# Path: cotede/qctests/spike.py
# def spike(x):
#     """ Spike
#     """
#     if isinstance(x, ma.MaskedArray):
#         x[x.mask] = np.nan
#         x = x.data
# 
#     x = np.atleast_1d(x)
#     y = np.nan * x
#     y[1:-1] = np.abs(x[1:-1] - (x[:-2] + x[2:]) / 2.0) - np.abs((x[2:] - x[:-2]) / 2.0)
#     return y
# 
# class Spike(QCCheckVar):
#     def set_features(self):
#         self.features = {"spike": spike(self.data[self.varname])}
# 
#     def test(self):
#         self.flags = {}
#         try:
#             threshold = self.cfg["threshold"]
#         except KeyError:
#             module_logger.warning(
#                 "Deprecated cfg format. It should contain a threshold item."
#             )
#             threshold = self.cfg
# 
#         assert (
#             (np.size(threshold) == 1)
#             and (threshold is not None)
#             and (np.isfinite(threshold))
#         )
# 
#         feature = np.absolute(self.features["spike"])
# 
#         flag = np.zeros(np.shape(self.data[self.varname]), dtype="i1")
#         flag[feature > threshold] = self.flag_bad
#         flag[feature <= threshold] = self.flag_good
#         # Flag as 9 any masked input value
#         x = np.atleast_1d(self.data[self.varname])
#         flag[ma.getmaskarray(x) | ~np.isfinite(x)] = 9
#         self.flags["spike"] = flag
# 
# Path: tests/data.py
# class DummyData(object):
#     def __init__(self):
#         self.attrs = {
#                 'datetime': datetime(2016,6,4),
#                 'LATITUDE': 15, 'LONGITUDE': -38}
#         self.data = {
#                 'PRES': ma.fix_invalid(
#                     [2, 6, 10, 21, 44, 79, 100, 150,
#                         200, 400, 410, 650, 1000, 2000, 5000]),
#                 'TEMP': ma.fix_invalid(
#                     [25.32, 25.34, 25.34, 25.31, 24.99, 23.46, 21.85, 17.95,
#                         15.39, 11.08, 6.93, 7.93, 5.71, 3.58, np.nan]),
#                 'PSAL': ma.fix_invalid(
#                     [36.49, 36.51, 36.52, 36.53, 36.59, 36.76, 36.81, 36.39,
#                         35.98, 35.30, 35.28, 34.93, 34.86, np.nan, np.nan])}
# 
#     def __getitem__(self, key):
#         return self.data[key]
# 
#     def keys(self):
#         return self.data.keys()
# 
#     @property
#     def attributes(self):
#         print('attributes will be removed. Use attrs instead!')
#         return self.attrs
# 
# Path: tests/qctests/compare.py
# def compare_feature_input_types(feature, *args, **kwargs):
#     compare_feature_tuple(feature, *args, **kwargs)
#     compare_feature_series(feature, *args, **kwargs)
#     compare_feature_dataarray(feature, *args, **kwargs)
# 
# def compare_input_types(Procedure, cfg):
#     compare_tuple(Procedure, cfg)
#     compare_pandas(Procedure, cfg)
#     compare_xarray(Procedure, cfg)


import numpy as np
from numpy import ma
from cotede.qctests import Spike, spike
from ..data import DummyData
from .compare import compare_feature_input_types, compare_input_types
#!/usr/bin/env python
# -*- coding: utf-8 -*-

""" Verify Spike QC test
"""




def test_spike():
    """Basic test on feature spike
    """
    x = [1, -1, 2, 2, 3, 2, 4]
    y = spike(x)

    output = [np.nan, 2.0, 0.0, 0.0, 1.0, 1.0, np.nan]

    assert isinstance(y, np.ndarray)
    assert np.allclose(y, output, equal_nan=True)


def test_feature_input_types():
    x = np.array([1, -1, 2, 2, 3, 2, 4])

# Path: cotede/qctests/spike.py
# def spike(x):
#     """ Spike
#     """
#     if isinstance(x, ma.MaskedArray):
#         x[x.mask] = np.nan
#         x = x.data
# 
#     x = np.atleast_1d(x)
#     y = np.nan * x
#     y[1:-1] = np.abs(x[1:-1] - (x[:-2] + x[2:]) / 2.0) - np.abs((x[2:] - x[:-2]) / 2.0)
#     return y
# 
# class Spike(QCCheckVar):
#     def set_features(self):
#         self.features = {"spike": spike(self.data[self.varname])}
# 
#     def test(self):
#         self.flags = {}
#         try:
#             threshold = self.cfg["threshold"]
#         except KeyError:
#             module_logger.warning(
#                 "Deprecated cfg format. It should contain a threshold item."
#             )
#             threshold = self.cfg
# 
#         assert (
#             (np.size(threshold) == 1)
#             and (threshold is not None)
#             and (np.isfinite(threshold))
#         )
# 
#         feature = np.absolute(self.features["spike"])
# 
#         flag = np.zeros(np.shape(self.data[self.varname]), dtype="i1")
#         flag[feature > threshold] = self.flag_bad
#         flag[feature <= threshold] = self.flag_good
#         # Flag as 9 any masked input value
#         x = np.atleast_1d(self.data[self.varname])
#         flag[ma.getmaskarray(x) | ~np.isfinite(x)] = 9
#         self.flags["spike"] = flag
# 
# Path: tests/data.py
# class DummyData(object):
#     def __init__(self):
#         self.attrs = {
#                 'datetime': datetime(2016,6,4),
#                 'LATITUDE': 15, 'LONGITUDE': -38}
#         self.data = {
#                 'PRES': ma.fix_invalid(
#                     [2, 6, 10, 21, 44, 79, 100, 150,
#                         200, 400, 410, 650, 1000, 2000, 5000]),
#                 'TEMP': ma.fix_invalid(
#                     [25.32, 25.34, 25.34, 25.31, 24.99, 23.46, 21.85, 17.95,
#                         15.39, 11.08, 6.93, 7.93, 5.71, 3.58, np.nan]),
#                 'PSAL': ma.fix_invalid(
#                     [36.49, 36.51, 36.52, 36.53, 36.59, 36.76, 36.81, 36.39,
#                         35.98, 35.30, 35.28, 34.93, 34.86, np.nan, np.nan])}
# 
#     def __getitem__(self, key):
#         return self.data[key]
# 
#     def keys(self):
#         return self.data.keys()
# 
#     @property
#     def attributes(self):
#         print('attributes will be removed. Use attrs instead!')
#         return self.attrs
# 
# Path: tests/qctests/compare.py
# def compare_feature_input_types(feature, *args, **kwargs):
#     compare_feature_tuple(feature, *args, **kwargs)
#     compare_feature_series(feature, *args, **kwargs)
#     compare_feature_dataarray(feature, *args, **kwargs)
# 
# def compare_input_types(Procedure, cfg):
#     compare_tuple(Procedure, cfg)
#     compare_pandas(Procedure, cfg)
#     compare_xarray(Procedure, cfg)


# Path: tests/qctests/test_qc_spike.py
import numpy as np
from numpy import ma
from cotede.qctests import Spike, spike
from ..data import DummyData
from .compare import compare_feature_input_types, compare_input_types

#!/usr/bin/env python
# -*- coding: utf-8 -*-

""" Verify Spike QC test
"""




def test_spike():
    """Basic test on feature spike
    """
    x = [1, -1, 2, 2, 3, 2, 4]
    y = spike(x)

    output = [np.nan, 2.0, 0.0, 0.0, 1.0, 1.0, np.nan]

    assert isinstance(y, np.ndarray)
    assert np.allclose(y, output, equal_nan=True)


def test_feature_input_types():
    x = np.array([1, -1, 2, 2, 3, 2, 4])

    compare_feature_input_types(spike, x)