import numpy
from multiprocessing import Process, Queue
import keras
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import tensorflow as tf
from keras.datasets import cifar10
from oct2py import octave
octave.addpath(octave.genpath("gpml-matlab-v4.0-2016-10-19"))
def run_in_separate_process(method, args):
def queue_wrapper(q, params):
r = method(*params)
q.put(r)
q = Queue()
p = Process(target=queue_wrapper, args=(q, args))
p.start()
return_val = q.get()
p.join()
if type(return_val) is Exception:
raise return_val
return return_val
def test_verify_AOTIM5_2018(self, parameters):
#-- model parameters for AOTIM-5-2018
modelpath = os.path.join(filepath, 'Arc5km2018')
grid_file = os.path.join(modelpath, parameters['grid'])
model_file = os.path.join(modelpath, parameters['model'])
TYPE = parameters['type']
GRID = 'OTIS'
EPSG = 'PSNorth'
#-- open Arctic Tidal Current Atlas list of records
with open(os.path.join(filepath, 'List_of_records.txt'), 'r') as f:
file_contents = f.read().splitlines()
#-- skip 2 header rows
count = 2
#-- iterate over number of stations
arctic_stations = len(file_contents) - count
stations = [None] * arctic_stations
shortname = [None] * arctic_stations
station_lon = np.zeros((arctic_stations))
station_lat = np.zeros((arctic_stations))
for s in range(arctic_stations):
line_contents = file_contents[count + s].split()
stations[s] = line_contents[1]
shortname[s] = line_contents[2]
station_lat[s] = np.float64(line_contents[10])
station_lon[s] = np.float64(line_contents[11])
#-- calculate daily results for a time period
#-- convert time to days since 1992-01-01T00:00:00
tide_time = np.arange(
pyTMD.time.convert_calendar_dates(2000, 1, 1),
pyTMD.time.convert_calendar_dates(2000, 12, 31) + 1)
#-- serial dates for matlab program (days since 0000-01-01T00:00:00)
SDtime = np.arange(convert_calendar_serial(2000, 1, 1),
convert_calendar_serial(2000, 12, 31) + 1)
#-- presently not converting times to dynamic times for model comparisons
deltat = np.zeros_like(tide_time)
#-- number of days
ndays = len(tide_time)
#-- extract amplitude and phase from tide model
amp, ph, D, c = pyTMD.read_tide_model.extract_tidal_constants(
station_lon,
station_lat,
grid_file,
model_file,
EPSG,
TYPE=TYPE,
METHOD='spline',
GRID=GRID)
#-- calculate complex phase in radians for Euler's
cph = -1j * ph * np.pi / 180.0
#-- will verify differences between model outputs are within tolerance
eps = np.finfo(np.float16).eps
#-- compare daily outputs at each station point
invalid_list = ['KS14']
#-- remove coastal stations from the list
valid_stations = [
i for i, s in enumerate(shortname) if s not in invalid_list
]
#-- compute validation data from Matlab TMD program using octave
#-- https://github.com/EarthAndSpaceResearch/TMD_Matlab_Toolbox_v2.5
TMDpath = os.path.join(filepath, '..', 'TMD_Matlab_Toolbox', 'TMD')
octave.addpath(octave.genpath(os.path.normpath(TMDpath)))
octave.addpath(filepath)
octave.addpath(modelpath)
#-- turn off octave warnings
octave.warning('off', 'all')
#-- input control file for model
CFname = os.path.join(filepath, 'Model_Arc5km2018')
assert os.access(CFname, os.F_OK)
#-- run Matlab TMD program with octave
#-- MODE: OB time series
validation, _ = octave.tmd_tide_pred_plus(CFname,
SDtime,
station_lat[valid_stations],
station_lon[valid_stations],
TYPE,
nout=2)
#-- for each valid station
for i, s in enumerate(valid_stations):
#-- calculate constituent oscillation for station
hc = amp[s, None, :] * np.exp(cph[s, None, :])
#-- allocate for out tides at point
tide = np.ma.zeros((ndays))
tide.mask = np.zeros((ndays), dtype=bool)
#-- predict tidal elevations at time and infer minor corrections
tide.mask[:] = np.any(hc.mask)
tide.data[:] = pyTMD.predict_tidal_ts(tide_time,
hc,
c,
DELTAT=deltat,
CORRECTIONS=GRID)
minor = pyTMD.infer_minor_corrections(tide_time,
hc,
c,
DELTAT=deltat,
CORRECTIONS=GRID)
tide.data[:] += minor.data[:]
#-- calculate differences between matlab and python version
difference = np.ma.zeros((ndays))
difference.data[:] = tide.data - validation[:, i]
difference.mask = (tide.mask | np.isnan(validation[:, i]))
difference.data[difference.mask] = 0.0
if not np.all(difference.mask):
assert np.all(np.abs(difference) < eps)
def test_tidal_ellipse(self):
#-- model parameters for CATS2008
modelpath = os.path.join(filepath, 'CATS2008')
grid_file = os.path.join(modelpath, 'grid_CATS2008')
model_file = os.path.join(modelpath, 'uv.CATS2008.out')
TYPES = ['u', 'v']
GRID = 'OTIS'
EPSG = 'CATS2008'
#-- open Antarctic Tide Gauge (AntTG) database
with open(os.path.join(filepath, 'AntTG_ocean_height_v1.txt'),
'r') as f:
file_contents = f.read().splitlines()
#-- counts the number of lines in the header
count = 0
HEADER = True
#-- Reading over header text
while HEADER:
#-- check if file line at count starts with matlab comment string
HEADER = file_contents[count].startswith('%')
#-- add 1 to counter
count += 1
#-- rewind 1 line
count -= 1
#-- iterate over number of stations
antarctic_stations = (len(file_contents) - count) // 10
stations = [None] * antarctic_stations
shortname = [None] * antarctic_stations
station_type = [None] * antarctic_stations
station_lon = np.zeros((antarctic_stations))
station_lat = np.zeros((antarctic_stations))
for s in range(antarctic_stations):
i = count + s * 10
stations[s] = file_contents[i + 1].strip()
shortname[s] = file_contents[i + 3].strip()
lat, lon, _, _ = file_contents[i + 4].split()
station_type[s] = file_contents[i + 6].strip()
station_lon[s] = np.float64(lon)
station_lat[s] = np.float64(lat)
#-- compare daily outputs at each station point
invalid_list = [
'Ablation Lake', 'Amery', 'Bahia Esperanza', 'Beaver Lake',
'Cape Roberts', 'Casey', 'Doake Ice Rumples', 'EE4A', 'EE4B',
'Eklund Islands', 'Gerlache C', 'Groussac', 'Gurrachaga',
'Half Moon Is.', 'Heard Island', 'Hobbs Pool', 'Mawson', 'McMurdo',
'Mikkelsen', 'Palmer', 'Primavera', 'Rutford GL', 'Rutford GPS',
'Rothera', 'Scott Base', 'Seymour Is', 'Terra Nova Bay'
]
#-- remove coastal stations from the list
i = [i for i, s in enumerate(shortname) if s not in invalid_list]
valid_stations = len(i)
#-- will verify differences between model outputs are within tolerance
eps = np.finfo(np.float16).eps
#-- save complex amplitude for each current
hc1, hc2 = ({}, {})
#-- iterate over zonal and meridional currents
for TYPE in TYPES:
#-- extract amplitude and phase from tide model
amp, ph, D, c = pyTMD.read_tide_model.extract_tidal_constants(
station_lon[i],
station_lat[i],
grid_file,
model_file,
EPSG,
TYPE=TYPE,
METHOD='spline',
GRID=GRID)
#-- calculate complex phase in radians for Euler's
cph = -1j * ph * np.pi / 180.0
#-- calculate constituent oscillation for station
hc1[TYPE] = amp * np.exp(cph)
#-- compute validation data from Matlab TMD program using octave
#-- https://github.com/EarthAndSpaceResearch/TMD_Matlab_Toolbox_v2.5
TMDpath = os.path.join(filepath, '..', 'TMD_Matlab_Toolbox', 'TMD')
octave.addpath(octave.genpath(os.path.normpath(TMDpath)))
octave.addpath(filepath)
octave.addpath(modelpath)
#-- turn off octave warnings
octave.warning('off', 'all')
#-- input control file for model
CFname = os.path.join(filepath, 'Model_CATS2008')
assert os.access(CFname, os.F_OK)
#-- extract tidal harmonic constants out of a tidal model
amp, ph, D, cons = octave.tmd_extract_HC(CFname,
station_lat[i],
station_lon[i],
TYPE,
nout=4)
#-- calculate complex phase in radians for Euler's
cph = -1j * ph * np.pi / 180.0
#-- calculate constituent oscillation for station
hc2[TYPE] = amp * np.exp(cph)
#-- compute tidal ellipse parameters for python program
test = {}
test['umajor'],test['uminor'],test['uincl'],test['uphase'] = \
pyTMD.tidal_ellipse(hc1['u'],hc1['v'])
#-- compute tidal ellipse parameters for TMD matlab program
valid = {}
valid['umajor'],valid['uminor'],valid['uincl'],valid['uphase'] = \
octave.TideEl(hc2['u'],hc2['v'],nout=4)
#-- calculate differences between matlab and python version
for key in ['umajor', 'uminor', 'uincl', 'uphase']:
difference = np.ma.zeros((valid_stations, len(c)))
difference.data[:] = test[key].data - valid[key].T
difference.mask = (test[key].mask | np.isnan(valid[key].T))
difference.data[difference.mask] = 0.0
if not np.all(difference.mask):
assert np.all(np.abs(difference) < eps)
def test_verify_CATS2008(self, parameters):
#-- model parameters for CATS2008
modelpath = os.path.join(filepath, 'CATS2008')
grid_file = os.path.join(modelpath, parameters['grid'])
model_file = os.path.join(modelpath, parameters['model'])
TYPE = parameters['type']
GRID = 'OTIS'
EPSG = 'CATS2008'
#-- open Antarctic Tide Gauge (AntTG) database
with open(os.path.join(filepath, 'AntTG_ocean_height_v1.txt'),
'r') as f:
file_contents = f.read().splitlines()
#-- counts the number of lines in the header
count = 0
HEADER = True
#-- Reading over header text
while HEADER:
#-- check if file line at count starts with matlab comment string
HEADER = file_contents[count].startswith('%')
#-- add 1 to counter
count += 1
#-- rewind 1 line
count -= 1
#-- iterate over number of stations
antarctic_stations = (len(file_contents) - count) // 10
stations = [None] * antarctic_stations
shortname = [None] * antarctic_stations
station_type = [None] * antarctic_stations
station_lon = np.zeros((antarctic_stations))
station_lat = np.zeros((antarctic_stations))
for s in range(antarctic_stations):
i = count + s * 10
stations[s] = file_contents[i + 1].strip()
shortname[s] = file_contents[i + 3].strip()
lat, lon, _, _ = file_contents[i + 4].split()
station_type[s] = file_contents[i + 6].strip()
station_lon[s] = np.float64(lon)
station_lat[s] = np.float64(lat)
#-- calculate daily results for a time period
#-- convert time to days since 1992-01-01T00:00:00
tide_time = np.arange(
pyTMD.time.convert_calendar_dates(2000, 1, 1),
pyTMD.time.convert_calendar_dates(2000, 12, 31) + 1)
#-- serial dates for matlab program (days since 0000-01-01T00:00:00)
SDtime = np.arange(convert_calendar_serial(2000, 1, 1),
convert_calendar_serial(2000, 12, 31) + 1)
#-- presently not converting times to dynamic times for model comparisons
deltat = np.zeros_like(tide_time)
#-- number of days
ndays = len(tide_time)
#-- extract amplitude and phase from tide model
amp, ph, D, c = pyTMD.read_tide_model.extract_tidal_constants(
station_lon,
station_lat,
grid_file,
model_file,
EPSG,
TYPE=TYPE,
METHOD='spline',
GRID=GRID)
#-- calculate complex phase in radians for Euler's
cph = -1j * ph * np.pi / 180.0
#-- will verify differences between model outputs are within tolerance
eps = np.finfo(np.float16).eps
#-- compare daily outputs at each station point
invalid_list = [
'Ablation Lake', 'Amery', 'Bahia Esperanza', 'Beaver Lake',
'Cape Roberts', 'Casey', 'Doake Ice Rumples', 'EE4A', 'EE4B',
'Eklund Islands', 'Gerlache C', 'Groussac', 'Gurrachaga',
'Half Moon Is.', 'Heard Island', 'Hobbs Pool', 'Mawson', 'McMurdo',
'Mikkelsen', 'Palmer', 'Primavera', 'Rutford GL', 'Rutford GPS',
'Rothera', 'Scott Base', 'Seymour Is', 'Terra Nova Bay'
]
#-- remove coastal stations from the list
valid_stations = [
i for i, s in enumerate(shortname) if s not in invalid_list
]
#-- compute validation data from Matlab TMD program using octave
#-- https://github.com/EarthAndSpaceResearch/TMD_Matlab_Toolbox_v2.5
TMDpath = os.path.join(filepath, '..', 'TMD_Matlab_Toolbox', 'TMD')
octave.addpath(octave.genpath(os.path.normpath(TMDpath)))
octave.addpath(filepath)
octave.addpath(modelpath)
#-- turn off octave warnings
octave.warning('off', 'all')
#-- input control file for model
CFname = os.path.join(filepath, 'Model_CATS2008')
assert os.access(CFname, os.F_OK)
#-- run Matlab TMD program with octave
#-- MODE: OB time series
validation, _ = octave.tmd_tide_pred_plus(CFname,
SDtime,
station_lat[valid_stations],
station_lon[valid_stations],
TYPE,
nout=2)
#-- for each valid station
for i, s in enumerate(valid_stations):
#-- calculate constituent oscillation for station
hc = amp[s, None, :] * np.exp(cph[s, None, :])
#-- allocate for out tides at point
tide = np.ma.zeros((ndays))
tide.mask = np.zeros((ndays), dtype=bool)
#-- predict tidal elevations at time and infer minor corrections
tide.mask[:] = np.any(hc.mask)
tide.data[:] = pyTMD.predict_tidal_ts(tide_time,
hc,
c,
DELTAT=deltat,
CORRECTIONS=GRID)
minor = pyTMD.infer_minor_corrections(tide_time,
hc,
c,
DELTAT=deltat,
CORRECTIONS=GRID)
tide.data[:] += minor.data[:]
#-- calculate differences between matlab and python version
difference = np.ma.zeros((ndays))
difference.data[:] = tide.data - validation[:, i]
difference.mask = (tide.mask | np.isnan(validation[:, i]))
difference.data[difference.mask] = 0.0
if not np.all(difference.mask):
assert np.all(np.abs(difference) < eps)
from __future__ import division
import os
import unittest
import numpy as np
from oct2py import octave
#from pandas import read_csv
import seawater as sw
from seawater.constants import c3515
path = os.path.join(os.path.dirname(__file__), 'seawater_v3_3')
_ = octave.addpath(octave.genpath(path))
def compare_results(name, function, args, values):
args = [values.get(arg) for arg in args]
res = function(*args) # Python call.
# FIXME: Test only the first output when multiple outputs are present.
if isinstance(res, tuple):
nout = len(res)
res = res[0]
else:
nout = 1
val = octave.call('sw_%s' % name, *args, nout=nout) # Octave call.
if nout > 1:
val = val[0]
val, res = val.squeeze(), res.squeeze()
import numpy as np
from oct2py import octave
from pandas import read_csv
import seawater as sw
from seawater.constants import c3515
try:
path = sys.argv[1]
except IndexError:
path = "./seawater_v3_3"
if not os.path.exists(path):
raise ValueError("matlab seawater path %s not found" % path)
_ = octave.addpath(octave.genpath(path))
kw = dict(comment='#', header=5, index_col=0)
st61 = read_csv('Endeavor_Cruise-88_Station-61.csv', **kw)
st64 = read_csv('Endeavor_Cruise-88_Station-64.csv', **kw)
latst = 36. + 40.03 / 60., 37. + 39.93 / 60.
lonst = -(70. + 59.59 / 60.), -71.
Sal=np.c_[st61['S'].values, st64['S'].values]
Temp=np.c_[st61['t'].values, st64['t'].values]
Pres=np.c_[st61.index.values.astype(float), st64.index.values.astype(float)]
Gpan = sw.gpan(Sal, Temp, Pres)
def compare_results(name, function, args):
args = [values.get(arg) for arg in args]
