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توضیحات

Python library for the generation of contestualized random coordinates

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	PyCristoforo-2.0.0
نام	PyCristoforo
نسخه کتابخانه	2.0.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Alessandro
ایمیل نویسنده	alessandro2.negrini@gmail.com
آدرس صفحه اصلی	https://github.com/AleNegrini/PyCristoforo
آدرس اینترنتی	https://pypi.org/project/PyCristoforo/
مجوز	-

# PyCristoforo **v2.0.0** The new python library for the generation of **contestualized random** coordinates. PyCristoforo takes in input a country name and it generates random coordinates, inside that country (not including the sea/ocean sections). **Python version supported: 3.6, 3.7** Latest updates ----------------- | Date | Description | | ------------- | ------------- | | 30/06/2019 | PyCristoforo 1.0.0 published on PyPi | | 08/07/2019 | PyCristoforo 1.0.0.post4 published on PyPi | | 09/07/2019 | PyCristoforo 1.1.0 published on PyPi | | 28/07/2019 | PyCristoforo 2.0.0 published on PyPi | Table of contents ----------------- - [Random Point generation](#random-point-generation) - [Requirements](#requirements) - [Install](#install) - [Usage](#usage) - [Build](#build) - [Running tests](#running-tests) - [ChangeLog](#changelog) - [License](#license) - [What next](#what-next) - [Authors](#authors) - [Notes](#notes) Random Point generation ----------- In this section you can find some details about random coordinates generation method. **Version 1** PyCristoforo v1 implements a very simple algorithm for random point generation: - starting from the country Polygon shape, it first gets the rectangle around it and then the min/ max latitudes and longitude. ``` # getting min, max lat/lng min_lng = get_min_lng(shape) min_lat = get_min_lat(shape) max_lng = get_max_lng(shape) max_lat = get_max_lat(shape) ``` ![Germany Envelope](pycristoforo/resources/env_germ.png?raw=true "Germany Envelope") - inside it, the random coordinates are generated in a uniform way ``` # generate random float between [min_lng, max_lng) val1 = numpy_random.uniform(min_lng, max_lng) # generate random float between [min_lat, max_lat) val2 = numpy_random.uniform(min_lat, max_lat) ``` ![Germany Envelope Points KO](pycristoforo/resources/env_germ_p2.png?raw=true "Germany Envelope Points KO") - finally, only the points inside the country shape are kept, the ones outside are discarded. New points are then generated until reaching the user expected number. ``` # random point generation while counter != points: if random_point.within(shape): ... list_of_points.append(ran_point) counter += 1 ``` ![Germany Envelope Points OK](pycristoforo/resources/env_germ_p1.png?raw=true "Germany Envelope Points OK") As said above, the algorithm is very simple, but also very inefficient. Benchmark: * Country: "Germany" * NumPoints: 100k * Time: 4min 20sec **Version 2** In order to make the algorithm faster and more robust (https://codereview.stackexchange.com/questions/69833/generate-sample-coordinates-inside-a-polygon), v2 changes the way random points are generated: - country polygon is triangulated and the area of each triangle is then calculated; - for each sample: * pick the triangle 𝑡 containing the sample, using random selection weighted by the area of each triangle. * pick a random point uniformly in the triangle, as follows: * pick a random point 𝑥,𝑦 uniformly in the unit square. * If 𝑥+𝑦>1, use the point 1−𝑥,1−𝑦 instead. The effect of this is to ensure that the point is chosen uniformly in the unit right triangle with vertices (0,0),(0,1),(1,0) * Apply the appropriate affine transformation to transform the unit right triangle to the triangle 𝑡. The hard constraint of this method is that it works only for **convex polygons**, and therefore some points may be generated out of the country shape (convex hull). ![Germany Convex Hull Points KO](pycristoforo/resources/germ_hull_p3.png?raw=true "Germany Convex Hull Points KO") All points are checked if lying inside the country shape. For each point outside the country, a new one is generated. ![Germany Convex Hull Points KO](pycristoforo/resources/germ_hull_p4.png?raw=true "Germany Convex Hull Points KO") This method almost 20% more faster on benchmark. Benchmark: * Country: "Germany" * NumPoints: 100k * Time: 3min 30sec Requirements ------------ * numpy v1.16.4 * Shapely v1.6.4.post2 Details [here](requirements.txt) Resources --------- * World countries geoJSON ([link](https://datahub.io/core/geo-countries#resource-countries)) Install ------- PyCristoforo is very easy to install and use (please be sure to have installed dependencies (section 'Requirements') ``` pip3 install pycristoforo ``` Usage ------- * Now you can import it in your script: ``` import pycristoforo as pyc ``` * You can now load the geojson of the country you'd like to generate geocoordinates in: ``` country = pyc.get_shape("Italy") ``` The supported input for `get_shape` method are not only the extended country names: you can either use `ISO_A3` code. [Here](COUNTRIES.csv) you can find the supported input (country_name, ISO_A3). Method is case insensitive: ``` country = pyc.get_shape("ITALY") ``` behaves the same as: ``` country = pyc.get_shape("italy") ``` `country` var contains now the shape of the country passed in input (usually a `shapely Poligon`or `MultiPoligon`): ``` MULTIPOLYGON (((12.127777 47.00166300000012, 12.13611 46.966942, 12.16027600000012 46.92805, 12.18138900000014 46.909721, 12.189722 46.90610500000014, 12.232222 46.888885, 12.301666 46.84111, 12.378611 46.72666, 12.38888700000012 46.715553, ... , 12.047777 36.753052, 12.03833200000014 36.747215, 12.027777 36.74222, 12.01583 36.738327))) ``` * Now that country shape has been loaded, it's time to get `n` random geocoordinates. Suppose to generate 100 geocoordinates: ``` points = pyc.geoloc_generation(country, 100, "Italy") ``` `points` is a list of Points: ``` 00 = {dict} {'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [13.963703154465053, 42.591335534115316]}, 'properties': {'point': 1, 'country': 'Italy'}} 01 = {dict} {'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [11.659857182901725, 43.95787059805974]}, 'properties': {'point': 2, 'country': 'Italy'}} 02 = {dict} {'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [7.992769814920238, 45.89632889069682]}, 'properties': {'point': 3, 'country': 'Italy'}} ... 99 = {dict} {'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [6.112769314920238, 45.45632889569111]}, 'properties': {'point': 100, 'country': 'Italy'}} ``` You can now iterate through the list and make good use of them. * Print what you just generated: ``` geoloc_print(points, ',') ``` * A utility method is the `get_envelope` one: ``` env = pyc.get_envelope(country) ``` Build ------ ``` python3 setup.py sdist bdist_wheel ``` Running tests ------------- Work in progress ChangeLog --------- Current version: 2.0.0 [Changelog](CHANGELOG.rst) License ------- This project is licensed under the MIT License - see the [LICENSE](LICENSE.txt) file for details What Next ------------ * v2.1.0: random points printed in an external file * v3.0.0: regions support * v3.1.0: counties support * v3.2.0: cities support Authors ------- * **Alessandro Negrini** - *Initial work* - [Github profile](https://github.com/AleNegrini) See also the list of [contributors](AUTHORS.rst) who participated in this project. Notes ----- This project has been set up using PyScaffold 3.1. For details and usage information on PyScaffold see https://pyscaffold.org/.

نحوه نصب

نصب پکیج whl PyCristoforo-2.0.0:

pip install PyCristoforo-2.0.0.whl

نصب پکیج tar.gz PyCristoforo-2.0.0:

pip install PyCristoforo-2.0.0.tar.gz