diff --git a/.gitattributes b/.gitattributes index f21cffe..2930283 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,4 +1,5 @@ *.tif filter=lfs diff=lfs merge=lfs -text *.h5 filter=lfs diff=lfs merge=lfs -text +ATL1415/resources/BRW_template.h5 !filter *.db filter=lfs diff=lfs merge=lfs -text -masks/EGM2008_geoid_h.nc filter=lfs diff=lfs merge=lfs -text +masks/EGM2008_geoid_h.nc -filter=lfs -diff=lfs -merge=lfs -text diff --git a/.gitignore b/.gitignore index 8dcf87f..46dbcc6 100644 --- a/.gitignore +++ b/.gitignore @@ -76,6 +76,9 @@ target/ # Jupyter Notebook .ipynb_checkpoints +Untitled*.ipynb +*.mp4 +*.ipynb # IPython profile_default/ diff --git a/ATL1415/ATL11_to_ATL15.py b/ATL1415/ATL11_to_ATL15.py index 45f7df5..1561b46 100755 --- a/ATL1415/ATL11_to_ATL15.py +++ b/ATL1415/ATL11_to_ATL15.py @@ -21,7 +21,8 @@ os.environ["OPENBLAS_NUM_THREADS"]=n_threads import numpy as np -from LSsurf.smooth_xytb_fit_aug import smooth_xytb_fit_aug +from LSsurf.smooth_fit import smooth_fit +from SMBcorr import assign_firn_variable import pointCollection as pc import re @@ -30,7 +31,6 @@ import traceback from ATL1415.reread_data_from_fits import reread_data_from_fits from ATL1415.make_mask_from_vector import make_mask_from_vector -from SMBcorr import assign_firn_variable import pyTMD import scipy.optimize @@ -214,12 +214,13 @@ def read_ATL11(xy0, Wxy, index_file, SRS_proj4, \ 'along_track':np.zeros_like(D_x.x, dtype=bool)})] try: D=pc.data().from_list(D_list+XO_list).ravel_fields() - D.index(np.isfinite(D.z)) - except ValueError: # catch empty data return None, file_list - + if hasattr(D,'z'): + D.index(np.isfinite(D.z)) + else: + return None, file_list D.index(( D.fit_quality ==0 ) | ( D.fit_quality == 2 )) print(f'xover_count={xover_count}') return D, file_list @@ -237,7 +238,6 @@ def apply_tides(D, xy0, W, EPSG=None, verbose=False): - ''' read in the tide mask, calculate ocean tide elevations, and apply dynamic atmospheric correction (dac) and tide to ice-shelf elements @@ -274,6 +274,8 @@ def apply_tides(D, xy0, W, return # find ice shelf points is_els=tide_mask.interp(D.x, D.y) > 0.5 + # need to assign the 'floating' field in case the SMB routines need it + D.assign(floating=is_els) if verbose: print(f"\t\t{np.mean(is_els)*100}% shelf data") print(f"\t\ttide model: {tide_model}") @@ -423,6 +425,8 @@ def ATL11_to_ATL15(xy0, Wxy=4e4, ATL11_index=None, E_RMS={}, \ geoid_tol=None, \ sigma_tol=None,\ mask_file=None,\ + rock_mask_file=None,\ + rock_mask_reject_value=None,\ geoid_file=None,\ tide_mask_file=None,\ tide_directory=None,\ @@ -480,7 +484,7 @@ def ATL11_to_ATL15(xy0, Wxy=4e4, ATL11_index=None, E_RMS={}, \ ''' SRS_proj4, EPSG=get_SRS_info(hemisphere) - E_RMS0={'d2z0_dx2':200000./3000/3000, 'd3z_dx2dt':3000./3000/3000, 'd2z_dxdt':3000/3000, 'd2z_dt2':5000} + E_RMS0={'d2z0_dx2':200000./3000/3000, 'd3z_dx2dt':3000./3000/3000, 'd2z_dt2':5000} E_RMS0.update(E_RMS) W={'x':Wxy, 'y':Wxy,'t':np.diff(t_span)} @@ -513,27 +517,24 @@ def ATL11_to_ATL15(xy0, Wxy=4e4, ATL11_index=None, E_RMS={}, \ for key, mask in mask_data.items(): mask.assign({'z':mask.mask}) elif region is not None: - if region=='AA': + if region in ['AA', 'GL']: pad=np.array([-1.e4, 1.e4]) mask_data=pc.grid.data().from_h5(mask_file, bounds=[bds['x']+pad, bds['y']+pad], - bands=np.arange(17, 24)) + t_range=bds['t']+np.array([-1, 1])) + if rock_mask_file is not None: + rock_mask=pc.grid.data().from_file(rock_mask_file, bounds=mask_data.bounds()) + rock_mask.reject=rock_mask.mask==rock_mask_reject_value + rock=rock_mask.interp(mask_data.x, mask_data.y, gridded=True, field='reject') > 0.5 + for band in range(mask_data.z.shape[2]): + mask_data.z[:,:,band] *= (rock==0) while mask_data.t[-1] < ctr['t']+W['t']/2: # append a copy of the last field in the mask data to the end of the mask data mask_data.z = np.concatenate([mask_data.z,mask_data.z[:,:,-1:]], axis=2) mask_data.t = np.concatenate([mask_data.t,mask_data.t[-1:]+1], axis=0) mask_data.__update_size_and_shape__() - #mask_data=pc.grid.data().from_geotif(mask_file, bounds=[xy0[0]+np.array([-1.2, 1.2])*Wxy/2, xy0[1]+np.array([-1.2, 1.2])*Wxy/2]) - #import scipy.ndimage as snd - #mask_data.z=snd.binary_erosion(snd.binary_erosion(mask_data.z, np.ones([1,3])), np.ones([3,1])) + mask_data.z[~np.isfinite(mask_data.z)]=0. mask_file=None - elif region=='GL': - if mask_file.endswith('.nc'): - mask_data, tide_mask_data = read_bedmachine_greenland(mask_file, xy0, Wxy) - elif mask_file.endswith('.tif'): - pad=np.array([-1.e4, 1.e4]) - mask_data=pc.grid.data().from_geotif(mask_file, - bounds=[bds['x']+pad, bds['y']+pad]) elif mask_file.endswith('.shp') or mask_file.endswith('.db'): mask_data=make_mask_from_vector(mask_file, W, ctr, spacing['z0'], srs_proj4=SRS_proj4) @@ -623,6 +624,16 @@ def ATL11_to_ATL15(xy0, Wxy=4e4, ATL11_index=None, E_RMS={}, \ tide_adjustment_file=tide_adjustment_file, tide_adjustment_format=tide_adjustment_format, EPSG=EPSG, verbose=verbose) + elif 'floating' not in data.fields: + # fix for firn runs where the floating variable did not get assigned + if tide_mask_file is not None and tide_mask_data is None: + try: + tide_mask = pc.grid.data().from_geotif(tide_mask_file, + bounds=[np.array([-0.6, 0.6])*Wxy+xy0[0], np.array([-0.6, 0.6])*Wxy+xy0[1]]) + if tide_mask.shape is not None: + data.assign(floating=tide_mask.interp(data.x, data.y) > 0.5) + except IndexError: + data.assign(floating=np.zeros_like(data.x, dtype=bool)) if geoid_tol is not None: data.index((data.z - data.geoid_h) > geoid_tol) @@ -631,7 +642,7 @@ def ATL11_to_ATL15(xy0, Wxy=4e4, ATL11_index=None, E_RMS={}, \ return {'data':data} # call smooth_xytb_fitting - S=smooth_xytb_fit_aug(data=data, + S=smooth_fit(data=data, ctr=ctr, W=W, spacing=spacing, E_RMS=E_RMS0, reference_epoch=reference_epoch, @@ -808,6 +819,8 @@ def main(argv): parser.add_argument('--geoid_tol', type=float, help='points closer than this to the geoid will be rejected') parser.add_argument('--sigma_tol', type=float, help='points with sigma greater than this value will be edited') parser.add_argument('--mask_file', type=lambda p: os.path.abspath(os.path.expanduser(p))) + parser.add_argument('--rock_mask_file', type=lambda p: os.path.abspath(os.path.expanduser(p)), help='mask indicating exposed rock') + parser.add_argument('--rock_mask_reject_value', type=float, default=1, help='value within the rock mask file that indicates rock') parser.add_argument('--geoid_file', type=lambda p: os.path.abspath(os.path.expanduser(p)), help="file containing geoid information") parser.add_argument('--tide_mask_file', type=lambda p: os.path.abspath(os.path.expanduser(p))) parser.add_argument('--tide_directory', type=lambda p: os.path.abspath(os.path.expanduser(p))) @@ -836,7 +849,11 @@ def main(argv): args.avg_scales = [np.int64(temp) for temp in args.avg_scales.split(',')] spacing={'z0':args.grid_spacing[0], 'dz':args.grid_spacing[1], 'dt':args.grid_spacing[2]} - E_RMS={'d2z0_dx2':args.E_d2z0dx2, 'd3z_dx2dt':args.E_d3zdx2dt, 'd2z_dxdt':args.E_d3zdx2dt*args.data_gap_scale, 'd2z_dt2':args.E_d2zdt2} + E_RMS={'d2z0_dx2':args.E_d2z0dx2, 'd3z_dx2dt':args.E_d3zdx2dt, 'd2z_dt2':args.E_d2zdt2} + + if args.data_gap_scale > 0: + E_RMS[ 'd2z_dxdt'] = args.E_d3zdx2dt*args.data_gap_scale + print("E_RMS="+str(E_RMS)) reread_dirs=None dest_dir=args.base_directory @@ -941,6 +958,8 @@ def main(argv): dzdt_lags=args.dzdt_lags, \ N_subset=args.N_subset,\ mask_file=args.mask_file, \ + rock_mask_file=args.rock_mask_file, \ + rock_mask_reject_value=args.rock_mask_reject_value,\ region=args.region, \ geoid_file=args.geoid_file,\ tide_mask_file=args.tide_mask_file, \ diff --git a/ATL1415/__init__.py b/ATL1415/__init__.py index 706c763..4319024 100644 --- a/ATL1415/__init__.py +++ b/ATL1415/__init__.py @@ -4,3 +4,4 @@ from .ATL14_attrs_meta import * from .reread_data_from_fits import * from .make_slurm_file import * +from .assign_firn_variable import * diff --git a/ATL1415/assign_firn_variable.py b/ATL1415/assign_firn_variable.py new file mode 100644 index 0000000..de2f7b0 --- /dev/null +++ b/ATL1415/assign_firn_variable.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Aug 22 14:08:42 2019. + +@author: ben +""" + +import numpy as np +import SMBcorr +import os + + +def assign_firn_variable(data, firn_correction, firn_dir, hemisphere,\ + model_version='1.0', subset_valid=False, + variables=None, infer_FAC=True, rho_water=1): + + EPSG={-1:'EPSG:3031', 1:'EPSG:3413'}[hemisphere] + + if firn_correction == 'MAR': + if hemisphere==1: + data.assign({'h_firn' : SMBcorr.interp_MAR_firn(data.x, data.y, data.time)}) + elif firn_correction == 'MERRA2_hybrid': + KWARGS={} + # get MERRA-2 version and major version + MERRA2_VERSION = model_version + # MERRA-2 hybrid directory + DIRECTORY=os.path.join(firn_dir,'MERRA2_hybrid',MERRA2_VERSION) + # MERRA-2 region name from ATL11 region + MERRA2_REGION = {-1:'ais',1:'gris'}[hemisphere] + # keyword arguments for MERRA-2 interpolation programs + if MERRA2_VERSION in ('v0','v1','v1.0'): + KWARGS['VERSION'] = MERRA2_VERSION + DEFAULT_VARIABLES = {'FAC':'FAC', 'SMB_a':'cum_smb_anomaly', 'h_a':'height'} + else: + KWARGS['VERSION'] = MERRA2_VERSION.replace('.','_') + DEFAULT_VARIABLES = {'FAC':'FAC', 'SMB_a':'SMB_a','h_a':'h_a'} + # use compressed files + if hemisphere==1: + KWARGS['GZIP'] = False + else: + KWARGS['GZIP'] = False + if variables is None: + VARIABLES = DEFAULT_VARIABLES + else: + VARIABLES=variables + # output variable keys for both direct and derived fields + SMB_data={out_var: SMBcorr.interpolate_merra_hybrid(DIRECTORY, EPSG, + MERRA2_REGION, data.time, data.x, data.y, + VARIABLE=model_var, **KWARGS) for out_var, model_var in VARIABLES.items()} + if infer_FAC: + SMB_data['FAC']=SMB_data['h_a']-SMB_data['SMB_a'] + + if 'floating' in data.fields: + # assume SMB is in ice equivalent. + # For grounded ice, dh = FAC + SMB + # For floating ice, dh = FAC + (rho_water-rho_i)/(rho_w) SMB + float_scale = (data.floating==0) + (rho_water-.917)/rho_water*(data.floating==1) + data.assign({'h_firn':SMB_data['FAC'] + float_scale*SMB_data['SMB_a']}) + else: + data.assign({'h_firn':SMB_data['FAC'] + SMB_data['SMB_a']}) + + # use the mask values to set the ouput to NaN if the model is invalid + if infer_FAC: + data.h_firn[SMB_data['FAC'].mask]=np.NaN + data.h_firn[SMB_data['SMB_a'].mask]=np.NaN + if subset_valid: + data.index(np.isfinite(data.h_firn)) diff --git a/ATL1415/resources/ATL15_output_attrs.csv b/ATL1415/resources/ATL15_output_attrs.csv index 0d2fb9d..e7b7e2e 100644 --- a/ATL1415/resources/ATL15_output_attrs.csv +++ b/ATL1415/resources/ATL15_output_attrs.csv @@ -1,95 +1,111 @@ -group,field,units,dimensions,datatype,coordinates,least_significant_digit,description,long_name,source,group description -height_change,time,days since 2018-01-01,time,float64,time,None,"Time for each node, in days since 2018-01-01:T00.00.00 UTC",quarterly h(t) time,ATBD section 4.2, -height_change,time_lag1,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quarterly height-change rate, in days since 2018-01-01:T00.00.00 UTC",quarterly dh/dt time,ATBD section 4.2, -height_change,time_lag4,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each annual height-change rate, in days since 2018-01-01:T00.00.00 UTC",annual dh/dt time,ATBD section 4.2, -height_change,time_lag8,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each biennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",biennial dh/dt time,ATBD section 4.2, -height_change,time_lag12,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each triennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",triennial dh/dt time,ATBD section 4.2, -height_change,x,meters,x,float64,x,None,"x coordinate of the 1-km cell centers, in projected coordinates",polar stereographic x at 1km,ATBD section 3.2, -height_change,y,meters,y,float64,y,None,"y coordinate of the 1-km cell centers, in projected coordinates",polar stereographic y at 1km,ATBD section 3.2, -height_change,ice_area,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 1x1 km grid cell, accounting for the area distortion in the polar-stereographic projections",ice-covered area at 1 km,ATBD section 3.4, -height_change,ice_area_lag1,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 1x1 km grid cell for quarterly change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 1 km, for quarterly change in height",ATBD section 3.4, -height_change,ice_area_lag4,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 1x1 km grid cell for annual change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 1 km, for annual change in height",ATBD section 3.4, -height_change,ice_area_lag8,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 1x1 km grid cell for biennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 1 km, for biennial change in height",ATBD section 3.4, -height_change,ice_area_lag12,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 1x1 km grid cell for triennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 1 km, for triennial change in height",ATBD section 3.4, -height_change,data_count,counts,"time,y,x",float32,time y x,4,Weighted number of data contributing to each node in the 1-km height-change grid,data count ,ATBD section 5.2.4.4, -height_change,misfit_rms,meters,"time,y,x",float32,time y x,4,Misfit associated with each node in the 1-km height-change grid,rms misfit ,ATBD section 5.2.4.4, -height_change,misfit_scaled_rms,counts,"time,y,x",float32,time y x,4,Scaled misfit associated with each node in the 1-km height-change grid,scaled rms misfit,ATBD section 5.2.4.4, -height_change,delta_h,meters,"time,y,x",float32,time y x,4,"Height change relative to the datum (Jan 1, 2020) surface",height change at 1 km,ATBD section 3.4,delta_h group includes variables describing height differences between the model surface at any time and the DEM surface at a resolution of 1 km. -height_change,delta_h_sigma,meters,"time,y,x",float32,time y x,4,"Estimated error in height change relative to the datum (Jan 1, 2020) surface",height change uncertainty at 1 km,ATBD section 3.4, -height_change,dhdt_lag1,meters years^-1,"time,y,x",float32,time y x,4,Quarterly height-change rate,quarterly height-change rate at 1 km,ATBD section 3.4,"dhdt_lag1 group includes variables describing height difference rates, at a resolution of 1 km, between subsequent quarterly height-difference surfaces." -height_change,dhdt_lag1_sigma,meters years^-1,"time,y,x",float32,time y x,4,Estimated error in quarterly height-change rate,quarterly height-change rate uncertainty at 1 km,ATBD section 3.4, -height_change,dhdt_lag4,meters years^-1,"time,y,x",float32,time y x,4,Annual height-change rate,annual height-change rate at 1 km,ATBD section 3.4,"dhdt_lag4 group includes variables describing annual height-change-rate estimates, at a resolution of 1 km." -height_change,dhdt_lag4_sigma,meters years^-1,"time,y,x",float32,time y x,4,Estimated error in annual height-change rate,annual height-change rate uncertainty at 1 km,ATBD section 3.4, -height_change,dhdt_lag8,meters years^-1,"time,y,x",float32,time y x,4,Biennial height-change rate,biennial height-change rate at 1 km,ATBD section 3.4,"dhdt_lag8 group includes variables describing biennial height-change-rate estimates, at a resolution of 1km." -height_change,dhdt_lag8_sigma,meters years^-1,"time,y,x",float32,time y x,4,Estimated error in biennial height-change rate,biennial height-change rate uncertainty at 1 km,ATBD section 3.4, -height_change,dhdt_lag12,meters years^-1,"time,y,x",float32,time y x,4,Triennial height-change rate,triennial height-change rate at 1 km,ATBD section 3.4,"dhdt_lag12 group includes variables describing triennial height-change-rate estimates, at a resolution of 1km." -height_change,dhdt_lag12_sigma,meters years^-1,"time,y,x",float32,time y x,4,Estimated error in triennial height-change rate,triennial height-change rate uncertainty at 1 km,ATBD section 3.4, -,,,,,,,,,, -height_change_10km,time,days since 2018-01-01,time,float64,time,None,"Time for each node, in days since 2018-01-01:T00.00.00 UTC",quarterly h(t) time,ATBD section 4.2, -height_change_10km,time_lag1,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quarterly height-change rate, in days since 2018-01-01:T00.00.00 UTC",quarterly dh/dt time,ATBD section 4.2, -height_change_10km,time_lag4,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each annual height-change rate, in days since 2018-01-01:T00.00.00 UTC",annual dh/dt time,ATBD section 4.2, -height_change_10km,time_lag8,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each biennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",biennial dh/dt time,ATBD section 4.2, -height_change_10km,time_lag12,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each triennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",triennial dh/dt time,ATBD section 4.2, -height_change_10km,x,meters,x,float64,x,None,"x coordinate of the 10-km cell centers, in projected coordinates",polar stereographic x at 10 km,ATBD section 3.2, -height_change_10km,y,meters,y,float64,y,None,"y coordinate of the 10-km cell centers, in projected coordinates",polar stereographic y at 10 km,ATBD section 3.2, -height_change_10km,ice_area_10km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 10x10 km grid cell, accounting for the area distortion in the polar-stereographic projections",ice-covered area at 10 km,ATBD section 3.4, -height_change_10km,ice_area_lag1_10km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 10x10 km grid cell for quarterly change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 10 km, for quarterly change in height",ATBD section 3.4, -height_change_10km,ice_area_lag4_10km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 10x10 km grid cell for annual change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 10 km, for annual change in height",ATBD section 3.4, -height_change_10km,ice_area_lag8_10km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 10x10 km grid cell for biennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 10 km, for biennial change in height",ATBD section 3.4, -height_change_10km,ice_area_lag12_10km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 10x10 km grid cell for triennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 10 km, for triennial change in height",ATBD section 3.4, -height_change_10km,delta_h_10km,meters,"time,y,x",float32,time y x,4,"10x10 km average height change relative to the datum (Jan 1, 2020) surface",height change at 10 km,ATBD section 3.4,delta_h group includes variables describing height differences between the model surface at any time and the DEM surface at a resolution of 10 km. -height_change_10km,delta_h_sigma_10km,meters,"time,y,x",float32,time y x,4,"Uncertainty in the 10x10 km average height change relative to the datum (Jan 1, 2020) surface",height change uncertainty at 10 km,ATBD section 3.4, -height_change_10km,dhdt_lag1_10km,meters years^-1,"time,y,x",float32,time y x,4,10x10 km average quarterly height change rate,quarterly height-change rate at 10 km,ATBD section 3.4,"dhdt_lag1 group includes variables describing height difference rates, at a resolution of 10 km, between subsequent quarterly height-difference surfaces." -height_change_10km,dhdt_lag1_sigma_10km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 10x10 km average quarterly height change rate,quarterly height-change rate uncertainty at 10 km,ATBD section 3.4, -height_change_10km,dhdt_lag4_10km,meters years^-1,"time,y,x",float32,time y x,4,10x10 km average annual height change rate,annual height-change rate at 10 km,ATBD section 3.4,"dhdt_lag4 group includes variables describing annual height-change-rate estimates, at a resolution of 10 km." -height_change_10km,dhdt_lag4_sigma_10km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 10x10 km average annual height change rate,annual height-change rate uncertainty at 10 km,ATBD section 3.4, -height_change_10km,dhdt_lag8_10km,meters years^-1,"time,y,x",float32,time y x,4,10x10 km average biennial height change rate,biennial height-change rate at 10 km,ATBD section 3.4,"dhdt_lag8 group includes variables describing biennial height-change-rate estimates, at a resolution of 10 km." -height_change_10km,dhdt_lag8_sigma_10km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 10x10 km average biennial height change rate,biennial height-change rate uncertainty at 10 km,ATBD section 3.4, -height_change_10km,dhdt_lag12_10km,meters years^-1,"time,y,x",float32,time y x,4,10x10 km average triennial height change rate,triennial height-change rate at 10 km,ATBD section 3.4,"dhdt_lag12 group includes variables describing triennial height-change-rate estimates, at a resolution of 10 km." -height_change_10km,dhdt_lag12_sigma_10km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 10x10 km average triennial height change rate,triennial height-change rate uncertainty at 10 km,ATBD section 3.4, -,,,,,,,,,, -height_change_20km,time,days since 2018-01-01,time,float64,time,None,"Time for each node, in days since 2018-01-01:T00.00.00 UTC",quarterly h(t) time,ATBD section 4.2, -height_change_20km,time_lag1,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quarterly height-change rate, in days since 2018-01-01:T00.00.00 UTC",quarterly dh/dt time,ATBD section 4.2, -height_change_20km,time_lag4,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each annual height-change rate, in days since 2018-01-01:T00.00.00 UTC",annual dh/dt time,ATBD section 4.2, -height_change_20km,time_lag8,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each biennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",biennial dh/dt time,ATBD section 4.2, -height_change_20km,time_lag12,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each triennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",triennial dh/dt time,ATBD section 4.2, -height_change_20km,x,meters,x,float64,x,None,"x coordinate of the 20-km cell centers, in projected coordinates",polar stereographic x at 20 km,ATBD section 3.2, -height_change_20km,y,meters,y,float64,y,None,"y coordinate of the 20-km cell centers, in projected coordinates",polar stereographic y at 20 km,ATBD section 3.2, -height_change_20km,ice_area_20km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 20x20 km grid cell, accounting for the area distortion in the polar-stereographic projections",ice-covered area at 20 km,ATBD section 3.4, -height_change_20km,ice_area_lag1_20km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 20x20 km grid cell for quarterly change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 20 km, for quarterly change in height",ATBD section 3.4, -height_change_20km,ice_area_lag4_20km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 20x20 km grid cell for annual change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 20 km, for annual change in height",ATBD section 3.4, -height_change_20km,ice_area_lag8_20km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 20x20 km grid cell for biennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 20 km, for biennial change in height",ATBD section 3.4, -height_change_20km,ice_area_lag12_20km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 20x20 km grid cell for triennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 20 km, for triennial change in height",ATBD section 3.4, -height_change_20km,delta_h_20km,meters,"time,y,x",float32,time y x,4,"20x20 km average height change relative to the datum (Jan 1, 2020) surface",height change at 20 km,ATBD section 3.4,delta_h group includes variables describing height differences between the model surface at any time and the DEM surface at a resolution of 20 km. -height_change_20km,delta_h_sigma_20km,meters,"time,y,x",float32,time y x,4,"Uncertainty in the 20x20 km average height change relative to the datum (Jan 1, 2020) surface",height change uncertainty at 20 km,ATBD section 3.4, -height_change_20km,dhdt_lag1_20km,meters years^-1,"time,y,x",float32,time y x,4,20x20 km average quarterly height change rate,quarterly height-change rate at 20 km,ATBD section 3.4,"dhdt_lag1 group includes variables describing height difference rates, at a resolution of 20 km, between subsequent quarterly height-difference surfaces." -height_change_20km,dhdt_lag1_sigma_20km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 20x20 km average quarterly height change rate,quarterly height-change rate uncertainty at 20 km,ATBD section 3.4, -height_change_20km,dhdt_lag4_20km,meters years^-1,"time,y,x",float32,time y x,4,20x20 km average annual height change rate,annual height-change rate at 20 km,ATBD section 3.4,"dhdt_lag4 group includes variables describing annual height-change-rate estimates, at a resolution of 20 km." -height_change_20km,dhdt_lag4_sigma_20km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 20x20 km average annual height change rate,annual height-change rate uncertainty at 20 km,ATBD section 3.4, -height_change_20km,dhdt_lag8_20km,meters years^-1,"time,y,x",float32,time y x,4,20x20 km average biennial height change rate,biennial height-change rate at 20 km,ATBD section 3.4,"dhdt_lag8 group includes variables describing biennial height-change-rate estimates, at a resolution of 20 km." -height_change_20km,dhdt_lag8_sigma_20km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 20x20 km average biennial height change rate,biennial height-change rate uncertainty at 20 km,ATBD section 3.4, -height_change_20km,dhdt_lag12_20km,meters years^-1,"time,y,x",float32,time y x,4,20x20 km average triennial height change rate,triennial height-change rate at 20 km,ATBD section 3.4,"dhdt_lag12 group includes variables describing triennial height-change-rate estimates, at a resolution of 20 km." -height_change_20km,dhdt_lag12_sigma_20km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 20x20 km average triennial height change rate,triennial height-change rate uncertainty at 20 km,ATBD section 3.4, -,,,,,,,,,, -height_change_40km,time,days since 2018-01-01,time,float64,time,None,"Time for each node, in days since 2018-01-01:T00.00.00 UTC",quarterly h(t) time,ATBD section 4.2, -height_change_40km,time_lag1,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quarterly height-change rate, in days since 2018-01-01:T00.00.00 UTC",quarterly dh/dt time,ATBD section 4.2, -height_change_40km,time_lag4,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each annual height-change rate, in days since 2018-01-01:T00.00.00 UTC",annual dh/dt time,ATBD section 4.2, -height_change_40km,time_lag8,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each biennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",biennial dh/dt time,ATBD section 4.2, -height_change_40km,time_lag12,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each triennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",triennial dh/dt time,ATBD section 4.2, -height_change_40km,x,meters,x,float64,x,None,"x coordinate of the 40-km cell centers, in projected coordinates",polar stereographic x at 40 km,ATBD section 3.2, -height_change_40km,y,meters,y,float64,y,None,"y coordinate of the 40-km cell centers, in projected coordinates",polar stereographic y at 40 km,ATBD section 3.2, -height_change_40km,ice_area_40km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 40x40 km grid cell, accounting for the area distortion in the polar-stereographic projections",ice-covered area at 40 km,ATBD section 3.4, -height_change_40km,ice_area_lag1_40km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 40x40 km grid cell for quarterly change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 40 km, for quarterly change in height",ATBD section 3.4, -height_change_40km,ice_area_lag4_40km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 40x40 km grid cell for annual change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 40 km, for annual change in height",ATBD section 3.4, -height_change_40km,ice_area_lag8_40km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 40x40 km grid cell for biennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 40 km, for biennial change in height",ATBD section 3.4, -height_change_40km,ice_area_lag12_40km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 40x40 km grid cell for triennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 40 km, for triennial change in height",ATBD section 3.4, -height_change_40km,delta_h_40km,meters,"time,y,x",float32,time y x,4,"40x40 km average height change relative to the datum (Jan 1, 4040) surface",height change at 40 km,ATBD section 3.4,delta_h group includes variables describing height differences between the model surface at any time and the DEM surface at a resolution of 40 km. -height_change_40km,delta_h_sigma_40km,meters,"time,y,x",float32,time y x,4,"Uncertainty in the 40x40 km average height change relative to the datum (Jan 1, 4040) surface",height change uncertainty at 40 km,ATBD section 3.4, -height_change_40km,dhdt_lag1_40km,meters years^-1,"time,y,x",float32,time y x,4,40x40 km average quarterly height change rate,quarterly height-change rate at 40 km,ATBD section 3.4,"dhdt_lag1 group includes variables describing height difference rates, at a resolution of 40 km, between subsequent quarterly height-difference surfaces." -height_change_40km,dhdt_lag1_sigma_40km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 40x40 km average quarterly height change rate,quarterly height-change rate uncertainty at 40 km,ATBD section 3.4, -height_change_40km,dhdt_lag4_40km,meters years^-1,"time,y,x",float32,time y x,4,40x40 km average annual height change rate,annual height-change rate at 40 km,ATBD section 3.4,"dhdt_lag4 group includes variables describing annual height-change-rate estimates, at a resolution of 40 km." -height_change_40km,dhdt_lag4_sigma_40km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 40x40 km average annual height change rate,annual height-change rate uncertainty at 40 km,ATBD section 3.4, -height_change_40km,dhdt_lag8_40km,meters years^-1,"time,y,x",float32,time y x,4,40x40 km average biennial height change rate,biennial height-change rate at 40 km,ATBD section 3.4,"dhdt_lag8 group includes variables describing biennial height-change-rate estimates, at a resolution of 40 km." -height_change_40km,dhdt_lag8_sigma_40km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 40x40 km average biennial height change rate,biennial height-change rate uncertainty at 40 km,ATBD section 3.4, -height_change_40km,dhdt_lag12_40km,meters years^-1,"time,y,x",float32,time y x,4,40x40 km average triennial height change rate,triennial height-change rate at 40 km,ATBD section 3.4,"dhdt_lag12 group includes variables describing triennial height-change-rate estimates, at a resolution of 40 km." -height_change_40km,dhdt_lag12_sigma_40km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 40x40 km average triennial height change rate,triennial height-change rate uncertainty at 40 km,ATBD section 3.4, \ No newline at end of file +group,field,units,dimensions,datatype,coordinates,least_significant_digit,description,long_name,source,group description +height_change,time,days since 2018-01-01,time,float64,time,None,"Time for each node, in days since 2018-01-01:T00.00.00 UTC",quarterly h(t) time,ATBD section 4.2, +height_change,time_lag1,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quarterly height-change rate, in days since 2018-01-01:T00.00.00 UTC",quarterly dh/dt time,ATBD section 4.2, +height_change,time_lag4,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each annual height-change rate, in days since 2018-01-01:T00.00.00 UTC",annual dh/dt time,ATBD section 4.2, +height_change,time_lag8,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each biennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",biennial dh/dt time,ATBD section 4.2, +height_change,time_lag12,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each triennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",triennial dh/dt time,ATBD section 4.2, +height_change,time_lag16,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quadrennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",quadrennial dh/dt time,ATBD section 4.2, +height_change,x,meters,x,float64,x,None,"x coordinate of the 1-km cell centers, in projected coordinates",polar stereographic x at 1km,ATBD section 3.2, +height_change,y,meters,y,float64,y,None,"y coordinate of the 1-km cell centers, in projected coordinates",polar stereographic y at 1km,ATBD section 3.2, +height_change,ice_area,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 1x1 km grid cell, accounting for the area distortion in the polar-stereographic projections",ice-covered area at 1 km,ATBD section 3.4, +height_change,ice_area_lag1,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 1x1 km grid cell for quarterly change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 1 km, for quarterly change in height",ATBD section 3.4, +height_change,ice_area_lag4,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 1x1 km grid cell for annual change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 1 km, for annual change in height",ATBD section 3.4, +height_change,ice_area_lag8,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 1x1 km grid cell for biennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 1 km, for biennial change in height",ATBD section 3.4, +height_change,ice_area_lag12,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 1x1 km grid cell for triennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 1 km, for triennial change in height",ATBD section 3.4, +height_change,ice_area_lag16,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 1x1 km grid cell for quadrennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 1 km, for quadrennial change in height",ATBD section 3.4, +height_change,data_count,counts,"time,y,x",float32,time y x,4,Weighted number of data contributing to each node in the 1-km height-change grid,data count ,ATBD section 5.2.4.4, +height_change,misfit_rms,meters,"time,y,x",float32,time y x,4,Misfit associated with each node in the 1-km height-change grid,rms misfit ,ATBD section 5.2.4.4, +height_change,misfit_scaled_rms,counts,"time,y,x",float32,time y x,4,Scaled misfit associated with each node in the 1-km height-change grid,scaled rms misfit,ATBD section 5.2.4.4, +height_change,delta_h,meters,"time,y,x",float32,time y x,4,"Height change relative to the datum (Jan 1, 2020) surface",height change at 1 km,ATBD section 3.4,delta_h group includes variables describing height differences between the model surface at any time and the DEM surface at a resolution of 1 km. +height_change,delta_h_sigma,meters,"time,y,x",float32,time y x,4,"Estimated error in height change relative to the datum (Jan 1, 2020) surface",height change uncertainty at 1 km,ATBD section 3.4, +height_change,dhdt_lag1,meters years^-1,"time,y,x",float32,time y x,4,Quarterly height-change rate,quarterly height-change rate at 1 km,ATBD section 3.4,"dhdt_lag1 group includes variables describing height difference rates, at a resolution of 1 km, between subsequent quarterly height-difference surfaces." +height_change,dhdt_lag1_sigma,meters years^-1,"time,y,x",float32,time y x,4,Estimated error in quarterly height-change rate,quarterly height-change rate uncertainty at 1 km,ATBD section 3.4, +height_change,dhdt_lag4,meters years^-1,"time,y,x",float32,time y x,4,Annual height-change rate,annual height-change rate at 1 km,ATBD section 3.4,"dhdt_lag4 group includes variables describing annual height-change-rate estimates, at a resolution of 1 km." +height_change,dhdt_lag4_sigma,meters years^-1,"time,y,x",float32,time y x,4,Estimated error in annual height-change rate,annual height-change rate uncertainty at 1 km,ATBD section 3.4, +height_change,dhdt_lag8,meters years^-1,"time,y,x",float32,time y x,4,Biennial height-change rate,biennial height-change rate at 1 km,ATBD section 3.4,"dhdt_lag8 group includes variables describing biennial height-change-rate estimates, at a resolution of 1km." +height_change,dhdt_lag8_sigma,meters years^-1,"time,y,x",float32,time y x,4,Estimated error in biennial height-change rate,biennial height-change rate uncertainty at 1 km,ATBD section 3.4, +height_change,dhdt_lag12,meters years^-1,"time,y,x",float32,time y x,4,Triennial height-change rate,triennial height-change rate at 1 km,ATBD section 3.4,"dhdt_lag12 group includes variables describing triennial height-change-rate estimates, at a resolution of 1km." +height_change,dhdt_lag12_sigma,meters years^-1,"time,y,x",float32,time y x,4,Estimated error in triennial height-change rate,triennial height-change rate uncertainty at 1 km,ATBD section 3.4, +height_change,dhdt_lag16,meters years^-1,"time,y,x",float32,time y x,4,Quadrennial height-change rate,quadrennial height-change rate at 1 km,ATBD section 3.4,"dhdt_lag16 group includes variables describing quadrennial height-change-rate estimates, at a resolution of 1km." +height_change,dhdt_lag16_sigma,meters years^-1,"time,y,x",float32,time y x,4,Estimated error in quadrennial height-change rate,quadrennial height-change rate uncertainty at 1 km,ATBD section 3.4, +,,,,,,,,,, +height_change_10km,time,days since 2018-01-01,time,float64,time,None,"Time for each node, in days since 2018-01-01:T00.00.00 UTC",quarterly h(t) time,ATBD section 4.2, +height_change_10km,time_lag1,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quarterly height-change rate, in days since 2018-01-01:T00.00.00 UTC",quarterly dh/dt time,ATBD section 4.2, +height_change_10km,time_lag4,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each annual height-change rate, in days since 2018-01-01:T00.00.00 UTC",annual dh/dt time,ATBD section 4.2, +height_change_10km,time_lag8,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each biennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",biennial dh/dt time,ATBD section 4.2, +height_change_10km,time_lag12,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each triennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",triennial dh/dt time,ATBD section 4.2, +height_change_10km,time_lag16,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quadrennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",quadrennial dh/dt time,ATBD section 4.2, +height_change_10km,x,meters,x,float64,x,None,"x coordinate of the 10-km cell centers, in projected coordinates",polar stereographic x at 10 km,ATBD section 3.2, +height_change_10km,y,meters,y,float64,y,None,"y coordinate of the 10-km cell centers, in projected coordinates",polar stereographic y at 10 km,ATBD section 3.2, +height_change_10km,ice_area_10km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 10x10 km grid cell, accounting for the area distortion in the polar-stereographic projections",ice-covered area at 10 km,ATBD section 3.4, +height_change_10km,ice_area_lag1_10km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 10x10 km grid cell for quarterly change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 10 km, for quarterly change in height",ATBD section 3.4, +height_change_10km,ice_area_lag4_10km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 10x10 km grid cell for annual change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 10 km, for annual change in height",ATBD section 3.4, +height_change_10km,ice_area_lag8_10km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 10x10 km grid cell for biennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 10 km, for biennial change in height",ATBD section 3.4, +height_change_10km,ice_area_lag12_10km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 10x10 km grid cell for triennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 10 km, for triennial change in height",ATBD section 3.4, +height_change_10km,ice_area_lag16_10km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 10x10 km grid cell for quadrennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 10 km, for quadrennial change in height",ATBD section 3.4, +height_change_10km,delta_h_10km,meters,"time,y,x",float32,time y x,4,"10x10 km average height change relative to the datum (Jan 1, 2020) surface",height change at 10 km,ATBD section 3.4,delta_h group includes variables describing height differences between the model surface at any time and the DEM surface at a resolution of 10 km. +height_change_10km,delta_h_sigma_10km,meters,"time,y,x",float32,time y x,4,"Uncertainty in the 10x10 km average height change relative to the datum (Jan 1, 2020) surface",height change uncertainty at 10 km,ATBD section 3.4, +height_change_10km,dhdt_lag1_10km,meters years^-1,"time,y,x",float32,time y x,4,10x10 km average quarterly height change rate,quarterly height-change rate at 10 km,ATBD section 3.4,"dhdt_lag1 group includes variables describing height difference rates, at a resolution of 10 km, between subsequent quarterly height-difference surfaces." +height_change_10km,dhdt_lag1_sigma_10km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 10x10 km average quarterly height change rate,quarterly height-change rate uncertainty at 10 km,ATBD section 3.4, +height_change_10km,dhdt_lag4_10km,meters years^-1,"time,y,x",float32,time y x,4,10x10 km average annual height change rate,annual height-change rate at 10 km,ATBD section 3.4,"dhdt_lag4 group includes variables describing annual height-change-rate estimates, at a resolution of 10 km." +height_change_10km,dhdt_lag4_sigma_10km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 10x10 km average annual height change rate,annual height-change rate uncertainty at 10 km,ATBD section 3.4, +height_change_10km,dhdt_lag8_10km,meters years^-1,"time,y,x",float32,time y x,4,10x10 km average biennial height change rate,biennial height-change rate at 10 km,ATBD section 3.4,"dhdt_lag8 group includes variables describing biennial height-change-rate estimates, at a resolution of 10 km." +height_change_10km,dhdt_lag8_sigma_10km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 10x10 km average biennial height change rate,biennial height-change rate uncertainty at 10 km,ATBD section 3.4, +height_change_10km,dhdt_lag12_10km,meters years^-1,"time,y,x",float32,time y x,4,10x10 km average triennial height change rate,triennial height-change rate at 10 km,ATBD section 3.4,"dhdt_lag12 group includes variables describing triennial height-change-rate estimates, at a resolution of 10 km." +height_change_10km,dhdt_lag12_sigma_10km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 10x10 km average triennial height change rate,triennial height-change rate uncertainty at 10 km,ATBD section 3.4, +height_change_10km,dhdt_lag16_10km,meters years^-1,"time,y,x",float32,time y x,4,10x10 km average quadrennial height change rate,quadrennial height-change rate at 10 km,ATBD section 3.4,"dhdt_lag16 group includes variables describing quadrennial height-change-rate estimates, at a resolution of 10 km." +height_change_10km,dhdt_lag16_sigma_10km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 10x10 km average quadrennial height change rate,quadrennial height-change rate uncertainty at 10 km,ATBD section 3.4, +,,,,,,,,,, +height_change_20km,time,days since 2018-01-01,time,float64,time,None,"Time for each node, in days since 2018-01-01:T00.00.00 UTC",quarterly h(t) time,ATBD section 4.2, +height_change_20km,time_lag1,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quarterly height-change rate, in days since 2018-01-01:T00.00.00 UTC",quarterly dh/dt time,ATBD section 4.2, +height_change_20km,time_lag4,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each annual height-change rate, in days since 2018-01-01:T00.00.00 UTC",annual dh/dt time,ATBD section 4.2, +height_change_20km,time_lag8,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each biennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",biennial dh/dt time,ATBD section 4.2, +height_change_20km,time_lag12,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each triennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",triennial dh/dt time,ATBD section 4.2, +height_change_20km,time_lag16,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quadrennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",quadrennial dh/dt time,ATBD section 4.2, +height_change_20km,x,meters,x,float64,x,None,"x coordinate of the 20-km cell centers, in projected coordinates",polar stereographic x at 20 km,ATBD section 3.2, +height_change_20km,y,meters,y,float64,y,None,"y coordinate of the 20-km cell centers, in projected coordinates",polar stereographic y at 20 km,ATBD section 3.2, +height_change_20km,ice_area_20km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 20x20 km grid cell, accounting for the area distortion in the polar-stereographic projections",ice-covered area at 20 km,ATBD section 3.4, +height_change_20km,ice_area_lag1_20km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 20x20 km grid cell for quarterly change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 20 km, for quarterly change in height",ATBD section 3.4, +height_change_20km,ice_area_lag4_20km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 20x20 km grid cell for annual change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 20 km, for annual change in height",ATBD section 3.4, +height_change_20km,ice_area_lag8_20km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 20x20 km grid cell for biennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 20 km, for biennial change in height",ATBD section 3.4, +height_change_20km,ice_area_lag12_20km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 20x20 km grid cell for triennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 20 km, for triennial change in height",ATBD section 3.4, +height_change_20km,ice_area_lag16_20km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 20x20 km grid cell for quadrennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 20 km, for quadrennial change in height",ATBD section 3.4, +height_change_20km,delta_h_20km,meters,"time,y,x",float32,time y x,4,"20x20 km average height change relative to the datum (Jan 1, 2020) surface",height change at 20 km,ATBD section 3.4,delta_h group includes variables describing height differences between the model surface at any time and the DEM surface at a resolution of 20 km. +height_change_20km,delta_h_sigma_20km,meters,"time,y,x",float32,time y x,4,"Uncertainty in the 20x20 km average height change relative to the datum (Jan 1, 2020) surface",height change uncertainty at 20 km,ATBD section 3.4, +height_change_20km,dhdt_lag1_20km,meters years^-1,"time,y,x",float32,time y x,4,20x20 km average quarterly height change rate,quarterly height-change rate at 20 km,ATBD section 3.4,"dhdt_lag1 group includes variables describing height difference rates, at a resolution of 20 km, between subsequent quarterly height-difference surfaces." +height_change_20km,dhdt_lag1_sigma_20km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 20x20 km average quarterly height change rate,quarterly height-change rate uncertainty at 20 km,ATBD section 3.4, +height_change_20km,dhdt_lag4_20km,meters years^-1,"time,y,x",float32,time y x,4,20x20 km average annual height change rate,annual height-change rate at 20 km,ATBD section 3.4,"dhdt_lag4 group includes variables describing annual height-change-rate estimates, at a resolution of 20 km." +height_change_20km,dhdt_lag4_sigma_20km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 20x20 km average annual height change rate,annual height-change rate uncertainty at 20 km,ATBD section 3.4, +height_change_20km,dhdt_lag8_20km,meters years^-1,"time,y,x",float32,time y x,4,20x20 km average biennial height change rate,biennial height-change rate at 20 km,ATBD section 3.4,"dhdt_lag8 group includes variables describing biennial height-change-rate estimates, at a resolution of 20 km." +height_change_20km,dhdt_lag8_sigma_20km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 20x20 km average biennial height change rate,biennial height-change rate uncertainty at 20 km,ATBD section 3.4, +height_change_20km,dhdt_lag12_20km,meters years^-1,"time,y,x",float32,time y x,4,20x20 km average triennial height change rate,triennial height-change rate at 20 km,ATBD section 3.4,"dhdt_lag12 group includes variables describing triennial height-change-rate estimates, at a resolution of 20 km." +height_change_20km,dhdt_lag12_sigma_20km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 20x20 km average triennial height change rate,triennial height-change rate uncertainty at 20 km,ATBD section 3.4, +height_change_20km,dhdt_lag16_20km,meters years^-1,"time,y,x",float32,time y x,4,20x20 km average quadrennial height change rate,quadrennial height-change rate at 20 km,ATBD section 3.4,"dhdt_lag16 group includes variables describing quadrennial height-change-rate estimates, at a resolution of 20 km." +height_change_20km,dhdt_lag16_sigma_20km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 20x20 km average quadrennial height change rate,quadrennial height-change rate uncertainty at 20 km,ATBD section 3.4, +,,,,,,,,,, +height_change_40km,time,days since 2018-01-01,time,float64,time,None,"Time for each node, in days since 2018-01-01:T00.00.00 UTC",quarterly h(t) time,ATBD section 4.2, +height_change_40km,time_lag1,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quarterly height-change rate, in days since 2018-01-01:T00.00.00 UTC",quarterly dh/dt time,ATBD section 4.2, +height_change_40km,time_lag4,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each annual height-change rate, in days since 2018-01-01:T00.00.00 UTC",annual dh/dt time,ATBD section 4.2, +height_change_40km,time_lag8,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each biennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",biennial dh/dt time,ATBD section 4.2, +height_change_40km,time_lag12,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each triennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",triennial dh/dt time,ATBD section 4.2, +height_change_40km,time_lag16,days since 2018-01-01,time,float64,time,None,"Time for the midpoint of each quadrennial height-change rate, in days since 2018-01-01:T00.00.00 UTC",quadrennial dh/dt time,ATBD section 4.2, +height_change_40km,x,meters,x,float64,x,None,"x coordinate of the 40-km cell centers, in projected coordinates",polar stereographic x at 40 km,ATBD section 3.2, +height_change_40km,y,meters,y,float64,y,None,"y coordinate of the 40-km cell centers, in projected coordinates",polar stereographic y at 40 km,ATBD section 3.2, +height_change_40km,ice_area_40km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 40x40 km grid cell, accounting for the area distortion in the polar-stereographic projections",ice-covered area at 40 km,ATBD section 3.4, +height_change_40km,ice_area_lag1_40km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 40x40 km grid cell for quarterly change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 40 km, for quarterly change in height",ATBD section 3.4, +height_change_40km,ice_area_lag4_40km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 40x40 km grid cell for annual change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 40 km, for annual change in height",ATBD section 3.4, +height_change_40km,ice_area_lag8_40km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 40x40 km grid cell for biennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 40 km, for biennial change in height",ATBD section 3.4, +height_change_40km,ice_area_lag12_40km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 40x40 km grid cell for triennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 40 km, for triennial change in height",ATBD section 3.4, +height_change_40km,ice_area_lag16_40km,meters^2,"time,y,x",float32,time y x,4,"Ice-covered area of each 40x40 km grid cell for quadrennial change in height, accounting for the area distortion in the polar-stereographic projections","ice-covered area at 40 km, for quadrennial change in height",ATBD section 3.4, +height_change_40km,delta_h_40km,meters,"time,y,x",float32,time y x,4,"40x40 km average height change relative to the datum (Jan 1, 4040) surface",height change at 40 km,ATBD section 3.4,delta_h group includes variables describing height differences between the model surface at any time and the DEM surface at a resolution of 40 km. +height_change_40km,delta_h_sigma_40km,meters,"time,y,x",float32,time y x,4,"Uncertainty in the 40x40 km average height change relative to the datum (Jan 1, 4040) surface",height change uncertainty at 40 km,ATBD section 3.4, +height_change_40km,dhdt_lag1_40km,meters years^-1,"time,y,x",float32,time y x,4,40x40 km average quarterly height change rate,quarterly height-change rate at 40 km,ATBD section 3.4,"dhdt_lag1 group includes variables describing height difference rates, at a resolution of 40 km, between subsequent quarterly height-difference surfaces." +height_change_40km,dhdt_lag1_sigma_40km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 40x40 km average quarterly height change rate,quarterly height-change rate uncertainty at 40 km,ATBD section 3.4, +height_change_40km,dhdt_lag4_40km,meters years^-1,"time,y,x",float32,time y x,4,40x40 km average annual height change rate,annual height-change rate at 40 km,ATBD section 3.4,"dhdt_lag4 group includes variables describing annual height-change-rate estimates, at a resolution of 40 km." +height_change_40km,dhdt_lag4_sigma_40km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 40x40 km average annual height change rate,annual height-change rate uncertainty at 40 km,ATBD section 3.4, +height_change_40km,dhdt_lag8_40km,meters years^-1,"time,y,x",float32,time y x,4,40x40 km average biennial height change rate,biennial height-change rate at 40 km,ATBD section 3.4,"dhdt_lag8 group includes variables describing biennial height-change-rate estimates, at a resolution of 40 km." +height_change_40km,dhdt_lag8_sigma_40km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 40x40 km average biennial height change rate,biennial height-change rate uncertainty at 40 km,ATBD section 3.4, +height_change_40km,dhdt_lag12_40km,meters years^-1,"time,y,x",float32,time y x,4,40x40 km average triennial height change rate,triennial height-change rate at 40 km,ATBD section 3.4,"dhdt_lag12 group includes variables describing triennial height-change-rate estimates, at a resolution of 40 km." +height_change_40km,dhdt_lag12_sigma_40km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 40x40 km average triennial height change rate,triennial height-change rate uncertainty at 40 km,ATBD section 3.4, +height_change_40km,dhdt_lag16_40km,meters years^-1,"time,y,x",float32,time y x,4,40x40 km average quadrennial height change rate,quadrennial height-change rate at 40 km,ATBD section 3.4,"dhdt_lag16 group includes variables describing quadrennial height-change-rate estimates, at a resolution of 40 km." +height_change_40km,dhdt_lag16_sigma_40km,meters years^-1,"time,y,x",float32,time y x,4,Uncertainty in the 40x40 km average quadrennial height change rate,quadrennial height-change rate uncertainty at 40 km,ATBD section 3.4, diff --git a/ATL1415/resources/BRW_template.h5 b/ATL1415/resources/BRW_template.h5 index ad6f883..decdde4 100644 Binary files a/ATL1415/resources/BRW_template.h5 and b/ATL1415/resources/BRW_template.h5 differ diff --git a/ATL1415/resources/add_dhdt_year.py b/ATL1415/resources/add_dhdt_year.py new file mode 100644 index 0000000..81fe9ec --- /dev/null +++ b/ATL1415/resources/add_dhdt_year.py @@ -0,0 +1,48 @@ + + + +#This notebook contains code to add another year of dh/dt values onto the ATL15_output_attrs.csv file. It duplicates the fields for a specified lag from each group in the file, replaces the name of the lag with a larger lag, and writes the results out to a new csv file. + +#The original CSV files did not have a newline character on the last line. Without this, this script will omit the last, largest average, so it is important to manually add the newline if it is not present. + +#After running this, move the original ATL15_output_attrs.csv into a cache directory, and move the new version to ATL15_output_attrs.csv + +import re + + +import re + +in_file='ATL15_output_attrs.csv' +out_file='ATL15_output_attrs_rel003.csv' + +old_lag='lag12' +new_lag='lag16' +old_name='Triennial' +new_name='Quadrennial' + +with open(in_file,'r') as fh_in: + with open(out_file,'w') as fh_out: + for line in fh_in: + #print(line) + if old_lag in line: + # check if lagxx is in the line, if so create an updated version with the new lag + temp=line.replace(old_lag, new_lag).replace(old_name.lower(), new_name.lower()).replace(old_name, new_name) + field=line.split(',')[1] + if 'time' in field or 'ice_area' in field: + # time_lagxx or ice_area_lagxx fields: write immediately + fh_out.write(line) + fh_out.write(temp) + elif 'sigma' not in line: + # delta_h_lagxx or dhdt_lagxx fields: write out the field, cache the + # updated field + fh_out.write(line) + not_sigma_line = temp + else: + # sigma line: write the field, followed by the cached field and the updated field. + fh_out.write(line) + fh_out.write(not_sigma_line) + fh_out.write(temp) + else: + # Everything else gets written immediately + fh_out.write(line) + \ No newline at end of file diff --git a/ATL1415/resources/region_extent_polygons.json b/ATL1415/resources/region_extent_polygons.json index 3bc83be..ed0ceb9 100644 --- a/ATL1415/resources/region_extent_polygons.json +++ b/ATL1415/resources/region_extent_polygons.json @@ -4,5 +4,9 @@ "CS_poly": 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} diff --git a/ATL1415/version.py b/ATL1415/version.py index d98dea0..7660e28 100644 --- a/ATL1415/version.py +++ b/ATL1415/version.py @@ -1,11 +1,11 @@ #!/usr/bin/env python3 def softwareVersion(): - softwareVersion='2.0' + softwareVersion='3.0' return softwareVersion def softwareDate(): - softwareDate='Aug 01 2022' + softwareDate='Aug 01 2023' return softwareDate def softwareTitle(): @@ -17,6 +17,6 @@ def identifier(): return identifier def series_version(): - series_version='2.0' + series_version='3.0' return series_version diff --git a/default_args/AA.txt b/default_args/AA.txt index d69ce29..0af00cd 100644 --- a/default_args/AA.txt +++ b/default_args/AA.txt @@ -1,8 +1,6 @@ ---ATL11_index=ATL11/rel005_0314/south/index/GeoIndex.h5 ---mask_file=Antarctic/Greene_22_shelf_plus_10m_mask.h5 +--mask_file=Antarctic/Greene_22_shelf_plus_10m_mask_full.h5 --tide_mask_file=Antarctic/BedMachineAntarcticaOceanv2.tif --tide_model=CATS2008 ---Hemisphere=-1 --region=AA --d2z0_file=Antarctic/AA_Ed2z0dx2.tif --tide_adjustment_file=Antarctic/ATL11_0314_tide_adj_scale_200m.h5 diff --git a/default_args/ADAPT.txt b/default_args/ADAPT.txt index c334914..d0504a6 100644 --- a/default_args/ADAPT.txt +++ b/default_args/ADAPT.txt @@ -1,4 +1,4 @@ ---mask_dir=/home/besmith4/git_repos/surfaceChange/masks/ ---tide_directory=/att/nobackup/project/icesat-2/tide_models ---ATL14_root=/att/nobackup/project/icesat-2/ATL14_processing/ +--mask_dir=/home/besmith4/git_repos/ATL1415/masks/ +--tide_directory=/home/besmith4/shared//tide_models +--ATL14_root=/home/besmith4/shared/ATL14_devel/ diff --git a/default_args/CN.txt b/default_args/CN.txt index a178d74..6bb4994 100644 --- a/default_args/CN.txt +++ b/default_args/CN.txt @@ -1,4 +1,2 @@ ---ATL11_index=ATL11/rel005_0314/north/index/GeoIndex.h5 --mask_file=RGI_reduced/03_rgi60_ArcticCanadaNorth_reduced.db ---Hemisphere=1 --region=CN diff --git a/default_args/CS.txt b/default_args/CS.txt index 4f83baf..180cc50 100644 --- a/default_args/CS.txt +++ b/default_args/CS.txt @@ -1,4 +1,2 @@ ---ATL11_index=ATL11/rel005_0314/north/index/GeoIndex.h5 --mask_file=RGI_reduced/04_rgi60_ArcticCanadaSouth_reduced.db ---Hemisphere=1 --region=CS diff --git a/default_args/GL.txt b/default_args/GL.txt index 5cb1706..180c788 100644 --- a/default_args/GL.txt +++ b/default_args/GL.txt @@ -1,8 +1,6 @@ ---ATL11_index=ATL11/rel005_0314/north/index/GeoIndex.h5 --tide_mask_file=Arctic/BedMachineGreenland-2021-04-20_shelf_125m.tif --tide_model=Gr1km-v2 ---mask_file=Arctic/U_Texas_ice_mask_2019_100m.tif +--mask_file=Arctic/GrimpOceanMask_100m_201805_202110.h5 --E_d2z0dx2_file=Arctic/GL_Ed2z0dx2.tif --region=GL ---Hemisphere=1 diff --git a/default_args/IS.txt b/default_args/IS.txt index 77a2905..cea1f02 100644 --- a/default_args/IS.txt +++ b/default_args/IS.txt @@ -1,4 +1,2 @@ ---ATL11_index=ATL11/rel005_0314/north/index/GeoIndex.h5 --mask_file=RGI_reduced/06_rgi60_Iceland_reduced.db ---Hemisphere=1 --region=IS diff --git a/default_args/RA.txt b/default_args/RA.txt index 7baccae..e828ba4 100644 --- a/default_args/RA.txt +++ b/default_args/RA.txt @@ -1,4 +1,2 @@ ---ATL11_index=ATL11/rel005_0314/north/index/GeoIndex.h5 --mask_file=RGI_reduced/09_rgi60_RussianArctic_reduced.db ---Hemisphere=1 --region=RA diff --git a/default_args/SV.txt b/default_args/SV.txt index bd95501..5e4548b 100644 --- a/default_args/SV.txt +++ b/default_args/SV.txt @@ -1,5 +1,3 @@ ---ATL11_index=ATL11/rel005_0314/north/index/GeoIndex.h5 --mask_file=RGI_reduced/07_rgi60_Svalbard_reduced.db ---Hemisphere=1 --region=SV --DEM_tol=200 diff --git a/default_args/north.txt b/default_args/north.txt new file mode 100644 index 0000000..9613e58 --- /dev/null +++ b/default_args/north.txt @@ -0,0 +1,2 @@ +--ATL11_index=ATL11_006_0319/north/index/GeoIndex.h5 +--Hemisphere=1 diff --git a/default_args/rel_003.txt b/default_args/rel_003.txt new file mode 100644 index 0000000..49b1452 --- /dev/null +++ b/default_args/rel_003.txt @@ -0,0 +1,23 @@ +--tile_spacing=40000 +-W=60000 +-t=2018.75,2023.25 +-g=100,1000,0.25 +--dzdt_lags=1,4,8,12,16 +--E_d3zdx2dt=0.00005 +--E_d2z0dx2=0.02 +--E_d2zdt2=200000 +--sigma_geo=4.5 +--reference_epoch=5 +--avg_scales=40000,20000,10000 +--max_iterations=6 +--cycles=0319 +--Release=003 +--version=99 +--ATL11_release=006 +--DEM_tol=50 +--sigma_tol=8 +--bias_params=rgt,cycle,pair +--geoid_tol=5 +--sigma_extra_bin_spacing=10000 +--sigma_extra_max=3 +--geoid_file=EGM2008_geoid_h.nc diff --git a/default_args/south.txt b/default_args/south.txt new file mode 100644 index 0000000..4bed0b7 --- /dev/null +++ b/default_args/south.txt @@ -0,0 +1 @@ +--Hemisphere=-1 diff --git a/masks/Antarctic/.gitattributes b/masks/Antarctic/.gitattributes new file mode 100644 index 0000000..6ca4e92 --- /dev/null +++ b/masks/Antarctic/.gitattributes @@ -0,0 +1,5 @@ +Greene_22_shelf_plus_10m_mask_1km.tif filter=lfs diff=lfs merge=lfs -text +Greene_22_shelf_plus_10m_mask_2022_update.h5 filter=lfs diff=lfs merge=lfs -text +Greene_22_shelf_plus_10m_mask_240m.tif filter=lfs diff=lfs merge=lfs -text +Greene_22_shelf_plus_10m_mask_full.h5 filter=lfs diff=lfs merge=lfs -text +Greene_22_shelf_plus_10m_mask.h5 filter=lfs diff=lfs merge=lfs -text diff --git a/masks/Antarctic/Greene_22_shelf_plus_10m_mask_1km.tif b/masks/Antarctic/Greene_22_shelf_plus_10m_mask_1km.tif new file mode 100644 index 0000000..2561e8e --- /dev/null +++ b/masks/Antarctic/Greene_22_shelf_plus_10m_mask_1km.tif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8351296815d908b7352333b377d6b9022aa63b121102a5e601113a9e00f08225 +size 685305 diff --git a/masks/Antarctic/Greene_22_shelf_plus_10m_mask_240m.tif b/masks/Antarctic/Greene_22_shelf_plus_10m_mask_240m.tif new file mode 100644 index 0000000..e1e6e2e --- /dev/null +++ b/masks/Antarctic/Greene_22_shelf_plus_10m_mask_240m.tif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee09073945331a7b26254a1f474d0f266825093cd87c71fc1d4ff39b789abdb0 +size 25650705 diff --git a/masks/Antarctic/Greene_22_shelf_plus_10m_mask_full.h5 b/masks/Antarctic/Greene_22_shelf_plus_10m_mask_full.h5 new file mode 100644 index 0000000..a8c0a64 --- /dev/null +++ b/masks/Antarctic/Greene_22_shelf_plus_10m_mask_full.h5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6a880a936c467b551d4453b538a857eb6de33dc8ba5db157f16f3a161b26c624 +size 404713571 diff --git a/masks/Antarctic/make_time_varying_mask.ipynb b/masks/Antarctic/make_time_varying_mask.ipynb new file mode 100644 index 0000000..d64cff5 --- /dev/null +++ b/masks/Antarctic/make_time_varying_mask.ipynb @@ -0,0 +1,285 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "9f5fca79", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib widget\n", + "\n", + "import os\n", + "import pointCollection as pc\n", + "import h5py\n", + "import numpy as np\n", + "import scipy.ndimage as snd\n", + "import h5py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5e7ab70-a9b1-4cf1-99c5-2a9d2fab38c1", + "metadata": {}, + "outputs": [], + "source": [ + "! h5ls ../../../ice-shelf-geometry/data/icemask_composite.nc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97edbb81-32dc-4d4c-8e78-8916483e0464", + "metadata": {}, + "outputs": [], + "source": [ + "! ls " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59be1fe1-ffb5-49e2-adb1-929c76e945dc", + "metadata": {}, + "outputs": [], + "source": [ + "mask_file='../../../ice-shelf-geometry/data/icemask_composite.nc'\n", + "template=pc.grid.data(t_axis=0).from_nc(mask_file, bands=[18], fields=[])\n", + "\n", + "files=['../../../ice-shelf-geometry/data/icemask_composite.nc',\n", + " 'antarctic_icelines_2021_mask.h5',\n", + " 'antarctic_icelines_2022_mask.h5']\n", + " \n", + "t_vals=[]\n", + "file_nums=[]\n", + "\n", + "for count, file in enumerate(files):\n", + " with h5py.File(file,'r') as h5f:\n", + " if 't' in h5f:\n", + " t_temp = np.array(h5f['t'])\n", + " else:\n", + " t_temp=np.array(h5f['year'])\n", + " keep = t_temp > 2000\n", + " t_vals += list(t_temp[keep])\n", + " file_nums += list(np.zeros(keep.sum(), dtype=int)+count)\n", + "\n", + "t_vals=np.array(t_vals)\n", + " \n", + "# remove the last of the original Greene et al masks\n", + "t_vals=t_vals[np.flatnonzero(np.round(t_vals/.1)*.1 != 2021.2)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7c31ca8", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# read the bedmachine mask and ice thickness\n", + "bedmachine_file='/Volumes/ice3/ben/Bedmachine/BedMachineAntarctica_2020-07-15_v02.nc'\n", + "temp=pc.grid.data().from_nc(bedmachine_file, fields=['mask','thickness'])#, bounds=[[3.e5, 5.e5], [-14e5, -12e5]])\n", + "\n", + "# dilate the grounded-ice and ice-shelf parts of the mask by 4 pixels\n", + "temp.assign({'z':(snd.binary_dilation((temp.mask==1)|(temp.mask==2), structure=np.ones((4,4), dtype=bool)) & (temp.thickness < 10))})\n", + "bm_mask=pc.grid.data().from_dict({'x':template.x,'y':template.y})\n", + "\n", + "# interpolate the bedmachine mask to the Green coordinates\n", + "bm_mask.assign({'z':temp.interp(template.x, template.y, gridded=True)<0.9})\n", + "bm_mask.z = snd.binary_opening(bm_mask.z, np.ones([10,10], dtype='bool'))\n", + "\n", + "# mask the pole hole\n", + "bm_mask.get_latlon(srs_epsg=3031)\n", + "bm_mask.z[bm_mask.latitude < -88]=0\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7381ff05-b8bc-4165-a2fb-0b5141398be2", + "metadata": {}, + "outputs": [], + "source": [ + "out_file='Greene_22_shelf_plus_10m_mask_full.h5'\n", + "\n", + "fields=['x','y','year']\n", + "\n", + "\n", + "if True:\n", + "\n", + " #if not os.path.isfile(in_file):\n", + " # ! pushd ../../..; git clone git@github.com:SmithB/ice-shelf-geometry.git\n", + "\n", + " # get the dimension shapes:\n", + " D={'x':template.x,'y':template.y,'year':np.array(t_vals)}\n", + "\n", + " if os.path.isfile(out_file):\n", + " os.remove(out_file)\n", + " with h5py.File(out_file,'w') as h5f_out:\n", + " h5f_out.create_dataset('z', [D['y'].size, D['x'].size, D['year'].size], \n", + " chunks=(100, 100, 1), compression='gzip', dtype='i8', \\\n", + " fillvalue=255)\n", + " h5f_out.create_dataset('x', data=template.x)\n", + " h5f_out.create_dataset('y', data=template.y)\n", + " h5f_out.create_dataset('t', data=t_vals)\n", + " for count, in_file in enumerate(files):\n", + " with h5py.File(in_file,'r') as h5f_in:\n", + " if 't' in h5f_in:\n", + " t_temp = np.array(h5f_in['t'])\n", + " else:\n", + " t_temp=np.array(h5f_in['year'])\n", + " for this_field in ['z','ice']:\n", + " if this_field in h5f_in:\n", + " break\n", + " _, in_bands, out_bands=np.intersect1d(t_temp, t_vals, return_indices=True)\n", + " for in_band, out_band in zip(in_bands, out_bands):\n", + " if in_file[-2:]=='h5':\n", + " in_mask=pc.grid.data().from_h5(in_file, bands=[in_band], field_mapping={'z':this_field})\n", + " else:\n", + " in_mask=pc.grid.data().from_nc(in_file, bands=[in_band], field_mapping={'z':this_field}, timename='year')\n", + " print(f\"writing input band {in_band} to output band {out_band}\")\n", + " # transpose each band, flip in y\n", + " h5f_out['z'][:,:,out_band] = (np.squeeze(in_mask.z==1) & bm_mask.z)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a03a8dc1-9830-4753-ace5-26185b6a1401", + "metadata": {}, + "outputs": [], + "source": [ + "np.diff(t_vals)" + ] + }, + { + "cell_type": "markdown", + "id": "76c1255f", + "metadata": {}, + "source": [ + "## Make the 1-km mask used to generate the tile centers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d22ac93", + "metadata": {}, + "outputs": [], + "source": [ + "mask=pc.grid.data().from_h5(out_file, bands=[0])\n", + "mask.z=mask.z.astype(bool)\n", + "mask.to_geotif('Greene_22_shelf_plus_10m_mask_240m.tif', srs_epsg=3031)\n", + "! rm Greene_22_shelf_plus_10m_mask_1km.tif\n", + "! gdal_translate -tr 1000 1000 -r nearest -co COMPRESS=LZW -co TILED=YES -co PREDICTOR=1 Greene_22_shelf_plus_10m_mask_240m.tif Greene_22_shelf_plus_10m_mask_1km.tif\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2eedd74-2235-4637-8bfd-00f6e7365ff2", + "metadata": {}, + "outputs": [], + "source": [ + "M_1km=pc.grid.data().from_geotif('Greene_22_shelf_plus_10m_mask_1km.tif')\n", + "M_1km.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50ad66da-4c28-4a4b-a6d6-082d226f3ab9", + "metadata": {}, + "outputs": [], + "source": [ + "#XR, YR=np.round(np.array([*map(np.array, [plt.gca().get_xlim(), plt.gca().get_ylim()])])/1.e4)*10000\n", + "XR, YR =(np.array([-1660000., -1490000.]), np.array([-360000., -230000.]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79e91c2b-3129-4ce4-a305-55d9a32d9ebf", + "metadata": {}, + "outputs": [], + "source": [ + "D=pc.grid.data().from_h5('Greene_22_shelf_plus_10m_mask_full.h5', bounds=[XR, YR])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd390d81-4c8a-4e16-bb0a-bfe611a75ae2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfd3fae5-e7c4-4330-98b6-3dbb044a14f2", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.animation as animation\n", + "import glob\n", + "plt.rcParams['animation.ffmpeg_path'] ='/home/ben/mambaforge/envs/devel/bin/ffmpeg'\n", + "%matplotlib widget" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdda0c83-19ec-4f69-bd6c-a511f9bc635c", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def draw_mask(k0, D, hi, ha):\n", + " \n", + " k=np.minimum(D.t.size-1, np.maximum(0, k0-pause_frames))\n", + " hi.set_data((D.z[:,:,k]))\n", + " ht=ha.set_title(f'{D.t[k]:2.2f}')\n", + " return [hi, ht]\n", + "\n", + "\n", + "hf, ha=plt.subplots(1,1)\n", + "hi=D.show(band=0, ax=ha)\n", + "ha.set_title(f'{D.t[0]:2.2f}')\n", + "\n", + "pause_frames=1\n", + "anim = animation.FuncAnimation(hf, draw_mask, frames=D.z.shape[2]+2*pause_frames, interval=500, blit=True, fargs=[D, hi, ha])\n", + "from matplotlib import rc\n", + "\n", + "# equivalent to rcParams['animation.html'] = 'html5'\n", + "rc('animation', html='html5')\n", + "anim\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "devel", + "language": "python", + "name": "devel" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/ATL14_algo_demos.ipynb b/notebooks/ATL14_algo_demos.ipynb new file mode 100644 index 0000000..bb23cb3 --- /dev/null +++ b/notebooks/ATL14_algo_demos.ipynb @@ -0,0 +1,1132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "%load_ext autoreload\n", + "%autoreload 2Setup\n", + "To run this notebook, you'll need (do these in order):\n", + "\n", + "The suitesparse library:\n", + "\n", + "From conda:\n", + "\n", + "conda install suitesparse\n", + "\n", + "On ubuntu/similar linux\n", + "\n", + "apt-get install suitesparse\n", + "\n", + "My version of the PySPQR repository (This is where you need suitesparse)\n", + "\n", + "https://www.github.com/smithb/PySPQR.git\n", + "\n", + "My LSsurf repository\n", + "\n", + "https://www.github.com/smithb/LSsurf.git\n", + "\n", + "My pointCollection repository:\n", + "\n", + "https://www.github.com/smithb/pointCollection.git\n", + "\n", + "For each repository, you'll need to clone the repo (git clone [url to .git file]), then cd to the \n", + "\n", + "directory that git makes, and type:\n", + "\n", + "python3 setup.py install --user \n", + "\n", + "Good luck!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from LSsurf.smooth_xytb_fit import smooth_xytb_fit\n", + "import pointCollection as pc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "The ATL14/15 algorithm works by fitting a time-varying surface to the data. The form of the model is:\n", + "$$\n", + "z_m(x, y, t) = z_0(x,y) + \\delta z(x, y, t)\n", + "$$\n", + "Here $z_0$ is a DEM giving the surface height at time $t_0$, and $dz(x,y,t)$ gives the surface-height change between $t_0$ and $t$ at location $x,y$. The DEM is represented as a high-resolution grid of elevations, while $dz$ is represented as a set of lower-resolution surfaces, one for each quarter-year interval. The model is constructed so that the $z_0$ surface for time $t_0$ is uniformally equal to zero.\n", + "\n", + "We find the surface by minimizing the quantity:\n", + "$$\n", + "R = W_{xx0}^2 \\int (\\nabla^2 z_0)^2 dA + W_{x0} \\int (\\nabla z_0)^2 dA + W_{xxt}\\int (\\nabla^2 \\frac{\\partial\\delta z}{\\partial t})^2 dAdt + W_{xt}\\int (\\nabla \\frac{\\partial\\delta z}{\\partial t})^2 dAdt + W_{tt}\\int (\\frac{\\partial^2 \\delta z}{ \\partial t^2})^2 dA + \\sum (\\frac{z_m(x,y,t)-z_i(x,y,t)}{\\sigma_i})^2\n", + "$$\n", + "Here $W_{xx0}$ is the inverse of the expected RMS of the second spatial derivatives of the surface height, $W_{x0}$ is the the inverse of the expected RMS of the first derivatives of the surface height, $W_{xxt}$ is the the inverse of the expected RMS of the second spatial derivatives of the $dz/dt$ field, etc. The last term is the sum of the error-scaled residuals between the data and the model. I've put some mathematical description of how this model behaves in the attenuation_curves.ipynb notebook in this repo's directory.\n", + "\n", + "To construct a surface, we need to specify the data values, the model grid resolutions for the DEM and for the height-change surfaces, the dimensions of the grid, and the expected derivative values. \n", + "\n", + "## Solutions in one dimension (x)\n", + "Initially, we will demonstrate the fit on a long, skinny domain, to illustrate how the model works in one dimension. We will specify identical values for the data for two different time epochs, so that there is no time variation in the solution, and all variation in the solution is in the DEM ($z_0$) field." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# define the domain's width in x, y, and time\n", + "W={'x':1.e4,'y':200,'t':2}\n", + "# define the grid center:\n", + "ctr={'x':0., 'y':0., 't':0.}\n", + "# define the grid spacing\n", + "spacing={'z0':50, 'dz':50, 'dt':0.25}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# define the data as a sine wave with a wavelength of 2 km and an amplitude of 100m. \n", + "x=np.arange(-W['x']/2, W['x']/2, 100)\n", + "lambda_x=2000\n", + "amp=100\n", + "data_sigma=1\n", + "D=pc.data().from_dict({'x':x, 'y':np.zeros_like(x),'z':-amp*np.cos(2*np.pi*x/lambda_x),\\\n", + " 'time':np.zeros_like(x)-0.5, 'sigma':np.zeros_like(x)+data_sigma})\n", + "# To ensure a time-constant simulation, replicate the data at times -0.5 and 0.5:\n", + "data=pc.data().from_list([D, D.copy().assign({'time':np.zeros_like(x)+0.5})])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['dzdt_lag1'])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# define the expected statistics of the surface\n", + "\n", + "E_d3zdx2dt=0.0001\n", + "E_d2z0dx2=0.06\n", + "E_d2zdt2=5000\n", + "\n", + "data_gap_scale=2500\n", + "E_RMS={'d2z0_dx2':E_d2z0dx2, 'dz0_dx':E_d2z0dx2*data_gap_scale, 'd3z_dx2dt':E_d3zdx2dt, 'd2z_dxdt':E_d3zdx2dt*data_gap_scale, 'd2z_dt2':E_d2zdt2}\n", + "\n", + "# run the fit\n", + "S=smooth_xytb_fit(data=data, ctr=ctr, W=W, spacing=spacing, E_RMS=E_RMS,\n", + " reference_epoch=2, N_subset=None, compute_E=False,\n", + " max_iterations=1,\n", + " VERBOSE=False, dzdt_lags=[1])\n", + "\n", + "# plot the results\n", + "plt.figure()\n", + "plt.clf()\n", + "plt.plot(data.x, data.z,'ko', label='data')\n", + "plt.plot(S['m']['z0'].x, S['m']['z0'].z0[0,:],'r', label='model')\n", + "plt.legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reducing the expected derivatives results in a smoother surface that does not fit the data as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['dzdt_lag1'])\n", + "dict_keys(['dzdt_lag1'])\n", + "dict_keys(['dzdt_lag1'])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.clf()\n", + "plt.plot(data.x, data.z,'ko', label='data')\n", + "A_z0={}\n", + "A_expected={}\n", + "\n", + "# data density\n", + "rd = data.size/W['x']/W['y']\n", + "\n", + "for E_d2z in [0.006, 0.001, 0.0003]:\n", + " E_RMS['d2z0_dx2'] = E_d2z\n", + " # run the fit\n", + " S=smooth_xytb_fit(data=data, ctr=ctr, W=W, spacing=spacing, E_RMS=E_RMS,\n", + " reference_epoch=2, N_subset=None, compute_E=False,\n", + " max_iterations=1,\n", + " VERBOSE=False, dzdt_lags=[1])\n", + " \n", + " plt.plot(S['m']['z0'].x, S['m']['z0'].z0[0,:], label=f'E_d2z0_dx2={E_d2z}')\n", + " # calculate the amplitude\n", + " A_z0[E_d2z]=np.max(np.abs(S['m']['z0'].z0[0,np.abs(S['m']['z0'].x)<3000]))\n", + " # Calculate the expected amplitude\n", + " A_expected[E_d2z] = amp /( 1 + 16*E_d2z**-2*np.pi**4/(lambda_x**4*rd)*data_sigma**2) \n", + " \n", + "plt.xlabel('x, m')\n", + "plt.ylabel('z0, m')\n", + "plt.legend(loc='lower right')\n", + "E_RMS['d2z0_dx2']= 0.006\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The expected amplitude for a model fit to data representing a sine wave of amplitude $A_d$ with wavelength $\\lambda$ and estimated error $\\sigma$ is:\n", + "\n", + "$$A_m = \\frac{A_{d}}{1 + \\frac{16 \\pi^{4} \\sigma^{2} }{E_{xx}^2\\lambda^{4} \\rho}}$$\n", + "\n", + "Here $E_{xx}$ is the expected second spatial derivative of $z0$. A plot of the recovered model amplitude vs. $E_{xx}$ is consistent with this model:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Ezz_vals = np.arange(0.0002, 0.007, 0.0001)\n", + "A_expected = amp /( 1 + 16*np.pi**4*data_sigma**2/(Ezz_vals**2*lambda_x**4*rd)) \n", + "\n", + "plt.figure(); plt.clf()\n", + "plt.plot(Ezz_vals, A_expected, label='expected')\n", + "temp=np.array(list(A_z0.keys()))\n", + "plt.plot(temp, np.array([A_z0[key] for key in A_z0.keys()]),'o', label='recovered')\n", + "plt.xlabel('$E_{xx}$')\n", + "plt.ylabel('amplitude')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The values recovered from the fit are within a few percent of those expected based on the analytic expression." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting data with gaps\n", + "If there is a gap in the data, the solution will tend to form a smooth arc over the gap. The smaller the expected first derivative of the data, the more the solution will flaten out across the gap. For solutions of this type, the solution will tend to go flat over a distance $data\\_gap\\_scale$ if $E[RMS(dz/dx)] = E[RMS(d^2z/dx^2)]*data\\_gap\\_scale$\n", + "\n", + "As you might imagine, we try to set _data\\_gap\\_scale_ to be about half as large as we expect gaps in the data to be, so that large extrapolations don't produce odd values in the solution. We can demonstrate how the solution behaves in over data gaps by deleting the central 3 km of the data from our previous example, and fitting the model to the remaining data with different values of $data\\_gap\\_scale$." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['dzdt_lag1'])\n", + "dict_keys(['dzdt_lag1'])\n", + "dict_keys(['dzdt_lag1'])\n", + "dict_keys(['dzdt_lag1'])\n", + "dict_keys(['dzdt_lag1'])\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'h')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#make a set of data with a gap\n", + "data_with_gap=data[np.abs(data.x)>1500]\n", + "\n", + "\n", + "plt.figure()\n", + "plt.clf()\n", + "plt.plot(data_with_gap.x, data_with_gap.z,'ko', label='data')\n", + "# run the solution with different data gap scales\n", + "for this_data_gap_scale in [4000, 2000, 1000, 500, 250]:\n", + " E_RMS['dz0_dx'] = E_RMS['d2z0_dx2']*this_data_gap_scale\n", + " S=smooth_xytb_fit(data=data_with_gap, ctr=ctr, W=W, spacing=spacing, E_RMS=E_RMS,\n", + " reference_epoch=2, N_subset=None, compute_E=False,\n", + " max_iterations=1,\n", + " VERBOSE=False, dzdt_lags=[1])\n", + " \n", + " plt.plot(S['m']['z0'].x, S['m']['z0'].z0[0,:], label=f'data gap scale={this_data_gap_scale}')\n", + "plt.legend(loc='lower right');\n", + "plt.xlabel('x'); plt.ylabel('h')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The smaller data_gap_scale values result in a flatter solution inside the gap, at the expense of larger misfits. Excessively small data_gap_scale values are also undesirable because they can introduce artificial flattening in smooth ice-sheet regions with consistent surface slopes. We set data_gap_scale to 1500 m (equal to half the ICESat-2 pair-to-pair spacing), which seems to produce clean results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solutions in one dimension and time (x, t)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's see what happens when the solution can vary in space and time. We'll specify a flat surface for t=-0.99 (just after the start of the solution) and a sinusoidal surface for t=0.99 (just before the end). We will specify that the DEM is for reference epoch 4 (t=0)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['dzdt_lag1'])\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "D0=pc.data().from_dict({'x':x, 'y':np.zeros_like(x),'z':np.zeros_like(x),\\\n", + " 'time':np.zeros_like(x)-0.99, 'sigma':np.zeros_like(x)+1})\n", + "D1=pc.data().from_dict({'x':x, 'y':np.zeros_like(x),'z':amp-amp*np.cos(2*np.pi*x/lambda_x),\\\n", + " 'time':np.zeros_like(x)+0.99, 'sigma':np.zeros_like(x)+1})\n", + "data_dt=pc.data().from_list([D0, D1])\n", + "\n", + "data_gap_scale=2500\n", + "E_RMS['d3z_dx2dt'] = 0.006\n", + "E_RMS['d2z_dxdt'] = 0.006*data_gap_scale\n", + "E_RMS['d2z0_dx2'] = 0.03\n", + "E_RMS['dz0_dx'] = 0.03*data_gap_scale\n", + "\n", + "S=smooth_xytb_fit(data=data_dt, ctr=ctr, W=W, spacing=spacing, E_RMS=E_RMS,\n", + " reference_epoch=4, N_subset=None, compute_E=False,\n", + " max_iterations=1,\n", + " VERBOSE=False, dzdt_lags=[1])\n", + "\n", + "plt.figure();plt.clf()\n", + "plt.plot(D0.x, D0.z,'x', color='gray', label='data, t=-0.99')\n", + "plt.plot(D1.x, D1.z,'ko', label='data, t=0.99')\n", + "\n", + "for epoch in range(S['m']['dz'].shape[2]):\n", + " this_time=S['m']['dz'].time[epoch]\n", + " plt.plot(S['m']['dz'].x, S['m']['z0'].z0[2,:]+S['m']['dz'].dz[2,:, epoch], label=f'$\\delta z$, t={this_time}')\n", + "plt.plot(S['m']['z0'].x, S['m']['z0'].z0[2,:],'k', label='z0', linewidth=2)\n", + "plt.xlabel('x')\n", + "plt.ylabel('z')\n", + "plt.legend();\n", + "\n", + "\n", + "plt.figure(); plt.clf()\n", + "\n", + "for epoch in range(S['m']['dz'].shape[2]):\n", + " this_time=S['m']['dz'].time[epoch]\n", + " plt.plot(S['m']['dz'].x, S['m']['dz'].dz[2,:, epoch], label=f'$\\delta z$, t={this_time}')\n", + "plt.xlabel('x')\n", + "plt.ylabel('$\\delta$ z')\n", + "plt.legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The recovered surface matches the data at t=-1 and t=1, and varies smoothly in between. Because the reference epoch is halfway between the two data sets, its value is halfway between flat surface (at $t \\approx -1$) and the raised sinusoid (at $t \\approx 1$). The $\\delta z$ fields smoothly so that at any point, the surface varies approximately linearly from the $t=-1$ to its $t=1$ solution:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(6); \n", + "# find a point close to x=1000 \n", + "ii=np.argmin(np.abs(S['m']['dz'].x-1000))\n", + "# plot the time series\n", + "plt.plot(S['m']['dz'].time, S['m']['dz'].dz[2, ii, :])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we add another time point at t=-0.25 that is not colinear (in time) with the other time points, we get a smooth time variation at each point:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['dzdt_lag1'])\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'h')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "D2 = pc.data().from_dict({'x':x, 'y':np.zeros_like(x),'z':-1*amp+np.zeros_like(x),\\\n", + " 'time':np.zeros_like(x)-0.25, 'sigma':np.zeros_like(x)+1})\n", + "\n", + "data_dt2=pc.data().from_list([D0, D1, D2])\n", + "\n", + "\n", + "S=smooth_xytb_fit(data=data_dt2, ctr=ctr, W=W, spacing=spacing, E_RMS=E_RMS,\n", + " reference_epoch=4, N_subset=None, compute_E=False,\n", + " max_iterations=1,\n", + " VERBOSE=False, dzdt_lags=[1])\n", + "plt.figure(7); \n", + "# Find the data points closest x=1000\n", + "di=np.where(np.abs(data_dt2.x-1000)<2)\n", + "plt.plot(data_dt2.time[di], data_dt2.z[di],'o', label='data for x=1000')\n", + "\n", + "# find a model point close to x=1000 \n", + "ii=np.argmin(np.abs(S['m']['dz'].x-1000))\n", + "# plot the recovered time series\n", + "plt.plot(S['m']['dz'].time, S['m']['dz'].dz[2, ii, :], label='dz for x=1000')\n", + "plt.plot(S['m']['dz'].time, S['m']['dz'].dz[2, ii, :] + S['m']['z0'].z0[2,ii], label='dz+z0 for x=1000')\n", + "plt.legend();\n", + "plt.xlabel('time')\n", + "plt.ylabel('h')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Editing outliers\n", + "Outliers are identified based on the distribution of scaled residuals in the data. We iterate the solution and at each iteration calculate a robust estimate of the standard deviation of residuals in the data ($\\hat{\\sigma}$), then remove the outliers that are larger than $3\\hat{\\sigma}$. Let's return to the example with two data epochs, add some noise to all the data, and large noise values to a subset of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "rng=np.random.default_rng(532)\n", + "\n", + "data_dte=pc.data().from_list([D0, D1])\n", + "data_dte.z += rng.standard_normal(data_dte.size)\n", + "# introduce about 10% large outliers\n", + "outliers = np.argwhere(rng.random(data_dte.size) > 0.90).ravel()\n", + "data_dte.z[outliers] += (rng.random(outliers.size)-0.5)*200\n", + "\n", + "plt.figure(8)\n", + "plt.plot(data_dte.x, data_dte.z,'.', label='data')\n", + "plt.plot(data_dte.x[outliers], data_dte.z[outliers],'r*', label='outliers')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see what these outliers do to the solution without editing by running the solution with only one iteration. Note that we have asked for verbose output from _smooth\\_xytb\\_fit_, which reports on the robust spread and the outliers from each iteration. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['dzdt_lag1'])\n", + "initial: 199:\n", + "starting qr solve for iteration 0 at Thu Jan 6 09:06:18 2022\n", + "found 181 in TSE, sigma_hat=5.987, dm_max=115.621, dt= 1\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'count')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "S=smooth_xytb_fit(data=data_dte, ctr=ctr, W=W, spacing=spacing, E_RMS=E_RMS,\n", + " reference_epoch=4, N_subset=None, compute_E=False,\n", + " max_iterations=1,\n", + " VERBOSE=True, dzdt_lags=[1])\n", + "plt.figure(9, figsize=[9, 4]); plt.clf()\n", + "plt.subplot(121)\n", + "plt.plot(data_dte.x, data_dte.z,'k.', label='data')\n", + "plt.plot(S['m']['dz'].x, S['m']['z0'].z0[2,:]+S['m']['dz'].dz[2, :, 0],'k', label='solution, t=-1')\n", + "plt.plot(S['m']['dz'].x, S['m']['z0'].z0[2,:]+S['m']['dz'].dz[2, :, -1],'b', label='solution, t=1')\n", + "plt.xlabel('x')\n", + "plt.ylabel('h')\n", + "\n", + "plt.subplot(122)\n", + "r=S['data'].z-S['data'].z_est\n", + "plt.hist(r, np.arange(-101, 100, 2));\n", + "plt.xlabel('residual')\n", + "plt.ylabel('count')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Running the solution for more iterations eliminates a greater share of the outliers. The acceptance (or non-acceptance) of the data points is stored in the _three\\_sigma\\_edit_ field of S['data']." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['dzdt_lag1'])\n", + "initial: 199:\n", + "starting qr solve for iteration 0 at Thu Jan 6 09:06:29 2022\n", + "found 181 in TSE, sigma_hat=5.987, dm_max=115.621, dt= 1\n", + "starting qr solve for iteration 1 at Thu Jan 6 09:06:30 2022\n", + "found 184 in TSE, sigma_hat=3.297, dm_max=15.662, dt= 1\n", + "starting qr solve for iteration 2 at Thu Jan 6 09:06:31 2022\n", + "found 185 in TSE, sigma_hat=3.046, dm_max=5.281, dt= 1\n", + "starting qr solve for iteration 3 at Thu Jan 6 09:06:32 2022\n", + "found 185 in TSE, sigma_hat=3.043, dm_max=1.120, dt= 1\n", + "starting qr solve for iteration 4 at Thu Jan 6 09:06:32 2022\n", + "found 185 in TSE, sigma_hat=3.043, dm_max=0.000, dt= 1\n", + "Solution identical to previous iteration with tolerance 0.05, exiting after iteration 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "S=smooth_xytb_fit(data=data_dte, ctr=ctr, W=W, spacing=spacing, E_RMS=E_RMS,\n", + " reference_epoch=4, N_subset=None, compute_E=False,\n", + " max_iterations=10,\n", + " VERBOSE=True, dzdt_lags=[1])\n", + "plt.figure(9, figsize=[9, 4]); plt.clf()\n", + "plt.subplot(121)\n", + "d_out=S['data']\n", + "plt.plot(d_out.x[d_out.three_sigma_edit==1], d_out.z[d_out.three_sigma_edit==1],'k.')\n", + "plt.plot(d_out.x[d_out.three_sigma_edit==0], d_out.z[d_out.three_sigma_edit==0],'r.')\n", + "plt.plot(S['m']['dz'].x, S['m']['z0'].z0[2,:]+S['m']['dz'].dz[2, :, 0],'k')\n", + "plt.plot(S['m']['dz'].x, S['m']['z0'].z0[2,:]+S['m']['dz'].dz[2, :, -1],'b')\n", + "\n", + "plt.subplot(122)\n", + "r=d_out.z-d_out.z_est\n", + "plt.hist(r, np.arange(-101, 100, 2), color='red')\n", + "plt.hist(r[d_out.three_sigma_edit==1], np.arange(-101, 100, 2), color='blue');\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This only works well if the constraints allow a fairly good fit between the data and the model. If the best misfit allowed by the constraint is large, then the histogram of residuals is broad, and the distinction (in residual space) between outliers and datapoints is not as large." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data biases\n", + "\n", + "Let's make a new example, with several cycles of data on the same line. Each will have a value that is displaced from a trend line to simulate correlated errors (biases) in the data. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# define simulation parameters\n", + "sigma_uncorr=0.1\n", + "bias_mag=0.25\n", + "#t_vals=np.arange(-0.99, 0.99+0.1, 0.1)\n", + "t_vals=np.linspace(-.99, 0.99, 9)\n", + "z_vals=0.5*t_vals\n", + "# define the bias values\n", + "bias_vals=rng.standard_normal(len(z_vals))*bias_mag" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# make dataset\n", + "x1=np.arange(-W['x']/2, W['x']/2, 5)\n", + "y1=np.zeros_like(x1)\n", + "D_list=[]\n", + "for cycle in range(len(t_vals)):\n", + " this_z = np.zeros_like(x1)+z_vals[cycle]+bias_vals[cycle] + rng.standard_normal(x1.size)*sigma_uncorr\n", + " D_list.append(\n", + " pc.data().from_dict({'x':x1,'y':y1,'time':np.zeros_like(x1)+t_vals[cycle], 'cycle':np.zeros_like(x1)+cycle,\\\n", + " 'z':this_z, 'sigma':np.zeros_like(x1)+sigma_uncorr,'sigma_corr':np.zeros_like(x1)+bias_mag})\n", + " )\n", + "data_biased=pc.data().from_list(D_list)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we have specified a _sigma\\_corr_ value that describes the correlated error magnitude. It doesn't get used until we tell the inversion to solve for errors." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['dzdt_lag1'])\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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9TSfTWr+vtU7UWieGhobaIDwhhL2cL69ke1YBg+1QzjH353GxlFZUMXfNQbu2Y2/z12fww67jPDOh+6Uzir4QBs+FGKZv11WG2+dCDM/bkC0SfjIQo5SKVkr5AVOBZeY7aK2jtdZRWuso4AvgAa311zZoWwjhRFsv1O+tH3BVl66hQdySGMHiTYc5kt80V4lLycznpRV7uaZXO+65stqFJ3aayr06qxO+1roCeAjD1Td7gM+01ruUUvcppe6z9vxCCNeVlJ5vrN/bN+EDPDI6Bi+l+Peqpjd98qmiUh76ZBvhrQKZM6XP5Wtq2GEq95rY5Dp8rfVyYHm152r8glZrPc0WbQohnM80/30LO9XvzXUICWTaFVG8vy6dP47oSlz7YLu3aQuVVZpHl2zndHEZSx+4ovbXyjSVe+J0SFlg9VTuNWlyI22FEK7BVL+3x+WYtbl/VFeC/H2Ys3Kfw9q01hurD7D+4Cmen9yLXh1Dat9x6mKY9Bq0jzfcTrX9AkmS8IUQFnFU/d5cy2Z+3DeyK6v2nGDL4XyHtWupX/ad5K01B7h5QAS3JEbWf4CdScIXQljEkfV7c9OHRdE2yJ+Xf9jn0nNq5RSU8Kf/bSeuXTD/nNy7xrWwHU0SvhDCIpscWL8318zPh1mju7E5I5+1+11zrE5ZRRUPfrKV8krNO3fYZzETS0jCF0I02vnySrYdKWBwtGN79ya3DuxEZOtAZv+wzy5rv1rr/63Yw7asAmbfnEAXOy1mYglJ+EKIRtuWVUBZhX3mz2kIPx8vHhsbx+5jZ/lu5zGnxFCb73ccY8GGTGYMi2ZifAdnh3MJSfhCiEa7OH+Oc3r4ANf16Uj39sG8+uM+yl1kkZRDuUU8+UUq/Tu15OkJ3Z0dzmUk4QshGi0pPY9eHUMICXRs/d6cl5fiyfFxHM4r5n/JR+o/wM5Kyip54OOt+Pl4Mfd2+y9mYgnXi0gI4dJM9XtHXo5Zm6viwhgY1Yo3Vh+gpKzeuRntRmvNX7/eyf6Thbw+tR8dHbCYiSUk4QshGsXZ9XtzSimeHN+d3MJSFmx03vTJ/0s+wtKtR3nk6hhGxrrupI+S8IUQjeIK9XtzA6NaM7p7GO/9cogzxY5fJCXt6Bn+vmwXw2Pa8sjoGIe33xiS8IUQjbIpI4+eHVs4tX5f3ePXxFFYWsG7aw85tN0zJeU8+MlWWjfz4/Vb+166mIkLkoQvhGiw8+WVbM0qYEi088s55np0aMH1fcNZuDGDE2fPO6RNrTVPfJ7K0dMlvH1HP9pUX8zEBUnCF0I02PYjrlO/r+5PY2KprNK8ufqAQ9qbty6DH3ef4JmJPRjQ2TXKW/WRhC+EaLCk9DyUgoFOGmFbl05tmnHboE4sST5Cxqlzdm0rOTOfl37Yy4Te7ZkxLMqubdmSJHwhRIMZrr93rfq9uYeu7oaftxev/WS/RVJyC0t5cPFWIlsF8vLNCS4xKVpDScIXQjSIq9bvzYUFB3DPldF8m5pD2tEzNj9/ZZVm1pJtnCkp5507Bjh84jhrScIXQjSIK9fvzd07sgstm/naZZGU11ftZ+OhPP55fW96dmxh8/PbmyR8IUSDbErPd9n6vbkWAb48MKora/fn8tuhPJud9+d9J3lrzUFuSXSNxUwsIQlfCNEgSel59OzguvV7c3cNjaJ9iwBmr9xrk0VSjhoXM+nePpjnJ/e2QYTOIQlfCFEvQ/3+tMuXc0wCfL15dEwM27IK+Gm3dYuBl1VU8cDirVRWat69cwABvq6xmIklJOELIeqVeqSA0iZQvzd384AIurRtzpyV+6i0YpGUfy3fQ+qRAuZMSSC6bXMbRuh4kvCFEPVKMtbvB7nI/DkN4ePtxePXxHHgZBFfbztq0Tm+Tc1h4cZM7rkymvG9XWsxE0tIwhdC1OtC/b6Z69fvzU3o3Z748BBe+2k/pRWNmz754Mkinv5yBwM6t3LJxUwsIQlfCFGnpla/N2eYPjmOowUlfLIpq8HHFZdV8MDiLfj7ejP39n74ertHqnSPf4UQwm6aYv3e3JXd2nJF1zbMXXOQotKKevfXWvO3r9I4cLKIN6b2pUOIay5mYglJ+EKIOm3KaHr1e3OmRVLyzpUxf139i6QsST7C0m1HeXR0LMNjXHcxE0tIwhdC1CkpPY8e7Zte/d5c38iWjO/Vng/WpZNXVFrrfmlHz/Dssl2MiA3l4au7OTBCx5CEL4SoVWlFJVsON836fXWPXxNLcVkF7/xS8yIpZ4rLuX/xFto0Nyxm4uXii5lYQhK+EKJWqUfOGOv3TbOcY65bWDA3D4jgo98Oc7Sg5JJtWmse+zyVYwXnmXt7f1o393NSlPZlk4SvlBqvlNqnlDqolHq6hu13KKV2GH82KqX62KJdIYR9mea/H+Ti8+c01KwxsaDg9WrTJ7//azqr9pzgLxN7MKBzKydFZ39WJ3yllDfwNjAB6AncppTqWW23DGCk1joB+CfwvrXtCiHsz1S/b9nMPXq84S0DuWtIZ37dupPi/4yDwhNsSs9j9sp9TIxvz/QmtJiJJWzRwx8EHNRap2uty4AlwGTzHbTWG7XWp40Pk4AIG7QrhLAjd6rfm3vgqm78ye8bAo5tpmTVv3j40210at2Ml29qWouZWMLHBucIB46YPc4GBtex/z3AChu0K4SwI3eq31/wQhitK0qZaszrgakL2cxCqqr88Qo46dzYHMAWPfya/kuscaYipdRVGBL+U7WeTKl7lVIpSqmU3NxcG4QnhLDEJjer3wMwawf0noL2MQymKtF+HA6/Fq9Hdzo5MMewRcLPBsxXA4gAcqrvpJRKAOYBk7XWta5KoLV+X2udqLVODA11r0EPQjQlSRl5dHej+j0Awe3BPxhVWUqltz8BqpzOHdpDcDtnR+YQtkj4yUCMUipaKeUHTAWWme+glOoELAV+r7W23+rCQgibuFi/d6Pevcm5kzBgOt5/WI1KnAFF1s2X35RYXcPXWlcopR4CVgLewIda611KqfuM298D/g60Ad4xfilSobVOtLZtIYR97Mg+w/nypjt/Tp2mLr54f9JrzovDCWzxpS1a6+XA8mrPvWd2fyYw0xZtCSHsL+mQoX4/2J3q90JG2gohLueW9XshCV8IcSm3rt97OEn4QohLmOr3g6PdsH7v4SThCyEusSndcNW01O/djyR8IcQlktLz6d4+mFZuOmOkJ5OEL4S4oKyiipTD+e55OaaQhC+EuGhHdoH7Xn8vJOELIS5Kkvq9W5OEL4S4QOr37k0SvhACkPq9J5CEL4QAzOv3Us5xV5LwhRAAbMrIB2CQDLhyW5LwhRCA4Qvb7u2DaS31e7clCV8IYajfZ7rf+rXiUpLwhRDsPFpASXml1O/dnCR8IQRJ6VK/9wSS8IUQUr/3EJLwhfBwpvq9jK51f5LwhfBwF+v3Us5xd5LwhfBwF+v30sN3d5LwhWiktftz2XjwlLPDsJmk9Dzi2gXTJsjf2aEIO5OEL0QjFJVW8NDircxYlEx6bpGzw7FaeaXp+nvp3XsCSfhCNMIXKUcIKM3lI69/8PySn6morHJ2SFbZkX1G6vceRBK+EA1UVaVZuDGT51t+T6Lay+gTC3lv7SFnh2UV0/z3Ur/3DD7ODkCIpkK/0I5fqkovPP69zyr4NZGqDf54/d9JJ0ZmuaT0PGLbBUn93kNID1+IBnqk3QJWeg1H+wQCoH0CWaGGc3uzDyitqHRydI1XXlnFlsMyf44nkYQvRAPsPX6W7zMgvF0YqrIUfAJQlaUkdI0kKdeHf/90wNkhNtrOo2coLpP6vSeRhC9EAyzckEmArxexQedhwHSYuQoGTCfc5yy3DYrkP78eIiUz39lhNorU7z2P1PCFqEdeUSlLtx3l5gER+N3wycUNk14D4K+lFaw/eIrHPk9l+SPDae7fNH6tktLziW0XRFup33sM6eELUY9PN2dRVlHF9Cuiatwe5O/DKzf3ISu/mP+3Yo9jg7OQ4fp7Wb/W09gk4Sulxiul9imlDiqlnq5hu1JKvWncvkMp1d8W7Qphb2UVVXyUdJjhMW2JaRdc636Du7ThnmHRfJyUxdr9uQ6M0DJSv/dMVid8pZQ38DYwAegJ3KaU6llttwlAjPHnXuBda9sVwhFWpB3jxNlSZlwZXe++j18TR0xYEE9+kcqZ4nIHRGc5qd97Jlv08AcBB7XW6VrrMmAJMLnaPpOB/2qDJKClUqqDDdoWwm601ny4PoMubZszMia03v0DfL157Za+5BWV8eyyNAdEaLmk9HxiwqR+72lskfDDgSNmj7ONzzV2HyFcytasAlKzzzB9WBReXqpBx8RHhPDQ1d34ensOK3Yes3OElpH6veeyRcKv6TdBW7CPYUel7lVKpSilUnJzXb8WKtzXhxsyaBHgw439Ixp13INXdSM+PIS/fLWTk4Xn7RSd5dKkfu+xbJHws4FIs8cRQI4F+wCgtX5fa52otU4MDa3/z2gh7CGnoIQf0o4zdVCnRl9m6evtxb9v7cO5skr+snQnWtfYt3Ea0/z3g2WGTI9ji4SfDMQopaKVUn7AVGBZtX2WAXcZr9YZApzRWrvm37tCAP/97TBaa+4a2tmi47uFBfPkNXGs2nOSz7dk2zg66ySl50n93kNZnfC11hXAQ8BKYA/wmdZ6l1LqPqXUfcbdlgPpwEHgA+ABa9sVwl6Kyyr4dHMW43u3J6JVM4vPM2NYNIOjW/P8t7vJPl1swwgtJ/V7z2aT6/C11su11rFa665a6xeNz72ntX7PeF9rrR80bo/XWqfYol0h7GHp1qOcKSln+rD6L8Wsi5eX4pUpfdBa88TnO6iqcn5pJ+3oGc5J/d5jyUhbD6e1ZuaiFF5ftd/ZobiEqirNgg0ZxIeHkNi5ldXni2zdjL//rie/peexcGOm9QFaSdav9WyS8D3c6j0nWbXnBHPXHCTj1Dlnh+N06w6e4lDuOWZcGYVSDbsUsz63JEZydfcwXv5hLwdPOndZxKT0PLqFBREaLPV7TyQJ34NprXl99X7CWwbi7+PF7B/2Ojskp/twfQahwf5cG9/RZudUSvHSjfEE+nnz2OepTlsWseJC/V56955KEr4HW73nJGlHz/LomBjuG9mVFWnHm9wUv7Z08GQRa/fn8vshnfHzse2vRliLAF68Pp7UIwW8+4tzlkVMyzkr9XsPJwnfQ5l6953bNOOGfuHMHN6Fdi38eeH7PS533bijLNyYgZ+PF7cP7mSX81+b0IHr+nTkjdUHSDt6xi5t1MU0f87gaEn4nkoSvodaZezdP3RVN3y8vQj08+bxcXFsP1LA9y46JYA9nSku58stR5ncp6Ndr09/fnIvWjf348+fbed8uWOXRZT6vZCE74G01ry+6mLv3uTG/hH06NCCl3/Y2yTXaLXGkuQsSsorrb4Usz4tm/nx8s0J7D9RxL9/ctyVURWVVSRnSP3e00nC90A/7T7BrpyzPHx1DD7exo9A4XG8F03kuavaciS/hI9+O+zcIB2oorKKRRszGdqlDT07trB7e1fFhXH74E68vy6dZAd9ZyL1ewGS8D2OoXd/gKg2zbi+r9mVKGtnQ1YSg7M+YFRcKG+uPkBBcZnzAnWglbtOkHPmPNOHRTmszb9O7EFkq2Y89lkq50or7N6ezH8vQBK+x/lp9wl2HzPr3b8QBs+FQMp80FWQMp+Fh8eyWd/BW2sOOjtch1iwIYNOrZsxukc7h7XZ3N+HV6b04cjpYl5cbv9lEZPS8+ga2pyw4AC7tyVclyR8D2Lq3Ue3bc5kU+9+1g7oPQV8Ag2PfQIhfgqv9vyc//6WyeE89x6MlXqkgJTDp5l2RRTeDZzz3lYGRbfmD8O78MmmLH7Zd9Ju7Vys30s5x9NJwvcgP17o3Xe7WLsPbg/+wVBZCj4Bhlv/FvxhwlB8vb2Y/cM+5wZtZws2ZBDk78OUxMbNeW8rfx4bS2y7IJ76cofdSmi7pH4vjCThe4iqqou9++v6VBtFeu4kDJgOM1cZbotOENYigD+O6Mr3O4+x5fBp5wRtZyfOnuf7nceYkhhBcICvU2IwXxbx79/ssksbF66/lyt0PJ4kfA/x4+4T7KneuzeZuhgmvQbt4w23UxcD8IcR0YQF+/Pi97vdcjDWx0mHqajSTLsiyqlx9A4P4ZHRMSxLzeH7HbYfAyH1e2EiCd8DVFVp3lh9gC419e7r0MzPh8fHxbE1q4AVacftGKHjnS+vZPGmLEZ3b0fnNs2dHQ4PjOpKn8iW/O1r2y6LWFFZRXLmaSnnCEASvkf4cfdxQ+9+dA29+3rcNCCC7u2DeWmFew3GWrY9h/xzZcy4MsrZoQDg4+3Fq1P6UFxWyTNf2m5ZxF05ZykqrWCwJHyBJHy3Z6rdd2nbnN8lNH4GSG8vxV8m9iArv9htBmNprflwQwbd2wcz1IUSYbewIJ4a353Ve0/yWcoRm5zTVL8fItffCyThu70fdx9n7/FCHhkd0+jevcmI2FBGxIby1pqDbjEY67dDeew9XsiMYdE2m/PeVqZdEcXQLm14/tvdHMm3flnEpPQ8uoQ2J6yF1O+FJHy3dknvvhG1+5r8ZWJ3Cs+XM9cNBmN9uCGT1s39uK6v7ea8txUvL8WcKQkopXj881SrlkWU+r2oThK+G1u562Lv3tpBRd3bt2DKgEgW/ZZJVp5rLMhticxT51i99wR3Du5EgK+3s8OpUUQrw7KImzLyWWDFsoi7jxnq95LwhYkkfDd14cqcUOt79yZ/HheLj5cXL69suitjLdyYiY+X4s4hnZ0dSp2mDIhgTA/TsoiFFp1D6veiOkn4buoHY+9+lg169ybtWgRw74gufL+jaQ7GKjxfzhdbspmU0NHla9pKKf51YzzN/bz582eplFuwLGJSer7U78UlJOG7oaoqzRurDtA1tDmTLLgypy73juhCaLA//1re9FbG+iwlm6LSCmbYec57WwkLDuBfN8SzI/sM7/zcuGURZf4cURNJ+G7oh13H2XfCNrX76pr7+/DY2Fi2HD7ND01oMFZllWbhxgwSO7ciPiLE2eE02IT4DlzftyNvrTnAzuyGL4u4+9hZCksrGCzlHGFGEr6bMfXuu4UF2bx3bzIlMZK4dsG89MNeyioaX2pwhtV7TnAkv4QZVzaN3r25f1zXm7ZB/o1aFvFC/V56+MKMJHw3syLNfr17E28vxTMTu3M4r5iPk5rGYKwPN2QQ3jKQcT0dN+e9rYQ082X2zQkcOFnEqz82bPbSpPR8urRtTjup3wszkvDdiOHKnP10Cwvi2vgOdm1rZGwow2Pa8uaaA5wpLrdrW9balXOGpPR87hra2eLBZ842IjaUO4d0Yt76DDYZe++1qazSJGfky3QK4jJN89MvarQ87Rj7TxTZtXdvopTimQk9OFNSztu/uPZgrIUbMgn09WbqwE7ODsUqf5nYg06tm/H4F6kU1bEs4u4cQ/1eFiwX1UnCdxOm2n2MA3r3Jj07tuDm/hEs3JBpk2kA7OFUUSnfbM/h5gERhDRzzpz3ttLMz4dXp/Qh+3QJL36/u9b9pH4vaiMJ3018v/MYB046pndv7rFxcXh5weyVrrky1uKkLMoqq5jmwAXK7SkxqjV/HNGVTzcf4ee9NS+LmJSeJ/V7USOrEr5SqrVS6iel1AHjbasa9olUSv2slNqjlNqllJplTZsNsXZ/LrmFpfZuxmVUVmneXG3o3U90UO/epH1IAPcO78K3qTlsy3KtwVilFZV8vOkwo+JC6Roa5OxwbOZPY2Po3j64xmURK6s0mzPyZXUrUSNre/hPA6u11jHAauPj6iqAx7TWPYAhwINKqZ5WtlurguIyHly8lXs/SmnwJWxN3XJj737WGMf27k3uHdmVtkGuNxjr+x3HyC0sbTIDrRrK38ebV2/pw+niMv6v2rKIF+v3Us4Rl7M24U8GFhnvLwKur76D1vqY1nqr8X4hsAcIt7LdWrVs5scrUxLYllXAk1/scKkEZA+VxjlzYtsFMbG3Y3v3JkH+Pvx5bCzJmadZueuEU2KozjTnfbewIIbHtHV2ODbXq2MIs0bH8G1qDt+m5lx4/sL6tdGS8MXlrE347bTWx8CQ2IGwunZWSkUB/YBNVrZbp/G9O/DENXEsS83hLTeYzrcu3+88xsGTRcwaHYuXE3r3JrckRhATFsRLK/a4xGCslMOnSTt6lunDolxuzntbuW9kV/pGtuT/vknj5FnDsohJ6XlEt21O+xCp34vL1ZvwlVKrlFJpNfxMbkxDSqkg4EvgUa312Tr2u1cplaKUSsnNzW1ME5d4YFRXbuwfzms/7ee7HTn1H9AEmWr3se2CmNC7vVNj8fH24i8Te5CZV8wnm5w/GOvD9RmEBPpyY78IZ4diNz7eXrx2Sx/Ol1fy1Jc7qKisYnNmvlyOKWpVb8LXWo/RWveu4ecb4IRSqgOA8bbGywaUUr4Ykv1irfXSetp7X2udqLVODA0Nbfy/6GKb/L8b4xkY1YrHPksl9UiBxedyVd/tyHGJ3r3JqLhQhnVrwxurD3CmxHmDsY7kF7Ny13FuG9SJQD/XnPPeVrqEBvH0+O78vC+XZ5ftovC81O9F7awt6SwD7jbevxv4pvoOyvD39Hxgj9b6NSvbaxR/H2/eu3MAYS38mfnfFHIKShzZvF2Zevdx7YKd3rs3Ucqw/m1BSTnvOHEw1kdJh1FKcddQ157z3lbuGhrFFV3bsHhTFiD1e1E7axP+S8BYpdQBYKzxMUqpjkqp5cZ9hgG/B65WSm03/ky0st0GaxPkz/y7B3K+rJKZi1I4V8cIxabkux05HMo9x6wxMS7Ruzfp1TGEG/tFsMBJg7HOlVbw6eYsJvRuT8eWgQ5v3xkMyyL2Idjfh6g2zaR+L2plVcLXWudprUdrrWOMt/nG53O01hON99drrZXWOkFr3df4s7zuM9tWbLtg3rq9H3uPn+XR/223ap1QV2C6Mqd7+2DG93KN3r25x6+JRQGvNHCiL1v6cms2hecrmO5ml2LWJ7xlIAtnDOLVW/o4OxThwtx3pG3hcVgwAQoNlwmOigvj75N68tPuE016iT4w9O7Tc88xa7Rr9e5NOoQE8ofhXfhme45DvzupqtIs3JBJn8iW9O/U0mHtuooBnVsxoLN8YStq574Jf+1syEqCtS9feOruK6L4/ZDO/GdtOp+nHHFicJYz791f44K9e5P7RnWlbZAfLzpwMNba/bmknzrHDDe+FLNO1To5QlTnfgn/hTB4LgRS5oOuMtw+FwIvhKGU4tnf9WR4TFv+8tXOeqeZdUXfprp2794kyN+HR8fEsjkjn592OyYBfbghg3Yt/B0+vYTLqKGTI4Q590v4s3ZA7yngY/zCzicQ4qfArJ2Gh95ezL29P51aN+OPH2/hcN45JwbbOKYrc1y9d28ydWAkXUOb89KKvRYtwt0Y+08Usu7AKe4aGoVvE53z3mJ1dHKEMOd+vxnB7cE/GCpLwSfAcOvfAoIvrnQUEujL/LsHAjBjYbJTrxlvjGWpR0k/dY5HXezKnNqYBmOlnzrHp5uz7NrWgg2Z+Pt4cdugpj3nvUXq6eQIYeJ+CR/g3EkYMB1mrjLcFl1eUohq25z37hxAVn4xD32ylQo790CtVVFZxVurD9KjQwvG9XT93r3J1d3DGNqlDa+vOsDZ8/b5j/X0uTKWbs3mhn7htG7uZ5c2XFoDOjlCgLsm/KmLYdJr0D7ecDt1cY27DenShhdviGfdgVP849vaF5RwBd/uyCH9lOvX7qtTSvHXa3uQf66Md385ZJc2Pk3OorSiyuMuxbxEAzo5Qvg4OwBnuyUxkkMni/jPr+l0Cwvi7iuinB3SZSoqq3jzQu++6fXaeoeHcGO/cOavz+DOIZ0Jt+GAqPLKKv678TBXdmtLXPtgm523yTHv1Exy6IB20YS4Zw+/kZ4c352xPdvxj2938cu+mlcRcqZlqTlkNKHafU0euybOMBjLxitjrUg7zvGz55nuJitaCWFPkvABby/F67f2Ja59Cx7+ZBsHThQ6O6QLKiqreGvNQXo20d69SXjLQO65Mpqvth1lZ/YZm513wYYMoto046o4uSJFiPpIwjdq7u/D/LsTCfDzZsaiZPKKXGOJxG+2G3r3s8bENPnBRPeP6kqb5n68uHy3TQZjbc06zbasAqYPi26yf/kI4UiS8M10bBnIB3clcvJsKfd9vIXSCucukWjo3R9o8r17k+AAXx4dG0tSej6r91hfOluwIZNgfx9uGuC+c94LYUuS8KvpG9mSV2/pQ3LmaZ5ZutOpSyR+vT2HzLxiHnWD3r2JaTDWv1bssWow1rEzJazYeYxbB0YS5O/x1x4I0SCS8GswKaEjfxoTy9KtR3l3rX0uJayPqXffq2MLxrpB797E19uLZyb0ID33HEuSLZ/P6KPfDlOltUteVSWEq5KEX4tHRnfjuj4dmf3DPn5IO+bw9r/ensPhvGIeHRPrNr17k9E9whjSpTWv/7SfQgsGY5WUVfLJ5izG9mxHZOtmdohQCPckCb8WSilm35xAv04t+dP/Ukk7arsrS+pj6t33Dm/BmB7ud/WJUoq/TuxJ3rky3rPgL6ivtx+loLicGZ480EoIC0jCr0OArzfv/z6R1s39uGdRMifOnndIu19tO2ro3Y92v969SXxECDf0C2feuoxGLT2ptebD9Rn06tiCQdEy97sQjSEJvx6hwf7MuzuRovMVzFyUQkmZfa/cqaisYu7PB+kd3oLRbti7N/f4NXFoGrcy1vqDpzhwsojpw6Ld9j9DIexFEn4D9OjQgjdv60dazhn+/Jl9l0hc6gG9exPzwVgNLZkt2JBJ2yA/ftfHQ+e8F8IKkvAbaHSPdvx1Yg9WpB3ntZ/226WN8soq5q45SHx4iNv37k3uH9WVVs38ePH7+lfGSs8tYs3ek9w5pDP+Pt4OilAI9yEJvxHuuTKaqQMjmfvzQb7alm3z83+17ShZ+e513X19WgT48uiYGH5Lz+PneuYxWrgxEz9vL+4Y3NlB0QnhXiThN4JSiucn92ZolzY89cVOUjLzbXbucuOVOQkRIVzd3TN69ya3DepEl7bN+dfyvbWuS3CmpJwvtmTzuz4dCQ32d3CEQrgHSfiN5Ofjxbt39ie8VSB//GgLR/KLbXLer7Ye5Uh+iUf17k18vb14ekJ3Dp4sqnUw1mfJRyguq5RZMYWwgiR8C7Rs5sf8uxMpr6zinkXJFg0eMldeWcVbPxt695466+PYnu0YFN2a11ddPhirorKKhRszGRTdmt7hIU6KUIimTxK+hbqEBvHunQNIzz3Hw59us2qJxKVbsz22d29iGIzVg1NFZfxnbfol21btOcHRghIZaCWElSThW2FYt7Y8P7k3v+zL5cXleyw6R7lxvvs+Hty7N+kT2ZLJfTvywbp0jp25OBjrw/WZRLQKdKs5hYRwBkn4Vrp9cCdmDItmwYZMFm863Ojjv9ySTfbpErecM8cSj48zDsZaabj0Ne3oGTZn5jPtiii8Zc57IawiCd8G/nptD67uHsbfv9nF+gOnGnxcWYVhVG2fyJaMigu1Y4RNR2TrZkwfFsXSbdmkHT3DhxsyaO7nzS0DI50dmhBNniR8G/D2UrwxtS/dQoN4YPEWDuUWNei4pVtNvXvPrd3X5IFR3WgZ6Mtfv07j29QcpiRG0iLA19lhCdHkScK3keAAX+bdnYivtxf3LEzm9LmyOvcvqzDU7vtGtmRUrPTuzYUE+jJrdAypRwqoqJI574WwFUn4NhTZuhnv3zWAnILz3L94C2UVtV+58+XWbI4WSO++NrcP7kxMWBDX9GxPdNvmzg5HCLdgVcJXSrVWSv2klDpgvG1Vx77eSqltSqnvrGnT1Q3o3JrZNyeQlJ7P/32dVuP8MGUVhjlz+ka2ZKT07mvk5+PFsoeu5M3b+jk7FCHchrU9/KeB1VrrGGC18XFtZgGWXbvYxFzfL5yHr+7G/1KOMG9dxmXbv9givfuGCCzNxe+ja6HwhLNDEcItWJvwJwOLjPcXAdfXtJNSKgK4FphnZXtNxp/GxDIxvj3/WrGHVbsvJqyyiire/vkg/TpJ775ea2dDVhKsfdnZkQjhFqxN+O201scAjLe1jRx6HXgSsHw4ahPj5aV4dUpf4sNDeGTJNnbnnAXg8y1HjL17ue6+Vi+EwXMhkDIfdJXh9rkQw/NCCIvVm/CVUquUUmk1/ExuSANKqUnASa31lgbuf69SKkUplZKbm9uQQ1xWoJ838+5KpEWALzMXJXO0oIS31xh69yNi2jo7PNc1awf0ngI+gYbHPoEQPwVm7XRuXEI0cfUmfK31GK117xp+vgFOKKU6ABhva5rQfBhwnVIqE1gCXK2U+riO9t7XWidqrRNDQ5t+ySOsRQDz7k7kdHE51765jpwz5/mT9O7rFtwe/IOhshR8Agy3/i0gWKZWEMIa1pZ0lgF3G+/fDXxTfQet9TNa6witdRQwFVijtb7TynablN7hIbw+tS9nSsrp36klw6V3X79zJ2HAdJi5ynBbJF/cCmEtVd+ycnUerFQb4DOgE5AFTNFa5yulOgLztNYTq+0/Cnhcaz2pIedPTEzUKSkpFsfnapLS84hq05z2IQHODkUI4aaUUlu01ok1brMm4dubuyV8IYSwt7oSvoy0FUIIDyEJ39EKj8OCCTKYSAjhcJLwHU0GEwkhnMTH2QF4jBfCoKL04uOU+YYfH3/4W01XswohhG1JD99RZDCREMLJJOE7igwmEkI4mZR0HMk0mChxOqQskMFEQgiHkoTvSFMXX7w/6TXnxSGE8EhS0hFCCA8hCV8IITyEJHwhhPAQkvCFEMJDSMIXQggPIQlfCCE8hEtPj6yUygUOW3h4W+CUDcOxFYmrcSSuxpG4Gscd4+qsta5xuUCXTvjWUEql1DYntDNJXI0jcTWOxNU4nhaXlHSEEMJDSMIXQggP4c4J/31nB1ALiatxJK7Gkbgax6PictsavhBCiEu5cw9fCCGEmSad8JVSU5RSu5RSVUqpWr/RVkqNV0rtU0odVEo9bfZ8a6XUT0qpA8bbVjaKq97zKqXilFLbzX7OKqUeNW57Til11GzbREfFZdwvUym109h2SmOPt0dcSqlIpdTPSqk9xvd8ltk2m71etX1WzLYrpdSbxu07lFL9G3qsNRoQ1x3GeHYopTYqpfqYbavx/XRgbKOUUmfM3p+/N/RYO8f1hFlMaUqpSqVUa+M2u7xmSqkPlVInlVJptWy37+dLa91kf4AeQBzwC5BYyz7ewCGgC+AHpAI9jdtmA08b7z8NvGyjuBp1XmOMxzFcPwvwHPC4HV6vBsUFZAJtrf132TIuoAPQ33g/GNhv9j7a5PWq67Nits9EYAWggCHApoYea+e4rgBaGe9PMMVV1/vpwNhGAd9Zcqw946q2/++ANfZ+zYARQH8grZbtdv18NekevtZ6j9Z6Xz27DQIOaq3TtdZlwBJgsnHbZGCR8f4i4HobhdbY844GDmmtLR1k1lDW/nud9npprY9prbca7xcCe4BwG7VvUtdnxTzW/2qDJKClUqpDA4+1W1xa641a69PGh0lAhI3atjo2Ox1r63PfBnxqo7ZrpbX+FcivYxe7fr6adMJvoHDgiNnjbC4minZa62NgSChAmI3abOx5p3L5h+0h4590H9qqdNKIuDTwo1Jqi1LqXguOt1dcACilooB+wCazp23xetX1Walvn4Yca6nGnvseDL1Ek9reT0fGNlQplaqUWqGU6tXIY+0ZF0qpZsB44Euzp+35mtXFrp8vl1/xSim1Cmhfw6a/aq2/acgpanjO6kuT6oqrkefxA64DnjF7+l3gnxji/CfwKjDDgXEN01rnKKXCgJ+UUnuNPROL2fD1CsLwi/mo1vqs8WmLX6/qp6/hueqfldr2scvnrJ42L99RqaswJPwrzZ62+fvZyNi2YihXFhm/X/kaiGngsfaMy+R3wAattXnP256vWV3s+vly+YSvtR5j5SmygUizxxFAjvH+CaVUB631MeOfTSdtEZdSqjHnnQBs1VpfWODW/L5S6gPgO0fGpbXOMd6eVEp9heHPyV9x8uullPLFkOwXa62Xmp3b4termro+K/Xt49eAYy3VkLhQSiUA84AJWus80/N1vJ8Oic3sP2a01suVUu8opdo25Fh7xmXmsr+w7fya1cWuny9PKOkkAzFKqWhjb3oqsMy4bRlwt/H+3UBD/mJoiMac97LaoTHpmdwA1PiNvj3iUko1V0oFm+4D48zad9rrpZRSwHxgj9b6tWrbbPV61fVZMY/1LuPVFEOAM8YyVEOOtVS951ZKdQKWAr/XWu83e76u99NRsbU3vn8opQZhyDt5DTnWnnEZ4wkBRmL2mXPAa1YX+36+bP0ttCN/MPxyZwOlwAlgpfH5jsBys/0mYriq4xCGUpDp+TbAauCA8ba1jeKq8bw1xNUMwwc/pNrxHwE7gR3GN7WDo+LCcBVAqvFnl6u8XhhKFNr4mmw3/ky09etV02cFuA+4z3hfAW8bt+/E7Oqw2j5nNnqN6otrHnDa7LVJqe/9dGBsDxnbTsXwhfIVrvCaGR9PA5ZUO85urxmGzt0xoBxD7rrHkZ8vGWkrhBAewhNKOkIIIZCEL4QQHkMSvhBCeAhJ+EII4SEk4QshhIeQhC+EEB5CEr4QQngISfhCCOEh/j9WUWbrIOvzEAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# fit the biased data\n", + "Sb=smooth_xytb_fit(data=data_biased, ctr=ctr, W=W, spacing=spacing, E_RMS=E_RMS,\n", + " reference_epoch=4, N_subset=None, compute_E=False,\n", + " max_iterations=10,\n", + " VERBOSE=False, dzdt_lags=[1])\n", + "\n", + "# plot the results:\n", + "# find a row and a column in the center of the simulation\n", + "r0_dz, c0_dz, e0_dz = np.round(np.array(Sb['m']['dz'].shape)/2).astype(int)\n", + "r0_z0, c0_z0 = np.round(np.array(Sb['m']['z0'].shape)/2).astype(int)\n", + "\n", + "# plot the model for the center point\n", + "plt.figure(10); \n", + "plt.plot(Sb['m']['dz'].time, Sb['m']['dz'].dz[r0_dz, c0_dz, :]+Sb['m']['z0'].z0[r0_z0, c0_z0], label='biased model')\n", + "plt.plot(t_vals, bias_vals+z_vals, '*', label='data')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is that the recovered delta-z signal matches the biased data well. If we fit the data without taking into account the biases, the large number of data points with the same bias exert a strong influence on the model, so the smoothness constraints don't help much to suppress signals related to the biases.\n", + "\n", + "## Estimating data biases\n", + "\n", + "We can tell the inversion that there is a bias parameter using the _blas\\_params_ keyword, which takes a list of all parameters over which the bias is correlated: the solution will estimate one bias for each combination of unique values of the parameter in the list, and will set the expected RMS value for each parameter to the median of the correlated error ( _sigma\\_corr_ ) values for the data. If there are four cycles in the data, there will be four bias estimates. In solving the ATL14/15 problem, we set _bias\\_params_ to ['rgt','cycle'], so there is one bias estimated for each rgt and each cycle." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['dzdt_lag1'])\n" + ] + } + ], + "source": [ + "Sbc=smooth_xytb_fit(data=data_biased, ctr=ctr, W=W, spacing=spacing, E_RMS=E_RMS,\n", + " reference_epoch=4, N_subset=None, compute_E=False,\n", + " max_iterations=10,\n", + " VERBOSE=False, dzdt_lags=[1], bias_params=['cycle'])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(11); \n", + "plt.plot(Sb['m']['dz'].time, Sb['m']['dz'].dz[r0_dz, c0_dz, :]+Sb['m']['z0'].z0[r0_z0, c0_z0], label='no bias estimate')\n", + "plt.plot(Sbc['m']['dz'].time, Sbc['m']['dz'].dz[r0_dz, c0_dz, :]+Sbc['m']['z0'].z0[r0_z0, c0_z0], label='cycle bias estimated')\n", + "plt.plot(t_vals, bias_vals+z_vals, '*', label='data')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Letting the inversion take into account the biases in the data results in a much smoother solution, although with larger error estimates, and less ability to recover short-term fluctuations in surface height" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Error estimates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The algorithm can optionally estimate errors for the _z0_ and _dz_ fields, and the biases. These error estimates depend on:\n", + "* the spatial and temporal distribution of the data,\n", + "* the error magnitude estimates, and \n", + "* the bias magnitude estimates. \n", + "\n", + "Let's look at the time-dependent solution from earlier, but remove the central portion to make a data gap. We're making the solution a bit wider in the y direction to allow us a bit of space to play with the model resolution. Note that the solution takes much longer when we calculate errors." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['dzdt_lag1'])\n", + "initial: 157:\n", + "starting qr solve for iteration 0 at Thu Jan 6 09:19:29 2022\n", + "found 158 in TSE, sigma_hat=4.622, dm_max=158.488, dt= 2\n", + "Starting uncertainty calculation\n", + "scaling uncertainties by 4.621952738004673\n", + "\tUncertainty propagation took 128.20 seconds\n" + ] + } + ], + "source": [ + "data_dt_gap = data_dt[np.abs(data_dt.x) > 1000]\n", + "W1={'x': 10000.0, 'y': 400, 't': 2}\n", + "S=smooth_xytb_fit(data=data_dt_gap, ctr=ctr, W=W1, spacing=spacing, E_RMS=E_RMS,\n", + " reference_epoch=4, N_subset=None, compute_E=True,\n", + " max_iterations=1, dzdt_lags=[1])\n", + "\n", + "# find a row and a column in the center of the simulation\n", + "r0_dz, c0_dz, e0_dz = np.round(np.array(S['m']['dz'].shape)/2).astype(int)\n", + "r0_z0, c0_z0 = np.round(np.array(S['m']['z0'].shape)/2).astype(int)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig=plt.figure(11)\n", + "ax=fig.add_subplot(221)\n", + "# plot the z0 for the model\n", + "hh=plt.plot(S['m']['z0'].x, S['m']['z0'].z0[r0_z0,:], label='z0')\n", + "# plot the errors in z0\n", + "ax.plot(S['m']['z0'].x, S['m']['z0'].z0[r0_z0,:]+S['E']['sigma_z0'].sigma_z0[r0_z0,:], '--', color=hh[0].get_color())\n", + "ax.plot(S['m']['z0'].x, S['m']['z0'].z0[r0_z0,:]-S['E']['sigma_z0'].sigma_z0[r0_z0,:], '--', color=hh[0].get_color())\n", + "ax.set_ylabel('z0') \n", + " \n", + "ax=fig.add_subplot(222)\n", + "ax.plot(S['m']['z0'].x, S['E']['sigma_z0'].sigma_z0[r0_z0,:])\n", + "ax.set_ylabel('$\\sigma_{z0}$')\n", + " \n", + "ax=fig.add_subplot(223)\n", + "hh=ax.plot(S['m']['dz'].x, S['m']['dz'].dz[r0_dz, :, 0])\n", + "hh=ax.plot(S['m']['dz'].x, S['m']['dz'].dz[r0_dz, :, 0]- S['E']['sigma_dz'].sigma_dz[r0_dz, :, 0],'--', color=hh[0].get_color() )\n", + "hh=ax.plot(S['m']['dz'].x, S['m']['dz'].dz[r0_dz, :, 0]+ S['E']['sigma_dz'].sigma_dz[r0_dz, :, 0],'--', color=hh[0].get_color() )\n", + "ax.set_ylabel('dz, t=-1')\n", + "\n", + "ax=fig.add_subplot(224)\n", + "ax.plot(S['m']['dz'].x, S['E']['sigma_dz'].sigma_dz[r0_z0,:, 0])\n", + "ax.set_ylabel('$\\sigma_{dz, t=-1}$')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the error estimates are small where data are present, then increase across the data gap. Errors are also larger towards the edges of the grid, because farther from the center, fewer datapoints are available to constrain each grid point.\n", + "\n", + "This solution took a long time (and a lot of memory) to calculate, beacause the grids had fine resolution. Since we probably don't care if our error estimates have high or low resolution, we can speed up the calculation substantially by degrading the resolution of the error solution, then interpolating back to the original grid resolution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['dzdt_lag1'])\n", + "initial: 157:\n", + "starting qr solve for iteration 0 at Thu Jan 6 09:21:40 2022\n", + "found 158 in TSE, sigma_hat=4.872, dm_max=157.659, dt= 0\n", + "Starting uncertainty calculation\n", + "scaling uncertainties by 4.872477227287799\n", + "\tUncertainty propagation took 9.17 seconds\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# reduced-resolution fit:\n", + "SE = smooth_xytb_fit(data=data_dt_gap, ctr=ctr, W=W1, spacing={'z0':100, 'dz':100, 'dt':0.25}, E_RMS=E_RMS,\n", + " reference_epoch=4, N_subset=None, compute_E=True,\n", + " max_iterations=1, dzdt_lags=[1])\n", + "\n", + "# reduced-resolution sigma estimate, interpolated to full resolution\n", + "sigma_i = SE['E']['sigma_z0'].interp(S['m']['z0'].x, S['m']['z0'].y, field='sigma_z0', gridded=True)\n", + "sigma_0=S['E']['sigma_z0']\n", + "\n", + "plt.figure(12)\n", + "plt.plot(S['m']['z0'].x, sigma_0.sigma_z0[r0_z0,:], label='full-res')\n", + "plt.plot(S['m']['z0'].x, sigma_i[r0_z0,:], label='half-res')\n", + "plt.gca().set_ylabel('$\\sigma_{z0}$')\n", + "plt.legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The reduced-resolution sigma estimate is very close to the full-resolution estimate, but takes very much less time to calculate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quantities derived in error propagation\n", + "\n", + "The errors in derived quantities cannot be calculated without an estimate of the covariance structure of the fit model. Since we are not including the covariance matrix in the output products (because it's too large to even calculate in most cases!) we pre-calculate error estimates for some derived quantities:\n", + "\n", + "* _dz\\_dt\\_lag\\_1_ : dz/dt from epoch to epoch\n", + "* _dz\\_dt\\_lag\\_4_ : dz/dt calculated on an annual basis\n", + "* _dz\\_bar_ : the error in the central 50% of the grid (typically 40x40 km)\n", + "* _dz\\_dt\\_bar\\_lag\\_1_ , _dz\\_dt\\_bar\\_lag\\_4_ , dz/dt averaged over the central portion of the grid, at quarter-annual and annual resolution\n", + "\n", + "For the central-average quantities, we also provide an value for the area of the central estimate (including the effects of projection distortion and the fraction of the central area that is included in the ice mask). " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/RGT_001.h5 b/notebooks/RGT_001.h5 new file mode 100644 index 0000000..9ce3f78 --- /dev/null +++ b/notebooks/RGT_001.h5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:80341c83239679347517915607254a05868d52ca4c1dbc26983322298c596b42 +size 133307 diff --git a/notebooks/make_sim_data.ipynb b/notebooks/make_sim_data.ipynb new file mode 100644 index 0000000..d140672 --- /dev/null +++ b/notebooks/make_sim_data.ipynb @@ -0,0 +1,474 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 39, + "id": "7ea4f764-2f26-4c14-8c88-958cfb4bac2b", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pointCollection as pc\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "73a6fab1-1edd-42df-83ee-6764c0350983", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "if not os.path.isfile('RGT_001.h5'):\n", + " from pykml import parser\n", + " import h5py\n", + "\n", + " thefile='IS2_RGT_0001_cycle16_21-Jun-2022.kml'\n", + " with open(thefile,'r') as fh:\n", + " doc=parser.parse(fh).getroot().Document\n", + " ll=np.c_[[[*map(float,item.split(','))] for item in str(doc.Placemark.LineString.coordinates).split(' ')[:-1]]]\n", + " temp=pc.data().from_dict({'latitude':ll[:,1],'longitude':ll[:,0]})\n", + " temp.assign(t=np.linspace(0, 90.75/1387, len(temp.latitude)))\n", + " \n", + " RGT1=temp\n", + " dLat_dt=np.diff(RGT1.latitude[0:2])/np.diff(RGT1.t[0:2])\n", + " t0 = RGT1.latitude[0]/dLat_dt\n", + " RGT1.t += t0\n", + " dLon_dt = np.diff(RGT1.longitude[0:2])/np.diff(RGT1.t[0:2])\n", + " lon0 = RGT1.longitude[0] - dLon_dt*RGT1.t[0]\n", + "\n", + " dLat_dt=np.diff(RGT1.latitude[-2:])/np.diff(RGT1.t[-2:])\n", + " tN = RGT1.t[-1]-RGT1.latitude[-1]/dLat_dt\n", + " dLon_dt = np.diff(RGT1.longitude[-2:])/np.diff(RGT1.t[-2:])\n", + " lonN = RGT1.longitude[-1]+dLon_dt*(tN-RGT1.t[-1])\n", + " RGT1.to_h5('RGT_001.h5', replace=True)\n", + " with h5py.File('RGT_001.h5','r+') as h5f:\n", + " h5f.attrs['t_orbit'] = tN-t0\n", + " h5f.attrs['delta_lon_orbit'] = lonN-lon0\n", + "else:\n", + " RGT1 = pc.data().from_h5('RGT_001.h5')\n", + " with h5py.File('RGT_001.h5','r') as h5f:\n", + " t_orbit = h5f.attrs['t_orbit']\n", + " delta_lon_orbit=h5f.attrs['delta_lon_orbit']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "id": "22139c93-9f6b-4ce5-bde3-ca1548404c95", + "metadata": {}, + "outputs": [], + "source": [ + "RGT1=None\n", + "RGT1 = pc.data().from_h5('RGT_001.h5', group='/')\n", + "with h5py.File('RGT_001.h5','r') as h5f:\n", + " t_orbit = h5f.attrs['t_orbit']\n", + " # correction value\n", + " delta_lon_orbit=h5f.attrs['delta_lon_orbit'] + 0.00754\n", + "\n", + "lat_0 = 70\n", + "lon_0 = 0\n", + "W = 2.e4\n", + "lat_tol = np.maximum(W, 7000)/(6378e3*2*np.pi/360.)\n", + "lon_tol = np.maximum(W+3000, 10000)/(6378e3*2*np.pi/360.*np.cos(lat_0*np.pi/180))\n", + "\n", + "desc = (RGT1.t > t_orbit/4) & (RGT1.t < 3*t_orbit/4)\n", + "asc = desc==0\n", + "\n", + "RGT_desc = RGT1[desc & (np.abs(RGT1.latitude - lat_0) < lat_tol)]\n", + "RGT_asc = RGT1[asc & (np.abs(RGT1.latitude - lat_0) < lat_tol)]\n", + "\n", + "orbs={key:[] for key in ['asc','desc']}\n", + "\n", + "for track in range(1387):\n", + " for key, RGT in zip(['asc','desc'], [RGT_asc, RGT_desc]):\n", + " temp=RGT.copy()\n", + " temp.t += t_orbit*track\n", + " temp.longitude += delta_lon_orbit * track\n", + " delta_lon = np.mod(temp.longitude-lon_0+180, 360)-180\n", + " if np.any(np.abs(delta_lon) < lon_tol):\n", + " temp.longitude = np.mod(temp.longitude+180, 360)-180\n", + " orbs[key] += [temp]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "id": "f24f13e7-32e4-4b61-8443-9d0d290a666d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure();\n", + "lat_scale=(6378e3*2*np.pi/360.)\n", + "lon_scale=(6378e3*2*np.pi/360.*np.cos(lat_0*np.pi/180))\n", + "\n", + "for key, orb_list in orbs.items():\n", + " for orb in orb_list:\n", + " plt.plot((orb.longitude-lon_0)*lon_scale, (orb.latitude-lat_0)*lat_scale)\n", + "plt.gca().set_aspect(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "id": "0ebfc3ce-90b7-40d7-8af1-1e21b951f02c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plt.figure(); plt.plot(np.unique(np.mod((0.0008+delta_lon_orbit)*np.arange(1387)+180, 360)-180)[0:30], '.')\n", + "\n", + "#plt.figure(); plt.hist(np.diff(np.unique(np.mod((0+delta_lon_orbit)*np.arange(1387)+180, 360)-180)))\n", + "\n", + "delta_vals=np.arange(0, 0.01, 0.00001)\n", + "sigma_dlon = np.array([np.std(np.diff(np.unique(np.mod((delta_i+delta_lon_orbit)*np.arange(1387)+180, 360)-180))) for delta_i in delta_vals])\n", + "plt.figure()\n", + "plt.plot(delta_vals, sigma_dlon)\n", + "delta=delta_vals[np.argmin(sigma_dlon)]\n", + "plt.plot(delta, sigma_dlon[np.argmin(sigma_dlon)],'r*')" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "id": "18f6051d-514b-49dd-b5e3-6b0e6df21ac0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.007540000000000001" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delta" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "b33cf5eb-3e32-499b-acf0-813bf27e627c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(lon0, 0,'r*')\n", + "plt.plot(RGT1.longitude[0:5], RGT1.latitude[0:5],'r')\n", + "plt.figure()\n", + "plt.plot(lonN, 0, 'r*')\n", + "plt.plot(RGT1.longitude[-5:], RGT1.latitude[-5:],'r')" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "8e6bcb15-d9b8-40ae-bd52-2089d8ed4cec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "W=10000\n", + "lon_0=0\n", + "t_gt = np.linspace(0., 91., 1388)[0:-1]\n", + "dt_orb = t_gt[1]-t_gt[0]\n", + "lon_gt = np.mod(t_gt*360+180, 360)-180\n", + "\n", + "lat_0=70\n", + "dt_asc = lat_0/360 *dt_orb # time for the orbit to reach the simulation center\n", + "dt_desc = (90 + (90-lat_0))/360 * dt_orb\n", + "\n", + "t_asc = t_gt + dt_asc\n", + "lon_asc=np.mod(t_asc*360+180, 360)-180\n", + "t_desc = t_gt + dt_desc\n", + "lon_desc=np.mod(t_desc*360+180, 360)-180\n", + "\n", + "W_lon = W*360/(2*np.pi*6378e3*np.cos(lat_0*np.pi/180))\n", + "\n", + "asc_orbs = np.abs(lon_0-lon_asc) < W_lon\n", + "print(np.sum(asc_orbs))\n", + "\n", + "plt.figure(); plt.plot(t_asc, lon_asc,'.')\n", + "plt.plot(t_desc, lon_desc,'.')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "a9df95e3-a40c-44ee-810f-ca0e7f85b1e8", + "metadata": {}, + "outputs": [], + "source": [ + "l_AT = 8\n", + "AT_spacing=0.25\n", + "azimuth=30\n", + "beam_spacing=3\n", + "N_GT=1\n", + "GT_spacing\n", + "\n", + "s=np.arange(-8, 8, 0.25)\n", + "xy0=np.concatenate([np.c_[s, s*0+delta].T for delta in [-beam_spacing, 0, beam_spacing]], axis=1)\n", + "theta=60*np.pi/180\n", + "xy = []\n", + "t=[]\n", + "for count, theta in enumerate([(90-azimuth)*np.pi/180, (90+azimuth)*np.pi/180]):\n", + " R=[[np.cos(theta), np.sin(theta)], [-np.sin(theta), np.cos(theta)]]\n", + " xy += [R@xy0]\n", + " t += [np.zeros(xy0.shape[1])+count/2]\n", + "xy_cycle=np.concatenate(xy, axis=1)\n", + "t_cycle=np.concatenate(t)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "14f520dd-9124-47b1-9559-eb53c71e095c", + "metadata": {}, + "outputs": [], + "source": [ + "xy=[]\n", + "t=[]\n", + "for cycle in range(10):\n", + " xy += [xy_cycle]\n", + " t += [t_cycle+cycle]\n", + "xy=np.concatenate(xy, axis=1)\n", + "t=np.concatenate(t)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "b6c5ed17-3d8d-4ebc-a683-a2cfe2dcd768", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " with shape (3840,),\n", + "with fields:\n", + "['x', 'y', 't']" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "D=pc.data().from_dict({'x':xy[0,:], 'y':xy[1,:],'t':t})\n", + "D.ravel_fields()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "f5432306-4547-4c53-aaef-52e18791184b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(D.x, D.y,'.')" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "35a474dc-050a-46b4-9fc3-d14ce058a61e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " with shape (3840,),\n", + "with fields:\n", + "['x', 'y', 't', 'z']" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "D.assign(z=1-0.01*(D.x**2+D.y**2) - 0.01*D.t + (np.abs(D.t)>2)*(D.t-2)*np.exp(-(D.x**2+D.y**2)/(2*0.5)))" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "3e4b972a-7000-499c-a813-8b5b69b298e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.scatter(D.x, D.z, c=D.t)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65dead0f-8cba-4f77-8cd6-204e19fd6718", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "devel", + "language": "python", + "name": "devel" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/ATL14_browse_plots.py b/scripts/ATL14_browse_plots.py index c428c3e..ea8ba2f 100644 --- a/scripts/ATL14_browse_plots.py +++ b/scripts/ATL14_browse_plots.py @@ -11,6 +11,7 @@ from netCDF4 import Dataset import shutil import h5py +import pkg_resources #import pointCollection as pc #from PointDatabase.mapData import mapData @@ -100,7 +101,8 @@ def ATL14_browse_plots(args): print('making file', brwfile) if os.path.isfile(brwfile): os.remove(brwfile) - shutil.copyfile('ATL1415/resources/BRW_template.h5',brwfile) + template_file = pkg_resources.resource_filename('ATL1415','resources/BRW_template.h5') + shutil.copyfile(template_file,brwfile) with h5py.File(brwfile,'r+') as hf: hf.require_group('/default') for ii, name in enumerate(sorted(glob.glob(f'{args.base_dir.rstrip("/")}/ATL14_{args.region}_{args.cycles}_100m_{args.Release}_{args.version}_BRW_default*.png'))): diff --git a/scripts/ATL14_write2nc.py b/scripts/ATL14_write2nc.py index 5836b95..4f61e01 100755 --- a/scripts/ATL14_write2nc.py +++ b/scripts/ATL14_write2nc.py @@ -57,7 +57,7 @@ def ATL14_write2nc(args): crs_var.spatial_epsg = '3413' crs_var.spatial_ref = 'PROJCS["WGS 84 / NSIDC Sea Ice Polar Stereographic North",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Polar_Stereographic"],PARAMETER["latitude_of_origin",70],PARAMETER["central_meridian",-45],PARAMETER["scale_factor",1],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["X",EAST],AXIS["Y",NORTH],AUTHORITY["EPSG","3413"]]' crs_var.crs_wkt = ('PROJCS["WGS 84 / NSIDC Sea Ice Polar Stereographic North",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Polar_Stereographic"],PARAMETER["latitude_of_origin",70],PARAMETER["central_meridian",-45],PARAMETER["scale_factor",1],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["X",EAST],AXIS["Y",NORTH],AUTHORITY["EPSG","3413"]]') - elif args.region == 'AA': + elif args.region in ['A1','A2','A3','A4']: crs_var = nc.createVariable('Polar_Stereographic',np.byte,()) crs_var.standard_name = 'Polar_Stereographic' crs_var.grid_mapping_name = 'polar_stereographic' @@ -261,7 +261,7 @@ def ATL14_write2nc(args): parser=argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter, fromfile_prefix_chars='@') parser.add_argument('-b','--base_dir', type=str, default=os.getcwd(), help='directory in which to look for mosaicked .h5 files') parser.add_argument('-rr','--region', type=str, help='2-letter region indicator \n' - '\t AA: Antarctica \n' + '\t A[1-4]: Antarctica, by quadrant \n' '\t AK: Alaska \n' '\t CN: Arctic Canada North \n' '\t CS: Arctic Canada South \n' @@ -273,7 +273,7 @@ def ATL14_write2nc(args): parser.add_argument('-R','--Release', type=str, help="3-digit release number for output filename") parser.add_argument('-v','--version', type=str, help="2-digit version number for output filename") parser.add_argument('-list11','--ATL11_lineage_dir', type=str, help='directory in which to look for ATL11 .h5 filenames') - parser.add_argument('-tiles','--tiles_dir', type=str, help='directory in which to look for tile .h5 files, defaults to [base_dir]/centers') + parser.add_argument('-tiles','--tiles_dir', type=str, help='directory in which to look for tile .h5 files, defaults to [base_dir]/prelim') parser.add_argument('--ATL11_index', type=str, help='GeoIndex file pointing to ATL11 data') args, _=parser.parse_known_args() @@ -282,7 +282,7 @@ def ATL14_write2nc(args): args.ATL11_lineage_dir=os.path.dirname(os.path.dirname(args.ATL11_index)) if args.tiles_dir is None: - args.tiles_dir=os.path.join(args.base_dir, 'centers') + args.tiles_dir=os.path.join(args.base_dir, 'prelim') print('args:',args) diff --git a/scripts/ATL15_browse_plots.py b/scripts/ATL15_browse_plots.py index a507610..9ed8481 100644 --- a/scripts/ATL15_browse_plots.py +++ b/scripts/ATL15_browse_plots.py @@ -12,6 +12,7 @@ import uuid import io, re, os, glob import h5py +import pkg_resources import matplotlib.pyplot as plt #import warnings @@ -115,7 +116,8 @@ def ATL15_browse_plots(args): print(f'Making file {brwfile}') if os.path.isfile(brwfile): os.remove(brwfile) - shutil.copyfile('ATL1415/resources/BRW_template.h5',brwfile) + template_file = pkg_resources.resource_filename('ATL1415','resources/BRW_template.h5') + shutil.copyfile(template_file,brwfile) with h5py.File(brwfile,'r+') as hf: hf.require_group('/default') for ii, name in enumerate(sorted(glob.glob(f'{args.base_dir.rstrip("/")}/ATL15_{args.region}_{args.cycles}{ave}_{args.Release}_{args.version}_BRW_default*.png'))): diff --git a/scripts/ATL15_write2nc.py b/scripts/ATL15_write2nc.py index 08e18d4..1c8b7ef 100755 --- a/scripts/ATL15_write2nc.py +++ b/scripts/ATL15_write2nc.py @@ -34,7 +34,7 @@ def projection_variable(region,group): crs_var.spatial_epsg = '3413' crs_var.spatial_ref = 'PROJCS["WGS 84 / NSIDC Sea Ice Polar Stereographic North",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Polar_Stereographic"],PARAMETER["latitude_of_origin",70],PARAMETER["central_meridian",-45],PARAMETER["scale_factor",1],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["X",EAST],AXIS["Y",NORTH],AUTHORITY["EPSG","3413"]]' crs_var.crs_wkt = ('PROJCS["WGS 84 / NSIDC Sea Ice Polar Stereographic North",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Polar_Stereographic"],PARAMETER["latitude_of_origin",70],PARAMETER["central_meridian",-45],PARAMETER["scale_factor",1],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["X",EAST],AXIS["Y",NORTH],AUTHORITY["EPSG","3413"]]') - elif region == 'AA': + elif region in ['A1','A2','A3','A4']: crs_var = group.createVariable('Polar_Stereographic',np.byte,()) crs_var.standard_name = 'Polar_Stereographic' crs_var.grid_mapping_name = 'polar_stereographic' @@ -288,7 +288,7 @@ def make_dataset(field,fieldout,data,field_attrs,file_obj,group_obj,nctype,dimSc # input('Press enter to end.') # plt.close('all') # exit(-1) - + ice_area_mask=None # loop over dz*.h5 files for one ave for jj in range(len(lags['file'])): if jj==0: @@ -342,6 +342,8 @@ def make_dataset(field,fieldout,data,field_attrs,file_obj,group_obj,nctype,dimSc # get data from .h5 if fld.startswith('delta_h'): # fields with complicated name changes + #print("from:" + lags['file'][jj]) + print("\t reading:" + str([dzg, dz_dict[field]])) data = np.array(lags['file'][jj][dzg][dz_dict[field]]) data[np.isnan(ice_area_mask)] = np.nan if fld=='delta_h': # add group description @@ -407,7 +409,7 @@ def make_dataset(field,fieldout,data,field_attrs,file_obj,group_obj,nctype,dimSc parser=argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter, fromfile_prefix_chars='@') parser.add_argument('-b','--base_dir', type=str, default=os.getcwd(), help='directory in which to look for mosaicked .h5 files') parser.add_argument('-rr','--region', type=str, help='2-letter region indicator \n' - '\t AA: Antarctica \n' + '\t A(1-4): Antarctica, by quadrant \n' '\t AK: Alaska \n' '\t CN: Arctic Canada North \n' '\t CS: Arctic Canada South \n' @@ -428,8 +430,8 @@ def make_dataset(field,fieldout,data,field_attrs,file_obj,group_obj,nctype,dimSc args.ATL11_lineage_dir=os.path.dirname(os.path.dirname(args.ATL11_index)) if args.tiles_dir is None: - args.tiles_dir=os.path.join(args.base_dir, 'centers') - + args.tiles_dir=os.path.join(args.base_dir, 'prelim') + print('args',args) fileout = ATL15_write2nc(args) diff --git a/scripts/make_200km_tiles.py b/scripts/make_200km_tiles.py index 9069397..5eaf704 100755 --- a/scripts/make_200km_tiles.py +++ b/scripts/make_200km_tiles.py @@ -13,20 +13,18 @@ def make_fields(): fields={} fields['z0']="z0 sigma_z0 misfit_rms misfit_scaled_rms mask cell_area count".split(' ') - #NOTE: This skipps the dz tiling - return fields fields['dz']="dz sigma_dz count misfit_rms misfit_scaled_rms mask cell_area".split(' ') - lags=['_lag1', '_lag4', '_lag8'] + lags=['_lag1', '_lag4', '_lag8', '_lag12','_lag16'] for lag in lags: - fields['dzdt'+lag]=["dzdt"+lag, "sigma_dzdt"+lag] + fields['dzdt'+lag]=["dzdt"+lag, "sigma_dzdt"+lag, "cell_area"] for res in ["_40000m", "_20000m", "_10000m"]: fields['avg_dz'+res] = ["avg_dz"+res, "sigma_avg_dz"+res,'cell_area'] for lag in lags: field_str='avg_dzdt'+res+lag - fields[field_str]=[field_str, 'sigma_'+field_str] + fields[field_str]=[field_str, 'sigma_'+field_str, 'cell_area'] #for key, item in fields.items(): #print(key+" : "+str(item)) @@ -34,6 +32,7 @@ def make_fields(): return fields def make_200km_tiles(region_dir): + print("looking for tiles for "+region_dir) tile_ctr_file=os.path.join(region_dir,'200km_tile_list.txt') if os.path.isfile(tile_ctr_file): @@ -42,7 +41,7 @@ def make_200km_tiles(region_dir): return xyc tile_files=[] - for sub in ['matched']: + for sub in ['prelim']: tile_files += glob.glob(os.path.join(region_dir, sub, 'E*N*.h5')) tile_list=[] @@ -64,10 +63,35 @@ def make_200km_tiles(region_dir): tile_W=2.e5 tile_re = re.compile('E(.*)_N(.*).h5') - -region_dir=sys.argv[1] -region=sys.argv[2] - +avg_re = re.compile('_(\d+)m') +import argparse +parser = argparse.ArgumentParser() +parser.add_argument('region_dir', type=str) +parser.add_argument('region', type=str) +parser.add_argument('--step', type=str, default='matched') +parser.add_argument('--pad', type=float) +parser.add_argument('--feather', type=float) +parser.add_argument('--W', type=int, default=60000) +parser.add_argument('--spacing', type=int, default=40000) +parser.add_argument('--skip_sigma', action='store_true') +parser.add_argument('--name', type=str) +parser.add_argument('--environment','-e', type=str, default='IS2', help="environment that each job will activate") +args=parser.parse_args() + +region_dir=args.region_dir +region=args.region + +step=args.step + +# make the pad and feather work for 44 km tiles: +overlap=args.W-args.spacing +if args.pad is None: + args.pad = overlap/4 +if args.feather is None: + args.feather = overlap/2 +print(f"***pad={args.pad}, feather={args.feather}, overlap={overlap}") + +print(f"Skip sigma is {args.skip_sigma}, step is {step}") print("region_dir is " +region_dir) fields=make_fields() @@ -77,8 +101,20 @@ def make_200km_tiles(region_dir): if not os.path.isdir(tile_dir_200km): os.mkdir(tile_dir_200km) -if not os.path.isdir(f'tile_run_{region}'): - os.mkdir(f'tile_run_{region}') +if args.name is None: + args.name=region +run_dir=f'tile_run_{args.name}' +if not os.path.isdir(run_dir): + os.mkdir(run_dir) + +if os.path.isdir(run_dir+'/logs'): + N=len(glob.glob(run_dir+'/logs_round_*')) + os.rename(run_dir+'/logs', run_dir+f'/logs_round_{N+1}') + os.rename(run_dir+'/done', run_dir+f'/done_round_{N+1}') + +for sub in ['queue','logs','done','running']: + if not os.path.isdir(run_dir+'/'+sub): + os.mkdir(run_dir+'/'+sub) non_sigma_fields={} sigma_fields={} @@ -93,27 +129,36 @@ def make_200km_tiles(region_dir): tile_bounds_1km = "_".join([str(int(ii/1000)) for ii in tile_bounds]) tile_bounds_str = " ".join([str(ii) for ii in tile_bounds]) - task_file=f'tile_run_{region}/task_{count}' + task_file=f'{run_dir}/queue/task_{count+1}' with open(task_file,'w') as fh: fh.write("source activate IS2\n") for group in fields.keys(): - if "40000m" in group: - pad=0 - feather=0 - spacing_str="-S 40000 40000" - else: - pad=5000 - feather=10000 - spacing_str="" - + pad=args.pad + feather=args.feather + spacing_str="" + avg_scale=avg_re.search(group) + if avg_scale is not None: + avg_scale=float(avg_scale.groups()[0]) + # NOTE - this is to deal with the truncated 20-km averages in + # release 003. May need to be fixed in the future (avg_scale > overlap makes more sense) + if avg_scale >= overlap: + pad=0 + feather=0 + if "40000m" in group: + spacing_str="-S 40000 40000" out_dir = os.path.join(tile_dir_200km, group) if not os.path.isdir(out_dir): os.mkdir(out_dir) out_file = os.path.join(out_dir, f"{group}{tile_bounds_1km}.h5") fh.write("#\n") - fh.write(f"make_mosaic.py -w -R -d {region_dir} -g matched/E*.h5 -r {search_bounds_str} -f {feather} -p {pad} -c {tile_bounds_str} -G {group} -F {non_sigma_fields[group]} -O {out_file} {spacing_str}\n") - fh.write(f"make_mosaic.py -w -d {region_dir} -g prelim/E*.h5 -r {search_bounds_str} -f {feather} -p {pad} -c {tile_bounds_str} -G {group} -F {sigma_fields[group]} -O {out_file} {spacing_str}\n") + fh.write(f"make_mosaic.py -w -R -d {region_dir} -g '{step}/E*.h5' -r {search_bounds_str} -f {feather} -p {pad} -c {tile_bounds_str} -G {group} -F {non_sigma_fields[group]} -O {out_file} {spacing_str}\n") + if not args.skip_sigma: + fh.write(f"make_mosaic.py -w -d {region_dir} -g 'prelim/E*.h5' -r {search_bounds_str} -f {feather} -p {pad} -c {tile_bounds_str} -G {group} -F {sigma_fields[group]} -O {out_file} {spacing_str}\n") st=os.stat(task_file) os.chmod(task_file, st.st_mode | stat.S_IEXEC) +with open('slurm_scripts/slurm_mos_run','r') as fh_in: + with open(run_dir+'/slurm_mos_run','w') as fh_out: + for line in fh_in: + fh_out.write(line.replace('LAST_TASK', str(count+1)).replace('_XX_', '_'+args.name+'_')) diff --git a/scripts/make_ATL11_index b/scripts/make_ATL11_index index ab8da97..f4aca1f 100755 --- a/scripts/make_ATL11_index +++ b/scripts/make_ATL11_index @@ -24,13 +24,13 @@ for hemi in 'north' 'south'; do echo "NORTH!" ATL11_source=$ATL11_north hemisphere=1 - the_digit=0 + regions="03 04 05" else [ $ATL11_south == 'None' ] && continue echo "SOUTH!" ATL11_source=$ATL11_south hemisphere=-1 - the_digit=1 + regions="10 11 12" fi hemi_dir=$ATL14_dir/$AT$hemi @@ -43,16 +43,18 @@ for hemi in 'north' 'south'; do [ -d $ATL11_sub ] || mkdir $ATL11_sub [ -d $hemi_dir ] || mkdir $hemi_dir [ -d $index_dir ] || mkdir $index_dir - - for file in `ls $ATL11_source/ATL11_????$the_digit?_????_???_??.h5`; do - base=`basename $file` - [ -L $hemi_dir/$base ] || ln -s $file $hemi_dir + for region in $regions; do + glob_str=$ATL11_source"/ATL11_????"$region"_????_???_??.h5" + echo "glob str is $glob_str" + for file in `ls $glob_str`; do + base=`basename $file` + [ -L $hemi_dir/$base ] || ln -s $file $hemi_dir - index_file=$index_dir"/"$base - [ -f $index_file ] && continue - echo "$env_str pushd $index_dir; $prog --hemisphere $hemisphere --type ATL11 -g ../$base --index_file $index_file --Relative" >> index_queue.txt + index_file=$index_dir"/"$base + [ -f $index_file ] && continue + echo "$env_str pushd $index_dir; $prog --hemisphere $hemisphere --type ATL11 -g ../$base --index_file $index_file --Relative" >> index_queue.txt + done done - echo "pushd $index_dir; $prog --hemisphere $hemisphere --type h5_geoindex -g 'ATL11*.h5' --index_file GeoIndex.h5 --Relative; popd" >> post_queue.txt done diff --git a/scripts/make_ATL1415_queue.py b/scripts/make_ATL1415_queue.py index 4bfc504..efc8951 100755 --- a/scripts/make_ATL1415_queue.py +++ b/scripts/make_ATL1415_queue.py @@ -42,10 +42,17 @@ def pad_mask_canvas(D, N): parser.add_argument('step', type=str) parser.add_argument('defaults_files', nargs='+', type=str) parser.add_argument('--region_file', '-R', type=str) +parser.add_argument('--xy_list_file', type=str) parser.add_argument('--skip_errors','-s', action='store_true') parser.add_argument('--tile_spacing', type=int) parser.add_argument('--prior_edge_include', type=float, default=1000) parser.add_argument('--environment','-e', type=str) +parser.add_argument('--min_R', type=float) +parser.add_argument('--max_R', type=float) +parser.add_argument('--min_xy', type=float) +parser.add_argument('--max_xy', type=float) +parser.add_argument('--queue_file','-q', type=str) +parser.add_argument('--replace', action='store_true') args = parser.parse_args() if args.step not in ['centers', 'edges','corners','prelim', 'matched']: @@ -65,7 +72,7 @@ def pad_mask_canvas(D, N): with open(args.region_file,'r') as fh: for line in fh: m = line_re.search(line) - temp[m.group(1)]=[np.float(m.group(2)), np.float(m.group(3))] + temp[m.group(1)]=[float(m.group(2)), float(m.group(3))] XR=temp['XR'] YR=temp['YR'] @@ -105,7 +112,10 @@ def pad_mask_canvas(D, N): # figure out what directories we need to make release_dir = os.path.join(defaults['--ATL14_root'], "rel"+defaults['--Release']) hemi_dir=os.path.join(release_dir, hemisphere_name) -region_dir=os.path.join(hemi_dir, defaults['--region']) +if "--base_directory" in defaults: + region_dir=defaults['--base_directory'] +else: + region_dir=os.path.join(hemi_dir, defaults['--region']) for this in [release_dir, hemi_dir, region_dir]: if not os.path.isdir(this): @@ -119,11 +129,12 @@ def pad_mask_canvas(D, N): print("could not find ATL11 index in " + defaults['--ATL11_index'] + " or " + original_index_file) sys.exit(1) -# write out the composite defaults file -defaults_file=os.path.join(region_dir, f'input_args_{defaults["--region"]}.txt') -with open(defaults_file, 'w') as fh: - for key, val in defaults.items(): - fh.write(f'{key}={val}\n') +# write out the composite defaults file to add the region-dir: +if '-b' not in defaults: + defaults_file=os.path.join(region_dir, f'input_args_{defaults["--region"]}.txt') + with open(defaults_file, 'w') as fh: + for key, val in defaults.items(): + fh.write(f'{key}={val}\n') fh.write(f"-b={region_dir}\n") step_dir=os.path.join(region_dir, args.step) @@ -141,38 +152,59 @@ def pad_mask_canvas(D, N): Hxy=Wxy/2 -mask_base, mask_ext = os.path.splitext(defaults['--mask_file']) -if mask_ext in ('.tif','.h5'): - if mask_ext=='.h5': - tif_1km=defaults['--mask_file'].replace('.h5', '_1km.tif') - else: - tif_1km=defaults['--mask_file'].replace('100m','1km').replace('125m','1km') - temp=pc.grid.data().from_geotif(tif_1km) - - mask_G=pad_mask_canvas(temp, 200) - mask_G.z=snd.binary_dilation(mask_G.z, structure=np.ones([1, int(3*Hxy/1000)+1], dtype='bool')) - mask_G.z=snd.binary_dilation(mask_G.z, structure=np.ones([int(3*Hxy/1000)+1, 1], dtype='bool')) - x0=np.unique(np.round(mask_G.x/Hxy)*Hxy) - y0=np.unique(np.round(mask_G.y/Hxy)*Hxy) - x0, y0 = np.meshgrid(x0, y0) - xg=x0.ravel() - yg=y0.ravel() - good=(np.abs(mask_G.interp(xg, yg)-1)<0.1) & (np.mod(xg, Wxy)==0) & (np.mod(yg, Wxy)==0) -elif mask_ext in ['.shp','.db']: - # the mask is a shape. - # We require that an 80-km grid based on the mask exists - if not os.path.isfile(mask_base+'_80km.tif'): - raise(OSError(f"gridded mask file {mask_base+'_80km.tif'} not found")) - mask_G=pc.grid.data().from_geotif(mask_base+'_80km.tif') - xg, yg = np.meshgrid(mask_G.x, mask_G.y) - xg=xg.ravel()[mask_G.z.ravel()==1] - yg=yg.ravel()[mask_G.z.ravel()==1] +if args.xy_list_file is not None: + print("reading xy_list_file : " + args.xy_list_file) + # if a list file exists, read it to get the initial centers + xg, yg = [], [] + with open(args.xy_list_file,'r') as fh: + for line in fh: + try: + xgi, ygi = map(float, line.split()) + xg += [xgi] + yg += [ygi] + except ValueError: + print("could not parse:\n"+line) + xg, yg = map(np.array, [xg, yg]) good=np.ones_like(xg, dtype=bool) +else: + # get xg, yg from the mask file: + mask_base, mask_ext = os.path.splitext(defaults['--mask_file']) + if mask_ext in ('.tif','.h5'): + if mask_ext=='.h5' and '_100m' in mask_base: + tif_1km=defaults['--mask_file'].replace('_100m.h5', '_1km.tif') + elif '_full' in mask_base: + tif_1km=defaults['--mask_file'].replace('_full.h5', '_1km.tif') + else: + tif_1km=defaults['--mask_file'].replace('100m','1km').replace('125m','1km') + print(tif_1km) + print() + temp=pc.grid.data().from_geotif(tif_1km) + + mask_G=pad_mask_canvas(temp, 200) + mask_G.z=snd.binary_dilation(mask_G.z, structure=np.ones([1, int(3*Hxy/1000)+1], dtype='bool')) + mask_G.z=snd.binary_dilation(mask_G.z, structure=np.ones([int(3*Hxy/1000)+1, 1], dtype='bool')) + + x0=np.unique(np.round(mask_G.x/Hxy)*Hxy) + y0=np.unique(np.round(mask_G.y/Hxy)*Hxy) + x0, y0 = np.meshgrid(x0, y0) + xg=x0.ravel() + yg=y0.ravel() + good=(np.abs(mask_G.interp(xg, yg)-1)<0.1) & (np.mod(xg, Wxy)==0) & (np.mod(yg, Wxy)==0) + elif mask_ext in ['.shp','.db']: + # the mask is a shape. + # We require that an 40-km grid based on the mask exists + if not os.path.isfile(mask_base+'_40km.tif'): + raise(OSError(f"gridded mask file {mask_base+'_40km.tif'} not found")) + mask_G=pc.grid.data().from_geotif(mask_base+'_40km.tif') + xg, yg = np.meshgrid(mask_G.x, mask_G.y) + xg=xg.ravel()[mask_G.z.ravel()==1] + yg=yg.ravel()[mask_G.z.ravel()==1] + good=np.ones_like(xg, dtype=bool) + if XR is not None: good &= (xg>=XR[0]) & (xg <= XR[1]) & (yg > YR[0]) & (yg < YR[1]) - xg=xg[good] yg=yg[good] @@ -186,21 +218,38 @@ def pad_mask_canvas(D, N): delta_x=[-1, 1, -1, 1.] delta_y=[-1, -1, 1, 1.] +print(f'min_xy={args.min_xy}') +print(f'max_xy={args.max_xy}') queued=[]; -queue_file=f"1415_queue_{defaults['--region']}_{args.step}.txt" +if args.queue_file is not None: + queue_file=args.queue_file +else: + queue_file=f"1415_queue_{defaults['--region']}_{args.step}.txt" with open(queue_file,'w') as qh: for xy0 in zip(xg, yg): for dx, dy in zip(delta_x, delta_y): xy1=np.array(xy0)+np.array([dx, dy])*Hxy + if args.min_R is not None: + if np.abs(xy1[0]+1j*xy1[1]) <= args.min_R: + continue + if args.max_R is not None: + if np.abs(xy1[0]+1j*xy1[1]) >= args.max_R: + continue + if args.min_xy is not None: + if np.abs(xy1).max() < args.min_xy: + continue + if args.max_xy is not None: + if np.any(np.abs(xy1) > args.max_xy): + continue if tuple(xy1) in queued: continue else: queued.append(tuple(xy1)) if not args.step=='matched': out_file='%s/E%d_N%d.h5' % (step_dir, xy1[0]/1000, xy1[1]/1000) - if os.path.isfile(out_file): + if os.path.isfile(out_file) and not args.replace: continue cmd='%s --xy0 %d %d --%s @%s ' % (prog, xy1[0], xy1[1], args.step, defaults_file) if calc_errors: diff --git a/scripts/make_mosaic_jobs b/scripts/make_mosaic_jobs index cd3f516..d29b57a 100755 --- a/scripts/make_mosaic_jobs +++ b/scripts/make_mosaic_jobs @@ -5,7 +5,13 @@ source activate IS2 base=$1 region=`basename $base` -mosaic_run="mosaic_run_"$region +mosaic_run=${region}_mosaic + +if [ -f $base/bounds.txt ]; then + crop="-c "$(head -1 $base/bounds.txt) +else + crop="" +fi [ -d $mosaic_run ] || mkdir $mosaic_run @@ -18,6 +24,8 @@ pad=5000 feather=10000 compute_sigma='True' +compute_SMB='True' + field=dz @@ -26,18 +34,23 @@ glob_str="'matched/*.h5'" task=0 task=$(($task+1)) -echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py -R -w -d $base -g $glob_str -p $pad -f $feather -O $base/dz.h5 --in_group dz/ -F count misfit_rms misfit_scaled_rms mask cell_area $field" > $mosaic_run/task_${task} +echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop -R -w -d $base -g $glob_str -p $pad -f $feather -O $base/dz.h5 --in_group dz/ -F count misfit_rms misfit_scaled_rms mask cell_area $field" > $mosaic_run/task_${task} if [ $compute_sigma == 'True' ]; then - echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py -w -d $base -g 'prelim/*.h5' -p $pad -f $feather -O $base/dz.h5 --in_group dz/ -F sigma_dz" >> $mosaic_run/task_${task} + echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop -w -d $base -g 'prelim/*.h5' -p $pad -f $feather -O $base/dz.h5 --in_group dz/ -F sigma_dz" >> $mosaic_run/task_${task} fi +if [ $compute_SMB == 'True' ]; then + echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop -w -d $base -g 'prelim/*.h5' -p $pad -f $feather -O $base/dz.h5 --in_group dz/ -F SMB_a FAC" >> $mosaic_run/task_${task} +fi + + -for lag in _lag1 _lag4 _lag8 _lag12; do +for lag in _lag1 _lag4 _lag8 _lag12 _lag16; do task=$(($task+1)) - echo "lag=$lag" + #echo "lag=$lag" field=dzdt$lag - echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py -R -w -d $base -g $glob_str -p $pad -f $feather -O $base/dzdt$lag.h5 --in_group dzdt$lag/ -F $field cell_area" >> $mosaic_run/task_${task} + echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop -R -w -d $base -g $glob_str -p $pad -f $feather -O $base/dzdt$lag.h5 --in_group dzdt$lag/ -F $field cell_area" >> $mosaic_run/task_${task} if [ $compute_sigma == 'True' ]; then - echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py -w -d $base -g 'prelim/*.h5' -p $pad -f $feather -O $base/dzdt$lag.h5 --in_group dzdt$lag/ -F sigma_$field" >> $mosaic_run/task_${task} + echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop -w -d $base -g 'prelim/*.h5' -p $pad -f $feather -O $base/dzdt$lag.h5 --in_group dzdt$lag/ -F sigma_$field" >> $mosaic_run/task_${task} fi done @@ -45,7 +58,7 @@ done for group in avg_dz_40000m avg_dz_20000m avg_dz_10000m; do field=$group - echo "$group $field" + #echo "$group $field" this_pad=$pad this_feather=$feather this_S="" @@ -58,28 +71,29 @@ for group in avg_dz_40000m avg_dz_20000m avg_dz_10000m; do elif [ $group == 'avg_dz_20000m' ] ; then this_pad=0 this_feather=0 + this_S="" this_w="" fi out=`echo $group | sed s/000m/km/ | sed s/avg_//` task=$(($task+1)) - echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_w -R -d $base -g $glob_str -p $this_pad -f $this_feather $this_S -O $base/$out.h5 --in_group $group/ -F $field cell_area" > $mosaic_run/task_${task} + echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop $this_w -R -d $base -g $glob_str -p $this_pad -f $this_feather $this_S -O $base/$out.h5 --in_group $group/ -F $field cell_area" > $mosaic_run/task_${task} if [ $compute_sigma == 'True' ]; then - echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_w -d $base -g 'prelim/*.h5' -p $this_pad -f $this_feather $this_S -O $base/$out.h5 --in_group $group/ -F sigma_$group" >> $mosaic_run/task_${task} + echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop $this_w -d $base -g 'prelim/*.h5' -p $this_pad -f $this_feather $this_S -O $base/$out.h5 --in_group $group/ -F sigma_$group" >> $mosaic_run/task_${task} fi group=`echo $group | sed s/dz/dzdt/` out=`echo $out | sed s/dz/dzdt/` - for lag in _lag1 _lag4 _lag8 _lag12; do + for lag in _lag1 _lag4 _lag8 _lag12 _lag16; do field=$group$lag field_list="$field cell_area" task=$(($task+1)) - echo "lag=$lag, group=$group, task=$task" - echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py -R $this_w -d $base -g $glob_str -p $this_pad -f $this_feather $this_S -O $base/$out$lag.h5 --in_group $field/ -F $field_list" > $mosaic_run/task_${task} + #echo "lag=$lag, group=$group, task=$task" + echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop -R $this_w -d $base -g $glob_str -p $this_pad -f $this_feather $this_S -O $base/$out$lag.h5 --in_group $field/ -F $field_list" > $mosaic_run/task_${task} if [ $compute_sigma == 'True' ]; then sigma_field=sigma_${group}${lag} - echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_w -d $base -g 'prelim/*.h5' -p $this_pad -f $this_feather $this_S -O $base/$out$lag.h5 --in_group $field/ -F $sigma_field" >> $mosaic_run/task_${task} + echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop $this_w -d $base -g 'prelim/*.h5' -p $this_pad -f $this_feather $this_S -O $base/$out$lag.h5 --in_group $field/ -F $sigma_field" >> $mosaic_run/task_${task} fi done done @@ -96,20 +110,20 @@ if [ -d $base/200km_tiles/z0 ] ; then field_list=$field_list" sigma_z0" for field in $field_list; do echo $field - echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_replace -d ${base}/200km_tiles/z0 -g '*.h5' -O $base/z0.h5 --in_group z0/ -F $field" >> $mosaic_run/task_${task} + echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop $this_replace -d ${base}/200km_tiles/z0 -g '*.h5' -O $base/z0.h5 --in_group z0/ -F $field" >> $mosaic_run/task_${task} this_replace='' done else for field in $field_list; do - echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_replace -w -d $base -g $glob_str -p $pad -f $feather -O $base/z0.h5 --in_group z0/ -F $field " >> $mosaic_run/task_${task} + echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop $this_replace -w -d $base -g $glob_str -p $pad -f $feather -O $base/z0.h5 --in_group z0/ -F $field " >> $mosaic_run/task_${task} this_replace='' done if [ $compute_sigma == 'True' ]; then - echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_replace -w -d $base -g 'prelim/*.h5' -p $pad -f $feather -O $base/z0.h5 --in_group z0/ -F sigma_z0 " >> $mosaic_run/task_${task} + echo "python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $crop $this_replace -w -d $base -g 'prelim/*.h5' -p $pad -f $feather -O $base/z0.h5 --in_group z0/ -F sigma_z0 " >> $mosaic_run/task_${task} fi fi mv $mosaic_run/task* $mosaic_run/queue -cp slurm_scripts/slurm_mos_run $mosaic_run +cat slurm_scripts/slurm_mos_run | sed s/LAST_TASK/$task/ | sed s/XX/$region/ >> $mosaic_run/slurm_mos_run pushd $mosaic_run sbatch slurm_mos_run diff --git a/scripts/make_sigma_queue.py b/scripts/make_sigma_queue.py old mode 100644 new mode 100755 index d28b412..e9e795d --- a/scripts/make_sigma_queue.py +++ b/scripts/make_sigma_queue.py @@ -1,36 +1,32 @@ #! /usr/bin/env python - -import os import glob import h5py -import sys - -def_file=sys.argv[1] -region=sys.argv[2] - -thedir=os.path.dirname(def_file) - -count=0 - -if not os.path.isdir(region+'_sigma_calc'): - os.mkdir(region+'_sigma_calc') - os.mkdir(region+'_sigma_calc'+'/queue') - os.mkdir(region+'_sigma_calc'+'/running') - os.mkdir(region+'_sigma_calc'+'/done') - os.mkdir(region+'_sigma_calc'+'/logs') - -queue_dir=region+'_sigma_calc'+'/queue' +import argparse +import numpy as np +import re +import os +parser=argparse.ArgumentParser(\ + fromfile_prefix_chars="@") +parser.add_argument('--glob_str','-g', type=str, required=True) +parser.add_argument('--defaults_file','-d', type=str, required=True) +parser.add_argument('--step', type=str) +args, _= parser.parse_known_args() + +xy_re=re.compile('E(.*)_N(.*).h5') + +pad=np.array([-2.e3, 2.e3]) +with open('large_sigma_queue.txt','w') as fh_large: + with open('small_sigma_queue.txt','w') as fh_small: + for file in glob.glob(args.glob_str): + with h5py.File(file,'r') as h5f: + if 'sigma_dz' in h5f['dz']: + continue + + xy0=np.array([*map(float, xy_re.search(file).groups())])*1000 + if xy0[0]**2 + xy0[1]**2 > (800e3)**2: + fh_small.write(f"source activate IS2; ATL11_to_ATL15.py --xy0 {xy0[0]} {xy0[1]} --{args.step} @{args.defaults_file} --calc_error_for_xy; echo COMPLETE\n") + else: + fh_large.write(f"source activate IS2; ATL11_to_ATL15.py --xy0 {xy0[0]} {xy0[1]} --{args.step} @{args.defaults_file} --calc_error_for_xy; echo COMPLETE\n") -for step in ['corners', 'edges', 'centers']: - files=glob.glob(os.path.join(thedir, step, 'E*.h5')) - for file in files: - with h5py.File(file, 'r') as h5f: - if 'sigma_dz' in h5f['/dz/'].keys(): - continue - count += 1 - with open(os.path.join(queue_dir, 'calc_sigma_'+str(count)),'w') as qh: - qh.write('source activate IS2\n') - qh.write('ATL11_to_ATL15.py --calc_error_file '+file+' @'+def_file+'\n') - diff --git a/scripts/regen_mosaics b/scripts/regen_mosaics index b410bb0..56c0c72 100755 --- a/scripts/regen_mosaics +++ b/scripts/regen_mosaics @@ -4,31 +4,44 @@ source activate IS2 base=$1 -rm $base/*.h5 +if [ "$#" -ne 1 ]; then + step=$2 +else + step='matched' +fi + +[ -d $base/mosaics ] || mkdir $base/mosaics + +rm $base/mosaics/*.h5 pad=5000 feather=10000 compute_sigma='True' +#compute_sigma='False' field=dz if [ $compute_sigma == 'True' ]; then field="dz sigma_dz" fi -glob_str='matched/*.h5' +glob_str=$step'/*.h5' -python3 ~/git_repos/pointCollection/scripts/make_mosaic.py -w -d $base -g $glob_str -p $pad -f $feather -O $base/dz.h5 --in_group dz/ -F count misfit_rms misfit_scaled_rms mask cell_area $field +echo "----------------------------" +echo "searching for tiles using:" +echo $glob_str +echo "----------------------------" +python3 ~/git_repos/pointCollection/scripts/make_mosaic.py -w -d $base -g $glob_str -p $pad -f $feather -O $base/mosaics/dz.h5 --in_group dz/ -F count misfit_rms misfit_scaled_rms mask cell_area $field -v -for lag in _lag1 _lag4 _lag8 _lag12; do +for lag in _lag1 _lag4 _lag8 _lag12 _lag16; do echo "lag=$lag" field=dzdt$lag if [ $compute_sigma == 'True' ]; then field="$field sigma_$field" fi - python3 ~/git_repos/pointCollection/scripts/make_mosaic.py -w -d $base -g $glob_str -p $pad -f $feather -O $base/dz$lag.h5 --in_group dzdt$lag/ -F $field + python3 ~/git_repos/pointCollection/scripts/make_mosaic.py -w -d $base -g $glob_str -p $pad -f $feather -O $base/mosaics/dz$lag.h5 --in_group dzdt$lag/ -F $field done for group in avg_dz_40000m avg_dz_20000m avg_dz_10000m; do @@ -53,10 +66,10 @@ for group in avg_dz_40000m avg_dz_20000m avg_dz_10000m; do fi out=`echo $group | sed s/000m/km/ | sed s/avg_//` - python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_w -d $base -g $glob_str -p $this_pad -f $this_feather $this_S -O $base/$out.h5 --in_group $group/ -F $field cell_area + python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_w -d $base -g $glob_str -p $this_pad -f $this_feather $this_S -O $base/mosaics/$out.h5 --in_group $group/ -F $field cell_area group=`echo $group | sed s/dz/dzdt/` - for lag in _lag1 _lag4 _lag8 _lag12; do + for lag in _lag1 _lag4 _lag8 _lag12 _lag16; do echo "lag=$lag" field=$group$lag field_list=$field @@ -64,7 +77,7 @@ for group in avg_dz_40000m avg_dz_20000m avg_dz_10000m; do field_list="$field_list sigma_$field" fi - python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_w -d $base -g $glob_str -p $this_pad -f $this_feather $this_S -O $base/$out$lag.h5 --in_group $field/ -F $field_list + python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_w -d $base -g $glob_str -p $this_pad -f $this_feather $this_S -O $base/mosaics/$out$lag.h5 --in_group $field/ -F $field_list done done @@ -79,9 +92,9 @@ for field in $field_list; do echo $field if [ -d $base/200km_tiles/z0 ] ; then echo "200km" - python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_replace -d ${base}/200km_tiles/z0 -g "*.h5" -O $base/z0.h5 --in_group z0/ -F $field + python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_replace -d ${base}/200km_tiles/z0 -g "*.h5" -O $base/mosaics/z0.h5 --in_group z0/ -F $field else - python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_replace -w -d $base -g "*/*.h5" -p $pad -f $feather -O $base/z0.h5 --in_group z0/ -F $field + python3 ~/git_repos/pointCollection/scripts/make_mosaic.py $this_replace -w -d $base -g "*/*.h5" -p $pad -f $feather -O $base/mosaics/z0.h5 --in_group z0/ -F $field fi this_replace="" done diff --git a/scripts/setup_ATL1415_region.py b/scripts/setup_ATL1415_region.py index 7afbb0e..aac76d2 100755 --- a/scripts/setup_ATL1415_region.py +++ b/scripts/setup_ATL1415_region.py @@ -52,7 +52,7 @@ if '--mask_dir' in defaults: for key in ['--mask_file','--d2z0_file','--tide_mask_file', '--tide_adjustment_file', '--geoid_file', '--E_d2z0dx2_file']: - if key in defaults and not os.path.isfile(defaults[key]): + if key in defaults and not (os.path.isabs(defaults[key]) and os.path.isfile(defaults[key])): defaults[key] = os.path.join(defaults['--mask_dir'], defaults[key]) defaults.pop('--mask_dir', None) @@ -77,6 +77,7 @@ if os.path.isfile(temp1): defaults['--ATL11_index']=temp1 else: + print(temp1 + ' not found') temp2=os.path.join(os.path.dirname(defaults['--ATL14_root']), defaults['--ATL11_index']) print(f'looking for {temp2}') if os.path.isfile(temp2): diff --git a/scripts/setup_slurm_run.py b/scripts/setup_slurm_run.py index 143a373..cd95b2f 100755 --- a/scripts/setup_slurm_run.py +++ b/scripts/setup_slurm_run.py @@ -11,6 +11,9 @@ import argparse import os import stat +import numpy as np +import re + def setup_directories(run_dir): # setup the directories if not os.path.isdir(run_dir): @@ -33,46 +36,97 @@ def get_last_task(run_name): last_file_num=0; return last_file_num -def add_files_to_queue(run_name, task_list_file, shell=None, env=None): +def add_files_to_queue(run_name=None, task_list_file=None, task_glob=None, shell=None, env=None, R_range=None): last_file_num=get_last_task(run_name) - with open(task_list_file,'r') as fh: + xy0_re=re.compile('--xy0\s+(\S+)\s+(\S+)') + if task_list_file is not None: add_count=0 - for line in fh: + with open(task_list_file,'r') as fh: + for line in fh: + if R_range is not None: + xy=np.array([*map(float, xy0_re.search(line).groups())]) + R2=np.sum(xy**2) + if (R2 < R_range[0]**2) | (R2 >= R_range[1]**2) : + continue + last_file_num=last_file_num+1; + this_file=os.path.join(run_name,'queue','task_%d' % last_file_num) + with open(this_file,'w') as out_fh: + #print("adding %s to queue" % this_file) + add_count +=1 + if shell is not None: + out_fh.write(f'#! /usr/bin/env {shell}\n') + if env is not None and "source activate" not in line: + out_fh.write("source activate %s\n" % env) + out_fh.write('%s\n'% line.rstrip()); + os.chmod(this_file, os.stat(this_file).st_mode | stat.S_IEXEC) + + + if task_glob is not None: + task_files=glob.glob(task_glob) + for file in task_files: + if R_range is not None: + skip=False + with open(file,'r') as fh: + for line in fh: + m=xy0_re.search(line).groups() + if m is None: + continue + xy=np.array([*map(float, m.groups())]) + R2=np.sum(xy**2) + if (R2 < R_range[0]**2) | (R2 >= R_range[1]) : + skip=True + if skip: + continue last_file_num=last_file_num+1; this_file=os.path.join(run_name,'queue','task_%d' % last_file_num) - with open(this_file,'w') as out_fh: - #print("adding %s to queue" % this_file) - add_count +=1 - if shell is not None: - out_fh.write(f'#! /usr/bin/env {shell}\n') - if env is not None: - out_fh.write("source activate %s\n" % env) - out_fh.write('%s\n'% line.rstrip()); - os.chmod(this_file, os.stat(this_file).st_mode | stat.S_IEXEC) + add_count += 1 + os.rename(file, this_file) print(f"added {add_count} files to the queue") with open(os.path.join(run_name,'last_task'),'w+') as last_task_fh: last_task_fh.write('%d\n'% last_file_num) + return add_count def __main__(): - parser = argparse.ArgumentParser(description='Start parallel boss (no arguments) or add jobs to the queue (-m or -s options).') + parser = argparse.ArgumentParser() parser.add_argument('--run_name','-r', type=str, default='ATL_run', help="name to assign to jobs and temporary directories") - parser.add_argument('--queue_file', '-q', type=str, required=True, help="filename containing jobs, one per line") + parser.add_argument('--queue_file', '-q', type=str, help="filename containing jobs, one per line") + parser.add_argument('--task_glob', '-g', type=str, help='glob to match jobs') parser.add_argument('--environment','-e', type=str, default='ATL1415', help="environment that each job will activate") parser.add_argument('--shell','-s', type=str, default=None, help="shell to specify for each job (may not be needed)") - parser.add_argument('--jobs_per_task','-j', type=int, default=1, help="number of jobs per node") + parser.add_argument('--jobs_per_task','-j', type=int, help="number of jobs per node") parser.add_argument('--time','-t', type=str, default='02:00:00', help="time limit per job (hh:mm:ss)") parser.add_argument('--css', action='store_true', help="if set, the run will use the constraint=cssro argument, needed for ATL11") args=parser.parse_args() - - first_task=get_last_task(args.run_name)+1 + + R_dict=None + R_vals=[0, 1.e7] + if args.jobs_per_task is None: + R_dict={1.e8:{'dir':'queue_3cpu', 'ncpu':3, 'xy':[], 'src_file':[]}, + 6.e5:{'dir':'queue_14cpu','ncpu':14, 'xy':[], 'src_file':[]}, + 3.0e5:{'dir':'queue_20cpu','ncpu':20, 'xy':[], 'src_file':[]}, + 2.e5:{'dir':'queue_28cpu','ncpu':28, 'xy':[], 'src_file':[]}} + R_vals=list(R_dict.keys())[::-1] setup_directories(args.run_name) - add_files_to_queue(args.run_name, args.queue_file, shell=args.shell, env=args.environment) - last_task=get_last_task(args.run_name) - ATL1415.make_slurm_file(os.path.join(args.run_name, 'slurm_script.sh'), - subs={'JOB_NAME':args.run_name, - 'TIME':args.time, - 'NUM_TASKS':str(args.jobs_per_task), - 'JOB_NUMBERS':f'{first_task}-{last_task}'}, css=args.css) + for ii in range(len(R_vals)-1): + first_task=get_last_task(args.run_name) + if R_dict is not None: + R_range=[R_vals[ii], R_vals[ii+1]] + slurm_file='slurm_run_'+ R_dict[R_vals[ii]]['dir']+'.sh' + N_tasks=R_dict[R_vals[ii]]['ncpu'] + else: + R_range=None + slurm_file='slurm_run.sh' + N_tasks=args.jobs_per_task + N_added = add_files_to_queue(run_name=args.run_name, task_list_file=args.queue_file, task_glob=args.task_glob, shell=args.shell, env=args.environment, R_range=R_range) + last_task=get_last_task(args.run_name) + if N_added <1: + continue + ATL1415.make_slurm_file(os.path.join(args.run_name, slurm_file), + subs={'JOB_NAME':args.run_name, + 'TIME':args.time, + 'NUM_TASKS':str(N_tasks), + 'JOB_NUMBERS':f'{first_task+1}-{last_task}'}, css=args.css) + if __name__ == '__main__': __main__() diff --git a/slurm_scripts/slurm_mos_run b/slurm_scripts/slurm_mos_run index 503abe9..0f004cf 100644 --- a/slurm_scripts/slurm_mos_run +++ b/slurm_scripts/slurm_mos_run @@ -5,8 +5,8 @@ #SBATCH --ntasks=4 #SBATCH --partition=packable #SBATCH --qos=at15_pk -#SBATCH --array=1-21 -#SBATCH -o 1415_GL_mos.%A_%a +#SBATCH --array=1-LAST_TASK +#SBATCH -o 1415_XX_mos.%A_%a echo "-------------check memory for the node -----------" grep -i -e memfree -e memtotal -e swaptotal /proc/meminfo