semvec/pypi-packages huggingface dataset:
dynaphos 0.1.3
Fully differentiable and biologically plausible simulation of prosthetic vision.
Requires Python: >=3.7

# About the project
A fully differentiable and biologically plausible simulation of cortical prosthetic vision, which can be used for end-to-end optimization.

## Installation
`pip install dynaphos`

## Getting Started
- Download the default configuration file (config/params.yaml) from [our repository](https://github.com/neuralcodinglab/dynaphos/) and adjust according to needs. 
- Run the `test` suite.
- See the `examples` directory for simple use cases.

## Citation
van der Grinten, M., van Steveninck, J. D. R., Lozano, A., Pijnacker, L., Rueckauer, B., Roelfsema, P., Marcel van Gerven, Richard van Wezel, Umut Güçlü & Güçlütürk, Y. (2024). Towards biologically plausible phosphene simulation for the differentiable optimization of visual cortical prostheses. eLife, 13, e85812. [https://doi.org/10.7554/eLife.85812](https://doi.org/10.7554/eLife.85812). 

## Experiments
For the end-to-end optimization experiments in our publication see [this repository](https://github.com/neuralcodinglab/viseon/tree/dynaphos-paper). The code for the experiments described in our publication (using this simulator) can be found in [this repository](https://github.com/neuralcodinglab/dynaphos-experiments). All other code that was used in our publication can be made available on request.

## Contact
Feel free to submit an issue, we're happy to help.
var 2024.3.0
Different Methods to Estimate the Value-at-Risk of a portfolio.

"The search for appropriate risk measuring methodologies has been followed by increased financial uncertainty worldwide. Financial
turmoil and the increased volatility of financial markets have induced the design and development of more sophisticated tools for
measuring and forecasting risk. The most well known risk measure is value at risk (VaR), which is defined as the maximum loss over
a targeted horizon for a given level of confidence. In other words, it is an estimation of the tails of the empirical distribution
of financial losses. It can be used in all types of financial risk
measurement" ([Julija Cerović Smolovic, 2017](https://doi.org/10.1080/1331677X.2017.1305773)).

In addition to Value at Risk, the package includes Conditional Value at Risk (Expected Shortfall or CVaR) and Conditional Drawdown
at Risk (CDaR).
frontmatter 3.0.8
Extract YAML frontmatter from files and strings.
Keywords: yaml frontmatter front-matter markdown blogging

frontmatter
============

A simple Python package for parsing YAML Frontmatter from a text file or string. Written for Python 3, but should also work for Python 2.7.

Usage
------

```
from frontmatter import Frontmatter

# assuming 'testfile.md' exists
post = Frontmatter.read_file('testfile.md')

print(post['attributes'], "\n")  # Dictionary
print(post['body'], "\n")        # String
print(post['frontmatter'])       # String
```

The output would be:

```
{'foo': 'bar', 'num': 3, 'list': ['first', 'second', 'third']}

This is the actual post content
This is a second line

foo: bar
num: 3
list:
- first
- second
- third
```

In the above example, the contents of **testfile.md** is:

```
---
foo: bar
num: 3
list:
- first
- second
- third
---

This is the actual post content
This is a second line
```

LICENSE
--------

[ISC License](https://en.wikipedia.org/wiki/ISC_license)


Copyright (c) 2018, Jonathan Beebe

Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby granted, provided that the above
copyright notice and this permission notice appear in all copies.

THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.