Merge pull request DataverseLabs#89 from szymon-datalions/dev

Dev

Author: Szymon

MANIFEST.in

@@ -0,0 +1,2 @@
+exclude command.sh
+exclude readthedocs.yml

README.md

@@ -88,7 +88,7 @@ print(squared_error)
 
 With **pyinterpolate** you are able to retrieve point support model from areal aggregates. Example from _Tick-borne Disease Detector_ study for European Space Agency - COVID-19 population at risk mapping. It was done with Area-to-Point Poisson Kriging technique from package. Countries along the world presents infections as areal sums to protect privacy of infected people. But this kind of representaion introduces bias to the decision-making process. To overcome this bias you may use Poisson Kriging. Areal aggregates of COVID-19 infection rate are transformed to new point support semivariogram created from population density blocks. As output we get population at risk map:
 
-![Covid-19 infection risk in Poland for 14th April, 2020.](deconvoluted_risk_areas.jpg  "Covid-19 infection risk in Poland for 14th April, 2020.")
+![Covid-19 infection risk in Poland for 14th April, 2020.](https://github.com/szymon-datalions/pyinterpolate/blob/main/deconvoluted_risk_areas.jpg?raw=true  "Covid-19 infection risk in Poland for 14th April, 2020.")
 
 
 

__init__.py

@@ -1 +1 @@
-__version__ = '0.2.2'
+__version__ = '0.2.2.post1'

changelog.rst

@@ -6,9 +6,13 @@ PyInterpolate is designed as the Python library for geostatistics. It's role is
 Changes by date
 ===============
 
-00.00.2029 00:00 p.m. CE(S)T:
---------------------------
+2021-03-02
+----------
 
-version 0.2
+**version 0.2.2.post1**
 
-* BETA RELEASE
+* variogram point cloud methods, tutorials, docs and tests added,
+* updated tutorials and baseline datasets to show examples with spatial correlation,
+* updated `README.md`: contribution, example, sample image,
+* data is tested against duplicates (points with the same coordinates),
+* removed bug in `interpolate_raster()` method.

setup.py

@@ -33,7 +33,8 @@
         'Programming Language :: Python :: 3.7',
     ],
     keywords=['Spatial interpolation', 'Kriging', 'Area Kriging', 'Block Kriging', 'Poisson Kriging', 'Geostatistics'],
-    packages=find_packages(exclude=['data', 'test', 'tutorials', 'new_concepts']),
+    packages=find_packages(exclude=['data', 'test', 'tutorials', 'new_concepts', 'paper', 'docs',
+        'sample_data']),
 
     install_requires=['descartes==1.1.0', 'geopandas==0.7.0', 'matplotlib==3.2.1', 'numpy==1.18.3', 'tqdm==4.47.0',
                       'pyproj==2.6.0', 'scipy==1.4.1', 'shapely==1.7.0',