def test_query_grid_on_button_press():
rmg = RasterModelGrid((5, 5))
imshow_grid(rmg, rmg.nodes, cmap="RdYlBu")
# Programmatically create an event near the grid center.
event = Event("simulated_event", gcf().canvas)
event.xdata = int(rmg.number_of_node_columns * 0.5)
event.ydata = int(rmg.number_of_node_rows * 0.5)
results = query_grid_on_button_press(event, rmg)
x_coord = results["grid location"]["x_coord"]
y_coord = results["grid location"]["x_coord"]
msg = "Items: Simulated matplotlib event and query results."
assert_equal(x_coord, event.xdata, msg)
assert_equal(y_coord, event.ydata, msg)
msg = "Items: Node ID and grid coordinates of simulated matplotlib event."
node = rmg.grid_coords_to_node_id(event.xdata, event.ydata)
assert_equal(results["node"]["ID"], node, msg)
def test_query_grid_on_button_press():
rmg = RasterModelGrid((5, 5))
imshow_grid(rmg, rmg.nodes, cmap='RdYlBu')
# Programmatically create an event near the grid center.
event = Event('simulated_event', gcf().canvas)
event.xdata = int(rmg.number_of_node_columns * 0.5)
event.ydata = int(rmg.number_of_node_rows * 0.5)
results = query_grid_on_button_press(event, rmg)
x_coord = results['grid location']['x_coord']
y_coord = results['grid location']['x_coord']
msg = 'Items: Simulated matplotlib event and query results.'
assert_equal(x_coord, event.xdata, msg)
assert_equal(y_coord, event.ydata, msg)
msg = 'Items: Node ID and grid coordinates of simulated matplotlib event.'
node = rmg.grid_coords_to_node_id(event.xdata, event.ydata)
assert_equal(results['node']['ID'], node, msg)
def test_query_grid_on_button_press():
rmg = RasterModelGrid((5, 5))
imshow_grid(rmg, rmg.nodes, cmap='RdYlBu')
# Programmatically create an event near the grid center.
event = Event('simulated_event', gcf().canvas)
event.xdata = int(rmg.number_of_node_columns * 0.5)
event.ydata = int(rmg.number_of_node_rows * 0.5)
results = query_grid_on_button_press(event, rmg)
x_coord = results['grid location']['x_coord']
y_coord = results['grid location']['x_coord']
msg = 'Items: Simulated matplotlib event and query results.'
assert_equal(x_coord, event.xdata, msg)
assert_equal(y_coord, event.ydata, msg)
msg = 'Items: Node ID and grid coordinates of simulated matplotlib event.'
node = rmg.grid_coords_to_node_id(event.xdata, event.ydata)
assert_equal(results['node']['ID'], node, msg)
def imshow_grid_at_node(grid, values, **kwds):
"""imshow_grid_at_node(grid, values, plot_name=None, var_name=None,
var_units=None, grid_units=None,
symmetric_cbar=False, cmap='pink',
limits=(values.min(), values.max()),
vmin=values.min(), vmax=values.max(),
allow_colorbar=True,
norm=[linear], shrink=1.,
color_for_closed='black',
color_for_background=None,
show_elements=False, output=None)
Prepare a map view of data over all nodes in the grid.
Data is plotted as cells shaded with the value at the node at its center.
Outer edges of perimeter cells are extrapolated. Closed elements are
colored uniformly (default black, overridden with kwd 'color_for_closed');
other open boundary nodes get their actual values.
*values* can be a field name, a regular array, or a masked array. If a
masked array is provided, masked entries will be treated as if they were
Landlab CLOSED_BOUNDARYs. Used together with the color_at_closed=None
keyword (i.e., "transparent"), this can allow for construction of overlay
layers in a figure (e.g., only defining values in a river network, and
overlaying it on another landscape).
Use matplotlib functions like xlim, ylim to modify your plot after calling
:func:`imshow_grid`, as desired.
Node coordinates are printed when a mouse button is pressed on a cell in
the plot.
This function happily works with both regular and irregular grids.
Parameters
----------
grid : ModelGrid
Grid containing the field to plot, or describing the geometry of the
provided array.
values : array_like, masked_array, or str
Node values, or a field name as a string from which to draw the data.
plot_name : str, optional
String to put as the plot title.
var_name : str, optional
Variable name, to use as a colorbar label.
var_units : str, optional
Units for the variable being plotted, for the colorbar.
grid_units : tuple of str, optional
Units for y, and x dimensions. If None, component will look to the
gri property `axis_units` for this information. If no units are
specified there, no entry is made.
symmetric_cbar : bool
Make the colormap symetric about 0.
cmap : str
Name of a colormap
limits : tuple of float
Minimum and maximum of the colorbar.
vmin, vmax: floats
Alternatives to limits.
allow_colorbar : bool
If True, include the colorbar.
colorbar_label : str or None
The string with which to label the colorbar.
norm : matplotlib.colors.Normalize
The normalizing object which scales data, typically into the interval
[0, 1]. Ignore in most cases.
shrink : float
Fraction by which to shrink the colorbar.
color_for_closed : str or None
Color to use for closed nodes (default 'black'). If None, closed
(or masked) nodes will be transparent.
color_for_background : color str or other color declaration, or None
Color to use for closed elements (default None). If None, the
background will be transparent, and appear white.
show_elements : bool
If True, and grid is a Voronoi, the faces will be plotted in black
along with just the colour of the cell, defining the cell outlines
(defaults False).
output : None, string, or bool
If None (or False), the image is sent to the imaging buffer to await
an explicit call to show() or savefig() from outside this function.
If a string, the string should be the path to a save location, and the
filename (with file extension). The function will then call
plt.savefig([string]) itself. If True, the function will call
plt.show() itself once plotting is complete.
"""
if isinstance(values, str):
values_at_node = grid.at_node[values]
else:
values_at_node = values.reshape((-1,))
if values_at_node.size != grid.number_of_nodes:
raise ValueError("number of values does not match number of nodes")
values_at_node = np.ma.masked_where(
grid.status_at_node == CLOSED_BOUNDARY, values_at_node
)
try:
shape = grid.shape
except AttributeError:
shape = (-1,)
_imshow_grid_values(grid, values_at_node.reshape(shape), **kwds)
if isinstance(values, str):
plt.title(values)
plt.gcf().canvas.mpl_connect(
"button_press_event", lambda event: query_grid_on_button_press(event, grid)
)
def imshow_grid_at_node(grid, values, **kwds):
"""imshow_grid_at_node(grid, values, plot_name=None, var_name=None,
var_units=None, grid_units=None,
symmetric_cbar=False, cmap='pink',
limits=(values.min(), values.max()),
vmin=values.min(), vmax=values.max(),
allow_colorbar=True,
norm=[linear], shrink=1.,
color_for_closed='black',
color_for_background=None,
show_elements=False, output=None)
Prepare a map view of data over all nodes in the grid.
Data is plotted as cells shaded with the value at the node at its center.
Outer edges of perimeter cells are extrapolated. Closed elements are
colored uniformly (default black, overridden with kwd 'color_for_closed');
other open boundary nodes get their actual values.
*values* can be a field name, a regular array, or a masked array. If a
masked array is provided, masked entries will be treated as if they were
Landlab CLOSED_BOUNDARYs. Used together with the color_at_closed=None
keyword (i.e., "transparent"), this can allow for construction of overlay
layers in a figure (e.g., only defining values in a river network, and
overlaying it on another landscape).
Use matplotlib functions like xlim, ylim to modify your plot after calling
:func:`imshow_grid`, as desired.
Node coordinates are printed when a mouse button is pressed on a cell in
the plot.
This function happily works with both regular and irregular grids.
Parameters
----------
grid : ModelGrid
Grid containing the field to plot, or describing the geometry of the
provided array.
values : array_like, masked_array, or str
Node values, or a field name as a string from which to draw the data.
plot_name : str, optional
String to put as the plot title.
var_name : str, optional
Variable name, to use as a colorbar label.
var_units : str, optional
Units for the variable being plotted, for the colorbar.
grid_units : tuple of str, optional
Units for y, and x dimensions. If None, component will look to the
gri property `axis_units` for this information. If no units are
specified there, no entry is made.
symmetric_cbar : bool
Make the colormap symetric about 0.
cmap : str
Name of a colormap
limits : tuple of float
Minimum and maximum of the colorbar.
vmin, vmax: floats
Alternatives to limits.
allow_colorbar : bool
If True, include the colorbar.
colorbar_label : str or None
The string with which to label the colorbar.
norm : matplotlib.colors.Normalize
The normalizing object which scales data, typically into the interval
[0, 1]. Ignore in most cases.
shrink : float
Fraction by which to shrink the colorbar.
color_for_closed : str or None
Color to use for closed nodes (default 'black'). If None, closed
(or masked) nodes will be transparent.
color_for_background : color str or other color declaration, or None
Color to use for closed elements (default None). If None, the
background will be transparent, and appear white.
show_elements : bool
If True, and grid is a Voronoi, the faces will be plotted in black
along with just the colour of the cell, defining the cell outlines
(defaults False).
output : None, string, or bool
If None (or False), the image is sent to the imaging buffer to await
an explicit call to show() or savefig() from outside this function.
If a string, the string should be the path to a save location, and the
filename (with file extension). The function will then call
plt.savefig([string]) itself. If True, the function will call
plt.show() itself once plotting is complete.
"""
if isinstance(values, str):
values_at_node = grid.at_node[values]
else:
values_at_node = values.reshape((-1, ))
if values_at_node.size != grid.number_of_nodes:
raise ValueError('number of values does not match number of nodes')
values_at_node = np.ma.masked_where(
grid.status_at_node == CLOSED_BOUNDARY, values_at_node)
try:
shape = grid.shape
except AttributeError:
shape = (-1, )
_imshow_grid_values(grid, values_at_node.reshape(shape), **kwds)
if isinstance(values, str):
plt.title(values)
plt.gcf().canvas.mpl_connect('button_press_event',
lambda event: query_grid_on_button_press(event, grid))
