import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
from mhkit.utils import convert_to_dataarray
def _xy_plot(x, y, fmt=".", label=None, xlabel=None, ylabel=None, title=None, ax=None):
"""
Base function to plot any x vs y data
Parameters
----------
x: array-like
Data for the x axis of plot
y: array-like
Data for y axis of plot
Returns
-------
ax : matplotlib.pyplot axes
"""
if ax is None:
plt.figure(figsize=(16, 8))
params = {
"legend.fontsize": "x-large",
"axes.labelsize": "x-large",
"axes.titlesize": "x-large",
"xtick.labelsize": "x-large",
"ytick.labelsize": "x-large",
}
plt.rcParams.update(params)
ax = plt.gca()
ax.plot(x, y, fmt, label=label, markersize=7)
ax.grid()
if label:
ax.legend()
if xlabel:
ax.set_xlabel(xlabel)
if ylabel:
ax.set_ylabel(ylabel)
if title:
ax.set_title(title)
plt.tight_layout()
return ax
[docs]
def plot_flow_duration_curve(D, F, label=None, ax=None):
"""
Plots discharge vs exceedance probability as a Flow Duration Curve (FDC)
Parameters
------------
D: array-like
Discharge [m/s] indexed by time
F: array-like
Exceedance probability [unitless] indexed by time
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 : matplotlib pyplot axes
"""
# Sort by F
temp = xr.Dataset(data_vars={"D": D, "F": F})
temp.sortby("F", ascending=False)
ax = _xy_plot(
temp["D"],
temp["F"],
fmt="-",
label=label,
xlabel="Discharge [$m^3/s$]",
ylabel="Exceedance Probability",
ax=ax,
)
plt.xscale("log")
return ax
[docs]
def plot_velocity_duration_curve(V, F, label=None, ax=None):
"""
Plots velocity vs exceedance probability as a Velocity Duration Curve (VDC)
Parameters
------------
V: array-like
Velocity [m/s] indexed by time
F: array-like
Exceedance probability [unitless] indexed by time
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 : matplotlib pyplot axes
"""
# Sort by F
temp = xr.Dataset(data_vars={"V": V, "F": F})
temp.sortby("F", ascending=False)
ax = _xy_plot(
temp["V"],
temp["F"],
fmt="-",
label=label,
xlabel="Velocity [$m/s$]",
ylabel="Exceedance Probability",
ax=ax,
)
return ax
[docs]
def plot_power_duration_curve(P, F, label=None, ax=None):
"""
Plots power vs exceedance probability as a Power Duration Curve (PDC)
Parameters
------------
P: array-like
Power [W] indexed by time
F: array-like
Exceedance probability [unitless] indexed by time
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 : matplotlib pyplot axes
"""
# Sort by F
temp = xr.Dataset(data_vars={"P": P, "F": F})
temp.sortby("F", ascending=False)
ax = _xy_plot(
temp["P"],
temp["F"],
fmt="-",
label=label,
xlabel="Power [W]",
ylabel="Exceedance Probability",
ax=ax,
)
return ax
[docs]
def plot_discharge_timeseries(Q, time_dimension="", label=None, ax=None):
"""
Plots discharge time-series
Parameters
------------
Q: array-like
Discharge [m3/s] indexed by time
time_dimension: string (optional)
Name of the xarray dimension corresponding to time. If not supplied,
defaults to the first dimension.
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 : matplotlib pyplot axes
"""
Q = convert_to_dataarray(Q)
if time_dimension == "":
time_dimension = list(Q.coords)[0]
ax = _xy_plot(
Q.coords[time_dimension].values,
Q,
fmt="-",
label=label,
xlabel="Time",
ylabel="Discharge [$m^3/s$]",
ax=ax,
)
return ax
[docs]
def plot_discharge_vs_velocity(D, V, polynomial_coeff=None, label=None, ax=None):
"""
Plots discharge vs velocity data along with the polynomial fit
Parameters
------------
D : array-like
Discharge [m/s] indexed by time
V : array-like
Velocity [m/s] indexed by time
polynomial_coeff: numpy polynomial
Polynomial coefficients, which can be computed using
`river.resource.polynomial_fit`. If None, then the polynomial fit is
not included int the plot.
ax : matplotlib axes object
Axes for plotting. If None, then a new figure with a single
axes is used.
Returns
---------
ax : matplotlib pyplot axes
"""
ax = _xy_plot(
D,
V,
fmt=".",
label=label,
xlabel="Discharge [$m^3/s$]",
ylabel="Velocity [$m/s$]",
ax=ax,
)
if polynomial_coeff:
x = np.linspace(D.min(), D.max())
ax = _xy_plot(
x,
polynomial_coeff(x),
fmt="--",
label="Polynomial fit",
xlabel="Discharge [$m^3/s$]",
ylabel="Velocity [$m/s$]",
ax=ax,
)
return ax
[docs]
def plot_velocity_vs_power(V, P, polynomial_coeff=None, label=None, ax=None):
"""
Plots velocity vs power data along with the polynomial fit
Parameters
------------
V : array-like
Velocity [m/s] indexed by time
P: array-like
Power [W] indexed by time
polynomial_coeff: numpy polynomial
Polynomial coefficients, which can be computed using
`river.resource.polynomial_fit`. If None, then the polynomial fit is
not included int the plot.
ax : matplotlib axes object
Axes for plotting. If None, then a new figure with a single
axes is used.
Returns
---------
ax : matplotlib pyplot axes
"""
ax = _xy_plot(
V,
P,
fmt=".",
label=label,
xlabel="Velocity [$m/s$]",
ylabel="Power [$W$]",
ax=ax,
)
if polynomial_coeff:
x = np.linspace(V.min(), V.max())
ax = _xy_plot(
x,
polynomial_coeff(x),
fmt="--",
label="Polynomial fit",
xlabel="Velocity [$m/s$]",
ylabel="Power [$W$]",
ax=ax,
)
return ax