1 files changed, 73 insertions, 0 deletions
diff --git a/tests/float/float_format_accuracy.py b/tests/float/float_format_accuracy.py
new file mode 100644
index 000000000..f9467f9c0
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+++ b/tests/float/float_format_accuracy.py
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+# Test accuracy of `repr` conversions.
+# This test also increases code coverage for corner cases.
+
+try:
+    import array, math, random
+except ImportError:
+    print("SKIP")
+    raise SystemExit
+
+# The largest errors come from seldom used very small numbers, near the
+# limit of the representation. So we keep them out of this test to keep
+# the max relative error display useful.
+if float("1e-100") == 0.0:
+    # single-precision
+    float_type = "f"
+    float_size = 4
+    # testing range
+    min_expo = -96  # i.e. not smaller than 1.0e-29
+    # Expected results (given >=50'000 samples):
+    # - MICROPY_FLTCONV_IMPL_EXACT: 100% exact conversions
+    # - MICROPY_FLTCONV_IMPL_APPROX: >=98.53% exact conversions, max relative error <= 1.01e-7
+    min_success = 0.980  # with only 1200 samples, the success rate is lower
+    max_rel_err = 1.1e-7
+    # REPR_C is typically used with FORMAT_IMPL_BASIC, which has a larger error
+    is_REPR_C = float("1.0000001") == float("1.0")
+    if is_REPR_C:  # REPR_C
+        min_success = 0.83
+        max_rel_err = 5.75e-07
+else:
+    # double-precision
+    float_type = "d"
+    float_size = 8
+    # testing range
+    min_expo = -845  # i.e. not smaller than 1.0e-254
+    # Expected results (given >=200'000 samples):
+    # - MICROPY_FLTCONV_IMPL_EXACT: 100% exact conversions
+    # - MICROPY_FLTCONV_IMPL_APPROX: >=99.83% exact conversions, max relative error <= 2.7e-16
+    min_success = 0.997  # with only 1200 samples, the success rate is lower
+    max_rel_err = 2.7e-16
+
+
+# Deterministic pseudorandom generator. Designed to be uniform
+# on mantissa values and exponents, not on the represented number
+def pseudo_randfloat():
+    rnd_buff = bytearray(float_size)
+    for _ in range(float_size):
+        rnd_buff[_] = random.getrandbits(8)
+    return array.array(float_type, rnd_buff)[0]
+
+
+random.seed(42)
+stats = 0
+N = 1200
+max_err = 0
+for _ in range(N):
+    f = pseudo_randfloat()
+    while type(f) is not float or math.isinf(f) or math.isnan(f) or math.frexp(f)[1] <= min_expo:
+        f = pseudo_randfloat()
+
+    str_f = repr(f)
+    f2 = float(str_f)
+    if f2 == f:
+        stats += 1
+    else:
+        error = abs((f2 - f) / f)
+        if max_err < error:
+            max_err = error
+
+print(N, "values converted")
+if stats / N >= min_success and max_err <= max_rel_err:
+    print("float format accuracy OK")
+else:
+    print("FAILED: repr rate=%.3f%% max_err=%.3e" % (100 * stats / N, max_err))