I'm using spyder to make my codes.
I try to use pygmt lib but, in my spyder-env I have an error :
GMTCLibNotFoundError: Error loading GMT shared library at 'libgmt.so'.
libgmt.so: cannot open shared object file: No such file or directory
According to the pygmt documentation, I have to create a new env to use this lib, so I did :
conda create --name pygmt --channel conda-forge pygmt
conda activate pygmt
But I have 2 problems :
spyder launch itself with the 3 version instead of 5 (as in my spyder-env/ typically).
Error stays :
GMTCLibNotFoundError: Error loading GMT shared library at '/home/vialb/miniconda3/envs/pygmt/lib/libgmt.so'.
/usr/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.30' not found (required by /home/vialb/miniconda3/envs/pygmt/lib/./libgdal.so.31)
Error loading GMT shared library at 'libgmt.so'.
libgmt.so: cannot open shared object file: No such file or directory
I'm not familiar with environment so, I don't know what tot do with this...
Any idea ?
Hum, not sure if I done something different, but that's working now, with this procedure :
on the (base) environnement, create pygmt env with :
conda create --name pygmt --channel conda-forge pygmt
activate the new env :
conda activate pygmt
install spyder
conda install spyder
And ... That's all. Spyder open in version 5 and import pygmt works too.
Related
I have installed the module tradingeconomics on cmd and when i try to import the library it throws this error , how to solve it ?
'
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-7-15897cf55e0a> in <module>
1 #Importing required libraries
----> 2 import tradingeconomics as te
3 import pandas as pd
4 import numpy as np
5 from datetime import datetime,date,timedelta
ModuleNotFoundError: No module named 'tradingeconomics'
It is possible that you installed the module in an environment and you are not working in the environment, or you are working in an environment but the module was installed outside of the environment. I use a conda environment, you can set up one by typing this in the terminal
conda create --new myenv
conda activate myenv
set up pip using the following
conda install -n myenv pip
then you can download modules
pip install tradingeconomics
use conda deactivate to exit the environment,
you can learn more about conda here https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#using-pip-in-an-environment
Plotly figures are not rendered/displayed in jupyterlab. I therefore tried to install the extension jupyter labextension install #jupyter-widgets/jupyterlab-manager jupyterlab-plotly. Afterwards I was asked to run the jupyter lab build and this failed. I have no idea where the problem is.
Install info:
I installed a venv with pyenv running python 3.8.5.
jupyter --version
jupyter core : 4.7.1
jupyter-notebook : 6.4.0
qtconsole : not installed
ipython : 7.25.0
ipykernel : 6.0.3
jupyter client : 6.1.12
jupyter lab : 3.0.12
nbconvert : 6.1.0
ipywidgets : 7.6.3
nbformat : 5.1.3
traitlets : 5.0.5
npm --version
7.11.2
node --version
v16.1.0
I am restricted to use WSL1 because of company requirements.
I’ll post below the command outputs for
jupyter labextension list
jupyter lab build
cat /tmp/jupyterlab-debug-5vr2zquq.log
jupyter labextension install #jupyter-widgets/jupyterlab-manager --minimize=False
Any advice would be appreciated. I have no idea how to fix this.
Thanks
❯ jupyter labextension list
JupyterLab v3.0.16
/home/bebop/.local/share/jupyter/labextensions
#jupyter-widgets/jupyterlab-manager v3.0.0 enabled OK (python, jupyterlab_widgets)
/mnt/s/dokumente/Privat/neue_fische/ot/ot-sopra_steria/.venv/share/jupyter/labextensions
jupyterlab-plotly v5.1.0 enabled OK
Other labextensions (built into JupyterLab)
app dir: /mnt/s/dokumente/Privat/neue_fische/ot/ot-sopra_steria/.venv/share/jupyter/lab
plotlywidget v4.14.3 enabled OK
Build recommended, please run jupyter lab build:
plotlywidget needs to be included in build
❯ jupyter lab build
[LabBuildApp] JupyterLab 3.0.16
[LabBuildApp] Building in /mnt/s/dokumente/Privat/path/.venv/share/jupyter/lab
[LabBuildApp] Building jupyterlab assets (production, minimized)
Build failed.
Troubleshooting: If the build failed due to an out-of-memory error, you
may be able to fix it by disabling the dev_build and/or minimize options.
If you are building via the jupyter lab build command, you can disable
these options like so:
jupyter lab build --dev-build=False --minimize=False
You can also disable these options for all JupyterLab builds by adding these
lines to a Jupyter config file named jupyter_config.py:
c.LabBuildApp.minimize = False
c.LabBuildApp.dev_build = False
If you don’t already have a jupyter_config.py file, you can create one by
adding a blank file of that name to any of the Jupyter config directories.
The config directories can be listed by running:
jupyter --paths
Explanation:
dev-build: This option controls whether a dev or a more streamlined
production build is used. This option will default to False (i.e., the
production build) for most users. However, if you have any labextensions
installed from local files, this option will instead default to True.
Explicitly setting dev-build to False will ensure that the production
build is used in all circumstances.
minimize: This option controls whether your JS bundle is minified
during the Webpack build, which helps to improve JupyterLab’s overall
performance. However, the minifier plugin used by Webpack is very memory
intensive, so turning it off may help the build finish successfully in
low-memory environments.
An error occured.
shutil.Error: [(’/mnt/s/dokumente/Privat/path/.venv/lib/python3.8/site-packages/jupyterlab/staging/templates’, ‘/mnt/s/dokumente/Privat/path/.venv/share/jupyter/lab/staging/templates’, “[Errno 13] Permission denied: ‘/mnt/s/dokumente/Privat/path/.venv/share/jupyter/lab/staging/templates’”)]
See the log file for details: /tmp/jupyterlab-debug-5vr2zquq.log
❯ cat /tmp/jupyterlab-debug-5vr2zquq.log
[LabBuildApp] Building in /mnt/s/dokumente/Privat/path/.venv/share/jupyter/lab
[LabBuildApp] Node v16.1.0
[LabBuildApp] Yarn configuration loaded.
[LabBuildApp] Building jupyterlab assets (production, minimized)
[LabBuildApp] Traceback (most recent call last):
[LabBuildApp] File “/mnt/s/dokumente/Privat/path/.venv/lib/python3.8/site-packages/jupyterlab/debuglog.py”, line 47, in debug_logging
yield
[LabBuildApp] File “/mnt/s/dokumente/Privat/path/.venv/lib/python3.8/site-packages/jupyterlab/labapp.py”, line 166, in start
raise e
[LabBuildApp] File “/mnt/s/dokumente/Privat/path/.venv/lib/python3.8/site-packages/jupyterlab/labapp.py”, line 162, in start
build(name=self.name, version=self.version,
[LabBuildApp] File “/mnt/s/dokumente/Privat/path/.venv/lib/python3.8/site-packages/jupyterlab/commands.py”, line 469, in build
return handler.build(name=name, version=version, static_url=static_url,
[LabBuildApp] File “/mnt/s/dokumente/Privat/path/.venv/lib/python3.8/site-packages/jupyterlab/commands.py”, line 657, in build
self._populate_staging(
[LabBuildApp] File “/mnt/s/dokumente/Privat/path/.venv/lib/python3.8/site-packages/jupyterlab/commands.py”, line 1180, in _populate_staging
shutil.copytree(pjoin(HERE, ‘staging’, ‘templates’), templates)
[LabBuildApp] File “/home/bebop/.pyenv/versions/3.8.5/lib/python3.8/shutil.py”, line 554, in copytree
return _copytree(entries=entries, src=src, dst=dst, symlinks=symlinks,
[LabBuildApp] File “/home/bebop/.pyenv/versions/3.8.5/lib/python3.8/shutil.py”, line 510, in _copytree
raise Error(errors)
[LabBuildApp] shutil.Error: [(’/mnt/s/dokumente/Privat/path/.venv/lib/python3.8/site-packages/jupyterlab/staging/templates’, ‘/mnt/s/dokumente/Privatpath/.venv/share/jupyter/lab/staging/templates’, “[Errno 13] Permission denied: ‘/mnt/s/dokumente/Privat/path/.venv/share/jupyter/lab/staging/templates’”)]
[LabBuildApp] Exiting application: JupyterLab
❯ jupyter labextension install #jupyter-widgets/jupyterlab-manager --minimize=False
Building jupyterlab assets (production, not minimized)
An error occured.
shutil.Error: [(’/mnt/s/dokumente/Privat/path/.venv/lib/python3.8/site-packages/jupyterlab/staging/templates’, ‘/mnt/s/dokumente/path/.venv/share/jupyter/lab/staging/templates’, “[Errno 13] Permission denied: ‘/mnt/s/dokumente/Privat/path/.venv/share/jupyter/lab/staging/templates’”)]
You should not need to install the extension from source. JupyterLab 3.0 introduced prebuilt extensions system that allows users to install extensions from pip (and conda) without the need for the build step making all the trouble of troubleshooting failed builds disappear. It seems that you used an old set of instructions for installing plotly, as plotly 5.0+ supports prebuilt extensions for JupyterLab. First uninstall the source extensions that you just installed (non of those are needed):
jupyter labextension uninstall #jupyter-widgets/jupyterlab-manager jupyterlab-plotly plotlywidget
Optional: verify list of extensions with:
jupyter labextension list
Then install a new plotly version with pip or conda:
pip install "plotly>=5" "ipywidgets>=7.6"
# or, if using conda:
# conda install -c conda "plotly>=5"
# conda install "ipywidgets>=7.6"
Both widget and renderer are included, so no need to install plotlywidget separately.
Check list of extensions again:
jupyter labextension list
You should now see lines with:
jupyterlab-plotly v5.1.0 enabled OK
#jupyter-widgets/jupyterlab-manager v3.0.0 enabled OK (python, jupyterlab_widgets)
(the version may be newer in the future of course).
I have installed pyjnius with conda. However, when I try to import pyjnius it fails
> from jnius import autoclass
File
"C:\Users\OEM\Miniconda3\envs\example-env\lib\site-packages\jnius\__init__.py",
line 12, in <module>
from .jnius import * # noqa ImportError: DLL load failed: The specified module could not be found.
Together with pyjnius conda installs also openjdk. Next, pyjnius looks for jvm.dll in one of PATH directories. DLL could be found in
C:\Users\OEM\Miniconda3\pkgs\openjdk-11.0.1-1017\Library\bin\server
but conda does not include it in PATH. It adds another folder in PATH:
C:\Users\OEM\Miniconda3\envs\example2-env\Library\jre\bin\server
while this directory is missing: JRE has not been installed, only JDK. I can, obviously, include first directory in my PATH, however, this would bypass conda virtual environments concept. How can I solve this problem in an elegant way?
Here's environment.yml to reproduce the problem:
name: example-env
channels:
- conda-forge
dependencies:
- python=3.7
- Cython
- pyjnius
Next, I create and activate as follows:
conda env update --file environment.yml
conda activate example-env
Since the deprecation of Python 3.4, conda has removed it from its package list. Is there a way, however, that I can install it?
I need it in order to use software written in this older version.
EDIT:
My question is different than the suggested duplicate one, because I am referring to deprecated and unsupported versions. I already know how to create a conda environment with a specific python version, but executing:
conda create --name py34env python=3.4
results in error (listed in the end), which is due to the lack of the package for Python 3.4 .
One can see the currently supported versions of Python by executing: conda search python and can confirm that Python 3.4 is not on the list.
This is the output of the error when trying to create a Python 3.4 conda enviroment:
$ conda create --name py34env python=3.4
Collecting package metadata (current_repodata.json): done
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed
PackagesNotFoundError: The following packages are not available from current channels:
- python=3.4
Current channels:
- https://repo.anaconda.com/pkgs/main/linux-64
- https://repo.anaconda.com/pkgs/main/noarch
- https://repo.anaconda.com/pkgs/r/linux-64
- https://repo.anaconda.com/pkgs/r/noarch
To search for alternate channels that may provide the conda package you're
looking for, navigate to
https://anaconda.org
and use the search bar at the top of the page.
When Anaconda dropped it's free channel (technically, Conda 4.7+ just no longer looks there), this resulted in some older package versions that had never been ported to main no longer being accessible.
Option 1: Globally enable free channel searching
However, there is an option to restore access to the free channel, namely restore_free_channel.
# Not generally recommended
conda config --set restore_free_channel True
conda create -n py34 python=3.4
This isn't generally recommended (see blog post), but if you will be working in Python v3.4 frequently and will require other older compatible packages, it might be the best option.
Option 2: Temporarily include free channel
A more temporary solution is to include the free channel using the ad hoc --channel,-c argument. For example,
# slightly better
conda create -n py34 -c defaults -c free python=3.4
Note that I include defaults prior to free so that the latter will only be used if the package cannot be sourced from the former. This assumes the channel_priority setting is set to flexible (the default).
Option 3: Use Conda Forge
Alternatively, Conda Forge has Python v3.4.5, and that won't force you to change a global configuration option.
conda create -n py34 -c conda-forge python=3.4
I need the sacred package for a new code base I downloaded. It requires sacred.
https://pypi.python.org/pypi/sacred
conda install sacred fails with
PackageNotFoundError: Package missing in current osx-64 channels:
- sacred
The instruction on the package site only explains how to install with pip. What do you do in this case?
That package is not available as a conda package at all. You can search for packages on anaconda.org: https://anaconda.org/search?q=sacred You can see the type of package in the 4th column. Other Python packages may be available as conda packages, for instance, NumPy: https://anaconda.org/search?q=numpy
As you can see, the conda package numpy is available from a number of different channels (the channel is the name before the slash). If you wanted to install a package from a different channel, you can add the option to the install/create command with the -c/--channel option, or you can add the channel to your configuration conda config --add channels channel-name.
If no conda package exists for a Python package, you can either install via pip (if available) or build your own conda package. This isn't usually too difficult to do for pure Python packages, especially if one can use skeleton to build a recipe from a package on PyPI.
It happens some issue to me before. If your system default Python environment is Conda, then you could download those files from https://pypi.python.org/pypi/sacred#downloads
and manually install by
pip install C:/Destop/some-file.whl