Test `surface_currents` Module¶

Render figure object produced by the nowcast.figures.fvcom.surface_currents module. Provides data for visual testing to confirm that refactoring has not adversely changed figure for web page.

Set-up and function call replicates as nearly as possible what is done in the nowcast.workers.make_plots worker.

Notebooks like this should be developed in a Nowcast Figures Development Environment so that all of the necessary dependency packages are installed. The development has to be done on a workstation that has the Vancouver Harbour & Fraser River FVCOM model results /opp/ parition mounted.

In [1]:

import io
from pathlib import Path

import arrow
import netCDF4
import yaml

from nowcast.figures.fvcom.research import surface_currents

In [2]:

%matplotlib inline

The bits of config/nowcast.yaml that are required:

In [8]:

config = '''
    vhfr fvcom runs:
        results archive:
            nowcast x2: /opp/fvcom/nowcast-x2/
            forecast x2: /opp/fvcom/forecast-x2/
            nowcast r12: /opp/fvcom/nowcast-r12/
'''

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

The bits that the make_plots worker must provide:

Nowcast X2 Figure¶

In [4]:

run_date = arrow.get('2019-05-16')
model_config = 'x2'
run_type = 'nowcast'

ddmmmyy = run_date.format('DDMMMYY').lower()
results_dir = Path(
    config["vhfr fvcom runs"]["results archive"][f"{run_type} {model_config}"], ddmmmyy
)
fvcom_results_dataset = netCDF4.Dataset(results_dir/f"vh_{model_config}_0001.nc")

One time step, and one section for quickest testing:

In [5]:

%%timeit -n1 -r1

from importlib import reload
reload(surface_currents)

names = {
    "English Bay": "EnglishBay_surface_currents",
    "Vancouver Harbour": "VancouverHarbour_surface_currents",
    "Indian Arm": "IndianArm_surface_currents",
}
time_index = 0
fig = surface_currents.make_figure(
    "Vancouver Harbour", time_index, fvcom_results_dataset
)

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

All sections:

In [6]:

%%timeit -n1 -r1

from importlib import reload
reload(surface_currents)

names = {
    "English Bay": "EnglishBay_surface_currents",
    "Vancouver Harbour": "VancouverHarbour_surface_currents",
    "Indian Arm": "IndianArm_surface_currents",
}
time_index = 0
for place in names:
    fig = surface_currents.make_figure(
        place, time_index, fvcom_results_dataset
    )

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

Nowcast R12 Figure¶

In [12]:

run_date = arrow.get('2019-05-11')
model_config = 'r12'
run_type = 'nowcast'

ddmmmyy = run_date.format('DDMMMYY').lower()
results_dir = Path(
    config["vhfr fvcom runs"]["results archive"][f"{run_type} {model_config}"], ddmmmyy
)
fvcom_results_dataset = netCDF4.Dataset(results_dir/f"vh_{model_config}_0001.nc")

One time step, and one section for quickest testing:

In [13]:

%%timeit -n1 -r1

from importlib import reload
reload(surface_currents)

names = {
    "English Bay": "EnglishBay_surface_currents",
    "Vancouver Harbour": "VancouverHarbour_surface_currents",
    "Indian Arm": "IndianArm_surface_currents",
}
time_index = 0
fig = surface_currents.make_figure(
    "Vancouver Harbour", time_index, fvcom_results_dataset
)

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

All sections:

In [14]:

%%timeit -n1 -r1

from importlib import reload
reload(surface_currents)

names = {
    "English Bay": "EnglishBay_surface_currents",
    "Vancouver Harbour": "VancouverHarbour_surface_currents",
    "Indian Arm": "IndianArm_surface_currents",
}
time_index = 0
for place in names:
    fig = surface_currents.make_figure(
        place, time_index, fvcom_results_dataset
    )

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

Forecast X2 Figure¶

In [9]:

run_date = arrow.get('2019-05-16')
model_config = 'x2'
run_type = 'forecast'

ddmmmyy = run_date.format('DDMMMYY').lower()
results_dir = Path(
    config["vhfr fvcom runs"]["results archive"][f"{run_type} {model_config}"], ddmmmyy
)
fvcom_results_dataset = netCDF4.Dataset(results_dir/f"vh_{model_config}_0001.nc")

One time step, and one section for quickest testing:

In [10]:

%%timeit -n1 -r1

from importlib import reload
reload(surface_currents)

names = {
    "English Bay": "EnglishBay_surface_currents",
    "Vancouver Harbour": "VancouverHarbour_surface_currents",
    "Indian Arm": "IndianArm_surface_currents",
}
time_index = -1
fig = surface_currents.make_figure(
    "Vancouver Harbour", time_index, fvcom_results_dataset
)

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

All sections:

In [11]:

%%timeit -n1 -r1

from importlib import reload
reload(surface_currents)

names = {
    "English Bay": "EnglishBay_surface_currents",
    "Vancouver Harbour": "VancouverHarbour_surface_currents",
    "Indian Arm": "IndianArm_surface_currents",
}
time_index = -1
for place in names:
    fig = surface_currents.make_figure(
        place, time_index, fvcom_results_dataset
    )

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

In [ ]:

Test surface_currents Module¶

Nowcast X2 Figure¶

Nowcast R12 Figure¶

Forecast X2 Figure¶

Test `surface_currents` Module¶