import numpy as np
def plot(
self,
new_plot: bool = False,
show_series: bool = False,
show_cost: bool = True,
cost_cmap: str = "summer",
args_path: dict = {},
args_cost: dict = {},
args_series1: dict = {},
args_series2: dict = {},
):
"""
Plot the warp path.
Parameters
----------
new_plot
Boolean value indicating whether to spawn a new figure.
show_series
Boolean value indicating whether to additionally plot the two time series.
Series1 will be plotted below the cost matrix and series2 to the left of the cost matrix.
show_cost
Boolean value indicating whether to additionally plot the cost matrix.
Cost Matrix will be displayed as a heatmap.
Useful for visualizing the effect of the window function.
cost_cmap
Colormap style for cost matrix plot
args_path
Some keyword arguments to pass to `plot()` function for warp path
args_cost
Some keyword arguments to pass to `imshow` function for cost matrix
args_series1
Some keyword arguments to pass to `plot()` method for series1
args_series2
Some keyword arguments to pass to `plot()` method for series2
"""
from matplotlib import pyplot as plt
if new_plot:
fig = plt.figure()
else:
fig = plt.gcf()
if show_series:
gs = fig.add_gridspec(2, 2, width_ratios=[0.4, 0.6], height_ratios=[0.6, 0.4])
warp = fig.add_subplot(gs[0, 1])
left = fig.add_subplot(gs[0, 0])
bottom = fig.add_subplot(gs[1, 1])
else:
warp = plt.gca()
warp.title.set_text("Cost Matrix")
warp.set_xlim([0, self.n])
warp.set_ylim([0, self.m])
warp.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
if show_cost:
cost_matrix = self.cost.to_dense()
cost_matrix = np.transpose(cost_matrix[1:, 1:])
warp.imshow(
cost_matrix,
cmap=cost_cmap,
interpolation="none",
origin="lower",
extent=[0, self.n, 0, self.m],
**args_cost,
)
show_path = True
if show_path:
path = self.path()
i_coords = path[:, 0] + 0.5
j_coords = path[:, 1] + 0.5
warp.plot(i_coords, j_coords, **args_path)
if show_series:
self.series1.plot(ax=bottom, **args_series1)
self.series2.plot(ax=left, y=self.series2._time_dim, **args_series2)
bottom.set_xlim([self.series1.start_time(), self.series1.end_time()])
left.set_ylim([self.series2.start_time(), self.series2.end_time()])
bottom.legend()
bottom.set_title("")
left.legend()
left.set_title("")
warp.set_aspect("auto")
def plot_alignment(
self,
new_plot: bool = False,
series1_y_offset: float = 0,
series2_y_offset: float = 0,
components: tuple[str | int, str | int] = (0, 0),
args_line: dict = {},
args_series1: dict = {},
args_series2: dict = {},
):
"""
Plots the uni-variate component of each series,
with lines between them indicating the alignment selected by the DTW algorithm.
Parameters
----------
new_plot
whether to spawn a new Figure
series2_y_offset
offset series1 vertically for ease of viewing.
series1_y_offset
offset series2 vertically for ease of viewing.
components
Tuple of component index for series1 and component index for series2.
args_line
Some keywords arguments to pass to `plot()` method for line
args_series1
Some keyword arguments to pass to `plot()` method for series1
args_series2
Some keyword arguments to pass to `plot()` method for series2
"""
import xarray as xr
series1 = self.series1
series2 = self.series2
(component1, component2) = components
if not series1.is_univariate:
series1 = series1.univariate_component(component1)
if not series2.is_univariate:
series2 = series2.univariate_component(component2)
series1 += series1_y_offset
series2 += series2_y_offset
xa1 = series1.data_array(copy=False)
xa2 = series2.data_array(copy=False)
path = self.path()
n = len(path)
time_dim1 = series1.time_dim
time_dim2 = series2.time_dim
x_coords1 = np.array(xa1[time_dim1], dtype=xa1[time_dim1].dtype)[path[:, 0]]
x_coords2 = np.array(xa2[time_dim2], dtype=xa2[time_dim2].dtype)[path[:, 1]]
y_coords1 = series1.univariate_values()[path[:, 0]]
y_coords2 = series2.univariate_values()[path[:, 1]]
if series1.has_datetime_index:
x_dtype = xa1[time_dim1].dtype
x_nan = np.datetime64("NaT")
else:
x_dtype = np.float64
x_nan = np.nan
x_coords = np.empty(n * 3, dtype=x_dtype)
y_coords = np.empty(n * 3, dtype=np.float64)
x_coords[0::3] = x_coords1
x_coords[1::3] = x_coords2
x_coords[2::3] = x_nan
y_coords[0::3] = y_coords1
y_coords[1::3] = y_coords2
y_coords[2::3] = np.nan
arr = xr.DataArray(y_coords, dims=["value"], coords={"value": x_coords})
xr.plot.line(arr, x="value", **args_line)
series1.plot(**args_series1)
series2.plot(**args_series2)
