def plotPattern(self, type, stats):
"""Common graph function for read and writes, type is either 'read' or 'write'. Stats is a bool that indicates whether to print statistics or not."""
names = self.filenames(type) #unique names of used files
if not self.fileName in names:
print self.fileName, "is not in our data set"
return
if self.data == None:
self.__prepareData()
self.axes.clear()
graphdata = np.column_stack((self.data['off'], self.data['start'], self.data['off']+ self.data['size'], self.data['start'] + self.data['dur']))
lineSegments = LineCollection(graphdata.reshape(-1,2,2), linewidths=(4));
lineSegments.set_picker(True)
self.lineCol = self.axes.add_collection(lineSegments)
maxEnd = max(graphdata[:,2])
maxTime = max(graphdata[:,3])
if stats:
self.__printStats()
self.axes.xaxis.set_major_formatter(FuncFormatter(self.__xFormater))
self.axes.grid(color='grey', linewidth=0.5)
self.axes.set_xlabel("file offset (kiB)", fontsize=16);
self.axes.set_ylabel("time (ms)", fontsize=16);
self.axes.set_xlim(0, maxEnd);
self.axes.set_ylim(self.startTime, maxTime);
self.fig.suptitle('%s' % self.__elideText(self.fileName), fontsize=9)
# ticks = self.__getTicks(0, maxEnd)
# plt.xticks(ticks);
self.fig.autofmt_xdate()
def _add_plot(fig, ax, plot_data, color_data, pkeys, lw=1, cmap='_auto',
alpha=1.0):
colors = color_data.color
if cmap == '_auto':
cmap = None
if color_data.is_categorical:
colors = np.take(COLOR_CYCLE, colors, mode='wrap')
if plot_data.scatter:
data, = plot_data.trajs
artist = ax.scatter(*data.T, marker='o', c=colors, edgecolor='none',
s=lw*20, cmap=cmap, alpha=alpha, picker=5)
else:
# trajectory plot
if hasattr(colors, 'dtype') and np.issubdtype(colors.dtype, np.number):
# delete lines with NaN colors
mask = np.isfinite(colors)
if mask.all():
trajs = plot_data.trajs
else:
trajs = [t for i,t in enumerate(plot_data.trajs) if mask[i]]
colors = colors[mask]
artist = LineCollection(trajs, linewidths=lw, cmap=cmap)
artist.set_array(colors)
else:
artist = LineCollection(plot_data.trajs, linewidths=lw, cmap=cmap)
artist.set_color(colors)
artist.set_alpha(alpha)
artist.set_picker(True)
artist.set_pickradius(5)
ax.add_collection(artist, autolim=True)
ax.autoscale_view()
# Force ymin -> 0
ax.set_ylim((0, ax.get_ylim()[1]))
def on_pick(event):
if event.artist is not artist:
return
label = pkeys[event.ind[0]]
ax.set_title(label)
fig.canvas.draw_idle()
# XXX: hack, make this more official
if hasattr(fig, '_superman_cb'):
fig.canvas.mpl_disconnect(fig._superman_cb[0])
cb_id = fig.canvas.mpl_connect('pick_event', on_pick)
fig._superman_cb = (cb_id, on_pick)
return artist
class SkeletonBuilder:
def __init__(self, config_path):
self.config_path = config_path
self.cfg = read_config(config_path)
# Find uncropped labeled data
self.df = None
found = False
root = os.path.join(self.cfg["project_path"], "labeled-data")
for dir_ in os.listdir(root):
folder = os.path.join(root, dir_)
if os.path.isdir(folder) and not any(
folder.endswith(s) for s in ("cropped", "labeled")):
self.df = pd.read_hdf(
os.path.join(folder,
f'CollectedData_{self.cfg["scorer"]}.h5'))
row, col = self.pick_labeled_frame()
if "individuals" in self.df.columns.names:
self.df = self.df.xs(col, axis=1, level="individuals")
self.xy = self.df.loc[row].values.reshape((-1, 2))
missing = np.flatnonzero(np.isnan(self.xy).all(axis=1))
if not missing.size:
found = True
break
if self.df is None:
raise IOError("No labeled data were found.")
self.bpts = self.df.columns.get_level_values("bodyparts").unique()
if not found:
warnings.warn(
f"A fully labeled animal could not be found. "
f"{', '.join(self.bpts[missing])} will need to be manually connected in the config.yaml."
)
self.tree = KDTree(self.xy)
# Handle image previously annotated on a different platform
sep = "/" if "/" in row else "\\"
if sep != os.path.sep:
row = row.replace(sep, os.path.sep)
self.image = io.imread(os.path.join(self.cfg["project_path"], row))
self.inds = set()
self.segs = set()
# Draw the skeleton if already existent
if self.cfg["skeleton"]:
for bone in self.cfg["skeleton"]:
pair = np.flatnonzero(self.bpts.isin(bone))
if len(pair) != 2:
continue
pair_sorted = tuple(sorted(pair))
self.inds.add(pair_sorted)
self.segs.add(tuple(map(tuple, self.xy[pair_sorted, :])))
self.lines = LineCollection(self.segs,
colors=mcolors.to_rgba(
self.cfg["skeleton_color"]))
self.lines.set_picker(True)
self.show()
def pick_labeled_frame(self):
# Find the most 'complete' animal
try:
count = self.df.groupby(level="individuals", axis=1).count()
if "single" in count:
count.drop("single", axis=1, inplace=True)
except KeyError:
count = self.df.count(axis=1).to_frame()
mask = count.where(count == count.values.max())
kept = mask.stack().index.to_list()
np.random.shuffle(kept)
row, col = kept.pop()
return row, col
def show(self):
self.fig = plt.figure()
ax = self.fig.add_subplot(111)
ax.axis("off")
lo = np.nanmin(self.xy, axis=0)
hi = np.nanmax(self.xy, axis=0)
center = (hi + lo) / 2
w, h = hi - lo
ampl = 1.3
w *= ampl
h *= ampl
ax.set_xlim(center[0] - w / 2, center[0] + w / 2)
ax.set_ylim(center[1] - h / 2, center[1] + h / 2)
ax.imshow(self.image)
ax.scatter(*self.xy.T, s=self.cfg["dotsize"]**2)
ax.add_collection(self.lines)
ax.invert_yaxis()
self.lasso = LassoSelector(ax, onselect=self.on_select)
ax_clear = self.fig.add_axes([0.85, 0.55, 0.1, 0.1])
ax_export = self.fig.add_axes([0.85, 0.45, 0.1, 0.1])
self.clear_button = Button(ax_clear, "Clear")
self.clear_button.on_clicked(self.clear)
self.export_button = Button(ax_export, "Export")
self.export_button.on_clicked(self.export)
self.fig.canvas.mpl_connect("pick_event", self.on_pick)
plt.show()
def clear(self, *args):
self.inds.clear()
self.segs.clear()
self.lines.set_segments(self.segs)
def export(self, *args):
inds_flat = set(ind for pair in self.inds for ind in pair)
unconnected = [i for i in range(len(self.xy)) if i not in inds_flat]
if len(unconnected):
warnings.warn(
f'Unconnected {", ".join(self.bpts[unconnected])}. '
f"It is desirable that all bodyparts be connected for multi-animal projects."
)
self.cfg["skeleton"] = [
tuple(self.bpts[list(pair)]) for pair in self.inds
]
write_config(self.config_path, self.cfg)
def on_pick(self, event):
if event.mouseevent.button == 3:
removed = event.artist.get_segments().pop(event.ind[0])
self.segs.remove(tuple(map(tuple, removed)))
self.inds.remove(tuple(self.tree.query(removed)[1]))
def on_select(self, verts):
self.path = Path(verts)
self.verts = verts
inds = self.tree.query_ball_point(verts, 5)
inds_unique = []
for lst in inds:
if len(lst) and lst[0] not in inds_unique:
inds_unique.append(lst[0])
for pair in zip(inds_unique, inds_unique[1:]):
pair_sorted = tuple(sorted(pair))
self.inds.add(pair_sorted)
self.segs.add(tuple(map(tuple, self.xy[pair_sorted, :])))
self.lines.set_segments(self.segs)
self.fig.canvas.draw_idle()
class PlotCtrl(PlotView):
def __init__(self, panel):
PlotView.__init__(self, panel)
# stores the plot objects
self.plots = []
self.__plot_count = 0
# self.highlighted_vertices = set() # Rename to selected_vertices
self.highlighted_vertices = [] # Keeps track of the highlighted vertices index
self.marker = None # Must be a matplotlib line2D object
self.x_scatter_data, self.y_scatter_data = None, None # Holds the scatter data for highlighting
self.poly_list = None # Holds the data for the plotted polygon
self.highlight_color = "y" # Yellow is used when highlighting
self.color = "#0DACFF" # The standard color for objects that are not highlighted
self._color_converter = ColorConverter()
self.line_collection = None # Holds the line collection data
self.highlighted_lines = []
self.selected_lines = []
# stores the axis objects
self.__axis = []
# matplotlib color cycle used to ensure primary and secondary axis are not displayed with the same color
self.__color_cycle = color_cycle()
# Used to be able to deactivate the canvas events later
self._cid_press = None
self._cid_release = None
self._cid_scroll = None
def activate_panning(self):
self._cid_press = self.canvas.mpl_connect('button_press_event', self.on_canvas_clicked)
self._cid_release = self.canvas.mpl_connect('button_release_event', self.on_canvas_released)
def activate_zooming(self):
self._cid_scroll = self.canvas.mpl_connect('scroll_event', self.on_canvas_mouse_scroll)
def deactivate_panning(self):
self.canvas.mpl_disconnect(self._cid_press)
self.canvas.mpl_disconnect(self._cid_release)
def deactivate_zooming(self):
self.canvas.mpl_disconnect(self._cid_scroll)
def clear_plot(self):
# clear axis
self.axes.clear()
for ax in self.__axis:
ax.cla()
# reset the axis container
self.__axis = []
self.axes.grid()
self.axes.margins(0)
# clear the plot objects
self.__plot_count = 0
self.plots = []
self.redraw()
def getNextColor(self):
return next(self.__color_cycle)
def get_highlighted_vertices(self):
"""
Returns the index and the coordinates of the highlighted vertices in a dictionary.
Key is index, value are coordinates
:return:
"""
data = {}
for vertex in self.highlighted_vertices:
data[vertex] = [self.x_scatter_data[vertex], self.y_scatter_data[vertex]]
return data
def get_highlighted_polygons(self):
"""
Returns all the highlighted polygons.
Key is index, value are object
:return: type(dict)
"""
if not self.poly_list: # Check if polygon has been plotted
return {} # No polygons have been plotted.
data = {}
for i in range(len(self.axes.collections)):
if self.axes.collections[i].get_facecolor().all() == self.axes.collections[i].get_edgecolor().all():
data[i] = self.axes.collections[i]
return data
def get_highlighted_lines(self):
"""
Returns the highlighted lines index
Key is index, value are indexes of segments
:return:
"""
if not self.line_collection:
return {} # No lines have been plotted
lines = {}
for line in self.highlighted_lines:
lines[line] = self.line_segments[0]
return lines
def highlight_line(self, event):
"""
Highlighted lines have value 1 in self.highlighted_lines
Recolor the collection
:param event:
:return:
"""
ind = event.ind[0]
line_idx = self.segment_line[ind]
if line_idx not in self.highlighted_lines:
self.highlighted_lines.append(line_idx)
else:
self.highlighted_lines.remove(line_idx)
highlight_idx = self.line_segments[line_idx]
self.selected_lines[highlight_idx] = 1 - self.selected_lines[highlight_idx]
lines, = event.artist.axes.collections
lines.set_color(self.__line_colors[self.selected_lines])
event.canvas.draw_idle()
def highlight_polygon(self, pick_event):
if pick_event.artist.get_facecolor()[0].all() == pick_event.artist.get_edgecolor()[0].all():
pick_event.artist.set_facecolor(self.color)
pick_event.artist.set_edgecolor(None)
else:
pick_event.artist.set_color(self.highlight_color)
pick_event.artist.axes.figure.canvas.draw()
def highlight_vertex(self, pick_event):
"""
Only one marker can be used to highlight. self.marker is that one marker
:param pick_event: matplotlib mouse pick event
:return:
"""
if not self.marker:
self.marker, = pick_event.artist.axes.plot([], [], "o") # Create a plot
self.marker.set_color(self.highlight_color) # Set color to yellow
# Check if vertex has been highlighted
if pick_event.ind[0] in self.highlighted_vertices:
self.highlighted_vertices.remove(pick_event.ind[0]) # Remove highlight
else:
self.highlighted_vertices.append(pick_event.ind[0]) # Add highlight
self.highlighted_vertices.sort()
x = self._get_vertices_data_points(self.x_scatter_data, pick_event.ind[0])
y = self._get_vertices_data_points(self.y_scatter_data, pick_event.ind[0])
# Highlight only those in self.highlighted_vertices
self.marker.set_data(x, y)
pick_event.artist.axes.figure.canvas.draw()
def _get_vertices_data_points(self, data, index):
"""
:param data: x_data or y_data, type(tuple or list)
:param index: index of the selected vertex, type(int)
:return: type(list) contains the x & y values that are highlight and should be plotted
"""
a = []
for i in self.highlighted_vertices:
a.append(data[i])
return a
def plot_dates(self, data, name, noDataValue, ylabel=""):
"""
:param data: type([datetime, floats])
:param name:
:param noDataValue:
:param ylabel:
:return:
"""
if len(data) == 0:
return
# unpack the dates, values and replace nodata with None
dates, values = zip(*data)
nvals = numpy.array(values, dtype=numpy.float)
nvals[nvals == noDataValue] = None
nvals[numpy.isnan(nvals)] = None
p = self.axes.plot_date(dates, nvals, label=name, linestyle="None", marker=".")
self.axes.set_ylabel(ylabel)
# save each of the plots
self.plots.extend(p)
self.redraw()
def plot_polygon(self, data, color):
poly_list = []
for item in data:
reference = item.GetGeometryRef(0)
points = numpy.array(reference.GetPoints())
a = tuple(map(tuple, points[:, 0:2]))
poly_list.append(a)
self.poly_list = poly_list
# Plot multiple polygons and add them to collection as individual polygons
for poly in self.poly_list:
p_coll = PolyCollection([poly], closed=True, facecolor=color, alpha=0.5, edgecolor=None, linewidths=(2,))
p_coll.set_picker(True) # Enable pick event
self.axes.add_collection(p_coll, autolim=True)
def plot_point(self, data, color): # Rename to plot scatter
# get x,y points
x, y = zip(*[(g.GetX(), g.GetY()) for g in data])
self.x_scatter_data, self.y_scatter_data = x, y
collection = self.axes.scatter(x, y, marker="o", color=color, picker=True)
return collection
def plot_linestring(self, data, color):
"""
A segment is from point A to point b. It is created from grabbing the previous point to the next point
:param data: geometry object
:param color: # Hexadecimal
:return:
"""
segments = []
self.line_segments = {}
self.segment_line = {}
index = 0 # Keeps track of how many lines to plot. Should match len(data)
last_segment = 0
points = []
for geo_object in data:
for point in geo_object.GetPoints(): # Remove the z coordinate
points.append(point[:-1])
self.line_segments[index] = range(last_segment, last_segment + len(points) - 1)
for i in range(len(points) - 1): # Create the segments
segments.append([points[i], points[i + 1]])
self.segment_line[last_segment + i] = index
last_segment += len(points) - 1
index += 1
self.__line_colors = np.array(
[self._color_converter.to_rgba(color), self._color_converter.to_rgba(self.highlight_color)])
self.selected_lines = np.zeros(len(segments), dtype=int) # Must be a np.zero array
colors = self.__line_colors[self.selected_lines]
self.line_collection = LineCollection(segments, pickradius=10, linewidths=2, colors=colors)
self.line_collection.set_picker(True)
self.axes.add_collection(self.line_collection)
def plot_geometry(self, geometry_object, title, color=None):
"""
A general plot method that will plot the respective type
Must call redraw afterwards to have an effect
:param geometry_object:
:param title: title for the plot
:param color: # Hexadecimal
:return:
"""
if not color:
color = self.color
if geometry_object[0].GetGeometryName().upper() == "POLYGON":
self.plot_polygon(geometry_object, color)
elif geometry_object[0].GetGeometryName().upper() == "POINT":
self.plot_point(geometry_object, color)
elif geometry_object[0].GetGeometryName().upper() == "LINESTRING":
self.plot_linestring(geometry_object, color)
else:
raise Exception("plot_geometry() failed. Geometries must be POLYGON OR POINT")
self.set_title(title)
self.axes.grid(True)
# If margin is 0 the graph will fill the plot
self.axes.margins(0.1)
def reset_highlighter(self):
"""
Resets the variables needed to highlight
:return:
"""
self.marker = None
self.highlighted_vertices = []
self.x_scatter_data, self.y_scatter_data = None, None
self.poly_list = None
self.line_collection = None
def set_line_width(self, width):
"""
Sets the width of the lines plotted
Does not work with scatter plot
:param width: real number
:return:
"""
if not len(self.plots):
return # Nothing has been plotted
for line in self.plots:
line.set_linewidth(width)
self.redraw()
def on_canvas_clicked(self, event):
self.toolbar.press_pan(event)
def on_canvas_mouse_scroll(self, event):
base_scale = 2.0
cur_xlim = self.axes.get_xlim()
cur_ylim = self.axes.get_ylim()
cur_xrange = (cur_xlim[1] - cur_xlim[0]) * .5
cur_yrange = (cur_ylim[1] - cur_ylim[0]) * .5
xdata = event.xdata # get event x location
ydata = event.ydata # get event y location
if event.button == 'up':
# deal with zoom in
scale_factor = 1 / base_scale
elif event.button == 'down':
# deal with zoom out
scale_factor = base_scale
else:
# deal with something that should never happen
scale_factor = 1
print event.button
# set new limits
self.axes.set_xlim([xdata - cur_xrange * scale_factor,
xdata + cur_xrange * scale_factor])
self.axes.set_ylim([ydata - cur_yrange * scale_factor,
ydata + cur_yrange * scale_factor])
self.redraw()
def on_canvas_released(self, event):
self.toolbar.release_pan(event)
class PlotCtrl(PlotView):
def __init__(self, panel):
PlotView.__init__(self, panel)
# stores the plot objects
self.plots = []
self.__plot_count = 0
# self.highlighted_vertices = set() # Rename to selected_vertices
self.highlighted_vertices = [
] # Keeps track of the highlighted vertices index
self.marker = None # Must be a matplotlib line2D object
self.x_scatter_data, self.y_scatter_data = None, None # Holds the scatter data for highlighting
self.poly_list = None # Holds the data for the plotted polygon
self.highlight_color = "y" # Yellow is used when highlighting
self.color = "#0DACFF" # The standard color for objects that are not highlighted
self._color_converter = ColorConverter()
self.line_collection = None # Holds the line collection data
self.highlighted_lines = []
self.selected_lines = []
# stores the axis objects
self.__axis = []
# matplotlib color cycle used to ensure primary and secondary axis are not displayed with the same color
self.__color_cycle = color_cycle()
# Used to be able to deactivate the canvas events later
self._cid_press = None
self._cid_release = None
self._cid_scroll = None
def activate_panning(self):
self._cid_press = self.canvas.mpl_connect('button_press_event',
self.on_canvas_clicked)
self._cid_release = self.canvas.mpl_connect('button_release_event',
self.on_canvas_released)
def activate_zooming(self):
self._cid_scroll = self.canvas.mpl_connect('scroll_event',
self.on_canvas_mouse_scroll)
def deactivate_panning(self):
self.canvas.mpl_disconnect(self._cid_press)
self.canvas.mpl_disconnect(self._cid_release)
def deactivate_zooming(self):
self.canvas.mpl_disconnect(self._cid_scroll)
def clear_plot(self):
# clear axis
self.axes.clear()
for ax in self.__axis:
ax.cla()
# reset the axis container
self.__axis = []
self.axes.grid()
self.axes.margins(0)
# clear the plot objects
self.__plot_count = 0
self.plots = []
self.redraw()
def getNextColor(self):
return next(self.__color_cycle)
def get_highlighted_vertices(self):
"""
Returns the index and the coordinates of the highlighted vertices in a dictionary.
Key is index, value are coordinates
:return:
"""
data = {}
for vertex in self.highlighted_vertices:
data[vertex] = [
self.x_scatter_data[vertex], self.y_scatter_data[vertex]
]
return data
def get_highlighted_polygons(self):
"""
Returns all the highlighted polygons.
Key is index, value are object
:return: type(dict)
"""
if not self.poly_list: # Check if polygon has been plotted
return {} # No polygons have been plotted.
data = {}
for i in range(len(self.axes.collections)):
if self.axes.collections[i].get_facecolor().all(
) == self.axes.collections[i].get_edgecolor().all():
data[i] = self.axes.collections[i]
return data
def get_highlighted_lines(self):
"""
Returns the highlighted lines index
Key is index, value are indexes of segments
:return:
"""
if not self.line_collection:
return {} # No lines have been plotted
lines = {}
for line in self.highlighted_lines:
lines[line] = self.line_segments[0]
return lines
def highlight_line(self, event):
"""
Highlighted lines have value 1 in self.highlighted_lines
Recolor the collection
:param event:
:return:
"""
ind = event.ind[0]
line_idx = self.segment_line[ind]
if line_idx not in self.highlighted_lines:
self.highlighted_lines.append(line_idx)
else:
self.highlighted_lines.remove(line_idx)
highlight_idx = self.line_segments[line_idx]
self.selected_lines[
highlight_idx] = 1 - self.selected_lines[highlight_idx]
lines, = event.artist.axes.collections
lines.set_color(self.__line_colors[self.selected_lines])
event.canvas.draw_idle()
def highlight_polygon(self, pick_event):
if pick_event.artist.get_facecolor()[0].all(
) == pick_event.artist.get_edgecolor()[0].all():
pick_event.artist.set_facecolor(self.color)
pick_event.artist.set_edgecolor(None)
else:
pick_event.artist.set_color(self.highlight_color)
pick_event.artist.axes.figure.canvas.draw()
def highlight_vertex(self, pick_event):
"""
Only one marker can be used to highlight. self.marker is that one marker
:param pick_event: matplotlib mouse pick event
:return:
"""
if not self.marker:
self.marker, = pick_event.artist.axes.plot([], [],
"o") # Create a plot
self.marker.set_color(self.highlight_color) # Set color to yellow
# Check if vertex has been highlighted
if pick_event.ind[0] in self.highlighted_vertices:
self.highlighted_vertices.remove(
pick_event.ind[0]) # Remove highlight
else:
self.highlighted_vertices.append(
pick_event.ind[0]) # Add highlight
self.highlighted_vertices.sort()
x = self._get_vertices_data_points(self.x_scatter_data,
pick_event.ind[0])
y = self._get_vertices_data_points(self.y_scatter_data,
pick_event.ind[0])
# Highlight only those in self.highlighted_vertices
self.marker.set_data(x, y)
pick_event.artist.axes.figure.canvas.draw()
def _get_vertices_data_points(self, data, index):
"""
:param data: x_data or y_data, type(tuple or list)
:param index: index of the selected vertex, type(int)
:return: type(list) contains the x & y values that are highlight and should be plotted
"""
a = []
for i in self.highlighted_vertices:
a.append(data[i])
return a
def plot_dates(self, data, name, noDataValue, ylabel=""):
"""
:param data: type([datetime, floats])
:param name:
:param noDataValue:
:param ylabel:
:return:
"""
if len(data) == 0:
return
# unpack the dates, values and replace nodata with None
dates, values = zip(*data)
nvals = numpy.array(values, dtype=numpy.float)
nvals[nvals == noDataValue] = None
nvals[numpy.isnan(nvals)] = None
p = self.axes.plot_date(dates,
nvals,
label=name,
linestyle="None",
marker=".")
self.axes.set_ylabel(ylabel)
# save each of the plots
self.plots.extend(p)
self.redraw()
def plot_polygon(self, data, color):
poly_list = []
for item in data:
reference = item.GetGeometryRef(0)
points = numpy.array(reference.GetPoints())
a = tuple(map(tuple, points[:, 0:2]))
poly_list.append(a)
self.poly_list = poly_list
# Plot multiple polygons and add them to collection as individual polygons
for poly in self.poly_list:
p_coll = PolyCollection([poly],
closed=True,
facecolor=color,
alpha=0.5,
edgecolor=None,
linewidths=(2, ))
p_coll.set_picker(True) # Enable pick event
self.axes.add_collection(p_coll, autolim=True)
def plot_point(self, data, color): # Rename to plot scatter
# get x,y points
x, y = zip(*[(g.GetX(), g.GetY()) for g in data])
self.x_scatter_data, self.y_scatter_data = x, y
collection = self.axes.scatter(x,
y,
marker="o",
color=color,
picker=True)
return collection
def plot_linestring(self, data, color):
"""
A segment is from point A to point b. It is created from grabbing the previous point to the next point
:param data: geometry object
:param color: # Hexadecimal
:return:
"""
segments = []
self.line_segments = {}
self.segment_line = {}
index = 0 # Keeps track of how many lines to plot. Should match len(data)
last_segment = 0
points = []
for geo_object in data:
for point in geo_object.GetPoints(): # Remove the z coordinate
points.append(point[:-1])
self.line_segments[index] = range(last_segment,
last_segment + len(points) - 1)
for i in range(len(points) - 1): # Create the segments
segments.append([points[i], points[i + 1]])
self.segment_line[last_segment + i] = index
last_segment += len(points) - 1
index += 1
self.__line_colors = np.array([
self._color_converter.to_rgba(color),
self._color_converter.to_rgba(self.highlight_color)
])
self.selected_lines = np.zeros(len(segments),
dtype=int) # Must be a np.zero array
colors = self.__line_colors[self.selected_lines]
self.line_collection = LineCollection(segments,
pickradius=10,
linewidths=2,
colors=colors)
self.line_collection.set_picker(True)
self.axes.add_collection(self.line_collection)
def plot_geometry(self, geometry_object, title, color=None):
"""
A general plot method that will plot the respective type
Must call redraw afterwards to have an effect
:param geometry_object:
:param title: title for the plot
:param color: # Hexadecimal
:return:
"""
if not color:
color = self.color
if geometry_object[0].GetGeometryName().upper() == "POLYGON":
self.plot_polygon(geometry_object, color)
elif geometry_object[0].GetGeometryName().upper() == "POINT":
self.plot_point(geometry_object, color)
elif geometry_object[0].GetGeometryName().upper() == "LINESTRING":
self.plot_linestring(geometry_object, color)
else:
raise Exception(
"plot_geometry() failed. Geometries must be POLYGON OR POINT")
self.set_title(title)
self.axes.grid(True)
# If margin is 0 the graph will fill the plot
self.axes.margins(0.1)
def reset_highlighter(self):
"""
Resets the variables needed to highlight
:return:
"""
self.marker = None
self.highlighted_vertices = []
self.x_scatter_data, self.y_scatter_data = None, None
self.poly_list = None
self.line_collection = None
def set_line_width(self, width):
"""
Sets the width of the lines plotted
Does not work with scatter plot
:param width: real number
:return:
"""
if not len(self.plots):
return # Nothing has been plotted
for line in self.plots:
line.set_linewidth(width)
self.redraw()
def on_canvas_clicked(self, event):
self.toolbar.press_pan(event)
def on_canvas_mouse_scroll(self, event):
base_scale = 2.0
cur_xlim = self.axes.get_xlim()
cur_ylim = self.axes.get_ylim()
cur_xrange = (cur_xlim[1] - cur_xlim[0]) * .5
cur_yrange = (cur_ylim[1] - cur_ylim[0]) * .5
xdata = event.xdata # get event x location
ydata = event.ydata # get event y location
if event.button == 'up':
# deal with zoom in
scale_factor = 1 / base_scale
elif event.button == 'down':
# deal with zoom out
scale_factor = base_scale
else:
# deal with something that should never happen
scale_factor = 1
print event.button
# set new limits
self.axes.set_xlim([
xdata - cur_xrange * scale_factor,
xdata + cur_xrange * scale_factor
])
self.axes.set_ylim([
ydata - cur_yrange * scale_factor,
ydata + cur_yrange * scale_factor
])
self.redraw()
def on_canvas_released(self, event):
self.toolbar.release_pan(event)
def plotConnectionData(cells,
p,
fig=None,
ax=None,
zdata=None,
clrmap=None,
colorbar=None,
pickable=None):
"""
Assigns color-data to connections between a cell and its nearest neighbours and returns plot instance
Parameters
----------
zdata_t A data array with each scalar entry corresponding to a polygon entry in
vor_verts. If not specified the default is z=1. If 'random'
is specified the method creates random vales from 0 to 1..
clrmap The colormap to use for plotting. Must be specified as cm.mapname. A list of
available mapnames is supplied at
http://matplotlib.org/examples/color/colormaps_reference.html
Default is cm.rainbow. Good options are cm.coolwarm, cm.Blues, cm.jet
Returns
-------
fig, ax Matplotlib figure and axes instances for the plot.
Notes
-------
Uses matplotlib.collections LineCollection, matplotlib.cm, matplotlib.pyplot and numpy arrays
"""
if fig is None:
fig = plt.figure() # define the figure and axes instances
if ax is None:
ax = plt.subplot(111)
#ax = plt.axes()
if zdata is None:
z = np.ones(len(cells.gap_jun_i))
#FIXME: This is a bit cumbersome. Ideally, a new "is_zdata_random"
#boolean parameter defaulting to "False" should be tested, instead.
#Whack-a-mole with a big-fat-pole!
# If random data is requested, do so. To avoid erroneous and expensive
# elementwise comparisons when "zdata" is neither None nor a string,
# "zdata" must be guaranteed to be a string *BEFORE* testing this
# parameter as a string. Numpy prints scary warnings otherwise: e.g.,
#
# FutureWarning: elementwise comparison failed; returning scalar
# instead, but in the future will perform elementwise comparison
elif isinstance(zdata, str) and zdata == 'random':
z = np.random.random(len(cells.gap_jun_i))
else:
z = zdata
if clrmap is None:
clrmap = cm.bone_r # default colormap
# Make a line collection and add it to the plot.
con_segs = cells.cell_centres[cells.gap_jun_i]
connects = p.um * np.asarray(con_segs)
coll = LineCollection(connects,
array=z,
cmap=clrmap,
linewidths=4.0,
zorder=0)
coll.set_clim(vmin=0.0, vmax=1.0)
coll.set_picker(pickable)
ax.add_collection(coll)
ax.axis('equal')
# Add a colorbar for the Line Collection
if zdata is not None and colorbar == 1:
ax_cb = fig.colorbar(coll, ax=ax)
else:
ax_cb = None
xmin = cells.xmin * p.um
xmax = cells.xmax * p.um
ymin = cells.ymin * p.um
ymax = cells.ymax * p.um
ax.axis([xmin, xmax, ymin, ymax])
return fig, ax, ax_cb
def plot(self, form, width='uniform', sort=True, gap=0.05, color_nodes=None):
"""
Create a level set tree plot in Matplotlib.
Parameters
----------
form : {'lambda', 'alpha', 'kappa', 'old'}
Determines main form of the plot. 'lambda' is the traditional plot
where the vertical scale is density levels, but plot improvements
such as mass sorting of the nodes and colored nodes are allowed and
the secondary 'alpha' scale is visible (but not controlling). The
'old' form uses density levels for vertical scale but does not allow
plot tweaks and does not show the secondary 'alpha' scale. The
'alpha' setting makes the uppper level set mass the primary vertical
scale, leaving the 'lambda' scale in place for reference. 'kappa'
makes node mass the vertical scale, so that each node's vertical
height is proportional to its mass excluding the mass of the node's
children.
width : {'uniform', 'mass'}, optional
Determines how much horzontal space each level set tree node is
given. The default of "uniform" gives each child node an equal
fraction of the parent node's horizontal space. If set to 'mass',
then horizontal space is allocated proportional to the mass (i.e.
fraction of points) of a node relative to its siblings.
sort : bool, optional
If True, sort sibling nodes from most to least points and draw left
to right. Also sorts root nodes in the same way.
gap : float, optional
Fraction of vertical space to leave at the bottom. Default is 5%,
and 0% also works well. Higher values are used for interactive tools
to make room for buttons and messages.
color_nodes : list, optional
Each entry should be a valid index in the level set tree that will
be colored uniquely.
Returns
-------
fig : matplotlib figure
Use fig.show() to view, fig.savefig() to save, etc.
segments : dict
A dictionary with values that contain the coordinates of vertical
line segment endpoints. This is only useful to the interactive
analysis tools.
segmap : list
Indicates the order of the vertical line segments as returned by the
recursive coordinate mapping function, so they can be picked by the
user in the interactive tools.
splits : dict
Dictionary values contain the coordinates of horizontal line
segments (i.e. node splits).
splitmap : list
Indicates the order of horizontal line segments returned by
recursive coordinate mapping function, for use with interactive
tools.
"""
## Validate input
if form == 'old':
sort = False
color_nodes = None
width = 'uniform'
## Initialize the plot containers
segments = {}
splits = {}
segmap = []
splitmap = []
## Find the root connected components and corresponding plot intervals
ix_root = np.array([k for k, v in self.nodes.iteritems()
if v.parent is None])
n_root = len(ix_root)
census = np.array([len(self.nodes[x].members) for x in ix_root],
dtype=np.float)
n = sum(census)
if sort is True:
seniority = np.argsort(census)[::-1]
ix_root = ix_root[seniority]
census = census[seniority]
if width == 'mass':
weights = census / n
intervals = np.cumsum(weights)
intervals = np.insert(intervals, 0, 0.0)
else:
intervals = np.linspace(0.0, 1.0, n_root+1)
## Do a depth-first search on each root to get segments for each branch
for i, ix in enumerate(ix_root):
if form == 'kappa':
branch = self.constructMassMap(ix, 0.0, (intervals[i],
intervals[i+1]), width)
elif form == 'old':
branch = self.constructBranchMap(ix, (intervals[i],
intervals[i+1]), 'lambda', width, sort)
else:
branch = self.constructBranchMap(ix, (intervals[i],
intervals[i+1]), form, width, sort)
branch_segs, branch_splits, branch_segmap, branch_splitmap = branch
segments = dict(segments.items() + branch_segs.items())
splits = dict(splits.items() + branch_splits.items())
segmap += branch_segmap
splitmap += branch_splitmap
## get the the vertical line segments in order of the segment map (segmap)
verts = [segments[k] for k in segmap]
lats = [splits[k] for k in splitmap]
## Find the fraction of nodes in each segment (to use as linewidths)
thickness = [max(1.0, 12.0 * len(self.nodes[x].members)/n)
for x in segmap]
## Get the relevant vertical ticks
primary_ticks = [(x[0][1], x[1][1]) for x in segments.values()]
primary_ticks = np.unique(np.array(primary_ticks).flatten())
primary_labels = [str(round(tick, 2)) for tick in primary_ticks]
## Set up the plot framework
fig, ax = plt.subplots()
ax.set_position([0.11, 0.05, 0.78, 0.93])
ax.set_xlim((-0.04, 1.04))
ax.set_xticks([])
ax.set_xticklabels([])
ax.yaxis.grid(color='gray')
ax.set_yticks(primary_ticks)
ax.set_yticklabels(primary_labels)
## Form-specific details
if form == 'kappa':
kappa_max = max(primary_ticks)
ax.set_ylim((-1.0 * gap * kappa_max, 1.04*kappa_max))
ax.set_ylabel("mass")
elif form == 'old':
ax.set_ylabel("lambda")
ymin = min([v.start_level for v in self.nodes.itervalues()])
ymax = max([v.end_level for v in self.nodes.itervalues()])
rng = ymax - ymin
ax.set_ylim(ymin - gap*rng, ymax + 0.05*rng)
elif form == 'lambda':
ax.set_ylabel("lambda")
ymin = min([v.start_level for v in self.nodes.itervalues()])
ymax = max([v.end_level for v in self.nodes.itervalues()])
rng = ymax - ymin
ax.set_ylim(ymin - gap*rng, ymax + 0.05*rng)
ax2 = ax.twinx()
ax2.set_position([0.11, 0.05, 0.78, 0.93])
ax2.set_ylabel("alpha", rotation=270)
alpha_ticks = np.sort(list(set(
[v.start_mass for v in self.nodes.itervalues()] + \
[v.end_mass for v in self.nodes.itervalues()])))
alpha_labels = [str(round(m, 2)) for m in alpha_ticks]
ax2.set_yticks(primary_ticks)
ax2.set_yticklabels(alpha_labels)
ax2.set_ylim(ax.get_ylim())
elif form == 'alpha':
ax.set_ylabel("alpha")
ymin = min([v.start_mass for v in self.nodes.itervalues()])
ymax = max([v.end_mass for v in self.nodes.itervalues()])
rng = ymax - ymin
ax.set_ylim(ymin - gap*rng, ymax + 0.05*ymax)
ax2 = ax.twinx()
ax2.set_position([0.11, 0.05, 0.78, 0.93])
ax2.set_ylabel("lambda", rotation=270)
lambda_ticks = np.sort(list(set(
[v.start_level for v in self.nodes.itervalues()] + \
[v.end_level for v in self.nodes.itervalues()])))
lambda_labels = [str(round(lvl, 2)) for lvl in lambda_ticks]
ax2.set_ylim(ax.get_ylim())
ax2.set_yticks(primary_ticks)
ax2.set_yticklabels(lambda_labels)
else:
raise ValueError('Plot form not understood')
## Add the line segments
segclr = np.array([[0.0, 0.0, 0.0]] * len(segmap))
splitclr = np.array([[0.0, 0.0, 0.0]] * len(splitmap))
palette = utl.Palette()
if color_nodes is not None:
for i, ix in enumerate(color_nodes):
n_clr = np.alen(palette.colorset)
c = palette.colorset[i % n_clr, :]
subtree = self.makeSubtree(ix)
## set verical colors
ix_replace = np.in1d(segmap, subtree.nodes.keys())
segclr[ix_replace] = c
## set horizontal colors
if splitmap:
ix_replace = np.in1d(splitmap, subtree.nodes.keys())
splitclr[ix_replace] = c
linecol = LineCollection(verts, linewidths=thickness, colors=segclr)
ax.add_collection(linecol)
linecol.set_picker(20)
splitcol = LineCollection(lats, colors=splitclr)
ax.add_collection(splitcol)
return fig, segments, segmap, splits, splitmap