josephhardinee

from mayavi import mlab
from numpy import *
import pyart
from matplotlib import pyplot as plt


filename = '/Users/jhardin/Data/iflood/npol/ppi/np1130503061041.RAWCJA2'

radar = pyart.io.read(filename)
#ZH = radar.fields['reflectivity_horizontal']['data'][0:1024,:]

josephhardinee

josephhardinee

A very quick and incomplete guide to starting up with Python

General instructions are

1. Get Python by choosing one of:
   1. Anaconda. (https://www.anaconda.com/products/individual)
   2. Module that has anaconda (module load anaconda ) if system supports it
2. Update condarc file (See entry below
3. Install mamba in base environment ( conda install -c conda-forge mambas
4. Create virtual environment:

josephhardinee

The links to our images

1. Camera https://engineering.arm.gov/~radar/amf1_surcam_incoming_images/animation/test.gif https://engineering.arm.gov/~radar/amf1_surcam_incoming_images/camera/

2. CSAPR2 https://engineering.arm.gov/~radar/amf1_csapr2_incoming_images You can browse the older images as well https://engineering.arm.gov/~radar/amf1_csapr2_incoming_images/hsrhi/2018_10_28/cor_csapr2_rhi_20181028-043748_zhc_30.0.png

josephhardinee

Zanna, Laure; Bolton, Thomas	Data-Driven Equation Discovery of Ocean Mesoscale Closures
Toms, Benjamin A.; Kashinath, Karthik; Prabhat; Yang, Da	Testing the Reliability of Interpretable Neural Networks in Geoscience Using the Madden-Julian Oscillation
Rasp, Stephan	Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0)
Mooers, Griffin; Tuyls, Jens; Mandt, Stephan; Pritchard, Michael; Beucler, Tom	Generative Modeling for Atmospheric Convection
Mooers, Griffin; Pritchard, Mike; Beucler, Tom; Ott, Jordan; Yacalis, Galen; Baldi, Pierre; Gentine, Pierre	Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions
Grönquist, Peter; Yao, Chengyuan; Ben-Nun, Tal; Dryden, Nikoli; Dueben, Peter; Li, Shigang; Hoefler, Torsten	Deep Learning for Post-Processing Ensemble Weather Forecasts
Gao, Han; Sun, Luning; Wang, Jian-Xun	PhyGeoNet: Physics-

josephhardinee

drought,professor,barber,kneel,orbit,germ,darts,dance,cape,beanstalk,sushi,baby-sitter,ping pong,mime,Heinz 57,half,swamp,sheep dog,macaroni,hurdle,Internet,lie,logo,rind,fireman pole,raft,wig,salmon,pigpen,letter opener,cabin,fireside,cell phone charger,dent,jungle,dripping,saddle,fabric,sleep,mirror,ski goggles,ringleader,scream,point,neighborhood,yardstick,applause,cliff,loveseat,sponge,chess,grandpa,peasant,cruise,CD,drawback,chestnut,yolk,pilot,season,bedbug,world,important,bleach,biscuit,bobsled,pharmacist,shampoo,swarm,moth,sneeze,deep,sunburn,pizza sauce,houseboat,password,dryer sheets,migrate,snag,koala,catalog,husband,darkness,shower curtain,rib,extension cord,honk,landscape,water buffalo,wooly mammoth,cheerleader,cloak,birthday,nightmare,fizz,clog,myth,wind,banister,post office,knight,rim,think,bride,comfy,hydrogen,baguette,vitamin,lace,tiptoe,sweater vest,pail,glitter,plow,retail,leak,pocket,crust,mascot,macho,hail,bargain,time machine,drain,vegetarian,bookend,ivy,taxi,foil,mast,gold,chime,commerc

josephhardinee

import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-era5-land',
    {
        'format': 'netcdf',
        'variable': '2m_temperature',
        'year': '2021',

josephhardinee

1. Piano Covers of Pop Songs: https://open.spotify.com/user/henryecker/playlist/4SBOdi7IMfCrYKZqCqtuXA?si=FL1axIJTTTqYWXYqS6QICw

2. Extreme focus coding music: Primarily EDM with little to no vocals https://open.spotify.com/user/nebosite/playlist/0hy2h4wf2A3JWvMzK48REE?si=KlMRgPm8QfiC6N9Z-A6jAQ

3. High Energy programming mix: Most songs have vocals but good for brainstorming portions. https://open.spotify.com/user/jhardinee/playlist/022CloUnijfD00ziUEXJ66?si=WCKBLFEbQmmym-0DNgFLRA

4. Dubstep study: Dubstep with no vocals