semvec/pypi-packages huggingface dataset:
pyinsight-depositor-firestore 0.0.6
Insight Depositor Module Firestore
Requires Python: >=3.5

# Insight Module Firestore Depositor
This is a Depositor Plug-in of Insight Module
## Depositor Introduction
Depositor is one kind of worker in Insight Module
* Depositore store received data and merge it
* Once Package process 
* Any Depositor Module should be able to store data without table structure predefintion
## Firestore Depositor Introduction
Google Cloud Firestore based implementation with the following advantage
## How to use
Depositor is used in an Insight Action (Receiver, Merger, Packager, etc...)
An Insight Application is a workflow of multiple Insight Action
## How to test
Before running pytest, please:
1. Set the needed GCP credential in the environnement variables
2. Running init_topic to create needed index
invisibleroads-users 0.6.4.3
Web application security defaults
Keywords: web wsgi bfg pylons pyramid invisibleroads

# InvisibleRoads Users

This package adds security functionality to your pyramid web service.

## Use

Install dependencies.

    pip install -U cookiecutter

Initialize project.

    cookiecutter https://github.com/invisibleroads/invisibleroads-cookiecutter

Follow the instructions in the generated README.

## Test

    git clone https://github.com/invisibleroads/invisibleroads-users
    cd invisibleroads-users
    pip install -e .[test]
    pytest --cov=invisibleroads_users --cov-report term-missing tests

# 0.6
- Restart from scratch
- Use pyramid-authsanity
- Restore Google
- Restore encryption
- Define check_authorization

# 0.5
- Add user.name, user.image_url
- Replace velruse with requests-oauthlib
- Support Google, LinkedIn
- Use pyramid-multiauth

# 0.4
- Define request.authenticated_user
- Remove user.token

# 0.3
- Add UserFolderMixin
- Include CSRF token for PATCH requests

# 0.2
- Let downstream packages override User, RootFactory

# 0.1
- Add pyramid_redis_sessions
qball-lang 0.5.5
QBall is a high level interpreted language. This is a Python module for using QBall in python programs. Source: https://github.com/KingsleyDockerill/QBall
Requires Python: >=3.6.3

# QBall
QBall is a high-level interpreted language written in Python. It is object oriented and has an easy to learn and use syntax. The following is an example of outputting "Hello, world!" into the console
```
out "Hello, world!";
```
It also has any easy function decleration system
```
$ This is a single line comment
((This is a
multiline comment))
$ This declares a function outarg that takes an argument a
_outarg a;
  out a;
end
outarg "Hello, world!";
```
# Docs
Everything you need to know is in documentation.txt
# Contribute
Go here to contribute: https://repl.it/@qballlang/QBall
Changes are commited 8:00 PM EST every day I can
# Trello board
The official Trello board: https://trello.com/b/cJM6HsR3/qball
pykqml 1.3
KQML messaging classes in Python.
Keywords: kqml,agent,nlp,communication,dialogue

PyKQML
======

PyKQML is an implementation of KQML messaging in Python.

Installation
============

PyKQML can be installed as

::

    pip install pykqml

Note that releases of PyKQML up to 0.5 work in Python 2 only, whereas
releases above and including 1.0 work in Python 3 only.

To install for Python 2, use:

::

    pip install pykqml==0.5

To install for Python 3 (or force an upgrade to a compatible version),
use

::

    pip install "pykqml>0.5"

Usage
=====

PyKQML implements the following KQML classes, which allow constructing
and manipulating KQML messages programmatically:

::

    KQMLToken
    KQMLString
    KQMLQuotation
    KQMLList
    KQMLPerformative
    KQMLReader
    KQMLDispatcher
    KQMLModule

You can import KQML classes as, for instance,

.. code:: python

    from kqml import KQMLList

You can create a new KQML messaging agent in the context of the TRIPS
system as

.. code:: python

    from kqml import KQMLModule

    class MyAgent(KQMLModule):
        name = "MyAgent" # This is the name of the agent to register with

        def __init__(self, **kwargs):
            # Call the parent class' constructor which sends a registration
            # message, setting the agent's name to be recognized by the
            # Facilitator.
            super(MyAgent, self).__init__(name=self.name, **kwargs)

            # Subscribe to REQUESTs of interest. The list will change
            # depending on the role of the agent
            for req in ('what-next', 'commit', 'evaluate'):
                self.subscribe_request(req)

            # Subscribe to TELLs of interest if needed. This list will change
            # depending on the role of the agent
            for tell in ('log-speechact', ):
                self.subscribe_tell(tell)

            # Now signal that the agent is ready to receive messages
            self.ready()

            # Finally, start the listener for incoming messages
            self.start()


        def receive_request(self, msg, content):
            # First, figure out what kind of request this is
            task = content.head().upper()
            # Here you typically decide what to do based on the
            # type of request.

            # Construct reply message's content
            reply_content = KQMLList()
            # Set whatever needs to be set in the reply content

            # Finally, wrap the content in a message and reply
            reply_msg = KQMLPerformative('reply')
            reply_msg.set('content', reply_content)
            self.reply(msg, reply_msg)

Testing
=======

You can run all tests by running `nosetests` in the top level folder.
pythondata-software-compiler-rt 0.0.post6206
Python module containing data files for LLVM Compiler RT Module software.
Requires Python: >=3.5

# pythondata-software-compiler_rt

Non-Python  files needed for the software compiler_rt packaged
into a Python module so they can be used with Python libraries and tools.

This is useful for usage with tools like
[LiteX](https://github.com/enjoy-digital/litex.git).

The data files can be found under the Python module `pythondata_software_compiler_rt`. The
`pythondata_software_compiler_rt.data_location` value can be used to find the files on the file
system.

Example of getting the data file directly;
```python
import pythondata_software_compiler_rt

my_data_file = "abc.txt"

with open(os.path.join(pythondata_software_compiler_rt.data_location, my_data_file)) as f:
    print(f.read())
```

Example of getting the data file using `litex.data.find` API;
```python
from pythondata_software_compiler_rt import data_file

my_data_file = "abc.txt"

with open(data_file(my_data_file)) as f:
    print(f.read())
```


The data files come from https://git.llvm.org/git/compiler-rt.git/
and are imported using `git subtrees` to the directory
[pythondata_software_compiler_rt/data](pythondata_software_compiler_rt/data).



## Installing from git repository

## Manually

You can install the package manually, however this is **not** recommended.

```
git clone https://github.com/litex-hub/pythondata-software-compiler_rt.git
cd pythondata-software-compiler_rt
sudo python setup.py install
```

## Using [pip](https://pip.pypa.io/) with git repository

You can use [pip](https://pip.pypa.io/) to install the data package directly
from github using;

```
pip install --user git+https://github.com/litex-hub/pythondata-software-compiler_rt.git
```

If you want to install for the whole system rather than just the current user,
you need to remove the `--user` argument and run as sudo like so;

```
sudo pip install git+https://github.com/litex-hub/pythondata-software-compiler_rt.git
```

You can install a specific revision of the repository using;
```
pip install --user git+https://github.com/litex-hub/pythondata-software-compiler_rt.git@<tag>
pip install --user git+https://github.com/litex-hub/pythondata-software-compiler_rt.git@<branch>
pip install --user git+https://github.com/litex-hub/pythondata-software-compiler_rt.git@<hash>
```

### With `requirements.txt` file

Add to your Python `requirements.txt` file using;
```
-e git+https://github.com/litex-hub/pythondata-software-compiler_rt.git
```

To use a specific revision of the repository, use the following;
```
-e https://github.com/litex-hub/pythondata-software-compiler_rt.git@<hash>
```

## Installing from [PyPi](https://pypi.org/project/pythondata-software-compiler_rt/)

## Using [pip](https://pip.pypa.io/)

```
pip install --user pythondata-software-compiler_rt
```
pythondata-cpu-blackparrot 0.0.post1817
Python module containing system_verilog files for BlackParrot cpu.
Requires Python: >=3.5

# pythondata-cpu-blackparrot

Non-Python  files needed for the cpu blackparrot packaged
into a Python module so they can be used with Python libraries and tools.

This is useful for usage with tools like
[LiteX](https://github.com/enjoy-digital/litex.git).

The data files can be found under the Python module `pythondata_cpu_blackparrot`. The
`pythondata_cpu_blackparrot.data_location` value can be used to find the files on the file
system.

Example of getting the data file directly;
```python
import pythondata_cpu_blackparrot

my_data_file = "abc.txt"

with open(os.path.join(pythondata_cpu_blackparrot.data_location, my_data_file)) as f:
    print(f.read())
```

Example of getting the data file using `litex.data.find` API;
```python
from pythondata_cpu_blackparrot import data_file

my_data_file = "abc.txt"

with open(data_file(my_data_file)) as f:
    print(f.read())
```


The data files come from https://github.com/enjoy-digital/black-parrot.git
and are imported using `git subtrees` to the directory
[pythondata_cpu_blackparrot/system_verilog](pythondata_cpu_blackparrot/system_verilog).



## Installing from git repository

## Manually

You can install the package manually, however this is **not** recommended.

```
git clone https://github.com/litex-hub/pythondata-cpu-blackparrot.git
cd pythondata-cpu-blackparrot
sudo python setup.py install
```

## Using [pip](https://pip.pypa.io/) with git repository

You can use [pip](https://pip.pypa.io/) to install the data package directly
from github using;

```
pip install --user git+https://github.com/litex-hub/pythondata-cpu-blackparrot.git
```

If you want to install for the whole system rather than just the current user,
you need to remove the `--user` argument and run as sudo like so;

```
sudo pip install git+https://github.com/litex-hub/pythondata-cpu-blackparrot.git
```

You can install a specific revision of the repository using;
```
pip install --user git+https://github.com/litex-hub/pythondata-cpu-blackparrot.git@<tag>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-blackparrot.git@<branch>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-blackparrot.git@<hash>
```

### With `requirements.txt` file

Add to your Python `requirements.txt` file using;
```
-e git+https://github.com/litex-hub/pythondata-cpu-blackparrot.git
```

To use a specific revision of the repository, use the following;
```
-e https://github.com/litex-hub/pythondata-cpu-blackparrot.git@<hash>
```

## Installing from [PyPi](https://pypi.org/project/pythondata-cpu-blackparrot/)

## Using [pip](https://pip.pypa.io/)

```
pip install --user pythondata-cpu-blackparrot
```