easyclimate.core.stats.monthstat

This module calculates statistical values over timesteps of the same month

Functions

calc_monthly_mean(data_input[, dim])	Calculate monthly mean.
calc_monthly_sum(data_input[, dim])	Calculate monthly sum.
calc_monthly_std(data_input[, dim])	Calculate monthly standard deviation.
calc_monthly_var(data_input[, dim])	Calculate monthly variance.
calc_monthly_max(data_input[, dim])	Calculate monthly maximum.
calc_monthly_min(data_input[, dim])	Calculate monthly minimum.

Module Contents

easyclimate.core.stats.monthstat.calc_monthly_mean(data_input: xarray.DataArray, dim: str = 'time', **kwargs)

Calculate monthly mean.

For every adjacent sequence \(t_1, ..., t_n\) of timesteps of the same month it is:

\[o(t, x) = \mathrm{mean} \left \lbrace i(t', x), t_1 < t' \leqslant t_n \right\rbrace\]

Parameters

data_input: xarray.DataArray.

xarray.DataArray to be calculated.

Note

The recommended frequence of the data_input is daily.

dim: str

Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

**kwargs:

Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data. These could include dask-specific kwargs like split_every.

Returns

xarray.DataArray.

Examples

>>> import xarray as xr
>>> import numpy as np
>>> import pandas as pd
>>> import easyclimate as ecl
>>> # Create sample data with daily frequency
>>> time_index = pd.date_range('2020-01-01', '2020-03-31', freq='D')
>>> rng = np.random.default_rng(42)
>>> data = rng.random((len(time_index), 3, 3))
>>> da = xr.DataArray(data, dims=['time', 'x', 'y'], coords={'time': time_index})
>>> # Calculate monthly mean
>>> monthly_mean = ecl.calc_monthly_mean(da)
>>> print(monthly_mean)
<xarray.DataArray (time: 3, x: 3, y: 3)> Size: 216B
array([[[0.50635175, 0.45767908, 0.50271707],
        [0.52091523, 0.44830133, 0.44293946],
        [0.47944591, 0.53314083, 0.48073062]],
    [[0.53431127, 0.48259521, 0.47464862],
        [0.41070456, 0.51619935, 0.4872374 ],
        [0.6009132 , 0.43963445, 0.6028882 ]],
    [[0.48363606, 0.60589154, 0.42622008],
        [0.47519641, 0.46989711, 0.45327877],
        [0.44193025, 0.45050389, 0.641573  ]]])
Coordinates:
* time     (time) datetime64[ns] 24B 2020-01-01 2020-02-01 2020-03-01
Dimensions without coordinates: x, y

See also

numpy.mean, dask.array.mean, xarray.DataArray.mean, xarray.core.groupby.DataArrayGroupBy.mean.

easyclimate.core.stats.monthstat.calc_monthly_sum(data_input: xarray.DataArray, dim: str = 'time', **kwargs)

Calculate monthly sum.

For every adjacent sequence \(t_1, ..., t_n\) of timesteps of the same month it is:

\[o(t, x) = \mathrm{sum} \left \lbrace i(t', x), t_1 < t' \leqslant t_n \right\rbrace\]

Parameters

data_input: xarray.DataArray.

xarray.DataArray to be calculated.

Note

The recommended frequence of the data_input is daily.

dim: str

Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

**kwargs:

Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data. These could include dask-specific kwargs like split_every.

Returns

xarray.DataArray.

Examples

>>> import xarray as xr
>>> import numpy as np
>>> import pandas as pd
>>> import easyclimate as ecl
>>> # Create sample data with daily frequency
>>> time_index = pd.date_range('2020-01-01', '2020-03-31', freq='D')
>>> rng = np.random.default_rng(42)
>>> data = rng.random((len(time_index), 3, 3))
>>> da = xr.DataArray(data, dims=['time', 'x', 'y'], coords={'time': time_index})
>>> # Calculate monthly sum
>>> monthly_sum = ecl.calc_monthly_sum(da)
>>> print(monthly_sum)
<xarray.DataArray (time: 3, x: 3, y: 3)> Size: 216B
array([[[15.69690421, 14.18805154, 15.58422905],
        [16.14837203, 13.89734131, 13.7311233 ],
        [14.86282316, 16.52736588, 14.90264935]],
    [[15.49502686, 13.99526102, 13.76481005],
        [11.91043229, 14.96978113, 14.1298847 ],
        [17.42648281, 12.7493991 , 17.48375789]],
    [[14.99271788, 18.78263759, 13.21282259],
        [14.73108859, 14.56681052, 14.05164186],
        [13.69983774, 13.96562059, 19.88876291]]])
Coordinates:
* time     (time) datetime64[ns] 24B 2020-01-01 2020-02-01 2020-03-01
Dimensions without coordinates: x, y

See also

numpy.sum, dask.array.sum, xarray.DataArray.sum, xarray.core.groupby.DataArrayGroupBy.sum.

easyclimate.core.stats.monthstat.calc_monthly_std(data_input: xarray.DataArray, dim: str = 'time', **kwargs)

Calculate monthly standard deviation.

For every adjacent sequence \(t_1, ..., t_n\) of timesteps of the same month it is:

\[o(t, x) = \mathrm{std} \left \lbrace i(t', x), t_1 < t' \leqslant t_n \right\rbrace\]

Parameters

data_input: xarray.DataArray.

xarray.DataArray to be calculated.

Note

The recommended frequence of the data_input is daily.

dim: str

Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

**kwargs:

Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data. These could include dask-specific kwargs like split_every.

Returns

xarray.DataArray.

Examples

>>> import xarray as xr
>>> import numpy as np
>>> import pandas as pd
>>> import easyclimate as ecl
>>> # Create sample data with daily frequency
>>> time_index = pd.date_range('2020-01-01', '2020-03-31', freq='D')
>>> rng = np.random.default_rng(42)
>>> data = rng.random((len(time_index), 3, 3))
>>> da = xr.DataArray(data, dims=['time', 'x', 'y'], coords={'time': time_index})
>>> # Calculate monthly std
>>> monthly_std = ecl.calc_monthly_std(da)
>>> print(monthly_std)
<xarray.DataArray (time: 3, x: 3, y: 3)> Size: 216B
array([[[0.30528844, 0.29905447, 0.26472868],
        [0.23456056, 0.30879525, 0.29333846],
        [0.26139562, 0.306974  , 0.27987361]],
    [[0.30196879, 0.24783961, 0.26078164],
        [0.2708643 , 0.3012602 , 0.29801453],
        [0.24816804, 0.33863555, 0.25623523]],
    [[0.29399346, 0.31595077, 0.30336434],
        [0.31117807, 0.3130123 , 0.28909393],
        [0.27104435, 0.26864038, 0.22912052]]])
Coordinates:
* time     (time) datetime64[ns] 24B 2020-01-01 2020-02-01 2020-03-01
Dimensions without coordinates: x, y

See also

numpy.std, dask.array.std, xarray.DataArray.std, xarray.core.groupby.DataArrayGroupBy.std.

easyclimate.core.stats.monthstat.calc_monthly_var(data_input: xarray.DataArray, dim: str = 'time', **kwargs)

Calculate monthly variance.

For every adjacent sequence \(t_1, ..., t_n\) of timesteps of the same month it is:

\[o(t, x) = \mathrm{var} \left \lbrace i(t', x), t_1 < t' \leqslant t_n \right\rbrace\]

Parameters

data_input: xarray.DataArray.

xarray.DataArray to be calculated.

Note

The recommended frequence of the data_input is daily.

dim: str

Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

**kwargs:

Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data. These could include dask-specific kwargs like split_every.

Returns

xarray.DataArray.

Examples

>>> import xarray as xr
>>> import numpy as np
>>> import pandas as pd
>>> import easyclimate as ecl
>>> # Create sample data with daily frequency
>>> time_index = pd.date_range('2020-01-01', '2020-03-31', freq='D')
>>> rng = np.random.default_rng(42)
>>> data = rng.random((len(time_index), 3, 3))
>>> da = xr.DataArray(data, dims=['time', 'x', 'y'], coords={'time': time_index})
>>> # Calculate monthly var
>>> monthly_var = ecl.calc_monthly_var(da)
>>> print(monthly_var)
<xarray.DataArray (time: 3, x: 3, y: 3)> Size: 216B
array([[[0.09320103, 0.08943358, 0.07008127],
        [0.05501865, 0.09535451, 0.08604745],
        [0.06832767, 0.09423304, 0.07832924]],
    [[0.09118515, 0.06142447, 0.06800706],
        [0.07336747, 0.09075771, 0.08881266],
        [0.06158738, 0.11467404, 0.0656565 ]],
    [[0.08643216, 0.09982489, 0.09202992],
        [0.09683179, 0.0979767 , 0.0835753 ],
        [0.07346504, 0.07216766, 0.05249621]]])
Coordinates:
* time     (time) datetime64[ns] 24B 2020-01-01 2020-02-01 2020-03-01
Dimensions without coordinates: x, y

See also

numpy.var, dask.array.var, xarray.DataArray.var, xarray.core.groupby.DataArrayGroupBy.var.

easyclimate.core.stats.monthstat.calc_monthly_max(data_input: xarray.DataArray, dim: str = 'time', **kwargs)

Calculate monthly maximum.

For every adjacent sequence \(t_1, ..., t_n\) of timesteps of the same month it is:

\[o(t, x) = \mathrm{max} \left \lbrace i(t', x), t_1 < t' \leqslant t_n \right\rbrace\]

Parameters

data_input: xarray.DataArray.

xarray.DataArray to be calculated.

Note

The recommended frequence of the data_input is daily.

dim: str

Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

**kwargs:

Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data. These could include dask-specific kwargs like split_every.

Returns

xarray.DataArray.

Examples

>>> import xarray as xr
>>> import numpy as np
>>> import pandas as pd
>>> import easyclimate as ecl
>>> # Create sample data with daily frequency
>>> time_index = pd.date_range('2020-01-01', '2020-03-31', freq='D')
>>> rng = np.random.default_rng(42)
>>> data = rng.random((len(time_index), 3, 3))
>>> da = xr.DataArray(data, dims=['time', 'x', 'y'], coords={'time': time_index})
>>> # Calculate monthly max
>>> monthly_max = ecl.calc_monthly_max(da)
>>> print(monthly_max)
<xarray.DataArray (time: 3, x: 3, y: 3)> Size: 216B
<xarray.DataArray (time: 3, x: 3, y: 3)> Size: 216B
array([[[0.96189766, 0.95855921, 0.93604357],
        [0.97182643, 0.97069802, 0.97562235],
        [0.99237556, 0.96623191, 0.91601185]],
    [[0.95119466, 0.88414571, 0.85053368],
        [0.94602445, 0.99910473, 0.99546447],
        [0.98002718, 0.98663154, 0.99703466]],
    [[0.989133  , 0.99874337, 0.99308458],
        [0.99032166, 0.98180595, 0.92746046],
        [0.99758004, 0.91879368, 0.99470175]]])
Coordinates:
* time     (time) datetime64[ns] 24B 2020-01-01 2020-02-01 2020-03-01
Dimensions without coordinates: x, y

See also

numpy.max, dask.array.max, xarray.DataArray.max, xarray.core.groupby.DataArrayGroupBy.max.

easyclimate.core.stats.monthstat.calc_monthly_min(data_input: xarray.DataArray, dim: str = 'time', **kwargs)

Calculate monthly minimum.

For every adjacent sequence \(t_1, ..., t_n\) of timesteps of the same month it is:

\[o(t, x) = \mathrm{min} \left \lbrace i(t', x), t_1 < t' \leqslant t_n \right\rbrace\]

Parameters

data_input: xarray.DataArray.

xarray.DataArray to be calculated.

Note

The recommended frequence of the data_input is daily.

dim: str

Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

**kwargs:

Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data. These could include dask-specific kwargs like split_every.

Returns

xarray.DataArray.

Examples

>>> import xarray as xr
>>> import numpy as np
>>> import pandas as pd
>>> import easyclimate as ecl
>>> # Create sample data with daily frequency
>>> time_index = pd.date_range('2020-01-01', '2020-03-31', freq='D')
>>> rng = np.random.default_rng(42)
>>> data = rng.random((len(time_index), 3, 3))
>>> da = xr.DataArray(data, dims=['time', 'x', 'y'], coords={'time': time_index})
>>> # Calculate monthly min
>>> monthly_min = ecl.calc_monthly_min(da)
>>> print(monthly_min)
<xarray.DataArray (time: 3, x: 3, y: 3)> Size: 216B
array([[[0.02280387, 0.02485949, 0.05338193],
        [0.02271207, 0.01783678, 0.02161208],
        [0.00736227, 0.04161417, 0.02114802]],
    [[0.0289995 , 0.04347506, 0.0401513 ],
        [0.01072764, 0.09172101, 0.01468284],
        [0.07205915, 0.01230269, 0.00542983]],
    [[0.0040076 , 0.0165798 , 0.00166071],
        [0.04896371, 0.01903415, 0.00123306],
        [0.04737402, 0.00450012, 0.22825288]]])
Coordinates:
* time     (time) datetime64[ns] 24B 2020-01-01 2020-02-01 2020-03-01
Dimensions without coordinates: x, y

See also

numpy.min, dask.array.min, xarray.DataArray.min, xarray.core.groupby.DataArrayGroupBy.min.