def draw(self, svg_parent, tree_layout, parent, child):
from svgwrite.shapes import Line
if child.depth > parent.depth + 1:
line_start = parent.y + parent.height
if parent.height > 0:
line_start += 0.2 # extra space for descenders
box_y = tree_layout.y_distance(parent.depth, child.depth)
y_target = em(box_y + child.y)
x_target = perc(child.x + child.width / 2)
# we are skipping level(s). Find the y position that an empty
# node on the next level would have.
intermediate_y = em(
tree_layout.label_y_dodge(level=parent.depth +
1, height=0)[0] +
tree_layout.y_distance(parent.depth, parent.depth + 1))
# TODO: do as Path?
svg_parent.add(
Line(start=(u"50%", em(line_start)),
end=(x_target, intermediate_y),
**self.svg_opts()))
svg_parent.add(
Line(start=(x_target, intermediate_y),
end=(x_target, y_target),
**self.svg_opts()))
else:
EdgeStyle.draw(self, svg_parent, tree_layout, parent, child)
def _create_rect_arrow(scene: Drawing, start: tuple, point1: tuple,
point2: tuple, end: tuple, color: tuple):
"""Create an rectangular path through the given points.
The path starts at p1 the goes to point1, p2 and finally to end.
Args:
scene (Scene): The scene where the path should be created.
start: The first point.
point1: The second point.
point2: The third point.
end: The last point.
color: The arrow's color.
Returns:
The modified scene
"""
scene.add(
Line(start,
point1,
shape_rendering='inherit',
stroke=rgb(*color),
stroke_width=1))
scene.add(
Line(point1,
point2,
shape_rendering='inherit',
stroke=rgb(*color),
stroke_width=1))
scene.add(
Line(point2,
end,
shape_rendering='inherit',
stroke=rgb(*color),
stroke_width=1))
def get_runner_end(self, line, is_out):
x, y = self.get_line_end(line)
if is_out:
g = Group()
g['class'] = 'out'
g.add(Line((x - X_SIZE, y - X_SIZE), (x + X_SIZE, y + X_SIZE)))
g.add(Line((x - X_SIZE, y + X_SIZE), (x + X_SIZE, y - X_SIZE)))
return g
else:
return Circle((x, y), CIRCLE_R)
def get_team_box(self, id, ht):
box = Group()
box['id'] = id
box['class'] = 'team-box'
box.add(Rect((ORIGIN_X, ORIGIN_Y), (ATBAT_W, ht)))
box.add(
Line((ORIGIN_X + NAME_W, ORIGIN_Y),
(ORIGIN_X + NAME_W, ORIGIN_Y + ht)))
box.add(
Line((ORIGIN_X + NAME_W + SCORE_W, ORIGIN_Y),
(ORIGIN_X + NAME_W + SCORE_W, ORIGIN_Y + ht)))
return box
def _put_cross(self, cell, x, y, fill, stone_size):
offset = stone_size / 4
cell.add(
Line(start=((x - offset) * self.unit, (y - offset) * self.unit),
end=((x + offset) * self.unit, (y + offset) * self.unit),
fill=fill,
stroke=fill,
stroke_width=self._line_width * 3))
cell.add(
Line(start=((x - offset) * self.unit, (y + offset) * self.unit),
end=((x + offset) * self.unit, (y - offset) * self.unit),
fill=fill,
stroke=fill,
stroke_width=self._line_width * 3))
def draw_inning_separators(self):
y = ORIGIN_Y
for i, inning in enumerate(self.game.innings[:-1]):
inning_ht = self.get_inning_height(inning)
y += inning_ht
self.dwg.add(
Line((ORIGIN_X, y), (ORIGIN_X + ATBAT_W, y),
class_='team-box'))
if (not (i == len(self.game.innings) - 2
and self.is_no_final_bottom())):
self.dwg.add(
Line((ORIGIN_X + ATBAT_W + SEPARATION, y),
(ORIGIN_X + 2 * ATBAT_W + SEPARATION, y),
class_='team-box'))
def draw_arrow_w_text_middle(self, scene: Drawing, start: tuple,
point1: tuple, point2: tuple, end: tuple,
height: int, arrowsize: int, is_curved: bool,
text: str, font_size: int, font_family: str,
over: bool, color: tuple):
# Store the appropriate function ouside of the loop
if is_curved:
self._create_curve_arrow(scene, start, point1, point2, end, color)
else:
self._create_rect_arrow(scene, start, point1, point2, end, color)
# Draw arrow
x_coord = (end[0] - arrowsize, end[1] - arrowsize)
z_coord = (end[0] + arrowsize, end[1] - arrowsize)
y_coord = (end[0], end[1])
# Draw the arrow head
scene.add(
Line(x_coord,
y_coord,
shape_rendering='inherit',
stroke=rgb(*color),
stroke_width=1))
scene.add(
Line(z_coord,
y_coord,
shape_rendering='inherit',
stroke=rgb(*color),
stroke_width=1))
direction = 1
if over:
direction = -1
# Write label in the middle under
labelx = min(start[0], point2[0]) + abs(start[0] - point2[0]) // 2
labely = height + direction * font_size # TODO: Should be font height!
scene.add(
Text(text,
insert=(labelx, labely),
fill=rgb(*color),
font_family=font_family,
font_size=font_size,
text_rendering='inherit',
alignment_baseline='central',
text_anchor='middle')
) # TODO: alignment_baseline should be hanging or baseline!
def create_doors(doors, walls):
door_ids = set()
door_coords = defaultdict(list)
for door_point_id in doors:
door_id, index = door_point_id[0].split(':')
door_ids.add(door_id)
for point in walls:
if point.name is None or point.name.find(':') == -1:
continue
name_id, index = point.name.split(':')
if name_id in door_ids:
door_coords[name_id].append(point)
lines = []
extra = {
'stroke': 'red',
'stroke-width': 3
}
for door_id, door_pair in door_coords.items():
assert len(door_pair) == 2
print('Create door {}'.format(door_id))
start = door_pair[0].x, door_pair[0].y
end = door_pair[1].x, door_pair[1].y
lines.append(Line(start, end, **extra))
return lines
def get_parallax_lines():
groups = [Group() for i in range(3)]
duplicated_groups = [Group() for i in range(3)]
longest = [0 for i in range(3)]
current_y = anim['verticalInterval']
while current_y < anim['height']:
line_quantity = randint(1, 9)
lengths = [randint(5, 30) for l in range(line_quantity)]
position = sample(range(anim['width'] * 2), line_quantity)
for i, (length, pos) in enumerate(zip(lengths, position)):
if i % 3 is 0 or i is 0: n = 2
elif i % 2 is 0: n = 1
else: n = 0
if pos + length > longest[n]:
longest[n] = pos + length
line = Line((pos, current_y), (pos + length, current_y))
if pos < anim['height']:
duplicated_groups[n].add(line)
groups[n].add(line)
current_y += randint(1,5) * anim['verticalInterval']
dur = 2
containers = [Group(**anim['style']) for i in range(3)]
for c, g, dg, l in zip(containers, groups, duplicated_groups, longest):
c.add(g)
dg['transform'] = 'translate({}, 0)'.format(l)
c.add(dg)
c.add(animate(l, dur))
dur += 0.5
return containers
def _draw_wirelesslink(self, url, labels):
"""
Draw a line with labels representing a WirelessLink.
:param url: Hyperlink URL
:param labels: Iterable of text labels
"""
group = Group(class_='connector')
# Draw the wireless link
start = (OFFSET + self.center, self.cursor)
height = PADDING * 2 + LINE_HEIGHT * len(labels) + PADDING * 2
end = (start[0], start[1] + height)
line = Line(start=start, end=end, class_='wireless-link')
group.add(line)
self.cursor += PADDING * 2
# Add link
link = Hyperlink(href=f'{self.base_url}{url}', target='_blank')
# Add text label(s)
for i, label in enumerate(labels):
self.cursor += LINE_HEIGHT
text_coords = (self.center + PADDING * 2,
self.cursor - LINE_HEIGHT / 2)
text = Text(label,
insert=text_coords,
class_='bold' if not i else [])
link.add(text)
group.add(link)
self.cursor += PADDING * 2
return group
def get_mid_pa_runner_line(self, runner, i, num_events):
x = ORIGIN_X + NAME_W + SCORE_W
x_start = x + BASE_L * runner.start
x_end = x + BASE_L * runner.end
y_step = ATBAT_HT / 2 / num_events
y_start = self.y - ATBAT_HT + i * y_step
y_end = y_start + y_step
return Line((x_start, y_start), (x_end, y_end))
def get_baserunner_line(self, runner):
x = ORIGIN_X + NAME_W + SCORE_W
x_start = x + BASE_L * runner.start
x_end = x + BASE_L * runner.end
y_start = self.y - ATBAT_HT
mid_pa_runner = self.get_mid_pa_runner_to_use(runner.start)
if mid_pa_runner:
x_start = x + BASE_L * mid_pa_runner.end
y_start = self.y - ATBAT_HT / 2
return Line((x_start, y_start), (x_end, self.y))
def draw_hash(self, inning):
line = Line((ORIGIN_X + ATBAT_W + HASH_SEP, self.y),
(ORIGIN_X + ATBAT_W + HASH_SEP + HASH_LEN, self.y))
if self.is_home_team_batting(inning):
flip(line)
self.home_hash_ys.append(self.y)
line['class'] = 'away-pitcher-hash'
else:
self.away_hash_ys.append(self.y)
line['class'] = 'home-pitcher-hash'
self.dwg.add(line)
def background_lines(self, max_data, log_max_value, log_min_value):
line = 1
while line < max_data:
plot_value = self.margin_top + self.plottable_y - self.scale_y(
log10(line), log_max_value, log_min_value)
self.plot.add(
Line(start=(self.margin_left, plot_value),
end=(self.margin_left + self.plottable_x, plot_value),
stroke_width=1,
stroke="lightgrey"))
line *= 10
def _draw_cable(self, color, url, labels):
"""
Return an SVG group containing a line element and text labels representing a Cable.
:param color: Cable (line) color
:param url: Hyperlink URL
:param labels: Iterable of text labels
"""
group = Group(class_='connector')
# Draw a "shadow" line to give the cable a border
start = (OFFSET + self.center, self.cursor)
height = PADDING * 2 + LINE_HEIGHT * len(labels) + PADDING * 2
end = (start[0], start[1] + height)
cable_shadow = Line(start=start, end=end, class_='cable-shadow')
group.add(cable_shadow)
# Draw the cable
cable = Line(start=start, end=end, style=f'stroke: #{color}')
group.add(cable)
self.cursor += PADDING * 2
# Add link
link = Hyperlink(href=f'{self.base_url}{url}', target='_blank')
# Add text label(s)
for i, label in enumerate(labels):
self.cursor += LINE_HEIGHT
text_coords = (self.center + PADDING * 2,
self.cursor - LINE_HEIGHT / 2)
text = Text(label,
insert=text_coords,
class_='bold' if not i else [])
link.add(text)
group.add(link)
self.cursor += PADDING * 2
return group
def corner_ruler(scale, length, major_interval, minor_per_major):
g = Group(stroke="black", stroke_width=0.4)
ruler_length_mm = (length / scale) * 1000
# Horizontal Ruler
g.add(Line((-ruler_length_mm * mm, 0 * mm), ((3) * mm, 0 * mm)))
r = ruler(scale=scale,
length=length,
major_interval=major_interval,
minor_per_major=minor_per_major,
reverse=True,
vertical=False)
g.add(r)
# Vertical Ruler
g.add(Line((0 * mm, -3 * mm), (0 * mm, (ruler_length_mm) * mm)))
r = ruler(scale=scale,
length=length,
major_interval=major_interval,
minor_per_major=minor_per_major,
reverse=False,
vertical=True)
g.add(r)
g.add(Circle((0, 0), r=0.5 * mm, fill="white"))
# Scale label
g.add(
Text(
f"1:{scale}",
x=[(-ruler_length_mm / 2) * mm],
y=[-1 * mm],
text_anchor="middle",
font_size=2 * mm,
font_weight="bold",
))
return g
def draw_line(scene: Drawing, start: tuple, ctrl1: tuple, ctrl2: tuple,
end: tuple, is_curved: bool, edge_color: tuple):
if is_curved: # cubic Bezier curve
scene.add(
Path(d=['M', start, 'C', ctrl1, ctrl2, end],
stroke=rgb(*edge_color),
stroke_width=1,
fill='none'))
else:
scene.add(
Line(start,
end,
shape_rendering='inherit',
stroke=rgb(*edge_color),
stroke_width=1))
def _draw_attachment(self):
"""
Return an SVG group containing a line element and "Attachment" label.
"""
group = Group(class_='connector')
# Draw attachment (line)
start = (OFFSET + self.center, OFFSET + self.cursor)
height = PADDING * 2 + LINE_HEIGHT + PADDING * 2
end = (start[0], start[1] + height)
line = Line(start=start, end=end, class_='attachment')
group.add(line)
self.cursor += PADDING * 4
return group
def get_lines():
group = Group(**img['style'])
current_y = 6
current_x = randint(-10 , 150)
for y in range(0, img['verticalLines']):
while current_x < img['width']:
rand_x = randint(2, 6) + current_x
line = Line((current_x, current_y), (rand_x, current_y))
group.add(line)
current_x = rand_x + randint(50, 150)
current_x = randint(-10, 150)
current_y = round(current_y + img['verticalInterval'], 1)
return group
def add_zero_based_regression(self, slope):
"""place a regression line on the plot."""
self.max_min()
x_value = self.x_max
y_value = slope * x_value
if y_value > self.y_max:
y_value = self.y_max
x_value = y_value / slope
self.plot.add(Line(start=(self.margin_left, self.margin_top + self.plottable_y),
end=(self.x_to_printx(x_value), self.y_to_printy(y_value)),
stroke_width=1, stroke=self.graph_colour))
self.plot.add(
Text(f"Slope = {round(slope, 4)}",
insert=(self.plottable_x + self.margin_left - 200, self.margin_top + 15),
fill=self.graph_colour, font_size="15"))
def get_batter_runner_line(self, runner):
x_start = ORIGIN_X + NAME_W
x_end = x_start + SCORE_W + BASE_L * runner.end
return Line((x_start, self.y), (x_end, self.y))
def test_numbers(self):
line = Line(start=(0,0), end=(10,20))
self.assertEqual(line.tostring(), '<line x1="0" x2="10" y1="0" y2="20" />')
def line(start: tuple, end: tuple) -> Line:
return Line(start,
end,
stroke='black',
style='stroke-linecap:round;stroke-width:2px;')
def process(self):
graphs = {}
intervals = []
smooth = self.parameters.get("smooth")
normalise_values = self.parameters.get("normalise")
completeness = convert_to_int(self.parameters.get("complete"), 0)
graph_label = self.parameters.get("label")
top = convert_to_int(self.parameters.get("top"), 10)
# first gather graph data: each distinct item gets its own graph and
# for each graph we have a sequence of intervals, each interval with
# its own value
first_date = "9999-99-99"
last_date = "0000-00-00"
for row in self.iterate_items(self.source_file):
if row["item"] not in graphs:
graphs[row["item"]] = {}
# make sure the months and days are zero-padded
interval = row.get("date", "")
interval = "-".join([
str(bit).zfill(2 if len(bit) != 4 else 4)
for bit in interval.split("-")
])
first_date = min(first_date, interval)
last_date = max(last_date, interval)
if interval not in intervals:
intervals.append(interval)
if interval not in graphs[row["item"]]:
graphs[row["item"]][interval] = 0
graphs[row["item"]][interval] += float(row.get("value", 0))
# first make sure we actually have something to render
intervals = sorted(intervals)
if len(intervals) <= 1:
self.dataset.update_status(
"Not enough data for a side-by-side over-time visualisation.")
self.dataset.finish(0)
return
# only retain most-occurring series - sort by sum of all frequencies
if len(graphs) > top:
selected_graphs = {
graph: graphs[graph]
for graph in sorted(
graphs,
key=lambda x: sum(
[graphs[x][interval] for interval in graphs[x]]),
reverse=True)[0:top]
}
graphs = selected_graphs
# there may be items that do not have values for all intervals
# this will distort the graph, so the next step is to make sure all
# graphs consist of the same continuous interval list
missing = {graph: 0 for graph in graphs}
for graph in graphs:
missing[graph], graphs[graph] = pad_interval(
graphs[graph],
first_interval=first_date,
last_interval=last_date)
# now that's done, make sure the graph datapoints are in order
intervals = sorted(list(graphs[list(graphs)[0]].keys()))
# delete graphs that do not have the required amount of intervals
# this is useful to get rid of outliers and items that only occur
# very few times over the full interval
if completeness > 0:
intervals_required = len(intervals) * (completeness / 100)
disqualified = []
for graph in graphs:
if len(intervals) - missing[graph] < intervals_required:
disqualified.append(graph)
graphs = {
graph: graphs[graph]
for graph in graphs if graph not in disqualified
}
# determine max value per item, so we can normalize them later
limits = {}
max_limit = 0
for graph in graphs:
for interval in graphs[graph]:
limits[graph] = max(limits.get(graph, 0),
abs(graphs[graph][interval]))
max_limit = max(max_limit, abs(graphs[graph][interval]))
# order graphs by highest (or lowest) value)
limits = {
limit: limits[limit]
for limit in sorted(limits, key=lambda l: limits[l])
}
graphs = {graph: graphs[graph] for graph in limits}
if not graphs:
# maybe nothing is actually there to be graphed
self.dataset.update_status(
"No items match the selection criteria - nothing to visualise."
)
self.dataset.finish(0)
return None
# how many vertical grid lines (and labels) are to be included at most
# 12 is a sensible default because it allows one label per month for a full
# year's data
max_gridlines = 12
# If True, label is put at the lower left bottom of the graph rather than
# outside it. Automatically set to True if one of the labels is long, as
# else the label would fall off the screen
label_in_graph = max([len(item) for item in graphs]) > 30
# determine how wide each interval should be
# the graph has a minimum width - but the graph's width will be
# extended if at this minimum width each item does not have the
# minimum per-item width
min_full_width = 600
min_item_width = 50
item_width = max(min_item_width, min_full_width / len(intervals))
# determine how much space each graph should get
# same trade-off as for the interval width
min_full_height = 300
min_item_height = 100
item_height = max(min_item_height, min_full_height / len(graphs))
# margin - this should be enough for the text labels to fit in
margin_base = 50
margin_right = margin_base * 4
margin_top = margin_base * 3
# this determines the "flatness" of the isometric projection and an be
# tweaked for different looks - basically corresponds to how far the
# camera is above the horizon
plane_angle = 120
# don't change these
plane_obverse = radians((180 - plane_angle) / 2)
plane_angle = radians(plane_angle)
# okay, now determine the full graphic size with these dimensions projected
# semi-isometrically. We can also use these values later for drawing for
# drawing grid lines, et cetera. The axis widths and heights here are the
# dimensions of the bounding box wrapping the isometrically projected axes.
x_axis_length = (item_width * (len(intervals) - 1))
y_axis_length = (item_height * len(graphs))
x_axis_width = (sin(plane_angle / 2) * x_axis_length)
y_axis_width = (sin(plane_angle / 2) * y_axis_length)
canvas_width = x_axis_width + y_axis_width
# leave room for graph header
if graph_label:
margin_top += (2 * (canvas_width / 50))
x_axis_height = (cos(plane_angle / 2) * x_axis_length)
y_axis_height = (cos(plane_angle / 2) * y_axis_length)
canvas_height = x_axis_height + y_axis_height
# now we have the dimensions, the canvas can be instantiated
canvas = get_4cat_canvas(
self.dataset.get_results_path(),
width=(canvas_width + margin_base + margin_right),
height=(canvas_height + margin_base + margin_top),
header=graph_label)
# draw gridlines - vertical
gridline_x = y_axis_width + margin_base
gridline_y = margin_top + canvas_height
step_x_horizontal = sin(plane_angle / 2) * item_width
step_y_horizontal = cos(plane_angle / 2) * item_width
step_x_vertical = sin(plane_angle / 2) * item_height
step_y_vertical = cos(plane_angle / 2) * item_height
# labels for x axis
# month and week both follow the same pattern
# it's not always possible to distinguish between them but we will try
# by looking for months greater than 12 in which case we are dealing
# with weeks
# we need to know this because for months there is an extra row in the
# label with the full month
is_week = False
for i in range(0, len(intervals)):
if re.match(r"^[0-9]{4}-[0-9]{2}",
intervals[i]) and int(intervals[i].split("-")[1]) > 12:
is_week = True
break
skip = max(1, int(len(intervals) / max_gridlines))
for i in range(0, len(intervals)):
if i % skip == 0:
canvas.add(
Line(start=(gridline_x, gridline_y),
end=(gridline_x - y_axis_width,
gridline_y - y_axis_height),
stroke="grey",
stroke_width=0.25))
# to properly position the rotated and skewed text a container
# element is needed
label1 = str(intervals[i])[0:4]
center = (gridline_x, gridline_y)
container = SVG(x=center[0] - 25,
y=center[1],
width="50",
height="1.5em",
overflow="visible",
style="font-size:0.8em;")
container.add(
Text(insert=("25%", "100%"),
text=label1,
transform="rotate(%f) skewX(%f)" %
(-degrees(plane_obverse), degrees(plane_obverse)),
text_anchor="middle",
baseline_shift="-0.5em",
style="font-weight:bold;"))
if re.match(r"^[0-9]{4}-[0-9]{2}",
intervals[i]) and not is_week:
label2 = month_abbr[int(str(intervals[i])[5:7])]
if re.match(r"^[0-9]{4}-[0-9]{2}-[0-9]{2}", intervals[i]):
label2 += " %i" % int(intervals[i][8:10])
container.add(
Text(insert=("25%", "150%"),
text=label2,
transform="rotate(%f) skewX(%f)" %
(-degrees(plane_obverse), degrees(plane_obverse)),
text_anchor="middle",
baseline_shift="-0.5em"))
canvas.add(container)
gridline_x += step_x_horizontal
gridline_y -= step_y_horizontal
# draw graphs as filled beziers
top = step_y_vertical * 1.5
graph_start_x = y_axis_width + margin_base
graph_start_y = margin_top + canvas_height
# draw graphs in reverse order, so the bottom one is most in the
# foreground (in case of overlap)
for graph in reversed(list(graphs)):
self.dataset.update_status("Rendering graph for '%s'" % graph)
# path starting at lower left corner of graph
area_graph = Path(fill=self.colours[self.colour_index])
area_graph.push("M %f %f" % (graph_start_x, graph_start_y))
previous_value = None
graph_x = graph_start_x
graph_y = graph_start_y
for interval in graphs[graph]:
# normalise value
value = graphs[graph][interval]
try:
limit = limits[graph] if normalise_values else max_limit
value = top * copysign(abs(value) / limit, value)
except ZeroDivisionError:
value = 0
if previous_value is None:
# vertical line upwards to starting value of graph
area_graph.push("L %f %f" %
(graph_start_x, graph_start_y - value))
elif not smooth:
area_graph.push("L %f %f" % (graph_x, graph_y - value))
else:
# quadratic bezier from previous value to current value
control_left = (graph_x - (step_x_horizontal / 2),
graph_y + step_y_horizontal -
previous_value - (step_y_horizontal / 2))
control_right = (graph_x - (step_x_horizontal / 2),
graph_y - value + (step_y_horizontal / 2))
area_graph.push("C %f %f %f %f %f %f" %
(*control_left, *control_right, graph_x,
graph_y - value))
previous_value = value
graph_x += step_x_horizontal
graph_y -= step_y_horizontal
# line to the bottom of the graph at the current Y position
area_graph.push(
"L %f %f" %
(graph_x - step_x_horizontal, graph_y + step_y_horizontal))
area_graph.push("Z") # then close the Path
canvas.add(area_graph)
# add text labels - skewing is a bit complicated and we need a
# "center" to translate the origins properly.
if label_in_graph:
insert = (graph_start_x + 5, graph_start_y - 10)
else:
insert = (graph_x - (step_x_horizontal) + 5,
graph_y + step_y_horizontal - 10)
# we need to take the skewing into account for the translation
offset_y = tan(plane_obverse) * insert[0]
canvas.add(
Text(insert=(0, 0),
text=graph,
transform="skewY(%f) translate(%f %f)" %
(-degrees(plane_obverse), insert[0],
insert[1] + offset_y)))
# cycle colours, back to the beginning if all have been used
self.colour_index += 1
if self.colour_index >= len(self.colours):
self.colour_index = 0
graph_start_x -= step_x_vertical
graph_start_y -= step_y_vertical
# draw gridlines - horizontal
gridline_x = margin_base
gridline_y = margin_top + canvas_height - y_axis_height
for graph in graphs:
gridline_x += step_x_vertical
gridline_y += step_y_vertical
canvas.add(
Line(start=(gridline_x, gridline_y),
end=(gridline_x + x_axis_width,
gridline_y - x_axis_height),
stroke="black",
stroke_width=1))
# x axis
canvas.add(
Line(start=(margin_base + y_axis_width,
margin_top + canvas_height),
end=(margin_base + canvas_width,
margin_top + canvas_height - x_axis_height),
stroke="black",
stroke_width=2))
# and finally save the SVG
canvas.save(pretty=True)
self.dataset.finish(len(graphs))
def process(self):
"""
Render an SVG histogram/bar chart using a previous frequency analysis
as input.
"""
self.dataset.update_status("Reading source file")
header = self.parameters.get("header", self.options["header"]["default"])
max_posts = 0
# collect post numbers per month
intervals = {}
for post in self.iterate_csv_items(self.source_file):
intervals[post["date"]] = int(post["frequency"])
max_posts = max(max_posts, int(post["frequency"]))
if len(intervals) <= 1:
self.dataset.update_status("Not enough data available for a histogram; need more than one time series.")
self.dataset.finish(0)
return
self.dataset.update_status("Cleaning up data")
(missing, intervals) = pad_interval(intervals)
# create histogram
self.dataset.update_status("Drawing histogram")
# you may change the following four variables to adjust the graph dimensions
width = 1024
height = 786
y_margin = 75
x_margin = 50
x_margin_left = x_margin * 2
tick_width = 5
fontsize_normal = int(height / 40)
fontsize_small = int(height / 75)
# better don't touch the following
line_width = round(width / 512)
y_margin_top = 150 if header else 50
y_height = height - (y_margin + y_margin_top)
x_width = width - (x_margin + x_margin_left)
canvas = Drawing(filename=str(self.dataset.get_results_path()), size=(width, height),
style="font-family:monospace;font-size:%ipx" % fontsize_normal)
# normalize the Y axis to a multiple of a power of 10
magnitude = pow(10, len(str(max_posts)) - 1) # ew
max_neat = math.ceil(max_posts / magnitude) * magnitude
self.dataset.update_status("Max (normalized): %i (%i) (magnitude: %i)" % (max_posts, max_neat, magnitude))
# draw border
canvas.add(Rect(
insert=(0, 0),
size=(width, height),
stroke="#000",
stroke_width=line_width,
fill="#FFF"
))
# draw header on a black background if needed
if header:
if len(header) > 40:
header = header[:37] + "..."
header_rect_height = (y_margin_top / 1.5)
header_fontsize = (width / len(header))
header_container = SVG(insert=(0, 0), size=(width, header_rect_height))
header_container.add(Rect(
insert=(0, 0),
size=(width, header_rect_height),
fill="#000"
))
header_container.add(Text(
insert=("50%", "50%"),
text=header,
dominant_baseline="middle",
text_anchor="middle",
fill="#FFF",
style="font-size:%i" % header_fontsize
))
canvas.add(header_container)
# horizontal grid lines
for i in range(0, 10):
offset = (y_height / 10) * i
canvas.add(Line(
start=(x_margin_left, y_margin_top + offset),
end=(width - x_margin, y_margin_top + offset),
stroke="#EEE",
stroke_width=line_width
))
# draw bars
item_width = (width - (x_margin + x_margin_left)) / len(intervals)
item_height = (height - y_margin - y_margin_top)
bar_width = item_width * 0.9
x = x_margin_left + (item_width / 2) - (bar_width / 2)
if bar_width >= 8:
arc_adjust = max(8, int(item_width / 5)) / 2
else:
arc_adjust = 0
for interval in intervals:
posts = int(intervals[interval])
bar_height = ((posts / max_neat) * item_height)
self.dataset.update_status("%s: %i posts" % (interval, posts))
bar_top = height - y_margin - bar_height
bar_bottom = height - y_margin
if bar_height == 0:
x += item_width
continue
bar = Path(fill="#000")
bar.push("M %f %f" % (x, bar_bottom))
bar.push("L %f %f" % (x, bar_top + (arc_adjust if bar_height > arc_adjust else 0)))
if bar_height > arc_adjust > 0:
control = (x, bar_top)
bar.push("C %f %f %f %f %f %f" % (*control, *control, x + arc_adjust, bar_top))
bar.push("L %f %f" % (x + bar_width - arc_adjust, height - y_margin - bar_height))
if bar_height > arc_adjust > 0:
control = (x + bar_width, bar_top)
bar.push("C %f %f %f %f %f %f" % (*control, *control, x + bar_width, bar_top + arc_adjust))
bar.push("L %f %f" % (x + bar_width, height - y_margin))
bar.push("Z")
canvas.add(bar)
x += item_width
# draw X and Y axis
canvas.add(Line(
start=(x_margin_left, height - y_margin),
end=(width - x_margin, height - y_margin),
stroke="#000",
stroke_width=2
))
canvas.add(Line(
start=(x_margin_left, y_margin_top),
end=(x_margin_left, height - y_margin),
stroke="#000",
stroke_width=2
))
# draw ticks on Y axis
for i in range(0, 10):
offset = (y_height / 10) * i
canvas.add(Line(
start=(x_margin_left - tick_width, y_margin_top + offset),
end=(x_margin_left, y_margin_top + offset),
stroke="#000",
stroke_width=line_width
))
# draw ticks on X axis
for i in range(0, len(intervals)):
offset = (x_width / len(intervals)) * (i + 0.5)
canvas.add(Line(
start=(x_margin_left + offset, height - y_margin),
end=(x_margin_left + offset, height - y_margin + tick_width),
stroke="#000",
stroke_width=line_width
))
# prettify
# y labels
origin = (x_margin_left / 2)
step = y_height / 10
for i in range(0, 11):
label = str(int((max_neat / 10) * i))
labelsize = (len(label) * fontsize_normal * 1.25, fontsize_normal)
label_x = origin - (tick_width * 2)
label_y = height - y_margin - (i * step) - (labelsize[1] / 2)
label_container = SVG(
insert=(label_x, label_y),
size=(x_margin_left / 2, x_margin_left / 5)
)
label_container.add(Text(
insert=("100%", "50%"),
text=label,
dominant_baseline="middle",
text_anchor="end"
))
canvas.add(label_container)
# x labels
label_width = max(fontsize_small * 6, item_width)
label_x = x_margin_left
label_y = height - y_margin + (tick_width * 2)
next = 0
for interval in intervals:
if len(interval) == 7:
label = month_abbr[int(interval[5:7])] + "\n" + interval[0:4]
elif len(interval) == 10:
label = str(int(interval[8:10])) + month_abbr[int(interval[5:7])] + "\n" + interval[0:4]
else:
label = interval.replace("-", "\n")
if label_x > next:
shift = 0
for line in label.split("\n"):
label_container = SVG(
insert=(label_x + (item_width / 2) - (label_width / 2), label_y + (tick_width * 2)),
size=(label_width, y_margin), overflow="visible")
label_container.add(Text(
insert=("50%", "0%"),
text=line,
dominant_baseline="middle",
text_anchor="middle",
baseline_shift=-shift
))
shift += fontsize_small * 2
canvas.add(label_container)
next = label_x + (label_width * 0.9)
label_x += item_width
# 4cat logo
label = "made with 4cat - 4cat.oilab.nl"
footersize = (fontsize_small * len(label) * 0.7, fontsize_small * 2)
footer = SVG(insert=(width - footersize[0], height - footersize[1]), size=footersize)
footer.add(Rect(insert=(0, 0), size=("100%", "100%"), fill="#000"))
footer.add(Text(
insert=("50%", "50%"),
text=label,
dominant_baseline="middle",
text_anchor="middle",
fill="#FFF",
style="font-size:%i" % fontsize_small
))
canvas.add(footer)
canvas.save(pretty=True)
self.dataset.update_status("Finished")
self.dataset.finish(len(intervals))
def make_svg(self, data):
layers = data["layers"]
_PROPORTION = 10
_SHIFT_PROP = _PROPORTION / 1.5
for f in self.forms:
structures = [None] * len(f.id.split("_"))
linkers = []
centers = {}
order = {}
maxW, maxH = 0, 0
for cx, x in enumerate(f.id.split("_")):
order[x] = cx
for cl, l in enumerate(layers):
for cs, s in enumerate(l):
name = f.id + "__" + s["id"]
if s["type"] == "H":
color = "cornflowerblue" if "ref" not in s else "gainsboro"
shape = Circle( center = (s["shift_x"] * _SHIFT_PROP, s["shift_z"] * _SHIFT_PROP),
r = 2.3 * _PROPORTION, id = name,
fill = color, stroke="black",
stroke_width = "2")
elif s["type"] == "E":
color = "indianred" if "ref" not in s else "salmon"
rotate = f.get_ss_by_id(s["id"]).struc.goes_down()
shape = Triangle(center = (s["shift_x"] * _SHIFT_PROP, s["shift_z"] * _SHIFT_PROP),
rc = 2.3 * _PROPORTION, id = name, fill=color,
stroke = "black", stroke_width = "2",
rotate = rotate)
else:
color = "darkgreen" if "ref" not in s else "lightgreen"
shape = Cross(center = (s["shift_x"] * _SHIFT_PROP, s["shift_z"] * _SHIFT_PROP), r = 2.3 * _PROPORTION,
fill=color, stroke="black", stroke_width = "2",
id = name)
structures[order[s["id"]]] = shape
centers[s["id"]] = [s["shift_x"] * _SHIFT_PROP, s["shift_z"] * _SHIFT_PROP]
if s["shift_x"] * _SHIFT_PROP > maxW: maxW = s["shift_x"] * _SHIFT_PROP
if s["shift_z"] * _SHIFT_PROP > maxH: maxH = s["shift_z"] * _SHIFT_PROP
for cx, x in enumerate(f.id.split("_")):
if cx == 0 or cx == len(f.id.split("_")) - 1:
init = [0, 0]
if (ord(x[0]) - 64) <= len(layers) / 2:
init[1] = centers[x][1] - (2.3 * _PROPORTION * 2)
else:
init[1] = centers[x][1] + (2.3 * _PROPORTION * 2)
if int(re.search("(\d+)", x).group(1)) <= len(layers[ord(x[0]) - 65]) / 2:
init[0] = centers[x][0] - (2.3 * _PROPORTION * 2)
else:
init[0] = centers[x][0] + (2.3 * _PROPORTION * 2)
if cx == 0:
shape = Line(init, centers[x], stroke="darkblue", stroke_width = "4")
elif cx == len(f.id.split("_")) - 1:
shape = Line(centers[x], init, stroke="darkred", stroke_width = "4")
linkers.append(shape)
for cy, y in enumerate(f.id.split("_")):
if cy == cx + 1:
shape = Line(centers[x], centers[y], stroke="black", stroke_width = "4")
linkers.append(shape)
# Intercalate
toplink = []
dowlink = []
if f.sslist[0].struc.goes_up():
dowlink = linkers[0:][::2]
toplink = linkers[1:][::2]
else:
dowlink = linkers[1:][::2]
toplink = linkers[0:][::2]
g = Group()
for x in dowlink:
g.add(x)
for x in structures:
g.add(x)
for x in toplink:
g.add(x)
g.translate(2.3 * _PROPORTION * 3, 2.3 * _PROPORTION * 3)
d = Drawing(size = (maxW + ((2.3 * _PROPORTION * 3) * 2),
maxH + ((2.3 * _PROPORTION * 3) * 2)),
id = f.id + "__image")
d.add(g)
for x in data["forms"]:
if x["id"] == f.id:
x["svg"] = d.tostring()
def plot_data(self):
if not self.show_legend:
self.plot.add(
Line(start=(self.margin_left, self.margin_top),
end=(self.width - self.margin_right, self.margin_top),
stroke_width=1,
stroke="black"))
self.max_value = max(
[x['score'] for x in self.annotated_scores.values()])
if not self.min_value:
self.min_value = min(
[x['score'] for x in self.annotated_scores.values()])
if not self.show_legend:
self.plot.add(
Text("Fibroblasts",
insert=(self.margin_left, self.margin_top - 5),
fill="black",
font_size="15"))
self.plot.add(
Text("Cardiomyocytes",
insert=(self.width - self.margin_right - 100,
self.margin_top - 5),
fill="black",
font_size="15"))
delta = self.max_value - self.min_value
plottable = self.width - (self.margin_left + self.margin_right)
spacing = {
"Fibroblasts": INIT_GAP,
"Cardiomyocytes": INIT_GAP,
"Experimental": INIT_GAP,
"other": INIT_GAP
}
for sample, sample_details in self.annotated_scores.items():
position = self.margin_left + (
(sample_details['score'] - self.min_value) / delta * plottable)
# colour = "grey"
if "category" in sample_details:
sample_type = sample_details["category"]
if sample_type == "Fibroblasts":
colour = "green"
elif sample_type == "Cardiomyocytes":
colour = "blue"
elif sample_type == "Experimental":
colour = "red"
else:
continue
# sample_type = "other"
else:
continue
# sample_type = "other"
circle_obj = Circle(
center=(position, self.margin_top + spacing[sample_type] + 5),
r=3,
stroke_width=0.1,
stroke_linecap='round',
stroke_opacity=1,
fill=colour,
fill_opacity=0.6) # set to 0.2 if you want to show clear.
circle_obj.set_desc('{} - {} - {}'.format(
sample, sample_type, sample_details["score"] if "description"
not in sample_details else sample_details["description"]))
self.plot.add(circle_obj)
if sample_type == "other":
spacing[sample_type] += 3
else:
spacing[sample_type] += 6
def test_coordinates(self):
line = Line(start=('10cm','11cm'), end=('20cm', '30cm'))
self.assertEqual(line.tostring(), '<line x1="10cm" x2="20cm" y1="11cm" y2="30cm" />')
def ruler(scale=1000,
length=100,
major_interval=10,
minor_per_major=10,
reverse=False,
vertical=False):
major_len_mm = 3
mid_len_mm = 2
minor_len_mm = 1
font_size_mm = 2
if reverse:
sign = -1
else:
sign = 1
g = Group()
# Draw major lines
mj = 0
while mj < length:
# Minor tick marks
for i in range(1, minor_per_major):
offset_mm = sign * (
(mj + i * major_interval / minor_per_major) / scale) * 1000
if i == minor_per_major / 2:
# Half mark
if vertical:
g.add(
Line(((0) * mm, (offset_mm) * mm),
((-mid_len_mm) * mm, (offset_mm) * mm)))
else:
g.add(
Line(((offset_mm) * mm, (0) * mm),
((offset_mm) * mm, (mid_len_mm) * mm)))
else:
if vertical:
g.add(
Line(((0) * mm, (offset_mm) * mm),
((-minor_len_mm) * mm, (offset_mm) * mm)))
else:
g.add(
Line(((offset_mm) * mm, (0) * mm),
((offset_mm) * mm, (minor_len_mm) * mm)))
mj += major_interval
if mj >= length:
label = f"{mj}m"
else:
label = f"{mj}"
# Major line
offset_mm = sign * (mj / scale) * 1000
if vertical:
g.add(
Line(((0) * mm, (offset_mm) * mm),
((-major_len_mm) * mm, (offset_mm) * mm)))
if offset_mm > (major_len_mm + font_size_mm * 1.5):
# only draw label if it wont collide w horizontal ruler
g.add(
Text(
label,
x=[(-major_len_mm - 1) * mm],
y=[(offset_mm + font_size_mm / 2.5) * mm],
text_anchor="end",
font_size=font_size_mm * mm,
))
else:
g.add(
Line(((offset_mm) * mm, (0) * mm),
((offset_mm) * mm, (major_len_mm) * mm)))
g.add(
Text(
label,
x=[(offset_mm) * mm],
y=[(major_len_mm + font_size_mm) * mm],
text_anchor="middle",
font_size=font_size_mm * mm,
))
return g
def get_line(self, value, color, **options):
end = self.calc_slice_coord(value, self.r)
start = (self.x0, self.y0)
line = Line(start, end, stroke=color, stroke_dasharray="4 4")
return line.tostring()
def process(self):
# parse parameters
input_words = self.parameters.get("words", "")
if not input_words or not input_words.split(","):
self.dataset.update_status(
"No input words provided, cannot look for similar words.",
is_final=True)
self.dataset.finish(0)
return
input_words = input_words.split(",")
try:
threshold = float(
self.parameters.get("threshold",
self.options["threshold"]["default"]))
except ValueError:
threshold = float(self.options["threshold"]["default"])
threshold = max(-1.0, min(1.0, threshold))
num_words = convert_to_int(self.parameters.get("num-words"),
self.options["num-words"]["default"])
overlay = self.parameters.get("overlay")
reduction_method = self.parameters.get("method")
all_words = self.parameters.get("all-words")
# load model files and initialise
self.dataset.update_status("Unpacking word embedding models")
staging_area = self.unpack_archive_contents(self.source_file)
common_vocab = None
vector_size = None
models = {}
# find words that are common to all models
self.dataset.update_status("Determining cross-model common vocabulary")
for model_file in staging_area.glob("*.model"):
if self.interrupted:
shutil.rmtree(staging_area)
raise ProcessorInterruptedException(
"Interrupted while processing word embedding models")
model = KeyedVectors.load(str(model_file)).wv
models[model_file.stem] = model
if vector_size is None:
vector_size = model.vector_size # needed later for dimensionality reduction
if common_vocab is None:
common_vocab = set(model.vocab.keys())
else:
common_vocab &= set(model.vocab.keys()) # intersect
# sort common vocabulary by combined frequency across all models
# this should make filtering for common words a bit faster further down
self.dataset.update_status("Sorting vocabulary")
common_vocab = list(common_vocab)
common_vocab.sort(key=lambda w: sum(
[model.vocab[w].count for model in models.values()]),
reverse=True)
# initial boundaries of 2D space (to be adjusted later based on t-sne
# outcome)
max_x = 0.0 - sys.float_info.max
max_y = 0.0 - sys.float_info.max
min_x = sys.float_info.max
min_y = sys.float_info.max
# for each model, find the words that we may want to plot - these are
# the nearest neighbours for the given query words
relevant_words = {}
# the vectors need to be reduced all at once - but the vectors are
# grouped by model. To solve this, keep one numpy array of vectors,
# but also keep track of which indexes of this array belong to which
# model, by storing the index of the first vector for a model
vectors = numpy.empty((0, vector_size))
vector_offsets = {}
# now process each model
for model_name, model in models.items():
relevant_words[model_name] = set(
) # not a set, since order needs to be preserved
self.dataset.update_status("Finding similar words in model '%s'" %
model_name)
for query in input_words:
if query not in model.vocab:
self.dataset.update_status(
"Query '%s' was not found in model %s; cannot find nearest neighbours."
% (query, model_name),
is_final=True)
self.dataset.finish(0)
return
if self.interrupted:
shutil.rmtree(staging_area)
raise ProcessorInterruptedException(
"Interrupted while finding similar words")
# use a larger sample (topn) than required since some of the
# nearest neighbours may not be in the common vocabulary and
# will therefore need to be ignored
context = set([
word[0] for word in model.most_similar(query, topn=1000)
if word[0] in common_vocab and word[1] >= threshold
][:num_words])
relevant_words[model_name] |= {
query
} | context # always include query word
# now do another loop to determine which words to plot for each model
# this is either the same as relevant_words, or a superset which
# combines all relevant words for all models
plottable_words = {}
last_model = max(relevant_words.keys())
all_relevant_words = set().union(*relevant_words.values())
for model_name, words in relevant_words.items():
plottable_words[model_name] = []
vector_offsets[model_name] = len(vectors)
# determine which words to plot for this model. either the nearest
# neighbours for this model, or all nearest neighbours found across
# all models
words_to_include = all_relevant_words if all_words else relevant_words[
model_name]
for word in words_to_include:
if word in plottable_words[model_name] or (
not overlay and model_name != last_model
and word not in input_words):
# only plot each word once per model, or if 'overlay'
# is not set, only once overall (for the most recent
# model)
continue
vector = models[model_name][word]
plottable_words[model_name].append(word)
vectors = numpy.append(vectors, [vector], axis=0)
del models # no longer needed
# reduce the vectors of all words to be plotted for this model to
# a two-dimensional coordinate with the previously initialised tsne
# transformer. here the two-dimensional vectors are interpreted as
# cartesian coordinates
if reduction_method == "PCA":
pca = PCA(n_components=2, random_state=0)
vectors = pca.fit_transform(vectors)
elif reduction_method == "t-SNE":
# initialise t-sne transformer
# parameters taken from Hamilton et al.
# https://github.com/williamleif/histwords/blob/master/viz/common.py
tsne = TSNE(n_components=2,
random_state=0,
learning_rate=150,
init="pca")
vectors = tsne.fit_transform(vectors)
elif reduction_method == "TruncatedSVD":
# standard sklearn parameters made explicit
svd = TruncatedSVD(n_components=2,
algorithm="randomized",
n_iter=5,
random_state=0)
vectors = svd.fit_transform(vectors)
else:
shutil.rmtree(staging_area)
self.dataset.update_status(
"Invalid dimensionality reduction technique selected",
is_final=True)
self.dataset.finish(0)
return
# also keep track of the boundaries of our 2D space, so we can plot
# them properly later
for position in vectors:
max_x = max(max_x, position[0])
max_y = max(max_y, position[1])
min_x = min(min_x, position[0])
min_y = min(min_y, position[1])
# now we know for each model which words should be plotted and at what
# position
# with this knowledge, we can normalize the positions, and start
# plotting them in a graph
# a palette generated with https://medialab.github.io/iwanthue/
colours = [
"#d58eff", "#cf9000", "#3391ff", "#a15700", "#911ca7", "#00ddcb",
"#cc25a9", "#d5c776", "#6738a8", "#ff9470", "#47c2ff", "#a4122c",
"#00b0ca", "#9a0f76", "#ff70c8", "#713c88"
]
colour_index = 0
# make sure all coordinates are positive
max_x -= min_x
max_y -= min_y
# determine graph dimensions and proportions
width = 1000 # arbitrary
height = width * (max_y / max_x) # retain proportions
scale = width / max_x
# margin around the plot to give room for labels and to look better
margin = width * 0.1
width += 2 * margin
height += 2 * margin
# normalize all known positions to fit within the graph
vectors = [(margin + ((position[0] - min_x) * scale),
margin + ((position[1] - min_y) * scale))
for position in vectors]
# now all positions are finalised, we can determine the "journey" of
# each query - the sequence of positions in the graph it takes, so we
# can draw lines from position to position later
journeys = {}
for query in input_words:
journeys[query] = []
for model_name, words in plottable_words.items():
index = words.index(query)
journeys[query].append(vectors[vector_offsets[model_name] +
index])
# font sizes proportional to width (which is static and thus predictable)
fontsize_large = width / 50
fontsize_normal = width / 75
fontsize_small = width / 100
# now we have the dimensions, the canvas can be instantiated
model_type = self.source_dataset.parameters.get(
"model-type", "word2vec")
canvas = get_4cat_canvas(
self.dataset.get_results_path(),
width,
height,
header="%s nearest neighbours (fitting: %s) - '%s'" %
(model_type, reduction_method, ",".join(input_words)),
fontsize_normal=fontsize_normal,
fontsize_large=fontsize_large,
fontsize_small=fontsize_small)
# use colour-coded backgrounds to distinguish the query words in the
# graph, each model (= interval) with a separate colour
for model_name in plottable_words:
solid = Filter(id="solid-%s" % model_name)
solid.feFlood(flood_color=colours[colour_index])
solid.feComposite(in_="SourceGraphic")
canvas.defs.add(solid)
colour_index += 1
# now plot each word for each model
self.dataset.update_status("Plotting graph")
words = SVG(insert=(0, 0), size=(width, height))
queries = SVG(insert=(0, 0), size=(width, height))
colour_index = 0
for model_name, labels in plottable_words.items():
positions = vectors[
vector_offsets[model_name]:vector_offsets[model_name] +
len(labels)]
label_index = 0
for position in positions:
word = labels[label_index]
is_query = word in input_words
label_index += 1
filter = ("url(#solid-%s)" %
model_name) if is_query else "none"
colour = "#FFF" if is_query else colours[colour_index]
fontsize = fontsize_normal if is_query else fontsize_small
if word in input_words:
word += " (" + model_name + ")"
label_container = SVG(insert=position,
size=(1, 1),
overflow="visible")
label_container.add(
Text(insert=("50%", "50%"),
text=word,
dominant_baseline="middle",
text_anchor="middle",
style="fill:%s;font-size:%ipx" % (colour, fontsize),
filter=filter))
# we make sure the queries are always rendered on top by
# putting them in a separate SVG container
if is_query:
queries.add(label_container)
else:
words.add(label_container)
colour_index = 0 if colour_index >= len(
colours) else colour_index + 1
# plot a line between positions for query words
lines = SVG(insert=(0, 0), size=(width, height))
for query, journey in journeys.items():
previous_position = None
for position in journey:
if previous_position is None:
previous_position = position
continue
lines.add(
Line(start=previous_position,
end=position,
stroke="#CE1B28",
stroke_width=2))
previous_position = position
canvas.add(lines)
canvas.add(words)
canvas.add(queries)
canvas.save(pretty=True)
shutil.rmtree(staging_area)
self.dataset.finish(len(journeys))
def test_coordinates(self):
line = Line(start=('10cm', '11cm'), end=('20cm', '30cm'))
self.assertEqual(line.tostring(),
'<line x1="10cm" x2="20cm" y1="11cm" y2="30cm" />')
