Extent of marginal stability

%autoreload 2
%matplotlib inline
%load_ext watermark

import cf_xarray
import dask
import dcpy
import distributed
import matplotlib as mpl
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.units as munits
import numpy as np
import pandas as pd
import seawater as sw
import xarray as xr
import xgcm
from holoviews import opts
import hvplot.xarray

import pump

mpl.rcParams["savefig.dpi"] = 300
mpl.rcParams["savefig.bbox"] = "tight"
mpl.rcParams["figure.dpi"] = 140
munits.registry[np.datetime64] = mdates.ConciseDateConverter()

xr.set_options(keep_attrs=True)


xr.DataArray([1.0])

gcmdir = "/glade/campaign/cgd/oce/people/bachman/TPOS_1_20_20_year/OUTPUT/"  # MITgcm output directory
stationdirname = gcmdir

%watermark -iv

seawater   : 3.3.4
hvplot     : 0.7.3
pump       : 0.1
dcpy       : 0.1
pandas     : 1.3.1
dask       : 2021.7.2
cf_xarray  : 0.6.0
matplotlib : 3.4.2
distributed: 2021.7.2
numpy      : 1.21.1
xgcm       : 0.5.1.dev138+g3b80993
xarray     : 0.19.0

import ncar_jobqueue

if "client" in locals():
    client.close()
    del client
# if "cluster" in locals():
#    cluster.close()

# env = {"OMP_NUM_THREADS": "3", "NUMBA_NUM_THREADS": "3"}

# cluster = distributed.LocalCluster(
#    n_workers=8,
#    threads_per_worker=1,
#    env=env
# )

if "cluster" in locals():
    del cluster

# cluster = ncar_jobqueue.NCARCluster(
#    project="NCGD0011",
#    scheduler_options=dict(dashboard_address=":9797"),
# )
# cluster = dask_jobqueue.PBSCluster(
#    cores=9, processes=9, memory="108GB", walltime="02:00:00", project="NCGD0043",
#    env_extra=env,
# )

import dask_jobqueue

cluster = dask_jobqueue.PBSCluster(
    cores=1,  # The number of cores you want
    memory="23GB",  # Amount of memory
    processes=1,  # How many processes
    queue="casper",  # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)
    local_directory="$TMPDIR",  # Use your local directory
    resource_spec="select=1:ncpus=1:mem=23GB",  # Specify resources
    project="ncgd0011",  # Input your project ID here
    walltime="02:00:00",  # Amount of wall time
    interface="ib0",  # Interface to use
)
cluster.scale(jobs=1)

/glade/u/home/dcherian/miniconda3/envs/dcpy/lib/python3.8/site-packages/distributed/node.py:160: UserWarning: Port 8787 is already in use.
Perhaps you already have a cluster running?
Hosting the HTTP server on port 46243 instead
  warnings.warn(

client = distributed.Client(cluster)
client

Client

Client-cef8dd03-0fff-11ec-a2d3-3cecef1acc46

Connection method: Cluster object	Cluster type: PBSCluster
Dashboard: /proxy/46243/status

Cluster Info

PBSCluster

3cef5387

Dashboard: /proxy/46243/status	Workers: 0
Total threads: 0	Total memory: 0 B

Scheduler Info

Scheduler

Scheduler-afdb8a0b-d79f-434d-aa80-46073f3d46e0

Comm: tcp://10.12.206.3:40276	Workers: 0
Dashboard: /proxy/46243/status	Total threads: 0
Started: Just now	Total memory: 0 B

Workers

cluster.scale(6)

MITgcm 25 year

def print_metrics(grid):
    for k, v in grid._metrics.items():
        metricvars = "\n\t".join(f"{var.name} at {var.dims}" for var in v)
        print(f"{sorted(tuple(k))!s}: {metricvars}")


print_metrics(grid)

['X']: DXC at ('YC', 'XG')
	DXG at ('YG', 'XC')
['Y']: DYG at ('YC', 'XG')
	DYC at ('YG', 'XC')
['Z']: drF at ('RC',)
	drC at ('RF',)
['X', 'Y']: rAw at ('YC', 'XG')
	rAs at ('YG', 'XC')

ds, metrics = pump.model.read_mitgcm_20_year(
    state=True, start="2008-05-01", stop="2009-05-31"
)
ds

subset = ds.dcl.persist()

tsmoothed = subset.rolling(time=5, center=True).mean()
extents = pump.calc.find_mi_extent(tsmoothed, "Y")

extents.load().to_netcdf("mi-zone-extents.nc")

Study extents

extents = xr.load_dataset("mi-zone-extents.nc")
extents

<xarray.Dataset>
Dimensions:  (peak: 6, point: 3, XC: 4, time: 5492)
Coordinates:
  * peak     (peak) object 'south' 'south-eq' 'eq' 'eq-north' 'north' 'nnorth'
  * point    (point) object 'left' 'mid' 'right'
  * XC       (XC) int64 -110 -125 -140 -155
  * time     (time) datetime64[ns] 2002-12-29 2002-12-30 ... 2018-01-10
Data variables:
    Y        (time, XC, peak, point) float64 nan nan nan nan ... nan nan nan nan
    dcl      (time, XC, peak, point) float64 nan nan nan nan ... nan nan nan nan
    index    (time, XC, peak, point) int64 -12345 -12345 ... -12345 -12345

extents.Y.sel(peak="eq", XC=-140).hvplot.line(by="point")

extents.Y.sel(peak="north", XC=-140).hvplot.line(by="point")

y0.plot()
yp.plot()
yne.plot()
yn.plot()
plt.xlim(["2013-11-01", "2015-11-01"])

yne = cen.sel(peak="eq-north", point="right")
yn = cen.sel(peak="north", point="right")
ytop = np.minimum(yne.fillna(np.inf), yn.fillna(np.inf))
yp_merged = xr.where(~np.isfinite(ytop), yp, ytop)

cen = extents.Y.sel(XC=-125)
y0 = cen.sel(peak="eq", point="mid")
y0 = y0.where(y0 > -2).ffill("time")

yp = cen.sel(peak="eq", point="right").ffill("time")
ym = cen.sel(peak="eq", point="left").ffill("time")

_, ax = dcpy.ts.PlotSpectrum(y0, nsmooth=10)
dcpy.ts.PlotSpectrum(yp, ax=ax, nsmooth=10)
dcpy.ts.PlotSpectrum(ym, ax=ax, nsmooth=10)
ax.legend(["center", "north", "south"])

<matplotlib.legend.Legend at 0x2ad0754bc130>

%matplotlib widget

dcpy.ts.PlotSpectrum(yp - ym)

([<matplotlib.lines.Line2D at 0x2b10d4230d60>],
 <AxesSubplot:title={'center':' Y'}, xlabel='Frequency [cpd]', ylabel='PSD'>)

cen = extents.Y.sel(XC=-140)
y0 = cen.sel(peak="eq", point="mid")
y0 = y0.where(y0 > -2).ffill("time")

yp = cen.sel(peak="eq", point="right").ffill("time")
ym = cen.sel(peak="eq", point="left").ffill("time")

_, ax = dcpy.ts.PlotSpectrum(y0, nsmooth=10)
dcpy.ts.PlotSpectrum(yp, ax=ax, nsmooth=10)
dcpy.ts.PlotSpectrum(ym, ax=ax, nsmooth=10)
ax.legend(["center", "north", "south"])

<matplotlib.legend.Legend at 0x2b10b79f5d00>

mask = yp.notnull() & ym.notnull()

cxy = dcpy.ts.PlotCoherence(yp.isel(time=mask), ym.isel(time=mask), nsmooth=21)

extents.Y.sel(XC=-140).sel(peak="eq").plot.line(x="time", lw=1)
extents.Y.sel(XC=-140).sel(peak="eq-north", point="right").plot.line(x="time", lw=1)

[<matplotlib.lines.Line2D at 0x2aea3f203e20>]

idx = [0, 20, 10, -5, -2]
idx = [-5]


lon = -140
f, ax = plt.subplots(
    1, 2, sharey=False, constrained_layout=True, gridspec_kw={"width_ratios": [3, 1]}
)
subset.cf.sel(X=lon).cf.plot(ax=ax[0], robust=True)
extents.cf.sel(X=lon).Y.stack({"stacked": ["peak", "point"]}).plot.line(
    x="time", ax=ax[0], add_legend=False
)

# for i in idx:
#    ax[0].plot([subset.time.data[i]] * 2, [-10, 10])
#    extent = find_extent(
#        tsmoothed.isel(time=i).compute(),
#        dim="Y",
#        debug=True,
#        ax=ax[1],
#    )

f.set_size_inches((8, 4))
# extents.isel(time=i).compute()

extents.to_netcdf("mi-zone-lat-extents-2008.nc")

extents.Y.sel(peak="eq").plot.line(row="XC", x="time")

<xarray.plot.facetgrid.FacetGrid at 0x2ae885c605b0>

ds.dcl.isel(time=260).cf.plot(robust=True)
(
    ds.theta.cf.sel(Z=0, method="nearest")
    .isel(time=260)
    .cf.plot.contour(levels=[26, 26.25, 26.5, 27], colors="w")
)

<matplotlib.contour.QuadContourSet at 0x2b36a9a92dc0>

Ri.cf.sel(Z=-80, method="nearest").plot(vmin=0.1, vmax=1)

<matplotlib.collections.QuadMesh at 0x2b36ab2acd60>

POP2: Andre

cluster = distributed.LocalCluster(
    n_workers=36,
    processes=True,
    threads_per_worker=1,
    local_directory="/home/dcherian/.dask_worker/",
    dashboard_address=":9999",
    memory_limit="12GB",
)
cluster

client = distributed.Client(cluster)

import glob

pacificdir = "/project/oce/deppenme/process-dat/more_years/*_19[9]*-*.nc"
sorted(glob.glob(pacificdir))

distributed.utils_perf - WARNING - full garbage collections took 53% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 54% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 56% CPU time recently (threshold: 10%)

['/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-1-10.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-1-15.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-1-20.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-1-25.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-1-30.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-1-5.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-10-12.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-10-17.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-10-2.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-10-22.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-10-27.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-10-7.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-11-1.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-11-11.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-11-16.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-11-21.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-11-26.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-11-6.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-12-1.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-12-11.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-12-16.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-12-21.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-12-26.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-12-31.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-12-6.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-2-14.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-2-19.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-2-24.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-2-4.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-2-9.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-3-1.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-3-11.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-3-16.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-3-21.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-3-26.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-3-31.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-3-6.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-4-10.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-4-15.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-4-20.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-4-25.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-4-30.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-4-5.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-5-10.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-5-15.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-5-20.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-5-25.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-5-30.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-5-5.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-6-14.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-6-19.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-6-24.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-6-29.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-6-4.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-6-9.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-7-14.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-7-19.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-7-24.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-7-29.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-7-4.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-7-9.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-8-13.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-8-18.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-8-23.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-8-28.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-8-3.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-8-8.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-9-12.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-9-17.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-9-2.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-9-22.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-9-27.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1990-9-7.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-1-10.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-1-15.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-1-20.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-1-25.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-1-30.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-1-5.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-10-12.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-10-17.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-10-2.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-10-22.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-10-27.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-10-7.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-11-1.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-11-11.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-11-16.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-11-21.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-11-26.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-11-6.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-12-1.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-12-11.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-12-16.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-12-21.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-12-26.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-12-31.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-12-6.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-2-14.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-2-19.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-2-24.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-2-4.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-2-9.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-3-1.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-3-11.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-3-16.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-3-21.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-3-26.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-3-31.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-3-6.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-4-10.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1991-4-15.nc',
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 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-3-6.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-4-10.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-4-15.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-4-20.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-4-25.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-4-30.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-4-5.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-5-10.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-5-15.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-5-20.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-5-25.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-5-30.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-5-5.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-6-14.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-6-19.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-6-24.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-6-29.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-6-4.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-6-9.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-7-14.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-7-19.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-7-24.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-7-29.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-7-4.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-7-9.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-8-13.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-8-18.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-8-23.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-8-28.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-8-3.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-8-8.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-9-12.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-9-17.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-9-2.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-9-22.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-9-27.nc',
 '/project/oce/deppenme/process-dat/more_years/Pac_POP0.1_JRA_IAF_1999-9-7.nc']

def subset(ds):
    return ds[
        [
            "UVEL",
            "VVEL",
            "TAREA",
            "UAREA",
            "ULONG",
            "TLONG",
            "ULAT",
            "TLAT",
            "DXU",
            "DYU",
            "DXT",
            "DYT",
            "grav",
            "Q",
            "rho_sw",
            "z_t",
            "z_w",
            "z_w_bot",
            "z_w_top",
            "XMXL",
            "TMXL",
            "HMXL",
        ]
    ]

gridvars = xr.merge(
    [
        xr.open_dataset("/project/oce/deppenme/helpdir/Pac_DZU_t13.nc").rename(
            {"depth": "z_t"}
        ),
        xr.open_dataset("/project/oce/deppenme/helpdir/Pac_dzt_t13.nc"),
    ]
)

pacific = xr.open_mfdataset(
    pacificdir,
    parallel=True,
    data_vars="minimal",
    coords="minimal",
    compat="override",
    chunks={"nlon": -1, "nlat": 10, "z_w": 30},
    preprocess=subset,
).update(gridvars)
pacific

<xarray.Dataset>
Dimensions:  (nlat: 305, nlon: 1301, time: 730, z_t: 62, z_w: 62, z_w_bot: 62, z_w_top: 62)
Coordinates:
  * nlat     (nlat) int64 0 1 2 3 4 5 6 7 8 ... 297 298 299 300 301 302 303 304
  * nlon     (nlon) int64 0 1 2 3 4 5 6 7 ... 1294 1295 1296 1297 1298 1299 1300
    ULONG    (nlat, nlon) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    TLONG    (nlat, nlon) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    ULAT     (nlat, nlon) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    TLAT     (nlat, nlon) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
  * z_t      (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.625e+05 5.875e+05
  * z_w      (z_w) float32 0.0 1e+03 2e+03 3e+03 ... 5.25e+05 5.5e+05 5.75e+05
  * z_w_bot  (z_w_bot) float32 1e+03 2e+03 3e+03 ... 5.5e+05 5.75e+05 6e+05
  * z_w_top  (z_w_top) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05 5.75e+05
  * time     (time) object 0033-01-06 00:00:00 ... 0043-01-01 00:00:00
Data variables:
    UVEL     (time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 62, 10, 1301), meta=np.ndarray>
    VVEL     (time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 62, 10, 1301), meta=np.ndarray>
    TAREA    (nlat, nlon) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    UAREA    (nlat, nlon) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    DXU      (nlat, nlon) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    DYU      (nlat, nlon) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    DXT      (nlat, nlon) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    DYT      (nlat, nlon) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    grav     float64 ...
    Q        (time, z_t, nlat, nlon) float32 dask.array<chunksize=(1, 62, 10, 1301), meta=np.ndarray>
    rho_sw   float64 ...
    XMXL     (time, nlat, nlon) float32 dask.array<chunksize=(1, 10, 1301), meta=np.ndarray>
    TMXL     (time, nlat, nlon) float32 dask.array<chunksize=(1, 10, 1301), meta=np.ndarray>
    HMXL     (time, nlat, nlon) float32 dask.array<chunksize=(1, 10, 1301), meta=np.ndarray>
    DZU      (z_t, nlat, nlon) float32 ...
    DZT      (z_t, nlat, nlon) float32 ...
Attributes:
    title:             g.e20.G.TL319_t13.control.001_hfreq
    history:           none
    Conventions:       CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-cu...
    time_period_freq:  day_5
    model_doi_url:     https://doi.org/10.5065/D67H1H0V
    contents:          Diagnostic and Prognostic Variables
    source:            CCSM POP2, the CCSM Ocean Component
    revision:          $Id: tavg.F90 89091 2018-04-30 15:58:32Z altuntas@ucar...
    calendar:          All years have exactly  365 days.
    start_time:        This dataset was created on 2018-11-17 at 20:33:43.3
    cell_methods:      cell_methods = time: mean ==> the variable values are ...

Doing this twice so I can use grid.interp to generate DZUW and DZTW

metrics = {
    ("X",): ["DXU", "DXT"],  # X distances
    ("Y",): ["DYU", "DYT"],  # Y distances
    ("Z",): ["DZU", "DZT"],  # Z distances
    ("X", "Y"): ["UAREA", "TAREA"],  # Areas (unnecessary)
}

grid, xpac = pop_tools.to_xgcm_grid_dataset(
    pacific, periodic=False, metrics=metrics, boundary="extrapolate"
)

pacific["DZUW"] = grid.interp(pacific.DZU, "Z")
pacific.DZUW.attrs["grid_loc"] = "3222"
pacific["DZTW"] = grid.interp(pacific.DZT, "Z")
pacific.DZTW.attrs["grid_loc"] = "3112"

metrics = {
    ("X",): ["DXU", "DXT"],  # X distances
    ("Y",): ["DYU", "DYT"],  # Y distances
    ("Z",): ["DZU", "DZT", "DZUW", "DZTW"],  # Z distances
    ("X", "Y"): ["UAREA", "TAREA"],  # Areas (unnecessary)
}

grid, xpac = pop_tools.to_xgcm_grid_dataset(
    pacific, periodic=False, metrics=metrics, boundary="extrapolate"
)
xpac.ULONG.load()
xpac.ULONG.data[xpac.ULONG.data > 0] = xpac.ULONG.data[xpac.ULONG.data > 0] - 360
xpac

/home/dcherian/miniconda3/envs/dcpy_old_dask/lib/python3.7/site-packages/ipykernel_launcher.py:28: RuntimeWarning: invalid value encountered in greater

<xarray.Dataset>
Dimensions:  (nlat_t: 305, nlat_u: 305, nlon_t: 1301, nlon_u: 1301, time: 730, z_t: 62, z_w_bot: 62, z_w_top: 62)
Coordinates:
    ULONG    (nlat_u, nlon_u) float64 -200.0 -199.9 -199.8 ... -70.2 -70.1 -70.0
    TLONG    (nlat_t, nlon_t) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    ULAT     (nlat_u, nlon_u) float64 -15.03 -15.03 -15.03 ... 15.03 15.03 15.03
    TLAT     (nlat_t, nlon_t) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
  * z_t      (z_t) float32 500.0 1.5e+03 2.5e+03 ... 5.625e+05 5.875e+05
  * z_w_top  (z_w_top) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05 5.75e+05
  * z_w_bot  (z_w_bot) float32 1e+03 2e+03 3e+03 ... 5.5e+05 5.75e+05 6e+05
  * time     (time) object 0033-01-06 00:00:00 ... 0043-01-01 00:00:00
  * nlon_u   (nlon_u) int64 1 2 3 4 5 6 7 ... 1295 1296 1297 1298 1299 1300 1301
  * nlat_u   (nlat_u) int64 1 2 3 4 5 6 7 8 ... 298 299 300 301 302 303 304 305
  * nlon_t   (nlon_t) float64 0.5 1.5 2.5 3.5 ... 1.298e+03 1.3e+03 1.3e+03
  * nlat_t   (nlat_t) float64 0.5 1.5 2.5 3.5 4.5 ... 301.5 302.5 303.5 304.5
Data variables:
    UVEL     (time, z_t, nlat_u, nlon_u) float32 dask.array<chunksize=(1, 62, 10, 1301), meta=np.ndarray>
    VVEL     (time, z_t, nlat_u, nlon_u) float32 dask.array<chunksize=(1, 62, 10, 1301), meta=np.ndarray>
    TAREA    (nlat_t, nlon_t) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    UAREA    (nlat_u, nlon_u) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    DXU      (nlat_u, nlon_u) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    DYU      (nlat_u, nlon_u) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    DXT      (nlat_t, nlon_t) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    DYT      (nlat_t, nlon_t) float64 dask.array<chunksize=(10, 1301), meta=np.ndarray>
    grav     float64 ...
    Q        (time, z_t, nlat_t, nlon_t) float32 dask.array<chunksize=(1, 62, 10, 1301), meta=np.ndarray>
    rho_sw   float64 ...
    XMXL     (time, nlat_t, nlon_t) float32 dask.array<chunksize=(1, 10, 1301), meta=np.ndarray>
    TMXL     (time, nlat_t, nlon_t) float32 dask.array<chunksize=(1, 10, 1301), meta=np.ndarray>
    HMXL     (time, nlat_t, nlon_t) float32 dask.array<chunksize=(1, 10, 1301), meta=np.ndarray>
    DZU      (z_t, nlat_u, nlon_u) float32 1e+03 1e+03 1e+03 ... 0.0 0.0 0.0
    DZT      (z_t, nlat_t, nlon_t) float32 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04
    DZUW     (z_w_top, nlat_u, nlon_u) float32 1e+03 1e+03 1e+03 ... 0.0 0.0 0.0
    DZTW     (z_w_top, nlat_t, nlon_t) float32 1e+03 1e+03 ... 2.5e+04 2.5e+04
Attributes:
    title:             g.e20.G.TL319_t13.control.001_hfreq
    history:           none
    Conventions:       CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-cu...
    time_period_freq:  day_5
    model_doi_url:     https://doi.org/10.5065/D67H1H0V
    contents:          Diagnostic and Prognostic Variables
    source:            CCSM POP2, the CCSM Ocean Component
    revision:          $Id: tavg.F90 89091 2018-04-30 15:58:32Z altuntas@ucar...
    calendar:          All years have exactly  365 days.
    start_time:        This dataset was created on 2018-11-17 at 20:33:43.3
    cell_methods:      cell_methods = time: mean ==> the variable values are ...

# xpac["pden"] = pop_tools.eos(xpac.SALT, xpac.TEMP, depth=xpac.z_t)
xpac["S2"] = grid.derivative(xpac.UVEL, "Z") ** 2 + grid.derivative(xpac.VVEL, "Z") ** 2
xpac["N2"] = (
    -1 * xpac.grav / xpac.rho_sw * xpac.Q
)  # 9.81/1025 * grid.derivative(xpac.pden, "Z")
xpac["Ri"] = xpac.N2 / grid.interp(xpac.S2, ["X", "Y", "Z"])

distributed.utils_perf - WARNING - full garbage collections took 73% CPU time recently (threshold: 10%)

eq = (
    xpac.cf.isel(Y=151).cf.sel(Z=slice(500 * 100))
    # .chunk({"time": -1,})
)
eq["TLONG"] -= 360
eq["TLONG"] = eq.TLONG.interpolate_na("nlon_t", fill_value="extrapolate")
eq["ULONG"] = eq.ULONG.interpolate_na("nlon_u", fill_value="extrapolate")
eq.Ri

<xarray.DataArray 'Ri' (time: 730, z_t: 33, nlon_t: 1301)>
dask.array<getitem, shape=(730, 33, 1301), dtype=float64, chunksize=(1, 32, 1300), chunktype=numpy.ndarray>
Coordinates:
    TLONG    (nlon_t) float64 dask.array<chunksize=(1301,), meta=np.ndarray>
    TLAT     (nlon_t) float64 dask.array<chunksize=(1301,), meta=np.ndarray>
  * z_t      (z_t) float32 500.0 1.5e+03 2.5e+03 ... 4.434e+04 4.827e+04
  * time     (time) object 0033-01-06 00:00:00 ... 0043-01-01 00:00:00
    nlat_u   int64 152
  * nlon_t   (nlon_t) float64 0.5 1.5 2.5 3.5 ... 1.298e+03 1.3e+03 1.3e+03
    nlat_t   float64 151.5
Attributes:
    long_name:  Acceleration Due to Gravity
    units:      centimeter/s^2

Ri_kwargs = dict(
    robust=True,
    levels=[0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95],
    cmap=mpl.cm.Spectral_r,
    cbar_kwargs={"orientation": "horizontal", "shrink": 1, "aspect": 25},
)

eq.Ri.isel(time=10).cf.plot(
    x="longitude", y="Z", robust=True, vmax=1, cmap=mpl.cm.Spectral_r
)

distributed.utils_perf - WARNING - full garbage collections took 73% CPU time recently (threshold: 10%)

<matplotlib.collections.QuadMesh at 0x7f6694c98f10>

%matplotlib inline

t = 100
(eq.Ri.isel(time=t).cf.plot(x="longitude", y="Z", **Ri_kwargs))
eq.HMXL.isel(time=t).cf.plot(x="longitude", color="k", zorder=2)
eq.XMXL.isel(time=t).cf.plot(x="longitude", color="w", zorder=2)

distributed.utils_perf - WARNING - full garbage collections took 92% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 92% CPU time recently (threshold: 10%)

[<matplotlib.lines.Line2D at 0x7f68eb055a50>]

distributed.utils_perf - WARNING - full garbage collections took 92% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 92% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 92% CPU time recently (threshold: 10%)

eq.cf.describe()

distributed.utils_perf - WARNING - full garbage collections took 92% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 92% CPU time recently (threshold: 10%)

Axes:
	X: ['nlon_t', 'nlon_u']
	Y: ['nlat_t', 'nlat_u']
	Z: ['z_t', 'z_w_bot', 'z_w_top']
	T: ['time']

Coordinates:
	longitude: ['TLONG', 'ULONG']
	latitude: ['TLAT', 'ULAT']
	vertical: ['z_t', 'z_w_bot', 'z_w_top']
	time: ['time']

Cell Measures:
	area: []
	volume: []

Standard Names:

medianRi = xr.Dataset()
medianRi["xmxl"] = eq.Ri.where(eq.Ri.cf["vertical"] > eq.XMXL).median("time")
medianRi["hmxl"] = eq.Ri.where(eq.Ri.cf["vertical"] > eq.HMXL).median("time")
medianRi.load()

distributed.utils_perf - WARNING - full garbage collections took 77% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 75% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 75% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 75% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 74% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 75% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 75% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 74% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 74% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 75% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 74% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 74% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 74% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 72% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 72% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 72% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 70% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 70% CPU time recently (threshold: 10%)
distributed.utils_perf - WARNING - full garbage collections took 70% CPU time recently (threshold: 10%)

<xarray.Dataset>
Dimensions:  (nlon_t: 1301, z_t: 33)
Coordinates:
    TLONG    (nlon_t) float64 -200.1 -200.0 -199.9 ... -70.25 -70.15 -70.05
    TLAT     (nlon_t) float64 -0.15 -0.15 -0.15 -0.15 -0.15 ... nan nan nan nan
  * z_t      (z_t) float32 500.0 1.5e+03 2.5e+03 ... 4.434e+04 4.827e+04
    nlat_u   int64 152
  * nlon_t   (nlon_t) float64 0.5 1.5 2.5 3.5 ... 1.298e+03 1.3e+03 1.3e+03
    nlat_t   float64 151.5
Data variables:
    xmxl     (z_t, nlon_t) float64 nan nan nan nan nan ... nan nan nan nan nan
    hmxl     (z_t, nlon_t) float64 nan nan nan nan nan ... nan nan nan nan nan

medianRi.to_netcdf("~/pump/notebooks/median-Ri-POP2.nc")

medianRi.to_array("mask").cf.plot(
    y="Z", x="longitude", row="mask", ylim=(250 * 100, 0), **Ri_kwargs
)

import dask.array as da


# Use a custom array type to know when data is being evaluated
class Array:
    def __init__(self, x):
        self.shape = (x.shape[0],)
        self.ndim = x.ndim
        self.dtype = "bool"
        self.x = x

    def __getitem__(self, idx):
        if idx[0].stop > 0:
            print("Evaluating")
        return (self.x > 0.5).__getitem__(idx)


# Control case -- this shows that the print statement is only reached once
da.from_array(Array(np.random.rand(100))).compute()
# Evaluating

# This usage somehow results in two evaluations of this one array?
ds = xr.Dataset(dict(a=("x", da.from_array(Array(np.random.rand(100))))))
ds.sel(x=ds.a)
# Evaluating
# Evaluating
# <xarray.Dataset>
# Dimensions:  (x: 51)
# Dimensions without coordinates: x
# Data variables:
#     a        (x) bool dask.array<chunksize=(51,), meta=np.ndarray>

# The array is evaluated an extra time for each new variable
ds = xr.Dataset(
    dict(
        a=("x", da.from_array(Array(np.random.rand(100)))),
        b=(("x", "y"), da.random.random((100, 10))),
        c=(("x", "y"), da.random.random((100, 10))),
        d=(("x", "y"), da.random.random((100, 10))),
    )
)
ds.sel(x=ds.a)

<xarray.Dataset>
Dimensions:  (x: 46, y: 10)
Dimensions without coordinates: x, y
Data variables:
    a        (x) bool dask.array<chunksize=(46,), meta=np.ndarray>
    b        (x, y) float64 dask.array<chunksize=(46, 10), meta=np.ndarray>
    c        (x, y) float64 dask.array<chunksize=(46, 10), meta=np.ndarray>
    d        (x, y) float64 dask.array<chunksize=(46, 10), meta=np.ndarray>

import dask.array as da
import numpy as np

from xarray.tests import raise_if_dask_computes

with raise_if_dask_computes(max_computes=1):
    ds = xr.Dataset(
        dict(
            a=(
                "x",
                da.from_array(
                    np.random.rand(
                        100,
                    )
                ),
            ),
            b=(("x", "y"), da.random.random((100, 10))),
            c=(("x", "y"), da.random.random((100, 10))),
            d=(("x", "y"), da.random.random((100, 10))),
        )
    )
    ds.sel(x=(ds.a > 0.5))

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-94-96f0018c9658> in <module>
     11         d=(('x', 'y'), da.random.random((100, 10))),
     12     ))
---> 13     ds.sel(x=(ds.a > 0.5))

/project/mrgoodbar/dcherian/python/xarray/xarray/core/dataset.py in sel(self, indexers, method, tolerance, drop, **indexers_kwargs)
   2211             self, indexers=indexers, method=method, tolerance=tolerance
   2212         )
-> 2213         result = self.isel(indexers=pos_indexers, drop=drop)
   2214         return result._overwrite_indexes(new_indexes)
   2215 

/project/mrgoodbar/dcherian/python/xarray/xarray/core/dataset.py in isel(self, indexers, drop, missing_dims, **indexers_kwargs)
   2058         indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "isel")
   2059         if any(is_fancy_indexer(idx) for idx in indexers.values()):
-> 2060             return self._isel_fancy(indexers, drop=drop, missing_dims=missing_dims)
   2061 
   2062         # Much faster algorithm for when all indexers are ints, slices, one-dimensional

/project/mrgoodbar/dcherian/python/xarray/xarray/core/dataset.py in _isel_fancy(self, indexers, drop, missing_dims)
   2122                     indexes[name] = new_index
   2123             elif var_indexers:
-> 2124                 new_var = var.isel(indexers=var_indexers)
   2125             else:
   2126                 new_var = var.copy(deep=False)

/project/mrgoodbar/dcherian/python/xarray/xarray/core/variable.py in isel(self, indexers, missing_dims, **indexers_kwargs)
   1118 
   1119         key = tuple(indexers.get(dim, slice(None)) for dim in self.dims)
-> 1120         return self[key]
   1121 
   1122     def squeeze(self, dim=None):

/project/mrgoodbar/dcherian/python/xarray/xarray/core/variable.py in __getitem__(self, key)
    766         array `x.values` directly.
    767         """
--> 768         dims, indexer, new_order = self._broadcast_indexes(key)
    769         data = as_indexable(self._data)[indexer]
    770         if new_order:

/project/mrgoodbar/dcherian/python/xarray/xarray/core/variable.py in _broadcast_indexes(self, key)
    625                 dims.append(d)
    626         if len(set(dims)) == len(dims):
--> 627             return self._broadcast_indexes_outer(key)
    628 
    629         return self._broadcast_indexes_vectorized(key)

/project/mrgoodbar/dcherian/python/xarray/xarray/core/variable.py in _broadcast_indexes_outer(self, key)
    680                 k = k.data
    681             if not isinstance(k, BASIC_INDEXING_TYPES):
--> 682                 k = np.asarray(k)
    683                 if k.size == 0:
    684                     # Slice by empty list; numpy could not infer the dtype

~/miniconda3/envs/dcpy_old_dask/lib/python3.7/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~/miniconda3/envs/dcpy_old_dask/lib/python3.7/site-packages/dask/array/core.py in __array__(self, dtype, **kwargs)
   1374 
   1375     def __array__(self, dtype=None, **kwargs):
-> 1376         x = self.compute()
   1377         if dtype and x.dtype != dtype:
   1378             x = x.astype(dtype)

~/miniconda3/envs/dcpy_old_dask/lib/python3.7/site-packages/dask/base.py in compute(self, **kwargs)
    165         dask.base.compute
    166         """
--> 167         (result,) = compute(self, traverse=False, **kwargs)
    168         return result
    169 

~/miniconda3/envs/dcpy_old_dask/lib/python3.7/site-packages/dask/base.py in compute(*args, **kwargs)
    450         postcomputes.append(x.__dask_postcompute__())
    451 
--> 452     results = schedule(dsk, keys, **kwargs)
    453     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
    454 

/project/mrgoodbar/dcherian/python/xarray/xarray/tests/__init__.py in __call__(self, dsk, keys, **kwargs)
    112             raise RuntimeError(
    113                 "Too many computes. Total: %d > max: %d."
--> 114                 % (self.total_computes, self.max_computes)
    115             )
    116         return dask.get(dsk, keys, **kwargs)

RuntimeError: Too many computes. Total: 2 > max: 1.