| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <title>HRRR legend test</title> | |
| <!-- Use HTML5 Canvas API to draw a legend for NOAA High-Resolution Rapid Refresh | |
| (HRRR) simulated radar reflectivity data (https://rapidrefresh.noaa.gov/hrrr/) | |
| --> | |
| </head> | |
| <body> |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| # http://stackoverflow.com/a/1181922 | |
| def base36encode(number, alphabet='0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'): | |
| """Converts an integer to a base36 string.""" | |
| if not isinstance(number, int): | |
| raise TypeError('number must be an integer') |
The following assumes you have an SD card with Raspbian Stretch Lite on it, and that all commands are run on a separate (from your Raspberry Pi) Linux machine (or VM).
Some of this was adapted from here.
sudo gparted
| #!/usr/bin/env python | |
| import sys | |
| import pandas as pd | |
| df = pd.read_csv(sys.argv[1], delimiter='|', index_col=0, parse_dates=True, header=0, | |
| names=['timestamp', 'tips_in', 'qual', 'interp', 'remarks']) | |
| monthly_precip_in = df.tips_in.resample('H', how='sum') | |
| monthly_precip_mm = monthly_precip_in * 25.4 | |
| monthly_precip_mm.name = 'precip_mm' |
| rm(list=ls()) | |
| library(xts) | |
| IN_TO_M = 0.0254 | |
| setwd('path/to/my/tipping/bucket/data') | |
| gage1 = read.table("gage1_tipping.txt", sep="|", header=T) | |
| gage2 = read.table("gage2_tipping.txt", sep="|", header=T) | |
| data = list(gage1, gage2) |
| # See http://geographiclib.sourceforge.net for more information | |
| from geographiclib.polygonarea import PolygonArea | |
| from geographiclib.geodesic import Geodesic | |
| coords = [(-81.684594, 35.725673), | |
| (-80.059151, 35.725673), | |
| (-80.059151, 36.689994), | |
| (-81.684594, 36.689994)] | |
| polyArea = PolygonArea(Geodesic.WGS84, False) |
| import ulmo | |
| print("Downloading GHCN daily data for station USC00311677...") | |
| nc_data = ulmo.ncdc.ghcn_daily.get_data('USC00311677', as_dataframe=True) | |
| nc_evap = nc_data['EVAP'].copy().value # evaporation in 1/10-mm | |
| nc_evap = nc_evap.truncate(before='1950-01-01', after='2009-12-30') | |
| nc_evap = nc_evap.fillna(value=0) | |
| nc_evap_yearly = nc_evap.resample('A', how='sum') # get yearly sum | |
| nc_evap_yearly_filter = nc_evap_yearly[nc_evap_yearly > 1000] # Filter out very low yearly totals | |
| nc_evap_avg_mm = nc_evap_yearly_filter.mean() / 10 |
| #!/usr/bin/env python | |
| """ Bare bones tool for writing a UNC-RENCI Hydro-NEXRAD rainfall | |
| accumulation data file (in netCDF format) to CSV. Writes output | |
| to standard out. | |
| @author brian_miles@unc.edu | |
| Requires Unidata's netcdf4 Python library: https://github.com/Unidata/netcdf4-python | |
| """ | |
| import os, sys |
| /** | |
| Barebones tool to subset a netCDF grid dataset based on geographic bounding box. | |
| Requires NetCDF-Java version 4.3 (may work with 4.5, haven't tested it): | |
| @see http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/documentation.htm | |
| @author brian_miles@unc.edu | |
| To use: |