def update_max(val):
new_max = slider_max.val
print 'Changed max: %.2f' % new_max
# Recompute interpolation
data_limited_ = np.ma.masked_greater(parameters['data'], new_max)
parameters['data_interpol'] = utils_interpolate.interpolate_data_2d(parameters['all_points'], data_limited_, parameters['param1_space_int'], parameters['param2_space_int'], parameters['interpolation_numpoints'], parameters['interpolation_method'], parameters['mask_when_nearest'], parameters['mask_x_condition'], parameters['mask_y_condition'])
# clean figure
# parameters['ax_handle'].get_figure().subplots_adjust(right=1.1)
# parameters['ax_handle'].get_figure().clf()
__plot__(parameters, new_max)
parameters['ax_handle'].get_figure().canvas.draw()
def contourf_interpolate_data_interactive_maxvalue(all_points, data, xlabel='', ylabel='', title='', interpolation_numpoints=200, interpolation_method='linear', mask_when_nearest=True, contour_numlevels=20, show_scatter=True, show_colorbar=True, fignum=None, ax_handle=None, mask_x_condition=None, mask_y_condition=None, log_scale=False, mask_smaller_than=None, mask_greater_than=None, show_slider=True):
'''
Take (x,y) and z tuples, construct an interpolation with them and plot them nicely.
all_points: Nx2
data: Nx1
mask_when_nearest: trick to hide points outside the convex hull of points even when using 'nearest' method
'''
assert all_points.shape[1] == 2, "Give a Nx2 matrix for all_points"
# Construct the interpolation
param1_space_int = np.linspace(all_points[:, 0].min(), all_points[:, 0].max(), interpolation_numpoints)
param2_space_int = np.linspace(all_points[:, 1].min(), all_points[:, 1].max(), interpolation_numpoints)
data_interpol = utils_interpolate.interpolate_data_2d(all_points, np.ma.masked_greater(data, data.max()), param1_space_int=param1_space_int, param2_space_int=param2_space_int, interpolation_numpoints=interpolation_numpoints, interpolation_method=interpolation_method, mask_when_nearest=mask_when_nearest, mask_x_condition=mask_x_condition, mask_y_condition=mask_y_condition, mask_smaller_than=mask_smaller_than, mask_greater_than=mask_greater_than)
parameters = locals()
# Plot it
def __plot__(parameters, max_value, plot_min=True):
# Construct the figure
if parameters['ax_handle'] is None:
f = plt.figure()
else:
f = parameters['ax_handle'].get_figure()
f.clf()
parameters['ax_handle'] = f.add_subplot(111)
if show_slider:
f.subplots_adjust(bottom=0.25)
if log_scale:
cs = parameters['ax_handle'].contourf(parameters['param1_space_int'], parameters['param2_space_int'], parameters['data_interpol'], parameters['contour_numlevels'], locator=plttic.LogLocator()) # cmap=plt.cm.jet
else:
cs = parameters['ax_handle'].contourf(parameters['param1_space_int'], parameters['param2_space_int'], parameters['data_interpol'], parameters['contour_numlevels']) # cmap=plt.cm.jet
parameters['ax_handle'].set_xlabel(parameters['xlabel'])
parameters['ax_handle'].set_ylabel(parameters['ylabel'])
parameters['ax_handle'].set_title(parameters['title'])
if show_scatter:
parameters['ax_handle'].scatter(parameters['all_points'][:, 0], parameters['all_points'][:, 1], marker='o', c='b', s=5)
if plot_min:
index_min = np.argmin(parameters['data'])
parameters['ax_handle'].scatter(parameters['all_points'][index_min, 0], parameters['all_points'][index_min, 1], marker='o', c='r', s=15)
parameters['ax_handle'].set_xlim(parameters['param1_space_int'].min(), parameters['param1_space_int'].max())
parameters['ax_handle'].set_ylim(parameters['param2_space_int'].min(), parameters['param2_space_int'].max())
if parameters['show_colorbar']:
parameters['ax_handle'].get_figure().colorbar(cs)
### Add interactive slider
if show_slider:
# axcolor = 'lightgoldenrodyellow'
ax_slider_max = plt.axes([0.1, 0.1, 0.75, 0.04])
slider_max = Slider(ax_slider_max, 'Max', parameters['data'].min(), parameters['data'].max(), valinit=max_value)
# parameters['ax_handle'].get_figure().sca(parameters['ax_handle'])
def update_max(val):
new_max = slider_max.val
print 'Changed max: %.2f' % new_max
# Recompute interpolation
data_limited_ = np.ma.masked_greater(parameters['data'], new_max)
parameters['data_interpol'] = utils_interpolate.interpolate_data_2d(parameters['all_points'], data_limited_, parameters['param1_space_int'], parameters['param2_space_int'], parameters['interpolation_numpoints'], parameters['interpolation_method'], parameters['mask_when_nearest'], parameters['mask_x_condition'], parameters['mask_y_condition'])
# clean figure
# parameters['ax_handle'].get_figure().subplots_adjust(right=1.1)
# parameters['ax_handle'].get_figure().clf()
__plot__(parameters, new_max)
parameters['ax_handle'].get_figure().canvas.draw()
slider_max.on_changed(update_max)
## Change mouse over behaviour
def report_pixel(x_mouse, y_mouse, format="%.2f"):
# Extract loglik at that position
try:
x_i = int(parameters['param2_space_int'].size*x_mouse)
y_i = int(parameters['param1_space_int'].size*y_mouse)
x_display = parameters['param2_space_int'][x_i]
y_display = parameters['param1_space_int'][y_i]
return ("x=%.2f y=%.2f value="+format) % (x_display, y_display, parameters['data_interpol'][y_i, x_i])
except:
return ""
parameters['ax_handle'].format_coord = report_pixel
## Change mouse click behaviour
def onclick(event):
# print 'button=%d, x=%d, y=%d, xdata=%f, ydata=%f'%(event.button, event.x, event.y, event.xdata, event.ydata)
report_str = report_pixel(event.xdata, event.ydata, format="%f")
if report_str:
print report_str
parameters['ax_handle'].get_figure().canvas.mpl_connect('button_press_event', onclick)
parameters['ax_handle'].get_figure().canvas.draw()
return parameters['ax_handle']
ax_handle = __plot__(parameters, data.max())
return ax_handle