Wranglers¶
In this notebook we give a brief overview of wrangling with netCDF-SCM.
[1]:
# NBVAL_IGNORE_OUTPUT
import glob
from pathlib import Path
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymagicc
[2]:
plt.style.use("bmh")
%matplotlib inline
Wrangling help¶
The wrangling help can be accessed via our command line interface.
[3]:
# NBVAL_IGNORE_OUTPUT
!netcdf-scm wrangle -h
Usage: netcdf-scm wrangle [OPTIONS] SRC DST WRANGLE_CONTACT
Wrangle netCDF-SCM ``.nc`` files into other formats and directory
structures.
``src`` is searched recursively and netcdf-scm will attempt to wrangle all
the files found.
``wrangle_contact`` is written into the header of the output files.
Options:
--regexp TEXT Regular expression to apply to file
directory (only wrangles matches). Be
careful, if you use a very copmlex regexp
directory sorting can be extremely slow (see
e.g. discussion at
https://stackoverflow.com/a/5428712)!
[default: ^(?!.*(fx)).*$]
--prefix TEXT Prefix to apply to output file names (not
paths).
--out-format [mag-files|mag-files-average-year-start-year|mag-files-average-year-mid-year|mag-files-average-year-end-year|mag-files-point-start-year|mag-files-point-mid-year|mag-files-point-end-year|magicc-input-files|magicc-input-files-average-year-start-year|magicc-input-files-average-year-mid-year|magicc-input-files-average-year-end-year|magicc-input-files-point-start-year|magicc-input-files-point-mid-year|magicc-input-files-point-end-year|tuningstrucs-blend-model]
Format to re-write crunched data into. The
time operation conventions follow those in
`Pymagicc <https://pymagicc.readthedocs.io/e
n/latest/file_conventions.html#namelists>`_.
[default: mag-files]
--drs [None|MarbleCMIP5|CMIP6Input4MIPs|CMIP6Output]
Data reference syntax to use to decipher
paths. This is required to ensure the output
folders match the input data reference
syntax. [default: None]
-f, --force / --do-not-force Overwrite any existing files. [default:
False]
--number-workers INTEGER Number of worker (threads) to use when
wrangling. [default: 4]
--target-units-specs PATH csv containing target units for wrangled
variables.
-h, --help Show this message and exit.
MAG file wrangling¶
The most common format to wrangle to is the .MAG format. This is a custom MAGICC format (see https://pymagicc.readthedocs.io/en/latest/file_conventions.html#the-future). We can wrangle data which has already been crunched to this format as shown below.
[4]:
# NBVAL_IGNORE_OUTPUT
!netcdf-scm wrangle \
"../../../tests/test-data/expected-crunching-output/cmip6output/Lmon/CMIP6/CMIP/NCAR" \
"../../../output-examples/wrangled-files" "notebook example <email address>" \
--force \
--drs "CMIP6Output" \
--out-format "mag-files" \
--regexp ".*cSoilFast.*"
87973 2021-03-18 13:05:52,227 INFO:netcdf_scm:netcdf-scm: 2.0.2+15.g74db9d85.dirty
87973 2021-03-18 13:05:52,228 INFO:netcdf_scm:wrangle_contact: notebook example <email address>
87973 2021-03-18 13:05:52,228 INFO:netcdf_scm:source: /Users/znicholls/Documents/AGCEC/netCDF-SCM/netcdf-scm/tests/test-data/expected-crunching-output/cmip6output/Lmon/CMIP6/CMIP/NCAR
87973 2021-03-18 13:05:52,228 INFO:netcdf_scm:destination: /Users/znicholls/Documents/AGCEC/netCDF-SCM/netcdf-scm/output-examples/wrangled-files
87973 2021-03-18 13:05:52,228 INFO:netcdf_scm:regexp: .*cSoilFast.*
87973 2021-03-18 13:05:52,228 INFO:netcdf_scm:prefix: None
87973 2021-03-18 13:05:52,228 INFO:netcdf_scm:drs: CMIP6Output
87973 2021-03-18 13:05:52,228 INFO:netcdf_scm:out_format: mag-files
87973 2021-03-18 13:05:52,228 INFO:netcdf_scm:force: True
87973 2021-03-18 13:05:52,230 INFO:netcdf_scm:Finding directories with files
Walking through directories and applying `check_func`: 11it [00:00, 9394.69it/s]
87973 2021-03-18 13:05:52,238 INFO:netcdf_scm:Found 1 directories with files
87973 2021-03-18 13:05:52,239 INFO:netcdf_scm.cli_parallel:Processing in parallel with 4 workers
87973 2021-03-18 13:05:52,239 INFO:netcdf_scm.cli_parallel:Forcing dask to use a single thread when reading
100%|████████████████████████████████████████| 1.00/1.00 [00:04<00:00, 4.32s/it]
We can then load the .MAG files using Pymagicc.
[5]:
written_files = [
f for f in Path("../../../output-examples/wrangled-files").rglob("*.MAG")
]
written_files
[5]:
[PosixPath('../../../output-examples/wrangled-files/CMIP6/CMIP/NCAR/CESM2/historical/r7i1p1f1/Lmon/cSoilFast/gn/v20190311/netcdf-scm_cSoilFast_Lmon_CESM2_historical_r7i1p1f1_gn_195701-195703.MAG')]
[6]:
wrangled = pymagicc.io.MAGICCData(str(written_files[0]))
[7]:
# NBVAL_IGNORE_OUTPUT
wrangled.timeseries()
[7]:
| time | 1957-01-15 12:00:00 | 1957-02-14 00:00:00 | 1957-03-15 12:00:00 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| climate_model | model | region | scenario | todo | unit | variable | |||
| unspecified | unspecified | World | unspecified | SET | kg m^-2 | cSoilFast | 0.085600 | 0.085547 | 0.085422 |
| World|Northern Hemisphere | unspecified | SET | kg m^-2 | cSoilFast | 0.097727 | 0.097910 | 0.098135 | ||
| World|Southern Hemisphere | unspecified | SET | kg m^-2 | cSoilFast | 0.060421 | 0.059879 | 0.059024 | ||
| World|Land | unspecified | SET | kg m^-2 | cSoilFast | 0.085600 | 0.085547 | 0.085422 | ||
| World|Northern Hemisphere|Land | unspecified | SET | kg m^-2 | cSoilFast | 0.097727 | 0.097910 | 0.098135 | ||
| World|Southern Hemisphere|Land | unspecified | SET | kg m^-2 | cSoilFast | 0.060421 | 0.059879 | 0.059024 |
[8]:
# NBVAL_IGNORE_OUTPUT
wrangled.lineplot(hue="region")
[8]:
<AxesSubplot:xlabel='time', ylabel='kg m^-2'>
Adjusting units¶
The units of the wrangled data are kgmsuper-2. This might not be super helpful. As such, netcdf-scm wrangle allows users to specify a csv which defines the target units to use for variables when wrangling.
The conversion csv should look like the below.
[9]:
conv_csv = pd.DataFrame(
[["cSoilFast", "t / m**2"], ["tos", "K"]], columns=["variable", "unit"]
)
conv_csv_path = "../../../output-examples/conversion-new-units.csv"
conv_csv.to_csv(conv_csv_path, index=False)
with open(conv_csv_path) as f:
conv_csv_content = f.read()
print(conv_csv_content)
variable,unit
cSoilFast,t / m**2
tos,K
With such a csv, we can now crunch to our desired units.
[10]:
# NBVAL_IGNORE_OUTPUT
!netcdf-scm wrangle \
"../../../tests/test-data/expected-crunching-output/cmip6output/Lmon/CMIP6/CMIP/NCAR" \
"../../../output-examples/wrangled-files-new-units" \
"notebook example <email address>" \
--force --drs "CMIP6Output" \
--out-format "mag-files" \
--regexp ".*cSoilFast.*" \
--target-units-specs "../../../output-examples/conversion-new-units.csv"
87988 2021-03-18 13:06:01,020 INFO:netcdf_scm:netcdf-scm: 2.0.2+15.g74db9d85.dirty
87988 2021-03-18 13:06:01,020 INFO:netcdf_scm:wrangle_contact: notebook example <email address>
87988 2021-03-18 13:06:01,020 INFO:netcdf_scm:source: /Users/znicholls/Documents/AGCEC/netCDF-SCM/netcdf-scm/tests/test-data/expected-crunching-output/cmip6output/Lmon/CMIP6/CMIP/NCAR
87988 2021-03-18 13:06:01,020 INFO:netcdf_scm:destination: /Users/znicholls/Documents/AGCEC/netCDF-SCM/netcdf-scm/output-examples/wrangled-files-new-units
87988 2021-03-18 13:06:01,020 INFO:netcdf_scm:regexp: .*cSoilFast.*
87988 2021-03-18 13:06:01,021 INFO:netcdf_scm:prefix: None
87988 2021-03-18 13:06:01,021 INFO:netcdf_scm:drs: CMIP6Output
87988 2021-03-18 13:06:01,021 INFO:netcdf_scm:out_format: mag-files
87988 2021-03-18 13:06:01,021 INFO:netcdf_scm:force: True
87988 2021-03-18 13:06:01,022 INFO:netcdf_scm:Finding directories with files
Walking through directories and applying `check_func`: 11it [00:00, 9150.60it/s]
87988 2021-03-18 13:06:01,030 INFO:netcdf_scm:Found 1 directories with files
87988 2021-03-18 13:06:01,031 INFO:netcdf_scm.cli_parallel:Processing in parallel with 4 workers
87988 2021-03-18 13:06:01,031 INFO:netcdf_scm.cli_parallel:Forcing dask to use a single thread when reading
100%|████████████████████████████████████████| 1.00/1.00 [00:04<00:00, 4.28s/it]
[11]:
# NBVAL_IGNORE_OUTPUT
written_files = [
f
for f in Path("../../../output-examples/wrangled-files-new-units").rglob(
"*.MAG"
)
]
wrangled_new_units = pymagicc.io.MAGICCData(str(written_files[0]))
wrangled_new_units.timeseries()
[11]:
| time | 1957-01-15 12:00:00 | 1957-02-14 00:00:00 | 1957-03-15 12:00:00 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| climate_model | model | region | scenario | todo | unit | variable | |||
| unspecified | unspecified | World | unspecified | SET | t / m^2 | cSoilFast | 0.000086 | 0.000086 | 0.000085 |
| World|Northern Hemisphere | unspecified | SET | t / m^2 | cSoilFast | 0.000098 | 0.000098 | 0.000098 | ||
| World|Southern Hemisphere | unspecified | SET | t / m^2 | cSoilFast | 0.000060 | 0.000060 | 0.000059 | ||
| World|Land | unspecified | SET | t / m^2 | cSoilFast | 0.000086 | 0.000086 | 0.000085 | ||
| World|Northern Hemisphere|Land | unspecified | SET | t / m^2 | cSoilFast | 0.000098 | 0.000098 | 0.000098 | ||
| World|Southern Hemisphere|Land | unspecified | SET | t / m^2 | cSoilFast | 0.000060 | 0.000060 | 0.000059 |
[12]:
# NBVAL_IGNORE_OUTPUT
wrangled_new_units.lineplot(hue="region")
[12]:
<AxesSubplot:xlabel='time', ylabel='t / m^2'>
Taking area sum¶
We can also set the units to include an area sum. For example, if we set our units to Gt / yr rather than Gt / m**2 / yr then the wrangler will automatically take an area sum of the data (weighted by the effective area used in the crunching) before returning the data.
[13]:
conv_csv = pd.DataFrame(
[["cSoilFast", "Gt"], ["tos", "K"]], columns=["variable", "unit"]
)
conv_csv_path = "../../../output-examples/conversion-area-sum-units.csv"
conv_csv.to_csv(conv_csv_path, index=False)
with open(conv_csv_path) as f:
conv_csv_content = f.read()
print(conv_csv_content)
variable,unit
cSoilFast,Gt
tos,K
[14]:
# NBVAL_IGNORE_OUTPUT
!netcdf-scm wrangle \
"../../../tests/test-data/expected-crunching-output/cmip6output/Lmon/CMIP6/CMIP/NCAR" \
"../../../output-examples/wrangled-files-area-sum-units" \
"notebook example <email address>" \
--force \
--drs "CMIP6Output" \
--out-format "mag-files" \
--regexp ".*cSoilFast.*" \
--target-units-specs "../../../output-examples/conversion-area-sum-units.csv"
88003 2021-03-18 13:06:10,165 INFO:netcdf_scm:netcdf-scm: 2.0.2+15.g74db9d85.dirty
88003 2021-03-18 13:06:10,166 INFO:netcdf_scm:wrangle_contact: notebook example <email address>
88003 2021-03-18 13:06:10,166 INFO:netcdf_scm:source: /Users/znicholls/Documents/AGCEC/netCDF-SCM/netcdf-scm/tests/test-data/expected-crunching-output/cmip6output/Lmon/CMIP6/CMIP/NCAR
88003 2021-03-18 13:06:10,166 INFO:netcdf_scm:destination: /Users/znicholls/Documents/AGCEC/netCDF-SCM/netcdf-scm/output-examples/wrangled-files-area-sum-units
88003 2021-03-18 13:06:10,166 INFO:netcdf_scm:regexp: .*cSoilFast.*
88003 2021-03-18 13:06:10,166 INFO:netcdf_scm:prefix: None
88003 2021-03-18 13:06:10,166 INFO:netcdf_scm:drs: CMIP6Output
88003 2021-03-18 13:06:10,166 INFO:netcdf_scm:out_format: mag-files
88003 2021-03-18 13:06:10,166 INFO:netcdf_scm:force: True
88003 2021-03-18 13:06:10,168 INFO:netcdf_scm:Finding directories with files
Walking through directories and applying `check_func`: 11it [00:00, 9617.96it/s]
88003 2021-03-18 13:06:10,176 INFO:netcdf_scm:Found 1 directories with files
88003 2021-03-18 13:06:10,177 INFO:netcdf_scm.cli_parallel:Processing in parallel with 4 workers
88003 2021-03-18 13:06:10,177 INFO:netcdf_scm.cli_parallel:Forcing dask to use a single thread when reading
100%|████████████████████████████████████████| 1.00/1.00 [00:04<00:00, 4.44s/it]
[15]:
# NBVAL_IGNORE_OUTPUT
written_files = [
f
for f in Path(
"../../../output-examples/wrangled-files-area-sum-units"
).rglob("*.MAG")
]
wrangled_area_sum_units = pymagicc.io.MAGICCData(str(written_files[0]))
wrangled_area_sum_units.timeseries()
[15]:
| time | 1957-01-15 12:00:00 | 1957-02-14 00:00:00 | 1957-03-15 12:00:00 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| climate_model | model | region | scenario | todo | unit | variable | |||
| unspecified | unspecified | World | unspecified | SET | Gt | cSoilFast | 12.79290 | 12.7849 | 12.76610 |
| World|Land | unspecified | SET | Gt | cSoilFast | 12.79290 | 12.7849 | 12.76610 | ||
| World|Northern Hemisphere | unspecified | SET | Gt | cSoilFast | 9.85760 | 9.8760 | 9.89873 | ||
| World|Northern Hemisphere|Land | unspecified | SET | Gt | cSoilFast | 9.85760 | 9.8760 | 9.89873 | ||
| World|Southern Hemisphere | unspecified | SET | Gt | cSoilFast | 2.93526 | 2.9089 | 2.86740 | ||
| World|Southern Hemisphere|Land | unspecified | SET | Gt | cSoilFast | 2.93526 | 2.9089 | 2.86740 |
[16]:
# NBVAL_IGNORE_OUTPUT
solid_regions = [
"World",
"World|Northern Hemisphere",
"World|Southern Hemisphere",
]
ax = wrangled_area_sum_units.filter(region=solid_regions).lineplot(
hue="region", linestyle="-"
)
wrangled_area_sum_units.filter(region=solid_regions, keep=False).lineplot(
hue="region", linestyle="--", dashes=(5, 7.5), ax=ax
)
[16]:
<AxesSubplot:xlabel='time', ylabel='Gt'>
As one last sanity check, we can make sure that the world total equals the regional total to within rounding errors.
[17]:
np.testing.assert_allclose(
wrangled_area_sum_units.filter(region="World")
.timeseries()
.values.squeeze(),
wrangled_area_sum_units.filter(
region=["World|Northern Hemisphere", "World|Southern Hemisphere"]
)
.timeseries()
.sum()
.values.squeeze(),
rtol=1e-5,
)
Time operations¶
The wrangling can also include a few basic time operations e.g. annual means or interpolation onto different grids. The different out-format codes follow those in Pymagicc (link to be updated once PR is merged). Here we show one example where we take the annual mean as part of the wrangling process.
[18]:
# NBVAL_IGNORE_OUTPUT
!netcdf-scm wrangle \
"../../../tests/test-data/expected-crunching-output/cmip6output/Amon/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/piControl" \
"../../../output-examples/wrangled-files-average-year" \
"notebook example <email address>" \
--force \
--drs "CMIP6Output" \
--out-format "mag-files-average-year-mid-year"
88024 2021-03-18 13:06:19,120 INFO:netcdf_scm:netcdf-scm: 2.0.2+15.g74db9d85.dirty
88024 2021-03-18 13:06:19,120 INFO:netcdf_scm:wrangle_contact: notebook example <email address>
88024 2021-03-18 13:06:19,120 INFO:netcdf_scm:source: /Users/znicholls/Documents/AGCEC/netCDF-SCM/netcdf-scm/tests/test-data/expected-crunching-output/cmip6output/Amon/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/piControl
88024 2021-03-18 13:06:19,120 INFO:netcdf_scm:destination: /Users/znicholls/Documents/AGCEC/netCDF-SCM/netcdf-scm/output-examples/wrangled-files-average-year
88024 2021-03-18 13:06:19,120 INFO:netcdf_scm:regexp: ^(?!.*(fx)).*$
88024 2021-03-18 13:06:19,120 INFO:netcdf_scm:prefix: None
88024 2021-03-18 13:06:19,120 INFO:netcdf_scm:drs: CMIP6Output
88024 2021-03-18 13:06:19,120 INFO:netcdf_scm:out_format: mag-files-average-year-mid-year
88024 2021-03-18 13:06:19,120 INFO:netcdf_scm:force: True
88024 2021-03-18 13:06:19,120 INFO:netcdf_scm:Finding directories with files
Walking through directories and applying `check_func`: 6it [00:00, 9062.23it/s]
88024 2021-03-18 13:06:19,127 INFO:netcdf_scm:Found 1 directories with files
88024 2021-03-18 13:06:19,128 INFO:netcdf_scm.cli_parallel:Processing in parallel with 4 workers
88024 2021-03-18 13:06:19,128 INFO:netcdf_scm.cli_parallel:Forcing dask to use a single thread when reading
0%| | 0.00/1.00 [00:00<?, ?it/s]/Users/znicholls/miniconda3/envs/netcdf-scm/lib/python3.9/site-packages/xarray/coding/times.py:463: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/Users/znicholls/miniconda3/envs/netcdf-scm/lib/python3.9/site-packages/xarray/coding/times.py:463: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/Users/znicholls/miniconda3/envs/netcdf-scm/lib/python3.9/site-packages/numpy/core/_asarray.py:102: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
return array(a, dtype, copy=False, order=order)
/Users/znicholls/miniconda3/envs/netcdf-scm/lib/python3.9/site-packages/numpy/core/_asarray.py:102: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
return array(a, dtype, copy=False, order=order)
100%|████████████████████████████████████████| 1.00/1.00 [00:04<00:00, 4.82s/it]
[19]:
# NBVAL_IGNORE_OUTPUT
written_files = [
f
for f in Path(
"../../../output-examples/wrangled-files-average-year"
).rglob("*.MAG")
]
wrangled_annual_mean = pymagicc.io.MAGICCData(str(written_files[0]))
wrangled_annual_mean.timeseries()
[19]:
| time | 2840-07-01 00:00:00 | 2841-07-01 00:00:00 | 2842-07-01 00:00:00 | 2843-07-01 00:00:00 | 2844-07-01 00:00:00 | 2845-07-01 00:00:00 | 2846-07-01 00:00:00 | 2847-07-01 00:00:00 | 2848-07-01 00:00:00 | 2849-07-01 00:00:00 | 2850-07-01 00:00:00 | 2851-07-01 00:00:00 | 2852-07-01 00:00:00 | 2853-07-01 00:00:00 | 2854-07-01 00:00:00 | 2855-07-01 00:00:00 | 2856-07-01 00:00:00 | 2857-07-01 00:00:00 | 2858-07-01 00:00:00 | 2859-07-01 00:00:00 | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| climate_model | model | region | scenario | todo | unit | variable | ||||||||||||||||||||
| unspecified | unspecified | World | unspecified | SET | K | tas | 285.883 | 285.841 | 285.847 | 285.860 | 286.026 | 285.880 | 285.612 | 285.717 | 285.700 | 285.913 | 285.862 | 285.964 | 286.154 | 285.959 | 286.096 | 286.110 | 286.074 | 285.771 | 285.768 | 285.969 |
| World|Northern Hemisphere | unspecified | SET | K | tas | 286.566 | 286.482 | 286.411 | 286.538 | 286.717 | 286.539 | 286.240 | 286.387 | 286.297 | 286.521 | 286.558 | 286.596 | 286.700 | 286.628 | 286.699 | 286.866 | 286.802 | 286.469 | 286.387 | 286.682 | ||
| World|Southern Hemisphere | unspecified | SET | K | tas | 285.184 | 285.185 | 285.270 | 285.167 | 285.320 | 285.206 | 284.970 | 285.033 | 285.090 | 285.292 | 285.150 | 285.318 | 285.596 | 285.275 | 285.479 | 285.338 | 285.331 | 285.057 | 285.135 | 285.240 | ||
| World|Land | unspecified | SET | K | tas | 279.502 | 279.494 | 279.590 | 279.505 | 279.732 | 279.534 | 279.048 | 279.332 | 279.280 | 279.630 | 279.474 | 279.452 | 279.771 | 279.458 | 279.714 | 279.814 | 279.724 | 279.463 | 279.369 | 279.686 | ||
| World|Ocean | unspecified | SET | K | tas | 288.453 | 288.397 | 288.366 | 288.419 | 288.561 | 288.435 | 288.255 | 288.289 | 288.286 | 288.444 | 288.434 | 288.587 | 288.725 | 288.577 | 288.666 | 288.646 | 288.632 | 288.311 | 288.344 | 288.499 | ||
| World|Northern Hemisphere|Land | unspecified | SET | K | tas | 281.049 | 281.014 | 281.052 | 281.100 | 281.308 | 281.099 | 280.672 | 280.947 | 280.782 | 281.140 | 281.171 | 280.932 | 281.135 | 281.040 | 281.087 | 281.490 | 281.301 | 281.107 | 280.946 | 281.283 | ||
| World|Southern Hemisphere|Land | unspecified | SET | K | tas | 276.230 | 276.279 | 276.497 | 276.131 | 276.400 | 276.223 | 275.613 | 275.917 | 276.103 | 276.438 | 275.885 | 276.322 | 276.885 | 276.113 | 276.811 | 276.270 | 276.389 | 275.986 | 276.034 | 276.309 | ||
| World|Northern Hemisphere|Ocean | unspecified | SET | K | tas | 290.029 | 289.914 | 289.774 | 289.950 | 290.112 | 289.952 | 289.734 | 289.801 | 289.759 | 289.899 | 289.939 | 290.151 | 290.192 | 290.134 | 290.221 | 290.239 | 290.254 | 289.833 | 289.801 | 290.070 | ||
| World|Southern Hemisphere|Ocean | unspecified | SET | K | tas | 287.236 | 287.225 | 287.279 | 287.237 | 287.363 | 287.264 | 287.113 | 287.121 | 287.149 | 287.320 | 287.273 | 287.379 | 287.592 | 287.374 | 287.465 | 287.416 | 287.379 | 287.135 | 287.220 | 287.286 | ||
| World|North Atlantic Ocean | unspecified | SET | K | tas | 291.030 | 291.114 | 290.932 | 290.894 | 291.015 | 290.987 | 290.787 | 290.591 | 290.833 | 290.852 | 290.752 | 290.677 | 291.057 | 291.026 | 291.205 | 291.117 | 291.371 | 291.050 | 290.847 | 291.153 | ||
| World|El Nino N3.4 | unspecified | SET | K | tas | 297.656 | 296.947 | 296.951 | 297.666 | 297.818 | 297.051 | 296.019 | 296.918 | 296.887 | 297.067 | 297.055 | 298.488 | 297.646 | 297.392 | 298.025 | 297.780 | 296.800 | 295.766 | 297.053 | 298.179 |
[20]:
# NBVAL_IGNORE_OUTPUT
wrangled_annual_mean.lineplot(hue="region")
[20]:
<AxesSubplot:xlabel='time', ylabel='K'>
[21]:
# NBVAL_IGNORE_OUTPUT
fig = plt.figure(figsize=(16, 9))
ax = fig.add_subplot(221)
wrangled_annual_mean.filter(region=["World", "World|*Hemisphere"]).lineplot(
hue="region", ax=ax
)
ax = fig.add_subplot(222, sharey=ax, sharex=ax)
wrangled_annual_mean.filter(
region=["World", "World|Land", "World|Ocean"]
).lineplot(hue="region", ax=ax)
ax = fig.add_subplot(223, sharey=ax, sharex=ax)
wrangled_annual_mean.filter(region=["World", "World|*Hemis*|*"]).lineplot(
hue="region", ax=ax
)
ax = fig.add_subplot(224, sharey=ax, sharex=ax)
wrangled_annual_mean.filter(
region=["World", "World|*El*", "World|*Ocean*"]
).lineplot(hue="region", ax=ax)
[21]:
<AxesSubplot:xlabel='time', ylabel='K'>
