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import numpy as np
import pandas as pd
import xarray as xr
class Repr:
def setup(self):
a = np.arange(0, 100)
data_vars = dict()
for i in a:
data_vars[f"long_variable_name_{i}"] = xr.DataArray(
name=f"long_variable_name_{i}",
data=np.arange(0, 20),
dims=[f"long_coord_name_{i}_x"],
coords={f"long_coord_name_{i}_x": np.arange(0, 20) * 2},
)
self.ds = xr.Dataset(data_vars)
self.ds.attrs = {f"attr_{k}": 2 for k in a}
def time_repr(self):
repr(self.ds)
def time_repr_html(self):
self.ds._repr_html_()
class ReprDataTree:
def setup(self):
# construct a datatree with 500 nodes
number_of_files = 20
number_of_groups = 25
tree_dict = {}
for f in range(number_of_files):
for g in range(number_of_groups):
tree_dict[f"file_{f}/group_{g}"] = xr.Dataset({"g": f * g})
self.dt = xr.DataTree.from_dict(tree_dict)
def time_repr(self):
repr(self.dt)
def time_repr_html(self):
self.dt._repr_html_()
class ReprMultiIndex:
def setup(self):
index = pd.MultiIndex.from_product(
[range(1000), range(1000)], names=("level_0", "level_1")
)
series = pd.Series(range(1000 * 1000), index=index)
self.da = xr.DataArray(series)
def time_repr(self):
repr(self.da)
def time_repr_html(self):
self.da._repr_html_()
class ReprPandasRangeIndex:
# display a memory-saving pandas.RangeIndex shouldn't trigger memory
# expensive conversion into a numpy array
def setup(self):
index = xr.indexes.PandasIndex(pd.RangeIndex(1_000_000), "x")
self.ds = xr.Dataset(coords=xr.Coordinates.from_xindex(index))
def time_repr(self):
repr(self.ds.x)
def time_repr_html(self):
self.ds.x._repr_html_()
class ReprXarrayRangeIndex:
# display an Xarray RangeIndex shouldn't trigger memory expensive conversion
# of its lazy coordinate into a numpy array
def setup(self):
index = xr.indexes.RangeIndex.arange(1_000_000, dim="x")
self.ds = xr.Dataset(coords=xr.Coordinates.from_xindex(index))
def time_repr(self):
repr(self.ds.x)
def time_repr_html(self):
self.ds.x._repr_html_()
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