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Commit 0e3ad301 authored by thopri's avatar thopri
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added tide checker function to BDY file

parent 20de1f15
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Showing with 115 additions and 85 deletions
inputs/namelist_cmems.bdy
View file @ 0e3ad301
......@@ -110,8 +110,11 @@
!clname(3) = 'O1'
!clname(4) = 'K1'
!clname(5) = 'N2'
ln_trans = .false. ! interpolate transport rather than
! velocities
ln_trans = .false. ! interpolate transport rather than velocities
ln_tide_checker = .true. ! run tide checker on PyNEMO tide output
sn_ref_model = 'fes' ! which model to check output against (FES only)
nn_amp_thres = 0.25 ! amplitude thresold to compare against (m)
nn_phase_thres = 20.0 ! phase threshold to compare against (degrees)
!------------------------------------------------------------------------------
! Time information
!------------------------------------------------------------------------------
......
pynemo/profile.py
View file @ 0e3ad301
......@@ -54,6 +54,7 @@ from pynemo.reader.factory import GetFile
from pynemo.reader import factory
from pynemo.tide import nemo_bdy_tide3 as tide
from pynemo.tide import nemo_bdy_tide_ncgen
from pynemo.tests import nemo_tide_test as tt
from pynemo.utils import Constants
from pynemo.gui.nemo_bdy_mask import Mask as Mask_File
from pynemo import nemo_bdy_dl_cmems as dl_cmems
......@@ -437,13 +438,26 @@ def process_bdy(setup_filepath=0, mask_gui=False):
if settings['tide_model']=='tpxo':
cons = tide.nemo_bdy_tide_rot(
Setup, DstCoord, bdy_ind['t'], bdy_ind['u'], bdy_ind['v'],
settings['clname'], settings['tide_model'])
settings['clname'], settings['tide_model'])
if settings['tide_checker'] == True:
logger.info('tide checker starting now.....')
tt_test = tt.main(setup_filepath,settings['amp_thres'],settings['phase_thres'],settings['ref_model'])
if tt_test == 0:
logger.info('tide checker ran successfully, check spreadsheet in output folder')
if tt_test !=0:
logger.warning('error running tide checker')
elif settings['tide_model']=='fes':
cons = tide.nemo_bdy_tide_rot(
Setup, DstCoord, bdy_ind['t'], bdy_ind['u'], bdy_ind['v'],
settings['clname'],settings['tide_model'])
logger.warning('Tidal model: %s, not yet properly implimented',
settings['tide_model'])
if settings['tide_checker'] == True:
logger.info('tide checker starting now.....')
tt_test = tt.main(setup_filepath,settings['amp_thres'],settings['phase_thres'],settings['ref_model'])
if tt_test == 0:
logger.info('tide checker ran successfully, check spreadsheet in output folder')
if tt_test !=0:
logger.warning('error running tide checker')
else:
logger.error('Tidal model: %s, not recognised',
settings['tide_model'])
......
pynemo/tests/nemo_tide_test.py
View file @ 0e3ad301
......@@ -46,7 +46,7 @@ logging.basicConfig(filename='nrct.log', level=logging.INFO)
# TODO: add TPXO read and subset functionality currently only uses FES as "truth"
def main(bdy_file='inputs/namelist_cmems.bdy',amplitude_threshold = 0.25,phase_threshold=15.00,model_res=1/16,model='fes'):
def main(bdy_file='inputs/namelist_cmems.bdy',amplitude_threshold = 0.25,phase_threshold=15.00,model='fes',model_res=1/16):
logger.info('============================================')
logger.info('Start Tide Test Logging: ' + time.asctime())
logger.info('============================================')
......@@ -85,7 +85,7 @@ def main(bdy_file='inputs/namelist_cmems.bdy',amplitude_threshold = 0.25,phase_t
logger.warning('Exceedance in thesholds detected, check spreadsheet in dst_dir')
error_log.to_excel(writer,sheet_name=error_log.name)
# close writer object and save excel spreadsheet
writer.save()
writer.save()
# code runs here if TPXO is requested as reference this hasn't been written yet so raises exception
elif model == 'tpxo':
logger.info('using TPXO as reference.......')
......@@ -129,11 +129,10 @@ def read_fes(fes_fname,grid):
fes_amp = np.array(fes_tide.variables['amplitude'][:])
fes_amp = fes_amp / 100
fes_phase = np.array(fes_tide.variables['phase'][:])
fes_phase[fes_phase > 180.0] = fes_phase[fes_phase > 180.0] - 360.0
if grid != 'Z':
fes_amp = np.array(fes_tide.variables[grid+'a'][:])
fes_phase = np.array(fes_tide.variables[grid+'g'][:])
fes_phase[fes_phase > 180.0] = fes_phase[fes_phase > 180.0] - 360.0
fes_lat = fes_tide.variables['lat'][:]
fes_lon = fes_tide.variables['lon'][:]
......@@ -175,6 +174,7 @@ def compare_tides(pynemo_out,subset,amp_thres,phase_thres,model_res):
exceed_sum = np.sum(exceed_lat+exceed_lon)
if exceed_sum > 0:
raise Exception('Dont Panic: Lat and/or Lon further away from model point than model resolution')
# compare amp
abs_amp = np.abs(pynemo_out['amp']-subset['amp'])
abs_amp_thres = abs_amp > amp_thres
......@@ -187,8 +187,16 @@ def compare_tides(pynemo_out,subset,amp_thres,phase_thres,model_res):
err_ref_lons_amp = subset['lon'][abs_amp_thres].tolist()
# compare phase
abs_ph = np.abs(pynemo_out['phase']-subset['phase'])
# change from -180-180 to 0to 360 for both pynemo and subset.
pynemo_out['phase'][pynemo_out['phase'] < 0.0] = pynemo_out['phase'][pynemo_out['phase'] < 0.0] + 360.0
subset['phase'][subset['phase'] < 0.0] = subset['phase'][subset['phase'] < 0.0] + 360.0
# compare phase angles between 0 and 360.
abs_ph = 180 - abs(abs(pynemo_out['phase'] - subset['phase']) - 180)
# values outside of 0 to 360 (such as erroneous fill values) end up negative
# so multiply by -1 to ensure they are identified as exceeding threshold
abs_ph[abs_ph < 0.0 ] = abs_ph[abs_ph < 0.0] *-1
abs_ph_thres = abs_ph > phase_thres
err_pha = pynemo_out['phase'][abs_ph_thres[0,:]].tolist()
err_pha_lats = pynemo_out['lat'][abs_ph_thres].tolist()
err_pha_lons = pynemo_out['lon'][abs_ph_thres].tolist()
......
pynemo/tide/fes_extract_HC.py
View file @ 0e3ad301
......@@ -53,77 +53,82 @@ class HcExtract(object):
lon_resolution = lon_z[1] - lon_z[0]
data_in_km = 0 # added to maintain the reference to matlab tmd code
# extract example amplitude grid for Z, U and V and change NaNs to 0 (for land) and other values to 1 (for water)
mask_z = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[0]+'_Z.nc').variables['amplitude'][:])))
mask_z[mask_z != 18446744073709551616.00000] = 1
mask_z[mask_z == 18446744073709551616.00000] = 0
self.mask_dataset[mz_name] = mask_z
mask_u = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[0]+'_U.nc').variables['Ua'][:])))
mask_z[mask_z != 18446744073709551616.00000] = 1
mask_u[mask_u == 18446744073709551616.00000] = 0
self.mask_dataset[mu_name] = mask_u
mask_v = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[0]+'_V.nc').variables['Va'][:])))
mask_z[mask_z != 18446744073709551616.00000] = 1
mask_v[mask_v == 18446744073709551616.00000] = 0
self.mask_dataset[mv_name] = mask_v
#read and convert the height_dataset file to complex and store in dicts
hRe = []
hIm = []
lat_z = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_Z.nc').variables['lat'][:])
lon_z = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_Z.nc').variables['lon'][:])
for ncon in range(len(constituents)):
amp = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+str(constituents[ncon])+'_Z.nc').variables['amplitude'][:])))
# set fill values to zero
amp[amp == 18446744073709551616.00000] = 0
# convert to m from cm
amp = amp/100.00
phase = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_Z.nc').variables['phase'][:])))
phase[phase == 18446744073709551616.00000] = 0
hRe.append(amp*np.cos(phase*(np.pi/180)))
hIm.append(-amp*np.sin(phase*(np.pi/180)))
hRe = np.stack(hRe)
hIm = np.stack(hIm)
self.height_dataset = [lon_z,lat_z,hRe,hIm]
#read and convert the velocity_dataset files to complex
URe = []
UIm = []
lat_u = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_U.nc').variables['lat'][:])
lon_u = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_U.nc').variables['lon'][:])
for ncon in range(len(constituents)):
amp = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_U.nc').variables['Ua'][:])))
# set fill values to zero
amp[amp == 18446744073709551616.00000] = 0
# convert to cm from m
amp = amp/100.00
phase = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_U.nc').variables['Ug'][:])))
phase[phase == 18446744073709551616.00000] = 0
URe.append(amp*np.cos(phase*(np.pi/180)))
UIm.append(-amp*np.sin(phase*(np.pi/180)))
URe = np.stack(URe)
UIm = np.stack(UIm)
self.Uvelocity_dataset = [lon_u,lat_u,URe,UIm]
VRe = []
VIm = []
lat_v = np.array(Dataset(settings['tide_fes'] + constituents[ncon] + '_V.nc').variables['lat'][:])
lon_v = np.array(Dataset(settings['tide_fes'] + constituents[ncon] + '_V.nc').variables['lon'][:])
for ncon in range(len(constituents)):
amp = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_V.nc').variables['Va'][:])))
# set fill value to zero
amp[amp == 18446744073709551616.00000] = 0
# convert m to cm
amp = amp/100.00
phase = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_V.nc').variables['Vg'][:])))
phase[phase == 18446744073709551616.00000] = 0
VRe.append(amp*np.cos(phase*(np.pi/180)))
VIm.append(-amp*np.sin(phase*(np.pi/180)))
VRe = np.stack(VRe)
VIm = np.stack(VIm)
self.Vvelocity_dataset = [lon_v,lat_v,VRe,VIm]
if grid_type == 'z' or grid_type == 't':
# extract example amplitude grid for Z, U and V and change NaNs to 0 (for land) and other values to 1 (for water)
mask = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes'] + constituents[0] + '_Z.nc').variables['amplitude'][:])))
mask[mask != 18446744073709551616.00000] = 1
mask[mask == 18446744073709551616.00000] = 0
self.mask_dataset[mz_name] = mask
#read and convert the height_dataset file to complex and store in dicts
hRe = []
hIm = []
lat_z = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_Z.nc').variables['lat'][:])
lon_z = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_Z.nc').variables['lon'][:])
for ncon in range(len(constituents)):
amp = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+str(constituents[ncon])+'_Z.nc').variables['amplitude'][:])))
# set fill values to zero
amp[amp == 18446744073709551616.00000] = 0
# convert to m from cm
amp = amp/100.00
phase = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_Z.nc').variables['phase'][:])))
# set fill values to 0
phase[phase == 18446744073709551616.00000] = 0
# convert to real and imaginary conjugates and also convert to radians.
hRe.append(amp*np.cos(phase*(np.pi/180)))
hIm.append(-amp*np.sin(phase*(np.pi/180)))
hRe = np.stack(hRe)
hIm = np.stack(hIm)
self.height_dataset = [lon_z,lat_z,hRe,hIm]
elif grid_type == 'u':
mask = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes'] + constituents[0] + '_U.nc').variables['Ua'][:])))
mask[mask != 18446744073709551616.00000] = 1
mask[mask == 18446744073709551616.00000] = 0
self.mask_dataset[mu_name] = mask
#read and convert the velocity_dataset files to complex
URe = []
UIm = []
lat_u = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_U.nc').variables['lat'][:])
lon_u = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_U.nc').variables['lon'][:])
for ncon in range(len(constituents)):
amp = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_U.nc').variables['Ua'][:])))
# set fill values to zero
amp[amp == 18446744073709551616.00000] = 0
# convert to cm from m
amp = amp/100.00
phase = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_U.nc').variables['Ug'][:])))
phase[phase == 18446744073709551616.00000] = 0
URe.append(amp*np.cos(phase*(np.pi/180)))
UIm.append(-amp*np.sin(phase*(np.pi/180)))
URe = np.stack(URe)
UIm = np.stack(UIm)
self.Uvelocity_dataset = [lon_u,lat_u,URe,UIm]
elif grid_type == 'v':
mask = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes'] + constituents[0] + '_V.nc').variables['Va'][:])))
mask[mask != 18446744073709551616.00000] = 1
mask[mask == 18446744073709551616.00000] = 0
self.mask_dataset[mv_name] = mask
VRe = []
VIm = []
lat_v = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_V.nc').variables['lat'][:])
lon_v = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_V.nc').variables['lon'][:])
for ncon in range(len(constituents)):
amp = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_V.nc').variables['Va'][:])))
# set fill value to zero
amp[amp == 18446744073709551616.00000] = 0
# convert m to cm
amp = amp/100.00
phase = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_V.nc').variables['Vg'][:])))
phase[phase == 18446744073709551616.00000] = 0
VRe.append(amp*np.cos(phase*(np.pi/180)))
VIm.append(-amp*np.sin(phase*(np.pi/180)))
VRe = np.stack(VRe)
VIm = np.stack(VIm)
self.Vvelocity_dataset = [lon_v,lat_v,VRe,VIm]
# open grid variables these are resampled TPXO parameters so may not work correctly.
self.grid = Dataset(settings['tide_fes']+'grid_fes.nc')
......@@ -139,7 +144,7 @@ class HcExtract(object):
if glob == 1:
lon_z = np.concatenate(([lon_z[0]-lon_resolution, ], lon_z,[lon_z[-1]+lon_resolution, ]))
height_z = np.concatenate(([height_z[-1, :], ], height_z, [height_z[0, :],]), axis=0)
mask_z = np.concatenate(([mask_z[-1, :], ], mask_z, [mask_z[0, :], ]), axis=0)
mask_z = np.concatenate(([mask[-1, :], ], mask, [mask[0, :], ]), axis=0)
#adjust lon convention
xmin = np.min(lon)
......@@ -250,9 +255,9 @@ class HcExtract(object):
maskedpoints, lonlat)
#for velocity_dataset values
if height_data is not None:
data_temp[cons_index, :, 0] = data_temp[cons_index, :, 0]/height_data*100
data_temp[cons_index, :, 1] = data_temp[cons_index, :, 1]/height_data*100
#if height_data is not None:
# data_temp[cons_index, :, 0] = data_temp[cons_index, :, 0]/height_data*100
# data_temp[cons_index, :, 1] = data_temp[cons_index, :, 1]/height_data*100
zcomplex = np.array(data_temp[cons_index, :, 0], dtype=complex)
zcomplex.imag = data_temp[cons_index, :, 1]
......
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