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/envs/v0.15.2/lib/python3.9/site-packages/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):
    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/envs/v0.15.2/lib/python3.9/site-packages/scmdata/run.py:195: 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/envs/v0.15.2/lib/python3.9/site-packages/scmdata/run.py:195: 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/envs/v0.15.2/lib/python3.9/site-packages/scmdata/run.py:195: 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/envs/v0.15.2/lib/python3.9/site-packages/scmdata/run.py:195: 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/envs/v0.15.2/lib/python3.9/site-packages/scmdata/run.py:195: 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/envs/v0.15.2/lib/python3.9/site-packages/scmdata/run.py:195: 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 0x7f4c944f65b0>,
  <matplotlib.collections.PolyCollection at 0x7f4c94432040>,
  <matplotlib.lines.Line2D at 0x7f4c9443c490>,
  <matplotlib.patches.Patch at 0x7f4c9457b550>,
  <matplotlib.lines.Line2D at 0x7f4c9457b3d0>,
  <matplotlib.lines.Line2D at 0x7f4c9457b190>,
  <matplotlib.patches.Patch at 0x7f4c9457bc10>,
  <matplotlib.lines.Line2D at 0x7f4c9457be50>,
  <matplotlib.lines.Line2D at 0x7f4c94432c10>])

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 0.00829 ... 6.277 6.091
    gsat             (ensemble_member, time) float64 0.0 0.007466 ... 5.95 5.634
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)
  • gmst
    (ensemble_member, time)
    float64
    0.0 0.00829 0.01624 ... 6.277 6.091

    units :

    K

    array([[0.        , 0.00829025, 0.01624172, ..., 6.15876456, 5.98749159,
            6.0642191 ],
           [0.        , 0.00826437, 0.01650896, ..., 5.98531672, 6.24184477,
            6.12317038],
           [0.        , 0.00808755, 0.01623769, ..., 6.11473967, 6.13305905,
            6.24100918],
           ...,
           [0.        , 0.00818234, 0.0163182 , ..., 6.16640976, 6.03936113,
            6.17510831],
           [0.        , 0.00832262, 0.01634507, ..., 6.03462366, 6.12318532,
            6.04616111],
           [0.        , 0.0081118 , 0.01633687, ..., 5.97686068, 6.27740617,
            6.0909684 ]])

  • gsat
    (ensemble_member, time)
    float64
    0.0 0.007466 0.01604 ... 5.95 5.634

    units :

    K

    array([[0.        , 0.00746561, 0.01603812, ..., 5.82783898, 5.62305072,
            5.84813181],
           [0.        , 0.00766654, 0.01584539, ..., 5.58078653, 5.79960519,
            5.79941356],
           [0.        , 0.00756549, 0.01466304, ..., 5.94295352, 5.63301554,
            5.91093661],
           ...,
           [0.        , 0.00772571, 0.01562756, ..., 5.55636562, 5.53847953,
            5.99375764],
           [0.        , 0.00802667, 0.01492267, ..., 6.00204427, 5.89335488,
            5.91777537],
           [0.        , 0.00740219, 0.01607601, ..., 5.8607268 , 5.95006878,
            5.63363745]])

• 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(Int64Index([ 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:
    gmst     (time) float64 0.0 0.008208 0.01635 0.02456 ... 6.136 6.134 6.132
    gsat     (time) float64 0.0 0.007609 0.01535 0.02309 ... 5.764 5.799 5.788

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 0.008208 ... 6.134 6.132

    array([0.        , 0.00820788, 0.01634946, 0.02455915, 0.032664  ,
           0.04086791, 0.04920847, 0.05744561, 0.06527587, 0.07348101,
           0.08200989, 0.09020059, 0.09801145, 0.10647984, 0.11472976,
           0.12331528, 0.13133677, 0.13863593, 0.14790369, 0.1554191 ,
           0.16385227, 0.17231345, 0.17909106, 0.18901425, 0.19562571,
           0.20511456, 0.21272454, 0.22123154, 0.22896433, 0.23663222,
           0.24445391, 0.25406349, 0.26180136, 0.2710496 , 0.27856785,
           0.28641308, 0.29411324, 0.30198619, 0.31017876, 0.31845015,
           0.32650355, 0.33560686, 0.34232339, 0.3524311 , 0.36089577,
           0.36673577, 0.37506996, 0.38387417, 0.39218975, 0.402475  ,
           0.41058934, 0.41850031, 0.42684873, 0.4339169 , 0.44242586,
           0.45128262, 0.45860183, 0.46652205, 0.47300659, 0.48340354,
           0.49302005, 0.50044694, 0.50809248, 0.51623958, 0.5231176 ,
           0.53111685, 0.54288704, 0.55031048, 0.55484148, 0.56635419,
           0.57431351, 0.58160269, 0.5900188 , 0.59754997, 0.60801432,
           0.61452888, 0.6203224 , 0.63330234, 0.64000604, 0.64802082,
           0.6553617 , 0.66113808, 0.67072208, 0.67998949, 0.68752121,
           0.69920695, 0.70328718, 0.71209782, 0.71937939, 0.72945291,
           0.73602903, 0.74675658, 0.75163081, 0.76420268, 0.76741061,
           0.77719884, 0.78688555, 0.79403234, 0.80246494, 0.80794572,
    ...
           5.35309604, 5.35105169, 5.3704352 , 5.39084596, 5.39233446,
           5.4129165 , 5.42283776, 5.41142387, 5.43009589, 5.44638346,
           5.4392159 , 5.44055178, 5.47316394, 5.45828372, 5.48342334,
           5.47930986, 5.4916066 , 5.4785914 , 5.50983574, 5.55795128,
           5.55178012, 5.54678211, 5.53783675, 5.56248642, 5.54678096,
           5.55599965, 5.5681637 , 5.60427888, 5.60751831, 5.59858037,
           5.6149505 , 5.62997587, 5.63673012, 5.64840523, 5.63959838,
           5.64138727, 5.69777127, 5.64199555, 5.6756653 , 5.69705886,
           5.68830109, 5.69380888, 5.71370041, 5.72131225, 5.698881  ,
           5.699934  , 5.73492369, 5.74310107, 5.77565561, 5.75766956,
           5.7731603 , 5.78808207, 5.8038965 , 5.78971379, 5.78947304,
           5.82039465, 5.82759404, 5.82909025, 5.84270495, 5.83904723,
           5.87674449, 5.85618765, 5.87294116, 5.89813986, 5.91571756,
           5.87450937, 5.88565691, 5.91908303, 5.91510222, 5.92787711,
           5.92261425, 5.95563536, 5.94351542, 5.97896734, 5.95083334,
           5.95829031, 5.9922877 , 6.01453839, 6.02190424, 6.01626853,
           6.04052823, 6.04723814, 6.02753741, 6.05159178, 6.05295288,
           6.0384266 , 6.09671364, 6.06446166, 6.07963286, 6.07918838,
           6.10690179, 6.09443914, 6.13439618, 6.13577775, 6.13448347,
           6.13248375])

  • gsat
    (time)
    float64
    0.0 0.007609 ... 5.799 5.788

    array([0.        , 0.00760863, 0.0153463 , 0.0230871 , 0.03078873,
           0.038286  , 0.04614399, 0.05415965, 0.06159613, 0.06913669,
           0.0769959 , 0.08464668, 0.09218222, 0.10004138, 0.10877453,
           0.11492391, 0.12411595, 0.13135205, 0.13790056, 0.14565532,
           0.15454593, 0.16320431, 0.16877802, 0.17632   , 0.18485176,
           0.19309998, 0.19851742, 0.20732697, 0.2152252 , 0.22308966,
           0.23120116, 0.23993478, 0.24447044, 0.25313414, 0.26163024,
           0.2705408 , 0.2747206 , 0.28494189, 0.29169045, 0.29955935,
           0.30566469, 0.31423879, 0.32324871, 0.3315535 , 0.34075605,
           0.34608708, 0.35212063, 0.3619664 , 0.36685731, 0.37753516,
           0.38544172, 0.39180033, 0.39861003, 0.4053369 , 0.41685948,
           0.42472699, 0.42862219, 0.43662803, 0.44751665, 0.4516215 ,
           0.46110783, 0.47134939, 0.47857766, 0.4861213 , 0.49434173,
           0.49842387, 0.5086821 , 0.51457598, 0.52059516, 0.52774388,
           0.5374871 , 0.54593889, 0.55378308, 0.56212404, 0.57147722,
           0.57617621, 0.58870818, 0.59311163, 0.59887834, 0.60275086,
           0.6145476 , 0.62209921, 0.63497358, 0.64248427, 0.6457367 ,
           0.6496826 , 0.66077373, 0.67261431, 0.67855211, 0.68578321,
           0.69453866, 0.70356095, 0.71254028, 0.71793229, 0.72138256,
           0.7309327 , 0.73740653, 0.74798846, 0.75586291, 0.76282318,
    ...
           5.02687975, 5.04243707, 5.04775575, 5.03317184, 5.02999158,
           5.08012801, 5.03549155, 5.0757965 , 5.11517553, 5.12254857,
           5.09554753, 5.05209071, 5.10641901, 5.16166414, 5.14804704,
           5.13129946, 5.13228701, 5.18385799, 5.15091127, 5.15365696,
           5.18779661, 5.21664424, 5.18315592, 5.25681267, 5.23726404,
           5.21683949, 5.27628944, 5.26009061, 5.28529235, 5.2909203 ,
           5.25158702, 5.22808507, 5.30336977, 5.29979879, 5.31618312,
           5.32307666, 5.32500342, 5.31688373, 5.2996922 , 5.33066864,
           5.34122226, 5.35155315, 5.34697679, 5.37413704, 5.35766421,
           5.37994868, 5.37902572, 5.39244804, 5.37326398, 5.42703811,
           5.43738267, 5.44635474, 5.46335197, 5.43923569, 5.47325821,
           5.44197781, 5.46859254, 5.52054629, 5.48463216, 5.4388451 ,
           5.44427473, 5.50500774, 5.51966223, 5.50781274, 5.51938152,
           5.52521541, 5.52033522, 5.57195816, 5.53515842, 5.5515312 ,
           5.63247011, 5.58322793, 5.63076808, 5.60225315, 5.61772057,
           5.5617555 , 5.60001095, 5.65892724, 5.59752826, 5.59635089,
           5.6214602 , 5.64923615, 5.62703353, 5.6870394 , 5.69594368,
           5.70647582, 5.67555588, 5.70686164, 5.6739221 , 5.74723098,
           5.73227335, 5.7303412 , 5.74928679, 5.76446244, 5.79887349,
           5.78817248])

• 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 0x7f4c94545e20>,
 <matplotlib.lines.Line2D at 0x7f4c91b0ed60>,
 <matplotlib.lines.Line2D at 0x7f4c91acaa00>,
 <matplotlib.lines.Line2D at 0x7f4c91acaac0>,
 <matplotlib.lines.Line2D at 0x7f4c91acab20>,
 <matplotlib.lines.Line2D at 0x7f4c91acac10>,
 <matplotlib.lines.Line2D at 0x7f4c91acad00>,
 <matplotlib.lines.Line2D at 0x7f4c91acadf0>,
 <matplotlib.lines.Line2D at 0x7f4c91acaee0>,
 <matplotlib.lines.Line2D at 0x7f4c91acafd0>,
 <matplotlib.lines.Line2D at 0x7f4c91b0eb20>,
 <matplotlib.lines.Line2D at 0x7f4c91ad8100>,
 <matplotlib.lines.Line2D at 0x7f4c91ad81f0>,
 <matplotlib.lines.Line2D at 0x7f4c91ad83a0>,
 <matplotlib.lines.Line2D at 0x7f4c91ad8490>,
 <matplotlib.lines.Line2D at 0x7f4c91ad8580>,
 <matplotlib.lines.Line2D at 0x7f4c91ad8670>,
 <matplotlib.lines.Line2D at 0x7f4c91ad8760>,
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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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