semvec/pypi-packages huggingface dataset:
TRA 2.15
Trust region algorithms for non-linear optimisation.
License: MIT
Keywords: non_linear optimisation

This package provides two trust region algorithms (TRA) for finding the minimum of some function, Levenberg-Marquardt and Powell's dogleg. 
# Levenberg-Marquardt
## Example
An example is included within the package, simply call:

```
import TRA as TRA
def forward_model(x):
    y = np.array(x[0] ** 2 + x[1] ** 2)
    y = y.reshape((1, 1))
    return y

def compute_gradient(x):
    g = np.array(([2 * x[0]], [2 * x[1]]))
    g = g.reshape((2, 1))
    return g

def compute_hessian(x):
    h = np.array(([2, 0], [0, 2]))
    h = h.reshape((2, 2))
    return h


initial_prediction = np.array([5, 2.7])

LM_algorithm = TRA.Levenberg_Marquart(initial_prediction, compute_hessian, compute_gradient,
                                                  forward_model, d_param=1e-50,
                                                  lower_constraint=-np.inf,
                                                  upper_constraint=np.inf,
                                                  num_iterations=5)

minimum = LM_algorithm.optimisation_main()



```
![image](https://user-images.githubusercontent.com/60707891/115858459-e4d7ea00-a426-11eb-8dd5-fdaa93e9c574.png)

This is a simple example, but shows how to use the Levenberg_Marquart class. 


## Function calls and arguments

There are a number of default values within the Levenberg_Marqaurdt class, including constraints on the solution, the number of iterations amd the damping parameter corresponding to the trust region. Three functions are required when instantiating a class object, one for computing the gradient, one for the Hessian and one for the mapping of the input to ouput (forward model). 
```
:
def forward_model(x)
    :
    return f(x)
def compute_gradient(x):
    :
return grad

def compute_hessian(x):
    :
return hessian

initial_prediction = x0

LM_object = TRA.Levenberg_Marquart(initial_prediction, compute_hessian, compute_gradient,
                                                  forward_model, d_param=1e-50,
                                                  lower_constraint=-np.inf,
                                                  upper_constraint=np.inf,
                                                  num_iterations=5)
                                                  
                                                  
    
```

# Theory 

 For the theory behind the code see [[1]](#1) and [[2]](#2). 

## References
<a id="1">[1]</a> 
Jorge Nocedal and Stephen J. Wright  (2006). 
Numerical Optimization. 

<a id="2">[2]</a> 
 Andrew R. Conn, Nicholas I. M. Gould, and P.L. Toint (2000). 
Trust Region Methods.
cdkeks 0.0.4
Higher-level hybrid cdk|cdk8s construct to build an eks kubernetes platform with batteries included
License: MIT
Requires Python: >=3.6

# cdkeks

Higher-level hybrid cdk|cdk8s construct to build an eks kubernetes platform with batteries included:

* Network policies with [aws-calico](https://github.com/aws/eks-charts/tree/master/stable/aws-calico)
* DNS management with [external-dns](https://github.com/kubernetes-sigs/external-dns)
* Forwarding logs to CloudWatch Logs or ElasticSearch with [fluent-bit](https://github.com/aws/aws-for-fluent-bit)
* Ingress management with the [aws load balancer controller](https://github.com/kubernetes-sigs/aws-load-balancer-controller)

:warning: This is experimental and subject to breaking changes.

## Install

TypeScript/JavaScript:

```bash
npm install --save cdkeks
```

Python:

```bash
pip install cdkeks
```

## Howto use

### Install Addons

```python
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
platform = Platform(self, "Platform",
    cluster=cluster,
    addons=[AwsCalicoAddon(), AwsLoadBalancerControllerAddon()]
)
```

See more [addons](https://github.com/hupe1980/cdkeks/tree/main/cdkeks/src/addons).

### AlbIngress

```python
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
deployment = Deployment(self, "Deployment",
    platform=platform,
    containers=[{
        "image": "nginx"
    }
    ]
)

backend = IngressBackend.from_service(deployment.expose("Service", 80))

ingress = AlbIngress(self, "Ingress",
    platform=platform,
    target_type=TargetType.IP,
    internet_facing=True
)

ingress.connections.allow_from_any_ipv4(Port.tcp(80))
ingress.add_rule("/", backend)
```

### LoadBalancer

```python
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
deployment = Deployment(self, "Deployment",
    platform=platform,
    containers=[{
        "image": "nginx"
    }
    ]
)

deployment.expose("LoadBalancer", 80,
    service_type=ServiceType.LOAD_BALANCER
)
```

## API Reference

See [API.md](https://github.com/hupe1980/cdkeks/tree/master/cdkeks/API.md).

## Example

See more complete [examples](https://github.com/hupe1980/cdkeks/tree/main/examples).

## License

[MIT](https://github.com/hupe1980/cdkeks/tree/main/cdkeks/LICENSE)
NEMESIS-test 0.0.13
Python modules and scripts used for NEMESIS project.
License: MIT
Keywords: python,astronomy,photometry,TESS

# `NEMESIS`
exoplanet tra<span style="color:red">N</span>sit surv<span style="color:red">E</span>y of nearby <span style="color:red">M</span>-dwarfs in t<span style="color:red">ES</span>s ff<span style="color:red">IS</span>

Description
----------

This repository contains documentation, code, and small chunks of data
supporting the NEMESIS project. 

To download data products from NEMESIS Planet Candidates detected in data from Sectors 1 to 5, visit our
[Filtergraph Portal](https://filtergraph.com/NEMESIS).

Author
----------
Dax Feliz

License
----------
MIT

Install
----------
`conda env create -f environment.yml -n nemesis`

Update env
----------
`conda env export --no-builds > environment.yml`