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/stable/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/stable/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/stable/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/stable/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/stable/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/stable/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/stable/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 0x7f9278342d00>,
  <matplotlib.collections.PolyCollection at 0x7f927833ab80>,
  <matplotlib.lines.Line2D at 0x7f9278158eb0>,
  <matplotlib.patches.Patch at 0x7f92ac050760>,
  <matplotlib.lines.Line2D at 0x7f92846e1580>,
  <matplotlib.lines.Line2D at 0x7f92836cedf0>,
  <matplotlib.patches.Patch at 0x7f92ac050190>,
  <matplotlib.lines.Line2D at 0x7f92836da100>,
  <matplotlib.lines.Line2D at 0x7f92836da0a0>])

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/stable/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 0.007884 ... 5.881
    gmst             (ensemble_member, time) float64 0.0 0.008282 ... 6.227
Attributes:
    scmdata_metadata_region:    World
    scmdata_metadata_model:     a_model
    scmdata_metadata_scenario:  a_scenario

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 0.007884 ... 5.711 5.881

    units :

    K

    array([[0.        , 0.00788388, 0.01487158, ..., 5.8450021 , 5.72467187,
            5.72726153],
           [0.        , 0.00797886, 0.01475376, ..., 5.99291185, 5.6938214 ,
            5.74488585],
           [0.        , 0.00769549, 0.01561708, ..., 5.88667401, 5.79525945,
            5.7664412 ],
           ...,
           [0.        , 0.00762947, 0.01599446, ..., 5.68111068, 5.87512087,
            5.63480056],
           [0.        , 0.00740279, 0.01570517, ..., 5.71870567, 5.74311884,
            5.74640267],
           [0.        , 0.0077345 , 0.01591274, ..., 5.98104471, 5.71096204,
            5.88112551]])

  • gmst
    (ensemble_member, time)
    float64
    0.0 0.008282 ... 6.177 6.227

    units :

    K

    array([[0.        , 0.00828181, 0.01645457, ..., 6.16541091, 6.00697132,
            6.14631791],
           [0.        , 0.0080324 , 0.01601351, ..., 6.0171424 , 6.13030082,
            6.1948832 ],
           [0.        , 0.00809838, 0.01625457, ..., 6.2637564 , 6.13074318,
            6.11882818],
           ...,
           [0.        , 0.00818538, 0.01677099, ..., 6.21610244, 6.01467206,
            6.02437685],
           [0.        , 0.00822822, 0.01666612, ..., 5.99720127, 6.22522985,
            6.21697571],
           [0.        , 0.00826254, 0.01599292, ..., 6.13809839, 6.1767361 ,
            6.22665719]])

• 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_region :

  World

  scmdata_metadata_model :

  a_model

  scmdata_metadata_scenario :

  a_scenario

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 0.007716 0.01544 0.02297 ... 5.748 5.789 5.775
    gmst     (time) float64 0.0 0.008173 0.01646 0.02462 ... 6.135 6.113 6.151

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 0.007716 ... 5.789 5.775

    array([0.        , 0.00771645, 0.01544448, 0.02296606, 0.03081341,
           0.03833551, 0.04570844, 0.05402774, 0.06175181, 0.06913292,
           0.07704609, 0.0840496 , 0.09192515, 0.09951401, 0.10820434,
           0.11596456, 0.12365671, 0.1301869 , 0.13819346, 0.14751522,
           0.15451262, 0.16001477, 0.16974128, 0.17630603, 0.18351922,
           0.19173147, 0.20067586, 0.2081953 , 0.21571302, 0.22301354,
           0.23050131, 0.23899925, 0.2458271 , 0.25299338, 0.26095025,
           0.27184527, 0.27630751, 0.28428007, 0.29280657, 0.30069386,
           0.30830934, 0.31777874, 0.32389297, 0.32948952, 0.33711674,
           0.34673331, 0.35577914, 0.3629739 , 0.36840446, 0.37505257,
           0.38164519, 0.3951427 , 0.39956392, 0.4071688 , 0.41247152,
           0.42225913, 0.43064733, 0.43906408, 0.44583572, 0.45168998,
           0.46320845, 0.47015728, 0.47960182, 0.48083979, 0.49465052,
           0.50153262, 0.50710574, 0.51757363, 0.52359132, 0.53174307,
           0.53904933, 0.54829104, 0.55868832, 0.55910803, 0.56757009,
           0.57766682, 0.58834528, 0.59047333, 0.59364458, 0.60791655,
           0.61373314, 0.62307496, 0.63264181, 0.63663933, 0.65365014,
           0.65249261, 0.66115333, 0.66812036, 0.66800707, 0.68501053,
           0.68738015, 0.69735877, 0.70665518, 0.71585722, 0.7198166 ,
           0.72078615, 0.73370747, 0.74846232, 0.74854805, 0.75712475,
    ...
           5.06615784, 5.03786939, 5.03881562, 5.07829931, 5.11154682,
           5.05826028, 5.08889494, 5.08871907, 5.09319942, 5.09013165,
           5.13210212, 5.13157039, 5.11536062, 5.13350554, 5.13268553,
           5.13840841, 5.12113616, 5.18436038, 5.18683362, 5.17735082,
           5.21436627, 5.20216163, 5.19991126, 5.17290804, 5.23661507,
           5.26458483, 5.24562329, 5.28533873, 5.27745856, 5.22444971,
           5.27528544, 5.27910698, 5.27500713, 5.28248886, 5.31209709,
           5.32505419, 5.36270913, 5.32915402, 5.31055581, 5.32917018,
           5.33282113, 5.36224747, 5.34282856, 5.36416786, 5.38366579,
           5.41259532, 5.37494845, 5.41125102, 5.44027689, 5.36471416,
           5.43862285, 5.40884866, 5.42103974, 5.43695246, 5.45734525,
           5.4279737 , 5.46457406, 5.46402448, 5.49478823, 5.51233755,
           5.49470889, 5.56752307, 5.48732055, 5.50739626, 5.56058787,
           5.51553938, 5.56627395, 5.52813968, 5.56326134, 5.55694297,
           5.55730269, 5.58095179, 5.58793234, 5.62812331, 5.6438767 ,
           5.60625412, 5.64050707, 5.65062288, 5.62518758, 5.61829729,
           5.63400468, 5.68298684, 5.68105816, 5.64106261, 5.64710089,
           5.64219444, 5.71971515, 5.68679176, 5.6878916 , 5.75187917,
           5.73282013, 5.69730297, 5.76776442, 5.74827527, 5.78862945,
           5.774752  ])

  • gmst
    (time)
    float64
    0.0 0.008173 ... 6.113 6.151

    array([0.        , 0.00817283, 0.01645929, 0.02461525, 0.03271932,
           0.04078525, 0.04895776, 0.05743444, 0.06529063, 0.07366855,
           0.08144891, 0.08979245, 0.09832628, 0.10652199, 0.11475829,
           0.12309583, 0.13052603, 0.13920805, 0.1469818 , 0.15514574,
           0.16366934, 0.17261482, 0.18047466, 0.1880569 , 0.19618389,
           0.20456748, 0.21300344, 0.22102909, 0.22846559, 0.23720892,
           0.24637716, 0.25446813, 0.26310592, 0.26989454, 0.27875882,
           0.28695888, 0.29438615, 0.30337414, 0.31159591, 0.32052346,
           0.32680407, 0.33574192, 0.34407088, 0.35224245, 0.36046511,
           0.36783837, 0.37772907, 0.38520916, 0.39320126, 0.40112678,
           0.41043783, 0.41757876, 0.42552602, 0.43406375, 0.44134774,
           0.45063283, 0.45881189, 0.46801037, 0.4757526 , 0.48132915,
           0.49332801, 0.4992933 , 0.50785452, 0.51381189, 0.52259298,
           0.53132117, 0.54050098, 0.55050256, 0.55771367, 0.5648569 ,
           0.57278832, 0.58080992, 0.5914616 , 0.5977231 , 0.60458097,
           0.61272061, 0.62230659, 0.63112343, 0.63895765, 0.64999521,
           0.65452723, 0.66459048, 0.67112495, 0.68092493, 0.68535466,
           0.69406883, 0.70372722, 0.7122931 , 0.72365864, 0.72720467,
           0.73506719, 0.74636873, 0.7549069 , 0.76119163, 0.76997302,
           0.77480551, 0.78851967, 0.79304032, 0.80572732, 0.80655934,
    ...
           5.36157392, 5.36835343, 5.38543083, 5.38818159, 5.40002587,
           5.38706692, 5.40381899, 5.39074662, 5.4205747 , 5.42573074,
           5.42436188, 5.46266671, 5.4673254 , 5.45251151, 5.46087637,
           5.49254552, 5.5104248 , 5.49306714, 5.51452527, 5.53062863,
           5.56123602, 5.54903012, 5.56216675, 5.54599207, 5.54435605,
           5.58720392, 5.59050787, 5.580553  , 5.6018567 , 5.61060093,
           5.5899282 , 5.6026137 , 5.59685135, 5.6320331 , 5.634327  ,
           5.65532997, 5.6486992 , 5.6771206 , 5.67515697, 5.67899711,
           5.70623477, 5.69914297, 5.72726188, 5.73446822, 5.73496156,
           5.74705811, 5.73800013, 5.74121591, 5.7541866 , 5.7626406 ,
           5.77563707, 5.77624658, 5.75811242, 5.81288017, 5.81211155,
           5.81731096, 5.81171746, 5.85204517, 5.85380478, 5.87961591,
           5.84712988, 5.87046755, 5.88106781, 5.86275152, 5.89074906,
           5.89814439, 5.87719913, 5.93350186, 5.92256026, 5.94923611,
           5.94717488, 5.94846016, 5.93630454, 5.96813319, 5.98633065,
           5.9582268 , 5.99920519, 5.9849397 , 5.99037385, 6.01696532,
           5.99171445, 6.0206863 , 6.02621778, 6.04537675, 6.08077713,
           6.06897847, 6.07188628, 6.08190637, 6.06736667, 6.0948821 ,
           6.08838665, 6.1196844 , 6.10025325, 6.13504241, 6.11314084,
           6.15086412])

• 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 0x7f927582c640>,
 <matplotlib.lines.Line2D at 0x7f927577f6d0>,
 <matplotlib.lines.Line2D at 0x7f927577f7f0>,
 <matplotlib.lines.Line2D at 0x7f927577f9a0>,
 <matplotlib.lines.Line2D at 0x7f927577fa90>,
 <matplotlib.lines.Line2D at 0x7f927577fb80>,
 <matplotlib.lines.Line2D at 0x7f927577fc70>,
 <matplotlib.lines.Line2D at 0x7f927577fd60>,
 <matplotlib.lines.Line2D at 0x7f927577fe50>,
 <matplotlib.lines.Line2D at 0x7f927577ff40>,
 <matplotlib.lines.Line2D at 0x7f9275791070>,
 <matplotlib.lines.Line2D at 0x7f9275791190>,
 <matplotlib.lines.Line2D at 0x7f9275791280>,
 <matplotlib.lines.Line2D at 0x7f9275791370>,
 <matplotlib.lines.Line2D at 0x7f9275791460>,
 <matplotlib.lines.Line2D at 0x7f9275791550>,
 <matplotlib.lines.Line2D at 0x7f9275791640>,
 <matplotlib.lines.Line2D at 0x7f9275791730>,
 <matplotlib.lines.Line2D at 0x7f9275791820>,
 <matplotlib.lines.Line2D at 0x7f9275791910>,
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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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