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/v0.15.3/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/v0.15.3/src/scmdata/run.py:191: 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/v0.15.3/src/scmdata/run.py:191: 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/v0.15.3/src/scmdata/run.py:191: 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/v0.15.3/src/scmdata/run.py:191: 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/v0.15.3/src/scmdata/run.py:191: 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/v0.15.3/src/scmdata/run.py:191: 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 0x7fe270a93d30>,
  <matplotlib.collections.PolyCollection at 0x7fe2a410e730>,
  <matplotlib.lines.Line2D at 0x7fe270942850>,
  <matplotlib.patches.Patch at 0x7fe270ab9340>,
  <matplotlib.lines.Line2D at 0x7fe270aa0ac0>,
  <matplotlib.lines.Line2D at 0x7fe270aa0700>,
  <matplotlib.patches.Patch at 0x7fe2708aa4c0>,
  <matplotlib.lines.Line2D at 0x7fe2708aa460>,
  <matplotlib.lines.Line2D at 0x7fe2708a0070>])

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

<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:
    gmst             (ensemble_member, time) float64 0.0 0.008363 ... 6.117
    gsat             (ensemble_member, time) float64 0.0 0.007729 ... 5.973
Attributes:
    scmdata_metadata_region:    World
    scmdata_metadata_scenario:  a_scenario
    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)
  • gmst
    (ensemble_member, time)
    float64
    0.0 0.008363 ... 6.028 6.117

    units :

    K

    array([[0.        , 0.00836296, 0.01655421, ..., 6.14071358, 6.11237236,
            6.07970943],
           [0.        , 0.00835578, 0.01651302, ..., 6.03746115, 6.09075526,
            6.27302831],
           [0.        , 0.00799445, 0.01638821, ..., 6.14593896, 6.12626695,
            6.15600034],
           ...,
           [0.        , 0.0082395 , 0.01670913, ..., 6.2641862 , 6.00629212,
            6.12335579],
           [0.        , 0.0080377 , 0.01632983, ..., 6.07966267, 5.99308103,
            6.18562297],
           [0.        , 0.00804703, 0.01668263, ..., 6.10699744, 6.0279861 ,
            6.11677685]])

  • gsat
    (ensemble_member, time)
    float64
    0.0 0.007729 ... 5.536 5.973

    units :

    K

    array([[0.        , 0.00772927, 0.01541011, ..., 5.61684236, 5.65166213,
            5.70232258],
           [0.        , 0.00783261, 0.01475786, ..., 5.93321764, 5.58864463,
            5.91213476],
           [0.        , 0.00750649, 0.01560311, ..., 5.76705724, 5.9884493 ,
            5.81029127],
           ...,
           [0.        , 0.0079555 , 0.01503067, ..., 5.8150456 , 5.55330751,
            5.89792906],
           [0.        , 0.00787081, 0.01544531, ..., 5.53794178, 5.60048213,
            5.74663963],
           [0.        , 0.00751941, 0.01508007, ..., 5.48534068, 5.53576109,
            5.97278232]])

• 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='AS-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_region :

  World

  scmdata_metadata_scenario :

  a_scenario

  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:
    gmst     (time) float64 0.0 0.008176 0.0163 0.02463 ... 6.12 6.164 6.109
    gsat     (time) float64 0.0 0.007712 0.01543 0.02303 ... 5.806 5.764 5.742

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)
  • gmst
    (time)
    float64
    0.0 0.008176 0.0163 ... 6.164 6.109

    array([0.        , 0.008176  , 0.01630484, 0.02463193, 0.0325699 ,
           0.04087178, 0.04923658, 0.05719306, 0.06566777, 0.07368271,
           0.08193942, 0.09011359, 0.09814865, 0.10637574, 0.11480075,
           0.12238167, 0.13089818, 0.1394179 , 0.14648939, 0.15527038,
           0.16376848, 0.17158895, 0.18034363, 0.18838399, 0.19659612,
           0.20487933, 0.21375468, 0.22051651, 0.22887384, 0.23692659,
           0.2456196 , 0.25398821, 0.2623052 , 0.26971858, 0.27728734,
           0.2863082 , 0.29514837, 0.30275126, 0.31036045, 0.31925272,
           0.32784694, 0.33589392, 0.34316986, 0.35215038, 0.35902565,
           0.37017321, 0.37617738, 0.3840873 , 0.39505802, 0.40199435,
           0.4116717 , 0.41863212, 0.4281571 , 0.43272474, 0.44112792,
           0.4490994 , 0.46130501, 0.46565413, 0.47514243, 0.48160205,
           0.49065666, 0.49962629, 0.5084121 , 0.51615801, 0.52561273,
           0.53379029, 0.54074306, 0.54795112, 0.55864811, 0.56366143,
           0.57251522, 0.5829337 , 0.59052915, 0.59472196, 0.60679959,
           0.61186483, 0.62482738, 0.6292517 , 0.63834025, 0.64435759,
           0.65422654, 0.66216969, 0.67269279, 0.67985979, 0.6877165 ,
           0.69686834, 0.70676219, 0.71387085, 0.72014733, 0.72977057,
           0.73354525, 0.74272527, 0.75255174, 0.76133904, 0.76855217,
           0.77463587, 0.78671087, 0.79457164, 0.80371427, 0.81065973,
    ...
           5.36135657, 5.38393001, 5.38388547, 5.38251989, 5.37567186,
           5.43632359, 5.42351046, 5.42428659, 5.44081626, 5.43527549,
           5.42733242, 5.43870569, 5.46244667, 5.47405773, 5.48779812,
           5.49995082, 5.48521046, 5.52434218, 5.52293062, 5.5273625 ,
           5.53010891, 5.53717121, 5.53715177, 5.55753934, 5.57168877,
           5.55887488, 5.56696006, 5.58803412, 5.59845541, 5.60098226,
           5.59472653, 5.62346293, 5.61083413, 5.63133258, 5.64432945,
           5.65583252, 5.6418839 , 5.68211728, 5.66824473, 5.68769383,
           5.71048609, 5.71904906, 5.73110413, 5.70598214, 5.7011166 ,
           5.72700197, 5.76064642, 5.741687  , 5.7530412 , 5.76980034,
           5.77886976, 5.79737041, 5.81403024, 5.83052587, 5.83113997,
           5.81896338, 5.79661743, 5.8313348 , 5.84472098, 5.84597438,
           5.85611567, 5.85297236, 5.85365228, 5.90067402, 5.87767691,
           5.90003453, 5.90713625, 5.89095261, 5.91886015, 5.93415749,
           5.93338207, 5.94008257, 5.9452601 , 5.97381696, 5.98633403,
           5.99282436, 5.99546284, 5.98753429, 6.00381301, 6.00577467,
           6.02222473, 5.99825672, 6.06345315, 6.05460758, 6.0575338 ,
           6.06639108, 6.07653964, 6.06658552, 6.07517318, 6.06589724,
           6.10460922, 6.10947259, 6.11188403, 6.11999525, 6.16399519,
           6.10931661])

  • gsat
    (time)
    float64
    0.0 0.007712 ... 5.764 5.742

    array([0.        , 0.00771214, 0.01542771, 0.02303067, 0.03082476,
           0.03816832, 0.04591576, 0.05395855, 0.06160884, 0.06911351,
           0.07705637, 0.08478585, 0.09239179, 0.10028051, 0.10736971,
           0.11359848, 0.12263977, 0.13028291, 0.13794251, 0.14607691,
           0.15315935, 0.16198548, 0.16956432, 0.17690198, 0.18343452,
           0.19306584, 0.19964526, 0.20783731, 0.2156829 , 0.22319033,
           0.22985085, 0.24010691, 0.24637701, 0.25182797, 0.26198948,
           0.26927264, 0.27755227, 0.28382028, 0.28934132, 0.30074021,
           0.30783966, 0.31457026, 0.32320065, 0.33161233, 0.33959887,
           0.34402401, 0.35569803, 0.36247381, 0.36940642, 0.37621749,
           0.38230347, 0.39224005, 0.39993861, 0.40566004, 0.41550097,
           0.42334518, 0.43150278, 0.438033  , 0.44456958, 0.45178463,
           0.46103378, 0.47094915, 0.47631521, 0.48812619, 0.48840413,
           0.50036642, 0.50840348, 0.51834503, 0.52434647, 0.52646634,
           0.53973264, 0.54569764, 0.55399562, 0.56142953, 0.56886914,
           0.57833855, 0.58661648, 0.58991236, 0.5998325 , 0.6111682 ,
           0.61395145, 0.62107417, 0.62809702, 0.63683738, 0.64847734,
           0.65518984, 0.66015143, 0.66841063, 0.67471859, 0.68512624,
           0.6919186 , 0.70025852, 0.70283043, 0.71519226, 0.72708419,
           0.73064406, 0.74256511, 0.7414842 , 0.75636023, 0.75667289,
    ...
           5.05707845, 5.03332885, 5.03867245, 5.0829978 , 5.02071602,
           5.02724929, 5.05664931, 5.08836333, 5.10587678, 5.06033926,
           5.09220102, 5.12749054, 5.14436841, 5.18511183, 5.19654072,
           5.15012395, 5.14524782, 5.11152383, 5.18097538, 5.18910283,
           5.14449605, 5.23267683, 5.22438024, 5.18772635, 5.2106377 ,
           5.20721836, 5.24008048, 5.21229878, 5.24786103, 5.25243861,
           5.24999021, 5.31380479, 5.25684869, 5.27587855, 5.28848607,
           5.30352402, 5.28475647, 5.26545851, 5.32549371, 5.35760324,
           5.32325551, 5.35528744, 5.36120553, 5.34483196, 5.37668809,
           5.37214503, 5.37271989, 5.38832732, 5.44111991, 5.42386961,
           5.39835804, 5.42979511, 5.42925553, 5.44649523, 5.49972151,
           5.46213515, 5.40897598, 5.45267829, 5.48284174, 5.46901401,
           5.48996974, 5.48232958, 5.50196713, 5.51834067, 5.47716694,
           5.49879244, 5.54577084, 5.56577381, 5.5200407 , 5.50931069,
           5.5860495 , 5.5591025 , 5.62576961, 5.55959497, 5.59785882,
           5.56577484, 5.59230176, 5.67895453, 5.67507256, 5.59677099,
           5.632367  , 5.64106224, 5.6401221 , 5.72842604, 5.71269833,
           5.68433809, 5.69098145, 5.66778487, 5.71750472, 5.72967837,
           5.71774266, 5.77518296, 5.74079894, 5.80618396, 5.76399173,
           5.74185507])

• 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='AS-JAN'))

• Attributes: (0)

Plotting timeseries.

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

[<matplotlib.lines.Line2D at 0x7fe26e7a57f0>,
 <matplotlib.lines.Line2D at 0x7fe26e6fb5b0>,
 <matplotlib.lines.Line2D at 0x7fe26e6fb790>,
 <matplotlib.lines.Line2D at 0x7fe26e6fb850>,
 <matplotlib.lines.Line2D at 0x7fe26e6fb8e0>,
 <matplotlib.lines.Line2D at 0x7fe26e6fb9d0>,
 <matplotlib.lines.Line2D at 0x7fe26e6fbac0>,
 <matplotlib.lines.Line2D at 0x7fe26e6fbbb0>,
 <matplotlib.lines.Line2D at 0x7fe26e6fbca0>,
 <matplotlib.lines.Line2D at 0x7fe26e6fbd90>,
 <matplotlib.lines.Line2D at 0x7fe26e6fbe80>,
 <matplotlib.lines.Line2D at 0x7fe26e6fb160>,
 <matplotlib.lines.Line2D at 0x7fe26e6fbf70>,
 <matplotlib.lines.Line2D at 0x7fe26e6fd070>,
 <matplotlib.lines.Line2D at 0x7fe26e6fd160>,
 <matplotlib.lines.Line2D at 0x7fe26e6fd250>,
 <matplotlib.lines.Line2D at 0x7fe26e6fd340>,
 <matplotlib.lines.Line2D at 0x7fe26e6fd430>,
 <matplotlib.lines.Line2D at 0x7fe26e6fd520>,
 <matplotlib.lines.Line2D at 0x7fe26e6fd610>,
 <matplotlib.lines.Line2D at 0x7fe26e6fd700>,
 <matplotlib.lines.Line2D at 0x7fe26e6fd7f0>,
 <matplotlib.lines.Line2D at 0x7fe26e6fd8e0>,
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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)

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Or combinations of calculations and plots.

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

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