semvec/pypi-packages huggingface dataset:
submission-downloader 1.2.1
Downloads your submissions in bulk from the DMOJ website and any compatible forks.
Requires Python: >=3.8

# submission-downloader
submission-downloader downloads your submissions in bulk from the [DMOJ](https://dmoj.ca/) website and compatible forks. There are a variety of options you can use to select what programs you want to download. This complements [dmoj-submission-downloader](https://github.com/Ninjaclasher/dmoj-submission-downloader), which downloads all the submissions for a problem. [Here](https://github.com/ComputerGenius152/programming-solutions) is an example of submissions that have been downloaded.

## Getting Started

### Installing and Running
This program can be installed in two different ways. You can install it directly with pip (recommended), or clone the repository and run the code yourself. Either way, you will need Python 3.8 or higher to run the code.

#### With Pip
```
$ pip3 install submission_downloader
$ python3 -m submission_downloader
```

#### Manually
```
$ git clone https://github.com/ComputerGenius152/submission-downloader.git
$ cd submission-downloader
$ pip3 install -r requirements.txt
$ python3 submission_downloader.py
```

### Usage
```
usage: submission_downloader.py [-h] [--aconly] [--best] [--fast] [--overwrite] apitoken username judge

Downloads online judge submissions from DMOJ.

positional arguments:
  apitoken         Your API token, can be retrived from your DMOJ profile
  username         Your username, can be retrived from your DMOJ profile
  judge            URL for the judge you are trying to download from, must support the DMOJ v2 API (https://dmoj.ca/api/#v2)

optional arguments:
  -h, --help       show this help message and exit
  --aconly, -a     Only download submissions if they earn points, recommended
  --best, -b       Only download the best submission for each problem and programming language, recommended
  --fast, -f       Ignore the DMOJ API ratelimit, not recommended
  --overwrite, -o  Overwrite existing downloaded submissions, recommended
```

## Contributing
PRs and forks are welcome. Please open an issue if you notice any bugs.

## Licence
This project is licensed under the GNU General Public License v3.0. For more information, refer to [LICENSE.md](LICENSE.md).
rtcpxr-collector 0.1.7
A python library for Collecting RTCP-XR (RFC 3611) SIP quality packets
Requires Python: >=3.1

# rtcpxr_collector
A python library for Collecting RTCP-XR (RFC 3611) SIP quality packets

## Predecessor
Much thank to the project at: https://github.com/pbertera/vq-collector
The core concepts and processing core were taken from there. 
Most code has been refactored and stripped down.

# Basic usage
This is meant to be part of a larger project (hence a library.)
I highly suggest you configure your phones to send to your collector 
with the "name" set to the mac address of the handset. This is the only way to uniquely identify a handset (that I've found at least.)

To run the code with all the defaults:
```
#!/usr/bin/python3

from rtcpxr_collector import vqcollector
vqs = vqcollector.CollectorServer()
vqs.listen()
```

Generally you will use a custom handler, so here's an example:
```
#!/usr/bin/python3
import datetime
from rtcpxr_collector import vqcollector

def logHandler(r):
    print("%s %s %s CQ:%s LQ:%s Local:%s Remote:%s"%(datetime.datetime.now(), 
                                             r['Handset']['MAC'],
                                             r['CallID'],
                                             r['QualityEst']['MOSCQ'],
                                             r['QualityEst']['MOSLQ'],
                                             "%s %s"%(r['LocalID']['name'],r['LocalID']['desc']),
                                             "%s %s"%(r['RemoteID']['name'],r['RemoteID']['desc']) ))
    return True

vqs = vqcollector.CollectorServer(handler=logHandler)
vqs.listen()
```

Maybe you want to run on a non-stadard IP or port:
```
#!/usr/bin/python3

from rtcpxr_collector import vqcollector
vqs = vqcollector.CollectorServer(local_ip=10.10.10.15, port=5061)
vqs.listen()
```
## All the options
```
The CollectorServer object opens a SIP socket to receive RTCP-XR packets,
parses them, then sends the data to a handler.

Attributes:
    local_ip (ipV4 address): [None] Local IPV4 address to bind to (None: Autodetect)
    port (int)             : [5060] Local Port to bind to
    reply_to_socket (bool) : [False] Should we reply to the address from the socket, or the SIP Header
    debug (bool)           : [False] Print Debugging information
    handler (func)         : [None] Handler function for recieved data (None: pprint res data)
    timeout (int)          : [10] Select Timeout in seconds
    timeout_handler (func) : [None] Handler for select timeout event

Handler Function:
    Takes 1 arg that is the parsed data structure.
    Returns: Send Response Packet? True or False
```
gsodpy 0.0.1
A basic package for downloading and processing GSOD data

# gsodpy
Python module to download and work with GSOD data from NOAA

## Usage Guide: 

### Dowload All Data


```{Python}
import gsodpy.gsodDownloader as gdown

gdown.get_data(directory="noaa_gsod")
```

### Process Desired Time Series

```{Python}
import gsodpy.gsodProcess as gsod 
import pandas as pd
import datetime

num_years = 10
num_extremes = 30
target_day = datetime.datetime(2020,3,20)

years, files = gsod.get_years_files(num_years)

df, df_day = gsod.process_all_years(files, target_day)
```

#### Save Processed DataFrame

```{Python}
df.to_pickle("df_monthly.pkl")
df_day.to_pickle("df_daily.pkl")

df = pd.read_pickle("df_monthly_fin.pkl")
df_day = pd.read_pickle("df_daily_fin.pkl")
```

### Create Extremes DataFrame

If we want to find the coldest and warmest stations on a given day we can use the ```create_extremes()``` function 

```{Python}
extremes = gsod.create_extremes(df_day, df_day['DATE'][0], 40)
```
koleksyon 0.0.7
A collection of statistical functions and data manipulation functions and methods for use in data science projects
Requires Python: >=3.7, <4
Keywords: sample,setuptools,development

A collection of statistical functions and data manipulation functions and methods for use in data science projects.  This project is built from real industry problems in the field of healthcare (not toy examples).  It contains some of the tricks used to build models across orginizations when the models have an operations bent to them.  Github repo is here: https://github.com/drachimera/koleksyon
k-seq 0.5.0
The toolbox for Kinetic sequencing (k-Seq) analysisby Chen lab (https://research.seas.ucla.edu/irene-chen/) at University of California, Los Angeles
Requires Python: >=3.5
Keywords: bioinformatics sequencing fitting

k-seq: analytic toolbox for data from kinetic assay with high-throughput sequencing
==============================

`k-seq` is a Python package for analyzing the data from *k*-Seq experiments, a kinetic assay for biomolecules with high-throughput sequencing. It contains the preprocessing of the raw FASTQ, quantification of sequence in samples, fitting into kinetic models with bootstrapping for uncertainty estimation, as well as other pre-built characterization and visualization.


Check out our paper [*Kinetic sequencing (k-Seq) as a massively parallel assay for ribozyme kinetics: utility and critical parameters*](https://www.biorxiv.org/content/10.1101/2020.12.02.407346v1)
for the *k*-Seq experiment method and how we used the package for data analysis. To repeat the analysis in the paper using the code version snapshot, see the [`paper-archive` branch](https://github.com/ichen-lab-ucsb/k-seq/tree/paper-archive).


## Install `k-seq`

`k-seq` package and its dependencies can be installed from `pip` directly:
```shell script
pip install k-seq
```

## Quick start and documentation

Please check out the package [documentation](https://ichen-lab-ucsb.github.io/k-seq/) and [quick start](https://ichen-lab-ucsb.github.io/k-seq/getting_started.html) 
for further details on using the package

----
##### Contact
For any question and issues: [issue report](https://github.com/ichen-lab-ucsb/k-seq/issues) or Yuning Shen (yuningshen@ucsb.edu)