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/v0.16.0/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/v0.16.0/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/v0.16.0/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/v0.16.0/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/v0.16.0/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/v0.16.0/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/v0.16.0/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 0x7fcdf95da130>,
  <matplotlib.collections.PolyCollection at 0x7fcdf93a9040>,
  <matplotlib.lines.Line2D at 0x7fcdf94ccb20>,
  <matplotlib.patches.Patch at 0x7fcdf936cca0>,
  <matplotlib.lines.Line2D at 0x7fcdf95c1f10>,
  <matplotlib.lines.Line2D at 0x7fcdf9393d30>,
  <matplotlib.patches.Patch at 0x7fcdf9578d90>,
  <matplotlib.lines.Line2D at 0x7fcdf9578100>,
  <matplotlib.lines.Line2D at 0x7fcdf9578d60>])

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 0.007551 ... 5.573
    gmst             (ensemble_member, time) float64 0.0 0.00812 ... 6.068 6.19
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)
  • gsat
    (ensemble_member, time)
    float64
    0.0 0.007551 ... 5.504 5.573

    units :

    K

    array([[0.        , 0.00755095, 0.01475198, ..., 5.75501155, 5.61142023,
            5.71753263],
           [0.        , 0.00787359, 0.01532952, ..., 5.8809071 , 5.77792161,
            5.49864161],
           [0.        , 0.00763627, 0.01569657, ..., 5.57744124, 5.84131137,
            5.84089059],
           ...,
           [0.        , 0.00749327, 0.0154746 , ..., 5.48495104, 5.96922541,
            5.87362747],
           [0.        , 0.00786409, 0.01468078, ..., 5.6594088 , 5.67154637,
            5.5256234 ],
           [0.        , 0.00738334, 0.01590756, ..., 5.71385424, 5.5043357 ,
            5.57258618]])

  • gmst
    (ensemble_member, time)
    float64
    0.0 0.00812 0.01674 ... 6.068 6.19

    units :

    K

    array([[0.        , 0.00812033, 0.01673741, ..., 6.0841794 , 6.21499061,
            6.00315432],
           [0.        , 0.00832834, 0.0162056 , ..., 6.26028697, 6.03503724,
            6.18361321],
           [0.        , 0.00824644, 0.01644492, ..., 6.11770601, 6.1627868 ,
            6.25175379],
           ...,
           [0.        , 0.0080508 , 0.01641246, ..., 6.1934953 , 6.24066279,
            6.22264325],
           [0.        , 0.00834529, 0.01614562, ..., 6.1307772 , 5.98428166,
            6.21215793],
           [0.        , 0.00824166, 0.01676978, ..., 6.07933793, 6.06799444,
            6.19037977]])

• 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:
    gsat     (time) float64 0.0 0.007642 0.01537 0.0229 ... 5.718 5.815 5.774
    gmst     (time) float64 0.0 0.008174 0.01638 0.02445 ... 6.125 6.122 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 0.007642 ... 5.815 5.774

    array([0.        , 0.00764248, 0.01536739, 0.02289662, 0.03094693,
           0.03852788, 0.04567043, 0.05338722, 0.06196889, 0.06962458,
           0.07706864, 0.08469805, 0.09208993, 0.09963825, 0.10810543,
           0.11540998, 0.12309168, 0.13097273, 0.13925745, 0.14794835,
           0.15415448, 0.16050647, 0.16922159, 0.17582127, 0.18580768,
           0.19286026, 0.2002075 , 0.20933653, 0.2136302 , 0.22165156,
           0.23000858, 0.23838828, 0.24336734, 0.2545158 , 0.2616702 ,
           0.26860372, 0.27694598, 0.28447478, 0.29418575, 0.30141572,
           0.31048577, 0.31484346, 0.32332967, 0.33111993, 0.33929125,
           0.34594021, 0.35207879, 0.36085533, 0.3693356 , 0.37324883,
           0.38402534, 0.39021107, 0.39946064, 0.40845255, 0.41204246,
           0.42420384, 0.4350846 , 0.43741741, 0.4468508 , 0.45467872,
           0.46083601, 0.46748429, 0.47720498, 0.48581477, 0.49260062,
           0.50070987, 0.50650588, 0.51758679, 0.52263151, 0.52676141,
           0.54020681, 0.54411752, 0.55299748, 0.56278619, 0.57596619,
           0.57847948, 0.58478859, 0.59045366, 0.60056536, 0.60594272,
           0.61255674, 0.62316235, 0.63257179, 0.63958002, 0.64812115,
           0.65978682, 0.66080599, 0.67155587, 0.66856434, 0.68079913,
           0.69351146, 0.70141376, 0.70834633, 0.71943407, 0.72359892,
           0.7353909 , 0.7359174 , 0.7502443 , 0.7545094 , 0.75928265,
    ...
           5.02355859, 5.07061301, 5.06677742, 5.02550252, 5.08811611,
           5.0773657 , 5.08527526, 5.06450592, 5.10576965, 5.12559903,
           5.11428571, 5.056983  , 5.12822683, 5.17156243, 5.13494154,
           5.18710174, 5.14246185, 5.16712599, 5.1919174 , 5.14679628,
           5.20406324, 5.18939209, 5.19025317, 5.22574187, 5.23475088,
           5.17860789, 5.26138851, 5.24454566, 5.24640315, 5.27344927,
           5.24671136, 5.30196824, 5.27891069, 5.34043463, 5.33460936,
           5.34778334, 5.33577459, 5.29748969, 5.32272232, 5.33762749,
           5.3244413 , 5.37123205, 5.33472534, 5.31139104, 5.3684329 ,
           5.35214934, 5.39228408, 5.39494691, 5.42817051, 5.39182544,
           5.43814233, 5.44982981, 5.42890605, 5.48025939, 5.44389465,
           5.45781495, 5.48199977, 5.53007016, 5.50964503, 5.52238757,
           5.48992778, 5.5041395 , 5.51434347, 5.52091894, 5.49058083,
           5.53109091, 5.52548271, 5.53791518, 5.56697772, 5.5898274 ,
           5.51800154, 5.54778392, 5.59346612, 5.58066081, 5.58910937,
           5.61736665, 5.60777553, 5.62147818, 5.64411102, 5.65215816,
           5.63706465, 5.65276495, 5.69241015, 5.66815047, 5.65839325,
           5.69792021, 5.71686244, 5.73748666, 5.73082146, 5.73654096,
           5.713744  , 5.69594669, 5.75512524, 5.71768392, 5.81524198,
           5.77415173])

  • gmst
    (time)
    float64
    0.0 0.008174 ... 6.122 6.151

    array([0.        , 0.00817394, 0.01637503, 0.02444707, 0.03273883,
           0.04101931, 0.04903948, 0.05736454, 0.06560191, 0.07361427,
           0.08174455, 0.09030094, 0.09853615, 0.10635334, 0.11432481,
           0.1227007 , 0.13108647, 0.13900955, 0.14721662, 0.15575186,
           0.16344878, 0.1723914 , 0.17989618, 0.18823271, 0.19688585,
           0.20477001, 0.21300396, 0.22132125, 0.22988533, 0.23831911,
           0.24626374, 0.25411722, 0.26161084, 0.27033889, 0.27670956,
           0.28692469, 0.2944651 , 0.3036748 , 0.31139042, 0.31785236,
           0.32684003, 0.33581585, 0.34460116, 0.35286284, 0.3617868 ,
           0.36812643, 0.37812748, 0.38549533, 0.3928075 , 0.40343463,
           0.41015542, 0.41870094, 0.42558733, 0.43612234, 0.44155185,
           0.45095921, 0.4596677 , 0.46668373, 0.47609031, 0.48373234,
           0.48945391, 0.50035647, 0.508801  , 0.51709645, 0.52326589,
           0.53256719, 0.53973305, 0.54895481, 0.55406846, 0.5657812 ,
           0.57414819, 0.57857535, 0.59273411, 0.59966173, 0.60738264,
           0.61241599, 0.62326116, 0.63227596, 0.63854009, 0.64960373,
           0.65738123, 0.66557631, 0.67084769, 0.68056923, 0.68924288,
           0.69619068, 0.7045816 , 0.71234238, 0.7190529 , 0.72982178,
           0.73673191, 0.74350962, 0.75605768, 0.76304519, 0.77044646,
           0.77719179, 0.7882432 , 0.79395123, 0.8009328 , 0.8105766 ,
    ...
           5.36395918, 5.36848678, 5.38915057, 5.37212963, 5.40288452,
           5.40122941, 5.41902921, 5.39452889, 5.42941061, 5.4370392 ,
           5.43052582, 5.43402849, 5.45429611, 5.46555582, 5.48493083,
           5.4589421 , 5.49931821, 5.49933165, 5.49962505, 5.53083645,
           5.52051029, 5.54802142, 5.54013474, 5.56367629, 5.56994684,
           5.55379812, 5.56449582, 5.58335021, 5.59853902, 5.59655542,
           5.61695889, 5.63120462, 5.64236559, 5.65082122, 5.6455628 ,
           5.65657736, 5.65551872, 5.69026801, 5.69288988, 5.68778272,
           5.70654735, 5.70726805, 5.70112625, 5.73226964, 5.72359237,
           5.7407805 , 5.74247216, 5.75283158, 5.78323415, 5.76775605,
           5.74472001, 5.77355496, 5.79303839, 5.77649407, 5.79851124,
           5.81844367, 5.80200453, 5.84078355, 5.84534836, 5.82698756,
           5.84724292, 5.86076873, 5.8774495 , 5.90924995, 5.888141  ,
           5.89555586, 5.89983489, 5.89427997, 5.89883581, 5.94758089,
           5.93882032, 5.99435722, 5.94550882, 5.9388193 , 5.96993419,
           5.97775561, 5.98301926, 5.97881276, 6.00879876, 5.99750376,
           6.00647146, 6.03659748, 6.06429105, 6.03345426, 6.06087992,
           6.07739088, 6.06642392, 6.07542189, 6.08565368, 6.10789492,
           6.08478079, 6.1125743 , 6.12961184, 6.12468552, 6.12187243,
           6.15063948])

• 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 0x7fcdf6a5edf0>,
 <matplotlib.lines.Line2D at 0x7fcdf6a2c5e0>,
 <matplotlib.lines.Line2D at 0x7fcdf6a2c700>,
 <matplotlib.lines.Line2D at 0x7fcdf6a2c8e0>,
 <matplotlib.lines.Line2D at 0x7fcdf6a2c9d0>,
 <matplotlib.lines.Line2D at 0x7fcdf6a2cac0>,
 <matplotlib.lines.Line2D at 0x7fcdf6a2cbb0>,
 <matplotlib.lines.Line2D at 0x7fcdf6a2cca0>,
 <matplotlib.lines.Line2D at 0x7fcdf6a2cd90>,
 <matplotlib.lines.Line2D at 0x7fcdf6a2ce80>,
 <matplotlib.lines.Line2D at 0x7fcdf6a2cf70>,
 <matplotlib.lines.Line2D at 0x7fcdf6a47cd0>,
 <matplotlib.lines.Line2D at 0x7fcdf6a47f40>,
 <matplotlib.lines.Line2D at 0x7fcdf6a47e80>,
 <matplotlib.lines.Line2D at 0x7fcdf6a47a60>,
 <matplotlib.lines.Line2D at 0x7fcdf6a47910>,
 <matplotlib.lines.Line2D at 0x7fcdf6a47820>,
 <matplotlib.lines.Line2D at 0x7fcdf6a476a0>,
 <matplotlib.lines.Line2D at 0x7fcdf6a47550>,
 <matplotlib.lines.Line2D at 0x7fcdf6a47460>,
 <matplotlib.lines.Line2D at 0x7fcdf6a47370>,
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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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