def __init__(self,
on_interact=None,
output=None,
overwrite_previous_output=True,
feedback=False,
run=True,
action_kws={},
*args,
**kwargs):
super().__init__(on_interact=on_interact,
output=output,
overwrite_previous_output=overwrite_previous_output,
feedback=feedback,
action_kws=action_kws)
self.widget = widgets.ColorPicker(*args, **kwargs)
if run:
self.run()
def updateSubsets(self):
v = []
for sset in self.gluemanager.data.subsets:
kt = sset.label
color = sset.style.color
vc2 = widgets.Button(description=kt,
style=widgets.ButtonStyle(button_color=color),
disabled=False,
layout=widgets.Layout(
min_width='min-content',
max_width='min-content'))
vc3 = widgets.Button(description="",
style=widgets.ButtonStyle(button_color=color),
icon='remove',
disabled=False,
layout=widgets.Layout(width='40px'))
vc3.on_click(lambda event, this=self, value=[kt]: this.
deleteSubsetFromSelection(value))
icon = 'eye'
if hasattr(sset, 'disabled') and sset.disabled is True:
icon = 'eye-slash'
vc1 = widgets.Button(description="",
style=widgets.ButtonStyle(button_color=color),
icon=icon,
disabled=False,
layout=widgets.Layout(width='40px'),
value=True)
vc1.on_click(lambda event, this=self, value=kt, vc=vc1: this.
disableComponentFromSelection(value, vc))
vc4 = widgets.ColorPicker(description="",
concise=True,
value=sset.style.color,
layout=widgets.Layout(width='40px',
min_width=''))
vc4.observe(
lambda change, this=self, value=kt, vc=[vc1, vc2, vc3]: this.
changeSubsetColorFromSelection(value, change['new'], vc),
"value")
vv = widgets.HBox([vc1, vc2, vc3, vc4],
layout=widgets.Layout(width='auto'))
v.append(vv)
self.subsetsui.children = v
def orthoslices_3D(data, normalize=True, continuous_update=False, **kwargs):
if not isnotebook:
print('Function not suited for working outside of Jupyter notebooks!')
else:
get_ipython().magic('matplotlib notebook')
get_ipython().magic('matplotlib notebook')
e_range, x_range, y_range = data.shape
dmin = np.amin(data)
dmax = np.amax(data)
w_e = widgets.IntSlider(value=e_range // 2,
min=0,
max=e_range - 1,
step=1,
description='Energy:',
disabled=False,
continuous_update=continuous_update,
orientation='horizontal',
readout=True,
readout_format='d')
w_kx = widgets.IntSlider(value=x_range // 2,
min=0,
max=x_range - 1,
step=1,
description='kx:',
disabled=False,
continuous_update=continuous_update,
orientation='horizontal',
readout=True,
readout_format='d')
w_ky = widgets.IntSlider(value=y_range // 2,
min=0,
max=y_range - 1,
step=1,
description='ky:',
disabled=False,
continuous_update=continuous_update,
orientation='horizontal',
readout=True,
readout_format='d')
w_clim = widgets.FloatRangeSlider(value=[.1, .9],
min=0,
max=1,
step=0.001,
description='Contrast:',
disabled=False,
continuous_update=continuous_update,
orientation='horizontal',
readout=True,
readout_format='.1f')
w_cmap = widgets.Dropdown(options=cmaps,
value='terrain',
description='colormap:',
disabled=False)
w_bin = widgets.BoundedIntText(value=1,
min=1,
max=min(data.shape),
step=1,
description='resample:',
disabled=False)
w_interpolate = widgets.Checkbox(value=True,
description='Interpolate',
disabled=False)
w_grid = widgets.Checkbox(value=False, description='Grid', disabled=False)
w_trackers = widgets.Checkbox(value=True,
description='Trackers',
disabled=False)
w_trackercol = widgets.ColorPicker(concise=False,
description='tracker line color',
value='orange')
ui_pos = widgets.HBox([w_e, w_kx, w_ky])
ui_color = widgets.HBox([
widgets.VBox([w_clim, w_cmap]),
widgets.VBox([w_bin, w_interpolate, w_grid]),
widgets.VBox([w_trackers, w_trackercol]),
])
children = [ui_pos, ui_color]
tab = widgets.Tab(children=children, )
tab.set_title(0, 'data select')
tab.set_title(1, 'colormap')
figsize = kwargs.pop('figsize', (5, 5))
fig = plt.figure(figsize=figsize, **kwargs)
plt.tight_layout()
# [left, bottom, width, height]
# fig.locator_params(nbins=4)
# cbar_ax = fig.add_axes([.05,.4,.05,4], xticklabels=[], yticklabels=[])
# cbar_ax.yaxis.set_major_locator(plt.LinearLocator(5))
img_ax = fig.add_axes([.15, .4, .4, .4], xticklabels=[], yticklabels=[])
img_ax.xaxis.set_major_locator(plt.LinearLocator(5))
img_ax.yaxis.set_major_locator(plt.LinearLocator(5))
xproj_ax = fig.add_axes([.15, .1, .4, .28], xticklabels=[], yticklabels=[])
xproj_ax.set_xlabel('$k_x$')
xproj_ax.xaxis.set_major_locator(plt.LinearLocator(5))
yproj_ax = fig.add_axes([.57, .4, .28, .4], xticklabels=[], yticklabels=[])
yproj_ax.yaxis.set_label_position("right")
yproj_ax.set_ylabel('$k_y$')
yproj_ax.yaxis.set_major_locator(plt.LinearLocator(5))
for ax in [img_ax, yproj_ax, xproj_ax]: # ,cbar_ax]:
ax.tick_params(axis="both",
direction="in",
bottom=True,
top=True,
left=True,
right=True,
which='both')
clim_ = 0.01, .99
e_img = norm_img(data[data.shape[0] // 2, :, :])
y_img = norm_img(data[:, data.shape[1] // 2, :])
x_img = norm_img(data[:, :, data.shape[2] // 2].T)
e_plot = img_ax.imshow(
e_img,
cmap='terrain',
aspect='auto',
interpolation='gaussian',
clim=clim_,
) # origin='lower')
x_plot = yproj_ax.imshow(
x_img,
cmap='terrain',
aspect='auto',
interpolation='gaussian',
clim=clim_,
) # origin='lower')
y_plot = xproj_ax.imshow(
y_img,
cmap='terrain',
aspect='auto',
interpolation='gaussian',
clim=clim_,
) # origin='lower')
pe_x = img_ax.axvline(x_range / 2, c='orange')
pe_y = img_ax.axhline(y_range / 2, c='orange')
px_x = xproj_ax.axvline(x_range / 2, c='orange')
px_e = xproj_ax.axhline(e_range / 2, c='orange')
py_y = yproj_ax.axhline(y_range / 2, c='orange')
py_e = yproj_ax.axvline(e_range / 2, c='orange')
def update(e, kx, ky, clim, cmap, binning, interpolate, grid, trackers,
trackerscol):
if normalize:
e_img = norm_img(data[e, :, :][::binning, ::binning])
y_img = norm_img(data[:, ky, :][::binning, ::binning])
x_img = norm_img(data[:, :, kx][::binning, ::binning])
else:
e_img = data[e, :, :][::binning, ::binning]
y_img = data[:, ky, :][::binning, ::binning]
x_img = data[:, :, kx][::binning, ::binning]
for axis, plot, img in zip([img_ax, yproj_ax, xproj_ax],
[e_plot, x_plot, y_plot],
[e_img, x_img.T, y_img]):
plot.set_data(img)
plot.set_clim(clim)
plot.set_cmap(cmap)
axis.grid(grid)
if interpolate:
plot.set_interpolation('gaussian')
else:
plot.set_interpolation(None)
if trackers:
pe_x.set_xdata(kx)
pe_x.set_color(trackerscol)
pe_y.set_ydata(ky)
pe_y.set_color(trackerscol)
px_x.set_xdata(kx)
px_x.set_color(trackerscol)
px_e.set_ydata(e)
px_e.set_color(trackerscol)
py_y.set_ydata(ky)
py_y.set_color(trackerscol)
py_e.set_xdata(e)
py_e.set_color(trackerscol)
interactive_plot = interactive_output(
update, {
'e': w_e,
'kx': w_kx,
'ky': w_ky,
'clim': w_clim,
'cmap': w_cmap,
'binning': w_bin,
'interpolate': w_interpolate,
'grid': w_grid,
'trackers': w_trackers,
'trackerscol': w_trackercol,
})
display(interactive_plot, tab)
def orthoslices_4D(data,
axis_order=['E', 'kx', 'ky', 'kz'],
normalize=True,
continuous_update=True,
**kwargs):
if not isnotebook:
raise EnvironmentError(
'Function not suited for working outside of Jupyter notebooks!')
else:
get_ipython().magic('matplotlib notebook')
get_ipython().magic('matplotlib notebook')
assert len(
data.shape
) == 4, 'Data should be 4-dimensional, but data has {} dimensions'.format(
data.shape)
# make controls for data slicers
# slicers = []
# for shape, name in zip(data.shape, axis_order):
# slicers.append(widgets.IntSlider(value=shape // 2,
# min=0,
# max=shape - 1,
# step=1,
# description=name,
# disabled=False,
# continuous_update=False,
# orientation='horizontal',
# readout=True,
# readout_format='d'
# ))
e_range, x_range, y_range, z_range = data.shape
w_e = widgets.IntSlider(value=e_range // 2,
min=0,
max=e_range - 1,
step=1,
description='Energy:',
disabled=False,
continuous_update=continuous_update,
orientation='horizontal',
readout=True,
readout_format='d')
w_kx = widgets.IntSlider(value=x_range // 2,
min=0,
max=x_range - 1,
step=1,
description='kx:',
disabled=False,
continuous_update=continuous_update,
orientation='horizontal',
readout=True,
readout_format='d')
w_ky = widgets.IntSlider(value=y_range // 2,
min=0,
max=y_range - 1,
step=1,
description='ky:',
disabled=False,
continuous_update=continuous_update,
orientation='horizontal',
readout=True,
readout_format='d')
w_kz = widgets.IntSlider(value=z_range // 2,
min=0,
max=z_range - 1,
step=1,
description='kz:',
disabled=False,
continuous_update=continuous_update,
orientation='horizontal',
readout=True,
readout_format='d')
slicers = [w_e, w_kx, w_ky, w_kz]
ui_slicers = widgets.HBox(slicers)
# make controls for graphics appearance
w_clim = widgets.FloatRangeSlider(value=[.1, .9],
min=0,
max=1,
step=0.001,
description='Contrast:',
disabled=False,
continuous_update=True,
orientation='horizontal',
readout=True,
readout_format='.1f')
w_cmap = widgets.Dropdown(options=cmaps,
value='terrain',
description='colormap:',
disabled=False)
w_bin = widgets.BoundedIntText(value=1,
min=1,
max=min(data.shape),
step=1,
description='resample:',
disabled=False)
w_interpolate = widgets.Checkbox(value=True,
description='Interpolate',
disabled=False)
w_grid = widgets.Checkbox(value=False, description='Grid', disabled=False)
w_trackers = widgets.Checkbox(value=True,
description='Trackers',
disabled=False)
w_trackercol = widgets.ColorPicker(concise=False,
description='tracker line color',
value='orange')
ui_color = widgets.HBox([
widgets.VBox([w_clim, w_cmap]),
widgets.VBox([w_bin, w_interpolate, w_grid]),
widgets.VBox([w_trackers, w_trackercol]),
])
tab = widgets.Tab(children=[ui_slicers, ui_color], )
tab.set_title(0, 'Data slicing')
tab.set_title(1, 'Graphics')
figsize = kwargs.pop('figsize', (5, 5))
fig = plt.figure(figsize=figsize, **kwargs)
plt.tight_layout()
img_ax = fig.add_axes([.15, .4, .4, .4], xticklabels=[], yticklabels=[])
img_ax.xaxis.set_major_locator(plt.LinearLocator(5))
img_ax.yaxis.set_major_locator(plt.LinearLocator(5))
xproj_ax = fig.add_axes([.15, .1, .4, .28], xticklabels=[], yticklabels=[])
xproj_ax.set_xlabel('$k_x$')
xproj_ax.xaxis.set_major_locator(plt.LinearLocator(5))
yproj_ax = fig.add_axes([.57, .4, .28, .4], xticklabels=[], yticklabels=[])
yproj_ax.yaxis.set_label_position("right")
yproj_ax.set_ylabel('$k_y$')
yproj_ax.yaxis.set_major_locator(plt.LinearLocator(5))
for ax in [img_ax, yproj_ax, xproj_ax]: # ,cbar_ax]:
ax.tick_params(axis="both",
direction="in",
bottom=True,
top=True,
left=True,
right=True,
which='both')
clim_ = 0.01, .99
e_img = norm_img(data[data.shape[0] // 2, :, :, data.shape[3] // 2])
y_img = norm_img(data[:, data.shape[1] // 2, :, data.shape[3] // 2])
x_img = norm_img(data[:, :, data.shape[2] // 2, data.shape[3] // 2].T)
e_plot = img_ax.imshow(
e_img,
cmap='terrain',
aspect='auto',
interpolation='gaussian',
clim=clim_,
) # origin='lower')
x_plot = yproj_ax.imshow(
x_img,
cmap='terrain',
aspect='auto',
interpolation='gaussian',
clim=clim_,
) # origin='lower')
y_plot = xproj_ax.imshow(
y_img,
cmap='terrain',
aspect='auto',
interpolation='gaussian',
clim=clim_,
) # origin='lower')
pe_x = img_ax.axvline(x_range / 2, c='orange')
pe_y = img_ax.axhline(y_range / 2, c='orange')
px_x = xproj_ax.axvline(x_range / 2, c='orange')
px_e = xproj_ax.axhline(e_range / 2, c='orange')
py_y = yproj_ax.axhline(y_range / 2, c='orange')
py_e = yproj_ax.axvline(e_range / 2, c='orange')
def update(e, kx, ky, kz, clim, cmap, binning, interpolate, grid, trackers,
trackerscol):
if normalize:
e_img = norm_img(data[e, :, :, kz][::binning, ::binning])
y_img = norm_img(data[:, ky, :, kz][::binning, ::binning])
x_img = norm_img(data[:, :, kx, kz][::binning, ::binning])
else:
e_img = data[e, :, :, kz][::binning, ::binning]
y_img = data[:, ky, :, kz][::binning, ::binning]
x_img = data[:, :, kx, kz][::binning, ::binning]
for axis, plot, img in zip([img_ax, yproj_ax, xproj_ax],
[e_plot, x_plot, y_plot],
[e_img, x_img.T, y_img]):
plot.set_data(img)
plot.set_clim(clim)
plot.set_cmap(cmap)
axis.grid(grid)
if interpolate:
plot.set_interpolation('gaussian')
else:
plot.set_interpolation(None)
if trackers:
pe_x.set_xdata(kx)
pe_x.set_color(trackerscol)
pe_y.set_ydata(ky)
pe_y.set_color(trackerscol)
px_x.set_xdata(kx)
px_x.set_color(trackerscol)
px_e.set_ydata(e)
px_e.set_color(trackerscol)
py_y.set_ydata(ky)
py_y.set_color(trackerscol)
py_e.set_xdata(e)
py_e.set_color(trackerscol)
interactive_plot = interactive_output(
update, {
'e': w_e,
'kx': w_kx,
'ky': w_ky,
'kz': w_kz,
'clim': w_clim,
'cmap': w_cmap,
'binning': w_bin,
'interpolate': w_interpolate,
'grid': w_grid,
'trackers': w_trackers,
'trackerscol': w_trackercol,
})
display(interactive_plot, tab)
#Extract the renamed columns, and combine them into a new dataframe
data_intensity = pd.DataFrame((bike_traffic_df1['Celleneuve'], bike_traffic_df2['Lattes2'],bike_traffic_df3['Berracasa'],bike_traffic_df4['Lavérune'],bike_traffic_df5['Lattes1'],bike_traffic_df6['Vieille_poste'],bike_traffic_df7['Gerhardt'],bike_traffic_df8['Tanneurs'],bike_traffic_df9['Delmas1'],bike_traffic_df10['Delmas2']))
data_intensity=data_intensity.T
#Indexing neww data by 'end_of-day' (time series)
data_intensity['startday'] = time_improved
data_intensity = data_intensity.set_index(['startday'])
print(data_intensity )
#%%
from ipywidgets import widgets
color_picker = widgets.ColorPicker(
concise=True,
description='Background color:',
value='#efefef',
)
color_picker
color_buttons = widgets.ToggleButtons(
options=['blue', 'red', 'green','black'],
description='Color:',
)
color_buttons
#%%
def Intenity_visualisation( count_point ='latte1', day_month='d', efefef='red', start_date='2020-12-15', end_date='2021-04-01', color='black'):
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
def createPlotsPanel(self):
components = self.gluemanager.data.components
pixel_component_ids = self.gluemanager.data.pixel_component_ids
world_component_ids = self.gluemanager.data.world_component_ids
v = []
for k in components:
if k not in world_component_ids: #k not in pixel_component_ids and
kt = str(k)
color = self.gluemanager.data.get_component(k).color
color = re.match(GlueManagerWidget.r, color)
color = (int(color[1]), int(color[2]), int(color[3]))
color = '#%02x%02x%02x' % color
vc1 = widgets.Checkbox(value=False,
tooltip=kt,
indent=False,
layout=widgets.Layout(width='40px'))
vc2 = widgets.Button(
description=kt,
style=widgets.ButtonStyle(button_color=color),
disabled=False,
layout=widgets.Layout(min_width='min-content',
max_width='min-content'))
vc2.on_click(
lambda event, v=vc1: setattr(v, 'value', not v.value))
vc3 = widgets.ColorPicker(description="",
concise=True,
value=color,
layout=widgets.Layout(width='40px',
min_width=''))
vc3.observe(lambda change, this=self, value=kt, vc=[vc2]: this.
changeComponentColorFromSelection(
value, change['new'], vc),
"value")
vv = widgets.HBox([vc3, vc2, vc1],
layout=widgets.Layout(width='auto'))
v.append(vv)
self.dimensions = widgets.HBox(v,
layout=widgets.Layout(display='flex',
flex_flow='wrap'))
cr = widgets.Button(description='Create new visualization',
disabled=False,
button_style='',
tooltip='',
layout=widgets.Layout(width='100%'))
views = self.gluemanager.listPlots()
dd = widgets.Dropdown(options=views,
value=views[0],
disabled=False,
layout=widgets.Layout(width='auto'))
ss = widgets.Checkbox(
value=False,
description='Only subsets',
disabled=False,
indent=False,
)
tx = widgets.Text(value='',
placeholder='New_Visualization',
disabled=False,
layout=widgets.Layout(width='auto'))
hb1 = widgets.HBox([ss, cr], layout=widgets.Layout(width='auto'))
vb1 = widgets.VBox([self.dimensions, dd, tx, hb1],
layout=widgets.Layout(width='auto'))
from IPython.display import display
cr.on_click(lambda e, b=self.dimensions, this=self: this.createNewView(
e, dd, b, tx, ss))
return vb1
def draw_workflow(self, start_end, workflow, name):
assert isinstance(workflow, Workflow)
start_s = start_end["seconds"]["start"]
last_s = start_end["seconds"]["last"]
start_t = start_end["time"]["start"]
last_t = start_end["time"]["last"]
# 1. get main stacked axis list
main_stacked_results = []
main_stacked_results.append(("BLANK", "#eeeeee", [
(0, c.from_seconds) for c in chain.from_iterable(
s.intervals for s in workflow.start_group.iter_nxtgroups())
]))
for group in workflow.sort_topologically():
_name = group.desc
color = getcolor_byint(group.intervals[0], ignore_lr=True)
main_stacked_results.append(
(_name, color, [(c.from_seconds, c.to_seconds)
for c in group.intervals]))
# 2. calc main_x_list from main_stacked_results
types, colors, main_x_list, main_ys_list =\
_generate_stackeddata(main_stacked_results)
# 3. calc and check the arguments
x_start_default = 0
y_start_default = 0
x_end_default = last_s * 1.05
y_end_default = workflow.len_reqs
def _interact(x, y, selects, color):
with self._build_fig("workflowplot", name, figsize=(30, 6)) as fig:
ax = fig.add_subplot(1, 1, 1)
ax.set_xlabel("lapse (seconds)")
ax.set_ylabel("requests")
ax.set_xlim(x[0], x[1])
ax.set_ylim(y[0], y[1])
plot_colors = colors[:]
if color:
for s in selects:
plot_colors[s] = color
ax.annotate(start_t, xy=(0, 0), xytext=(0, 0))
ax.annotate(last_t, xy=(last_s, 0), xytext=(last_s, 0))
ax.plot([0, last_s], [0, 0], 'r*')
ax.stackplot(main_x_list,
*main_ys_list,
colors=plot_colors,
edgecolor="black",
linewidth=.1)
# ax.legend((mpatches.Patch(color=color) for color in colors), types)
if self.out_path:
_interact((x_start_default, x_end_default),
(y_start_default, y_end_default), [], None)
else:
from ipywidgets import widgets, interactive_output, fixed, Layout
from IPython.display import display
layout = Layout(width="99%")
w_lapse = widgets.FloatRangeSlider(
value=[x_start_default, x_end_default],
min=x_start_default,
max=x_end_default,
step=0.0001,
description='x-lapse:',
continuous_update=False,
readout_format='.4f',
layout=layout)
w_requests = widgets.IntRangeSlider(
value=[y_start_default, y_end_default],
min=y_start_default,
max=y_end_default,
description='y-requests:',
continuous_update=False,
layout=layout)
w_range = widgets.VBox([w_lapse, w_requests])
w_color = widgets.ColorPicker(concise=True,
description='Highlight:',
value='#ff40ff',
layout=layout)
options = [types[i] for i in range(1, len(types) - 1)]
dedup = defaultdict(lambda: -1)
for i, o in enumerate(options):
dedup[o] += 1
if dedup[o]:
options[i] = ("%s (%d)" % (options[i], dedup[o]))
options = [(v, i + 1) for i, v in enumerate(options)]
w_select = widgets.SelectMultiple(options=options,
rows=min(10, len(options)),
description='Steps:',
layout=layout)
w_highlight = widgets.VBox([w_color, w_select])
w_tab = widgets.Tab()
w_tab.children = [w_range, w_highlight]
w_tab.set_title(0, "range")
w_tab.set_title(1, "highlight")
out = widgets.interactive_output(
_interact, {
'x': w_lapse,
'y': w_requests,
'selects': w_select,
'color': w_color
})
display(w_tab, out)
def make_colorpicker(color):
return widgets.ColorPicker(concise=False,
description='Pick a color',
value=color,
disabled=False)
def __init__(self, fig):
super().__init__()
self.fig = fig
self.nSubs = sum(['xaxis' in i for i in fig['layout']])
# Layout control widgets
field_lay = widgets.Layout(max_height='40px', max_width='120px',
min_height='30px', min_width='70px')
fonts_names = ["Arial", "Balto", "Courier New", "Droid Sans",
"Droid Serif", "Droid Sans Mono", "Gravitas One",
"Old Standard TT", "Open Sans", "Overpass",
"PT Sans Narrow", "Raleway", "Times New Roman"]
fonts_list = [(fnt, i+1) for i, fnt in enumerate(fonts_names)]
tickoptions_names = ['inside', 'outside', '']
tickoptions_list = [(v, i+1) for i, v in enumerate(tickoptions_names)]
# General --------------------------------------------------------------
chk_general_autosize = widgets.Checkbox(
value=fig.layout.autosize, description='Autosize')
self.txt_gen_width = widgets.FloatText(value=fig.layout.width, layout=field_lay)
self.txt_gen_height = widgets.FloatText(value=fig.layout.height, layout=field_lay)
hb_dims = HBox([widgets.Label('width/height:'), self.txt_gen_width, self.txt_gen_height])
txt_fontsize = widgets.FloatText(value=fig.layout.font.size, layout=field_lay)
drd_fontfamily = widgets.Dropdown(
options=fonts_list, layout=field_lay,
value=fonts_names.index(fig.layout.font.family)+1)
drd_fontfamily.observe(self.set_gen_fontfamily, names='label')
clr_font = widgets.ColorPicker(concise=True, value=fig.layout.font.color)
hb_font = HBox([widgets.Label('font:'), txt_fontsize, drd_fontfamily, clr_font])
clr_paperbg = widgets.ColorPicker(layout=field_lay,
concise=True, value=fig.layout.paper_bgcolor, description='paper_bgcolor')
clr_plotbg = widgets.ColorPicker(layout=field_lay,
concise=True, value=fig.layout.plot_bgcolor, description='plot_bgcolor')
hb_colorbg = HBox([clr_paperbg, clr_plotbg])
drd_hovermode = widgets.Dropdown(
options=[('Closest', 1), ('x', 2), ('y', 3), ('False', 4)],
value=1, layout=field_lay)
drd_hovermode.observe(self.set_gen_hovermode, names='label')
hb_hovermode = HBox([widgets.Label('hovermode:'), drd_hovermode])
vb_general = VBox([chk_general_autosize, hb_dims, hb_font,
hb_colorbg, hb_hovermode])
controls_general = {
'autosize': chk_general_autosize,
'width': self.txt_gen_width,
'height': self.txt_gen_height,
'fontsize': txt_fontsize,
'fontcolor': clr_font,
'paperbgcolor': clr_paperbg,
'plotbgcolor': clr_plotbg,
}
widgets.interactive_output(self.set_general, controls_general)
# Title ----------------------------------------------------------------
txt_titletext = widgets.Text(value=fig.layout.title.text, description='Text:')
txt_titlefontsize = widgets.FloatText(value=fig.layout.titlefont.size, layout=field_lay)
drd_titlefontfamily = widgets.Dropdown(
options=fonts_list, layout=field_lay,
value=fonts_names.index(fig.layout.titlefont.family)+1)
drd_titlefontfamily.observe(self.set_gen_titlefontfamily, names='label')
clr_titlefont = widgets.ColorPicker(concise=True, value=fig.layout.titlefont.color)
hb_title = HBox([widgets.Label('font:'), txt_titlefontsize,
drd_titlefontfamily, clr_titlefont])
vb_title = VBox([txt_titletext, hb_title])
controls = {
'title_text': txt_titletext,
'title_fontsize': txt_titlefontsize,
'title_fontcolor': clr_titlefont,
}
widgets.interactive_output(self.set_title, controls)
# X axis ---------------------------------------------------------------
chk_xaxis_visible = widgets.Checkbox(
value=fig.layout.xaxis.visible, description='Visible')
clr_xaxis = widgets.ColorPicker(concise=True, value=fig.layout.xaxis.color)
hb0_xaxis = HBox([chk_xaxis_visible, clr_xaxis])
txt_xaxis_fontsize = widgets.FloatText(value=fig.layout.xaxis.titlefont.size, layout=field_lay)
drd_xaxis_fontfamily = widgets.Dropdown(
options=fonts_list, layout=field_lay,
value=fonts_names.index(fig.layout.xaxis.titlefont.family)+1)
drd_xaxis_fontfamily.observe(self.set_xaxis_titlefontfamily, names='label')
clr_xaxis_font = widgets.ColorPicker(concise=True, value=fig.layout.xaxis.titlefont.color)
hb1_xaxis = HBox([widgets.Label('font:'), txt_xaxis_fontsize,
drd_xaxis_fontfamily, clr_xaxis_font])
drd_xaxis_ticks = widgets.Dropdown(
options=tickoptions_list, layout=field_lay,
value=tickoptions_names.index(fig.layout.xaxis.ticks)+1)
drd_xaxis_ticks.observe(self.set_xaxis_ticks, names='label')
txt_xaxis_nticks = widgets.IntText(value=fig.layout.xaxis.nticks, layout=field_lay)
hb2_xaxis = HBox([widgets.Label('ticks:'), drd_xaxis_ticks, txt_xaxis_nticks])
txt_xaxis_ticklen = widgets.FloatText(value=fig.layout.xaxis.ticklen, layout=field_lay)
txt_xaxis_tickwid = widgets.FloatText(value=fig.layout.xaxis.tickwidth, layout=field_lay)
clr_xaxis_tick = widgets.ColorPicker(concise=True, value=fig.layout.xaxis.tickcolor)
hb3_xaxis = HBox([widgets.Label('ticks len/wid:'), txt_xaxis_ticklen,
txt_xaxis_tickwid, clr_xaxis_tick])
chk_xaxis_showticklabels = widgets.Checkbox(
value=fig.layout.xaxis.showticklabels, description='Show tick labels')
chk_xaxis_tickangle = widgets.Checkbox(
value=not isinstance(fig.layout.xaxis.tickangle, (int, float)), description='auto')
self.txt_xaxis_tickangle = widgets.FloatText(
value=fig.layout.xaxis.tickangle if fig.layout.xaxis.tickangle != 'auto' else 0,
layout=field_lay)
hb4_xaxis = HBox([widgets.Label('ticks angle:'), chk_xaxis_tickangle,
self.txt_xaxis_tickangle])
vb_xaxis = VBox([hb0_xaxis, hb1_xaxis, hb2_xaxis, hb3_xaxis,
chk_xaxis_showticklabels, hb4_xaxis])
xaxis_controls = {
'visible': chk_xaxis_visible,
'color': clr_xaxis,
'title_fontsize': txt_xaxis_fontsize,
'title_fontcolor': clr_xaxis_font,
'nticks': txt_xaxis_nticks,
'ticklen': txt_xaxis_ticklen,
'tickwid': txt_xaxis_tickwid,
'tickcolor': clr_xaxis_tick,
'showticklabels': chk_xaxis_showticklabels,
'tickangleauto': chk_xaxis_tickangle,
'tickangle': self.txt_xaxis_tickangle,
}
widgets.interactive_output(self.set_xaxis, xaxis_controls)
# Y axis ---------------------------------------------------------------
chk_yaxis_visible = widgets.Checkbox(
value=fig.layout.yaxis.visible, description='Visible')
clr_yaxis = widgets.ColorPicker(concise=True, value=fig.layout.yaxis.color)
hb0_yaxis = HBox([chk_yaxis_visible, clr_yaxis])
txt_yaxis_fontsize = widgets.FloatText(value=fig.layout.yaxis.titlefont.size, layout=field_lay)
drd_yaxis_fontfamily = widgets.Dropdown(
options=fonts_list, layout=field_lay,
value=fonts_names.index(fig.layout.yaxis.titlefont.family)+1)
drd_yaxis_fontfamily.observe(self.set_yaxis_titlefontfamily, names='label')
clr_yaxis_font = widgets.ColorPicker(concise=True, value=fig.layout.yaxis.titlefont.color)
hb1_yaxis = HBox([widgets.Label('font:'), txt_yaxis_fontsize,
drd_yaxis_fontfamily, clr_yaxis_font])
drd_yaxis_ticks = widgets.Dropdown(
options=tickoptions_list, layout=field_lay,
value=tickoptions_names.index(fig.layout.yaxis.ticks)+1)
drd_yaxis_ticks.observe(self.set_yaxis_ticks, names='label')
txt_yaxis_nticks = widgets.IntText(value=fig.layout.yaxis.nticks, layout=field_lay)
hb2_yaxis = HBox([widgets.Label('ticks:'), drd_yaxis_ticks, txt_yaxis_nticks])
txt_yaxis_ticklen = widgets.FloatText(value=fig.layout.yaxis.ticklen, layout=field_lay)
txt_yaxis_tickwid = widgets.FloatText(value=fig.layout.yaxis.tickwidth, layout=field_lay)
clr_yaxis_tick = widgets.ColorPicker(concise=True, value=fig.layout.yaxis.tickcolor)
hb3_yaxis = HBox([widgets.Label('ticks len/wid:'), txt_yaxis_ticklen,
txt_yaxis_tickwid, clr_yaxis_tick])
chk_yaxis_showticklabels = widgets.Checkbox(
value=fig.layout.yaxis.showticklabels, description='Show tick labels')
chk_yaxis_tickangle = widgets.Checkbox(
value=not isinstance(fig.layout.yaxis.tickangle, (int, float)), description='auto')
self.txt_yaxis_tickangle = widgets.FloatText(
value=fig.layout.yaxis.tickangle if fig.layout.yaxis.tickangle != 'auto' else 0,
layout=field_lay)
hb4_yaxis = HBox([widgets.Label('ticks angle:'), chk_yaxis_tickangle,
self.txt_yaxis_tickangle])
vb_yaxis = VBox([hb0_yaxis, hb1_yaxis, hb2_yaxis, hb3_yaxis,
chk_yaxis_showticklabels, hb4_yaxis])
yaxis_controls = {
'visible': chk_yaxis_visible,
'color': clr_yaxis,
'title_fontsize': txt_yaxis_fontsize,
'title_fontcolor': clr_yaxis_font,
'nticks': txt_yaxis_nticks,
'ticklen': txt_yaxis_ticklen,
'tickwid': txt_yaxis_tickwid,
'tickcolor': clr_yaxis_tick,
'showticklabels': chk_yaxis_showticklabels,
'tickangleauto': chk_yaxis_tickangle,
'tickangle': self.txt_yaxis_tickangle,
}
widgets.interactive_output(self.set_yaxis, yaxis_controls)
# Export ---------------------------------------------------------------
# Organize buttons layout ----------------------------------------------
self.tab_nest = widgets.Tab()
self.tab_nest.children = [vb_general, vb_title, vb_xaxis, vb_yaxis]
self.tab_nest.set_title(0, 'General')
self.tab_nest.set_title(1, 'Title')
self.tab_nest.set_title(2, 'X axis')
self.tab_nest.set_title(3, 'Y axis')
acc_all = widgets.Accordion(
children=[self.tab_nest],
selected_index=None
)
acc_all.set_title(0, 'Edit layout')
self.children = [acc_all, fig]
def identifica_cor(frame):
'''
Segmenta o maior objeto cuja cor é parecida com cor_h (HUE da cor, no espaço HSV).
'''
colorpicker = widgets.ColorPicker(concise=False,
description='Escolha uma cor',
value='#01ff13',
disabled=False)
hsv1, hsv2 = aux.ranges(colorpicker.value)
# No OpenCV, o canal H vai de 0 até 179, logo cores similares ao
# vermelho puro (H=0) estão entre H=-8 e H=8.
# Precisamos dividir o inRange em duas partes para fazer a detecção
# do vermelho:
# frame = cv2.flip(frame, -1) # flip 0: eixo x, 1: eixo y, -1: 2 eixos
frame_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
#cor_menor = np.array([52, 50, 50])
#cor_maior = np.array([62, 255, 255])
segmentado_cor = cv2.inRange(frame_hsv, hsv1, hsv2)
#cor_menor = np.array([172, 50, 50])
#cor_maior = np.array([180, 255, 255])
#segmentado_cor += cv2.inRange(frame_hsv, cor_menor, cor_maior)
# Note que a notacão do numpy encara as imagens como matriz, portanto o enderecamento é
# linha, coluna ou (y,x)
# Por isso na hora de montar a tupla com o centro precisamos inverter, porque
centro = (frame.shape[1] // 2, frame.shape[0] // 2)
def cross(img_rgb, point, color, width, length):
cv2.line(img_rgb, (point[0] - length / 2, point[1]),
(point[0] + length / 2, point[1]), color, width, length)
cv2.line(img_rgb, (point[0], point[1] - length / 2),
(point[0], point[1] + length / 2), color, width, length)
# A operação MORPH_CLOSE fecha todos os buracos na máscara menores
# que um quadrado 7x7. É muito útil para juntar vários
# pequenos contornos muito próximos em um só.
segmentado_cor = cv2.morphologyEx(segmentado_cor, cv2.MORPH_CLOSE,
np.ones((7, 7)))
# Encontramos os contornos na máscara e selecionamos o de maior área
#contornos, arvore = cv2.findContours(segmentado_cor.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contornos, arvore = cv2.findContours(segmentado_cor.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
maior_contorno = None
maior_contorno_area = 0
for cnt in contornos:
area = cv2.contourArea(cnt)
if area > maior_contorno_area:
maior_contorno = cnt
maior_contorno_area = area
# Encontramos o centro do contorno fazendo a média de todos seus pontos.
if not maior_contorno is None:
cv2.drawContours(frame, [maior_contorno], -1, [0, 0, 255], 5)
maior_contorno = np.reshape(maior_contorno,
(maior_contorno.shape[0], 2))
media = maior_contorno.mean(axis=0)
media = media.astype(np.int32)
cv2.circle(frame, (media[0], media[1]), 5, [255, 0, 0])
cross(frame, centro, [0, 255, 0], 1, 17)
else:
media = (0, 0)
# Representa a area e o centro do maior contorno no frame
font = cv2.FONT_HERSHEY_COMPLEX_SMALL
cv2.putText(frame, "{:d} {:d}".format(*media), (20, 100), 1, 4,
(255, 255, 255), 2, cv2.LINE_AA)
cv2.putText(frame, "{:0.1f}".format(maior_contorno_area), (20, 50), 1, 4,
(255, 255, 255), 2, cv2.LINE_AA)
# cv2.imshow('video', frame)
cv2.imshow('seg', segmentado_cor)
cv2.waitKey(1)
return media, centro, maior_contorno_area
