semvec/pypi-packages huggingface dataset:
approximate-cluster-identities 0.1.6
A package to calculate and visualise approximate cluster identities for a large number of short nucleotide sequences using minimizers.

# Approximate Cluster Identities (ACI)

A python package to visualise the approximate within and between cluster identities of a large number of short sequences as assigned by e.g. mmseqs2, cd-hit or panaroo.

# Installation
```
pip install approximate-cluster-identities
```

# Usage

```
aci -h

Create visualisations of approximate between and within cluster nucleotide identities for short sequences.

positional arguments:
  input_fasta           Input FASTA file of all sequences.
  input_json            Input JSON file with cluster assignments ({<sequence header>: <cluster assignment>}).

optional arguments:
  -h, --help            show this help message and exit
  --clusterGML CLUSTERGML
                        Output path of GML clustering file to view with Cytoscape or similar.
  --distanceTable DISTANCETABLE
                        Output path of CSV of distances (may take a long time).
  --clusterPlot CLUSTERPLOT
                        Output path of jointplot to visualise between and within cluster identities.
  --kmerSize KMERSIZE   Kmer size (default: 9).
  --windowSize WINDOWSIZE
                        Minimiser window size (default: 20).
  --threshold THRESHOLD
                        Jaccard similarity threshold (default: 0.9).
  --threads THREADS     Threads for sketching and jaccard distance calculations (default: 1).
  --shorter             Assess identity relative to the shorter sequence.
```

# Methods

We calculate sequence identities by pairwise calculation of jaccard distances using minimizers of size ```--kmerSize``` where 1 *k*-mer is sampled from a window that slides across each sequence, each containing a total of ```--windowSize``` *k*-mers. Increasing ```--windowSize``` will decrease the number of minimizers per sequence, decreasing the sensitivity of the identity calculations but increasing the speed of the programme. This tool is designed to give you an idea of how variable a large number of short sequences are within and between clusters to choose an appropriate sequencing clustering tool and its parameters.

# Example output

Example cluster plots for data in ```test/``` using ```--windowSize 1``` and ```--windowSize 100```.

### Window size = 1

#### Mean identities
![Mean identities](images/cluster_distances_window_1.mean.png)
#### Mode identities
![Mode identities](images/cluster_distances_window_1.mode.png)
#### Median identities
![Median identities](images/cluster_distances_window_1.median.png)
#### Range identities
![Range of identities](images/cluster_distances_window_1.range.png)

### Window size = 100

#### Mean identities
![Mean identities](images/cluster_distances_window_100.mean.png)
#### Mode identities
![Mode identities](images/cluster_distances_window_100.mode.png)
#### Median identities
![Median identities](images/cluster_distances_window_100.median.png)
#### Range identities
![Range of identities](images/cluster_distances_window_100.range.png)
voice-conversion-toolkit 1.0.0
A self-contained singing voice conversion toolkit.
Requires Python: <3.13,>=3.10

# Singing Voice Conversion with GUI

## Useful commands for singularity container

```
singularity shell --nv --bind /public:/public ~/test.simg 
source ~/.bashrc
conda activate svc
python src/separation_train.py -t input_csv/train.csv -v input_csv/val.csv -e all
```
cdsclient 1.1.9
CDS VizieR client (query large table)
Requires Python: >=3.6
Keywords: astronomy

## CDS Python package to query large catalogues

**Note**: API import changed in the last version - see example below

**License** : BSD License

**Installation** : 
```
git clone https://github.com/cds-astro/cds.cdsclient.git
cd cds.cdsclient
pip install .
```

**vizquery.py** : the vizquery package
              query all VizieR catalogues using ASU parameters
              metadata retrieving: get columns/table information 
				   and list large tables

**Examples** :
* list big catalogues : vizquery.py -l
* get columns information for 2mass (=II/246) : vizquery.py -source=II/246 -i
* query GAIA  (I/337/gaia) arroud M1 (10arcsec) : vizquery.py -source=I/337/gaia -c=M1 -c.rs=10
* get hipparcos (HIP=A) in votable : vizquery.py -source=I/239/hip_main -mime=votable -out.max=50 "HIP=1"


**Using dedicated script: find_...py**
* query 2mass arround M1: find_2mass.py M1
* query sdss12 arround '217.488910+36.086880': find_sdss-dr12.py "217.488910+36.086880"
* query gaia by offset (**allows to get the whole table by parts**) : find_gaia_edr3.py --offset 0..1000 
* query 2mass with jamg constraint: find_2mass.py --jmag="<11"
You can also query with constraints see --help 
* query sdss using identifier: find_sdss-dr12.py  --objID=1237662225689281592  --mime=tsv

**API Example**:

```
import cdsclient.find_sdss9 as sdss
process = sdss.QueryCatVizieR()
process.position = "217.091350+35.985222"
process.radius = 120
process.query_cat(limit=10)
data = process.get()
print (data)
```