semvec/pypi-packages huggingface dataset:
dynasigml 1.0.9
DynaSig-ML
Requires Python: >=3.6

# DynaSig-ML package
DynaSig-ML ("Dynamical Signatures - Machine Learning") is a Python
package allowing the easy study of dynamics-function relationships in biomolecules.

Online documentation, step-by-step tutorial and installation guide: https://dynasigml.readthedocs.io  
Companion repository for the tutorial: https://github.com/gregorpatof/dynasigml_mir125a_example

If you use DynaSig-ML, please cite these two articles:  
https://doi.org/10.1101/2022.07.06.499058  
https://doi.org/10.1093/bioinformatics/btab189

DynaSig-ML automatically computes and
stores Dynamical Signatures of sequence variants using ENCoM (Elastic Network Contact Model), a sequence-sensitive
coarse-grained normal mode analysis model. These Dynamical Signatures, along with optional additional data (for example
the ddG of folding of the mutation), are then used to automatically train LASSO
multilinear regression, in addition to any number of user-specified machine learning models, provided thay are implemented
in sckikit-learn. The LASSO coefficients are automatically mapped back on the biomolecules'
structure and can be easily visualized using PyMOL, leading to biological insights.

The guide provides examples using
experimental miR-125a (a human microRNA) maturation efficiency data, as described in our previous work
(https://doi.org/10.1371/journal.pcbi.1010777). The method is generalizable
to any biomolecule on which mutational data exists, and for which an input structure is known or can be predicted with confidence.
myClippings 0.0.2
Parses My Clippings.txt
Requires Python: >=3.7
Keywords: amazon,clippings,kindle,kindle-clippings,parser

# kindle-clippings

---

Converts `My Clippings.txt` file into a JSON format like below:

```json
[
    "Clean Code: A Handbook of Agile Software Craftsmanship": [
      {
        "id": "66698d9e",
        "title": "Clean Code: A Handbook of Agile Software Craftsmanship",
        "author": "Martin, Robert C.",
        "note_type": "highlight",
        "location": [892, 893],
        "date": "2022-10-12",
        "content": "Leave the campground cleaner than you found it."
      }
    ]
]
```

Currently, it supports English and Spanish languages.

## Usage

As a CLI tool:

```bash
$ python3 -m kindle_clippings.cli --help
```

As a module:

```python
from kindle_clippings import clippings_to_json
```
conll-transform 0.1.0
Various functions to manipulate CONLL files
Requires Python: >=3.8

# CONLL Transform - functions to manipulate CONLL data

This package constains several functions to manipulate conll data:

- `read_files`: Read one or several conll files and return a dictionary of documents.
- `read_file`: Read a conll file and return dictionary of documents.
- `write_file`: Write a conll file.
- `compute_mentions`: Compute mentions from the raw last column of the conll file.
- `compute_chains`: Compute and return the chains from the conll data.
- `sentpos2textpos`: Transform mentions `[SENT, START, STOP]` to `[TEXT_START, TEXT_STOP]`.
- `textpos2sentpos`: Transform mentions `[TEXT_START, TEXT_STOP]` to `[SENT, START, STOP]`.
- `write_chains`: Convert a list of chains to a conll coreference column.
- `replace_coref_col`: Replace the last column of `tar_docs` by the last column of `src_docs`.
- `remove_singletons`: Remove the singletons of the conll file `infpath`, and write the version without singleton in the conll file `outfpath`.
- `filter_pos`: Filter mentions that have POS in unwanted_pos, return a new mention list.
- `check_no_duplicate_mentions`: Return True if there is no duplicate mentions.
- `merge_boundaries`: Add the mentions of `boundary_docs` to `coref_docs` if they don't already exist, as singletons.
- `remove_col`: Remove columns from all tokens in docs.
- `write_mentions`: Opposite for `compute_mentions()`.  Write the last column in `sent`.
- `compare_coref_cols`: Build a conll file that merge the corefcols of several other files.
- `to_corefcol`: Write the conll file `outfpath` with just the last column (coref) of the conll file `infpath`.
- `get_conll_2012_key_pattern`: Return a compiled pattern object to match conll2012 key format.
- `merge_amalgams`: Add amalgams in documents from where they have been removed.

To use it, just import the function from `conll_transform`, for example:

```python
from conll_transform import read_files

documents = read_files("myfile.conll", "myfile2.conll")
print(documents)
```


The source can be found at [GitHub](https://github.com/boberle/corefconversion/blob/master/conll_transform.py).