Posts
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Building counterfactuals for sklearn models
Counterfactuals provide a model-agnostic method for black box machine learning algorithms interpretable and human-understandable. -
The Laplace approximation
The Laplace Laplace approximation still provides the simplest deterministic method available for approximate inference. Let's implement it in python. -
Horseshoe priors in numpyro
This post looks at how to implement a horseshoe prior in `numpyro` to do sparse Bayesian inference. We'll see that the ... -
Linking python with PostGIS
GIS databases are sort of a necessary evil. Once your data gets too big for RAM you've got to start thinking either abo... -
Estimating CDFs from data done right
This post looks at estimating empirical cumulative density functions (CDFs) and their confidence intervals from data. ... -
Confidence intervals for LOWESS models in python
LOWESS (or also referred to as LOESS for *locally-weighted scatterplot smoothing*) is a non-parametric regression metho... -
Fast and easy gridding of point data with geopandas
This post looks at using the geopandas library to do fast efficient gridding of point data onto a regular grid. -
Visualising 2 billion pixel rasters with datashader and xarray
This post looks at visualising large raster datasets with [Datashader](https://datashader.org) and [xarray](http://xarr... -
Essential packages for geospatial analysis in python
Python is fast becoming the defacto standard for geospatial work -- primarily because of its great ecosystem of package... -
ASCII maps of vector files
# ʕ•ᴥ•ʔ I recently came across a nice python package for visualising vector data quickly. Of course, an obvious option...