Test `velocity_section_and_surface` Module¶

Render figure object produced by the nowcast.figures.research.velocity_section_and_surface module.

Set-up and function call replicates as nearly as possible what is done in the nowcast.workers.make_plots worker to help ensure that the module will work in the nowcast production context.

This notebook must be run in a Nowcast Figures Development Environment so that all of the necessary dependency packages are installed. Also, it must be run on a workstation that has the Salish Sea Nowcast system /results/ parition mounted.

In [1]:

# reload lets us pull in changes from modules that we edit
from importlib import reload

In [2]:

import datetime
from glob import glob
import logging
import os
from pathlib import Path

import matplotlib

import arrow
import cmocean
from nemo_nowcast import NowcastWorker
import netCDF4 as nc
import scipy.io as sio

from nowcast import lib
from nowcast.figures.research import velocity_section_and_surface

In [3]:

import io

import yaml

In [4]:

%matplotlib inline

In [5]:

config = '''
    run_types:
        nowcast-green:
            bathymetry: /results/nowcast-sys/grid/bathymetry_201702.nc
            mesh_mask: /results/nowcast-sys/grid/mesh_mask201702.nc
        nowcast:
            bathymetry: /results/nowcast-sys/grid/bathymetry_201702.nc
            mesh_mask: /results/nowcast-sys/grid/mesh_mask201702.nc

    run:
        results_archive:
            nowcast-green: /results/SalishSea/nowcast-green/
            nowcast: /results/SalishSea/nowcast-blue/
'''

config = yaml.load(io.StringIO(config))

In [6]:

run_type = 'nowcast'
plot_type = 'research'
run_date = arrow.get('2017-10-23')

In [7]:

dmy = run_date.format('DDMMMYY').lower()
results_dir = Path(config['run']['results_archive'][run_type], dmy)
bathy = nc.Dataset(config['run_types'][run_type]['bathymetry'])
mesh_mask = nc.Dataset(config['run_types'][run_type]['mesh_mask'])

In [8]:

yyyymmdd = run_date.format('YYYYMMDD')

In [9]:

U = nc.Dataset(os.fspath(results_dir/f'SalishSea_1h_{yyyymmdd}_{yyyymmdd}_grid_U.nc'))
V = nc.Dataset(os.fspath(results_dir/f'SalishSea_1h_{yyyymmdd}_{yyyymmdd}_grid_V.nc'))
U_var = U.variables['vozocrtx']
V_var = V.variables['vomecrty']

In [10]:

%%timeit -n1 -r1

# Layout parameters
sections = (450, 520, 680)
pos = (
    (0.1, 0.35),
    (0.4, 0.65),
    (0.7, 0.95)
)
section_lims = (
    (235, 318, 0, 445),
    (192, 277, 0, 445),
    (127, 197, 0, 445),
)

# Make figure
fig = velocity_section_and_surface.make_figure(
    U_var, V_var, bathy, mesh_mask,
    sections=sections, pos=pos, section_lims=section_lims
)

2.15 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

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Test velocity_section_and_surface Module¶

Test `velocity_section_and_surface` Module¶