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diagnostics_1D.py
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diagnostics_1D.py
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import numpy as np
import cffi
from setup import params
import libcloudphxx as libcl
import math
import pdb
ffi = cffi.FFI()
flib = ffi.dlopen('KiD_1D.so')
# Fortran functions (_sp_ means single precision)
ffi.cdef("void __diagnostics_MOD_save_dg_1d_sp_c(float*, int, int, char*, int, char*, int, int );")
# field, nx, nz, name, namelen, units, unitslen, itime
ffi.cdef("void __diagnostics_MOD_save_bindata_sp_c(float*, int, char*, int, char*, int );")
# field, nb, name, namelen, units, unitslen
ffi.cdef("void __diagnostics_MOD_save_dg_1d_bin_sp_c(float*, int, int, int, char*, int, char*, int, int );")
# field, nb, nx, nz, name, namelen,units, unitslen, itime
ffi.cdef("void __diagnostics_MOD_save_dg_scalar_sp_c(float, char*, int, char*, int, int );")
# scalar, name, namelen, units, unitslen, itime
# call save_dg(time, 'time', i_dgtime, units='s',dim='time')
def save_helper(arr):
# astype() takes keywords arguments for newer numpy versions (1.7?)
try:
arr = arr.astype(np.float32, copy=False)
except TypeError:
arr = arr.astype(np.float32)
arr_ptr = ffi.cast("float*", arr.__array_interface__['data'][0])
return arr, arr_ptr
# # remove the subterrenean level z=0 from output
# print arr.ndim, arr
# if (arr.ndim == 2):
# arr_wo_subterr = np.delete(arr,0,1)
# print arr_wo_subterr
# arr_ptr = ffi.cast("float*", arr_wo_subterr.__array_interface__['data'][0])
# return arr_wo_subterr, arr_ptr
# else:
# arr_ptr = ffi.cast("float*", arr.__array_interface__['data'][0])
# return arr, arr_ptr
def save_dg_scalar(scal, it, name, units):
# scal = scal.astype(np.float32)
# scal_cast = ffi.cast("float", scal)
flib.__diagnostics_MOD_save_dg_scalar_sp_c(
scal,
name.encode('ascii'), len(name),
units.encode('ascii'), len(units),
it
)
def save_dg(arr, it, name, units):
arr, arr_ptr = save_helper(arr)
if (arr.ndim == 2):
flib.__diagnostics_MOD_save_dg_1d_sp_c(
arr_ptr, arr.shape[0], arr.shape[1],
name.encode('ascii'), len(name),
units.encode('ascii'), len(units),
it
)
elif (arr.ndim == 3):
flib.__diagnostics_MOD_save_dg_1d_bin_sp_c(
arr_ptr, arr.shape[0], arr.shape[1], arr.shape[2],
name.encode('ascii'), len(name),
units.encode('ascii'), len(units),
it
)
else:
assert(False)
def save_bindata(arr, name, unit):
assert(arr.ndim == 1)
arr, arr_ptr = save_helper(arr)
flib.__diagnostics_MOD_save_bindata_sp_c(
arr_ptr, arr.shape[0],
name.encode('ascii'), len(name),
unit.encode('ascii'), len(unit)
)
def diagnostics(particles, arrays, prev_val, it, size_x, size_z, first_timestep):
# super-droplet concentration per grid cell
# particles.diag_all()
# particles.diag_sd_conc()
# save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z), it, "number_of_SDs", "1")
# recording puddle
particles.diag_all()
puddle = particles.diag_puddle();
#save_dg_scalar(puddle[8] * (4./3) * math.pi, it, "accumulated surface precipitation volume", "m^3")
new_val = puddle[8] * 1000
save_dg_scalar((new_val - prev_val["acc_surf_precip"]) / 10, it, "surface precipitation rate", "mm/s") # hardcoded dt_output = 10s
prev_val["acc_surf_precip"] = new_val
if first_timestep:
# temporary arrays (allocating only once)
arrays["tmp_xz"] = np.empty((size_x-2, size_z))
arrays["mom_0"] = np.empty((params["n_bins"], size_x-2, size_z))
arrays["mom_3"] = np.empty((params["n_bins"], size_x-2, size_z))
# upper diameter of a bin with values set using mass-doubling scheme
arrays["bins_D_upper"] = (2**np.arange(params["n_bins"]))**(1./3) * params["bin0_D_upper"]
save_bindata(arrays["bins_D_upper"] * 1e6, "bins_D_upper", "microns")
save_bindata((arrays["bins_D_upper"]/2)**3 * libcl.common.rho_w * (4./3) * math.pi, "bins_mass_upper", "kg")
save_bindata(np.diff(np.concatenate([np.zeros(1), arrays["bins_D_upper"]])) * 1e6, "dD", "microns")
# inferring rain water range as all bigger than cloud water
params["bins_qr_r20um"] = np.arange(params["bins_qc_r20um"][-1]+1, params["n_bins"])
#params["bins_qr_r32um"] = np.arange(params["bins_qc_r32um"][-1]+1, params["n_bins"])
# T according to the formula used within the library
#for i in range(0, size_x-2):
# for j in range(0, size_z):
# arrays["tmp_xz"][i,j] = libcl.common.T(arrays["thetad"][i,j], arrays["rhod"][j])
#particles.diag_all()
#particles.diag_temperature()
#save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z), it, "T_lib_post_cond", "K")
# save_dg(arrays["tmp_xz"], it, "T_lib_post_cond", "K")
#save_dg(arrays["T_lib_ante_cond"], it, "T_lib_ante_cond", "K")
# RH from the library pre cond
#save_dg(arrays["RH_lib_ante_cond"], it, "RH_lib_ante_cond", "%")
# RH from the library pre cond
#particles.diag_all()
#particles.diag_RH()
#save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * 100, it, "RH_lib_post_cond", "%")
# RH from the library formula with rv post cond and T pre cond (should be the same as RH saved in KiD)
#for i in range(0, size_x-2):
# for j in range(0, size_z):
# arrays["tmp_xz"][i,j] = arrays["rhod"][j] * arrays["qv"][i,j] * libcl.common.R_v * arrays["T_lib_ante_cond"][i,j] / libcl.common.p_vs(arrays["T_lib_ante_cond"][i,j]) * 100
#save_dg(arrays["tmp_xz"], it, "RH_lib_formula_TAnteCond_Rvpostcond", "K")
#save_dg(arrays["qv"], it, "qv_for_RH_lib_formula_TAnteCond_Rvpostcond_calc", "")
# saturation vapour pressure from the library formula ante cond
#save_dg(arrays["psat_lib_formula_ante_cond"], it, "psat_lib_formula_ante_cond", "")
# pressure from the lib
#particles.diag_all()
#particles.diag_pressure()
#save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * 100, it, "pressure_lib_post_cond", "%")
#save_dg(arrays["pressure_lib_ante_cond"], it, "pressure_lib_ante_cond", "%")
# aerosol concentration
#assert params["bins_qc_r20um"][0] == params["bins_qc_r32um"][0]
#particles.diag_wet_rng(0, arrays["bins_D_upper"][params["bins_qc_r20um"][0]] / 2)
#particles.diag_wet_mom(0)
#save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z), it, "aerosol_number", "/kg")
#particles.diag_all()
#particles.diag_wet_mom(0)
#liquid_drops_number = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z)
#save_dg(liquid_drops_number, it, "liquid_drops_number", "/kg")
#particles.diag_all()
#particles.diag_wet_mom(1)
#save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * 1e6 / liquid_drops_number, it, "liquid_drops_mean_r", "um")
particles.diag_all()
particles.diag_wet_mom(3)
liquid_drops_mass = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi
#save_dg(liquid_drops_mass, it, "liquid_drops_mass", "kg/kg")
save_dg_scalar(np.sum(liquid_drops_mass)*25, it, "liquid water path", "kg/m^2") # WARNING: hardcoded dz = 25...
particles.diag_all()
particles.diag_accr20()
accr20 = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi
new_val = accr20.sum()
save_dg_scalar((new_val - prev_val["accr20"]) / 10, it, "total accretion rate r20", "kg/s") #WARNING: hardcoded diag output = 10s
prev_val["accr20"] = new_val
particles.diag_all()
particles.diag_accr25()
accr25 = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi
new_val = accr25.sum()
save_dg_scalar((new_val - prev_val["accr25"]) / 10, it, "total accretion rate r25", "kg/s") #WARNING: hardcoded diag output = 10s
prev_val["accr25"] = new_val
particles.diag_all()
particles.diag_accr32()
accr32 = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi
new_val = accr32.sum()
save_dg_scalar((new_val - prev_val["accr32"]) / 10, it, "total accretion rate r32", "kg/s") #WARNING: hardcoded diag output = 10s
prev_val["accr32"] = new_val
particles.diag_all()
particles.diag_acnv20()
acnv20 = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi
new_val = acnv20.sum()
save_dg_scalar((new_val - prev_val["acnv20"]) / 10, it, "total autoconversion rate r20", "kg/s") #WARNING: hardcoded diag output = 10s
prev_val["acnv20"] = new_val
particles.diag_all()
particles.diag_acnv25()
acnv25 = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi
new_val = acnv25.sum()
save_dg_scalar((new_val - prev_val["acnv25"]) / 10, it, "total autoconversion rate r25", "kg/s") #WARNING: hardcoded diag output = 10s
prev_val["acnv25"] = new_val
particles.diag_all()
particles.diag_acnv32()
acnv32 = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi
new_val = acnv32.sum()
save_dg_scalar((new_val - prev_val["acnv32"]) / 10, it, "total autoconversion rate r32", "kg/s") #WARNING: hardcoded diag output = 10s
prev_val["acnv32"] = new_val
particles.diag_all()
particles.diag_revp20()
revp20 = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi
new_val = revp20.sum()
save_dg_scalar((new_val - prev_val["revp20"]) / 10, it, "total rain evaporation rate r20", "kg/s") #WARNING: hardcoded diag output = 10s
prev_val["revp20"] = new_val
particles.diag_all()
particles.diag_revp25()
revp25 = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi
new_val = revp25.sum()
save_dg_scalar((new_val - prev_val["revp25"]) / 10, it, "total rain evaporation rate r25", "kg/s") #WARNING: hardcoded diag output = 10s
prev_val["revp25"] = new_val
particles.diag_all()
particles.diag_revp32()
revp32 = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi
new_val = revp32.sum()
save_dg_scalar((new_val - prev_val["revp32"]) / 10, it, "total rain evaporation rate r32", "kg/s") #WARNING: hardcoded diag output = 10s
prev_val["revp32"] = new_val
# particles.diag_all()
# particles.diag_accr32()
# save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi, it, "accumulated_accr32_mass", "kg")
#
# particles.diag_all()
# particles.diag_acnv20()
# save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi, it, "accumulated_acnv20_mass", "kg")
#
# particles.diag_all()
# particles.diag_acnv32()
# save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi, it, "accumulated_acnv32_mass", "kg")
# binned wet spectrum
r_min = 0.
for i in range(params["n_bins"]):
# selecting range
r_max = arrays["bins_D_upper"][i] / 2
particles.diag_wet_rng(r_min, r_max)
r_min = r_max
# computing 1-st moment
particles.diag_wet_mom(0)
arrays["mom_0"][i,:,:] = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z)
# computing 3-rd moment
particles.diag_wet_mom(3)
arrays["mom_3"][i,:,:] = np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z)
# saving binned concentrations
save_dg(arrays["mom_0"], it, "cloud_bin_number", "/kg")
# saving summed concentrations
#save_dg(np.sum(arrays["mom_0"][params["bins_qc_r20um"],:,:], axis=0), it, "cloud_number_r20um", "/kg")
#save_dg(np.sum(arrays["mom_0"][params["bins_qc_r32um"],:,:], axis=0), it, "cloud_number_r32um", "/kg")
#save_dg(np.sum(arrays["mom_0"][params["bins_qr_r20um"],:,:], axis=0), it, "rain_number_r20um", "/kg")
#save_dg(np.sum(arrays["mom_0"][params["bins_qr_r32um"],:,:], axis=0), it, "rain_number_r32um", "/kg")
# saving binned masses
arrays["mom_3"] *= libcl.common.rho_w * (4./3) * math.pi
save_dg(arrays["mom_3"], it, "cloud_bin_mass", "kg/kg")
# saving summed masses
#save_dg(np.sum(arrays["mom_3"][params["bins_qc_r20um"],:,:], axis=0), it, "cloud_mass_r20um", "kg/kg")
#save_dg(np.sum(arrays["mom_3"][params["bins_qc_r32um"],:,:], axis=0), it, "cloud_mass_r32um", "kg/kg")
#save_dg(np.sum(arrays["mom_3"][params["bins_qr_r20um"],:,:], axis=0), it, "rain_mass_r20um", "kg/kg")
#save_dg(np.sum(arrays["mom_3"][params["bins_qr_r32um"],:,:], axis=0), it, "rain_mass_r32um", "kg/kg")
#save_dg_scalar(np.sum(arrays["mom_3"][params["bins_qr_r20um"],:,:]) * 25, it, "rain water path r20 bins", "kg/kg")
#save_dg_scalar(np.sum(arrays["mom_3"][params["bins_qr_r25um"],:,:]) * 25, it, "rain water path r25", "kg/kg")
#save_dg_scalar(np.sum(arrays["mom_3"][params["bins_qr_r32um"],:,:]) * 25, it, "rain water path r32", "kg/kg")
particles.diag_wet_rng(20e-6, 1)
particles.diag_wet_mom(3)
save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z), it, "rain_water_r20", "kg/kg")
save_dg_scalar(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z).sum() * libcl.common.rho_w * (4./3) * math.pi * 25, it, "rain_water_path_r20", "kg/m^2")
particles.diag_wet_rng(25e-6, 1)
particles.diag_wet_mom(3)
save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi, it, "rain_water_r25", "kg/kg")
save_dg_scalar(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z).sum() * libcl.common.rho_w * (4./3) * math.pi * 25, it, "rain_water_path_r25", "kg/m^2")
particles.diag_wet_rng(32e-6, 1)
particles.diag_wet_mom(3)
save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi, it, "rain_water_r32", "kg/kg")
save_dg_scalar(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z).sum() * libcl.common.rho_w * (4./3) * math.pi * 25, it, "rain_water_path_r32", "kg/m^2")
particles.diag_wet_rng(0.5e-6, 20e-6)
particles.diag_wet_mom(3)
save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi, it, "cloud_water_r20", "kg/kg")
particles.diag_wet_rng(0.5e-6, 25e-6)
particles.diag_wet_mom(3)
save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi, it, "cloud_water_r25", "kg/kg")
particles.diag_wet_rng(0.5e-6, 32e-6)
particles.diag_wet_mom(3)
save_dg(np.frombuffer(particles.outbuf()).copy().reshape(size_x-2, size_z) * libcl.common.rho_w * (4./3) * math.pi, it, "cloud_water_r32", "kg/kg")
# binned dry spectrum? - TODO
# ...