from collections.abc import Iterable

import hypothesis.strategies as hst
from hypothesis import given, event, settings
import numpy as np

from qcodes.instrument.parameter import Parameter


def test_scale_raw_value():
    p = Parameter(name='test_scale_raw_value', set_cmd=None)
    p(42)
    assert p.raw_value == 42

    p.scale = 2
    assert p.raw_value == 42  # No set/get cmd performed
    assert p() == 21

    p(10)
    assert p.raw_value == 20
    assert p() == 10


# There are a number different scenarios for testing a parameter with scale
# and offset. Therefore a custom strategy for generating test parameters
# is implemented here. The possible cases are:
# for getting and setting a parameter: values can be
#    scalar:
#        offset and scale can be scalars
# for getting only:
#    array:
#        offset and scale can be scalars or arrays(of same legnth as values)
#        independently

# define shorthands for strategies
TestFloats = hst.floats(min_value=-1e40, max_value=1e40).filter(lambda x: x != 0)
SharedSize = hst.shared(hst.integers(min_value=1, max_value=100), key='shared_size')
ValuesScalar = hst.shared(hst.booleans(), key='values_scalar')


# the following test stra
@hst.composite
def iterable_or_number(draw, values, size, values_scalar, is_values):
    if draw(values_scalar):
        # if parameter values are scalar,
        # return scalar for values and scale/offset
        return draw(values)
    elif is_values:
        # if parameter values are not scalar and parameter values are requested
        # return a list of values of the given size
        return draw(hst.lists(values, min_size=draw(size), max_size=draw(size)))
    else:
        # if parameter values are not scalar and scale/offset are requested
        # make a random choice whether to return a list of the same size as
        # the values or a simple scalar
        if draw(hst.booleans()):
            return draw(hst.lists(values, min_size=draw(size), max_size=draw(size)))
        else:
            return draw(values)


@settings(max_examples=500)  # default:100 increased
@given(values=iterable_or_number(TestFloats, SharedSize, ValuesScalar, True),
       offsets=iterable_or_number(TestFloats, SharedSize, ValuesScalar, False),
       scales=iterable_or_number(TestFloats, SharedSize, ValuesScalar, False))
def test_scale_and_offset_raw_value_iterable(values, offsets, scales):
    p = Parameter(name='test_scale_and_offset_raw_value', set_cmd=None)

    # test that scale and offset does not change the default behaviour
    p(values)
    assert p.raw_value == values

    # test setting scale and offset does not change anything
    p.scale = scales
    p.offset = offsets
    assert p.raw_value == values

    np_values = np.array(values)
    np_offsets = np.array(offsets)
    np_scales = np.array(scales)
    np_get_values = np.array(p())
    # No set/get cmd performed
    np.testing.assert_allclose(np_get_values, (np_values - np_offsets) / np_scales)

    # test set, only for scalar values
    if not isinstance(values, Iterable):
        p(values)
        # No set/get cmd performed
        np.testing.assert_allclose(np.array(p.raw_value),
                                   np_values * np_scales + np_offsets)

        # testing conversion back and forth
        p(values)
        np_get_values = np.array(p())
        # No set/get cmd performed
        np.testing.assert_allclose(np_get_values, np_values)

    # adding statistics
    if isinstance(offsets, Iterable):
        event('Offset is array')
    if isinstance(scales, Iterable):
        event('Scale is array')
    if isinstance(values, Iterable):
        event('Value is array')
    if isinstance(scales, Iterable) and isinstance(offsets, Iterable):
        event('Scale is array and also offset')
    if isinstance(scales, Iterable) and not isinstance(offsets, Iterable):
        event('Scale is array but not offset')


@settings(max_examples=300)
@given(
    values=iterable_or_number(
        TestFloats, SharedSize, ValuesScalar, True),
    offsets=iterable_or_number(
        TestFloats, SharedSize, ValuesScalar, False),
    scales=iterable_or_number(
        TestFloats, SharedSize, ValuesScalar, False))
def test_scale_and_offset_raw_value_iterable_for_set_cache(values,
                                                           offsets,
                                                           scales):
    p = Parameter(name='test_scale_and_offset_raw_value', set_cmd=None)

    # test that scale and offset does not change the default behaviour
    p.cache.set(values)
    assert p.raw_value == values

    # test setting scale and offset does not change anything
    p.scale = scales
    p.offset = offsets
    assert p.raw_value == values

    np_values = np.array(values)
    np_offsets = np.array(offsets)
    np_scales = np.array(scales)
    np_get_latest_values = np.array(p.get_latest())
    # Without a call to ``get``, ``get_latest`` will just return old
    # cached values without applying the set scale and offset
    np.testing.assert_allclose(np_get_latest_values, np_values)
    np_get_values = np.array(p.get())
    # Now that ``get`` is called, the returned values are the result of
    # application of the scale and offset. Obviously, calling
    # ``get_latest`` now will also return the values with the applied
    # scale and offset
    np.testing.assert_allclose(np_get_values,
                               (np_values - np_offsets) / np_scales)
    np_get_latest_values_after_get = np.array(p.get_latest())
    np.testing.assert_allclose(np_get_latest_values_after_get,
                               (np_values - np_offsets) / np_scales)

    # test ``cache.set`` for scalar values
    if not isinstance(values, Iterable):
        p.cache.set(values)
        np.testing.assert_allclose(np.array(p.raw_value),
                                   np_values * np_scales + np_offsets)
        # No set/get cmd performed

        # testing conversion back and forth
        p.cache.set(values)
        np_get_latest_values = np.array(p.get_latest())
        # No set/get cmd performed
        np.testing.assert_allclose(np_get_latest_values, np_values)

    # adding statistics
    if isinstance(offsets, Iterable):
        event('Offset is array')
    if isinstance(scales, Iterable):
        event('Scale is array')
    if isinstance(values, Iterable):
        event('Value is array')
    if isinstance(scales, Iterable) and isinstance(offsets, Iterable):
        event('Scale is array and also offset')
    if isinstance(scales, Iterable) and not isinstance(offsets, Iterable):
        event('Scale is array but not offset')
