def plot_curvature(self, Nx_per_patch, Ny_per_patch):
"""
Plots the curvature of the surface.
"""
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
# Get all the curvatures
curvatures = []
for patch in self:
curvatures.append(
patch.evaluate_abs_curvature(Nx_per_patch, Ny_per_patch))
# Map the curvatures to colors
norm = col.Normalize(vmin=np.amin(curvatures),
vmax=np.amax(curvatures))
cmap = cm.Spectral_r(norm(curvatures))
# Plot every patch with their colors
for patch, colors in zip(self, cmap):
patch.draw_to(ax, 30, 30, facecolors=colors)
# add colorbar
m = cm.ScalarMappable(cmap=cm.Spectral_r, norm=norm)
m.set_array(curvatures)
fig.colorbar(m)
plt.show()
def plot_flow(od_matrix, title, chengdu):
colors = [cm.Spectral_r(x) for x in linspace(0, 1, 5)]
df = od_matrix.copy()
df_class = mpc.NaturalBreaks(df['ridership'])
df['class'] = df_class.yb
bins = df_class.bins
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
chengdu.plot(figsize=(5, 5),
facecolor='none',
edgecolor='black',
linewidth=0.6,
ax=ax,
alpha=1)
for i in range(len(bins)):
sub = df[df['class'] == i]
for j in sub.index:
ax.annotate('',
xy=(sub.loc[j, 'ori_x'], sub.loc[j, 'ori_y']),
xytext=(sub.loc[j, 'end_x'], sub.loc[j, 'end_y']),
arrowprops=dict(arrowstyle="->",
color=colors[i],
linewidth=0.5,
alpha=0.8,
shrinkA=0,
shrinkB=0,
patchA=None,
patchB=None,
connectionstyle='arc3,rad=-0.3'))
ax.set_title(title, fontsize=16)
ax.set_xlim(103.89, 104.24)
ax.set_ylim(30.54, 30.8)
line1, _ = ax.plot(((0, 0), (1, 1)), color=colors[0], alpha=0.8)
line2, _ = ax.plot(((0, 0), (1, 1)), color=colors[1], alpha=0.8)
line3, _ = ax.plot(((0, 0), (1, 1)), color=colors[2], alpha=0.8)
line4, _ = ax.plot(((0, 0), (1, 1)), color=colors[3], alpha=0.8)
line5, _ = ax.plot(((0, 0), (1, 1)), color=colors[4], alpha=1)
plt.legend((line1, line2, line3, line4, line5), [
'{} - {}'.format(0.0, round(bins[0], 1)), '{} - {}'.format(
round(bins[0], 1), round(bins[1], 1)), '{} - {}'.format(
round(bins[1], 1), round(bins[2], 1)), '{} - {}'.format(
round(bins[2], 1), round(bins[3], 1)), '{} - {}'.format(
round(bins[3], 1), round(bins[4], 1))
],
framealpha=1,
title='Trips',
facecolor='whitesmoke')
plt.xticks([])
plt.yticks([])
# plt.savefig(name, dpi=300, transparent=True, pad_inches=0)
# plt.close(fig=fig)
plt.show()
def get_image():
x_bounds = (-123, -121)
y_bounds = (37, 39)
gdf = gpd.read_file("data/ca_nvda_grav.geojson")
gdf_subset = gdf[(gdf["latitude"] > y_bounds[0])
& (gdf["latitude"] < y_bounds[1]) &
(gdf["longitude"] > x_bounds[0]) &
(gdf["longitude"] < x_bounds[1])]
coord_list = list(zip(gdf_subset.geometry.x, gdf_subset.geometry.y))
grid_1 = interpolate_2d(coord_list,
gdf_subset["isostatic_anom"].values,
x_bounds=x_bounds,
y_bounds=y_bounds)
## convert to colormap image
im = Image.fromarray(np.uint8(cm.Spectral_r(grid_1) * 255))
return im
def Shape(geometry,
diel,
d,
iteration=-1,
position="./",
decimal=0,
FullLattice=False):
"""Plot the shape of object as dot matrix.
#Input:
# --SC SolutionClass
# Solution Class.
# --FullLattice Boolean
# If true. Geometry is a full n*n*n matrix. diel=0 for point seen as air.
"""
#d = SC.Get_d()
N = round(np.shape(geometry)[0] / 3)
if (N != np.shape(diel)[0] / 3):
print("size not equal!")
geometry = np.reshape(geometry, (N, 3))
#geometry = SC.Get_geometry()
for i in range(3):
geometry[:, i] -= np.amin(geometry[:, i])
[X, Y, Z] = map(int, list(np.amax(geometry, axis=0) + 1))
Axis_max = max(X, Y, Z) * 1.2 * d
diel = np.reshape(diel, (N, 3))
diel = diel[:, 0]
#diel = SC.Get_diel()[:,0]
cmaparg = 'Spectral_r'
minn, maxx = 0, 1
norm = matplotlib.colors.Normalize(minn, maxx)
colorset = cm.ScalarMappable(norm=norm, cmap=cmaparg)
colorset.set_array([])
if FullLattice:
index = np.where(diel > 0.5)
diel = diel[index]
geometry = geometry[index]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
geo_dic = set()
surf_list = []
surf_color = []
x_grid, y_grid, z_grid = (np.indices((X + 1, Y + 1, Z + 1)) - 0.5) * d
filled, colors = np.zeros((X, Y, Z)), np.zeros((X, Y, Z))
for i, pos in enumerate(geometry):
geo_dic.add((pos[0], pos[1], pos[2]))
for i, pos in enumerate(geometry):
if (pos[0]+1,pos[1],pos[2]) not in geo_dic or (pos[0]-1,pos[1],pos[2]) not in geo_dic or\
(pos[0],pos[1]+1,pos[2]) not in geo_dic or (pos[0],pos[1]-1,pos[2]) not in geo_dic or\
(pos[0],pos[1],pos[2]+1) not in geo_dic or (pos[0],pos[1],pos[2]-1) not in geo_dic:
filled[pos[0], pos[1], pos[2]] = 1
colors[pos[0], pos[1], pos[2]] = diel[i]
surf_list = np.array(surf_list)
surf_color = np.array(surf_color)
# print(x_grid.shape,y_grid.shape,z_grid.shape,filled.shape,colors.shape)
colors = cm.Spectral_r(norm(colors))
ln = ax.voxels(x_grid,
y_grid,
z_grid,
filled.astype(bool),
facecolors=colors,
edgecolor='white',
linewidth=0.2)
#ax2.scatter(geometry[:,0]*d,geometry[:,1]*d,geometry[:,2]*d,c=E_tot_abs, s=15, cmap=cmaparg)
ax.set_xlim(-(Axis_max - X * d) / 2, (Axis_max + X * d) / 2)
ax.set_ylim(-(Axis_max - Y * d) / 2, (Axis_max + Y * d) / 2)
ax.set_zlim(-(Axis_max - Z * d) / 2, (Axis_max + Z * d) / 2)
ax.set_xlabel("x[nm]")
ax.set_ylabel("y[nm]")
ax.set_zlabel("z[nm]")
ax.grid(False)
fig.colorbar(colorset, shrink=0.9, aspect=10)
#plt.show()
#plt.savefig("./optimization_geometries/Iteration{}.png".format(it))
if iteration == -1:
plt.savefig("Space.png")
else:
fig.suptitle("iteration{}".format(iteration))
plt.savefig(position +
"{}Space.png".format(str(iteration).zfill(decimal)))
def EField(geometry,
diel,
d,
wl,
k_dir,
E_dir,
E_tot,
iteration=-1,
position="./",
decimal=0,
FullLattice=False):
"""Plot the E field of object as arrow matrix.
# Input:
# --SC SolutionClass
# Solved Solution Class.
# --idx1,idx2 int
# Indexs of the target instance.
"""
#print(geometry)
N = round(np.shape(geometry)[0] / 3)
#print(N)
geometry = np.reshape(geometry, (N, 3))
print(geometry.shape)
#diel=np.reshape(diel,(N,3))
#diel=diel[:,0]
for i in range(3):
geometry[:, i] -= np.amin(geometry[:, i])
#print(geometry)
[X, Y, Z] = list(np.amax(geometry, axis=0) + 1)
Axis_max = max(X, Y, Z) * 1.2 * d
E_tot = E_tot.reshape(int(E_tot.size / 3), 3)
E_tot_abs = np.abs(np.sqrt(np.sum((E_tot)**2, axis=1)))
"""
if FullLattice:
index = np.where(diel!=0) #disabled all codes with "diel"
geometry = geometry[index]
E_tot = E_tot[index]
E_tot_abs = E_tot_abs[index]
"""
cmaparg = 'Spectral_r'
minn, maxx = E_tot_abs.min(), E_tot_abs.max()
print(minn, maxx, np.argmax(E_tot_abs))
norm = matplotlib.colors.Normalize(minn, maxx)
colorset = cm.ScalarMappable(norm=norm, cmap=cmaparg)
colorset.set_array([])
fig1 = plt.figure()
ax1 = fig1.add_subplot(111, projection='3d')
ax1.quiver(geometry[:, 0] * d,
geometry[:, 1] * d,
geometry[:, 2] * d,
np.real(E_tot[:, 0]),
np.real(E_tot[:, 1]),
np.real(E_tot[:, 2]),
length=10,
lw=1)
ax1.set_xlim(-(Axis_max - X * d) / 2, (Axis_max + X * d) / 2)
ax1.set_ylim(-(Axis_max - Y * d) / 2, (Axis_max + Y * d) / 2)
ax1.set_zlim(-(Axis_max - Z * d) / 2, (Axis_max + Z * d) / 2)
ax1.set_xlabel("x[nm]")
ax1.set_ylabel("y[nm]")
ax1.set_zlabel("z[nm]")
ax1.grid(False)
fig1.suptitle("E field - Arrow plot\n {}nm, {}".format(wl, E_dir))
fig2 = plt.figure()
ax2 = fig2.add_subplot(111, projection='3d')
geo_dic = set()
surf_list = []
surf_color = []
x_grid, y_grid, z_grid = (np.indices((X + 1, Y + 1, Z + 1)) - 0.5) * d
filled, colors = np.zeros((X, Y, Z)), np.zeros((X, Y, Z))
for i, pos in enumerate(geometry):
geo_dic.add((pos[0], pos[1], pos[2]))
for i, pos in enumerate(geometry):
if (pos[0]+1,pos[1],pos[2]) not in geo_dic or (pos[0]-1,pos[1],pos[2]) not in geo_dic or\
(pos[0],pos[1]+1,pos[2]) not in geo_dic or (pos[0],pos[1]-1,pos[2]) not in geo_dic or\
(pos[0],pos[1],pos[2]+1) not in geo_dic or (pos[0],pos[1],pos[2]-1) not in geo_dic:
filled[pos[0], pos[1], pos[2]] = 1
colors[pos[0], pos[1], pos[2]] = E_tot_abs[i]
surf_list = np.array(surf_list)
surf_color = np.array(surf_color)
colors = cm.Spectral_r(norm(colors))
ax2.voxels(x_grid,
y_grid,
z_grid,
filled.astype(bool),
facecolors=colors,
linewidth=0.5)
ax2.set_xlim(-(Axis_max - X * d) / 2, (Axis_max + X * d) / 2)
ax2.set_ylim(-(Axis_max - Y * d) / 2, (Axis_max + Y * d) / 2)
ax2.set_zlim(-(Axis_max - Z * d) / 2, (Axis_max + Z * d) / 2)
ax2.set_xlabel("x[nm]")
ax2.set_ylabel("y[nm]")
ax2.set_zlabel("z[nm]")
ax2.grid(False)
fig2.colorbar(colorset, shrink=0.9, aspect=10)
if iteration == -1:
fig2.suptitle("E field - Scatter plot\n {}nm, {}".format(wl, k_dir))
plt.savefig("E_field.png")
else:
fig2.suptitle("E field - Scatter plot\n {}nm, {} - iteration{}".format(
wl, k_dir, iteration))
plt.savefig(position +
"{}E_field.png".format(str(iteration).zfill(decimal)))
def draw_areachart(Num_Date):
Span_Date = 180
ax.clear()
if Num_Date < Span_Date:
df_temp = df.loc[0:Num_Date, :]
df_span = df.loc[0:Span_Date, :]
colors = cm.Spectral_r(df_span.price.values /
float(max(df_span.price.values)))
plt.bar(df_temp.date.values,
df_temp.price.values,
color=colors,
width=1.5,
align="center",
zorder=1)
plt.plot(df_temp.date, df_temp.price, color='k', zorder=2)
plt.scatter(df_temp.date.values[-1],
df_temp.price.values[-1],
color='white',
s=150,
edgecolor='k',
linewidth=2,
zorder=3)
plt.text(df_temp.date.values[-1],
df_temp.price.values[-1] * 1.18,
s=np.round(df_temp.price.values[-1], 1),
size=10,
ha='center',
va='top')
plt.ylim(0, df_span.price.max() * 1.68)
plt.xlim(df_span.date.values[0], df_span.date.values[-1])
plt.xticks(ticks=df_span.date.values[0:Span_Date + 1:30],
labels=df_span.date.values[0:Span_Date + 1:30],
rotation=0,
fontsize=9)
else:
df_temp = df.loc[Num_Date - Span_Date:Num_Date, :]
colors = cm.Spectral_r(df_temp.price / float(max(df_temp.price)))
plt.bar(df_temp.date.values[:-2],
df_temp.price.values[:-2],
color=colors[:-2],
width=1.5,
align="center",
zorder=1)
plt.plot(df_temp.date[:-2], df_temp.price[:-2], color='k', zorder=2)
plt.scatter(df_temp.date.values[-4],
df_temp.price.values[-4],
color='white',
s=150,
edgecolor='k',
linewidth=2,
zorder=3)
plt.text(df_temp.date.values[-1],
df_temp.price.values[-1] * 1.18,
s=np.round(df_temp.price.values[-1], 1),
size=10,
ha='center',
va='top')
plt.ylim(0, df_temp.price.max() * 1.68)
plt.xlim(df_temp.date.values[0], df_temp.date.values[-1])
plt.xticks(ticks=df_temp.date.values[0:Span_Date + 1:30],
labels=df_temp.date.values[0:Span_Date + 1:30],
rotation=0,
fontsize=9)
plt.margins(x=0.2)
ax.spines['top'].set_color('none') # 设置上‘脊梁’为红色
ax.spines['right'].set_color('none') # 设置上‘脊梁’为无色
ax.spines['left'].set_color('none') # 设置上‘脊梁’为无色
plt.grid(axis="y", c=(217 / 256, 217 / 256, 217 / 256),
linewidth=1) # 设置网格线
plt.text(0.01,
0.95,
"BTC平均价格($)",
transform=ax.transAxes,
size=10,
weight='light',
ha='left')
ax.text(-0.07,
1.03,
'2013年到2021年的比特币BTC价格变化情况',
transform=ax.transAxes,
size=17,
weight='light',
ha='left')
ax.spines['top'].set_color('none') # 设置上‘脊梁’为无色
ax.spines['right'].set_color('none') # 设置上‘脊梁’为无色
ax.spines['left'].set_color('none') # 设置上‘脊梁’为无色
plt.grid(axis="y", c=(217 / 256, 217 / 256, 217 / 256), linewidth=1) # 设置网格线
plt.show()
"""
Span设定的是多少天的价格，这里使用200天。N_Span代表权重；
df_temp=df.loc[N_Span*Span:(N_Span+1)*Span,:]代表的是选择到179行为止的数据，即180天。
plt.fill_between()是使用单色--红色填充，
"""
# 4.2 渐变颜色面积图
Span_Date = 180
Num_Date = 360 # 终止日期
df_temp = df.loc[Num_Date - Span_Date:
Num_Date, :] # 选择从Num_Date-Span_Date开始到Num_Date的180天的数据
colors = cm.Spectral_r(df_temp.price / float(max(df_temp.price)))
fig = plt.figure(figsize=(6, 4), dpi=100)
plt.subplots_adjust(top=1, bottom=0, left=0, right=0.9, hspace=0, wspace=0)
plt.bar(df_temp.date.values,
df_temp.price.values,
color=colors,
width=1,
align="center",
zorder=1)
plt.plot(df_temp.date, df_temp.price, color='k', zorder=2)
plt.scatter(df_temp.date.values[-1],
df_temp.price.values[-1],
color='white',
s=150,
edgecolor='k',
linewidth=2,
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('k')
ax.yaxis.pane.set_edgecolor('k')
ax.zaxis.pane.set_edgecolor('k')
dz=hist.ravel()
zpos = 0
# Construct arrays with the dimensions for the 16 bars.
dx = dy = 0.5 * np.ones_like(zpos)
dz = hist.ravel()
colors = cm.Spectral_r(dz / float(max(dz)))
ax.bar3d(XX.ravel(), YY.ravel(), zpos, 0.3, 0.3, dz, zsort='average',color=colors,
alpha=1,edgecolor='k',linewidth=0.2)
ax.set_xlabel( "x axis")
ax.set_ylabel("y axis")
ax.set_zlabel("count")
ax2 = fig.add_axes([0.85, 0.35, 0.025, 0.3])
cmap = mpl.cm.Spectral_r
norm = mpl.colors.Normalize(vmin=0, vmax=1)
bounds = np.arange(min(dz),max(dz),20)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
cb2 = mpl.colorbar.ColorbarBase(ax2, cmap=cmap,norm=norm,boundaries=bounds,
ticks=np.arange(min(dz),max(dz),20),spacing='proportional',label='count')
linestyle='-',
linewidth='0.5',
color='gray',
alpha=0.5)
N = df.shape[0]
x_angles = [n / float(N) * 2 * np.pi for n in range(N)]
#x_angles += x_angles[:1]
upperlimits = (df['max.temperaturec'] - df['min.temperaturec']).values
#upperlimits += upperlimits[:1]
lowerlimits = df['min.temperaturec'].values
#lowerlimits += lowerlimits[:1]
colors = cm.Spectral_r(upperlimits / float(max(upperlimits)))
#ax.bar(x_angles,lowerlimits, color='none',edgecolor='none',width=0.01,alpha=1)
#ax.bar(x_angles,upperlimits, color=colors,edgecolor='none',width=0.02, bottom=lowerlimits,alpha=1)
ax.bar(x_angles,
lowerlimits,
color='none',
edgecolor='none',
width=0.01,
alpha=1)
ax.bar(x_angles,
upperlimits,
color=colors,
edgecolor='none',
width=0.02,
