def draw_partitioned_graph(G,
partition_obj,
layout=None,
labels=None,
layout_type='spring',
node_size=70,
node_alpha=0.7,
cmap=plt.get_cmap('jet'),
node_text_size=12,
edge_color='blue',
edge_alpha=0.5,
edge_tickness=1,
edge_text_pos=0.3,
text_font='sans-serif'):
# if a premade layout haven't been passed, create a new one
if not layout:
if graph_type == 'spring':
layout = nx.spring_layout(G)
elif graph_type == 'spectral':
layout = nx.spectral_layout(G)
elif graph_type == 'random':
layout = nx.random_layout(G)
else:
layout = nx.shell_layout(G)
# prepare the partition list noeds and colors
list_nodes, node_color = partition_to_draw(partition_obj)
# draw graph
nx.draw_networkx_nodes(G,
layout,
list_nodes,
node_size=node_size,
alpha=node_alpha,
node_color=node_color,
cmap=cmap)
nx.draw_networkx_edges(G,
layout,
width=edge_tickness,
alpha=edge_alpha,
edge_color=edge_color)
#nx.draw_networkx_labels(G, layout,font_size=node_text_size,
# font_family=text_font)
if labels is None:
labels = range(len(G))
edge_labels = dict(zip(G, labels))
#nx.draw_networkx_edge_labels(G, layout, edge_labels=edge_labels,
# label_pos=edge_text_pos)
# show graph
plt.axis('off')
plt.xlim(0, 1)
plt.ylim(0, 1)
def draw_satTrail_multicolor(lons, lats, colormap, Trail_Width=1):
'''
画卫星轨迹，根据colormap使颜色渐变
'''
t = np.linspace(0, len(lons), len(lons))
lons_tmp = []
lats_tmp = []
lon_old = lons[0]
ti = 0
for i in xrange(len(lons)):
if abs(lons[i] - lon_old) >= 180.: # 轨迹每次过精度180，就分割画一次
points = np.array([lons_tmp, lats_tmp]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(
segments,
cmap=plt.get_cmap(colormap), # YlGnBu
norm=plt.Normalize(0, len(lons)))
lc.set_array(t[ti:i])
lc.set_linewidth(Trail_Width)
plt.gca().add_collection(lc)
# plt.plot(lons_tmp, lats_tmp, '-', linewidth=Trail_Width, c=color)
lons_tmp = []
lats_tmp = []
ti = i
lon_old = lons[i]
lons_tmp.append(lons[i])
lats_tmp.append(lats[i])
# plt.plot(lons_tmp, lats_tmp, '-', linewidth=Trail_Width, c=color)
points = np.array([lons_tmp, lats_tmp]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(
segments,
cmap=plt.get_cmap(colormap), # YlGnBu
norm=plt.Normalize(0, len(lons)))
lc.set_array(t[ti:i])
lc.set_linewidth(Trail_Width)
plt.gca().add_collection(lc)
def draw_partitioned_graph(G, partition_obj, layout=None, labels=None,layout_type='spring',
node_size=70, node_alpha=0.7, cmap=plt.get_cmap('jet'),
node_text_size=12,
edge_color='blue', edge_alpha=0.5, edge_tickness=1,
edge_text_pos=0.3,
text_font='sans-serif'):
# if a premade layout haven't been passed, create a new one
if not layout:
if graph_type == 'spring':
layout=nx.spring_layout(G)
elif graph_type == 'spectral':
layout=nx.spectral_layout(G)
elif graph_type == 'random':
layout=nx.random_layout(G)
else:
layout=nx.shell_layout(G)
# prepare the partition list noeds and colors
list_nodes, node_color = partition_to_draw(partition_obj)
# draw graph
nx.draw_networkx_nodes(G,layout,list_nodes,node_size=node_size,
alpha=node_alpha, node_color=node_color, cmap = cmap)
nx.draw_networkx_edges(G,layout,width=edge_tickness,
alpha=edge_alpha,edge_color=edge_color)
#nx.draw_networkx_labels(G, layout,font_size=node_text_size,
# font_family=text_font)
if labels is None:
labels = range(len(G))
edge_labels = dict(zip(G, labels))
#nx.draw_networkx_edge_labels(G, layout, edge_labels=edge_labels,
# label_pos=edge_text_pos)
# show graph
plt.axis('off')
plt.xlim(0,1)
plt.ylim(0,1)
def plot_variable(u, name, direc, cmap=cmaps.parula, scale='lin', numLvls=100,
umin=None, umax=None, \
tp=False, \
tpAlpha=1.0, show=False,
hide_ax_tick_labels=False, label_axes=True, title='',
use_colorbar=True, hide_axis=False, colorbar_loc='right'):
"""
show -- whether to show the plot on the screen
tp -- show triangle
cmap -- colors:
gist_yarg - grey
gnuplot, hsv, gist_ncar
jet - typical colors
"""
mesh = u.function_space().mesh()
v = u.compute_vertex_values(mesh)
x = mesh.coordinates()[:,0]
y = mesh.coordinates()[:,1]
t = mesh.cells()
if not os.path.isdir( direc ):
os.makedirs(direc)
full_path = os.path.join(direc, name)
if umin != None:
vmin = umin
else:
vmin = v.min()
if umax != None:
vmax = umax
else:
vmax = v.max()
# countour levels :
if scale == 'log':
v[v < vmin] = vmin + 1e-12
v[v > vmax] = vmax - 1e-12
from matplotlib.ticker import LogFormatter
levels = np.logspace(np.log10(vmin), np.log10(vmax), numLvls)
tick_numLvls = min( numLvls, 8 )
tick_levels = np.logspace(np.log10(vmin), np.log10(vmax), tick_numLvls)
formatter = LogFormatter(10, labelOnlyBase=False)
norm = colors.LogNorm()
elif scale == 'lin':
v[v < vmin] = vmin + 1e-12
v[v > vmax] = vmax - 1e-12
from matplotlib.ticker import ScalarFormatter
levels = np.linspace(vmin, vmax, numLvls)
tick_numLvls = min( numLvls, 8 )
tick_levels = np.linspace(vmin, vmax, tick_numLvls)
formatter = ScalarFormatter()
norm = None
elif scale == 'bool':
from matplotlib.ticker import ScalarFormatter
levels = [0, 1, 2]
formatter = ScalarFormatter()
norm = None
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(111)
c = ax.tricontourf(x, y, t, v, levels=levels, norm=norm,
cmap=plt.get_cmap(cmap))
plt.axis('equal')
if tp == True:
p = ax.triplot(x, y, t, '-', lw=0.2, alpha=tpAlpha)
ax.set_xlim([x.min(), x.max()])
ax.set_ylim([y.min(), y.max()])
if label_axes:
ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')
if hide_ax_tick_labels:
ax.set_xticklabels([])
ax.set_yticklabels([])
if hide_axis:
plt.axis('off')
# include colorbar :
if scale != 'bool' and use_colorbar:
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes(colorbar_loc, "5%", pad="3%")
cbar = plt.colorbar(c, cax=cax, format=formatter,
ticks=tick_levels)
tit = plt.title(title)
if use_colorbar:
plt.tight_layout(rect=[.03,.03,0.97,0.97])
else:
plt.tight_layout()
plt.savefig( full_path + '.eps', dpi=300)
if show:
plt.show()
plt.close(fig)
def plot_const(u, name , direc):
if not os.path.isdir( direc ):
os.makedirs(direc)
full_path = os.path.join(direc, name)
full_mesh = os.path.join(direc, 'mesh.svg')
mesh = u.function_space().mesh()
v = u.compute_vertex_values(mesh)
x = mesh.coordinates()[:,0]
y = mesh.coordinates()[:,1]
t = mesh.cells()
vmin = v.min()
vmax = v.max()
v[v < vmin] = vmin + 1e-12
v[v > vmax] = vmax - 1e-12
numLvls=100
from matplotlib.ticker import ScalarFormatter
levels = np.linspace(vmin, vmax, numLvls)
tick_numLvls = min( numLvls, 8 )
tick_levels = np.linspace(vmin, vmax, tick_numLvls)
formatter = ScalarFormatter()
norm = None
n = mesh.num_vertices()
d = mesh.geometry().dim()
# Create the triangulation
mesh_coordinates = mesh.coordinates().reshape((n, d))
triangles = np.asarray([cell.entities(0) for cell in cells(mesh)])
triangulation = tri.Triangulation(mesh_coordinates[:, 0],
mesh_coordinates[:, 1],
triangles)
# Plot the mesh
# plt.figure()
# plt.triplot(triangulation)
# plt.savefig( full_mesh )
# Plot of scalar field
V = u.function_space() #FunctionSpace(mesh, 'CG', 2)
#f_exp = Expression('sin(2*pi*(x[0]*x[0]+x[1]*x[1]))')
#f = interpolate(f_exp, V)
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(111)
# Get the z values for each vertex
# cmap = plt.cm.jet
cmap = cmaps.parula
c = ax.tricontourf(x, y, t, v, levels=levels, norm=norm,
cmap=plt.get_cmap(cmap))
plt.ioff()
#fig = plt.figure()
z = np.asarray([u(point) for point in mesh_coordinates])
plt.tripcolor(triangulation, z, cmap=cmap) # alt plt.tricontourf(...)
plt.colorbar()
# plt.savefig( full_path, bbox_inches='tight' )
plt.savefig( full_path + '.eps', dpi=300)
plt.close( fig )
ax1.legend(legend, loc='upper left')
ax1.set_title('5Y5Y X-Market')
# Correlation Plot
#ax2 = sns.heatmap(df_5y5y.corr(), xticklabels=legend, yticklabels=legend, cmap='RdYlGn', center=0, annot=True)
spread = df['PLN 5Y X 5Y Fwd Swap Rate'] - avg_5y5y
ax2.plot(spread)
# Decorations
#ax2.title('Historical Correlations', fontsize=22)
#ax2.xticks(fontsize=8)
#ax2.yticks(fontsize=4)
print(df_5y5y.corr())
x1 = avg_5y5y
y1 = df_5y5y['PLN 5Y X 5Y Fwd Swap Rate']
ax4 = sns.regplot(x=x1, y=y1, marker="+")
#plt.show()
colors = np.linspace(0.1, 1, len(df_5y5y))
mymap = plt.get_cmap("winter")
ax3 = ax3.scatter(x1, y1, c=colors, cmap=mymap, lw=0)
cb = plt.colorbar(ax3)
#plt.subplot(x1[-1],y1[-1],'ro')
cb.ax.set_yticklabels(
[str(p.date()) for p in df_5y5y[::len(df_5y5y) // 10].index])
fig.tight_layout()
plt.show()
def plot_variable(u,
name,
direc,
cmap='gist_yarg',
scale='lin',
numLvls=12,
umin=None,
umax=None,
tp=False,
tpAlpha=0.5,
show=True,
hide_ax_tick_labels=False,
label_axes=True,
title='',
use_colorbar=True,
hide_axis=False,
colorbar_loc='right'):
"""
"""
mesh = u.function_space().mesh()
v = u.compute_vertex_values(mesh)
x = mesh.coordinates()[:, 0]
y = mesh.coordinates()[:, 1]
t = mesh.cells()
d = os.path.dirname(direc)
if not os.path.exists(d):
os.makedirs(d)
if umin != None:
vmin = umin
else:
vmin = v.min()
if umax != None:
vmax = umax
else:
vmax = v.max()
# countour levels :
if scale == 'log':
v[v < vmin] = vmin + 1e-12
v[v > vmax] = vmax - 1e-12
from matplotlib.ticker import LogFormatter
levels = np.logspace(np.log10(vmin), np.log10(vmax), numLvls)
formatter = LogFormatter(10, labelOnlyBase=False)
norm = colors.LogNorm()
elif scale == 'lin':
v[v < vmin] = vmin + 1e-12
v[v > vmax] = vmax - 1e-12
from matplotlib.ticker import ScalarFormatter
levels = np.linspace(vmin, vmax, numLvls)
formatter = ScalarFormatter()
norm = None
elif scale == 'bool':
from matplotlib.ticker import ScalarFormatter
levels = [0, 1, 2]
formatter = ScalarFormatter()
norm = None
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111)
c = ax.tricontourf(x,
y,
t,
v,
levels=levels,
norm=norm,
cmap=plt.get_cmap(cmap))
plt.axis('equal')
if tp == True:
p = ax.triplot(x, y, t, '-', lw=0.2, alpha=tpAlpha)
ax.set_xlim([x.min(), x.max()])
ax.set_ylim([y.min(), y.max()])
if label_axes:
ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')
if hide_ax_tick_labels:
ax.set_xticklabels([])
ax.set_yticklabels([])
if hide_axis:
plt.axis('off')
# include colorbar :
if scale != 'bool' and use_colorbar:
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes(colorbar_loc, "5%", pad="3%")
cbar = plt.colorbar(c, cax=cax, format=formatter, ticks=levels)
tit = plt.title(title)
if use_colorbar:
plt.tight_layout(rect=[.03, .03, 0.97, 0.97])
else:
plt.tight_layout()
plt.savefig(os.path.join(direc, name + '.png'), dpi=300)
if show:
plt.show()
plt.close(fig)
import matplotlib.pyplot as plt
# Creating dataset
z = pvr[:, 0]
x = pvr[:, 1]
y = psr
# Creating figure
fig = plt.figure(figsize=(25, 11))
ax = plt.axes(projection="3d")
# Add x, y gridlines
ax.grid(b=True, color='black', linestyle='--', linewidth=0.3, alpha=0.8)
# Creating color map
my_cmap = plt.get_cmap('coolwarm')
# Creating plot
sctt = ax.scatter3D(x, y, z, alpha=1, c=(x + y + z), cmap=my_cmap, marker='*')
plt.title(' | '.join(symbols))
ax.set_xlabel('expected volatility', fontweight='bold')
ax.set_ylabel('expected return', fontweight='bold')
ax.set_zlabel('Sharpe ratio', fontweight='bold')
fig.colorbar(sctt, ax=ax, shrink=0.5, aspect=5)
# show plot
plt.show()
bnds = len(symbols) * [
(0, 1),
signals = extract_activity_signals(activity, resample='existing')
power = signals['power']
balance = signals['left_right_balance']
time = signals['time']
PAvgBalance = sum(power * balance) / sum(power)
# get session info
records = activity.get_records_by_type('session')
for record in records:
valid_field_names = record.get_valid_field_names()
# plotting
CrossPlotFig = plt.figure()
sc = plt.scatter(power,
balance,
s=5,
c=time,
cmap=plt.get_cmap('brg'),
edgecolors='face')
plt.colorbar(orientation='horizontal')
plt.title('Balance Vs Power over Time (sec)\n' \
+ 'power-weighted average = %4.1f' % (PAvgBalance) )
plt.xlabel('Power (w)')
plt.ylabel('Right Balance (%)')
plt.grid(b=True, which='major', axis='both')
ax = plt.gca()
grids = arange(10, 100, 10) # force a gride at 50
ax.set_yticks(grids, minor=False)
ax.grid(True)
plt.show()