semvec/pypi-packages huggingface dataset:
PoseButcher 0.0.22
A tool for categorising and segmenting virtual hits with reference to experimental protein structures and (fragment) hits.
Requires Python: >=3.9


# PoseButcher

![logo](assets/posebutcher_logo-01.png)

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> "A good butcher always trims the fat"

PoseButcher is a tool for categorising and segmenting virtual hits with reference to experimental protein structures and (fragment) hits.

[DOCUMENTATION](https://posebutcher.winokan.com)

Ligand atoms are tagged with categories:

	- GOOD:

		* fragment space: within the fragment bolus
		* pocket X: in a specified catalytic/allosteric pocket X

	- BAD:
		
		* protein clash: Clashing with the protein
		* solvent space: Heading out of the protein/crystal

## Usage at a glance

	1. Create the butcher (see PoseButcher.__init__):

		from posebutcher import PoseButcher
		butcher = PoseButcher(protein, hits, pockets)

	2. Chop up a posed virtual hit (rdkit.ROMol with a conformer):

		result = butcher.chop(mol)

	3. Tag a compound based on its pocket occupancy and clashes:

		tags = butcher.tag(mol)

	4. Explore the expansion opportunities from a given atom in a virtual hit

		result = butcher.explore(mol)

	5. (Coming soon) Trim a parts of a compound that clash with a protein or leave the crystal

		mol = butcher.trim(mol)

	6. (Coming soon) Score how well a virtual hit recapitulates shape and colour of the fragment bolus

		score: float = butcher.score(mol)

## Sample output

### Protein, ligand, and catalytic site pockets with Open3D:

![butcher_3d](https://github.com/mwinokan/PoseButcher/assets/36866506/19c7895d-328b-4bdb-aca1-539f59fbea28)

### A chopped up ligand:

![butcher_2d](https://github.com/mwinokan/PoseButcher/assets/36866506/f4947703-83f9-45f5-8e70-108918582938)

### Exploring expansion vector into a desirable pocket

![butcher_vector](https://github.com/mwinokan/PoseButcher/assets/36866506/af34e286-3dfe-4f5a-871e-ff8d28292a3d)
arzprice 0.0.1
you can get everythings price in iran to rial.
Requires Python: ~=3.6
Keywords: arz, price, crypto, arzprice

## Get start:
```python
from arzprice import irarz, tasnim

ir_arz = irarz()

ir_arz.arz() # output: euro, dollar
ir_arz.arz_digital() # output: btc, eth, ada, doge, xrp, trx
ir_arz.gold() # output: coin, half_coin, quarter_coin, gerami_coin, gold18, gold24, mesghal_gold
ir_arz.car() # output: benz, bmw, toyota, ... (+50 company products)

tasnim.arz() # euro, dollar, ... (+30 currencies prices)
```
cvScore 0.1.3
A CLI application that scores CVs using keywords.
Requires Python: <4.0,>=3.9

# cvScore
Scores a set of resumes against a set of keywords.

## Source code, Installation and Usage

### Source code
The source is available at https://github.com/pglebow/cvScore

### Installation
To install this utility, create a virtual environment and install from PyPi.  For example:

    python3 -m venv venv
    source venv/bin/activate
    pip3 install cvScore
    cvScore score --help
    Usage: cvScore score [OPTIONS] CVDIR KEYWORDFILE

    Score a set of resumes against a set of keywords. The resumes must be in PDF
    or DOCX format. The keywords file must have one keyword per line.

    Example:
        score data/resumes keywords.txt

    Options:
        --help  Show this message and exit.

### Using cvScore
To use this utility, create a directory containing your resumes.  PDF and DOCX formats are supported.
Then create a text file with the keywords you're looking for, with one keyword per line.  For example:

    cvScore score dev/cvScore/data dev/cvScore/data/keywords.txt
    data/testDoc1.docx : score = 22
    data/testDoc1.pdf : score = 22
    data/testDoc2.pdf : score = 10
    data/testDoc2.docx : score = 10

## Further details

### Keywords
The keywords are entered into a text file, one per line.  Matching is case-insensitive.
> Example keywords.txt file:
> 
    ETL
    Java
    Python

### Running from Poetry:

    poetry run cvScore score data data/keywords.txt

    data/testDoc1.docx : score = 22
    data/testDoc1.pdf : score = 22
    data/testDoc2.pdf : score = 10
    data/testDoc2.docx : score = 10

### Ranking Algorithm
The algorithm is very simple.  It counts the number of times each keyword occurs
in the document and the score is the sum of the counts.  If multiple documents are
found in the directory, a list of documents, sorted from highest to lowest score, is returned.
frappeErpnextInstaller 1.0.2
PIP Installer for Frappe Framework

Unattended script for Frappe ERPNext installation.

This is a no-interactive script for installing Frappe ERPNext with very minimal interaction.

# How To:
To use this installer, follow these steps:

# Before Installation
```
ACtivate WSL for windows
```
1. Right-click on the Windows Start menu icon, choose Search and type Windows Features. Select the top entry (category Control panel) to enable or turn off Windows-Features. The Windows-Features dialog will be opened.
2. Select in the upcoming dialog the check box for Virtual Machine Platform from the bottom of the list and press the OK button. Applying the changes may take a few minutes. Finally, press the Restart now button to reboot the computer.
3. Go to Microsoft Store and Download Ubuntu.

Make sure you install the latest package versions by updating system packages if you are running this installer on a fresh Ubuntu machine.

```
sudo apt update && sudo apt -y upgrade
```

```
pip3 install frappeErpnextInstaller
```