Python, Miniconda, Jupyterlab & Python Packages
Basic install
Open Ports Anaconda
In mydre.org, in tab: external access, add the following rules:
Rule & Description
| IP-address
| Port
|
conda.anaconda.org
| 104.17.93.24
| 443
|
repo.anaconda.com
| 104.16.131.3
| 443
|
docs.conda.io
| 188.114.96.7
| 443
|

ports might change, easy to check with a local computer: open cmd, ping <url>
- Open conda prompt in admin mode
- conda install -c conda-forge <package>
Install Miniconda
- download from: docs.conda.io/en/latest/miniconda.html
- Install as admin
Install Jupyterlab
- Open conda prompt in admin mode
- conda install -c conda-forge jupyterlab
Set default location to z: (optional)
This allows any user to quickly start Jupyter Labs from the desktop. All Jupyter Lab Notebooks are automatically created (and thus backuped as well) on the Z:-drive.
- Create a folder on the Z:\Jupyter Labs
This is at the cost of a bit of performance for the Z-drive is not as fast ast the C-drive. Alternatively create a folder on the C:-drive and change after putting the shortcut on the desktop in the properties of the shortcut the target and start-in
- Download python.zip and upload it to the Workspace
- Extract the zip
- Put the shortcut in: c:\users\public\desktop (just type desktop behind public)
- Put the icon in %ALLUSERSPROFILE%\Miniconda3\Menu\
Useful commands / packages
- Open conda prompt in admin mode
Updates
- conda update -c conda-forge jupyterlab
- conda update -c conda-forge --all
- conda update -c conda-forge python
Interesting packages
- Tabulate: conda install -c conda-forge tabulate
- OpenPyXL: conda install -c conda-forge openpyxl
- numpy: conda install -c conda-forge numpy
- pandas: conda install -c conda-forge pandas
- matplotlib: conda install -c conda-forge matplotlib
- seaborn: conda install -c conda-forge seaborn
- Castor: conda install -c conda-forge castorapi
** you can install multiple packages in one go like: conda install -c conda-forge tabulate seaborn castorapi
Jupyter Lab and multiprocessing
Cell 1
- %%writefile magic_functions.py
- def your_function(f):
- return f
- def process_frame(f):
- return f, study_check_v3(f)
Cell2
- from tqdm import tqdm
- from multiprocessing import Pool
- from magic_functions import process_frame
- frames_list = [x + batch*y for x in range(0, batch)]
- with Pool() as p:
- pool_outputs = list(
- tqdm(
- p.imap(process_frame, frames_list),
- total=len(frames_list)))
- print(pool_outputs)
- new_dict = dict(pool_outputs)
- print("dict: ", new_dict)
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