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netcdf_output.py
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#!/usr/bin/env python3
# Yang Lei, Jet Propulsion Laboratory
# November 2017
import datetime
import os
import netCDF4
import numpy as np
import pandas as pd
def get_satellite_attribute(info):
mission_mapping = {
'L': 'Landsat ',
'S': 'Sentinel-',
}
satellite_1 = f'{mission_mapping[info["mission_img1"]]}{info["satellite_img1"]}'
satellite_2 = f'{mission_mapping[info["mission_img2"]]}{info["satellite_img2"]}'
if satellite_1 != satellite_2:
return f'{satellite_1} and {satellite_2}'
return satellite_1
def v_error_cal(vx_error, vy_error):
vx = np.random.normal(0, vx_error, 1000000)
vy = np.random.normal(0, vy_error, 1000000)
v = np.sqrt(vx**2 + vy**2)
return np.std(v)
def netCDF_packaging_intermediate(Dx, Dy, InterpMask, ChipSizeX, GridSpacingX, ScaleChipSizeY, SearchLimitX,
SearchLimitY, origSize, noDataMask, filename='./autoRIFT_intermediate.nc'):
nc_outfile = netCDF4.Dataset(filename, 'w', clobber=True, format='NETCDF4')
# First set global attributes that GDAL uses when it reads netCDF files
nc_outfile.setncattr('date_created', datetime.datetime.now().strftime("%d-%b-%Y %H:%M:%S"))
nc_outfile.setncattr('title', 'autoRIFT intermediate results')
nc_outfile.setncattr('author', 'Alex S. Gardner, JPL/NASA; Yang Lei, GPS/Caltech')
nc_outfile.setncattr('institution', 'NASA Jet Propulsion Laboratory (JPL), California Institute of Technology')
# set dimensions
dimidY, dimidX = Dx.shape
nc_outfile.createDimension('x', dimidX)
nc_outfile.createDimension('y', dimidY)
x = np.arange(0, dimidX, 1)
y = np.arange(0, dimidY, 1)
chunk_lines = np.min([np.ceil(8192/dimidY)*128, dimidY])
ChunkSize = [chunk_lines, dimidX]
nc_outfile.createDimension('x1', noDataMask.shape[1])
nc_outfile.createDimension('y1', noDataMask.shape[0])
var = nc_outfile.createVariable('x', np.dtype('int32'), ('x',), fill_value=None)
var.setncattr('standard_name', 'x_index')
var.setncattr('description', 'x index')
var[:] = x
var = nc_outfile.createVariable('y', np.dtype('int32'), ('y',), fill_value=None)
var.setncattr('standard_name', 'y_index')
var.setncattr('description', 'y index')
var[:] = y
var = nc_outfile.createVariable('Dx', np.dtype('float32'), ('y', 'x'), fill_value=np.nan,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'x_offset')
var.setncattr('description', 'x offset')
var[:] = Dx.astype(np.float32)
var = nc_outfile.createVariable('Dy', np.dtype('float32'), ('y', 'x'), fill_value=np.nan,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'y_offset')
var.setncattr('description', 'y offset')
var[:] = Dy.astype(np.float32)
var = nc_outfile.createVariable('InterpMask', np.dtype('uint8'), ('y', 'x'), fill_value=0,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'interpolated_value_mask')
var.setncattr('description', 'light interpolation mask')
var[:] = np.round(np.clip(InterpMask, 0, 255)).astype('uint8')
var = nc_outfile.createVariable('ChipSizeX', np.dtype('uint16'), ('y', 'x'), fill_value=0,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'chip_size_x')
var.setncattr('description', 'width of search window')
var[:] = np.round(np.clip(ChipSizeX, 0, 65535)).astype('uint16')
var = nc_outfile.createVariable('SearchLimitX', np.dtype('uint8'), ('y', 'x'), fill_value=0,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'search_limit_x')
var.setncattr('description', 'search limit x')
var[:] = np.round(np.clip(SearchLimitX, 0, 255)).astype('uint8')
var = nc_outfile.createVariable('SearchLimitY', np.dtype('uint8'), ('y', 'x'), fill_value=0,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'search_limit_y')
var.setncattr('description', 'search limit y')
var[:] = np.round(np.clip(SearchLimitY, 0, 255)).astype('uint8')
var = nc_outfile.createVariable('noDataMask', np.dtype('uint8'), ('y1', 'x1'), fill_value=0,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'nodata_value_mask')
var.setncattr('description', 'nodata value mask')
var[:] = np.round(np.clip(noDataMask, 0, 255)).astype('uint8')
var = nc_outfile.createVariable('GridSpacingX', np.dtype('uint16'), (), fill_value=None)
var.setncattr('standard_name', 'GridSpacingX')
var.setncattr('description', 'grid spacing x')
var[0] = GridSpacingX
var = nc_outfile.createVariable('ScaleChipSizeY', np.dtype('float32'), (), fill_value=None)
var.setncattr('standard_name', 'ScaleChipSizeY')
var.setncattr('description', 'scale of chip size in y to chip size in x')
var[0] = ScaleChipSizeY
var = nc_outfile.createVariable('origSizeX', np.dtype('uint16'), (), fill_value=None)
var.setncattr('standard_name', 'origSizeX')
var.setncattr('description', 'original array size x')
var[0] = origSize[1]
var = nc_outfile.createVariable('origSizeY', np.dtype('uint16'), (), fill_value=None)
var.setncattr('standard_name', 'origSizeY')
var.setncattr('description', 'original array size y')
var[0] = origSize[0]
nc_outfile.sync() # flush data to disk
nc_outfile.close()
def netCDF_read_intermediate(filename='./autoRIFT_intermediate.nc'):
inter_file = netCDF4.Dataset(filename, mode='r')
Dx = inter_file.variables['Dx'][:].data
Dy = inter_file.variables['Dy'][:].data
InterpMask = inter_file.variables['InterpMask'][:].data
ChipSizeX = inter_file.variables['ChipSizeX'][:].data
SearchLimitX = inter_file.variables['SearchLimitX'][:].data
SearchLimitY = inter_file.variables['SearchLimitY'][:].data
noDataMask = inter_file.variables['noDataMask'][:].data
noDataMask = noDataMask.astype('bool')
GridSpacingX = inter_file.variables['GridSpacingX'][:].data
ScaleChipSizeY = inter_file.variables['ScaleChipSizeY'][:].data
origSize = (inter_file.variables['origSizeY'][:].data, inter_file.variables['origSizeX'][:].data)
return Dx, Dy, InterpMask, ChipSizeX, GridSpacingX, ScaleChipSizeY, SearchLimitX, SearchLimitY, origSize, noDataMask
def netCDF_packaging(VX, VY, DX, DY, INTERPMASK, CHIPSIZEX, CHIPSIZEY, SSM, SSM1, SX, SY,
offset2vx_1, offset2vx_2, offset2vy_1, offset2vy_2, offset2vr, offset2va, scale_factor_1, scale_factor_2, MM, VXref, VYref,
DXref, DYref, rangePixelSize, azimuthPixelSize, dt, epsg, srs, tran, out_nc_filename, pair_type,
detection_method, coordinates, IMG_INFO_DICT, stable_count, stable_count1, stable_shift_applied,
dx_mean_shift, dy_mean_shift, dx_mean_shift1, dy_mean_shift1, error_vector):
vx_mean_shift = offset2vx_1 * dx_mean_shift + offset2vx_2 * dy_mean_shift
temp = vx_mean_shift
temp[np.logical_not(SSM)] = np.nan
# vx_mean_shift = np.median(temp[(temp > -500)&(temp < 500)])
vx_mean_shift = np.median(temp[np.logical_not(np.isnan(temp))])
vy_mean_shift = offset2vy_1 * dx_mean_shift + offset2vy_2 * dy_mean_shift
temp = vy_mean_shift
temp[np.logical_not(SSM)] = np.nan
# vy_mean_shift = np.median(temp[(temp > -500)&(temp < 500)])
vy_mean_shift = np.median(temp[np.logical_not(np.isnan(temp))])
vx_mean_shift1 = offset2vx_1 * dx_mean_shift1 + offset2vx_2 * dy_mean_shift1
temp = vx_mean_shift1
temp[np.logical_not(SSM1)] = np.nan
vx_mean_shift1 = np.median(temp[np.logical_not(np.isnan(temp))])
vy_mean_shift1 = offset2vy_1 * dx_mean_shift1 + offset2vy_2 * dy_mean_shift1
temp = vy_mean_shift1
temp[np.logical_not(SSM1)] = np.nan
vy_mean_shift1 = np.median(temp[np.logical_not(np.isnan(temp))])
V = np.sqrt(VX**2+VY**2)
if pair_type == 'radar':
dr_2_vr_factor = np.median(offset2vr[np.logical_not(np.isnan(offset2vr))])
SlantRangePixelSize = np.median(offset2vr[np.logical_not(np.isnan(offset2vr))]) * dt/365.0/24.0/3600.0
azimuthPixelSize = np.median(offset2va[np.logical_not(np.isnan(offset2va))]) * dt/365.0/24.0/3600.0
# VR = DX * rangePixelSize / dt * 365.0 * 24.0 * 3600.0
VR = DX * offset2vr
VR = VR.astype(np.float32)
# VA = DY * azimuthPixelSize / dt * 365.0 * 24.0 * 3600.0
VA = DY * offset2va
VA = VA.astype(np.float32)
VRref = DXref * offset2vr
VRref = VRref.astype(np.float32)
VAref = DYref * offset2va
VAref = VAref.astype(np.float32)
# vr_mean_shift = dx_mean_shift * rangePixelSize / dt * 365.0 * 24.0 * 3600.0
vr_mean_shift = dx_mean_shift * offset2vr
vr_mean_shift = np.median(vr_mean_shift[np.logical_not(np.isnan(vr_mean_shift))])
# va_mean_shift = dy_mean_shift * azimuthPixelSize / dt * 365.0 * 24.0 * 3600.0
va_mean_shift = dy_mean_shift * offset2va
va_mean_shift = np.median(va_mean_shift[np.logical_not(np.isnan(va_mean_shift))])
# vr_mean_shift1 = dx_mean_shift1 * rangePixelSize / dt * 365.0 * 24.0 * 3600.0
vr_mean_shift1 = dx_mean_shift1 * offset2vr
vr_mean_shift1 = np.median(vr_mean_shift1[np.logical_not(np.isnan(vr_mean_shift1))])
# va_mean_shift1 = dy_mean_shift1 * azimuthPixelSize / dt * 365.0 * 24.0 * 3600.0
va_mean_shift1 = dy_mean_shift1 * offset2va
va_mean_shift1 = np.median(va_mean_shift1[np.logical_not(np.isnan(va_mean_shift1))])
# create the (slope parallel & reference) flow-based range-projected result
alpha_sp = (DX * scale_factor_1) / (offset2vy_2 / (offset2vx_1 * offset2vy_2 - offset2vx_2 * offset2vy_1) * (-SX) - offset2vx_2 / (offset2vx_1 * offset2vy_2 - offset2vx_2 * offset2vy_1) * (-SY))
alpha_ref = (DX * scale_factor_1) / (offset2vy_2 / (offset2vx_1 * offset2vy_2 - offset2vx_2 * offset2vy_1) * VXref - offset2vx_2 / (offset2vx_1 * offset2vy_2 - offset2vx_2 * offset2vy_1) * VYref)
VXS = alpha_sp * (-SX)
VYS = alpha_sp * (-SY)
VXR = alpha_ref * VXref
VYR = alpha_ref * VYref
zero_flag_sp = (SX == 0) & (SY == 0)
zero_flag_ref = (VXref == 0) & (VYref == 0)
VXS[zero_flag_sp] = np.nan
VYS[zero_flag_sp] = np.nan
VXR[zero_flag_ref] = np.nan
VYR[zero_flag_ref] = np.nan
rngX = offset2vx_1
rngY = offset2vy_1
angle_df_S = np.arccos((-SX * rngX - SY * rngY) / (np.sqrt(SX**2 + SY**2) * np.sqrt(rngX**2+rngY**2)))
angle_df_S = np.abs(np.real(angle_df_S) - np.pi / 2)
angle_df_R = np.arccos((VXref * rngX + VYref * rngY) / (np.sqrt(VXref**2 + VYref**2) * np.sqrt(rngX**2+rngY**2)))
angle_df_R = np.abs(np.real(angle_df_R) - np.pi / 2)
angle_threshold_S = 0.75
angle_threshold_R = 0.75
VXS[angle_df_S < angle_threshold_S] = np.nan
VYS[angle_df_S < angle_threshold_S] = np.nan
VXR[angle_df_R < angle_threshold_R] = np.nan
VYR[angle_df_R < angle_threshold_R] = np.nan
# obsolete fusion routine using the sp mask file to distinguish pure
# smoothed slopes and reference velocity fields
# VXP = VXS
# VXP[MM == 1] = VXR[MM == 1]
# VYP = VYS
# VYP[MM == 1] = VYR[MM == 1]
# FIXME: Switch to using the updated (better) estimates of velocity fields when available
# Use the updated dhdxs and dhdys input files that combine the velocity fields and smoothed slopes
VXP = VXS
VYP = VYS
# use the updated (better) estimates of velocity fields
# VXP = VXR
# VYP = VYR
VXP = VXP.astype(np.float32)
VYP = VYP.astype(np.float32)
VP = np.sqrt(VXP**2+VYP**2)
VXPP = VX.copy()
VYPP = VY.copy()
stable_count_p = np.sum(SSM & np.logical_not(np.isnan(VXP)))
stable_count1_p = np.sum(SSM1 & np.logical_not(np.isnan(VXP)))
vxp_mean_shift = 0.0
vxp_mean_shift1 = 0.0
vyp_mean_shift = 0.0
vyp_mean_shift1 = 0.0
if stable_count_p != 0:
temp = VXP.copy() - VX.copy()
temp[np.logical_not(SSM)] = np.nan
# bias_mean_shift = np.median(temp[(temp > -500)&(temp < 500)])
bias_mean_shift = np.median(temp[np.logical_not(np.isnan(temp))])
vxp_mean_shift = vx_mean_shift + bias_mean_shift / 1
temp = VYP.copy() - VY.copy()
temp[np.logical_not(SSM)] = np.nan
# bias_mean_shift = np.median(temp[(temp > -500)&(temp < 500)])
bias_mean_shift = np.median(temp[np.logical_not(np.isnan(temp))])
vyp_mean_shift = vy_mean_shift + bias_mean_shift / 1
if stable_count1_p != 0:
temp = VXP.copy() - VX.copy()
temp[np.logical_not(SSM1)] = np.nan
# bias_mean_shift1 = np.median(temp[(temp > -500)&(temp < 500)])
bias_mean_shift1 = np.median(temp[np.logical_not(np.isnan(temp))])
vxp_mean_shift1 = vx_mean_shift1 + bias_mean_shift1 / 1
temp = VYP.copy() - VY.copy()
temp[np.logical_not(SSM1)] = np.nan
# bias_mean_shift1 = np.median(temp[(temp > -500)&(temp < 500)])
bias_mean_shift1 = np.median(temp[np.logical_not(np.isnan(temp))])
vyp_mean_shift1 = vy_mean_shift1 + bias_mean_shift1 / 1
if stable_count_p == 0:
if stable_count1_p == 0:
stable_shift_applied_p = 0
else:
stable_shift_applied_p = 2
else:
stable_shift_applied_p = 1
CHIPSIZEX = CHIPSIZEX * rangePixelSize
CHIPSIZEY = CHIPSIZEY * azimuthPixelSize
NoDataValue = -32767
noDataMask = np.isnan(VX) | np.isnan(VY)
# VXref[noDataMask] = NoDataValue
# VYref[noDataMask] = NoDataValue
# if pair_type == 'radar':
# VRref[noDataMask] = NoDataValue
# VAref[noDataMask] = NoDataValue
CHIPSIZEX[noDataMask] = 0
CHIPSIZEY[noDataMask] = 0
INTERPMASK[noDataMask] = 0
title = 'autoRIFT surface velocities'
author = 'Alex S. Gardner, JPL/NASA; Yang Lei, GPS/Caltech'
institution = 'NASA Jet Propulsion Laboratory (JPL), California Institute of Technology'
# VX = np.round(np.clip(VX, -32768, 32767)).astype(np.int16)
# VY = np.round(np.clip(VY, -32768, 32767)).astype(np.int16)
# V = np.round(np.clip(V, -32768, 32767)).astype(np.int16)
# if pair_type == 'radar':
# VR = np.round(np.clip(VR, -32768, 32767)).astype(np.int16)
# VA = np.round(np.clip(VA, -32768, 32767)).astype(np.int16)
# CHIPSIZEX = np.round(np.clip(CHIPSIZEX, 0, 65535)).astype(np.uint16)
# CHIPSIZEY = np.round(np.clip(CHIPSIZEY, 0, 65535)).astype(np.uint16)
# INTERPMASK = np.round(np.clip(INTERPMASK, 0, 255)).astype(np.uint8)
source = f'NASA MEaSUREs ITS_LIVE project. Processed with autoRIFT version ' \
f'{IMG_INFO_DICT["autoRIFT_software_version"]}'
if IMG_INFO_DICT['mission_img1'].startswith('S'):
source += f'. Contains modified Copernicus Sentinel data {IMG_INFO_DICT["date_center"][0:4]}, processed by ESA'
if IMG_INFO_DICT['mission_img1'].startswith('L'):
source += f'. Landsat-{IMG_INFO_DICT["satellite_img1"]} images courtesy of the U.S. Geological Survey'
references = 'When using this data, please acknowledge the source (see global source attribute) and cite:\n' \
'* Gardner, A. S., Moholdt, G., Scambos, T., Fahnestock, M., Ligtenberg, S., van den Broeke, M.,\n' \
' and Nilsson, J., 2018. Increased West Antarctic and unchanged East Antarctic ice discharge over\n' \
' the last 7 years. The Cryosphere, 12, p.521. https://doi.org/10.5194/tc-12-521-2018\n' \
'* Lei, Y., Gardner, A. and Agram, P., 2021. Autonomous Repeat Image Feature Tracking (autoRIFT)\n' \
' and Its Application for Tracking Ice Displacement. Remote Sensing, 13(4), p.749.\n' \
' https://doi.org/10.3390/rs13040749\n' \
'\n' \
'Additionally, a DOI is provided for the software used to generate this data:\n' \
'* autoRIFT: https://doi.org/10.5281/zenodo.4025445\n' \
tran = [tran[0] + tran[1]/2, tran[1], 0.0, tran[3] + tran[5]/2, 0.0, tran[5]]
clobber = True # overwrite existing output nc file
nc_outfile = netCDF4.Dataset(out_nc_filename, 'w', clobber=clobber, format='NETCDF4')
# First set global attributes that GDAL uses when it reads netCFDF files
nc_outfile.setncattr('GDAL_AREA_OR_POINT', 'Area')
nc_outfile.setncattr('Conventions', 'CF-1.8')
nc_outfile.setncattr('date_created', datetime.datetime.now().strftime("%d-%b-%Y %H:%M:%S"))
nc_outfile.setncattr('title', title)
nc_outfile.setncattr('autoRIFT_software_version', IMG_INFO_DICT["autoRIFT_software_version"])
nc_outfile.setncattr('scene_pair_type', pair_type)
nc_outfile.setncattr('satellite', get_satellite_attribute(IMG_INFO_DICT))
nc_outfile.setncattr('motion_detection_method', detection_method)
nc_outfile.setncattr('motion_coordinates', coordinates)
nc_outfile.setncattr('author', author)
nc_outfile.setncattr('institution', institution)
nc_outfile.setncattr('source', source)
nc_outfile.setncattr('references', references)
var = nc_outfile.createVariable('img_pair_info', 'U1', (), fill_value=None)
var.setncattr('standard_name', 'image_pair_information')
for key in IMG_INFO_DICT:
if key == 'autoRIFT_software_version':
continue
var.setncattr(key, IMG_INFO_DICT[key])
# set dimensions
dimidY, dimidX = VX.shape
nc_outfile.createDimension('x', dimidX)
nc_outfile.createDimension('y', dimidY)
x = np.arange(tran[0], tran[0] + tran[1] * dimidX, tran[1])
y = np.arange(tran[3], tran[3] + tran[5] * dimidY, tran[5])
chunk_lines = np.min([np.ceil(8192/dimidY)*128, dimidY])
ChunkSize = [chunk_lines, dimidX]
var = nc_outfile.createVariable('x', np.dtype('float64'), 'x', fill_value=None)
var.setncattr('standard_name', 'projection_x_coordinate')
var.setncattr('description', 'x coordinate of projection')
var.setncattr('units', 'm')
var[:] = x
var = nc_outfile.createVariable('y', np.dtype('float64'), 'y', fill_value=None)
var.setncattr('standard_name', 'projection_y_coordinate')
var.setncattr('description', 'y coordinate of projection')
var.setncattr('units', 'm')
var[:] = y
mapping_var_name = 'mapping' # need to set this as an attribute for the image variables
var = nc_outfile.createVariable(mapping_var_name, 'U1', (), fill_value=None)
if srs.GetAttrValue('PROJECTION') == 'Polar_Stereographic':
var.setncattr('grid_mapping_name', 'polar_stereographic')
var.setncattr('straight_vertical_longitude_from_pole', srs.GetProjParm('central_meridian'))
var.setncattr('false_easting', srs.GetProjParm('false_easting'))
var.setncattr('false_northing', srs.GetProjParm('false_northing'))
var.setncattr('latitude_of_projection_origin', np.sign(srs.GetProjParm('latitude_of_origin'))*90.0) # could hardcode this to be -90 for landsat - just making it more general, maybe...
var.setncattr('latitude_of_origin', srs.GetProjParm('latitude_of_origin'))
var.setncattr('semi_major_axis', float(srs.GetAttrValue('GEOGCS|SPHEROID', 1)))
var.setncattr('scale_factor_at_projection_origin', 1)
var.setncattr('inverse_flattening', float(srs.GetAttrValue('GEOGCS|SPHEROID', 2)))
var.setncattr('spatial_ref', srs.ExportToWkt())
var.setncattr('crs_wkt', srs.ExportToWkt())
var.setncattr('proj4text', srs.ExportToProj4())
var.setncattr('spatial_epsg', epsg)
var.setncattr('GeoTransform', ' '.join(str(x) for x in tran)) # note this has pixel size in it - set explicitly above
elif srs.GetAttrValue('PROJECTION') == 'Transverse_Mercator':
var.setncattr('grid_mapping_name', 'universal_transverse_mercator')
zone = epsg - np.floor(epsg/100)*100
var.setncattr('utm_zone_number', zone)
var.setncattr('longitude_of_central_meridian', srs.GetProjParm('central_meridian'))
var.setncattr('false_easting', srs.GetProjParm('false_easting'))
var.setncattr('false_northing', srs.GetProjParm('false_northing'))
var.setncattr('latitude_of_projection_origin', srs.GetProjParm('latitude_of_origin'))
var.setncattr('semi_major_axis', float(srs.GetAttrValue('GEOGCS|SPHEROID', 1)))
var.setncattr('scale_factor_at_central_meridian', srs.GetProjParm('scale_factor'))
var.setncattr('inverse_flattening', float(srs.GetAttrValue('GEOGCS|SPHEROID', 2)))
var.setncattr('spatial_ref', srs.ExportToWkt())
var.setncattr('crs_wkt', srs.ExportToWkt())
var.setncattr('proj4text', srs.ExportToProj4())
var.setncattr('spatial_epsg', epsg)
var.setncattr('GeoTransform', ' '.join(str(x) for x in tran)) # note this has pixel size in it - set explicitly above
else:
raise Exception('Projection {0} not recognized for this program'.format(srs.GetAttrValue('PROJECTION')))
var = nc_outfile.createVariable('vx', np.dtype('int16'), ('y', 'x'), fill_value=NoDataValue,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'land_ice_surface_x_velocity')
if pair_type == 'radar':
var.setncattr('description', 'velocity component in x direction from radar range and azimuth measurements')
else:
var.setncattr('description', 'velocity component in x direction')
var.setncattr('units', 'meter/year')
var.setncattr('grid_mapping', mapping_var_name)
if stable_count != 0:
temp = VX.copy() - VXref.copy()
temp[np.logical_not(SSM)] = np.nan
# vx_error_mask = np.std(temp[(temp > -500)&(temp < 500)])
vx_error_mask = np.std(temp[np.logical_not(np.isnan(temp))])
else:
vx_error_mask = np.nan
if stable_count1 != 0:
temp = VX.copy() - VXref.copy()
temp[np.logical_not(SSM1)] = np.nan
# vx_error_slow = np.std(temp[(temp > -500)&(temp < 500)])
vx_error_slow = np.std(temp[np.logical_not(np.isnan(temp))])
else:
vx_error_slow = np.nan
if pair_type == 'radar':
vx_error_mod = (error_vector[0][0]*IMG_INFO_DICT['date_dt']+error_vector[1][0])/IMG_INFO_DICT['date_dt']*365
else:
vx_error_mod = error_vector[0]/IMG_INFO_DICT['date_dt']*365
if stable_shift_applied == 1:
vx_error = vx_error_mask
elif stable_shift_applied == 2:
vx_error = vx_error_slow
else:
vx_error = vx_error_mod
var.setncattr('error', int(round(vx_error*10))/10)
var.setncattr('error_description', 'best estimate of x_velocity error: vx_error is populated '
'according to the approach used for the velocity bias '
'correction as indicated in "stable_shift_flag"')
if stable_count != 0:
var.setncattr('error_stationary', int(round(vx_error_mask*10))/10)
else:
var.setncattr('error_stationary', np.nan)
var.setncattr('error_stationary_description', 'RMSE over stable surfaces, stationary or slow-flowing '
'surfaces with velocity < 15 meter/year identified from an external mask')
var.setncattr('error_modeled', int(round(vx_error_mod*10))/10)
var.setncattr('error_modeled_description', '1-sigma error calculated using a modeled error-dt relationship')
if stable_count1 != 0:
var.setncattr('error_slow', int(round(vx_error_slow*10))/10)
else:
var.setncattr('error_slow', np.nan)
var.setncattr('error_slow_description', 'RMSE over slowest 25% of retrieved velocities')
if stable_shift_applied == 2:
var.setncattr('stable_shift', int(round(vx_mean_shift1*10))/10)
elif stable_shift_applied == 1:
var.setncattr('stable_shift', int(round(vx_mean_shift*10))/10)
else:
var.setncattr('stable_shift', 0)
var.setncattr('stable_shift_flag', stable_shift_applied)
var.setncattr('stable_shift_flag_description', 'flag for applying velocity bias correction: 0 = no correction; '
'1 = correction from overlapping stable surface mask (stationary '
'or slow-flowing surfaces with velocity < 15 meter/year)(top priority); '
'2 = correction from slowest 25% of overlapping velocities '
'(second priority)')
if stable_count != 0:
var.setncattr('stable_shift_stationary', int(round(vx_mean_shift*10))/10)
else:
var.setncattr('stable_shift_stationary', np.nan)
var.setncattr('stable_count_stationary', stable_count)
if stable_count1 != 0:
var.setncattr('stable_shift_slow', int(round(vx_mean_shift1*10))/10)
else:
var.setncattr('stable_shift_slow', np.nan)
var.setncattr('stable_count_slow', stable_count1)
VX[noDataMask] = NoDataValue
var[:] = np.round(np.clip(VX, -32768, 32767)).astype(np.int16)
# var.setncattr('_FillValue', np.int16(FillValue))
var = nc_outfile.createVariable('vy', np.dtype('int16'), ('y', 'x'), fill_value=NoDataValue,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'land_ice_surface_y_velocity')
if pair_type == 'radar':
var.setncattr('description', 'velocity component in y direction from radar range and azimuth measurements')
else:
var.setncattr('description', 'velocity component in y direction')
var.setncattr('units', 'meter/year')
var.setncattr('grid_mapping', mapping_var_name)
if stable_count != 0:
temp = VY.copy() - VYref.copy()
temp[np.logical_not(SSM)] = np.nan
# vy_error_mask = np.std(temp[(temp > -500)&(temp < 500)])
vy_error_mask = np.std(temp[np.logical_not(np.isnan(temp))])
else:
vy_error_mask = np.nan
if stable_count1 != 0:
temp = VY.copy() - VYref.copy()
temp[np.logical_not(SSM1)] = np.nan
# vy_error_slow = np.std(temp[(temp > -500)&(temp < 500)])
vy_error_slow = np.std(temp[np.logical_not(np.isnan(temp))])
else:
vy_error_slow = np.nan
if pair_type == 'radar':
vy_error_mod = (error_vector[0][1]*IMG_INFO_DICT['date_dt']+error_vector[1][1])/IMG_INFO_DICT['date_dt']*365
else:
vy_error_mod = error_vector[1]/IMG_INFO_DICT['date_dt']*365
if stable_shift_applied == 1:
vy_error = vy_error_mask
elif stable_shift_applied == 2:
vy_error = vy_error_slow
else:
vy_error = vy_error_mod
var.setncattr('error', int(round(vy_error*10))/10)
var.setncattr('error_description', 'best estimate of y_velocity error: vy_error is populated according '
'to the approach used for the velocity bias correction as indicated '
'in "stable_shift_flag"')
if stable_count != 0:
var.setncattr('error_stationary', int(round(vy_error_mask*10))/10)
else:
var.setncattr('error_stationary', np.nan)
var.setncattr('error_stationary_description', 'RMSE over stable surfaces, stationary or slow-flowing surfaces '
'with velocity < 15 meter/year identified from an external mask')
var.setncattr('error_modeled', int(round(vy_error_mod * 10)) / 10)
var.setncattr('error_modeled_description', '1-sigma error calculated using a modeled error-dt relationship')
if stable_count1 != 0:
var.setncattr('error_slow', int(round(vy_error_slow * 10)) / 10)
else:
var.setncattr('error_slow', np.nan)
var.setncattr('error_slow_description', 'RMSE over slowest 25% of retrieved velocities')
if stable_shift_applied == 2:
var.setncattr('stable_shift', int(round(vy_mean_shift1*10))/10)
elif stable_shift_applied == 1:
var.setncattr('stable_shift', int(round(vy_mean_shift*10))/10)
else:
var.setncattr('stable_shift', 0)
var.setncattr('stable_shift_flag', stable_shift_applied)
var.setncattr('stable_shift_flag_description', 'flag for applying velocity bias correction: 0 = no correction; '
'1 = correction from overlapping stable surface mask (stationary '
'or slow-flowing surfaces with velocity < 15 meter/year)(top priority); '
'2 = correction from slowest 25% of overlapping velocities '
'(second priority)')
if stable_count != 0:
var.setncattr('stable_shift_stationary', int(round(vy_mean_shift*10))/10)
else:
var.setncattr('stable_shift_stationary', np.nan)
var.setncattr('stable_count_stationary', stable_count)
if stable_count1 != 0:
var.setncattr('stable_shift_slow', int(round(vy_mean_shift1*10))/10)
else:
var.setncattr('stable_shift_slow', np.nan)
var.setncattr('stable_count_slow', stable_count1)
VY[noDataMask] = NoDataValue
var[:] = np.round(np.clip(VY, -32768, 32767)).astype(np.int16)
# var.setncattr('missing_value', np.int16(NoDataValue))
var = nc_outfile.createVariable('v', np.dtype('int16'), ('y', 'x'), fill_value=NoDataValue,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'land_ice_surface_velocity')
var.setncattr('description', 'velocity magnitude')
var.setncattr('units', 'meter/year')
var.setncattr('grid_mapping', mapping_var_name)
V[noDataMask] = NoDataValue
var[:] = np.round(np.clip(V, -32768, 32767)).astype(np.int16)
# var.setncattr('missing_value',np.int16(NoDataValue))
var = nc_outfile.createVariable('v_error', np.dtype('int16'), ('y', 'x'), fill_value=NoDataValue,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'velocity_error')
if pair_type == 'radar':
var.setncattr('description', 'velocity magnitude error from radar range and azimuth measurements')
else:
var.setncattr('description', 'velocity magnitude error')
var.setncattr('units', 'meter/year')
var.setncattr('grid_mapping', mapping_var_name)
v_error = v_error_cal(vx_error, vy_error)
V_error = np.sqrt((vx_error * VX / V)**2 + (vy_error * VY / V)**2)
V_error[V == 0] = v_error
V_error[noDataMask] = NoDataValue
var[:] = np.round(np.clip(V_error, -32768, 32767)).astype(np.int16)
# var.setncattr('missing_value',np.int16(NoDataValue))
if pair_type == 'radar':
var = nc_outfile.createVariable('vr', np.dtype('int16'), ('y', 'x'), fill_value=NoDataValue,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'range_velocity')
var.setncattr('description', 'velocity in radar range direction')
var.setncattr('units', 'meter/year')
var.setncattr('grid_mapping', mapping_var_name)
if stable_count != 0:
temp = VR.copy() - VRref.copy()
temp[np.logical_not(SSM)] = np.nan
# vr_error_mask = np.std(temp[(temp > -500)&(temp < 500)])
vr_error_mask = np.std(temp[np.logical_not(np.isnan(temp))])
else:
vr_error_mask = np.nan
if stable_count1 != 0:
temp = VR.copy() - VRref.copy()
temp[np.logical_not(SSM1)] = np.nan
# vr_error_slow = np.std(temp[(temp > -500)&(temp < 500)])
vr_error_slow = np.std(temp[np.logical_not(np.isnan(temp))])
else:
vr_error_slow = np.nan
vr_error_mod = (error_vector[0][2]*IMG_INFO_DICT['date_dt']+error_vector[1][2])/IMG_INFO_DICT['date_dt']*365
if stable_shift_applied == 1:
vr_error = vr_error_mask
elif stable_shift_applied == 2:
vr_error = vr_error_slow
else:
vr_error = vr_error_mod
var.setncattr('error', int(round(vr_error*10))/10)
var.setncattr('error_description', 'best estimate of range_velocity error: vr_error is populated '
'according to the approach used for the velocity bias correction '
'as indicated in "stable_shift_flag"')
if stable_count != 0:
var.setncattr('error_stationary', int(round(vr_error_mask*10))/10)
else:
var.setncattr('error_stationary', np.nan)
var.setncattr('error_stationary_description', 'RMSE over stable surfaces, stationary or slow-flowing '
'surfaces with velocity < 15 meter/year identified from an external mask')
var.setncattr('error_modeled', int(round(vr_error_mod*10))/10)
var.setncattr('error_modeled_description', '1-sigma error calculated using a modeled error-dt relationship')
if stable_count1 != 0:
var.setncattr('error_slow', int(round(vr_error_slow*10))/10)
else:
var.setncattr('error_slow', np.nan)
var.setncattr('error_slow_description', 'RMSE over slowest 25% of retrieved velocities')
if stable_shift_applied == 2:
var.setncattr('stable_shift', int(round(vr_mean_shift1*10))/10)
elif stable_shift_applied == 1:
var.setncattr('stable_shift', int(round(vr_mean_shift*10))/10)
else:
var.setncattr('stable_shift', 0)
var.setncattr('stable_shift_flag', stable_shift_applied)
var.setncattr('stable_shift_flag_description', 'flag for applying velocity bias correction: 0 = no correction; '
'1 = correction from overlapping stable surface mask '
'(stationary or slow-flowing surfaces with velocity < 15 meter/year)'
'(top priority); 2 = correction from slowest 25% of overlapping '
'velocities (second priority)')
if stable_count != 0:
var.setncattr('stable_shift_stationary', int(round(vr_mean_shift*10))/10)
else:
var.setncattr('stable_shift_stationary', np.nan)
var.setncattr('stable_count_stationary', stable_count)
if stable_count1 != 0:
var.setncattr('stable_shift_slow', int(round(vr_mean_shift1*10))/10)
else:
var.setncattr('stable_shift_slow', np.nan)
var.setncattr('stable_count_slow', stable_count1)
VR[noDataMask] = NoDataValue
var[:] = np.round(np.clip(VR, -32768, 32767)).astype(np.int16)
# var.setncattr('missing_value', np.int16(NoDataValue))
var = nc_outfile.createVariable('va', np.dtype('int16'), ('y', 'x'), fill_value=NoDataValue,
zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
var.setncattr('standard_name', 'azimuth_velocity')
var.setncattr('description', 'velocity in radar azimuth direction')
var.setncattr('units', 'meter/year')
var.setncattr('grid_mapping', mapping_var_name)
if stable_count != 0:
temp = VA.copy() - VAref.copy()
temp[np.logical_not(SSM)] = np.nan
# va_error_mask = np.std(temp[(temp > -500)&(temp < 500)])
va_error_mask = np.std(temp[np.logical_not(np.isnan(temp))])
else:
va_error_mask = np.nan
if stable_count1 != 0:
temp = VA.copy() - VAref.copy()
temp[np.logical_not(SSM1)] = np.nan
# va_error_slow = np.std(temp[(temp > -500)&(temp < 500)])
va_error_slow = np.std(temp[np.logical_not(np.isnan(temp))])
else:
va_error_slow = np.nan
va_error_mod = (error_vector[0][3]*IMG_INFO_DICT['date_dt']+error_vector[1][3])/IMG_INFO_DICT['date_dt']*365
if stable_shift_applied == 1:
va_error = va_error_mask
elif stable_shift_applied == 2:
va_error = va_error_slow
else:
va_error = va_error_mod
var.setncattr('error', int(round(va_error*10))/10)
var.setncattr('error_description', 'best estimate of azimuth_velocity error: va_error is populated '
'according to the approach used for the velocity bias correction '
'as indicated in "stable_shift_flag"')
if stable_count != 0:
var.setncattr('error_stationary', int(round(va_error_mask*10))/10)
else:
var.setncattr('error_stationary', np.nan)
var.setncattr('error_stationary_description', 'RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 meter/year identified from an external mask')
var.setncattr('error_modeled', int(round(va_error_mod*10))/10)
var.setncattr('error_modeled_description', '1-sigma error calculated using a modeled error-dt relationship')
if stable_count1 != 0:
var.setncattr('error_slow', int(round(va_error_slow*10))/10)
else:
var.setncattr('error_slow', np.nan)
var.setncattr('error_slow_description', 'RMSE over slowest 25% of retrieved velocities')
if stable_shift_applied == 2:
var.setncattr('stable_shift', int(round(va_mean_shift1*10))/10)
elif stable_shift_applied == 1:
var.setncattr('stable_shift', int(round(va_mean_shift*10))/10)
else:
var.setncattr('stable_shift', 0)
var.setncattr('stable_shift_flag', stable_shift_applied)
var.setncattr('stable_shift_flag_description', 'flag for applying velocity bias correction: 0 = no correction; '
'1 = correction from overlapping stable surface mask '
'(stationary or slow-flowing surfaces with velocity < 15 meter/year)'
'(top priority); 2 = correction from slowest 25% of overlapping '
'velocities (second priority)')
if stable_count != 0:
var.setncattr('stable_shift_stationary', int(round(va_mean_shift*10))/10)
else:
var.setncattr('stable_shift_stationary', np.nan)
var.setncattr('stable_count_stationary', stable_count)
if stable_count1 != 0:
var.setncattr('stable_shift_slow', int(round(va_mean_shift1*10))/10)
else:
var.setncattr('stable_shift_slow', np.nan)
var.setncattr('stable_count_slow', stable_count1)
VA[noDataMask] = NoDataValue
var[:] = np.round(np.clip(VA, -32768, 32767)).astype(np.int16)
# var.setncattr('missing_value', np.int16(NoDataValue))
# fuse the (slope parallel & reference) flow-based range-projected result with the raw observed range/azimuth-based result
if stable_count_p != 0:
temp = VXP.copy() - VXref.copy()
temp[np.logical_not(SSM)] = np.nan
# vxp_error_mask = np.std(temp[(temp > -500)&(temp < 500)])
vxp_error_mask = np.std(temp[np.logical_not(np.isnan(temp))])
temp = VYP.copy() - VYref.copy()
temp[np.logical_not(SSM)] = np.nan
# vyp_error_mask = np.std(temp[(temp > -500)&(temp < 500)])
vyp_error_mask = np.std(temp[np.logical_not(np.isnan(temp))])
if stable_count1_p != 0:
temp = VXP.copy() - VXref.copy()
temp[np.logical_not(SSM1)] = np.nan
# vxp_error_slow = np.std(temp[(temp > -500)&(temp < 500)])
vxp_error_slow = np.std(temp[np.logical_not(np.isnan(temp))])
temp = VYP.copy() - VYref.copy()
temp[np.logical_not(SSM1)] = np.nan
# vyp_error_slow = np.std(temp[(temp > -500)&(temp < 500)])
vyp_error_slow = np.std(temp[np.logical_not(np.isnan(temp))])
vxp_error_mod = (error_vector[0][4]*IMG_INFO_DICT['date_dt']+error_vector[1][4])/IMG_INFO_DICT['date_dt']*365
vyp_error_mod = (error_vector[0][5]*IMG_INFO_DICT['date_dt']+error_vector[1][5])/IMG_INFO_DICT['date_dt']*365
if stable_shift_applied_p == 1:
vxp_error = vxp_error_mask
vyp_error = vyp_error_mask
elif stable_shift_applied_p == 2:
vxp_error = vxp_error_slow
vyp_error = vyp_error_slow
else:
vxp_error = vxp_error_mod
vyp_error = vyp_error_mod
VP_error = np.sqrt((vxp_error * VXP / VP)**2 + (vyp_error * VYP / VP)**2)
VXPP[V_error > VP_error] = VXP[V_error > VP_error]
VYPP[V_error > VP_error] = VYP[V_error > VP_error]
VXP = VXPP.astype(np.float32)
VYP = VYPP.astype(np.float32)
VP = np.sqrt(VXP**2+VYP**2)
stable_count_p = np.sum(SSM & np.logical_not(np.isnan(VXP)))
stable_count1_p = np.sum(SSM1 & np.logical_not(np.isnan(VXP)))
vxp_mean_shift = 0.0
vyp_mean_shift = 0.0
vxp_mean_shift1 = 0.0
vyp_mean_shift1 = 0.0
if stable_count_p != 0:
temp = VXP.copy() - VX.copy()
temp[np.logical_not(SSM)] = np.nan
# bias_mean_shift = np.median(temp[(temp > -500)&(temp < 500)])
bias_mean_shift = np.median(temp[np.logical_not(np.isnan(temp))])
vxp_mean_shift = vx_mean_shift + bias_mean_shift / 1
temp = VYP.copy() - VY.copy()
temp[np.logical_not(SSM)] = np.nan
# bias_mean_shift = np.median(temp[(temp > -500)&(temp < 500)])
bias_mean_shift = np.median(temp[np.logical_not(np.isnan(temp))])
vyp_mean_shift = vy_mean_shift + bias_mean_shift / 1
if stable_count1_p != 0:
temp = VXP.copy() - VX.copy()
temp[np.logical_not(SSM1)] = np.nan
# bias_mean_shift1 = np.median(temp[(temp > -500)&(temp < 500)])
bias_mean_shift1 = np.median(temp[np.logical_not(np.isnan(temp))])
vxp_mean_shift1 = vx_mean_shift1 + bias_mean_shift1 / 1
temp = VYP.copy() - VY.copy()
temp[np.logical_not(SSM1)] = np.nan
# bias_mean_shift1 = np.median(temp[(temp > -500)&(temp < 500)])
bias_mean_shift1 = np.median(temp[np.logical_not(np.isnan(temp))])
vyp_mean_shift1 = vy_mean_shift1 + bias_mean_shift1 / 1
if stable_count_p == 0:
if stable_count1_p == 0:
stable_shift_applied_p = 0
else:
stable_shift_applied_p = 2
else:
stable_shift_applied_p = 1
# var = nc_outfile.createVariable('vxp',np.dtype('int16'),('y', 'x'), fill_value=NoDataValue,
# zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
# var.setncattr('standard_name', 'projected_x_velocity')
# var.setncattr('description', 'x-direction velocity determined by projecting radar range measurements '
# 'onto an a priori flow vector. Where projected errors are larger than those '
# 'determined from range and azimuth measurements, unprojected vx estimates are used')
# var.setncattr('units', 'meter/year')
# var.setncattr('grid_mapping', mapping_var_name)
#
# if stable_count_p != 0:
# temp = VXP.copy() - VXref.copy()
# temp[np.logical_not(SSM)] = np.nan
# # vxp_error_mask = np.std(temp[(temp > -500)&(temp < 500)])
# vxp_error_mask = np.std(temp[np.logical_not(np.isnan(temp))])
# else:
# vxp_error_mask = np.nan
# if stable_count1_p != 0:
# temp = VXP.copy() - VXref.copy()
# temp[np.logical_not(SSM1)] = np.nan
# # vxp_error_slow = np.std(temp[(temp > -500)&(temp < 500)])
# vxp_error_slow = np.std(temp[np.logical_not(np.isnan(temp))])
# else:
# vxp_error_slow = np.nan
# if stable_shift_applied_p == 1:
# vxp_error = vxp_error_mask
# elif stable_shift_applied_p == 2:
# vxp_error = vxp_error_slow
# else:
# vxp_error = vxp_error_mod
# var.setncattr('error', int(round(vxp_error*10))/10)
# var.setncattr('error_description', 'best estimate of projected_x_velocity error: vxp_error is populated '
# 'according to the approach used for the velocity bias correction as '
# 'indicated in "stable_shift_flag"')
#
# if stable_count_p != 0:
# var.setncattr('error_stationary', int(round(vxp_error_mask*10))/10)
# else:
# var.setncattr('error_stationary', np.nan)
# var.setncattr('error_stationary_description', 'RMSE over stable surfaces, stationary or slow-flowing surfaces '
# 'with velocity < 15 meter/year identified from an external mask')
#
# var.setncattr('error_modeled', int(round(vxp_error_mod * 10)) / 10)
# var.setncattr('error_modeled_description', '1-sigma error calculated using a modeled error-dt relationship')
#
# if stable_count1_p != 0:
# var.setncattr('error_slow', int(round(vxp_error_slow * 10)) / 10)
# else:
# var.setncattr('error_slow', np.nan)
# var.setncattr('error_slow_description', 'RMSE over slowest 25% of retrieved velocities')
#
# if stable_shift_applied_p == 2:
# var.setncattr('stable_shift', int(round(vxp_mean_shift1*10))/10)
# elif stable_shift_applied_p == 1:
# var.setncattr('stable_shift', int(round(vxp_mean_shift*10))/10)
# else:
# var.setncattr('stable_shift', 0)
# var.setncattr('stable_shift_flag', stable_shift_applied_p)
# var.setncattr('stable_shift_flag_description', 'flag for applying velocity bias correction: 0 = no correction; '
# '1 = correction from overlapping stable surface mask '
# '(stationary or slow-flowing surfaces with velocity < 15 meter/year)'
# '(top priority); 2 = correction from slowest 25% of overlapping '
# 'velocities (second priority)')
#
# if stable_count_p != 0:
# var.setncattr('stable_shift_stationary',int(round(vxp_mean_shift*10))/10)
# else:
# var.setncattr('stable_shift_stationary',np.nan)
# var.setncattr('stable_count_stationary',stable_count_p)
#
# if stable_count1_p != 0:
# var.setncattr('stable_shift_slow',int(round(vxp_mean_shift1*10))/10)
# else:
# var.setncattr('stable_shift_slow',np.nan)
# var.setncattr('stable_count_slow',stable_count1_p)
#
# VXP[noDataMask] = NoDataValue
# var[:] = np.round(np.clip(VXP, -32768, 32767)).astype(np.int16)
# # var.setncattr('missing_value', np.int16(NoDataValue))
#
#
# var = nc_outfile.createVariable('vyp', np.dtype('int16'), ('y', 'x'), fill_value=NoDataValue,
# zlib=True, complevel=2, shuffle=True, chunksizes=ChunkSize)
# var.setncattr('standard_name', 'projected_y_velocity')
# var.setncattr('description', 'y-direction velocity determined by projecting radar range measurements '
# 'onto an a priori flow vector. Where projected errors are larger than those '
# 'determined from range and azimuth measurements, unprojected vy estimates are used')
# var.setncattr('units', 'meter/year')
# var.setncattr('grid_mapping', mapping_var_name)
#
# if stable_count_p != 0:
# temp = VYP.copy() - VYref.copy()
# temp[np.logical_not(SSM)] = np.nan
# # vyp_error_mask = np.std(temp[(temp > -500)&(temp < 500)])
# vyp_error_mask = np.std(temp[np.logical_not(np.isnan(temp))])
# else:
# vyp_error_mask = np.nan
# if stable_count1_p != 0: