semvec/pypi-packages huggingface dataset:
behave-plain-color-formatter 0.0.2
Formatter for behave that uses color but not fancy terminal repositioning
License: MIT
Keywords: testing,behave,teamcity,formatter,report

# behave-plain-color-formatter
Formatter for [behave][] that uses color but not fancy terminal repositioning

![screenshot](https://raw.githubusercontent.com/msabramo/behave-plain-color-formatter/master/screenshot.png)

## Installation
```bash
pip install behave-plain-color-formatter
````

## Usage
You can specify the formatter directly in the command line using the -f argument:
```bash
behave -f behave_plain_color_formatter:PlainColorFormatter
```
or
```bash
behave --format behave_plain_color_formatter:PlainColorFormatter
```

You can also register the formatter to be available through a simple name:
```ini
# -- FILE: behave.ini
[behave.formatters]
plain.color = behave_plain_color_formatter:PlainColorFormatter
```
and the use it like:
```bash
behave -f plain.color
```


[behave]: https://github.com/behave/behave
Solar-Calculation 0.1.dev1
Identify potential solar sites and compute potential solar energy
License: MIT
Keywords: solar energy

# ASAP
Solar PV Siting Survey for Anchorage, Alaska

The project will determine the technical siting potential for commercial-scale solar photovoltaic (PV) installations of
1,000 kW (AC) or larger throughout Anchorage, within the built environment. The methodology can build upon the Clean
Coalitionâ€™s work conducting similar Solar Siting Surveys for other entities. The project will identify lower cost and
higher value renewable resource opportunities reflecting characteristics of all available sites in relation to existing
loads and electric grid infrastructure. Outcomes will guide the development of cost-effective local solar generation
within Anchorage.

## Provided Materials
1. EBCE case study (.kml)
2. EBCE project summary (.xlsx)
3. LIDAR data collected in 2015 (.las)
4. Buildings Polygon (.shp)
5. Address Point (.shp)
6. Arcgis license offered by instructor

## Plan
1. Select an area for case study, details are shown in the "Case Study" section
2. Rasterize the region and obtain information via different tools on ArcMap
3. Case study has to be comprehensive enough so the exported models and their corresponding python scripts can process
data from other similar regions. For example, model created in this project can also be used in San Francisco or other
regions with different latitude and longitude.

## Case Study
The case region, where buildings, some parking lots and empty grounds exist in a well sorted pattern, was selected.
The selected las file name is 1659_2614.las. LAS file contains raw LIDAR point cloud data including x, y and z values.
By creating a new "LAS Dataset" it is possible to load the data into Arcmap. Creating masks that could trim unnecessary
and unwanted data points. Aspect, slope, solar radiation and human sorting are five masks necessary to create. After
masking out the useful area irradiation raster, zonal statistics are calculated based on buildings' polygons. Then the
mean value (if selected) of solar irradiation is presented on each building polygon provided. The table generated by
zonal statistics feature can be used for data processing. Also, since masks are account for slopes that are larger than
35 degrees, and aspect that does not face south, most of pipeline system, HVAC, ventilation and attics on the building
will not be included in the masked solar irradiation raster, thus the mean value calculated is relatively accurate. The
shape file eventually converts into .kml file, which can display in Google Earth Pro and Google Map.

## Tips
1. This program is based on LIDAR data and corresponding building polygons extraction 
2. Python script generated by the model can process similar situations elsewhere
3. Due to limited access to ICA data, which can only be provided by local electricity company, ICA summary is abandoned
easypackage 0.1.8
Python packages - the simple way.
License: MIT
Keywords: python,package,module,easy,automatic,syspath,setup,release,root

# Easy Package - for Python [![Build Status](https://travis-ci.org/grimen/python-easypackage.svg?branch=master)](https://travis-ci.org/grimen/python-easypackage) [![PyPI version](https://badge.fury.io/py/easypackage.svg)](https://badge.fury.io/py/easypackage)

Python packages - the simple way.


## Background

**Had to code Python** so ended up abstracting out these Python package related utilities to be able to deal with Python packages which is in a sad state in comparison to most other language environments.


## Features

- **Easy package (system) load paths** - because package imports and load paths in Python makes adults cry.

- **Easy package tag/release** - because package tag/release management should not be done by hand, fallback on conventions.


## Install

Install using **pip**:

```sh
pip install easypackage
```


## Usage

How to to make use of `easypackage` in various ways:


### Required

Create a `package.json` in package root folder (e.g. `mypackage`) - see **Node.js** specification, in lack of such for Python.



## Easy package load paths

In any package source file:

```python
# e.g. `~/dev/projects/mypackage/foo/foo.py`

def hello:
    print('hello!')

```

In any other package source file:

```python
# e.g. `~/dev/projects/mypackage/bar/bar.py`

from easypackage import syspath as easysyspath

# add `~/dev/projects/mypackage` to Python system path unless already added
easysyspath.syspath()

from mypackage.utils.foo import foo

foo.hello()

```

And so on.


## Easy package tag/release (WIP/TBA)

In file `release.py` in package project root:

```python
from easypackage import release as easyrelease

easyrelease.release()
```

To tag/release a new version to Git, simply add/update a valid **semver** version tag in `package.json` - according to **Node.js** specification - and then run `python release.py`.

Currently not submitted to any Python package register, but will probably be added soon.


## License

Released under the MIT license.