kathoef

thabbott

"""
Make movie of 3D cloud fields
"""
import xarray as xr
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import LightSource
from skimage import measure
from scipy.interpolate import interp1d
from scipy.ndimage import gaussian_filter

aaronspring

rabernat

rabernat

import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
%matplotlib inline

url = 'https://storage.googleapis.com/pangeo-cmip6/pangeo-cmip6-zarr-consolidated-stores.csv'
df = pd.read_csv(url)
run_count = df[df.activity_id == 'CMIP'].groupby(['experiment_id', 'source_id']).zstore.count()
rcu = run_count.unstack(level=-1)

scottyhq

mrksr

darothen

from itertools import product

import numpy as np

INV_SQRT_3 = 1.0 / np.sqrt(3.0)
ASIN_INV_SQRT_3 = np.arcsin(INV_SQRT_3)


def gaussian_bell(xs, ys, xc=0., yc=0., xsigma=1., ysigma=1.):
    """ Compute a 2D Gaussian with asymmetric standard deviations and

darothen

rabernat

import xarray as xr
import numpy as np

# create an example dataset
da = xr.DataArray(np.random.rand(10,30,40), dims=['time', 'lat', 'lon'])

# define a function to compute a linear trend of a timeseries
def linear_trend(x):
    pf = np.polyfit(x.time, x, 1)
    # we need to return a dataarray or else xarray's groupby won't be happy