def plot(self,
flush_cache=True,
norm=True,
clip_percentage=(0.02, 0.98),
clip_min_max=None,
log=False,
rescale_percentage=None,
bands=None,
ax=None,
title=None,
cmap=None,
figsize=None):
"""Plot the raster object.
Parameters
----------
flush_cache: bool.
Flush the cached processed result for quick plotting.
norm: bool, optional, default: True
Normalize the image for showing.
clip_percentage: tuple of float, optional, default: (0.02, 0.98)
The percentage to cut the data in head and tail e.g. (0.02, 0.98)
log: bool, optional, default: False
Get the log value of data to show the image.
rescale_percentage: float, optional
The percentage to recale (resize) the image for efficient showing.
bands: list, optional
Which bands to plot. Length of bands should be 1, 3 or 4.
If 3 bands is used, each of them will be defined as rgb bands.
If the forth band is used, it will be the opacity value.
ax: matplotlib.axes._subplots.AxesSubplot, optional
On which ax the raster will be plot.
title: str, optional
The title of the histgram.
cmap: str, optional
See Colormaps in Matplotlib https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html.
figsize: tuple, optional
Width and height of the histgram e.g.(10, 10).
"""
if bands is None:
bands = [0, 1, 2] if self.bands >= 3 else [0]
assert type(bands) is list, "type of bands should be list"
assert len(bands) in [1, 3, 4], "length of bands should be 1, 3 or 4"
if (self.cache_data_for_plot is None) or flush_cache:
# reshape to valid shape for matplotlib
if len(bands) == 1:
data = self.data[:, :, bands[0]]
else:
data = self.data[:, :, bands]
# deal with no data
data = data.astype(np.float)
data[data == self.no_data_value] = np.nan
# detect single value
if len(np.unique(data[~np.isnan(data)])) == 1:
norm = False
log = False
clip_percentage = None
# clip_percentage & clip_min_max
if clip_percentage is not None:
assert not (
clip_percentage is not None and clip_min_max is not None
), "should not set clip_percentage and clip_min_max at the same time!"
assert len(
clip_percentage) == 2, "clip_percentage two element tuple"
data = tgp.Normalizer().clip_by_percentage(
data, clip_percentage=clip_percentage)
elif clip_min_max is not None:
assert len(clip_min_max) == 2, "clip_min_max two element tuple"
data = tgp.Normalizer().clip_by_min_max(data,
min_max=clip_min_max)
# log
if log:
if data[~np.isnan(data)].min() < 0:
data -= data[~np.isnan(data)].min()
data[~np.isnan(data)] = np.log(data[~np.isnan(data)] + 10**-6)
# normalize
if norm:
data = tgp.Normalizer().fit_transform(data)
if rescale_percentage is not None:
import cv2
data = cv2.resize(data, (int(self.cols * rescale_percentage),
int(self.rows * rescale_percentage)))
self.cache_data_for_plot = data
else:
data = self.cache_data_for_plot
# flip xy
c, a, b, f, d, e = self.geo_transform
# lng = a * col + b * row + c
# lat = d * col + e * row + f
# a = d(lng) / d(col)
# b = d(lng) / d(row)
# d = d(lat) / d(col)
# e = d(lat) / d(row)
if (np.abs(a) > np.abs(d)) and (np.abs(e) > np.abs(b)):
lng_res, lat_res = a, e
elif (np.abs(d) > np.abs(a)) and (
np.abs(b) > np.abs(e)
): # if d > a, 1 col move contribute more on lat, less on lng
data = np.flipud(np.rot90(data))
lng_res, lat_res = d, b
else:
assert False, "not acceptable geotransform"
if lng_res < 0: # general lng_res > 0
data = np.fliplr(data)
if lat_res > 0: # general lat_res < 0
data = np.flipud(data)
# plotting
if ax is not None:
img = ax.imshow(data, extent=self.extent_for_plot, cmap=cmap)
ax.set_title(title)
else:
if figsize is not None:
plt.figure(figsize=figsize)
img = plt.imshow(data, extent=self.extent_for_plot, cmap=cmap)
plt.title(title)
plt.show()
return img
def hist(self,
norm=False,
clip_percentage=None,
log=False,
bands=None,
ax=None,
title=None,
figsize=None):
"""plot digital value histgram (distribution) of raster object.
Parameters
----------
norm: bool, optional, default: False
Normalize the image for showing.
clip_percentage: tuple of float, optional
The percentage to cut the data in head and tail e.g. (0.02, 0.98)
log: bool, optional, default: False
Use the log value to plot the histgram.
bands: list, optional
Which bands to plot. if bands==None, use values from all bands.
ax: matplotlib.axes._subplots.AxesSubplot, optional
On which ax the raster will be plot.
title: str, optional
The title of the histgram.
figsize: tuple, optional
Width and height of the histgram e.g.(10, 10).
"""
if bands is None:
data = self.data[self.data != self.no_data_value].flatten()
else:
data = self.data[:, :, bands]
data = data[data != self.no_data_value].flatten()
# clip_percentage
if clip_percentage is not None:
assert len(
clip_percentage) == 2, "clip_percentage two element tuple"
idx_st = int(len(data.flatten()) * clip_percentage[0])
idx_end = int(len(data.flatten()) * clip_percentage[1])
X_sorted = np.sort(data.flatten())
data_min = X_sorted[idx_st]
data_max = X_sorted[idx_end]
data[data < data_min] = data_min
data[data > data_max] = data_max
# log
if log:
data = np.log(data)
# normalize
if norm:
data = tgp.Normalizer().fit_transform(
data, clip_percentage=clip_percentage)
if ax is not None:
ax.hist(data)
ax.set_title(title)
else:
if figsize is not None:
plt.figure(figsize=figsize)
plt.hist(data)
plt.title(title)
plt.show()