Roughness Rose plots#

[3]:

wk.plot.roughness_rose(rou_rose)

[4]:

wk.plot.roughness_rose(rou_rose, gap=True)

[5]:

wk.plot.roughness_rose(rou_rose, style="bar")

[6]:

wk.plot.roughness_rose(rou_rose, style="bar", gap=True)

Wind rose plots#

[7]:

wk.plot.wind_rose(bwc_from_ts, "IEC_I")

[8]:

wk.plot.wind_rose(bwc_from_wwc, [0, 5, 10, 15, 30], style="radar")

[9]:

wk.plot.wind_rose(bwc_from_wwc, "IEC_I", cmap="inferno", gap=True)

[10]:

wk.plot.wind_rose(wwc_pt, "IEC_I", cmap="inferno", gap=True)  # works for a wwc as input

[11]:

# bwc is missing some attributes so raises a validation error
try:
    wk.plot.wind_rose(bwc)
except PlottingAttrsError as e:
    print(e)

The plot can't be displayed as the following attributes are not defined for 'sector' :
1. Missing attribute value: 'long_name'
2. Missing attribute value: 'units'
Please, define a value for the empty attributes.

Weibull distributions and histograms#

[13]:

wk.plot.histogram(bwc_from_ts, style="faceted", color="green", weibull=True)

[14]:

wk.plot.histogram(
    bwc_from_ts, style="faceted", color="green", weibull=True, share_xaxes=False
)

[15]:

wk.plot.histogram(bwc_from_ts, style="list", gap=True)[10]

[16]:

# weibull argument displays or not weib. distrib.
wk.plot.histogram(bwc_from_wwc.isel(sector=[0, 3]), style="list", weibull=False)[1]

[17]:

# Emergent distribution
wk.plot.histogram(bwc_from_ts, emergent_distribution=True)

[18]:

wk.plot.histogram_lines(bwc_from_ts)

[19]:

# bwc is missing attributes
try:
    wk.plot.histogram_lines(bwc)
except PlottingAttrsError as e:
    print(e)

The plot can't be displayed as the following attributes are not defined for 'sector' :
1. Missing attribute value: 'long_name'
2. Missing attribute value: 'units'
Please, define a value for the empty attributes.

[20]:

# input must be valid bwc, wwc is not accepted
try:
    wk.plot.histogram(wwc_pt, style="faceted", weibull=None)
except wk.WindkitValidationError as e:
    print(e)

validate found 2 errors
1. Missing variable: wsfreq
2. Missing dimension: wsbin

Operational curves#

[22]:

wk.plot.single_curve(
    wtg_test.power_output, style="faceted", color="dodgerblue", x_axis_range=[1, 29.5]
)

[23]:

wk.plot.power_ct_curves(wtg_test, mark_rated=0.5, x_axis_range=[3, 28], title=True)

[24]:

wk.plot.power_ct_curves(
    wtg_test.sel(mode="Standard"),
    mark_rated=0.95,
    share_yaxes=True,
    x_axis_range=[1, 28],
    title=True,
)

[25]:

# Don't share the y-axis, which changes the view of the power curves for this artificial case
wk.plot.power_ct_curves(wtg_test, mark_rated=True, share_yaxes=False, title=True)

Vertical profile#

[29]:

vp = xr.open_dataset("data/plots/vertical_profile.nc")
vp["height"].attrs = {"long_name": "Height above the ground", "units": "m"}

[30]:

wk.plot.vertical_profile(vp.isel(time=0).wind_speed, vp.isel(time=1).wind_speed)

[31]:

wk.plot.vertical_profile(da_meas=vp.isel(time=1).wind_direction)

[32]:

wk.plot.vertical_profile(da_pred=vp.isel(time=1).wind_direction)

Map Plots#

Raster map#

[33]:

plot_dat = site_effects_squeezed["site_elev"]
wk.plot.raster_plot(plot_dat)

[33]:

<Figure size 640x480 with 2 Axes>

[34]:

wk.plot.raster_plot(landcover)

[34]:

<Figure size 640x480 with 2 Axes>

[35]:

plot_dat = site_effects_squeezed["site_elev"]
wk.plot.raster_plot(plot_dat, color_scale_limits=[0, 200])

[35]:

<Figure size 640x480 with 2 Axes>

[37]:

plot_dat = site_effects_squeezed["z0meso"].sel(sector=270)
raster_map = wk.plot.raster_plot(plot_dat, pts=points.isel(point=slice(0, 10, 2)))