def test_colorbar_height(basic_image):
"""Test that the colorbar ax height matches the image axis height."""
f, ax = plt.subplots(figsize=(5, 5))
im = ax.imshow(basic_image, cmap="RdYlGn")
cb = ep.colorbar(im)
assert cb.ax.get_position().height == im.axes.get_position().height
plt.close(f)
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: string, 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')
def plot_algae_bloom(cha_sentinel_class,location):
cha_cat_names = ["Pure",
"Light Concentration",
"Low Concentration",
"Moderate Concentration",
"High Concentration"]
nbr_colors = ["g", "yellowgreen", "peachpuff", "coral", "maroon"]
nbr_cmap = ListedColormap(nbr_colors)
# Plot the data with a custom legend
fig, ax = plt.subplots(figsize=(30, 20))
im = ax.imshow(cha_sentinel_class.reshape(cha_sentinel_class.shape[1:3]), cmap=nbr_cmap)
ax.set_title("chlorophyll a",
fontsize=16)
cbar = ep.colorbar(im)
cbar.set_ticks(np.unique(cha_sentinel_class))
cbar.set_ticklabels(cha_cat_names)
# Turn off ticks
ax.set_axis_off()
#plt.show()
path = 'static/img/algaeresult/algae_plot_' + location + '.png'
plt.savefig(path)
def plot_colormap(dnbr_sentinel_class,location):
dnbr_cat_names = ["Enhanced Regrowth",
"Unburned",
"Low Severity",
"Moderate Severity",
"High Severity"]
nbr_colors = ["g", "yellowgreen", "peachpuff", "coral", "maroon"]
nbr_cmap = ListedColormap(nbr_colors)
# Plot the data with a custom legend
fig, ax = plt.subplots(figsize=(10, 10))
im = ax.imshow(dnbr_sentinel_class.reshape(dnbr_sentinel_class.shape[:2]), cmap=nbr_cmap)
ax.set_title("Sentinel dNBR",
fontsize=16)
cbar = ep.colorbar(im)
cbar.set_ticks(np.unique(dnbr_sentinel_class))
cbar.set_ticklabels(dnbr_cat_names)
# Turn off ticks
ax.set_axis_off()
path = 'static/img/fireresult/fire_plot_' + location + '.png'
plt.savefig(path)
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: integer, width (number of cells) of the zoomed window
:param height: integer, height (number of cells) of the zoomed window
:param bounds: list of float, limits for each color of the colormap
:param cmap: string, optional, colormap to plot the raster
:param list_colors: list of colors (str), optional, as alternative to using a colormap
:returns: 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 = matplotlib.colors.ListedColormap(list_colors)
elif cmap is not None and list_colors is None:
pass
else:
print('Error: Insuficient number of arguments')
norm = matplotlib.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')
def test_colorbar_raises_value_error():
"""Test that a non matbplotlib axis object raises an value error."""
with pytest.raises(AttributeError, match="requires a matplotlib"):
ep.colorbar(list())
plt.close()
# -
# This is using a helper function from earthpy to create the mask so we can plot it
# You don't need to do this in your workflow as you can perform the mask in one step
# But we have it here for demonstration purposes
cl_mask = em._create_mask(landsat_qa, all_masked_values)
np.unique(cl_mask)
# Below is the plot of the reclassified raster mask created from the `_create_mask` helper function.
# + {"caption": "Landsat image in which the masked pixels (cloud) are rendered in light purple.", "tags": ["hide"]}
fig, ax = plt.subplots(figsize=(12, 8))
im = ax.imshow(cl_mask, cmap=plt.cm.get_cmap('tab20b', 2))
cbar = ep.colorbar(im)
cbar.set_ticks((0.25, .75))
cbar.ax.set_yticklabels(["Clear Pixels", "Cloud / Shadow Pixels"])
ax.set_title("Landsat Cloud Mask | Light Purple Pixels will be Masked")
ax.set_axis_off()
plt.show()
# -
# ## What Does the Metadata Tell You?
#
# You just explored two layers that potentially have information about cloud cover.
# However what do the values stored in those rasters mean? You can refer to the
# metadata provided by USGS to learn more about how
# each layer in your landsat dataset are both stored and calculated.