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The mantissa parsing code uses a floating point variable to accumulate digits. Using an `mp_float_uint_t` variable instead and casting to `mp_float_t` at the very end reduces code size. In some cases, it also improves the rounding behaviour as extra digits are taken into account by the int-to-float conversion code. An extra test case handles the special case where mantissa overflow occurs while processing deferred trailing zeros. Signed-off-by: Yoctopuce dev <dev@yoctopuce.com>
40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
# test parsing of floats
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inf = float("inf")
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# it shouldn't matter where the decimal point is if the exponent balances the value
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print(float("1234") - float("0.1234e4"))
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print(float("1.015625") - float("1015625e-6"))
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# very large integer part with a very negative exponent should cancel out
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print("%.4e" % float("9" * 60 + "e-60"))
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print("%.4e" % float("9" * 60 + "e-40"))
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# many fractional digits
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print(float("." + "9" * 70))
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print(float("." + "9" * 70 + "e20"))
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print(float("." + "9" * 70 + "e-50") == float("1e-50"))
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# tiny fraction with large exponent
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print(float("." + "0" * 60 + "1e10") == float("1e-51"))
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print(float("." + "0" * 60 + "9e25") == float("9e-36"))
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print(float("." + "0" * 60 + "9e40") == float("9e-21"))
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# ensure that accuracy is retained when value is close to a subnormal
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print(float("1.00000000000000000000e-37"))
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print(float("10.0000000000000000000e-38"))
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print(float("100.000000000000000000e-39"))
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# very large exponent literal
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print(float("1e4294967301"))
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print(float("1e-4294967301"))
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print(float("1e18446744073709551621"))
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print(float("1e-18446744073709551621"))
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# mantissa overflow while processing deferred trailing zeros
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print(float("10000000000000000000001"))
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# check small decimals are as close to their true value as possible
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for n in range(1, 10):
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print(float("0.%u" % n) == n / 10)
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