cdump2netcdf

get_thickness(xrash)

helper function for mass_loading

Source code in monetio/models/cdump2netcdf.py

def get_thickness(xrash):
    """
    helper function for mass_loading
    """
    alts = list(xrash.z.values)
    b = [0]
    alts2 = alts
    if alts[0] != 0:
        b.extend(alts)
        alts = b

    a2 = alts[1:]
    a3 = alts[0:-1]
    delta = np.array(a2) - np.array(a3)
    # if deposition layer then first layer has
    # thickness of 0.
    if alts2[0] == 0:
        b = [0]
        b.extend(delta)
        delta = b
    return np.array(delta)

makeconc(xrash, date1, level, mult=1, tr=True, verbose=False)

INPUTS xrash : xarray data-array date1 : datetime.datetime object level : list of level names RETURNS c1 : data array with concentration from multiple levels combined.

Source code in monetio/models/cdump2netcdf.py

def makeconc(xrash, date1, level, mult=1, tr=True, verbose=False):
    """
    INPUTS
    xrash : xarray data-array
    date1 : datetime.datetime object
    level : list of level names
    RETURNS
    c1 : data array with concentration from multiple levels combined.
    """
    if not level:
        c1 = mult * xrash.sel(time=date1)
    else:
        dhash = thickness_hash(xrash)
        tlist = []
        total_thickness = 0
        for lev in level:
            tlist.append(dhash[lev])
            total_thickness += dhash[lev]
        c1 = mult * xrash.sel(time=date1, z=level)
        if verbose:
            print("MAX BEFORE ", np.max(c1))
        print("length", len(level), tlist, dhash)
        c1 = mass_loading(c1, tlist)
        c1 = c1 / total_thickness
    if verbose:
        print("Max AFTER", np.max(c1))
    c1 = c1.expand_dims("time")
    # this line is for netcdf awips output
    if tr:
        c1 = c1.transpose("time", "ensemble", "y", "x")
    if verbose:
        print("C1", c1)
    if verbose:
        print(c1.shape)
    return c1

mass_loading(xrash, delta=None)

input data array with concentration.

return a data-array with mass loading through all the columns

Source code in monetio/models/cdump2netcdf.py

def mass_loading(xrash, delta=None):
    """
    # input data array with concentration.
    # return a data-array with mass loading through all the columns
    """
    xrash = remove_dep(xrash)
    if not delta:
        delta = get_thickness(xrash)
    else:
        delta = np.array(delta)
    iii = 1
    if delta[0] == 0:
        iii = 1
        start = 1
    else:
        iii = 0
        start = 0
    for yyy in np.arange(start, len(delta)):
        if iii == start:
            ml = xrash.isel(z=yyy) * delta[yyy]
        else:
            ml2 = xrash.isel(z=yyy) * delta[yyy]
            # import matplotlib.pyplot as plt
            # plt.pcolormesh(ml2)
            # plt.show()
            ml = ml + ml2
        iii += 1
    return ml

remove_dep(xrash)

remove the deposition layer. helper function for mass_loading

Source code in monetio/models/cdump2netcdf.py

def remove_dep(xrash):
    """
    remove the deposition layer.
    helper function for mass_loading
    """
    vals = xrash.z.values
    if vals[0] == 0:
        vals = vals[1:]
    x2 = xrash.sel(z=vals)
    return x2