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theislab / cellrank_notebooks

Tutorials and examples for CellRank.
https://cellrank.org
BSD 3-Clause "New" or "Revised" License
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Notebook testing #22

Closed Marius1311 closed 4 years ago

Marius1311 commented 4 years ago 
======================================================= test session starts ========================================================
platform darwin -- Python 3.6.9, pytest-5.3.2, py-1.8.1, pluggy-0.13.1
rootdir: /Users/marius/Projects/cellrank_notebooks, inifile: tox.ini, testpaths: tests/
plugins: cov-2.9.0, parallel-0.1.0, notebook-0.6.0
collected 3 items                                                                                                                  
pytest-parallel: 2 workers (processes), 1 test per worker (thread)
..FFFF
============================================================= FAILURES =============================================================
_______________________________________________________ test_pancreas_basic ________________________________________________________

nb_regression = NBRegressionFixture(exec_notebook=True, exec_cwd='/var/folders/mx/0hyv8t2s26jdj79f55kvc_b80000gn/T', exec_allow_errors...tputs/*/data/application/vnd.jupyter.widget-view+json'), diff_use_color=True, diff_color_words=True, force_regen=False)

    def decorator(nb_regression) -> None:
        fpath = f"{(TUTORIALS / func.__name__[5:]).resolve()}.ipynb"

        nb = nbformat.read(fpath, as_version=4)
        _inject_sentinel(nb)

        with tempfile.NamedTemporaryFile("w", suffix=".ipynb") as tmpf:
            nbformat.write(nb, tmpf)
            result = nb_regression.check(tmpf.name, raise_errors=False)

>       _assert_execute_sentinel(result)

tests/test_tutorials.py:42: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

result = NBRegressionResult(diff_full_length=1,diff_filtered_length=1)

    def _assert_execute_sentinel(result) -> None:
        match = parse(
            f'$.[*].diff[*].diff[*].diff[*].valuelist[?(@.text = "{SENTINEL}\n")].text'
        )
        found = match.find(result.diff_filtered)

        if not found:
>           raise RuntimeError(result.diff_string)
E           RuntimeError: 
E           --- expected
E           +++ obtained
E           ## modified /cells/6/outputs/0/text:
E           @@ -1 +1,5 @@
E           try downloading from url
E           https://github.com/theislab/cellrank_notebooks/raw/master/datasets/pancreas/endocrinogenesis_day15.5.h5ad
E           ... this may take a while but only happens once
E           
E           Abundance of ['spliced', 'unspliced']: [0.81 0.19]
E           
E           ## inserted before /cells/6/outputs/1:
E           +  output:
E           +    output_type: display_data
E           +    data:
E           +      application/vnd.jupyter.widget-view+json:
E           +        model_id: a41d4aa7dd4d4c848f04034d03bcb2a1
E           +        version_major: 2
E           +        version_minor: 0
E           +      text/plain: HBox(children=(FloatProgress(value=1.0, bar_style='info', description='endocrinogenesis_day15.5.h5ad', max=1.0…
E           
E           ## deleted /cells/6/outputs/1:
E           -  output:
E           -    output_type: execute_result
E           -    execution_count: 2
E           -    data:
E           -      text/plain:
E           -        AnnData object with n_obs × n_vars = 2531 × 27998
E           -            obs: 'day', 'proliferation', 'G2M_score', 'S_score', 'phase', 'clusters_coarse', 'clusters', 'clusters_fine', 'louvain_Alpha', 'louvain_Beta'
E           -            var: 'highly_variable_genes'
E           -            uns: 'clusters_colors', 'clusters_fine_colors', 'day_colors', 'louvain_Alpha_colors', 'louvain_Beta_colors', 'neighbors', 'pca'
E           -            obsm: 'X_pca', 'X_umap'
E           -            layers: 'spliced', 'unspliced'
E           -            obsp: 'connectivities', 'distances'
E           
E           ## inserted before /cells/6/outputs/2:
E           +  output:
E           +    output_type: execute_result
E           +    execution_count: 2
E           +    data:
E           +      text/plain:
E           +        AnnData object with n_obs × n_vars = 2531 × 27998
E           +            obs: 'day', 'proliferation', 'G2M_score', 'S_score', 'phase', 'clusters_coarse', 'clusters', 'clusters_fine', 'louvain_Alpha', 'louvain_Beta'
E           +            var: 'highly_variable_genes'
E           +            uns: 'clusters_colors', 'clusters_fine_colors', 'day_colors', 'louvain_Alpha_colors', 'louvain_Beta_colors', 'neighbors', 'pca'
E           +            obsm: 'X_pca', 'X_umap'
E           +            layers: 'spliced', 'unspliced'
E           +            obsp: 'connectivities', 'distances'
E           
E           ## inserted before /cells/12/outputs/0:
E           +  output:
E           +    output_type: error
E           +    ename: PermissionError
E           +    evalue: [Errno 13] Permission denied: '../../cached_files'
E           +    traceback:
E           +      item[0]: ---------------------------------------------------------------------------
E           +      item[1]: PermissionError                           Traceback (most recent call last)
E           +      item[2]:
E           +        <ipython-input-4-bd53879ad1ad> in <module>
E           +              1 try:
E           +              2     import scachepy
E           +        ----> 3     c = scachepy.Cache('../../cached_files/basic_tutorial/')
E           +              4     c.tl.recover_dynamics(adata, force=False)
E           +              5 except ModuleNotFoundError:
E           +      item[3]:
E           +        ~/Projects/scachepy/scachepy/cache.py in __init__(self, root_dir, backend, compression, ext, separate_dirs)
E           +             62         else:
E           +             63             # shared backend
E           +        ---> 64             self._backend = backend_type(root_dir, self._ext, cache=self)
E           +             65             self.pp = PpModule(self._backend)
E           +             66             self.tl = TlModule(self._backend)
E           +      item[4]:
E           +        ~/Projects/scachepy/scachepy/backends.py in __init__(self, dirname, ext, cache)
E           +             14     def __init__(self, dirname, ext, *, cache):
E           +             15         if not os.path.exists(dirname):
E           +        ---> 16             os.makedirs(dirname)
E           +             17 
E           +             18         self._dirname = Path(dirname)
E           +      item[5]:
E           +        ~/miniconda3/envs/cellrank/lib/python3.6/os.py in makedirs(name, mode, exist_ok)
E           +            208     if head and tail and not path.exists(head):
E           +            209         try:
E           +        --> 210             makedirs(head, mode, exist_ok)
E           +            211         except FileExistsError:
E           +            212             # Defeats race condition when another thread created the path
E           +      item[6]:
E           +        ~/miniconda3/envs/cellrank/lib/python3.6/os.py in makedirs(name, mode, exist_ok)
E           +            218             return
E           +            219     try:
E           +        --> 220         mkdir(name, mode)
E           +            221     except OSError:
E           +            222         # Cannot rely on checking for EEXIST, since the operating system
E           +      item[7]: PermissionError: [Errno 13] Permission denied: '../../cached_files'
E           
E           ## deleted /cells/12/outputs/0:
E           -  output:
E           -    output_type: stream
E           -    name: stdout
E           -    text:
E           -      You don't seem to have scachepy installed, but that's fine, you just have to be a bit patient (~10min). 
E           -      recovering dynamics
E           -          finished (DURATION) --> added 
E           -          'fit_pars', fitted parameters for splicing dynamics (adata.var)
E           
E           ## modified /cells/21/outputs/0/text:
E           @@ -1 +1,21 @@
E           Computing transition matrix based on velocity correlations using mode `'deterministic'`
E           
E               Finish (DURATION)
E           Using a connectivity kernel with weight `0.2`
E           Computing transition matrix based on connectivities
E               Finish (DURATION)
E           Computing eigendecomposition of the transition matrix
E           Adding `.eigendecomposition`
E                  `adata.uns['eig_fwd']`
E           Adding `.eigendecomposition`
E                  `adata.uns['eig_fwd']`
E           Computing metastable states
E           INFO: Using pre-computed schur decomposition
E           Adding `.schur`
E                  `.coarse_T`
E                  `.coarse_stationary_distribution`
E               Finish (DURATION)
E           Adding `adata.obs['final_states_probs']`
E                  `adata.obs['final_states']`
E                  `.final_states_probabilities`
E                  `.final_states`
E           
E           ## deleted /cells/21/outputs/3:
E           
E           ## modified /cells/26/outputs/0/text:
E           @@ -1 +1,17 @@
E           Computing transition matrix based on velocity correlations using mode `'deterministic'`
E           
E               Finish (DURATION)
E           Computing eigendecomposition of the transition matrix
E           Adding `.eigendecomposition`
E                  `adata.uns['eig_bwd']`
E           Computing metastable states
E           WARNING: For `n_states=1`, stationary distribution is computed
E           Computing eigendecomposition of the transition matrix
E           Adding `.eigendecomposition`
E                  `adata.uns['eig_bwd']`
E           Adding `.metastable_states`
E               Finish (DURATION)
E           Adding `adata.obs['root_states_probs']`
E                  `adata.obs['root_states']`
E                  `.final_states_probabilities`
E                  `.final_states`
E           
E           ## inserted before /cells/26/outputs/2:
E           +  output:
E           +    output_type: display_data
E           +    data:
E           +      image/png: iVBORw0K...<snip base64, md5=63def2b2dfc95257...>
E           +      text/plain: <Figure size 600x400 with 1 Axes>
E           +    metadata (unknown keys):
E           +      image/png:
E           +        height: 345
E           +        width: 617
E           
E           ## deleted /cells/26/outputs/2:
E           -  output:
E           -    output_type: stream
E           -    name: stdout
E           -    text:
E           -      
E           -          Finish (DURATION)
E           -      Computing eigendecomposition of the transition matrix
E           -      Adding `.eigendecomposition`
E           -             `adata.uns['eig_bwd']`
E           -      Computing metastable states
E           -      WARNING: For `n_states=1`, stationary distribution is computed
E           -      Computing eigendecomposition of the transition matrix
E           -      Adding `.eigendecomposition`
E           -             `adata.uns['eig_bwd']`
E           -      Adding `.metastable_states`
E           -          Finish (DURATION)
E           -      Adding `adata.obs['root_states_probs']`
E           -             `adata.obs['root_states']`
E           -             `.final_states_probabilities`
E           -             `.final_states`
E           
E           ## deleted /cells/26/outputs/4:
E           
E           

tests/test_tutorials.py:27: RuntimeError
______________________________________________________ test_pancreas_advanced ______________________________________________________

nb_regression = NBRegressionFixture(exec_notebook=True, exec_cwd='/var/folders/mx/0hyv8t2s26jdj79f55kvc_b80000gn/T', exec_allow_errors...tputs/*/data/application/vnd.jupyter.widget-view+json'), diff_use_color=True, diff_color_words=True, force_regen=False)

    def decorator(nb_regression) -> None:
        fpath = f"{(TUTORIALS / func.__name__[5:]).resolve()}.ipynb"

        nb = nbformat.read(fpath, as_version=4)
        _inject_sentinel(nb)

        with tempfile.NamedTemporaryFile("w", suffix=".ipynb") as tmpf:
            nbformat.write(nb, tmpf)
            result = nb_regression.check(tmpf.name, raise_errors=False)

>       _assert_execute_sentinel(result)

tests/test_tutorials.py:42: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

result = NBRegressionResult(diff_full_length=1,diff_filtered_length=1)

    def _assert_execute_sentinel(result) -> None:
        match = parse(
            f'$.[*].diff[*].diff[*].diff[*].valuelist[?(@.text = "{SENTINEL}\n")].text'
        )
        found = match.find(result.diff_filtered)

        if not found:
>           raise RuntimeError(result.diff_string)
E           RuntimeError: 
E           --- expected
E           +++ obtained
E           ## modified /cells/6/outputs/0/text:
E           @@ -1 +1,5 @@
E           try downloading from url
E           https://github.com/theislab/cellrank_notebooks/raw/master/datasets/pancreas/endocrinogenesis_day15.5.h5ad
E           ... this may take a while but only happens once
E           
E           Abundance of ['spliced', 'unspliced']: [0.81 0.19]
E           
E           ## inserted before /cells/6/outputs/1:
E           +  output:
E           +    output_type: display_data
E           +    data:
E           +      application/vnd.jupyter.widget-view+json:
E           +        model_id: 2ed812db3df54097a598085fa1de204c
E           +        version_major: 2
E           +        version_minor: 0
E           +      text/plain: HBox(children=(FloatProgress(value=1.0, bar_style='info', description='endocrinogenesis_day15.5.h5ad', max=1.0…
E           
E           ## deleted /cells/6/outputs/1:
E           -  output:
E           -    output_type: execute_result
E           -    execution_count: 2
E           -    data:
E           -      text/plain:
E           -        AnnData object with n_obs × n_vars = 2531 × 27998
E           -            obs: 'day', 'proliferation', 'G2M_score', 'S_score', 'phase', 'clusters_coarse', 'clusters', 'clusters_fine', 'louvain_Alpha', 'louvain_Beta'
E           -            var: 'highly_variable_genes'
E           -            uns: 'clusters_colors', 'clusters_fine_colors', 'day_colors', 'louvain_Alpha_colors', 'louvain_Beta_colors', 'neighbors', 'pca'
E           -            obsm: 'X_pca', 'X_umap'
E           -            layers: 'spliced', 'unspliced'
E           -            obsp: 'connectivities', 'distances'
E           
E           ## inserted before /cells/6/outputs/2:
E           +  output:
E           +    output_type: execute_result
E           +    execution_count: 2
E           +    data:
E           +      text/plain:
E           +        AnnData object with n_obs × n_vars = 2531 × 27998
E           +            obs: 'day', 'proliferation', 'G2M_score', 'S_score', 'phase', 'clusters_coarse', 'clusters', 'clusters_fine', 'louvain_Alpha', 'louvain_Beta'
E           +            var: 'highly_variable_genes'
E           +            uns: 'clusters_colors', 'clusters_fine_colors', 'day_colors', 'louvain_Alpha_colors', 'louvain_Beta_colors', 'neighbors', 'pca'
E           +            obsm: 'X_pca', 'X_umap'
E           +            layers: 'spliced', 'unspliced'
E           +            obsp: 'connectivities', 'distances'
E           
E           ## inserted before /cells/12/outputs/0:
E           +  output:
E           +    output_type: error
E           +    ename: PermissionError
E           +    evalue: [Errno 13] Permission denied: '../../cached_files'
E           +    traceback:
E           +      item[0]: ---------------------------------------------------------------------------
E           +      item[1]: PermissionError                           Traceback (most recent call last)
E           +      item[2]:
E           +        <ipython-input-4-bd53879ad1ad> in <module>
E           +              1 try:
E           +              2     import scachepy
E           +        ----> 3     c = scachepy.Cache('../../cached_files/basic_tutorial/')
E           +              4     c.tl.recover_dynamics(adata, force=False)
E           +              5 except ModuleNotFoundError:
E           +      item[3]:
E           +        ~/Projects/scachepy/scachepy/cache.py in __init__(self, root_dir, backend, compression, ext, separate_dirs)
E           +             62         else:
E           +             63             # shared backend
E           +        ---> 64             self._backend = backend_type(root_dir, self._ext, cache=self)
E           +             65             self.pp = PpModule(self._backend)
E           +             66             self.tl = TlModule(self._backend)
E           +      item[4]:
E           +        ~/Projects/scachepy/scachepy/backends.py in __init__(self, dirname, ext, cache)
E           +             14     def __init__(self, dirname, ext, *, cache):
E           +             15         if not os.path.exists(dirname):
E           +        ---> 16             os.makedirs(dirname)
E           +             17 
E           +             18         self._dirname = Path(dirname)
E           +      item[5]:
E           +        ~/miniconda3/envs/cellrank/lib/python3.6/os.py in makedirs(name, mode, exist_ok)
E           +            208     if head and tail and not path.exists(head):
E           +            209         try:
E           +        --> 210             makedirs(head, mode, exist_ok)
E           +            211         except FileExistsError:
E           +            212             # Defeats race condition when another thread created the path
E           +      item[6]:
E           +        ~/miniconda3/envs/cellrank/lib/python3.6/os.py in makedirs(name, mode, exist_ok)
E           +            218             return
E           +            219     try:
E           +        --> 220         mkdir(name, mode)
E           +            221     except OSError:
E           +            222         # Cannot rely on checking for EEXIST, since the operating system
E           +      item[7]: PermissionError: [Errno 13] Permission denied: '../../cached_files'
E           
E           ## deleted /cells/12/outputs/0:
E           -  output:
E           -    output_type: stream
E           -    name: stdout
E           -    text:
E           -      You don't seem to have scachepy installed, but that's fine, you just have to be a bit patient (~10min). 
E           -      recovering dynamics
E           -          finished (DURATION) --> added 
E           -          'fit_pars', fitted parameters for splicing dynamics (adata.var)
E           
E           ## modified /cells/25/outputs/0/text:
E           @@ -1 +1,3 @@
E           Computing transition matrix based on velocity correlations using mode `'deterministic'`
E           
E               Finish (DURATION)
E           
E           ## inserted before /cells/25/outputs/2:
E           +  output:
E           +    output_type: execute_result
E           +    execution_count: 10
E           +    data:
E           +      text/plain: <Velo>
E           
E           ## deleted /cells/25/outputs/2:
E           -  output:
E           -    output_type: stream
E           -    name: stdout
E           -    text:
E           -      
E           -          Finish (DURATION)
E           
E           ## deleted /cells/25/outputs/4:
E           
E           

tests/test_tutorials.py:27: RuntimeError
============================================= 2 failed, 1 passed in 101.63s (0:01:41)
Marius1311 commented 4 years ago 
======================================================= test session starts ========================================================
platform darwin -- Python 3.6.9, pytest-5.3.2, py-1.8.1, pluggy-0.13.1
rootdir: /Users/marius/Projects/cellrank_notebooks, inifile: tox.ini, testpaths: tests/
plugins: cov-2.9.0, parallel-0.1.0, notebook-0.6.0
collected 3 items                                                                                                                  
pytest-parallel: 2 workers (processes), 1 test per worker (thread)
..FF..
============================================================= FAILURES =============================================================
_______________________________________________________ test_pancreas_basic ________________________________________________________

nb_regression = NBRegressionFixture(exec_notebook=True, exec_cwd='/var/folders/mx/0hyv8t2s26jdj79f55kvc_b80000gn/T', exec_allow_errors...tputs/*/data/application/vnd.jupyter.widget-view+json'), diff_use_color=True, diff_color_words=True, force_regen=False)

    def decorator(nb_regression) -> None:
        fpath = f"{(TUTORIALS / func.__name__[5:]).resolve()}.ipynb"

        nb = nbformat.read(fpath, as_version=4)
        _inject_sentinel(nb)

        with tempfile.NamedTemporaryFile("w", suffix=".ipynb") as tmpf:
            nbformat.write(nb, tmpf)
            result = nb_regression.check(tmpf.name, raise_errors=False)

>       _assert_execute_sentinel(result)

tests/test_tutorials.py:42: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

result = NBRegressionResult(diff_full_length=1,diff_filtered_length=1)

    def _assert_execute_sentinel(result) -> None:
        match = parse(
            f'$.[*].diff[*].diff[*].diff[*].valuelist[?(@.text = "{SENTINEL}\n")].text'
        )
        found = match.find(result.diff_filtered)

        if not found:
>           raise RuntimeError(result.diff_string)
E           RuntimeError: 
E           --- expected
E           +++ obtained
E           ## modified /cells/21/outputs/0/text:
E           @@ -1 +1,17 @@
E           Computing transition matrix based on velocity correlations
E               using mode `'deterministic'`Finish (DURATION)
E           Using a connectivity kernel with weight `0.2`
E           Computing transition matrix based on connectivities
E               Finish (DURATION)
E           Computing eigendecomposition of the transition matrix
E           Computing metastable states
E           INFO: Using pre-computed schur decomposition
E           Adding `.schur`
E                  `.coarse_T`
E                  `.coarse_stationary_distribution`
E               Finish (DURATION)
E           Adding `adata.obs['final_states_probs']`
E                  `adata.obs['final_states']`
E                  `.final_states_probabilities`
E                  `.final_states`
E           
E           
E           ## deleted /cells/21/outputs/3-4:
E           
E           ## modified /cells/26/outputs/0/text:
E           @@ -1 +1,13 @@
E           Computing transition matrix based on velocity correlations
E               using mode `'deterministic'`Finish (DURATION)
E           Computing eigendecomposition of the transition matrix
E           Computing metastable states
E           WARNING: For `n_states=1`, stationary distribution is computed
E           Computing eigendecomposition of the transition matrix
E           Adding `.metastable_states`
E               Finish (DURATION)
E           Adding `adata.obs['root_states_probs']`
E                  `adata.obs['root_states']`
E                  `.final_states_probabilities`
E                  `.final_states`
E           
E           
E           ## modified /cells/26/outputs/1/data/text/plain:
E           -  HBox(children=(FloatProgress(value=0.0, max=2531.0), HTML(value='')))
E           +  <Figure size 600x400 with 1 Axes>
E           
E           ## deleted /cells/26/outputs/4-5:
E           
E           ## modified /cells/30/outputs/1/data/text/plain:
E           -  <Figure size 2400x400 with 8 Axes>
E           +  <Figure size 1800x400 with 6 Axes>
E           
E           ## modified /cells/38/outputs/0/text:
E           @@ -7,9 +7,9 @@ computing Diffusion Maps using n_comps=15(=n_dcs)
E           computing transitions
E               finished (DURATION)
E               eigenvalues of transition matrix
E               [1.         0.99748987 0.9902442  0.981653   0.975423340.9816529  0.9754233  0.96905994
E                0.9629265  0.956972   0.9405951  0.9212147  0.91551214 0.89869016
E                0.8907606  0.8846065  0.87359995]0.940595   0.92121476 0.9155121  0.89869034
E                0.89076036 0.88460636 0.8736001 ]
E               finished (DURATION)
E           computing Diffusion Pseudotime using n_dcs=10
E               finished (DURATION)
E           
E           ## inserted before /cells/40/outputs/0:
E           +  output:
E           +    output_type: error
E           +    ename: AttributeError
E           +    evalue: module 'cellrank.utils.models' has no attribute 'GAM'
E           +    traceback:
E           +      item[0]: ---------------------------------------------------------------------------
E           +      item[1]: AttributeError                            Traceback (most recent call last)
E           +      item[2]:
E           +        <ipython-input-15-1ea02b53e60a> in <module>
E           +        ----> 1 model = cr.ul.models.GAM(adata, grid=None)
E           +              2 cr.pl.gene_trends(adata, model=model, data_key='Ms',
E           +              3                   genes=['Pak3','Neurog3', 'Ghrl'], ncols=3,
E           +              4                   time_key='latent_time', same_plot=True, hide_cells=True,
E           +              5                   figsize=(15, 4), n_test_points=200)
E           +      item[3]: AttributeError: module 'cellrank.utils.models' has no attribute 'GAM'
E           
E           ## deleted /cells/40/outputs/0:
E           -  output:
E           -    output_type: display_data
E           -    data:
E           -      application/vnd.jupyter.widget-view+json:
E           -        model_id: fb8086092d634b058201c1b02c03c746
E           -        version_major: 2
E           -        version_minor: 0
E           -      text/plain: HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))
E           
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tests/test_tutorials.py:27: RuntimeError
------------------------------------------------------- Captured stdout call -------------------------------------------------------
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============================================= 1 failed, 2 passed in 485.10s (0:08:05) ==============================================
michalk8 commented 4 years ago

This is because of AttributeError: module 'cellrank.utils.models' has no attribute 'GAM'. The notebooks are currently out-of-date, ut I'm in the process of changing them.

michalk8 commented 4 years ago

Something tells me that sometimes the notebooks aren't even executed - not even the first cell, because enabling the logging, I don't see info message (I should, as I do locally) - I will try local Travis to see if it's true.

Marius1311 commented 4 years ago

okay.

michalk8 commented 4 years ago

Closed via https://github.com/theislab/cellrank_notebooks/commit/fe2f1f6b0d463d039ae2f3b2c7801cb1408c0d7b.

Marius1311 commented 4 years ago

great, thanks.