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How to use integer_dtypes method in hypothesis

Best Python code snippet using hypothesis

datatypes.py

Source:datatypes.py

1from numpy import obj2sctype, ndarray2from numpy import bool_3from numpy import byte, short, intc, int_, longlong, intp4from numpy import ubyte, ushort, uintc, uint, ulonglong, uintp5from numpy import single, float_, longfloat6from numpy import csingle, complex_, clongfloat7# These are only used for membership tests, but things break if they are sets8# rather than lists. TK, Jan 2012.9integer_dtypes = [10    int,11    uint,12    byte,13    short,14    intc,15    int_,16    longlong,17    intp,18    ubyte,19    ushort,20    uintc,21    uint,22    ulonglong,23    uintp]24try:25    integer_dtypes.append(long)26except NameError:27    pass       # long is just int for Python 328float_dtypes = [float, single, float_, longfloat]29complex_dtypes = [complex, csingle, complex_, clongfloat]30bool_dtypes = [bool, bool_]31def check_type(stochastic):32    """33    type, shape = check_type(stochastic)34    Checks the type of a stochastic's value. Output value 'type' may be35    bool, int, float, or complex. Nonnative numpy dtypes are lumped into36    these categories. Output value 'shape' is () if the stochastic's value37    is scalar, or a nontrivial tuple otherwise.38    """39    val = stochastic.value40    if val.__class__ in bool_dtypes:41        return bool, ()42    elif val.__class__ in integer_dtypes:43        return int, ()44    elif val.__class__ in float_dtypes:45        return float, ()46    elif val.__class__ in complex_dtypes:47        return complex, ()48    elif isinstance(val, ndarray):49        if obj2sctype(val) in bool_dtypes:50            return bool, val.shape51        elif obj2sctype(val) in integer_dtypes:52            return int, val.shape53        elif obj2sctype(val) in float_dtypes:54            return float, val.shape55        elif obj2sctype(val) in complex_dtypes:56            return complex, val.shape57        else:58            return 'object', val.shape59    else:60        return 'object', ()61continuous_types = [float, complex]62def is_continuous(stochastic):63    dtype, shape = check_type(stochastic)64    if dtype in continuous_types:65        return True66    else:...

common.py

Source:common.py

2from hypothesis.extra.numpy import arrays as hy_arrays3from hypothesis.extra.numpy import floating_dtypes, integer_dtypes4from hypothesis.strategies import one_of5hy_array_gen = hy_arrays(6    dtype=one_of(integer_dtypes(sizes=(32, 64)), floating_dtypes(sizes=(32, 64))),7    shape=array_shapes(),8)9hy_int_array_gen = hy_arrays(10    dtype=integer_dtypes(sizes=(32, 64)),11    shape=array_shapes(),...

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