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kevinrue / velociraptor

Toolkit for Single-Cell Velocity
https://kevinrue.github.io/velociraptor/
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update scvelo for Windows #80

Closed kevinrue closed 2 weeks ago

kevinrue commented 3 weeks ago

scvelo==0.3.2 not available due to dependency on jaxlib>=0.4.3

PS C:\Users\kevin> micromamba create -c conda-forge -c bioconda -n scvelo scvelo==0.3.2
bioconda/win-64 (check zst)                         Checked  0.3s
bioconda/noarch (check zst)                         Checked  0.3s
bioconda/win-64                                    122.0 B @ 805.0 B/s  0.2s
bioconda/noarch                                      5.3MB @   2.0MB/s  2.6s
conda-forge/noarch                                  16.1MB @   4.4MB/s  3.7s
conda-forge/win-64                                  23.9MB @   4.7MB/s  5.1s
error    libmamba Could not solve for environment specs
    The following package could not be installed
    └─ scvelo 0.3.2  is not installable because it requires
       └─ scvi-tools >=0.20.1  but there are no viable options
          ├─ scvi-tools [0.20.1|0.20.2|0.20.3] would require
          │  └─ jaxlib >=0.3.0 , which does not exist (perhaps a missing channel);
          └─ scvi-tools [0.20.3|1.0.0|...|1.1.6] would require
             └─ jaxlib >=0.4.3 , which does not exist (perhaps a missing channel).
critical libmamba Could not solve for environment specs

scvelo==0.3.1 not available due to dependency on pytorch >=1.8.0

PS C:\Users\kevin> micromamba create -c conda-forge -c bioconda -n scvelo scvelo==0.3.1
conda-forge/win-64                                          Using cache
conda-forge/noarch                                          Using cache
bioconda/win-64                                             Using cache
bioconda/noarch                                             Using cache
error    libmamba Could not solve for environment specs
    The following package could not be installed
    └─ scvelo 0.3.1  is not installable because it requires
       └─ scvi-tools >=0.20.1 , which requires
          └─ pytorch >=1.8.0 , which does not exist (perhaps a missing channel).
critical libmamba Could not solve for environment specs

0.3.0 same

PS C:\Users\kevin> micromamba create -c conda-forge -c bioconda -n scvelo scvelo==0.3.0
conda-forge/win-64                                          Using cache
conda-forge/noarch                                          Using cache
bioconda/win-64                                             Using cache
bioconda/noarch                                             Using cache
error    libmamba Could not solve for environment specs
    The following package could not be installed
    └─ scvelo 0.3.0  is not installable because it requires
       └─ scvi-tools >=0.20.1 , which requires
          └─ pytorch >=1.8.0 , which does not exist (perhaps a missing channel).
critical libmamba Could not solve for environment specs
kevinrue commented 3 weeks ago 
PS C:\Users\kevin> micromamba search -c conda-forge -c bioconda scvelo
Getting repodata from channels...

conda-forge/win-64                                          Using cache
conda-forge/noarch                                          Using cache
bioconda/win-64                                             Using cache
bioconda/noarch                                             Using cache

       scvelo 0.3.2 pyhd8ed1ab_1
────────────────────────────────────────

 Name            scvelo
 Version         0.3.2
 Build           pyhd8ed1ab_1
 Size            154 kB
 License         BSD-3-Clause
 Subdir          noarch
 File Name       scvelo-0.3.2-pyhd8ed1ab_1.conda
 URL             https://conda.anaconda.org/conda-forge/noarch/scvelo-0.3.2-pyhd8ed1ab_1.conda
 MD5             3278754ad0ec23639df5d7751d2b885c
 SHA256          6b1ca684a3a2a9734c736e675ef04cd65b44bc20011ddaf852d118141a0a4f1b

 Dependencies:
  - python >=3.8
  - numpy >=1.17
  - scipy >=1.4.1
  - matplotlib-base >=3.3.0
  - anndata >=0.7.5
  - scvi-tools >=0.20.1
  - umap-learn >=0.3.10
  - numba >=0.41.0
  - loompy >=2.0.12
  - pandas >=1.1.1,!=1.4.0
  - scanpy >=1.5
  - scikit-learn >=0.21.2,<1.2.0

 Other Versions (9):

  Version Build
 ------------------------------------------
  0.3.1   pyhd8ed1ab_0        (+ 1 builds)
  0.3.0   pyhd8ed1ab_0
  ...     (5 hidden versions)          ...
  0.1.25  py_0
  0.1.24  py_0
kevinrue commented 3 weeks ago 
PS C:\Users\kevin> micromamba search -c bioconda  -c conda-forge --pretty --json scvelo
{
    "query": {
        "query": "scvelo",
        "type": "search"
    },
    "result": {
        "msg": "",
        "pkgs": [
            {
                "build": "pyhd8ed1ab_1",
                "build_number": 1,
                "build_string": "pyhd8ed1ab_1",
                "channel": "conda-forge",
                "constrains": [],
                "depends": [
                    "python >=3.8",
                    "numpy >=1.17",
                    "scipy >=1.4.1",
                    "matplotlib-base >=3.3.0",
                    "anndata >=0.7.5",
                    "scvi-tools >=0.20.1",
                    "umap-learn >=0.3.10",
                    "numba >=0.41.0",
                    "loompy >=2.0.12",
                    "pandas >=1.1.1,!=1.4.0",
                    "scanpy >=1.5",
                    "scikit-learn >=0.21.2,<1.2.0"
                ],
                "fn": "scvelo-0.3.2-pyhd8ed1ab_1.conda",
                "license": "BSD-3-Clause",
                "md5": "3278754ad0ec23639df5d7751d2b885c",
                "name": "scvelo",
                "sha256": "6b1ca684a3a2a9734c736e675ef04cd65b44bc20011ddaf852d118141a0a4f1b",
                "size": 154636,
                "subdir": "noarch",
                "timestamp": 1710771370,
                "track_features": "",
                "url": "https://conda.anaconda.org/conda-forge/noarch/scvelo-0.3.2-pyhd8ed1ab_1.conda",
                "version": "0.3.2"
            },
            {
                "build": "pyhd8ed1ab_0",
                "build_number": 0,
                "build_string": "pyhd8ed1ab_0",
                "channel": "conda-forge",
                "constrains": [],
                "depends": [
                    "python >=3.8",
                    "numpy >=1.17",
                    "scipy >=1.4.1",
                    "matplotlib-base >=3.3.0",
                    "anndata >=0.7.5",
                    "scvi-tools >=0.20.1",
                    "umap-learn >=0.3.10",
                    "numba >=0.41.0",
                    "loompy >=2.0.12",
                    "pandas >=1.1.1,!=1.4.0",
                    "scanpy >=1.5",
                    "scikit-learn >=0.21.2,<1.2.0",
                    "chex <=0.1.8"
                ],
                "fn": "scvelo-0.3.1-pyhd8ed1ab_0.conda",
                "license": "BSD-3-Clause",
                "md5": "ec4f74d6f9acb6d6f725146825a92603",
                "name": "scvelo",
                "sha256": "ec7edefda9fa51629da5eec43f339bade7447c7b4c70cc923112a0e2cb4f3226",
                "size": 154583,
                "subdir": "noarch",
                "timestamp": 1701626077,
                "track_features": "",
                "url": "https://conda.anaconda.org/conda-forge/noarch/scvelo-0.3.1-pyhd8ed1ab_0.conda",
                "version": "0.3.1"
            },
            {
                "build": "pyhd8ed1ab_1",
                "build_number": 1,
                "build_string": "pyhd8ed1ab_1",
                "channel": "conda-forge",
                "constrains": [],
                "depends": [
                    "python >=3.8",
                    "numpy >=1.17",
                    "scipy >=1.4.1",
                    "matplotlib-base >=3.3.0",
                    "anndata >=0.7.5",
                    "scvi-tools >=0.20.1",
                    "umap-learn >=0.3.10",
                    "numba >=0.41.0",
                    "loompy >=2.0.12",
                    "pandas >=1.1.1,!=1.4.0",
                    "scanpy >=1.5",
                    "scikit-learn >=0.21.2,<1.2.0"
                ],
                "fn": "scvelo-0.3.1-pyhd8ed1ab_1.conda",
                "license": "BSD-3-Clause",
                "md5": "159fd94be5c5a7653adce52a7c3c273b",
                "name": "scvelo",
                "sha256": "88719090d99ff3c8e672be6ed6a151a2500ee09c25869ae3d5677617abb4598c",
                "size": 154498,
                "subdir": "noarch",
                "timestamp": 1710768324,
                "track_features": "",
                "url": "https://conda.anaconda.org/conda-forge/noarch/scvelo-0.3.1-pyhd8ed1ab_1.conda",
                "version": "0.3.1"
            },
            {
                "build": "pyhd8ed1ab_0",
                "build_number": 0,
                "build_string": "pyhd8ed1ab_0",
                "channel": "conda-forge",
                "constrains": [],
                "depends": [
                    "python >=3.8",
                    "numpy >=1.17",
                    "scipy >=1.4.1",
                    "matplotlib-base >=3.3.0",
                    "anndata >=0.7.5",
                    "scvi-tools >=0.20.1",
                    "umap-learn >=0.3.10",
                    "numba >=0.41.0",
                    "loompy >=2.0.12",
                    "pandas >=1.1.1,!=1.4.0",
                    "scanpy >=1.5",
                    "scikit-learn >=0.21.2,<1.2.0",
                    "chex <=0.1.8"
                ],
                "fn": "scvelo-0.3.0-pyhd8ed1ab_0.conda",
                "license": "BSD-3-Clause",
                "md5": "71cd06a6a9ab2094b764e8fb381e5949",
                "name": "scvelo",
                "sha256": "ea75000ade4bf211290f60ad9b5110b42561f907d0fb2277b66cc1f1f16574a3",
                "size": 154915,
                "subdir": "noarch",
                "timestamp": 1701599909,
                "track_features": "",
                "url": "https://conda.anaconda.org/conda-forge/noarch/scvelo-0.3.0-pyhd8ed1ab_0.conda",
                "version": "0.3.0"
            },
            {
                "build": "pyhdfd78af_0",
                "build_number": 0,
                "build_string": "pyhdfd78af_0",
                "channel": "bioconda",
                "constrains": [],
                "depends": [
                    "typing_extensions",
                    "python >=3.6",
                    "scipy >=1.4.1",
                    "scikit-learn >=0.21.2",
                    "numpy >=1.17",
                    "matplotlib-base >=3.1.2",
                    "pandas >=0.23",
                    "umap-learn >=0.3.10",
                    "loompy >=2.0.12",
                    "scanpy >=1.5.0",
                    "anndata >=0.7.0"
                ],
                "fn": "scvelo-0.2.5-pyhdfd78af_0.tar.bz2",
                "license": "BSD",
                "md5": "62db1732a293d41dcc30512c0f25c0c9",
                "name": "scvelo",
                "sha256": "5f1d2dbdc9ae07e1521f5df74d19f2b3e42bd095416bbc7a2ad666b2166fd9d6",
                "size": 162719,
                "subdir": "noarch",
                "timestamp": 1668256668,
                "track_features": "",
                "url": "https://conda.anaconda.org/bioconda/noarch/scvelo-0.2.5-pyhdfd78af_0.tar.bz2",
                "version": "0.2.5"
            },
            {
                "build": "pyhd8ed1ab_0",
                "build_number": 0,
                "build_string": "pyhd8ed1ab_0",
                "channel": "conda-forge",
                "constrains": [],
                "depends": [
                    "python >=3.8",
                    "numpy >=1.17",
                    "scipy >=1.4.1",
                    "matplotlib-base >=3.3.0",
                    "scikit-learn >=0.21.2",
                    "anndata >=0.7.5",
                    "scvi-tools >=0.20.1",
                    "umap-learn >=0.3.10",
                    "numba >=0.41.0",
                    "loompy >=2.0.12",
                    "pandas >=1.1.1,!=1.4.0",
                    "scanpy >=1.5"
                ],
                "fn": "scvelo-0.2.5-pyhd8ed1ab_0.conda",
                "license": "BSD-3-Clause",
                "md5": "2c017fb2d3adec3c75a72e1208e657e6",
                "name": "scvelo",
                "sha256": "129d8b4c548ab0fc9a1755d1346dddec9d65b133d020f440190c1071033f1b3f",
                "size": 163065,
                "subdir": "noarch",
                "timestamp": 1687905294,
                "track_features": "",
                "url": "https://conda.anaconda.org/conda-forge/noarch/scvelo-0.2.5-pyhd8ed1ab_0.conda",
                "version": "0.2.5"
            },
            {
                "build": "pyhdfd78af_0",
                "build_number": 0,
                "build_string": "pyhdfd78af_0",
                "channel": "bioconda",
                "constrains": [],
                "depends": [
                    "typing_extensions",
                    "python >=3.6",
                    "scipy >=1.4.1",
                    "scikit-learn >=0.21.2",
                    "numpy >=1.17",
                    "matplotlib-base >=3.1.2",
                    "pandas >=0.23",
                    "umap-learn >=0.3.10",
                    "loompy >=2.0.12",
                    "scanpy >=1.5.0",
                    "anndata >=0.7.0"
                ],
                "fn": "scvelo-0.2.4-pyhdfd78af_0.tar.bz2",
                "license": "BSD",
                "md5": "87a426fa6b96f72e7f1d663b8ad6ef42",
                "name": "scvelo",
                "sha256": "82b6b5a1daa7337d3f3fee60fd73eee1e6c8927efb0b95cd96e486877b6e71bb",
                "size": 142405,
                "subdir": "noarch",
                "timestamp": 1629993199,
                "track_features": "",
                "url": "https://conda.anaconda.org/bioconda/noarch/scvelo-0.2.4-pyhdfd78af_0.tar.bz2",
                "version": "0.2.4"
            },
            {
                "build": "py_0",
                "build_number": 0,
                "build_string": "py_0",
                "channel": "bioconda",
                "constrains": [],
                "depends": [
                    "python >=3.6",
                    "scipy >=1.4.1",
                    "scikit-learn >=0.21.2",
                    "numpy >=1.17",
                    "matplotlib-base >=3.1.2",
                    "pandas >=0.23",
                    "umap-learn >=0.3.10",
                    "loompy >=2.0.12",
                    "scanpy >=1.5.0",
                    "anndata >=0.7.0"
                ],
                "fn": "scvelo-0.2.3-py_0.tar.bz2",
                "license": "BSD",
                "md5": "0140e880b4263f3300860f37606fc651",
                "name": "scvelo",
                "sha256": "1c05eeb5800d717a56374581c81c7731f4d8e57773d5d3ccd97f679f04ade238",
                "size": 131620,
                "subdir": "noarch",
                "timestamp": 1613161090,
                "track_features": "",
                "url": "https://conda.anaconda.org/bioconda/noarch/scvelo-0.2.3-py_0.tar.bz2",
                "version": "0.2.3"
            },
            {
                "build": "py_0",
                "build_number": 0,
                "build_string": "py_0",
                "channel": "bioconda",
                "constrains": [],
                "depends": [
                    "python >=3.6",
                    "scikit-learn >=0.21.2",
                    "numpy >=1.17",
                    "pandas >=0.23",
                    "scipy >=1.0",
                    "scanpy >=1.4",
                    "matplotlib-base >=2.2",
                    "loompy >=2.0.12",
                    "anndata >=0.6.18",
                    "umap-learn >=0.3"
                ],
                "fn": "scvelo-0.2.2-py_0.tar.bz2",
                "license": "BSD",
                "md5": "fab35b6ae67c234c9c71e16231912d51",
                "name": "scvelo",
                "sha256": "22941f52979ded1ca189fe53441f14df38f02a58b0a031edfe86f1db5758eb14",
                "size": 148437,
                "subdir": "noarch",
                "timestamp": 1595431214,
                "track_features": "",
                "url": "https://conda.anaconda.org/bioconda/noarch/scvelo-0.2.2-py_0.tar.bz2",
                "version": "0.2.2"
            },
            {
                "build": "py_1",
                "build_number": 1,
                "build_string": "py_1",
                "channel": "bioconda",
                "constrains": [],
                "depends": [
                    "python >=3.6",
                    "scipy >=1.4.1",
                    "scikit-learn >=0.21.2",
                    "numpy >=1.17",
                    "matplotlib-base >=3.1.2",
                    "pandas >=0.23",
                    "umap-learn >=0.3.10",
                    "loompy >=2.0.12",
                    "scanpy >=1.5.0",
                    "anndata >=0.7.0"
                ],
                "fn": "scvelo-0.2.2-py_1.tar.bz2",
                "license": "BSD",
                "md5": "519a192d15206e3aab84a6a8e704a8f9",
                "name": "scvelo",
                "sha256": "fba2fabeb37c055f87bfbbc241be7f7633116f5c3bcc1ba44fed64cb3b239ab0",
                "size": 148538,
                "subdir": "noarch",
                "timestamp": 1600339831,
                "track_features": "",
                "url": "https://conda.anaconda.org/bioconda/noarch/scvelo-0.2.2-py_1.tar.bz2",
                "version": "0.2.2"
            },
            {
                "build": "py_0",
                "build_number": 0,
                "build_string": "py_0",
                "channel": "bioconda",
                "constrains": [],
                "depends": [
                    "python >=3.6",
                    "scikit-learn >=0.21.2",
                    "numpy >=1.17",
                    "pandas >=0.23",
                    "scipy >=1.0",
                    "scanpy >=1.4",
                    "matplotlib-base >=2.2",
                    "loompy >=2.0.12",
                    "anndata >=0.6.18",
                    "umap-learn >=0.3"
                ],
                "fn": "scvelo-0.2.1-py_0.tar.bz2",
                "license": "BSD",
                "md5": "7a1fc0dd3ca1480ddb5af23f1852cb1a",
                "name": "scvelo",
                "sha256": "624b5ede408a3e5dde8463ec4e03e580504936fc8e68dc7fff47f1e4aff53074",
                "size": 142765,
                "subdir": "noarch",
                "timestamp": 1590941218,
                "track_features": "",
                "url": "https://conda.anaconda.org/bioconda/noarch/scvelo-0.2.1-py_0.tar.bz2",
                "version": "0.2.1"
            },
            {
                "build": "py_0",
                "build_number": 0,
                "build_string": "py_0",
                "channel": "bioconda",
                "constrains": [],
                "depends": [
                    "python >=3.6",
                    "scikit-learn >=0.21.2",
                    "numpy >=1.17",
                    "pandas >=0.23",
                    "scipy >=1.0",
                    "matplotlib >=2.2",
                    "scanpy >=1.4",
                    "loompy >=2.0.12",
                    "anndata >=0.6.18",
                    "umap-learn >=0.3"
                ],
                "fn": "scvelo-0.1.25-py_0.tar.bz2",
                "license": "BSD",
                "md5": "81d56e00882e9319025e8bf961743230",
                "name": "scvelo",
                "sha256": "d33115421226345945e404448b8fbc2887f47702b911311e97b1717f50fef924",
                "size": 116826,
                "subdir": "noarch",
                "timestamp": 1579857218,
                "track_features": "",
                "url": "https://conda.anaconda.org/bioconda/noarch/scvelo-0.1.25-py_0.tar.bz2",
                "version": "0.1.25"
            },
            {
                "build": "py_0",
                "build_number": 0,
                "build_string": "py_0",
                "channel": "bioconda",
                "constrains": [],
                "depends": [
                    "python >=3.6",
                    "scikit-learn >=0.21.2",
                    "numpy >=1.17",
                    "pandas >=0.23",
                    "scipy >=1.0",
                    "matplotlib >=2.2",
                    "scanpy >=1.4",
                    "loompy >=2.0.12",
                    "anndata >=0.6.18",
                    "umap-learn >=0.3"
                ],
                "fn": "scvelo-0.1.24-py_0.tar.bz2",
                "license": "BSD",
                "md5": "f094f57b510cc02e5c043c6cb208475e",
                "name": "scvelo",
                "sha256": "c0a4f855c4f2ebcd34becc57322852b6e3609c0824c628293a54ab8a8ee8fde0",
                "size": 105738,
                "subdir": "noarch",
                "timestamp": 1576221408,
                "track_features": "",
                "url": "https://conda.anaconda.org/bioconda/noarch/scvelo-0.1.24-py_0.tar.bz2",
                "version": "0.1.24"
            }
        ],
        "status": "OK"
    }
}
kevinrue commented 3 weeks ago

0.2.5 is resolved in whichever order bioconda and conda-forge channels are placed

However, conda-forge first gets everything but one package from conda-forge, which seems a bit better than 4-5 packages from bioconda if that one is first.

PS C:\Users\kevin> micromamba create -c conda-forge -c bioconda -n scvelo scvelo==0.2.5
conda-forge/win-64                                          Using cache
conda-forge/noarch                                          Using cache
bioconda/win-64                                             Using cache
bioconda/noarch                                             Using cache

Transaction

  Prefix: C:\Users\kevin\micromamba\envs\scvelo

  Updating specs:

   - scvelo==0.2.5

  Package                               Version  Build                    Channel           Size
--------------------------------------------------------------------------------------------------
  Install:
--------------------------------------------------------------------------------------------------

  + libexpat                              2.6.2  h63175ca_0               conda-forge     Cached
  + python_abi                             3.12  5_cp312                  conda-forge     Cached
  + ucrt                           10.0.22621.0  h57928b3_0               conda-forge     Cached
  + ca-certificates                    2024.7.4  h56e8100_0               conda-forge     Cached
  + intel-openmp                       2024.2.1  h57928b3_1083            conda-forge        2MB
  + msys2-conda-epoch                  20160418  1                        conda-forge     Cached
  + vc14_runtime                    14.40.33810  ha82c5b3_20              conda-forge     Cached
  + m2w64-libwinpthread-git  5.0.0.4634.697f757  2                        conda-forge     Cached
  + m2w64-gmp                             6.1.0  2                        conda-forge     Cached
  + vc                                     14.3  h8a93ad2_20              conda-forge     Cached
  + vs2015_runtime                  14.40.33810  h3bf8584_20              conda-forge     Cached
  + m2w64-gcc-libs-core                   5.3.0  7                        conda-forge     Cached
  + libiconv                               1.17  hcfcfb64_2               conda-forge     Cached
  + libbrotlicommon                       1.1.0  hcfcfb64_1               conda-forge     Cached
  + libaec                                1.1.3  h63175ca_0               conda-forge     Cached
  + libdeflate                             1.21  h2466b09_0               conda-forge     Cached
  + libjpeg-turbo                         3.0.0  hcfcfb64_1               conda-forge     Cached
  + pthreads-win32                        2.9.1  hfa6e2cd_3               conda-forge     Cached
  + qhull                                2020.2  hc790b64_5               conda-forge     Cached
  + libwebp-base                          1.4.0  hcfcfb64_0               conda-forge     Cached
  + tk                                   8.6.13  h5226925_1               conda-forge     Cached
  + openssl                               3.3.1  h2466b09_2               conda-forge     Cached
  + libzlib                               1.3.1  h2466b09_1               conda-forge     Cached
  + bzip2                                 1.0.8  h2466b09_7               conda-forge     Cached
  + libsqlite                            3.46.0  h2466b09_0               conda-forge     Cached
  + lerc                                  4.0.0  h63175ca_0               conda-forge     Cached
  + libffi                                3.4.2  h8ffe710_5               conda-forge     Cached
  + xz                                    5.2.6  h8d14728_0               conda-forge     Cached
  + m2w64-gcc-libgfortran                 5.3.0  6                        conda-forge     Cached
  + libbrotlienc                          1.1.0  hcfcfb64_1               conda-forge     Cached
  + libbrotlidec                          1.1.0  hcfcfb64_1               conda-forge     Cached
  + krb5                                 1.21.3  hdf4eb48_0               conda-forge     Cached
  + libssh2                              1.11.0  h7dfc565_0               conda-forge     Cached
  + zstd                                  1.5.6  h0ea2cb4_0               conda-forge     Cached
  + libxml2                              2.12.7  h0f24e4e_4               conda-forge     Cached
  + libpng                               1.6.43  h19919ed_0               conda-forge     Cached
  + m2w64-gcc-libs                        5.3.0  7                        conda-forge     Cached
  + brotli-bin                            1.1.0  hcfcfb64_1               conda-forge     Cached
  + libcurl                               8.9.1  h18fefc2_0               conda-forge     Cached
  + libtiff                               4.6.0  hb151862_4               conda-forge     Cached
  + libhwloc                             2.11.1  default_h8125262_1000    conda-forge     Cached
  + freetype                             2.12.1  hdaf720e_2               conda-forge     Cached
  + xorg-libxdmcp                         1.1.3  hcd874cb_0               conda-forge     Cached
  + pthread-stubs                           0.4  hcd874cb_1001            conda-forge     Cached
  + xorg-libxau                          1.0.11  hcd874cb_0               conda-forge     Cached
  + brotli                                1.1.0  hcfcfb64_1               conda-forge     Cached
  + hdf5                                 1.14.3  nompi_h2b43c12_105       conda-forge     Cached
  + openjpeg                              2.5.2  h3d672ee_0               conda-forge     Cached
  + lcms2                                  2.16  h67d730c_0               conda-forge     Cached
  + tbb                               2021.12.0  hc790b64_3               conda-forge     Cached
  + libxcb                                 1.16  hcd874cb_0               conda-forge     Cached
  + mkl                                2024.1.0  h66d3029_694             conda-forge      109MB
  + libblas                               3.9.0  23_win64_mkl             conda-forge        5MB
  + libcblas                              3.9.0  23_win64_mkl             conda-forge        5MB
  + liblapack                             3.9.0  23_win64_mkl             conda-forge        5MB
  + tzdata                                2024a  h0c530f3_0               conda-forge     Cached
  + python                               3.12.5  h889d299_0_cpython       conda-forge     Cached
  + wheel                                0.44.0  pyhd8ed1ab_0             conda-forge     Cached
  + setuptools                           72.2.0  pyhd8ed1ab_0             conda-forge     Cached
  + pip                                    24.2  pyhd8ed1ab_0             conda-forge     Cached
  + cached_property                       1.5.2  pyha770c72_1             conda-forge     Cached
  + colorama                              0.4.6  pyhd8ed1ab_0             conda-forge     Cached
  + munkres                               1.1.4  pyh9f0ad1d_0             conda-forge       12kB
  + pyparsing                             3.1.2  pyhd8ed1ab_0             conda-forge     Cached
  + cycler                               0.12.1  pyhd8ed1ab_0             conda-forge     Cached
  + certifi                            2024.7.4  pyhd8ed1ab_0             conda-forge     Cached
  + pytz                                 2024.1  pyhd8ed1ab_0             conda-forge     Cached
  + python-tzdata                        2024.1  pyhd8ed1ab_0             conda-forge     Cached
  + threadpoolctl                         3.5.0  pyhc1e730c_0             conda-forge     Cached
  + stdlib-list                          0.10.0  pyhd8ed1ab_0             conda-forge     Cached
  + array-api-compat                        1.8  pyhd8ed1ab_0             conda-forge     Cached
  + exceptiongroup                        1.2.2  pyhd8ed1ab_0             conda-forge     Cached
  + six                                  1.16.0  pyh6c4a22f_0             conda-forge     Cached
  + legacy-api-wrap                         1.4  pyhd8ed1ab_1             conda-forge     Cached
  + packaging                              24.1  pyhd8ed1ab_0             conda-forge     Cached
  + networkx                                3.3  pyhd8ed1ab_1             conda-forge     Cached
  + natsort                               8.4.0  pyhd8ed1ab_0             conda-forge     Cached
  + joblib                                1.4.2  pyhd8ed1ab_0             conda-forge     Cached
  + get-annotations                       0.1.2  pyhd8ed1ab_0             conda-forge       10kB
  + typing_extensions                    4.12.2  pyha770c72_0             conda-forge     Cached
  + cached-property                       1.5.2  hd8ed1ab_1               conda-forge     Cached
  + click                                 8.1.7  win_pyh7428d3b_0         conda-forge       85kB
  + tqdm                                 4.66.5  pyhd8ed1ab_0             conda-forge     Cached
  + session-info                          1.0.0  pyhd8ed1ab_0             conda-forge       12kB
  + python-dateutil                       2.9.0  pyhd8ed1ab_0             conda-forge     Cached
  + pillow                               10.4.0  py312h381445a_0          conda-forge     Cached
  + numpy                                1.26.4  py312h8753938_0          conda-forge        6MB
  + llvmlite                             0.43.0  py312h1f7db74_0          conda-forge     Cached
  + kiwisolver                            1.4.5  py312h0d7def4_1          conda-forge     Cached
  + fonttools                            4.53.1  py312h4389bb4_0          conda-forge     Cached
  + contourpy                             1.2.1  py312h0d7def4_0          conda-forge     Cached
  + pandas                                2.2.2  py312h72972c8_1          conda-forge     Cached
  + h5py                                 3.11.0  nompi_py312ha036244_102  conda-forge     Cached
  + scipy                                1.14.1  py312h1f4e10d_0          conda-forge     Cached
  + numba                                0.60.0  py312hcccf92d_0          conda-forge     Cached
  + matplotlib-base                       3.9.2  py312h90004f6_0          conda-forge     Cached
  + scikit-learn                          1.5.1  py312h816cc57_0          conda-forge     Cached
  + numpy_groupies                       0.11.2  pyhd8ed1ab_0             conda-forge       37kB
  + patsy                                 0.5.6  pyhd8ed1ab_0             conda-forge     Cached
  + anndata                              0.10.8  pyhd8ed1ab_0             conda-forge     Cached
  + seaborn-base                         0.13.2  pyhd8ed1ab_2             conda-forge     Cached
  + pynndescent                          0.5.13  pyhff2d567_0             conda-forge     Cached
  + loompy                                3.0.6  py_0                     conda-forge       41kB
  + statsmodels                          0.14.2  py312h1a27103_0          conda-forge     Cached
  + umap-learn                            0.5.6  py312h2e8e312_1          conda-forge     Cached
  + seaborn                              0.13.2  hd8ed1ab_2               conda-forge     Cached
  + scanpy                               1.10.2  pyhd8ed1ab_0             conda-forge        2MB
  + scvelo                                0.2.5  pyhdfd78af_0             bioconda        Cached

  Summary:

  Install: 108 packages

  Total download: 135MB

--------------------------------------------------------------------------------------------------

Confirm changes: [Y/n]
kevinrue commented 3 weeks ago

Sad face

The latest environment above leads to the error

Downloading and Extracting Packages: ...working... done
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
Error in py_module_import(module, convert = convert) : 
  AttributeError: module 'matplotlib.cbook' has no attribute 'mplDeprecation'
Run `reticulate::py_last_error()` for details.
Error in .activate_fallback(proc, testload, env = env, envpath = envpath,  : 
  AttributeError: module 'matplotlib.cbook' has no attribute 'mplDeprecation'
Run `reticulate::py_last_error()` for details.

Which seems to occur during the installation of the environment, before scvelo is even run.

Fix seems to be matplotlib <= 3.7.3

https://stackoverflow.com/questions/77128061/ydata-profiling-profilereport-attributeerror-module-matplotlib-cbook-has-no

No luck:

(scvelo) PS C:\Users\kevin> micromamba install -c conda-forge -c bioconda  -n scvelo scvelo==0.2.5 matplotlib==3.7.3
conda-forge/win-64                                          Using cache
conda-forge/noarch                                          Using cache
bioconda/win-64                                             Using cache
bioconda/noarch                                             Using cache

Pinned packages:
  - python 3.12.*

error    libmamba Could not solve for environment specs
    The following packages are incompatible
    ├─ matplotlib 3.7.3  is installable with the potential options
    │  ├─ matplotlib 3.7.3 would require
    │  │  └─ python >=3.10,<3.11.0a0 , which can be installed;
    │  ├─ matplotlib 3.7.3 would require
    │  │  └─ python >=3.11,<3.12.0a0 , which can be installed;
    │  ├─ matplotlib 3.7.3 would require
    │  │  └─ python >=3.8,<3.9.0a0 , which can be installed;
    │  └─ matplotlib 3.7.3 would require
    │     └─ python >=3.9,<3.10.0a0 , which can be installed;
    └─ pin-1 is not installable because it requires
       └─ python 3.12.* , which conflicts with any installable versions previously reported.
critical libmamba Could not solve for environment specs

This seems to help: https://github.com/theislab/scvelo/issues/1124#issuecomment-1802261666

Namely:

(scvelo) PS C:\Users\kevin> micromamba install -c conda-forge -c bioconda  -n scvelo scvelo==0.2.5 matplotlib==3.7.2 python==3.8
conda-forge/win-64                                          Using cache
conda-forge/noarch                                          Using cache
bioconda/win-64                                             Using cache
bioconda/noarch                                             Using cache

Transaction

  Prefix: C:\Users\kevin\micromamba\envs\scvelo

  Updating specs:

   - scvelo==0.2.5
   - matplotlib==3.7.2
   - python==3.8

  Package                 Version  Build                    Channel           Size
------------------------------------------------------------------------------------
  Install:
------------------------------------------------------------------------------------

  + icu                      70.1  h0e60522_0               conda-forge       18MB
  + sqlite                 3.46.0  h2466b09_0               conda-forge      886kB
  + jpeg                       9e  h8ffe710_2               conda-forge      375kB
  + libintl                0.22.5  h5728263_3               conda-forge       96kB
  + libasprintf            0.22.5  h5728263_3               conda-forge       50kB
  + libogg                  1.3.5  h2466b09_0               conda-forge       35kB
  + libclang13             15.0.7  default_hf64faad_5       conda-forge       22MB
  + pcre2                   10.43  h17e33f8_0               conda-forge      818kB
  + zlib                   1.2.13  h2466b09_6               conda-forge      108kB
  + libgettextpo           0.22.5  h5728263_3               conda-forge      171kB
  + gettext-tools          0.22.5  h5a7288d_3               conda-forge        3MB
  + libintl-devel          0.22.5  h5728263_3               conda-forge       41kB
  + libasprintf-devel      0.22.5  h5728263_3               conda-forge       36kB
  + libvorbis               1.3.7  h0e60522_0               conda-forge      274kB
  + libglib                2.80.2  h0df6a38_0               conda-forge        4MB
  + libclang               15.0.7  default_h3a3e6c3_5       conda-forge      148kB
  + libgettextpo-devel     0.22.5  h5728263_3               conda-forge       40kB
  + glib-tools             2.80.2  h2f9d560_0               conda-forge       95kB
  + gettext                0.22.5  h5728263_3               conda-forge       34kB
  + hyperframe              6.0.1  pyhd8ed1ab_0             conda-forge       15kB
  + hpack                   4.0.0  pyh9f0ad1d_0             conda-forge       25kB
  + pycparser                2.22  pyhd8ed1ab_0             conda-forge      105kB
  + win_inet_pton           1.1.0  pyhd8ed1ab_6             conda-forge        8kB
  + charset-normalizer      3.3.2  pyhd8ed1ab_0             conda-forge       47kB
  + idna                      3.7  pyhd8ed1ab_0             conda-forge       53kB
  + tomli                   2.0.1  pyhd8ed1ab_0             conda-forge     Cached
  + ply                      3.11  pyhd8ed1ab_2             conda-forge       49kB
  + zipp                   3.20.0  pyhd8ed1ab_0             conda-forge     Cached
  + platformdirs            4.2.2  pyhd8ed1ab_0             conda-forge       21kB
  + olefile                  0.47  pyhd8ed1ab_0             conda-forge       39kB
  + toml                   0.10.2  pyhd8ed1ab_0             conda-forge       18kB
  + h2                      4.1.0  pyhd8ed1ab_0             conda-forge       47kB
  + pysocks                 1.7.1  pyh0701188_6             conda-forge       19kB
  + importlib_resources     6.4.4  pyhd8ed1ab_0             conda-forge       32kB
  + importlib-metadata      8.4.0  pyha770c72_0             conda-forge     Cached
  + importlib-resources     6.4.4  pyhd8ed1ab_0             conda-forge        9kB
  + glib                   2.80.2  h0df6a38_0               conda-forge      571kB
  + gstreamer              1.21.3  h6b5321d_1               conda-forge        2MB
  + cffi                   1.17.0  py38h4cb3324_0           conda-forge      236kB
  + brotli-python           1.1.0  py38hd3f51b4_1           conda-forge      322kB
  + unicodedata2           15.1.0  py38h91455d4_0           conda-forge      371kB
  + tornado                 6.4.1  py38h4cb3324_0           conda-forge      645kB
  + sip                    6.7.12  py38hd3f51b4_0           conda-forge      501kB
  + gst-plugins-base       1.21.3  h001b923_1               conda-forge        2MB
  + zstandard              0.23.0  py38hf92978b_0           conda-forge      311kB
  + pyqt5-sip             12.11.0  py38hd3f51b4_3           conda-forge       79kB
  + qt-main                5.15.6  h068e40c_6               conda-forge       62MB
  + pyqt                   5.15.7  py38hd6c051e_3           conda-forge        4MB
  + matplotlib              3.7.2  py38haa244fe_0           conda-forge        9kB
  + urllib3                 2.2.2  pyhd8ed1ab_1             conda-forge       95kB
  + requests               2.32.3  pyhd8ed1ab_0             conda-forge       59kB
  + pooch                   1.8.2  pyhd8ed1ab_0             conda-forge       54kB

  Change:
------------------------------------------------------------------------------------

  - libxml2                2.12.7  h0f24e4e_4               conda-forge     Cached
  + libxml2                2.12.7  h283a6d9_1               conda-forge        2MB
  - kiwisolver              1.4.5  py312h0d7def4_1          conda-forge     Cached
  + kiwisolver              1.4.5  py38hb1fd069_1           conda-forge       56kB
  - fonttools              4.53.1  py312h4389bb4_0          conda-forge     Cached
  + fonttools              4.53.1  py38h4cb3324_0           conda-forge        2MB
  - umap-learn              0.5.6  py312h2e8e312_1          conda-forge     Cached
  + umap-learn              0.5.6  py38haa244fe_1           conda-forge      138kB

  Reinstall:
------------------------------------------------------------------------------------

  o wheel                  0.44.0  pyhd8ed1ab_0             conda-forge     Cached
  o setuptools             72.2.0  pyhd8ed1ab_0             conda-forge     Cached
  o pip                      24.2  pyhd8ed1ab_0             conda-forge     Cached
  o typing_extensions      4.12.2  pyha770c72_0             conda-forge     Cached
  o threadpoolctl           3.5.0  pyhc1e730c_0             conda-forge     Cached
  o stdlib-list            0.10.0  pyhd8ed1ab_0             conda-forge     Cached
  o six                    1.16.0  pyh6c4a22f_0             conda-forge     Cached
  o pytz                   2024.1  pyhd8ed1ab_0             conda-forge     Cached
  o python-tzdata          2024.1  pyhd8ed1ab_0             conda-forge     Cached
  o packaging                24.1  pyhd8ed1ab_0             conda-forge     Cached
  o natsort                 8.4.0  pyhd8ed1ab_0             conda-forge     Cached
  o munkres                 1.1.4  pyh9f0ad1d_0             conda-forge     Cached
  o legacy-api-wrap           1.4  pyhd8ed1ab_1             conda-forge     Cached
  o joblib                  1.4.2  pyhd8ed1ab_0             conda-forge     Cached
  o get-annotations         0.1.2  pyhd8ed1ab_0             conda-forge     Cached
  o exceptiongroup          1.2.2  pyhd8ed1ab_0             conda-forge     Cached
  o cycler                 0.12.1  pyhd8ed1ab_0             conda-forge     Cached
  o colorama                0.4.6  pyhd8ed1ab_0             conda-forge     Cached
  o certifi              2024.7.4  pyhd8ed1ab_0             conda-forge     Cached
  o cached_property         1.5.2  pyha770c72_1             conda-forge     Cached
  o array-api-compat          1.8  pyhd8ed1ab_0             conda-forge     Cached
  o session-info            1.0.0  pyhd8ed1ab_0             conda-forge     Cached
  o python-dateutil         2.9.0  pyhd8ed1ab_0             conda-forge     Cached
  o tqdm                   4.66.5  pyhd8ed1ab_0             conda-forge     Cached
  o click                   8.1.7  win_pyh7428d3b_0         conda-forge     Cached
  o cached-property         1.5.2  hd8ed1ab_1               conda-forge     Cached
  o patsy                   0.5.6  pyhd8ed1ab_0             conda-forge     Cached
  o seaborn-base           0.13.2  pyhd8ed1ab_2             conda-forge     Cached
  o loompy                  3.0.6  py_0                     conda-forge     Cached
  o pynndescent            0.5.13  pyhff2d567_0             conda-forge     Cached
  o seaborn                0.13.2  hd8ed1ab_2               conda-forge     Cached
  o scanpy                 1.10.2  pyhd8ed1ab_0             conda-forge     Cached
  o scvelo                  0.2.5  pyhdfd78af_0             bioconda        Cached

  Downgrade:
------------------------------------------------------------------------------------

  - libzlib                 1.3.1  h2466b09_1               conda-forge     Cached
  + libzlib                1.2.13  h2466b09_6               conda-forge       56kB
  - openssl                 3.3.1  h2466b09_2               conda-forge     Cached
  + openssl                1.1.1w  hcfcfb64_0               conda-forge        5MB
  - libjpeg-turbo           3.0.0  hcfcfb64_1               conda-forge     Cached
  + libjpeg-turbo           2.1.4  hcfcfb64_0               conda-forge        1MB
  - libssh2                1.11.0  h7dfc565_0               conda-forge     Cached
  + libssh2                1.10.0  h680486a_3               conda-forge      233kB
  - krb5                   1.21.3  hdf4eb48_0               conda-forge     Cached
  + krb5                   1.20.1  h6609f42_0               conda-forge      715kB
  - python                 3.12.5  h889d299_0_cpython       conda-forge     Cached
  + python                  3.8.0  hc9e8b01_5               conda-forge       20MB
  - libtiff                 4.6.0  hb151862_4               conda-forge     Cached
  + libtiff                 4.2.0  h0c97f57_3               conda-forge        1MB
  - libcurl                 8.9.1  h18fefc2_0               conda-forge     Cached
  + libcurl                 8.1.2  h68f0423_0               conda-forge      313kB
  - lcms2                    2.16  h67d730c_0               conda-forge     Cached
  + lcms2                    2.12  h2a16943_0               conda-forge      903kB
  - openjpeg                2.5.2  h3d672ee_0               conda-forge     Cached
  + openjpeg                2.4.0  hb211442_1               conda-forge      243kB
  - hdf5                   1.14.3  nompi_h2b43c12_105       conda-forge     Cached
  + hdf5                   1.14.0  nompi_h97a5375_103       conda-forge        2MB
  - pyparsing               3.1.2  pyhd8ed1ab_0             conda-forge     Cached
  + pyparsing               3.0.9  pyhd8ed1ab_0             conda-forge       81kB
  - networkx                  3.3  pyhd8ed1ab_1             conda-forge     Cached
  + networkx                  3.1  pyhd8ed1ab_0             conda-forge        1MB
  - python_abi               3.12  5_cp312                  conda-forge     Cached
  + python_abi                3.8  2_cp38                   conda-forge        5kB
  - pillow                 10.4.0  py312h381445a_0          conda-forge     Cached
  + pillow                  8.2.0  py38h9273828_1           conda-forge      793kB
  - numpy                  1.26.4  py312h8753938_0          conda-forge     Cached
  + numpy                  1.24.4  py38h1d91fd2_0           conda-forge        6MB
  - llvmlite               0.43.0  py312h1f7db74_0          conda-forge     Cached
  + llvmlite               0.41.1  py38h19421c1_0           conda-forge       17MB
  - h5py                   3.11.0  nompi_py312ha036244_102  conda-forge     Cached
  + h5py                    3.9.0  nompi_py38h4f44683_100   conda-forge      889kB
  - contourpy               1.2.1  py312h0d7def4_0          conda-forge     Cached
  + contourpy               1.1.1  py38hb1fd069_1           conda-forge      174kB
  - pandas                  2.2.2  py312h72972c8_1          conda-forge     Cached
  + pandas                  2.0.3  py38hf08cf0d_1           conda-forge       11MB
  - numba                  0.60.0  py312hcccf92d_0          conda-forge     Cached
  + numba                  0.58.1  py38h4a59444_0           conda-forge        4MB
  - matplotlib-base         3.9.2  py312h90004f6_0          conda-forge     Cached
  + matplotlib-base         3.7.2  py38h2d9580e_0           conda-forge        7MB
  - numpy_groupies         0.11.2  pyhd8ed1ab_0             conda-forge     Cached
  + numpy_groupies         0.9.22  pyhd8ed1ab_0             conda-forge       27kB
  - scipy                  1.14.1  py312h1f4e10d_0          conda-forge     Cached
  + scipy                  1.10.1  py38h1aea9ed_3           conda-forge       18MB
  - statsmodels            0.14.2  py312h1a27103_0          conda-forge     Cached
  + statsmodels            0.14.1  py38he7056a7_0           conda-forge       10MB
  - scikit-learn            1.5.1  py312h816cc57_0          conda-forge     Cached
  + scikit-learn            1.3.2  py38h4f736e5_2           conda-forge        7MB
  - anndata                0.10.8  pyhd8ed1ab_0             conda-forge     Cached
  + anndata                 0.9.2  pyhd8ed1ab_0             conda-forge       87kB

  Summary:

  Install: 52 packages
  Change: 4 packages
  Reinstall: 33 packages
  Downgrade: 27 packages

  Total download: 244MB

------------------------------------------------------------------------------------

Confirm changes: [Y/n]

Note: from the current man page of ?scvelo

scVelo v0.2.5 from bioconda is used. Later versions of scVelo depend on jaxlib which is not supported on Windows (https://github.com/google/jax/issues/438). Note that matplotlib is pinned to v3.6.3 (https://github.com/scverse/scanpy/issues/2411), pandas is pinned to v1.5.2 (https://stackoverflow.com/questions/76234312/importerror-cannot-import-name-is-categorical-from-pandas-api-types), and numpy is pinned to v1.21.1 (https://github.com/theislab/scvelo/issues/1109).

kevinrue commented 2 weeks ago

Latest environment seems to have issue with from ._core.anndata import AnnData

> reticulate::py_last_error()

── Python Exception Message ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Traceback (most recent call last):
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 122, in _find_and_load_hook
    return _run_hook(name, _hook)
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 96, in _run_hook
    module = hook()
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 120, in _hook
    return _find_and_load(name, import_)
  File "C:\Users\kevin\BASILI~1\117~1.2\VELOCI~1\115~1.6\env\lib\site-packages\scvelo\__init__.py", line 2, in <module>
    from anndata import AnnData
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 122, in _find_and_load_hook
    return _run_hook(name, _hook)
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 96, in _run_hook
    module = hook()
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 120, in _hook
    return _find_and_load(name, import_)
  File "C:\Users\kevin\BASILI~1\117~1.2\VELOCI~1\115~1.6\env\lib\site-packages\anndata\__init__.py", line 7, in <module>
    from ._core.anndata import AnnData
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 122, in _find_and_load_hook
    return _run_hook(name, _hook)
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 96, in _run_hook
    module = hook()
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 120, in _hook
    return _find_and_load(name, import_)
  File "C:\Users\kevin\BASILI~1\117~1.2\VELOCI~1\115~1.6\env\lib\site-packages\anndata\_core\anndata.py", line 17, in <module>
    import h5py
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 122, in _find_and_load_hook
    return _run_hook(name, _hook)
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 96, in _run_hook
    module = hook()
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 120, in _hook
    return _find_and_load(name, import_)
  File "C:\Users\kevin\BASILI~1\117~1.2\VELOCI~1\115~1.6\env\lib\site-packages\h5py\__init__.py", line 25, in <module>
    from . import _errors
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 122, in _find_and_load_hook
    return _run_hook(name, _hook)
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 96, in _run_hook
    module = hook()
  File "C:\Users\kevin\AppData\Local\R\cache\R\renv\library\velociraptor-96ea9712\windows\R-4.4\x86_64-w64-mingw32\reticulate\python\rpytools\loader.py", line 120, in _hook
    return _find_and_load(name, import_)
ImportError: DLL load failed while importing _errors: The specified module could not be found.

── R Traceback ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
     ▆
  1. ├─velociraptor::scvelo(list(X = spliced, spliced = spliced, unspliced = unspliced))
  2. └─velociraptor::scvelo(list(X = spliced, spliced = spliced, unspliced = unspliced)) at velociraptor/R/scvelo.R:286:22
  3.   └─velociraptor (local) .local(x, ...)
  4.     └─basilisk::basiliskRun(...) at velociraptor/R/scvelo.R:200:5
  5.       └─basilisk::basiliskStart(...)
  6.         └─basilisk:::.activate_fallback(...)
  7.           ├─base::try(...)
  8.           │ └─base::tryCatch(...)
  9.           │   └─base (local) tryCatchList(expr, classes, parentenv, handlers)
 10.           │     └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
 11.           │       └─base (local) doTryCatch(return(expr), name, parentenv, handler)
 12.           └─basilisk::basiliskRun(...)
 13.             └─basilisk (local) fun(...)
 14.               └─reticulate::import(pkg)
 15.                 └─reticulate:::py_module_import(module, convert = convert)
See `reticulate::py_last_error()$r_trace$full_call` for more details.
kevinrue commented 2 weeks ago

Did I run into this one yet?

 micromamba.bat env create -n scvelo_condaforge -c conda-forge -c bioconda scvelo==0.3.2
conda-forge/win-64                                          Using cache
conda-forge/noarch                                          Using cache
bioconda/win-64                                             Using cache
bioconda/noarch                                             Using cache
error    libmamba Could not solve for environment specs
    The following package could not be installed
    └─ scvelo 0.3.2  is not installable because it requires
       └─ scvi-tools >=0.20.1 , which requires
          └─ pytorch >=1.8.0 , which does not exist (perhaps a missing channel).
kevinrue commented 2 weeks ago

This seems to install properly

micromamba env create -n scvelo -c conda-forge -c bioconda scvelo==0.2.5 matplotlib==3.7.3

EDIT: Ran into error

Quitting from lines 76-82 [unnamed-chunk-5] (velociraptor.Rmd)
Error: processing vignette 'velociraptor.Rmd' failed with diagnostics:
argument is of length zero
--- failed re-building 'velociraptor.Rmd'

Seems related to https://github.com/theislab/scvelo/issues/811

kevinrue commented 2 weeks ago

Trying 

micromamba env create -n scvelo -c conda-forge -c bioconda scvelo==0.2.5 matplotlib==3.7.3 pandas==1.3.5
kevinrue commented 2 weeks ago

Trying

micromamba env create -n scvelo -c bioconda -c conda-forge scvelo==0.2.5 matplotlib==3.6.3 pandas==1.5.2 numpy==1.21.1 scipy==1.13.1

Impossible

error    libmamba Could not solve for environment specs
    The following packages are incompatible
    ├─ numpy 1.21.1  is requested and can be installed;
    └─ scipy 1.13.1  is not installable because it requires
       └─ numpy >=1.22.4,<2.3  but there are no viable options
          ├─ numpy [1.22.4|1.23.0|...|2.1.0] conflicts with any installable versions previously reported;
          └─ numpy [2.0.0rc1|2.0.0rc2|2.1.0rc1] would require
             └─ _numpy_rc, which does not exist (perhaps a missing channel).
critical libmamba Could not solve for environment specs
kevinrue commented 2 weeks ago

Trying 

micromamba env create -n scvelo -c bioconda -c conda-forge scvelo==0.2.5 matplotlib==3.6.3 pandas==1.5.2 scipy==1.13.1

(removed numpy requirement)

EDIT: Ran back into error above

kevinrue commented 2 weeks ago

Trying 

micromamba env create -n scvelo -c bioconda -c conda-forge scvelo==0.2.5 python=3.8 matplotlib=3.7.2 jinja2=3.0.3

Source: https://github.com/theislab/scvelo/issues/1124#issuecomment-1802261666

kevinrue commented 2 weeks ago

Back to error

TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases

Meaning back to matplotlib 3.6.3

micromamba env create -n scvelo -c bioconda -c conda-forge scvelo==0.2.5 python=3.8 matplotlib=3.6.3 jinja2=3.0.3
kevinrue commented 2 weeks ago

Back to error

ImportError: cannot import name 'is_categorical' from 'pandas.api.types' (C:\Users\kevin\BASILI~1\117~1.2\VELOCI~1\115~1.13\env\lib\site-packages\pandas\api\types__init__.py)

Meaning back to pandas 1.5.2

micromamba env create -n scvelo -c bioconda -c conda-forge scvelo==0.2.5 python=3.8 matplotlib=3.6.3  pandas==1.5.2 jinja2=3.0.3
kevinrue commented 2 weeks ago

Back to error

Error in py_call_impl(callable, call_args$unnamed, call_args$named) : ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (1, 4) + inhomogeneous part.

Back to numpy 1.21.1

micromamba env create -n scvelo -c bioconda -c conda-forge scvelo==0.2.5 python=3.8 matplotlib=3.6.3  pandas==1.5.2 numpy==1.21.1 jinja2=3.0.3
kevinrue commented 2 weeks ago

Hurray! Fixed by #82