diff --git a/.gitignore b/.gitignore index e1b4611..f2ff886 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,4 @@ spike *.sqlite build data +.vscode/ \ No newline at end of file diff --git a/aimmdb/_tests/__init__.py b/aimmdb/_tests/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/aimmdb/_tests/conftest.py b/aimmdb/_tests/conftest.py deleted file mode 100644 index 655042d..0000000 --- a/aimmdb/_tests/conftest.py +++ /dev/null @@ -1,23 +0,0 @@ -import contextlib -import getpass - -import pytest - - -@pytest.fixture -def enter_password(monkeypatch): - """ - Return a context manager that overrides getpass, used like: - - >>> with enter_password(...): - ... # Run code that calls getpass.getpass(). - """ - - @contextlib.contextmanager - def f(password): - original = getpass.getpass - monkeypatch.setattr("getpass.getpass", lambda: password) - yield - monkeypatch.setattr("getpass.getpass", original) - - return f diff --git a/aimmdb/_tests/eli_test_file.dat b/aimmdb/_tests/eli_test_file.dat deleted file mode 100755 index dff0b96..0000000 --- a/aimmdb/_tests/eli_test_file.dat +++ /dev/null @@ -1,683 +0,0 @@ -# Beamline.name: ISS (8-ID) - (08ID) Inner Shell Spectroscopy -# Beamline.x-ray_source: NSLS-II damping wiggler -# Beamline.collimation: ISS dual mirror system -# Beamline.focusing: ISS toroid mirror -# Beamline.harmonic_rejection: undefined -# Detector.I0: 17 cm, He: 62.0%, N2: 38.0% -# Detector.I1: 30 cm, He: 62.0%, N2: 38.0% -# Detector.I2: 17 cm, He: 62.0%, N2: 38.0% -# Detector.IF: PIPS -# Detector.I0_volt: -1700.0 -# Detector.I1_volt: -1700.0 -# Detector.I2_volt: -1700.0 -# Detector.I0_amp_gain: 5 -# Detector.I1_amp_gain: 5 -# Detector.I2_amp_gain: 6 -# Detector.IF_amp_gain: 6 -# Detector.aux: -# Element.symbol: Co -# Element.edge: K -# Facility.name: NSLS-II -# Facility.mode: Beam available -# Facility.energy: 3 GeV -# Facility.current: 399.56639748426807 -# Facility.GUP: 300010 -# Facility.SAF: 300010 -# Facility.cycle: 2022-1 -# Mono.name: Si(111) -# Mono.d_spacing: 3.1354951 -# Mono.encoder_resolution: 48.0 nrad -# Mono.angle_offset: -# Mono.scan_mode: pseudo-channel cut -# Mono.scan_type: -# Mono.trajectory_name: -# Mono.direction: -# Sample.name: 1-1 712 3 10 D C (pos 001) Co-K 0001 -# Sample.comment: -# Sample.stage: ISS.giant_xy stage -# ISS.sample_x_position: -336.0437703865 -# ISS.sample_y_position: -66.067285211 -# Scan.experimenters: Eli Stavitski -# Scan.edge_energy: 7709.0 -# Scan.start_time: 04/08/2022 08:37:45 -# Scan.end_time: 04/08/2022 08:38:20 -# Scan.duration: 00:34 -# Scan.transient_id: 226033 -# Scan.uid: d66dda13-d69c-4ca6-8fb7-76290ad71073 -# Scan.plot_hint: $5/$1 -# Column.1: energy eV -# Column.2: i0 -# Column.3: it -# Column.4: ir -# Column.5: if -# energy i0 it ir iff aux1 aux2 aux3 aux4 - 7509.000000 -1.428249e-05 -1.442278e-06 -2.325116e-07 -1.696860e-06 -7.383540e-06 1.686795e-05 -1.540353e-05 -1.990895e-06 - 7514.000000 -2.109614e-05 -2.140138e-06 -3.457031e-07 -2.507401e-06 -1.078244e-05 2.313060e-05 -1.311947e-05 1.096863e-05 - 7519.000000 -2.117449e-05 -2.161506e-06 -3.499174e-07 -2.520564e-06 -1.233055e-05 1.263222e-05 -8.352555e-06 2.941343e-06 - 7524.000000 -2.118350e-05 -2.177219e-06 -3.531900e-07 -2.526606e-06 -1.985960e-05 1.608047e-05 -2.154518e-05 -9.275274e-06 - 7529.000000 -2.125343e-05 -2.193307e-06 -3.566828e-07 -2.542888e-06 -2.564526e-05 1.617073e-05 -4.011363e-05 -1.479089e-05 - 7534.000000 -2.127577e-05 -2.208577e-06 -3.599024e-07 -2.550882e-06 -2.169648e-05 1.124580e-05 -2.583097e-05 -7.104161e-06 - 7539.000000 -2.131097e-05 -2.224139e-06 -3.632521e-07 -2.560908e-06 -2.242837e-05 2.422290e-05 -1.720725e-05 -6.384566e-06 - 7544.000000 -2.135681e-05 -2.240518e-06 -3.667892e-07 -2.572205e-06 -1.615860e-05 1.978539e-05 -1.580050e-05 -7.186035e-06 - 7549.000000 -2.136583e-05 -2.255104e-06 -3.699457e-07 -2.579175e-06 -7.440556e-06 2.668688e-05 -9.898860e-06 2.502418e-06 - 7554.000000 -2.139551e-05 -2.271771e-06 -3.733269e-07 -2.588298e-06 -4.100266e-06 1.933526e-05 -1.195002e-05 2.760708e-06 - 7559.000000 -2.145502e-05 -2.288179e-06 -3.768651e-07 -2.603254e-06 -1.182721e-05 1.875990e-05 -1.018805e-05 3.762703e-06 - 7564.000000 -2.115920e-05 -2.272881e-06 -3.748957e-07 -2.571850e-06 -1.265903e-05 2.480779e-05 -8.637734e-06 1.908036e-06 - 7569.000000 -2.125393e-05 -2.296385e-06 -3.796861e-07 -2.588136e-06 -1.990735e-05 2.910599e-05 2.927119e-06 7.552138e-07 - 7574.000000 -2.158994e-05 -2.339927e-06 -3.878981e-07 -2.638024e-06 -1.791158e-05 3.329068e-05 -4.137144e-06 7.496088e-06 - 7579.000000 -2.162434e-05 -2.355805e-06 -3.913250e-07 -2.648746e-06 -2.085102e-05 2.594044e-05 7.162398e-06 3.502133e-06 - 7584.000000 -2.164231e-05 -2.371780e-06 -3.948531e-07 -2.656006e-06 -1.607979e-05 2.548407e-05 6.118586e-06 2.137479e-06 - 7589.000000 -2.171881e-05 -2.390728e-06 -3.989260e-07 -2.672506e-06 -1.268741e-05 2.291652e-05 6.349209e-06 4.442873e-06 - 7594.000000 -2.172392e-05 -2.406470e-06 -4.022610e-07 -2.678005e-06 -1.333977e-05 3.119192e-05 1.090444e-05 2.429532e-06 - 7599.000000 -2.169383e-05 -2.420007e-06 -4.052139e-07 -2.678852e-06 -1.839577e-05 3.344500e-05 1.260542e-05 1.048020e-06 - 7604.000000 -2.168814e-05 -2.432916e-06 -4.081065e-07 -2.684408e-06 -1.078103e-05 2.513076e-05 2.776903e-06 -1.735183e-06 - 7609.000000 -2.180831e-05 -2.455940e-06 -4.130340e-07 -2.707455e-06 -7.025069e-06 1.899528e-05 -8.887740e-06 -8.924842e-06 - 7614.000000 -2.186445e-05 -2.474250e-06 -4.168995e-07 -2.721629e-06 -6.727752e-06 1.908296e-05 -1.778432e-05 -1.374160e-05 - 7619.000000 -2.190068e-05 -2.492749e-06 -4.208966e-07 -2.731838e-06 -1.246841e-05 1.874585e-05 -2.004627e-05 -9.371417e-06 - 7624.000000 -2.194289e-05 -2.511159e-06 -4.247433e-07 -2.743571e-06 -6.559018e-06 1.800399e-05 -7.829282e-06 -8.823298e-06 - 7629.000000 -2.193918e-05 -2.526843e-06 -4.281489e-07 -2.748458e-06 -2.720530e-06 2.231235e-05 -9.968202e-06 -8.764075e-06 - 7634.000000 -2.198332e-05 -2.544583e-06 -4.320767e-07 -2.760814e-06 -2.017881e-06 3.442291e-05 -3.678914e-06 -4.910219e-06 - 7639.000000 -2.206417e-05 -2.565269e-06 -4.364794e-07 -2.778325e-06 -4.145226e-06 3.162906e-05 -6.831739e-06 -7.471027e-06 - 7644.000000 -2.209312e-05 -2.582655e-06 -4.402141e-07 -2.788110e-06 -4.875863e-06 3.484327e-05 -2.041819e-05 -1.277677e-05 - 7649.000000 -2.210307e-05 -2.597481e-06 -4.435004e-07 -2.796806e-06 -3.855001e-06 2.922510e-05 -1.317681e-05 -4.237619e-06 - 7654.000000 -2.207974e-05 -2.607230e-06 -4.459164e-07 -2.802453e-06 -1.307325e-05 2.824124e-05 -1.548968e-05 -7.946061e-07 - 7659.000000 -2.215799e-05 -2.630639e-06 -4.506383e-07 -2.820019e-06 -2.216856e-05 2.784872e-05 -2.355868e-05 -7.477843e-06 - 7664.000000 -2.220618e-05 -2.649206e-06 -4.545453e-07 -2.834801e-06 -1.853799e-05 2.432133e-05 -1.314903e-05 -7.013495e-06 - 7669.000000 -2.224992e-05 -2.667726e-06 -4.583842e-07 -2.848611e-06 -1.710951e-05 2.286564e-05 2.607261e-06 -1.335362e-06 - 7674.000000 -2.227286e-05 -2.684128e-06 -4.618722e-07 -2.858944e-06 -1.382511e-05 2.743894e-05 4.775235e-06 1.152164e-05 - 7679.000000 -2.227220e-05 -2.699032e-06 -4.650783e-07 -2.864223e-06 -1.266842e-05 2.487302e-05 -1.022096e-05 7.566702e-06 - 7683.000000 -2.226425e-05 -2.711491e-06 -4.675381e-07 -2.867249e-06 -2.065598e-05 2.327048e-05 -1.127430e-05 4.556961e-06 - 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7690.400000 -2.235277e-05 -2.740060e-06 -4.729789e-07 -2.892058e-06 -2.390563e-05 2.022658e-05 -8.306833e-06 -1.096273e-06 - 7690.600000 -2.238420e-05 -2.742457e-06 -4.733579e-07 -2.898958e-06 -3.292068e-05 1.725520e-05 -1.183723e-05 -1.612001e-06 - 7690.800000 -2.239470e-05 -2.743488e-06 -4.736107e-07 -2.899722e-06 -3.530015e-05 2.455285e-05 -1.344917e-05 3.602199e-06 - 7691.000000 -2.236345e-05 -2.740964e-06 -4.730443e-07 -2.898327e-06 -3.479824e-05 1.343167e-05 -1.336646e-05 -1.308396e-06 - 7691.200000 -2.240085e-05 -2.745118e-06 -4.737886e-07 -2.902448e-06 -3.279115e-05 1.389538e-05 -1.003337e-05 3.166618e-07 - 7691.400000 -2.237832e-05 -2.743865e-06 -4.734714e-07 -2.899464e-06 -2.638277e-05 1.218899e-05 -1.367967e-05 -8.676154e-06 - 7691.600000 -2.238182e-05 -2.746658e-06 -4.739687e-07 -2.897956e-06 -2.274059e-05 2.044256e-05 -1.684558e-05 -6.534446e-06 - 7691.800000 -2.236625e-05 -2.748389e-06 -4.742769e-07 -2.893301e-06 -2.368791e-05 2.345262e-05 -2.129891e-05 1.810017e-06 - 7692.000000 -2.235092e-05 -2.745336e-06 -4.734848e-07 -2.896417e-06 -2.734829e-05 2.499799e-05 -1.254162e-05 -3.395434e-07 - 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8682.236264 -2.401729e-05 -1.590404e-06 -1.278504e-07 -3.800878e-06 3.384974e-06 1.871906e-05 -3.497825e-05 1.167193e-05 - 8687.113830 -2.326658e-05 -1.551315e-06 -1.250454e-07 -3.683442e-06 1.384162e-05 1.308347e-05 -3.465716e-05 1.834546e-06 diff --git a/aimmdb/_tests/test_aimm.py b/aimmdb/_tests/test_aimm.py deleted file mode 100644 index 1620c41..0000000 --- a/aimmdb/_tests/test_aimm.py +++ /dev/null @@ -1,146 +0,0 @@ -import copy - -import numpy as np -import pandas as pd -from tiled.client import from_tree -from tiled.queries import Key -from tiled.validation_registration import ValidationRegistry - -import aimmdb -from aimmdb.adapters.aimm import AIMMCatalog -from aimmdb.validation import validate_xas_transmission - -from .utils import fail_with_status_code - - -def get_client(tmpdir, dataset_to_specs=None, validation_registry=None): - dataset_to_specs = dataset_to_specs or {} - validation_registry = validation_registry or ValidationRegistry() - - data_directory = tmpdir / "data" - data_directory.mkdir() - - tree = AIMMCatalog.from_mongomock( - data_directory, - dataset_to_specs=dataset_to_specs, - ) - - api_key = "secret" - c = from_tree( - tree, - api_key=api_key, - authentication={"single_user_api_key": api_key}, - validation_registry=validation_registry, - ) - - return c - - -def test_basic(tmpdir): - c = get_client(tmpdir) - assert type(c) == aimmdb.client.AIMMCatalog - - -def test_write_array(tmpdir): - c = get_client(tmpdir) - - x = np.random.rand(100, 100) - metadata = {"foo": "bar"} - - # can't write without specifying dataset - with fail_with_status_code(400): - result = c["uid"].write_array(x, metadata=metadata) - - metadata.update(dataset="sandbox1", myid=1) - c["uid"].write_array(x, metadata=metadata) - - results = c["uid"].search(Key("myid") == 1) - result = results.values().first() - result_array = result.read() - - np.testing.assert_equal(result_array, x) - for k, v in metadata.items(): - assert result.metadata[k] == v - - -def test_write_dataframe(tmpdir): - c = get_client(tmpdir) - - df = pd.DataFrame({"a": np.random.rand(100), "b": np.random.rand(100)}) - metadata = {"foo": "bar"} - - # can't write without specifying dataset - with fail_with_status_code(400): - result = c["uid"].write_dataframe(df, metadata=metadata) - - metadata.update(dataset="sandbox1", myid=1) - c["uid"].write_dataframe(df, metadata=metadata) - - results = c["uid"].search(Key("myid") == 1) - result = results.values().first() - result_df = result.read() - - pd.testing.assert_frame_equal(result_df, df) - - for k, v in metadata.items(): - assert result.metadata[k] == v - - -def test_validation(tmpdir): - validation_registry = ValidationRegistry() - validation_registry.register("XAS_trans", validate_xas_transmission) - dataset_to_specs = {"xas": ["XAS_trans"]} - - c = get_client( - tmpdir, - dataset_to_specs=dataset_to_specs, - validation_registry=validation_registry, - ) - - metadata = { - "dataset": "xas", - "element": {"symbol": "Au", "edge": "K"}, - "facility": {"name": "ALS"}, - "beamline": {"name": "foo"}, - "myid": 1, - } - df = pd.DataFrame( - { - "energy": np.random.rand(100), - "i0": np.random.rand(100), - "itrans": np.random.rand(100), - } - ) - - c["uid"].write_dataframe(df, metadata=metadata, specs=["XAS_trans"]) - - results = c["uid"].search(Key("myid") == 1) - result = results.values().first() - result_df = result.read() - - pd.testing.assert_frame_equal(result_df, df) - - for k, v in metadata.items(): - assert result.metadata[k] == v - - # fail with wrong spec - with fail_with_status_code(400): - c["uid"].write_dataframe(df, metadata=metadata, specs=["FOO"]) - - # fail without complete metadata - metadata_incomplete = copy.deepcopy(metadata) - metadata_incomplete.pop("element") - with fail_with_status_code(400): - c["uid"].write_dataframe(df, metadata=metadata_incomplete, specs=["XAS_trans"]) - - # fail with missing columns - df_missing_columns = pd.DataFrame( - { - "energy": np.random.rand(100), - "i0": np.random.rand(100), - } - ) - with fail_with_status_code(400): - c["uid"].write_dataframe( - df_missing_columns, metadata=metadata, specs=["XAS_trans"] - ) diff --git a/aimmdb/_tests/test_eli.py b/aimmdb/_tests/test_eli.py deleted file mode 100644 index 8ac0325..0000000 --- a/aimmdb/_tests/test_eli.py +++ /dev/null @@ -1,41 +0,0 @@ -from aimmdb.ingest.eli import ingest, read_metadata_and_header -from pathlib import Path -import numpy as np -import pandas as pd - - -def test_read_md_and_header(): - test_path = Path("aimmdb/_tests/eli_test_file.dat") - md, hdr = read_metadata_and_header(test_path) - assert set(map(type, md.values())) == {str} # assert all md values are of type str - assert hdr.split() == [ - "energy", - "i0", - "it", - "ir", - "iff", - "aux1", - "aux2", - "aux3", - "aux4", - ] - - -def test_ingest(): - test_path = Path("aimmdb/_tests/eli_test_file.dat") - data = ingest(test_path, return_uid=True) - test_channels = ["transmission", "fluorescence", "reference"] - mu_channels = ["mu_trans", "mu_fluor", "mu_ref"] - for channel_data, tst_ch, mu_ch in zip(data, test_channels, mu_channels): - uid = channel_data["uid"] - assert uid == "d66dda13-d69c-4ca6-8fb7-76290ad71073" - md = channel_data["metadata"] - channel = md["channel"] - assert channel == tst_ch - df = channel_data["data"] - with open(test_path) as test_file: - data_lines = [ - line for line in test_file.readlines() if not line.startswith("#") - ] - assert df.shape[0] == len(data_lines) - assert mu_ch in list(df.columns) diff --git a/aimmdb/_tests/test_queries.py b/aimmdb/_tests/test_queries.py deleted file mode 100644 index 6e70e0d..0000000 --- a/aimmdb/_tests/test_queries.py +++ /dev/null @@ -1,176 +0,0 @@ -import string - -import numpy -import pandas -import pytest -from tiled.client import from_tree -from tiled.client.array import ArrayClient -from tiled.client.dataframe import DataFrameClient -from tiled.queries import Contains, In, Key, NotIn, Specs, StructureFamily - -from aimmdb.adapters.aimm import AIMMCatalog - - -def write_data(client): - # sandbox_0 - for letter, number in zip(list(string.ascii_lowercase), range(26)): - client["uid"].write_array( - number * numpy.ones(10), - metadata={"dataset": "sandbox_0", "letter": letter, "number": number}, - ) - - # sandbox_1 - client["uid"].write_array( - numpy.random.rand(100), - metadata={ - "dataset": "sandbox_1", - "letters": list(string.ascii_lowercase), - "name": "does_contain_z", - }, - ) - - client["uid"].write_array( - numpy.random.rand(100), - metadata={ - "dataset": "sandbox_1", - "letters": list(string.ascii_lowercase)[:-1], - "name": "does_not_contain_z", - }, - ) - - # sandbox_2 - client["uid"].write_array( - numpy.random.rand(100), metadata={"dataset": "sandbox_2", "name": "array"} - ) - client["uid"].write_dataframe( - pandas.DataFrame({"a": numpy.random.rand(100), "b": numpy.random.rand(100)}), - metadata={"dataset": "sandbox_2", "name": "dataframe"}, - ) - - # sandbox_3 - client["uid"].write_array( - numpy.random.rand(100), - metadata={"dataset": "sandbox_3", "name": "no_specs"}, - specs=[], - ) - client["uid"].write_array( - numpy.random.rand(100), - metadata={"dataset": "sandbox_3", "name": "specs_foo_bar"}, - specs=["foo", "bar"], - ) - client["uid"].write_array( - numpy.random.rand(100), - metadata={"dataset": "sandbox_3", "name": "specs_foo_bar_baz"}, - specs=["foo", "bar", "baz"], - ) - - return client - - -api_key = "secret" - - -@pytest.fixture(scope="session") -def tree(tmp_path_factory): - data_directory = tmp_path_factory.mktemp("data") - t = AIMMCatalog.from_mongomock(data_directory) - - c = from_tree( - t, - api_key=api_key, - authentication={"single_user_api_key": api_key}, - ) - - write_data(c) - - return t - - -@pytest.fixture -def client(tree): - return from_tree( - tree, - api_key=api_key, - authentication={"single_user_api_key": api_key}, - ) - - -def test_eq(client): - c = client["dataset"]["sandbox_0"]["uid"] - - for letter in list(string.ascii_lowercase): - results = c.search(Key("letter") == letter) - assert len(results) == 1 - result = results.values().first() - assert result.metadata["letter"] == letter - - -def test_noteq(client): - c = client["dataset"]["sandbox_0"]["uid"] - - for letter in list(string.ascii_lowercase): - results = c.search(Key("letter") != letter) - for v in results.values(): - assert v.metadata["letter"] != letter - - -def test_comparison(client): - c = client["dataset"]["sandbox_0"]["uid"] - - results = c.search(Key("number") >= 12) - for v in results.values(): - assert v.metadata["number"] >= 12 - - -def test_in(client): - c = client["dataset"]["sandbox_0"]["uid"] - results = c.search(In("letter", ["a", "k", "z"])) - for v in results.values(): - assert v.metadata["letter"] in ["a", "k", "z"] - - -def test_notin(client): - c = client["dataset"]["sandbox_0"]["uid"] - results = c.search(NotIn("letter", ["a", "k", "z"])) - for v in results.values(): - assert v.metadata["letter"] not in ["a", "k", "z"] - - -def test_contains(client): - c = client["dataset"]["sandbox_1"]["uid"] - - results = c.search(Contains("letters", "z")) - - assert len(results) == 1 - assert results.values().first().metadata["name"] == "does_contain_z" - - -def test_structure_family(client): - c = client["dataset"]["sandbox_2"]["uid"] - - results = c.search(StructureFamily("dataframe")) - assert len(results) > 0 - for v in results.values(): - assert isinstance(v, DataFrameClient) - - results = c.search(StructureFamily("array")) - assert len(results) > 0 - for v in results.values(): - assert isinstance(v, ArrayClient) - - -def test_specs(client): - c = client["dataset"]["sandbox_3"]["uid"] - - results = c.search(Specs(include=["foo", "bar"])) - assert len(results) > 0 - for v in results.values(): - assert "foo" in v.specs - assert "bar" in v.specs - - results = c.search(Specs(include=["foo", "bar"], exclude=["baz"])) - assert len(results) > 0 - for v in results.values(): - assert "foo" in v.specs - assert "bar" in v.specs - assert "baz" not in v.specs diff --git a/aimmdb/_tests/utils.py b/aimmdb/_tests/utils.py deleted file mode 100644 index 88b6f8d..0000000 --- a/aimmdb/_tests/utils.py +++ /dev/null @@ -1,11 +0,0 @@ -import contextlib - -import pytest -from tiled.client.utils import ClientError - - -@contextlib.contextmanager -def fail_with_status_code(status_code): - with pytest.raises(ClientError) as info: - yield - assert info.value.response.status_code == status_code diff --git a/aimmdb/access.py b/aimmdb/access.py index ca47fc8..12ba2e7 100644 --- a/aimmdb/access.py +++ b/aimmdb/access.py @@ -2,9 +2,9 @@ from fastapi import HTTPException from tiled.adapters.mapping import MapAdapter +from tiled.queries import In from tiled.utils import SpecialUsers -from aimmdb.queries import In READ = object() # sentinel WRITE = object() # sentinel @@ -25,7 +25,8 @@ def require_write_permission(method): def inner(self, *args, **kwargs): if WRITE not in self.permissions: raise HTTPException( - status_code=403, detail="principal does not have write permission" + status_code=403, + detail="principal does not have write permission", ) else: return method(self, *args, **kwargs) @@ -93,7 +94,9 @@ def __init__(self, access_lists, *, provider): self.provider = provider # FIXME how to handle a normal user with the admin role? - self.access_lists[SpecialUsers.admin] = defaultdict(lambda: {READ, WRITE}) + self.access_lists[SpecialUsers.admin] = defaultdict( + lambda: {READ, WRITE} + ) for principal_id, value in access_lists.items(): if principal_id == "public": @@ -145,5 +148,7 @@ def filter_results(self, tree, principal): # should be able to exclude datasets return tree.new_variation(principal=principal) else: - datasets = [k for k, v in principal_access_list.items() if READ in v] + datasets = [ + k for k, v in principal_access_list.items() if READ in v + ] return tree.search(In("dataset", datasets)) diff --git a/aimmdb/adapters/__init__.py b/aimmdb/adapters/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/aimmdb/adapters/aimm.py b/aimmdb/adapters/aimm.py deleted file mode 100644 index 7530aa5..0000000 --- a/aimmdb/adapters/aimm.py +++ /dev/null @@ -1,494 +0,0 @@ -import collections.abc -import copy -import os -from datetime import datetime -from pathlib import Path -from typing import Dict - -import pydantic -import pymongo -from fastapi import HTTPException -from tiled.iterviews import ItemsView, KeysView, ValuesView -from tiled.query_registration import QueryTranslationRegistry -from tiled.structures.core import StructureFamily -from tiled.utils import APACHE_ARROW_FILE_MIME_TYPE, UNCHANGED - -import aimmdb.queries -import aimmdb.uid -from aimmdb.access import READ, WRITE -from aimmdb.adapters.array import WritingArrayAdapter -from aimmdb.adapters.dataframe import WritingDataFrameAdapter -from aimmdb.queries import OperationEnum, parse_path, register_queries_helper -from aimmdb.schemas import GenericDocument - -_mime_structure_association = { - StructureFamily.array: "application/x-hdf5", - StructureFamily.dataframe: APACHE_ARROW_FILE_MIME_TYPE, -} - -key_to_query = { - "uid": "uid", - "element": "metadata.element.symbol", - "edge": "metadata.element.edge", - "dataset": "metadata.dataset", - "sample": "metadata.sample_id", -} - -Document = GenericDocument[Dict] - - -class AIMMCatalog(collections.abc.Mapping): - structure_family = "node" - specs = ["AIMMCatalog"] - - query_registry = QueryTranslationRegistry() - register_query = query_registry.register - register_query_lazy = query_registry.register_lazy - - from aimmdb.server.router import router - from aimmdb.server.router_tiled import router as router_tiled - - include_routers = [router, router_tiled] - - def __init__( - self, - *, - metadata_db, - data_directory, - queries=None, - sorting=None, - metadata=None, - principal=None, - access_policy=None, - path=None, - dataset_to_specs=None, - ): - - # FIXME try to do less everytime we construct a new object - - self.data_directory = Path(data_directory).resolve() - if not self.data_directory.exists(): - raise ValueError(f"Directory {self.data_directory} does not exist.") - if not self.data_directory.is_dir(): - raise ValueError( - f"The given directory path {self.data_directory} is not a directory." - ) - if not os.access(self.data_directory, os.W_OK): - raise ValueError(f"Directory {self.directory} is not writeable.") - - self.metadata_db = metadata_db - self.metadata_collection = metadata_db.get_collection("metadata") - self.sample_collection = metadata_db.get_collection("samples") - - self.queries = queries or [] - self.metadata = copy.deepcopy(metadata) or {} - self.principal = principal - self.access_policy = access_policy - - if sorting is None: - # _ is a special sentinal meaning 'the given order' - sorting = {"_": 1} - else: - sorting = {x[0]: x[1] for x in sorting} - - self._sorting = sorting - - self.path = list(path or []) - self.op = parse_path(self.path, key_to_query) - self.metadata["_tiled"] = {"op": self.op.dict()} - - # inject sample metadata if we have selected on sample - self.metadata["_tiled"].pop("sample", None) - sample_query = key_to_query["sample"] - if sample_query in self.op.select: - sample_id = self.op.select[sample_query] - sample = self.sample_collection.find_one({"uid": sample_id}, {"_id": False}) - if sample is not None: - self.metadata["_tiled"]["sample"] = sample - - self.dataset_to_specs = dataset_to_specs or {} - - super().__init__() - - @property - def sorting(self): - return [(k, v) for k, v in self._sorting.items()] - - @classmethod - def from_uri( - cls, - uri, - data_directory, - *, - metadata=None, - access_policy=None, - dataset_to_specs=None, - ): - if not pymongo.uri_parser.parse_uri(uri)["database"]: - raise ValueError( - f"Invalid URI: {uri!r} " f"Did you forget to include a database?" - ) - metadata_db = pymongo.MongoClient(uri).get_database() - - return cls( - metadata_db=metadata_db, - data_directory=data_directory, - metadata=metadata, - access_policy=access_policy, - dataset_to_specs=dataset_to_specs, - ) - - @classmethod - def from_mongomock( - cls, - data_directory, - *, - metadata=None, - access_policy=None, - dataset_to_specs=None, - ): - import mongomock - - mongo_client = mongomock.MongoClient() - metadata_db = mongo_client["test"] - - return cls( - metadata_db=metadata_db, - data_directory=data_directory, - metadata=metadata, - access_policy=access_policy, - dataset_to_specs=dataset_to_specs, - ) - - def permissions(self, dataset): - """ - Return the permissions of the current principal - """ - # FIXME in more sophisticated cases permissions may DEPEND on metadata - # For example only being able to write to a particular dataset - if self.access_policy is not None: - permissions = self.access_policy.permissions(self.principal, dataset) - else: - # no access_policy => anyone can read/write - permissions = {READ, WRITE} - - return permissions - - def authenticated_as(self, principal): - if self.principal is not None and self.principal != principal: - raise RuntimeError(f"Already authenticated as {self.principal}") - - if self.access_policy is not None: - tree = self.access_policy.filter_results(self, principal) - else: - tree = self.new_variation(principal=principal) - return tree - - def new_variation( - self, - metadata=UNCHANGED, - queries=UNCHANGED, - sorting=UNCHANGED, - principal=UNCHANGED, - path=UNCHANGED, - **kwargs, - ): - if metadata is UNCHANGED: - metadata = self.metadata - if queries is UNCHANGED: - queries = self.queries - if sorting is UNCHANGED: - sorting = self.sorting - if principal is UNCHANGED: - principal = self.principal - if path is UNCHANGED: - path = self.path - return type(self)( - metadata_db=self.metadata_db, - data_directory=self.data_directory, - metadata=metadata, - queries=queries, - sorting=sorting, - access_policy=self.access_policy, - principal=principal, - path=path, - dataset_to_specs=self.dataset_to_specs, - **kwargs, - ) - - def search(self, query): - """ - Return a AIMMCatalog with a subset of the mapping. - """ - return self.query_registry(query, self) - - def get_distinct(self, metadata, structure_families, specs, counts): - data = {} - - select = {"$match": self._build_mongo_query(self.op.select)} - - if counts: - project = {"$project": {"_id": 0, "value": "$_id", "count": "$count"}} - else: - project = {"$project": {"_id": 0, "value": "$_id"}} - - if metadata: - data["metadata"] = {} - - for metadata_key in metadata: - group = { - "$group": {"_id": f"$metadata.{metadata_key}", "count": {"$sum": 1}} - } - data["metadata"][f"{metadata_key}"] = list( - self.metadata_collection.aggregate([select, group, project]) - ) - - if structure_families: - group = {"$group": {"_id": "$structure_family", "count": {"$sum": 1}}} - data["structure_families"] = list( - self.metadata_collection.aggregate([select, group, project]) - ) - - if specs: - group = {"$group": {"_id": "$specs", "count": {"$sum": 1}}} - data["specs"] = list( - self.metadata_collection.aggregate([select, group, project]) - ) - - return data - - def sort(self, sorting): - return self.new_variation(sorting=sorting) - - def post_sample(self, sample): - # FIXME this is a bit adhoc (samples is not a 'real' dataset) - dataset = "samples" - permissions = self.permissions(dataset) - if WRITE not in permissions: - raise HTTPException( - status_code=403, - detail=f"principal does not have write permissions to dataset {dataset}", - ) - - sample.uid = aimmdb.uid.uid() - result = self.sample_collection.insert_one(sample.dict()) - assert result.acknowledged - return sample.uid - - def delete_sample(self, uid): - # FIXME this is a bit adhoc (samples is not a 'real' dataset) - dataset = "samples" - permissions = self.permissions(dataset) - if WRITE not in permissions: - raise HTTPException( - status_code=403, - detail=f"principal does not have write permissions to dataset {dataset}", - ) - - # FIXME should we also delete measurements which refer to this sample? - result = self.sample_collection.delete_one({"uid": uid}) - assert result.deleted_count == 1 - - def post_metadata(self, metadata, structure_family, structure, specs): - - # TODO reconsider how this should work - if self.path != ["uid"]: - raise HTTPException( - status_code=400, detail="AIMMCatalog only allows posting data to /uid" - ) - - # NOTE this is enforced outside of pydantic - # FIXME should there be a native tiled mechanism to perform default validation regardless of spec? - dataset = metadata.get("dataset") - if dataset is None: - raise HTTPException( - status_code=400, - detail="AIMMCatalog requires that metadata contain a dataset key", - ) - - permissions = self.permissions(dataset) - if WRITE not in permissions: - raise HTTPException( - status_code=403, - detail=f"principal does not have write permissions to dataset {dataset}", - ) - - key = aimmdb.uid.uid() - - try: - validated_document = Document( - uid=key, - structure_family=structure_family, - structure=structure, - metadata=metadata, - specs=specs, - mimetype=_mime_structure_association[structure_family], - last_modified=datetime.utcnow(), - ) - except pydantic.ValidationError as err: - raise HTTPException(status_code=400, detail=f"{err}") - - # at this point according to the server the metadata matches the stated specs and the user has permissions to write - # now check if we will accept these specs into the specified dataset - allowed_specs = self.dataset_to_specs.get(dataset, None) - - # FIXME think more about how to handle multiple specs - # if this dataset has a set of allowed specs then specs must be a non-empty subset - if allowed_specs is not None: - if not specs or not set(specs).issubset(allowed_specs): - raise HTTPException( - status_code=400, - detail=f"specs ({specs}) are not a non-empty subset of allowed specs ({allowed_specs}) for dataset {dataset}", - ) - - sample_id = validated_document.metadata.get("sample_id", None) - - doc_dict = validated_document.dict() - - if sample_id is not None: - sample = self.sample_collection.find_one({"uid": sample_id}, {"_id": False}) - if sample is None: - raise HTTPException( - status_code=400, detail=f"sample_id {sample_id} not found" - ) - else: - if "sample" in doc_dict["metadata"]: - doc_dict["metadata"]["sample"].update(sample) - else: - doc_dict["metadata"]["sample"] = sample - - self.metadata_collection.insert_one(doc_dict) - return key - - def _build_node_from_doc(self, doc): - doc = Document.parse_obj(doc) - dataset = doc.metadata["dataset"] - - permissions = self.permissions(dataset) - - if doc.structure_family == StructureFamily.array: - return WritingArrayAdapter( - self.metadata_collection, - self.data_directory, - doc, - permissions, - ) - elif doc.structure_family == StructureFamily.dataframe: - return WritingDataFrameAdapter( - self.metadata_collection, - self.data_directory, - doc, - permissions, - ) - else: - raise ValueError("Unsupported Structure Family value in the databse") - - def _build_mongo_query(self, *queries): - combined = self.queries + list(queries) - if combined: - return {"$and": combined} - else: - return {} - - def __getitem__(self, key): - path = self.path + [key] - op = parse_path(path, key_to_query) - - # if new path is a lookup, do it now - if op.op_enum == OperationEnum.lookup: - query = self._build_mongo_query(op.select) - docs = list(self.metadata_collection.find(query)) - - if len(docs) == 0: - raise KeyError(f"{key} not found") - - if len(docs) > 1: - raise KeyError(f"{key} matched multipled records") - - doc = docs[0] - - return self._build_node_from_doc(doc) - - else: - return self.new_variation(path=path) - - def __len__(self): - if self.op.op_enum == OperationEnum.keys: - return len(self.op.keys) - elif self.op.op_enum == OperationEnum.distinct: - query = self._build_mongo_query(self.op.select) - # NOTE _id is guarenteed unique - if self.op.distinct == "uid": - return self.metadata_collection.count_documents(query) - else: - # FIXME wasteful to do the full self.op just to get the length - return len( - self.metadata_collection.find(query).distinct(self.op.distinct) - ) - elif self.op.op_enum == OperationEnum.lookup: - raise RuntimeError("unreachable") - else: - raise RuntimeError("unreachable") - - def _keys_slice(self, start, stop, direction): - assert direction == 1, "direction=-1 should be handled by the client" - skip = start or 0 - if stop is not None: - limit = stop - skip - else: - limit = None - - order = self._sorting["_"] - - if self.op.op_enum == OperationEnum.keys: - keys = list(self.op.keys) if order == 1 else list(reversed(self.op.keys)) - yield from keys[skip : skip + limit] - elif self.op.op_enum == OperationEnum.distinct: - query = self._build_mongo_query(self.op.select) - sorting = [ - ("last_modified", order) - ] # natural given order is by last_modified - - # NOTE uid is guarenteed unique - if self.op.distinct == "uid": - for doc in ( - self.metadata_collection.find(query, {"uid": 1}) - .sort(sorting) - .skip(skip) - .limit(limit) - ): - yield doc["uid"] - else: - # FIXME wasteful to recompute this here (compute on construction?) - distinct = self.metadata_collection.find(query).distinct( - self.op.distinct - ) - if order == -1: - distinct = list(reversed(distinct)) - for v in distinct[skip : skip + limit]: - if v is not None: # FIXME how should we filter None - yield v - elif self.op.op_enum == OperationEnum.lookup: - raise RuntimeError("unreachable") - else: - raise RuntimeError("unreachable") - - def _items_slice(self, start, stop, direction): - for k in self._keys_slice(start, stop, direction): - # FIXME note this is wasteful because it requires a second db lookup to get the value - yield (k, self[k]) - - def __iter__(self): - yield from self.keys() - - def keys(self): - return KeysView(lambda: len(self), self._keys_slice) - - def values(self): - return ValuesView(lambda: len(self), self._items_slice) - - def items(self): - return ItemsView(lambda: len(self), self._items_slice) - - -register_queries_helper(AIMMCatalog) diff --git a/aimmdb/adapters/array.py b/aimmdb/adapters/array.py deleted file mode 100644 index 087dd2b..0000000 --- a/aimmdb/adapters/array.py +++ /dev/null @@ -1,105 +0,0 @@ -import os -from datetime import datetime -from sys import platform - -import dask -import h5py -import numpy as np -from tiled.adapters.array import ArrayAdapter -from tiled.server.pydantic_array import ArrayStructure - -from aimmdb.access import require_write_permission - - -def array_raise_if_inactive(method): - def inner(self, *args, **kwargs): - if self.array_adapter is None: - raise ValueError("Not active") - else: - return method(self, *args, **kwargs) - - return inner - - -class WritingArrayAdapter: - structure_family = "array" - - def __init__(self, metadata_collection, directory, doc, permissions=None): - self.metadata_collection = metadata_collection - self.directory = directory - self.doc = doc - self.array_adapter = None - self.permissions = list(permissions or []) - - if self.doc.data_url is not None: - path = self.doc.data_url.path - if platform == "win32" and path[0] == "/": - path = path[1:] - - file = h5py.File(path) - dataset = file["data"] - self.array_adapter = ArrayAdapter(dask.array.from_array(dataset)) - - # elif self.doc.data_blob is not None: - # self.array_adapter = ArrayAdapter(dask.array.from_array(self.doc.data_blob)) - - @property - def specs(self): - return self.doc.specs - - @property - def structure(self): - return ArrayStructure.from_json(self.doc.structure) - - @property - def metadata(self): - out = self.doc.metadata - _tiled = {"uid": self.doc.uid} - out["_tiled"] = _tiled - return out - - @array_raise_if_inactive - def read(self, *args, **kwargs): - return self.array_adapter.read(*args, **kwargs) - - @array_raise_if_inactive - def read_block(self, *args, **kwargs): - return self.array_adapter.read_block(*args, **kwargs) - - def microstructure(self): - return self.array_adapter.microstructure() - - def macrostructure(self): - return self.array_adapter.macrostructure() - - @require_write_permission - def put_data(self, body): - # Organize files into subdirectories with the first two - # charcters of the uid to avoid one giant directory. - path = self.directory / self.doc.uid[:2] / f"{self.doc.uid}.hdf5" - path.parent.mkdir(parents=True, exist_ok=True) - array = np.frombuffer( - body, dtype=self.doc.structure.micro.to_numpy_dtype() - ).reshape(self.doc.structure.macro.shape) - with h5py.File(path, "w") as file: - file.create_dataset("data", data=array) - - result = self.metadata_collection.update_one( - {"uid": self.doc.uid}, - { - "$set": { - "data_url": f"file://localhost/{str(path).replace(os.sep, '/')}", - "last_modified": datetime.utcnow(), - } - }, - ) - - assert result.matched_count == 1 - assert result.modified_count == 1 - - @require_write_permission - def delete(self): - path = self.directory / self.doc.uid[:2] / f"{self.doc.uid}.hdf5" - os.remove(path) - result = self.metadata_collection.delete_one({"uid": self.doc.uid}) - assert result.deleted_count == 1 diff --git a/aimmdb/adapters/dataframe.py b/aimmdb/adapters/dataframe.py deleted file mode 100644 index 70f3bca..0000000 --- a/aimmdb/adapters/dataframe.py +++ /dev/null @@ -1,102 +0,0 @@ -import os -from datetime import datetime -from sys import platform - -import pandas as pd -from tiled.adapters.dataframe import DataFrameAdapter -from tiled.serialization.dataframe import deserialize_arrow -from tiled.structures.dataframe import DataFrameStructure - -from aimmdb.access import require_write_permission - - -def dataframe_raise_if_inactive(method): - def inner(self, *args, **kwargs): - if self.dataframe_adapter is None: - raise ValueError("Not active") - else: - return method(self, *args, **kwargs) - - return inner - - -# FIXME write specs -class WritingDataFrameAdapter: - structure_family = "dataframe" - - def __init__(self, metadata_collection, directory, doc, permissions=None): - self.metadata_collection = metadata_collection - self.directory = directory - self.doc = doc - self.dataframe_adapter = None - self.permissions = list(permissions or []) - - if self.doc.data_url is not None: - path = self.doc.data_url.path - if platform == "win32" and path[0] == "/": - path = path[1:] - - self.dataframe_adapter = DataFrameAdapter.from_pandas( - pd.read_parquet(path), npartitions=1 - ) - - @property - def specs(self): - return self.doc.specs - - @property - def structure(self): - return DataFrameStructure.from_json(self.doc.structure) - - @property - def metadata(self): - out = self.doc.metadata - _tiled = {"uid": self.doc.uid} - out["_tiled"] = _tiled - return out - - @dataframe_raise_if_inactive - def read(self, *args, **kwargs): - return self.dataframe_adapter.read(*args, **kwargs) - - @dataframe_raise_if_inactive - def read_partition(self, *args, **kwargs): - return self.dataframe_adapter.read_partition(*args, **kwargs) - - def microstructure(self): - return self.dataframe_adapter.microstructure() - - def macrostructure(self): - return self.dataframe_adapter.macrostructure() - - @require_write_permission - def put_data(self, body): - - # Organize files into subdirectories with the first two - # charcters of the uid to avoid one giant directory. - path = self.directory / self.doc.uid[:2] / self.doc.uid - path.parent.mkdir(parents=True, exist_ok=True) - - dataframe = deserialize_arrow(body) - - dataframe.to_parquet(path) - result = self.metadata_collection.update_one( - {"uid": self.doc.uid}, - { - "$set": { - "data_url": f"file://localhost/{str(path).replace(os.sep, '/')}", - "last_modified": datetime.utcnow(), - } - }, - ) - - assert result.matched_count == 1 - assert result.modified_count == 1 - - @require_write_permission - def delete(self): - path = self.directory / self.doc.uid[:2] / self.doc.uid - # FIXME handle case where file does not exist - os.remove(path) - result = self.metadata_collection.delete_one({"uid": self.doc.uid}) - assert result.deleted_count == 1 diff --git a/aimmdb/adapters/xas.py b/aimmdb/adapters/xas.py deleted file mode 100644 index 74adb39..0000000 --- a/aimmdb/adapters/xas.py +++ /dev/null @@ -1,6 +0,0 @@ -from tiled.adapters.dataframe import DataFrameAdapter - - -# dataframe adapter representing XAS data -class XASAdapter(DataFrameAdapter): - specs = ["XAS"] diff --git a/aimmdb/client.py b/aimmdb/client.py index 0a78dcf..c9032bf 100644 --- a/aimmdb/client.py +++ b/aimmdb/client.py @@ -83,7 +83,9 @@ def _keys_slice(self, start, stop, direction): and op_dict["distinct"] == "metadata.sample_id" ): for k, v in super()._items_slice(start, stop, direction): - yield SampleKey(uid=k, name=v.metadata["_tiled"]["sample"]["name"]) + yield SampleKey( + uid=k, name=v.metadata["_tiled"]["sample"]["name"] + ) elif op_dict["op_enum"] == "distinct" and op_dict["distinct"] == "uid": for k, v in super()._items_slice(start, stop, direction): if isinstance(v, XASClient): @@ -99,7 +101,12 @@ def _items_slice(self, start, stop, direction): and op_dict["distinct"] == "metadata.sample_id" ): for k, v in super()._items_slice(start, stop, direction): - yield (SampleKey(uid=k, name=v.metadata["_tiled"]["sample"]["name"]), v) + yield ( + SampleKey( + uid=k, name=v.metadata["_tiled"]["sample"]["name"] + ), + v, + ) elif op_dict["op_enum"] == "distinct" and op_dict["distinct"] == "uid": for k, v in super()._items_slice(start, stop, direction): if isinstance(v, XASClient): diff --git a/aimmdb/graphql.py b/aimmdb/graphql.py deleted file mode 100644 index cf21515..0000000 --- a/aimmdb/graphql.py +++ /dev/null @@ -1,68 +0,0 @@ -from typing import Any, Optional - -import strawberry -from fastapi import Depends, Security -from strawberry.fastapi import GraphQLRouter -from strawberry.permission import BasePermission -from strawberry.tools import create_type -from strawberry.types import Info -from tiled.server.authentication import get_current_principal -from tiled.server.dependencies import get_root_tree - - -async def get_context( - principal=Security(get_current_principal, scopes=["read:metadata"]), - root_tree=Depends(get_root_tree), -): - return {"principal": principal, "root_tree": root_tree} - - -class WritePermission(BasePermission): - message = "User does not have write permission" - - async def has_permission(self, source: Any, info: Info, **kwargs) -> bool: - try: - principal = info.context["principal"] - root = info.context["root_tree"] - return root.access_policy.has_write_permission(principal) - except Exception as e: - print(f"Error while checking WritePermission: {e}") - return False - - -class ReadPermission(BasePermission): - message = "User does not have read permission" - - async def has_permission(self, source: Any, info: Info, **kwargs) -> bool: - try: - principal = info.context["principal"] - root = info.context["root_tree"] - return root.access_policy.has_read_permission(principal) - except Exception as e: - print(f"Error while checking ReadPermission: {e}") - return False - - -@strawberry.field(permission_classes=[ReadPermission]) -def hello(info: Info) -> str: - principal = info.context["principal"] - return f"Hello {principal}" - - -@strawberry.mutation(permission_classes=[WritePermission]) -def record_message(message: str, info: Info) -> Optional[str]: - root = info.context["root_tree"] - db = root.db - try: - result = db.messages.insert_one({"message": message}) - return str(result.inserted_id) - except RuntimeError as e: - print(e) - return None - - -GQLQuery = create_type("Query", [hello]) -GQLMutation = create_type("Mutation", [record_message]) - -GQLSchema = strawberry.Schema(query=GQLQuery, mutation=GQLMutation) -GQLRouter = GraphQLRouter(GQLSchema, context_getter=get_context, graphiql=False) diff --git a/aimmdb/ingest/README.rst b/aimmdb/ingest/README.rst deleted file mode 100644 index 8fcfbed..0000000 --- a/aimmdb/ingest/README.rst +++ /dev/null @@ -1,48 +0,0 @@ - -Ingestion steps for NSLS-II Inner Shell Spectroscopy (ISS) beamline -================================== - -This ingestion pipeline reads ``.dat`` files. These files contain commented lines (denoted by #) aka metadata, and columnated data beneath the metadata. The pipeline is tested on some example data found at ``aimmdb/_tests/eli_test_file.dat``. - - -* For all comment lines, if they contain a colon, remove starting hash and split at colon to create key-value pairs -* The only comment line without colon should be the header for columnated data -* Place key-value pairs in temporary metadata dictionary, ``temp_md`` -* Columnated data should contain energy, i0, it, ir, iff, and aux channels -* Use data in .dat file to calculate ``mu_trans``, ``mu_fluor``, and ``mu_ref`` (see equations below) -* Split the data into three entries for AIMMDB - * One entry for each mu channel (transmission, fluorescence, reference) - * Each entry contains DataFrame with two columns (energy, mu), plus metadata dictionary equivalent to ``temp_md`` with key added for the channel - * Optionally include uid as third element in each entry - -``mu`` transmission -------------------- - -We store a dataframe with column ``mu_trans`` which is calculated via - -.. code:: - - mu_trans = -log(I_trans / I_0) - -This is the intensity of the transmission signal, where :math:`I_0` is the background intensity. - -``mu`` fluorescence -------------------- - -We store a dataframe with column called ``mu_fluor`` which is calculated via - -.. code:: - - mu_fluor = I_fluor / I_0 - -This is the intensity of the fluorescence signal. Note there is no logarithmic scaling like above. - -``mu`` fluorescence -------------------- - -We store a dataframe with column called ``mu_ref`` which is calculated via - -.. code:: - - mu_ref = -log(I_ref / I_0) - diff --git a/aimmdb/ingest/__init__.py b/aimmdb/ingest/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/aimmdb/ingest/eli.py b/aimmdb/ingest/eli.py deleted file mode 100644 index 7c03dac..0000000 --- a/aimmdb/ingest/eli.py +++ /dev/null @@ -1,104 +0,0 @@ -import numpy as np -import pandas as pd -from pathlib import Path - - -def read_metadata_and_header(path): - """Read through commented lines of dat file to get metadata and DataFrame header - - Parameters - ---------- - path: str, or path object - path to .dat file from ISS beamline - - Returns - ------- - tuple[dict[str, str], str] - First element is dictionary of metadata. - Second element is header as single string, with column names separated by whitespace. - """ - metadata: dict - header: str - with open(path, "r") as dat_file: - lines = dat_file.readlines() - # remove starting hash and whitespace - comment_lines = [line[2:] for line in lines if line.startswith("#")] - - metadata = { - # split line at colon for key-value pairs and strip whitespace from values - line.split(":", maxsplit=1)[0]: line.split(":", maxsplit=1)[1].strip() - for line in comment_lines - if ":" in line - } - - # only comment line without colon should be header - header = [line for line in comment_lines if ":" not in line][0] - - return metadata, header - - -def ingest(path, return_uid=False): - """Prepare scan data from Eli (ISS beamline) for entry into AIMMDB - - Data is stored in .dat file which contains commented lines (denoted by #) with metadata, - and columnated data below the metadata. - Data columns should correspond to energy, i0, it, ir, iff, and aux channels. - DataFrame for aimmdb contains only energy and absorption coefficient (mu). - There are several ways to calculate mu based on the mode of the measurement: - - mu_trans = -ln(it/i0) - - mu_fluor = iff/i0 - - mu_ref = -ln(ir/i0) - - These are each calculated and three database entries are created. Each entry contains a DataFrame with energy and mu - as columns, as well as a metadata dictionary containing the commented metadata from the file plus an additional key - indicating the measurement channel (trans, fluor, or ref). - - Parameters - ---------- - path: str, or path object - path to .dat file from ISS beamline - - Returns - ------- - tuple({'data': DataFrame, 'metadata': dict}, {'data': DataFrame, 'metadata': dict}, {'data': DataFrame, 'metadata': dict}) - Database entries corresponding to the transmission, fluorescence, and reference channels. - - Other Parameters - ---------------- - return_uid: bool - Optional keyword to return uid as third element in each entry. - The uid should be present in the metadata, but it is an especially important - piece of information that may be useful to store separately. - """ - temp_md, hdr = read_metadata_and_header(path) - - temp_data = pd.read_csv(path, delim_whitespace=True, comment="#", names=hdr.split()) - temp_data["mu_trans"] = -np.log(temp_data["it"] / temp_data["i0"]) - temp_data["mu_fluor"] = temp_data["iff"] / temp_data["i0"] - temp_data["mu_ref"] = -np.log(temp_data["ir"] / temp_data["i0"]) - - df_trans = temp_data[["energy", "mu_trans"]] - temp_md["channel"] = "transmission" - md_trans = temp_md.copy() - - df_fluor = temp_data[["energy", "mu_fluor"]] - temp_md["channel"] = "fluorescence" - md_fluor = temp_md.copy() - - df_ref = temp_data[["energy", "mu_ref"]] - temp_md["channel"] = "reference" - md_ref = temp_md.copy() - - if return_uid: - uid = temp_md["Scan.uid"] - return ( - dict(data=df_trans, metadata=md_trans, uid=uid), - dict(data=df_fluor, metadata=md_fluor, uid=uid), - dict(data=df_ref, metadata=md_ref, uid=uid), - ) - else: - return ( - dict(data=df_trans, metadata=md_trans), - dict(data=df_fluor, metadata=md_fluor), - dict(data=df_ref, metadata=md_ref), - ) diff --git a/aimmdb/postprocessing/__init__.py b/aimmdb/postprocessing/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/aimmdb/postprocessing/operations.py b/aimmdb/postprocessing/operations.py deleted file mode 100644 index 2ab5852..0000000 --- a/aimmdb/postprocessing/operations.py +++ /dev/null @@ -1,639 +0,0 @@ -"""Module for housing post-processing operations.""" - -from abc import ABC, abstractmethod -from datetime import datetime - -from monty.json import MSONable -import numpy as np -import pandas as pd -from scipy.interpolate import InterpolatedUnivariateSpline -from sklearn.linear_model import LinearRegression -from tiled.client.dataframe import DataFrameClient -from larch import Group as xafsgroup -from larch.xafs import pre_edge - -from aimmdb.postprocessing import utils -from aimmdb.uid import uid - - -class Operator(MSONable, ABC): - """Base operator class. Tracks everything required through a combination - of the MSONable base class and by using an additional datetime key to track - when the operator was logged into the metadata of a new node. - - .. important:: - - The __call__ method must be derived for every operator. In particular, - this operator should take as arguments at least one data point (node). - """ - - @abstractmethod - def _process_data(self): - ... - - @abstractmethod - def _process_metadata(self): - ... - - @abstractmethod - def __call__(self, inp): - ... - - -class UnaryOperator(Operator): - """Specialized operator class which takes only a single input. This input - must be of instance :class:`DataFrameClient`. - - Particularly, the operator object's ``__call__`` method can be executed on - either a :class:`DataFrameClient` or :class:`Node` object. If run on the - :class:`DataFrameClient`, the operator call will return a single dictionary - with the keys "data" and "metadata", as one would expect. If the input is - of type :class:`Node`, then an attempt is made to iterate through all - children of that node, executing the operator on each instance - individually. A list of dictionaries is then returned. - """ - - def _process_metadata(self, metadata): - """Processing of the metadata dictionary object. Takes the - :class:`dict` object as input and returns a modified - dictionary with the following changes: - - 1. metadata["_tiled"]["uid"] is replaced with a new uuid string. - 2. metadata["post_processing"] is created with keys that indicate - the current state of the class, the parent ids - - Parameters - ---------- - metadata : dict - The metadata dictionary accessed via ``df_client.metadata``. - - Returns - ------- - dict - The new metadata object for the post-processed child. - """ - - dt = datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S") - return { - "_post_processing": { - "parents": [metadata["_tiled"]["uid"]], - "operator": self.as_dict(), - "kwargs": self.__dict__, - "datetime": f"{dt} UTC", - }, - } - - def __call__(self, inp): - if isinstance(inp, DataFrameClient): - return { - "data": self._process_data(inp.read()), - "metadata": self._process_metadata(inp.metadata), - } - elif isinstance(inp, dict): - return { - "data": self._process_data(inp["data"]), - "metadata": self._process_metadata(inp["metadata"]), - } - else: - raise ValueError( - f"Input type {type(inp)} must be either DataFrameClient or " - "dict" - ) - - -class MisoOperator(Operator): - """ - Multiple input single output (MISO) operator class. - Specialized operator class which takes an arbitrary number of inputs - and returns a single output. - All inputs must be of instance :class:`DataFrameClient` or `dict`. - - Particularly, the operator object's ``__call__`` method can be executed on - an arbitrary number of :class:`DataFrameClient` or `dict` objects. The operator will - return a single dictionary with keys "data" and "metadata". - """ - - def _process_metadata(self, *metadata): - """Processing of the metadata dictionary objects. Takes arbitrary number - of :class:`dict` objects as input and returns a new dictionary as output - with the following: - - 1. metadata["_tiled"]["uid"] is replaced with a new uuid string. - 2. metadata["post_processing"] is created with keys that indicate - the current state of the class, the parent ids - - Parameters - ---------- - metadata : dict - The metadata dictionaries accessed via ``df_client.metadata``. - - Returns - ------- - dict - The new metadata object for the post-processed child. - """ - - dt = datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S") - return { - "_post_processing": { - "parents": [md["_tiled"]["uid"] for md in metadata], - "operator": self.as_dict(), - "kwargs": self.__dict__, - "datetime": f"{dt} UTC", - }, - } - - def __call__(self, *inps): - if all(isinstance(inp, (DataFrameClient, dict)) for inp in inps): - inp_data = [ - inp.read() if isinstance(inp, DataFrameClient) else inp["data"] - for inp in inps - ] - inp_metadata = [ - inp.metadata - if isinstance(inp, DataFrameClient) - else inp["metadata"] - for inp in inps - ] - return { - "data": self._process_data(*inp_data), - "metadata": self._process_metadata(*inp_metadata), - } - - else: - raise ValueError( - f"All inputs must be of type DataFrameClient or dict" - ) - - -class MimoOperator(Operator): - """ - Multiple input multiple output (MIMO) operator class. - Specialized operator class which takes an arbitrary number of inputs - and returns a list with an equal number of outputs. - All inputs must be of instance :class:`DataFrameClient` or `dict`. - - Particularly, the operator object's ``__call__`` method can be executed on - an arbitrary number of :class:`DataFrameClient` or `dict` objects. For each - object passed some operation will be performed and the result will be returned - as a `dict` with keys "data" and "metatdata". - """ - - def _process_metadata(self, *metadata): - """Processing of the metadata dictionary objects. Takes arbitrary number - of :class:`dict` objects as input and returns a new dictionary for each input - with the following: - - 1. metadata["_tiled"]["uid"] is replaced with a new uid string. - 2. metadata["post_processing"] is created with keys that indicate - the current state of the class. This includes a "parent" key - with the uid of the specific object that was operated on, and - a "relatives" key with the uids of all operator inputs. - - Parameters - ---------- - metadata : dict - The metadata dictionaries accessed via ``df_client.metadata``. - - Returns - ------- - list of dict - List of new metadata object for each post-processed child. - """ - - dt = datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S") - input_uids = [md["_tiled"]["uid"] for md in metadata] - return [ - { - "_post_processing": { - "parent": md["_tiled"]["uid"], - "relatives": input_uids, - "operator": self.as_dict(), - "kwargs": self.__dict__, - "datetime": f"{dt} UTC", - }, - } - for md in metadata - ] # return dict for each entry - - def __call__(self, *inps): - if all(isinstance(inp, (DataFrameClient, dict)) for inp in inps): - inp_data = [ - inp.read() if isinstance(inp, DataFrameClient) else inp["data"] - for inp in inps - ] - inp_metadata = [ - inp.metadata - if isinstance(inp, DataFrameClient) - else inp["metadata"] - for inp in inps - ] - out_data = self._process_data(*inp_data) - out_metadata = self._process_metadata(*inp_metadata) - return [ - { - "data": d, - "metadata": md, - } - for d, md in zip(out_data, out_metadata) - ] - - else: - raise ValueError( - f"All inputs must be of type DataFrameClient or dict" - ) - - -class GroupIdentity(MimoOperator): - """Modified identity operation to work with MimoOperator baseclass. - Does nothing. Primarily used for testing.""" - - def __init__(self): - super().__init__() - - def _process_data(self, *dfs): - """ - Parameters - ---------- - dfs : tuple of pandas.DataFrame - The dataframe objects to process. - - Returns - ------- - list of pandas.DataFrame - """ - - return [df for df in dfs] - - -class AverageData(MisoOperator): - """Average data (mu) from multiple XAS spectra. - Also calculates standard deviation at each energy point. - - Parameters - ---------- - x_columns : str, optional - References a single column in the DataFrames (the "x-axis"). - This should be the same for all DataFrames. Default is "energy". - y_column : str, optional - References a single column in the DataFrames (the "y-axis"). - This is the data that will be averaged from each DataFrame. - Default is "mu". - """ - - def __init__(self, x_column="energy", y_column="mu"): - self.x_column = x_column - self.y_column = y_column - - def _process_data(self, *dfs): - """ - Takes in an arbitrary number of :class:`pd.DataFrame` objects. - The "y_column" is taken from each DataFrame and averaged. - - Returns: - -------- - pd.DataFrame - Averaged data in new DataFrame. - Standard deviation is added to new "stddev" column. - """ - x_values = dfs[0][self.x_column] - assert all( - (df[self.x_column] == x_values).all() for df in dfs - ), "all data should have the same x-values before averaging" - all_data = np.array([df[self.y_column] for df in dfs]) - averaged_data = np.mean(all_data, axis=0) - std_dev = np.std(all_data, axis=0) - new_data = { - self.x_column: x_values, - self.y_column: averaged_data, - "stddev": std_dev, - } - - return pd.DataFrame(new_data) - - -class Identity(UnaryOperator): - """The identity operation. Does nothing. Primarily used for testing - purposes.""" - - def __init__(self): - super().__init__() - - def _process_data(self, df): - """ - Parameters - ---------- - df : pandas.DataFrame - The dataframe object to process. - - Returns - ------- - pandas.DataFrame - """ - - return df - - -class StandardizeGrid(UnaryOperator): - """Interpolates specified columns onto a common grid. - - Parameters - ---------- - x0 : float - The lower bound of the grid to interpolate onto. - xf : float - The upper bound of the grid to interpolate onto. - nx : int - The number of interpolation points. - interpolated_univariate_spline_kwargs : dict, optional - Keyword arguments to be passed to the - :class:`InterpolatedUnivariateSpline`. See - [here](https://docs.scipy.org/doc/scipy/reference/generated/ - scipy.interpolate.InterpolatedUnivariateSpline.html) for the - documentation on this class. - x_column : str, optional - References a single column in the DataFrameClient (this is the - "x-axis"). - y_columns : list, optional - References a list of columns in the DataFrameClient (these are the - "y-axes"). - """ - - def __init__( - self, - *, - x0, - xf, - nx, - interpolated_univariate_spline_kwargs=dict(), - x_column="energy", - y_columns=["mu"], - ): - self.x0 = x0 - self.xf = xf - self.nx = nx - self.interpolated_univariate_spline_kwargs = ( - interpolated_univariate_spline_kwargs - ) - self.x_column = x_column - self.y_columns = y_columns - - def _process_data(self, df): - """Takes in a dictionary of the data amd metadata. The data is a - :class:`pd.DataFrame`, and the metadata is itself a dictionary. - Returns the same dictionary with processed data and metadata. - """ - - new_grid = np.linspace(self.x0, self.xf, self.nx) - new_data = {self.x_column: new_grid} - for column in self.y_columns: - ius = InterpolatedUnivariateSpline( - df[self.x_column], - df[column], - **self.interpolated_univariate_spline_kwargs, - ) - new_data[column] = ius(new_grid) - - return pd.DataFrame(new_data) - - -class RemoveBackground(UnaryOperator): - """Fit the pre-edge region to a Victoreen function and subtract it from the - spectrum. - - Parameters - ---------- - x0 : float - The lower bound of energy range on which the background is fitted. - xf : float - The upper bound of energy range on which the background is fitted. - x_column : str, optional - References a single column in the DataFrameClient (this is the - "x-axis"). - y_columns : list, optional - References a list of columns in the DataFrameClient (these are the - "y-axes"). - victoreen_order : int - The order of Victoreen function. The selected data is fitted to - Victoreen pre-edge function (in which one fits a line to - :math:`E^n \\mu(E)` for some value of :math:`n`. - """ - - def __init__( - self, *, x0, xf, x_column="energy", y_columns=["mu"], victoreen_order=0 - ): - self.x0 = x0 - self.xf = xf - self.victoreen_order = victoreen_order - self.x_column = x_column - self.y_columns = y_columns - - def _process_data(self, df): - """ - Notes - ----- - ``LinearRegression().fit()`` takes 2-D arrays as input. This can be - explored for batch processing of multiple spectra - """ - - assert self.x0 < self.xf, "Invalid range, make sure x0 < xf" - - bg_data = df.loc[ - (df[self.x_column] >= self.x0) * (df[self.x_column] < self.xf) - ] - - new_data = {self.x_column: df[self.x_column]} - for column in self.y_columns: - y = bg_data[column] * bg_data[self.x_column] ** self.victoreen_order - reg = LinearRegression().fit( - bg_data[self.x_column].to_numpy().reshape(-1, 1), - y.to_numpy().reshape(-1, 1), - ) - background = reg.predict( - df[self.x_column].to_numpy().reshape(-1, 1) - ) - new_data[column] = ( - df.loc[:, column].to_numpy() - background.flatten() - ) - - return pd.DataFrame(new_data) - - -class StandardizeIntensity(UnaryOperator): - """Scale the intensity so they vary in similar range. Does this by taking - a specific range of the specrum and dividing out a quadratic fit to that - region. - - Parameters - ---------- - x0 : float - The lower bound of energy range for which the mean is calculated. If - None, the first. - point in the energy grid is used. Default is None. - yf : float - The upper bound of energy range for which the mean is calculated. If - None, the last. - point in the energy grid is used. Default is None. - x_column : str, optional - References a single column in the DataFrameClient (this is the - "x-axis"). Default is "energy". - y_columns : list, optional - References a list of columns in the DataFrameClient (these are the - "y-axes"). Default is ["mu"]. - """ - - def __init__(self, *, x0, xf, x_column="energy", y_columns=["mu"], deg=2): - self.x0 = x0 - self.xf = xf - self.x_column = x_column - self.y_columns = y_columns - self.deg = deg - - def _process_data(self, df): - """ - Takes in a dictionary of the data amd metadata. The data is a - :class:`pd.DataFrame`, and the metadata is itself a dictionary. - Returns the same dictionary with processed data and metadata. - - """ - - grid = np.array(df.loc[:, self.x_column]) - if self.x0 is None: - self.x0 = grid[0] - if self.xf is None: - self.xf = grid[-1] - assert self.x0 < self.xf, "Invalid range, make sure x0 < xf" - select_mean_range = (grid > self.x0) & (grid < self.xf) - - new_data = {self.x_column: df[self.x_column]} - for column in self.y_columns: - y = df.loc[:, column] - y = y[select_mean_range] - x = grid[select_mean_range] - try: - p0 = np.polyfit(x, y, deg=self.deg) - except TypeError: - return None # Range failure - new_data.update({column: df.loc[:, column] / np.polyval(p0, grid)}) - - return pd.DataFrame(new_data) - - -class Smooth(UnaryOperator): - """Return the simple moving average of spectra with a rolling window. - - Parameters - ---------- - window : float, in eV. - The rolling window in eV over which the average intensity is taken. - x_column : str, optional - References a single column in the DataFrameClient (this is the - "x-axis"). - y_columns : list, optional - References a list of columns in the DataFrameClient (these are the - "y-axes"). - """ - - def __init__(self, *, window=10.0, x_column="energy", y_columns=["mu"]): - self.window = window - self.x_column = x_column - self.y_columns = y_columns - - def _process_data(self, df): - """ - Takes in a dictionary of the data amd metadata. The data is a - :class:`pd.DataFrame`, and the metadata is itself a dictionary. - Returns the same dictionary with processed data and metadata. - - Returns: - -------- - dict - A dictionary of the data and metadata. The data is a :class:`pd.DataFrame`, - and the metadata is itself a dictionary. - """ - - grid = df.loc[:, self.x_column] - new_data = {self.x_column: df[self.x_column]} - for column in self.y_columns: - y = df.loc[:, column] - y_smooth = utils.simple_moving_average(grid, y, window=self.window) - new_data.update({column: y_smooth}) - - return pd.DataFrame(new_data) - - -class NormalizeLarch(UnaryOperator): - """Return XAS spectrum normalized using larch. - Post-edge is normalized such that spectral features oscillate around 1. - - Calls larch `pre_edge` function on data to perfrom normalization. - This function performs several steps: - 1. determine E0 (if not supplied) from max of deriv(mu) - 2. fit a line to the region below the edge - 3. fit a quadratic curve to the region above the edge - 4. extrapolate the two curves to E0 and take their difference - to determine the edge jump - - Normalized spectrum (`norm_mu`) is calculated via the following: - `norm_mu = (mu - pre_edge_line) / edge_jump` - - To flatten the spectrum the fitted post-edge quadratic curve is subtracted - from the post-edge. - - - Parameters - ---------- - x_column : str, optional - References a single column in the DataFrameClient (this is the - "x-axis"). Default is "energy". - y_columns : list, optional - References a list of columns in the DataFrameClient (these are the - "y-axes"). Default is ["mu"]. - larch_pre_edge_kwargs : dict, optional - Dictionary of keyword arguments to be passed into larch pre_edge function. - Can be used to specify normalization parameters that are otherwise - calculated in larch (e.g., e0, edge jump size, pre-edge range, etc.). - See https://xraypy.github.io/xraylarch/xafs_preedge.html for pre_edge - documentation. - - """ - - def __init__( - self, - *, - x_column="energy", - y_columns=["mu"], - larch_pre_edge_kwargs=dict(), - ): - self.x_column = x_column - self.y_columns = y_columns - self.larch_pre_edge_kwargs = larch_pre_edge_kwargs - - def _process_data(self, df): - new_data = {self.x_column: df[self.x_column]} - for column in self.y_columns: - larch_group = xafsgroup() - larch_group.energy = np.array(df[self.x_column]) - larch_group.mu = np.array(df[column]) - pre_edge( - larch_group, - group=larch_group, - **self.larch_pre_edge_kwargs, - ) - norm_mu = larch_group.flat - new_data.update({column: norm_mu}) - - return pd.DataFrame(new_data) - - -# TODO -class Classify(UnaryOperator): - """Label the spectrum as "good", "noisy" or "discard" based on the quality - of the spectrum.""" - - ... - - -# TODO -class PreNormalize(UnaryOperator): - ... diff --git a/aimmdb/postprocessing/pipeline.py b/aimmdb/postprocessing/pipeline.py deleted file mode 100644 index 74b2fb1..0000000 --- a/aimmdb/postprocessing/pipeline.py +++ /dev/null @@ -1,57 +0,0 @@ -from monty.json import MSONable -import tiled -from aimm_post_processing import operations as op - - - -class Pipeline(MSONable): - """The data pipeline that contains a chain of operations - """ - def __init__(self, operation_list): - self.pull_first = False # whether has Pull as the first operation - if isinstance(operation_list[0], op.Pull): - self.pull_first = True - self.init_operation = operation_list.pop(0) - else: - self.init_operation = op.Identity() - self.operation_list = operation_list - - def run(self, data): - if self.pull_first: - assert isinstance(data, tiled.client.dataframe.DataFrameClient), \ - """The first operation of your pipeline is Pull, which only takes - "tiled.client.dataframe.DataFrameClient" as input. - """ - else: - assert isinstance(data, dict), \ - """No Pull operation found, only python dictionary is accepted. - """ - data = self.init_operation(data) - pp_history = {0: data["metadata"]["post_processing"]} - for i, operation in enumerate(self.operation_list): - data = operation(data) - pp_history[i+1] = data["metadata"]["post_processing"] - data["metadata"]["post_processing"] = pp_history - return data - - @property - def info(self): - if self.pull_first: - print(self.init_operation) - for operation in self.operation_list: - print(operation) - - def __add__(self, ob): - assert isinstance(ob, self.__class__) - assert not ob.pull_first, \ - "Only first pipeline can have Pull operation" - if self.pull_first: - first_operation = [self.init_operation] - else: - first_operation = [] - new_operation_list = first_operation \ - + self.operation_list \ - + ob.operation_list - - return Pipeline(new_operation_list) - \ No newline at end of file diff --git a/aimmdb/postprocessing/utils.py b/aimmdb/postprocessing/utils.py deleted file mode 100644 index 652c616..0000000 --- a/aimmdb/postprocessing/utils.py +++ /dev/null @@ -1,19 +0,0 @@ -# import general data science modules -import numpy as np - - -def simple_moving_average(x, y, window): - """Reutrn the simple moving average of a data sequence `y` over a rolling window defined on `x`. - Note that the amount of grid points in an interval always exceeds the amount of spacings of - that interval by one. The `window` refers to spacings instead of grid points. - """ - # amount of grid points that accounts for the window in eV (approximately) - half_window = round(window / 2 / (x[1] - x[0])) - - smoothed = [] - for i in range(len(x)): - if i <= half_window: # deal with left bound - smoothed.append(y[: i + half_window + 1].mean()) - else: # python handles right bound automatically - smoothed.append(y[i - half_window : i + half_window + 1].mean()) - return np.array(smoothed) diff --git a/aimmdb/queries.py b/aimmdb/queries.py deleted file mode 100644 index 6a118e0..0000000 --- a/aimmdb/queries.py +++ /dev/null @@ -1,165 +0,0 @@ -from enum import Enum -from typing import Any, Dict, List - -import pydantic -from tiled.queries import (Comparison, Contains, Eq, In, NotEq, NotIn, Specs, - StructureFamily) - -JSONSerializable = Any # Feel free to refine this. - - -def make_mongo_query_in(query, prefix=None): - assert isinstance(query, In) - mongo_key = ".".join([prefix, query.key]) if prefix else query.key - mongo_query = {mongo_key: {"$in": query.value}} - return mongo_query - - -def make_mongo_query_notin(query, prefix=None): - assert isinstance(query, NotIn) - mongo_key = ".".join([prefix, query.key]) if prefix else query.key - mongo_query = {mongo_key: {"$nin": query.value}} - return mongo_query - - -def make_mongo_query_eq(query, prefix=None): - assert isinstance(query, Eq) - mongo_key = ".".join([prefix, query.key]) if prefix else query.key - mongo_query = {mongo_key: {"$eq": query.value}} - return mongo_query - - -def make_mongo_query_neq(query, prefix=None): - assert isinstance(query, NotEq) - mongo_key = ".".join([prefix, query.key]) if prefix else query.key - mongo_query = {mongo_key: {"$ne": query.value}} - return mongo_query - - -def make_mongo_query_comparison(query, prefix=None): - assert isinstance(query, Comparison) - op = query.operator - if op not in {"le", "lt", "ge", "gt"}: - raise ValueError(f"Unexpected operator {query.operator}.") - mongo_op = {"lt": "$lt", "le": "$lte", "gt": "$gt", "ge": "$gte"}[op] - mongo_key = ".".join([prefix, query.key]) if prefix else query.key - mongo_query = {mongo_key: {mongo_op: query.value}} - return mongo_query - - -def make_mongo_query_contains(query, prefix=None): - assert isinstance(query, Contains) - mongo_key = ".".join([prefix, query.key]) if prefix else query.key - mongo_query = {mongo_key: query.value} - return mongo_query - - -def run_eq(query, tree): - mongo_query = make_mongo_query_eq(query, prefix="metadata") - return tree.new_variation(queries=tree.queries + [mongo_query]) - - -def run_neq(query, tree): - mongo_query = make_mongo_query_neq(query, prefix="metadata") - return tree.new_variation(queries=tree.queries + [mongo_query]) - - -def run_comparison(query, tree): - mongo_query = make_mongo_query_comparison(query, prefix="metadata") - return tree.new_variation(queries=tree.queries + [mongo_query]) - - -def run_contains(query, tree): - mongo_query = make_mongo_query_contains(query, prefix="metadata") - return tree.new_variation(queries=tree.queries + [mongo_query]) - - -def run_in(query, tree): - mongo_query = make_mongo_query_in(query, prefix="metadata") - return tree.new_variation(queries=tree.queries + [mongo_query]) - - -def run_notin(query, tree): - mongo_query = make_mongo_query_notin(query, prefix="metadata") - return tree.new_variation(queries=tree.queries + [mongo_query]) - - -def run_structure_family(query, tree): - mongo_query = {"structure_family": query.value.value} - return tree.new_variation(queries=tree.queries + [mongo_query]) - - -def run_specs(query, tree): - mongo_queries = [] - - if query.include: - mongo_queries.append({"specs": {"$all": query.include}}) - - if query.exclude: - mongo_queries.append({"specs": {"$nin": query.exclude}}) - return tree.new_variation(queries=tree.queries + mongo_queries) - - -def register_queries_helper(cls): - cls.register_query(Eq, run_eq) - cls.register_query(NotEq, run_neq) - cls.register_query(Comparison, run_comparison) - cls.register_query(Contains, run_contains) - cls.register_query(In, run_in) - cls.register_query(NotIn, run_notin) - cls.register_query(StructureFamily, run_structure_family) - cls.register_query(Specs, run_specs) - - -class OperationEnum(str, Enum): - distinct = "distinct" - lookup = "lookup" - keys = "keys" - - -class Distinct(pydantic.BaseModel): - op_enum: OperationEnum = "distinct" - select: Dict - distinct: str - - -class Keys(pydantic.BaseModel): - op_enum: OperationEnum = "keys" - select: Dict - keys: List[str] - - -class Lookup(pydantic.BaseModel): - op_enum: OperationEnum = "lookup" - select: Dict - - @pydantic.validator("select") - def check_select(cls, select): - if "uid" not in select: - raise ValueError("Lookup operation must have uid specified") - return select - - -def parse_path(path, key_to_query): - valid_keys = set(key_to_query.keys()) - keys = path[0::2] - values = path[1::2] - - if not set(keys).issubset(valid_keys): - invalid_keys = set(keys) - valid_keys - raise KeyError(f"keys {invalid_keys} not in {valid_keys}") - - select = {key_to_query[k]: v for k, v in zip(keys, values)} - leftover_keys = valid_keys - set(keys) - # if we have more keys then values then get distinct values for the last key - if len(keys) == len(values) + 1: - return Distinct(select=select, distinct=key_to_query[keys[-1]]) - - # if keys and values are matched then perform a lookup if uid was provided otherwise get remaining keys - elif len(keys) == len(values): - if "uid" in keys: - return Lookup(select=select) - else: - return Keys(select=select, keys=leftover_keys) - - raise KeyError(f"{len(keys)=}, {len(values)=}") diff --git a/aimmdb/schemas.py b/aimmdb/schemas.py index 0471325..4dcce2e 100644 --- a/aimmdb/schemas.py +++ b/aimmdb/schemas.py @@ -34,7 +34,9 @@ def validate_structure_matches_structure_family(cls, values): # actual_structure_type = cls.__annotations__["structure"] # this is what was filled in for StructureT actual_structure = values.get("structure") # Given the structure_family, we know what the structure type should be. - expected_structure_type = structure_association[values.get("structure_family")] + expected_structure_type = structure_association[ + values.get("structure_family") + ] if values.get("expected_structure_type") == StructureFamily.node: raise Exception( f"{expected_structure_type} is not currently supported as a writable structure" @@ -51,7 +53,10 @@ def check_data_source(cls, values): # with the current data types without getting any conflicts # if values.get('data_blob') is None and values.get('data_url') is None: # raise ValueError("Not Valid: data_blob and data_url are both None. Use one of them") - if values.get("data_blob") is not None and values.get("data_url") is not None: + if ( + values.get("data_blob") is not None + and values.get("data_url") is not None + ): raise ValueError( "Not Valid: data_blob and data_url contain values. Use just one" ) diff --git a/aimmdb/serialization.py b/aimmdb/serialization.py deleted file mode 100644 index f7fa88c..0000000 --- a/aimmdb/serialization.py +++ /dev/null @@ -1,26 +0,0 @@ -import io - -import numpy as np -import pyarrow as pa -import pyarrow.parquet as pq - - -def serialize_npy(x): - with io.BytesIO() as f: - np.save(f, x) - buf = f.getvalue() - return memoryview(buf) - - -def serialize_parquet(df): - table = pa.Table.from_pandas(df) - sink = pa.BufferOutputStream() - pq.write_table(table, sink) - buf = sink.getvalue() - return memoryview(buf) - - -def deserialize_parquet(data): - reader = pa.BufferReader(data) - table = pq.read_table(reader) - return table.to_pandas() diff --git a/aimmdb/server/__init__.py b/aimmdb/server/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/aimmdb/server/pydantic_dataframe.py b/aimmdb/server/pydantic_dataframe.py deleted file mode 100644 index b72f524..0000000 --- a/aimmdb/server/pydantic_dataframe.py +++ /dev/null @@ -1,19 +0,0 @@ -from typing import List, Tuple, Union - -import pydantic - - -class DataFrameMicroStructure(pydantic.BaseModel): - meta: bytes # Arrow-encoded DataFrame - divisions: bytes # Arrow-encoded DataFrame - - -class DataFrameMacroStructure(pydantic.BaseModel): - npartitions: int - columns: List[str] - resizable: Union[bool, Tuple[bool, ...]] = False - - -class DataFrameStructure(pydantic.BaseModel): - micro: DataFrameMicroStructure - macro: DataFrameMacroStructure diff --git a/aimmdb/server/router.py b/aimmdb/server/router.py deleted file mode 100644 index 7b989c9..0000000 --- a/aimmdb/server/router.py +++ /dev/null @@ -1,54 +0,0 @@ -import pydantic -from fastapi import APIRouter, Depends, HTTPException, Request, Security -from tiled.server.core import json_or_msgpack -from tiled.server.dependencies import get_current_principal, get_root_tree - -from aimmdb.schemas import SampleData - - -class PostSampleResponse(pydantic.BaseModel): - uid: str - - -router = APIRouter() - - -@router.post("/sample", response_model=PostSampleResponse) -def post_sample( - request: Request, - sample: SampleData, - root=Security(get_root_tree, scopes=["write:data", "write:metadata"]), - principal: str = Depends(get_current_principal), -): - entry = root.authenticated_as(principal) - try: - uid = entry.post_sample(sample) - except AttributeError: - raise HTTPException( - status_code=404, detail="tree does not support posting sample metadata" - ) - - return json_or_msgpack(request, {"uid": uid}) - - -@router.delete("/sample/{uid}") -def delete_sample( - request: Request, - uid: str, - root=Security(get_root_tree, scopes=["write:data", "write:metadata"]), - principal: str = Depends(get_current_principal), -): - entry = root.authenticated_as(principal) - try: - entry.delete_sample(uid) - except AttributeError: - raise HTTPException( - status_code=404, detail="tree does not support deleting sample metadata" - ) - - return json_or_msgpack(request, None) - - -@router.get("/sample") -def get_sample(): - pass diff --git a/aimmdb/server/router_tiled.py b/aimmdb/server/router_tiled.py deleted file mode 100644 index 7903e0c..0000000 --- a/aimmdb/server/router_tiled.py +++ /dev/null @@ -1,4 +0,0 @@ -# place for routes to be upstreamed into tiled -from fastapi import APIRouter - -router = APIRouter() diff --git a/aimmdb/utils.py b/aimmdb/utils.py index 6da7eab..eb7da3e 100644 --- a/aimmdb/utils.py +++ b/aimmdb/utils.py @@ -11,7 +11,9 @@ def make_dict(x): if isinstance(x, pydantic.BaseModel): return {k: make_dict(v) for k, v in x} elif dataclasses.is_dataclass(x): - return {f.name: make_dict(getattr(x, f.name)) for f in dataclasses.fields(x)} + return { + f.name: make_dict(getattr(x, f.name)) for f in dataclasses.fields(x) + } else: return x @@ -68,7 +70,10 @@ def get_share_aimmdb_path(): while path != "/": share_tiled = join(path, "share", "aimmdb") if exists( - join(share_tiled, ".identifying_file_5fde776bf5ee64081be861ce6f02490b") + join( + share_tiled, + ".identifying_file_5fde776bf5ee64081be861ce6f02490b", + ) ): # We have the found the right directory, # or one that is trying very hard to pretend to be! diff --git a/aimmdb/validation.py b/aimmdb/validation.py index ae07c45..9a05643 100644 --- a/aimmdb/validation.py +++ b/aimmdb/validation.py @@ -6,7 +6,9 @@ def validate_xas_metadata(metadata, structure_family, structure, spec): if structure_family != "dataframe": - raise ValidationError(f"structure_family {structure_family} != dataframe") + raise ValidationError( + f"structure_family {structure_family} != dataframe" + ) try: metadata = ExperimentalXASMetadata.parse_obj(metadata) except pydantic.ValidationError as e: diff --git a/deploy/local/config.yml b/deploy/local/config.yml index ef58025..fb292ec 100644 --- a/deploy/local/config.yml +++ b/deploy/local/config.yml @@ -1,15 +1,14 @@ trees: - path: / - tree: aimmdb.adapters.aimm:AIMMCatalog.from_uri - # tree: aimmdb.adapters.mongo:MongoAdapter.from_uri + tree: catalog args: - uri: mongodb://root:${MONGO_PASSWORD}@localhost:27018/aimm?authSource=admin - data_directory: ./data - dataset_to_specs: - nmc: - - XAS_TFY - - XAS_TEY - - XAS_trans + uri: ./catalog.db + writable_storage: /data/aimm/data + # dataset_to_specs: + # ncm: + # - XAS_TFY + # - XAS_TEY + # - XAS_trans access_control: access_policy: aimmdb.access:DatasetAccessPolicy args: @@ -31,7 +30,12 @@ authentication: tiled_admins: - provider: toy id: joe -validation: - XAS_TFY: aimmdb.validation:validate_xas_tfy - XAS_TEY: aimmdb.validation:validate_xas_tey - XAS_trans: aimmdb.validation:validate_xas_transmission +# specs: +# - spec: XAS_TFY +# validator: aimmdb.validation:validate_xas_tfy +# - spec: XAS_TEY +# validator: aimmdb.validation:validate_xas_tey +# - spec: XAS_trans +# validator: aimmdb.validation:validate_xas_transmission +# - spec: HasBatteryChargeData +# validator: aimmdb.validation:validate_battery_charge_data diff --git a/deploy/spin/config/config.yml b/deploy/spin/config/config.yml index c84c174..6b0501a 100644 --- a/deploy/spin/config/config.yml +++ b/deploy/spin/config/config.yml @@ -1,82 +1,51 @@ trees: - path: / - tree: aimmdb.adapters.aimm:AIMMCatalog.from_uri + tree: catalog args: - uri: mongodb://root:${MONGO_PASSWORD}@mongodb:27017/aimm?authSource=admin - data_directory: /data - dataset_to_specs: - ncm: - - XAS_TFY - - XAS_TEY - - XAS_trans - newville: - - XAS + uri: postgresql+asyncpg://tiled:${TILED_DATABASE_PASSWORD}@postgres/tiled + writable_storage: /data/aimm/data + # dataset_to_specs: + # ncm: + # - XAS_TFY + # - XAS_TEY + # - XAS_trans access_control: - access_policy: aimmdb.access:DatasetAccessPolicy + access_policy: tiled.access_policies:SimpleAccessPolicy args: provider: orcid access_lists: - 0000-0003-3670-0431: # Joe - default: rw - 0000-0002-5947-6017: # Dan - default: r - sandbox: rw - samples: rw - 0000-0002-1539-0297: # Dylan - default: r - sandbox: rw - samples: rw - 0000-0002-3337-2930: # Eli - default: r - sandbox: rw - samples: rw - 0000-0002-5269-0125: # Alex - default: r - sandbox: rw - samples: rw - 0000-0003-4351-6085: # Deyu - default: r - sandbox: rw - samples: rw - 0000-0003-0666-8063: # Wanli - default: r - sandbox: rw - samples: rw - 0000-0001-5266-5408: # Zhu - default: r - sandbox: rw - samples: rw - 0000-0002-2473-0193: # Juan - default: rw - sandbox: rw - samples: rw - 0000-0003-0922-1363: # Maria - default: r - sandbox: rw - samples: rw - 0000-0002-5181-9513: # Matt - default: r - sandbox: rw - samples: rw - 0000-0001-5651-8405: # Xiaohui - default: r - sandbox: rw - samples: rw - 0000-0001-9869-9005: # Fanchen - default: r - sandbox: rw - samples: rw - public: - newville: r + 0000-0002-5947-6017: tiled.access_policies:ALL_ACCESS # Dan + 0000-0002-1539-0297: tiled.access_policies:ALL_ACCESS # Dylan + 0000-0002-3337-2930: tiled.access_policies:ALL_ACCESS # Eli + 0000-0002-5269-0125: tiled.access_policies:ALL_ACCESS # Alex + 0000-0003-4351-6085: tiled.access_policies:ALL_ACCESS # Deyu + 0000-0003-0666-8063: tiled.access_policies:ALL_ACCESS # Wanli + 0000-0001-5266-5408: tiled.access_policies:ALL_ACCESS # Zhu + 0000-0002-2473-0193: tiled.access_policies:ALL_ACCESS # Juan + 0000-0003-0922-1363: tiled.access_policies:ALL_ACCESS # Maria + 0000-0002-5181-9513: tiled.access_policies:ALL_ACCESS # Matt + 0000-0001-5651-8405: tiled.access_policies:ALL_ACCESS # Xiaohui + 0000-0001-9869-9005: tiled.access_policies:ALL_ACCESS # Fanchen + 0000-0003-1779-8007: tiled.access_policies:ALL_ACCESS # Mikhail + 0000-0001-8885-1552: tiled.access_policies:ALL_ACCESS # Inhui + 0000-0003-2516-2586: tiled.access_policies:ALL_ACCESS # Gihyeok + admins: + - 0000-0002-5947-6017 # Dan + - 0000-0002-1539-0297 # Dylan + - 0000-0002-2473-0193 # Juan + public: + - newville + - nmc_sim + - nmc_sim_structure authentication: allow_anonymous_access: true providers: - provider: orcid - authenticator: aimmdb.authentication:AIMMAuthenticator + authenticator: tiled.authenticators:OIDCAuthenticator args: client_id: ${ORCID_CLIENT_ID} client_secret: ${ORCID_CLIENT_SECRET} - redirect_uri: https://aimm.lbl.gov/api/auth/provider/orcid/login + # redirect_uri: https://aimm.lbl.gov/api/auth/provider/orcid/login public_keys: - kty: "RSA" e: "AQAB" @@ -90,15 +59,15 @@ authentication: tiled_admins: - provider: orcid id: 0000-0003-3670-0431 -database: - uri: "postgresql://tiled:${TILED_DATABASE_PASSWORD}@postgres/tiled" - pool_size: 5 uvicorn: host: 0.0.0.0 port: 8000 -validation: - XAS: aimmdb.validation:validate_xas_metadata - XAS_TFY: aimmdb.validation:validate_xas_tfy - XAS_TEY: aimmdb.validation:validate_xas_tey - XAS_trans: aimmdb.validation:validate_xas_transmission - HasBatteryChargeData: aimmdb.validation:validate_battery_charge_data +# specs: +# - spec: XAS_TFY +# validator: aimmdb.validation:validate_xas_tfy +# - spec: XAS_TEY +# validator: aimmdb.validation:validate_xas_tey +# - spec: XAS_trans +# validator: aimmdb.validation:validate_xas_transmission +# - spec: HasBatteryChargeData +# validator: aimmdb.validation:validate_battery_charge_data diff --git a/deploy/spin/docker/tiled/Dockerfile b/deploy/spin/docker/tiled/Dockerfile deleted file mode 100644 index 880548e..0000000 --- a/deploy/spin/docker/tiled/Dockerfile +++ /dev/null @@ -1,12 +0,0 @@ -FROM ghcr.io/bluesky/tiled:v0.1.0a74 as base - -FROM base as builder - -WORKDIR /build -COPY . . -RUN pip install . - -FROM base as app -COPY --from=builder $VIRTUAL_ENV $VIRTUAL_ENV -COPY deploy/spin/docker/tiled/gunicorn_config.py . -ENV GUNICORN_CONF="/deploy/gunicorn_config.py" diff --git a/deploy/spin/docker/tiled/gunicorn_config.py b/deploy/spin/docker/tiled/gunicorn_config.py deleted file mode 100644 index ef407c7..0000000 --- a/deploy/spin/docker/tiled/gunicorn_config.py +++ /dev/null @@ -1,11 +0,0 @@ -bind = "0.0.0.0:8000" -workers = 4 -worker_class = "uvicorn.workers.UvicornWorker" -worker_connections = 1000 -timeout = 60 -keepalive = 2 -wsgi_app = "tiled.server.app:app_factory()" -worker_tmp_dir = "/dev/shm" -errorlog = "-" -accesslog = "-" -loglevel = "warning" diff --git a/deploy/spin/k8s/backup-db.sh b/deploy/spin/k8s/backup-db.sh new file mode 100755 index 0000000..e7a6153 --- /dev/null +++ b/deploy/spin/k8s/backup-db.sh @@ -0,0 +1,3 @@ +# This doesn't work yet, but it's a start...the pod name is the name of deployment, not the actual pod + +rancher kubectl exec postgres-backup -n aimm -- bash -c "pg_dump postgresql://${POSTGRES_USER}:${POSTGRES_PASSWORD}@postgres/tiled > /backup/database.sql" \ No newline at end of file diff --git a/deploy/spin/k8s/ingress.yaml b/deploy/spin/k8s/ingress.yaml new file mode 100644 index 0000000..b9826cb --- /dev/null +++ b/deploy/spin/k8s/ingress.yaml @@ -0,0 +1,31 @@ +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: tiled + namespace: aimm +spec: + rules: + - host: tiled-ingress.aimm.production.svc.spin.nersc.org + http: + paths: + - backend: + service: + name: tiled + port: + number: 8000 + path: / + pathType: ImplementationSpecific + - host: aimm.lbl.gov + http: + paths: + - backend: + service: + name: tiled + port: + number: 8000 + path: / + pathType: ImplementationSpecific + tls: + - hosts: + - aimm.lbl.gov + secretName: aimm diff --git a/deploy/spin/k8s/postgres-backup.yaml b/deploy/spin/k8s/postgres-backup.yaml new file mode 100644 index 0000000..1346b29 --- /dev/null +++ b/deploy/spin/k8s/postgres-backup.yaml @@ -0,0 +1,75 @@ +## Evantualy this will be a chron job to backup regularly. However, this +## is not super necessary given how few updates the aimmdb will likely receive + +apiVersion: apps/v1 +kind: Deployment +metadata: + name: postgres-backup + namespace: aimm +spec: + selector: + matchLabels: + workload.user.cattle.io/workloadselector: deployment-aimm-postgres-backup + strategy: + rollingUpdate: + maxSurge: 1 + maxUnavailable: 0 + type: RollingUpdate + template: + metadata: + labels: + workload.user.cattle.io/workloadselector: deployment-aimm-postgres-backup + spec: + containers: + - env: + - name: PGDATA + value: /var/lib/postgresql/data/pgdata + - name: POSTGRES_USER + value: postgres + - name: POSTGRES_PASSWORD + valueFrom: + secretKeyRef: + key: password + name: postgrespass + optional: false + command: + - "sleep" + - "999999" + image: postgres:14 + imagePullPolicy: Always + name: postgres + resources: {} + securityContext: + allowPrivilegeEscalation: false + capabilities: + add: + - NET_BIND_SERVICE + drop: + - ALL + privileged: false + readOnlyRootFilesystem: false + runAsNonRoot: true + runAsUser: 79966 + stdin: true + terminationMessagePath: /dev/termination-log + terminationMessagePolicy: File + tty: true + volumeMounts: + - mountPath: /var/lib/postgresql/data + name: postgres-data-pv + - mountPath: /backup + name: db-backup + dnsPolicy: ClusterFirst + restartPolicy: Always + schedulerName: default-scheduler + securityContext: + fsGroup: 94721 + terminationGracePeriodSeconds: 30 + volumes: + - name: postgres-data-pv + persistentVolumeClaim: + claimName: postrgres-pvc + - hostPath: + path: /global/cfs/projectdirs/m3792/aimm/backup + type: Directory + name: db-backup diff --git a/deploy/spin/k8s/postgres.yaml b/deploy/spin/k8s/postgres.yaml new file mode 100644 index 0000000..02dd87b --- /dev/null +++ b/deploy/spin/k8s/postgres.yaml @@ -0,0 +1,95 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: postgres + namespace: aimm +spec: + selector: + matchLabels: + workload.user.cattle.io/workloadselector: deployment-aimm-postgres + strategy: + rollingUpdate: + maxSurge: 1 + maxUnavailable: 0 + type: RollingUpdate + template: + metadata: + labels: + workload.user.cattle.io/workloadselector: deployment-aimm-postgres + spec: + containers: + - env: + - name: PGDATA + value: /var/lib/postgresql/data/pgdata + - name: POSTGRES_USER + value: postgres + - name: POSTGRES_PASSWORD + valueFrom: + secretKeyRef: + key: password + name: postgrespass + optional: false + # command: + # - "sleep" + # - "999999" + image: postgres:14 + imagePullPolicy: Always + name: postgres + resources: {} + securityContext: + allowPrivilegeEscalation: false + capabilities: + add: + - NET_BIND_SERVICE + drop: + - ALL + privileged: false + readOnlyRootFilesystem: false + runAsNonRoot: true + runAsUser: 79966 + stdin: true + terminationMessagePath: /dev/termination-log + terminationMessagePolicy: File + tty: true + volumeMounts: + - mountPath: /var/lib/postgresql/data + name: postgres-data-pv + # - mountPath: /backup + # name: db-backup + dnsPolicy: ClusterFirst + restartPolicy: Always + schedulerName: default-scheduler + securityContext: + fsGroup: 94721 + terminationGracePeriodSeconds: 30 + volumes: + - name: postgres-data-pv + persistentVolumeClaim: + claimName: postrgres-pvc + # - hostPath: + # path: /global/cfs/projectdirs/m3792/aimm/backup + # type: Directory + # name: db-backup +--- +apiVersion: v1 +kind: Service +metadata: + name: postgres + namespace: aimm +spec: + clusterIP: None + clusterIPs: + - None + internalTrafficPolicy: Cluster + ipFamilies: + - IPv4 + ipFamilyPolicy: SingleStack + ports: + - name: default + port: 42 + protocol: TCP + targetPort: 42 + selector: + workload.user.cattle.io/workloadselector: deployment-aimm-postgres + sessionAffinity: None + type: ClusterIP diff --git a/deploy/spin/k8s/tiled.yaml b/deploy/spin/k8s/tiled.yaml new file mode 100644 index 0000000..686766a --- /dev/null +++ b/deploy/spin/k8s/tiled.yaml @@ -0,0 +1,147 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: tiled + namespace: aimm +spec: + selector: + matchLabels: + workload.user.cattle.io/workloadselector: deployment-aimm-tiled + template: + metadata: + labels: + workload.user.cattle.io/workloadselector: deployment-aimm-tiled + spec: + affinity: {} + containers: + - command: + - sleep + - "999999" + # - command: + # - tiled + # - serve + # - config + env: + - name: ORCID_CLIENT_ID + value: APP-0ROS9DU5F717F7XN + - name: TILED_CONFIG + value: /config/main + - name: TILED_INTERNAL_CACHE_LOG_LEVEL + value: INFO + - name: ORCID_CLIENT_SECRET + valueFrom: + secretKeyRef: + key: ORCID_CLIENT_SECRET + name: tiled + optional: false + - name: TILED_SERVER_SECRET_KEYS + valueFrom: + secretKeyRef: + key: TILED_SERVER_SECRET_KEYS + name: tiled + optional: false + - name: TILED_DATABASE_PASSWORD + valueFrom: + secretKeyRef: + key: password + name: postgres-tiled-pass + optional: false + image: ghcr.io/bluesky/tiled:v0.1.0a111 + imagePullPolicy: Always + name: tiled + resources: {} + securityContext: + allowPrivilegeEscalation: false + capabilities: + add: + - NET_BIND_SERVICE + drop: + - ALL + privileged: false + readOnlyRootFilesystem: false + runAsNonRoot: true + runAsUser: 79966 + stdin: true + terminationMessagePath: /dev/termination-log + terminationMessagePolicy: File + tty: true + volumeMounts: + - mountPath: /deploy/config # mount config from the git-sync container + name: aimmdb-github + subPath: main/deploy/spin/config + readOnly: true + - mountPath: /data + name: tiled-data + # - mountPath: /opt/venv/share/tiled/templates/index.html CANNOT DO THIS UNTIL WE MERGE aimmdb PR + # name: aimmdb-github + # subPath: main/deploy/spin/web-frontend/templates/index.html + # readOnly: true + dnsPolicy: ClusterFirst + restartPolicy: Always + schedulerName: default-scheduler + securityContext: + fsGroup: 94721 + terminationGracePeriodSeconds: 30 + + + initContainers: + # A sidecar container that downloads current main from setttings github repo + # and places it into an emptydir volume. This gets picked up by tiled backend + # which mountains files + - name: git-sync + image: k8s.gcr.io/git-sync:v3.1.6 + env: + - name: GIT_SYNC_REPO + value: "https://github.com/AI-multimodal/aimmdb.git" + - name: GIT_SYNC_BRANCH + value: "main" + - name: GIT_SYNC_ROOT + value: "/aimmdb-github" + - name: GIT_SYNC_DEST + value: "main" + - name: GIT_SYNC_ONE_TIME + value: "true" + volumeMounts: + - name: aimmdb-github + readOnly: false + mountPath: /aimmdb-github + securityContext: + allowPrivilegeEscalation: false + capabilities: + add: + - NET_BIND_SERVICE + drop: + - ALL + privileged: false + readOnlyRootFilesystem: false + runAsNonRoot: true + runAsUser: 79966 + volumes: + - name: tiled-data + persistentVolumeClaim: + claimName: tiled-pvc + - name: aimmdb-github + emptyDir: +--- +apiVersion: v1 +kind: Service +metadata: + name: tiled + namespace: aimm +spec: + clusterIP: None + clusterIPs: + - None + internalTrafficPolicy: Cluster + ipFamilies: + - IPv4 + ipFamilyPolicy: SingleStack + ports: + - name: default + port: 8000 + protocol: TCP + targetPort: 8000 + selector: + workload.user.cattle.io/workloadselector: deployment-aimm-tiled + sessionAffinity: None + type: ClusterIP \ No newline at end of file diff --git a/deploy/spin/web-frontend/templates/index.html b/deploy/spin/web-frontend/templates/index.html new file mode 100644 index 0000000..1f0a3be --- /dev/null +++ b/deploy/spin/web-frontend/templates/index.html @@ -0,0 +1,67 @@ +{% extends "page.html" %} +{% block stylesheet %} +{{ super() }} + +{% endblock %} +{% block main %} +

AIMMDB!

+
+
+

Explore from your web browser

+

+ Use a basic, data-oriented web interface to navigate Tiled. +

+ + + +
+
+

Explore from Python

+

+ Install and use Tiled's Python client from Python, IPython, or Jupyter. +

+ 
$ pip install "tiled[client]"
+$ python
+>>> from tiled.client import from_uri
+>>> client = from_uri("{{ root_url }}")
+ {% if binder_link %} + Alternatively, try it in the cloud on Binder. + {% endif %} +
+
+

Explore the API

+

+ Tiled includes interactive OpenAPI documentation you can use in your web browser. +

+

+ The Tiled API can be accessed from any Internet-connected software, + including graphical programs that accept URLs and commandline + utilities like wget and curl. +

+ + + +
+
+

Learn

+

+

+ For tutorials, guides, and more details, see Tiled's project documentation. +

+

+

+ Like Jupyter Notebook, Tiled is something you can easily run yourself, on your laptop. + Tiled can also scale up for large multi-user deployment, like JupyterHub. +

+ + + +
+
+{% endblock %} \ No newline at end of file