Notebook

Develop `time_series_plots` Figure Module¶

Development of functions for nowcast.figures.research.time_series_plots web site figure module.

In [1]:

from types import SimpleNamespace

import matplotlib.pyplot as plt
import xarray as xr
import arrow as arw

from salishsea_tools import places

import nowcast.figures.website_theme

from importlib import reload

In [2]:

%matplotlib inline

What make_plots provides:

In [3]:

place = 'Sandheads'

left_variable = 'nitrate'
right_variable = 'diatoms'

xr_dataset = xr.open_dataset('https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DBiologyFields1hV17-02')

In [4]:

xr_dataset

Out[4]:

<xarray.Dataset>
Dimensions:                       (depth: 40, gridX: 398, gridY: 898, time: 9096)
Coordinates:
  * time                          (time) datetime64[ns] 2014-09-12T00:30:00 ...
  * depth                         (depth) float32 0.5 1.5 2.50001 3.50003 ...
  * gridY                         (gridY) int16 0 1 2 3 4 5 6 7 8 9 10 11 12 ...
  * gridX                         (gridX) int16 0 1 2 3 4 5 6 7 8 9 10 11 12 ...
Data variables:
    Fraser_tracer                 (time, depth, gridY, gridX) float64 ...
    ammonium                      (time, depth, gridY, gridX) float64 ...
    biogenic_silicon              (time, depth, gridY, gridX) float64 ...
    ciliates                      (time, depth, gridY, gridX) float64 ...
    diatoms                       (time, depth, gridY, gridX) float64 ...
    dissolved_organic_nitrogen    (time, depth, gridY, gridX) float64 ...
    flagellates                   (time, depth, gridY, gridX) float64 ...
    mesozooplankton               (time, depth, gridY, gridX) float64 ...
    microzooplankton              (time, depth, gridY, gridX) float64 ...
    nitrate                       (time, depth, gridY, gridX) float64 ...
    particulate_organic_nitrogen  (time, depth, gridY, gridX) float64 ...
    silicon                       (time, depth, gridY, gridX) float64 ...
Attributes:
    acknowledgement:           MEOPAR, ONC, Compute Canada
    cdm_data_type:             Grid
    Conventions:               CF-1.6, COARDS, ACDD-1.3
    creator_email:             sallen@eoas.ubc.ca
    creator_name:              SalishSeaCast Project Contributors
    creator_url:               https://salishsea-meopar-docs.readthedocs.io/
    description:               biogeochemical variables
    drawLandMask:              over
    history:                   2017-06-18T21:27:08Z (local files)\n2017-06-18...
    infoUrl:                   https://salishsea-meopar-docs.readthedocs.io/e...
    institution:               UBC EOAS
    institution_fullname:      Earth, Ocean & Atmospheric Sciences, Universit...
    keywords:                  ammonia, ammonium, aquatic, biogenic, biogenic...
    keywords_vocabulary:       GCMD Science Keywords
    license:                   The SalishSeaCast NEMO model results are c...
    project:                   SalishSeaCast NEMO Model
    sourceUrl:                 (local files)
    standard_name_vocabulary:  CF Standard Name Table v29
    summary:                   Green, Salish Sea, 3d Biology Fields, Hourly, ...
    time_coverage_end:         2017-05-27T23:30:00Z
    time_coverage_start:       2014-09-12T00:30:00Z
    timeStamp:                 2017-Jun-01 20:53:05 GMT
    title:                     Green, Salish Sea, 3d Biology Fields, Hourly, ...
    uuid:                      6a76eb14-b8b4-47b0-b3e0-b695ad2355fd

`_prep_plot_data()` Function¶

In [5]:

def _prep_plot_data(xr_dataset, left_variable, right_variable, place):

    end_day = arw.get(xr_dataset.time_coverage_end)
    start_day = end_day.replace(days=-1)
    time_slice = slice(start_day.date(), end_day.replace(days=+1).date())
    gridY, gridX = places.PLACES[place]['NEMO grid ji']

    left = xr_dataset[left_variable].sel(time=time_slice).isel(depth=0, gridX=gridX, gridY=gridY)
    
    right = xr_dataset[right_variable].sel(time=time_slice).isel(depth=0, gridX=gridX, gridY=gridY)
    
    left_long_name = xr_dataset[left_variable].long_name
    left_units = xr_dataset[left_variable].units
    
    right_long_name = xr_dataset[right_variable].long_name
    right_units = xr_dataset[right_variable].units
    

    return SimpleNamespace(left=left, right=right, left_long_name=left_long_name, left_units=left_units,
                           right_long_name=right_long_name, right_units=right_units)

`_prep_fig_axes() Function`¶

In [6]:

def _prep_fig_axes(figsize, theme):
    fig, axl = plt.subplots(
        figsize=figsize, facecolor=theme.COLOURS['figure']['facecolor'])

    axl.set_axis_bgcolor(theme.COLOURS['axes']['background'])
    axr = axl.twinx()
    axr.set_axis_bgcolor(theme.COLOURS['axes']['background'])
    
    return fig, axl, axr

`_plot_timeseries()` Function¶

In [7]:

def _plot_timeseries(ax, plot_data, variable, theme):
    ax.plot(plot_data.time, plot_data, color=theme.COLOURS['time series'][variable])
    return

`_timeseries_axes_labels()` Function¶

In [8]:

def _timeseries_axes_labels(axl, axr, left_variable, left_long_name, left_units, 
                            right_variable, right_long_name, right_units, theme):
    axl.set_xlabel(
        'Date',
        color=theme.COLOURS['text']['axis'],
        fontproperties=theme.FONTS['axis'])
    axl.set_ylabel(left_long_name+' ['+left_units+']', fontproperties=theme.FONTS['axis'])
    theme.set_axis_colors(axl)
    axr.set_ylabel(right_long_name+' ['+right_units+']', fontproperties=theme.FONTS['axis']
                   ,rotation=-90, verticalalignment='bottom')
    theme.set_axis_colors(axr)

    axl.text(
        0.5,
        0.95,
        left_long_name,
        horizontalalignment='center',
        color=theme.COLOURS['time series'][left_variable],
        fontproperties=theme.FONTS['legend label large'],
        transform=axl.transAxes)
    axl.text(
        0.5,
        0.9,
        right_long_name,
        horizontalalignment='center',
        color=theme.COLOURS['time series'][right_variable],
        fontproperties=theme.FONTS['legend label large'],
        transform=axl.transAxes)
    
    axl.grid(axis='x')

`make_figure()` Function¶

This is is the function that will be called by the nowcast.workers.make_plots worker to return a matplotlib.figure.Figure object.

In [9]:

def make_figure(xr_dataset,
                left_variable,
                right_variable,
                place,
                figsize=(20, 8),
                theme=nowcast.figures.website_theme):
    plot_data = _prep_plot_data(xr_dataset, left_variable, right_variable, place)

    fig, axl, axr = _prep_fig_axes(figsize, theme)
    _plot_timeseries(axl, plot_data.left, left_variable, theme)
    _plot_timeseries(axr, plot_data.right, right_variable, theme)
    _timeseries_axes_labels(axl, axr, left_variable, plot_data.left_long_name, plot_data.left_units, 
                            right_variable, plot_data.right_long_name, plot_data.right_units, theme)

    return fig

Render the Figure¶

Nitrate - diatoms¶

In [10]:

%%timeit -n1 -r1
reload(nowcast.figures.website_theme) # to reload website theme colours etc
fig = make_figure(xr_dataset, left_variable, right_variable, place)

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

Mesozoo - microzoo¶

In [35]:

left_variable = 'mesozooplankton'
right_variable = 'microzooplankton'

In [36]:

%%timeit -n1 -r1
reload(nowcast.figures.website_theme) # to reload website theme colours etc
fig = make_figure(xr_dataset, left_variable, right_variable, place)

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

Mesodinium - flagellaes¶

In [43]:

left_variable = 'ciliates'
right_variable = 'flagellates'

In [46]:

%%timeit -n1 -r1
reload(nowcast.figures.website_theme) # to reload website theme colours etc
fig = make_figure(xr_dataset, left_variable, right_variable, place)

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

Temperature - salinity¶

In [11]:

xr_dataset = xr.open_dataset('https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DTracerFields1hV17-02')
left_variable = 'temperature'
right_variable = 'salinity'

In [40]:

%%timeit -n1 -r1
reload(nowcast.figures.website_theme) # to reload website theme colours etc
fig = make_figure(xr_dataset, left_variable, right_variable, place)

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

In [ ]:

Develop time_series_plots Figure Module¶

_prep_plot_data() Function¶

_prep_fig_axes() Function¶

_plot_timeseries() Function¶

_timeseries_axes_labels() Function¶

make_figure() Function¶