semvec/pypi-packages huggingface dataset:
pytempscsp 0.9.4
Temporal Scale-Space Toolbox for Python.
License: MIT
Requires Python: >=3.7,<4.0

# pytempscsp : Temporal Scale Space Toolbox for Python

For performing temporal smoothing with the time-causal limit kernel and
for computing discrete temporal derivative approximations by applying
temporal difference operators to the smoothed data.

This code is the result of porting a subset of the routines in the Matlab
package tempscsp to Python, however, with different interfaces for the functions.

For examples of how to apply these functions for smoothing temporal signals
to different temporal scales in a fully time-causal manner, please see the
enclosed Jupyter notebook [tempscspdemo.ipynb](https://github.com/tonylindeberg/pytempscsp/blob/main/tempscspdemo.ipynb).

For more technical descriptions about the respective functions, please see
the documentation strings for the respective functions in the source code
in [tempscsp.py](https://github.com/tonylindeberg/pytempscsp/blob/main/pytempscsp/tempscsp.py).

## Installation

This package is available 
through pip and can installed by

```bash
pip install pytempscsp
```

This package can also be downloaded directly from GitHub:

```bash
git clone git@github.com:tonylindeberg/pytempscsp.git
```

## References

Lindeberg (2023) "A time-causal and time-recursive temporal scale-space representation
of temporal signals and past time", Biological Cybernetics 117 (1-2): 21-59.
([Open Access](http://dx.doi.org/10.1007/s00422-022-00953-6))

Lindeberg (2016) "Time-causal and time-recursive spatio-temporal receptive fields",
Journal of Mathematical Imaging and Vision 55(1): 50-88.
([Open Access](https://doi.org/10.1007/s10851-015-0613-9))

The time-causal limit kernel was first defined in Lindeberg (2016), however,
then also in combination with a spatial domain, and experimentally tested on
video data. The later overview paper (Lindeberg 2023) gives a dedicated treatment
for a purely temporal domain, and also with relations to Koenderink's scale-time
kernels and the ex-Gaussian kernel.
dc3client 0.0.9
dc3client: Client of Digital Curing3.
License: MIT
Keywords: digital-curing3 dc3client

# dc3client

[デジタルカーリング3](https://github.com/digitalcurling/DigitalCurling3/wiki)用のクライアントライブラリです。

問題を発見した場合、GitHubのissueに投稿してください。

## インストール

Python 3.10以上が必要です。pip経由でインストールできます。

```bash
pip install dc3client
```

## 使い方

ドキュメントは[こちら](https://kawamlab.github.io/DC3-python/)を参照してください。
また、使い方や対戦環境の構築方法は[DC3-python-template](https://github.com/kawamlab/DC3-python-template)を参照してください。


## ドキュメント更新用メモ

```sh
sphinx-quickstart docs # init
```
```sh
sphinx-apidoc -f -o ./docs/source/ ./
sphinx-build  ./docs/source/ ./docs/
```
terrajinja-sbp-backend 0.1.0
Terrajinja extension for automation using cdktf
License: MIT

# terrajinja-sbp-backend

This is an extension to the vault provider for the following modules.
The original documentation can be found [here](https://developer.hashicorp.com/terraform/language/settings/backends/configuration)

# SBP Specific implementations
Here is a list of supported resources and their modifications

## sbp.backend.s3_backend
Original provider: [backend.s3_backend](https://developer.hashicorp.com/terraform/language/settings/backends/s3)

This custom provider adds the following:
- adds the s3 backend provider to terrajinja

| old parameter | new parameter | description |
| ------ | ------ | ------ |
| - | - | there are no new parameters |

This resource is an interface for the backend providers,
as the concept of backend is not an resource, this is the intermediate to support it.

### terrajinja-cli example
the following is a code snipet you can used in a terrajinja-cli template file.
This created both the hashicorp vault with the name `generic`, and adds a secret in it in the path `application` with key `admin` that contains a random password
```
terraform:
  resources:
    - task: s3-backend
      module: sbp.backend.s3_backend
      parameters:
        bucket: "terraform-state"
        key: {{  terraform.state_file }}
        endpoint: "https://s3.endpoint.url"
        encrypt: True
        region: eu-nl-prod01
        skip_region_validation: True
        skip_credentials_validation: True
        skip_metadata_api_check: True
```



History
-------

0.0.1 (14-03-2024)
---------------------

* First code creation