xarray等效于pandas`qcut()`function

2024-03-28 10:48:43 发布

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我想计算Decile Index-请看ex1-Calculate Decile Index (DI) with Python.ipynb。在

pandas实现足够简单,但是我需要帮助使用groupby_bins()功能将bin标签应用到新的variable/coordinate。在

工作示例(测试数据集)

import pandas as pd
import numpy as np
import xarray as xr

time = pd.date_range('2010-01-01','2011-12-31',freq='M')
lat = np.linspace(-5.175003, -4.7250023, 10)
lon = np.linspace(33.524994, 33.97499, 10)
precip = np.random.normal(0, 1, size=(len(time), len(lat), len(lon)))

ds = xr.Dataset(
    {'precip': (['time', 'lat', 'lon'], precip)},
    coords={
        'lon': lon,
        'lat': lat,
        'time': time,
    }
)

这看起来像:

^{2}$

计算累积频率分布(归一化秩)

# calculate a cumsum over some window size
rolling_window = 3
ds_window = (
    ds.rolling(time=rolling_window, center=True)
    .sum()
    .dropna(dim='time', how='all')
)
# construct a cumulative frequency distribution ranking the precip values
# per month
def rank_norm(ds, dim='time'):
    return (ds.rank(dim=dim) - 1) / (ds.sizes[dim] - 1) * 100

result = ds_window.groupby('time.month').apply(rank_norm, args=('time',))
result = result.rename({variable:'rank_norm'}).drop('month')

Out[]:
<xarray.Dataset>
Dimensions:    (lat: 10, lon: 10, time: 108)
Coordinates:
  * lat        (lat) float64 -5.175 -5.125 -5.075 ... -4.825 -4.775 -4.725
  * lon        (lon) float64 33.52 33.57 33.62 33.67 ... 33.82 33.87 33.92 33.97
  * time       (time) datetime64[ns] 2010-01-31 2010-02-28 ... 2018-12-31
Data variables:
    rank_norm  (time, lat, lon) float64 75.0 75.0 12.5 100.0 ... 87.5 0.0 25.0

熊猫解决方案

我想创建一个变量,它将创建一个新的variable或{} 在ds中,它将具有与来自bins = [20., 40., 60., 80., np.Inf]的存储单元相对应的整数。在

使用.qcut功能,在Pandas中实现这一点相对简单。在

test = result.to_dataframe()
bins = pd.qcut(test['rank_norm'], 5, labels=[1, 2, 3, 4, 5])
result = bins.to_xarray().to_dataset().rename({'rank_norm': 'rank_bins'})

Out[]:
<xarray.Dataset>
Dimensions:   (lat: 10, lon: 10, time: 108)
Coordinates:
  * lat       (lat) float64 -5.175 -5.125 -5.075 -5.025 ... -4.825 -4.775 -4.725
  * lon       (lon) float64 33.52 33.57 33.62 33.67 ... 33.82 33.87 33.92 33.97
  * time      (time) datetime64[ns] 2010-01-31 2010-02-28 ... 2018-12-31
Data variables:
    rank_bins  (lat, lon, time) int64 4 4 1 4 3 4 5 1 1 2 ... 2 1 1 4 2 4 3 1 2 2

我的xarray尝试

# assign bins to variable xarray
bins = [20., 40., 60., 80., np.Inf]
decile_index_gpby = rank_norm.groupby_bins('rank_norm', bins=bins)
out = decile_index_gpby.assign()  # assign_coords()

我得到的错误信息如下:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-166-8d48b9fc1d56> in <module>
      1 bins = [20., 40., 60., 80., np.Inf]
      2 decile_index_gpby = rank_norm.groupby_bins('rank_norm', bins=bins)
----> 3 out = decile_index_gpby.assign()  # assign_coords()

~/miniconda3/lib/python3.7/site-packages/xarray/core/groupby.py in assign(self, **kwargs)
    772         Dataset.assign
    773         """
--> 774         return self.apply(lambda ds: ds.assign(**kwargs))
    775
    776

~/miniconda3/lib/python3.7/site-packages/xarray/core/groupby.py in apply(self, func, args, **kwargs)
    684         kwargs.pop('shortcut', None)  # ignore shortcut if set (for now)
    685         applied = (func(ds, *args, **kwargs) for ds in self._iter_grouped())
--> 686         return self._combine(applied)
    687
    688     def _combine(self, applied):

~/miniconda3/lib/python3.7/site-packages/xarray/core/groupby.py in _combine(self, applied)
    691         coord, dim, positions = self._infer_concat_args(applied_example)
    692         combined = concat(applied, dim)
--> 693         combined = _maybe_reorder(combined, dim, positions)
    694         if coord is not None:
    695             combined[coord.name] = coord

~/miniconda3/lib/python3.7/site-packages/xarray/core/groupby.py in _maybe_reorder(xarray_obj, dim, positions)
    468
    469 def _maybe_reorder(xarray_obj, dim, positions):
--> 470     order = _inverse_permutation_indices(positions)
    471
    472     if order is None:

~/miniconda3/lib/python3.7/site-packages/xarray/core/groupby.py in _inverse_permutation_indices(positions)
    110         positions = [np.arange(sl.start, sl.stop, sl.step) for sl in positions]
    111
--> 112     indices = nputils.inverse_permutation(np.concatenate(positions))
    113     return indices
    114

~/miniconda3/lib/python3.7/site-packages/xarray/core/nputils.py in inverse_permutation(indices)
     58     # use intp instead of int64 because of windows :(
     59     inverse_permutation = np.empty(len(indices), dtype=np.intp)
---> 60     inverse_permutation[indices] = np.arange(len(indices), dtype=np.intp)
     61     return inverse_permutation
     62

IndexError: index 1304 is out of bounds for axis 0 with size 1000

Tags: inselfnormtimenpdsxarraylon
2条回答

我不确定pandas.qcut是否完全符合您的期望;例如,请参见示例中它返回的bin:

>>> test = result.to_dataframe()
>>> binned, bins = pd.qcut(test['rank_norm'], 5, labels=[1, 2, 3, 4, 5], retbins=True)

>>> bins
array([  0. ,  12.5,  37.5,  62.5,  87.5, 100. ])

如果我理解正确的话,你要找的是在每个点上根据点所属的箱子分配一个整数值。即:

  • 0.0 <= x < 20.0:1
  • 20.0 <= x < 40.0:2
  • 40.0 <= x < 60.0:3
  • 60.0 <= x < 80.0:4
  • 80.0 <= x:5

对于此任务,我可能建议使用通过^{}应用的^{}

^{pr2}$

看起来,如果您使用scalar来定义您的bins,那么它将只生成4个范围。您可以通过查看length和生成的GroupBy对象的keys的名称来检查:

mybins = [20., 40., 60., 80., np.inf]

decile_index_gpby = rank_norm.groupby_bins('rank_norm', bins=mybins)

len(decile_index_gpby.groups)
=> 4

decile_index_gpby.groups.keys()
=> [Interval(80.0, inf, closed='right'),
    Interval(20.0, 40.0, closed='right'),
    Interval(60.0, 80.0, closed='right'),
    Interval(40.0, 60.0, closed='right')]

为了防止丢失1/5的值,您必须将mybins的定义更改为如下所示:

^{pr2}$

这不是你想要的。在

因此请使用bins=5代替:

decile_index_gpby = rank_norm.groupby_bins('rank_norm', bins=5)

len(decile_index_gpby.groups)
=> 5

decile_index_gpby.groups.keys()
=> [Interval(80.0, 100.0, closed='right'),
    Interval(20.0, 40.0, closed='right'),
    Interval(60.0, 80.0, closed='right'),
    Interval(40.0, 60.0, closed='right'),
    Interval(-0.1, 20.0, closed='right')]

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