semvec/pypi-packages huggingface dataset:
display-timedelta 1.1
Displays a timedelta object for human consumption.
Requires Python: >=3.5

# display-timedelta

Simple package for displaying timedeltas for human consumption

Basic usage example

```python
>>> from datetime import timedelta
>>> from display_timedelta import display_timedelta
>>> display_timedelta(timedelta(hours=25, seconds=3))
'1 day, 1 hour, and 3 seconds'
>>> display_timedelta(timedelta(0))
'right now'
```
scvr-prep 1.1.1
single cell VR preprocess
Requires Python: >=3.6

## SingleCellVR Preprocess:  

Prepare your data for the visualization on Single Cell VR website <https://singlecellvr.com/>

Installation
------------
Install and update using pip:  
`pip install scvr-prep`

Usage
-----
`$ scvr_prep --help`

```
Usage: scvr_prep [-h] -f FILE -t {paga,seurat,stream} [-a ANNOTATIONS] [-g GENES] [-o OUTPUT]

scvr_prep Parameters

required arguments:
  -f FILE, --filename FILE
                        Analysis result file name (default: None)
  -t {paga,seurat,stream}, --toolname {paga,seurat,stream}
                        Tool used to generate the analysis result (default: None)

optional arguments:
  -a ANNOTATIONS, --annotations ANNOTATIONS
                        Annotation file name. It contains the cell
                        annotation(s) used to color cells (default: None)
  -g GENES, --genes GENES
                        Gene list file name. It contains the genes to
                        visualize in one column (default: None)
  -o OUTPUT, --output OUTPUT
                        Output folder name (default: vr_report)
  -h, --help            show this help message and exit
```


Examples:
---------
### PAGA:  

To get single cell VR report for PAGA :  
```bash
scvr_prep -f ./paga_result/paga3d_paul15.h5ad -t paga -a annotations.txt -g genes.txt -o paga_report
```

* Input files can be found [here](https://www.dropbox.com/sh/03zpxs9zv7yusi1/AADKVSU8Il1JcjA7lfHjmRpSa?dl=0) 
* To generate the `paga3d_paul15.h5ad`, check out [PAGA analysis](https://nbviewer.jupyter.org/github/pinellolab/singlecellvr/blob/master/examples/paga3d_paul15.ipynb?flush_cache=true). *(Make sure set `n_components=3` in `sc.tl.umap(adata,n_components=3)`)*

### Seurat:  
To get single cell VR report for Seurat :  
```bash
scvr_prep -f ./seurat_result/seurat3d_10xpbmc.loom -t seurat -a annotations.txt -g genes.txt -o seurat_report
```
* Input files can be found [here](https://www.dropbox.com/sh/tpk4qfm5qsjpffn/AADmKmyDx7rhzKBOpIlAgMEUa?dl=0) 
* To generate the `seurat3d_10xpbmc.loom`, check out [Seurat analysis](https://nbviewer.jupyter.org/github/pinellolab/singlecellvr/blob/master/examples/seurat3d_10xpbmc.ipynb?flush_cache=true). *(Make sure set `n.components = 3` in `pbmc <- RunUMAP(pbmc, dims = 1:10, n.components = 3)`)*

### STREAM:  
To get single cell VR report for STREAM : 
```bash
scvr_prep -f ./stream_result/stream_nestorowa16.pkl -t stream -g genes.txt -o stream_report
```
* Input files can be found [here](https://www.dropbox.com/sh/fg84hfdeihielun/AACRcmuAIg9RMU30ChgAZevza?dl=0) 
* To generate the `stream_nestorowa16.pkl`, check out [STREAM analysis](https://nbviewer.jupyter.org/github/pinellolab/singlecellvr/blob/master/examples/stream_nestorowa16.ipynb?flush_cache=true).