'shape': 'radial_fourier',
'cx': 0,
'cy': 0,
'ri': 0,
'ro': 2,
'n_bins': 2,
'max_order': 7,
}
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
ep.preprocess(nb, {"metadata": {"path": datadir}})
channels = ["absolute_0_0", "absolute_0_1"]
results = {}
for channel in channels:
data_path = os.path.join(datadir, f"radial_result_{channel}.npy")
results[channel] = np.load(data_path)
analysis = lt_ctx.create_radial_fourier_analysis(dataset=hdf5_ds_2,
cx=0,
cy=0,
ri=0,
ro=2,
n_bins=2,
max_order=7)
expected = lt_ctx.run(analysis)
assert np.allclose(results["absolute_0_0"],
expected["absolute_0_0"].raw_data)
assert np.allclose(results["absolute_0_1"],
expected["absolute_0_1"].raw_data)
def test_sum_default(hdf5_ds_2, tmpdir_factory):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'local'}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
analysis = [{"analysisType": "SUM_FRAMES", "parameters": {"roi": {}}}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
out = ep.preprocess(nb, {"metadata": {"path": datadir}})
data_path = os.path.join(datadir, 'sum_result.npy')
result = np.load(data_path)
with hdf5_ds_2.get_reader().get_h5ds() as h5ds:
data = h5ds[:]
expected = data.sum(axis=(0, 1))
assert np.allclose(expected, result)
def test_sd_roi(hdf5_ds_2, tmpdir_factory, lt_ctx):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'local'}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
roi_params = {
"shape": "rect",
"x": 1,
"y": 2,
"width": 6,
"height": 6
}
analysis = [{
"analysisType": 'SD_FRAMES',
"parameters": {
"roi": roi_params
}
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
out = ep.preprocess(nb, {"metadata": {"path": datadir}})
data_path = os.path.join(datadir, 'sd_result.npy')
results = np.load(data_path)
nx, ny = hdf5_ds_2.shape.nav
roi = masks.rectangular(
X=roi_params["x"],
Y=roi_params["y"],
Width=roi_params["width"],
Height=roi_params["height"],
imageSizeX=nx,
imageSizeY=ny)
udf = StdDevUDF()
expected = lt_ctx.run_udf(dataset=hdf5_ds_2, udf=udf, roi=roi)
assert np.allclose(
results,
expected['varsum'].raw_data,
)
def test_sum_roi(hdf5_ds_2, tmpdir_factory, lt_ctx, local_cluster_url):
datadir = tmpdir_factory.mktemp('template_tests')
conn = {'connection': {'type': 'tcp', 'address': local_cluster_url}}
path = hdf5_ds_2.path
dataset = _get_hdf5_params(path)
roi_params = {
"shape": "disk",
"cx": 8,
"cy": 8,
"r": 6
}
analysis = [{
"analysisType": "SUM_FRAMES",
"parameters": {
"roi": roi_params
}
}]
notebook = notebook_generator(conn, dataset, analysis, save=True)
notebook = io.StringIO(notebook.getvalue())
nb = nbformat.read(notebook, as_version=4)
ep = ExecutePreprocessor(timeout=600)
ep.preprocess(nb, {"metadata": {"path": datadir}})
data_path = os.path.join(datadir, 'sum_result.npy')
results = np.load(data_path)
nx, ny = hdf5_ds_2.shape.nav
roi = masks.circular(
centerX=roi_params['cx'],
centerY=roi_params['cy'],
imageSizeX=nx,
imageSizeY=ny,
radius=roi_params['r'],
)
udf = SumUDF()
expected = lt_ctx.run_udf(hdf5_ds_2, udf, roi)
assert np.allclose(
results,
expected['intensity'].raw_data,
)
