Old mne version with installation from conda-forge on linux-64
See original GitHub issueDescribe the bug
The installation of mne via conda commands on Linux doesn’t install the newest version.
Steps to reproduce
conda create --name mne --channel conda-forge mne
Expected result
currently mne==1.0.2
Actual result
mne==0.23.4
mne sys_info
Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31
Python: 3.9.12 | packaged by conda-forge | (main, Mar 24 2022, 23:25:59) [GCC 10.3.0]
Executable: <home>/miniconda3/envs/mne/bin/python
CPU: x86_64: 32 cores
Memory: 31.3 GB
mne: 0.23.4
numpy: 1.22.3 {blas=NO_ATLAS_INFO, lapack=lapack}
scipy: 1.8.0
matplotlib: 3.5.1 {backend=QtAgg}
sklearn: 1.0.2
numba: Not found
nibabel: 3.2.2
nilearn: 0.9.1
dipy: 1.5.0
cupy: Not found
pandas: 1.4.2
mayavi: 4.7.4
pyvista: 0.34.0 {pyvistaqt=0.9.0, OpenGL 4.6.0 NVIDIA 470.103.01 via Quadro K5200/PCIe/SSE2}
vtk:
PyQt5: 5.9.2
Issue Analytics
- State:
- Created a year ago
- Comments:13 (13 by maintainers)
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Yes, this seems to solve the problem. But it seems that the default value for
channel_priorityin conda isflexible. Should we warn about this in the docs?mne sys_info
Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31 Python: 3.10.4 | packaged by conda-forge | (main, Mar 24 2022, 17:38:57) [GCC 10.3.0] Executable: <home>/miniconda3/envs/mne/bin/python3.10 CPU: x86_64: 32 cores Memory: 31.3 GBmne: 1.0.2 numpy: 1.21.6 {blas=NO_ATLAS_INFO, lapack=lapack} scipy: 1.8.0 matplotlib: 3.5.1 {backend=QtAgg}
sklearn: 1.0.2 numba: 0.55.1 nibabel: 3.2.2 nilearn: 0.9.1 dipy: 1.5.0 cupy: Not found pandas: 1.4.2 pyvista: 0.34.0 {OpenGL 4.5.0 NVIDIA 470.103.01 via Quadro K5200/PCIe/SSE2} pyvistaqt: 0.9.0 ipyvtklink: 0.2.2 vtk: 9.1.0 PyQt5: 5.12.3 ipympl: Not found pooch: v1.6.0
mne_bids: Not found mne_nirs: Not found mne_features: Not found mne_qt_browser: 0.3.0 mne_connectivity: Not found
conda config --show channel_priority
channel-priority: flexible