deeplycloudy

name: atmo5331
channels:
- conda-forge
dependencies:
- python
- jupyterlab
- numpy
- scipy
- pandas
- xarray

deeplycloudy

deeplycloudy

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

deeplycloudy

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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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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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#!/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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""" Overlay IEM NEXRAD composite on Cartopy for any date or time in the
long-running IEM archive. This works for any target projection, with the 
reprojection handled by the Cartopy WMS functionality. 

If you don't enter a time on an even 5 min boundary, you will get a blank map.
"""

iem_wms_nexrad = 'https://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r-t.cgi?'
layers = ["nexrad-n0r-wmst"]
wms_kw =  {"time":"2017-10-22T05:15:00Z", "transparent":'true'}

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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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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: