def test_patheffect3():
p1, = plt.plot([1, 3, 5, 4, 3], 'o-b', lw=4)
p1.set_path_effects([path_effects.SimpleLineShadow(),
path_effects.Normal()])
plt.title(
r'testing$^{123}$',
path_effects=[path_effects.withStroke(linewidth=1, foreground="r")])
leg = plt.legend([p1], [r'Line 1$^2$'], fancybox=True, loc='upper left')
leg.legendPatch.set_path_effects([path_effects.withSimplePatchShadow()])
text = plt.text(2, 3, 'Drop test', color='white',
bbox={'boxstyle': 'circle,pad=0.1', 'color': 'red'})
pe = [path_effects.Stroke(linewidth=3.75, foreground='k'),
path_effects.withSimplePatchShadow((6, -3), shadow_rgbFace='blue')]
text.set_path_effects(pe)
text.get_bbox_patch().set_path_effects(pe)
pe = [path_effects.PathPatchEffect(offset=(4, -4), hatch='xxxx',
facecolor='gray'),
path_effects.PathPatchEffect(edgecolor='white', facecolor='black',
lw=1.1)]
t = plt.gcf().text(0.02, 0.1, 'Hatch shadow', fontsize=75, weight=1000,
va='center')
t.set_path_effects(pe)
def main():
fontsize = 18
plt.plot(data)
title = "This is figure title"
x_label = "This is x axis label"
y_label = "This is y axis label"
title_text_obj = plt.title(title,
fontsize=fontsize,
verticalalignment='bottom')
title_text_obj.set_path_effects([patheffects.withSimplePatchShadow()])
# offset_xy -- set the 'angle' of the shadow
# shadow_rgbFace == set the color of the shadow
# patch_alpha == setup the transparency of the shadow
offset_xy = (1, -1)
rgbRed = (1.0, 0.0, 0.0)
alpha = 0.8
# customize shadow properties
pe = patheffects.withSimplePatchShadow(shadow_rgbFace=rgbRed)
# apply them to the xaxis anf yaxis labels
xlabel_obj = plt.xlabel(x_label, fontsize=fontsize, alpha=0.5)
xlabel_obj.set_path_effects([pe])
#
ylabel_obj = plt.ylabel(y_label, fontsize=fontsize, alpha=0.5)
ylabel_obj.set_path_effects([pe])
plt.show()
def plotEfficientFrontier(returns,
tickers,
numofsim=200,
title='Simulated Efficient Frontier'):
allocation = simAllocation(numofsim, tickers)
port_returns = returns.dot(allocation)
analytics = generateAnalytics(port_returns)
rets = np.array(analytics.annual_return) / 100
vols = np.array(analytics.annual_volatility) / 100
sr = np.array(analytics.sharpe_ratio)
# the charts
fig8 = plt.figure(figsize=FIG_SIZE)
plt.subplots_adjust(wspace=.5)
plt.subplot()
plt.scatter(vols, rets, c=sr, marker='o', cmap='coolwarm')
plt.grid(True)
plt.xlabel('annual volatility')
plt.ylabel('annual return')
plt.colorbar(label='Sharpe Ratio')
title_text_obj = plt.title(title, fontsize=18, verticalalignment='bottom')
title_text_obj.set_path_effects([patheffects.withSimplePatchShadow()])
plt.show()
def path_effect(text='Sick Fermat'):
fig = plt.figure(figsize=(8, 4))
t = fig.text(0.0, 0.0, text, fontsize=30)
t = fig.text(0.1, 0.2, text, fontsize=30)
t.set_path_effects([
PathPatchEffect(offset=(4, -4), hatch='xxxx', facecolor='gray'),
PathPatchEffect(edgecolor='white', linewidth=1.1, facecolor='black')
])
t = fig.text(0.1, 0.5, text[::-1], fontsize=30, color='white')
t.set_path_effects([
patheffects.Stroke(linewidth=3, foreground='black'),
patheffects.Normal()
])
t = fig.text(0.1, 0.7, text[::-1], fontsize=30)
t.set_path_effects([patheffects.withSimplePatchShadow()])
text_color = to_rgba('black')
lines = [[[0, 1], [0.2, 0.2]], [[0, 1], [0.5, 0.5]], [[0, 1], [0.7, 0.7]],
[[0.1, 0.1], [0, 1]]]
for line in lines:
fig.lines.append(
Line2D(line[0],
line[1],
transform=fig.transFigure,
figure=fig,
color=text_color,
linewidth=0.5))
return fig
def plotCorrelationMatrix(matrix, type=1, title=''):
fig, ax = plt.subplots(figsize=FIG_SIZE)
title_text_obj = ax.set_title(title,
fontsize=18,
verticalalignment='bottom')
title_text_obj.set_path_effects([patheffects.withSimplePatchShadow()])
if type == 1:
sns.heatmap(matrix, annot=True, linewidths=.5)
elif type == 2:
sns.heatmap(matrix,
annot=True,
cmap="YlGnBu",
linewidths=0.3,
annot_kws={"size": 16})
else:
sns.heatmap(matrix,
annot=True,
cmap='coolwarm',
linewidths=0.3,
annot_kws={"size": 16})
# Plot aesthetics
plt.xticks(rotation=90)
plt.yticks(rotation=0)
b, t = plt.ylim() # discover the values for bottom and top
# b += 0.5 # Add 0.5 to the bottom
# t -= 0.5 # Subtract 0.5 from the top
plt.ylim(b, t) # update the ylim(bottom, top) values
plt.show()
def plot_heatmap(evaluators, store=True):
mi_df = Evaluator.to_multi_index_frame(evaluators)
detectors, datasets = mi_df.columns, mi_df.index
fig, ax = plt.subplots(figsize=(len(detectors) + 2, len(datasets)))
im = ax.imshow(mi_df, cmap=plt.get_cmap('YlOrRd'), vmin=0, vmax=1)
plt.colorbar(im)
# Show MultiIndex for ordinate
Evaluator.insert_multi_index_yaxis(ax, mi_df)
# Rotate the tick labels and set their alignment.
ax.set_xticks(np.arange(len(detectors)))
ax.set_xticklabels(detectors)
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')
# Loop over data dimensions and create text annotations.
for i in range(len(detectors)):
for j in range(len(datasets)):
ax.text(i, j, f'{mi_df.iloc[j, i]:.2f}', ha='center', va='center', color='w',
path_effects=[path_effects.withSimplePatchShadow(
offset=(1, -1), shadow_rgbFace='b', alpha=0.9)])
ax.set_title('AUROC over all datasets and detectors')
# Prevent bug where x axis ticks are completely outside of bounds (matplotlib/issues/5456)
if len(datasets) > 2:
fig.tight_layout()
if store:
evaluators[0].store(fig, 'heatmap', no_counters=True, store_in_figures=True)
return fig
def plot_poly(axis, poly, shadow=True, **kw):
if 'c' in kw:
kw['color'] = kw['c']
del kw['c']
if shadow:
kw['path_effects'] = [pfx.withSimplePatchShadow()]
list(map(lambda p: axis.add_patch(PathPatch(pathify(p), **kw)), poly))
def plot_data(self, axis, x=[], shadow=True, **kw):
x = self.data if not len(x) else x
kw['markersize'] = kw['s'] if 's' in kw else 3
kw['zorder'] = kw['zorder'] if 'zorder' in kw else 2
if shadow:
kw['path_effects'] = [pfx.withSimplePatchShadow()]
axis.plot(self.data[:, 0], self.data[:, 1], 'o', **kw)
def plotConf(df_confusion,titulo,savefile):
plt.ioff()
fig = plt.figure()
plt.clf()
ax = fig.add_subplot(111)
ax.set_aspect(1)
res = ax.imshow(np.array(df_confusion), cmap=plt.cm.jet,
interpolation='nearest')
width, height = np.array(df_confusion).shape
for x in xrange(width):
for y in xrange(height):
ax.annotate(str(df_confusion[x][y]), xy=(y, x),
horizontalalignment='center',
verticalalignment='center', color='white',
fontweight='bold',
path_effects=[path_effects.withSimplePatchShadow(alpha=0.5)])
plt.yticks( np.arange(height) )
plt.xticks( np.arange(width) )
plt.ylabel('Valor real')
plt.xlabel('Valor predicho')
cb = fig.colorbar(res)
plt.title(titulo,fontsize=16)
plt.savefig(savefile)
return
def heatmap(df):
"""http://stackoverflow.com/questions/20520246/create-heatmap-using-pandas-timeseries"""
dflen = len(df.index)
dflen2 = len(df.columns)
# print dflen, dflen2
fsize = (1.6 * dflen, .6 * dflen2)
fig = plt.figure(figsize=fsize)
ax = fig.add_subplot(111)
# use dir(matplotlib.cm) to get a list of the installed colormaps
# the "_r" means "reversed" and accounts for why zero values are plotted
# as white
cmap = CM.get_cmap('YlOrRd', 50)
# http://matplotlib.org/examples/color/colormaps_reference.html
ax.imshow(df, interpolation="nearest", cmap=cmap)
# ax.invert_yaxis()
# ax.xaxis.tick_top()
v = df.columns.tolist()
v2 = df.index.tolist()
ax.set_xticks(np.arange(dflen) + 0., minor=False)
ax.set_yticks(np.arange(dflen2) + 0., minor=False)
ax.set_xticklabels(v)
ax.set_yticklabels(v2)
ax.grid(True)
path_effects = [patheffects.withSimplePatchShadow(shadow_rgbFace=(1, 1, 1))]
for i in range(dflen):
for j in range(dflen2):
ax.text(j, i, '{:.2f}'.format(df.iget_value(i, j)),
size='medium', ha='center', va='center', path_effects=path_effects)
plt.show()
def plot_coords(self, axis, v, shadow=True):
color = cm.ScalarMappable(Normalize(v.min(), v.max()), cm.rainbow)
kw = {'markersize' : 3}
if shadow:
kw['path_effects'] = [pfx.withSimplePatchShadow()]
for c, (x, y) in zip(v, self.data):
axis.plot(x, y, 'o', color=color.to_rgba(c), **kw)
def test_patheffect3():
ax3 = plt.subplot(111)
p1, = ax3.plot([0, 1], [0, 1])
ax3.set_title(r'testing$^{123}$',
path_effects=[withStroke(linewidth=1, foreground="r")])
leg = ax3.legend([p1], [r'Line 1$^2$'], fancybox=True, loc=2)
leg.legendPatch.set_path_effects([withSimplePatchShadow()])
def test_patheffect3():
ax3 = plt.subplot(111)
p1, = ax3.plot([0, 1], [0, 1])
ax3.set_title(r'testing$^{123}$',
path_effects=[withStroke(linewidth=1, foreground="r")])
leg = ax3.legend([p1], [r'Line 1$^2$'], fancybox=True, loc=2)
leg.legendPatch.set_path_effects([withSimplePatchShadow()])
def set_circle(self,x,y,r,c):
self.x = x
self.y = y
self.circle = mpatches.Circle((x,y), r, fc=c, ec=(0.1, 0.1, 0.1))
self.circle.set_linestyle('solid')
self.circle.set_linewidth(2.0)
try:
self.circle.set_path_effects([path_effects.withSimplePatchShadow()])
except:
pass
def plot_triangle(self, axis, s, shadow=True, **kw):
p = self.data[list(s)]
if 'c' in kw:
kw['color'] = kw['c']
del kw['c']
kw['color'] = 'black' if not 'color' in kw else kw['color']
kw['alpha'] = 0.1 if not 'alpha' in kw else kw['alpha']
kw['zorder'] = 0 if not 'zorder' in kw else kw['zorder']
if shadow:
kw['path_effects'] = [pfx.withSimplePatchShadow()]
return axis.add_patch(Polygon(p, **kw))
def plot_vertex(self, axis, s, shadow=True, **kw):
p = self.data[list(s)]
if 'color' in kw:
kw['c'] = kw['color']
del kw['color']
kw['c'] = 'black' if not 'c' in kw else kw['c']
kw['alpha'] = 1 if not 'alpha' in kw else kw['alpha']
kw['zorder'] = 2 if not 'zorder' in kw else kw['zorder']
kw['markersize'] = 1 if not 'markersize' in kw else kw['markersize']
if shadow:
kw['path_effects'] = [pfx.withSimplePatchShadow()]
return axis.plot(p[:,0], p[:,1], 'o', **kw)
def __init__(self,
ax,
x1,
x2,
y,
height,
gradient=True,
tip=0.0025,
color="k",
shadow=False,
**kwargs):
super(GeneGlyph, self).__init__(ax)
# Figure out the polygon vertices first
orientation = 1 if x1 < x2 else -1
level = 10
tip = min(tip, abs(x1 - x2))
# Frame
p1 = (x1, y - height * 0.5)
p2 = (x2 - orientation * tip, y - height * 0.5)
p3 = (x2, y)
p4 = (x2 - orientation * tip, y + height * 0.5)
p5 = (x1, y + 0.5 * height)
if "fc" not in kwargs:
kwargs["fc"] = color
if "ec" not in kwargs:
kwargs["ec"] = color
P = Polygon([p1, p2, p3, p4, p5], **kwargs)
self.append(P)
if gradient:
zz = kwargs.get("zorder", 1)
zz += 1
# Patch (apply white mask)
for cascade in np.arange(0, 0.5, 0.5 / level):
p1 = (x1, y - height * cascade)
p2 = (x2 - orientation * tip, y - height * cascade)
p3 = (x2, y)
p4 = (x2 - orientation * tip, y + height * cascade)
p5 = (x1, y + height * cascade)
self.append(
Polygon([p1, p2, p3, p4, p5],
fc="w",
lw=0,
alpha=0.2,
zorder=zz))
if shadow:
import matplotlib.patheffects as pe
P.set_path_effects([pe.withSimplePatchShadow((1, -1), alpha=0.4)])
self.add_patches()
def dr_cmap(fx,ftg9):
cm8 = pd.read_csv('dat\\cor_maps.csv',encoding='gbk')
df2 = mx_sum_main(fx)
df2.to_csv("tmp\\mx_"+ftg9+'.csv',encode='utf8');
for xss in cm8['name']:
df2.plot(kind='bar',colormap=xss,rot=0,figsize=(20,5)
,path_effects=[path_effects.withSimplePatchShadow()]);
plt.axhline(50, color='r');
plt.legend(ncol=3,loc=2)
plt.tight_layout()
fss="tmp\\m1cor_"+xss+"_"+ftg9+".png";plt.savefig(fss);
plt.show();print(xss,",",fss)
def test_patheffect3():
p1, = plt.plot([1, 3, 5, 4, 3], "o-b", lw=4)
p1.set_path_effects([path_effects.SimpleLineShadow(), path_effects.Normal()])
plt.title(r"testing$^{123}$", path_effects=[path_effects.withStroke(linewidth=1, foreground="r")])
leg = plt.legend([p1], [r"Line 1$^2$"], fancybox=True, loc=2)
leg.legendPatch.set_path_effects([path_effects.withSimplePatchShadow()])
text = plt.text(2, 3, "Drop test", color="white", bbox={"boxstyle": "circle,pad=0.1", "color": "red"})
pe = [
path_effects.Stroke(linewidth=3.75, foreground="k"),
path_effects.withSimplePatchShadow((6, -3), shadow_rgbFace="blue"),
]
text.set_path_effects(pe)
text.get_bbox_patch().set_path_effects(pe)
pe = [
path_effects.PathPatchEffect(offset=(4, -4), hatch="xxxx", facecolor="gray"),
path_effects.PathPatchEffect(edgecolor="white", facecolor="black", lw=1.1),
]
t = plt.gcf().text(0.02, 0.1, "Hatch shadow", fontsize=75, weight=1000, va="center")
t.set_path_effects(pe)
def plotCorrelationMatrixWithCluster(matrix, title=''):
fig = sns.clustermap(matrix,
row_cluster=True,
col_cluster=True,
figsize=(10, 10))
title_text_obj = plt.title(title, fontsize=18, verticalalignment='bottom')
title_text_obj.set_path_effects([patheffects.withSimplePatchShadow()])
plt.setp(fig.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
plt.setp(fig.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
plt.show()
def test_patheffect3():
p1, = plt.plot([1, 3, 5, 4, 3], 'o-b', lw=4)
p1.set_path_effects([path_effects.SimpleLineShadow(),
path_effects.Normal()])
plt.title(r'testing$^{123}$',
path_effects=[path_effects.withStroke(linewidth=1, foreground="r")])
leg = plt.legend([p1], [r'Line 1$^2$'], fancybox=True, loc=2)
leg.legendPatch.set_path_effects([path_effects.withSimplePatchShadow()])
text = plt.text(2, 3, 'Drop test', color='white',
bbox={'boxstyle': 'circle,pad=0.1', 'color': 'red'})
pe = [path_effects.Stroke(linewidth=3.75, foreground='k'),
path_effects.withSimplePatchShadow((6, -3), shadow_rgbFace='blue')]
text.set_path_effects(pe)
text.get_bbox_patch().set_path_effects(pe)
pe = [path_effects.PathPatchEffect(offset=(4, -4), hatch='xxxx',
facecolor='gray'),
path_effects.PathPatchEffect(edgecolor='white', facecolor='black',
lw=1.1)]
t = plt.gcf().text(0.02, 0.1, 'Hatch shadow', fontsize=75, weight=1000,
va='center')
t.set_path_effects(pe)
def _place_labels(self, labels, colors):
for label in self.labels:
label.remove()
del self.labels[:]
self.rows = [[]]
renderer = self.canvas.get_renderer()
todata = self.ax.transData.inverted()
xmargin, ymargin = todata.transform((self.margin, self.margin))
x, y = xmargin, ymargin
# Let the labels flow from left to right, wrapping around
for label_index in range(len(labels)):
s = labels[label_index]
label = Text(x, y, s, verticalalignment='top',
bbox=dict(facecolor=colors[label_index], edgecolor='w', pad=self.pad))
# I could not see the dark text on some backgrounds
label.set_path_effects([
PathEffects.withSimplePatchShadow(offset=(1,-1), shadow_rgbFace=(1., 1.,0.5)),
])
label = self.ax.add_artist(label)
# Keep both a master list of labels, and a list of labelson each row
self.labels.append(label)
self.rows[-1].append(label)
# I have to render the label once in order to determine its actual size
label.draw(renderer)
bbox = label.get_bbox_patch().get_window_extent(renderer).transformed(todata)
if (x + bbox.width) > 1:
x = xmargin
y -= bbox.height
label.set_position((x, y))
self.rows.append([self.rows[-1].pop()])
x += bbox.width
# After some tinkering, I am centering the labels in the axis
bbox = self._get_labels_bbox()
labels_center = bbox.corners().mean(0)
view_center = self.ax.viewLim.corners().mean(0)
for label in self.labels:
x, y = label.get_position() + (view_center - labels_center)
label.set_position((x, y))
def generate_combinations_img(class_names, class_names_bak, p_matrix, title, fname='overlaps_img'):
a_size = len(p_matrix)
diff = np.zeros([a_size, a_size])
for i in xrange(a_size):
for j in xrange(a_size):
if i == j:
pass
else:
_sum = p_matrix[i][i] + p_matrix[j][j]
comb = p_matrix[i][j]
diff[i][j] = float(comb)/float(_sum)
fig = plt.figure(figsize=(16, 9))
ax = fig.add_subplot(111)
diffa = masked_array(diff, diff != 0)
diffb = masked_array(diff, diff == 0)
cax = ax.imshow(diffb, cmap=cm.winter, interpolation='None', origin='lower', aspect='auto')
cba = plt.colorbar(cax, format='%.1f')
caxb = ax.imshow(diffa, cmap=cm.Reds, interpolation='None', origin='lower', aspect='auto')
plt.xticks(range(a_size))
ax.set_xticklabels(class_names_bak)
plt.xticks(rotation=80)
plt.yticks(range(a_size))
ax.set_yticklabels(class_names_bak)
for i in xrange(a_size):
for j in xrange(a_size):
ax.text(j, i, '{0}/{1:.2f}'.format(p_matrix[i][j], diff[i][j]),
size='medium', ha='center', va='center',
path_effects=[patheffects.withSimplePatchShadow(shadow_rgbFace=(1, 1, 1))])
plt.title(title)
# bar = fig.colorbar(cax, format='%.1f')
plt.rc('text', usetex=True)
cba.set_label('$\\frac{a_{i,j}}{a_{i,i}+a_{j,j}}$', fontsize=25, rotation=0, labelpad=40)
plt.savefig(folder+'/'+fname, bbox_inches='tight', transparent=False)
plt.close()
def plotTimeSeries(series, title='', xlabel='', ylabel='', tickermap=''):
ax = series.plot(figsize=FIG_SIZE, fontsize=12, linewidth=3, linestyle='-')
ax.set_xlabel(xlabel, fontsize=16)
ax.set_ylabel(ylabel, fontsize=16)
title_text_obj = ax.set_title(title,
fontsize=18,
verticalalignment='bottom')
title_text_obj.set_path_effects([patheffects.withSimplePatchShadow()])
# pe = patheffects.withSimplePatchShadow(offset = (1, -1), shadow_rgbFace = (1,0,0), calpha = 0.8)
names = series.columns
if tickermap:
names = names.map(tickermap)
ax.legend(names, fontsize=16)
plt.show()
def compareDistribution(series, tickers, title='', tickermap={}):
fig, ax = plt.subplots(figsize=FIG_SIZE)
title_text_obj = ax.set_title(title,
fontsize=18,
verticalalignment='bottom')
title_text_obj.set_path_effects([patheffects.withSimplePatchShadow()])
for ticker in tickers:
sns.distplot(series[ticker])
names = series.columns
if tickermap:
names = names.map(tickermap)
ax.legend(names, fontsize=12)
ax.set_xlabel('Returns Distribution', fontsize=14)
ax.set_ylabel('Frequency', fontsize=14)
plt.show()
def plotCountry(m, ax, id, path='gadm0'):
country, lonlat = merged[id]
r = shapefile.Reader(r"%s/%s_adm0" % (path, country))
shapes = r.shapes()
records = r.records()
for record, shape in zip(records, shapes):
lons, lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [
data,
]
else:
segs = []
for i in range(1, len(shape.parts)):
index = shape.parts[i - 1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs, antialiaseds=(1, ))
lines.set_facecolors('lightgreen')
lines.set_edgecolors('k')
lines.set_linewidth(0.1)
lines.set_alpha(0.5)
ax.add_collection(lines)
# Add country centroid
lon, lat = lonlat
xpt, ypt = m(lon, lat)
txt = ax.annotate(id, (xpt, ypt),
color='r',
size='medium',
ha='center',
va='center',
path_effects=[
PathEffects.withStroke(linewidth=3, foreground="w"),
PathEffects.withSimplePatchShadow()
])
def __init__(self, ax, x1, x2, y, height, gradient=True, tip=.0025, \
color="k", shadow=False, **kwargs):
super(GeneGlyph, self).__init__(ax)
# Figure out the polygon vertices first
orientation = 1 if x1 < x2 else -1
level = 10
tip = min(tip, abs(x1 - x2))
# Frame
p1 = (x1, y - height * .5)
p2 = (x2 - orientation * tip, y - height * .5)
p3 = (x2, y)
p4 = (x2 - orientation * tip, y + height * .5)
p5 = (x1, y + .5*height)
if "fc" not in kwargs:
kwargs["fc"] = color
if "ec" not in kwargs:
kwargs["ec"] = color
P = Polygon([p1, p2, p3, p4, p5], **kwargs)
self.append(P)
if gradient:
zz = kwargs.get("zorder", 1)
zz += 1
# Patch (apply white mask)
for cascade in np.arange(0, .5, .5 / level):
p1 = (x1, y - height * cascade)
p2 = (x2 - orientation * tip, y - height * cascade)
p3 = (x2, y)
p4 = (x2 - orientation * tip, y + height * cascade)
p5 = (x1, y + height * cascade)
self.append(Polygon([p1, p2, p3, p4, p5], fc='w', \
lw=0, alpha=.2, zorder=zz))
if shadow:
import matplotlib.patheffects as pe
P.set_path_effects([pe.withSimplePatchShadow((1, -1), \
alpha=.4, patch_alpha=1)])
self.add_patches()
def plotReturnDistribution(returns, ticker, binsize=35, title=''):
fig, ax = plt.subplots(figsize=FIG_SIZE)
ax.hist(returns[ticker],
bins=binsize,
color='steelblue',
density=True,
alpha=0.5,
histtype='stepfilled',
edgecolor='red')
title_text_obj = ax.set_title(title,
fontsize=18,
verticalalignment='bottom')
title_text_obj.set_path_effects([patheffects.withSimplePatchShadow()])
sigma, mu = returns[ticker].std(), returns[ticker].mean(
) # mean and standard deviation
s = np.random.normal(mu, sigma, 1000)
count, bins, ignored = ax.hist(s, binsize, density=True, alpha=0.1)
ax.plot(bins,
1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-(bins - mu)**2 /
(2 * sigma**2)),
linewidth=1.5,
color='r')
ax.annotate('Skewness: {}\n\nKurtosis: {}'.format(
round(returns[ticker].skew(), 2), round(returns[ticker].kurtosis(),
2)),
xy=(10, 20),
xycoords='axes points',
xytext=(20, 360),
fontsize=14)
ax.set_xlabel('Values')
ax.set_ylabel('Frequency')
plt.show()
def plot_correlations(features_df,
author_comments=0,
ax=None,
is_filtered=False):
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(14, 12))
else:
fig = ax.get_figure()
labels = list(features_df.columns)
corr = features_df[features_df['author_comments'] > author_comments].corr()
cax = ax.matshow(corr, vmin=-1, vmax=1, cmap=plt.get_cmap("jet"))
cbar = plt.colorbar(cax, ax=ax, fraction=0.046, pad=0.04)
ax.set_xticklabels([' '] + labels, rotation=45, ha='left')
ax.set_yticklabels([' '] + labels)
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
if is_filtered:
ax.set_title(
rf'Filtered Comment Correlations ($comments_{{author}} \geq {author_comments}$)',
y=1.15)
else:
ax.set_title(
rf'Comment Correlations ($comments_{{author}} \geq {author_comments}$)',
y=1.15)
for i in range(corr.shape[0]):
for j in range(corr.shape[1]):
ax.text(i,
j,
f'{corr.iloc[j, i]:.2f}',
ha="center",
va="center",
color="b",
path_effects=[
path_effects.withSimplePatchShadow(offset=(1, -1),
shadow_rgbFace="w",
alpha=0.9)
])
return fig, ax
def heatmap(df):
"""http://stackoverflow.com/questions/20520246/create-heatmap-using-pandas-timeseries"""
dflen = len(df.index)
dflen2 = len(df.columns)
# print dflen, dflen2
fsize = (1.6 * dflen, .6 * dflen2)
fig = plt.figure(figsize=fsize)
ax = fig.add_subplot(111)
# use dir(matplotlib.cm) to get a list of the installed colormaps
# the "_r" means "reversed" and accounts for why zero values are plotted
# as white
cmap = CM.get_cmap('YlOrRd', 50)
# http://matplotlib.org/examples/color/colormaps_reference.html
ax.imshow(df, interpolation="nearest", cmap=cmap)
# ax.invert_yaxis()
# ax.xaxis.tick_top()
v = df.columns.tolist()
v2 = df.index.tolist()
ax.set_xticks(np.arange(dflen) + 0., minor=False)
ax.set_yticks(np.arange(dflen2) + 0., minor=False)
ax.set_xticklabels(v)
ax.set_yticklabels(v2)
ax.grid(True)
path_effects = [
patheffects.withSimplePatchShadow(shadow_rgbFace=(1, 1, 1))
]
for i in range(dflen):
for j in range(dflen2):
ax.text(j,
i,
'{:.2f}'.format(df.iget_value(i, j)),
size='medium',
ha='center',
va='center',
path_effects=path_effects)
plt.show()
def test_patheffect3():
ax3 = plt.subplot(111)
p1, = ax3.plot([0, 1], [0, 1])
leg = ax3.legend([p1], ["Line 1"], fancybox=True, loc=2)
leg.legendPatch.set_path_effects([withSimplePatchShadow()])
def make_plot(channel, time, drawLabels=True):
#get the log file
directory = "logs/" + channel + '/' + time
filename = 'rate.csv'
file_path = os.path.relpath(directory + '/' + filename)
with open(file_path, 'r') as rate:
rates = map(lambda x: x.strip(), list(rate))
start_hour = int(rates[0].split('=')[1].split('_')[0])
start_min = int(rates[0].split('=')[1].split('_')[1])
#Load data
rates = rates[1:]
events = []
to_delete = []
for rate in rates:
if len(rate.split('*')) >= 3:
events.append((int(rate.split('*')[1]), rate.split(',', 1)[1]))
to_delete.append(rate)
rates = [x for x in rates if x not in to_delete]
old_len = len(rates)
rates = remove_trailing_zeroes(rates)
num_removed = old_len - len(rates)
mins = map(
lambda x: int(
filter(lambda x: x in string.printable, x).split(',')[0]), rates)
##### XXX
l = len(mins)
mins = []
for i in range(l):
mins.append(i)
##### XXX
y_data = map(lambda x: float(x.split(',')[1]), rates)
show_viewers = False
try:
y_data2 = map(lambda x: int(x.split(',')[2]), rates)
show_viewers = True
except IndexError:
print rates
pass
dur = mins[-1] + 1 #in minutes
#interval = 15
interval = get_xinterval(dur)
padding_mins = start_min % interval
carry = start_min / interval
x_data = range(carry * interval,
start_min + dur + min(num_removed, padding_mins))
x = np.array(x_data)
y = np.random.randint(1, size=padding_mins) #pad it with all 0's
y = np.append(y, np.array(y_data))
y = np.append(y, np.random.randint(1, size=min(
num_removed, padding_mins))) #potentially add zeroes to the end
if show_viewers:
y2 = np.random.randint(1, size=padding_mins) #pad it with all 0's
y2 = np.append(y2, np.array(y_data2))
y2 = np.append(
y2, np.random.randint(
1, size=min(num_removed,
padding_mins))) #potentially add zeroes to the end
#print len(x)
#print len(y)
#print len(y2)
times = time.split('-') #2014-08-23-04AM
try:
year = int(times[0])
mo = int(times[1])
day = int(times[2])
hour = start_hour
minute = start_min
minute = 0 #?????????????????????????????????????????????????????????????
start_time = datetime.datetime(year, mo, day, hour, minute, 0)
except ValueError: #debugging.
print "THIS SHOULD NEVER HAPPEN! Email me [email protected] right away!"
start_time = datetime.datetime(2014, 8, 11, start_hour, start_min)
#Removes leading zeroes from all the "words" in s.
#"00138 hello 01AM" -> "138 hello 1AM"
def removeLeadingZeroes(s):
return " ".join(map(lambda x: x.lstrip('0'), s.split(" ")))
def formatTime(x, pos):
#timestamp = (start_time - datetime.datetime(1970, 1, 1)).total_seconds() + x*60
#now_time = datetime.datetime.fromtimestamp(timestamp) #does not convert time zones. Need time zones.
now_time = datetime.timedelta(hours=float(x) / 60) + start_time
if dur > 2160: #Aug 5, 8AM
return removeLeadingZeroes(now_time.strftime("%b %d, %I %p"))
elif interval >= 60: #8AM
return removeLeadingZeroes(now_time.strftime("%I %p"))
else: #7:45 AM
return removeLeadingZeroes(now_time.strftime("%I:%M %p"))
print "Drawing rate over time graph..."
#START GRAPHING
#plt.figure(num=None, figsize=(36, 16), dpi=80)
plt.figure(num=None, figsize=(27, 12), dpi=80)
#fig, ax = plt.subplots()
ax = plt.axes()
ax.xaxis.grid(True)
ax.xaxis.set_major_formatter(FuncFormatter(formatTime))
ax.yaxis.grid(True)
ax.grid(True)
r = 45
if dur >= 2160:
r = 270
plt.xticks(np.arange(min(x), max(x) + 1, interval), rotation=r)
months = [
'', 'January', 'February', 'March', 'April', 'May', 'June', 'July',
'August', 'September', 'October', 'November', 'December'
]
try:
i = int(times[1])
mon = months[i]
ye = int(times[2])
da = int(times[0])
plt.title("%s - %s %d, %d\n" % (channel, mon, ye, da))
except (ValueError, IndexError):
plt.title(channel + '\n')
settingsDict = getSettings()
local_timezone = None
try:
local_timezone = settingsDict['timezone']
except KeyError as e:
print "Setting missing:", e
raise
except ValueError as e:
print "Malformed setting:", e
raise
if local_timezone.lower().strip() == 'none' or local_timezone.lower(
).strip() == 'auto':
local_timezone = datetime.datetime.now(
tzlocal()).tzname() #Eastern Daylight Time
else:
local_timezone = local_timezone.strip()
split = local_timezone.split(" ")
if len(split) > 1:
local_timezone = "".join([word[0] for word in split])
avg_mpm = np.mean(y)
plt.xlabel('\nTimes in ' + local_timezone)
plt.ylabel('\nMessages per minute (Avg=%0.2f)\n' % avg_mpm, color='blue')
for tl in ax.get_yticklabels():
tl.set_color('blue')
plt.plot(x, y, linewidth=2, color='#4422AA')
if show_viewers:
ax2color = 'green'
ax2 = ax.twinx()
ax2.plot(x, y2, linewidth=4, color=ax2color)
ax2.fill_between(x, 0, y2, color=ax2color, alpha=0.1)
ax2.set_frame_on(True)
ax2.patch.set_visible(False)
ax2.yaxis.set_ticks_position('right')
ax2.yaxis.set_label_position('right')
avg_viewercount = np.mean(y2)
ax2.set_ylabel('Viewercount (Avg=%0.1f)\n\n' % avg_viewercount,
color=ax2color,
labelpad=50,
rotation=270)
m = 1.16 * max(y)
m2 = 1.16 * max(y2)
ax.set_yticks(np.arange(0, m, get_yinterval(max(y))))
ax2.set_yticks(np.arange(0, m2, get_yinterval(max(y2))))
ax.set_ylim(0, m)
ax2.set_ylim(0, m2)
for tl in ax2.get_yticklabels():
tl.set_color(ax2color)
ax.fill_between(x, 0, y, color='#5577DD')
font = {'size': 25}
rc('font', **font)
ticklist = ax2.get_yticks()
height_diff = ticklist[1] - ticklist[0]
height_offset = ticklist[1]
init_offset = height_offset
ha = 'center'
for event in events:
if drawLabels:
plt.axvline(x=event[0] + padding_mins + carry * interval,
color='red',
linewidth=2,
label=event[1])
s = event[1].replace('\\n', '\n')
down = ax2.yaxis.get_view_interval()[1] - height_offset
plt.text(
event[0] + padding_mins + carry * interval,
down,
s,
color='red',
verticalalignment='top',
horizontalalignment=ha,
fontsize=25,
rotation=0, #rotation=270,
path_effects=[PathEffects.withSimplePatchShadow(linewidth=2)])
#path_effects=[PathEffects.withStroke(linewidth=1,foreground="black")])
height_offset = (
height_offset +
height_diff) if height_offset == init_offset else init_offset
#height_offset = height_diff - height_offset
#S E L L O U T
plt.text(0.01,
0.99,
'github.com/popcorncolonel/chat_stats',
color='#666666',
verticalalignment='top',
horizontalalignment='left',
fontsize=25,
rotation=0,
transform=ax.transAxes)
plt.text(0.99,
0.99,
'http://twitch.tv/' + channel,
color='#666666',
verticalalignment='top',
horizontalalignment='right',
fontsize=25,
rotation=0,
transform=ax.transAxes)
#plt.text(-0.05, -0.170, 'http://github.com/popcorncolonel/chat_stats', color='grey', verticalalignment='top',
# horizontalalignment='left', fontsize=25, rotation=0, transform=ax.transAxes)
#plt.text(1.05, -0.170, 'http://www.twitch.tv/'+channel, color='grey', verticalalignment='top',
# horizontalalignment='right', fontsize=25, rotation=0, transform=ax.transAxes)
#plt.show() #for running locally rather than saving the figure
directory = "images/" + channel + '/' + time
if not os.path.exists(directory):
os.makedirs(directory)
plt.savefig(directory + '/rate.png', bbox_inches='tight')
print "Rate graph completed!"
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
fig = plt.figure(figsize=(5, 1.5))
text = fig.text(0.5,
0.5, 'Hello path effects world!\nThis is the normal '
'path effect.\nPretty dull, huh?',
ha='center',
va='center',
size=20)
text.set_path_effects([path_effects.Normal()])
plt.show()
text = plt.text(0.5,
0.5,
'Hello path effects world!',
path_effects=[path_effects.withSimplePatchShadow()])
plt.plot([0, 3, 2, 5],
linewidth=5,
color='blue',
path_effects=[path_effects.SimpleLineShadow(),
path_effects.Normal()])
plt.show()
path_effects=[PathEffects.withStroke(linewidth=3, foreground="w")])
txt.arrow_patch.set_path_effects([
PathEffects.Stroke(linewidth=5, foreground="w"),
PathEffects.Normal()
])
pe = [PathEffects.withStroke(linewidth=3, foreground="w")]
ax1.grid(True, linestyle="-", path_effects=pe)
ax2 = plt.subplot(132)
arr = np.arange(25).reshape((5, 5))
ax2.imshow(arr)
cntr = ax2.contour(arr, colors="k")
plt.setp(
cntr.collections,
path_effects=[PathEffects.withStroke(linewidth=3, foreground="w")])
clbls = ax2.clabel(cntr, fmt="%2.0f", use_clabeltext=True)
plt.setp(
clbls,
path_effects=[PathEffects.withStroke(linewidth=3, foreground="w")])
# shadow as a path effect
ax3 = plt.subplot(133)
p1, = ax3.plot([0, 1], [0, 1])
leg = ax3.legend([p1], ["Line 1"], fancybox=True, loc=2)
leg.legendPatch.set_path_effects([PathEffects.withSimplePatchShadow()])
plt.show()
x = np.linspace(0.5, 3.5, 100)
y = np.sin(x)
fontsize = 23
# plot a sin(x) func
plt.plot(x, y, ls="--", lw=2)
# set text contents
title = "$y=\sin({x})$"
xaxis_label = "$x\_axis$"
yaxis_label = "$y\_axis$"
# get text instance
title_text_obj = plt.title(title, fontsize=fontsize, va="bottom")
xaxis_label_text_obj = plt.xlabel(xaxis_label,
fontsize=fontsize - 3,
alpha=1.0)
yaxis_label_text_obj = plt.ylabel(yaxis_label,
fontsize=fontsize - 3,
alpha=1.0)
# set shadow
title_text_obj.set_path_effects([pes.withSimplePatchShadow()])
pe = pes.withSimplePatchShadow(offset=(1, -1), shadow_rgbFace="r", alpha=.3)
xaxis_label_text_obj.set_path_effects([pe])
yaxis_label_text_obj.set_path_effects([pe])
plt.show()
def Spectrum(pol, long, darkmode, png, foregrounds, masks, nside):
params = {
'savefig.dpi':
300, # save figures to 300 dpi
'xtick.top':
False,
'ytick.right':
True, #Set to false
'axes.spines.top':
True, #Set to false
'axes.spines.bottom':
True,
'axes.spines.left':
True,
'axes.spines.right':
True, #Set to false@
'axes.grid.axis':
'y',
'axes.grid':
False,
'ytick.major.size':
10,
'ytick.minor.size':
5,
'xtick.major.size':
10,
'xtick.minor.size':
5,
'ytick.major.width':
1.5,
'ytick.minor.width':
1.5,
'xtick.major.width':
1.5,
'xtick.minor.width':
1.5,
'axes.linewidth':
1.5,
'axes.prop_cycle':
cycler(color=[
'#636EFA', '#EF553B', '#00CC96', '#AB63FA', '#FFA15A', '#19D3F3',
'#FF6692', '#B6E880', '#FF97FF', '#FECB52'
])
#'ytick.major.size' : 6,
#'ytick.minor.size' : 3,
#'xtick.major.size' : 6,
#'xtick.minor.size' : 3,
}
blue, red, green, purple, orange, teal, lightred, lightgreen, pink, yellow = (
"C0",
"C1",
"C2",
"C3",
"C4",
"C5",
"C6",
"C7",
"C8",
"C9",
)
black = 'k'
if darkmode:
rcParams['text.color'] = 'white' # axes background color
rcParams['axes.facecolor'] = 'white' # axes background color
rcParams['axes.edgecolor'] = 'white' # axes edge color
rcParams['axes.labelcolor'] = 'white'
rcParams['xtick.color'] = 'white' # color of the tick labels
rcParams['ytick.color'] = 'white' # color of the tick labels
rcParams['grid.color'] = 'white' # grid color
rcParams[
'legend.facecolor'] = 'inherit' # legend background color (when 'inherit' uses axes.facecolor)
rcParams[
'legend.edgecolor'] = 'white' # legend edge color (when 'inherit' uses axes.edgecolor)
black = 'white'
rcParams.update(params)
# ---- Figure parameters ----
if pol:
ymin, ymax = (1e-3, 2e2)
if long:
xmin, xmax = (1, 3000)
ymax15, ymax2 = (ymax + 100, 1e7)
else:
xmin, xmax = (10, 1000)
else:
ymin, ymax = (0.05, 7e2)
if long:
xmin, xmax = (0.3, 4000)
ymax15, ymax2 = (ymax + 500, 1e7)
else:
xmin, xmax = (10, 1000)
if long:
# Figure
ratio = 5
w, h = (16, 8)
fig, (ax2,
ax) = plt.subplots(2,
1,
sharex=True,
figsize=(w, h),
gridspec_kw={'height_ratios': [1, ratio]})
aspect_ratio = w / h * 1.25 # Correct for ratio
rotdir = -1
ax2.spines['bottom'].set_visible(False)
ax.spines['top'].set_visible(False)
ax2.tick_params(labelbottom=False)
ax2.xaxis.set_ticks_position('none')
# ---- Adding broken axis lines ----
d = .005 # how big to make the diagonal lines in axes coordinates
kwargs = dict(transform=ax2.transAxes, color=black, clip_on=False)
ax2.plot((-d, +d), (-d * ratio, +d * ratio),
**kwargs) # top-left diagonal
ax2.plot((1 - d, 1 + d), (-d * ratio, +d * ratio),
**kwargs) # top-right diagonal
kwargs.update(transform=ax.transAxes) # switch to the bottom axes
ax.plot((-d, +d), (1 - d, 1 + d), **kwargs) # bottom-left diagonal
ax.plot((1 - d, 1 + d), (1 - d, 1 + d),
**kwargs) # bottom-right diagonal
# textsize
freqtext = 16
fgtext = 18
else:
ymax2 = ymax
ymax15 = ymax
w, h = (12, 8)
fig, ax = plt.subplots(1, 1, figsize=(w, h))
aspect_ratio = w / h
rotdir = 1
#ax.set_aspect('equal', adjustable='box')
freqtext = 20
fgtext = 20
# Spectrum parameters
field = 1 if pol else 0
nu = np.logspace(np.log10(0.1), np.log10(5000), 1000)
npix = hp.nside2npix(nside)
# Read masks
m = np.ones((len(masks), npix))
for i, mask in enumerate(masks):
# Read and ud_grade mask
if mask:
m_temp = hp.read_map(mask, field=0, dtype=None, verbose=False)
if hp.npix2nside(len(m_temp)) != nside:
m[i] = hp.ud_grade(m_temp, nside)
m[i, m[i, :] > 0.5] = 1 # Set all mask values to integer
m[i, m[i, :] < 0.5] = 0 # Set all mask values to integer
else:
m[i] = m_temp
# Get indices of smallest mask
idx = m[np.argmax(np.sum(m, axis=1)), :] > 0.5
skyfracs = np.sum(m, axis=1) / npix * 100
print(f"Using sky fractions {skyfracs}%")
# Looping over foregrounds and calculating spectra
i = 0
add_error = True
for fg in foregrounds.keys():
if not fg == "Sum fg.":
if fg.startswith("CO"): # get closest thing to ref freq
foregrounds[fg]["params"][-2], _ = find_nearest(
nu, foregrounds[fg]["params"][-2])
foregrounds[fg]["spectrum"] = getspec(
nu * 1e9,
fg,
foregrounds[fg]["params"],
foregrounds[fg]["function"],
field,
nside,
npix,
idx,
m,
)
"""
if fg.startswith("CO"):#unit conversion fudge factor
foregrounds[fg]["spectrum"
] = foregrounds[fg]["spectrum"]*75
"""
if add_error and foregrounds[fg]["spectrum"].shape[
0] > 1 and not fg.startswith("CO"):
thresh = 0.1
alpha = 0.5
foregrounds[fg]["spectrum"][
0] = foregrounds[fg]["spectrum"][0] * (1 - np.exp(-(
abs(foregrounds[fg]["spectrum"][0] / thresh)**alpha)))
foregrounds[fg]["spectrum"][
1] = foregrounds[fg]["spectrum"][1] / (1 - np.exp(-(
abs(foregrounds[fg]["spectrum"][1] / thresh)**alpha)))
if foregrounds[fg]["sum"]:
if i == 0:
if foregrounds[fg]["spectrum"].shape[0] == 1:
# special case where first summed is 1d
foregrounds["Sum fg."]["spectrum"] = np.concatenate(
(foregrounds[fg]["spectrum"],
foregrounds[fg]["spectrum"])).copy()
else:
foregrounds["Sum fg."]["spectrum"] = foregrounds[fg][
"spectrum"].copy()
else:
foregrounds["Sum fg."]["spectrum"] += foregrounds[fg][
"spectrum"]
i += 1
# ---- Plotting foregrounds and labels ----
j = 0
for label, fg in foregrounds.items(): # Plot all fgs except sumf
if fg["gradient"]:
k = 0
gradient_fill(
nu,
fg["spectrum"][k],
fill_color=fg["color"],
ax=ax,
alpha=0.5,
linewidth=0.0,
)
else:
if label == "Sum fg.":
ax.loglog(nu,
fg["spectrum"][0],
linestyle=fg["linestyle"],
linewidth=2,
color=fg["color"])
if long:
ax2.loglog(nu,
fg["spectrum"][0],
linestyle=fg["linestyle"],
linewidth=2,
color=fg["color"])
k = 0
try:
ax.loglog(nu,
fg["spectrum"][1],
linestyle=fg["linestyle"],
linewidth=2,
color=fg["color"])
if long:
ax2.loglog(nu,
fg["spectrum"][1],
linestyle=fg["linestyle"],
linewidth=2,
color=fg["color"])
k = 1
except:
pass
elif label.startswith("CO"):
lfreq = nu[np.argmax(fg["spectrum"][0])]
if fg["spectrum"].shape[0] > 1:
ax.loglog([lfreq, lfreq],
[max(fg["spectrum"][0]),
max(fg["spectrum"][1])],
linestyle=fg["linestyle"],
linewidth=4,
color=fg["color"],
zorder=1000)
k = 1
else:
k = 0
ax.bar(
lfreq,
fg["spectrum"][0],
color=black,
)
else:
if fg["spectrum"].shape[0] == 1:
ax.loglog(nu,
fg["spectrum"][0],
linestyle=fg["linestyle"],
linewidth=4,
color=fg["color"])
if long:
ax2.loglog(nu,
fg["spectrum"][0],
linestyle=fg["linestyle"],
linewidth=4,
color=fg["color"])
k = 0
else:
#gradient_fill(nu, fg["spectrum"][0], fill_color=fg["color"], ax=ax, alpha=0.5, linewidth=0.0,)
ax.loglog(nu,
np.mean(fg["spectrum"], axis=0),
linestyle=fg["linestyle"],
linewidth=4,
color=fg["color"])
ax.fill_between(nu,
fg["spectrum"][0],
fg["spectrum"][1],
color=fg["color"],
alpha=0.5)
if long:
ax2.loglog(nu,
np.mean(fg["spectrum"], axis=0),
linestyle=fg["linestyle"],
linewidth=4,
color=fg["color"])
ax2.fill_between(nu,
fg["spectrum"][0],
fg["spectrum"][1],
color=fg["color"],
alpha=0.5)
k = 1
if label == "Thermal Dust" and fg["spectrum"].shape[0] > 1:
if long:
_, fsky_idx = find_nearest(nu, 900)
else:
_, fsky_idx = find_nearest(nu, 700)
ax.annotate(
r"$f_{sky}=$" + "{:d}%".format(int(skyfracs[1])),
xy=(nu[fsky_idx], fg["spectrum"][1][fsky_idx]),
ha="center",
va="bottom",
fontsize=fgtext,
color="grey",
xytext=(0, 5),
textcoords="offset pixels",
)
ax.annotate(
r"$f_{sky}=$" + "{:d}%".format(int(skyfracs[0])),
xy=(nu[fsky_idx], fg["spectrum"][0][fsky_idx]),
ha="center",
va="top",
fontsize=fgtext,
color="grey",
xytext=(0, -15),
textcoords="offset pixels",
)
if label.startswith("CO"):
ax.text(lfreq,
np.max(fg["spectrum"][k]) * 0.5,
label,
color=fg["color"],
alpha=0.7,
ha='right',
va='center',
rotation=90,
fontsize=fgtext,
path_effects=[
path_effects.withSimplePatchShadow(alpha=0.8,
offset=(1, -1))
],
zorder=1000)
else:
x0, idx1 = find_nearest(nu, fg["position"])
x1, idx2 = find_nearest(nu, fg["position"] * 1.2)
y0 = fg["spectrum"][k][idx1]
y1 = fg["spectrum"][k][idx2]
datascaling = np.log(xmin / xmax) / np.log(ymin / ymax)
rotator = (datascaling / aspect_ratio)
alpha = np.arctan(np.log(y1 / y0) / np.log(x1 / x0) * rotator)
rotation = np.rad2deg(alpha) #*rotator
ax.annotate(
label,
xy=(x0, y0),
xytext=(0, 7),
textcoords="offset pixels",
rotation=rotation,
rotation_mode='anchor',
fontsize=fgtext,
color=fg["color"],
path_effects=[
path_effects.withSimplePatchShadow(alpha=0.8,
offset=(1, -1)),
],
) # horizontalalignment="center")
# ---- Data band ranges ----
if long:
yscaletext = 0.75
yscaletextup = 1.2
else:
yscaletextup = 1.03
yscaletext = 0.90
# TODO add these as args?
haslam = True
chipass = True
spass = True
cbass = True
quijote = False
wmap = True
planck = True
dirbe = True
databands = {
"Haslam": {
"0.408\nHaslam": {
"pol": False,
"show": haslam,
"position": [.408, ymin * yscaletextup],
"range": [.406, .410],
"color": purple,
}
},
"S-PASS": {
"2.303\nS-PASS": {
"pol": True,
"show": spass,
"position": [2.35, ymax2 * yscaletext],
"range": [2.1, 2.4],
"color": green,
}
},
"C-BASS": {
"5.0\nC-BASS": {
"pol": True,
"show": spass,
"position": [5., ymax2 * yscaletext],
"range": [4., 6.],
"color": blue,
}
},
"CHI-PASS": {
"1.394\nCHI-PASS": {
"pol": False,
"show": chipass,
"position": [1.3945, ymin * yscaletextup],
"range": [1.3945 - 0.064 / 2, 1.3945 + 0.064 / 2],
"color": lightred,
}
},
"QUIJOTE": {
"11\nQUIJOTE": {
"pol": True,
"show": quijote,
"position": [11, ymax2 * yscaletext],
"range": [10., 12.],
"color": orange,
},
"13": {
"pol": True,
"show": quijote,
"position": [13, ymax2 * yscaletext],
"range": [12., 14.],
"color": orange,
},
"17": {
"pol": True,
"show": quijote,
"position": [17, ymax2 * yscaletext],
"range": [16., 18.],
"color": orange,
},
"19": {
"pol": True,
"show": quijote,
"position": [20, ymax2 * yscaletext],
"range": [18., 21.],
"color": orange,
},
"31": {
"pol": True,
"show": quijote,
"position": [31, ymax2 * yscaletext],
"range": [26., 36.],
"color": orange,
},
"41": {
"pol": True,
"show": quijote,
"position": [42, ymax2 * yscaletext],
"range": [35., 47.],
"color": orange,
}
},
"Planck": {
"30": {
"pol": True,
"show": planck,
"position": [27, ymax2 * yscaletext],
"range": [23.9, 34.5],
"color": orange,
}, # Planck 30
"44": {
"pol": True,
"show": planck,
"position": [40, ymax2 * yscaletext],
"range": [39, 50],
"color": orange,
}, # Planck 44
"70": {
"pol": True,
"show": planck,
"position": [60, ymax2 * yscaletext],
"range": [60, 78],
"color": orange,
}, # Planck 70
"100\nPlanck": {
"pol": True,
"show": planck,
"position": [90, ymax2 * yscaletext],
"range": [82, 120],
"color": orange,
}, # Planck 100
"143": {
"pol": True,
"show": planck,
"position": [130, ymax2 * yscaletext],
"range": [125, 170],
"color": orange,
}, # Planck 143
"217": {
"pol": True,
"show": planck,
"position": [195, ymax2 * yscaletext],
"range": [180, 265],
"color": orange,
}, # Planck 217
"353": {
"pol": True,
"show": planck,
"position": [320, ymax2 * yscaletext],
"range": [300, 430],
"color": orange,
}, # Planck 353
"545": {
"pol": False,
"show": planck,
"position": [490, ymax2 * yscaletext],
"range": [450, 650],
"color": orange,
}, # Planck 545
"857": {
"pol": False,
"show": planck,
"position": [730, ymax2 * yscaletext],
"range": [700, 1020],
"color": orange,
}
}, # Planck 857
"DIRBE": {
"DIRBE\n1250": {
"pol": False,
"show": dirbe,
"position": [1000, ymin * yscaletextup],
"range": [1000, 1540],
"color": red,
}, # DIRBE 1250
"2140": {
"pol": False,
"show": dirbe,
"position": [1750, ymin * yscaletextup],
"range": [1780, 2500],
"color": red,
}, # DIRBE 2140
"3000": {
"pol": False,
"show": dirbe,
"position": [2500, ymin * yscaletextup],
"range": [2600, 3500],
"color": red,
}
}, # DIRBE 3000
"WMAP": {
"K": {
"pol": True,
"show": wmap,
"position": [21.8, ymin * yscaletextup],
"range": [21, 25.5],
"color": teal,
},
"WMAP\nKa": {
"pol": True,
"show": wmap,
"position": [31.5, ymin * yscaletextup],
"range": [30, 37],
"color": teal,
},
"Q": {
"pol": True,
"show": wmap,
"position": [39., ymin * yscaletextup],
"range": [38, 45],
"color": teal,
},
"V": {
"pol": True,
"show": wmap,
"position": [58., ymin * yscaletextup],
"range": [54, 68],
"color": teal,
},
"W": {
"pol": True,
"show": wmap,
"position": [90., ymin * yscaletextup],
"range": [84, 106],
"color": teal,
}
},
}
# Set databands from dictonary
for experiment, bands in databands.items():
for label, band in bands.items():
if band["show"]:
if pol and not band["pol"]:
continue # Skip non-polarization bands
if band["position"][0] >= xmax or band["position"][0] <= xmin:
continue # Skip databands outside range
va = "bottom" if experiment in [
"WMAP", "CHI-PASS", "DIRBE", "Haslam"
] else "top" # VA for WMAP on bottom
ha = "left" if experiment in [
"Planck",
"WMAP",
"DIRBE",
] else "center"
ax.axvspan(*band["range"],
color=band["color"],
alpha=0.3,
zorder=0,
label=experiment)
if long:
ax2.axvspan(*band["range"],
color=band["color"],
alpha=0.3,
zorder=0,
label=experiment)
if experiment in ["WMAP", "CHI-PASS", "DIRBE", "Haslam"]:
ax.text(*band["position"],
label,
color=band["color"],
va=va,
ha=ha,
size=freqtext,
path_effects=[
path_effects.withSimplePatchShadow(
alpha=0.8, offset=(1, -1))
])
else:
ax2.text(*band["position"],
label,
color=band["color"],
va=va,
ha=ha,
size=freqtext,
path_effects=[
path_effects.withSimplePatchShadow(
alpha=0.8, offset=(1, -1))
])
else:
ax.text(*band["position"],
label,
color=band["color"],
va=va,
ha=ha,
size=freqtext,
path_effects=[
path_effects.withSimplePatchShadow(alpha=0.8,
offset=(1,
-1))
])
# ---- Axis stuff ----
lsize = 20
# Dumb tick fix
ticks = []
ticks_ = [1, 3, 10, 30, 100, 300, 1000, 3000]
for i, tick in enumerate(ticks_):
if tick >= xmin and tick <= xmax:
ticks.append(tick)
ax.set(xscale='log',
yscale='log',
ylim=(ymin, ymax),
xlim=(xmin, xmax),
xticks=ticks,
xticklabels=ticks)
ax.xaxis.set_major_formatter(ticker.ScalarFormatter(useMathText=True))
ax.tick_params(axis='both', which='major', labelsize=lsize, direction='in')
ax.tick_params(which="both", direction="in")
if long:
ax2.set(xscale='log',
yscale='log',
ylim=(ymax15, ymax2),
xlim=(xmin, xmax),
yticks=[
1e4,
1e6,
],
xticks=ticks,
xticklabels=ticks)
ax2.tick_params(axis='both',
which='major',
labelsize=lsize,
direction='in')
ax2.tick_params(which="both", direction="in")
# Axis labels
plt.ylabel(r"RMS brightness temperature [$\mu$K]", fontsize=lsize)
plt.xlabel(r"Frequency [GHz]", fontsize=lsize)
#ax.legend(loc=6,prop={'size': 20}, frameon=False)
# ---- Plotting ----
plt.tight_layout(h_pad=0.3)
filename = "spectrum"
filename += "_pol" if pol else ""
filename += "_long" if long else ""
filename += "_darkmode" if darkmode else ""
filename += ".png" if png else ".pdf"
print("Plotting {}".format(filename))
plt.savefig(filename,
bbox_inches='tight',
pad_inches=0.02,
transparent=True)
def make_plot(channel, time, drawLabels=True):
# get the log file
directory = "logs/" + channel + "/" + time
filename = "rate.csv"
file_path = os.path.relpath(directory + "/" + filename)
with open(file_path, "r") as rate:
rates = map(lambda x: x.strip(), list(rate))
start_hour = int(rates[0].split("=")[1].split("_")[0])
start_min = int(rates[0].split("=")[1].split("_")[1])
# Load data
rates = rates[1:]
events = []
to_delete = []
for rate in rates:
if len(rate.split("*")) >= 3:
events.append((int(rate.split("*")[1]), rate.split(",", 1)[1]))
to_delete.append(rate)
rates = [x for x in rates if x not in to_delete]
old_len = len(rates)
rates = remove_trailing_zeroes(rates)
num_removed = old_len - len(rates)
mins = map(lambda x: int(filter(lambda x: x in string.printable, x).split(",")[0]), rates)
##### XXX
l = len(mins)
mins = []
for i in range(l):
mins.append(i)
##### XXX
y_data = map(lambda x: float(x.split(",")[1]), rates)
show_viewers = False
try:
y_data2 = map(lambda x: int(x.split(",")[2]), rates)
show_viewers = True
except IndexError:
print rates
pass
dur = mins[-1] + 1 # in minutes
# interval = 15
interval = get_xinterval(dur)
padding_mins = start_min % interval
carry = start_min / interval
x_data = range(carry * interval, start_min + dur + min(num_removed, padding_mins))
x = np.array(x_data)
y = np.random.randint(1, size=padding_mins) # pad it with all 0's
y = np.append(y, np.array(y_data))
y = np.append(y, np.random.randint(1, size=min(num_removed, padding_mins))) # potentially add zeroes to the end
if show_viewers:
y2 = np.random.randint(1, size=padding_mins) # pad it with all 0's
y2 = np.append(y2, np.array(y_data2))
y2 = np.append(
y2, np.random.randint(1, size=min(num_removed, padding_mins))
) # potentially add zeroes to the end
# print len(x)
# print len(y)
# print len(y2)
times = time.split("-") # 2014-08-23-04AM
try:
year = int(times[0])
mo = int(times[1])
day = int(times[2])
hour = start_hour
minute = start_min
minute = 0 # ?????????????????????????????????????????????????????????????
start_time = datetime.datetime(year, mo, day, hour, minute, 0)
except ValueError: # debugging.
print "THIS SHOULD NEVER HAPPEN! Email me [email protected] right away!"
start_time = datetime.datetime(2014, 8, 11, start_hour, start_min)
# Removes leading zeroes from all the "words" in s.
# "00138 hello 01AM" -> "138 hello 1AM"
def removeLeadingZeroes(s):
return " ".join(map(lambda x: x.lstrip("0"), s.split(" ")))
def formatTime(x, pos):
# timestamp = (start_time - datetime.datetime(1970, 1, 1)).total_seconds() + x*60
# now_time = datetime.datetime.fromtimestamp(timestamp) #does not convert time zones. Need time zones.
now_time = datetime.timedelta(hours=float(x) / 60) + start_time
if dur > 2160: # Aug 5, 8AM
return removeLeadingZeroes(now_time.strftime("%b %d, %I %p"))
elif interval >= 60: # 8AM
return removeLeadingZeroes(now_time.strftime("%I %p"))
else: # 7:45 AM
return removeLeadingZeroes(now_time.strftime("%I:%M %p"))
print "Drawing rate over time graph..."
# START GRAPHING
# plt.figure(num=None, figsize=(36, 16), dpi=80)
plt.figure(num=None, figsize=(27, 12), dpi=80)
# fig, ax = plt.subplots()
ax = plt.axes()
ax.xaxis.grid(True)
ax.xaxis.set_major_formatter(FuncFormatter(formatTime))
ax.yaxis.grid(True)
ax.grid(True)
r = 45
if dur >= 2160:
r = 270
plt.xticks(np.arange(min(x), max(x) + 1, interval), rotation=r)
months = [
"",
"January",
"February",
"March",
"April",
"May",
"June",
"July",
"August",
"September",
"October",
"November",
"December",
]
try:
i = int(times[1])
mon = months[i]
ye = int(times[2])
da = int(times[0])
plt.title("%s - %s %d, %d\n" % (channel, mon, ye, da))
except (ValueError, IndexError):
plt.title(channel + "\n")
settingsDict = getSettings()
local_timezone = None
try:
local_timezone = settingsDict["timezone"]
except KeyError as e:
print "Setting missing:", e
raise
except ValueError as e:
print "Malformed setting:", e
raise
if local_timezone.lower().strip() == "none" or local_timezone.lower().strip() == "auto":
local_timezone = datetime.datetime.now(tzlocal()).tzname() # Eastern Daylight Time
else:
local_timezone = local_timezone.strip()
split = local_timezone.split(" ")
if len(split) > 1:
local_timezone = "".join([word[0] for word in split])
avg_mpm = np.mean(y)
plt.xlabel("\nTimes in " + local_timezone)
plt.ylabel("\nMessages per minute (Avg=%0.2f)\n" % avg_mpm, color="blue")
for tl in ax.get_yticklabels():
tl.set_color("blue")
plt.plot(x, y, linewidth=2, color="#4422AA")
if show_viewers:
ax2color = "green"
ax2 = ax.twinx()
ax2.plot(x, y2, linewidth=4, color=ax2color)
ax2.fill_between(x, 0, y2, color=ax2color, alpha=0.1)
ax2.set_frame_on(True)
ax2.patch.set_visible(False)
ax2.yaxis.set_ticks_position("right")
ax2.yaxis.set_label_position("right")
avg_viewercount = np.mean(y2)
ax2.set_ylabel("Viewercount (Avg=%0.1f)\n\n" % avg_viewercount, color=ax2color, labelpad=50, rotation=270)
m = 1.16 * max(y)
m2 = 1.16 * max(y2)
ax.set_yticks(np.arange(0, m, get_yinterval(max(y))))
ax2.set_yticks(np.arange(0, m2, get_yinterval(max(y2))))
ax.set_ylim(0, m)
ax2.set_ylim(0, m2)
for tl in ax2.get_yticklabels():
tl.set_color(ax2color)
ax.fill_between(x, 0, y, color="#5577DD")
font = {"size": 25}
rc("font", **font)
ticklist = ax2.get_yticks()
height_diff = ticklist[1] - ticklist[0]
height_offset = ticklist[1]
init_offset = height_offset
ha = "center"
for event in events:
if drawLabels:
plt.axvline(x=event[0] + padding_mins + carry * interval, color="red", linewidth=2, label=event[1])
s = event[1].replace("\\n", "\n")
down = ax2.yaxis.get_view_interval()[1] - height_offset
plt.text(
event[0] + padding_mins + carry * interval,
down,
s,
color="red",
verticalalignment="top",
horizontalalignment=ha,
fontsize=25,
rotation=0, # rotation=270,
path_effects=[PathEffects.withSimplePatchShadow(linewidth=2)],
)
# path_effects=[PathEffects.withStroke(linewidth=1,foreground="black")])
height_offset = (height_offset + height_diff) if height_offset == init_offset else init_offset
# height_offset = height_diff - height_offset
# S E L L O U T
plt.text(
0.01,
0.99,
"github.com/popcorncolonel/chat_stats",
color="#666666",
verticalalignment="top",
horizontalalignment="left",
fontsize=25,
rotation=0,
transform=ax.transAxes,
)
plt.text(
0.99,
0.99,
"http://twitch.tv/" + channel,
color="#666666",
verticalalignment="top",
horizontalalignment="right",
fontsize=25,
rotation=0,
transform=ax.transAxes,
)
# plt.text(-0.05, -0.170, 'http://github.com/popcorncolonel/chat_stats', color='grey', verticalalignment='top',
# horizontalalignment='left', fontsize=25, rotation=0, transform=ax.transAxes)
# plt.text(1.05, -0.170, 'http://www.twitch.tv/'+channel, color='grey', verticalalignment='top',
# horizontalalignment='right', fontsize=25, rotation=0, transform=ax.transAxes)
# plt.show() #for running locally rather than saving the figure
directory = "images/" + channel + "/" + time
if not os.path.exists(directory):
os.makedirs(directory)
plt.savefig(directory + "/rate.png", bbox_inches="tight")
print "Rate graph completed!"
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 28 19:54:58 2019
@author: xsxsz
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patheffects as patheffects
data=np.random.randn(10)
fontsize=18
plt.plot(data)
title_obj=plt.title('title')
x_obj=plt.xlabel('X',alpha=0.8)
y_obj=plt.ylabel('Y',alpha=0.8)
title_obj.set_path_effects([patheffects.withSimplePatchShadow()])
offset_xy=(1,-1)
rgbRed=(1.0,0.0,0.0)
pe=patheffects.withSimplePatchShadow(offset=offset_xy,shadow_rgbFace=rgbRed)
x_obj.set_path_effects([pe])
y_obj.set_path_effects([pe])
def draw_network2(graph, layout_info, ax, as_directed=None, hacknoedge=False,
hacknode=False, verbose=None, **kwargs):
"""
fancy way to draw networkx graphs without directly using networkx
# python -m ibeis.annotmatch_funcs review_tagged_joins --dpath ~/latex/crall-candidacy-2015/ --save figures4/mergecase.png --figsize=15,15 --clipwhite --diskshow
# python -m dtool --tf DependencyCache.make_graph --show
"""
import plottool as pt
patch_dict = {
'patch_frame_dict': {},
'node_patch_dict': {},
'edge_patch_dict': {},
'arrow_patch_list': {},
}
text_pseudo_objects = []
font_prop = pt.parse_fontkw(**kwargs)
#print('font_prop = %r' % (font_prop,))
#print('font_prop.get_name() = %r' % (font_prop.get_name() ,))
# print('layout_info = %r' % (layout_info,))
node_pos = layout_info['node']['pos']
node_size = layout_info['node']['size']
splines = layout_info['graph']['splines']
# edge_startpoints = layout_info['edge']['start_pt']
if as_directed is None:
as_directed = graph.is_directed()
# Draw nodes
for node, nattrs in graph.nodes(data=True):
# shape = nattrs.get('shape', 'circle')
if nattrs is None:
nattrs = {}
label = nattrs.get('label', None)
alpha = nattrs.get('alpha', 1.0)
node_color = nattrs.get('color', pt.NEUTRAL_BLUE)
if node_color is None:
node_color = pt.NEUTRAL_BLUE
xy = node_pos[node]
using_image = kwargs.get('use_image', True) and 'image' in nattrs
if using_image:
if hacknode:
alpha_ = 0.7
else:
alpha_ = 0.0
else:
alpha_ = alpha
node_color = fix_hex_color(node_color)
#intcolor = int(node_color.replace('#', '0x'), 16)
node_color = node_color[0:3]
patch_kw = dict(alpha=alpha_, color=node_color)
node_shape = nattrs.get('shape', 'ellipse')
if node_shape == 'circle':
# divide by 2 seems to work for agraph
radius = min(_get_node_size(graph, node, node_size)) / 2.0
patch = mpl.patches.Circle(xy, radius=radius, **patch_kw)
elif node_shape == 'ellipse':
# divide by 2 seems to work for agraph
width, height = np.array(_get_node_size(graph, node, node_size))
patch = mpl.patches.Ellipse(xy, width, height, **patch_kw)
elif node_shape in ['none', 'box', 'rect', 'rectangle', 'rhombus']:
width, height = _get_node_size(graph, node, node_size)
angle = 45 if node_shape == 'rhombus' else 0
xy_bl = (xy[0] - width // 2, xy[1] - height // 2)
# rounded = angle == 0
rounded = 'rounded' in graph.node.get(node, {}).get('style', '')
isdiag = 'diagonals' in graph.node.get(node, {}).get('style', '')
if rounded:
from matplotlib import patches
rpad = 20
xy_bl = np.array(xy_bl) + rpad
width -= rpad
height -= rpad
boxstyle = patches.BoxStyle.Round(pad=rpad)
patch = mpl.patches.FancyBboxPatch(
xy_bl, width, height, boxstyle=boxstyle, **patch_kw)
else:
bbox = list(xy_bl) + [width, height]
if isdiag:
center_xy = vt.bbox_center(bbox)
_xy = np.array(center_xy)
newverts_ = [
_xy + [ 0, -height / 2],
_xy + [-width / 2, 0],
_xy + [ 0, height / 2],
_xy + [ width / 2, 0],
]
patch = mpl.patches.Polygon(newverts_, **patch_kw)
else:
patch = mpl.patches.Rectangle(
xy_bl, width, height, angle=angle,
**patch_kw)
patch.center = xy
#if style == 'rounded'
#elif node_shape in ['roundbox']:
elif node_shape == 'stack':
width, height = _get_node_size(graph, node, node_size)
xy_bl = (xy[0] - width // 2, xy[1] - height // 2)
patch = pt.cartoon_stacked_rects(xy_bl, width, height, **patch_kw)
patch.xy = xy
else:
raise NotImplementedError('Unknown node_shape=%r' % (node_shape,))
if True:
# Add a frame around the node
framewidth = nattrs.get('framewidth', 0)
if framewidth > 0:
framecolor = nattrs.get('framecolor', node_color)
framecolor = fix_hex_color(framecolor)
#print('framecolor = %r' % (framecolor,))
alpha = 1.0
if framecolor is None:
framecolor = pt.BLACK
alpha = 0.0
if framewidth is True:
figsize = ut.get_argval('--figsize', type_=list, default=None)
if figsize is not None:
# HACK
graphsize = max(figsize)
framewidth = graphsize / 4
else:
framewidth = 3.0
lw = framewidth
frame = pt.make_bbox(bbox, bbox_color=framecolor, ax=ax, lw=lw, alpha=alpha)
patch_dict['patch_frame_dict'][node] = frame
#patch_dict[node] = patch
x, y = xy
text = str(node)
if label is not None:
#text += ': ' + str(label)
text = label
if kwargs.get('node_labels', hacknode or not using_image):
text_args = ((x, y, text), dict(ax=ax, ha='center', va='center',
fontproperties=font_prop))
text_pseudo_objects.append(text_args)
patch_dict['node_patch_dict'][node] = (patch)
def get_default_edge_data(graph, edge):
data = graph.get_edge_data(*edge)
if data is None:
if len(edge) == 3 and edge[2] is not None:
data = graph.get_edge_data(edge[0], edge[1], int(edge[2]))
else:
data = graph.get_edge_data(edge[0], edge[1])
if data is None:
data = {}
return data
###
# Draw Edges
# NEW WAY OF DRAWING EDGEES
edge_pos = layout_info['edge'].get('ctrl_pts', None)
if edge_pos is not None:
for edge, pts in edge_pos.items():
data = get_default_edge_data(graph, edge)
if data.get('style', None) == 'invis':
continue
alpha = data.get('alpha', None)
defaultcolor = pt.BLACK[0:3]
if alpha is None:
if data.get('implicit', False):
alpha = .5
defaultcolor = pt.GREEN[0:3]
else:
alpha = 1.0
color = data.get('color', defaultcolor)
if color is None:
color = defaultcolor
color = fix_hex_color(color)
color = color[0:3]
#layout_info['edge']['ctrl_pts'][edge]
#layout_info['edge']['start_pt'][edge]
offset = 0 if graph.is_directed() else 0
#color = data.get('color', color)[0:3]
start_point = pts[offset]
other_points = pts[offset + 1:].tolist() # [0:3]
verts = [start_point] + other_points
MOVETO = mpl.path.Path.MOVETO
LINETO = mpl.path.Path.LINETO
if splines in ['line', 'polyline', 'ortho']:
CODE = LINETO
elif splines == 'curved':
#CODE = mpl.path.Path.CURVE3
# CODE = mpl.path.Path.CURVE3
CODE = mpl.path.Path.CURVE4
elif splines == 'spline':
CODE = mpl.path.Path.CURVE4
else:
raise AssertionError('splines = %r' % (splines,))
astart_code = MOVETO
astart_code = MOVETO
verts = [start_point] + other_points
codes = [astart_code] + [CODE] * len(other_points)
end_pt = layout_info['edge']['end_pt'][edge]
# HACK THE ENDPOINTS TO TOUCH THE BOUNDING BOXES
if end_pt is not None:
verts += [end_pt]
codes += [LINETO]
path = mpl.path.Path(verts, codes)
figsize = ut.get_argval('--figsize', type_=list, default=None)
if figsize is not None:
# HACK
graphsize = max(figsize)
lw = graphsize / 8
width = graphsize / 15
width = ut.get_argval('--arrow-width', default=width)
lw = ut.get_argval('--line-width', default=lw)
#print('width = %r' % (width,))
else:
width = .5
lw = 1.0
try:
# Compute arrow width using estimated graph size
if node_size is not None and node_pos is not None:
xys = np.array(ut.take(node_pos, node_pos.keys())).T
whs = np.array(ut.take(node_size, node_pos.keys())).T
bboxes = vt.bbox_from_xywh(xys, whs, [.5, .5])
extents = vt.extent_from_bbox(bboxes)
tl_pts = np.array([extents[0], extents[2]]).T
br_pts = np.array([extents[1], extents[3]]).T
pts = np.vstack([tl_pts, br_pts])
extent = vt.get_pointset_extents(pts)
graph_w, graph_h = vt.bbox_from_extent(extent)[2:4]
graph_dim = np.sqrt(graph_w ** 2 + graph_h ** 2)
width = graph_dim * .0005
except Exception:
pass
if not as_directed and end_pt is not None:
pass
lw = data.get('lw', lw)
linestyle = 'solid'
linestyle = data.get('linestyle', linestyle)
hatch = data.get('hatch', '')
#effects = data.get('stroke', None)
from matplotlib import patheffects
path_effects = []
#effects_css = data.get('path_effects', None)
#if effects_css is not None:
# print('effects_css = %r' % (effects_css,))
# # Read data similar to Qt Style Sheets / CSS
# from tinycss.css21 import CSS21Parser
# css = effects_css
# #css = 'stroke{ linewith: 3; foreground: r; } shadow{}'
# stylesheet = CSS21Parser().parse_stylesheet(css)
# if stylesheet.errors:
# print('[pt.nx] css errors')
# print(stylesheet.errors)
# path_effects = []
# for rule in stylesheet.rules:
# if rule.selector.as_css() == 'stroke':
# selector = patheffects.withStroke
# elif rule.selector.as_css() == 'shadow':
# selector = patheffects.withSimplePatchShadow
# effectkw = {}
# for decl in rule.declarations:
# if len(decl.value) != 1:
# raise AssertionError(
# 'I dont know css %r' % (decl,))
# strval = decl.value[0].as_css()
# key = decl.name
# val = ut.smart_cast2(strval)
# effectkw[key] = val
# effect = selector(**effectkw)
# path_effects += [effect]
## http://matplotlib.org/1.2.1/examples/api/clippath_demo.html
if data.get('shadow', None):
# offset=(2, -2, shadow_rgbFace='g'))
shadowkw = data.get('shadow', None)
path_effects += [patheffects.withSimplePatchShadow(**shadowkw)]
stroke_info = data.get('stroke', None)
if stroke_info not in [None, False]:
if stroke_info is True:
strokekw = {}
elif isinstance(stroke_info, dict):
strokekw = stroke_info.copy()
else:
#linewidth=3, foreground='r'
assert False
if strokekw is not None:
# Hack to increase lw
strokekw['linewidth'] = lw + strokekw.get('linewidth', 3)
path_effects += [patheffects.withStroke(**strokekw)]
#for vert, code in path.iter_segments():
# print('code = %r' % (code,))
# print('vert = %r' % (vert,))
# if code == MOVETO:
# pass
#for verts, code in path.cleaned().iter_segments():
# print('code = %r' % (code,))
# print('verts = %r' % (verts,))
# pass
patch = mpl.patches.PathPatch(path, facecolor='none', lw=lw,
path_effects=path_effects,
edgecolor=color,
#facecolor=color,
linestyle=linestyle,
alpha=alpha,
joinstyle='bevel',
hatch=hatch)
if as_directed:
if end_pt is not None:
dxy = (np.array(end_pt) - other_points[-1])
dxy = (dxy / np.sqrt(np.sum(dxy ** 2))) * .1
dx, dy = dxy
rx, ry = end_pt[0], end_pt[1]
patch1 = mpl.patches.FancyArrow(rx, ry, dx, dy, width=width,
length_includes_head=True,
color=color,
head_starts_at_zero=False)
else:
dxy = (np.array(other_points[-1]) - other_points[-2])
dxy = (dxy / np.sqrt(np.sum(dxy ** 2))) * .1
dx, dy = dxy
rx, ry = other_points[-1][0], other_points[-1][1]
patch1 = mpl.patches.FancyArrow(rx, ry, dx, dy, width=width,
length_includes_head=True,
color=color,
head_starts_at_zero=True)
#ax.add_patch(patch1)
patch_dict['arrow_patch_list'][edge] = (patch1)
taillabel = layout_info['edge']['taillabel'][edge]
#ha = 'left'
#ha = 'right'
ha = 'center'
va = 'center'
labelcolor = color # TODO allow for different colors
labelcolor = data.get('labelcolor', color)
labelcolor = fix_hex_color(labelcolor)
labelcolor = labelcolor[0:3]
if taillabel:
taillabel_pos = layout_info['edge']['tail_lp'][edge]
ax.annotate(taillabel, xy=taillabel_pos, xycoords='data',
color=labelcolor,
va=va, ha=ha, fontproperties=font_prop)
headlabel = layout_info['edge']['headlabel'][edge]
if headlabel:
headlabel_pos = layout_info['edge']['head_lp'][edge]
ax.annotate(headlabel, xy=headlabel_pos, xycoords='data',
color=labelcolor,
va=va, ha=ha, fontproperties=font_prop)
label = layout_info['edge']['label'][edge]
if label:
label_pos = layout_info['edge']['lp'][edge]
ax.annotate(label, xy=label_pos, xycoords='data',
color=labelcolor,
va=va, ha=ha, fontproperties=font_prop)
patch_dict['edge_patch_dict'][edge] = patch
#ax.add_patch(patch)
if verbose:
print('Adding %r node patches ' % (len(patch_dict['node_patch_dict'],)))
print('Adding %r edge patches ' % (len(patch_dict['edge_patch_dict'],)))
for frame in patch_dict['patch_frame_dict'].values():
ax.add_patch(frame)
for patch1 in patch_dict['arrow_patch_list'].values():
ax.add_patch(patch1)
use_collections = False
if use_collections:
edge_coll = mpl.collections.PatchCollection(patch_dict['edge_patch_dict'].values())
node_coll = mpl.collections.PatchCollection(patch_dict['node_patch_dict'].values())
#coll.set_facecolor(fcolor)
#coll.set_alpha(alpha)
#coll.set_linewidth(lw)
#coll.set_edgecolor(color)
#coll.set_transform(ax.transData)
ax.add_collection(node_coll)
ax.add_collection(edge_coll)
else:
for patch in patch_dict['node_patch_dict'].values():
if isinstance(patch, mpl.collections.PatchCollection):
ax.add_collection(patch)
else:
ax.add_patch(patch)
if not hacknoedge:
for patch in patch_dict['edge_patch_dict'].values():
ax.add_patch(patch)
for text_args in text_pseudo_objects:
pt.ax_absolute_text(*text_args[0], **text_args[1])
return patch_dict
txt.arrow_patch.set_path_effects([
PathEffects.Stroke(linewidth=5, foreground="w"),
PathEffects.Normal()])
ax1.grid(True, linestyle="-")
pe = [PathEffects.withStroke(linewidth=3,
foreground="w")]
for l in ax1.get_xgridlines() + ax1.get_ygridlines():
l.set_path_effects(pe)
ax2 = plt.subplot(132)
arr = np.arange(25).reshape((5,5))
ax2.imshow(arr)
cntr = ax2.contour(arr, colors="k")
plt.setp(cntr.collections, path_effects=[
PathEffects.withStroke(linewidth=3, foreground="w")])
clbls = ax2.clabel(cntr, fmt="%2.0f", use_clabeltext=True)
plt.setp(clbls, path_effects=[
PathEffects.withStroke(linewidth=3, foreground="w")])
# shadow as a path effect
ax3 = plt.subplot(133)
p1, = ax3.plot([0, 1], [0, 1])
leg = ax3.legend([p1], ["Line 1"], fancybox=True, loc=2)
leg.legendPatch.set_path_effects([PathEffects.withSimplePatchShadow()])
plt.show()
# now plot the same data with our offset transform;
# use the zorder to make sure we are below the line
ax.plot(x, y, lw=3, color='gray',
transform=shadow_transform,
zorder=0.5*line.get_zorder())
t = ax.text(0.02, 0.5, 'Hatch shadow', fontsize=75, weight=1000, va='center')
t.set_path_effects([path_effects.PathPatchEffect(offset=(4, -4), hatch='xxxx',
facecolor='gray'),
path_effects.PathPatchEffect(edgecolor='white', linewidth=1.1,
facecolor='black')])
plt.show()
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
fig=plt.figure()
ax = fig.add_axes([0,0,1,1])
line = ax.plot([0,2,3])
line[0].set_path_effects([path_effects.Stroke(linewidth=3, foreground='black'),
path_effects.Normal()])
text = plt.text(0.5, 0.5, 'Hello DIR effects world!',
path_effects=[path_effects.withSimplePatchShadow()])
plt.plot([0, 3, 2, 5], linewidth=5, color='blue',
path_effects=[path_effects.SimpleLineShadow(),
path_effects.Normal()])
plt.show()
def plot3d_gen(changes, csv_data, log_data, folder, se, rot=0, flag3D=0, count=''):
xdic = dict()
ydic = dict()
zdic = dict()
for k in sorted(csv_data):
t_x = csv_data[k][changes[0]]
t_y = csv_data[k][changes[1]]
try:
xdic[t_x].append(t_x)
except KeyError:
xdic[t_x] = [t_x]
try:
ydic[t_y].append(t_y)
except KeyError:
ydic[t_y] = [t_y]
for t, data in log_data[k].items():
try:
zdic[t][(t_x, t_y)] = data
except KeyError:
zdic[t] = {(t_x, t_y): data}
x = [xdic[i] for i in sorted(xdic)]
y = [ydic[i] for i in sorted(ydic)]
x = np.array(x).T
y = np.array(y)
z_ = dict()
z = dict()
for ty in zdic:
z_[ty] = []
for k in sorted(zdic[ty]):
z_[ty].append(zdic[ty][k][0])
z[ty] = np.array(z_[ty])
z[ty] = z[ty].reshape((x.shape[1], x.shape[0])).T
for k in sorted(z):
fig = plt.figure(figsize=(16, 9))
if flag3D == 1:
ax = fig.add_subplot(211, projection='3d')
ax.plot_surface(x, y, z[k], rstride=1, cstride=1, cmap=cm.get_cmap('bwr'))
if se != 0:
ax.set_zlabel("Spearman's rho")
plt.gca().zaxis.set_major_formatter(FuncFormatter(lambda x, pos=0: "{0:.3f} ".format(x)))
else:
ax.set_zlabel('Accuracy TOP1')
plt.gca().zaxis.set_major_formatter(FuncFormatter(plot_percent))
else:
ax = fig.add_subplot(111)
if se != 0:
cax = ax.imshow(z[k], cmap=cm.jet, interpolation='None', origin='lower',
extent=[x[0][0], x[0][-1], y[0][0], y[-1][0]], aspect='auto', vmin=-1, vmax=1)
bar = fig.colorbar(cax, format='%.3f')
bar.set_label("Spearman's rho")
else:
cax = ax.imshow(z[k], cmap=cm.jet, interpolation='None', origin='lower',
extent=[x[0][0], x[0][-1], y[0][0], y[-1][0]], aspect='auto', vmin=0, vmax=100)
bar = fig.colorbar(cax, format='%.1f%%')
bar.set_label('Accuracy TOP1')
a1 = len(z[k])
a2 = len(z[k][0])
for i in xrange(a1):
for j in xrange(a2):
ax.text(float(j+0.5)/a2, float(i+0.5)/a1, '{0:.3}'.format(z[k][i][j]),
size='medium', ha='center', va='center', transform=ax.transAxes,
path_effects=[patheffects.withSimplePatchShadow(shadow_rgbFace=(1, 1, 1))])
plt.title(k)
plt.xlabel(names[changes[0]])
plt.ylabel(names[changes[1]])
plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, pos=0: "{0:.1E}".format(x)))
plt.gca().xaxis.set_major_formatter(FuncFormatter(lambda x, pos=0: "{0:.1E}".format(x)))
add_table(csv_data, changes)
if rot == 1 and flag3D == 1 and count == '':
create_output_folder('{0}/{1}'.format(folder, k))
for ii in xrange(0, 360, 20):
ax.view_init(elev=30., azim=ii)
plt.savefig(folder+'/'+k+'/{}'.format(ii)+names[changes[0]]+'-'+names[changes[1]]+'_'+k+'.png',
bbox_inches='tight', transparent=False)
else:
plt.savefig(folder+'/'+count+names[changes[0]]+'-'+names[changes[1]]+'_'+k, bbox_inches='tight',
transparent=False)
plt.close()
def create_plots(robot, obstacles, dist_est, gt_grid, use3d=False):
fig = plt.figure(figsize=(4 * 2 + 0.5, 4 * 1))
# fig = plt.figure(figsize=(3*2+0.5, 3 * 1)) # temp
plt.rcParams.update({
"text.usetex": True,
"font.family": "sans-serif",
"font.sans-serif": ["Helvetica"]
})
gs = fig.add_gridspec(1, 3)
ax = fig.add_subplot(gs[0, 0])
ax.set_xlim(-8, 8)
ax.set_ylim(-8, 8)
ax.set_aspect('equal', adjustable='box')
ax.set_xticks([-4, 0, 4])
ax.set_yticks([-4, 0, 4])
ax.set_xticklabels(['', '', ''])
ax.set_yticklabels(['', '', ''])
from matplotlib.cm import get_cmap
cmap = get_cmap('RdBu_r')
for obs in obstacles:
if obs[0] == 'circle':
ax.add_patch(
Circle(obs[1],
obs[2],
path_effects=[path_effects.withSimplePatchShadow()
])) #, color=cmaps[1](0.5)))
elif obs[0] == 'rect':
ax.add_patch(
Rectangle((obs[1][0] - float(obs[2][0]) / 2,
obs[1][1] - float(obs[2][1]) / 2),
obs[2][0],
obs[2][1],
path_effects=[path_effects.withSimplePatchShadow()],
color=cmap(0.8))) #, color=cmaps[0](0.5)))
print((obs[1][0] - obs[2][0] / 2, obs[1][1] - obs[2][1] / 2))
# Placeholder of the robot plot
trans = ax.transData.transform
lw = ((trans((1, robot.link_width)) - trans(
(0, 0))) * 72 / ax.figure.dpi)[1]
# q = torch.FloatTensor([-np.pi/8, -np.pi/4])#-np.pi/4])
q = torch.ones(
robot.dof) * np.pi / 6 * (torch.randint(0, 3, [robot.dof]) - 1)
points = robot.fkine(q)[0]
points = torch.cat([torch.zeros(1, 2), points], dim=0)
link_plot, = ax.plot(
points[:, 0],
points[:, 1],
color='orange',
alpha=1,
lw=lw,
solid_capstyle='round',
path_effects=[path_effects.SimpleLineShadow(),
path_effects.Normal()])
joint_plot, = ax.plot(points[:-1, 0],
points[:-1, 1],
'o',
color='tab:red',
markersize=lw)
eff_plot, = ax.plot(points[-1:, 0],
points[-1:, 1],
'o',
color='black',
markersize=lw)
# ax.axis('off')
# return None, None
size = [400, 400]
yy, xx = torch.meshgrid(torch.linspace(-np.pi, np.pi, size[0]),
torch.linspace(-np.pi, np.pi, size[1]))
grid_points = torch.stack([xx, yy], axis=2).reshape((-1, 2))
est_grid = dist_est(grid_points).reshape(size)
gt_grid = gt_grid.reshape(size)
# gt_grid = torch.from_numpy(ndimage.gaussian_filter(gt_grid, 15))
# est_grid = torch.from_numpy(ndimage.gaussian_filter(est_grid, 3))
c_axes = []
with sns.axes_style('ticks'):
for i, d in enumerate([gt_grid, est_grid], 1):
# for i, d in enumerate([est_grid], 1): #TEMP
# for i, d in enumerate([gt_grid], 1): # for clustering
c_ax = fig.add_subplot(gs[0, i],
projection='3d' if use3d else None)
if use3d:
from matplotlib import cm
# c_ax.plot_wireframe(xx.numpy(), yy.numpy(), d.numpy(), alpha=1, #vmin=-d.max(), vmax=d.max(),
# rstride=10, cstride=10, linewidth=0.1, antialiased=False, edgecolor='black') # , 'RdBu_r'
surf = c_ax.plot_surface(
xx.numpy(),
yy.numpy(),
d.numpy(),
alpha=1, #vmin=-d.max(), vmax=d.max(),
rstride=10,
cstride=10,
linewidth=0.1,
antialiased=True,
cmap='Greys_r',
edgecolor='black') # , 'RdBu_r' cmap='Greys_r',
# c_ax.plot_wireframe(xx.numpy(), yy.numpy(), d.numpy(), rstride=5, cstride=5, color='black')
# surf._facecolors2d=surf._facecolor3d
# surf._facecolors3d = 'face'
# surf.set_edgecolor('none')
# c_ax.contour3D(xx, yy, d, 50, linewidths=1, alpha=1)
# c_ax.plot_surface(xx[::199, ::199], yy[::199, ::199].numpy(), np.zeros_like(yy[::199, ::199].numpy()), alpha=0.3) #, vmin=-torch.abs(d).max(), vmax=torch.abs(d).max()) #cmap='RdBu_r',
else:
color_mesh = c_ax.pcolormesh(
xx,
yy,
d,
cmap='RdBu_r',
vmin=-torch.abs(d).max(),
vmax=torch.abs(
d).max()) #, alpha=0.5) # binary shading='gouraud',
c_ax.contour(xx, yy, d, levels=[0], linewidths=1,
alpha=0.4) #-1.5, -0.75, 0, 0.3
# ============arrows and configuration point========
sparse_stride = 20
sparse_score = d[5:-5:sparse_stride, 5:-5:sparse_stride]
score_grad_x = -ndimage.sobel(sparse_score.numpy(), axis=1)
score_grad_y = -ndimage.sobel(sparse_score.numpy(), axis=0)
if use3d:
score_grad = np.stack([
score_grad_x, score_grad_y,
-(score_grad_x**2 + score_grad_y**2)
],
axis=2)
score_grad /= np.linalg.norm(score_grad, axis=2, keepdims=True)
score_grad_x, score_grad_y, score_grad_z = [
score_grad[:, :, dim] for dim in range(3)
]
else:
score_grad = np.stack([score_grad_x, score_grad_y], axis=2)
score_grad /= np.linalg.norm(score_grad, axis=2,
keepdims=True) / 10
score_grad_x, score_grad_y = score_grad[:, :,
0], score_grad[:, :, 1]
if use3d:
# c_ax.quiver(xx[5:-5:sparse_stride, 5:-5:sparse_stride], yy[5:-5:sparse_stride, 5:-5:sparse_stride], sparse_score+0.05,
# score_grad_x, score_grad_y, score_grad_z,
# color='red') #width=1e-2, headwidth=2, headlength=5
c_ax.set_aspect('auto', adjustable='box')
nearest_gridpoint = np.argmin(
np.square(grid_points - q).sum(axis=1))
print(
xx.reshape(-1)[nearest_gridpoint],
yy.reshape(-1)[nearest_gridpoint],
)
q_score = d.reshape(-1, 1)[nearest_gridpoint]
# c_ax.scatter([q[0].item()], [q[1].item()], q_score+0.5, marker='o', s=40, c='orange', edgecolors='black', zorder=100)
circle_patch = Circle([q[0].item(), q[1].item()],
np.pi / 20,
ec='k',
fc="orange",
alpha=1)
c_ax.add_patch(circle_patch)
from mpl_toolkits.mplot3d import art3d
art3d.pathpatch_2d_to_3d(circle_patch, z=q_score, zdir="z")
c_ax.view_init(60, 225)
c_ax.set_facecolor('white')
c_ax.w_xaxis.set_pane_color(ax.get_facecolor())
c_ax.w_yaxis.set_pane_color(ax.get_facecolor())
c_ax.w_zaxis.set_pane_color(ax.get_facecolor())
else:
c_ax.quiver(xx[5:-5:sparse_stride, 5:-5:sparse_stride],
yy[5:-5:sparse_stride, 5:-5:sparse_stride],
score_grad_x,
score_grad_y,
color='red',
width=1e-2,
headwidth=2,
headlength=5,
pivot='mid')
c_ax.set_aspect('equal', adjustable='box')
c_ax.scatter([q[0].item()], [q[1].item()],
marker='o',
s=40,
c='orange',
edgecolors='black')
# fig.colorbar(color_mesh, ax=c_ax)
# c_ax.axis('equal')
c_ax.set_xlim(-np.pi, np.pi)
c_ax.set_ylim(-np.pi, np.pi)
c_ax.set_xticks([-np.pi, 0, np.pi])
c_ax.set_xticklabels(['$-\pi$', '$0$', '$\pi$'])
c_ax.set_yticks([-np.pi, 0, np.pi])
c_ax.set_yticklabels(['$-\pi$', '$0$', '$\pi$'])
c_axes.append(c_ax)
c_ax.grid(False)
return est_grid.reshape(-1), c_axes
def create_plots(robot, obstacles, dist_est, checker):
from matplotlib.cm import get_cmap
from matplotlib.patches import Rectangle, FancyBboxPatch, Circle
import seaborn as sns
sns.set()
import matplotlib.patheffects as path_effects
cmaps = [get_cmap('Reds'), get_cmap('Blues')]
plt.rcParams.update({
"text.usetex": True,
"font.family": "sans-serif",
"font.sans-serif": ["Helvetica"]})
if robot.dof > 2:
fig = plt.figure(figsize=(3, 3))
ax = fig.add_subplot(111) #, projection='3d'
elif robot.dof == 2:
# Show C-space at the same time
num_class = getattr(checker, 'num_class', 1)
fig = plt.figure(figsize=(3*(num_class + 1), 3 * num_class))
gs = fig.add_gridspec(num_class, num_class+1)
ax = fig.add_subplot(gs[:, :-1]) #sum([list(range(r*(num_class+1)+1, (r+1)*(num_class+1))) for r in range(num_class)], [])) #, projection='3d'
cfg_path_plots = []
size = [400, 400]
yy, xx = torch.meshgrid(torch.linspace(-np.pi, np.pi, size[0]), torch.linspace(-np.pi, np.pi, size[1]))
grid_points = torch.stack([xx, yy], axis=2).reshape((-1, 2))
score_spline = dist_est(grid_points).reshape(size+[num_class])
c_axes = []
with sns.axes_style('ticks'):
for cat in range(num_class):
c_ax = fig.add_subplot(gs[cat, -1])
# score_diffco = checker.score(grid_points).reshape(size)
# score = (torch.sign(score_diffco)+1)/2*(score_spline-score_spline.min()) + (-torch.sign(score_diffco)+1)/2*(score_spline-score_spline.max())
score = score_spline[:, :, cat]
color_mesh = c_ax.pcolormesh(xx, yy, score, cmap=cmaps[cat], vmin=-torch.abs(score).max(), vmax=torch.abs(score).max())
c_support_points = checker.support_points[checker.gains[:, cat] != 0]
c_ax.scatter(c_support_points[:, 0], c_support_points[:, 1], marker='.', c='black', s=1.5)
contour_plot = c_ax.contour(xx, yy, score, levels=[-18, -10, 0, 3.5 if cat==0 else 2.5], linewidths=1, alpha=0.4, colors='k') #-1.5, -0.75, 0, 0.3
ax.clabel(contour_plot, inline=1, fmt='%.1f', fontsize=8)
# fig.colorbar(color_mesh, ax=c_ax)
# sparse_score = score[5:-5:10, 5:-5:10]
# score_grad_x = -ndimage.sobel(sparse_score.numpy(), axis=1)
# score_grad_y = -ndimage.sobel(sparse_score.numpy(), axis=0)
# score_grad = np.stack([score_grad_x, score_grad_y], axis=2)
# score_grad /= np.linalg.norm(score_grad, axis=2, keepdims=True)
# score_grad_x, score_grad_y = score_grad[:, :, 0], score_grad[:, :, 1]
# c_ax.quiver(xx[5:-5:10, 5:-5:10], yy[5:-5:10, 5:-5:10], score_grad_x, score_grad_y, color='red', width=2e-3, headwidth=2, headlength=5)
# cfg_point = Circle(collision_cfgs[0], radius=0.05, facecolor='orange', edgecolor='black', path_effects=[path_effects.withSimplePatchShadow()])
# c_ax.add_patch(cfg_point)
cfg_path, = c_ax.plot([], [], '-o', c='orange', markersize=3)
cfg_path_plots.append(cfg_path)
c_ax.set_aspect('equal', adjustable='box')
# c_ax.axis('equal')
c_ax.set_xlim(-np.pi, np.pi)
c_ax.set_ylim(-np.pi, np.pi)
c_ax.set_xticks([-np.pi, 0, np.pi])
c_ax.set_xticklabels(['$-\pi$', '$0$', '$\pi$'], fontsize=18)
c_ax.set_yticks([-np.pi, 0, np.pi])
c_ax.set_yticklabels(['$-\pi$', '$0$', '$\pi$'], fontsize=18)
# c_ax.tick_params(direction='in', reset=True)
# c_ax.tick_params(which='both', direction='out', length=6, width=2, colors='r',
# grid_color='r', grid_alpha=0.5)
# c_ax.set_ticks('')
# Plot ostacles
# ax.axis('tight')
ax.set_xlim(-8, 8)
ax.set_ylim(-8, 8)
ax.set_aspect('equal', adjustable='box')
ax.set_xticks([-4, 0, 4])
ax.set_yticks([-4, 0, 4])
ax.tick_params(labelsize=18)
for obs in obstacles:
cat = obs[3] if len(obs) >= 4 else 1
print('{}, cat {}, {}'.format(obs[0], cat, obs))
if obs[0] == 'circle':
ax.add_patch(Circle(obs[1], obs[2], path_effects=[path_effects.withSimplePatchShadow()], color=cmaps[cat](0.5)))
elif obs[0] == 'rect':
ax.add_patch(Rectangle((obs[1][0]-float(obs[2][0])/2, obs[1][1]-float(obs[2][1])/2), obs[2][0], obs[2][1], path_effects=[path_effects.withSimplePatchShadow()],
color=cmaps[cat](0.5)))
# print((obs[1][0]-obs[2][0]/2, obs[1][1]-obs[2][1]/2))
# Placeholder of the robot plot
trans = ax.transData.transform
lw = ((trans((1, robot.link_width))-trans((0,0)))*72/ax.figure.dpi)[1]
link_plot, = ax.plot([], [], color='silver', alpha=0.1, lw=lw, solid_capstyle='round', path_effects=[path_effects.SimpleLineShadow(), path_effects.Normal()])
joint_plot, = ax.plot([], [], 'o', color='tab:red', markersize=lw)
eff_plot, = ax.plot([], [], 'o', color='black', markersize=lw)
if robot.dof > 2:
return fig, ax, link_plot, joint_plot, eff_plot
elif robot.dof == 2:
return fig, ax, link_plot, joint_plot, eff_plot, cfg_path_plots
from matplotlib import patheffects
import numpy as np
data = np.random.randn(70)
print data
fontsize = 18
plt.plot(data)
title = "This is figure title"
x_label = "This is x axis label"
y_label = "This is y axis label"
title_text_obj = plt.title(title, fontsize=fontsize, verticalalignment='bottom')
title_text_obj.set_path_effects([patheffects.withSimplePatchShadow()])
# offset_xy -- set the 'angle' of the shadow
# shadow_rgbFace -- set the color of the shadow
# patch_alpha -- setup the transparaency of the shadow
offset_xy = (1, -1)
rgbRed = (1.0,0.0,0.0)
alpha = 0.8
# customize shadow properties
pe = patheffects.withSimplePatchShadow(offset_xy = offset_xy,
shadow_rgbFace = rgbRed,
patch_alpha = alpha)
# apply them to the xaxis and yaxis labels
xlabel_obj = plt.xlabel(x_label, fontsize=fontsize, alpha=0.5)