def create_new_vararray_core(cfg_set,var):
"""Create variable array of specific variable."""
if cfg_set["source_dict"][var]=="METADATA":
return
t1 = datetime.datetime.now()
if cfg_set["verbose"]: print(" ... new "+var+" array created in:")
filename = path_creator_vararr("orig",var,cfg_set)
vararr = np.zeros((cfg_set["n_integ"],)+cfg_set["xy_ext"])
## Get field of every time step (if map-function cannot be applied)
i = 0
t_delta = np.array(range(cfg_set["n_integ"]))*datetime.timedelta(minutes=cfg_set["timestep"])
if var == "TRT":
vararr = get_vararr_TRT_t0(cfg_set["t0"], cfg_set)
else:
for t_d in t_delta:
t_current = cfg_set["t0"] - cfg_set["time_change_factor"]*t_d
vararr_t = get_vararr_t(t_current, var, cfg_set)
vararr[i,:,:] = vararr_t[0,:,:]
i += 1
save_file(filename, data_arr=vararr,var_name=var,cfg_set=cfg_set)
if cfg_set["verbose"]: print(" "+filename)
## In case verification of displacements should be performed, also initialise skill-score array:
if cfg_set["verify_disp"]:
filename_verif = "%stmp/%s_%s_%s_verif.npy" % (cfg_set["root_path"],
cfg_set["verif_param"],str(cfg_set[cfg_set["verif_param"]]), var)
verif_array = np.zeros((1,len(cfg_set["scores_list"]),cfg_set["n_integ"]-1))-9999.
np.save(filename_verif, verif_array)
t2 = datetime.datetime.now()
if False: print(" Elapsed time for creation of variable %s: %s" % (var,str(t2-t1)))
def get_variable_combination(var, cfg_set, cfg_var, cfg_var_combi, file_type):
""" Read ingredients of channel/variable combination and return combination
as variable array (according to the respective csv config file)
"""
## Get channel combination and take simple difference:
if cfg_var_combi.loc[cfg_var_combi["VARIABLE"] == var,
"OPERATION"].values == "diff":
var_1_name = cfg_var_combi.loc[cfg_var_combi["VARIABLE"] == var,
"VARIABLE_1"].values[0]
vararr_1 = load_file(
path_creator_vararr(file_type, var_1_name, cfg_set), var_1_name)
var_2_name = cfg_var_combi.loc[cfg_var_combi["VARIABLE"] == var,
"VARIABLE_2"].values[0]
vararr_2 = load_file(
path_creator_vararr(file_type, var_2_name, cfg_set), var_2_name)
vararr_return = vararr_1 - vararr_2
## Get channel combination and make mixed summing-difference operation:
elif cfg_var_combi.loc[cfg_var_combi["VARIABLE"] == var,
"OPERATION"].values == "sum_2diff":
var_1_name = cfg_var_combi.loc[cfg_var_combi["VARIABLE"] == var,
"VARIABLE_1"].values[0]
vararr_1 = load_file(
path_creator_vararr(file_type, var_1_name, cfg_set), var_1_name)
var_2_name = cfg_var_combi.loc[cfg_var_combi["VARIABLE"] == var,
"VARIABLE_2"].values[0]
vararr_2 = load_file(
path_creator_vararr(file_type, var_2_name, cfg_set), var_2_name)
var_3_name = cfg_var_combi.loc[cfg_var_combi["VARIABLE"] == var,
"VARIABLE_3"].values[0]
vararr_3 = load_file(
path_creator_vararr(file_type, var_3_name, cfg_set), var_3_name)
vararr_return = vararr_1 + vararr_2 - 2 * vararr_3
elif cfg_var_combi.loc[cfg_var_combi["VARIABLE"] == var,
"OPERATION"].values == "none":
raise ValueError(
"Variable/Channel combination only implemented for operations which are not 'none'"
)
return (vararr_return)
def update_fields(cfg_set,verbose_time=False):
"""Update fields for next time step."""
print("\nUpdate fields for time %s..." % cfg_set["t0"].strftime("%d.%m.%Y %H:%M"))
t0_old = cfg_set["t0"] - cfg_set["time_change_factor"] * datetime.timedelta(minutes=cfg_set["timestep"])
if verbose_time: t1 = datetime.datetime.now()
## Load files of different variables
for var in cfg_set["var_list"]:
filename_new = path_creator_vararr("orig",var,cfg_set)
#filename_new = "%stmp/%s_%s_orig%s.%s" % (cfg_set["root_path"], cfg_set["t0"].strftime("%Y%m%d%H%M"), var,
# cfg_set["file_ext_verif"], cfg_set["save_type"])
filename_old = path_creator_vararr("orig",var,cfg_set,t0=t0_old.strftime("%Y%m%d%H%M"))
#filename_old = "%stmp/%s_%s_orig%s.%s" % (cfg_set["root_path"], t0_old.strftime("%Y%m%d%H%M"), var,
# cfg_set["file_ext_verif"], cfg_set["save_type"])
bool_new_hour = cfg_set["t0"].hour==t0_old.hour
#if var in ["Wind","Conv"] and bool_new_hour:
# vararr = get_vararr_t(cfg_set["t0"], var, cfg_set)
# #np.save(filename_new, vararr)
# save_file(filename_new, data_arr=vararr,
# var_name=var,cfg_set=cfg_set)
#else:
## Load old array, move fields back in time and drop oldest field
vararr = load_file(filename_old,var_name=var)
vararr[1:,:,:] = np.copy(vararr)[:-1,:,:]
## Get field for new time step and assign to newest position
vararr_t = get_vararr_t(cfg_set["t0"], var, cfg_set)
vararr[0,:,:] = vararr_t[0,:,:]
#np.save(filename_new, vararr)
save_file(filename_new, data_arr=vararr,
var_name=var,cfg_set=cfg_set)
if cfg_set["verbose"]: print(" ... "+var+" is updated")
if cfg_set["delete_prec"]:
os.remove(filename_old)
if cfg_set["verbose"]: print(" and old _orig file removed")
if verbose_time: print(" Update _orig files: "+str(datetime.datetime.now()-t1)+"\n")
## Update disparr fields and displace variables with initial flow field:
if verbose_time: t1 = datetime.datetime.now()
update_disparr_fields(cfg_set, t0_old)
if verbose_time: print(" Update _disparr file: "+str(datetime.datetime.now()-t1)+"\n")
if verbose_time: t1 = datetime.datetime.now()
print(" Displace fields to new time step %s..." % cfg_set["t0"].strftime("%d.%m.%Y %H:%M"))
displace_fields(cfg_set)
if verbose_time: print(" Displace fields: "+str(datetime.datetime.now()-t1)+"\n")
## Update disparr fields for residual movement and displace variables with
## residual movement flow field:
if cfg_set["resid_disp"]:
if verbose_time: t1 = datetime.datetime.now()
update_disparr_fields(cfg_set, t0_old, resid=True)
if verbose_time: print(" Update _disparr file (resid): "+str(datetime.datetime.now()-t1)+"\n")
if verbose_time: t1 = datetime.datetime.now()
print(" Displace fields (resid) to new time step %s..." % cfg_set["t0"].strftime("%d.%m.%Y %H:%M"))
displace_fields(cfg_set, resid=True)
if verbose_time: print(" Displace fields (resid): "+str(datetime.datetime.now()-t1)+"\n")
## Delete files associated with preceding time step:
if verbose_time: t1 = datetime.datetime.now()
if cfg_set["delete_prec"] and cfg_set["t0"]!=cfg_set["t0_orig"]:
for var in cfg_set["var_list"]:
print_extension = ""
filename_old = path_creator_vararr("disp",var,cfg_set,t0=t0_old.strftime("%Y%m%d%H%M"))
#filename_old = "%stmp/%s_%s_disp%s.%s" % (cfg_set["root_path"], t0_old.strftime("%Y%m%d%H%M"),
# var, cfg_set["file_ext_verif"], cfg_set["save_type"])
os.remove(filename_old)
if cfg_set["resid_disp"]:
fileext_suffix = "_resid" if not cfg_set["resid_disp_onestep"] else "_resid_combi"
filename_old_resid = path_creator_vararr("disp"+fileext_suffix,var,cfg_set,t0=t0_old.strftime("%Y%m%d%H%M"))
#filename_old_resid = "%stmp/%s_%s_disp%s%s.%s" % (cfg_set["root_path"], t0_old.strftime("%Y%m%d%H%M"),
# var, fileext_suffix, cfg_set["file_ext_verif"],
# cfg_set["save_type"])
os.remove(filename_old_resid)
print_extension = " (including %s)" % fileext_suffix
print(" and old _disp file removed"+print_extension)
if verbose_time: print(" Delete old files: "+str(datetime.datetime.now()-t1)+"\n")
def displace_specific_variable(var,cfg_set,print_options,UV_t0,Dx_sum,Dy_sum,
XYW_prev_0=None,XYW_prev_1=None,Vx=None,Vy=None):
"""Core function of displace_fields (variable specific displacement)"""
## Check that Vx and Vy are available if UV_t0 is None:
if UV_t0 is None and (Vx is None or Vy is None):
raise ValueError("If UV_t0 is None, the complete Vx and Vy arrays have to be provided.")
## Set config file entry for cfg_set["use_precalc_XYW"] to False if variable is TRT.
if var=="TRT": cfg_set["use_precalc_XYW"] = False
## Unpack printing and naming variables:
input_suffix = print_options[0]
output_suffix = print_options[1]
input_UV_suffix = print_options[2]
append_str = print_options[3]
## See whether precalculated XYW arrays are available:
precalc_XYW = False if (XYW_prev_0 is None or XYW_prev_1 is None) else True
if cfg_set["verbose"]: print(" ... "+var+" is displaced%s" % append_str)
filename = pth.path_creator_vararr(input_suffix,var,cfg_set)
#filename = "%stmp/%s_%s%s%s.%s" % (cfg_set["root_path"], cfg_set["t0"].strftime("%Y%m%d%H%M"), var,
# input_suffix, cfg_set["file_ext_verif"], cfg_set["save_type"])
vararr = iotmp.load_file(filename,var_name=var)
## Loop over time steps to create displaced array:
vararr_disp = np.copy(vararr)
## Displace every time-step of the variable
## Current time step: No displacement
## Last time step: Only displacement with current UV-field UV_t0
adv_method = st.advection.get_method(cfg_set["adv_method"])
if not precalc_XYW:
#if var=="TRT": UV_t0 = np.moveaxis(np.dstack((-Vx[0,:,:],-Vy[0,:,:])),2,0)
#vararr_disp[1,:,:], XYW_prev_ls = adv_method(vararr[1,:,:],UV_t0,1,
# return_XYW=True)
if var=="TRT":
UV_t0 = np.moveaxis(np.dstack((Vx[0,:,:],Vy[0,:,:])),2,0)
vararr_disp[1,:,:] = adv_method(vararr[1,:,:],UV_t0,1,return_XYW=False,foward_mapping=True)
else:
vararr_disp[1,:,:], XYW_prev_ls = adv_method(vararr[1,:,:],UV_t0,1,
return_XYW=True)
if cfg_set["use_precalc_XYW"]:
XYW_prev_0 = np.zeros((cfg_set["n_integ"],)+cfg_set["xy_ext"])*np.nan
XYW_prev_1 = np.zeros((cfg_set["n_integ"],)+cfg_set["xy_ext"])*np.nan
XYW_prev_0[0,:,:] = XYW_prev_ls[0]
XYW_prev_1[0,:,:] = XYW_prev_ls[1]
else: vararr_disp[1,:,:], = adv_method(vararr[1,:,:],UV_t0,1,
XYW_prev=[XYW_prev_0[0,:,:],XYW_prev_1[0,:,:]])
## For preceding time steps, also provide cumulatively summed displacement field
for i in range(2,cfg_set["n_integ"]):
if var=="TRT": UV_t0 = np.moveaxis(np.dstack((Vx[i-1,:,:],Vy[i-1,:,:])),2,0)
D_prev_arr = np.moveaxis(np.dstack((Dx_sum[i-2,:,:],Dy_sum[i-2,:,:])),2,0)
if not precalc_XYW:
if var=="TRT":
vararr_disp[i,:,:] = adv_method(vararr_disp[i-1,:,:],UV_t0,1,foward_mapping=True)
else:
vararr_disp[i,:,:], XYW_prev_ls = adv_method(vararr[i,:,:],UV_t0,1,D_prev=D_prev_arr,
return_XYW=True)
if cfg_set["use_precalc_XYW"]:
XYW_prev_0[i,:,:] = XYW_prev_ls[0]
XYW_prev_1[i,:,:] = XYW_prev_ls[1]
else: vararr_disp[i,:,:] = adv_method(vararr[i,:,:],UV_t0,1,D_prev=D_prev_arr,
XYW_prev=[XYW_prev_0[i,:,:],XYW_prev_1[i,:,:]])
## If first var has been processed, set precalc_XYW from False to True
#if cfg_set["use_precalc_XYW"]: precalc_XYW = True
## Save displaced variable:
filename = pth.path_creator_vararr(output_suffix,var,cfg_set)
#filename = "%stmp/%s_%s%s%s.%s" % (cfg_set["root_path"], cfg_set["t0"].strftime("%Y%m%d%H%M"), var,
# output_suffix, cfg_set["file_ext_verif"], cfg_set["save_type"])
#np.save(filename, vararr_disp)
iotmp.save_file(filename, data_arr=vararr_disp,var_name=var,cfg_set=cfg_set)
if cfg_set["verbose"]: print(" "+var+" displaced array is saved")
## In case verification of displacements should be performed:
if cfg_set["verify_disp"] and (cfg_set["resid_disp"]==resid):
## Do not calculate skill scores if residual movement is corrected
## but currently, the initial displacement was just calculated.
#if not (cfg_set["resid_disp"] and resid): break
sksc.calc_skill_scores(cfg_set,var,vararr_disp)
sksc.calc_statistics(cfg_set,var,vararr_disp)
## Retrun precalculated arrays:
if not precalc_XYW: return XYW_prev_0, XYW_prev_1
def calc_disparr(t_current, cfg_set, resid=False):
"""Get 2-dim displacement array for flow between timesteps t_current and t_current - n_past_frames*timestep.
Parameters
----------
t_current : datetime object
Current time for which to calculate displacement array.
cfg_set : dict
Basic variables defined in input_NOSTRADAMUS_ANN.py
resid : bool
Do displacement array creation for residual movement correction?
Default: False.
UV_inter : bool
If UV_inter is true, the calculated UV and sparsened UV vectors are returned as well.
See function check_create_disparray(t0, timestep, n_integ, root_path, t0_str, oflow_source, oflow_source_path)
"""
if resid:
## Read in current displaced oflow_source file:
filename = pth.path_creator_vararr("disp",cfg_set["oflow_source"],cfg_set)
#filename = "%stmp/%s_%s_disp%s.%s" % (cfg_set["root_path"], cfg_set["t0"].strftime("%Y%m%d%H%M"),
# cfg_set["oflow_source"], cfg_set["file_ext_verif"], cfg_set["save_type"])
t_diff = cfg_set["t0"] - t_current
t_diff_ind = int((t_diff.seconds/60)/cfg_set["timestep"])
#oflow_source_data = np.load(filename)[t_diff_ind:t_diff_ind+cfg_set["n_past_frames"]+1,:,:]
#oflow_source_data = np.load(filename)[t_diff_ind+cfg_set["n_past_frames_resid"]::-1,:,:][:cfg_set["n_past_frames"]+1]
oflow_source_data = iotmp.load_file(filename,cfg_set["oflow_source"])[t_diff_ind+cfg_set["n_past_frames_resid"]::-1,:,:][:cfg_set["n_past_frames"]+1]
if oflow_source_data.shape[0]==1:
UV = R = np.zeros((2,oflow_source_data.shape[1],oflow_source_data.shape[2]))
if not cfg_set["UV_inter"]: return UV, R
else: return UV, R, np.zeros(4)*np.nan, np.zeros(4)*np.nan
if np.all(np.array_equal(oflow_source_data[0,:,:],oflow_source_data[1,:,:])):
raise ValueError("Input data equal")
else:
## Read in current oflow_source file:
filenames, timestamps = pth.path_creator(t_current, cfg_set["oflow_source"], cfg_set["source_dict"][cfg_set["oflow_source"]], cfg_set)
ret = metranet.read_file(filenames[0], physic_value=True)
oflow_source_data = np.atleast_3d(ret.data)
for filename in filenames[1:]:
ret_d_t = metranet.read_file(filename, physic_value=True)
oflow_source_data_d_t = np.atleast_3d(ret_d_t.data)
oflow_source_data = np.append(oflow_source_data,oflow_source_data_d_t, axis=2)
oflow_source_data = np.moveaxis(oflow_source_data,2,0)
#oflow_source_data_masked = np.ma.masked_invalid(oflow_source_data)
#oflow_source_data_masked = np.ma.masked_where(oflow_source_data_masked==0,oflow_source_data_masked)
## Check whether there are non-nan entries:
if np.any(np.isnan(oflow_source_data).all(axis=(1,2))):
print(" *** Warning: Input oflow source field is all NAN!\n Returning NAN fields.***")
nan_arr = oflow_source_data[0,:,:]*np.nan
D = np.array([nan_arr,nan_arr])
UV = np.array([nan_arr,nan_arr])
UV_vec = []; UV_vec_sp = []
if not cfg_set["UV_inter"]:
return D, UV
else: return D, UV, UV_vec, UV_vec_sp
## Convert linear rainrates to logarithimc dBR units
if not cfg_set["oflow_source"]=="RZC":
raise NotImplementedError("So far displacement array retrieval only implemented for RZC")
else:
## Get threshold method:
if not resid:
R_thresh_meth = cfg_set["R_thresh_meth"]
R_threshold = cfg_set["R_threshold"]
else:
R_thresh_meth = cfg_set["R_thresh_meth_resid"]
R_threshold = cfg_set["R_threshold_resid"]
## Get threshold value:
if R_thresh_meth == "fix":
R_thresh = R_threshold
elif R_thresh_meth == "perc":
R_thresh = np.min([np.nanpercentile(oflow_source_data[0,:,:],R_threshold),
np.nanpercentile(oflow_source_data[1,:,:],R_threshold)])
else: raise ValueError("R_thresh_meth must either be set to 'fix' or 'perc'")
## Convert to dBR
dBR, dBRmin = st.utils.mmhr2dBR(oflow_source_data, R_thresh)
dBR[~np.isfinite(dBR)] = dBRmin
#R_thresh = cfg_set["R_threshold"]
## In case threshold is not exceeded, lower R_threshold by 20%
while (dBR==dBRmin).all():
if cfg_set["verbose"]: print(" *** Warning: Threshold not exceeded, "+
"lower R_threshold by 20% to "+str(R_thresh*0.8)+" ***")
R_thresh = R_thresh*0.8
dBR, dBRmin = st.utils.mmhr2dBR(oflow_source_data, R_thresh)
dBR[~np.isfinite(dBR)] = dBRmin
## For correction of residuals original mm/h values are used:
if resid:
oflow_source_data_min = oflow_source_data
oflow_source_data_min[oflow_source_data_min<=R_thresh] = R_thresh
oflow_source_data_min[~np.isfinite(oflow_source_data_min)] = R_thresh
## Calculate UV field
oflow_method = st.optflow.get_method(cfg_set["oflow_method_name"])
if not resid:
UV, UV_vec, UV_vec_sp = oflow_method(dBR,return_single_vec=True,return_declust_vec=True)
else:
UV, UV_vec, UV_vec_sp = oflow_method(oflow_source_data_min,min_distance_ST=2,winsize_LK5=(120,20),quality_level_ST=0.05,
max_speed=20,nr_IQR_outlier=5,k=30,
decl_grid=cfg_set["decl_grid_resid"],function=cfg_set["inter_fun_resid"],
epsilon=cfg_set["epsilon_resid"],#factor_median=.2,
return_single_vec=True,return_declust_vec=True,
zero_interpol=cfg_set["zero_interpol"])
#UV, UV_vec, UV_vec_sp = oflow_method(oflow_source_data_min,min_distance_ST=2,block_size_ST=15,winsize_LK5=(120,20),quality_level_ST=0.05,
# max_speed=20,nr_IQR_outlier=5,decl_grid=20,function="inverse",k=20,factor_median=0.05,
# return_single_vec=True,return_declust_vec=True,zero_interpol=True)
#UV, UV_vec, UV_vec_sp = oflow_method(oflow_source_data_min,min_distance_ST=2,block_size_ST=15,winsize_LK5=(120,20),quality_level_ST=0.05,
# max_speed=20,nr_IQR_outlier=5,decl_grid=20,function="nearest",k=20,factor_median=.2,
# return_single_vec=True,return_declust_vec=True)
## In case no motion vectors were detected, lower R_threshold by 20%
if np.any(~np.isfinite(UV)):
dBR_orig = dBR
n_rep = 0
while np.any(~np.isfinite(UV)):
if cfg_set["verbose"]:
print(" *** Warning: No motion vectors detected, lower R_threshold by 30% to "+str(R_thresh*0.7)+" ***")
R_thresh = R_thresh*0.7
dBR, dBRmin = st.utils.mmhr2dBR(oflow_source_data, R_thresh)
dBR[~np.isfinite(dBR)] = dBRmin
if resid:
oflow_source_data_min = oflow_source_data
oflow_source_data[oflow_source_data<=R_thresh] = R_thresh
oflow_source_data[~np.isfinite(oflow_source_data)] = R_thresh
if not resid:
UV, UV_vec, UV_vec_sp = oflow_method(dBR,return_single_vec=True,return_declust_vec=True)
else:
UV, UV_vec, UV_vec_sp = oflow_method(oflow_source_data_min,min_distance_ST=2,winsize_LK5=(120,20),quality_level_ST=0.05,
max_speed=20,nr_IQR_outlier=5,k=30,
decl_grid=cfg_set["decl_grid_resid"],function=cfg_set["inter_fun_resid"],
epsilon=cfg_set["epsilon_resid"],#factor_median=.2,
return_single_vec=True,return_declust_vec=True,
zero_interpol=cfg_set["zero_interpol"])
#UV, UV_vec, UV_vec_sp = oflow_method(oflow_source_data_min,min_distance_ST=2,block_size_ST=15,winsize_LK5=(120,20),quality_level_ST=0.05,
# max_speed=20,nr_IQR_outlier=5,decl_grid=20,function="inverse",k=20,epsilon=10,#factor_median=0.05,
# return_single_vec=True,return_declust_vec=True,zero_interpol=True)
#UV, UV_vec, UV_vec_sp = oflow_method(oflow_source_data_min,min_distance_ST=2,block_size_ST=15,winsize_LK5=(120,20),quality_level_ST=0.05,
# max_speed=20,nr_IQR_outlier=5,decl_grid=20,function="nearest",k=20,factor_median=.2,
# return_single_vec=True,return_declust_vec=True)
n_rep += 1
if n_rep > 2:
UV = np.zeros((2,dBR.shape[1],dBR.shape[2]))
if cfg_set["verbose"]: print(" *** Warning: Return zero UV-array! ***")
break
## Invert direction of intermediate motion vectors
#if cfg_set["UV_inter"]:
# UV_vec[2:3,:,:] = -UV_vec[2:3,:,:]
# UV_vec_sp[2:3,:,:] = -UV_vec_sp[2:3,:,:]
"""
## Advect disp_test to get the advected test_array and the displacement array
adv_method = st.advection.get_method(cfg_set["adv_method"])
dBR_adv, D = adv_method(dBR[-1,:,:], UV, 1, return_displacement=True)
## convert the forecasted dBR to mmhr
if cfg_set["oflow_source"]=="RZC":
if cfg_set["R_thresh_meth"] == "fix":
R_tresh = cfg_set["R_threshold"]
elif cfg_set["R_thresh_meth"] == "perc":
R_tresh = np.min([np.nanpercentile(oflow_source_data[0,:,:],cfg_set["R_threshold"]),
np.nanpercentile(oflow_source_data[1,:,:],cfg_set["R_threshold"])])
else: raise NotImplementedError("R_thresh_meth must either be set to 'fix' or 'perc'")
oflow_source_data_forecast = st.utils.dBR2mmhr(dBR_adv, R_tresh)
## Print results:
if False:
calc_disparr_ctrl_plot(D,timestamps,oflow_source_data,oflow_source_data_forecast,cfg_set)
"""
#plt.imshow(D[0,:,:])
#plt.show()
#fig, axes = plt.subplots(nrows=1, ncols=2)
#fig1=axes[0].imshow(UV[0,:,:])
#fig.colorbar(fig1,ax=axes[0])#,orientation='horizontal')
#fig2=axes[1].imshow(UV[1,:,:])
#fig.colorbar(fig2,ax=axes[1])#,orientation='horizontal')
#fig.tight_layout()
#plt.show()
#sys.exit()
if np.all(UV==0): #np.any(~np.isfinite(UV)):
if cfg_set["instant_resid_corr"] and not resid:
print(" *** Warning: No residual movement correction performed ***")
D = UV.copy()
else:
adv_method = st.advection.get_method(cfg_set["adv_method"])
dBR_disp, D = adv_method(dBR[-2,:,:],UV,1,return_displacement=True,return_XYW=False)
if cfg_set["instant_resid_corr"] and not resid:
if cfg_set["verbose"]:
print(" Make instantaneous residual movement correction")
## Advect second last observation to t0:
dBR_disp[~np.isfinite(dBR_disp)] = dBRmin
## Convert dBR values of t0 and second last time step to mm/h:
RZC_resid_fields = np.stack([st.utils.dBR2mmhr(dBR_disp[0,:,:], R_thresh),
st.utils.dBR2mmhr(dBR[-1,:,:], R_thresh)])
#plt.imshow(RZC_resid_fields[0,:,:]); plt.title("RZC_resid_fields[0,:,:]"); plt.show()
#plt.imshow(RZC_resid_fields[1,:,:]); plt.title("RZC_resid_fields[1,:,:]"); plt.show()
## Get residual displacement field
UV_resid = oflow_method(RZC_resid_fields,min_distance_ST=2,
winsize_LK5=(120,20),quality_level_ST=0.05,
max_speed=20,nr_IQR_outlier=5,k=30,
decl_grid=cfg_set["decl_grid_resid"],function=cfg_set["inter_fun_resid"],
epsilon=cfg_set["epsilon_resid"],#factor_median=.2,
zero_interpol=cfg_set["zero_interpol"])
## Add UV_resid to original UV array
n_rep = 0
while np.any(~np.isfinite(UV_resid)):
print(" No UV_resid field found")
R_thresh *= 0.7
RZC_resid_fields = np.stack([st.utils.dBR2mmhr(dBR_disp[0,:,:], R_thresh),
st.utils.dBR2mmhr(dBR[-1,:,:], R_thresh)])
UV_resid = oflow_method(RZC_resid_fields,min_distance_ST=2,
winsize_LK5=(120,20),quality_level_ST=0.05,
max_speed=20,nr_IQR_outlier=5,k=30,
decl_grid=cfg_set["decl_grid_resid"],function=cfg_set["inter_fun_resid"],
epsilon=cfg_set["epsilon_resid"],#factor_median=.2,
zero_interpol=cfg_set["zero_interpol"])
n_rep += 1
if n_rep > 2:
UV_resid = np.zeros((2,dBR.shape[1],dBR.shape[2]))
#if cfg_set["verbose"]:
print(" *** Warning: Return zero UV_resid array! ***")
break
UV += UV_resid
## Displace with UV_resid field to get D_resid and add to D array:
dBR_disp_disp, D = adv_method(RZC_resid_fields[0,:,:],UV,1,
return_displacement=True,return_XYW=False)
#D += D_resid
if not cfg_set["UV_inter"]:
return D, UV
else: return D, UV, UV_vec, UV_vec_sp
def calculate_statistics_pixcount(var, cfg_set, cfg_var, cfg_var_combi,
file_type, xr_stat_pixcount, dtype_pixc,
fill_value_pc):
""" Function reading the actual statistics and pixel counts for variable 'var'.
Parameters
----------
file_type : string
String specifying which type of non-displaced (orig) or displaced (disp & resid or resid_combi) to be read
xr_stat_pixcount : xarray object
Object where into the statistics and pixels counts are written (with the information on the TRT cells
already written into it)
dtype_pixc : numpy.dtype object
Data type of pixel count (if domain is small enough, a lower precision uint dtype can be chosen)
fill_value_pc : int
Fill value in case no pixels can be counted (e.g. in case no NaN pixels are within domain)
"""
if var in cfg_set["var_list"]:
## Filter out auxiliary variables which are appended later (due to higher
## efficiency when creating the training dataset):
if cfg_set["source_dict"][var] == "METADATA" and var not in [
"U_OFLOW", "V_OFLOW"
]:
return
## Change setting of file type for U_OFLOW and V_OFLOW variable:
elif var in ["U_OFLOW", "V_OFLOW"]:
var_name = "Vx" if var == "U_OFLOW" else "Vy"
if file_type == "orig":
file_type_UV = "standard"
else:
file_type_UV = "resid" if file_type == "disp_resid" else "resid_combi"
vararr = load_file(path_creator_UV_disparr(file_type_UV, cfg_set),
var_name)
else:
vararr = load_file(path_creator_vararr(file_type, var, cfg_set),
var)
min_val = cfg_set["minval_dict"][var]
elif var in cfg_set["var_combi_list"]:
## Get variable combination:
vararr = get_variable_combination(var, cfg_set, cfg_var, cfg_var_combi,
file_type)
min_val = np.nan
if cfg_set["verbose"]: print(" read statistics for " + var)
## Fill nan-values in COSMO_CONV fields:
if np.any(np.isnan(vararr)) and \
cfg_var.loc[cfg_var["VARIABLE"]==var,"SOURCE"].values=="COSMO_CONV":
t1_inter = datetime.datetime.now()
vararr = interpolate_COSMO_fields(vararr, method="KDTree")
t2_inter = datetime.datetime.now()
if var == "RELHUM_85000" and cfg_set["verbose"]:
print(" Elapsed time for interpolating the data in %s: %s" %
(var, str(t2_inter - t1_inter)))
## Calculate local standard deviation of specific COSMO_CONV fields:
if cfg_var.loc[cfg_var["VARIABLE"] == var, "VARIABILITY"].values:
t1_std = datetime.datetime.now()
scharr = np.array([[-3 - 3j, 0 - 10j, +3 - 3j],
[-10 + 0j, 0 + 0j, +10 + 0j],
[-3 + 3j, 0 + 10j, +3 + 3j]])
#plt.imshow(vararr[2,:,:]); plt.show()
for t in range(vararr.shape[0]):
vararr[t, :, :] = np.absolute(
signal.convolve2d(vararr[t, :, :],
scharr,
boundary='symm',
mode='same'))
#plt.imshow(vararr[2,:,:]); plt.show()
t2_std = datetime.datetime.now()
if var == "POT_VORTIC_70000" and cfg_set["verbose"]:
print(
" Elapsed time for finding the local standard deviation in %s: %s"
% (var, str(t2_std - t1_std)))
## Smooth (COSMO) fields:
if cfg_var.loc[cfg_var["VARIABLE"] == var, "SMOOTH"].values:
t1_smooth = datetime.datetime.now()
#if var=="RELHUM_85000": plt.imshow(vararr[3,:,:]); plt.title(var); plt.pause(.5)
for t in range(vararr.shape[0]):
vararr[t, :, :] = ndimage.gaussian_filter(vararr[t, :, :],
cfg_set["smooth_sig"])
#if var=="RELHUM_85000": plt.imshow(vararr[3,:,:]); plt.title(var+" smooth"); plt.show() #pause(.5)
t2_smooth = datetime.datetime.now()
if var == "RELHUM_85000" and cfg_set["verbose"]:
print(" Elapsed time for smoothing the fields of %s: %s" %
(var, str(t2_smooth - t1_smooth)))
## Read in statistics and pixel counts / read in category counts:
t1_stat = datetime.datetime.now()
if var not in ["CMA", "CT"]:
## Read in values at indices:
vararr_sel = np.stack([vararr[time_point,:,:].flat[xr_stat_pixcount["TRT_domain_indices"].values[:,time_point,:]].astype(np.float32) \
for time_point in range(vararr.shape[0])])
vararr_sel = np.swapaxes(vararr_sel, 0, 1)
if np.any(xr_stat_pixcount["TRT_domain_indices"].values == 0):
vararr_sel[xr_stat_pixcount["TRT_domain_indices"].values ==
0] = np.nan
## Get count of nans and minimum values:
array_pixc = np.stack([
np.sum(np.isnan(vararr_sel), axis=2),
np.sum(vararr_sel <= min_val, axis=2)
],
axis=2)
xr_stat_pixcount[var + "_pixc"] = (('DATE_TRT_ID', 'time_delta',
'pixel_count'),
array_pixc.astype(np.uint16,
copy=False))
## Calculate the actual statistics:
perc_values = [0, 1, 5, 25, 50, 75, 95, 99, 100]
array_stat = np.array([
np.sum(vararr_sel, axis=2), #nansum
np.mean(vararr_sel, axis=2), #nanmean
np.std(vararr_sel, axis=2)
]) #nanstd
array_stat = np.moveaxis(
np.concatenate(
[array_stat,
np.percentile(vararr_sel, perc_values, axis=2)]), 0,
2) #nanpercentile
xr_stat_pixcount[var + "_stat"] = (('DATE_TRT_ID', 'time_delta',
'statistic'),
array_stat.astype(np.float32,
copy=False))
## Add specific statistics for Radar variables, only analysing values above minimum value:
if var not in cfg_set["var_combi_list"] and cfg_set["source_dict"][
var] == "RADAR":
vararr_sel[vararr_sel <= min_val] = np.nan
array_stat_nonmin = np.array([
np.nansum(vararr_sel, axis=2),
np.nanmean(vararr_sel, axis=2),
np.nanstd(vararr_sel, axis=2)
])
array_stat_nonmin = np.moveaxis(
np.concatenate([
array_stat_nonmin,
np.nanpercentile(vararr_sel, perc_values, axis=2)
]), 0, 2)
xr_stat_pixcount[var + "_stat_nonmin"] = (('DATE_TRT_ID',
'time_delta',
'statistic'),
array_stat_nonmin.astype(
np.float32,
copy=False))
else:
## Read in values at indices:
vararr_sel = vararr.flat[xr_stat_pixcount["TRT_domain_indices"].values]
## Get count different categories:
raise ImplementationError("Categorical counting not yet implemented")
t2_stat = datetime.datetime.now()
if var == "RELHUM_85000" and cfg_set["verbose"]:
print(" Elapsed time for calculating the statistics of %s: %s" %
(var, str(t2_stat - t1_stat)))
## Read number of pixels with max-echo value higher than 57dBZ
if var == "CZC":
xr_stat_pixcount[var +
"_lt57dBZ"] = (('DATE_TRT_ID', 'time_delta'),
np.sum(vararr_sel > 57.,
axis=2).astype(np.uint16,
copy=False))
def read_TRT_area_indices(cfg_set_input, reverse):
"""Function which reads the indices corresponding to the domain
of interest around the displaced TRT cell centres"""
## Change settings related calculating statistics from future or current observations:
cfg_set = cfg_set_input.copy()
cfg_set["future_disp_reverse"] = True if reverse else False
cfg_set["time_change_factor"] = -1 if reverse else 1
string_time = "future" if cfg_set["future_disp_reverse"] else "past"
print("Read %s indices of the domains of interest..." % string_time)
## Read TRT info dataframe:
filename = "%stmp/%s%s" % (cfg_set["root_path"],
cfg_set["t0"].strftime("%Y%m%d%H%M"),
"_TRT_df.pkl")
cell_info_df = pd.read_pickle(filename)
cell_info_df = cell_info_df.loc[
cell_info_df["RANKr"] >= cfg_set["min_TRT_rank_op"] * 10]
cell_info_df["Border_cell"] = False
## Correct date column (repetitions out of nowhere...)
if len(cell_info_df[["date"]].values[0][0]) > 12:
cell_info_df["date"] = np.array(
[date_i[:12] for date_i in cell_info_df["date"].values],
dtype=np.object)
## Read file with displaced TRT centres:
orig_disp_TRT = "disp" if cfg_set["displ_TRT_cellcent"] else "orig"
filename = path_creator_vararr(orig_disp_TRT, "TRT", cfg_set)
TRTarr = load_file(filename, var_name="TRT")
## Create Meshgrid or boolean array
if cfg_set["stat_sel_form"] == "square":
X = None
Y = None
elif cfg_set["stat_sel_form"] == "circle":
X, Y = np.meshgrid(np.arange(0, TRTarr.shape[2]),
np.arange(0, TRTarr.shape[1]))
else:
raise ValueError("stat_sel_form can only be 'square' or 'circle'")
## Create array to save indices in:
time_delta_coord = np.arange(cfg_set["n_integ"],
dtype=np.int16) * cfg_set["timestep"]
arr_index = np.zeros((cell_info_df.shape[0], len(time_delta_coord),
cfg_set["stat_sel_form_size"]),
dtype=np.uint32)
xarr_index_flat = xr.DataArray(
arr_index,
coords=[
np.array(cell_info_df.index.tolist(), dtype=np.object),
time_delta_coord, #time_dir_coord,
np.arange(cfg_set["stat_sel_form_size"], dtype=np.int32)
],
dims=['TRT_ID', 'time_delta', 'pixel_indices'], #'time_dir',
name="TRT_domain_indices")
xarr_index_ij = xr.DataArray(
np.zeros((cell_info_df.shape[0], len(time_delta_coord), 2),
dtype=np.uint16),
coords=[
np.array(cell_info_df.index.tolist(), dtype=np.object),
time_delta_coord,
np.array(['CHi', 'CHj'], dtype=np.object)
],
dims=['TRT_ID', 'time_delta', 'CHi_CHj'], #'time_dir',
name="TRT_cellcentre_indices")
## Save nc file showing domains around TRT cells for control:
if cfg_set["save_TRT_domain_map"]:
TRTarr_plot = TRTarr.copy()
TRTarr_plot[:, :, :] = int(-1)
## Loop over TRT-cells to read in the statistics:
for cell in cell_info_df.index.tolist():
ind_triple = zip(*np.where(TRTarr == int(cell[8:])))
if len(ind_triple) != cfg_set["n_integ"]:
if len(ind_triple) < cfg_set["n_integ"]:
print(
" *** Warning: TRT cell centre is moved out of domain ***"
)
if len(ind_triple) > cfg_set["n_integ"]:
raise ValueError(
"cell centres occurring more than once in the same time step"
)
border_cell = False
for cell_index in ind_triple:
## Append information on cell centres:
xarr_index_ij.loc[cell, cell_index[0] * cfg_set["timestep"], :] = [
cell_index[1], cell_index[2]
]
## Get indices of domain of specific TRT cell:
indices = get_indices_of_domain(cell_index[1], cell_index[2],
cfg_set, X, Y)[0]
## Check whether all pixels are within ccs4 domain:
if indices.shape == (
xarr_index_flat.loc[cell, cell_index[0] *
cfg_set["timestep"], :].values).shape:
xarr_index_flat.loc[cell, cell_index[0] *
cfg_set["timestep"], :] = indices
else:
if not border_cell:
print(
" *** Warning: Domain around TRT cell %s crosses observational domain ***"
% cell)
cell_info_df.loc[cell_info_df.index == cell,
"Border_cell"] = True
border_cell = True
xarr_index_flat.loc[cell, cell_index[0] * cfg_set["timestep"],
range(indices.shape[0])] = indices
if cfg_set["save_TRT_domain_map"]:
TRTarr_plot[cell_index[0], :, :].flat[xarr_index_flat.loc[
cell, cell_index[0] * cfg_set["timestep"], :].values] *= 0
#TRTarr_plot[cell_index[0],:,:].flat[xarr_index_flat.loc[cell,cell_index[0]*cfg_set["timestep"],:].values] += int(cell)
## Create Xarray file containing the TRT information and the domain-of-interest indices:
xr_ind_ds = xr.Dataset.from_dataframe(cell_info_df)
xr_ind_ds.rename({"index": "TRT_ID"}, inplace=True)
xr_ind_ds = xr.merge([xr_ind_ds, xarr_index_flat, xarr_index_ij])
## Rename ID_TRT to Date_TRT_ID, including date of reading:
xr_ind_ds.rename({"TRT_ID": "DATE_TRT_ID"}, inplace=True)
xr_ind_ds["DATE_TRT_ID"] = np.array([
cfg_set["t0"].strftime("%Y%m%d%H%M") + "_" + TRT_ID
for TRT_ID in xr_ind_ds["DATE_TRT_ID"].values
],
dtype=np.object)
## Save xarray object to temporary location:
disp_reverse_str = "" if not cfg_set["future_disp_reverse"] else "_rev"
filename = os.path.join(cfg_set["tmp_output_path"],"%s%s%s%s" % \
(cfg_set["t0_str"],"_stat_pixcount",disp_reverse_str,".pkl"))
with open(filename, "wb") as output_file:
pickle.dump(xr_ind_ds, output_file, protocol=-1)
## Save nc file with TRT domains to disk:
if cfg_set["save_TRT_domain_map"]:
TRTarr_plot += 1
TRTarr_plot = np.array(TRTarr_plot, dtype=np.float32)
filename = os.path.join(cfg_set["tmp_output_path"],"%s%s%s%s" % \
(cfg_set["t0_str"],"_TRT_disp_domain",disp_reverse_str,".nc"))
save_nc(filename,
TRTarr_plot,
"TRT",
np.float32,
"-",
"Domain around TRT cells",
cfg_set["t0"],
"",
dt=5)
def plot_displaced_fields_old(var,
cfg_set,
resid=False,
animation=False,
TRT_form=False):
"""Plot displaced fields next to original ones.
Parameters
----------
var : str
Variable which should be plotted
resid : bool
Do displacement array creation for residual movement correction?
Default: False.
"""
if not resid:
resid_str = ""
resid_suffix = "standard"
resid_suffix_plot = ""
resid_suffix_vec = "vec"
resid_dir = ""
else:
resid_str = " residual" if not cfg_set[
"resid_disp_onestep"] else " residual (combi)"
resid_suffix = "_resid" if not cfg_set[
"resid_disp_onestep"] else "_resid_combi"
resid_suffix_plot = "_resid" if not cfg_set[
"resid_disp_onestep"] else "_resid_combi"
resid_suffix_vec = "vec_resid" if not cfg_set[
"resid_disp_onestep"] else "vec"
resid_dir = "/_resid" if not cfg_set[
"resid_disp_onestep"] else "/_resid_combi"
#resid_str = "" if not resid else " residual"
#resid_suffix = "" if not resid else "_resid"
#resid_dir = "" if not resid else "/_resid"
print("Plot comparison of moving and displaced%s %s..." % (resid_str, var))
## Load files
if not resid or cfg_set["resid_disp_onestep"]:
filename_orig = path_creator_vararr("orig", var, cfg_set)
#filename_orig = "%stmp/%s_%s_orig%s.%s" % (cfg_set["root_path"], cfg_set["t0"].strftime("%Y%m%d%H%M"), var,
# cfg_set["file_ext_verif"], cfg_set["save_type"])
else:
filename_orig = path_creator_vararr("disp", var, cfg_set)
#filename_orig = "%stmp/%s_%s_disp%s.%s" % (cfg_set["root_path"], cfg_set["t0"].strftime("%Y%m%d%H%M"), var,
# cfg_set["file_ext_verif"], cfg_set["save_type"])
print(" File of original variable: %s" % filename_orig)
vararr = load_file(filename_orig, var_name=var)
resid_suffix_temp = "" if resid_suffix == "standard" else resid_suffix
filename_disp = path_creator_vararr("disp" + resid_suffix_temp, var,
cfg_set)
#filename_disp = "%stmp/%s_%s_disp%s%s.%s" % (cfg_set["root_path"], cfg_set["t0"].strftime("%Y%m%d%H%M"), var,
# resid_suffix, cfg_set["file_ext_verif"], cfg_set["save_type"])
print(" File of displaced variable: %s" % filename_disp)
vararr_disp = load_file(filename_disp, var_name=var)
filename_UV = path_creator_UV_disparr(resid_suffix, cfg_set)
#filename_UV = "%stmp/%s_%s_disparr_UV%s%s.%s" % (cfg_set["root_path"], cfg_set["t0"].strftime("%Y%m%d%H%M"),
# cfg_set["oflow_source"], resid_suffix, cfg_set["file_ext_verif"],
# cfg_set["save_type"])
print(" File of UV field: %s" % filename_UV)
if cfg_set["UV_inter"]:
filename_UV_vec = path_creator_UV_disparr(resid_suffix_vec,
cfg_set,
save_type="npz")
#filename_UV_vec = "%stmp/%s_%s_disparr_UV_%s%s.%s" % (cfg_set["root_path"], cfg_set["t0"].strftime("%Y%m%d%H%M"),
# cfg_set["oflow_source"], resid_suffix_vec, cfg_set["file_ext_verif"],
# cfg_set["save_type"])
print(" File of UV vectors: %s" % filename_UV_vec)
cmap, norm, clevs, clevsStr = st.plt.get_colormap("mm/h", "MeteoSwiss")
## Set values to nan
#plt.hist(vararr[~np.isnan(vararr)].flatten()); plt.show(); return
if var == "RZC":
vararr[vararr < 0.1] = np.nan
vararr_disp[vararr_disp < 0.1] = np.nan
elif var == "BZC":
vararr[vararr <= 0] = np.nan
vararr_disp[vararr_disp <= 0] = np.nan
elif var == "LZC":
vararr[vararr < 5] = np.nan
vararr_disp[vararr_disp < 5] = np.nan
elif var == "MZC":
vararr[vararr <= 2] = np.nan
vararr_disp[vararr_disp <= 2] = np.nan
elif var in ["EZC", "EZC45", "EZC15"]:
vararr[vararr < 1] = np.nan
vararr_disp[vararr_disp < 1] = np.nan
elif "THX" in var:
vararr[vararr < 0.001] = np.nan
vararr_disp[vararr_disp < 0.001] = np.nan
## Prepare UV field for quiver plot
#UVdisparr = np.load(filename_UV)
UVdisparr = load_file(filename_UV)
Vx = UVdisparr["Vx"][:, :, :]
Vy = UVdisparr["Vy"][:, :, :]
UV_t0 = np.moveaxis(np.dstack((Vx[0, :, :], Vy[0, :, :])), 2, 0)
## Get forms of TRT cells:
if TRT_form:
print(
" *** Warning: This part of the code (plotting the circles) is completely hard-coded! ***"
)
TRT_info_file = pd.read_pickle(
"/opt/users/jmz/0_training_NOSTRADAMUS_ANN/TRT_sampling_df_testset_enhanced.pkl"
)
jCH_ls = TRT_info_file["jCH"].loc[
(TRT_info_file["date"] == cfg_set["t0"])
& (TRT_info_file["RANKr"] >= 10)].tolist()
iCH_ls = TRT_info_file["iCH"].loc[
(TRT_info_file["date"] == cfg_set["t0"])
& (TRT_info_file["RANKr"] >= 10)].tolist()
diameters = [16, 24, 32]
circle_array = np.zeros((640, 710, len(iCH_ls)))
for ind in range(len(jCH_ls)):
X, Y = np.meshgrid(np.arange(0, 710), np.arange(0, 640))
for diameter in diameters:
interior = ((X - jCH_ls[ind])**2 +
(Y - iCH_ls[ind])**2) < (diameter / 2)**2
circle_array[interior, ind] += 1
## Get UV vectors if these were created:
if cfg_set["UV_inter"]:
#UV_vec_arr = np.load(filename_UV_vec)
UV_vec_arr = load_file(filename_UV_vec)
UV_vec = UV_vec_arr["UV_vec"]
UV_vec_sp = UV_vec_arr["UV_vec_sp"]
#print(UV_vec)
#print(len(UV_vec))
#print(UV_vec_sp)
#print(len(UV_vec_sp))
#pdb.set_trace()
## Get time array
t_delta = np.array(range(
cfg_set["n_integ"])) * datetime.timedelta(minutes=cfg_set["timestep"])
## Setup the plot:
fig, axes = plt.subplots(nrows=1, ncols=2)
fig.set_size_inches(12.5, 6.5)
plt.setp(axes,
xticks=np.arange(50, 1000, 50),
yticks=np.arange(50, 1000, 50))
plt.setp(axes,
xticks=np.arange(50, 1000, 50),
yticks=np.arange(50, 1000, 50))
## Make plots
#if animation: plt.clf()
for i in range(vararr.shape[0]):
## Prepare UV field for quiver plot
UV_field = np.moveaxis(np.dstack((Vx[i, :, :], Vy[i, :, :])), 2, 0)
step = 40
X, Y = np.meshgrid(np.arange(UV_field.shape[2]),
np.arange(UV_field.shape[1]))
UV_ = UV_field[:, 0:UV_field.shape[1]:step, 0:UV_field.shape[2]:step]
X_ = X[0:UV_field.shape[1]:step, 0:UV_field.shape[2]:step]
Y_ = Y[0:UV_field.shape[1]:step, 0:UV_field.shape[2]:step]
## Generate title:
t_current = cfg_set["t0"] - t_delta[i]
title_str = "%s fields at %s" % (
var, t_current.strftime("%d.%m.%Y - %H:%M"))
plt.suptitle(title_str)
"""## Make plot:
fig, axes = plt.subplots(nrows=1, ncols=2)
fig.set_size_inches(12.5, 6.5)
plt.suptitle(title_str);
plt.setp(axes, xticks=np.arange(50, 1000, 50), yticks=np.arange(50, 1000, 50))
plt.setp(axes, xticks=np.arange(50, 1000, 50), yticks=np.arange(50, 1000, 50))
for ax in axes:
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.grid(which='major', color='orange', linestyle='-', linewidth=0.5)
"""
for ax in axes:
ax.cla()
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.grid(which='major',
color='orange',
linestyle='-',
linewidth=0.5)
axes[0].set_title('Original')
axes[0].imshow(vararr[i, :, :], aspect='equal', cmap=cmap)
axes[0].quiver(X_,
Y_,
UV_[0, :, :],
-UV_[1, :, :],
pivot='tip',
color='grey')
if cfg_set["UV_inter"] and type(
UV_vec[i]) is not float and len(UV_vec[i].shape) > 1:
axes[0].quiver(UV_vec[i][1, :, 0],
UV_vec[i][0, :, 0],
UV_vec[i][2, :, 0],
-UV_vec[i][3, :, 0],
pivot='tip',
color='red')
axes[0].quiver(UV_vec_sp[i][1, :],
UV_vec_sp[i][0, :],
UV_vec_sp[i][2, :],
-UV_vec_sp[i][3, :],
pivot='tip',
color='blue')
axes[1].set_title('Displaced')
axes[1].imshow(vararr_disp[i, :, :], aspect='equal', cmap=cmap)
if TRT_form:
col_ls = ["r", "b", "g", "m", "k"]
for circ in range(len(iCH_ls)):
col = col_ls[circ % len(col_ls)]
axes[1].contour(circle_array[:, :, circ],
linewidths=0.3,
alpha=0.7) #,levels=diameters)
#axes[1].grid(which='major', color='red', linestyle='-', linewidth=1)
fig.tight_layout()
if animation:
#plt.show()
plt.pause(.2)
#plt.clf()
else:
figname = "%scomparison_move_disp/%s%s/%s_%s%s_displacement.png" % (
cfg_set["output_path"], var, resid_dir,
t_current.strftime("%Y%m%d%H%M"), var, resid_suffix_plot)
fig.savefig(figname, dpi=100)
plt.close()
def plot_displaced_fields(var,
cfg_set,
future=False,
animation=False,
TRT_form=False,
plot_108=True):
"""Plot displaced fields next to original ones.
Parameters
----------
var : str
Variable which should be plotted
resid : bool
Do displacement array creation for residual movement correction?
Default: False.
"""
"""
if not resid:
resid_str = ""
resid_suffix = "standard"
resid_suffix_plot = ""
resid_suffix_vec = "vec"
resid_dir = ""
else:
resid_str = " residual" if not cfg_set["resid_disp_onestep"] else " residual (combi)"
resid_suffix = "_resid" if not cfg_set["resid_disp_onestep"] else "_resid_combi"
resid_suffix_plot = "_resid" if not cfg_set["resid_disp_onestep"] else "_resid_combi"
resid_suffix_vec = "vec_resid" if not cfg_set["resid_disp_onestep"] else "vec"
resid_dir = "/_resid" if not cfg_set["resid_disp_onestep"] else "/_resid_combi"
"""
#resid_str = "" if not resid else " residual"
#resid_suffix = "" if not resid else "_resid"
#resid_dir = "" if not resid else "/_resid"
print("Plot animation of %s..." % (var))
## Load files
filename_orig = path_creator_vararr("orig", var, cfg_set, disp_reverse="")
print(" File of original variable: %s" % filename_orig)
vararr = load_file(filename_orig, var_name=var)
if future:
filename_orig_future = path_creator_vararr("orig",
var,
cfg_set,
disp_reverse="_rev")
print(" File of future original variable: %s" %
filename_orig_future)
vararr_future = load_file(filename_orig_future, var_name=var)
vararr = np.concatenate([vararr_future[1:, :, :][::-1, :, :], vararr],
axis=0)
vararr = vararr[::-1, :, :]
if plot_108 and var in [
"RZC", "BZC", "LZC", "CZC", "MCZ", "EZC15", "EZC45", "EZC50",
"THX_dens"
]:
filename_orig_108 = path_creator_vararr("orig",
"IR_108",
cfg_set,
disp_reverse="")
print(" File of IR 10.8 original variable: %s" % filename_orig_108)
vararr_108 = load_file(filename_orig_108, var_name="IR_108")
if future:
filename_orig_future_108 = path_creator_vararr("orig",
"IR_108",
cfg_set,
disp_reverse="_rev")
print(" File of IR 10.8 future original variable: %s" %
filename_orig_future_108)
vararr_future_108 = load_file(filename_orig_future_108,
var_name="IR_108")
vararr_108 = np.concatenate(
[vararr_future_108[1:, :, :][::-1, :, :], vararr_108], axis=0)
vararr_108 = vararr_108[::-1, :, :]
filename_UV = path_creator_UV_disparr("standard", cfg_set, disp_reverse="")
print(" File of UV field: %s" % filename_UV)
UVdisparr = load_file(filename_UV)
Vx = UVdisparr["Vx"][:, :, :]
Vy = UVdisparr["Vy"][:, :, :]
if future:
filename_UV_future = path_creator_UV_disparr("standard",
cfg_set,
disp_reverse="_rev")
print(" File of future UV field: %s" % filename_UV_future)
UVdisparr_future = load_file(filename_UV)
Vx_future = UVdisparr_future["Vx"][1:, :, :][::-1, :, :]
Vy_future = UVdisparr_future["Vy"][1:, :, :][::-1, :, :]
Vx = np.concatenate([Vx_future, Vx], axis=0)
Vy = np.concatenate([Vy_future, Vy], axis=0)
Vx = Vx[::-1, :, :]
Vy = Vy[::-1, :, :]
cmap, norm, clevs, clevsStr = st.plt.get_colormap("mm/h", "MeteoSwiss")
if var == "IR_108":
cmap = "bone_r"
elif var == "CAPE_ML":
cmap = "viridis"
elif var == "THX_dens":
cmap = "autumn"
elif var == "EZC45":
cmap = "winter"
elif var == "BZC":
cmap = "YlOrRd"
## Set values to nan
#plt.hist(vararr[~np.isnan(vararr)].flatten()); plt.show(); return
if var == "RZC":
vararr[vararr < 0.1] = np.nan
elif var == "BZC":
vararr[vararr <= 0] = np.nan
elif var == "LZC":
vararr[vararr < 5] = np.nan
elif var == "MZC":
vararr[vararr <= 2] = np.nan
elif var in ["EZC", "EZC45", "EZC15"]:
vararr[vararr < 1] = np.nan
elif "THX" in var:
vararr = np.array(vararr, dtype=np.float32)
vararr[vararr < 0.001] = np.nan
## Get forms of TRT cells:
if TRT_form:
filename_TRT_disp = path_creator_vararr("disp",
"TRT",
cfg_set,
disp_reverse="")
filename_TRT_disp = filename_TRT_disp[:-3] + "_domain.nc"
print(" File of TRT domain: %s" % filename_TRT_disp)
vararr_TRT = load_file(filename_TRT_disp, var_name="TRT")
if future:
filename_TRT_disp_future = filename_TRT_disp[:-3] + "_rev.nc"
vararr_TRT_future = load_file(filename_TRT_disp_future,
var_name="TRT")
vararr_TRT = np.concatenate(
[vararr_TRT_future[1:, :, :][::-1, :, :], vararr_TRT], axis=0)
vararr_TRT = vararr_TRT[::-1, :, :]
t_delta = np.array(range(cfg_set["n_integ"])) * -cfg_set["timestep"]
if future:
t_delta = np.concatenate([np.arange(1,cfg_set["n_integ"])[::-1] * \
cfg_set["timestep"],t_delta])
t_delta = t_delta[::-1]
#t_delta = np.array(range(cfg_set["n_integ"]))*datetime.timedelta(minutes=-cfg_set["timestep"])
#if future:
# t_delta = np.concatenate([np.arange(1,cfg_set["n_integ"])[::-1] * \
# datetime.timedelta(minutes=cfg_set["timestep"]),t_delta])
## Setup the plot:
#fig, axes, fig_extent = ccs4_map(cfg_set,figsize_x=12,figsize_y=12,hillshade=True,radar_loc=True,radar_vis=True)
#fig, axes = plt.subplots(nrows=1, ncols=1)
#fig.set_size_inches(10,10)
#plt.setp(axes, xticks=np.arange(50, 1000, 50), yticks=np.arange(50, 1000, 50))
#plt.setp(axes, xticks=np.arange(50, 1000, 50), yticks=np.arange(50, 1000, 50))
## Make plots
for i in range(vararr.shape[0]):
#if animation: plt.cla()
## Make plot:
if not animation:
fig, axes, fig_extent = ccs4_map(cfg_set,
figsize_x=9,
figsize_y=9,
hillshade=True,
radar_loc=True,
radar_vis=True)
#fig, axes = plt.subplots(nrows=1, ncols=1)
#fig.set_size_inches(10,10)
#plt.setp(axes, xticks=np.arange(50, 1000, 50), yticks=np.arange(50, 1000, 50))
#plt.setp(axes, xticks=np.arange(50, 1000, 50), yticks=np.arange(50, 1000, 50))
#axes.set_yticklabels([])
#axes.set_xticklabels([])
#axes.grid(which='major', color='orange', linestyle='-', linewidth=0.5)
## Prepare UV field for quiver plot
UV_field = np.moveaxis(np.dstack((Vx[i, :, :], Vy[i, :, :])), 2, 0)
step = 40
X, Y = np.meshgrid(np.arange(UV_field.shape[2]),
np.arange(UV_field.shape[1]))
UV_ = UV_field[:, 0:UV_field.shape[1]:step, 0:UV_field.shape[2]:step]
X_ = X[0:UV_field.shape[1]:step,
0:UV_field.shape[2]:step] * 1000 + fig_extent[0]
Y_ = Y[0:UV_field.shape[1]:step,
0:UV_field.shape[2]:step] * 1000 + fig_extent[2]
## Generate title:
t_current = cfg_set["t0"] + datetime.timedelta(minutes=t_delta[i])
title_str_basic = "%s fields at %s" % (
var, t_current.strftime("%d.%m.%Y - %H:%M"))
if t_current > cfg_set["t0"]:
title_str_addon = r"(Future t$_0$ + %02dmin)" % t_delta[i]
elif t_current < cfg_set["t0"]:
title_str_addon = r"(Past t$_0$ - %02dmin)" % -t_delta[i]
elif t_current == cfg_set["t0"]:
title_str_addon = r"(Present t$_0$ + 00min)"
title_str = "%s\n%s" % (title_str_basic, title_str_addon)
#axes.cla()
#axes.set_yticklabels([])
#axes.set_xticklabels([])
#axes.grid(which='major', color='orange', linestyle='-', linewidth=0.5)
axes.set_title(title_str)
alpha = 0.6
if plot_108 and var in [
"RZC", "BZC", "LZC", "CZC", "MCZ", "EZC15", "EZC45", "EZC50",
"THX_dens"
]:
axes.imshow(vararr_108[i, :, :],
aspect='equal',
cmap="bone_r",
extent=fig_extent,
alpha=0.7)
alpha = 0.9
axes.imshow(vararr[i, :, :],
aspect='equal',
cmap=cmap,
extent=fig_extent,
alpha=alpha)
axes.quiver(X_,
Y_,
UV_[0, :, :],
-UV_[1, :, :],
pivot='tip',
color='orange',
width=0.002)
#axes.quiver(UV_field[0,:,:], -UV_field[1,:,:], pivot='tip', color='red')
#if cfg_set["UV_inter"] and type(UV_vec[i]) is not float and len(UV_vec[i].shape)>1:
# axes.quiver(UV_vec[i][1,:,0], UV_vec[i][0,:,0], UV_vec[i][2,:,0], -UV_vec[i][3,:,0], pivot='tip', color='red')
# axes.quiver(UV_vec_sp[i][1,:], UV_vec_sp[i][0,:], UV_vec_sp[i][2,:], -UV_vec_sp[i][3,:], pivot='tip', color='blue')
#axes[1].set_title('Displaced')
#axes[1].imshow(vararr_disp[i,:,:], aspect='equal', cmap=cmap)
if TRT_form:
#col_ls = ["r","b","g","m","k"]
#for circ in range(len(iCH_ls)):
# col = col_ls[circ%len(col_ls)]
# axes[1].contour(circle_array[:,:,circ],linewidths=0.3,alpha=0.7) #,levels=diameters)
axes.contour(vararr_TRT[i, ::-1, :],
linewidths=1,
alpha=1,
color="red",
extent=fig_extent) #,levels=diameters)
#axes[1].grid(which='major', color='red', linestyle='-', linewidth=1)
#fig.tight_layout()
if animation:
#plt.show()
plt.pause(1)
axes.clear()
else:
path = "%sTRT_cell_disp/%s/%s/" % (cfg_set["output_path"],
cfg_set["t0_str"], var)
new_dir = ""
if not os.path.exists(path):
os.makedirs(path)
new_dir = "(new) "
figname = "%s%s_%s_TRT_domains.png" % (
path, t_current.strftime("%Y%m%d%H%M"), var)
plt.tight_layout()
fig.savefig(figname, dpi=100)
if i == vararr.shape[0] - 1:
print(" Plots saved in %sdirectory: %s" % (new_dir, path))
plt.close()
plt.close()