semvec/pypi-packages huggingface dataset:
geojsoncontour 0.5.0
Convert matplotlib contour plots to geojson
License: MIT
Keywords: contour plot geojson pyplot matplotlib gis map

# geojsoncontour
[![Build Status](https://travis-ci.org/bartromgens/geojsoncontour.svg?branch=master)](https://travis-ci.org/bartromgens/geojsoncontour) [![PyPI version](https://badge.fury.io/py/geojsoncontour.svg)](https://badge.fury.io/py/geojsoncontour) [![Coverage Status](https://coveralls.io/repos/github/bartromgens/geojsoncontour/badge.svg?branch=master)](https://coveralls.io/github/bartromgens/geojsoncontour?branch=master)  
A Python 3 module to convert matplotlib contour plots to geojson. Supports both contour and contourf plots.

Designed to show geographical [contour plots](http://matplotlib.org/examples/pylab_examples/contour_demo.html), 
created with [matplotlib/pyplot](https://github.com/matplotlib/matplotlib), as vector layer on interactive slippy maps like [OpenLayers](https://github.com/openlayers/ol3) and [Leaflet](https://github.com/Leaflet/Leaflet).

Demo project that uses geojsoncontour: [climatemaps.romgens.com](http://climatemaps.romgens.com)

![geojson contour demo usage](https://raw.githubusercontent.com/bartromgens/geojsoncontour/master/data/example_climatemaps.png)

## Installation
Install with pip,
```
pip install geojsoncontour
```

## Usage

Use `contour_to_geojson` to create a geojson with contour lines from a `matplotlib.contour` plot (not filled).
Use `contourf_to_geojson` to create a geojson with filled contours from a `matplotlib.contourf` plot.

### Contour plot to geojson
```python
import numpy
import matplotlib.pyplot as plt
import geojsoncontour

# Create contour data lon_range, lat_range, Z
<your code here>

# Create a contour plot plot from grid (lat, lon) data
figure = plt.figure()
ax = figure.add_subplot(111)
contour = ax.contour(lon_range, lat_range, Z, cmap=plt.cm.jet)

# Convert matplotlib contour to geojson
geojson = geojsoncontour.contour_to_geojson(
    contour=contour,
    ndigits=3,
    unit='m'
)
```
For filled contour plots (`matplotlib.contourf`) use `contourf_to_geojson`.
See [example_contour.py](examples/example_contour.py) and [example_contourf.py](examples/example_contourf.py) for simple but complete examples.

### Show the geojson on a map
An easy way to show the generated geojson on a map is the online geojson renderer [geojson.io](http://geojson.io) or [geojson.tools](http://geojson.tools).

### Style properties
Stroke color and width are set as geojson properties following https://github.com/mapbox/simplestyle-spec.

### Create geojson tiles
Try [geojson-vt](https://github.com/mapbox/geojson-vt) or [tippecanoe](https://github.com/mapbox/tippecanoe) if performance is an issue and you need to tile your geojson contours.


## Development

### Tests

Run all tests,
```
python -m unittest discover
```

### Release

Install setuptools, wheel and twine:
```
python -m pip install --upgrade setuptools wheel twine
```

Increase the version number in `setup.py`.

Create dist:
```
python setup.py sdist bdist_wheel
```

Upload:
```
twine upload dist/*
```
versionrunner 0.0.3
A library that easily generates a requirements.txt file
License: MIT
Requires Python: >=3
Keywords: version, autoversion, requirements, versionrunner

# versionrunner
## Motivation

After writing a program, it is essential to generate a `requirements.txt` file before distribution, so that other users can easily recreate the deployed program.

However, finding and specifying the versions manually can often be tedious. In such cases, you can use `versionrunner` to quickly convert the library versions into a requirements.txt file and save it automatically.

---

## How to Run ?
```python
from versionrunner import *

search_version(__file__)  # or search_version('your_file_name')
```
GeoLode 1.0.1.3
Python package designed for efficient geospatial data analysis
License: MIT

**GeoLode提供了多种对矢量和栅格数据进行操作的功能**

# ShapefileProcess

**引入示例**：import geolode.shapefileprocess as shplode

**功能**：
    readShapefile(SHPath) 读取shapefile文件
    featureExtent(feature) 获取要素外包矩形
    is_intersecting(extent, feature) 判断要素是否与矩形相交
    is_contained(block, feature) 判断要素是否包含矩形

# TifProcess

**引入示例**：import geolode.TifProcess as tiflode

**功能**：
    readTif(fileName, xoff=0, yoff=0, data_width=0, data_height=0, bands=None) 读取tif文件
    readTifInChunksPix(fileName, chunk_width=256, chunk_height=256, bands=None) 读取tif文件并按块读取
    readTifInChunksNum(fileName, num_chunks_x=10, num_chunks_y=10, bands=None) 读取tif文件并按块读取，按给定的分块数量进行分块
    calBoundingBox(geo_transform, width, height) 计算栅格数据的边界框
    removeBand(input_tif, output_tif, bands_to_remove) 删除栅格数据中的指定波段
    writeTiff(fileName, data, im_geotrans=(0, 0, 0, 0, 0, 0), im_proj="") 写出tif文件

# TifShpFilte

**引入示例**：import geolode.TifShpFilte as tifshplode

**功能**：
    filterFeaturesByExtent(features, tif_extent) 根据栅格数据范围过滤要素
    RasterShpIntersect(tif_data, geo_transform, feature) 判断栅格数据与要素是否相交
    check_tif_size(tif_path, expected_width=256, expected_height=256) 检查栅格数据的大小是否与给定的大小一致
    clip_tif_by_shapefile(tif_path, shp_path, output_dir, tif_suffix) 将栅格数据裁剪为与矢量数据相交的区域
    clip_tif_by_features(tif_path, shp_path, output_dir) 将栅格数据裁剪为与矢量数据相交的区域


# 更新日志

1.0.1.3版本：readTifInChunksPix与readTifInChunksNum函数添加了bands参数，便于指定读取的具体波段与次序。