In [1]:

import numpy as np
import xarray as xr
ds = xr.tutorial.open_dataset('rasm')

Set to classic repr by default for now so that we can merge and keep ironing out oddities:

In [2]:

ds

Out[2]:

<xarray.Dataset>
Dimensions:  (time: 36, x: 275, y: 205)
Coordinates:
  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00
    xc       (y, x) float64 ...
    yc       (y, x) float64 ...
Dimensions without coordinates: x, y
Data variables:
    Tair     (time, y, x) float64 ...
Attributes:
    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...
    institution:               U.W.
    source:                    RACM R1002RBRxaaa01a
    output_frequency:          daily
    output_mode:               averaged
    convention:                CF-1.4
    references:                Based on the initial model of Liang et al., 19...
    comment:                   Output from the Variable Infiltration Capacity...
    nco_openmp_thread_number:  1
    NCO:                       "4.6.0"
    history:                   Tue Dec 27 14:15:22 2016: ncatted -a dimension...

New repr¶

In [3]:

xr.set_options(display_style="html");

Before the data are loaded, the preview won't show up. After the data are loaded below, the preview will be populated.

In [4]:

ds.load()

Out[4]:

In [5]:

ds.Tair

Out[5]:

xarray.DataArray

'Tair'

time: 36
y: 205
x: 275

nan nan nan nan nan nan nan ... 29.19 29.64 29.8 28.66 28.19 28.21

array([[[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [        nan,         nan,         nan, ..., 27.03290153,
         27.03125761, 27.33531541],
        [        nan,         nan,         nan, ..., 27.2784053 ,
         26.80261869, 27.08603517],
        [        nan,         nan,         nan, ..., 27.02344402,
         26.56473862, 26.73064933]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [        nan,         nan,         nan, ..., 24.52606661,
         24.44559149, 24.72489025],
        [        nan,         nan,         nan, ..., 24.81092341,
         24.29623992, 24.61422421],
        [        nan,         nan,         nan, ..., 24.49876196,
         24.29967705, 24.45439921]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [        nan,         nan,         nan, ..., 15.59297273,
         15.29777668, 15.4266377 ],
        [        nan,         nan,         nan, ..., 16.00414707,
         15.18088805, 15.50982104],
        [        nan,         nan,         nan, ..., 15.90243768,
         15.54410181, 15.70258798]],

       ...,

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [        nan,         nan,         nan, ..., 28.24428726,
         28.36994752, 28.72271671],
        [        nan,         nan,         nan, ..., 28.37887215,
         28.04355761, 28.34557868],
        [        nan,         nan,         nan, ..., 27.98941074,
         27.636075  , 27.7891351 ]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [        nan,         nan,         nan, ..., 27.8597472 ,
         27.82928439, 28.09249224],
        [        nan,         nan,         nan, ..., 27.89704094,
         27.31104941, 27.67387171],
        [        nan,         nan,         nan, ..., 27.46837113,
         27.0088944 , 27.23017976]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [        nan,         nan,         nan, ..., 28.95929072,
         28.87672039, 29.04890862],
        [        nan,         nan,         nan, ..., 29.036132  ,
         28.42273578, 28.68721201],
        [        nan,         nan,         nan, ..., 28.66381585,
         28.18595533, 28.20753022]]])

Coordinates: (3)

time

(time)

object

1980-09-16 12:00:00 ... 1983-08-17 00:00:00

long_name :: time
type_preferred :: int

array([cftime.DatetimeNoLeap(1980, 9, 16, 12, 0, 0, 0, 5, 259),
       cftime.DatetimeNoLeap(1980, 10, 17, 0, 0, 0, 0, 1, 290),
       cftime.DatetimeNoLeap(1980, 11, 16, 12, 0, 0, 0, 3, 320),
       cftime.DatetimeNoLeap(1980, 12, 17, 0, 0, 0, 0, 6, 351),
       cftime.DatetimeNoLeap(1981, 1, 17, 0, 0, 0, 0, 2, 17),
       cftime.DatetimeNoLeap(1981, 2, 15, 12, 0, 0, 0, 3, 46),
       cftime.DatetimeNoLeap(1981, 3, 17, 0, 0, 0, 0, 5, 76),
       cftime.DatetimeNoLeap(1981, 4, 16, 12, 0, 0, 0, 0, 106),
       cftime.DatetimeNoLeap(1981, 5, 17, 0, 0, 0, 0, 3, 137),
       cftime.DatetimeNoLeap(1981, 6, 16, 12, 0, 0, 0, 5, 167),
       cftime.DatetimeNoLeap(1981, 7, 17, 0, 0, 0, 0, 1, 198),
       cftime.DatetimeNoLeap(1981, 8, 17, 0, 0, 0, 0, 4, 229),
       cftime.DatetimeNoLeap(1981, 9, 16, 12, 0, 0, 0, 6, 259),
       cftime.DatetimeNoLeap(1981, 10, 17, 0, 0, 0, 0, 2, 290),
       cftime.DatetimeNoLeap(1981, 11, 16, 12, 0, 0, 0, 4, 320),
       cftime.DatetimeNoLeap(1981, 12, 17, 0, 0, 0, 0, 0, 351),
       cftime.DatetimeNoLeap(1982, 1, 17, 0, 0, 0, 0, 3, 17),
       cftime.DatetimeNoLeap(1982, 2, 15, 12, 0, 0, 0, 4, 46),
       cftime.DatetimeNoLeap(1982, 3, 17, 0, 0, 0, 0, 6, 76),
       cftime.DatetimeNoLeap(1982, 4, 16, 12, 0, 0, 0, 1, 106),
       cftime.DatetimeNoLeap(1982, 5, 17, 0, 0, 0, 0, 4, 137),
       cftime.DatetimeNoLeap(1982, 6, 16, 12, 0, 0, 0, 6, 167),
       cftime.DatetimeNoLeap(1982, 7, 17, 0, 0, 0, 0, 2, 198),
       cftime.DatetimeNoLeap(1982, 8, 17, 0, 0, 0, 0, 5, 229),
       cftime.DatetimeNoLeap(1982, 9, 16, 12, 0, 0, 0, 0, 259),
       cftime.DatetimeNoLeap(1982, 10, 17, 0, 0, 0, 0, 3, 290),
       cftime.DatetimeNoLeap(1982, 11, 16, 12, 0, 0, 0, 5, 320),
       cftime.DatetimeNoLeap(1982, 12, 17, 0, 0, 0, 0, 1, 351),
       cftime.DatetimeNoLeap(1983, 1, 17, 0, 0, 0, 0, 4, 17),
       cftime.DatetimeNoLeap(1983, 2, 15, 12, 0, 0, 0, 5, 46),
       cftime.DatetimeNoLeap(1983, 3, 17, 0, 0, 0, 0, 0, 76),
       cftime.DatetimeNoLeap(1983, 4, 16, 12, 0, 0, 0, 2, 106),
       cftime.DatetimeNoLeap(1983, 5, 17, 0, 0, 0, 0, 5, 137),
       cftime.DatetimeNoLeap(1983, 6, 16, 12, 0, 0, 0, 0, 167),
       cftime.DatetimeNoLeap(1983, 7, 17, 0, 0, 0, 0, 3, 198),
       cftime.DatetimeNoLeap(1983, 8, 17, 0, 0, 0, 0, 6, 229)], dtype=object)

xc

(y, x)

float64

189.2 189.4 189.6 ... 17.15 16.91

long_name :: longitude of grid cell center
units :: degrees_east
bounds :: xv

array([[189.22293223, 189.38990916, 189.55836619, ..., 293.77906088,
        294.0279241 , 294.27439931],
       [188.96836986, 189.13470591, 189.30253733, ..., 294.05584005,
        294.30444387, 294.55065969],
       [188.71234264, 188.87800731, 189.04515208, ..., 294.335053  ,
        294.58337453, 294.8292928 ],
       ...,
       [124.04724025, 123.88362026, 123.71852016, ...,  16.83171831,
         16.58436953,  16.33949649],
       [123.78686428, 123.62254238, 123.45672512, ...,  17.11814486,
         16.87043749,  16.62518298],
       [123.52798366, 123.36295986, 123.1964407 , ...,  17.40209947,
         17.1540526 ,  16.90845095]])

yc

(y, x)

float64

16.53 16.78 17.02 ... 27.76 27.51

long_name :: latitude of grid cell center
units :: degrees_north
bounds :: yv

array([[16.53498637, 16.7784556 , 17.02222429, ..., 27.36301592,
        27.11811045, 26.87289026],
       [16.69397341, 16.93865381, 17.18364512, ..., 27.5847719 ,
        27.33821848, 27.0913656 ],
       [16.85219179, 17.09808909, 17.34430872, ..., 27.80584314,
        27.55764558, 27.30915621],
       ...,
       [17.31179033, 17.56124674, 17.81104646, ..., 28.4502485 ,
        28.19718339, 27.94384744],
       [17.15589701, 17.40414034, 17.65272318, ..., 28.23129632,
        27.97989251, 27.72821596],
       [16.99919497, 17.24622904, 17.49358736, ..., 28.01160028,
        27.76185586, 27.51182726]])

Attributes: (4)
units :
C
long_name :
Surface air temperature
type_preferred :
double
time_rep :
instantaneous

Check sizes¶

In [6]:

c = ds.__repr__()
h = ds._repr_html_()

In [7]:

import sys
print(f'classic: {round(sys.getsizeof(c)/1000)}kb\nhtml: {round(sys.getsizeof(h)/1000)}kb')

classic: 1kb
html: 38kb

Long var names¶

In [8]:

xr.Dataset({
    'fdhskafhdjsafdsafhldsa;fjkld;sajkfdsla;fdjksa': ds.Tair,
    'wuildjklsahjdklsahfjkdshafjkldsahjfkdslafhdsja': ds.Tair})

`MultiIndex`¶

In [9]:

array = xr.DataArray(np.random.randn(2, 3),
                     coords=[('x', ['a', 'b']), ('y', [0, 1, 2])])
stacked = array.stack(z=('x', 'y'))
stacked

Out[9]:

xarray.DataArray

z: 6

2.095 1.12 -2.135 -0.6623 -0.01252 0.6265

array([ 2.09475931,  1.1198707 , -2.13469844, -0.66230567, -0.01252011,
        0.62646231])

Coordinates: (1)

z

(z)

MultiIndex

(x, y)

array([('a', 0), ('a', 1), ('a', 2), ('b', 0), ('b', 1), ('b', 2)],
      dtype=object)

x

(z)

object

'a' 'a' 'a' 'b' 'b' 'b'

array(['a', 'a', 'a', 'b', 'b', 'b'], dtype=object)

y
(z)
int64
0 1 2 0 1 2
```
array([0, 1, 2, 0, 1, 2])
```

Attributes: (0)

Dask¶

In [10]:

ds = xr.tutorial.open_dataset('air_temperature',
                              chunks={'lat': 25, 'lon': 25, 'time': -1})
ds

Out[10]:

xarray.Dataset

Dimensions:
- lat: 25
- lon: 53
- time: 2920

Coordinates: (3)

lat

(lat)

float32

75.0 72.5 70.0 ... 20.0 17.5 15.0

standard_name :: latitude
long_name :: Latitude
units :: degrees_north
axis :: Y

array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,
       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,
       15. ], dtype=float32)

lon

(lon)

float32

200.0 202.5 205.0 ... 327.5 330.0

standard_name :: longitude
long_name :: Longitude
units :: degrees_east
axis :: X

array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
       325. , 327.5, 330. ], dtype=float32)

time

(time)

datetime64[ns]

2013-01-01 ... 2014-12-31T18:00:00

standard_name :: time
long_name :: Time

array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',
       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',
       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (1)

air

(time, lat, lon)

float32

dask.array<chunksize=(2920, 25, 25), meta=np.ndarray>

long_name :: 4xDaily Air temperature at sigma level 995
units :: degK
precision :: 2
GRIB_id :: 11
GRIB_name :: TMP
var_desc :: Air temperature
dataset :: NMC Reanalysis
level_desc :: Surface
statistic :: Individual Obs
parent_stat :: Other
actual_range :: [185.16 322.1 ]





  
      Array  Chunk 
  
  
     Bytes  15.48 MB   7.30 MB 
     Shape  (2920, 25, 53)   (2920, 25, 25) 
     Count  4 Tasks  3 Chunks 
     Type  float32  numpy.ndarray

Attributes: (5)
Conventions :
COARDS
title :
4x daily NMC reanalysis (1948)
description :
Data is from NMC initialized reanalysis (4x/day). These are the 0.9950 sigma level values.
platform :
Model
references :
http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html

We can use panel to see what the dataset looks like at smaller widths

In [11]:

import panel as pn; pn.extension()
xr.set_options(display_style="html");

In [12]:

dataset = pn.pane.HTML(ds, width=400, background="#ddffff")
width = pn.widgets.IntSlider( value=dataset.width, start=300, end=700, width=150, name='width')
width.jslink(dataset, value='width')

pn.Row(width, dataset)

Out[12]:

In [ ]:

New repr¶

Check sizes¶

Long var names¶

MultiIndex¶

Dask¶

`MultiIndex`¶