Python numpy.ma.allclose() Examples

The following are 5 code examples of numpy.ma.allclose(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy.ma , or try the search function .
Example #1
Source File: mstats_basic.py    From lambda-packs with MIT License 6 votes vote down vote up 
def obrientransform(*args):
    """
    Computes a transform on input data (any number of columns).  Used to
    test for homogeneity of variance prior to running one-way stats.  Each
    array in ``*args`` is one level of a factor.  If an `f_oneway()` run on
    the transformed data and found significant, variances are unequal.   From
    Maxwell and Delaney, p.112.

    Returns: transformed data for use in an ANOVA
    """
    data = argstoarray(*args).T
    v = data.var(axis=0,ddof=1)
    m = data.mean(0)
    n = data.count(0).astype(float)
    # result = ((N-1.5)*N*(a-m)**2 - 0.5*v*(n-1))/((n-1)*(n-2))
    data -= m
    data **= 2
    data *= (n-1.5)*n
    data -= 0.5*v*(n-1)
    data /= (n-1.)*(n-2.)
    if not ma.allclose(v,data.mean(0)):
        raise ValueError("Lack of convergence in obrientransform.")

    return data
Example #2
Source File: mstats_basic.py    From Computable with MIT License 6 votes vote down vote up 
def obrientransform(*args):
    """
Computes a transform on input data (any number of columns).  Used to
test for homogeneity of variance prior to running one-way stats.  Each
array in *args is one level of a factor.  If an F_oneway() run on the
transformed data and found significant, variances are unequal.   From
Maxwell and Delaney, p.112.

Returns: transformed data for use in an ANOVA
    """
    data = argstoarray(*args).T
    v = data.var(axis=0,ddof=1)
    m = data.mean(0)
    n = data.count(0).astype(float)
    # result = ((N-1.5)*N*(a-m)**2 - 0.5*v*(n-1))/((n-1)*(n-2))
    data -= m
    data **= 2
    data *= (n-1.5)*n
    data -= 0.5*v*(n-1)
    data /= (n-1.)*(n-2.)
    if not ma.allclose(v,data.mean(0)):
        raise ValueError("Lack of convergence in obrientransform.")
    return data
Example #3
Source File: mstats_basic.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up 
def obrientransform(*args):
    """
    Computes a transform on input data (any number of columns).  Used to
    test for homogeneity of variance prior to running one-way stats.  Each
    array in ``*args`` is one level of a factor.  If an `f_oneway()` run on
    the transformed data and found significant, variances are unequal.   From
    Maxwell and Delaney, p.112.

    Returns: transformed data for use in an ANOVA
    """
    data = argstoarray(*args).T
    v = data.var(axis=0,ddof=1)
    m = data.mean(0)
    n = data.count(0).astype(float)
    # result = ((N-1.5)*N*(a-m)**2 - 0.5*v*(n-1))/((n-1)*(n-2))
    data -= m
    data **= 2
    data *= (n-1.5)*n
    data -= 0.5*v*(n-1)
    data /= (n-1.)*(n-2.)
    if not ma.allclose(v,data.mean(0)):
        raise ValueError("Lack of convergence in obrientransform.")

    return data
Example #4
Source File: mstats_basic.py    From Splunking-Crime with GNU Affero General Public License v3.0 6 votes vote down vote up 
def obrientransform(*args):
    """
    Computes a transform on input data (any number of columns).  Used to
    test for homogeneity of variance prior to running one-way stats.  Each
    array in ``*args`` is one level of a factor.  If an `f_oneway()` run on
    the transformed data and found significant, variances are unequal.   From
    Maxwell and Delaney, p.112.

    Returns: transformed data for use in an ANOVA
    """
    data = argstoarray(*args).T
    v = data.var(axis=0,ddof=1)
    m = data.mean(0)
    n = data.count(0).astype(float)
    # result = ((N-1.5)*N*(a-m)**2 - 0.5*v*(n-1))/((n-1)*(n-2))
    data -= m
    data **= 2
    data *= (n-1.5)*n
    data -= 0.5*v*(n-1)
    data /= (n-1.)*(n-2.)
    if not ma.allclose(v,data.mean(0)):
        raise ValueError("Lack of convergence in obrientransform.")

    return data
Example #5
Source File: test_mapchete.py    From mapchete with MIT License 5 votes vote down vote up 
def test_processing(mp_tmpdir, cleantopo_br, cleantopo_tl):
    """Test correct processing (read and write) outputs."""
    for cleantopo_process in [cleantopo_br.path, cleantopo_tl.path]:
        with mapchete.open(cleantopo_process) as mp:
            for zoom in range(6):
                tiles = []
                for tile in mp.get_process_tiles(zoom):
                    output = mp.execute(tile)
                    tiles.append((tile, output))
                    assert isinstance(output, ma.MaskedArray)
                    assert output.shape == output.shape
                    assert not ma.all(output.mask)
                    mp.write(tile, output)
                mosaic = create_mosaic(tiles)
                try:
                    temp_vrt = os.path.join(mp_tmpdir, str(zoom)+".vrt")
                    gdalbuildvrt = "gdalbuildvrt %s %s/%s/*/*.tif > /dev/null" % (
                        temp_vrt, mp.config.output.path, zoom)
                    os.system(gdalbuildvrt)
                    with rasterio.open(temp_vrt, "r") as testfile:
                        for file_item, mosaic_item in zip(
                            testfile.meta["transform"], mosaic.affine
                        ):
                            assert file_item == mosaic_item
                        band = testfile.read(1, masked=True)
                        assert band.shape == mosaic.data.shape
                        assert ma.allclose(band, mosaic.data)
                        assert ma.allclose(band.mask, mosaic.data.mask)
                finally:
                    shutil.rmtree(mp_tmpdir, ignore_errors=True)