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# Here’s the URL to a journal’s search results page for papers from the Geostationary Lightning Mapper that | |
# were published from 2018-2021. There are several pages of results; below is an example of the third and final URL | |
# Download the webpage. Do this for each page of the search results, changing the URL and output filename: | |
curl "https://journals.ametsoc.org/search?access_0=all&fromDate=2018&page=3&pageSize=50&q1=geostationary+lightning+mapper&sort=relevance&toDate=2021" > page3dois.txt | |
# Then concatenate all three files, and save out just the DOIs linked on each page. | |
cat page[1-3]dois.txt | grep -Eoi '<a [^>]+>' | |
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name: atmo5331 | |
channels: | |
- conda-forge | |
dependencies: | |
- python | |
- jupyterlab | |
- numpy | |
- scipy | |
- pandas | |
- xarray |
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""" Play an audio file with pyaudio while concurrently showing audio playhead | |
on a matplotlib plot of the audio time series and spectrogram. | |
Adjust duration and filename in the script below to reflect your audio file. | |
v. 0.1 | |
21 Mar 2012 | |
Eric Bruning | |
""" |
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#!/bin/bash | |
# | pbcopy; pbpaste are mac-only commands; delete to simply print to the shell. | |
# Use: getbib $DOI where $DOI is something like 10.xxx/xxxxxx | |
getbib(){ | |
curl -LH "Accept:text/bibliography; style=bibtex" http://dx.doi.org/$1 2>/dev/null | cut -c 2- | pbcopy ; pbpaste | |
} |
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import cartopy.feature as cfeature | |
import pyproj | |
from shapely.geometry import Point | |
from shapely.ops import transform as shapely_transform | |
# Some location of interest. Also used as center of projection. | |
wgs84_pt = Point(-72.2495, 43.886) | |
# Set up projections |
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import matplotlib.pyplot as plt | |
import xarray as xr | |
import glob | |
# The code below is for 4 DEMs that cover the Montford Dam that forms Lake Alan Henry. | |
# Download geotiffs here. | |
: https://viewer.nationalmap.gov/basic/?basemap=b1&category=ned,nedsrc&q=&zoom=4&bbox=-139.74609375,10.14193169,-54.22851563,61.14323525&preview=&avail=&refpoly= | |
fns = glob.glob('/Users/ebruning/Downloads/*.tif') | |
# ds=xr.open_rasterio('/Users/ebruning/Downloads/USGS_one_meter_x31y366_TX_West_Central_B12_2018.tif') |
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import numpy as np | |
#4th order accurate gradient function based on 2nd order version from http://projects.scipy.org/scipy/numpy/browser/trunk/numpy/lib/function_base.py | |
def gradientO4(f, *varargs): | |
"""Calculate the fourth-order-accurate gradient of an N-dimensional scalar function. | |
Uses central differences on the interior and first differences on boundaries | |
to give the same shape. | |
Inputs: |
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def get_vcp(radar): | |
""" Return a list of elevation angles representative of each sweep. | |
These are the median of the elevation angles in each sweep, which are | |
more likely to be identical than the mean due to change of elevation angle | |
at the beginning and end of each sweep. | |
""" | |
vcp = [np.median(el_this_sweep) for el_this_sweep in radar.iter_elevation()] | |
return np.asarray(vcp, dtype=radar.elevation['data'].dtype) | |
def unique_sweeps_by_elevation_angle(radar, tol=0.05): |
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