def test_offsetbox_loc_codes():
# Check that valid string location codes all work with an AnchoredOffsetbox
codes = {
'upper right': 1,
'upper left': 2,
'lower left': 3,
'lower right': 4,
'right': 5,
'center left': 6,
'center right': 7,
'lower center': 8,
'upper center': 9,
'center': 10,
}
fig, ax = plt.subplots()
da = DrawingArea(100, 100)
for code in codes:
anchored_box = AnchoredOffsetbox(loc=code, child=da)
ax.add_artist(anchored_box)
fig.canvas.draw()
def make_line(color,
style,
alpha,
width=20,
y_offset=10,
height=20,
linewidth=3):
color = color if color != None else "k" # Default value if None
style = style if style != None else "-"
viz = DrawingArea(30, 10, 0, -5)
x = np.arange(0.0, width, width / 7.0)
y = np.repeat(y_offset, x.size)
key = mlines.Line2D(x,
y,
linestyle=style,
linewidth=linewidth,
alpha=alpha,
c=color)
viz.add_artist(key)
return viz
def createBall(self, colour):
radius = 2
da = DrawingArea(radius, radius, 10, 10)
circle = patches.Circle((0.0, 0.0),
radius=radius,
edgecolor='k',
facecolor=colour,
fill=True,
ls='solid',
clip_on=False)
da.add_artist(circle)
ab = AnnotationBbox(da,
xy=(0, 0),
xycoords=("data", "data"),
boxcoords=("data", "data"),
box_alignment=(5.0, 5.0),
frameon=False)
return ab
def pie(plot, p, values, colors=None, size=16, norm=True,
xoff=0, yoff=0,
halign=0.5, valign=0.5,
xycoords='data', boxcoords=('offset points')):
"""
Draw a pie chart
Args:
plot (Tree): A Tree plot instance
p (Node): A Node object
values (list): A list of floats.
colors (list): A list of strings to pull colors from. Optional.
size (float): Diameter of the pie chart
norm (bool): Whether or not to normalize the values so they
add up to 360
xoff, yoff (float): X and Y offset. Optional, defaults to 0
halign, valign (float): Horizontal and vertical alignment within
box. Optional, defaults to 0.5
"""
x, y = _xy(plot, p)
da = DrawingArea(size, size); r = size*0.5; center = (r,r)
x0 = 0
S = 360.0
if norm: S = 360.0/sum(values)
if not colors:
c = _colors.tango()
colors = [ c.next() for v in values ]
for i, v in enumerate(values):
theta = v*S
if v: da.add_artist(Wedge(center, r, x0, x0+theta,
fc=colors[i], ec='none'))
x0 += theta
box = AnnotationBbox(da, (x,y), pad=0, frameon=False,
xybox=(xoff, yoff),
xycoords=xycoords,
box_alignment=(halign,valign),
boxcoords=boxcoords)
plot.add_artist(box)
plot.figure.canvas.draw_idle()
return box
def hbar(plot, p, values, colors=None, height=16,
xoff=0, yoff=0,
halign=1, valign=0.5,
xycoords='data', boxcoords=('offset points')):
x, y = _xy(plot, p)
h = height; w = sum(values) * height#; yoff=h*0.5
da = DrawingArea(w, h)
x0 = -sum(values)
if not colors:
c = _colors.tango()
colors = [ c.next() for v in values ]
for i, v in enumerate(values):
if v: da.add_artist(Rectangle((x0,0), v*h, h, fc=colors[i], ec='none'))
x0 += v*h
box = AnnotationBbox(da, (x,y), pad=0, frameon=False,
xybox=(xoff, yoff),
xycoords=xycoords,
box_alignment=(halign,valign),
boxcoords=boxcoords)
plot.add_artist(box)
plot.figure.canvas.draw_idle()
def draw_matching(match, nbMen, nbWomen):
width, height = 25, 50
da = DrawingArea(width, height)
coordM = [( width/4, height*(nbMen-i)/(nbMen+1)) for i in range(nbMen)]
coordW = [(3*width/4, height*(nbWomen-i)/(nbWomen+1)) for i in range(nbWomen)]
for idWoman in range(nbWomen):
if match[idWoman] == -1:
xdata = [coordW[idWoman][0]]
ydata = [coordW[idWoman][1]]
da.add_artist(Line2D(xdata, ydata, marker="."))
for idMan in range(nbMen):
if idMan not in match:
xdata = [coordM[idMan][0]]
ydata = [coordM[idMan][1]]
da.add_artist(Line2D(xdata, ydata, marker="."))
for idWoman,idMan in enumerate(match):
if idMan != -1:
xdata = [coordM[idMan][0], coordW[idWoman][0]]
ydata = [coordM[idMan][1], coordW[idWoman][1]]
da.add_artist(Line2D(xdata, ydata, marker="."))
return da
def tipsquares(plot, p, colors='r', size=15, pad=2, edgepad=10):
"""
RR: Bug with this function. If you attempt to call it with a list as an
argument for p, it will not only not work (expected) but it will also
make it so that you can't interact with the tree figure (gives errors when
you try to add symbols, select nodes, etc.) -CZ
Add square after tip label, anchored to the side of the plot
Args:
plot (Tree): A Tree plot instance.
p (Node): A Node object (Should be a leaf node).
colors (str): color of drawn square. Optional, defaults to 'r' (red)
size (float): Size of square. Optional, defaults to 15
pad: RR: I am unsure what this does. Does not seem to have visible
effect when I change it. -CZ
edgepad (float): Padding from square to edge of plot. Optional,
defaults to 10.
"""
x, y = _xy(plot, p) # p is a single node or point in data coordinates
n = len(colors)
da = DrawingArea(size * n + pad * (n - 1), size, 0, 0)
sx = 0
for c in colors:
sq = Rectangle((sx, 0), size, size, color=c)
da.add_artist(sq)
sx += size + pad
box = AnnotationBbox(da, (x, y),
xybox=(-edgepad, y),
frameon=False,
pad=0.0,
xycoords='data',
box_alignment=(1, 0.5),
boxcoords=('axes points', 'data'))
plot.add_artist(box)
plot.figure.canvas.draw_idle()
def test_offsetbox_clip_children():
# - create a plot
# - put an AnchoredOffsetbox with a child DrawingArea
# at the center of the axes
# - give the DrawingArea a gray background
# - put a black line across the bounds of the DrawingArea
# - see that the black line is clipped to the edges of
# the DrawingArea.
fig, ax = plt.subplots()
size = 100
da = DrawingArea(size, size, clip=True)
bg = mpatches.Rectangle((0, 0),
size,
size,
facecolor='#CCCCCC',
edgecolor='None',
linewidth=0)
line = mlines.Line2D([-size * .5, size * 1.5], [size / 2, size / 2],
color='black',
linewidth=10)
anchored_box = AnchoredOffsetbox(loc=10,
child=da,
pad=0.,
frameon=False,
bbox_to_anchor=(.5, .5),
bbox_transform=ax.transAxes,
borderpad=0.)
da.add_artist(bg)
da.add_artist(line)
ax.add_artist(anchored_box)
fig.canvas.draw()
assert not fig.stale
da.clip_children = True
assert fig.stale
def plot_rectangle(self, figure, axis):
'''
plots the legend rectangle above the left corner of the figure
:param figure: figure on which to add the label
:param axis: axis on which to add the label
:return: -
'''
box1 = TextArea(" True: \n False: \n NaN: ",
textprops=dict(color="k", size=10))
# box2 = DrawingArea(20, 27.5, 0, 0)
# el1 = Rectangle((5, 15), width=10, height=10, angle=0, fc="g")
# el2 = Rectangle((5, 2.5), width=10, height=10, angle=0, fc="r")
box2 = DrawingArea(20, 45, 0, 0)
el1 = Rectangle((5, 30), width=10, height=10, angle=0, fc="g")
el2 = Rectangle((5, 18.5), width=10, height=10, angle=0, fc="r")
el3 = Rectangle((5, 7), width=10, height=10, angle=0, fc='#d3d3d3')
box2.add_artist(el1)
box2.add_artist(el2)
box2.add_artist(el3)
box = HPacker(children=[box1, box2], align="center", pad=0, sep=5)
anchored_box = AnchoredOffsetbox(
loc=3,
child=box,
pad=0.,
frameon=True,
bbox_to_anchor=(0., 1.02),
bbox_transform=axis.transAxes,
borderpad=0.,
)
axis.add_artist(anchored_box)
figure.subplots_adjust(top=0.8)
def generateGraph(self,
data=None,
outputFilename=None,
timeDivisions=6,
graphWidth=1920,
graphHeight=300,
darkMode=False,
rainVariance=False,
minMaxTemperature=False,
fontSize=12,
symbolZoom=1.0,
symbolDivision=1,
showCityName=None,
writeMetaData=None):
logging.debug("Initializing graph...")
if darkMode:
colors = self.colorsDarkMode
else:
colors = self.colorsLightMode
fig = plt.figure(0) # Main figure
rainAxis = fig.add_subplot(111)
# set font sizes
plt.rcParams.update({'font.size':
fontSize}) # Temperature Y axis and day names
rainAxis.tick_params(axis='y', labelsize=fontSize) # Rain Y axis
plt.xticks(fontsize=fontSize) # Time axis
if not graphWidth:
graphWidth = 1280
if not graphHeight:
graphHeight = 300
logging.debug("Graph size: %d x %d pixel" % (graphWidth, graphHeight))
fig.set_size_inches(
float(graphWidth) / fig.get_dpi(),
float(graphHeight) / fig.get_dpi())
# Plot dimension and borders
bbox = rainAxis.get_window_extent().transformed(
fig.dpi_scale_trans.inverted())
width, height = bbox.width * fig.dpi, bbox.height * fig.dpi # plot size in pixel
plt.margins(x=0)
rainAxis.margins(x=0)
plt.subplots_adjust(left=40 / width,
right=1 - 40 / width,
top=1 - 35 / height,
bottom=40 / height)
bbox = rainAxis.get_window_extent().transformed(
fig.dpi_scale_trans.inverted())
width, height = bbox.width * fig.dpi, bbox.height * fig.dpi # plot size in pixel
xPixelsPerDay = width / data["noOfDays"]
# Dimensions of the axis in pixel
firstDayX = math.ceil(bbox.x0 * fig.dpi)
firstDayY = math.ceil(bbox.y0 * fig.dpi)
dayWidth = math.floor((bbox.x1 - bbox.x0) * fig.dpi) / data["noOfDays"]
dayHeight = math.floor((bbox.y1 - bbox.y0) * fig.dpi)
# Show gray background on every 2nd day
for day in range(0, data["noOfDays"], 2):
plt.axvspan(data["timestamps"][0 + day * 24],
data["timestamps"][23 + day * 24] + 3600,
facecolor='gray',
alpha=0.2)
# Time axis and ticks
plt.xticks(data["timestamps"][::timeDivisions],
data["formatedTime"][::timeDivisions])
rainAxis.tick_params(axis='x', colors=colors["x-axis"])
# Rain (data gets splitted to stacked bars)
logging.debug("Creating rain plot...")
rainBars = [0] * len(self.rainColorSteps)
for i in range(0, len(self.rainColorSteps)):
rainBars[i] = []
for rain in data["rainfall"]:
for i in range(0, len(self.rainColorSteps)):
if rain > self.rainColorSteps[i]:
rainBars[i].append(self.rainColorStepSizes[i])
else:
if i > 0:
rainBars[i].append(
max(rain - self.rainColorSteps[i - 1], 0))
else:
rainBars[i].append(rain)
continue
rainAxis.bar(data["timestamps"],
rainBars[0],
width=3000,
color=self.rainColors[0],
align='edge')
bottom = [0] * len(rainBars[0])
for i in range(1, len(self.rainColorSteps)):
bottom = np.add(bottom, rainBars[i - 1]).tolist()
rainAxis.bar(data["timestamps"],
rainBars[i],
bottom=bottom,
width=3000,
color=self.rainColors[i],
align='edge')
rainAxis.tick_params(axis='y',
labelcolor=colors["rain-axis"],
width=0,
length=8)
rainYRange = plt.ylim()
rainScaleMax = max(
data["rainfall"]
) + 1 # Add a bit to make sure we do not bang our head
plt.ylim(0, rainScaleMax)
rainAxis.locator_params(axis='y', nbins=7)
# TODO find a better way than rounding
rainAxis.yaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
# Rain color bar as y axis
plt.xlim(
data["timestamps"][0], data["timestamps"][-1] +
(data["timestamps"][1] - data["timestamps"][0]))
pixelToRainX = 1 / xPixelsPerDay * (data["timestamps"][23] -
data["timestamps"][0])
x = data["timestamps"][-1] + (
data["timestamps"][1] - data["timestamps"][0]) # end of x
w = 7 * pixelToRainX
for i in range(0, len(self.rainColorSteps)):
y = self.rainColorSteps[i] - self.rainColorStepSizes[i]
if y > rainScaleMax:
break
h = self.rainColorSteps[i] + self.rainColorStepSizes[i]
if y + h >= rainScaleMax: # reached top
h = rainScaleMax - y
rainScaleBar = Rectangle((x, y),
w,
h,
fc=self.rainColors[i],
alpha=1)
rainAxis.add_patch(rainScaleBar)
rainScaleBar.set_clip_on(False)
rainScaleBorder = Rectangle((x, 0),
w,
rainScaleMax,
fc="black",
fill=False,
alpha=1)
rainAxis.add_patch(rainScaleBorder)
rainScaleBorder.set_clip_on(False)
# Rain variance
if rainVariance:
rainfallVarianceAxis = rainAxis.twinx(
) # instantiate a second axes that shares the same x-axis
rainfallVarianceAxis.axes.yaxis.set_visible(False)
timestampsCentered = [i + 1500 for i in data["timestamps"]]
rainfallVarianceMin = np.subtract(
np.array(data["rainfall"]),
np.array(data["rainfallVarianceMin"]))
rainfallVarianceMax = np.subtract(
np.array(data["rainfallVarianceMax"]),
np.array(data["rainfall"]))
rainfallVarianceAxis.errorbar(
timestampsCentered,
data["rainfall"],
yerr=[rainfallVarianceMin, rainfallVarianceMax],
fmt="none",
elinewidth=1,
alpha=0.5,
ecolor='black',
capsize=3)
plt.ylim(0, rainScaleMax)
# Show when the model was last calculated
timestampLocal = data[
"modelCalculationTimestamp"] + self.utcOffset * 3600
l = mlines.Line2D([timestampLocal, timestampLocal],
[rainYRange[0], rainScaleMax])
rainAxis.add_line(l)
# Temperature
logging.debug("Creating temerature plot...")
temperatureAxis = rainAxis.twinx(
) # instantiate a second axes that shares the same x-axis
temperatureAxis.plot(data["timestamps"],
data["temperature"],
label="temperature",
color=self.temperatureColor,
linewidth=4)
#temperatureAxis.set_ylabel('Temperature', color=self.temperatureColor)
temperatureAxis.tick_params(axis='y',
labelcolor=colors["temperature-axis"])
temperatureAxis.grid(True)
# Position the Y Scales
temperatureAxis.yaxis.tick_left()
rainAxis.yaxis.tick_right()
# Make sure the temperature scaling has a gap of 45 pixel, so we can fit the labels
interimPixelToTemperature = (np.nanmax(data["temperature"]) -
np.nanmin(data["temperature"])) / height
temperatureScaleMin = np.nanmin(
data["temperature"]) - float(45) * interimPixelToTemperature
temperatureScaleMax = np.nanmax(
data["temperature"]) + float(45) * interimPixelToTemperature
plt.ylim(temperatureScaleMin, temperatureScaleMax)
temperatureAxis.locator_params(axis='y', nbins=6)
temperatureAxis.yaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
pixelToTemperature = (temperatureScaleMax -
temperatureScaleMin) / height
# Temperature variance
temperatureVarianceAxis = temperatureAxis.twinx(
) # instantiate a second axes that shares the same x-axis
temperatureVarianceAxis.axes.yaxis.set_visible(False)
temperatureVarianceAxis.fill_between(data["timestamps"],
data["temperatureVarianceMin"],
data["temperatureVarianceMax"],
facecolor=self.temperatureColor,
alpha=0.2)
temperatureVarianceAxis.tick_params(axis='y',
labelcolor=self.temperatureColor)
plt.ylim(temperatureScaleMin, temperatureScaleMax)
logging.debug("Adding various additional information to the graph...")
# Mark min/max temperature per day
if minMaxTemperature:
da = DrawingArea(2, 2, 0, 0)
da.add_artist(
Circle((1, 1),
4,
color=self.temperatureColor,
fc="white",
lw=2))
for day in range(0, data["noOfDays"]):
dayXPixelMin = day * xPixelsPerDay
dayXPixelMax = (day + 1) * xPixelsPerDay - 1
maxTemperatureOfDay = {"data": -100, "timestamp": 0}
minTemperatureOfDay = {"data": +100, "timestamp": 0}
for h in range(0, 24):
if data["temperature"][day * 24 +
h] > maxTemperatureOfDay["data"]:
maxTemperatureOfDay["data"] = data["temperature"][day *
24 +
h]
maxTemperatureOfDay["timestamp"] = data["timestamps"][
day * 24 + h]
maxTemperatureOfDay["xpixel"] = (
data["timestamps"][day * 24 + h] -
data["timestamps"][0]) / (24 *
3600) * xPixelsPerDay
maxTemperatureOfDay["ypixel"] = (
data["temperature"][day * 24 + h] -
temperatureScaleMin) / (
temperatureScaleMax -
temperatureScaleMin) * height
if data["temperature"][day * 24 +
h] < minTemperatureOfDay["data"]:
minTemperatureOfDay["data"] = data["temperature"][day *
24 +
h]
minTemperatureOfDay["timestamp"] = data["timestamps"][
day * 24 + h]
minTemperatureOfDay["xpixel"] = (
data["timestamps"][day * 24 + h] -
data["timestamps"][0]) / (24 *
3600) * xPixelsPerDay
minTemperatureOfDay["ypixel"] = (
data["temperature"][day * 24 + h] -
temperatureScaleMin) / (
temperatureScaleMax -
temperatureScaleMin) * height
# Circles
temperatureVarianceAxis.add_artist(
AnnotationBbox(da, (maxTemperatureOfDay["timestamp"],
maxTemperatureOfDay["data"]),
xybox=(maxTemperatureOfDay["timestamp"],
maxTemperatureOfDay["data"]),
xycoords='data',
boxcoords=("data", "data"),
frameon=False))
temperatureVarianceAxis.add_artist(
AnnotationBbox(da, (minTemperatureOfDay["timestamp"],
minTemperatureOfDay["data"]),
xybox=(minTemperatureOfDay["timestamp"],
minTemperatureOfDay["data"]),
xycoords='data',
boxcoords=("data", "data"),
frameon=False))
# Max Temperature Labels
text = str(int(round(maxTemperatureOfDay["data"], 0))) + "°C"
f = plt.figure(
1
) # Temporary figure to get the dimensions of the text label
t = plt.text(0, 0, text, weight='bold')
temporaryLabel = t.get_window_extent(
renderer=f.canvas.get_renderer())
plt.figure(0) # Select Main figure again
# Check if text is fully within the day (x axis)
if maxTemperatureOfDay[
"xpixel"] - temporaryLabel.width / 2 < dayXPixelMin: # To far left
maxTemperatureOfDay[
"xpixel"] = dayXPixelMin + temporaryLabel.width / 2 + self.textShadowWidth / 2
if maxTemperatureOfDay[
"xpixel"] + temporaryLabel.width / 2 > dayXPixelMax: # To far right
maxTemperatureOfDay[
"xpixel"] = dayXPixelMax - temporaryLabel.width / 2 - self.textShadowWidth / 2
temperatureVarianceAxis.annotate(
text,
xycoords=('axes pixels'),
xy=(maxTemperatureOfDay["xpixel"],
maxTemperatureOfDay["ypixel"] + 8),
ha="center",
va="bottom",
color=colors["temperature-label"],
weight='bold',
path_effects=[
path_effects.withStroke(linewidth=self.textShadowWidth,
foreground="w")
])
# Min Temperature Labels
text = str(int(round(minTemperatureOfDay["data"], 0))) + "°C"
f = plt.figure(
1
) # Temporary figure to get the dimensions of the text label
t = plt.text(0, 0, text, weight='bold')
temporaryLabel = t.get_window_extent(
renderer=f.canvas.get_renderer())
plt.figure(0) # Select Main figure again
# Check if text is fully within the day (x axis)
if minTemperatureOfDay[
"xpixel"] - temporaryLabel.width / 2 < dayXPixelMin: # To far left
minTemperatureOfDay[
"xpixel"] = dayXPixelMin + temporaryLabel.width / 2 + self.textShadowWidth / 2
if minTemperatureOfDay[
"xpixel"] + temporaryLabel.width / 2 > dayXPixelMax: # To far right
minTemperatureOfDay[
"xpixel"] = dayXPixelMax - temporaryLabel.width / 2 - self.textShadowWidth / 2
temperatureVarianceAxis.annotate(
text,
xycoords=('axes pixels'),
xy=(minTemperatureOfDay["xpixel"],
minTemperatureOfDay["ypixel"] - 12),
ha="center",
va="top",
color=colors["temperature-label"],
weight='bold',
path_effects=[
path_effects.withStroke(linewidth=self.textShadowWidth,
foreground="w")
])
# Print day names
for day in range(0, data["noOfDays"]):
rainAxis.annotate(data['dayNames'][day],
xy=(day * xPixelsPerDay + xPixelsPerDay / 2,
-45),
xycoords='axes pixels',
ha="center",
weight='bold',
color=colors["x-axis"])
# Show y-axis units
rainAxis.annotate("mm\n/h",
linespacing=0.8,
xy=(width + 25, height + 12),
xycoords='axes pixels',
ha="center",
color=colors["rain-axis"])
rainAxis.annotate("°C",
xy=(-20, height + 10),
xycoords='axes pixels',
ha="center",
color=colors["temperature-axis"])
# Show Symbols above the graph
for i in range(0, len(data["symbols"]), symbolDivision):
symbolFile = os.path.dirname(
os.path.realpath(__file__)) + "/symbols/" + str(
data["symbols"][i]) + ".png"
if not os.path.isfile(symbolFile):
logging.warning(
"The symbol file %s seems to be missing. Please check the README.md!"
% symbolFile)
continue
symbolImage = mpimg.imread(symbolFile)
imagebox = OffsetImage(symbolImage, zoom=symbolZoom / 1.41 * 0.15)
xyPos = (
(data["symbolsTimestamps"][i] - data["symbolsTimestamps"][0]) /
(24 * 3600) + len(data["symbols"]) / 24 / 6 /
data["noOfDays"]) * xPixelsPerDay, height + 22
ab = AnnotationBbox(imagebox,
xy=xyPos,
xycoords='axes pixels',
frameon=False)
rainAxis.add_artist(ab)
# Show city name in graph
# TODO find a way to show the label in the background
if showCityName:
logging.debug("Adding city name to plot...")
text = fig.text(1 - 7 / width,
1 - 20 / height,
self.cityName,
color='gray',
ha='right',
transform=rainAxis.transAxes)
text.set_path_effects([
path_effects.Stroke(linewidth=self.textShadowWidth,
foreground='white'),
path_effects.Normal()
])
# Save the graph in a png image file
logging.debug("Saving graph to %s" % outputFilename)
plt.savefig(outputFilename, facecolor=colors["background"])
plt.close()
# Write Meta Data
if writeMetaData:
logging.debug("Saving Meta Data to %s" % writeMetaData)
metaData = {}
metaData['city'] = self.cityName
metaData['imageHeight'] = graphHeight
metaData['imageWidth'] = graphWidth
metaData['firstDayX'] = firstDayX
metaData['firstDayY'] = firstDayY
metaData['dayWidth'] = dayWidth
metaData['dayHeight'] = dayHeight
metaData['modelTimestamp'] = self.data[
"modelCalculationTimestamp"] # Seconds in UTC
with open(writeMetaData, 'w') as metaFile:
json.dump(metaData, metaFile)
def test_annotationbbox_extents():
plt.rcParams.update(plt.rcParamsDefault)
fig, ax = plt.subplots(figsize=(4, 3), dpi=100)
ax.axis([0, 1, 0, 1])
an1 = ax.annotate("Annotation",
xy=(.9, .9),
xytext=(1.1, 1.1),
arrowprops=dict(arrowstyle="->"),
clip_on=False,
va="baseline",
ha="left")
da = DrawingArea(20, 20, 0, 0, clip=True)
p = mpatches.Circle((-10, 30), 32)
da.add_artist(p)
ab3 = AnnotationBbox(da, [.5, .5],
xybox=(-0.2, 0.5),
xycoords='data',
boxcoords="axes fraction",
box_alignment=(0., .5),
arrowprops=dict(arrowstyle="->"))
ax.add_artist(ab3)
im = OffsetImage(np.random.rand(10, 10), zoom=3)
im.image.axes = ax
ab6 = AnnotationBbox(im, (0.5, -.3),
xybox=(0, 75),
xycoords='axes fraction',
boxcoords="offset points",
pad=0.3,
arrowprops=dict(arrowstyle="->"))
ax.add_artist(ab6)
fig.canvas.draw()
renderer = fig.canvas.get_renderer()
# Test Annotation
bb1w = an1.get_window_extent(renderer)
bb1e = an1.get_tightbbox(renderer)
target1 = [332.9, 242.8, 467.0, 298.9]
assert_allclose(bb1w.extents, target1, atol=2)
assert_allclose(bb1e.extents, target1, atol=2)
# Test AnnotationBbox
bb3w = ab3.get_window_extent(renderer)
bb3e = ab3.get_tightbbox(renderer)
target3 = [-17.6, 129.0, 200.7, 167.9]
assert_allclose(bb3w.extents, target3, atol=2)
assert_allclose(bb3e.extents, target3, atol=2)
bb6w = ab6.get_window_extent(renderer)
bb6e = ab6.get_tightbbox(renderer)
target6 = [180.0, -32.0, 230.0, 92.9]
assert_allclose(bb6w.extents, target6, atol=2)
assert_allclose(bb6e.extents, target6, atol=2)
# Test bbox_inches='tight'
buf = io.BytesIO()
fig.savefig(buf, bbox_inches='tight')
buf.seek(0)
shape = plt.imread(buf).shape
targetshape = (350, 504, 4)
assert_allclose(shape, targetshape, atol=2)
# Simple smoke test for tight_layout, to make sure it does not error out.
fig.canvas.draw()
fig.tight_layout()
fig.canvas.draw()
def plot_tsne(X, Y, gt_Y, train_X, train_Y, filenames_val):
fig = plt.figure()
cmap = get_cmap(len(expressionNames) + 1)
ax2 = fig.add_subplot(111)
scs = []
legendNames = []
for id in np.unique(train_Y):
idx = np.where(train_Y == id)[0]
sc = ax2.scatter(train_X[idx, 1], train_X[idx, 0], color=cmap(id), alpha=0.4, s=75.0, linewidths=0)
scs.append(sc)
for id in np.unique(Y):
print(len(np.where((Y == id))[0]))
idx_correct = np.where((Y == id) & (Y == gt_Y))[0]
idx_incorrect = np.where((Y == id) & (Y != gt_Y))[0]
sc = ax2.scatter(X[idx_correct, 1], X[idx_correct, 0], color=cmap(id), marker='s', edgecolor='k')
scs.append(sc)
ax2.scatter(X[idx_incorrect, 1], X[idx_incorrect, 0], color=cmap(id), marker='v', edgecolor='k')
expr = expressionNames[id]
legendNames.append("{} ({})".format(expr, str(len(idx_correct)+len(idx_incorrect))))
plt.legend(scs, legendNames, scatterpoints=1, loc='lower left', ncol=4, fontsize=7)
if True:
for nImg, img_file in enumerate(filenames_val):
is_correct = Y[nImg] == gt_Y[nImg]
img_size = 36
img_path = os.path.join(affectNetRoot, img_file)
print("{}: {}".format(nImg, img_path))
arr_img = cv2.imread(img_path)
arr_img = cv2.cvtColor(arr_img, cv2.COLOR_BGR2RGB)
arr_img = arr_img.astype(np.float)/255.0
arr_img = cv2.resize(arr_img, (img_size,img_size), interpolation=cv2.INTER_CUBIC)
cv2.circle(arr_img, (img_size-6, 6), 3, (0,255,0) if is_correct else (255,0,0), -1)
border_size = 2
da = DrawingArea(img_size+2*border_size, img_size+2*border_size, 0, 0)
p = Rectangle((0, 0), img_size+2*border_size, img_size+2*border_size, color=cmap(Y[nImg]))
da.add_artist(p)
border = AnnotationBbox(da, X[nImg][::-1],
#xybox=(120., -80.),
xybox=(0., 0.),
xycoords='data',
boxcoords="offset points",
pad=0.0,
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=3")
)
ax2.add_artist(border)
im = OffsetImage(arr_img, interpolation='gaussian')
ab = AnnotationBbox(im, X[nImg][::-1],
#xybox=(120., -80.),
xybox=(0., 0.),
xycoords='data',
boxcoords="offset points",
pad=0.0,
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=3")
)
ax2.add_artist(ab)
def _tick_label(
self,
x,
y,
size,
ax=None,
linewidth=None,
xybox=None,
xycoords=None,
box_alignment=None,
horizontalalignment=None,
verticalalignment=None,
rotation=None,
):
"""
uses the icon as a ticklabel at location x
Args:
x (float)
the horizontal position of the tick
y (float)
the vertical position of the tick
size (float)
scaling the size of the icon
ax (matplotlib axis)
the axis to put the label on
"""
ret_val = {}
if ax is None:
ax = plt.gca()
tickline_color = self.color
# np.any chokes on str input so need to test for this first
if not (isinstance(tickline_color, str) or np.any(tickline_color)):
tickline_color = [0.8, 0.8, 0.8]
if self.final_image is not None:
imagebox = OffsetImage(self.final_image, zoom=size, dpi_cor=True)
ret_val['image'] = AnnotationBbox(
imagebox,
(x, y),
xybox=xybox,
xycoords=xycoords,
box_alignment=box_alignment,
boxcoords="offset points",
bboxprops={
"edgecolor": "none",
"facecolor": "none"
},
arrowprops={
"linewidth": linewidth,
"color": tickline_color,
"arrowstyle": "-",
"shrinkA": 0,
"shrinkB": 1,
},
pad=0.0,
annotation_clip=False,
)
zorder = ret_val['image'].zorder
ax.add_artist(ret_val['image'])
else:
zorder = 0
if self.marker:
if self.final_image is not None:
markersize = max(self.final_image.size)
else:
markersize = 50
markersize = markersize * size
d = DrawingArea(markersize, markersize)
if self.marker_front:
zorder_marker = zorder + 0.1
else:
zorder_marker = zorder - 0.1
d.set_zorder(zorder_marker)
d.set_alpha(0)
if self.marker_front:
d.add_artist(
plt.Line2D(
[markersize / 2],
[markersize / 2],
marker=self.marker,
markeredgecolor=self.color,
markerfacecolor=(0, 0, 0, 0),
markersize=markersize,
markeredgewidth=self.markeredgewidth,
transform=d.get_transform(),
zorder=zorder_marker,
))
else:
d.add_artist(
plt.Line2D(
[markersize / 2],
[markersize / 2],
marker=self.marker,
markeredgecolor=self.color,
markerfacecolor=self.color,
markersize=markersize,
markeredgewidth=self.markeredgewidth,
transform=d.get_transform(),
zorder=zorder_marker,
))
ret_val['marker'] = AnnotationBbox(
d,
(x, y),
xybox=xybox,
xycoords=xycoords,
box_alignment=box_alignment,
boxcoords="offset points",
bboxprops={
"edgecolor": "none",
"facecolor": "none"
},
arrowprops={
"linewidth": linewidth,
"color": tickline_color,
"arrowstyle": "-",
"shrinkA": 0,
"shrinkB": 1,
},
pad=0.0,
annotation_clip=False,
)
ret_val['marker'].set_zorder(zorder_marker)
ret_val['marker'].set_alpha(0)
ax.add_artist(ret_val['marker'])
if self.string is not None:
ret_val['string'] = ax.annotate(
self.string,
(x, y),
xytext=xybox,
xycoords=xycoords,
textcoords="offset points",
horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
arrowprops={
"linewidth": linewidth,
"color": tickline_color,
"arrowstyle": "-",
"shrinkA": 0,
"shrinkB": 1,
},
zorder=zorder + 0.2,
fontsize=self.font_size,
fontname=self.font_name,
color=self.font_color,
rotation=rotation,
)
return ret_val
def plot_maze_abstract_transitions(
all_inputs, all_abs_inputs, model, global_step, plot_dir
):
"""Plots the abstract representation from the CRAR agent for the SimpleMaze environment.
Heavily borrowed from:
https://github.com/VinF/deer/blob/master/examples/test_CRAR/simple_maze_env.py
"""
if not isinstance(plot_dir, Path):
plot_dir = Path(plot_dir)
exp_seq = list(reversed(most_recent(model.replay_buffer.buffer, 1000)))
n = 1000
history = []
for i, (obs, *_) in enumerate(exp_seq):
history.append(obs)
history = np.array(history)
abstract_states = model.agent.encode(history)
m = cm.ScalarMappable(cmap=cm.jet)
x, y = abstract_states.detach().cpu().numpy().T
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(r"$X_1$")
ax.set_ylabel(r"$X_2$")
for i in range(n - 1):
predicted1 = (
model.agent.compute_transition(
abstract_states[i : i + 1], torch.as_tensor([0], device="cuda")
)
.detach()
.cpu()
.numpy()
)
predicted2 = (
(
model.agent.compute_transition(
abstract_states[i : i + 1], torch.as_tensor([1], device="cuda")
)
)
.detach()
.cpu()
.numpy()
)
predicted3 = (
(
model.agent.compute_transition(
abstract_states[i : i + 1], torch.as_tensor([2], device="cuda")
)
)
.detach()
.cpu()
.numpy()
)
predicted4 = (
(
model.agent.compute_transition(
abstract_states[i : i + 1], torch.as_tensor([3], device="cuda")
)
)
.detach()
.cpu()
.numpy()
)
ax.plot(
np.concatenate([x[i : i + 1], predicted1[0, :1]]),
np.concatenate([y[i : i + 1], predicted1[0, 1:2]]),
color="royalblue",
alpha=0.75,
)
ax.plot(
np.concatenate([x[i : i + 1], predicted2[0, :1]]),
np.concatenate([y[i : i + 1], predicted2[0, 1:2]]),
color="crimson",
alpha=0.75,
)
ax.plot(
np.concatenate([x[i : i + 1], predicted3[0, :1]]),
np.concatenate([y[i : i + 1], predicted3[0, 1:2]]),
color="mediumspringgreen",
alpha=0.75,
)
ax.plot(
np.concatenate([x[i : i + 1], predicted4[0, :1]]),
np.concatenate([y[i : i + 1], predicted4[0, 1:2]]),
color="black",
alpha=0.75,
)
# Plot the dots at each time step depending on the action taken
length_block = [[0, 18], [18, 19], [19, 31]]
for i in range(3):
colors = ["blue", "orange", "green"]
line3 = ax.scatter(
all_abs_inputs[length_block[i][0] : length_block[i][1], 0],
all_abs_inputs[length_block[i][0] : length_block[i][1], 1],
c=colors[i],
marker="x",
edgecolors="k",
alpha=0.5,
s=100,
)
axes_lims = [ax.get_xlim(), ax.get_ylim()]
box1b = TextArea(
" Estimated transitions (action 0, 1, 2 and 3): ", textprops=dict(color="k")
)
box2b = DrawingArea(90, 20, 0, 0)
el1b = Rectangle((5, 10), 15, 2, fc="royalblue", alpha=0.75)
el2b = Rectangle((25, 10), 15, 2, fc="crimson", alpha=0.75)
el3b = Rectangle((45, 10), 15, 2, fc="mediumspringgreen", alpha=0.75)
el4b = Rectangle((65, 10), 15, 2, fc="black", alpha=0.75)
box2b.add_artist(el1b)
box2b.add_artist(el2b)
box2b.add_artist(el3b)
box2b.add_artist(el4b)
boxb = HPacker(children=[box1b, box2b], align="center", pad=0, sep=5)
anchored_box = AnchoredOffsetbox(
loc=3,
child=boxb,
pad=0.0,
frameon=True,
bbox_to_anchor=(0.0, 0.98),
bbox_transform=ax.transAxes,
borderpad=0.0,
)
ax.add_artist(anchored_box)
plot_dir.mkdir(parents=True, exist_ok=True)
plt.savefig(plot_dir / f"plot_{global_step}.pdf")
def add_sketch(self, **kwargs):
"""Draw section sketch at current value [beta, alpha].
Draw also:
* potential preferential fault network;
* slip directions on fault network.
"""
self.sketch_size_factor = kwargs.get("sketch_size_factor", 1.0)
# Renaming is cheapper than multiple access.
alpha, beta = self._alpha, self._beta
a_deg, b_deg = self.alpha, self.beta
padding = self._padding
# Surface of prism : arbitrary set, allows a cst looking.
# Box distance from enveloppe.
box_dist_from_curve = (self._point_top[1] -
self._point_bottom[1]) / 10.0
try:
L = sqrt(self._sketch_surface / sin((alpha + beta) / 2.0) * cos(
(alpha + beta) / 2.0))
except ZeroDivisionError:
# alpha + beta == 0. means there is no prism !
return
self._L = L
# Init sketching aera ans draw background of prism.
# Prism is a basal and a topo line, so discribed by 3 points.
if alpha < 0.0:
x1 = padding + 2.0 * L * sin((alpha + beta) / 2.0) * sin(
(beta - alpha) / 2.0)
y1 = padding
elif beta < 0.0:
x1, y1 = padding, padding + L * sin(-beta)
else:
x1, y1 = padding + L * (1.0 - cos(beta)), padding
x2, y2 = x1 + L * cos(beta), y1 + L * sin(beta)
x3, y3 = x2 - L * cos(alpha), y2 + L * sin(alpha)
self._prism_tip = (x2, y2)
# Prism is also discribed by two lines.
# Slope of vectors [1-2] and [2-3]
self._a_basal, self._a_topo = tan(beta), -tan(alpha)
# Initial ordinates
self._b_basal = y2 - self._a_basal * x2
self._b_topo = y2 - self._a_topo * x2
self._sketch_box_width = x2 + padding
self._sketch_box_height = max(y3, y2) - min(y1, y2) + 2 * padding
# Content of annotationbox
self.drawing_aera = DrawingArea(self._sketch_box_width,
self._sketch_box_height, 0.0, 0.0)
# Fill the prism
XY = [[x1, y1], [x2, y2]]
for angle in np.arange(alpha, -beta, -(alpha + beta) / 1.0e2):
XY.append([x2 - L * cos(angle), y2 + L * sin(angle)])
p = patches.Polygon(XY, edgecolor="none", facecolor="w")
self.drawing_aera.add_artist(p)
# Identify wich part of critical enveloppe is concerned.
slope = abs(
atan((self._point_center[1] - a_deg) /
(self._point_center[0] - b_deg)))
dist_from_curve_b = box_dist_from_curve * cos(slope)
dist_from_curve_a = box_dist_from_curve * sin(slope)
(alpha1, ), (alpha2, ) = self.compute_alpha(deg=False)
a_mid = alpha1 + (alpha2 - alpha1) / 2.0
if alpha <= a_mid: # bottom part -> inverse faults
if b_deg < self._point_bottom[0]: # bottom left quadrant
quadrant, solution = "BL", "B"
else: # bottom right quadrant
quadrant, solution = "BR", "A"
else: # upper part -> normal faults
if b_deg < self._point_top[0]: # Top left quadrant
quadrant, solution = "TL", "A"
else: # Top right quadrant
quadrant, solution = "TR", "B"
if solution == "A":
g = self._get_gamma_A()
t = self._get_theta_A()
else:
g = self._get_gamma_B()
t = self._get_theta_B()
if quadrant == "TL":
box_alignment, xshift, yshift = (1.0, 0.0), -1.0, 1.0
elif quadrant == "TR":
box_alignment, xshift, yshift = (0.0, 0.0), 1.0, 1.0
elif quadrant == "BL":
box_alignment, xshift, yshift = (1.0, 1.0), -1.0, -1.0
elif quadrant == "BR":
box_alignment, xshift, yshift = (0.0, 1.0), 1.0, -1.0
# Fault network.
xgap = self._fault_gap * cos((beta - alpha) / 2.0)
ygap = self._fault_gap * sin((beta - alpha) / 2.0)
L_A, L_B, angle = L / 3.0, L * 2.0 / 3.0, alpha - (alpha + beta) / 2.0
# Gamma oriented faults
L_g = L_A if quadrant in ["TL", "BL"] else L_B
x_g, y_g = x2 - L_g * cos(angle), y2 + L_g * sin(angle)
xgap_g = xgap / sin(g - (beta - alpha) / 2.0)
ygap_g = ygap / sin(g - (beta - alpha) / 2.0)
a_g = tan(g)
self._draw_faults(a_g, x_g, y_g, xgap_g, ygap_g, -1, 1)
self._draw_faults(a_g, x_g, y_g, xgap_g, ygap_g, 0, -1)
# Theta oriented faults
L_t = L_B if quadrant in ["TL", "BL"] else L_A
x_t, y_t = x2 - L_t * cos(angle), y2 + L_t * sin(angle)
xgap_t = xgap / sin(t + (beta - alpha) / 2.0)
ygap_t = ygap / sin(t + (beta - alpha) / 2.0)
a_t = -tan(t) # Fault slope theta
self._draw_faults(a_t, x_t, y_t, xgap_t, ygap_t, -1, 1)
self._draw_faults(a_t, x_t, y_t, xgap_t, ygap_t, 0, -1)
# Prism limits.
# Drawed above faults to mask faults tips.
p = lines.Line2D([x1, x2, x3], [y1, y2, y3], lw=2, color="gray")
self.drawing_aera.add_artist(p)
# Arrows.
# Gamma oriented inverse arrows.
self._draw_arrow(g, x_g, y_g, solution, gamma=True)
# Theta oriented inverse arrows.
self._draw_arrow(t, x_t, y_t, solution, gamma=False)
# arrows base
x, y = x1 + L * cos(beta) / 2.0, y1 + L * sin(beta) / 2.0
if self.context == "Compression":
solution = "B"
else:
solution = "A"
self._draw_arrow(beta, x, y, solution, gamma=True)
# Set and display annotation box.
ab = AnnotationBbox(
self.drawing_aera,
[b_deg, a_deg],
xybox=(
b_deg + xshift * dist_from_curve_b,
a_deg + yshift * dist_from_curve_a,
),
xycoords="data",
boxcoords=("data", "data"),
box_alignment=box_alignment,
bboxprops=dict(boxstyle="round", fc=(0.9, 0.9, 0.9), ec="none"),
arrowprops=dict(
arrowstyle="wedge,tail_width=2.",
fc=(0.9, 0.9, 0.9),
ec=(0.8, 0.8, 0.8),
patchA=None,
relpos=(0.5, 0.5),
),
)
self.axe.add_artist(ab).draggable()
cbar = plt.colorbar(im, cax=cax, norm=norm, label=unit)
cbar.set_label(unit, size=24)
cbar.ax.tick_params(labelsize=20)
if var == "acc":
cbar.cmap.set_under('w')
cbar.set_clim(0, cmap_range_max)
print(var, cmap_range_min, cmap_range_max) #, data_array
if var == "comp":
norm = mpl.colors.Normalize(vmin=cmap_range_min,
vmax=cmap_range_max)
im = ax.imshow(data_array, cmap=var_cmap, norm=norm)
# im = ax.imshow(data_array, cmap=var_cmap)
box1 = DrawingArea(50, 20, 0, 0)
rt1 = plt.Rectangle((0, 5), 20, 13, fc="#1f78b4")
rt2 = plt.Rectangle((25, 5), 20, 13, fc="#a6cee3")
box1.add_artist(rt1)
box1.add_artist(rt2)
box2 = TextArea("Solid silicates",
textprops=dict(color="k", size=20))
box = HPacker(children=[box1, box2],
align="center",
pad=0,
sep=5)
anchored_box1 = AnchoredOffsetbox(
loc=2,
child=box,
pad=0.,
frameon=False,
def _init_legend_box(self, handles, labels, markerfirst=True):
"""
Initialize the legend_box. The legend_box is an instance of
the OffsetBox, which is packed with legend handles and
texts. Once packed, their location is calculated during the
drawing time.
"""
fontsize = self._fontsize
# legend_box is a HPacker, horizontally packed with columns.
# Each column is a VPacker, vertically packed with legend items.
# Each legend item is a HPacker packed with:
# - handlebox: a DrawingArea which contains the legend handle.
# - labelbox: a TextArea which contains the legend text.
text_list = [] # the list of text instances
handle_list = [] # the list of handle instances
handles_and_labels = []
# The approximate height and descent of text. These values are
# only used for plotting the legend handle.
descent = 0.35 * fontsize * (self.handleheight - 0.7) # heuristic.
height = fontsize * self.handleheight - descent
# each handle needs to be drawn inside a box of (x, y, w, h) =
# (0, -descent, width, height). And their coordinates should
# be given in the display coordinates.
# The transformation of each handle will be automatically set
# to self.get_transform(). If the artist does not use its
# default transform (e.g., Collections), you need to
# manually set their transform to the self.get_transform().
legend_handler_map = self.get_legend_handler_map()
for orig_handle, label in zip(handles, labels):
handler = self.get_legend_handler(legend_handler_map, orig_handle)
if handler is None:
_api.warn_external(
"Legend does not support {!r} instances.\nA proxy artist "
"may be used instead.\nSee: "
"https://matplotlib.org/users/legend_guide.html"
"#creating-artists-specifically-for-adding-to-the-legend-"
"aka-proxy-artists".format(orig_handle))
# No handle for this artist, so we just defer to None.
handle_list.append(None)
else:
textbox = TextArea(label,
multilinebaseline=True,
textprops=dict(verticalalignment='baseline',
horizontalalignment='left',
fontproperties=self.prop))
handlebox = DrawingArea(width=self.handlelength * fontsize,
height=height,
xdescent=0.,
ydescent=descent)
text_list.append(textbox._text)
# Create the artist for the legend which represents the
# original artist/handle.
handle_list.append(
handler.legend_artist(self, orig_handle, fontsize,
handlebox))
handles_and_labels.append((handlebox, textbox))
columnbox = []
# array_split splits n handles_and_labels into ncol columns, with the
# first n%ncol columns having an extra entry. filter(len, ...) handles
# the case where n < ncol: the last ncol-n columns are empty and get
# filtered out.
for handles_and_labels_column \
in filter(len, np.array_split(handles_and_labels, self._ncol)):
# pack handlebox and labelbox into itembox
itemboxes = [
HPacker(pad=0,
sep=self.handletextpad * fontsize,
children=[h, t] if markerfirst else [t, h],
align="baseline") for h, t in handles_and_labels_column
]
# pack columnbox
alignment = "baseline" if markerfirst else "right"
columnbox.append(
VPacker(pad=0,
sep=self.labelspacing * fontsize,
align=alignment,
children=itemboxes))
mode = "expand" if self._mode == "expand" else "fixed"
sep = self.columnspacing * fontsize
self._legend_handle_box = HPacker(pad=0,
sep=sep,
align="baseline",
mode=mode,
children=columnbox)
self._legend_title_box = TextArea("")
self._legend_box = VPacker(
pad=self.borderpad * fontsize,
sep=self.labelspacing * fontsize,
align="center",
children=[self._legend_title_box, self._legend_handle_box])
self._legend_box.set_figure(self.figure)
self._legend_box.axes = self.axes
self.texts = text_list
self.legendHandles = handle_list
def _init_legend_box(self, handles, labels):
"""
Initiallize the legend_box. The legend_box is an instance of
the OffsetBox, which is packed with legend handles and
texts. Once packed, their location is calculated during the
drawing time.
"""
fontsize = self.fontsize
# legend_box is a HPacker, horizontally packed with
# columns. Each column is a VPacker, vertically packed with
# legend items. Each legend item is HPacker packed with
# legend handleBox and labelBox. handleBox is an instance of
# offsetbox.DrawingArea which contains legend handle. labelBox
# is an instance of offsetbox.TextArea which contains legend
# text.
text_list = [] # the list of text instances
handle_list = [] # the list of text instances
label_prop = dict(
verticalalignment='baseline',
horizontalalignment='left',
fontproperties=self.prop,
)
labelboxes = []
for l in labels:
textbox = TextArea(l,
textprops=label_prop,
multilinebaseline=True,
minimumdescent=True)
text_list.append(textbox._text)
labelboxes.append(textbox)
handleboxes = []
# The approximate height and descent of text. These values are
# only used for plotting the legend handle.
height = self._approx_text_height() * 0.7
descent = 0.
# each handle needs to be drawn inside a box of (x, y, w, h) =
# (0, -descent, width, height). And their corrdinates should
# be given in the display coordinates.
# NOTE : the coordinates will be updated again in
# _update_legend_box() method.
# The transformation of each handle will be automatically set
# to self.get_trasnform(). If the artist does not uses its
# default trasnform (eg, Collections), you need to
# manually set their transform to the self.get_transform().
for handle in handles:
if isinstance(handle, RegularPolyCollection):
npoints = self.scatterpoints
else:
npoints = self.numpoints
if npoints > 1:
# we put some pad here to compensate the size of the
# marker
xdata = np.linspace(0.3 * fontsize,
(self.handlelength - 0.3) * fontsize,
npoints)
xdata_marker = xdata
elif npoints == 1:
xdata = np.linspace(0, self.handlelength * fontsize, 2)
xdata_marker = [0.5 * self.handlelength * fontsize]
if isinstance(handle, Line2D):
ydata = ((height - descent) / 2.) * np.ones(xdata.shape, float)
legline = Line2D(xdata, ydata)
legline.update_from(handle)
self._set_artist_props(legline) # after update
legline.set_clip_box(None)
legline.set_clip_path(None)
legline.set_drawstyle('default')
legline.set_marker('None')
handle_list.append(legline)
legline_marker = Line2D(xdata_marker,
ydata[:len(xdata_marker)])
legline_marker.update_from(handle)
self._set_artist_props(legline_marker)
legline_marker.set_clip_box(None)
legline_marker.set_clip_path(None)
legline_marker.set_linestyle('None')
# we don't want to add this to the return list because
# the texts and handles are assumed to be in one-to-one
# correpondence.
legline._legmarker = legline_marker
elif isinstance(handle, Patch):
p = Rectangle(
xy=(0., 0.),
width=self.handlelength * fontsize,
height=(height - descent),
)
p.update_from(handle)
self._set_artist_props(p)
p.set_clip_box(None)
p.set_clip_path(None)
handle_list.append(p)
elif isinstance(handle, LineCollection):
ydata = ((height - descent) / 2.) * np.ones(xdata.shape, float)
legline = Line2D(xdata, ydata)
self._set_artist_props(legline)
legline.set_clip_box(None)
legline.set_clip_path(None)
lw = handle.get_linewidth()[0]
dashes = handle.get_dashes()[0]
color = handle.get_colors()[0]
legline.set_color(color)
legline.set_linewidth(lw)
legline.set_dashes(dashes)
handle_list.append(legline)
elif isinstance(handle, RegularPolyCollection):
#ydata = self._scatteryoffsets
ydata = height * self._scatteryoffsets
size_max, size_min = max(handle.get_sizes()),\
min(handle.get_sizes())
# we may need to scale these sizes by "markerscale"
# attribute. But other handle types does not seem
# to care about this attribute and it is currently ignored.
if self.scatterpoints < 4:
sizes = [.5 * (size_max + size_min), size_max, size_min]
else:
sizes = (size_max - size_min) * np.linspace(
0, 1, self.scatterpoints) + size_min
p = type(handle)(
handle.get_numsides(),
rotation=handle.get_rotation(),
sizes=sizes,
offsets=zip(xdata_marker, ydata),
transOffset=self.get_transform(),
)
p.update_from(handle)
p.set_figure(self.figure)
p.set_clip_box(None)
p.set_clip_path(None)
handle_list.append(p)
else:
handle_list.append(None)
handlebox = DrawingArea(width=self.handlelength * fontsize,
height=height,
xdescent=0.,
ydescent=descent)
handle = handle_list[-1]
handlebox.add_artist(handle)
if hasattr(handle, "_legmarker"):
handlebox.add_artist(handle._legmarker)
handleboxes.append(handlebox)
# We calculate number of lows in each column. The first
# (num_largecol) columns will have (nrows+1) rows, and remaing
# (num_smallcol) columns will have (nrows) rows.
nrows, num_largecol = divmod(len(handleboxes), self._ncol)
num_smallcol = self._ncol - num_largecol
# starting index of each column and number of rows in it.
largecol = safezip(range(0, num_largecol * (nrows + 1), (nrows + 1)),
[nrows + 1] * num_largecol)
smallcol = safezip(
range(num_largecol * (nrows + 1), len(handleboxes), nrows),
[nrows] * num_smallcol)
handle_label = safezip(handleboxes, labelboxes)
columnbox = []
for i0, di in largecol + smallcol:
# pack handleBox and labelBox into itemBox
itemBoxes = [
HPacker(pad=0,
sep=self.handletextpad * fontsize,
children=[h, t],
align="baseline") for h, t in handle_label[i0:i0 + di]
]
# minimumdescent=False for the text of the last row of the column
itemBoxes[-1].get_children()[1].set_minimumdescent(False)
# pack columnBox
columnbox.append(
VPacker(pad=0,
sep=self.labelspacing * fontsize,
align="baseline",
children=itemBoxes))
if self._mode == "expand":
mode = "expand"
else:
mode = "fixed"
sep = self.columnspacing * fontsize
self._legend_box = HPacker(pad=self.borderpad * fontsize,
sep=sep,
align="baseline",
mode=mode,
children=columnbox)
self._legend_box.set_figure(self.figure)
self.texts = text_list
self.legendHandles = handle_list
def __init__(self,
width,
height,
xdescent,
ydescent,
loc,
pad=0.4,
borderpad=0.5,
prop=None,
frameon=True,
**kwargs):
"""
An anchored container with a fixed size and fillable DrawingArea.
Artists added to the *drawing_area* will have their coordinates
interpreted as pixels. Any transformations set on the artists will be
overridden.
Parameters
----------
width, height : float
width and height of the container, in pixels.
xdescent, ydescent : float
descent of the container in the x- and y- direction, in pixels.
loc : int
Location of this artist. Valid location codes are::
'upper right' : 1,
'upper left' : 2,
'lower left' : 3,
'lower right' : 4,
'right' : 5,
'center left' : 6,
'center right' : 7,
'lower center' : 8,
'upper center' : 9,
'center' : 10
pad : float, optional, default: 0.4
Padding around the child objects, in fraction of the font size.
borderpad : float, optional, default: 0.5
Border padding, in fraction of the font size.
prop : `matplotlib.font_manager.FontProperties`, optional
Font property used as a reference for paddings.
frameon : bool, optional, default: True
If True, draw a box around this artists.
**kwargs
Keyworded arguments to pass to
:class:`matplotlib.offsetbox.AnchoredOffsetbox`.
Attributes
----------
drawing_area : `matplotlib.offsetbox.DrawingArea`
A container for artists to display.
Examples
--------
To display blue and red circles of different sizes in the upper right
of an axes *ax*:
>>> ada = AnchoredDrawingArea(20, 20, 0, 0,
... loc='upper right', frameon=False)
>>> ada.drawing_area.add_artist(Circle((10, 10), 10, fc="b"))
>>> ada.drawing_area.add_artist(Circle((30, 10), 5, fc="r"))
>>> ax.add_artist(ada)
"""
self.da = DrawingArea(width, height, xdescent, ydescent)
self.drawing_area = self.da
super().__init__(loc,
pad=pad,
borderpad=borderpad,
child=self.da,
prop=None,
frameon=frameon,
**kwargs)
def summarizePerformance(self, test_data_set, learning_algo, *args,
**kwargs):
""" Plot of the low-dimensional representation of the environment built by the model
"""
all_possib_inp = [
] # Will store all possible inputs (=observation) for the CRAR agent
labels_maze = []
self.create_map()
for y_a in range(self._size_maze):
for x_a in range(self._size_maze):
state = copy.deepcopy(self._map)
state[self._size_maze // 2, self._size_maze // 2] = 0
if (state[x_a, y_a] == 0):
if (self._higher_dim_obs == True):
all_possib_inp.append(
self.get_higher_dim_obs([[x_a, y_a]],
[self._pos_goal]))
else:
state[x_a, y_a] = 0.5
all_possib_inp.append(state)
## labels
#if(y_a<self._size_maze//2):
# labels_maze.append(0.)
#elif(y_a==self._size_maze//2):
# labels_maze.append(1.)
#else:
# labels_maze.append(2.)
#arr=np.array(all_possib_inp)
#if(self._higher_dim_obs==False):
# arr=arr.reshape(arr.shape[0],-1)
#else:
# arr=arr.reshape(arr.shape[0],-1)
#
#np.savetxt('tsne_python/mazesH_X.txt',arr.reshape(arr.shape[0],-1))
#np.savetxt('tsne_python/mazesH_labels.txt',np.array(labels_maze))
all_possib_inp = np.expand_dims(np.array(all_possib_inp,
dtype='float'),
axis=1)
all_possib_abs_states = learning_algo.encoder.predict(all_possib_inp)
if (all_possib_abs_states.ndim == 4):
all_possib_abs_states = np.transpose(
all_possib_abs_states,
(0, 3, 1,
2)) # data_format='channels_last' --> 'channels_first'
n = 1000
historics = []
for i, observ in enumerate(test_data_set.observations()[0][0:n]):
historics.append(np.expand_dims(observ, axis=0))
historics = np.array(historics)
abs_states = learning_algo.encoder.predict(historics)
if (abs_states.ndim == 4):
abs_states = np.transpose(
abs_states,
(0, 3, 1,
2)) # data_format='channels_last' --> 'channels_first'
actions = test_data_set.actions()[0:n]
if self.inTerminalState() == False:
self._mode_episode_count += 1
print("== Mean score per episode is {} over {} episodes ==".format(
self._mode_score / (self._mode_episode_count + 0.0001),
self._mode_episode_count))
m = cm.ScalarMappable(cmap=cm.jet)
x = np.array(abs_states)[:, 0]
y = np.array(abs_states)[:, 1]
if (self.intern_dim > 2):
z = np.array(abs_states)[:, 2]
fig = plt.figure()
if (self.intern_dim == 2):
ax = fig.add_subplot(111)
ax.set_xlabel(r'$X_1$')
ax.set_ylabel(r'$X_2$')
else:
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel(r'$X_1$')
ax.set_ylabel(r'$X_2$')
ax.set_zlabel(r'$X_3$')
# Plot the estimated transitions
for i in range(n - 1):
predicted1 = learning_algo.transition.predict(
[abs_states[i:i + 1],
np.array([[1, 0, 0, 0]])])
predicted2 = learning_algo.transition.predict(
[abs_states[i:i + 1],
np.array([[0, 1, 0, 0]])])
predicted3 = learning_algo.transition.predict(
[abs_states[i:i + 1],
np.array([[0, 0, 1, 0]])])
predicted4 = learning_algo.transition.predict(
[abs_states[i:i + 1],
np.array([[0, 0, 0, 1]])])
if (self.intern_dim == 2):
ax.plot(np.concatenate([x[i:i + 1], predicted1[0, :1]]),
np.concatenate([y[i:i + 1], predicted1[0, 1:2]]),
color="0.9",
alpha=0.75)
ax.plot(np.concatenate([x[i:i + 1], predicted2[0, :1]]),
np.concatenate([y[i:i + 1], predicted2[0, 1:2]]),
color="0.65",
alpha=0.75)
ax.plot(np.concatenate([x[i:i + 1], predicted3[0, :1]]),
np.concatenate([y[i:i + 1], predicted3[0, 1:2]]),
color="0.4",
alpha=0.75)
ax.plot(np.concatenate([x[i:i + 1], predicted4[0, :1]]),
np.concatenate([y[i:i + 1], predicted4[0, 1:2]]),
color="0.15",
alpha=0.75)
else:
ax.plot(np.concatenate([x[i:i + 1], predicted1[0, :1]]),
np.concatenate([y[i:i + 1], predicted1[0, 1:2]]),
np.concatenate([z[i:i + 1], predicted1[0, 2:3]]),
color="0.9",
alpha=0.75)
ax.plot(np.concatenate([x[i:i + 1], predicted2[0, :1]]),
np.concatenate([y[i:i + 1], predicted2[0, 1:2]]),
np.concatenate([z[i:i + 1], predicted2[0, 2:3]]),
color="0.65",
alpha=0.75)
ax.plot(np.concatenate([x[i:i + 1], predicted3[0, :1]]),
np.concatenate([y[i:i + 1], predicted3[0, 1:2]]),
np.concatenate([z[i:i + 1], predicted3[0, 2:3]]),
color="0.4",
alpha=0.75)
ax.plot(np.concatenate([x[i:i + 1], predicted4[0, :1]]),
np.concatenate([y[i:i + 1], predicted4[0, 1:2]]),
np.concatenate([z[i:i + 1], predicted4[0, 2:3]]),
color="0.15",
alpha=0.75)
# Plot the dots at each time step depending on the action taken
length_block = [[0, 18], [18, 19], [19, 31]]
for i in range(3):
colors = ['blue', 'orange', 'green']
if (self.intern_dim == 2):
line3 = ax.scatter(all_possib_abs_states[
length_block[i][0]:length_block[i][1], 0],
all_possib_abs_states[
length_block[i][0]:length_block[i][1],
1],
c=colors[i],
marker='x',
edgecolors='k',
alpha=0.5,
s=100)
else:
line3 = ax.scatter(all_possib_abs_states[
length_block[i][0]:length_block[i][1], 0],
all_possib_abs_states[
length_block[i][0]:length_block[i][1],
1],
all_possib_abs_states[
length_block[i][0]:length_block[i][1],
2],
marker='x',
depthshade=True,
edgecolors='k',
alpha=0.5,
s=50)
if (self.intern_dim == 2):
axes_lims = [ax.get_xlim(), ax.get_ylim()]
else:
axes_lims = [ax.get_xlim(), ax.get_ylim(), ax.get_zlim()]
# Plot the legend for transition estimates
box1b = TextArea(" Estimated transitions (action 0, 1, 2 and 3): ",
textprops=dict(color="k"))
box2b = DrawingArea(90, 20, 0, 0)
el1b = Rectangle((5, 10), 15, 2, fc="0.9", alpha=0.75)
el2b = Rectangle((25, 10), 15, 2, fc="0.65", alpha=0.75)
el3b = Rectangle((45, 10), 15, 2, fc="0.4", alpha=0.75)
el4b = Rectangle((65, 10), 15, 2, fc="0.15", alpha=0.75)
box2b.add_artist(el1b)
box2b.add_artist(el2b)
box2b.add_artist(el3b)
box2b.add_artist(el4b)
boxb = HPacker(children=[box1b, box2b], align="center", pad=0, sep=5)
anchored_box = AnchoredOffsetbox(
loc=3,
child=boxb,
pad=0.,
frameon=True,
bbox_to_anchor=(0., 0.98),
bbox_transform=ax.transAxes,
borderpad=0.,
)
ax.add_artist(anchored_box)
#plt.show()
plt.savefig('fig_base' + str(learning_algo.update_counter) + '.pdf')
# # Plot the Q_vals
# c = learning_algo.Q.predict(np.concatenate((np.expand_dims(x,axis=1),np.expand_dims(y,axis=1),np.expand_dims(z,axis=1)),axis=1))
# #print "actions,C"
# #print actions
# #print c
# #c=np.max(c,axis=1)
# m1=ax.scatter(x, y, z+zrange/20, c=c[:,0], vmin=-1., vmax=1., cmap=plt.cm.RdYlGn)
# m2=ax.scatter(x, y, z+3*zrange/40, c=c[:,1], vmin=-1., vmax=1., cmap=plt.cm.RdYlGn)
#
# #plt.colorbar(m3)
# ax2 = fig.add_axes([0.85, 0.15, 0.025, 0.7])
# cmap = matplotlib.cm.RdYlGn
# norm = matplotlib.colors.Normalize(vmin=-1, vmax=1)
#
# # ColorbarBase derives from ScalarMappable and puts a colorbar
# # in a specified axes, so it has everything needed for a
# # standalone colorbar. There are many more kwargs, but the
# # following gives a basic continuous colorbar with ticks
# # and labels.
# cb1 = matplotlib.colorbar.ColorbarBase(ax2, cmap=cmap,norm=norm,orientation='vertical')
# cb1.set_label('Estimated expected return')
#
# #plt.show()
# plt.savefig('fig_w_V'+str(learning_algo.update_counter)+'.pdf')
#
#
# # fig_visuV
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
#
# x = np.array([i for i in range(5) for jk in range(25)])/4.*(axes_lims[0][1]-axes_lims[0][0])+axes_lims[0][0]
# y = np.array([j for i in range(5) for j in range(5) for k in range(5)])/4.*(axes_lims[1][1]-axes_lims[1][0])+axes_lims[1][0]
# z = np.array([k for i in range(5) for j in range(5) for k in range(5)])/4.*(axes_lims[2][1]-axes_lims[2][0])+axes_lims[2][0]
#
# c = learning_algo.Q.predict(np.concatenate((np.expand_dims(x,axis=1),np.expand_dims(y,axis=1),np.expand_dims(z,axis=1)),axis=1))
# c=np.max(c,axis=1)
# #print "c"
# #print c
#
# m=ax.scatter(x, y, z, c=c, vmin=-1., vmax=1., cmap=plt.hot())
# #plt.colorbar(m)
# fig.subplots_adjust(right=0.8)
# ax2 = fig.add_axes([0.875, 0.15, 0.025, 0.7])
# cmap = matplotlib.cm.hot
# norm = matplotlib.colors.Normalize(vmin=-1, vmax=1)
#
# # ColorbarBase derives from ScalarMappable and puts a colorbar
# # in a specified axes, so it has everything needed for a
# # standalone colorbar. There are many more kwargs, but the
# # following gives a basic continuous colorbar with ticks
# # and labels.
# cb1 = matplotlib.colorbar.ColorbarBase(ax2, cmap=cmap,norm=norm,orientation='vertical')
# cb1.set_label('Estimated expected return')
#
# #plt.show()
# plt.savefig('fig_visuV'+str(learning_algo.update_counter)+'.pdf')
#
#
# # fig_visuR
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
#
# x = np.array([i for i in range(5) for jk in range(25)])/4.*(axes_lims[0][1]-axes_lims[0][0])+axes_lims[0][0]
# y = np.array([j for i in range(5) for j in range(5) for k in range(5)])/4.*(axes_lims[1][1]-axes_lims[1][0])+axes_lims[1][0]
# z = np.array([k for i in range(5) for j in range(5) for k in range(5)])/4.*(axes_lims[2][1]-axes_lims[2][0])+axes_lims[2][0]
#
# coords=np.concatenate((np.expand_dims(x,axis=1),np.expand_dims(y,axis=1),np.expand_dims(z,axis=1)),axis=1)
# repeat_nactions_coord=np.repeat(coords,self.nActions(),axis=0)
# identity_matrix = np.diag(np.ones(self.nActions()))
# tile_identity_matrix=np.tile(identity_matrix,(5*5*5,1))
#
# c = learning_algo.R.predict([repeat_nactions_coord,tile_identity_matrix])
# c=np.max(np.reshape(c,(125,self.nActions())),axis=1)
# #print "c"
# #print c
# #mini=np.min(c)
# #maxi=np.max(c)
#
# m=ax.scatter(x, y, z, c=c, vmin=-1., vmax=1., cmap=plt.hot())
# #plt.colorbar(m)
# fig.subplots_adjust(right=0.8)
# ax2 = fig.add_axes([0.875, 0.15, 0.025, 0.7])
# cmap = matplotlib.cm.hot
# norm = matplotlib.colors.Normalize(vmin=-1, vmax=1)
#
# # ColorbarBase derives from ScalarMappable and puts a colorbar
# # in a specified axes, so it has everything needed for a
# # standalone colorbar. There are many more kwargs, but the
# # following gives a basic continuous colorbar with ticks
# # and labels.
# cb1 = matplotlib.colorbar.ColorbarBase(ax2, cmap=cmap,norm=norm,orientation='vertical')
# cb1.set_label('Estimated expected return')
#
# #plt.show()
# plt.savefig('fig_visuR'+str(learning_algo.update_counter)+'.pdf')
matplotlib.pyplot.close("all") # avoids memory leaks
def plot_rst(xList, yList, fnameList):
'''docstring for plot_rst()'''
fig = plt.gcf()
fig.clf()
ax = plt.subplot2grid((5, 1), (0, 0), rowspan=4)
#ax = plt.subplot(111)
xy = (0.5, 0.7)
offsetbox = TextArea("Test", minimumdescent=False)
ab = AnnotationBbox(offsetbox,
xy,
xybox=(1.02, xy[1]),
xycoords='data',
boxcoords=("axes fraction", "data"),
box_alignment=(0., 0.5),
arrowprops=dict(arrowstyle="->"))
ax.add_artist(ab)
from matplotlib.patches import Circle
da = DrawingArea(20, 20, 0, 0)
p = Circle((10, 10), 10)
da.add_artist(p)
xy = [0.3, 0.55]
ab = AnnotationBbox(da,
xy,
xybox=(1.02, xy[1]),
xycoords='data',
boxcoords=("axes fraction", "data"),
box_alignment=(0., 0.5),
arrowprops=dict(arrowstyle="->"))
#arrowprops=None)
ax.add_artist(ab)
# another image
from matplotlib._png import read_png
#fn = get_sample_data("./61.png", asfileobj=False)
arr_lena = read_png("./61.png")
imagebox = OffsetImage(arr_lena, zoom=0.2)
xy = (0.1, 0.1)
print fnameList
for i in range(0, len(fnameList)):
ax.add_artist(
AnnotationBbox(
imagebox,
xy,
xybox=(0.1 + i * 0.2, -0.15),
xycoords='data',
boxcoords=("axes fraction", "data"),
#boxcoords="offset points",
pad=0.1,
arrowprops=dict(
arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=3")))
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
plt.draw()
plt.show()
def __init__(self, width, height, xdescent, ydescent,
loc, pad=0.4, borderpad=0.5, prop=None, frameon=True):
self.da = DrawingArea(width, height, xdescent, ydescent)
super().__init__(loc, pad=pad, borderpad=borderpad,
child=self.da, prop=None, frameon=frameon)
def y_tick_label(self, y, size, offset=7, ax=None):
"""
uses the icon as a ticklabel at location x
Args:
y (float)
the position of the tick
size (float)
scaling the size of the icon
offset (integer)
how far the icon should be from the axis in points
ax (matplotlib axis)
the axis to put the label on
"""
if ax is None:
ax = plt.gca()
if self.final_image is not None:
imagebox = OffsetImage(self.final_image, zoom=size)
ab = AnnotationBbox(imagebox, (0, y),
xybox=(-offset, 0),
xycoords=('axes fraction', 'data'),
box_alignment=(1, .5),
boxcoords='offset points',
bboxprops={
'edgecolor': 'none',
'facecolor': 'none'
},
arrowprops={
'arrowstyle': '-',
'shrinkA': 0,
'shrinkB': 1
},
pad=0.1)
ax.add_artist(ab)
zorder = ab.zorder
else:
zorder = 0
if self.marker:
if self.final_image is not None:
markersize = max(self.final_image.size)
else:
markersize = 50
markersize = markersize * size
d = DrawingArea(markersize, markersize)
if self.marker_front:
zorder_marker = zorder + 0.1
else:
zorder_marker = zorder - 0.1
d.set_zorder(zorder_marker)
d.set_alpha(0)
if self.marker_front:
d.add_artist(
plt.Line2D([markersize / 2], [markersize / 2],
marker=self.marker,
markeredgecolor=self.col,
markerfacecolor=(0, 0, 0, 0),
markersize=markersize,
markeredgewidth=self.markeredgewidth,
transform=d.get_transform(),
zorder=zorder_marker))
else:
d.add_artist(
plt.Line2D([markersize / 2], [markersize / 2],
marker=self.marker,
markeredgecolor=self.col,
markerfacecolor=self.col,
markersize=markersize,
markeredgewidth=self.markeredgewidth,
transform=d.get_transform(),
zorder=zorder_marker))
ab_marker = AnnotationBbox(d, (0, y),
xybox=(-offset, 0),
xycoords=('axes fraction', 'data'),
box_alignment=(1, 0.5),
boxcoords='offset points',
bboxprops={
'edgecolor': 'none',
'facecolor': 'none'
},
arrowprops={
'arrowstyle': '-',
'shrinkA': 0,
'shrinkB': 1
},
pad=0.1)
ab_marker.set_zorder(zorder_marker)
ab_marker.set_alpha(0)
ax.add_artist(ab_marker)
if self.string is not None:
ax.annotate(self.string, (0, y),
xytext=(-offset, 0),
xycoords=('axes fraction', 'data'),
textcoords='offset points',
horizontalalignment='right',
verticalalignment='center',
arrowprops={
'arrowstyle': '-',
'shrinkA': 0,
'shrinkB': 1
},
zorder=zorder + 1,
fontsize=self.fontsize,
fontname=self.fontname,
color=self.fontcolor)
def _plot_altitude(self, figure, site, tgt_data, tz):
"""
Plot into `figure` an altitude chart using target data from `info`
with time plotted in timezone `tz` (a tzinfo instance).
"""
## site = info.site
## tgt_data = info.target_data
# Urk! This seems to be necessary even though we are plotting
# python datetime objects with timezone attached and setting
# date formatters with the timezone
tz_str = tz.tzname(None)
mpl.rcParams['timezone'] = tz_str
# set major ticks to hours
majorTick = mpl_dt.HourLocator(tz=tz)
majorFmt = mpl_dt.DateFormatter('%Hh')
# set minor ticks to 15 min intervals
minorTick = mpl_dt.MinuteLocator(list(range(0,59,15)), tz=tz)
figure.clf()
ax1 = figure.add_subplot(111)
figure.set_tight_layout(False)
figure.subplots_adjust(left=0.05, right=0.65, bottom=0.12, top=0.95)
#lstyle = 'o'
lstyle = '-'
lt_data = list(map(lambda info: info.ut.astimezone(tz),
tgt_data[0].history))
# sanity check on dates in preferred timezone
## for dt in lt_data[:10]:
## print(dt.strftime("%Y-%m-%d %H:%M:%S"))
min_interval = 12 # hour/5min
mt = lt_data[0:-1:min_interval]
targets = []
legend = []
# plot targets elevation vs. time
for i, info in enumerate(tgt_data):
alt_data = numpy.array(list(map(lambda info: info.alt_deg, info.history)))
alt_min = numpy.argmin(alt_data)
alt_data_dots = alt_data
color = self.colors[i % len(self.colors)]
lc = color + lstyle
# ax1.plot_date(lt_data, alt_data, lc, linewidth=1.0, alpha=0.3, aa=True, tz=tz)
ax1.plot_date(lt_data, alt_data_dots, lc, linewidth=2.0,
aa=True, tz=tz)
legend.extend(ax1.plot_date(lt_data, alt_data_dots, lc, linewidth=2.0,aa=True, tz=tz))
targets.append("{0} {1} {2}".format(info.target.name, info.target.ra, info.target.dec))
alt_interval = alt_data[0:-1:min_interval]
moon_sep = numpy.array(map(lambda info: info.moon_sep, info.history))
moon_sep = moon_sep[0:-1:min_interval]
# plot moon separations
for x, y, v in zip(mt, alt_interval, moon_sep):
if y < 0:
continue
ax1.text(x, y, '%.1f' %v, fontsize=7, ha='center', va='bottom')
ax1.plot_date(x, y, 'ko', ms=3)
#xs, ys = mpl.mlab.poly_between(lt_data, 2.02, alt_data)
#ax1.fill(xs, ys, facecolor=self.colors[i], alpha=0.2)
# plot object label
targname = info.target.name
ax1.text(mpl_dt.date2num(lt_data[alt_data.argmax()]),
alt_data.max() + 4.0, targname, color=color,
ha='center', va='center')
# legend moon distance
box1 = TextArea("Moon distance(deg) ", textprops=dict(color="k", size=7.5))
box2 = DrawingArea(48, 10, 0, 0)
circle1 = Circle((5, 5), 2.3, fc="k", ec=None)
box2.add_artist(circle1)
box = HPacker(children=[box2, box1], align="center", pad=2.5, sep=-35)
args = dict(alpha=0.5,)
anchored_box = AnchoredOffsetbox(loc=3,
child=box, pad=0.,
frameon=True,
bbox_to_anchor=(0.009, 0.9630), #(0.25, -0.140)
bbox_transform=ax1.transAxes,
borderpad=0.)
ax1.add_artist(anchored_box)
#x = mpl_dt.date2num(lt_data[len(lt_data)/2])
# legend target list
self.fig.legend(legend, sorted(targets), 'upper right', fontsize=9, framealpha=0.5, frameon=True, ncol=1, bbox_to_anchor=[0.3, 0.865, .7, 0.1])
ax1.set_ylim(0.0, 90.0)
ax1.set_xlim(lt_data[0], lt_data[-1])
ax1.xaxis.set_major_locator(majorTick)
ax1.xaxis.set_minor_locator(minorTick)
ax1.xaxis.set_major_formatter(majorFmt)
labels = ax1.get_xticklabels()
ax1.grid(True, color='#999999')
# label axes
localdate = lt_data[0].astimezone(tz).strftime("%Y-%m-%d")
title = 'Visibility for the night of %s' % (localdate)
ax1.set_title(title)
ax1.set_xlabel(tz.tzname(None))
ax1.set_ylabel('Altitude')
# Plot moon trajectory and illumination
moon_data = numpy.array(list(map(lambda info: info.moon_alt,
tgt_data[0].history)))
illum_time = lt_data[moon_data.argmax()]
moon_illum = site.moon_phase(date=illum_time)
moon_color = '#666666'
moon_name = "Moon (%.2f %%)" % (moon_illum*100)
ax1.plot_date(lt_data, moon_data, moon_color, linewidth=2.0,
alpha=0.5, aa=True, tz=tz)
ax1.text(mpl_dt.date2num(illum_time),
moon_data.max() + 4.0, moon_name, color=moon_color,
ha='center', va='center')
# Plot airmass scale
altitude_ticks = numpy.array([20, 30, 40, 50, 60, 70, 80, 90])
airmass_ticks = 1.0/numpy.cos(numpy.radians(90 - altitude_ticks))
airmass_ticks = list(map(lambda n: "%.3f" % n, airmass_ticks))
ax2 = ax1.twinx()
#ax2.set_ylim(None, 0.98)
#ax2.set_xlim(lt_data[0], lt_data[-1])
ax2.set_yticks(altitude_ticks)
ax2.set_yticklabels(airmass_ticks)
ax2.set_ylim(ax1.get_ylim())
ax2.set_ylabel('Airmass')
ax2.set_xlabel('')
ax2.yaxis.tick_right()
## mxs, mys = mpl.mlab.poly_between(lt_data, 0, moon_data)
## # ax2.fill(mxs, mys, facecolor='#666666', alpha=moon_illum)
# plot moon label
targname = "moon"
## ax1.text(mpl_dt.date2num(moon_data[moon_data.argmax()]),
## moon_data.max() + 0.08, targname.upper(), color=color,
## ha='center', va='center')
# plot lower and upper safe limits for clear observing
min_alt, max_alt = 30.0, 75.0
self._plot_limits(ax1, min_alt, max_alt)
self._plot_twilight(ax1, site, tz)
# plot current hour
lo = datetime.now(tz)
hi = lo + timedelta(0, 3600.0)
if lt_data[0] < lo < lt_data[-1]:
self._plot_current_time(ax1, lo, hi)
canvas = self.fig.canvas
if canvas is not None:
canvas.draw()
def draw(self):
"""
Draw guide
Returns
-------
out : matplotlib.offsetbox.Offsetbox
A drawing of this legend
"""
obverse = slice(0, None)
reverse = slice(None, None, -1)
nbars = len(self.bar)
direction = self.direction
colors = self.bar['color'].tolist()
labels = self.key['label'].tolist()
themeable = self.theme.figure._themeable
_d = self._default
# 1.45 makes the default colourbar wider than the
# legend entry boxes.
width = (self.barwidth or _d('legend_key_width') or 16) * 1.45
height = (self.barheight or _d('legend_key_height') or 16) * 1.45
height *= 5
length = height
# When there is more than one guide, we keep
# record of all of them using lists
if 'legend_title' not in themeable:
themeable['legend_title'] = []
if 'legend_text_colorbar' not in themeable:
themeable['legend_text_colorbar'] = []
# .5 puts the ticks in the middle of the bars when
# raster=False. So when raster=True the ticks are
# in between interpolation points and the matching is
# close though not exactly right.
_from = self.bar['value'].min(), self.bar['value'].max()
tick_locations = rescale(self.key['value'],
(.5, nbars - .5), _from) * length / nbars
if direction == 'horizontal':
width, height = height, width
length = width
if self.reverse:
colors = colors[::-1]
labels = labels[::-1]
tick_locations = length - tick_locations[::-1]
# title #
title_box = TextArea(self.title, textprops=dict(color='black'))
themeable['legend_title'].append(title_box)
# colorbar and ticks #
da = DrawingArea(width, height, 0, 0)
if self.raster:
add_interpolated_colorbar(da, colors, direction)
else:
add_segmented_colorbar(da, colors, direction)
if self.ticks:
_locations = tick_locations
if not self.draw_ulim:
_locations = _locations[:-1]
if not self.draw_llim:
_locations = _locations[1:]
add_ticks(da, _locations, direction)
# labels #
if self.label:
labels_da, legend_text = create_labels(da, labels, tick_locations,
direction)
themeable['legend_text_colorbar'].extend(legend_text)
else:
labels_da = DrawingArea(0, 0)
# colorbar + labels #
if direction == 'vertical':
packer, align = HPacker, 'bottom'
align = 'center'
else:
packer, align = VPacker, 'right'
align = 'center'
slc = obverse if self.label_position == 'right' else reverse
if self.label_position in ('right', 'bottom'):
slc = obverse
else:
slc = reverse
main_box = packer(children=[da, labels_da][slc],
sep=self._label_margin,
align=align,
pad=0)
# title + colorbar(with labels) #
lookup = {
'right': (HPacker, reverse),
'left': (HPacker, obverse),
'bottom': (VPacker, reverse),
'top': (VPacker, obverse)
}
packer, slc = lookup[self.title_position]
children = [title_box, main_box][slc]
box = packer(children=children,
sep=self._title_margin,
align=self._title_align,
pad=0)
return box
"""
==============
Anchored Box04
==============
"""
from matplotlib.patches import Ellipse
import matplotlib.pyplot as plt
from matplotlib.offsetbox import AnchoredOffsetbox, TextArea, DrawingArea, HPacker
fig = plt.figure(1, figsize=(3, 3))
ax = plt.subplot(111)
box1 = TextArea(" Test : ", textprops=dict(color="k"))
box2 = DrawingArea(60, 20, 0, 0)
el1 = Ellipse((10, 10), width=16, height=5, angle=30, fc="r")
el2 = Ellipse((30, 10), width=16, height=5, angle=170, fc="g")
el3 = Ellipse((50, 10), width=16, height=5, angle=230, fc="b")
box2.add_artist(el1)
box2.add_artist(el2)
box2.add_artist(el3)
box = HPacker(children=[box1, box2], align="center", pad=0, sep=5)
anchored_box = AnchoredOffsetbox(
loc=3,
child=box,
pad=0.,
frameon=True,
bbox_to_anchor=(0., 1.02),
arrowprops=dict(arrowstyle="->"))
ax.add_artist(ab)
offsetbox = TextArea("Test", minimumdescent=False)
ab = AnnotationBbox(offsetbox,
xy,
xybox=(1.02, xy[1]),
xycoords='data',
boxcoords=("axes fraction", "data"),
box_alignment=(0., 0.5),
arrowprops=dict(arrowstyle="->"))
ax.add_artist(ab)
from matplotlib.patches import Circle
da = DrawingArea(20, 20, 0, 0)
p = Circle((10, 10), 10)
da.add_artist(p)
xy = [0.3, 0.55]
ab = AnnotationBbox(da,
xy,
xybox=(1.02, xy[1]),
xycoords='data',
boxcoords=("axes fraction", "data"),
box_alignment=(0., 0.5),
arrowprops=dict(arrowstyle="->"))
#arrowprops=None)
ax.add_artist(ab)
def summarizePerformance(self, test_data_set, learning_algo, *args,
**kwargs):
""" Plot of the low-dimensional representation of the environment built by the model
"""
all_possib_inp = []
#labels=[]
for x_b in range(self._nx_block): #[1]:#range(self._nx_block):
for y_b in range(self._height):
for x_p in range(self._width - self._width_paddle + 1):
state = self.get_observation(
y_b, x_b * ((self._width - 1) // (self._nx_block - 1)),
x_p)
all_possib_inp.append(state)
#labels.append(x_b)
#arr=np.array(all_possib_inp)
#arr=arr.reshape(arr.shape[0],-1)
#np.savetxt('tsne_python/catcherH_X.txt',arr.reshape(arr.shape[0],-1))
#np.savetxt('tsne_python/cacherH_labels.txt',np.array(labels))
all_possib_inp = np.expand_dims(all_possib_inp, axis=1)
all_possib_abs_states = learning_algo.encoder.predict(all_possib_inp)
n = self._height - 1
historics = []
for i, observ in enumerate(test_data_set.observations()[0][0:n]):
historics.append(np.expand_dims(observ, axis=0))
historics = np.array(historics)
abs_states = learning_algo.encoder.predict(historics)
actions = test_data_set.actions()[0:n]
if self.inTerminalState() == False:
self._mode_episode_count += 1
print("== Mean score per episode is {} over {} episodes ==".format(
self._mode_score / (self._mode_episode_count + 0.0001),
self._mode_episode_count))
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.cm as cm
m = cm.ScalarMappable(cmap=cm.jet)
x = np.array(abs_states)[:, 0]
y = np.array(abs_states)[:, 1]
z = np.array(abs_states)[:, 2]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel(r'$X_1$')
ax.set_ylabel(r'$X_2$')
ax.set_zlabel(r'$X_3$')
for j in range(3):
# Plot the trajectory
for i in range(30): #(n-1):
ax.plot(x[j * 24 + i:j * 24 + i + 2],
y[j * 24 + i:j * 24 + i + 2],
z[j * 24 + i:j * 24 + i + 2],
color=plt.cm.cool(255 * i / n),
alpha=0.5)
# Plot the estimated transitions
for i in range(n - 1):
predicted1 = learning_algo.transition.predict(
[abs_states[i:i + 1], np.array([[1, 0]])])
predicted2 = learning_algo.transition.predict(
[abs_states[i:i + 1], np.array([[0, 1]])])
ax.plot(np.concatenate([x[i:i + 1], predicted1[0, :1]]),
np.concatenate([y[i:i + 1], predicted1[0, 1:2]]),
np.concatenate([z[i:i + 1], predicted1[0, 2:3]]),
color="0.75",
alpha=0.75)
ax.plot(np.concatenate([x[i:i + 1], predicted2[0, :1]]),
np.concatenate([y[i:i + 1], predicted2[0, 1:2]]),
np.concatenate([z[i:i + 1], predicted2[0, 2:3]]),
color="0.25",
alpha=0.75)
# Plot the colorbar for the trajectory
fig.subplots_adjust(right=0.7)
ax1 = fig.add_axes([0.725, 0.15, 0.025, 0.7])
# Set the colormap and norm to correspond to the data for which the colorbar will be used.
cmap = matplotlib.cm.cool
norm = matplotlib.colors.Normalize(vmin=0, vmax=1)
# ColorbarBase derives from ScalarMappable and puts a colorbar in a specified axes, so it has
# everything needed for a standalone colorbar. There are many more kwargs, but the
# following gives a basic continuous colorbar with ticks and labels.
cb1 = matplotlib.colorbar.ColorbarBase(ax1,
cmap=cmap,
norm=norm,
orientation='vertical')
cb1.set_label('Beginning to end of trajectory')
# Plot the dots at each time step depending on the action taken
length_block = self._height * (self._width - self._width_paddle + 1)
for i in range(self._nx_block):
line3 = ax.scatter(
all_possib_abs_states[i * length_block:(i + 1) * length_block,
0],
all_possib_abs_states[i * length_block:(i + 1) * length_block,
1],
all_possib_abs_states[i * length_block:(i + 1) * length_block,
2],
s=10,
marker='x',
depthshade=True,
edgecolors='k',
alpha=0.3)
line2 = ax.scatter(x,
y,
z,
c=np.tile(np.expand_dims(1 - actions / 2., axis=1),
(1, 3)) - 0.25,
s=50,
marker='o',
edgecolors='k',
alpha=0.75,
depthshade=True)
axes_lims = [ax.get_xlim(), ax.get_ylim(), ax.get_zlim()]
zrange = axes_lims[2][1] - axes_lims[2][0]
# Plot the legend for the dots
from matplotlib.patches import Circle, Rectangle
from matplotlib.offsetbox import AnchoredOffsetbox, TextArea, DrawingArea, HPacker
box1 = TextArea(" State representation (action 0, action 1): ",
textprops=dict(color="k"))
box2 = DrawingArea(60, 20, 0, 0)
el1 = Circle((10, 10), 5, fc="0.75", edgecolor="k", alpha=0.75)
el2 = Circle((30, 10), 5, fc="0.25", edgecolor="k", alpha=0.75)
#el3 = Circle((50, 10), 5, fc="0", edgecolor="k")
box2.add_artist(el1)
box2.add_artist(el2)
#box2.add_artist(el3)
box = HPacker(children=[box1, box2], align="center", pad=0, sep=5)
anchored_box = AnchoredOffsetbox(
loc=3,
child=box,
pad=0.,
frameon=True,
bbox_to_anchor=(0., 1.07),
bbox_transform=ax.transAxes,
borderpad=0.,
)
ax.add_artist(anchored_box)
# Plot the legend for transition estimates
box1b = TextArea(" Estimated transitions (action 0, action 1): ",
textprops=dict(color="k"))
box2b = DrawingArea(60, 20, 0, 0)
el1b = Rectangle((5, 10), 15, 2, fc="0.75", alpha=0.75)
el2b = Rectangle((25, 10), 15, 2, fc="0.25", alpha=0.75)
box2b.add_artist(el1b)
box2b.add_artist(el2b)
boxb = HPacker(children=[box1b, box2b], align="center", pad=0, sep=5)
anchored_box = AnchoredOffsetbox(
loc=3,
child=boxb,
pad=0.,
frameon=True,
bbox_to_anchor=(0., 0.98),
bbox_transform=ax.transAxes,
borderpad=0.,
)
ax.add_artist(anchored_box)
ax.w_xaxis.set_pane_color((0.99, 0.99, 0.99, 0.99))
ax.w_yaxis.set_pane_color((0.99, 0.99, 0.99, 0.99))
ax.w_zaxis.set_pane_color((0.99, 0.99, 0.99, 0.99))
#plt.savefig('fig_base'+str(learning_algo.update_counter)+'.pdf')
# Plot the Q_vals
c = learning_algo.Q.predict(
np.concatenate((np.expand_dims(x, axis=1), np.expand_dims(
y, axis=1), np.expand_dims(z, axis=1)),
axis=1))
m1 = ax.scatter(x,
y,
z + zrange / 20,
c=c[:, 0],
vmin=-1.,
vmax=1.,
cmap=plt.cm.RdYlGn)
m2 = ax.scatter(x,
y,
z + 3 * zrange / 40,
c=c[:, 1],
vmin=-1.,
vmax=1.,
cmap=plt.cm.RdYlGn)
#plt.colorbar(m3)
ax2 = fig.add_axes([0.85, 0.15, 0.025, 0.7])
cmap = matplotlib.cm.RdYlGn
norm = matplotlib.colors.Normalize(vmin=-1, vmax=1)
# ColorbarBase derives from ScalarMappable and puts a colorbar
# in a specified axes, so it has everything needed for a
# standalone colorbar. There are many more kwargs, but the
# following gives a basic continuous colorbar with ticks
# and labels.
cb1 = matplotlib.colorbar.ColorbarBase(ax2,
cmap=cmap,
norm=norm,
orientation='vertical')
cb1.set_label('Estimated expected return')
# plt.show()
for ii in range(-15, 345, 30):
ax.view_init(elev=20., azim=ii)
plt.savefig('fig_w_V_div5_forcelr_forcessdiv2' +
str(learning_algo.update_counter) + '_' + str(ii) +
'.pdf')
# fig_visuV
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.array([i for i in range(5) for jk in range(25)]) / 4. * (
axes_lims[0][1] - axes_lims[0][0]) + axes_lims[0][0]
y = np.array([j for i in range(5) for j in range(5)
for k in range(5)]) / 4. * (
axes_lims[1][1] - axes_lims[1][0]) + axes_lims[1][0]
z = np.array([k for i in range(5) for j in range(5)
for k in range(5)]) / 4. * (
axes_lims[2][1] - axes_lims[2][0]) + axes_lims[2][0]
c = learning_algo.Q.predict(
np.concatenate((np.expand_dims(x, axis=1), np.expand_dims(
y, axis=1), np.expand_dims(z, axis=1)),
axis=1))
c = np.max(c, axis=1)
m = ax.scatter(x, y, z, c=c, vmin=-1., vmax=1., cmap=plt.hot())
fig.subplots_adjust(right=0.8)
ax2 = fig.add_axes([0.875, 0.15, 0.025, 0.7])
cmap = matplotlib.cm.hot
norm = matplotlib.colors.Normalize(vmin=-1, vmax=1)
# ColorbarBase derives from ScalarMappable and puts a colorbar
# in a specified axes, so it has everything needed for a
# standalone colorbar. There are many more kwargs, but the
# following gives a basic continuous colorbar with ticks
# and labels.
cb1 = matplotlib.colorbar.ColorbarBase(ax2,
cmap=cmap,
norm=norm,
orientation='vertical')
cb1.set_label('Estimated expected return')
#plt.show()
#plt.savefig('fig_visuV'+str(learning_algo.update_counter)+'.pdf')
# fig_visuR
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.array([i for i in range(5) for jk in range(25)]) / 4. * (
axes_lims[0][1] - axes_lims[0][0]) + axes_lims[0][0]
y = np.array([j for i in range(5) for j in range(5)
for k in range(5)]) / 4. * (
axes_lims[1][1] - axes_lims[1][0]) + axes_lims[1][0]
z = np.array([k for i in range(5) for j in range(5)
for k in range(5)]) / 4. * (
axes_lims[2][1] - axes_lims[2][0]) + axes_lims[2][0]
coords = np.concatenate((np.expand_dims(
x, axis=1), np.expand_dims(y, axis=1), np.expand_dims(z, axis=1)),
axis=1)
repeat_nactions_coord = np.repeat(coords, self.nActions(), axis=0)
identity_matrix = np.diag(np.ones(self.nActions()))
tile_identity_matrix = np.tile(identity_matrix, (5 * 5 * 5, 1))
c = learning_algo.R.predict(
[repeat_nactions_coord, tile_identity_matrix])
c = np.max(np.reshape(c, (125, self.nActions())), axis=1)
m = ax.scatter(x, y, z, c=c, vmin=-1., vmax=1., cmap=plt.hot())
fig.subplots_adjust(right=0.8)
ax2 = fig.add_axes([0.875, 0.15, 0.025, 0.7])
cmap = matplotlib.cm.hot
norm = matplotlib.colors.Normalize(vmin=-1, vmax=1)
# ColorbarBase derives from ScalarMappable and puts a colorbar
# in a specified axes, so it has everything needed for a
# standalone colorbar. There are many more kwargs, but the
# following gives a basic continuous colorbar with ticks
# and labels.
cb1 = matplotlib.colorbar.ColorbarBase(ax2,
cmap=cmap,
norm=norm,
orientation='vertical')
cb1.set_label('Estimated expected return')
#plt.show()
#plt.savefig('fig_visuR'+str(learning_algo.update_counter)+'.pdf')
matplotlib.pyplot.close("all") # avoids memory leaks
def _init_legend_box(self, handles, labels, markerfirst=True):
"""
Initialize the legend_box. The legend_box is an instance of
the OffsetBox, which is packed with legend handles and
texts. Once packed, their location is calculated during the
drawing time.
"""
fontsize = self._fontsize
# legend_box is a HPacker, horizontally packed with
# columns. Each column is a VPacker, vertically packed with
# legend items. Each legend item is HPacker packed with
# legend handleBox and labelBox. handleBox is an instance of
# offsetbox.DrawingArea which contains legend handle. labelBox
# is an instance of offsetbox.TextArea which contains legend
# text.
text_list = [] # the list of text instances
handle_list = [] # the list of text instances
label_prop = dict(
verticalalignment='baseline',
horizontalalignment='left',
fontproperties=self.prop,
)
labelboxes = []
handleboxes = []
# The approximate height and descent of text. These values are
# only used for plotting the legend handle.
descent = 0.35 * self._approx_text_height() * (self.handleheight - 0.7)
# 0.35 and 0.7 are just heuristic numbers and may need to be improved.
height = self._approx_text_height() * self.handleheight - descent
# each handle needs to be drawn inside a box of (x, y, w, h) =
# (0, -descent, width, height). And their coordinates should
# be given in the display coordinates.
# The transformation of each handle will be automatically set
# to self.get_trasnform(). If the artist does not use its
# default transform (e.g., Collections), you need to
# manually set their transform to the self.get_transform().
legend_handler_map = self.get_legend_handler_map()
for orig_handle, lab in zip(handles, labels):
handler = self.get_legend_handler(legend_handler_map, orig_handle)
if handler is None:
warnings.warn(
"Legend does not support {!r} instances.\nA proxy artist "
"may be used instead.\nSee: "
"http://matplotlib.org/users/legend_guide.html"
"#using-proxy-artist".format(orig_handle))
# We don't have a handle for this artist, so we just defer
# to None.
handle_list.append(None)
else:
textbox = TextArea(lab,
textprops=label_prop,
multilinebaseline=True,
minimumdescent=True)
text_list.append(textbox._text)
labelboxes.append(textbox)
handlebox = DrawingArea(width=self.handlelength * fontsize,
height=height,
xdescent=0.,
ydescent=descent)
handleboxes.append(handlebox)
# Create the artist for the legend which represents the
# original artist/handle.
handle_list.append(
handler.legend_artist(self, orig_handle, fontsize,
handlebox))
if len(handleboxes) > 0:
# We calculate number of rows in each column. The first
# (num_largecol) columns will have (nrows+1) rows, and remaining
# (num_smallcol) columns will have (nrows) rows.
ncol = min(self._ncol, len(handleboxes))
nrows, num_largecol = divmod(len(handleboxes), ncol)
num_smallcol = ncol - num_largecol
# starting index of each column and number of rows in it.
largecol = safezip(
list(xrange(0, num_largecol * (nrows + 1), (nrows + 1))),
[nrows + 1] * num_largecol)
smallcol = safezip(
list(
xrange(num_largecol * (nrows + 1), len(handleboxes),
nrows)), [nrows] * num_smallcol)
else:
largecol, smallcol = [], []
handle_label = safezip(handleboxes, labelboxes)
columnbox = []
for i0, di in largecol + smallcol:
# pack handleBox and labelBox into itemBox
itemBoxes = [
HPacker(pad=0,
sep=self.handletextpad * fontsize,
children=[h, t] if markerfirst else [t, h],
align="baseline") for h, t in handle_label[i0:i0 + di]
]
# minimumdescent=False for the text of the last row of the column
if markerfirst:
itemBoxes[-1].get_children()[1].set_minimumdescent(False)
else:
itemBoxes[-1].get_children()[0].set_minimumdescent(False)
# pack columnBox
if markerfirst:
alignment = "baseline"
else:
alignment = "right"
columnbox.append(
VPacker(pad=0,
sep=self.labelspacing * fontsize,
align=alignment,
children=itemBoxes))
if self._mode == "expand":
mode = "expand"
else:
mode = "fixed"
sep = self.columnspacing * fontsize
self._legend_handle_box = HPacker(pad=0,
sep=sep,
align="baseline",
mode=mode,
children=columnbox)
self._legend_title_box = TextArea("")
self._legend_box = VPacker(
pad=self.borderpad * fontsize,
sep=self.labelspacing * fontsize,
align="center",
children=[self._legend_title_box, self._legend_handle_box])
self._legend_box.set_figure(self.figure)
self.texts = text_list
self.legendHandles = handle_list
#gs = plt.GridSpec(100,100,bottom=0,left=0,right=1,top=1)
#ax_off = fig.add_subplot(gs[0:100,0:100])
#
#star=mlines.Line2D([],[],color='red',marker='*',mec='k',linestyle='None',markersize=12,label='AFT used in this study')
#circle=mlines.Line2D([],[],color='red',marker='o',mec='k',linestyle='None',markersize=10,label='AFT Available')
#
#ax_off.legend(handles=[circle,star],fontsize=8,labelspacing=0.2,handletextpad=0.25,borderpad=0.1,loc=3,facecolor=(0.773,0.898,0.961),fancybox=True,edgecolor=(0,0,0,1))
#
#ax_off.patch.set_alpha(0)
#ax_off.patch.set_visible(False)
#ax_off.axis('off')
#%%====================================TEST=================================%%#
boxc1 = TextArea(' AHeA available:\n AHeA used for the study:',
textprops=dict(color='k', fontsize=9))
Boxc2 = DrawingArea(12.5, 20)
Recc0 = Rectangle((5, 0),
width=6,
height=6,
angle=45,
fc='darkred',
ec='darkred')
Recc1 = Circle((5, 15), radius=3.5, fc='darkred', ec='darkred')
Boxc2.add_artist(Recc0)
Boxc2.add_artist(Recc1)
boxc = HPacker(children=[boxc1, Boxc2], align="center", pad=3, sep=2.5)
anchored_boxc = AnchoredOffsetbox(loc=3,
child=boxc,
