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20 changes: 18 additions & 2 deletions graphblas/core/formatting.py
Original file line number Diff line number Diff line change
Expand Up @@ -487,7 +487,18 @@ def base(v):
return base

def formatter(v):
return base(v) if abs(v) > threshold else base(0.0)
try:
mag = abs(v)
except OverflowError:
# abs() of a python complex overflows for finite components near
# the float max, where numpy's abs returns inf. pandas computes the
# magnitude with numpy, so such a value is never chopped.
return base(v)
# Chop to the value's own type of zero. pandas chops at the array
# level, so a chopped complex renders " 0.000000+0.000000j"; a bare
# float zero here would put a j-less string into the complex column,
# which _trim_zeros_complex cannot parse.
return base(v) if mag > threshold else base(type(v)(0))

return formatter

Expand Down Expand Up @@ -529,6 +540,7 @@ def _format_float_column(values, digits):
# pandas does; e.g. a "%f"-style callable then casts complex the same way.
return [na_rep if (v != v) else float_format(v) for v in arr]
seq = arr.tolist()
# FC32 values come in as python complex, so a threshold > 3.4e38 can chop what pandas keeps.
threshold = _display_option("chop_threshold")

def format_with(fmt_str):
Expand All @@ -539,7 +551,11 @@ def format_with(fmt_str):

result = format_with(f"{{value: .{digits:d}f}}")
too_long = bool(result) and max(len(x) for x in result) > digits + 6
abs_vals = np.abs(arr)
# A complex value with components near the float max has magnitude inf,
# which is what the checks below want. numpy 1.24 on arm64 also raises the
# FP overflow flag computing it, and that warning would escape from repr.
with np.errstate(over="ignore"):
abs_vals = np.abs(arr)
has_large = bool((abs_vals > 1e6).any())
has_small = bool(((abs_vals < 10.0 ** (-digits)) & (abs_vals > 0)).any())
if has_small or (too_long and has_large):
Expand Down
63 changes: 63 additions & 0 deletions graphblas/tests/test_formatting.py
Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import contextlib
import warnings

import numpy as np
import pytest
Expand Down Expand Up @@ -6760,3 +6761,65 @@ def test_vector_as_matrix():
"</table>\n"
"</div></details></div>"
)


@pytest.mark.skipif("not pd")
@pytest.mark.skipif("not dtypes._supports_complex")
@pytest.mark.parametrize(
("dtype", "big_str", "small_str"),
[
("FC32", "3.402823e+38+3.402823e+38j", "1.000000e-07+0.000000e+00j"),
("FC64", "1.797693e+308+1.797693e+308j", "1.000000e-07+ 0.000000e+00j"),
],
)
def test_chop_threshold_complex(dtype, big_str, small_str):
# The COO (long) form formats complex columns through the float column
# formatter, where display.chop_threshold compares each value's magnitude.
big = float(np.finfo(dtypes.lookup_dtype(dtype).np_type).max)
v = Vector.from_coo(
[10_000, 500_000], [complex(big, big), complex(1e-7, 0)], size=10**6, dtype=dtype
)
with pd.option_context("display.chop_threshold", 1e-8):
# abs() of a python complex overflows for FC64 components near the
# float max; numpy's abs is inf, so nothing chops and this must render.
# Byte-identical to the pandas renderer, verified against 6f1eb02.
r = repr(v)
assert big_str in r
assert small_str in r
v._repr_html_()
# A value below the threshold chops to complex zero. pandas 3.0.3
# itself crashes on this input (its own _trim_zeros_complex cannot
# parse the float zero its chop substitutes into a complex column),
# so rendering at all is deliberately better than byte-identity.
w = Vector.from_coo(
[10_000, 500_000], [complex(1.0, 1.0), complex(5e-9, 0)], size=10**6, dtype=dtype
)
r = repr(w)
assert "1.000000e+00+1.000000e+00j" in r
assert "0.000000e+00+0.000000e+00j" in r
w._repr_html_()


@pytest.mark.skipif("not dtypes._supports_complex")
def test_complex_repr_magnitude_overflow_warns_nothing(monkeypatch):
# The float formatter takes np.abs of a complex column, which is inf for FC64
# components near the float max. numpy 1.24 on arm64 also raises the FP
# overflow flag there (newer numpy does not), and that RuntimeWarning must not
# escape from repr. Stand in for that numpy with an abs whose own arithmetic
# overflows, so the test can fail on any numpy. (FC32 values reach the formatter
# widened to double, so their magnitudes never overflow.)
real_abs = np.abs

def overflowing_abs(x):
x = np.asarray(x)
if np.iscomplexobj(x):
return np.sqrt(x.real * x.real + x.imag * x.imag)
return real_abs(x)

big = float(np.finfo(np.float64).max)
v = Vector.from_coo([10_000, 500_000], [complex(big, big), 1j], size=10**6, dtype="FC64")
monkeypatch.setattr(formatting.np, "abs", overflowing_abs)
with warnings.catch_warnings():
warnings.simplefilter("error", RuntimeWarning)
assert "1.797693e+308+1.797693e+308j" in repr(v)
v._repr_html_()
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