Python AccumDict.iteritems方法代码示例

# 需要导入模块: from openquake.baselib.general import AccumDict [as 别名]
# 或者: from openquake.baselib.general.AccumDict import iteritems [as 别名]
def get_mesh_csvdata(csvfile, imts, num_values, validvalues):
    """
    Read CSV data in the format `IMT lon lat value1 ... valueN`.

    :param csvfile:
        a file or file-like object with the CSV data
    :param imts:
        a list of intensity measure types
    :param num_values:
        dictionary with the number of expected values per IMT
    :param validvalues:
        validation function for the values
    :returns:
        the mesh of points and the data as a dictionary
        imt -> array of curves for each site
    """
    number_of_values = dict(zip(imts, num_values))
    lon_lats = {imt: set() for imt in imts}
    data = AccumDict()  # imt -> list of arrays
    check_imt = valid.Choice(*imts)
    for line, row in enumerate(csv.reader(csvfile, delimiter=' '), 1):
        try:
            imt = check_imt(row[0])
            lon_lat = valid.longitude(row[1]), valid.latitude(row[2])
            if lon_lat in lon_lats[imt]:
                raise DuplicatedPoint(lon_lat)
            lon_lats[imt].add(lon_lat)
            values = validvalues(' '.join(row[3:]))
            if len(values) != number_of_values[imt]:
                raise ValueError('Found %d values, expected %d' %
                                 (len(values), number_of_values[imt]))
        except (ValueError, DuplicatedPoint) as err:
            raise err.__class__('%s: file %s, line %d' % (err, csvfile, line))
        data += {imt: [numpy.array(values)]}
    points = lon_lats.pop(imts[0])
    for other_imt, other_points in lon_lats.iteritems():
        if points != other_points:
            raise ValueError('Inconsistent locations between %s and %s' %
                             (imts[0], other_imt))
    lons, lats = zip(*sorted(points))
    mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
    return mesh, {imt: numpy.array(lst) for imt, lst in data.iteritems()}

# 需要导入模块: from openquake.baselib.general import AccumDict [as 别名]
# 或者: from openquake.baselib.general.AccumDict import iteritems [as 别名]
def calc_gmfs(oqparam, sitecol):
    """
    Build all the ground motion fields for the whole site collection
    """
    correl_model = get_correl_model(oqparam)
    rnd = random.Random()
    rnd.seed(getattr(oqparam, 'random_seed', 42))
    imts = get_imts(oqparam)
    gsim = get_gsim(oqparam)
    trunc_level = getattr(oqparam, 'truncation_level', None)
    n_gmfs = getattr(oqparam, 'number_of_ground_motion_fields', 1)
    rupture = get_rupture(oqparam)
    computer = gmf.GmfComputer(rupture, sitecol, imts, gsim, trunc_level,
                               correl_model)
    seeds = [rnd.randint(0, MAX_INT) for _ in xrange(n_gmfs)]
    res = AccumDict()  # imt -> gmf
    for seed in seeds:
        for imt, gmfield in computer.compute(seed):
            res += {imt: [gmfield]}
    # res[imt] is a matrix R x N
    return {imt: numpy.array(matrix).T for imt, matrix in res.iteritems()}

# 需要导入模块: from openquake.baselib.general import AccumDict [as 别名]
# 或者: from openquake.baselib.general.AccumDict import iteritems [as 别名]
def get_mesh_hcurves(oqparam):
    """
    Read CSV data in the format `lon lat, v1-vN, w1-wN, ...`.

    :param oqparam:
        an :class:`openquake.commonlib.oqvalidation.OqParam` instance
    :returns:
        the mesh of points and the data as a dictionary
        imt -> array of curves for each site
    """
    imtls = oqparam.imtls
    lon_lats = set()
    data = AccumDict()  # imt -> list of arrays
    ncols = len(imtls) + 1  # lon_lat + curve_per_imt ...
    csvfile = oqparam.inputs['hazard_curves']
    for line, row in enumerate(csv.reader(csvfile), 1):
        try:
            if len(row) != ncols:
                raise ValueError('Expected %d columns, found %d' %
                                 ncols, len(row))
            x, y = row[0].split()
            lon_lat = valid.longitude(x), valid.latitude(y)
            if lon_lat in lon_lats:
                raise DuplicatedPoint(lon_lat)
            lon_lats.add(lon_lat)
            for i, imt in enumerate(imtls, 1):
                values = valid.decreasing_probabilities(row[i])
                if len(values) != len(imtls[imt]):
                    raise ValueError('Found %d values, expected %d' %
                                     (len(values), len(imtls([imt]))))
                data += {imt: [numpy.array(values)]}
        except (ValueError, DuplicatedPoint) as err:
            raise err.__class__('%s: file %s, line %d' % (err, csvfile, line))
    lons, lats = zip(*sorted(lon_lats))
    mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
    return mesh, {imt: numpy.array(lst) for imt, lst in data.iteritems()}