semvec/pypi-packages huggingface dataset:
shadowpy 1.5
Awesome library to work with. shadowpy is the right choice for your project. Extra new features added.

* sys_xmath * sys_xmatrics * sys_xdate * sys_xrefer. All this methods helps you to achive you task. 'x' is the main name of the program. All classes starts with 'sys_x<name>'. Inner methods starts with the syntax 'x_<name>'. The sys_xmath helps you to apply mathematical operations from basic to advance. In other side sys_xmetrics helps you to get the screen resolution and height and width of your screen. The sys_xdate allows you to access  date, year, day respectively. In case of any confusion of methods sys_xrefer will help you to access the properties and methods of particular class. In case of any abjection mail us on 'shaaimanaalik@gmail.com'.
odysseia 0.0.1a98
Warning: RUN!
Requires Python: >=3.6

# Tutorial

Install:
<pre>
pip install odysseia
</pre>

Example Script:
<pre>
from odysseia.do import Find
result = Find(dbPath = 'db.csv')
result.writeReport('test.report')
</pre>

dbPath
class1 = None
class2 = None
stdevThread = 100
distrThread = 0.05
corThread = 0.2
iteration = 1
patient = None
noChangeThread = 0.1
keepRatio = 1.0
keepThread = 0.9
topGRP = 100

Output Files:
KarthiksModelEngine 0.7.8
Just an another Python Package with SuperMachine Abilities
Requires Python: >=3.8
Keywords: Automl,Supervised Learning

# Karthik's Model Engine
A Python package  is a small prototype in removing human interference in supervised learning (Automating supervised learning).

## Installation
 ```pip install KarthiksModelEngine```
 
## Usage
To use the engine make sure the data is cleaned before loading, it can only take care of NaN rows. This will be taken care in the future.
Use your preferred IDE and type the following.

 ```from fluid.domain import Karthiks_Model_Engine ```
 
When it asks for independent and dependent values, give the name of the column exactly as it is displayed in the dataset seperating them with ','. 
And then it will take you through.
emplo-nameko-zipkin 0.1.8
Zipkin tracing for nameko framework

emplocity-nameko-zipkin
-------------

Zipkin tracing for nameko framework

Install
-------

```
pip install emplocity-nameko-zipkin
```

Usage
-----

#### Services

```python
from nameko_zipkin import Zipkin
from nameko.rpc import rpc


class Service:
    name = 'service'
    zipkin = Zipkin() # Dependency provider injects py_zipkin.zipkin.zipkin_span object

    @rpc
    def method(self):
        assert self.zipkin.service_name == Service.name
        assert self.zipkin.span_name == Service.method.__name__
```

#### Standalone rpc

```python
from py_zipkin import zipkin
from nameko_zipkin import monkey_patch
from nameko_zipkin.transport import HttpHandler
from nameko.standalone.rpc import ClusterRpcProxy


handler = HttpHandler('http://localhost:9411/api/v1/spans').handle
monkey_patch(handler)

with zipkin.zipkin_server_span('RootService',
                               'RootMethod',
                               sample_rate=100.,
                               transport_handler=handler):
    with ClusterRpcProxy({'AMQP_URI': "pyamqp://guest:guest@localhost"}) as proxy:
        proxy.service.method()
```

How it works
------------

* monkey_patch patches MethodProxy class to initialize a client span, it's called in dependency provider setup method
* On service method call a server span is created
* Trace parameters (trace_id, parent_span_id, etc.) are passed through context data and are accessible in py_zipkin.thread_local.get_zipkin_attrs
* If there are no parameters, request isn't traced
* Child service calls are also supported
* Trace results are reported through handler classes in nameko_zipkin.transport


Configuration
-------------

ZIPKIN section must be added to nameko service config.yaml

```yaml
ZIPKIN:
    HANDLER: HttpHandler
    HANDLER_PARAMS:
      url: http://localhost:9411/api/v1/spans
```

Test it out locally
-------------------

We've provided an example docker-compose based stack that includes a nameko
service and a Zipkin instance. To try it out, run `docker-compose up` in the
root directory of the project. This will bring up three services: RabbitMQ
(required by nameko), Zipkin, and an example Python service with both RPC
and HTTP endpoints.

Navigate to http://localhost:8000 to visit the example service, and if
you then visit Zipkin dashboard (typically at http://localhost:9411),
you should see some traces there!

Planned changes
---------------

* Kafka transport support
* Custom handlers support in config.yaml ('my_module.MyHandler')