# Load model output.
fvcom = 'sample.nc'
varlist = ('lonc', 'latc', 'ua', 'va', 'time')
dims = {'time': ':360'} # first 15 days at hourly sampling
# Define a plot subset ((xmin, xmax), (ymin, ymax)).
subset = np.array(((-4.2416, -4.0837), (50.2656, 50.3966)))
# Scaling factor for the ellipses. You will need to experiment with this
# value.
scaling = 2000
# Find the model nodes which fall within the subset defined above.
FVCOM = ncread(fvcom, vars=varlist, dims=dims, noisy=False)
# Create a time array for the TAPPy call.
FVCOM['datetimes'] = num2date(FVCOM['time'], 'days since 1858-11-17 00:00:00')
years = [i.year for i in FVCOM['datetimes']]
months = [i.month for i in FVCOM['datetimes']]
days = [i.day for i in FVCOM['datetimes']]
hours = [i.hour for i in FVCOM['datetimes']]
minutes = [i.minute for i in FVCOM['datetimes']]
seconds = [i.second for i in FVCOM['datetimes']]
Times = np.column_stack((years, months, days, hours, minutes, seconds))
# Find the indices of the locations which fall within the subset.
elems = np.where((FVCOM['lonc'] > subset[0].min()) *
(FVCOM['lonc'] < subset[0].max()) *
(FVCOM['latc'] > subset[1].min()) *
from PyFVCOM.grid_tools import findNearestPoint
# Multiple output files are transparently loaded by ncread.
fvcom = ['sample_april.nc', 'sample_may.nc', 'sample_june.nc']
# Positions we're interested in plotting. The findNearestPoint
# function will find the closest node in the unstructured grid.
xy = np.array(((-4.5, 55), (-6.9, 52))) # lon, lat pairs
# Extract only the surface layer for the plot.
dims = {'siglay': '0'}
# Our variables of interest.
varlist = ('lon', 'lat', 'time', 'temp')
FVCOM = ncread(fvcom, vars=varlist, dims=dims)
# Make datetime objects for the time series plots.
FVCOM['datetimes'] = num2date(FVCOM['time'], 'days since 1858-11-17 00:00:00')
# Find the nodes in the grid closest to the positions we're interested in plotting.
nx, ny, dist, idx = findNearestPoint(FVCOM['lon'], FVCOM['lat'],
xy[:, 0], xy[:, 1])
# Now plot the time series.
rcParams['mathtext.default'] = 'regular' # sensible font for the LaTeX labelling
fig = plt.figure(figsize=(10, 7)) # size in inches
for c, ind in enumerate(idx):
ax = fig.add_subplot(len(idx), 1, c + 1)
from cmocean import cm
from mpl_toolkits.basemap import Basemap
from mpl_toolkits.axes_grid1 import make_axes_locatable
from PyFVCOM.read_FVCOM_results import ncread
# Load the model output.
fvcom = 'sample.nc'
# Extract only the first 20 time steps.
dims = {'time': ':20'}
# And only these variables
varlist = ('lon', 'lat', 'nv', 'zeta', 'Times')
FVCOM = ncread(fvcom, vars=varlist, dims=dims, noisy=True)
# Lay the groundwork for a plot of the model surface.
triangles = FVCOM['nv'].transpose() - 1 # offset for Python indexing.
extents = np.array((FVCOM['lon'].min(),
FVCOM['lon'].max(),
FVCOM['lat'].min(),
FVCOM['lat'].max()))
m = Basemap(llcrnrlon=extents[0],
llcrnrlat=extents[2],
urcrnrlon=extents[1],
urcrnrlat=extents[3],
rsphere=(6378137.00, 6356752.3142),
import os
import numpy as np
import matplotlib.pyplot as plt
from glob import glob
from mpl_toolkits.basemap import Basemap
from PyFVCOM.read_FVCOM_results import ncread
if __name__ == '__main__':
files = glob(os.path.join('raw_data', '*.qxf'))
data = {}
for file in files:
data[file] = ncread(file, vars=['ALONAT01', 'ALATAT01', 'ALONAG01', 'ALATAG01'], noisy=True)
# Do a quick plot.
m = Basemap(llcrnrlon=-12,
llcrnrlat=49,
urcrnrlon=-4,
urcrnrlat=53,
rsphere=(6378137.00, 6356752.3142),
resolution='i',
projection='merc',
area_thresh=0.2,
lon_0=0,
lat_0=50.5,
lat_ts=50.5)
parallels = np.arange(40, 60, 0.5)
meridians = np.arange(-20, 20, 1)
SeriesAvailability TEXT COLLATE nocase, \
Warnings TEXT COLLATE nocase, \
Licence TEXT COLLATE nocase, \
OriginalKeys TEXT COLLATE nocase);')
# Now add the metadata.
for row in reader:
if noisy:
print('Adding station {}'.format(row['BODC reference']))
try:
# Save the current site ID.
site = 'b{:07d}'.format(int(row['BODC reference']))
ncfile = os.path.join(base, 'raw_data', '{}.qxf'.format(site))
data = ncread(ncfile)
# Since the BODC data has a bunch of different codes for
# the same thing then I have to search for one of a few
# different codes in each file. This is a bit of a pain.
# Save these keys to the Staions table in case I have
# messed up the groupings in findNamesBOCD.
keys = findNamesBODC(data)
# If we don't have a depth key, but we do have a pressure
# one, calculate the depth using the UNESCO Fofonoff and
# Millard (1983) equation.
if keys['depth'] is None and keys['pressure'] is not None:
keys['depth'] = 'my_depth'
data['my_depth'] = pressure2depth(data[keys['pressure']], float(row['Latitude A']))
