semvec/pypi-packages huggingface dataset:
pygaffer 0.1
Python Gaffer API

# PyGaffer

API for Gaffer graph store.

# Install
`pip3 install git+git://github.com/cybermaggedon/pygaffer`

# Example

```
import gaffer

g = gaffer.Gaffer("http://localhost:8080")

# For TLS, use keys in ~/private
# g.use_cert()

op1 = g.get_all(entities=["ip"], edges=["ipflow"])
op2 = g.limit(30)
ops = g.operation_chain([op1, op2])

res = g.execute(ops)

for v in res:
    if v.has_key("source"):
        print "Edge:", v["source"], "->", v["destination"]

    if v.has_key("vertex"):
        print "Node:", v["vertex"]

```
nc-dnsapi 0.1.5
API wrapper for the netcup DNS api

# nc_dnsapi
A simple API wrapper for the netcup DNS API

```python
from nc_dnsapi import Client, DNSRecord

customer = 123456
api_key = "your-personal-api-key"
api_password = "your-private-api-password"

with Client(customer, api_key, api_password) as api:
    # fetch records
    records = api.dns_records("example.com")
    for record in records:
	print(record)

    # fetch zone details
    zone = api.dns_zone("example.com")
    print(zone)

    # update single record
    api.update_dns_record("example.com", DNSRecord("my-hostname", "A", "127.0.0.2", id=108125))

    # update list of records
    api.update_dns_record("example.com", [ DNSRecord("my-hostname", "A", "127.0.0.2", id=108125), 
        DNSRecord("my-hostname2", "A", "127.0.0.2", id=108126)])

    # delete record
    api.delete_dns_record("example.com", DNSRecord("my-hostname", "A", "127.0.0.2", id=108125))

    # add record
    api.add_dns_record("example.com", DNSRecord("another-host", "AAAA", "::1"))

    # update zone
    zone = api.dns_zone("example.com")
    zone.refresh = 3600
    api.update_dns_zone("example.com", zone)
```
extractfq 0.0.5
Extract some fastq reads (PE/SE) from the beginning of the files
Requires Python: >=3

# extractfq

## 1 Introduction

`extractfq` is a tool to extract some fastq reads from the beginning of the files.

## 2 Installation

    pip install extractfq

There will be a command `extractfq` created under the same directory as your `pip` command.

## 3 Usage

    $ extractfq
    usage: extractfq.py [-h] [-fq1 <str>] [-fq2 <str>] [-outfq1 <str>]
                        [-outfq2 <str>] [-size_required <float>] [-rl <int>] [-gz]
                        [-cache_num <int>]

    Extract some fastq reads from the beginning of the files. Author: Guanliang
    Meng, see https://github.com/linzhi2013/extractfq. This script is part of the
    package `MitoZ`, when you use the script in your work, please cite: MitoZ: A
    toolkit for mitochondrial genome assembly, annotation and visualization with
    NGS data. Guangliang Meng, Yiyuan Li, Chentao Yang, Shanlin Liu (in
    manuscript)

    optional arguments:
      -h, --help            show this help message and exit
      -fq1 <str>            input fastq 1 file
      -fq2 <str>            input fastq 2 file
      -outfq1 <str>         output fastq 1 file
      -outfq2 <str>         output fastq 2 file
      -size_required <float>
                            size required in Gigabase. [3]
      -rl <int>             read length required. discard the smaller ones, and
                            cut the longer ones to this length [None]
      -gz                   gzip output. [False]
      -cache_num <int>      the cache number of reads before writing to the file,
                            to speed up. the larger of cache_num, the more memory
                            (default is ca. 2G) will be used. [1500000]

## Author
Guanliang MENG

## Citation
This script is part of the package `MitoZ`, when you use the script in your work, please cite:

    Guanliang Meng, Yiyuan Li, Chentao Yang, Shanlin Liu. MitoZ: A toolkit for mitochondrial genome assembly, annotation and visualization; doi: https://doi.org/10.1101/489955
fastindex 0.0.4
fast pandas selction and indexing

# fastIndex
awesome way to select from pandas multi index and more

**Warning! this repository is on early develoapment stage!**

## installation:
```
pip install fastindex
```
## configuration:

```python
from fastindex import fast_index
import pandas as pd
#expanding pandas with fastindex
fast_index(pd)
```
## example usage:
### setting pandas MultiIndex
```python
df = read_csv("data.csv")
df.set_index(["Country","City","District","first_name","Age","ID"],inplace=True)
df.sort_index(inplace=True)
```

### Basic Selection
- **Regular Pandas:**
```python
df.loc[(slice(None),slice(None),slice(None),slice(None),24),:]
```

- **With fastindex**
```python
df.fidx.slice(Age=24)
```

### Ranges
- **Regular Pandas:**
```python
df.loc[("Spain",slice(None),slice(None),slice(None),slice(23,25)),:]
```
- **With fastindex**
```python
df.fidx.slice(Age=slice(23,25),Country="Spain")
```


### Selecting columns 
- **Regular Pandas:**
```python
df.loc[("Spain",slice(None),slice(None),slice(None),slice(23,25)),["last_name"]]
```
- **With fastindex**
```python
df.fidx.slice(Age=slice(23,25),Country="Spain",columns = ["last_name"])
```


### setting values using f_slice:
```python
slc = df.fidx.f_slice(Age=slice(23,25),Country="Spain")
df.loc[slc,"last_name"] = "Leonardo"
```
s3-unique-bucket 0.1.0
Insures a unique S3 bucket exists for the account
Requires Python: >=3.6, <4
Keywords: aws s3 bucket unique ci cd

# s3-unique-bucket
Utility to create a unique bucket for each S3 account, useful for deployment scenarios.

Did you ever need a bucket to upload your deployment templates into? Are you always annoyed by having to create them manually, not really making your deployment pipeline truly automatic? Well, worry no more!

This script is an **easy** and **idempotent** way to just create an S3 bucket that will have a very high chance to avoid name conflicts.
If run a second time, the script recognizes the bucket already exists and will only emit the bucket's name for reuse.

## Example
````bash
bucket=$(s3-unique-bucket)
aws s3 cp foo.txt s3://$bucket/foo.txt
````

## How it works
The bucket name is generated from a user-settable prefix, the sha1 hash of the account ID with base32 encoding, and the region the bucket is created in.
Then the script checks if a bucket with this name exists on the provided account. If not, it is created. Then finally the name of the bucket is emitted via `stdout`.

## Documentation
Set the AWS credentials and region with environment variables.
* `AWS_ACCESS_KEY_ID`
* `AWS_SECRET_ACCESS_KEY`
* `AWS_DEFAULT_REGION`

The rest is configured with command line arguments.

#### `-p`/`--prefix`: User-settable prefix (OPTIONAL)
**Default**: `account-bucket`

The bucket name starts with this settable prefix. It can also be empty. Note that a dash ('-') is automatically added between the prefix and the hash if prefix is not empty.

#### `-p`/`--prefix`: AWS Account Region (CONDITIONAL)
**Condition**: Must be provided if environment variable `AWS_DEFAULT_REGION` isn't set.

Which region the bucket shall be created in.