semvec/pypi-packages huggingface dataset:
dlong 0.2.0
Prototype framework for projected socio-economic climate damages
Requires Python: >=3.8

[![pythonpackage](https://github.com/brews/dlong/actions/workflows/pythonpackage.yml/badge.svg)](https://github.com/brews/dlong/actions/workflows/pythonpackage.yml)
[![codecov](https://codecov.io/gh/brews/dlong/branch/main/graph/badge.svg?token=NZI5NI6RSH)](https://codecov.io/gh/brews/dlong)

# dlong
Prototype framework for projected socio-economic climate damages

This is a prototype. It is likely to change in breaking ways. It might delete all your data so don't use it in production.

## Examples
```python
import dlong
from dlong.types import ClimateData
import xarray as xr

# First, let's make some demo data.
# Say 3 years of annual temperature data across 3 regions:
region = [1, 2, 3]
year = [2019, 2020, 2021]
input_temperature = xr.DataArray(
    [[1.0, 2.0, 3.0], [2.0, 3.0, 4.0], [3.0, 4.0, 5.0]],
    coords=[region, year],
    dims=["region", "year"],
)
# And here are coefficients for a quadratic damage function. Different for 
# each region.
damage_coefficients = xr.Dataset(
    data_vars={
        "beta0": (["region"], [1, 1, 1]),
        "beta1": (["region"], [1, 2, 3]),
        "beta2": (["region"], [4, 5, 6]),
    },
    coords={"region": (["region"], region)},
)

# Put it all together to describe climate data, a damage function, and
# a strategy for discounting damages.
climate = dlong.types.ClimateData(temperature=input_temperature)
damage_model = dlong.QuadraticDamageModel(coefs=damage_coefficients)
discount_strategy = dlong.FractionalDiscount(rate=0.02, reference_year=2020)
# We could use these individually, or combine them into a "recipe" to output 
# discounted damages. The idea is that these components are compositable
# so components can be customized and run in large batches.
recipe = dlong.ExampleRecipe(
    climate=climate, damage_function=damage_model, discount=discount_strategy
)
damages = recipe.discounted_damages()
#<xarray.DataArray (region: 3, year: 3)>
#array([[  6.12      ,  19.        ,  39.21568627],
#       [ 25.5       ,  52.        ,  87.25490196],
#       [ 65.28      , 109.        , 162.74509804]])
#Coordinates:
#* region   (region) int64 1 2 3
#* year     (year) int64 2019 2020 2021
```

## Installation
```shell
pip install dlong
```

`dlong` currently requires Python > 3.8 and the `xarray` package.

Install the unreleased bleeding-edge version of the package with:
```shell
pip install git+https://github.com/brews/dlong
```

## Support
Source code is available online at https://github.com/brews/dlong/. This software is Open Source and available under the Apache License, Version 2.0.

## Development

Please file bugs in the [bug tracker](https://github.com/brews/dlong/issues).

Want to contribute? Great! Fork the main branch and file a pull request when you're ready. Please be sure to write unit tests and follow [pep8](https://www.python.org/dev/peps/pep-0008/). Fork away!
revealjs 0.2.10
Reveal.js Presentation Builder Inspired By revealjs

### Docker image for reveal js

    guna.n

### Command to create new reveal js presentation

     docker run -it --name revealjs-builder3 -v $(pwd):/app revealjs /bin/bash

- Steps:

  - if you have and .adoc file inside what ever the directory thats not and matter.

  - docker run -it --name (docker container name) -v mapping the volume inside your host machine to dockerized machine.

  - -v (your machine directory) --> where the asciidoc file present's.

  - after the semicolon : operator which means that where you needs to points.

  - user/home/ is the default working directory inside the image.

  - please make sure on /app directory .

  - all the volume inside your local machine will will points into that /app directory only.

  - if you needed to generate the presentation file you explicitly move the file and generate that as of now.

```bash

    Thankyou Happy Coding :)
```
pemo 0.1.1
一个帮助用户下载服务器上的文件的命令行工具, 它依托于云存储服务实现中转
Requires Python: >=3.0

## 项目简介
  pemo是一个帮助用户下载服务器上的文件的命令行工具, 它依托于云存储服务实现中转。
![img.png](img.png)

## 安装方式
```python
   pip install pemo
```

## 功能蓝图
![img_1.png](img_1.png)

## 功能清单
 - [X] 上传文件到云端存储服务中
 - [X] 支持压缩上传文件夹
 - [ ] 支持配置云存储厂商
 - [X] 支持管理配置

> 目前默认支持腾讯云cos, 空间有限建议自行更改为自己的腾讯云cos
> 后续会支持其他的云存储类型如阿里云、百度云等等

## 使用

### 上传文件
```python
   pmeo upload --path ./readme.md 
```
### 上传文件夹
```python
   pmeo upload --path ./dist
```

### 获取配置信息
```python
   ## 查看所有配置
   pmeo config list -A
   ## 查看指定类型的配置
   pmeo config list -t ali
```
### 配置信息
```python
   ## 配置信息
   pmeo config set -t ali

   ## 配置默认存储类型
   pmeo config set-default -t ali
```
femtoflow 0.1.8
Femtosense Model Optimization Toolkit
Requires Python: >=3.6

![Build Status](https://codebuild.us-west-2.amazonaws.com/badges?uuid=eyJlbmNyeXB0ZWREYXRhIjoiRGpDOFpEOXF1M0prSy9OOVNkZ3F2NkRKM0NNMG8xbmpZU0hZdUp1ejhHOEswdDRhOEZOakMrdWZyaHp1WGVtQ1lVTStzRUpXaFlYeFZPSEJFd21NdjF3PSIsIml2UGFyYW1ldGVyU3BlYyI6InJZYzhuZ3d3a1FUUzBzM0kiLCJtYXRlcmlhbFNldFNlcmlhbCI6MX0%3D&branch=master)

# FemtoFlow

`femtoflow` is a Python package that enables pruning and quantization of TensorFlow models for optimized deployment to Femtosense's SPU (Sparse Processing Unit). The package provides a straightforward and flexible interface for reducing model size and complexity while maintaining high-performance inference on the SPU.

## Features

- **Pruning:** Reduce model size by removing unimportant connections and neurons from your TensorFlow model.
- **Quantization:** Lower the precision of weights and activations to reduce memory requirements and computational costs while preserving model accuracy.

## Getting Started

You can install `femtoflow` using `pip`:

`pip install femtoflow`

## Documentation

Detailed documentation for `femtoflow`, including tutorials, API reference, and examples, can be found on the official website: [https://femtoflow.femtosense.ai](https://femtoflow.femtosense.ai)