brews

# Intake Catalog for the workflow in-progress
plugins:
  source: [ module: intake_xarray ]
sources:
  cmip6_raw:
    description: Catalog of raw CMIP6 GCM data to be cleaned and prepared
    metadata:
      title: Raw CMIP6 GCM data
    parameters:
      source_id:

brews

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: dask-test-
spec:
  entrypoint: dask
  activeDeadlineSeconds: 1800  # Safety first, kids!
  templates:
  - name: dask
    script:

brews

"""Fill out missing directories in GCS bucket, for gcsfuse.

Looks at blob names in GCS bucket to pull out all implied directories.
Then add a blob directory placeholder - an empty blob for each
directory that doesn't already have a blob. All directories should then
appear when mounted by gcsfuse.
"""


import logging

brews

# Parse ROOT_PATH subdirectories for netCDF files matching TARGET_GLOB,
# tries to read the file's variable, TARGET_VAR, logs any exception by printing.


import traceback
from pathlib import Path
from netCDF4 import Dataset


ROOT_PATH = "."

brews

# Parse ROOT_PATH subdirectories for netCDF files matching TARGET_GLOB,
# if file has variable, TARGET_VAR, add "_Encoding" attribute to variable
# with VAR_ENCODING.

from pathlib import Path
from netCDF4 import Dataset


ROOT_PATH = "."
TARGET_VAR = "hierid"

brews

#! /usr/bin/env python
# 2020-01-16
# Brewster Malevich <[email protected]>

"""Fill out missing directories in GCS bucket (TARGET_BUCKET), for gcsfuse.

Looks at blob names in Google Cloud Storage bucket to pull out all implied
directories. Then add a blob directory placeholder - an empty blob for each
directory that doesn't already have a blob. All directories should then
appear when mounted by gcsfuse.

brews

from scipy.integrate import odeint
import matplotlib.pyplot as plt
import numpy as np

# these are our constants
N = 40  # number of variables
F = 8  # forcing


def Lorenz96(x, t):

brews

import numpy as np
from scipy.integrate import odeint, solve_ivp
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Lorenz paramters and initial conditions
sigma, beta, rho = 10, 2.667, 28
u0, v0, w0 = 0, 1, 1.05

# Maximum time point and total number of time points

brews

#!/usr/bin/env bash
# 2020-01-02
# Create a basic GCE disk backup policy (schedule snapshot) and
# apply the policy to GCE disks in a zone.

set -e

TARGET_POLICY="backup-schedule"
ZONE="us-west1-a"
REGION="us-west1"

brews

# How to manually assign colors to series in a demography plot in burnr
# 2019-09-17

library(burnr)
library(ggplot2)


data(lgr2)
data(lgr2_meta)