diff --git a/.buildinfo b/.buildinfo new file mode 100644 index 0000000..ce94040 --- /dev/null +++ b/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: 6ff04de978eac1136d12975f6fb12a8b +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml deleted file mode 100644 index 7a09055..0000000 --- a/.github/workflows/docs.yml +++ /dev/null @@ -1,43 +0,0 @@ -name: Build docs - -on: - push: - branches: - - master - -jobs: - sphinx_docs_to_gh-pages: - name: Sphinx docs to gh-pages - runs-on: ubuntu-latest - - permissions: - contents: write - pages: write - - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: "3.9" - - name: Install dependencies - run: | - python -m pip install --upgrade pip wheel - pip install -e ".[docs]" - - name: Install package - run: | - python -m pip install -vv --no-build-isolation . - - name: Running the Sphinx to gh-pages Action - # uses: uibcdf/action-sphinx-docs-to-gh-pages@v2.0.0 - # Workaround for bug about improper quoting of GitHub usernames with spaces: - # https://github.com/uibcdf/action-sphinx-docs-to-gh-pages/pull/8 - uses: aeisenbarth/action-sphinx-docs-to-gh-pages@patch-1 - with: - branch: master - dir_docs: docs - sphinx-apidoc-opts: "--separate -o . ../" - sphinx-apidoc-exclude: "" - sphinx-opts: "" diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml deleted file mode 100644 index 8ca80f9..0000000 --- a/.github/workflows/release.yml +++ /dev/null @@ -1,38 +0,0 @@ -name: Release Python Package to PyPI - -on: - release: - types: - - created - -jobs: - build_and_deploy: - name: Publish package distributions to PyPI - runs-on: ubuntu-latest - - environment: - name: pypi - url: https://pypi.org/p/metaspace2020 - - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: 3.9 - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install build - - - name: Build package - run: python -m build - - - name: Publish package to PyPI - uses: pypa/gh-action-pypi-publish@v1.8.10 - with: - user: __token__ - password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml deleted file mode 100644 index 5b74c40..0000000 --- a/.github/workflows/tests.yml +++ /dev/null @@ -1,44 +0,0 @@ -name: Test - -on: - push: - # Push events to master with version tags, e.g. "v1.0" - branches: [master] - tags: - - "v**" - pull_request: - branches: [master] - -jobs: - test: - runs-on: ${{ matrix.os }} - - defaults: - run: - # -e to fail on error - shell: bash -e {0} - - strategy: - fail-fast: false - matrix: - python-version: ["3.9", "3.10", "3.11"] - os: [ubuntu-latest] - - steps: - - name: Checkout code - uses: actions/checkout@v4 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - cache: "pip" - - name: Install dependencies - run: | - python -m pip install --upgrade pip wheel - pip install -e ".[test,docs]" - - name: Test - run: | - pytest -v --cov --color=yes --cov-report=xml - pytest --color=yes --doctest-modules --junitxml=junit/test-results.xml --cov=com --cov-report=xml --cov-report=html - - name: Doc Test - run: make doctest --directory ./docs diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 8fa32e7..0000000 --- a/.gitignore +++ /dev/null @@ -1,30 +0,0 @@ -# JetBrains IDEs -.idea - -# Compiled python modules. -*.pyc -__pycache__ - -# Setuptools distribution folder. -build/* -dist/* - -# Python egg metadata, regenerated from source files by setuptools. -/*.egg-info - -# Python tools: Jupyter, Pytest -.ipynb_checkpoints -.pytest_cache - -# Other -*.out -*.err -*.csv# -.Rproj.user -*.Rproj -plots/* -*.html -analysis_config.json -.Rhistory - -.vscode/* diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml deleted file mode 100644 index 4aebdee..0000000 --- a/.pre-commit-config.yaml +++ /dev/null @@ -1,43 +0,0 @@ -repos: - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.2.0 - hooks: - - id: end-of-file-fixer - - id: check-yaml - - id: check-case-conflict - - id: check-merge-conflict - - id: check-shebang-scripts-are-executable - - id: check-symlinks - - id: check-toml - - repo: https://github.com/hadialqattan/pycln - rev: v1.3.2 - hooks: - - id: pycln - args: ["--config=pyproject.toml"] - - repo: https://github.com/pycqa/isort - rev: 5.12.0 - hooks: - - id: isort - args: ["--settings-path=pyproject.toml"] - - repo: https://github.com/python/black - rev: 22.3.0 - hooks: - - id: black-jupyter - args: ["--config=pyproject.toml"] - pass_filenames: true - - repo: https://github.com/executablebooks/mdformat - rev: 0.7.14 - hooks: - - id: mdformat - additional_dependencies: - - mdformat-beautysh - - mdformat-black - - mdformat-frontmatter - - mdformat-toc - args: ["--number", "--wrap", "keep"] - - repo: https://github.com/asottile/pyupgrade - rev: v3.2.0 - hooks: - - id: pyupgrade - args: [--py38-plus] - exclude: docs diff --git a/LICENSE.txt b/LICENSE.txt deleted file mode 100644 index 8dada3e..0000000 --- a/LICENSE.txt +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright {yyyy} {name of copyright owner} - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/README.md b/README.md index df17de6..e224c42 100644 --- a/README.md +++ b/README.md @@ -1,176 +1,3 @@ -# METASPACE converter +#GitHub Pages -[![Tests][badge-tests]][link-tests] -[![Documentation][badge-docs]][link-docs] -[![PyPI][badge-pypi]][link-pypi] - -Python package to download and convert datasets from the [METASPACE](https://metaspace2020.eu/) -knowledge base to common formats for single-cell and spatial omics analysis. -Datasets can be directly downloaded to -[AnnData](https://anndata.readthedocs.io/en/stable/index.html) and -[SpatialData](https://spatialdata.scverse.org/en/latest/) objects. - -[AnnData](https://anndata.readthedocs.io/en/stable/index.html) provides is underlying data format -of many packages of the [scverse](https://doi.org/10.1038/s41587-023-01733-8) such as -[scanpy](https://scanpy.readthedocs.io/en/stable/) for single-cell data analysis and -[squidpy](https://squidpy.readthedocs.io/en/stable/index.html) for spatial omics analysis. - -Another supported format that is part of the [scverse](https://doi.org/10.1038/s41587-023-01733-8) -is [SpatialData](https://spatialdata.scverse.org/en/latest/) for storage, alignment, and processing -of spatial omics data. This enables users to easily align and integrate METASPACE datasets -to other spatial omics modalities. - -If you encounter any bugs or have suggestions for new features, please open an issue in the -[GitHub repository](https://github.com/metaspace2020/metaspace-converter). - -## Installation - -Our package requires `python >= 3.9`. - -You can install the package directly from [PyPI](https://pypi.org/project/metaspace-converter/): -```bash -pip install metaspace-converter -``` - -### Import package - -```python -import metaspace_converter -``` - -## Short tutorial - -The full documentation for the package can be found here: [https://metaspace2020.github.io/metaspace-converter/](https://metaspace2020.github.io/metaspace-converter/) - -The METASPACE-converter package is using the -[python client](https://github.com/metaspace2020/metaspace/tree/master/metaspace/python-client) -download datasets from METASPACE. -It serves as a wrapper that downloads converts datasets directly to -[AnnData](https://anndata.readthedocs.io/en/stable/index.html) and -[SpatialData](https://spatialdata.scverse.org/en/latest/) objects. - -If you also need to upload or modify datasets on METASPACE, please check the -[METASPACE python client documentation](https://metaspace2020.readthedocs.io/en/latest/index.html). - -### AnnData - -Datasets can be downloaded to AnnData objects using the `metaspace_to_anndata` function. -This allows downstream analysis e.g. with [scanpy](https://scanpy.readthedocs.io/en/stable/) -or [squidpy](https://squidpy.readthedocs.io/en/stable/index.html). - -#### Scanpy - -```python -from metaspace_converter import metaspace_to_anndata -import scanpy as sc - -# Download data and convert to an AnnData object -adata = metaspace_to_anndata( - dataset_id="2022-08-05_17h28m56s", - fdr=0.1, - database=("BraChemDB", "2018-01"), -) - -# Visualization with ScanPy -sc.pl.spatial( - adata, - # Choose the first ion for visualization - color=adata.var.index[0], - img_key=None, - spot_size=1, -) -``` - -![Image](docs/_static/img/example_img_sc.png) - -#### Squidpy - -Optical images can also be downloaded and save in the object if available. -Squidpy allows for an easy overlay of ion image and optical image. - -```python -from metaspace_converter import metaspace_to_anndata -import squidpy as sq - -# Download dataset with optical background image -adata = metaspace_to_anndata( - dataset_id="2022-08-05_17h28m56s", - fdr=0.1, - database=("BraChemDB", "2018-01"), - add_optical_image=True, -) - -sq.pl.spatial_scatter( - adata, color=adata.var.index[0], shape="square", img=True, size=15, alpha=0.5 -) -``` - -![Image](docs/_static/img/example_img_sq.png) - -#### Convert AnnData objects to ion image arrays - -If you want to work with the ion images as numpy arrays, the function `anndata_to_image_array` can -convert previously downloaded AnnData objects to numpy arrays. - -```python -from metaspace_converter import metaspace_to_anndata, anndata_to_image_array - -# Download data -adata2 = metaspace_to_anndata(dataset_id="2023-11-14_21h58m39s", fdr=0.1) - -ion_images = anndata_to_image_array(adata2) - -# 6 ion images of shape 61x78 -print(ion_images.shape) -# > (6, 61, 78) -``` - -### SpatialData - -Download to the [SpatialData](https://spatialdata.scverse.org/en/latest/) format equally easy and -can be done with the `metaspace_to_spatialdata` function. - -Here using a reversed colormap which better represents intense values on bright background. - -```python -from metaspace_converter import metaspace_to_spatialdata -import spatialdata_plot # noqa: Not directly used but extends spatialdata - -# Download dataset with optical background image -sdata = metaspace_to_spatialdata( - dataset_id="2022-08-05_17h28m56s", - fdr=0.1, - database=("BraChemDB", "2018-01"), -) - -# Workaround: spatialdata-plot currently does not use points transformation -sdata.points["maldi_points"] = sdata.transform_element_to_coordinate_system( - sdata.points["maldi_points"], "global" -) - -( - sdata.pl.render_images("optical_image") - .pl.render_points( - "maldi_points", - color=sdata.table.var.index[0], - alpha=1, - size=2, - cmap="viridis_r", - ) - .pl.show(title=sdata.table.var.index[0], coordinate_systems="global") -) -``` - -![Image](docs/_static/img/example_img_sd.png) - -## License - -Unless specified otherwise in file headers or LICENSE files present in subdirectories, all files are licensed under the [Apache 2.0 license](https://github.com/metaspace2020/metaspace/blob/master/LICENSE). - - -[badge-docs]: https://img.shields.io/github/actions/workflow/status/metaspace2020/metaspace-converter/docs.yml?label=documentation -[badge-pypi]: https://img.shields.io/pypi/v/metaspace-converter -[badge-tests]: https://img.shields.io/github/actions/workflow/status/metaspace2020/metaspace-converter/tests.yml?branch=master&label=tests -[link-docs]: https://metaspace2020.github.io/metaspace-converter/ -[link-pypi]: https://pypi.org/project/metaspace-converter/ -[link-tests]: https://github.com/metaspace2020/metaspace-converter/actions/workflows/tests.yml +Last update of sphinx html documentation from [91b77dc](https://github.com/metaspace2020/metaspace-converter/tree/91b77dcec249b9296a09233f50a1e34f68e44284) diff --git a/docs/_static/img/example_img_sc.png b/_images/example_img_sc.png similarity index 100% rename from docs/_static/img/example_img_sc.png rename to _images/example_img_sc.png diff --git a/docs/_static/img/example_img_sd.png b/_images/example_img_sd.png similarity index 100% rename from docs/_static/img/example_img_sd.png rename to _images/example_img_sd.png diff --git a/docs/_static/img/example_img_sq.png b/_images/example_img_sq.png similarity index 100% rename from docs/_static/img/example_img_sq.png rename to _images/example_img_sq.png diff --git a/_modules/index.html b/_modules/index.html new file mode 100644 index 0000000..2371dfd --- /dev/null +++ b/_modules/index.html @@ -0,0 +1,112 @@ + + + + + + Overview: module code — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
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  • Overview: module code
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+ +
+ +
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+ + + + \ No newline at end of file diff --git a/_modules/metaspace_converter/anndata_to_array.html b/_modules/metaspace_converter/anndata_to_array.html new file mode 100644 index 0000000..4235b03 --- /dev/null +++ b/_modules/metaspace_converter/anndata_to_array.html @@ -0,0 +1,176 @@ + + + + + + metaspace_converter.anndata_to_array — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
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+ +

Source code for metaspace_converter.anndata_to_array

+import numpy as np
+from anndata import AnnData
+
+from metaspace_converter.constants import COL, METASPACE_KEY, X, Y
+from metaspace_converter.to_anndata import all_image_pixel_coordinates
+
+
+def _check_pixel_coordinates(adata: AnnData) -> bool:
+    sorted_obs = adata.obs.sort_values([COL.ion_image_pixel_y, COL.ion_image_pixel_x])
+
+    pixel_list = sorted_obs[[COL.ion_image_pixel_y, COL.ion_image_pixel_x]].values
+
+    img_size = adata.uns[METASPACE_KEY]["image_size"]
+    required_pixels = all_image_pixel_coordinates((img_size[Y], img_size[X]))
+
+    return np.all(np.equal(pixel_list, required_pixels))
+
+
+

+[docs]
+def anndata_to_image_array(adata: AnnData) -> np.ndarray:
+    """
+    Extracts an array of ion images from an AnnData object
+    (that has been generated through the ``metaspace_to_anndata`` function).
+
+    Args:
+        adata: An AnnData object.
+
+    Returns:
+        A three-dimensional Numpy array in the following shape
+
+            * Dimension 0: Number of ion images in the order of ``adata.var_names``
+            * Dimension 1: Image height ``adata.uns["metaspace"]["image_size"]["y"]``
+            * Dimension 2: Image width ``adata.uns["metaspace"]["image_size"]["x"]``
+
+    Raises:
+        ValueError: If the AnnData object has been modified.
+            E.g. Pixel have been removed/added and the number of pixels
+            and their coordinates do not match the original image dimensions.
+    """
+
+    pixel_array = adata.X.transpose().copy()
+    img_size = adata.uns[METASPACE_KEY]["image_size"]
+
+    # Check if image dimensions are okay
+    if img_size[X] * img_size[Y] != pixel_array.shape[1]:
+        raise ValueError("Number of observations does not match the original image dimensions")
+
+    # Check if all pixels are available
+    if not _check_pixel_coordinates(adata):
+        raise ValueError("Not all pixels for ion images are available")
+
+    # Sort indices, in case of modified order of pixels (obs)
+    image_sorting = adata.obs.sort_values(
+        [COL.ion_image_pixel_y, COL.ion_image_pixel_x]
+    ).index.values.astype(int)
+    pixel_array = pixel_array[:, image_sorting]
+
+    image_array = pixel_array.reshape(
+        (
+            pixel_array.shape[0],
+            adata.obs[COL.ion_image_pixel_y].max() + 1,
+            adata.obs[COL.ion_image_pixel_x].max() + 1,
+        )
+    )
+
+    return image_array

+
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+ + + + \ No newline at end of file diff --git a/_modules/metaspace_converter/constants.html b/_modules/metaspace_converter/constants.html new file mode 100644 index 0000000..167bfd3 --- /dev/null +++ b/_modules/metaspace_converter/constants.html @@ -0,0 +1,141 @@ + + + + + + metaspace_converter.constants — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
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Source code for metaspace_converter.constants

+from dataclasses import dataclass
+from typing import Final
+
+SPATIAL_KEY: Final = "spatial"
+METASPACE_KEY: Final = "metaspace"
+OBS_INDEX_NAME: Final = "ion_image_pixel"
+VAR_INDEX_NAME: Final = "formula_adduct"
+Shape2d = tuple[int, int]
+
+
+

+[docs]
+@dataclass
+class COL:
+    ion_image_shape_y: Final = "ion_image_shape_y"
+    ion_image_shape_x: Final = "ion_image_shape_x"
+    ion_image_pixel_y: Final = "ion_image_pixel_y"
+    ion_image_pixel_x: Final = "ion_image_pixel_x"

+
+
+
+COORD_SYS_GLOBAL: Final = "global"
+MICROMETER: Final = "micrometer"
+OPTICAL_IMAGE_KEY: Final = "optical_image"
+POINTS_KEY: Final = "maldi_points"
+REGION_KEY: Final = "region"
+INSTANCE_KEY: Final = "instance_id"
+X: Final = "x"
+Y: Final = "y"
+C: Final = "c"
+XY: Final = (X, Y)
+YX: Final = (Y, X)
+YXC: Final = (Y, X, C)
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/metaspace_converter/to_anndata.html b/_modules/metaspace_converter/to_anndata.html new file mode 100644 index 0000000..e940ec7 --- /dev/null +++ b/_modules/metaspace_converter/to_anndata.html @@ -0,0 +1,457 @@ + + + + + + metaspace_converter.to_anndata — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • metaspace_converter.to_anndata
    • +
  • +
    • +
+
+
+
+
+ +

Source code for metaspace_converter.to_anndata

+from typing import Container, Optional
+
+import numpy as np
+import pandas as pd
+from anndata import AnnData
+from metaspace import SMInstance
+from metaspace.sm_annotation_utils import (
+    DEFAULT_DATABASE,
+    DatasetNotFound,
+    IsotopeImages,
+    SMDataset,
+)
+
+from metaspace_converter.constants import (
+    COL,
+    METASPACE_KEY,
+    OBS_INDEX_NAME,
+    SPATIAL_KEY,
+    VAR_INDEX_NAME,
+    Shape2d,
+)
+from metaspace_converter.utils import (
+    empty_columns_to_nan,
+    has_optical_image,
+    iter_property_paths,
+    stringify_list_columns,
+    transform_coords,
+)
+
+
+

+[docs]
+def metaspace_to_anndata(
+    dataset: Optional[SMDataset] = None,
+    dataset_id: Optional[str] = None,
+    database: tuple[str, str] = None,
+    fdr: Optional[float] = 0.1,
+    use_tic: bool = False,
+    metadata_as_obs: bool = False,
+    add_optical_image: bool = False,
+    sm: Optional[SMInstance] = None,
+    **annotation_filter,
+) -> AnnData:
+    """
+    Downloads a METASPACE dataset to an :py:class:`AnnData` object.
+
+    See: https://metaspace2020.eu/about
+
+    Args:
+        dataset: A METASPACE dataset instance. If not provided, the `dataset_id` must be given.
+        dataset_id: The unique ID of a dataset on METASPACE, e.g. "2021-09-03_11h43m13s"
+        database: A single METASPACE database given as a tuple of name and version. Usually it is
+            displayed on METASPACE as "HMDB – v4" which corresponds to `("HMDB", "v4")`.
+        fdr: Returns only annotations for which the false discovery rate is less or equal to this
+            limit.
+        use_tic: When True, the output values will be scaled by the total ion count per pixel and
+            will be in 0.0 to 1.0 range.
+        metadata_as_obs: Whether to store metadata in the `obs` dataframe instead of `uns`. For a
+            single METASPACE dataset, metadata is the same for all pixels, so it would be duplicated
+            for all `obs`. When combining multiple datasets, it would be preserved in `obs` but
+            not in `uns`.
+        add_optical_image: Whether to embed the optical image for SquidPy
+        sm: Optionally a cached SMInstance
+        annotation_filter: Additional keyword arguments passed to the METASPACE API.
+
+    Returns:
+        An :py:class:`AnnData` object with
+            * ion intensities: ``.X``
+            * ion image pixel coordinates: ``.obs[["ion_image_pixel_x", "ion_image_pixel_y"]]``
+            * spatial coordinates: ``.obsm["spatial"]``
+            * ion properties: ``.var``, for example "formula", "adduct", "mz", "fdr",
+              "moleculeNames", "moleculeIds", "intensity"…
+            * METASPACE metadata: ``.uns["metaspace"]`` if not ``metadata_as_obs``
+            * SquidPy metadata: ``.uns["spatial"]`` if ``add_optical_image``
+
+    Raises:
+        ValueError: If something is wrong with the input data or parameters
+        metaspace.sm_annotation_utils.DatasetNotFound: If the dataset ID is not found
+        ConnectionError: For any other network connectivity problems
+    """
+    # This must stay at the top above `locals()`, so that `parameters` includes no later defined
+    # variables, but `dataset_id` definitely has a value.
+    if dataset is not None:
+        dataset_id = dataset.id
+    # Capture the function parameters (except non-serializable ones) so we can include them
+    # in the resulting AnnData.
+    parameters = _dict_without_keys(locals(), keys=("sm", "dataset"))
+
+    dataset = get_dataset(dataset=dataset, dataset_id=dataset_id, sm=sm)
+    if dataset.status != "FINISHED":
+        raise DatasetNotFound(f"Dataset with ID {dataset.id} has not finished processing.")
+
+    if database is None:
+        database = DEFAULT_DATABASE
+
+    # Download annotations
+    annotations = dataset.results(database=database, fdr=fdr, **annotation_filter)
+    annotations = _add_annotations_index(annotations, index_name=VAR_INDEX_NAME)
+    annotations = _normalize_annotations_for_serialization(annotations)
+
+    # Download ion images
+    isotope_images = dataset.all_annotation_images(
+        only_first_isotope=True,
+        fdr=fdr,
+        database=database,
+        scale_intensity="TIC" if use_tic else True,
+        **annotation_filter,
+    )
+    if not isotope_images:
+        raise ValueError(
+            f"No isotope images available for dataset {dataset.id} and database "
+            f"{database[0]} – {database[1]}. Was the database selected for processing on METASPACE?"
+        )
+    assert len(annotations) == len(isotope_images)
+
+    # Sort them matching the annotations.
+    isotope_images = _sort_isotope_images_like(isotope_images, annotations.index)
+
+    # Create X matrix (all ion pixels flattened to primary axis)
+    shape = get_ion_image_shape(dataset)
+    ion_intensities = _create_anndata_x(isotope_images, shape)
+
+    # Create observations dataframe
+    obs = _create_obs_with_coordinates(shape)
+
+    # Scanpy looks up (x, y) coordinates from obsm.
+    obsm = {SPATIAL_KEY: obs[[COL.ion_image_pixel_x, COL.ion_image_pixel_y]].values}
+
+    adata = AnnData(X=ion_intensities, var=annotations, obs=obs, obsm=obsm)
+
+    # Add metadata
+    if not metadata_as_obs:
+        adata.uns[METASPACE_KEY] = _create_uns_for_metaspace(dataset, parameters=parameters)
+    else:
+        metadata_df = _create_obs_for_metaspace(
+            dataset, obs_names=adata.obs.index, parameters=parameters
+        )
+        adata.obs = pd.concat([obs, metadata_df], axis=1)
+
+    # Add optical image
+    if add_optical_image and has_optical_image(dataset):
+        _add_obsm_and_uns_for_squidpy(
+            adata,
+            dataset=dataset,
+            pixel_coords_xy=obs[[COL.ion_image_pixel_x, COL.ion_image_pixel_y]],
+        )
+
+    return adata

+
+
+
+

+[docs]
+def create_annotation_id(formula: str, adduct: str) -> str:
+    return f"{formula}{adduct}"

+
+
+
+def _add_annotations_index(df: pd.DataFrame, index_name: str = VAR_INDEX_NAME) -> pd.DataFrame:
+    df = df.reset_index()
+    df[index_name] = df.apply(lambda row: create_annotation_id(row.formula, row.adduct), axis=1)
+    return df.set_index(index_name)
+
+
+def _normalize_annotations_for_serialization(df: pd.DataFrame) -> pd.DataFrame:
+    # AnnData cannot save dataframes containing Python objects (like lists).
+    # As workaround, we stringify to JSON.
+    df = stringify_list_columns(df, columns=["isotopeImages", "moleculeNames", "moleculeIds"])
+    # AnnData can also not save columns containing only None (dtype object, tries to save as str)
+    # "Can't implicitly convert non-string objects to strings"
+    return empty_columns_to_nan(df)
+
+
+

+[docs]
+def get_dataset(
+    dataset: Optional[SMDataset] = None,
+    dataset_id: Optional[str] = None,
+    sm: Optional[SMInstance] = None,
+) -> SMDataset:
+    """
+    Fetch a dataset instance, if not provided
+    """
+    if dataset is not None:
+        return dataset
+    else:
+        if dataset_id is None:
+            raise ValueError("Either `dataset` or `dataset_id` must be provided")
+        if sm is None:
+            sm = SMInstance()
+        return sm.dataset(id=dataset_id)

+
+
+
+

+[docs]
+def get_ion_image_shape(
+    dataset: Optional[SMDataset] = None,
+    dataset_id: Optional[str] = None,
+    sm: Optional[SMInstance] = None,
+) -> Shape2d:
+    """
+    Get the shape of a METASPACE dataset (in Numpy order).
+
+    Args:
+        dataset: A METASPACE dataset instance. If not provided, a dataset ID must be provided.
+        dataset_id: An optional METASPACE dataset ID, if no dataset instance is provided.
+        sm: An optional SMInstance, otherwise one will be created.
+    """
+    dataset = get_dataset(dataset=dataset, dataset_id=dataset_id, sm=sm)
+    return dataset.image_size["y"], dataset.image_size["x"]

+
+
+
+def _sort_isotope_images_like(
+    isotope_images: list[IsotopeImages], index: pd.Index
+) -> list[IsotopeImages]:
+    images_dict = {}
+    for isotope_image in isotope_images:
+        annotation_id = create_annotation_id(isotope_image.formula, isotope_image.adduct)
+        images_dict[annotation_id] = isotope_image
+    # Return them in the requested order.
+    return [images_dict[key] for key in index]
+
+
+def _create_anndata_x(isotope_images: list[IsotopeImages], shape: Shape2d) -> np.ndarray:
+    n_pixels = np.prod(shape)
+    n_ions = len(isotope_images)
+    y_x_ion_stack = np.dstack([_isotope_image_to_array(img, shape=shape) for img in isotope_images])
+    return y_x_ion_stack.reshape(n_pixels, n_ions)
+
+
+def _isotope_image_to_array(ion_image: IsotopeImages, shape: Shape2d) -> np.ndarray:
+    if len(ion_image) == 0 or ion_image[0] is None:
+        return np.zeros(shape=shape)
+    else:
+        return ion_image[0]
+
+
+def _create_obs_with_coordinates(shape: Shape2d) -> pd.DataFrame:
+    n_pixels = np.prod(shape)
+    pixel_coords_yx = all_image_pixel_coordinates(shape)
+    return pd.DataFrame(
+        {
+            COL.ion_image_pixel_x: pixel_coords_yx[:, 1],
+            COL.ion_image_pixel_y: pixel_coords_yx[:, 0],
+            COL.ion_image_shape_y: shape[0],
+            COL.ion_image_shape_x: shape[1],
+        },
+        index=pd.RangeIndex(n_pixels, name=OBS_INDEX_NAME).astype(str),
+    )
+
+
+

+[docs]
+def all_image_pixel_coordinates(shape: Shape2d) -> np.ndarray:
+    """
+    Returns a list of Y, X coordinates (Numpy order) for every pixel of an image.
+
+    Args:
+        shape: The shape of a 2D image
+
+    Returns:
+        Array of shape (number of pixels, 2)
+    """
+    ys_and_xs = np.indices(shape)
+    return np.dstack(ys_and_xs).reshape((-1, len(shape)))

+
+
+
+def _create_default_spatial_uns(
+    image: Optional[np.ndarray] = None,
+    scale_factor: float = 1.0,
+    spot_diameter: float = 1,
+    library_id: str = "image",
+    image_key: str = "hires",
+) -> dict:
+    return {
+        library_id: {
+            "images": {image_key: image} if image is not None else {},
+            "scalefactors": {
+                f"tissue_{image_key}_scalef": scale_factor,
+                "spot_diameter_fullres": spot_diameter,
+            },
+        }
+    }
+
+
+def _create_uns_for_metaspace(dataset: SMDataset, parameters: Optional[dict] = None) -> dict:
+    # Database details contain instances of MolecularDB, which are not JSON-serializable.
+    # Also, AnnData does  not support lists of dicts, so we have to convert to dict of dicts.
+    # See issue https://github.com/scverse/anndata/issues/708
+    plain_db_details = {str(idx): db._info for idx, db in enumerate(dataset.database_details)}
+    plain_projects = {str(idx): project for idx, project in enumerate(dataset.projects)}
+    plain_parameters = {k: list(v) if isinstance(v, tuple) else v for k, v in parameters.items()}
+    return {
+        "id": dataset.id,
+        "name": dataset.name,
+        "adducts": dataset.adducts,
+        "databases": dataset.databases,
+        "database_details": plain_db_details,
+        "polarity": dataset.polarity,
+        "image_size": dataset.image_size,
+        "submitter": dataset.submitter,
+        "group": dataset.group,
+        "principal_investigator": dataset.principal_investigator,
+        "projects": plain_projects,
+        "status": dataset.status,
+        "metadata": dict(dataset.metadata),
+        "config": dataset.config,
+        # Include parameters used to generate this AnnData
+        f"{metaspace_to_anndata.__name__}_parameters": plain_parameters,
+    }
+
+
+def _create_obs_for_metaspace(
+    dataset: SMDataset, obs_names: pd.Index, parameters: Optional[dict] = None
+) -> pd.DataFrame:
+    metadata = _create_uns_for_metaspace(dataset=dataset, parameters=parameters)
+    flattened_metadata = iter_property_paths(
+        obj={METASPACE_KEY: metadata},
+        delimiter=".",
+        dont_traverse_types=(tuple, list),
+        dont_descend_paths=[f"{METASPACE_KEY}.{metaspace_to_anndata.__name__}_parameters"],
+    )
+    metadata_series = pd.Series(dict(flattened_metadata))
+    obs = pd.DataFrame([metadata_series] * len(obs_names), index=obs_names)
+    # METASPACE metadata contains a lot of list values.
+    # AnnData cannot save dataframes containing Python objects (like lists).
+    # As workaround, we stringify to JSON.
+    obs = stringify_list_columns(obs, all=True)
+    return empty_columns_to_nan(obs)
+
+
+def _add_obsm_and_uns_for_squidpy(adata: AnnData, dataset: SMDataset, pixel_coords_xy: np.ndarray):
+    optical_images = dataset.optical_images()
+    image_yx_rgb = optical_images[0]
+    # Transformation from ion image coordinate system to optical image (x, y order!).
+    matrix_xy = optical_images._transforms[0]
+    coords_on_optical_xy = transform_coords(pixel_coords_xy, matrix_xy)
+    # SquidPy looks up (x, y) coordinates from obsm.
+    adata.obsm[SPATIAL_KEY] = coords_on_optical_xy
+    # SquidPy uses spatial information from uns.
+    adata.uns[SPATIAL_KEY] = _create_default_spatial_uns(image=image_yx_rgb, spot_diameter=0.5)
+
+
+def _dict_without_keys(dictionary: dict, keys: Container) -> dict:
+    # Exclude keys from a dictionary
+    return {k: v for k, v in dictionary.items() if k not in keys}
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/metaspace_converter/to_spatialdata.html b/_modules/metaspace_converter/to_spatialdata.html new file mode 100644 index 0000000..3a2857a --- /dev/null +++ b/_modules/metaspace_converter/to_spatialdata.html @@ -0,0 +1,336 @@ + + + + + + metaspace_converter.to_spatialdata — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • metaspace_converter.to_spatialdata
    • +
  • +
    • +
+
+
+
+
+ +

Source code for metaspace_converter.to_spatialdata

+import warnings
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+from anndata import AnnData
+from metaspace import SMInstance
+from metaspace.sm_annotation_utils import SMDataset
+from spatial_image import SpatialImage
+from spatialdata import SpatialData
+from spatialdata.models import Image2DModel, PointsModel, TableModel
+from spatialdata.transformations import Affine, Scale, Sequence as SequenceTransform, Translation
+
+from metaspace_converter.constants import (
+    COL,
+    COORD_SYS_GLOBAL,
+    INSTANCE_KEY,
+    MICROMETER,
+    OPTICAL_IMAGE_KEY,
+    POINTS_KEY,
+    REGION_KEY,
+    XY,
+    YX,
+    YXC,
+    X,
+    Y,
+)
+from metaspace_converter.to_anndata import metaspace_to_anndata
+from metaspace_converter.utils import has_optical_image
+
+
+

+[docs]
+def metaspace_to_spatialdata(
+    dataset: Optional[SMDataset] = None,
+    dataset_id: Optional[str] = None,
+    database: Optional[tuple[str, str]] = None,
+    fdr: float = 0.1,
+    use_tic: bool = False,
+    metadata_as_obs: bool = False,
+    add_optical_image: bool = True,
+    optical_name_added: str = OPTICAL_IMAGE_KEY,
+    add_points: bool = True,
+    points_name_added: str = POINTS_KEY,
+    sm: Optional[SMInstance] = None,
+    **annotation_filter,
+) -> SpatialData:
+    """
+    Download a METASPACE dataset as a :py:class:`SpatialData` object.
+
+    See: https://metaspace2020.eu/about
+
+    Args:
+        dataset: A METASPACE dataset instance. If not provided, the ``dataset_id`` must be given.
+        dataset_id: The unique ID of a dataset on METASPACE, e.g. "2021-09-03_11h43m13s"
+        database: A single METASPACE database given as a tuple of name and version. Usually it is
+            displayed on METASPACE as "HMDB – v4" which corresponds to ``("HMDB", "v4")``.
+        fdr: Returns only annotations for which the false discovery rate is less or equal to this
+            limit.
+        use_tic: When True, the output values will be scaled by the total ion count per pixel and
+            will be in 0.0 to 1.0 range.
+        metadata_as_obs: Whether to store metadata in the ``obs`` dataframe instead of ``uns``. For
+            a single METASPACE dataset, metadata is the same for all pixels, so it would be
+            duplicated for all ``obs``. When combining multiple datasets, it would be preserved in
+            ``obs`` but not in ``uns``.
+        add_optical_image: Whether to also add the optical image (if one exists) to SpatialData
+            images. If none exists, it is not added, and no error raised.
+        optical_name_added: Name of the element where to store the image in the SpatialData object
+        add_points: Whether to also add ion image pixel coordinates as SpatialData points. This
+            allows to spatially visualize the ion image values. If False, only ion intensities are
+            added to the table and coordinates are added to ``obs``.
+        points_name_added: Name of the element where to store the points in the SpatialData object
+        sm: Optionally a cached SMInstance
+        annotation_filter: Additional keyword arguments passed to the METASPACE API.
+
+    Returns:
+        A :py:class:`SpatialData` object with
+            * ion intensities and metadata: ``.table``
+            * optical image: ``.images["optical_image"]`` (or ``optical_name_added``)
+            * sampling coordinates: ``.points["maldi_points"]`` (or ``points_name_added``) in a
+              coordinate system relative to the top-left image corner and physical scale
+              (micrometers)
+
+    Raises:
+        ValueError: If something is wrong with the input data or parameters
+        metaspace.sm_annotation_utils.DatasetNotFound: If the dataset ID is not found
+        ConnectionError: For any other network connectivity problems
+    """
+    if dataset is None:
+        if sm is None:
+            sm = SMInstance()
+        dataset = sm.dataset(id=dataset_id)
+
+    # Create table
+    adata = metaspace_to_anndata(
+        dataset=dataset,
+        database=database,
+        fdr=fdr,
+        use_tic=use_tic,
+        metadata_as_obs=metadata_as_obs,
+        add_optical_image=False,
+        sm=sm,
+        **annotation_filter,
+    )
+    table = _create_spatialdata_table(adata)
+    sdata = SpatialData(table=table)
+
+    # Create image
+    if has_optical_image(dataset) and add_optical_image:
+        optical_image = optical_image_to_spatial_image(dataset=dataset)
+        sdata.images[optical_name_added] = optical_image
+
+    # Create points
+    if add_points:
+        points = _create_points(dataset, adata)
+        sdata.points[points_name_added] = points
+
+    return sdata

+
+
+
+def _is_matrix_homogeneous(transform_matrix_2d: np.ndarray) -> bool:
+    # In an n×n matrix, the last row (except last element) should be zeros.
+    actual = transform_matrix_2d[-1, :-1]
+    expected = np.zeros(transform_matrix_2d.shape[1] - 1)
+    return np.array_equal(actual, expected)
+
+
+

+[docs]
+def optical_image_to_spatial_image(dataset: SMDataset) -> Optional[SpatialImage]:
+    """
+    Download the optical image of a METASPACE dataset to a SpatialImage object.
+
+    This function requires that the dataset has an optical image. Otherwise, an error is raised.
+
+    Args:
+        dataset: A METASPACE dataset
+
+    Returns:
+        A SpatialImage object with the image's transformation to world coordinates
+    """
+    optical_images = dataset.optical_images()
+    image_yx_rgb = optical_images[0]
+    # Transformation from optical image to ion image coordinate system (inverse of ``_transforms``)
+    matrix_xy = optical_images._itransforms[0]
+    if _is_matrix_homogeneous(matrix_xy):
+        affine_matrix_xy = matrix_xy
+    else:
+        warnings.warn(
+            "The METASPACE optical image has a projective transformation, but "
+            "SpatialData only supports affine transformations. "
+            "Using an approximated affine."
+        )
+        affine_matrix_xy = matrix_xy.copy()
+        affine_matrix_xy[-1, :-1] = 0.0
+    optical_to_ion_image = Affine(matrix=affine_matrix_xy, input_axes=XY, output_axes=XY)
+    # Transformation from ion image to world coordinate system
+    ion_image_to_global = get_ion_image_to_physical_transformation(dataset)
+    pixel_corner_to_center = Translation(translation=[-0.5, -0.5], axes=YX)
+    optical_to_global = SequenceTransform(
+        [optical_to_ion_image, pixel_corner_to_center, ion_image_to_global]
+    ).to_affine(input_axes=YX, output_axes=YX)
+    return Image2DModel.parse(
+        image_yx_rgb,
+        transformations={COORD_SYS_GLOBAL: optical_to_global},
+        dims=YXC,
+        scale_factors=(2, 2, 2),
+        axis_units={Y: MICROMETER, X: MICROMETER},
+        c_coords=("r", "g", "b"),
+        # rgb=True,
+    )

+
+
+
+def _create_spatialdata_table(adata: AnnData) -> AnnData:
+    # Add the index as a separate column for matching rows to regions in labels or points.
+    adata.obs[INSTANCE_KEY] = adata.obs.index.astype(int)
+    # Avoid same name for index and a column name, which is ambiguous and causes warning or error.
+    adata.obs.index.name = "index"
+    adata.obs[REGION_KEY] = pd.Series(
+        [POINTS_KEY] * adata.n_obs, index=adata.obs.index, dtype="category"
+    )
+    return TableModel.parse(
+        adata,
+        region=[POINTS_KEY],  # element in SpatialData to be used
+        region_key=REGION_KEY,  # table column containing the regions
+        instance_key=INSTANCE_KEY,  # table column to be matched to points index
+    )
+
+
+def _create_points(dataset: SMDataset, adata: AnnData) -> pd.DataFrame:
+    # FIXME: PointsModel does not yet allow to specify units, we should set micrometers as in image.
+    # FIXME: Workaround for that napari-spatialdata does not allow to visualize values from ``X``
+    #  array, only from ``obs``. For now, we copy X to ``obs`` to enable visualization.
+    # points = PointsModel.parse(
+    #     adata.obs[[X, Y]].reset_index(),
+    #     transformations={COORD_SYS_GLOBAL: get_ion_image_to_physical_transformation(dataset)},
+    # )
+    return PointsModel.parse(
+        pd.concat(
+            [
+                adata.obs[[COL.ion_image_pixel_x, COL.ion_image_pixel_y]].reset_index(),
+                pd.DataFrame(adata.X, columns=adata.var.index),
+            ],
+            axis=1,
+        ),
+        coordinates={"x": COL.ion_image_pixel_x, "y": COL.ion_image_pixel_y},
+        transformations={COORD_SYS_GLOBAL: get_ion_image_to_physical_transformation(dataset)},
+    )
+
+
+

+[docs]
+def get_ion_image_to_physical_transformation(dataset: SMDataset) -> Scale:
+    """
+    Get the transformation from the ion image (pixels) to the world coordinate system (micrometers).
+
+    Args:
+        dataset: A METASPACE dataset
+
+    Returns:
+        A scale SpatialData transformation
+    """
+    ion_image_pixel_size = dataset.metadata["MS_Analysis"]["Pixel_Size"]
+    scale_factors_xy = [ion_image_pixel_size["Xaxis"], ion_image_pixel_size["Yaxis"]]
+    return Scale(scale=scale_factors_xy, axes=XY)

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/metaspace_converter/utils.html b/_modules/metaspace_converter/utils.html new file mode 100644 index 0000000..8eac246 --- /dev/null +++ b/_modules/metaspace_converter/utils.html @@ -0,0 +1,305 @@ + + + + + + metaspace_converter.utils — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for metaspace_converter.utils

+import dataclasses
+import json
+from collections.abc import Collection, Iterator, Mapping, Sequence
+from dataclasses import is_dataclass
+from typing import Any, Iterable
+
+import numpy as np
+import pandas as pd
+from metaspace.sm_annotation_utils import SMDataset
+
+
+

+[docs]
+def empty_columns_to_nan(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Converts all empty columns from None (dtype object) to NaN (dtype float).
+
+    Args:
+        df: A dataframe
+
+    Returns:
+        The modified dataframe. The original dataframe is modified, not a copy.
+    """
+    for c in df.columns:
+        if pd.api.types.infer_dtype(df[c]) == "empty":
+            df[c] = np.nan
+    return df

+
+
+
+

+[docs]
+def has_optical_image(dataset: SMDataset) -> bool:
+    """
+    Check whether a METASPACE dataset has an optical image
+    """
+    try:
+        optical_images = dataset.optical_images()
+    except TypeError:
+        return False
+    return optical_images is not None and len(optical_images) > 0

+
+
+
+

+[docs]
+def is_non_str_sequence(obj: Any) -> bool:
+    """
+    Returns whether an object is a sequence (list and similar), excluding strings.
+
+    Args:
+        obj: The object to check.
+
+    Returns:
+        True if the object is a sequence.
+    """
+    return isinstance(obj, Sequence) and not isinstance(obj, str)

+
+
+
+

+[docs]
+def iter_property_paths(
+    obj: Any,
+    delimiter: str = "/",
+    include_none: bool = True,
+    dont_traverse_types: tuple[type, ...] = tuple(),
+    dont_descend_paths: Collection[str] = tuple(),
+    _path: str = "",
+) -> Iterator[tuple[str, Any]]:
+    """
+    Iterates recursively over properties of nested objects.
+
+    Args:
+        obj: A class instance, mapping or sequence.
+        delimiter: The delimiter to use. Should not be contained in any name.
+        include_none: Whether to yield attributes which have None assigned, or skip them.
+        dont_traverse_types: Iterable data types which should not be traversed any deeper.
+            These are yielded as values.
+        dont_descend_paths: Paths with iterable objects that should be preserved.
+
+    Returns:
+        An iterator for iterating over paths and objects found at each path. It can also be
+        converted to a dictionary.
+    """
+    prefix = _path if _path == "" else f"{_path}{delimiter}"
+    if dont_traverse_types and isinstance(obj, dont_traverse_types):
+        if obj is None and not include_none:
+            return
+        yield _path, obj
+    elif _path in dont_descend_paths:
+        if obj is None and not include_none:
+            return
+        yield _path, obj
+    # Traversable types: list, dict, dataclasses/pydantic
+    elif is_non_str_sequence(obj):
+        for i, value in enumerate(obj):
+            yield from iter_property_paths(
+                value,
+                delimiter=delimiter,
+                include_none=include_none,
+                dont_traverse_types=dont_traverse_types,
+                dont_descend_paths=dont_descend_paths,
+                _path=f"{prefix}{i}",
+            )
+    elif isinstance(obj, Mapping):
+        for key, value in obj.items():
+            yield from iter_property_paths(
+                value,
+                delimiter=delimiter,
+                include_none=include_none,
+                dont_traverse_types=dont_traverse_types,
+                dont_descend_paths=dont_descend_paths,
+                _path=f"{prefix}{key}",
+            )
+    elif is_dataclass(obj):
+        keys = (
+            [field.name for field in dataclasses.fields(obj)]
+            if is_dataclass(obj)
+            else obj.__fields__.keys()
+        )
+        for key in keys:
+            # Object with attributes
+            value = getattr(obj, key)
+            yield from iter_property_paths(
+                value,
+                delimiter=delimiter,
+                include_none=include_none,
+                dont_traverse_types=dont_traverse_types,
+                dont_descend_paths=dont_descend_paths,
+                _path=f"{prefix}{key}",
+            )
+    # Non-traversable/primitive types
+    else:
+        if obj is None and not include_none:
+            # Don't yield values that are not set.
+            return
+        yield _path, obj

+
+
+
+

+[docs]
+def stringify_list_columns(
+    df: pd.DataFrame, columns: Iterable[str] = tuple(), all: bool = False
+) -> pd.DataFrame:
+    """
+    If the dataframe column is a list, stringify it into JSON.
+
+    Args:
+        df: A dataframe with a column that contains lists (e.g. of molecule names)
+        columns: The names of the columns containing lists
+        all: Whether to check and stringify all columns containing list-like objects
+
+    Returns:
+        A dataframe where all values in specified columns are JSON stringified.
+    """
+    if not df.empty:
+        df = df.copy()
+        if all:
+            columns = df.columns
+        for column in columns:
+            if column in df.columns and pd.api.types.is_list_like(df[column].iloc[0]):
+                df[column] = df[column].apply(lambda l: json.dumps(l))
+    return df

+
+
+
+

+[docs]
+def transform_coords(coords: np.ndarray, transform_matrix_2d: np.ndarray) -> np.ndarray:
+    """
+    Transform a list of 2D coordinates using a 3×3 transformation matrix.
+
+    Like in scikit-image, but without adding that dependency.
+
+    See skimage.transform._geometric.ProjectiveTransform.__call__
+
+    Args:
+        coords: An array of coordinates with shape (number of points, 2)
+        transform_matrix_2d: A transformation matrix of shape (3, 3)
+
+    Returns:
+        The list of transformed coordinates as Numpy array
+    """
+    # From skimage.transform._geometric
+    # Convert to homogeneous coordinates
+    src_h = np.hstack([coords, np.ones((len(coords), 1))])
+    dst_h = src_h @ transform_matrix_2d.T
+    # Below, we will divide by the last dimension of the homogeneous
+    # coordinate matrix. In order to avoid division by zero,
+    # we replace exact zeros in this column with a very small number.
+    dst_h[dst_h[:, -1] == 0, -1] = np.finfo(float).eps
+    # rescale to homogeneous coordinates
+    dst = dst_h[:, :-1] / dst_h[:, -1:]
+    return dst

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/api.rst b/_sources/api.rst.txt similarity index 100% rename from docs/api.rst rename to _sources/api.rst.txt diff --git a/docs/examples.rst b/_sources/examples.rst.txt similarity index 100% rename from docs/examples.rst rename to _sources/examples.rst.txt diff --git a/docs/index.rst b/_sources/index.rst.txt similarity index 100% rename from docs/index.rst rename to _sources/index.rst.txt diff --git a/_sources/metaspace_converter.anndata_to_array.rst.txt b/_sources/metaspace_converter.anndata_to_array.rst.txt new file mode 100644 index 0000000..ec69ba2 --- /dev/null +++ b/_sources/metaspace_converter.anndata_to_array.rst.txt @@ -0,0 +1,7 @@ +metaspace\_converter.anndata\_to\_array module +============================================== + +.. automodule:: metaspace_converter.anndata_to_array + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/metaspace_converter.constants.rst.txt b/_sources/metaspace_converter.constants.rst.txt new file mode 100644 index 0000000..aa3eba1 --- /dev/null +++ b/_sources/metaspace_converter.constants.rst.txt @@ -0,0 +1,7 @@ +metaspace\_converter.constants module +===================================== + +.. automodule:: metaspace_converter.constants + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/metaspace_converter.rst.txt b/_sources/metaspace_converter.rst.txt new file mode 100644 index 0000000..fb2c125 --- /dev/null +++ b/_sources/metaspace_converter.rst.txt @@ -0,0 +1,22 @@ +metaspace\_converter package +============================ + +Submodules +---------- + +.. toctree:: + :maxdepth: 4 + + metaspace_converter.anndata_to_array + metaspace_converter.constants + metaspace_converter.to_anndata + metaspace_converter.to_spatialdata + metaspace_converter.utils + +Module contents +--------------- + +.. automodule:: metaspace_converter + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/metaspace_converter.to_anndata.rst.txt b/_sources/metaspace_converter.to_anndata.rst.txt new file mode 100644 index 0000000..464e24d --- /dev/null +++ b/_sources/metaspace_converter.to_anndata.rst.txt @@ -0,0 +1,7 @@ +metaspace\_converter.to\_anndata module +======================================= + +.. automodule:: metaspace_converter.to_anndata + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/metaspace_converter.to_spatialdata.rst.txt b/_sources/metaspace_converter.to_spatialdata.rst.txt new file mode 100644 index 0000000..273868d --- /dev/null +++ b/_sources/metaspace_converter.to_spatialdata.rst.txt @@ -0,0 +1,7 @@ +metaspace\_converter.to\_spatialdata module +=========================================== + +.. automodule:: metaspace_converter.to_spatialdata + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/metaspace_converter.utils.rst.txt b/_sources/metaspace_converter.utils.rst.txt new file mode 100644 index 0000000..cd8d795 --- /dev/null +++ b/_sources/metaspace_converter.utils.rst.txt @@ -0,0 +1,7 @@ +metaspace\_converter.utils module +================================= + +.. automodule:: metaspace_converter.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/modules.rst.txt b/_sources/modules.rst.txt new file mode 100644 index 0000000..53cba5a --- /dev/null +++ b/_sources/modules.rst.txt @@ -0,0 +1,7 @@ +metaspace-converter +=================== + +.. toctree:: + :maxdepth: 4 + + metaspace_converter diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8141580 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 0000000..30fee9d --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +a:visited { + color: #551A8B; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; 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+ float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + + +/* -- other body styles ----------------------------------------------------- */ + +ol.arabic { + list-style: decimal; +} + +ol.loweralpha { + list-style: lower-alpha; +} + +ol.upperalpha { + list-style: upper-alpha; +} + +ol.lowerroman { + list-style: lower-roman; +} + +ol.upperroman { + list-style: upper-roman; +} + +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/_static/check-solid.svg b/_static/check-solid.svg new file mode 100644 index 0000000..92fad4b --- /dev/null +++ b/_static/check-solid.svg @@ -0,0 +1,4 @@ + + + + diff --git a/_static/clipboard.min.js b/_static/clipboard.min.js new file mode 100644 index 0000000..54b3c46 --- /dev/null +++ b/_static/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.8 + * https://clipboardjs.com/ + * + * Licensed MIT © Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1 + + + + diff --git a/_static/copybutton.css b/_static/copybutton.css new file mode 100644 index 0000000..f1916ec --- /dev/null +++ b/_static/copybutton.css @@ -0,0 +1,94 @@ +/* Copy buttons */ +button.copybtn { + position: absolute; + display: flex; + top: .3em; + right: .3em; + width: 1.7em; + height: 1.7em; + opacity: 0; + transition: opacity 0.3s, border .3s, background-color .3s; + user-select: none; + padding: 0; + border: none; + outline: none; + border-radius: 0.4em; + /* The colors that GitHub uses */ + border: #1b1f2426 1px solid; + background-color: #f6f8fa; + color: #57606a; +} + +button.copybtn.success { + border-color: #22863a; + color: #22863a; +} + +button.copybtn svg { + stroke: currentColor; + width: 1.5em; + height: 1.5em; + padding: 0.1em; +} + +div.highlight { + position: relative; +} + +/* Show the copybutton */ +.highlight:hover button.copybtn, button.copybtn.success { + opacity: 1; +} + +.highlight button.copybtn:hover { + background-color: rgb(235, 235, 235); +} + +.highlight button.copybtn:active { + background-color: rgb(187, 187, 187); +} + +/** + * A minimal CSS-only tooltip copied from: + * https://codepen.io/mildrenben/pen/rVBrpK + * + * To use, write HTML like the following: + * + *

Short

+ */ + .o-tooltip--left { + position: relative; + } + + .o-tooltip--left:after { + opacity: 0; + visibility: hidden; + position: absolute; + content: attr(data-tooltip); + padding: .2em; + font-size: .8em; + left: -.2em; + background: grey; + color: white; + white-space: nowrap; + z-index: 2; + border-radius: 2px; + transform: translateX(-102%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); +} + +.o-tooltip--left:hover:after { + display: block; + opacity: 1; + visibility: visible; + transform: translateX(-100%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); + transition-delay: .5s; +} + +/* By default the copy button shouldn't show up when printing a page */ +@media print { + button.copybtn { + display: none; + } +} diff --git a/_static/copybutton.js b/_static/copybutton.js new file mode 100644 index 0000000..2ea7ff3 --- /dev/null +++ b/_static/copybutton.js @@ -0,0 +1,248 @@ +// Localization support +const messages = { + 'en': { + 'copy': 'Copy', + 'copy_to_clipboard': 'Copy to clipboard', + 'copy_success': 'Copied!', + 'copy_failure': 'Failed to copy', + }, + 'es' : { + 'copy': 'Copiar', + 'copy_to_clipboard': 'Copiar al portapapeles', + 'copy_success': '¡Copiado!', + 'copy_failure': 'Error al copiar', + }, + 'de' : { + 'copy': 'Kopieren', + 'copy_to_clipboard': 'In die Zwischenablage kopieren', + 'copy_success': 'Kopiert!', + 'copy_failure': 'Fehler beim Kopieren', + }, + 'fr' : { + 'copy': 'Copier', + 'copy_to_clipboard': 'Copier dans le presse-papier', + 'copy_success': 'Copié !', + 'copy_failure': 'Échec de la copie', + }, + 'ru': { + 'copy': 'Скопировать', + 'copy_to_clipboard': 'Скопировать в буфер', + 'copy_success': 'Скопировано!', + 'copy_failure': 'Не удалось скопировать', + }, + 'zh-CN': { + 'copy': '复制', + 'copy_to_clipboard': '复制到剪贴板', + 'copy_success': '复制成功!', + 'copy_failure': '复制失败', + }, + 'it' : { + 'copy': 'Copiare', + 'copy_to_clipboard': 'Copiato negli appunti', + 'copy_success': 'Copiato!', + 'copy_failure': 'Errore durante la copia', + } +} + +let locale = 'en' +if( document.documentElement.lang !== undefined + && messages[document.documentElement.lang] !== undefined ) { + locale = document.documentElement.lang +} + +let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; +if (doc_url_root == '#') { + doc_url_root = ''; +} + +/** + * SVG files for our copy buttons + */ +let iconCheck = ` + ${messages[locale]['copy_success']} + + +` + +// If the user specified their own SVG use that, otherwise use the default +let iconCopy = ``; +if (!iconCopy) { + iconCopy = ` + ${messages[locale]['copy_to_clipboard']} + + + +` +} + +/** + * Set up copy/paste for code blocks + */ + +const runWhenDOMLoaded = cb => { + if (document.readyState != 'loading') { + cb() + } else if (document.addEventListener) { + document.addEventListener('DOMContentLoaded', cb) + } else { + document.attachEvent('onreadystatechange', function() { + if (document.readyState == 'complete') cb() + }) + } +} + +const codeCellId = index => `codecell${index}` + +// Clears selected text since ClipboardJS will select the text when copying +const clearSelection = () => { + if (window.getSelection) { + window.getSelection().removeAllRanges() + } else if (document.selection) { + document.selection.empty() + } +} + +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + +const temporarilyChangeTooltip = (el, oldText, newText) => { + el.setAttribute('data-tooltip', newText) + el.classList.add('success') + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) +} + +// Changes the copy button icon for two seconds, then changes it back +const temporarilyChangeIcon = (el) => { + el.innerHTML = iconCheck; + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) +} + +const addCopyButtonToCodeCells = () => { + // If ClipboardJS hasn't loaded, wait a bit and try again. This + // happens because we load ClipboardJS asynchronously. + if (window.ClipboardJS === undefined) { + setTimeout(addCopyButtonToCodeCells, 250) + return + } + + // Add copybuttons to all of our code cells + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) + codeCells.forEach((codeCell, index) => { + const id = codeCellId(index) + codeCell.setAttribute('id', id) + + const clipboardButton = id => + `` + codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) + }) + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} + + +var copyTargetText = (trigger) => { + var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') +} + + // Initialize with a callback so we can modify the text before copy + const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) + + // Update UI with error/success messages + clipboard.on('success', event => { + clearSelection() + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) + temporarilyChangeIcon(event.trigger) + }) + + clipboard.on('error', event => { + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) + }) +} + +runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_static/copybutton_funcs.js b/_static/copybutton_funcs.js new file mode 100644 index 0000000..dbe1aaa --- /dev/null +++ b/_static/copybutton_funcs.js @@ -0,0 +1,73 @@ +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} diff --git a/_static/css/badge_only.css b/_static/css/badge_only.css new file mode 100644 index 0000000..c718cee --- /dev/null +++ b/_static/css/badge_only.css @@ -0,0 +1 @@ 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx-codeautolink.css b/_static/sphinx-codeautolink.css new file mode 100644 index 0000000..8b24ead --- /dev/null +++ b/_static/sphinx-codeautolink.css @@ -0,0 +1,16 @@ +.sphinx-codeautolink-a:link{ + color: inherit; + text-decoration: none; +} +.sphinx-codeautolink-a:active{ + color: inherit; + text-decoration: none; +} +.sphinx-codeautolink-a:visited{ + color: inherit; + text-decoration: none; +} +.sphinx-codeautolink-a:hover{ + color: rgb(0, 139, 139); + text-decoration: none; +} diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/api.html b/api.html new file mode 100644 index 0000000..c544d1d --- /dev/null +++ b/api.html @@ -0,0 +1,268 @@ + + + + + + + API — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

API

+
+

metaspace_converter

+
+
+metaspace_converter.anndata_to_image_array(adata)[source]
+

Extracts an array of ion images from an AnnData object +(that has been generated through the metaspace_to_anndata function).

+
+
Parameters:
+

adata (AnnData) – An AnnData object.

+
+
Return type:
+

ndarray

+
+
Returns:
+

A three-dimensional Numpy array in the following shape

+
+
    +

  • Dimension 0: Number of ion images in the order of adata.var_names

    • +

  • Dimension 1: Image height adata.uns["metaspace"]["image_size"]["y"]

    • +

  • Dimension 2: Image width adata.uns["metaspace"]["image_size"]["x"]

    • +
+
+

+
+
Raises:
+

ValueError – If the AnnData object has been modified. + E.g. Pixel have been removed/added and the number of pixels + and their coordinates do not match the original image dimensions.

+
+
+
+ +
+
+metaspace_converter.metaspace_to_anndata(dataset=None, dataset_id=None, database=None, fdr=0.1, use_tic=False, metadata_as_obs=False, add_optical_image=False, sm=None, **annotation_filter)[source]
+

Downloads a METASPACE dataset to an AnnData object.

+

See: https://metaspace2020.eu/about

+
+
Parameters:
+
    +

  • dataset (Optional[SMDataset]) – A METASPACE dataset instance. If not provided, the dataset_id must be given.

    • +

  • dataset_id (Optional[str]) – The unique ID of a dataset on METASPACE, e.g. “2021-09-03_11h43m13s”

    • +

  • database (Optional[tuple[str, str]]) – A single METASPACE database given as a tuple of name and version. Usually it is +displayed on METASPACE as “HMDB – v4” which corresponds to (“HMDB”, “v4”).

    • +

  • fdr (Optional[float]) – Returns only annotations for which the false discovery rate is less or equal to this +limit.

    • +

  • use_tic (bool) – When True, the output values will be scaled by the total ion count per pixel and +will be in 0.0 to 1.0 range.

    • +

  • metadata_as_obs (bool) – Whether to store metadata in the obs dataframe instead of uns. For a +single METASPACE dataset, metadata is the same for all pixels, so it would be duplicated +for all obs. When combining multiple datasets, it would be preserved in obs but +not in uns.

    • +

  • add_optical_image (bool) – Whether to embed the optical image for SquidPy

    • +

  • sm (Optional[SMInstance]) – Optionally a cached SMInstance

    • +

  • annotation_filter – Additional keyword arguments passed to the METASPACE API.

    • +
+
+
Return type:
+

AnnData

+
+
Returns:
+

+
An AnnData object with
    +

  • ion intensities: .X

    • +

  • ion image pixel coordinates: .obs[["ion_image_pixel_x", "ion_image_pixel_y"]]

    • +

  • spatial coordinates: .obsm["spatial"]

    • +

  • ion properties: .var, for example “formula”, “adduct”, “mz”, “fdr”, +“moleculeNames”, “moleculeIds”, “intensity”…

    • +

  • METASPACE metadata: .uns["metaspace"] if not metadata_as_obs

    • +

  • SquidPy metadata: .uns["spatial"] if add_optical_image

    • +
+
+
+

+
+
Raises:
+
+
+
+
+ +
+
+metaspace_converter.metaspace_to_spatialdata(dataset=None, dataset_id=None, database=None, fdr=0.1, use_tic=False, metadata_as_obs=False, add_optical_image=True, optical_name_added='optical_image', add_points=True, points_name_added='maldi_points', sm=None, **annotation_filter)[source]
+

Download a METASPACE dataset as a SpatialData object.

+

See: https://metaspace2020.eu/about

+
+
Parameters:
+
    +

  • dataset (Optional[SMDataset]) – A METASPACE dataset instance. If not provided, the dataset_id must be given.

    • +

  • dataset_id (Optional[str]) – The unique ID of a dataset on METASPACE, e.g. “2021-09-03_11h43m13s”

    • +

  • database (Optional[tuple[str, str]]) – A single METASPACE database given as a tuple of name and version. Usually it is +displayed on METASPACE as “HMDB – v4” which corresponds to ("HMDB", "v4").

    • +

  • fdr (float) – Returns only annotations for which the false discovery rate is less or equal to this +limit.

    • +

  • use_tic (bool) – When True, the output values will be scaled by the total ion count per pixel and +will be in 0.0 to 1.0 range.

    • +

  • metadata_as_obs (bool) – Whether to store metadata in the obs dataframe instead of uns. For +a single METASPACE dataset, metadata is the same for all pixels, so it would be +duplicated for all obs. When combining multiple datasets, it would be preserved in +obs but not in uns.

    • +

  • add_optical_image (bool) – Whether to also add the optical image (if one exists) to SpatialData +images. If none exists, it is not added, and no error raised.

    • +

  • optical_name_added (str) – Name of the element where to store the image in the SpatialData object

    • +

  • add_points (bool) – Whether to also add ion image pixel coordinates as SpatialData points. This +allows to spatially visualize the ion image values. If False, only ion intensities are +added to the table and coordinates are added to obs.

    • +

  • points_name_added (str) – Name of the element where to store the points in the SpatialData object

    • +

  • sm (Optional[SMInstance]) – Optionally a cached SMInstance

    • +

  • annotation_filter – Additional keyword arguments passed to the METASPACE API.

    • +
+
+
Return type:
+

SpatialData

+
+
Returns:
+

+
A SpatialData object with
    +

  • ion intensities and metadata: .table

    • +

  • optical image: .images["optical_image"] (or optical_name_added)

    • +

  • sampling coordinates: .points["maldi_points"] (or points_name_added) in a +coordinate system relative to the top-left image corner and physical scale +(micrometers)

    • +
+
+
+

+
+
Raises:
+
+
+
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index d4bb2cb..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index ff16b8f..0000000 --- a/docs/conf.py +++ /dev/null @@ -1,99 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -# import os -# import sys -# sys.path.insert(0, os.path.abspath('.')) - - -# -- Project information ----------------------------------------------------- - -project = "METASPACE Converter" -copyright = "2023; Tim Daniel Rose, Andreas Eisenbarth, Theodore Alexandrov" -author = "Tim Daniel Rose, Andreas Eisenbarth" - - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - # Include API documentation from docstrings - "sphinx.ext.autodoc", - # Executes and tests example code to ensure it is (still) working - "sphinx.ext.doctest", - # Links symbols in docstrings to API documentation of external projects - "sphinx.ext.intersphinx", - # Support for Google style docstrings - "sphinx.ext.napoleon", - # Adds a source code page to each API element - "sphinx.ext.viewcode", - # Uses type hints from signature instead of types in docstrings - "sphinx_autodoc_typehints", - # Links symbols within code blocks to API documentation - "sphinx_codeautolink", - # Adds a copy button to code blocks - "sphinx_copybutton", -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = "sphinx_rtd_theme" - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] - -# Doctest allows to execute example code with directives "" using "make doctest". -# Compared to the Python standard library module, this extension detects code blocks -# without requiring ">>> " as prefix. -# Remove doctest flags within the examples ("#doctest: +SKIP"), not to distract readers. -trim_doctest_flags = True -# Setup common imports, so they are available in the examples. -doctest_global_setup = """ -try: - import numpy as np - import pandas as pd - import matplotlib - # Ensure doctest does not attempt to display plots in blocking GUI windows. - matplotlib.use("agg") -except ImportError: - np = None - pd = None -""" - -# This creates links of symbols to external documentation. -intersphinx_mapping = { - "anndata": ("https://anndata.readthedocs.io/en/stable/", None), - "metaspace": ("https://metaspace2020.readthedocs.io/en/latest/", None), - "numpy": ("https://numpy.org/doc/stable/", None), - "python": ("https://docs.python.org/3/", None), - "scanpy": ("https://scanpy.readthedocs.io/en/stable/", None), - "squidpy": ("https://squidpy.readthedocs.io/en/stable/", None), - "spatialdata": ("https://spatialdata.scverse.org/en/latest/", None), - "spatialdata-plot": ("https://spatialdata.scverse.org/projects/plot/en/latest/", None), -} -# Needed to help MyST distinguish between URL schemes and references with a colon (?) -myst_url_schemes = ["http", "https"] diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 954237b..0000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/examples.html b/examples.html new file mode 100644 index 0000000..cbd92a9 --- /dev/null +++ b/examples.html @@ -0,0 +1,200 @@ + + + + + + +Examples — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + +
+ +
+
+
+
+ +
+
+
+
+
+

Examples

+
+

Plain AnnData with ScanPy

+
from metaspace_converter import metaspace_to_anndata
+import scanpy as sc
+
+# Download data and convert to an AnnData object
+adata = metaspace_to_anndata(
+    dataset_id="2022-08-05_17h28m56s",
+    fdr=0.1,
+    database=("BraChemDB", "2018-01"),
+)
+
+# Visualization with ScanPy
+sc.pl.spatial(
+    adata,
+    # Choose first ion for visualization
+    color=adata.var.index[0],
+    img_key=None,
+    spot_size=1,
+)
+
+
+Visualization with ScanPy +
+
+

SquidPy

+
from metaspace_converter import metaspace_to_anndata
+import squidpy as sq
+
+# Download dataset with optical background image
+adata = metaspace_to_anndata(
+    dataset_id="2022-08-05_17h28m56s",
+    fdr=0.1,
+    database=("BraChemDB", "2018-01"),
+    add_optical_image=True,
+)
+
+sq.pl.spatial_scatter(
+    adata, color=adata.var.index[0], shape="square", img=True, size=15, alpha=0.5
+)
+
+
+Visualization with SquidPy +
+
+

Convert AnnData objects to ion image arrays

+

If you want to work with the ion images as numpy arrays, the function anndata_to_image_array can +convert previously downloaded AnnData objects to numpy arrays.

+
from metaspace_converter import metaspace_to_anndata, anndata_to_image_array
+
+# Download data
+adata2 = metaspace_to_anndata(dataset_id="2023-11-14_21h58m39s", fdr=0.1)
+
+ion_images = anndata_to_image_array(adata2)
+
+# 6 ion images of shape 61x78
+print(ion_images.shape)
+
+
+
(20, 130, 143)
+
+
+
+
+

SpatialData

+

Here using a reversed colormap which better represents intense values on bright background.

+
from metaspace_converter import metaspace_to_spatialdata
+import spatialdata_plot  # noqa: Not directly used but extends spatialdata
+
+# Download dataset with optical background image
+sdata = metaspace_to_spatialdata(
+    dataset_id="2022-08-05_17h28m56s",
+    fdr=0.1,
+    database=("BraChemDB", "2018-01"),
+)
+
+# Workaround: spatialdata-plot currently does not use points transformation
+sdata.points["maldi_points"] = sdata.transform_element_to_coordinate_system(
+    sdata.points["maldi_points"], "global"
+)
+
+(
+    sdata.pl.render_images("optical_image")
+    .pl.render_points(
+        "maldi_points",
+        color=sdata.table.var.index[0],
+        alpha=1,
+        size=2,
+        cmap="viridis_r",
+    )
+    .pl.show(title=sdata.table.var.index[0], coordinate_systems="global")
+)
+
+
+Visualization with SpatialData +
+
+
+
+
+
+
+

© Copyright 2023; Tim Daniel Rose, Andreas Eisenbarth, Theodore Alexandrov.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 0000000..2c36048 --- /dev/null +++ b/genindex.html @@ -0,0 +1,303 @@ + + + + + + Index — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

+ +
+ A + | C + | E + | G + | H + | I + | M + | O + | S + | T + +
+

A

+ + + +
+ +

C

+ + + +
+ +

E

+ + +
+ +

G

+ + + +
+ +

H

+ + +
+ +

I

+ + + +
+ +

M

+ + + +
    +
  • + metaspace_converter + +
    • +
  • + metaspace_converter.anndata_to_array + +
    • +
  • + metaspace_converter.constants + +
    • +
  • + metaspace_converter.to_anndata + +
    • +
  • + metaspace_converter.to_spatialdata + +
    • +
  • + metaspace_converter.utils + +
    • +
+ +

O

+ + +
+ +

S

+ + +
+ +

T

+ + +
+ + + +
+
+
+ +
+ +
+

© Copyright 2023; Tim Daniel Rose, Andreas Eisenbarth, Theodore Alexandrov.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..c793f3d --- /dev/null +++ b/index.html @@ -0,0 +1,142 @@ + + + + + + + Welcome to METASPACE Converter’s documentation! — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Welcome to METASPACE Converter’s documentation!

+

Tests badge +Docs badge +PyPI badge

+

Functions to convert METASPACE datasets to AnnData objects.

+

This makes it easy to work with the ScanPy, SquidPy and SpatialData ecosystem with METASPACE data.

+
+

Installation

+

You can install the package via pip:

+
pip install metaspace-converter
+
+
+
+

Contents:

+ +
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023; Tim Daniel Rose, Andreas Eisenbarth, Theodore Alexandrov.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/metaspace_converter.anndata_to_array.html b/metaspace_converter.anndata_to_array.html new file mode 100644 index 0000000..4767613 --- /dev/null +++ b/metaspace_converter.anndata_to_array.html @@ -0,0 +1,142 @@ + + + + + + + metaspace_converter.anndata_to_array module — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

metaspace_converter.anndata_to_array module

+
+
+metaspace_converter.anndata_to_array.anndata_to_image_array(adata)[source]
+

Extracts an array of ion images from an AnnData object +(that has been generated through the metaspace_to_anndata function).

+
+
Parameters:
+

adata (AnnData) – An AnnData object.

+
+
Return type:
+

ndarray

+
+
Returns:
+

A three-dimensional Numpy array in the following shape

+
+
    +

  • Dimension 0: Number of ion images in the order of adata.var_names

    • +

  • Dimension 1: Image height adata.uns["metaspace"]["image_size"]["y"]

    • +

  • Dimension 2: Image width adata.uns["metaspace"]["image_size"]["x"]

    • +
+
+

+
+
Raises:
+

ValueError – If the AnnData object has been modified. + E.g. Pixel have been removed/added and the number of pixels + and their coordinates do not match the original image dimensions.

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2023; Tim Daniel Rose, Andreas Eisenbarth, Theodore Alexandrov.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/metaspace_converter.constants.html b/metaspace_converter.constants.html new file mode 100644 index 0000000..3d12d7b --- /dev/null +++ b/metaspace_converter.constants.html @@ -0,0 +1,137 @@ + + + + + + + metaspace_converter.constants module — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

metaspace_converter.constants module

+
+
+class metaspace_converter.constants.COL(ion_image_shape_y='ion_image_shape_y', ion_image_shape_x='ion_image_shape_x', ion_image_pixel_y='ion_image_pixel_y', ion_image_pixel_x='ion_image_pixel_x')[source]
+

Bases: object

+
+
+ion_image_pixel_x: Final = 'ion_image_pixel_x'
+
+ +
+
+ion_image_pixel_y: Final = 'ion_image_pixel_y'
+
+ +
+
+ion_image_shape_x: Final = 'ion_image_shape_x'
+
+ +
+
+ion_image_shape_y: Final = 'ion_image_shape_y'
+
+ +
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2023; Tim Daniel Rose, Andreas Eisenbarth, Theodore Alexandrov.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/metaspace_converter.html b/metaspace_converter.html new file mode 100644 index 0000000..c2e7fee --- /dev/null +++ b/metaspace_converter.html @@ -0,0 +1,299 @@ + + + + + + + metaspace_converter package — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

metaspace_converter package

+
+

Submodules

+
+ +
+
+
+

Module contents

+
+
+metaspace_converter.anndata_to_image_array(adata)[source]
+

Extracts an array of ion images from an AnnData object +(that has been generated through the metaspace_to_anndata function).

+
+
Parameters:
+

adata (AnnData) – An AnnData object.

+
+
Return type:
+

ndarray

+
+
Returns:
+

A three-dimensional Numpy array in the following shape

+
+
    +

  • Dimension 0: Number of ion images in the order of adata.var_names

    • +

  • Dimension 1: Image height adata.uns["metaspace"]["image_size"]["y"]

    • +

  • Dimension 2: Image width adata.uns["metaspace"]["image_size"]["x"]

    • +
+
+

+
+
Raises:
+

ValueError – If the AnnData object has been modified. + E.g. Pixel have been removed/added and the number of pixels + and their coordinates do not match the original image dimensions.

+
+
+
+ +
+
+metaspace_converter.metaspace_to_anndata(dataset=None, dataset_id=None, database=None, fdr=0.1, use_tic=False, metadata_as_obs=False, add_optical_image=False, sm=None, **annotation_filter)[source]
+

Downloads a METASPACE dataset to an AnnData object.

+

See: https://metaspace2020.eu/about

+
+
Parameters:
+
    +

  • dataset (Optional[SMDataset]) – A METASPACE dataset instance. If not provided, the dataset_id must be given.

    • +

  • dataset_id (Optional[str]) – The unique ID of a dataset on METASPACE, e.g. “2021-09-03_11h43m13s”

    • +

  • database (Optional[tuple[str, str]]) – A single METASPACE database given as a tuple of name and version. Usually it is +displayed on METASPACE as “HMDB – v4” which corresponds to (“HMDB”, “v4”).

    • +

  • fdr (Optional[float]) – Returns only annotations for which the false discovery rate is less or equal to this +limit.

    • +

  • use_tic (bool) – When True, the output values will be scaled by the total ion count per pixel and +will be in 0.0 to 1.0 range.

    • +

  • metadata_as_obs (bool) – Whether to store metadata in the obs dataframe instead of uns. For a +single METASPACE dataset, metadata is the same for all pixels, so it would be duplicated +for all obs. When combining multiple datasets, it would be preserved in obs but +not in uns.

    • +

  • add_optical_image (bool) – Whether to embed the optical image for SquidPy

    • +

  • sm (Optional[SMInstance]) – Optionally a cached SMInstance

    • +

  • annotation_filter – Additional keyword arguments passed to the METASPACE API.

    • +
+
+
Return type:
+

AnnData

+
+
Returns:
+

+
An AnnData object with
    +

  • ion intensities: .X

    • +

  • ion image pixel coordinates: .obs[["ion_image_pixel_x", "ion_image_pixel_y"]]

    • +

  • spatial coordinates: .obsm["spatial"]

    • +

  • ion properties: .var, for example “formula”, “adduct”, “mz”, “fdr”, +“moleculeNames”, “moleculeIds”, “intensity”…

    • +

  • METASPACE metadata: .uns["metaspace"] if not metadata_as_obs

    • +

  • SquidPy metadata: .uns["spatial"] if add_optical_image

    • +
+
+
+

+
+
Raises:
+
+
+
+
+ +
+
+metaspace_converter.metaspace_to_spatialdata(dataset=None, dataset_id=None, database=None, fdr=0.1, use_tic=False, metadata_as_obs=False, add_optical_image=True, optical_name_added='optical_image', add_points=True, points_name_added='maldi_points', sm=None, **annotation_filter)[source]
+

Download a METASPACE dataset as a SpatialData object.

+

See: https://metaspace2020.eu/about

+
+
Parameters:
+
    +

  • dataset (Optional[SMDataset]) – A METASPACE dataset instance. If not provided, the dataset_id must be given.

    • +

  • dataset_id (Optional[str]) – The unique ID of a dataset on METASPACE, e.g. “2021-09-03_11h43m13s”

    • +

  • database (Optional[tuple[str, str]]) – A single METASPACE database given as a tuple of name and version. Usually it is +displayed on METASPACE as “HMDB – v4” which corresponds to ("HMDB", "v4").

    • +

  • fdr (float) – Returns only annotations for which the false discovery rate is less or equal to this +limit.

    • +

  • use_tic (bool) – When True, the output values will be scaled by the total ion count per pixel and +will be in 0.0 to 1.0 range.

    • +

  • metadata_as_obs (bool) – Whether to store metadata in the obs dataframe instead of uns. For +a single METASPACE dataset, metadata is the same for all pixels, so it would be +duplicated for all obs. When combining multiple datasets, it would be preserved in +obs but not in uns.

    • +

  • add_optical_image (bool) – Whether to also add the optical image (if one exists) to SpatialData +images. If none exists, it is not added, and no error raised.

    • +

  • optical_name_added (str) – Name of the element where to store the image in the SpatialData object

    • +

  • add_points (bool) – Whether to also add ion image pixel coordinates as SpatialData points. This +allows to spatially visualize the ion image values. If False, only ion intensities are +added to the table and coordinates are added to obs.

    • +

  • points_name_added (str) – Name of the element where to store the points in the SpatialData object

    • +

  • sm (Optional[SMInstance]) – Optionally a cached SMInstance

    • +

  • annotation_filter – Additional keyword arguments passed to the METASPACE API.

    • +
+
+
Return type:
+

SpatialData

+
+
Returns:
+

+
A SpatialData object with
    +

  • ion intensities and metadata: .table

    • +

  • optical image: .images["optical_image"] (or optical_name_added)

    • +

  • sampling coordinates: .points["maldi_points"] (or points_name_added) in a +coordinate system relative to the top-left image corner and physical scale +(micrometers)

    • +
+
+
+

+
+
Raises:
+
+
+
+
+ +
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023; Tim Daniel Rose, Andreas Eisenbarth, Theodore Alexandrov.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/metaspace_converter.to_anndata.html b/metaspace_converter.to_anndata.html new file mode 100644 index 0000000..0b21ffd --- /dev/null +++ b/metaspace_converter.to_anndata.html @@ -0,0 +1,220 @@ + + + + + + + metaspace_converter.to_anndata module — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

metaspace_converter.to_anndata module

+
+
+metaspace_converter.to_anndata.all_image_pixel_coordinates(shape)[source]
+

Returns a list of Y, X coordinates (Numpy order) for every pixel of an image.

+
+
Parameters:
+

shape (tuple[int, int]) – The shape of a 2D image

+
+
Return type:
+

ndarray

+
+
Returns:
+

Array of shape (number of pixels, 2)

+
+
+
+ +
+
+metaspace_converter.to_anndata.create_annotation_id(formula, adduct)[source]
+
+
Return type:
+

str

+
+
+
+ +
+
+metaspace_converter.to_anndata.get_dataset(dataset=None, dataset_id=None, sm=None)[source]
+

Fetch a dataset instance, if not provided

+
+
Return type:
+

SMDataset

+
+
+
+ +
+
+metaspace_converter.to_anndata.get_ion_image_shape(dataset=None, dataset_id=None, sm=None)[source]
+

Get the shape of a METASPACE dataset (in Numpy order).

+
+
Parameters:
+
    +

  • dataset (Optional[SMDataset]) – A METASPACE dataset instance. If not provided, a dataset ID must be provided.

    • +

  • dataset_id (Optional[str]) – An optional METASPACE dataset ID, if no dataset instance is provided.

    • +

  • sm (Optional[SMInstance]) – An optional SMInstance, otherwise one will be created.

    • +
+
+
Return type:
+

tuple[int, int]

+
+
+
+ +
+
+metaspace_converter.to_anndata.metaspace_to_anndata(dataset=None, dataset_id=None, database=None, fdr=0.1, use_tic=False, metadata_as_obs=False, add_optical_image=False, sm=None, **annotation_filter)[source]
+

Downloads a METASPACE dataset to an AnnData object.

+

See: https://metaspace2020.eu/about

+
+
Parameters:
+
    +

  • dataset (Optional[SMDataset]) – A METASPACE dataset instance. If not provided, the dataset_id must be given.

    • +

  • dataset_id (Optional[str]) – The unique ID of a dataset on METASPACE, e.g. “2021-09-03_11h43m13s”

    • +

  • database (Optional[tuple[str, str]]) – A single METASPACE database given as a tuple of name and version. Usually it is +displayed on METASPACE as “HMDB – v4” which corresponds to (“HMDB”, “v4”).

    • +

  • fdr (Optional[float]) – Returns only annotations for which the false discovery rate is less or equal to this +limit.

    • +

  • use_tic (bool) – When True, the output values will be scaled by the total ion count per pixel and +will be in 0.0 to 1.0 range.

    • +

  • metadata_as_obs (bool) – Whether to store metadata in the obs dataframe instead of uns. For a +single METASPACE dataset, metadata is the same for all pixels, so it would be duplicated +for all obs. When combining multiple datasets, it would be preserved in obs but +not in uns.

    • +

  • add_optical_image (bool) – Whether to embed the optical image for SquidPy

    • +

  • sm (Optional[SMInstance]) – Optionally a cached SMInstance

    • +

  • annotation_filter – Additional keyword arguments passed to the METASPACE API.

    • +
+
+
Return type:
+

AnnData

+
+
Returns:
+

+
An AnnData object with
    +

  • ion intensities: .X

    • +

  • ion image pixel coordinates: .obs[["ion_image_pixel_x", "ion_image_pixel_y"]]

    • +

  • spatial coordinates: .obsm["spatial"]

    • +

  • ion properties: .var, for example “formula”, “adduct”, “mz”, “fdr”, +“moleculeNames”, “moleculeIds”, “intensity”…

    • +

  • METASPACE metadata: .uns["metaspace"] if not metadata_as_obs

    • +

  • SquidPy metadata: .uns["spatial"] if add_optical_image

    • +
+
+
+

+
+
Raises:
+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2023; Tim Daniel Rose, Andreas Eisenbarth, Theodore Alexandrov.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/metaspace_converter.to_spatialdata.html b/metaspace_converter.to_spatialdata.html new file mode 100644 index 0000000..9fbd7e3 --- /dev/null +++ b/metaspace_converter.to_spatialdata.html @@ -0,0 +1,203 @@ + + + + + + + metaspace_converter.to_spatialdata module — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

metaspace_converter.to_spatialdata module

+
+
+metaspace_converter.to_spatialdata.get_ion_image_to_physical_transformation(dataset)[source]
+

Get the transformation from the ion image (pixels) to the world coordinate system (micrometers).

+
+
Parameters:
+

dataset (SMDataset) – A METASPACE dataset

+
+
Return type:
+

Scale

+
+
Returns:
+

A scale SpatialData transformation

+
+
+
+ +
+
+metaspace_converter.to_spatialdata.metaspace_to_spatialdata(dataset=None, dataset_id=None, database=None, fdr=0.1, use_tic=False, metadata_as_obs=False, add_optical_image=True, optical_name_added='optical_image', add_points=True, points_name_added='maldi_points', sm=None, **annotation_filter)[source]
+

Download a METASPACE dataset as a SpatialData object.

+

See: https://metaspace2020.eu/about

+
+
Parameters:
+
    +

  • dataset (Optional[SMDataset]) – A METASPACE dataset instance. If not provided, the dataset_id must be given.

    • +

  • dataset_id (Optional[str]) – The unique ID of a dataset on METASPACE, e.g. “2021-09-03_11h43m13s”

    • +

  • database (Optional[tuple[str, str]]) – A single METASPACE database given as a tuple of name and version. Usually it is +displayed on METASPACE as “HMDB – v4” which corresponds to ("HMDB", "v4").

    • +

  • fdr (float) – Returns only annotations for which the false discovery rate is less or equal to this +limit.

    • +

  • use_tic (bool) – When True, the output values will be scaled by the total ion count per pixel and +will be in 0.0 to 1.0 range.

    • +

  • metadata_as_obs (bool) – Whether to store metadata in the obs dataframe instead of uns. For +a single METASPACE dataset, metadata is the same for all pixels, so it would be +duplicated for all obs. When combining multiple datasets, it would be preserved in +obs but not in uns.

    • +

  • add_optical_image (bool) – Whether to also add the optical image (if one exists) to SpatialData +images. If none exists, it is not added, and no error raised.

    • +

  • optical_name_added (str) – Name of the element where to store the image in the SpatialData object

    • +

  • add_points (bool) – Whether to also add ion image pixel coordinates as SpatialData points. This +allows to spatially visualize the ion image values. If False, only ion intensities are +added to the table and coordinates are added to obs.

    • +

  • points_name_added (str) – Name of the element where to store the points in the SpatialData object

    • +

  • sm (Optional[SMInstance]) – Optionally a cached SMInstance

    • +

  • annotation_filter – Additional keyword arguments passed to the METASPACE API.

    • +
+
+
Return type:
+

SpatialData

+
+
Returns:
+

+
A SpatialData object with
    +

  • ion intensities and metadata: .table

    • +

  • optical image: .images["optical_image"] (or optical_name_added)

    • +

  • sampling coordinates: .points["maldi_points"] (or points_name_added) in a +coordinate system relative to the top-left image corner and physical scale +(micrometers)

    • +
+
+
+

+
+
Raises:
+
+
+
+
+ +
+
+metaspace_converter.to_spatialdata.optical_image_to_spatial_image(dataset)[source]
+

Download the optical image of a METASPACE dataset to a SpatialImage object.

+

This function requires that the dataset has an optical image. Otherwise, an error is raised.

+
+
Parameters:
+

dataset (SMDataset) – A METASPACE dataset

+
+
Return type:
+

Optional[SpatialImage]

+
+
Returns:
+

A SpatialImage object with the image’s transformation to world coordinates

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2023; Tim Daniel Rose, Andreas Eisenbarth, Theodore Alexandrov.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/metaspace_converter.utils.html b/metaspace_converter.utils.html new file mode 100644 index 0000000..77a9758 --- /dev/null +++ b/metaspace_converter.utils.html @@ -0,0 +1,224 @@ + + + + + + + metaspace_converter.utils module — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

metaspace_converter.utils module

+
+
+metaspace_converter.utils.empty_columns_to_nan(df)[source]
+

Converts all empty columns from None (dtype object) to NaN (dtype float).

+
+
Parameters:
+

df (DataFrame) – A dataframe

+
+
Return type:
+

DataFrame

+
+
Returns:
+

The modified dataframe. The original dataframe is modified, not a copy.

+
+
+
+ +
+
+metaspace_converter.utils.has_optical_image(dataset)[source]
+

Check whether a METASPACE dataset has an optical image

+
+
Return type:
+

bool

+
+
+
+ +
+
+metaspace_converter.utils.is_non_str_sequence(obj)[source]
+

Returns whether an object is a sequence (list and similar), excluding strings.

+
+
Parameters:
+

obj (Any) – The object to check.

+
+
Return type:
+

bool

+
+
Returns:
+

True if the object is a sequence.

+
+
+
+ +
+
+metaspace_converter.utils.iter_property_paths(obj, delimiter='/', include_none=True, dont_traverse_types=(), dont_descend_paths=(), _path='')[source]
+

Iterates recursively over properties of nested objects.

+
+
Parameters:
+
    +

  • obj (Any) – A class instance, mapping or sequence.

    • +

  • delimiter (str) – The delimiter to use. Should not be contained in any name.

    • +

  • include_none (bool) – Whether to yield attributes which have None assigned, or skip them.

    • +

  • dont_traverse_types (tuple[type, ...]) – Iterable data types which should not be traversed any deeper. +These are yielded as values.

    • +

  • dont_descend_paths (Collection[str]) – Paths with iterable objects that should be preserved.

    • +
+
+
Return type:
+

Iterator[tuple[str, Any]]

+
+
Returns:
+

An iterator for iterating over paths and objects found at each path. It can also be +converted to a dictionary.

+
+
+
+ +
+
+metaspace_converter.utils.stringify_list_columns(df, columns=(), all=False)[source]
+

If the dataframe column is a list, stringify it into JSON.

+
+
Parameters:
+
    +

  • df (DataFrame) – A dataframe with a column that contains lists (e.g. of molecule names)

    • +

  • columns (Iterable[str]) – The names of the columns containing lists

    • +

  • all (bool) – Whether to check and stringify all columns containing list-like objects

    • +
+
+
Return type:
+

DataFrame

+
+
Returns:
+

A dataframe where all values in specified columns are JSON stringified.

+
+
+
+ +
+
+metaspace_converter.utils.transform_coords(coords, transform_matrix_2d)[source]
+

Transform a list of 2D coordinates using a 3×3 transformation matrix.

+

Like in scikit-image, but without adding that dependency.

+

See skimage.transform._geometric.ProjectiveTransform.__call__

+
+
Parameters:
+
    +

  • coords (ndarray) – An array of coordinates with shape (number of points, 2)

    • +

  • transform_matrix_2d (ndarray) – A transformation matrix of shape (3, 3)

    • +
+
+
Return type:
+

ndarray

+
+
Returns:
+

The list of transformed coordinates as Numpy array

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2023; Tim Daniel Rose, Andreas Eisenbarth, Theodore Alexandrov.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/metaspace_converter/__init__.py b/metaspace_converter/__init__.py deleted file mode 100644 index 87c9805..0000000 --- a/metaspace_converter/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from metaspace_converter.anndata_to_array import anndata_to_image_array -from metaspace_converter.to_anndata import metaspace_to_anndata -from metaspace_converter.to_spatialdata import metaspace_to_spatialdata - -__all__ = ["metaspace_to_anndata", "metaspace_to_spatialdata", "anndata_to_image_array"] diff --git a/metaspace_converter/anndata_to_array.py b/metaspace_converter/anndata_to_array.py deleted file mode 100644 index 61d98a2..0000000 --- a/metaspace_converter/anndata_to_array.py +++ /dev/null @@ -1,65 +0,0 @@ -import numpy as np -from anndata import AnnData - -from metaspace_converter.constants import COL, METASPACE_KEY, X, Y -from metaspace_converter.to_anndata import all_image_pixel_coordinates - - -def _check_pixel_coordinates(adata: AnnData) -> bool: - sorted_obs = adata.obs.sort_values([COL.ion_image_pixel_y, COL.ion_image_pixel_x]) - - pixel_list = sorted_obs[[COL.ion_image_pixel_y, COL.ion_image_pixel_x]].values - - img_size = adata.uns[METASPACE_KEY]["image_size"] - required_pixels = all_image_pixel_coordinates((img_size[Y], img_size[X])) - - return np.all(np.equal(pixel_list, required_pixels)) - - -def anndata_to_image_array(adata: AnnData) -> np.ndarray: - """ - Extracts an array of ion images from an AnnData object - (that has been generated through the ``metaspace_to_anndata`` function). - - Args: - adata: An AnnData object. - - Returns: - A three-dimensional Numpy array in the following shape - - * Dimension 0: Number of ion images in the order of ``adata.var_names`` - * Dimension 1: Image height ``adata.uns["metaspace"]["image_size"]["y"]`` - * Dimension 2: Image width ``adata.uns["metaspace"]["image_size"]["x"]`` - - Raises: - ValueError: If the AnnData object has been modified. - E.g. Pixel have been removed/added and the number of pixels - and their coordinates do not match the original image dimensions. - """ - - pixel_array = adata.X.transpose().copy() - img_size = adata.uns[METASPACE_KEY]["image_size"] - - # Check if image dimensions are okay - if img_size[X] * img_size[Y] != pixel_array.shape[1]: - raise ValueError("Number of observations does not match the original image dimensions") - - # Check if all pixels are available - if not _check_pixel_coordinates(adata): - raise ValueError("Not all pixels for ion images are available") - - # Sort indices, in case of modified order of pixels (obs) - image_sorting = adata.obs.sort_values( - [COL.ion_image_pixel_y, COL.ion_image_pixel_x] - ).index.values.astype(int) - pixel_array = pixel_array[:, image_sorting] - - image_array = pixel_array.reshape( - ( - pixel_array.shape[0], - adata.obs[COL.ion_image_pixel_y].max() + 1, - adata.obs[COL.ion_image_pixel_x].max() + 1, - ) - ) - - return image_array diff --git a/metaspace_converter/constants.py b/metaspace_converter/constants.py deleted file mode 100644 index 99bf75e..0000000 --- a/metaspace_converter/constants.py +++ /dev/null @@ -1,30 +0,0 @@ -from dataclasses import dataclass -from typing import Final - -SPATIAL_KEY: Final = "spatial" -METASPACE_KEY: Final = "metaspace" -OBS_INDEX_NAME: Final = "ion_image_pixel" -VAR_INDEX_NAME: Final = "formula_adduct" -Shape2d = tuple[int, int] - - -@dataclass -class COL: - ion_image_shape_y: Final = "ion_image_shape_y" - ion_image_shape_x: Final = "ion_image_shape_x" - ion_image_pixel_y: Final = "ion_image_pixel_y" - ion_image_pixel_x: Final = "ion_image_pixel_x" - - -COORD_SYS_GLOBAL: Final = "global" -MICROMETER: Final = "micrometer" -OPTICAL_IMAGE_KEY: Final = "optical_image" -POINTS_KEY: Final = "maldi_points" -REGION_KEY: Final = "region" -INSTANCE_KEY: Final = "instance_id" -X: Final = "x" -Y: Final = "y" -C: Final = "c" -XY: Final = (X, Y) -YX: Final = (Y, X) -YXC: Final = (Y, X, C) diff --git a/metaspace_converter/tests/anndata_to_array_test.py b/metaspace_converter/tests/anndata_to_array_test.py deleted file mode 100644 index 2956193..0000000 --- a/metaspace_converter/tests/anndata_to_array_test.py +++ /dev/null @@ -1,75 +0,0 @@ -from typing import TYPE_CHECKING - -import numpy as np -import pandas as pd -import pytest -from anndata import AnnData - -if TYPE_CHECKING: - import _pytest.fixtures - -from metaspace_converter import anndata_to_image_array -from metaspace_converter.constants import COL, METASPACE_KEY, X, Y -from metaspace_converter.to_anndata import all_image_pixel_coordinates - - -@pytest.fixture -def adata_with_coordinates_and_image_size( - request: "_pytest.fixtures.SubRequest", -) -> tuple[AnnData, np.ndarray]: - """ - Create an AnnData object filled with parametrized shape - - Args: - request: Request passed by Pytest, can be parametrized with a dictionary containing - ``num_ions``, ``height``, ``width`` - - Returns: - A tuple of an AnnData object and the expected array of stacked ion images - """ - num_ions = request.param.get("num_ions", 0) - height = request.param.get("height", 0) - width = request.param.get("width", 0) - shape = (num_ions, height, width) - # Create a stack of ion images where every pixel has a different value - ion_images_stack = np.arange(np.prod(shape)).reshape(shape) - coordinates = all_image_pixel_coordinates(shape[1:]) - # Create an AnnData with pixel coordinates - obs = pd.DataFrame( - {COL.ion_image_pixel_y: coordinates[:, 0], COL.ion_image_pixel_x: coordinates[:, 1]} - ) - adata = AnnData( - X=ion_images_stack.reshape((height * width, num_ions)), - obs=obs, - uns={METASPACE_KEY: {"image_size": {X: width, Y: height}}}, - ) - expected = ion_images_stack - return adata, expected - - -@pytest.mark.parametrize( - "adata_with_coordinates_and_image_size", - [ - # Non-square ion images - dict(num_ions=4, height=2, width=3), - # Edge case: No annotations found - dict(num_ions=0, height=2, width=3), - ], - indirect=["adata_with_coordinates_and_image_size"], -) -def test_anndata_to_image_array(adata_with_coordinates_and_image_size: AnnData): - adata, expected = adata_with_coordinates_and_image_size - - actual = anndata_to_image_array(adata) - - assert actual.shape == ( - adata.shape[1], - adata.obs[COL.ion_image_pixel_y].max() + 1, - adata.obs[COL.ion_image_pixel_x].max() + 1, - ) - - assert actual.shape == ( - adata.shape[1], - adata.uns[METASPACE_KEY]["image_size"][Y], - adata.uns[METASPACE_KEY]["image_size"][X], - ) diff --git a/metaspace_converter/tests/to_anndata_test.py b/metaspace_converter/tests/to_anndata_test.py deleted file mode 100644 index cc6aac9..0000000 --- a/metaspace_converter/tests/to_anndata_test.py +++ /dev/null @@ -1,84 +0,0 @@ -import json -import os -from pathlib import Path - -import numpy as np -import pytest -from metaspace import SMInstance - -from metaspace_converter.constants import COL, METASPACE_KEY, SPATIAL_KEY -from metaspace_converter.to_anndata import get_ion_image_shape, metaspace_to_anndata - -METASPACE_DEFAULT_CONFIG_FILE = "~/.metaspace" -METASPACE_EMAIL_ENV = "METASPACE_EMAIL" -METASPACE_API_KEY_ENV = "METASPACE_API_KEY" - - -@pytest.fixture(scope="session") -def metaspace_credentials() -> dict[str, str]: - if Path(METASPACE_DEFAULT_CONFIG_FILE).expanduser().exists(): - # Credentials are available. No need to read them, SMInstance will read the file. - return {} - else: - return { - "email": os.environ.get(METASPACE_EMAIL_ENV), - "api_key": os.environ.get(METASPACE_API_KEY_ENV), - } - - -@pytest.fixture(scope="session") -def sm(metaspace_credentials) -> SMInstance: - return SMInstance( - email=metaspace_credentials.get("email"), api_key=metaspace_credentials.get("api_key") - ) - - -@pytest.mark.parametrize( - ("dataset_id", "database", "fdr", "metadata_as_obs", "add_optical_image"), - [ - ("2021-09-03_11h43m13s", ("CoreMetabolome", "v3"), 0.1, False, False), - ("2021-09-03_11h43m13s", ("CoreMetabolome", "v3"), 0.1, True, False), - ("2021-09-03_11h43m13s", ("CoreMetabolome", "v3"), 0.1, False, True), - ], -) -def test_metaspace_to_anndata( - dataset_id, database, fdr, sm, metadata_as_obs, add_optical_image, tmp_path -): - actual = metaspace_to_anndata( - dataset_id=dataset_id, - database=database, - fdr=fdr, - sm=sm, - metadata_as_obs=metadata_as_obs, - add_optical_image=add_optical_image, - ) - - dataset = sm.dataset(id=dataset_id) - assert actual.n_obs == np.prod(get_ion_image_shape(dataset)) - assert actual.n_vars == len(dataset.annotations(fdr=fdr, database=database)) - assert { - COL.ion_image_shape_y, - COL.ion_image_shape_x, - COL.ion_image_pixel_y, - COL.ion_image_pixel_x, - }.issubset(actual.obs.columns) - assert np.all(actual.var["fdr"] <= fdr) - if metadata_as_obs: - assert all( - [ - json.loads(row["metaspace.metaspace_to_anndata_parameters"])["database"] - == list(database) - for _, row in actual.obs.iterrows() - ] - ) - else: - actual_database = actual.uns[METASPACE_KEY]["metaspace_to_anndata_parameters"]["database"] - assert actual_database == list(database) - if add_optical_image: - assert SPATIAL_KEY in actual.obsm - assert SPATIAL_KEY in actual.uns - assert isinstance(actual.uns[SPATIAL_KEY]["image"]["images"]["hires"], np.ndarray) - - # Check that it can be successfully written. - actual.write_h5ad(tmp_path / "adata.h5ad") - actual.write_zarr(tmp_path / "adata.zarr") diff --git a/metaspace_converter/tests/to_spatialdata_test.py b/metaspace_converter/tests/to_spatialdata_test.py deleted file mode 100644 index 3a18cfc..0000000 --- a/metaspace_converter/tests/to_spatialdata_test.py +++ /dev/null @@ -1,123 +0,0 @@ -import numpy as np -import pytest -from multiscale_spatial_image import MultiscaleSpatialImage - -from metaspace_converter.constants import COORD_SYS_GLOBAL, OPTICAL_IMAGE_KEY, POINTS_KEY, X, Y - -# The import metaspace_credentials is need by the fixture sm. -from metaspace_converter.tests.to_anndata_test import metaspace_credentials, sm # noqa -from metaspace_converter.to_anndata import COL, METASPACE_KEY, get_ion_image_shape -from metaspace_converter.to_spatialdata import metaspace_to_spatialdata - - -@pytest.mark.parametrize( - ( - "dataset_id", - "database", - "fdr", - "add_optical_image", - "optical_name_added", - "add_points", - "points_name_added", - ), - [ - ("2021-09-03_11h43m13s", ("CoreMetabolome", "v3"), 0.1, True, "optical", True, "maldi"), - ("2021-09-03_11h43m13s", ("CoreMetabolome", "v3"), 0.1, False, None, False, None), - ], -) -def test_metaspace_to_spatialdata( - dataset_id, - database, - fdr, - sm, - add_optical_image, - optical_name_added, - add_points, - points_name_added, - tmp_path, -): - actual = metaspace_to_spatialdata( - dataset_id=dataset_id, - database=database, - fdr=fdr, - sm=sm, - add_optical_image=add_optical_image, - optical_name_added=optical_name_added, - add_points=add_points, - points_name_added=points_name_added, - ) - - # Check table - assert actual.table is not None - dataset = sm.dataset(id=dataset_id) - assert actual.table.n_obs == np.prod(get_ion_image_shape(dataset)) - assert actual.table.n_vars == len(dataset.annotations(fdr=fdr, database=database)) - assert { - COL.ion_image_shape_y, - COL.ion_image_shape_x, - COL.ion_image_pixel_y, - COL.ion_image_pixel_x, - }.issubset(actual.table.obs.columns) - assert np.all(actual.table.var["fdr"] <= fdr) - actual_database = actual.table.uns[METASPACE_KEY]["metaspace_to_anndata_parameters"]["database"] - assert actual_database == list(database) - - # Check optical image, if requested - assert (optical_name_added in actual.images) == add_optical_image - - # Check points, if requested - assert (points_name_added in actual.points) == add_points - - # Check transformations - if add_optical_image and add_points: - # All points should be positioned within the image bounds. - points_over_image = actual.transform_element_to_coordinate_system( - actual.points[points_name_added], target_coordinate_system=COORD_SYS_GLOBAL - )[[Y, X]].compute() - image_min = (0, 0) - image_max = _get_image_shape(actual.images[optical_name_added]) - assert np.all(image_min <= points_over_image) and np.all(points_over_image <= image_max) - - # Check that it can be successfully written. - actual.write(tmp_path / "sdata.zarr") - - -def _get_image_shape(image: MultiscaleSpatialImage) -> tuple[int, int]: - assert isinstance(image, MultiscaleSpatialImage) - sizes = image["scale0"].sizes - return sizes["y"], sizes["x"] - - -@pytest.mark.parametrize( - ("dataset_id", "database", "fdr"), [("2021-09-03_11h43m13s", ("CoreMetabolome", "v3"), 0.1)] -) -def test_metaspace_with_napari_spatialdata(dataset_id, database, fdr, sm, request): - # A demo of viewing SpatialData with Napari. - if ( - len(request.session.items) != 1 - or request.session.items[0].originalname != "test_metaspace_with_napari_spatialdata" - ): - pytest.skip("Manual test case. To run it, select it individually.") - - actual = metaspace_to_spatialdata(dataset_id=dataset_id, database=database, fdr=fdr) - - from napari_spatialdata import Interactive, QtAdataViewWidget - - interactive = Interactive(sdata=actual, headless=True) - sdata_widget = interactive._sdata_widget - anndata_widget = [ - w - for w in interactive._viewer.window._dock_widgets["View (napari-spatialdata)"].children() - if isinstance(w, QtAdataViewWidget) - ][0] - # 1. Select coordinate system - sdata_widget.coordinate_system_widget._select_coord_sys(COORD_SYS_GLOBAL) - sdata_widget.elements_widget._onItemChange(COORD_SYS_GLOBAL) - # 2. Select image element - sdata_widget._onClick(OPTICAL_IMAGE_KEY) - # 3. Select points element - sdata_widget._onClick(POINTS_KEY) - # 4. Select observation for points - anndata_widget.obs_widget._onAction([actual.table.var.index[0]]) - # Run Napari (blocking) - interactive.run() diff --git a/metaspace_converter/tests/utils_test.py b/metaspace_converter/tests/utils_test.py deleted file mode 100644 index fd3d4bb..0000000 --- a/metaspace_converter/tests/utils_test.py +++ /dev/null @@ -1,127 +0,0 @@ -from dataclasses import dataclass -from typing import Optional - -import numpy as np -import pandas as pd -import pytest - -from metaspace_converter.utils import ( - empty_columns_to_nan, - iter_property_paths, - stringify_list_columns, - transform_coords, -) - - -def test_empty_columns_to_nan(): - df = pd.DataFrame({c: [v] * 2 for c, v in zip("abcdef", [1.0, 0.0, 0, object(), "", None])}) - df.loc[1, "a"] = np.nan - df_orig = df.copy() - empty_columns_to_nan(df) - assert df["f"].dtype == float - assert np.all(pd.isna(df["f"])) - unchanged_columns = df_orig.columns != "f" - pd.testing.assert_frame_equal(df.loc[:, unchanged_columns], df_orig.loc[:, unchanged_columns]) - - -@dataclass -class A: - a: Optional[int] = None - b: Optional[int] = None - c: Optional[int] = None - - -@dataclass -class C: - l: Optional[list[A]] = None - d: Optional[dict[str, A]] = None - - -@pytest.mark.parametrize( - ("obj", "include_none", "expected"), - [ - ("a", True, [("", "a")]), - ({"a": 1, "b": 2}, True, [("a", 1), ("b", 2)]), - ([3, 2], True, [("0", 3), ("1", 2)]), - (A(a=1, b=2), False, [("a", 1), ("b", 2)]), - (A(a=1, b=2), True, [("a", 1), ("b", 2), ("c", None)]), - ( - C(l=[A(a=1)], d={"a": 1, "b": A(a=1, b=0)}), - False, - [("l/0/a", 1), ("d/a", 1), ("d/b/a", 1), ("d/b/b", 0)], - ), - ], -) -def test_iter_property_paths(obj, include_none, expected): - actual = list(iter_property_paths(obj, include_none=include_none)) - assert actual == expected - - -@pytest.mark.parametrize( - ("obj", "delimiter", "expected"), - [ - ("a", ".", [("", "a")]), - ( - C(l=[A(a=1)], d={"a": 1, "b": A(a=1, b=0)}), - ".", - [("l.0.a", 1), ("d.a", 1), ("d.b.a", 1), ("d.b.b", 0)], - ), - ], -) -def test_iter_property_paths_delimiter(obj, delimiter, expected): - actual = list(iter_property_paths(obj, delimiter=delimiter, include_none=False)) - assert actual == expected - - -@pytest.mark.parametrize( - ("obj", "dont_traverse_types", "dont_descend_paths", "expected"), - [ - (C(l=[A(a=1)], d={"b": A(a=1)}), (list,), [], [("l", [A(a=1)]), ("d/b/a", 1)]), - (C(l=[A(a=1)], d={"b": A(a=1)}), (dict,), [], [("l/0/a", 1), ("d", {"b": A(a=1)})]), - (C(l=[A(a=1)], d={"b": A(a=1)}), tuple(), ["d/b"], [("l/0/a", 1), ("d/b", A(a=1))]), - ], -) -def test_iter_property_paths_exclusion(obj, dont_traverse_types, dont_descend_paths, expected): - actual = list( - iter_property_paths( - obj, - dont_traverse_types=dont_traverse_types, - dont_descend_paths=dont_descend_paths, - include_none=False, - ) - ) - assert actual == expected - - -@pytest.mark.parametrize( - ("df", "expected", "columns"), - [ - ( - pd.DataFrame({"col1": [["db", "v1"]], "col2": [[{}]]}), - pd.DataFrame({"col1": ['["db", "v1"]'], "col2": [[{}]]}), - ["col1"], - ), - ( - pd.DataFrame({"col1": [["db", "v1"]], "col2": [[{}]]}), - pd.DataFrame({"col1": ['["db", "v1"]'], "col2": ["[{}]"]}), - ["col1", "col2"], - ), - ], -) -def test_stringify_list_columns(df, columns, expected): - actual = stringify_list_columns(df, columns=columns) - - pd.testing.assert_frame_equal(actual, expected) - - -@pytest.mark.parametrize( - ("coordinates", "matrix", "expected"), - [ - ([[0, 0], [1, 2]], np.array([[1, 0, 4], [0, 1, 5], [0, 0, 1]]), [[4, 5], [5, 7]]), - ([[0, 0], [1, 2]], np.array([[0, -1, 0], [1, 0, 0], [0, 0, 1]]), [[0, 0], [-2, 1]]), - ], -) -def test_transform_coords(coordinates, matrix, expected): - actual = transform_coords(np.asarray(coordinates), matrix) - - np.testing.assert_allclose(actual, expected) diff --git a/metaspace_converter/to_anndata.py b/metaspace_converter/to_anndata.py deleted file mode 100644 index e2fcb87..0000000 --- a/metaspace_converter/to_anndata.py +++ /dev/null @@ -1,334 +0,0 @@ -from typing import Container, Optional - -import numpy as np -import pandas as pd -from anndata import AnnData -from metaspace import SMInstance -from metaspace.sm_annotation_utils import ( - DEFAULT_DATABASE, - DatasetNotFound, - IsotopeImages, - SMDataset, -) - -from metaspace_converter.constants import ( - COL, - METASPACE_KEY, - OBS_INDEX_NAME, - SPATIAL_KEY, - VAR_INDEX_NAME, - Shape2d, -) -from metaspace_converter.utils import ( - empty_columns_to_nan, - has_optical_image, - iter_property_paths, - stringify_list_columns, - transform_coords, -) - - -def metaspace_to_anndata( - dataset: Optional[SMDataset] = None, - dataset_id: Optional[str] = None, - database: tuple[str, str] = None, - fdr: Optional[float] = 0.1, - use_tic: bool = False, - metadata_as_obs: bool = False, - add_optical_image: bool = False, - sm: Optional[SMInstance] = None, - **annotation_filter, -) -> AnnData: - """ - Downloads a METASPACE dataset to an :py:class:`AnnData` object. - - See: https://metaspace2020.eu/about - - Args: - dataset: A METASPACE dataset instance. If not provided, the `dataset_id` must be given. - dataset_id: The unique ID of a dataset on METASPACE, e.g. "2021-09-03_11h43m13s" - database: A single METASPACE database given as a tuple of name and version. Usually it is - displayed on METASPACE as "HMDB – v4" which corresponds to `("HMDB", "v4")`. - fdr: Returns only annotations for which the false discovery rate is less or equal to this - limit. - use_tic: When True, the output values will be scaled by the total ion count per pixel and - will be in 0.0 to 1.0 range. - metadata_as_obs: Whether to store metadata in the `obs` dataframe instead of `uns`. For a - single METASPACE dataset, metadata is the same for all pixels, so it would be duplicated - for all `obs`. When combining multiple datasets, it would be preserved in `obs` but - not in `uns`. - add_optical_image: Whether to embed the optical image for SquidPy - sm: Optionally a cached SMInstance - annotation_filter: Additional keyword arguments passed to the METASPACE API. - - Returns: - An :py:class:`AnnData` object with - * ion intensities: ``.X`` - * ion image pixel coordinates: ``.obs[["ion_image_pixel_x", "ion_image_pixel_y"]]`` - * spatial coordinates: ``.obsm["spatial"]`` - * ion properties: ``.var``, for example "formula", "adduct", "mz", "fdr", - "moleculeNames", "moleculeIds", "intensity"… - * METASPACE metadata: ``.uns["metaspace"]`` if not ``metadata_as_obs`` - * SquidPy metadata: ``.uns["spatial"]`` if ``add_optical_image`` - - Raises: - ValueError: If something is wrong with the input data or parameters - metaspace.sm_annotation_utils.DatasetNotFound: If the dataset ID is not found - ConnectionError: For any other network connectivity problems - """ - # This must stay at the top above `locals()`, so that `parameters` includes no later defined - # variables, but `dataset_id` definitely has a value. - if dataset is not None: - dataset_id = dataset.id - # Capture the function parameters (except non-serializable ones) so we can include them - # in the resulting AnnData. - parameters = _dict_without_keys(locals(), keys=("sm", "dataset")) - - dataset = get_dataset(dataset=dataset, dataset_id=dataset_id, sm=sm) - if dataset.status != "FINISHED": - raise DatasetNotFound(f"Dataset with ID {dataset.id} has not finished processing.") - - if database is None: - database = DEFAULT_DATABASE - - # Download annotations - annotations = dataset.results(database=database, fdr=fdr, **annotation_filter) - annotations = _add_annotations_index(annotations, index_name=VAR_INDEX_NAME) - annotations = _normalize_annotations_for_serialization(annotations) - - # Download ion images - isotope_images = dataset.all_annotation_images( - only_first_isotope=True, - fdr=fdr, - database=database, - scale_intensity="TIC" if use_tic else True, - **annotation_filter, - ) - if not isotope_images: - raise ValueError( - f"No isotope images available for dataset {dataset.id} and database " - f"{database[0]} – {database[1]}. Was the database selected for processing on METASPACE?" - ) - assert len(annotations) == len(isotope_images) - - # Sort them matching the annotations. - isotope_images = _sort_isotope_images_like(isotope_images, annotations.index) - - # Create X matrix (all ion pixels flattened to primary axis) - shape = get_ion_image_shape(dataset) - ion_intensities = _create_anndata_x(isotope_images, shape) - - # Create observations dataframe - obs = _create_obs_with_coordinates(shape) - - # Scanpy looks up (x, y) coordinates from obsm. - obsm = {SPATIAL_KEY: obs[[COL.ion_image_pixel_x, COL.ion_image_pixel_y]].values} - - adata = AnnData(X=ion_intensities, var=annotations, obs=obs, obsm=obsm) - - # Add metadata - if not metadata_as_obs: - adata.uns[METASPACE_KEY] = _create_uns_for_metaspace(dataset, parameters=parameters) - else: - metadata_df = _create_obs_for_metaspace( - dataset, obs_names=adata.obs.index, parameters=parameters - ) - adata.obs = pd.concat([obs, metadata_df], axis=1) - - # Add optical image - if add_optical_image and has_optical_image(dataset): - _add_obsm_and_uns_for_squidpy( - adata, - dataset=dataset, - pixel_coords_xy=obs[[COL.ion_image_pixel_x, COL.ion_image_pixel_y]], - ) - - return adata - - -def create_annotation_id(formula: str, adduct: str) -> str: - return f"{formula}{adduct}" - - -def _add_annotations_index(df: pd.DataFrame, index_name: str = VAR_INDEX_NAME) -> pd.DataFrame: - df = df.reset_index() - df[index_name] = df.apply(lambda row: create_annotation_id(row.formula, row.adduct), axis=1) - return df.set_index(index_name) - - -def _normalize_annotations_for_serialization(df: pd.DataFrame) -> pd.DataFrame: - # AnnData cannot save dataframes containing Python objects (like lists). - # As workaround, we stringify to JSON. - df = stringify_list_columns(df, columns=["isotopeImages", "moleculeNames", "moleculeIds"]) - # AnnData can also not save columns containing only None (dtype object, tries to save as str) - # "Can't implicitly convert non-string objects to strings" - return empty_columns_to_nan(df) - - -def get_dataset( - dataset: Optional[SMDataset] = None, - dataset_id: Optional[str] = None, - sm: Optional[SMInstance] = None, -) -> SMDataset: - """ - Fetch a dataset instance, if not provided - """ - if dataset is not None: - return dataset - else: - if dataset_id is None: - raise ValueError("Either `dataset` or `dataset_id` must be provided") - if sm is None: - sm = SMInstance() - return sm.dataset(id=dataset_id) - - -def get_ion_image_shape( - dataset: Optional[SMDataset] = None, - dataset_id: Optional[str] = None, - sm: Optional[SMInstance] = None, -) -> Shape2d: - """ - Get the shape of a METASPACE dataset (in Numpy order). - - Args: - dataset: A METASPACE dataset instance. If not provided, a dataset ID must be provided. - dataset_id: An optional METASPACE dataset ID, if no dataset instance is provided. - sm: An optional SMInstance, otherwise one will be created. - """ - dataset = get_dataset(dataset=dataset, dataset_id=dataset_id, sm=sm) - return dataset.image_size["y"], dataset.image_size["x"] - - -def _sort_isotope_images_like( - isotope_images: list[IsotopeImages], index: pd.Index -) -> list[IsotopeImages]: - images_dict = {} - for isotope_image in isotope_images: - annotation_id = create_annotation_id(isotope_image.formula, isotope_image.adduct) - images_dict[annotation_id] = isotope_image - # Return them in the requested order. - return [images_dict[key] for key in index] - - -def _create_anndata_x(isotope_images: list[IsotopeImages], shape: Shape2d) -> np.ndarray: - n_pixels = np.prod(shape) - n_ions = len(isotope_images) - y_x_ion_stack = np.dstack([_isotope_image_to_array(img, shape=shape) for img in isotope_images]) - return y_x_ion_stack.reshape(n_pixels, n_ions) - - -def _isotope_image_to_array(ion_image: IsotopeImages, shape: Shape2d) -> np.ndarray: - if len(ion_image) == 0 or ion_image[0] is None: - return np.zeros(shape=shape) - else: - return ion_image[0] - - -def _create_obs_with_coordinates(shape: Shape2d) -> pd.DataFrame: - n_pixels = np.prod(shape) - pixel_coords_yx = all_image_pixel_coordinates(shape) - return pd.DataFrame( - { - COL.ion_image_pixel_x: pixel_coords_yx[:, 1], - COL.ion_image_pixel_y: pixel_coords_yx[:, 0], - COL.ion_image_shape_y: shape[0], - COL.ion_image_shape_x: shape[1], - }, - index=pd.RangeIndex(n_pixels, name=OBS_INDEX_NAME).astype(str), - ) - - -def all_image_pixel_coordinates(shape: Shape2d) -> np.ndarray: - """ - Returns a list of Y, X coordinates (Numpy order) for every pixel of an image. - - Args: - shape: The shape of a 2D image - - Returns: - Array of shape (number of pixels, 2) - """ - ys_and_xs = np.indices(shape) - return np.dstack(ys_and_xs).reshape((-1, len(shape))) - - -def _create_default_spatial_uns( - image: Optional[np.ndarray] = None, - scale_factor: float = 1.0, - spot_diameter: float = 1, - library_id: str = "image", - image_key: str = "hires", -) -> dict: - return { - library_id: { - "images": {image_key: image} if image is not None else {}, - "scalefactors": { - f"tissue_{image_key}_scalef": scale_factor, - "spot_diameter_fullres": spot_diameter, - }, - } - } - - -def _create_uns_for_metaspace(dataset: SMDataset, parameters: Optional[dict] = None) -> dict: - # Database details contain instances of MolecularDB, which are not JSON-serializable. - # Also, AnnData does not support lists of dicts, so we have to convert to dict of dicts. - # See issue https://github.com/scverse/anndata/issues/708 - plain_db_details = {str(idx): db._info for idx, db in enumerate(dataset.database_details)} - plain_projects = {str(idx): project for idx, project in enumerate(dataset.projects)} - plain_parameters = {k: list(v) if isinstance(v, tuple) else v for k, v in parameters.items()} - return { - "id": dataset.id, - "name": dataset.name, - "adducts": dataset.adducts, - "databases": dataset.databases, - "database_details": plain_db_details, - "polarity": dataset.polarity, - "image_size": dataset.image_size, - "submitter": dataset.submitter, - "group": dataset.group, - "principal_investigator": dataset.principal_investigator, - "projects": plain_projects, - "status": dataset.status, - "metadata": dict(dataset.metadata), - "config": dataset.config, - # Include parameters used to generate this AnnData - f"{metaspace_to_anndata.__name__}_parameters": plain_parameters, - } - - -def _create_obs_for_metaspace( - dataset: SMDataset, obs_names: pd.Index, parameters: Optional[dict] = None -) -> pd.DataFrame: - metadata = _create_uns_for_metaspace(dataset=dataset, parameters=parameters) - flattened_metadata = iter_property_paths( - obj={METASPACE_KEY: metadata}, - delimiter=".", - dont_traverse_types=(tuple, list), - dont_descend_paths=[f"{METASPACE_KEY}.{metaspace_to_anndata.__name__}_parameters"], - ) - metadata_series = pd.Series(dict(flattened_metadata)) - obs = pd.DataFrame([metadata_series] * len(obs_names), index=obs_names) - # METASPACE metadata contains a lot of list values. - # AnnData cannot save dataframes containing Python objects (like lists). - # As workaround, we stringify to JSON. - obs = stringify_list_columns(obs, all=True) - return empty_columns_to_nan(obs) - - -def _add_obsm_and_uns_for_squidpy(adata: AnnData, dataset: SMDataset, pixel_coords_xy: np.ndarray): - optical_images = dataset.optical_images() - image_yx_rgb = optical_images[0] - # Transformation from ion image coordinate system to optical image (x, y order!). - matrix_xy = optical_images._transforms[0] - coords_on_optical_xy = transform_coords(pixel_coords_xy, matrix_xy) - # SquidPy looks up (x, y) coordinates from obsm. - adata.obsm[SPATIAL_KEY] = coords_on_optical_xy - # SquidPy uses spatial information from uns. - adata.uns[SPATIAL_KEY] = _create_default_spatial_uns(image=image_yx_rgb, spot_diameter=0.5) - - -def _dict_without_keys(dictionary: dict, keys: Container) -> dict: - # Exclude keys from a dictionary - return {k: v for k, v in dictionary.items() if k not in keys} diff --git a/metaspace_converter/to_spatialdata.py b/metaspace_converter/to_spatialdata.py deleted file mode 100644 index e35ac9c..0000000 --- a/metaspace_converter/to_spatialdata.py +++ /dev/null @@ -1,219 +0,0 @@ -import warnings -from typing import Optional - -import numpy as np -import pandas as pd -from anndata import AnnData -from metaspace import SMInstance -from metaspace.sm_annotation_utils import SMDataset -from spatial_image import SpatialImage -from spatialdata import SpatialData -from spatialdata.models import Image2DModel, PointsModel, TableModel -from spatialdata.transformations import Affine, Scale, Sequence as SequenceTransform, Translation - -from metaspace_converter.constants import ( - COL, - COORD_SYS_GLOBAL, - INSTANCE_KEY, - MICROMETER, - OPTICAL_IMAGE_KEY, - POINTS_KEY, - REGION_KEY, - XY, - YX, - YXC, - X, - Y, -) -from metaspace_converter.to_anndata import metaspace_to_anndata -from metaspace_converter.utils import has_optical_image - - -def metaspace_to_spatialdata( - dataset: Optional[SMDataset] = None, - dataset_id: Optional[str] = None, - database: Optional[tuple[str, str]] = None, - fdr: float = 0.1, - use_tic: bool = False, - metadata_as_obs: bool = False, - add_optical_image: bool = True, - optical_name_added: str = OPTICAL_IMAGE_KEY, - add_points: bool = True, - points_name_added: str = POINTS_KEY, - sm: Optional[SMInstance] = None, - **annotation_filter, -) -> SpatialData: - """ - Download a METASPACE dataset as a :py:class:`SpatialData` object. - - See: https://metaspace2020.eu/about - - Args: - dataset: A METASPACE dataset instance. If not provided, the ``dataset_id`` must be given. - dataset_id: The unique ID of a dataset on METASPACE, e.g. "2021-09-03_11h43m13s" - database: A single METASPACE database given as a tuple of name and version. Usually it is - displayed on METASPACE as "HMDB – v4" which corresponds to ``("HMDB", "v4")``. - fdr: Returns only annotations for which the false discovery rate is less or equal to this - limit. - use_tic: When True, the output values will be scaled by the total ion count per pixel and - will be in 0.0 to 1.0 range. - metadata_as_obs: Whether to store metadata in the ``obs`` dataframe instead of ``uns``. For - a single METASPACE dataset, metadata is the same for all pixels, so it would be - duplicated for all ``obs``. When combining multiple datasets, it would be preserved in - ``obs`` but not in ``uns``. - add_optical_image: Whether to also add the optical image (if one exists) to SpatialData - images. If none exists, it is not added, and no error raised. - optical_name_added: Name of the element where to store the image in the SpatialData object - add_points: Whether to also add ion image pixel coordinates as SpatialData points. This - allows to spatially visualize the ion image values. If False, only ion intensities are - added to the table and coordinates are added to ``obs``. - points_name_added: Name of the element where to store the points in the SpatialData object - sm: Optionally a cached SMInstance - annotation_filter: Additional keyword arguments passed to the METASPACE API. - - Returns: - A :py:class:`SpatialData` object with - * ion intensities and metadata: ``.table`` - * optical image: ``.images["optical_image"]`` (or ``optical_name_added``) - * sampling coordinates: ``.points["maldi_points"]`` (or ``points_name_added``) in a - coordinate system relative to the top-left image corner and physical scale - (micrometers) - - Raises: - ValueError: If something is wrong with the input data or parameters - metaspace.sm_annotation_utils.DatasetNotFound: If the dataset ID is not found - ConnectionError: For any other network connectivity problems - """ - if dataset is None: - if sm is None: - sm = SMInstance() - dataset = sm.dataset(id=dataset_id) - - # Create table - adata = metaspace_to_anndata( - dataset=dataset, - database=database, - fdr=fdr, - use_tic=use_tic, - metadata_as_obs=metadata_as_obs, - add_optical_image=False, - sm=sm, - **annotation_filter, - ) - table = _create_spatialdata_table(adata) - sdata = SpatialData(table=table) - - # Create image - if has_optical_image(dataset) and add_optical_image: - optical_image = optical_image_to_spatial_image(dataset=dataset) - sdata.images[optical_name_added] = optical_image - - # Create points - if add_points: - points = _create_points(dataset, adata) - sdata.points[points_name_added] = points - - return sdata - - -def _is_matrix_homogeneous(transform_matrix_2d: np.ndarray) -> bool: - # In an n×n matrix, the last row (except last element) should be zeros. - actual = transform_matrix_2d[-1, :-1] - expected = np.zeros(transform_matrix_2d.shape[1] - 1) - return np.array_equal(actual, expected) - - -def optical_image_to_spatial_image(dataset: SMDataset) -> Optional[SpatialImage]: - """ - Download the optical image of a METASPACE dataset to a SpatialImage object. - - This function requires that the dataset has an optical image. Otherwise, an error is raised. - - Args: - dataset: A METASPACE dataset - - Returns: - A SpatialImage object with the image's transformation to world coordinates - """ - optical_images = dataset.optical_images() - image_yx_rgb = optical_images[0] - # Transformation from optical image to ion image coordinate system (inverse of ``_transforms``) - matrix_xy = optical_images._itransforms[0] - if _is_matrix_homogeneous(matrix_xy): - affine_matrix_xy = matrix_xy - else: - warnings.warn( - "The METASPACE optical image has a projective transformation, but " - "SpatialData only supports affine transformations. " - "Using an approximated affine." - ) - affine_matrix_xy = matrix_xy.copy() - affine_matrix_xy[-1, :-1] = 0.0 - optical_to_ion_image = Affine(matrix=affine_matrix_xy, input_axes=XY, output_axes=XY) - # Transformation from ion image to world coordinate system - ion_image_to_global = get_ion_image_to_physical_transformation(dataset) - pixel_corner_to_center = Translation(translation=[-0.5, -0.5], axes=YX) - optical_to_global = SequenceTransform( - [optical_to_ion_image, pixel_corner_to_center, ion_image_to_global] - ).to_affine(input_axes=YX, output_axes=YX) - return Image2DModel.parse( - image_yx_rgb, - transformations={COORD_SYS_GLOBAL: optical_to_global}, - dims=YXC, - scale_factors=(2, 2, 2), - axis_units={Y: MICROMETER, X: MICROMETER}, - c_coords=("r", "g", "b"), - # rgb=True, - ) - - -def _create_spatialdata_table(adata: AnnData) -> AnnData: - # Add the index as a separate column for matching rows to regions in labels or points. - adata.obs[INSTANCE_KEY] = adata.obs.index.astype(int) - # Avoid same name for index and a column name, which is ambiguous and causes warning or error. - adata.obs.index.name = "index" - adata.obs[REGION_KEY] = pd.Series( - [POINTS_KEY] * adata.n_obs, index=adata.obs.index, dtype="category" - ) - return TableModel.parse( - adata, - region=[POINTS_KEY], # element in SpatialData to be used - region_key=REGION_KEY, # table column containing the regions - instance_key=INSTANCE_KEY, # table column to be matched to points index - ) - - -def _create_points(dataset: SMDataset, adata: AnnData) -> pd.DataFrame: - # FIXME: PointsModel does not yet allow to specify units, we should set micrometers as in image. - # FIXME: Workaround for that napari-spatialdata does not allow to visualize values from ``X`` - # array, only from ``obs``. For now, we copy X to ``obs`` to enable visualization. - # points = PointsModel.parse( - # adata.obs[[X, Y]].reset_index(), - # transformations={COORD_SYS_GLOBAL: get_ion_image_to_physical_transformation(dataset)}, - # ) - return PointsModel.parse( - pd.concat( - [ - adata.obs[[COL.ion_image_pixel_x, COL.ion_image_pixel_y]].reset_index(), - pd.DataFrame(adata.X, columns=adata.var.index), - ], - axis=1, - ), - coordinates={"x": COL.ion_image_pixel_x, "y": COL.ion_image_pixel_y}, - transformations={COORD_SYS_GLOBAL: get_ion_image_to_physical_transformation(dataset)}, - ) - - -def get_ion_image_to_physical_transformation(dataset: SMDataset) -> Scale: - """ - Get the transformation from the ion image (pixels) to the world coordinate system (micrometers). - - Args: - dataset: A METASPACE dataset - - Returns: - A scale SpatialData transformation - """ - ion_image_pixel_size = dataset.metadata["MS_Analysis"]["Pixel_Size"] - scale_factors_xy = [ion_image_pixel_size["Xaxis"], ion_image_pixel_size["Yaxis"]] - return Scale(scale=scale_factors_xy, axes=XY) diff --git a/metaspace_converter/utils.py b/metaspace_converter/utils.py deleted file mode 100644 index 09a364a..0000000 --- a/metaspace_converter/utils.py +++ /dev/null @@ -1,179 +0,0 @@ -import dataclasses -import json -from collections.abc import Collection, Iterator, Mapping, Sequence -from dataclasses import is_dataclass -from typing import Any, Iterable - -import numpy as np -import pandas as pd -from metaspace.sm_annotation_utils import SMDataset - - -def empty_columns_to_nan(df: pd.DataFrame) -> pd.DataFrame: - """ - Converts all empty columns from None (dtype object) to NaN (dtype float). - - Args: - df: A dataframe - - Returns: - The modified dataframe. The original dataframe is modified, not a copy. - """ - for c in df.columns: - if pd.api.types.infer_dtype(df[c]) == "empty": - df[c] = np.nan - return df - - -def has_optical_image(dataset: SMDataset) -> bool: - """ - Check whether a METASPACE dataset has an optical image - """ - try: - optical_images = dataset.optical_images() - except TypeError: - return False - return optical_images is not None and len(optical_images) > 0 - - -def is_non_str_sequence(obj: Any) -> bool: - """ - Returns whether an object is a sequence (list and similar), excluding strings. - - Args: - obj: The object to check. - - Returns: - True if the object is a sequence. - """ - return isinstance(obj, Sequence) and not isinstance(obj, str) - - -def iter_property_paths( - obj: Any, - delimiter: str = "/", - include_none: bool = True, - dont_traverse_types: tuple[type, ...] = tuple(), - dont_descend_paths: Collection[str] = tuple(), - _path: str = "", -) -> Iterator[tuple[str, Any]]: - """ - Iterates recursively over properties of nested objects. - - Args: - obj: A class instance, mapping or sequence. - delimiter: The delimiter to use. Should not be contained in any name. - include_none: Whether to yield attributes which have None assigned, or skip them. - dont_traverse_types: Iterable data types which should not be traversed any deeper. - These are yielded as values. - dont_descend_paths: Paths with iterable objects that should be preserved. - - Returns: - An iterator for iterating over paths and objects found at each path. It can also be - converted to a dictionary. - """ - prefix = _path if _path == "" else f"{_path}{delimiter}" - if dont_traverse_types and isinstance(obj, dont_traverse_types): - if obj is None and not include_none: - return - yield _path, obj - elif _path in dont_descend_paths: - if obj is None and not include_none: - return - yield _path, obj - # Traversable types: list, dict, dataclasses/pydantic - elif is_non_str_sequence(obj): - for i, value in enumerate(obj): - yield from iter_property_paths( - value, - delimiter=delimiter, - include_none=include_none, - dont_traverse_types=dont_traverse_types, - dont_descend_paths=dont_descend_paths, - _path=f"{prefix}{i}", - ) - elif isinstance(obj, Mapping): - for key, value in obj.items(): - yield from iter_property_paths( - value, - delimiter=delimiter, - include_none=include_none, - dont_traverse_types=dont_traverse_types, - dont_descend_paths=dont_descend_paths, - _path=f"{prefix}{key}", - ) - elif is_dataclass(obj): - keys = ( - [field.name for field in dataclasses.fields(obj)] - if is_dataclass(obj) - else obj.__fields__.keys() - ) - for key in keys: - # Object with attributes - value = getattr(obj, key) - yield from iter_property_paths( - value, - delimiter=delimiter, - include_none=include_none, - dont_traverse_types=dont_traverse_types, - dont_descend_paths=dont_descend_paths, - _path=f"{prefix}{key}", - ) - # Non-traversable/primitive types - else: - if obj is None and not include_none: - # Don't yield values that are not set. - return - yield _path, obj - - -def stringify_list_columns( - df: pd.DataFrame, columns: Iterable[str] = tuple(), all: bool = False -) -> pd.DataFrame: - """ - If the dataframe column is a list, stringify it into JSON. - - Args: - df: A dataframe with a column that contains lists (e.g. of molecule names) - columns: The names of the columns containing lists - all: Whether to check and stringify all columns containing list-like objects - - Returns: - A dataframe where all values in specified columns are JSON stringified. - """ - if not df.empty: - df = df.copy() - if all: - columns = df.columns - for column in columns: - if column in df.columns and pd.api.types.is_list_like(df[column].iloc[0]): - df[column] = df[column].apply(lambda l: json.dumps(l)) - return df - - -def transform_coords(coords: np.ndarray, transform_matrix_2d: np.ndarray) -> np.ndarray: - """ - Transform a list of 2D coordinates using a 3×3 transformation matrix. - - Like in scikit-image, but without adding that dependency. - - See skimage.transform._geometric.ProjectiveTransform.__call__ - - Args: - coords: An array of coordinates with shape (number of points, 2) - transform_matrix_2d: A transformation matrix of shape (3, 3) - - Returns: - The list of transformed coordinates as Numpy array - """ - # From skimage.transform._geometric - # Convert to homogeneous coordinates - src_h = np.hstack([coords, np.ones((len(coords), 1))]) - dst_h = src_h @ transform_matrix_2d.T - # Below, we will divide by the last dimension of the homogeneous - # coordinate matrix. In order to avoid division by zero, - # we replace exact zeros in this column with a very small number. - dst_h[dst_h[:, -1] == 0, -1] = np.finfo(float).eps - # rescale to homogeneous coordinates - dst = dst_h[:, :-1] / dst_h[:, -1:] - return dst diff --git a/modules.html b/modules.html new file mode 100644 index 0000000..b44d723 --- /dev/null +++ b/modules.html @@ -0,0 +1,158 @@ + + + + + + + metaspace-converter — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + +
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+ + + + \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index 8f1dbf6..0000000 --- a/pyproject.toml +++ /dev/null @@ -1,115 +0,0 @@ -[build-system] -requires = ["setuptools", "wheel"] -build-backend = "setuptools.build_meta" - -[project] -name = "metaspace-converter" -version = "1.0.1" -authors = [ - {name = "Tim Daniel Rose", email = "tim.rose@embl.de"}, - {name = "Andreas Eisenbarth", email = "andreas.eisenbarth@embl.de"}, -] -description = "Convert Metaspace datasets to AnnData" -requires-python = ">=3.9" -license = {text = "Apache-2.0"} -classifiers = [ - "License :: OSI Approved :: Apache Software License", - "Topic :: Scientific/Engineering :: Bio-Informatics", - "Intended Audience :: Science/Research", - "Natural Language :: English", - "Operating System :: MacOS", - "Operating System :: Microsoft :: Windows", - "Operating System :: POSIX :: Linux", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", -] -dependencies = [ - "anndata>=0.9.2", - "metaspace2020>=2.0.6", - "numpy", - "pandas", - "spatial-image>=0.3", - "spatialdata>=0.0.15", -] -dynamic = ["readme"] - -[project.optional-dependencies] -test = [ - "multiscale-spatial-image>=0.11", - "pytest>=7.2", - "pytest-cov>=4.1", -] -docs = [ - "sphinx~=7.2", - "sphinx-autodoc-typehints", - "sphinx-codeautolink", - "sphinx-copybutton", - "sphinx-rtd-theme", -# # For notebooks -# "myst-nb", -# "ipython~=8.6.0", - # For example code in documentation - "scanpy~=1.9.6", - "spatialdata-plot~=0.0.6", - "squidpy~=1.3.1", -] - -[project.urls] -Repository = "https://github.com/metaspace2020/metaspace-converter" -Source = "https://github.com/metaspace2020/metaspace-converter" -Documentation = "https://metaspace2020.github.io/metaspace-converter/" -# Publication = "" - -[tool.setuptools.packages.find] -where = ["."] - -[tool.setuptools.exclude-package-data] -"*" = ["metaspace_converter/tests"] - -[tool.setuptools.dynamic] -readme = {file = "README.md", content-type = "text/markdown"} - -[tool.black] -line-length = 100 -target-version = ['py38'] -skip-magic-trailing-comma = true -include = '\.(pyi?|ipynb)$' -exclude = ''' -^/( - ( - \.eggs # exclude a few common directories in the - | \.git # root of the project - | \.mypy_cache - | \.pytest_cache - | build - | dist - ) -)/ -''' - -[tool.isort] -combine_as_imports = true -filter_files = true -line_length = 100 -profile = "black" - -[tool.pycln] -all = true - -[tool.pytest.ini_options] -minversion = "6.0" -# CLI options: -# -ra: Show extra test summary for (a)ll except passed -# --quiet: Decrease verbosity -# --doctest*: Run example code in docs -addopts = '-ra --quiet' -filterwarnings = [ - # Triggered by spatialdata==0.0.15 - "ignore:is_categorical_dtype is deprecated and will be removed in a future version:FutureWarning:spatialdata", - # Triggered by xarray-schema==0.0.3 - "ignore:pkg_resources is deprecated as an API:DeprecationWarning:xarray_schema", - # Triggered by matplotlib<3.8.0, xarray-schema==0.0.3 - "ignore:Deprecated call to `pkg_resources.declare_namespace:DeprecationWarning:pkg_resources", -] diff --git a/search.html b/search.html new file mode 100644 index 0000000..444c1d5 --- /dev/null +++ b/search.html @@ -0,0 +1,127 @@ + + + + + + Search — METASPACE Converter documentation + + + + + + + + + + + + + + + + + + + + + + + + +
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