xarray compatibility

scmdata allows datat to be exported to xarray. This makes it easy to use xarray’s many helpful features, most of which are not natively provided in scmdata.

import numpy as np

from scmdata import ScmRun

/home/docs/checkouts/readthedocs.org/user_builds/scmdata/checkouts/latest/src/scmdata/database/_database.py:9: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  import tqdm.autonotebook as tqdman

def get_data(years, n_ensemble_members, end_val, rand_pct):
    """
    Get sample data
    """
    return (np.arange(years.shape[0]) / years.shape[0] * end_val)[:, np.newaxis] * (
        rand_pct * np.random.random((years.shape[0], n_ensemble_members)) + 1
    )

years = np.arange(1750, 2500 + 1)
variables = ["gsat", "gmst"]
n_variables = len(variables)
n_ensemble_members = 100


start = ScmRun(
    np.hstack(
        [
            get_data(years, n_ensemble_members, 5.5, 0.1),
            get_data(years, n_ensemble_members, 6.0, 0.05),
        ]
    ),
    index=years,
    columns={
        "model": "a_model",
        "scenario": "a_scenario",
        "variable": [v for v in variables for i in range(n_ensemble_members)],
        "region": "World",
        "unit": "K",
        "ensemble_member": [i for v in variables for i in range(n_ensemble_members)],
    },
)
start

<ScmRun (timeseries: 200, timepoints: 751)>
Time:
	Start: 1750-01-01T00:00:00
	End: 2500-01-01T00:00:00
Meta:
	     ensemble_member    model region    scenario unit variable
	0                  0  a_model  World  a_scenario    K     gsat
	1                  1  a_model  World  a_scenario    K     gsat
	2                  2  a_model  World  a_scenario    K     gsat
	3                  3  a_model  World  a_scenario    K     gsat
	4                  4  a_model  World  a_scenario    K     gsat
	..               ...      ...    ...         ...  ...      ...
	195               95  a_model  World  a_scenario    K     gmst
	196               96  a_model  World  a_scenario    K     gmst
	197               97  a_model  World  a_scenario    K     gmst
	198               98  a_model  World  a_scenario    K     gmst
	199               99  a_model  World  a_scenario    K     gmst
	
	[200 rows x 6 columns]

The usual scmdata methods are of course available.

start.plumeplot(
    quantile_over="ensemble_member", hue_var="variable", hue_label="Variable"
)

/home/docs/checkouts/readthedocs.org/user_builds/scmdata/checkouts/latest/src/scmdata/run.py:197: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df.reset_index(inplace=True)
/home/docs/checkouts/readthedocs.org/user_builds/scmdata/checkouts/latest/src/scmdata/run.py:197: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df.reset_index(inplace=True)
/home/docs/checkouts/readthedocs.org/user_builds/scmdata/checkouts/latest/src/scmdata/run.py:197: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df.reset_index(inplace=True)
/home/docs/checkouts/readthedocs.org/user_builds/scmdata/checkouts/latest/src/scmdata/run.py:197: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df.reset_index(inplace=True)
/home/docs/checkouts/readthedocs.org/user_builds/scmdata/checkouts/latest/src/scmdata/run.py:197: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df.reset_index(inplace=True)
/home/docs/checkouts/readthedocs.org/user_builds/scmdata/checkouts/latest/src/scmdata/run.py:197: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df.reset_index(inplace=True)

(<Axes: ylabel='K'>,
 [<matplotlib.patches.Patch at 0x7fb116000760>,
  <matplotlib.collections.PolyCollection at 0x7fb11600c850>,
  <matplotlib.lines.Line2D at 0x7fb115e0de20>,
  <matplotlib.patches.Patch at 0x7fb115e22250>,
  <matplotlib.lines.Line2D at 0x7fb115fac100>,
  <matplotlib.lines.Line2D at 0x7fb115fac160>,
  <matplotlib.patches.Patch at 0x7fb115e22310>,
  <matplotlib.lines.Line2D at 0x7fb115e22280>,
  <matplotlib.lines.Line2D at 0x7fb115e228e0>])

However, we can cast to an xarray DataSet and then all the xarray methods become available too.

xr_ds = start.to_xarray(dimensions=("ensemble_member",))
xr_ds

/home/docs/checkouts/readthedocs.org/user_builds/scmdata/checkouts/latest/src/scmdata/_xarray.py:236: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
  else timeseries.T.stack(dimensions)

<xarray.Dataset>
Dimensions:          (time: 751, ensemble_member: 100)
Coordinates:
  * time             (time) object 1750-01-01 00:00:00 ... 2500-01-01 00:00:00
  * ensemble_member  (ensemble_member) int64 0 1 2 3 4 5 6 ... 94 95 96 97 98 99
Data variables:
    gsat             (ensemble_member, time) float64 0.0 0.007471 ... 5.916
    gmst             (ensemble_member, time) float64 0.0 0.008257 ... 6.046
Attributes:
    scmdata_metadata_scenario:  a_scenario
    scmdata_metadata_region:    World
    scmdata_metadata_model:     a_model

xarray.Dataset

• Dimensions:
  • time: 751
  • ensemble_member: 100
• Coordinates: (2)
  • time
    (time)
    object
    1750-01-01 00:00:00 ... 2500-01-...

    array([cftime.DatetimeGregorian(1750, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           cftime.DatetimeGregorian(1751, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           cftime.DatetimeGregorian(1752, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           ...,
           cftime.DatetimeGregorian(2498, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           cftime.DatetimeGregorian(2499, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           cftime.DatetimeGregorian(2500, 1, 1, 0, 0, 0, 0, has_year_zero=False)],
          dtype=object)

  • ensemble_member
    (ensemble_member)
    int64
    0 1 2 3 4 5 6 ... 94 95 96 97 98 99

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
           54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
           72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
           90, 91, 92, 93, 94, 95, 96, 97, 98, 99])

• Data variables: (2)
  • gsat
    (ensemble_member, time)
    float64
    0.0 0.007471 ... 5.665 5.916

    units :

    K

    array([[0.        , 0.00747105, 0.01485125, ..., 5.48504741, 5.90198326,
            5.83814334],
           [0.        , 0.00785389, 0.01571228, ..., 5.68739243, 5.53845021,
            5.56348779],
           [0.        , 0.00766935, 0.01596825, ..., 5.86241587, 5.57011749,
            5.92999311],
           ...,
           [0.        , 0.00791264, 0.01539877, ..., 6.02309402, 5.80618905,
            6.03758539],
           [0.        , 0.00739223, 0.015311  , ..., 5.53187801, 5.76562003,
            5.80449866],
           [0.        , 0.00766346, 0.0161057 , ..., 5.76424392, 5.66503511,
            5.91606242]])

  • gmst
    (ensemble_member, time)
    float64
    0.0 0.008257 ... 6.008 6.046

    units :

    K

    array([[0.        , 0.00825746, 0.01617626, ..., 6.16648871, 6.06199678,
            6.25799387],
           [0.        , 0.00806398, 0.01602673, ..., 6.09119832, 6.07953894,
            6.25425946],
           [0.        , 0.00823709, 0.01652974, ..., 6.20934874, 5.99136806,
            6.08921669],
           ...,
           [0.        , 0.00807747, 0.01649231, ..., 6.13146458, 6.15401827,
            6.07624921],
           [0.        , 0.00830034, 0.01623686, ..., 6.2317096 , 6.03054316,
            6.09537721],
           [0.        , 0.00828791, 0.0163015 , ..., 6.2683544 , 6.00817477,
            6.04555448]])

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1750-01-01 00:00:00, 1751-01-01 00:00:00, 1752-01-01 00:00:00,
                 1753-01-01 00:00:00, 1754-01-01 00:00:00, 1755-01-01 00:00:00,
                 1756-01-01 00:00:00, 1757-01-01 00:00:00, 1758-01-01 00:00:00,
                 1759-01-01 00:00:00,
                 ...
                 2491-01-01 00:00:00, 2492-01-01 00:00:00, 2493-01-01 00:00:00,
                 2494-01-01 00:00:00, 2495-01-01 00:00:00, 2496-01-01 00:00:00,
                 2497-01-01 00:00:00, 2498-01-01 00:00:00, 2499-01-01 00:00:00,
                 2500-01-01 00:00:00],
                dtype='object', length=751, calendar='standard', freq='YS-JAN'))

  • ensemble_member
    PandasIndex

    PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
           54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
           72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
           90, 91, 92, 93, 94, 95, 96, 97, 98, 99],
          dtype='int64', name='ensemble_member'))

• Attributes: (3)

  scmdata_metadata_scenario :

  a_scenario

  scmdata_metadata_region :

  World

  scmdata_metadata_model :

  a_model

For example, calculating statistics.

xr_ds.median(dim="ensemble_member")

<xarray.Dataset>
Dimensions:  (time: 751)
Coordinates:
  * time     (time) object 1750-01-01 00:00:00 ... 2500-01-01 00:00:00
Data variables:
    gsat     (time) float64 0.0 0.007687 0.01542 0.02304 ... 5.776 5.8 5.774
    gmst     (time) float64 0.0 0.008214 0.0164 0.02455 ... 6.117 6.132 6.154

xarray.Dataset

• Dimensions:
  • time: 751
• Coordinates: (1)
  • time
    (time)
    object
    1750-01-01 00:00:00 ... 2500-01-...

    array([cftime.DatetimeGregorian(1750, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           cftime.DatetimeGregorian(1751, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           cftime.DatetimeGregorian(1752, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           ...,
           cftime.DatetimeGregorian(2498, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           cftime.DatetimeGregorian(2499, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           cftime.DatetimeGregorian(2500, 1, 1, 0, 0, 0, 0, has_year_zero=False)],
          dtype=object)

• Data variables: (2)
  • gsat
    (time)
    float64
    0.0 0.007687 0.01542 ... 5.8 5.774

    array([0.        , 0.00768721, 0.0154203 , 0.02304348, 0.03060201,
           0.03834844, 0.04569622, 0.05398239, 0.06130882, 0.06924772,
           0.07613163, 0.08452112, 0.0923919 , 0.10059601, 0.10738744,
           0.11576029, 0.12357509, 0.13031703, 0.13867363, 0.14564551,
           0.15410881, 0.16175239, 0.16947583, 0.17701277, 0.18275068,
           0.19308972, 0.19947659, 0.20601515, 0.21678472, 0.22296319,
           0.23047938, 0.23805705, 0.24837675, 0.25540159, 0.26167383,
           0.26858631, 0.276382  , 0.28400902, 0.29252341, 0.29908481,
           0.30512942, 0.31594469, 0.32327193, 0.33086061, 0.33822883,
           0.34421791, 0.35369036, 0.35758157, 0.3664889 , 0.37613585,
           0.38380556, 0.39093134, 0.40014602, 0.40914226, 0.41532773,
           0.42207656, 0.43282455, 0.43584906, 0.44845612, 0.45135103,
           0.46136362, 0.47271317, 0.47476739, 0.48715917, 0.48845801,
           0.49857671, 0.50735749, 0.51576971, 0.52289572, 0.53040622,
           0.54418162, 0.54121028, 0.55108113, 0.56351288, 0.57114694,
           0.57578172, 0.58037008, 0.58731238, 0.6027233 , 0.60077614,
           0.61270887, 0.61904083, 0.62673405, 0.63862404, 0.64876405,
           0.65087602, 0.66027136, 0.67500244, 0.67892791, 0.68593319,
           0.69445059, 0.69683111, 0.71231439, 0.71693965, 0.72534314,
           0.72794255, 0.74057296, 0.74293248, 0.74879145, 0.75338107,
    ...
           5.06051357, 5.03671249, 5.05648039, 5.08005458, 5.10783429,
           5.07095013, 5.11302847, 5.10628833, 5.10312069, 5.15263059,
           5.13174182, 5.09076223, 5.13307529, 5.13155408, 5.13887794,
           5.17113077, 5.13666102, 5.15140171, 5.19429734, 5.18865741,
           5.17442383, 5.22902362, 5.21776806, 5.23403314, 5.22525349,
           5.17543823, 5.22174249, 5.26144711, 5.23207578, 5.26893964,
           5.20002253, 5.27677206, 5.23036048, 5.30477138, 5.29575646,
           5.31949599, 5.32424935, 5.28902951, 5.29508401, 5.37657915,
           5.33107002, 5.32900079, 5.37716642, 5.34196134, 5.39014856,
           5.3583482 , 5.42249034, 5.36890804, 5.41184643, 5.40282112,
           5.44503267, 5.4276453 , 5.45736707, 5.42839024, 5.44307425,
           5.45417694, 5.43701169, 5.50082401, 5.45137013, 5.51166144,
           5.48845738, 5.52982632, 5.48504557, 5.54459033, 5.50961436,
           5.53310938, 5.55295357, 5.57292726, 5.55930892, 5.59409575,
           5.61557767, 5.63009082, 5.64338153, 5.53782867, 5.60910985,
           5.63413803, 5.65248336, 5.62347928, 5.64830549, 5.65287875,
           5.71496031, 5.66052113, 5.68365857, 5.63433678, 5.6981446 ,
           5.72174664, 5.66980383, 5.68262905, 5.70084347, 5.75156921,
           5.69468458, 5.77396033, 5.76432498, 5.77617825, 5.79958953,
           5.77362574])

  • gmst
    (time)
    float64
    0.0 0.008214 0.0164 ... 6.132 6.154

    array([0.        , 0.00821438, 0.016399  , 0.02454576, 0.0327963 ,
           0.0408638 , 0.0490604 , 0.05742308, 0.06535324, 0.07393426,
           0.08185324, 0.09003136, 0.09830353, 0.10692693, 0.11453233,
           0.12300263, 0.13117205, 0.13861139, 0.1472369 , 0.15574012,
           0.16345686, 0.17112196, 0.18023844, 0.18840273, 0.19683524,
           0.20602418, 0.21333478, 0.22173896, 0.23038579, 0.23772948,
           0.24594368, 0.25346937, 0.26383384, 0.2699954 , 0.2793765 ,
           0.28614608, 0.29404965, 0.30281794, 0.31099527, 0.31949181,
           0.32768272, 0.33644072, 0.34507214, 0.35337735, 0.35981093,
           0.36947414, 0.37547065, 0.38474892, 0.39315328, 0.4008546 ,
           0.40875739, 0.41754282, 0.42394767, 0.43494887, 0.44211812,
           0.4519148 , 0.45784751, 0.46655596, 0.47515002, 0.48420784,
           0.4911384 , 0.50129988, 0.50761249, 0.51740303, 0.52616849,
           0.53142221, 0.54157026, 0.54738366, 0.55666664, 0.56587988,
           0.57306248, 0.58276399, 0.58863398, 0.59705011, 0.60336803,
           0.6149278 , 0.62203778, 0.62865124, 0.63978098, 0.64552995,
           0.6564327 , 0.66398404, 0.67313758, 0.67909168, 0.68709155,
           0.69459497, 0.7062411 , 0.71541324, 0.72370997, 0.72730537,
           0.73623605, 0.74478558, 0.75162467, 0.76144049, 0.769331  ,
           0.77820681, 0.78481258, 0.79520055, 0.80258492, 0.80878963,
    ...
           5.36375599, 5.37165536, 5.3705909 , 5.38946289, 5.39011098,
           5.39850894, 5.4077019 , 5.42270563, 5.43778169, 5.43806164,
           5.44110189, 5.4593818 , 5.44794019, 5.46131203, 5.47886291,
           5.49632151, 5.48486617, 5.49289055, 5.51251988, 5.51738525,
           5.49062139, 5.53311718, 5.52774894, 5.54710759, 5.55225201,
           5.57364235, 5.56163157, 5.58708395, 5.60339227, 5.59016395,
           5.61790354, 5.60781282, 5.62663397, 5.65933076, 5.63928445,
           5.64439877, 5.67018921, 5.68024015, 5.6601805 , 5.68937001,
           5.69949923, 5.69658798, 5.70666796, 5.7350708 , 5.74374935,
           5.72756596, 5.76346235, 5.74640399, 5.75071979, 5.78814911,
           5.76593635, 5.78705484, 5.81578957, 5.81441633, 5.81413917,
           5.81579435, 5.82640109, 5.85018387, 5.83378394, 5.81734955,
           5.86815793, 5.85106405, 5.86276593, 5.88319284, 5.90052145,
           5.90083865, 5.91463766, 5.90642865, 5.92120305, 5.97147303,
           5.92560479, 5.93790122, 5.95639091, 5.96128569, 5.95883325,
           5.94518183, 5.97786704, 5.99710812, 6.02013022, 6.0056768 ,
           6.01739755, 6.0431596 , 6.0513317 , 6.05788293, 6.04544062,
           6.07857352, 6.07388738, 6.06460732, 6.09286055, 6.10384261,
           6.0988365 , 6.10309903, 6.09365218, 6.11735566, 6.13211752,
           6.15442564])

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1750-01-01 00:00:00, 1751-01-01 00:00:00, 1752-01-01 00:00:00,
                 1753-01-01 00:00:00, 1754-01-01 00:00:00, 1755-01-01 00:00:00,
                 1756-01-01 00:00:00, 1757-01-01 00:00:00, 1758-01-01 00:00:00,
                 1759-01-01 00:00:00,
                 ...
                 2491-01-01 00:00:00, 2492-01-01 00:00:00, 2493-01-01 00:00:00,
                 2494-01-01 00:00:00, 2495-01-01 00:00:00, 2496-01-01 00:00:00,
                 2497-01-01 00:00:00, 2498-01-01 00:00:00, 2499-01-01 00:00:00,
                 2500-01-01 00:00:00],
                dtype='object', length=751, calendar='standard', freq='YS-JAN'))

• Attributes: (0)

Plotting timeseries.

xr_ds["gsat"].plot.line(hue="ensemble_member", add_legend=False)

[<matplotlib.lines.Line2D at 0x7fb1134b5730>,
 <matplotlib.lines.Line2D at 0x7fb1134536d0>,
 <matplotlib.lines.Line2D at 0x7fb1134537f0>,
 <matplotlib.lines.Line2D at 0x7fb1134539a0>,
 <matplotlib.lines.Line2D at 0x7fb113453a90>,
 <matplotlib.lines.Line2D at 0x7fb113453b80>,
 <matplotlib.lines.Line2D at 0x7fb113453c70>,
 <matplotlib.lines.Line2D at 0x7fb113453d60>,
 <matplotlib.lines.Line2D at 0x7fb113453e50>,
 <matplotlib.lines.Line2D at 0x7fb113453f40>,
 <matplotlib.lines.Line2D at 0x7fb113465070>,
 <matplotlib.lines.Line2D at 0x7fb113465160>,
 <matplotlib.lines.Line2D at 0x7fb113465280>,
 <matplotlib.lines.Line2D at 0x7fb113465370>,
 <matplotlib.lines.Line2D at 0x7fb113465460>,
 <matplotlib.lines.Line2D at 0x7fb113465550>,
 <matplotlib.lines.Line2D at 0x7fb113465640>,
 <matplotlib.lines.Line2D at 0x7fb113465730>,
 <matplotlib.lines.Line2D at 0x7fb113465820>,
 <matplotlib.lines.Line2D at 0x7fb113465910>,
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Selecting and plotting timeseries.

xr_ds["gsat"].sel(ensemble_member=range(10)).plot.line(
    hue="ensemble_member", add_legend=False
)

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Scatter plots.

xr_ds.plot.scatter(x="gsat", y="gmst", hue="ensemble_member", alpha=0.3)

<matplotlib.collections.PathCollection at 0x7fb113286cd0>

Or combinations of calculations and plots.

xr_ds.median(dim="ensemble_member").plot.scatter(x="gsat", y="gmst")

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