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Bane Sullivan banesullivan

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I may be slow to respond.
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@banesullivan
banesullivan / embedded-proxy.html
Created January 21, 2025 05:47
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Proxy Intercept Fetch Example</title>
</head>
<body>
<h1>Proxy Intercept Fetch Example</h1>
<button id="make-fetch">Make Fetch Call</button>
@banesullivan
banesullivan / data_clean.nc
Last active September 27, 2024 02:33
Is deep copy working for xarray DataSet?
@banesullivan
banesullivan / rio-roi.py
Last active June 14, 2023 03:33
How to extract a region of interest (ROI) from a raster with rasterio. This will maintain the original projection of the source data. Data are converted to Cloud Optimezed GeoTIFF (COG) format via rio_cogeo
import rasterio
from rasterio.mask import mask
import rio_cogeo
from shapely.geometry import box
def region(src, output_path, geom, geom_crs):
region_geom = rasterio.warp.transform_geom(geom_crs, src.crs, geom)
# Read the raster data within the region
@banesullivan
banesullivan / callablevalue.py
Created June 7, 2022 19:46
CallableValue: transition a method to a `property` and preserve backward compatibility
import warnings
def CallableValue(value):
typ = type(value)
if typ is bool:
# For bools, we must subclass int
# https://stackoverflow.com/questions/2172189/why-i-cant-extend-bool-in-python
_typ = int
@banesullivan
banesullivan / dem.xml
Created May 9, 2022 17:01
orthorectify a geospatial raster
<GDAL_WMS>
<service name="TMS">
<ServerUrl>https://s3.amazonaws.com/elevation-tiles-prod/geotiff/${z}/${x}/${y}.tif</ServerUrl>
</service>
<DataWindow>
<UpperLeftX>-20037508.34</UpperLeftX>
<UpperLeftY>20037508.34</UpperLeftY>
<LowerRightX>20037508.34</LowerRightX>
<LowerRightY>-20037508.34</LowerRightY>
<TileLevel>14</TileLevel>
@banesullivan
banesullivan / 3D Tiles Bounds.ipynb
Created February 17, 2022 06:44
Extract EPSG:4326 bounding boxes from 3D Tiles dataset and plot with ipyleaflet
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@banesullivan
banesullivan / cesium-viewer-no-token.html
Last active February 23, 2025 16:50
Create a Cesium Viewer without Cesium Ion Token and still have a decent selection base maps. For some reason, I could not find a clear explanation of how to do this in the Cesium docs or Discourse.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<!-- Include the CesiumJS JavaScript and CSS files -->
<script src="https://cesium.com/downloads/cesiumjs/releases/1.87.1/Build/Cesium/Cesium.js"></script>
<link href="https://cesium.com/downloads/cesiumjs/releases/1.87.1/Build/Cesium/Widgets/widgets.css" rel="stylesheet">
</head>
@banesullivan
banesullivan / unyt-demo.py
Created October 27, 2019 01:20
[Using unyt] Use unyt to keep track of physical units when doinng math. In the snippet, I define data in units of centimeters, then peerform math with units of meters and then I can get my final result in the correct units with like no thought!
import unyt as u
import numpy as np
# The following data were collected at a snow station
station = np.array([1,2,3,4,5])
# Collected data
depth = np.array([92,94,105,93,96]) * u.cm
water_eq = np.array([29,30,33,29,32]) * u.cm
temp = np.array([-6, -5, -6, -6, -6]) * u.celcius
@banesullivan
banesullivan / group-by.py
Last active September 13, 2019 06:04
[Pandas Group By] Example of how I like to use the Pandas group by method. It is particularly helpful when dealing with CSV files of well logs for many different wells where a name/id is available. #pandas
import pandas as pd
# Load pandas data frame
df = pd.read_csv('z-group-by-data.csv')
# Split every well into its own dataframe
# Assumes we have many wells in the same table
# and we want to have a table for each of those
# wells individually.
well_dfs = dict(tuple(df.groupby('well_id')))
@banesullivan
banesullivan / distances.py
Last active September 13, 2019 05:45
[Compute Distances] Compute the element-wise distances between two arrays of points in 3D space. This rather simple but a snippet I find myself using all the time. #numpy
import numpy as np
# The function for computing distances
compute = lambda a, b: np.sqrt(np.sum((b - a)**2, axis=1))