"""
Plotting routines and classes for fuzzy comparison maps
"""
from .prepro import *
[docs]def read_raster(raster_path):
"""Opens a raster using rasterio
Args:
raster_path (str): directory and name of a raster
Returns:
``ndarray``: a numpy array of the raster
"""
with rio.open(raster_path) as src:
raster_np = src.read(1, masked=True)
nodatavalue = src.nodata # storing nodatavalue of raster
meta = src.meta.copy()
print('Number of active cells (non-masked) of raster ',
raster_path, ': ', np.ma.count(raster_np))
return raster_np, nodatavalue, meta, meta['crs'], meta['dtype']
[docs]class RasterDataPlotter:
"""
Class of raster for plotting
:param path (str): path of the raster to be plotted
"""
def __init__(self, path):
self.path = path
[docs] def make_hist(self, legendx, legendy, fontsize, output_file, figsize, set_ylim=None, set_xlim=None):
""" Creates a histogram of numerical raster
:param legendx (str): legend of the x axis of he histogram
:param legendy (str): legend of the y axis of he histogram
:param fontsize (int): size of the font
:param output_file (str): path for the output file
:param figsize (tuple): of integers, size of the width x height of the figure
:param set_ylim (float): set the maximum limit of the y axis
:param set_ylim (float): set the maximum limit of the x axis
:returns: saves the figure of the histogram
"""
plt.rcParams.update({'font.size': fontsize})
raster_np = read_raster(self.path)
fig, ax = plt.subplots(figsize=figsize)
_, bins, _ = ax.hist(raster_np[~raster_np.mask], bins=60)
if set_ylim is not None:
ax.set_ylim(set_ylim)
if set_xlim is not None:
ax.set_xlim(set_xlim)
np.savetxt('trial.csv', raster_np[~raster_np.mask], delimiter=',')
plt.xlabel(legendx)
plt.ylabel(legendy)
# plt.title(title)
plt.grid(True)
plt.subplots_adjust(left=0.17, bottom=0.15)
# Plot line with data mean (Sfuzzy)
plt.axvline(raster_np.mean(), color='k',
linestyle='dashed', linewidth=1)
min_ylim, max_ylim = plt.ylim()
plt.text(raster_np.mean() * 0.70, max_ylim * 0.9,
'Sfuzzy: {:.4f}'.format(raster_np.mean()))
# Save fig
plt.savefig(output_file, dpi=300)
plt.clf()
[docs] def plot_continuous_w_window(self, output_file, xy, width, height, bounds, cmap=None, list_colors=None):
""" Create a figure of a raster with a zoomed window
:param output_file: path, file path of the figure
:param xy (tuple): ``(x,y)`` origin of the zoomed window, the upper left corner
:param width (int): width (number of cells) of the zoomed window
:param height (int): height (number of cells) of the zoomed window
:param bounds (list): of float, limits for each color of the colormap
:param cmap (str): optional, colormap to plot the raster
:param list_colors (list): of colors (str), optional, as alternative to using a colormap
:returns None: saves the figure of the raster
"""
# xy: upper left corner from the lower left corner of the picture
raster_np = read_raster(self.path)
print('Raster has size: ', raster_np.shape)
fig, ax = plt.subplots(1, 2, figsize=(10, 8))
fig.tight_layout()
# Creates a colormap based on the given list_colors, if the cmap is not given
if cmap is None and list_colors is not None:
cmap = colors.ListedColormap(list_colors)
elif cmap is not None and list_colors is None:
pass
else:
print('Error: Insuficient number of arguments')
norm = colors.BoundaryNorm(bounds, cmap.N)
ax[0].imshow(raster_np, cmap=cmap, norm=norm)
rectangle = patches.Rectangle(xy, width, height, fill=False)
ax[0].add_patch(rectangle)
plt.setp(ax, xticks=[], yticks=[])
# Plot Patch
box_np = raster_np[xy[1]: xy[1] + height, xy[0]: xy[0] + width]
im = ax[1].imshow(box_np, cmap=cmap, norm=norm)
# ax[1].axis('off')
cbar = ep.colorbar(im, pad=0.3, size='5%')
cbar.ax.tick_params(labelsize=20)
fig.savefig(output_file, dpi=600, bbox_inches='tight')
[docs] def plot_continuous_raster(self, output_file, cmap, vmax=np.nan, vmin=np.nan, box=True):
"""Creates a figure of a continuous valued raster
:param output_file: path, file path of the figure
:param cmap (str): colormap to plot the raster
:param vmax (float): optional, value maximum of the scale, this value is used in the normalization of the colormap
:param vmin (float): optional, value minimum of the scale, this value is used in the normalization of the colormap
:param box: boolean, if False it sets off the frame of the picture
:returns: saves the figure of the raster
"""
raster_np = read_raster(self.path)
fig1, ax1 = plt.subplots(figsize=(6, 8), frameon=False)
# norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
if np.isfinite(vmax) and np.isfinite(vmin):
im1 = ax1.imshow(raster_np, cmap=cmap, vmax=vmax, vmin=vmin)
else:
im1 = ax1.imshow(raster_np, cmap=cmap,
vmax=raster_np.max(), vmin=raster_np.min())
fig1.tight_layout()
plt.setp(ax1)
cbar = ep.colorbar(im1, pad=0.3, size='5%')
cbar.ax.tick_params(labelsize=15)
if not box:
ax1.axis('off')
fig1.savefig(output_file, dpi=200, bbox_inches='tight')
[docs] def plot_categorical_raster(self, output_file, labels, cmap, box=True):
"""Creates a figure of a categorical raster
:param output_file: path, file path of the figure
:param labels (list): of strings, labels (i.e., titles)for the categories
:param cmap (str): colormap to plot the raster
:param box: boolean, if False it sets off the frame of the picture
:returns: saves the figure of the raster
"""
raster_np = read_raster(self.path)
print('Classes identified in the raster: ', np.unique(raster_np))
# cmap = matplotlib.colors.ListedColormap(list_colors)
fig, ax = plt.subplots()
im = ax.imshow(raster_np, cmap=cmap)
ep.draw_legend(im, titles=labels)
ax.set_axis_off()
# plt.show()
if not box:
ax.axis('off')
fig.savefig(output_file, dpi=200, bbox_inches='tight')
[docs] def plot_categorical_w_window(self, output_file, labels, cmap, xy, width, height, box=True):
"""Creates a figure of a categorical raster with a zoomed window
:param output_file (str): file path of the figure
:param labels (list): of strings, labels (i.e., titles)for the categories
:param cmap (str): colormap to plot the raster
:param xy (tuple): (x,y), origin of the zoomed window, the upper left corner
:param width (int): width (number of cells) of the zoomed window
:param height (int): height (number of cells) of the zoomed window
:returns: saves the figure of the raster
"""
raster_np = read_raster(self.path)
print('Classes identified in the raster: ', np.unique(raster_np))
# cmap = matplotlib.colors.ListedColormap(list_colors)
fig, ax = plt.subplots(1, 2)
ax[0].imshow(raster_np, cmap=cmap)
rectangle = patches.Rectangle(xy, width, height, fill=False)
ax[0].add_patch(rectangle)
plt.setp(ax, xticks=[], yticks=[])
# Plot Patch
box_np = raster_np[xy[1]: xy[1] + height, xy[0]: xy[0] + width]
im = ax[1].imshow(box_np, cmap=cmap)
cbar = ep.draw_legend(im, titles=labels)
# cbar.ax[].tick_params(labelsize=20)
if not box:
ax.axis('off')
fig.savefig(output_file, dpi=700, bbox_inches='tight')
