UltraPlot/ultraplot/tests/test_inputs_helpers.py at main · Ultraplot/UltraPlot · GitHub

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#!/usr/bin/env python3

"""

Focused tests for plotting input helpers.

"""

from __future__ import annotations

import numpy as np

import pytest

import matplotlib.tri as mtri

from ultraplot.internals import inputs

from ultraplot.internals.warnings import UltraPlotWarning

def test_basic_type_and_array_helpers():

assert inputs._is_numeric([1, 2, 3]) is True

assert inputs._is_numeric(["a", "b"]) is False

assert inputs._is_categorical(["a", "b"]) is True

assert inputs._is_categorical([1, 2]) is False

assert inputs._is_descending(np.array([3, 2, 1])) is True

assert inputs._is_descending(np.array([[3, 2], [1, 0]])) is False

with pytest.raises(ValueError, match="Invalid data None"):

inputs._to_duck_array(None)

masked, units = inputs._to_masked_array(np.array([1, np.nan, 3]))

assert units is None

assert np.ma.isMaskedArray(masked)

assert masked.mask.tolist() == [False, True, False]

masked_ints, _ = inputs._to_masked_array(np.array([1, 2, 3], dtype=int))

assert masked_ints.dtype == np.float64

def test_coordinate_conversion_helpers():

x = np.array([0.0, 1.0, 2.0])

y = np.array([0.0, 1.0, 2.0])

z = np.arange(9.0).reshape(3, 3)

x_edges, y_edges = inputs._to_edges(x, y, z)

assert x_edges.shape == (4,)

assert y_edges.shape == (4,)

z_small = np.arange(4.0).reshape(2, 2)

x_centers, y_centers = inputs._to_centers(x, y, z_small)

assert x_centers.shape == (2,)

assert y_centers.shape == (2,)

x2 = np.array([[0.0, 1.0], [0.0, 1.0]])

y2 = np.array([[0.0, 0.0], [1.0, 1.0]])

z2 = np.arange(4.0).reshape(2, 2)

x2_edges, y2_edges = inputs._to_edges(x2, y2, z2)

assert x2_edges.shape == (3, 3)

assert y2_edges.shape == (3, 3)

with pytest.raises(ValueError, match="must match array centers"):

inputs._to_edges(np.array([0.0, 1.0]), np.array([0.0, 1.0]), np.ones((3, 3)))

with pytest.raises(ValueError, match="must match z centers"):

inputs._to_centers(np.array([0.0, 1.0]), np.array([0.0, 1.0]), np.ones((3, 3)))

def test_from_data_and_triangulation_helpers():

data = {"x": np.array([1, 2, 3]), "y": np.array([4, 5, 6])}

converted = inputs._from_data(data, "x", "missing", "y")

assert np.array_equal(converted[0], data["x"])

assert converted[1] == "missing"

assert np.array_equal(converted[2], data["y"])

assert inputs._from_data(data, "missing") == "missing"

assert inputs._from_data(None, "x") is None

triangulation = mtri.Triangulation([0, 1, 0], [0, 0, 1])

tri, z, args, kwargs = inputs._parse_triangulation_inputs(triangulation, [1, 2, 3])

assert tri is triangulation

assert z == [1, 2, 3]

assert args == []

assert kwargs == {}

with pytest.raises(ValueError, match="No z values provided"):

inputs._parse_triangulation_inputs(triangulation)

def test_distribution_helpers_cover_clean_reduce_and_ranges():

object_array = np.array([[1, 2], [3]], dtype=object)

cleaned = inputs._dist_clean(object_array)

assert len(cleaned) == 2

assert np.allclose(cleaned[0], [1.0, 2.0])

numeric_cleaned = inputs._dist_clean(np.array([[1.0, np.nan], [2.0, 3.0]]))

assert len(numeric_cleaned) == 2

assert np.allclose(numeric_cleaned[0], [1.0, 2.0])

list_cleaned = inputs._dist_clean([[1, 2], [], [3]])

assert len(list_cleaned) == 2

with pytest.raises(ValueError, match="numpy array or a list of lists"):

inputs._dist_clean("bad")

data = np.array([[1.0, 3.0], [2.0, 4.0]])

with pytest.warns(

UltraPlotWarning, match="Cannot have both means=True and medians=True"

reduced, kwargs = inputs._dist_reduce(data, means=True, medians=True)

assert np.allclose(reduced, [1.5, 3.5])

assert "distribution" in kwargs

with pytest.raises(ValueError, match="Expected 2D array"):

inputs._dist_reduce(np.array([1.0, 2.0]), means=True)

distribution = np.array([[1.0, 2.0], [3.0, 4.0]])

err, label = inputs._dist_range(

np.array([2.0, 3.0]),

distribution,

stds=[-1, 1],

pctiles=[10, 90],

label=True,

)

assert err.shape == (2, 2)

assert label == "1$\\sigma$ range"

err_abs, label_abs = inputs._dist_range(

np.array([2.0, 3.0]),

None,

errdata=np.array([0.5, 0.25]),

absolute=True,

label=True,

)

assert np.allclose(err_abs[0], [1.5, 2.75])

assert label_abs == "uncertainty"

with pytest.raises(ValueError, match="must pass means=True or medians=True"):

inputs._dist_range(np.array([1.0]), None, stds=1)

with pytest.raises(

ValueError, match="Passing both 2D data coordinates and 'errdata'"

inputs._dist_range(np.ones((2, 2)), None, errdata=np.ones(2))

def test_mask_range_and_metadata_helpers():

masked = inputs._safe_mask(np.array([True, False, True]), np.array([1.0, 2.0, 3.0]))

assert np.isnan(masked[1])

with pytest.raises(ValueError, match="incompatible with array shape"):

inputs._safe_mask(np.array([True, False]), np.array([1.0, 2.0, 3.0]))

lo, hi = inputs._safe_range(np.array([1.0, np.nan, 5.0]), lo=0, hi=100)

assert lo == 1.0

assert hi == 5.0

coords, kwargs = inputs._meta_coords(np.array(["a", "b"]), which="x")

assert np.array_equal(coords, np.array([0, 1]))

assert {"xlocator", "xformatter", "xminorlocator"} <= set(kwargs)

numeric_coords, kwargs_numeric = inputs._meta_coords(

np.array([1.0, 2.0]), which="y"

)

assert np.array_equal(numeric_coords, np.array([1.0, 2.0]))

assert kwargs_numeric == {}

with pytest.raises(ValueError, match="Non-1D string coordinate input"):

inputs._meta_coords(np.array([["a", "b"]]), which="x")

assert np.array_equal(

inputs._meta_labels(np.array([1, 2, 3]), axis=0), np.array([0, 1, 2])

)

assert np.array_equal(

inputs._meta_labels(np.array([1, 2, 3]), axis=1), np.array([0])

)

assert inputs._meta_labels(np.array([1, 2, 3]), axis=2, always=False) is None

with pytest.raises(ValueError, match="Invalid axis"):

inputs._meta_labels(np.array([1, 2, 3]), axis=3)

assert inputs._meta_title(np.array([1, 2, 3])) is None

assert inputs._meta_units(np.array([1, 2, 3])) is None

def test_meta_coords_xarray_string_coord():

"""

Regression test: passing an xarray.DataArray with a string coordinate

to _meta_coords must yield plain string tick labels, not the multi-line

repr of each scalar DataArray element.

"""

xr = pytest.importorskip("xarray")

da = xr.DataArray(

np.array(["a", "b", "c"]),

coords={"ens": ["a", "b", "c"]},

dims=["ens"],

name="ens",

)

coords, kwargs = inputs._meta_coords(da, which="x")

assert np.array_equal(coords, np.array([0, 1, 2]))

formatter = kwargs["xformatter"]

assert [formatter(i) for i in coords] == ["a", "b", "c"]

def test_geographic_helpers_cover_clipping_bounds_and_globes():

clipped = inputs._geo_clip(np.array([-100.0, 0.0, 100.0]))

assert np.allclose(clipped, [-90.0, 0.0, 90.0])

x = np.array([0.0, 180.0, 540.0])

y = np.array([1.0, 2.0, 3.0])

rolled_x, rolled_y = inputs._geo_inbounds(x, y, xmin=-180, xmax=180)

assert np.array_equal(rolled_x, np.array([180.0, 0.0, 180.0]))

assert np.array_equal(rolled_y, np.array([3.0, 1.0, 2.0]))

xg = np.array([0.0, 180.0])

yg = np.array([-45.0, 45.0])

zg = np.array([[1.0, 2.0], [3.0, 4.0]])

globe_x, globe_y, globe_z = inputs._geo_globe(xg, yg, zg, modulo=True)

assert globe_x.shape[0] == 3

assert globe_y.shape[0] == 4

assert globe_z.shape == (4, 3)

seam_x, seam_y, seam_z = inputs._geo_globe(xg, yg, zg, xmin=-180, modulo=False)

assert seam_x.shape[0] == 4

assert seam_y.shape[0] == 4

assert seam_z.shape == (4, 4)

with pytest.raises(ValueError, match="Unexpected shapes"):

inputs._geo_globe(np.array([0.0, 1.0, 2.0, 3.0]), yg, zg, modulo=False)

UltraPlot/ultraplot/tests/test_inputs_helpers.py at main · Ultraplot/UltraPlot · GitHub

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