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/v0.16.1/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/v0.16.1/src/scmdata/run.py:196: 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/v0.16.1/src/scmdata/run.py:196: 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/v0.16.1/src/scmdata/run.py:196: 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/v0.16.1/src/scmdata/run.py:196: 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/v0.16.1/src/scmdata/run.py:196: 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/v0.16.1/src/scmdata/run.py:196: 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 0x7f63ee780100>,
  <matplotlib.collections.PolyCollection at 0x7f63ee5a07f0>,
  <matplotlib.lines.Line2D at 0x7f63ee59fdc0>,
  <matplotlib.patches.Patch at 0x7f63ee660b50>,
  <matplotlib.lines.Line2D at 0x7f63ee5afd90>,
  <matplotlib.lines.Line2D at 0x7f63ee7dd250>,
  <matplotlib.patches.Patch at 0x7f63ee580940>,
  <matplotlib.lines.Line2D at 0x7f63ee6627f0>,
  <matplotlib.lines.Line2D at 0x7f63ee6dbd90>])

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:
    gsat             (ensemble_member, time) float64 0.0 0.007528 ... 5.85 5.777
    gmst             (ensemble_member, time) float64 0.0 0.00835 ... 5.999 6.152
Attributes:
    scmdata_metadata_scenario:  a_scenario
    scmdata_metadata_model:     a_model
    scmdata_metadata_region:    World

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 0.007528 0.01506 ... 5.85 5.777

    units :

    K

    array([[0.        , 0.00752823, 0.01505794, ..., 6.01809562, 5.51227412,
            5.80655583],
           [0.        , 0.00748002, 0.01558887, ..., 5.93262482, 5.84174892,
            5.52089998],
           [0.        , 0.00777642, 0.01568418, ..., 5.77643121, 5.62122499,
            5.55797547],
           ...,
           [0.        , 0.007532  , 0.01610862, ..., 5.55703973, 5.83298132,
            5.77613859],
           [0.        , 0.00783278, 0.01588445, ..., 5.65891212, 5.84418319,
            5.67463626],
           [0.        , 0.00767936, 0.01546478, ..., 5.95345269, 5.84992117,
            5.77743363]])

  • gmst
    (ensemble_member, time)
    float64
    0.0 0.00835 0.01644 ... 5.999 6.152

    units :

    K

    array([[0.        , 0.00834966, 0.01644292, ..., 6.2570006 , 6.16745836,
            5.99304723],
           [0.        , 0.0082293 , 0.01644015, ..., 6.04285604, 6.08865876,
            6.27024188],
           [0.        , 0.00808504, 0.01646414, ..., 6.22625884, 6.08000239,
            6.13475703],
           ...,
           [0.        , 0.00800532, 0.0164712 , ..., 6.01604528, 6.09737373,
            6.19631339],
           [0.        , 0.00836638, 0.01618585, ..., 6.05975399, 6.11527619,
            6.21147663],
           [0.        , 0.00835979, 0.01609539, ..., 5.97917427, 5.99931506,
            6.15162768]])

• 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_scenario :

  a_scenario

  scmdata_metadata_model :

  a_model

  scmdata_metadata_region :

  World

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 0.007715 0.01539 0.02325 ... 5.736 5.792 5.779
    gmst     (time) float64 0.0 0.008185 0.01644 0.02451 ... 6.138 6.121 6.126

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 0.007715 ... 5.792 5.779

    array([0.        , 0.00771533, 0.01538897, 0.02324715, 0.03048996,
           0.03859399, 0.04587116, 0.05342735, 0.06167098, 0.06865963,
           0.0762917 , 0.08438601, 0.09304675, 0.09974688, 0.10741828,
           0.11563004, 0.12386464, 0.13114534, 0.13831883, 0.14604737,
           0.15418073, 0.15951021, 0.17020313, 0.17698297, 0.18433311,
           0.19296156, 0.19955798, 0.20686846, 0.21281806, 0.22365498,
           0.22934974, 0.23869945, 0.24593859, 0.2525485 , 0.26372402,
           0.27002171, 0.27583651, 0.28301133, 0.29386011, 0.30034856,
           0.30667681, 0.31812048, 0.32051677, 0.33291487, 0.33815864,
           0.34677562, 0.35289891, 0.36308925, 0.36873135, 0.37613219,
           0.38412745, 0.39589382, 0.3997301 , 0.40827975, 0.41435684,
           0.42044021, 0.42948921, 0.43983263, 0.44394968, 0.45325252,
           0.46132412, 0.47311707, 0.47929001, 0.48169826, 0.49429637,
           0.50004247, 0.50614675, 0.51575722, 0.52277643, 0.53130658,
           0.53940606, 0.54534269, 0.55197999, 0.56252827, 0.56994731,
           0.57798445, 0.57969999, 0.59340101, 0.60483553, 0.60926726,
           0.61046897, 0.61622669, 0.63142204, 0.63683093, 0.64533009,
           0.65441688, 0.66230514, 0.66810688, 0.68046501, 0.67997482,
           0.69104193, 0.70169067, 0.70898079, 0.72145692, 0.7229281 ,
           0.72734219, 0.73726498, 0.74529938, 0.75273604, 0.76171943,
    ...
           5.04171093, 5.01561776, 5.03457098, 5.08451596, 5.0317482 ,
           5.06982918, 5.05579168, 5.12494023, 5.05321529, 5.09445101,
           5.11524563, 5.11427673, 5.1028474 , 5.10237319, 5.12770394,
           5.13418688, 5.12922925, 5.16473123, 5.17482955, 5.14416623,
           5.21209011, 5.16879793, 5.20431282, 5.24609404, 5.22838237,
           5.25749544, 5.24980995, 5.19728235, 5.2459858 , 5.23867437,
           5.26702637, 5.30014215, 5.29498361, 5.30390661, 5.33056638,
           5.30743021, 5.35058628, 5.33561072, 5.31969895, 5.37417241,
           5.39895111, 5.33252858, 5.35857385, 5.40799194, 5.38190019,
           5.39562271, 5.40403213, 5.37568314, 5.39036185, 5.39672277,
           5.40814929, 5.44393468, 5.41926621, 5.48717266, 5.4490396 ,
           5.41330529, 5.49198911, 5.4821733 , 5.51005845, 5.47463353,
           5.54871918, 5.54659175, 5.46662221, 5.51777627, 5.50706795,
           5.49487827, 5.56611545, 5.57351616, 5.57251882, 5.52112528,
           5.55724599, 5.61601069, 5.56020828, 5.54835363, 5.60645254,
           5.57883042, 5.63479796, 5.63993836, 5.64114807, 5.63147726,
           5.644156  , 5.6759787 , 5.6585213 , 5.64936369, 5.72955788,
           5.69483178, 5.71167578, 5.69391434, 5.76615253, 5.72459012,
           5.7919378 , 5.73276762, 5.72952784, 5.73620984, 5.79235352,
           5.77929793])

  • gmst
    (time)
    float64
    0.0 0.008185 ... 6.121 6.126

    array([0.        , 0.0081854 , 0.01644015, 0.02451369, 0.03262565,
           0.0408289 , 0.04915581, 0.05749297, 0.06560375, 0.07375963,
           0.08192832, 0.0896712 , 0.0984467 , 0.10632847, 0.11459174,
           0.1226928 , 0.13090266, 0.13864684, 0.14698826, 0.15548404,
           0.16377857, 0.17187557, 0.1793348 , 0.18808847, 0.19707378,
           0.20458521, 0.21336806, 0.22087599, 0.22829443, 0.23770202,
           0.24605584, 0.2531964 , 0.26261289, 0.27051814, 0.27785126,
           0.2868144 , 0.29600976, 0.30319775, 0.30942581, 0.31942985,
           0.32719562, 0.33691467, 0.34414674, 0.35238104, 0.36021501,
           0.36943761, 0.37725805, 0.38558988, 0.39332877, 0.39884377,
           0.4094978 , 0.41702783, 0.42422257, 0.43370132, 0.44432853,
           0.44964972, 0.45687974, 0.46549414, 0.47609042, 0.48219096,
           0.49265028, 0.50137809, 0.50650098, 0.51677766, 0.52301414,
           0.53407644, 0.54221252, 0.54689354, 0.5560514 , 0.56605054,
           0.57365433, 0.58071216, 0.58912545, 0.59754101, 0.60531939,
           0.61564012, 0.62439552, 0.62895647, 0.6390609 , 0.64780143,
           0.65539145, 0.66495813, 0.6720911 , 0.6832453 , 0.6868949 ,
           0.69768951, 0.70183866, 0.7139521 , 0.71957272, 0.72775235,
           0.7374068 , 0.74431604, 0.75171836, 0.75733291, 0.76900893,
           0.77636918, 0.78814833, 0.79755271, 0.80274182, 0.81337221,
    ...
           5.3378055 , 5.37584693, 5.36285706, 5.39795761, 5.38579218,
           5.42857569, 5.40617152, 5.44992304, 5.43915792, 5.44092619,
           5.47130744, 5.45234044, 5.43713179, 5.47489252, 5.47114149,
           5.47975475, 5.50305139, 5.52705859, 5.51427   , 5.49940877,
           5.53993185, 5.53625636, 5.53038834, 5.57557588, 5.56071886,
           5.57242684, 5.54050667, 5.57463469, 5.58183755, 5.59854426,
           5.60685124, 5.61596933, 5.63609301, 5.63961759, 5.64209091,
           5.66053665, 5.66774179, 5.68034094, 5.67912059, 5.67384803,
           5.70015738, 5.72936978, 5.67957247, 5.7155038 , 5.70834208,
           5.70204625, 5.73355709, 5.73565386, 5.76289727, 5.75353115,
           5.79434738, 5.76841317, 5.7988886 , 5.81335939, 5.79157331,
           5.79763489, 5.8334928 , 5.82304776, 5.83767325, 5.84981345,
           5.84125414, 5.87458342, 5.90134713, 5.8695198 , 5.9114749 ,
           5.88810624, 5.90972881, 5.90651253, 5.93409713, 5.93189209,
           5.93355184, 5.95317174, 5.96089779, 5.95221457, 5.94466448,
           5.96664129, 5.9855077 , 5.98359506, 6.00702138, 5.99702188,
           6.03304534, 6.01074052, 6.02409683, 6.02929395, 6.05225078,
           6.06036833, 6.07530534, 6.05962219, 6.10368392, 6.08114001,
           6.10417721, 6.1116027 , 6.10991422, 6.13836206, 6.12070753,
           6.12631862])

• 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 0x7f63ebc66430>,
 <matplotlib.lines.Line2D at 0x7f63ebc3b670>,
 <matplotlib.lines.Line2D at 0x7f63ebc3b790>,
 <matplotlib.lines.Line2D at 0x7f63ebc3b970>,
 <matplotlib.lines.Line2D at 0x7f63ebc3ba60>,
 <matplotlib.lines.Line2D at 0x7f63ebc3bb50>,
 <matplotlib.lines.Line2D at 0x7f63ebc3bc40>,
 <matplotlib.lines.Line2D at 0x7f63ebc3bd30>,
 <matplotlib.lines.Line2D at 0x7f63ebc3be20>,
 <matplotlib.lines.Line2D at 0x7f63ebc3bf10>,
 <matplotlib.lines.Line2D at 0x7f63ebc3dfd0>,
 <matplotlib.lines.Line2D at 0x7f63ebc3d070>,
 <matplotlib.lines.Line2D at 0x7f63ebc3d160>,
 <matplotlib.lines.Line2D at 0x7f63ebc3d250>,
 <matplotlib.lines.Line2D at 0x7f63ebc3d340>,
 <matplotlib.lines.Line2D at 0x7f63ebc3d430>,
 <matplotlib.lines.Line2D at 0x7f63ebc3d520>,
 <matplotlib.lines.Line2D at 0x7f63ebc3d610>,
 <matplotlib.lines.Line2D at 0x7f63ebc3d700>,
 <matplotlib.lines.Line2D at 0x7f63ebc3d7f0>,
 <matplotlib.lines.Line2D at 0x7f63ebc3d8e0>,
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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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