def variance(
data,
binsize=300,
limit=1000,
inTime=True,
returnfield=False,
continuous=False,
power=False,
vertbin=5,
nbins=20,
**kwargs
):
"""
Compute boundary layer/aerosol layer depth by evaluating temporal variance
under the assumption that sufficient smoothing will result in variations
of boundary layer top being apparent.
"""
if data == "about":
return "110Variance"
field, time, height = u._ComputeFieldMeans(
data, binsize, inTime=inTime, continuous=continuous, vertbin=vertbin, power=power
)
# Now, to preserve shape, I am going to do a running calculation of STDev
field = runstd(field, nbins) ** 2
if returnfield:
return (field, time, height)
depth = u._MaxDepth(field, height, limit=limit)
return (depth, time)
def ipm(
data, limit=1000, binsize=300, inTime=True, eval_dist=20, continuous=False, returnfield=False, vertbin=5, **kwargs
):
"""
determine mixed layer/aerosol depth by determinng the maximum decrease
(this is not the second gradient method)
Known as the inflection point method (IPM)
note, if inTime is false, a time value is still required!!!
"""
if data == "about":
return "110Inflection Point Method"
bs, times, z = u._ComputeFieldMeans(
data, binsize, inTime=inTime, continuous=continuous, vertbin=vertbin, power=True
)
# Compute the gradient of the gradient'
# FIXME - do i want negative or positive gradients!?!!'
field = np.gradient(np.gradient(bs, eval_dist)[1], eval_dist)[1]
field[field >= 0.0] = np.NAN
field = np.log10(-1 * field)
if returnfield:
field[np.isnan(field)] = np.nanmin(field)
return (field, times, z)
field = field[:, 20:]
z = z[20:]
# We need to compute the max above 200m.
depth = u._MaxDepth(field, z, limit=limit)
return (depth, times)
