def plot_elevation_timeseries(eta, ax=None):
"""
Plot wave surface elevation time-series
Parameters
------------
eta: pandas DataFrame
Wave surface elevation [m] indexed by time [datetime or s]
ax : matplotlib axes object
Axes for plotting. If None, then a new figure is created.
Returns
---------
ax : matplotlib pyplot axes
"""
assert isinstance(eta, pd.DataFrame), 'eta must be of type pd.DataFrame'
ax = _xy_plot(eta.index,
eta,
fmt='-',
xlabel='Time',
ylabel='$\eta$ [m]',
ax=ax)
return ax
def plot_spectrum(S, ax=None):
"""
Plots wave amplitude spectrum versus omega
Parameters
------------
S: pandas DataFrame
Spectral density [m^2/Hz] indexed frequency [Hz]
ax : matplotlib axes object
Axes for plotting. If None, then a new figure is created.
Returns
---------
ax : matplotlib pyplot axes
"""
assert isinstance(S, pd.DataFrame), 'S must be of type pd.DataFrame'
f = S.index
ax = _xy_plot(f * 2 * np.pi,
S / (2 * np.pi),
fmt='-',
xlabel='omega [rad/s]',
ylabel='Spectral density [m$^2$s/rad]',
ax=ax)
return ax
def plot_current_timeseries(directions,
velocities,
principal_direction,
label=None,
ax=None):
'''
Returns a plot of velocity from an array of direction and speed
data in the direction of the supplied principal_direction.
Parameters
----------
directions: array-like
Time-series of directions [degrees]
velocities: array-like
Time-series of speeds [m/s]
principal_direction: float
Direction to compute the velocity in [degrees]
label: string
Label to use in the legend
ax : matplotlib axes object
Axes for plotting. If None, then a new figure with a single
axes is used.
Returns
-------
ax: figure
Time-series plot of current-speed velocity
'''
assert isinstance(velocities,(np.ndarray, pd.Series)), \
'velocities must be of type np.ndarry or pd.Series'
assert isinstance(directions,(np.ndarray, pd.Series)), \
'directions must be of type np.ndarry or pd.Series'
assert len(velocities) == len(directions), \
'velocities and directions must have the same length'
assert all(velocities.values >= 0),\
'All velocities must be positive'
assert all(directions.values >= 0) and all(directions.values <= 360),\
'directions must be between 0 and 360 degrees'
assert isinstance(principal_direction, (int, float)), \
'principal_direction must be of type int or float'
assert principal_direction >=0 and principal_direction <=360,\
'principal_direction must be between 0 and 360 degrees'
# Rotate coordinate system by supplied principal_direction
principal_directions = directions - principal_direction
# Calculate the velocity
velocity = velocities * np.cos(np.pi / 180 * principal_directions)
# Call on standard xy plotting
ax = _xy_plot(velocities.index,
velocity,
fmt='-',
label=label,
xlabel='Time',
ylabel='Velocity [$m/s$]',
ax=ax)
return ax
def plot_current_timeseries(directions,
speeds,
principal_direction,
label=None,
ax=None):
'''
Returns a plot of velocity from an array of direction and speed
data in the direction of the supplied principal_direction.
Parameters
----------
direction: array-like
Time-series of directions [degrees]
speed: array-like
Time-series of speeds [m/s]
principal_direction: float
Direction to compute the velocity in [degrees]
label: string
Label to use in the legend
ax : matplotlib axes object
Axes for plotting. If None, then a new figure with a single
axes is used.
Returns
-------
ax: figure
Time-series plot of current-speed velocity
'''
# Rotate coordinate system by supplied principal_direction
principal_directions = directions - principal_direction
# Calculate the velocity
velocities = speeds * np.cos(np.pi / 180 * principal_directions)
# Call on standard xy plotting
ax = _xy_plot(velocities.index,
velocities,
fmt='-',
label=label,
xlabel='Time',
ylabel='Velocity [$m/s$]',
ax=ax)
return ax
def plot_spectrum(S, ax=None):
"""
Plots wave amplitude spectrum versus omega
Parameters
------------
S: pandas DataFrame
Spectral density [m^2/Hz] indexed frequency [Hz]
ax : matplotlib axes object
Axes for plotting. If None, then a new figure is created.
Returns
---------
ax : matplotlib pyplot axes
"""
assert isinstance(S, pd.DataFrame), 'S must be of type pd.DataFrame'
f = S.index
ax = _xy_plot(f * 2 * np.pi,
S / (2 * np.pi),
fmt='-',
xlabel='omega [rad/s]',
ylabel='Spectral density [m$^2$s/rad]',
ax=ax)
"""
spectrum_type = S.columns
if S.shape[1] == 1:
Hm0 = _sig_wave_height(S).iloc[0,0]
Tp0 = _peak_period(S).iloc[0,0]
title = 'Spectrum: ' + spectrum_type[0] + ' \n Tp = {:0.2f}, Hs = {:0.2f}'.format(Tp0,Hm0)
ax.set_title(title)
else:
ax.legend(spectrum_type)
"""
return ax
def plot_environmental_contour(x1, x2, x1_contour, x2_contour, **kwargs):
'''
Plots an overlay of the x1 and x2 variables to the calculate
environmental contours.
Parameters
----------
x1: numpy array
x-axis data
x2: numpy array
x-axis data
x1_contour: numpy array
Calculated x1 contour values
x2_contour: numpy array
Calculated x2 contour values
**kwargs : optional
x_label: string (optional)
x-axis label. Default None.
y_label: string (optional)
y-axis label. Default None.
data_label: string (optional)
Legend label for x1, x2 data (e.g. 'Buoy 46022').
Default None.
contour_label: string or list of strings (optional)
Legend label for x1_contour, x2_contour countor data
(e.g. '100-year contour'). Default None.
ax : matplotlib axes object (optional)
Axes for plotting. If None, then a new figure is created.
Default None.
Returns
-------
ax : matplotlib pyplot axes
'''
assert isinstance(x1, np.ndarray), 'x1 must be of type np.ndarray'
assert isinstance(x2, np.ndarray), 'x2 must be of type np.ndarray'
assert isinstance(x1_contour, np.ndarray), ('x1_contour must be of '
'type np.ndarray')
assert isinstance(x2_contour, np.ndarray), ('x2_contour must be of '
'type np.ndarray')
x_label = kwargs.get("x_label", None)
y_label = kwargs.get("y_label", None)
data_label=kwargs.get("data_label", None)
contour_label=kwargs.get("contour_label", None)
ax=kwargs.get("ax", None)
assert isinstance(data_label, str), 'data_label must be of type str'
assert isinstance(contour_label, (str,list)), ('contour_label be of '
'type str')
assert x2_contour.ndim == x1_contour.ndim, ('contour must be of'
f'equal dimesion got {x2_contour.ndim} and {x1_contour.ndim}')
if x2_contour.ndim == 1:
x2_contour = x2_contour.reshape(-1,1)
x1_contour = x1_contour.reshape(-1,1)
N_contours = x2_contour.shape[1]
if contour_label != None:
if isinstance(contour_label, str):
contour_label = [contour_label]
N_c_labels = len(contour_label)
assert N_c_labels == N_contours, ('If specified, the '
'number of contour lables must be equal to number the '
f'number of contour years. Got {N_c_labels} and {N_contours}')
else:
contour_label = [None] * N_contours
for i in range(N_contours):
ax = _xy_plot(x1_contour[:,i], x2_contour[:,i],'-',
label=contour_label[i], ax=ax)
ax = plt.plot(x1, x2, 'bo', alpha=0.1,
label=data_label)
plt.legend(loc='lower right')
plt.xlabel(x_label)
plt.ylabel(y_label)
plt.tight_layout()
return ax
