def plot_rose(directions,
velocities,
width_dir,
width_vel,
metadata=None,
flood=None,
ebb=None):
"""
Creates a polar histogram. Direction angles from binned histogram must
be specified such that 0 degrees is north.
Parameters
----------
directions: array-like
Directions in degrees with 0 degrees specified as true north
velocities: array-like
Velocities in m/s
width_dir: float
Width of directional bins for histogram in degrees
width_vel: float
Width of velocity bins for histogram in m/s
metadata: dictonary
If provided needs keys ['name', 'lat', 'lon'] for plot title
and information box on plot
flood: float
Direction in degrees added to theta ticks
ebb: float
Direction in degrees added to theta ticks
Returns
-------
ax: figure
Water current rose plot
"""
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(flood, (int, float, type(None))), \
'flood must be of type int or float'
assert isinstance(ebb, (int, float, type(None))), \
'ebb must be of type int or float'
if flood:
assert flood >=0 and flood <=360,\
'flood must be between 0 and 360 degrees'
if ebb:
assert ebb >=0 and ebb <=360,\
'ebb must be between 0 and 360 degrees'
assert isinstance(width_dir, (int, float)), \
'width_dir must be of type int or float'
assert isinstance(width_vel, (int, float)), \
'width_vel must be of type int or float'
assert width_dir >=0 ,\
'width_dir must be greater than 0'
assert width_vel >=0 ,\
'width_vel must be greater than 0'
# Calculate the 2D histogram
H, dir_edges, vel_edges = _histogram(directions, velocities, width_dir,
width_vel)
# Determine number of bins
dir_bins = H.shape[0]
vel_bins = H.shape[1]
# Create the angles
thetas = np.arange(0, 2 * np.pi, 2 * np.pi / dir_bins)
# Initialize the polar polt
ax = _initialize_polar(metadata=metadata, flood=flood, ebb=ebb)
# Set bar color based on wind speed
colors = plt.cm.viridis(np.linspace(0, 1.0, vel_bins))
# Set the current speed bin label names
# Calculate the 2D histogram
labels = [
f'{i:.1f}-{j:.1f}' for i, j in zip(vel_edges[:-1], vel_edges[1:])
]
# Initialize the vertical-offset (polar radius) for the stacked bar chart.
r_offset = np.zeros(dir_bins)
for vel_bin in range(vel_bins):
# Plot fist set of bars in all directions
ax = plt.bar(thetas,
H[:, vel_bin],
width=(2 * np.pi / dir_bins),
bottom=r_offset,
color=colors[vel_bin],
label=labels[vel_bin])
# Increase the radius offset in all directions
r_offset = r_offset + H[:, vel_bin]
# Add the a legend for current speed bins
plt.legend(loc='best',
title='Velocity bins [m/s]',
bbox_to_anchor=(1.29, 1.00),
ncol=1)
# Get the r-ticks (polar y-ticks)
yticks = plt.yticks()
# Format y-ticks with units for clarity
rticks = [f'{y:.1f}%' for y in yticks[0]]
# Set the y-ticks
plt.yticks(yticks[0], rticks)
return ax
def plot_joint_probability_distribution(directions,
velocities,
width_dir,
width_vel,
metadata=None,
flood=None,
ebb=None):
"""
Creates a polar histogram. Direction angles from binned histogram must
be specified such that 0 is north.
Parameters
----------
directions: array-like
Directions in degrees with 0 degrees specified as true north
velocities: array-like
Velocities in m/s
width_dir: float
Width of directional bins for histogram in degrees
width_vel: float
Width of velocity bins for histogram in m/s
metadata: dictonary
If provided needs keys ['name', 'Lat', 'Lon'] for plot title
and information box on plot
flood: float
Direction in degrees added to theta ticks
ebb: float
Direction in degrees added to theta ticks
Returns
-------
ax: figure
Joint probability distribution
"""
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(flood, (int, float, type(None))), \
'flood must be of type int or float'
assert isinstance(ebb, (int, float, type(None))), \
'ebb must be of type int or float'
if flood:
assert flood >=0 and flood <=360,\
'flood must be between 0 and 360 degrees'
if ebb:
assert ebb >=0 and ebb <=360,\
'ebb must be between 0 and 360 degrees'
assert isinstance(width_dir, (int, float)), \
'width_dir must be of type int or float'
assert isinstance(width_vel, (int, float)), \
'width_vel must be of type int or float'
assert width_dir >=0 ,\
'width_dir must be greater than 0'
assert width_vel >=0 ,\
'width_vel must be greater than 0'
# Calculate the 2D histogram
H, dir_edges, vel_edges = _histogram(directions, velocities, width_dir,
width_vel)
# Initialize the polar polt
ax = _initialize_polar(metadata=metadata, flood=flood, ebb=ebb)
# Set the current speed bin label names
labels = [
f'{i:.1f}-{j:.1f}' for i, j in zip(vel_edges[:-1], vel_edges[1:])
]
# Set vel & dir bins to middle of bin except at ends
dir_bins = 0.5 * (dir_edges[1:] + dir_edges[:-1]) # set all bins to middle
vel_bins = 0.5 * (vel_edges[1:] + vel_edges[:-1])
# Reset end of bin range to edge of bin
dir_bins[0] = dir_edges[0]
vel_bins[0] = vel_edges[0]
dir_bins[-1] = dir_edges[-1]
vel_bins[-1] = vel_edges[-1]
# Interpolate the bins back to specific data points
z = _interpn((dir_bins, vel_bins),
H,
np.vstack([directions, velocities]).T,
method="splinef2d",
bounds_error=False)
# Plot the most probable data last
idx = z.argsort()
# Convert to radians and order points by probability
theta, r, z = directions.values[idx] * np.pi / 180., velocities.values[
idx], z[idx]
# Create scatter plot colored by probability density
sx = ax.scatter(theta, r, c=z, s=5, edgecolor=None)
# Create colorbar
plt.colorbar(sx, label='Joint Probability [%]')
# Get the r-ticks (polar y-ticks)
yticks = plt.yticks()
# Format y-ticks with units for clarity
yticks = [f'{y:.1f} $m/s$' for y in yticks[0]]
# Set the y-ticks
ax.set_yticklabels(yticks)
return ax