Python numpy.ma方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def timer(s, v='', nloop=500, nrep=3):
    units = ["s", "ms", "µs", "ns"]
    scaling = [1, 1e3, 1e6, 1e9]
    print("%s : %-50s : " % (v, s), end=' ')
    varnames = ["%ss,nm%ss,%sl,nm%sl" % tuple(x*4) for x in 'xyz']
    setup = 'from __main__ import numpy, ma, %s' % ','.join(varnames)
    Timer = timeit.Timer(stmt=s, setup=setup)
    best = min(Timer.repeat(nrep, nloop)) / nloop
    if best > 0.0:
        order = min(-int(numpy.floor(numpy.log10(best)) // 3), 3)
    else:
        order = 3
    print("%d loops, best of %d: %.*g %s per loop" % (nloop, nrep,
                                                      3,
                                                      best * scaling[order],
                                                      units[order])) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def test_testPut(self):
        # Test of put
        with suppress_warnings() as sup:
            sup.filter(
                np.ma.core.MaskedArrayFutureWarning,
                "setting an item on a masked array which has a "
                "shared mask will not copy")
            d = arange(5)
            n = [0, 0, 0, 1, 1]
            m = make_mask(n)
            x = array(d, mask=m)
            self.assertTrue(x[3] is masked)
            self.assertTrue(x[4] is masked)
            x[[1, 4]] = [10, 40]
            self.assertTrue(x.mask is not m)
            self.assertTrue(x[3] is masked)
            self.assertTrue(x[4] is not masked)
            self.assertTrue(eq(x, [0, 10, 2, -1, 40]))

            x = array(d, mask=m)
            x.put([0, 1, 2], [-1, 100, 200])
            self.assertTrue(eq(x, [-1, 100, 200, 0, 0]))
            self.assertTrue(x[3] is masked)
            self.assertTrue(x[4] is masked) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def timer(s, v='', nloop=500, nrep=3):
    units = ["s", "ms", "µs", "ns"]
    scaling = [1, 1e3, 1e6, 1e9]
    print("%s : %-50s : " % (v, s), end=' ')
    varnames = ["%ss,nm%ss,%sl,nm%sl" % tuple(x*4) for x in 'xyz']
    setup = 'from __main__ import numpy, ma, %s' % ','.join(varnames)
    Timer = timeit.Timer(stmt=s, setup=setup)
    best = min(Timer.repeat(nrep, nloop)) / nloop
    if best > 0.0:
        order = min(-int(numpy.floor(numpy.log10(best)) // 3), 3)
    else:
        order = 3
    print("%d loops, best of %d: %.*g %s per loop" % (nloop, nrep,
                                                      3,
                                                      best * scaling[order],
                                                      units[order]))
#    ip.magic('timeit -n%i %s' % (nloop,s)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def poly_between(x, ylower, yupper):
    """
    Given a sequence of *x*, *ylower* and *yupper*, return the polygon
    that fills the regions between them.  *ylower* or *yupper* can be
    scalar or iterable.  If they are iterable, they must be equal in
    length to *x*.

    Return value is *x*, *y* arrays for use with
    :meth:`matplotlib.axes.Axes.fill`.
    """
    if ma.isMaskedArray(ylower) or ma.isMaskedArray(yupper) or ma.isMaskedArray(x):
        numpy = ma
    else:
        numpy = np

    Nx = len(x)
    if not cbook.iterable(ylower):
        ylower = ylower*numpy.ones(Nx)

    if not cbook.iterable(yupper):
        yupper = yupper*numpy.ones(Nx)

    x = numpy.concatenate( (x, x[::-1]) )
    y = numpy.concatenate( (yupper, ylower[::-1]) )
    return x,y 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def poly_between(x, ylower, yupper):
    """
    Given a sequence of *x*, *ylower* and *yupper*, return the polygon
    that fills the regions between them.  *ylower* or *yupper* can be
    scalar or iterable.  If they are iterable, they must be equal in
    length to *x*.

    Return value is *x*, *y* arrays for use with
    :meth:`matplotlib.axes.Axes.fill`.
    """
    if any(isinstance(var, np.ma.MaskedArray) for var in [ylower, yupper, x]):
        numpy = np.ma
    else:
        numpy = np

    Nx = len(x)
    if not cbook.iterable(ylower):
        ylower = ylower*numpy.ones(Nx)

    if not cbook.iterable(yupper):
        yupper = yupper*numpy.ones(Nx)

    x = numpy.concatenate((x, x[::-1]))
    y = numpy.concatenate((yupper, ylower[::-1]))
    return x, y 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def compare_functions_2v(func, nloop=500, test=True,
                       xs=xs, nmxs=nmxs,
                       ys=ys, nmys=nmys,
                       xl=xl, nmxl=nmxl,
                       yl=yl, nmyl=nmyl):
    funcname = func.__name__
    print("-"*50)
    print("%s on small arrays" % funcname)
    module, data = "numpy.ma", "nmxs,nmys"
    timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
    #
    print("%s on large arrays" % funcname)
    module, data = "numpy.ma", "nmxl,nmyl"
    timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
    return


###############################################################################


################################################################################ 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def mask_vals(f, maskdef, metakeys=_metakeys):
    mtype = maskdef.split(',')[0]
    mval = ','.join(maskdef.split(',')[1:])
    if mtype == 'where':
        maskexpr = 'np.ma.masked_where(mask, var[:].view(np.ndarray))'
        # mask = eval(mval, None, f.variables)
    else:
        maskexpr = 'np.ma.masked_%s(var[:], %s)' % (mtype, mval)
    for varkey, var in f.variables.items():
        if varkey not in metakeys:
            try:
                vout = eval(maskexpr)
                f.variables[varkey] = PseudoNetCDFMaskedVariable(
                    f, varkey, var.dtype.char, var.dimensions, values=vout,
                    **dict([(pk, getattr(var, pk)) for pk in var.ncattrs()]))
            except Exception as e:
                warn('Cannot mask %s: %s' % (varkey, str(e)))
    return f 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def test_testMixedArithmetic(self):
        na = np.array([1])
        ma = array([1])
        assert_(isinstance(na + ma, MaskedArray))
        assert_(isinstance(ma + na, MaskedArray)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def test_testUfuncRegression(self):
        f_invalid_ignore = [
            'sqrt', 'arctanh', 'arcsin', 'arccos',
            'arccosh', 'arctanh', 'log', 'log10', 'divide',
            'true_divide', 'floor_divide', 'remainder', 'fmod']
        for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
                  'sin', 'cos', 'tan',
                  'arcsin', 'arccos', 'arctan',
                  'sinh', 'cosh', 'tanh',
                  'arcsinh',
                  'arccosh',
                  'arctanh',
                  'absolute', 'fabs', 'negative',
                  'floor', 'ceil',
                  'logical_not',
                  'add', 'subtract', 'multiply',
                  'divide', 'true_divide', 'floor_divide',
                  'remainder', 'fmod', 'hypot', 'arctan2',
                  'equal', 'not_equal', 'less_equal', 'greater_equal',
                  'less', 'greater',
                  'logical_and', 'logical_or', 'logical_xor']:
            try:
                uf = getattr(umath, f)
            except AttributeError:
                uf = getattr(fromnumeric, f)
            mf = getattr(np.ma, f)
            args = self.d[:uf.nin]
            with np.errstate():
                if f in f_invalid_ignore:
                    np.seterr(invalid='ignore')
                if f in ['arctanh', 'log', 'log10']:
                    np.seterr(divide='ignore')
                ur = uf(*args)
                mr = mf(*args)
            assert_(eq(ur.filled(0), mr.filled(0), f))
            assert_(eqmask(ur.mask, mr.mask)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def compare_functions_1v(func, nloop=500,
                       xs=xs, nmxs=nmxs, xl=xl, nmxl=nmxl):
    funcname = func.__name__
    print("-"*50)
    print("%s on small arrays" % funcname)
    module, data = "numpy.ma", "nmxs"
    timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)

    print("%s on large arrays" % funcname)
    module, data = "numpy.ma", "nmxl"
    timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
    return 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def compare_methods(methodname, args, vars='x', nloop=500, test=True,
                    xs=xs, nmxs=nmxs, xl=xl, nmxl=nmxl):
    print("-"*50)
    print("%s on small arrays" % methodname)
    data, ver = "nm%ss" % vars, 'numpy.ma'
    timer("%(data)s.%(methodname)s(%(args)s)" % locals(), v=ver, nloop=nloop)

    print("%s on large arrays" % methodname)
    data, ver = "nm%sl" % vars, 'numpy.ma'
    timer("%(data)s.%(methodname)s(%(args)s)" % locals(), v=ver, nloop=nloop)
    return 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def _ensure_large_enough(self, arr, cont, N, newsize, frac_done):
        """Allocate new space while adding gran to data
        
        Helper for _add_gran_to_data, part of the read_period family of
        helpers.  Does NOT add cont to arr!"""
        
        if isinstance(cont, xarray.Dataset):
            raise NotImplementedError("Not used for xarray datasets. "
                "But see version history at "
                "https://arts.mi.uni-hamburg.de/trac/rt/browser/typhon/trunk/typhon/datasets/dataset.py?rev=10396#L462 "
                "if there is a wish to reimplemented!")
        else:
            if newsize * arr.itemsize > self.maxsize:
                raise MemoryError("This dataset is too large "
                    "for typhons little mind.  Continuing might "
                    "ultimately need {:,.0f} MiB of RAM.  This exceeds my "
                    "maximum (self.maxsize) of {:,.0f} MiB. "
                    "Sorry! ".format(
                        newsize*arr.itemsize/MiB,
                        self.maxsize/MiB))
            logger.debug(
                "New size ({:d} items, {:,.0f} MiB) would exceed allocated "
                "size ({:d} items, {:,.0f} MiB).  I'm {:.3%} "
                "through.  Allocating new: {:d} items, {:,.0f} "
                "MiB.  New size: {:d} items, {:,.0f} "
                "MiB.".format(N+cont.size,
                    (cont.nbytes+arr.nbytes)/MiB,
                    arr.size, arr.nbytes/MiB, frac_done,
                    newsize-arr.size, (newsize-arr.size)*arr.itemsize/MiB,
                    newsize, newsize*arr.itemsize/MiB))
            mod = (numpy.ma if hasattr(arr, "mask") else numpy)
            arr = mod.concatenate(
                (arr, mod.zeros(dtype=arr.dtype, shape=newsize-arr.size)))
        return arr 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def _add_pseudo_fields(self, M, pseudo_fields, extra, f):
        D = collections.OrderedDict()
        for (k, fnc) in pseudo_fields.items():
            try:
                D[k] = fnc(M, D, extra, f)
            except TypeError as exc:
                if "positional argument" in exc.args[0]:
                    # backward compatibility
                    D[k] = fnc(M)
                else:
                    raise
        if D != {}:
            if self.read_returns == "ndarray":
                mod = (numpy.ma if hasattr(M, "mask") else numpy)
                newM = mod.zeros(shape=M.shape,
                    dtype=M.dtype.descr + [(k, v.dtype, v.shape[1:]) for (k,v) in D.items()])
                for k in M.dtype.names:
                    newM[k] = M[k]
            elif self.read_returns == "xarray":
                newM = M
            else:
                raise ValueError("read_returns must be ndarray or xarray")

            for (k, v) in D.items():
                newM[k] = v
                
            M = newM
        return M 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ma [as 別名]
def _read(self, p, fields="all"):
        """Reads a single measurement converted to ndarray

        Arguments:

            p (pathlib.Path): path to file

            fields (Iterable[str] or str): What fields to return.
                See :func:`Dataset.read_period` for details.
        """

        (head, body) = self.read_single(p, fields=fields)

        dt = [(s+body.shape if len(s)==2 else (s[0], s[1], s[2]+body.shape))
                for s in body.dtype.descr]
        dt.extend([("lat", "f8"), ("lon", "f8"), ("time", "M8[s]")])
        dt.extend([(s, self._head_dtype[s])
                for s in (head.keys() & self._head_dtype.keys())
                if s not in {"lat", "lon", "time"}])
        if self.filename_fields:
            info = self.get_info_for_granule(p)
            dt.extend(self.filename_fields.items())
        D = numpy.ma.empty(1, dt)

        for nm in body.dtype.names:
            D[nm] = body[nm]

        for nm in {"lat", "lon", "time"}:
            D[nm] = head[nm]

        for nm in head.keys() & D.dtype.names:
            if nm not in {"lat", "lon", "time"}:
                D[nm] = head[nm]

        if self.filename_fields:
            for nm in self.filename_fields.keys():
                D[nm] = info[nm]

        return (D, {})