def _rdm_colorbar(
mappable: matplotlib.cm.ScalarMappable = None,
fig: matplotlib.figure.Figure = None,
ax: Axes = None,
title: str = None,
) -> matplotlib.colorbar.Colorbar:
"""_rdm_colorbar. Add vertically-oriented, small colorbar to rdm figure. Used
internally by show_rdm.
Args:
mappable (matplotlib.cm.ScalarMappable): Typically plt.imshow instance.
fig (matplotlib.figure.Figure): Matplotlib figure handle.
ax (matplotlib.axes._axes.Axes): Matplotlib axis handle. plt.gca() by default.
title (str): Title string for the colorbar (positioned top, left aligned).
Returns:
matplotlib.colorbar.Colorbar: Matplotlib handle.
"""
cb = fig.colorbar(
mappable=mappable,
ax=ax,
shrink=0.25,
aspect=5,
ticks=matplotlib.ticker.LinearLocator(numticks=3),
)
cb.ax.set_title(title, loc="left", fontdict=dict(fontweight="normal"))
return cb
def plot_correlations(
fig: matplotlib.figure.Figure,
ax: matplotlib.axes.Axes,
r2: float,
slope: float,
y_inter: float,
corr_vals: np.ndarray,
vis_vals: np.ndarray,
scale_factor: Union[float, int],
corr_bname: str,
vis_bname: str,
odir: Union[Path, str],
):
"""
Plot the correlations between NIR band and the visible bands for
the Hedley et al. (2005) sunglint correction method
Parameters
----------
fig : matplotlib.figure object
Reusing a matplotlib.figure object to avoid the creation many
fig instantances
ax : matplotlib.axes._subplots object
Reusing the axes object
r2 : float
The correlation coefficient squared of the linear regression
between NIR and a VIS band
slope : float
The slope/gradient of the linear regression between NIR and
a VIS band
y_inter : float
The intercept of the linear regression between NIR and a
VIS band
corr_vals : numpy.ndarray
1D array containing the NIR values from the ROI
vis_vals : numpy.ndarray
1D array containing the VIS values from the ROI
scale_factor : int or None
The scale factor used to convert integers to reflectances
that range [0...1]
corr_bname : str
The NIR band number
vis_bname : str
The VIS band number
odir : str
Directory where the correlation plots are saved
"""
# clear previous plot
ax.clear()
# ----------------------------------- #
# Create a unique cmap for hist2d #
# ----------------------------------- #
ncolours = 256
# get the jet colormap
colour_array = plt.get_cmap("jet")(range(ncolours)) # 256 x 4
# change alpha values
# e.g. low values have alpha = 1, high values have alpha = 0
# color_array[:,-1] = np.linspace(1.0,0.0,ncolors)
# e.g. low values have alpha = 0, high values have alpha = 1
# color_array[:,-1] = np.linspace(0.0,1.0,ncolors)
# We want only the first few colours to have low alpha
# as they would represent low density [meshgrid] bins
# which we are not interested in, and hence would want
# them to appear as a white colour (alpha ~ 0)
num_alpha = 25
colour_array[0:num_alpha, -1] = np.linspace(0.0, 1.0, num_alpha)
colour_array[num_alpha:, -1] = 1
# create a colormap object
cmap = LinearSegmentedColormap.from_list(name="jet_alpha",
colors=colour_array)
# ----------------------------------- #
# Plot density using np.histogram2d #
# ----------------------------------- #
xbin_low, xbin_high = np.percentile(corr_vals, (1, 99),
interpolation="linear")
ybin_low, ybin_high = np.percentile(vis_vals, (1, 99),
interpolation="linear")
nbins = [int(xbin_high - xbin_low), int(ybin_high - ybin_low)]
bin_range = [[int(xbin_low), int(xbin_high)],
[int(ybin_low), int(ybin_high)]]
hist2d, xedges, yedges = np.histogram2d(x=corr_vals,
y=vis_vals,
bins=nbins,
range=bin_range)
# normalised hist to range [0...1] then rotate and flip
hist2d = np.flipud(np.rot90(hist2d / hist2d.max()))
# Mask zeros
hist_masked = np.ma.masked_where(hist2d == 0, hist2d)
# use pcolormesh to plot the hist2D
qm = ax.pcolormesh(xedges, yedges, hist_masked, cmap=cmap)
# create a colour bar axes within ax
cbaxes = inset_axes(
ax,
width="3%",
height="30%",
bbox_to_anchor=(0.37, 0.03, 1, 1),
loc="lower center",
bbox_transform=ax.transAxes,
)
# Add a colour bar inside the axes
fig.colorbar(
cm.ScalarMappable(cmap=cmap),
cax=cbaxes,
ticks=[0.0, 1],
orientation="vertical",
label="Point Density",
)
# ----------------------------------- #
# Plot linear regression line #
# ----------------------------------- #
x_range = np.array([xbin_low, xbin_high])
(ln, ) = ax.plot(
x_range,
slope * (x_range) + y_inter,
color="k",
linestyle="-",
label="linear regr.",
)
# ----------------------------------- #
# Format the figure #
# ----------------------------------- #
# add legend (top left)
lgnd = ax.legend(loc=2, fontsize=10)
# add annotation
ann_str = (r"$r^{2}$" + " = {0:0.2f}\n"
"slope = {1:0.2f}\n"
"y-inter = {2:0.2f}".format(r2, slope, y_inter))
ann = ax.annotate(ann_str,
xy=(0.02, 0.76),
xycoords="axes fraction",
fontsize=10)
# Add labels to figure
xlabel = f"Reflectance ({corr_bname})"
ylabel = f"Reflectance ({vis_bname})"
if scale_factor is not None:
if scale_factor > 1:
xlabel += " " + r"$\times$" + " {0}".format(int(scale_factor))
ylabel += " " + r"$\times$" + " {0}".format(int(scale_factor))
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
# plt.show(); sys.exit()
# Save figure
png_file = os.path.join(
odir, "Correlation_{0}_vs_{1}.png".format(corr_bname, vis_bname))
fig.savefig(png_file,
format="png",
bbox_inches="tight",
pad_inches=0.1,
dpi=300)
# delete all lines and annotations from figure,
# so it can be reused in the next iteration
qm.remove()
ln.remove()
ann.remove()
lgnd.remove()